Generate text by using the ML.GENERATE_TEXT function

This document shows you how to create a BigQuery ML remote model that represents a hosted Vertex AI model. The hosted Vertex AI model can be a built-in Vertex AI text or multimodal model, or an Anthropic Claude model. Depending on the Vertex AI model that you choose, you can then use the ML.GENERATE_TEXT function to analyze unstructured data in object tables or text in standard tables.

Required permissions

• To create a connection, you need membership in the following Identity and Access Management (IAM) role:

  • roles/bigquery.connectionAdmin

• To grant permissions to the connection's service account, you need the following permission:

  • resourcemanager.projects.setIamPolicy

• To create the model using BigQuery ML, you need the following IAM permissions:

  • bigquery.jobs.create
  • bigquery.models.create
  • bigquery.models.getData
  • bigquery.models.updateData
  • bigquery.models.updateMetadata

• To run inference, you need the following permissions:

  • bigquery.tables.getData on the table
  • bigquery.models.getData on the model
  • bigquery.jobs.create

Before you begin

1. 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

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 connection

Create a Cloud resource connection and get the connection's service account.

Select one of the following options:

Console

1. Go to the BigQuery page.

   Go to BigQuery

2. To create a connection, click Add, and then click Connections to external data sources.

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

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

5. Click Create connection.

6. Click Go to connection.

7. 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 service account access

Grant the connection's service account the Vertex AI User 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/text-embedding-004', 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 = 'text-embedding-004', 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 Add.

   The Add principals dialog opens.

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

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

5. 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.user' --condition=None

Replace the following:

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

Enable the Vertex AI model

This step is only needed if you are using a Claude model.

1. In the Google Cloud console, go to the Vertex AI Model Garden page.

   Go to Model Garden

2. Search or browse for the Claude model that you want to use.

3. Click the model card.

4. On the model page, click Enable.

5. Fill out the requested enablement information, and then click Next.

6. In the Terms and conditions section, select the checkbox.

7. Click Agree to agree to the terms and conditions and enable the model.

Create a BigQuery ML remote model

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

   Go to BigQuery

2. Using the SQL editor, create a remote model:

   CREATE OR REPLACE MODEL
   `PROJECT_ID.DATASET_ID.MODEL_NAME`
   REMOTE WITH CONNECTION `PROJECT_ID.REGION.CONNECTION_ID`
   OPTIONS (ENDPOINT = 'ENDPOINT');

   Replace the following:

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

   • ENDPOINT: the name of the supported Vertex AI model to use.

     For some types of models, you can specify a particular version of the model. For information about supported model versions for different model types, see ENDPOINT.

   Important: The text-bison and text-unicorn models discontinue in April 2025. If you have remote models that use either of these models, migrate to a Gemini 1.5 model as soon as possible.
   Note: If you have remote models that use the gemini-1.0-pro-vision model, consider migrating to a Gemini 1.5 model for improved performance.

Generate text from text data by using a prompt from a table

Generate text by using the ML.GENERATE_TEXT function with a remote model, and using prompt data from a table column:

gemini-2.0-flash

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

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,
  GROUND_WITH_GOOGLE_SEARCH AS ground_with_google_search,
  SAFETY_SETTINGS AS safety_settings)
);

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 that's named prompt, 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 that controls the degree of randomness in token selection. The temperature value must be greater than 0.0 and less than or equal to 1.0.

  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.

• 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.

Example

The following example shows a request with these characteristics:

• Uses the prompt column of the prompts table for the prompt.
• Returns a longer response.
• Flattens the JSON response into separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.flash_2_model`,
    TABLE mydataset.prompts,
    STRUCT(
      0.4 AS temperature, 8192 AS max_output_tokens,
      TRUE AS flatten_json_output));

gemini-1.5-flash

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,
  GROUND_WITH_GOOGLE_SEARCH AS ground_with_google_search,
  SAFETY_SETTINGS AS safety_settings)
);

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 that's named prompt, 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 1.0.

  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.

