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GraphRAG4OpenWebUI

GraphRAG4OpenWebUI is an API interface specifically designed for Open WebUI, aiming to integrate Microsoft Research's GraphRAG (Graph-based Retrieval-Augmented Generation) technology. This project provides a powerful information retrieval system that supports multiple search models, particularly suitable for use in open web user interfaces.

Project Overview

The main goal of this project is to provide a convenient interface for Open WebUI to leverage the powerful features of GraphRAG. It integrates three main retrieval methods and offers a comprehensive search option, allowing users to obtain thorough and precise search results.

Key Retrieval Features

1. Local Search

   • Utilizes GraphRAG technology for efficient retrieval in local knowledge bases
   • Suitable for quick access to pre-defined structured information
   • Leverages graph structures to improve retrieval accuracy and relevance

2. Global Search

   • Searches for information in a broader scope, beyond local knowledge bases
   • Suitable for queries requiring more comprehensive information
   • Utilizes GraphRAG's global context understanding capabilities to provide richer search results

3. Tavily Search

   • Integrates external Tavily search API
   • Provides additional internet search capabilities, expanding information sources
   • Suitable for queries requiring the latest or extensive web information

4. Full Model Search

   • Combines all three search methods above
   • Provides the most comprehensive search results, meeting complex information needs
   • Automatically integrates and ranks information from different sources

Local LLM and Embedding Model Support

GraphRAG4OpenWebUI now supports the use of local language models (LLMs) and embedding models, increasing the project's flexibility and privacy. Specifically, we support the following local models:

1. Ollama

   • Supports various open-source LLMs run through Ollama, such as Llama 2, Mistral, etc.
   • Can be configured by setting the API_BASE environment variable to point to Ollama's API endpoint

2. LM Studio

   • Compatible with models run by LM Studio
   • Connect to LM Studio's service by configuring the API_BASE environment variable

3. Local Embedding Models

   • Supports the use of locally run embedding models, such as SentenceTransformers
   • Specify the embedding model to use by setting the GRAPHRAG_EMBEDDING_MODEL environment variable

This support for local models allows GraphRAG4OpenWebUI to run without relying on external APIs, enhancing data privacy and reducing usage costs.

Installation

Ensure that you have Python 3.8 or higher installed on your system. Then, follow these steps to install:

1. Clone the repository:

   git clone https://github.com/your-username/GraphRAG4OpenWebUI.git
   cd GraphRAG4OpenWebUI
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

   Note: The graphrag package might need to be installed from a specific source. If the above command fails to install graphrag, please refer to Microsoft Research's specific instructions or contact the maintainer for the correct installation method.

Configuration

Before running the API, you need to set the following environment variables. You can do this by creating a .env file or exporting them directly in your terminal:

# Set the TAVILY API key 
export TAVILY_API_KEY="your_tavily_api_key"

export INPUT_DIR="/path/to/your/input/directory"

# Set the API key for LLM
export GRAPHRAG_API_KEY="your_actual_api_key_here"

# Set the API key for embedding (if different from GRAPHRAG_API_KEY)
export GRAPHRAG_API_KEY_EMBEDDING="your_embedding_api_key_here"

# Set the LLM model 
export GRAPHRAG_LLM_MODEL="gemma2"

# Set the API base URL 
export API_BASE="http://localhost:11434/v1"

# Set the embedding API base URL (default is OpenAI's API)
export API_BASE_EMBEDDING="https://api.openai.com/v1"

# Set the embedding model (default is "text-embedding-3-small")
export GRAPHRAG_EMBEDDING_MODEL="text-embedding-3-small"

Make sure to replace the placeholders in the above commands with your actual API keys and paths.

Usage

1. Start the server:

   python main-en.py
   

   The server will run on http://localhost:8012.

2. API Endpoints:

   • /v1/chat/completions: POST request for performing searches
   • /v1/models: GET request to retrieve the list of available models

3. Integration with Open WebUI: In the Open WebUI configuration, set the API endpoint to http://localhost:8012/v1/chat/completions. This will allow Open WebUI to use the search functionality of GraphRAG4OpenWebUI.

4. Example search request:

   import requests
   import json
   
   url = "http://localhost:8012/v1/chat/completions"
   headers = {"Content-Type": "application/json"}
   data = {
       "model": "full-model:latest",
       "messages": [{"role": "user", "content": "Your search query"}],
       "temperature": 0.7
   }
   
   response = requests.post(url, headers=headers, data=json.dumps(data))
   print(response.json())
   

Available Models

• graphrag-local-search:latest: Local search
• graphrag-global-search:latest: Global search
• tavily-search:latest: Tavily search
• full-model:latest: Comprehensive search (includes all search methods above)

Notes

• Ensure that you have the correct input files (such as Parquet files) in the INPUT_DIR directory.
• The API uses asynchronous programming, make sure your environment supports async operations.
• For large-scale deployment, consider using a production-grade ASGI server.
• This project is specifically designed for Open WebUI and can be easily integrated into various web-based applications.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

Apache-2.0 License