快速入門：代理檢索

註

Azure AI 搜尋服務可透過 Azure 入口網站、REST API 及 Azure SDK 取得。它同時也是 Foundry IQ 的基礎，這是一個管理式知識層，能將企業內容轉化為可重複使用、權限感知的知識庫，供 Microsoft Foundry 入口網站中的代理使用。

重要

標記（預覽）的功能、能力或屬性不受服務等級協議涵蓋，也不建議用於生產工作負載，且在正式上架前可能會有所變動或受限。 Azure AI 搜尋服務預覽條款適用於所有預覽功能，無論是獨立功能還是正式推出功能的一部分。

在這個快速入門中，你將使用 agentic retrieval，建立一個由 Azure AI 搜尋服務索引的文件及來自 Azure OpenAI Foundry 模型的大型語言模型（LLM）驅動的對話式搜尋體驗。

知識庫利用基於 LLM 的查詢規劃（預覽）將複雜查詢分解為子查詢。接著它會對一個或多個 知識來源 執行子查詢，並回傳帶有元資料的結果。預設情況下，知識庫會從來源回傳原始內容，但此快速入門則使用答案綜合（預覽）來產生自然語言答案。

雖然你可以使用自己的資料，但這個快速入門使用了 NASA 電子書《Earth at Night》電子書中的範例 JSON 文件。

提示

想馬上開始嗎？請於GitHub下載原始碼。

先決條件

一個有有效訂閱的 Azure 帳號。免費註冊帳號。
在任何提供 Agent 擷取功能的區域中的 Azure AI 搜尋服務服務。此快速入門需要 Basic 等級或以上的管理身份支援。
一個Microsoft Foundry 專案和資源。當你建立專案時，資源會自動建立。
用於文本到向量轉換的嵌入模型已部署到你的專案中。你可以使用任何 text-embedding 模型，例如 text-embedding-3-large。
LLM 已部署至您的專案，用於查詢規劃與產生答案。你可以使用任何支援的大型語言模型，例如 gpt-5-mini。
.NET 8或更晚。
git 來複製樣本庫。
Azure CLI 用於與 Microsoft Entra ID 的無金鑰認證。

設定存取權限

在開始之前，請確保你有權限存取內容和操作。此快速入門使用 Microsoft Entra ID 進行驗證，並以角色為基礎的存取控制來授權。您必須是 擁有者 或 使用者存取管理員 才能指派角色。如果角色不可行，改用金鑰驗證。

若要為此快速入門設定存取：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 啟用基於角色的存取權限。
2. 建立系統指派的管理身份。
3. 為您的使用者帳號指派以下角色：搜尋服務貢獻者、搜尋索引資料貢獻者，以及搜尋索引資料閱讀器。
在您的 Microsoft Foundry 資源中，將 Cognitive Services User 指派給您的搜尋服務的管理身份。

重要

代理檢索有兩種基於代幣的收費模式：

Azure AI 搜尋服務用於 Agent 擷取的計費。
來自 Azure OpenAI，使用查詢規劃和答案合成的計費。

欲了解更多資訊，請參閱區域可用性、限額及計費。

取得端點

每個Azure AI 搜尋服務服務與 Microsoft Foundry 資源皆有一個 endpoint，這是一個唯一用於識別並提供資源網路存取的 URL。在後面的章節中，你指定這些端點以程式方式連接你的資源。

若要取得此快速入門的端點：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 從左側窗格選擇 「概覽」。
2. 複製網址，應該看起來像 https://my-service.search.windows.net。
關於你的 Microsoft Foundry 資源：
1. 從左側窗格選擇 資源管理>鍵與端點。
2. 複製 OpenAI 分頁上的網址，應該會看起來像 https://my-resource.openai.azure.com/。

設定環境

用 Git 複製樣本庫。

git clone https://github.com/Azure-Samples/azure-search-dotnet-samples

進入快速啟動資料夾。

cd azure-search-dotnet-samples/quickstart-agentic-retrieval

在 sample.env 中，將 SEARCH_ENDPOINT 和 AOAI_ENDPOINT 的預留位置值替換為您在取得端點中取得的 URL。
重新命名 sample.env 為 .env。
```
mv sample.env .env
```
安裝相依性。
```
dotnet restore AgenticRetrievalQuickstart.csproj
```
還原完成後，請確保輸出中沒有錯誤。
若要使用 Microsoft Entra ID 進行無鑰匙認證，請登入您的 Azure 帳號。如果你有多個訂閱，請選擇包含你 Azure AI 搜尋服務和 Microsoft Foundry 資源的訂閱。
```
az login
```

執行程式碼

執行應用程式建立索引、上傳文件、設定知識來源與知識庫，並執行代理式檢索查詢。

dotnet run --project AgenticRetrievalQuickstart.csproj

產出

申請的輸出應與以下內容相似：

Index 'earth-at-night' created or updated successfully.
Documents uploaded to index 'earth-at-night' successfully.
Knowledge source 'earth-knowledge-source' created or updated successfully.
Knowledge base 'earth-knowledge-base' created or updated successfully.
Running the query...Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?
Response:
Suburban belts brighten more (in relative terms) in December because holiday lighting is concentrated in yards and single-family suburbs where extra decorative lights add a large percentage increase over baseline residential lighting, whereas dense urban cores already have high absolute light levels so the same added holiday lights produce a smaller relative change [ref_id:6][ref_id:2]. The documents note suburbs and outskirts show the biggest holiday increases and central urban areas show smaller percent increases (20-30% in cores, larger in suburbs) linked to available yard space and prevalence of single-family homes [ref_id:6][ref_id:2]. Phoenix's street grid appears very sharp from space because the city's regular, closely spaced north-south/east-west street lighting and bright nodes at intersections and commercial strips produce a strong, high-contrast patterned signal (grid and major corridors like Grand Avenue), while long Midwestern interstates are comparatively dim because roadway lighting is more sparse and continuous between cities and navigable rivers/long highways lack the dense, closely spaced light sources that produce visible nodes and grid patterns at night [ref_id:3][ref_id:7]. In addition, the Black Marble processing accounts for atmospheric, lunar, snow/seasonal and stray-light effects and isolates artificial emissions, so concentrated urban lighting (as in Phoenix) stands out more in the corrected radiance product than dispersed or spaced sources like isolated stretches of interstate or sparsely lit rivers and plains [ref_id:1][ref_id:4][ref_id:7].
Activity:
Activity Type: KnowledgeBaseModelQueryPlanningActivityRecord
{
  "InputTokens": 1489,
  "OutputTokens": 326,
  "Id": 0,
  "ElapsedMs": 4558,
  "Error": null
}
Activity Type: KnowledgeBaseSearchIndexActivityRecord
{
  "SearchIndexArguments": {
    "Search": "December brightening suburban belts vs urban cores light pollution causes seasonal variation \"December brightening\" satellite night lights",
    "Filter": null,
    "SourceDataFields": [
      {
        "Name": "page_chunk"
      },
      {
        "Name": "id"
      },
      {
        "Name": "page_number"
      }
    ],
    "SearchFields": [],
    "SemanticConfigurationName": "semantic_config"
  },
  "KnowledgeSourceName": "earth-knowledge-source",
  "QueryTime": "2026-02-24T14:59:41.536+00:00",
  "Count": 21,
  "Id": 1,
  "ElapsedMs": 623,
  "Error": null
}
... // Trimmed for brevity
References:
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_105_verbalized",
  "Id": "0",
  "ActivitySource": 2,
  "SourceData": {},
  "RerankerScore": 2.7294974
}
... // Trimmed for brevity
Continue the conversation with this query: How do I find lava at night?
Response:
... // Trimmed for brevity
Activity:
... // Trimmed for brevity
References:
... // Trimmed for brevity
Knowledge base 'earth-knowledge-base' deleted successfully.
Knowledge source 'earth-knowledge-source' deleted successfully.
Index 'earth-at-night' deleted successfully.

了解程式碼

註

本節的程式碼片段可能已經過修改以提升可讀性。完整工作範例請參考原始碼。

現在你已經執行過程式碼，讓我們來拆解幾個關鍵步驟：

建立搜尋索引
將文件上傳至索引
建立知識來源
建立知識庫
設定訊息
執行檢索管道
繼續對話

建立搜尋索引

在 Azure AI 搜尋服務中，索引是一組結構化的資料集合。以下程式碼定義了一個名為 earth-at-night的索引。

索引結構包含文件識別與頁面內容、嵌入及編號欄位。該架構還包含語意排序與向量搜尋的配置，利用您的 text-embedding-3-large 部署工具將文字向量化並根據語意或概念相似性匹配文件。

// Define fields for the index
var fields = new List<SearchField>
{
    new SimpleField("id", SearchFieldDataType.String) { IsKey = true, IsFilterable = true, IsSortable = true, IsFacetable = true },
    new SearchField("page_chunk", SearchFieldDataType.String) { IsFilterable = false, IsSortable = false, IsFacetable = false },
    new SearchField("page_embedding_text_3_large", SearchFieldDataType.Collection(SearchFieldDataType.Single)) { VectorSearchDimensions = 3072, VectorSearchProfileName = "hnsw_text_3_large" },
    new SimpleField("page_number", SearchFieldDataType.Int32) { IsFilterable = true, IsSortable = true, IsFacetable = true }
};

// Define a vectorizer
var vectorizer = new AzureOpenAIVectorizer(vectorizerName: "azure_openai_text_3_large")
{
    Parameters = new AzureOpenAIVectorizerParameters
    {
        ResourceUri = new Uri(aoaiEndpoint),
        DeploymentName = aoaiEmbeddingDeployment,
        ModelName = aoaiEmbeddingModel
    }
};

// Define a vector search profile and algorithm
var vectorSearch = new VectorSearch()
{
    Profiles =
    {
        new VectorSearchProfile(
            name: "hnsw_text_3_large",
            algorithmConfigurationName: "alg"
        )
        {
            VectorizerName = "azure_openai_text_3_large"
        }
    },
    Algorithms =
    {
        new HnswAlgorithmConfiguration(name: "alg")
    },
    Vectorizers =
    {
        vectorizer
    }
};

// Define a semantic configuration
var semanticConfig = new SemanticConfiguration(
    name: "semantic_config",
    prioritizedFields: new SemanticPrioritizedFields
    {
        ContentFields = { new SemanticField("page_chunk") }
    }
);

var semanticSearch = new SemanticSearch()
{
    DefaultConfigurationName = "semantic_config",
    Configurations = { semanticConfig }
};

// Create the index
var index = new SearchIndex(indexName)
{
    Fields = fields,
    VectorSearch = vectorSearch,
    SemanticSearch = semanticSearch
};

// Create the index client, deleting and recreating the index if it exists
var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
await indexClient.CreateOrUpdateIndexAsync(index);
Console.WriteLine($"Index '{indexName}' created or updated successfully.");

參考資料：SearchField、 SimpleField、 VectorSearch、 SemanticSearch、 SearchIndex、 SearchIndexClient

將文件上傳至索引

目前，該 earth-at-night 指數是空白的。以下程式碼將索引中填充來自 NASA 的《Earth at Night》電子書的 JSON 文件。依照 Azure AI 搜尋服務的要求，每份文件都符合索引結構中定義的欄位與資料型態。

// Upload sample documents from the GitHub URL
string url = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";
var httpClient = new HttpClient();
var response = await httpClient.GetAsync(url);
response.EnsureSuccessStatusCode();
var json = await response.Content.ReadAsStringAsync();
var documents = JsonSerializer.Deserialize<List<Dictionary<string, object>>>(json);
var searchClient = new SearchClient(new Uri(searchEndpoint), indexName, credential);
var searchIndexingBufferedSender = new SearchIndexingBufferedSender<Dictionary<string, object>>(
    searchClient,
    new SearchIndexingBufferedSenderOptions<Dictionary<string, object>>
    {
        KeyFieldAccessor = doc => doc["id"].ToString(),
    }
);

await searchIndexingBufferedSender.UploadDocumentsAsync(documents);
await searchIndexingBufferedSender.FlushAsync();
Console.WriteLine($"Documents uploaded to index '{indexName}' successfully.");

參考資料：SearchClient， SearchIndexingBufferedSender

建立知識來源

知識來源是可重複使用的來源資料參考。以下程式碼定義了一個針對該earth-knowledge-source索引的earth-at-night知識來源。

SourceDataFields 指定引用引用中包含哪些索引欄位。此範例僅包含人類可讀欄位，以避免回應中冗長且無法解釋的嵌入。

// Create a knowledge source
var indexKnowledgeSource = new SearchIndexKnowledgeSource(
    name: knowledgeSourceName,
    searchIndexParameters: new SearchIndexKnowledgeSourceParameters(searchIndexName: indexName)
    {
        SourceDataFields = { new SearchIndexFieldReference(name: "id"), new SearchIndexFieldReference(name: "page_chunk"), new SearchIndexFieldReference(name: "page_number") }
    }
);

await indexClient.CreateOrUpdateKnowledgeSourceAsync(indexKnowledgeSource);
Console.WriteLine($"Knowledge source '{knowledgeSourceName}' created or updated successfully.");

參考資料：SearchIndexKnowledgeSource

建立知識庫

若要在查詢時以 earth-knowledge-source 和部署 gpt-5-mini 為目標，您需要知識庫。以下程式碼定義了一個名為 earth-knowledge-base的知識庫。

OutputMode（預覽）設定為 AnswerSynthesis，可產生會引用檢索到的文件並遵循所提供 AnswerInstructions 的自然語言回答。 RetrievalReasoningEffort（預覽）設為 low，以控制用於查詢規劃的推理量。

// Create a knowledge base
var openAiParameters = new AzureOpenAIVectorizerParameters
{
    ResourceUri = new Uri(aoaiEndpoint),
    DeploymentName = aoaiGptDeployment,
    ModelName = aoaiGptModel
};

var model = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: openAiParameters);

var knowledgeBase = new KnowledgeBase(
    name: knowledgeBaseName,
    knowledgeSources: new KnowledgeSourceReference[] { new KnowledgeSourceReference(knowledgeSourceName) }
)
{
    RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort(),
    OutputMode = KnowledgeRetrievalOutputMode.AnswerSynthesis,
    AnswerInstructions = "Provide a two sentence concise and informative answer based on the retrieved documents.",
    Models = { model }
};

await indexClient.CreateOrUpdateKnowledgeBaseAsync(knowledgeBase);
Console.WriteLine($"Knowledge base '{knowledgeBaseName}' created or updated successfully.");

