In similarity searches of the vectors, cosine similarity is most widely used. It is a measure of similarity between two non-zero vectors of an inner product space — this measures cosine of the angle between them. In NLP, cosine similarity is often used to compare the similarity of words or documents represented as vectors in a high-dimensional space (word embeddings or document embeddings).