As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

ModelService

class google.ai.generativelanguage_v1beta.services.model_service.ModelServiceAsyncClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.ai.generativelanguage_v1beta.services.model_service.transports.base.ModelServiceTransport, typing.Callable[[...], google.ai.generativelanguage_v1beta.services.model_service.transports.base.ModelServiceTransport]]] = 'grpc_asyncio', client_options: typing.Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)[source]

Provides methods for getting metadata information about Generative Models.

Instantiates the model service async client.

Parameters

  • credentials (Optional[google.auth.credentials.Credentials]) – The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.

  • transport (Optional[Union[str,ModelServiceTransport,Callable[..., ModelServiceTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport to use. If a Callable is given, it will be called with the same set of initialization arguments as used in the ModelServiceTransport constructor. If set to None, a transport is chosen automatically.

  • client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]) –

    Custom options for the client.

    1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “always” (always use the default mTLS endpoint), “never” (always use the default regular endpoint) and “auto” (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value).

    2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is “false” or not set, no client certificate will be used.

    3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.

  • client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises

google.auth.exceptions.MutualTlsChannelError – If mutual TLS transport creation failed for any reason.

property api_endpoint

Return the API endpoint used by the client instance.

Returns

The API endpoint used by the client instance.

Return type

str

static common_billing_account_path(billing_account: str) → str

Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str

Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str

Returns a fully-qualified location string.

static common_organization_path(organization: str) → str

Returns a fully-qualified organization string.

static common_project_path(project: str) → str

Returns a fully-qualified project string.

async create_tuned_model(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.CreateTunedModelRequest, dict]] = None, *, tuned_model: Optional[google.ai.generativelanguage_v1beta.types.tuned_model.TunedModel] = None, tuned_model_id: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation_async.AsyncOperation[source]

Creates a tuned model. Check intermediate tuning progress (if any) through the [google.longrunning.Operations] service.

Access status and results through the Operations service. Example: GET /v1/tunedModels/az2mb0bpw6i/operations/000-111-222

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_create_tuned_model():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceAsyncClient()

    # Initialize request argument(s)
    tuned_model = generativelanguage_v1beta.TunedModel()
    tuned_model.tuning_task.training_data.examples.examples.text_input = "text_input_value"
    tuned_model.tuning_task.training_data.examples.examples.output = "output_value"

    request = generativelanguage_v1beta.CreateTunedModelRequest(
        tuned_model=tuned_model,
    )

    # Make the request
    operation = client.create_tuned_model(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.CreateTunedModelRequest, dict]]) – The request object. Request to create a TunedModel.

  • tuned_model (google.ai.generativelanguage_v1beta.types.TunedModel) – Required. The tuned model to create. This corresponds to the tuned_model field on the request instance; if request is provided, this should not be set.

  • tuned_model_id (str) –

    Optional. The unique id for the tuned model if specified. This value should be up to 40 characters, the first character must be a letter, the last could be a letter or a number. The id must match the regular expression: [a-z]([a-z0-9-]{0,38}[a-z0-9])?.

    This corresponds to the tuned_model_id field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

An object representing a long-running operation.

The result type for the operation will be google.ai.generativelanguage_v1beta.types.TunedModel A fine-tuned model created using ModelService.CreateTunedModel.

Return type

google.api_core.operation_async.AsyncOperation

async delete_tuned_model(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.DeleteTunedModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]

Deletes a tuned model.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_delete_tuned_model():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.DeleteTunedModelRequest(
        name="name_value",
    )

    # Make the request
    await client.delete_tuned_model(request=request)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.DeleteTunedModelRequest, dict]]) – The request object. Request to delete a TunedModel.

  • name (str) –

    Required. The resource name of the model. Format: tunedModels/my-model-id

    This corresponds to the name field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

ModelServiceAsyncClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

info.

