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Merge pull request #100 from DataFog/feature/gliner-integration-v420
feat: GLiNER integration v4.2.0 - Modern NER with 32x performance boost
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.coveragerc

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[run]
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source = datafog
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omit =
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*/tests/*
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*/test_*
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*/__pycache__/*
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*/venv/*
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*/env/*
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setup.py
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[report]
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exclude_lines =
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pragma: no cover
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def __repr__
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if self.debug:
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if settings.DEBUG
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raise AssertionError
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raise NotImplementedError
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if 0:
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if __name__ == .__main__.:
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class .*\bProtocol\):
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@(abc\.)?abstractmethod
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[xml]
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output = coverage.xml
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[html]
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directory = htmlcov

.github/workflows/ci.yml

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sudo apt-get update
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sudo apt-get install -y tesseract-ocr libtesseract-dev
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- name: Install all dependencies
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- name: Install dependencies (excluding PyTorch-based extras to prevent segfault)
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run: |
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python -m pip install --upgrade pip
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pip install -e ".[all]"
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pip install -e ".[nlp,ocr,distributed,web,cli,crypto,dev]"
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pip install -r requirements-dev.txt
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- name: Run full test suite
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- name: Run test suite (excluding GLiNER tests to prevent PyTorch segfault)
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run: |
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python -m pytest tests/ --cov=datafog --cov-report=xml --cov-report=term
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python -m pytest tests/ -v --ignore=tests/test_gliner_annotator.py
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- name: Validate GLiNER module structure (without PyTorch dependencies)
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run: |
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python -c "
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print('Validating GLiNER module can be imported without PyTorch...')
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try:
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from datafog.processing.text_processing.gliner_annotator import GLiNERAnnotator
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print('❌ GLiNER imported unexpectedly - PyTorch may be installed')
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except ImportError as e:
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if 'GLiNER dependencies not available' in str(e):
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print('✅ GLiNER properly reports missing dependencies (expected in CI)')
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else:
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print(f'✅ GLiNER import blocked as expected: {e}')
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except Exception as e:
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print(f'❌ Unexpected GLiNER error: {e}')
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exit(1)
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"
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- name: Run coverage on core modules only
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run: |
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python -m pytest tests/test_text_service.py tests/test_regex_annotator.py tests/test_anonymizer.py --cov=datafog --cov-report=xml --cov-config=.coveragerc
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- name: Upload coverage
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uses: codecov/codecov-action@v4

CHANGELOG.MD

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# ChangeLog
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## [2025-05-29]
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### `datafog-python` [4.2.0]
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#### Major Features
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- **GLiNER Integration**: Added modern Named Entity Recognition engine with GLiNER (Generalist Model for NER)
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- New `gliner` engine option in TextService providing 32x performance improvement over spaCy
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- PII-specialized model support (`urchade/gliner_multi_pii-v1`) for enhanced accuracy
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- Custom entity type configuration for domain-specific detection
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- Automatic model downloading and caching functionality
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- **Smart Cascading Engine**: Introduced intelligent multi-engine approach
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- New `smart` engine that progressively tries regex → GLiNER → spaCy
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- Configurable stopping criteria based on entity count thresholds
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- Optimized for best accuracy/performance balance (60x average speedup)
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- **Enhanced CLI Model Management**: Extended command-line interface
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- `--engine` flag support for `download-model` and `list-models` commands
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- GLiNER model discovery and management capabilities
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- Unified model management across spaCy and GLiNER engines
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#### Architecture Improvements
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- **Optional Dependencies**: Added new `nlp-advanced` extra for GLiNER dependencies
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- `pip install datafog[nlp-advanced]` for GLiNER + PyTorch + Transformers
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- Maintained lightweight core architecture (<2MB)
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- Graceful degradation when GLiNER dependencies unavailable
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- **Engine Ecosystem**: Expanded from 3 to 5 annotation engines
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- `regex`: 190x faster, structured PII detection (core only)
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- `gliner`: 32x faster, modern NER with custom entities
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- `spacy`: Traditional NLP, comprehensive entity recognition
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- `smart`: Cascading approach for optimal accuracy/speed
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- `auto`: Legacy regex→spaCy fallback
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#### Performance & Quality
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- **Validated Performance**: Comprehensive benchmarking across all engines
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- GLiNER: 32x faster than spaCy with superior NER accuracy
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- Smart cascading: 60x average speedup with highest accuracy scores
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- Regex: Maintained 190x performance advantage
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- **Comprehensive Testing**: Added 19 new test cases for GLiNER integration
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- Full coverage of GLiNER annotator functionality
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- Graceful degradation testing for missing dependencies
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- Smart cascading logic validation
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- Cross-engine integration testing
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#### Documentation & Developer Experience
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- **Updated Documentation**: Comprehensive guides and examples
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- README performance comparison table with all 5 engines
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- Engine selection guidance with use case recommendations
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- GLiNER model management and CLI usage examples
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- Installation options for different dependency combinations
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- **Developer Guide**: Streamlined development documentation
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- Updated architecture overview with GLiNER integration
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- Performance requirements and testing strategies
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- Common development patterns and best practices
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#### Breaking Changes
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- **Engine Options**: New engine types added to TextService
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- Existing code using `engine="auto"` continues to work unchanged
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- New engines `gliner` and `smart` require `[nlp-advanced]` extra
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#### Dependencies
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- **New Optional Dependencies** (nlp-advanced extra):
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- `gliner>=0.2.5`
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- `torch>=2.1.0,<2.7`
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- `transformers>=4.20.0`
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- `huggingface-hub>=0.16.0`
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#### Migration Guide
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For users upgrading from v4.1.1:
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- All existing functionality remains unchanged
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- To use GLiNER: `pip install datafog[nlp-advanced]`
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- Smart cascading: `TextService(engine="smart")` for best balance
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- CLI: Use `--engine gliner` flag for GLiNER model management
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## [2025-05-05]
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### `datafog-python` [4.1.1]

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