Building AI Applications with Microsoft Semantic Kernel : Easily Integrate Generative AI Capabilities and Copilot Experiences into Your Applications.

Format:

Book

Author/Creator:

Meyer, Lucas A.

Language:

English

Subjects (All):

Artificial intelligence.

Open source software.

Physical Description:

1 online resource (252 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2024.

Biography/History:

Meyer Lucas A. : Lucas A. Meyer is a Computer Scientist and Financial Economist with over two decades of experience in technology. Lucas joined Microsoft in 2002 to work with databases in Finance, joined Amazon in 2020 to work with fraud detection and prevention, and returned to Microsoft in 2022 as a Principal Research Scientist in the Microsoft's AI for Good Lab, where he works with Large Language Models (LLMs) and the Microsoft Semantic Kernel daily. Lucas' first NLP project, released in 2016, was a chatbot that streamlined several corporate finance operations and won the Adam Smith Award from London's Treasury Today. Lucas has an MBA and an M. Sc. in Finance from the University of Washington in Seattle.

Summary:

Unlock the power of GenAI by effortlessly linking your C# and Python apps with cutting-edge models, orchestrating diverse AI services with finesse, and crafting bespoke applications through immersive, real-world examplesKey FeaturesLink your C# and Python applications with the latest AI models from OpenAICombine and orchestrate different AI services such as text and image generatorsCreate your own AI apps with real-world use case examples that show you how to use basic generative AI, create images, process documents, use a vector databasePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn 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.What you will learnWrite reusable AI prompts and connect to different AI providersCreate new plugins that extend the capabilities of AI servicesUnderstand how to combine multiple plugins to execute complex actionsOrchestrate multiple AI services to accomplish a taskLeverage the powerful planner to automatically create appropriate AI callsUse vector databases as additional memory for your AI tasksDeploy your application to ChatGPT, making it available to hundreds of millions of usersWho this book is forThis book is for beginner-level to experienced .NET or Python software developers who want to quickly incorporate the latest AI technologies into their applications, without having to learn the details of every new AI service. Product managers with some development experience will find this book helpful while creating proof-of-concept applications. This book requires working knowledge of programming basics.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Part 1: Introduction to Generative AI and Microsoft Semantic Kernel

Chapter 1: Introducing Microsoft Semantic Kernel

Technical requirements

Obtaining an OpenAI API key

Obtaining an Azure OpenAI API key

Generative AI and how to use it

Text generation models

Understanding the difference between applications and models

Generating text using consumer applications

Generating images

Microsoft Semantic Kernel

Installing the Microsoft Semantic Kernel package

Using Semantic Kernel to connect to AI services

Connecting to OpenAI Services using Python

Connecting to OpenAI services using C#

Running a simple prompt

Running a simple prompt in Python

Running a simple prompt in C#

Using generative AI to solve simple problems

Creating semantic functions

Creating native functions

Plugins

The config.json file for the knock-knock joke function

The skprompt.txt file for the knock-knock joke function

The config.json file for the semantic function that explains jokes

The skprompt.txt file for the explain joke function

Loading the plugin from a directory into the kernel

Using a planner to run a multistep task

Calling the Function Calling Stepwise planner with Python

Summary

References

Chapter 2: Creating Better Prompts

A simple plugin template

The skprompt.txt file

The config.json file

Calling the plugin from Python

Calling the plugin from C#

Results

Improving the prompt to get better results

Revising the skprompt.txt file

The result

Prompts with multiple variables

Requesting a complex itinerary with Python

Requesting a complex itinerary with C#

The result of the complex itinerary

Issues when answering math problems.

Multistage prompts

CoT - "Let's think step by step"

Implementing CoT with Python

Implementing CoT with C#

Results for CoT

An ensemble of answers

Part 2: Creating AI Applications with Semantic Kernel

Chapter 3: Extending Semantic Kernel

Getting to know the core plugins

An example - Using the TimePlugin

Introducing the application - Validating grants

Directory structure of our application

Developing native plugins

The directory structure of our plugins

Checking the structure of our Excel spreadsheet

Adding additional checks

Evaluating the Word document

Developing semantic plugins

Evaluating the grant proposal with a semantic plugin

Chapter 4: Performing Complex Actions by Chaining Functions

Creating a native plugin that generates images

Writing a DALL-E 3 wrapper in Python

Writing a DALL-E 3 wrapper in C#

Using multiple steps to solve a problem

Generating an image from a clue

Chaining semantic and native functions with C#

Chaining semantic and native functions with Python

Dealing with larger, more complex chains

Preparing our directory structure

Understanding the flow of our process

Creating the native function to process a folder

Modifying the Excel native plugin

Modifying the Word native plugin

Modifying the semantic functions

Creating and calling the pipeline

Chapter 5: Programming with Planners

What is a planner?

When to use a planner

Instantiating a planner

Creating and running a plan

An example of how a planner can help

How do planners work?

Controlling home automation with the planner

Creating the native functions

Adding a semantic function to suggest movies.

Invoking the planner

Chapter 6: Adding Memories to Your AI Application

Defining memory and embeddings

How does semantic memory work?

Embeddings in action

Using memory within chats and LLMs

Using memory with Microsoft Semantic Kernel

Using memory in chats

Reducing history size with summarization

Part 3: Real-World Use Cases

Chapter 7: Real-World Use Case - Retrieval-Augmented Generation

Why would you need to customize GPT models?

Retrieval-augmented generation

Creating an index

Uploading documents to the index

Using the index to find academic articles

Using RAG to create a summary of several articles on a topic

Chapter 8: Real-World Use Case - Making Your Application Available on ChatGPT

Custom GPT agents

Creating a custom GPT

Creating a custom GPT that supports actions

Creating a web API wrapper for the native function

Deploying your application to an Azure Web App

Connecting the custom GPT with your custom GPT action

Index

Other Books You May Enjoy.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Meyer, Lucas A. Building AI Applications with Microsoft Semantic Kernel

ISBN:

9781835469590

OCLC:

1439005024