semantica/docs/modules.md

Modules

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

!!! tip "Just need a quick reference?" Jump to the Module Index at the bottom of this page.

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Architecture Overview

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

• Input Layer

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  Data ingestion and preparation

  Modules: Ingest, Parse, Split, Normalize

• Core Processing

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  Intelligence and understanding

  Modules: Semantic Extract, Knowledge Graph, Ontology, Reasoning

• Storage

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  Persistent data storage

  Modules: Embeddings, Vector Store, Graph Store, Triplet Store

• Quality Assurance

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  Data quality and consistency

  Modules: Deduplication, Conflicts

• Context & Memory

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  Agent memory and foundation data

  Modules: Context, Seed, LLM Providers

• Output & Orchestration

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  Export, visualization, and workflows

  Modules: Export, Visualization, Pipeline

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Module Index

Module	Purpose	Key Classes	Use Cases
Ingest	Data ingestion	FileIngestor, WebIngestor	File processing, web scraping
Parse	Document parsing	DocumentParser	PDF processing, text extraction
Split	Text chunking	TextSplitter	RAG systems, preprocessing
Normalize	Data cleaning	DataNormalizer	Quality improvement
Semantic Extract	Information extraction	NERExtractor, RelationExtractor	Knowledge graphs
Knowledge Graph	Graph management	GraphBuilder, GraphAnalyzer	Graph construction
Ontology	Schema management	OntologyManager	Data modeling
Reasoning	Logical inference	ReasoningEngine	Knowledge discovery
Embeddings	Vector embeddings	EmbeddingGenerator	Semantic search
Vector Store	Vector database	VectorStore	Similarity search
Graph Store	Graph database	GraphStore	Graph storage
Triplet Store	Triple storage	TripletStore	Semantic web
Deduplication	Entity resolution	EntityResolver	Data quality
Conflicts	Conflict resolution	ConflictDetector	Consistency
Context	Context management	ContextManager	AI agents
Seed	Foundation data	SeedData	Domain knowledge
LLM Providers	LLM integration	LLMProvider	AI generation
Export	Data export	GraphExporter	Data sharing
Visualization	Graph visualization	GraphVisualizer	Data exploration
Pipeline	Workflow orchestration	Pipeline	Process automation
Change Management	Version control	TemporalVersionManager	Audit trails
Provenance	Data lineage	ProvenanceManager	Source tracking
Core	Framework orchestration	Semantica, Config	System management

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