Class AgentsClient (2.20.0)

Service for managing Agents.

Returns the transport used by the client instance.

Instantiates the agents client.

Releases underlying transport's resources.

Returns a fully-qualified agent string.

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.

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.

Deletes the specified agent.

# 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_agent():
    # Create a client
    client = dialogflow_v2.AgentsClient()

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

    # Make the request
    client.delete_agent(request=request)

Exports the specified agent to a ZIP file.

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: An empty Struct message <https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#struct>__
• response: xref_ExportAgentResponse

# 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_export_agent():
    # Create a client
    client = dialogflow_v2.AgentsClient()

    # Initialize request argument(s)
    request = dialogflow_v2.ExportAgentRequest(
        parent="parent_value",
        agent_uri="agent_uri_value",
    )

    # Make the request
    operation = client.export_agent(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 agent.

# 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_agent():
    # Create a client
    client = dialogflow_v2.AgentsClient()

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

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

    # Handle the response
    print(response)

Gets information about a location.

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.

Gets the latest state of a long-running operation.

Gets agent validation result. Agent validation is performed during training time and is updated automatically when training is completed.

# 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_validation_result():
    # Create a client
    client = dialogflow_v2.AgentsClient()

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

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

    # Handle the response
    print(response)

Imports the specified agent from a ZIP file.

Uploads new intents and entity types without deleting the existing ones. Intents and entity types with the same name are replaced with the new versions from xref_ImportAgentRequest. After the import, the imported draft agent will be trained automatically (unless disabled in agent settings). However, once the import is done, training may not be completed yet. Please call xref_TrainAgent and wait for the operation it returns in order to train explicitly.

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

The operation only tracks when importing is complete, not when it is done training.

Note: You should always train an agent prior to sending it queries. See the training documentation <https://cloud.google.com/dialogflow/es/docs/training>__.

# 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_import_agent():
    # Create a client
    client = dialogflow_v2.AgentsClient()

    # Initialize request argument(s)
    request = dialogflow_v2.ImportAgentRequest(
        agent_uri="agent_uri_value",
        parent="parent_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)

Lists information about the supported locations for this service.

Lists operations that match the specified filter in the request.

Parses a agent path into its component segments.

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.

Restores the specified agent from a ZIP file.

Replaces the current agent version with a new one. All the intents and entity types in the older version are deleted. After the restore, the restored draft agent will be trained automatically (unless disabled in agent settings). However, once the restore is done, training may not be completed yet. Please call xref_TrainAgent and wait for the operation it returns in order to train explicitly.

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

The operation only tracks when restoring is complete, not when it is done training.

Note: You should always train an agent prior to sending it queries. See the training documentation <https://cloud.google.com/dialogflow/es/docs/training>__.

# 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_restore_agent():
    # Create a client
    client = dialogflow_v2.AgentsClient()

    # Initialize request argument(s)
    request = dialogflow_v2.RestoreAgentRequest(
        agent_uri="agent_uri_value",
        parent="parent_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)

Returns the list of agents.

Since there is at most one conversational agent per project, this method is useful primarily for listing all agents across projects the caller has access to. One can achieve that with a wildcard project collection id "-". Refer to List Sub-Collections <https://cloud.google.com/apis/design/design_patterns#list_sub-collections>__.

# 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_search_agents():
    # Create a client
    client = dialogflow_v2.AgentsClient()

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

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

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

Creates/updates the specified agent.

Note: You should always train an agent prior to sending it queries. See the training documentation <https://cloud.google.com/dialogflow/es/docs/training>__.

# 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_set_agent():
    # Create a client
    client = dialogflow_v2.AgentsClient()

    # Initialize request argument(s)
    agent = dialogflow_v2.Agent()
    agent.parent = "parent_value"
    agent.display_name = "display_name_value"
    agent.default_language_code = "default_language_code_value"
    agent.time_zone = "time_zone_value"

    request = dialogflow_v2.SetAgentRequest(
        agent=agent,
    )

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

    # Handle the response
    print(response)

Trains the specified agent.

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

Note: You should always train an agent prior to sending it queries. See the training documentation <https://cloud.google.com/dialogflow/es/docs/training>__.

# 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_train_agent():
    # Create a client
    client = dialogflow_v2.AgentsClient()

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

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

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

    response = operation.result()

    # Handle the response
    print(response)