Search by Meaning - Embeddings and Vector Databases

Transform your text chunks into a searchable knowledge base. Learn to create semantic embeddings, perform similarity searches, and store your vectors in a production-ready database like Supabase with pgvector.

You've broken down documents into perfect chunks, but now comes the real challenge: how does your system find the right chunk when a user asks a question? Here's the problem: you could have thousands of chunks, and scanning through them one by one isn't going to work. Your system needs to understand what each chunk means and instantly find the most relevant one for any query.

The solution is embeddings - a way to convert text into numbers that capture meaning. Words and chunks with similar meanings get similar numbers, letting you use math to find semantic matches in milliseconds. This tutorial teaches you the core retrieval engine that powers every RAG system. You'll start with a simple example to build intuition, then use real embedding models to turn your chunks into searchable vectors, and finally store them in a production database that can handle millions of documents.