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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

What is an embedding model?

Embedding models are a type of tool used in machine learning and artificial intelligence that simplifies large and complex data into a more manageable form. This process, known as embedding, involves reducing the data’s dimensions.

Imagine going from a detailed world map with highways, railroads, rivers, trails, and so on, to a simpler, summarized version with only country boundaries and capital cities. This not only makes computation faster and less resource-intensive, but also helps identify and understand relationships within the data. Because embedding models streamline the processing and analyzing of large datasets, they are particularly useful in areas of language (text) processing, image and sound recognition, and recommendation systems.

Consider a vast library where each book stands for one point in high dimensions. Embedding models can help reorganize the library to improve ease of navigation, such as by grouping the books on related...

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