Perform intelligent SQL queries using AI functions

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Note: This experimental launch is a Pre-GA offering.

This page describes how to query using AI powered SQL operators provided by AI functions. 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 and creates the ai schema.

Before you begin

Before you use natural language in SQL operators, do the following:

• Verify that the google_ml_integration extension is installed.
• Verify that the google_ml_integration.enable_model_support flag is set to on.
• Integrate with Vertex AI.
• Use a Gemini model that's supported in your region.

Integrate with Vertex AI and install the extension

1. Configure user access to Vertex AI models.
2. Verify that the latest version of google_ml_integration is installed.

   1. To check the installed version, run the following command:

              SELECT extversion FROM pg_extension WHERE extname = 'google_ml_integration';
              extversion 
              ------------
              1.5.2
              (1 row)
            

   2. If the extension isn't installed or if the installed version is earlier than 1.5.2, update the extension.

              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 1.5.2 after you run the preceding commands, contact Google Cloud support.

3. To use the AlloyDB AI query engine functionality, set the google_ml_integration.enable_ai_query_engine flag to true.

   SQL

   1. Enable the AI query engine for the current session.
      

                    SET google_ml_integration.enable_ai_query_engine = true;
                    

   2. Enable features for a specific database across sessions.
      

                    ALTER DATABASE DATABASE_NAME SET google_ml_integration.enable_ai_query_engine = 'on';
                    

   3. Enable the AI query engine for a specific user across sessions and databases.
      

                    ALTER ROLE postgres SET google_ml_integration.enable_ai_query_engine = 'on';
                  

   Console

   To modify the value of the google_ml_integration.enable_ai_query_engine flag, follow the steps in Configure an instance's database flags.

   gcloud

   To use the gcloud CLI, you can install and initialize the Google Cloud CLI, or you can use Cloud Shell.

   You can modify the value of the google_ml_integration.enable_ai_query_engine flag. For more information, see Configure an instance's database flags.

                gcloud alloydb instances update INSTANCE_ID \
                  --database-flags google_ml_integration.enable_ai_query_engine=on \
                  --region=REGION_ID \
                  --cluster=CLUSTER_ID \
                  --project=PROJECT_ID
               

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.

Note: Verify model availability in your region. For more information, see Gemini 2.0 Flash.

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

The ai.generate function produces text by combining provided data with the user's 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 sort query results using custom, natural language instructions, 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 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) DESC
LIMIT 20;

What's next

• Register a model endpoint with model endpoint management.

• Rank and score search results for RAG.

• Semantically filter SQL queries and rank results.