🐳 Complete Docker setup implementation for AI Learning Platform

[Copilot]

Summary

This PR implements a complete Docker-based development and deployment solution for the AI Learning Platform. The existing Docker files had several configuration issues that prevented the application from running properly. This implementation provides a fully functional containerized environment with proper service orchestration.

Issues Fixed

Docker Configuration Problems

• Build context mismatch: docker-compose.yml was trying to build from root directory but Dockerfile was in ./backend
• Obsolete version field: Docker Compose v2 doesn't require the version field
• Missing dependencies: pydantic-settings package was missing from requirements.txt
• SQLAlchemy naming conflict: Model used reserved metadata field name

SSL/Network Issues

• Added trusted hosts for pip installation to handle certificate issues in sandboxed environments
• Improved Dockerfile with system dependencies (gcc) for proper package compilation

Key Features Implemented

🚀 One-Command Startup

docker compose up

Starts all services with proper dependency ordering and health checks.

🛠️ Management Helper Script

Created docker-helper.sh with intuitive commands:

./docker-helper.sh start    # Start all services
./docker-helper.sh stop     # Stop all services  
./docker-helper.sh status   # Check service health
./docker-helper.sh logs     # View logs

🔧 Service Architecture

• FastAPI Backend (port 8000) - Main application API
• PostgreSQL Database (port 5432) - Data persistence with auto-initialization
• pgAdmin (port 5050) - Database administration interface

📚 Documentation & Configuration

• Comprehensive README.md with setup instructions and troubleshooting
• .env.example template for environment configuration
• Clear service access points and credentials

Technical Changes

Database Model Fix

# Before: Reserved 'metadata' field caused SQLAlchemy errors
metadata = Column(JSON)

# After: Renamed to avoid conflict
lesson_metadata = Column(JSON)

Docker Compose Improvements

# Fixed build context and volume mapping
api:
  build: ./backend          # Was: build: .
  volumes:
    - ./backend:/app        # Was: ./backend:/backend
  environment:
    PYTHONPATH: /app        # Was: /backend

Enhanced Dockerfile

# Added system dependencies and SSL handling
RUN pip install --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host files.pythonhosted.org -r requirements.txt

Testing Verified

✅ API Endpoints: All REST endpoints working correctly
✅ Database: PostgreSQL connection and auto-initialization
✅ Admin Interface: pgAdmin accessible and functional
✅ Service Health: All containers start and communicate properly
✅ Development Workflow: Hot-reload working for code changes

Usage

Quick Start

# Clone and start
git clone <repo>
cd Ai-Learning-Platform
docker compose up

# Access services
# API: http://localhost:8000
# API Docs: http://localhost:8000/docs  
# pgAdmin: http://localhost:5050

Development

The setup includes volume mounting for live code reloading, making it perfect for development workflows.

This implementation transforms the AI Learning Platform into a fully containerized application that can be deployed anywhere Docker runs, with zero configuration required for development and easy customization for production deployments.

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