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Contributing to DataTalk

Development Setup

git clone https://github.com/vtsaplin/datatalk-cli.git
cd datatalk-cli

# Install dependencies (first time setup)
uv sync

# Option 1: Run from source (recommended, no installation needed)
uv run python -m datatalk.main sample_data/sales_data.csv

# Option 2: Install in editable mode (changes reflected immediately)
uv pip install -e .
dtalk sample_data/sales_data.csv

# Uninstall editable install (if needed)
uv pip uninstall datatalk-cli

# Run tests
uv run python -m pytest

# Build package
python -m build

Testing

# Run all tests
uv run python -m pytest

# Run specific test
uv run python -m pytest tests/test_e2e.py::TestCLI::test_load_csv_file

Provider Configuration

Tests work with any LLM provider. Configure in .env:

# OpenAI
LLM_MODEL=gpt-4o
OPENAI_API_KEY=your-key

# Anthropic / Google / Ollama
LLM_MODEL=claude-3-5-sonnet-20241022
ANTHROPIC_API_KEY=your-key

VS Code / Cursor

Repository includes .vscode/ config:

  • Testing panel (🧪) - auto-discover, run and debug tests
  • Launch configs (F5) - run/debug DataTalk CLI in single query or interactive mode

Creating Demo

Generate animated GIF using VHS:

# Install VHS
brew install vhs  # macOS
# or download from https://github.com/charmbracelet/vhs/releases

# Generate demo.gif from demo.tape
vhs demo.tape

Edit demo.tape to change demo script. Result saved to docs/demo.gif.

Making a Release

Release process is automated via release.sh:

# 1. Update version in pyproject.toml
# 2. Run release script
./release.sh

What it does:

  • Runs tests
  • Checks you're on main branch
  • Creates and pushes git tag (e.g., v0.2.1)
  • GitHub Actions automatically publishes to PyPI

Requirements:

  • Clean working directory (or will prompt to commit)
  • All tests passing
  • On main branch

Contributing Guidelines

Contributions welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes with tests (uv run python -m pytest)
  4. Submit a PR

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