Reference：KnowledgeBaseAzureOpenAIModel， KnowledgeBase

設定訊息

訊息是檢索路徑的輸入，包含對話歷史。每則訊息都包含一個角色來指示其來源，例如 system 或 user，以及自然語言內容。您使用的 LLM 會決定哪些角色有效。

以下程式碼會產生系統訊息， earth-knowledge-base 指示在夜間回答有關地球的問題，當無法回答時則回答「我不知道」。

// Set up messages
string instructions = @"A Q&A agent that can answer questions about the Earth at night.
If you don't have the answer, respond with ""I don't know"".";

var messages = new List<Dictionary<string, string>>
{
    new Dictionary<string, string>
    {
        { "role", "system" },
        { "content", instructions }
    }
};

執行檢索流程

你已經準備好運行代理檢索程序了。以下程式碼會將兩部分的使用者查詢發送到 earth-knowledge-base，該查詢：

分析整個對話，推斷使用者的資訊需求。
將複合查詢分解為聚焦子查詢。
針對您的知識來源並行執行子查詢。
使用語意排名器重新排序並過濾結果。
將最優秀的結果綜合成自然語言的答案。

// Run agentic retrieval
var baseClient = new KnowledgeBaseRetrievalClient(
    endpoint: new Uri(searchEndpoint),
    knowledgeBaseName: knowledgeBaseName,
    tokenCredential: new DefaultAzureCredential()
);

messages.Add(new Dictionary<string, string>
{
    { "role", "user" },
    { "content", @"Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?" }
});

var retrievalRequest = new KnowledgeBaseRetrievalRequest();
foreach (Dictionary<string, string> message in messages) {
    if (message["role"] != "system") {
        retrievalRequest.Messages.Add(new KnowledgeBaseMessage(content: new[] { new KnowledgeBaseMessageTextContent(message["content"]) }) { Role = message["role"] });
    }
}
retrievalRequest.RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort();
var retrievalResult = await baseClient.RetrieveAsync(retrievalRequest);

messages.Add(new Dictionary<string, string>
{
    { "role", "assistant" },
    { "content", (retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text }
});

參考資料：KnowledgeBaseRetrievalClient， KnowledgeBaseRetrievalRequest

檢視回應、活動及參考資料

以下程式碼顯示檢索管線的回應、活動與參考，其中：

Response 提供一個綜合的、由大型語言模型生成的答案，並引用所檢索文件來回應查詢。當未啟用答案綜合時，本節內容會直接從文件中擷取。
Activity 追蹤檢索過程中所採取的步驟，包括部署 gpt-5-mini 產生的子查詢，以及用於語意排序、查詢規劃和答案綜合的標記。
References 列出對回應有貢獻的文件，每份文件皆以 DocKey。

// Print the response, activity, and references
Console.WriteLine("Response:");
Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text);

Console.WriteLine("Activity:");
foreach (var activity in retrievalResult.Value.Activity)
{
    Console.WriteLine($"Activity Type: {activity.GetType().Name}");
    string activityJson = JsonSerializer.Serialize(
        activity,
        activity.GetType(),
        new JsonSerializerOptions { WriteIndented = true }
    );
    Console.WriteLine(activityJson);
}

Console.WriteLine("References:");
foreach (var reference in retrievalResult.Value.References)
{
    Console.WriteLine($"Reference Type: {reference.GetType().Name}");
    string referenceJson = JsonSerializer.Serialize(
        reference,
        reference.GetType(),
        new JsonSerializerOptions { WriteIndented = true }
    );
    Console.WriteLine(referenceJson);
}

繼續對話

以下程式碼將延續與 earth-knowledge-base 的對話。在你發送這個使用者查詢後，知識庫會從訊息清單中擷取相關內容 earth-knowledge-source ，並將回應附加到訊息清單中。

// Continue the conversation
messages.Add(new Dictionary<string, string>
{
    { "role", "user" },
    { "content", "How do I find lava at night?" }
});

retrievalRequest = new KnowledgeBaseRetrievalRequest();
foreach (Dictionary<string, string> message in messages) {
    if (message["role"] != "system") {
        retrievalRequest.Messages.Add(new KnowledgeBaseMessage(content: new[] { new KnowledgeBaseMessageTextContent(message["content"]) }) { Role = message["role"] });
    }
}
retrievalRequest.RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort();
retrievalResult = await baseClient.RetrieveAsync(retrievalRequest);

messages.Add(new Dictionary<string, string>
{
    { "role", "assistant" },
    { "content", (retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text }
});

檢視新的回應、活動及參考資料

以下程式碼顯示來自檢索管線的新回應、活動及參考資料。

// Print the new response, activity, and references
Console.WriteLine("Response:");
Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text);

Console.WriteLine("Activity:");
foreach (var activity in retrievalResult.Value.Activity)
{
    Console.WriteLine($"Activity Type: {activity.GetType().Name}");
    string activityJson = JsonSerializer.Serialize(
        activity,
        activity.GetType(),
        new JsonSerializerOptions { WriteIndented = true }
    );
    Console.WriteLine(activityJson);
}

Console.WriteLine("References:");
foreach (var reference in retrievalResult.Value.References)
{
    Console.WriteLine($"Reference Type: {reference.GetType().Name}");
    string referenceJson = JsonSerializer.Serialize(
        reference,
        reference.GetType(),
        new JsonSerializerOptions { WriteIndented = true }
    );
    Console.WriteLine(referenceJson);
}

清理資源

當您在自己的訂用帳戶中工作時，建議您在完成專案後移除不再需要的資源。若您讓資源繼續執行，則可能會產生費用。

在Azure入口網站中，從左側窗格選擇 所有資源或 資源群組以尋找並管理資源。你可以單獨刪除資源，或是一次性刪除資源群組，移除所有資源。

否則，以下程式碼 program.cs 會刪除你在這個快速入門中建立的物件。

刪除知識庫

await indexClient.DeleteKnowledgeBaseAsync(knowledgeBaseName);
Console.WriteLine($"Knowledge base '{knowledgeBaseName}' deleted successfully.");

刪除知識來源

await indexClient.DeleteKnowledgeSourceAsync(knowledgeSourceName);
Console.WriteLine($"Knowledge source '{knowledgeSourceName}' deleted successfully.");

刪除搜尋索引

await indexClient.DeleteIndexAsync(indexName);
Console.WriteLine($"Index '{indexName}' deleted successfully.");

雖然你可以使用自己的資料，但這個快速入門使用了 NASA 電子書《Earth at Night》電子書中的範例 JSON 文件。

提示

想馬上開始嗎？請於GitHub下載原始碼。

先決條件

一個有有效訂閱的 Azure 帳號。免費註冊帳號。
在任何提供 Agent 擷取功能的區域中的 Azure AI 搜尋服務服務。此快速入門需要 Basic 等級或以上的管理身份支援。
一個Microsoft Foundry 專案和資源。當你建立專案時，資源會自動建立。
用於文本到向量轉換的嵌入模型已部署到你的專案中。你可以使用任何 text-embedding 模型，例如 text-embedding-3-large。
LLM 已部署至您的專案，用於查詢規劃與產生答案。你可以使用任何支援的大型語言模型，例如 gpt-5-mini。
Java 11或更高，Maven。
git 來複製樣本庫。
Azure CLI 用於與 Microsoft Entra ID 的無金鑰認證。

設定存取權限

若要為此快速入門設定存取：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 啟用基於角色的存取權限。
2. 建立系統指派的管理身份。
3. 為您的使用者帳號指派以下角色：搜尋服務貢獻者、搜尋索引資料貢獻者，以及搜尋索引資料閱讀器。
在您的 Microsoft Foundry 資源中，將 Cognitive Services User 指派給您的搜尋服務的管理身份。

重要

代理檢索有兩種基於代幣的收費模式：

Azure AI 搜尋服務用於 Agent 擷取的計費。
來自 Azure OpenAI，使用查詢規劃和答案合成的計費。

欲了解更多資訊，請參閱區域可用性、限額及計費。

取得端點

若要取得此快速入門的端點：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 從左側窗格選擇 「概覽」。
2. 複製網址，應該看起來像 https://my-service.search.windows.net。
關於你的 Microsoft Foundry 資源：
1. 從左側窗格選擇 資源管理>鍵與端點。
2. 複製 OpenAI 分頁上的網址，應該會看起來像 https://my-resource.openai.azure.com/。

設定環境

用 Git 複製樣本庫。

git clone https://github.com/Azure-Samples/azure-search-java-samples

進入快速啟動資料夾。

cd azure-search-java-samples/quickstart-agentic-retrieval

在 sample.env 中，將 SEARCH_ENDPOINT 和 AOAI_ENDPOINT 的預留位置值替換為您在取得端點中取得的 URL。
重新命名 sample.env 為 .env。
```
mv sample.env .env
```
安裝相依性。
```
mvn clean dependency:copy-dependencies
```
若要使用 Microsoft Entra ID 進行無鑰匙認證，請登入您的 Azure 帳號。如果你有多個訂閱，請選擇包含你 Azure AI 搜尋服務和 Microsoft Foundry 資源的訂閱。
```
az login
```

執行程式碼

建立並執行應用程式以建立索引、上傳文件、設定知識來源與知識庫，並執行代理式檢索查詢。

javac AgenticRetrievalQuickstart.java -cp ".;target\dependency\*"
java -cp ".;target\dependency\*" AgenticRetrievalQuickstart

javac AgenticRetrievalQuickstart.java -cp ".:target/dependency/*"
java -cp ".:target/dependency/*" AgenticRetrievalQuickstart

javac AgenticRetrievalQuickstart.java -cp ".:target/dependency/*"
java -cp ".:target/dependency/*" AgenticRetrievalQuickstart

產出

申請的輸出應與以下內容相似：

Index 'earth-at-night' created or updated successfully.
Documents uploaded to index 'earth-at-night' successfully.
Knowledge source 'earth-knowledge-source' created or updated successfully.
Knowledge base 'earth-knowledge-base' created or updated successfully.
Running the query...Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?
Response:
December percent brightening is larger in suburban belts because many houses add seasonal residential/holiday lighting on yards and roofs, so a relatively dark suburban baseline can increase by 20-50% when those lights turn on, while dense urban cores already have high continuous lighting so the same added lights make a smaller percentage change [ref_id:2][ref_id:5][ref_id:8]. Phoenix's street grid appears sharply from space because the metropolitan layout is a regular, continuous north-south/east-west street and block grid with a major diagonal artery (Grand Avenue) and concentrated, continuous arterial and commercial lighting along intersections and corridors [ref_id:3][ref_id:0][ref_id:1]. ...
Activity:
Activity Type: KnowledgeBaseModelQueryPlanningActivityRecord
{
  "id" : 0,
  "elapsedMs" : 5229,
  "type" : "modelQueryPlanning",
  "inputTokens" : 1489,
  "outputTokens" : 383
}
Activity Type: KnowledgeBaseSearchIndexActivityRecord
{
  "id" : 1,
  "elapsedMs" : 2670,
  "knowledgeSourceName" : "earth-knowledge-source",
  "queryTime" : "2026-02-24T15:28:36.776Z",
  "count" : 3,
  "type" : "searchIndex",
  "searchIndexArguments" : {
    "search" : "December brightening suburban belts vs urban cores light pollution causes seasonal variation reasons \"December brightening\"",
    "sourceDataFields" : [ {
      "name" : "page_chunk"
    }, {
      "name" : "id"
    }, {
      "name" : "page_number"
    } ],
    "searchFields" : [ ],
    "semanticConfigurationName" : "semantic_config"
  }
}
... // Trimmed for brevity
References:
Reference Type: KnowledgeBaseSearchIndexReference
{
  "id" : "0",
  "activitySource" : 2,
  "rerankerScore" : 2.7486389,
  "type" : "searchIndex",
  "docKey" : "earth_at_night_508_page_105_verbalized"
}
... // Trimmed for brevity
Continue the conversation with this query: How do I find lava at night?
Response:
... // Trimmed for brevity
Activity:
... // Trimmed for brevity
References:
... // Trimmed for brevity
Knowledge base 'earth-knowledge-base' deleted successfully.
Knowledge source 'earth-knowledge-source' deleted successfully.
Index 'earth-at-night' deleted successfully.

了解程式碼

註

本節的程式碼片段可能已經過修改以提升可讀性。完整工作範例請參考原始碼。

現在你已經執行過程式碼，讓我們來拆解幾個關鍵步驟：

建立搜尋索引
將文件上傳至索引
建立知識來源
建立知識庫
設定訊息
執行檢索管道
繼續對話

建立搜尋索引

在 Azure AI 搜尋服務中，索引是一組結構化的資料集合。以下程式碼定義了一個名為 earth-at-night的索引。

List<SearchField> fields = Arrays.asList(
    new SearchField("id", SearchFieldDataType.STRING)
        .setKey(true)
        .setFilterable(true)
        .setSortable(true)
        .setFacetable(true),
    new SearchField("page_chunk", SearchFieldDataType.STRING)
        .setFilterable(false)
        .setSortable(false)
        .setFacetable(false),
    new SearchField("page_embedding_text_3_large",
            SearchFieldDataType.collection(
                SearchFieldDataType.SINGLE))
        .setVectorSearchDimensions(3072)
        .setVectorSearchProfileName("hnsw_text_3_large"),
    new SearchField("page_number", SearchFieldDataType.INT32)
        .setFilterable(true)
        .setSortable(true)
        .setFacetable(true)
);

AzureOpenAIVectorizer vectorizer = new AzureOpenAIVectorizer(
        "azure_openai_text_3_large")
    .setParameters(new AzureOpenAIVectorizerParameters()
        .setResourceUrl(aoaiEndpoint)
        .setDeploymentName(aoaiEmbeddingDeployment)
        .setModelName(
            AzureOpenAIModelName.fromString(
                aoaiEmbeddingModel)));

VectorSearch vectorSearch = new VectorSearch()
    .setProfiles(Arrays.asList(
        new VectorSearchProfile("hnsw_text_3_large", "alg")
            .setVectorizerName("azure_openai_text_3_large")
    ))
    .setAlgorithms(Arrays.asList(
        new HnswAlgorithmConfiguration("alg")
    ))
    .setVectorizers(Arrays.asList(vectorizer));

