# Azure OpenAI
export AZURE_OPENAI_API_KEY=AAA....

# 或直接用 OpenAI
export OPENAI_API_KEY=sk-...

pip install semantic-kernel

dotnet add package Microsoft.SemanticKernel
dotnet add package Microsoft.SemanticKernel.Agents.Core

import asyncio
from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

async def main():
    agent = ChatCompletionAgent(
        service=AzureChatCompletion(),
        name="SK-Assistant",
        instructions="You are a helpful assistant.",
    )
    response = await agent.get_response(messages="Write a haiku about Semantic Kernel.")
    print(response.content)

asyncio.run(main())

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents;

var builder = Kernel.CreateBuilder();
builder.AddAzureOpenAIChatCompletion(
                Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT"),
                Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT"),
                Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY"));
var kernel = builder.Build();

ChatCompletionAgent agent = new()
{
    Name = "SK-Agent",
    Instructions = "You are a helpful assistant.",
    Kernel = kernel,
};

await foreach (AgentResponseItem<ChatMessageContent> response
    in agent.InvokeAsync("Write a haiku about Semantic Kernel."))
{
    Console.WriteLine(response.Message);
}

git clone https://github.com/microsoft/semantic-kernel.git
cd semantic-kernel

import asyncio
from typing import Annotated
from pydantic import BaseModel
from semantic_kernel.agents import ChatCompletionAgent
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, OpenAIChatPromptExecutionSettings
from semantic_kernel.functions import kernel_function, KernelArguments

class MenuPlugin:
    @kernel_function(description="Provides a list of specials from the menu.")
    def get_specials(self) -> Annotated[str, "Returns the specials from the menu."]:
        return "Special Soup: Clam Chowder\nSpecial Salad: Cobb Salad\nSpecial Drink: Chai Tea"

    @kernel_function(description="Provides the price of the requested menu item.")
    def get_item_price(
        self, menu_item: Annotated[str, "The name of the menu item."]
    ) -> Annotated[str, "Returns the price of the menu item."]:
        return "$9.99"

class MenuItem(BaseModel):
    price: float
    name: str

async def main():
    settings = OpenAIChatPromptExecutionSettings()
    settings.response_format = MenuItem

    agent = ChatCompletionAgent(
        service=AzureChatCompletion(),
        name="SK-Assistant",
        instructions="You are a helpful assistant.",
        plugins=[MenuPlugin()],
        arguments=KernelArguments(settings)
    )
    response = await agent.get_response(messages="What is the price of the soup special?")
    print(response.content)

asyncio.run(main())

billing_agent = ChatCompletionAgent(
    service=AzureChatCompletion(),
    name="BillingAgent",
    instructions="You handle billing issues like charges, payment methods, cycles, fees, discrepancies, and payment failures."
)

refund_agent = ChatCompletionAgent(
    service=AzureChatCompletion(),
    name="RefundAgent",
    instructions="Assist users with refund inquiries, including eligibility, policies, processing, and status updates."
)

triage_agent = ChatCompletionAgent(
    service=OpenAIChatCompletion(),
    name="TriageAgent",
    instructions="Evaluate user requests and forward them to BillingAgent or RefundAgent for targeted assistance.",
    plugins=[billing_agent, refund_agent],
)

settings = OpenAIChatPromptExecutionSettings()
settings.response_format = MenuItem  # pydantic BaseModel

# ---- Python ----
pip install semantic-kernel
pip show semantic-kernel

# ---- .NET ----
dotnet add package Microsoft.SemanticKernel
dotnet add package Microsoft.SemanticKernel.Agents.Core
dotnet build

# ---- 凭据 ----
export OPENAI_API_KEY=sk-...
export AZURE_OPENAI_API_KEY=...
export AZURE_OPENAI_ENDPOINT=...
export AZURE_OPENAI_DEPLOYMENT=...

# ---- 本地模型 ----
# 配合 Ollama / LMStudio / ONNX 使用，按官方文档配置