Model endpoint management reference

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This page lists parameters for different functions provided by the google_ml_integration extension to register and manage model endpoints. The page also lists the secrets that you can manage by using model endpoint management. To use AI models in production environments, see Build generative AI applications using Cloud SQL.

Note: Before you can start using the extension for model endpoint management, you must set the google_ml_integration.enable_model_support database flag to on. Also, for more information about model endpoint management, see Register and call remote AI models in Cloud SQL.

Models

Use this reference to understand parameters for functions that let you manage model endpoints.

google_ml.create_model()

The following shows how to call the google_ml.create_model() SQL function used to register model endpoint metadata:

  CALL
    google_ml.create_model(
      model_id => 'MODEL_ID',
      model_request_url => 'REQUEST_URL',
      model_provider => 'PROVIDER_ID',
      model_type => 'MODEL_TYPE',
      model_qualified_name => 'MODEL_QUALIFIED_NAME',
      model_auth_type => 'AUTH_TYPE',
      model_auth_id => 'AUTH_ID',
      generate_headers_fn => 'GENERATE_HEADER_FUNCTION',
      model_in_transform_fn => 'INPUT_TRANSFORM_FUNCTION',
      model_out_transform_fn => 'OUTPUT_TRANSFORM_FUNCTION');

Parameter	Required	Description
MODEL_ID	required for all model endpoints	A unique ID for the model endpoint that you define.
REQUEST_URL	optional for text embedding model endpoints with built-in support	The model-specific endpoint when adding other text embedding and generic model endpoints. The request URL that the function generates for built-in model endpoints refers to your cluster's project and region or location. If you want to refer to another project, then ensure that you specify the model_request_url explicitly. For custom hosted model endpoints, ensure that the model endpoint is accessible through the internet.
PROVIDER_ID	required for text embedding model endpoints with built-in support	The provider of the model endpoint. The default value is custom. For Cloud SQL, set the provider to google for Vertex AI model endpoints, open_ai for OpenAI model endpoints, anthropic for Anthropic model endpoints, hugging_face for Hugging Face model endpoints, or custom for custom-hosted model endpoints.
MODEL_TYPE	optional for generic model endpoints	The model type. You can set this value to text_embedding for text embedding model endpoints or generic for all other model endpoints.
MODEL_QUALIFIED_NAME	required for OpenAI model endpoints; optional for other model endpoints	The fully qualified name in case the model endpoint has multiple versions or if the model endpoint defines it—for example, textembedding-gecko@001 or textembedding-gecko@002. Since the textembedding-gecko@001 model is pre-registered with model endpoint management, you can generate embeddings using textembedding-gecko@001 as the model ID.
AUTH_TYPE	optional unless model endpoint has specific authentication requirement	The authentication type used by the model endpoint. You can set it to either cloudsql_service_agent_iam for Vertex AI models or secret_manager for other providers.
AUTH_ID	don't set for Vertex AI model endpoints; required for all other model endpoints that store secrets in Secret Manager	The secret ID that you set and is subsequently used when registering a model endpoint.
GENERATE_HEADER_FUNCTION	optional	The function name you set to generate custom headers. The signature of this function depends on the google_ml.predict_row() function. See HTTP Header generation function.
INPUT_TRANSFORM_FUNCTION	optional for text embedding model endpoints with built-in support;don't set for generic model endpoints	The function to transform input of the corresponding prediction function to the model-specific input. See Transform functions.
OUTPUT_TRANSFORM_FUNCTION	optional for text embedding model endpoints with built-in support;don't set for generic model endpoints	The function to transform model specific output to the prediction function output. See Transform functions.

google_ml.alter_model()

The following shows how to call the google_ml.alter_model() SQL function used to update model endpoint metadata:

    CALL
    google_ml.alter_model(
      model_id => 'MODEL_ID',
      model_request_url => 'REQUEST_URL',
      model_provider => 'PROVIDER_ID',
      model_type => 'MODEL_TYPE',
      model_qualified_name => 'MODEL_QUALIFIED_NAME',
      model_auth_type => 'AUTH_TYPE',
      model_auth_id => 'AUTH_ID',
      generate_headers_fn => 'GENERATE_HEADER_FUNCTION',
      model_in_transform_fn => 'INPUT_TRANSFORM_FUNCTION',
      model_out_transform_fn => 'OUTPUT_TRANSFORM_FUNCTION');

google_ml.drop_model()

The following shows how to call the google_ml.drop_model() SQL function used to drop a model endpoint:

