This is a document question answering app made with LangChain and deployed on Streamlit where you can upload a .txt or .docx file, ask questions based on the file and an LLM like Falcon-7B or Dolly-V2-3B answers it. ChromaDB is used as the vector database.

Ask any question to your documents, PDFs, or SQL databases using GenAI for quick and accurate answers.

AI-powered document question answering app using GenAI + Streamlit. Upload documents and ask questions — get accurate, context-aware answers instantly.

Retriever-Augmented Generation (RAG) system for question answering using provided documents and data(GenAI)

GenAI Document Assistant is a smart AI-powered Streamlit application that allows you to upload PDF or TXT documents and interact with them intelligently. Use it to generate summaries, ask and answer questions, create logic-based questions, and evaluate your answers — all powered by state-of-the-art language models.

MediInsight AI is a full-stack healthcare GenAI application built with React, TypeScript, Node.js, Groq, and a local retrieval pipeline. It combines multimodal imaging support, wellness planning, appointment intake, mental wellness chat, and document question answering into one interview-ready product.

​The Tender Intelligence Assistant is a GenAI-powered tool that enables users to upload tender or RFP PDFs and receive instant, structured answers to natural language questions, with contextual references extracted directly from the documents.

GenAI PDF Insight Bot is a Streamlit app that uses generative AI to provide answers from uploaded PDF documents. Upload your PDFs, ask questions, and receive detailed responses based on the document content. Enjoy an intuitive and colorful interface for seamless interaction.

This project is a Streamlit web application that allows users to upload one or more PDF documents and interactively ask questions based on the contents of the uploaded PDFs. The application uses Google's Gemini Pro (via langchain_google_genai) for answering questions and FAISS for storing and retrieving relevant chunks of text from the PDFs.

An AI-powered application that generates multiple-choice and short-answer questions from GenAI-related PDFs using Retrieval-Augmented Generation (RAG). Built with LangChain, FAISS, and Azure OpenAI (GPT-4o), it enables efficient assessment content creation through intelligent document understanding.

DocQuery is a RAG-based GenAI app for students that enables Q&A over uploaded PDFs. Users can upload study material, ask natural-language questions, and get document-grounded answers using Gemini free-tier models with daily usage limit notifications.

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Document-based Question Answering System (GenAI)

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DocMind AI is a RAG-based GenAI platform that answers questions over large documentation sets by combining vector search with large language models and real-time streaming responses.

GenAI chatbot for answering questions from legal documents using RAG

PDF Document Question Answering LLM System using LangChain, Pinecone, Google GenAI

GenAI chatbot using RAG to answer questions from organizational documents

GenAI-powered Retrieval-Augmented Generation (RAG) assistant for answering questions from Cohesity documents.

GenAI ML project: Document question and answering (RAG), comparing two models; Deepseek-R1 and Llama3.1

GenAI project that Creates Question and Answers from Pdf documents using Large Language Models.

A GenAI-powered assistant that summarizes uploaded documents and answers questions based on content.

A chatbot that allows teachers to upload their documents so that students can ask questions and receive answers based on the content. This project is built using the Docker GenAI Stack, which leverages modern AI tools like LangChain, Neo4j, and Ollama to create a retrieval-augmented generation (RAG) chatbot.

Cloud-hosted GenAI RAG application for semantic document search and grounded question answering using Streamlit, Gemini, and ChromaDB.

A fully serverless GenAI document assistant that extracts text from uploaded documents and enables AI-powered summarization and question answering using AWS.

document processing pipeline that leverages GenAI and traditional NLP to extract, classify, validate, and answer questions about documents.

A GenAI-powered smart assistant that summarizes documents, answers questions, and generates logic-based challenges.

A fast, offline GenAI document assistant that summarizes, answers questions, and quizzes users based on PDFs.

Ask any question to your documents, PDFs, or SQL databases using GenAI for quick and accurate answers.