class IntentEngineTests(unittest.TestCase):
def setUp(self):
self.engine = IntentDeterminationEngine()
def testRegisterIntentParser(self):
assert len(self.engine.intent_parsers) == 0
try:
self.engine.register_intent_parser("NOTAPARSER")
assert "Did not fail to register invalid intent parser" and False
except ValueError as e:
pass
parser = IntentBuilder("Intent").build()
self.engine.register_intent_parser(parser)
assert len(self.engine.intent_parsers) == 1
def testRegisterRegexEntity(self):
assert len(self.engine._regex_strings) == 0
assert len(self.engine.regular_expressions_entities) == 0
self.engine.register_regex_entity(".*")
assert len(self.engine._regex_strings) == 1
assert len(self.engine.regular_expressions_entities) == 1
def testSelectBestIntent(self):
parser1 = IntentBuilder("Parser1").require("Entity1").build()
self.engine.register_entity("tree", "Entity1")
self.engine.register_intent_parser(parser1)
utterance = "go to the tree house"
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser1'
parser2 = IntentBuilder("Parser2").require("Entity1").require(
"Entity2").build()
self.engine.register_entity("house", "Entity2")
self.engine.register_intent_parser(parser2)
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser2'
def testIntentMissingEntity(self):
utterance1 = "give me One home"
utterance2 = "give me One or Two"
parser3 = IntentBuilder("Parser3").require("One").require(
"Two").build()
self.engine.register_entity("One", "One")
self.engine.register_entity("Two", "Two")
self.engine.register_intent_parser(parser3)
intent2 = self.engine.determine_intent(utterance2)
try:
intent2 = next(intent2)
except BaseException:
pass
intent1 = self.engine.determine_intent(utterance1)
try:
intent1 = next(intent1)
except BaseException:
pass
class ContextManagerIntegrationTest(unittest.TestCase):
def setUp(self):
self.context_manager = ContextManager()
self.engine = IntentDeterminationEngine()
def testBasicContextualFollowup(self):
intent1 = IntentBuilder("TimeQueryIntent")\
.require("TimeQuery")\
.require("Location")\
.build()
intent2 = IntentBuilder("WeatherQueryIntent")\
.require("WeatherKeyword")\
.require("Location")\
.build()
self.engine.register_intent_parser(intent1)
self.engine.register_intent_parser(intent2)
self.engine.register_entity("what time is it", "TimeQuery")
self.engine.register_entity("seattle", "Location")
self.engine.register_entity("miami", "Location")
self.engine.register_entity("weather", "WeatherKeyword")
utterance1 = "what time is it in seattle"
intent = next(self.engine.determine_intent(utterance1, include_tags=True, context_manager=self.context_manager))
assert intent
assert intent['intent_type'] == 'TimeQueryIntent'
assert '__tags__' in intent
for tag in intent['__tags__']:
context_entity = tag.get('entities')[0]
self.context_manager.inject_context(context_entity)
utterance2 = "what's the weather like?"
intent = next(self.engine.determine_intent(utterance2, context_manager=self.context_manager))
assert intent
assert intent['intent_type'] == 'WeatherQueryIntent'
def testContextOnlyUsedOnce(self):
intent_parser = IntentBuilder("DummyIntent")\
.require("Foo")\
.optionally("Foo", "Foo2")\
.build()
context_entity = {'confidence': 1.0, 'data': [('foo', 'Foo')], 'match': 'foo', 'key': 'foo'}
self.context_manager.inject_context(context_entity)
self.engine.register_intent_parser(intent_parser)
self.engine.register_entity("foo", "Foo")
self.engine.register_entity("fop", "Foo")
intent = next(self.engine.determine_intent("foo", include_tags=True, context_manager=self.context_manager))
assert intent
assert intent['intent_type'] == "DummyIntent"
assert not (intent.get("Foo") and intent.get("Foo2"))
class ContextManagerIntegrationTest(unittest.TestCase):
def setUp(self):
self.context_manager = ContextManager()
self.engine = IntentDeterminationEngine()
def testBasicContextualFollowup(self):
intent1 = IntentBuilder("TimeQueryIntent")\
.require("TimeQuery")\
.require("Location")\
.build()
intent2 = IntentBuilder("WeatherQueryIntent")\
.require("WeatherKeyword")\
.require("Location")\
.build()
self.engine.register_intent_parser(intent1)
self.engine.register_intent_parser(intent2)
self.engine.register_entity("what time is it", "TimeQuery")
self.engine.register_entity("seattle", "Location")
self.engine.register_entity("miami", "Location")
self.engine.register_entity("weather", "WeatherKeyword")
utterance1 = "what time is it in seattle"
intent = next(self.engine.determine_intent(utterance1, include_tags=True, context_manager=self.context_manager))
assert intent
assert intent['intent_type'] == 'TimeQueryIntent'
assert '__tags__' in intent
for tag in intent['__tags__']:
context_entity = tag.get('entities')[0]
self.context_manager.inject_context(context_entity)
utterance2 = "what's the weather like?"
intent = next(self.engine.determine_intent(utterance2, context_manager=self.context_manager))
assert intent
assert intent['intent_type'] == 'WeatherQueryIntent'
def testContextOnlyUsedOnce(self):
intent_parser = IntentBuilder("DummyIntent")\
.require("Foo")\
.optionally("Foo", "Foo2")\
.build()
context_entity = {'confidence': 1.0, 'data': [('foo', 'Foo')], 'match': 'foo', 'key': 'foo'}
self.context_manager.inject_context(context_entity)
self.engine.register_intent_parser(intent_parser)
self.engine.register_entity("foo", "Foo")
self.engine.register_entity("fop", "Foo")
intent = next(self.engine.determine_intent("foo", include_tags=True, context_manager=self.context_manager))
assert intent
assert intent['intent_type'] == "DummyIntent"
assert not (intent.get("Foo") and intent.get("Foo2"))
class SmartHomeIntentParser():
def __init__(self):
self.engine = IntentDeterminationEngine()
# smart home intent vocabalory
switch_tasks_keyword = ["switch", "turn"]
for stk in switch_tasks_keyword:
self.engine.register_entity(stk, "SwitchTasksKeyword")
on_off_keyword = ["on", "off"]
for stk in on_off_keyword:
self.engine.register_entity(stk, "OnOffKeyword")
equipment_keyword = ["lights", "light", "fan"]
for stk in equipment_keyword:
self.engine.register_entity(stk, "EquipmentKeyword")
smart_home_intent = IntentBuilder("SmartHomeIntent")\
.require("SwitchTasksKeyword")\
.require("OnOffKeyword")\
.require("EquipmentKeyword")\
.build()
self.engine.register_intent_parser(smart_home_intent)
def parse(self, sentence):
for intent in self.engine.determine_intent(sentence):
if intent.get('confidence') > 0:
return intent
class MusicPlayerIntentParser():
def __init__(self):
self.engine = IntentDeterminationEngine()
# define music vocabulary
music_verbs = ["listen", "hear", "play", "stop"]
for mv in music_verbs:
self.engine.register_entity(mv, "MusicVerb")
music_keywords = ["songs", "music"]
for mk in music_keywords:
self.engine.register_entity(mk, "MusicKeyword")
self.engine.register_regex_entity(
"(play|hear|listen|listen to)\s*(the)?\s*(song|album)?\s*(?P<Media>.*)$" # NoQA
)
music_intent = IntentBuilder("MusicIntent")\
.require("MusicVerb")\
.optionally("MusicKeyword")\
.optionally("Media")\
.build()
self.engine.register_intent_parser(music_intent)
def parse(self, sentence):
for intent in self.engine.determine_intent(sentence):
if intent.get('confidence') > 0:
return intent
class IntentSkill(MycroftSkill):
def __init__(self):
MycroftSkill.__init__(self, name="IntentSkill")
self.engine = IntentDeterminationEngine()
def initialize(self):
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
def handle_utterance(self, message):
utterances = message.data.get('utterances', '')
best_intent = None
for utterance in utterances:
try:
best_intent = next(self.engine.determine_intent(
utterance, 100))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration, e:
logger.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
reply = message.reply(best_intent.get('intent_type'), best_intent)
self.emitter.emit(reply)
elif len(utterances) == 1:
self.emitter.emit(
Message("intent_failure", {"utterance": utterances[0]}))
else:
self.emitter.emit(
Message("multi_utterance_intent_failure",
{"utterances": utterances}))
class JokeIntentParser():
def __init__(self):
self.engine = IntentDeterminationEngine()
joke_verbs = [
"tell",
"crack"
]
for mv in joke_verbs:
self.engine.register_entity(mv, "JokeVerb")
joke_keywords = [
"joke"
]
for mk in joke_keywords:
self.engine.register_entity(mk, "JokeKeyword")
joke_intent = IntentBuilder("JokeIntent")\
.require("JokeVerb")\
.optionally("JokeKeyword")\
.build()
self.engine.register_intent_parser(joke_intent)
def parse(self, sentence):
for intent in self.engine.determine_intent(sentence):
if intent.get('confidence') > 0:
return intent
def testContextAndOneOf(self):
# test to cover https://github.com/MycroftAI/adapt/issues/86
engine = IntentDeterminationEngine()
context_manager = ContextManager()
# define vocabulary
weather_keyword = ["weather"]
for wk in weather_keyword:
engine.register_entity(wk, "WeatherKeyword")
# structure intent
weather_intent = IntentBuilder("WeatherIntent") \
.require("WeatherKeyword") \
.one_of("Location", "LocationContext").build()
engine.register_intent_parser(weather_intent)
word = 'lizard'
context = 'LocationContext'
entity = {}
entity['data'] = [(word, context)]
entity['match'] = word
entity['key'] = word
context_manager.inject_context(entity)
intents = list(
engine.determine_intent('weather',
context_manager=context_manager))
self.assertEqual(1, len(intents), "Incorrect number of intents")
result = intents[0]
self.assertEqual("lizard", result.get("LocationContext"),
"Context not matched")
self.assertEqual(0.75, result.get('confidence'),
"Context confidence not properly applied.")
class IntentSkill(MycroftSkill):
def __init__(self):
MycroftSkill.__init__(self, name="IntentSkill")
self.engine = IntentDeterminationEngine()
def initialize(self):
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
def handle_utterance(self, message):
utterances = message.metadata.get('utterances', '')
best_intent = None
for utterance in utterances:
try:
best_intent = next(self.engine.determine_intent(utterance, num_results=100))
best_intent['utterance'] = utterance # TODO - Should Adapt handle this?
except StopIteration, e:
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
reply = message.reply(best_intent.get('intent_type'), metadata=best_intent)
self.emitter.emit(reply)
elif len(utterances) == 1:
self.emitter.emit(Message("intent_failure", metadata={"utterance": utterances[0]}))
else:
self.emitter.emit(Message("multi_utterance_intent_failure", metadata={"utterances": utterances}))
def testEmptyTags(self):
# Validates https://github.com/MycroftAI/adapt/issues/114
engine = IntentDeterminationEngine()
engine.register_entity("Kevin",
"who") # same problem if several entities
builder = IntentBuilder("Buddies")
builder.optionally("who") # same problem if several entity types
engine.register_intent_parser(builder.build())
intents = [i for i in engine.determine_intent("Julien is a friend")]
assert len(intents) == 0
class input_engine:
"""Manages the intent engine and natural language input parser"""
def __init__(self):
self.engine = IntentDeterminationEngine()
def register_entity(self, keywords, name):
"""Registers an intenty to be found in an input"""
for k in keywords:
self.engine.register_entity(k, name)
def register_intent(self, intent):
"""Registers an intent that can be found in an input"""
self.engine.register_intent_parser(intent)
def get_intent(self, input_string):
"""Returns an intent from an input string if one is found"""
intent = self.engine.determine_intent(input_string)
for intent in self.engine.determine_intent(input_string):
if intent.get("confidence") > 0:
return intent
return None
class IntentEngineTests(unittest.TestCase):
def setUp(self):
self.engine = IntentDeterminationEngine()
def testRegisterIntentParser(self):
assert len(self.engine.intent_parsers) == 0
try:
self.engine.register_intent_parser("NOTAPARSER")
assert "Did not fail to register invalid intent parser" and False
except ValueError as e:
pass
parser = IntentBuilder("Intent").build()
self.engine.register_intent_parser(parser)
assert len(self.engine.intent_parsers) == 1
def testRegisterRegexEntity(self):
assert len(self.engine._regex_strings) == 0
assert len(self.engine.regular_expressions_entities) == 0
self.engine.register_regex_entity(".*")
assert len(self.engine._regex_strings) == 1
assert len(self.engine.regular_expressions_entities) == 1
def testSelectBestIntent(self):
parser1 = IntentBuilder("Parser1").require("Entity1").build()
self.engine.register_intent_parser(parser1)
self.engine.register_entity("tree", "Entity1")
utterance = "go to the tree house"
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser1'
parser2 = IntentBuilder("Parser2").require("Entity1").require("Entity2").build()
self.engine.register_intent_parser(parser2)
self.engine.register_entity("house", "Entity2")
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser2'
class IntentSkill(MycroftSkill):
def __init__(self):
MycroftSkill.__init__(self, name="IntentSkill")
self.engine = IntentDeterminationEngine()
self.reload_skill = False
def initialize(self):
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
def handle_utterance(self, message):
# Get language of the utterance
lang = message.data.get('lang', None)
if not lang:
lang = "en-us"
utterances = message.data.get('utterances', '')
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy", etc.
best_intent = next(
self.engine.determine_intent(normalize(utterance, lang),
100))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration, e:
logger.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
reply = message.reply(best_intent.get('intent_type'), best_intent)
self.emitter.emit(reply)
elif len(utterances) == 1:
self.emitter.emit(
Message("intent_failure", {
"utterance": utterances[0],
"lang": lang
}))
else:
self.emitter.emit(
Message("multi_utterance_intent_failure", {
"utterances": utterances,
"lang": lang
}))
class IntentService(object):
def __init__(self, emitter):
self.engine = IntentDeterminationEngine()
self.emitter = emitter
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
self.emitter.on('detach_skill', self.handle_detach_skill)
def handle_utterance(self, message):
# Get language of the utterance
lang = message.data.get('lang', None)
if not lang:
lang = "en-us"
utterances = message.data.get('utterances', '')
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy", etc.
