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 10: Conversation-Driven Development and Interactive Learning

Compared with traditional software development, the challenge of developing a chatbot is far greater. This is largely due to the fact that the user could say anything to the dialogue bot. Of course, as a developer, you cannot cope with all possible situations for your robot. Therefore, it is extremely important to understand your user's queries.

In this chapter, we will introduce a methodology in which to develop a dialogue system called Conversation-Driven Development (CDD). This methodology improves dialogue robots by observing, summarizing, and modifying the dialogue process. Additionally, we will introduce a tool for CDD: Rasa X. In a step-by-step manner, we will learn how to use Rasa X to complete all stages of CDD. Finally, we will also introduce you to Interactive Learning, which is a technical solution that allows developers to interact with the dialogue system to test system capabilities and quickly...