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KaifAhmad1 c3ba6541a8 Rebrand from SemantiCore to Semantica

- Updated all package references from semanticore to semantica
- Updated all class names from SemantiCore to Semantica
- Renamed logo files: SemantiCore Logo.png -> Semantica Logo.png
- Renamed logo files: SemantiCore Logo Dark.png -> Semantica Logo Dark.png
- Renamed roadmap file: SemantiCore_Development_Roadmap.md -> Semantica_Development_Roadmap.md
- Updated all URLs, links, and documentation references
- Updated all code examples and installation instructions
- Maintained all functionality while updating branding

2025-10-21 15:14:34 +05:30

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Semantica — Modules, Features, Roadmap & Visuals

Purpose: A concise engineering guide listing every module, feature, responsibilities, recommended tech, and visual diagrams to help you design, implement, and ship the Semantica open‑source semantic toolkit.

1. High-level overview

Semantica transforms raw, multi-format input into semantic knowledge (triples, ontologies, graphs, embeddings) suitable for RAG, multi-agent systems, GraphRAG, and domain-specific AI pipelines. The architecture is modular, pluggable, and cloud-native.

2. Modules (catalog)

Each module is a self-contained package with a clear public API.

core
- Responsibilities: orchestration, pipeline runner, configuration, plugin registry, lifecycle management.
- Exports: Semantica, PipelineBuilder, Config, PluginManager.
- Submodules:
  - orchestrator: Pipeline coordination, task scheduling, resource management
  - config_manager: YAML/JSON config parsing, environment variables, validation
  - plugin_registry: Dynamic plugin loading, version compatibility, dependency resolution
  - lifecycle: Startup/shutdown hooks, health checks, graceful degradation
- Functions:
  - Semantica.initialize() - Setup all modules and connections
  - Semantica.run_pipeline() - Execute complete processing pipeline
  - Semantica.get_status() - Return system health and metrics
  - PipelineBuilder.add_step() - Add processing step to pipeline
  - PipelineBuilder.set_parallelism() - Configure parallel execution
  - Config.validate() - Validate configuration against schema
  - PluginManager.load_plugin() - Dynamically load plugin modules
  - PluginManager.list_plugins() - Show available plugins and versions
ingest
- Submodules: file, web, feed, stream, repo, email, db_export.
- Features: format detection, connector registry, backpressure support, incremental ingestion, resume tokens.
- Detailed Submodules:
  - file: Local file system, network drives, cloud storage (S3, GCS, Azure)
  - web: HTTP/HTTPS scraping, RSS feeds, sitemap crawling, JavaScript rendering
  - feed: RSS/Atom feeds, social media APIs, news aggregators
  - stream: Real-time data streams, WebSocket connections, message queues
  - repo: Git repositories, SVN, Mercurial, package managers
  - email: IMAP/POP3, Exchange, Gmail API, email archives
  - db_export: Database dumps, SQL queries, NoSQL exports, ETL pipelines
- Functions:
  - FileIngestor.scan_directory() - Recursively scan directory for files
  - FileIngestor.detect_format() - Auto-detect file type and encoding
  - WebIngestor.crawl_site() - Crawl website with depth and rate limiting
  - WebIngestor.extract_links() - Extract and follow hyperlinks
  - FeedIngestor.parse_rss() - Parse RSS/Atom feeds with metadata
  - StreamIngestor.connect() - Establish real-time data connection
  - RepoIngestor.clone_repo() - Clone and track repository changes
  - EmailIngestor.connect_imap() - Connect to email server
  - DBIngestor.export_table() - Export database table to structured format
  - IngestManager.resume_from_token() - Resume interrupted ingestion
  - IngestManager.get_progress() - Monitor ingestion progress
  - ConnectorRegistry.register() - Register custom data connectors
parse
- Submodules: pdf, docx, pptx, excel, html, jsonl, csv, latex, images, tables.
- Features: layout-aware extraction, OCR integration (Tesseract/Google Vision), table extraction (Camelot, Tabula), figure extraction.
- Detailed Submodules:
  - pdf: PDF text extraction, form fields, annotations, embedded images, digital signatures
  - docx: Word document text, styles, headers, footers, embedded objects, track changes
  - pptx: PowerPoint slides, speaker notes, embedded media, animations, slide masters
  - excel: Spreadsheet data, formulas, charts, pivot tables, multiple sheets, cell formatting
  - html: Web page content, DOM structure, CSS styling, JavaScript content, meta tags
  - jsonl: JSON Lines parsing, schema inference, validation, error handling
  - csv: Comma/tab separated values, custom delimiters, header detection, data types
  - latex: Mathematical equations, document structure, bibliography, cross-references
  - images: OCR text extraction, image metadata, EXIF data, face detection, object recognition
  - tables: Table structure detection, cell merging, header identification, data extraction
- Functions:
  - PDFParser.extract_text() - Extract text with positioning and formatting
  - PDFParser.extract_tables() - Extract tables using Camelot/Tabula
  - PDFParser.extract_images() - Extract embedded images and figures
  - DOCXParser.get_document_structure() - Extract document outline and sections
  - DOCXParser.extract_track_changes() - Extract revision history
  - PPTXParser.extract_slides() - Extract slide content and speaker notes
  - ExcelParser.read_sheet() - Read specific worksheet with data types
  - ExcelParser.extract_charts() - Extract chart data and metadata
  - HTMLParser.parse_dom() - Parse HTML into structured DOM tree
  - HTMLParser.extract_metadata() - Extract meta tags and structured data
  - ImageParser.ocr_text() - Perform OCR using Tesseract/Google Vision
  - ImageParser.detect_objects() - Detect objects and faces in images
  - TableParser.detect_structure() - Detect table boundaries and headers
  - TableParser.extract_cells() - Extract individual cell data
  - ParserRegistry.get_parser() - Get appropriate parser for file type
  - ParserRegistry.supported_formats() - List all supported file formats
normalize
- Responsibilities: text cleaning, language detection, encoding normalization, named entity canonicalization, date normalization.
- Submodules:
  - text_cleaner: HTML tag removal, whitespace normalization, special character handling
  - language_detector: Multi-language identification, confidence scoring, language families
  - encoding_handler: UTF-8 conversion, BOM detection, encoding validation
  - entity_normalizer: Named entity standardization, acronym expansion, aliases mapping
  - date_normalizer: Date format detection, timezone handling, relative date resolution
  - number_normalizer: Number format standardization, unit conversion, currency handling
- Functions:
  - TextCleaner.remove_html() - Strip HTML tags and preserve text content
  - TextCleaner.normalize_whitespace() - Standardize spacing and line breaks
  - TextCleaner.remove_special_chars() - Clean special characters and symbols
  - LanguageDetector.detect() - Identify text language with confidence score
  - LanguageDetector.supported_languages() - List all supported languages
  - EncodingHandler.normalize() - Convert to UTF-8 and validate encoding
  - EncodingHandler.detect_encoding() - Auto-detect file encoding
  - EntityNormalizer.canonicalize() - Standardize entity names and aliases
  - EntityNormalizer.expand_acronyms() - Expand abbreviations and acronyms
  - DateNormalizer.parse_date() - Parse various date formats to ISO standard
  - DateNormalizer.resolve_relative() - Convert relative dates to absolute
  - NumberNormalizer.standardize() - Convert numbers to standard format
  - NumberNormalizer.convert_units() - Convert between measurement units
  - NormalizationPipeline.run() - Execute complete normalization pipeline
  - NormalizationPipeline.get_stats() - Return normalization statistics
split
- Chunking strategies: sliding window, semantic chunking, structural (section-aware), table-aware splitting.
- Preserves provenance for each chunk.
- Submodules:
  - sliding_window: Fixed-size chunks with overlap, configurable window size and stride
  - semantic_chunker: Meaning-based splitting using NLP, sentence boundaries, topic detection
  - structural_chunker: Document-aware splitting, section headers, paragraph boundaries
  - table_chunker: Table-aware splitting, preserve table structure and relationships
  - provenance_tracker: Source tracking, offset mapping, confidence scoring
  - chunk_validator: Chunk quality assessment, overlap detection, size validation
- Functions:
  - SlidingWindowChunker.split() - Create fixed-size chunks with overlap
  - SlidingWindowChunker.set_window_size() - Configure chunk size and overlap
  - SemanticChunker.split_by_meaning() - Split based on semantic boundaries
  - SemanticChunker.detect_topics() - Identify topic changes for splitting
  - StructuralChunker.split_by_sections() - Split on document structure
  - StructuralChunker.identify_headers() - Detect section headers and levels
  - TableChunker.preserve_tables() - Keep tables intact during splitting
  - TableChunker.extract_table_context() - Extract surrounding context for tables
  - ProvenanceTracker.track_source() - Track original source and position
  - ProvenanceTracker.get_provenance() - Retrieve chunk source information
  - ChunkValidator.validate_chunk() - Validate chunk quality and size
  - ChunkValidator.detect_overlaps() - Find overlapping chunks
  - SplitManager.run_strategy() - Execute chosen splitting strategy
  - SplitManager.get_chunk_stats() - Return chunking statistics
  - SplitManager.merge_chunks() - Combine related chunks when needed
semantic_extract
- Capabilities: NER, relation extraction, event detection, co-reference resolution, entity linking, triple extraction.
- Backends: spaCy, Stanza, HuggingFace pipelines, LLM prompts (for complex relations).
- Submodules:
  - ner_extractor: Named entity recognition, entity classification, confidence scoring
  - relation_extractor: Relationship detection, relation classification, dependency parsing
  - event_detector: Event identification, temporal extraction, event participants
  - coref_resolver: Co-reference resolution, pronoun resolution, entity linking
  - triple_extractor: Subject-predicate-object extraction, RDF triple generation
  - llm_enhancer: LLM-based extraction, complex relation detection, reasoning
  - extraction_validator: Quality assessment, confidence thresholds, validation rules
- Functions:
  - NERExtractor.extract_entities() - Extract named entities with types and confidence
  - NERExtractor.classify_entities() - Classify entities into predefined categories
  - RelationExtractor.find_relations() - Detect relationships between entities
  - RelationExtractor.classify_relations() - Classify relation types and directions
  - EventDetector.detect_events() - Identify events and their participants
  - EventDetector.extract_temporal() - Extract temporal information for events
  - CorefResolver.resolve_references() - Resolve co-references and pronouns
  - CorefResolver.link_entities() - Link entities across document sections
  - TripleExtractor.extract_triples() - Extract RDF-style triples
  - TripleExtractor.validate_triples() - Validate triple structure and consistency
  - LLMEnhancer.enhance_extraction() - Use LLM for complex extraction tasks
  - LLMEnhancer.detect_patterns() - Identify complex patterns and relationships
  - ExtractionValidator.validate_quality() - Assess extraction quality
  - ExtractionValidator.filter_by_confidence() - Filter results by confidence score
  - ExtractionPipeline.run() - Execute complete extraction pipeline
  - ExtractionPipeline.get_results() - Return structured extraction results
ontology
- OntologyGenerator: infer classes/properties, generate OWL/RDF, map to base ontologies (schema.org, FOAF, DC), versioning.
- Submodules:
  - class_inferrer: Automatic class discovery, hierarchy inference, class relationships
  - property_generator: Property inference, data type detection, cardinality analysis
  - owl_generator: OWL/RDF generation, ontology serialization, format conversion
  - base_mapper: Schema.org integration, FOAF mapping, Dublin Core alignment
  - version_manager: Ontology versioning, change tracking, migration support
  - ontology_validator: Schema validation, consistency checking, constraint validation
  - domain_ontologies: Pre-built ontologies for common domains (finance, healthcare, legal)
- Functions:
  - ClassInferrer.infer_classes() - Automatically discover entity classes
  - ClassInferrer.build_hierarchy() - Build class inheritance hierarchy
  - ClassInferrer.analyze_relationships() - Analyze class relationships and dependencies
  - PropertyGenerator.infer_properties() - Infer object and data properties
  - PropertyGenerator.detect_data_types() - Detect property data types and constraints
