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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.
!!! tip "How to use this Cookbook" - Beginners: Start with the Core Tutorials to learn the basics. - Developers: Check out Advanced Concepts for deep dives into specific features. - Architects: Explore Industry Use Cases for end-to-end solutions.
!!! note "Prerequisites" Before running these notebooks, ensure you have: - Python 3.8+ installed - A basic understanding of Python and Jupyter - An OpenAI API key (for most examples)
!!! success "Installation" Install Semantica from PyPI (recommended):
```bash
pip install semantica
# Or with all optional dependencies:
pip install semantica[all]
```
For more installation options, see the [Installation Guide](installation.md).
� Featured Recipes
Hand-picked tutorials to show you the power of Semantica.
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:material-robot: GraphRAG Complete
Build a production-ready Graph Retrieval Augmented Generation system.
Topics: RAG, LLMs, Vector Search, Graph Traversal
Difficulty: Advanced
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:material-scale-balance: RAG vs. GraphRAG Comparison
Side-by-side comparison of Standard RAG vs. GraphRAG using real-world data.
Topics: RAG, GraphRAG, Benchmarking, Visualization
Difficulty: Intermediate
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:material-graph: Your First Knowledge Graph
Go from raw text to a queryable knowledge graph in 20 minutes.
Topics: Extraction, Graph Construction, Visualization
Difficulty: Beginner
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:material-shield-alert: Real-Time Anomaly Detection
Detect anomalies in streaming data using dynamic graphs.
Topics: Streaming, Security, Dynamic Graphs
Difficulty: Advanced
🏁 Core Tutorials
Essential guides to master the Semantica framework.
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:material-hand-wave: Welcome to Semantica
An interactive introduction to the framework's core philosophy and all modules including ingestion, parsing, extraction, knowledge graphs, embeddings, and more.
Topics: Framework Overview, Architecture, All Modules
Difficulty: Beginner
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:material-database-import: Data Ingestion
Techniques for loading data from multiple sources using FileIngestor, WebIngestor, FeedIngestor, StreamIngestor, RepoIngestor, EmailIngestor, DBIngestor, and MCPIngestor.
Topics: File Ingestion, Web Scraping, Database Integration, Streams, Feeds, Repositories, Email, MCP
Difficulty: Beginner
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:material-file-document-outline: Document Parsing
Extracting clean text from complex formats like PDF, DOCX, and HTML.
Topics: OCR, PDF Parsing, Text Extraction
Difficulty: Beginner
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:material-broom: Data Normalization
Pipelines for cleaning, normalizing, and preparing text.
Topics: Text Cleaning, Unicode, Formatting
Difficulty: Beginner
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:material-account-search: Entity Extraction
Using NER to identify people, organizations, and custom entities.
Topics: NER, Spacy, LLM Extraction
Difficulty: Beginner
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:material-relation-many-to-many: Relation Extraction
Discovering and classifying relationships between entities.
Topics: Relation Classification, Dependency Parsing
Difficulty: Beginner
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:material-vector-square: Embedding Generation
Creating and managing vector embeddings for semantic search.
Topics: Embeddings, OpenAI, HuggingFace
Difficulty: Intermediate
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:material-database-search: Vector Store
Setting up vector stores for similarity search and retrieval.
Difficulty: Intermediate
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:material-database-settings: Graph Store
Persisting knowledge graphs in Neo4j or FalkorDB.
Topics: Neo4j, Cypher, Persistence
Difficulty: Intermediate
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:material-sitemap: Ontology
Defining domain schemas and ontologies to structure your data.
Topics: OWL, RDF, Schema Design
Difficulty: Intermediate
🧠 Advanced Concepts
Deep dive into advanced features, customization, and complex workflows.
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:material-flask: Advanced Extraction
Custom extractors, LLM-based extraction, and complex pattern matching.
Topics: Custom Models, Regex, LLMs
Difficulty: Advanced
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:material-chart-network: Advanced Graph Analytics
Centrality, community detection, and pathfinding algorithms.
Topics: PageRank, Louvain, Shortest Path
Difficulty: Advanced
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:material-monitor-dashboard: Complete Visualization Suite
Creating interactive, publication-ready visualizations of your graphs.
