돋보기 오케스트레이션

마젠틱 오케스트레이션은 AutoGen에서 발명한 Magentic-One 시스템을 기반으로 설계되었습니다. 동적 협업이 필요한 복잡한 개방형 작업을 위해 설계된 유연한 범용 다중 에이전트 패턴입니다. 이 패턴에서 전용 Magentic 관리자는 특수 에이전트 팀을 조정하여 진화하는 컨텍스트, 작업 진행률 및 에이전트 기능에 따라 다음에 조치를 취해야 하는 에이전트를 선택합니다.

Magentic 관리자는 공유 컨텍스트를 유지하고, 진행률을 추적하고, 워크플로를 실시간으로 조정합니다. 이를 통해 시스템은 복잡한 문제를 분석하고, 하위 작업을 위임하고, 에이전트 협업을 통해 솔루션을 반복적으로 구체화할 수 있습니다. 오케스트레이션은 솔루션 경로를 미리 알 수 없으며 여러 라운드의 추론, 연구 및 계산이 필요할 수 있는 시나리오에 특히 적합합니다.

패턴을 사용하는 시기 또는 워크로드에서 패턴을 피해야 하는 경우와 같은 패턴에 대한 자세한 내용은 Magentic 오케스트레이션을 참조하세요.

일반 사용 예

사용자는 다양한 기계 학습 모델의 에너지 효율 및 CO 보류 배출을 비교하는 포괄적인 보고서를 요청합니다. Magentic 관리자는 먼저 연구 에이전트를 할당하여 관련 데이터를 수집한 다음, 분석 및 계산을 코더 에이전트에 위임합니다. 관리자는 여러 라운드의 연구 및 계산을 조정하고, 결과를 집계하고, 상세하고 구조화된 보고서를 최종 출력으로 생성합니다.

학습 내용

• Magentic 오케스트레이션에 대한 에이전트를 정의하고 구성하는 방법
• 에이전트 협업을 조정하도록 Magentic 관리자를 설정하는 방법
• 계획, 진행률 추적 및 최종 답변 합성을 포함하여 오케스트레이션 프로세스의 작동 방식

using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents;
using Microsoft.SemanticKernel.Agents.AzureAI;
using Microsoft.SemanticKernel.Agents.Magentic;
using Microsoft.SemanticKernel.Agents.Orchestration;
using Microsoft.SemanticKernel.Agents.Runtime.InProcess;
using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel.Connectors.OpenAI;
using Azure.AI.Agents.Persistent;
using Azure.Identity;

// Helper function to create a kernel with chat completion
public static Kernel CreateKernelWithChatCompletion(...)
{
    ...
}

// Create a kernel with OpenAI chat completion for the research agent
Kernel researchKernel = CreateKernelWithChatCompletion("gpt-4o-search-preview");
ChatCompletionAgent researchAgent = new ChatCompletionAgent {
    Name = "ResearchAgent",
    Description = "A helpful assistant with access to web search. Ask it to perform web searches.",
    Instructions = "You are a Researcher. You find information without additional computation or quantitative analysis.",
    Kernel = researchKernel,
};

// Create a persistent Azure AI agent for code execution
PersistentAgentsClient agentsClient = AzureAIAgent.CreateAgentsClient(endpoint, new AzureCliCredential());
PersistentAgent definition = await agentsClient.Administration.CreateAgentAsync(
    modelId,
    name: "CoderAgent",
    description: "Write and executes code to process and analyze data.",
    instructions: "You solve questions using code. Please provide detailed analysis and computation process.",
    tools: [new CodeInterpreterToolDefinition()]);
AzureAIAgent coderAgent = new AzureAIAgent(definition, agentsClient);

Kernel managerKernel = CreateKernelWithChatCompletion("o3-mini");
StandardMagenticManager manager = new StandardMagenticManager(
    managerKernel.GetRequiredService<IChatCompletionService>(),
    new OpenAIPromptExecutionSettings())
{
    MaximumInvocationCount = 5,
};

ChatHistory history = [];

ValueTask responseCallback(ChatMessageContent response)
{
    history.Add(response);
    return ValueTask.CompletedTask;
}

MagenticOrchestration orchestration = new MagenticOrchestration(
    manager,
    researchAgent,
    coderAgent)
{
    ResponseCallback = responseCallback,
};

