Methods for Measuring Similarity between Embeddings
Explore the main techniques for measuring the similarity between vector embeddings, such as Euclidean distance, cosine similarity, and dot product. Understand their characteristics, differences, and appropriate use cases to accurately compare vectors in AI models and applications.
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
Let’s see the syntax to find the Euclidean distance between two embeddings using ...