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Similarity Metrics

Distance metrics are used to measure similarities among vectors. The choice of metric depends on the method of obtaining vectors, particularly on the neural network encoder training method.

KDB.AI supports the following distance metrics:

  • Euclidean distance: Represented as L2
  • Inner product: Represented as IP
  • Cosine similarity: Represented as CS