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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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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
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Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
Appendix: Further Reading: Index

Your (soon-to-be) intelligent app

With LLMs, embedding models, vector databases, and model hosting, you have the key building blocks for creating intelligent applications. While the specific architecture will vary depending on your use case, a common pattern emerges:

  • LLMs for reasoning and generation
  • Embeddings and vector search for retrieval and memory
  • Model hosting to serve these components at scale

This AI stack is integrated with traditional application components, such as backend services, APIs, frontend user interfaces, databases, and data pipelines. Additionally, intelligent applications often include components for AI-specific concerns, such as prompt management and optimization, data preparation and embedding generation, and AI safety, testing, and monitoring.

The rest of this section walks through an example architecture for a RAG-powered chatbot, showcasing how these components work together. The subsequent chapters will dive deeper into the end...

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