Added azure openai lightrag server to the api install and fused documentation.

README.md

@@ -598,120 +598,6 @@ if __name__ == "__main__":
 | **convert\_response\_to\_json\_func** | `callable` | Not used | `convert_response_to_json` |
 | **embedding\_cache\_config** | `dict` | Configuration for question-answer caching. Contains three parameters:<br>- `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers.<br>- `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM.<br>- `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` |
 
-## API Server Implementation
-
-LightRAG also provides a FastAPI-based server implementation for RESTful API access to RAG operations. This allows you to run LightRAG as a service and interact with it through HTTP requests.
-
-### Setting up the API Server
-<details>
-<summary>Click to expand setup instructions</summary>
-
-1. First, ensure you have the required dependencies:
-```bash
-pip install fastapi uvicorn pydantic
-```
-
-2. Set up your environment variables:
-```bash
-export RAG_DIR="your_index_directory"  # Optional: Defaults to "index_default"
-export OPENAI_BASE_URL="Your OpenAI API base URL"  # Optional: Defaults to "https://api.openai.com/v1"
-export OPENAI_API_KEY="Your OpenAI API key"  # Required
-export LLM_MODEL="Your LLM model" # Optional: Defaults to "gpt-4o-mini"
-export EMBEDDING_MODEL="Your embedding model" # Optional: Defaults to "text-embedding-3-large"
-```
-
-3. Run the API server:
-```bash
-python examples/lightrag_api_openai_compatible_demo.py
-```
-
-The server will start on `http://0.0.0.0:8020`.
-</details>
-
-### API Endpoints
-
-The API server provides the following endpoints:
-
-#### 1. Query Endpoint
-<details>
-<summary>Click to view Query endpoint details</summary>
-
-- **URL:** `/query`
-- **Method:** POST
-- **Body:**
-```json
-{
-    "query": "Your question here",
-    "mode": "hybrid",  // Can be "naive", "local", "global", or "hybrid"
-    "only_need_context": true // Optional: Defaults to false, if true, only the referenced context will be returned, otherwise the llm answer will be returned
-}
-```
-- **Example:**
-```bash
-curl -X POST "http://127.0.0.1:8020/query" \
-     -H "Content-Type: application/json" \
-     -d '{"query": "What are the main themes?", "mode": "hybrid"}'
-```
-</details>
-
-#### 2. Insert Text Endpoint
-<details>
-<summary>Click to view Insert Text endpoint details</summary>
-
-- **URL:** `/insert`
-- **Method:** POST
-- **Body:**
-```json
-{
-    "text": "Your text content here"
-}
-```
-- **Example:**
-```bash
-curl -X POST "http://127.0.0.1:8020/insert" \
-     -H "Content-Type: application/json" \
-     -d '{"text": "Content to be inserted into RAG"}'
-```
-</details>
-
-#### 3. Insert File Endpoint
-<details>
-<summary>Click to view Insert File endpoint details</summary>
-
-- **URL:** `/insert_file`
-- **Method:** POST
-- **Body:**
-```json
-{
-    "file_path": "path/to/your/file.txt"
-}
-```
-- **Example:**
-```bash
-curl -X POST "http://127.0.0.1:8020/insert_file" \
-     -H "Content-Type: application/json" \
-     -d '{"file_path": "./book.txt"}'
-```
-</details>
-
-#### 4. Health Check Endpoint
-<details>
-<summary>Click to view Health Check endpoint details</summary>
-
-- **URL:** `/health`
-- **Method:** GET
-- **Example:**
-```bash
-curl -X GET "http://127.0.0.1:8020/health"
-```
-</details>
-
-### Configuration
-
-The API server can be configured using environment variables:
-- `RAG_DIR`: Directory for storing the RAG index (default: "index_default")
-- API keys and base URLs should be configured in the code for your specific LLM and embedding model providers
-
 ### Error Handling
 <details>
 <summary>Click to view error handling details</summary>
@@ -989,6 +875,12 @@ def extract_queries(file_path):
 │   ├── lightrag_siliconcloud_demo.py
 │   └── vram_management_demo.py
 ├── lightrag/
+│   ├── api/
+│   │   ├── lollms_lightrag_server.py
+│   │   ├── ollama_lightrag_server.py
+│   │   ├── openai_lightrag_server.py
+│   │   ├── azure_openai_lightrag_server.py
+│   │   └── requirements.txt
 │   ├── kg/
 │   │   ├── __init__.py
 │   │   ├── oracle_impl.py
@@ -1033,7 +925,7 @@ pip install "lightrag-hku[api]"
 
 ```bash
 # Clone the repository
-git clone https://github.com/ParisNeo/lightrag.git
+git clone https://github.com/HKUDS/lightrag.git
 
 # Change to the repository directory
 cd lightrag
@@ -1060,6 +952,27 @@ Before running any of the servers, ensure you have the corresponding backend ser
 - Requires valid OpenAI API credentials set in environment variables
 - OPENAI_API_KEY must be set
 
+#### For Azure OpenAI Server
+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.
+
+
+
 ### Configuration Options
 
 Each server has its own specific configuration options:
@@ -1112,6 +1025,22 @@ Each server has its own specific configuration options:
 | --input-dir | ./inputs | Input directory for documents |
 | --log-level | INFO | Logging level |
 
+#### OpenAI AZURE Server 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 |
+
+
 ### Example Usage
 
 #### LoLLMs RAG Server
@@ -1140,17 +1069,25 @@ ollama-lightrag-server --model mistral-nemo:latest --embedding-model bge-m3 --em
 # Using GPT-4 with text-embedding-3-large
 openai-lightrag-server --port 9624 --model gpt-4 --embedding-model text-embedding-3-large
 ```
+#### Azure OpenAI RAG Server
+```bash
+# Using GPT-4 with text-embedding-3-large
+azure-openai-lightrag-server --model gpt-4o --port 8080 --working-dir ./custom_rag --embedding-model text-embedding-3-large
+```
+
 
