Register and call remote AI models using model endpoint management

To invoke predictions or generate embeddings using a model, register the model endpoint with model endpoint management.

Before you begin

Before you register a model endpoint with model endpoint management, you must enable the google_ml_integration extension and set up authentication based on the model provider, if your model endpoint requires authentication.

Make sure that you access your database with the postgres default username.

Enable the extension

You must add and enable the google_ml_integration extension before you can start using the associated functions. Model endpoint management requires that the google_ml_integration extension is installed.

1. Verify that the google_ml_integration.enable_model_support database flag is set to on for an instance. For more information about setting database flags, see Configure an instance's database flags.

2. Connect to your database using psql or AlloyDB for PostgreSQL Studio.

3. Optional: If the google_ml_integration extension is already installed, alter it to update to the latest version:

       ALTER EXTENSION google_ml_integration UPDATE;
   

4. Add the google_ml_integration extension using psql:

     CREATE EXTENSION google_ml_integration;
   

5. Optional: Grant permission to a non-super PostgreSQL user to manage model metadata:

     GRANT SELECT, INSERT, UPDATE, DELETE ON ALL TABLES IN SCHEMA google_ml TO NON_SUPER_USER;
   

   Replace NON_SUPER_USER with the non-super PostgreSQL username.

6. Ensure that outbound IP is enabled to access models hosted outside of your VPC, such as third-party models. For more information, see Add outbound connectivity.

Set up authentication

The following sections show how to set up authentication before registering a model endpoint.

Set up authentication for Vertex AI

To use the Google Vertex AI model endpoints, you must add Vertex AI permissions to the IAM-based AlloyDB service account you use to connect to the database. For more information about integrating with Vertex AI, see Integrate with Vertex AI.

Set up authentication using Secret Manager

This section explains how to set up authentication if you are using Secret Manager to store authentication details for third party providers.

This step is optional if your model endpoint doesn't handle authentication through Secret Manager—for example, if your model endpoint uses HTTP headers to pass authentication information or doesn't use authentication at all.

To create and use an API key or a bearer token, complete the following steps:

1. Create the secret in Secret Manager. For more information, see Create a secret and access a secret version.

   The secret path is used in the google_ml.create_sm_secret() SQL function.

2. Grant permissions to the AlloyDB cluster to access the secret.

     gcloud secrets add-iam-policy-binding 'SECRET_NAME' \
         --member="serviceAccount:SERVICE_ACCOUNT_ID" \
         --role="roles/secretmanager.secretAccessor"
   

   Replace the following:

   • SECRET_NAME: the secret name in Secret Manager.

   • SERVICE_ACCOUNT_ID: the ID of the IAM-based service account in the serviceAccount:[e-mail] format—for example, [e-mail].

     You can also grant this role to the service account at the project level. For more information, see Add Identity and Access Management policy binding

Set up authentication using headers

The following example shows how to set up authentication using a function. The function returns a JSON object that contains the headers required to make a request to the embedding model.

  CREATE OR REPLACE FUNCTION HEADER_GEN_FUNCTION(
    model_id VARCHAR(100),
    input_text TEXT
  )
  RETURNS JSON
  LANGUAGE plpgsql
  AS $$
  #variable_conflict use_variable
  DECLARE
    api_key VARCHAR(255) := 'API_KEY';
    header_json JSON;
  BEGIN
    header_json := json_build_object(
      'Content-Type', 'application/json',
      'Authorization', 'Bearer ' || api_key
    );
    RETURN header_json;
  END;
  $$;

Replace the following:

• HEADER_GEN_FUNCTION: the name of the header generation function that you can use when registering a model.
• API_KEY: the API key of the model provider.

Text embedding models

This section shows how to register model endpoints with model endpoint management.

The model endpoint management supports some text embedding and generic Vertex AI models as pre-registered model endpoints. You can directly use the model ID to generate embeddings or invoke predictions, based on the model type. For more information about supported pre-registered models, see Pre-registered Vertex AI models.

Note: Vertex AI model support is governed by Vertex AI model versioning and lifecycle guidelines. For more information about stable versions, see Model versions and lifecycle.

For example, to call the pre-registered textembedding-gecko model, you can directly call the model using the embedding function:

SELECT
      google_ml.embedding(
        model_id => 'textembedding-gecko',
        content => 'AlloyDB is a managed, cloud-hosted SQL database service');

Similarly, to call the pre-registered gemini-1.5-pro:generateContent model, you can directly call the model using the prediction function:

 SELECT
        json_array_elements(
        google_ml.predict_row(
            model_id => 'gemini-1.5-pro:generateContent',
            request_body => '{
        "contents": [
            {
                "role": "user",
                "parts": [
                    {
                        "text": "For TPCH database schema as mentioned here https://www.tpc.org/TPC_Documents_Current_Versions/pdf/TPC-H_v3.0.1.pdf , generate a SQL query to find all supplier names which are located in the India nation."
                    }
                ]
            }
        ]
        }'))-> 'candidates' -> 0 -> 'content' -> 'parts' -> 0 -> 'text'; 

To generate embeddings, see how to generate embedding for pre-registered model endpoints. To invoke predictions, see how to invoke predictions for pre-registered model endpoints.

