The AI.GENERATE_BOOL function

This document describes the AI.GENERATE_BOOL function, which lets you analyze any combination of text and unstructured data. For each row in the table, the function generates a STRUCT that contains a BOOL value.

The function works by sending requests to a Vertex AI Gemini model, and then returning that model's response.

You can use the AI.GENERATE_BOOL function to perform tasks such as classification and sentiment analysis.

Prompt design can strongly affect the responses returned by the model. For more information, see Introduction to prompting .

Input

Using the AI.GENERATE_BOOL function, you can use the following types of input:

Text data from standard tables.
ObjectRefRuntime values that are generated by the OBJ.GET_ACCESS_URL function . You can use ObjectRef values from standard tables as input to the OBJ.GET_ACCESS_URL function. ( Preview )

When you analyze unstructured data, that data must meet the following requirements:

Content must be in one of the supported formats that are described in the Gemini API model mimeType parameter .
If you are analyzing a video, the maximum supported length is two minutes. If the video is longer than two minutes, AI.GENERATE_BOOL only returns results based on the first two minutes.

Syntax

 AI 
 . 
 GENERATE_BOOL 
 ( 
  
 [ 
  
 prompt 
  
 => 
  
 ] 
  
 ' PROMPT 
' 
 , 
  
 [ 
 , 
  
 endpoint 
  
 => 
  
 ' ENDPOINT 
' 
 ] 
  
 [ 
 , 
  
 model_params 
  
 => 
  
  MODEL_PARAMS 
 
 ] 
  
 [ 
 , 
  
 connection_id 
  
 => 
  
 ' CONNECTION 
' 
 ] 
  
 [ 
 , 
  
 request_type 
  
 => 
  
 ' REQUEST_TYPE 
' 
 ] 
 )

Arguments

AI.GENERATE_BOOL takes the following arguments:

PROMPT : a STRING or STRUCT value that specifies the PROMPT value to send to the model. The prompt must be the first argument that you specify. You can provide the prompt value in the following ways:

Specify a STRING value. For example, 'Is Seattle a US city?' .

Specify a STRUCT value that contains one or more fields. You can use the following types of fields, or arrays containing these types, within the STRUCT value:

Field type Description Examples

Field type	Description	Examples
`STRING`	A string literal, or the name of a `STRING` column.	String literal: `'Is Seattle a US city?'` String column name: `my_string_column`
`ObjectRefRuntime`	An `ObjectRefRuntime` value returned by the `OBJ.GET_ACCESS_URL` function . The `OBJ.GET_ACCESS_URL` function takes an `ObjectRef` value as input, which you can provide by either specifying the name of a column that contains `ObjectRef` values, or by constructing an `ObjectRef` value. `ObjectRefRuntime` values must have the `access_url.read_url` and `details.gcs_metadata.content_type` elements of the JSON value populated.	Function call with `ObjectRef` column: `OBJ.GET_ACCESS_URL(my_objectref_column, 'r')` Function call with constructed `ObjectRef` value: `OBJ.GET_ACCESS_URL(OBJ.MAKE_REF('gs://image.jpg', 'myconnection'), 'r')`

STRING

A string literal, or the name of a STRING column.

String literal:
'Is Seattle a US city?'

String column name:
my_string_column

ObjectRefRuntime

An ObjectRefRuntime value returned by the OBJ.GET_ACCESS_URL function . The OBJ.GET_ACCESS_URL function takes an ObjectRef value as input, which you can provide by either specifying the name of a column that contains ObjectRef values, or by constructing an ObjectRef value.

ObjectRefRuntime values must have the access_url.read_url and details.gcs_metadata.content_type elements of the JSON value populated.

Function call with ObjectRef column:
OBJ.GET_ACCESS_URL(my_objectref_column, 'r')

Function call with constructed ObjectRef value:
OBJ.GET_ACCESS_URL(OBJ.MAKE_REF('gs://image.jpg', 'myconnection'), 'r')

The function combines STRUCT fields similarly to a CONCAT operation and concatenates the fields in their specified order. The same is true for the elements of any arrays used within the struct. The following table shows some examples of STRUCT prompt values and how they are interpreted:

Struct field types	Struct value	Semantic equivalent
`STRUCT<STRING, STRING, STRING>`	`('Is the city of ', my_city_column, ' in the US?')`	'Is the city of `my_city_column_value` in the US?'
`STRUCT<STRING, ObjectRefRuntime>`	`('Is the city in the following image in the US?', OBJ.GET_ACCESS_URL(image_objectref_column, 'r'))`	'Is the city in the following image in the US?' `image`

ENDPOINT : a STRING value that specifies the Vertex AI endpoint to use for the model. You can specify any generally available or preview Gemini model. If you specify the model name, BigQuery ML automatically identifies and uses the full endpoint of the model. If you don't specify an ENDPOINT value, BigQuery ML selects a recent stable version of Gemini to use.