SemanticSearch semanticSearch = new SemanticSearch()
    .setDefaultConfigurationName("semantic_config")
    .setConfigurations(Arrays.asList(
        new SemanticConfiguration("semantic_config",
            new SemanticPrioritizedFields()
                .setContentFields(Arrays.asList(
                    new SemanticField("page_chunk")
                ))
        )
    ));

SearchIndex index = new SearchIndex(indexName)
    .setFields(fields)
    .setVectorSearch(vectorSearch)
    .setSemanticSearch(semanticSearch);

indexClient.createOrUpdateIndex(index);

參考資料：SearchField、 VectorSearch、 SemanticSearch、 SearchIndex、 SearchIndexClient

將文件上傳至索引

String url = "https://raw.githubusercontent.com/Azure-Samples/"
    + "azure-search-sample-data/refs/heads/main/nasa-e-book/"
    + "earth-at-night-json/documents.json";

java.net.http.HttpClient httpClient =
    java.net.http.HttpClient.newHttpClient();
java.net.http.HttpRequest httpRequest =
    java.net.http.HttpRequest.newBuilder()
        .uri(URI.create(url))
        .build();

java.net.http.HttpResponse<String> response =
    httpClient.send(httpRequest,
        java.net.http.HttpResponse.BodyHandlers.ofString());

if (response.statusCode() != 200) {
    throw new IOException(
        "Failed to fetch documents: " + response.statusCode());
}

ObjectMapper mapper = new ObjectMapper();
JsonNode jsonArray = mapper.readTree(response.body());

List<SearchDocument> documents = new ArrayList<>();
for (int i = 0; i < jsonArray.size(); i++) {
    JsonNode doc = jsonArray.get(i);
    SearchDocument searchDoc = new SearchDocument();

    searchDoc.put("id", doc.has("id")
        ? doc.get("id").asText() : String.valueOf(i + 1));
    searchDoc.put("page_chunk", doc.has("page_chunk")
        ? doc.get("page_chunk").asText() : "");

    if (doc.has("page_embedding_text_3_large")
            && doc.get("page_embedding_text_3_large")
                .isArray()) {
        List<Double> embeddings = new ArrayList<>();
        for (JsonNode embedding
                : doc.get("page_embedding_text_3_large")) {
            embeddings.add(embedding.asDouble());
        }
        searchDoc.put(
            "page_embedding_text_3_large", embeddings);
    } else {
        List<Double> fallback = new ArrayList<>();
        for (int j = 0; j < 3072; j++) {
            fallback.add(0.1);
        }
        searchDoc.put(
            "page_embedding_text_3_large", fallback);
    }

    searchDoc.put("page_number",
        doc.has("page_number")
            ? doc.get("page_number").asInt() : i + 1);

    documents.add(searchDoc);
}

SearchClient searchClient = new SearchClientBuilder()
    .endpoint(searchEndpoint)
    .indexName(indexName)
    .credential(credential)
    .buildClient();

searchClient.uploadDocuments(documents);

參考資料：SearchClient， SearchDocument

建立知識來源

知識來源是可重複使用的來源資料參考。以下程式碼定義了一個針對該earth-knowledge-source索引的earth-at-night知識來源。

sourceDataFields 指定引用引用中包含哪些索引欄位。此範例僅包含人類可讀欄位，以避免回應中冗長且無法解釋的嵌入。

SearchIndexKnowledgeSource indexKnowledgeSource =
    new SearchIndexKnowledgeSource(
        knowledgeSourceName,
        new SearchIndexKnowledgeSourceParameters(indexName)
            .setSourceDataFields(Arrays.asList(
                new SearchIndexFieldReference("id"),
                new SearchIndexFieldReference("page_chunk"),
                new SearchIndexFieldReference("page_number")
            ))
    );

indexClient.createOrUpdateKnowledgeSource(indexKnowledgeSource);

參考資料：SearchIndexKnowledgeSource

建立知識庫

若要在查詢時以 earth-knowledge-source 和部署 gpt-5-mini 為目標，您需要知識庫。以下程式碼定義了一個名為 earth-knowledge-base的知識庫。

OutputMode（預覽）設定為 ANSWER_SYNTHESIS，以啟用會引用擷取的文件並遵循所提供 AnswerInstructions 的自然語言答案。 RetrievalReasoningEffort（預覽版）設定為 low，以控制用於查詢規劃的推理量。

AzureOpenAIVectorizerParameters openAiParameters =
    new AzureOpenAIVectorizerParameters()
        .setResourceUrl(aoaiEndpoint)
        .setDeploymentName(aoaiGptDeployment)
        .setModelName(
            AzureOpenAIModelName.fromString(aoaiGptModel));

KnowledgeBaseAzureOpenAIModel model =
    new KnowledgeBaseAzureOpenAIModel(openAiParameters);

KnowledgeBase knowledgeBase = new KnowledgeBase(
        knowledgeBaseName,
        Arrays.asList(
            new KnowledgeSourceReference(knowledgeSourceName))
    )
    .setRetrievalReasoningEffort(
        new KnowledgeRetrievalLowReasoningEffort())
    .setOutputMode(
        KnowledgeRetrievalOutputMode.ANSWER_SYNTHESIS)
    .setAnswerInstructions(
        "Provide a two sentence concise and informative answer "
        + "based on the retrieved documents.")
    .setModels(Arrays.asList(model));

indexClient.createOrUpdateKnowledgeBase(knowledgeBase);

Reference：KnowledgeBaseAzureOpenAIModel， KnowledgeBase

設定訊息

以下程式碼會產生系統訊息， earth-knowledge-base 指示在夜間回答有關地球的問題，當無法回答時則回答「我不知道」。

String instructions =
    "A Q&A agent that can answer questions about the "
    + "Earth at night.\n"
    + "If you don't have the answer, respond with "
    + "\"I don't know\".";

List<Map<String, String>> messages = new ArrayList<>();
Map<String, String> systemMessage = new HashMap<>();
systemMessage.put("role", "system");
systemMessage.put("content", instructions);
messages.add(systemMessage);

執行檢索流程

你已經準備好運行代理檢索程序了。以下程式碼會將兩部分的使用者查詢發送到 earth-knowledge-base，該查詢：

分析整個對話，推斷使用者的資訊需求。
將複合查詢分解為聚焦子查詢。
針對您的知識來源並行執行子查詢。
使用語意排名器重新排序並過濾結果。
將最優秀的結果綜合成自然語言的答案。

KnowledgeBaseRetrievalClient baseClient =
    new KnowledgeBaseRetrievalClientBuilder()
        .endpoint(searchEndpoint)
        .knowledgeBaseName(knowledgeBaseName)
        .credential(
            new DefaultAzureCredentialBuilder().build())
        .buildClient();

String query = "Why do suburban belts display larger "
    + "December brightening than urban cores even "
    + "though absolute light levels are higher "
    + "downtown? Why is the Phoenix nighttime street "
    + "grid is so sharply visible from space, whereas "
    + "large stretches of the interstate between "
    + "midwestern cities remain comparatively dim?";

messages.add(Map.of("role", "user", "content", query));

KnowledgeBaseRetrievalResult retrievalResult =
    retrieve(baseClient, messages, knowledgeSourceName);

String responseText =
    ((KnowledgeBaseMessageTextContent) retrievalResult
        .getResponse().get(0).getContent().get(0))
        .getText();

messages.add(
    Map.of("role", "assistant", "content", responseText));

retrieve 輔助函式會根據對話歷史建立 KnowledgeBaseRetrievalOptions、設定檢索推理的工作量、附加知識來源參數，並回傳 KnowledgeBaseRetrievalResult：

private static KnowledgeBaseRetrievalResult retrieve(
        KnowledgeBaseRetrievalClient client,
        List<Map<String, String>> messages,
        String knowledgeSourceName) {
    List<KnowledgeBaseMessage> retrievalMessages = new ArrayList<>();
    for (Map<String, String> message : messages) {
        String role = message.get("role");
        if ("system".equals(role)) {
            continue;
        }
        retrievalMessages.add(
            new KnowledgeBaseMessage(
                new KnowledgeBaseMessageTextContent(
                    message.get("content")))
                .setRole(role));
    }

    KnowledgeBaseRetrievalOptions request =
        new KnowledgeBaseRetrievalOptions()
            .setMessages(retrievalMessages)
            .setRetrievalReasoningEffort(
                new KnowledgeRetrievalLowReasoningEffort())
            .setIncludeActivity(true)
            .setKnowledgeSourceParams(Arrays.asList(
                new SearchIndexKnowledgeSourceParams(knowledgeSourceName)
                    .setIncludeReferences(true)
                    .setIncludeReferenceSourceData(true)
            ));

    return client.retrieve(request);
}

參考資料：KnowledgeBaseRetrievalClient， KnowledgeBaseRetrievalOptions（知識庫檢索選項）

檢視回應、活動及參考資料

以下程式碼顯示檢索管線的回應、活動與參考，其中：

Response 提供一個綜合的、由大型語言模型生成的答案，並引用所檢索文件來回應查詢。當未啟用答案綜合時，本節內容會直接從文件中擷取。
Activity 追蹤檢索過程中所採取的步驟，包括部署 gpt-5-mini 產生的子查詢，以及用於語意排序、查詢規劃和答案綜合的標記。
References 列出對回應有貢獻的文件，每份文件皆以 docKey。

System.out.println("Response:");
System.out.println(responseText);

System.out.println("Activity:");
for (KnowledgeBaseActivityRecord activity
        : retrievalResult.getActivity()) {
    System.out.println("Activity Type: "
        + activity.getClass().getSimpleName());
    System.out.println(toJsonString(activity));
}

System.out.println("References:");
for (KnowledgeBaseReference reference
        : retrievalResult.getReferences()) {
    System.out.println("Reference Type: "
        + reference.getClass().getSimpleName());
    System.out.println(toJsonString(reference));
}

繼續對話

String nextQuery = "How do I find lava at night?";
messages.add(
    Map.of("role", "user", "content", nextQuery));

retrievalResult = retrieve(baseClient, messages, knowledgeSourceName);

檢視新的回應、活動及參考資料

以下程式碼擷取回應文字並呼叫 printResult 顯示新的回應、活動與參考。

responseText =
    ((KnowledgeBaseMessageTextContent) retrievalResult
        .getResponse().get(0).getContent().get(0))
        .getText();
messages.add(
    Map.of("role", "assistant", "content", responseText));

printResult(responseText, retrievalResult);

清理資源

當您在自己的訂用帳戶中工作時，建議您在完成專案後移除不再需要的資源。若您讓資源繼續執行，則可能會產生費用。

在Azure入口網站中，從左側窗格選擇 所有資源或 資源群組以尋找並管理資源。你可以單獨刪除資源，或是一次性刪除資源群組，移除所有資源。

否則，以下程式碼 AgenticRetrievalQuickstart.java 會刪除你在這個快速入門中建立的物件。

刪除知識庫

indexClient.deleteKnowledgeBase(knowledgeBaseName);
System.out.println("Knowledge base '" + knowledgeBaseName
    + "' deleted successfully.");

刪除知識來源

indexClient.deleteKnowledgeSource(knowledgeSourceName);
System.out.println("Knowledge source '" + knowledgeSourceName
    + "' deleted successfully.");

刪除搜尋索引

indexClient.deleteIndex(indexName);
System.out.println("Index '" + indexName
    + "' deleted successfully.");

雖然你可以使用自己的資料，但這個快速入門使用了 NASA 電子書《Earth at Night》電子書中的範例 JSON 文件。

提示

想馬上開始嗎？請於GitHub下載原始碼。

先決條件

一個有有效訂閱的 Azure 帳號。免費註冊帳號。
在任何提供 Agent 擷取功能的區域中的 Azure AI 搜尋服務服務。此快速入門需要 Basic 等級或以上的管理身份支援。
一個Microsoft Foundry 專案和資源。當你建立專案時，資源會自動建立。
用於文本到向量轉換的嵌入模型已部署到你的專案中。你可以使用任何 text-embedding 模型，例如 text-embedding-3-large。
LLM 已部署至您的專案，用於查詢規劃與產生答案。你可以使用任何支援的大型語言模型，例如 gpt-5-mini。
Node.js 20 LTS 或更新版本。
git 來複製樣本庫。
Azure CLI 用於與 Microsoft Entra ID 的無金鑰認證。

設定存取權限

若要為此快速入門設定存取：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 啟用基於角色的存取權限。
2. 建立系統指派的管理身份。
3. 為您的使用者帳號指派以下角色：搜尋服務貢獻者、搜尋索引資料貢獻者，以及搜尋索引資料閱讀器。
在您的 Microsoft Foundry 資源中，將 Cognitive Services User 指派給您的搜尋服務的管理身份。

重要

代理檢索有兩種基於代幣的收費模式：

Azure AI 搜尋服務用於 Agent 擷取的計費。
來自 Azure OpenAI，使用查詢規劃和答案合成的計費。

欲了解更多資訊，請參閱區域可用性、限額及計費。

取得端點

若要取得此快速入門的端點：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 從左側窗格選擇 「概覽」。
2. 複製網址，應該看起來像 https://my-service.search.windows.net。
關於你的 Microsoft Foundry 資源：
1. 從左側窗格選擇 資源管理>鍵與端點。
2. 複製 OpenAI 分頁上的網址，應該會看起來像 https://my-resource.openai.azure.com/。

設定環境

用 Git 複製樣本庫。

git clone https://github.com/Azure-Samples/azure-search-javascript-samples

進入快速啟動資料夾。

cd azure-search-javascript-samples/quickstart-agentic-retrieval-js

在 sample.env 中，將 AZURE_SEARCH_ENDPOINT 和 AZURE_OPENAI_ENDPOINT 的預留位置值替換為您在取得端點中取得的 URL。
重新命名 sample.env 為 .env。
```
mv sample.env .env
```
安裝相依性。
```
npm install
```
安裝完成後，你會看到專案目錄中有一個 node_modules 資料夾。
若要使用 Microsoft Entra ID 進行無鑰匙認證，請登入您的 Azure 帳號。如果你有多個訂閱，請選擇包含你 Azure AI 搜尋服務和 Microsoft Foundry 資源的訂閱。
```
az login
```

執行程式碼

執行應用程式建立索引、上傳文件、設定知識來源與知識庫，並執行代理式檢索查詢。

npm start

產出

申請的輸出應與以下內容相似：

Waiting for indexing to complete...
Expected documents: 194
Current indexed count: 194
✓ All 194 documents indexed successfully!
✅ Knowledge source 'earth-knowledge-source' created successfully.
✅ Knowledge base 'earth-knowledge-base' created successfully.

