用于 Microsoft Foundry 代理的代码解释器工具

在 Python SDK 中将代理与代码解释器工具配合使用的示例

以下Python示例演示如何使用代码解释器工具创建代理、上传 CSV 文件进行分析，以及基于数据请求条形图。 它演示了完整的工作流：上传文件、创建启用了代码解释器的代理、请求数据可视化并下载生成的图表。

import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition, CodeInterpreterTool, AutoCodeInterpreterToolParam

# Load the CSV file to be processed
asset_file_path = os.path.abspath(
    os.path.join(os.path.dirname(__file__), "../assets/synthetic_500_quarterly_results.csv")
)

# Format: "https://resource_name.ai.azure.com/api/projects/project_name"
PROJECT_ENDPOINT = "your_project_endpoint"

# Create clients to call Foundry API
project = AIProjectClient(
    endpoint=PROJECT_ENDPOINT,
    credential=DefaultAzureCredential(),
)
openai = project.get_openai_client()

# Upload the CSV file for the code interpreter to use
file = openai.files.create(purpose="assistants", file=open(asset_file_path, "rb"))

# Create agent with code interpreter tool
agent = project.agents.create_version(
    agent_name="MyAgent",
    definition=PromptAgentDefinition(
        model="gpt-5-mini",
        instructions="You are a helpful assistant.",
        tools=[CodeInterpreterTool(container=AutoCodeInterpreterToolParam(file_ids=[file.id]))],
    ),
    description="Code interpreter agent for data analysis and visualization.",
)

# Create a conversation for the agent interaction
conversation = openai.conversations.create()

# Send request to create a chart and generate a file
response = openai.responses.create(
    conversation=conversation.id,
    input="Could you please create bar chart in TRANSPORTATION sector for the operating profit from the uploaded csv file and provide file to me?",
    extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
)

# Extract file information from response annotations
file_id = ""
filename = ""
container_id = ""

# Get the last message which should contain file citations
last_message = response.output[-1]  # ResponseOutputMessage
if (
    last_message.type == "message"
    and last_message.content
    and last_message.content[-1].type == "output_text"
    and last_message.content[-1].annotations
):
    file_citation = last_message.content[-1].annotations[-1]  # AnnotationContainerFileCitation
    if file_citation.type == "container_file_citation":
        file_id = file_citation.file_id
        filename = file_citation.filename
        container_id = file_citation.container_id
        print(f"Found generated file: {filename} (ID: {file_id})")

# Clean up resources
project.agents.delete_version(agent_name=agent.name, agent_version=agent.version)

# Download the generated file if available
if file_id and filename:
    file_content = openai.containers.files.content.retrieve(file_id=file_id, container_id=container_id)
    print(f"File ready for download: {filename}")
    file_path = os.path.join(os.path.dirname(__file__), filename)
    with open(file_path, "wb") as f:
        f.write(file_content.read())
    print(f"File downloaded successfully: {file_path}")
else:
    print("No file generated in response")

预期输出

示例代码生成类似于以下示例的输出：

Found generated file: transportation_operating_profit_bar_chart.png (ID: file-xxxxxxxxxxxxxxxxxxxx)
File ready for download: transportation_operating_profit_bar_chart.png
File downloaded successfully: transportation_operating_profit_bar_chart.png

代理将 CSV 文件上传到Azure存储、创建沙盒Python环境、分析数据以筛选运输部门记录、生成按季度显示营业利润的 PNG 条形图，并将图表下载到本地目录。 响应中的文件注释提供检索生成的图表所需的文件 ID 和容器信息。

在 C 中使用代码解释器创建图表#

以下 C# 示例演示如何使用代码解释器工具创建代理、上传 CSV 文件进行分析和下载生成的图表。 有关异步用法，请参阅 代码示例，在GitHub上的.NET存储库中找到Azure SDK。

using System;
using System.IO;
using Azure.AI.Projects;
using Azure.AI.Extensions.OpenAI;
using Azure.Identity;
using OpenAI.Files;

// Format: "https://resource_name.ai.azure.com/api/projects/project_name"
var projectEndpoint = "your_project_endpoint";

// Create project client to call Foundry API
AIProjectClient projectClient = new(
    endpoint: new Uri(projectEndpoint),
    tokenProvider: new DefaultAzureCredential());

