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Building AI Intensive Python Applications
Building AI Intensive Python Applications

Building AI Intensive Python Applications: Create intelligent apps with LLMs and vector databases

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Building AI Intensive Python Applications

Building Blocks of Intelligent Applications

In the rapidly evolving landscape of software development, a new class of applications is emerging: intelligent applications. Intelligent applications are a superset of traditional full stack applications. These applications use artificial intelligence (AI) to deliver highly personalized, context-aware experiences that go beyond the capabilities of traditional software.

Intelligent applications understand complex, unstructured data and use this understanding to make decisions and create natural, adaptive interactions.

The goal of this chapter is to provide you with an overview of the logical and technical building blocks of intelligent applications. The chapter explores how intelligent applications extend the capability of traditional full-stack applications, the core structures that define them, and how these components function to create dynamic, context-aware experiences. By the end of this chapter, you will understand how these...

Technical requirements

This chapter is theoretical. It covers the logical components of intelligent applications and how they fit together.

This chapter assumes fundamental knowledge of traditional full stack application development components, such as servers, clients, databases, and APIs.

Defining intelligent applications

Traditional applications typically consist of a client-side user interface, a server-side backend, and a database for data storage and retrieval. They perform tasks following a strict set of instructions. Intelligent applications require a client, server, and database as well, but they augment the traditional stack with AI components.

Intelligent applications stand out by understanding complex, unstructured data to enable natural, adaptive interactions and decision-making. Intelligent applications can engage in open-ended interactions, generate novel content, and make autonomous decisions.

Examples of intelligent applications include the following:

  • Chatbots that provide natural language responses based on external data using retrieval-augmented generation (RAG). For example, Perplexity.ai (https://www.perplexity.ai/) is an AI-powered search engine and chatbot that provides users with AI-generated answers to their queries based on sources...

LLMs – reasoning engines for intelligent apps

LLMs are the key technology of intelligent applications, unlocking whole new classes of AI-powered systems. These models are trained on vast amounts of text data to understand language, generate human-like text, answer questions, and engage in dialogue.

LLMs undergo continuous improvement with the release of new models. featuring billions or trillions of parameters and enhanced reasoning, memory, and multi-modal capabilities.

Use cases for LLM reasoning engines

LLMs have emerged as a powerful general-purpose technology for AI systems, analogous to the central processing unit (CPU) in traditional computing. Much like CPUs, LLMs serve as general-purpose computational engines that can be programmed for many tasks and play a similar role in language-based reasoning and generation. The general-purpose nature of LLMs lets developers use their capabilities for a wide range of reasoning tasks.

A crop of techniques to leverage...

Embedding models and vector databases – semantic long-term memory

In addition to the reasoning capabilities provided by LLMs, intelligent applications require semantic long-term memory for storing and retrieving information.

Semantic memory typically consists of two core components—AI vector embedding models and vector databases. Vector embedding models represent the semantic meaning of unstructured data, such as text or images, in large arrays of numbers. Vector databases efficiently store and retrieve these vectors to support semantic search and context retrieval. These components work together to enable the reasoning engine to access relevant context and information as needed.

Embedding models

Embedding models are AI models that map text and other data types, such as images and audio, into high-dimensional vector representations. These vector representations capture the semantic meaning of the input data, allowing for efficient similarity comparisons and semantic...

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...

Summary

Intelligent applications represent a new paradigm in software development, combining AI with traditional application components to deliver highly personalized, context-aware experiences. This chapter details the core components of intelligent applications, highlighting the pivotal role of LLMs as reasoning engines. LLMs serve as versatile computational tools capable of performing diverse tasks, including chat, summarization, and classification, due to their general-purpose design.

Complementing these reasoning engines are embedding models and vector databases, which function as the semantic memory of intelligent applications. These components enable the reasoning engine to retrieve pertinent context and information as needed. Additionally, the hosting of AI models demands dedicated infrastructure, as their unique hardware requirements differ significantly from traditional software needs. Using building blocks such as LLMs, embedding models, vector databases, and model hosting...

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Key benefits

  • Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks
  • Implement effective retrieval-augmented generation strategies with MongoDB Atlas
  • Optimize AI models for performance and accuracy with model compression and deployment optimization
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

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.

Who is this book for?

This book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.

What you will learn

  • Understand the architecture and components of the generative AI stack
  • Explore the role of vector databases in enhancing AI applications
  • Master Python frameworks for AI development
  • Implement Vector Search in AI applications
  • Find out how to effectively evaluate LLM output
  • Overcome common failures and challenges in AI development
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Table of Contents

16 Chapters
Chapter 1: Getting Started with Generative AI Chevron down icon Chevron up icon
Chapter 2: Building Blocks of Intelligent Applications Chevron down icon Chevron up icon
Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design Chevron down icon Chevron up icon
Chapter 3: Large Language Models Chevron down icon Chevron up icon
Chapter 4: Embedding Models Chevron down icon Chevron up icon
Chapter 5: Vector Databases Chevron down icon Chevron up icon
Chapter 6: AI/ML Application Design Chevron down icon Chevron up icon
Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search Chevron down icon Chevron up icon
Chapter 7: Useful Frameworks, Libraries, and APIs Chevron down icon Chevron up icon
Chapter 8: Implementing Vector Search in AI Applications Chevron down icon Chevron up icon
Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics Chevron down icon Chevron up icon
Chapter 9: LLM Output Evaluation Chevron down icon Chevron up icon
Chapter 10: Refining the Semantic Data Model to Improve Accuracy Chevron down icon Chevron up icon
Chapter 11: Common Failures of Generative AI Chevron down icon Chevron up icon
Chapter 12: Correcting and Optimizing Your Generative AI Application Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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