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