CodeMentor AI - Intelligent Programming Learning Platform

A revolutionary full-stack application that combines artificial intelligence with proven educational methods to create the most effective programming learning platform. Built with cutting-edge technologies and designed for scalability.

🚀 Features

🤖 AI-Powered Learning

• Adaptive AI Tutors: Personalized guidance that adapts to your learning style
• Dynamic Challenge Generation: AI creates unique coding challenges based on your progress
• Intelligent Code Analysis: Real-time feedback on code quality, performance, and best practices
• Smart Learning Path Recommendations: ML-driven curriculum that evolves with you

💻 Interactive Development Environment

• Advanced Code Editor: Monaco Editor with IntelliSense and syntax highlighting
• Multi-Language Support: Python, JavaScript, TypeScript, Java, C++, Go, Rust, and more
• Real-time Code Execution: Secure sandbox environment for testing code
• Collaborative Coding: Pair programming with real-time synchronization

🎮 Gamified Learning Experience

• Achievement System: Unlock badges and trophies as you progress
• XP and Leveling: Gain experience points and level up your skills
• Leaderboards: Compete with peers and track your ranking
• Streak Tracking: Maintain daily coding streaks for bonus rewards

🏗 Enterprise-Grade Architecture

• Microservices: Scalable backend with Node.js and Python services
• Real-time Communication: WebSocket support for live collaboration
• Advanced Caching: Redis for optimal performance
• Monitoring & Analytics: Comprehensive tracking with Prometheus and Grafana

🛠 Tech Stack

Frontend

• Framework: Next.js 14+ (App Router)
• Language: TypeScript
• Styling: Tailwind CSS
• UI Components: Custom components with Framer Motion
• State Management: Zustand
• Code Editor: Monaco Editor

Backend

• Runtime: Node.js with Express
• Database: MongoDB with Mongoose
• Authentication: JWT with bcrypt
• Real-time: Socket.io
• API Documentation: Swagger/OpenAPI

AI Engine

• Framework: Python Flask
• AI/ML: Custom ML Models (TinyLlama, CodeT5), TensorFlow, scikit-learn
• Local Models: No external API dependencies
• NLP: Custom language processing for code analysis
• Recommendation Engine: Collaborative filtering algorithms

DevOps & Infrastructure

• Containerization: Docker & Docker Compose
• Reverse Proxy: Nginx
• Monitoring: Prometheus + Grafana
• Code Execution: Judge0 sandbox
• Caching: Redis
• CI/CD: GitHub Actions (configured)

🚀 Quick Start

Prerequisites

• Node.js 18+
• Python 3.9+
• Docker & Docker Compose
• MongoDB (or use Docker)
• Redis (or use Docker)

1. Clone and Setup

git clone https://github.com/NickScherbakov/codementor-ai-platform.git
cd codementor-ai-platform

# Install frontend dependencies
npm install

# Install backend dependencies
cd backend && npm install && cd ..

# Install AI engine dependencies
cd ai-engine && pip install -r requirements.txt && cd ..

# Download ML models (first time only)
cd ai-engine && python init_models.py && cd ..

2. Environment Configuration

⚠️ Important: You need to configure environment variables before running the application.

# Copy the example environment file
cp .env.example .env

Required Environment Variables:

• MONGODB_URI: MongoDB connection string (or use Docker)
• JWT_SECRET: Generate a secure random string
• REDIS_URL: Redis connection URL (or use Docker)

Optional:

• OPENAI_API_KEY: No longer required! Using custom ML models

Example .env setup:

# No OpenAI API key needed anymore!

# Database (use Docker or local MongoDB)
MONGODB_URI=mongodb://localhost:27017/codementor-ai

# Security
JWT_SECRET=your-super-secure-jwt-secret-here

# Cache (use Docker or local Redis)
REDIS_URL=redis://localhost:6379

# Development URLs
NEXT_PUBLIC_API_URL=http://localhost:3001/api
NEXT_PUBLIC_AI_API_URL=http://localhost:5000

💡 Tip: See .env.example for all available configuration options.

3. Start with Docker (Recommended)

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

4. Manual Development Setup

# Terminal 1 - Frontend
npm run dev

# Terminal 2 - Backend
npm run backend

# Terminal 3 - AI Engine
npm run ai-engine

# Terminal 4 - Database (if not using Docker)
mongod
redis-server

5. Access the Application

• Frontend: http://localhost:3000
• Backend API: http://localhost:3001
• AI Engine: http://localhost:5000
• Monitoring: http://localhost:3002 (Grafana)

📚 Documentation

📖 Complete Documentation Hub

• 📋 Documentation Index - Complete documentation overview and navigation
• 🚀 Quick Start Tutorial - Step-by-step getting started guide
• 💡 Examples & Demos - Interactive examples and real-world use cases
• 🤝 Contributing Guide - How to contribute to the project

🏗️ Architecture & Design

• 🏛️ System Architecture - High-level system overview and design principles
• 🔄 Service Interactions - Microservices communication patterns
• 📊 Data Flow Diagrams - Request/response flows and data processing
• 🗄️ Database Schema - Data models and relationships

