# 1. Set OpenAI API key
export OPENAI_API_KEY='your-api-key-here'

# 2. Start all services
docker compose up -d

# 3. Access the application
# Frontend: http://localhost
# API: http://localhost/backend/api
# API Docs: http://localhost/backend/api/docs

# Required
OPENAI_API_KEY=your_openai_api_key

# Optional (with defaults)
DB_NAME=content_management
DB_USER=postgres
DB_PASSWORD=postgres
DB_HOST=db
DB_PORT=5432
REDIS_URL=redis://redis:6379/0
QDRANT_URL=http://qdrant:6333
DEBUG=True
SECRET_KEY=your-secret-key

# Create a user
curl -X POST http://localhost/backend/api/users \
  -H "Content-Type: application/json" \
  -d '{
    "username": "student1",
    "email": "[email protected]"
  }'
# Response: {"id": 1, "username": "student1", ...}

# Submit content for analysis
curl -X POST http://localhost/backend/api/content \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Introduction to Machine Learning",
    "content": "Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed...",
    "user_id": 1
  }'
# Response: {"id": 1, "name": "Introduction to Machine Learning", "processed": false, ...}

# Check analysis status
curl http://localhost/backend/api/content/1/status
# Response: {"status": "processing"} or {"status": "completed"}

# Get analyzed content
curl http://localhost/backend/api/content/1
# Response includes key_concepts, difficulty_level, estimated_study_time

# Create a conversation session
curl -X POST http://localhost/backend/api/users/1/sessions \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Learning about ML"
  }'
# Response: {"id": "550e8400-e29b-41d4-a716-446655440000", "title": "Learning about ML", ...}

# Send a message
curl -X POST http://localhost/backend/api/sessions/550e8400-e29b-41d4-a716-446655440000/messages \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Can you explain the key concepts from my machine learning content?"
  }'
# Response: {"role": "assistant", "content": "Based on your content...", "relevant_contents": [...]}

# Get conversation history
curl http://localhost/backend/api/sessions/550e8400-e29b-41d4-a716-446655440000/messages
# Response: Array of messages with context

# Create test user
username: testuser123
password: password123
email: [email protected]

# Create test content
name: "Test Content"
content: "Any educational material you want to analyze..."

# Backend development
cd backend
uv sync                      # Install dependencies
python manage.py migrate     # Run migrations
python manage.py runserver   # Start API server
python run_worker.py         # Start worker (separate terminal)

# Frontend development  
cd frontend
npm install                  # Install dependencies
npm start                    # Start dev server

# Start required services
docker compose up -d redis db qdrant

# Rebuild and restart all services
docker compose down && docker compose up --build -d

# View logs
docker compose logs -f
docker compose logs -f backend worker

# Access container shell
docker compose exec backend bash
docker compose exec frontend sh

# Scale workers
docker compose up -d --scale worker=3

# Clean up
docker compose down -v        # Remove volumes
docker system prune -a       # Clean unused resources

# Test complete Docker environment
./test-docker-setup.sh       # Validates all services and connections

# The script checks:
# - Docker and Docker Compose installation
# - Service health and connectivity  
# - API endpoints and responses
# - Container logs and status

# Run all tests with coverage
cd backend
python run_tests.py all

# Test options
python run_tests.py unit      # Unit tests only
python run_tests.py integration # Integration tests only
python run_tests.py fast      # Without coverage
python run_tests.py watch     # Watch mode
python run_tests.py coverage  # Generate HTML report

# Run in Docker
docker compose run --rm backend uv run pytest

# Frontend unit tests
cd frontend
npm test                     # Run React unit tests

# Cypress E2E tests
npm run cy:open             # Open Cypress UI for interactive testing
npm run cy:run              # Run all Cypress tests headlessly

# Individual test suites
npm run cy:app-navigation   # Test app navigation
npm run cy:content-management # Test content management
npm run cy:chat-functionality # Test chat features
npm run cy:full-workflow    # Test complete user workflow

# Full-stack E2E testing
npm run e2e                 # Start frontend and run E2E tests
npm run e2e:open           # Start frontend and open Cypress UI

# Quick rebuild
rebuild: docker compose down && docker compose up --build -d