python-chat

A Python project skeleton for developing an LLM-powered chatbot.

Status: Foundational setup complete. The actual chatbot implementation is NOT included (per requirements). This provides a clean, typed, tested base ready for adding LLM features (e.g., using OpenAI, LangChain, Gradio UI, etc.).

Features of the Skeleton

• Modern Python packaging with uv (fast, reliable) and pyproject.toml
• src/ layout for the python_chat package
• Testing: pytest with configuration
• Type checking: mypy in strict mode
• Placeholder code: hello_world() function + passing test
• Editor support: VS Code settings for pytest + mypy
• Copilot instructions: .github/copilot-instructions.md for consistent AI assistance
• Git ready: .gitignore tailored for Python/uv/mypy

Project Structure

python-chat/
├── .github/
│   └── copilot-instructions.md
├── .vscode/
│   └── settings.json
├── src/
│   └── python_chat/
│       ├── __init__.py
│       ├── hello.py          # Placeholder: hello_world()
│       └── py.typed
├── tests/
│   └── test_hello.py
├── .gitignore
├── pyproject.toml
├── README.md
└── uv.lock (after first sync)

Getting Started

Prerequisites

• Python >= 3.11
• uv (v0.4+ recommended)

Setup (one-time)

# Clone or cd into the project
cd python-chat

# Create virtualenv + install project + dev dependencies (pytest, mypy)
uv sync --group dev

# Activate the env (uv will suggest or use `uv run`)
# On Windows PowerShell:
. .venv\Scripts\Activate.ps1

This creates .venv/ and uv.lock.

Running Tests

# Using uv (recommended, no manual activate needed)
uv run pytest

# Or with activated venv
pytest

# Verbose / specific
uv run pytest -v tests/test_hello.py

All tests should pass ✅

Type Checking

uv run mypy src
# or simply
uv run mypy

Should report no errors with strict mode.

Running Code / Python Shell

uv run python
>>> from python_chat.hello import hello_world
>>> hello_world()
'Hello World'

Or for package:

uv run python -c "from python_chat import hello_world; print(hello_world())"

Adding the LLM Chatbot Later

When ready to implement:

1. Add runtime dependencies: uv add openai langchain-core gradio ...
2. Create modules under src/python_chat/ (e.g., llm.py, chatbot.py)
3. Add corresponding tests in tests/
4. Update copilot-instructions.md as needed
5. For Gradio web app (Supabase deploy): add a examples/app.py or src/python_chat/app.py that imports your chatbot logic and launches gr.Interface(...). Use uv run python -m python_chat.app or add [project.scripts].

Never put actual LLM keys or full chatbot code in the initial skeleton.

Development Commands

Command	Description
uv sync --group dev	Install/update all deps + editable
uv run pytest	Run the test suite
uv run mypy	Strict static type check
uv run ruff check .	(Future) Linting
uv add <pkg>	Add runtime dependency
uv add --group dev <pkg>	Add dev-only tool
uv run python -m pytest --cov	(Future) with coverage

VS Code Integration

The .vscode/settings.json configures:

• Pytest as default test runner
• Mypy as type checker
• Python interpreter from .venv
• Format on save, etc.

Open the folder in VS Code and select the interpreter from .venv\Scripts\python.exe if prompted.

License

This project is unlicensed (or add your license here). For personal/educational use in building LLM chatbots.

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Generated as part of initial project scaffolding. Happy coding!