best_intent = next(self.engine.determine_intent(
normalize(utterance, lang), 100))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration, e:
logger.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
reply = message.reply(
best_intent.get('intent_type'), best_intent)
self.emitter.emit(reply)
elif len(utterances) == 1:
self.emitter.emit(Message("intent_failure", {
"utterance": utterances[0],
"lang": lang
}))
else:
self.emitter.emit(Message("multi_utterance_intent_failure", {
"utterances": utterances,
"lang": lang
}))
def put_in_engin(text):
engine = IntentDeterminationEngine()
# Register entities on engine
for entity, keywords in entities.items():
for keyword in keywords:
engine.register_entity(keyword, entity)
for entity in multi_regex_entities:
for regex in entity:
engine.register_regex_entity(regex)
# Register intents on engine
for intent in intents:
engine.register_intent_parser(intent)
for intent in engine.determine_intent(text):
# return dict[intenct] # will return function
print(intent)
print('finished')
class WeatherIntentParser():
def __init__(self):
self.engine = IntentDeterminationEngine()
weather_verbs = ["weather", "temperature", "forecast"]
for mv in weather_verbs:
self.engine.register_entity(mv, "WeatherVerb")
self.engine.register_regex_entity(
"in\s*(?P<Location>[A-Z][^\s]*\s*?)+.*$" # NoQA
)
weather_intent = IntentBuilder("WeatherIntent")\
.require("WeatherVerb")\
.require("Location")\
.build()
self.engine.register_intent_parser(weather_intent)
def parse(self, sentence):
for intent in self.engine.determine_intent(sentence):
if intent.get('confidence') > 0:
return intent
def get_intent(message):
engine = IntentDeterminationEngine()
keywords = [
'service',
'med',
'clinic',
'walk in',
]
for key in keywords:
engine.register_entity(key, "KeyWords")
print(os.getcwd())
with open(os.getcwd() + '/home/addresses.csv', 'rb') as csvfile:
records = csv.reader(csvfile, delimiter=',')
street_number = []
street_name = []
for row in records:
street_number.append(row[8])
street_name.append(row[11])
for key in street_number:
engine.register_entity(key, "StreetNumber")
for key in street_name:
engine.register_entity(key, "StreetName")
address_intent = IntentBuilder("AddressIntent")\
.require("KeyWords")\
.optionally("StreetNumber")\
.optionally("StreetName")\
.build()
engine.register_intent_parser(address_intent)
for intent in engine.determine_intent(''.join(message)):
return intent
class IntentService(object):
def __init__(self, emitter):
self.config = Configuration.get().get('context', {})
self.engine = IntentDeterminationEngine()
# Dictionary for translating a skill id to a name
self.skill_names = {}
# Context related intializations
self.context_keywords = self.config.get('keywords', [])
self.context_max_frames = self.config.get('max_frames', 3)
self.context_timeout = self.config.get('timeout', 2)
self.context_greedy = self.config.get('greedy', False)
self.context_manager = ContextManager(self.context_timeout)
self.emitter = emitter
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
self.emitter.on('detach_skill', self.handle_detach_skill)
# Context related handlers
self.emitter.on('add_context', self.handle_add_context)
self.emitter.on('remove_context', self.handle_remove_context)
self.emitter.on('clear_context', self.handle_clear_context)
# Converse method
self.emitter.on('skill.converse.response',
self.handle_converse_response)
self.emitter.on('mycroft.speech.recognition.unknown',
self.reset_converse)
self.emitter.on('mycroft.skills.loaded', self.update_skill_name_dict)
def add_active_skill_handler(message):
self.add_active_skill(message.data['skill_id'])
self.emitter.on('active_skill_request', add_active_skill_handler)
self.active_skills = [] # [skill_id , timestamp]
self.converse_timeout = 5 # minutes to prune active_skills
def update_skill_name_dict(self, message):
"""
Messagebus handler, updates dictionary of if to skill name
conversions.
"""
self.skill_names[message.data['id']] = message.data['name']
def get_skill_name(self, skill_id):
"""
Get skill name from skill ID.
Args
skill_id: a skill id as encoded in Intent handlers.
Returns: (str) Skill name or the skill id if the skill
wasn't found in the dict.
"""
return self.skill_names.get(int(skill_id), skill_id)
def reset_converse(self, message):
"""Let skills know there was a problem with speech recognition"""
lang = message.data.get('lang', "en-us")
for skill in self.active_skills:
self.do_converse(None, skill[0], lang)
def do_converse(self, utterances, skill_id, lang):
self.emitter.emit(Message("skill.converse.request", {
"skill_id": skill_id, "utterances": utterances, "lang": lang}))
self.waiting = True
self.result = False
start_time = time.time()
t = 0
while self.waiting and t < 5:
t = time.time() - start_time
time.sleep(0.1)
self.waiting = False
return self.result
def handle_converse_response(self, message):
# id = message.data["skill_id"]
# no need to crosscheck id because waiting before new request is made
# no other skill will make this request is safe assumption
result = message.data["result"]
self.result = result
self.waiting = False
def remove_active_skill(self, skill_id):
for skill in self.active_skills:
if skill[0] == skill_id:
self.active_skills.remove(skill)
def add_active_skill(self, skill_id):
# search the list for an existing entry that already contains it
# and remove that reference
self.remove_active_skill(skill_id)
# add skill with timestamp to start of skill_list
self.active_skills.insert(0, [skill_id, time.time()])
def update_context(self, intent):
"""
updates context with keyword from the intent.
NOTE: This method currently won't handle one_of intent keywords
since it's not using quite the same format as other intent
keywords. This is under investigation in adapt, PR pending.
Args:
intent: Intent to scan for keywords
"""
for tag in intent['__tags__']:
if 'entities' not in tag:
continue
context_entity = tag['entities'][0]
if self.context_greedy:
self.context_manager.inject_context(context_entity)
elif context_entity['data'][0][1] in self.context_keywords:
self.context_manager.inject_context(context_entity)
def send_metrics(self, intent, context, stopwatch):
"""
Send timing metrics to the backend.
"""
LOG.debug('Sending metric')
ident = context['ident'] if context else None
if intent:
# Recreate skill name from skill id
parts = intent.get('intent_type', '').split(':')
intent_type = self.get_skill_name(parts[0])
if len(parts) > 1:
intent_type = ':'.join([intent_type] + parts[1:])
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': intent_type})
else:
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': 'intent_failure'})
def handle_utterance(self, message):
"""
Messagebus handler for the recognizer_loop:utterance message
"""
try:
# Get language of the utterance
lang = message.data.get('lang', "en-us")
utterances = message.data.get('utterances', '')
stopwatch = Stopwatch()
with stopwatch:
# Parse the sentence
converse = self.parse_converse(utterances, lang)
if not converse:
# no skill wants to handle utterance
intent = self.parse_utterances(utterances, lang)
if converse:
# Report that converse handled the intent and return
ident = message.context['ident'] if message.context else None
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': 'converse'})
return
elif intent:
# Send the message on to the intent handler
reply = message.reply(intent.get('intent_type'), intent)
else:
# or if no match send sentence to fallback system
reply = message.reply('intent_failure',
{'utterance': utterances[0],
'lang': lang})
self.emitter.emit(reply)
self.send_metrics(intent, message.context, stopwatch)
except Exception as e:
LOG.exception(e)
def parse_converse(self, utterances, lang):
"""
Converse, check if a recently invoked skill wants to
handle the utterance and override normal adapt handling.
Returns: True if converse handled the utterance, else False.
"""
# check for conversation time-out
self.active_skills = [skill for skill in self.active_skills
if time.time() - skill[
1] <= self.converse_timeout * 60]
# check if any skill wants to handle utterance
for skill in self.active_skills:
if self.do_converse(utterances, skill[0], lang):
# update timestamp, or there will be a timeout where
# intent stops conversing whether its being used or not
self.add_active_skill(skill[0])
return True
return False
def parse_utterances(self, utterances, lang):
"""
Parse the utteracne using adapt to find a matching intent.
Args:
utterances (list): list of utterances
lang (string): 4 letter ISO language code
Returns: Intent structure, or None if no match was found.
"""
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy", etc.
best_intent = next(self.engine.determine_intent(
normalize(utterance, lang), 100,
include_tags=True,
context_manager=self.context_manager))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration:
# don't show error in log
continue
except Exception as e:
LOG.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
self.update_context(best_intent)
# update active skills
skill_id = int(best_intent['intent_type'].split(":")[0])
self.add_active_skill(skill_id)
return best_intent
def handle_register_vocab(self, message):
start_concept = message.data.get('start')
end_concept = message.data.get('end')
regex_str = message.data.get('regex')
alias_of = message.data.get('alias_of')
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(
start_concept, end_concept, alias_of=alias_of)
def handle_register_intent(self, message):
intent = open_intent_envelope(message)
self.engine.register_intent_parser(intent)
def handle_detach_intent(self, message):
intent_name = message.data.get('intent_name')
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name]
self.engine.intent_parsers = new_parsers
def handle_detach_skill(self, message):
skill_id = message.data.get('skill_id')
new_parsers = [
p for p in self.engine.intent_parsers if
not p.name.startswith(skill_id)]
self.engine.intent_parsers = new_parsers
def handle_add_context(self, message):
"""
Handles adding context from the message bus.
The data field must contain a context keyword and
may contain a word if a specific word should be injected
as a match for the provided context keyword.
"""
entity = {'confidence': 1.0}
context = message.data.get('context')
word = message.data.get('word') or ''
# if not a string type try creating a string from it
if not isinstance(word, basestring):
word = str(word)
entity['data'] = [(word, context)]
entity['match'] = word
entity['key'] = word
self.context_manager.inject_context(entity)
def handle_remove_context(self, message):
"""
Handles removing context from the message bus. The
data field must contain the 'context' to remove.
"""
context = message.data.get('context')
if context:
self.context_manager.remove_context(context)
def handle_clear_context(self, message):
"""
Clears all keywords from context.
"""
self.context_manager.clear_context()
class IntentService(object):
def __init__(self, emitter):
self.engine = IntentDeterminationEngine()
self.emitter = emitter
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
self.emitter.on('detach_skill', self.handle_detach_skill)
def handle_utterance(self, message):
# Get language of the utterance
lang = message.data.get('lang', None)
if not lang:
lang = "en-us"
utterances = message.data.get('utterances', '')
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy", etc.
best_intent = next(self.engine.determine_intent(
normalize(utterance, lang), 100))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration as e:
logger.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
reply = message.reply(
best_intent.get('intent_type'), best_intent)
self.emitter.emit(reply)
elif len(utterances) == 1:
self.emitter.emit(Message("intent_failure", {
"utterance": utterances[0],
"lang": lang
}))
else:
self.emitter.emit(Message("multi_utterance_intent_failure", {
"utterances": utterances,
"lang": lang
}))
def handle_register_vocab(self, message):
start_concept = message.data.get('start')
end_concept = message.data.get('end')
regex_str = message.data.get('regex')
alias_of = message.data.get('alias_of')
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(
start_concept, end_concept, alias_of=alias_of)
def handle_register_intent(self, message):
intent = open_intent_envelope(message)
self.engine.register_intent_parser(intent)
def handle_detach_intent(self, message):
intent_name = message.data.get('intent_name')
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name]
self.engine.intent_parsers = new_parsers
def handle_detach_skill(self, message):
skill_name = message.data.get('skill_name')
new_parsers = [
p for p in self.engine.intent_parsers if
not p.name.startswith(skill_name)]
self.engine.intent_parsers = new_parsers
class IntentService(object):
def __init__(self, emitter):
self.config = Configuration.get().get('context', {})
self.engine = IntentDeterminationEngine()
self.context_keywords = self.config.get('keywords', [])
self.context_max_frames = self.config.get('max_frames', 3)
self.context_timeout = self.config.get('timeout', 2)
self.context_greedy = self.config.get('greedy', False)
self.context_manager = ContextManager(self.context_timeout)
self.emitter = emitter
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
self.emitter.on('detach_skill', self.handle_detach_skill)
# Context related handlers
self.emitter.on('add_context', self.handle_add_context)
self.emitter.on('remove_context', self.handle_remove_context)
self.emitter.on('clear_context', self.handle_clear_context)
# Converse method
self.emitter.on('skill.converse.response',
self.handle_converse_response)
self.emitter.on('mycroft.speech.recognition.unknown',
self.reset_converse)
def add_active_skill_handler(message):
self.add_active_skill(message.data['skill_id'])
self.emitter.on('active_skill_request', add_active_skill_handler)
self.active_skills = [] # [skill_id , timestamp]
self.converse_timeout = 5 # minutes to prune active_skills
def reset_converse(self, message):
"""Let skills know there was a problem with speech recognition"""
lang = message.data.get('lang', "en-us")
for skill in self.active_skills:
self.do_converse(None, skill[0], lang)
def do_converse(self, utterances, skill_id, lang):
self.emitter.emit(Message("skill.converse.request", {
"skill_id": skill_id, "utterances": utterances, "lang": lang}))
self.waiting = True
self.result = False
start_time = time.time()
t = 0
while self.waiting and t < 5:
t = time.time() - start_time
time.sleep(0.1)
self.waiting = False
return self.result
def handle_converse_response(self, message):
# id = message.data["skill_id"]
# no need to crosscheck id because waiting before new request is made
# no other skill will make this request is safe assumption
result = message.data["result"]
self.result = result
self.waiting = False
def remove_active_skill(self, skill_id):
for skill in self.active_skills:
if skill[0] == skill_id:
self.active_skills.remove(skill)
def add_active_skill(self, skill_id):
# search the list for an existing entry that already contains it
# and remove that reference
self.remove_active_skill(skill_id)
# add skill with timestamp to start of skill_list
self.active_skills.insert(0, [skill_id, time.time()])
def update_context(self, intent):
"""
updates context with keyword from the intent.
NOTE: This method currently won't handle one_of intent keywords
since it's not using quite the same format as other intent
keywords. This is under investigation in adapt, PR pending.
Args:
intent: Intent to scan for keywords
"""
for tag in intent['__tags__']:
if 'entities' not in tag:
continue
context_entity = tag['entities'][0]
if self.context_greedy:
self.context_manager.inject_context(context_entity)
elif context_entity['data'][0][1] in self.context_keywords:
self.context_manager.inject_context(context_entity)
def handle_utterance(self, message):
# Get language of the utterance
lang = message.data.get('lang', "en-us")
utterances = message.data.get('utterances', '')
# check for conversation time-out
self.active_skills = [skill for skill in self.active_skills
if time.time() - skill[
1] <= self.converse_timeout * 60]
# check if any skill wants to handle utterance
for skill in self.active_skills:
if self.do_converse(utterances, skill[0], lang):
# update timestamp, or there will be a timeout where
# intent stops conversing whether its being used or not
self.add_active_skill(skill[0])
return
# no skill wants to handle utterance
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy", etc.
best_intent = next(self.engine.determine_intent(
normalize(utterance, lang), 100,
include_tags=True,
context_manager=self.context_manager))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration:
# don't show error in log
continue
except e:
LOG.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
self.update_context(best_intent)
reply = message.reply(
best_intent.get('intent_type'), best_intent)
self.emitter.emit(reply)
# update active skills
skill_id = int(best_intent['intent_type'].split(":")[0])
self.add_active_skill(skill_id)
else:
self.emitter.emit(Message("intent_failure", {
"utterance": utterances[0],
"lang": lang
}))
def handle_register_vocab(self, message):
start_concept = message.data.get('start')
end_concept = message.data.get('end')
regex_str = message.data.get('regex')
alias_of = message.data.get('alias_of')
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(
start_concept, end_concept, alias_of=alias_of)
def handle_register_intent(self, message):
print "Registering: " + str(message.data)
intent = open_intent_envelope(message)
self.engine.register_intent_parser(intent)
def handle_detach_intent(self, message):
intent_name = message.data.get('intent_name')
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name]
self.engine.intent_parsers = new_parsers
def handle_detach_skill(self, message):
skill_id = message.data.get('skill_id')
new_parsers = [
p for p in self.engine.intent_parsers if
not p.name.startswith(skill_id)]
self.engine.intent_parsers = new_parsers
def handle_add_context(self, message):
"""
Handles adding context from the message bus.
The data field must contain a context keyword and
may contain a word if a specific word should be injected
as a match for the provided context keyword.
