[P] I built a system that lets you ask questions about any GitHub repo and get answers grounded in the actual source code [P]

Reddit r/MachineLearning Tools
gitrag code-question-answering rag retrieval-augmented-generation open-source developer-tool github

Summary

GitRAG is a tool that allows users to paste any public GitHub URL and ask questions about the codebase, returning answers grounded in the source code with exact file paths and line numbers, using AST-aware chunking, hybrid search (dense + BM25), reranking, and a language model for generation.

Hi guys I've been working on **GitRAG** — paste any public GitHub URL, and ask it anything about the codebase. It answers with exact file paths and line numbers, no hallucination. **How it works under the hood:** 1. Clones the repo and splits files into semantic chunks using AST-aware parsing (not just line splits) 2. Builds a hybrid index — dense embeddings + BM25 keyword index 3. At query time, fuses both signals with Reciprocal Rank Fusion, then runs Cohere reranking to cut 20 candidates down to 5 4. Sends those 5 chunks to Groq's llama-3.3-70b which generates a grounded answer The retrieval pipeline is what I'm most proud of — the BM25 + semantic fusion catches things that pure vector search misses (exact function names, error codes, etc.) **Stack:** FastAPI · ChromaDB · text-embedding-3-small · Cohere rerank-v3.5 · Groq llama-3.3-70b · React + Vite **Supports 15+ languages:** Python, JS/TS, C#, Java, Go, Rust, C/C++, Swift, Kotlin, Dart, Ruby, PHP, Vue, Svelte, Shell... Curious what repos people try it on — drop your results below 👇
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