def _parse_item(self, line: Text) -> None:
"""Parses an md list item line based on the current section type."""
import rasa.shared.nlu.training_data.lookup_tables_parser as lookup_tables_parser # noqa: E501
import rasa.shared.nlu.training_data.synonyms_parser as synonyms_parser
from rasa.shared.nlu.training_data import entities_parser
match = re.match(item_regex, line)
if match:
item = match.group(1)
if self.current_section == INTENT:
parsed = entities_parser.parse_training_example(
item, self.current_title)
synonyms_parser.add_synonyms_from_entities(
parsed.get(TEXT), parsed.get("entities", []),
self.entity_synonyms)
self.training_examples.append(parsed)
elif self.current_section == SYNONYM:
synonyms_parser.add_synonym(item, self.current_title,
self.entity_synonyms)
elif self.current_section == REGEX:
self.regex_features.append({
"name": self.current_title,
"pattern": item
})
elif self.current_section == LOOKUP:
lookup_tables_parser.add_item_to_lookup_tables(
self.current_title, item, self.lookup_tables)
def parse_e2e_message(line: Text) -> Message:
"""Parses an md list item line based on the current section type.
Matches expressions of the form `<intent>:<example>`. For the
syntax of `<example>` see the Rasa docs on NLU training data."""
# Match three groups:
# 1) Potential "form" annotation
# 2) The correct intent
# 3) Optional entities
# 4) The message text
form_group = fr"({FORM_PREFIX}\s*)*"
item_regex = re.compile(r"\s*" + form_group +
r"([^{}]+?)({.*})*:\s*(.*)")
match = re.match(item_regex, line)
if not match:
raise ValueError(
"Encountered invalid test story format for message "
"`{}`. Please visit the documentation page on "
"end-to-end testing at {}/user-guide/testing-your-assistant/"
"#end-to-end-testing/".format(line, LEGACY_DOCS_BASE_URL))
from rasa.shared.nlu.training_data import entities_parser
intent = match.group(2)
message = match.group(4)
example = entities_parser.parse_training_example(message, intent)
# If the message starts with the `INTENT_MESSAGE_PREFIX` potential entities
# are annotated in the json format (e.g. `/greet{"name": "Rasa"})
if message.startswith(INTENT_MESSAGE_PREFIX):
parsed = RegexInterpreter().synchronous_parse(message)
example.data["entities"] = parsed["entities"]
return example
def extract_text_and_entities(text: Text):
"""
extract entities and plain text from markdown intent example
:param text: markdown intent example
:return: plain intent, list of extracted entities
"""
example = entities_parser.parse_training_example(text)
return example.get(TEXT), example.get('entities', [])
def test_parse_training_example_with_entities():
message = entities_parser.parse_training_example(
"I am from [Berlin](city).", intent="inform"
)
assert message.get("intent") == "inform"
assert message.get(TEXT) == "I am from Berlin."
assert message.get("entities") == [
{"start": 10, "end": 16, "value": "Berlin", "entity": "city"}
]
def extract_text_and_entities(text: Text):
"""
extract entities and plain text from markdown intent example
:param text: markdown intent example
:return: plain intent, list of extracted entities
"""
from rasa.shared.nlu.constants import TEXT
from rasa.shared.nlu.training_data import entities_parser
example = entities_parser.parse_training_example(text)
return example.get(TEXT), example.get('entities', None)
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}
)
def test_parse_empty_example():
message = entities_parser.parse_training_example("")
assert message.get("intent") is None
assert message.get(TEXT) == ""
def test_parse_training_example():
message = entities_parser.parse_training_example("Hello!", intent="greet")
assert message.get("intent") == "greet"
assert message.get(TEXT) == "Hello!"