"""
entity = {'confidence': 1.0}
context = message.data.get('context')
word = message.data.get('word') or ''
# if not a string type try creating a string from it
if not isinstance(word, basestring):
word = str(word)
entity['data'] = [(word, context)]
entity['match'] = word
entity['key'] = word
self.context_manager.inject_context(entity)
def handle_remove_context(self, message):
"""
Handles removing context from the message bus. The
data field must contain the 'context' to remove.
"""
context = message.data.get('context')
if context:
self.context_manager.remove_context(context)
def handle_clear_context(self, message):
"""
Clears all keywords from context.
"""
self.context_manager.clear_context()
class AdaptService:
"""Intent service wrapping the Apdapt intent Parser."""
def __init__(self, config):
self.config = config
self.engine = IntentDeterminationEngine()
# Context related intializations
self.context_keywords = self.config.get('keywords', [])
self.context_max_frames = self.config.get('max_frames', 3)
self.context_timeout = self.config.get('timeout', 2)
self.context_greedy = self.config.get('greedy', False)
self.context_manager = ContextManager(self.context_timeout)
def update_context(self, intent):
"""Updates context with keyword from the intent.
NOTE: This method currently won't handle one_of intent keywords
since it's not using quite the same format as other intent
keywords. This is under investigation in adapt, PR pending.
Args:
intent: Intent to scan for keywords
"""
for tag in intent['__tags__']:
if 'entities' not in tag:
continue
context_entity = tag['entities'][0]
if self.context_greedy:
self.context_manager.inject_context(context_entity)
elif context_entity['data'][0][1] in self.context_keywords:
self.context_manager.inject_context(context_entity)
def match_intent(self, utterances, _=None, __=None):
"""Run the Adapt engine to search for an matching intent.
Arguments:
utterances (iterable): iterable of utterances, expected order
[raw, normalized, other]
Returns:
Intent structure, or None if no match was found.
"""
best_intent = {}
def take_best(intent, utt):
nonlocal best_intent
best = best_intent.get('confidence', 0.0) if best_intent else 0.0
conf = intent.get('confidence', 0.0)
if conf > best:
best_intent = intent
# TODO - Shouldn't Adapt do this?
best_intent['utterance'] = utt
for utt_tup in utterances:
for utt in utt_tup:
try:
intents = [
i for i in self.engine.determine_intent(
utt,
100,
include_tags=True,
context_manager=self.context_manager)
]
if intents:
take_best(intents[0], utt_tup[0])
except Exception as err:
LOG.exception(err)
if best_intent:
self.update_context(best_intent)
skill_id = best_intent['intent_type'].split(":")[0]
ret = IntentMatch('Adapt', best_intent['intent_type'], best_intent,
skill_id)
else:
ret = None
return ret
def register_vocab(self, start_concept, end_concept, alias_of, regex_str):
"""Register vocabulary."""
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(start_concept,
end_concept,
alias_of=alias_of)
def register_intent(self, intent):
"""Register new intent with adapt engine.
Arguments:
intent (IntentParser): IntentParser to register
"""
self.engine.register_intent_parser(intent)
def detach_skill(self, skill_id):
"""Remove all intents for skill.
Arguments:
skill_id (str): skill to process
"""
new_parsers = [
p for p in self.engine.intent_parsers
if not p.name.startswith(skill_id)
]
self.engine.intent_parsers = new_parsers
def detach_intent(self, intent_name):
"""Detatch a single intent
Arguments:
intent_name (str): Identifier for intent to remove.
"""
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name
]
self.engine.intent_parsers = new_parsers
class AdaptExtractor(IntentExtractor):
keyword_based = True
regex_entity_support = True
def __init__(self, normalize=False, *args, **kwargs):
super().__init__(*args, **kwargs)
self.normalize = normalize
self.engine = IntentDeterminationEngine()
def register_entity(self, entity_name, samples=None):
samples = samples or [entity_name]
for kw in samples:
self.engine.register_entity(kw, entity_name)
super().register_entity(entity_name, samples)
def register_regex_entity(self, entity_name, samples):
if isinstance(samples, str):
self.engine.register_regex_entity(samples)
if isinstance(samples, list):
for s in samples:
self.engine.register_regex_entity(s)
def register_regex_intent(self, intent_name, samples):
self.register_regex_entity(intent_name + "_adapt_rx", samples)
self.register_intent(intent_name, [intent_name + "_adapt_rx"])
def register_intent(self, intent_name, samples=None,
optional_samples=None, rx_samples=None):
"""
:param intent_name: intent_name
:param samples: list of required registered entities (names)
:param optional_samples: list of optional registered samples (names)
:return:
"""
super().register_entity(intent_name, samples)
if not samples:
samples = [intent_name]
self.register_entity(intent_name, samples)
optional_samples = optional_samples or []
# structure intent
intent = IntentBuilder(intent_name)
for kw in samples:
intent.require(kw)
for kw in optional_samples:
intent.optionally(kw)
self.engine.register_intent_parser(intent.build())
return intent
def calc_intent(self, utterance):
utterance = utterance.strip()
if self.normalize:
utterance = normalize(utterance, self.lang, True)
for intent in self.engine.determine_intent(utterance, 100,
include_tags=True,
context_manager=self.context_manager):
if intent and intent.get('confidence') > 0:
intent.pop("target")
matches = {k: v for k, v in intent.items() if
k not in ["intent_type", "confidence", "__tags__"]}
intent["entities"] = {}
for k in matches:
intent["entities"][k] = intent.pop(k)
intent["conf"] = intent.pop("confidence")
intent["utterance"] = utterance
intent["intent_engine"] = "adapt"
remainder = get_utterance_remainder(
utterance, samples=[v for v in matches.values()])
intent["utterance_remainder"] = remainder
return intent
return {"conf": 0, "intent_type": "unknown", "entities": {},
"utterance_remainder": utterance,
"utterance": utterance, "intent_engine": "adapt"}
def calc_intents(self, utterance, min_conf=0.5):
bucket = {}
for ut in self.segmenter.segment(utterance):
intent = self.calc_intent(ut)
if intent["conf"] < min_conf:
bucket[ut] = None
else:
bucket[ut] = intent
return bucket
def calc_intents_list(self, utterance, min_conf=0.5):
utterance = utterance.strip() # spaces should not mess with exact matches
bucket = {}
for ut in self.segmenter.segment(utterance):
if self.normalize:
ut = normalize(ut, self.lang, True)
bucket[ut] = []
for intent in self.engine.determine_intent(ut, 100,
include_tags=True,
context_manager=self.context_manager):
if intent:
intent.pop("target")
matches = {k: v for k, v in intent.items() if
k not in ["intent_type", "confidence",
"__tags__"]}
intent["entities"] = {}
for k in matches:
intent["entities"][k] = intent.pop(k)
intent["conf"] = intent.pop("confidence")
intent["utterance"] = ut
intent["intent_engine"] = "adapt"
remainder = get_utterance_remainder(
utterance, samples=[v for v in matches.values()])
intent["utterance_remainder"] = remainder
if intent["conf"] >= min_conf:
bucket[ut] += [intent]
return bucket
def intent_scores(self, utterance):
utterance = utterance.strip() # spaces should not mess with exact matches
bucket = []
for intent in self.engine.determine_intent(utterance, 100,
include_tags=True,
context_manager=self.context_manager):
if intent:
intent.pop("target")
matches = {k: v for k, v in intent.items() if
k not in ["intent_type", "confidence", "__tags__"]}
intent["entities"] = {}
for k in matches:
intent["entities"][k] = intent.pop(k)
intent["conf"] = intent.pop("confidence")
intent["intent_engine"] = "adapt"
intent["utterance"] = utterance
remainder = get_utterance_remainder(
utterance, samples=[v for v in matches.values()])
intent["utterance_remainder"] = remainder
bucket += [intent]
return bucket
def intent_remainder(self, utterance, _prev=""):
utterance = utterance.strip() # spaces should not mess with exact matches
if self.normalize:
utterance = normalize(utterance, self.lang, True)
return IntentExtractor.intent_remainder(self, utterance)
def intents_remainder(self, utterance, min_conf=0.5):
"""
segment utterance and for each chunk recursively check for intents in utterance remainer
:param utterance:
:param min_conf:
:return:
"""
utterance = utterance.strip() # spaces should not mess with exact matches
bucket = {}
for utterance in self.segmenter.segment(utterance):
if self.normalize:
utterance = normalize(utterance, self.lang, True)
bucket[utterance] = self.intent_remainder(utterance)
return bucket
def segment(self, text):
if self.normalize:
text = normalize(text, self.lang, True)
return self.segment(text)
def detach_intent(self, intent_name):
LOG.debug("detaching adapt intent: " + intent_name)
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name]
self.engine.intent_parsers = new_parsers
def detach_skill(self, skill_id):
LOG.debug("detaching adapt skill: " + skill_id)
new_parsers = [
p.name for p in self.engine.intent_parsers if
p.name.startswith(skill_id)]
for intent_name in new_parsers:
self.detach_intent(intent_name)
def manifest(self):
# TODO vocab, skill ids, intent_data
return {
"intent_names": [p.name for p in self.engine.intent_parsers]
}
class IntentService:
def __init__(self, bus):
self.config = Configuration.get().get('context', {})
self.engine = IntentDeterminationEngine()
# Dictionary for translating a skill id to a name
self.skill_names = {}
# Context related intializations
self.context_keywords = self.config.get('keywords', [])
self.context_max_frames = self.config.get('max_frames', 3)
self.context_timeout = self.config.get('timeout', 2)
self.context_greedy = self.config.get('greedy', False)
self.context_manager = ContextManager(self.context_timeout)
self.bus = bus
self.bus.on('register_vocab', self.handle_register_vocab)
self.bus.on('register_intent', self.handle_register_intent)
self.bus.on('recognizer_loop:utterance', self.handle_utterance)
self.bus.on('detach_intent', self.handle_detach_intent)
self.bus.on('detach_skill', self.handle_detach_skill)
# Context related handlers
self.bus.on('add_context', self.handle_add_context)
self.bus.on('remove_context', self.handle_remove_context)
self.bus.on('clear_context', self.handle_clear_context)
# Converse method
self.bus.on('skill.converse.response', self.handle_converse_response)
self.bus.on('skill.converse.error', self.handle_converse_error)
self.bus.on('mycroft.speech.recognition.unknown', self.reset_converse)
self.bus.on('mycroft.skills.loaded', self.update_skill_name_dict)
def add_active_skill_handler(message):
self.add_active_skill(message.data['skill_id'])
self.bus.on('active_skill_request', add_active_skill_handler)
self.active_skills = [] # [skill_id , timestamp]
self.converse_timeout = 5 # minutes to prune active_skills
self.waiting_for_converse = False
self.converse_result = False
self.converse_skill_id = ""
# Intents API
self.registered_intents = []
self.registered_vocab = []
self.bus.on('intent.service.adapt.get', self.handle_get_adapt)
self.bus.on('intent.service.intent.get', self.handle_get_intent)
self.bus.on('intent.service.skills.get', self.handle_get_skills)
self.bus.on('intent.service.active_skills.get',
self.handle_get_active_skills)
self.bus.on('intent.service.adapt.manifest.get', self.handle_manifest)
self.bus.on('intent.service.adapt.vocab.manifest.get',
self.handle_vocab_manifest)
def update_skill_name_dict(self, message):
"""
Messagebus handler, updates dictionary of if to skill name
conversions.
"""
self.skill_names[message.data['id']] = message.data['name']
def get_skill_name(self, skill_id):
""" Get skill name from skill ID.
Args:
skill_id: a skill id as encoded in Intent handlers.
Returns:
(str) Skill name or the skill id if the skill wasn't found
"""
return self.skill_names.get(skill_id, skill_id)
def reset_converse(self, message):
"""Let skills know there was a problem with speech recognition"""
lang = message.data.get('lang', "en-us")
set_active_lang(lang)
for skill in self.active_skills:
self.do_converse(None, skill[0], lang)
def do_converse(self, utterances, skill_id, lang, message):
self.waiting_for_converse = True
self.converse_result = False
self.converse_skill_id = skill_id
self.bus.emit(
message.reply("skill.converse.request", {
"skill_id": skill_id,
"utterances": utterances,
"lang": lang
}))
start_time = time.time()
t = 0
while self.waiting_for_converse and t < 5:
t = time.time() - start_time
time.sleep(0.1)
self.waiting_for_converse = False
self.converse_skill_id = ""
return self.converse_result
def handle_converse_error(self, message):
skill_id = message.data["skill_id"]
if message.data["error"] == "skill id does not exist":
self.remove_active_skill(skill_id)
if skill_id == self.converse_skill_id:
self.converse_result = False
self.waiting_for_converse = False
def handle_converse_response(self, message):
skill_id = message.data["skill_id"]
if skill_id == self.converse_skill_id:
self.converse_result = message.data.get("result", False)
self.waiting_for_converse = False
def remove_active_skill(self, skill_id):
for skill in self.active_skills:
if skill[0] == skill_id:
self.active_skills.remove(skill)
def add_active_skill(self, skill_id):
# search the list for an existing entry that already contains it
# and remove that reference
self.remove_active_skill(skill_id)
# add skill with timestamp to start of skill_list
self.active_skills.insert(0, [skill_id, time.time()])
def update_context(self, intent):
""" Updates context with keyword from the intent.
NOTE: This method currently won't handle one_of intent keywords
since it's not using quite the same format as other intent
keywords. This is under investigation in adapt, PR pending.
Args:
intent: Intent to scan for keywords
"""
for tag in intent['__tags__']:
if 'entities' not in tag:
continue
context_entity = tag['entities'][0]
if self.context_greedy:
self.context_manager.inject_context(context_entity)
elif context_entity['data'][0][1] in self.context_keywords:
self.context_manager.inject_context(context_entity)
def send_metrics(self, intent, context, stopwatch):
"""
Send timing metrics to the backend.
NOTE: This only applies to those with Opt In.
"""
ident = context['ident'] if 'ident' in context else None
if intent:
# Recreate skill name from skill id
parts = intent.get('intent_type', '').split(':')
intent_type = self.get_skill_name(parts[0])
if len(parts) > 1:
intent_type = ':'.join([intent_type] + parts[1:])
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': intent_type})
else:
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': 'intent_failure'})
def handle_utterance(self, message):
""" Main entrypoint for handling user utterances with Mycroft skills
Monitor the messagebus for 'recognizer_loop:utterance', typically
generated by a spoken interaction but potentially also from a CLI
or other method of injecting a 'user utterance' into the system.
