semantica/docs/learning-more.md

Learning More

Additional resources, tutorials, and advanced learning materials for Semantica.

!!! info "About This Guide" This guide provides structured learning paths, quick references, troubleshooting guides, and advanced topics to help you master Semantica.

---

Structured Learning Paths

• :material-school: Beginner Path

  Perfect for those new to Semantica and knowledge graphs.

  Start Path

• :material-compass: Intermediate Path

  For users comfortable with basics who want to build production applications.

  Start Path

• :material-rocket: Advanced Path

  For experienced users building enterprise applications.

  Start Path

---

Beginner Path (1-2 hours)

1. Installation & Setup (15 min)

   • Installation Guide
   • Welcome to Semantica Cookbook: Comprehensive introduction
     • Topics: Framework overview, all modules, architecture, configuration
     • Difficulty: Beginner
     • Time: 30-45 minutes
     • Use Cases: First-time users, understanding the framework

2. Core Concepts (30 min)

   • Core Concepts
   • Getting Started Guide
   • Data Ingestion Cookbook: Learn to ingest from multiple sources
     • Topics: File, web, feed, stream, database ingestion
     • Difficulty: Beginner
     • Time: 15-20 minutes
     • Use Cases: Loading data from various sources

3. First Knowledge Graph (30 min)

   • Quickstart Tutorial
   • Your First Knowledge Graph Cookbook: Build your first knowledge graph
     • Topics: Entity extraction, relationship extraction, graph construction, visualization
     • Difficulty: Beginner
     • Time: 20-30 minutes
     • Use Cases: Learning the basics, quick start

4. Basic Operations (30 min)

   • Examples
   • Entity Extraction Cookbook: Learn entity extraction
     • Topics: Named entity recognition, entity types, extraction methods
     • Difficulty: Beginner
     • Time: 15-20 minutes
     • Use Cases: Understanding entity extraction

---

Intermediate Path (4-6 hours)

1. Advanced Concepts (1 hour)

   • Modules Guide
   • Building Knowledge Graphs Cookbook: Advanced graph construction
     • Topics: Graph building, entity merging, conflict resolution, temporal graphs
     • Difficulty: Intermediate
     • Time: 30-45 minutes
     • Use Cases: Production graph construction
   • Embeddings Cookbook: Learn embeddings
     • Topics: Embedding generation, similarity search, vector operations
     • Difficulty: Beginner
     • Time: 20-30 minutes
     • Use Cases: Understanding embeddings, semantic search

2. Use Cases (1 hour)

   • Use Cases Guide
   • GraphRAG Complete Cookbook: Build production GraphRAG
     • Topics: GraphRAG, hybrid retrieval, graph traversal, LLM integration
     • Difficulty: Advanced
     • Time: 1-2 hours
     • Use Cases: Production GraphRAG systems

3. Advanced Examples (1 hour)

   • Examples
   • Advanced Extraction Cookbook: Advanced extraction patterns
     • Topics: Custom entity types, domain-specific extraction, hybrid methods
     • Difficulty: Intermediate
     • Time: 30-45 minutes
     • Use Cases: Domain-specific extraction

4. Quality & Optimization (1 hour)

   • Quality Assurance
   • Performance Optimization
   • Multi-Source Data Integration Cookbook: Integrate multiple sources
     • Topics: Multi-source integration, entity resolution, conflict handling
     • Difficulty: Intermediate
     • Time: 30-45 minutes
     • Use Cases: Building unified knowledge graphs

---

Advanced Path (8+ hours)

1. Advanced Architecture (2 hours)

   • Architecture Guide
   • Temporal Graphs Cookbook: Build temporal graphs
     • Topics: Time-stamped entities, temporal relationships, historical queries
     • Difficulty: Intermediate
     • Time: 30-45 minutes
     • Use Cases: Time-aware knowledge graphs
   • Ontology Cookbook: Generate ontologies
     • Topics: Ontology generation, OWL, schema design
     • Difficulty: Intermediate
     • Time: 30-45 minutes
     • Use Cases: Formal knowledge representation

