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🧠 Semantica
Open Source Framework for Semantic Layer & Knowledge Engineering
Transform chaotic data into intelligent knowledge.
The missing fabric between raw data and AI engineering. A comprehensive open-source framework for building semantic layers and knowledge engineering systems that transform unstructured data into AI-ready knowledge — powering Knowledge Graph-Powered RAG (GraphRAG), AI Agents, Multi-Agent Systems, and AI applications with structured semantic knowledge.
100% Open Source • MIT Licensed • Production Ready • Community Driven
What is Semantica?
Semantica bridges the gap between raw data chaos and AI-ready knowledge. It's a semantic intelligence platform that transforms unstructured data into structured, queryable knowledge graphs powering GraphRAG, AI agents, and multi-agent systems.
What Makes Semantica Different?
Unlike traditional approaches that process isolated documents and extract text into vectors, Semantica understands semantic relationships across all content, provides automated ontology generation, and builds a unified semantic layer with production-grade QA.
| Traditional Approaches | Semantica's Approach |
|---|---|
| Process data as isolated documents | Understands semantic relationships across all content |
| Extract text and store vectors | Builds knowledge graphs with meaningful connections |
| Generic entity recognition | General-purpose ontology generation and validation |
| Manual schema definition | Automatic semantic modeling from content patterns |
| Disconnected data silos | Unified semantic layer across all data sources |
| Basic quality checks | Production-grade QA with conflict detection & resolution |
🎯 The Problem We Solve
The Semantic Gap
Organizations today face a fundamental mismatch between how data exists and how AI systems need it.
The Semantic Gap: Problem vs. Solution
Organizations have unstructured data (PDFs, emails, logs), messy data (inconsistent formats, duplicates, conflicts), and disconnected silos (no shared context, missing relationships). AI systems need clear rules (formal ontologies), structured entities (validated, consistent), and relationships (semantic connections, context-aware reasoning).
| What Organizations Have | What AI Systems Require |
|---|---|
| Unstructured Data | Clear Rules |
| PDFs, emails, logs | Formal ontologies |
| Mixed schemas | Graphs & Networks |
| Conflicting facts | |
| Messy, Noisy Data | Structured Entities |
| Inconsistent formats | Validated entities |
| Duplicate records | Domain Knowledge |
| Missing relationships | |
| Disconnected, Siloed Data | Relationships |
| Data in separate systems | Semantic connections |
| No shared context | Context-Aware Reasoning |
| Isolated knowledge |
SEMANTICA FRAMEWORK
Semantica operates through three integrated layers that transform raw data into AI-ready knowledge:
Input Layer — Universal ingestion from 50+ data formats (PDFs, DOCX, HTML, JSON, CSV, databases, live feeds, APIs, streams, archives, multi-modal content) into a unified pipeline.
Semantic Layer — Core intelligence engine performing entity extraction, relationship mapping, ontology generation, context engineering, and quality assurance. This is where unstructured data transforms into structured knowledge.
Output Layer — Production-ready knowledge graphs, vector embeddings, and validated ontologies that power GraphRAG systems, AI agents, and multi-agent systems.
Powers: GraphRAG, AI Agents, Multi-Agent Systems
Semantica Processing Flow
View Interactive Flowchart
flowchart TD
A[Raw Data Sources<br/>PDFs, Emails, Logs, Databases<br/>50+ Formats] --> B[Input Layer<br/>Universal Data Ingestion]
B --> C[Format Detection<br/>& Parsing]
C --> D[Normalization<br/>& Preprocessing]
D --> E[Semantic Layer<br/>Core Intelligence]
E --> F[Entity Extraction<br/>NER + LLM Enhancement]
E --> G[Relationship Mapping<br/>Triple Generation]
E --> H[Ontology Generation<br/>6-Stage Pipeline]
E --> I[Context Engineering<br/>Semantic Enrichment]
E --> J[Quality Assurance<br/>Conflict Detection]
F --> K[Output Layer]
G --> K
H --> K
I --> K
J --> K
K --> L[Knowledge Graphs<br/>Production-Ready]
K --> M[Vector Embeddings<br/>Semantic Search]
K --> N[Ontologies<br/>OWL Validated]
L --> O[Application Layer]
M --> O
N --> O
O --> P[GraphRAG Engine<br/>91% Accuracy]
O --> Q[AI Agents<br/>Persistent Memory]
O --> R[Multi-Agent Systems<br/>Shared Models]
O --> S[Analytics & BI<br/>Graph Insights]
style A fill:#e1f5ff
style E fill:#fff4e1
style K fill:#e8f5e9
style O fill:#f3e5f5
What Happens Without Semantics?