Utterances then work through this sequence to be handled:
1) Active skills attempt to handle using converse()
2) Padatious high match intents (conf > 0.95)
3) Adapt intent handlers
5) Fallbacks:
- Padatious near match intents (conf > 0.8)
- General fallbacks
- Padatious loose match intents (conf > 0.5)
- Unknown intent handler
Args:
message (Message): The messagebus data
"""
try:
# Get language of the utterance
lang = message.data.get('lang', "en-us")
set_active_lang(lang)
utterances = message.data.get('utterances', [])
# normalize() changes "it's a boy" to "it is a boy", etc.
norm_utterances = [
normalize(u.lower(), remove_articles=False) for u in utterances
]
# Build list with raw utterance(s) first, then optionally a
# normalized version following.
combined = utterances + list(
set(norm_utterances) - set(utterances))
LOG.debug("Utterances: {}".format(combined))
stopwatch = Stopwatch()
intent = None
padatious_intent = None
with stopwatch:
# Give active skills an opportunity to handle the utterance
converse = self._converse(combined, lang, message)
if not converse:
# No conversation, use intent system to handle utterance
intent = self._adapt_intent_match(utterances,
norm_utterances, lang)
for utt in combined:
_intent = PadatiousService.instance.calc_intent(utt)
if _intent:
best = padatious_intent.conf if padatious_intent \
else 0.0
if best < _intent.conf:
padatious_intent = _intent
LOG.debug("Padatious intent: {}".format(padatious_intent))
LOG.debug(" Adapt intent: {}".format(intent))
if converse:
# Report that converse handled the intent and return
LOG.debug("Handled in converse()")
ident = None
if message.context and 'ident' in message.context:
ident = message.context['ident']
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': 'converse'})
return
elif (intent and intent.get('confidence', 0.0) > 0.0 and
not (padatious_intent and padatious_intent.conf >= 0.95)):
# Send the message to the Adapt intent's handler unless
# Padatious is REALLY sure it was directed at it instead.
self.update_context(intent)
# update active skills
skill_id = intent['intent_type'].split(":")[0]
self.add_active_skill(skill_id)
# Adapt doesn't handle context injection for one_of keywords
# correctly. Workaround this issue if possible.
try:
intent = workaround_one_of_context(intent)
except LookupError:
LOG.error('Error during workaround_one_of_context')
reply = message.reply(intent.get('intent_type'), intent)
else:
# Allow fallback system to handle utterance
# NOTE: A matched padatious_intent is handled this way, too
# TODO: Need to redefine intent_failure when STT can return
# multiple hypothesis -- i.e. len(utterances) > 1
reply = message.reply(
'intent_failure', {
'utterance': utterances[0],
'norm_utt': norm_utterances[0],
'lang': lang
})
self.bus.emit(reply)
self.send_metrics(intent, message.context, stopwatch)
except Exception as e:
LOG.exception(e)
def _converse(self, utterances, lang, message):
""" Give active skills a chance at the utterance
Args:
utterances (list): list of utterances
lang (string): 4 letter ISO language code
message (Message): message to use to generate reply
Returns:
bool: True if converse handled it, False if no skill processes it
"""
# check for conversation time-out
self.active_skills = [
skill for skill in self.active_skills
if time.time() - skill[1] <= self.converse_timeout * 60
]
# check if any skill wants to handle utterance
for skill in self.active_skills:
if self.do_converse(utterances, skill[0], lang, message):
# update timestamp, or there will be a timeout where
# intent stops conversing whether its being used or not
self.add_active_skill(skill[0])
return True
return False
def _adapt_intent_match(self, raw_utt, norm_utt, lang):
""" Run the Adapt engine to search for an matching intent
Args:
raw_utt (list): list of utterances
norm_utt (list): same list of utterances, normalized
lang (string): language code, e.g "en-us"
Returns:
Intent structure, or None if no match was found.
"""
best_intent = None
def take_best(intent, utt):
nonlocal best_intent
best = best_intent.get('confidence', 0.0) if best_intent else 0.0
conf = intent.get('confidence', 0.0)
if conf > best:
best_intent = intent
# TODO - Shouldn't Adapt do this?
best_intent['utterance'] = utt
for idx, utt in enumerate(raw_utt):
try:
intents = [
i for i in self.engine.determine_intent(
utt,
100,
include_tags=True,
context_manager=self.context_manager)
]
if intents:
take_best(intents[0], utt)
# Also test the normalized version, but set the utterance to
# the raw version so skill has access to original STT
norm_intents = [
i for i in self.engine.determine_intent(
norm_utt[idx],
100,
include_tags=True,
context_manager=self.context_manager)
]
if norm_intents:
take_best(norm_intents[0], utt)
except Exception as e:
LOG.exception(e)
return best_intent
def handle_register_vocab(self, message):
start_concept = message.data.get('start')
end_concept = message.data.get('end')
regex_str = message.data.get('regex')
alias_of = message.data.get('alias_of')
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(start_concept,
end_concept,
alias_of=alias_of)
self.registered_vocab.append(message.data)
def handle_register_intent(self, message):
intent = open_intent_envelope(message)
self.engine.register_intent_parser(intent)
def handle_detach_intent(self, message):
intent_name = message.data.get('intent_name')
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name
]
self.engine.intent_parsers = new_parsers
def handle_detach_skill(self, message):
skill_id = message.data.get('skill_id')
new_parsers = [
p for p in self.engine.intent_parsers
if not p.name.startswith(skill_id)
]
self.engine.intent_parsers = new_parsers
def handle_add_context(self, message):
""" Add context
Args:
message: data contains the 'context' item to add
optionally can include 'word' to be injected as
an alias for the context item.
"""
entity = {'confidence': 1.0}
context = message.data.get('context')
word = message.data.get('word') or ''
origin = message.data.get('origin') or ''
# if not a string type try creating a string from it
if not isinstance(word, str):
word = str(word)
entity['data'] = [(word, context)]
entity['match'] = word
entity['key'] = word
entity['origin'] = origin
self.context_manager.inject_context(entity)
def handle_remove_context(self, message):
""" Remove specific context
Args:
message: data contains the 'context' item to remove
"""
context = message.data.get('context')
if context:
self.context_manager.remove_context(context)
def handle_clear_context(self, message):
""" Clears all keywords from context """
self.context_manager.clear_context()
def handle_get_adapt(self, message):
utterance = message.data["utterance"]
lang = message.data.get("lang", "en-us")
norm = normalize(utterance, lang, remove_articles=False)
intent = self._adapt_intent_match([utterance], [norm], lang)
self.bus.emit(
message.reply("intent.service.adapt.reply", {"intent": intent}))
def handle_get_intent(self, message):
utterance = message.data["utterance"]
lang = message.data.get("lang", "en-us")
norm = normalize(utterance, lang, remove_articles=False)
intent = self._adapt_intent_match([utterance], [norm], lang)
# Adapt intent's handler is used unless
# Padatious is REALLY sure it was directed at it instead.
padatious_intent = PadatiousService.instance.calc_intent(utterance)
if not padatious_intent and norm != utterance:
padatious_intent = PadatiousService.instance.calc_intent(norm)
if intent is None or (padatious_intent
and padatious_intent.conf >= 0.95):
intent = padatious_intent.__dict__
self.bus.emit(
message.reply("intent.service.intent.reply", {"intent": intent}))
def handle_get_skills(self, message):
self.bus.emit(
message.reply("intent.service.skills.reply",
{"skills": self.skill_names}))
def handle_get_active_skills(self, message):
self.bus.emit(
message.reply("intent.service.active_skills.reply",
{"skills": [s[0] for s in self.active_skills]}))
def handle_manifest(self, message):
self.bus.emit(
message.reply("intent.service.adapt.manifest",
{"intents": self.registered_intents}))
def handle_vocab_manifest(self, message):
self.bus.emit(
message.reply("intent.service.adapt.vocab.manifest",
{"vocab": self.registered_vocab}))
class ContextManagerIntegrationTest(unittest.TestCase):
def setUp(self):
self.context_manager = ContextManager()
self.engine = IntentDeterminationEngine()
def testBasicContextualFollowup(self):
intent1 = IntentBuilder("TimeQueryIntent")\
.require("TimeQuery")\
.require("Location")\
.build()
intent2 = IntentBuilder("WeatherQueryIntent")\
.require("WeatherKeyword")\
.require("Location")\
.build()
self.engine.register_intent_parser(intent1)
self.engine.register_intent_parser(intent2)
self.engine.register_entity("what time is it", "TimeQuery")
self.engine.register_entity("seattle", "Location")
self.engine.register_entity("miami", "Location")
self.engine.register_entity("weather", "WeatherKeyword")
utterance1 = "what time is it in seattle"
intent = next(
self.engine.determine_intent(utterance1,
include_tags=True,
context_manager=self.context_manager))
assert intent
assert intent['intent_type'] == 'TimeQueryIntent'
assert '__tags__' in intent
for tag in intent['__tags__']:
context_entity = tag.get('entities')[0]
self.context_manager.inject_context(context_entity)
utterance2 = "what's the weather like?"
intent = next(
self.engine.determine_intent(utterance2,
context_manager=self.context_manager))
assert intent
assert intent['intent_type'] == 'WeatherQueryIntent'
def testContextOnlyUsedOnce(self):
intent_parser = IntentBuilder("DummyIntent")\
.require("Foo")\
.optionally("Foo", "Foo2")\
.build()
context_entity = {
'confidence': 1.0,
'data': [('foo', 'Foo')],
'match': 'foo',
'key': 'foo'
}
self.context_manager.inject_context(context_entity)
self.engine.register_intent_parser(intent_parser)
self.engine.register_entity("foo", "Foo")
self.engine.register_entity("fop", "Foo")
intent = next(
self.engine.determine_intent("foo",
include_tags=True,
context_manager=self.context_manager))
assert intent
assert intent['intent_type'] == "DummyIntent"
assert not (intent.get("Foo") and intent.get("Foo2"))
def testContextAndOneOf(self):
# test to cover https://github.com/MycroftAI/adapt/issues/86
engine = IntentDeterminationEngine()
context_manager = ContextManager()
# define vocabulary
weather_keyword = ["weather"]
for wk in weather_keyword:
engine.register_entity(wk, "WeatherKeyword")
# structure intent
weather_intent = IntentBuilder("WeatherIntent") \
.require("WeatherKeyword") \
.one_of("Location", "LocationContext").build()
engine.register_intent_parser(weather_intent)
word = 'lizard'
context = 'LocationContext'
entity = {}
entity['data'] = [(word, context)]
entity['match'] = word
entity['key'] = word
context_manager.inject_context(entity)
intents = list(
engine.determine_intent('weather',
context_manager=context_manager))
self.assertEqual(1, len(intents), "Incorrect number of intents")
result = intents[0]
self.assertEqual("lizard", result.get("LocationContext"),
"Context not matched")
self.assertEqual(0.75, result.get('confidence'),
"Context confidence not properly applied.")
if text.startswith("b'update"):
engine = IntentDeterminationEngine()
DOMTree = parse('config.xml')
for intent in DOMTree.getElementsByTagName("intent"):
builder = IntentBuilder(intent.attributes["name"].value)
for node in intent.getElementsByTagName("keyword"):
keyword = node.attributes["name"].value
required = node.attributes["required"].value
ktype = node.attributes["type"].value
if ktype == 'normal':
for child in node.getElementsByTagName("item"):
engine.register_entity(child.childNodes[0].nodeValue,
keyword)
else:
engine.register_regex_entity(node.childNodes[0].nodeValue)
if required == 'true':
builder.require(keyword)
else:
builder.optionally(keyword)
engine.register_intent_parser(builder.build())
else:
for intent in engine.determine_intent(" " + text):
if intent.get('confidence') > 0:
client.send(
str(json.dumps(intent, indent=4)).encode(encoding='utf_8',
errors='strict'))
client.close()
server.close()
class AdaptService:
"""Intent service wrapping the Apdapt intent Parser."""
def __init__(self, config):
self.config = config
self.engine = IntentDeterminationEngine()
# Context related intializations
self.context_keywords = self.config.get('keywords', [])
self.context_max_frames = self.config.get('max_frames', 3)
self.context_timeout = self.config.get('timeout', 2)
self.context_greedy = self.config.get('greedy', False)
self.context_manager = ContextManager(self.context_timeout)
self.lock = Lock()
def update_context(self, intent):
"""Updates context with keyword from the intent.
NOTE: This method currently won't handle one_of intent keywords
since it's not using quite the same format as other intent
keywords. This is under investigation in adapt, PR pending.
Args:
intent: Intent to scan for keywords
"""
for tag in intent['__tags__']:
if 'entities' not in tag:
continue
context_entity = tag['entities'][0]
if self.context_greedy:
self.context_manager.inject_context(context_entity)
elif context_entity['data'][0][1] in self.context_keywords:
self.context_manager.inject_context(context_entity)
def match_intent(self, utterances, _=None, __=None):
"""Run the Adapt engine to search for an matching intent.
Args:
utterances (iterable): utterances for consideration in intent
matching. As a practical matter, a single utterance will be
passed in most cases. But there are instances, such as
streaming STT that could pass multiple. Each utterance
is represented as a tuple containing the raw, normalized, and
possibly other variations of the utterance.
Returns:
Intent structure, or None if no match was found.
"""
best_intent = {}
def take_best(intent, utt):
nonlocal best_intent
best = best_intent.get('confidence', 0.0) if best_intent else 0.0
conf = intent.get('confidence', 0.0)
if conf > best:
best_intent = intent
# TODO - Shouldn't Adapt do this?
best_intent['utterance'] = utt
for utt_tup in utterances:
for utt in utt_tup:
try:
intents = [
i for i in self.engine.determine_intent(
utt,
100,
include_tags=True,
context_manager=self.context_manager)
]
if intents:
utt_best = max(intents,
key=lambda x: x.get('confidence', 0.0))
take_best(utt_best, utt_tup[0])
except Exception as err:
LOG.exception(err)
if best_intent:
self.update_context(best_intent)
skill_id = best_intent['intent_type'].split(":")[0]
ret = IntentMatch('Adapt', best_intent['intent_type'], best_intent,
skill_id)
else:
ret = None
return ret
# TODO 22.02: Remove this deprecated method
def register_vocab(self, start_concept, end_concept, alias_of, regex_str):
"""Register Vocabulary. DEPRECATED
This method should not be used, it has been replaced by
register_vocabulary().
"""
self.register_vocabulary(start_concept, end_concept, alias_of,
regex_str)
def register_vocabulary(self, entity_value, entity_type, alias_of,
regex_str):
"""Register skill vocabulary as adapt entity.
This will handle both regex registration and registration of normal
keywords. if the "regex_str" argument is set all other arguments will
be ignored.
Argument:
entity_value: the natural langauge word
entity_type: the type/tag of an entity instance
alias_of: entity this is an alternative for
"""
with self.lock:
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(entity_value,
entity_type,
alias_of=alias_of)
def register_intent(self, intent):
"""Register new intent with adapt engine.
Args:
intent (IntentParser): IntentParser to register
"""
with self.lock:
self.engine.register_intent_parser(intent)
def detach_skill(self, skill_id):
"""Remove all intents for skill.
Args:
skill_id (str): skill to process
"""
with self.lock:
skill_parsers = [
p.name for p in self.engine.intent_parsers
if p.name.startswith(skill_id)
]
self.engine.drop_intent_parser(skill_parsers)
self._detach_skill_keywords(skill_id)
self._detach_skill_regexes(skill_id)
def _detach_skill_keywords(self, skill_id):
"""Detach all keywords registered with a particular skill.
Arguments:
skill_id (str): skill identifier
"""
skill_id = _entity_skill_id(skill_id)
def match_skill_entities(data):
return data and data[1].startswith(skill_id)
self.engine.drop_entity(match_func=match_skill_entities)
def _detach_skill_regexes(self, skill_id):
"""Detach all regexes registered with a particular skill.
