Tune a model using your data

This document shows you how to create a BigQuery ML remote model that references a Vertex AI model, and then configure the model to perform supervised tuning. The Vertex AI model must be one of the following:

• gemini-2.5-pro
• gemini-2.5-flash-lite
• gemini-2.0-flash-001
• gemini-2.0-flash-lite-001

After you create the remote model, you use the ML.EVALUATE function to evaluate the model and confirm that the model's performance suits your use case. You can then use the model in conjunction with the ML.GENERATE_TEXT function to analyze text in a BigQuery table.

For more information, see Vertex AI Gemini API model supervised tuning.

Required roles

To create and evaluate a tuned model, you need the following Identity and Access Management (IAM) roles:

• Create and use BigQuery datasets, tables, and models: BigQuery Data Editor (roles/bigquery.dataEditor) on your project.

• Create, delegate, and use BigQuery connections: BigQuery Connections Admin (roles/bigquery.connectionsAdmin) on your project.

  If you don't have a default connection configured, you can create and set one as part of running the CREATE MODEL statement. To do so, you must have BigQuery Admin (roles/bigquery.admin) on your project. For more information, see Configure the default connection.

• Grant permissions to the connection's service account: Project IAM Admin (roles/resourcemanager.projectIamAdmin) on the project that contains the Vertex AI endpoint. This is the current project for remote models that you create by specifying the model name as an endpoint. This is the project identified in the URL for remote models that you create by specifying a URL as an endpoint.

• Create BigQuery jobs: BigQuery Job User (roles/bigquery.jobUser) on your project.

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:

Required permissions

• Create a dataset: bigquery.datasets.create
• Create, delegate, and use a connection: bigquery.connections.*
• Set service account permissions: resourcemanager.projects.getIamPolicy and resourcemanager.projects.setIamPolicy
• 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.

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 (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.
   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

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

3. Enable the BigQuery, BigQuery Connection,Vertex AI, and Compute Engine 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 contain your resources:

Console

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

   Go to BigQuery

2. In the left pane, click Explorer:

   

   If you don't see the left pane, click Expand left pane to open the pane.

3. In the Explorer pane, click your project name.

4. Click View actions > Create dataset.

5. On the Create dataset page, do the following:

   1. For Dataset ID, type a name for the dataset.

   2. For Location type, select Region or Multi-region.

      • If you selected Region, then select a location from the Region list.
      • If you selected Multi-region, then select US or Europe from the Multi-region list.

   3. Click Create dataset.

bq

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

Create a connection

You can skip this step if you either have a default connection configured, or you have the BigQuery Admin role.

Create a Cloud resource connection for the remote model to use, and get the connection's service account. Create the connection in the same location as the dataset that you created in the previous step.

Select one of the following options:

Console

1. Go to the BigQuery page.

   Go to BigQuery

2. In the Explorer pane, click Add data:

   

   The Add data dialog opens.

3. In the Filter By pane, in the Data Source Type section, select Business Applications.

   Alternatively, in the Search for data sources field, you can enter Vertex AI.

4. In the Featured data sources section, click Vertex AI.

5. Click the Vertex AI Models: BigQuery Federation solution card.

6. In the Connection type list, select Vertex AI remote models, remote functions, BigLake and Spanner (Cloud Resource).

7. In the Connection ID field, enter a name for your connection.

8. Click Create connection.

9. Click Go to connection.

10. In the Connection info pane, copy the service account ID for use in a later step.

bq

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": "[e-mail]"}
   

Terraform

Use the google_bigquery_connection resource.

Note: To create BigQuery objects using Terraform, you must enable the Cloud Resource Manager API.

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.

Note: Terraform samples typically assume that the required APIs are enabled in your Google Cloud project.

Give the connection's service account access

Grant the connection's service account the Vertex AI Service Agent role.

If you plan to specify the endpoint as a URL when you create the remote model, for example endpoint = 'https://us-central1-aiplatform.googleapis.com/v1/projects/myproject/locations/us-central1/publishers/google/models/gemini-2.0-flash', grant this role in the same project you specify in the URL.

