Class ConversationModelsClient (2.33.0) 

ConversationModelsClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.dialogflow_v2.services.conversation_models.transports.base.ConversationModelsTransport, typing.Callable[[...], google.cloud.dialogflow_v2.services.conversation_models.transports.base.ConversationModelsTransport]]] = 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>)

Manages a collection of models for human agent assistant.

Properties

api_endpoint

Return the API endpoint used by the client instance.

Returns
Type Description
str The API endpoint used by the client instance.

transport

Returns the transport used by the client instance.

Returns
Type Description
ConversationModelsTransport The transport used by the client instance.

universe_domain

Return the universe domain used by the client instance.

Returns
Type Description
str The universe domain used by the client instance.

Methods

ConversationModelsClient

ConversationModelsClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.dialogflow_v2.services.conversation_models.transports.base.ConversationModelsTransport, typing.Callable[[...], google.cloud.dialogflow_v2.services.conversation_models.transports.base.ConversationModelsTransport]]] = 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>)

Instantiates the conversation models client.

Parameters
Name Description
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,ConversationModelsTransport,Callable[..., ConversationModelsTransport]]]

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 ConversationModelsTransport 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.
Exceptions
Type Description
google.auth.exceptions.MutualTLSChannelError If mutual TLS transport creation failed for any reason.

__exit__

__exit__(type, value, traceback)

Releases underlying transport's resources.

cancel_operation

cancel_operation(
    request: typing.Optional[
        google.longrunning.operations_pb2.CancelOperationRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Parameters
Name Description
request .operations_pb2.CancelOperationRequest

The request object. Request message for CancelOperation method.
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.

common_billing_account_path

common_billing_account_path(billing_account: str) -> str

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str) -> str

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str) -> str

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str) -> str

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str) -> str

Returns a fully-qualified project string.

conversation_dataset_path

conversation_dataset_path(
    project: str, location: str, conversation_dataset: str
) -> str

Returns a fully-qualified conversation_dataset string.

conversation_model_evaluation_path

conversation_model_evaluation_path(
    project: str, conversation_model: str, evaluation: str
) -> str

Returns a fully-qualified conversation_model_evaluation string.

conversation_model_path

conversation_model_path(
    project: str, location: str, conversation_model: str
) -> str

Returns a fully-qualified conversation_model string.

create_conversation_model

create_conversation_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.dialogflow_v2.types.conversation_model.CreateConversationModelRequest,
            dict,
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    conversation_model: typing.Optional[
        google.cloud.dialogflow_v2.types.conversation_model.ConversationModel
    ] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

Creates a model.

This method is a long-running operation <https://cloud.google.com/dialogflow/es/docs/how/long-running-operations>__. The returned Operation type has the following method-specific fields:

  • metadata: xref_CreateConversationModelOperationMetadata
  • response: xref_ConversationModel
# 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.cloud import dialogflow_v2

def sample_create_conversation_model():
    # Create a client
    client = dialogflow_v2.ConversationModelsClient()

    # Initialize request argument(s)
    conversation_model = dialogflow_v2.ConversationModel()
    conversation_model.display_name = "display_name_value"
    conversation_model.datasets.dataset = "dataset_value"

    request = dialogflow_v2.CreateConversationModelRequest(
        conversation_model=conversation_model,
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.dialogflow_v2.types.CreateConversationModelRequest, dict]

The request object. The request message for ConversationModels.CreateConversationModel
parent str

The project to create conversation model for. Format: projects/ This corresponds to the parent field on the request instance; if request is provided, this should not be set.
conversation_model google.cloud.dialogflow_v2.types.ConversationModel

Required. The conversation model to create. This corresponds to the conversation_model 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
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be ConversationModel Represents a conversation model.

create_conversation_model_evaluation

create_conversation_model_evaluation(
    request: typing.Optional[
        typing.Union[
            google.cloud.dialogflow_v2.types.conversation_model.CreateConversationModelEvaluationRequest,
            dict,
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    conversation_model_evaluation: typing.Optional[
        google.cloud.dialogflow_v2.types.conversation_model.ConversationModelEvaluation
    ] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

Creates evaluation of a conversation 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.cloud import dialogflow_v2

def sample_create_conversation_model_evaluation():
    # Create a client
    client = dialogflow_v2.ConversationModelsClient()

    # Initialize request argument(s)
    request = dialogflow_v2.CreateConversationModelEvaluationRequest(
        parent="parent_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.dialogflow_v2.types.CreateConversationModelEvaluationRequest, dict]

The request object. The request message for ConversationModels.CreateConversationModelEvaluation
parent str

Required. The conversation model resource name. Format: projects/ This corresponds to the parent field on the request instance; if request is provided, this should not be set.
conversation_model_evaluation google.cloud.dialogflow_v2.types.ConversationModelEvaluation

Required. The conversation model evaluation to be created. This corresponds to the conversation_model_evaluation 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
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be ConversationModelEvaluation Represents evaluation result of a conversation model.

delete_conversation_model

delete_conversation_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.dialogflow_v2.types.conversation_model.DeleteConversationModelRequest,
            dict,
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

Deletes a model.