Text embedding models with built-in support

The model endpoint management provides built-in support for some models by Vertex AI and OpenAI. For the list of models with built-in support, see Models with built-in support.

For models with built-in support, you can set the qualified name as the model qualified name and specify the request URL. Model endpoint management automatically identifies the model and sets up default transform functions.

Vertex AI embedding models

The following steps show how to register Vertex AI models with built-in support. The textembedding-gecko@002 and the text-multilingual-embedding-002 model endpoint is used as an example.

Note: Only the text-multilingual-embedding-002 model endpoint requires setting up transform functions provided by model endpoint management.

Ensure that both the AlloyDB cluster and the Vertex AI model you are querying are in the same region.

1. Connect to your database using psql.

2. Create and enable the google_ml_integration extension.

3. Call the create model function to add the model endpoint:

   textembedding-gecko@002

     CALL
       google_ml.create_model(
         model_id => 'textembedding-gecko@002',
         model_request_url => 'publishers/google/models/textembedding-gecko@002',
         model_provider => 'google',
         model_qualified_name => 'textembedding-gecko@002',
         model_type => 'text_embedding',
         model_auth_type => 'alloydb_service_agent_iam');
   

   text-multilingual-embedding-002

     CALL
       google_ml.create_model(
         model_id => 'text-multilingual-embedding-002',
         model_request_url => 'publishers/google/models/text-multilingual-embedding-002',
         model_provider => 'google',
         model_qualified_name => 'text-multilingual-embedding-002',
         model_type => 'text_embedding',
         model_auth_type => 'alloydb_service_agent_iam'
         model_in_transform_fn => 'google_ml.vertexai_text_embedding_input_transform',
         model_out_transform_fn => 'google_ml.vertexai_text_embedding_output_transform');
   

If the model is stored in the another project and region than your AlloyDB cluster, then set the request URL to projects/PROJECT_ID/locations/REGION_ID/publishers/google/models/MODEL_ID, where REGION_ID is the region where your model is hosted, and the MODEL_ID is the qualified model name.

In addition, grant the Vertex AI User (roles/aiplatform.user) role to AlloyDB service account of the project where AlloyDB instance resides so that AlloyDB can access the model hosted in the other project.

Open AI text embedding model

The google_ml_integration extension automatically sets up default transform functions and invokes calls to the remote OpenAI models. For the list of OpenAI models with built-in support, see Models with built-in support.

The following example adds the text-embedding-ada-002 OpenAI model endpoint. You can register the OpenAI text-embedding-3-small and text-embedding-3-large model endpoints using the same steps and setting the model qualified names specific to the models.

1. Connect to your database using psql.
2. Create and enable the google_ml_integration extension.
3. Add the OpenAI API key as a secret to the Secret Manager for authentication.

4. Call the secret stored in the 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');
   

   Replace the following:

   • SECRET_ID: the secret ID that you set and is subsequently used when registering a model endpoint—for example, key1.
   • SECRET_MANAGER_SECRET_ID: the secret ID set in Secret Manager when you created the secret.
   • PROJECT_ID: the ID of your Google Cloud project.
   • VERSION_NUMBER: the version number of the secret ID.

5. Call the create model function to register the text-embedding-ada-002 model endpoint:

   CALL
     google_ml.create_model(
       model_id => 'MODEL_ID',
       model_provider => 'open_ai',
       model_type => 'text_embedding',
       model_qualified_name => 'text-embedding-ada-002',
       model_auth_type => 'secret_manager',
       model_auth_id => 'SECRET_ID');
   

   Replace the following:

   • MODEL_ID: a unique ID for the model endpoint that you define. This model ID is referenced for metadata that the model endpoint needs to generate embeddings or invoke predictions.
   • SECRET_ID: the secret ID you used earlier in the google_ml.create_sm_secret() procedure.

To generate embeddings, see how to generate embedding for model endpoints with built-in support.

Custom-hosted text embedding model

This section shows how to register a custom-hosted model endpoint along with creating transform functions, and optionally, custom HTTP headers. All custom-hosted model endpoints are supported regardless of where they are hosted.

