semantica/docs/cookbook.md

🍳 Semantica Cookbook

Welcome to the Semantica Cookbook!

This collection of Jupyter notebooks is designed to take you from a beginner to an expert in building semantic AI applications. Whether you're looking for quick recipes or deep-dive tutorials, you'll find it here.

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🏁 Introduction

Start here if you are new to the framework. These notebooks cover the essentials.

• Welcome to Semantica: An interactive introduction to the framework.
• Configuration Basics: Learn how to configure API keys and settings.
• Your First Knowledge Graph: Build a simple graph from scratch.
• Data Ingestion: Loading data from files and web sources.
• Document Parsing: Extracting text from PDFs, DOCX, and more.
• Data Normalization: Cleaning and preparing text.
• Entity Extraction: Identifying people, places, and organizations.
• Relation Extraction: Finding connections between entities.
• Embedding Generation: Creating vector representations.
• Vector Store: Storing and searching vectors.
• Ontology: Defining the structure of your knowledge.
• Conflict Detection: Handling contradictory information.
• Deduplication: Merging duplicate entities.
• Building Knowledge Graphs: Putting it all together.
• Graph Analytics: Analyzing graph structure.
• Graph Quality: Ensuring data quality.
• Visualization: Visualizing your graphs.
• Export: Exporting data to other formats.

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🧠 Advanced Concepts

Deep dive into advanced features and customization.

• Advanced Extraction: Techniques for complex entity and relation extraction.
• Advanced Graph Analytics: In-depth graph analysis algorithms.
• Complete Visualization Suite: Comprehensive guide to visualization tools.
• Conflict Resolution Strategies: Advanced methods for resolving data conflicts.
• Multi-Format Export: Exporting data to various formats.
• Multi-Source Data Integration: Integrating data from disparate sources.
• Pipeline Orchestration: Building and managing complex pipelines.
• Reasoning and Inference: Applying logical reasoning to your graph.
• Semantic Layer Construction: Building a robust semantic layer.
• Temporal Knowledge Graphs: Working with time-aware graphs.
• Text Chunking Strategies: Optimizing text processing.
• Unstructured to Ontology: Automatically generating ontologies from text.

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💡 Use Cases

Real-world examples and applications across various industries.

Advanced RAG

• GraphRAG Complete: End-to-end implementation of Graph Retrieval Augmented Generation.

Biomedical

• Drug Discovery Pipeline: Accelerating drug discovery with knowledge graphs.
• Genomic Variant Analysis: Analyzing genomic variants and their implications.

Blockchain

• DeFi Protocol Intelligence: Analyzing decentralized finance protocols.
• Transaction Network Analysis: Investigating blockchain transaction networks.

Cybersecurity

• Anomaly Detection Real-Time: Detecting anomalies in real-time streams.
• Incident Analysis: Analyzing security incidents and breaches.
• Threat Correlation: Correlating threats across different vectors.
• Threat Intelligence Hybrid RAG: Combining RAG with threat intelligence.
• Threat Intelligence Integration: Integrating threat feeds into a knowledge graph.
• Vulnerability Tracking: Tracking and managing system vulnerabilities.

Finance

• Financial Data Integration: Merging financial data from multiple sources.
• Financial Reports Analysis: Extracting insights from financial reports.
• Fraud Detection: Identifying fraudulent activities and patterns.
• Investment Analysis Hybrid RAG: AI-powered investment analysis.
• Market Intelligence: Gathering and analyzing market intelligence.
• Regulatory Compliance: Ensuring compliance with financial regulations.

Healthcare

• Clinical Reports Processing: Processing and structuring clinical reports.
• Disease Network Analysis: Analyzing disease networks and comorbidities.
• Drug Interactions Analysis: Identifying potential drug interactions.
• Healthcare GraphRAG Hybrid: Hybrid RAG for healthcare applications.
• Medical Database Integration: Integrating medical databases.
• Medical Literature GraphRAG: Querying medical literature with GraphRAG.
• Patient Records Temporal: Analyzing patient records over time.

Intelligence

• Network Analysis Intelligence Reports: Analyzing intelligence reports for network insights.
• Criminal Network Analysis: Analyze criminal networks with graph analytics, key player detection, and intelligence reporting using agent-based workflows and MCP integration.
• Law Enforcement and Forensics: Process forensic evidence, analyze case files, build temporal case timelines, and generate forensic analysis reports with agent-based workflows.
• Intelligence Analysis: Comprehensive intelligence analysis using Orchestrator-Worker pattern with complete graph analytics, hybrid RAG, ontology building, threat assessment, and geospatial intelligence.

Renewable Energy

• Energy Market Analysis: Analyzing trends in the energy market.
• Environmental Impact: Assessing environmental impact.
• Grid Management: Optimizing power grid management.
• Resource Optimization: Optimizing renewable resources.
• Supply Chain Analysis: Analyzing the renewable energy supply chain.

Supply Chain

• Supply Chain Data Integration: Integrating supply chain data.
• Supply Chain Risk Management: Managing and mitigating supply chain risks.

Trading

• Market Data Analysis: Analyzing trading market data.
• News Sentiment Analysis: Analyzing news sentiment for trading signals.
• Real Time Market Data: Processing real-time market data.
• Real Time Monitoring: Monitoring trading systems in real-time.
• Risk Assessment: Assessing trading risks.
• Strategy Backtesting: Backtesting trading strategies.

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🛠️ How to Run

To run these notebooks locally:

1. Clone the repository:

   git clone https://github.com/Hawksight-AI/semantica.git
   cd semantica
   

2. Install dependencies:

   pip install -e .[all]
   pip install jupyter
   

3. Launch Jupyter:

   jupyter notebook