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chore: import upstream snapshot with attribution
2026-07-13 13:24:47 +08:00
..

Reminderbot

The reminderbot example demonstrates how your bot can respond to external events or reminders.

Whats inside this example?

This example contains some training data and the main files needed to build an assistant on your local machine. The reminderbot consists of the following files:

  • data/nlu.yml contains training examples for the NLU model
  • data/rules.yml contains rules for the Core model
  • config.yml contains the model configuration
  • domain.yml contains the domain of the assistant
  • credentials.yml contains credentials for the different channels
  • endpoints.yml contains the different endpoints reminderbot can use
  • actions/actions.py contains the custom actions that deal with external events and reminders

How to use this example?

To train and chat with reminderbot, execute the following steps:

  1. Train a Rasa Open Source model containing the Rasa NLU and Rasa Core models by running:

    rasa train
    

    The model will be stored in the /models directory as a zipped file.

  2. Run a Rasa SDK action server with

    rasa run actions
    
  3. To test this example, run a callback channel. In a separate console window from where you ran the step 2 command:

    python callback_server.py
    

    This will run a server that prints the bot's responses to the console.

    Start your Rasa server in a third console window:

    rasa run --enable-api
    

    You can then send messages to the bot via the callback channel endpoint:

    curl -XPOST http://localhost:5005/webhooks/callback/webhook \
       -d '{"sender": "tester", "message": "hello"}' \
       -H "Content-type: application/json"
    

For more information about the individual commands, please check out our documentation.

Encountered any issues?

Let us know about it by posting on Rasa Community Forum!