crossword-app/backend-py/README.md

Python Backend with Vector Similarity Search

This is the Python implementation of the crossword generator backend, featuring true AI word generation via vector similarity search.

🚀 Features

• True Vector Search: Uses sentence-transformers + FAISS for semantic word discovery
• 30K+ Vocabulary: Searches through full model vocabulary instead of limited static lists
• FastAPI: Modern, fast Python web framework
• Same API: Compatible with existing React frontend
• Hybrid Approach: AI vector search with static word fallback

🔄 Differences from JavaScript Backend

Feature	JavaScript Backend	Python Backend
Word Generation	Embedding filtering of static lists	True vector similarity search
Vocabulary Size	~100 words per topic	30K+ words from model
AI Approach	Semantic similarity filtering	Nearest neighbor search
Performance	Fast but limited	Slower startup, better results
Dependencies	Node.js + HuggingFace API	Python + ML libraries

🛠️ Setup & Installation

Prerequisites

• Python 3.11+ (3.11 recommended for Docker compatibility)
• pip (Python package manager)

Basic Setup (Core Functionality)

# Clone and navigate to backend directory
cd crossword-app/backend-py

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install core dependencies
pip install -r requirements.txt

# Start the server
python app.py

Full Development Setup (with AI features)

# Install development dependencies including AI/ML libraries
pip install -r requirements-dev.txt

# This includes:
# - All core dependencies
# - AI/ML libraries (torch, sentence-transformers, etc.)
# - Development tools (pytest, coverage, etc.)

Requirements Files

• requirements.txt: Core dependencies for basic functionality
• requirements-dev.txt: Full development environment with AI features

Note: The AI/ML dependencies are large (~2GB). For basic testing without AI features, use requirements.txt only.

Python Version: Both local development and Docker use Python 3.11+ for optimal performance and latest package compatibility.

📁 Structure

backend-py/
├── app.py                          # FastAPI application entry point
├── requirements.txt                # Core Python dependencies
├── requirements-dev.txt            # Full development dependencies
├── src/
│   ├── services/
│   │   ├── vector_search.py        # Core vector similarity search
│   │   └── crossword_generator.py  # Puzzle generation logic
│   └── routes/
│       └── api.py                  # API endpoints (matches JS backend)
├── test-unit/                      # Unit tests (pytest framework) - 5 files
│   ├── test_crossword_generator.py
│   ├── test_api_routes.py
│   └── test_vector_search.py
├── test-integration/               # Integration tests (standalone scripts) - 16 files
│   ├── test_simple_generation.py
│   ├── test_boundary_fix.py
│   └── test_local.py               # (+ 13 more test files)
├── data/ -> ../backend/data/       # Symlink to shared word data
└── public/                         # Frontend static files (copied during build)

🛠 Dependencies

Core ML Stack

• sentence-transformers: Local model loading and embeddings
• faiss-cpu: Fast vector similarity search
• torch: PyTorch for model inference
• numpy: Vector operations

Web Framework

• fastapi: Modern Python web framework
• uvicorn: ASGI server
• pydantic: Data validation

Testing

• pytest: Testing framework
• pytest-asyncio: Async test support

🧪 Testing

📁 Test Organization (Reorganized for Clarity)

We've reorganized the test structure for better developer experience:

Test Type	Location	Purpose	Framework	Count
Unit Tests	test-unit/	Test individual components in isolation	pytest	5 files
Integration Tests	test-integration/	Test complete workflows end-to-end	Standalone scripts	16 files

Benefits of this structure:

• ✅ Clear separation between unit and integration testing
• ✅ Intuitive naming - developers immediately understand test types
• ✅ Better tooling - can run different test types independently
• ✅ Easier maintenance - organized by testing strategy

Note: Previously tests were mixed in tests/ folder and root-level test_*.py files. The new structure provides much better organization.

Unit Tests Details (test-unit/)

What they test: Individual components with mocking and isolation

• test_crossword_generator.py - Core crossword generation logic
• test_api_routes.py - FastAPI endpoint handlers
• test_crossword_generator_wrapper.py - Service wrapper layer
• test_index_bug_fix.py - Specific bug fix validations
• test_vector_search.py - AI vector search functionality (requires torch)

Run Unit Tests (Formal Test Suite)

# Run all unit tests
python run_tests.py

# Run specific test modules  
python run_tests.py crossword_generator
pytest test-unit/test_crossword_generator.py -v

# Run core tests (excluding AI dependencies)
pytest test-unit/ -v --ignore=test-unit/test_vector_search.py

# Run individual unit test classes
pytest test-unit/test_crossword_generator.py::TestCrosswordGenerator::test_init -v

Integration Tests Details (test-integration/)

What they test: Complete workflows without mocking - real functionality

• test_simple_generation.py - End-to-end crossword generation
• test_boundary_fix.py - Word boundary validation (our major fix!)
• test_local.py - Local environment and dependencies
• test_word_boundaries.py - Comprehensive boundary testing
• test_bounds_comprehensive.py - Advanced bounds checking
• test_final_validation.py - API integration testing
• And 10 more specialized feature tests...

