Register and call remote AI models in Cloud SQL overview

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MySQL   |  PostgreSQL   |  SQL Server

This page describes how to register an AI model endpoint and invoke predictions with model endpoint management in Cloud SQL. To use AI models in production environments, see Generate and manage vector embeddings.

Overview

Model endpoint management lets you register a model endpoint, manage model endpoint metadata in your Cloud SQL instance, and then interact with the models using SQL queries. You can use these models to generate vector embeddings or invoke predictions.

You can register the following model types by using model endpoint management:

• Vertex AI text embedding models.
• Custom-hosted text embedding models hosted in networks within Google Cloud.

• Generic models with a JSON-based API. Examples of these models include the following:

  • The gemini-flash model from the Vertex AI Model Garden
  • The open_ai model for OpenAI models
  • Models hosted in networks within Google Cloud

How it works

You can use model endpoint management to register a model endpoint that complies to the following:

• The model input and output support the JSON format.
• You can use the REST protocol to call the model.

When you register a model endpoint with model endpoint management, model endpoint management registers each endpoint with a unique model ID as a reference to the model. You can use this model ID to query models, as follows:

• Generate embeddings to translate text prompts to numerical vectors. You can store generated embeddings as vector data when you enable vector embedding support on your instance. For more information, see Enable and disable vector embeddings on your instance.

• Invoke predictions to call a model using SQL within a transaction.

To register and call remote AI models with your Cloud SQL instance, your instance must be installed with maintenance version MYSQL_VERSION.R20250531.01_14 or later. If your instance is running a maintenance version earlier than MYSQL_VERSION.R20250531.01_14, then you can only use the following functions as documented with the integration of Cloud SQL and Vertex AI.

• mysql.ml_embedding()
• mysql.ml_predict_row()

To upgrade your instance to maintenance version MYSQL_VERSION.R20250531.01_14 or later, see Self-service maintenance. After you upgrade your instance, you can use the following functions:

• mysql.ml_create_model_registration(): registers the model endpoint that's used in the prediction or embedding function
• mysql.ml_create_sm_secret_registration(): uses secrets in Google Cloud Secret Manager, where the API keys are stored

In addition, you can use the following functions with your registered model provider:

• mysql.ml_embedding(): generates text embeddings
• mysql.ml_predict_row(): generates predictions when you call generic models that support the JSON input and output formats

Required database user privileges

To register and call remote AI models, you must be logged in as a MySQL database user who has been granted SELECT and EXECUTE on mysql.* privileges.

By default, any user with the cloudsqlsuperuser role has these privileges or can create a user and grant the required privileges.

For more information about the cloudsqlsuperuser role in Cloud SQL, see MySQL 8.0 user privileges and MySQL 8.4 user privileges.

Key concepts

Before you start using model endpoint management, understand the concepts required to connect to and use the models.

Model provider

Model provider is the supported model hosting provider. The following table shows the model provider value you must set based on the model provider that you use:

Model provider	Set in function as…
Vertex AI (includes Gemini)	google
Anthropic	anthropic
Hugging Face	hugging_face
OpenAI	open_ai
Other models hosted outside of Vertex AI, Anthropic, Hugging Face, and OpenAI	custom

The default model provider is custom.

If your Cloud SQL instance isn't configured with a public IP address, then you can't use a model provider other than Vertex AI.

Model types

Model types are the types of the AI model. When you register a model endpoint, you can set the text_embedding or generic model types for the endpoint.

If you use Vertex AI as your model provider, then you don't need to register a model endpoint because the endpoints are supported automatically. By default, with Vertex AI, you use the text-embedding-005 model.

Other text embedding models

For other text embedding models, you need to create transform functions to handle the input and output formats that the model supports. Optionally, you can use the HTTP header generation function that generates custom headers required by your model.

The model type for these models is text_embedding.

Generic models

Model endpoint management also supports registering of all other model types apart from text embedding models. To invoke predictions for generic models, use the mysql.ml_predict_row() function. You can set model endpoint metadata, such as a request endpoint and HTTP headers that are specific to your model.

