def _parse_intent(self, data: Dict[Text, Any]) -> None:
from rasa.nlu.training_data import Message
import rasa.nlu.training_data.entities_parser as entities_parser
import rasa.nlu.training_data.synonyms_parser as synonyms_parser
import rasa.nlu.constants as nlu_constants
intent = data.get(KEY_INTENT, "")
if not intent:
raise_warning(
f"Issue found while processing '{self.filename}': "
f"The intent has an empty name. "
f"Intents should have a name defined under the {KEY_INTENT} key. "
f"It will be skipped.",
docs=DOCS_URL_TRAINING_DATA_NLU,
)
return
examples = data.get(KEY_INTENT_EXAMPLES, "")
for example, entities in self._parse_training_examples(examples, intent):
synonyms_parser.add_synonyms_from_entities(
example, entities, self.entity_synonyms
)
plain_text = entities_parser.replace_entities(example)
message = Message.build(plain_text, intent)
if entities:
message.set(nlu_constants.ENTITIES, entities)
self.training_examples.append(message)
def test_markdown_entity_regex(
example: Text, expected_entities: List[Dict[Text, Any]], expected_text: Text
):
result = entities_parser.find_entities_in_training_example(example)
assert result == expected_entities
replaced_text = entities_parser.replace_entities(example)
assert replaced_text == expected_text
def _parse_raw_user_utterance(self, step: Dict[Text, Any]) -> Optional[UserUttered]:
intent_name = self._user_intent_from_step(step)
intent = {"name": intent_name, "confidence": 1.0}
if KEY_USER_MESSAGE in step:
user_message = step[KEY_USER_MESSAGE].strip()
entities = entities_parser.find_entities_in_training_example(user_message)
plain_text = entities_parser.replace_entities(user_message)
else:
raw_entities = step.get(KEY_ENTITIES, [])
entities = self._parse_raw_entities(raw_entities)
plain_text = intent_name
return UserUttered(plain_text, intent, entities)
def parse_training_example(self, example: Text) -> "Message":
"""Extract entities and synonyms, and convert to plain text."""
from rasa.nlu.training_data import Message
import rasa.nlu.training_data.entities_parser as entities_parser
import rasa.nlu.training_data.synonyms_parser as synonyms_parser
entities = entities_parser.find_entities_in_training_example(example)
plain_text = entities_parser.replace_entities(example)
synonyms_parser.add_synonyms_from_entities(plain_text, entities,
self.entity_synonyms)
message = Message.build(plain_text, self.current_title)
if len(entities) > 0:
message.set("entities", entities)
return message