def read_from_json(self, js: Dict[Text, Any], **_: Any) -> "TrainingData":
"""Loads training data stored in the rasa NLU data format."""
import rasa.shared.nlu.training_data.schemas.data_schema as schema
import rasa.shared.utils.validation as validation_utils
validation_utils.validate_training_data(js,
schema.rasa_nlu_data_schema())
data = js["rasa_nlu_data"]
common_examples = data.get("common_examples", [])
entity_synonyms = data.get("entity_synonyms", [])
regex_features = data.get("regex_features", [])
lookup_tables = data.get("lookup_tables", [])
entity_synonyms = transform_entity_synonyms(entity_synonyms)
training_examples = []
for ex in common_examples:
# taking care of custom entries
msg = Message.build(
text=ex.pop(TEXT, ""),
intent=ex.pop(INTENT, None),
entities=ex.pop(ENTITIES, None),
**ex,
)
training_examples.append(msg)
return TrainingData(training_examples, entity_synonyms, regex_features,
lookup_tables)
def _read_entities(entity_js, examples_js) -> "TrainingData":
entity_synonyms = transform_entity_synonyms(examples_js)
name = entity_js.get("name")
lookup_tables = DialogflowReader._extract_lookup_tables(
name, examples_js)
return TrainingData([], entity_synonyms, [], lookup_tables)
def _read_entities(
entity: Dict[Text, Any], examples: List[Dict[Text, Any]]
) -> "TrainingData":
entity_synonyms = transform_entity_synonyms(examples)
if entity["isRegexp"]:
regex_features = DialogflowReader._extract_regex_features(entity, examples)
return TrainingData([], entity_synonyms, regex_features, [])
else:
lookup_tables = DialogflowReader._extract_lookup_tables(entity, examples)
return TrainingData([], entity_synonyms, [], lookup_tables)