chore: import upstream snapshot with attribution

dotnet/samples/Demos/AotCompatibility/AotCompatibility.csproj

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+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <RootNamespace>SemanticKernel.AotCompatibility</RootNamespace>
+    <TargetFramework>net10.0</TargetFramework>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <Nullable>enable</Nullable>
+    <PublishAot>true</PublishAot>
+    <TrimmerSingleWarn>false</TrimmerSingleWarn>
+    <NoWarn>VSTHRD111,CA2007;IDE1006,SKEXP0120</NoWarn>
+    <UserSecretsId>5ee045b0-aea3-4f08-8d31-32d1a6f8fed0</UserSecretsId>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Microsoft.Extensions.Configuration" />
+    <PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Connectors\Connectors.Onnx\Connectors.Onnx.csproj" />
+    <ProjectReference Include="..\..\..\src\SemanticKernel.Abstractions\SemanticKernel.Abstractions.csproj" />
+    <ProjectReference Include="..\..\..\src\SemanticKernel.Core\SemanticKernel.Core.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/Demos/AotCompatibility/JsonSerializerContexts/LocationJsonSerializerContext.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json.Serialization;
+using SemanticKernel.AotCompatibility.Plugins;
+
+namespace SemanticKernel.AotCompatibility.JsonSerializerContexts;
+
+[JsonSerializable(typeof(Location))]
+internal sealed partial class LocationJsonSerializerContext : JsonSerializerContext
+{
+}

dotnet/samples/Demos/AotCompatibility/JsonSerializerContexts/WeatherJsonSerializerContext.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json.Serialization;
+using SemanticKernel.AotCompatibility.Plugins;
+
+namespace SemanticKernel.AotCompatibility.JsonSerializerContexts;
+
+[JsonSerializable(typeof(Weather))]
+internal sealed partial class WeatherJsonSerializerContext : JsonSerializerContext
+{
+}

dotnet/samples/Demos/AotCompatibility/KernelFunctionSamples.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json;
+using Microsoft.Extensions.Configuration;
+using Microsoft.SemanticKernel;
+using SemanticKernel.AotCompatibility.JsonSerializerContexts;
+using SemanticKernel.AotCompatibility.Plugins;
+
+namespace SemanticKernel.AotCompatibility;
+
+/// <summary>
+/// This class contains samples of how to create and invoke kernel functions in AOT applications.
+/// </summary>
+internal static class KernelFunctionSamples
+{
+    /// <summary>
+    /// Creates a kernel function from a lambda and invokes it.
+    /// </summary>
+    /// <remarks>
+    /// Other overloads of KernelFunctionFactory.CreateFromMethod can also be used to create functions,
+    /// as well as the Kernel.CreateFunctionFromMethod extension methods.
+    /// </remarks>
+    public static async Task CreateFunctionFromLambda(IConfigurationRoot _)
+    {
+        Kernel kernel = new();
+
+        // Create JsonSerializerOptions with custom JsonSerializerContexts for the Location and Weather types that are used in the lambda below.
+        // This is necessary for JsonSerializer to infer the type information for these types in AOT applications.  
+        JsonSerializerOptions options = new();
+        options.TypeInfoResolverChain.Add(WeatherJsonSerializerContext.Default);
+        options.TypeInfoResolverChain.Add(LocationJsonSerializerContext.Default);
+
+        // Create a kernel function.
+        KernelFunction function = KernelFunctionFactory.CreateFromMethod(
+            method: (Location location) => location.City == "Boston" ? new Weather { Temperature = 61, Condition = "rainy" } : throw new NotImplementedException(),
+            jsonSerializerOptions: options);
+
+        // Invoke the function
+        KernelArguments arguments = new() { ["location"] = new Location("USA", "Boston") };
+
+        FunctionResult functionResult = await function.InvokeAsync(kernel, arguments);
+
+        // Display the result
+        Weather weather = functionResult.GetValue<Weather>()!;
+        Console.WriteLine($"Temperature: {weather.Temperature}, Condition: {weather.Condition}");
+    }
+}