Arguments:
skill_id (str): skill identifier
"""
skill_id = _entity_skill_id(skill_id)
def match_skill_regexes(regexp):
return any(
[r.startswith(skill_id) for r in regexp.groupindex.keys()])
self.engine.drop_regex_entity(match_func=match_skill_regexes)
def detach_intent(self, intent_name):
"""Detatch a single intent
Args:
intent_name (str): Identifier for intent to remove.
"""
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name
]
self.engine.intent_parsers = new_parsers
# Register entities in engine
for entity, keywords in entities.items():
for keyword in keywords:
engine.register_entity(keyword, entity)
for entity in multi_regex_entities:
for regex in entity:
engine.register_regex_entity(regex)
# Register intents on engine
for intent in intents:
engine.register_intent_parser(intent)
text1 = 'what is the number of active jobs today?'
text2 = 'create assignment buy mic for john'
for intent in engine.determine_intent(text1):
print(intent)
for intent in engine.determine_intent(text2):
print(intent)
def f1():
print(' i am connecting todo')
h1 = Handler.Handler(intents[0], f1)
h1.compute() #
self.intent = 'courtesyIntent'
# define vocabulary
self.courtesy_keyword = [
"bonjour"
]
# structure intent
self.courtesy_intent = IntentBuilder(self.intent)\
.require("CourtesyKeyword")\
.build()
def register(self, engine):
for wk in self.courtesy_keyword:
engine.register_entity(wk, "courtesyKeyword")
engine.register_intent_parser(self.courtesy_intent)
def process(self, json):
result = sympy.sympify(json.get('CourtesyKeyword'))
if json.get('CourtesyKeyword') == "bonjour":
return "Bonjour monsieur"
if __name__ == "__main__":
engine = IntentDeterminationEngine()
skill = courtesySkill()
skill.register(engine)
for intent in engine.determine_intent(' '.join(sys.argv[1:])):
if intent and intent.get('confidence') > 0:
print(json.dumps(intent, indent=4))
class CrmnextChatBot:
def __init__(self):
"""
Init for logger Intent engine Corpus
"""
self.engine = IntentDeterminationEngine()
self.bot = BotLogger()
self.cr = BotNextCorpus()
self.first_time = True
self.intent = []
self.train_data = {'intent': "", 'user_text': "", 'bot_response': ""}
self.train_intent()
self.senti = SentimentIntensityAnalyzer()
def train_intent(self):
"""
Training intent engine to classify text intent
:return:
"""
for k in self.cr.train_intent_list.keys():
for w in self.cr.train_intent_list[k]:
self.engine.register_entity(w, k)
for w in self.cr.train_intent.keys():
self.intent.append(IntentBuilder(w) \
.require(self.cr.train_intent[w]) \
.build())
for obj in self.intent:
self.engine.register_intent_parser(obj)
def run_bot(self, conv):
"""
Thread starts from here.
:return:
"""
self.bot.log_debug("Received Json in run_bot: {}".format(conv))
msg = conv["user_text"]
isFirst = True if conv["intent_type"] == "" else False
if self.sent(msg):
if isFirst:
try:
intent = json.loads(self.intent_parser(msg))
except Exception:
intent = {
"intent_type": "",
"Card": "",
"CardLost": "",
"target": "",
"confidence": 0.0
}
conv["intent_type"] = intent["intent_type"]
return self.p_flow(self.cr.chat_data, conv)
else:
return self.p_flow(self.cr.chat_data, conv)
else:
return self.neg_res(self.cr.chat_data, conv)
def intent_parser(self, conv):
"""
Method to get intent of user text.
:param conv: user data
:return:
"""
for intent in self.engine.determine_intent(conv):
if intent.get('confidence') > 0:
return json.dumps(intent, indent=4)
else:
pass
def p_flow(self, corpus, ud):
"""
Method to implement process for user.
:param corpus: It provides data for chatbot
:param ud: User related data.
:return: response
"""
response = dict(userId="123",
user_intent="",
response_text="",
card_type="",
recommendation=[],
entities=[],
user_stage=0)
self.bot.log_debug("User Data: {}".format(ud))
p_data = corpus[ud['intent_type']][ud['user_stage']]
user_txt = self.clean_text(ud['user_text'])
if user_txt in p_data.keys():
response['response_text'] = random.choice(p_data[user_txt])
response['card_type'] = ""
elif user_txt in p_data['user_text']:
print("p_data: >>" + str(p_data))
response = self.update_res(response, p_data)
elif user_txt == 'quit':
q_data = corpus['EXIT'][0]
response['user_intent'] = q_data['intent_type']
response['user_stage'] = q_data['user_stage']
response['response_text'] = random.choice(q_data['response'])
response['card_type'] = q_data['card_type']
else:
self.build_train_cr(ud)
if 'default_speech' in p_data.keys(
) and p_data['default_speech'] is not '':
response['response_text'] = random.choice(
p_data['default_speech'])
else:
response[
'response_text'] = "Please select below option to move forward"
response['card_type'] = "#IntentOptions"
response['user_stage'] = ud['user_stage']
response['user_intent'] = ud['intent_type']
if len(p_data['recommendation']) == 0:
response['recommendation'] = self.cr.all_skills
else:
response['recommendation'] = p_data['recommendation']
print("response: " + str(response))
return response
def update_res(self, response, p_data):
"""
:param response:
:param p_data:
:return:
"""
response['response_text'] = random.choice(p_data['response'])
response['user_intent'] = p_data['intent_type']
response['card_type'] = p_data['card_type']
response['user_stage'] = p_data['user_stage']
response['entities'] = p_data['entities']
return response
def neg_res(self, cr_data, conv):
"""
Generate response if sentiment is -ve
:param cr_data:
:param conv:
:return:
"""
neg_data = cr_data["neg_sent"][0]
return dict(user_intent="",
response_text=random.choice(neg_data["response"]),
user_stage=0,
card_type='',
recommendation=[])
def sent(self, conv):
"""
Polarity is considered pos if > -0.2 (for tuning our scenario)
:param conv:
:return:
"""
t = TextBlob(conv)
vs = self.senti.polarity_scores(conv)
return False if t.polarity < -0.3 else True
def clean_text(self, statement):
"""
Remove any consecutive whitespace characters from the statement text.
"""
return re.sub(r'[^a-zA-Z0-9 ]', r'', statement).rstrip().lower()
def build_train_cr(self, ud):
intent = ud['intent_type']
if intent is not '':
self.train_data['intent'] = intent
self.train_data['user_text'] = ud['user_text']
else:
self.train_data['intent'] = ""
self.train_data['user_text'] = ud['user_text']
with open('train_data.json', 'a') as f:
json.dump(self.train_data, f, indent=True)
# bot = CrmnextChatBot()
# user_stage = 0
# intent = ''
# print(bot.clean_text("Hi I am #avinash ? "))
# while True:
#
# text = input("Enter text: ")
# reconnect = input('re-connect: ')
# print(bool(reconnect))
# data = {'userId': '123', "intent_type":intent, "user_text": text, 'user_name': 'Avinash Gaur',
# 'contactNumber': '89892398128', 'cardCount': 2, 'user_stage': user_stage, 're_connect': bool(reconnect)}
# print(data)
# print(user_stage)
# print(intent)
# d = bot.run_bot(data)
# intent = d['user_intent']
# user_stage = d['user_stage']
#
#
# print(d)
class IntentService(object):
def __init__(self, emitter):
self.config = Configuration.get().get('context', {})
self.engine = IntentDeterminationEngine()
self.context_keywords = self.config.get('keywords', [])
self.context_max_frames = self.config.get('max_frames', 3)
self.context_timeout = self.config.get('timeout', 2)
self.context_greedy = self.config.get('greedy', False)
self.context_manager = ContextManager(self.context_timeout)
self.emitter = emitter
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
self.emitter.on('detach_skill', self.handle_detach_skill)
# Context related handlers
self.emitter.on('add_context', self.handle_add_context)
self.emitter.on('remove_context', self.handle_remove_context)
self.emitter.on('clear_context', self.handle_clear_context)
# Converse method
self.emitter.on('skill.converse.response',
self.handle_converse_response)
self.active_skills = [] # [skill_id , timestamp]
self.converse_timeout = 5 # minutes to prune active_skills
def do_converse(self, utterances, skill_id, lang):
self.emitter.emit(Message("skill.converse.request", {
"skill_id": skill_id, "utterances": utterances, "lang": lang}))
self.waiting = True
self.result = False
start_time = time.time()
t = 0
while self.waiting and t < 5:
t = time.time() - start_time
time.sleep(0.1)
self.waiting = False
return self.result
def handle_converse_response(self, message):
# id = message.data["skill_id"]
# no need to crosscheck id because waiting before new request is made
# no other skill will make this request is safe assumption
result = message.data["result"]
self.result = result
self.waiting = False
def remove_active_skill(self, skill_id):
for skill in self.active_skills:
if skill[0] == skill_id:
self.active_skills.remove(skill)
def add_active_skill(self, skill_id):
# search the list for an existing entry that already contains it
# and remove that reference
self.remove_active_skill(skill_id)
# add skill with timestamp to start of skill_list
self.active_skills.insert(0, [skill_id, time.time()])
def update_context(self, intent):
"""
updates context with keyword from the intent.
NOTE: This method currently won't handle one_of intent keywords
since it's not using quite the same format as other intent
keywords. This is under investigation in adapt, PR pending.
Args:
intent: Intent to scan for keywords
"""
for tag in intent['__tags__']:
if 'entities' not in tag:
continue
context_entity = tag['entities'][0]
if self.context_greedy:
self.context_manager.inject_context(context_entity)
elif context_entity['data'][0][1] in self.context_keywords:
self.context_manager.inject_context(context_entity)
def handle_utterance(self, message):
# Get language of the utterance
lang = message.data.get('lang', None)
if not lang:
lang = "en-us"
utterances = message.data.get('utterances', '')
# check for conversation time-out
self.active_skills = [skill for skill in self.active_skills
if time.time() - skill[
1] <= self.converse_timeout * 60]
# check if any skill wants to handle utterance
for skill in self.active_skills:
if self.do_converse(utterances, skill[0], lang):
# update timestamp, or there will be a timeout where
# intent stops conversing whether its being used or not
self.add_active_skill(skill[0])
return
# no skill wants to handle utterance
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy", etc.
best_intent = next(self.engine.determine_intent(
normalize(utterance, lang), 100,
include_tags=True,
context_manager=self.context_manager))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration:
# don't show error in log
continue
except e:
LOG.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
self.update_context(best_intent)
reply = message.reply(
best_intent.get('intent_type'), best_intent)
self.emitter.emit(reply)
# update active skills
skill_id = int(best_intent['intent_type'].split(":")[0])
self.add_active_skill(skill_id)
else:
self.emitter.emit(Message("intent_failure", {
"utterance": utterances[0],
"lang": lang
}))
def handle_register_vocab(self, message):
start_concept = message.data.get('start')
end_concept = message.data.get('end')
regex_str = message.data.get('regex')
alias_of = message.data.get('alias_of')
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(
start_concept, end_concept, alias_of=alias_of)
def handle_register_intent(self, message):
print "Registering: " + str(message.data)
intent = open_intent_envelope(message)
self.engine.register_intent_parser(intent)
def handle_detach_intent(self, message):
intent_name = message.data.get('intent_name')
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name]
self.engine.intent_parsers = new_parsers
def handle_detach_skill(self, message):
skill_id = message.data.get('skill_id')
new_parsers = [
p for p in self.engine.intent_parsers if
not p.name.startswith(skill_id)]
self.engine.intent_parsers = new_parsers
def handle_add_context(self, message):
"""
Handles adding context from the message bus.
The data field must contain a context keyword and
may contain a word if a specific word should be injected
as a match for the provided context keyword.
"""
entity = {'confidence': 1.0}
context = message.data.get('context')
word = message.data.get('word') or ''
# if not a string type try creating a string from it
if not isinstance(word, basestring):
word = str(word)
entity['data'] = [(word, context)]
entity['match'] = word
entity['key'] = word
self.context_manager.inject_context(entity)
def handle_remove_context(self, message):
"""
Handles removing context from the message bus. The
data field must contain the 'context' to remove.
"""
context = message.data.get('context')
if context:
self.context_manager.remove_context(context)
def handle_clear_context(self, message):
"""
Clears all keywords from context.
"""
self.context_manager.clear_context()
class IntentEngineTests(unittest.TestCase):
def setUp(self):
self.engine = IntentDeterminationEngine()
def testRegisterIntentParser(self):
assert len(self.engine.intent_parsers) == 0
try:
self.engine.register_intent_parser("NOTAPARSER")
assert "Did not fail to register invalid intent parser" and False
except ValueError as e:
pass
parser = IntentBuilder("Intent").build()
self.engine.register_intent_parser(parser)
assert len(self.engine.intent_parsers) == 1
def testRegisterRegexEntity(self):
assert len(self.engine._regex_strings) == 0
assert len(self.engine.regular_expressions_entities) == 0
self.engine.register_regex_entity(".*")
assert len(self.engine._regex_strings) == 1
assert len(self.engine.regular_expressions_entities) == 1
def testSelectBestIntent(self):
parser1 = IntentBuilder("Parser1").require("Entity1").build()
self.engine.register_intent_parser(parser1)
self.engine.register_entity("tree", "Entity1")
utterance = "go to the tree house"
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser1'
parser2 = IntentBuilder("Parser2").require("Entity1").require(
"Entity2").build()
self.engine.register_intent_parser(parser2)
self.engine.register_entity("house", "Entity2")
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser2'
def testDropIntent(self):
parser1 = IntentBuilder("Parser1").require("Entity1").build()
self.engine.register_intent_parser(parser1)
self.engine.register_entity("tree", "Entity1")
parser2 = (IntentBuilder("Parser2").require("Entity1").require(
"Entity2").build())
self.engine.register_intent_parser(parser2)
self.engine.register_entity("house", "Entity2")
utterance = "go to the tree house"
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser2'
assert self.engine.drop_intent_parser('Parser2') is True
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser1'
def testDropEntity(self):
parser1 = IntentBuilder("Parser1").require("Entity1").build()
self.engine.register_intent_parser(parser1)
self.engine.register_entity("laboratory", "Entity1")
self.engine.register_entity("lab", "Entity1")
utterance = "get out of my lab"
utterance2 = "get out of my laboratory"
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser1'
intent = next(self.engine.determine_intent(utterance2))
assert intent
assert intent['intent_type'] == 'Parser1'
