操作说明：打开 AI 助手代理 文件搜索

警告

语义内核代理框架 处于预览状态，可能会更改。

概述

在此示例中，我们将探讨如何使用 Open AI 助手代理的文件搜索工具完成理解任务。 该方法将分步实现，确保在整个过程中的清晰度和精确性。 作为任务的一部分，代理将在响应中提供文档引文。

流式处理将用于传送代理的响应。 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。

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

如果在 Visual Studio 中管理 NuGet 包，请确保已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>

代理框架是实验性的，需要警告抑制。 这可以作为项目文件（.csproj）：

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

此外，从Grimms-The-White-Snake.txt复制LearnResources公共域内容。 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="Grimms-The-King-of-the-Golden-Mountain.txt">
      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
    </None>
    <None Include="Grimms-The-Water-of-Life.txt">
      <CopyToOutputDirectory>Always</CopyToOutputDirectory>
    </None>
    <None Include="Grimms-The-White-Snake.txt">
      <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。 在文件顶部 .py 包括以下导入：

import asyncio
import os

from semantic_kernel.agents.open_ai.azure_assistant_agent import AzureAssistantAgent
from semantic_kernel.contents.chat_message_content import ChatMessageContent
from semantic_kernel.contents.streaming_annotation_content import StreamingAnnotationContent
from semantic_kernel.contents.utils.author_role import AuthorRole
from semantic_kernel.kernel import Kernel

此外，从Grimms-The-White-Snake.txt复制LearnResources公共域内容。 在项目文件夹中添加这些文件。

代理当前在 Java 中不可用。

配置

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

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

# Azure Open AI
dotnet user-secrets set "AzureOpenAISettings:ApiKey" "<api-key>" # Not required if using token-credential
dotnet user-secrets set "AzureOpenAISettings:Endpoint" "https://lightspeed-team-shared-openai-eastus.openai.azure.com/"
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。 此类充当以下示例的基础组件。

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();
    }
}

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://..."
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="..."
AZURE_OPENAI_API_VERSION="..."

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

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。

代理当前在 Java 中不可用。

编码

此示例的编码过程涉及：

1. 安装程序 - 初始化设置和插件。
2. 代理定义 - 使用模板化指令和插件创建 Chat_Completion_Agent 。
3. 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 Open AI 助手代理之前，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。 使用设置还可以创建将用于OpenAIClientProvider一个，以及文件上传和创建 。VectorStore

Settings settings = new();

OpenAIClientProvider clientProvider =
    OpenAIClientProvider.ForAzureOpenAI(
        new AzureCliCredential(),
        new Uri(settings.AzureOpenAI.Endpoint));

代理当前在 Java 中不可用。

现在，创建一个空_Vector存储，用于 文件搜索 工具：

使用 OpenAIClientProvider/&a0VectorStoreClient> 访问和创建一个 VectorStore。

Console.WriteLine("Creating store...");
VectorStoreClient storeClient = clientProvider.Client.GetVectorStoreClient();
CreateVectorStoreOperation operation = await storeClient.CreateVectorStoreAsync(waitUntilCompleted: true);
string storeId = operation.VectorStoreId;

def get_filepath_for_filename(filename: str) -> str:
    base_directory = os.path.dirname(os.path.realpath(__file__))
    return os.path.join(base_directory, filename)

代理当前在 Java 中不可用。

让我们声明上一 配置 部分中介绍的三个内容文件：

private static readonly string[] _fileNames =
    [
        "Grimms-The-King-of-the-Golden-Mountain.txt",
        "Grimms-The-Water-of-Life.txt",
        "Grimms-The-White-Snake.txt",
    ];

filenames = [
    "Grimms-The-King-of-the-Golden-Mountain.txt",
    "Grimms-The-Water-of-Life.txt",
    "Grimms-The-White-Snake.txt",
]

代理当前在 Java 中不可用。

现在，请上传这些文件并将其添加到 Vector Store，方法是使用以前创建的客户端上传每个文件VectorStoreClient，并将其添加到 OpenAIFileClient，从而保留生成的文件引用。

Dictionary<string, OpenAIFile> fileReferences = [];

