An AI-powered chatbot that answers student questions using university PDFs with the help of Google's Gemini API and RAG (Retrieval-Augmented Generation) architecture.

A Streamlit-based app for asking questions directly from uploaded documents using Gemini embeddings and a language model. Supports PDF, TXT, and DOCX files. Fast, simple, and powerful document-based QA.

An advanced, fully local, and GPU-accelerated RAG pipeline. Features a sophisticated LLM-based preprocessing engine, state-of-the-art Parent Document Retriever with RAG Fusion, and a modular, Hydra-configurable architecture. Built with LangChain, Ollama, and ChromaDB for 100% private, high-performance document Q&A.

Streamlit-based chatbot to interact with PDFs using Retrieval-Augmented Generation (RAG), FAISS, Sentence Transformers, and Mistral LLM

Turn your documents into instant answers with FAISS + Streamlit.

A lightweight, modular Retrieval-Augmented Generation (RAG) system built with Streamlit, FAISS, and LLMs like OpenAI and Ollama. Upload documents, embed them, and ask intelligent questions with real-time context-aware responses.

This is a Document Question Answering (Doc-QA) system built with Python and Streamlit. Users can upload a PDF, and ask questions related to the document content. The system searches the document and provides the most relevant answers.

📄 Create a local, free Retrieval-Augmented Q&A system to easily extract answers from your personal documents in minutes.