使用指南：`OpenAIAssistantAgent` 代码解释器

2025-05-06

重要

此功能处于候选发布阶段。此阶段的功能几乎完整且一般稳定，尽管这些功能可能会在全面普遍可用之前进行轻微的改进或优化。

概述

在此示例中，我们将探讨如何使用代码 OpenAIAssistantAgent 解释器工具来完成数据分析任务。该方法将逐步分解以突出编码过程中的关键部分。作为任务的一部分，代理将生成图像和文本响应。这将演示此工具在执行定量分析方面的多功能性。

流式处理将用于传送代理人的响应。这将在任务进行时提供实时更新。

入门

在继续执行功能编码之前，请确保开发环境已完全设置和配置。

首先创建控制台项目。然后，包括以下包引用，以确保所有必需的依赖项都可用。

若要从命令行添加包依赖项，请使用 dotnet 以下命令：

dotnet add package Azure.Identity
dotnet add package Microsoft.Extensions.Configuration
dotnet add package Microsoft.Extensions.Configuration.Binder
dotnet add package Microsoft.Extensions.Configuration.UserSecrets
dotnet add package Microsoft.Extensions.Configuration.EnvironmentVariables
dotnet add package Microsoft.SemanticKernel
dotnet add package Microsoft.SemanticKernel.Agents.OpenAI --prerelease

重要

如果在 Visual Studio 中管理 NuGet 包，请确保选中 Include prerelease。

项目文件（.csproj）应包含以下 PackageReference 定义：

  <ItemGroup>
    <PackageReference Include="Azure.Identity" Version="<stable>" />
    <PackageReference Include="Microsoft.Extensions.Configuration" Version="<stable>" />
    <PackageReference Include="Microsoft.Extensions.Configuration.Binder" Version="<stable>" />
    <PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="<stable>" />
    <PackageReference Include="Microsoft.Extensions.Configuration.EnvironmentVariables" Version="<stable>" />
    <PackageReference Include="Microsoft.SemanticKernel" Version="<latest>" />
    <PackageReference Include="Microsoft.SemanticKernel.Agents.OpenAI" Version="<latest>" />
  </ItemGroup>

Agent Framework 是实验性的，需要抑制警告。这可以在项目文件（.csproj）中作为属性来解决。

  <PropertyGroup>
    <NoWarn>$(NoWarn);CA2007;IDE1006;SKEXP0001;SKEXP0110;OPENAI001</NoWarn>
  </PropertyGroup>

此外，复制PopulationByAdmin1.csv和PopulationByCountry.csv数据文件从语义内核LearnResources项目。在项目文件夹中添加这些文件，并将其配置为将它们复制到输出目录：

  <ItemGroup>
    <None Include="PopulationByAdmin1.csv">
      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
    </None>
    <None Include="PopulationByCountry.csv">
      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
    </None>
  </ItemGroup>

首先创建一个将保存脚本（.py 文件）和示例资源的文件夹。在 .py 文件的顶部包含以下导入：

import asyncio
import os

from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent
from semantic_kernel.contents import StreamingFileReferenceContent

此外，将PopulationByAdmin1.csv和PopulationByCountry.csv数据文件从语义内核learn_resources/resources目录中复制出来。将这些文件添加到工作目录。

配置

此示例需要配置设置才能连接到远程服务。需要定义 OpenAI 或 Azure OpenAI 的设置。

# OpenAI
dotnet user-secrets set "OpenAISettings:ApiKey" "<api-key>"
dotnet user-secrets set "OpenAISettings:ChatModel" "gpt-4o"

# Azure OpenAI
dotnet user-secrets set "AzureOpenAISettings:ApiKey" "<api-key>" # Not required if using token-credential
dotnet user-secrets set "AzureOpenAISettings:Endpoint" "<model-endpoint>"
dotnet user-secrets set "AzureOpenAISettings:ChatModelDeployment" "gpt-4o"

