This project implements a tool-driven Retrieval-Augmented Generation (RAG) assistant that answers questions strictly from an indexed research publication. The system demonstrates controlled, hallucination-free responses using a ReAct-based agent over a single CLIP-focused publication.
A selected publication is chunked, embedded, and stored in a vector database, with queries resolved via similarity search. A ReAct agent enforces mandatory tool usage, generating answers solely from retrieved content.
The assistant accurately answers publication-specific questions while rejecting out-of-scope queries with deterministic “I don’t know” responses. This confirms reliable grounding, reduced hallucination risk, and predictable production behavior.