Book Image

Conversational AI with Rasa

By : Xiaoquan Kong, Guan Wang
Book Image

Conversational AI with Rasa

By: Xiaoquan Kong, Guan Wang

Overview of this book

The Rasa framework enables developers to create industrial-strength chatbots using state-of-the-art natural language processing (NLP) and machine learning technologies quickly, all in open source. Conversational AI with Rasa starts by showing you how the two main components at the heart of Rasa work – Rasa NLU (natural language understanding) and Rasa Core. You'll then learn how to build, configure, train, and serve different types of chatbots from scratch by using the Rasa ecosystem. As you advance, you'll use form-based dialogue management, work with the response selector for chitchat and FAQ-like dialogs, make use of knowledge base actions to answer questions for dynamic queries, and much more. Furthermore, you'll understand how to customize the Rasa framework, use conversation-driven development patterns and tools to develop chatbots, explore what your bot can do, and easily fix any mistakes it makes by using interactive learning. Finally, you'll get to grips with deploying the Rasa system to a production environment with high performance and high scalability and cover best practices for building an efficient and robust chat system. By the end of this book, you'll be able to build and deploy your own chatbots using Rasa, addressing the common pain points encountered in the chatbot life cycle.
Table of Contents (16 chapters)
1
Section 1: The Rasa Framework
5
Section 2: Rasa in Action
11
Section 3: Best Practices

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...