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Contributing

Thank you for your interest in contributing to LLM Data Processer!

Ways to Contribute

  • 🐛 Report bugs
  • 💡 Suggest features
  • 📝 Improve documentation
  • 🔧 Submit code changes
  • ✅ Add tests
  • 💬 Help others in discussions

Getting Started

1. Fork and Clone

# Fork the repository on GitHub, then:
git clone https://github.com/YOUR-USERNAME/LLM-data-processer.git
cd LLM-data-processer

2. Set Up Environment

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

pip install -r requirements.txt
pip install -e ".[dev]"

3. Create a Branch

git checkout -b feature/your-feature-name

Development Guidelines

Code Style

Follow PEP 8 and use type hints:

def add_guideline(self, guideline: str) -> None:
    """Add a custom guideline to influence model behavior.

    Args:
        guideline: A string describing the desired behavior

    Returns:
        None
    """
    self.guideline.append(guideline)

Docstring Format

Use Google-style docstrings:

def ask(self, prompt: str, with_history: bool = True) -> str:
    """Generate a response from the LLM.

    Args:
        prompt: The question or instruction to send
        with_history: Whether to include conversation context

    Returns:
        The model's response as a string

    Raises:
        ValueError: If prompt is empty
    """

Testing

Test your changes manually:

# Test basic functionality
from llm_helper import AIHelper

ai = AIHelper(model_name='Llama-3.1', display_response=False)
response = ai.ask("Test question")
assert isinstance(response, str)

Adding New Features

Adding a New LLM Provider

  1. Create a new class in llm_helper/ai_helper.py:
class AIHelper_NewProvider:
    def __init__(self, model: str = 'default-model', display_response: bool = True):
        self.client = NewProviderClient()
        self.model = model
        self.display_response = display_response
        self.history = []

    def ask(self, prompt: str, display_response: bool = None) -> str:
        """Generate response using NewProvider."""
        # Implementation
        pass
  1. Update __init__.py:
from .ai_helper import AIHelper, AIHelper_Google, AIHelper_NewProvider

__all__ = ["AIHelper", "AIHelper_Google", "AIHelper_NewProvider"]
  1. Add documentation
  2. Add examples
  3. Update README

Adding New Methods

  1. Add method to class
  2. Add docstring
  3. Update API reference
  4. Add usage example
  5. Test thoroughly

Submitting Changes

Commit Guidelines

# Good commit messages
git commit -m "Add: Support for Claude API"
git commit -m "Fix: Memory leak in chat_widget"
git commit -m "Docs: Update installation guide"

# Bad commit messages
git commit -m "updates"
git commit -m "fix stuff"

Pull Request Process

  1. Update documentation if needed
  2. Test your changes
  3. Update CHANGELOG.md
  4. Push your branch: 
    git push origin feature/your-feature-name
  5. Open a Pull Request on GitHub
  6. Address review feedback

PR Template

## Description
Brief description of changes

## Type of Change
- [ ] Bug fix
- [ ] New feature
- [ ] Documentation update
- [ ] Performance improvement

## Testing
How you tested your changes

## Checklist
- [ ] Code follows style guidelines
- [ ] Documentation updated
- [ ] CHANGELOG.md updated
- [ ] Examples added (if applicable)

Documentation

Updating Docs

Documentation is in the docs/ folder using Markdown.

# Edit docs
vim docs/usage.md

# Preview locally (if MkDocs is installed)
mkdocs serve

Adding Examples

Add to examples/ folder with clear comments:

"""
Example: Analyzing customer data with AI.

This example shows how to...
"""

from llm_helper import AIHelper
import pandas as pd

# Step 1: Load data
df = pd.DataFrame({...})

# Step 2: Initialize AI
ai = AIHelper(...)

# Step 3: Query
response = ai.ask(...)

Community Guidelines

Be Respectful

  • Be welcoming to newcomers
  • Provide constructive feedback
  • Respect different viewpoints
  • Focus on the code, not the person

Getting Help

Development Tips

Testing Locally

# Quick smoke test
python -c "from llm_helper import AIHelper; ai = AIHelper(); print('OK')"

# Run examples
python examples/basic_usage.py

Debugging

# Enable verbose logging
import logging
logging.basicConfig(level=logging.DEBUG)

# Test in interactive mode
python -i -m llm_helper.ai_helper

Performance

  • Keep responses under 2 seconds for simple queries
  • Optimize DataFrame serialization for large datasets
  • Cache repeated queries when appropriate

Release Process

Maintainers follow this process:

  1. Update version in __init__.py and setup.py
  2. Update CHANGELOG.md
  3. Create git tag: git tag v0.0.2
  4. Push tag: git push --tags
  5. Create GitHub release
  6. Deploy documentation

Recognition

Contributors will be:

  • Added to CONTRIBUTORS.md
  • Mentioned in release notes
  • Credited in documentation

Questions?

Open an issue with the question label or ask in Discussions.

License

By contributing, you agree that your contributions will be licensed under the MIT License.

Thank you for contributing! 🎉