Modules & Architecture¶

Complete guide to Semantica's modular architecture and how to use each component.

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"])

What it does: - 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

Use Cases: - 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"]

What it does: - Text extraction - Extract clean text from documents - Metadata parsing - Extract titles, authors, dates - Structure analysis - Identify sections, headings - OCR support - Handle scanned documents

Use Cases: - 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)

What it does: - 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

Use Cases: - 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")

What it does: - Text cleaning - Remove noise and artifacts - Date standardization - Convert to ISO format - Name normalization - Standardize person names - Entity normalization - Clean up company names

Use Cases: - 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)

What it does: - 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

Use Cases: - 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)

What it does: - 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

Use Cases: - 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)

What it does: - Schema definition - Define data structure - Data validation - Ensure data conforms to schema - Inheritance - Create hierarchical relationships - Constraints - Enforce data quality rules

Use Cases: - 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"])

What it does: - Logical inference - Derive new facts from existing ones - Pattern matching - Find complex patterns in data - Consistency checking - Detect contradictions - Decision support - Automated reasoning

Use Cases: - 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])

What it does: - Text embeddings - Convert text to vectors - Similarity search - Find similar content - Clustering - Group related items - AI integration - Provide context to LLMs

Use Cases: - 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)

What it does: - Vector storage - Efficient vector database - Fast search - Approximate nearest neighbor search - Indexing - Optimize for performance - Batch operations - Handle large datasets

Use Cases: - 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")

What it does: - Graph persistence - Store graphs in databases - Graph queries - Cypher and Gremlin support - Graph algorithms - Path finding, centrality - Transactions - ACID compliance

Use Cases: - 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.")

What it does: - Triple storage - Store (subject, predicate, object) triples - Pattern matching - Find specific patterns - RDF support - Semantic web standards - Bulk operations - Efficient batch processing

Use Cases: - 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")

What it does: - Duplicate detection - Find similar entities - Entity resolution - Merge duplicate records - Similarity scoring - Quality assessment - Record linkage - Connect related records

Use Cases: - 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")

What it does: - Conflict detection - Find contradictory information - Resolution strategies - Automated conflict resolution - Source reliability - Trustworthiness assessment - Temporal analysis - Time-based conflict handling

Use Cases: - 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)

What it does: - Context tracking - Maintain conversation context - Memory management - Store and retrieve context - Relevance scoring - Find relevant context - Session management - Handle multiple conversations

Use Cases: - 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")

What it does: - Seed knowledge - Foundation data for domains - Knowledge bases - Pre-built domain knowledge - Quick start - Bootstrap applications - Domain models - Industry-specific data

Use Cases: - 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)

What it does: - LLM integration - Connect to various LLM providers - Prompt engineering - Optimize prompts for results - Context injection - Provide knowledge graph context - Response parsing - Extract structured outputs

Use Cases: - 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")

What it does: - Multiple formats - JSON, CSV, RDF, GraphML - Database export - Export to various databases - Streaming - Handle large datasets - Filtering - Export specific data subsets

Use Cases: - 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")

What it does: - Graph visualization - Interactive graph plots - Custom styling - Tailored visual appearance - Analytics charts - Statistics and metrics - Exploration tools - Interactive data exploration

Use Cases: - 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/")

What it does: - Workflow orchestration - Coordinate multiple steps - Parallel processing - Run steps concurrently - Progress tracking - Monitor pipeline execution - Error handling - Robust error management

Use Cases: - Data processing - Workflow automation - Batch processing - System integration

New Features & 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")

What it does: - Version control - Track changes over time - Audit trails - Complete change history - Data integrity - SHA-256 checksums - Change comparison - Detailed diff analysis

Use Cases: - 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")

What it does: - W3C PROV-O compliant - Industry standard tracking - Complete lineage - End-to-end traceability - Source attribution - Track data origins - Integrity verification - Tamper detection

Use Cases: - 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/")

What it does: - Framework orchestration - Central coordination - Configuration management - Settings and preferences - Lifecycle management - Start/stop/restart - Plugin system - Extensible architecture

Use Cases: - Framework initialization - Configuration management - Plugin development - System orchestration

Getting Started¶

Quick Start Example¶

# Complete pipeline example
from semantica.ingest import FileIngestor
from semantica.semantic_extract import NERExtractor, RelationExtractor
from semantica.kg import GraphBuilder
from semantica.pipeline import Pipeline

# Create pipeline
pipeline = Pipeline()
pipeline.add_step("ingest", FileIngestor())
pipeline.add_step("ner", NERExtractor())
pipeline.add_step("relations", RelationExtractor())
pipeline.add_step("build", GraphBuilder())

# Run pipeline
kg = pipeline.run("documents/")
print(f"Built graph with {len(kg['entities'])} entities")

Choose Your Modules¶

For Document Processing: - Ingest → Parse → Split → Semantic Extract → Knowledge Graph

For Web Scraping: - Ingest (Web) → Normalize → Semantic Extract → Graph Store

For AI Agents: - Context → LLM Providers → Reasoning → Export

For Analytics: - Knowledge Graph → Graph Store → Visualization → Export

Module Reference¶

Need Help?¶

• Documentation: Getting Started
• Examples: Cookbook
• Community: Discord
• Issues: GitHub Issues
• Support: Contact Us