semantica/docs/learning-more.md

Learning More

Structured learning paths, quick references, and performance guidance for going deeper with Semantica.

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Learning Paths

• :material-school: Beginner (1–2 hours)

  New to Semantica and knowledge graphs.

  Start here

• :material-compass: Intermediate (4–6 hours)

  Comfortable with basics, building production applications.

  Start here

• :material-rocket: Advanced (8+ hours)

  Enterprise applications and customization.

  Start here

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Beginner Path

1. Installation & Setup — Installation Guide
2. Core Concepts — Core Concepts + Getting Started
3. First Knowledge Graph — Quickstart Tutorial
4. Interactive Introduction — Welcome to Semantica notebook
5. Hands-On Practice — Your First Knowledge Graph notebook

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Intermediate Path

1. All Modules — Modules Guide
2. Advanced Graph Construction — Building Knowledge Graphs notebook
3. Embeddings & Search — Embeddings notebook
4. GraphRAG — GraphRAG Complete notebook
5. Multi-Source Integration — Multi-Source Data Integration notebook
6. Use Case Examples — Use Cases

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Advanced Path

1. Architecture Deep Dive — Architecture Guide
2. Temporal Graphs — Temporal Graphs notebook
3. Ontologies — Ontology notebook
4. Visualization — Complete Visualization Suite notebook
5. Export Pipelines — Multi-Format Export notebook
6. Production GraphRAG — GraphRAG Complete notebook

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Configuration Reference

Setting	Environment Variable	Default
OpenAI API Key	OPENAI_API_KEY	None
Embedding Provider	SEMANTICA_EMBEDDING_PROVIDER	"openai"
Graph Backend	SEMANTICA_GRAPH_BACKEND	"networkx"

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Troubleshooting

• :material-alert: Import Errors

  ModuleNotFoundError

  Verify installation: pip list | grep semantica. Ensure Python 3.8+.

• :material-key: API Key Errors

  AuthenticationError

  Set OPENAI_API_KEY (or the relevant provider key) as an environment variable.

• :material-memory: Memory Errors

  MemoryError or OOM crashes

  Reduce batch sizes or switch to a persistent graph backend (Neo4j, FalkorDB).

• :material-speedometer: Slow Processing

  Long runtimes on large datasets

  Enable parallel processing (PipelineBuilder workers) and GPU acceleration.

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Performance Optimization

Batch Processing

Process documents in batches rather than one at a time. Configure chunk sizes based on available RAM.

Parallel Execution

PipelineBuilder supports configurable worker counts per stage for independent operations.

Backend Selection

Operation	NetworkX	Neo4j / FalkorDB
Graph construction	Fast	Moderate
Query performance	Moderate	Fast
Scalability	Low (in-memory)	High (persistent)

Use NetworkX for development and smaller graphs; switch to a persistent backend for production at scale.

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Security Best Practices

API keys

• Store in environment variables or a secrets manager
• Never hardcode keys or commit them to version control
• Rotate keys regularly

Data privacy

• Use local embedding models for sensitive data
• Avoid sending PII to external APIs without appropriate data handling agreements
• Encrypt sensitive graph exports at rest

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Next Steps

• Cookbook — interactive Jupyter notebook tutorials
• API Reference — complete technical documentation
• Use Cases — real-world domain examples
• FAQ — common questions

!!! info "Questions or feedback?" Open an issue or start a discussion.