dotnet-semantic-kernel

dotnet-semantic-kernel

Microsoft Semantic Kernel for AI and LLM orchestration in .NET applications. Covers kernel setup and configuration, plugin/function calling, prompt templates with Handlebars and Liquid syntax, memory and vector store integration, planners, the agents framework, and integration with Azure OpenAI, OpenAI, and local models.

Out of scope: General async/await patterns and cancellation token propagation -- see [skill:dotnet-csharp-async-patterns]. DI container mechanics and service lifetime management -- see [skill:dotnet-csharp-dependency-injection]. HTTP client resilience and retry policies -- see [skill:dotnet-resilience]. Configuration binding (options pattern, secrets) -- see [skill:dotnet-csharp-configuration].

Cross-references: [skill:dotnet-csharp-async-patterns] for async streaming patterns used with chat completions, [skill:dotnet-csharp-dependency-injection] for kernel service registration in ASP.NET Core, [skill:dotnet-resilience] for retry policies on AI service calls, [skill:dotnet-csharp-configuration] for managing API keys and model configuration.

---

Kernel Setup

The Kernel is the central object in Semantic Kernel. It manages AI service connections, plugins, and function invocation.

Package Landscape

Package	Purpose
Microsoft.SemanticKernel	Core kernel, function calling, prompt templates
Microsoft.SemanticKernel.Connectors.AzureOpenAI	Azure OpenAI chat/embedding/image services
Microsoft.SemanticKernel.Connectors.OpenAI	OpenAI chat/embedding/image services
Microsoft.SemanticKernel.Connectors.Ollama	Ollama local model integration
Microsoft.SemanticKernel.Plugins.Core	Built-in plugins (time, math, text)
Microsoft.SemanticKernel.Agents.Core	Agent framework (chat agents, group chat)
Microsoft.Extensions.VectorData.Abstractions	Vector store abstraction layer
Microsoft.SemanticKernel.Connectors.Qdrant	Qdrant vector store connector
Microsoft.SemanticKernel.Connectors.AzureAISearch	Azure AI Search vector store connector

Basic Kernel Configuration

using Microsoft.SemanticKernel;

var builder = Kernel.CreateBuilder();

// Azure OpenAI
builder.AddAzureOpenAIChatCompletion(
    deploymentName: "gpt-4o",
    endpoint: Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!,
    apiKey: Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY")!);

var kernel = builder.Build();

DI Integration with ASP.NET Core

var builder = WebApplication.CreateBuilder(args);

builder.Services.AddKernel();

builder.Services.AddAzureOpenAIChatCompletion(
    deploymentName: builder.Configuration["AI:DeploymentName"]!,
    endpoint: builder.Configuration["AI:Endpoint"]!,
    apiKey: builder.Configuration["AI:ApiKey"]!);

// Register plugins
builder.Services.AddSingleton<OrderPlugin>();
builder.Services.AddSingleton(sp =>
{
    var kernel = sp.GetRequiredService<Kernel>();
    kernel.Plugins.AddFromObject(sp.GetRequiredService<OrderPlugin>());
    return kernel;
});

Multiple AI Services

Register multiple AI services and select by service ID:

var builder = Kernel.CreateBuilder();

builder.AddAzureOpenAIChatCompletion(
    deploymentName: "gpt-4o",
    endpoint: endpoint,
    apiKey: apiKey,
    serviceId: "gpt4o");

builder.AddAzureOpenAIChatCompletion(
    deploymentName: "gpt-4o-mini",
    endpoint: endpoint,
    apiKey: apiKey,
    serviceId: "gpt4o-mini");

var kernel = builder.Build();

// Select service at invocation time
var settings = new PromptExecutionSettings { ServiceId = "gpt4o-mini" };
var result = await kernel.InvokePromptAsync("Summarize: {{$input}}", new(settings)
{
    ["input"] = longDocument
});

Local Models with Ollama

#pragma warning disable SKEXP0070  // Ollama connector is experimental

var builder = Kernel.CreateBuilder();

builder.AddOllamaChatCompletion(
    modelId: "llama3.2",
    endpoint: new Uri("http://localhost:11434"));

var kernel = builder.Build();

---

Plugins and Function Calling

Plugins expose .NET methods as functions that the AI model can invoke. This is the primary mechanism for grounding LLM responses in real data and actions.

