def _parse_item(self, line: Text) -> None:
"""Parses an md list item line based on the current section type."""
import rasa.nlu.training_data.lookup_tables_parser as lookup_tables_parser
import rasa.nlu.training_data.synonyms_parser as synonyms_parser
from rasa.nlu.training_data import entities_parser
match = re.match(item_regex, line)
if match:
item = match.group(1)
if self.current_section == INTENT:
parsed = entities_parser.parse_training_example(
item, self.current_title)
synonyms_parser.add_synonyms_from_entities(
parsed.text, parsed.get("entities", []),
self.entity_synonyms)
self.training_examples.append(parsed)
elif self.current_section == SYNONYM:
synonyms_parser.add_synonym(item, self.current_title,
self.entity_synonyms)
elif self.current_section == REGEX:
self.regex_features.append({
"name": self.current_title,
"pattern": item
})
elif self.current_section == LOOKUP:
lookup_tables_parser.add_item_to_lookup_tables(
self.current_title, item, self.lookup_tables)
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_add_synonyms_from_entities():
training_example = "I want to fly from Berlin to LA"
entities = [
{
"start": 19,
"end": 25,
"value": "Berlin",
"entity": "city",
"role": "to"
},
{
"start": 29,
"end": 31,
"value": "Los Angeles",
"entity": "city",
"role": "from",
},
]
result = {}
synonyms_parser.add_synonyms_from_entities(training_example, entities,
result)
assert result == {"LA": "Los Angeles"}
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