dotnet/samples/Demos/AotCompatibility/KernelPluginSamples.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+using System.Text.Json;
+using Microsoft.Extensions.Configuration;
+using Microsoft.SemanticKernel;
+using SemanticKernel.AotCompatibility.JsonSerializerContexts;
+using SemanticKernel.AotCompatibility.Plugins;
+
+namespace SemanticKernel.AotCompatibility;
+
+/// <summary>
+/// This class contains samples of how to create, import and add kernel plugins and invoke their functions in AOT applications.
+/// </summary>
+internal static class KernelPluginSamples
+{
+    /// <summary>
+    /// Creates a kernel plugin from a type and invokes its function.
+    /// </summary>
+    /// <remarks>
+    /// The KernelPluginFactory class provides other methods such as CreateFromObject and CreateFromFunctions,
+    /// which can be used to create a plugin from a class instance or a list of functions.
+    /// Additionally, the Kernel.CreatePluginFrom* extension methods are available for similar purposes.
+    /// </remarks>
+    public static async Task CreatePluginFromType(IConfigurationRoot _)
+    {
+        Kernel kernel = new();
+
+        // Create JsonSerializerOptions with custom JsonSerializerContexts for the Location and Weather types that are used by the plugin below.
+        // This is necessary for JsonSerializer to infer the type information for these types in AOT applications.  
+        JsonSerializerOptions options = new();
+        options.TypeInfoResolverChain.Add(WeatherJsonSerializerContext.Default);
+        options.TypeInfoResolverChain.Add(LocationJsonSerializerContext.Default);
+
+        // Create a kernel plugin
+        KernelPlugin plugin = KernelPluginFactory.CreateFromType<WeatherPlugin>(options, "weather_utils");
+
+        // Invoke the function
+        KernelFunction function = plugin["GetCurrentWeather"];
+        KernelArguments arguments = new() { ["location"] = new Location("USA", "Boston") };
+
+        FunctionResult functionResult = await function.InvokeAsync(kernel, arguments);
+
+        // Display the result
+        Weather weather = functionResult.GetValue<Weather>()!;
+        Console.WriteLine($"Temperature: {weather.Temperature}, Condition: {weather.Condition}");
+    }
+
+    /// <summary>
+    /// Imports a kernel plugin into the kernel's plugin collection from a type and invokes its function.
+    /// </summary>
+    /// <remarks>
+    /// The kernel provides extension methods like ImportFromObject, ImportFromFunctions and ImportPluginFromPromptDirectory,
+    /// allowing the import of a plugin from a class instance, a collection of functions or a prompt directory.
+    /// </remarks>
+    public static async Task ImportPluginFromType(IConfigurationRoot _)
+    {
+        Kernel kernel = new();
+
+        // Create JsonSerializerOptions with custom JsonSerializerContexts for the Location and Weather types that are used by the plugin below.
+        // This is necessary for JsonSerializer to infer the type information for these types in AOT applications.  
+        JsonSerializerOptions options = new();
+        options.TypeInfoResolverChain.Add(WeatherJsonSerializerContext.Default);
+        options.TypeInfoResolverChain.Add(LocationJsonSerializerContext.Default);
+
+        // Create a kernel plugin
+        KernelPlugin plugin = kernel.ImportPluginFromType<WeatherPlugin>(options, "weather_utils");
+
+        // Invoke the function
+        KernelFunction function = kernel.Plugins["weather_utils"]["GetCurrentWeather"];
+        KernelArguments arguments = new() { ["location"] = new Location("USA", "Boston") };
+
+        FunctionResult functionResult = await function.InvokeAsync(kernel, arguments);
+
+        // Display the result
+        Weather weather = functionResult.GetValue<Weather>()!;
+        Console.WriteLine($"Temperature: {weather.Temperature}, Condition: {weather.Condition}");
+    }
+
+    /// <summary>
+    /// Adds a kernel plugin into the kernel's plugin collection from a type and invokes its function.
+    /// </summary>
+    /// <remarks>
+    /// Other extension methods like AddFromObject, AddFromFunctions
+    /// can be used to create a plugin and add it to the kernel's plugins collection.
+    /// </remarks>
+    public static async Task AddPluginFromType(IConfigurationRoot _)
+    {
+        Kernel kernel = new();
+
+        // Create JsonSerializerOptions with custom JsonSerializerContexts for the Location and Weather types that are used by the plugin below.
+        // This is necessary for JsonSerializer to infer the type information for these types in AOT applications.  
+        JsonSerializerOptions options = new();
+        options.TypeInfoResolverChain.Add(WeatherJsonSerializerContext.Default);
+        options.TypeInfoResolverChain.Add(LocationJsonSerializerContext.Default);
+
+        // Create a kernel plugin
+        KernelPlugin plugin = kernel.Plugins.AddFromType<WeatherPlugin>(options, "weather_utils");
+
+        // Invoke the function
+        KernelFunction function = kernel.Plugins["weather_utils"]["GetCurrentWeather"];
+        KernelArguments arguments = new() { ["location"] = new Location("USA", "Boston") };
+
+        FunctionResult functionResult = await function.InvokeAsync(kernel, arguments);
+
+        // Display the result
+        Weather weather = functionResult.GetValue<Weather>()!;
+        Console.WriteLine($"Temperature: {weather.Temperature}, Condition: {weather.Condition}");
+    }
+}

