RAG Knowledge Search
A streaming RAG powered knowledge search tool that lets users ingest their own documents, runs paragraph level embeddings, and returns grounded answers with source citations in real time. Built with OpenAI embeddings, GPT-4o-mini, a custom in-memory vector store, and a Next 16 API layer hardened with rate limits and basic abuse protections.
Technologies Used
- Next.js 16
- React 19
- TypeScript
- OpenAI API
- Embeddings
- Vector Search
- Streaming
- Framer Motion
Project Details
Overview
RAG Knowledge Search is a small AI lab project that I built to show what applied AI engineering looks like when it is treated as a real product instead of a toy.
It lets users:
- Add their own documents into a knowledge base
- Ask natural language questions about that content
- Get streaming answers from GPT-4o-mini that are grounded in retrieved text
- See which source chunks were used for each answer, with match scores
Under the hood it uses:
- OpenAI text-embedding-3-small for embeddings
- An in-memory vector store for similarity search (ready to swap for Pinecone or pgvector later)
- A Next 16 API layer with shared state, chunking, and security constraints
- A React-based chat UI that handles streaming and context visualization
This project lives at /ai/rag and powers the first tile in my AI lab.
Problem
Most AI demos show a model that answers questions out of thin air. That is fine for a quick prototype, but it is not how teams actually ship reliable AI features in production.
I wanted a small, end-to-end example that solves a more realistic problem:
Given a set of arbitrary documents, let a user ask questions and get answers that are grounded in those documents, with sources and safety limits, in a way that feels like a real product.
The constraints I set for myself:
- No offline preprocessing step. The system should be able to ingest documents on demand.