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Contributing to llm-kit-pro

First off, thank you for considering contributing to llm-kit-pro! It's people like you who make this toolkit better for everyone.

This document provides guidelines and rules for contributing to the project. These are not just rules but best practices to ensure the project remains high-quality, maintainable, and reliable.

🚀 Getting Started

1. Prerequisites

  • Python 3.11+: The project uses modern Python features.
  • Poetry: We use Poetry for dependency management and packaging. Install Poetry.

2. Local Setup

  1. Fork the repository on GitHub.
  2. Clone your fork locally: 
    git clone https://github.com/YOUR_USERNAME/llm-kit-pro.git
cd llm-kit-pro
  3. Install dependencies: 
    poetry install --all-extras
  4. Activate the virtual environment: 
    poetry shell

🛠 Development Workflow

1. Branching Strategy

  • Always create a new branch for your work:
    • feat/description for new features.
    • fix/description for bug fixes.
    • docs/description for documentation changes.
    • refactor/description for code refactoring.
  • Keep your branches up-to-date with the main branch.

2. Coding Standards

We aim for clean, idiomatic, and type-safe Python code.

  • Type Hints: All new functions and classes must have complete type hints.
  • Pydantic: Use Pydantic models for data validation and configuration where appropriate.
  • Formatting: We use black for formatting and ruff for linting. 
    # Format code
black .
# Lint code
ruff check .
  • Docstrings: Use Google-style docstrings for all public modules, classes, and methods.

3. Testing Requirements

We maintain high test coverage to ensure stability across multiple LLM providers.

  • New Features: Must include unit tests and, where applicable, integration tests (using mocks).
  • Bug Fixes: Should include a regression test that fails without the fix.
  • Running Tests: 
    pytest
  • Async Testing: We use pytest-asyncio. Ensure your async tests are properly decorated with @pytest.mark.asyncio.

4. Documentation

  • If you add a feature, update the README.md and relevant docstrings.
  • Documentation is built using Sphinx. To build docs locally: 
    cd docs
make html

📤 Submitting a Pull Request

  1. Commit Messages: Use clear, descriptive commit messages. Follow Conventional Commits if possible (e.g., feat: add Anthropic provider).
  2. Pull Request Template:
    • Describe the changes in detail.
    • Explain the motivation (link to an issue if it exists).
    • List any breaking changes.
    • Confirm that tests pass and linting is clean.
  3. Review Process:
    • At least one maintainer must approve the PR.
    • Address all review comments promptly.
    • Squash commits before merging if requested.

🐛 Reporting Issues

  • Use the GitHub Issue tracker.
  • Use a clear and descriptive title.
  • Provide a minimal, reproducible example (MRE) for bugs.
  • Describe the expected vs. actual behavior.
  • Include your environment details (Python version, OS, llm-kit-pro version).

⚖️ Code of Conduct

Be respectful and professional in all interactions. We follow the standard Contributor Covenant Code of Conduct.

Thank you for contributing to the future of multi-provider LLM integration!