使用說明：
重要
這項功能位於候選版階段。 此階段的功能幾乎完整且一般穩定，不過在達到完整正式運作之前，它們可能會經歷輕微的精簡或優化。
概觀
在此範例中，我們將探索如何使用 的程式 OpenAIAssistantAgent 代碼解釋器工具來完成數據分析工作。 此方法會逐步細分，以強調程式編寫過程的關鍵部分。 在工作中，代理程式會產生影像和文字回應。 這將示範此工具在執行量化分析方面的多功能性。
串流將用來傳遞代理程序的回應。 這會在工作進行時提供即時更新。
快速入門
繼續進行功能程式代碼撰寫之前，請確定您的開發環境已完全設定和設定。
從建立主控台項目開始。 然後，請包含下列套件參考，以確保所有必要的相依組件都可用。
若要從指令列新增套件相依性，請使用 dotnet 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 套件，請確定 Include prerelease 已核取 。
項目檔 （.csproj） 應包含下列 PackageReference 定義：
<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 是實驗性的，需要警告抑制措施。 可能會將這個設為項目檔 (.csproj) 中的一個屬性。
<PropertyGroup>
<NoWarn>$(NoWarn);CA2007;IDE1006;SKEXP0001;SKEXP0110;OPENAI001</NoWarn>
</PropertyGroup>
此外，請從PopulationByAdmin1.csv複製 PopulationByCountry.csv 和 LearnResources 數據檔。 在項目資料夾中新增這些檔案，並設定將它們複製到輸出目錄：
<ItemGroup>
<None Include="PopulationByAdmin1.csv">
<CopyToOutputDirectory>Always</CopyToOutputDirectory>
</None>
<None Include="PopulationByCountry.csv">
<CopyToOutputDirectory>Always</CopyToOutputDirectory>
</None>
</ItemGroup>
首先，建立一個資料夾來保存您的腳本（.py 檔案）和範例資源。 在.py檔案的頂端包含下列匯入：
import asyncio
import os
from semantic_kernel.agents import AssistantAgentThread, AzureAssistantAgent
from semantic_kernel.contents import StreamingFileReferenceContent
此外，請從 PopulationByAdmin1.csv複製 PopulationByCountry.csv 和 learn_resources/resources 資料檔。 將這些檔案新增至您的工作目錄。
Java 中目前無法使用的功能。
組態
此範例需要組態設定，才能連線到遠端服務。 您必須定義 OpenAI 或 Azure OpenAI 的設定。
# 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"
下列類別用於所有 Agent 範例中。 請務必將它包含在專案中，以確保適當的功能。 這個類別可作為後續範例的基礎元件。
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();
}
}
若要開始使用適當的組態來執行範例程序代碼，最快的方式是在專案的根目錄建立 .env 檔案（執行腳本的位置）。
在您的 .env 檔案中為 Azure OpenAI 或 OpenAI 配置以下設定：
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=""
小提示
Azure 小幫手至少需要 2024-05-01-preview 的 API 版本。 隨著新功能的推出，API 版本會據以更新。 截至本文所述，最新版本為 2025-01-01-preview。 如需最新版本控制詳細資訊，請參閱 Azure OpenAI API 預覽生命週期。
設定之後，個別的 AI 服務類別會挑選必要的變數，並在具現化期間使用這些變數。
Java 中目前無法使用的功能。
撰寫程式碼
這個範例的編碼程式牽涉到：
完整範例程式代碼會在 Final 區段中提供。 如需完整的實作，請參閱該區段。
設定
建立 OpenAIAssistantAgent之前，請確定組態設定可供使用，並準備文件資源。
實例化在上一節Settings部分所參考的類別。 使用設定來建立將用於AzureOpenAIClient和檔案上傳的。
Settings settings = new();
AzureOpenAIClient client = OpenAIAssistantAgent.CreateAzureOpenAIClient(new AzureCliCredential(), new Uri(settings.AzureOpenAI.Endpoint));
Java 中目前無法使用的功能。
使用 AzureOpenAIClient 來存取 OpenAIFileClient，並上傳在上一配置節中所述的兩個資料檔，保留檔案參照以供最後清除。
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);
在建立 AzureAssistantAgent 或 OpenAIAssistantAgent之前，請確定組態設定可供使用，並準備文件資源。
小提示
視檔案所在的位置而定，您可能需要調整檔案路徑。
# 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,
)
我們先設定 Azure OpenAI 資源，以取得用戶端和模型。 接下來，我們會使用用戶端的檔案 API，從指定的路徑上傳 CSV 檔案。 接著，我們會使用上傳的檔案 ID 來設定 code_interpreter_tool，這些 ID 在創建時會連結到助理，並且包括模型、指示和名稱。
