4.8 KiB
LightRAG API Server
A powerful FastAPI-based server for managing and querying documents using LightRAG (Light Retrieval-Augmented Generation). This server provides a REST API interface for document management and intelligent querying using OpenAI's language models.
Features
- 🔍 Multiple search modes (naive, local, global, hybrid)
- 📡 Streaming and non-streaming responses
- 📝 Document management (insert, batch upload, clear)
- ⚙️ Highly configurable model parameters
- 📚 Support for text and file uploads
- 🔧 RESTful API with automatic documentation
- 🚀 Built with FastAPI for high performance
Prerequisites
- Python 3.8+
- Azure OpenAI API key
- Azure OpenAI Deployments (gpt-4o, text-embedding-3-large)
- Required Python packages:
- fastapi
- uvicorn
- lightrag
- pydantic
- openai
- nest-asyncio
Installation
If you are using Windows, you will need to download and install visual c++ build tools from https://visualstudio.microsoft.com/visual-cpp-build-tools/ Make sure you install the VS 2022 C++ x64/x86 Build tools from individual components tab.
- Clone the repository:
git clone https://github.com/ParisNeo/LightRAG.git
cd api
- Install dependencies:
python -m venv venv
source venv/bin/activate
#venv\Scripts\activate for Windows
pip install -r requirements.txt
- Set up environment variables:
use the
.envfile to set the environment variables (you can copy the.env.aoi.examplefile and rename it to.env), or set them manually:
export AZURE_OPENAI_API_VERSION='2024-08-01-preview'
export AZURE_OPENAI_DEPLOYMENT='gpt-4o'
export AZURE_OPENAI_API_KEY='myapikey'
export AZURE_OPENAI_ENDPOINT='https://myendpoint.openai.azure.com'
export AZURE_EMBEDDING_DEPLOYMENT='text-embedding-3-large'
export AZURE_EMBEDDING_API_VERSION='2023-05-15'
Configuration
The server can be configured using command-line arguments:
python azure_openai_lightrag_server.py --help
Available options:
| Parameter | Default | Description |
|---|---|---|
| --host | 0.0.0.0 | Server host |
| --port | 9621 | Server port |
| --model | gpt-4 | OpenAI model name |
| --embedding-model | text-embedding-3-large | OpenAI embedding model |
| --working-dir | ./rag_storage | Working directory for RAG |
| --max-tokens | 32768 | Maximum token size |
| --max-embed-tokens | 8192 | Maximum embedding token size |
| --input-dir | ./inputs | Input directory for documents |
| --enable-cache | True | Enable response cache |
| --log-level | INFO | Logging level |
Quick Start
- Basic usage with default settings:
python azure_openai_lightrag_server.py
- Custom configuration:
python azure_openai_lightrag_server.py --model gpt-4o --port 8080 --working-dir ./custom_rag
API Endpoints
Query Endpoints
POST /query
Query the RAG system with options for different search modes.
curl -X POST "http://localhost:9621/query" \
-H "Content-Type: application/json" \
-d '{"query": "Your question here", "mode": "hybrid"}'
POST /query/stream
Stream responses from the RAG system.
curl -X POST "http://localhost:9621/query/stream" \
-H "Content-Type: application/json" \
-d '{"query": "Your question here", "mode": "hybrid"}'
Document Management Endpoints
POST /documents/text
Insert text directly into the RAG system.
curl -X POST "http://localhost:9621/documents/text" \
-H "Content-Type: application/json" \
-d '{"text": "Your text content here", "description": "Optional description"}'
POST /documents/file
Upload a single file to the RAG system.
curl -X POST "http://localhost:9621/documents/file" \
-F "file=@/path/to/your/document.txt" \
-F "description=Optional description"
POST /documents/batch
Upload multiple files at once.
curl -X POST "http://localhost:9621/documents/batch" \
-F "files=@/path/to/doc1.txt" \
-F "files=@/path/to/doc2.txt"
DELETE /documents
Clear all documents from the RAG system.
curl -X DELETE "http://localhost:9621/documents"
Utility Endpoints
GET /health
Check server health and configuration.
curl "http://localhost:9621/health"
Development
Running in Development Mode
uvicorn azure_openai_lightrag_server:app --reload --port 9621
API Documentation
When the server is running, visit:
- Swagger UI: http://localhost:9621/docs
- ReDoc: http://localhost:9621/redoc
License
This project is licensed under the MIT License - see the LICENSE file for details.