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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

Dealing with larger, more complex chains

In the previous chapter, we created three plugins:

  • CheckSpreadsheet: A native plugin that checks that the Excel spreadsheet contains the required fields and that they fulfill some rules
  • ParseWordDocument: A native plugin that extracts text from a Word document
  • ProposalChecker: A semantic plugin that checks whether text blocks fulfill some requirements, such as “does this text block describe a team that has a Ph.D. and a medical doctor?”

With these three plugins, you can already solve the business problem of checking proposals by calling each plugin separately and writing the logic to handle whether there was an error. This is likely sufficient for problems that have a small number of steps.

While we are still going to use a small number of steps and a small number of documents for didactic purposes, the approach to analyzing and making decisions on a large number of documents presented in this chapter...

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