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181 lines
4.2 KiB
Markdown
181 lines
4.2 KiB
Markdown
# Architecture
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Semantica's modular, extensible framework for semantic intelligence and knowledge engineering.
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---
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## Design Principles
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- **Modular**: Independent, reusable components
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- **Extensible**: Easy to add new functionality
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- **Scalable**: Handle large-scale data processing
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- **Maintainable**: Clear separation of concerns
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---
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## System Architecture
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```mermaid
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graph TB
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A[Data Ingestion Layer] --> B[Semantic Processing Layer]
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B --> C[Application Layer]
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A1[Files • Web • APIs • Streams] --> A
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B1[Parse • Normalize • Extract • Build] --> B
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C1[GraphRAG • AI Agents • Analytics] --> C
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```
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### Three-Layer Architecture
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**1. Data Ingestion Layer**
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- 50+ file formats (PDF, DOCX, JSON, CSV, etc.)
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- Web scraping and APIs
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- Real-time streams (Kafka, RabbitMQ)
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- Database connectors (SQL, NoSQL)
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**2. Semantic Processing Layer**
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- Document parsing and normalization
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- Entity and relationship extraction
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- Embedding generation
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- Knowledge graph construction
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- Quality assurance and deduplication
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**3. Application Layer**
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- GraphRAG for enhanced retrieval
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- AI agent memory and context
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- Multi-agent systems
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- Analytics and visualization
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---
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## Core Modules
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### Orchestration
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- **`semantica.core`** - Main framework class and coordination
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- **`semantica.pipeline`** - Pipeline management and execution
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### Data Processing
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- **`semantica.ingest`** - Universal data ingestion
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- **`semantica.parse`** - Document parsing
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- **`semantica.normalize`** - Data cleaning and normalization
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### Semantic Intelligence
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- **`semantica.semantic_extract`** - Entity and relationship extraction
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- **`semantica.embeddings`** - Vector embedding generation
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- **`semantica.ontology`** - Ontology generation and management
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### Knowledge Graphs
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- **`semantica.kg`** - Knowledge graph construction
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- **`semantica.vector_store`** - Vector storage (Pinecone, Weaviate, FAISS)
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- **`semantica.triple_store`** - RDF triple storage (Jena, Blazegraph)
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- **`semantica.graph_store`** - Property graphs (Neo4j, FalkorDB)
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### Quality Assurance
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- **`semantica.deduplication`** - Entity deduplication
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- **`semantica.conflicts`** - Conflict detection and resolution
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---
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## Data Flow
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```
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1. Ingestion → Raw data from sources
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2. Parsing → Structured content extraction
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3. Normalization → Cleaned data
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4. Semantic Extraction → Entities, relationships, events
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5. Graph Construction → Entity resolution, conflict resolution
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6. Quality Assurance → Deduplication, validation
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7. Storage → Vector, triple, and graph stores
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8. Application → GraphRAG, agents, analytics
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```
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---
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## Extension Points
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### Custom Ingestors
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```python
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from semantica.ingest import BaseIngestor
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class CustomIngestor(BaseIngestor):
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def ingest(self, source):
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# Custom ingestion logic
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pass
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```
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### Custom Extractors
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```python
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from semantica.semantic_extract import BaseExtractor
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class CustomExtractor(BaseExtractor):
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def extract(self, text):
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# Custom extraction logic
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pass
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```
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### Custom Validators
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Validators can be implemented within domain-specific modules (e.g., graph or ontology) as needed.
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---
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## Design Decisions
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### Modularity
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Independent components that can be used standalone or together. Easy to test, maintain, and extend.
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### Plugin System
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Extensible architecture allowing custom functionality without modifying core code.
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### Configuration Management
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Centralized configuration with environment variable support for different deployment environments.
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### Error Handling
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Comprehensive error handling with graceful degradation and recovery mechanisms.
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---
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## Performance
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**Scalability**
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- Parallel processing support
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- Streaming for large datasets
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- Efficient memory usage
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- Intelligent caching
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**Optimization**
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- Lazy loading
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- Batch processing
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- Connection pooling
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- Query optimization
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---
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## Security
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**Data Security**
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- Secure credential handling
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- Input validation and output sanitization
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- Audit logging
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**Access Control**
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- Authentication and authorization
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- API key management
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- Role-based access control
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---
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## Future Roadmap
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- Distributed processing
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- Real-time streaming improvements
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- Advanced reasoning capabilities
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- Multi-modal expansion
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- Enhanced visualization
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---
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For detailed module documentation, see [Modules Guide](modules.md)
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