A vector store is a database that finds things by meaning, not exact match. You convert text into a numerical vector (an embedding), store it, and later search for semantically similar vectors. That's the retrieval half of RAG. Which store you pick depends on scale, whether you want persistence, and …

Before you can build a retrieval system, you need to understand what embeddings are and why they exist. Skip this and the rest of RAG won't make sense — you'll be configuring things without knowing what they do.

What an embedding is

An embedding is a list of numbers that represents …