You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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.
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.