Bot Responses¶

If you want your bot to respond to user messages, you need to manage the bots responses. In the training data for your bot, your stories, you specify the actions your bot should execute. These actions can use utterances to send messages back to the user.

There are two ways to manage these utterances:

include your bot utterances in your domain file or
use an external service to generate the responses

Including the utterances in the domain¶

The default format is, to include the utterances into your domain file. This file then contains references to all your custom actions, available entities, slots and intents.

# all hashtags are comments :)
intents:
 - greet
 - default
 - goodbye
 - affirm
 - thank_you
 - change_bank_details
 - simple
 - hello
 - why
 - next_intent

entities:
 - name

slots:
  name:
    type: text

templates:
  utter_greet:
    - "hey there {name}!"  # {name} will be filled by slot (same name) or by custom action code
  utter_goodbye:
    - "goodbye 😢"
    - "bye bye 😢"   # multiple templates - bot will randomly pick one of them
  utter_default:
    - "default message"

actions:
  - utter_default
  - utter_greet
  - utter_goodbye

In this example domain file, the section templates contains the template the bot uses to send messages to the user.

If you want to change the text, or any other part of the bots response, you need to retrain the bot before these changes will be picked up.

More details about the format of these responses can be found in the documentation about the domain file format: Utterance templates.

Managing bot utterances using an external CMS¶

Retraining the bot, just to change the text copy can be suboptimal for some workflows. That’s why Core also allows you to outsource the response generation and separate it from the dialogue learning.

The bot will still learn to predict actions and to react to user input based on past dialogues, but the responses it sends back to the user are generated outside of Rasa Core.

If the bot wants to send a message to the user, it will call an external HTTP server with a POST request. To configure this endpoint, you need to create an endpoints.yml and pass it either to the run or server script. The content of the endpoints.yml should be

nlg:
  url: http://localhost:5055/nlg    # url of the nlg endpoint
  # you can also specify additional parameters, if you need them:
  # headers:
  #   my-custom-header: value
  # token: "my_authentication_token"    # will be passed as a get parameter
  # basic_auth:
  #   username: user
  #   password: pass
# example of redis external tracker store config
tracker_store:
  type: redis
  url: localhost
  port: 6379
  db: 0
  password: password
  record_exp: 30000
# example of mongoDB external tracker store config
#tracker_store:
  #type: mongod
  #url: mongodb://localhost:27017
  #db: rasa
  #user: username
  #password: password
  

and you can use this file like this:

$ python -m rasa_core.run \
   --enable_api \
   -d examples/babi/models/policy/current \
   -u examples/babi/models/nlu/current_py2 \
   -o out.log \
   --endpoints endpoints.yml

The body of the POST request sent to the endpoint will look like this:

{
  "tracker": {
    "latest_message": {
      "text": "/greet",
      "intent_ranking": [
        {
          "confidence": 1.0,
          "name": "greet"
        }
      ],
      "intent": {
        "confidence": 1.0,
        "name": "greet"
      },
      "entities": []
    },
    "sender_id": "22ae96a6-85cd-11e8-b1c3-f40f241f6547",
    "paused": false,
    "latest_event_time": 1531397673.293572,
    "slots": {
      "name": null
    },
    "events": [
      {
        "timestamp": 1531397673.291998,
        "event": "action",
        "name": "action_listen"
      },
      {
        "timestamp": 1531397673.293572,
        "parse_data": {
          "text": "/greet",
          "intent_ranking": [
            {
              "confidence": 1.0,
              "name": "greet"
            }
          ],
          "intent": {
            "confidence": 1.0,
            "name": "greet"
          },
          "entities": []
        },
        "event": "user",
        "text": "/greet"
      }
    ]
  },
  "arguments": {},
  "template": "utter_greet",
  "channel": {
    "name": "collector"
  }
}

The endpoint then needs to respond with the generated response:

{
    "text": "hey there",
    "buttons": [],
    "image": null,
    "elements": [],
    "attachments": []
}

The bot will then use this response and sent it back to the user.

Have questions or feedback?¶

We have a very active support community on Rasa Community Forum that is happy to help you with your questions. If you have any feedback for us or a specific suggestion for improving the docs, feel free to share it by creating an issue on Rasa Core GitHub repository.