  CALL google_ml.drop_model('MODEL_ID');

Parameter	Description
MODEL_ID	A unique ID for the model endpoint that you define.

google_ml.list_model()

The following shows how to call the google_ml.list_model() SQL function used to list model endpoint information:

  SELECT google_ml.list_model('MODEL_ID');

Parameter	Description
MODEL_ID	A unique ID for the model endpoint that you define.

google_ml.model_info_view

The following shows how to call the google_ml.model_info_view view that is used to list model endpoint information for all model endpoints:

  SELECT * FROM google_ml.model_info_view;

Secrets

Use this reference to understand parameters for functions that let you manage secrets.

google_ml.create_sm_secret()

The following shows how to call the google_ml.create_sm_secret() SQL function used to add the secret created in Secret Manager:

    CALL
    google_ml.create_sm_secret(
      secret_id => 'SECRET_ID',
      secret_path => 'projects/PROJECT_ID/secrets/SECRET_MANAGER_SECRET_ID/versions/VERSION_NUMBER');

Parameter	Description
SECRET_ID	The secret ID that you set and is subsequently used when registering a model endpoint.
PROJECT_ID	The ID of your Google Cloud project that contains the secret. This project can be different from the project that contains your Cloud SQL instance.
SECRET_MANAGER_SECRET_ID	The secret ID set in Secret Manager when you created the secret.
VERSION_NUMBER	The version number of the secret ID.

google_ml.alter_sm_secret()

The following shows how to call the google_ml.alter_sm_secret() SQL function used to update secret information:

  CALL
    google_ml.alter_sm_secret(
      secret_id => 'SECRET_ID',
      secret_path => 'projects/PROJECT_ID/secrets/SECRET_MANAGER_SECRET_ID/versions/VERSION_NUMBER');

Parameter	Description
SECRET_ID	The secret ID that you set and is subsequently used when registering a model endpoint.
PROJECT_ID	The ID of your Google Cloud project that contains the secret. This project can be different from the project that contains your Cloud SQL instance.
SECRET_MANAGER_SECRET_ID	The secret ID set in Secret Manager when you created the secret.
VERSION_NUMBER	The version number of the secret ID.

google_ml.drop_sm_secret()

The following shows how to call the google_ml.drop_sm_secret() SQL function used to drop a secret:

  CALL google_ml.drop_sm_secret('SECRET_ID');

Parameter	Description
SECRET_ID	The secret ID that you set and is subsequently used when registering a model endpoint.

Prediction functions

Use this reference to understand parameters for functions that let you generate embeddings or invoke predictions.

google_ml.embedding()

The following shows how to generate embeddings:

SELECT
  google_ml.embedding(
    model_id => 'MODEL_ID',
    contents => 'CONTENT');

Parameter	Description
MODEL_ID	A unique ID for the model endpoint that you define.
CONTENT	the text to translate into a vector embedding.

For examples of SQL queries to generate text embeddings, see Examples.

google_ml.predict_row()

The following shows how to invoke predictions:

SELECT
  google_ml.predict_row(
    model_id => 'MODEL_ID',
    request_body => 'REQUEST_BODY');

Parameter	Description
MODEL_ID	A unique ID for the model endpoint that you define.
REQUEST_BODY	the parameters to the prediction function, in JSON format.

For examples of SQL queries to invoke predictions, see Examples.

Transform functions

Use this reference to understand parameters for input and output transform functions.

Input transform function

The following shows the signature for the prediction function for text embedding model endpoints:

  CREATE OR REPLACE FUNCTION INPUT_TRANSFORM_FUNCTION(model_id VARCHAR(100), input_text TEXT) RETURNS JSON;

Parameter	Description
INPUT_TRANSFORM_FUNCTION	The function to transform input of the corresponding prediction function to the model endpoint-specific input.

Output transform function

The following shows the signature for the prediction function for text embedding model endpoints:

  CREATE OR REPLACE FUNCTION OUTPUT_TRANSFORM_FUNCTION(model_id VARCHAR(100), response_json JSON) RETURNS real[];

Parameter	Description
OUTPUT_TRANSFORM_FUNCTION	The function to transform model endpoint-specific output to the prediction function output.

Transform functions example

To better understand how to create transform functions for your model endpoint, consider a custom-hosted text embedding model endpoint that requires JSON input and output.