If you plan to specify the endpoint by using the model name when you create the remote model, for example endpoint = 'gemini-2.0-flash', grant this role in the same project where you plan to create the remote model.

Granting the role in a different project results in the error [e-mail] does not have the permission to access resource.

To grant the role, follow these steps:

Console

1. Go to the IAM & Admin page.

   Go to IAM & Admin

2. Click Grant Access.

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

4. Click Select a role.

5. In Filter, type Vertex AI Service Agent and then select that role.

6. Click Save.

gcloud

Use the gcloud projects add-iam-policy-binding command:

gcloud projects add-iam-policy-binding 'PROJECT_NUMBER' --member='serviceAccount:MEMBER' --role='roles/aiplatform.serviceAgent' --condition=None

Replace the following:

• PROJECT_NUMBER: your project number.
• MEMBER: the service account ID that you copied earlier.

The service account associated with your connection is an instance of the BigQuery Connection Delegation Service Agent, so it is OK to assign a service agent role to it.

Create a model with supervised tuning

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

   Go to BigQuery

2. In the query editor, run the following query to create a remote model:

   CREATE OR REPLACE MODEL
   `PROJECT_ID.DATASET_ID.MODEL_NAME`
   REMOTE WITH CONNECTION {DEFAULT | `PROJECT_ID.REGION.CONNECTION_ID`}
   OPTIONS (
     ENDPOINT = 'ENDPOINT',
     MAX_ITERATIONS = MAX_ITERATIONS,
     LEARNING_RATE_MULTIPLIER = LEARNING_RATE_MULTIPLIER,
     DATA_SPLIT_METHOD = 'DATA_SPLIT_METHOD',
     DATA_SPLIT_EVAL_FRACTION = DATA_SPLIT_EVAL_FRACTION,
     DATA_SPLIT_COL = 'DATA_SPLIT_COL',
     EVALUATION_TASK = 'EVALUATION_TASK',
     PROMPT_COL = 'INPUT_PROMPT_COL',
     INPUT_LABEL_COLS = INPUT_LABEL_COLS)
   AS SELECT PROMPT_COLUMN, LABEL_COLUMN
   FROM `TABLE_PROJECT_ID.TABLE_DATASET.TABLE_NAME`;

   Replace the following:

   • PROJECT_ID: the project ID of the project in which to create the model.
   • DATASET_ID: the ID of the dataset to contain the model. This dataset must be in a supported Vertex AI region.
   • MODEL_NAME: the name of the model.
   • REGION: the region used by the connection.
   • CONNECTION_ID: the ID of your BigQuery connection. This connection must be in the same location as the dataset that you are using.

     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.

   • ENDPOINT: a STRING value that specifies the name of the model to use.
   • MAX_ITERATIONS: an INT64 value that specifies the number of steps to run for supervised tuning. The MAX_ITERATIONS value must be between 1 and ∞.

     Gemini models train using epochs rather than steps, so BigQuery ML converts the MAX_ITERATIONS value to epochs. The default value for MAX_ITERATIONS is the number of rows in the input data, which is equivalent to one epoch. To use multiple epochs, specify a multiple of the number of rows in your training data. For example, if you have 100 rows of input data and you want to use two epochs, specify 200 for the argument value. If you provide a value that isn't a multiple of the number of rows in the input data, BigQuery ML rounds up to the nearest epoch. For example, if you have 100 rows of input data and you specify 101 for the MAX_ITERATIONS value, training is performed with two epochs.

     For more information about the parameters used to tune Gemini models, see Create a tuning job.

   • DATA_SPLIT_METHOD: a STRING value that specifies the method used to split input data into training and evaluation sets. The valid options are the following:
     • AUTO_SPLIT: BigQuery ML automatically splits the data. The way in which the data is split varies depending on the number of rows in the input table. This is the default value.
     • RANDOM: data is randomized before being split into sets. To customize the data split, you can use this option with the DATA_SPLIT_EVAL_FRACTION option.
     • CUSTOM: data is split using the column provided in the DATA_SPLIT_COL option. The DATA_SPLIT_COL value must be the name of a column of type BOOL. Rows with a value of TRUE or NULL are used as evaluation data, and rows with a value of FALSE are used as training data.
     • SEQ: split data using the column provided in the DATA_SPLIT_COL option. The DATA_SPLIT_COL value must be the name of a column of one of the following types:
       • NUMERIC
       • BIGNUMERIC
       • STRING
       • TIMESTAMP

       The data is sorted smallest to largest based on the specified column.

