Choose among vector distance functions to measure vector embeddings similarity

This page describes how to choose among the vector distance functions provided in Spanner to measure similarity between vector embeddings.

After you've generated embeddings from your Spanner data, you can perform a similarity search using vector distance functions. The following table describes the vector distance functions in Spanner.

FunctionDescriptionFormulaRelationship to increasing similarity
Dot product Calculates the cosine of angle \(\theta\) multiplied by the product of corresponding vector magnitudes. \(a_1b_1+a_2b_2+...+a_nb_n\) \(=|a||b|cos(\theta)\) Increases
Cosine distance The cosine distance function subtracts the cosine similarity from one (cosine_distance() = 1 - cosine similarity). The cosine similarity measures the cosine of angle \(\theta\) between two vectors. 1 - \(\frac{a^T b}{|a| \cdot |b|}\) Decreases
Euclidean distance Measures the straight line distance between two vectors. \(\sqrt{(a_1-b_1)^2+(a_2-b_2)^2+...+(a_N-b_N)^2}\) Decreases

Choose a similarity measure

Depending on whether or not all your vector embeddings are normalized, you can determine which similarity measure to use to find similarity. A normalized vector embedding has a magnitude (length) of exactly 1.0.

In addition, if you know which distance function your model was trained with, use that distance function to measure similarity between your vector embeddings.

Normalized data

If you have a dataset where all vector embeddings are normalized, then all three functions provide the same semantic search results. In essence, although each function returns a different value, those values sort the same way. When embeddings are normalized, DOT_PRODUCT() is usually the most computationally efficient, but the difference is negligible in most cases. However, if your application is highly performance sensitive, DOT_PRODUCT() might help with performance tuning.

Non-normalized data

If you have a dataset where vector embeddings aren't normalized, then it's not mathematically correct to use DOT_PRODUCT() as a distance function because dot product as a function doesn't measure distance. Depending on how the embeddings were generated and what type of search is preferred, either the COSINE_DISTANCE() or EUCLIDEAN_DISTANCE() function produces search results that are subjectively better than the other function. Experimentation with either COSINE_DISTANCE() or EUCLIDEAN_DISTANCE() might be necessary to determine which is best for your use case.

Unsure if data is normalized or non-normalized

If you're unsure whether or not your data is normalized and you want to use DOT_PRODUCT(), we recommend that you use COSINE_DISTANCE() instead. COSINE_DISTANCE() is like DOT_PRODUCT() with normalization built-in. Similarity measured using COSINE_DISTANCE() ranges from 0 to 2. A result that is close to 0 indicates the vectors are very similar.

What's next