// Upload a CSV file for Code Interpreter to analyze
OpenAIFileClient fileClient = projectClient.ProjectOpenAIClient.GetOpenAIFileClient();
OpenAIFile uploadedFile = fileClient.UploadFile(
    filePath: "synthetic_500_quarterly_results.csv",
    purpose: FileUploadPurpose.Assistants);
Console.WriteLine($"Uploaded file: {uploadedFile.Id}");

// Create an agent with Code Interpreter enabled
DeclarativeAgentDefinition agentDefinition = new(model: "gpt-5-mini")
{
    Instructions = "You are a helpful assistant.",
    Tools = {
        ResponseTool.CreateCodeInterpreterTool(
            new CodeInterpreterToolContainer(
                CodeInterpreterToolContainerConfiguration.CreateAutomaticContainerConfiguration(
                    fileIds: [uploadedFile.Id]
                )
            )
        ),
    }
};
ProjectsAgentVersion agentVersion = projectClient.AgentAdministrationClient.CreateAgentVersion(
    agentName: "myChartAgent",
    options: new(agentDefinition));

// Request chart generation from the uploaded CSV data
AgentReference agentReference = new(name: agentVersion.Name, version: agentVersion.Version);
ProjectResponsesClient responseClient = projectClient.ProjectOpenAIClient.GetProjectResponsesClientForAgent(agentReference);

ResponseResult response = responseClient.CreateResponse(
    "Could you please create bar chart in TRANSPORTATION sector for the operating profit " +
    "from the uploaded csv file and provide file to me?");

Console.WriteLine(response.GetOutputText());

// Extract file information from response annotations
ContainerFileCitationMessageAnnotation containerAnnotation = null;
foreach (ResponseItem item in response.OutputItems)
{
    if (item is MessageResponseItem messageItem)
    {
        foreach (ResponseContentPart content in messageItem.Content)
        {
            foreach (ResponseMessageAnnotation annotation in content.OutputTextAnnotations)
            {
                if (annotation is ContainerFileCitationMessageAnnotation cntrAnnotation)
                {
                    containerAnnotation = cntrAnnotation;
                }
            }
        }
    }
}

// Download the generated chart if available
if (containerAnnotation is not null)
{
    ContainerClient containerClient = projectClient.ProjectOpenAIClient.GetContainerClient();
    BinaryData fileData = containerClient.DownloadContainerFile(
        containerId: containerAnnotation.ContainerId,
        fileId: containerAnnotation.FileId);
    File.WriteAllBytes("chart.png", fileData.ToArray());
    Console.WriteLine($"Chart downloaded: {Path.GetFullPath("chart.png")}");
}
else
{
    Console.WriteLine("No file generated in response");
}

// Clean up resources
projectClient.AgentAdministrationClient.DeleteAgentVersion(
    agentName: agentVersion.Name, agentVersion: agentVersion.Version);

预期输出

示例代码生成类似于以下示例的输出：

Uploaded file: file-xxxxxxxxxxxxxxxxxxxx
Here is the bar chart showing operating profit by quarter in the TRANSPORTATION sector...
Chart downloaded: C:\Users\you\chart.png

代理将 CSV 文件上传到Azure存储，创建沙盒Python环境，分析数据以筛选运输扇区记录，并生成 PNG 条形图。 批注分析从响应中提取容器 ID 和文件 ID，用于将图表下载到本地目录。

在 TypeScript SDK 中将代理与代码解释器工具配合使用的示例

以下 TypeScript 示例演示如何使用代码解释器工具创建代理、上传 CSV 文件进行分析，以及基于数据请求条形图。 有关 JavaScript 版本，请参阅 GitHub 上的 javaScript 存储库Azure SDK中的 JavaScript 示例。

import { DefaultAzureCredential } from "@azure/identity";
import { AIProjectClient } from "@azure/ai-projects";
import * as fs from "fs";
import * as path from "path";
import { fileURLToPath } from "url";

// Format: "https://resource_name.ai.azure.com/api/projects/project_name"
const PROJECT_ENDPOINT = "your_project_endpoint";

// Helper to resolve asset file path
const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);

export async function main(): Promise<void> {
  // Create clients to call Foundry API
  const project = new AIProjectClient(PROJECT_ENDPOINT, new DefaultAzureCredential());
  const openai = project.getOpenAIClient();