They Break — Systems crash due to inconsistent formats and missing structure.
They Hallucinate — AI models generate false information without semantic context to validate outputs.
They Fail Silently — Systems return wrong answers without warnings, leading to bad decisions.
Why? Systems have data — not semantics. They can't connect concepts, understand relationships, validate against domain rules, or detect conflicts.
💡 The Semantica Solution
Semantica is an open-source framework that closes the semantic gap between real-world messy data and the structured semantic layers required by advanced AI systems — GraphRAG, agents, multi-agent systems, reasoning models, and more.
How Semantica Solves These Problems
Universal Data Ingestion — Handles 50+ formats (PDF, DOCX, HTML, JSON, CSV, databases, APIs, streams) with unified pipeline, no custom parsers needed.
Automated Semantic Extraction — NER, relationship extraction, and triple generation with LLM enhancement discovers entities and relationships automatically.
Knowledge Graph Construction — Production-ready graphs with entity resolution, temporal support, and graph analytics. Queryable knowledge ready for AI applications.
GraphRAG Engine — Hybrid vector + graph retrieval achieves 91% accuracy (30% improvement) via semantic search + graph traversal for multi-hop reasoning.
AI Agent Context Engineering — Persistent memory with RAG + knowledge graphs enables context maintenance, action validation, and structured knowledge access.
Automated Ontology Generation — 6-stage LLM pipeline generates validated OWL ontologies with HermiT/Pellet validation, eliminating manual engineering.
Production-Grade QA — Conflict detection, deduplication, quality scoring, and provenance tracking ensure trusted, production-ready knowledge graphs.
Pipeline Orchestration — Flexible pipeline builder with parallel execution enables scalable processing via orchestrator-worker pattern.
Core Features at a Glance
| Feature Category | Capabilities | Key Benefits |
|---|---|---|
| Data Ingestion | 50+ formats (PDF, DOCX, HTML, JSON, CSV, databases, APIs, streams, archives) | Universal ingestion, no custom parsers needed |
| Semantic Extraction | NER, relationship extraction, triple generation, LLM enhancement | Automated discovery of entities and relationships |
| Knowledge Graphs | Entity resolution, temporal support, graph analytics, query interface | Production-ready, queryable knowledge structures |
| Ontology Generation | 6-stage LLM pipeline, OWL generation, HermiT/Pellet validation | Automated ontology creation from documents |
| GraphRAG | Hybrid vector + graph retrieval, multi-hop reasoning | 91% accuracy, 30% improvement over vector-only |
| Agent Memory | Persistent memory, RAG integration, MCP-compatible tools | Context-aware agents with semantic understanding |
| Pipeline Orchestration | Parallel execution, custom steps, orchestrator-worker pattern | Scalable, flexible data processing |
| Quality Assurance | Conflict detection, deduplication, quality scoring, provenance | Trusted knowledge graphs ready for production |
👥 Who Is This For?
Semantica is designed for developers, data engineers, and organizations building the next generation of AI applications that require semantic understanding and knowledge graphs.
Who Uses Semantica
AI/ML Engineers & Data Scientists — Build GraphRAG systems, AI agents, and multi-agent systems.
Data Engineers — Build scalable pipelines with semantic enrichment.
Knowledge Engineers & Ontologists — Create knowledge graphs and ontologies with automated pipelines.
Enterprise Data Teams — Unify semantic layers, improve data quality, resolve conflicts.
Software & DevOps Engineers — Build semantic APIs and infrastructure with production-ready SDK.
Analysts & Researchers — Transform data into queryable knowledge graphs for insights.
Security & Compliance Teams — Threat intelligence, regulatory reporting, audit trails.
Product Teams & Startups — Rapid prototyping of AI products and semantic features.
Skill Levels: Beginner (Python basics) • Intermediate (NLP/knowledge graphs) • Advanced (custom pipelines, ontology engineering)
📦 Installation
Prerequisites: Python 3.8+ (3.9+ recommended) • pip (latest version)
Install from PyPI (Recommended)
# Install latest version from PyPI
pip install semantica
# Or install with optional dependencies
pip install semantica[all]
# Verify installation
python -c "import semantica; print(semantica.__version__)"
Current Version: • View on PyPI
Install from Source (Development)
# Clone and install in editable mode
git clone https://github.com/Hawksight-AI/semantica.git
cd semantica
pip install -e .
