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Mohd KaifandClaude Sonnet 4.6
b282487b17
- 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>
150 lines
4.8 KiB
Markdown
150 lines
4.8 KiB
Markdown
# Frequently Asked Questions
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Common questions about Semantica. Use Ctrl+F to find what you need.
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---
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## General
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### What is Semantica?
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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.
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### What can I build with Semantica?
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- Knowledge graphs from documents and multi-source data
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- GraphRAG systems with graph-grounded retrieval
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- AI agents with structured decision history and semantic memory
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- Compliance-ready pipelines with W3C PROV-O lineage
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### What makes Semantica different from other frameworks?
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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.
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### Is Semantica free?
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Yes — MIT licensed, no vendor lock-in. Some features require third-party API keys (e.g., OpenAI embeddings), but Semantica itself is free.
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---
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## Installation
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### How do I install Semantica?
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```bash
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pip install semantica
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```
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See [Installation](installation.md) for virtual environment setup, optional extras, and troubleshooting.
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### What Python version do I need?
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Python 3.8 or higher. Python 3.11+ is recommended for best performance.
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### What are the system requirements?
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- Python 3.8+
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- 4 GB RAM minimum; 16 GB+ recommended for larger graphs
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- Optional GPU for embedding generation and ML inference
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---
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## Getting Started
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### Where do I start?
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1. [Installation](installation.md) — get set up
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2. [Getting Started](getting-started.md) — core concepts and first example
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3. [Quickstart Tutorial](quickstart.md) — full step-by-step pipeline
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4. [Cookbook](cookbook.md) — interactive Jupyter notebooks
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### What data sources does Semantica support?
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- **Files** — PDF, DOCX, HTML, JSON, CSV, Excel, PPTX, archives
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- **Web** — crawl with `WebIngestor`, RSS feeds
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- **Databases** — PostgreSQL, MySQL, Snowflake via `DBIngestor` / `SnowflakeIngestor`
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- **Streams** — Kafka, real-time ingestion
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- **Media** — image OCR, audio/video metadata
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---
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## Features
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### Can I use my own models?
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Yes. Semantica supports custom entity extraction models, embedding models, LLM providers (via LiteLLM — 100+ models), and custom pipeline processors.
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### Does Semantica support GPUs?
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Yes. When available, GPUs are used automatically for embedding generation, ML model inference, and vector operations. Install `semantica[gpu]` for CUDA support.
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### How does Semantica handle large datasets?
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- **Batching** — process documents in configurable chunks
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- **Parallel processing** — `PipelineBuilder` supports configurable worker counts
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- **Delta processing** — update graphs incrementally without full recompute
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- **Graph backends** — swap in-memory NetworkX for Neo4j, FalkorDB, or Apache AGE at scale
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---
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## Technical
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### What graph databases are supported?
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Neo4j, FalkorDB, Apache AGE (PostgreSQL), Amazon Neptune, and in-memory NetworkX for development.
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### What export formats are available?
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RDF (Turtle, JSON-LD, N-Triples, XML), Apache Parquet, ArangoDB AQL, CSV, YAML, and OWL ontologies.
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### Is Semantica production-ready?
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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](index.md#whats-new-in-v030) for details.
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---
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## Troubleshooting
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### Import error: `ModuleNotFoundError: No module named 'semantica'`
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Ensure you have the correct Python environment active, then:
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```bash
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pip list | grep semantica
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pip install --upgrade semantica
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```
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### Installation fails with dependency errors
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```bash
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pip install --upgrade pip wheel
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pip install semantica
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```
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### Memory errors during processing
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Reduce batch sizes, enable streaming ingestion, or switch to a persistent graph backend (Neo4j, FalkorDB).
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### Slow embedding or inference
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Install GPU support (`pip install semantica[gpu]`) and ensure CUDA is available on your system.
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---
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## Support
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### Where can I get help?
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- [Discord](https://discord.gg/sV34vps5hH) — community chat and support
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- [GitHub Issues](https://github.com/Hawksight-AI/semantica/issues) — bug reports and feature requests
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- [GitHub Discussions](https://github.com/Hawksight-AI/semantica/discussions) — questions and ideas
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### How do I report a bug?
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1. Search [existing issues](https://github.com/Hawksight-AI/semantica/issues) first
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2. Open a new issue with: description, reproduction steps, expected vs actual behavior, and your environment (Python version, OS, Semantica version)
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### How do I contribute?
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See the [Contributing Guide](contributing.md).
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