Example

The following example shows a request with these characteristics:

• Uses the prompt column of the prompts table for the prompt.
• Returns a short response.
• Flattens the JSON response into separate columns.
• Retrieves and returns public web data for response grounding.
• Filters out unsafe responses by using two safety settings.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    TABLE mydataset.prompts,
    STRUCT(
      100 AS max_output_tokens, 0.5 AS top_p,
      TRUE AS flatten_json_output,
      TRUE AS ground_with_google_search,
      [STRUCT('HARM_CATEGORY_HATE_SPEECH' AS category,
        'BLOCK_LOW_AND_ABOVE' AS threshold),
      STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category,
        'BLOCK_MEDIUM_AND_ABOVE' AS threshold)] AS safety_settings));

gemini-1.5-pro

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,
  GROUND_WITH_GOOGLE_SEARCH AS ground_with_google_search,
  SAFETY_SETTINGS AS safety_settings)
);

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 that's named prompt, 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 1.0.

  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.

Example

The following example shows a request with these characteristics:

• Uses the prompt column of the prompts table for the prompt.
• Returns a short response.
• Flattens the JSON response into separate columns.
• Retrieves and returns public web data for response grounding.
• Filters out unsafe responses by using two safety settings.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    TABLE mydataset.prompts,
    STRUCT(
      100 AS max_output_tokens, 0.5 AS top_p,
      TRUE AS flatten_json_output,
      TRUE AS ground_with_google_search,
      [STRUCT('HARM_CATEGORY_HATE_SPEECH' AS category,
        'BLOCK_LOW_AND_ABOVE' AS threshold),
      STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category,
        'BLOCK_MEDIUM_AND_ABOVE' AS threshold)] AS safety_settings));

gemini-pro

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_K AS top_k, TOP_P AS top_p, FLATTEN_JSON AS flatten_json_output,
  STOP_SEQUENCES AS stop_sequences,
  GROUND_WITH_GOOGLE_SEARCH AS ground_with_google_search,
  SAFETY_SETTINGS AS safety_settings)
);

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 that's named prompt, 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_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 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.

Example

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.
• Flattens the JSON response into separate columns.
• Retrieves and returns public web data for response grounding.
• Filters out unsafe responses by using two safety settings.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    TABLE mydataset.prompts,
    STRUCT(
      0.4 AS temperature, 100 AS max_output_tokens, 0.5 AS top_p,
      40 AS top_k, TRUE AS flatten_json_output,
      TRUE AS ground_with_google_search,
      [STRUCT('HARM_CATEGORY_HATE_SPEECH' AS category,
        'BLOCK_LOW_AND_ABOVE' AS threshold),
      STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category,
        'BLOCK_MEDIUM_AND_ABOVE' AS threshold)] AS safety_settings));

Claude

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

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, TOP_K AS top_k,
  TOP_P AS top_p, FLATTEN_JSON AS flatten_json_output)
);

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 that's named prompt, 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 [1,4096]. Specify a lower value for shorter responses and a higher value for longer responses. The default is 128.
• 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. If you don't specify a value, the model determines an appropriate value.
• 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. If you don't specify a value, the model determines an appropriate value.
• FLATTEN_JSON: a BOOL value that determines whether to return the generated text and the safety attributes in separate columns. The default is FALSE.

Example

The following example shows a request with these characteristics:

• Uses the prompt column of the prompts table for the prompt.
• Returns the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    TABLE mydataset.prompts,
    STRUCT(TRUE AS flatten_json_output));

text-bison

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_K AS top_k, 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 that's named prompt, 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 [1,1024]. 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 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.

Example

The following example shows a request with these characteristics:

• Uses the prompt column of the prompts table for the prompt.
• Returns the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    TABLE mydataset.prompts,
    STRUCT(TRUE AS flatten_json_output));

text-bison32

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_K AS top_k, 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 that's named prompt, 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_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 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.

Example

The following example shows a request with these characteristics:

• Uses the prompt column of the prompts table for the prompt.
• Returns the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    TABLE mydataset.prompts,
    STRUCT(TRUE AS flatten_json_output));

text-unicorn

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_K AS top_k, 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 that's named prompt, 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 [1,1024]. 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 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.

