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    Building AI Applications with Microsoft Semantic Kernel: Easily integrate generative AI capabilities and copilot experiences

    Posted By: naag
    Building AI Applications with Microsoft Semantic Kernel: Easily integrate generative AI capabilities and copilot experiences

    Building AI Applications with Microsoft Semantic Kernel: Easily integrate generative AI capabilities and copilot experiences into your applications
    English | 2024 | ASIN: B0D2318CJC | 384 pages | EPUB (True) | 7.94 MB

    Unlock the power of GenAI by effortlessly linking your C# and Python apps with cutting-edge models, orchestrating diverse AI services with finesse, and crafting bespoke applications through immersive, real-world examples

    Key Features
    Link your C# and Python applications with the latest AI models from OpenAI
    Combine and orchestrate different AI services such as text and image generators
    Create your own AI apps with real-world use case examples that show you how to use basic generative AI, create images, process documents, use a vector database
    Purchase of the print or Kindle book includes a free PDF eBook
    Book Description
    In the fast-paced world of AI, developers are constantly seeking efficient ways to integrate AI capabilities into their apps. Microsoft Semantic Kernel simplifies this process by using the GenAI features from Microsoft and OpenAI.

    Written by Lucas A. Meyer, a Principal Research Scientist in Microsoft’s AI for Good Lab, this book helps you get hands on with Semantic Kernel. It begins by introducing you to different generative AI services such as GPT-3.5 and GPT-4, demonstrating their integration with Semantic Kernel. You’ll then learn to craft prompt templates for reuse across various AI services and variables. Next, you’ll learn how to add functionality to Semantic Kernel by creating your own plugins. The second part of the book shows you how to combine multiple plugins to execute complex actions, and how to let Semantic Kernel use its own AI to solve complex problems by calling plugins, including the ones made by you. The book concludes by teaching you how to use vector databases to expand the memory of your AI services and how to help AI remember the context of earlier requests. You’ll also be guided through several real-world examples of applications, such as RAG and custom GPT agents.

    By the end of this book, you'll have gained the knowledge you need to start using Semantic Kernel to add AI capabilities to your applications.

    What you will learn
    Write reusable AI prompts and connect to different AI providers
    Create new plugins that extend the capabilities of AI services
    Understand how to combine multiple plugins to execute complex actions
    Orchestrate multiple AI services to accomplish a task
    Leverage the powerful planner to automatically create appropriate AI calls
    Use vector databases as additional memory for your AI tasks
    Deploy your application to ChatGPT, making it available to hundreds of millions of users
    Who this book is for
    This book is for beginner-level to experienced .NET or Python software developers who want to quickly incorporate the latest AI technologies into their applications, without having to learn the details of every new AI service. Product managers with some development experience will find this book helpful while creating proof-of-concept applications. This book requires working knowledge of programming basics.

    Table of Contents
    Introducing Microsoft Semantic Kernel
    Creating Better Prompts
    Extending Semantic Kernel
    Performing Complex Actions by Chaining Functions
    Programming with Planners
    Adding Memories to Your AI Application
    Real-World Use Case – Retrieval-Augmented Generation
    Real-World Use Case – Making Your Application Available on ChatGPT