以下类在所有代理示例中均使用。请确保将其包含在项目中，以确保适当的功能。此类作为后续示例的基础类。

using System.Reflection;
using Microsoft.Extensions.Configuration;

namespace AgentsSample;

public class Settings
{
    private readonly IConfigurationRoot configRoot;

    private AzureOpenAISettings azureOpenAI;
    private OpenAISettings openAI;

    public AzureOpenAISettings AzureOpenAI => this.azureOpenAI ??= this.GetSettings<Settings.AzureOpenAISettings>();
    public OpenAISettings OpenAI => this.openAI ??= this.GetSettings<Settings.OpenAISettings>();

    public class OpenAISettings
    {
        public string ChatModel { get; set; } = string.Empty;
        public string ApiKey { get; set; } = string.Empty;
    }

    public class AzureOpenAISettings
    {
        public string ChatModelDeployment { get; set; } = string.Empty;
        public string Endpoint { get; set; } = string.Empty;
        public string ApiKey { get; set; } = string.Empty;
    }

    public TSettings GetSettings<TSettings>() =>
        this.configRoot.GetRequiredSection(typeof(TSettings).Name).Get<TSettings>()!;

    public Settings()
    {
        this.configRoot =
            new ConfigurationBuilder()
                .AddEnvironmentVariables()
                .AddUserSecrets(Assembly.GetExecutingAssembly(), optional: true)
                .Build();
    }
}

运行示例代码的正确配置入门的最快方法是在项目的根目录（运行脚本的位置）创建 .env 文件。

在 .env 文件中为 Azure OpenAI 或 OpenAI 配置以下设置：

AZURE_OPENAI_API_KEY="..."
AZURE_OPENAI_ENDPOINT="https://<resource-name>.openai.azure.com/"
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="..."
AZURE_OPENAI_API_VERSION="..."

OPENAI_API_KEY="sk-..."
OPENAI_ORG_ID=""
OPENAI_CHAT_MODEL_ID=""

提示

Azure 助手至少需要 2024-05-01-preview 的 API 版本。随着新功能的引入，API 版本会相应地更新。截至本文撰写，最新版本为 2025-01-01-preview。有关最新版本信息的详细内容，请参阅 Azure OpenAI API 预览生命周期。

配置后，相应的 AI 服务类将选取所需的变量，并在实例化期间使用这些变量。

编码

此示例的编码过程涉及：

安装程序 - 初始化设置和插件。
代理定义 - 使用模板化说明和插件创建_OpenAI_AssistantAgent。
聊天循环 - 编写驱动用户/代理交互的循环。

最终部分提供了完整的示例代码。有关完整实现，请参阅该部分。

安装

在创建 OpenAIAssistantAgent之前，请确保配置设置可用并准备文件资源。

实例化 Settings 上一配置部分中引用的类。使用设置来创建一个AzureOpenAIClient，该AzureOpenAIClient将用于代理定义和文件上传。

Settings settings = new();

AzureOpenAIClient client = OpenAIAssistantAgent.CreateAzureOpenAIClient(new AzureCliCredential(), new Uri(settings.AzureOpenAI.Endpoint));

使用AzureOpenAIClient访问OpenAIFileClient，上传上一节配置中描述的两个数据文件，并保留文件引用以供最终清理。

Console.WriteLine("Uploading files...");
OpenAIFileClient fileClient = client.GetOpenAIFileClient();
OpenAIFile fileDataCountryDetail = await fileClient.UploadFileAsync("PopulationByAdmin1.csv", FileUploadPurpose.Assistants);
OpenAIFile fileDataCountryList = await fileClient.UploadFileAsync("PopulationByCountry.csv", FileUploadPurpose.Assistants);

在创建 AzureAssistantAgent 或 OpenAIAssistantAgent之前，请确保配置设置可用并准备文件资源。

提示

可能需要根据文件所在的位置调整文件路径。

# Let's form the file paths that we will use as part of file upload
csv_file_path_1 = os.path.join(
    os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
    "resources",
    "PopulationByAdmin1.csv",
)

csv_file_path_2 = os.path.join(
    os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
    "resources",
    "PopulationByCountry.csv",
)

# Create the client using Azure OpenAI resources and configuration
client, model = AzureAssistantAgent.setup_resources()

# Upload the files to the client
file_ids: list[str] = []
for path in [csv_file_path_1, csv_file_path_2]:
    with open(path, "rb") as file:
        file = await client.files.create(file=file, purpose="assistants")
        file_ids.append(file.id)

# Get the code interpreter tool and resources
code_interpreter_tools, code_interpreter_tool_resources = AzureAssistantAgent.configure_code_interpreter_tool(
    file_ids=file_ids
)

# Create the assistant definition
definition = await client.beta.assistants.create(
    model=model,
    instructions="""
        Analyze the available data to provide an answer to the user's question.
        Always format response using markdown.
        Always include a numerical index that starts at 1 for any lists or tables.
        Always sort lists in ascending order.
        """,
    name="SampleAssistantAgent",
    tools=code_interpreter_tools,
    tool_resources=code_interpreter_tool_resources,
)