Defining a Plugin

using Microsoft.SemanticKernel;
using System.ComponentModel;

public sealed class OrderPlugin
{
    private readonly IOrderRepository _repository;

    public OrderPlugin(IOrderRepository repository) => _repository = repository;

    [KernelFunction("get_order")]
    [Description("Retrieves an order by its ID")]
    public async Task<OrderSummary?> GetOrderAsync(
        [Description("The unique order identifier")] string orderId,
        CancellationToken ct = default)
    {
        var order = await _repository.GetByIdAsync(orderId, ct);
        return order is null ? null : new OrderSummary(order);
    }

    [KernelFunction("list_recent_orders")]
    [Description("Lists the most recent orders for a customer")]
    public async Task<IReadOnlyList<OrderSummary>> ListRecentOrdersAsync(
        [Description("The customer ID")] string customerId,
        [Description("Maximum number of orders to return")] int limit = 10,
        CancellationToken ct = default)
    {
        var orders = await _repository.GetRecentAsync(customerId, limit, ct);
        return orders.Select(o => new OrderSummary(o)).ToList();
    }
}

Registering Plugins

var kernel = builder.Build();

// From an object instance (DI-friendly)
kernel.Plugins.AddFromObject(new OrderPlugin(orderRepo), "Orders");

// From a type (kernel creates the instance)
kernel.Plugins.AddFromType<TimePlugin>("Time");

// From functions directly
kernel.Plugins.AddFromFunctions("Math",
[
    KernelFunctionFactory.CreateFromMethod(
        ([Description("First number")] double a, [Description("Second number")] double b) => a + b,
        "Add",
        "Adds two numbers")
]);

Automatic Function Calling

Enable the model to call functions automatically during chat:

var settings = new AzureOpenAIPromptExecutionSettings
{
    FunctionChoiceBehavior = FunctionChoiceBehavior.Auto()
};

var chatHistory = new ChatHistory();
chatHistory.AddUserMessage("What's the status of order ORD-12345?");

var result = await kernel.GetRequiredService<IChatCompletionService>()
    .GetChatMessageContentAsync(chatHistory, settings, kernel);

// The model calls get_order("ORD-12345") automatically and responds with the result
Console.WriteLine(result.Content);

Function Filters

Intercept function calls for logging, authorization, or modification:

public sealed class AuthorizationFilter : IFunctionInvocationFilter
{
    public async Task OnFunctionInvocationAsync(
        FunctionInvocationContext context,
        Func<FunctionInvocationContext, Task> next)
    {
        // Check authorization before function execution
        if (context.Function.Name == "get_order")
        {
            var orderId = context.Arguments["orderId"]?.ToString();
            // Validate access...
        }

        await next(context);

        // Post-execution: log or modify result
    }
}

// Register the filter
builder.Services.AddSingleton<IFunctionInvocationFilter, AuthorizationFilter>();

---

Prompt Templates

Prompt templates support variable substitution and function calling within structured prompts.

Inline Prompts

var result = await kernel.InvokePromptAsync(
    "Summarize the following text in {{$style}} style:\n\n{{$input}}",
    new KernelArguments
    {
        ["input"] = articleText,
        ["style"] = "concise bullet points"
    });

Handlebars Templates

Handlebars templates support conditionals, loops, and function calls:

var templateString = """
    <message role="system">
    You are a helpful customer service agent.
    {{#if isVip}}You are speaking with a VIP customer. Be extra attentive.{{/if}}
    </message>
    <message role="user">
    Customer: {{customerName}}
    Query: {{query}}

    Recent orders:
    {{#each orders}}
    - Order {{this.Id}}: {{this.Status}} ({{this.Date}})
    {{/each}}
    </message>
    """;

var factory = new HandlebarsPromptTemplateFactory();
var template = factory.Create(new PromptTemplateConfig(templateString)
{
    TemplateFormat = HandlebarsPromptTemplateFactory.HandlebarsTemplateFormat
});

var result = await template.RenderAsync(kernel, new KernelArguments
{
    ["customerName"] = "Alice",
    ["query"] = "Where is my order?",
    ["isVip"] = true,
    ["orders"] = recentOrders
});

YAML Prompt Configuration

Define prompts as YAML files for separation of concerns:

# prompts/summarize.yaml
name: Summarize
description: Summarizes text to a specified length
template_format: handlebars
template: |
  <message role="system">
  Summarize the following text in approximately {{maxWords}} words.
  Focus on key facts and actionable items.
  </message>
  <message role="user">{{input}}</message>
input_variables:
  - name: input
    description: The text to summarize
    is_required: true
  - name: maxWords
    description: Target word count
    default: "100"
execution_settings:
  default:
    temperature: 0.3
    max_tokens: 500

var yamlContent = File.ReadAllText("prompts/summarize.yaml");
var function = kernel.CreateFunctionFromPromptYaml(yamlContent);

var result = await kernel.InvokeAsync(function, new KernelArguments
{
    ["input"] = longText,
    ["maxWords"] = "50"
});

---

Memory and Vector Stores

Semantic Kernel provides abstractions for vector storage, enabling retrieval-augmented generation (RAG) patterns.

