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**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 β€” powering Knowledge Graph-Powered RAG (GraphRAG), AI Agents, Multi-Agent Systems, and AI applications with structured semantic knowledge.* **100% Open Source** β€’ **MIT Licensed** β€’ **Production Ready** β€’ **Community Driven** [**Discord**](https://discord.gg/semantica) β€’ [**GitHub**](https://github.com/Hawksight-AI/semantica)
## 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** β€” 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** β€” 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** β€” 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 ```mermaid flowchart TD A[Raw Data Sources
PDFs, Emails, Logs, Databases
50+ Formats] --> B[Input Layer
Universal Data Ingestion] B --> C[Format Detection
& Parsing] C --> D[Normalization
& Preprocessing] D --> E[Semantic Layer
Core Intelligence] E --> F[Entity Extraction
NER + LLM Enhancement] E --> G[Relationship Mapping
Triple Generation] E --> H[Ontology Generation
6-Stage Pipeline] E --> I[Context Engineering
Semantic Enrichment] E --> J[Quality Assurance
Conflict Detection] F --> K[Output Layer] G --> K H --> K I --> K J --> K K --> L[Knowledge Graphs
Production-Ready] K --> M[Vector Embeddings
Semantic Search] K --> N[Ontologies
OWL Validated] L --> O[Application Layer] M --> O N --> O O --> P[GraphRAG Engine
91% Accuracy] O --> Q[AI Agents
Persistent Memory] O --> R[Multi-Agent Systems
Shared Models] O --> S[Analytics & BI
Graph Insights] style A fill:#e1f5ff style E fill:#fff4e1 style K fill:#e8f5e9 style O fill:#f3e5f5 ```
### What Happens Without Semantics? **They Break** β€” Systems crash due to inconsistent formats and missing structure. **They Hallucinate** β€” AI models generate false information without semantic context to validate outputs. **They Fail Silently** β€” Systems return wrong answers without warnings, leading to bad decisions. **Why?** Systems have data β€” not semantics. They can't connect concepts, understand relationships, validate against domain rules, or detect conflicts. --- ## πŸ’‘ 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 β€” GraphRAG, agents, multi-agent systems, reasoning models, and more. ### How Semantica Solves These Problems **Universal Data Ingestion** β€” Handles 50+ formats (PDF, DOCX, HTML, JSON, CSV, databases, APIs, streams) with unified pipeline, no custom parsers needed. **Automated Semantic Extraction** β€” NER, relationship extraction, and triple generation with LLM enhancement discovers entities and relationships automatically. **Knowledge Graph Construction** β€” Production-ready graphs with entity resolution, temporal support, and graph analytics. Queryable knowledge ready for AI applications. **GraphRAG Engine** β€” Hybrid vector + graph retrieval achieves 91% accuracy (30% improvement) via semantic search + graph traversal for multi-hop reasoning. **AI Agent Context Engineering** β€” Persistent memory with RAG + knowledge graphs enables context maintenance, action validation, and structured knowledge access. **Automated Ontology Generation** β€” 6-stage LLM pipeline generates validated OWL ontologies with HermiT/Pellet validation, eliminating manual engineering. **Production-Grade QA** β€” Conflict detection, deduplication, quality scoring, and provenance tracking ensure trusted, production-ready knowledge graphs. **Pipeline Orchestration** β€” 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 | --- ## πŸ‘₯ 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** β€” Build GraphRAG systems, AI agents, and multi-agent systems. **Data Engineers** β€” Build scalable pipelines with semantic enrichment. **Knowledge Engineers & Ontologists** β€” Create knowledge graphs and ontologies with automated pipelines. **Enterprise Data Teams** β€” Unify semantic layers, improve data quality, resolve conflicts. **Software & DevOps Engineers** β€” Build semantic APIs and infrastructure with production-ready SDK. **Analysts & Researchers** β€” Transform data into queryable knowledge graphs for insights. **Security & Compliance Teams** β€” Threat intelligence, regulatory reporting, audit trails. **Product Teams & Startups** β€” Rapid prototyping of AI products and semantic features. --- ## πŸ“¦ Installation **Prerequisites:** Python 3.8+ (3.9+ recommended) β€’ pip (latest version) ### Install from PyPI (Recommended) ```bash # 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:** [![PyPI version](https://badge.fury.io/py/semantica.svg)](https://pypi.org/project/semantica/0.0.1/) β€’ [View on PyPI](https://pypi.org/project/semantica/0.0.1/) ### Install from Source (Development) ```bash # 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]" ``` ## πŸ“š Resources > **New to Semantica?