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

Testing Rasa projects

In this section, we will start by discussing how to validate data and stories. This step is used to find obvious bugs. Later, we will discuss how to evaluate NLU performance and how to read the corresponding reports. Finally, we will introduce the test story format and learn how to use test stories to evaluate the performance of Dialogue management.

Validating data and stories

If developers can quickly detect whether there are errors and where these potential errors are in NLU data and stories, this can help developers greatly improve work efficiency. In Rasa, there is a command for this purpose:

rasa data validate

The preceding command will detect errors in the data and configuration. Common errors include the following:

  • Inconsistency of the training data (the same training data appearing in two or more different intents)
  • The intents in the training data being inconsistent with the intents in the domain file (fewer or more intents)
  • ...