def log_bot_utterances_on_tracker(tracker: DialogueStateTracker,
dispatcher: Dispatcher) -> None:
if dispatcher.latest_bot_messages:
for m in dispatcher.latest_bot_messages:
bot_utterance = BotUttered(text=m.text, data=m.data)
logger.debug("Bot utterance '{}'".format(bot_utterance))
tracker.update(bot_utterance)
dispatcher.latest_bot_messages = []
def test_session_start(default_domain: Domain):
tracker = DialogueStateTracker("default", default_domain.slots)
# the retrieved tracker should be empty
assert len(tracker.events) == 0
# add a SessionStarted event
tracker.update(SessionStarted())
# tracker has one event
assert len(tracker.events) == 1
def _collect_action_executed_predictions(
processor: "MessageProcessor",
partial_tracker: DialogueStateTracker,
event: ActionExecuted,
fail_on_prediction_errors: bool,
circuit_breaker_tripped: bool,
) -> Tuple[EvaluationStore, Optional[Text], Optional[float]]:
from rasa.core.policies.form_policy import FormPolicy
action_executed_eval_store = EvaluationStore()
gold = event.action_name
if circuit_breaker_tripped:
predicted = "circuit breaker tripped"
policy = None
confidence = None
else:
action, policy, confidence = processor.predict_next_action(
partial_tracker)
predicted = action.name()
if policy and predicted != gold and FormPolicy.__name__ in policy:
# FormPolicy predicted wrong action
# but it might be Ok if form action is rejected
_emulate_form_rejection(processor, partial_tracker)
# try again
action, policy, confidence = processor.predict_next_action(
partial_tracker)
predicted = action.name()
action_executed_eval_store.add_to_store(action_predictions=predicted,
action_targets=gold)
if action_executed_eval_store.has_prediction_target_mismatch():
partial_tracker.update(
WronglyPredictedAction(gold, predicted, event.policy,
event.confidence, event.timestamp))
if fail_on_prediction_errors:
error_msg = ("Model predicted a wrong action. Failed Story: "
"\n\n{}".format(partial_tracker.export_stories()))
if FormPolicy.__name__ in policy:
error_msg += ("FormAction is not run during "
"evaluation therefore it is impossible to know "
"if validation failed or this story is wrong. "
"If the story is correct, add it to the "
"training stories and retrain.")
raise ValueError(error_msg)
else:
partial_tracker.update(event)
return action_executed_eval_store, policy, confidence
def test_traveling_back_in_time(default_domain):
tracker = DialogueStateTracker("default", default_domain.slots)
# the retrieved tracker should be empty
assert len(tracker.events) == 0
intent = {"name": "greet", "confidence": 1.0}
tracker.update(ActionExecuted(ACTION_LISTEN_NAME))
tracker.update(UserUttered("/greet", intent, []))
import time
time.sleep(1)
time_for_timemachine = time.time()
time.sleep(1)
tracker.update(ActionExecuted("my_action"))
tracker.update(ActionExecuted(ACTION_LISTEN_NAME))
# Expecting count of 4:
# +3 executed actions
# +1 final state
assert tracker.latest_action_name == ACTION_LISTEN_NAME
assert len(tracker.events) == 4
assert len(list(tracker.generate_all_prior_trackers())) == 4
tracker = tracker.travel_back_in_time(time_for_timemachine)
# Expecting count of 2:
# +1 executed actions
# +1 final state
assert tracker.latest_action_name == ACTION_LISTEN_NAME
assert len(tracker.events) == 2
assert len(list(tracker.generate_all_prior_trackers())) == 2
