Model endpoint management reference

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MySQL | PostgreSQL | SQL Server

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 
' 
 );

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:

sql
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.

Model endpoint management reference Stay organized with collections Save and categorize content based on your preferences.

Models

google_ml.create_model()

google_ml.alter_model()

google_ml.drop_model()

google_ml.list_model()

google_ml.model_info_view