Hands-on RAG Implementation

Ask questions. Get answers grounded in your data.

A hands-on implementation of Retrieval-Augmented Generation. Upload documents, ask questions, see the magic happen.

"What are the key features of the product?"

Based on product-docs.pdf:"The key features include real-time sync, AI-powered search..."

How RagXGen Works

A complete RAG pipeline from document upload to grounded answers

Document Upload

Upload PDFs and text files. Watch them transform into searchable vectors.

Semantic Search

Ask natural language questions. Find relevant context instantly.

Grounded Answers

LLM responses backed by your actual documents. No hallucinations.

Vector Storage

FAISS-powered similarity search. Fast and efficient retrieval.

Why Retrieval-Augmented Generation?

Reduce Hallucinations

Ground LLM responses in actual documents, not training data fantasies.

Access Fresh Data

Query documents from today, not just the LLM's training cutoff.

Cite Your Sources

Know exactly which documents informed each answer.

Tech Stack

Next.js

Frontend

FastAPI

Backend

LangChain

RAG

FAISS

Vector DB

OpenAI

LLM

Ready to see RAG in action?

Upload your documents and ask questions. Experience the power of retrieval-augmented generation first-hand.

Launch Live Demo