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  • Book Overview & Buying Building AI Intensive Python Applications
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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
Buy this Book

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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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Download a free PDF copy of this book
Free Chapter
1
Chapter 1: Getting Started with Generative AI
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Chapter 1: Getting Started with Generative AI
Technical requirements
Defining the terminology
The generative AI stack
Important features of generative AI
Summary
2
Chapter 2: Building Blocks of Intelligent Applications
Chapter 2: Building Blocks of Intelligent Applications
Technical requirements
Defining intelligent applications
LLMs – reasoning engines for intelligent apps
Embedding models and vector databases – semantic long-term memory
Your (soon-to-be) intelligent app
Summary
3
Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
4
Chapter 3: Large Language Models
Chapter 3: Large Language Models
Technical requirements
Probabilistic framework
Machine learning for language modelling
ANNs for natural language processing
Dealing with sequential data
LLMs in practice
Summary
5
Chapter 4: Embedding Models
Chapter 4: Embedding Models
Technical requirements
What is an embedding model?
Choosing embedding models
Best practices
Summary
6
Chapter 5: Vector Databases
Chapter 5: Vector Databases
Technical requirements
What is a vector embedding?
Graph connectivity
The need for vector databases
Case studies and real-world applications
Vector search best practices
Summary
7
Chapter 6: AI/ML Application Design
Chapter 6: AI/ML Application Design
Technical requirements
Data modeling
Data storage
Data flow
Freshness and retention
Security and RBAC
Best practices for AI/ML application design
Summary
8
Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
9
Chapter 7: Useful Frameworks, Libraries, and APIs
Chapter 7: Useful Frameworks, Libraries, and APIs
Technical requirements
Python for AI/ML
AI/ML frameworks
Key Python libraries
AI/ML APIs
Summary
10
Chapter 8: Implementing Vector Search in AI Applications
Chapter 8: Implementing Vector Search in AI Applications
Technical requirements
Information retrieval with MongoDB Atlas Vector Search
Building RAG architecture systems
Summary
11
Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
12
Chapter 9: LLM Output Evaluation
Chapter 9: LLM Output Evaluation
Technical requirements
What is LLM evaluation?
Model benchmarking
Evaluation metrics
Summary
13
Chapter 10: Refining the Semantic Data Model to Improve Accuracy
Chapter 10: Refining the Semantic Data Model to Improve Accuracy
Technical requirements
Embeddings
Embedding metadata
Optimizing retrieval-augmented generation
Summary
14
Chapter 11: Common Failures of Generative AI
Chapter 11: Common Failures of Generative AI
Technical requirements
Hallucinations
Sycophancy
Data leakage
Cost
Performance issues in generative AI applications
Summary
15
Chapter 12: Correcting and Optimizing Your Generative AI Application
Chapter 12: Correcting and Optimizing Your Generative AI Application
Technical requirements
Baselining
Testing and red teaming
Information post-processing
Other remedies
Summary
16
Other Books You May Enjoy
Other Books You May Enjoy
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Appendix: Further Reading: Index
Appendix: Further Reading: Index
Why subscribe?

Getting Started with Generative AI

There are a plethora of options for building generative AI (GenAI) applications. The landscape is, quite frankly, overwhelming to navigate, and many of the tools that satisfy one criterion may fall short in another. GenAI applications evolve so quickly that within weeks of this book being published, some of the new AI companies might no longer exist. Therefore, this chapter focuses on long-lived, high-level concepts related to technologies that are used to create GenAI applications.

You will learn ways from which your next web development project might benefit. This chapter will examine not just what these ways are but how they work, which will give you a broader understanding and perspective of GenAI. This should help you decide when to use GenAI and how, as well as make the applications you create generally more accurate.

By the end of this chapter, you will have a good understanding of the benefits that individual AI/ML stack components bring to a development project, how they relate to each other, and why GenAI technologies are a revolution in software—both in terms of the data handled and desired functionalities.

This chapter gives an introduction to GenAI and provides a quick overview of the following topics:

  • Definitions for common terminology
  • A GenAI stack of choice
  • Python and GenAI
  • The OpenAI API
  • An introduction to MongoDB Vector Search
  • Important features of GenAI
  • Why use GenAI?
  • The ethics and risks of GenAI
End of Section 1
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