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

Why do we need entity roles and entity groups?

Sometimes, it is not enough to only have the entity type and entity value to accomplish a complicated task. We need to distinguish entities at a more granular level. Rasa provides two additional pieces of information about an entity: its role and group.

You can use entity roles to distinguish entities from the same entity type. For example, in your bot system, you can use an entity of the city type to mark a traveler's departure and arrival cities. Since both the departure and the destination are marked as city types, the bot cannot distinguish which one is the departure. In this case, you can use the entity role information to determine which city entity is the departure city and which one is the arrival city.

For more complex expressions, users could express two or more different requests at the same time. This time, understanding how to distinguish between which entities belong to the same group of requests will be critical...