Generate text by using a remote model and the ML.GENERATE_TEXT function

This tutorial shows you how to create a remote model that's based on the text-bison large language model, and then use that model together with the ML.GENERATE_TEXT function to perform several text-generation tasks. This tutorial uses the bigquery-public-data.imdb.reviews public table.

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 service that's represented by the remote 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 in the BigQuery documentation.

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

Before you begin

1. In the Google Cloud console, on the project selector page, select or create a Google Cloud 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 more_vert 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:

 ## This creates a cloud resource connection.
 ## Note: The cloud resource nested object has only one output only field - serviceAccountId.
 resource "google_bigquery_connection" "connection" {
    connection_id = "CONNECTION_ID"
    project = "PROJECT_ID"
    location = "REGION"
    cloud_resource {}
}        

Grant permissions to the connection's service account

To grant the connection's service account an appropriate role to access the Vertex AI service, follow these steps:

1. Go to the IAM & Admin page.

   Go to IAM & Admin

2. Click person_add Grant Access.

3. In the New principals field, enter the service account ID that you copied earlier.

4. In the Select a role field, choose Vertex AI, and then select Vertex AI User role.

5. Click Save.

Create the remote model

Create a remote model that represents a hosted Vertex AI large language model (LLM):

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

   Go to BigQuery

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

   CREATE OR REPLACE MODEL `bqml_tutorial.llm_model`
     REMOTE WITH CONNECTION `LOCATION.CONNECTION_ID`
     OPTIONS (ENDPOINT = 'text-bison');
   

   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 model llm_model appears in the bqml_tutorial dataset in the Explorer pane. Because the query uses a CREATE MODEL statement to create a model, there are no query results.

Perform keyword extraction

Perform keyword extraction on IMDB movie reviews by using the remote model and the ML.GENERATE_TEXT function:

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

   Go to BigQuery

2. In the query editor, enter the following statement to perform keyword extraction on five movie reviews:

   SELECT
     ml_generate_text_result['predictions'][0]['content'] AS generated_text,
     ml_generate_text_result['predictions'][0]['safetyAttributes']
       AS safety_attributes,
     * EXCEPT (ml_generate_text_result)
   FROM
     ML.GENERATE_TEXT(
       MODEL `bqml_tutorial.llm_model`,
       (
         SELECT
           CONCAT('Extract the key words from the text below: ', review) AS prompt,
           *
         FROM
           `bigquery-public-data.imdb.reviews`
         LIMIT 5
       ),
       STRUCT(
         0.2 AS temperature,
         100 AS max_output_tokens));
   

   The output is similar to the following:

   

   The results include the following columns:

   • generated_text: the generated text.
   • safety_attributes: the safety attributes, along with information about whether the content is blocked due to one of the blocking categories. For more information about the safety attributes, see Vertex PaLM API.
   • ml_generate_text_status: the API response status for the corresponding row. If the operation was successful, this value is empty.
   • prompt: the prompt that is used for the sentiment analysis.
   • All of the columns from the bigquery-public-data.imdb.reviews table.

3. Optional: Instead of manually parsing the JSON returned by the function, as you did in the previous step, use the flatten_json_output argument to return the generated text and the safety attributes in separate columns.

   In the query editor, run the following statement:

   SELECT
     *
   FROM
     ML.GENERATE_TEXT(
       MODEL `bqml_tutorial.llm_model`,
       (
         SELECT
           CONCAT('Extract the key words from the text below: ', review) AS prompt,
           *
         FROM
           `bigquery-public-data.imdb.reviews`
         LIMIT 5
       ),
       STRUCT(
         0.2 AS temperature,
         100 AS max_output_tokens,
         TRUE AS flatten_json_output));
   

   The output is similar to the following:

   

   The results include the following columns:

   • ml_generate_text_llm_result: the generated text.
   • ml_generate_text_rai_result: the safety attributes, along with information about whether the content is blocked due to one of the blocking categories. For more information about the safety attributes, see Vertex PaLM API.
   • ml_generate_text_status: the API response status for the corresponding row. If the operation was successful, this value is empty.
   • prompt: the prompt that is used for the keyword extraction.
   • All of the columns from the bigquery-public-data.imdb.reviews table.

Perform sentiment analysis

Perform sentiment analysis on IMDB movie reviews by using the remote model and the ML.GENERATE_TEXT function:

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

   Go to BigQuery

2. In the query editor, run the following statement to perform sentiment analysis on five movie reviews:

   SELECT
     ml_generate_text_result['predictions'][0]['content'] AS generated_text,
     ml_generate_text_result['predictions'][0]['safetyAttributes']
       AS safety_attributes,
     * EXCEPT (ml_generate_text_result)
   FROM
     ML.GENERATE_TEXT(
       MODEL `bqml_tutorial.llm_model`,
       (
         SELECT
           CONCAT(
             'perform sentiment analysis on the following text, return one the following categories: positive, negative: ',
             review) AS prompt,
           *
         FROM
           `bigquery-public-data.imdb.reviews`
         LIMIT 5
       ),
       STRUCT(
         0.2 AS temperature,
         100 AS max_output_tokens));
   

   The output is similar to the following:

   

   The results include the same columns documented for Perform keyword extraction.

Clean up

1. In the Google 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.