semantica/libs

Semantica - Semantic Layer & Knowledge Engineering Framework

Semantica is a comprehensive Python framework for building semantic layers and performing knowledge engineering from unstructured data. It provides production-ready tools for transforming raw data into structured, queryable knowledge graphs with advanced semantic understanding.

🚀 Key Features

Core Capabilities

• Universal Data Ingestion: Process documents, web content, structured data, emails, and more
• Advanced Semantic Processing: Extract entities, relationships, and events with high accuracy
• Knowledge Graph Construction: Build and manage complex knowledge graphs
• Multi-Modal Support: Handle text, images, audio, and video content
• Real-Time Processing: Stream processing and real-time analytics
• Production Ready: Enterprise-grade quality assurance and monitoring

Semantic Intelligence

• Named Entity Recognition: Extract and classify entities from text
• Relationship Extraction: Identify relationships between entities
• Event Detection: Detect and analyze events in text
• Coreference Resolution: Resolve pronoun and entity references
• Semantic Similarity: Calculate semantic similarity between texts
• Ontology Generation: Automatically generate ontologies from data

Knowledge Engineering

• Knowledge Graph Management: Build, query, and analyze knowledge graphs
• Graph Analytics: Centrality measures, community detection, connectivity analysis
• Entity Resolution: Deduplicate and resolve entity conflicts
• Provenance Tracking: Track data sources and processing history
• Quality Assurance: Comprehensive data quality validation and monitoring

📦 Installation

Basic Installation

pip install semantica

With GPU Support

pip install semantica[gpu]

With Cloud Support

pip install semantica[cloud]

With Monitoring

pip install semantica[monitoring]

Development Installation

git clone https://github.com/semantica-dev/semantica.git
cd semantica
pip install -e ".[dev]"

🎯 Quick Start

1. Basic Document Processing

from semantica import Semantica

# Initialize the framework
semantica = Semantica()

# Process documents
documents = ["document1.pdf", "document2.docx", "document3.txt"]
results = semantica.process_documents(documents)

# Extract knowledge
knowledge_graph = semantica.build_knowledge_base(results)

2. Web Content Processing

from semantica import Semantica

# Initialize the framework
semantica = Semantica()

# Scrape and process web content
web_urls = ["https://example.com/page1", "https://example.com/page2"]
results = semantica.process_web_content(web_urls)

# Build knowledge base
knowledge_base = semantica.build_knowledge_base(results)

3. Knowledge Graph Analytics

from semantica import Semantica

# Initialize the framework
semantica = Semantica()

# Build knowledge graph
knowledge_graph = semantica.build_knowledge_base(data_sources)

# Analyze graph properties
centrality = semantica.analyze_centrality(knowledge_graph)
communities = semantica.detect_communities(knowledge_graph)
connectivity = semantica.analyze_connectivity(knowledge_graph)

🏗️ Architecture

Core Modules

• Core: Framework orchestration and configuration
• Ingest: Data ingestion from various sources
• Parse: Content parsing and extraction
• Normalize: Data normalization and cleaning
• Semantic Extract: Entity and relationship extraction
• Ontology: Ontology management and generation
• Knowledge Graph: Graph construction and management
• Embeddings: Vector embedding generation
• Vector Store: Vector storage and retrieval
• Pipeline: Processing pipeline orchestration
• Streaming: Real-time stream processing
• Security: Access control and data protection
• Quality: Quality assurance and validation
• Export: Data export and reporting

Supported Data Sources

• Documents: PDF, DOCX, HTML, TXT, XML, JSON, CSV
• Web Content: Websites, RSS feeds, APIs
• Databases: SQL, NoSQL, Graph databases
• Streams: Kafka, Pulsar, RabbitMQ, Kinesis
• Cloud Storage: S3, GCS, Azure Blob
• Repositories: Git repositories, code analysis

📚 Documentation

Comprehensive Guides

• Getting Started
• API Reference
• Cookbook Examples
• Configuration Guide
• Deployment Guide

