using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Connectors.OpenAI;
var builder = Kernel.CreateBuilder;
// Choose your provider: OpenAI or Azure OpenAI
builder.AddOpenAIChatCompletion(
modelId: "gpt-4o-mini", // or your preferred model
apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY")
);
var kernel = builder.Build;

using Microsoft.SemanticKernel.SemanticFunctions;
var prompt = @"คุณคือผู้ให้คำอธิบายที่กระชับ
อธิบายแนวคิดของ '{topic}' ใน 3 bullet point สำหรับผู้เริ่มต้น";
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)
{
// Call your weather API here
var temp = 22; // placeholder
return $"Weather in {city}: {temp}°C and clear";
}
}
// Register plugin
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) // swap for a vector DB (Qdrant, Pinecone, Azure AI Search)
.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."
);
// Later: retrieve and stuff into prompt
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) Define plugins (semantic + native) as before
// 2) Use a planner (API surface may vary by version)
var goal = "Summarize the attached policy, classify risk, and email a report";
// Assume we have plugins: files, summarize, classify, email
// Planner will assemble a plan: files.Load → summarize.Run → classify.Run → email.Send
// Execute plan sequentially, validate JSON outputs between steps

var kernel = Kernel.CreateBuilder
.AddOpenAIChatCompletion("gpt-4o-mini", apiKey)
.Build;
var fastKernel = Kernel.CreateBuilder
.AddOpenAIChatCompletion("gpt-4o-mini", apiKey) // speed-optimized
.Build;
// Route simpler prompts to fastKernel; complex tasks to kernel

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) Ingest docs into memory
await memory.SaveInformationAsync("handbook", "vacation-policy",
"Employees accrue 1.5 days of PTO per month and can carry over 5 days.");
// 2) Retrieve context for a question
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) Ask with context and request citations
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);