AI Chat with RAG Search | Demos

AI Chat with RAG Search

Upload your documents, ask questions in plain language, get answers based only on what's inside those files. That's the core idea behind this demo project.

Retrieval Augmented Generation (RAG) makes it possible. Instead of keyword matching, the system understands the meaning behind your question and finds relevant sections across all uploaded documents. The AI then generates a response using only that context, nothing else.

How RAG works

The process has two phases:

Document Processing

Upload PDF or DOCX files via drag-and-drop

Text gets extracted and split into semantic chunks (~2000 characters each)

Each chunk converts to a vector embedding, a numerical representation of its meaning

Everything stores in MongoDB with real-time status updates via Socket.IO

Query Processing

Your question becomes a vector embedding too

MongoDB's vector search finds the most relevant chunks by semantic similarity

Those chunks get injected into the AI prompt as context

Gemini generates an answer based only on your documents

This pattern works for internal knowledge bases, customer support tools, legal document analysis, or any product where users need to find specific information across large document sets.