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Add comprehensive user documentation for tc_pipeline.py module

William A.requested to merge
feature/tc-pipeline-documentation into main

Overview

This merge request adds comprehensive end-user documentation for the tc_pipeline.py module, which is the core topic modeling component of the ML Pipeline project.

What's Added

📚 Complete User Guide (docs/tc_pipeline_user_guide.md)

  • Detailed module overview and architecture explanation
  • Step-by-step installation instructions
  • Data requirements and expected formats
  • Comprehensive usage examples (basic and advanced)
  • Complete API reference for all functions
  • Configuration options and parameter tuning
  • Troubleshooting section with common issues
  • Performance optimization tips
  • Best practices for topic modeling

Key Features Documented

🔧 Core Functionality:

  • custom_preprocessing() - Data preprocessing pipeline
  • calculate_topics() - Main topic modeling function
  • Integration with BERTopic, UMAP, HDBSCAN, and SentenceTransformers

📊 Technical Details:

  • Model configuration parameters
  • Directory structure and file organization
  • Output formats and model artifacts
  • Memory management and performance considerations

💡 User-Friendly Examples:

  • Basic usage patterns
  • Advanced workflows
  • Visualization examples
  • Complete end-to-end pipeline examples

Benefits

✅ For End Users:

  • Clear understanding of how to use the module
  • Reduced onboarding time for new team members
  • Self-service troubleshooting capabilities
  • Best practices guidance

✅ For Maintainers:

  • Reduced support requests
  • Standardized usage patterns
  • Better adoption of the module

Documentation Quality

  • Comprehensive: Covers all aspects from installation to advanced usage
  • Practical: Includes working code examples and real-world scenarios
  • Accessible: Written for users with varying levels of ML expertise
  • Maintainable: Structured format that's easy to update

Testing

The documentation has been validated against the actual module code to ensure:

  • All function signatures are accurate
  • Code examples are syntactically correct
  • Parameter descriptions match implementation
  • Dependencies are correctly listed

This documentation will significantly improve the developer experience for anyone working with the topic modeling pipeline. Ready for review! 🚀

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