Register and call remote AI models using model endpoint management

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MySQL   |  PostgreSQL   |  SQL Server

This page describes how to invoke predictions or generate embeddings using a model, and then register the model endpoint with model endpoint management.

For more information about the mysql.ml_create_model_registration() function, see Model endpoint management reference.

Before you begin

• Based on the model provider, set up authentication.

• Connect to your primary instance using either a mysql client or Cloud SQL Studio.

• Make sure that you connect to the database as a MySQL database user with the database privileges described in Required database user privileges.

Set up authentication

The following sections show how to set up authentication before adding a Vertex AI model endpoint or model endpoints hosted within Google Cloud.

Set up authentication for Vertex AI

To use the Google Vertex AI model endpoints, you must add Vertex AI permissions to the IAM-based Cloud SQL service account you use to connect to the database. For more information about integrating with Vertex AI, see Integrate Cloud SQL with Vertex AI.

Set up authentication for custom-hosted models

This section explains how to set up authentication if you're using Secret Manager. For all models except Vertex AI model endpoints, you can store your API keys or bearer tokens in Secret Manager.

If your model endpoint doesn't handle authentication through Secret Manager, then this section is optional. For example, if your model endpoint uses HTTP headers to pass authentication information or doesn't use authentication at all, then don't complete the steps in this section.

To create and use an API key or a bearer token, complete the following steps:

1. Create a secret in Secret Manager. For more information, see Create a secret and access a secret version.

   The secret name and the secret path are used in the mysql.ml_create_sm_secret_registration() SQL function.

2. Grant permissions to the Cloud SQL instance to access the secret.

     gcloud secrets add-iam-policy-binding SECRET_ID \
         --member="serviceAccount:SERVICE_ACCOUNT_EMAIL" \
         --role="roles/secretmanager.secretAccessor"
   

   Replace the following:

   • SECRET_ID: the secret ID in Secret Manager.
   • SERVICE_ACCOUNT_EMAIL: the email address of the IAM-based Cloud SQL service account. To find this email address, use the gcloud sql instances describe INSTANCE_NAME command and replace INSTANCE_NAME with the name of the instance. The value that appears next to the serviceAccountEmailAddress parameter is the email address.

Text embedding models with built-in support

This section shows how to register model endpoints for model endpoint management.

Vertex AI embedding models

Model endpoint management provides built-in support for all versions of the text-embedding-gecko, text-embedding, and gemini-embedding models by Vertex AI. Use the qualified name to set the model version to either textembedding-gecko@001 or textembedding-gecko@002.

Because Vertex AI embedding model endpoint IDs are supported by default with model endpoint management, you can use any of them directly as the model ID. For these models, the embedding function automatically performs input and output transformation.

Ensure that both the Cloud SQL instance and the Vertex AI model that you're querying are in the same region.

To register the gemini-embedding-001 model endpoint, call the ml_create_model_registration function:

  CALL
    mysql.ml_create_model_registration(
      'gemini-embedding-001',
      'publishers/google/models/gemini-embedding-001',
      'google','text_embedding', 'gemini-embedding-001',
      'AUTH_TYPE_CLOUDSQL_SERVICE_AGENT_IAM',
       NULL,
      'mysql.cloudsql_ml_text_embedding_input_transform',
      'mysql.cloudsql_ml_text_embedding_output_transform', NULL);

Custom-hosted text embedding models

This section shows how to register custom model endpoints hosted in networks within Google Cloud.

Adding custom-hosted text embedding model endpoints involves creating transform functions, and optionally, custom HTTP headers. On the other hand, adding custom-hosted generic model endpoints involves optionally generating custom HTTP headers and setting the model request URL.

The following example adds the custom-embedding-model text embedding model endpoint hosted by Cymbal, which is hosted within Google Cloud. The cymbal_text_input_transform and cymbal_text_output_transform transform functions are used to transform the input and output format of the model to the input and output format of the prediction function.

