Choosing Between Cosine Similarity, Dot Product, and Euclidean Distance for RAG Applications

Choosing the right vector similarity measure is crucial for Retrieval-Augmented Generation (RAG) applications. Cosine similarity measures the angle between vectors, making it ideal for text where direction matters more than magnitude. Dot product considers both direction and magnitude, which is useful when vector size is significant. Euclidean distance calculates the straight-line distance between points, best suited for spatial concepts or clustering tasks.