Parameters

  • info (dict) – The service account private key info.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

ModelServiceAsyncClient

classmethod from_service_account_json(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

ModelServiceAsyncClient

async get_model(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.GetModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.model.Model[source]

Gets information about a specific Model such as its version number, token limits, parameters and other metadata. Refer to the Gemini models guide for detailed model information.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_get_model():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.GetModelRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_model(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.GetModelRequest, dict]]) – The request object. Request for getting information about a specific Model.

  • name (str) –

    Required. The resource name of the model.

    This name should match a model name returned by the ListModels method.

    Format: models/{model}

    This corresponds to the name field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Information about a Generative Language Model.

Return type

google.ai.generativelanguage_v1beta.types.Model

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[google.api_core.client_options.ClientOptions] = None)[source]

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not “true”, the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “always”, use the default mTLS endpoint; if the environment variable is “never”, use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameters

client_options (google.api_core.client_options.ClientOptions) – Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.

Returns

returns the API endpoint and the

client cert source to use.

Return type

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

classmethod get_transport_class(label: Optional[str] = None) → Type[google.ai.generativelanguage_v1beta.services.model_service.transports.base.ModelServiceTransport]

Returns an appropriate transport class.

Parameters

label – The name of the desired transport. If none is provided, then the first transport in the registry is used.

Returns

The transport class to use.

async get_tuned_model(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.GetTunedModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.tuned_model.TunedModel[source]

Gets information about a specific TunedModel.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_get_tuned_model():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.GetTunedModelRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_tuned_model(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.GetTunedModelRequest, dict]]) – The request object. Request for getting information about a specific Model.

  • name (str) –

    Required. The resource name of the model.

    Format: tunedModels/my-model-id

    This corresponds to the name field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

A fine-tuned model created using ModelService.CreateTunedModel.

Return type

google.ai.generativelanguage_v1beta.types.TunedModel

async list_models(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.ListModelsRequest, dict]] = None, *, page_size: Optional[int] = None, page_token: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.services.model_service.pagers.ListModelsAsyncPager[source]

Lists the `Models <https://ai.google.dev/gemini-api/docs/models/gemini>`__ available through the Gemini API.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_list_models():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.ListModelsRequest(
    )

    # Make the request
    page_result = client.list_models(request=request)

    # Handle the response
    async for response in page_result:
        print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.ListModelsRequest, dict]]) – The request object. Request for listing all Models.

  • page_size (int) –

    The maximum number of Models to return (per page).

    If unspecified, 50 models will be returned per page. This method returns at most 1000 models per page, even if you pass a larger page_size.

    This corresponds to the page_size field on the request instance; if request is provided, this should not be set.

  • page_token (str) –

    A page token, received from a previous ListModels call.

    Provide the page_token returned by one request as an argument to the next request to retrieve the next page.

    When paginating, all other parameters provided to ListModels must match the call that provided the page token.

    This corresponds to the page_token field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Response from ListModel containing a paginated list of Models.

Iterating over this object will yield results and resolve additional pages automatically.

Return type

google.ai.generativelanguage_v1beta.services.model_service.pagers.ListModelsAsyncPager

async list_tuned_models(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.ListTunedModelsRequest, dict]] = None, *, page_size: Optional[int] = None, page_token: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.services.model_service.pagers.ListTunedModelsAsyncPager[source]

Lists created tuned models.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_list_tuned_models():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.ListTunedModelsRequest(
    )

    # Make the request
    page_result = client.list_tuned_models(request=request)

    # Handle the response
    async for response in page_result:
        print(response)
Parameters

  • request (Optional[Union[google.ai.generativelanguage_v1beta.types.ListTunedModelsRequest, dict]]) – The request object. Request for listing TunedModels.

  • page_size (int) –

    Optional. The maximum number of TunedModels to return (per page). The service may return fewer tuned models.

    If unspecified, at most 10 tuned models will be returned. This method returns at most 1000 models per page, even if you pass a larger page_size.

    This corresponds to the page_size field on the request instance; if request is provided, this should not be set.

  • page_token (str) –

    Optional. A page token, received from a previous ListTunedModels call.

