快速入门：创建新代理

Prerequisites

• Azure 订阅 - 免费创建订阅。
• 确保创建帐户和项目的个人在订阅范围内具有 Azure AI 帐户所有者 角色，这将授予创建项目所需的权限
  • 或者，在订阅级别具有“参与者”或“认知服务参与者”角色将允许创建项目
• 创建项目后，请确保项目中的单个创建代理在项目级别具有 Azure AI 用户 角色

在 Azure AI Foundry 门户中创建 Foundry 帐户和项目

若要在 Azure AI Foundry 中创建帐户和项目，请执行以下步骤：

1. 转到 Azure AI Foundry。 如果你已进入项目，请选择页面左上角的“Azure AI Foundry”以转到“主页”。

2. 使用“代理入门”创建流程以获得最快的体验。 单击“ 创建代理”。

   

3. 输入项目的名称。 如果要自定义默认值，请选择 “高级”选项。

   

4. 选择 创建。

5. 等待资源预配。

   1. 将创建帐户和项目（帐户的子资源）。
   2. gpt-4o 模型将自动部署
   3. 将创建默认代理

6. 完成后，你将直接进入代理工作区，可以开始创建代理。 你可以给代理提供关于该做什么以及如何去做的说明。 例如： “你是一个有用的代理，可以回答有关地理的问题。 然后，可以开始与代理聊天。

   

   Note

   如果在尝试配置或创建代理时遇到权限错误，请确保项目上有 Azure AI 用户 。

| 参考文档 | 示例 | 库源代码 | 包 (NuGet) |

Prerequisites

• 设置代理环境
• 将 Azure AI 用户RBAC 角色 分配给需要使用 SDK 或代理 Playground 创建或编辑代理的每个团队成员
  • 必须在项目范围内分配此角色
  • 所需的最低权限： agents/*/read、 agents/*/action、 agents/*/delete

配置并运行代理

创建 .NET 控制台项目。

dotnet new console

将 .NET 包安装到项目。 例如，如果使用 .NET CLI，请运行以下命令。

dotnet add package Azure.AI.Agents.Persistent
dotnet add package Azure.Identity

接下来，为了验证 API 请求并运行程序，请使用 az login 命令登录 Azure 订阅。

az login

使用以下代码创建并运行代理。 若要运行此代码，需要获取项目的终结点。 此字符串采用以下格式：

https://<AIFoundryResourceName>.services.ai.azure.com/api/projects/<ProjectName>

您可以在项目的 概述中，通过 Azure AI Foundry 门户下的库>Azure AI Foundry找到您的终结点。

在名为 . ProjectEndpoint. 的环境变量中设置此终结点。

您还需要模型的部署名称。 可以在左侧导航菜单中的 “模型 + 终结点 ”中找到它。

将模型部署名称保存为名为ModelDeploymentName的环境变量。

using Azure;
using Azure.AI.Agents.Persistent;
using Azure.Identity;
using Microsoft.Extensions.Configuration;
using System.Diagnostics;

IConfigurationRoot configuration = new ConfigurationBuilder()
    .SetBasePath(AppContext.BaseDirectory)
    .AddJsonFile("appsettings.json", optional: false, reloadOnChange: true)
    .Build();

var projectEndpoint = configuration["ProjectEndpoint"];
var modelDeploymentName = configuration["ModelDeploymentName"];

//Create a PersistentAgentsClient and PersistentAgent.
PersistentAgentsClient client = new(projectEndpoint, new DefaultAzureCredential());

//Give PersistentAgent a tool to execute code using CodeInterpreterToolDefinition.
PersistentAgent agent = client.Administration.CreateAgent(
    model: modelDeploymentName,
    name: "My Test Agent",
    instructions: "You politely help with math questions. Use the code interpreter tool when asked to visualize numbers.",
    tools: [new CodeInterpreterToolDefinition()]
);

//Create a thread to establish a session between Agent and a User.
PersistentAgentThread thread = client.Threads.CreateThread();

//Ask a question of the Agent.
client.Messages.CreateMessage(
    thread.Id,
    MessageRole.User,
    "Hi, Agent! Draw a graph for a line with a slope of 4 and y-intercept of 9.");

//Have Agent begin processing user's question with some additional instructions associated with the ThreadRun.
ThreadRun run = client.Runs.CreateRun(
    thread.Id,
    agent.Id,
    additionalInstructions: "Please address the user as Jane Doe. The user has a premium account.");