       The first n rows are used as evaluation data, where n is the value specified for DATA_SPLIT_EVAL_FRACTION. The remaining rows are used as training data.

     • NO_SPLIT: no data split; all input data is used as training data.

     For more information about these data split options, see DATA_SPLIT_METHOD.

   • DATA_SPLIT_EVAL_FRACTION: a FLOAT64 value that specifies the fraction of the data to use as evaluation data when performing supervised tuning. Must be a value in the range [0, 1.0]. The default value is 0.2.

     Use this option when you specify RANDOM or SEQ as the value for the DATA_SPLIT_METHOD option. To customize the data split, you can use the DATA_SPLIT_METHOD option with the DATA_SPLIT_EVAL_FRACTION option.

   • DATA_SPLIT_COL: a STRING value that specifies the name of the column to use to sort input data into the training or evaluation set. Use when you are specifying CUSTOM or SEQ as the value for the DATA_SPLIT_METHOD option.
   • EVALUATION_TASK: a STRING value that specifies the type of task that you want to tune the model to perform. The valid options are:
     • TEXT_GENERATION
     • CLASSIFICATION
     • SUMMARIZATION
     • QUESTION_ANSWERING
     • UNSPECIFIED

     The default value is UNSPECIFIED.

   • INPUT_PROMPT_COL: a STRING value that contains the name of the prompt column in the training data table to use when performing supervised tuning. The default value is prompt.
   • INPUT_LABEL_COLS: an ARRAY<<STRING> value that contains the name of the label column in the training data table to use in supervised tuning. You can only specify one element in the array. The default value is an empty array. This causes label to be the default value of the LABEL_COLUMN argument.
   • PROMPT_COLUMN: the column in the training data table that contains the prompt for evaluating the content in the LABEL_COLUMN column. This column must be of STRING type or be cast to STRING. If you specify a value for the INPUT_PROMPT_COL option, you must specify the same value for PROMPT_COLUMN. Otherwise this value must be prompt. If your table does not have a prompt column, use an alias to specify an existing table column. For example, AS SELECT hint AS prompt, label FROM mydataset.mytable.
   • LABEL_COLUMN: the column in the training data table that contains the examples to train the model with. This column must be of STRING type or be cast to STRING. If you specify a value for the INPUT_LABEL_COLS option, you must specify the same value for LABEL_COLUMN. Otherwise this value must be label. If your table does not have a label column, use an alias to specify an existing table column. For example, AS SELECT prompt, feature AS label FROM mydataset.mytable.
   • TABLE_PROJECT_ID: the project ID of the project that contains the training data table.
   • TABLE_DATASET: the name of the dataset that contains the training data table.
   • TABLE_NAME: the name of the table that contains the data to use to train the model.

Evaluate the tuned model

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

   Go to BigQuery

2. In the query editor, run the following query to evaluate the tuned model:

   SELECT
   *
   FROM
   ML.EVALUATE(
     MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
     TABLE `TABLE_PROJECT_ID.TABLE_DATASET.TABLE_NAME`,
     STRUCT('TASK_TYPE' AS task_type, TOKENS AS max_output_tokens,
       TEMPERATURE AS temperature, TOP_K AS top_k,
       TOP_P AS top_p));

   Replace the following:

   • PROJECT_ID: the project ID of the project that contains the model.
   • DATASET_ID: the ID of the dataset that contains the model.
   • MODEL_NAME: the name of the model.
   • TABLE_PROJECT_ID: the project ID of the project that contains the evaluation data table.
   • TABLE_DATASET: the name of the dataset that contains the evaluation data table.
   • TABLE_NAME: the name of the table that contains the evaluation data.

     The table must have a column whose name matches the prompt column name that is provided during model training. You can provide this value by using the prompt_col option during model training. If prompt_col is unspecified, the column named prompt in the training data is used. An error is returned if there is no column named prompt.

