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Building AI Intensive Python Applications

You're reading from   Building AI Intensive Python Applications Create intelligent apps with LLMs and vector databases

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781836207252
Length 298 pages
Edition 1st Edition
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Getting Started with Generative AI 2. Chapter 2: Building Blocks of Intelligent Applications FREE CHAPTER 3. Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
4. Chapter 3: Large Language Models 5. Chapter 4: Embedding Models 6. Chapter 5: Vector Databases 7. Chapter 6: AI/ML Application Design 8. Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
9. Chapter 7: Useful Frameworks, Libraries, and APIs 10. Chapter 8: Implementing Vector Search in AI Applications 11. Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
12. Chapter 9: LLM Output Evaluation 13. Chapter 10: Refining the Semantic Data Model to Improve Accuracy 14. Chapter 11: Common Failures of Generative AI 15. Chapter 12: Correcting and Optimizing Your Generative AI Application 16. Other Books You May Enjoy Appendix: Further Reading: Index

Getting Started with Generative AI

There are a plethora of options for building generative AI (GenAI) applications. The landscape is, quite frankly, overwhelming to navigate, and many of the tools that satisfy one criterion may fall short in another. GenAI applications evolve so quickly that within weeks of this book being published, some of the new AI companies might no longer exist. Therefore, this chapter focuses on long-lived, high-level concepts related to technologies that are used to create GenAI applications.

You will learn ways from which your next web development project might benefit. This chapter will examine not just what these ways are but how they work, which will give you a broader understanding and perspective of GenAI. This should help you decide when to use GenAI and how, as well as make the applications you create generally more accurate.

By the end of this chapter, you will have a good understanding of the benefits that individual AI/ML stack components bring to a development project, how they relate to each other, and why GenAI technologies are a revolution in software—both in terms of the data handled and desired functionalities.

This chapter gives an introduction to GenAI and provides a quick overview of the following topics:

  • Definitions for common terminology
  • A GenAI stack of choice
  • Python and GenAI
  • The OpenAI API
  • An introduction to MongoDB Vector Search
  • Important features of GenAI
  • Why use GenAI?
  • The ethics and risks of GenAI
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