Start learning how to use Semantic Kernel

In just a few steps, you can start running the getting started guides for Semantic Kernel in either C# or Python. After completing the guides, you'll know how to...

• Configure your local machine to run Semantic Kernel
• Run AI prompts from the kernel
• Make AI prompts dynamic with variables
• Create a simple AI agent
• Automatically combine functions together with planners
• Store and retrieve memory with embeddings

If you are an experienced developer, you can skip the guides and directly access the packages from the Nuget feed or PyPI.

C#

Instructions for accessing the SemanticKernel Nuget feed is available here. It's as easy as:

#r "nuget: Microsoft.SemanticKernel, *-*"

Java

The SemanticKernel bom is available here. Using the package is as easy as:

    <dependencyManagement>
        <dependencies>
            <dependency>
                <groupId>com.microsoft.semantic-kernel</groupId>
                <artifactId>semantickernel-bom</artifactId>
                <version>${semantickernel.version}</version>
                <scope>import</scope>
                <type>pom</type>
            </dependency>
        </dependencies>
    </dependencyManagement>

    <dependency>
        <groupId>com.microsoft.semantic-kernel</groupId>
        <artifactId>semantickernel-api</artifactId>
    </dependency>

Python

Instructions for accessing the SemanticKernel Python package is available here. It's as easy as:

pip install semantic-kernel

Requirements to run the guides

Before running the guides in C#, make sure you have the following installed on your local machine.

• git or the GitHub app
• VSCode or Visual Studio
• An OpenAI key via either Azure OpenAI Service or OpenAI
• .Net 7 SDK - for C# notebook guides
• In VS Code the Polyglot Notebook - for notebook guides

If you are using the Python guides, you just need git and python. These guides have been tested on python versions 3.8-3.11.

Download and run the guides

To setup the guides, follow the steps below.

Tip

Have your OpenAI or Azure OpenAI keys ready to enter when prompted by the Jupyter notebook.

1. Use your web browser to visit aka.ms/sk/repo on GitHub.

2. Clone or fork the repo to your local machine.

   Note

   If you are new to using GitHub and have never cloned a repo to your local machine, please review this guide.

   Note

   If you are a new contributor to open source, please fork the repo to start your journey.

   If you have trouble cloning or forking the repo, you can watch the video below.

3. While the repository is open in VS Code, navigate to the /dotnet/notebooks or /python/notebooks folder.

4. Choose either the dotnet or python folder based on your preferred programming language.

5. Open the 00-getting-started.ipynb notebook.

6. Activate each code snippet with the "play" button on the left hand side.

   If you need help running the 00-getting-started.ipynb notebook, you can watch the video below.

7. Repeat for the remaining notebooks.

Run Java Samples

Details of running the Java samples can be found [here] (https://github.com/microsoft/semantic-kernel/tree/java-v1/java/samples/sample-code).

AZURE_CLIENT_KEY="my-key" \
CLIENT_ENDPOINT="https://<MY_INSTANCE>.openai.azure.com/" \
../../mvnw exec:java -Dsample=Example13_ConversationSummaryPlugin -Dexec.cleanupDaemonThreads=false

Like what you see?

If you are a fan of Semantic Kernel, please give the repo a ⭐️ star to show your support.

Keep learning

The guides are an easy way run sample code and learn how to use Semantic Kernel. If you want to learn more about the concepts behind Semantic Kernel, keep reading the docs. Based on your experience level, you can jump to the section that best fits your needs.

Experience level	Next step
For beginners who are just starting to learn about AI	Learn prompt engineering
For people who are well versed in prompt engineering	Orchestrate AI plugins
For people familiar with using AI plugins	Store and retrieve memory

Learn how to Orchestrate AI