semantica/docs/architecture.md

Architecture

This document describes the architecture of the Semantica framework.

Overview

Semantica is built as a modular, extensible framework for semantic intelligence and knowledge engineering. The architecture is designed to be:

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

System Architecture

┌─────────────────────────────────────────────────────────────┐
│                    SEMANTICA FRAMEWORK                       │
├─────────────────────────────────────────────────────────────┤
│                                                               │
│  ┌─────────────────────────────────────────────────────┐    │
│  │           DATA INGESTION LAYER                      │    │
│  │  ┌──────┬──────┬──────┬──────┬──────┬──────────┐   │    │
│  │  │Files │ Web  │Feeds │ APIs │Stream│ Archives │   │    │
│  │  └──────┴──────┴──────┴──────┴──────┴──────────┘   │    │
│  │        50+ Formats • Real-time • Multi-modal        │    │
│  └─────────────────────────────────────────────────────┘    │
│                          ↓                                    │
│  ┌─────────────────────────────────────────────────────┐    │
│  │        SEMANTIC PROCESSING LAYER                    │    │
│  │  ┌──────────┬────────────┬────────────┬──────────┐ │    │
│  │  │  Parse   │ Normalize  │  Extract  │  Build  │ │    │
│  │  │          │            │ Semantics │  Graph  │ │    │
│  │  └──────────┴────────────┴────────────┴──────────┘ │    │
│  │   NLP • Embeddings • Ontologies • Quality Assurance│ │    │
│  └─────────────────────────────────────────────────────┘    │
│                          ↓                                    │
│  ┌─────────────────────────────────────────────────────┐    │
│  │            APPLICATION LAYER                        │    │
│  │  ┌──────────┬────────────┬────────────┬──────────┐ │    │
│  │  │ GraphRAG │ AI Agents │Multi-Agent │Analytics │ │    │
│  │  │          │            │  Systems  │ Copilots │ │    │
│  │  └──────────┴────────────┴────────────┴──────────┘ │    │
│  │   Hybrid Retrieval • Context • Reasoning            │ │    │
│  └─────────────────────────────────────────────────────┘    │
│                                                               │
└─────────────────────────────────────────────────────────────┘

Module Architecture

Core Modules

semantica.core

The core orchestration module that coordinates all framework components.

Key Components:

• Semantica: Main framework class
• Orchestrator: Pipeline coordination
• ConfigManager: Configuration management
• PluginRegistry: Plugin system
• LifecycleManager: System lifecycle

Responsibilities:

• Initialize and configure components
• Coordinate data flow between modules
• Manage plugin system
• Handle errors and recovery

semantica.pipeline

Pipeline management and execution.

Key Components:

• PipelineBuilder: Pipeline construction DSL
• ExecutionEngine: Pipeline execution
• PipelineValidator: Validation
• ParallelismManager: Parallel execution
• ResourceScheduler: Resource allocation

Responsibilities:

• Define processing pipelines
• Execute pipelines with parallelism
• Manage resources
• Handle failures

Data Processing Modules

semantica.ingest

Universal data ingestion from multiple sources.

Supported Sources:

• Files (50+ formats)
• Web (scraping, APIs)
• Databases (SQL, NoSQL)
• Streams (Kafka, RabbitMQ)
• Archives (ZIP, TAR, etc.)

Key Components:

• FileIngestor: File processing
• WebIngestor: Web scraping
• DBIngestor: Database access
• StreamIngestor: Real-time streams

semantica.parse

Document parsing and extraction.

Key Components:

• DocumentParser: PDF, DOCX, etc.
• WebParser: HTML, XML
• StructuredDataParser: JSON, CSV
• EmailParser: Email formats

semantica.normalize

Data normalization and cleaning.

Key Components:

• TextNormalizer: Text normalization
• EntityNormalizer: Entity name normalization
• DateNormalizer: Date format normalization
• EncodingHandler: Character encoding

Semantic Intelligence Modules

semantica.semantic_extract

Entity and relationship extraction.