# Remove Entity and re-register laboratory and make sure only that
# matches.
self.engine.drop_entity(entity_type='Entity1')
self.engine.register_entity("laboratory", "Entity1")
# Sentence containing lab should not produce any results
with self.assertRaises(StopIteration):
intent = next(self.engine.determine_intent(utterance))
# But sentence with laboratory should
intent = next(self.engine.determine_intent(utterance2))
assert intent
assert intent['intent_type'] == 'Parser1'
def testCustomDropEntity(self):
parser1 = (IntentBuilder("Parser1").one_of("Entity1",
"Entity2").build())
self.engine.register_intent_parser(parser1)
self.engine.register_entity("laboratory", "Entity1")
self.engine.register_entity("lab", "Entity2")
utterance = "get out of my lab"
utterance2 = "get out of my laboratory"
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser1'
intent = next(self.engine.determine_intent(utterance2))
assert intent
assert intent['intent_type'] == 'Parser1'
def matcher(data):
return data[1].startswith('Entity')
self.engine.drop_entity(match_func=matcher)
self.engine.register_entity("laboratory", "Entity1")
# Sentence containing lab should not produce any results
with self.assertRaises(StopIteration):
intent = next(self.engine.determine_intent(utterance))
# But sentence with laboratory should
intent = next(self.engine.determine_intent(utterance2))
assert intent
def testDropRegexEntity(self):
self.engine.register_regex_entity(r"the dog (?P<Dog>.*)")
self.engine.register_regex_entity(r"the cat (?P<Cat>.*)")
assert len(self.engine._regex_strings) == 2
assert len(self.engine.regular_expressions_entities) == 2
self.engine.drop_regex_entity(entity_type='Cat')
assert len(self.engine._regex_strings) == 1
assert len(self.engine.regular_expressions_entities) == 1
def testCustomDropRegexEntity(self):
self.engine.register_regex_entity(r"the dog (?P<SkillADog>.*)")
self.engine.register_regex_entity(r"the cat (?P<SkillACat>.*)")
self.engine.register_regex_entity(r"the mangy dog (?P<SkillBDog>.*)")
assert len(self.engine._regex_strings) == 3
assert len(self.engine.regular_expressions_entities) == 3
def matcher(regexp):
"""Matcher for all match groups defined for SkillB"""
match_groups = regexp.groupindex.keys()
return any([k.startswith('SkillB') for k in match_groups])
self.engine.drop_regex_entity(match_func=matcher)
assert len(self.engine._regex_strings) == 2
assert len(self.engine.regular_expressions_entities) == 2
class IntentService(object):
def __init__(self, emitter):
self.config = ConfigurationManager.get().get('context', {})
self.engine = IntentDeterminationEngine()
self.context_keywords = self.config.get('keywords', ['Location'])
self.context_max_frames = self.config.get('max_frames', 3)
self.context_timeout = self.config.get('timeout', 2)
self.context_greedy = self.config.get('greedy', False)
self.context_manager = ContextManager(self.context_timeout)
self.emitter = emitter
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
self.emitter.on('detach_skill', self.handle_detach_skill)
# Context related handlers
self.emitter.on('add_context', self.handle_add_context)
self.emitter.on('remove_context', self.handle_remove_context)
self.emitter.on('clear_context', self.handle_clear_context)
# Converse method
self.emitter.on('skill.converse.response',
self.handle_converse_response)
self.active_skills = [] # [skill_id , timestamp]
self.converse_timeout = 5 # minutes to prune active_skills
def do_converse(self, utterances, skill_id, lang):
self.emitter.emit(Message("skill.converse.request", {
"skill_id": skill_id, "utterances": utterances, "lang": lang}))
self.waiting = True
self.result = False
start_time = time.time()
t = 0
while self.waiting and t < 5:
t = time.time() - start_time
time.sleep(0.1)
self.waiting = False
return self.result
def handle_converse_response(self, message):
# id = message.data["skill_id"]
# no need to crosscheck id because waiting before new request is made
# no other skill will make this request is safe assumption
result = message.data["result"]
self.result = result
self.waiting = False
def remove_active_skill(self, skill_id):
for skill in self.active_skills:
if skill[0] == skill_id:
self.active_skills.remove(skill)
def add_active_skill(self, skill_id):
# search the list for an existing entry that already contains it
# and remove that reference
self.remove_active_skill(skill_id)
# add skill with timestamp to start of skill_list
self.active_skills.insert(0, [skill_id, time.time()])
def update_context(self, intent):
"""
updates context with keyword from the intent.
NOTE: This method currently won't handle one_of intent keywords
since it's not using quite the same format as other intent
keywords. This is under investigation in adapt, PR pending.
Args:
intent: Intent to scan for keywords
"""
for tag in intent['__tags__']:
if 'entities' not in tag:
continue
context_entity = tag['entities'][0]
if self.context_greedy:
self.context_manager.inject_context(context_entity)
elif context_entity['data'][0][1] in self.context_keywords:
self.context_manager.inject_context(context_entity)
def handle_utterance(self, message):
# Get language of the utterance
lang = message.data.get('lang', None)
if not lang:
lang = "en-us"
utterances = message.data.get('utterances', '')
# check for conversation time-out
self.active_skills = [skill for skill in self.active_skills
if time.time() - skill[
1] <= self.converse_timeout * 60]
# check if any skill wants to handle utterance
for skill in self.active_skills:
if self.do_converse(utterances, skill[0], lang):
# update timestamp, or there will be a timeout where
# intent stops conversing whether its being used or not
self.add_active_skill(skill[0])
return
# no skill wants to handle utterance
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy", etc.
best_intent = next(self.engine.determine_intent(
normalize(utterance, lang), 100,
include_tags=True,
context_manager=self.context_manager))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration, e:
LOG.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
self.update_context(best_intent)
reply = message.reply(
best_intent.get('intent_type'), best_intent)
self.emitter.emit(reply)
# update active skills
skill_id = int(best_intent['intent_type'].split(":")[0])
self.add_active_skill(skill_id)
else:
self.emitter.emit(Message("intent_failure", {
"utterance": utterances[0],
"lang": lang
}))
from adapt.intent import IntentBuilder
from adapt.engine import IntentDeterminationEngine
engine = IntentDeterminationEngine()
schema = json.loads(sys.argv[1])
for entity in schema["entities"]:
if entity["type"] == "string":
for value in entity["values"]:
engine.register_entity(value, entity["name"])
elif entity["type"] == "regex":
engine.register_regex_entity(entity["pattern"])
for intent in schema["intents"]:
ib = IntentBuilder(intent["name"].encode("utf-8"))
for requirement in intent["requirements"]:
ib.require(requirement["entity"], requirement["attribute"])
for optional in intent["optionals"]:
ib.optionally(optional["entity"], optional["attribute"])
engine.register_intent_parser(ib.build())
if __name__ == "__main__":
while True:
line = sys.stdin.readline()
query = json.loads(line)
intents = list(engine.determine_intent(query["input"]))
response = {"intents": intents}
print(json.dumps(response))
sys.stdout.flush()
class IntentService(object):
def __init__(self, bus):
self.config = Configuration.get().get('context', {})
self.engine = IntentDeterminationEngine()
# Dictionary for translating a skill id to a name
self.skill_names = {}
# Context related intializations
self.context_keywords = self.config.get('keywords', [])
self.context_max_frames = self.config.get('max_frames', 3)
self.context_timeout = self.config.get('timeout', 2)
self.context_greedy = self.config.get('greedy', False)
self.context_manager = ContextManager(self.context_timeout)
self.bus = bus
self.bus.on('register_vocab', self.handle_register_vocab)
self.bus.on('register_intent', self.handle_register_intent)
self.bus.on('recognizer_loop:utterance', self.handle_utterance)
self.bus.on('detach_intent', self.handle_detach_intent)
self.bus.on('detach_skill', self.handle_detach_skill)
# Context related handlers
self.bus.on('add_context', self.handle_add_context)
self.bus.on('remove_context', self.handle_remove_context)
self.bus.on('clear_context', self.handle_clear_context)
# Converse method
self.bus.on('skill.converse.response', self.handle_converse_response)
self.bus.on('skill.converse.error', self.handle_converse_error)
self.bus.on('mycroft.speech.recognition.unknown', self.reset_converse)
self.bus.on('mycroft.skills.loaded', self.update_skill_name_dict)
def add_active_skill_handler(message):
self.add_active_skill(message.data['skill_id'])
self.bus.on('active_skill_request', add_active_skill_handler)
self.active_skills = [] # [skill_id , timestamp]
self.converse_timeout = 5 # minutes to prune active_skills
self.waiting_for_converse = False
self.converse_result = False
self.converse_skill_id = ""
def update_skill_name_dict(self, message):
"""
Messagebus handler, updates dictionary of if to skill name
conversions.
"""
self.skill_names[message.data['id']] = message.data['name']
def get_skill_name(self, skill_id):
""" Get skill name from skill ID.
Args:
skill_id: a skill id as encoded in Intent handlers.
Returns:
(str) Skill name or the skill id if the skill wasn't found
"""
return self.skill_names.get(skill_id, skill_id)
def reset_converse(self, message):
"""Let skills know there was a problem with speech recognition"""
lang = message.data.get('lang', "en-us")
for skill in self.active_skills:
self.do_converse(None, skill[0], lang)
def do_converse(self, utterances, skill_id, lang):
self.waiting_for_converse = True
self.converse_result = False
self.converse_skill_id = skill_id
self.bus.emit(Message("skill.converse.request", {
"skill_id": skill_id, "utterances": utterances, "lang": lang}))
start_time = time.time()
t = 0
while self.waiting_for_converse and t < 5:
t = time.time() - start_time
time.sleep(0.1)
self.waiting_for_converse = False
self.converse_skill_id = ""
return self.converse_result
def handle_converse_error(self, message):
skill_id = message.data["skill_id"]
if message.data["error"] == "skill id does not exist":
self.remove_active_skill(skill_id)
if skill_id == self.converse_skill_id:
self.converse_result = False
self.waiting_for_converse = False
def handle_converse_response(self, message):
skill_id = message.data["skill_id"]
if skill_id == self.converse_skill_id:
self.converse_result = message.data.get("result", False)
self.waiting_for_converse = False
def remove_active_skill(self, skill_id):
for skill in self.active_skills:
if skill[0] == skill_id:
self.active_skills.remove(skill)
def add_active_skill(self, skill_id):
# search the list for an existing entry that already contains it
# and remove that reference
self.remove_active_skill(skill_id)
# add skill with timestamp to start of skill_list
self.active_skills.insert(0, [skill_id, time.time()])
def update_context(self, intent):
""" Updates context with keyword from the intent.
NOTE: This method currently won't handle one_of intent keywords
since it's not using quite the same format as other intent
keywords. This is under investigation in adapt, PR pending.
Args:
intent: Intent to scan for keywords
"""
for tag in intent['__tags__']:
if 'entities' not in tag:
continue
context_entity = tag['entities'][0]
if self.context_greedy:
self.context_manager.inject_context(context_entity)
elif context_entity['data'][0][1] in self.context_keywords:
self.context_manager.inject_context(context_entity)
def send_metrics(self, intent, context, stopwatch):
"""
Send timing metrics to the backend.
NOTE: This only applies to those with Opt In.
"""
ident = context['ident'] if context else None
if intent:
# Recreate skill name from skill id
parts = intent.get('intent_type', '').split(':')
intent_type = self.get_skill_name(parts[0])
if len(parts) > 1:
intent_type = ':'.join([intent_type] + parts[1:])
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': intent_type})
else:
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': 'intent_failure'})
def handle_utterance(self, message):
""" Main entrypoint for handling user utterances with Mycroft skills
Monitor the messagebus for 'recognizer_loop:utterance', typically
generated by a spoken interaction but potentially also from a CLI
or other method of injecting a 'user utterance' into the system.
Utterances then work through this sequence to be handled:
1) Active skills attempt to handle using converse()
2) Adapt intent handlers
3) Padatious intent handlers
4) Other fallbacks
Args:
message (Message): The messagebus data
"""
try:
# Get language of the utterance
lang = message.data.get('lang', "en-us")
utterances = message.data.get('utterances', '')
stopwatch = Stopwatch()
with stopwatch:
# Give active skills an opportunity to handle the utterance
converse = self._converse(utterances, lang)
if not converse:
# No conversation, use intent system to handle utterance
intent = self._adapt_intent_match(utterances, lang)
padatious_intent = PadatiousService.instance.calc_intent(
utterances[0])
if converse:
# Report that converse handled the intent and return
ident = message.context['ident'] if message.context else None
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': 'converse'})
return
elif intent and not (padatious_intent and
padatious_intent.conf >= 0.95):
# Send the message to the Adapt intent's handler unless
# Padatious is REALLY sure it was directed at it instead.
reply = message.reply(intent.get('intent_type'), intent)
else:
# Allow fallback system to handle utterance
# NOTE: Padatious intents are handled this way, too
reply = message.reply('intent_failure',
{'utterance': utterances[0],
'lang': lang})
self.bus.emit(reply)
self.send_metrics(intent, message.context, stopwatch)
except Exception as e:
LOG.exception(e)
def _converse(self, utterances, lang):
""" Give active skills a chance at the utterance
Args:
utterances (list): list of utterances
lang (string): 4 letter ISO language code
Returns:
bool: True if converse handled it, False if no skill processes it
"""
# check for conversation time-out
self.active_skills = [skill for skill in self.active_skills
if time.time() - skill[
1] <= self.converse_timeout * 60]
# check if any skill wants to handle utterance
for skill in self.active_skills:
if self.do_converse(utterances, skill[0], lang):
# update timestamp, or there will be a timeout where
# intent stops conversing whether its being used or not
self.add_active_skill(skill[0])
return True
return False
def _adapt_intent_match(self, utterances, lang):
""" Run the Adapt engine to search for an matching intent
Args:
utterances (list): list of utterances
lang (string): 4 letter ISO language code
Returns:
Intent structure, or None if no match was found.
"""
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy", etc.
best_intent = next(self.engine.determine_intent(
normalize(utterance, lang), 100,
include_tags=True,
context_manager=self.context_manager))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration:
# don't show error in log
continue
except Exception as e:
LOG.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
self.update_context(best_intent)
# update active skills
skill_id = best_intent['intent_type'].split(":")[0]
self.add_active_skill(skill_id)
# adapt doesn't handle context injection for one_of keywords
# correctly. Workaround this issue if possible.
try:
best_intent = workaround_one_of_context(best_intent)
except LookupError:
LOG.error('Error during workaround_one_of_context')
return best_intent
def handle_register_vocab(self, message):
start_concept = message.data.get('start')
end_concept = message.data.get('end')
regex_str = message.data.get('regex')
alias_of = message.data.get('alias_of')
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(
start_concept, end_concept, alias_of=alias_of)
def handle_register_intent(self, message):
intent = open_intent_envelope(message)
self.engine.register_intent_parser(intent)
def handle_detach_intent(self, message):
intent_name = message.data.get('intent_name')
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name]
self.engine.intent_parsers = new_parsers
def handle_detach_skill(self, message):
skill_id = message.data.get('skill_id')
new_parsers = [
p for p in self.engine.intent_parsers if
not p.name.startswith(skill_id)]
self.engine.intent_parsers = new_parsers
def handle_add_context(self, message):
""" Add context
Args:
message: data contains the 'context' item to add
optionally can include 'word' to be injected as
an alias for the context item.
