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Version: 0.11

Vector Functions

This page lists all vector-related functions supported in GreptimeDB. Vector functions are primarily used for operations such as distance calculation, similarity measurement, and more.

Distance Calculations​

  • vec_l2sq_distance(vec1, vec2): Computes the squared L2 distance between two vectors.
  • vec_cos_distance(vec1, vec2): Computes the cosine distance between two vectors.
  • vec_dot_product(vec1, vec2): Computes the dot product of two vectors.

These functions accept vector values as parameters. You can use the parse_vec function to convert a string into a vector value, such as parse_vec('[1.0, 2.0, 3.0]'). Also, vector strings (e.g., [1.0, 2.0, 3.0]) can be used directly and will be automatically converted. Regardless of the method used, the dimensionality of the vectors must remain consistent.

vec_l2sq_distance​

Calculates the squared Euclidean distance (squared L2 distance) between two vectors. L2 distance is the straight-line distance between two points in geometric space. This function returns the squared value to improve computational efficiency.

Example:

SELECT vec_l2sq_distance(parse_vec('[1.0, 2.0, 3.0]'), parse_vec('[2.0, 1.0, 4.0]'));

Or

SELECT vec_l2sq_distance('[1.0, 2.0, 3.0]', '[2.0, 1.0, 4.0]');

Details:

  • Parameters are two vectors with consistent dimensions.
  • Output: A scalar value of type Float32.

cos_distance​

Calculates the cosine distance between two vectors. Cosine distance measures the cosine of the angle between two vectors and is used to quantify similarity.

Example:

SELECT vec_cos_distance(parse_vec('[1.0, 2.0, 3.0]'), parse_vec('[2.0, 1.0, 4.0]'));

Or

SELECT vec_cos_distance('[1.0, 2.0, 3.0]', '[2.0, 1.0, 4.0]');

Details:

  • Parameters are two vectors with consistent dimensions.
  • Output: A scalar value of type Float32.

dot_product​

Computes the dot product of two vectors. The dot product is the sum of the element-wise multiplications of two vectors. It is commonly used to measure similarity or for linear transformations in machine learning.

Example:

SELECT vec_dot_product(parse_vec('[1.0, 2.0, 3.0]'), parse_vec('[2.0, 1.0, 4.0]'));

Or

SELECT vec_dot_product('[1.0, 2.0, 3.0]', '[2.0, 1.0, 4.0]');

Details:

  • Parameters are two vectors with consistent dimensions.
  • Output: A scalar value of type Float32.

Conversion Functions​

When dealing with vector data in the database, GreptimeDB provides convenient functions for converting between strings and vector values.

parse_vec​

Converts a string to a vector value. The string must be enclosed in square brackets [] and contain elements of type Float32, separated by commas.

Example:

CREATE TABLE vectors (
    ts TIMESTAMP,
    vec_col VECTOR(3)
);

INSERT INTO vectors (ts, vec_col) VALUES ('2024-11-18 00:00:01', parse_vec('[1.0, 2.0, 3.0]'));

vec_to_string​

Converts a vector object to a string. The converted string format is [<float32>, <float32>, ...].

Example:

SELECT vec_to_string(vec_col) FROM vectors;