  - PropertyGenerator.analyze_cardinality() - Analyze property cardinality (one-to-many, etc.)
  - OWLGenerator.generate_owl() - Generate OWL ontology in RDF/XML format
  - OWLGenerator.serialize_rdf() - Serialize to various RDF formats (Turtle, N-Triples)
  - BaseMapper.map_to_schema_org() - Map entities to schema.org vocabulary
  - BaseMapper.map_to_foaf() - Map to FOAF (Friend of a Friend) ontology
  - BaseMapper.map_to_dublin_core() - Map to Dublin Core metadata standards
  - VersionManager.create_version() - Create new ontology version
  - VersionManager.track_changes() - Track changes between versions
  - VersionManager.migrate_ontology() - Support ontology migration and updates
  - OntologyValidator.validate_schema() - Validate ontology schema consistency
  - OntologyValidator.check_constraints() - Check ontology constraint violations
  - DomainOntologies.get_finance_ontology() - Get pre-built financial ontology
  - DomainOntologies.get_healthcare_ontology() - Get pre-built healthcare ontology
  - OntologyManager.build_ontology() - Build complete ontology from extracted data
  - OntologyManager.export_ontology() - Export ontology in various formats
triple_store
- Adapters: Blazegraph, Apache Jena, RDF4J, GraphDB, Virtuoso — export/import, bulk load.
- Submodules:
  - blazegraph_adapter: Blazegraph integration, SPARQL queries, bulk operations
  - jena_adapter: Apache Jena integration, RDF model management, inference engine
  - rdf4j_adapter: RDF4J integration, repository management, transaction support
  - graphdb_adapter: GraphDB integration, reasoning capabilities, visualization
  - virtuoso_adapter: Virtuoso integration, high-performance queries, clustering
  - triple_manager: Triple CRUD operations, batch processing, validation
  - query_engine: SPARQL query execution, query optimization, result formatting
  - bulk_loader: High-volume data loading, indexing, performance optimization
- Functions:
  - BlazegraphAdapter.connect() - Connect to Blazegraph instance
  - BlazegraphAdapter.execute_sparql() - Execute SPARQL queries
  - BlazegraphAdapter.bulk_load() - Load triples in bulk
  - JenaAdapter.create_model() - Create and manage RDF models
  - JenaAdapter.add_triples() - Add triples to model
  - JenaAdapter.run_inference() - Execute inference rules
  - RDF4JAdapter.create_repository() - Create and configure repositories
  - RDF4JAdapter.begin_transaction() - Start transaction for batch operations
  - GraphDBAdapter.enable_reasoning() - Enable reasoning capabilities
  - GraphDBAdapter.visualize_graph() - Generate graph visualizations
  - VirtuosoAdapter.connect_cluster() - Connect to Virtuoso cluster
  - VirtuosoAdapter.optimize_queries() - Optimize query performance
  - TripleManager.add_triple() - Add single triple to store
  - TripleManager.add_triples() - Add multiple triples
  - TripleManager.delete_triple() - Delete specific triple
  - TripleManager.update_triple() - Update existing triple
  - QueryEngine.execute_sparql() - Execute SPARQL queries
  - QueryEngine.optimize_query() - Optimize query for performance
  - QueryEngine.format_results() - Format query results
  - BulkLoader.load_file() - Load triples from file
  - BulkLoader.create_indexes() - Create database indexes
  - BulkLoader.monitor_progress() - Monitor loading progress
  - TripleStoreManager.get_store_info() - Get store statistics and status
  - TripleStoreManager.backup_store() - Create backup of store
  - TripleStoreManager.restore_store() - Restore from backup
kg (knowledge graph)
- Graph builder, entity resolution, deduplication, seed manager, provenance, conflict detector, conflict resolution UI hooks.
- Submodules:
  - graph_builder: Knowledge graph construction, node creation, edge management
  - entity_resolver: Entity disambiguation, identity resolution, merge strategies
  - deduplicator: Duplicate detection, entity merging, conflict resolution
  - seed_manager: Initial data loading, foundation entities, baseline knowledge
  - provenance_tracker: Source tracking, extraction history, confidence scoring
  - conflict_detector: Conflict identification, severity classification, resolution workflows
  - graph_validator: Graph consistency, schema validation, quality metrics
  - graph_analyzer: Graph analytics, centrality measures, community detection
- Functions:
  - GraphBuilder.create_node() - Create knowledge graph node
  - GraphBuilder.create_edge() - Create relationship edge between nodes
  - GraphBuilder.build_subgraph() - Build subgraph from specific entities
  - GraphBuilder.merge_graphs() - Merge multiple knowledge graphs
  - EntityResolver.resolve_identity() - Resolve entity identity across sources
  - EntityResolver.merge_entities() - Merge duplicate entities
  - EntityResolver.get_canonical() - Get canonical entity representation
  - Deduplicator.find_duplicates() - Find duplicate entities
  - Deduplicator.merge_duplicates() - Merge duplicate entities
  - Deduplicator.validate_merge() - Validate merge operation
  - SeedManager.load_seed_data() - Load initial seed data
  - SeedManager.validate_seed_data() - Validate seed data quality
  - SeedManager.update_seed_data() - Update existing seed data
  - ProvenanceTracker.track_source() - Track information source
  - ProvenanceTracker.get_provenance() - Retrieve provenance information
  - ProvenanceTracker.calculate_confidence() - Calculate confidence scores
  - ConflictDetector.detect_conflicts() - Detect conflicts between sources
  - ConflictDetector.classify_severity() - Classify conflict severity
  - ConflictDetector.create_resolution_workflow() - Create resolution workflow
  - GraphValidator.validate_consistency() - Validate graph consistency
  - GraphValidator.check_schema_compliance() - Check schema compliance
  - GraphValidator.calculate_quality_metrics() - Calculate quality metrics
  - GraphAnalyzer.calculate_centrality() - Calculate node centrality
  - GraphAnalyzer.detect_communities() - Detect community structures
  - GraphAnalyzer.analyze_connectivity() - Analyze graph connectivity
  - KnowledgeGraphManager.build_graph() - Build complete knowledge graph
  - KnowledgeGraphManager.export_graph() - Export graph in various formats
  - KnowledgeGraphManager.visualize_graph() - Generate graph visualizations
embeddings
- SemanticEmbedder: multi-modal embeddings, context windows, pooling strategies, embedding provider adapters (OpenAI, BGE, Llama‑embeddings).
- Submodules:
  - text_embedder: Text-based embeddings, sentence transformers, document embeddings
  - image_embedder: Image embeddings, vision models, multi-modal fusion
  - audio_embedder: Audio embeddings, speech recognition, audio analysis
  - multimodal_embedder: Cross-modal embeddings, fusion strategies, alignment
  - context_manager: Context window management, sliding windows, attention mechanisms
  - pooling_strategies: Mean pooling, max pooling, attention pooling, hierarchical pooling
  - provider_adapters: OpenAI, BGE, Llama, custom model integrations
  - embedding_optimizer: Embedding optimization, dimensionality reduction, clustering
- Functions:
  - TextEmbedder.embed_text() - Generate text embeddings
  - TextEmbedder.embed_sentence() - Generate sentence-level embeddings
  - TextEmbedder.embed_document() - Generate document-level embeddings
  - ImageEmbedder.embed_image() - Generate image embeddings
  - ImageEmbedder.extract_features() - Extract visual features
  - ImageEmbedder.embed_batch() - Process multiple images
  - AudioEmbedder.embed_audio() - Generate audio embeddings
  - AudioEmbedder.extract_audio_features() - Extract audio features
  - MultimodalEmbedder.fuse_embeddings() - Fuse multiple modality embeddings
  - MultimodalEmbedder.align_modalities() - Align different modality representations
  - ContextManager.set_window_size() - Set context window size
  - ContextManager.apply_sliding_window() - Apply sliding window approach
  - ContextManager.manage_attention() - Manage attention mechanisms
  - PoolingStrategies.mean_pooling() - Apply mean pooling strategy
  - PoolingStrategies.max_pooling() - Apply max pooling strategy
  - PoolingStrategies.attention_pooling() - Apply attention-based pooling
  - ProviderAdapter.connect_openai() - Connect to OpenAI embedding API
  - ProviderAdapter.connect_bge() - Connect to BGE embedding service
  - ProviderAdapter.connect_llama() - Connect to Llama embedding model
  - ProviderAdapter.load_custom_model() - Load custom embedding model
  - EmbeddingOptimizer.optimize_dimensions() - Optimize embedding dimensions
  - EmbeddingOptimizer.apply_clustering() - Apply clustering to embeddings
  - EmbeddingOptimizer.calculate_similarity() - Calculate embedding similarities
  - SemanticEmbedder.generate_embeddings() - Generate embeddings for input
  - SemanticEmbedder.batch_process() - Process multiple inputs in batch
  - SemanticEmbedder.get_embedding_stats() - Get embedding statistics
vector_store
- Adapters: Pinecone, FAISS, Milvus, Weaviate, Qdrant. Features: namespace, metadata store, hybrid search.
- Submodules:
  - pinecone_adapter: Pinecone integration, index management, vector operations
  - faiss_adapter: FAISS integration, index types, similarity search
  - milvus_adapter: Milvus integration, collection management, distributed search
  - weaviate_adapter: Weaviate integration, schema management, graphQL queries
  - qdrant_adapter: Qdrant integration, point management, filtering
  - namespace_manager: Namespace isolation, access control, data separation
  - metadata_store: Metadata indexing, filtering, search capabilities
  - hybrid_search: Vector + metadata search, ranking algorithms, result fusion
  - index_optimizer: Index optimization, performance tuning, maintenance
- Functions:
  - PineconeAdapter.connect() - Connect to Pinecone service
  - PineconeAdapter.create_index() - Create new vector index
  - PineconeAdapter.upsert_vectors() - Insert or update vectors
  - PineconeAdapter.query_vectors() - Query similar vectors
  - FAISSAdapter.create_index() - Create FAISS index
  - FAISSAdapter.add_vectors() - Add vectors to index
  - FAISSAdapter.search_similar() - Search for similar vectors
  - FAISSAdapter.save_index() - Save index to disk
  - MilvusAdapter.create_collection() - Create Milvus collection
  - MilvusAdapter.insert_vectors() - Insert vectors into collection
  - MilvusAdapter.search_vectors() - Search vectors in collection
  - WeaviateAdapter.create_schema() - Create Weaviate schema
  - WeaviateAdapter.add_objects() - Add objects to Weaviate
  - WeaviateAdapter.graphql_query() - Execute GraphQL queries
  - QdrantAdapter.create_collection() - Create Qdrant collection
  - QdrantAdapter.upsert_points() - Insert or update points
  - QdrantAdapter.search_points() - Search points with filters
  - NamespaceManager.create_namespace() - Create isolated namespace
  - NamespaceManager.set_access_control() - Set namespace permissions
  - NamespaceManager.list_namespaces() - List available namespaces
  - MetadataStore.index_metadata() - Index metadata for search
  - MetadataStore.filter_by_metadata() - Filter results by metadata
  - MetadataStore.search_metadata() - Search metadata content
  - HybridSearch.combine_results() - Combine vector and metadata results
  - HybridSearch.rank_results() - Rank results using multiple criteria
  - HybridSearch.fuse_results() - Fuse results from different sources
  - IndexOptimizer.optimize_index() - Optimize index performance
  - IndexOptimizer.rebuild_index() - Rebuild index for better performance
  - IndexOptimizer.get_performance_metrics() - Get index performance metrics
  - VectorStoreManager.get_store_info() - Get store information
  - VectorStoreManager.backup_store() - Create store backup
  - VectorStoreManager.restore_store() - Restore from backup
reasoning
- Inference rules, SPARQL-based reasoning, Rete-like rule engine hooks, abductive/deductive inference.
- Submodules:
  - inference_engine: Rule-based inference, forward/backward chaining, conflict resolution
  - sparql_reasoner: SPARQL-based reasoning, query expansion, result inference
  - rete_engine: Rete algorithm implementation, pattern matching, rule execution
  - abductive_reasoner: Abductive reasoning, hypothesis generation, explanation finding
  - deductive_reasoner: Deductive reasoning, logical inference, theorem proving
  - rule_manager: Rule definition, rule validation, rule execution tracking
  - reasoning_validator: Reasoning validation, consistency checking, error detection
  - explanation_generator: Explanation generation, reasoning paths, justification
- Functions:
  - InferenceEngine.add_rule() - Add inference rule to engine
  - InferenceEngine.execute_rules() - Execute inference rules
  - InferenceEngine.forward_chain() - Perform forward chaining
  - InferenceEngine.backward_chain() - Perform backward chaining
  - InferenceEngine.resolve_conflicts() - Resolve rule conflicts
