MedSimMentor Telegram Bot

A Telegram bot for medical simulation training and mentoring, powered by Google Vertex AI for dynamic scenario generation.

Features

• Dynamic medical case generation using Google Vertex AI
• Real-time feedback on medical decisions
• Progress tracking and performance analytics
• Multiple difficulty levels for different experience levels
• Support for emergency medicine scenarios

Setup

1. Install dependencies:

npm install

2. Create a .env file in the root directory with the following variables:

# Bot Configuration
BOT_TOKEN=your_telegram_bot_token
NODE_ENV=development

# Google Cloud Configuration
GOOGLE_CLOUD_PROJECT=your_project_id
VERTEX_LOCATION=us-central1

# Database Configuration
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_DB=postgres
POSTGRES_USER=postgres
POSTGRES_PASSWORD=your_password

3. Set up Google Cloud Authentication:

   • Install Google Cloud SDK
   • Run gcloud auth application-default login
   • Ensure you have the necessary IAM roles:
     • roles/aiplatform.user
     • roles/viewer

4. Build the TypeScript code:

npm run build

Running the Bot

Development mode:

npm run dev

Production mode:

npm start

Available Commands

• /start - Start the bot and set your experience level
• /help - Show help message
• /progress - Check your progress and performance stats
• /practice - Start a practice session with a new medical case
• /feedback - Get detailed feedback on your last case
• /settings - Adjust your preferences and difficulty level

Development

• npm run build - Compile TypeScript to JavaScript
• npm run watch - Watch for changes and recompile
• npm run dev - Run the bot in development mode with hot-reload
• npm run lint - Run ESLint
• npm test - Run tests

Architecture

The bot uses:

• Telegraf.js for Telegram bot functionality
• Google Vertex AI for medical case generation
• PostgreSQL for data persistence
• TypeScript for type safety
• Node.js runtime

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.