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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

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781836207252

Length 298 pages

Edition 1st Edition

Languages

Python

Tools

MongoDB

Concepts

Artificial Intelligence

Table of Contents (18) Chapters

Preface

1. Chapter 1: Getting Started with Generative AI FREE CHAPTER

2. Chapter 2: Building Blocks of Intelligent Applications

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

Why subscribe?

Technical requirements

To perform the steps shown in this chapter, you will need the following:

Latest major version of Python.
A free tier Atlas cluster running MongoDB version 6.0.11, 7.0.2, or later.
Your current IP address added to your Atlas project access list.
An environment set up to run Python code in an interactive environment, such as Jupyter Notebook or Colab. This chapter uses Jupyter Notebook.

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