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Conversational AI with Rasa

You're reading from Conversational AI with Rasa

Product type Book

Published in Oct 2021

Publisher Packt

ISBN-13 9781801077057

Pages 264 pages

Edition 1st Edition

Languages

Concepts

Mobile Application Development

Authors (2):

Xiaoquan Kong

Guan Wang

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Table of Contents (16) Chapters

Preface

Section 1: The Rasa Framework

Chapter 1: Introduction to Chatbots and the Rasa Framework

Chapter 2: Natural Language Understanding in Rasa

Chapter 3: Rasa Core

Section 2: Rasa in Action

Chapter 4: Handling Business Logic

Chapter 5: Working with Response Selector to Handle Chitchat and FAQs

Chapter 6: Knowledge Base Actions to Handle Question Answering

Chapter 7: Entity Roles and Groups for Complex Named Entity Recognition

Chapter 8: Working Principles and Customization of Rasa

Section 3: Best Practices

Chapter 9: Testing and Production Deployment

Chapter 10: Conversation-Driven Development and Interactive Learning

Chapter 11: Debugging, Optimization, and Community Ecosystem

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Using channels to communicate with instant messaging software

In most cases, users will be using all kinds of instant messaging (IM) apps to interact with chatbots.

Rasa is one of the best platforms for seamlessly integrating with different IMs. Rasa supports most of the mainstream IMs on the market that support OpenAPI. Currently, it includes Facebook Messenger, Slack, Telegram, Twilio, Microsoft Bot Framework, Cisco Webex Teams, RocketChat, Mattermost, and Google Hangouts Chat.

Community developers have also developed many open source IMs for Rasa, and those open source IMs are often used by start-ups and developers for product demonstration purposes. Rasa Webchat (https://github.com/botfront/rasa-webchat) and Chatroom (https://github.com/scalableminds/chatroom) have the most mature functionalities.

In Rasa, the connector is responsible for connecting a Rasa system to an IM. The Connect feature handles the communication protocol. Since different IMs may share the same communication...

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Authors (2)

Xiaoquan Kong

Xiaoquan is a machine learning expert specializing in NLP applications. He has extensive experience in leading teams to build NLP platforms in several Fortune Global 500 companies. He is a Google developer expert in Machine Learning and has been actively involved in contributions to TensorFlow for many years. He also has actively contributed to the development of the Rasa framework since the early stage and became a Rasa Superhero in 2018. He manages the Rasa Chinese community and has also participated in the Chinese localization of TensorFlow documents as a technical reviewer.

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Guan Wang

Guan is currently working on Al applications and research for the insurance industry. Prior to that, he was a machine learning researcher at several industry Al labs. He was raised and educated in Mainland China, lived in Hong Kong for 10 years before relocating to Singapore in 2020. Guan holds BSc degrees in Physics and Computer Science from Peking University, and an MPhil degree in Physics from HKUST. Guan is an active tech blogger and community contributor to open source projects including Rasa, receiving more than10,000 stars for his own projects on Github.

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