semantica/docs/modules.md

Modules & Architecture

Semantica is built with a modular architecture, designed to be flexible and extensible. This guide provides an overview of the key modules and their responsibilities.

🏗️ Architecture Overview

The framework is organized into several core layers, each handling specific aspects of the semantic processing pipeline.

graph TD
    subgraph Ingest [Ingestion Layer]
        I[Ingest Module] --> P[Parse Module]
        P --> N[Normalize Module]
    end

    subgraph Core [Core Processing]
        N --> SE[Semantic Extract]
        SE --> KG[Knowledge Graph]
        KG --> O[Ontology]
    end

    subgraph Storage [Storage Layer]
        KG --> VS[Vector Store]
        KG --> TS[Triple Store]
    end

    subgraph Output [Output & Analysis]
        KG --> E[Export]
        KG --> V[Visualization]
        KG --> R[Reasoning]
    end
    
    style Ingest fill:#e1f5fe,stroke:#01579b
    style Core fill:#e8f5e9,stroke:#1b5e20
    style Storage fill:#fff3e0,stroke:#e65100
    style Output fill:#f3e5f5,stroke:#4a148c

---

📦 Core Modules

Semantica is organized into 12 core modules. Below is a detailed breakdown of each.

1. Ingest Module

Purpose: Ingest data from various sources into a unified format.

The ingest module is the entry point for data. It handles the complexity of connecting to different data sources, from local files to web streams.

• Components:
  • FileIngestor: Read files (PDF, DOCX, HTML, JSON, CSV, etc.)
  • WebIngestor: Scrape and ingest web pages
  • FeedIngestor: Process RSS/Atom feeds
  • StreamIngestor: Real-time data streaming
  • DBIngestor: Database queries and ingestion
  • EmailIngestor: Process email messages
  • RepoIngestor: Git repository analysis

from semantica.ingest import FileIngestor, WebIngestor

# Ingest local files
file_ingestor = FileIngestor()
documents = file_ingestor.ingest("data/")

# Ingest web content
web_ingestor = WebIngestor()
web_docs = web_ingestor.ingest("https://example.com")

2. Parse Module

Purpose: Parse and extract content from various raw formats.

Once data is ingested, the parse module extracts the raw text and metadata. It supports a wide range of formats and includes OCR capabilities.

• Components:
  • DocumentParser: Main parser orchestrator
  • PDFParser: Extract text, tables, images from PDFs
  • DOCXParser: Parse Word documents
  • HTMLParser: Extract content from HTML
  • JSONParser: Parse structured JSON data
  • ExcelParser: Process spreadsheets
  • ImageParser: OCR and image analysis
  • CodeParser: Parse source code files

from semantica.parse import DocumentParser

parser = DocumentParser()
parsed_docs = parser.parse(documents)

3. Normalize Module

Purpose: Clean and normalize text for processing.

Raw text is often noisy. The normalize module cleans, standardizes, and prepares text for semantic extraction.

• Components:
  • TextNormalizer: Main normalization orchestrator
  • TextCleaner: Remove noise, fix encoding
  • DataCleaner: Clean structured data
  • EntityNormalizer: Normalize entity names
  • DateNormalizer: Standardize date formats
  • NumberNormalizer: Normalize numeric values
  • LanguageDetector: Detect document language
  • EncodingHandler: Handle character encoding

from semantica.normalize import TextNormalizer

normalizer = TextNormalizer()
normalized = normalizer.normalize(parsed_docs)

4. Semantic Extract Module

Purpose: Extract entities, relationships, and semantic information.

This is the brain of the operation. It uses LLMs and NLP techniques to understand the text and extract structured knowledge.

• Components:
  • NERExtractor: Named Entity Recognition
  • RelationExtractor: Extract relationships between entities
  • SemanticAnalyzer: Deep semantic analysis
  • SemanticNetworkExtractor: Extract semantic networks

from semantica.semantic_extract import NERExtractor, RelationExtractor

# Extract entities
extractor = NERExtractor()
entities = extractor.extract(normalized_docs)

# Extract relationships
relation_extractor = RelationExtractor()
relationships = relation_extractor.extract(normalized_docs, entities)

5. Knowledge Graph (KG) Module

Purpose: Build and manage knowledge graphs.

The kg module constructs the graph from extracted entities and relationships, handling complex tasks like resolution and analysis.

