docify-ai 0.1.5

An AI-powered README.md generator for local projects.

Docify AI 🚀

Instantly generate beautiful, comprehensive README.md files for your local projects using the power of AI.

Table of Contents

• What is Docify?
• Key Features ✨
• Project Structure 📂
• Technologies Used 🛠️
• Installation 📦
• Usage 🚀
  • 1. Set Your API Key
  • 2. Run Docify
  • Command-Line Options
• How It Works 🧠
• Contributing 🤝
• License 📄

What is Docify?

Every great project needs great documentation, but writing it is often a tedious, manual process. Docify is a command-line tool that automates this entire workflow. It intelligently scans your project's file structure and source code, understands what your project does, and uses a powerful language model to generate a high-quality README.md file in seconds.

Stop writing docs and start shipping code.

Key Features ✨

• 🤖 AI-Powered Content Generation: Leverages large language models (specifically Google Gemini) to write human-like, technical documentation.
• 📂 Intelligent Code Analysis: Scans your entire project directory, intelligently ignoring irrelevant files and directories, to understand its purpose, technologies, and structure.
• ⚙️ Simple Command-Line Interface: Generate a complete README with a single, intuitive command: docify.
• 🚀 Fast and Efficient: Go from a messy project folder to a polished README in under a minute.
• 🔧 Customizable: Easily specify the project path to analyze and the desired output file name.

Project Structure 📂

The project is structured to be modular and easy to navigate:

.
├── LICENSE
├── README.md
├── pyproject.toml
└── docify_tool/
    ├── __init__.py
    ├── cli.py
    ├── generator.py
    └── scanner.py

• LICENSE: Contains the MIT License details for the project.
• README.md: This very file, providing an overview and instructions.
• pyproject.toml: Project metadata, dependencies, and build configuration.
• docify_tool/: The core source code for the Docify CLI.
  • __init__.py: Marks the directory as a Python package.
  • cli.py: Handles command-line argument parsing and orchestrates the scanning and generation process.
  • generator.py: Interfaces with the AI model (Google Gemini) to generate README content.
  • scanner.py: Traverses the project directory, collects file content, and builds the project context.

Technologies Used 🛠️

• Python: The core programming language for the application (Python 3.8+).
• LangChain: Framework for developing applications powered by language models.
  • langchain-openai (used for langchain_google_genai integration)
  • langchain-core
• Google Gemini API: The underlying large language model used for content generation.
• Setuptools: Used for packaging and distribution of the Python project.
• Argparse: Python's standard library for parsing command-line arguments.

Installation 📦

Docify is published on PyPI and can be installed on any machine with Python 3.8+ using pip.

pip install docify-ai

Usage 🚀

Using Docify is incredibly simple.

1. Set Your API Key

First, you need to make your Google Gemini API key available as an environment variable. This is a one-time setup per machine. You can get a key from the Google AI Studio for free.

macOS / Linux:

export GEMINI_API_KEY='your-secret-api-key'

(To make this permanent, add the line to your ~/.zshrc or ~/.bashrc file.)

Windows (PowerShell):

$Env:GEMINI_API_KEY="your-secret-api-key"

2. Run Docify

Navigate to any project directory you want to document and run the command:

docify

That's it! The tool will scan the current directory and create a README.md file with the AI-generated content.

Command-Line Options

You can also specify the path to your project and the desired output file using the following options:

• --path: The root directory of the project to document. Defaults to the current directory (.).
• --output: The name of the output file. Defaults to README.md.

Example:

docify --path /path/to/my-other-project --output DOCS.md

How It Works 🧠

Docify is built with a clean, modular architecture:

1. Scanner (scanner.py): Traverses the specified project directory, ignoring unnecessary files (like .git, venv, node_modules, __pycache__), and aggregates all relevant source code into a single context string.
2. Generator (generator.py): Takes the aggregated project context string and feeds it to the Google Gemini API via LangChain, using a carefully crafted prompt to generate the Markdown content for the README.
3. CLI (cli.py): Provides the user-friendly command-line interface using argparse, orchestrating the scanner and generator to deliver the final README.md file.

Contributing 🤝

Contributions are welcome! If you have ideas for new features, bug fixes, or improvements, please open an issue or submit a pull request on our GitHub repository (replace with actual link).

License 📄

This project is licensed under the MIT License. See the LICENSE file for details.