Note: Using Gemini 2.5 models incurs charges for the thinking process . You can set a budget for the thinking process for Gemini 2.5 Flash and Gemini 2.5 Flash-Lite models by using the MODEL_PARAMS argument to set the thinking_budget parameter. For an example, see Set the thinking budget for a Gemini 2.5 Flash model . You can't set a budget for Gemini 2.5 Pro models.
MODEL_PARAMS : a JSON literal that provides additional parameters to the model. The MODEL_PARAMS value must conform to the generateContent request body format . You can provide a value for any field in the request body except for the contents field; the contents field is populated with the PROMPT argument value.
CONNECTION : a STRING value specifying the connection to use to communicate with the model, in the format [ PROJECT_ID ]. LOCATION . CONNECTION_ID . For example, myproject.us.myconnection .

If you don't specify a connection, then the query uses your end-user credentials .

For information about configuring permissions, see Set permissions for BigQuery ML generative AI functions that call Vertex AI models .
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:
- SHARED : The function only uses dynamic shared quota (DSQ) .
- DEDICATED : The function only uses Provisioned Throughput quota. The function returns an invalid query error if Provisioned Throughput quota isn't available. For more information, see Use Vertex AI Provisioned Throughput .
- UNSPECIFIED : The function uses quota as follows:
  - If you haven't purchased Provisioned Throughput quota, the function uses DSQ quota.
  - If you have purchased Provisioned Throughput quota, the 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 .

Output

AI.GENERATE_BOOL returns a STRUCT value for each row in the table. The struct contains the following fields:

result : a BOOL value containing the model's response to the prompt. The result is NULL if the request fails or is filtered by responsible AI .
full_response : a JSON value containing the response from the projects.locations.endpoints.generateContent call to the model. The generated text is in the text element.
status : a STRING value that contains the API response status for the corresponding row. This value is empty if the operation was successful.

Examples

The following examples assume that you have granted the Vertex AI User role to your personal account. For more information, see Run generative AI queries with end-user credentials .

Use string input

To determine whether each city is located in the US, call the AI.GENERATE_BOOL function and select the result field in the output by running the following query:

 SELECT 
  
 city 
 , 
  
 AI 
 . 
 GENERATE_BOOL 
 (( 
 'Is ' 
 , 
  
 city 
 , 
  
 ' a US city?' 
 )). 
 result 
 FROM 
  
 UNNEST 
 ( 
 [ 
 "Seattle" 
 , 
  
 "Beijing" 
 , 
  
 "Paris" 
 , 
  
 "London" 
 ] 
 ) 
  
 city 
 ;

The result is similar to the following:

+---------+--------+
| city    | result |
+---------+--------+
| Seattle | true   |
| Beijing | false  |
| Paris   | false  |
| London  | false  |
+---------+--------+

Process images in a Cloud Storage bucket

The following query creates an external table from images of pet products stored in a publicly available Cloud Storage bucket:

 CREATE 
  
 SCHEMA 
  
 IF 
  
 NOT 
  
 EXISTS 
  
 bqml_tutorial 
 ; 
 CREATE 
  
 OR 
  
 REPLACE 
  
 EXTERNAL 
  
 TABLE 
  
 bqml_tutorial 
 . 
 product_images 
  
 WITH 
  
 CONNECTION 
  
 DEFAULT 
  
 OPTIONS 
  
 ( 
  
 object_metadata 
  
 = 
  
 'SIMPLE' 
 , 
  
 uris 
  
 = 
  
 [ 
 'gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/images/*.png' 
 ] 
 );

To determine which animals are mammals, call the AI.GENERATE_BOOL function and select the result field in the output by running the following query:

 SELECT 
  
 uri 
 , 
  
 STRING 
 ( 
 OBJ 
 . 
 GET_ACCESS_URL 
 ( 
 ref 
 , 
 'r' 
 ). 
 access_urls 
 . 
 read_url 
 ) 
  
 AS 
  
 signed_url 
 , 
  
 AI 
 . 
 GENERATE_BOOL 
 (( 
 "Is " 
 , 
  
 OBJ 
 . 
 GET_ACCESS_URL 
 ( 
 ref 
 , 
  
 'r' 
 ), 
  
 " an aquarium?" 
 )). 
 result 
 FROM 
  
 bqml_tutorial 
 . 
 product_images 
 LIMIT 
  
 3 
 ;

The result is similar to the following:

AI_GENERATE_WITH_IMAGE

Set the thinking budget for a Gemini 2.5 Flash model

The following query shows how to set the model_params argument to set the model's thinking budget to 0 for the request:

 SELECT 
  
 city 
 , 
  
 AI 
 . 
 GENERATE_BOOL 
 (( 
 'Is ' 
 , 
  
 city 
 , 
  
 ' a US city?' 
 ), 
  
 endpoint 
  
 => 
  
 'gemini-2.5-flash' 
 , 
  
 model_params 
  
 => 
  
 JSON 
  
 '{"generation_config":{"thinking_config": {"thinking_budget": 0}}}' 
 ) 
 FROM 
  
 mydataset 
 . 
 cities 
 ;

Best Practices

This function passes your input to a Gemini model and incurs charges in Vertex AI each time it's called. For information about how to view these charges, see Track costs . To minimize Vertex AI charges when you use AI.GENERATE_BOOL on filtered data using the WHERE clause, materialize the filtered data to a table first. For example, the first of the following examples is preferable to the second one:

 CREATE 
  
 TABLE 
  
 mydataset 
 . 
 cities 
 AS 
  
 ( 
  
 SELECT 
  
 city_name 
  
 from 
  
 mydataset 
 . 
 customers 
  
 WHERE 
 ... 
 ); 
 SELECT 
  
 city 
 , 
  
 AI 
 . 
 GENERATE_BOOL 
 ( 
  
 ( 
 'Is ' 
 , 
  
 city 
 , 
  
 ' a US city?' 
 )). 
 result 
 FROM 
  
 mydataset 
 . 
 cities 
 ;

 SELECT 
  
 city 
 , 
  
 AI 
 . 
 GENERATE_BOOL 
 ( 
  
 ( 
 'Is ' 
 , 
  
 city 
 , 
  
 ' a US city?' 
 )). 
 result 
 FROM 
  
 ( 
 SELECT 
  
 city_name 
  
 from 
  
 mydataset 
 . 
 customers 
  
 WHERE 
 ...);

Writing the query results to a table beforehand helps you to ensure that you are sending as few rows as possible to the model.

Use Vertex AI Provisioned Throughput

You can use Vertex AI Provisioned Throughput with the AI.GENERATE_BOOL function to provide consistent high throughput for requests. The remote model that you reference in the AI.GENERATE_BOOL function must use a supported Gemini model in order for you to use Provisioned Throughput.

To use Provisioned Throughput, calculate your Provisioned Throughput requirements and then purchase Provisioned Throughput quota before running the AI.GENERATE_BOOL function. When you purchase Provisioned Throughput, do the following:

For Model, select the same Gemini model as the one used by the remote model that you reference in the AI.GENERATE_BOOL function.
For Region, select the same region as the dataset that contains the remote model that you reference in the AI.GENERATE_BOOL function, with the following exceptions:
- If the dataset is in the US multi-region, select the us-central1 region.
- If the dataset is in the EU multi-region, select the europe-west4 region.

After you submit the order, wait for the order to be approved and appear on the Orders page.

After you have purchased Provisioned Throughput quota, use the REQUEST_TYPE argument to determine how the AI.GENERATE_BOOL function uses the quota.

Locations

You can run AI.GENERATE_BOOL in all of the regions that support Gemini models, and also in the US and EU multi-regions.

Quotas

See Vertex AI and Cloud AI service functions quotas and limits .

What's next

For more information about using Vertex AI models to generate text and embeddings, see Generative AI overview .
For more information about using Cloud AI APIs to perform AI tasks, see AI application overview .
For more information about supported SQL statements and functions for generative AI models, see End-to-end user journeys for generative AI models .