Agen Deklaratif

Agen deklaratif memungkinkan Anda menentukan konfigurasi agen menggunakan file YAML atau JSON alih-alih menulis kode terprogram. Pendekatan ini membuat agen lebih mudah untuk menentukan, memodifikasi, dan berbagi di seluruh tim.

Contoh berikut menunjukkan cara membuat agen deklaratif dari konfigurasi YAML:

using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;

// Create the chat client
IChatClient chatClient = new AIProjectClient(
    new Uri("<your-foundry-project-endpoint>"),
    new DefaultAzureCredential())
        .GetProjectOpenAIClient()
        .GetProjectResponsesClient()
        .AsIChatClient("gpt-4o-mini");

// Define the agent using a YAML definition.
var yamlDefinition =
    """
    kind: Prompt
    name: Assistant
    description: Helpful assistant
    instructions: You are a helpful assistant. You answer questions in the language specified by the user. You return your answers in a JSON format.
    model:
        options:
            temperature: 0.9
            topP: 0.95
    outputSchema:
        properties:
            language:
                type: string
                required: true
                description: The language of the answer.
            answer:
                type: string
                required: true
                description: The answer text.
    """;

// Create the agent from the YAML definition.
var agentFactory = new ChatClientPromptAgentFactory(chatClient);
var agent = await agentFactory.CreateFromYamlAsync(yamlDefinition);

// Invoke the agent and output the text result.
Console.WriteLine(await agent!.RunAsync("Tell me a joke about a pirate in English."));

// Invoke the agent with streaming support.
await foreach (var update in agent!.RunStreamingAsync("Tell me a joke about a pirate in French."))
{
    Console.WriteLine(update);
}

Peringatan

DefaultAzureCredential nyaman untuk pengembangan tetapi membutuhkan pertimbangan yang cermat dalam produksi. Dalam produksi, pertimbangkan untuk menggunakan kredensial tertentu (misalnya, ManagedIdentityCredential) untuk menghindari masalah latensi, pemeriksaan kredensial yang tidak diinginkan, dan potensi risiko keamanan dari mekanisme fallback.

Menentukan agen sebaris dengan YAML

Anda dapat menentukan spesifikasi YAML lengkap sebagai string langsung dalam kode Anda:

import asyncio

from agent_framework.declarative import AgentFactory
from azure.identity.aio import AzureCliCredential


async def main():
    """Create an agent from an inline YAML definition and run it."""
    yaml_definition = """kind: Prompt
name: DiagnosticAgent
displayName: Diagnostic Assistant
instructions: Specialized diagnostic and issue detection agent for systems with critical error protocol and automatic handoff capabilities
description: An agent that performs diagnostics on systems and can escalate issues when critical errors are detected.

model:
  id: =Env.AZURE_OPENAI_MODEL
  connection:
    kind: remote
    endpoint: =Env.FOUNDRY_PROJECT_ENDPOINT
"""
    async with (
        AzureCliCredential() as credential,
        AgentFactory(client_kwargs={"credential": credential}).create_agent_from_yaml(yaml_definition) as agent,
    ):
        response = await agent.run("What can you do for me?")
        print("Agent response:", response.text)


if __name__ == "__main__":
    asyncio.run(main())

Memuat agen dari file YAML

Anda juga dapat memuat definisi YAML dari file:

import asyncio
from pathlib import Path

from agent_framework.declarative import AgentFactory
from azure.identity import AzureCliCredential


async def main():
    """Create an agent from a declarative YAML file and run it."""
    yaml_path = Path(__file__).parent / "agent-config.yaml"

    with yaml_path.open("r") as f:
        yaml_str = f.read()

    agent = AgentFactory(client_kwargs={"credential": AzureCliCredential()}).create_agent_from_yaml(yaml_str)
    response = await agent.run("Why is the sky blue?")
    print("Agent response:", response.text)


if __name__ == "__main__":
    asyncio.run(main())

Langkah selanjutnya

Observabilitas