2. Production Deployment (2 hours)

   • Security Best Practices
   • GraphRAG Complete Cookbook: Production GraphRAG
     • Topics: Production deployment, scalability, optimization
     • Difficulty: Advanced
     • Time: 1-2 hours
     • Use Cases: Production systems

3. Customization (2 hours)

   • Complete Visualization Suite Cookbook: Advanced visualization
     • Topics: Custom layouts, filtering, styling, multiple graph types
     • Difficulty: Intermediate
     • Time: 30-45 minutes
     • Use Cases: Production visualizations
   • Multi-Format Export Cookbook: Advanced export patterns
     • Topics: Batch export, custom formats, format conversion
     • Difficulty: Intermediate
     • Time: 30-45 minutes
     • Use Cases: Production exports

---

Quick Reference

Common Operations

The typical workflow involves these steps:

1. Ingest documents using `FileIngestor`
2. Parse documents using `DocumentParser`
3. Extract entities and relationships using `NERExtractor` and `RelationExtractor`
4. Build knowledge graph using `GraphBuilder`
5. Generate embeddings using `TextEmbedder`

For complete examples, see:

• Your First Knowledge Graph Cookbook: Complete workflow example
• Welcome to Semantica Cookbook: All modules overview

Configuration Reference

Setting	Environment Variable	Config File	Default
OpenAI API Key	OPENAI_API_KEY	api_keys.openai	None
Embedding Provider	SEMANTICA_EMBEDDING_PROVIDER	embedding.provider	"openai"
Graph Backend	SEMANTICA_GRAPH_BACKEND	knowledge_graph.backend	"networkx"

---

Troubleshooting Guide

• :material-alert: Import Errors

  ModuleNotFoundError

  Solution: Verify installation (pip list) and Python version (3.8+).

• :material-key: API Key Errors

  AuthenticationError

  Solution: Set OPENAI_API_KEY environment variable.

• :material-memory: Memory Errors

  MemoryError

  Solution: Use batch processing and graph stores (Neo4j).

• :material-speedometer: Slow Processing

  Long processing times

  Solution: Enable parallel processing and GPU acceleration.

---

Performance Optimization

1. Batch Processing

Process multiple documents together for better throughput. Use batch processing when working with large document collections.

For examples, see:

• Data Ingestion Cookbook: Batch ingestion patterns
• Multi-Source Data Integration Cookbook: Advanced integration

2. Parallel Execution

Use parallel processing for independent operations to improve performance on multi-core systems.

3. Backend Selection

Operation	NetworkX	Neo4j
Graph Construction	⚡⚡⚡	⚡⚡
Query Performance	⚡⚡	⚡⚡⚡
Scalability	Low	High

---

Security Best Practices

API Key Management

• DO: Use environment variables, rotate keys regularly.
• DON'T: Hardcode keys, commit to version control.

Data Privacy

• DO: Encrypt sensitive data, use local models.
• DON'T: Send PII to external APIs without protection.

---

FAQ

Q: What is Semantica? A: A framework for building knowledge graphs and semantic applications.

Q: Is Semantica free? A: Yes, it is open source. Some features (e.g., OpenAI) require paid APIs.

Q: Can I use Semantica in production? A: Yes, it is designed for production with proper configuration.

---

Next Steps

Continue your learning journey:

• Cookbook - Interactive Jupyter notebook tutorials
• API Reference - Complete API documentation
• Use Cases - Real-world applications
• Examples - Code examples and patterns

🍳 Recommended Next Cookbooks

• GraphRAG Complete: Production GraphRAG system

  • Topics: GraphRAG, hybrid retrieval, LLM integration
  • Difficulty: Advanced
  • Time: 1-2 hours
  • Use Cases: Production RAG applications

• RAG vs. GraphRAG Comparison: Understand the differences

  • Topics: RAG comparison, reasoning gap, inference engines
  • Difficulty: Intermediate
  • Time: 45-60 minutes
  • Use Cases: Choosing the right approach

---

!!! info "Contribute" Have questions? Open an issue or start a discussion!