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

Understanding Rasa's NLU module

Let's start by looking at how the components in Rasa's NLU module work. We will introduce them separately in their two working processes, namely the training process and the inference process.

How does the NLU training work?

The main implementation of the training process is in the rasa.nlu.train.train function and the rasa.nlu.model.Trainer class. In this section, we introduce how Rasa's NLU module works during the training process.

Initializing the trainer object

The instantiation step is implemented in the rasa.nlu.model.Trainer.__init__() method. During the training process, Rasa reads the pipeline field in the config.yaml configuration file, and gets the detailed definition of every component in the pipeline.

Rasa takes the component configuration and pipeline configuration as the parameters to call the create() class method of the component. This method returns an instance of this class.

In this way, we can...