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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 our agent for complex interactions

Our agent is going to be powered by tools that provide the functionality to meet our requirements. The main concept is that the agent will be able to take our question and use an LLM call to reason and split this task down into smaller tasks, while deciding which tools are appropriate to use to satisfy our request and deliver an answer to our question. The reasoning processes the agent uses areillustrated in the following diagram:

Figure 8.1 –  The agent’s reasoning process

Figure 8.1 – The agent’s reasoning process

Begin by creating a dedicated folder that will contain all the code for our agent. This will help organize our project files and make the code base easier to navigate. I’m going to name this folder streamlit, as it’s where we’ll create our Streamlit app later on.

Copy over the Chroma database we’ve created to store the vectors in this folder: path to your project\streamlit\chapter8db\.

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