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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

Freshness and retention

Fresh data and effective retention strategies ensure that your content is relevant and delivered on time. Freshness keeps users engaged with the latest articles, comments, and recommendations. Retention strategies manage the data lifecycle, preserving valuable historical data for analytics while purging obsolete data. This section explores methods for ensuring up-to-date content and efficient data flow.

Real-time updates

The primary concern is to ingest and update new data in real time, making it available across all cloud regions. For the news site, this means new articles and their vector embeddings should be promptly persisted and replicated for global access.

To achieve this with a distributed data model and application, use an ACID transaction to ensure that the article and its content embeddings are written together as a single unit. For an example of creating MongoDB transactions in Python, see https://learn.mongodb.com/learn/course/mongodb-crud...

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