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Getting Started

Welcome to the MLContext documentation! This guide will help you understand our project structure and get you up and running quickly.

🎯 Project Overview​

MLContext is a comprehensive movie recommendation platform consisting of three main components:

  1. DAGGH Frontend: Next.js-based collaborative movie discovery interface
  2. ML Service: Python FastAPI service with advanced recommendation algorithms
  3. Documentation Platform: This Docusaurus-powered documentation hub

🚀 Quick Setup​

Prerequisites​

  • Node.js 18+ and pnpm (for frontend development)
  • Python 3.9+ (for ML service)
  • Git (for version control)
  • Supabase account (for database and authentication)

Choose Your Path​

🎬 Frontend Development (DAGGH)​

If you're working on the movie discovery interface:

  1. Navigate to the DAGGH Overview
  2. Follow the Quick Start Guide
  3. Review the Tech Stack

🤖 ML Service Development​

If you're working on the recommendation engine:

  1. Check the ML Service Overview
  2. Follow the Setup Guide
  3. Understand the Architecture

📚 Documentation Contribution​

If you're contributing to documentation:

  1. Review the Documentation Platform Overview
  2. Follow the Setup Guide
  3. Check the Best Practices

🗂️ Documentation Structure​

Our documentation is organized by application:

  • Shared: Cross-project documentation and guides
  • DAGGH: Frontend application documentation
  • ML Service: Machine learning service documentation
  • API: API reference for all services
  • Documentation Platform: Documentation site management

Each app section contains:

  • LLM Context: AI-specific context for development
  • General Context: Overview and setup information
  • Feature/Implementation Docs: Detailed technical documentation
  • Versioning: Release history and changelog

🔗 Key Links​

🆘 Need Help?​

Next Steps: Choose your development path above and dive into the specific documentation for your area of interest!