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

Debugging Rasa systems

A chatbot is a complex software system. Therefore, we need to design and configure Rasa projects carefully. It is pretty common for developers to get different kinds of bugs when building Rasa-based chatbots. In general, those bugs can be of two types: one is that the prediction results are not as expected; another is that there is a code error in the Rasa system, and the bot cannot run normally. We will cover both types of bugs in the following subsections.

Wrong prediction of results

Two problems may cause the wrong prediction of results. It can be that the Natural Language Understanding (NLU) module makes the wrong prediction on user intent and entities, or it can be that a policy makes the wrong prediction on the next action. It is crucial to first make sure which of these problems is causing the wrong predictions.

Fortunately, most of the commands in Rasa have the debug function. Developers can turn on the debugging option to obtain critical internal...