"""
entity = {'confidence': 1.0}
context = message.data.get('context')
word = message.data.get('word') or ''
origin = message.data.get('origin') or ''
# if not a string type try creating a string from it
if not isinstance(word, str):
word = str(word)
entity['data'] = [(word, context)]
entity['match'] = word
entity['key'] = word
entity['origin'] = origin
self.context_manager.inject_context(entity)
def handle_remove_context(self, message):
""" Remove specific context
Args:
message: data contains the 'context' item to remove
"""
context = message.data.get('context')
if context:
self.context_manager.remove_context(context)
def handle_clear_context(self, message):
""" Clears all keywords from context """
self.context_manager.clear_context()
class IntentService(object):
def __init__(self, emitter):
self.engine = IntentDeterminationEngine()
self.emitter = emitter
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('onyx_recognizer:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
self.emitter.on('detach_skill', self.handle_detach_skill)
def handle_utterance(self, message):
lang = message.data.get('lang', None)
if not lang:
lang = "en-US"
user = message.data.get('user', None)
url = message.data.get('url', None)
utterances = message.data.get('utterances', '')
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy"
best_intent = next(
self.engine.determine_intent(normalize(utterance, lang),
100))
best_intent['utterance'] = utterance
except StopIteration as e:
logger.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
best_intent['lang'] = lang
best_intent['user'] = user
reply = message.reply(best_intent.get('intent_type'), best_intent)
self.emitter.emit(reply)
elif len(utterances) == 1:
self.emitter.emit(
Message("intent_failure", {
"utterance": utterances[0],
"lang": lang
}))
else:
self.emitter.emit(
Message("multi_utterance_intent_failure", {
"utterances": utterances,
"lang": lang
}))
def handle_register_vocab(self, message):
start_concept = message.data.get('start')
end_concept = message.data.get('end')
regex_str = message.data.get('regex')
alias_of = message.data.get('alias_of')
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(start_concept,
end_concept,
alias_of=alias_of)
def handle_register_intent(self, message):
intent = open_intent_envelope(message)
self.engine.register_intent_parser(intent)
def handle_detach_intent(self, message):
intent_name = message.data.get('intent_name')
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name
]
self.engine.intent_parsers = new_parsers
def handle_detach_skill(self, message):
skill_name = message.data.get('skill_name')
new_parsers = [
p for p in self.engine.intent_parsers
if not p.name.startswith(skill_name)
]
self.engine.intent_parsers = new_parsers
class IntentEngineTests(unittest.TestCase):
def setUp(self):
self.engine = IntentDeterminationEngine()
def testRegisterIntentParser(self):
assert len(self.engine.intent_parsers) == 0
try:
self.engine.register_intent_parser("NOTAPARSER")
assert "Did not fail to register invalid intent parser" and False
except ValueError as e:
pass
parser = IntentBuilder("Intent").build()
self.engine.register_intent_parser(parser)
assert len(self.engine.intent_parsers) == 1
def testRegisterRegexEntity(self):
assert len(self.engine._regex_strings) == 0
assert len(self.engine.regular_expressions_entities) == 0
self.engine.register_regex_entity(".*")
assert len(self.engine._regex_strings) == 1
assert len(self.engine.regular_expressions_entities) == 1
def testSelectBestIntent(self):
parser1 = IntentBuilder("Parser1").require("Entity1").build()
self.engine.register_intent_parser(parser1)
self.engine.register_entity("tree", "Entity1")
utterance = "go to the tree house"
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser1'
parser2 = IntentBuilder("Parser2").require("Entity1").require(
"Entity2").build()
self.engine.register_intent_parser(parser2)
self.engine.register_entity("house", "Entity2")
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser2'
def testDropIntent(self):
parser1 = IntentBuilder("Parser1").require("Entity1").build()
self.engine.register_intent_parser(parser1)
self.engine.register_entity("tree", "Entity1")
parser2 = (IntentBuilder("Parser2").require("Entity1").require(
"Entity2").build())
self.engine.register_intent_parser(parser2)
self.engine.register_entity("house", "Entity2")
utterance = "go to the tree house"
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser2'
assert self.engine.drop_intent_parser('Parser2') is True
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser1'
def testDropEntity(self):
parser1 = IntentBuilder("Parser1").require("Entity1").build()
self.engine.register_intent_parser(parser1)
self.engine.register_entity("laboratory", "Entity1")
self.engine.register_entity("lab", "Entity1")
utterance = "get out of my lab"
utterance2 = "get out of my laboratory"
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser1'
intent = next(self.engine.determine_intent(utterance2))
assert intent
assert intent['intent_type'] == 'Parser1'
# Remove Entity and re-register laboratory and make sure only that
# matches.
self.engine.drop_entity(entity_type='Entity1')
self.engine.register_entity("laboratory", "Entity1")
# Sentence containing lab should not produce any results
with self.assertRaises(StopIteration):
intent = next(self.engine.determine_intent(utterance))
# But sentence with laboratory should
intent = next(self.engine.determine_intent(utterance2))
assert intent
assert intent['intent_type'] == 'Parser1'
def testCustomDropEntity(self):
parser1 = (IntentBuilder("Parser1").one_of("Entity1",
"Entity2").build())
self.engine.register_intent_parser(parser1)
self.engine.register_entity("laboratory", "Entity1")
self.engine.register_entity("lab", "Entity2")
utterance = "get out of my lab"
utterance2 = "get out of my laboratory"
intent = next(self.engine.determine_intent(utterance))
assert intent
assert intent['intent_type'] == 'Parser1'
intent = next(self.engine.determine_intent(utterance2))
assert intent
assert intent['intent_type'] == 'Parser1'
def matcher(data):
return data[1].startswith('Entity')
self.engine.drop_entity(match_func=matcher)
self.engine.register_entity("laboratory", "Entity1")
# Sentence containing lab should not produce any results
with self.assertRaises(StopIteration):
intent = next(self.engine.determine_intent(utterance))
# But sentence with laboratory should
intent = next(self.engine.determine_intent(utterance2))
assert intent
def testDropRegexEntity(self):
self.engine.register_regex_entity(r"the dog (?P<Dog>.*)")
self.engine.register_regex_entity(r"the cat (?P<Cat>.*)")
assert len(self.engine._regex_strings) == 2
assert len(self.engine.regular_expressions_entities) == 2
self.engine.drop_regex_entity(entity_type='Cat')
assert len(self.engine._regex_strings) == 1
assert len(self.engine.regular_expressions_entities) == 1
def testCustomDropRegexEntity(self):
self.engine.register_regex_entity(r"the dog (?P<SkillADog>.*)")
self.engine.register_regex_entity(r"the cat (?P<SkillACat>.*)")
self.engine.register_regex_entity(r"the mangy dog (?P<SkillBDog>.*)")
assert len(self.engine._regex_strings) == 3
assert len(self.engine.regular_expressions_entities) == 3
def matcher(regexp):
"""Matcher for all match groups defined for SkillB"""
match_groups = regexp.groupindex.keys()
return any([k.startswith('SkillB') for k in match_groups])
self.engine.drop_regex_entity(match_func=matcher)
assert len(self.engine._regex_strings) == 2
assert len(self.engine.regular_expressions_entities) == 2
def testAddingOfRemovedRegexp(self):
self.engine.register_regex_entity(r"the cool (?P<thing>.*)")
def matcher(regexp):
"""Matcher for all match groups defined for SkillB"""
match_groups = regexp.groupindex.keys()
return any([k.startswith('thing') for k in match_groups])
self.engine.drop_regex_entity(match_func=matcher)
assert len(self.engine.regular_expressions_entities) == 0
self.engine.register_regex_entity(r"the cool (?P<thing>.*)")
assert len(self.engine.regular_expressions_entities) == 1
def testUsingOfRemovedRegexp(self):
self.engine.register_regex_entity(r"the cool (?P<thing>.*)")
parser = IntentBuilder("Intent").require("thing").build()
self.engine.register_intent_parser(parser)
def matcher(regexp):
"""Matcher for all match groups defined for SkillB"""
match_groups = regexp.groupindex.keys()
return any([k.startswith('thing') for k in match_groups])
self.engine.drop_regex_entity(match_func=matcher)
assert len(self.engine.regular_expressions_entities) == 0
utterance = "the cool cat"
intents = [match for match in self.engine.determine_intent(utterance)]
assert len(intents) == 0
def testEmptyTags(self):
# Validates https://github.com/MycroftAI/adapt/issues/114
engine = IntentDeterminationEngine()
engine.register_entity("Kevin",
"who") # same problem if several entities
builder = IntentBuilder("Buddies")
builder.optionally("who") # same problem if several entity types
engine.register_intent_parser(builder.build())
intents = [i for i in engine.determine_intent("Julien is a friend")]
assert len(intents) == 0
def testResultsAreSortedByConfidence(self):
self.engine.register_entity('what is', 'Query', None)
self.engine.register_entity('weather', 'Weather', None)
self.engine.register_regex_entity('(at|in) (?P<Location>.+)')
self.engine.register_regex_entity('(?P<Entity>.*)')
i = IntentBuilder("CurrentWeatherIntent").require(
"Weather").optionally("Location").build()
self.engine.register_intent_parser(i)
utterance = "what is the weather like in stockholm"
intents = [
i for i in self.engine.determine_intent(utterance, num_results=100)
]
confidences = [intent.get('confidence', 0.0) for intent in intents]
assert len(confidences) > 1
assert all(confidences[i] >= confidences[i + 1]
for i in range(len(confidences) - 1))
class IntentSkill(BoomerSkill):
def __init__(self):
BoomerSkill.__init__(self, name="IntentSkill")
self.engine = IntentDeterminationEngine()
def initialize(self):
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
def handle_utterance(self, message):
timer = Stopwatch()
timer.start()
metrics = MetricsAggregator()
utterances = message.data.get('utterances', '')
best_intent = None
for utterance in utterances:
metrics.increment("utterances.count")
for intent in self.engine.determine_intent(
utterance, num_results=100):
metrics.increment("intents.count")
intent['utterance'] = utterance
best_confidence = best_intent.get('confidence') \
if best_intent else 0.0
cur_confidence = intent.get('confidence', 0.0)
if best_confidence < cur_confidence:
best_intent = intent
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
reply = message.reply(
best_intent.get('intent_type'), data=best_intent)
self.emitter.emit(reply)
elif len(utterances) == 1:
self.emitter.emit(
Message("intent_failure",
data={"utterance": utterances[0]}))
else:
self.emitter.emit(
Message("multi_utterance_intent_failure",
data={"utterances": utterances}))
metrics.timer("parse.time", timer.stop())
metrics.flush()
def handle_register_vocab(self, message):
start_concept = message.data.get('start')
end_concept = message.data.get('end')
regex_str = message.data.get('regex')
alias_of = message.data.get('alias_of')
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(
start_concept, end_concept, alias_of=alias_of)
def handle_register_intent(self, message):
intent = open_intent_envelope(message)
self.engine.register_intent_parser(intent)
def handle_detach_intent(self, message):
intent_name = message.data.get('intent_name')
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name]
self.engine.intent_parsers = new_parsers
def stop(self):
pass
class IntentService(object):
def __init__(self, emitter):
self.config = ConfigurationManager.get().get('context', {})
self.engine = IntentDeterminationEngine()
self.context_keywords = self.config.get('keywords', ['Location'])
self.context_max_frames = self.config.get('max_frames', 3)
self.context_timeout = self.config.get('timeout', 2)
self.context_greedy = self.config.get('greedy', False)
self.context_manager = ContextManager(self.context_timeout)
self.emitter = emitter
self.emitter.on('register_vocab', self.handle_register_vocab)
self.emitter.on('register_intent', self.handle_register_intent)
self.emitter.on('recognizer_loop:utterance', self.handle_utterance)
self.emitter.on('detach_intent', self.handle_detach_intent)
self.emitter.on('detach_skill', self.handle_detach_skill)
# Context related handlers
self.emitter.on('add_context', self.handle_add_context)
self.emitter.on('remove_context', self.handle_remove_context)
self.emitter.on('clear_context', self.handle_clear_context)
# Converse method
self.emitter.on('skill.converse.response',
self.handle_converse_response)
self.active_skills = [] # [skill_id , timestamp]
self.converse_timeout = 5 # minutes to prune active_skills
def do_converse(self, utterances, skill_id, lang):
self.emitter.emit(
Message("skill.converse.request", {
"skill_id": skill_id,
"utterances": utterances,
"lang": lang
}))
self.waiting = True
self.result = False
start_time = time.time()
t = 0
while self.waiting and t < 5:
t = time.time() - start_time
time.sleep(0.1)
self.waiting = False
return self.result
def handle_converse_response(self, message):
# id = message.data["skill_id"]
# no need to crosscheck id because waiting before new request is made
# no other skill will make this request is safe assumption
result = message.data["result"]
self.result = result
self.waiting = False
def remove_active_skill(self, skill_id):
for skill in self.active_skills:
if skill[0] == skill_id:
self.active_skills.remove(skill)
def add_active_skill(self, skill_id):
# search the list for an existing entry that already contains it
# and remove that reference
self.remove_active_skill(skill_id)
# add skill with timestamp to start of skill_list
self.active_skills.insert(0, [skill_id, time.time()])
def update_context(self, intent):
"""
updates context with keyword from the intent.
NOTE: This method currently won't handle one_of intent keywords
since it's not using quite the same format as other intent
keywords. This is under investigation in adapt, PR pending.
Args:
intent: Intent to scan for keywords
"""
for tag in intent['__tags__']:
if 'entities' not in tag:
continue
context_entity = tag['entities'][0]
if self.context_greedy:
self.context_manager.inject_context(context_entity)
elif context_entity['data'][0][1] in self.context_keywords:
self.context_manager.inject_context(context_entity)
def handle_utterance(self, message):
# Get language of the utterance
lang = message.data.get('lang', None)
if not lang:
lang = "en-us"
utterances = message.data.get('utterances', '')
# check for conversation time-out
self.active_skills = [
skill for skill in self.active_skills
if time.time() - skill[1] <= self.converse_timeout * 60
]
# check if any skill wants to handle utterance
for skill in self.active_skills:
if self.do_converse(utterances, skill[0], lang):
# update timestamp, or there will be a timeout where
# intent stops conversing whether its being used or not
self.add_active_skill(skill[0])
return
# no skill wants to handle utterance
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy", etc.
