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RAG-based Document Q&A App Built with Next.js, Pinecone DB, and shadcn/ui, this application allows users to upload multiple documents and ask questions based on the content. It uses vector embeddings for semantic search, storing and retrieving document chunks from Pinecone to provide accurate and context-aware responses. The UI is clean and respons

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Akshay-hub-007/AI-Document-Summarizer

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🧠 RAG-based Document Q&A App

A lightweight RAG (Retrieval-Augmented Generation) application built with Next.js, Pinecone, and shadcn/ui. This tool allows users to upload multiple files and ask natural language questions based on the content of those documents.

🔗 Live Demo: ai-document-summarizer-rho.vercel.app


✨ Features

  • 📁 Upload and manage multiple documents
  • 📄 Automatic chunking and embedding of text
  • 🔍 Fast and accurate semantic search via Pinecone vector DB
  • 🤖 AI-powered question answering using OpenAI or Google models
  • 💅 Clean and responsive UI using shadcn/ui

🛠 Tech Stack

Layer Technology
Frontend Next.js, React
UI Library shadcn/ui
Backend API Routes in Next.js
AI Model OpenAI or Google APIs
Vector DB Pinecone

🚀 Getting Started

1. Clone the repository

git clone https://github.com/Akshay-hub-007/AI-Document-Summarizer.git

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RAG-based Document Q&A App Built with Next.js, Pinecone DB, and shadcn/ui, this application allows users to upload multiple documents and ask questions based on the content. It uses vector embeddings for semantic search, storing and retrieving document chunks from Pinecone to provide accurate and context-aware responses. The UI is clean and respons

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