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 the memory of your bot (slots)

The slot is the memory of the chatbot. The slot is represented as a key-value pair, such as city: New York. It records the key information from conversations. The key information can come from a user's input (intents and entities), or from backend systems (for example, the result from a payment action: success or failure). Normally, the information is crucial for the flow of the conversation and will be used by the dialogue management system to predict the next action.

Let's take an example. In a simple application of a weather forecast, the information of location and date is key for the dialogue management system to decide the next action. If the system finds either the location or date missing, it will ask users for the corresponding information until both are present. Then the system will start to query some weather APIs.

Here, the system only cares about whether the location and date slots are filled. It does not care...