//Poll for completion.
do
{
    Thread.Sleep(TimeSpan.FromMilliseconds(500));
    run = client.Runs.GetRun(thread.Id, run.Id);
}
while (run.Status == RunStatus.Queued
    || run.Status == RunStatus.InProgress
    || run.Status == RunStatus.RequiresAction);

//Get the messages in the PersistentAgentThread. Includes Agent (Assistant Role) and User (User Role) messages.
Pageable<PersistentThreadMessage> messages = client.Messages.GetMessages(
    threadId: thread.Id,
    order: ListSortOrder.Ascending);

//Display each message and open the image generated using CodeInterpreterToolDefinition.
foreach (PersistentThreadMessage threadMessage in messages)
{
    foreach (MessageContent content in threadMessage.ContentItems)
    {
        switch (content)
        {
            case MessageTextContent textItem:
                Console.WriteLine($"[{threadMessage.Role}]: {textItem.Text}");
                break;
            case MessageImageFileContent imageFileContent:
                Console.WriteLine($"[{threadMessage.Role}]: Image content file ID = {imageFileContent.FileId}");
                BinaryData imageContent = client.Files.GetFileContent(imageFileContent.FileId);
                string tempFilePath = Path.Combine(AppContext.BaseDirectory, $"{Guid.NewGuid()}.png");
                File.WriteAllBytes(tempFilePath, imageContent.ToArray());
                client.Files.DeleteFile(imageFileContent.FileId);

                ProcessStartInfo psi = new()
                {
                    FileName = tempFilePath,
                    UseShellExecute = true
                };
                Process.Start(psi);
                break;
        }
    }
}

//Clean up test resources.
client.Threads.DeleteThread(threadId: thread.Id);
client.Administration.DeleteAgent(agentId: agent.Id);

| 参考文档 | 示例 | 库源代码 | 包 (PyPi) |

Prerequisites

• 设置代理环境
• 将 Azure AI 用户RBAC 角色 分配给需要使用 SDK 或代理 Playground 创建或编辑代理的每个团队成员
  • 必须在项目范围内分配此角色
  • 所需的最低权限： agents/*/read、 agents/*/action、 agents/*/delete

配置并运行代理

运行以下命令以安装 python 包。

pip install azure-ai-projects
pip install azure-identity

接下来，为了验证 API 请求并运行程序，请使用 az login 命令登录 Azure 订阅。

az login

使用以下代码创建并运行代理。 若要运行此代码，需要获取项目的终结点。 此字符串采用以下格式：

https://<AIFoundryResourceName>.services.ai.azure.com/api/projects/<ProjectName>

您可以在项目的 概述中，通过 Azure AI Foundry 门户下的库>Azure AI Foundry找到您的终结点。

将此终结点设置为名为 PROJECT_ENDPOINT 的环境变量。

您还需要模型的部署名称。 可以在左侧导航菜单中的 “模型 + 终结点 ”中找到它。

将模型部署名称保存为名为MODEL_DEPLOYMENT_NAME的环境变量。

import os
from pathlib import Path
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential
from azure.ai.agents.models import CodeInterpreterTool

# Create an AIProjectClient from an endpoint, copied from your Azure AI Foundry project.
# You need to login to Azure subscription via Azure CLI and set the environment variables
project_endpoint = os.environ["PROJECT_ENDPOINT"]  # Ensure the PROJECT_ENDPOINT environment variable is set

# Create an AIProjectClient instance
project_client = AIProjectClient(
    endpoint=project_endpoint,
    credential=DefaultAzureCredential(),  # Use Azure Default Credential for authentication
)

code_interpreter = CodeInterpreterTool()
with project_client:
    # Create an agent with the Code Interpreter tool
    agent = project_client.agents.create_agent(
        model=os.environ["MODEL_DEPLOYMENT_NAME"],  # Model deployment name
        name="my-agent",  # Name of the agent
        instructions="You politely help with math questions. Use the Code Interpreter tool when asked to visualize numbers.",  # Instructions for the agent
        tools=code_interpreter.definitions,  # Attach the tool
    )
    print(f"Created agent, ID: {agent.id}")

    # Create a thread for communication
    thread = project_client.agents.threads.create()
    print(f"Created thread, ID: {thread.id}")