The following example adds the custom-embedding-model custom model endpoint hosted by Cymbal. The cymbal_text_input_transform and cymbal_text_output_transform transform functions are used to transform the input and output format of the model to the input and output format of the prediction function.

To register custom-hosted text embedding model endpoints, complete the following steps:

1. Connect to your database using psql.

2. Create and enable the google_ml_integration extension.

3. Optional: Add the API key as a secret to the Secret Manager for authentication.

4. Call the secret stored in the 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');
   

   Replace the following:

   • SECRET_ID: the secret ID that you set and is subsequently used when registering a model endpoint—for example, key1.
   • SECRET_MANAGER_SECRET_ID: the secret ID set in Secret Manager when you created the secret.
   • PROJECT_ID: the ID of your Google Cloud project.
   • VERSION_NUMBER: the version number of the secret ID.
   Note: Secret Manager generates an Authorization: Bearer SECRET_VALUE_FROM_SECRET_MANAGER header for authentication by default. If this format matches your model endpoint's authorization bearer token format, then you don't have to generate auth headers using the header generation function.

5. Create the input and output transform functions based on the following signature for the prediction function for text embedding model endpoints. For more information about how to create transform functions, see Transform functions example.

   The following are example transform functions that are specific to the custom-embedding-model text embedding model endpoint:

   -- Input Transform Function corresponding to the custom model endpoint
   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 corresponding to the custom model endpoint
   CREATE OR REPLACE FUNCTION cymbal_text_output_transform(model_id VARCHAR(100), response_json JSON)
   RETURNS REAL[]
   LANGUAGE plpgsql
   AS $$
   DECLARE
     transformed_output REAL[];
   BEGIN
     SELECT ARRAY(SELECT json_array_elements_text(response_json->0)) INTO transformed_output;
     RETURN transformed_output;
   END;
   $$;
   

6. Call the create model function to register the custom embedding model endpoint:

   CALL
     google_ml.create_model(
       model_id => 'MODEL_ID',
       model_request_url => 'REQUEST_URL',
       model_provider => 'custom',
       model_type => 'text_embedding',
       model_auth_type => 'secret_manager',
       model_auth_id => 'SECRET_ID',
       model_qualified_name => 'MODEL_QUALIFIED_NAME',
       model_in_transform_fn => 'cymbal_text_input_transform',
       model_out_transform_fn => 'cymbal_text_output_transform');
   

   Replace the following:

   • MODEL_ID: required. A unique ID for the model endpoint that you define-for example custom-embedding-model. This model ID is referenced for metadata that the model endpoint needs to generate embeddings or invoke predictions.
   • REQUEST_URL: required. The model-specific endpoint when adding custom text embedding and generic model endpoints—for example, https://cymbal.com/models/text/embeddings/v1. Ensure that the model endpoint is accessible through an internal IP address. Model endpoint management doesn't support public IP addresses.
   • MODEL_QUALIFIED_NAME: required if your model endpoint uses a qualified name. The fully qualified name in case the model endpoint has multiple versions.
   • SECRET_ID: the secret ID you used earlier in the google_ml.create_sm_secret() procedure.

Generic models

This section shows how to register any generic model endpoint that is available on a hosted model provider such as Hugging Face, OpenAI, Vertex AI, Anthropic, or any other provider. This section shows examples to register a generic model endpoint hosted on Hugging Face, a generic gemini-pro model from Vertex AI Model Garden, and the claude-haiku model endpoint.

You can register any generic model endpoint as long as the input and output is in the JSON format. Based on your model endpoint metadata, you might need to generate HTTP headers or define request URLs.

For more information about pre-registered generic models and models with built-in support, see Supported models.

Gemini model

Since some gemini-pro models are pre-registered, you can directly call the model ID to invoke predictions.

The following example uses the gemini-1.5-pro:generateContent model endpoint from the Vertex AI Model Garden.

1. Connect to your database using psql.
2. Create and enable the google_ml_integration extension.

3. Invoke predictions using the pre-registered model ID:

   SELECT
       json_array_elements(
       google_ml.predict_row(
           model_id => 'gemini-1.5-pro:generateContent',
           request_body => '{
       "contents": [
           {
               "role": "user",
               "parts": [
                   {
                       "text": "For TPCH database schema as mentioned here https://www.tpc.org/TPC_Documents_Current_Versions/pdf/TPC-H_v3.0.1.pdf , generate a SQL query to find all supplier names which are located in the India nation."
                   }
               ]
           }
       ]
       }'))-> 'candidates' -> 0 -> 'content' -> 'parts' -> 0 -> 'text'; 
   

Generic model on Hugging Face

The following example adds the facebook/bart-large-mnli custom classification model endpoint hosted on Hugging Face.

