快速入门：代理检索

注释

此功能目前处于公开预览状态。此预览版未随附服务级别协议，建议不要用于生产工作负载。某些功能可能不受支持或者受限。有关详细信息，请参阅 Azure 预览版的使用条款。

在本快速入门中，你将使用agentic retrieval来创建一个由 Azure AI Search 中编制索引的文档和 Foundry 模型中的 Azure OpenAI 大型语言模型（LLM）提供支持的对话式搜索体验。

knowledge base通过将复杂查询分解为子查询来协调代理检索，对一个或多个 knowledge 源运行子查询，并使用元数据返回结果。默认情况下，知识库从源中输出原始内容，但本快速入门指南使用答案合成输出模式来生成自然语言答案。

虽然可以使用自己的数据，但在本快速入门中，我们使用来自美国宇航局《地球夜间观测》电子书中的样本 JSON 文档。

小窍门

想立即开始吗？在 GitHub 上下载源代码。

先决条件

具有活动订阅的Azure帐户。创建试用版订阅。
在任何提供代理检索的区域中设置的 Azure AI 搜索服务。本快速入门需要基本层或更高级别来支持托管身份。
使用 Microsoft Entra ID 进行免密身份验证的 Azure CLI。
.NET 8 或更高版本。
Visual Studio Code。
Git 克隆示例存储库。
使用 Microsoft Entra ID 进行免密身份验证的 Azure CLI。

配置访问

在开始之前，请确保你有权访问内容和操作。本快速入门指南使用 Microsoft Entra ID 进行身份验证，并通过基于角色的访问来进行授权。必须是 Owner 或 User Access Administrator 才能分配角色。如果角色设置不可行，请改用基于密钥的身份验证方式。

若要为本快速入门配置访问权限，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 基于角色的访问。
2. 创建系统分配的托管标识。
3. 将以下角色分配给用户帐户： 搜索服务参与者、 搜索索引数据参与者和 搜索索引数据读取者。
在你的资源上，将 认知服务用户 分配给搜索服务的托管身份。

重要

代理检索具有两种基于标记的计费模型：

用于代理检索的Azure AI Search计费。
Azure OpenAI 的计费，用于查询规划和答案合成的服务。

有关详细信息，请参阅代理检索的可用性和定价。

获取终结点

每个 Azure AI Search 服务和 Foundry 资源都有一个 endpoint，这是一个唯一的 URL，用于标识资源并提供网络访问。在后面的部分中，指定这些终结点以便通过编程方式连接到你的资源。

若要获取本快速入门的终结点，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 在左窗格中，选择“ 概述”。
记下终结点，该终结点应类似于 https://my-service.search.azure.cn。
在“Foundry”资源中：
1. 在左窗格中，选择 “资源管理>密钥和终结点”。
2. 复制 OpenAI 选项卡上的 URL，如下所示 https://my-resource.openai.azure.com/。

设置环境

使用 Git 克隆示例存储库。

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

导航到快速入门文件夹，并在 Visual Studio Code 中打开它。
```
cd azure-search-dotnet-samples/quickstart-agentic-retrieval
code .
```
在sample.env中，将SEARCH_ENDPOINT和AOAI_ENDPOINT这两个占位符值替换为从获取终结点中得到的URL。
将 sample.env 重命名为 .env。
```
mv sample.env .env
```
安装依赖项。
```
dotnet restore AgenticRetrievalQuickstart.csproj
```
还原完成后，请验证输出中是否未显示任何错误。
若要使用 Microsoft Entra ID 进行无密钥身份验证，请登录到Azure帐户。如果有多个订阅，请选择包含 Azure AI 搜索和Microsoft Foundry 资源的订阅。
```
az login
```

运行代码

运行应用程序以创建索引、上传文档、配置知识源和知识库，并运行代理检索查询。

dotnet run --project AgenticRetrievalQuickstart.csproj

输出

应用程序的输出应如下所示：

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.

了解代码

注释

本部分中的代码片段可能已修改为可读性。有关完整的工作示例，请参阅源代码。

运行代码后，让我们分解关键步骤：

创建搜索索引
将文档上传到索引
创建知识来源
创建 knowledge base
设置消息
运行检索管道
继续对话

创建搜索索引

在Azure AI Search中，索引是结构化数据集合。以下代码定义名为的 earth-at-night索引。

索引架构包含文档标识和页面内容、嵌入和数字的字段。该架构还包含用于语义排序和向量搜索的配置，该配置利用你部署的 text-embedding-3-large 模型将文本转换为向量，并根据语义或概念相似度匹配相关文档。

// 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.");

