Overview
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. An n8n automation workflow template by Md Khalid Ali.
Md Khalid Ali
web
free
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