def get_attribute(tracker: Tracker) -> Text:
"""
Get the attribute mentioned by the user. As the user may use a synonym for
an attribute, we need to map the mentioned attribute to the
attribute name used in the knowledge base.
:param tracker: tracker
:return: attribute (same type as used in the knowledge base)
"""
graph_database = GraphDatabase()
attribute = tracker.get_slot("attribute")
return graph_database.map("attribute-mapping", attribute)
def get_entity_type(tracker: Tracker) -> Text:
"""
Get the entity type mentioned by the user. As the user may speak of an
entity type in plural, we need to map the mentioned entity type to the
type used in the knowledge base.
:param tracker: tracker
:return: entity type (same type as used in the knowledge base)
"""
graph_database = GraphDatabase()
entity_type = tracker.get_slot("entity_type")
return graph_database.map("entity-type-mapping", entity_type)
def resolve_mention(tracker: Tracker) -> Text:
"""
Resolves a mention of an entity, such as first, to the actual entity.
If multiple entities are listed during the conversation, the entities
are stored in the slot 'listed_items' as a list. We resolve the mention,
such as first, to the list index and retrieve the actual entity.
:param tracker: tracker
:return: name of the actually entity
"""
graph_database = GraphDatabase()
mention = tracker.get_slot("mention")
listed_items = tracker.get_slot("listed_items")
if mention is not None and listed_items is not None:
idx = int(graph_database.map("mention-mapping", mention))
if type(idx) is int and idx < len(listed_items):
return listed_items[idx]