def prepare(self, text):
tracker = DialogueStateTracker("default", self.domain.slots)
parse_data = self.interpreter.parse(text)
# print(parse_data)
tracker.update(
UserUttered(text, parse_data["intent"], parse_data["entities"],
parse_data))
# store all entities as slots
for e in self.domain.slots_for_entities(parse_data["entities"]):
tracker.update(e)
print("Logged UserUtterance - "
"tracker now has {} events".format(len(tracker.events)))
# print(tracker.latest_message)
return tracker
def test_tracker_entity_retrieval(default_domain):
tracker = DialogueStateTracker("default", default_domain.slots)
# the retrieved tracker should be empty
assert len(tracker.events) == 0
assert list(tracker.get_latest_entity_values("entity_name")) == []
intent = {"name": "greet", "confidence": 1.0}
tracker.update(UserUttered("/greet", intent, [{
"start": 1,
"end": 5,
"value": "greet",
"entity": "entity_name",
"extractor": "manual"
}]))
assert list(tracker.get_latest_entity_values("entity_name")) == ["greet"]
assert list(tracker.get_latest_entity_values("unknown")) == []
async def cancel_reminder_and_check(
tracker: DialogueStateTracker,
default_processor: MessageProcessor,
reminder_canceled_event: ReminderCancelled,
num_jobs_before: int,
num_jobs_after: int,
) -> None:
# cancel the sixth reminder
tracker.update(reminder_canceled_event)
# check that the jobs were added
assert len((await jobs.scheduler()).get_jobs()) == num_jobs_before
await default_processor._cancel_reminders(tracker.events, tracker)
# check that only one job was removed
assert len((await jobs.scheduler()).get_jobs()) == num_jobs_after
def _emulate_form_rejection(processor: "MessageProcessor",
partial_tracker: DialogueStateTracker) -> None:
from rasa.core.policies.form_policy import FormPolicy
from rasa.core.events import ActionExecutionRejected
if partial_tracker.active_form.get("name"):
for p in processor.policy_ensemble.policies:
if isinstance(p, FormPolicy):
# emulate form rejection
partial_tracker.update(
ActionExecutionRejected(
partial_tracker.active_form["name"]))
# check if unhappy path is covered by the train stories
if not p.state_is_unhappy(partial_tracker, processor.domain):
# this state is not covered by the stories
del partial_tracker.events[-1]
partial_tracker.active_form["rejected"] = False
async def trigger_external_user_uttered(
self,
intent_name: Text,
entities: Optional[Union[List[Dict[Text, Any]], Dict[Text, Text]]],
tracker: DialogueStateTracker,
output_channel: OutputChannel,
) -> None:
"""Triggers an external message.
Triggers an external message (like a user message, but invisible;
used, e.g., by a reminder or the trigger_intent endpoint).
Args:
intent_name: Name of the intent to be triggered.
entities: Entities to be passed on.
tracker: The tracker to which the event should be added.
output_channel: The output channel.
"""
if isinstance(entities, list):
entity_list = entities
elif isinstance(entities, dict):
# Allow for a short-hand notation {"ent1": "val1", "ent2": "val2", ...}.
# Useful if properties like 'start', 'end', or 'extractor' are not given,
# e.g. for external events.
entity_list = [
{"entity": ent, "value": val} for ent, val in entities.items()
]
elif not entities:
entity_list = []
else:
rasa.shared.utils.io.raise_warning(
f"Invalid entity specification: {entities}. Assuming no entities."
)
entity_list = []
