def update_answer_record(
self,
request: gcd_answer_record.UpdateAnswerRecordRequest = None,
*,
answer_record: gcd_answer_record.AnswerRecord = None,
update_mask: field_mask.FieldMask = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> gcd_answer_record.AnswerRecord:
r"""Updates the specified answer record.
Args:
request (google.cloud.dialogflow_v2.types.UpdateAnswerRecordRequest):
The request object. Request message for
[AnswerRecords.UpdateAnswerRecord][google.cloud.dialogflow.v2.AnswerRecords.UpdateAnswerRecord].
answer_record (google.cloud.dialogflow_v2.types.AnswerRecord):
Required. Answer record to update.
This corresponds to the ``answer_record`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
update_mask (google.protobuf.field_mask_pb2.FieldMask):
Required. The mask to control which
fields get updated.
This corresponds to the ``update_mask`` 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:
google.cloud.dialogflow_v2.types.AnswerRecord:
Answer records are records to manage answer history and feedbacks for
Dialogflow.
Currently, answer record includes:
- human agent assistant article suggestion
- human agent assistant faq article
It doesn't include:
- DetectIntent intent matching
- DetectIntent knowledge
Answer records are not related to the conversation
history in the Dialogflow Console. A Record is
generated even when the end-user disables
conversation history in the console. Records are
created when there's a human agent assistant
suggestion generated.
A typical workflow for customers provide feedback to
an answer is:
1. For human agent assistant, customers get
suggestion via ListSuggestions API. Together with
the answers,
[AnswerRecord.name][google.cloud.dialogflow.v2.AnswerRecord.name]
are returned to the customers.
2. The customer uses the
[AnswerRecord.name][google.cloud.dialogflow.v2.AnswerRecord.name]
to call the [UpdateAnswerRecord][] method to send
feedback about a specific answer that they believe is
wrong.
"""
# Create or coerce a protobuf request object.
# Sanity check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([answer_record, update_mask])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# Minor optimization to avoid making a copy if the user passes
# in a gcd_answer_record.UpdateAnswerRecordRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, gcd_answer_record.UpdateAnswerRecordRequest):
request = gcd_answer_record.UpdateAnswerRecordRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if answer_record is not None:
request.answer_record = answer_record
if update_mask is not None:
request.update_mask = update_mask
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.update_answer_record]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata(
(("answer_record.name", request.answer_record.name),)
),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Done; return the response.
return response
async def update_answer_record(
self,
request: Union[gcd_answer_record.UpdateAnswerRecordRequest,
dict] = None,
*,
answer_record: gcd_answer_record.AnswerRecord = None,
update_mask: field_mask_pb2.FieldMask = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> gcd_answer_record.AnswerRecord:
r"""Updates the specified answer record.
.. code-block:: python
from google.cloud import dialogflow_v2
async def sample_update_answer_record():
# Create a client
client = dialogflow_v2.AnswerRecordsAsyncClient()
# Initialize request argument(s)
request = dialogflow_v2.UpdateAnswerRecordRequest(
)
# Make the request
response = await client.update_answer_record(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.dialogflow_v2.types.UpdateAnswerRecordRequest, dict]):
The request object. Request message for
[AnswerRecords.UpdateAnswerRecord][google.cloud.dialogflow.v2.AnswerRecords.UpdateAnswerRecord].
answer_record (:class:`google.cloud.dialogflow_v2.types.AnswerRecord`):
Required. Answer record to update.
This corresponds to the ``answer_record`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`):
Required. The mask to control which
fields get updated.
This corresponds to the ``update_mask`` 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:
google.cloud.dialogflow_v2.types.AnswerRecord:
Answer records are records to manage answer history and feedbacks for
Dialogflow.
Currently, answer record includes:
- human agent assistant article suggestion
- human agent assistant faq article
It doesn't include:
- DetectIntent intent matching
- DetectIntent knowledge
Answer records are not related to the conversation
history in the Dialogflow Console. A Record is
generated even when the end-user disables
conversation history in the console. Records are
created when there's a human agent assistant
suggestion generated.
A typical workflow for customers provide feedback to
an answer is:
1. For human agent assistant, customers get
suggestion via ListSuggestions API. Together with
the answers,
[AnswerRecord.name][google.cloud.dialogflow.v2.AnswerRecord.name]
are returned to the customers.
2. The customer uses the
[AnswerRecord.name][google.cloud.dialogflow.v2.AnswerRecord.name]
to call the [UpdateAnswerRecord][] method to send
feedback about a specific answer that they believe
is wrong.
"""
# Create or coerce a protobuf request object.
# Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([answer_record, update_mask])
if request is not None and has_flattened_params:
raise ValueError("If the `request` argument is set, then none of "
"the individual field arguments should be set.")
request = gcd_answer_record.UpdateAnswerRecordRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if answer_record is not None:
request.answer_record = answer_record
if update_mask is not None:
request.update_mask = update_mask
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = gapic_v1.method_async.wrap_method(
self._client._transport.update_answer_record,
default_timeout=None,
client_info=DEFAULT_CLIENT_INFO,
)
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata(
(("answer_record.name", request.answer_record.name), )), )
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