 **Important Notes:**
 - For LoLLMs: Make sure the specified models are installed in your LoLLMs instance
 - For Ollama: Make sure the specified models are installed in your Ollama instance
 - For OpenAI: Ensure you have set up your OPENAI_API_KEY environment variable
+- For Azure OpenAI: Build and configure your server as stated in the Prequisites section
 
 For help on any server, use the --help flag:
 ```bash
 lollms-lightrag-server --help
 ollama-lightrag-server --help
 openai-lightrag-server --help
+azure-openai-lightrag-server --help
 ```
 
 Note: If you don't need the API functionality, you can install the base package without API support using:
@@ -1160,7 +1097,7 @@ pip install lightrag-hku
 
 ## API Endpoints
 
-All servers (LoLLMs, Ollama, and OpenAI) provide the same REST API endpoints for RAG functionality.
+All servers (LoLLMs, Ollama, OpenAI and Azure OpenAI) provide the same REST API endpoints for RAG functionality.
 
 ### Query Endpoints
 
@@ -1245,7 +1182,10 @@ For OpenAI:
 ```bash
 uvicorn openai_lightrag_server:app --reload --port 9621
 ```
-
+For Azure OpenAI:
+```bash
+uvicorn azure_openai_lightrag_server:app --reload --port 9621
+```
 ### API Documentation
 