Console.WriteLine("Uploading files...");
OpenAIFileClient fileClient = clientProvider.Client.GetOpenAIFileClient();
foreach (string fileName in _fileNames)
{
    OpenAIFile fileInfo = await fileClient.UploadFileAsync(fileName, FileUploadPurpose.Assistants);
    await storeClient.AddFileToVectorStoreAsync(storeId, fileInfo.Id, waitUntilCompleted: true);
    fileReferences.Add(fileInfo.Id, fileInfo);
}

代理当前在 Java 中不可用。

代理定义

我们现在已准备好实例化 OpenAI 助手代理。 代理配置了其目标模型、 指令和 启用了文件搜索 工具。 此外，我们显式将矢量存储与文件搜索工具相关联。

我们将再次利用OpenAIClientProvider创建：OpenAIAssistantAgent

Console.WriteLine("Defining agent...");
OpenAIAssistantAgent agent =
    await OpenAIAssistantAgent.CreateAsync(
        clientProvider,
        new OpenAIAssistantDefinition(settings.AzureOpenAI.ChatModelDeployment)
        {
            Name = "SampleAssistantAgent",
            Instructions =
                """
                The document store contains the text of fictional stories.
                Always analyze the document store to provide an answer to the user's question.
                Never rely on your knowledge of stories not included in the document store.
                Always format response using markdown.
                """,
            EnableFileSearch = true,
            VectorStoreId = storeId,
        },
        new Kernel());

agent = await AzureAssistantAgent.create(
    kernel=Kernel(),
    service_id="agent",
    name="SampleAssistantAgent",
    instructions="""
        The document store contains the text of fictional stories.
        Always analyze the document store to provide an answer to the user's question.
        Never rely on your knowledge of stories not included in the document store.
        Always format response using markdown.
        """,
    enable_file_search=True,
    vector_store_filenames=[get_filepath_for_filename(filename) for filename in filenames],
)

代理当前在 Java 中不可用。

聊天循环

最后，我们能够协调用户和 代理之间的交互。 首先创建 助理线程 来维护聊天状态和创建空循环。

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...");
string threadId = await agent.CreateThreadAsync();

Console.WriteLine("Ready!");

try
{
    bool isComplete = false;
    do
    {
        // Processing occurrs here
    } while (!isComplete);
}
finally
{
    Console.WriteLine();
    Console.WriteLine("Cleaning-up...");
    await Task.WhenAll(
        [
            agent.DeleteThreadAsync(threadId),
            agent.DeleteAsync(),
            storeClient.DeleteVectorStoreAsync(storeId),
            ..fileReferences.Select(fileReference => fileClient.DeleteFileAsync(fileReference.Key))
        ]);
}

print("Creating thread...")
thread_id = await agent.create_thread()

try:
    is_complete: bool = False
    while not is_complete:
        # Processing occurs here

finally:
    print("Cleaning up resources...")
    if agent is not None:
        [await agent.delete_file(file_id) for file_id in agent.file_search_file_ids]
        await agent.delete_thread(thread_id)
        await agent.delete()

代理当前在 Java 中不可用。

现在，让我们在上一循环中捕获用户输入。 在这种情况下，将忽略空输入，术语 EXIT 将指示会话已完成。 有效的 nput 将作为用户消息添加到助手线程。

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

await agent.AddChatMessageAsync(threadId, new ChatMessageContent(AuthorRole.User, input));
Console.WriteLine();

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

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

await agent.add_chat_message(
    thread_id=thread_id, message=ChatMessageContent(role=AuthorRole.USER, content=user_input)
)

代理当前在 Java 中不可用。

在调用 代理 响应之前，让我们添加一个帮助程序方法来将 unicode 批注括号重新设置为 ANSI 括号。

private static string ReplaceUnicodeBrackets(this string content) =>
    content?.Replace('【', '[').Replace('】', ']');

# No special handling required.

代理当前在 Java 中不可用。

若要生成对用户输入的代理响应，请通过指定助手线程来调用代理。 在此示例中，我们选择流式响应并捕获任何关联的 引文批注 ，以便在响应周期结束时显示。 请注意，每个流区块正在使用以前的帮助程序方法重新格式化。