    # Add a message to the thread
    message = project_client.agents.messages.create(
        thread_id=thread.id,
        role="user",  # Role of the message sender
        content="Hi, Agent! Draw a graph for a line with a slope of 4 and y-intercept of 9.",  # Message content
    )
    print(f"Created message, ID: {message['id']}")

    # Create and process an agent run
    run = project_client.agents.runs.create_and_process(
        thread_id=thread.id,
        agent_id=agent.id,
        additional_instructions="Please address the user as Jane Doe. The user has a premium account",
    )
    print(f"Run finished with status: {run.status}")

    # Check if the run failed
    if run.status == "failed":
        print(f"Run failed: {run.last_error}")

    # Fetch and log all messages
    messages = project_client.agents.messages.list(thread_id=thread.id)
    for message in messages:
        print(f"Role: {message.role}, Content: {message.content}")

        # Save every image file in the message
        for img in message.image_contents:
            file_id = img.image_file.file_id
            file_name = f"{file_id}_image_file.png"
            project_client.agents.files.save(file_id=file_id, file_name=file_name)
            print(f"Saved image file to: {Path.cwd() / file_name}")

    # Delete the agent when done
    project_client.agents.delete_agent(agent.id)
    print("Deleted agent")

| 参考文档 | 示例 | 库源代码 | 包 (npm) |

Prerequisites

• 设置代理环境
• 将 Azure AI 用户RBAC 角色 分配给需要使用 SDK 或代理 Playground 创建或编辑代理的每个团队成员
  • 必须在项目范围内分配此角色
  • 所需的最低权限： agents/*/read、 agents/*/action、 agents/*/delete

配置并运行代理

此代码中的关键对象包括：

• AgentsClient
• ToolUtility

首先，通过运行来初始化新项目：

npm init -y

运行以下命令以安装所需的 npm 包。

npm install @azure/ai-agents @azure/identity
npm install dotenv

接下来，为了验证 API 请求并运行程序，请使用 az login 命令登录 Azure 订阅。

az login

使用以下代码创建并运行上传 CSV 数据的 代理，然后从该数据生成条形图。 若要运行此代码，需要获取项目的终结点。 此字符串采用以下格式：

https://<AIFoundryResourceName>.services.ai.azure.com/api/projects/<ProjectName>

您可以在项目的 概述中，通过 Azure AI Foundry 门户下的库>Azure AI Foundry找到您的终结点。

将此终结点设置为文件中命名 PROJECT_ENDPOINT 的 .env 环境变量。

您还需要模型的部署名称。 可以在左侧导航菜单中的 “模型 + 终结点 ”中找到它。

将模型部署名称保存为名为MODEL_DEPLOYMENT_NAME的环境变量。

接下来，创建一个 index.js 文件并粘贴以下代码：

// Copyright (c) Microsoft Corporation.
// Licensed under the MIT License.

/**
 * This sample demonstrates how to use agent operations with code interpreter from the Azure Agents service.
 *
 * @summary demonstrates how to use agent operations with code interpreter.
 */
// @ts-nocheck
import type {
  MessageDeltaChunk,
  MessageDeltaTextContent,
  MessageImageFileContent,
  MessageTextContent,
  ThreadRun,
} from "@azure/ai-agents";
import {
  RunStreamEvent,
  MessageStreamEvent,
  DoneEvent,
  ErrorEvent,
  AgentsClient,
  isOutputOfType,
  ToolUtility,
} from "@azure/ai-agents";
import { DefaultAzureCredential } from "@azure/identity";

import * as fs from "fs";
import path from "node:path";
import "dotenv/config";

const projectEndpoint = process.env["PROJECT_ENDPOINT"] || "<project endpoint>";
const modelDeploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "gpt-4o";

export async function main(): Promise<void> {
  // Create an Azure AI Client
  const client = new AgentsClient(projectEndpoint, new DefaultAzureCredential());

  // Upload file and wait for it to be processed
  const filePath = "./data/nifty500QuarterlyResults.csv";
  const localFileStream = fs.createReadStream(filePath);
  const localFile = await client.files.upload(localFileStream, "assistants", {
    fileName: "myLocalFile",
  });

  console.log(`Uploaded local file, file ID : ${localFile.id}`);

  // Create code interpreter tool
  const codeInterpreterTool = ToolUtility.createCodeInterpreterTool([localFile.id]);

  // Notice that CodeInterpreter must be enabled in the agent creation, otherwise the agent will not be able to see the file attachment
  const agent = await client.createAgent(modelDeploymentName, {
    name: "my-agent",
    instructions: "You are a helpful agent",
    tools: [codeInterpreterTool.definition],
    toolResources: codeInterpreterTool.resources,
  });
  console.log(`Created agent, agent ID: ${agent.id}`);

