Learning More

Whether you’re running your first pipeline or deploying Semantica in production, this page gives you a structured path forward: from beginner to enterprise-grade usage.

Learning Paths

Beginner (1–2 hrs) — New to Semantica and knowledge graphs. Start with Installation →
Intermediate (4–6 hrs) — Comfortable with basics, building real applications. Start with Modules →
Advanced (8+ hrs) — Enterprise deployments, customization, and extension. Start with Architecture →

Beginner (1–2 hrs)
Intermediate (4–6 hrs)
Advanced (8+ hrs)

New to Semantica and knowledge graphs. No prior graph database experience required.

Set up your environment

Installation Guide: virtual environments, optional extras, platform-specific fixes.

Understand the core ideas

Core Concepts: what knowledge graphs are, how embeddings work, what extraction does.

Run your first example

Getting Started: 5-minute code walkthrough with pattern-based extraction (no API key needed).

Build your first knowledge graph

Quickstart Tutorial: full 6-step pipeline from ingestion to visualization.

Explore interactively

Welcome to Semantica notebook: Jupyter walkthrough of every module.

Enterprise deployments, customization, and extension. Assumes production usage experience.

Understand the architecture

Architecture Guide: four-layer design, extension points, and design decisions.

Temporal intelligence

Temporal Graphs notebook: valid_from/valid_until, Allen interval algebra, point-in-time queries.

Ontology-driven knowledge bases

Ontology notebook: auto-generation, SHACL validation, Ontology Hub (v0.5.0).

Advanced visualization

Complete Visualization Suite notebook: UMAP, t-SNE, community layouts, embedding projections.

Enterprise export

Multi-Format Export notebook: RDF with PROV-O, Parquet, Neo4j Cypher, Arrow, OWL.

Configuration Reference

All settings can be overridden with environment variables: no code changes needed.

Setting	Environment Variable	Default
OpenAI API Key	`OPENAI_API_KEY`	`None`
Groq API Key	`GROQ_API_KEY`	`None`
Anthropic API Key	`ANTHROPIC_API_KEY`	`None`
Embedding Provider	`SEMANTICA_EMBEDDING_PROVIDER`	`"openai"`
Graph Backend	`SEMANTICA_GRAPH_BACKEND`	`"networkx"`
Log Level	`SEMANTICA_LOG_LEVEL`	`"INFO"`
Log Format	`SEMANTICA_LOG_FORMAT`	`"text"`

Troubleshooting

ModuleNotFoundError: No module named 'semantica'

Verify installation and that the correct Python environment is active:

pip list | grep semantica
pip install --upgrade semantica

For optional features, install the relevant extra:

pip install "semantica[llm-openai]"   # OpenAI provider
pip install "semantica[gpu]"          # GPU acceleration

AuthenticationError

Set your API key as an environment variable — never hardcode keys in source files:

export OPENAI_API_KEY="sk-..."
export GROQ_API_KEY="gsk_..."

MemoryError or OOM crashes

Switch from the default in-memory NetworkX backend to a persistent graph database:

from semantica.graph_store import FalkorDBStore
from semantica.kg import GraphBuilder

store   = FalkorDBStore(host="localhost", port=6379)
builder = GraphBuilder(merge_entities=True, graph_store=store)

Also reduce batch sizes and enable streaming ingestion for large corpora.

Slow processing on large datasets

Enable parallel execution and GPU acceleration:

from semantica.pipeline import Pipeline

pipeline = Pipeline(workers=8, batch_size=32)
pipeline.run(sources)

pip install "semantica[gpu]"  # CUDA-backed embeddings

Windows [all] installation fails

Fixed in v0.5.0. Upgrade:

pip install --upgrade semantica

Or install extras individually: pip install "semantica[core]", then add [llm-openai], [gpu], etc. as needed.

cp1252 encoding crash on Windows

Fixed in v0.5.0. For earlier versions, set the encoding environment variable:

set PYTHONIOENCODING=utf-8

Performance Optimization

Backend selection: development vs. production

Operation	NetworkX (default)	Neo4j / FalkorDB
Graph construction	Fast	Moderate
Query performance	Moderate	Fast
Scalability	In-memory only	Persistent, production-scale
Recommended for	Development, small graphs	Production, large corpora

Use NetworkX for local development and prototyping. Switch to a persistent backend before deploying to production.

Batch processing for large corpora

Process documents in batches rather than one at a time. Configure chunk_size based on available RAM: a good starting point is 1,000 documents per batch on a 16 GB machine.

from semantica.pipeline import Pipeline

pipeline = Pipeline(workers=8, batch_size=32)
pipeline.run(sources)

Deduplication v2: up to 7× faster

If deduplication is a bottleneck, switch from v1 strategies to the v2 engine:

resolver = EntityResolver()
merged   = resolver.resolve(entities, strategy="semantic_v2")  # up to 7x faster

The blocking_v2, hybrid_v2, and semantic_v2 strategies reduce O(n²) comparisons via candidate blocking before similarity scoring.

Security Best Practices

API keys: store in environment variables or a secrets manager; never commit them to version control; rotate on a schedule
Sensitive data: use local embedding models (Ollama, HuggingFace) for PII or classified content; avoid sending sensitive data to external APIs without data handling agreements
Graph exports: encrypt sensitive exports at rest; use the v0.5.0 SSRF-safe base_url validation when configuring custom LLM gateways
XML ingestion: always use XMLIngestor (v0.5.0), which uses the XXE-safe lxml backend; never parse untrusted XML with the standard library parser
Cookbook — Interactive Jupyter notebooks from beginner to advanced.
FAQ — Common questions answered.
API Reference — Complete technical documentation.

Overview

Core Concepts

Guides

Integrations

Graph Stores

Vector Stores

Learning Paths

Configuration Reference

Troubleshooting

Performance Optimization

Security Best Practices

​Learning Paths

​Configuration Reference

​Troubleshooting

​Performance Optimization

​Security Best Practices

Learning Paths

Configuration Reference

Troubleshooting

Performance Optimization

Security Best Practices