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

    This tutorial shows you how to create a remote model that's based on the Gemma model , and then how to use that model with the ML.GENERATE_TEXT function to extract keywords and perform sentiment analysis on movie reviews from the bigquery-public-data.imdb.reviews public table.

    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 ).
    • Deploy and undeploy models in Vertex AI: Vertex AI Administrator ( roles/aiplatform.admin ).

    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 permissionssection:

    Required permissions

    • Create a dataset: bigquery.datasets.create
    • Create, delegate, and use a connection: bigquery.connections.*
    • Set the default connection: bigquery.config.*
    • Set service account permissions: resourcemanager.projects.getIamPolicy and resourcemanager.projects.setIamPolicy
    • Deploy and undeploy a Vertex AI model:
      • aiplatform.endpoints.deploy
      • aiplatform.endpoints.undeploy
    • Create a model and run inference:
      • bigquery.jobs.create
      • bigquery.models.create
      • bigquery.models.getData
      • bigquery.models.updateData
      • bigquery.models.updateMetadata

    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 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 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.

    Open models that you deploy to Vertex AI are charged per machine-hour. This means billing starts as soon as the endpoint is fully set up, and continues until you un-deploy it. 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.

      Roles required to select or create a project

      • Select a project : Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
      • Create a project : To create a project, you need the Project Creator role ( roles/resourcemanager.projectCreator ), which contains the resourcemanager.projects.create permission. Learn how to grant roles .

      Go to project selector

    2. Verify that billing is enabled for your Google Cloud project .

    3. Enable the BigQuery, BigQuery Connection, and Vertex AI APIs.

      Roles required to enable APIs

      To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

      Enable the APIs

    Create a dataset

    Create a BigQuery dataset to store your ML model.

    Console

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to the BigQuery page

    2. In the Explorerpane, click your project name.

    3. Click View actions > Create dataset

    4. On the Create datasetpage, do the following:

      • For Dataset ID, enter bqml_tutorial .

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

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

    bq

    To create a new dataset, use the bq mk command with the --location flag. For a full list of possible parameters, see the bq mk --dataset command reference.

    1. Create a dataset named bqml_tutorial with the data location set to US and a description of BigQuery ML tutorial dataset :

      bq --location=US mk -d \
 --description "BigQuery ML tutorial dataset." \
 bqml_tutorial

      Instead of using the --dataset flag, the command uses the -d shortcut. If you omit -d and --dataset , the command defaults to creating a dataset.

    2. Confirm that the dataset was created:

      bq  
ls

    API

    Call the datasets.insert method with a defined dataset resource .

     { 
  
 "datasetReference" 
 : 
  
 { 
  
 "datasetId" 
 : 
  
 "bqml_tutorial" 
  
 } 
 }

    BigQuery DataFrames

    Before trying this sample, follow the BigQuery DataFrames setup instructions in the BigQuery quickstart using BigQuery DataFrames . For more information, see the BigQuery DataFrames reference documentation .

    To authenticate to BigQuery, set up Application Default Credentials. For more information, see Set up ADC for a local development environment .

      import 
  
 google.cloud.bigquery 
 bqclient 
 = 
 google 
 . 
 cloud 
 . 
  bigquery 
 
 . 
  Client 
 
 () 
 bqclient 
 . 
  create_dataset 
 
 ( 
 "bqml_tutorial" 
 , 
 exists_ok 
 = 
 True 
 )

    Create the remote model

    Create a remote model that represents a hosted Vertex AI model:

    1. In the Google Cloud console, go to the BigQuerypage.

      Go to BigQuery

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

     CREATE 
  
 OR 
  
 REPLACE 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 gemma_model 
 ` 
  
 REMOTE 
  
 WITH 
  
 CONNECTION 
  
 DEFAULT 
  
 OPTIONS 
  
 ( 
  
 MODEL_GARDEN_MODEL_NAME 
  
 = 
  
 'publishers/google/models/gemma3@gemma-3-270m-it' 
 , 
  
 MACHINE_TYPE 
  
 = 
  
 'g2-standard-12' 
  
 );

    The query takes up to 20 minutes to complete, after which the gemma_model model appears in the bqml_tutorial dataset in the Explorerpane. 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 BigQuerypage.

      Go to BigQuery

    2. In the query editor, enter the following statement to perform keyword extraction on 10 movie reviews:

       -- This function takes your instruction and wraps it with chat template for 
 -- better output quality. 
 -- This is usually the recommended way when using Gemma instruction-tuned models. 
 CREATE 
  
 TEMP 
  
 FUNCTION 
  
 FormatPromptWithChatTemplate 
 ( 
 user_instruction 
  
 STRING 
 ) 
  
 AS 
  
 ( 
  
 CONCAT 
 ( 
  
 '<start_of_turn>user\n' 
 , 
  
 user_instruction 
 , 
  
 '<end_of_turn>\n<start_of_turn>model\n' 
  
 ) 
 ); 
 SELECT 
  
 * 
 FROM 
  
 ML 
 . 
 GENERATE_TEXT 
 ( 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 gemma_model 
 ` 
 , 
  
 ( 
  
 SELECT 
  
 FormatPromptWithChatTemplate 
 ( 
  
 'Extract the key words from the movie review below: ' 
  
 || 
  
 review 
 ) 
  
 AS 
  
 prompt 
 , 
  
 * 
  
 FROM 
  
 ` 
 bigquery 
 - 
 public 
 - 
 data 
 . 
 imdb 
 . 
 reviews 
 ` 
  
 LIMIT 
  
 10 
  
 ), 
  
 STRUCT 
 ( 
  
 0 
 . 
 2 
  
 AS 
  
 temperature 
 , 
  
 100 
  
 AS 
  
 max_output_tokens 
 , 
  
 TRUE 
  
 AS 
  
 flatten_json_output 
 ));

      For more information about using chat templates with Gemma, see Gemma formatting and system instructions .