     The table must have a column whose name matches the label column name that is provided during model training. You can provide this value by using the input_label_cols option during model training. If input_label_cols is unspecified, the column named label in the training data is used. An error is returned if there is no column named label.

   • TASK_TYPE: a STRING value that specifies the type of task that you want to evaluate the model for. The valid options are:
     • TEXT_GENERATION
     • CLASSIFICATION
     • SUMMARIZATION
     • QUESTION_ANSWERING
     • UNSPECIFIED
   • TOKENS: an INT64 value that sets the maximum number of tokens that can be generated in the response. This value must be in the range [1,1024]. Specify a lower value for shorter responses and a higher value for longer responses. The default is 128.
   • TEMPERATURE: a FLOAT64 value in the range [0.0,1.0] that controls the degree of randomness in token selection. The default is 0.

     Lower values for temperature are good for prompts that require a more deterministic and less open-ended or creative response, while higher values for temperature can lead to more diverse or creative results. A value of 0 for temperature is deterministic, meaning that the highest probability response is always selected.

   • TOP_K: an INT64 value in the range [1,40] that determines the initial pool of tokens the model considers for selection. Specify a lower value for less random responses and a higher value for more random responses. The default is 40.
   • TOP_P: a FLOAT64 value in the range [0.0,1.0] helps determine which tokens from the pool determined by TOP_K are selected. Specify a lower value for less random responses and a higher value for more random responses. The default is 0.95.

Generate text

Generate text with the ML.GENERATE_TEXT function:

Prompt column

Generate text by using a table column to provide the prompt.

SELECT *
FROM ML.GENERATE_TEXT(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  TABLE PROJECT_ID.DATASET_ID.TABLE_NAME,
  STRUCT(TOKENS AS max_output_tokens, TEMPERATURE AS temperature,
  TOP_P AS top_p, FLATTEN_JSON AS flatten_json_output,
  STOP_SEQUENCES AS stop_sequences)
);

Replace the following:

• PROJECT_ID: your project ID.
• DATASET_ID: the ID of the dataset that contains the model.
• MODEL_NAME: the name of the model.
• TABLE_NAME: the name of the table that contains the prompt. This table must have a column whose name matches the name of the feature column in the tuned model. The feature column name in the model can be set by using the PROMPT_COL option when creating the model. Otherwise, the feature column name in the model is prompt by default, or you can use an alias to use a differently named column.
• TOKENS: an INT64 value that sets the maximum number of tokens that can be generated in the response. This value must be in the range [1,8192]. Specify a lower value for shorter responses and a higher value for longer responses. The default is 128.
• TEMPERATURE: a FLOAT64 value in the range [0.0,1.0] that controls the degree of randomness in token selection. The default is 0.

  Lower values for temperature are good for prompts that require a more deterministic and less open-ended or creative response, while higher values for temperature can lead to more diverse or creative results. A value of 0 for temperature is deterministic, meaning that the highest probability response is always selected.

• TOP_P: a FLOAT64 value in the range [0.0,1.0] helps determine the probability of the tokens selected. Specify a lower value for less random responses and a higher value for more random responses. The default is 0.95.
• FLATTEN_JSON: a BOOL value that determines whether to return the generated text and the safety attributes in separate columns. The default is FALSE.
• STOP_SEQUENCES: an ARRAY<STRING> value that removes the specified strings if they are included in responses from the model. Strings are matched exactly, including capitalization. The default is an empty array.
• GROUND_WITH_GOOGLE_SEARCH: a BOOL value that determines whether the Vertex AI model uses Grounding with Google Search when generating responses. Grounding lets the model use additional information from the internet when generating a response, in order to make model responses more specific and factual. When both flatten_json_output and this field are set to True, an additional ml_generate_text_grounding_result column is included in the results, providing the sources that the model used to gather additional information. The default is FALSE.
• SAFETY_SETTINGS: an ARRAY<STRUCT<STRING AS category, STRING AS threshold>> value that configures content safety thresholds to filter responses. The first element in the struct specifies a harm category, and the second element in the struct specifies a corresponding blocking threshold. The model filters out content that violate these settings. You can only specify each category once. For example, you can't specify both STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category, 'BLOCK_MEDIUM_AND_ABOVE' AS threshold) and STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category, 'BLOCK_ONLY_HIGH' AS threshold). If there is no safety setting for a given category, the BLOCK_MEDIUM_AND_ABOVE safety setting is used.

