Architecture¶

Semantica's modular, extensible framework for semantic intelligence and knowledge engineering.

Design Principles¶

• Modular: Independent, reusable components
• Extensible: Easy to add new functionality
• Scalable: Handle large-scale data processing
• Maintainable: Clear separation of concerns

System Architecture¶

graph TB
    A[Data Ingestion Layer] --> B[Semantic Processing Layer]
    B --> C[Application Layer]

    A1[Files • Web • APIs • Streams] --> A
    B1[Parse • Normalize • Extract • Build] --> B
    C1[GraphRAG • AI Agents • Analytics] --> C

Three-Layer Architecture¶

1. Data Ingestion Layer - Multiple file formats (PDF, DOCX, JSON, CSV, etc.) - Web scraping and APIs - Real-time streams (Kafka, RabbitMQ) - Database connectors (SQL, NoSQL)

2. Semantic Processing Layer - Document parsing and normalization - Entity and relationship extraction - Embedding generation - Knowledge graph construction - Quality assurance and deduplication

3. Application Layer - GraphRAG for enhanced retrieval - AI agent memory and context - Multi-agent systems - Analytics and visualization

Core Modules¶

Orchestration¶

• semantica.core - Main framework class and coordination
• semantica.pipeline - Pipeline management and execution

Data Processing¶

• semantica.ingest - Universal data ingestion
• semantica.parse - Document parsing
• semantica.normalize - Data cleaning and normalization

Semantic Intelligence¶

• semantica.semantic_extract - Entity and relationship extraction
• semantica.embeddings - Vector embedding generation
• semantica.ontology - Ontology generation and management

Knowledge Graphs¶

• semantica.kg - Knowledge graph construction
• semantica.vector_store - Vector storage (Weaviate, FAISS)
• semantica.triplet_store - RDF triplet storage (Jena, Blazegraph)
• semantica.graph_store - Property graphs (Neo4j, FalkorDB)

Quality Assurance¶

• semantica.deduplication - Entity deduplication
• semantica.conflicts - Conflict detection and resolution

Data Flow¶

1. Ingestion → Raw data from sources
2. Parsing → Structured content extraction
3. Normalization → Cleaned data
4. Semantic Extraction → Entities, relationships, events
5. Graph Construction → Entity resolution, conflict resolution
6. Quality Assurance → Deduplication, validation
7. Storage → Vector, triplet, and graph stores
8. Application → GraphRAG, agents, analytics

Extension Points¶

Custom Ingestors¶

from semantica.ingest import BaseIngestor

class CustomIngestor(BaseIngestor):
    def ingest(self, source):
        # Custom ingestion logic
        pass

Custom Extractors¶

from semantica.semantic_extract import BaseExtractor

class CustomExtractor(BaseExtractor):
    def extract(self, text):
        # Custom extraction logic
        pass

Custom Validators¶

Validators can be implemented within domain-specific modules (e.g., graph or ontology) as needed.

Design Decisions¶

Modularity¶

Independent components that can be used standalone or together. Easy to test, maintain, and extend.

Plugin System¶

Extensible architecture allowing custom functionality without modifying core code.

Configuration Management¶

Centralized configuration with environment variable support for different deployment environments.

Error Handling¶

Comprehensive error handling with graceful degradation and recovery mechanisms.

Performance¶

Scalability - Parallel processing support - Streaming for large datasets - Efficient memory usage - Intelligent caching

Optimization - Lazy loading - Batch processing - Connection pooling - Query optimization

Security¶

Data Security - Secure credential handling - Input validation and output sanitization - Audit logging

Access Control - Authentication and authorization - API key management - Role-based access control

Future Roadmap¶

• Distributed processing
• Real-time streaming improvements
• Advanced reasoning capabilities
• Multi-modal expansion
• Enhanced visualization

For detailed module documentation, see Modules Guide