Reference：SearchField、SimpleField、 VectorSearch、SemanticSearch、SearchIndex、SearchIndexClient

将文档上传到索引

目前，索引 earth-at-night 为空。以下代码使用来自 NASA 地球的夜间电子书中的 JSON 文档填充索引。根据Azure AI Search的要求，每个文档都符合索引架构中定义的字段和数据类型。

// 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.");

Reference：SearchClient，SearchIndexingBufferedSender

创建知识来源

知识源是对源数据的可重用引用。以下代码定义了一个名为 earth-knowledge-source 的知识源，目标是 earth-at-night 索引。

SourceDataFields 指定引文引用中包含哪些索引字段。此示例仅包含人类可读字段，以避免在响应中产生冗长、难以解释的嵌入。

// 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.");

Reference：SearchIndexKnowledgeSource

创建知识库

若要在查询时以 earth-knowledge-source 和 gpt-5-mini 部署为目标，需要一个知识库。以下代码定义名为 earth-knowledge-base 的knowledge base。

OutputMode 设置为 AnswerSynthesis，可启用以检索到的文档为依据并遵循所提供的 AnswerInstructions 的自然语言答复。

// 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

设置消息

消息是检索路由的输入，包含对话历史记录。每条消息都包含一个角色，用于指示其来源（例如 system 或 user）以及自然语言中的内容。使用的 LLM 决定了哪些角色有效。

以下代码创建一条系统消息，该消息指示 earth-knowledge-base 在夜间回答有关地球的问题，并在答案不可用时使用“我不知道”进行回答。

// 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 }
    }
};

运行检索管道

你已准备好运行智能体检索。以下代码将由两个部分组成的用户查询发送到 earth-knowledge-base：

分析整个对话以推断用户的信息需求。
将复合查询分解为有针对性的子查询。
并发地针对知识来源运行子查询。
使用语义排序器对结果进行重新排序和筛选。
将排名靠前的结果合成为自然语言答案。

// 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 }
});

Reference：KnowledgeBaseRetrievalClient，KnowledgeBaseRetrievalRequest

查看响应、活动和引用

以下代码显示检索管道中的响应、活动和引用，其中：

Response 会根据检索到的文档，生成一个由大型语言模型 (LLM) 合成的答案。如果未启用答案合成功能，则此部分将直接显示从文档中提取的内容。
Activity 会追踪检索过程中的所有步骤，包括 gpt-5-mini 部署在检索过程中生成的子查询，以及用于语义排序、查询规划和答案合成所使用的标记。
References 列出构成该响应的文档，每个文档都由其 DocKey标识。

// 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);
}

继续对话

以下代码继续与 earth-knowledge-base 对话。发送此用户查询后，knowledge base从 earth-knowledge-source 提取相关内容，并将响应追加到消息列表。

// 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 }
});

查看新的响应、活动和引用

以下代码显示检索管道中的新响应、活动和引用。

// 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);
}

清理资源

在您自己的订阅计划中工作时，最好通过删除不再需要的资源来完成项目。持续运行的资源可能会产生费用。

在Azure portal中，从左窗格中选择“所有资源或resource 组以查找和管理资源。可以单独删除资源，也可以删除资源组以一次性删除所有资源。

否则，以下代码 program.cs 将删除您在本快速入门中创建的对象。

删除知识库

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

删除知识来源

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

删除搜索索引

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

注释

虽然可以使用自己的数据，但在本快速入门中，我们使用来自美国宇航局《地球夜间观测》电子书中的样本 JSON 文档。

小窍门

想立即开始吗？在 GitHub 上下载源代码。

先决条件

具有活动订阅的Azure帐户。免费创建帐户。
在任何提供代理检索的区域中设置的 Azure AI 搜索服务。本快速入门需要基本层或更高级别来支持托管身份。
Java 11 或更高版本和 Maven。
Visual Studio Code。
Git 克隆示例存储库。
使用 Microsoft Entra ID 进行免密身份验证的 Azure CLI。

配置访问

若要为本快速入门配置访问权限，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 基于角色的访问。
2. 创建系统分配的托管标识。
3. 将以下角色分配给用户帐户： 搜索服务参与者、 搜索索引数据参与者和 搜索索引数据读取者。
在您的资源中，将 认知服务用户 分配到搜索服务的托管身份。

重要

代理检索具有两种基于标记的计费模型：

用于代理检索的Azure AI Search计费。
Azure OpenAI 的计费，用于查询规划和答案合成的服务。

有关详细信息，请参阅代理检索的可用性和定价。

获取终结点

若要获取本快速入门的终结点，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 在左窗格中，选择“ 概述”。
记下终结点，该终结点应类似于 https://my-service.search.azure.cn。
在“Foundry”资源中：
1. 在左窗格中，选择 “资源管理>密钥和终结点”。
2. 复制 OpenAI 选项卡上的 URL，如下所示 https://my-resource.openai.azure.com/。

