Embeddings and the Vector Store

Embeddings

Part of: Build a RAG System

You've got chunks. Now: when a user asks "How was revenue growth?", how do you find the chunk that answers it? Keyword matching won't cut it. The question says "revenue growth" but the relevant chunk might say "earnings increased", zero word overlap, same meaning. You need to search by meaning , not by exact words. That's what embeddings give you. An embedding is a list of numbers that captures meaning An embedding model turns a piece of text into a vector, a fixed-length list of floats. The magic property: texts with similar meaning land close together in that number-space, even if they share no words. "Revenue growth" and "earnings increased" point in nearly the same direction; "office dog" points somewhere else entirely. Real embedding models (the production kind) output vectors with hundreds or thousands of dimensions, trained on huge text corpora. The principle is identical to the toy version you'll build here, which just counts keyword occurrences into a small vector. Measuring closeness: cosine similarity To find the closest chunk, you measure the angle between two vectors. Cosine similarity does this: 1.0 means pointing the same direction (very similar), 0.0 means perpendic

Challenge: The Vector-Store Retriever