NinerConnect

A production-ready monorepo with React frontend, Node.js backend, and Python ML service.

Architecture

• client: React + Vite + TypeScript + Tailwind CSS + React Router + Zustand + Socket.io + Axios
• server: Node.js + Express + TypeScript + Mongoose + Socket.io + Zod + Winston
• ml-service: Python FastAPI + sentence-transformers + scikit-learn + uvicorn + pydantic

Prerequisites

• Node.js 18+ and pnpm
• Python 3.9+
• Docker and Docker Compose

Quick Start

1. Clone and install dependencies:

   git clone <repository-url>
   cd ninerconnect
   pnpm install

2. Set up environment variables:

   cp client/.env.example client/.env
   cp server/.env.example server/.env
   cp ml-service/.env.example ml-service/.env

3. Start development environment:

   pnpm dev

4. Or start with Docker:

   docker-compose up -d

Development

Available Scripts

• pnpm dev - Start all services in development mode
• pnpm build - Build all packages
• pnpm test - Run tests for all packages
• pnpm lint - Lint all packages
• pnpm typecheck - Type check all packages

Individual Service Scripts

Client:

• pnpm --filter client dev - Start React dev server
• pnpm --filter client build - Build for production
• pnpm --filter client preview - Preview production build

Server:

• pnpm --filter server dev - Start Express server in dev mode
• pnpm --filter server build - Build TypeScript
• pnpm --filter server start - Start production server

ML Service:

• pnpm --filter ml-service dev - Start FastAPI in dev mode
• pnpm --filter ml-service start - Start production FastAPI

Environment Variables

Client (.env)

VITE_API_URL=http://localhost:3001
VITE_SOCKET_URL=http://localhost:3001

Server (.env)

PORT=3001
NODE_ENV=development
MONGODB_URI=mongodb://localhost:27017/ninerconnect
JWT_SECRET=your-jwt-secret-here
CORS_ORIGIN=http://localhost:5173

ML Service (.env)

ML_SERVICE_PORT=8000
MODEL_PATH=./models
LOG_LEVEL=info

API Endpoints

Server (Express)

• GET /api/health - Health check
• POST /api/auth/login - User authentication
• GET /api/users - Get users

ML Service (FastAPI)

• GET /health - Health check
• POST /embed - Text embedding
• POST /predict - ML prediction

Socket.io Events

• connection - Client connection
• message - Chat messages
• notification - Real-time notifications

Docker

The project includes Docker Compose configuration for local development:

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

CI/CD

GitHub Actions workflows are configured for:

• Type checking
• Linting
• Testing
• Build verification

Project Structure

ninerconnect/
├── client/                 # React frontend
│   ├── src/
│   │   ├── components/     # Reusable components
│   │   ├── pages/         # Route components
│   │   ├── store/         # Zustand stores
│   │   ├── utils/         # Utility functions
│   │   └── types/         # TypeScript types
│   └── package.json
├── server/                # Node.js backend
│   ├── src/
│   │   ├── controllers/   # Route controllers
│   │   ├── models/        # Mongoose models
│   │   ├── routes/        # Express routes
│   │   ├── services/      # Business logic
│   │   ├── middleware/    # Custom middleware
│   │   └── utils/         # Utility functions
│   └── package.json
├── ml-service/            # Python ML service
│   ├── app/
│   │   ├── api/          # FastAPI routes
│   │   ├── models/       # ML models
│   │   ├── services/     # ML services
│   │   └── utils/        # Utility functions
│   └── requirements.txt
├── docker-compose.yml     # Docker services
├── package.json          # Root package.json
└── README.md

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests and linting
5. Submit a pull request

License

MIT License - see LICENSE file for details.