I made a small tool to inspect retrieval results before feeding them into RAG

Reddit r/LocalLLaMA 05/27/26, 07:16 AM Tools

rag retrieval-augmented-generation search-quality tool web-retrieval inspection open-source

Summary

A developer created a small local tool for inspecting retrieval results from search providers like Brave, Serper, Tavily, and Exa before feeding them into a RAG pipeline, checking signals such as source diversity, duplicates, freshness, and SEO/GEO pollution risk.

I’ve been messing around with live web retrieval for RAG, and the part that kept annoying me wasn’t the search call itself. It was figuring out whether the returned results were actually usable as evidence. A result can look relevant, but still be stale, duplicated, SEO-heavy, or just not good enough to put into the context window. So I cleaned up a small local tool for inspecting retrieval/search results before feeding them into a RAG pipeline: [https://github.com/mameirolabs/rag-search-quality-lab-public](https://github.com/mameirolabs/rag-search-quality-lab-public) It currently supports mock, Brave, Serper, Tavily, and Exa. It looks at rough signals like source diversity, duplicates, freshness, citation readiness, SEO/GEO pollution risk, and provider differences. Not trying to make a benchmark or declare which provider is “best”. The scoring is still very rough. I mostly use it to compare outputs side by side and spot bad evidence before it reaches the model. Curious how others handle this: What signals do you check before trusting retrieved web results in a RAG pipeline?

Original Article

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