1. Connect to your database using psql.
2. Create and enable the google_ml_integration extension.
3. Add the OpenAI API key as a secret to the Secret Manager for authentication. If you have already created a secret for any other OpenAI model, you can reuse the same secret.

4. Call the secret stored in the 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');
   

   Replace the following:

   • SECRET_ID: the secret ID that you set and is subsequently used when registering a model endpoint.
   • SECRET_MANAGER_SECRET_ID: the secret ID set in Secret Manager when you created the secret.
   • PROJECT_ID: the ID of your Google Cloud project.
   • VERSION_NUMBER: the version number of the secret ID.

5. Call the create model function to register the facebook/bart-large-mnli model endpoint:

   CALL
     google_ml.create_model(
       model_id => 'MODEL_ID',
       model_provider => 'hugging_face',
       model_request_url => 'REQUEST_URL',
       model_qualified_name => 'MODEL_QUALIFIED_NAME',
       model_auth_type => 'secret_manager',
       model_auth_id => 'SECRET_ID');
   

   Replace the following:

   • MODEL_ID: a unique ID for the model endpoint that you define—for example, custom-classification-model. This model ID is referenced for metadata that the model endpoint needs to generate embeddings or invoke predictions.
   • REQUEST_URL: the model-specific endpoint when adding custom text embedding and generic model endpoints—for example, https://api-inference.huggingface.co/models/facebook/bart-large-mnli.
   • MODEL_QUALIFIED_NAME: the fully qualified name of the model endpoint version-for example, facebook/bart-large-mnli.
   • SECRET_ID: the secret ID you used earlier in the google_ml.create_sm_secret() procedure.

Anthropic generic model

The following example adds the claude-3-opus-20240229 model endpoint. Model endpoint management provides the header function required for registering Anthropic models.

1. Connect to your database using psql.

2. Create and enable the google_ml_integration extension.

   Secret Manager

   1. Add the bearer token as a secret to the Secret Manager for authentication.

   2. Call the secret stored in the 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');
      

      Replace the following:

      • SECRET_ID: the secret ID that you set and is subsequently used when registering a model endpoint.
      • SECRET_MANAGER_SECRET_ID: the secret ID set in Secret Manager when you created the secret.
      • PROJECT_ID: the ID of your Google Cloud project.
      • VERSION_NUMBER: the version number of the secret ID.

   3. Call the create model function to register the claude-3-opus-20240229 model endpoint.

      CALL
        google_ml.create_model(
          model_id => 'MODEL_ID',
          model_provider => 'anthropic',
          model_request_url => 'REQUEST_URL',
          model_auth_type => 'secret_manager',
          model_auth_id => 'SECRET_ID',
          generate_headers_fn => 'google_ml.anthropic_claude_header_gen_fn');
      

      Replace the following:

      • MODEL_ID: a unique ID for the model endpoint that you define—for example, anthropic-opus. This model ID is referenced for metadata that the model endpoint needs to generate embeddings or invoke predictions.
      • REQUEST_URL: the model-specific endpoint when adding custom text embedding and generic model endpoints—for example, https://api.anthropic.com/v1/messages.

   Auth header

   1. Use the google_ml.anthropic_claude_header_gen_fn default header generation function or create a header generation function.

        CREATE OR REPLACE FUNCTION anthropic_sample_header_gen_fn(model_id VARCHAR(100), request_body JSON)
        RETURNS JSON
        LANGUAGE plpgsql
        AS $$
        #variable_conflict use_variable
        BEGIN
              RETURN json_build_object('x-api-key', 'ANTHROPIC_API_KEY', 'anthropic-version', 'ANTHROPIC_VERSION')::JSON;
        END;
        $$;
      

      Replace the following:

      • ANTHROPIC_API_KEY: the anthropic API key.
      • ANTHROPIC_VERSION (Optional): the specific model version you want to use—for example, 2023-06-01.

   2. Call the create model function to register the claude-3-opus-20240229 model endpoint.

      CALL
        google_ml.create_model(
          model_id => 'MODEL_ID',
          model_provider => 'anthropic',
          model_request_url => 'REQUEST_URL',
          generate_headers_fn => 'google_ml.anthropic_claude_header_gen_fn');
      

      Replace the following:

      • MODEL_ID: a unique ID for the model endpoint that you define—for example, anthropic-opus. This model ID is referenced for metadata that the model endpoint needs to generate embeddings or invoke predictions.
      • REQUEST_URL: the model-specific endpoint when adding custom text embedding and generic model endpoints—for example, https://api.anthropic.com/v1/messages.

For more information, see how to invoke predictions for generic model endpoints.

What's next

• Learn about the model endpoint management reference
• Use sample templates for registering model endpoints