best_intent = next(
self.engine.determine_intent(
normalize(utterance, lang),
100,
include_tags=True,
context_manager=self.context_manager))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration, e:
LOG.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
self.update_context(best_intent)
reply = message.reply(best_intent.get('intent_type'), best_intent)
self.emitter.emit(reply)
# update active skills
skill_id = int(best_intent['intent_type'].split(":")[0])
self.add_active_skill(skill_id)
else:
self.emitter.emit(
Message("intent_failure", {
"utterance": utterances[0],
"lang": lang
}))
engine.register_regex_entity(regex)
for regex in location_regex_keywords:
engine.register_regex_entity(regex)
for regex in with_regex_keywords:
engine.register_regex_entity(regex)
# construt an intent parser
add_event_intent = IntentBuilder('EventIntent') \
.require('AddKeyword') \
.require('EventKeyword') \
.require('Personkeyword') \
.optionally('LocationKeyword') \
.optionally('Date') \
.build()
engine.register_intent_parser(add_event_intent)
x='Add event with Hana Bouzid in Midoune Room starts 10 am'
for intent in engine.determine_intent(x):
clist=[]
alist = x.split("with ")
print(alist)
blist=alist[1].split(" in")
lliste=blist[1].split("starts")
print(blist)
print(lliste)
if("and")in blist[0]:
clist = blist[0].split("and")
print(clist) # liste des attendees cités dans x
print(type(clist))
else :
clist.append(blist[0])
print(clist) #liste des attendees cités dans x
print(type(clist))
class VoiceCommands:
ACTIONS = 'Action'
NAME = 'Name'
ACTUATOR_TYPE = 'ActuatorType'
@typechecked()
def __init__(self, job_controll: AsyncActuatorCommands,
logging: RootLogger):
self.__job_controll = job_controll
self.__logging = logging
def configure(self):
self.__engine = IntentDeterminationEngine()
actions = ['on', 'off']
self.__register_entity(actions, self.ACTIONS)
locations = ['living', 'kitchen', 'hollway', 'wemo']
self.__register_entity(locations, self.NAME)
actuator_types = ['light', 'switch', 'courtains', 'door']
self.__register_entity(actuator_types, self.ACTUATOR_TYPE)
actuator_intent = IntentBuilder("ActuatorIntent") \
.require(self.ACTIONS) \
.require(self.ACTUATOR_TYPE) \
.require(self.NAME) \
.build()
self.__engine.register_intent_parser(actuator_intent)
self.__commands_map = [
{
'entities': {
'name': 'living',
'type': 'light'
},
'actuator': 'livingLight'
},
{
'entities': {
'name': 'living',
'type': 'courtains'
},
'actuator': 'livingCourtains'
},
{
'entities': {
'name': 'hollway',
'type': 'light'
},
'actuator': 'holwayLight'
},
{
'entities': {
'name': 'wemo',
'type': 'switch'
},
'actuator': 'wemoSwitch1'
},
]
return self
@typechecked()
def execute(self, command: str) -> None:
command = self.__normalize_command(command)
for intent in self.__engine.determine_intent(command):
if intent and intent.get('confidence') > 0:
self.__logging.info(intent)
command = self.__get_matching_command(intent)
self.__run_command(command, intent)
def __register_entity(self, wordlist, name):
for action in wordlist:
self.__engine.register_entity(action, name)
def __get_matching_command(self, intent):
for command in self.__commands_map:
if command['entities']['name'] == intent[self.NAME] and \
command['entities']['type'] == intent[self.ACTUATOR_TYPE]:
return command
def __run_command(self, command, intent):
if None == command:
return
actuator_state = (False, True)[intent[self.ACTIONS] == 'on']
self.__logging.info('Changin actuator {0} value: {1}'.format(
command['actuator'], actuator_state))
self.__job_controll.change_actuator(command['actuator'],
actuator_state)
def __normalize_command(self, command):
replaces = [('life', 'light'), ('leaving', 'living'),
('hallway', 'hollway'), ('quarters', 'courtains')]
for replace in replaces:
command = re.sub(replace[0], replace[1], command)
return command
class Brain(AppDaemon):
def initialize(self):
self.morph = pymorphy2.MorphAnalyzer()
self.engine = IntentDeterminationEngine()
self.context_managers = {}
self.last_request = {}
def query(self, query, connector):
nquery = self.normalize(query)
if not self.context_managers.get(connector):
self.context_managers[connector] = ContextManager()
if not self.last_request.get(connector):
self.last_request[connector] = datetime.datetime.now()
last_rq_delta = datetime.datetime.now() - self.last_request[connector]
if last_rq_delta.seconds > 300:
# resetting context, if last conversation was more than 15 minutes
# ago
self.context_managers[connector].frame_stack = []
message = "Прости, что то пошло не так"
for intent in self.engine.determine_intent(
nquery,
include_tags=True,
context_manager=self.context_managers[connector]):
# after enabling context management, it can become more important
if intent and intent.get('confidence') > 0:
if self.get_app(intent["intent_type"]).context_sensitive:
for tag in intent["__tags__"]:
context_entity = tag.get('entities')[0]
if self.get_app(intent["intent_type"]).context_blacklist\
and context_entity['data'][0][1]\
in self.get_app(intent["intent_type"]).context_blacklist:
continue
self.context_managers[connector].inject_context(
context_entity)
message = self.get_app(intent["intent_type"]).handle(intent)
self.last_request[connector] = datetime.datetime.now()
# Some code for self-analyze
self.get_app("analysis").add_entry(
connector, intent["intent_type"], query,
len(self.context_managers[connector].frame_stack))
self.get_app(connector).answer(message)
def normalize(self, text):
"""Remove punctuation, set all words to normal form"""
nwords = []
text = re.sub('\?|\!|\.|\,', '', text).lower()
for word in text.split(' '):
nword = self.morph.parse(word)[0].normal_form
nwords.append(nword)
nwords = " ".join(nwords)
nwords = text_to_number(nwords)
return nwords
def read_dialog_files(self, dialog_file):
"""
Reads file with phrases and returns list of them
"""
dialog = []
with open(dialog_file, 'r') as d:
for line in d:
dialog.append(line[:-1])
return dialog
def my_name(self):
names = ["Jumper"]
return random.choice(names)
def greeting(self):
phrases = [
"Голосовой интерфейс активирован", "%s, я тут!", "Привет, %s!"
]
phrase = random.choice(phrases)
try:
return phrase % self.my_name()
except TypeError:
return phrase
def dont_understand(self):
phrases = [
"Прости, %s, я не поняла", "Повтори пожалуйста", "Прости, что?"
]
phrase = random.choice(phrases)
try:
return phrase % self.my_name()
except TypeError:
return phrase
class IntentService(object):
def __init__(self, bus):
self.config = Configuration.get().get('context', {})
self.engine = IntentDeterminationEngine()
# Dictionary for translating a skill id to a name
self.skill_names = {}
# Context related intializations
self.context_keywords = self.config.get('keywords', [])
self.context_max_frames = self.config.get('max_frames', 3)
self.context_timeout = self.config.get('timeout', 2)
self.context_greedy = self.config.get('greedy', False)
self.context_manager = ContextManager(self.context_timeout)
self.bus = bus
self.bus.on('register_vocab', self.handle_register_vocab)
self.bus.on('register_intent', self.handle_register_intent)
self.bus.on('recognizer_loop:utterance', self.handle_utterance)
self.bus.on('detach_intent', self.handle_detach_intent)
self.bus.on('detach_skill', self.handle_detach_skill)
# Context related handlers
self.bus.on('add_context', self.handle_add_context)
self.bus.on('remove_context', self.handle_remove_context)
self.bus.on('clear_context', self.handle_clear_context)
# Converse method
self.bus.on('skill.converse.response', self.handle_converse_response)
self.bus.on('skill.converse.error', self.handle_converse_error)
self.bus.on('mycroft.speech.recognition.unknown', self.reset_converse)
self.bus.on('mycroft.skills.loaded', self.update_skill_name_dict)
def add_active_skill_handler(message):
self.add_active_skill(message.data['skill_id'])
self.bus.on('active_skill_request', add_active_skill_handler)
self.active_skills = [] # [skill_id , timestamp]
self.converse_timeout = 5 # minutes to prune active_skills
self.waiting_for_converse = False
self.converse_result = False
self.converse_skill_id = ""
def update_skill_name_dict(self, message):
"""
Messagebus handler, updates dictionary of if to skill name
conversions.
"""
self.skill_names[message.data['id']] = message.data['name']
def get_skill_name(self, skill_id):
""" Get skill name from skill ID.
Args:
skill_id: a skill id as encoded in Intent handlers.
Returns:
(str) Skill name or the skill id if the skill wasn't found
"""
return self.skill_names.get(skill_id, skill_id)
def reset_converse(self, message):
"""Let skills know there was a problem with speech recognition"""
lang = message.data.get('lang', "en-us")
for skill in self.active_skills:
self.do_converse(None, skill[0], lang)
def do_converse(self, utterances, skill_id, lang):
self.waiting_for_converse = True
self.converse_result = False
self.converse_skill_id = skill_id
self.bus.emit(
Message("skill.converse.request", {
"skill_id": skill_id,
"utterances": utterances,
"lang": lang
}))
start_time = time.time()
t = 0
while self.waiting_for_converse and t < 5:
t = time.time() - start_time
time.sleep(0.1)
self.waiting_for_converse = False
self.converse_skill_id = ""
return self.converse_result
def handle_converse_error(self, message):
skill_id = message.data["skill_id"]
if message.data["error"] == "skill id does not exist":
self.remove_active_skill(skill_id)
if skill_id == self.converse_skill_id:
self.converse_result = False
self.waiting_for_converse = False
def handle_converse_response(self, message):
skill_id = message.data["skill_id"]
if skill_id == self.converse_skill_id:
self.converse_result = message.data.get("result", False)
self.waiting_for_converse = False
def remove_active_skill(self, skill_id):
for skill in self.active_skills:
if skill[0] == skill_id:
self.active_skills.remove(skill)
def add_active_skill(self, skill_id):
# search the list for an existing entry that already contains it
# and remove that reference
self.remove_active_skill(skill_id)
# add skill with timestamp to start of skill_list
self.active_skills.insert(0, [skill_id, time.time()])
def update_context(self, intent):
""" Updates context with keyword from the intent.
NOTE: This method currently won't handle one_of intent keywords
since it's not using quite the same format as other intent
keywords. This is under investigation in adapt, PR pending.
Args:
intent: Intent to scan for keywords
"""
for tag in intent['__tags__']:
if 'entities' not in tag:
continue
context_entity = tag['entities'][0]
if self.context_greedy:
self.context_manager.inject_context(context_entity)
elif context_entity['data'][0][1] in self.context_keywords:
self.context_manager.inject_context(context_entity)
def send_metrics(self, intent, context, stopwatch):
"""
Send timing metrics to the backend.
NOTE: This only applies to those with Opt In.
"""
ident = context['ident'] if context else None
if intent:
# Recreate skill name from skill id
parts = intent.get('intent_type', '').split(':')
intent_type = self.get_skill_name(parts[0])
if len(parts) > 1:
intent_type = ':'.join([intent_type] + parts[1:])
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': intent_type})
else:
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': 'intent_failure'})
def handle_utterance(self, message):
""" Main entrypoint for handling user utterances with Mycroft skills
Monitor the messagebus for 'recognizer_loop:utterance', typically
generated by a spoken interaction but potentially also from a CLI
or other method of injecting a 'user utterance' into the system.
Utterances then work through this sequence to be handled:
1) Active skills attempt to handle using converse()
2) Adapt intent handlers
3) Padatious intent handlers
4) Other fallbacks
Args:
message (Message): The messagebus data
"""
try:
# Get language of the utterance
lang = message.data.get('lang', "en-us")
utterances = message.data.get('utterances', '')
stopwatch = Stopwatch()
with stopwatch:
# Give active skills an opportunity to handle the utterance
converse = self._converse(utterances, lang)
if not converse:
# No conversation, use intent system to handle utterance
intent = self._adapt_intent_match(utterances, lang)
padatious_intent = PadatiousService.instance.calc_intent(
utterances[0])
if converse:
# Report that converse handled the intent and return
ident = message.context['ident'] if message.context else None
report_timing(ident, 'intent_service', stopwatch,
{'intent_type': 'converse'})
return
elif intent and not (padatious_intent
and padatious_intent.conf >= 0.95):
# Send the message to the Adapt intent's handler unless
# Padatious is REALLY sure it was directed at it instead.
reply = message.reply(intent.get('intent_type'), intent)
else:
# Allow fallback system to handle utterance
# NOTE: Padatious intents are handled this way, too
reply = message.reply('intent_failure', {
'utterance': utterances[0],
'lang': lang
})
self.bus.emit(reply)
self.send_metrics(intent, message.context, stopwatch)
except Exception as e:
LOG.exception(e)
def _converse(self, utterances, lang):
""" Give active skills a chance at the utterance
Args:
utterances (list): list of utterances
lang (string): 4 letter ISO language code
Returns:
bool: True if converse handled it, False if no skill processes it
"""
# check for conversation time-out
self.active_skills = [
skill for skill in self.active_skills
if time.time() - skill[1] <= self.converse_timeout * 60
]
# check if any skill wants to handle utterance
for skill in self.active_skills:
if self.do_converse(utterances, skill[0], lang):
# update timestamp, or there will be a timeout where
# intent stops conversing whether its being used or not
self.add_active_skill(skill[0])
return True
return False
def _adapt_intent_match(self, utterances, lang):
""" Run the Adapt engine to search for an matching intent
Args:
utterances (list): list of utterances
lang (string): 4 letter ISO language code
Returns:
Intent structure, or None if no match was found.
"""
best_intent = None
for utterance in utterances:
try:
# normalize() changes "it's a boy" to "it is boy", etc.
best_intent = next(
self.engine.determine_intent(
normalize(utterance, lang),
100,
include_tags=True,
context_manager=self.context_manager))
# TODO - Should Adapt handle this?
best_intent['utterance'] = utterance
except StopIteration:
# don't show error in log
continue
except Exception as e:
LOG.exception(e)
continue
if best_intent and best_intent.get('confidence', 0.0) > 0.0:
self.update_context(best_intent)
# update active skills
skill_id = best_intent['intent_type'].split(":")[0]
self.add_active_skill(skill_id)
# adapt doesn't handle context injection for one_of keywords
# correctly. Workaround this issue if possible.
try:
best_intent = workaround_one_of_context(best_intent)
except LookupError:
LOG.error('Error during workaround_one_of_context')
return best_intent
def handle_register_vocab(self, message):
start_concept = message.data.get('start')
end_concept = message.data.get('end')
regex_str = message.data.get('regex')
alias_of = message.data.get('alias_of')
if regex_str:
self.engine.register_regex_entity(regex_str)
else:
self.engine.register_entity(start_concept,
end_concept,
alias_of=alias_of)
def handle_register_intent(self, message):
intent = open_intent_envelope(message)
self.engine.register_intent_parser(intent)
def handle_detach_intent(self, message):
intent_name = message.data.get('intent_name')
new_parsers = [
p for p in self.engine.intent_parsers if p.name != intent_name
]
self.engine.intent_parsers = new_parsers
def handle_detach_skill(self, message):
skill_id = message.data.get('skill_id')
new_parsers = [
p for p in self.engine.intent_parsers
if not p.name.startswith(skill_id)
]
self.engine.intent_parsers = new_parsers
def handle_add_context(self, message):
""" Add context
Args:
message: data contains the 'context' item to add
optionally can include 'word' to be injected as
an alias for the context item.
"""
entity = {'confidence': 1.0}
context = message.data.get('context')
word = message.data.get('word') or ''
origin = message.data.get('origin') or ''
# if not a string type try creating a string from it
if not isinstance(word, str):
word = str(word)
entity['data'] = [(word, context)]
entity['match'] = word
entity['key'] = word
entity['origin'] = origin
self.context_manager.inject_context(entity)
def handle_remove_context(self, message):
""" Remove specific context
Args:
message: data contains the 'context' item to remove
"""
context = message.data.get('context')
if context:
self.context_manager.remove_context(context)
def handle_clear_context(self, message):
""" Clears all keywords from context """
self.context_manager.clear_context()
"third eye blind", "the who", "the clash", "john mayer", "kings of leon",
"adelle"
]
for a in artists:
engine.register_entity(a, "Artist")
music_verbs = ["listen", "hear", "play"]
for mv in music_verbs:
engine.register_entity(mv, "MusicVerb")
music_keywords = ["songs", "music"]
for mk in music_keywords:
engine.register_entity(mk, "MusicKeyword")
music_intent = IntentBuilder("MusicIntent")\
.require("MusicVerb")\
.optionally("MusicKeyword")\
.optionally("Artist")\
.build()
engine.register_intent_parser(weather_intent)
engine.register_intent_parser(music_intent)
if __name__ == "__main__":
for intent in engine.determine_intent(' '.join(sys.argv[1:])):
if intent and intent.get('confidence') > 0:
print(json.dumps(intent, indent=4))
