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## Documentation Changes ### 📚 Major Improvements - **Cleaned up all documentation files** - Removed redundant content and improved clarity - **Restructured Resources section** - Removed unnecessary files, kept only essential ones - **Added Snowflake integration** - Complete integration guide with examples - **Improved navigation** - Better organization and user experience ### 🗂️ File Changes - **docs/concepts.md** - Rewritten to be clean and user-friendly - **docs/modules.md** - Updated with current modules and removed emojis - **docs/glossary.md** - Reorganized thematically instead of alphabetically - **docs/getting-started.md** - Made more concise and practical - **docs/community.md** - Clean, focused community guide - **docs/contributing.md** - Clear contribution guidelines - **docs/faq.md** - Comprehensive FAQ with practical answers - **docs/license.md** - Clean license explanation - **docs/css/custom.css** - Fixed CSS syntax and organization ### 🔧 Technical Changes - **mkdocs.yml** - Updated navigation, removed redundant files - **docs/integrations/snowflake.md** - New comprehensive Snowflake guide - **docs/reference/ingest.md** - Added Snowflake references - **Removed files**: changelog.md, release-guide.md, change_management_usage.md, community-projects.md, architecture.md, governance.md, citation.md ### 🎯 Benefits - **Better user experience** - Clean, easy to navigate documentation - **Reduced redundancy** - No duplicate or unnecessary content - **Professional quality** - Enterprise-ready documentation - **Consistent style** - Uniform formatting across all files This commit includes all documentation improvements while maintaining the main branch's stability.
149 lines
4.0 KiB
Markdown
149 lines
4.0 KiB
Markdown
# Frequently Asked Questions
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**Common questions about Semantica and how to use it.**
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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 knowledge graphs from unstructured data. It transforms documents, web pages, and databases into structured, queryable knowledge.
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### What can I do with Semantica?
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- **Build knowledge graphs** from documents and data
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- **Extract entities and relationships** automatically
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- **Power AI applications** with structured knowledge
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- **Create semantic search** and GraphRAG systems
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- **Integrate multiple data sources** into unified graphs
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### Is Semantica free?
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Yes! Semantica is open source under the MIT License.
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### What makes Semantica different?
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- **Modular architecture** - Use only what you need
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- **Production-ready** - Built for scale and reliability
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- **Extensible** - Add custom models and components
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- **Open source** - Transparent and community-driven
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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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### What Python version do I need?
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Python 3.8 or higher. Python 3.11+ is recommended.
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### What are the system requirements?
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- Python 3.8+
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- 4GB+ RAM for basic use
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- Optional GPU for embeddings and ML models
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---
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## Getting Started
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### How do I start using Semantica?
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```python
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from semantica.semantic_extract import NERExtractor
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from semantica.kg import GraphBuilder
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# Extract entities
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ner = NERExtractor()
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entities = ner.extract("Apple Inc. was founded by Steve Jobs.")
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# Build knowledge graph
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kg = GraphBuilder().build({"entities": entities})
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```
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### Where can I find examples?
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- **[Getting Started Guide](getting-started.md)** - Quick introduction
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- **[Cookbook](cookbook.md)** - Practical examples
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- **[GitHub Examples](https://github.com/Hawksight-AI/semantica/tree/main/examples)** - Code samples
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---
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## Features
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### What data sources does Semantica support?
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- **Files**: PDF, DOCX, TXT, JSON, CSV
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- **Web**: Websites, RSS feeds, APIs
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- **Databases**: PostgreSQL, MySQL, Snowflake, MongoDB
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- **Streams**: Kafka, RabbitMQ, real-time data
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### Can I use custom models?
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Yes! Semantica supports custom:
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- **Entity extraction models**
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- **Embedding models**
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- **Language models**
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- **Custom processors**
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### Does Semantica support GPUs?
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Yes, Semantica automatically uses GPUs when available for:
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- **Embedding generation**
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- **ML model inference**
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- **Vector operations**
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---
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## Technical
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### How does Semantica handle large datasets?
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- **Batching** - Process data in chunks
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- **Streaming** - Handle real-time data
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- **Parallel processing** - Use multiple cores
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- **Memory management** - Efficient resource usage
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### Can I deploy Semantica in production?
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Yes! Semantica is production-ready with:
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- **Scalable architecture**
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- **Error handling**
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- **Monitoring support**
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- **Container deployment**
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### How do I customize Semantica?
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- **Custom processors** - Add new extraction logic
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- **Custom models** - Use your own ML models
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- **Plugins** - Extend functionality
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- **Configuration** - Adjust behavior
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---
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## Troubleshooting
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### Installation issues
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- **Python version**: Ensure Python 3.8+
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- **Dependencies**: Install with `pip install -e .[dev]`
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- **Permissions**: Use virtual environments
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### Performance issues
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- **Memory**: Increase available RAM
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- **GPU**: Install CUDA for GPU acceleration
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- **Batching**: Use smaller chunk sizes
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### Common errors
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- **Import errors**: Check installation path
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- **Model loading**: Verify model availability
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- **Memory errors**: Reduce batch sizes
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---
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## Support
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### Where can I get help?
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- **[GitHub Issues](https://github.com/Hawksight-AI/semantica/issues)** - Report problems
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- **[Discussions](https://github.com/Hawksight-AI/semantica/discussions)** - Ask questions
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- **[Documentation](index.md)** - Browse guides and references
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### How do I report bugs?
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1. **Search** existing issues first
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2. **Create** a new issue with details
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3. **Include** reproduction steps
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4. **Add** environment information
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### Can I contribute?
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Yes! See the [Contributing Guide](contributing.md) for details on how to help improve Semantica.
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