GEN_AI_Course

A comprehensive Python project demonstrating Domain-Driven Design (DDD), MLOps best practices, and modern development workflows.

🚀 Quick Start

This project uses GitHub Codespaces with pre-configured conda environment:

1. Open in Codespace: Click the "Code" button → "Create codespace on main"
2. Environment: Automatically configured with genai-course conda environment
3. Development: Start coding immediately - all dependencies are installed

Manual Setup (if not using Codespaces)

# Install Miniconda/Anaconda
# Create environment
conda env create -f settings/environment.yml
# Activate environment
conda activate genai-course

🏗️ Project Structure

• src/: Source code organized by DDD layers
  • domain/: Business logic and entities
  • application/: Use cases and workflows
  • infrastructure/: External integrations and adapters
• tests/: Hierarchical test structure
  • unit/: Unit tests by DDD layer
  • integration/: End-to-end workflow tests
• settings/: Environment configuration
• skills/: Development best practices documentation
• scripts/: Automation utilities

Running Tests

• Run all tests: pytest tests/
• Run unit tests only: pytest tests/unit/
• Run integration tests only: pytest tests/integration/
• Run tests for a specific layer: pytest tests/unit/domain/

Environment Setup

This project requires a Conda environment for proper dependency management:

1. Install Miniconda or Anaconda
2. Create environment: conda env create -f settings/environment.yml
3. Activate environment: conda activate genai-course
4. Run code/tests with environment activated
5. Deactivate when done: conda deactivate

All dependencies are version-pinned for reproducibility.

Environment Status: ✅ Created and tested - all tests pass with Python 3.12.1

Documentation Summary

• Generated: 2026-04-09 10:08:16 UTC
• Commit message: Update documentation for linting improvements
• Updated files:
• No tracked changes detected.

This section is generated automatically by scripts/generate_documentation_commit_push.py.