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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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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

Bringing it all together – building Your own LangChain Chatbot with Streamlit

Let’s begin constructing our web-based chatbot so that we can interact with ChatGPT using LangChain. To accomplish this, we’ll use the Streamlit Python library to build the interface. Streamlit’s wealth of ready-to-use components and utilities, as well as great documentation, allows us to swiftly develop a functional web-based chatbot. Its straightforward yet powerful features support quick prototyping and iterative refinement, making it perfectly suited for our chatbot project. Streamlit simplifies the integration of interactive elements and provides a lot of out-of-the-box functionality, which will cut our development time and allow us to concentrate on the LangChain project.

Make sure you’ve installed Streamlit with pip install streamlit, or by using one of the many other ways to install. I’m using Anaconda, so I’m using this distribution. You can test...

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