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 9: Testing and Production Deployment

In this chapter, we will introduce how to test Rasa projects. We will then discuss how to verify NLU data and stories, as well as how to evaluate the performance of NLU models and Dialogue management models. Through testing, we can find errors in projects as early as possible. We can also comprehensively evaluate the performance of bots.

Moving on, we will discuss how to deploy Rasa applications in production environments. We will discuss the choice of deployment methods, model storage, tracker stores, and locker stores. By properly deploying Rasa applications, we can implement model version management, load balancing, service expansion, and other functions in production environments.

We will cover the following topics:

  • Testing Rasa projects
  • Deploying your Rasa assistant to production

Let's talk about validation and evaluation first, because they are executed before deployment in the software development process...