contextmaker 1.7.0

A simple context builder and converter toolkit

ContextMaker

ContextMaker is a powerful Python toolkit that converts library documentation into formats optimized for AI agent ingestion. It automatically detects and processes various documentation formats including Sphinx, Jupyter notebooks, Python docstrings, and raw source code.

Feature to enrich the CMBAgents: Multi-Agent System for Science, Made by Cosmologists, Powered by AG2.

Features

• 🔍 Automatic Format Detection: Intelligently detects Sphinx, notebooks, docstrings, or source code
• 📚 Multi-Format Support: Handles Sphinx documentation, Jupyter notebooks, Python docstrings, and raw source
• 🚀 Smart Fallbacks: Multiple conversion methods with automatic fallbacks for maximum compatibility
• 📝 Flexible Output: Generate clean text (.txt) or markdown (.md) files
• 🎯 AI-Optimized: Output formatted specifically for Large Language Model ingestion
• 🔧 Robust Processing: Handles edge cases and provides detailed logging

Supported Documentation Formats

1. Sphinx Documentation (Highest Priority)

   • Automatic detection of conf.py and index.rst
   • Support for Makefile-based builds
   • Fallback to direct Sphinx building

2. Jupyter Notebooks

   • Recursive search for .ipynb files
   • Conversion to markdown using jupytext
   • Integration with other documentation sources

3. Python Docstrings

   • AST-based extraction of module, class, and function docstrings
   • Structured markdown output with proper headers

4. Raw Source Code

   • Fallback for projects without structured documentation
   • Preserves code formatting and structure

Installation

Install ContextMaker from PyPI:

python3 -m venv context_env
source context_env/bin/activate
pip install contextmaker

Usage

From the Command Line

ContextMaker automatically finds libraries on your system and generates complete documentation with function signatures and docstrings.

# Convert a library's documentation (automatic search)
contextmaker library_name

# Example: convert pixell documentation
contextmaker pixell

# Example: convert numpy documentation
contextmaker numpy

Advanced Usage

# Specify custom output path
contextmaker pixell --output ~/Documents/my_docs

# Specify manual input path (overrides automatic search)
contextmaker pixell --input_path /path/to/library/source

# Choose output format (txt or md)
contextmaker pixell --extension md

Output

• Default location: ~/your_context_library/library_name.txt
• Content: Complete documentation with function signatures, docstrings, examples, and API references
• Format: Clean text optimized for AI agent ingestion

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From a Python Script

You can also use ContextMaker programmatically in your Python scripts:

import contextmaker

# Minimal usage (automatic search, default output path)
contextmaker.make("pixell")

# With custom output path
contextmaker.make("pixell", output_path="/tmp")

# With manual input path
contextmaker.make("pixell", input_path="/path/to/pixell/source")

# Choose output format (txt or md)
contextmaker.make("pixell", extension="md")

Examples

Processing a Sphinx Project

contextmaker my_library --input_path /path/to/sphinx/docs

Processing Notebooks Only

contextmaker tutorial_project --input_path /path/to/notebooks

Custom Output Format

contextmaker numpy --extension md --output ~/my_docs

Running the Jupyter Notebook

To launch and use the notebooks provided in this project, follow these steps:

1. Install Jupyter
   If Jupyter is not already installed, you can install it with:

pip install jupyter

2. Launch Jupyter Notebook
   Navigate to the project directory and run:

jupyter notebook

This will open the Jupyter interface in your web browser.

Dependencies

• Core: Python 3.8+
• Documentation: Sphinx, jupytext, sphinx-rtd-theme
• Processing: markdownify, beautifulsoup4, html2text
• Utilities: rich, numpy, docutils, jinja2

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

Acknowledgments

This project uses the CAMB code developed by Antony Lewis and collaborators. Please see the CAMB website and documentation for more information.