Java 中目前無法使用的功能。
代理程式定義
我們現在已準備好先建立助理定義來實例化 OpenAIAssistantAgent。 小幫手已設定其目標模型，指示，並啟用 程式代碼解釋器 工具。 此外，我們會明確地將這兩個數據檔與程式 代碼解釋器 工具產生關聯。
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);
我們現在已準備好建立 AzureAssistantAgent 的實例。 代理程式由用戶端和助理定義來設定。
# Create the agent using the client and the assistant definition
agent = AzureAssistantAgent(
client=client,
definition=definition,
)
Java 中目前無法使用的功能。
聊天迴圈
最後，我們能夠協調使用者與 Agent之間的互動。 首先，建立 AgentThread 來維護交談狀態並建立空迴圈。
我們也確保資源會在執行結束時移除，以將不必要的費用降到最低。
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)
Java 中目前無法使用的功能。
現在讓我們在上一個迴圈中擷取用戶輸入。 在此情況下，將會忽略空的輸入，而字詞 EXIT 會發出交談已完成的訊號。
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
Java 中目前無法使用的功能。
在叫用 Agent 回應之前，讓我們新增一些協助函式來下載任何可能由 Agent產生的檔案。
在這裡，我們會將檔案內容放在系統定義的暫存目錄中，然後啟動系統定義的查看器應用程式。
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 = $"/C start {filePath}"
});
}
}
}
import os
async def download_file_content(agent, file_id: str):
try:
# Fetch the content of the file using the provided method
response_content = await agent.client.files.content(file_id)
# Get the current working directory of the file
current_directory = os.path.dirname(os.path.abspath(__file__))
# Define the path to save the image in the current directory
file_path = os.path.join(
current_directory, # Use the current directory of the file
f"{file_id}.png" # You can modify this to use the actual filename with proper extension
)
# Save content to a file asynchronously
with open(file_path, "wb") as file:
file.write(response_content.content)
print(f"File saved to: {file_path}")
except Exception as e:
print(f"An error occurred while downloading file {file_id}: {str(e)}")
async def download_response_image(agent, file_ids: list[str]):
if file_ids:
# Iterate over file_ids and download each one
for file_id in file_ids:
await download_file_content(agent, file_id)
Java 中目前無法使用的功能。
若要產生 Agent 使用者輸入的回應，請藉由提供訊息和 AgentThread來叫用代理程式。 在此範例中，我們會選擇串流回應，並擷取任何產生的 檔案參考 ，以在回應週期結束時下載和檢閱。 請務必注意，生成的代碼是透過回應訊息中Metadata索引鍵的存在來識別，並將其與交談回覆區分開。
bool isCode = false;
await foreach (StreamingChatMessageContent response in agent.InvokeStreamingAsync(message, agentThread))
{
if (isCode != (response.Metadata?.ContainsKey(OpenAIAssistantAgent.CodeInterpreterMetadataKey) ?? false))
{
Console.WriteLine();
isCode = !isCode;
}
// Display response.
Console.Write($"{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()
Java 中目前無法使用的功能。
最終
將所有步驟結合在一起，我們有此範例的最終程序代碼。 以下提供完整的實作。
請嘗試使用這些建議的輸入：
- 比較檔案，以確定沒有定義州或省的國家數目，並與總計數進行比較。
- 為已定義州或省的國家/地區建立數據表。 包括州或省的數目和總人口
- 為名稱以相同字母開頭的國家/地區提供條形圖，並以最高計數排序 x 軸到最低（包括所有國家/地區）
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())
您可能會在存放庫中找到完整的 程式代碼，如上所示。
Java 中目前無法使用的功能。