From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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Contributing Guidelines
Thank you for your interest in contributing to the CodeXchange AI project! This document provides guidelines and instructions for contributing to the project.
How to Contribute
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature
) - Commit your changes (
git commit -m 'Add amazing feature'
) - Push to the branch (
git push origin feature/amazing-feature
) - Open a Pull Request
Development Workflow
- Check the issues page for open tasks or create a new issue for the feature/bug you want to work on
- Assign yourself to the issue
- Implement your changes following the best practices outlined below
- Write tests for your changes
- Update documentation as needed
- Submit a pull request referencing the issue
Best Practices
Code Style
- Follow existing patterns for consistency
- Follow PEP 8 style guidelines for Python code
- Use descriptive variable and function names
- Add type hints for all new functions
- Keep functions small and focused on a single responsibility
- Use docstrings for all public functions and classes
Error Handling
- Add comprehensive error handling
- Use specific exception types
- Provide helpful error messages
- Log errors with appropriate context
Logging
- Include detailed logging
- Use the existing logging framework
- Log at appropriate levels (DEBUG, INFO, WARNING, ERROR)
- Include relevant context in log messages
Documentation
- Update documentation for any changes
- Document new features, configuration options, and APIs
- Keep the README and docs directory in sync
- Use clear, concise language
Testing
- Write unit tests for new functionality
- Ensure all tests pass before submitting a PR
- Test edge cases and error conditions
- Aim for good test coverage
Pull Request Process
- Ensure your code follows the style guidelines
- Update documentation as needed
- Include tests for new functionality
- Link the PR to any related issues
- Wait for code review and address any feedback
Code Review
All submissions require review. We use GitHub pull requests for this purpose:
- Reviewers will check code quality, test coverage, and documentation
- Address any comments or requested changes
- Once approved, maintainers will merge your PR
Acknowledgments
We would like to thank the following organizations and projects that make CodeXchange AI possible:
- OpenAI for GPT models
- Anthropic for Claude
- Google for Gemini
- DeepSeek and GROQ for their AI models
- The Gradio team for the web interface framework
License
By contributing to this project, you agree that your contributions will be licensed under the project's MIT License.