Model endpoint management reference

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.

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');

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');

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');

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');

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');

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');

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');

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');

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;

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[];

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:

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

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;

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)