document-qa

Document Q&A chatbot

RAG-based document Q&A system (FastAPI + React) using FAISS vector search, sentence-transformer embeddings, and Gemini grounded answers with citations.

AI powered document question answering system that lets users upload files and get accurate, contextaware answers using a fullstack FastAPI and React architecture.

Lightweight RAG pipeline for local document Q&A

LocalDoc RAG: browser-only local document RAG for PDF/TXT/DOCX/CSV chat with Ollama, Qdrant, and plain JavaScript.

Upload documents (PDFs, text, Word). Ask questions. Get context-aware answers using RAG (embedding + LLM) - optimized for RTX 4050 GPU.

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.

Full-stack React and Express AI workspace with streaming replies, model selection, document Q&A, backups, safeguards, and automated tests.

Multi-Document AI Research Assistant — RAG with FastAPI, LangChain, FAISS, React

A full-stack MERN RAG application that enables users to upload PDFs, perform semantic vector search with MongoDB Atlas, and chat with documents using real-time AI responses with page-level source citations.

Upload docs, ask questions, get grounded answers with source citations. RAG pipeline with FastAPI, LangChain, ChromaDB, GPT-4o-mini, and a streaming React UI.

Local-first RAG app for document Q&A upload a PDF and ask questions about it, powered entirely by open-source models (Ollama) with zero API costs.

AI-Powered Multi-Document Reader & Voice Assistant

RAG chatbot template with document upload and question answering. Node.js + Express + OpenAI embeddings.

RAG document Q&A portfolio demo built with React and Vite

Panzoq is designed to break down information silos. Instead of managing separate tools for document reading, image analysis, and database queries

RAG-powered document Q&A application with OpenAI GPT-4 and Embeddings

AI-powered document QA platform using RAG + DeepSeek

AI-powered Retrieval-Augmented Generation (RAG) chatbot supporting multiple PDFs, citations, conversation memory, and multiple LLM providers.

AI-powered RAG document assistant that enables conversational PDF search using Google Gemini, Redis queues, AWS S3, MongoDB Atlas, and a scalable cloud-native architecture.