Implementation of RAG Based Question-Answering Application

This project proposes the use of Streamlit, Python, LangChain, Chroma, and Google's Gemini Model to develop a Retrieval-Augmented Generation (RAG) based questionanswering chat bot. With the use of cutting-edge language models and RAG architecture, this chatbot can ac curately answer user questions and retrieve pertinent document portions. After loading docu ments, the system uses the Gemini model to create succinct responses after processing them through a vector storage for similarity-based retrieval. For those looking for exact information from large text sources, this solution offers an effective, interactive experience with Streamlit as the user interface. To provide a strong, contextually aware question-answering tool, the project uses Google's Gemini Model for intelligent replies, Chroma for vector storage, and LangChain for document management.