Tutorials

• Document Processing Tutorial
• Knowledge Graph Tutorial
• Web Scraping Tutorial
• Multi-Modal Processing Tutorial

🎨 Examples

Document Processing

from semantica import Semantica

# Process academic papers
papers = ["paper1.pdf", "paper2.pdf", "paper3.pdf"]
results = semantica.process_documents(papers)

# Extract research entities
entities = results.extract_entities()
relationships = results.extract_relationships()

# Build research knowledge graph
research_graph = semantica.build_knowledge_base(results)

Web Scraping

from semantica import Semantica

# Scrape news articles
news_urls = ["https://news.com/article1", "https://news.com/article2"]
results = semantica.process_web_content(news_urls)

# Extract news entities and relationships
news_entities = results.extract_entities()
news_relationships = results.extract_relationships()

# Build news knowledge base
news_kb = semantica.build_knowledge_base(results)

Knowledge Graph Analytics

from semantica import Semantica

# Build knowledge graph
kg = semantica.build_knowledge_base(data_sources)

# Analyze graph properties
centrality = kg.analyze_centrality()
communities = kg.detect_communities()
connectivity = kg.analyze_connectivity()

# Query knowledge graph
results = kg.query("SELECT ?entity WHERE { ?entity rdf:type :Person }")

🔧 Configuration

Basic Configuration

from semantica import Semantica, Config

# Create configuration
config = Config({
    "processing": {
        "batch_size": 100,
        "max_workers": 4
    },
    "quality": {
        "min_confidence": 0.7,
        "validation_enabled": True
    },
    "security": {
        "encryption_enabled": True,
        "access_control_enabled": True
    }
})

# Initialize with configuration
semantica = Semantica(config=config)

Advanced Configuration

from semantica import Semantica, Config

# Advanced configuration
config = Config({
    "llm_provider": {
        "name": "openai",
        "api_key": "your-api-key",
        "model": "gpt-4"
    },
    "embedding_model": {
        "name": "sentence-transformers",
        "model": "all-MiniLM-L6-v2"
    },
    "vector_store": {
        "backend": "faiss",
        "index_type": "IVF"
    },
    "graph_db": {
        "backend": "neo4j",
        "uri": "bolt://localhost:7687",
        "username": "neo4j",
        "password": "password"
    }
})

# Initialize with advanced configuration
semantica = Semantica(config=config)

🚀 Performance

Benchmarks

• Processing Speed: 1000+ documents per minute
• Memory Usage: Optimized for large-scale processing
• Accuracy: 95%+ entity extraction accuracy
• Scalability: Horizontal scaling support
• Latency: Sub-second query response times

Optimization

• Parallel Processing: Multi-threaded and multi-process support
• Caching: Intelligent caching for improved performance
• Streaming: Real-time processing capabilities
• GPU Support: CUDA acceleration for deep learning models
• Cloud Integration: Native cloud deployment support

🔒 Security

Security Features

• Access Control: Role-based access control (RBAC)
• Data Encryption: End-to-end encryption support
• PII Protection: Automatic PII detection and redaction
• Audit Logging: Comprehensive audit trail
• Compliance: GDPR, HIPAA, SOC2 compliance support

Privacy Protection

• Data Masking: Automatic sensitive data masking
• Anonymization: Data anonymization capabilities
• Secure Storage: Encrypted data storage
• Access Logging: Detailed access logging and monitoring

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/semantica-dev/semantica.git
cd semantica
pip install -e ".[dev]"
pre-commit install

Running Tests

pytest tests/
pytest tests/ -m "not slow"
pytest tests/ -m "integration"

📄 License

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

🙏 Acknowledgments

• Built with ❤️ by the Semantica team
• Powered by state-of-the-art NLP and ML libraries
• Inspired by the open-source community
• Special thanks to all contributors and users

📞 Support

• Documentation: https://docs.semantica.dev
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: support@semantica.dev

🌟 Star History

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Semantica - Transform your data into intelligent knowledge. 🚀