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docs: add gunicorn deployment guide and update server

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yangdx
2025-03-02 02:43:11 +08:00
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lightrag/api/README.md
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@@ -24,6 +24,8 @@ pip install -e ".[api]"
### Starting API Server with Default Settings ### Starting API Server with Default Settings
After installing LightRAG with API support, you can start LightRAG by this command: `lightrag-server`
LightRAG requires both LLM and Embedding Model to work together to complete document indexing and querying tasks. LightRAG supports binding to various LLM/Embedding backends: LightRAG requires both LLM and Embedding Model to work together to complete document indexing and querying tasks. LightRAG supports binding to various LLM/Embedding backends:
* ollama * ollama
@@ -92,10 +94,40 @@ LLM_BINDING_API_KEY=your_api_key Light_server --llm-binding openai-ollama
LLM_BINDING_API_KEY=your_api_key Light_server --llm-binding openai --embedding-binding openai LLM_BINDING_API_KEY=your_api_key Light_server --llm-binding openai --embedding-binding openai
# start with ollama llm and ollama embedding (no apikey is needed) # start with ollama llm and ollama embedding (no apikey is needed)
Light_server --llm-binding ollama --embedding-binding ollama light-server --llm-binding ollama --embedding-binding ollama
``` ```
### Starting API Server with Gunicorn (Production)
For production deployments, it's recommended to use Gunicorn as the WSGI server to handle concurrent requests efficiently. LightRAG provides a dedicated Gunicorn startup script that handles shared data initialization, process management, and other critical functionalities.
```bash
# Start with run_with_gunicorn.py
python run_with_gunicorn.py --workers 4
```
The `--workers` parameter is crucial for performance:
- Determines how many worker processes Gunicorn will spawn to handle requests
- Each worker can handle concurrent requests using asyncio
- Recommended value is (2 x number_of_cores) + 1
- For example, on a 4-core machine, use 9 workers: (2 x 4) + 1 = 9
- Consider your server's memory when setting this value, as each worker consumes memory
Other important startup parameters:
- `--host`: Server listening address (default: 0.0.0.0)
- `--port`: Server listening port (default: 9621)
- `--timeout`: Request handling timeout (default: 150 seconds)
- `--log-level`: Logging level (default: INFO)
- `--ssl`: Enable HTTPS
- `--ssl-certfile`: Path to SSL certificate file
- `--ssl-keyfile`: Path to SSL private key file
The command line parameters and enviroment variable run_with_gunicorn.py is exactly the same as `light-server`.
### For Azure OpenAI Backend ### For Azure OpenAI Backend
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)): 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 ```bash
# Change the resource group name, location and OpenAI resource name as needed # Change the resource group name, location and OpenAI resource name as needed