Skip to main content

An AI-powered tool to automatically document, refactor, and format Python code.

Project description

AutoDoc AI

PyPI version License: MIT

AutoDoc AI is a smart command-line developer tool that automates the tedious parts of maintaining high-quality Python code. It leverages Large Language Models (LLMs) to automatically generate docstrings, suggest intelligent refactorings, and integrate seamlessly into a modern development workflow.

This project was built on a foundation of solid Object-Oriented Programming principles and design patterns, including the Visitor, Strategy, Factory, and Adapter patterns.


Core Features

  • AI-Powered Docstring Generation: Automatically create high-quality, context-aware docstrings for any function or class that's missing them.
  • Intelligent Docstring Correction: Uses AI to evaluate existing docstrings and replaces poor-quality or placeholder documentation with new, improved versions.
  • AI-Powered Refactoring:
    • Safe Variable Renaming: Automatically renames poorly named local variables within function scope.
    • Intelligent Naming Suggestions: Acts as an AI-powered linter, suggesting better names for classes and functions.
  • Seamless Git Integration: Use the --diff flag to only process files that have been changed in your current Git branch.
  • Codebase-Wide Scanning: Run autodoc on a single file or an entire directory, with automatic .gitignore respect.
  • Automatic Formatting: Integrates with the black code formatter to ensure all generated and refactored code is perfectly styled.
  • Highly Configurable: Set project-wide defaults for style, AI provider, and behavior in your pyproject.toml file.

Installation

You can install AutoDoc AI directly from PyPI:

pip install autodoc-ai-paudelnirajan

First-Time Setup

Before you can use the AI features, you need to configure your API key. The easiest way is to use the built-in init command in your project's root directory.

autodoc init

This will guide you through creating or updating a .env file with your Groq API key and preferred model.

Configuration

AutoDoc uses a .env file for secrets and a pyproject.toml file for project-wide settings.

1. API Credentials (.env)

The autodoc init command will create this for you. It should look like this:

GROQ_API_KEY="gsk_YourActualGroqApiKeyHere"
GROQ_MODEL_NAME="llama3-8b-8192"

2. Project Defaults (pyproject.toml)

You can set default behaviors for your project in pyproject.toml. These settings will be used unless they are overridden by a command-line flag.

# In your pyproject.toml

[tool.autodoc]
# Settings for your AutoDoc tool
strategy = "groq"
style = "google"           # 'google', 'numpy', or 'rst'
overwrite_existing = true  # Regenerate poor-quality docstrings
refactor = true            # Enable AI-powered refactoring

Usage

AutoDoc is a flexible command-line tool with two main commands: init and run.

Get a list of commands:

autodoc --help

Get detailed help for the run command and all its flags:

autodoc run --help

Run on a specific file (dry run, prints to console):

autodoc run path/to/your/file.py

Run on an entire directory and save changes in-place:

autodoc run src/ --in-place

Run in "Git mode" to only process changed files (most common use case):

autodoc run . --diff --in-place

Enable all AI features to perform a full quality pass:

autodoc run . --in-place --overwrite-existing --refactor

How It Works

AutoDoc uses a "Surgical Hybrid" architecture that combines the strengths of traditional and modern tooling:

  1. Fast Local Analysis: It uses Python's ast (Abstract Syntax Tree) module to rapidly parse code and identify potential issues.
  2. Targeted AI Intelligence: Only when a potential issue is found does it send the small, relevant code snippet to an LLM for intelligent analysis.
  3. Precise Code Modification: The AI's response is used to perform a safe and precise modification of the AST, which is then unparsed back into valid Python code.
  4. Toolchain Integration: Finally, it uses black to ensure the final code is perfectly formatted, integrating seamlessly into the existing Python ecosystem.

License

This project is licensed under the MIT License.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autodoc_ai_paudelnirajan-0.1.2.tar.gz (14.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

autodoc_ai_paudelnirajan-0.1.2-py3-none-any.whl (13.5 kB view details)

Uploaded Python 3

File details

Details for the file autodoc_ai_paudelnirajan-0.1.2.tar.gz.

File metadata

File hashes

Hashes for autodoc_ai_paudelnirajan-0.1.2.tar.gz
Algorithm Hash digest
SHA256 5fbfd9b2e2a21be51b3afe97067b5f6fdfdd030f9b8803641acfcecc67a7c549
MD5 c9913b4161834c95b93950036658a5a8
BLAKE2b-256 598302654d3c7840ba21769cfffbd4d54fb1f0b4f8363bcd4ed821e903aeb97a

See more details on using hashes here.

File details

Details for the file autodoc_ai_paudelnirajan-0.1.2-py3-none-any.whl.

File metadata

File hashes

Hashes for autodoc_ai_paudelnirajan-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 d622a8d77297dc363aa981ac4114cbc5a346e51ce01d381011253108e40fb2f1
MD5 b2389ca4a921650baa298615f49ddd0d
BLAKE2b-256 e9a40e39361b3ad9e30aa24a5281a5dbef0e79374ccd6f5d85f467161036d6c0

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page