📝 ANSWER:
────────────────────────────────────────────────────────────────────────────────
Suburban belts show larger December brightening (20–50% increases) because residential holiday lighting and seasonal decorations are concentrated there, so relative (fractional) increases over the baseline are bigger even though absolute downtown radiances remain higher; urban cores already emit strong baseline light while many suburbs add a large seasonal increment visible in VIIRS DNB observations [ref_id:0][ref_id:1]. The Phoenix street grid appears sharply from space because continuous, street‑oriented lighting with regular residential lot spacing and little vegetative masking produces strong, linear emissions, whereas long interstate stretches between Midwestern cities have sparser, access‑limited lighting, fewer adjacent developments and more shielded fixtures so they register comparatively dim on night‑light sensors like VIIRS/DNB [ref_id:0][ref_id:1].
────────────────────────────────────────────────────────────────────────────────

Activities:
Activity Type: modelQueryPlanning
{
  "id": 0,
  "type": "modelQueryPlanning",
  "elapsedMs": 5883,
  "inputTokens": 1489,
  "outputTokens": 326
}
Activity Type: searchIndex
{
  "id": 1,
  "type": "searchIndex",
  "elapsedMs": 527,
  "knowledgeSourceName": "earth-knowledge-source",
  "queryTime": "2025-12-19T15:38:23.462Z",
  "count": 1,
  "searchIndexArguments": {
    "search": "December brightening suburban belts vs urban cores light pollution causes December increase in night lights suburban vs urban",
    "filter": null,
    "sourceDataFields": [
      {
        "name": "page_chunk"
      },
      {
        "name": "id"
      },
      {
        "name": "page_number"
      }
    ],
    "searchFields": [],
    "semanticConfigurationName": "semantic_config"
  }
}
... // Trimmed for brevity
Activity Type: agenticReasoning
{
  "id": 4,
  "type": "agenticReasoning",
  "reasoningTokens": 70397,
  "retrievalReasoningEffort": {
    "kind": "low"
  }
}
Activity Type: modelAnswerSynthesis
{
  "id": 5,
  "type": "modelAnswerSynthesis",
  "elapsedMs": 4908,
  "inputTokens": 4013,
  "outputTokens": 187
}

References:
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "0",
  "activitySource": 3,
  "sourceData": {
    "id": "earth_at_night_508_page_174_verbalized",
    "page_chunk": "<!-- PageHeader=\"Holiday Lights\" -->\n\n## Holiday Lights\n\n### Bursting with Holiday Energy-United States\n\nNASA researchers found that nighttime lights in the United States shine 20 to 50 percent brighter in December due to holiday light displays and other activities during Christmas and New Year's when compared to light output during the rest of the year.\n\nThe next five maps (see also pages 161-163), created using data from the VIIRS DNB on the Suomi NPP satellite, show changes in lighting intensity and location around many major cities, comparing the nighttime light signals from December 2012 and beyond.\n\n---\n\n#### Figure 1. Location Overview\n\nA map of the western hemisphere with a marker indicating the mid-Atlantic region of the eastern United States, where the study of holiday lighting intensity was focused.\n\n---\n\n#### Figure 2. Holiday Lighting Intensity: Mid-Atlantic United States (2012–2014)\n\nA map showing Maryland, New Jersey, Delaware, Virginia, West Virginia, Ohio, Kentucky, Tennessee, North Carolina, South Carolina, and surrounding areas. Major cities labeled include Washington, D.C., Richmond, Norfolk, and Raleigh.\n\nThe map uses colors to indicate changes in holiday nighttime lighting intensity between 2012 and 2014:\n\n- **Green/bright areas**: More holiday lighting (areas shining 20–50% brighter in December).\n- **Yellow areas**: No change in lighting.\n- **Dim/grey areas**: Less holiday lighting.\n\nKey observations from the map:\n\n- The Washington, D.C. metropolitan area shows significant increases in lighting during the holidays, extending into Maryland and Virginia.\n- Urban centers such as Richmond (Virginia), Norfolk (Virginia), Raleigh (North Carolina), and clusters in Tennessee and South Carolina also experience notable increases in light intensity during December.\n- Rural areas and the interiors of West Virginia, Kentucky, and North Carolina show little change or less holiday lighting, corresponding to population density and urbanization.\n\n**Legend:**\n\n| Holiday Lighting Change | Color on Map   |\n|------------------------|---------------|\n| More                   | Green/bright  |\n| No Change              | Yellow        |\n| Less                   | Dim/grey      |\n\n_The scale bar indicates a distance of 100 km for reference._\n\n---\n\n<!-- PageFooter=\"158 Earth at Night\" -->",
    "page_number": 174
  },
  "rerankerScore": 2.6692379,
  "docKey": "earth_at_night_508_page_174_verbalized"
}
... // Trimmed for brevity

❓ Follow-up question: How do I find lava at night?

📝 ANSWER:
────────────────────────────────────────────────────────────────────────────────
... // Trimmed for brevity
────────────────────────────────────────────────────────────────────────────────

Activities:
... // Trimmed for brevity

References:
... // Trimmed for brevity

✅ Quickstart completed successfully!

🗑️  Cleaned up resources.

了解程式碼

既然你已經掌握了程式碼，讓我們來拆解一下關鍵組成部分：

建立搜尋索引
將文件上傳至索引
建立知識來源
建立知識庫
執行檢索管道
檢視回應、活動及參考資料
繼續對話

建立搜尋索引

在 Azure AI 搜尋服務中，索引是一組結構化的資料集合。以下程式碼定義了一個名為 earth_at_night的索引。

索引結構包含文件識別與頁面內容、嵌入及編號欄位。該架構還包含語意排序與向量搜尋的配置，後者利用你的 text-embedding-3-large 部署方式將文字向量化並根據語意相似度匹配文件。

const index = {
    name: 'earth_at_night',
    fields: [
        {
            name: "id",
            type: "Edm.String",
            key: true,
            filterable: true,
            sortable: true,
            facetable: true
        },
        {
            name: "page_chunk",
            type: "Edm.String",
            searchable: true,
            filterable: false,
            sortable: false,
            facetable: false
        },
        {
            name: "page_embedding_text_3_large",
            type: "Collection(Edm.Single)",
            searchable: true,
            filterable: false,
            sortable: false,
            facetable: false,
            vectorSearchDimensions: 3072,
            vectorSearchProfileName: "hnsw_text_3_large"
        },
        {
            name: "page_number",
            type: "Edm.Int32",
            filterable: true,
            sortable: true,
            facetable: true
        }
    ],
    vectorSearch: {
        profiles: [
            {
                name: "hnsw_text_3_large",
                algorithmConfigurationName: "alg",
                vectorizerName: "azure_openai_text_3_large"
            }
        ],
        algorithms: [
            {
                name: "alg",
                kind: "hnsw"
            }
        ],
        vectorizers: [
            {
                vectorizerName: "azure_openai_text_3_large",
                kind: "azureOpenAI",
                parameters: {
                    resourceUrl: process.env.AZURE_OPENAI_ENDPOINT,
                    deploymentId: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT,
                    modelName: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT
                }
            }
        ]
    },
    semanticSearch: {
        defaultConfigurationName: "semantic_config",
        configurations: [
            {
                name: "semantic_config",
                prioritizedFields: {
                    contentFields: [
                        { name: "page_chunk" }
                    ]
                }
            }
        ]
    }
};

const credential = new DefaultAzureCredential();

const searchIndexClient = new SearchIndexClient(process.env.AZURE_SEARCH_ENDPOINT, credential);
const searchClient = new SearchClient(process.env.AZURE_SEARCH_ENDPOINT, 'earth_at_night', credential);

await searchIndexClient.createOrUpdateIndex(index);

參考資料：SearchField、 VectorSearch、 SemanticSearch、 SearchIndex、 SearchIndexClient、 SearchClient、 DefaultAzureCredential

將文件上傳至索引

const response = await fetch("https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json");

if (!response.ok) {
    throw new Error(`Failed to fetch documents: ${response.status} ${response.statusText}`);
}
const documents = await response.json();

const bufferedClient = new SearchIndexingBufferedSender(
    searchClient,
    documentKeyRetriever,
    {
        autoFlush: true,
    },
);

await bufferedClient.uploadDocuments(documents);
await bufferedClient.flush();
await bufferedClient.dispose();

console.log(`Waiting for indexing to complete...`);
console.log(`Expected documents: ${documents.length}`);
await delay(WAIT_TIME);

let count = await searchClient.getDocumentsCount();
console.log(`Current indexed count: ${count}`);

while (count !== documents.length) {
    await delay(WAIT_TIME);
    count = await searchClient.getDocumentsCount();
    console.log(`Current indexed count: ${count}`);
}

console.log(`✓ All ${documents.length} documents indexed successfully!`);

參考資料：SearchIndexingBufferedSender

建立知識來源

知識來源是可重複使用的來源資料參考。以下程式碼定義了一個針對該earth-knowledge-source索引的earth-at-night知識來源。

sourceDataFields 指定引用引用中包含哪些索引欄位。此範例僅包含人類可讀欄位，以避免回應中冗長且無法解釋的嵌入。

await searchIndexClient.createKnowledgeSource({
    name: 'earth-knowledge-source',
    description: "Knowledge source for Earth at Night e-book content",
    kind: "searchIndex",
    searchIndexParameters: {
        searchIndexName: 'earth_at_night',
        sourceDataFields: [
            { name: "id" },
            { name: "page_number" }
        ]
    }
});

console.log(`✅ Knowledge source 'earth-knowledge-source' created successfully.`);

參考資料：SearchIndexKnowledgeSource

建立知識庫

若要在查詢時以 earth-knowledge-source 和部署 gpt-5-mini 為目標，您需要知識庫。以下程式碼定義了一個名為 earth-knowledge-base的知識庫。

outputMode（預覽）設為 answerSynthesis，即可啟用自然語言回答，引用檢索到的文件並遵循提供的 answerInstructions。

await searchIndexClient.createKnowledgeBase({
    name: 'earth-knowledge-base',
    knowledgeSources: [
        {
            name: 'earth-knowledge-source'
        }
    ],
    models: [
        {
            kind: "azureOpenAI",
            azureOpenAIParameters: {
                resourceUrl: process.env.AZURE_OPENAI_ENDPOINT,
                deploymentId: process.env.AZURE_OPENAI_GPT_DEPLOYMENT,
                modelName: process.env.AZURE_OPENAI_GPT_DEPLOYMENT
            }
        }
    ],
    outputMode: "answerSynthesis",
    answerInstructions: "Provide a two sentence concise and informative answer based on the retrieved documents."
});

console.log(`✅ Knowledge base 'earth-knowledge-base' created successfully.`);

參考資料：知識庫

執行檢索流程

你已經準備好運行代理檢索程序了。以下程式碼會將兩部分的使用者查詢發送到 earth-knowledge-base，該查詢：

分析整個對話，推斷使用者的資訊需求。
將複合查詢分解為聚焦子查詢。
針對您的知識來源並行執行子查詢。
使用語意排名器重新排序並過濾結果。
將最優秀的結果綜合成自然語言的答案。

retrievalReasoningEffort（預覽）設定為 low，以控制用於查詢規劃的推理量。

const knowledgeRetrievalClient = new KnowledgeRetrievalClient(
    process.env.AZURE_SEARCH_ENDPOINT,
    'earth-knowledge-base',
    credential
)

const query1 = `Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?`;

const retrievalRequest = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text",
                    text: query1
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex",
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" }
};

const result = await knowledgeRetrievalClient.retrieve(retrievalRequest);

參考資料：KnowledgeRetrievalClient， KnowledgeBaseRetrievalRequest

檢視回應、活動及參考資料

以下程式碼顯示檢索管線的回應、活動與參考，其中：

Answer 提供一個綜合的、由大型語言模型生成的答案，並引用所檢索文件來回應查詢。當未啟用答案綜合時，本節內容會直接從文件中擷取。
Activities 追蹤檢索過程中所採取的步驟，包括部署 gpt-5-mini 產生的子查詢，以及用於語意排序、查詢規劃和答案綜合的標記。
References 列出對回應有貢獻的文件，每份文件皆以 docKey。

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result.response && result.response.length > 0) {
    result.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result.activity) {
    console.log("\nActivities:");
    result.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result.references) {
    console.log("\nReferences:");
    result.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

繼續對話

const query2 = "How do I find lava at night?";
console.log(`\n❓ Follow-up question: ${query2}`);

const retrievalRequest2 = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text",
                    text: query2
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex",
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" }
};

const result2 = await knowledgeRetrievalClient.retrieve(retrievalRequest2);

檢視新的回應、活動及參考資料

以下程式碼顯示來自檢索管線的新回應、活動及參考資料。

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result2.response && result2.response.length > 0) {
    result2.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result2.activity) {
    console.log("\nActivities:");
    result2.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result2.references) {
    console.log("\nReferences:");
    result2.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

清理資源

當您在自己的訂用帳戶中工作時，建議您在完成專案後移除不再需要的資源。若您讓資源繼續執行，則可能會產生費用。

在Azure入口網站中，從左側窗格選擇 所有資源或 資源群組以尋找並管理資源。你可以單獨刪除資源，或是一次性刪除資源群組，移除所有資源。

否則，以下程式碼 index.js 會刪除你在這個快速入門中建立的物件。

await searchIndexClient.deleteKnowledgeBase('earth-knowledge-base');
await searchIndexClient.deleteKnowledgeSource('earth-knowledge-source');
await searchIndexClient.deleteIndex('earth_at_night');

console.log(`\n🗑️  Cleaned up resources.`);

雖然你可以使用自己的資料，但這個快速入門使用了 NASA 電子書《Earth at Night》電子書中的範例 JSON 文件。

提示

想馬上開始嗎？請於GitHub下載原始碼。

先決條件

一個有有效訂閱的 Azure 帳號。免費註冊帳號。
在任何提供 Agent 擷取功能的區域中的 Azure AI 搜尋服務服務。此快速入門需要 Basic 等級或以上的管理身份支援。
一個Microsoft Foundry 專案和資源。當你建立專案時，資源會自動建立。
用於文本到向量轉換的嵌入模型已部署到你的專案中。你可以使用任何 text-embedding 模型，例如 text-embedding-3-large。
LLM 已部署至您的專案，用於查詢規劃與產生答案。你可以使用任何支援的大型語言模型，例如 gpt-5-mini。
Python 3.8或更高。
Visual Studio Code，並搭配Python及Jupyter擴充功能。
git 來複製樣本庫。
Azure CLI 用於與 Microsoft Entra ID 的無金鑰認證。

設定存取權限

若要為此快速入門設定存取：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 啟用基於角色的存取權限。
2. 建立系統指派的管理身份。
3. 為您的使用者帳號指派以下角色：搜尋服務貢獻者、搜尋索引資料貢獻者，以及搜尋索引資料閱讀器。
在您的 Microsoft Foundry 資源中，將 Cognitive Services User 指派給您的搜尋服務的管理身份。

重要

代理檢索有兩種基於代幣的收費模式：

Azure AI 搜尋服務用於 Agent 擷取的計費。
來自 Azure OpenAI，使用查詢規劃和答案合成的計費。

欲了解更多資訊，請參閱區域可用性、限額及計費。

取得端點

若要取得此快速入門的端點：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 從左側窗格選擇 「概覽」。
2. 複製網址，應該看起來像 https://my-service.search.windows.net。
關於你的 Microsoft Foundry 資源：
1. 從左側窗格選擇 資源管理>鍵與端點。
2. 複製 OpenAI 分頁上的網址，應該會看起來像 https://my-resource.openai.azure.com/。

設定環境

用 Git 複製樣本庫。

git clone https://github.com/Azure-Samples/azure-search-python-samples

到快速啟動資料夾，用 Visual Studio Code 打開它。

cd azure-search-python-samples/Quickstart-Agentic-Retrieval
code .

