Similarity Metrics for Vector Search

This article discusses vector similarity search metrics and how they work. It covers three primary distance metrics: L2 or Euclidean distance, cosine similarity, and inner product. Additionally, it mentions other interesting vector similarity or distance metrics such as Hamming Distance and Jaccard Index. The article explains the concept of vectors in terms of orientation and magnitude, and how these metrics can be used to compare any data that can be vectorized. It also provides examples of when each metric should be used.