 When any server is running, visit:
@@ -1301,6 +1241,14 @@ ollama-lightrag-server --input-dir ./my_documents --port 8080
 openai-lightrag-server --input-dir ./my_documents --port 9624
 ```
 
+#### Azure OpenAI RAG Server
+
+```bash
+# Start server with automatic document vectorization
+# Existing documents are retrieved from cache, only new ones are processed
+azure-openai-lightrag-server --input-dir ./my_documents --port 9624
+```
+
 **Important Notes:**
 - The `--input-dir` parameter enables automatic document processing at startup
 - Documents already in the database are not re-vectorized

examples/openai_README.md

@@ -0,0 +1,114 @@
+
+## API Server Implementation
+
+LightRAG also provides a FastAPI-based server implementation for RESTful API access to RAG operations. This allows you to run LightRAG as a service and interact with it through HTTP requests.
+
+### Setting up the API Server
+<details>
+<summary>Click to expand setup instructions</summary>
+
+1. First, ensure you have the required dependencies:
+```bash
+pip install fastapi uvicorn pydantic
+```
+
+2. Set up your environment variables:
+```bash
+export RAG_DIR="your_index_directory"  # Optional: Defaults to "index_default"
+export OPENAI_BASE_URL="Your OpenAI API base URL"  # Optional: Defaults to "https://api.openai.com/v1"
+export OPENAI_API_KEY="Your OpenAI API key"  # Required
+export LLM_MODEL="Your LLM model" # Optional: Defaults to "gpt-4o-mini"
+export EMBEDDING_MODEL="Your embedding model" # Optional: Defaults to "text-embedding-3-large"
+```
+
+3. Run the API server:
+```bash
+python examples/lightrag_api_openai_compatible_demo.py
+```
+
+The server will start on `http://0.0.0.0:8020`.
+</details>
+
+### API Endpoints
+
+The API server provides the following endpoints:
+
+#### 1. Query Endpoint
+<details>
+<summary>Click to view Query endpoint details</summary>
+
+- **URL:** `/query`
+- **Method:** POST
+- **Body:**
+```json
+{
+    "query": "Your question here",
+    "mode": "hybrid",  // Can be "naive", "local", "global", or "hybrid"
+    "only_need_context": true // Optional: Defaults to false, if true, only the referenced context will be returned, otherwise the llm answer will be returned
+}
+```
+- **Example:**
+```bash
+curl -X POST "http://127.0.0.1:8020/query" \
+     -H "Content-Type: application/json" \
+     -d '{"query": "What are the main themes?", "mode": "hybrid"}'
+```
+</details>
+
+#### 2. Insert Text Endpoint
+<details>
+<summary>Click to view Insert Text endpoint details</summary>
+
+- **URL:** `/insert`
+- **Method:** POST
+- **Body:**
+```json
+{
+    "text": "Your text content here"
+}
+```
+- **Example:**
+```bash
+curl -X POST "http://127.0.0.1:8020/insert" \
+     -H "Content-Type: application/json" \
+     -d '{"text": "Content to be inserted into RAG"}'
+```
+</details>
+
+#### 3. Insert File Endpoint
+<details>
+<summary>Click to view Insert File endpoint details</summary>
+
+- **URL:** `/insert_file`
+- **Method:** POST
+- **Body:**
+```json
+{
+    "file_path": "path/to/your/file.txt"
+}
+```
+- **Example:**
+```bash
+curl -X POST "http://127.0.0.1:8020/insert_file" \
+     -H "Content-Type: application/json" \
+     -d '{"file_path": "./book.txt"}'
+```
+</details>
+
+#### 4. Health Check Endpoint
+<details>
+<summary>Click to view Health Check endpoint details</summary>
+
+- **URL:** `/health`
+- **Method:** GET
+- **Example:**
+```bash
+curl -X GET "http://127.0.0.1:8020/health"
+```
+</details>
+
+### Configuration
+
+The API server can be configured using environment variables:
+- `RAG_DIR`: Directory for storing the RAG index (default: "index_default")
+- API keys and base URLs should be configured in the code for your specific LLM and embedding model providers

lightrag/api/README_AZURE_OPENAI.md

@@ -1,202 +0,0 @@
-
-# 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

lightrag/api/azure_openai_lightrag_server.py

@@ -435,9 +435,13 @@ def create_app(args):
     return app
 
 
-if __name__ == "__main__":
+def main():
     args = parse_args()
     import uvicorn
 
     app = create_app(args)
     uvicorn.run(app, host=args.host, port=args.port)
+
+
+if __name__ == "__main__":
+    main()

setup.py

@@ -103,6 +103,7 @@ setuptools.setup(
             "lollms-lightrag-server=lightrag.api.lollms_lightrag_server:main [api]",
             "ollama-lightrag-server=lightrag.api.ollama_lightrag_server:main [api]",
             "openai-lightrag-server=lightrag.api.openai_lightrag_server:main [api]",
+            "azure-openai-lightrag-server=lightrag.api.azure_openai_lightrag_server:main [api]",
         ],
     },
 )