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ChatGPT for Conversational AI and Chatbots

ChatGPT for Conversational AI and Chatbots

By : Adrian Thompson
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ChatGPT for Conversational AI and Chatbots

ChatGPT for Conversational AI and Chatbots

5 (3)
By: Adrian Thompson

Overview of this book

ChatGPT for Conversational AI and Chatbots is a definitive resource for exploring conversational AI, ChatGPT, and large language models. This book introduces the fundamentals of ChatGPT and conversational AI automation. You’ll explore the application of ChatGPT in conversation design, the use of ChatGPT as a tool to create conversational experiences, and a range of other practical applications. As you progress, you’ll delve into LangChain, a dynamic framework for LLMs, covering topics such as prompt engineering, chatbot memory, using vector stores, and validating responses. Additionally, you’ll learn about creating and using LLM-enabling tools, monitoring and fine tuning, LangChain UI tools such as LangFlow, and the LangChain ecosystem. You’ll also cover popular use cases, such as using ChatGPT in conjunction with your own data. Later, the book focuses on creating a ChatGPT-powered chatbot that can comprehend and respond to queries directly from your unique data sources. The book then guides you through building chatbot UIs with ChatGPT API and some of the tools and best practices available. By the end of this book, you’ll be able to confidently leverage ChatGPT technologies to build conversational AI solutions.
Table of Contents (15 chapters)
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Part 1: Foundations of Conversational AI
4
Part 2: Using ChatGPT, Prompt Engineering, and Exploring LangChain
9
Part 3: Building and Enhancing ChatGPT-Powered Applications

Exploring LangChain memory

As we work with LLMs, a key challenge emerges as they cannot inherently recall past interactions. So, in essence, they are stateless. A stateless operation won’t persist information from one request to the next, which is a problem if you want to create a chatbot. The way around this is to add the full conversation to the context. The ChatGPT client itself will be passing the full conversation into each prompt as it progresses.

What we want our ChatGPT applications to do is offer stateful interactions where information is remembered across requests and sessions. To achieve this, we need to use a memory mechanism. Different representations of memory will then be included in an LLM prompt. This section is dedicated to exploring the concept of memory in the context of LLMs. We will delve into different types of memory and the challenges you’ll face in using memory with LLMs before taking a deep dive into how to use LangChain to provide memory...

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