Semantic Kernel Chat Streaming in C

This sample demonstrates how to use the Semantic Kernel Chat API with streaming in a C# console application.

Program.cs
SemanticKernelChatCompletionsStreamingClass.cs

How to generate this sample

Command

ai dev new sk-chat-streaming --csharp

Output

AI - Azure AI CLI, Version 1.0.0
Copyright (c) 2024 Microsoft Corporation. All Rights Reserved.

This PUBLIC PREVIEW version may change at any time.
See: https://aka.ms/azure-ai-cli-public-preview

Generating 'sk-chat-streaming' in 'sk-chat-streaming-cs' (3 files)...

sk-chat-streaming.csproj
SemanticKernelChatCompletionsStreamingClass.cs
Program.cs

Generating 'sk-chat-streaming' in 'sk-chat-streaming-cs' (3 files)... DONE!

Program.cs

STEP 1: Read the configuration settings from environment variables.

Program.cs

var AZURE_OPENAI_API_KEY = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY") ?? "<insert your Azure OpenAI API key here>";
var AZURE_OPENAI_ENDPOINT = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? "<insert your Azure OpenAI endpoint here>";
var AZURE_OPENAI_CHAT_DEPLOYMENT = Environment.GetEnvironmentVariable("AZURE_OPENAI_CHAT_DEPLOYMENT") ?? "<insert your Azure OpenAI chat deployment name here>";
var AZURE_OPENAI_SYSTEM_PROMPT = Environment.GetEnvironmentVariable("AZURE_OPENAI_SYSTEM_PROMPT") ?? "You are a helpful AI assistant.";

STEP 2: Check if the required environment variables are set.

Program.cs

var azureOk = 
    AZURE_OPENAI_API_KEY != null && !AZURE_OPENAI_API_KEY.StartsWith("<insert") &&
    AZURE_OPENAI_CHAT_DEPLOYMENT != null && !AZURE_OPENAI_CHAT_DEPLOYMENT.StartsWith("<insert") &&
    AZURE_OPENAI_ENDPOINT != null && !AZURE_OPENAI_ENDPOINT.StartsWith("<insert");

var ok = azureOk &&
    AZURE_OPENAI_SYSTEM_PROMPT != null && !AZURE_OPENAI_SYSTEM_PROMPT.StartsWith("<insert");

if (!ok)
{
    Console.WriteLine(
        "To use Azure OpenAI, set the following environment variables:\n" +
        "\n  AZURE_OPENAI_SYSTEM_PROMPT" +
        "\n  AZURE_OPENAI_API_KEY" +
        "\n  AZURE_OPENAI_CHAT_DEPLOYMENT" +
        "\n  AZURE_OPENAI_ENDPOINT"
    );
    Console.WriteLine(
        "\nYou can easily do that using the Azure AI CLI by doing one of the following:\n" +
        "\n  ai init" +
        "\n  ai dev shell" +
        "\n  dotnet run" +
        "\n" +
        "\n  or" +
        "\n" +
        "\n  ai init" +
        "\n  ai dev shell --run \"dotnet run\""
    );

    return 1;
}

STEP 3: Initialize the kernel with the configuration settings and create the streaming chat completions helper.

Program.cs

var builder = Kernel.CreateBuilder();
builder.AddAzureOpenAIChatCompletion(AZURE_OPENAI_CHAT_DEPLOYMENT!, AZURE_OPENAI_ENDPOINT!, AZURE_OPENAI_API_KEY!);
var kernel = builder.Build();
var chat = new SemanticKernelChatCompletionsStreamingClass(AZURE_OPENAI_SYSTEM_PROMPT!, kernel);

STEP 4: Obtain user input, use the helper class to get the assistant's response, and display responses as they are received.

Program.cs

while (true)
{
    Console.Write("User: ");
    var userPrompt = Console.ReadLine();
    if (string.IsNullOrEmpty(userPrompt) || userPrompt == "exit") break;

    Console.Write("\nAssistant: ");
    await chat.GetStreamingChatMessageContentsAsync(userPrompt, content =>
        Console.Write(content.Content)
    );
    Console.WriteLine("\n");
}

SemanticKernelChatCompletionsStreamingClass.cs

STEP 1: Initialize the kernel and chat message history with a system message.

SemanticKernelChatCompletionsStreamingClass.cs

public SemanticKernelChatCompletionsStreamingClass(string systemPrompt, Kernel kernel)
{
    _systemPrompt = systemPrompt;
    _kernel = kernel;

    _history = new ChatHistory(_systemPrompt);
    _chatCompletionService = _kernel.GetRequiredService<IChatCompletionService>();
}

public void ClearConversation()
{
    _history.RemoveRange(1, _history.Count);
}

STEP 2: When the user provides input, add the user message to the chat message history.

SemanticKernelChatCompletionsStreamingClass.cs

public async Task<string> GetStreamingChatMessageContentsAsync(string userPrompt, Action<StreamingChatMessageContent>? callback = null)
{
    _history.AddUserMessage(userPrompt);

STEP 3: Send the chat message history to the streaming chat API.

SemanticKernelChatCompletionsStreamingClass.cs

    var responseContent = string.Empty;
    var response = _chatCompletionService.GetStreamingChatMessageContentsAsync(_history);

STEP 4: For each non-empty update, accumulate the assistant's response and invoke the callback for the update.

SemanticKernelChatCompletionsStreamingClass.cs

    await foreach (var content in response)
    {
        if (!string.IsNullOrEmpty(content.Content))
        {
            responseContent += content.Content;
            if (callback != null) callback(content);
        }
    }

STEP 5: Finally, add the assistant's response to the chat message history, and return the response.

SemanticKernelChatCompletionsStreamingClass.cs

    _history.AddAssistantMessage(responseContent);
    return responseContent;
}