Class ConversationDatasetsAsyncClient (2.13.0)

Conversation datasets. Conversation datasets contain raw conversation files and their customizable metadata that can be used for model training.

Returns the transport used by the client instance.

Instantiates the conversation datasets client.

Returns a fully-qualified billing_account string.

Returns a fully-qualified folder string.

Returns a fully-qualified location string.

Returns a fully-qualified organization string.

Returns a fully-qualified project string.

Returns a fully-qualified conversation_dataset string.

Creates a new conversation dataset.

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_CreateConversationDatasetOperationMetadata
• response: xref_ConversationDataset

from google.cloud import dialogflow_v2

def sample_create_conversation_dataset():
    # Create a client
    client = dialogflow_v2.ConversationDatasetsClient()

    # Initialize request argument(s)
    conversation_dataset = dialogflow_v2.ConversationDataset()
    conversation_dataset.display_name = "display_name_value"

    request = dialogflow_v2.CreateConversationDatasetRequest(
        parent="parent_value",
        conversation_dataset=conversation_dataset,
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)

Deletes the specified conversation dataset.

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_DeleteConversationDatasetOperationMetadata
• 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_dataset():
    # Create a client
    client = dialogflow_v2.ConversationDatasetsClient()

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

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

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

    response = operation.result()

    # Handle the response
    print(response)

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

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

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

Retrieves the specified conversation dataset.

from google.cloud import dialogflow_v2

def sample_get_conversation_dataset():
    # Create a client
    client = dialogflow_v2.ConversationDatasetsClient()

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

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

    # Handle the response
    print(response)

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.

Returns an appropriate transport class.

Import data into the specified conversation dataset. Note that it is not allowed to import data to a conversation dataset that already has data in it.

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_ImportConversationDataOperationMetadata
• response: xref_ImportConversationDataOperationResponse

from google.cloud import dialogflow_v2

def sample_import_conversation_data():
    # Create a client
    client = dialogflow_v2.ConversationDatasetsClient()

    # Initialize request argument(s)
    input_config = dialogflow_v2.InputConfig()
    input_config.gcs_source.uris = ['uris_value_1', 'uris_value_2']

    request = dialogflow_v2.ImportConversationDataRequest(
        name="name_value",
        input_config=input_config,
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)

Returns the list of all conversation datasets in the specified project and location.

from google.cloud import dialogflow_v2

def sample_list_conversation_datasets():
    # Create a client
    client = dialogflow_v2.ConversationDatasetsClient()

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

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

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

Parse a billing_account path into its component segments.

Parse a folder path into its component segments.

Parse a location path into its component segments.

Parse a organization path into its component segments.

Parse a project path into its component segments.

Parses a conversation_dataset path into its component segments.