# Install dependencies
pip install -r src/backend/requirements.txt
pip install -r src/ai-service/requirements.txt
cd src/frontend && npm install

# Run tests
pytest tests/

# Start development environment
docker-compose up -d
Architecture

Backend: FastAPI application handling GitHub webhooks
AI Service: Microservice for LLM-based code analysis
Frontend: React dashboard for analytics
Database: PostgreSQL for data persistence
Cache: Redis for caching and queuing

Deployment
AWS Deployment
bash# Deploy infrastructure
cd infrastructure/terraform
terraform init
terraform plan
terraform apply

# Deploy application
./scripts/deploy.sh
Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests
Submit a pull request

License
MIT License - see LICENSE file for details

This repository structure follows all the blueprint requirements:

1. **Planning & Design**: Comprehensive design documents and API contracts
2. **Git Best Practices**: Feature branch workflow with PR templates
3. **AI Model Development**: Structured notebooks and model versioning
4. **Containerization**: Dockerfiles for all components with Docker Compose
5. **CI/CD Pipeline**: GitHub Actions for automated testing and deployment
6. **Infrastructure as Code**: Terraform modules for AWS deployment
7. **Monitoring**: Prometheus/Grafana setup for observability

The structure provides a solid foundation for building a production-ready AI-powered documentation assistant.