Tune a model using your data

Console

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

   Go to BigQuery

2. In the left pane, click explore Explorer:

   

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

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

4. Click more_vert 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

Console

1. Go to the BigQuery page.

   Go to BigQuery

2. In the Explorer pane, click add 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": "connection-1234-9u56h9@gcp-sa-bigquery-condel.iam.gserviceaccount.com"}
   

Terraform

Use the google_bigquery_connection resource.

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.

Console

1. Go to the IAM & Admin page.

   Go to IAM & Admin

2. Click person_add 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.

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));