The following example cURL request creates embeddings based on the prompt and the model endpoint:

  curl -m 100 -X POST https://cymbal.com/models/text/embeddings/v1 \
    -H "Content-Type: application/json"
    -d '{"prompt": ["Cloud SQL Embeddings"]}'

The following example response is returned:

[[ 0.3522231  -0.35932037  0.10156056  0.17734447 -0.11606089 -0.17266059
   0.02509351  0.20305622 -0.09787305 -0.12154685 -0.17313677 -0.08075467
   0.06821183 -0.06896557  0.1171584  -0.00931572  0.11875633 -0.00077482
   0.25604948  0.0519384   0.2034983  -0.09952664  0.10347155 -0.11935943
  -0.17872004 -0.08706985 -0.07056875 -0.05929353  0.4177883  -0.14381726
   0.07934926  0.31368294  0.12543282  0.10758053 -0.30210832 -0.02951015
   0.3908268  -0.03091059  0.05302926 -0.00114946 -0.16233777  0.1117468
  -0.1315904   0.13947351 -0.29569918 -0.12330773 -0.04354299 -0.18068913
   0.14445548  0.19481727]]

Based on this input and response, you can infer the following:

• The model expects JSON input through the prompt field. This field accepts an array of inputs. As the google_ml.embedding() function is a row level function, it expects one text input at a time. Thus, you need to create an input transform function that builds an array with a single element.

• The response from the model is an array of embeddings, one for each prompt input to the model. As the google_ml.embedding() function is a row level function, it returns a single input at a time. Thus, you need to create an output transform function that can be used to extract the embedding from the array.

The following example shows the input and output transform functions that is used for this model endpoint when it is registered with model endpoint management:

input transform function

CREATE OR REPLACE FUNCTION cymbal_text_input_transform(model_id VARCHAR(100), input_text TEXT)
RETURNS JSON
LANGUAGE plpgsql
AS $$
DECLARE
  transformed_input JSON;
  model_qualified_name TEXT;
BEGIN
  SELECT json_build_object('prompt', json_build_array(input_text))::JSON INTO transformed_input;
  RETURN transformed_input;
END;
$$;

output transform function

CREATE OR REPLACE FUNCTION cymbal_text_output_transform(model_id VARCHAR(100), response_json JSON)
RETURNS REAL[]
LANGUAGE plpgsql
AS $$
DECLARE
  transformed_output REAL[];
BEGIN
SELECT ARRAY(SELECT json_array_elements_text(response_json->0)) INTO transformed_output;
RETURN transformed_output;
END;
$$;

HTTP header generation function

The following shows signature for the header generation function that can be used with the google_ml.embedding() prediction function when registering other text embedding model endpoints.

  CREATE OR REPLACE FUNCTION GENERATE_HEADERS(model_id VARCHAR(100), input_text TEXT) RETURNS JSON;

For the google_ml.predict_row() prediction function, the signature is as follows:

CREATE OR REPLACE FUNCTION GENERATE_HEADERS(model_id TEXT, input JSON) RETURNS JSON;

Parameter	Description
GENERATE_HEADERS	The function to generate custom headers. You can also pass the authorization header generated by the header generation function while registering the model endpoint.

Header generation function example

To better understand how to create a function that generates output in JSON key value pairs that are used as HTTP headers, consider a custom-hosted text embedding model endpoint.

The following example cURL request passes the version HTTP header, which is used by the model endpoint:

  curl -m 100 -X POST https://cymbal.com/models/text/embeddings/v1 \
      -H "Content-Type: application/json" \
      -H "version: 2024-01-01" \
      -d '{"prompt": ["Cloud SQL Embeddings"]}'

The model expects text input through the version field and returns the version value in JSON format. The following example shows the header generation function that is used for this text embedding model endpoint when it is registered with model endpoint management:

CREATE OR REPLACE FUNCTION header_gen_fn(model_id VARCHAR(100), input_text TEXT)
RETURNS JSON
LANGUAGE plpgsql
AS $$
BEGIN
  RETURN json_build_object('version', '2024-01-01')::JSON;
END;
$$;

Request URL generation

Use the request URL generation function to infer the request URLs for the model endpoints with built-in support. The following shows the signature for this function:

CREATE OR REPLACE FUNCTION GENERATE_REQUEST_URL(provider google_ml.model_provider, model_type google_ml.MODEL_TYPE, model_qualified_name VARCHAR(100), model_region VARCHAR(100) DEFAULT NULL)

Parameter	Description
GENERATE_REQUEST_URL	The function to generate request URL generated by the extension for model endpoints with built-in support.