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

Summary

In this chapter, we discussed two very important stages in the development of a Dialogue system: testing and deployment. Testing is very important for us to ensure the intelligence of a Dialogue system. We must find the current problems of the Dialogue system through testing and correct these problems. We also discussed how to deploy Rasa projects to production environments. A real large-scale Dialogue system needs to be accessed by tens of thousands or even millions of users at the same time. Such a Dialogue system must have very good horizontal scalability. Fortunately, Rasa considered these issues at the beginning of the design and provided corresponding solutions. By using a central storage system, tracker store, and lock store, we are able to extend our service smoothly.

In the next chapter, we will discuss a user-centered methodology and the tools required for developing Dialogue systems.