iflow-mcp_alibaba-damo-academy-lingshu-mcp

# Lingshu FastMCP Medical AI Service This project implements a FastMCP server for the Lingshu medical AI model and a corresponding client for testing and integration. ## Components 1. `mcp_server_lingshu.py`: FastMCP server wrapping the Lingshu model 2. `mcp_client_lingshu.py`: Test client demonstrating interaction with the Lingshu FastMCP server ## Server Features - Medical image analysis - Structured medical report generation - Medical Q&A ## Prerequisites - FastMCP framework - OpenAI API compatible LLM server (e.g., vLLM) - Required Python packages (install via `pip install -r requirements.txt`) ## Setup 1. Clone the repository 2. Install dependencies: `pip install -r requirements.txt` ## Usage ### Use vLLM to serve the Lingshu Model ```bash vllm serve lingshu-medical-mllm/Lingshu-7B --dtype float16 --api_key api_key --port 8000 --max-model-len 32768 ``` ### Wrap the server with FastMCP ```python export LINGSHU_SERVER_URL="http://localhost:8000/v1" export LINGSHU_SERVER_API="api_key" export LINGSHU_MODEL="lingshu-medical-mllm/Lingshu-7B" # the above config depends on your vllm server config python mcp_server_lingshu.py --host 127.0.0.1 --port 4200 --path /lingshu --log-level info ``` ### Try connecting Lingshu with MCP ```python export LLM_SERVER_URL="xxx" export LLM_SERVER_API="xxx" export LLM_MODEL="xxx" ## this is your own model python mcp_client_lingshu.py --mcp-url http://127.0.0.1:4200/lingshu # the mcp-url should depend on the mcp server you deployed in the last step ```