  Supported categories are as follows:

  • HARM_CATEGORY_HATE_SPEECH
  • HARM_CATEGORY_DANGEROUS_CONTENT
  • HARM_CATEGORY_HARASSMENT
  • HARM_CATEGORY_SEXUALLY_EXPLICIT

  Supported thresholds are as follows:

  • BLOCK_NONE (Restricted)
  • BLOCK_LOW_AND_ABOVE
  • BLOCK_MEDIUM_AND_ABOVE (Default)
  • BLOCK_ONLY_HIGH
  • HARM_BLOCK_THRESHOLD_UNSPECIFIED

  For more information, refer to the definition of safety category and blocking threshold.

• REQUEST_TYPE: a STRING value that specifies the type of inference request to send to the Gemini model. The request type determines what quota the request uses. Valid values are as follows:
  • DEDICATED: The ML.GENERATE_TEXT function only uses Provisioned Throughput quota. The ML.GENERATE_TEXT function returns the error Provisioned throughput is not purchased or is not active if Provisioned Throughput quota isn't available.
  • SHARED: The ML.GENERATE_TEXT function only uses dynamic shared quota (DSQ), even if you have purchased Provisioned Throughput quota.
  • UNSPECIFIED: The ML.GENERATE_TEXT function uses quota as follows:
    • If you haven't purchased Provisioned Throughput quota, the ML.GENERATE_TEXT function uses DSQ quota.
    • If you have purchased Provisioned Throughput quota, the ML.GENERATE_TEXT function uses the Provisioned Throughput quota first. If requests exceed the Provisioned Throughput quota, the overflow traffic uses DSQ quota.

  The default value is UNSPECIFIED.

  For more information, see Use Vertex AI Provisioned Throughput.

  The following example shows a request with these characteristics:

  • Uses the prompt column of the prompts table for the prompt.
  • Returns a short and moderately probable response.
  • Returns the generated text and the safety attributes in separate columns.

  SELECT *
  FROM
    ML.GENERATE_TEXT(
      MODEL `mydataset.mymodel`,
      TABLE mydataset.prompts,
      STRUCT(
        0.4 AS temperature, 100 AS max_output_tokens, 0.5 AS top_p,
        TRUE AS flatten_json_output));

Prompt query

Generate text by using a query to provide the prompt.

SELECT *
FROM ML.GENERATE_TEXT(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  (PROMPT_QUERY),
  STRUCT(TOKENS AS max_output_tokens, TEMPERATURE AS temperature,
  TOP_P AS top_p, FLATTEN_JSON AS flatten_json_output,
  STOP_SEQUENCES AS stop_sequences)
);

Replace the following:

• PROJECT_ID: your project ID.
• DATASET_ID: the ID of the dataset that contains the model.
• MODEL_NAME: the name of the model.
• PROMPT_QUERY: a query that provides the prompt data.
• TOKENS: an INT64 value that sets the maximum number of tokens that can be generated in the response. This value must be in the range [1,8192]. Specify a lower value for shorter responses and a higher value for longer responses. The default is 128.
• TEMPERATURE: a FLOAT64 value in the range [0.0,1.0] that controls the degree of randomness in token selection. The default is 0.

  Lower values for temperature are good for prompts that require a more deterministic and less open-ended or creative response, while higher values for temperature can lead to more diverse or creative results. A value of 0 for temperature is deterministic, meaning that the highest probability response is always selected.