# Or with all optional dependencies
pip install -e ".[all]"
# Development setup
pip install -e ".[dev]"
📚 Resources
New to Semantica? Check out the Cookbook for hands-on examples!
- Cookbook - 50+ interactive notebooks
- Introduction - Getting started tutorials
- Advanced - Advanced techniques
- Use Cases - Real-world applications
✨ Core Capabilities
| Data Ingestion | Semantic Extract | Knowledge Graphs | Ontology |
|---|---|---|---|
| 50+ Formats | Entity & Relations | Graph Analytics | Auto Generation |
| Context | GraphRAG | Pipeline | QA |
| Agent Memory | Hybrid RAG | Parallel Workers | Conflict Resolution |
Universal Data Ingestion
50+ file formats • PDF, DOCX, HTML, JSON, CSV, databases, feeds, archives
from semantica.ingest import FileIngestor, WebIngestor, DBIngestor
file_ingestor = FileIngestor(recursive=True)
web_ingestor = WebIngestor(max_depth=3)
db_ingestor = DBIngestor(connection_string="postgresql://...")
sources = []
sources.extend(file_ingestor.ingest("documents/"))
sources.extend(web_ingestor.ingest("https://example.com"))
sources.extend(db_ingestor.ingest(query="SELECT * FROM articles"))
print(f" Ingested {len(sources)} sources")
Semantic Intelligence Engine
Entity & Relation Extraction • NER, Relationships, Events, Triples with LLM Enhancement
from semantica import Semantica
text = "Apple Inc., founded by Steve Jobs in 1976, acquired Beats Electronics for $3 billion."
core = Semantica(ner_model="transformer", relation_strategy="hybrid")
results = core.extract_semantics(text)
print(f"Entities: {len(results.entities)}, Relationships: {len(results.relationships)}")
Cookbook: Entity Extraction • Relation Extraction
Knowledge Graph Construction
Production-Ready KGs • Entity Resolution • Temporal Support • Graph Analytics
from semantica import Semantica
from semantica.kg import GraphAnalyzer
documents = ["doc1.txt", "doc2.txt", "doc3.txt"]
core = Semantica(graph_db="neo4j", merge_entities=True)
kg = core.build_knowledge_graph(documents, generate_embeddings=True)
analyzer = GraphAnalyzer()
pagerank = analyzer.compute_centrality(kg, method="pagerank")
communities = analyzer.detect_communities(kg, method="louvain")
result = kg.query("Who founded the company?", return_format="structured")
print(f"Nodes: {kg.node_count}, Answer: {result.answer}")
Cookbook: Building Knowledge Graphs • Graph Analytics
Ontology Generation & Management
6-Stage LLM Pipeline • Automatic OWL Generation • HermiT/Pellet Validation
from semantica.ontology import OntologyGenerator, OntologyValidator
generator = OntologyGenerator(llm_provider="openai", model="gpt-4")
ontology = generator.generate_from_documents(sources=["domain_docs/"])
validator = OntologyValidator(reasoner="hermit")
validation = validator.validate(ontology)
print(f"Classes: {len(ontology.classes)}, Valid: {validation.is_consistent}")
Context Engineering for AI Agents
Persistent Memory • RAG + Knowledge Graphs • MCP-Compatible Tools
from semantica.context import AgentMemory, ContextRetriever
from semantica.vector_store import VectorStore
memory = AgentMemory(vector_store=VectorStore(backend="faiss"), retention_policy="unlimited")
memory.store("User prefers technical docs", metadata={"user_id": "user_123"})
retriever = ContextRetriever(memory_store=memory)
context = retriever.retrieve("What are user preferences?", max_results=5)
Knowledge Graph-Powered RAG (GraphRAG)
30% Accuracy Improvement • Vector + Graph Hybrid Search • 91% Accuracy
from semantica.qa_rag import GraphRAGEngine
from semantica.vector_store import VectorStore
graphrag = GraphRAGEngine(
vector_store=VectorStore(backend="faiss"),
knowledge_graph=kg
)
result = graphrag.query("Who founded the company?", top_k=5, expand_graph=True)
print(f"Answer: {result.answer} (Confidence: {result.confidence:.2f})")
Pipeline Orchestration & Parallel Processing
Orchestrator-Worker Pattern • Parallel Execution • Scalable Processing
from semantica.pipeline import PipelineBuilder, ExecutionEngine
pipeline = PipelineBuilder() \
.add_step("ingest", "custom", func=ingest_data) \
.add_step("extract", "custom", func=extract_entities) \
.add_step("build", "custom", func=build_graph) \
.build()
result = ExecutionEngine().execute_pipeline(pipeline, parallel=True)
Cookbook: Pipeline Orchestration
Production-Ready Quality Assurance
Enterprise-Grade QA • Conflict Detection • Deduplication • Quality Scoring
from semantica.kg_qa import QualityAssessor
from semantica.deduplication import DuplicateDetector
from semantica.conflicts import ConflictDetector
assessor = QualityAssessor()
report = assessor.assess(kg, check_completeness=True, check_consistency=True)
detector = DuplicateDetector()
duplicates = detector.find_duplicates(entities=kg.entities, similarity_threshold=0.85)
print(f"Quality Score: {report.overall_score}/100, Duplicates: {len(duplicates)}")
Cookbook: Conflict Detection • Deduplication • Graph Quality
🚀 Quick Start
For comprehensive examples, see the Cookbook with 50+ interactive notebooks!