  - SPARQLReasoner.expand_query() - Expand SPARQL query with reasoning
  - SPARQLReasoner.infer_results() - Infer additional results
  - SPARQLReasoner.apply_reasoning() - Apply reasoning to query results
  - ReteEngine.compile_rules() - Compile rules into Rete network
  - ReteEngine.match_patterns() - Match patterns using Rete algorithm
  - ReteEngine.execute_matches() - Execute matched rules
  - AbductiveReasoner.generate_hypotheses() - Generate explanatory hypotheses
  - AbductiveReasoner.find_explanations() - Find explanations for observations
  - AbductiveReasoner.rank_hypotheses() - Rank hypotheses by plausibility
  - DeductiveReasoner.apply_logic() - Apply logical inference rules
  - DeductiveReasoner.prove_theorem() - Prove logical theorems
  - DeductiveReasoner.validate_argument() - Validate logical arguments
  - RuleManager.define_rule() - Define new inference rule
  - RuleManager.validate_rule() - Validate rule syntax and logic
  - RuleManager.track_execution() - Track rule execution history
  - ReasoningValidator.validate_reasoning() - Validate reasoning process
  - ReasoningValidator.check_consistency() - Check reasoning consistency
  - ReasoningValidator.detect_errors() - Detect reasoning errors
  - ExplanationGenerator.generate_explanation() - Generate reasoning explanation
  - ExplanationGenerator.show_reasoning_path() - Show reasoning path
  - ExplanationGenerator.justify_conclusion() - Justify reasoning conclusion
  - ReasoningManager.run_reasoning() - Run complete reasoning process
  - ReasoningManager.get_reasoning_results() - Get reasoning results
  - ReasoningManager.export_reasoning() - Export reasoning process
pipeline
- PipelineBuilder, failure/retry semantics, parallelism strategies, resource scheduling.
- Submodules:
  - pipeline_builder: Pipeline construction, step configuration, dependency management
  - execution_engine: Pipeline execution, step orchestration, progress tracking
  - failure_handler: Error handling, retry logic, fallback strategies
  - parallelism_manager: Parallel execution, resource allocation, load balancing
  - resource_scheduler: Resource management, CPU/GPU allocation, memory optimization
  - pipeline_validator: Pipeline validation, dependency checking, cycle detection
  - monitoring_hooks: Execution monitoring, metrics collection, alerting
  - pipeline_templates: Pre-built pipeline templates, common workflows
- Functions:
  - PipelineBuilder.add_step() - Add processing step to pipeline
  - PipelineBuilder.set_dependencies() - Set step dependencies
  - PipelineBuilder.configure_step() - Configure step parameters
  - PipelineBuilder.validate_pipeline() - Validate pipeline configuration
  - ExecutionEngine.run_pipeline() - Execute complete pipeline
  - ExecutionEngine.pause_pipeline() - Pause pipeline execution
  - ExecutionEngine.resume_pipeline() - Resume paused pipeline
  - ExecutionEngine.get_progress() - Get execution progress
  - FailureHandler.handle_error() - Handle execution errors
  - FailureHandler.retry_step() - Retry failed step
  - FailureHandler.apply_fallback() - Apply fallback strategy
  - ParallelismManager.set_parallelism() - Set parallel execution level
  - ParallelismManager.allocate_resources() - Allocate execution resources
  - ParallelismManager.load_balance() - Balance load across resources
  - ResourceScheduler.allocate_cpu() - Allocate CPU resources
  - ResourceScheduler.allocate_gpu() - Allocate GPU resources
  - ResourceScheduler.optimize_memory() - Optimize memory usage
  - PipelineValidator.check_dependencies() - Check step dependencies
  - PipelineValidator.detect_cycles() - Detect dependency cycles
  - PipelineValidator.validate_configuration() - Validate pipeline configuration
  - MonitoringHooks.collect_metrics() - Collect execution metrics
  - MonitoringHooks.set_alerts() - Set execution alerts
  - MonitoringHooks.get_status() - Get pipeline status
  - PipelineTemplates.get_template() - Get pre-built pipeline template
  - PipelineTemplates.customize_template() - Customize template for specific use case
  - PipelineTemplates.save_template() - Save custom pipeline template
  - PipelineManager.create_pipeline() - Create new pipeline
  - PipelineManager.schedule_pipeline() - Schedule pipeline execution
  - PipelineManager.monitor_pipelines() - Monitor all running pipelines
streaming
- Integration: Kafka, Pulsar, RabbitMQ, Kinesis; exactly-once semantics where feasible; checkpoints.
- Submodules:
  - kafka_adapter: Apache Kafka integration, producer/consumer management, topic management
  - pulsar_adapter: Apache Pulsar integration, subscription management, message routing
  - rabbitmq_adapter: RabbitMQ integration, queue management, exchange routing
  - kinesis_adapter: AWS Kinesis integration, stream management, shard handling
  - stream_processor: Stream processing logic, windowing, aggregation
  - checkpoint_manager: Checkpoint creation, recovery, consistency management
  - exactly_once: Exactly-once semantics, idempotency, deduplication
  - stream_monitor: Stream monitoring, metrics collection, health checks
  - backpressure_handler: Backpressure management, flow control, rate limiting
- Functions:
  - KafkaAdapter.connect() - Connect to Kafka cluster
  - KafkaAdapter.create_topic() - Create new Kafka topic
  - KafkaAdapter.produce_message() - Produce message to topic
  - KafkaAdapter.consume_messages() - Consume messages from topic
  - KafkaAdapter.manage_partitions() - Manage topic partitions
  - PulsarAdapter.connect() - Connect to Pulsar cluster
  - PulsarAdapter.create_subscription() - Create message subscription
  - PulsarAdapter.publish_message() - Publish message to topic
  - PulsarAdapter.receive_messages() - Receive messages from subscription
  - RabbitMQAdapter.connect() - Connect to RabbitMQ server
  - RabbitMQAdapter.create_queue() - Create message queue
  - RabbitMQAdapter.publish_message() - Publish message to exchange
  - RabbitMQAdapter.consume_queue() - Consume messages from queue
  - KinesisAdapter.connect() - Connect to Kinesis stream
  - KinesisAdapter.create_stream() - Create new Kinesis stream
  - KinesisAdapter.put_record() - Put record to stream
  - KinesisAdapter.get_records() - Get records from stream
  - StreamProcessor.process_stream() - Process incoming stream data
  - StreamProcessor.apply_windowing() - Apply time-based windowing
  - StreamProcessor.aggregate_data() - Aggregate stream data
  - CheckpointManager.create_checkpoint() - Create processing checkpoint
  - CheckpointManager.recover_from_checkpoint() - Recover from checkpoint
  - CheckpointManager.manage_consistency() - Manage checkpoint consistency
  - ExactlyOnce.enable_idempotency() - Enable idempotent processing
  - ExactlyOnce.deduplicate_messages() - Remove duplicate messages
  - ExactlyOnce.ensure_consistency() - Ensure exactly-once consistency
  - StreamMonitor.collect_metrics() - Collect stream processing metrics
  - StreamMonitor.check_health() - Check stream health status
  - StreamMonitor.set_alerts() - Set monitoring alerts
  - BackpressureHandler.manage_flow() - Manage data flow control
  - BackpressureHandler.apply_rate_limiting() - Apply rate limiting
  - BackpressureHandler.handle_backpressure() - Handle backpressure situations
  - StreamingManager.create_stream() - Create new data stream
  - StreamingManager.monitor_streams() - Monitor all active streams
  - StreamingManager.get_stream_stats() - Get stream statistics
domains
- Domain-specific processors (cybersecurity, biomedical, finance, legal). Each provides templates, mapping rules, ontologies, and extractors.
- Submodules:
  - cybersecurity_processor: Security domain processing, threat detection, vulnerability analysis
  - biomedical_processor: Medical domain processing, drug discovery, clinical data analysis
  - finance_processor: Financial domain processing, market analysis, risk assessment
  - legal_processor: Legal domain processing, contract analysis, regulation compliance
  - domain_templates: Domain-specific templates, schemas, data models
  - mapping_rules: Domain mapping rules, entity relationships, vocabulary mapping
  - domain_ontologies: Domain-specific ontologies, taxonomies, classification systems
  - domain_extractors: Specialized extractors for domain entities and relationships
  - domain_validator: Domain-specific validation rules, compliance checking
- Functions:
  - CybersecurityProcessor.detect_threats() - Detect security threats and vulnerabilities
  - CybersecurityProcessor.analyze_attacks() - Analyze attack patterns and techniques
  - CybersecurityProcessor.assess_risks() - Assess security risks and impact
  - BiomedicalProcessor.analyze_clinical_data() - Analyze clinical trial data
  - BiomedicalProcessor.drug_discovery() - Support drug discovery processes
  - BiomedicalProcessor.medical_entity_extraction() - Extract medical entities
  - FinanceProcessor.market_analysis() - Analyze market trends and patterns
  - FinanceProcessor.risk_assessment() - Assess financial risks
  - FinanceProcessor.compliance_checking() - Check regulatory compliance
  - LegalProcessor.contract_analysis() - Analyze legal contracts
  - LegalProcessor.regulation_compliance() - Check regulatory compliance
  - LegalProcessor.case_law_analysis() - Analyze case law and precedents
  - DomainTemplates.get_template() - Get domain-specific template
  - DomainTemplates.customize_template() - Customize template for specific use case
  - DomainTemplates.validate_template() - Validate template compliance
  - MappingRules.define_rules() - Define domain mapping rules
  - MappingRules.apply_mapping() - Apply mapping to data
  - MappingRules.validate_mapping() - Validate mapping consistency
  - DomainOntologies.get_ontology() - Get domain-specific ontology
  - DomainOntologies.extend_ontology() - Extend ontology with new concepts
  - DomainOntologies.validate_ontology() - Validate ontology consistency
  - DomainExtractors.extract_entities() - Extract domain-specific entities
  - DomainExtractors.extract_relationships() - Extract domain relationships
  - DomainExtractors.validate_extraction() - Validate extraction quality
  - DomainValidator.check_compliance() - Check domain compliance
  - DomainValidator.validate_data() - Validate domain data quality
  - DomainValidator.generate_report() - Generate compliance report
  - DomainManager.register_domain() - Register new domain processor
  - DomainManager.get_domain() - Get domain processor
  - DomainManager.list_domains() - List available domains
qa_rag
- RAG-optimised tools: semantic chunker, prompt templates, retrieval policies, answer justification, provenance-aware answer builder.
- Submodules:
  - semantic_chunker: Intelligent text chunking, context-aware splitting, overlap management
  - prompt_templates: RAG prompt templates, question formatting, context injection
  - retrieval_policies: Retrieval strategies, ranking algorithms, result filtering
  - answer_builder: Answer construction, context integration, source attribution
  - provenance_tracker: Source tracking, confidence scoring, evidence linking
  - answer_validator: Answer validation, fact checking, consistency verification
  - rag_optimizer: RAG performance optimization, query enhancement, result ranking
  - conversation_manager: Multi-turn conversations, context management, history tracking
- Functions:
  - SemanticChunker.chunk_text() - Create semantic chunks with context
  - SemanticChunker.optimize_chunks() - Optimize chunk size and overlap
  - SemanticChunker.merge_chunks() - Merge related chunks when needed
  - PromptTemplates.get_template() - Get RAG prompt template
  - PromptTemplates.format_question() - Format question for retrieval
  - PromptTemplates.inject_context() - Inject retrieved context into prompt
  - RetrievalPolicies.set_strategy() - Set retrieval strategy
  - RetrievalPolicies.rank_results() - Rank retrieval results
  - RetrievalPolicies.filter_results() - Filter results by criteria
  - AnswerBuilder.construct_answer() - Construct answer from retrieved context
  - AnswerBuilder.integrate_context() - Integrate multiple context sources
  - AnswerBuilder.attribute_sources() - Attribute answer to source documents
  - ProvenanceTracker.track_sources() - Track information sources
  - ProvenanceTracker.calculate_confidence() - Calculate answer confidence
  - ProvenanceTracker.link_evidence() - Link answer to supporting evidence
  - AnswerValidator.validate_answer() - Validate answer accuracy
  - AnswerValidator.fact_check() - Perform fact checking
  - AnswerValidator.verify_consistency() - Verify answer consistency
  - RAGOptimizer.optimize_retrieval() - Optimize retrieval performance
  - RAGOptimizer.enhance_queries() - Enhance user queries
  - RAGOptimizer.improve_ranking() - Improve result ranking
  - ConversationManager.start_conversation() - Start new conversation
  - ConversationManager.add_context() - Add context to conversation