Vector Store Abstractions

using Microsoft.Extensions.VectorData;

public sealed class DocumentRecord
{
    [VectorStoreRecordKey]
    public string Id { get; set; } = string.Empty;

    [VectorStoreRecordData(IsFilterable = true)]
    public string Source { get; set; } = string.Empty;

    [VectorStoreRecordData(IsFullTextSearchable = true)]
    public string Content { get; set; } = string.Empty;

    [VectorStoreRecordVector(Dimensions: 1536)]
    public ReadOnlyMemory<float> Embedding { get; set; }
}

Registering a Vector Store

using Microsoft.SemanticKernel.Connectors.Qdrant;

var builder = Kernel.CreateBuilder();

// Register embedding generation
builder.AddAzureOpenAITextEmbeddingGeneration(
    deploymentName: "text-embedding-3-small",
    endpoint: endpoint,
    apiKey: apiKey);

// Register vector store
builder.Services.AddQdrantVectorStore("localhost", 6334);

RAG Pattern

public sealed class RagService
{
    private readonly IVectorStoreRecordCollection<string, DocumentRecord> _collection;
    private readonly ITextEmbeddingGenerationService _embeddingService;
    private readonly IChatCompletionService _chatService;

    public RagService(
        IVectorStore vectorStore,
        ITextEmbeddingGenerationService embeddingService,
        IChatCompletionService chatService)
    {
        _collection = vectorStore.GetCollection<string, DocumentRecord>("documents");
        _embeddingService = embeddingService;
        _chatService = chatService;
    }

    public async Task<string> AskAsync(string question, CancellationToken ct = default)
    {
        // 1. Generate embedding for the question
        var questionEmbedding = await _embeddingService
            .GenerateEmbeddingAsync(question, cancellationToken: ct);

        // 2. Search for relevant documents
        var searchResults = _collection.VectorizedSearchAsync(
            questionEmbedding,
            new VectorSearchOptions { Top = 5 },
            ct);

        // 3. Build context from search results
        var contextBuilder = new StringBuilder();
        await foreach (var result in searchResults)
        {
            contextBuilder.AppendLine(result.Record.Content);
            contextBuilder.AppendLine("---");
        }

        // 4. Generate answer with context
        var chatHistory = new ChatHistory();
        chatHistory.AddSystemMessage(
            $"Answer based on the following context:\n\n{contextBuilder}");
        chatHistory.AddUserMessage(question);

        var response = await _chatService
            .GetChatMessageContentAsync(chatHistory, cancellationToken: ct);

        return response.Content ?? string.Empty;
    }
}

Ingesting Documents

public async Task IngestAsync(
    string documentId,
    string content,
    string source,
    CancellationToken ct = default)
{
    await _collection.CreateCollectionIfNotExistsAsync(ct);

    var embedding = await _embeddingService
        .GenerateEmbeddingAsync(content, cancellationToken: ct);

    await _collection.UpsertAsync(new DocumentRecord
    {
        Id = documentId,
        Content = content,
        Source = source,
        Embedding = embedding
    }, cancellationToken: ct);
}

---

Agents Framework

The Semantic Kernel agents framework enables building multi-agent systems where specialized agents collaborate on tasks.

Chat Completion Agent

#pragma warning disable SKEXP0110  // Agents framework is experimental

using Microsoft.SemanticKernel.Agents;

var agent = new ChatCompletionAgent
{
    Name = "OrderAssistant",
    Instructions = """
        You are an order management assistant. Help customers check order status,
        process returns, and answer questions about their orders.
        Always verify the customer's identity before sharing order details.
        """,
    Kernel = kernel,
    Arguments = new KernelArguments(new AzureOpenAIPromptExecutionSettings
    {
        FunctionChoiceBehavior = FunctionChoiceBehavior.Auto()
    })
};

// Invoke via a thread (required -- agents do not accept bare strings)
var thread = new ChatHistoryAgentThread();
await foreach (var message in agent.InvokeAsync(
    "What's the status of my order ORD-12345?", thread))
{
    Console.WriteLine(message.Content);
}

