The AI.IF function

This document describes the AI.IF function, which uses a Vertex AI Gemini model to evaluate a condition described in natural language and returns a BOOL.

You can use the AI.IF function to filter and join data based on conditions described in natural language or multimodal input. The following are common use cases:

  • Sentiment analysis: Find customer reviews with negative sentiment.
  • Topic analysis: Identify news articles related to a specific subject.
  • Image analysis: Select images that contain a specific item.
  • Security: Identify suspicious emails.

Input

AI.IF accepts the following types of input:

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.
  • For more information about accepted multimodal input, see the technical specifications for Gemini.

This function passes your input to a Gemini model and incurs charges in Vertex AI each time it's called.

Syntax

AI.IF(  [ prompt => ] 'PROMPT',  connection_id => 'CONNECTION' )

Arguments

AI.IF takes the following arguments:

  • PROMPT: a STRING or STRUCT value that specifies the prompt 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 within the STRUCT value:

      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
      ARRAY<STRING> You can only use string literals in the array. Array of string literals:
      ['Is ', 'Seattle', ' a US city?']
      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')
      ARRAY<ObjectRefRuntime>

      ObjectRefRuntime values returned from multiple calls to 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 calls with ObjectRef columns:
      [OBJ.GET_ACCESS_URL(my_objectref_column1, 'r'), OBJ.GET_ACCESS_URL(my_objectref_column2, 'r')]

      Function calls with constructed ObjectRef values:
      [OBJ.GET_ACCESS_URL(OBJ.MAKE_REF('gs://image1.jpg', 'myconnection'), 'r'), OBJ.GET_ACCESS_URL(OBJ.MAKE_REF('gs://image2.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> ('Is Seattle a US city?') 'Is Seattle a US city?'
      STRUCT<STRING, STRING, STRING> ('Is the city of ', my_city_column, ' in India?') 'Is the city of my_city_column_value in India?'
      STRUCT<STRING, ARRAY<STRING>> ('Is the city of ', ['Oslo', ' in India?']) 'Is the city of Oslo in India?'
      STRUCT<STRING, ObjectRefRuntime> ('Is the city in the following image in Canada?', OBJ.GET_ACCESS_URL(image_objectref_column, 'r')) 'Is the city in the following image in Canada?' image
      STRUCT<STRING, ObjectRefRuntime, ObjectRefRuntime> ('Is the city in the first image within the country of the second image?',
      OBJ.GET_ACCESS_URL(city_image_objectref_column, 'r'),
      OBJ.GET_ACCESS_URL(country_image_objectref_column, 'r'))      		 'Is the city in the first image within the country of the second image?' city_image country_image

  • CONNECTION: a STRING value specifying the Cloud resource connection to use. The following forms are accepted:

    • Connection name: [PROJECT_ID].LOCATION.CONNECTION_ID

      For example, myproject.us.myconnection.

    • Fully qualified connection ID: projects/PROJECT_ID/locations/LOCATION/connections/CONNECTION_ID

      For example, projects/myproject/locations/us/connections/myconnection.

    Replace the following:

    • PROJECT_ID: the project ID of the project that contains the connection.
    • LOCATION: the location used by the connection.
    • CONNECTION_ID: the connection ID—for example, myconnection.

      You can get this value by viewing the connection details in the Google Cloud console and copying 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.

Output

AI.IF returns a BOOL based on evaluation of the condition in the input prompt.

If the call to Vertex AI is unsuccessful for any reason, such as exceeding quota or model unavailability, then the function returns NULL.

Examples

The following examples show how to use the AI.IF function to filter text and join multimodal data.

Filter text by topic

The following query uses the AI.IF function to filter news stories to those that cover a natural disaster:

SELECT  title, body FROM  `bigquery-public-data.bbc_news.fulltext` WHERE  AI.IF((  'The following news story is about a natural disaster: ', body),  connection_id => 'us.example_connection');

Combine filters

BigQuery optimizes queries to reduce the number of calls to Gemini. The following query first filters by the equality operator, and then filters using the AI.IF function:

SELECT  title, body  FROM  `bigquery-public-data.bbc_news.fulltext` WHERE  AI.IF(  ('This news is related to Google: ', body),  connection_id => 'us.example_connection')  AND category = 'tech'; -- Non-AI filters are evaluated first

Filter images

The following query creates an external table from images of pet products stored in a publicly available Cloud Storage bucket. Then, it filters the results to images that contain a ball.

