DocChameleon is an intelligent documentation enhancement tool that augments TensorFlow API docs using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). It tackles common developer pain points such as missing examples, ambiguous explanations, and lack of practical learning resources by generating executable code, clarifications, and curated references from Stack Overflow and YouTube.
As machine learning libraries like TensorFlow evolve rapidly, API documentation often fails to keep pace with updates, leaving developers frustrated and dependent on external sources for clarity. A deep dive into Stack Overflow questions revealed that most TensorFlow documentation-related queries stem from:
- A lack of clear, executable code examples
- Ambiguous or insufficient explanations
- The absence of supporting resources or references
To bridge this gap, DocChameleon uses LLMs and RAG to enrich TensorFlow API documentation automatically. It aims to reduce friction for developers by providing:
- Executable examples tailored to each API
- Clearer, AI-generated explanations for confusing or undocumented behaviors
- Curated external resources to accelerate learning and troubleshooting
- OpenAI & Cohere API Keys
- Python 3.9+
- Clone the repo
git clone https://github.com/sharukat/docchameleon.git cd docchameleon