** Check out the [**Cookbook**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook) for hands-on examples! - [**Cookbook**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook) - 50+ interactive notebooks - [Introduction](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction) - Getting started tutorials - [Advanced](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced) - Advanced techniques - [Use Cases](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/use_cases) - Real-world applications ## ✨ Core Capabilities | **Data Ingestion** | **Semantic Extract** | **Knowledge Graphs** | **Ontology** | |:--------------------:|:----------------------:|:----------------------:|:--------------:| | [50+ Formats](#universal-data-ingestion) | [Entity & Relations](#semantic-intelligence-engine) | [Graph Analytics](#knowledge-graph-construction) | [Auto Generation](#ontology-generation--management) | | **Context** | **GraphRAG** | **Pipeline** | **QA** | | [Agent Memory](#context-engineering-for-ai-agents) | [Hybrid RAG](#knowledge-graph-powered-rag-graphrag) | [Parallel Workers](#pipeline-orchestration--parallel-processing) | [Conflict Resolution](#production-ready-quality-assurance) | --- ### Universal Data Ingestion > **50+ file formats** β€’ PDF, DOCX, HTML, JSON, CSV, databases, feeds, archives ```python 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") ``` [**Cookbook: Data Ingestion**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/02_Data_Ingestion.ipynb) β€’ [**Document Parsing**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/03_Document_Parsing.ipynb) β€’ [**Data Normalization**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/04_Data_Normalization.ipynb) β€’ [**Chunking & Splitting**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/11_Chunking_and_Splitting.ipynb) ### Semantic Intelligence Engine > **Entity & Relation Extraction** β€’ NER, Relationships, Events, Triples with LLM Enhancement ```python 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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/05_Entity_Extraction.ipynb) β€’ [**Relation Extraction**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/06_Relation_Extraction.ipynb) β€’ [**Advanced Extraction**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/01_Advanced_Extraction.ipynb) ### Knowledge Graph Construction > **Production-Ready KGs** β€’ Entity Resolution β€’ Temporal Support β€’ Graph Analytics ```python 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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/07_Building_Knowledge_Graphs.ipynb) β€’ [**Graph Store**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/09_Graph_Store.ipynb) β€’ [**Triple Store**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/20_Triple_Store.ipynb) β€’ [**Visualization**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/16_Visualization.ipynb) [**Graph Analytics**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/10_Graph_Analytics.ipynb) β€’ [**Advanced Graph Analytics**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/02_Advanced_Graph_Analytics.ipynb) ### Ontology Generation & Management > **6-Stage LLM Pipeline** β€’ Automatic OWL Generation β€’ HermiT/Pellet Validation ```python 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}") ``` [**Cookbook: Ontology**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/14_Ontology.ipynb) ### Context Engineering for AI Agents > **Persistent Memory** β€’ RAG + Knowledge Graphs β€’ MCP-Compatible Tools ```python 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) ``` [**Cookbook: Vector Store**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/13_Vector_Store.ipynb) β€’ [**Embedding Generation**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/12_Embedding_Generation.ipynb) β€’ [**Context Module**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/19_Context_Module.ipynb) β€’ [**Advanced Vector Store**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/Advanced_Vector_Store_and_Search.ipynb) ### Knowledge Graph-Powered RAG (GraphRAG) > **30% Accuracy Improvement** β€’ Vector + Graph Hybrid Search β€’ 91% Accuracy ```python 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})") ``` [**Cookbook: GraphRAG**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb) ### Pipeline Orchestration & Parallel Processing > **Orchestrator-Worker Pattern** β€’ Parallel Execution β€’ Scalable Processing ```python 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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/07_Pipeline_Orchestration.ipynb) ### Production-Ready Quality Assurance > **Enterprise-Grade QA** β€’ Conflict Detection β€’ Deduplication ```python from semantica.deduplication