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 PDF Question-Answering App built with RAG (Retrieval-Augmented Generation), allowing users to upload PDFs and ask context-based questions. Powered by Streamlit, LangChain, Ollama, and Chroma for efficient and accurate answers.

📄 Transform your PDF documents into actionable insights with this RAG-based Question-Answering App for efficient and accurate responses.

Air-gapped, production-ready RAG workspace featuring hybrid search, semantic reranking, cross-encoder reranking, and verifiable citations.

Build a Production-Ready RAG system for intelligent Q&A over PDFs (policies, contracts, resumes). Powered by OpenAI Structured Outputs, Pydantic for schema enforcement, ChromaDB for local vector storage, and a Streamlit UI. Ideal for enterprise AI in legal, banking, and HR. Scalable, deterministic, and easy to deploy locally.

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.

Dynamic Retrieval-Augmented Generation framework for context-aware PDF question answering with adaptive retrieval strategies.

📄 IEEE-Published AI-powered PDF Question Answering System using Google Gemini, FAISS, HuggingFace Embeddings, and Streamlit for semantic document search, contextual question answering, and intelligent document analysis.

FastAPI RAG document question answering system using embeddings and semantic search

PDFMate.AI is a Django-based app that lets you upload PDFs, indexes them into a vector database, and ask natural-language questions to get grounded answers with evidence. It uses PyMuPDF for PDF parsing, Transformers + PyTorch for embeddings, and Pinecone for fast semantic search. Clean templates provide Q&A views with cited contexts.

RAG-based PDF Question Answering AI Agent using Python, Streamlit, Qdrant Vector DB, semantic search, and LLM-powered retrieval for context-aware document intelligence.

Production-grade Multi-Modal RAG system for intelligent document Q&A with structural extraction and real-time observability.

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.

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.

Document Intelligence is a lightweight, minimalistic PDF question-answering system built using LlamaIndex, HuggingFace models, and Streamlit. It allows users to upload PDFs, index their content, and ask natural language questions to retrieve precise, source-backed answers with page-level context.

Hybrid RAG over PDFs with FAISS, BM25, cross-encoder reranking and grounded citations

📄 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.

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.