  // Load and upload CSV file
  const assetFilePath = path.resolve(
    __dirname,
    "../assets/synthetic_500_quarterly_results.csv",
  );
  const fileStream = fs.createReadStream(assetFilePath);

  // Upload CSV file
  const uploadedFile = await openai.files.create({
    file: fileStream,
    purpose: "assistants",
  });

  // Create agent with Code Interpreter tool
  const agent = await project.agents.createVersion("MyAgent", {
    kind: "prompt",
    model: "gpt-5-mini",
    instructions: "You are a helpful assistant.",
    tools: [
      {
        type: "code_interpreter",
        container: {
          type: "auto",
          file_ids: [uploadedFile.id],
        },
      },
    ],
  });

  // Create a conversation
  const conversation = await openai.conversations.create();

  // Request chart generation
  const response = await openai.responses.create(
    {
      conversation: conversation.id,
      input:
        "Could you please create bar chart in TRANSPORTATION sector for the operating profit from the uploaded csv file and provide file to me?",
    },
    {
      body: { agent: { name: agent.name, type: "agent_reference" } },
    },
  );

  // Extract file information from response annotations
  let fileId = "";
  let filename = "";
  let containerId = "";

  // Get the last message which should contain file citations
  const lastMessage = response.output?.[response.output.length - 1];
  if (lastMessage && lastMessage.type === "message") {
    // Get the last content item
    const textContent = lastMessage.content?.[lastMessage.content.length - 1];
    if (textContent && textContent.type === "output_text" && textContent.annotations) {
      // Get the last annotation (most recent file)
      const fileCitation = textContent.annotations[textContent.annotations.length - 1];
      if (fileCitation && fileCitation.type === "container_file_citation") {
        fileId = fileCitation.file_id;
        filename = fileCitation.filename;
        containerId = fileCitation.container_id;
        console.log(`Found generated file: ${filename} (ID: ${fileId})`);
      }
    }
  }

  // Download the generated file if available
  if (fileId && filename) {
    const safeFilename = path.basename(filename);
    const fileContent = await openai.containers.files.content.retrieve({
      file_id: fileId,
      container_id: containerId,
    });

    // Read the readable stream into a buffer
    const chunks: Buffer[] = [];
    for await (const chunk of fileContent.body) {
      chunks.push(Buffer.from(chunk));
    }
    const buffer = Buffer.concat(chunks);

    fs.writeFileSync(safeFilename, buffer);
    console.log(`File ${safeFilename} downloaded successfully.`);
    console.log(`File ready for download: ${safeFilename}`);
  } else {
    console.log("No file generated in response");
  }

  // Clean up resources
  await project.agents.deleteVersion(agent.name, agent.version);
}

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

预期输出

示例代码生成类似于以下示例的输出：

Found generated file: transportation_operating_profit_bar_chart.png (ID: file-xxxxxxxxxxxxxxxxxxxx)
File transportation_operating_profit_bar_chart.png downloaded successfully.
File ready for download: transportation_operating_profit_bar_chart.png

代理将 CSV 文件上传到Azure存储、创建沙盒Python环境、分析数据以筛选运输部门记录、生成按季度显示营业利润的 PNG 条形图，并将图表下载到本地目录。 响应中的文件注释提供检索生成的图表所需的文件 ID 和容器信息。

在 Java 中使用代码解释器创建图表

将依赖项添加到pom.xml：

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-agents</artifactId>
    <version>2.0.0</version>
</dependency>

创建代理并生成图表

import com.azure.ai.agents.AgentsClient;
import com.azure.ai.agents.AgentsClientBuilder;
import com.azure.ai.agents.ResponsesClient;
import com.azure.ai.agents.models.AgentReference;
import com.azure.ai.agents.models.AgentVersionDetails;
import com.azure.ai.agents.models.AzureCreateResponseOptions;
import com.azure.ai.agents.models.CodeInterpreterTool;
import com.azure.ai.agents.models.PromptAgentDefinition;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.openai.models.responses.Response;
import com.openai.models.responses.ResponseCreateParams;

import java.util.Collections;

public class CodeInterpreterChartExample {
    public static void main(String[] args) {
        // Format: "https://resource_name.ai.azure.com/api/projects/project_name"
        String projectEndpoint = "your_project_endpoint";

