Knowledge Base

Chat With Your Codebase Using LlamaIndex RAG

Index a GitHub repository with LlamaIndex, store embeddings in a vector database, and answer natural-language questions about the code with citations back to the exact files.

What This Builds

This recipe builds a question-answering system over a single codebase. You point it at a GitHub repository, it loads and chunks the source files, embeds them into a vector store, and then answers questions like “where is auth handled?” or “what does this function call?” — pointing you at the relevant files instead of making you grep.

The Argilla LlamaIndex tutorial demonstrates exactly this pattern: a RAG system that answers questions about a specific GitHub repository, using the repo as example data.

Product Shape

This is a knowledge base, not a coding agent. It does not edit code; it retrieves and explains. Build the index once, then serve queries cheaply. Because the answer cites the source chunks, a human (or a downstream agent) can verify the claim against the actual file.

The Stack

  • LlamaIndex — handles loading, chunking, embedding, retrieval, and the query engine.
  • LlamaHub — provides the GitHub repository reader/loader so you do not hand-roll file ingestion.
  • Qdrant Cloud Free Tier — stores the code embeddings for fast nearest-neighbor retrieval.
  • GitHub repository — the codebase being indexed.
  • An embeddings + LLM provider such as the Google AI Studio / Gemini API free tier for embeddings and answer synthesis.

Step-by-Step Outline

  1. Load the repository with the LlamaHub GitHub reader (or clone locally and use SimpleDirectoryReader).
  2. Chunk source files into nodes; keep file path metadata on each node so answers can cite locations.
  3. Embed the nodes and upsert them into a Qdrant collection via the LlamaIndex Qdrant vector store integration.
  4. Build a VectorStoreIndex query engine over the Qdrant store.
  5. Ask natural-language questions; the engine retrieves the most relevant code chunks and synthesizes an answer.
  6. Return the cited file paths alongside the answer so the user can jump straight to the source.

Why This Shape Works

Keeping file-path metadata on every chunk turns retrieval into navigation: the system tells you both the answer and where to look. Using a managed vector store (Qdrant) instead of an in-memory index means the index survives restarts and scales to large repos.

Source