在 sample.env 中，將 SEARCH_ENDPOINT 和 AOAI_ENDPOINT 的預留位置值替換為您在取得端點中取得的 URL。
重新命名 sample.env 為 .env。
```
mv sample.env .env
```
打開 quickstart-agentic-retrieval.ipynb。
按 Ctrl+Shift+P，選擇 筆記本：選擇筆記本核心，並依照指示建立虛擬環境。選擇 requirements.txt 來指定相依性。

完成後，你應該會在專案目錄中看到一個 .venv 資料夾。
若要使用 Microsoft Entra ID 進行無鑰匙認證，請登入您的 Azure 帳號。如果你有多個訂閱，請選擇包含你 Azure AI 搜尋服務和 Microsoft Foundry 資源的訂閱。
```
az login
```

執行程式碼

執行 Load connections 儲存單元來安裝所需的套件並載入環境變數。
依序執行剩餘儲存格以建立索引、上傳文件、設定知識來源與知識庫，並執行代理檢索查詢。

產出

每個程式碼區塊會將其輸出顯示在筆記本上。以下範例顯示執行所有儲存格後的輸出：

Documents uploaded to index 'earth-at-night' successfully.
Knowledge source 'earth-knowledge-source' created or updated successfully.
Knowledge base 'earth-knowledge-base' created or updated successfully.
Retrieved content from 'earth-knowledge-base' successfully.
response_content:
 Suburban belts brighten more in December because holiday lighting is concentrated in suburbs and outskirts—where yard space and single-family homes allow more displays—while central urban cores already have much higher absolute light levels so their fractional increase is smaller [ref_id:4][ref_id:7].
The Phoenix street grid is sharply visible from space because its regular block pattern plus continuous street, commercial, and corridor lighting (including the diagonal Grand Avenue) produce a bright, grid-like signature at night [ref_id:3][ref_id:0], whereas interstate corridors between Midwestern cities often appear comparatively dim because light is concentrated at urban nodes and ports while long stretches of highway and rivers lack continuous lighting [ref_id:7][ref_id:2].

activity_content:
 [
  {
    "id": 0,
    "type": "modelQueryPlanning",
    "elapsed_ms": 16946,
    "input_tokens": 1354,
    "output_tokens": 906
  },
  {
    "id": 1,
    "type": "searchIndex",
    "elapsed_ms": 887,
    "knowledge_source_name": "earth-knowledge-source",
    "query_time": "2025-11-05T16:17:48.345Z",
    "count": 22,
    "search_index_arguments": {
      "search": "December brightening in satellite nighttime lights: why do suburban belts show larger relative increases in December than urban cores despite higher absolute downtown light levels?"
    }
  },
  ... // Trimmed for brevity
  {
    "id": 4,
    "type": "agenticReasoning",
    "reasoning_tokens": 72191,
    "retrieval_reasoning_effort": {
      "kind": "low"
    }
  },
  {
    "id": 5,
    "type": "modelAnswerSynthesis",
    "elapsed_ms": 22353,
    "input_tokens": 7564,
    "output_tokens": 1645
  }
]

references_content:
 [
  {
    "type": "searchIndex",
    "id": "0",
    "activity_source": 2,
    "source_data": {
      "id": "earth_at_night_508_page_105_verbalized",
      "page_chunk": "# Urban Structure\n\n## March 16, 2013\n\n### Phoenix Metropolitan Area at Night\n\nThis figure presents a nighttime satellite view of the Phoenix metropolitan area, highlighting urban structure and transport corridors. City lights illuminate the layout of several cities and major thoroughfares.\n\n**Labeled Urban Features:**\n\n- **Phoenix:** Central and brightest area in the right-center of the image.\n- **Glendale:** Located to the west of Phoenix, this city is also brightly lit.\n- **Peoria:** Further northwest, this area is labeled and its illuminated grid is seen.\n- **Grand Avenue:** Clearly visible as a diagonal, brightly lit thoroughfare running from Phoenix through Glendale and Peoria.\n- **Salt River Channel:** Identified in the southeast portion, running through illuminated sections.\n- **Phoenix Mountains:** Dark, undeveloped region to the northeast of Phoenix.\n- **Agricultural Fields:** Southwestern corner of the image, grid patterns are visible but with much less illumination, indicating agricultural land use.\n\n**Additional Notes:**\n\n- The overall pattern shows a grid-like urban development typical of western U.S. cities, with scattered bright nodes at major intersections or city centers.\n- There is a clear transition from dense urban development to sparsely populated or agricultural land, particularly evident towards the bottom and left of the image.\n- The illuminated areas follow the existing road and street grids, showcasing the extensive spread of the metropolitan area.\n\n**Figure Description:**  \nA satellite nighttime image captured on March 16, 2013, showing Phoenix and surrounding areas (including Glendale and Peoria). Major landscape and infrastructural features, such as the Phoenix Mountains, Grand Avenue, the Salt River Channel, and agricultural fields, are labeled. The image reveals the extent of urbanization and the characteristic street grid illuminated by city lights.\n\n---\n\nPage 89",
      "page_number": 105
    },
    "reranker_score": 2.722408,
    "doc_key": "earth_at_night_508_page_105_verbalized"
  },
  ... // Trimmed for brevity
]
Retrieved content from 'earth-knowledge-base' successfully.
response_content:
  ... // Trimmed for brevity

activity_content:
 [
  ... // Trimmed for brevity
]

references_content:
 [
  ... // Trimmed for brevity
]
Knowledge base 'earth-knowledge-base' deleted successfully.
Knowledge source 'earth-knowledge-source' deleted successfully.
Index 'earth-at-night' deleted successfully.

了解程式碼

註

本節的程式碼片段可能已經過修改以提升可讀性。完整工作範例請參考原始碼。

現在你已經執行過程式碼，讓我們來拆解幾個關鍵步驟：

建立搜尋索引
將文件上傳至索引
建立知識來源
建立知識庫
設定訊息
執行檢索管道
繼續對話

建立搜尋索引

在 Azure AI 搜尋服務中，索引是一組結構化的資料集合。以下程式碼定義了一個名為 earth-at-night的索引。

# Create an index
azure_openai_token_provider = get_bearer_token_provider(credential, "https://ai.azure.com/.default")

index = SearchIndex(
    name=index_name,
    fields=[
        SearchField(name="id", type="Edm.String", key=True, filterable=True, sortable=True, facetable=True),
        SearchField(name="page_chunk", type="Edm.String", filterable=False, sortable=False, facetable=False),
        SearchField(name="page_embedding_text_3_large", type="Collection(Edm.Single)", stored=False, vector_search_dimensions=3072, vector_search_profile_name="hnsw_text_3_large"),
        SearchField(name="page_number", type="Edm.Int32", filterable=True, sortable=True, facetable=True)
    ],
    vector_search=VectorSearch(
        profiles=[VectorSearchProfile(name="hnsw_text_3_large", algorithm_configuration_name="alg", vectorizer_name="azure_openai_text_3_large")],
        algorithms=[HnswAlgorithmConfiguration(name="alg")],
        vectorizers=[
            AzureOpenAIVectorizer(
                vectorizer_name="azure_openai_text_3_large",
                parameters=AzureOpenAIVectorizerParameters(
                    resource_url=aoai_endpoint,
                    deployment_name=aoai_embedding_deployment,
                    model_name=aoai_embedding_model
                )
            )
        ]
    ),
    semantic_search=SemanticSearch(
        default_configuration_name="semantic_config",
        configurations=[
            SemanticConfiguration(
                name="semantic_config",
                prioritized_fields=SemanticPrioritizedFields(
                    content_fields=[
                        SemanticField(field_name="page_chunk")
                    ]
                )
            )
        ]
    )
)

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.create_or_update_index(index)
print(f"Index '{index_name}' created or updated successfully.")

參考資料：SearchField、 VectorSearch、 SemanticSearch、 SearchIndex、 SearchIndexClient

將文件上傳至索引

# Upload documents
url = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json"
documents = requests.get(url).json()

with SearchIndexingBufferedSender(endpoint=search_endpoint, index_name=index_name, credential=credential) as client:
    client.upload_documents(documents=documents)

print(f"Documents uploaded to index '{index_name}' successfully.")

參考資料：SearchIndexingBufferedSender

建立知識來源

知識來源是可重複使用的來源資料參考。以下程式碼定義了一個針對該earth-knowledge-source索引的earth-at-night知識來源。

source_data_fields 指定引用引用中包含哪些索引欄位。此範例僅包含人類可讀欄位，以避免回應中冗長且無法解釋的嵌入。

# Create a knowledge source
ks = SearchIndexKnowledgeSource(
    name=knowledge_source_name,
    description="Knowledge source for Earth at night data",
    search_index_parameters=SearchIndexKnowledgeSourceParameters(
        search_index_name=index_name,
        source_data_fields=[SearchIndexFieldReference(name="id"), SearchIndexFieldReference(name="page_number")]
    ),
)

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.create_or_update_knowledge_source(knowledge_source=ks)
print(f"Knowledge source '{knowledge_source_name}' created or updated successfully.")

參考資料：SearchIndexKnowledgeSource

建立知識庫

若要在查詢時以 earth-knowledge-source 和部署 gpt-5-mini 為目標，您需要知識庫。以下程式碼定義了一個名為 earth-knowledge-base的知識庫。

output_mode（預覽）設為 answerSynthesis，即可啟用自然語言回答，這些回答會引用檢索到的文件，並遵循所提供的 answer_instructions。

# Create a knowledge base
aoai_params = AzureOpenAIVectorizerParameters(
    resource_url=aoai_endpoint,
    deployment_name=aoai_gpt_deployment,
    model_name=aoai_gpt_model,
)

knowledge_base = KnowledgeBase(
    name=knowledge_base_name,
    models=[KnowledgeBaseAzureOpenAIModel(azure_open_ai_parameters=aoai_params)],
    knowledge_sources=[
        KnowledgeSourceReference(
            name=knowledge_source_name
        )
    ],
    output_mode="answerSynthesis",
    answer_instructions="Provide a 2 sentence concise and informative answer based on the retrieved documents."
)

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.create_or_update_knowledge_base(knowledge_base)
print(f"Knowledge base '{knowledge_base_name}' created or updated successfully.")

參考資料：知識庫

設定訊息

以下程式碼會產生系統訊息，指示 earth-knowledge-base 在夜間回答地球相關問題，當無法回答時則回應「我不知道」。

# Set up messages
instructions = """
A Q&A agent that can answer questions about the Earth at night.
If you don't have the answer, respond with "I don't know".
"""

messages = [
    {
        "role": "system",
        "content": instructions
    }
]

執行檢索流程

你已經準備好運行代理檢索程序了。以下程式碼會將兩部分的使用者查詢發送到 earth-knowledge-base，該查詢：

分析整個對話，推斷使用者的資訊需求。
將複合查詢分解為聚焦子查詢。
針對您的知識來源並行執行子查詢。
使用語意排名器重新排序並過濾結果。
將最優秀的結果綜合成自然語言的答案。

retrieval_reasoning_effort（預覽）設為 low，以控制用於查詢規劃的推理量。

# Run agentic retrieval
agent_client = KnowledgeBaseRetrievalClient(endpoint=search_endpoint, knowledge_base_name=knowledge_base_name, credential=credential)
query_1 = """
    Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown?
    Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?
    """

messages.append({
    "role": "user",
    "content": query_1
})

req = KnowledgeBaseRetrievalRequest(
    messages=[
        KnowledgeBaseMessage(
            role=m["role"],
            content=[KnowledgeBaseMessageTextContent(text=m["content"])]
        ) for m in messages if m["role"] != "system"
    ],
    knowledge_source_params=[
        SearchIndexKnowledgeSourceParams(
            knowledge_source_name=knowledge_source_name,
            include_references=True,
            include_reference_source_data=True,
            always_query_source=True
        )
    ],
    include_activity=True,
    retrieval_reasoning_effort=KnowledgeRetrievalLowReasoningEffort()
)

result = agent_client.retrieve(retrieval_request=req)
print(f"Retrieved content from '{knowledge_base_name}' successfully.")

