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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

Summary

In this chapter, we have dived deeper into LangChain, looking at some more advanced topics. We started by looking at debugging techniques and introduced LangSmith, the go-to tool for advanced logging and monitoring for LangChain applications. We also looked at LangChain agents and understood how to use them to provide different functionalities to our LLM project, as well as how they tie in with OpenAI function calling. We focused on the out-of-the-box tools provided by LangChain and covered creating custom tools by looking at practical examples to help our ChatGPT application answer questions about real-time news and weather. Finally, we covered the concept of providing memory to our agents while looking at the different types of memory, the challenges, and the techniques involved in providing memory in LangChain.

In the next chapter, we’ll look at RAG. You’ll understand what it is, the concepts and processes involved, and how to implement retrieval in LangChain...

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