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Frequently Asked Questions
Common questions and answers about Semantica.
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General Questions
What is Semantica?
Semantica is an open-source framework for building semantic layers and knowledge graphs from unstructured data. It transforms raw data into structured, queryable knowledge that powers AI applications.
What can I use Semantica for?
- Building knowledge graphs from documents
- Creating semantic layers for AI applications
- Extracting entities and relationships
- Powering GraphRAG systems
- Integrating multi-source data
- Building AI agent memory
Is Semantica free?
Yes! Semantica is 100% open source and free to use under the MIT License.
What makes Semantica different?
- Modular: Use only what you need
- Extensible: Plug in custom models
- Production-ready: Built for scale
- Open source: Fully transparent
Installation & Setup
How do I install Semantica?
pip install semantica
See the Installation Guide for details.
What Python version do I need?
Python 3.8 or higher. Python 3.11+ is recommended for best performance.
Do I need a GPU?
No, GPU is optional. Semantica works on CPU, but GPU acceleration is available for faster processing.
How do I get started?
- Install:
pip install semantica - Follow the Quick Start Guide
- Try the Examples
Knowledge Graphs
What is a knowledge graph?
A structured representation where entities (nodes) are connected by relationships (edges). It captures semantic meaning and relationships in data.
How do I build a knowledge graph?
from semantica import Semantica
semantica = Semantica()
result = semantica.build_knowledge_base(["document.pdf"])
kg = result["knowledge_graph"]
Can I merge multiple knowledge graphs?
Yes! Use the merge method:
merged = semantica.kg.merge([kg1, kg2, kg3])
How do I visualize a knowledge graph?
semantica.kg.visualize(kg, output_path="graph.html")
Usage & Features
Can I process PDF files?
Yes! Semantica supports PDF, DOCX, HTML, JSON, CSV, and many other formats.
How do I extract entities from text?
from semantica import Semantica
semantica = Semantica()
result = semantica.semantic_extract.extract_entities("Your text")
entities = result["entities"]
Can I use my own models?
Yes, Semantica is extensible. You can plug in custom models for entity extraction, embeddings, and more.
What export formats are supported?
- RDF/XML
- OWL (Ontology)
- JSON
- CSV
- YAML
- And more
Conflict Resolution
What is conflict resolution?
When the same entity appears in multiple sources with different information, conflict resolution determines which information to use.
What strategies are available?
- Voting: Majority wins
- Credibility Weighted: Weight by source credibility
- Most Recent: Use latest information
- Highest Confidence: Use highest confidence score
How do I set a resolution strategy?
from semantica.conflicts import ConflictResolver
resolver = ConflictResolver(default_strategy="voting")
Integration
Can I use Semantica with other tools?
Yes! Semantica exports to standard formats that work with:
- Neo4j
- Graph databases
- RDF stores
- Vector databases
- Any tool that accepts RDF/JSON/CSV
Does it work with LangChain?
Yes, Semantica can be integrated with LangChain for RAG applications.
Can I connect to databases?
Yes, Semantica supports connections to Neo4j, FalkorDB, and other graph databases.
Performance
How fast is Semantica?
Performance depends on:
- Document size
- Number of documents
- Hardware (CPU/GPU)
- Configuration options
For typical documents, processing takes seconds to minutes.
Can I process large datasets?
Yes, but consider:
- Processing in batches
- Using GPU acceleration
- Incremental building
- Optimizing configuration
How can I improve performance?
- Enable GPU if available
- Process in smaller batches
- Use faster models
- Optimize configuration
- Cache embeddings
Troubleshooting
Installation fails
- Upgrade pip:
pip install --upgrade pip - Use virtual environment
- Check Python version:
python --version
No entities extracted
- Verify document contains text (not just images)
- Check document format is supported
- Review extraction configuration
Memory errors
- Process documents one at a time
- Reduce batch sizes
- Use smaller models
- Increase available RAM
Slow processing
- Enable GPU if available
- Process in smaller batches
- Optimize configuration
- Use faster models
Getting Help
Where can I get help?
- Documentation: This site
- GitHub Issues: Report bugs or ask questions
How do I report a bug?
Open an issue on GitHub with:
- Description of the problem
- Steps to reproduce
- Expected vs actual behavior
- Environment details
Can I contribute?
Yes! We welcome contributions. See our Contributing Guide.
How do I request a feature?
Open a feature request on GitHub Issues with:
- Use case description
- Proposed solution
- Benefits to the community
!!! question "Still have questions?" Check the API Reference, browse the Cookbook, or ask on GitHub Issues