This project is a Flask web application that allows users to ask questions and get answers using a Retrieval-Augmented Generation (RAG) model. Users input their questions, and the application returns relevant answers based on the indexed documents.
An AI-powered solution for efficient document querying. It uses Llama Index for vector-based indexing and OpenAI's GPT to interpret natural language queries, providing accurate search results.
A PDF Question Answering System leveraging Retrieval-Augmented Generation (RAG) and advanced natural language processing. Combines vector-based indexing, semantic similarity, and HuggingFace Transformers to deliver precise insights from PDFs. Streamlit interface ensures seamless, data-driven exploration. Built with Python.
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