Run Integration Tests (End-to-End Scripts)

# Test core functionality
python test-integration/test_simple_generation.py
python test-integration/test_boundary_fix.py
python test-integration/test_local.py

# Test specific features
python test-integration/test_word_boundaries.py
python test-integration/test_bounds_comprehensive.py

# Test API integration
python test-integration/test_final_validation.py

Test Coverage

# Run core tests with coverage (requires requirements-dev.txt)
pytest test-unit/test_crossword_generator.py --cov=src --cov-report=html
pytest test-unit/test_crossword_generator.py --cov=src --cov-report=term

# Full coverage report (may fail without AI dependencies)
pytest test-unit/ --cov=src --cov-report=html --ignore=test-unit/test_vector_search.py

Test Status

• ✅ Core crossword generation: 15/19 unit tests passing
• ✅ Boundary validation: All integration tests passing
• ⚠️ AI/Vector search: Requires torch dependencies
• ⚠️ Some async mocking: Minor test infrastructure issues

🔄 Migration Guide (For Existing Developers)

If you had previous commands, update them:

Old Command	New Command
pytest tests/	pytest test-unit/
python test_simple_generation.py	python test-integration/test_simple_generation.py
pytest tests/ --cov=src	pytest test-unit/ --cov=src

All functionality is preserved - just organized better!

🔧 Configuration

Environment variables (set in HuggingFace Spaces):

# Core settings
PORT=7860
NODE_ENV=production

# AI Configuration
EMBEDDING_MODEL=sentence-transformers/all-mpnet-base-v2
WORD_SIMILARITY_THRESHOLD=0.65

# Optional
LOG_LEVEL=INFO

🎯 Vector Search Process

1. Initialization:

   • Load sentence-transformers model locally
   • Extract 30K+ vocabulary from model tokenizer
   • Pre-compute embeddings for all vocabulary words
   • Build FAISS index for fast similarity search

2. Word Generation:

   • Get topic embedding: "Animals" → [768-dim vector]
   • Search FAISS index for nearest neighbors
   • Filter by similarity threshold (0.65+)
   • Filter by difficulty (word length)
   • Return top matches with generated clues

3. Fallback:

   • If vector search fails → use static word lists
   • If insufficient AI words → supplement with static words

🧪 Testing

# Local testing (without full vector search)
cd backend-py
python test_local.py

# Start development server
python app.py

🐳 Docker Deployment

The Dockerfile has been updated to use Python backend:

FROM python:3.9-slim
# ... install dependencies
# ... build frontend (same as before)
# ... copy to backend-py/public/
CMD ["python", "app.py"]

🧪 Testing

Quick Test

# Basic functionality test (no model download)
python test_local.py

Comprehensive Unit Tests

# Run all unit tests
python run_tests.py

# Or use pytest directly
pytest tests/ -v

# Run specific test file
python run_tests.py crossword_generator_fixed
pytest tests/test_crossword_generator_fixed.py -v

# Run with coverage
pytest tests/ --cov=src --cov-report=html

Test Structure

• tests/test_crossword_generator_fixed.py - Core grid generation logic
• tests/test_vector_search.py - Vector similarity search
• tests/test_crossword_generator_wrapper.py - Service wrapper
• tests/test_api_routes.py - FastAPI endpoints

Key Test Features

• ✅ Index alignment fix: Tests the list index out of range bug fix
• ✅ Mocked vector search: Tests without downloading models
• ✅ API validation: Tests all endpoints and error cases
• ✅ Async support: Full pytest-asyncio integration
• ✅ Error handling: Tests malformed inputs and edge cases

📊 Performance Comparison

Startup Time:

• JavaScript: ~2 seconds
• Python: ~30-60 seconds (model download + index building)

Word Quality:

• JavaScript: Limited by static word lists
• Python: Access to full model vocabulary with semantic understanding

Memory Usage:

• JavaScript: ~100MB
• Python: ~500MB-1GB (model + embeddings + FAISS index)

API Response Time:

• JavaScript: ~100ms (after cache warm-up)
• Python: ~200-500ms (vector search + filtering)

🔄 Migration Strategy

1. Phase 1 ✅: Basic Python backend structure
2. Phase 2: Test vector search functionality
3. Phase 3: Docker deployment and production testing
4. Phase 4: Compare with JavaScript backend
5. Phase 5: Production switch with rollback capability

🎯 Next Steps

• Test vector search with real model
• Optimize FAISS index performance
• Add more sophisticated crossword grid generation
• Implement LLM-based clue generation
• Add caching for frequently requested topics