我们首先设置 Azure OpenAI 资源来获取客户端和模型。接下来，我们使用客户端的文件 API 从指定路径上传 CSV 文件。然后，我们使用上传的文件 ID 配置 code_interpreter_tool，这些 ID 在创建时会与智能助手、模型、说明和名称一起链接。

代理定义

我们现在准备先创建一个助手定义来实例化 OpenAIAssistantAgent。助理已配置其目标模型、指令，并启用了 代码解释器 工具。此外，我们显式将这两个数据文件与 代码解释器 工具相关联。

Console.WriteLine("Defining agent...");
AssistantClient assistantClient = client.GetAssistantClient();
        Assistant assistant =
            await assistantClient.CreateAssistantAsync(
                settings.AzureOpenAI.ChatModelDeployment,
                name: "SampleAssistantAgent",
                instructions:
                        """
                        Analyze the available data to provide an answer to the user's question.
                        Always format response using markdown.
                        Always include a numerical index that starts at 1 for any lists or tables.
                        Always sort lists in ascending order.
                        """,
                enableCodeInterpreter: true,
                codeInterpreterFileIds: [fileDataCountryList.Id, fileDataCountryDetail.Id]);

// Create agent
OpenAIAssistantAgent agent = new(assistant, assistantClient);

我们现在已准备好实例化 AzureAssistantAgent。代理与客户端和助理定义一起配置。

# Create the agent using the client and the assistant definition
agent = AzureAssistantAgent(
    client=client,
    definition=definition,
)

聊天循环

最后，我们能够协调用户与 Agent之间的交互。首先创建一个 AgentThread 来维护聊天状态和创建一个空循环。

此外，请确保在执行结束时删除资源，以最大程度地减少不必要的费用。

Console.WriteLine("Creating thread...");
AssistantAgentThread agentThread = new();

Console.WriteLine("Ready!");

try
{
    bool isComplete = false;
    List<string> fileIds = [];
    do
    {

    } while (!isComplete);
}
finally
{
    Console.WriteLine();
    Console.WriteLine("Cleaning-up...");
    await Task.WhenAll(
        [
            agentThread.DeleteAsync(),
            assistantClient.DeleteAssistantAsync(assistant.Id),
            fileClient.DeleteFileAsync(fileDataCountryList.Id),
            fileClient.DeleteFileAsync(fileDataCountryDetail.Id),
        ]);
}

thread: AssistantAgentThread = None

try:
    is_complete: bool = False
    file_ids: list[str] = []
    while not is_complete:
        # agent interaction logic here
finally:
    print("\nCleaning up resources...")
    [await client.files.delete(file_id) for file_id in file_ids]
    await thread.delete() if thread else None
    await client.beta.assistants.delete(agent.id)

现在，让我们在前一个循环中捕获用户的输入。在这种情况下，将忽略空输入，术语 EXIT 将指示会话已完成。

Console.WriteLine();
Console.Write("> ");
string input = Console.ReadLine();
if (string.IsNullOrWhiteSpace(input))
{
    continue;
}
if (input.Trim().Equals("EXIT", StringComparison.OrdinalIgnoreCase))
{
    isComplete = true;
    break;
}

var message = new ChatMessageContent(AuthorRole.User, input);

Console.WriteLine();

user_input = input("User:> ")
if not user_input:
    continue

if user_input.lower() == "exit":
    is_complete = True
    break

在调用 Agent 响应之前，让我们添加一些帮助程序方法来下载 Agent可能生成的任何文件。

在这里，我们将文件内容放置在系统定义的临时目录中，然后启动系统定义的查看器应用程序。

private static async Task DownloadResponseImageAsync(OpenAIFileClient client, ICollection<string> fileIds)
{
    if (fileIds.Count > 0)
    {
        Console.WriteLine();
        foreach (string fileId in fileIds)
        {
            await DownloadFileContentAsync(client, fileId, launchViewer: true);
        }
    }
}

private static async Task DownloadFileContentAsync(OpenAIFileClient client, string fileId, bool launchViewer = false)
{
    OpenAIFile fileInfo = client.GetFile(fileId);
    if (fileInfo.Purpose == FilePurpose.AssistantsOutput)
    {
        string filePath =
            Path.Combine(
                Path.GetTempPath(),
                Path.GetFileName(Path.ChangeExtension(fileInfo.Filename, ".png")));