设置环境

使用 Git 克隆示例存储库。

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

导航到快速入门文件夹，并在 Visual Studio Code 中打开它。
```
cd azure-search-java-samples/quickstart-agentic-retrieval
code .
```
在sample.env中，将SEARCH_ENDPOINT和AOAI_ENDPOINT这两个占位符值替换为从获取终结点中得到的URL。
将 sample.env 重命名为 .env。
```
mv sample.env .env
```
安装依赖项，包括 Azure AI 搜索客户端库和用于 Java 的 Azure 标识客户端库。
```
mvn clean dependency:copy-dependencies
```
若要使用 Microsoft Entra ID 进行无密钥身份验证，请登录到Azure帐户。如果有多个订阅，请选择包含 Azure AI 搜索和Microsoft Foundry 资源的订阅。
```
az login
```

运行代码

生成并运行应用程序以创建索引、上传文档、配置知识库和运行代理检索查询。

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

输出

应用程序的输出应如下所示：

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.

了解代码

注释

本部分中的代码片段可能已修改为可读性。有关完整的工作示例，请参阅源代码。

运行代码后，让我们分解关键步骤：

创建搜索索引
将文档上传到索引
创建知识来源
创建 knowledge base
设置消息
运行检索管道
继续对话

创建搜索索引

在Azure AI Search中，索引是结构化数据集合。以下代码定义名为的 earth-at-night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);

Reference：SearchField， VectorSearch、SemanticSearch、SearchIndex、SearchIndexClient

将文档上传到索引

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);

Reference：SearchClient、 SearchDocument

创建知识来源

知识源是对源数据的可重用引用。以下代码定义了一个名为 earth-knowledge-source 的知识源，目标是 earth-at-night 索引。

sourceDataFields 指定引文引用中包含哪些索引字段。此示例仅包含人类可读字段，以避免在响应中产生冗长、难以解释的嵌入。

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);

Reference：SearchIndexKnowledgeSource

创建知识库

若要在查询时以 earth-knowledge-source 和 gpt-5-mini 部署为目标，需要一个知识库。以下代码定义名为 earth-knowledge-base 的knowledge base。

OutputMode 设置为 ANSWER_SYNTHESIS，可启用以检索到的文档为依据并遵循所提供的 AnswerInstructions 的自然语言答复。

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

设置消息

以下代码创建一条系统消息，该消息指示 earth-knowledge-base 在夜间回答有关地球的问题，并在答案不可用时使用“我不知道”进行回答。

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);

运行检索管道

你已准备好运行智能体检索。以下代码将由两个部分组成的用户查询发送到 earth-knowledge-base：

分析整个对话以推断用户的信息需求。
将复合查询分解为有针对性的子查询。
并发地针对知识来源运行子查询。
使用语义排序器对结果进行重新排序和筛选。
将排名靠前的结果合成为自然语言答案。

SearchKnowledgeBaseClient baseClient =
    new SearchKnowledgeBaseClientBuilder()
        .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));

KnowledgeBaseRetrievalResponse retrievalResult =
    retrieve(baseClient, messages);

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

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

retrieve帮助程序方法从聊天历史记录生成KnowledgeBaseMessage对象，并发送检索请求：

private static KnowledgeBaseRetrievalResponse retrieve(
        SearchKnowledgeBaseClient client,
        List<Map<String, String>> messages) {
    List<KnowledgeBaseMessage> kbMessages = new ArrayList<>();
    for (Map<String, String> msg : messages) {
        if (!"system".equals(msg.get("role"))) {
            kbMessages.add(
                new KnowledgeBaseMessage(Arrays.asList(
                    new KnowledgeBaseMessageTextContent(
                        msg.get("content"))
                )).setRole(msg.get("role"))
            );
        }
    }

    KnowledgeBaseRetrievalRequest request =
        new KnowledgeBaseRetrievalRequest();
    request.setMessages(kbMessages);
    request.setRetrievalReasoningEffort(
        new KnowledgeRetrievalLowReasoningEffort());

    return client.retrieve(request, null);
}

参考：SearchKnowledgeBaseClient、 KnowledgeBaseRetrievalRequest

查看响应、活动和引用

以下代码显示检索管道中的响应、活动和引用，其中：

Response 会根据检索到的文档，生成一个由大型语言模型 (LLM) 合成的答案。如果未启用答案合成功能，则此部分将直接显示从文档中提取的内容。
Activity 会追踪检索过程中的所有步骤，包括 gpt-5-mini 部署在检索过程中生成的子查询，以及用于语义排序、查询规划和答案合成所使用的标记。
References 列出构成该响应的文档，每个文档都由其 docKey标识。

