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

How do you customize knowledge base actions?

The default knowledge base action has several disadvantages. First, the message returned to the user is not very user-friendly, the reply format is fixed, and it does not have any personality. Second, the built-in memory-based knowledge base is limited by the size of the memory and cannot support a very large-scale knowledge base. Additionally, there is no way to modify the content of the knowledge base externally in real time. In the following sections, we will solve these problems one by one.

Modifying ActionQueryKnowledgeBase to customize the behavior

Here, we introduce how to customize the output message from ActionQueryKnowledgeBase. This is especially important for Rasa developers who use multiple languages, as the default return message is always English.

Custom ways to express the object list

When a user requests the bot system to return the list of objects, utter_objects() will be called. The function of utter_objects...