    Provide the page_token returned by one request as an argument to the next request to retrieve the next page.

    When paginating, all other parameters provided to ListTunedModels must match the call that provided the page token.

    This corresponds to the page_token field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry_async.AsyncRetry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Response from ListTunedModels containing a paginated list of Models.

Iterating over this object will yield results and resolve additional pages automatically.

Return type

google.ai.generativelanguage_v1beta.services.model_service.pagers.ListTunedModelsAsyncPager

static model_path(model: str) → str

Returns a fully-qualified model string.

static parse_common_billing_account_path(path: str) → Dict[str, str]

Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str]

Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str]

Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str]

Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str]

Parse a project path into its component segments.

static parse_model_path(path: str) → Dict[str, str]

Parses a model path into its component segments.

static parse_tuned_model_path(path: str) → Dict[str, str]

Parses a tuned_model path into its component segments.

property transport: google.ai.generativelanguage_v1beta.services.model_service.transports.base.ModelServiceTransport

Returns the transport used by the client instance.

Returns

The transport used by the client instance.

Return type

ModelServiceTransport

static tuned_model_path(tuned_model: str) → str

Returns a fully-qualified tuned_model string.

property universe_domain: str

Return the universe domain used by the client instance.

Returns

The universe domain used

by the client instance.

Return type

str

async update_tuned_model(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.UpdateTunedModelRequest, dict]] = None, *, tuned_model: Optional[google.ai.generativelanguage_v1beta.types.tuned_model.TunedModel] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.tuned_model.TunedModel[source]

Updates a tuned model.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

async def sample_update_tuned_model():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceAsyncClient()

    # Initialize request argument(s)
    tuned_model = generativelanguage_v1beta.TunedModel()
    tuned_model.tuning_task.training_data.examples.examples.text_input = "text_input_value"
    tuned_model.tuning_task.training_data.examples.examples.output = "output_value"

    request = generativelanguage_v1beta.UpdateTunedModelRequest(
        tuned_model=tuned_model,
    )

    # Make the request
    response = await client.update_tuned_model(request=request)

    # Handle the response
    print(response)
Parameters
Returns

A fine-tuned model created using ModelService.CreateTunedModel.

Return type

google.ai.generativelanguage_v1beta.types.TunedModel

class google.ai.generativelanguage_v1beta.services.model_service.ModelServiceClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.ai.generativelanguage_v1beta.services.model_service.transports.base.ModelServiceTransport, typing.Callable[[...], google.ai.generativelanguage_v1beta.services.model_service.transports.base.ModelServiceTransport]]] = None, client_options: typing.Optional[typing.Union[google.api_core.client_options.ClientOptions, dict]] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)[source]

Provides methods for getting metadata information about Generative Models.

Instantiates the model service client.

Parameters

  • credentials (Optional[google.auth.credentials.Credentials]) – The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.

  • transport (Optional[Union[str,ModelServiceTransport,Callable[..., ModelServiceTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport. If a Callable is given, it will be called with the same set of initialization arguments as used in the ModelServiceTransport constructor. If set to None, a transport is chosen automatically.

  • client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]) –

    Custom options for the client.

    1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “always” (always use the default mTLS endpoint), “never” (always use the default regular endpoint) and “auto” (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value).

    2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is “false” or not set, no client certificate will be used.

    3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that the api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.

  • client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises

google.auth.exceptions.MutualTLSChannelError – If mutual TLS transport creation failed for any reason.

__exit__(type, value, traceback)[source]

Releases underlying transport’s resources.

Warning

ONLY use as a context manager if the transport is NOT shared with other clients! Exiting the with block will CLOSE the transport and may cause errors in other clients!

property api_endpoint

Return the API endpoint used by the client instance.

Returns

The API endpoint used by the client instance.

