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

1. Upload a document via form trigger
2. Content is split into searchable chunks
3. Gemini generates embeddings
4. Data is stored in a vector store
5. 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