# Set the new event's input channel to the latest input channel, so
# that we don't lose this property.
input_channel = tracker.get_latest_input_channel()
tracker.update(
UserUttered.create_external(intent_name, entity_list, input_channel)
)
await self._predict_and_execute_next_action(output_channel, tracker)
# save tracker state to continue conversation from this state
self._save_tracker(tracker)
def _collect_user_uttered_predictions(
event: UserUttered,
partial_tracker: DialogueStateTracker,
fail_on_prediction_errors: bool,
) -> EvaluationStore:
user_uttered_eval_store = EvaluationStore()
intent_gold = event.parse_data.get("true_intent")
predicted_intent = event.parse_data.get("intent", {}).get("name")
if not predicted_intent:
predicted_intent = [None]
user_uttered_eval_store.add_to_store(
intent_predictions=predicted_intent, intent_targets=intent_gold
)
entity_gold = event.parse_data.get("true_entities")
predicted_entities = event.parse_data.get("entities")
if entity_gold or predicted_entities:
user_uttered_eval_store.add_to_store(
entity_targets=_clean_entity_results(event.text, entity_gold),
entity_predictions=_clean_entity_results(event.text, predicted_entities),
)
if user_uttered_eval_store.has_prediction_target_mismatch():
partial_tracker.update(
WronglyClassifiedUserUtterance(event, user_uttered_eval_store)
)
if fail_on_prediction_errors:
raise ValueError(
"NLU model predicted a wrong intent. Failed Story:"
" \n\n{}".format(partial_tracker.export_stories())
)
else:
end_to_end_user_utterance = EndToEndUserUtterance(
event.text, event.intent, event.entities
)
partial_tracker.update(end_to_end_user_utterance)
return user_uttered_eval_store
def _collect_user_uttered_predictions(
event: UserUttered,
predicted: Dict[Text, Any],
partial_tracker: DialogueStateTracker,
fail_on_prediction_errors: bool,
) -> EvaluationStore:
user_uttered_eval_store = EvaluationStore()
intent_gold = event.intent.get("name")
predicted_intent = predicted.get(INTENT, {}).get("name")
user_uttered_eval_store.add_to_store(
intent_predictions=[predicted_intent], intent_targets=[intent_gold]
)
entity_gold = event.entities
predicted_entities = predicted.get(ENTITIES)
if entity_gold or predicted_entities:
user_uttered_eval_store.add_to_store(
entity_targets=_clean_entity_results(event.text, entity_gold),
entity_predictions=_clean_entity_results(event.text, predicted_entities),
)
if user_uttered_eval_store.has_prediction_target_mismatch():
partial_tracker.update(
WronglyClassifiedUserUtterance(event, user_uttered_eval_store)
)
if fail_on_prediction_errors:
raise ValueError(
"NLU model predicted a wrong intent. Failed Story:"
" \n\n{}".format(
YAMLStoryWriter().dumps(partial_tracker.as_story().story_steps)
)
)
else:
end_to_end_user_utterance = EndToEndUserUtterance(
event.text, event.intent, event.entities
)
partial_tracker.update(end_to_end_user_utterance)
return user_uttered_eval_store
def test_get_latest_entity_values(entities: List[Dict[Text, Any]],
expected_values: List[Text],
default_domain: Domain):
entity_type = entities[0].get("entity")
entity_role = entities[0].get("role")
entity_group = entities[0].get("group")
tracker = DialogueStateTracker("default", default_domain.slots)
# the retrieved tracker should be empty
assert len(tracker.events) == 0
assert list(tracker.get_latest_entity_values(entity_type)) == []
intent = {"name": "greet", "confidence": 1.0}
tracker.update(UserUttered("/greet", intent, entities))
assert (list(
tracker.get_latest_entity_values(
entity_type, entity_role=entity_role,
entity_group=entity_group)) == expected_values)
assert list(tracker.get_latest_entity_values("unknown")) == []
def test_missing_classes_filled_correctly(
self, default_domain, trackers, tracker, featurizer, priority
):
