Class ConversationModelsAsyncClient (2.13.0)

ConversationModelsAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.dialogflow_v2.services.conversation_models.transports.base.ConversationModelsTransport] = 'grpc_asyncio', client_options: 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>)

Manages a collection of models for human agent assistant.

Properties

transport

Returns the transport used by the client instance.

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

Methods

ConversationModelsAsyncClient

ConversationModelsAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.dialogflow_v2.services.conversation_models.transports.base.ConversationModelsTransport] = 'grpc_asyncio', client_options: 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>)

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`	`Union[str, .ConversationModelsTransport]` The transport to use. If set to None, a transport is chosen automatically.
`client_options`	`ClientOptions` Custom options for the client. It won't take effect if a `transport` instance is provided. (1) The `api_endpoint` property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "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). However, the `api_endpoint` property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the `client_cert_source` property can be used to provide client certificate for mutual TLS 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.

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

common_billing_account_path

common_billing_account_path(billing_account: str)

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str)

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str)

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str)

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str)

Returns a fully-qualified project string.

conversation_dataset_path

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

Returns a fully-qualified conversation_dataset string.

conversation_model_evaluation_path

conversation_model_evaluation_path(project: str, conversation_model: str)

Returns a fully-qualified conversation_model_evaluation string.

conversation_model_path

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

Returns a fully-qualified conversation_model string.

create_conversation_model

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

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

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`	`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_async.AsyncOperation`	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: Optional[Union[google.cloud.dialogflow_v2.types.conversation_model.CreateConversationModelEvaluationRequest, dict]] = None, *, parent: Optional[str] = None, conversation_model_evaluation: Optional[google.cloud.dialogflow_v2.types.conversation_model.ConversationModelEvaluation] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates evaluation of a conversation model.

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`	`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_async.AsyncOperation`	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: Optional[Union[google.cloud.dialogflow_v2.types.conversation_model.DeleteConversationModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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>__

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_async.AsyncOperation 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); } The JSON representation for Empty is empty JSON object {}.

deploy_conversation_model

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

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>__

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

document_path

document_path(project: str, knowledge_base: str, document: 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
`ConversationModelsAsyncClient`	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
`ConversationModelsAsyncClient`	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
`ConversationModelsAsyncClient`	The constructed client.

get_conversation_model

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

Gets conversation model.

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

Gets an evaluation of conversation model.

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_mtls_endpoint_and_cert_source

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

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 variabel 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.

Parameter
Name	Description
`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.

Exceptions
Type	Description
`google.auth.exceptions.MutualTLSChannelError`	If any errors happen.

Returns
Type	Description
`Tuple[str, Callable[[], Tuple[bytes, bytes]]]`	returns the API endpoint and the client cert source to use.

get_transport_class

get_transport_class()

Returns an appropriate transport class.

list_conversation_model_evaluations

list_conversation_model_evaluations(request: Optional[Union[google.cloud.dialogflow_v2.types.conversation_model.ListConversationModelEvaluationsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists evaluations of a conversation model.

from google.cloud import dialogflow_v2

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

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

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

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

Parameters
Name	Description
`request`	`Union[google.cloud.dialogflow_v2.types.ListConversationModelEvaluationsRequest, dict]` The request object. The request message for ConversationModels.ListConversationModelEvaluations
`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.
`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.services.conversation_models.pagers.ListConversationModelEvaluationsAsyncPager`	The response message for ConversationModels.ListConversationModelEvaluations Iterating over this object will yield results and resolve additional pages automatically.

list_conversation_models

list_conversation_models(request: Optional[Union[google.cloud.dialogflow_v2.types.conversation_model.ListConversationModelsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists conversation models.

from google.cloud import dialogflow_v2

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

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

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

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

Parameters
Name	Description
`request`	`Union[google.cloud.dialogflow_v2.types.ListConversationModelsRequest, dict]` The request object. The request message for ConversationModels.ListConversationModels
`parent`	`str` Required. The project to list all conversation models for. Format: `projects/` This corresponds to the `parent` 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.services.conversation_models.pagers.ListConversationModelsAsyncPager`	The response message for ConversationModels.ListConversationModels Iterating over this object will yield results and resolve additional pages automatically.

parse_common_billing_account_path

parse_common_billing_account_path(path: str)

Parse a billing_account path into its component segments.

parse_common_folder_path

parse_common_folder_path(path: str)

Parse a folder path into its component segments.

parse_common_location_path

parse_common_location_path(path: str)

Parse a location path into its component segments.

parse_common_organization_path

parse_common_organization_path(path: str)

Parse a organization path into its component segments.

parse_common_project_path

parse_common_project_path(path: str)

Parse a project path into its component segments.

parse_conversation_dataset_path

parse_conversation_dataset_path(path: str)

Parses a conversation_dataset path into its component segments.

parse_conversation_model_evaluation_path

parse_conversation_model_evaluation_path(path: str)

Parses a conversation_model_evaluation path into its component segments.

parse_conversation_model_path

parse_conversation_model_path(path: str)

Parses a conversation_model path into its component segments.

parse_document_path

parse_document_path(path: str)

Parses a document path into its component segments.

undeploy_conversation_model

undeploy_conversation_model(request: Optional[Union[google.cloud.dialogflow_v2.types.conversation_model.UndeployConversationModelRequest, dict]] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Undeploys a model. If the model is not deployed this method has no effect. If the model is currently being used:

For article suggestion, article suggestion will fallback to the default model if model is undeployed.

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_UndeployConversationModelOperationMetadata
response: An Empty message <https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#empty>__

from google.cloud import dialogflow_v2

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

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

    # Make the request
    operation = client.undeploy_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.UndeployConversationModelRequest, dict]` The request object. The request message for ConversationModels.UndeployConversationModel
`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