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, you learned how to debug a Rasa system and to optimize the performance of Rasa. You also learned about some excellent tools from the community that can help you.

When discussing how to debug a Rasa system, we introduced how to use the debugging information of the Rasa shell to deal with the problem of incorrect results run by Rasa. We then introduced how to use the pdb module and the IDE's debugging function to debug code errors.

When discussing how to optimize the performance of Rasa, we introduced how to use TensorBoard to observe the changes in metrics to determine how to adjust the learning rate and epoch settings.

Finally, we introduced you to some excellent tools from the Rasa community. By using these tools, your work efficiency can be greatly improved.

This is the last chapter of the book, so let's quickly review its main sections.

We started with an introduction to the architecture and underlying principles of the Rasa framework...