• Components:
  • GraphBuilder: Construct knowledge graphs from entities/relationships
  • GraphAnalyzer: Analyze graph structure and properties
  • GraphValidator: Validate graph quality and consistency
  • EntityResolver: Resolve entity conflicts and duplicates
  • ConflictDetector: Detect conflicting information
  • CentralityCalculator: Calculate node importance metrics
  • CommunityDetector: Detect communities in graphs
  • ConnectivityAnalyzer: Analyze graph connectivity
  • TemporalQuery: Query temporal knowledge graphs
  • Deduplicator: Remove duplicate entities/relationships

from semantica.kg import GraphBuilder, GraphAnalyzer

# Build graph
builder = GraphBuilder()
kg = builder.build(entities, relationships)

# Analyze graph
analyzer = GraphAnalyzer()
metrics = analyzer.analyze(kg)

6. Embeddings Module

Purpose: Generate vector embeddings for various data types.

Embeddings are crucial for semantic search. This module generates vectors for text, images, and graph nodes.

• Components:
  • EmbeddingGenerator: Main embedding orchestrator
  • TextEmbedder: Generate text embeddings
  • ImageEmbedder: Generate image embeddings
  • AudioEmbedder: Generate audio embeddings
  • MultimodalEmbedder: Combine multiple modalities
  • EmbeddingOptimizer: Optimize embedding quality
  • ProviderAdapters: Support for OpenAI, Cohere, etc.

from semantica.embeddings import EmbeddingGenerator

generator = EmbeddingGenerator()
embeddings = generator.generate(documents)

7. Vector Store Module

Purpose: Store and search vector embeddings.

Manages the storage and retrieval of high-dimensional vectors, supporting hybrid search strategies.

• Components:
  • VectorStore: Main vector store interface
  • FAISSAdapter: FAISS integration
  • HybridSearch: Combine vector and keyword search
  • VectorRetriever: Retrieve relevant vectors

from semantica.vector_store import VectorStore, HybridSearch

vector_store = VectorStore()
vector_store.store(embeddings, documents, metadata)

hybrid_search = HybridSearch(vector_store)
results = hybrid_search.search(query, top_k=10)

8. Reasoning Module

Purpose: Perform logical inference and reasoning.

Goes beyond simple retrieval to infer new facts and validate existing knowledge using logical rules.

• Components:
  • InferenceEngine: Main inference orchestrator
  • RuleManager: Manage inference rules
  • DeductiveReasoner: Deductive reasoning
  • AbductiveReasoner: Abductive reasoning
  • ExplanationGenerator: Generate explanations for inferences
  • RETEEngine: RETE algorithm for rule matching

from semantica.reasoning import InferenceEngine, RuleManager

inference_engine = InferenceEngine()
rule_manager = RuleManager()
new_facts = inference_engine.forward_chain(kg, rule_manager)

9. Ontology Module

Purpose: Generate and manage ontologies.

Defines the schema and structure of your knowledge domain, ensuring consistency and enabling interoperability.

• Components:
  • OntologyGenerator: Generate ontologies from knowledge graphs
  • OntologyValidator: Validate ontology structure
  • OWLGenerator: Generate OWL format ontologies
  • PropertyGenerator: Generate ontology properties
  • ClassInferrer: Infer ontology classes

from semantica.ontology import OntologyGenerator

generator = OntologyGenerator()
ontology = generator.generate_from_graph(kg)

10. Export Module

Purpose: Export data in various formats.

Allows you to take your knowledge graph and data out of Semantica for use in other tools.

• Components:
  • JSONExporter: Export to JSON
  • RDFExporter: Export to RDF/XML
  • CSVExporter: Export to CSV
  • GraphExporter: Export to graph formats (GraphML, GEXF)
  • OWLExporter: Export to OWL
  • VectorExporter: Export vectors

from semantica.export import JSONExporter, RDFExporter

json_exporter = JSONExporter()
json_exporter.export(kg, "output.json")

11. Visualization Module

Purpose: Visualize knowledge graphs and analytics.

Provides tools to visually explore your data, making it easier to understand complex relationships.

• Components:
  • KGVisualizer: Visualize knowledge graphs
  • EmbeddingVisualizer: Visualize embeddings (t-SNE, PCA, UMAP)
  • QualityVisualizer: Visualize quality metrics
  • AnalyticsVisualizer: Visualize graph analytics
  • TemporalVisualizer: Visualize temporal data

from semantica.visualization import KGVisualizer

visualizer = KGVisualizer()
visualizer.visualize(kg)

12. Pipeline Module

Purpose: Build and execute processing pipelines.

Orchestrates the entire flow, connecting modules together into robust, executable workflows.

• Components:
  • PipelineBuilder: Build complex pipelines
  • ExecutionEngine: Execute pipelines
  • FailureHandler: Handle pipeline failures
  • ParallelismManager: Enable parallel processing
  • ResourceScheduler: Schedule resources

from semantica.pipeline import PipelineBuilder

builder = PipelineBuilder()
pipeline = builder.add_step("ingest", FileIngestor()) \
              .add_step("parse", DocumentParser()) \
              .build()