參考資料：KnowledgeBaseRetrievalClient， KnowledgeBaseRetrievalRequest

檢視回應、活動及參考資料

以下程式碼顯示檢索管線的回應、活動與參考，其中：

response_contents 提供一個綜合的、由大型語言模型生成的答案，並引用所檢索文件來回應查詢。當未啟用答案綜合時，本節內容會直接從文件中擷取。
activity_contents 追蹤檢索過程中所採取的步驟，包括部署 gpt-5-mini 產生的子查詢，以及用於語意排序、查詢規劃和答案綜合的標記。
references_contents 列出對回應有貢獻的文件，每份文件皆以 doc_key。

# Display the response, activity, and references
response_contents = []
activity_contents = []
references_contents = []

response_parts = []
for resp in result.response:
    for content in resp.content:
        response_parts.append(content.text)
response_content = "\n\n".join(response_parts) if response_parts else "No response found on 'result'"

response_contents.append(response_content)

# Print the three string values
print("response_content:\n", response_content, "\n")

messages.append({
    "role": "assistant",
    "content": response_content
})

if result.activity:
    activity_content = json.dumps([a.as_dict() for a in result.activity], indent=2)
else:
    activity_content = "No activity found on 'result'"
    
activity_contents.append(activity_content)
print("activity_content:\n", activity_content, "\n")

if result.references:
    references_content = json.dumps([r.as_dict() for r in result.references], indent=2)
else:
    references_content = "No references found on 'result'"
    
references_contents.append(references_content)
print("references_content:\n", references_content)

繼續對話

# Continue the conversation
query_2 = "How do I find lava at night?"
messages.append({
    "role": "user",
    "content": query_2
})

req = KnowledgeBaseRetrievalRequest(
    messages=[
        KnowledgeBaseMessage(
            role=m["role"],
            content=[KnowledgeBaseMessageTextContent(text=m["content"])]
        ) for m in messages if m["role"] != "system"
    ],
    knowledge_source_params=[
        SearchIndexKnowledgeSourceParams(
            knowledge_source_name=knowledge_source_name,
            include_references=True,
            include_reference_source_data=True,
            always_query_source=True
        )
    ],
    include_activity=True,
    retrieval_reasoning_effort=KnowledgeRetrievalLowReasoningEffort()
)

result = agent_client.retrieve(retrieval_request=req)
print(f"Retrieved content from '{knowledge_base_name}' successfully.")

檢視新的回應、活動及參考資料

以下程式碼顯示來自檢索管線的新回應、活動及參考資料。

# Display the new retrieval response, activity, and references
response_parts = []
for resp in result.response:
    for content in resp.content:
        response_parts.append(content.text)
response_content = "\n\n".join(response_parts) if response_parts else "No response found on 'result'"

response_contents.append(response_content)

# Print the three string values
print("response_content:\n", response_content, "\n")

if result.activity:
    activity_content = json.dumps([a.as_dict() for a in result.activity], indent=2)
else:
    activity_content = "No activity found on 'result'"
    
activity_contents.append(activity_content)
print("activity_content:\n", activity_content, "\n")

if result.references:
    references_content = json.dumps([r.as_dict() for r in result.references], indent=2)
else:
    references_content = "No references found on 'result'"
    
references_contents.append(references_content)
print("references_content:\n", references_content)

清理資源

當您在自己的訂用帳戶中工作時，建議您在完成專案後移除不再需要的資源。若您讓資源繼續執行，則可能會產生費用。

在Azure入口網站中，從左側窗格選擇 所有資源或 資源群組以尋找並管理資源。你可以單獨刪除資源，或是一次性刪除資源群組，移除所有資源。

否則，以下程式碼 quickstart-agentic-retrieval.ipynb 會刪除你在這個快速入門中建立的物件。

刪除知識庫

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.delete_knowledge_base(knowledge_base_name)
print(f"Knowledge base '{knowledge_base_name}' deleted successfully.")

刪除知識來源

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.delete_knowledge_source(knowledge_source=knowledge_source_name)
print(f"Knowledge source '{knowledge_source_name}' deleted successfully.")

刪除搜尋索引

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.delete_index(index_name)
print(f"Index '{index_name}' deleted successfully.")

雖然你可以使用自己的資料，但這個快速入門使用了 NASA 電子書《Earth at Night》電子書中的範例 JSON 文件。

提示

想馬上開始嗎？請於GitHub下載原始碼。

先決條件

一個有有效訂閱的 Azure 帳號。免費註冊帳號。
在任何提供 Agent 擷取功能的區域中的 Azure AI 搜尋服務服務。此快速入門需要 Basic 等級或以上的管理身份支援。
一個Microsoft Foundry 專案和資源。當你建立專案時，資源會自動建立。
用於文本到向量轉換的嵌入模型已部署到你的專案中。你可以使用任何 text-embedding 模型，例如 text-embedding-3-large。
LLM 已部署至您的專案，用於查詢規劃與產生答案。你可以使用任何支援的大型語言模型，例如 gpt-5-mini。
Node.js 20 LTS 或更新版本。
TypeScript 將 TypeScript 編譯成 JavaScript。
git 來複製樣本庫。
Azure CLI 用於與 Microsoft Entra ID 的無金鑰認證。

設定存取權限

若要為此快速入門設定存取：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 啟用基於角色的存取權限。
2. 建立系統指派的管理身份。
3. 為您的使用者帳號指派以下角色：搜尋服務貢獻者、搜尋索引資料貢獻者，以及搜尋索引資料閱讀器。
在您的 Microsoft Foundry 資源中，將 Cognitive Services User 指派給您的搜尋服務的管理身份。

重要

代理檢索有兩種基於代幣的收費模式：

Azure AI 搜尋服務用於 Agent 擷取的計費。
來自 Azure OpenAI，使用查詢規劃和答案合成的計費。

欲了解更多資訊，請參閱區域可用性、限額及計費。

取得端點

若要取得此快速入門的端點：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 從左側窗格選擇 「概覽」。
2. 複製網址，應該看起來像 https://my-service.search.windows.net。
關於你的 Microsoft Foundry 資源：
1. 從左側窗格選擇 資源管理>鍵與端點。
2. 複製 OpenAI 分頁上的網址，應該會看起來像 https://my-resource.openai.azure.com/。

設定環境

用 Git 複製樣本庫。

git clone https://github.com/Azure-Samples/azure-search-javascript-samples

進入快速啟動資料夾。

cd azure-search-javascript-samples/quickstart-agentic-retrieval-ts

在 sample.env 中，將 AZURE_SEARCH_ENDPOINT 和 AZURE_OPENAI_ENDPOINT 的預留位置值替換為您在取得端點中取得的 URL。
重新命名 sample.env 為 .env。
```
mv sample.env .env
```
安裝相依性。
```
npm install
```
安裝完成後，你會看到專案目錄中有一個 node_modules 資料夾。
將 TypeScript 檔案編譯成 JavaScript。
```
npm run build
```
若要使用 Microsoft Entra ID 進行無鑰匙認證，請登入您的 Azure 帳號。如果你有多個訂閱，請選擇包含你 Azure AI 搜尋服務和 Microsoft Foundry 資源的訂閱。
```
az login
```

執行程式碼

執行應用程式建立索引、上傳文件、設定知識來源與知識庫，並執行代理式檢索查詢。

npm start

註

此指令會執行編譯的.js檔案，這些檔案位於dist資料夾中。 Node.js 需要先將 TypeScript 程式碼轉譯成 JavaScript 才能執行，這也是你之前執行 npm run build的原因。

產出

申請的輸出應與以下內容相似：

Waiting for indexing to complete...
Expected documents: 194
Current indexed count: 194
✓ All 194 documents indexed successfully!
✅ Knowledge source 'earth-knowledge-source' created successfully.
✅ Knowledge base 'earth-knowledge-base' created successfully.

📝 ANSWER:
────────────────────────────────────────────────────────────────────────────────
Suburban belts show larger December brightening (20–50% increases) because residential holiday lighting and seasonal decorations are concentrated there, so relative (fractional) increases over the baseline are bigger even though absolute downtown radiances remain higher; urban cores already emit strong baseline light while many suburbs add a large seasonal increment visible in VIIRS DNB observations [ref_id:0][ref_id:1]. The Phoenix street grid appears sharply from space because continuous, street‑oriented lighting with regular residential lot spacing and little vegetative masking produces strong, linear emissions, whereas long interstate stretches between Midwestern cities have sparser, access‑limited lighting, fewer adjacent developments and more shielded fixtures so they register comparatively dim on night‑light sensors like VIIRS/DNB [ref_id:0][ref_id:1].
────────────────────────────────────────────────────────────────────────────────

Activities:
Activity Type: modelQueryPlanning
{
  "id": 0,
  "type": "modelQueryPlanning",
  "elapsedMs": 5883,
  "inputTokens": 1489,
  "outputTokens": 326
}
Activity Type: searchIndex
{
  "id": 1,
  "type": "searchIndex",
  "elapsedMs": 527,
  "knowledgeSourceName": "earth-knowledge-source",
  "queryTime": "2025-12-19T15:38:23.462Z",
  "count": 1,
  "searchIndexArguments": {
    "search": "December brightening suburban belts vs urban cores light pollution causes December increase in night lights suburban vs urban",
    "filter": null,
    "sourceDataFields": [
      {
        "name": "page_chunk"
      },
      {
        "name": "id"
      },
      {
        "name": "page_number"
      }
    ],
    "searchFields": [],
    "semanticConfigurationName": "semantic_config"
  }
}
... // Trimmed for brevity
Activity Type: agenticReasoning
{
  "id": 4,
  "type": "agenticReasoning",
  "reasoningTokens": 70397,
  "retrievalReasoningEffort": {
    "kind": "low"
  }
}
Activity Type: modelAnswerSynthesis
{
  "id": 5,
  "type": "modelAnswerSynthesis",
  "elapsedMs": 4908,
  "inputTokens": 4013,
  "outputTokens": 187
}

References:
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "0",
  "activitySource": 3,
  "sourceData": {
    "id": "earth_at_night_508_page_174_verbalized",
    "page_chunk": "<!-- PageHeader=\"Holiday Lights\" -->\n\n## Holiday Lights\n\n### Bursting with Holiday Energy-United States\n\nNASA researchers found that nighttime lights in the United States shine 20 to 50 percent brighter in December due to holiday light displays and other activities during Christmas and New Year's when compared to light output during the rest of the year.\n\nThe next five maps (see also pages 161-163), created using data from the VIIRS DNB on the Suomi NPP satellite, show changes in lighting intensity and location around many major cities, comparing the nighttime light signals from December 2012 and beyond.\n\n---\n\n#### Figure 1. Location Overview\n\nA map of the western hemisphere with a marker indicating the mid-Atlantic region of the eastern United States, where the study of holiday lighting intensity was focused.\n\n---\n\n#### Figure 2. Holiday Lighting Intensity: Mid-Atlantic United States (2012–2014)\n\nA map showing Maryland, New Jersey, Delaware, Virginia, West Virginia, Ohio, Kentucky, Tennessee, North Carolina, South Carolina, and surrounding areas. Major cities labeled include Washington, D.C., Richmond, Norfolk, and Raleigh.\n\nThe map uses colors to indicate changes in holiday nighttime lighting intensity between 2012 and 2014:\n\n- **Green/bright areas**: More holiday lighting (areas shining 20–50% brighter in December).\n- **Yellow areas**: No change in lighting.\n- **Dim/grey areas**: Less holiday lighting.\n\nKey observations from the map:\n\n- The Washington, D.C. metropolitan area shows significant increases in lighting during the holidays, extending into Maryland and Virginia.\n- Urban centers such as Richmond (Virginia), Norfolk (Virginia), Raleigh (North Carolina), and clusters in Tennessee and South Carolina also experience notable increases in light intensity during December.\n- Rural areas and the interiors of West Virginia, Kentucky, and North Carolina show little change or less holiday lighting, corresponding to population density and urbanization.\n\n**Legend:**\n\n| Holiday Lighting Change | Color on Map   |\n|------------------------|---------------|\n| More                   | Green/bright  |\n| No Change              | Yellow        |\n| Less                   | Dim/grey      |\n\n_The scale bar indicates a distance of 100 km for reference._\n\n---\n\n<!-- PageFooter=\"158 Earth at Night\" -->",
    "page_number": 174
  },
  "rerankerScore": 2.6692379,
  "docKey": "earth_at_night_508_page_174_verbalized"
}
... // Trimmed for brevity

❓ Follow-up question: How do I find lava at night?

📝 ANSWER:
────────────────────────────────────────────────────────────────────────────────
... // Trimmed for brevity
────────────────────────────────────────────────────────────────────────────────

Activities:
... // Trimmed for brevity

References:
... // Trimmed for brevity

✅ Quickstart completed successfully!

🗑️  Cleaned up resources.

了解程式碼

既然你已經掌握了程式碼，讓我們來拆解一下關鍵組成部分：

建立搜尋索引
將文件上傳至索引
建立知識來源
建立知識庫
執行檢索管道
檢視回應、活動及參考資料
繼續對話

建立搜尋索引

在 Azure AI 搜尋服務中，索引是一組結構化的資料集合。以下程式碼定義了一個名為 earth_at_night的索引。

const index: SearchIndex = {
    name: 'earth_at_night',
    fields: [
        {
            name: "id",
            type: "Edm.String",
            key: true,
            filterable: true,
            sortable: true,
            facetable: true
        } as SearchField,
        {
            name: "page_chunk",
            type: "Edm.String",
            searchable: true,
            filterable: false,
            sortable: false,
            facetable: false
        } as SearchField,
        {
            name: "page_embedding_text_3_large",
            type: "Collection(Edm.Single)",
            searchable: true,
            filterable: false,
            sortable: false,
            facetable: false,
            vectorSearchDimensions: 3072,
            vectorSearchProfileName: "hnsw_text_3_large"
        } as SearchField,
        {
            name: "page_number",
            type: "Edm.Int32",
            filterable: true,
            sortable: true,
            facetable: true
        } as SearchField
    ],
    vectorSearch: {
        profiles: [
            {
                name: "hnsw_text_3_large",
                algorithmConfigurationName: "alg",
                vectorizerName: "azure_openai_text_3_large"
            } as VectorSearchProfile
        ],
        algorithms: [
            {
                name: "alg",
                kind: "hnsw"
            } as HnswAlgorithmConfiguration
        ],
        vectorizers: [
            {
                vectorizerName: "azure_openai_text_3_large",
                kind: "azureOpenAI",
                parameters: {
                    resourceUrl: process.env.AZURE_OPENAI_ENDPOINT!,
                    deploymentId: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT!,
                    modelName: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT!
                } as AzureOpenAIParameters
            } as AzureOpenAIVectorizer
        ]
    } as VectorSearch,
    semanticSearch: {
        defaultConfigurationName: "semantic_config",
        configurations: [
            {
                name: "semantic_config",
                prioritizedFields: {
                    contentFields: [
                        { name: "page_chunk" } as SemanticField
                    ]
                } as SemanticPrioritizedFields
            } as SemanticConfiguration
        ]
    } as SemanticSearch
};

const credential = new DefaultAzureCredential();

const searchIndexClient = new SearchIndexClient(process.env.AZURE_SEARCH_ENDPOINT!, credential);
const searchClient = new SearchClient<EarthAtNightDocument>(process.env.AZURE_SEARCH_ENDPOINT!, 'earth_at_night', credential);

await searchIndexClient.createOrUpdateIndex(index);

