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Contributing to LLM Data Normalization Pattern

First off, thank you for considering contributing to this project! 🎉

How Can I Contribute?

Reporting Bugs

Before creating bug reports, please check existing issues to avoid duplicates. When you create a bug report, include as many details as possible:

  • Use a clear and descriptive title
  • Describe the exact steps to reproduce the problem
  • Provide specific examples (code snippets, configuration files)
  • Describe the behavior you observed and what you expected
  • Include logs and error messages

Suggesting Enhancements

Enhancement suggestions are welcome! Please provide:

  • Use a clear and descriptive title
  • Provide a detailed description of the suggested enhancement
  • Explain why this enhancement would be useful
  • List any alternatives you've considered

Pull Requests

  1. Fork the repo and create your branch from main
  2. Follow the existing code style
  3. Add tests if applicable
  4. Update documentation to reflect your changes
  5. Ensure all tests pass
  6. Write a clear commit message

Development Setup

# Clone your fork
git clone https://github.com/YOUR_USERNAME/llm-data-normalization-pattern.git
cd llm-data-normalization-pattern

# Create a branch for your feature
git checkout -b feature/my-new-feature

# Make your changes and commit
git add .
git commit -m "feat: add my new feature"

# Push and create a PR
git push origin feature/my-new-feature

Code Style

  • JavaScript/Node.js: Follow existing patterns in the codebase
  • Documentation: Use clear, concise language
  • Commit messages: Follow Conventional Commits
    • feat: for new features
    • fix: for bug fixes
    • docs: for documentation
    • refactor: for code refactoring
    • test: for tests

Documentation Contributions

Documentation improvements are highly valued! This includes:

  • Fixing typos and grammar
  • Clarifying existing documentation
  • Adding examples and use cases
  • Translating to other languages
  • Improving diagrams

Areas for Contribution

We especially welcome contributions in these areas:

Prompt Engineering

  • Improved prompts for specific data types
  • Multi-language support
  • Domain-specific normalization rules

New LLM Providers

  • OpenAI integration
  • Cohere integration
  • Anthropic direct API (non-Bedrock)
  • Local LLM support (Ollama)

Performance Optimizations

  • Caching strategies
  • Batch processing improvements
  • Concurrent execution patterns

Statistical Validation

  • Additional quality metrics
  • Anomaly detection algorithms
  • Visualization tools

Cloud Providers

  • Azure OpenAI integration
  • GCP Vertex AI integration
  • Multi-cloud support

Code of Conduct

Our Pledge

We are committed to providing a friendly, safe, and welcoming environment for all contributors.

Our Standards

  • Be respectful and inclusive
  • Accept constructive criticism gracefully
  • Focus on what's best for the community
  • Show empathy towards others

Enforcement

Instances of unacceptable behavior may be reported to the project maintainers. All complaints will be reviewed and investigated promptly and fairly.

Questions?

Feel free to open an issue with the question label or reach out to the maintainer:

Thank you for contributing! 🙏