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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 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

Data storage

In this section, you will perform sizing, which is an educated estimate, for storage requirements. You will consider not just volume size and speed, but also several other aspects of the database cluster that are needed for harnessing the data of your application while following expected data access patterns.

MDN plans to publish 100 articles daily. Keeping the articles from the last 5 years, the number of articles would total 182,500. With 48 million subscribers and 24 million daily active users, peak access occurs for 30 minutes daily across three major time zones, as shown in Figure 6.4.

Figure 6.4: MDN subscriber time zones and peak times

First, you will estimate the total data size. Each article has one 1,024-dimension embedding for semantic search and five 768-dimension embeddings for image search, totaling 40 KB uncompressed (dimensions use the double type). With the title, summary, body (with and without markup), and other fields...

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