semantica/docs/architecture.md

Architecture

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

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Design Principles

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

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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

• 50+ 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

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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

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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

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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.

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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.

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Performance

Scalability

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

Optimization

• Lazy loading
• Batch processing
• Connection pooling
• Query optimization

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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

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Future Roadmap

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

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For detailed module documentation, see Modules Guide