using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Connectors.OpenAI;
var builder = Kernel.CreateBuilder;
// 제공자 선택: OpenAI 또는 Azure OpenAI
builder.AddOpenAIChatCompletion(
modelId: "gpt-4o-mini", // 선호하는 모델
apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY")
);
var kernel = builder.Build;

using Microsoft.SemanticKernel.SemanticFunctions;
var prompt = @"You are a concise explainer.
Explain the concept of '{topic}' in 3 bullet points for a beginner.";
var explainFunc = kernel.CreateFunctionFromPrompt(prompt);
var result = await explainFunc.InvokeAsync(kernel, new { ["topic"] = "vector embeddings" });
Console.WriteLine(result);

public class WeatherPlugin
{
[KernelFunction, Description("Get weather for a city")]
public async Task<string> GetWeatherAsync(
[Description("City name")] string city)
{
// 여기에 날씨 API 호출
var temp = 22; // 임시 값
return $"Weather in {city}: {temp}°C and clear";
}
}
// 플러그인 등록
var weather = new WeatherPlugin;
kernel.Plugins.AddFromObject(weather, pluginName: "weather");

var prompt = @"
You are a classification engine.
Task: Classify the `message` into one of [Billing, Tech Support, Sales].
Return strict JSON: { \"label\": string, \"confidence\": number }
message: {message}
";
var classify = kernel.CreateFunctionFromPrompt(prompt);
var output = await classify.InvokeAsync(kernel, new { ["message"] = "I can’t log into my account." });
Console.WriteLine(output); // {"label":"Tech Support","confidence":0.89}

using Microsoft.SemanticKernel.Memory;
var memory = new MemoryBuilder
.WithMemoryStore(new VolatileMemoryStore) // 벡터 DB(Qdrant, Pinecone, Azure AI Search)로 교체 가능
.WithTextEmbeddingGeneration(
new OpenAITextEmbeddingGeneration("text-embedding-3-small", Environment.GetEnvironmentVariable("OPENAI_API_KEY")))
.Build;
await memory.SaveInformationAsync(
collection: "policies", id: "refund-policy",
text: "Customers can request a refund within 30 days of purchase with proof of receipt."
);
// 이후: 프롬프트에 넣을 컨텍스트 검색
var results = memory.SearchAsync("refund window", collection: "policies", limit: 3, minRelevanceScore: 0.7);
await foreach (var item in results)
{
Console.WriteLine($"Relevant: {item.Metadata.Text}");
}

// 1) 이전과 같이 플러그인(시맨틱 + 네이티브) 정의
// 2) 플래너 사용 (버전에 따라 API 다름)
var goal = "첨부된 정책 요약, 위험 분류, 보고서 이메일 발송";
// 플러그인 가정: files, summarize, classify, email
// 플래너가 계획 조립: files.Load → summarize.Run → classify.Run → email.Send
// 계획 순차 실행, 단계 간 JSON 출력 검증

var kernel = Kernel.CreateBuilder
.AddOpenAIChatCompletion("gpt-4o-mini", apiKey)
.Build;
var fastKernel = Kernel.CreateBuilder
.AddOpenAIChatCompletion("gpt-4o-mini", apiKey) // 속도 최적화
.Build;
// 간단한 프롬프트는 fastKernel, 복잡한 작업은 kernel로 라우팅

record Classification(string label, double confidence);
bool TryParseClassification(string text, out Classification cls)
{
try { cls = System.Text.Json.JsonSerializer.Deserialize<Classification>(text)!; return true; }
catch { cls = default!; return false; }
}

// 1) 문서를 메모리에 저장
await memory.SaveInformationAsync("handbook", "vacation-policy",
"Employees accrue 1.5 days of PTO per month and can carry over 5 days.");
// 2) 질문에 대한 컨텍스트 검색
var top = memory.SearchAsync("carry over PTO", "handbook", limit: 3, minRelevanceScore: 0.75);
var contexts = new List<string>;
await foreach (var r in top) contexts.Add(r.Metadata.Text);
// 3) 컨텍스트와 인용 요청 포함 질문
var qaPrompt = @"
You answer strictly from the provided context. If missing, say you don't know.
Include inline citations like [source i] using the index of context items starting at 1.
Context:
1) {{ctx1}}
2) {{ctx2}}
Question: {{q}}
";
var qa = kernel.CreateFunctionFromPrompt(qaPrompt);
var variables = new KernelArguments
{
["ctx1"] = contexts.ElementAtOrDefault(0) ?? "",
["ctx2"] = contexts.ElementAtOrDefault(1) ?? "",
["q"] = "How many PTO days can I carry over?"
};
var answer = await qa.InvokeAsync(kernel, variables);
Console.WriteLine(answer);