def test_resolve_intent_response_key_from_label(
predicted_label, train_on_text, resolved_intent_response_key
):
# use data that include some responses
training_data = rasa.shared.nlu.training_data.loading.load_data(
"data/examples/rasa/demo-rasa.yml"
)
training_data_responses = rasa.shared.nlu.training_data.loading.load_data(
"data/examples/rasa/demo-rasa-responses.yml"
)
training_data = training_data.merge(training_data_responses)
response_selector = ResponseSelector(
component_config={"use_text_as_label": train_on_text}
)
response_selector.preprocess_train_data(training_data)
label_intent_response_key = response_selector._resolve_intent_response_key(
{"id": hash(predicted_label), "name": predicted_label}
)
assert resolved_intent_response_key == label_intent_response_key
assert (
response_selector.responses[
util.intent_response_key_to_template_key(label_intent_response_key)
]
== training_data.responses[
util.intent_response_key_to_template_key(resolved_intent_response_key)
]
)
def process(self, message: Message, **kwargs: Any) -> None:
"""Return the most likely response, the associated intent_response_key and its similarity to the input."""
out = self._predict(message)
top_label, label_ranking = self._predict_label(out)
# Get the exact intent_response_key and the associated
# response templates for the top predicted label
label_intent_response_key = (
self._resolve_intent_response_key(top_label)
or top_label[INTENT_NAME_KEY])
label_response_templates = self.responses.get(
util.intent_response_key_to_template_key(
label_intent_response_key))
if label_intent_response_key and not label_response_templates:
# response templates seem to be unavailable,
# likely an issue with the training data
# we'll use a fallback instead
rasa.shared.utils.io.raise_warning(
f"Unable to fetch response templates for {label_intent_response_key} "
f"This means that there is likely an issue with the training data."
f"Please make sure you have added response templates for this intent."
)
label_response_templates = [{TEXT: label_intent_response_key}]
for label in label_ranking:
label[INTENT_RESPONSE_KEY] = (
self._resolve_intent_response_key(label)
or label[INTENT_NAME_KEY])
# Remove the "name" key since it is either the same as
# "intent_response_key" or it is the response text which
# is not needed in the ranking.
label.pop(INTENT_NAME_KEY)
selector_key = (self.retrieval_intent if self.retrieval_intent else
RESPONSE_SELECTOR_DEFAULT_INTENT)
logger.debug(
f"Adding following selector key to message property: {selector_key}"
)
prediction_dict = {
RESPONSE_SELECTOR_PREDICTION_KEY: {
"id":
top_label["id"],
RESPONSE_SELECTOR_RESPONSES_KEY:
label_response_templates,
PREDICTED_CONFIDENCE_KEY:
top_label[PREDICTED_CONFIDENCE_KEY],
INTENT_RESPONSE_KEY:
label_intent_response_key,
RESPONSE_SELECTOR_TEMPLATE_NAME_KEY:
util.intent_response_key_to_template_key(
label_intent_response_key),
},
RESPONSE_SELECTOR_RANKING_KEY: label_ranking,
}
self._set_message_property(message, prediction_dict, selector_key)
def test_resolve_intent_response_key_from_label(
predicted_label: Text,
train_on_text: bool,
resolved_intent_response_key: Text,
response_selector_training_data: TrainingData,
create_response_selector: Callable[
[Dict[Text, Any]], ResponseSelectorGraphComponent
],
):
response_selector = create_response_selector({"use_text_as_label": train_on_text},)
response_selector.preprocess_train_data(response_selector_training_data)
label_intent_response_key = response_selector._resolve_intent_response_key(
{"id": hash(predicted_label), "name": predicted_label}
)
assert resolved_intent_response_key == label_intent_response_key
assert (
response_selector.responses[
util.intent_response_key_to_template_key(label_intent_response_key)
]
== response_selector_training_data.responses[
util.intent_response_key_to_template_key(resolved_intent_response_key)
]
)
def _needed_responses_for_examples(
self, examples: List[Message]
) -> Dict[Text, List[Dict[Text, Any]]]:
"""Get all responses used in any of the examples.