  // Create a thread
  const thread = await client.threads.create();
  console.log(`Created thread, thread ID: ${thread.id}`);

  // Create a message
  const message = await client.messages.create(
    thread.id,
    "user",
    "Could you please create a bar chart in the TRANSPORTATION sector for the operating profit from the uploaded CSV file and provide the file to me?",
  );

  console.log(`Created message, message ID: ${message.id}`);

  // Create and execute a run
  const streamEventMessages = await client.runs.create(thread.id, agent.id).stream();

  for await (const eventMessage of streamEventMessages) {
    switch (eventMessage.event) {
      case RunStreamEvent.ThreadRunCreated:
        console.log(`ThreadRun status: ${(eventMessage.data as ThreadRun).status}`);
        break;
      case MessageStreamEvent.ThreadMessageDelta:
        {
          const messageDelta = eventMessage.data as MessageDeltaChunk;
          messageDelta.delta.content.forEach((contentPart) => {
            if (contentPart.type === "text") {
              const textContent = contentPart as MessageDeltaTextContent;
              const textValue = textContent.text?.value || "No text";
              console.log(`Text delta received:: ${textValue}`);
            }
          });
        }
        break;

      case RunStreamEvent.ThreadRunCompleted:
        console.log("Thread Run Completed");
        break;
      case ErrorEvent.Error:
        console.log(`An error occurred. Data ${eventMessage.data}`);
        break;
      case DoneEvent.Done:
        console.log("Stream completed.");
        break;
    }
  }

  // Delete the original file from the agent to free up space (note: this does not delete your version of the file)
  await client.files.delete(localFile.id);
  console.log(`Deleted file, file ID : ${localFile.id}`);

  // Print the messages from the agent
  const messagesIterator = client.messages.list(thread.id);
  const messagesArray = [];
  for await (const m of messagesIterator) {
    messagesArray.push(m);
  }
  console.log("Messages:", messagesArray);

  // Get most recent message from the assistant
// Get most recent message from the assistant
  const assistantMessage = messagesArray.find((msg) => msg.role === "assistant");
  if (assistantMessage) {
    // Look for an image file in the assistant's message
    const imageFileOutput = assistantMessage.content.find(content => 
      content.type === "image_file" && content.imageFile?.fileId);
    
    if (imageFileOutput) {
      try {
        // Save the newly created file
        console.log(`Saving new files...`);
        const imageFile = imageFileOutput.imageFile.fileId;
        const imageFileName = path.resolve(
          "./data/" + (await client.files.get(imageFile)).filename + "ImageFile.png",
        );
        console.log(`Image file name : ${imageFileName}`);

        const fileContent = await client.files.getContent(imageFile).asNodeStream();
        if (fileContent && fileContent.body) {
          const chunks = [];
          for await (const chunk of fileContent.body) {
            chunks.push(Buffer.isBuffer(chunk) ? chunk : Buffer.from(chunk));
          }
          const buffer = Buffer.concat(chunks);
          fs.writeFileSync(imageFileName, buffer);
          console.log(`Successfully saved image to ${imageFileName}`);
        } else {
          console.error("No file content available in the response");
        }
      } catch (error) {
        console.error("Error saving image file:", error);
      }
    } else {
      console.log("No image file found in assistant's message");
    }
  } else {
    console.log("No assistant message found");
  }

  // Iterate through messages and print details for each annotation
  console.log(`Message Details:`);
  messagesArray.forEach((m) => {
    console.log(`File Paths:`);
    console.log(`Type: ${m.content[0].type}`);
    if (isOutputOfType<MessageTextContent>(m.content[0], "text")) {
      const textContent = m.content[0] as MessageTextContent;
      console.log(`Text: ${textContent.text.value}`);
    }
    console.log(`File ID: ${m.id}`);
    // firstId and lastId are properties of the paginator, not the messages array
    // Removing these references as they don't exist in this context
  });

  // Delete the agent once done
  await client.deleteAgent(agent.id);
  console.log(`Deleted agent, agent ID: ${agent.id}`);
}

main().catch((err) => {
  console.error("The sample encountered an error:", err);
});