List<StreamingAnnotationContent> footnotes = [];
await foreach (StreamingChatMessageContent chunk in agent.InvokeStreamingAsync(threadId))
{
    // Capture annotations for footnotes
    footnotes.AddRange(chunk.Items.OfType<StreamingAnnotationContent>());

    // Render chunk with replacements for unicode brackets.
    Console.Write(chunk.Content.ReplaceUnicodeBrackets());
}

Console.WriteLine();

// Render footnotes for captured annotations.
if (footnotes.Count > 0)
{
    Console.WriteLine();
    foreach (StreamingAnnotationContent footnote in footnotes)
    {
        Console.WriteLine($"#{footnote.Quote.ReplaceUnicodeBrackets()} - {fileReferences[footnote.FileId!].Filename} (Index: {footnote.StartIndex} - {footnote.EndIndex})");
    }
}

footnotes: list[StreamingAnnotationContent] = []
async for response in agent.invoke_stream(thread_id=thread_id):
    footnotes.extend([item for item in response.items if isinstance(item, StreamingAnnotationContent)])

    print(f"{response.content}", end="", flush=True)

print()

if len(footnotes) > 0:
    for footnote in footnotes:
        print(
            f"\n`{footnote.quote}` => {footnote.file_id} "
            f"(Index: {footnote.start_index} - {footnote.end_index})"
        )

代理当前在 Java 中不可用。

Final

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。

请尝试使用这些建议的输入：

1. 每个故事的段落计数是多少？
2. 创建一个表，用于标识每个故事的主角和对角。
3. 白蛇中的道德是什么？

using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;
using Azure.Identity;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.Agents.OpenAI;
using Microsoft.SemanticKernel.ChatCompletion;
using OpenAI.Files;
using OpenAI.VectorStores;

namespace AgentsSample;

public static class Program
{
    private static readonly string[] _fileNames =
        [
            "Grimms-The-King-of-the-Golden-Mountain.txt",
            "Grimms-The-Water-of-Life.txt",
            "Grimms-The-White-Snake.txt",
        ];

    /// <summary>
    /// The main entry point for the application.
    /// </summary>
    /// <returns>A <see cref="Task"/> representing the asynchronous operation.</returns>
    public static async Task Main()
    {
        // Load configuration from environment variables or user secrets.
        Settings settings = new();

        OpenAIClientProvider clientProvider =
            OpenAIClientProvider.ForAzureOpenAI(
                new AzureCliCredential(),
                new Uri(settings.AzureOpenAI.Endpoint));

        Console.WriteLine("Creating store...");
        VectorStoreClient storeClient = clientProvider.Client.GetVectorStoreClient();
        CreateVectorStoreOperation operation = await storeClient.CreateVectorStoreAsync(waitUntilCompleted: true);
        string storeId = operation.VectorStoreId;

        // Retain file references.
        Dictionary<string, OpenAIFile> fileReferences = [];

        Console.WriteLine("Uploading files...");
        OpenAIFileClient fileClient = clientProvider.Client.GetOpenAIFileClient();
        foreach (string fileName in _fileNames)
        {
            OpenAIFile fileInfo = await fileClient.UploadFileAsync(fileName, FileUploadPurpose.Assistants);
            await storeClient.AddFileToVectorStoreAsync(storeId, fileInfo.Id, waitUntilCompleted: true);
            fileReferences.Add(fileInfo.Id, fileInfo);
        }


        Console.WriteLine("Defining agent...");
        OpenAIAssistantAgent agent =
            await OpenAIAssistantAgent.CreateAsync(
                clientProvider,
                new OpenAIAssistantDefinition(settings.AzureOpenAI.ChatModelDeployment)
                {
                    Name = "SampleAssistantAgent",
                    Instructions =
                        """
                        The document store contains the text of fictional stories.
                        Always analyze the document store to provide an answer to the user's question.
                        Never rely on your knowledge of stories not included in the document store.
                        Always format response using markdown.
                        """,
                    EnableFileSearch = true,
                    VectorStoreId = storeId,
                },
                new Kernel());