Args:
examples: messages to select responses by.
Returns:
All responses that appear at least once in the list of examples.
"""
responses = {}
for ex in examples:
if ex.get(INTENT_RESPONSE_KEY) and ex.get(RESPONSE):
key = util.intent_response_key_to_template_key(ex.get_full_intent())
responses[key] = self.responses[key]
return responses
def _fill_response_phrases(self) -> None:
"""Set response phrase for all examples by looking up NLG stories."""
for example in self.training_examples:
# if intent_response_key is None, that means the corresponding intent is
# not a retrieval intent and hence no response text needs to be fetched.
# If intent_response_key is set, fetch the corresponding response text
if example.get(INTENT_RESPONSE_KEY) is None:
continue
# look for corresponding bot utterance
story_lookup_key = util.intent_response_key_to_template_key(
example.get_full_intent())
assistant_utterances = self.responses.get(story_lookup_key, [])
if assistant_utterances:
# Use the first response text as training label if needed downstream
for assistant_utterance in assistant_utterances:
if assistant_utterance.get(TEXT):
example.set(RESPONSE, assistant_utterance[TEXT])
# If no text attribute was found use the key for training
if not example.get(RESPONSE):
example.set(RESPONSE, story_lookup_key)
def process(self, messages: List[Message]) -> List[Message]:
"""Selects most like response for message.
Args:
messages: List containing latest user message.
Returns:
List containing the message augmented with the most likely response,
the associated intent_response_key and its similarity to the input.
"""
for message in messages:
out = self._predict(message)
top_label, label_ranking = self._predict_label(out)
# Get the exact intent_response_key and the associated
# responses for the top predicted label
label_intent_response_key = (
self._resolve_intent_response_key(top_label)
or top_label[INTENT_NAME_KEY])
label_responses = self.responses.get(
util.intent_response_key_to_template_key(
label_intent_response_key))
if label_intent_response_key and not label_responses:
# responses seem to be unavailable,
# likely an issue with the training data
# we'll use a fallback instead
rasa.shared.utils.io.raise_warning(
f"Unable to fetch responses for {label_intent_response_key} "
f"This means that there is likely an issue with the training data."
f"Please make sure you have added responses for this intent."
)
label_responses = [{TEXT: label_intent_response_key}]
for label in label_ranking:
label[INTENT_RESPONSE_KEY] = (
self._resolve_intent_response_key(label)
or label[INTENT_NAME_KEY])
# Remove the "name" key since it is either the same as
# "intent_response_key" or it is the response text which
# is not needed in the ranking.
label.pop(INTENT_NAME_KEY)
selector_key = (self.retrieval_intent if self.retrieval_intent else
RESPONSE_SELECTOR_DEFAULT_INTENT)
logger.debug(
f"Adding following selector key to message property: {selector_key}"
)
utter_action_key = util.intent_response_key_to_template_key(
label_intent_response_key)
prediction_dict = {
RESPONSE_SELECTOR_PREDICTION_KEY: {
RESPONSE_SELECTOR_RESPONSES_KEY: label_responses,
PREDICTED_CONFIDENCE_KEY:
top_label[PREDICTED_CONFIDENCE_KEY],
INTENT_RESPONSE_KEY: label_intent_response_key,
RESPONSE_SELECTOR_UTTER_ACTION_KEY: utter_action_key,
},
RESPONSE_SELECTOR_RANKING_KEY: label_ranking,
}
self._set_message_property(message, prediction_dict, selector_key)
if (self._execution_context.should_add_diagnostic_data and out
and DIAGNOSTIC_DATA in out):
message.add_diagnostic_data(self._execution_context.node_name,
out.get(DIAGNOSTIC_DATA))
return messages
def test_intent_response_key_to_template_key():
intent_response_key = "chitchat/ask_name"
template_key = "utter_chitchat/ask_name"
assert intent_response_key_to_template_key(intent_response_key) == template_key
