RAG
QueryWise
A multi-agent retrieval-augmented generation system enabling intelligent document querying with hallucination prevention and enterprise-grade answer verification.
Tech Stack
Python
LangChain
Gradio
The Problem
- Document QA tools hallucinated confident-sounding answers when queries fell outside uploaded document scope.
- Single-stage retrieval lacked a verification layer, returning plausible but unverified answers from collections.
- Non-technical users had no accessible interface for querying PDF, DOCX, and TXT documents through natural language.
- Keyword-only search missed semantically relevant passages, producing incomplete or misleading retrieval results.
- No system combined hybrid retrieval, multi-document handling, and structured parsing in a single accessible tool.
Gallery
Our Solution
- Built a multi-agent RAG system using LangChain and LangGraph with hybrid BM25 and vector embedding retrieval.
- Integrated Docling for structured, layout-aware parsing of PDFs, DOCX, and TXT documents.
- Implemented a dedicated fact-verification agent that cross-checks generated answers against retrieved source passages.
- Added scope detection to identify and decline out-of-context queries, preventing hallucinated responses.
- Developed a Gradio web interface supporting document upload and natural language querying for non-technical users.
Impact
Hallucination-freedocument Q&A
Delivered enterprise-grade document intelligence with multi-agent answer verification, suitable for research and business document workflows.
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