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
mcp_server_lingshu.py: FastMCP server wrapping the Lingshu modelmcp_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
- Clone the repository
- Install dependencies:
pip install -r requirements.txt
Usage
Use vLLM to serve the Lingshu Model
vllm serve lingshu-medical-mllm/Lingshu-7B --dtype float16 --api_key api_key --port 8000 --max-model-len 32768
Wrap the server with FastMCP
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
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
Metadata
Release files for iflow-mcp_alibaba-damo-academy-lingshu-mcp 0.1.2
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| File | Interpreter | ABI | Platform | Reset |
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Total release size: 20.1 kB
Release files / iflow_mcp_alibaba_damo_academy_lingshu_mcp-0.1.2.tar.gz
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