Vector functions

Vector Functions

Name Description
COSINE_DISTANCE Returns the cosine distance between two vectors
DOT_PRODUCT Returns the dot product between two vectors
EUCLIDEAN_DISTANCE Returns the euclidean distance between two vectors
MANHATTAN_DISTANCE Returns the manhattan distance between two vectors
VECTOR_LENGTH Returns the number of elements in a vector

COSINE_DISTANCE

Syntax:

cosine_distance(x: VECTOR, y: VECTOR) -> FLOAT64

Description:

Returns the cosine distance between x and y.

Node.js
const sampleVector = [0.0, 1, 2, 3, 4, 5];
const result = await db.pipeline()
  .collection("books")
  .select(
    field("embedding").cosineDistance(sampleVector).as("cosineDistance")
  )
  .execute();
test.firestore.js

Web

const sampleVector = [0.0, 1, 2, 3, 4, 5];
const result = await execute(db.pipeline()
  .collection("books")
  .select(
    field("embedding").cosineDistance(sampleVector).as("cosineDistance")));
test.firestore.js
Swift
let sampleVector = [0.0, 1, 2, 3, 4, 5]
let result = try await db.pipeline()
  .collection("books")
  .select([
    Field("embedding").cosineDistance(sampleVector).as("cosineDistance")
  ])
  .execute()
PipelineSnippets.swift

Kotlin

val sampleVector = doubleArrayOf(0.0, 1.0, 2.0, 3.0, 4.0, 5.0)
val result = db.pipeline()
    .collection("books")
    .select(
        field("embedding").cosineDistance(sampleVector).alias("cosineDistance")
    )
    .execute()
DocSnippets.kt

Java

double[] sampleVector = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
Task<Pipeline.Snapshot> result = db.pipeline()
    .collection("books")
    .select(
        field("embedding").cosineDistance(sampleVector).alias("cosineDistance")
    )
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field
from google.cloud.firestore_v1.vector import Vector

sample_vector = Vector([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
result = (
    client.pipeline()
    .collection("books")
    .select(
        Field.of("embedding").cosine_distance(sample_vector).as_("cosineDistance")
    )
    .execute()
)
firestore_pipelines.py
Java
double[] sampleVector = new double[] {0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
Pipeline.Snapshot result =
    firestore
        .pipeline()
        .collection("books")
        .select(cosineDistance(field("embedding"), sampleVector).as("cosineDistance"))
        .execute()
        .get();
PipelineSnippets.java

DOT_PRODUCT

Syntax:

dot_product(x: VECTOR, y: VECTOR) -> FLOAT64

Description:

Returns the dot product of x and y.

Node.js
const sampleVector = [0.0, 1, 2, 3, 4, 5];
const result = await db.pipeline()
  .collection("books")
  .select(
    field("embedding").dotProduct(sampleVector).as("dotProduct")
  )
  .execute();
test.firestore.js

Web

const sampleVector = [0.0, 1, 2, 3, 4, 5];
const result = await execute(db.pipeline()
  .collection("books")
  .select(
    field("embedding").dotProduct(sampleVector).as("dotProduct")
  )
);
test.firestore.js
Swift
let sampleVector = [0.0, 1, 2, 3, 4, 5]
let result = try await db.pipeline()
  .collection("books")
  .select([
    Field("embedding").dotProduct(sampleVector).as("dotProduct")
  ])
  .execute()
PipelineSnippets.swift

Kotlin

val sampleVector = doubleArrayOf(0.0, 1.0, 2.0, 3.0, 4.0, 5.0)
val result = db.pipeline()
    .collection("books")
    .select(
        field("embedding").dotProduct(sampleVector).alias("dotProduct")
    )
    .execute()
DocSnippets.kt

Java

double[] sampleVector = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
Task<Pipeline.Snapshot> result = db.pipeline()
    .collection("books")
    .select(
        field("embedding").dotProduct(sampleVector).alias("dotProduct")
    )
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field
from google.cloud.firestore_v1.vector import Vector

sample_vector = Vector([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
result = (
    client.pipeline()
    .collection("books")
    .select(Field.of("embedding").dot_product(sample_vector).as_("dotProduct"))
    .execute()
)
firestore_pipelines.py
Java
double[] sampleVector = new double[] {0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
Pipeline.Snapshot result =
    firestore
        .pipeline()
        .collection("books")
        .select(dotProduct(field("embedding"), sampleVector).as("dotProduct"))
        .execute()
        .get();
PipelineSnippets.java

EUCLIDEAN_DISTANCE

Syntax:

euclidean_distance(x: VECTOR, y: VECTOR) -> FLOAT64

Description:

Computes the euclidean distance between x and y.