# Pretend that a couple of classes are missing and check that
# those classes are predicted as 0, while the other class
# probabilities are predicted normally.
policy = self.create_policy(featurizer=featurizer, priority=priority, cv=None)
classes = [1, 3]
new_trackers = []
for tr in trackers:
new_tracker = DialogueStateTracker(
UserMessage.DEFAULT_SENDER_ID, default_domain.slots
)
for e in tr.applied_events():
if isinstance(e, ActionExecuted):
new_action = default_domain.action_for_index(
np.random.choice(classes), action_endpoint=None
).name()
new_tracker.update(ActionExecuted(new_action))
else:
new_tracker.update(e)
new_trackers.append(new_tracker)
policy.train(
new_trackers, domain=default_domain, interpreter=RegexInterpreter()
)
predicted_probabilities = policy.predict_action_probabilities(
tracker, default_domain
)
assert len(predicted_probabilities) == default_domain.num_actions
assert np.allclose(sum(predicted_probabilities), 1.0)
for i, prob in enumerate(predicted_probabilities):
if i in classes:
assert prob >= 0.0
else:
assert prob == 0.0
def predict_action_probabilities(self, tracker: DialogueStateTracker,
domain: Domain) -> List[float]:
"""Predicts the corresponding form action if there is an active form"""
result = [0.0] * domain.num_actions
if tracker.active_form.get('name'):
logger.debug("There is an active form '{}'"
"".format(tracker.active_form['name']))
if tracker.latest_action_name == ACTION_LISTEN_NAME:
# predict form action after user utterance
if tracker.active_form.get('rejected'):
# since it is assumed that training stories contain
# only unhappy paths, notify the form that
# it should not be validated if predicted by other policy
tracker_as_states = self.featurizer.prediction_states(
[tracker], domain)
states = tracker_as_states[0]
memorized_form = self.recall(states, tracker, domain)
if memorized_form == tracker.active_form['name']:
logger.debug("There is a memorized tracker state {}, "
"added `FormValidation(False)` event"
"".format(self._modified_states(states)))
tracker.update(FormValidation(False))
return result
idx = domain.index_for_action(tracker.active_form['name'])
result[idx] = 1.0
elif tracker.latest_action_name == tracker.active_form.get('name'):
# predict action_listen after form action
idx = domain.index_for_action(ACTION_LISTEN_NAME)
result[idx] = 1.0
else:
logger.debug("There is no active form")
return result
async def _handle_message_with_tracker(
self, message: UserMessage, tracker: DialogueStateTracker) -> None:
if message.parse_data:
parse_data = message.parse_data
else:
parse_data = await self._parse_message(message)
# don't ever directly mutate the tracker
# - instead pass its events to log
tracker.update(
UserUttered(message.text,
parse_data["intent"],
parse_data["entities"],
parse_data,
input_channel=message.input_channel,
message_id=message.message_id))
# store all entities as slots
for e in self.domain.slots_for_entities(parse_data["entities"]):
tracker.update(e)
logger.debug("Logged UserUtterance - "
"tracker now has {} events".format(len(tracker.events)))
def test_tracker_update_slots_with_entity(default_domain):
tracker = DialogueStateTracker("default", default_domain.slots)
test_entity = default_domain.entities[0]
expected_slot_value = "test user"
intent = {"name": "greet", "confidence": 1.0}
tracker.update(
UserUttered(
"/greet",
intent,
[{
"start": 1,
"end": 5,
"value": expected_slot_value,
"entity": test_entity,
"extractor": "manual",
}],
),
default_domain,
)
assert tracker.get_slot(test_entity) == expected_slot_value
async def test_action_session_start_with_slots(
default_channel: CollectingOutputChannel,
template_nlg: TemplatedNaturalLanguageGenerator,
template_sender_tracker: DialogueStateTracker,
default_domain: Domain,
session_config: SessionConfig,
expected_events: List[Event],
):
# set a few slots on tracker
slot_set_event_1 = SlotSet("my_slot", "value")
slot_set_event_2 = SlotSet("another-slot", "value2")
for event in [slot_set_event_1, slot_set_event_2]:
template_sender_tracker.update(event)
default_domain.session_config = session_config
events = await ActionSessionStart().run(default_channel, template_nlg,
template_sender_tracker,
default_domain)
assert events == expected_events
# make sure that the list of events has ascending timestamps
assert sorted(events, key=lambda x: x.timestamp) == events
def test_session_start_is_not_serialised(default_domain: Domain):
