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

Deploying your Rasa assistant to production

Here we introduce how to deploy your Rasa assistant to production.

When to deploy

It is common to use the minimum viable product or MVP strategy during the product development process. MVP is all about building a usable product prototype that fulfills the key requirements in the most efficient and simple way and then iterating to fine-tune the product details.

In Rasa, the official recommendation is that a product can be put into production as an MVP as soon as it can handle the most important (but not every) "happy path" of Dialogue. It's recommended to use Rasa X to have early users test the product prototype. This is in order to continuously improve the model until the product prototype reaches the MVP standard and you are ready to deploy it to a production environment.

Deployment options

When we want to deploy a Rasa assistant on a large scale, we normally use solutions based on Kubernetes or OpenShift. Rasa...