Added azure openai lightrag server to the api install and fused documentation.
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Saifeddine ALOUI
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# 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 OpenAI's language models.
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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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- Azure OpenAI API key
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- Azure OpenAI Deployments (gpt-4o, text-embedding-3-large)
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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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- openai
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- nest-asyncio
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## Installation
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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/](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
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Make sure you install the VS 2022 C++ x64/x86 Build tools from individual components tab.
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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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python -m venv venv
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source venv/bin/activate
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#venv\Scripts\activate for Windows
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pip install -r requirements.txt
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```
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3. Set up environment variables:
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use the `.env` file to set the environment variables (you can copy the `.env.aoi.example` file and rename it to `.env`),
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or set them manually:
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```bash
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export AZURE_OPENAI_API_VERSION='2024-08-01-preview'
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export AZURE_OPENAI_DEPLOYMENT='gpt-4o'
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export AZURE_OPENAI_API_KEY='myapikey'
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export AZURE_OPENAI_ENDPOINT='https://myendpoint.openai.azure.com'
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export AZURE_EMBEDDING_DEPLOYMENT='text-embedding-3-large'
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export AZURE_EMBEDDING_API_VERSION='2023-05-15'
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```
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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 azure_openai_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 | 9621 | Server port |
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| --model | gpt-4 | OpenAI model name |
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| --embedding-model | text-embedding-3-large | OpenAI embedding model |
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| --working-dir | ./rag_storage | Working directory for RAG |
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| --max-tokens | 32768 | Maximum token size |
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| --max-embed-tokens | 8192 | Maximum embedding token size |
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| --input-dir | ./inputs | Input directory for documents |
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| --enable-cache | True | Enable response cache |
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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 azure_openai_lightrag_server.py
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```
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2. Custom configuration:
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```bash
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python azure_openai_lightrag_server.py --model gpt-4o --port 8080 --working-dir ./custom_rag
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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:9621/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:9621/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:9621/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:9621/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:9621/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:9621/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:9621/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 azure_openai_lightrag_server:app --reload --port 9621
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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:9621/docs
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- ReDoc: http://localhost:9621/redoc
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## Deployment
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Azure OpenAI API can be created using the following commands in Azure CLI (you need to install Azure CLI first from [https://docs.microsoft.com/en-us/cli/azure/install-azure-cli](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli)):
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```bash
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# Change the resource group name, location and OpenAI resource name as needed
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RESOURCE_GROUP_NAME=LightRAG
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LOCATION=swedencentral
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RESOURCE_NAME=LightRAG-OpenAI
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az login
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az group create --name $RESOURCE_GROUP_NAME --location $LOCATION
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az cognitiveservices account create --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --kind OpenAI --sku S0 --location swedencentral
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az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME --model-format OpenAI --name $RESOURCE_NAME --deployment-name gpt-4o --model-name gpt-4o --model-version "2024-08-06" --sku-capacity 100 --sku-name "Standard"
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az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME --model-format OpenAI --name $RESOURCE_NAME --deployment-name text-embedding-3-large --model-name text-embedding-3-large --model-version "1" --sku-capacity 80 --sku-name "Standard"
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az cognitiveservices account show --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --query "properties.endpoint"
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az cognitiveservices account keys list --name $RESOURCE_NAME -g $RESOURCE_GROUP_NAME
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```
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The output of the last command will give you the endpoint and the key for the OpenAI API. You can use these values to set the environment variables in the `.env` file.
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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 [OpenAI](https://openai.com/) for language model inference
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@@ -435,9 +435,13 @@ def create_app(args):
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return app
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if __name__ == "__main__":
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def main():
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args = parse_args()
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import uvicorn
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app = create_app(args)
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uvicorn.run(app, host=args.host, port=args.port)
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if __name__ == "__main__":
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main()
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