A production-grade modular learning platform with AI-enhanced features for personalized learning experiences, incorporating adaptive assessments, content generation, analytics, and more.

• Frontend: Next.js (TypeScript), Tailwind CSS, React Query
• Backend: Python (FastAPI, Langchain, Langgraph)
• Database: Supabase (PostgreSQL)
• AI: Langchain, Langgraph, Google's free LLM model
• Caching: Redis
• Monitoring: Prometheus
• Deployment: GitHub Actions, Docker, Nginx, Vercel

• Personalized Learning: AI-generated learning paths tailored to individual learning styles
• Adaptive Assessments: Dynamically adjust difficulty based on user performance
• Content Generation: AI-assisted content creation for instructors
• Analytics: Comprehensive analytics for tracking user progress and engagement
• Notifications: Real-time notifications for course updates and achievements
• Search: Advanced search functionality for courses and content
• User Preferences: Customizable learning styles, UI preferences, and notification settings
• Monitoring: Performance metrics and error tracking

.
├── frontend/                # Next.js frontend application
│   ├── app/                 # Next.js app router
│   ├── components/          # Reusable UI components
│   ├── lib/                 # Utility functions
│   ├── styles/              # Global styles
│   └── public/              # Static assets
│
├── backend/                 # FastAPI backend application
│   ├── app/                 # Application code
│   │   ├── api/             # API endpoints
│   │   ├── core/            # Core functionality
│   │   ├── models/          # Database models
│   │   ├── schemas/         # Pydantic schemas
│   │   └── services/        # Business logic
│   ├── tests/               # Test suite
│   └── main.py              # Application entry point
│
├── docs/                    # Documentation
│   ├── database/            # Database schema
│   └── assets/              # Documentation assets
│
├── .github/                 # GitHub configuration
│   └── workflows/           # GitHub Actions workflows
│
├── docker-compose.yml       # Docker Compose configuration
├── .env.example             # Example environment variables
└── README.md                # Project documentation

• Docker and Docker Compose
• Node.js 18+
• Python 3.11+
• Supabase account

1. Clone the repository:

   git clone https://github.com/yourusername/ai-learning-platform.git
   cd ai-learning-platform

2. Create a .env file based on .env.example:

   cp .env.example .env

3. Update the .env file with your Supabase credentials and other configuration.

4. Start the development environment:

   docker-compose up -d

5. Access the applications:

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000/api/v1/docs

cd frontend
npm install
npm run dev

cd backend
pip install -r requirements.txt
uvicorn main:app --reload

cd frontend
npm test

cd backend
pytest

The application is configured for deployment using GitHub Actions:

• Frontend: Vercel
• Backend: Docker container (deploy to your preferred cloud provider)
• Database: Supabase

1. Create a feature branch: git checkout -b feat/your-feature-name
2. Commit your changes: git commit -m "feat: add some feature"
3. Push to the branch: git push origin feat/your-feature-name
4. Submit a pull request

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