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You're reading from  Conversational AI with Rasa

Product typeBook
Published inOct 2021
PublisherPackt
ISBN-139781801077057
Edition1st Edition
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Authors (2):
Xiaoquan Kong
Xiaoquan Kong
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Xiaoquan Kong

Xiaoquan is a machine learning expert specializing in NLP applications. He has extensive experience in leading teams to build NLP platforms in several Fortune Global 500 companies. He is a Google developer expert in Machine Learning and has been actively involved in contributions to TensorFlow for many years. He also has actively contributed to the development of the Rasa framework since the early stage and became a Rasa Superhero in 2018. He manages the Rasa Chinese community and has also participated in the Chinese localization of TensorFlow documents as a technical reviewer.
Read more about Xiaoquan Kong

Guan Wang
Guan Wang
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Guan Wang

Guan is currently working on Al applications and research for the insurance industry. Prior to that, he was a machine learning researcher at several industry Al labs. He was raised and educated in Mainland China, lived in Hong Kong for 10 years before relocating to Singapore in 2020. Guan holds BSc degrees in Physics and Computer Science from Peking University, and an MPhil degree in Physics from HKUST. Guan is an active tech blogger and community contributor to open source projects including Rasa, receiving more than10,000 stars for his own projects on Github.
Read more about Guan Wang

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Making intents trigger actions

In actual application scenarios, it is very useful to trigger the execution of specific actions by sending intents. Fortunately, Rasa provides support for triggering between intentions and actions. There are two types of trigger sources: built-in and user-defined.

Let's start by talking about the built-in triggers.

Triggering actions by using built-in intents

Rasa allows developers to use a format such as /intent{"entity1": val1, "entity2": val2} as a simplified way of defining intent and entities. We can use this to test the bot. Another usage is to return payload to the system when a user clicks on a button. This format is very similar to the user message in story.md; however, here it must start with /.

RulePolicy gives the corresponding intents restat, back, and session_start for the session-level actions action_start, action_back, and action_session_start, and manages the mapping from intent to action so that session...

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Conversational AI with Rasa
Published in: Oct 2021Publisher: PacktISBN-13: 9781801077057

Authors (2)

author image
Xiaoquan Kong

Xiaoquan is a machine learning expert specializing in NLP applications. He has extensive experience in leading teams to build NLP platforms in several Fortune Global 500 companies. He is a Google developer expert in Machine Learning and has been actively involved in contributions to TensorFlow for many years. He also has actively contributed to the development of the Rasa framework since the early stage and became a Rasa Superhero in 2018. He manages the Rasa Chinese community and has also participated in the Chinese localization of TensorFlow documents as a technical reviewer.
Read more about Xiaoquan Kong

author image
Guan Wang

Guan is currently working on Al applications and research for the insurance industry. Prior to that, he was a machine learning researcher at several industry Al labs. He was raised and educated in Mainland China, lived in Hong Kong for 10 years before relocating to Singapore in 2020. Guan holds BSc degrees in Physics and Computer Science from Peking University, and an MPhil degree in Physics from HKUST. Guan is an active tech blogger and community contributor to open source projects including Rasa, receiving more than10,000 stars for his own projects on Github.
Read more about Guan Wang