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212 lines
4.8 KiB
Markdown
212 lines
4.8 KiB
Markdown
# Frequently Asked Questions
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Common questions and answers about Semantica.
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## General Questions
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### What is Semantica?
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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.
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### What can I use Semantica for?
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- Building knowledge graphs from documents
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- Creating semantic layers for AI applications
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- Extracting entities and relationships from text
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- Powering GraphRAG systems
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- Integrating data from multiple sources
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- Building AI agent memory systems
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### Is Semantica free?
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Yes! Semantica is 100% open source and free to use under the MIT License.
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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 the [Installation Guide](installation.md) for detailed instructions.
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### What Python version do I need?
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Python 3.8 or higher. Python 3.11+ is recommended.
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### Do I need GPU?
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No, GPU is optional. Semantica works on CPU, but GPU acceleration is available for faster processing.
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## Usage
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### How do I get started?
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1. Install Semantica: `pip install semantica`
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2. Follow the [Quick Start Guide](quickstart.md)
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3. Try the [Examples](examples.md)
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### Can I process PDF files?
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Yes! Semantica supports PDF, DOCX, HTML, JSON, CSV, and many other formats.
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### How do I extract entities from text?
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```python
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from semantica import Semantica
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semantica = Semantica()
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result = semantica.semantic_extract.extract_entities("Your text here")
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entities = result["entities"]
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```
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### Can I use my own models?
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Yes, Semantica is extensible. You can plug in custom models for entity extraction, embeddings, etc.
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## Knowledge Graphs
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### What is a knowledge graph?
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A knowledge graph is a structured representation where entities (nodes) are connected by relationships (edges). It captures semantic meaning and relationships in data.
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### How do I build a knowledge graph?
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```python
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from semantica import Semantica
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semantica = Semantica()
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result = semantica.build_knowledge_base(["document.pdf"])
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kg = result["knowledge_graph"]
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```
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### Can I merge multiple knowledge graphs?
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Yes! Use the `merge` method:
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```python
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merged = semantica.kg.merge([kg1, kg2, kg3])
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```
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### How do I visualize a knowledge graph?
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```python
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semantica.kg.visualize(kg, output_path="graph.html")
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```
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## Conflict Resolution
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### What is conflict resolution?
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When the same entity appears in multiple sources with different information, conflict resolution determines which information to use.
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### What strategies are available?
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- **Voting**: Majority wins
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- **Credibility Weighted**: Weight by source credibility
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- **Most Recent**: Use latest information
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- **Highest Confidence**: Use highest confidence score
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- **First Seen**: Use first encountered value
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### How do I set a resolution strategy?
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```python
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from semantica.conflicts import ConflictResolver
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resolver = ConflictResolver(default_strategy="voting")
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```
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## Export & Integration
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### What formats can I export to?
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- RDF/XML
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- OWL (Ontology)
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- JSON
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- CSV
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- YAML
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- And more
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### Can I use Semantica with other tools?
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Yes! Semantica exports to standard formats that work with:
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- Neo4j
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- Graph databases
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- RDF stores
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- Vector databases
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- Any tool that accepts RDF/JSON/CSV
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## Performance
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### How fast is Semantica?
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Performance depends on:
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- Document size
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- Number of documents
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- Hardware (CPU/GPU)
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- Configuration options
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For typical documents, processing takes seconds to minutes.
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### Can I process large datasets?
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Yes, but consider:
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- Processing in batches
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- Using GPU acceleration
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- Incremental building
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- Optimizing configuration
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## Troubleshooting
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### Installation fails
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- Upgrade pip: `pip install --upgrade pip`
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- Use virtual environment
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- Check Python version: `python --version`
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### No entities extracted
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- Verify document contains text (not just images)
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- Check document format is supported
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- Review extraction configuration
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### Memory errors
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- Process documents one at a time
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- Reduce batch sizes
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- Use smaller models
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- Increase available RAM
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### Slow processing
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- Enable GPU if available
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- Process in smaller batches
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- Optimize configuration
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- Use faster models
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## Getting Help
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### Where can I get help?
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- **Documentation**: This site
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- **GitHub Issues**: [Report bugs](https://github.com/Hawksight-AI/semantica/issues)
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- **Discussions**: [Ask questions](https://github.com/Hawksight-AI/semantica/discussions)
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### How do I report a bug?
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Open an issue on [GitHub](https://github.com/Hawksight-AI/semantica/issues) with:
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- Description of the problem
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- Steps to reproduce
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- Expected vs actual behavior
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- Environment details
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### Can I contribute?
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Yes! We welcome contributions. See our [Contributing Guide](https://github.com/Hawksight-AI/semantica/blob/main/CONTRIBUTING.md).
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---
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Still have questions? Check the [API Reference](api.md) or [open an issue](https://github.com/Hawksight-AI/semantica/issues).
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