Semantica Logo # 🧠 Semantica [![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![PyPI version](https://badge.fury.io/py/semantica.svg)](https://pypi.org/project/semantica/0.0.1/) [![Downloads](https://pepy.tech/badge/semantica)](https://pepy.tech/project/semantica) [![Discord](https://img.shields.io/discord/semantica?color=7289da&label=discord)](https://discord.gg/semantica) [![CI](https://github.com/Hawksight-AI/semantica/workflows/CI/badge.svg)](https://github.com/Hawksight-AI/semantica/actions) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![Contributors](https://img.shields.io/github/contributors/Hawksight-AI/semantica)](https://github.com/Hawksight-AI/semantica/graphs/contributors) [![Issues](https://img.shields.io/github/issues/Hawksight-AI/semantica)](https://github.com/Hawksight-AI/semantica/issues) [![Pull Requests](https://img.shields.io/github/issues-pr/Hawksight-AI/semantica)](https://github.com/Hawksight-AI/semantica/pulls) **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.* **πŸ†“ 100% Open Source** β€’ **πŸ“œ MIT Licensed** β€’ **πŸš€ Production Ready** β€’ **🌍 Community Driven** [πŸ’¬ **Discord**](https://discord.gg/semantica) β€’ [πŸ™ **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** | |:---------------------------|:-------------------------| | πŸ”Έ Process data as isolated documents | βœ… Understands semantic relationships across all content | | πŸ”Έ Extract text and store vectors | βœ… Builds knowledge graphs with meaningful connections | | πŸ”Έ Generic entity recognition | βœ… General-purpose ontology generation and validation | | πŸ”Έ Manual schema definition | βœ… Automatic semantic modeling from content patterns | | πŸ”Έ Disconnected data silos | βœ… Unified semantic layer across all data sources | | πŸ”Έ Basic quality checks | βœ… Production-grade QA with conflict detection & resolution | --- ## 🎯 The Problem We Solve ### πŸ”΄ The Semantic Gap Organizations today face a **fundamental mismatch** between how data exists and how AI systems need it. #### πŸ“Š 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). | **πŸ“Š What Organizations Have** | **πŸ€– What AI Systems Require** | |:------------------------------|:------------------------------| | **πŸ—‚οΈ Unstructured Data** | **πŸ“‹ Clear Rules** | | πŸ“„ PDFs, emails, logs | πŸ“š Formal ontologies | | πŸ“‹ Mixed schemas | πŸ•ΈοΈ Graphs & Networks | | βš”οΈ Conflicting facts | | | **🧹 Messy, Noisy Data** | **🏷️ Structured Entities** | | ⚠️ Inconsistent formats | βœ… Validated entities | | πŸ” Duplicate records | πŸ“– Domain Knowledge | | πŸ”— Missing relationships | | | **πŸ”— Disconnected, Siloed Data** | **πŸ”— Relationships** | | πŸ”’ Data in separate systems | πŸ”— 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: **πŸ“₯ 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. **πŸ“€ Output Layer** β€” Production-ready knowledge graphs, vector embeddings, and validated ontologies that power GraphRAG systems, AI agents, and multi-agent systems. **βœ… Powers: GraphRAG, AI Agents, Multi-Agent Systems** #### πŸ”„ Semantica Processing Flow
πŸ“Š 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? **πŸ’₯ 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. **πŸ”‡ 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 **πŸ“₯ 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. **πŸ•ΈοΈ 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. **πŸ”— AI Agent Context Engineering** β€” Persistent memory with RAG + knowledge graphs enables context maintenance, action validation, and structured knowledge access. **πŸ“š Automated Ontology Generation** β€” 6-stage LLM pipeline generates validated OWL ontologies with HermiT/Pellet validation, eliminating manual engineering. **πŸ”§ Production-Grade QA** β€” Conflict detection, deduplication, quality scoring, and provenance tracking ensure trusted, production-ready knowledge graphs. **πŸ”„ 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** | |:---------------------|:-----------------|:------------------| | **πŸ“₯ 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 | | **πŸ•ΈοΈ Knowledge Graphs** | Entity resolution, temporal support, graph analytics, query interface | Production-ready, queryable knowledge structures | | **πŸ“š 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 | | **πŸ”— Agent Memory** | Persistent memory, RAG integration, MCP-compatible tools | Context-aware agents with semantic understanding | | **πŸ”„ Pipeline Orchestration** | Parallel execution, custom steps, orchestrator-worker pattern | Scalable, flexible data processing | | **πŸ”§ 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 **πŸ‘¨β€πŸ’» AI/ML Engineers & Data Scientists** β€” Build GraphRAG systems, AI agents, and multi-agent systems. **πŸ‘· Data Engineers** β€” Build scalable pipelines with semantic enrichment. **πŸ“š Knowledge Engineers & Ontologists** β€” Create knowledge graphs and ontologies with automated pipelines. **🏒 Enterprise Data Teams** β€” Unify semantic layers, improve data quality, resolve conflicts. **πŸ’» Software & DevOps Engineers** β€” Build semantic APIs and infrastructure with production-ready SDK. **πŸ“Š Analysts & Researchers** β€” Transform data into queryable knowledge graphs for insights. **πŸ›‘οΈ Security & Compliance Teams** β€” Threat intelligence, regulatory reporting, audit trails. **πŸš€ Product