# Modules Every Semantica module works independently — use only what you need. !!! tip "Just need a quick reference?" Jump to the [Module Index](#module-index) at the bottom of this page. --- ## Architecture Overview Semantica is organized into **six logical layers** - each with specific responsibilities:
- **Input Layer** --- Data ingestion and preparation **Modules**: Ingest, Parse, Split, Normalize - **Core Processing** --- Intelligence and understanding **Modules**: Semantic Extract, Knowledge Graph, Ontology, Reasoning - **Storage** --- Persistent data storage **Modules**: Embeddings, Vector Store, Graph Store, Triplet Store - **Quality Assurance** --- Data quality and consistency **Modules**: Deduplication, Conflicts - **Context & Memory** --- Agent memory and foundation data **Modules**: Context, Seed, LLM Providers - **Output & Orchestration** --- Export, visualization, and workflows **Modules**: Export, Visualization, Pipeline
--- ## Input Layer ### Ingest Module **Data ingestion from multiple sources** ```python from semantica.ingest import FileIngestor, WebIngestor # File ingestion ingestor = FileIngestor() documents = ingestor.ingest_directory("data/") # Web ingestion web_ingestor = WebIngestor() pages = web_ingestor.ingest_urls(["https://example.com"]) ``` - **File formats** - PDF, DOCX, TXT, JSON, CSV - **Web scraping** - Extract content from websites - **Database** - Connect to SQL and NoSQL databases - **Batch processing** - Handle large datasets efficiently - Document processing pipelines - Web data extraction - Database integration - Multi-source data collection ### Parse Module **Document parsing and text extraction** ```python from semantica.parse import DocumentParser parser = DocumentParser() parsed = parser.parse_document("document.pdf") text = parsed["full_text"] metadata = parsed["metadata"] ``` - **Text extraction** - Extract clean text from documents - **Metadata parsing** - Extract titles, authors, dates - **Structure analysis** - Identify sections, headings - **OCR support** - Handle scanned documents - PDF processing - Document analysis - Content extraction - Metadata harvesting --- ### Split Module **Text chunking and segmentation** ```python from semantica.split import TextSplitter splitter = TextSplitter(method="semantic") chunks = splitter.split(text, chunk_size=1000, overlap=200) ``` - **Intelligent chunking** - Split text while preserving context - **Semantic splitting** - Break at natural boundaries - **Size control** - Manage chunk sizes for processing - **Overlap handling** - Maintain context between chunks - Document preprocessing - Embedding preparation - RAG systems - Large document processing --- ### Normalize Module **Data cleaning and standardization** ```python from semantica.normalize import DataNormalizer normalizer = DataNormalizer() clean_text = normalizer.normalize_text(text) standardized_date = normalizer.normalize_date("Jan 1st, 2020") ``` - **Text cleaning** - Remove noise and artifacts - **Date standardization** - Convert to ISO format - **Name normalization** - Standardize person names - **Entity normalization** - Clean up company names - Data preprocessing - Quality improvement - Standardization - Consistency enforcement --- ## Core Processing ### Semantic Extract Module **Entity and relationship extraction** ```python from semantica.semantic_extract import NERExtractor, RelationExtractor # Entity extraction ner = NERExtractor() entities = ner.extract("Apple Inc. was founded by Steve Jobs.") # Relationship extraction rel_extractor = RelationExtractor() relationships = rel_extractor.extract(text, entities) ``` - **Named Entity Recognition** - Find people, orgs, locations - **Relationship extraction** - Find connections between entities - **Custom entities** - Define your own entity types - **Confidence scoring** - Quality assessment for extractions - Knowledge graph construction - Document analysis - Information extraction - Content understanding --- ### Knowledge Graph Module **Graph construction and management** ```python from semantica.kg import GraphBuilder, GraphAnalyzer # Build graph builder = GraphBuilder() kg = builder.build({"entities": entities, "relationships": relationships}) # Analyze graph analyzer = GraphAnalyzer() stats = analyzer.analyze(kg) ``` - **Graph construction** - Build knowledge graphs from data - **Graph analysis** - Calculate metrics and statistics - **Graph querying** - Search and retrieve information - **Graph manipulation** - Merge, split, transform graphs - Knowledge base creation - Graph analytics - Information retrieval - Data integration --- ### Ontology Module **Schema definition and validation** ```python from semantica.ontology import OntologyManager # Define ontology ontology = OntologyManager() ontology.add_class("Person", ["name", "birth_date"]) ontology.add_relationship("works_for", "Person", "Organization") # Validate data is_valid = ontology.validate_graph(kg) ``` - **Schema definition** - Define data structure - **Data validation** - Ensure data conforms to schema - **Inheritance** - Create hierarchical relationships - **Constraints** - Enforce data quality rules - Data modeling - Quality assurance - Schema management - Rule enforcement --- ### Reasoning Module **Logical inference and deduction** ```python from semantica.reasoning import ReasoningEngine engine = ReasoningEngine() inferences = engine.infer(kg, rules=["transitivity", "symmetry"]) ``` - **Logical