[WhitneyLand]

For anyone new to this, the topic has gained interest in recent years alongside the rise of AI. Many ML models project text, images, etc, into an embedding layer in high dimensional space as a vector (an array of numbers). It then becomes possible to numerically compare these vectors to see how closely related they are, in others words to find the semantic similarity.

This nifty feature leads to the need to scale up. For example, given many vectors in a database which one is most similar to my word or image. Applications like this need ways to efficiently scale these kinds of searches and HNSW is one prominent technique to enable this.