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));
}

继续对话

以下代码继续与 earth-knowledge-base 对话。发送此用户查询后，knowledge base从 earth-knowledge-source 提取相关内容，并将响应追加到消息列表。

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

retrievalResult = retrieve(baseClient, messages);

查看新的响应、活动和引用

以下代码提取响应文本和调用 printResult 以显示新的响应、活动和引用。

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

printResult(responseText, retrievalResult);

清理资源

在您自己的订阅计划中工作时，最好通过删除不再需要的资源来完成项目。持续运行的资源可能会产生费用。

在Azure portal中，从左窗格中选择“所有资源或resource 组以查找和管理资源。可以单独删除资源，也可以删除资源组以一次性删除所有资源。

否则，以下代码 AgenticRetrievalQuickstart.java 将删除您在本快速入门中创建的对象。

删除知识库

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

删除知识来源

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

删除搜索索引

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

注释

虽然可以使用自己的数据，但在本快速入门中，我们使用来自美国宇航局《地球夜间观测》电子书中的样本 JSON 文档。

小窍门

此快速入门的 JavaScript 版本的源代码不可用。可以直接从本文复制代码。

先决条件

具有活动订阅的Azure帐户。免费创建帐户。
在任何提供代理检索的区域中设置的 Azure AI 搜索服务。本快速入门需要基本层或更高级别来支持托管身份。
Node.js 20 LTS 或更高版本。
Visual Studio Code。
使用 Microsoft Entra ID 进行免密身份验证的 Azure CLI。

配置访问

若要为本快速入门配置访问权限，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 基于角色的访问。
2. 创建系统分配的托管标识。
3. 将以下角色分配给用户帐户： 搜索服务参与者、 搜索索引数据参与者和 搜索索引数据读取者。
在您的资源上，将 认知服务用户 分配到搜索服务的托管身份。

重要

代理检索具有两种基于标记的计费模型：

用于代理检索的Azure AI Search计费。
Azure OpenAI 的计费，用于查询规划和答案合成的服务。

有关详细信息，请参阅代理检索的可用性和定价。

获取终结点

若要获取本快速入门的终结点，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 在左窗格中，选择“ 概述”。
记下终结点，该终结点应类似于 https://my-service.search.azure.cn。
在“Foundry”资源中：
1. 在左窗格中，选择 “资源管理>密钥和终结点”。
2. 复制 OpenAI 选项卡上的 URL，如下所示 https://my-resource.openai.azure.com/。

设置环境

创建一个名为 quickstart-agentic-retrieval 包含应用程序的文件夹。
在 Visual Studio Code 中打开文件夹。
选择终端>新终端，然后运行以下命令以初始化 package.json。
```
npm init -y
npm pkg set type=module
```
安装适用于 JavaScript 的 Azure AI Search 客户端库。
```
npm install @azure/search-documents@12.3.0-beta.1
```
若要使用 Microsoft Entra ID 进行无密钥身份验证，请安装 Azure Identity 客户端库用于 JavaScript。
```
npm install @azure/identity
```
若要使用 Microsoft Entra ID 进行无密钥身份验证，请登录到Azure帐户。如果有多个订阅，请选择包含 Azure AI 搜索和Microsoft Foundry 资源的订阅。
```
az login
```

运行代码

创建文件夹中命名.envquickstart-agentic-retrieval的文件，然后粘贴以下内容。将占位符值替换为在获取端点中获得的 URL。

AZURE_SEARCH_ENDPOINT = https://<your-search-service-name>.search.azure.cn
AZURE_OPENAI_GPT_DEPLOYMENT = gpt-5-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT = text-embedding-3-large

创建一个名为 index.js的文件，然后将以下代码粘贴到文件中。

import { DefaultAzureCredential } from '@azure/identity';
import {
    SearchIndexClient,
    SearchClient,
    KnowledgeRetrievalClient,
    SearchIndexingBufferedSender
} from '@azure/search-documents';

export const documentKeyRetriever = (document) => {
  return document.id;
};

export const WAIT_TIME = 4000;
export function delay(timeInMs) {
  return new Promise((resolve) => setTimeout(resolve, timeInMs));
}

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);

// get Documents with vectors
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!`);

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.`);

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.`);

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.retrieveKnowledge(retrievalRequest);

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));
    });
}

// Follow-up query - to demonstrate conversational context
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.retrieveKnowledge(retrievalRequest2);

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!");

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

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

生成并运行应用程序。
```
node --env-file=.env index.js
```

输出

应用程序的输出应如下所示：

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.

