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379 lines
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<title>Semantica - Technical Specification</title>
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<h1>Semantica Framework - Technical Specification</h1>
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<h2>1. Architecture Overview</h2>
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<h3>1.1 System Architecture</h3>
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<p>Semantica follows a modular, layered architecture designed for extensibility and production deployment:</p>
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<pre>
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┌─────────────────────────────────────────────────────────────┐
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│ APPLICATION LAYER │
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│ GraphRAG • AI Agents • Multi-Agent Systems • Analytics │
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└─────────────────────────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ SEMANTIC PROCESSING LAYER │
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│ Entity Extraction • Relation Extraction • Ontology Gen │
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│ Knowledge Graph Construction • Reasoning • QA │
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└─────────────────────────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ DATA PROCESSING LAYER │
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│ Parsing • Normalization • Chunking • Embeddings │
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└─────────────────────────────────────────────────────────────┘
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↓
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┌─────────────────────────────────────────────────────────────┐
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│ DATA INGESTION LAYER │
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│ Files • Web • Feeds • Databases • Streams • Archives │
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└─────────────────────────────────────────────────────────────┘
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</pre>
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<h3>1.2 Core Components</h3>
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<ul>
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<li><strong>Orchestrator:</strong> Coordinates pipeline execution across modules</li>
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<li><strong>Config Manager:</strong> Centralized configuration with environment variable support</li>
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<li><strong>Plugin Registry:</strong> Extensible plugin system for custom components</li>
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<li><strong>Lifecycle Manager:</strong> Handles initialization, execution, and cleanup</li>
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</ul>
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<h2>2. Module Breakdown (29 Production Modules)</h2>
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<h3>2.1 Core & Infrastructure (5 modules)</h3>
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<div class="module">
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<strong>semantica.core</strong> - Framework orchestration, configuration management, plugin system
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</div>
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<div class="module">
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<strong>semantica.pipeline</strong> - Pipeline construction, execution, validation, parallelism management
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</div>
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<div class="module">
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<strong>semantica.utils</strong> - Shared utilities, validators, helpers, logging, exceptions
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</div>
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<h3>2.2 Data Processing (5 modules)</h3>
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<div class="module">
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<strong>semantica.ingest</strong> - Universal data ingestion (50+ formats): files, web, feeds, databases, streams, emails, repositories
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</div>
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<div class="module">
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<strong>semantica.parse</strong> - Document parsing: PDF, DOCX, HTML, JSON, CSV, Excel, images, code, media
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</div>
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<div class="module">
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<strong>semantica.normalize</strong> - Data normalization: text cleaning, entity normalization, date/number formatting, encoding handling
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</div>
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<div class="module">
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<strong>semantica.split</strong> - Document chunking: semantic, structural, sliding window, table-aware chunking
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</div>
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<h3>2.3 Semantic Intelligence (4 modules)</h3>
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<div class="module">
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<strong>semantica.semantic_extract</strong> - Entity recognition, relation extraction, event detection, triple extraction, coreference resolution
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</div>
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<div class="module">
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<strong>semantica.embeddings</strong> - Multi-modal embeddings: text, image, audio, with optimization and context management
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</div>
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<div class="module">
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<strong>semantica.ontology</strong> - 6-stage automatic ontology generation pipeline with symbolic validation (F1 up to 0.99)
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</div>
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<h3>2.4 Knowledge Graph (3 modules)</h3>
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<div class="module">
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<strong>semantica.kg</strong> - Knowledge graph construction with temporal support, entity resolution, graph analytics, temporal queries
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</div>
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<div class="module">
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<strong>semantica.triple_store</strong> - RDF storage with adapters for Jena, Blazegraph, Virtuoso, RDF4J
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</div>
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<div class="module">
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<strong>semantica.vector_store</strong> - Vector storage with adapters for FAISS, Pinecone, Weaviate, Qdrant, Milvus
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</div>
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<h3>2.5 AI Applications (6 modules)</h3>
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<div class="module">
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<strong>semantica.qa_rag</strong> - GraphRAG engine with hybrid retrieval (vector + graph), 30% accuracy improvement
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</div>
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<div class="module">
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<strong>semantica.context</strong> - Context engineering for AI agents: context graphs, agent memory, entity linking
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</div>
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<div class="module">
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<strong>semantica.reasoning</strong> - Reasoning & inference: deductive, abductive, SPARQL reasoning, RETE engine
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</div>
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<h3>2.6 Quality Assurance (5 modules)</h3>
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<div class="module">
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<strong>semantica.deduplication</strong> - Entity deduplication with fuzzy matching, similarity calculation, merge strategies