  - ConversationManager.get_history() - Get conversation history
  - RAGManager.process_question() - Process user question
  - RAGManager.get_answer() - Get RAG-generated answer
  - RAGManager.evaluate_performance() - Evaluate RAG performance
agents
- Agent manager & orchestration for multiagent workflows, tools for tool‑use, orchestration policies, cost-awareness, sandboxing.
- Submodules:
  - agent_manager: Agent lifecycle management, registration, monitoring
  - orchestration_engine: Multi-agent coordination, workflow management, task distribution
  - tool_registry: Tool registration, discovery, version management
  - cost_tracker: Cost monitoring, budget management, resource optimization
  - sandbox_manager: Agent sandboxing, security isolation, resource limits
  - workflow_engine: Workflow definition, execution, monitoring
  - agent_communication: Inter-agent communication, message routing, protocol management
  - policy_enforcer: Policy enforcement, access control, compliance checking
  - agent_analytics: Agent performance analytics, behavior analysis, optimization
- Functions:
  - AgentManager.register_agent() - Register new agent
  - AgentManager.start_agent() - Start agent execution
  - AgentManager.stop_agent() - Stop agent execution
  - AgentManager.monitor_agent() - Monitor agent status
  - OrchestrationEngine.coordinate_agents() - Coordinate multiple agents
  - OrchestrationEngine.distribute_tasks() - Distribute tasks among agents
  - OrchestrationEngine.manage_workflows() - Manage agent workflows
  - ToolRegistry.register_tool() - Register tool for agent use
  - ToolRegistry.discover_tools() - Discover available tools
  - ToolRegistry.get_tool() - Get specific tool
  - CostTracker.monitor_costs() - Monitor agent execution costs
  - CostTracker.set_budget() - Set cost budget limits
  - CostTracker.optimize_resources() - Optimize resource usage
  - SandboxManager.create_sandbox() - Create agent sandbox
  - SandboxManager.isolate_agent() - Isolate agent execution
  - SandboxManager.set_resource_limits() - Set resource limits
  - WorkflowEngine.define_workflow() - Define agent workflow
  - WorkflowEngine.execute_workflow() - Execute defined workflow
  - WorkflowEngine.monitor_progress() - Monitor workflow progress
  - AgentCommunication.send_message() - Send message between agents
  - AgentCommunication.route_message() - Route message to appropriate agent
  - AgentCommunication.manage_protocols() - Manage communication protocols
  - PolicyEnforcer.enforce_policy() - Enforce access policies
  - PolicyEnforcer.check_compliance() - Check policy compliance
  - PolicyEnforcer.set_permissions() - Set agent permissions
  - AgentAnalytics.analyze_performance() - Analyze agent performance
  - AgentAnalytics.analyze_behavior() - Analyze agent behavior patterns
  - AgentAnalytics.optimize_agents() - Optimize agent performance
  - MultiAgentManager.create_team() - Create agent team
  - MultiAgentManager.orchestrate_workflow() - Orchestrate team workflow
  - MultiAgentManager.get_team_status() - Get team execution status
ui
- Web dashboard components: ingestion monitor, KG viewer (graph UI), conflict resolver, analytics dashboard, pipeline editor.
- Submodules:
  - ingestion_monitor: Real-time ingestion monitoring, progress tracking, error visualization
  - kg_viewer: Knowledge graph visualization, interactive graph exploration, node/edge inspection
  - conflict_resolver: Conflict resolution interface, conflict visualization, resolution workflows
  - analytics_dashboard: Data analytics, metrics visualization, trend analysis
  - pipeline_editor: Visual pipeline builder, step configuration, workflow design
  - data_explorer: Data exploration interface, search capabilities, result filtering
  - user_management: User authentication, role management, access control
  - notification_system: Alert notifications, status updates, system announcements
  - report_generator: Report creation, export functionality, template management
- Functions:
  - IngestionMonitor.show_progress() - Display ingestion progress
  - IngestionMonitor.track_errors() - Track and display errors
  - IngestionMonitor.show_statistics() - Show ingestion statistics
  - KGViewer.display_graph() - Display knowledge graph
  - KGViewer.zoom_graph() - Zoom and pan graph view
  - KGViewer.search_nodes() - Search for specific nodes
  - KGViewer.inspect_node() - Inspect node details
  - KGViewer.inspect_edge() - Inspect edge relationships
  - ConflictResolver.show_conflicts() - Display detected conflicts
  - ConflictResolver.resolve_conflict() - Resolve specific conflict
  - ConflictResolver.create_workflow() - Create resolution workflow
  - AnalyticsDashboard.show_metrics() - Display system metrics
  - AnalyticsDashboard.analyze_trends() - Analyze data trends
  - AnalyticsDashboard.generate_charts() - Generate data charts
  - PipelineEditor.create_pipeline() - Create new pipeline
  - PipelineEditor.edit_step() - Edit pipeline step
  - PipelineEditor.validate_pipeline() - Validate pipeline configuration
  - DataExplorer.search_data() - Search through data
  - DataExplorer.filter_results() - Filter search results
  - DataExplorer.export_results() - Export search results
  - UserManagement.authenticate_user() - Authenticate user login
  - UserManagement.manage_roles() - Manage user roles
  - UserManagement.set_permissions() - Set user permissions
  - NotificationSystem.send_alert() - Send system alert
  - NotificationSystem.update_status() - Update system status
  - NotificationSystem.announce_change() - Announce system changes
  - ReportGenerator.create_report() - Create custom report
  - ReportGenerator.export_report() - Export report in various formats
  - ReportGenerator.manage_templates() - Manage report templates
  - UIManager.initialize_dashboard() - Initialize dashboard
  - UIManager.update_components() - Update UI components
  - UIManager.get_user_preferences() - Get user preferences
monitoring
- Metrics, traces (OpenTelemetry), alerts, SLAs, data quality metrics, semantic quality score.
- Submodules:
  - metrics_collector: System metrics collection, performance monitoring, resource usage
  - tracing_system: OpenTelemetry integration, distributed tracing, span management
  - alert_manager: Alert generation, notification routing, escalation policies
  - sla_monitor: SLA tracking, performance thresholds, compliance monitoring
  - quality_metrics: Data quality assessment, semantic quality scoring, validation metrics
  - health_checker: System health monitoring, component status, dependency checking
  - performance_analyzer: Performance analysis, bottleneck detection, optimization suggestions
  - log_manager: Log collection, aggregation, analysis, retention policies
  - dashboard_renderer: Monitoring dashboard, visualization, real-time updates
- Functions:
  - MetricsCollector.collect_metrics() - Collect system metrics
  - MetricsCollector.monitor_performance() - Monitor system performance
  - MetricsCollector.track_resources() - Track resource usage
  - TracingSystem.start_trace() - Start distributed trace
  - TracingSystem.add_span() - Add span to trace
  - TracingSystem.end_trace() - End trace and collect data
  - AlertManager.generate_alert() - Generate system alert
  - AlertManager.route_notification() - Route alert notification
  - AlertManager.escalate_alert() - Escalate critical alerts
  - SLAMonitor.track_sla() - Track SLA compliance
  - SLAMonitor.check_thresholds() - Check performance thresholds
  - SLAMonitor.generate_report() - Generate SLA compliance report
  - QualityMetrics.assess_data_quality() - Assess data quality
  - QualityMetrics.calculate_semantic_score() - Calculate semantic quality score
  - QualityMetrics.validate_metrics() - Validate quality metrics
  - HealthChecker.check_system_health() - Check overall system health
  - HealthChecker.check_component_status() - Check individual component status
  - HealthChecker.verify_dependencies() - Verify system dependencies
  - PerformanceAnalyzer.analyze_performance() - Analyze system performance
  - PerformanceAnalyzer.detect_bottlenecks() - Detect performance bottlenecks
  - PerformanceAnalyzer.suggest_optimizations() - Suggest performance optimizations
  - LogManager.collect_logs() - Collect system logs
  - LogManager.aggregate_logs() - Aggregate log data
  - LogManager.analyze_logs() - Analyze log patterns
  - DashboardRenderer.render_dashboard() - Render monitoring dashboard
  - DashboardRenderer.update_visualizations() - Update dashboard visualizations
  - DashboardRenderer.refresh_data() - Refresh dashboard data
  - MonitoringManager.initialize_monitoring() - Initialize monitoring system
  - MonitoringManager.get_system_status() - Get overall system status
  - MonitoringManager.export_metrics() - Export monitoring metrics
quality
- QA, validation engine, schema validation, unit tests for extracted triples, confidence thresholds.
- Submodules:
  - qa_engine: Quality assurance engine, automated testing, validation workflows
  - validation_engine: Data validation, schema compliance, constraint checking
  - schema_validator: Schema validation, structure verification, format checking
  - triple_validator: Triple validation, consistency checking, quality scoring
  - confidence_calculator: Confidence scoring, reliability assessment, quality metrics
  - test_generator: Automated test generation, test case creation, coverage analysis
  - quality_reporter: Quality reports, issue tracking, improvement recommendations
  - data_profiler: Data profiling, statistics generation, anomaly detection
  - compliance_checker: Compliance verification, regulatory checking, audit support
- Functions:
  - QAEngine.run_qa_tests() - Run quality assurance tests
  - QAEngine.validate_data() - Validate data quality
  - QAEngine.generate_reports() - Generate QA reports
  - ValidationEngine.validate_data() - Validate data against schemas
  - ValidationEngine.check_constraints() - Check data constraints
  - ValidationEngine.verify_format() - Verify data format compliance
  - SchemaValidator.validate_schema() - Validate data schema
  - SchemaValidator.verify_structure() - Verify data structure
  - SchemaValidator.check_format() - Check data format
  - TripleValidator.validate_triple() - Validate individual triple
  - TripleValidator.check_consistency() - Check triple consistency
  - TripleValidator.score_quality() - Score triple quality
  - ConfidenceCalculator.calculate_confidence() - Calculate confidence score
  - ConfidenceCalculator.assess_reliability() - Assess data reliability
  - ConfidenceCalculator.generate_metrics() - Generate quality metrics
  - TestGenerator.generate_tests() - Generate automated tests
  - TestGenerator.create_test_cases() - Create specific test cases
  - TestGenerator.analyze_coverage() - Analyze test coverage
  - QualityReporter.generate_report() - Generate quality report
  - QualityReporter.track_issues() - Track quality issues
  - QualityReporter.suggest_improvements() - Suggest quality improvements
  - DataProfiler.profile_data() - Profile data characteristics
  - DataProfiler.generate_statistics() - Generate data statistics
  - DataProfiler.detect_anomalies() - Detect data anomalies
  - ComplianceChecker.verify_compliance() - Verify regulatory compliance
  - ComplianceChecker.check_regulations() - Check regulatory requirements
  - ComplianceChecker.generate_audit_report() - Generate audit report
  - QualityManager.initialize_qa() - Initialize quality assurance system
  - QualityManager.run_validation() - Run complete validation
  - QualityManager.get_quality_score() - Get overall quality score
security
- Access control (RBAC), data masking, PII redaction, audit logs, encryption helpers.
- Submodules:
  - access_control: Role-based access control, permission management, user authentication
  - data_masking: Sensitive data masking, anonymization, privacy protection
  - pii_redactor: PII detection, redaction, compliance with privacy regulations
  - audit_logger: Audit trail logging, activity tracking, compliance reporting
  - encryption_manager: Data encryption, key management, secure communication
  - security_validator: Security validation, vulnerability assessment, threat detection
  - compliance_manager: Regulatory compliance, policy enforcement, audit support
  - threat_monitor: Threat monitoring, intrusion detection, security alerts
  - vulnerability_scanner: Vulnerability assessment, security scanning, risk analysis
- Functions:
  - AccessControl.authenticate_user() - Authenticate user identity
  - AccessControl.authorize_access() - Authorize user access to resources
  - AccessControl.manage_roles() - Manage user roles and permissions
  - AccessControl.set_permissions() - Set resource permissions
  - DataMasking.mask_sensitive_data() - Mask sensitive information
  - DataMasking.anonymize_data() - Anonymize personal data
  - DataMasking.protect_privacy() - Protect data privacy
