Handle quota errors by calling ML.GENERATE_TEXT iteratively

This tutorial shows you how to use the BigQuery bqutil.procedure.bqml_generate_text public stored procedure to iterate through calls to the ML.GENERATE_TEXT 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_text stored procedure in GitHub, see bqml_generate_text.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 gemini-2.0-flash model.
• Iterating through calls to the ML.GENERATE_TEXT function, using the remote model and the bigquery-public-data.bbc_news.fulltext public data table with the bqutil.procedure.bqml_generate_text stored procedure.

Required permissions

To run this tutorial, you need the following Identity and Access Management (IAM) roles:

• Create and use BigQuery datasets, connections, and models: BigQuery Admin (roles/bigquery.admin).
• Grant permissions to the connection's service account: Project IAM Admin (roles/resourcemanager.projectIamAdmin).

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:

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

Costs

In this document, you use the following billable components of Trusted Cloud by S3NS:

• 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 Trusted 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

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

   Go to project selector

2. Make sure that billing is enabled for your Trusted 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 models and sample data:

1. In the Trusted 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:

   1. For Dataset ID, enter sample.

   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 the text generation model

Create a remote model that represents a hosted Vertex AI gemini-2.0-flash model:

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

   Go to BigQuery

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

   CREATE OR REPLACE MODEL `sample.generate_text`
     REMOTE WITH CONNECTION DEFAULT
     OPTIONS (ENDPOINT = 'gemini-2.0-flash');

   The query takes several seconds to complete, after which the generate_text 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_text stored procedure, which iterates through calls to the ML.GENERATE_TEXT function using the sample.generate_text model and the bigquery-public-data.bbc_news.fulltext public data table:

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

   Go to BigQuery

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

   CALL `bqutil.procedure.bqml_generate_text`(
       "bigquery-public-data.bbc_news.fulltext",   -- source table
       "PROJECT_ID.sample.news_generated_text",  -- destination table
       "PROJECT_ID.sample.generate_text",        -- model
       "body",                                     -- content column
       ["filename"],                               -- key columns
       '{}'                                        -- optional arguments
   );

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

   The stored procedure creates a sample.news_generated_text table to contain the output of the ML.GENERATE_TEXT function.

3. When the query is finished running, confirm that there are no rows in the sample.news_generated_text table that contain a retryable error. In the query editor, run the following statement:

   SELECT *
   FROM `sample.news_generated_text`
   WHERE ml_generate_text_status LIKE '%A retryable error occurred%';

   The query returns the message No data to display.

Clean up

1. In the Trusted Cloud console, go to the Manage resources page.

   Go to Manage resources

2. In the project list, select the project that you want to delete, and then click Delete.
3. In the dialog, type the project ID, and then click Shut down to delete the project.