This tutorial was originally published on IBM Developer.

Imagine you’re heads-down focused in a project, searching a GitHub repository’s Markdown files for that one small unit test command or an elusive detail about configuring an API. You’re flipping between READMEs, wikis, and scattered “docs” folders, losing time and patience. What if there was a way to just ask your documentation? "How do I run a single unit test from the suite?" or "What’s the retry policy for the endpoint?" and get a precise, context-aware answer in seconds? This is where, the technology of Retrieval-Augmented Generation (RAG) can help make your documentation conversational.

In this tutorial, we’ll build an intelligent documentation assistant that lets you chat with your project’s Markdown documentation (those .md files you see in GitHub, like READMEs). Using JavaScript, and a tool called LangChain, and the IBM Granite model via Ollama, we’ll create a command-line interface (CLI) that connects to your GitHub repository, pulls in your documentation, and answers your questions in plain language. It’s like having a super-seasoned teammate who knows every word of your project’s docs, akin to a pair programming buddy in your day to day workflows.

Why Markdown? Markdown is the lingua franca of developer documentation. It is lightweight, packed with critical info, readable, and ubiquitous in GitHub repos. They are the go-to format for project documentation on GitHub. Our assistant makes them interactive, saving you time and frustration. It’s the perfect starting point for a RAG-powered assistant.

Here’s what we’re building

We’re creating a command-line assistant that lets you chat with any markdown file instantly turning your documentation into an interactive, AI-powered resource.

• Ask Questions, Get Answers: Provide a public URL to a markdown file (like a README or guide from GitHub). The assistant downloads and processes it, so you can ask questions about its content in natural language.

• AI-Powered, Contextual Responses: When you ask a question, the assistant searches the document for the most relevant sections and uses a local large language model (IBM Granite 3.3 via Ollama) to generate accurate, context-aware answers.

• No Complex Setup: There’s no need to clone repositories, manage tokens, or set up databases. Just paste a markdown file URL and start chatting.

• Proof of Concept: This demo focuses on a single markdown file to showcase the Retrieval-Augmented Generation (RAG) workflow. The design is simple, but the approach can be extended to entire documentation sites, web chat interfaces, or large-scale document search.

Continue reading on IBM Developer to learn how to build a RAG-powered Markdown documentation assistant...