Example

The following example shows a request with these characteristics:

• Uses the prompt column of the prompts table for the prompt.
• Returns the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    TABLE mydataset.prompts,
    STRUCT(TRUE AS flatten_json_output));

Generate text from text data by using a prompt from a query

Generate text by using the ML.GENERATE_TEXT function with a remote model, and using a query that provides the prompt data:

gemini-2.0-flash

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

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,
  GROUND_WITH_GOOGLE_SEARCH AS ground_with_google_search,
  SAFETY_SETTINGS AS safety_settings)
);

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 that controls the degree of randomness in token selection. The temperature value must be greater than 0.0 and less than or equal to 1.0.

  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.

• 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.

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 the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .05 AS TEMPERATURE,
      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 response.
• Doesn't return the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT(question, 'Text:', description, 'Category') AS prompt
      FROM mydataset.input_table
    ),
    STRUCT(
      100 AS max_output_tokens,
      .1 AS TEMPERATURE,
      FALSE AS flatten_json_output));

Example 3

The following example shows a request with these characteristics:

• Prompts for a summary of the text in the body column of the articles table.
• Flattens the JSON response into separate columns.
• Retrieves and returns public web data for response grounding.
• Filters out unsafe responses by using two safety settings.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .1 AS TEMPERATURE,
      TRUE AS flatten_json_output, TRUE AS ground_with_google_search,
      [STRUCT('HARM_CATEGORY_HATE_SPEECH' AS category,
        'BLOCK_LOW_AND_ABOVE' AS threshold),
      STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category,
        'BLOCK_MEDIUM_AND_ABOVE' AS threshold)] AS safety_settings));

gemini-1.5-flash

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,
  GROUND_WITH_GOOGLE_SEARCH AS ground_with_google_search,
  SAFETY_SETTINGS AS safety_settings)
);

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 1.0.

  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.

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 the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .05 AS TEMPERATURE,
      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 response.
• Doesn't return the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT(question, 'Text:', description, 'Category') AS prompt
      FROM mydataset.input_table
    ),
    STRUCT(
      100 AS max_output_tokens,
      .1 AS TEMPERATURE,
      FALSE AS flatten_json_output));

Example 3

The following example shows a request with these characteristics:

• Prompts for a summary of the text in the body column of the articles table.
• Flattens the JSON response into separate columns.
• Retrieves and returns public web data for response grounding.
• Filters out unsafe responses by using two safety settings.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .1 AS TEMPERATURE,
      TRUE AS flatten_json_output, TRUE AS ground_with_google_search,
      [STRUCT('HARM_CATEGORY_HATE_SPEECH' AS category,
        'BLOCK_LOW_AND_ABOVE' AS threshold),
      STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category,
        'BLOCK_MEDIUM_AND_ABOVE' AS threshold)] AS safety_settings));

gemini-1.5-pro

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,
  GROUND_WITH_GOOGLE_SEARCH AS ground_with_google_search,
  SAFETY_SETTINGS AS safety_settings)
);

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 1.0.

  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.

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 the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .05 AS TEMPERATURE,
      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 response.
• Doesn't return the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT(question, 'Text:', description, 'Category') AS prompt
      FROM mydataset.input_table
    ),
    STRUCT(
      100 AS max_output_tokens,
      .1 AS TEMPERATURE,
      FALSE AS flatten_json_output));

Example 3

The following example shows a request with these characteristics:

• Prompts for a summary of the text in the body column of the articles table.
• Flattens the JSON response into separate columns.
• Retrieves and returns public web data for response grounding.
• Filters out unsafe responses by using two safety settings.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .1 AS TEMPERATURE,
      TRUE AS flatten_json_output, TRUE AS ground_with_google_search,
      [STRUCT('HARM_CATEGORY_HATE_SPEECH' AS category,
        'BLOCK_LOW_AND_ABOVE' AS threshold),
      STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category,
        'BLOCK_MEDIUM_AND_ABOVE' AS threshold)] AS safety_settings));

gemini-pro

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

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_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 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.

