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# Frequently Asked Questions
Common questions and answers about Semantica.
!!! tip "Can't find your question?"
Browse existing questions or [ask a new question on GitHub Issues](https://github.com/Hawksight-AI/semantica/issues/new)
---
## 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?
```bash
pip install semantica
```
See the [Installation Guide](installation.md) 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?
1. Install: `pip install semantica`
2. Follow the [Quick Start Guide](quickstart.md)
3. Try the [Examples](examples.md)
---
## 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?
```python
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:
```python
merged = semantica.kg.merge([kg1, kg2, kg3])
```
### How do I visualize a knowledge graph?
```python
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?
```python
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?
```python
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](https://github.com/Hawksight-AI/semantica/issues)
### How do I report a bug?
Open an issue on [GitHub](https://github.com/Hawksight-AI/semantica/issues) 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](https://github.com/Hawksight-AI/semantica/blob/main/CONTRIBUTING.md).
### How do I request a feature?
Open a feature request on [GitHub Issues](https://github.com/Hawksight-AI/semantica/issues) with:
- Use case description
- Proposed solution
- Benefits to the community
---
!!! question "Still have questions?"
Check the [API Reference](reference/core.md), browse the [Cookbook](cookbook.md), or [ask on GitHub Issues](https://github.com/Hawksight-AI/semantica/issues/new)