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🧠 Semantica

Python 3.8+ License: MIT PyPI version Downloads Documentation Discord

Open Source Framework for building Semantic Layers and 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.

πŸ†“ 100% Open Source β€’ πŸ“œ MIT Licensed β€’ πŸš€ Production Ready β€’ 🌍 Community Driven

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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**. You have mountains of dataβ€”PDFs, emails, documents, databasesβ€”but AI systems need structured, validated knowledge with semantic relationships. This gap is the #1 blocker for production AI. ```mermaid flowchart TD subgraph RawData ["πŸ“¦ RAW DATA CHAOS"] direction TB A["πŸ“„ PDFs & Documents"] B["πŸ“§ Emails & Chat"] C["πŸ’Ύ Databases"] D["🌐 Web Content"] end subgraph Gap ["❌ THE SEMANTIC GAP"] direction TB X["MISSING LAYER"] X1["No Context"] X2["No Relationships"] X3["No Validation"] X --> X1 X --> X2 X --> X3 end subgraph AI ["πŸ€– AI SYSTEMS NEEDS"] direction TB F["AI Agents"] G["GraphRAG"] H["Reasoning"] I["Multi-Agent"] end RawData ==>|"Unstructured Noise"| Gap Gap ==>|"Hallucinations & Errors"| AI style Gap fill:#ffebee,stroke:#c62828,stroke-width:3px,stroke-dasharray: 5 5 style RawData fill:#f5f5f5,stroke:#616161,stroke-width:2px style AI fill:#e3f2fd,stroke:#1565c0,stroke-width:2px ``` ### Real-World Consequences **Without a semantic layer, your AI systems fail:**
- **πŸ”΄ RAG Systems Fail** --- - Vector search alone misses crucial relationships - No graph traversal for context expansion - Significantly lower accuracy than hybrid approaches - Can't answer multi-hop questions - **πŸ”΄ AI Agents Hallucinate** --- - No ontological constraints to validate actions - Missing semantic routing for intent understanding - No persistent memory across conversations - Can't reason about domain rules - **πŸ”΄ Multi-Agent Coordination Fails** --- - No shared semantic models for collaboration - Unable to validate actions against domain rules - Conflicting knowledge representations - Agents work in silos, not as a team - **πŸ”΄ Knowledge Is Untrusted** --- - Duplicate entities pollute graphs - Conflicting facts from different sources - No provenance tracking or validation - Can't explain where knowledge came from
--- ## 🌟 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 libraryβ€”it's a complete **semantic intelligence platform** that transforms unstructured information into structured, queryable knowledge graphs. ### 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** | --- ## βœ… The Semantica Solution Semantica fills the semantic gap with a **complete intelligence framework**: ```mermaid flowchart TD subgraph Input ["πŸ“₯ INPUT LAYER"] direction TB I1["Files & Documents"] I2["API Streams"] I3["Databases"] end subgraph Engine ["🧠 SEMANTICA ENGINE"] direction TB S1["Entity Extraction"] S2["Relation Mapping"] S3["Ontology Generation"] S4["Conflict Resolution"] end subgraph Output ["πŸ“€ KNOWLEDGE OUTPUT"] direction TB O1["Knowledge Graph"] O2["Vector Store"] O3["Reasoning API"] end Input ====>|"Ingest & Parse"| Engine Engine ====>|"Synthesize & Validate"| Output style Engine fill:#e8f5e9,stroke:#2e7d32,stroke-width:3px style Input fill:#f5f5f5,stroke:#616161,stroke-width:2px style Output fill:#e3f2fd,stroke:#1565c0,stroke-width:2px ``` ### Powers Next-Gen AI Applications - **GraphRAG**: Hybrid retrieval combining vector search + graph traversal for improved accuracy - **AI Agents**: Ontology-constrained actions with semantic routing and persistent memory - **Multi-Agent Systems**: Shared semantic models for coordinated decision-making - **Knowledge Engineering**: Production-grade knowledge graphs with provenance and validation --- ## πŸš€ Choose Your Path
- :material-rocket-launch: **Quick Start** --- Get up and running with Semantica in minutes. Learn the basics of ingestion and extraction. [:arrow_right: Start Here](getting-started.md) - :material-book-open-page-variant: **Core Concepts** --- Deep dive into Knowledge Graphs, Ontologies, and Semantic Reasoning. [:arrow_right: Learn Concepts](concepts.md) - :material-code-braces: **API Reference** --- Detailed technical documentation for all Semantica modules and classes. [:arrow_right: View API](reference/core.md) - :material-chef-hat: **Cookbook** --- Interactive tutorials, real-world examples, and copy-paste recipes. [:arrow_right: Explore Cookbook](cookbook.md)