This method is a long-running operation <https://cloud.google.com/dialogflow/es/docs/how/long-running-operations>__. The returned Operation type has the following method-specific fields:

  • metadata: xref_DeleteConversationModelOperationMetadata
  • response: An Empty message <https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#empty>__
# 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.cloud import dialogflow_v2

def sample_delete_conversation_model():
    # Create a client
    client = dialogflow_v2.ConversationModelsClient()

    # Initialize request argument(s)
    request = dialogflow_v2.DeleteConversationModelRequest(
        name="name_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.dialogflow_v2.types.DeleteConversationModelRequest, dict]

The request object. The request message for ConversationModels.DeleteConversationModel
name str

Required. The conversation model to delete. Format: projects/ 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
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }

deploy_conversation_model

deploy_conversation_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.dialogflow_v2.types.conversation_model.DeployConversationModelRequest,
            dict,
        ]
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

Deploys a model. If a model is already deployed, deploying it has no effect. A model can only serve prediction requests after it gets deployed. For article suggestion, custom model will not be used unless it is deployed.

This method is a long-running operation <https://cloud.google.com/dialogflow/es/docs/how/long-running-operations>__. The returned Operation type has the following method-specific fields:

  • metadata: xref_DeployConversationModelOperationMetadata
  • response: An Empty message <https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#empty>__
# 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.cloud import dialogflow_v2

def sample_deploy_conversation_model():
    # Create a client
    client = dialogflow_v2.ConversationModelsClient()

    # Initialize request argument(s)
    request = dialogflow_v2.DeployConversationModelRequest(
        name="name_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.dialogflow_v2.types.DeployConversationModelRequest, dict]

The request object. The request message for ConversationModels.DeployConversationModel
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
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be google.protobuf.empty_pb2.Empty A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }

document_path

document_path(project: str, knowledge_base: str, document: str) -> str

Returns a fully-qualified document string.

from_service_account_file

from_service_account_file(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials file.

Parameter
Name Description
filename str

The path to the service account private key json file.
Returns
Type Description
ConversationModelsClient The constructed client.

from_service_account_info

from_service_account_info(info: dict, *args, **kwargs)

Creates an instance of this client using the provided credentials info.

Parameter
Name Description
info dict

The service account private key info.
Returns
Type Description
ConversationModelsClient The constructed client.

from_service_account_json

from_service_account_json(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials file.

Parameter
Name Description
filename str

The path to the service account private key json file.
Returns
Type Description
ConversationModelsClient The constructed client.

get_conversation_model

get_conversation_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.dialogflow_v2.types.conversation_model.GetConversationModelRequest,
            dict,
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.dialogflow_v2.types.conversation_model.ConversationModel

Gets conversation 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.cloud import dialogflow_v2

def sample_get_conversation_model():
    # Create a client
    client = dialogflow_v2.ConversationModelsClient()

    # Initialize request argument(s)
    request = dialogflow_v2.GetConversationModelRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.dialogflow_v2.types.GetConversationModelRequest, dict]

The request object. The request message for ConversationModels.GetConversationModel
name str

Required. The conversation model to retrieve. Format: projects/ 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
Type Description
google.cloud.dialogflow_v2.types.ConversationModel Represents a conversation model.

get_conversation_model_evaluation

get_conversation_model_evaluation(
    request: typing.Optional[
        typing.Union[
            google.cloud.dialogflow_v2.types.conversation_model.GetConversationModelEvaluationRequest,
            dict,
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.dialogflow_v2.types.conversation_model.ConversationModelEvaluation

Gets an evaluation of conversation 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.cloud import dialogflow_v2

def sample_get_conversation_model_evaluation():
    # Create a client
    client = dialogflow_v2.ConversationModelsClient()

    # Initialize request argument(s)
    request = dialogflow_v2.GetConversationModelEvaluationRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.dialogflow_v2.types.GetConversationModelEvaluationRequest, dict]

The request object. The request message for ConversationModels.GetConversationModelEvaluation
name str

Required. The conversation model evaluation resource name. Format: projects/ 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
Type Description
google.cloud.dialogflow_v2.types.ConversationModelEvaluation Represents evaluation result of a conversation model.

get_location

get_location(
    request: