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Generate text embeddings by using the ML.GENERATE_EMBEDDING function | BigQuery

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Generate text embeddings by using the ML.GENERATE_EMBEDDING function

This document shows you how to create a BigQuery ML remote model that references a Vertex AI embedding model. You then use that model with the ML.GENERATE_EMBEDDING function to create text embeddings by using data from a BigQuery standard table.

Required roles

To create a remote model and use the ML.GENERATE_EMBEDDING function, you need the following Identity and Access Management (IAM) roles:

These predefined roles contain the permissions required to perform the tasks in this document. To see the exact permissions that are required, expand the Required permissions section:

Required permissions

You might also be able to get these permissions with custom roles or other predefined roles.

Before you begin

  1. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

    Go to project selector

  2. Verify that billing is enabled for your Google Cloud project.

  3. Enable the BigQuery, BigQuery Connection, and Vertex AI APIs.

    Enable the APIs

Create a dataset

Create a BigQuery dataset to contain your resources:

Console

  1. In the Google Cloud console, go to the BigQuery page.

    Go to the BigQuery page

  2. In the Explorer pane, click your project name.

  3. Click more_vert View actions > Create dataset.

  4. On the Create dataset page, do the following:

bq

  1. To create a new dataset, use the bq mk command with the --location flag:

    bq --location=LOCATION mk -d DATASET_ID

    Replace the following:

  2. Confirm that the dataset was created:

    bq ls
Create a connection

You can skip this step if you either have a default connection configured, or you have the BigQuery Admin role.

Create a Cloud resource connection for the remote model to use, and get the connection's service account. Create the connection in the same location as the dataset that you created in the previous step.

Select one of the following options:

Console

  1. Go to the BigQuery page.

    Go to BigQuery

  2. In the Explorer pane, click add Add data:

    The Add data dialog opens.

  3. In the Filter By pane, in the Data Source Type section, select Business Applications.

    Alternatively, in the Search for data sources field, you can enter Vertex AI.

  4. In the Featured data sources section, click Vertex AI.

  5. Click the Vertex AI Models: BigQuery Federation solution card.

  6. In the Connection type list, select Vertex AI remote models, remote functions and BigLake (Cloud Resource).

  7. In the Connection ID field, enter a name for your connection.

  8. Click Create connection.

  9. Click Go to connection.

  10. In the Connection info pane, copy the service account ID for use in a later step.

bq

  1. In a command-line environment, create a connection:

    bq mk --connection --location=REGION --project_id=PROJECT_ID \
    --connection_type=CLOUD_RESOURCE CONNECTION_ID

    The --project_id parameter overrides the default project.

    Replace the following:

    When you create a connection resource, BigQuery creates a unique system service account and associates it with the connection.

    Troubleshooting: If you get the following connection error, update the Google Cloud SDK:

    Flags parsing error: flag --connection_type=CLOUD_RESOURCE: value should be one of...

  2. Retrieve and copy the service account ID for use in a later step:

    bq show --connection PROJECT_ID.REGION.CONNECTION_ID

    The output is similar to the following:

    name                          properties
1234.REGION.CONNECTION_ID     {"serviceAccountId": "connection-1234-9u56h9@gcp-sa-bigquery-condel.iam.gserviceaccount.com"}
Terraform

Use the google_bigquery_connection resource.

Note: To create BigQuery objects using Terraform, you must enable the Cloud Resource Manager API.

To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up authentication for client libraries.

The following example creates a Cloud resource connection named my_cloud_resource_connection in the US region:

To apply your Terraform configuration in a Google Cloud project, complete the steps in the following sections.

Prepare Cloud Shell
  1. Launch Cloud Shell.

  2. Set the default Google Cloud project where you want to apply your Terraform configurations.

    You only need to run this command once per project, and you can run it in any directory.

    export GOOGLE_CLOUD_PROJECT=PROJECT_ID

    Environment variables are overridden if you set explicit values in the Terraform configuration file.

Prepare the directory

Each Terraform configuration file must have its own directory (also called a root module).

  1. In Cloud Shell, create a directory and a new file within that directory. The filename must have the .tf extension—for example main.tf. In this tutorial, the file is referred to as main.tf. 
    mkdir DIRECTORY && cd DIRECTORY && touch main.tf

  2. If you are following a tutorial, you can copy the sample code in each section or step.

    Copy the sample code into the newly created main.tf.

