Handle quota errors by calling ML.GENERATE_EMBEDDING iteratively

This tutorial shows you how to use the BigQuery bqutil.procedure.bqml_generate_embeddings public stored procedure to iterate through calls to the ML.GENERATE_EMBEDDING function. Calling the function iteratively lets you address any retryable errors that occur due to exceeding the quotas and limits that apply to the function.

To review the source code for the bqutil.procedure.bqml_generate_embeddings stored procedure in GitHub, see bqml_generate_embeddings.sqlx. For more information about the stored procedure parameters and usage, see the README file.

This tutorial guides you through the following tasks:

Creating a remote model over a text-embedding-004 model.
Iterating through calls to the ML.GENERATE_EMBEDDING function, using the remote model and the bigquery-public-data.bbc_news.fulltext public data table with the bqutil.procedure.bqml_generate_embeddings stored procedure.

Required permissions

To create the dataset, you need the bigquery.datasets.create Identity and Access Management (IAM) permission.
To create the connection resource, you need the following IAM permissions:
- bigquery.connections.create
- bigquery.connections.get
To grant permissions to the connection's service account, you need the following permission:
- resourcemanager.projects.setIamPolicy
To create the model, you need the following permissions:
- bigquery.jobs.create
- bigquery.models.create
- bigquery.models.getData
- bigquery.models.updateData
- bigquery.connections.delegate
To run inference, you need the following permissions:
- bigquery.models.getData
- bigquery.jobs.create

Costs

In this document, you use the following billable components of Google Cloud:

BigQuery ML: You incur costs for the data that you process in BigQuery.
Vertex AI: You incur costs for calls to the Vertex AI model.

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

For more information about BigQuery pricing, see BigQuery pricing.

For more information about Vertex AI pricing, see Vertex AI pricing.

Before you begin

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
Make sure that billing is enabled for your Google Cloud project.
Enable the BigQuery, BigQuery Connection, and Vertex AI APIs.
Enable the APIs

Create a dataset

Create a BigQuery dataset to store your models and sample data:

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

Go to the BigQuery page
In the Explorer pane, click your project name.
Click View actions > Create dataset.
On the Create dataset page, do the following:
1. For Dataset ID, enter target_dataset.
2. For Location type, select Multi-region, and then select US (multiple regions in United States).
3. 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 ID. Create the connection in the same location as the dataset that you created in the previous step.

Select one of the following options:

Console

Go to the BigQuery page.

Go to BigQuery
To create a connection, click Add, and then click Connections to external data sources.
In the Connection type list, select Vertex AI remote models, remote functions and BigLake (Cloud Resource).
In the Connection ID field, enter a name for your connection.
Click Create connection.
Click Go to connection.
In the Connection info pane, copy the service account ID for use in a later step.

bq

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...
```

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.

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

Launch Cloud Shell.
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).

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
```
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.
Review and modify the sample parameters to apply to your environment.
Save your changes.
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

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.
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.
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.

Grant permissions to the connection's service account

To grant the connection's service account appropriate roles to access the Cloud Storage and Vertex AI services, follow these steps:

Go to the IAM & Admin page.

Go to IAM & Admin
Click Grant access.
In the New principals field, enter the service account ID that you copied earlier.
In the Select a role menu, choose Vertex AI > Vertex AI User.
Click Save.

Create the text embedding generation model

Create a remote model that represents a hosted Vertex AI text-embedding-004 model:

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

Go to BigQuery
In the query editor, run the following statement:
```
CREATE OR REPLACE MODEL `target_dataset.embedding_model`
  REMOTE WITH CONNECTION `LOCATION.CONNECTION_ID`
  OPTIONS (ENDPOINT = 'text-embedding-004');
```
Replace the following:
- LOCATION: the connection location.
- 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.
The query takes several seconds to complete, after which the embedding model appears in the sample dataset in the Explorer pane. Because the query uses a CREATE MODEL statement to create a model, there are no query results.

Run the stored procedure

Run the bqutil.procedure.bqml_generate_embeddings stored procedure, which iterates through calls to the ML.GENERATE_EMBEDDING function using the target_dataset.embedding_model model and the bigquery-public-data.bbc_news.fulltext public data table:

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

Go to BigQuery

In the query editor, run the following statement:

CALL `bqutil.procedure.bqml_generate_embeddings`(
    "bigquery-public-data.bbc_news.fulltext",            -- source table
    "PROJECT_ID.target_dataset.news_body_embeddings",  -- destination table
    "PROJECT_ID.target_dataset.embedding_model",       -- model
    "body",                                              -- content column
    ["filename"],                                        -- key columns
    '{}'                                                 -- optional arguments encoded as a JSON string
);

Replace PROJECT_ID with the project ID of the project you are using for this tutorial.

The stored procedure creates a target_dataset.news_body_embeddings table to contain the output of the ML.GENERATE_EMBEDDING function.

When the query is finished running, confirm that there are no rows in the target_dataset.news_body_embeddings table that contain a retryable error. In the query editor, run the following statement:
```
SELECT *
FROM `target_dataset.news_body_embeddings`
WHERE ml_generate_embedding_status LIKE '%A retryable error occurred%';
```
The query returns the message No data to display.

Clean up

In the Google Cloud console, go to the Manage resources page.
Go to Manage resources
In the project list, select the project that you want to delete, and then click Delete.
In the dialog, type the project ID, and then click Shut down to delete the project.