Automated codebase analysis and documentation platform powered by AI

CodeExp AI is a comprehensive platform that automatically analyzes codebases, generates documentation, creates dependency diagrams, and provides intelligent Q&A capabilities for software repositories.

• Automatic Code Analysis: Parse and understand codebases using tree-sitter
• AI-Powered Summaries: Generate function/class summaries and documentation
• Intelligent Q&A: Ask questions about your codebase and get grounded answers
• Visual Diagrams: Create dependency graphs and call diagrams
• Multi-Language Support: Python, JavaScript/TypeScript, Go, C++, and more
• Real-time Processing: Track analysis progress with live updates
• Export Capabilities: Generate documentation in multiple formats

• Frontend: React 18 + TypeScript + Tailwind CSS + shadcn/ui
• Backend API: Go (Gin framework) with JWT authentication
• AI Service: Python (FastAPI) + Hugging Face Transformers
• Database: PostgreSQL with pgvector extension
• Cache & Jobs: Redis for job queues and caching
• Infrastructure: Docker + Docker Compose

┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│   Frontend  │───▶│   Go API    │───▶│ Python AI   │
│   (React)   │    │ (Gateway)   │    │ (Analysis)  │
└─────────────┘    └─────────────┘    └─────────────┘
                          │                    │
                          ▼                    ▼
                   ┌─────────────┐    ┌─────────────┐
                   │ PostgreSQL  │    │    Redis    │
                   │ + pgvector  │    │ (Jobs/Cache)│
                   └─────────────┘    └─────────────┘

• Docker & Docker Compose
• Node.js 18+ with pnpm
• Git

1. Clone the repository

   git clone https://github.com/Tukesh1/codexp-ai.git
   cd codexp-ai

2. Run setup script

   make setup

3. Start development environment

   make dev

4. Access the application

   • Frontend: http://localhost:3000
   • API: http://localhost:8080
   • AI Service: http://localhost:8000

If you prefer manual setup:

# Install dependencies
pnpm install

# Create environment file
cp .env.example .env

# Start services
docker-compose up -d

codexp-ai/
├── apps/                    # Frontend applications
│   ├── web/                # Main web application
│   ├── admin/              # Admin dashboard
│   ├── landing/            # Landing page
│   └── mobile/             # Mobile app (React Native)
├── backend/                # Backend services
│   ├── api/                # Go API service
│   └── ai-service/         # Python AI service
├── packages/               # Shared packages
│   ├── ui/                 # UI component library
│   ├── typescript-config/  # TypeScript configurations
│   └── eslint-config/      # ESLint configurations
└── docker-compose.yml      # Development environment

# Development
make dev          # Start development environment
make logs         # View all service logs
make ps           # Check service status

# Building
make build        # Build all Docker images
make clean        # Clean up containers and volumes

# Testing
make test         # Run all tests
make lint         # Run linters

# Database
make db-shell     # Connect to PostgreSQL
make db-reset     # Reset database

1. Phase 1 (Week 1-2): Core infrastructure and repository parsing
2. Phase 2 (Week 3-4): AI-powered analysis and frontend
3. Phase 3 (Week 5-6): Advanced features and monetization
4. Phase 4 (Week 7-8): Production readiness and launch

Copy .env.example to .env and configure:

# Database
DATABASE_URL=postgresql://codeexp:secure_password@localhost:5432/codeexp

# Authentication (Clerk)
CLERK_SECRET_KEY=your_clerk_secret
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=your_clerk_publishable_key

# GitHub Integration
GITHUB_APP_ID=your_app_id
GITHUB_APP_PRIVATE_KEY=your_private_key

# AI Service
HUGGINGFACE_API_KEY=your_hf_token

# Payments (Stripe)
STRIPE_SECRET_KEY=your_stripe_secret

• postgres: PostgreSQL 16 with pgvector extension
• redis: Redis 7 for job queues and caching
• api: Go API service (port 8080)
• ai-service: Python AI service (port 8000)
• frontend: React application (port 3000)

• POST /auth/webhook - Clerk webhook handler
• GET /auth/me - Get current user

• GET /projects - List user projects
• POST /projects - Create new project
• GET /projects/{id} - Get project details
• DELETE /projects/{id} - Delete project

• POST /projects/{id}/analyze - Start repository analysis
• GET /projects/{id}/status - Get analysis status
• GET /projects/{id}/summary - Get project summary
• POST /projects/{id}/ask - Ask questions about code

• GET /projects/{id}/diagram - Get dependency diagram
• GET /projects/{id}/docs - Get generated documentation

# Run all tests
make test

# Run specific service tests
docker-compose exec api go test ./...
docker-compose exec ai-service python -m pytest

# Run frontend tests
cd apps/web && npm test

# Build production images
docker-compose -f docker-compose.prod.yml build

# Deploy to production
docker-compose -f docker-compose.prod.yml up -d

1. Database: PostgreSQL with pgvector on Railway/Supabase
2. Cache: Redis Cloud/Upstash
3. API: Deploy Go service to Fly.io/Render
4. AI Service: Deploy Python service to Render/Railway
5. Frontend: Deploy to Vercel
6. Storage: S3/Cloudflare R2 for artifacts

• Health Checks: /health endpoints on all services
• Logging: Structured JSON logs
• Metrics: Application performance monitoring
• Alerts: Error tracking with Sentry

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

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

• Documentation
• API Reference
• Development Guide
• Deployment Guide