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 3: Rasa Core

In this chapter, we introduce how to implement dialogue management in Rasa. Rasa Core is the component in Rasa that handles dialogue management. Dialogue management is responsible for keeping a record of the conversation context and choosing the next actions accordingly.

The dialogue management system can be divided into four parts. Dialogue state tracking updates the dialogue state according to the previous round of dialogue and the previous round of system actions, as well as the user's intentions and entities in the current round. The dialogue policy is responsible for outputting dialogue actions according to the dialogue state. The dialogue action is based on the decision of the dialogue strategy to interact with the backend interface to complete the actual task execution. And finally, the dialogue result output outputs the result of the system operation in a user-friendly way.

In Rasa Core, these functions have all been integrated, and users can...