  - PIIRedactor.detect_pii() - Detect personally identifiable information
  - PIIRedactor.redact_pii() - Redact PII from data
  - PIIRedactor.comply_regulations() - Ensure regulatory compliance
  - AuditLogger.log_activity() - Log user activities
  - AuditLogger.track_changes() - Track data changes
  - AuditLogger.generate_report() - Generate audit reports
  - EncryptionManager.encrypt_data() - Encrypt sensitive data
  - EncryptionManager.manage_keys() - Manage encryption keys
  - EncryptionManager.secure_communication() - Secure data communication
  - SecurityValidator.validate_security() - Validate security measures
  - SecurityValidator.assess_vulnerabilities() - Assess security vulnerabilities
  - SecurityValidator.detect_threats() - Detect security threats
  - ComplianceManager.check_compliance() - Check regulatory compliance
  - ComplianceManager.enforce_policies() - Enforce security policies
  - ComplianceManager.support_audits() - Support compliance audits
  - ThreatMonitor.monitor_threats() - Monitor security threats
  - ThreatMonitor.detect_intrusions() - Detect intrusion attempts
  - ThreatMonitor.alert_security() - Alert on security issues
  - VulnerabilityScanner.scan_vulnerabilities() - Scan for security vulnerabilities
  - VulnerabilityScanner.assess_risks() - Assess security risks
  - VulnerabilityScanner.generate_report() - Generate vulnerability report
  - SecurityManager.initialize_security() - Initialize security system
  - SecurityManager.monitor_security() - Monitor overall security status
  - SecurityManager.generate_security_report() - Generate security report
deploy
- K8s manifests, Helm charts, Dockerfiles, autoscaling policies, GPU scheduling guides.
- Submodules:
  - kubernetes_manifests: K8s deployment files, service definitions, config maps
  - helm_charts: Helm chart packaging, templating, value management
  - docker_builder: Docker image building, multi-stage builds, optimization
  - autoscaling_manager: Horizontal pod autoscaling, resource-based scaling, custom metrics
  - gpu_scheduler: GPU resource allocation, scheduling policies, workload distribution
  - infrastructure_as_code: Terraform configurations, cloud provider templates, environment setup
  - deployment_validator: Deployment validation, health checks, rollback procedures
  - environment_manager: Environment management, configuration profiles, secrets management
  - rollout_manager: Deployment rollouts, canary deployments, blue-green strategies
- Functions:
  - KubernetesManifests.create_deployment() - Create K8s deployment manifest
  - KubernetesManifests.create_service() - Create K8s service manifest
  - KubernetesManifests.create_configmap() - Create K8s config map
  - KubernetesManifests.create_secret() - Create K8s secret
  - HelmCharts.package_chart() - Package Helm chart
  - HelmCharts.template_values() - Template chart values
  - HelmCharts.install_chart() - Install Helm chart
  - DockerBuilder.build_image() - Build Docker image
  - DockerBuilder.optimize_image() - Optimize Docker image size
  - DockerBuilder.push_image() - Push image to registry
  - AutoscalingManager.configure_hpa() - Configure horizontal pod autoscaler
  - AutoscalingManager.set_metrics() - Set custom scaling metrics
  - AutoscalingManager.manage_scaling() - Manage scaling policies
  - GPUScheduler.allocate_gpu() - Allocate GPU resources
  - GPUScheduler.set_policies() - Set GPU scheduling policies
  - GPUScheduler.distribute_workload() - Distribute workload across GPUs
  - InfrastructureAsCode.create_terraform() - Create Terraform configuration
  - InfrastructureAsCode.setup_cloud() - Setup cloud infrastructure
  - InfrastructureAsCode.manage_resources() - Manage cloud resources
  - DeploymentValidator.validate_deployment() - Validate deployment configuration
  - DeploymentValidator.run_health_checks() - Run deployment health checks
  - DeploymentValidator.prepare_rollback() - Prepare rollback procedures
  - EnvironmentManager.create_environment() - Create deployment environment
  - EnvironmentManager.manage_configs() - Manage environment configurations
  - EnvironmentManager.manage_secrets() - Manage environment secrets
  - RolloutManager.deploy_canary() - Deploy canary version
  - RolloutManager.deploy_blue_green() - Deploy using blue-green strategy
  - RolloutManager.manage_rollout() - Manage deployment rollout
  - DeploymentManager.initialize_deployment() - Initialize deployment system
  - DeploymentManager.monitor_deployment() - Monitor deployment status
  - DeploymentManager.rollback_deployment() - Rollback failed deployment
cli
- Lightweight CLI for quick ingestion, building KB, exporting triples, running QA checks.
- Submodules:
  - ingestion_cli: Command-line ingestion tools, batch processing, progress tracking
  - kb_builder_cli: Knowledge base building commands, configuration management
  - export_cli: Data export commands, format conversion, bulk operations
  - qa_cli: Quality assurance commands, validation tools, testing utilities
  - monitoring_cli: System monitoring commands, status checking, health verification
  - pipeline_cli: Pipeline management commands, execution control, configuration
  - user_management_cli: User management commands, authentication, authorization
  - help_system: Command help, documentation, examples, tutorials
  - interactive_shell: Interactive command shell, command history, auto-completion
- Functions:
  - IngestionCLI.ingest_files() - Ingest files from command line
  - IngestionCLI.ingest_directory() - Ingest entire directory
  - IngestionCLI.batch_process() - Process files in batch
  - IngestionCLI.track_progress() - Track ingestion progress
  - KBBuilderCLI.build_kb() - Build knowledge base from command line
  - KBBuilderCLI.configure_build() - Configure build parameters
  - KBBuilderCLI.monitor_build() - Monitor build progress
  - ExportCLI.export_triples() - Export triples in various formats
  - ExportCLI.convert_format() - Convert between export formats
  - ExportCLI.bulk_export() - Perform bulk export operations
  - QACLI.run_qa_tests() - Run QA tests from command line
  - QACLI.validate_data() - Validate data quality
  - QACLI.generate_reports() - Generate QA reports
  - MonitoringCLI.check_status() - Check system status
  - MonitoringCLI.verify_health() - Verify system health
  - MonitoringCLI.get_metrics() - Get system metrics
  - PipelineCLI.create_pipeline() - Create pipeline from command line
  - PipelineCLI.run_pipeline() - Run pipeline execution
  - PipelineCLI.monitor_pipeline() - Monitor pipeline status
  - UserManagementCLI.authenticate() - Authenticate user
  - UserManagementCLI.manage_users() - Manage user accounts
  - UserManagementCLI.set_permissions() - Set user permissions
  - HelpSystem.show_help() - Show command help
  - HelpSystem.show_examples() - Show usage examples
  - HelpSystem.show_tutorials() - Show tutorials
  - InteractiveShell.start_shell() - Start interactive shell
  - InteractiveShell.command_history() - Manage command history
  - InteractiveShell.auto_complete() - Provide command auto-completion
  - CLIManager.initialize_cli() - Initialize CLI system
  - CLIManager.parse_commands() - Parse command line arguments
  - CLIManager.execute_commands() - Execute CLI commands
examples
- Cookbooks and minimal reproducible examples across domains.
- Submodules:
  - cookbooks: Step-by-step guides, best practices, common use cases
  - minimal_examples: Minimal working examples, quick start guides, basic implementations
  - domain_examples: Domain-specific examples, industry use cases, specialized workflows
  - integration_examples: Integration examples, API usage, third-party tool integration
  - performance_examples: Performance optimization examples, benchmarking, optimization guides
  - troubleshooting_examples: Common problems, solutions, debugging guides
  - advanced_examples: Advanced features, complex workflows, expert-level usage
  - tutorial_examples: Learning tutorials, progressive examples, skill building
  - testing_examples: Testing examples, test data, validation examples
- Functions:
  - Cookbooks.get_cookbook() - Get specific cookbook guide
  - Cookbooks.list_cookbooks() - List available cookbooks
  - Cookbooks.search_cookbooks() - Search cookbooks by topic
  - MinimalExamples.get_example() - Get minimal working example
  - MinimalExamples.run_example() - Run example code
  - MinimalExamples.customize_example() - Customize example for specific use case
  - DomainExamples.get_domain_example() - Get domain-specific example
  - DomainExamples.list_domains() - List available domains
  - DomainExamples.customize_domain() - Customize domain example
  - IntegrationExamples.get_integration() - Get integration example
  - IntegrationExamples.test_integration() - Test integration example
  - IntegrationExamples.customize_integration() - Customize integration
  - PerformanceExamples.get_optimization() - Get performance optimization example
  - PerformanceExamples.benchmark_performance() - Benchmark performance
  - PerformanceExamples.optimize_code() - Optimize code performance
  - TroubleshootingExamples.get_solution() - Get troubleshooting solution
  - TroubleshootingExamples.diagnose_problem() - Diagnose common problems
  - TroubleshootingExamples.apply_fix() - Apply problem fixes
  - AdvancedExamples.get_advanced_feature() - Get advanced feature example
  - AdvancedExamples.explain_complexity() - Explain complex workflows
  - AdvancedExamples.demonstrate_expertise() - Demonstrate expert-level usage
  - TutorialExamples.get_tutorial() - Get learning tutorial
  - TutorialExamples.progress_through_levels() - Progress through tutorial levels
  - TutorialExamples.build_skills() - Build specific skills
  - TestingExamples.get_test_data() - Get test data examples
  - TestingExamples.create_tests() - Create test examples
  - TestingExamples.validate_examples() - Validate example functionality
  - ExamplesManager.initialize_examples() - Initialize examples system
  - ExamplesManager.search_examples() - Search through examples
  - ExamplesManager.run_example() - Run specific example
docs
- Sphinx/ReadTheDocs-ready documentation with tutorials and API reference.
- Submodules:
  - api_reference: Complete API documentation, method signatures, parameter descriptions
  - user_guides: User tutorials, step-by-step instructions, best practices
  - architecture_docs: System architecture, component diagrams, design decisions
  - deployment_guides: Deployment instructions, configuration guides, environment setup
  - troubleshooting: Common issues, solutions, debugging procedures, FAQ
  - developer_docs: Developer guides, contribution guidelines, code examples
  - api_examples: API usage examples, code snippets, integration patterns
  - version_notes: Release notes, changelog, migration guides, deprecation notices
  - documentation_generator: Auto-generated docs, docstring processing, format conversion
- Functions:
  - APIReference.generate_docs() - Generate API documentation
  - APIReference.document_methods() - Document all methods and functions
  - APIReference.describe_parameters() - Describe method parameters
  - UserGuides.create_tutorial() - Create user tutorial
  - UserGuides.write_instructions() - Write step-by-step instructions
  - UserGuides.include_best_practices() - Include best practices
  - ArchitectureDocs.document_architecture() - Document system architecture
  - ArchitectureDocs.create_diagrams() - Create component diagrams
  - ArchitectureDocs.explain_design() - Explain design decisions
  - DeploymentGuides.write_deployment() - Write deployment instructions
  - DeploymentGuides.create_config_guide() - Create configuration guide
  - DeploymentGuides.setup_environment() - Setup environment guide
  - Troubleshooting.document_issues() - Document common issues
  - Troubleshooting.provide_solutions() - Provide solution steps
  - Troubleshooting.create_faq() - Create frequently asked questions
  - DeveloperDocs.write_guide() - Write developer guide
  - DeveloperDocs.create_contribution() - Create contribution guidelines
  - DeveloperDocs.provide_examples() - Provide code examples
  - APIExamples.create_examples() - Create API usage examples
  - APIExamples.write_snippets() - Write code snippets
  - APIExamples.demonstrate_integration() - Demonstrate integration patterns
  - VersionNotes.create_release_notes() - Create release notes
  - VersionNotes.write_changelog() - Write changelog
  - VersionNotes.create_migration() - Create migration guide
  - DocumentationGenerator.auto_generate() - Auto-generate documentation
  - DocumentationGenerator.process_docstrings() - Process docstrings
  - DocumentationGenerator.convert_formats() - Convert between formats
  - DocumentationManager.initialize_docs() - Initialize documentation system
  - DocumentationManager.build_docs() - Build complete documentation
  - DocumentationManager.deploy_docs() - Deploy documentation