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 the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .05 AS TEMPERATURE,
      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 response.
• Doesn't return the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT(question, 'Text:', description, 'Category') AS prompt
      FROM mydataset.input_table
    ),
    STRUCT(
      100 AS max_output_tokens,
      .1 AS TEMPERATURE,
      FALSE AS flatten_json_output));

Example 3

The following example shows a request with these characteristics:

• Prompts for a summary of the text in the body column of the articles table.
• Flattens the JSON response into separate columns.
• Retrieves and returns public web data for response grounding.
• Filters out unsafe responses by using two safety settings.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .1 AS TEMPERATURE,
      TRUE AS flatten_json_output, TRUE AS ground_with_google_search,
      [STRUCT('HARM_CATEGORY_HATE_SPEECH' AS category,
        'BLOCK_LOW_AND_ABOVE' AS threshold),
      STRUCT('HARM_CATEGORY_DANGEROUS_CONTENT' AS category,
        'BLOCK_MEDIUM_AND_ABOVE' AS threshold)] AS safety_settings));

Claude

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

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

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 [1,4096]. Specify a lower value for shorter responses and a higher value for longer responses. The default is 128.
• 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. If you don't specify a value, the model determines an appropriate value.
• 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. If you don't specify a value, the model determines an appropriate value.
• FLATTEN_JSON: a BOOL value that determines whether to return the generated text and the safety attributes in separate columns. The default is FALSE.

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 the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(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 response.
• Doesn't return the generated text and the safety attributes in separate columns.

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

text-bison

SELECT *
FROM ML.GENERATE_TEXT(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  (PROMPT_QUERY),
  STRUCT(TOKENS AS max_output_tokens, TEMPERATURE AS temperature,
  TOP_K AS top_k, 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 [1,1024]. 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 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.

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 the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .05 AS TEMPERATURE,
      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 response.
• Doesn't return the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT(question, 'Text:', description, 'Category') AS prompt
      FROM mydataset.input_table
    ),
    STRUCT(
      100 AS max_output_tokens,
      .1 AS TEMPERATURE,
      FALSE AS flatten_json_output));

text-bison32

SELECT *
FROM ML.GENERATE_TEXT(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  (PROMPT_QUERY),
  STRUCT(TOKENS AS max_output_tokens, TEMPERATURE AS temperature,
  TOP_K AS top_k, 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_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 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.

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 the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .05 AS TEMPERATURE,
      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 response.
• Doesn't return the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT(question, 'Text:', description, 'Category') AS prompt
      FROM mydataset.input_table
    ),
    STRUCT(
      100 AS max_output_tokens,
      .1 AS TEMPERATURE,
      FALSE AS flatten_json_output));

text-unicorn

SELECT *
FROM ML.GENERATE_TEXT(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  (PROMPT_QUERY),
  STRUCT(TOKENS AS max_output_tokens, TEMPERATURE AS temperature,
  TOP_K AS top_k, 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 [1,1024]. 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 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.

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 the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT('Summarize this text', body) AS prompt
      FROM mydataset.articles
    ),
    STRUCT(
      .05 AS TEMPERATURE,
      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 response.
• Doesn't return the generated text and the safety attributes in separate columns.

SELECT *
FROM
  ML.GENERATE_TEXT(
    MODEL `mydataset.text_model`,
    (
      SELECT CONCAT(question, 'Text:', description, 'Category') AS prompt
      FROM mydataset.input_table
    ),
    STRUCT(
      100 AS max_output_tokens,
      .1 AS TEMPERATURE,
      FALSE AS flatten_json_output));

Generate text from object table data

Generate text by using the ML.GENERATE_TEXT function with a remote model, using an object table to provide the content to analyze and providing the prompt data in the prompt parameter:

gemini-2.0-flash

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

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

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 object table that contains the content to analyze. For more information on what types of content you can analyze, see Input.

  The Cloud Storage bucket used by the object table should be in the same project where you have created the model and where you are calling the ML.GENERATE_TEXT function. If you want to call the ML.GENERATE_TEXT function in a different project than the one that contains the Cloud Storage bucket used by the object table, you must grant the Storage Admin role at the bucket level to the [e-mail] service account.

• PROMPT: the prompt to use to analyze the content.
• 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 that controls the degree of randomness in token selection. The temperature value must be greater than 0.0 and less than or equal to 1.0.

  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.

• 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.
• 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.

