Rasa SDK

These docs are for version 1.x of Rasa Open Source.

User Guide

• Installation
• Tutorial: Rasa Basics
• Tutorial: Building Assistants
• Command Line Interface
• Architecture
• Messaging and Voice Channels
• Evaluating Models
• Validate Data
• Configuring the HTTP API
• Deploying your Rasa Assistant
• Cloud Storage

NLU

• About
• Using NLU Only
• Training Data Format
• Choosing a Pipeline
• Language Support
• Entity Extraction
• Components

Core

• About
• Stories
• Domains
• Responses
• Actions
• Policies
• Slots
• Forms
• Retrieval Actions
• Interactive Learning
• Fallback Actions
• Knowledge Base Actions

Conversation Design

• Dialogue Elements
• Small Talk
• Completing Tasks
• Guiding Users

API Reference

• Action Server
• HTTP API
• Jupyter Notebooks
• Agent
• Custom NLU Components
• Rasa SDK
• Events
• Tracker
• Tracker Stores
• Event Brokers
• Lock Stores
• Training Data Importers
• Featurization of Conversations
• Migration Guide
• Rasa OSS Change Log

Migrate from (beta)

• Dialogflow
• Wit.ai
• LUIS
• IBM Watson

Reference

• Glossary

Rasa SDK

Rasa SDK provides the tools you need to write custom actions in python.

Installation

Use pip to install rasa-sdk on your action server.

pip install rasa-sdk

Running the Action Server

If you have rasa installed, run this command to start your action server:

rasa run actions

Otherwise, if you do not have rasa installed, run this command:

python -m rasa_sdk --actions actions

You can verify that the action server is up and running with the command:

curl http://localhost:5055/health

You can get the list of registered custom actions with the command:

curl http://localhost:5055/actions

The file that contains your custom actions should be called actions.py. Alternatively, you can use a package directory called actions or else manually specify an actions module or package with the --actions flag.

Actions

The Action class is the base class for any custom action. It has two methods that both need to be overwritten, name() and run().

from rasa_sdk import Action
from rasa_sdk.events import SlotSet

class ActionCheckRestaurants(Action):
   def name(self) -> Text:
      return "action_check_restaurants"

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

cuisine = tracker.get_slot('cuisine')
      q = "select * from restaurants where cuisine='{0}' limit 1".format(cuisine)
      result = db.query(q)

return [SlotSet("matches", result if result is not None else [])]

Details of the run() method:

Execute the side effects of this action.

Parameters

• dispatcher – used to send messages back to the user.
• tracker – the state tracker for the current user.
• domain – the bot’s domain

Returns a dictionary of rasa_sdk.events.Event instances returned through the endpoint.

Customising the session start action

The default behaviour of the session start action is to take all existing slots and to carry them over into the next session.

from typing import Text, List, Dict, Any

from rasa_sdk import Action, Tracker
from rasa_sdk.events import SlotSet, SessionStarted, ActionExecuted, EventType
from rasa_sdk.executor import CollectingDispatcher

class ActionSessionStart(Action):
    def name(self) -> Text:
        return "action_session_start"

async def run(
        self,
        dispatcher: CollectingDispatcher,
        tracker: Tracker,
        domain: Dict[Text, Any],
    ) -> List[EventType]:
        events = [SessionStarted()]
        return events

Events

An action’s run() method returns a list of events.

Tracker

The rasa_sdk.Tracker lets you access the bot’s memory in your custom actions. This allows you to get information about past events and the current state of the conversation through Tracker attributes and methods.

The following are available as attributes of a Tracker object:

• sender_id
• slots
• latest_message
• events
• active_form
• latest_action_name

You can get the value of a slot by using tracker.get_slot(key).