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 5: Working with Response Selector to Handle Chitchat and FAQs

Most chatbots have simple FAQ and chitchat functions. Both types of functions involve knowing how to choose an appropriate response to a user's request. These functions sound simple, but in reality, they actually involve a lot of work. If we use one intent to represent an FAQ or chitchat intent from the user and pair it with an action, the story will become both complicated and inefficient. Rasa offers the Natural Language Understanding (NLU) ResponseSelector component, which is specifically used for FAQ and chitchat tasks.

In this chapter, you will learn how to define a question and find its corresponding answer. Additionally, you will learn how to configure Rasa to automatically identify a query (by finding a question that is semantically closest to the query) and give the corresponding answer. Finally, you will develop a practical understanding of these concepts with the help of the hands-on exercise...