參考資料：SearchField、 VectorSearch、 SemanticSearch、 SearchIndex、 SearchIndexClient、 SearchClient、 DefaultAzureCredential

將文件上傳至索引

const response = await fetch("https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json");

if (!response.ok) {
    throw new Error(`Failed to fetch documents: ${response.status} ${response.statusText}`);
}
const documents = await response.json() as any[];

const bufferedClient = new SearchIndexingBufferedSender<EarthAtNightDocument>(
    searchClient,
    documentKeyRetriever,
    {
        autoFlush: true,
    },
);

await bufferedClient.uploadDocuments(documents);
await bufferedClient.flush();
await bufferedClient.dispose();

console.log(`Waiting for indexing to complete...`);
console.log(`Expected documents: ${documents.length}`);
await delay(WAIT_TIME);

let count = await searchClient.getDocumentsCount();
console.log(`Current indexed count: ${count}`);

while (count !== documents.length) {
    await delay(WAIT_TIME);
    count = await searchClient.getDocumentsCount();
    console.log(`Current indexed count: ${count}`);
}

console.log(`✓ All ${documents.length} documents indexed successfully!`);

參考資料：SearchIndexingBufferedSender

建立知識來源

知識來源是可重複使用的來源資料參考。以下程式碼定義了一個針對該earth-knowledge-source索引的earth-at-night知識來源。

sourceDataFields 指定引用引用中包含哪些索引欄位。此範例僅包含人類可讀欄位，以避免回應中冗長且無法解釋的嵌入。

await searchIndexClient.createKnowledgeSource({
    name: 'earth-knowledge-source',
    description: "Knowledge source for Earth at Night e-book content",
    kind: "searchIndex",
    searchIndexParameters: {
        searchIndexName: 'earth_at_night',
        sourceDataFields: [
            { name: "id" },
            { name: "page_number" }
        ]
    }
});

console.log(`✅ Knowledge source 'earth-knowledge-source' created successfully.`);

參考資料：SearchIndexKnowledgeSource

建立知識庫

若要在查詢時以 earth-knowledge-source 和部署 gpt-5-mini 為目標，您需要知識庫。以下程式碼定義了一個名為 earth-knowledge-base的知識庫。

outputMode（預覽）設定為 answerSynthesis，以啟用自然語言回答，這些回答會引用檢索到的文件，並遵循所提供的 answerInstructions。

await searchIndexClient.createKnowledgeBase({
    name: 'earth-knowledge-base',
    knowledgeSources: [
        {
            name: 'earth-knowledge-source'
        }
    ],
    models: [
        {
            kind: "azureOpenAI",
            azureOpenAIParameters: {
                resourceUrl: process.env.AZURE_OPENAI_ENDPOINT!,
                deploymentId: process.env.AZURE_OPENAI_GPT_DEPLOYMENT!,
                modelName: process.env.AZURE_OPENAI_GPT_DEPLOYMENT!
            }
        }
    ],
    outputMode: "answerSynthesis" as KnowledgeRetrievalOutputMode,
    answerInstructions: "Provide a two sentence concise and informative answer based on the retrieved documents."
});

console.log(`✅ Knowledge base 'earth-knowledge-base' created successfully.`);

參考資料：知識庫

執行檢索流程

你已經準備好運行代理檢索程序了。以下程式碼會將兩部分的使用者查詢發送到 earth-knowledge-base，該查詢：

分析整個對話，推斷使用者的資訊需求。
將複合查詢分解為聚焦子查詢。
針對您的知識來源並行執行子查詢。
使用語意排名器重新排序並過濾結果。
將最優秀的結果綜合成自然語言的答案。

retrievalReasoningEffort（預覽）會設為 low，以控制用於查詢規劃的推理量。

const knowledgeRetrievalClient = new KnowledgeRetrievalClient(
    process.env.AZURE_SEARCH_ENDPOINT!,
    'earth-knowledge-base',
    credential
);

const query1 = `Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?`;

const retrievalRequest = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text" as const,
                    text: query1
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex" as const,
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" as const }
};

const result = await knowledgeRetrievalClient.retrieve(retrievalRequest);

參考資料：KnowledgeRetrievalClient， KnowledgeBaseRetrievalRequest

檢視回應、活動及參考資料

以下程式碼顯示檢索管線的回應、活動與參考，其中：

Answer 提供一個綜合的、由大型語言模型生成的答案，並引用所檢索文件來回應查詢。當未啟用答案綜合時，本節內容會直接從文件中擷取。
Activities 追蹤檢索過程中所採取的步驟，包括部署 gpt-5-mini 產生的子查詢，以及用於語意排序、查詢規劃和答案綜合的標記。
References 列出對回應有貢獻的文件，每份文件皆以 docKey。

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result.response && result.response.length > 0) {
    result.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result.activity) {
    console.log("\nActivities:");
    result.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result.references) {
    console.log("\nReferences:");
    result.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

繼續對話

const query2 = "How do I find lava at night?";
console.log(`\n❓ Follow-up question: ${query2}`);

const retrievalRequest2 = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text" as const,
                    text: query2
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex" as const,
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" as const }
};

const result2 = await knowledgeRetrievalClient.retrieve(retrievalRequest2);

檢視新的回應、活動及參考資料

以下程式碼顯示來自檢索管線的新回應、活動及參考資料。

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result2.response && result2.response.length > 0) {
    result2.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result2.activity) {
    console.log("\nActivities:");
    result2.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result2.references) {
    console.log("\nReferences:");
    result2.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

console.log("\n✅ Quickstart completed successfully!");

清理資源

當您在自己的訂用帳戶中工作時，建議您在完成專案後移除不再需要的資源。若您讓資源繼續執行，則可能會產生費用。

在Azure入口網站中，從左側窗格選擇 所有資源或 資源群組以尋找並管理資源。你可以單獨刪除資源，或是一次性刪除資源群組，移除所有資源。

否則，以下程式碼 index.ts 會刪除你在這個快速入門中建立的物件。

await searchIndexClient.deleteKnowledgeBase('earth-knowledge-base');
await searchIndexClient.deleteKnowledgeSource('earth-knowledge-source');
await searchIndexClient.deleteIndex('earth_at_night');

console.log(`\n🗑️  Cleaned up resources.`);

雖然你可以使用自己的資料，但這個快速入門使用了 NASA 電子書《Earth at Night》電子書中的範例 JSON 文件。

提示

想馬上開始嗎？請於GitHub下載原始碼。

先決條件

一個有有效訂閱的 Azure 帳號。免費註冊帳號。
在任何提供 Agent 擷取功能的區域中的 Azure AI 搜尋服務服務。此快速入門需要 Basic 等級或以上的管理身份支援。
一個Microsoft Foundry 專案和資源。當你建立專案時，資源會自動建立。
用於文本到向量轉換的嵌入模型已部署到你的專案中。你可以使用任何 text-embedding 模型，例如 text-embedding-3-large。
LLM 已部署至您的專案，用於查詢規劃與產生答案。你可以使用任何支援的大型語言模型，例如 gpt-5-mini。
Visual Studio Code搭配 REST Client 擴充功能。
git 來複製樣本庫。
Azure CLI 用於與 Microsoft Entra ID 的無金鑰認證。

設定存取權限

若要為此快速入門設定存取：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 啟用基於角色的存取權限。
2. 建立系統指派的管理身份。
3. 為您的使用者帳號指派以下角色：搜尋服務貢獻者、搜尋索引資料貢獻者，以及搜尋索引資料閱讀器。
在您的 Microsoft Foundry 資源中，將 Cognitive Services User 指派給您的搜尋服務的管理身份。

重要

代理檢索有兩種基於代幣的收費模式：

Azure AI 搜尋服務用於 Agent 擷取的計費。
來自 Azure OpenAI，使用查詢規劃和答案合成的計費。

欲了解更多資訊，請參閱區域可用性、限額及計費。

取得端點

若要取得此快速入門的端點：

登入 Azure 入口網站。
關於你的 Azure AI 搜尋服務服務：
1. 從左側窗格選擇 「概覽」。
2. 複製網址，應該看起來像 https://my-service.search.windows.net。
關於你的 Microsoft Foundry 資源：
1. 從左側窗格選擇 資源管理>鍵與端點。
2. 複製 OpenAI 分頁上的網址，應該會看起來像 https://my-resource.openai.azure.com/。

設定環境

用 Git 複製樣本庫。

git clone https://github.com/Azure-Samples/azure-search-rest-samples

到快速啟動資料夾，用 Visual Studio Code 打開它。

cd azure-search-rest-samples/Quickstart-agentic-retrieval
code .

在 agentic-retrieval.rest 中，將 @search-url 和 @aoai-url 的預留位置值替換為您在取得端點中取得的 URL。
若要使用 Microsoft Entra ID 進行無鑰匙認證，請登入您的 Azure 帳號。如果你有多個訂閱，請選擇包含你 Azure AI 搜尋服務和 Microsoft Foundry 資源的訂閱。
```
az login
```

使用 Microsoft Entra ID 進行無鑰匙認證時，請產生存取權杖。

az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv

將的 @token 佔位值替換為前一步的標記。

執行程式碼

依序發送每個請求，從 ### Create an index開始。

每個請求都應該回傳一個 200 OK、 201 Created或 204 No Content 狀態碼。如果收到錯誤，請檢查請求是否有錯字，並確保你的令牌有效。

產出

每個請求會根據操作回傳不同的 JSON。鍵輸出來自 ### Run agentic retrieval，應該看起來像以下：

{
  "response": [
    {
      "content": [
        {
          "type": "text",
          "text": "Causes (mechanisms) — seasonal and lighting behavior: Residential suburban areas often show larger relative (percentage) December brightening because many homes turn on more outdoor and indoor lights in winter evenings (longer nights and holiday lighting) compared with their usual baseline, producing a bigger percent increase even if absolute downtown lighting remains higher [ref_id:1][ref_id:0]. Snow and increased surface reflectance in winter amplify light seen from orbit, increasing apparent brightness especially where lights are horizontal and near ground level (e.g., suburban streets and yards) [ref_id:0].\n\nResidential vs commercial/industrial lighting and activity patterns: Urban cores have high absolute lumen outputs from continuous commercial, industrial, and dense street lighting that change less seasonally, so percent brightening is smaller; suburbs have lower baseline light but larger seasonal/holiday additions, so their relative brightening is larger [ref_id:1][ref_id:0].\n\nWhy Phoenix’s nighttime street grid is sharply visible (causes and consequences): The Phoenix metropolitan area has a regular, dense grid of north–south/east–west streets and the diagonal Grand Avenue corridor with continuous street and commercial lighting whose spacing and lumen output produce linear, high-contrast features from low-Earth orbit; bright nodes occur at major intersections and commercial properties, making the grid and corridors stand out in satellite night images [ref_id:1][ref_id:0]. Darker pockets (Phoenix Mountains, Salt River channel, agricultural fields) increase contrast and make lit streets appear sharper [ref_id:1][ref_id:0].\n\nWhy long interstates between Midwestern cities appear comparatively dim: Rural interstate stretches often lack continuous high-intensity lighting (limited lamps, wider spacing, fewer ramps and commercial nodes), use lower-output or shielded fixtures, and have long unlit segments between cities, so linear continuity and contrast are much lower than a densely lit urban grid [ref_id:1][ref_id:0].\n\nSensor and dataset effects (consequences for observed brightness): Night-light datasets and orbital sensors (as used to capture images like the Phoenix photo) emphasize linear, continuous lighting patterns; sensor behaviors such as gain and saturation make very bright urban cores register high absolute values while limiting the apparent dynamic range, so relative changes (percentage brightening) in dimmer suburban areas can appear proportionally larger in some satellite products [ref_id:1][ref_id:0].\n\nOverall: larger December brightening in suburban belts is a percentage effect from seasonal/holiday increases, snow reflectance, and residential lighting behavior on a low baseline, whereas urban cores remain brighter in absolute terms but show smaller relative changes; Phoenix’s dense, continuous street-grid lighting and contrasting dark areas create sharp linear features from space, while long, sparsely lit interstates lack that continuity and contrast and therefore appear dimmer [ref_id:1][ref_id:0]."
        }
      ]
    }
  ],
  "activity": [
    {
      "type": "modelQueryPlanning",
      "id": 0,
      "inputTokens": 1350,
      "outputTokens": 1538,
      "elapsedMs": 20780
    },
    {
      "type": "searchIndex",
      "id": 1,
      "knowledgeSourceName": "earth-knowledge-source",
      "queryTime": "2025-11-05T19:42:09.673Z",
      "count": 0,
      "elapsedMs": 694,
      "searchIndexArguments": {
        "search": "December brightening in satellite night lights: why do suburban belts show larger December brightening than urban cores? causes: snow reflectance, holiday/residential lighting, leaf-off, VIIRS/DMSP sensor saturation",
        "filter": null
      }
    },
    {
      "type": "searchIndex",
      "id": 2,
      "knowledgeSourceName": "earth-knowledge-source",
      "queryTime": "2025-11-05T19:42:09.999Z",
      "count": 2,
      "elapsedMs": 325,
      "searchIndexArguments": {
        "search": "Why is the Phoenix nighttime street grid so sharply visible from space while long stretches of interstate between Midwestern cities remain comparatively dim? factors: streetlight spacing, lighting type/shielding, vegetation/tree cover, land use, VIIRS DNB detection",
        "filter": null
      }
    },
    {
      "type": "agenticReasoning",
      "id": 3,
      "retrievalReasoningEffort": {
        "kind": "low"
      },
      "reasoningTokens": 1566
    },
    {
      "type": "modelAnswerSynthesis",
      "id": 4,
      "inputTokens": 3656,
      "outputTokens": 1909,
      "elapsedMs": 21988
    }
  ],
  "references": [
    {
      "type": "searchIndex",
      "id": "0",
      "activitySource": 2,
      "sourceData": {
        "id": "earth_at_night_508_page_104_verbalized",
        "page_chunk": "<!-- PageHeader=\"Urban Structure\" -->\n\n### Location of Phoenix, Arizona\n\nThe image depicts a globe highlighting the location of Phoenix, Arizona, in the southwestern United States, marked with a blue pinpoint on the map of North America. Phoenix is situated in the central part of Arizona, which is in the southwestern region of the United States.\n\n---\n\n### Grid of City Blocks-Phoenix, Arizona\n\nLike many large urban areas of the central and western United States, the Phoenix metropolitan area is laid out along a regular grid of city blocks and streets. While visible during the day, this grid is most evident at night, when the pattern of street lighting is clearly visible from the low-Earth-orbit vantage point of the ISS.\n\nThis astronaut photograph, taken on March 16, 2013, includes parts of several cities in the metropolitan area, including Phoenix (image right), Glendale (center), and Peoria (left). While the major street grid is oriented north-south, the northwest-southeast oriented Grand Avenue cuts across the three cities at image center. Grand Avenue is a major transportation corridor through the western metropolitan area; the lighting patterns of large industrial and commercial properties are visible along its length. Other brightly lit properties include large shopping centers, strip malls, and gas stations, which tend to be located at the intersections of north-south and east-west trending streets.\n\nThe urban grid encourages growth outwards along a city's borders by providing optimal access to new real estate. Fueled by the adoption of widespread personal automobile use during the twentieth century, the Phoenix metropolitan area today includes 25 other municipalities (many of them largely suburban and residential) linked by a network of surface streets and freeways.\n\nWhile much of the land area highlighted in this image is urbanized, there are several noticeably dark areas. The Phoenix Mountains are largely public parks and recreational land. To the west, agricultural fields provide a sharp contrast to the lit streets of residential developments. The Salt River channel appears as a dark ribbon within the urban grid.\n\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"88\" -->",
        "page_number": 104
      },
      "rerankerScore": 2.6394622,
      "docKey": "earth_at_night_508_page_104_verbalized"
    },
    {
      "type": "searchIndex",
      "id": "1",
      "activitySource": 2,
      "sourceData": {
        "id": "earth_at_night_508_page_105_verbalized",
        "page_chunk": "# Urban Structure\n\n## March 16, 2013\n\n### Phoenix Metropolitan Area at Night\n\nThis figure presents a nighttime satellite view of the Phoenix metropolitan area, highlighting urban structure and transport corridors. City lights illuminate the layout of several cities and major thoroughfares.\n\n**Labeled Urban Features:**\n\n- **Phoenix:** Central and brightest area in the right-center of the image.\n- **Glendale:** Located to the west of Phoenix, this city is also brightly lit.\n- **Peoria:** Further northwest, this area is labeled and its illuminated grid is seen.\n- **Grand Avenue:** Clearly visible as a diagonal, brightly lit thoroughfare running from Phoenix through Glendale and Peoria.\n- **Salt River Channel:** Identified in the southeast portion, running through illuminated sections.\n- **Phoenix Mountains:** Dark, undeveloped region to the northeast of Phoenix.\n- **Agricultural Fields:** Southwestern corner of the image, grid patterns are visible but with much less illumination, indicating agricultural land use.\n\n**Additional Notes:**\n\n- The overall pattern shows a grid-like urban development typical of western U.S. cities, with scattered bright nodes at major intersections or city centers.\n- There is a clear transition from dense urban development to sparsely populated or agricultural land, particularly evident towards the bottom and left of the image.\n- The illuminated areas follow the existing road and street grids, showcasing the extensive spread of the metropolitan area.\n\n**Figure Description:**  \nA satellite nighttime image captured on March 16, 2013, showing Phoenix and surrounding areas (including Glendale and Peoria). Major landscape and infrastructural features, such as the Phoenix Mountains, Grand Avenue, the Salt River Channel, and agricultural fields, are labeled. The image reveals the extent of urbanization and the characteristic street grid illuminated by city lights.\n\n---\n\nPage 89",
        "page_number": 105
      },
      "rerankerScore": 2.565024,
      "docKey": "earth_at_night_508_page_105_verbalized"
    }
  ]
}