        BinaryData content = await client.DownloadFileAsync(fileId);
        await using FileStream fileStream = new(filePath, FileMode.CreateNew);
        await content.ToStream().CopyToAsync(fileStream);
        Console.WriteLine($"File saved to: {filePath}.");

        if (launchViewer)
        {
            Process.Start(
                new ProcessStartInfo
                {
                    FileName = "cmd.exe",
                    Arguments = $"/C start {filePath}"
                });
        }
    }
}

import os

async def download_file_content(agent, file_id: str):
    try:
        # Fetch the content of the file using the provided method
        response_content = await agent.client.files.content(file_id)

        # Get the current working directory of the file
        current_directory = os.path.dirname(os.path.abspath(__file__))

        # Define the path to save the image in the current directory
        file_path = os.path.join(
            current_directory,  # Use the current directory of the file
            f"{file_id}.png"  # You can modify this to use the actual filename with proper extension
        )

        # Save content to a file asynchronously
        with open(file_path, "wb") as file:
            file.write(response_content.content)

        print(f"File saved to: {file_path}")
    except Exception as e:
        print(f"An error occurred while downloading file {file_id}: {str(e)}")

async def download_response_image(agent, file_ids: list[str]):
    if file_ids:
        # Iterate over file_ids and download each one
        for file_id in file_ids:
            await download_file_content(agent, file_id)

若要生成 Agent 对用户输入的响应，请通过提供消息和 AgentThread 来调用代理。在此示例中，我们选择流式响应并捕获任何生成的 文件引用 ，以便在响应周期结束时下载和查看。请务必注意，生成的代码是通过响应消息中存在 元数据 密钥来标识的，这与对话回复区分开来。

bool isCode = false;
await foreach (StreamingChatMessageContent response in agent.InvokeStreamingAsync(message, agentThread))
{
    if (isCode != (response.Metadata?.ContainsKey(OpenAIAssistantAgent.CodeInterpreterMetadataKey) ?? false))
    {
        Console.WriteLine();
        isCode = !isCode;
    }

    // Display response.
    Console.Write($"{response.Content}");

    // Capture file IDs for downloading.
    fileIds.AddRange(response.Items.OfType<StreamingFileReferenceContent>().Select(item => item.FileId));
}
Console.WriteLine();

// Download any files referenced in the response.
await DownloadResponseImageAsync(fileClient, fileIds);
fileIds.Clear();

is_code = False
last_role = None
async for response in agent.invoke_stream(messages=user_input, thread=thread):
    current_is_code = response.metadata.get("code", False)

    if current_is_code:
        if not is_code:
            print("\n\n```python")
            is_code = True
        print(response.content, end="", flush=True)
    else:
        if is_code:
            print("\n```")
            is_code = False
            last_role = None
        if hasattr(response, "role") and response.role is not None and last_role != response.role:
            print(f"\n# {response.role}: ", end="", flush=True)
            last_role = response.role
        print(response.content, end="", flush=True)
    file_ids.extend([
        item.file_id for item in response.items if isinstance(item, StreamingFileReferenceContent)
    ])
    thread = response.thread
if is_code:
    print("```\n")
print()

await download_response_image(agent, file_ids)
file_ids.clear()

最终

将所有步骤组合在一起，我们提供了此示例的最终代码。下面提供了完整的实现。

尝试使用这些建议的输入：

比较文件，以确定在总计数中，哪些国家没有定义州或省的数量。
为拥有州或省的国家创建表。请包括州或省的数量以及总人口。
为名称以相同字母开头的国家/地区提供条形图，并将 x 轴按最高计数排序（包括所有国家/地区）

using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents.OpenAI;
using Microsoft.SemanticKernel.ChatCompletion;
using OpenAI.Assistants;
using OpenAI.Files;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.IO;
using System.Linq;
using System.Threading.Tasks;

namespace AgentsSample;

public static class Program
{
    public static async Task Main()
    {
        // Load configuration from environment variables or user secrets.
        Settings settings = new();

        // Initialize the clients
        AzureOpenAIClient client = OpenAIAssistantAgent.CreateAzureOpenAIClient(new AzureCliCredential(), new Uri(settings.AzureOpenAI.Endpoint));
        //OpenAIClient client = OpenAIAssistantAgent.CreateOpenAIClient(new ApiKeyCredential(settings.OpenAI.ApiKey)));
        AssistantClient assistantClient = client.GetAssistantClient();
        OpenAIFileClient fileClient = client.GetOpenAIFileClient();