🔌 API Reference

• 📡 Complete API Guide - Comprehensive API documentation with examples
• ⚡ OpenAPI Specification - Machine-readable API specification
• 🧪 API Examples - Code examples for all endpoints
• 🔗 Interactive API Explorer - Try APIs live in your browser

🚀 Deployment & Operations

• 🐳 Deployment Guide - Production deployment instructions
• ☸️ Kubernetes Setup - Enterprise Kubernetes deployment
• 🔧 Troubleshooting - Common issues and solutions
• 📊 Performance Metrics - Benchmarks and optimization guides

🚀 Quick API Reference

POST /api/auth/register     // User registration
POST /api/auth/login        // User login
POST /api/auth/refresh      // Refresh JWT token
POST /api/auth/logout       // User logout

GET  /api/challenges        // Get coding challenges
POST /api/challenges/generate // Generate adaptive challenge
POST /api/submissions       // Submit solution
GET  /api/progress         // Get learning progress
GET  /api/achievements     // Get user achievements

POST /ai-tutor/chat        // Chat with AI tutor
POST /code/analyze         // Analyze code quality
POST /learning-path/recommend // Get learning recommendations

📋 View Interactive API Documentation →

🏗 Project Structure

codementor-ai/
├── src/                    # Frontend source code
│   ├── app/               # Next.js app directory
│   ├── components/        # React components
│   ├── hooks/            # Custom React hooks
│   ├── lib/              # Utility libraries
│   ├── store/            # State management
│   └── types/            # TypeScript definitions
├── backend/               # Node.js backend
│   ├── controllers/      # Route controllers
│   ├── middleware/       # Express middleware
│   ├── models/           # MongoDB models
│   ├── routes/           # API routes
│   └── utils/            # Utility functions
├── ai-engine/            # Python AI service
│   ├── models/           # ML models
│   ├── services/         # AI services
│   └── utils/            # Helper functions
├── docs/                 # 📚 Comprehensive Documentation
│   ├── api/              # API documentation & Swagger UI
│   ├── architecture/     # System architecture diagrams
│   ├── deployment/       # Production deployment guides
│   ├── performance/      # Benchmarks & optimization
│   ├── EXAMPLES.md       # Code examples & demos
│   └── TUTORIAL.md       # Getting started guide
├── public/               # Static assets
├── tests/                # Test suites
└── docker-compose.yml    # Container orchestration

🔍 Explore Architecture Diagrams →

🧪 Testing

# Frontend tests
npm test
npm run test:e2e

# Backend tests
cd backend && npm test

# AI engine tests
cd ai-engine && pytest

📈 Performance & Monitoring

The platform includes enterprise-grade monitoring and performance optimization:

• 📊 Performance Benchmarks: Detailed performance metrics and load testing results
• 🎯 Response Time Targets: < 200ms API responses, < 2s AI responses
• 📈 Throughput: 1000+ RPS frontend, 500+ RPS backend, 50+ RPS AI engine
• 🔍 Real-time Monitoring: Prometheus + Grafana dashboards
• ⚡ Auto-scaling: Kubernetes HPA for dynamic scaling

Performance Highlights:

• API P95 Response Time: 156ms
• AI Tutor P95 Response Time: 3.2s
• Database Query P95: 78ms
• 99.94% Uptime SLA

Access Grafana at http://localhost:3002 (admin/admin123)

📋 View Complete Performance Documentation →

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📚 Documentation Contributions

• API Documentation: Update OpenAPI specs and examples
• Architecture Diagrams: Improve system visualization with Mermaid
• Performance Benchmarks: Add new test scenarios and metrics
• Deployment Guides: Enhance production setup instructions

Development Workflow

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

Code Standards

• Frontend: ESLint + Prettier
• Backend: ESLint + Prettier
• Python: Black + Flake8
• Commits: Conventional Commits
• Documentation: Markdown with Mermaid diagrams

📋 View Complete Contributing Guide →

📝 License

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

🔒 Security

Environment Variables

• Never commit .env files to version control
• Use strong secrets for JWT_SECRET and other sensitive values
• Rotate API keys regularly in production
• Use environment-specific configurations for different deployments

Development Security

• All API keys are loaded from environment variables
• No hardcoded secrets in the codebase
• Rate limiting enabled on API endpoints
• Input validation on all user inputs

Reporting Security Issues

If you discover a security vulnerability, please email [email protected] instead of using the public issue tracker.

🆘 Support

• Documentation: docs.codementor-ai.com
• Community Forum: community.codementor-ai.com
• Issues: GitHub Issues
• Email: [email protected]

🌟 Roadmap

Phase 1 (Current)

• Core learning platform
• AI-powered tutoring
• Code execution sandbox
• Basic gamification

Phase 2 (Next)

• Mobile applications (React Native)
• Advanced ML recommendations
• Corporate training modules
• Integration with popular IDEs

Phase 3 (Future)

• VR/AR coding environments
• Blockchain-based certificates
• Advanced collaboration tools
• Multi-tenant enterprise features

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Built with ❤️ by the CodeMentor AI Team

Empowering the next generation of developers through intelligent, adaptive learning.