inference** - Derive new facts from existing ones - **Pattern matching** - Find complex patterns in data - **Consistency checking** - Detect contradictions - **Decision support** - Automated reasoning - Knowledge discovery - Decision making - Consistency checking - Advanced analytics --- ## Storage Layer ### Embeddings Module **Vector embeddings and similarity** ```python from semantica.embeddings import EmbeddingGenerator generator = EmbeddingGenerator(model="sentence-transformers") embeddings = generator.generate(["text1", "text2"]) similarity = generator.similarity(embeddings[0], embeddings[1]) ``` - **Text embeddings** - Convert text to vectors - **Similarity search** - Find similar content - **Clustering** - Group related items - **AI integration** - Provide context to LLMs - Semantic search - Recommendation systems - Clustering - AI context --- ### Vector Store Module **Vector database management** ```python from semantica.vector_store import VectorStore store = VectorStore(backend="faiss") store.add_vectors(embeddings, ids) results = store.search(query_vector, top_k=10) ``` - **Vector storage** - Efficient vector database - **Fast search** - Approximate nearest neighbor search - **Indexing** - Optimize for performance - **Batch operations** - Handle large datasets - Semantic search - RAG systems - Recommendation engines - Similarity matching --- ### Graph Store Module **Graph database integration** ```python from semantica.graph_store import GraphStore store = GraphStore(backend="neo4j") store.add_nodes(entities) store.add_edges(relationships) results = store.query("MATCH (n)-[r]->(m) RETURN n, r, m") ``` - **Graph persistence** - Store graphs in databases - **Graph queries** - Cypher and Gremlin support - **Graph algorithms** - Path finding, centrality - **Transactions** - ACID compliance - Knowledge graph storage - Graph analytics - Network analysis - Relationship queries --- ### Triplet Store Module **Triple-based storage** ```python from semantica.triplet_store import TripletStore store = TripletStore() store.add_triplets(subject, predicate, object) triplets = store.get_triplets(entity="Apple Inc.") ``` - **Triple storage** - Store (subject, predicate, object) triples - **Pattern matching** - Find specific patterns - **RDF support** - Semantic web standards - **Bulk operations** - Efficient batch processing - Semantic web - Knowledge representation - Linked data - Triple stores --- ## Quality Assurance ### Deduplication Module **Entity deduplication and resolution** ```python from semantica.deduplication import EntityResolver resolver = EntityResolver() merged_entities = resolver.resolve(entities, strategy="semantic") ``` - **Duplicate detection** - Find similar entities - **Entity resolution** - Merge duplicate records - **Similarity scoring** - Quality assessment - **Record linkage** - Connect related records - Data cleaning - Master data management - Record linkage - Quality improvement --- ### Conflicts Module **Conflict detection and resolution** ```python from semantica.conflicts import ConflictDetector detector = ConflictDetector() conflicts = detector.detect_conflicts(kg) resolved = detector.resolve(conflicts, strategy="most_recent") ``` - **Conflict detection** - Find contradictory information - **Resolution strategies** - Automated conflict resolution - **Source reliability** - Trustworthiness assessment - **Temporal analysis** - Time-based conflict handling - Data quality - Consistency checking - Trust management - Conflict resolution --- ## Context & Memory ### Context Module **Context management for AI agents** ```python from semantica.context import ContextManager manager = ContextManager() context = manager.get_context(query, history) ``` - **Context tracking** - Maintain conversation context - **Memory management** - Store and retrieve context - **Relevance scoring** - Find relevant context - **Session management** - Handle multiple conversations - AI agents - Chatbots - Conversational AI - Context-aware systems --- ### Seed Module **Foundation data and knowledge** ```python from semantica.seed import SeedData seed = SeedData() knowledge = seed.get_knowledge("technology", "companies") ``` - **Seed knowledge** - Foundation data for domains - **Knowledge bases** - Pre-built domain knowledge - **Quick start** - Bootstrap applications - **Domain models** - Industry-specific data - Domain bootstrapping - Quick start data - Industry knowledge - Foundation models --- ### LLM Providers Module **Large Language Model integration** ```python from semantica.llms import LLMProvider provider = LLMProvider(model="gpt-4") response = provider.generate(prompt, context=kg) ``` - **LLM integration** - Connect to various LLM providers - **Prompt engineering** - Optimize prompts for results - **Context injection** - Provide knowledge graph context - **Response parsing** - Extract structured outputs - AI generation - Question answering - Text completion - Knowledge reasoning --- ## Output & Orchestration ### Export Module **Data export and serialization** ```python from semantica.export import GraphExporter exporter = GraphExporter() exporter.export(kg, format="json", filename="output.json") ``` - **Multiple formats** - JSON, CSV, RDF, GraphML - **Database export** - Export to various databases - **Streaming** - Handle large datasets - **Filtering** - Export specific data subsets - Data sharing - System integration - Backup and restore - Format conversion --- ### Visualization