GBC-MadAI: Intelligent Medical Assistant System 69

GBC-Madai is an intelligent medical assistant system built on FastAPI and Vue 3, integrating multiple AI models and intelligent search technologies to provide users with efficient medical information Q&A and analysis services. It supports various AI models such as DeepSeek and Ollama, offering intelligent dialogue, complex reasoning, and image understanding capabilities. It can also retrieve the latest medical information through search engines like BochAI and Baidu AI. The system supports multiple conversations, user registration and login, real-time communication, and features an anime-style UI that works smoothly on both desktop and mobile devices. The underlying AI capabilities are powered by LangGraph and GraphRag frameworks.

GBC-MadAI Features

• AI Capabilities: Supports multiple AI models, remembers conversation context, and handles complex reasoning and image analysis.

• Intelligent Search: Integrates multiple search engines to retrieve the latest medical information in real time, automatically selecting the optimal search strategy.

• Dialogue System: Supports multiple conversations with history saved, user registration, login, and settings, with real-time dialogue via WebSocket.

• UI Design: Anime-style interface supporting desktop and mobile, with smooth interactions.

GBC-MadAI Technical Architecture

• Backend: FastAPI framework, MySQL, Redis, and Neo4j databases, integration with multiple AI APIs, Redis caching, and structured logging.

• Frontend: Vue 3 and TypeScript framework, Element Plus UI library, Pinia state management, Vite build tool, CSS3 animations.

GBC-MadAI Installation and Usage

• Environment Requirements: Python 3.8+, Node.js 16+ (development mode), MySQL 8.0+, Redis 6.0+, optional Neo4j 4.0+.

• Installation Steps:

  Clone the project: git clone https://github.com/yourusername/gbc-madai.git

  Enter the backend directory: cd llm_backend

  Install dependencies: pip install -r requirements.txt

  Configure environment variables: copy .env.example to .env and edit it.

  Initialize the database: create a MySQL database named assist_gen.

  Start the service: python run.py.

• Development Mode: Enter the frontend directory cd ../gbc_madai_web, and run npm install && npm run dev to start the frontend development server.

✅Project URL: https://github.com/zhanlangerba/gbc-madai