      The output is similar to the following, with non-generated columns omitted for clarity:

       +----------------------------------------------+-------------------------+-----------------------------+-----+
| ml_generate_text_llm_result                  | ml_generate_text_status | prompt                      | ... |
+----------------------------------------------+-------------------------------------------------------+-----+
| Here are some key words from the             |                         | <start_of_turn>user         |     |
| movie review: * * 
*Romance:**                 |                         | Extract the key words from  |     |
| "romantic tryst," "elope" * * 
*Comedy:**      |                         | the movie review below:     |     |
| "Contrived Comedy" * * 
*Burglary:**           |                         | Linda Arvidson (as Jennie)  |     |
| "burglar," "rob," "booty" * * 
*Chase:**       |                         | and Harry Solter (as Frank) |     |
| "chases," "escape" * * 
*Director:** "D.W.     |                         | are enjoying a romantic     |     |
| Griffith" * * 
*Actors:** "Linda Arvidson,"... |                         | tryst, when in walks her... |     |
+----------------------------------------------+-------------------------+-----------------------------+-----+
| Here are some key words from the             |                         | <start_of_turn>user         |     |
| movie review: * * 
*Elderbush Gilch:** The     |                         | Extract the key words from  |     |
| name of the movie being reviewed. *          |                         | the movie review below:     |     | 
 | * 
*Disappointment:** The reviewer's           |                         | This is the second addition |     |
| overall feeling about the film. *            |                         | to Frank Baum's personally  |     | 
 | * 
*Dim-witted:** Describes the story          |                         | produced trilogy of Oz      |     |
| line negatively. * * 
*Moronic, sadistic,...   |                         | films. It's essentially ... |     |
+----------------------------------------------+-------------------------+-----------------------------+-----+

      The results include the following columns:

      • ml_generate_text_llm_result : the generated text.
      • 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.

    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 BigQuerypage.

      Go to BigQuery

    2. In the query editor, run the following statement to perform sentiment analysis on 10 movie reviews:

       -- This function takes your instruction and wraps it with chat template for 
 -- better output quality. 
 -- This is usually the recommended way when using Gemma instruction-tuned models. 
 CREATE 
  
 TEMP 
  
 FUNCTION 
  
 FormatPromptWithChatTemplate 
 ( 
 user_instruction 
  
 STRING 
 ) 
  
 AS 
  
 ( 
  
 CONCAT 
 ( 
  
 '<start_of_turn>user\n' 
 , 
  
 user_instruction 
 , 
  
 '<end_of_turn>\n<start_of_turn>model\n' 
  
 ) 
 ); 
 SELECT 
  
 * 
 FROM 
  
 ML 
 . 
 GENERATE_TEXT 
 ( 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 gemma_model 
 ` 
 , 
  
 ( 
  
 SELECT 
  
 FormatPromptWithChatTemplate 
 ( 
  
 'Analyze the sentiment of the following movie review and classify it as either POSITIVE or NEGATIVE. \nMovie Review: ' 
  
 || 
  
 review 
 ) 
  
 AS 
  
 prompt 
 , 
  
 * 
  
 FROM 
  
 ` 
 bigquery 
 - 
 public 
 - 
 data 
 . 
 imdb 
 . 
 reviews 
 ` 
  
 LIMIT 
  
 10 
  
 ), 
  
 STRUCT 
 ( 
  
 0 
 . 
 2 
  
 AS 
  
 temperature 
 , 
  
 128 
  
 AS 
  
 max_output_tokens 
 , 
  
 TRUE 
  
 AS 
  
 flatten_json_output 
 ));

      For more information about using chat templates with Gemma, see Gemma formatting and system instructions .

      The output is similar to the following, with non-generated columns omitted for clarity:

       +-----------------------------+-------------------------+-----------------------------+-----+
| ml_generate_text_llm_result | ml_generate_text_status | prompt                      | ... |
+-----------------------------+-------------------------------------------------------+-----+
| **NEGATIVE**                |                         | <start_of_turn>user         |     |
|                             |                         | Analyze the sentiment of    |     |
|                             |                         | movie review and classify   |     |
|                             |                         | it as either POSITIVE or    |     |
|                             |                         | NEGATIVE. Movie Review:     |     |
|                             |                         | Although Charlie Chaplin    |     |
|                             |                         | made some great short       |     |
|                             |                         | comedies in the late...     |     |
+-----------------------------+-------------------------+-----------------------------+-----+
| **NEGATIVE**                |                         | <start_of_turn>user         |     |
|                             |                         | Analyze the sentiment of    |     |
|                             |                         | movie review and classify   |     |
|                             |                         | it as either POSITIVE or    |     |
|                             |                         | NEGATIVE. Movie Review:     |     |
|                             |                         | Opulent sets and sumptuous  |     |
|                             |                         | costumes well photographed  |     |
|                             |                         | by Theodor Sparkuhl, and... |     |
+-----------------------------+-------------------------+-----------------------------+-----+

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

    Undeploy model

    If you choose not to delete your project as recommended , you must undeploy the Gemma model in Vertex AI to avoid continued billing for it. BigQuery automatically undeploys the model after a specified period of idleness (6.5 hours by default). Alternatively, you can immediately undeploy the model by using the ALTER MODEL statement , as shown in the following example:

     ALTER 
  
 MODEL 
  
 ` 
 bqml_tutorial 
 . 
 gemma_model 
 ` 
 SET 
  
 OPTIONS 
  
 ( 
 deploy_model 
  
 = 
  
 false 
 );

    For more information, see Automatic or immediate open model undeployment .

    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.
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