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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By : Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan
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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By: Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan

Overview of this book

The era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.
Table of Contents (18 chapters)
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3
Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
8
Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
11
Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
Appendix: Further Reading: Index

Performance issues in generative AI applications

The most obvious failures of GenAI are performance- and reliability-related issues. Since you’ve learned about accuracy in Chapter 10, Refining the Semantic Data Model to Improve Accuracy, performance in this chapter’s context means slowness. If a user asks your AI application a question and there is either no response, a metered response, or a partial response, it is typically much more apparent than if the response was hallucinated or sycophantic.

Several factors can contribute to the slowness of a GenAI application. Some of the most common causes of performance issues in GenAI are computational load, network latency, model serving strategies, and high input/output (I/O) operations.

There can be many more causes, of course. The rest of this section will explain some of these performance killers in detail and their impact on your application and users.

Computational load

As you already know, LLMs require significant...

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