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</div>
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<div class="module">
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<strong>semantica.conflicts</strong> - Conflict detection and resolution with source tracking and investigation guides
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</div>
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<div class="module">
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<strong>semantica.kg_qa</strong> - Knowledge graph quality assessment with comprehensive metrics
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</div>
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<div class="module">
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<strong>semantica.seed</strong> - Seed data management for building foundation graphs
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</div>
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<h2>3. Technology Stack</h2>
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<table>
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<tr>
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<th>Category</th>
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<th>Technologies</th>
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</tr>
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<tr>
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<td>Core Language</td>
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<td>Python 3.8+</td>
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</tr>
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<tr>
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<td>NLP & ML</td>
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<td>spaCy, Transformers, PyTorch, sentence-transformers, scikit-learn</td>
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</tr>
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<tr>
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<td>Graph Processing</td>
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<td>NetworkX, RDFLib, Neo4j, SPARQL</td>
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</tr>
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<tr>
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<td>Vector Stores</td>
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<td>FAISS, Pinecone, Weaviate, Qdrant, Milvus</td>
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</tr>
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<tr>
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<td>Data Processing</td>
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<td>Pandas, NumPy, BeautifulSoup4, lxml</td>
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</tr>
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<tr>
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<td>Document Parsing</td>
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<td>PyPDF2, python-docx, openpyxl, Pillow</td>
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</tr>
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<tr>
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<td>Streaming</td>
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<td>Kafka, RabbitMQ, Pulsar, Celery</td>
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</tr>
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<tr>
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<td>Cloud & Storage</td>
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<td>AWS (boto3), Azure, GCP, Redis, MongoDB, PostgreSQL</td>
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</tr>
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</table>
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<h2>4. Data Flow Architecture</h2>
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<pre>
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Input Sources
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↓
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[Ingestion Layer] → Parse → Normalize → Chunk
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↓
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[Semantic Extraction] → Entities → Relations → Events → Triples
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↓
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[Embedding Generation] → Vector Store
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↓
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[Knowledge Graph Construction] → Entity Resolution → Conflict Detection → Graph Store
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↓
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[Ontology Generation] → 6-Stage Pipeline → OWL/Turtle Export
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↓
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[Quality Assurance] → Deduplication → Conflict Resolution → Validation
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↓
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[Application Layer] → GraphRAG → AI Agents → Reasoning → Export
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</pre>
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<h2>5. API Design Principles</h2>
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<h3>5.1 High-Level API</h3>
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<pre><code>from semantica import Semantica
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core = Semantica(
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ner_model="transformer",
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relation_strategy="hybrid",
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graph_db="neo4j"
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)
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kg = core.build_knowledge_graph(sources=["documents/"])</code></pre>
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<h3>5.2 Low-Level API</h3>
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<pre><code>from semantica.semantic_extract import NamedEntityRecognizer
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from semantica.kg import GraphBuilder
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ner = NamedEntityRecognizer(model="transformer")
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entities = ner.extract(text)
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builder = GraphBuilder(merge_entities=True)
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kg = builder.build(sources)</code></pre>
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<h3>5.3 Plugin Architecture</h3>
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<p>Semantica supports custom plugins through the registry system:</p>
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<pre><code>from semantica.core import PluginRegistry
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registry = PluginRegistry()
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registry.register("custom_extractor", CustomExtractor)</code></pre>
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<h2>6. Integration Points</h2>
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<table>
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<tr>
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<th>Tool/Framework</th>
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<th>Integration Type</th>
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<th>Use Case</th>
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</tr>
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<tr>
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<td>LangChain</td>
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<td>Tool/Agent Integration</td>
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<td>Semantic layer for AI agents</td>
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</tr>
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<tr>
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<td>Haystack</td>
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<td>RAG Pipeline</td>
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<td>GraphRAG enhancement for retrieval</td>
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</tr>
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<tr>
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<td>Neo4j</td>
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<td>Graph Storage</td>
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<td>Production knowledge graph storage</td>
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</tr>
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<tr>
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<td>Stardog</td>
