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api/README.md

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+# 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 various LLM models through Ollama.
+
+## 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+
+- Ollama server running locally or remotely
+- Required Python packages:
+  - fastapi
+  - uvicorn
+  - lightrag
+  - pydantic
+
+## Installation
+
+1. Clone the repository:
+```bash
+git clone https://github.com/yourusername/lightrag-server.git
+cd lightrag-server
+```
+
+2. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+3. Make sure Ollama is running and accessible.
+
+## Configuration
+
+The server can be configured using command-line arguments:
+
+```bash
+python rag_server.py --help
+```
+
+Available options:
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| --host | 0.0.0.0 | Server host |
+| --port | 8000 | Server port |
+| --model | gemma2:2b | LLM model name |
+| --embedding-model | nomic-embed-text | Embedding model name |
+| --ollama-host | http://localhost:11434 | Ollama host URL |
+| --working-dir | ./dickens | Working directory for RAG |
+| --max-async | 4 | Maximum async operations |
+| --max-tokens | 32768 | Maximum token size |
+| --embedding-dim | 768 | Embedding dimensions |
+| --max-embed-tokens | 8192 | Maximum embedding token size |
+| --input-file | ./book.txt | Initial input file |
+| --log-level | INFO | Logging level |
+
+## Quick Start
+
+1. Basic usage with default settings:
+```bash
+python rag_server.py
+```
+
+2. Custom configuration:
+```bash
+python rag_server.py --model llama2:13b --port 8080 --working-dir ./custom_rag
+```
+
+3. Using the launch script:
+```bash
+chmod +x launch_rag_server.sh
+./launch_rag_server.sh
+```
+
+## API Endpoints
+
+### Query Endpoints
+
+#### POST /query
+Query the RAG system with options for different search modes.
+
+```bash
+curl -X POST "http://localhost:8000/query" \
+    -H "Content-Type: application/json" \
+    -d '{"query": "Your question here", "mode": "hybrid"}'
+```
+
+#### POST /query/stream
+Stream responses from the RAG system.
+
+```bash
+curl -X POST "http://localhost:8000/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.
+
+```bash
+curl -X POST "http://localhost:8000/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.
+
+```bash
+curl -X POST "http://localhost:8000/documents/file" \
+    -F "file=@/path/to/your/document.txt" \
+    -F "description=Optional description"
+```
+
+#### POST /documents/batch
+Upload multiple files at once.
+
+```bash
+curl -X POST "http://localhost:8000/documents/batch" \
+    -F "files=@/path/to/doc1.txt" \
+    -F "files=@/path/to/doc2.txt"
+```
+
+#### DELETE /documents
+Clear all documents from the RAG system.
+
+```bash
+curl -X DELETE "http://localhost:8000/documents"
+```
+
+### Utility Endpoints
+
+#### GET /health
+Check server health and configuration.
+
+```bash
+curl "http://localhost:8000/health"
+```
+
+## Development
+
+### Running in Development Mode
+
+```bash
+uvicorn rag_server:app --reload --port 8000
+```
+
+### API Documentation
+
+When the server is running, visit:
+- Swagger UI: http://localhost:8000/docs
+- ReDoc: http://localhost:8000/redoc
+
+## Contributing
+
+1. Fork the repository
+2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the branch (`git push origin feature/AmazingFeature`)
+5. Open a Pull Request
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Acknowledgments
+
+- Built with [FastAPI](https://fastapi.tiangolo.com/)
+- Uses [LightRAG](https://github.com/HKUDS/LightRAG) for document processing
+- Powered by [Ollama](https://ollama.ai/) for LLM inference
+
+## Support