- Rewrote index.md to match README (tagline, badges, Problem/Solution text) - Improved getting-started, concepts, quickstart, installation, faq, use-cases, contributing, glossary, learning-more, examples, modules, architecture, cookbook, deep-dive pages: tighter prose, fixed headings/bullets, removed inconsistencies and duplicate sections - Removed overuse of emojis from headings in integration pages (docling, snowflake) - Fixed change_management reference page: closed unclosed JSON code block that broke the right TOC, demoted noisy sub-headings to bold text - CSS layout: widened content area (max-width 1440px grid, left sidebar 11rem, right TOC narrowed to 11rem for broader content), tightened TOC spacing and font size, fixed word-wrap/overflow on TOC links - Added mkdocs_local.yml for local serving without mkdocs-jupyter plugin Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
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Frequently Asked Questions
Common questions about Semantica. Use Ctrl+F to find what you need.
General
What is Semantica?
Semantica is an open-source framework for building context graphs and decision intelligence layers for AI. It transforms unstructured data — documents, APIs, databases — into structured knowledge graphs with full provenance tracking, making AI systems explainable and auditable.
What can I build with Semantica?
- Knowledge graphs from documents and multi-source data
- GraphRAG systems with graph-grounded retrieval
- AI agents with structured decision history and semantic memory
- Compliance-ready pipelines with W3C PROV-O lineage
What makes Semantica different from other frameworks?
Most frameworks stop at retrieval or generation. Semantica adds an accountability layer: every decision is recorded, every fact links to a source, and every reasoning step is explainable. It's designed for environments where you need to audit why an AI reached a conclusion.
Is Semantica free?
Yes — MIT licensed, no vendor lock-in. Some features require third-party API keys (e.g., OpenAI embeddings), but Semantica itself is free.
Installation
How do I install Semantica?
pip install semantica
See Installation for virtual environment setup, optional extras, and troubleshooting.
What Python version do I need?
Python 3.8 or higher. Python 3.11+ is recommended for best performance.
What are the system requirements?
- Python 3.8+
- 4 GB RAM minimum; 16 GB+ recommended for larger graphs
- Optional GPU for embedding generation and ML inference
Getting Started
Where do I start?
- Installation — get set up
- Getting Started — core concepts and first example
- Quickstart Tutorial — full step-by-step pipeline
- Cookbook — interactive Jupyter notebooks
What data sources does Semantica support?
- Files — PDF, DOCX, HTML, JSON, CSV, Excel, PPTX, archives
- Web — crawl with
WebIngestor, RSS feeds - Databases — PostgreSQL, MySQL, Snowflake via
DBIngestor/SnowflakeIngestor - Streams — Kafka, real-time ingestion
- Media — image OCR, audio/video metadata
Features
Can I use my own models?
Yes. Semantica supports custom entity extraction models, embedding models, LLM providers (via LiteLLM — 100+ models), and custom pipeline processors.
Does Semantica support GPUs?
Yes. When available, GPUs are used automatically for embedding generation, ML model inference, and vector operations. Install semantica[gpu] for CUDA support.
How does Semantica handle large datasets?
- Batching — process documents in configurable chunks
- Parallel processing —
PipelineBuildersupports configurable worker counts - Delta processing — update graphs incrementally without full recompute
- Graph backends — swap in-memory NetworkX for Neo4j, FalkorDB, or Apache AGE at scale
Technical
What graph databases are supported?
Neo4j, FalkorDB, Apache AGE (PostgreSQL), Amazon Neptune, and in-memory NetworkX for development.
What export formats are available?
RDF (Turtle, JSON-LD, N-Triples, XML), Apache Parquet, ArangoDB AQL, CSV, YAML, and OWL ontologies.
Is Semantica production-ready?
Yes. v0.3.0 ships with 886+ passing tests, PipelineValidator, FailureHandler with exponential backoff, W3C PROV-O provenance, and change management with checksums. See What's New for details.
Troubleshooting
Import error: ModuleNotFoundError: No module named 'semantica'
Ensure you have the correct Python environment active, then:
pip list | grep semantica
pip install --upgrade semantica
Installation fails with dependency errors
pip install --upgrade pip wheel
pip install semantica
Memory errors during processing
Reduce batch sizes, enable streaming ingestion, or switch to a persistent graph backend (Neo4j, FalkorDB).
Slow embedding or inference
Install GPU support (pip install semantica[gpu]) and ensure CUDA is available on your system.
Support
Where can I get help?
- Discord — community chat and support
- GitHub Issues — bug reports and feature requests
- GitHub Discussions — questions and ideas
How do I report a bug?
- Search existing issues first
- Open a new issue with: description, reproduction steps, expected vs actual behavior, and your environment (Python version, OS, Semantica version)
How do I contribute?
See the Contributing Guide.