tracker = DialogueStateTracker("default", default_domain.slots)
# the retrieved tracker should be empty
assert len(tracker.events) == 0
# add SlotSet event
tracker.update(SlotSet("slot", "value"))
# add the two SessionStarted events and a user event
tracker.update(ActionExecuted(ACTION_SESSION_START_NAME))
tracker.update(SessionStarted())
tracker.update(UserUttered("say something"))
# make sure session start is not serialised
story = Story.from_events(tracker.events, "some-story01")
expected = """## some-story01
- slot{"slot": "value"}
* say something
"""
assert story.as_story_string(flat=True) == expected
def _find_action_from_rules(
self, tracker: DialogueStateTracker, domain: Domain
) -> Optional[Text]:
tracker_as_states = self.featurizer.prediction_states([tracker], domain)
states = tracker_as_states[0]
logger.debug(f"Current tracker state: {states}")
rule_keys = self._get_possible_keys(self.lookup[RULES], states)
predicted_action_name = None
best_rule_key = ""
if rule_keys:
# if there are several rules,
# it should mean that some rule is a subset of another rule
# therefore we pick a rule of maximum length
best_rule_key = max(rule_keys, key=len)
predicted_action_name = self.lookup[RULES].get(best_rule_key)
active_loop_name = tracker.active_loop_name
if active_loop_name:
# find rules for unhappy path of the loop
loop_unhappy_keys = self._get_possible_keys(
self.lookup[RULES_FOR_LOOP_UNHAPPY_PATH], states
)
# there could be several unhappy path conditions
unhappy_path_conditions = [
self.lookup[RULES_FOR_LOOP_UNHAPPY_PATH].get(key)
for key in loop_unhappy_keys
]
# Check if a rule that predicted action_listen
# was applied inside the loop.
# Rules might not explicitly switch back to the loop.
# Hence, we have to take care of that.
predicted_listen_from_general_rule = (
predicted_action_name == ACTION_LISTEN_NAME
and not get_active_loop_name(self._rule_key_to_state(best_rule_key)[-1])
)
if predicted_listen_from_general_rule:
if DO_NOT_PREDICT_LOOP_ACTION not in unhappy_path_conditions:
# negative rules don't contain a key that corresponds to
# the fact that active_loop shouldn't be predicted
logger.debug(
f"Predicted loop '{active_loop_name}' by overwriting "
f"'{ACTION_LISTEN_NAME}' predicted by general rule."
)
return active_loop_name
# do not predict anything
predicted_action_name = None
if DO_NOT_VALIDATE_LOOP in unhappy_path_conditions:
logger.debug("Added `FormValidation(False)` event.")
tracker.update(FormValidation(False))
if predicted_action_name is not None:
logger.debug(
f"There is a rule for the next action '{predicted_action_name}'."
)
else:
logger.debug("There is no applicable rule.")
return predicted_action_name
def predict_action_probabilities(
self,
tracker: DialogueStateTracker,
domain: Domain,
interpreter: NaturalLanguageInterpreter = RegexInterpreter(),
**kwargs: Any,
) -> List[float]:
"""Predicts the next action the bot should take after seeing the tracker.
Returns the list of probabilities for the next actions.
If memorized action was found returns 1 for its index,
else returns 0 for all actions.
"""
result = self._default_predictions(domain)
if not self.is_enabled:
return result
# Rasa Open Source default actions overrule anything. If users want to achieve
# the same, they need to a rule or make sure that their form rejects
# accordingly.
rasa_default_action_name = _should_run_rasa_default_action(tracker)
if rasa_default_action_name:
result[domain.index_for_action(rasa_default_action_name)] = 1
return result
active_form_name = tracker.active_form_name()
active_form_rejected = tracker.active_loop.get("rejected")
should_predict_form = (active_form_name and not active_form_rejected
and
tracker.latest_action_name != active_form_name)
should_predict_listen = (active_form_name and not active_form_rejected
and tracker.latest_action_name
== active_form_name)
# A form has priority over any other rule.
# The rules or any other prediction will be applied only if a form was rejected.
# If we are in a form, and the form didn't run previously or rejected, we can
# simply force predict the form.
if should_predict_form:
logger.debug(f"Predicted form '{active_form_name}'.")
result[domain.index_for_action(active_form_name)] = 1
return result
# predict `action_listen` if form action was run successfully
if should_predict_listen:
logger.debug(
f"Predicted '{ACTION_LISTEN_NAME}' after form '{active_form_name}'."