import DuplicateDetector from semantica.conflicts import ConflictDetector conflicts = ConflictDetector().detect_conflicts(kg) duplicates = DuplicateDetector().find_duplicates(entities=kg.entities, similarity_threshold=0.85) print(f"Conflicts: {len(conflicts)} | Duplicates: {len(duplicates)}") ``` [**Cookbook: Conflict Detection**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/17_Conflict_Detection.ipynb) β€’ [**Deduplication**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/18_Deduplication.ipynb) β€’ [**Graph Quality**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/11_Graph_Quality.ipynb) β€’ [**Conflict Resolution**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/04_Conflict_Resolution_Strategies.ipynb) ### Export & Integration > **Multi-Format Export** β€’ JSON, CSV, RDF, GraphML ```python from semantica.export import GraphExporter exporter = GraphExporter(kg) exporter.export("graph.json", format="json") exporter.export("graph.ttl", format="turtle") ``` [**Cookbook: Export**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/15_Export.ipynb) β€’ [**Multi-Format Export**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/05_Multi_Format_Export.ipynb) β€’ [**Multi-Source Integration**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/06_Multi_Source_Data_Integration.ipynb) ## πŸš€ Quick Start > **For comprehensive examples, see the [**Cookbook**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook) with 50+ interactive notebooks!** ```python 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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/08_Your_First_Knowledge_Graph.ipynb) ## 🎯 Use Cases **Enterprise Knowledge Engineering** β€” Unify data sources into knowledge graphs, breaking down silos. **AI Agents & Autonomous Systems** β€” Build agents with persistent memory and semantic understanding. **Multi-Format Document Processing** β€” Process 50+ formats through a unified pipeline. **Data Pipeline Processing** β€” Build scalable pipelines with parallel execution. **Intelligence & Security** β€” Analyze networks, threat intelligence, forensic analysis. **Finance & Trading** β€” Fraud detection, market intelligence, risk assessment. **Healthcare & Biomedical** β€” Clinical reports, drug discovery, medical literature analysis. [**Explore Use Case Examples**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/use_cases) β€” See real-world implementations in finance, healthcare, cybersecurity, trading, and more. ## πŸ”¬ Advanced Features **Incremental Updates** β€” Real-time stream processing with Kafka, RabbitMQ, Kinesis for live updates. **Multi-Language Support** β€” Process 50+ languages with automatic detection. **Custom Ontology Import** β€” Import and extend Schema.org and custom ontologies. **Advanced Reasoning** β€” Deductive, inductive, abductive reasoning with HermiT/Pellet. **Graph Analytics** β€” Centrality, community detection, path finding, temporal analysis. **Custom Pipelines** β€” Build custom pipelines with parallel execution. **API Integration** β€” Integrate external APIs for entity enrichment. [**See Advanced Examples**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced) β€” Advanced extraction, graph analytics, reasoning, and more. ## πŸ—ΊοΈ Roadmap ### Q1 2026 - [x] Core framework (v1.0) - [x] GraphRAG engine - [x] 6-stage ontology pipeline - [ ] Quality assurance features and Quality Assurance module - [ ] Enhanced multi-language support - [ ] Real-time streaming improvements - [ ] Advanced reasoning v2 ### Q2 2026 - [ ] Multi-modal processing --- ## 🀝 Community & Support ### Join Our Community | **Channel** | **Purpose** | |:-----------:|:-----------| | [**Discord**](https://discord.gg/semantica) | Real-time help, showcases | | [**GitHub Discussions**](https://github.com/Hawksight-AI/semantica/discussions) | Q&A, feature requests | ### 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](https://github.com/Hawksight-AI/semantica/issues) with "[Enterprise]" prefix ## 🀝 Contributing ### How to Contribute ```bash # 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](https://github.com/Hawksight-AI/semantica/issues/new) 4. **Feature Requests** - [Request feature](https://github.com/Hawksight-AI/semantica/issues/new) ### Recognition Contributors receive: - Recognition in [CONTRIBUTORS.md](https://github.com/Hawksight-AI/semantica/blob/main/CONTRIBUTORS.md) - GitHub badges - Semantica swag - Featured showcases ## πŸ“œ License Semantica is licensed under the **MIT License** - see the [LICENSE](https://github.com/Hawksight-AI/semantica/blob/main/LICENSE) file for details.
**Built by the Semantica Community** [GitHub](https://github.com/Hawksight-AI/semantica) β€’ [Discord](https://discord.gg/semantica)