🆕 NEW MODULES TO TACKLE KNOWLEDGE GRAPH PROBLEMS

template_manager

Purpose: Enforce fixed templates and predefined schemas to prevent AI from inventing entities/relationships. * Features:
- Schema validation against predefined templates
- Template registry with versioning
- Constraint enforcement (required fields, data types, relationships)
- Template inheritance and composition
- Domain-specific template libraries (finance, healthcare, legal) * Exports: TemplateRegistry, SchemaValidator, ConstraintEngine, TemplateBuilder. * Submodules:
- template_registry: Template storage, versioning, metadata management
- schema_validator: Schema validation, constraint checking, compliance verification
- constraint_engine: Constraint definition, enforcement, validation rules
- template_builder: Template creation, composition, inheritance management
- domain_templates: Domain-specific template libraries, industry standards
- template_versioning: Version control, change tracking, migration support
- template_validation: Template quality checks, consistency validation
- template_export: Template export, import, sharing capabilities * Functions:
- TemplateRegistry.register_template() - Register new template
- TemplateRegistry.get_template() - Get template by ID
- TemplateRegistry.list_templates() - List available templates
- TemplateRegistry.version_template() - Create new template version
- SchemaValidator.validate_schema() - Validate data against schema
- SchemaValidator.check_constraints() - Check constraint compliance
- SchemaValidator.verify_compliance() - Verify regulatory compliance
- ConstraintEngine.define_constraint() - Define new constraint
- ConstraintEngine.enforce_constraint() - Enforce constraint on data
- ConstraintEngine.validate_rules() - Validate constraint rules
- TemplateBuilder.create_template() - Create new template
- TemplateBuilder.compose_template() - Compose template from components
- TemplateBuilder.inherit_template() - Inherit from base template
- DomainTemplates.get_finance_templates() - Get financial templates
- DomainTemplates.get_healthcare_templates() - Get healthcare templates
- DomainTemplates.get_legal_templates() - Get legal templates
- TemplateVersioning.create_version() - Create new template version
- TemplateVersioning.track_changes() - Track template changes
- TemplateVersioning.migrate_template() - Migrate between versions
- TemplateValidation.validate_quality() - Validate template quality
- TemplateValidation.check_consistency() - Check template consistency
- TemplateValidation.assess_completeness() - Assess template completeness
- TemplateExport.export_template() - Export template to various formats
- TemplateExport.import_template() - Import template from external source
- TemplateExport.share_template() - Share template with other users
- TemplateManager.initialize_templates() - Initialize template system
- TemplateManager.validate_all_templates() - Validate all templates
- TemplateManager.get_template_statistics() - Get template statistics

seed_manager

Purpose: Initialize Knowledge Graph with existing, known data to build on "foundation of truth". * Features:
- Seed data import from CSV, JSON, databases
- Known entity registration (products, departments, employees)
- Seed data validation and conflict detection
- Incremental seed data updates
- Seed data provenance tracking * Exports: SeedManager, SeedValidator, SeedImporter, SeedRegistry. * Submodules:
- seed_registry: Central registry for all seed data sources
- seed_importer: Import seed data from various formats
- seed_validator: Validate seed data quality and consistency
- seed_updater: Incremental updates to existing seed data
- seed_provenance: Track origin and changes to seed data
- seed_conflict_resolver: Resolve conflicts in seed data
- seed_synchronizer: Sync seed data across systems
- seed_analytics: Analyze seed data coverage and quality * Functions:
- SeedRegistry.register_source(name, format, location) - Register new seed data source
- SeedRegistry.list_sources() - List all registered seed sources
- SeedRegistry.get_source_metadata(source_id) - Get source metadata
- SeedImporter.import_csv(file_path, schema) - Import CSV seed data
- SeedImporter.import_json(file_path, schema) - Import JSON seed data
- SeedImporter.import_database(connection, query) - Import from database
- SeedImporter.import_api(endpoint, authentication) - Import from API
- SeedValidator.validate_data(data, schema) - Validate seed data
- SeedValidator.check_consistency(data) - Check data consistency
- SeedValidator.assess_quality(data) - Assess data quality
- SeedUpdater.update_entity(entity_id, new_data) - Update existing entity
- SeedUpdater.bulk_update(updates) - Bulk update multiple entities
- SeedUpdater.rollback_update(update_id) - Rollback specific update
- SeedProvenance.track_origin(entity_id, source) - Track entity origin
- SeedProvenance.get_change_history(entity_id) - Get entity change history
- SeedProvenance.export_provenance(entity_ids) - Export provenance data
- SeedConflictResolver.detect_conflicts(data) - Detect data conflicts
- SeedConflictResolver.resolve_conflict(conflict_id, resolution) - Resolve conflict
- SeedConflictResolver.get_conflict_report() - Get conflict summary
- SeedSynchronizer.sync_with_external(source) - Sync with external system
- SeedSynchronizer.get_sync_status() - Get synchronization status
- SeedSynchronizer.force_sync() - Force immediate synchronization
- SeedAnalytics.get_coverage_report() - Get data coverage analysis
- SeedAnalytics.get_quality_metrics() - Get quality metrics
- SeedAnalytics.identify_gaps() - Identify data gaps
- SeedManager.initialize_seeds() - Initialize seed data system
- SeedManager.refresh_seeds() - Refresh all seed data
- SeedManager.get_seed_statistics() - Get seed data statistics

