Chat with PDF, CSV, and JSON documents using Google Gemini RAG

Overview

Turn documents into an AI-powered knowledge base.

Upload PDF, CSV, or JSON files and ask natural-language questions about their content using a Retrieval-Augmented Generation (RAG) workflow powered by Google Gemini. The workflow extracts, embeds, and semantically searches document data to generate accurate, source-grounded answers.

Designed as a simple and extensible starting point for building AI document assistants.

Key Features

Upload and analyze PDF, CSV, and JSON AI chatbot with semantic document search Retrieval-Augmented Generation (RAG) architecture Answers grounded in uploaded documents Beginner-friendly workflow with clear documentation Easy to extend for production use

How It Works

Upload a document via form trigger
Content is split into searchable chunks
Gemini generates embeddings
Data is stored in a vector store
The chatbot retrieves context and answers questions

Requirements

Google Gemini API credentials

Notes

Uses an in-memory vector store (data resets on restart) Can be replaced with Pinecone, Supabase, Weaviate, or other persistent databases Gemini API usage may incur costs depending on document size and query volume