You can't pass transform functions when you register a generic model endpoint. Ensure that when you invoke predictions the input to the function is in the JSON format, and that you parse the JSON output to derive the final output.

The model type for these models is generic. Because generic is the default model type, if you register model endpoints for this type, then setting the model type is optional.

With Vertex AI, model endpoint management includes pre-registered support for the gemini-2.5-flash model.

Authentication methods

You can enable support for vector embeddings in your Cloud SQL for MySQL instance and then specify different authentication methods to access your model. Setting these methods is optional and is required only if you need to authenticate to access your model.

For Vertex AI models, the Cloud SQL service account is used for authentication. For other models, the API key or bearer token that is stored as a secret in the Secret Manager can be used with the mysql.ml_create_sm_secret_registration() SQL function.

The following table shows the authentication methods that you can set:

Authentication method	Set in function as…	Model provider
Cloud SQL service agent	auth_type_cloudsql_service_agent_iam	Vertex AI provider
Secret Manager	auth_type_secret_manager	Models hosted outside of Vertex AI

Prediction functions

mysql.ml_embedding()

Calls a registered text embedding model endpoint to generate embeddings. It includes built-in support for all embedding models by Vertex AI.

For text embedding models without built-in support, the input and output parameters are unique to a model and need to be transformed for the function to call the model. Create a transform input function to transform input of the prediction function to the model specific input, and a transform output function to transform model specific output to the prediction function output.

mysql.ml_predict_row()

Calls a registered generic model endpoint, if the endpoint supports JSON-based APIs to invoke predictions.

Transform functions

Transform functions modify the input to a format that the model understands, and convert the model response to the format that the prediction function expects. The transform functions are used when registering the text-embedding model endpoint without built-in support. The signature of the transform functions depends on the prediction function for the model type.

You can't use transform functions when registering a generic model endpoint.

The following shows the signatures for the prediction function for text embedding models:

// define custom model specific input/output transform functions.
CREATE FUNCTION IF NOT EXISTS input_transform_function(model_id VARCHAR(100), input_text TEXT) RETURNS JSON DETERMINISTIC;

// the returned BLOB should be of type VECTOR
CREATE FUNCTION IF NOT EXISTS output_transform_function(model_id VARCHAR(100), response_json JSON) RETURNS BLOB DETERMINISTIC;

Note: If your instance has binary logging enabled and the log_bin_trust_function_creators flag set to off, then you must include a DETERMINISTIC, READS SQL DATA or NO SQL characteristic in your function. For more information, see log_bin_trust_function_creators in the MySQL documentation.

For more information about how to create transform functions, see Transform functions example.

HTTP header generation function

The HTTP header generation function generates the output in JSON key value pairs that are used as HTTP headers. The signature of the prediction function defines the signatures of the header generation function.

The following example shows the signature for the mysql.ml_embedding() prediction function:

CREATE FUNCTION IF NOT EXISTS generate_headers(model_id VARCHAR(100), input TEXT) RETURNS JSON DETERMINISTIC;

For the mysql.ml_predict_row() prediction function, the signature is as follows:

CREATE FUNCTION IF NOT EXISTS generate_headers(model_id VARCHAR(100), input JSON) RETURNS JSON DETERMINISTIC;

For more information about how to create a header generation function, see Header generation function example.

Limitations

• When you run any of the model registration or secret management functions, you implicitly commit any open transactions in the session. The prediction functions don't implicitly commit transactions.

• If you export or import your database with mysqldump, then the model endpoint catalog isn't exported.

• A user database that contains transform functions can't have a period character ('.') in its name. For example, a database called my.sql isn't supported.

• Model endpoint management is available only for Cloud SQL for MySQL version 8.0.36 and later.

What's next

• Set up authentication for model providers.
• Register a model endpoint with model endpoint management.
• Learn about the model endpoint management reference.