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

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
├─...