)
result[domain.index_for_action(ACTION_LISTEN_NAME)] = 1
return result
possible_keys = set(self.lookup.keys())
tracker_as_states = self.featurizer.prediction_states([tracker],
domain)
states = tracker_as_states[0]
logger.debug(f"Current tracker state: {states}")
for i, state in enumerate(reversed(states)):
possible_keys = set(
filter(lambda _key: self._rule_is_good(_key, i, state),
possible_keys))
if possible_keys:
# TODO rethink that
key = max(possible_keys, key=len)
recalled = self.lookup.get(key)
if active_form_name:
# Check if a rule that predicted action_listen
# was applied inside the form.
# Rules might not explicitly switch back to the `Form`.
# Hence, we have to take care of that.
predicted_listen_from_general_rule = recalled is None or (
domain.action_names[recalled] == ACTION_LISTEN_NAME
and f"active_form_{active_form_name}" not in key)
if predicted_listen_from_general_rule:
logger.debug(f"Predicted form '{active_form_name}'.")
result[domain.index_for_action(active_form_name)] = 1
return result
# Since rule snippets inside the form contain only unhappy paths,
# notify the form that
# it was predicted after an answer to a different question and
# therefore it should not validate user input for requested slot
predicted_form_from_form_rule = (
domain.action_names[recalled] == active_form_name
and f"active_form_{active_form_name}" in key)
if predicted_form_from_form_rule:
logger.debug("Added `FormValidation(False)` event.")
tracker.update(FormValidation(False))
if recalled is not None:
logger.debug(f"There is a rule for next action "
f"'{domain.action_names[recalled]}'.")
result[recalled] = 1
else:
logger.debug("There is no applicable rule.")
return result
def test_revert_user_utterance_event(default_domain):
tracker = DialogueStateTracker("default", default_domain.slots)
# the retrieved tracker should be empty
assert len(tracker.events) == 0
intent1 = {"name": "greet", "confidence": 1.0}
tracker.update(ActionExecuted(ACTION_LISTEN_NAME))
tracker.update(UserUttered("/greet", intent1, []))
tracker.update(ActionExecuted("my_action_1"))
tracker.update(ActionExecuted(ACTION_LISTEN_NAME))
intent2 = {"name": "goodbye", "confidence": 1.0}
tracker.update(UserUttered("/goodbye", intent2, []))
tracker.update(ActionExecuted("my_action_2"))
tracker.update(ActionExecuted(ACTION_LISTEN_NAME))
# Expecting count of 6:
# +5 executed actions
# +1 final state
assert tracker.latest_action_name == ACTION_LISTEN_NAME
assert len(list(tracker.generate_all_prior_trackers())) == 6
tracker.update(UserUtteranceReverted())
# Expecting count of 3:
# +5 executed actions
# +1 final state
# -2 rewound actions associated with the /goodbye
# -1 rewound action from the listen right before /goodbye
assert tracker.latest_action_name == "my_action_1"
assert len(list(tracker.generate_all_prior_trackers())) == 3
dialogue = tracker.as_dialogue()
recovered = DialogueStateTracker("default", default_domain.slots)
recovered.recreate_from_dialogue(dialogue)
assert recovered.current_state() == tracker.current_state()
assert tracker.latest_action_name == "my_action_1"
assert len(list(tracker.generate_all_prior_trackers())) == 3
def _collect_action_executed_predictions(
processor: "MessageProcessor",
partial_tracker: DialogueStateTracker,
event: ActionExecuted,
fail_on_prediction_errors: bool,
circuit_breaker_tripped: bool,
) -> Tuple[EvaluationStore, Optional[Text], Optional[float]]:
from rasa.core.policies.form_policy import FormPolicy
action_executed_eval_store = EvaluationStore()
gold = event.action_name
if circuit_breaker_tripped:
predicted = "circuit breaker tripped"
policy = None
confidence = None
else:
action, policy, confidence = processor.predict_next_action(partial_tracker)
predicted = action.name()
if (
policy
and predicted != gold
and _form_might_have_been_rejected(
processor.domain, partial_tracker, predicted
)
):
