pydocenhancer 1.0.0

AI-powered Python documentation enhancer supporting local LLMs (LLaMA, Ollama) and cloud providers.

PyDocEnhancer

AI-powered Python plugin to enhance documentation with summaries, code explanations, examples, semantic search, automated example testing, and multilingual documentation.

Features

• Auto-Generated Summaries: Summarize modules, classes, and functions.
• Code Explanations: Plain-English explanations of code logic.
• Semantic Search: Query documentation with natural language (e.g., "find data processing functions").
• Auto-Generated Examples: Create working code examples from docstrings.
• Automated Example Testing: Extracts and runs code examples from docstrings, reporting results in the docs.
• Multilingual Documentation: Generate documentation in multiple languages (e.g., English, French, Spanish, Chinese) using LLM translation.
• Local LLM Support: Privacy-first processing with local models (e.g., LLaMA 3.2, Ollama).
• Integrations: Works with Sphinx, MkDocs, and Jupyter Notebooks.

Installation

pip install pydocenhancer

Quick Start

from pydocenhancer import DocEnhancer

# Initialize with a local LLM (Ollama or LLaMA), generate docs in French
enhancer = DocEnhancer(provider="local", model="ollama/llama3", language="fr")
enhancer.generate_docs(module_path="my_project/utils.py", output_dir="docs", language="fr")

# Search documentation
results = enhancer.search_docs("file handling functions", "docs")
print(results)

CLI Usage

# Generate documentation with Ollama in Spanish, with example testing
pydocenhancer enhance --module my_project/utils.py --output docs/ --provider local --model ollama/llama3 --language es

# Search documentation
pydocenhancer search --query "data processing functions" --docs-dir docs/

Requirements

• Python 3.8+
• Local LLM (e.g., LLaMA 3.2 via llama-cpp-python or Ollama)
• Optional: Sphinx or MkDocs for integration

Documentation

Full documentation is available in this README and at GitHub.

License

MIT © Abdoullah Ndao [e-mail]