To register custom-hosted text embedding model endpoints, complete the following steps:

1. Call the secret stored in the Secret Manager:

   CALL
     mysql.ml_create_sm_secret_registration(
       'SECRET_ID',
       'projects/project-id/secrets/SECRET_MANAGER_SECRET_ID/versions/VERSION_NUMBER');
   

   Replace the following:

   • SECRET_ID: the secret ID that you set and is subsequently used when registering a model endpoint—for example, key1.
   • SECRET_MANAGER_SECRET_ID: the secret ID set in Secret Manager when you created the secret.
   • PROJECT_ID: the ID of your Google Cloud project.
   • VERSION_NUMBER: the version number of the secret ID.
   Note: Secret Manager generates an Authorization: Bearer SECRET_VALUE_FROM_SECRET_MANAGER header for authentication by default. If this format matches your model endpoint's authorization bearer token format, then you don't have to generate auth headers using the header generation function.

2. Create the input and output transform functions based on the following signature for the prediction function for text embedding model endpoints. For more information about how to create transform functions, see Transform functions example.

   The following are example transform functions that are specific to the custom-embedding-model text embedding model endpoint:

   -- Input Transform Function corresponding to the custom model endpoint
   DELIMITER $$
   CREATE FUNCTION IF NOT EXISTS cymbal_text_input_transform(model_id VARCHAR(100), input_text TEXT)
   RETURNS JSON
   DETERMINISTIC
   
   BEGIN
     RETURN JSON_OBJECT('prompt', JSON_ARRAY(input_text));
   END $$
   
   -- Output Transform Function corresponding to the custom model endpoint
   CREATE FUNCTION IF NOT EXISTS cymbal_text_output_transform(model_id VARCHAR(100), response_json JSON)
   RETURNS BLOB
   DETERMINISTIC
   
   BEGIN
     RETURN STRING_TO_VECTOR(
            JSON_EXTRACT(
                 content,
                 '$.predictions[0].embeddings.values'
               )
        );
   
   END $$
   DELIMITER ;
   

3. Call the create model function to register the custom embedding model endpoint:

   CALL
      mysql.ml_create_model_registration(
        'MODEL_ID',
        'REQUEST_URL',
        'custom',
        'text_embedding',
        'MODEL_QUALIFIED_NAME',
        'auth_type_secret_manager',
        'SECRET_ID'
        'database_name.cymbal_text_input_transform',
        'database_name.cymbal_text_output_transform', NULL);

Replace the following:

• MODEL_ID: required. A unique ID for the model endpoint that you define (for example, custom-embedding-model). This model ID is referenced for metadata that the model endpoint needs to generate embeddings or invoke predictions.
• REQUEST_URL: required. The model-specific endpoint when adding custom text embedding and generic model endpoints—for example, https://cymbal.com/models/text/embeddings/v1. Ensure that the model endpoint is accessible through an internal IP address. Model endpoint management doesn't support external IP addresses.
• MODEL_QUALIFIED_NAME: required if your model endpoint uses a qualified name. The fully qualified name in case the model endpoint has multiple versions.
• SECRET_ID: the secret ID you used earlier in the mysql.ml_create_sm_secret_registration() procedure.

Generic models

This section shows how to register a generic gemini-flash model endpoint from Vertex AI Model Garden, which is pre-registered in the catalog by default. You can register any generic model endpoint that is hosted within Google Cloud.

Cloud SQL only supports model endpoints that are available through Vertex AI Model Garden and model endpoints hosted in networks within Google Cloud.

Gemini model

The following example uses the gemini-2.5-flash model endpoint from the Vertex AI Model Garden.

To register the gemini-2.5-flash model endpoint, call the mysql.ml_create_model_registration function:

    CALL
      mysql.ml_create_model_registration(
        'MODEL_ID',
        'https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/gemini-2.5-flash:streamGenerateContent',
        'google',
        'auth_type_cloudsql_service_agent_iam',
        NULL, NULL, NULL, NULL);

Replace the following:

• MODEL_ID: a unique ID for the model endpoint that you define (for example,
  gemini-1). This model ID is referenced for metadata that the model endpoint needs to generate embeddings or invoke predictions.
• PROJECT_ID: the ID of your Google Cloud project.

For more information, see how to invoke predictions for generic model endpoints.

What's next

• Learn about the model endpoint management reference.