class IntentContainer(object):
"""
Creates an IntentContainer object used to load and match intents
Args:
cache_dir (str): Place to put all saved neural networks
"""
def __init__(self, cache_dir):
self.intents = IntentManager(cache_dir)
self.entities = EntityManager(cache_dir)
def add_intent(self, *args, **kwargs):
"""
Creates a new intent, optionally checking the cache first
Args:
name (str): The associated name of the intent
lines (list<str>): All the sentences that should activate the intent
reload_cache: Whether to ignore cached intent if exists
"""
self.intents.add(*args, **kwargs)
def add_entity(self, name, *args, **kwargs):
"""
Adds an entity that matches the given lines.
Example:
self.add_intent('weather', ['will it rain on {weekday}?'])
self.add_entity('{weekday}', ['monday', 'tuesday', 'wednesday']) # ...
Args:
name (str): The name of the entity
lines (list<str>): Lines of example extracted entities
reload_cache (bool): Whether to refresh all of cache
"""
Entity.verify_name(name)
self.entities.add(Entity.wrap_name(name), *args, **kwargs)
def load_entity(self, name, *args, **kwargs):
"""
Loads an entity, optionally checking the cache first
Args:
name (str): The associated name of the entity
file_name (str): The location of the entity file
reload_cache (bool): Whether to refresh all of cache
"""
Entity.verify_name(name)
self.entities.load(Entity.wrap_name(name), *args, **kwargs)
def load_file(self, *args, **kwargs):
"""Legacy. Use load_intent instead"""
self.load_intent(*args, **kwargs)
def load_intent(self, *args, **kwargs):
"""
Loads an intent, optionally checking the cache first
Args:
name (str): The associated name of the intent
file_name (str): The location of the intent file
reload_cache (bool): Whether to refresh all of cache
"""
self.intents.load(*args, **kwargs)
def train(self, *args, **kwargs):
"""
Trains all the loaded intents that need to be updated
If a cache file exists with the same hash as the intent file,
the intent will not be trained and just loaded from file
Args:
print_updates (bool): Whether to print a message to stdout
each time a new intent is trained
single_thread (bool): Whether to force running in a single thread
"""
self.intents.train(*args, **kwargs)
self.entities.train(*args, **kwargs)
self.entities.calc_ent_dict()
def calc_intents(self, query):
"""
Tests all the intents against the query and returns
data on how well each one matched against the query
Args:
query (str): Input sentence to test against intents
Returns:
list<MatchData>: List of intent matches
See calc_intent() for a description of the returned MatchData
"""
return self.intents.calc_intents(query, self.entities)
def calc_intent(self, query):
"""
Tests all the intents against the query and returns
match data of the best intent
Args:
query (str): Input sentence to test against intents
Returns:
MatchData: Best intent match
"""
return self.intents.calc_intent(query, self.entities)