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 use knowledge base actions?

To tackle the challenges that we introduced in the previous section, Rasa can be integrated with a knowledge base via a knowledge base action. A knowledge base action is a special action that has been developed to handle referential resolution and queries on objects and their properties.

In general, to use knowledge base actions, you need to do the following:

  • Create a knowledge base from where the bot can retrieve information that will be used to answer the questions that have been asked.
  • Create a knowledge base action using Rasa SDK, which will query the knowledge base according to the user's inputs and reply with relevant answers.
  • Define some Natural Language Understanding (NLU) data so that users can trigger the knowledge base action via the inputs.
  • Modify your knowledge base actions to make the responses more human-like.

Let's start by defining a knowledge base.

Creating a knowledge base

A knowledge...