- Fix: Provide versioned source history in ProvenanceManager.track_entity to support correct get_all_sources behavior.
- Fix: Ensure get_lineage aggregates and returns metadata fields correctly.
- Fix: Update est_real_module_integration.py and est_semantic_extract_provenance.py to match correct rack_relationship API signature.
- Verified: All provenance tests passed (237/237).
Verify that temperature parameter is omitted from API calls when None,
allowing models to use their defaults. Tests cover OpenAI, Groq, Gemini,
Ollama, and DeepSeek providers.
- Implemented provenance tracking across all 17 Semantica modules
- Added W3C PROV-O compliant schemas (prov:Entity, prov:Activity, prov:Agent, prov:wasDerivedFrom)
- Created ProvenanceManager with InMemory and SQLite storage backends
- Implemented SHA-256 integrity verification for tamper detection
- Added bridge axiom support for domain transformations (L1→L2→L3)
- Created provenance-enabled versions of all modules (opt-in with provenance=True)
- Added comprehensive test suite (237 tests covering edge cases and real scenarios)
- Updated README with accurate claims and compliance disclaimers
- Added complete documentation (usage guide and API reference)
- Zero breaking changes - fully backward compatible
- Fix variable shadowing in fetch_vectors (use vector_id instead of id)
- Remove redundant PINECONE_AVAILABLE check in create_index
- Add Pinecone imports and exports to __init__.py
- Add 'pinecone' to SUPPORTED_BACKENDS in vector_store.py
- Add vectorstore-pinecone dependency group to pyproject.toml
- Create vectorstore-all optional dependency group
- Fix duplicate MagicMock import in test_pinecone_store.py
- Update test_pinecone_removal.py with explanatory comment
- Update all docstrings to include Pinecone in supported backends
All fixes address code review feedback and ensure proper integration.
- Implemented 'Bring Your Own Model' (BYOM) support for NER, Relation, and Triplet extraction
- Added NER aggregation strategies (simple, max, average)
- Implemented Relation Extraction via Sequence Classification with entity markers
- Enhanced Triplet Extraction with REBEL post-processing and lazy loading
- Updated all extractors to prioritize runtime options over config defaults
- Added extensive tests and examples (huggingface_demo.py)
- Updated documentation and CHANGELOG
- Added OntologyIngestor in semantica/ingest/ontology_ingestor.py
- Updated semantica/ontology/__init__.py to export OntologyIngestor
- Updated semantica/ingest/methods.py to use OntologyIngestor
- Added tests for ontology ingestion
- Cleaned up temporary files
- Harden LLM relation extraction result handling to parse instructor/OpenAI/Groq variations
- Add structured JSON fallback when typed generation yields zero relations
- Strip acceptance of extra kwargs like max_tokens/max_entities_prompt in relation extraction internals
- Add comprehensive unit tests with mocked LLM provider
- Add integration tests for Groq provider with environment variable API key
- Ensure relation extraction completes and returns results when model identifies relations
- Implemented high-throughput parallel batch processing across all core extractors (NERExtractor, RelationExtractor, TripletExtractor, EventDetector, SemanticNetworkExtractor) using ThreadPoolExecutor.
- Added max_workers configuration parameter (default: 1) to all extractor extract() methods.
- Implemented parallel processing for large document chunking in _extract_entities_chunked and _extract_relations_chunked.
- Enhanced ProgressTracker to be thread-safe.
- Optimized setUpClass in tests to reduce Groq LLM initialization overhead.
- Updated documentation and usage examples.
- Implemented ML/LLM -> Pattern -> Last Resort fallback chains for NER, Relation, and Triplet extractors to prevent empty results.
- Added provenance metadata (batch_index, document_id) to all extraction schemas (Entity, Relation, Triplet, etc.).
- Unified batch processing API with progress tracking across all extractors.
- Updated documentation (module usage and reference docs) to reflect new features.
- Added robustness and batch provenance tests.
- Robust ID extraction in CentralityCalculator, CommunityDetector, and ConnectivityAnalyzer
- Support for direct Entity objects and dictionaries as node identifiers
- Improved Entity hashability in utils/types.py
- Added integration test to verify fix and prevent regression
- Added tests/reasoning/ directory with unit and integration tests
- Fixed indentation bug in Reasoner.add_fact for dictionary-based relationships
- Fixed regex variable matching in Reasoner._match_pattern
- Fixed variable handling in SPARQLReasoner query expansion
- Cleaned up cookbook and documentation references
Sameer6305