Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Building AI Applications with Microsoft Semantic Kernel

You're reading from   Building AI Applications with Microsoft Semantic Kernel Easily integrate generative AI capabilities and copilot experiences into your applications

Arrow left icon
Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781835463703
Length 252 pages
Edition 1st Edition
Languages
Tools
Concepts
Arrow right icon
Author (1):
Arrow left icon
Lucas A. Meyer Lucas A. Meyer
Author Profile Icon Lucas A. Meyer
Lucas A. Meyer
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Part 1:Introduction to Generative AI and Microsoft Semantic Kernel
2. Chapter 1: Introducing Microsoft Semantic Kernel FREE CHAPTER 3. Chapter 2: Creating Better Prompts 4. Part 2: Creating AI Applications with Semantic Kernel
5. Chapter 3: Extending Semantic Kernel 6. Chapter 4: Performing Complex Actions by Chaining Functions 7. Chapter 5: Programming with Planners 8. Chapter 6: Adding Memories to Your AI Application 9. Part 3: Real-World Use Cases
10. Chapter 7: Real-World Use Case – Retrieval-Augmented Generation 11. Chapter 8: Real-World Use Case – Making Your Application Available on ChatGPT 12. Index 13. Other Books You May Enjoy

Creating and running a plan

Now that we have a planner, we can use it to create a plan for a user’s request and then invoke the plan to get a result. In both languages, we use two steps, one to create the plan and another one to execute it.

For the next two code snippets, assume you have the user’s request loaded into the ask string. Let’s see how to call the planner:

C#

var plan = await planner.CreatePlanAsync(kernel, ask);
var result = await plan.InvokeAsync(kernel);
Console.Write ($"Results: {result}");

Python

result = await planner.invoke(kernel, ask)
print(result.final_answer)

You may remember from Chapter 1 that in Python, the result variable contains all the steps to create the plan, so in order to see the plan’s results, you need to print result.final_answer. If you print the result variable, you’ll get a large JSON object.

An example of how a planner can help

Let’s see a simple example that already shows...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image