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

Overview of Rasa NLU components

Rasa NLU is a pipeline-based general framework. This gives Rasa great flexibility.

A pipeline defines the data processing order for each component. There are dependencies between certain components. One failure in such dependency requirements will fail the whole pipeline. Rasa NLU checks the dependency requirements for each and every component. If any of those dependency requirements fail, Rasa will stop the program and give corresponding errors and warnings.

One NLU application normally includes both an intent recognition task and entity extraction task. To accomplish those tasks, here is a typical Rasa NLU pipeline:

Figure 2.3 – A typical Rasa NLU pipeline

Let's look at the components within this typical Rasa NLU pipeline:

  • Language model component: This loads the language model files to support the following components. For example, spaCy and MITIE can be initiated here.
  • Tokenizer component: This...