-- Create a dataset CREATE SCHEMA IF NOT EXISTS cymbal_pets; -- Create an object table CREATE OR REPLACE EXTERNAL TABLE cymbal_pets.product_images WITH CONNECTION us.example_connection  OPTIONS (  object_metadata = 'SIMPLE',  uris = ['gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/images/*.png'] ); -- Filter images in the object table SELECT  STRING(OBJ.GET_ACCESS_URL(ref,'r').access_urls.read_url) AS signed_url, FROM  `cymbal_pets.product_images` WHERE  AI.IF(('The image contains a ball.', OBJ.GET_ACCESS_URL(ref, 'r')),  connection_id => 'us.example_connection');

Join tables based on image content

The following queries create a table of product data and a table of product images. The tables are joined based on whether the image is of the product.

-- Create a dataset CREATE SCHEMA IF NOT EXISTS cymbal_pets; -- Load a non-object table LOAD DATA OVERWRITE cymbal_pets.products FROM  FILES(  format = 'avro',  uris = [  'gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/tables/products/products_*.avro']); -- Create an object table CREATE OR REPLACE EXTERNAL TABLE cymbal_pets.product_images  WITH CONNECTION us.example_connection  OPTIONS (  object_metadata = 'SIMPLE',  uris = ['gs://cloud-samples-data/bigquery/tutorials/cymbal-pets/images/*.png']); -- Join the standard table and object table SELECT product_name, brand, signed_url FROM  cymbal_pets.products INNER JOIN  EXTERNAL_OBJECT_TRANSFORM(TABLE `cymbal_pets.product_images`, ['SIGNED_URL']) as images ON  AI.IF(  (  """You will be provided an image of a pet product.  Determine if the image is of the following pet toy: """,  products.product_name,  images.ref  ),  connection_id => 'us.example_connection') WHERE  products.category = "Toys" AND  products.brand = "Fluffy Buns";

Filter audio by speech topic

The following queries create a table of audio data stored in a publicly available Cloud Storage bucket. The query filters the audio samples to those that contain speech discussing a large language model.

-- Create a dataset CREATE SCHEMA IF NOT EXISTS audio_repo; -- Create an object table with audios CREATE OR REPLACE EXTERNAL TABLE audio_repo.prompt_audio WITH CONNECTION us.test_connection  OPTIONS (  object_metadata = 'SIMPLE',  uris = ['gs://cloud-samples-data/generative-ai/audio/*.mp3'] ); -- Filter audios in the object table SELECT  STRING(OBJ.GET_ACCESS_URL(ref,'r').access_urls.read_url) AS signed_url, FROM  `audio_repo.prompt_audio` WHERE  AI.IF(('Does the audio talk about large language models? ', OBJ.GET_ACCESS_URL(ref, 'r')),  connection_id => 'us.test_connection');

The AI.IF and AI.GENERATE_BOOL functions both use models to generate a boolean value in response to a prompt. The following differences can help you choose which function to use:

  • Prompt Optimization: AI.IF automatically structures your prompts to improve the quality of the output.
  • Input: AI.IF automatically selects a Gemini model. AI.GENERATE_BOOL lets you specify a specific model endpoint and model parameters to use.
  • Output: AI.IF returns a BOOL value, which makes it easier to work with in queries. AI.GENERATE_BOOL returns a STRUCT that contains a BOOL value, as well as additional information about the model call, which is useful if you need to view details such as the safety rating or API response status.
  • Error handling: If AI.IF produces an error for any input, then the function returns NULL. AI.GENERATE_BOOL records details about the errors in its output.

Locations

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

Quotas

See Generative AI functions quotas and limits.

What's next