Conversational AI with Rasa: Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots

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Key benefits

Understand the architecture and put the underlying principles of the Rasa framework to practice

Learn how to quickly build different types of chatbots such as task-oriented, FAQ-like, and knowledge graph-based chatbots

Explore best practices for working with Rasa and its debugging and optimizing aspects

Description

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.

What you will learn

Use the response selector to handle chitchat and FAQs

Create custom actions using the Rasa SDK

Train Rasa to handle complex named entity recognition

Become skilled at building custom components in the Rasa framework

Validate and test dialogs end to end in Rasa

Develop and refine a chatbot system by using conversation-driven deployment processing

Use TensorBoard for tuning to find the best configuration options

Debug and optimize dialogue systems based on Rasa

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Amazon verified reviews

V. Wacker Nov 28, 2021

I bought the book because it was recommended to me (a complete newbie to conversational AI) by a Rasa developer. I really like it. The information in this book go beyond the “Rasa Developer Course” on udemy. You can read it cover to cover, but it also comes very handy when you need to look up something.

Amazon Verified review

Colbert Philippe Jan 27, 2023

I wanted to get seriously learn RASA. This book is must for that. The second part are the RASA videos on the RASA channel on YouTube. I started my effort by reading this book and like it for that. I am very pleased by this book.

NLP7 Feb 23, 2022

I like this book. Clear explanations and easy to follow.

Dishant Oct 21, 2021

Hi, I am Dishant, Chatbot developer and Rasa Superhero. I started reading this book to gain more in-depth knowledge of Rasa. I got to know some new functionalities, even after using it for like 1+ years. It started from the Basic to Intermediate level.I like the section which explains the NLU and NLG part of Rasa, Also, the section which teaches on building a Music bot🤫.Not going too deep into the book. I would recommend anyone who wants to learn about building Rasa chatbots(even a chatbot other than Rasa) should read this book.

FT Oct 08, 2021

Conversational AI with Rasa by Xiaoquan Kong and Guan Wang provides a comprehensive account of building and deploying conversational assistants with the Rasa framework, which has become increasingly popular among conversational AI practitioners during the last few years. The book starts with a digestible and relatable introduction to foundational concepts of dialogue systems, machine learning (ML), and natural language processing (NLP), such as supervised learning, transfer learning, etc., as well as a brief literature review of the field of NLP, providing a useful theoretical context for the reader. The authors, then, walk the reader through well-structured and articulated chapters on chatbots and the Rasa framework, tying in the concepts of ML and NLP they laid out in the first chapter. They explain the main components of Rasa, namely Rasa NLU and Rasa Core, in easy-to-follow details. Code samples, “code in text,” included throughout the book are an effective way of demonstrating the implementation of various concepts that are part of the framework. The book also provides links to where one can download code files and practice their learning. The final three chapters expand the technical details of the framework and provide a thorough coverage on deploying, testing, and optimizing Rasa chatbots and conclude the book with conversation-driven deployment, debugging, and existing tools that support the community of chatbot developers, providing the reader with all the necessary details of the Rasa framework and chatbot development in a coherent structure.The target audience of the book includes NLP professionals and ML and deep learning practitioners, as well as anyone with some beginner-level knowledge of NLP, but it also appeals to those with minimal to no prior knowledge. This book comes at a time when conversational AI has become a critical component of AI applications. The authors are able to take the reader through the stages of understanding the entire process of developing a chatbot and also equip them with enough knowledge tools to explore more if they would like to by providing and explaining the conventions, best practices, and important terminology used in the field.Using an accessible language and a methodical approach, Conversational AI with Rasa meets a timely demand for comprehensive accounts on conversational AI. Those who would like to learn and extend their knowledge of conversational AI and the ins and outs of one of the most popular chatbot frameworks should certainly read this book.

Conversational AI with Rasa: Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots

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