Generate image embeddings by using the ML.GENERATE_EMBEDDING function

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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:

   • LOCATION: the dataset's location.
   • DATASET_ID is the ID of the dataset that you're creating.

2. Confirm that the dataset was created:

   bq ls

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": "connection-1234-9u56h9@gcp-sa-bigquery-condel.iam.gserviceaccount.com"}
   

Use the google_bigquery_connection resource.

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.

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:

• PROJECT_NUMBER: the project number of the project in which to grant the role.
• MEMBER: the service account ID that you copied earlier.

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:

• BUCKET_PATH: the path to the Cloud Storage bucket that contains the images, in the format ['gs://bucket_name/[folder_name/]*'].

  The Cloud Storage bucket that you use 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 service-A@gcp-sa-aiplatform.iam.gserviceaccount.com service account.

• REGION: the region or multi-region that contains the connection.
• CONNECTION_ID: the ID of the connection that you created.

  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.

• STALENESS_INTERVAL: specifies whether cached metadata is used by operations against the object table, and how fresh the cached metadata must be in order for the operation to use it. For more information on metadata caching considerations, see Metadata caching for performance.

  To disable metadata caching, specify 0. This is the default.

  To enable metadata caching, specify an interval literal value between 30 minutes and 7 days. For example, specify INTERVAL 4 HOUR for a 4 hour staleness interval. With this value, operations against the table use cached metadata if it has been refreshed within the past 4 hours. If the cached metadata is older than that, the operation retrieves metadata from Cloud Storage instead.

• CACHE_MODE: specifies whether the metadata cache is refreshed automatically or manually. For more information on metadata caching considerations, see Metadata caching for performance.

  Set to AUTOMATIC for the metadata cache to be refreshed at a system-defined interval, usually somewhere between 30 and 60 minutes.

  Set to MANUAL if you want to refresh the metadata cache on a schedule you determine. In this case, you can call the BQ.REFRESH_EXTERNAL_METADATA_CACHE system procedure to refresh the cache.

  You must set CACHE_MODE if STALENESS_INTERVAL is set to a value greater than 0.

• PROJECT_ID: your project ID.
• DATASET_ID: the ID of the dataset that you created.
• TABLE_NAME: the name of the object table.