Return type

str

static common_billing_account_path(billing_account: str) → str[source]

Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str[source]

Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str[source]

Returns a fully-qualified location string.

static common_organization_path(organization: str) → str[source]

Returns a fully-qualified organization string.

static common_project_path(project: str) → str[source]

Returns a fully-qualified project string.

create_tuned_model(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.CreateTunedModelRequest, dict]] = None, *, tuned_model: Optional[google.ai.generativelanguage_v1beta.types.tuned_model.TunedModel] = None, tuned_model_id: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation.Operation[source]

Creates a tuned model. Check intermediate tuning progress (if any) through the [google.longrunning.Operations] service.

Access status and results through the Operations service. Example: GET /v1/tunedModels/az2mb0bpw6i/operations/000-111-222

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_create_tuned_model():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceClient()

    # Initialize request argument(s)
    tuned_model = generativelanguage_v1beta.TunedModel()
    tuned_model.tuning_task.training_data.examples.examples.text_input = "text_input_value"
    tuned_model.tuning_task.training_data.examples.examples.output = "output_value"

    request = generativelanguage_v1beta.CreateTunedModelRequest(
        tuned_model=tuned_model,
    )

    # Make the request
    operation = client.create_tuned_model(request=request)

    print("Waiting for operation to complete...")

    response = operation.result()

    # Handle the response
    print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.CreateTunedModelRequest, dict]) – The request object. Request to create a TunedModel.

  • tuned_model (google.ai.generativelanguage_v1beta.types.TunedModel) – Required. The tuned model to create. This corresponds to the tuned_model field on the request instance; if request is provided, this should not be set.

  • tuned_model_id (str) –

    Optional. The unique id for the tuned model if specified. This value should be up to 40 characters, the first character must be a letter, the last could be a letter or a number. The id must match the regular expression: [a-z]([a-z0-9-]{0,38}[a-z0-9])?.

    This corresponds to the tuned_model_id field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

An object representing a long-running operation.

The result type for the operation will be google.ai.generativelanguage_v1beta.types.TunedModel A fine-tuned model created using ModelService.CreateTunedModel.

Return type

google.api_core.operation.Operation

delete_tuned_model(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.DeleteTunedModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]

Deletes a tuned model.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_delete_tuned_model():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.DeleteTunedModelRequest(
        name="name_value",
    )

    # Make the request
    client.delete_tuned_model(request=request)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.DeleteTunedModelRequest, dict]) – The request object. Request to delete a TunedModel.

  • name (str) –

    Required. The resource name of the model. Format: tunedModels/my-model-id

    This corresponds to the name field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

ModelServiceClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

info.

Parameters

  • info (dict) – The service account private key info.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

ModelServiceClient

classmethod from_service_account_json(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

ModelServiceClient

get_model(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.GetModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.model.Model[source]

Gets information about a specific Model such as its version number, token limits, parameters and other metadata. Refer to the Gemini models guide for detailed model information.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_get_model():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.GetModelRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_model(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.GetModelRequest, dict]) – The request object. Request for getting information about a specific Model.

  • name (str) –

    Required. The resource name of the model.

    This name should match a model name returned by the ListModels method.

    Format: models/{model}

    This corresponds to the name field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Information about a Generative Language Model.

Return type

google.ai.generativelanguage_v1beta.types.Model

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[google.api_core.client_options.ClientOptions] = None)[source]

Deprecated. Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not “true”, the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “always”, use the default mTLS endpoint; if the environment variable is “never”, use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameters

client_options (google.api_core.client_options.ClientOptions) – Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.

Returns

returns the API endpoint and the

client cert source to use.

Return type

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

get_tuned_model(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.GetTunedModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.tuned_model.TunedModel[source]

Gets information about a specific TunedModel.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_get_tuned_model():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.GetTunedModelRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_tuned_model(request=request)

    # Handle the response
    print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.GetTunedModelRequest, dict]) – The request object. Request for getting information about a specific Model.

  • name (str) –

    Required. The resource name of the model.

    Format: tunedModels/my-model-id

    This corresponds to the name field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

A fine-tuned model created using ModelService.CreateTunedModel.

