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.

Build a powerful PDF Chat Assistant using Node.js, LangChain, and Google Gemini. Upload PDFs, extract content, and interact with them using natural language queries powered by Gemini LLM. Ideal for document Q&A, contract analysis, resume review, and more.

PDFSeek is a full-stack web app that allows users to securely upload PDF documents and ask questions based on their content. Built with Angular for the frontend and Flask for the backend, it uses MongoDB for authentication and Groq's language model API to extract and answer questions directly from the uploaded PDFs.

This is a Streamlit web application that allows users to upload a PDF document, extract text from it, and ask questions based on the extracted content using Google Gemini AI.

📄 QuestRAG: AI-powered PDF Question Answering & Summarizer Bot using LangChain, Flan-T5, and Streamlit: A GenAI mini-project that allows users to upload research PDFs, ask questions, and get intelligent summaries using Retrieval-Augmented Generation (RAG) with locally hosted Hugging Face models.

NeuroQuery is an AI-powered PDF question-answering system that lets you upload and interact with documents using natural language. Built with LangChain, Gemini AI, and Chroma, it delivers fast, context-aware answers from your files.

This Streamlit-based AI assistant allows you to upload documents (PDF, DOCX, TXT) and interact with them using natural language. Powered by Llama models via Groq API and LangChain, the bot intelligently understands your documents and provides accurate answers with source references.

Retrieval-Augmented Question Answering system for complex insurance documents using Ollama, LangChain, and ChromaDB. Designed for scalable, intuitive document navigation and decision support.

PDFSeek is a full-stack web app that allows users to securely upload PDF documents and ask questions based on their content. Built with Angular for the frontend and Flask for the backend, it uses MongoDB for authentication and Groq's language model API to extract and answer questions directly from the uploaded PDFs.

🤖 RAG-based chatbot for answering queries from 📄 customer support PDFs using 🧠 LLMs, 🔍 OCR, and 📚 FAISS vector search.