Node.js
const sampleVector = [0.0, 1, 2, 3, 4, 5];
const result = await db.pipeline()
  .collection("books")
  .select(
    field("embedding").euclideanDistance(sampleVector).as("euclideanDistance")
  )
  .execute();
test.firestore.js

Web

const sampleVector = [0.0, 1, 2, 3, 4, 5];
const result = await execute(db.pipeline()
  .collection("books")
  .select(
    field("embedding").euclideanDistance(sampleVector).as("euclideanDistance")
  )
);
test.firestore.js
Swift
let sampleVector = [0.0, 1, 2, 3, 4, 5]
let result = try await db.pipeline()
  .collection("books")
  .select([
    Field("embedding").euclideanDistance(sampleVector).as("euclideanDistance")
  ])
  .execute()
PipelineSnippets.swift

Kotlin

val sampleVector = doubleArrayOf(0.0, 1.0, 2.0, 3.0, 4.0, 5.0)
val result = db.pipeline()
    .collection("books")
    .select(
        field("embedding").euclideanDistance(sampleVector).alias("euclideanDistance")
    )
    .execute()
DocSnippets.kt

Java

double[] sampleVector = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
Task<Pipeline.Snapshot> result = db.pipeline()
    .collection("books")
    .select(
        field("embedding").euclideanDistance(sampleVector).alias("euclideanDistance")
    )
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field
from google.cloud.firestore_v1.vector import Vector

sample_vector = Vector([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
result = (
    client.pipeline()
    .collection("books")
    .select(
        Field.of("embedding")
        .euclidean_distance(sample_vector)
        .as_("euclideanDistance")
    )
    .execute()
)
firestore_pipelines.py
Java
double[] sampleVector = new double[] {0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
Pipeline.Snapshot result =
    firestore
        .pipeline()
        .collection("books")
        .select(euclideanDistance(field("embedding"), sampleVector).as("euclideanDistance"))
        .execute()
        .get();
PipelineSnippets.java

MANHATTAN_DISTANCE

Syntax:

manhattan_distance(x: VECTOR, y: VECTOR) -> FLOAT64

Description:

Computes the manhattan distance between x and y.

VECTOR_LENGTH

Syntax:

vector_length(vector: VECTOR) -> INT64

Description:

Returns the number of elements in a VECTOR.

Node.js
const result = await db.pipeline()
  .collection("books")
  .select(
    field("embedding").vectorLength().as("vectorLength")
  )
  .execute();
test.firestore.js

Web

const result = await execute(db.pipeline()
  .collection("books")
  .select(
    field("embedding").vectorLength().as("vectorLength")
  )
);
test.firestore.js
Swift
let result = try await db.pipeline()
  .collection("books")
  .select([
    Field("embedding").vectorLength().as("vectorLength")
  ])
  .execute()
PipelineSnippets.swift

Kotlin

val result = db.pipeline()
    .collection("books")
    .select(
        field("embedding").vectorLength().alias("vectorLength")
    )
    .execute()
DocSnippets.kt

Java

Task<Pipeline.Snapshot> result = db.pipeline()
    .collection("books")
    .select(
        field("embedding").vectorLength().alias("vectorLength")
    )
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field

result = (
    client.pipeline()
    .collection("books")
    .select(Field.of("embedding").vector_length().as_("vectorLength"))
    .execute()
)
firestore_pipelines.py
Java
Pipeline.Snapshot result =
    firestore
        .pipeline()
        .collection("books")
        .select(vectorLength(field("embedding")).as("vectorLength"))
        .execute()
        .get();
PipelineSnippets.java