    Optionally, copy the code from GitHub. This is recommended when the Terraform snippet is part of an end-to-end solution.

  3. Review and modify the sample parameters to apply to your environment.
  4. Save your changes.
  5. Initialize Terraform. You only need to do this once per directory. 
    terraform init

    Optionally, to use the latest Google provider version, include the -upgrade option:

    terraform init -upgrade
Apply the changes
  1. Review the configuration and verify that the resources that Terraform is going to create or update match your expectations: 
    terraform plan

    Make corrections to the configuration as necessary.

  2. Apply the Terraform configuration by running the following command and entering yes at the prompt: 
    terraform apply

    Wait until Terraform displays the "Apply complete!" message.

  3. Open your Google Cloud project to view the results. In the Google Cloud console, navigate to your resources in the UI to make sure that Terraform has created or updated them.
Note: Terraform samples typically assume that the required APIs are enabled in your Google Cloud project. Give the service account access

Grant the connection's service account the Vertex AI User role.

If you plan to specify the endpoint as a URL when you create the remote model, for example endpoint = 'https://us-central1-aiplatform.googleapis.com/v1/projects/myproject/locations/us-central1/publishers/google/models/text-embedding-005', grant this role in the same project you specify in the URL.

If you plan to specify the endpoint by using the model name when you create the remote model, for example endpoint = 'text-embedding-005', grant this role in the same project where you plan to create the remote model.

Granting the role in a different project results in the error bqcx-1234567890-wxyz@gcp-sa-bigquery-condel.iam.gserviceaccount.com does not have the permission to access resource.

To grant the role, follow these steps:

Console

  1. Go to the IAM & Admin page.

    Go to IAM & Admin

  2. Click person_add Grant access.

    The Add principals dialog opens.

  3. In the New principals field, enter the service account ID that you copied earlier.

  4. In the Select a role field, select Vertex AI, and then select Vertex AI User.

  5. Click Save.

gcloud

Use the gcloud projects add-iam-policy-binding command:

gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/aiplatform.user' --condition=None

Replace the following:

Create a model

  1. In the Google Cloud console, go to the BigQuery page.

    Go to BigQuery

  2. Using the SQL editor, create a remote model:

    CREATE OR REPLACE MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`
REMOTE WITH CONNECTION {DEFAULT | `PROJECT_ID.REGION.CONNECTION_ID`}
OPTIONS (ENDPOINT = 'ENDPOINT');

    Replace the following:

Generate text embeddings by using data from a table

Generate text embeddings with the ML.GENERATE_EMBEDDING function by using text data from a table column.

Typically, you want to use a text-embedding or text-multilingual-embedding model for text-only use cases, and use a multimodalembedding model for cross-modal search use cases, where embeddings for text and visual content are generated in the same semantic space.

text embedding

Generate text embeddings by using a remote model over an embedding model:

SELECT *
FROM ML.GENERATE_EMBEDDING(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  TABLE PROJECT_ID.DATASET_ID.TABLE_NAME,
  STRUCT(FLATTEN_JSON AS flatten_json_output,
    TASK_TYPE AS task_type,
    OUTPUT_DIMENSIONALITY AS output_dimensionality)
);

Replace the following:

multimodal embedding

Generate text embeddings by using a remote model over the multimodalembedding model:

SELECT *
FROM ML.GENERATE_EMBEDDING(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  TABLE PROJECT_ID.DATASET_ID.TABLE_NAME,
  STRUCT(FLATTEN_JSON AS flatten_json_output，
  OUTPUT_DIMENSIONALITY AS output_dimensionality)
);

Replace the following:

Generate text embeddings by using data from a query

Generate text embeddings with the ML.GENERATE_EMBEDDING function by using text data provided by a query and a remote model over an embedding model.

