ReportIQ
Evaluate RAG retrieval and explore your documents with AI
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ReportIQ - Evaluate RAG retrieval and explore your documents with AI

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ReportIQ is a document Q&A and RAG evaluation application built with Python and Streamlit. Ask questions about PDF documents, explore retrieval quality using BGE embeddings and FAISS, and inspect LLM workflows with LangSmith. Built for developers and AI practitioners experimenting with retrieval-augmented generation.

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Hey Product Hunt!

I built ReportIQ while exploring a question that goes beyond building another AI chatbot:

How do we know whether a RAG system is retrieving the right evidence, rather than just generating a convincing answer?

ReportIQ is my attempt to explore that problem through a working document Q&A application, retrieval evaluation, and LLM observability.

What you can explore:

  • Ask questions about PDF documents.

  • Explore a RAG pipeline built with BGE embeddings and FAISS.

  • Evaluate retrieval quality using metrics such as Recall@K and MRR.

  • Inspect LLM workflows using LangSmith.

I processed 8 PDFs into 721 chunks and evaluated retrieval on 50 questions. My latest recorded baseline achieved 91.7% Recall@3 an initial result from a evaluation set, not a claim of general performance.

Built with Python, Streamlit, FAISS, BGE embeddings, and LangSmith.

Try ReportIQ:

Explore the code:

I'd especially appreciate feedback on the retrieval evaluation approach, the user experience, and what you'd improve next.

Thanks for checking it out!