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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

Summary

Before having the help of AI, reading and interpreting a document required using the time of a person, or writing a specialized machine learning model. Semantic Kernel allows you to write code to analyze large and complex documents.

In our pipeline, the CheckSpreadsheet native plugin does not strictly require Semantic Kernel and could be done in a separate step, since it only runs code that is never read by AI. We added it to the pipeline to make our end-to-end solution more streamlined.

The ParseWordDocument native plugin, on the other hand, helps Semantic Kernel receive the information in parts. Breaking the document into parts makes the semantic functions simpler: each function can evaluate just a portion of the document. For example, the function that evaluates the Teams section of the document just needs to check the team qualifications. That makes the function a lot simpler to write than a function that reads the whole document and decides about all sections of...

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