Building a Medical-Grade AI Assistant: An Intelligent Health Monitoring System Based on RAG and Multi-Tool Agent

This article introduces the open-source project Healthcare-Monitoring-AI-Agent, which combines Retrieval-Augmented Generation (RAG) and multi-tool Agent workflows. It aims to provide intelligent medical consultation services while reducing the "hallucination" risk of large language models. The core idea is to ensure the accuracy and verifiability of information through a "retrieval-first" architecture, offering practical references for medical AI application development.

The medical information query scenario has extremely high accuracy requirements for AI systems. Traditional generative models lack traceability in outputs, and errors in key information can lead to serious consequences. This project adopts the "retrieval-first" principle: instead of relying on the model's parameterized knowledge, it retrieves evidence from structured medical datasets before generating answers, prioritizing information reliability even if it means sacrificing some fluency.

The system uses a front-end and back-end separation architecture: React for the front end and FastAPI for the back end. The core process is: User query → FastAPI → Medical Agent Controller → Hybrid Retrieval → Cross-Encoder Re-ranking → Security Check → Answer. Hybrid retrieval integrates semantic retrieval (FAISS vector database) and lexical retrieval (BM25); the former captures conceptual relevance, while the latter accurately matches medical terms. The all-MiniLM-L6-v2 model generates 384-dimensional vectors, ensuring result quality through "recall + fine ranking".

The Agent integrates multiple professional tools: drug interaction checker, medication reminder, health risk predictor, and real-time alert system. When a query is identified as a tool intent, it is routed to the corresponding module for execution. The knowledge base integrates datasets such as drugs, diseases, nutrition, and medical guidelines, with 23,000 to 25,000 document chunks. Each piece of knowledge is stored in structured JSON (including metadata like type, name, chapter, etc.), facilitating filtering and retrieval optimization.

The system has built-in security checks: if retrieval results cannot support a reliable answer, it clearly informs the user of no relevant information. The project states it is for educational reference only and cannot replace professional medical advice. Tech stack: Backend uses Python + FastAPI + FAISS/BM25; ML uses Sentence Transformers and Cross-Encoder; Frontend uses React + Vite + TS. Deployment supports Render (one-click deployment) and Dockerization solutions.

Practical insights: In vertical domains, RAG outperforms pure generative models; hybrid retrieval improves recall quality, and re-ranking optimizes relevance; multi-tool Agent enhances system flexibility. Future plans: Add functions like voice interaction, domain confidence optimization, role-adaptive response formatting, etc., to further meet the needs of actual medical scenarios.

Healthcare-Monitoring-AI-Agent demonstrates a responsible AI application approach in the high-risk medical field, balancing accuracy and practicality. For medical AI developers, this project provides a solid technical baseline and is a worthwhile starting point for reference.