semantic_deduplicator
- Purpose: Clean up and merge semantically similar entities to prevent graph messiness.
- Features:
  - Semantic similarity detection using embeddings
  - Fuzzy matching algorithms (Levenshtein, Jaro-Winkler)
  - Entity clustering and grouping
  - Merge strategies and conflict resolution
  - Duplicate detection confidence scoring
  - Batch deduplication with rollback support
- Exports: SemanticDeduplicator, SimilarityEngine, MergeEngine, ClusterAnalyzer.
- Submodules:
  - similarity_engine: Core similarity detection algorithms
  - fuzzy_matcher: String-based fuzzy matching
  - embedding_matcher: Semantic similarity using embeddings
  - cluster_analyzer: Entity clustering and grouping
  - merge_engine: Entity merging strategies
  - confidence_scorer: Duplicate detection confidence
  - batch_processor: Batch deduplication operations
  - rollback_manager: Rollback support for merges
- Functions:
  - SimilarityEngine.calculate_similarity(entity1, entity2) - Calculate similarity score
  - SimilarityEngine.find_similar_entities(entity, threshold) - Find similar entities
  - SimilarityEngine.batch_similarity_analysis(entities) - Batch similarity analysis
  - FuzzyMatcher.levenshtein_distance(str1, str2) - Calculate Levenshtein distance
  - FuzzyMatcher.jaro_winkler_similarity(str1, str2) - Calculate Jaro-Winkler similarity
  - FuzzyMatcher.fuzzy_match(query, candidates) - Fuzzy string matching
  - EmbeddingMatcher.semantic_similarity(entity1, entity2) - Semantic similarity
  - EmbeddingMatcher.embed_entity(entity) - Generate entity embedding
  - EmbeddingMatcher.find_semantic_duplicates(entities) - Find semantic duplicates
  - ClusterAnalyzer.create_clusters(entities, threshold) - Create entity clusters
  - ClusterAnalyzer.analyze_cluster(cluster_id) - Analyze specific cluster
  - ClusterAnalyzer.get_cluster_statistics() - Get clustering statistics
  - MergeEngine.merge_entities(entity_ids, strategy) - Merge entities
  - MergeEngine.define_merge_strategy(attributes) - Define merge strategy
  - MergeEngine.validate_merge(entity_ids) - Validate merge operation
  - ConfidenceScorer.calculate_confidence(match) - Calculate match confidence
  - ConfidenceScorer.get_confidence_threshold() - Get confidence threshold
  - ConfidenceScorer.set_confidence_threshold(threshold) - Set confidence threshold
  - BatchProcessor.process_batch(entities, batch_size) - Process batch deduplication
  - BatchProcessor.get_batch_status(batch_id) - Get batch processing status
  - BatchProcessor.cancel_batch(batch_id) - Cancel batch processing
  - RollbackManager.create_checkpoint(batch_id) - Create rollback checkpoint
  - RollbackManager.rollback_to_checkpoint(checkpoint_id) - Rollback to checkpoint
  - RollbackManager.list_checkpoints() - List available checkpoints
  - SemanticDeduplicator.initialize_deduplication() - Initialize deduplication system
  - SemanticDeduplicator.run_full_deduplication() - Run complete deduplication
  - SemanticDeduplicator.get_deduplication_report() - Get deduplication summary
conflict_detector
- Purpose: Flag disagreements when different sources provide conflicting information.
- Features:
  - Multi-source conflict detection
  - Conflict severity classification (minor, moderate, critical)
  - Source document tracking and highlighting
  - Conflict resolution workflow
  - Conflict history and audit trail
  - Automated conflict alerts and notifications
- Exports: ConflictDetector, ConflictResolver, ConflictTracker, AlertManager.
- Submodules:
  - conflict_detector: Core conflict detection engine
  - conflict_resolver: Conflict resolution strategies
  - conflict_tracker: Track and monitor conflicts
  - alert_manager: Automated conflict notifications
  - severity_classifier: Classify conflict severity
  - source_tracker: Track conflict sources
  - resolution_workflow: Conflict resolution process
  - audit_logger: Conflict audit trail
- Functions:
  - ConflictDetector.detect_conflicts(entities) - Detect conflicts in entities
  - ConflictDetector.scan_for_conflicts() - Scan entire KG for conflicts
  - ConflictDetector.get_conflict_summary() - Get conflict overview
  - ConflictResolver.resolve_conflict(conflict_id, resolution) - Resolve specific conflict
  - ConflictResolver.suggest_resolutions(conflict_id) - Suggest resolution options
  - ConflictResolver.apply_resolution(conflict_id, resolution) - Apply resolution
  - ConflictTracker.track_conflict(conflict_details) - Track new conflict
  - ConflictTracker.get_conflict_history(entity_id) - Get entity conflict history
  - ConflictTracker.get_active_conflicts() - Get unresolved conflicts
  - AlertManager.send_alert(conflict_id, recipients) - Send conflict alert
  - AlertManager.set_alert_rules(rules) - Configure alert rules
  - AlertManager.get_alert_history() - Get alert history
  - SeverityClassifier.classify_severity(conflict) - Classify conflict severity
  - SeverityClassifier.set_severity_thresholds(thresholds) - Set severity thresholds
  - SeverityClassifier.get_severity_distribution() - Get severity distribution
  - SourceTracker.identify_sources(conflict_id) - Identify conflict sources
  - SourceTracker.get_source_credibility(source_id) - Get source credibility score
  - SourceTracker.track_source_changes(source_id) - Track source changes
  - ResolutionWorkflow.create_workflow(conflict_id) - Create resolution workflow
  - ResolutionWorkflow.assign_resolver(conflict_id, user_id) - Assign conflict resolver
  - ResolutionWorkflow.track_progress(conflict_id) - Track resolution progress
  - AuditLogger.log_conflict_event(event_type, details) - Log conflict event
  - AuditLogger.get_audit_trail(conflict_id) - Get conflict audit trail
  - AuditLogger.export_audit_log(time_range) - Export audit log
  - ConflictDetector.initialize_detection() - Initialize conflict detection
  - ConflictDetector.run_conflict_scan() - Run conflict detection scan
  - ConflictDetector.get_detection_statistics() - Get detection statistics
provenance_tracker
- Purpose: Track the exact source of every piece of information for transparency and investigation.
- Features:
  - Source document linking (file, page, paragraph, line)
  - Extraction timestamp and confidence scores
  - Processing pipeline tracking
  - Source credibility scoring
  - Provenance visualization in UI
  - Export capabilities for audit purposes
- Submodules:
  - provenance_tracker: Core provenance tracking engine
  - source_linker: Link information to source documents
  - timestamp_manager: Track extraction and processing times
  - confidence_tracker: Track confidence scores
  - pipeline_tracker: Track processing pipeline steps
  - credibility_scorer: Score source credibility
  - provenance_visualizer: Visualize provenance in UI
  - audit_exporter: Export provenance for audit
- Functions:
  - ProvenanceTracker.track_entity(entity_id, source_info) - Track entity provenance
  - ProvenanceTracker.get_provenance(entity_id) - Get entity provenance
  - ProvenanceTracker.update_provenance(entity_id, new_info) - Update provenance
  - SourceLinker.link_to_document(entity_id, doc_id, location) - Link to document
  - SourceLinker.link_to_page(entity_id, page_number) - Link to specific page
  - SourceLinker.link_to_paragraph(entity_id, paragraph_id) - Link to paragraph
  - SourceLinker.link_to_line(entity_id, line_number) - Link to specific line
  - TimestampManager.record_extraction(entity_id, timestamp) - Record extraction time
  - TimestampManager.record_processing(entity_id, step, timestamp) - Record processing time
  - TimestampManager.get_timeline(entity_id) - Get entity timeline
  - ConfidenceTracker.set_confidence(entity_id, score) - Set confidence score
  - ConfidenceTracker.get_confidence(entity_id) - Get confidence score
  - ConfidenceTracker.track_confidence_changes(entity_id) - Track confidence changes
  - PipelineTracker.track_step(entity_id, step_name, status) - Track pipeline step
  - PipelineTracker.get_pipeline_history(entity_id) - Get pipeline history
  - PipelineTracker.get_step_status(entity_id, step_name) - Get step status
  - CredibilityScorer.score_source(source_id, criteria) - Score source credibility
  - CredibilityScorer.get_source_score(source_id) - Get source credibility score
  - CredibilityScorer.update_source_score(source_id, new_score) - Update source score
  - ProvenanceVisualizer.display_provenance(entity_id) - Display provenance in UI
  - ProvenanceVisualizer.create_provenance_graph(entity_id) - Create provenance graph
  - ProvenanceVisualizer.export_provenance_view(entity_id, format) - Export view
  - AuditExporter.export_provenance_report(entity_ids, format) - Export audit report
  - AuditExporter.export_source_analysis(time_range) - Export source analysis
  - AuditExporter.export_confidence_report() - Export confidence report
  - ProvenanceTracker.initialize_tracking() - Initialize provenance system
  - ProvenanceTracker.get_tracking_statistics() - Get tracking statistics - ProvenanceTracker.cleanup_old_provenance(retention_days) - Cleanup old data
- Exports: ProvenanceTracker, SourceLinker, CredibilityScorer, ProvenanceExporter.

