- Moved semantica folder from libs/ to root level
- Moved pyproject.toml from libs/ to root level
- Moved and renamed libs/README.md to LIBS_README.md at root
- Removed empty libs folder
- Add KGVisualizer for knowledge graph network, community, and centrality visualizations
- Add OntologyVisualizer for class hierarchy, property graphs, and semantic model visualization
- Add EmbeddingVisualizer for 2D/3D projections, similarity heatmaps, and clustering
- Add SemanticNetworkVisualizer for semantic network graph visualizations
- Add QualityVisualizer for quality metrics dashboards and issue tracking
- Add AnalyticsVisualizer for centrality rankings, community structures, and connectivity analysis
- Add TemporalVisualizer for timeline, animation, and temporal pattern visualizations
- Add visualization utilities (layout algorithms, color schemes, export formats)
- Enhanced support for semantic models, ontologies, and semantic networks with multiple input formats
- Auto-extraction of classes and properties from semantic networks
- Update pyproject.toml with optional viz dependencies (pyvis, graphviz, umap-learn)
- Add comprehensive visualization examples to README
- Clean imports without try-except complexity for core dependencies
- Implement FAISSAdapter with index creation and similarity search
- Implement PineconeAdapter with index management and namespace support
- Implement WeaviateAdapter with schema management and GraphQL queries
- Implement QdrantAdapter with collection and point management
- Implement MilvusAdapter with collection and partition management
- Implement HybridSearch with vector and metadata fusion
- Implement MetadataStore with indexing and querying capabilities
- Implement NamespaceManager with isolation and access control
- Add support for multiple vector database backends
- Add hybrid search with RRF and weighted average ranking
- Add metadata indexing and filtering
- Add namespace isolation for multi-tenant support
- Complete all vector store management capabilities
- Implement TripleManager with CRUD operations and multi-store support
- Implement BlazegraphAdapter with SPARQL query execution and bulk loading
- Implement JenaAdapter with rdflib integration and model management
- Implement RDF4JAdapter with repository management and transaction support
- Implement VirtuosoAdapter with cluster connection and query optimization
- Implement QueryEngine with query optimization, caching, and performance monitoring
- Implement BulkLoader with batch processing, retry logic, and progress tracking
- Add support for multiple triple store backends
- Add query caching and optimization
- Add bulk loading with progress monitoring
- Complete all triple store management capabilities
- Implement InferenceEngine with forward/backward chaining and multiple strategies
- Implement RuleManager with rule parsing, validation, and execution tracking
- Implement SPARQLReasoner with query expansion and result inference
- Implement ReteEngine with Rete algorithm for efficient rule matching
- Implement AbductiveReasoner with hypothesis generation and ranking strategies
- Implement DeductiveReasoner with logical inference and proof generation
- Implement ExplanationGenerator with natural language explanations and multiple detail levels
- Add rule parsing from natural language (IF-THEN, IFF patterns)
- Add Rete network construction for efficient pattern matching
- Add query caching for SPARQL reasoning
- Add execution history tracking and statistics
- Complete all reasoning and inference capabilities
- Implement PipelineBuilder with fluent DSL for pipeline construction
- Implement ExecutionEngine with dependency-aware execution and topological sorting
- Implement FailureHandler with retry mechanisms and multiple strategies (linear, exponential, fixed)
- Implement ParallelismManager with thread/process pool execution support
- Implement ResourceScheduler for CPU, GPU, memory, and disk allocation
- Implement PipelineValidator with circular dependency detection and reachability analysis
- Implement PipelineTemplateManager with pre-built templates (document processing, RAG, KG construction, ontology generation)
- Add pause/resume/stop execution control
- Add progress tracking and time estimation
- Add optional psutil dependency for resource detection with fallback
- Complete all pipeline orchestration and management capabilities
- Implement 6-stage ontology generation pipeline (Semantic Network → YAML → Types → Hierarchy → TTL → Validation)
- Implement ClassInferrer for automatic class discovery and hierarchy building
- Implement PropertyGenerator for property inference from data patterns
- Implement OWLGenerator with rdflib support and fallback formatting
- Implement OntologyValidator with symbolic reasoner integration (HermiT/Pellet)
- Implement OntologyEvaluator for quality evaluation against competency questions
- Implement NamespaceManager for IRI management and namespace handling
- Implement NamingConventions for enforcing naming best practices
- Implement VersionManager for version-aware IRI management
- Implement ModuleManager for modular ontology development
- Implement ReuseManager for ontology reuse and import management
- Implement RequirementsSpecManager and CompetencyQuestionsManager
- Implement OntologyDocumentationManager for documentation generation
- Implement DomainOntologies with pre-built templates (healthcare, finance)
- Implement AssociativeClassBuilder for complex relationship modeling
- Add comprehensive exports and documentation
- Implement DataCleaner with duplicate detection and validation
- Implement TextNormalizer with Unicode and whitespace handling
- Implement EntityNormalizer with alias resolution and disambiguation
- Implement DateNormalizer with timezone and relative date support
- Implement NumberNormalizer with unit conversion and currency handling
- Add optional dependency handling for dateutil library
- Complete all normalization utilities for text, entities, dates, and numbers
- Implement EntityLinker for cross-source entity linking with URI assignment
- Implement ContextGraphBuilder for formalizing context as graph connections
- Implement ContextRetriever with hybrid retrieval (vector + graph)
- Implement AgentMemory for persistent RAG-based agent memory
- Add context graph building from conversations
- Add entity linking with semantic similarity
- Add hybrid context retrieval with graph expansion
- Add memory management with retention policies
- Support conversation history and context accumulation
- Implemented text embedder with sentence-transformers support
- Added image embedder with CLIP integration
- Created audio embedder with librosa features
- Built provider adapters for OpenAI, BGE, and Llama
- Implemented multiple pooling strategies (mean, max, CLS, attention, hierarchical)
- Added multimodal embedder with cross-modal alignment
- Created embedding optimizer with PCA and quantization
- Implemented context manager for window management
- Built main embedding generator with unified interface
All 9 files in embeddings module fully implemented.
No linter errors. All TODOs resolved.
- Implemented NER extractor with spaCy support
- Added relation extractor with pattern matching
- Created semantic network extractor with YAML export
- Built LLM enhancer for OpenAI and Anthropic
- Implemented extraction validator with quality metrics
- Added named entity recognizer with classifiers
- Created triple extractor with RDF serialization
- Built event detector with temporal processing
- Implemented coreference resolver with chain building
- Added semantic analyzer with similarity and clustering
All 10 files in semantic_extract module fully implemented.
No linter errors. All TODOs resolved.
- Implemented semantic chunking with spaCy NLP support
- Added sliding window chunker with overlap capabilities
- Created structural chunker for document-aware splitting
- Built table-aware chunker for tabular data
- Implemented chunk validator with quality scoring
- Added provenance tracker for source tracking and lineage
- Implement config_manager.py with configuration management
- Implement lifecycle.py with lifecycle management
- Implement orchestrator.py with main Semantica orchestrator class
- Implement plugin_registry.py with plugin management system
- Update core __init__.py to export all components
Mohd Kaif