使用 node index.js 运行代码。 此代码从上传的 CSV 文件生成一个 TRANSPORTATION 部门的营业利润条形图 PNG 图像文件，并将该文件提供给你。 提供完整的 示例源代码 。

| 参考文档 | 样品 | 库源代码 | 包 （Maven） |

Prerequisites

• 设置代理环境
• 将 Azure AI 用户RBAC 角色 分配给需要使用 SDK 或代理 Playground 创建或编辑代理的每个团队成员
  • 必须在项目范围内分配此角色
  • 所需的最低权限： agents/*/read、 agents/*/action、 agents/*/delete

配置并运行代理

首先，创建新的 Java 控制台项目。 需要以下依赖项才能运行代码：

<dependencies>
    <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-ai-agents-persistent</artifactId>
        <version>1.0.0-beta.2</version>
    </dependency>
    <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-identity</artifactId>
        <version>1.17.0-beta.1</version>
    </dependency>
</dependencies>

接下来，为了验证 API 请求并运行程序，请使用 az login 命令登录 Azure 订阅。

az login

使用以下代码创建并运行代理。 若要运行此代码，需要获取项目的终结点。 此字符串采用以下格式：

https://<AIFoundryResourceName>.services.ai.azure.com/api/projects/<ProjectName>

您可以在项目的 概述中，通过 Azure AI Foundry 门户下的库>Azure AI Foundry找到您的终结点。

在名为 . PROJECT_ENDPOINT. 的环境变量中设置此终结点。

您还需要模型的部署名称。 可以在左侧导航菜单中的 “模型 + 终结点 ”中找到它。

将模型部署名称保存为名为MODEL_DEPLOYMENT_NAME的环境变量。

代码示例

package com.example.agents;

import com.azure.ai.agents.persistent.MessagesClient;
import com.azure.ai.agents.persistent.PersistentAgentsAdministrationClient;
import com.azure.ai.agents.persistent.PersistentAgentsClient;
import com.azure.ai.agents.persistent.PersistentAgentsClientBuilder;
import com.azure.ai.agents.persistent.RunsClient;
import com.azure.ai.agents.persistent.ThreadsClient;
import com.azure.ai.agents.persistent.models.CodeInterpreterToolDefinition;
import com.azure.ai.agents.persistent.models.CreateAgentOptions;
import com.azure.ai.agents.persistent.models.CreateRunOptions;
import com.azure.ai.agents.persistent.models.MessageImageFileContent;
import com.azure.ai.agents.persistent.models.MessageRole;
import com.azure.ai.agents.persistent.models.MessageTextContent;
import com.azure.ai.agents.persistent.models.PersistentAgent;
import com.azure.ai.agents.persistent.models.PersistentAgentThread;
import com.azure.ai.agents.persistent.models.RunStatus;
import com.azure.ai.agents.persistent.models.ThreadMessage;
import com.azure.ai.agents.persistent.models.ThreadRun;
import com.azure.ai.agents.persistent.models.MessageContent;
import com.azure.core.http.rest.PagedIterable;
import com.azure.identity.DefaultAzureCredentialBuilder;
import java.util.Arrays;

public class AgentSample {

    public static void main(String[] args) {
        // variables for authenticating requests to the agent service 
        String projectEndpoint = System.getenv("PROJECT_ENDPOINT");
        String modelName = System.getenv("MODEL_DEPLOYMENT_NAME");

        // initialize clients to manage various aspects of agent runtime
        PersistentAgentsClientBuilder clientBuilder = new PersistentAgentsClientBuilder()
            .endpoint(projectEndpoint)
            .credential(new DefaultAzureCredentialBuilder().build());
        PersistentAgentsClient agentsClient = clientBuilder.buildClient();
        PersistentAgentsAdministrationClient administrationClient = agentsClient.getPersistentAgentsAdministrationClient();
        ThreadsClient threadsClient = agentsClient.getThreadsClient();
        MessagesClient messagesClient = agentsClient.getMessagesClient();
        RunsClient runsClient = agentsClient.getRunsClient();
        
        
        String agentName = "my-agent"; // the name of the agent
        CreateAgentOptions createAgentOptions = new CreateAgentOptions(modelName)
            .setName(agentName)
            .setInstructions("You are a helpful agent") // system insturctions
            .setTools(Arrays.asList(new CodeInterpreterToolDefinition()));
        PersistentAgent agent = administrationClient.createAgent(createAgentOptions);

