lightrag/api/README_AZURE_OPENAI.md

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.

1. Clone the repository:

git clone https://github.com/ParisNeo/LightRAG.git
cd api

2. Install dependencies:

python -m venv venv
source venv/bin/activate
#venv\Scripts\activate for Windows
pip install -r requirements.txt

3. Set up environment variables: use the .env file to set the environment variables (you can copy the .env.aoi.example file 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

1. Basic usage with default settings:

python azure_openai_lightrag_server.py

2. 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

Deployment

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):

# Change the resource group name, location and OpenAI resource name as needed
RESOURCE_GROUP_NAME=LightRAG
LOCATION=swedencentral
RESOURCE_NAME=LightRAG-OpenAI

az login
az group create --name $RESOURCE_GROUP_NAME --location $LOCATION
az cognitiveservices account create --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME  --kind OpenAI --sku S0 --location swedencentral
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"
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"
az cognitiveservices account show --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --query "properties.endpoint"
az cognitiveservices account keys list --name $RESOURCE_NAME -g $RESOURCE_GROUP_NAME

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.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with FastAPI
• Uses LightRAG for document processing
• Powered by OpenAI for language model inference