Key Components:

• NamedEntityRecognizer: NER
• RelationExtractor: Relationship extraction
• EventDetector: Event detection
• CoreferenceResolver: Coreference resolution
• TripleExtractor: RDF triple extraction

semantica.embeddings

Vector embedding generation.

Key Components:

• EmbeddingGenerator: Main generator
• TextEmbedder: Text embeddings
• MultiModalEmbedder: Multi-modal embeddings
• ProviderAdapters: Provider-specific adapters

semantica.ontology

Ontology generation and management.

Key Components:

• OntologyGenerator: 6-stage generation pipeline
• ClassInferrer: Class discovery
• PropertyGenerator: Property inference
• OntologyValidator: Validation

Knowledge Graph Modules

semantica.kg

Knowledge graph construction and analysis.

Key Components:

• GraphBuilder: Graph construction
• EntityResolver: Entity resolution
• GraphAnalyzer: Graph analytics
• TemporalGraphQuery: Time-aware queries

semantica.vector_store

Vector storage and search.

Key Components:

• VectorStore: Main interface
• PineconeAdapter: Pinecone integration
• WeaviateAdapter: Weaviate integration
• FAISSAdapter: FAISS integration

semantica.triple_store

RDF triple storage.

Key Components:

• TripleManager: Triple management
• QueryEngine: SPARQL queries
• JenaAdapter: Apache Jena
• BlazegraphAdapter: Blazegraph

Quality Assurance Modules

semantica.deduplication

Entity deduplication.

Key Components:

• DuplicateDetector: Duplicate detection
• EntityMerger: Entity merging
• SimilarityCalculator: Similarity calculation

semantica.conflicts

Conflict detection and resolution.

Key Components:

• ConflictDetector: Conflict detection
• ConflictResolver: Conflict resolution
• ConflictAnalyzer: Conflict analysis

semantica.kg_qa

Knowledge graph quality assessment.

Key Components:

• QualityAssessor: Quality assessment
• Quality metrics calculation
• Validation rules

Data Flow

Typical Processing Flow

1. Ingestion
   └─> Raw data from various sources

2. Parsing
   └─> Structured content extraction

3. Normalization
   └─> Cleaned and normalized data

4. Semantic Extraction
   ├─> Entity extraction
   ├─> Relationship extraction
   └─> Event detection

5. Knowledge Graph Construction
   ├─> Entity resolution
   ├─> Conflict resolution
   └─> Graph building

6. Quality Assurance
   ├─> Deduplication
   ├─> Conflict detection
   └─> Quality assessment

7. Storage
   ├─> Knowledge graph storage
   ├─> Vector embeddings
   └─> Triple store

8. Application
   ├─> GraphRAG queries
   ├─> AI agent context
   └─> Analytics

Design Decisions

Modularity

Decision: Modular architecture with independent components

Rationale:

• Easy to understand and maintain
• Components can be used independently
• Easy to test in isolation
• Simple to extend

Plugin System

Decision: Plugin-based architecture for extensibility

Rationale:

• Users can add custom functionality
• Community can contribute plugins
• Core remains stable
• Easy to integrate third-party tools

Configuration Management

Decision: Centralized configuration with environment variable support

Rationale:

• Consistent configuration across modules
• Easy to override for different environments
• Supports both programmatic and file-based config
• Secure handling of sensitive data

Error Handling

Decision: Comprehensive error handling with recovery

Rationale:

• Production-ready reliability
• Graceful degradation
• Detailed error reporting
• Recovery mechanisms

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

from semantica.kg_qa import BaseValidator

class CustomValidator(BaseValidator):
    def validate(self, graph):
        # Custom validation logic
        pass

Performance Considerations

Scalability

• Parallel processing support
• Streaming for large datasets
• Efficient memory usage
• Caching strategies

Optimization

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

Security

Data Security

• Secure credential handling
• Input validation
• Output sanitization
• Audit logging

Access Control

• Authentication support
• Authorization mechanisms
• API key management
• Role-based access

Future Enhancements

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