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

Chapter 8: Working Principles and Customization of Rasa

In this chapter, we introduce the working principles behind Rasa. We will discuss exactly what happens after Rasa receives requests from its users. This is essential for you to understand how to debug a Rasa application, which we will discuss in Chapter 11, Debugging, Optimization, and the Community Ecosystem.

We will also learn how to extend and customize Rasa. Using detailed examples, you will learn to create and use custom components that allow you to use adapters or advanced features not included in Rasa. This will help you to create highly customized or complex chatbot applications.

In this chapter, we will cover the following topics:

  • Understanding Rasa's Natural Language Understanding (NLU) module
  • Understanding Rasa policies
  • Writing Rasa extensions
  • Practice: Creating your own custom English tokenizer