Module **Graph visualization and analysis** ```python from semantica.visualization import GraphVisualizer visualizer = GraphVisualizer() visualizer.plot(kg, layout="force_directed") ``` - **Graph visualization** - Interactive graph plots - **Custom styling** - Tailored visual appearance - **Analytics charts** - Statistics and metrics - **Exploration tools** - Interactive data exploration - Data exploration - Presentation - Analysis - Reporting --- ### Pipeline Module **Workflow orchestration** ```python from semantica.pipeline import Pipeline pipeline = Pipeline() pipeline.add_step("ingest", FileIngestor()) pipeline.add_step("extract", NERExtractor()) pipeline.add_step("build", GraphBuilder()) result = pipeline.run("data/") ``` - **Workflow orchestration** - Coordinate multiple steps - **Parallel processing** - Run steps concurrently - **Progress tracking** - Monitor pipeline execution - **Error handling** - Robust error management - Data processing - Workflow automation - Batch processing - System integration --- ## Additional Modules ### Change Management Module **Version control and audit trails** ```python from semantica.change_management import TemporalVersionManager manager = TemporalVersionManager(storage_path="versions.db") snapshot = manager.create_snapshot(kg, "v1.0", "user@example.com", "Initial version") ``` - **Version control** - Track changes over time - **Audit trails** - Complete change history - **Data integrity** - SHA-256 checksums - **Change comparison** - Detailed diff analysis - Knowledge graph versioning - Compliance tracking - Data governance - Change management --- ### Provenance Module **W3C PROV-O compliant tracking** ```python from semantica.provenance import ProvenanceManager manager = ProvenanceManager() manager.track_entity("entity_1", "document.pdf", "person") ``` - **W3C PROV-O compliant** - Industry standard tracking - **Complete lineage** - End-to-end traceability - **Source attribution** - Track data origins - **Integrity verification** - Tamper detection - Regulatory compliance - Data provenance - Audit trails - Source tracking --- ### Core Module **Framework orchestration and configuration** ```python from semantica.core import Semantica, Config # Initialize framework semantica = Semantica(config=Config()) result = semantica.process("data/") ``` - **Framework orchestration** - Central coordination - **Configuration management** - Settings and preferences - **Lifecycle management** - Start/stop/restart - **Plugin system** - Extensible architecture - Framework initialization - Configuration management - Plugin development - System orchestration --- ## Common Module Chains | Goal | Modules | |------|---------| | Document processing | Ingest → Parse → Split → Semantic Extract → KG | | Web scraping | Ingest (Web) → Normalize → Semantic Extract → Graph Store | | AI agents | Context → LLM Providers → Reasoning → Export | | Analytics | KG → Graph Store → Visualization → Export | --- ## Module Index | Module | Purpose | Key Classes | Use Cases | |--------|---------|-------------|-----------| | [Ingest](reference/ingest.md) | Data ingestion | FileIngestor, WebIngestor | File processing, web scraping | | [Parse](reference/parse.md) | Document parsing | DocumentParser | PDF processing, text extraction | | [Split](reference/split.md) | Text chunking | TextSplitter | RAG systems, preprocessing | | [Normalize](reference/normalize.md) | Data cleaning | DataNormalizer | Quality improvement | | [Semantic Extract](reference/semantic_extract.md) | Information extraction | NERExtractor, RelationExtractor | Knowledge graphs | | [Knowledge Graph](reference/kg.md) | Graph management | GraphBuilder, GraphAnalyzer | Graph construction | | [Ontology](reference/ontology.md) | Schema management | OntologyManager | Data modeling | | [Reasoning](reference/reasoning.md) | Logical inference | ReasoningEngine | Knowledge discovery | | [Embeddings](reference/embeddings.md) | Vector embeddings | EmbeddingGenerator | Semantic search | | [Vector Store](reference/vector_store.md) | Vector database | VectorStore | Similarity search | | [Graph Store](reference/graph_store.md) | Graph database | GraphStore | Graph storage | | [Triplet Store](reference/triplet_store.md) | Triple storage | TripletStore | Semantic web | | [Deduplication](reference/deduplication.md) | Entity resolution | EntityResolver | Data quality | | [Conflicts](reference/conflicts.md) | Conflict resolution | ConflictDetector | Consistency | | [Context](reference/context.md) | Context management | ContextManager | AI agents | | [Seed](reference/seed.md) | Foundation data | SeedData | Domain knowledge | | [LLM Providers](reference/llms.md) | LLM integration | LLMProvider | AI generation | | [Export](reference/export.md) | Data export | GraphExporter | Data sharing | | [Visualization](reference/visualization.md) | Graph visualization | GraphVisualizer | Data exploration | | [Pipeline](reference/pipeline.md) | Workflow orchestration | Pipeline | Process automation | | [Change Management](reference/change_management.md) | Version control | TemporalVersionManager | Audit trails | | [Provenance](reference/provenance.md) | Data lineage | ProvenanceManager | Source tracking | | [Core](reference/core.md) | Framework orchestration | Semantica, Config | System management | --- ## More - [Getting Started](getting-started.md) - [Examples](examples.md) - [Cookbook](cookbook.md) - [API Reference](reference/core.md)