Overview

This script is part of a Rasa project, which is used for building conversational AI, like chatbots. The code defines an action that the bot can perform, specifically extracting information about food from what a user has said.

Step-by-Step Explanation

1. Import Statements

from typing import Any, Text, Dict, List
from rasa_sdk import Action, Tracker
from rasa_sdk.executor import CollectingDispatcher

• from typing import Any, Text, Dict, List: This line is importing some helpful tools that let us define the type of data we are using. Think of it like labelling different types of boxes for organizing items.
  • Any means any type of data.
  • Text represents strings (like words or sentences).
  • Dict is for dictionaries, which store pairs of keys and values (like a mini database).
  • List is for lists, which are collections of items.
• from rasa_sdk import Action, Tracker: This line imports specific classes from the Rasa framework.
  • Action is a blueprint for creating actions that your bot can take.
  • Tracker helps keep track of the conversation history and what the user has said.
• from rasa_sdk.executor import CollectingDispatcher: This imports the CollectingDispatcher, which is used to send messages back to the user.

2. Class Definition

class ExtractFoodEntity(Action):

• Here, we are defining a new class called ExtractFoodEntity which is a type of Action. A class is like a blueprint for creating something. In this case, it’s a blueprint for an action that the bot can perform.

3. Name Method

def name(self) -> Text:
    return "action_extract_food_entity"

• def name(self) -> Text:: This defines a method called name which returns the name of the action as a string.
• return "action_extract_food_entity": This line specifies the action's name, which is used internally by Rasa to identify it.

4. Run Method

def run(self, dispatcher: CollectingDispatcher,
        tracker: Tracker,
        domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:

• def run(self, dispatcher, tracker, domain):: This defines a method called run, which contains the logic for what this action does.
  • dispatcher: This is used to send messages back to the user.
  • tracker: This keeps track of the conversation and retrieves information.
  • domain: This contains the context for the bot, such as available intents and entities.

5. Extracting the Food Entity

food_entity = next(tracker.get_latest_entity_values('food'), None)