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<td>RDF/OWL Storage</td>
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<td>Ontology and triple store</td>
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</tr>
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<tr>
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<td>Pinecone/Weaviate</td>
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<td>Vector Storage</td>
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<td>Embedding storage for semantic search</td>
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</tr>
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<tr>
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<td>Apache Kafka</td>
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<td>Stream Processing</td>
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<td>Real-time knowledge graph updates</td>
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</tr>
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</table>
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<h2>7. Performance Requirements</h2>
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<h3>7.1 Processing Speed</h3>
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<table>
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<tr>
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<th>Document Type</th>
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<th>Current (docs/hour)</th>
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<th>Target (docs/hour)</th>
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</tr>
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<tr>
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<td>PDF (10 pages)</td>
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<td>1,200</td>
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<td>5,000</td>
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</tr>
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<tr>
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<td>DOCX (5 pages)</td>
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<td>2,500</td>
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<td>10,000</td>
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</tr>
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<tr>
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<td>HTML (articles)</td>
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<td>5,000</td>
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<td>20,000</td>
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</tr>
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</table>
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<h3>7.2 Accuracy Metrics</h3>
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<table>
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<tr>
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<th>Task</th>
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<th>Current F1</th>
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<th>Target F1</th>
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</tr>
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<tr>
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<td>Entity Extraction</td>
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<td>0.92</td>
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<td>0.95</td>
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</tr>
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<tr>
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<td>Relationship Extraction</td>
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<td>0.87</td>
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<td>0.90</td>
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</tr>
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<tr>
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<td>Ontology Generation</td>
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<td>0.94</td>
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<td>0.97</td>
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</tr>
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</table>
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<h3>7.3 Scalability Targets</h3>
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<ul>
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<li>Process 1M+ documents in under 24 hours</li>
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<li>Support knowledge graphs with 100M+ entities</li>
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<li>Real-time updates with <100ms latency</li>
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<li>Concurrent query support for 1000+ users</li>
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</ul>
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<h2>8. Deployment Architecture</h2>
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<h3>8.1 Single Node Deployment</h3>
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<pre>
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┌─────────────────────────────────┐
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│ Semantica Application │
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│ ├─ Orchestrator │
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│ ├─ Processing Modules │
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│ └─ Local Storage (FAISS) │
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└─────────────────────────────────┘
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</pre>
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<h3>8.2 Distributed Deployment</h3>
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<pre>
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┌──────────────┐ ┌──────────────┐ ┌──────────────┐
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│ Worker 1 │ │ Worker 2 │ │ Worker N │
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│ (Ingestion) │ │ (Processing)│ │ (Graph) │
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└──────┬───────┘ └──────┬───────┘ └──────┬───────┘
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│ │ │
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└─────────────────┼─────────────────┘
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│
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┌──────────▼──────────┐
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│ Message Queue │
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│ (Kafka/RabbitMQ) │
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└──────────┬──────────┘
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│
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┌──────────▼──────────┐
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│ Graph Database │
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│ (Neo4j/Neptune) │
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└─────────────────────┘
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</pre>
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<h2>9. Security & Privacy</h2>
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<ul>
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<li><strong>Data Isolation:</strong> Namespace-based multi-tenancy</li>
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<li><strong>Access Control:</strong> Role-based permissions</li>
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<li><strong>Privacy:</strong> Support for federated learning and differential privacy</li>
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<li><strong>Encryption:</strong> Data at rest and in transit encryption</li>
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</ul>
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<h2>10. Open Standards Compliance</h2>
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<ul>
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<li><strong>RDF:</strong> Full RDF 1.1 support with Turtle, N-Triples, JSON-LD</li>
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<li><strong>OWL:</strong> OWL 2 DL support with automatic ontology generation</li>
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<li><strong>SPARQL:</strong> SPARQL 1.1 query support</li>
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<li><strong>JSON-LD:</strong> JSON-LD 1.1 serialization</li>
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<li><strong>Schema.org:</strong> Schema.org vocabulary support</li>
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</ul>
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<hr>
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<p><em>Document Version: 1.0 | Last Updated: 2025 | Semantica Framework</em></p>
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