Topics: PyVis, NetworkX, D3.js
Difficulty: Intermediate
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:material-scale-balance: Conflict Resolution
Strategies for handling contradictory information from multiple sources.
Topics: Truth Discovery, Voting, Confidence
Difficulty: Advanced
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:material-export: Multi-Format Export
Exporting to RDF, OWL, JSON-LD, and NetworkX formats.
Topics: Serialization, Interoperability
Difficulty: Intermediate
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:material-source-merge: Multi-Source Integration
Merging data from disparate sources into a unified graph.
Topics: Entity Resolution, Merging, Fusion
Difficulty: Advanced
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:material-brain: Reasoning and Inference
Using logical reasoning to infer new knowledge from existing facts.
Topics: Logic Rules, Inference Engines
Difficulty: Advanced
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:material-layers: Semantic Layer Construction
Building a semantic layer over your data warehouse or lake.
Topics: Semantic Layer, Data Warehouse
Difficulty: Advanced
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:material-clock-outline: Temporal Knowledge Graphs
Modeling and querying data that changes over time.
Topics: Time Series, Temporal Logic
Difficulty: Advanced
🏭 Industry Use Cases
Real-world examples and end-to-end applications across various industries.
Biomedical
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:material-pill: Drug Discovery Pipeline
Accelerating drug discovery by connecting genes, proteins, and drugs.
Topics: Bioinformatics, KG Construction
Difficulty: Advanced
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:material-dna: Genomic Variant Analysis
Analyzing genomic variants and their implications for disease.
Topics: Genomics, Variant Calling
Difficulty: Advanced
Healthcare
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:material-hospital-box: Clinical Reports Processing
Processing and structuring unstructured clinical reports.
Topics: NLP, Medical Records
Difficulty: Intermediate
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:material-virus: Disease Network Analysis
Analyzing disease networks and comorbidities for population health.
Topics: Disease Modeling, Comorbidity Networks
Difficulty: Advanced
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:material-pill-multiple: Drug Interactions Analysis
Identifying potential drug interactions and contraindications.
Topics: Pharmacology, Drug Safety
Difficulty: Advanced
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:material-robot-love: Healthcare GraphRAG Hybrid
Hybrid RAG system for healthcare knowledge retrieval.
Topics: RAG, Medical Knowledge, LLMs
Difficulty: Advanced
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:material-database-plus: Medical Database Integration
Integrating multiple medical databases into unified knowledge graphs.
Topics: Data Integration, Medical Databases
Difficulty: Intermediate
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:material-account-heart: Patient Records Temporal
Analyzing patient records over time to track health progression.
Topics: Temporal Analysis, Patient Journeys
Difficulty: Advanced
Finance
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:material-finance: Financial Data Integration
Merging financial data from reports, news, and market feeds.
Topics: Finance, Data Fusion
Difficulty: Intermediate
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:material-file-chart: Financial Reports Analysis
Extracting insights from financial reports and earnings calls.
Topics: Financial Analysis, NLP
Difficulty: Intermediate
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:material-incognito: Fraud Detection
Identifying fraudulent activities and patterns in transaction networks.
Topics: Anomaly Detection, Graph Mining
Difficulty: Advanced
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:material-chart-box: Investment Analysis Hybrid RAG
AI-powered investment analysis using hybrid RAG approach.
Topics: Investment Research, RAG, Financial Analysis
Difficulty: Advanced
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:material-gavel: Regulatory Compliance
Ensuring compliance with financial regulations using knowledge graphs.
Topics: Compliance, Regulatory Analysis
Difficulty: Advanced
Blockchain
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:material-bitcoin: DeFi Protocol Intelligence
Analyzing decentralized finance protocols and transaction flows.
Topics: Blockchain, DeFi, Smart Contracts
Difficulty: Advanced
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:material-network: Transaction Network Analysis
Mapping and analyzing blockchain transaction networks.
Topics: Blockchain Analytics, Network Analysis
Difficulty: Advanced
Cybersecurity
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:material-shield-alert: Anomaly Detection Real-Time
Detecting anomalies in real-time network traffic streams.
