def process_user_utterance(user_utterance: UserUttered,
is_test_story: bool = False) -> OrderedDict:
"""Converts a single user utterance into an ordered dict.
Args:
user_utterance: Original user utterance object.
is_test_story: Identifies if the user utterance should be added
to the final YAML or not.
Returns:
Dict with a user utterance.
"""
result = CommentedMap()
if user_utterance.intent_name and not user_utterance.use_text_for_featurization:
result[KEY_USER_INTENT] = user_utterance.intent_name
if hasattr(user_utterance, "inline_comment"):
comment = user_utterance.inline_comment()
if comment:
result.yaml_add_eol_comment(comment, KEY_USER_INTENT)
if user_utterance.text and (
# We only print the utterance text if it was an end-to-end prediction
user_utterance.use_text_for_featurization
# or if we want to print a conversation test story.
or is_test_story):
result[KEY_USER_MESSAGE] = LiteralScalarString(
rasa.shared.core.events.format_message(
user_utterance.text,
user_utterance.intent_name,
user_utterance.entities,
))
if len(user_utterance.entities) and not is_test_story:
entities = []
for entity in user_utterance.entities:
if "value" in entity:
if hasattr(user_utterance, "inline_comment_for_entity"):
for predicted in user_utterance.predicted_entities:
if predicted["start"] == entity["start"]:
commented_entity = user_utterance.inline_comment_for_entity( # noqa: E501
predicted, entity)
if commented_entity:
entity_map = CommentedMap([
(entity["entity"], entity["value"])
])
entity_map.yaml_add_eol_comment(
commented_entity,
entity["entity"],
)
entities.append(entity_map)
else:
entities.append(
OrderedDict([(entity["entity"],
entity["value"])]))
else:
entities.append(
OrderedDict([(entity["entity"], entity["value"])]))
else:
entities.append(entity["entity"])
result[KEY_ENTITIES] = entities
return result
def process_user_utterance(user_utterance: UserUttered,
is_test_story: bool = False) -> OrderedDict:
"""Converts a single user utterance into an ordered dict.
Args:
user_utterance: Original user utterance object.
is_test_story: Identifies if the user utterance should be added
to the final YAML or not.
Returns:
Dict with a user utterance.
"""
result = CommentedMap()
if user_utterance.intent_name and not user_utterance.use_text_for_featurization:
result[KEY_USER_INTENT] = (
user_utterance.full_retrieval_intent_name
if user_utterance.full_retrieval_intent_name else
user_utterance.intent_name)
entities = []
if len(user_utterance.entities) and not is_test_story:
for entity in user_utterance.entities:
if "value" in entity:
if hasattr(user_utterance, "inline_comment_for_entity"):
# FIXME: to fix this type issue, WronglyClassifiedUserUtterance
# needs to be imported but it's currently outside
# of `rasa.shared`
for predicted in user_utterance.predicted_entities: # type: ignore[attr-defined] # noqa: E501
if predicted["start"] == entity["start"]:
commented_entity = user_utterance.inline_comment_for_entity( # type: ignore[attr-defined] # noqa: E501
predicted, entity)
if commented_entity:
entity_map = CommentedMap([
(entity["entity"], entity["value"])
])
entity_map.yaml_add_eol_comment(
commented_entity, entity["entity"])
entities.append(entity_map)
else:
entities.append(
OrderedDict([(entity["entity"],
entity["value"])]))
else:
entities.append(
OrderedDict([(entity["entity"], entity["value"])]))
else:
entities.append(entity["entity"])
result[KEY_ENTITIES] = entities
if hasattr(user_utterance, "inline_comment"):
# FIXME: to fix this type issue, WronglyClassifiedUserUtterance needs to
# be imported but it's currently outside of `rasa.shared`
comment = user_utterance.inline_comment( # type: ignore[attr-defined]
force_comment_generation=not entities)
if comment:
result.yaml_add_eol_comment(comment, KEY_USER_INTENT)
if user_utterance.text and (
# We only print the utterance text if it was an end-to-end prediction
user_utterance.use_text_for_featurization
# or if we want to print a conversation test story.
or is_test_story):
result[KEY_USER_MESSAGE] = LiteralScalarString(
rasa.shared.core.events.format_message(
user_utterance.text,
user_utterance.intent_name,
user_utterance.entities,
))
return result
