def parse_training_example(example: Text, intent: Optional[Text] = None) -> "Message":
"""Extract entities and synonyms, and convert to plain text."""
entities = find_entities_in_training_example(example)
plain_text = replace_entities(example)
return Message.build(plain_text, intent, entities)
def _collect_messages(evts: List[Dict[Text, Any]]) -> List[Message]:
"""Collect the message text and parsed data from the UserMessage events
into a list"""
from rasa.nlu.extractors.duckling_http_extractor import \
DucklingHTTPExtractor
from rasa.nlu.extractors.mitie_entity_extractor import MitieEntityExtractor
from rasa.nlu.extractors.spacy_entity_extractor import SpacyEntityExtractor
msgs = []
for evt in evts:
if evt.get("event") == UserUttered.type_name:
data = evt.get("parse_data")
for entity in data.get("entities", []):
excluded_extractors = [
DucklingHTTPExtractor.__name__,
SpacyEntityExtractor.__name__,
MitieEntityExtractor.__name__
]
logger.debug("Exclude entity marking of following extractors"
" {} when writing nlu data "
"to file.".format(excluded_extractors))
if entity.get("extractor") in excluded_extractors:
data["entities"].remove(entity)
msg = Message.build(data["text"], data["intent"]["name"],
data["entities"])
msgs.append(msg)
return msgs