🎯 KNOWLEDGE GRAPH QUALITY MODULES COMPLETED

All 5 new Knowledge Graph quality modules have been fully detailed with:

✅ template_manager - Template-based KG construction with schema validation ✅ seed_manager - Seed data management and validation
✅ semantic_deduplicator - Entity deduplication and merging ✅ conflict_detector - Multi-source conflict detection and resolution ✅ provenance_tracker - Complete provenance tracking and audit

Total Functions Added: 150+ detailed functions across all submodules Total Submodules Added: 40+ specialized submodules for quality control Coverage: Complete implementation roadmap for KG trustworthiness

🚀 NEXT DEVELOPMENT PRIORITIES

Implementation Phase 1: Core quality modules (template_manager, seed_manager)
Implementation Phase 2: Processing modules (deduplicator, conflict_detector)
Implementation Phase 3: Tracking modules (provenance_tracker)
Integration Phase: Connect all modules into unified quality pipeline
Testing Phase: Comprehensive testing of quality workflows
Documentation Phase: User guides and API documentation

3. Features (by user story)

As a data engineer I want pluggable ingestors so I can add new connectors quickly.
As an ontology engineer I want automatic ontology suggestions and human-in-the-loop refinement.
As an analyst I want a KG browser that shows provenance and conflict highlights.
As an ML engineer I want RAG-ready semantic chunks with embeddings and retrieval tuning.
As a security engineer I want PII detection and masking before storing embeddings.

🆕 NEW USER STORIES FOR KNOWLEDGE GRAPH PROBLEMS

As a knowledge engineer I want to enforce fixed templates so the AI doesn't invent new entities or relationships.
As a domain expert I want to seed the Knowledge Graph with known data so the AI builds on existing knowledge.
As a data quality analyst I want automatic deduplication to merge semantically similar entities and keep the graph clean.
As a compliance officer I want to see exactly where conflicting information comes from so I can investigate discrepancies.
As a business user I want to trust that the Knowledge Graph represents the single source of truth without duplicates or conflicts.

4. Recommended tech stack (per module)

Language: Python 3.10+ (type hints, pydantic models)
Async: asyncio + anyio for I/O bound ingestion
Parsing: pdfplumber, PyMuPDF, python-docx, openpyxl, beautifulsoup4
OCR: tesserocr / Google Vision or AWS Textract adapters
NLP: spaCy, transformers, flair, LLM adapters
Vector stores: FAISS (local), Pinecone / Qdrant / Milvus
KG: Neo4j for property graph + RDF store for triples
Streaming: Kafka / Pulsar
Infra: Docker + Kubernetes + Helm
Monitoring: Prometheus + Grafana + OpenTelemetry
CI/CD: GitHub Actions, dependabot

🆕 NEW TECH STACK FOR KNOWLEDGE GRAPH MODULES

Template Management: JSON Schema, Pydantic, Cerberus for validation
Semantic Deduplication: SentenceTransformers, FAISS, scikit-learn clustering
Conflict Detection: Fuzzy matching with fuzzywuzzy, rapidfuzz
Provenance Tracking: GraphQL, Neo4j Cypher, RDF/SPARQL

5. Architecture — dataflow diagram (Mermaid)

flowchart LR
  A[Sources] -->|file/web/db/feeds| B(Ingest Layer)
  B --> C(Parse & Normalize)
  C --> D(Semantic Extract)
  D --> E{Branch}
  E -->|triples| F(Triple Store / RDF)
  E -->|embeddings| G(Vector Store)
  E -->|kg nodes| H(KG / Graph DB)
  F --> I[Reasoning & Ontology]
  G --> J[RAG & Agents]
  H --> K[Analytics / UI]
  J --> K
  I --> K
  
  %% NEW KNOWLEDGE GRAPH MODULES
  L[Seed Data] --> M(Seed Manager)
  M --> H
  N[Template Registry] --> O(Template Manager)
  O --> D
  P[Conflict Detector] --> Q(Conflict Resolution)
  Q --> H
  R[Semantic Deduplicator] --> H
  S[Provenance Tracker] --> T(Provenance Store)
  T --> K

6. Roadmap (quarterly milestones)

Q1 — MVP
 - core, ingest(file/web), parse(pdf/docx/html), split, embeddings(local FAISS), basic vector_store adapter
 - simple CLI

Q2 — KG + Triples
 - triple_extractor, ontology generator, triple store adapter, deduplication
 - basic KG UI

Q3 — Streaming & Domains
 - streaming connectors, feed processor, domain processors (cyber, biomedical)
 - monitoring & QA

Q4 — Agents & Enterprise
 - multi-agent orchestration, advanced reasoning, RBAC, production K8s charts, enterprise docs

🆕 Q5 — Knowledge Graph Quality & Trust
 - template_manager, seed_manager, semantic_deduplicator, conflict_detector
 - provenance_tracker, advanced conflict resolution UI
 - template libraries for common domains

🆕 Q6 — Enterprise Knowledge Graph Features
 - multi-tenant template management, advanced conflict workflows
 - compliance reporting, audit trails, enterprise integrations

7. Visual charts

7.1 Module size and priority (simple bar chart)

Modules Priority
core           ██████████ 10
ingest         █████████  9
parse          ████████   8
semantic_extract ████████ 8
embeddings     ███████    7
vector_store   ███████    7
kg             ████████   8
ontology       ██████     6
ui             █████      5
monitoring     █████      5
agents         ████       4
🆕 template_manager ████████ 8
🆕 seed_manager     ███████  7
🆕 semantic_deduplicator ████████ 8
🆕 conflict_detector     ████████ 8
🆕 provenance_tracker    ███████  7

7.2 Gantt-style timeline (Mermaid)

gantt
    dateFormat  YYYY-MM-DD
    title Semantica high-level roadmap
    section MVP
    Core & Ingest       :done, a1, 2025-01-01, 45d
    Parsing & Chunking  :done, a2, after a1, 30d
    Embeddings & FAISS  :done, a3, after a2, 20d
    section KG
    Triple extractor    :a4, after a3, 30d
    Ontology generator  :a5, after a4, 20d
    section Scale
    Streaming & Domains :a6, after a5, 60d
    Monitoring & QA     :a7, after a6, 30d
    Agents & Enterprise :a8, after a7, 60d
    🆕 section KG Quality
    Template Manager    :a9, after a8, 45d
    Seed Manager        :a10, after a9, 30d
    Semantic Deduplicator :a11, after a10, 45d
    Conflict Detector   :a12, after a11, 45d
    Provenance Tracker  :a13, after a12, 30d

8. Design notes & implementation guidance

Start small: implement core, ingest.file, parse.pdf, split and embeddings with local FAISS. Aim for end-to-end pipeline that takes a PDF and returns embeddings + triples.
Provenance-first: every chunk, triple, and entity MUST carry source, offset, and confidence.
Schema-first approach: let teams register Pydantic models (business entities) early — this simplifies downstream validation and UX.
Human-in-loop: provide UIs for ontology review, conflict resolution, and triple acceptance.
Test coverage: unit tests for extractors, golden files for parsers, and integration tests that simulate full pipelines.
Performance: make parsing idempotent and resumable; add caching at the ingest and embedder layers.

🆕 NEW DESIGN PRINCIPLES FOR KNOWLEDGE GRAPH QUALITY

Template-first: Define schemas before ingestion to prevent AI invention of entities/relationships.
Seed-before-extract: Always start with known data to establish foundation of truth.
Conflict-aware: Design for conflict detection from day one, not as an afterthought.
Provenance-everywhere: Track source of every piece of information for transparency.
Deduplication-continuous: Implement semantic deduplication as a continuous process, not just during ingestion.

9. CI/CD, release & contributor guidelines

Monorepo with packages under packages/ using poetry or flit.
Pre-commit hooks (black, isort, ruff, mypy), unit tests on GitHub Actions, release automation for PyPI and DockerHub.
CONTRIBUTING.md with code style, PR checklist, and issue template.

10. Deliverables (file list for initial repo scaffolding)

/README.md
/pyproject.toml
/packages/core/
/packages/ingest/
/packages/parse/
/packages/split/
/packages/semantic_extract/
/packages/embeddings/
/packages/vector_store/
/packages/kg/
/packages/ontology/
/packages/cli/
/docs/
/examples/
/infra/k8s/
/helm/semantica/
/.github/workflows/

🆕 /packages/template_manager/
🆕 /packages/seed_manager/
🆕 /packages/semantic_deduplicator/
🆕 /packages/conflict_detector/
🆕 /packages/provenance_tracker/

11. Starter tasks (first sprint — 2 weeks)

Scaffold repo and package layout.
Implement core and a file ingestor that accepts local directories and single files.
Implement pdf parser using pdfplumber that returns structured blocks and simple tables.
Implement split with structural and sliding-window modes.
Implement SemanticEmbedder with local huggingface mini-embedding and persist to FAISS.
Add basic unit tests and a demo notebook that shows E2E from PDF -> embeddings -> search.

🆕 NEW STARTER TASKS FOR KNOWLEDGE GRAPH QUALITY

Implement template_manager with basic JSON schema validation.
Create seed_manager for importing known entities from CSV/JSON.
Build semantic_deduplicator using sentence transformers for similarity detection.
Develop conflict_detector to identify source disagreements.
Add provenance_tracker to link all information to source documents.

12. Example API usage (reference)

from semantica import Semantica

core = Semantica(
    config_path="./config.yaml",
    llm_provider="local",
    vector_store="faiss"
)

kb = core.build_knowledge_base(["./sample_docs/"])
print(kb.search("key trends"))

🆕 NEW API EXAMPLES FOR KNOWLEDGE GRAPH QUALITY

from semantica import Semantica
from semantica.template_manager import TemplateRegistry
from semantica.seed_manager import SeedManager
from semantica.semantic_deduplicator import SemanticDeduplicator
from semantica.conflict_detector import ConflictDetector

# Initialize with quality modules
core = Semantica(
    config_path="./config.yaml",
    template_manager=TemplateRegistry("./templates/"),
    seed_manager=SeedManager("./seed_data/"),
    deduplicator=SemanticDeduplicator(),
    conflict_detector=ConflictDetector()
)

# Load predefined template
template = core.template_manager.load_template("financial_report")

# Seed with known data
core.seed_manager.load_seed_data("departments.csv", "employees.json")

# Build knowledge base with quality controls
kb = core.build_knowledge_base(
    ["./documents/"],
    template=template,
    enable_deduplication=True,
    enable_conflict_detection=True
)

# Check for conflicts
conflicts = core.conflict_detector.get_conflicts()
for conflict in conflicts:
    print(f"Conflict: {conflict.description}")
    print(f"Sources: {conflict.sources}")
    print(f"Severity: {conflict.severity}")

# Get provenance for any entity
provenance = core.provenance_tracker.get_provenance("Q1_Sales_Report")
print(f"Source: {provenance.source_document}")
print(f"Extracted: {provenance.extraction_timestamp}")
print(f"Confidence: {provenance.confidence_score}")

13. Next steps / how I can help

If you want, I can:

Expand each module into a README.md + __init__.py template for the repo scaffold.
Generate a prioritized ticket backlog (Jira/GitHub issues format).
Create starter code for core, file ingestor, pdf parser and a demo notebook.

🆕 NEW NEXT STEPS FOR KNOWLEDGE GRAPH QUALITY

Create detailed specifications for the 5 new Knowledge Graph quality modules.
Design the conflict resolution workflow and UI components.
Develop template libraries for common domains (finance, healthcare, legal).
Build integration examples showing how these modules work together.
Create quality metrics and validation frameworks for Knowledge Graph trustworthiness.

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