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 connection, you need membership in the following Identity and Access Management (IAM) role:

  • roles/bigquery.connectionAdmin

• To grant permissions to the connection's service account, you need the following permission:

  • resourcemanager.projects.setIamPolicy

• To create the model using BigQuery ML, you need the following IAM permissions:

  • bigquery.jobs.create
  • bigquery.models.create
  • bigquery.models.getData
  • bigquery.models.updateData
  • bigquery.models.updateMetadata

• To run inference, you need the following permissions:

  • bigquery.tables.getData on the table
  • bigquery.models.getData on the model
  • bigquery.jobs.create

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. Make sure 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 store your ML model:

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 View actions > Create dataset.

   

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

   • For Dataset ID, enter bqml_tutorial.

   • For Location type, select Multi-region, and then select US (multiple regions in United States).

     The public datasets are stored in the US multi-region. For simplicity, store your dataset in the same location.

   • Leave the remaining default settings as they are, and click Create dataset.

     

Create a connection

Create a Cloud resource connection and get the connection's service account. Create the connection in the same location as the dataset you created in the previous step.

Select one of the following options:

Console

1. Go to the BigQuery page.

   Go to BigQuery

2. To create a connection, click Add, and then click Connections to external data sources.

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

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

5. Click Create connection.

6. Click Go to connection.

7. 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:

   • REGION: your connection region
   • PROJECT_ID: your Google Cloud project ID
   • CONNECTION_ID: an ID for your connection

   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": "[e-mail]"}
   

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:

# This queries the provider for project information.
data "google_project" "default" {}

# This creates a cloud resource connection in the US region named my_cloud_resource_connection.
# Note: The cloud resource nested object has only one output field - serviceAccountId.
resource "google_bigquery_connection" "default" {
  connection_id = "my_cloud_resource_connection"
  project       = data.google_project.default.project_id
  location      = "US"
  cloud_resource {}
}

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-004', 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-004', 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 [e-mail] 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 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:

• PROJECT_NUMBER: your project number
• MEMBER: the service account ID that you copied earlier

Create an object table

Create an object table that stores video contents. The object table makes it possible to analyze the video without moving them from Cloud Storage.

The Cloud Storage bucket used by the object table should be in the same project where you plan to create the model and call the ML.GENERATE_EMBEDDING function. If you want to call the ML.GENERATE_EMBEDDING function in a different project than the one that contains the Cloud Storage bucket used by the object table, you must grant the Storage Admin role at the bucket level to the [e-mail] service account.

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 `PROJECT_ID.REGION.CONNECTION_ID`
   OPTIONS (ENDPOINT = 'ENDPOINT');

   Replace the following:

   • PROJECT_ID: your project ID
   • DATASET_ID: the ID of the dataset to contain the model
   • MODEL_NAME: the name of the model
   • REGION: the region used by the connection
   • CONNECTION_ID: the ID of your BigQuery connection

     When you view the connection details in the Google Cloud console, this is the value in the last section of the fully qualified connection ID that is shown in Connection ID, for example projects/myproject/locations/connection_location/connections/myconnection

   • ENDPOINT: the embedding LLM to use, in this case multimodalembedding@001.

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:

• PROJECT_ID: your project ID.
• DATASET_ID: the ID of the dataset that contains the model.
• MODEL_NAME: the name of the remote model over a multimodalembedding@001 model.
• TABLE_NAME: the name of the object table that contains the videos to embed.
• FLATTEN_JSON: a BOOL value that indicates whether to parse the embedding into a separate column. The default value is TRUE.
• START_SECOND: a FLOAT64 value that specifies the second in the video at which to start the embedding. The default value is 0. This value must be positive and less than the end_second value.
• END_SECOND: a FLOAT64 value that specifies the second in the video at which to end the embedding. The default value is 120. This value must be positive and greater than the start_second value.
• INTERVAL_SECONDS: a FLOAT64 value that specifies the interval to use when creating embeddings. For example, if you set start_second = 0, end_second = 120, and interval_seconds = 10, then the video is split into twelve 10 second segments ([0, 10), [10, 20), [20, 30)...) and embeddings are generated for each segment. This value must be greater than 4 and less than 120. The default value is 16.

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)
  );