Methods for Measuring Similarity between Embeddings

Vector embeddings are often compared using distance metrics, which quantify the difference or similarity between two vectors. Following are the three key similarity measures:

• Euclidean distance

• Cosine similarity

• Dot product

Euclidean distance

Euclidean distance is a measure of the straight-line distance between two points in Euclidean space. In the context of vector embeddings, it quantifies the geometric distance between two vectors in a multi-dimensional space. It is calculated as the square root of the sum of squared differences between the corresponding elements of the two vectors.

For two vectors $u$ and $v$ of dimension $n$, the Euclidean distance is calculated as follows:

Let’s see the syntax to find the Euclidean distance between two ...