1.1.2

ai-docify 🤖

A simple, secure, and cost-aware CLI tool for generating high-quality NumPy/Sphinx style docstrings using AI.

ai-docify helps you document your Python code instantly using either cloud-based models (OpenAI) or local privacy-focused models (Ollama). It is designed to be safe (using AST parsing), transparent (pre-flight cost checks), and non-destructive.

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✨ Key Features

• 💰 Cost-Aware Design: Calculates and displays the estimated input token cost via tiktoken before you spend a penny.
• 🔒 Privacy-First: Switch seamlessly between OpenAI (Cloud) and Ollama (Local) with a single flag. Keep proprietary code on your machine when needed.
• 🛡️ AST-Powered Safety: Unlike other tools that "guess" where to put text, ai-docify parses your code's Abstract Syntax Tree to surgically inject docstrings without breaking indentation or logic.
• ↩️ The "Undo" Button: Includes a strip command to safely remove all docstrings if you change your mind.
• ✌️ Dual Generation Modes: Choose between rewrite for speed/coverage, or inject for surgical precision.
• 🎯 Granular Control: Document an entire file or target a single function or class for surgical precision.
• ⚡ "Lean" Architecture: Optimized prompt engineering ensures high-quality documentation without wasting tokens on conversational fluff.

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🚀 Installation

Install ai-docify directly from PyPI:

pip install ai-docify

Prerequisites

• Python 3.8+
• Ollama (Optional, required only if using local models)

Setup (OpenAI Only)

If you plan to use OpenAI models, set your API key as an environment variable:

Mac/Linux:

export OPENAI_API_KEY=sk-your-api-key-here

Windows (PowerShell):

$env:OPENAI_API_KEY="sk-your-api-key-here"

(Alternatively, you can create a .env file in your project root)

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💻 VS Code Extension

Prefer a GUI? This CLI powers the AI Docify for VS Code extension. 👉 **Download AI Docify for VS Code

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📖 Usage

1. Generating Documentation

You must specify the Provider, Model, and Mode.

inject Mode (Recommended)

Best for: Surgical precision and preserving formatting. Uses AST parsing to find functions and classes, injecting docstrings exactly where they belong without touching a single line of your actual code.

ai-docify generate my_script.py --provider openai --model gpt-5-mini --mode inject

rewrite Mode

Best for: Speed and heavy refactoring. Asks the AI to rewrite the file with docs included. Good for initial drafts or undocumented legacy files.

# Using a local model (Free)
ai-docify generate my_script.py --provider ollama --model llama3 --mode rewrite

2. Targeting a Single Function

Best for: Quickly documenting a specific piece of code you're working on.

Use the --function flag to target a single function or class by name. This is faster, cheaper, and automatically uses the safe inject mode.

# Target only the 'calculate_total' function in the file
ai-docify generate utils.py --provider openai --model gpt-5-mini --function calculate_total

3. The Safety Check 🛡️

Before generating anything, the tool will pause and show you an exact cost estimate:

🤖 ai-docify: Checking my_script.py in INJECT mode

📊 Estimation (Input Only):
   Tokens: 350
   Est. Cost: $0.00009

Do you want to proceed? [y/n]:

3. Stripping Docstrings (Undo) ↩️

Need to start over? The strip command uses AST parsing to cleanly remove all docstrings from a file, leaving your logic intact. It saves the clean version to a stripped_scripts/ folder by default.

ai-docify strip my_script.py

4. Cleaning Output

To remove all generated files from the default ai_output/ directory:

ai-docify clean

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⚙️ Supported Models

ai-docify comes pre-configured with pricing and token limits for popular models.

OpenAI:

• o3-2025-04-16
• gpt-5 / gpt-5-mini / gpt-5-nano
• gpt-5.2

Ollama (Local):

• llama3.1:8b
• qwen2.5-coder:7b
• (Any model pulled via ollama pull works with the --provider ollama flag)

Missing a model? Feel free to open an Issue or Pull Request to update the internal pricing configuration!

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🤝 Contributing

We welcome contributions! Whether it's a bug fix, a new feature, or a better prompt template:

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/NewModel)
3. Commit your Changes (git commit -m 'Add GPT-6 support')
4. Push to the Branch (git push origin feature/NewModel)
5. Open a Pull Request

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📄 License

Distributed under the MIT License. See LICENSE for more information.