将flask改成fastapi

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2025-10-13 13:18:03 +08:00
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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.

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#
# 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"
]

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{
"api_key": "ragflow-***",
"host_address": "127.0.0.1:80"
}

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#
# 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)}"

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requests

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# 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.

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<svg width="32" height="34" viewBox="0 0 32 34" fill="none" xmlns="http://www.w3.org/2000/svg">
<path fill-rule="evenodd" clip-rule="evenodd"
d="M3.43265 20.7677C4.15835 21.5062 4.15834 22.7035 3.43262 23.4419L3.39546 23.4797C2.66974 24.2182 1.49312 24.2182 0.767417 23.4797C0.0417107 22.7412 0.0417219 21.544 0.767442 20.8055L0.804608 20.7677C1.53033 20.0292 2.70694 20.0293 3.43265 20.7677Z"
fill="#B2DDFF" />
<path fill-rule="evenodd" clip-rule="evenodd"
d="M12.1689 21.3375C12.8933 22.0773 12.8912 23.2746 12.1641 24.0117L7.01662 29.2307C6.2896 29.9678 5.11299 29.9657 4.38859 29.2259C3.66419 28.4861 3.66632 27.2888 4.39334 26.5517L9.54085 21.3327C10.2679 20.5956 11.4445 20.5977 12.1689 21.3375Z"
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fill="#1570EF" />
<path fill-rule="evenodd" clip-rule="evenodd"
d="M22.256 1.59299C22.9822 2.33095 22.983 3.52823 22.2578 4.26718L8.45055 18.3358C7.72533 19.0748 6.54871 19.0756 5.82251 18.3376C5.09631 17.5996 5.09552 16.4024 5.82075 15.6634L19.6279 1.59478C20.3532 0.855827 21.5298 0.855022 22.256 1.59299Z"
fill="#1570EF" />
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fill="#53B1FD" />
<path fill-rule="evenodd" clip-rule="evenodd"
d="M11.893 0.624023C12.9193 0.624023 13.7513 1.47063 13.7513 2.51497V2.70406C13.7513 3.7484 12.9193 4.59501 11.893 4.59501C10.8667 4.59501 10.0347 3.7484 10.0347 2.70406V2.51497C10.0347 1.47063 10.8667 0.624023 11.893 0.624023Z"
fill="#B2DDFF" />
</svg>

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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." });
}
});

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(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
\`\`\``;
})();

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{
"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": ["<all_urls>"],
"js": ["content.js"],
"css": ["styles/popup.css"]
}
],
"icons": {
"16": "icons/icon-16x16.png",
"48": "icons/icon-48x48.png",
"128": "icons/icon-128x128.png"
}
}

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>RagFlow option</title>
<link rel="stylesheet" href="styles/options.css" />
</head>
<body id="ragflow">
<div id="form-config">
<div class="header">
<img src="assets/logo-with-text.png" alt="Logo" class="logo" />
</div>
<div class="content">
<label for="base-url">Base URL:</label>
<input type="text" id="base-url" placeholder="Enter base URL" />
<label for="from">From:</label>
<select id="from">
<option selected value="agent">agent</option>
<option value="chat">chat</option>
</select>
<label for="auth">Auth:</label>
<input type="text" id="auth" placeholder="Enter auth" />
<label for="shared-id">Shared ID:</label>
<input type="text" id="shared-id" placeholder="Enter shared ID" />
<button id="save-config">🛖</button>
</div>
</div>
<script src="options.js"></script>
</body>
</html>

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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");
}
);
});
});

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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="viewport"
content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0" />
<title>RAGFLOW</title>
<link rel="stylesheet" href="styles/popup.css" />
</head>
<body id="ragflow">
<div class="window">
<textarea id="getHtml"></textarea>
<iframe src="" style="width: 100%; height: 100%; min-height: 600px" frameborder="0"></iframe>
</div>
<script src="popup.js"></script>
</body>
</html>

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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;
}
}
);
});
});

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#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;
}

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#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;
}

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# 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

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# 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.

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"""
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"]

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"""
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())

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"""
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)

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"""
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 ""

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"""
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

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"""
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"
]
}

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"""
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"
]

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"""
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"
]

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# 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