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Building AI Applications with OpenAI APIs

You're reading from   Building AI Applications with OpenAI APIs Leverage ChatGPT, Whisper, and DALL-E APIs to build 10 innovative AI projects

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781835884003
Length 252 pages
Edition 2nd Edition
Languages
Concepts
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Author (1):
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Martin Yanev Martin Yanev
Author Profile Icon Martin Yanev
Martin Yanev
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Table of Contents (19) Chapters Close

Preface 1. Part 1:Getting Started with OpenAI APIs FREE CHAPTER
2. Chapter 1: Getting Started with the ChatGPT API for NLP Tasks 3. Chapter 2: Building a ChatGPT Clone 4. Part 2: Build Web Applications with ChatGPT API
5. Chapter 3: Creating and Deploying a Code Bug-Fixing Application Using Flask 6. Chapter 4: Integrating the Code Bug-Fixing Application with a Payment Service 7. Chapter 5: Quiz Generation App with ChatGPT and Django 8. Part 3: ChatGPT, DALL-E, and Whisper APIs for Desktop Apps Development
9. Chapter 6: Language Translation Desktop App with the ChatGPT API and Microsoft Word 10. Chapter 7: Building an Outlook Email Reply Generator 11. Chapter 8: Essay Generation Tool with PyQt and the ChatGPT API 12. Chapter 9: Integrating the ChatGPT and DALL-E APIs: Building an End-to-End PowerPoint Presentation Generator 13. Chapter 10: Speech Recognition and Text-to-Speech with the Whisper API 14. Part 4: Advanced Concepts for Powering ChatGPT Apps
15. Chapter 11: Choosing the Right ChatGPT API Model 16. Chapter 12: Fine-Tuning ChatGPT to Create Unique API Models 17. Index 18. Other Books You May Enjoy

Summary

In this chapter, we explored various ChatGPT API models. In the ChatGPT API models – GPT-3.5, GPT-4, and beyond section, 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. This 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 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...

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