semantic-comparer 0.1.1

Advanced text alignment and semantic containment analysis tool

Semantic Comparer

Advanced text alignment and semantic containment analysis tool using modern Python practices.

Features

• Semantic Alignment: Uses Smith-Waterman algorithm with sentence transformers for intelligent text comparison
• Modern CLI: Built with Typer and Rich for beautiful, user-friendly interface
• Async Processing: High-performance asynchronous operations
• File Support: Direct text input or file-based processing
• Rich Output: Colorized, formatted results with detailed statistics
• Type Safety: Full type annotations and modern Python practices

Installation

# Install dependencies
uv add rich typer aiofiles

# Install the package
uv pip install -e .

# Or run directly as a module
python -m semantic_comparer compare "text1" "text2"

Usage

Basic Comparison

# Compare two texts directly
python -m semantic_comparer compare "This is the first text." "This is the second text."

# Compare with custom parameters
python -m semantic_comparer compare \
  "First text content" \
  "Second text content" \
  --model all-MiniLM-L6-v2 \
  --gap-penalty 0.3 \
  --similarity-threshold 0.5

File-based Comparison

# Compare text files (prefix with @)
python -m semantic_comparer compare @file1.txt @file2.txt

# Mix direct text and file
python -m semantic_comparer compare "Direct text" @file.txt

Output Options

# Quiet mode (summary only)
python -m semantic_comparer compare text1 text2 --quiet

# Save detailed results to JSON
python -m semantic_comparer compare text1 text2 --output results.json

Command Line Options

Option	Short	Description	Default
--model	-m	Sentence transformer model	all-MiniLM-L6-v2
--gap-penalty	-g	Penalty for gaps (0.0-1.0)	0.3
--similarity-threshold	-t	Minimum similarity for matches (0.0-1.0)	0.5
--output	-o	Output file for JSON results	None
--quiet	-q	Suppress detailed output	False

Understanding the Results

Alignment Types

• ✓ Match: Paragraphs that are semantically similar
• ⚠ Only in A/B: Paragraphs present in one text but not the other
• ✗ Unaligned: Paragraphs that couldn't be matched

Containment Score

The semantic containment score measures how much of text A's semantic content is found in text B:

• 0.0-0.4: Low similarity (Red)
• 0.4-0.7: Moderate similarity (Yellow)
• 0.7-1.0: High similarity (Green)

Advanced Usage

Custom Models

# Use a different sentence transformer model
python -m semantic_comparer compare text1 text2 --model all-mpnet-base-v2

Fine-tuning Parameters

# Stricter matching (higher threshold)
python -m semantic_comparer compare text1 text2 --similarity-threshold 0.8

# More lenient gap handling (lower penalty)
python -m semantic_comparer compare text1 text2 --gap-penalty 0.1

Development

Project Structure

semantic_comparer/
├── __init__.py          # Package initialization
├── core.py              # Core alignment logic
├── cli.py               # Command-line interface
└── utils.py             # Utility functions

Running Tests

# Install dev dependencies
uv add --dev pytest pytest-asyncio black isort mypy ruff

# Run tests
pytest

# Format code
black .
isort .

# Type checking
mypy .

# Linting
ruff check .

Technical Details

Algorithm

The tool uses the Smith-Waterman algorithm adapted for semantic similarity:

1. Text Segmentation: Split texts into paragraphs
2. Embedding Generation: Convert paragraphs to semantic vectors
3. Similarity Calculation: Compute cosine similarity between vectors
4. Dynamic Programming: Apply Smith-Waterman for optimal alignment
5. Score Calculation: Weighted containment score based on matches

Performance

• Async Processing: Non-blocking I/O operations
• Memory Efficient: Streaming file processing for large texts
• Progress Tracking: Real-time progress indicators
• Error Handling: Robust error handling with user-friendly messages

Security

• Input Validation: Comprehensive parameter validation
• File Safety: Secure file operations with size limits
• Text Sanitization: Removal of problematic characters
• Error Isolation: Graceful error handling without data exposure

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes with proper type annotations
4. Add tests for new functionality
5. Ensure code passes linting and type checking
6. Submit a pull request

License

MIT License - see LICENSE file for details.