🧠 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 Semantic Gap

Organizations today face a fundamental mismatch between how data exists and how AI systems need it.

📊 What Organizations Have

🗂️ Unstructured Data	🧹 Messy, Noisy Data	🔗 Disconnected, Siloed Data
📄 PDFs, emails, logs	⚠️ Inconsistent formats	🔒 Data in separate systems
📋 Mixed schemas	🔁 Duplicate records	❌ No shared context
⚔️ Conflicting facts	🔗 Missing relationships	🏝️ Isolated knowledge

🤖 What AI Systems Require

📋 Clear Rules	🏷️ Structured Entities	🔗 Relationships
📚 Formal ontologies	✅ Validated entities	🔗 Semantic connections
🕸️ Graphs & Networks	📖 Domain Knowledge	🧠 Context-Aware Reasoning

SEMANTICA FRAMEWORK

📥 Input Layer	🧠 Semantic Layer	📤 Output Layer
50+ data formats	Entity extraction	Knowledge graphs
Live feeds	Relationship mapping	Vector embeddings
APIs & streams	Ontology generation	Ontologies
Archives	Context engineering	—
Multi-modal	Quality assurance	—

### **✅ Powers: GraphRAG, AI Agents, Multi-Agent Systems**

💥 The Core Problem

Most AI tooling assumes your data is already clean, structured, and semantically rich.

But real-world enterprise data actually looks like this:

📦 Data Format Challenges

📄 PDFs	📧 Emails	📊 Logs	📋 Tables
📝 Unstructured text	💬 Informal language	📈 Raw events	🔀 Mixed schemas

⚠️ Data Quality Issues

⚠️ Mixed/Incomplete Schemas	🔀 Multiple Conflicting Facts	🔁 Duplicate Records
❌ No standardization	⚔️ Contradictory information	🔄 Same data, different sources

❌ Missing Semantic Infrastructure

❌ No Ontology	❌ No Relationships	❌ No Shared Context
📚 No domain model	🔗 Disconnected facts	🏝️ Isolated knowledge

And then we push this directly into:

• 🔍 RAG systems
• 🤖 AI agents
• ⚙️ Workflows
• 🧠 LLM pipelines

⚠️ What Happens?

💥 They Break	🎭 They Hallucinate	🔇 They Fail Silently
💣 System crashes	🎪 Generate false information	🤐 Return wrong answers without warning
📉 Poor performance	🧠 Make up facts	⚠️ No error indicators

❓ Why?

Because the system has data — not semantics.

Without semantic understanding, AI systems can't:

• 🔗 Connect related concepts
• 🧠 Understand context and relationships
• ✅ Validate information against domain rules
• 🎯 Reason about complex queries
• 🛡️ Detect conflicts and inconsistencies

Core Problems

1. Fragmented Data Landscape 📊

• Problem: Organizations store data across multiple formats (PDFs, Word docs, databases, APIs, web pages) with no unified way to extract meaning
• Impact: Data silos prevent comprehensive knowledge discovery and cross-domain insights
• Challenge: Each format requires custom parsing logic, normalization, and semantic extraction

2. Lack of Semantic Understanding 🧠

• Problem: Traditional data processing extracts text but doesn't understand relationships, context, or meaning
• Impact: AI systems receive disconnected facts instead of interconnected knowledge
• Challenge: Building semantic relationships requires domain expertise, NLP models, and graph construction capabilities

3. Inadequate RAG Performance 🔍

• Problem: Vector-only RAG systems miss crucial relationships and context, leading to incomplete or inaccurate answers
• Impact: 30% lower accuracy compared to hybrid graph+vector approaches, poor handling of complex multi-hop queries
• Challenge: No framework exists to combine vector search with graph traversal for comprehensive retrieval

4. AI Agent Limitations 🤖

• Problem: AI agents lack persistent memory, semantic routing, and ontological validation
• Impact: Agents hallucinate, make invalid decisions, and can't maintain context across conversations
• Challenge: No standardized way to build agent memory systems with semantic knowledge graphs