Topics: Network Security, Streaming
Difficulty: Advanced
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:material-shield-search: Incident Analysis
Analyzing security incidents and breaches using graph forensics.
Topics: Incident Response, Forensics
Difficulty: Intermediate
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:material-shield-link-variant: Threat Correlation
Correlating threats across different vectors to identify campaigns.
Topics: Threat Intel, Correlation
Difficulty: Advanced
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:material-robot-angry: Threat Intelligence Hybrid RAG
Combining RAG with threat intelligence for enhanced security insights.
Topics: Threat Intelligence, RAG, Security
Difficulty: Advanced
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:material-shield-plus: Threat Intelligence Integration
Integrating threat feeds into a unified knowledge graph.
Topics: STIX/TAXII, Threat Feeds, Integration
Difficulty: Advanced
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:material-bug: Vulnerability Tracking
Tracking and managing system vulnerabilities using knowledge graphs.
Topics: CVE, Vulnerability Management
Difficulty: Intermediate
Intelligence
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:material-account-network: Criminal Network Analysis
Analyze criminal networks with graph analytics and key player detection.
Topics: Forensics, Social Network Analysis
Difficulty: Advanced
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:material-file-search: Intelligence Analysis
Comprehensive intelligence analysis using orchestrator-worker pattern with graph analytics and hybrid RAG.
Topics: Intelligence Analysis, Orchestrator-Worker, Graph Analytics
Difficulty: Advanced
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:material-gavel: Law Enforcement Forensics
Forensic analysis pipeline for processing case files and evidence.
Topics: Forensics, Evidence Analysis, Case Correlation
Difficulty: Advanced
Trading
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:material-chart-areaspline: Market Data Analysis
Analyzing trading market data for patterns and opportunities.
Topics: Trading, Market Analysis
Difficulty: Intermediate
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:material-newspaper-variant: News Sentiment Analysis
Analyzing news sentiment for trading signals and market predictions.
Topics: Sentiment Analysis, Trading Signals
Difficulty: Intermediate
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:material-monitor-dashboard: Real-Time Monitoring
Monitoring trading systems and positions in real-time.
Topics: Monitoring, Real-Time Systems
Difficulty: Advanced
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:material-shield-check: Risk Assessment
Assessing trading risks using knowledge graphs and analytics.
Topics: Risk Management, Portfolio Analysis
Difficulty: Advanced
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:material-history: Strategy Backtesting
Backtesting trading strategies using historical data and graphs.
Topics: Backtesting, Strategy Optimization
Difficulty: Advanced
Renewable Energy
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:material-wind-turbine: Energy Market Analysis
Analyzing trends and pricing in the renewable energy market.
Topics: Energy, Time Series
Difficulty: Intermediate
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:material-leaf: Environmental Impact
Assessing environmental impact of energy projects and policies.
Topics: Environmental Science, Impact Analysis
Difficulty: Intermediate
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:material-transmission-tower: Grid Management
Optimizing power grid management and distribution.
Topics: Grid Optimization, Energy Distribution
Difficulty: Advanced
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:material-solar-power: Resource Optimization
Optimizing renewable energy resources and generation.
Topics: Resource Management, Optimization
Difficulty: Advanced
Supply Chain
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:material-truck-delivery: Supply Chain Data Integration
Integrating supply chain data to optimize logistics and reduce risk.
Topics: Logistics, Risk Management
Difficulty: Advanced
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:material-alert-octagon: Supply Chain Risk Management
Managing and mitigating supply chain risks using knowledge graphs.
Topics: Risk Management, Supply Chain Resilience
Difficulty: Advanced
🛠️ How to Run
To run these notebooks locally:
-
Install Semantica from PyPI (recommended):
pip install semantica[all] pip install jupyter -
Or install from source (for development):
git clone https://github.com/Hawksight-AI/semantica.git cd semantica pip install -e .[all] pip install jupyter -
Launch Jupyter:
jupyter notebook
!!! tip "Using Docker"
You can also run the cookbook using Docker:
bash docker run -p 8888:8888 hawksight/semantica-cookbook