def _find_entities_in_training_example(self, example: Text) -> List[Dict]:
"""Extracts entities from a markdown intent example.
Args:
example: markdown intent example
Returns: list of extracted entities
"""
entities = []
offset = 0
for match in re.finditer(entity_regex, example):
entity_attributes = self._extract_entity_attributes(match)
start_index = match.start() - offset
end_index = start_index + len(entity_attributes.text)
offset += len(match.group(0)) - len(entity_attributes.text)
entity = build_entity(
start_index,
end_index,
entity_attributes.value,
entity_attributes.type,
entity_attributes.role,
entity_attributes.group,
)
entities.append(entity)
return entities
def find_entities_in_training_example(example: Text) -> List[Dict[Text, Any]]:
"""Extracts entities from an intent example.
Args:
example: Intent example.
Returns:
Extracted entities.
"""
import rasa.nlu.utils as rasa_nlu_utils
entities = []
offset = 0
for match in re.finditer(ENTITY_REGEX, example):
entity_attributes = extract_entity_attributes(match)
start_index = match.start() - offset
end_index = start_index + len(entity_attributes.text)
offset += len(match.group(0)) - len(entity_attributes.text)
entity = rasa_nlu_utils.build_entity(
start_index,
end_index,
entity_attributes.value,
entity_attributes.type,
entity_attributes.role,
entity_attributes.group,
)
entities.append(entity)
return entities
def _extract_entity(chunk, current_offset):
"""Extract an entity from a chunk if present."""
entity = None
if "meta" in chunk or "alias" in chunk:
start = current_offset
text = chunk["text"]
end = start + len(text)
entity_type = chunk.get("alias", chunk["meta"])
if entity_type != "@sys.ignore":
entity = utils.build_entity(start, end, text, entity_type)
return entity
def _find_entities_in_training_example(example: Text) -> List[Dict]:
"""Extracts entities from a markdown intent example."""
entities = []
offset = 0
for match in re.finditer(ent_regex, example):
entity_text = match.groupdict()["entity_text"]
entity_type = match.groupdict()["entity"]
if match.groupdict()["value"]:
entity_value = match.groupdict()["value"]
else:
entity_value = entity_text
start_index = match.start() - offset
end_index = start_index + len(entity_text)
offset += len(match.group(0)) - len(entity_text)
entity = build_entity(start_index, end_index, entity_value, entity_type)
entities.append(entity)
return entities
