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

Summary

In this chapter, you learned to use ResponseSelector to handle chitchat and FAQs. This usually requires three steps. First, you need to define retrieval intents, given enough samples about the questions that users might ask. Note that the retrieval intents are slightly different from ordinary intents (if you do not remember the differences, please try to review this chapter). Second, you need to define your responses, that is, the answers to the questions. Remember that there is a rule about how to pair the answers with the questions. Third, you need to update the configuration (both in the pipeline field and the polices field) to use ResponseSelector and RulePolicy to make the bot work correctly.

In the next chapter, we will examine how to use knowledge base actions to handle knowledge base question answering.