        AgentsClientBuilder builder = new AgentsClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(projectEndpoint);

        AgentsClient agentsClient = builder.buildAgentsClient();
        ResponsesClient responsesClient = builder.buildResponsesClient();

        // Create code interpreter tool
        CodeInterpreterTool codeInterpreter = new CodeInterpreterTool();

        // Create agent with code interpreter for data visualization
        PromptAgentDefinition agentDefinition = new PromptAgentDefinition("gpt-5-mini")
            .setInstructions("You are a data visualization assistant. When asked to create charts, "
                + "write and run Python code using matplotlib to generate them.")
            .setTools(Collections.singletonList(codeInterpreter));

        AgentVersionDetails agent = agentsClient.createAgentVersion("chart-agent", agentDefinition);

        // Request a bar chart with inline data
        AgentReference agentReference = new AgentReference(agent.getName())
            .setVersion(agent.getVersion());

        Response response = responsesClient.createAzureResponse(
            new AzureCreateResponseOptions().setAgentReference(agentReference),
            ResponseCreateParams.builder()
                .input("Create a bar chart showing quarterly revenue for 2025: "
                    + "Q1=$2.1M, Q2=$2.8M, Q3=$3.2M, Q4=$2.9M. "
                    + "Use a blue color scheme, add data labels on each bar, "
                    + "and title the chart 'Quarterly Revenue 2025'. "
                    + "Save the chart as a PNG file."));

        System.out.println("Response: " + response.output());

        // Clean up
        agentsClient.deleteAgentVersion(agent.getName(), agent.getVersion());
    }
}

预期输出

Response: Here is the bar chart showing quarterly revenue for 2025 with Q1 ($2.1M), Q2 ($2.8M), Q3 ($3.2M), and Q4 ($2.9M) displayed in blue with data labels.

代理创建代码解释器会话，编写使用 matplotlib 生成图表的 Python 代码，并在沙盒环境中执行这些代码。 有关上传 CSV 文件并下载生成的图表的示例，请从本文顶部的语言选择器中选择 Python 或 TypeScript。 有关更多示例，请参阅 Azure AI 代理Java SDK 示例。

使用 REST API 通过代码解释器创建图表

以下示例演示如何上传 CSV 文件、使用代码解释器创建代理、请求图表以及下载生成的文件。

先决条件

设置以下环境变量：

• FOUNDRY_PROJECT_ENDPOINT：项目终结点 URL。
• AGENT_TOKEN：Foundry 的持有者令牌。

获取访问令牌：

export AGENT_TOKEN=$(az account get-access-token --scope "https://ai.azure.com/.default" --query accessToken -o tsv)

1.上传 CSV 文件

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/openai/v1/files" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -F "purpose=assistants" \
  -F "file=@quarterly_results.csv"

请保存来自响应中的id（例如file-abc123）。

2.使用代码解释器创建代理

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/agents?api-version=v1" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "name": "chart-agent",
    "definition": {
      "kind": "prompt",
      "model": "<MODEL_DEPLOYMENT>",
      "instructions": "You are a data visualization assistant. When asked to create charts, write and run Python code using matplotlib to generate them.",
      "tools": [
        {
          "type": "code_interpreter",
          "container": {
            "type": "auto",
            "file_ids": ["<FILE_ID>"]
          }
        }
      ]
    }
  }'

3. 生成图表

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/openai/v1/responses" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "agent_reference": {"type": "agent_reference", "name": "chart-agent"},
    "input": "Create a bar chart of operating profit by quarter from the uploaded CSV file. Use a blue color scheme and add data labels."
  }'

响应包含附有生成的文件详细信息的container_file_citation注释。 保存container_id和file_id值来自批注。

4.下载生成的图表

curl -X GET "$FOUNDRY_PROJECT_ENDPOINT/openai/v1/containers/<CONTAINER_ID>/files/<FILE_ID>/content" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  --output chart.png

5. 清理

curl -X DELETE "$FOUNDRY_PROJECT_ENDPOINT/agents/chart-agent?api-version=v1" \
  -H "Authorization: Bearer $AGENT_TOKEN"