dotnet/samples/Demos/AotCompatibility/OnnxChatCompletionSamples.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Extensions.Configuration;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.ChatCompletion;
+using Microsoft.SemanticKernel.Connectors.Onnx;
+
+namespace SemanticKernel.AotCompatibility;
+
+/// <summary>
+/// This class contains samples of how to use ONNX chat completion service in AOT applications.
+/// </summary>
+internal static class OnnxChatCompletionSamples
+{
+    /// <summary>
+    /// Sends a prompt to the ONNX model and gets the chat message content.
+    /// </summary>
+    public static async Task GetChatMessageContent(IConfigurationRoot config)
+    {
+        string chatModelPath = config["Onnx:ModelPath"]!;
+        string chatModelId = config["Onnx:ModelId"] ?? "phi-3";
+
+        // Create kernel builder and add OnnxRuntimeGenAIChatCompletion service.
+        // If you plan to use the service with Non-ONNX prompt execution settings,  
+        // supply JSON serializer options with a JSON serializer context for this setup.
+        IKernelBuilder builder = Kernel.CreateBuilder()
+            .AddOnnxRuntimeGenAIChatCompletion(chatModelId, chatModelPath);
+
+        // Build kernel and get the service instance
+        Kernel kernel = builder.Build();
+        IChatCompletionService chatService = kernel.GetRequiredService<IChatCompletionService>();
+
+        string prompt = "Hello, what is the weather in Boston, USA now?";
+
+        OnnxRuntimeGenAIPromptExecutionSettings executionSettings = new()
+        {
+            Temperature = 0.7f, // Adjusts creativity level  
+            TopP = 0.9f // Limits token choice diversity
+        };
+
+        // Prompt the ONNX model
+        ChatMessageContent messageContent = await chatService.GetChatMessageContentAsync(prompt, executionSettings);
+
+        // Display the result
+        Console.WriteLine(messageContent);
+    }
+
+    /// <summary>
+    /// Sends a prompt to the ONNX model and gets the chat message content in a streaming fashion.
+    /// </summary>
+    public static async Task GetStreamingChatMessageContents(IConfigurationRoot config)
+    {
+        string chatModelPath = config["Onnx:ModelPath"]!;
+        string chatModelId = config["Onnx:ModelId"] ?? "phi-3";
+
+        // Create kernel builder and add OnnxRuntimeGenAIChatCompletion service.
+        // If you plan to use the service with Non-ONNX prompt execution settings,  
+        // supply JSON serializer options with a JSON serializer context for this setup.
+        IKernelBuilder builder = Kernel.CreateBuilder()
+            .AddOnnxRuntimeGenAIChatCompletion(chatModelId, chatModelPath);
+
+        // Build kernel and get the service instance
+        Kernel kernel = builder.Build();
+        IChatCompletionService chatService = kernel.GetRequiredService<IChatCompletionService>();
+
+        string prompt = "Hello, what is the weather in Boston, USA now?";
+
+        OnnxRuntimeGenAIPromptExecutionSettings executionSettings = new()
+        {
+            Temperature = 0.7f, // Adjusts creativity level  
+            TopP = 0.9f // Limits token choice diversity
+        };
+
+        // Prompt the ONNX model
+        await foreach (StreamingChatMessageContent messageContent in chatService.GetStreamingChatMessageContentsAsync(prompt, executionSettings))
+        {
+            // Display the result
+            Console.WriteLine(messageContent);
+        }
+    }
+}

dotnet/samples/Demos/AotCompatibility/Plugins/Location.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+namespace SemanticKernel.AotCompatibility.Plugins;
+
+internal sealed class Location
+{
+    public string Country { get; set; }
+
+    public string City { get; set; }
+
+    public Location(string country, string city)
+    {
+        this.Country = country;
+        this.City = city;
+    }
+}

dotnet/samples/Demos/AotCompatibility/Plugins/Weather.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+namespace SemanticKernel.AotCompatibility.Plugins;
+
+internal sealed class Weather
+{
+    public int? Temperature { get; set; }
+    public string? Condition { get; set; }
+
+    public override string ToString() => $"Current weather(temperature: {this.Temperature}F, condition: {this.Condition})";
+}

dotnet/samples/Demos/AotCompatibility/Plugins/WeatherPlugin.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+using System.ComponentModel;
+using Microsoft.SemanticKernel;
+
+namespace SemanticKernel.AotCompatibility.Plugins;
+
+internal sealed class WeatherPlugin
+{
+    [KernelFunction]
+    [Description("Get the current weather in a given location.")]
+    public Weather GetCurrentWeather(Location location)
+    {
+        return location.City switch
+        {
+            "Boston" => new Weather { Temperature = 61, Condition = "rainy" },
+            "London" => new Weather { Temperature = 55, Condition = "cloudy" },
+            "Miami" => new Weather { Temperature = 80, Condition = "sunny" },
+            "Tokyo" => new Weather { Temperature = 50, Condition = "sunny" },
+            "Sydney" => new Weather { Temperature = 75, Condition = "sunny" },
+            _ => new Weather { Temperature = 31, Condition = "snowing" }
+        };
+    }
+}

