- 🔧 [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)
</details>
## 💡 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).
- [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:
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 `v0.21.1-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.21.1-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:v0.21.1` for the full edition `v0.21.1`.
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 `<YOUR_SERVING_PORT>: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.
