def read_from_json(self, js, **kwargs):
"""Loads training data stored in the rasa NLU data format."""
from rasa.nlu.training_data import Message, TrainingData
validate_rasa_nlu_data(js)
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", [])
entity_synonyms = transform_entity_synonyms(entity_synonyms)
if intent_examples or entity_examples:
logger.warning("DEPRECATION 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.")
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)
def _read_entities(entity_js, examples_js) -> "TrainingData":
from rasa.nlu.training_data import 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_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
)
