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

Connecting with other services via endpoints

As a mature dialogue system, Rasa supports communication with external services and internal components in a similar way to microservices. In Rasa's terminology, all links to these services are called endpoints. The endpoint is the connection between Rasa Core and other services and is defined in endpoints.yml. Currently, the supported endpoints are as follows:

  • Event broker: This allows you to connect your bot to other services that can process conversation data asynchronously. The event broker publishes messages to a message broker in order to forward conversations from Rasa to external services. This is useful for advanced users who want to analyze the conversations.
  • Tracker store: Rasa's conversations are stored within a tracker store. Rasa provides several built-in tracker stores. In general, all tracker stores can be divided into two categories: the tracker store that is exclusive to the process and the tracker...