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ChatGPT for Conversational AI and Chatbots

ChatGPT for Conversational AI and Chatbots

By : Adrian Thompson
5 (3)
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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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1
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

Creating different LangChain chains

In this section, we’ll explore how to create some of the various chains you can create in LangChain. Our aim is to guide you from basic chains to more advanced chains.

We’ll start with an example to introduce the basics of a simple chain. This will provide a foundation for understanding how chains work in LangChain. Next, we’ll learn how to create sequential chains, parallel chains, and finally, routing chains.

By the end of this section, you’ll have a clear understanding of how to create and utilize different LangChain chains and be equipped with the skills to apply these in your future LangChain projects. Let’s dive in.

Basic chain example

In this basic example, we’ll show the simplest chain with a prompt and LLM component, which forms the basis of many more complex implementations. In this example, we are using our model to translate text into any language:

prompt = ChatPromptTemplate.from_template...

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