Teams & Startups** β€” Rapid prototyping of AI products and semantic features. **Skill Levels:** Beginner (Python basics) β€’ Intermediate (NLP/knowledge graphs) β€’ Advanced (custom pipelines, ontology engineering) --- ## 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 > πŸ’‘ **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 - πŸ“– [Introduction](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction) - Getting started tutorials - πŸš€ [Advanced](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced) - Advanced techniques - πŸ’Ό [Use Cases](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/use_cases) - Real-world applications ## ✨ Core Capabilities | **πŸ“Š Data Ingestion** | **🧠 Semantic Extract** | **πŸ•ΈοΈ Knowledge Graphs** | **πŸ“š Ontology** | |:--------------------:|:----------------------:|:----------------------:|:--------------:| | [50+ Formats](#universal-data-ingestion) | [Entity & Relations](#semantic-intelligence-engine) | [Graph Analytics](#knowledge-graph-construction) | [Auto Generation](#ontology-generation--management) | | **πŸ”— Context** | **🎯 GraphRAG** | **πŸ”„ Pipeline** | **πŸ”§ 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) | --- ### πŸ“Š 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"βœ… Ingested {len(sources)} sources") ``` 🍳 [**Cookbook: Data Ingestion**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Data_Ingestion.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/Entity_Extraction.ipynb) β€’ [**Relation Extraction**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Relation_Extraction.ipynb) ### πŸ•ΈοΈ 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/Building_Knowledge_Graphs.ipynb) β€’ [**Graph Analytics**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Graph_Analytics.ipynb) ### πŸ“š 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/Ontology.ipynb) ### πŸ”— 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/Vector_Store.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/GraphRAG_Complete.ipynb) ### πŸ”„ 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/Pipeline_Orchestration.ipynb) ### πŸ”§ Production-Ready Quality Assurance > **Enterprise-Grade QA** β€’ Conflict Detection β€’ Deduplication β€’ Quality Scoring ```python 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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Conflict_Detection.ipynb) β€’ [**Deduplication**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Deduplication.ipynb) β€’ [**Graph Quality**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Graph_Quality.ipynb) ## πŸš€ Quick Start > πŸ’‘ **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/Your_First_Knowledge_Graph.ipynb) ## 🎯 Use Cases **🏒 Enterprise Knowledge Engineering** β€” Unify data sources into knowledge graphs, breaking down silos. **πŸ€– AI Agents & Autonomous Systems** β€” Build agents with persistent memory and semantic understanding. **πŸ“„ Multi-Format Document Processing** β€” Process 50+ formats through a unified pipeline. **πŸ”„ Data Pipeline Processing** β€” Build scalable pipelines with parallel execution. **πŸ›‘οΈ Intelligence & Security** β€” Analyze networks, threat intelligence, forensic analysis. **πŸ’° Finance & Trading** β€” Fraud detection, market intelligence, risk assessment. **πŸ₯ 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 **πŸ”„ Incremental Updates** β€” Real-time stream processing with Kafka, RabbitMQ, Kinesis for live updates. **🌍 Multi-Language Support** β€” Process 50+ languages with automatic detection. **πŸ“š Custom Ontology Import** β€” Import and extend Schema.org and custom ontologies. **🧠 Advanced Reasoning** β€” Deductive, inductive, abductive reasoning with HermiT/Pellet. **πŸ“Š Graph Analytics** β€” Centrality, community detection, path finding, temporal analysis. **πŸ”§ Custom Pipelines** β€” Build custom pipelines with parallel execution. **πŸ”Œ 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 - [x] Quality assurance features - [ ] Enhanced multi-language support - [ ] Real-time streaming improvements ### Q2 2026 - [ ] Multi-modal processing - [ ] Advanced reasoning v2 --- ## 🀝 Community & Support ### πŸ’¬ Join Our Community | **Channel** | **Purpose** | |:-----------:|:-----------| | πŸ’¬ [**Discord**](https://discord.gg/semantica) | Real-time help, showcases | | πŸ’‘ [**GitHub Discussions**](https://github.com/Hawksight-AI/semantica/discussions) | Q&A, feature requests | | 🐦 [**Twitter**](https://twitter.com/semantica_ai) | Updates, tips | | πŸ“Ί [**YouTube**](https://youtube.com/@semantica) | Tutorials, webinars | ### πŸ“š Learning Resources ### 🏒 Enterprise Support | **Tier** | **Features** | **SLA** | **Price** | |:--------:|:-----------|:-------:|:--------:| | πŸ†“ **Community** | Public support | Best effort | Free | | πŸ’Ό **Professional** | Email support | 48h | Contact | | 🏒 **Enterprise** | 24/7 support | 4h | Contact | | ⭐ **Premium** | Phone, custom dev | 1h | Contact | **Contact:** enterprise@semantica.io ## 🀝 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: - πŸ“œ Recognition in [CONTRIBUTORS.md](https://github.com/Hawksight-AI/semantica/blob/main/CONTRIBUTORS.md) - πŸ† 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 with ❀️ by the Semantica Community** [GitHub](https://github.com/Hawksight-AI/semantica) β€’ [Discord](https://discord.gg/semantica)