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Chat with a local AI model using .NET and Semantic Kernel

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Local AI models provide powerful and flexible options for building AI solutions. In this quickstart, you'll explore how to set up and connect to a local AI model using .NET and the Semantic Kernel SDK. For this example, you'll run the local AI model using Ollama.

Prerequisites

Run the local AI model

Complete the following steps to configure and run a local AI Model on your device. Many different AI models are available to run locally and are trained for different tasks, such as generating code, analyzing images, generative chat, or creating embeddings. For this quickstart, you'll use the general purpose phi3:mini model, which is a small but capable generative AI created by Microsoft.

  1. Open a terminal window and verify that Ollama is available on your device:

    ollama

    If Ollama is running, it displays a list of available commands.

  2. Pull the phi3:mini model from the Ollama registry and wait for it to download:

    ollama pull phi3:mini

  3. After the download completes, run the model:

    ollama run phi3:mini

    Ollama starts the phi3:mini model and provides a prompt for you to interact with it.

Create the .NET app

Complete the following steps to create a .NET console app that will connect to your local phi3:mini AI model:

  1. In a terminal window, navigate to an empty directory on your device and create a new app with the dotnet new command:

    dotnet new console

  2. Add the Semantic Kernel SDK package to your app:

    dotnet add package Microsoft.SemanticKernel

  3. Open the new app in your editor of choice, such as Visual Studio Code.

    code .

Connect to and chat with the AI model

The Semantic Kernel SDK provides many services and features to connect to AI models and manage interactions. In the steps ahead, you'll create a simple app that connects to the local AI and stores conversation history to improve the chat experience.

  1. Open the Program.cs file and replace the contents of the file with the following code:

    using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;

// Create a kernel with OpenAI chat completion
#pragma warning disable SKEXP0010
Kernel kernel = Kernel.CreateBuilder()
                    .AddOpenAIChatCompletion(
                        modelId: "phi3:mini",
                        endpoint: new Uri("http://localhost:11434"),
                        apiKey: "")
                    .Build();

var aiChatService = kernel.GetRequiredService<IChatCompletionService>();
var chatHistory = new ChatHistory();

while (true)
{
    // Get user prompt and add to chat history
    Console.WriteLine("Your prompt:");
    var userPrompt = Console.ReadLine();
    chatHistory.Add(new ChatMessageContent(AuthorRole.User, userPrompt));

    // Stream the AI response and add to chat history
    Console.WriteLine("AI Response:");
    var response = "";
    await foreach(var item in 
        aiChatService.GetStreamingChatMessageContentsAsync(chatHistory))
    {
        Console.Write(item.Content);
        response += item.Content;
    }
    chatHistory.Add(new ChatMessageContent(AuthorRole.Assistant, response));
    Console.WriteLine();
}

    Note

    The #pragma warning disable SKEXP0010 line is included due to the experimental state of some Semantic Kernel SDK features.

    The preceding code accomplishes the following tasks:

    • Creates a Kernel object and uses it to retrieve a chat completion service.
    • Creates a ChatHistory object to store the messages between the user and the AI model.
    • Retrieves a prompt from the user and stores it in the ChatHistory.
    • Sends the chat data to the AI model to generate a response.

    Note

    Ollama runs on port 11434 by default, which is why the AI model endpoint is set to http://localhost:11434.

  2. Run the app and enter a prompt into the console to receive a response from the AI, such as the following:

    Your prompt:
Tell me three facts about .NET.

AI response:
1. **Cross-Platform Development:** One of the significant strengths of .NET, 
particularly its newer iterations (.NET Core and .NET 5+), is cross-platform support.
It allows developers to build applications that run on Windows, Linux, macOS,
and various other operating systems seamlessly, enhancing flexibility and
reducing barriers for a wider range of users.

2. **Rich Ecosystem and Library Support:** .NET has an incredibly rich ecosystem,
comprising an extensive collection of libraries (such as those provided by the 
official NuGet Package Manager), tools, and services. This allows developers 
to work on web applications (.NET Framework for desktop apps and ASP.NET Core 
for modern web applications), mobile applications (.NET MAUI or Xamarin.Forms),
IoT solutions, AI/ML projects, and much more with a vast array of pre-built
components available at their disposal.

3. **Type Safety:** .NET operates under the Common Language Infrastructure (CLI) 
model and employs managed code for executing applications. This approach inherently
offers strong type safety checks which help in preventing many runtime errors that
are common in languages like C/C++. It also enables features such as garbage collection,
thus relieving developers from manual memory management. These characteristics enhance
the reliability of .NET-developed software and improve productivity by catching
issues early during development.

  3. The response from the AI is accurate, but also verbose. The stored chat history enables the AI to modify its response. Instruct the AI to shorten the list it provided:

    Your prompt:
Shorten the length of each item in the previous response.

AI Response:
 **Cross-platform Capabilities:** .NET allows building for various operating systems
through platforms like .NET Core, promoting accessibility (Windows, Linux, macOS).

**Extensive Ecosystem:** Offers a vast library selection via NuGet and tools for web
(.NET Framework), mobile development (Maui/Xamarin.Forms), IoT, AI, providing rich
capabilities to developers.

**Type Safety & Reliability:** .NET's CLI model enforces strong typing and automatic
garbage collection, mitigating runtime errors, thus enhancing application stability.

    The updated response from the AI is much shorter the second time. Due to the available chat history, the AI was able to assess the previous result and provide shorter summaries.

Next steps