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Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

By : Martin Yanev
4.4 (27)
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Building AI Applications with ChatGPT APIs

Building AI Applications with ChatGPT APIs

4.4 (27)
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)
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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

Summary

In the section titled ChatGPT API Models GPT-3, GPT-4, and Beyond, we explored the different ChatGPT API models. Then we provided you with a deeper understanding of these AI models and their features, enabling you to choose the most suitable model for your specific applications. The chapter emphasized the importance of considering factors such as cost, quality, and prompt length when selecting a model, as the most advanced and capable model may not always be the best choice. Additionally, we used Python to allow you to compare the responses and costs of different models, aiding in the decision-making process.

We also focused on the various parameters of the ChatGPT API and their impact on response quality. We highlighted key parameters such as model, messages, temperature, max_tokens, stop, and n, and explained how they can be manipulated to optimize interactions with the ChatGPT API. You learned about the importance of rate limits in maintaining the stability and...

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