dotnet/samples/Demos/AotCompatibility/Program.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+using Microsoft.Extensions.Configuration;
+
+namespace SemanticKernel.AotCompatibility;
+
+/// <summary>
+/// This application demonstrates how to use the Semantic Kernel in AOT applications.
+/// </summary>
+internal sealed class Program
+{
+    private static async Task<int> Main(string[] args)
+    {
+        var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
+
+        bool success = await RunAsync(s_samples, config);
+
+        return success ? 1 : 0;
+    }
+
+    private static readonly Func<IConfigurationRoot, Task>[] s_samples =
+    [
+        // Samples showing how to create a kernel function and invoke it in AOT applications.
+        KernelFunctionSamples.CreateFunctionFromLambda,
+
+        // Samples showing how to create, import and add a kernel plugin and invoke its functions in AOT applications.
+        KernelPluginSamples.CreatePluginFromType,
+        KernelPluginSamples.ImportPluginFromType,
+        KernelPluginSamples.AddPluginFromType,
+
+        // Samples showing how to use ONNX chat completion service in AOT applications.
+        OnnxChatCompletionSamples.GetChatMessageContent,
+        OnnxChatCompletionSamples.GetStreamingChatMessageContents
+    ];
+
+    private static async Task<bool> RunAsync(IEnumerable<Func<IConfigurationRoot, Task>> functionsToRun, IConfigurationRoot config)
+    {
+        bool failed = false;
+
+        foreach (var function in functionsToRun)
+        {
+            Console.Write($"Running - {function.Method.DeclaringType?.Name}.{function.Method.Name}");
+
+            try
+            {
+                await function(config);
+            }
+            catch (Exception)
+            {
+                failed = true;
+            }
+        }
+
+        return failed;
+    }
+}

dotnet/samples/Demos/AotCompatibility/README.md

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+# Native-AOT Samples
+This application demonstrates how to use the Semantic Kernel Native-AOT compatible API in a Native-AOT application.
+
+## Running Samples
+The samples be run either in a debug mode by just setting a break point and pressing `F5` in Visual Studio (make sure the `AotCompatibility` project is set as the startup project) in which case they are run in a regular CoreCLR application and not in Native-AOT one. This might be useful to understand how the API works and how to use it.
+
+To run the samples in a Native-AOT application, first publish it using the following command: `dotnet publish -r win-x64`. Then, execute the application by running the following command in the terminal: `.\bin\Release\net8.0\win-x64\publish\AotCompatibility.exe`.  
+
+## Samples
+Most of the samples don't require any additional setup, and can be run as is. However, some of them might require additional configuration.
+
+### 1. [ONNX Chat Completion Service](./OnnxChatCompletionSamples.cs)
+To configure the sample, you need to download the ONNX model from the Hugging Face repository. Go to a directory of your choice where the model should be downloaded and run the following command:
+```powershell
+git clone https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx
+```
+
+> [!IMPORTANT]
+The `Phi-3` model may be too large to download using the `git clone` command unless you have the [git-lfs extension](https://git-lfs.com/) installed. 
+You might need to download it manually using the following link: [Phi-3-Mini-4k CPU](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx/resolve/main/cpu_and_mobile/cpu-int4-rtn-block-32/phi3-mini-4k-instruct-cpu-int4-rtn-block-32.onnx.data?download=true) (approximately 2.7 GB).
+
+After downloading the model, you need to configure the sample by setting the `Onnx:ModelPath` and `Onnx:ModelId` secrets. 
+The `Onnx:ModelPath` should point to the directory where the model was downloaded, and the `Onnx:ModelId` should be set to `phi-3`.
+The secrets can be set using [Secret Manager](https://learn.microsoft.com/en-us/aspnet/core/security/app-secrets#secret-manager) in the following way:
+```powershell
+dotnet user-secrets set "Onnx:ModelId" "phi-3"
+dotnet user-secrets set "Onnx:ModelPath" "C:\path\to\huggingface\Phi-3-mini-4k-instruct-onnx\cpu_and_mobile\cpu-int4-rtn-block-32" 
+```
+
+### AOT Compatibility
+At the moment, the following Semantic Kernel packages are AOT compatible:
+
+| Package                   | AOT compatible |  
+|--------------------------|----------------|  
+| SemanticKernel.Abstractions | ✔️              |  
+| SemanticKernel.Core         | ✔️              |  
+| Connectors.Onnx            | ✔️              |  
+
+Other packages are not AOT compatible yet, but we plan to make them compatible in the future.