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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.
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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?
- Getting Started Guide - Quick introduction
- Cookbook - Practical examples
- GitHub Examples - Code samples
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?
- GitHub Issues - Report problems
- Discussions - Ask questions
- Documentation - Browse guides and references
How do I report bugs?
- Search existing issues first
- Create a new issue with details
- Include reproduction steps
- Add environment information
Can I contribute?
Yes! See the Contributing Guide for details on how to help improve Semantica.