Vector similarity search

Technique used to find vectors in a high-dimensional space that are similar to a given query vector. It is commonly used in applications like image retrieval, document search, and recommendation systems, utilizing similarity measures such as cosine similarity, Euclidean distance, and dot product, and indexing structures like KD-trees and Annoy for efficient searching. This method is particularly beneficial for developers and data scientists working on machine learning and artificial intelligence projects that require efficient and accurate similarity searches.

Vector similarity search was discovered on October 13th 2022 and it currently has a search volume of 720 with a growth of +1800%.