AgentsAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.dialogflow_v2beta1.services.agents.transports.base.AgentsTransport] = '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>)
Service for managing Agents.
Inheritance
builtins.object > AgentsAsyncClientProperties
transport
Returns the transport used by the client instance.
Type | Description |
AgentsTransport | The transport used by the client instance. |
Methods
AgentsAsyncClient
AgentsAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.dialogflow_v2beta1.services.agents.transports.base.AgentsTransport] = '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 agents client.
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, `.AgentsTransport`]
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 |
Type | Description |
google.auth.exceptions.MutualTlsChannelError | If mutual TLS transport creation failed for any reason. |
agent_path
agent_path(project: str)
Returns a fully-qualified agent string.
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.
delete_agent
delete_agent(request: Optional[Union[google.cloud.dialogflow_v2beta1.types.agent.DeleteAgentRequest, 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]] = ())
Deletes the specified agent.
from google.cloud import dialogflow_v2beta1
async def sample_delete_agent():
# Create a client
client = dialogflow_v2beta1.AgentsAsyncClient()
# Initialize request argument(s)
request = dialogflow_v2beta1.DeleteAgentRequest(
parent="parent_value",
)
# Make the request
await client.delete_agent(request=request)
Name | Description |
request |
Union[google.cloud.dialogflow_v2beta1.types.DeleteAgentRequest, dict]
The request object. The request message for Agents.DeleteAgent. |
parent |
`str`
Required. The project that the agent to delete is associated with. Format: |
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. |
export_agent
export_agent(request: Optional[Union[google.cloud.dialogflow_v2beta1.types.agent.ExportAgentRequest, 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]] = ())
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 emptyStruct message <https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#struct>
__response
: xref_ExportAgentResponse
from google.cloud import dialogflow_v2beta1
async def sample_export_agent():
# Create a client
client = dialogflow_v2beta1.AgentsAsyncClient()
# Initialize request argument(s)
request = dialogflow_v2beta1.ExportAgentRequest(
parent="parent_value",
)
# Make the request
operation = client.export_agent(request=request)
print("Waiting for operation to complete...")
response = await operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.dialogflow_v2beta1.types.ExportAgentRequest, dict]
The request object. The request message for Agents.ExportAgent. |
parent |
`str`
Required. The project that the agent to export is associated with. Format: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | An object representing a long-running operation. The result type for the operation will be ExportAgentResponse The response message for Agents.ExportAgent. |
from_service_account_file
from_service_account_file(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials file.
Name | Description |
filename |
str
The path to the service account private key json file. |
Type | Description |
AgentsAsyncClient | 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.
Name | Description |
info |
dict
The service account private key info. |
Type | Description |
AgentsAsyncClient | 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.
Name | Description |
filename |
str
The path to the service account private key json file. |
Type | Description |
AgentsAsyncClient | The constructed client. |
get_agent
get_agent(request: Optional[Union[google.cloud.dialogflow_v2beta1.types.agent.GetAgentRequest, 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]] = ())
Retrieves the specified agent.
from google.cloud import dialogflow_v2beta1
async def sample_get_agent():
# Create a client
client = dialogflow_v2beta1.AgentsAsyncClient()
# Initialize request argument(s)
request = dialogflow_v2beta1.GetAgentRequest(
parent="parent_value",
)
# Make the request
response = await client.get_agent(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.dialogflow_v2beta1.types.GetAgentRequest, dict]
The request object. The request message for Agents.GetAgent. |
parent |
`str`
Required. The project that the agent to fetch is associated with. Format: |
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. |
Type | Description |
google.cloud.dialogflow_v2beta1.types.Agent | A Dialogflow agent is a virtual agent that handles conversations with your end-users. It is a natural language understanding module that understands the nuances of human language. Dialogflow translates end-user text or audio during a conversation to structured data that your apps and services can understand. You design and build a Dialogflow agent to handle the types of conversations required for your system. For more information about agents, see the [Agent guide](\ https://cloud.google.com/dialogflow/docs/agents-overview). |
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.
Name | Description |
client_options |
google.api_core.client_options.ClientOptions
Custom options for the client. Only the |
Type | Description |
google.auth.exceptions.MutualTLSChannelError | If any errors happen. |
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.
get_validation_result
get_validation_result(request: Optional[Union[google.cloud.dialogflow_v2beta1.types.agent.GetValidationResultRequest, 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]] = ())
Gets agent validation result. Agent validation is performed during training time and is updated automatically when training is completed.
