What is the difference between cosine similarity, dot product, and Euclidean distance?

Cosine similarity measures the angle between vectors and ignores magnitude. Dot product accounts for both angle and magnitude. Euclidean distance measures the straight-line distance in vector space. For normalized embeddings, cosine similarity and dot product are equivalent. The choice depends on whether magnitude is meaningful for your embeddings; most modern embedding models are designed to be used with cosine similarity.