Rasa Chatbot Framework: Customizing the Session Start Experience

When building conversational AI solutions with Rasa, you may want to customize the session start experience to suit your specific requirements. In this article, we will explore how to do this by customizing the `ActionSessionStart` class in Rasa 2.4.

By default, when a user initiates a conversation with the `/session_start` action, Rasa sends an empty response back to the user. However, you may want to send a specific message to welcome the user and set the tone for the conversation. To achieve this, we will create a custom `ActionSessionStart` class that extends the default behavior.

Here is an example of how you can customize the `ActionSessionStart` class:

```

from rasa_sdk import Action

from rasa_sdk.executor import CollectingDispatcher

class ActionSessionStart(Action):

def name(self):

return 'action_session_start'

def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict):

dispatcher.utter_message(text='Bonjour Matthieu, bienvenu dans cette nouvelle session en ma compagnie !')

```

In this example, we have created a custom `ActionSessionStart` class that sends a welcome message to the user when the `/session_start` action is triggered.

Another important consideration is handling the events that occur during the conversation. By default, Rasa will append an `action_listen` event after the session start event. However, if you want to collect slots from the user before responding, you may need to add additional logic to handle this.

For example, you can use the following code to collect slots containing the user's name and phone number:

```

def fetch_slots(tracker: Tracker) -> List:

slots = ''

for slot in tracker.latest_message.get('slots'):

if slot''key'' == 'user_name' or slot''key'' == 'phone_number':

slots.append(SlotSet(key=slot''key'', value=slot''value''))

return slots

```

In this example, we are using the `fetch_slots` function to collect slots containing the user's name and phone number. You can then use these slots to personalize your responses and provide a more tailored experience for the user.

By customizing the `ActionSessionStart` class and handling events accordingly, you can create a more engaging and personalized conversation experience with Rasa 2.4. Whether you're building a chatbot for customer support, marketing, or another use case, this flexibility is essential for creating effective conversational AI solutions.

Rasa chatbot framework