Jevon-Zhong/Ai-doctor
📦 Open Source ProjectJevon-Zhong
An intelligent medical Q&A assistant leveraging RAG, MCP, and Qwen3 for professional healthcare insights.
Ai-doctor is a comprehensive full-stack medical AI assistant designed to bridge the gap between complex medical knowledge and user queries. The system architecture is built on a modern stack: Vue3 and TypeScript for the frontend, and a scalable Nest.js backend. It leverages Milvus for high-performance vector search, allowing the RAG pipeline to retrieve relevant medical documentation efficiently.
Key innovations include its implementation of the Model Context Protocol (MCP), which allows the LLM to dynamically interact with external tools, such as web scrapers, to fetch and synthesize real-time medical data. By utilizing the Qwen3 model, the system ensures high-quality natural language generation tailored to medical contexts. This project serves as a practical implementation of RAG and MCP in a sensitive, high-stakes domain, providing a template for developers looking to build domain-specific AI assistants that require both internal knowledge retrieval and external tool-use capabilities.
💡Highlights
- ├─RAG-powered medical Q&A
- ├─MCP-enabled web scraping
- └─Milvus vector database integration
🎯For
- ├─Healthcare developers
- └─AI engineers