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

Introduction to CDD

CDD is a methodology that enables you to develop a dialogue system; it was introduced by the Rasa team. It is an iterative and interactive process: developers observe the behavior of users and improve chatbot performance based on those observations.

CDD involves the following steps:

  1. Sharing your bot: We should distribute our product prototype for user testing as soon as possible. No matter how hard developers try, users will always have something new to input into the chatbot. Many teams spend months developing chatbots and focusing on conversations that, in reality, users never have.
  2. Reviewing conversations: We should spend time studying the conversation between users and our chatbot. It is very helpful to study real user conversations at each stage of development (from the prototype to the real product). Far too many teams only focus on simple attributes, such as how many users express certain intentions and so on. Instead, they should spend more...