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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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Chapter 1: Getting Started with Generative AI
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Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
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Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
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Chapter 7: Useful Frameworks, Libraries, and APIs
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Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
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Chapter 9: LLM Output Evaluation
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Chapter 10: Refining the Semantic Data Model to Improve Accuracy
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Chapter 12: Correcting and Optimizing Your Generative AI Application
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Other Books You May Enjoy
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Appendix: Further Reading: Index
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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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