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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

Defining memory and embeddings

LLMs provided by AI services such as OpenAI are stateless, meaning they don’t retain any memory of previous interactions. When you submit a request, the request itself contains all the information the model will use to respond. Any previous requests you submitted have already been forgotten by the model. While this stateless nature allows for many useful applications, some situations require the model to consider more context across multiple requests.

Despite their immense computing power, most LLMs can only work with small amounts of text, about one page at a time, although this has been increasing recently — the new GPT-4 Turbo, released in November 2023, can receive 128,000 tokens as input, which is about 200 pages of text. Sometimes, however, there are applications that require a model to consider more than 200 pages of text — for example, a model that answers questions about a large collection of academic papers.

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