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 6: Knowledge Base Actions to Handle Question Answering

In the previous chapter, we introduced, in detail, the process of using ResponseSelector to handle chitchat and FAQs. This chapter will teach you how to deal with more complex question answering problems: referential resolution and dynamic query. Referential resolution refers to correctly parsing the pronouns (such as it, the first, and the last) into corresponding concrete objects. The dynamic query problem means that the query result might change rapidly. It might be different each time, so it is impossible to use fixed reply content, as we did in the previous chapter.

In this chapter, you will learn how to create a knowledge base that can be used for answering questions. Additionally, you will learn to customize knowledge base actions, learn how referential resolution (mapping a mention to an object) works, and how to create a knowledge base. Finally, you will develop a practical understanding of these concepts with...