from semantica import Semantica
# Initialize and build knowledge graph
core = Semantica(ner_model="transformer", relation_strategy="hybrid")
documents = ["doc1.txt", "doc2.txt", "doc3.txt"]
kg = core.build_knowledge_graph(documents, merge_entities=True)
# Query the graph
result = kg.query("Who founded the company?", return_format="structured")
print(f"Answer: {result.answer} | Nodes: {kg.node_count}, Edges: {kg.edge_count}")
Cookbook: Your First Knowledge Graph
🎯 Use Cases
Enterprise Knowledge Engineering — Unify data sources into knowledge graphs, breaking down silos.
AI Agents & Autonomous Systems — Build agents with persistent memory and semantic understanding.
Multi-Format Document Processing — Process 50+ formats through a unified pipeline.
Data Pipeline Processing — Build scalable pipelines with parallel execution.
Intelligence & Security — Analyze networks, threat intelligence, forensic analysis.
Finance & Trading — Fraud detection, market intelligence, risk assessment.
Healthcare & Biomedical — Clinical reports, drug discovery, medical literature analysis.
Explore Use Case Examples — See real-world implementations in finance, healthcare, cybersecurity, trading, and more.
🔬 Advanced Features
Incremental Updates — Real-time stream processing with Kafka, RabbitMQ, Kinesis for live updates.
Multi-Language Support — Process 50+ languages with automatic detection.
Custom Ontology Import — Import and extend Schema.org and custom ontologies.
Advanced Reasoning — Deductive, inductive, abductive reasoning with HermiT/Pellet.
Graph Analytics — Centrality, community detection, path finding, temporal analysis.
Custom Pipelines — Build custom pipelines with parallel execution.
API Integration — Integrate external APIs for entity enrichment.
See Advanced Examples — Advanced extraction, graph analytics, reasoning, and more.
🗺️ Roadmap
Q1 2026
- Core framework (v1.0)
- GraphRAG engine
- 6-stage ontology pipeline
- Quality assurance features
- Enhanced multi-language support
- Real-time streaming improvements
Q2 2026
- Multi-modal processing
- Advanced reasoning v2
🤝 Community & Support
Join Our Community
| Channel | Purpose |
|---|---|
| Discord | Real-time help, showcases |
| GitHub Discussions | Q&A, feature requests |
| Updates, tips | |
| YouTube | Tutorials, webinars |
Learning Resources
Enterprise Support
| Tier | Features | SLA | Price |
|---|---|---|---|
| Community | Public support | Best effort | Free |
| Professional | Email support | 48h | Contact |
| Enterprise | 24/7 support | 4h | Contact |
| Premium | Phone, custom dev | 1h | Contact |
Contact: GitHub Issues with "[Enterprise]" prefix
🤝 Contributing
How to Contribute
# Fork and clone
git clone https://github.com/your-username/semantica.git
cd semantica
# Create branch
git checkout -b feature/your-feature
# Install dev dependencies
pip install -e ".[dev,test]"
# Make changes and test
pytest tests/
black semantica/
flake8 semantica/
# Commit and push
git commit -m "Add feature"
git push origin feature/your-feature
Contribution Types
- Code - New features, bug fixes
- Documentation - Improvements, tutorials
- Bug Reports - Create issue
- Feature Requests - Request feature
Recognition
Contributors receive:
- Recognition in CONTRIBUTORS.md
- GitHub badges
- Semantica swag
- Featured showcases
📜 License
Semantica is licensed under the MIT License - see the LICENSE file for details.