        // Upload files
        Console.WriteLine("Uploading files...");
        OpenAIFile fileDataCountryDetail = await fileClient.UploadFileAsync("PopulationByAdmin1.csv", FileUploadPurpose.Assistants);
        OpenAIFile fileDataCountryList = await fileClient.UploadFileAsync("PopulationByCountry.csv", FileUploadPurpose.Assistants);

        // Define assistant
        Console.WriteLine("Defining assistant...");
        Assistant assistant =
            await assistantClient.CreateAssistantAsync(
                settings.AzureOpenAI.ChatModelDeployment,
                name: "SampleAssistantAgent",
                instructions:
                        """
                        Analyze the available data to provide an answer to the user's question.
                        Always format response using markdown.
                        Always include a numerical index that starts at 1 for any lists or tables.
                        Always sort lists in ascending order.
                        """,
                enableCodeInterpreter: true,
                codeInterpreterFileIds: [fileDataCountryList.Id, fileDataCountryDetail.Id]);

        // Create agent
        OpenAIAssistantAgent agent = new(assistant, assistantClient);

        // Create the conversation thread
        Console.WriteLine("Creating thread...");
        AssistantAgentThread agentThread = new();

        Console.WriteLine("Ready!");

        try
        {
            bool isComplete = false;
            List<string> fileIds = [];
            do
            {
                Console.WriteLine();
                Console.Write("> ");
                string input = Console.ReadLine();
                if (string.IsNullOrWhiteSpace(input))
                {
                    continue;
                }
                if (input.Trim().Equals("EXIT", StringComparison.OrdinalIgnoreCase))
                {
                    isComplete = true;
                    break;
                }

                var message = new ChatMessageContent(AuthorRole.User, input);

                Console.WriteLine();

                bool isCode = false;
                await foreach (StreamingChatMessageContent response in agent.InvokeStreamingAsync(message, agentThread))
                {
                    if (isCode != (response.Metadata?.ContainsKey(OpenAIAssistantAgent.CodeInterpreterMetadataKey) ?? false))
                    {
                        Console.WriteLine();
                        isCode = !isCode;
                    }

                    // Display response.
                    Console.Write($"{response.Content}");

                    // Capture file IDs for downloading.
                    fileIds.AddRange(response.Items.OfType<StreamingFileReferenceContent>().Select(item => item.FileId));
                }
                Console.WriteLine();

                // Download any files referenced in the response.
                await DownloadResponseImageAsync(fileClient, fileIds);
                fileIds.Clear();

            } while (!isComplete);
        }
        finally
        {
            Console.WriteLine();
            Console.WriteLine("Cleaning-up...");
            await Task.WhenAll(
                [
                    agentThread.DeleteAsync(),
                    assistantClient.DeleteAssistantAsync(assistant.Id),
                    fileClient.DeleteFileAsync(fileDataCountryList.Id),
                    fileClient.DeleteFileAsync(fileDataCountryDetail.Id),
                ]);
        }
    }

    private static async Task DownloadResponseImageAsync(OpenAIFileClient client, ICollection<string> fileIds)
    {
        if (fileIds.Count > 0)
        {
            Console.WriteLine();
            foreach (string fileId in fileIds)
            {
                await DownloadFileContentAsync(client, fileId, launchViewer: true);
            }
        }
    }

    private static async Task DownloadFileContentAsync(OpenAIFileClient client, string fileId, bool launchViewer = false)
    {
        OpenAIFile fileInfo = client.GetFile(fileId);
        if (fileInfo.Purpose == FilePurpose.AssistantsOutput)
        {
            string filePath =
                Path.Combine(
                    Path.GetTempPath(),
                    Path.GetFileName(Path.ChangeExtension(fileInfo.Filename, ".png")));

            BinaryData content = await client.DownloadFileAsync(fileId);
            await using FileStream fileStream = new(filePath, FileMode.CreateNew);
            await content.ToStream().CopyToAsync(fileStream);
            Console.WriteLine($"File saved to: {filePath}.");

            if (launchViewer)
            {
                Process.Start(
                    new ProcessStartInfo
                    {
                        FileName = "cmd.exe",
                        Arguments = $"/C start {filePath}"
                    });
            }
        }
    }
}

import asyncio
import logging
import os

from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent
from semantic_kernel.contents import StreamingFileReferenceContent

logging.basicConfig(level=logging.ERROR)