        PersistentAgentThread thread = threadsClient.createThread();
        ThreadMessage createdMessage = messagesClient.createMessage(
            thread.getId(),
            MessageRole.USER,
            "I need to solve the equation `3x + 11 = 14`. Can you help me?"); // The message to the agent

        try {
            //run the agent
            CreateRunOptions createRunOptions = new CreateRunOptions(thread.getId(), agent.getId())
                .setAdditionalInstructions("");
            ThreadRun threadRun = runsClient.createRun(createRunOptions);
            // wait for the run to complete before printing the message
            waitForRunCompletion(thread.getId(), threadRun, runsClient);
            printRunMessages(messagesClient, thread.getId());
        } catch (InterruptedException e) {
            throw new RuntimeException(e);
        } finally {
            //cleanup - remove or comment out these lines if you want to keep the agent
            threadsClient.deleteThread(thread.getId());
            administrationClient.deleteAgent(agent.getId());
        }
    }

    // A helper function to print messages from the agent
    public static void printRunMessages(MessagesClient messagesClient, String threadId) {

        PagedIterable<ThreadMessage> runMessages = messagesClient.listMessages(threadId);
        for (ThreadMessage message : runMessages) {
            System.out.print(String.format("%1$s - %2$s : ", message.getCreatedAt(), message.getRole()));
            for (MessageContent contentItem : message.getContent()) {
                if (contentItem instanceof MessageTextContent) {
                    System.out.print((((MessageTextContent) contentItem).getText().getValue()));
                } else if (contentItem instanceof MessageImageFileContent) {
                    String imageFileId = (((MessageImageFileContent) contentItem).getImageFile().getFileId());
                    System.out.print("Image from ID: " + imageFileId);
                }
                System.out.println();
            }
        }
    }

    // a helper function to wait until a run has completed running
    public static void waitForRunCompletion(String threadId, ThreadRun threadRun, RunsClient runsClient)
        throws InterruptedException {

        do {
            Thread.sleep(500);
            threadRun = runsClient.getRun(threadId, threadRun.getId());
        }
        while (
            threadRun.getStatus() == RunStatus.QUEUED
                || threadRun.getStatus() == RunStatus.IN_PROGRESS
                || threadRun.getStatus() == RunStatus.REQUIRES_ACTION);

        if (threadRun.getStatus() == RunStatus.FAILED) {
            System.out.println(threadRun.getLastError().getMessage());
        }
    }
}

| 参考文档 |

Prerequisites

• 设置代理环境
• 将 Azure AI 用户RBAC 角色 分配给需要使用 SDK 或代理 Playground 创建或编辑代理的每个团队成员
  • 必须在项目范围内分配此角色
  • 所需的最低权限： agents/*/read、 agents/*/action、 agents/*/delete

配置并运行代理

API 调用信息

为了验证 API 请求，请使用 az login 命令登录 Azure 订阅。

az login

接下来，你需要获取 Entra ID 令牌，用作对 API 调用的授权。 使用 CLI 命令提取令牌：

az account get-access-token --resource 'https://ai.azure.com' | jq -r .accessToken | tr -d '"'

将访问令牌设置为名为 AGENT_TOKEN 的环境变量。

若要成功对 Azure AI Foundry 代理服务进行 REST API 调用，需要使用终结点，如下所示：

https://<your_ai_service_name>.services.ai.azure.com/api/projects/<your_project_name>

例如，终结点可能如下所示：

https://exampleaiservice.services.ai.azure.com/api/projects/project

将此终结点设置为名为 AZURE_AI_FOUNDRY_PROJECT_ENDPOINT 的环境变量。

创建代理

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/assistants?api-version=2025-05-01 \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "instructions": "You are a helpful agent.",
    "name": "my-agent",
    "tools": [{"type": "code_interpreter"}],
    "model": "gpt-4o-mini"
  }'

创建线程

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads?api-version=2025-05-01 \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d ''

将用户问题添加到线程

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/messages?api-version=2025-05-01 \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
      "role": "user",
      "content": "I need to solve the equation `3x + 11 = 14`. Can you help me?"
    }'

运行线程

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/runs?api-version=2025-05-01 \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "assistant_id": "asst_abc123",
  }'

获取运行状态

curl --request GET \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/runs/run_abc123?api-version=2025-05-01 \
  -H "Authorization: Bearer $AGENT_TOKEN"

检索代理响应

curl --request GET \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/messages?api-version=2025-05-01 \
  -H "Authorization: Bearer $AGENT_TOKEN"