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

Reddit r/LocalLLaMA Tools

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

Similar Articles

@GYLQ520: PixelRAG is an open-source project from UC Berkeley SkyLab and other teams, ready to play. Its approach is straightforward, no longer relying on HTML or text parsing. Instead, it directly captures web pages and PDFs as screenshots and uses visual indexing for RAG retrieval. Everything that HTML parsing loses, such as tables, charts, and layouts, is preserved…

X AI KOLs Timeline

PixelRAG is an open-source project from UC Berkeley SkyLab and other teams. By capturing web pages and PDFs as screenshots and using visual indexing for retrieval, it improves RAG accuracy and significantly reduces token costs for AI Agents.