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Conversational AI with Rasa

You're reading from  Conversational AI with Rasa

Product type Book
Published in Oct 2021
Publisher Packt
ISBN-13 9781801077057
Pages 264 pages
Edition 1st Edition
Languages
Authors (2):
Xiaoquan Kong Xiaoquan Kong
Profile icon Xiaoquan Kong
Guan Wang Guan Wang
Profile icon Guan Wang
View More author details

Table of Contents (16) Chapters

Preface 1. Section 1: The Rasa Framework
2. Chapter 1: Introduction to Chatbots and the Rasa Framework 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

Learning by doing – building a ticket and drink booking bot

We have designed this section to enhance your practical understanding. We will create a ticket and drink booking bot based on a homemade toy-level dataset. The robot can simulate the process of booking tickets and drinks for travelers (they will not actually book tickets or drinks).

What are the features of our bot?

By using a combination of entity roles and slot mapping in the form, we can map city entities into departure and destination slots. In this way, the user's request can be successfully processed.

By using entity groups, our bot system can easily group entities into subtasks, which will make it possible to process them.

How can we implement it?

Let's follow the official Rasa project structure:

.
├── actions
│   └── actions.py
├── config.yml
├── credentials.yml
├─...
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