🧠 Semantica

Open Source Framework for Semantic Intelligence & 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

📚 Documentation • 🍳 Cookbook • 💬 Discord • 🐙 GitHub

🌟 What is Semantica?

Semantica is the first comprehensive open-source framework that bridges the critical gap between raw data chaos and AI-ready knowledge. It's not just another data processing library—it's a complete semantic intelligence platform that transforms unstructured information into structured, queryable knowledge graphs that power the next generation of AI applications.

The Vision

In the era of AI agents and autonomous systems, data alone isn't enough. Context is king. Semantica provides the semantic infrastructure that enables AI systems to truly understand, reason about, and act upon information with human-like comprehension.

What Makes Semantica Different?

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

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🎯 The Problem We Solve

The Data-to-AI Gap

Modern organizations face a fundamental challenge: the semantic gap between raw data and AI systems.

┌─────────────────────────────────────────────────────────────────┐
│                    THE SEMANTIC GAP                             │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Raw Data (What You Have)          AI Systems (What They Need) │
│  ├─ PDFs, emails, docs             ├─ Structured entities      │
│  ├─ Multiple formats               ├─ Semantic relationships   │
│  ├─ Inconsistent schemas           ├─ Formal ontologies        │
│  ├─ Siloed sources                 ├─ Connected knowledge      │
│  ├─ No semantic meaning            ├─ Context-aware reasoning  │
│  └─ Unvalidated content            └─ Quality-assured knowledge│
│                                                                 │
│               ❌ Missing: The Semantic Layer                    │
└─────────────────────────────────────────────────────────────────┘

Real-World Consequences

Without a semantic layer:

1. RAG Systems Fail 🔴

   • Vector search alone misses crucial relationships
   • No graph traversal for context expansion
   • 30% lower accuracy than hybrid approaches

2. AI Agents Hallucinate 🔴

   • No ontological constraints to validate actions
   • Missing semantic routing for intent understanding
   • No persistent memory across conversations

3. Multi-Agent Systems Can't Coordinate 🔴

   • No shared semantic models for collaboration
   • Unable to validate actions against domain rules
   • Conflicting knowledge representations

4. Knowledge Is Untrusted 🔴

   • Duplicate entities pollute graphs
   • Conflicting facts from different sources
   • No provenance tracking or validation

The Semantica Solution

Semantica fills this gap with a complete semantic intelligence framework:

┌─────────────────────────────────────────────────────────────────┐
│                    SEMANTICA FRAMEWORK                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  📥 Input Layer          🧠 Semantic Layer       📤 Output Layer│
│  ├─ 50+ data formats    ├─ Entity extraction    ├─ Knowledge   │
│  ├─ Live feeds          ├─ Relationship mapping │   graphs     │
│  ├─ APIs & streams      ├─ Ontology generation  ├─ Vector      │
│  ├─ Archives            ├─ Context engineering  │   embeddings │
│  └─ Multi-modal         └─ Quality assurance    └─ Ontologies  │
│                                                                 │
│               ✅ Powers: GraphRAG, AI Agents, Multi-Agent       │
└─────────────────────────────────────────────────────────────────┘

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📦 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]"

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📚 Documentation & Resources

📖 Documentation	🍳 Cookbook	🎯 Use Cases	🚀 Quick Start
API Reference & Guides	50+ Interactive Notebooks	Industry Applications	Get Started in Minutes

💡 New to Semantica? Start with the Cookbook for hands-on examples!

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✨ 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

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📊 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")

📖 Guide • 🍳 Cookbook

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🧠 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)}")

📖 Guide • 🍳 Cookbook

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🕸️ 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}")

📖 Guide • 🍳 Cookbook

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📚 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}")

📖 Guide • 🍳 Cookbook

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🔗 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)

📖 Guide • 🍳 Cookbook

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🎯 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})")

📖 Guide • 🍳 Cookbook

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🔄 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)

📖 Guide • 🍳 Cookbook

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🔧 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)}")

📖 Guide • 🍳 Cookbook

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🏗️ Architecture Overview

System Architecture

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

---

🚀 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}")

🍳 See 50+ comprehensive examples in the Cookbook →

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🎯 Use Cases

Use Case	Description	Cookbook
🏢 Enterprise Knowledge Engineering	Process diverse enterprise data sources and build unified knowledge graphs	View →
🤖 AI Agents & Autonomous Systems	Build AI agents with access to structured knowledge and persistent memory	View →
📄 Multi-Format Document Processing	Process 50+ document formats uniformly through a single pipeline	View →
🔄 Data Pipeline Processing	Build custom processing pipelines with parallel execution	View →
🛡️ Intelligence & Security	Criminal network analysis, threat intelligence, forensic analysis	View →
💰 Finance & Trading	Fraud detection, market intelligence, risk assessment	View →
🏥 Healthcare & Biomedical	Clinical reports, drug discovery, medical literature analysis	View →

🍳 Explore all 50+ use case examples in the Cookbook →

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🔬 Advanced Features

Feature	Description	Documentation
🔄 Incremental Updates	Real-time stream processing with Kafka, RabbitMQ, Kinesis	Streaming Guide →
🌍 Multi-Language Support	Process documents in 50+ languages with auto-detection	Multi-Language Guide →
📚 Custom Ontology Import	Import and extend existing ontologies (Schema.org, custom)	Ontology Guide →
🧠 Advanced Reasoning	Deductive, inductive, and abductive reasoning with HermiT/Pellet	Reasoning Guide →
📊 Graph Analytics	Centrality, community detection, path finding, temporal analysis	Graph Analytics Guide →
🔧 Custom Pipelines	Build custom processing pipelines with parallel execution	Pipeline Guide →
🔌 API Integration	Integrate with external APIs for entity enrichment	Integration Guide →

🍳 See advanced examples in the Cookbook →

🗺️ Roadmap

Q1 2025

• Core framework (v1.0)
• GraphRAG engine
• 6-stage ontology pipeline
• Quality assurance features
• Enhanced multi-language support
• Real-time streaming improvements

Q2 2025

• Multi-modal processing
• Advanced reasoning v2
• AutoML for NER models
• Federated knowledge graphs
• Enterprise SSO

Q3 2025

• Temporal knowledge graphs
• Probabilistic reasoning
• Automated ontology alignment
• Graph neural networks
• Mobile SDK

Q4 2025

• Quantum-ready algorithms
• Neuromorphic computing
• Blockchain provenance
• Privacy-preserving techniques
• Version 2.0 release

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🤝 Community & Support

💬 Join Our Community

Channel	Purpose
Discord	Real-time help, showcases
GitHub Discussions	Q&A, feature requests
Twitter	Updates, tips
YouTube	Tutorials, webinars

📚 Learning Resources

• 📖 Documentation
• 🎯 Tutorials
• 💡 Examples
• 🎓 Academy
• 📝 Blog

🏢 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: enterprise@semantica.io

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🤝 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

1. Code - New features, bug fixes
2. Documentation - Improvements, tutorials
3. Bug Reports - Create issue
4. Feature Requests - Request feature

Recognition

Contributors receive:

• 📜 Recognition in CONTRIBUTORS.md
• 🏆 GitHub badges
• 🎁 Semantica swag
• 🌟 Featured showcases

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📜 License

Semantica is licensed under the MIT License - see the LICENSE file for details.

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Built with ❤️ by the Semantica Community

Website • Documentation • GitHub • Discord