Merge pull request #504 from congiuluc/feature-azureopenai-api

Added Azure OpenAI api sample with streaming

.gitignore

@@ -15,3 +15,7 @@ ignore_this.txt
 gui/
 *.log
 .vscode
+.env
+venv/
+examples/input/
+examples/output/

api/.env.aoi.example

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+AZURE_OPENAI_API_VERSION=2024-08-01-preview
+AZURE_OPENAI_DEPLOYMENT=gpt-4o
+AZURE_OPENAI_API_KEY=myapikey
+AZURE_OPENAI_ENDPOINT=https://myendpoint.openai.azure.com
+
+AZURE_EMBEDDING_DEPLOYMENT=text-embedding-3-large
+AZURE_EMBEDDING_API_VERSION=2023-05-15

api/README_AZURE_OPENAI.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 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/](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:
+```bash
+git clone https://github.com/ParisNeo/LightRAG.git
+cd api
+```
+
+2. Install dependencies:
+```bash
+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:
+```bash
+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:
+
+```bash
+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:
+```bash
+python azure_openai_lightrag_server.py
+```
+
+2. Custom configuration:
+```bash
+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.
+
+```bash
+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.
+
+```bash
+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.
+
+```bash
+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.
+
+```bash
+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.
+
+```bash
+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.
+
+```bash
+curl -X DELETE "http://localhost:9621/documents"
+```
+
+### Utility Endpoints
+
+#### GET /health
+Check server health and configuration.
+
+```bash
+curl "http://localhost:9621/health"
+```
+
+## Development
+
+### Running in Development Mode
+
+```bash
+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](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli)):
+```bash
+# 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](https://fastapi.tiangolo.com/)
+- Uses [LightRAG](https://github.com/HKUDS/LightRAG) for document processing
+- Powered by [OpenAI](https://openai.com/) for language model inference