188 lines
4.8 KiB
Markdown
188 lines
4.8 KiB
Markdown
# LightRAG API Server
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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 various LLM models through Ollama.
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## Features
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- 🔍 Multiple search modes (naive, local, global, hybrid)
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- 📡 Streaming and non-streaming responses
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- 📝 Document management (insert, batch upload, clear)
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- ⚙️ Highly configurable model parameters
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- 📚 Support for text and file uploads
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- 🔧 RESTful API with automatic documentation
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- 🚀 Built with FastAPI for high performance
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## Prerequisites
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- Python 3.8+
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- Ollama server running locally or remotely
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- Required Python packages:
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- fastapi
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- uvicorn
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- lightrag
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- pydantic
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## Installation
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If you are using windows, you will need to donwload and install visual c++ build tools from [https://visualstudio.microsoft.com/visual-cpp-build-tools/ ](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
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Make sure you install the VS 2022 C++ x64/x86 Build tools like from indivisual componants tab:
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This is mandatory for builmding some modules.
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1. Clone the repository:
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```bash
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git clone https://github.com/ParisNeo/LightRAG.git
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cd api
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```
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Make sure Ollama is running and accessible.
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## Configuration
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The server can be configured using command-line arguments:
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```bash
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python ollama_lightrag_server.py --help
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```
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Available options:
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| Parameter | Default | Description |
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|-----------|---------|-------------|
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| --host | 0.0.0.0 | Server host |
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| --port | 8000 | Server port |
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| --model | gemma2:2b | LLM model name |
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| --embedding-model | nomic-embed-text | Embedding model name |
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| --ollama-host | http://localhost:11434 | Ollama host URL |
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| --working-dir | ./dickens | Working directory for RAG |
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| --max-async | 4 | Maximum async operations |
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| --max-tokens | 32768 | Maximum token size |
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| --embedding-dim | 768 | Embedding dimensions |
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| --max-embed-tokens | 8192 | Maximum embedding token size |
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| --input-file | ./book.txt | Initial input file |
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| --log-level | INFO | Logging level |
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## Quick Start
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1. Basic usage with default settings:
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```bash
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python rag_server.py
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```
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2. Custom configuration:
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```bash
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python rag_server.py --model llama2:13b --port 8080 --working-dir ./custom_rag
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```
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3. Using the launch script:
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```bash
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chmod +x launch_rag_server.sh
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./launch_rag_server.sh
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```
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## API Endpoints
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### Query Endpoints
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#### POST /query
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Query the RAG system with options for different search modes.
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```bash
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curl -X POST "http://localhost:8000/query" \
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-H "Content-Type: application/json" \
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-d '{"query": "Your question here", "mode": "hybrid"}'
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```
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#### POST /query/stream
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Stream responses from the RAG system.
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```bash
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curl -X POST "http://localhost:8000/query/stream" \
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-H "Content-Type: application/json" \
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-d '{"query": "Your question here", "mode": "hybrid"}'
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```
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### Document Management Endpoints
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#### POST /documents/text
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Insert text directly into the RAG system.
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```bash
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curl -X POST "http://localhost:8000/documents/text" \
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-H "Content-Type: application/json" \
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-d '{"text": "Your text content here", "description": "Optional description"}'
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```
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#### POST /documents/file
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Upload a single file to the RAG system.
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```bash
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curl -X POST "http://localhost:8000/documents/file" \
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-F "file=@/path/to/your/document.txt" \
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-F "description=Optional description"
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```
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#### POST /documents/batch
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Upload multiple files at once.
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```bash
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curl -X POST "http://localhost:8000/documents/batch" \
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-F "files=@/path/to/doc1.txt" \
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-F "files=@/path/to/doc2.txt"
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```
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#### DELETE /documents
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Clear all documents from the RAG system.
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```bash
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curl -X DELETE "http://localhost:8000/documents"
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```
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### Utility Endpoints
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#### GET /health
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Check server health and configuration.
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```bash
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curl "http://localhost:8000/health"
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```
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## Development
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### Running in Development Mode
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```bash
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uvicorn rag_server:app --reload --port 8000
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```
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### API Documentation
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When the server is running, visit:
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- Swagger UI: http://localhost:8000/docs
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- ReDoc: http://localhost:8000/redoc
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## Contributing
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1. Fork the repository
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2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
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3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
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4. Push to the branch (`git push origin feature/AmazingFeature`)
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5. Open a Pull Request
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## License
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This project is licensed under the MIT License - see the LICENSE file for details.
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## Acknowledgments
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- Built with [FastAPI](https://fastapi.tiangolo.com/)
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- Uses [LightRAG](https://github.com/HKUDS/LightRAG) for document processing
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- Powered by [Ollama](https://ollama.ai/) for LLM inference
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## Support
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