Book Image

Building AI Applications with ChatGPT APIs

By : Martin Yanev
4.2 (5)
Book Image

Building AI Applications with ChatGPT APIs

4.2 (5)
By: Martin Yanev

Overview of this book

Combining ChatGPT APIs with Python opens doors to building extraordinary AI applications. By leveraging these APIs, you can focus on the application logic and user experience, while ChatGPT’s robust NLP capabilities handle the intricacies of human-like text understanding and generation. This book is a guide for beginners to master the ChatGPT, Whisper, and DALL-E APIs by building ten innovative AI projects. These projects offer practical experience in integrating ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt. Throughout this book, you’ll get to grips with performing NLP tasks, building a ChatGPT clone, and creating an AI-driven code bug fixing SaaS application. You’ll also cover speech recognition, text-to-speech functionalities, language translation, and generation of email replies and PowerPoint presentations. This book teaches you how to fine-tune ChatGPT and generate AI art using DALL-E APIs, and then offers insights into selling your apps by integrating ChatGPT API with Stripe. With practical examples available on GitHub, the book gradually progresses from easy to advanced topics, cultivating the expertise required to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs.
Table of Contents (19 chapters)
Free Chapter
1
Part 1:Getting Started with OpenAI APIs
4
Part 2: Building Web Applications with the ChatGPT API
8
Part 3: The ChatGPT, DALL-E, and Whisper APIs for Desktop Apps Development
14
Part 4:Advanced Concepts for Powering ChatGPT Apps

Using Chat Completion Parameters

In this section, we will be using ChatGPT API parameters and will look at their profound impact on the quality of responses generated by models. By understanding and harnessing the power of these parameters, you will gain the ability to optimize your interactions with the ChatGPT API, unlocking its true potential. Some of the key parameters to control the API response are as follows:

  • model: Specifies the specific ChatGPT model to use for generating responses.
  • messages: Provides the conversation history as a list of message objects, including user and assistant messages.
  • temperature: Controls the randomness of the generated responses. Higher values (for example, 0.8) make the responses more random, while lower values (for example, 0.2) make them more focused and deterministic.
  • max_tokens: Sets the maximum number of tokens in the generated response. Limiting this parameter can control the length of the response.
  • stop: Allows you...