Return type

google.ai.generativelanguage_v1beta.types.TunedModel

list_models(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.ListModelsRequest, dict]] = None, *, page_size: Optional[int] = None, page_token: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.services.model_service.pagers.ListModelsPager[source]

Lists the `Models <https://ai.google.dev/gemini-api/docs/models/gemini>`__ available through the Gemini API.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_list_models():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.ListModelsRequest(
    )

    # Make the request
    page_result = client.list_models(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.ListModelsRequest, dict]) – The request object. Request for listing all Models.

  • page_size (int) –

    The maximum number of Models to return (per page).

    If unspecified, 50 models will be returned per page. This method returns at most 1000 models per page, even if you pass a larger page_size.

    This corresponds to the page_size field on the request instance; if request is provided, this should not be set.

  • page_token (str) –

    A page token, received from a previous ListModels call.

    Provide the page_token returned by one request as an argument to the next request to retrieve the next page.

    When paginating, all other parameters provided to ListModels must match the call that provided the page token.

    This corresponds to the page_token field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Response from ListModel containing a paginated list of Models.

Iterating over this object will yield results and resolve additional pages automatically.

Return type

google.ai.generativelanguage_v1beta.services.model_service.pagers.ListModelsPager

list_tuned_models(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.ListTunedModelsRequest, dict]] = None, *, page_size: Optional[int] = None, page_token: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.services.model_service.pagers.ListTunedModelsPager[source]

Lists created tuned models.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_list_tuned_models():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceClient()

    # Initialize request argument(s)
    request = generativelanguage_v1beta.ListTunedModelsRequest(
    )

    # Make the request
    page_result = client.list_tuned_models(request=request)

    # Handle the response
    for response in page_result:
        print(response)
Parameters

  • request (Union[google.ai.generativelanguage_v1beta.types.ListTunedModelsRequest, dict]) – The request object. Request for listing TunedModels.

  • page_size (int) –

    Optional. The maximum number of TunedModels to return (per page). The service may return fewer tuned models.

    If unspecified, at most 10 tuned models will be returned. This method returns at most 1000 models per page, even if you pass a larger page_size.

    This corresponds to the page_size field on the request instance; if request is provided, this should not be set.

  • page_token (str) –

    Optional. A page token, received from a previous ListTunedModels call.

    Provide the page_token returned by one request as an argument to the next request to retrieve the next page.

    When paginating, all other parameters provided to ListTunedModels must match the call that provided the page token.

    This corresponds to the page_token field on the request instance; if request is provided, this should not be set.

  • retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.

  • timeout (float) – The timeout for this request.

  • metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns

Response from ListTunedModels containing a paginated list of Models.

Iterating over this object will yield results and resolve additional pages automatically.

Return type

google.ai.generativelanguage_v1beta.services.model_service.pagers.ListTunedModelsPager

static model_path(model: str) → str[source]

Returns a fully-qualified model string.

static parse_common_billing_account_path(path: str) → Dict[str, str][source]

Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str][source]

Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str][source]

Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str][source]

Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str][source]

Parse a project path into its component segments.

static parse_model_path(path: str) → Dict[str, str][source]

Parses a model path into its component segments.

static parse_tuned_model_path(path: str) → Dict[str, str][source]

Parses a tuned_model path into its component segments.

property transport: google.ai.generativelanguage_v1beta.services.model_service.transports.base.ModelServiceTransport

Returns the transport used by the client instance.

Returns

The transport used by the client

instance.

Return type

ModelServiceTransport

static tuned_model_path(tuned_model: str) → str[source]

Returns a fully-qualified tuned_model string.

property universe_domain: str

Return the universe domain used by the client instance.

Returns

The universe domain used by the client instance.