5. Multi-Agent Coordination Failures 👥

• Problem: Multiple AI agents can't share semantic models or validate actions against domain rules
• Impact: Agents work in isolation, produce conflicting outputs, and can't collaborate effectively
• Challenge: Missing shared knowledge representation and validation mechanisms

6. Knowledge Quality Issues ⚠️

• Problem: Knowledge graphs suffer from duplicates, conflicts, inconsistencies, and lack of provenance
• Impact: Untrusted knowledge leads to poor AI decisions and unreliable systems
• Challenge: No comprehensive QA framework for detecting and resolving knowledge graph issues

7. Manual Ontology Engineering 📚

• Problem: Creating domain ontologies requires manual work, domain expertise, and validation tools
• Impact: Slow, expensive, and error-prone ontology development
• Challenge: No automated pipeline for generating, validating, and managing ontologies from unstructured content

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
   • Can't answer complex multi-hop questions

2. AI Agents Hallucinate 🔴

   • No ontological constraints to validate actions
   • Missing semantic routing for intent understanding
   • No persistent memory across conversations
   • Make decisions based on incomplete context

3. Multi-Agent Systems Can't Coordinate 🔴

   • No shared semantic models for collaboration
   • Unable to validate actions against domain rules
   • Conflicting knowledge representations
   • Agents work in isolation without shared understanding

4. Knowledge Is Untrusted 🔴

   • Duplicate entities pollute graphs
   • Conflicting facts from different sources
   • No provenance tracking or validation
   • Poor data quality leads to bad AI decisions

5. Slow Time-to-Value ⏱️

   • Months of manual work to build knowledge graphs
   • Expensive custom development for each use case
   • No reusable framework for semantic intelligence

---

💡 The Semantica Solution

Semantica is an open-source framework for building semantic layers and intelligent knowledge structures from raw, unstructured information.

It 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.

Semantica fills this gap with a complete semantic intelligence framework that transforms raw data into AI-ready knowledge:

SEMANTICA FRAMEWORK

📥 Input Layer	🧠 Semantic Layer	📤 Output Layer
50+ data formats	Entity extraction	Knowledge graphs
Live feeds	Relationship mapping	Vector embeddings
APIs & streams	Ontology generation	Ontologies
Archives	Context engineering	—
Multi-modal	Quality assurance	—

✅ Powers: GraphRAG, AI Agents, Multi-Agent Systems

How Semantica Solves These Problems

1. Universal Data Ingestion 📥

• Solution: Single framework handles 50+ formats (PDF, DOCX, HTML, JSON, CSV, databases, feeds, APIs, streams)
• Benefit: No need for custom parsers - ingest everything uniformly
• Result: Unified data pipeline from any source to semantic knowledge

2. Automated Semantic Extraction 🧠

• Solution: Advanced NER, relationship extraction, and triple generation with LLM enhancement
• Benefit: Automatically discovers entities, relationships, and meaning from unstructured text
• Result: Rich semantic knowledge extracted without manual annotation

3. Knowledge Graph Construction 🕸️

• Solution: Production-ready knowledge graphs with entity resolution, temporal support, and graph analytics
• Benefit: Structured, queryable knowledge that captures relationships and context
• Result: Comprehensive knowledge graphs ready for AI applications

4. GraphRAG Engine 🎯

• Solution: Hybrid vector + graph retrieval with 30% accuracy improvement over vector-only RAG
• Benefit: Combines semantic search with graph traversal for comprehensive context retrieval
• Result: 91% accuracy on complex queries, handles multi-hop reasoning

5. AI Agent Context Engineering 🔗

• Solution: Persistent memory systems with RAG + knowledge graphs, MCP-compatible tools
• Benefit: Agents maintain context, validate actions, and access structured knowledge
• Result: Reliable AI agents with semantic understanding and memory