from google.cloud import dialogflow_v2beta1
async def sample_get_validation_result():
# Create a client
client = dialogflow_v2beta1.AgentsAsyncClient()
# Initialize request argument(s)
request = dialogflow_v2beta1.GetValidationResultRequest(
parent="parent_value",
)
# Make the request
response = await client.get_validation_result(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.dialogflow_v2beta1.types.GetValidationResultRequest, dict]
The request object. The request message for Agents.GetValidationResult. |
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. |
Type | Description |
google.cloud.dialogflow_v2beta1.types.ValidationResult | Represents the output of agent validation. |
import_agent
import_agent(request: Optional[Union[google.cloud.dialogflow_v2beta1.types.agent.ImportAgentRequest, 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]] = ())
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 emptyStruct message <https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#struct>
__response
: AnEmpty 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>
__.
from google.cloud import dialogflow_v2beta1
async def sample_import_agent():
# Create a client
client = dialogflow_v2beta1.AgentsAsyncClient()
# Initialize request argument(s)
request = dialogflow_v2beta1.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 = await operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.dialogflow_v2beta1.types.ImportAgentRequest, dict]
The request object. The request message for Agents.ImportAgent. |
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. |
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 {}. |
parse_agent_path
parse_agent_path(path: str)
Parses a agent path into its component segments.
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.
restore_agent
restore_agent(request: Optional[Union[google.cloud.dialogflow_v2beta1.types.agent.RestoreAgentRequest, 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]] = ())
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 emptyStruct message <https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#struct>
__response
: AnEmpty 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>
__.
from google.cloud import dialogflow_v2beta1
async def sample_restore_agent():
# Create a client
client = dialogflow_v2beta1.AgentsAsyncClient()
# Initialize request argument(s)
request = dialogflow_v2beta1.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 = await operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.dialogflow_v2beta1.types.RestoreAgentRequest, dict]
The request object. The request message for Agents.RestoreAgent. |
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. |
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 {}. |
search_agents
search_agents(request: Optional[Union[google.cloud.dialogflow_v2beta1.types.agent.SearchAgentsRequest, 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]] = ())
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>
__.
from google.cloud import dialogflow_v2beta1
async def sample_search_agents():
# Create a client
client = dialogflow_v2beta1.AgentsAsyncClient()
# Initialize request argument(s)
request = dialogflow_v2beta1.SearchAgentsRequest(
parent="parent_value",
)
# Make the request
page_result = client.search_agents(request=request)
# Handle the response
async for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.dialogflow_v2beta1.types.SearchAgentsRequest, dict]
The request object. The request message for Agents.SearchAgents. |
parent |
`str`
Required. The project to list agents from. Format: |
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. |
Type | Description |
google.cloud.dialogflow_v2beta1.services.agents.pagers.SearchAgentsAsyncPager | The response message for Agents.SearchAgents. Iterating over this object will yield results and resolve additional pages automatically. |
set_agent
set_agent(request: Optional[Union[google.cloud.dialogflow_v2beta1.types.agent.SetAgentRequest, dict]] = None, *, agent: Optional[google.cloud.dialogflow_v2beta1.types.agent.Agent] = 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/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>
__.
from google.cloud import dialogflow_v2beta1
async def sample_set_agent():
# Create a client
client = dialogflow_v2beta1.AgentsAsyncClient()
# Initialize request argument(s)
agent = dialogflow_v2beta1.Agent()
agent.parent = "parent_value"
request = dialogflow_v2beta1.SetAgentRequest(
agent=agent,
)
# Make the request
response = await client.set_agent(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.dialogflow_v2beta1.types.SetAgentRequest, dict]
The request object. The request message for Agents.SetAgent. |
agent |
Agent
Required. The agent to update. This corresponds to the |
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. |
Type | Description |
google.cloud.dialogflow_v2beta1.types.Agent | A Dialogflow agent is a virtual agent that handles conversations with your end-users. It is a natural language understanding module that understands the nuances of human language. Dialogflow translates end-user text or audio during a conversation to structured data that your apps and services can understand. You design and build a Dialogflow agent to handle the types of conversations required for your system. For more information about agents, see the [Agent guide](\ https://cloud.google.com/dialogflow/docs/agents-overview). |
train_agent
train_agent(request: Optional[Union[google.cloud.dialogflow_v2beta1.types.agent.TrainAgentRequest, 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]] = ())
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 emptyStruct message <https://developers.google.com/protocol-buffers/docs/reference/google.protobuf#struct>
__response
: AnEmpty 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>
__.
from google.cloud import dialogflow_v2beta1
async def sample_train_agent():
# Create a client
client = dialogflow_v2beta1.AgentsAsyncClient()
# Initialize request argument(s)
request = dialogflow_v2beta1.TrainAgentRequest(
parent="parent_value",
)
# Make the request
operation = client.train_agent(request=request)
print("Waiting for operation to complete...")
response = await operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.dialogflow_v2beta1.types.TrainAgentRequest, dict]
The request object. The request message for Agents.TrainAgent. |
parent |
`str`
Required. The project that the agent to train is associated with. Format: |
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. |
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 {}. |