def _get_validated_dict(json_str: Text) -> Dict[Text, Text]:
"""Converts the provided json_str to a valid dict containing the entity
attributes.
Users can specify entity roles, synonyms, groups for an entity in a dict, e.g.
[LA]{"entity": "city", "role": "to", "value": "Los Angeles"}
Args:
json_str: the entity dict as string without "{}"
Raises:
ValidationError if validation of entity dict fails.
JSONDecodeError if provided entity dict is not valid json.
Returns:
a proper python dict
"""
import json
import rasa.shared.utils.validation as validation_utils
import rasa.shared.nlu.training_data.schemas.data_schema as schema
# add {} as they are not part of the regex
try:
data = json.loads(f"{{{json_str}}}")
except JSONDecodeError as e:
rasa.shared.utils.io.raise_warning(
f"Incorrect training data format ('{{{json_str}}}'), make sure your "
f"data is valid. For more information about the format visit "
f"{LEGACY_DOCS_BASE_URL}/nlu/training-data-format/.")
raise e
validation_utils.validate_training_data(data,
schema.entity_dict_schema())
return data
def get_validated_dict(json_str: Text) -> Dict[Text, Text]:
"""Converts the provided `json_str` to a valid dict containing the entity
attributes.
Users can specify entity roles, synonyms, groups for an entity in a dict, e.g.
[LA]{"entity": "city", "role": "to", "value": "Los Angeles"}.
Args:
json_str: The entity dict as string without "{}".
Raises:
ValidationError if validation of entity dict fails.
JSONDecodeError if provided entity dict is not valid json.
Returns:
Deserialized and validated `json_str`.
"""
import json
import rasa.shared.utils.validation as validation_utils
import rasa.shared.nlu.training_data.schemas.data_schema as schema
# add {} as they are not part of the regex
try:
data = json.loads(f"{{{json_str}}}")
except JSONDecodeError as e:
rasa.shared.utils.io.raise_warning(
f"Incorrect training data format ('{{{json_str}}}'). Make sure your "
f"data is valid.",
docs=DOCS_URL_TRAINING_DATA_NLU,
)
raise e
validation_utils.validate_training_data(data, schema.entity_dict_schema())
return data
def test_entity_dict_is_valid(data):
validation_utils.validate_training_data(data, schema.entity_dict_schema())
def test_validate_entity_dict_is_throwing_exceptions(invalid_data):
with pytest.raises(SchemaValidationError):
validation_utils.validate_training_data(invalid_data,
schema.entity_dict_schema())