6. Automated Ontology Generation 📚

• Solution: 6-stage LLM pipeline for automatic OWL ontology generation with HermiT/Pellet validation
• Benefit: Generate domain ontologies automatically from documents
• Result: Validated, production-ready ontologies without manual engineering

7. Production-Grade Quality Assurance 🔧

• Solution: Conflict detection, deduplication, quality scoring, and provenance tracking
• Benefit: Trusted knowledge graphs with validated, conflict-free data
• Result: High-quality knowledge ready for production AI systems

8. Pipeline Orchestration 🔄

• Solution: Flexible pipeline builder with parallel execution and custom step integration
• Benefit: Scalable processing with orchestrator-worker pattern
• Result: Handle large-scale data processing efficiently

Key Differentiators

Feature	Semantica	Traditional Approaches
📊 Data Formats	✅ 50+ formats unified	❌ Custom parsers per format
🧠 Semantic Extraction	✅ Automated with LLM enhancement	❌ Manual or basic NER only
🕸️ Knowledge Graphs	✅ Production-ready with QA	❌ Basic graphs, no validation
🎯 RAG Performance	✅ Hybrid graph+vector (91% accuracy)	❌ Vector-only (60-70% accuracy)
📚 Ontology Generation	✅ Automated 6-stage pipeline	❌ Manual engineering required
🔧 Quality Assurance	✅ Conflict detection & resolution	❌ Basic checks or none
🔗 Agent Memory	✅ Built-in semantic memory	❌ Custom development needed
👥 Multi-Agent Support	✅ Shared semantic models	❌ No coordination framework

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👥 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.

🏗️ Built For

Role	Use Case
👨‍🔬 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

🎯 Primary Audiences

Semantica serves developers, engineers, and organizations who need to transform unstructured data into semantic knowledge:

Audience	Key Use Cases	Why Semantica
👨‍💻 AI/ML Engineers & Data Scientists	GraphRAG, AI agents, multi-agent systems	Complete semantic processing framework
📚 Knowledge Engineers & Ontologists	Knowledge graphs, automated ontologies, enterprise KBs	Automated pipeline with validation & QA
🏢 Enterprise Data Teams	Unified semantic layers, data quality, conflict resolution	Universal ingestion & scalable processing
💻 Software & DevOps Engineers	Semantic APIs, pipelines, infrastructure	Production-ready SDK with orchestration
📊 Analysts & Researchers	Data exploration, literature analysis, BI	Transform data into queryable knowledge graphs
🛡️ Security & Compliance Teams	Threat intelligence, regulatory reporting, audit trails	Relationship mapping & provenance tracking
🚀 Product Teams & Startups	Rapid prototyping, AI products, semantic intelligence	Production-ready framework for fast development

🎓 Skill Level Requirements

• Beginner: Python basics, understanding of data structures
• Intermediate: Experience with NLP, knowledge graphs, or AI applications
• Advanced: Custom pipeline development, ontology engineering, production deployments

💼 Use Cases by Organization Size

Organization Type	Primary Use Cases	Key Benefits
🚀 Startups	Rapid prototyping, MVP development	🆓 Open-source, ⚡ fast time-to-value
🏢 Mid-Size Companies	Knowledge management, AI applications	✅ Production-ready, 📈 scalable
🏛️ Enterprises	Enterprise knowledge graphs, multi-agent systems	🔧 Quality assurance, ⚔️ conflict resolution
🎓 Research Institutions	Academic research, knowledge synthesis	🔌 Extensible, 📚 well-documented

🚀 Getting Started Paths

• Quick Start: Want to build a knowledge graph in minutes? → Quick Start Guide
• RAG Systems: Building retrieval-augmented generation? → GraphRAG Engine
• AI Agents: Creating agents with memory? → Context Engineering
• Enterprise: Need production deployment? → Quality Assurance
• Learning: New to knowledge graphs? → Cookbook

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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

---

📊 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

---

🧠 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

---

🕸️ 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

---

🔗 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

---

🎯 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

---

🔄 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

---

🔧 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