- Updated README.md with new logo reference
- Updated docs/index.md with new logo reference
- Updated docs/DOCS_README.md documentation
- Added new clean, professional logo (Semantica Updated Logo.png)
- Removed old illustrated logo (semantica_logo.png)
The new logo is minimal, scales well, and better represents Semantica as an enterprise-grade semantic layer.
- 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
- 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
Summary of changes:
- Update version to 0.1.0 in pyproject.toml and __init__.py files
- Add semantica/cli.py with click-based interface
- Add semantica/server.py with FastAPI-based REST API
- Add semantica/worker.py for background task processing
- Update documentation and changelog for v0.1.0
- Deleted cookbook/use_cases/healthcare/02_Drug_Interactions_Analysis.ipynb
- Removed Healthcare section from README.md
- Removed Healthcare section from docs/cookbook.md
- Removed Drug Interactions references from STRATEGIES_SUMMARY.md
- Updated cookbook count from 18 to 17 in all documentation
- Updated docs/index.md to reflect 17 cookbooks
- 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
- Enhanced 18 cookbooks across 9 domains with real data sources, advanced chunking, temporal KGs, and GraphRAG
- Updated docs/cookbook.md with all 18 cookbook links and enhanced descriptions
- Updated docs/use-cases.md with corrected links and removed duplicates
- Updated README.md with comprehensive Industry Use Cases section
- Fixed all outdated notebook links and ensured consistency across all docs
- Added real data ingestion (RSS feeds, APIs, MCP servers, streams)
- Integrated advanced chunking strategies (entity-aware, relation-aware, ontology-aware, semantic_transformer, etc.)
- Added temporal knowledge graphs, GraphRAG, deduplication, conflict detection, and other Semantica modules
- 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
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