Generate text by using the ML.GENERATE_TEXT function

This tutorial shows you how to create a BigQuery ML remote model that references a Vertex AI natural language foundation model. You can then use that model in conjunction with the ML.GENERATE_TEXT function to analyze text in a BigQuery table.

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 data, and then click Connections to external data sources.

3. In the Connection type list, select BigLake and remote functions (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.

2. Retrieve and copy the service account ID because you need it 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

Append the following section into your main.tf file.

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

Replace the following:

• CONNECTION_ID: an ID for your connection
• PROJECT_ID: your Google Cloud project ID
• REGION: your connection region

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...

Give the service account access

Give your service account permission to use the connection. Failure to give permission results in an error. Select one of the following options:

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

Create a 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 text LLM to use. For example, ENDPOINT='text-bison-32k'.

     You can specify a particular version of the model by appending @version to the model name. For example, text-bison@001. If you don't specify a version, the latest version of the model is used.

Note: You can create a remote model over a Vertex AI LLM by specifying the REMOTE_SERVICE_TYPE value as 'CLOUD_AI_LARGE_LANGUAGE_MODEL_V1' for the text-bison model. However, the preferred method for creating a remote model over an LLM is to specify the LLM as an endpoint instead, as shown here.

Generate text

Generate text with the ML.GENERATE_TEXT function:

Prompt column

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

SELECT *
FROM ML.GENERATE_TEXT(
  MODEL `PROJECT_ID.DATASET_ID.MODEL_NAME`,
  TABLE PROJECT_ID.DATASET_ID.TABLE_NAME,
  STRUCT(TOKENS AS max_output_tokens, TEMPERATURE AS temperature,
  TOP_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 table that's named prompt, or you can use an alias to use a differently named column.
• TOKENS: an INT64 value in the range [1,1024] that sets the maximum number of tokens output by the model. Specify a lower value for shorter responses and a higher value for longer responses. The default is 50.
• 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_K: an INT64 value in the range [1,40] that determines the initial pool of tokens the model considers for selection. Specify a lower value for less random responses and a higher value for more random responses. The default is 40.
• TOP_P: a FLOAT64 value in the range [0.0,1.0] helps determine which tokens from the pool determined by TOP_K are selected. Specify a lower value for less random responses and a higher value for more random responses. The default is 1.0.
• FLATTEN_JSON: a BOOL value that determines whether to return the generated text and the safety attributes in separate columns. The default is FALSE.

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.llm_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));

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_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 in the range [1,1024] that sets the maximum number of tokens output by the model. Specify a lower value for shorter responses and a higher value for longer responses. The default is 50.
• 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_K: an INT64 value in the range [1,40] that determines the initial pool of tokens the model considers for selection. Specify a lower value for less random responses and a higher value for more random responses. The default is 40.
• TOP_P: a FLOAT64 value in the range [0.0,1.0] helps determine which tokens from the pool determined by TOP_K are selected. Specify a lower value for less random responses and a higher value for more random responses. The default is 1.0.
• FLATTEN_JSON: a BOOL value that determines whether to return the generated text and the safety attributes in separate columns.

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.llm_model`,
    (
      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,
      15 AS top_k, 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.llm_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,
      30 AS top_k, FALSE AS flatten_json_output));