Typically, you want to use a text-embedding or text-multilingual-embedding model for text-only use cases, and use a multimodalembedding model for cross-modal search use cases, where embeddings for text and visual content are generated in the same semantic space.

text embedding

Generate text embeddings by using a remote model over the embedding model:

SELECT *
FROM ML.GENERATE_EMBEDDING(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  (CONTENT_QUERY),
  STRUCT(FLATTEN_JSON AS flatten_json_output,
    TASK_TYPE AS task_type,
    OUTPUT_DIMENSIONALITY AS output_dimensionality)
  );

Replace the following:

multimodal embedding

Generate text embeddings by using a remote model over the multimodalembedding model:

SELECT *
FROM ML.GENERATE_EMBEDDING(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  (CONTENT_QUERY),
  STRUCT(FLATTEN_JSON AS flatten_json_output,
  OUTPUT_DIMENSIONALITY AS output_dimensionality)
);

Replace the following:

Examples

The following examples show how to call the ML.GENERATE_EMBEDDING function on a table and a query.

Embed text in a table

The following example shows a request to embed the content column of the text_data table:

SELECT *
FROM
  ML.GENERATE_EMBEDDING(
    MODEL `mydataset.embedding_model`,
    TABLE mydataset.text_data,
    STRUCT(TRUE AS flatten_json_output, 'CLASSIFICATION' AS task_type)
  );

Use embeddings to rank semantic similarity

The following example embeds a collection of movie reviews and orders them by cosine distance to the review "This movie was average" using the VECTOR_SEARCH function. A smaller distance indicates more semantic similarity.

For more information about vector search and vector index, see Introduction to vector search.

CREATE TEMPORARY TABLE movie_review_embeddings AS (
  SELECT *
  FROM
    ML.GENERATE_EMBEDDING(
      MODEL `bqml_tutorial.embedding_model`,
      (
        SELECT "This movie was fantastic" AS content
        UNION ALL
        SELECT "This was the best movie I've ever seen!!" AS content
        UNION ALL
        SELECT "This movie was just okay..." AS content
        UNION ALL
        SELECT "This movie was terrible." AS content
      ),
      STRUCT(TRUE AS flatten_json_output)
    )
);

WITH average_review_embedding AS (
  SELECT ml_generate_embedding_result
  FROM
    ML.GENERATE_EMBEDDING(
      MODEL `bqml_tutorial.embedding_model`,
      (SELECT "This movie was average" AS content),
      STRUCT(TRUE AS flatten_json_output)
    )
)
SELECT
  base.content AS content,
  distance AS distance_to_average_review
FROM
  VECTOR_SEARCH(
    TABLE movie_review_embeddings,
    "ml_generate_embedding_result",
    (SELECT ml_generate_embedding_result FROM average_review_embedding),
    distance_type=>"COSINE",
    top_k=>-1
  )
ORDER BY distance_to_average_review;

The result is the following:

+------------------------------------------+----------------------------+
| content                                  | distance_to_average_review |
+------------------------------------------+----------------------------+
| This movie was just okay...              | 0.062789813467745592       |
| This movie was fantastic                 |  0.18579561313064263       |
| This movie was terrible.                 |  0.35707466240930985       |
| This was the best movie I've ever seen!! |  0.41844932504542975       |
+------------------------------------------+----------------------------+
What's next

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-14 UTC.

[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-14 UTC."],[[["This guide details how to use BigQuery ML's `ML.GENERATE_EMBEDDING` function to create text embeddings from a BigQuery standard table by first establishing a remote model that references a Vertex AI embedding model."],["To set up the necessary environment, you need to create a BigQuery dataset, establish a connection to a cloud resource, and grant the appropriate IAM permissions, such as `roles/bigquery.connectionAdmin` and `roles/aiplatform.user`, to the connection's service account."],["You can create a remote model using the `CREATE OR REPLACE MODEL` statement in the BigQuery console, specifying the project ID, dataset ID, model name, connection ID, and endpoint, which should reference a supported embedding model like `text-embedding-005`."],["The `ML.GENERATE_EMBEDDING` function can generate embeddings from either a table column or the results of a query, and you can customize the embedding process with parameters like `FLATTEN_JSON`, `TASK_TYPE` (e.g., `RETRIEVAL_QUERY`, `SEMANTIC_SIMILARITY`), and `OUTPUT_DIMENSIONALITY` for either text-only or multimodal embeddings."],["The `ML.DISTANCE` function can be used in conjunction with embeddings to gauge the semantic similarity between text inputs, such as ordering movie reviews by their proximity in meaning to a specific review, as seen in the provided example."]]],[]]

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