# Wrong action was predicted,
# but it might be Ok if form action is rejected.
_emulate_form_rejection(partial_tracker)
# try again
action, policy, confidence = processor.predict_next_action(partial_tracker)
# Even if the prediction is also wrong, we don't have to undo the emulation
# of the action rejection as we know that the user explicitly specified
# that something else than the form was supposed to run.
predicted = action.name()
action_executed_eval_store.add_to_store(
action_predictions=[predicted], action_targets=[gold]
)
if action_executed_eval_store.has_prediction_target_mismatch():
partial_tracker.update(
WronglyPredictedAction(
gold, predicted, event.policy, event.confidence, event.timestamp
)
)
if fail_on_prediction_errors:
error_msg = (
"Model predicted a wrong action. Failed Story: "
"\n\n{}".format(
YAMLStoryWriter().dumps(partial_tracker.as_story().story_steps)
)
)
if FormPolicy.__name__ in policy:
error_msg += (
"FormAction is not run during "
"evaluation therefore it is impossible to know "
"if validation failed or this story is wrong. "
"If the story is correct, add it to the "
"training stories and retrain."
)
raise ValueError(error_msg)
else:
partial_tracker.update(event)
return action_executed_eval_store, policy, confidence
async def test_persist_form_story(tmpdir):
domain = Domain.load("data/test_domains/form.yml")
tracker = DialogueStateTracker("", domain.slots)
story = ("* greet\n"
" - utter_greet\n"
"* start_form\n"
" - some_form\n"
' - form{"name": "some_form"}\n'
"* default\n"
" - utter_default\n"
" - some_form\n"
"* stop\n"
" - utter_ask_continue\n"
"* affirm\n"
" - some_form\n"
"* stop\n"
" - utter_ask_continue\n"
" - action_listen\n"
"* form: inform\n"
" - some_form\n"
' - form{"name": null}\n'
"* goodbye\n"
" - utter_goodbye\n")
# simulate talking to the form
events = [
UserUttered(intent={"name": "greet"}),
ActionExecuted("utter_greet"),
ActionExecuted("action_listen"),
# start the form
UserUttered(intent={"name": "start_form"}),
ActionExecuted("some_form"),
Form("some_form"),
ActionExecuted("action_listen"),
# out of form input
UserUttered(intent={"name": "default"}),
ActionExecutionRejected("some_form"),
ActionExecuted("utter_default"),
ActionExecuted("some_form"),
ActionExecuted("action_listen"),
# out of form input
UserUttered(intent={"name": "stop"}),
ActionExecutionRejected("some_form"),
ActionExecuted("utter_ask_continue"),
ActionExecuted("action_listen"),
# out of form input but continue with the form
UserUttered(intent={"name": "affirm"}),
FormValidation(False),
ActionExecuted("some_form"),
ActionExecuted("action_listen"),
# out of form input
UserUttered(intent={"name": "stop"}),
ActionExecutionRejected("some_form"),
ActionExecuted("utter_ask_continue"),
ActionExecuted("action_listen"),
# form input
UserUttered(intent={"name": "inform"}),
FormValidation(True),
ActionExecuted("some_form"),
ActionExecuted("action_listen"),
Form(None),
UserUttered(intent={"name": "goodbye"}),
ActionExecuted("utter_goodbye"),
ActionExecuted("action_listen"),
]
[tracker.update(e) for e in events]
assert story in tracker.export_stories()
def _emulate_form_rejection(partial_tracker: DialogueStateTracker) -> None:
from rasa.core.events import ActionExecutionRejected
rejected_action_name: Text = partial_tracker.active_loop["name"]
partial_tracker.update(ActionExecutionRejected(rejected_action_name))