Vector databases and indexes

Exact nearest-neighbour search does not scale, so vector indexes trade a little recall for a lot of speed. Knowing the trade-off keeps you out of trouble.

Lesson 1.24m 23sAdvanced8.7k students

Chapters

4:23
  1. 0:00Intro
  2. 0:44Why do we need vector databases
  3. 1:29Vector embeddings and indexes
  4. 2:58Use cases
  5. 3:45Different vector databases

What this lesson covers

  • Understand approximate nearest neighbour search
  • Trade recall against latency deliberately
  • Store metadata alongside vectors for filtering

This lesson sits in Embeddings and Vector Search, part of Retrieval-Augmented Generation from Scratch. It assumes what came before it and leads directly into the next lesson in the module.

In this lesson you will

  • Understand approximate nearest neighbour search
  • Trade recall against latency deliberately
  • Store metadata alongside vectors for filtering

Resources