--- ## πŸ“¦ Installation !!! success "Now Available on PyPI!" Semantica is officially published on PyPI! Install it with a single command. === "From PyPI (Recommended)" Install Semantica directly from PyPI: ```bash # Install the core package pip install semantica # Or install with all optional dependencies pip install semantica[all] ``` === "From Source" Install from the local source for the latest development version: ```bash # Clone the repository git clone https://github.com/Hawksight-AI/semantica.git cd semantica # Install in editable mode with core dependencies pip install -e . # Or install with all optional dependencies pip install -e ".[all]" ``` === "Development" For contributors who want to modify the framework: ```bash # Clone the repository git clone https://github.com/Hawksight-AI/semantica.git cd semantica # Install in editable mode with dev dependencies pip install -e ".[dev]" ``` === "Docker" Run Semantica in a containerized environment: ```bash docker pull semantica/semantica:latest docker run -it semantica/semantica ``` --- ## ✨ Core Capabilities ### 1. πŸ“Š Universal Data Ingestion Process **50+ file formats** with intelligent semantic extraction:
- __πŸ“„ Documents__ --- - PDF (with OCR) - DOCX, XLSX, PPTX - TXT, RTF, ODT - EPUB, LaTeX, Markdown - __🌐 Web & Feeds__ --- - HTML, XHTML, XML - RSS, Atom feeds - JSON-LD, RDFa - Web scraping - __πŸ’Ύ Structured Data__ --- - JSON, YAML, TOML - CSV, TSV, Excel - Parquet, Avro, ORC - SQL/NoSQL databases - __πŸ“§ Communication__ --- - EML, MSG, MBOX - PST archives - Email threads - Attachment extraction - __πŸ—œοΈ Archives__ --- - ZIP, TAR, RAR, 7Z - Recursive processing - Multi-level extraction - __πŸ”¬ Scientific__ --- - BibTeX, EndNote, RIS - JATS XML - PubMed formats - Citation networks
### 2. 🧠 Semantic Intelligence Engine Transform raw text into structured semantic knowledge with state-of-the-art NLP and AI models: - **Named Entity Recognition (NER)**: Extract people, organizations, locations, dates, and custom entities - **Relationship Extraction**: Identify semantic, temporal, and causal relationships - **Event Detection**: Detect and classify events (acquisitions, partnerships, announcements) - **Coreference Resolution**: Resolve pronouns and entity mentions across documents - **Triple Extraction**: Generate RDF triples for knowledge graph construction ### 3. πŸ•ΈοΈ Knowledge Graph Construction Build production-ready knowledge graphs with: - **Automatic Entity Resolution**: Merge duplicate entities with fuzzy matching - **Conflict Detection & Resolution**: Handle contradictory information from multiple sources - **Temporal Knowledge Graphs**: Track changes over time with version history - **Graph Analytics**: Centrality, community detection, path finding - **Multi-Format Export**: Neo4j, RDF, JSON-LD, GraphML ### 4. πŸ“š Ontology Generation & Management Generate formal ontologies automatically using a **6-stage LLM-based pipeline**: 1. **Semantic Network Parsing** β†’ Extract domain concepts 2. **YAML-to-Definition** β†’ Transform into class definitions 3. **Definition-to-Types** β†’ Map to OWL types 4. **Hierarchy Generation** β†’ Build taxonomic structures 5. **TTL Generation** β†’ Generate OWL/Turtle syntax 6. **Symbolic Validation** β†’ HermiT/Pellet reasoning (F1 up to 0.99) ### 5. πŸ” Hybrid Search & Retrieval Power GraphRAG applications with: - **Vector Search**: Semantic similarity using embeddings - **Graph Traversal**: Multi-hop reasoning for context expansion - **Hybrid Retrieval**: Combine vector + graph for improved accuracy - **Temporal Queries**: Query knowledge at specific time points --- ## 🎯 Why Semantica?
- **πŸ†“ 100% Open Source** --- MIT licensed. No vendor lock-in. Full transparency. - **πŸš€ Production Ready** --- Battle-tested with quality assurance, conflict resolution, and validation. - **🧩 Modular Architecture** --- Use only what you need. Swap components easily. - **🌍 Community Driven** --- Built by developers, for developers. Active Discord community. - **πŸ“š Comprehensive** --- End-to-end solution from ingestion to reasoning. No duct-taping required. - **πŸ”¬ Research-Backed** --- Based on latest research in knowledge graphs, ontologies, and semantic web.
--- ## πŸ—οΈ Built For - **Data Scientists**: Transform messy data into clean knowledge graphs - **Data Engineers**: Build scalable data pipelines with semantic enrichment - **AI Engineers**: Build GraphRAG, AI agents, and multi-agent systems - **Knowledge Engineers**: Generate and manage formal ontologies - **Ontologists**: Design and validate domain-specific ontologies and taxonomies - **Researchers**: Analyze scientific literature and build citation networks - **ML Engineers**: Create semantic features for machine learning models - **Enterprises**: Unify data silos into a semantic layer --- ## 🚦 Quick Example ```python from semantica import Semantica # Initialize core = Semantica() # Ingest documents docs = core.ingest.load("documents/", recursive=True) # Build knowledge graph kg = core.kg.build_graph(docs, merge_entities=True) # Query result = kg.query("Who founded Apple Inc.?") print(result.answer) # Steve Jobs, Steve Wozniak, Ronald Wayne print(result.confidence) # 0.98 ``` --- ## οΏ½ Learn More - [Getting Started Guide](getting-started.md) - Your first knowledge graph in 5 minutes - [Core Concepts](concepts.md) - Deep dive into knowledge graphs and ontologies - [Cookbook](cookbook.md) - Real-world examples and tutorials - [API Reference](reference/core.md) - Complete technical documentation ---
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