"""
The following sample demonstrates how to create a simple,
OpenAI assistant agent that utilizes the code interpreter
to analyze uploaded files.
"""

# Let's form the file paths that we will later pass to the assistant
csv_file_path_1 = os.path.join(
    os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
    "resources",
    "PopulationByAdmin1.csv",
)

csv_file_path_2 = os.path.join(
    os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
    "resources",
    "PopulationByCountry.csv",
)


async def download_file_content(agent: AzureAssistantAgent, file_id: str):
    try:
        # Fetch the content of the file using the provided method
        response_content = await agent.client.files.content(file_id)

        # Get the current working directory of the file
        current_directory = os.path.dirname(os.path.abspath(__file__))

        # Define the path to save the image in the current directory
        file_path = os.path.join(
            current_directory,  # Use the current directory of the file
            f"{file_id}.png",  # You can modify this to use the actual filename with proper extension
        )

        # Save content to a file asynchronously
        with open(file_path, "wb") as file:
            file.write(response_content.content)

        print(f"File saved to: {file_path}")
    except Exception as e:
        print(f"An error occurred while downloading file {file_id}: {str(e)}")


async def download_response_image(agent: AzureAssistantAgent, file_ids: list[str]):
    if file_ids:
        # Iterate over file_ids and download each one
        for file_id in file_ids:
            await download_file_content(agent, file_id)


async def main():
    # Create the client using Azure OpenAI resources and configuration
    client, model = AzureAssistantAgent.setup_resources()

    # Upload the files to the client
    file_ids: list[str] = []
    for path in [csv_file_path_1, csv_file_path_2]:
        with open(path, "rb") as file:
            file = await client.files.create(file=file, purpose="assistants")
            file_ids.append(file.id)

    # Get the code interpreter tool and resources
    code_interpreter_tools, code_interpreter_tool_resources = AzureAssistantAgent.configure_code_interpreter_tool(
        file_ids=file_ids
    )

    # Create the assistant definition
    definition = await client.beta.assistants.create(
        model=model,
        instructions="""
            Analyze the available data to provide an answer to the user's question.
            Always format response using markdown.
            Always include a numerical index that starts at 1 for any lists or tables.
            Always sort lists in ascending order.
            """,
        name="SampleAssistantAgent",
        tools=code_interpreter_tools,
        tool_resources=code_interpreter_tool_resources,
    )

    # Create the agent using the client and the assistant definition
    agent = AzureAssistantAgent(
        client=client,
        definition=definition,
    )

    thread: AssistantAgentThread = None

    try:
        is_complete: bool = False
        file_ids: list[str] = []
        while not is_complete:
            user_input = input("User:> ")
            if not user_input:
                continue

            if user_input.lower() == "exit":
                is_complete = True
                break

            is_code = False
            last_role = None
            async for response in agent.invoke_stream(messages=user_input, thread=thread):
                current_is_code = response.metadata.get("code", False)

                if current_is_code:
                    if not is_code:
                        print("\n\n```python")
                        is_code = True
                    print(response.content, end="", flush=True)
                else:
                    if is_code:
                        print("\n```")
                        is_code = False
                        last_role = None
                    if hasattr(response, "role") and response.role is not None and last_role != response.role:
                        print(f"\n# {response.role}: ", end="", flush=True)
                        last_role = response.role
                    print(response.content, end="", flush=True)
                file_ids.extend([
                    item.file_id for item in response.items if isinstance(item, StreamingFileReferenceContent)
                ])
                thread = response.thread
            if is_code:
                print("```\n")
            print()

            await download_response_image(agent, file_ids)
            file_ids.clear()

    finally:
        print("\nCleaning up resources...")
        [await client.files.delete(file_id) for file_id in file_ids]
        await thread.delete() if thread else None
        await client.beta.assistants.delete(agent.id)


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

可以在存储库中找到完整的代码，如上所示。

后续步骤

操作指南：OpenAIAssistantAgent 代码文件搜索

通过

使用指南：OpenAIAssistantAgent 代码解释器

概述

入门

配置

编码

安装

代理定义

聊天循环

最终

后续步骤

其他资源

使用指南：`OpenAIAssistantAgent` 代码解释器