Skip to content

Frequently Asked Questions

Common questions about Semantica and how to use it.

General

What is Semantica?

Semantica is an open-source framework for building knowledge graphs from unstructured data. It transforms documents, web pages, and databases into structured, queryable knowledge.

What can I do with Semantica?

  • Build knowledge graphs from documents and data
  • Extract entities and relationships automatically
  • Power AI applications with structured knowledge
  • Create semantic search and GraphRAG systems
  • Integrate multiple data sources into unified graphs

Is Semantica free?

Yes! Semantica is open source under the MIT License.

What makes Semantica different?

  • Modular architecture - Use only what you need
  • Production-ready - Built for scale and reliability
  • Extensible - Add custom models and components
  • Open source - Transparent and community-driven

Installation

How do I install Semantica?

pip install semantica

What Python version do I need?

Python 3.8 or higher. Python 3.11+ is recommended.

What are the system requirements?

  • Python 3.8+
  • 4GB+ RAM for basic use
  • Optional GPU for embeddings and ML models

Getting Started

How do I start using Semantica?

from semantica.semantic_extract import NERExtractor
from semantica.kg import GraphBuilder

# Extract entities
ner = NERExtractor()
entities = ner.extract("Apple Inc. was founded by Steve Jobs.")

# Build knowledge graph
kg = GraphBuilder().build({"entities": entities})

Where can I find examples?

Features

What data sources does Semantica support?

  • Files: PDF, DOCX, TXT, JSON, CSV
  • Web: Websites, RSS feeds, APIs
  • Databases: PostgreSQL, MySQL, Snowflake, MongoDB
  • Streams: Kafka, RabbitMQ, real-time data

Can I use custom models?

Yes! Semantica supports custom: - Entity extraction models - Embedding models - Language models - Custom processors

Does Semantica support GPUs?

Yes, Semantica automatically uses GPUs when available for: - Embedding generation - ML model inference - Vector operations

Technical

How does Semantica handle large datasets?

  • Batching - Process data in chunks
  • Streaming - Handle real-time data
  • Parallel processing - Use multiple cores
  • Memory management - Efficient resource usage

Can I deploy Semantica in production?

Yes! Semantica is production-ready with: - Scalable architecture - Error handling - Monitoring support - Container deployment

How do I customize Semantica?

  • Custom processors - Add new extraction logic
  • Custom models - Use your own ML models
  • Plugins - Extend functionality
  • Configuration - Adjust behavior

Troubleshooting

Installation issues

  • Python version: Ensure Python 3.8+
  • Dependencies: Install with pip install -e .[dev]
  • Permissions: Use virtual environments

Performance issues

  • Memory: Increase available RAM
  • GPU: Install CUDA for GPU acceleration
  • Batching: Use smaller chunk sizes

Common errors

  • Import errors: Check installation path
  • Model loading: Verify model availability
  • Memory errors: Reduce batch sizes

Support

Where can I get help?

How do I report bugs?

  1. Search existing issues first
  2. Create a new issue with details
  3. Include reproduction steps
  4. Add environment information

Can I contribute?

Yes! See the Contributing Guide for details on how to help improve Semantica.