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(io_utils.DEFAULT_ENCODING),
suffix="_tmp_training_data.json",
mode="w+b",
)
data = io_utils.read_json_file(fname)
assert data is not None
validation_utils.validate_training_data(data, schema.rasa_nlu_data_schema())
def read_from_json(self, js: Dict[Text, Any], **_) -> "TrainingData":
"""Loads training data stored in the rasa NLU data format."""
from rasa.nlu.training_data import Message, TrainingData
import rasa.nlu.schemas.data_schema as schema
import rasa.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", [])
intent_examples = data.get("intent_examples", [])
entity_examples = data.get("entity_examples", [])
entity_synonyms = data.get("entity_synonyms", [])
regex_features = data.get("regex_features", [])
lookup_tables = data.get("lookup_tables", [])
gazette = data.get("gazette", [])
entity_synonyms = transform_entity_synonyms(entity_synonyms)
if intent_examples or entity_examples:
raise_warning(
"Your rasa data "
"contains 'intent_examples' "
"or 'entity_examples' which will be "
"removed in the future. Consider "
"putting all your examples "
"into the 'common_examples' section.",
FutureWarning,
docs=DOCS_URL_TRAINING_DATA_NLU,
)
all_examples = common_examples + intent_examples + entity_examples
training_examples = []
for ex in all_examples:
msg = Message.build(ex["text"], ex.get("intent"),
ex.get("entities"))
training_examples.append(msg)
return TrainingData(training_examples, entity_synonyms, regex_features,
lookup_tables, gazette)
def read_from_json(self, js: Dict[Text, Any], **_) -> "TrainingData":
"""Loads training data stored in the rasa NLU data format."""
from rasa.nlu.training_data import Message, TrainingData
import rasa.nlu.schemas.data_schema as schema
import rasa.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:
msg = Message.build(**ex)
training_examples.append(msg)
return TrainingData(
training_examples, entity_synonyms, regex_features, lookup_tables
)
def test_validate_training_data_is_throwing_exceptions(invalid_data):
with pytest.raises(ValidationError):
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 = io_utils.read_json_file(demo_json)
validation_utils.validate_training_data(data, schema.rasa_nlu_data_schema())