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Conversational AI with Rasa

You're reading from   Conversational AI with Rasa Build, test, and deploy AI-powered, enterprise-grade virtual assistants and chatbots

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801077057
Length 264 pages
Edition 1st Edition
Tools
Concepts
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Authors (2):
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Xiaoquan Kong Xiaoquan Kong
Author Profile Icon Xiaoquan Kong
Xiaoquan Kong
Guan Wang Guan Wang
Author Profile Icon Guan Wang
Guan Wang
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: The Rasa Framework
2. Chapter 1: Introduction to Chatbots and the Rasa Framework FREE CHAPTER 3. Chapter 2: Natural Language Understanding in Rasa 4. Chapter 3: Rasa Core 5. Section 2: Rasa in Action
6. Chapter 4: Handling Business Logic 7. Chapter 5: Working with Response Selector to Handle Chitchat and FAQs 8. Chapter 6: Knowledge Base Actions to Handle Question Answering 9. Chapter 7: Entity Roles and Groups for Complex Named Entity Recognition 10. Chapter 8: Working Principles and Customization of Rasa 11. Section 3: Best Practices
12. Chapter 9: Testing and Production Deployment 13. Chapter 10: Conversation-Driven Development and Interactive Learning 14. Chapter 11: Debugging, Optimization, and Community Ecosystem 15. Other Books You May Enjoy

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...

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