Conversational AI with Rasa
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Conversational AI with Rasa
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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)
Preface
Section 1: The Rasa Framework
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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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