Modules¶

Every Semantica module works independently — use only what you need.

Architecture Overview¶

Semantica is organized into six logical layers - each with specific responsibilities:

Input Layer¶

Ingest Module¶

Data ingestion from multiple sources

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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¶

Module Index¶

More¶

• Getting Started
• Examples
• Cookbook
• API Reference