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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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Free Chapter
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

Using multiple steps to solve a problem

Although programming solutions step by step can be very helpful, one of the best abilities that Semantic Kernel gives users is allowing them to make requests using natural language. This will require using planners, which we will use in Chapter 5, to break down a user request into multiple steps and then automatically call each step in the appropriate order.

In this section, we will solve problems by telling Semantic Kernel which functions to call. This is helpful for making sure that the solutions we make available to the planner work, and it is also helpful when we want to explicitly control how things are executed.

To illustrate the manual approach, we will see how to give Semantic Kernel clues about an animal, guess it with a semantic function, and then generate an image of the animal using the native function we created in the previous section.

Generating an image from a clue

In the following code, we have two steps. In the first...

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