了解程式碼

註

本節的程式碼片段可能已經過修改以提升可讀性。完整工作範例請參考原始碼。

現在你已經執行過程式碼，讓我們來拆解幾個關鍵步驟：

建立搜尋索引
將文件上傳至索引
建立知識來源
建立知識庫
執行檢索管道

建立搜尋索引

在 Azure AI 搜尋服務中，索引是一組結構化的資料集合。以下程式碼定義了一個名為 earth-at-night的索引。

### Create an index
PUT {{search-url}}/indexes/{{index-name}}?api-version={{api-version}}  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

{
    "name": "{{index-name}}",
    "fields": [
        {
            "name": "id",
            "type": "Edm.String",
            "key": true
        },
        {
            "name": "page_chunk",
            "type": "Edm.String",
            "searchable": true
        },
        {
            "name": "page_embedding_text_3_large",
            "type": "Collection(Edm.Single)",
            "stored": false,
            "dimensions": 3072,
            "vectorSearchProfile": "hnsw_text_3_large"
        },
        {
            "name": "page_number",
            "type": "Edm.Int32",
            "filterable": true
        }
    ],
    "semantic": {
        "defaultConfiguration": "semantic_config",
        "configurations": [
            {
                "name": "semantic_config",
                "prioritizedFields": {
                "prioritizedContentFields": [
                    {
                        "fieldName": "page_chunk"
                    }
                ]
                }
            }
        ]
    },
    "vectorSearch": {
        "profiles": [
            {
                "name": "hnsw_text_3_large",
                "algorithm": "alg",
                "vectorizer": "azure_openai_text_3_large"
            }
        ],
        "algorithms": [
            {
                "name": "alg",
                "kind": "hnsw"
            }
        ],
        "vectorizers": [
            {
                "name": "azure_openai_text_3_large",
                "kind": "azureOpenAI",
                "azureOpenAIParameters": {
                "resourceUri": "{{aoai-url}}",
                "deploymentId": "{{aoai-embedding-deployment}}",
                "modelName": "{{aoai-embedding-model}}"
                }
            }
        ]
    }
}

參考資料：索引 - 建立

將文件上傳至索引

目前，該 earth-at-night 指數是空白的。以下程式碼將 NASA 的《地球夜》電子書中的 JSON 文件填充索引。依照 Azure AI 搜尋服務的要求，每份文件都符合索引結構中定義的欄位與資料型態。

### Upload documents
POST {{search-url}}/indexes/{{index-name}}/docs/index?api-version={{api-version}}  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

{
    "value": [
        {
            "@search.action": "upload",
            "id": "earth_at_night_508_page_104_verbalized",
            "page_chunk": "<!-- PageHeader=\"Urban Structure\" -->\n\n### Location of Phoenix, Arizona\n\nThe image depicts a globe highlighting the location of Phoenix, Arizona, in the southwestern United States, marked with a blue pinpoint on the map of North America. Phoenix is situated in the central part of Arizona, which is in the southwestern region of the United States.\n\n---\n\n### Grid of City Blocks-Phoenix, Arizona\n\nLike many large urban areas of the central and western United States, the Phoenix metropolitan area is laid out along a regular grid of city blocks and streets. While visible during the day, this grid is most evident at night, when the pattern of street lighting is clearly visible from the low-Earth-orbit vantage point of the ISS.\n\nThis astronaut photograph, taken on March 16, 2013, includes parts of several cities in the metropolitan area, including Phoenix (image right), Glendale (center), and Peoria (left). While the major street grid is oriented north-south, the northwest-southeast oriented Grand Avenue cuts across the three cities at image center. Grand Avenue is a major transportation corridor through the western metropolitan area; the lighting patterns of large industrial and commercial properties are visible along its length. Other brightly lit properties include large shopping centers, strip malls, and gas stations, which tend to be located at the intersections of north-south and east-west trending streets.\n\nThe urban grid encourages growth outwards along a city's borders by providing optimal access to new real estate. Fueled by the adoption of widespread personal automobile use during the twentieth century, the Phoenix metropolitan area today includes 25 other municipalities (many of them largely suburban and residential) linked by a network of surface streets and freeways.\n\nWhile much of the land area highlighted in this image is urbanized, there are several noticeably dark areas. The Phoenix Mountains are largely public parks and recreational land. To the west, agricultural fields provide a sharp contrast to the lit streets of residential developments. The Salt River channel appears as a dark ribbon within the urban grid.\n\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"88\" -->",
            "page_embedding_text_3_large": [
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            "page_chunk": "# Urban Structure\n\n## March 16, 2013\n\n### Phoenix Metropolitan Area at Night\n\nThis figure presents a nighttime satellite view of the Phoenix metropolitan area, highlighting urban structure and transport corridors. City lights illuminate the layout of several cities and major thoroughfares.\n\n**Labeled Urban Features:**\n\n- **Phoenix:** Central and brightest area in the right-center of the image.\n- **Glendale:** Located to the west of Phoenix, this city is also brightly lit.\n- **Peoria:** Further northwest, this area is labeled and its illuminated grid is seen.\n- **Grand Avenue:** Clearly visible as a diagonal, brightly lit thoroughfare running from Phoenix through Glendale and Peoria.\n- **Salt River Channel:** Identified in the southeast portion, running through illuminated sections.\n- **Phoenix Mountains:** Dark, undeveloped region to the northeast of Phoenix.\n- **Agricultural Fields:** Southwestern corner of the image, grid patterns are visible but with much less illumination, indicating agricultural land use.\n\n**Additional Notes:**\n\n- The overall pattern shows a grid-like urban development typical of western U.S. cities, with scattered bright nodes at major intersections or city centers.\n- There is a clear transition from dense urban development to sparsely populated or agricultural land, particularly evident towards the bottom and left of the image.\n- The illuminated areas follow the existing road and street grids, showcasing the extensive spread of the metropolitan area.\n\n**Figure Description:**  \nA satellite nighttime image captured on March 16, 2013, showing Phoenix and surrounding areas (including Glendale and Peoria). Major landscape and infrastructural features, such as the Phoenix Mountains, Grand Avenue, the Salt River Channel, and agricultural fields, are labeled. The image reveals the extent of urbanization and the characteristic street grid illuminated by city lights.\n\n---\n\nPage 89",
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            ],
            "page_number": 105
        }
    ]
}

參考資料：文件 - 索引

建立知識來源

知識來源是可重複使用的來源資料參考。以下程式碼定義了一個針對該earth-knowledge-source索引的earth-at-night知識來源。

sourceDataFields 指定引用引用中包含哪些索引欄位。此範例僅包含人類可讀欄位，以避免回應中冗長且無法解釋的嵌入。

### Create a knowledge source
POST {{search-url}}/knowledgesources?api-version={{api-version}}  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

{
    "name": "{{knowledge-source-name}}",
    "description": "This knowledge source pulls from a search index that contains pages from the Earth at Night e-book.",
    "kind": "searchIndex",
    "searchIndexParameters": {
        "searchIndexName": "{{index-name}}",
        "sourceDataFields": [
            { "name": "id" },
            { "name": "page_chunk" },
            { "name": "page_number" }
        ]
    }
}

參考資料：知識來源 - 建立

建立知識庫

若要在查詢時以 earth-knowledge-source 和部署 gpt-5-mini 為目標，您需要知識庫。以下程式碼定義了一個名為 earth-knowledge-base的基底。

outputMode（預覽）設為 answerSynthesis，以便產生會引用檢索到的文件並遵循所提供 answerInstructions 的自然語言回答。

### Create a knowledge base
PUT {{search-url}}/knowledgebases/{{knowledge-base-name}}?api-version={{api-version}}  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

{
    "name": "{{knowledge-base-name}}",
    "knowledgeSources": [
        {
            "name": "{{knowledge-source-name}}"
        }
    ],
    "models": [
        {
            "kind": "azureOpenAI",
            "azureOpenAIParameters": {
                "resourceUri": "{{aoai-url}}",
                "deploymentId": "{{aoai-gpt-deployment}}",
                "modelName": "{{aoai-gpt-model}}"
            }
        }
    ],
    "outputMode": "answerSynthesis",
    "answerInstructions": "Provide a two sentence concise and informative answer based on the retrieved documents."
}

參考資料：知識庫 - 建立

執行檢索流程

你已經準備好運行代理檢索程序了。以下程式碼會將兩部分的使用者查詢發送到 earth-knowledge-base，該查詢：

分析整個對話，推斷使用者的資訊需求。
將複合查詢分解為聚焦子查詢。
針對您的知識來源並行執行子查詢。
使用語意排名器重新排序並過濾結果。此範例排除了重排序器分數為 2.5 或更低的回應。
將最優秀的結果綜合成自然語言的答案。

retrievalReasoningEffort（預覽）設為 low，以控制用於查詢規劃的推理量。

### Run agentic retrieval
POST {{search-url}}/knowledgebases/{{knowledge-base-name}}/retrieve?api-version={{api-version}}  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

{
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?"
                }
            ]
        }
    ],
    "knowledgeSourceParams": [
        {
            "knowledgeSourceName": "{{knowledge-source-name}}",
            "kind": "searchIndex",
            "includeReferences": true,
            "includeReferenceSourceData": true,
            "alwaysQuerySource": true,
            "rerankerThreshold": 2.5
        }
    ],
    "includeActivity": true,
    "retrievalReasoningEffort": { "kind": "low" }
}

參考資料：知識檢索 - 檢索

輸出包含以下組件：

response 提供一個綜合的、由大型語言模型生成的答案，並引用所檢索文件來回應查詢。當未啟用答案綜合時，本節內容會直接從文件中擷取。
activity 追蹤檢索過程中所採取的步驟，包括部署 gpt-5-mini 產生的子查詢，以及用於語意排序、查詢規劃和答案綜合的標記。
references 列出對回應有貢獻的文件，每份文件皆以 docKey。

清理資源

當您在自己的訂用帳戶中工作時，建議您在完成專案後移除不再需要的資源。若您讓資源繼續執行，則可能會產生費用。

在Azure入口網站中，從左側窗格選擇 所有資源或 資源群組以尋找並管理資源。你可以單獨刪除資源，或是一次性刪除資源群組，移除所有資源。

否則，來自 agentic-retrieval.rest 的下列請求會刪除你在本快速入門中建立的物件。

刪除知識庫

### Delete the knowledge base
DELETE {{search-url}}/knowledgebases/{{knowledge-base-name}}?api-version={{api-version}}  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

刪除知識來源

### Delete the knowledge source
DELETE {{search-url}}/knowledgesources/{{knowledge-source-name}}?api-version={{api-version}}  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

刪除搜尋索引

### Delete the index
DELETE {{search-url}}/indexes/{{index-name}}?api-version={{api-version}}  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

意見反應

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Last updated on 2026-09-17