def update_context(
self,
request: Union[gcd_context.UpdateContextRequest, dict] = None,
*,
context: gcd_context.Context = None,
update_mask: field_mask_pb2.FieldMask = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> gcd_context.Context:
r"""Updates the specified context.
.. code-block:: python
from google.cloud import dialogflow_v2
def sample_update_context():
# Create a client
client = dialogflow_v2.ContextsClient()
# Initialize request argument(s)
context = dialogflow_v2.Context()
context.name = "name_value"
request = dialogflow_v2.UpdateContextRequest(
context=context,
)
# Make the request
response = client.update_context(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.dialogflow_v2.types.UpdateContextRequest, dict]):
The request object. The request message for
[Contexts.UpdateContext][google.cloud.dialogflow.v2.Contexts.UpdateContext].
context (google.cloud.dialogflow_v2.types.Context):
Required. The context to update.
This corresponds to the ``context`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
update_mask (google.protobuf.field_mask_pb2.FieldMask):
Optional. 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.Context:
Dialogflow contexts are similar to natural language context. If a person says
to you "they are orange", you need context in order
to understand what "they" is referring to. Similarly,
for Dialogflow to handle an end-user expression like
that, it needs to be provided with context in order
to correctly match an intent.
Using contexts, you can control the flow of a
conversation. You can configure contexts for an
intent by setting input and output contexts, which
are identified by string names. When an intent is
matched, any configured output contexts for that
intent become active. While any contexts are active,
Dialogflow is more likely to match intents that are
configured with input contexts that correspond to the
currently active contexts.
For more information about context, see the [Contexts
guide](\ https://cloud.google.com/dialogflow/docs/contexts-overview).
"""
# 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([context, 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_context.UpdateContextRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, gcd_context.UpdateContextRequest):
request = gcd_context.UpdateContextRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if context is not None:
request.context = context
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_context]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata(
(("context.name", request.context.name), )), )
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
async def update_context(
self,
request: gcd_context.UpdateContextRequest = None,
*,
context: gcd_context.Context = None,
update_mask: field_mask.FieldMask = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> gcd_context.Context:
r"""Updates the specified context.
Args:
request (:class:`~.gcd_context.UpdateContextRequest`):
The request object. The request message for
[Contexts.UpdateContext][google.cloud.dialogflow.v2.Contexts.UpdateContext].
context (:class:`~.gcd_context.Context`):
Required. The context to update.
This corresponds to the ``context`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
update_mask (:class:`~.field_mask.FieldMask`):
Optional. 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:
~.gcd_context.Context:
Dialogflow contexts are similar to natural language
context. If a person says to you "they are orange", you
need context in order to understand what "they" is
referring to. Similarly, for Dialogflow to handle an
end-user expression like that, it needs to be provided
with context in order to correctly match an intent.
Using contexts, you can control the flow of a
conversation. You can configure contexts for an intent
by setting input and output contexts, which are
identified by string names. When an intent is matched,
any configured output contexts for that intent become
active. While any contexts are active, Dialogflow is
more likely to match intents that are configured with
input contexts that correspond to the currently active
contexts.
For more information about context, see the `Contexts
guide <https://cloud.google.com/dialogflow/docs/contexts-overview>`__.
"""
# 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([context, 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_context.UpdateContextRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if context is not None:
request.context = context
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_context,
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(
(("context.name", request.context.name), )), )
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response