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

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Generate video 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 foundation model. You then use that model with the ML.GENERATE_EMBEDDING function to create video embeddings by using data from a BigQuery object table.

Required roles

To create a remote model and generate embeddings, 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, Cloud Storage, 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 and Storage Object Viewer roles.

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/gemini-2.0-flash', 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 = 'gemini-2.0-flash', 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 these roles, follow these steps:

Console

  1. Go to the IAM & Admin page.

    Go to IAM & Admin

  2. Click person_add Add.

    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 Add another role.

  6. In the Select a role field, choose Cloud Storage, and then select Storage Object Viewer.

  7. 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
gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/storage.objectViewer' --condition=None

Replace the following:

Create an object table

To analyze videos without moving them from Cloud Storage, create an object table.

To create an object table:

SQL

Use the CREATE EXTERNAL TABLE statement.

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

    Go to BigQuery

  2. In the query editor, enter the following statement:

    CREATE EXTERNAL TABLE `PROJECT_ID.DATASET_ID.TABLE_NAME`
WITH CONNECTION {`PROJECT_ID.REGION.CONNECTION_ID`| DEFAULT}
OPTIONS(
  object_metadata = 'SIMPLE',
  uris = ['BUCKET_PATH'[,...]],
  max_staleness = STALENESS_INTERVAL,
  metadata_cache_mode = 'CACHE_MODE');

    Replace the following:

  3. Click play_circle Run.

For more information about how to run queries, see Run an interactive query.

bq

Use the bq mk command.

bq mk --table \
--external_table_definition=BUCKET_PATH@REGION.CONNECTION_ID \
--object_metadata=SIMPLE \
--max_staleness=STALENESS_INTERVAL \
--metadata_cache_mode=CACHE_MODE \
PROJECT_ID:DATASET_ID.TABLE_NAME

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 video embeddings

Generate video embeddings with the ML.GENERATE_EMBEDDING function by using video data from an object table:

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,
    START_SECOND AS start_second,
    END_SECOND AS end_second,
    INTERVAL_SECONDS AS interval_seconds)
);

Replace the following:

Example

The following example shows how to create embeddings for the videos in the videos object table. Embeddings are created for each 5 second interval between the 10 second and 40 second marks in each video.

SELECT *
FROM
  ML.GENERATE_EMBEDDING(
    MODEL `mydataset.embedding_model`,
    TABLE `mydataset.videos`,
    STRUCT(TRUE AS flatten_json_output,
    10 AS start_second,
    40 AS end_second,
    5 AS interval_seconds)
  );
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 2025-08-07 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 2025-08-07 UTC."],[[["This guide explains how to generate video embeddings using the `ML.GENERATE_EMBEDDING` function in BigQuery ML, leveraging data from a BigQuery object table."],["A remote model is created, referencing a Vertex AI embedding foundation model, which is then used with `ML.GENERATE_EMBEDDING` to create the video embeddings."],["Proper IAM permissions are necessary for creating connections, models, and running inferences, as well as for the connection's service accounts to access the model and the object table."],["An object table must be set up to store the video data, and the associated Cloud Storage bucket should generally be in the same project or have specific Storage Admin permissions granted to a specific service account."],["Video embeddings are generated by specifying a start and end time in seconds, and an interval in seconds, allowing the videos to be segmented and analyzed accordingly."]]],[]]

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