Distance Metrics in Vector Search

Vector databases like Weaviate use machine learning models to analyze data and calculate vector embeddings, which are stored together with the data in a database for later querying. Various distance metrics can be used to judge how similar or dissimilar two objects are based on their vector values. These metrics include Cosine Similarity, Dot Product, Squared Euclidean (L2-Squared), Manhattan (L1 Norm or Taxicab Distance), and Hamming. The choice of distance metric depends on the data, model, and application being used. Weaviate supports five different distance metrics and allows users to create their own.