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🍳 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.
🏁 Introduction
Start here if you are new to the framework. These notebooks cover the essentials.
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:material-hand-wave: Welcome to Semantica
An interactive introduction to the framework.
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:material-cog: Configuration Basics
Learn how to configure API keys and settings.
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:material-graph: Your First Knowledge Graph
Build a simple graph from scratch.
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:material-database-import: Data Ingestion
Loading data from files and web sources.
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:material-file-document-outline: Document Parsing
Extracting text from PDFs, DOCX, and more.
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:material-broom: Data Normalization
Cleaning and preparing text.
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:material-account-search: Entity Extraction
Identifying people, places, and organizations.
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:material-relation-many-to-many: Relation Extraction
Finding connections between entities.
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:material-vector-square: Embedding Generation
Creating vector representations.
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:material-database-search: Vector Store
Storing and searching vectors.
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:material-database-settings: Graph Store
Storing graphs in Neo4j, KuzuDB, or FalkorDB.
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:material-sitemap: Ontology
Defining the structure of your knowledge.
🧠 Advanced Concepts
Deep dive into advanced features and customization.
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:material-flask: Advanced Extraction
Techniques for complex entity and relation extraction.
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:material-chart-network: Advanced Graph Analytics
In-depth graph analysis algorithms.
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:material-monitor-dashboard: Complete Visualization Suite
Comprehensive guide to visualization tools.
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:material-scale-balance: Conflict Resolution
Advanced methods for resolving data conflicts.
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:material-export: Multi-Format Export
Exporting data to various formats.
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:material-source-merge: Multi-Source Integration
Integrating data from disparate sources.
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:material-pipe: Pipeline Orchestration
Building and managing complex pipelines.
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:material-brain: Reasoning and Inference
Applying logical reasoning to your graph.
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:material-layers: Semantic Layer Construction
Building a robust semantic layer.
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:material-clock-outline: Temporal Knowledge Graphs
Working with time-aware graphs.
💡 Use Cases
Real-world examples and applications across various industries.
Advanced RAG
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:material-robot: GraphRAG Complete
End-to-end implementation of Graph Retrieval Augmented Generation.
Biomedical
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:material-pill: Drug Discovery Pipeline
Accelerating drug discovery with knowledge graphs.
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:material-dna: Genomic Variant Analysis
Analyzing genomic variants and their implications.
Blockchain & Finance
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:material-bitcoin: DeFi Protocol Intelligence
Analyzing decentralized finance protocols.
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:material-finance: Financial Data Integration
Merging financial data from multiple sources.
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:material-incognito: Fraud Detection
Identifying fraudulent activities and patterns.
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:material-chart-line: Market Intelligence
Gathering and analyzing market intelligence.
Cybersecurity
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:material-shield-alert: Anomaly Detection
Detecting anomalies in real-time streams.
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:material-shield-search: Incident Analysis
Analyzing security incidents and breaches.
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:material-shield-link-variant: Threat Correlation
Correlating threats across different vectors.
Healthcare
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:material-hospital-box: Clinical Reports
Processing and structuring clinical reports.
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:material-virus: Disease Network Analysis
Analyzing disease networks and comorbidities.
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:material-account-heart: Patient Records Temporal
Analyzing patient records over time.
Intelligence
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:material-account-network: Criminal Network Analysis
Analyze criminal networks with graph analytics.
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:material-gavel: Law Enforcement
Process forensic evidence and analyze case files.
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:material-eye: Intelligence Analysis
Comprehensive intelligence analysis using Orchestrator-Worker pattern.
Renewable Energy & Supply Chain
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:material-wind-turbine: Energy Market Analysis
Analyzing trends in the energy market.
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:material-truck-delivery: Supply Chain Integration
Integrating supply chain data.
🛠️ How to Run
To run these notebooks locally:
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Clone the repository:
git clone https://github.com/Hawksight-AI/semantica.git cd semantica -
Install dependencies:
pip install -e .[all] pip install jupyter -
Launch Jupyter:
jupyter notebook