        Console.WriteLine("Creating thread...");
        string threadId = await agent.CreateThreadAsync();

        Console.WriteLine("Ready!");

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

                await agent.AddChatMessageAsync(threadId, new ChatMessageContent(AuthorRole.User, input));
                Console.WriteLine();

                List<StreamingAnnotationContent> footnotes = [];
                await foreach (StreamingChatMessageContent chunk in agent.InvokeStreamingAsync(threadId))
                {
                    // Capture annotations for footnotes
                    footnotes.AddRange(chunk.Items.OfType<StreamingAnnotationContent>());

                    // Render chunk with replacements for unicode brackets.
                    Console.Write(chunk.Content.ReplaceUnicodeBrackets());
                }

                Console.WriteLine();

                // Render footnotes for captured annotations.
                if (footnotes.Count > 0)
                {
                    Console.WriteLine();
                    foreach (StreamingAnnotationContent footnote in footnotes)
                    {
                        Console.WriteLine($"#{footnote.Quote.ReplaceUnicodeBrackets()} - {fileReferences[footnote.FileId!].Filename} (Index: {footnote.StartIndex} - {footnote.EndIndex})");
                    }
                }
            } while (!isComplete);
        }
        finally
        {
            Console.WriteLine();
            Console.WriteLine("Cleaning-up...");
            await Task.WhenAll(
                [
                    agent.DeleteThreadAsync(threadId),
                    agent.DeleteAsync(),
                    storeClient.DeleteVectorStoreAsync(storeId),
                    ..fileReferences.Select(fileReference => fileClient.DeleteFileAsync(fileReference.Key))
                ]);
        }
    }

    private static string ReplaceUnicodeBrackets(this string content) =>
        content?.Replace('【', '[').Replace('】', ']');
}

import asyncio
import os

from semantic_kernel.agents.open_ai.azure_assistant_agent import AzureAssistantAgent
from semantic_kernel.contents.chat_message_content import ChatMessageContent
from semantic_kernel.contents.streaming_annotation_content import StreamingAnnotationContent
from semantic_kernel.contents.utils.author_role import AuthorRole
from semantic_kernel.kernel import Kernel


def get_filepath_for_filename(filename: str) -> str:
    base_directory = os.path.dirname(os.path.realpath(__file__))
    return os.path.join(base_directory, filename)


filenames = [
    "Grimms-The-King-of-the-Golden-Mountain.txt",
    "Grimms-The-Water-of-Life.txt",
    "Grimms-The-White-Snake.txt",
]


async def main():
    agent = await AzureAssistantAgent.create(
        kernel=Kernel(),
        service_id="agent",
        name="SampleAssistantAgent",
        instructions="""
            The document store contains the text of fictional stories.
            Always analyze the document store to provide an answer to the user's question.
            Never rely on your knowledge of stories not included in the document store.
            Always format response using markdown.
            """,
        enable_file_search=True,
        vector_store_filenames=[get_filepath_for_filename(filename) for filename in filenames],
    )

    print("Creating thread...")
    thread_id = await agent.create_thread()

    try:
        is_complete: bool = False
        while not is_complete:
            user_input = input("User:> ")
            if not user_input:
                continue

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

            await agent.add_chat_message(
                thread_id=thread_id, message=ChatMessageContent(role=AuthorRole.USER, content=user_input)
            )

            footnotes: list[StreamingAnnotationContent] = []
            async for response in agent.invoke_stream(thread_id=thread_id):
                footnotes.extend([item for item in response.items if isinstance(item, StreamingAnnotationContent)])

                print(f"{response.content}", end="", flush=True)

            print()

            if len(footnotes) > 0:
                for footnote in footnotes:
                    print(
                        f"\n`{footnote.quote}` => {footnote.file_id} "
                        f"(Index: {footnote.start_index} - {footnote.end_index})"
                    )

    finally:
        print("Cleaning up resources...")
        if agent is not None:
            [await agent.delete_file(file_id) for file_id in agent.file_search_file_ids]
            await agent.delete_thread(thread_id)
            await agent.delete()


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

代理当前在 Java 中不可用。

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