Model endpoint management reference

Select a documentation version:

This page lists parameters for different functions provided by the google_ml_integration extension to register and manage model endpoints, and secrets with model endpoint management.

You must set the google_ml_integration.enable_model_support database flag to on before you can start using the extension.

For more information, see Use Model endpoint management with AlloyDB Omni for AI models .

Models

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

`google_ml.create_model()` function

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 other text embedding model endpoints with built-in support

The model-specific endpoint when adding other text embedding and generic model endpoints. For AlloyDB for PostgreSQL, provide an https URL.

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 a list of request URLs for Vertex AI model endpoints, see Vertex AI model endpoints request URL .

For custom hosted model endpoints, ensure that the model endpoint is accessible from the network where AlloyDB is located.

 PROVIDER_ID

required for text embedding model endpoints with built-in support

The provider of the model endpoint. The default value is custom .

Set to one of the following:

google for Vertex AI model endpoints
open_ai for OpenAI model endpoints
hugging_face for Hugging Face model endpoints
anthropic for Anthropic model endpoints
custom for other providers

 MODEL_TYPE

optional for generic model endpoints

The model type.

Set to one of the following:

text_embedding for text embedding model endpoints
generic for all other model endpoints

 MODEL_QUALIFIED_NAME

required for text embedding models with built-in support; optional for other model endpoints

The fully qualified name for text embedding models with built-in support.

For Vertex AI qualified names that you must use for pre-registered models, see Pre-registered Vertex AI models .

For qualified names that you must use for OpenAI models with built-in support, see Models with built-in support

 AUTH_TYPE

optional unless the model endpoint has specific authentication requirement

The authentication type used by the model endpoint.

You can set it to either alloydb_service_agent_iam for Vertex AI models or secret_manager for other providers, if they use Secret Manager for authentication.

You don't need to set this value if you are using authentication headers.

 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 name of the function that generates custom headers.

For Anthropic models, model endpoint management provides a google_ml.anthropic_claude_header_gen_fn function that you can use for default versions.

The signature of this function depends on the prediction function that you use. See 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 
' 
 );

For information about the values that you must set for each parameter, see Create a model .

`google_ml.drop_model()` function

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 defined.

`google_ml.list_model()` function

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 defined.

`google_ml.model_info_view` 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()` function

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.
`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()` function

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 
' 
 );

For information about the values that you must set for each parameter, see Create a secret .

`google_ml.drop_sm_secret()` function

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 was 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()` function

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 example SQL queries to generate text embeddings, see Transform function examples for AlloyDB Omni .

`google_ml.predict_row()` function

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 example SQL queries to invoke predictions, see Examples for AlloyDB Omni .

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": ["AlloyDB 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, we 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 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 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": ["AlloyDB 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 
 ; 
 $$ 
 ;

Header generation function using API Key

The following examples show how to set up authentication using the API key.

embedding model

  CREATE 
  
 OR 
  
 REPLACE 
  
 FUNCTION 
  
 header_gen_func 
 ( 
  
 model_id 
  
 VARCHAR 
 ( 
 100 
 ), 
  
 input_text 
  
 TEXT 
 ) 
 RETURNS 
  
 JSON 
 LANGUAGE 
  
 plpgsql 
 AS 
  
 $$ 
 # 
 variable_conflict 
  
 use_variable 
 BEGIN 
  
 RETURN 
  
 json_build_object 
 ( 
 'Authorization' 
 , 
  
 ' API_KEY 
' 
 ):: 
 JSON 
 ; 
 END 
 ; 
 $$ 
 ;

Replace the API_KEY with the API key of the model provider.

generic model

  CREATE 
  
 OR 
  
 REPLACE 
  
 FUNCTION 
  
 header_gen_func 
 ( 
  
 model_id 
  
 VARCHAR 
 ( 
 100 
 ), 
  
 response_json 
  
 JSON 
 ) 
 RETURNS 
  
 JSON 
 LANGUAGE 
  
 plpgsql 
 AS 
  
 $$ 
 # 
 variable_conflict 
  
 use_variable 
 DECLARE 
 transformed_output 
  
 REAL 
 []; 
 BEGIN 
  
 -- code to add Auth token to API request 
 RETURN 
  
 json_build_object 
 ( 
 'x-api-key' 
 , 
  
 ' API_KEY 
' 
 , 
  
 'anthropic-version' 
 , 
  
 '2023-06-01' 
 ):: 
 JSON 
 ; 
 END 
 ; 
 $$ 
 ;

Replace the API_KEY with the API key of the model provider.

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.

Supported models

You can use model endpoint management to register any text embedding or generic model endpoint. Model endpoint management also includes pre-registered Vertex AI models and models with built-in support. For more information about different model types, see Model type .

Pre-registered Vertex AI models

Model type

Model ID

Extension version

generic

gemini-1.5-pro:streamGenerateContent
gemini-1.5-pro:generateContent
gemini-1.0-pro:generateContent

version 1.4.2 and later

text_embedding

textembedding-gecko
text-embedding-gecko@001

version 1.3 and later

Models with built-in support

Vertex AI

Qualified model name	Model type
`text-embedding-gecko@001`	`text-embedding`
`text-embedding-gecko@003`	`text-embedding`
`text-embedding-004`	`text-embedding`
`text-embedding-005`	`text-embedding`
`text-embedding-preview-0815`	`text-embedding`
`text-multilingual-embedding-002`	`text-embedding`

OpenAI

Qualified model name	Model type
`text-embedding-ada-002`	`text-embedding`
`text-embedding-3-small`	`text-embedding`
`text-embedding-3-large`	`text-embedding`

Anthropic

Qualified model name	Model type
`claude-3-opus-20240229`	`generic`
`claude-3-sonnet-20240229`	`generic`
`claude-3-haiku-20240307`	`generic`