Distance Metrics in Vector Search: Cosine, Dot Product, Euclidean, Manhattan, Hamming

AI Impact Summary

This document explores various distance metrics used in vector search, including cosine similarity, dot product, Euclidean, Manhattan, and Hamming. It explains how these metrics quantify the similarity between vector embeddings, highlighting their applications in semantic search and similarity search. The document emphasizes the importance of choosing the right metric based on the underlying machine learning model and the dimensionality of the vectors, addressing considerations like computational cost and accuracy.