Similarity Search

The goal of similarity search is to find the most similar vectors to a given query vector, which is the vector representing the data the user is searching for. There are several algorithms that can be used for similarity search, each with its own strengths and weaknesses.

Inner Product Space (IPS)

The inner product space algorithm calculates the dOT product of the query vector and each vector in the database to find the most similar vectors. The dot product is a measure of the similarity between two vectors, and it can be calculated using the following formula:

dot product = sum(a_i * b_i)

where a_i and b_i are the i-th elements of the two vectors.