Return type

str

update_tuned_model(request: Optional[Union[google.ai.generativelanguage_v1beta.types.model_service.UpdateTunedModelRequest, dict]] = None, *, tuned_model: Optional[google.ai.generativelanguage_v1beta.types.tuned_model.TunedModel] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.ai.generativelanguage_v1beta.types.tuned_model.TunedModel[source]

Updates a tuned model.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.ai import generativelanguage_v1beta

def sample_update_tuned_model():
    # Create a client
    client = generativelanguage_v1beta.ModelServiceClient()

    # Initialize request argument(s)
    tuned_model = generativelanguage_v1beta.TunedModel()
    tuned_model.tuning_task.training_data.examples.examples.text_input = "text_input_value"
    tuned_model.tuning_task.training_data.examples.examples.output = "output_value"

    request = generativelanguage_v1beta.UpdateTunedModelRequest(
        tuned_model=tuned_model,
    )

    # Make the request
    response = client.update_tuned_model(request=request)

    # Handle the response
    print(response)
Parameters
Returns

A fine-tuned model created using ModelService.CreateTunedModel.

Return type

google.ai.generativelanguage_v1beta.types.TunedModel

class google.ai.generativelanguage_v1beta.services.model_service.pagers.ListModelsAsyncPager(method: Callable[[...], Awaitable[google.ai.generativelanguage_v1beta.types.model_service.ListModelsResponse]], request: google.ai.generativelanguage_v1beta.types.model_service.ListModelsRequest, response: google.ai.generativelanguage_v1beta.types.model_service.ListModelsResponse, *, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_models requests.

This class thinly wraps an initial google.ai.generativelanguage_v1beta.types.ListModelsResponse object, and provides an __aiter__ method to iterate through its models field.

If there are more pages, the __aiter__ method will make additional ListModels requests and continue to iterate through the models field on the corresponding responses.

All the usual google.ai.generativelanguage_v1beta.types.ListModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters
class google.ai.generativelanguage_v1beta.services.model_service.pagers.ListModelsPager(method: Callable[[...], google.ai.generativelanguage_v1beta.types.model_service.ListModelsResponse], request: google.ai.generativelanguage_v1beta.types.model_service.ListModelsRequest, response: google.ai.generativelanguage_v1beta.types.model_service.ListModelsResponse, *, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_models requests.

This class thinly wraps an initial google.ai.generativelanguage_v1beta.types.ListModelsResponse object, and provides an __iter__ method to iterate through its models field.

If there are more pages, the __iter__ method will make additional ListModels requests and continue to iterate through the models field on the corresponding responses.

All the usual google.ai.generativelanguage_v1beta.types.ListModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters
class google.ai.generativelanguage_v1beta.services.model_service.pagers.ListTunedModelsAsyncPager(method: Callable[[...], Awaitable[google.ai.generativelanguage_v1beta.types.model_service.ListTunedModelsResponse]], request: google.ai.generativelanguage_v1beta.types.model_service.ListTunedModelsRequest, response: google.ai.generativelanguage_v1beta.types.model_service.ListTunedModelsResponse, *, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_tuned_models requests.

This class thinly wraps an initial google.ai.generativelanguage_v1beta.types.ListTunedModelsResponse object, and provides an __aiter__ method to iterate through its tuned_models field.

If there are more pages, the __aiter__ method will make additional ListTunedModels requests and continue to iterate through the tuned_models field on the corresponding responses.

All the usual google.ai.generativelanguage_v1beta.types.ListTunedModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters
class google.ai.generativelanguage_v1beta.services.model_service.pagers.ListTunedModelsPager(method: Callable[[...], google.ai.generativelanguage_v1beta.types.model_service.ListTunedModelsResponse], request: google.ai.generativelanguage_v1beta.types.model_service.ListTunedModelsRequest, response: google.ai.generativelanguage_v1beta.types.model_service.ListTunedModelsResponse, *, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_tuned_models requests.

This class thinly wraps an initial google.ai.generativelanguage_v1beta.types.ListTunedModelsResponse object, and provides an __iter__ method to iterate through its tuned_models field.

If there are more pages, the __iter__ method will make additional ListTunedModels requests and continue to iterate through the tuned_models field on the corresponding responses.

All the usual google.ai.generativelanguage_v1beta.types.ListTunedModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters