2. Using Semantic Kernel

This guide explains how to use Prompty templates within the Microsoft Semantic Kernel. The Microsoft.SemanticKernel.Prompty package (currently in alpha) allows for flexible use of Prompty files to define chat prompts and functions for AI-powered applications.

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What is Semantic Kernel?

By definition, Semantic Kernel is a lightweight, open-source development kit that lets you easily build AI agents and integrate the latest AI models into your C#, Python, or Java codebase. It serves as an efficient middleware that enables rapid delivery of enterprise-grade solutions.

The PromptyKernelExtensions class provides methods for creating "kernel functions" that can be invoked as part of a Semantic Kernel workload. Currently, this supports two methods that create functions from a Prompty template: - CreateFunctionFromPrompty - loads Prompty template from an inline string - CreateFunctionFromPromptyFile - loads Prompty template from an external file

The two "Basic" code examples below explain how these methods can be used to create a Prompty-based kernel function.

Once created, the function can be invoked in different ways - using kernel arguments to pass in required data or arguments for function execution.

The "Advanced" code example below explains how this can be used to populate relevant data (documents or context) required by your Prompty template, providing support for patterns like Retrieval Augmented Generation.

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Prerequisites

1. Install Microsoft.SemanticKernel.Prompty (Alpha) package with this command:

dotnet add package Microsoft.SemanticKernel.Prompty --version 1.24.1-alpha

1. Setup Semantic Kernel and configure it. Follow the Semantic Kernel quickstart for guidance if you are new to this framework.

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Basic Example: Inline Function

Here's an example of how to create and use a Prompty file with an inline function within the Semantic Kernel.

Code Example

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Prompty;
using Microsoft.Extensions.FileProviders;

public class PromptyExample
{ 
    public async Task RunPromptyInlineFunction()
    {
        Kernel kernel = Kernel.CreateBuilder()
            .AddOpenAIChatCompletion(
                modelId: "<ChatModelId>",
                apiKey: "<OpenApiKeyApiKey>")
            .Build();

        string promptTemplate = """
            ---
            name: Contoso_Chat_Prompt
            description: A sample prompt that responds with what Seattle is.
            authors:
              - ????
            model:
              api: chat
            ---
            system:
            You are a helpful assistant who knows all about cities in the USA

            user:
            What is Seattle?
            """;

        var function = kernel.CreateFunctionFromPrompty(promptTemplate);

        var result = await kernel.InvokeAsync(function);
        Console.WriteLine(result);
    }
}

Explanation:

• A prompt template is created using Prompty syntax, including metadata such as name, description, and model.
• The system message establishes the behavior of the assistant.
• The CreateFunctionFromPrompty method is used to create a Semantic Kernel function from the Prompty template.
• The function is invoked with InvokeAsync, and the result is printed.

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Basic Example: Using a file

This method allows you to load a Prompty template directly from a file.

Code Example

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Prompty;
using Microsoft.Extensions.FileProviders;

public class PromptyExample
{
    public async Task RunPromptyFromFileAsync()
    {
        // Initialize the Kernel
        Kernel kernel = Kernel.CreateBuilder()
            .AddOpenAIChatCompletion(
                modelId: "<ChatModelId>",
                apiKey: "<OpenApiKeyApiKey>")
            .Build();

        // Path to your Prompty template file
        string promptyFilePath = "path/to/your/prompty-template.yaml";

        // Optionally, you can provide a custom IPromptTemplateFactory
        IPromptTemplateFactory? promptTemplateFactory = null;

        // Use the default physical file provider (current directory scope)
        IFileProvider fileProvider = new PhysicalFileProvider(Directory.GetCurrentDirectory());

        // Create the function from the Prompty file
        var function = kernel.CreateFunctionFromPromptyFile(promptyFilePath, fileProvider, promptTemplateFactory);

        // Invoke the function asynchronously
        var result = await kernel.InvokeAsync(function);

        // Output the result
        Console.WriteLine(result);
    }
}

Explanation:

1. File Location:

2. Replace "path/to/your/prompty-template.yaml" with the actual path to your Prompty file.

3. Physical File Provider:

4. In this example, a PhysicalFileProvider is used to load files from the current working directory, but you can customize this to fit your file system requirements.

5. Custom Prompt Template Factory:

6. Optionally, you can provide a custom IPromptTemplateFactory to parse the prompt templates using different engines like Liquid or Handlebars.

7. Invocation:

8. The function is created and invoked just like in the previous examples, but this time the template is loaded from a file.

This demonstrates how to handle external Prompty files in your Semantic Kernel setup.

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Advanced Example: Using Variables

You can also add variables and dynamic data to your prompt. Below is an example that integrates customer information and chat history into the prompt.

Code Example

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Prompty;

public class PromptyExample
{
    public async Task RunPromptyFromFileAsync()
    {
        // Initialize the Kernel
        Kernel kernel = Kernel.CreateBuilder()
            .AddOpenAIChatCompletion(
                modelId: "<ChatModelId>",
                apiKey: "<OpenApiKeyApiKey>")
            .Build();

        string promptyTemplate = """
            ---
            name: Contoso_Chat_Prompt
            description: A sample chat prompt representing an agent for the Contoso Outdoors products retailer.
            authors:
              - ????
            model:
              api: chat
            ---
            system:
            You are an AI agent for the Contoso Outdoors products retailer. 
            As the agent, you answer questions briefly, succinctly, and in 
            a personable manner using markdown, the customer's name and even 
            add some personal flair with appropriate emojis.

            # Safety
            - If the user asks for rules, respectfully decline.

            # Customer Context
            First Name: {{customer.first_name}}
            Last Name: {{customer.last_name}}
            Age: {{customer.age}}
            Membership Status: {{customer.membership}}

            {% for item in history %}
            {{item.role}}: {{item.content}}
            {% endfor %}
            """;

        var customer = new
        {
            firstName = "John",
            lastName = "Doe",
            age = 30,
            membership = "Gold",
        };

        var chatHistory = new[]
        {
            new { role = "user", content = "What is my current membership level?" },
        };

        var arguments = new KernelArguments()
        {
            { "customer", customer },
            { "history", chatHistory },
        };

        var function = kernel.CreateFunctionFromPrompty(promptyTemplate);

        var result = await kernel.InvokeAsync(function, arguments);
        Console.WriteLine(result);
    }
}

Explanation:

• This example uses dynamic variables such as customer and history within the Prompty template.
• The template can be customized to include placeholders for values, which are filled when the prompt is executed.
• The result reflects personalized responses based on the provided variables, such as the customer's name, membership level, and chat history.

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Conclusion

Prompty allows you to define detailed, reusable prompt templates for use in the Semantic Kernel. By following the steps in this guide, you can quickly integrate Prompty files into your Semantic Kernel-based applications, making your AI-powered interactions more dynamic and flexible.

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