- 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
- 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.
- Reduced code examples in all guide pages (getting-started, quickstart, concepts, modules, examples, use-cases, learning-more)
- Added comprehensive cookbook links with descriptions (topics, difficulty, time, use cases)
- Improved structure and organization across all guide pages
- Updated use-cases.md to only include use cases with corresponding cookbooks
- Removed 'Last Updated: 2024' from all documentation files
- Enhanced navigation with better 'Next Steps' sections
- Add semantica.llms module with Groq, OpenAI, HuggingFace, and LiteLLM providers
- Add query_with_reasoning() method for multi-hop reasoning with LLM-generated responses
- Update ContextRetriever and AgentContext with reasoning capabilities
- Add comprehensive documentation for LLM providers and GraphRAG reasoning
- Update README and docs with new features
- Update notebook examples to use new query_with_reasoning() method
- 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
- Removed all Pinecone references, adapters, and documentation to align with open-source, self-hosted focus.
- Removed PineconeAdapter and related dependencies.
- Updated VectorStore to enforce supported backends (FAISS, Weaviate, Qdrant, Milvus, InMemory).
- Updated cookbooks (e.g., 13_Vector_Store.ipynb) to use Weaviate/FAISS examples instead of Pinecone.
- Updated core documentation (modules.md, rchitecture.md, etc.) to reflect backend changes.
- Added new tests ( est_pinecone_removal.py, est_vector_store_deepdive.py) to verify removal and validate remaining backends.
- Verified all vector store tests pass.
- Renamed semantica/triple_store to semantica/triplet_store
- Updated all imports and class references in core modules and adapters
- Refactored Jupyter notebooks in cookbook/
- Updated documentation files (README, docs/, etc.)
- Updated tests and verified passing status
- Enhanced docs/reference/vector_store.md (~575 lines)
- All 32 classes documented
- All 10 convenience functions
- Complete adapter documentation
- Updated cookbook/introduction/13_Vector_Store.ipynb
- 10-step comprehensive guide
- Created cookbook/advanced/Advanced_Vector_Store_and_Search.ipynb
- 4 focused parts (removed error handling per user request)
- Part 1: Index selection (Flat, HNSW, IVF)
- Part 2: Smart filtering with metadata
- Part 3: Result fusion (RRF, weighted)
- Part 4: Multi-tenant data isolation
- Beginner-friendly with clear examples
- Quick reference guide included
All vector_store documentation complete and production-ready.
BREAKING CHANGE: Removed build() convenience function from semantic_extract module
- Removed build() function from semantic_extract/__init__.py
- Updated __all__ exports to remove 'build'
- Resolved merge conflicts in named_entity_recognizer.py, relation_extractor.py, triple_extractor.py
- Updated semantic_extract_usage.md with class-based examples
- Updated docs/reference/semantic_extract.md with detailed parameter documentation
- Fixed 01_GraphRAG_Complete.ipynb to use individual extractor classes
- Enhanced 05_Entity_Extraction.ipynb with comprehensive examples (9 sections)
- Enhanced 06_Relation_Extraction.ipynb with complete pipeline examples (9 sections)
Users should now use individual classes (NERExtractor, RelationExtractor, TripleExtractor, etc.)
instead of the build() function for better control and flexibility.
Migration guide available in documentation.
KaifAhmad1