def test_generate_message_raises_on_overlapping_but_not_identical_spans(
message_text: Text,
entities: List[Dict[Text, Any]],
):
message = Message.build(message_text, "dummy_intent", entities=entities)
with pytest.raises(ValueError):
TrainingDataWriter.generate_message(message)
def test_generate_message(
message_text: Text,
expected_text: Text,
entities: List[Dict[Text, Any]],
):
message = Message.build(message_text, "dummy_intent", entities=entities)
message_text = TrainingDataWriter.generate_message(message)
assert message_text == expected_text
def md_format_message(
text: Text, intent: Optional[Text], entities: Union[Text, List[Any]]
) -> Text:
"""Uses NLU parser information to generate a message with inline entity annotations.
Arguments:
text: text of the message
intent: intent of the message
entities: entities of the message
Return:
Message with entities annotated inline, e.g.
`I am from [Berlin]{"entity": "city"}`.
"""
from rasa.shared.nlu.training_data.formats.readerwriter import TrainingDataWriter
from rasa.shared.nlu.training_data import entities_parser
message_from_md = entities_parser.parse_training_example(text, intent)
deserialised_entities = deserialise_entities(entities)
return TrainingDataWriter.generate_message(
{"text": message_from_md.get(TEXT), "entities": deserialised_entities}
)