• TOP_P: a FLOAT64 value in the range [0.0,1.0] helps determine the probability of the tokens selected. Specify a lower value for less random responses and a higher value for more random responses. The default is 0.95.
• FLATTEN_JSON: a BOOL value that determines whether to return the generated text and the safety attributes in separate columns. The default is FALSE.
• STOP_SEQUENCES: an ARRAY<STRING> value that removes the specified strings if they are included in responses from the model. Strings are matched exactly, including capitalization. The default is an empty array.
• GROUND_WITH_GOOGLE_SEARCH: a BOOL value that determines whether the Vertex AI model uses Grounding with Google Search when generating responses. Grounding lets the model use additional information from the internet when generating a response, in order to make model responses more specific and factual. When both flatten_json_output and this field are set to True, an additional ml_generate_text_grounding_result column is included in the results, providing the sources that the model used to gather additional information. The default is FALSE.
• SAFETY_SETTINGS: an ARRAY<STRUCT<STRING AS category, STRING AS threshold>> value that configures content safety thresholds to filter responses. The first element in the struct specifies a harm category, and the second element in the struct specifies a corresponding blocking threshold. The model filters out content that violate these settings. You can only specify each category once. For example, you can't specify both STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category, 'BLOCK_MEDIUM_AND_ABOVE' AS threshold) and STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category, 'BLOCK_ONLY_HIGH' AS threshold). If there is no safety setting for a given category, the BLOCK_MEDIUM_AND_ABOVE safety setting is used.

  Supported categories are as follows:

  • HARM_CATEGORY_HATE_SPEECH
  • HARM_CATEGORY_DANGEROUS_CONTENT
  • HARM_CATEGORY_HARASSMENT
  • HARM_CATEGORY_SEXUALLY_EXPLICIT

  Supported thresholds are as follows:

  • BLOCK_NONE (Restricted)
  • BLOCK_LOW_AND_ABOVE
  • BLOCK_MEDIUM_AND_ABOVE (Default)
  • BLOCK_ONLY_HIGH
  • HARM_BLOCK_THRESHOLD_UNSPECIFIED

  For more information, refer to the definition of safety category and blocking threshold.

• REQUEST_TYPE: a STRING value that specifies the type of inference request to send to the Gemini model. The request type determines what quota the request uses. Valid values are as follows:
  • DEDICATED: The ML.GENERATE_TEXT function only uses Provisioned Throughput quota. The ML.GENERATE_TEXT function returns the error Provisioned throughput is not purchased or is not active if Provisioned Throughput quota isn't available.
  • SHARED: The ML.GENERATE_TEXT function only uses dynamic shared quota (DSQ), even if you have purchased Provisioned Throughput quota.
  • UNSPECIFIED: The ML.GENERATE_TEXT function uses quota as follows:
    • If you haven't purchased Provisioned Throughput quota, the ML.GENERATE_TEXT function uses DSQ quota.
    • If you have purchased Provisioned Throughput quota, the ML.GENERATE_TEXT function uses the Provisioned Throughput quota first. If requests exceed the Provisioned Throughput quota, the overflow traffic uses DSQ quota.

  The default value is UNSPECIFIED.

  For more information, see Use Vertex AI Provisioned Throughput.

  Example 1

  The following example shows a request with these characteristics:

  • Prompts for a summary of the text in the body column of the articles table.
  • Returns a moderately long and more probable response.
  • Returns the generated text and the safety attributes in separate columns.

  SELECT *
  FROM
    ML.GENERATE_TEXT(
      MODEL `mydataset.mymodel`,
      (
        SELECT CONCAT('Summarize this text', body) AS prompt
        FROM mydataset.articles
      ),
      STRUCT(
        0.2 AS temperature, 650 AS max_output_tokens, 0.2 AS top_p,
        TRUE AS flatten_json_output));

  Example 2

  The following example shows a request with these characteristics:

  • Uses a query to create the prompt data by concatenating strings that provide prompt prefixes with table columns.
  • Returns a short and moderately probable response.
  • Doesn't return the generated text and the safety attributes in separate columns.

  SELECT *
  FROM
    ML.GENERATE_TEXT(
      MODEL `mydataset.mytuned_model`,
      (
        SELECT CONCAT(question, 'Text:', description, 'Category') AS prompt
        FROM mydataset.input_table
      ),
      STRUCT(
        0.4 AS temperature, 100 AS max_output_tokens, 0.5 AS top_p,
        FALSE AS flatten_json_output));