Version: 2.8.x

Default Actions

Default actions are actions that are built into the dialogue manager by default. Most of these are automatically predicted based on certain conversation situations. You may want to customize these to personalize your assistant.

Each of these actions have a default behavior, described in the sections below. In order to overwrite this default behavior, write a custom action whose name() method returns the same name as the default action:

class ActionRestart(Action):
def name(self) -> Text:
return "action_restart"
async def run(
self, dispatcher, tracker: Tracker, domain: Dict[Text, Any]
) -> List[Dict[Text, Any]]:
# custom behavior
return [...]

Add this action to the actions section of your domain file so your assistant knows to use the custom definition instead of the default one:

actions:
- action_restart
caution

After adding this action to your domain file, re-train your model with rasa train --force. Otherwise Rasa won't know you've changed anything and may skip re-training your dialogue model.

action_listen

This action is predicted to signal that the assistant should do nothing and wait for the next user input.

action_restart

This action resets the whole conversation history, including any slots that were set during it.

It can be triggered by the user in a conversation by sending a "/restart" message, if the RulePolicy is included in the model configuration. If you define an utter_restart response in your domain, this will be sent to the user as well.

action_session_start

This action starts a new conversation session, and is executed in the following situations:

  • at the beginning of each new conversation
  • after a user was inactive for a period defined by the session_expiration_time parameter in the domain's session configuration
  • when a user sends a "/session_start" message during a conversation

The action will reset the conversation tracker, but by default will not clear any slots that were set.

Customization

The default behavior of the session start action is to take all existing slots and to carry them over into the next session. Let's say you do not want to carry over all slots, but only a user's name and their phone number. To do that, you'd override the action_session_start with a custom action that might look like this:

from typing import Any, Text, Dict, List
from rasa_sdk import Action, Tracker
from rasa_sdk.events import SlotSet, SessionStarted, ActionExecuted, EventType
class ActionSessionStart(Action):
def name(self) -> Text:
return "action_session_start"
@staticmethod
def fetch_slots(tracker: Tracker) -> List[EventType]:
"""Collect slots that contain the user's name and phone number."""
slots = []
for key in ("name", "phone_number"):
value = tracker.get_slot(key)
if value is not None:
slots.append(SlotSet(key=key, value=value))
return slots
async def run(
self, dispatcher, tracker: Tracker, domain: Dict[Text, Any]
) -> List[Dict[Text, Any]]:
# the session should begin with a `session_started` event
events = [SessionStarted()]
# any slots that should be carried over should come after the
# `session_started` event
events.extend(self.fetch_slots(tracker))
# an `action_listen` should be added at the end as a user message follows
events.append(ActionExecuted("action_listen"))
return events

If you want to access the metadata which was sent with the user message which triggered the session start, you can access the special slot session_started_metadata:

from typing import Any, Text, Dict, List
from rasa_sdk import Action, Tracker
from rasa_sdk.events import SessionStarted, ActionExecuted
class ActionSessionStart(Action):
def name(self) -> Text:
return "action_session_start"
async def run(
self, dispatcher, tracker: Tracker, domain: Dict[Text, Any]
) -> List[Dict[Text, Any]]:
metadata = tracker.get_slot("session_started_metadata")
# Do something with the metadata
print(metadata)
# the session should begin with a `session_started` event and an `action_listen`
# as a user message follows
return [SessionStarted(), ActionExecuted("action_listen")]

action_default_fallback

This action undoes the last user-bot interaction and sends the utter_default response if it is defined. It is triggered by low action prediction confidence, if you have this fallback mechanism enabled.

action_deactivate_loop

This action deactivates the active loop and resets the requested slot. This is used when handling unhappy paths in forms.

note

If you wish to reset all slots, we recommend using a custom action that returns the AllSlotsReset event after form deactivation.

action_revert_fallback_events

Deprecated!

The action_revert_fallback_events action is deprecated since the 2.0 release. The action_two_stage_fallback action now handles all two-stage fallback behavior

action_two_stage_fallback

This is a fallback loop that can be used to handle low NLU confidence. Read more about handling low NLU confidence.

action_default_ask_affirmation

This action is used by the action_two_stage_fallback loop. It asks the user to confirm the intent of their message. This action can be customized to be more personalized to your specific use case.

action_default_ask_rephrase

This action is used by the action_two_stage_fallback loop if the user denies the intent action_default_ask_affirmation displays. It asks the user to rephrase their message.

action_back

This action undoes the last user-bot interaction. It can be triggered by the user by sending a "/back" message to the assistant if the RulePolicy is configured. |

Form Action

By default Rasa Open Source uses FormAction for processing any form logic. You can override this default action with a custom action by adding a custom action with the form's name to the domain. Overriding the default action for forms should only be used during the process of migrating from Rasa Open Source 1 to 2. In this case you can override the default action to instruct Rasa Open Source to use the deprecated FormAction which is part of the Rasa SDK.

action_unlikely_intent

Rasa Open Source triggers action_unlikely_intent via UnexpecTEDIntentPolicy. You can control how often this action is predicted by tuning the tolerance parameter of UnexpecTEDIntentPolicy.

Customization

You can customize your assistant's behaviour to configure what should happen once action_unlikely_intent is triggered. For example, as a follow up you can trigger a hand-off to a human agent with a rule:

- rule: trigger human handoff with action_unlikely_intent
steps:
- action: action_unlikely_intent
- action: ask_human_handoff
- intent: affirm
- action: trigger_human_handoff

Alternatively, you can also override it's behaviour as a custom action by adding action_unlikely_intent to the list of actions in the domain and implementing the custom behaviour:

class ActionUnlikelyIntent(Action):
def name(self) -> Text:
return "action_unlikely_intent"
async def run(
self, dispatcher, tracker: Tracker, domain: Dict[Text, Any],
) -> List[Dict[Text, Any]]:
# Implement custom logic here
return []
note

Since action_unlikely_intent can be triggered at any conversation step during inference, all policies which are trained on only story data, for example - TEDPolicy, UnexpecTEDIntentPolicy, MemoizationPolicy ignore its presence in the tracker when making a prediction. However, RulePolicy takes its presence into account so that conversation behaviour is customizable.

note

action_unlikely_intent cannot be included in the training stories. It can only be added to rules.