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 _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 test_url_data_format():
data = """
{
"rasa_nlu_data": {
"entity_synonyms": [
{
"value": "nyc",
"synonyms": ["New York City", "nyc", "the big apple"]
}
],
"common_examples" : [
{
"text": "show me flights to New York City",
"intent": "unk",
"entities": [
{
"entity": "destination",
"start": 19,
"end": 32,
"value": "NYC"
}
]
}
]
}
}"""
fname = io_utils.create_temporary_file(
data.encode(rasa.shared.utils.io.DEFAULT_ENCODING),
suffix="_tmp_training_data.json",
mode="w+b",
)
data = rasa.shared.utils.io.read_json_file(fname)
assert data is not None
validation_utils.validate_training_data(data,
schema.rasa_nlu_data_schema())
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_validate_training_data_is_throwing_exceptions(invalid_data):
with pytest.raises(SchemaValidationError):
validation_utils.validate_training_data(invalid_data,
schema.rasa_nlu_data_schema())
def test_example_training_data_is_valid():
demo_json = "data/examples/rasa/demo-rasa.json"
data = rasa.shared.utils.io.read_json_file(demo_json)
validation_utils.validate_training_data(data,
schema.rasa_nlu_data_schema())
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())
