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  • Book Overview & Buying Building AI Applications with Microsoft Semantic Kernel
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Building AI Applications with Microsoft Semantic Kernel

Building AI Applications with Microsoft Semantic Kernel

By : Lucas A. Meyer
3.9 (9)
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Building AI Applications with Microsoft Semantic Kernel

Building AI Applications with Microsoft Semantic Kernel

3.9 (9)
By: Lucas A. Meyer

Overview of this book

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.
Table of Contents (14 chapters)
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1
Part 1:Introduction to Generative AI and Microsoft Semantic Kernel
4
Part 2: Creating AI Applications with Semantic Kernel
9
Part 3: Real-World Use Cases
11
Chapter 8: Real-World Use Case – Making Your Application Available on ChatGPT

Developing native plugins

Let’s start with native functions. Native functions are regular code in your language of choice (Python or C#) and do not necessarily require a specific directory structure, unlike semantic functions. However, it is easier to put the native functions together with the main code of your program. This will make it easier to import the classes.

Avoid duplicate names

Since semantic functions are directories and native functions are source code files, it’s possible to have a semantic function and a native function with the same name inside a plugin, for example a directory named my_function and a source code file named my_function.py. If you do that, the last function loaded (the source code file) will overwrite the first, leading to unexpected problems. Therefore, avoid duplicate names. At the time of writing, Semantic Kernel does not provide a warning when this happens.

Let’s look at the directory structure of our plugins.

The...

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Building AI Applications with Microsoft Semantic Kernel
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