Query using AI powered SQL operators
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This page describes how to query using AI powered SQL operators provided by the AlloyDB AI query engine. You can use the ai.if for filters, ai.rank, and ai.generate operators to combine natural language with SQL queries.

To use instructions on this page, you must have an understanding of AlloyDB and be familiar with generative AI concepts.

AlloyDB AI reserves the ai schema, and tries to create this schema when you install the google_ml_integration extension. If the schema creation fails, then use the functions with the same name in the google_ml schema.

Before you begin

Before you use natural language in SQL operators, make sure that you meet the following prerequisites.

Integrate with Vertex AI and install the extension

Integrate with Vertex AI.

Ensure that the latest version of google_ml_integration is installed.

To check the installed version, run the following command:

        SELECT extversion FROM pg_extension WHERE extname = 'google_ml_integration';
        extversion 
        ------------
        1.4.3
        (1 row)

If the extension isn't installed or if the installed version is earlier than 1.4.3, update the extension by running the following commands:
```
        CREATE EXTENSION IF NOT EXISTS google_ml_integration;

        ALTER EXTENSION google_ml_integration UPDATE;
      
```
If you experience issues when you run the preceding commands, or if the extension isn't updated to version 1.4.3 after you run the preceding commands, contact AlloyDB support.

After you ensure that the version is current, install the preview functionality by running the upgrade_to_preview_version procedure:

        CALL google_ml.upgrade_to_preview_version();

        SELECT extversion FROM pg_extension WHERE extname = 'google_ml_integration';
        extversion 
        ------------
        1.4.4
        (1 row)

Use a Gemini model that's supported in your region

If your AlloyDB for PostgreSQL cluster is in a region where gemini-2.0-flash isn't supported, you can use one of the other available Gemini models in your region using the model_id parameter.

Alternatively, you can register a Gemini model endpoint and provide that model ID to the AI operators. For more information, see Register and call remote AI models using model endpoint management.

The following example shows how to register another Gemini endpoint. In this example, this second Gemini endpoint is the global endpoint for gemini-2.0-flash. You can use this registered model with AI operators by passing model_id =>gemini-2.0-flash-global` as an additional argument.

CALL
  google_ml.create_model(
    model_id => 'gemini-2.0-flash-global',
    model_type => 'llm',
    model_provider => 'google',
    model_qualified_name => 'gemini-2.0-flash',
    model_request_url =>  'https://aiplatform.googleapis.com/v1/projects/<project_id>/locations/global/publishers/google/models/gemini-2.0-flash:generateContent',
    model_auth_type => 'alloydb_service_agent_iam'
);

Use filters in your queries

AlloyDB AI offers several AI-powered SQL functions that let you use natural language processing and LLMs directly within your database queries, including the ai.if and ai.rank operators.

Filters

To evaluate whether a condition stated in natural language is met, use the ai.if/google_ml.if operator. The function returns boolean true or false value, and returns false if the output isn't clearly detected.

- Function signature
FUNCTION ai.if(prompt TEXT, model_id VARCHAR(100) DEFAULT NULL) RETURNS bool

The following example shows the use of the ai.if operator as a filter to find restaurants with more than 500 positive reviews located in cities with a population greater than 100,000. The example uses restaurant_reviews and contains data like reviews and city location. The ai.if operator helps you understand the review sentiment and combine the locations from the database with Gemini general knowledge about the population in these locations.

SELECT r.name, r.location_city
FROM restaurant_reviews r
WHERE
  AI.IF(r.location_city || ' has a population OF more than 100,000 AND the following is a positive review; Review: ' || r.review)
GROUP BY r.name, r.location_city
HAVING COUNT(*) > 500;

The following shows the same example using the model that you registered in Use a Gemini model that's supported in your region.

SELECT r.name, r.location_city
FROM restaurant_reviews r
WHERE
  AI.IF(r.location_city || ' has a population of more than 100,000 AND the following is a positive review; Review: ' || r.review, model_id => 'gemini-2.0-flash-global')
GROUP BY r.name, r.location_city
HAVING COUNT(*) > 500;

Perform a join on a query that uses the if operator

To perform a join operation, use the ai.if/google_ml.if operator with join. The following sample query finds the number of reviews that mention each menu item from the restaurant menu.

    SELECT item_name, COUNT(*)
    FROM menu_items JOIN user_reviews
      ON ai.if(
        prompt => 'Does the following user review talk about the menu item mentioned ? review: ' || user_reviews.review_text || ' menu item: ' || item_name)
    GROUP BY item_name;

Text generation and summarization

Using an LLM, the ai.generate function allows generation of text from data in the database, based on a prompt.

-- Function Signature
FUNCTION ai.generate(prompt TEXT, model_id VARCHAR(100) DEFAULT NULL) RETURNS TEXT

For example, you can use the following query to generate a concise summary of each user review.

SELECT
  ai.generate(
    prompt => 'Summarize the review in 20 words or less. Review: ' || review) AS review_summary
FROM user_reviews

Score your query results

If you need to incorporate a ranking criteria which uses real-world knowledge or a custom natural language ranking description, use the ai.rank operator. This function lets you provide a prompt which describes the ranking criteria and returns a score for each item.

-- Function signature
FUNCTION ai.rank(prompt TEXT, model_id VARCHAR(100) DEFAULT NULL) RETURNS real

For example, the following query gets the top 20 most positive restaurant reviews, using scores from an LLM.

SELECT review AS top20
FROM user_reviews
ORDER BY score DESC
  ai.rank(
    'Score the following review according to these rules:
    (1) Score OF 8 to 10 IF the review says the food IS excellent.
    (2) 4 to 7 IF the review says the food is ok.
    (3) 1 to 3 IF the review says the food is not good. Here is the review:' || review)
LIMIT 20;