Semantic Kernel 2026：微软AI编排框架的成熟之路

// C# 示例：Semantic Kernel 2026
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Connectors.OpenAI;

var builder = Kernel.CreateBuilder();
builder.AddAzureOpenAIChatCompletion("gpt-4o", endpoint, apiKey);
builder.Plugins.AddFromType<TimePlugin>();
builder.Plugins.AddFromType<SearchPlugin>();
var kernel = builder.Build();

// 1. Semantic Function（AI生成）
var summarize = kernel.CreateFunctionFromPrompt(
    "总结以下文本的核心要点：\n{{$input}}",
    functionName: "Summarize",
    description: "文本摘要"
);

// 2. Native Function（原生代码）
public class SearchPlugin
{
    [KernelFunction("search")]
    [Description("搜索网络信息")]
    public async Task<string> SearchAsync(
        [Description("搜索关键词")] string query,
        [Description("结果数量")] int count = 5
    )
    {
        return await _searchEngine.QueryAsync(query, count);
    }
}

// 3. Hybrid Function（混合函数 - 2026新增）
var analyze = kernel.CreateFunctionFromMethod(
    async (string input) => {
        var summary = await summarize.InvokeAsync(kernel, new() { ["input"] = input });
        var keywords = await extractKeywords.InvokeAsync(kernel, new() { ["input"] = summary });
        return new { Summary = summary, Keywords = keywords };
    },
    functionName: "Analyze"
);

// 函数管道
var pipeline = kernel.CreatePipeline(
    "SearchPlugin.Search",
    "Summarize",
    "AnalysisPlugin.ExtractKeywords",
    "OutputPlugin.FormatReport"
);

var result = await pipeline.InvokeAsync("AI Agent frameworks 2026");

// 条件管道（2026新特性）
var conditionalPipeline = kernel.CreatePipeline()
    .Step("IntentClassifier.Classify")
    .When("technical", p => p
        .Step("SearchPlugin.Search")
        .Step("CodePlugin.GenerateExample")
        .Step("OutputPlugin.TechnicalReport"))
    .When("business", p => p
        .Step("SearchPlugin.Search")
        .Step("AnalysisPlugin.MarketAnalysis")
        .Step("OutputPlugin.BusinessReport"))
    .Otherwise(p => p
        .Step("GeneralPlugin.Respond"));

var result = await conditionalPipeline.InvokeAsync(userQuery);

from semantic_kernel import Kernel
from semantic_kernel.functions import kernel_function
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

# Python原生插件
class DataAnalysisPlugin:
    @kernel_function(description="分析CSV数据并生成统计摘要")
    async def analyze_csv(self, file_path: str, columns: list[str] = None) -> dict:
        import pandas as pd
        df = pd.read_csv(file_path)
        if columns:
            df = df[columns]
        return {
            "shape": df.shape,
            "describe": df.describe().to_dict(),
            "null_counts": df.isnull().sum().to_dict()
        }
    
    @kernel_function(description="生成数据可视化图表")
    async def create_chart(self, data: dict, chart_type: str = "bar") -> str:
        # 生成图表并返回文件路径
        ...

# 组合使用
kernel = Kernel()
kernel.add_service(OpenAIChatCompletion("gpt-4o", api_key))
kernel.add_plugin(DataAnalysisPlugin(), "DataAnalysis")

# Agent式自动编排
from semantic_kernel.agents import ChatCompletionAgent

agent = ChatCompletionAgent(
    kernel=kernel,
    instructions="""你是一个数据分析助手。
    可以使用DataAnalysis插件的工具来帮助用户分析数据。
    始终先理解用户需求，再选择合适的工具。""",
    # 2026新特性：自动函数选择
    function_choice_behavior="auto",
    # 执行限制
    max_iterations=10
)

response = await agent.get_response(
    messages=[{"role": "user", "content": "分析sales.csv文件的趋势"}]
)

// Azure AI Foundry集成
builder.AddAzureAIFoundry("my-deployment");

// Azure Monitor可观测性
builder.Services.AddApplicationInsightsTelemetry();
builder.Services.AddOpenTelemetry()
    .WithTracing(t => t.AddSource("SemanticKernel"))
    .WithMetrics(m => m.AddMeter("SemanticKernel"));

// Azure Key Vault密钥管理
builder.Services.AddSingleton<ICredential>(new DefaultAzureCredential());
builder.AddAzureOpenAIChatCompletion(
    deploymentName,
    endpoint,
    new DefaultAzureCredential()  // 托管身份认证
);

// 内容安全过滤器
builder.Services.AddSingleton<IPromptFilter>(new AzureContentSafetyFilter(
    new ContentSafetyConfig
    {
        HateThreshold = ContentSeverity.Medium,
        ViolenceThreshold = ContentSeverity.Medium,
        SelfHarmThreshold = ContentSeverity.High,
        SexualThreshold = ContentSeverity.High
    }
));

// 敏感信息脱敏
builder.Services.AddSingleton<IPromptFilter>(new PiiRedactionFilter(
    new PiiRedactionConfig
    {
        RedactionMode = RedactionMode.Mask,
        DetectTypes = new[] { "Email", "Phone", "SSN", "CreditCard" }
    }
));

// 审计日志
builder.Services.AddSingleton<IFunctionFilter>(new AuditLogFilter(
    logTo: "azure_log_analytics",
    includeArguments: true,
    includeResults: true,
    retentionDays: 90
));