InProcessRuntime runtime = new InProcessRuntime();
await runtime.StartAsync();

string input = @"I am preparing a report on the energy efficiency of different machine learning model architectures.\nCompare the estimated training and inference energy consumption of ResNet-50, BERT-base, and GPT-2 on standard datasets (e.g., ImageNet for ResNet, GLUE for BERT, WebText for GPT-2). Then, estimate the CO2 emissions associated with each, assuming training on an Azure Standard_NC6s_v3 VM for 24 hours. Provide tables for clarity, and recommend the most energy-efficient model per task type (image classification, text classification, and text generation).";
var result = await orchestration.InvokeAsync(input, runtime);

string output = await result.GetValueAsync(TimeSpan.FromSeconds(300));
Console.WriteLine($"\n# RESULT: {output}");
Console.WriteLine("\n\nORCHESTRATION HISTORY");
foreach (ChatMessageContent message in history)
{
    // Print each message
    Console.WriteLine($"# {message.Role} - {message.AuthorName}: {message.Content}");
}

await runtime.RunUntilIdleAsync();

# RESULT: ```markdown
# Report: Energy Efficiency of Machine Learning Model Architectures

This report assesses the energy consumption and related CO₂ emissions for three popular ...

ORCHESTRATION HISTORY

# Assistant - ResearchAgent: Comparing the energy efficiency of different machine learning ...

# assistant - CoderAgent: Below are tables summarizing the approximate energy consumption and ...

# assistant - CoderAgent: The estimates provided in our tables align with a general understanding ...

# assistant - CoderAgent: Here's the updated structure for the report integrating both the ...

from semantic_kernel.agents import ChatCompletionAgent, OpenAIAssistantAgent
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

research_agent = ChatCompletionAgent(
    name="ResearchAgent",
    description="A helpful assistant with access to web search. Ask it to perform web searches.",
    instructions="You are a Researcher. You find information without additional computation or quantitative analysis.",
    service=OpenAIChatCompletion(ai_model_id="gpt-4o-search-preview"),
)

# Create an OpenAI Assistant agent with code interpreter capability
client, model = OpenAIAssistantAgent.setup_resources()
code_interpreter_tool, code_interpreter_tool_resources = OpenAIAssistantAgent.configure_code_interpreter_tool()
definition = await client.beta.assistants.create(
    model=model,
    name="CoderAgent",
    description="A helpful assistant that writes and executes code to process and analyze data.",
    instructions="You solve questions using code. Please provide detailed analysis and computation process.",
    tools=code_interpreter_tool,
    tool_resources=code_interpreter_tool_resources,
)
coder_agent = OpenAIAssistantAgent(
    client=client,
    definition=definition,
)

from semantic_kernel.agents import StandardMagenticManager
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

manager = StandardMagenticManager(chat_completion_service=OpenAIChatCompletion())

from semantic_kernel.contents import ChatMessageContent

def agent_response_callback(message: ChatMessageContent) -> None:
    print(f"**{message.name}**\n{message.content}")

from semantic_kernel.agents import MagenticOrchestration

magentic_orchestration = MagenticOrchestration(
    members=[research_agent, coder_agent],
    manager=manager,
    agent_response_callback=agent_response_callback,
)

from semantic_kernel.agents.runtime import InProcessRuntime

runtime = InProcessRuntime()
runtime.start()

orchestration_result = await magentic_orchestration.invoke(
    task=(
        "I am preparing a report on the energy efficiency of different machine learning model architectures. "
        "Compare the estimated training and inference energy consumption of ResNet-50, BERT-base, and GPT-2 "
        "on standard datasets (e.g., ImageNet for ResNet, GLUE for BERT, WebText for GPT-2). "
        "Then, estimate the CO2 emissions associated with each, assuming training on an Azure Standard_NC6s_v3 VM "
        "for 24 hours. Provide tables for clarity, and recommend the most energy-efficient model "
        "per task type (image classification, text classification, and text generation)."
    ),
    runtime=runtime,
)

value = await orchestration_result.get()
print(f"\nFinal result:\n{value}")

await runtime.stop_when_idle()

**ResearchAgent**
Estimating the energy consumption and associated CO₂ emissions for training and inference of ResNet-50, BERT-base...

**CoderAgent**
Here is the comparison of energy consumption and CO₂ emissions for each model (ResNet-50, BERT-base, and GPT-2)
over a 24-hour period:

| Model     | Training Energy (kWh) | Inference Energy (kWh) | Total Energy (kWh) | CO₂ Emissions (kg) |
|-----------|------------------------|------------------------|---------------------|---------------------|
| ResNet-50 | 21.11                  | 0.08232                | 21.19232            | 19.50               |
| BERT-base | 0.048                  | 0.23736                | 0.28536             | 0.26                |
| GPT-2     | 42.22                  | 0.35604                | 42.57604            | 39.17               |

...

Final result:
Here is the comprehensive report on energy efficiency and CO₂ emissions for ResNet-50, BERT-base, and GPT-2 models...

다음 단계