Examples

This example translates and transcribes audio content from an object table that's named feedback:

SELECT * FROM
  ML.GENERATE_TEXT(
    MODEL
      `mydataset.audio_model`,
        TABLE `mydataset.feedback`,
          STRUCT('What is the content of this audio clip, translated into Spanish?' AS PROMPT,
          .01 AS TEMPERATURE,
          TRUE AS FLATTEN_JSON_OUTPUT));

This example classifies PDF content from an object table that's named invoices:

SELECT * FROM
  ML.GENERATE_TEXT(
    MODEL
      `mydataset.classify_model`,
        TABLE `mydataset.invoices`,
          STRUCT('Classify this document based on the invoice total, using the following categories: 0 to 100, 101 to 200, greater than 200' AS PROMPT,
          .5 AS TEMPERATURE,
          TRUE AS FLATTEN_JSON_OUTPUT));

gemini-1.5-flash

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

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 object table that contains the content to analyze. For more information on what types of content you can analyze, see Input.

  The Cloud Storage bucket used by the object table should be in the same project where you have created the model and where you are calling the ML.GENERATE_TEXT function. If you want to call the ML.GENERATE_TEXT function in a different project than the one that contains the Cloud Storage bucket used by the object table, you must grant the Storage Admin role at the bucket level to the [e-mail] service account.

• PROMPT: the prompt to use to analyze the content.
• 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 1.0.

  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.
• 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.

Examples

This example translates and transcribes audio content from an object table that's named feedback:

SELECT * FROM
  ML.GENERATE_TEXT(
    MODEL
      `mydataset.audio_model`,
        TABLE `mydataset.feedback`,
          STRUCT('What is the content of this audio clip, translated into Spanish?' AS PROMPT,
          TRUE AS FLATTEN_JSON_OUTPUT));

This example classifies PDF content from an object table that's named invoices:

SELECT * FROM
  ML.GENERATE_TEXT(
    MODEL
      `mydataset.classify_model`,
        TABLE `mydataset.invoices`,
          STRUCT('Classify this document based on the invoice total, using the following categories: 0 to 100, 101 to 200, greater than 200' AS PROMPT,
          TRUE AS FLATTEN_JSON_OUTPUT));

gemini-1.5-pro

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

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 object table that contains the content to analyze. For more information on what types of content you can analyze, see Input.

  The Cloud Storage bucket used by the object table should be in the same project where you have created the model and where you are calling the ML.GENERATE_TEXT function. If you want to call the ML.GENERATE_TEXT function in a different project than the one that contains the Cloud Storage bucket used by the object table, you must grant the Storage Admin role at the bucket level to the [e-mail] service account.

• PROMPT: the prompt to use to analyze the content.
• 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 1.0.

  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.
• 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.

Examples

This example translates and transcribes audio content from an object table that's named feedback:

SELECT * FROM
  ML.GENERATE_TEXT(
    MODEL
      `mydataset.audio_model`,
        TABLE `mydataset.feedback`,
          STRUCT('What is the content of this audio clip, translated into Spanish?' AS PROMPT,
          TRUE AS FLATTEN_JSON_OUTPUT));

This example classifies PDF content from an object table that's named invoices:

SELECT * FROM
  ML.GENERATE_TEXT(
    MODEL
      `mydataset.classify_model`,
        TABLE `mydataset.invoices`,
          STRUCT('Classify this document based on the invoice total, using the following categories: 0 to 100, 101 to 200, greater than 200' AS PROMPT,
          TRUE AS FLATTEN_JSON_OUTPUT));

gemini-pro-vision

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

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

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 object table that contains the content to analyze. For more information on what types of content you can analyze, see Input.

  The Cloud Storage bucket used by the object table should be in the same project where you have created the model and where you are calling the ML.GENERATE_TEXT function. If you want to call the ML.GENERATE_TEXT function in a different project than the one that contains the Cloud Storage bucket used by the object table, you must grant the Storage Admin role at the bucket level to the [e-mail] service account.

• PROMPT: the prompt to use to analyze the content.
• 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 [1,2048]. Specify a lower value for shorter responses and a higher value for longer responses. The default is 2048.
• TEMPERATURE: a FLOAT64 value in the range [0.0,1.0] that controls the degree of randomness in token selection. The default is 0.4.

  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 32.
• 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.
• 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.

Examples

This example analyzes video content from an object table that's named videos and describes the content in each video:

SELECT * FROM
  ML.GENERATE_TEXT(
    MODEL
      `mydataset.video_model`,
        TABLE `mydataset.videos`,
          STRUCT('What is happening in this video?' AS PROMPT,
          TRUE AS FLATTEN_JSON_OUTPUT));