Agent Group Chat

Multiple agents can collaborate in a group chat with termination conditions:

var analyst = new ChatCompletionAgent
{
    Name = "DataAnalyst",
    Instructions = "You analyze data and provide insights. Present findings clearly.",
    Kernel = kernel
};

var writer = new ChatCompletionAgent
{
    Name = "ReportWriter",
    Instructions = "You take analytical findings and write clear, actionable reports.",
    Kernel = kernel
};

var chat = new AgentGroupChat(analyst, writer)
{
    ExecutionSettings = new AgentGroupChatSettings
    {
        TerminationStrategy = new ApprovalTerminationStrategy
        {
            MaximumIterations = 6
        }
    }
};

chat.AddChatMessage(
    new ChatMessageContent(AuthorRole.User, "Analyze Q4 sales trends and write a summary report."));

await foreach (var message in chat.InvokeAsync())
{
    Console.WriteLine($"[{message.AuthorName}]: {message.Content}");
}

OpenAI Assistant Agent

For stateful conversations with built-in tools (code interpreter, file search):

#pragma warning disable SKEXP0110

// Create the assistant via the builder pattern
OpenAIAssistantAgent agent = await OpenAIAssistantAgent.CreateAsync(
    kernel,
    new OpenAIAssistantDefinition("gpt-4o")
    {
        Name = "DataProcessor",
        Instructions = "You process CSV data and generate insights.",
        EnableCodeInterpreter = true
    });

try
{
    // Assistant agents use threads for stateful conversations
    var thread = await agent.CreateThreadAsync();

    await foreach (var message in agent.InvokeAsync(
        "Analyze the attached sales data.", thread))
    {
        Console.WriteLine(message.Content);
    }
}
finally
{
    await agent.DeleteAsync();
}

Note: The agents framework is experimental (SKEXP0110). APIs change frequently between Semantic Kernel releases. Verify method signatures against the latest samples when adopting.

---

Streaming Responses

For chat applications, stream responses token-by-token:

var chatService = kernel.GetRequiredService<IChatCompletionService>();
var chatHistory = new ChatHistory("You are a helpful assistant.");
chatHistory.AddUserMessage(userInput);

var settings = new AzureOpenAIPromptExecutionSettings
{
    FunctionChoiceBehavior = FunctionChoiceBehavior.Auto()
};

await foreach (var chunk in chatService.GetStreamingChatMessageContentsAsync(
    chatHistory, settings, kernel))
{
    Console.Write(chunk.Content);
}

---

Key Principles

• Use function calling over prompt stuffing -- let the model call plugins to retrieve real-time data rather than injecting everything into the prompt
• Keep plugins focused -- each plugin should represent a single domain; use [Description] attributes on functions and parameters so the model knows when and how to call them
• Use YAML prompts for production -- separate prompt content from code for easier iteration and non-developer editing
• Do not store API keys in code -- use environment variables, Azure Key Vault, or the .NET secrets manager (see [skill:dotnet-csharp-configuration])
• Prefer vector store abstractions -- code against IVectorStore to allow switching between Qdrant, Azure AI Search, and other providers
• Handle experimental APIs explicitly -- suppress SKEXP* warnings per-call, not globally, so you notice when APIs graduate to stable

---

Agent Gotchas

1. Do not hardcode API keys or endpoints in Kernel builder calls -- use builder.Configuration or environment variables. Hardcoded secrets leak into source control and prevent environment-specific configuration.
2. Do not suppress all SKEXP* warnings globally -- experimental APIs change frequently. Suppress per-usage (#pragma warning disable SKEXP0110) so new experimental usage sites are flagged by the compiler.
3. Do not create a new Kernel instance per request in ASP.NET Core -- register the kernel in DI as a singleton (it is thread-safe) and clone with kernel.Clone() if per-request state is needed.
4. Do not ignore CancellationToken in plugin functions -- AI function calls can be cancelled by the user or timeout policies. Always propagate CancellationToken through plugin method signatures.
5. Do not return large objects from plugin functions -- the model receives the serialized result as context. Return summary DTOs, not full entity graphs, to avoid exceeding token limits.
6. Do not mix AddAzureOpenAIChatCompletion and AddOpenAIChatCompletion without serviceId -- without a service ID, the last registration wins. Use explicit serviceId when registering multiple AI services.

---

Prerequisites

• Microsoft.SemanticKernel NuGet package (1.x stable)
• An AI service endpoint (Azure OpenAI, OpenAI API key, or Ollama for local models)
• For vector stores: a running instance of the chosen provider (Qdrant, Azure AI Search, etc.)

---

References

• Semantic Kernel documentation
• Semantic Kernel .NET SDK
• Semantic Kernel plugins
• Semantic Kernel agents
• Vector store connectors
• Semantic Kernel samples