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

Writing Rasa extensions

Rasa is very flexible for extensions. Besides using the built-in functions, developers can freely extend it to have third-party functions.

There are two common scenarios for using custom components. The first scenario is to develop a custom adapter. Rasa and many Instant Messaging systems (IMs) can communicate with each other through the built-in connector, but if the IM used by the user is not supported (usually a product with fewer users or a private product), you can create an adapter for it yourself. The second scenario is to develop custom NLU or dialogue management components. Technology is developing rapidly in the field of chat robots, and most senior developers use their own models or techniques in their projects.

First of all, let's discuss how to write pipeline and policy extensions.

Writing pipeline and policy extensions

In practical applications, the probability of using custom pipeline components is much greater than using custom...