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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

Large Language Models

Language models are computational algorithms designed to process, understand, and generate natural language. The study, research, and development of these algorithms is known as natural language processing (NLP). NLP predates the field of machine learning (ML) and can be traced back to the 1950s and the development of the first computers. While the first language models relied heavily on rule-based approaches, NLP shifted in the 1980s toward statistical methods and began to converge with ML. The increase in computational power and text corpora led to the development of deep learning and neural network-based language models in the early 21st century, which have seen significant progress over the last decade.

Language models have a variety of applications in NLP for understanding and generating natural languages as well as more formal languages, such as programming and database query languages. Their use cases include tasks such as text labeling and sentiment...

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