了解代码

有了代码后，让我们分解关键组件：

创建搜索索引
将文档上传到索引
创建知识来源
创建 knowledge base
运行检索管道
查看响应、活动和引用
继续对话

创建搜索索引

在Azure AI Search中，索引是结构化数据集合。以下代码定义名为的 earth_at_night索引。

索引架构包含文档标识和页面内容、嵌入和数字的字段。该架构还包含用于语义排序和向量搜索的配置，该配置利用你部署的 text-embedding-3-large 模型将文本转换为向量，并根据语义相似度匹配相关文档。

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);

Reference：SearchField VectorSearch SemanticSearch， SearchIndex、SearchIndexClient、SearchClient、DefaultAzureCredential

将文档上传到索引

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!`);

Reference：SearchIndexingBufferedSender

创建知识来源

知识源是对源数据的可重用引用。以下代码定义了一个名为 earth-knowledge-source 的知识源，目标是 earth-at-night 索引。

sourceDataFields 指定引文引用中包含哪些索引字段。此示例仅包含人类可读字段，以避免在响应中产生冗长、难以解释的嵌入。

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.`);

Reference：SearchIndexKnowledgeSource

创建知识库

若要在查询时以 earth-knowledge-source 和 gpt-5-mini 部署为目标，需要一个知识库。以下代码定义名为 earth-knowledge-base 的knowledge base。

outputMode 设置为 answerSynthesis，可启用以检索到的文档为依据并遵循所提供的 answerInstructions 的自然语言答复。

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.`);

Reference：KnowledgeBase

运行检索管道

你已准备好运行智能体检索。以下代码将由两个部分组成的用户查询发送到 earth-knowledge-base：

分析整个对话以推断用户的信息需求。
将复合查询分解为有针对性的子查询。
并发地针对知识来源运行子查询。
使用语义排序器对结果进行重新排序和筛选。
将排名靠前的结果合成为自然语言答案。

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.retrieveKnowledge(retrievalRequest);

Reference：KnowledgeRetrievalClient，KnowledgeBaseRetrievalRequest

查看响应、活动和引用

以下代码显示检索管道中的响应、活动和引用，其中：

Answer 会根据检索到的文档，生成一个由大型语言模型 (LLM) 合成的答案。如果未启用答案合成功能，则此部分将直接显示从文档中提取的内容。
Activities 会追踪检索过程中的所有步骤，包括 gpt-5-mini 部署在检索过程中生成的子查询，以及用于语义排序、查询规划和答案合成所使用的标记。
References 列出构成该响应的文档，每个文档都由其 docKey标识。

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));
    });
}

继续对话

以下代码继续与 earth-knowledge-base 对话。发送此用户查询后，knowledge base从 earth-knowledge-source 提取相关内容，并将响应追加到消息列表。

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.retrieveKnowledge(retrievalRequest2);

查看新的响应、活动和引用

以下代码显示检索管道中的新响应、活动和引用。

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));
    });
}

清理资源

在您自己的订阅计划中工作时，最好通过删除不再需要的资源来完成项目。持续运行的资源可能会产生费用。

在Azure portal中，从左窗格中选择“所有资源或resource 组以查找和管理资源。可以单独删除资源，也可以删除资源组以一次性删除所有资源。

否则，以下代码 index.js 将删除您在本快速入门中创建的对象。

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

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

注释

虽然可以使用自己的数据，但在本快速入门中，我们使用来自美国宇航局《地球夜间观测》电子书中的样本 JSON 文档。

小窍门

想立即开始吗？在 GitHub 上下载源代码。

先决条件

具有活动订阅的Azure帐户。创建试用版订阅。
在任何提供代理检索的区域中设置的 Azure AI 搜索服务。本快速入门需要基本层或更高级别来支持托管身份。
Python 3.8 或更高版本。
Visual Studio Code 带有 Python 和 Jupyter 扩展。
Git 克隆示例存储库。
使用 Microsoft Entra ID 进行免密身份验证的 Azure CLI。

配置访问

若要为本快速入门配置访问权限，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 基于角色的访问。
2. 创建系统分配的托管标识。
3. 将以下角色分配给用户帐户： 搜索服务参与者、 搜索索引数据参与者和 搜索索引数据读取者。
在您的资源上，将 认知服务用户 分配给搜索服务的托管身份。

重要

代理检索具有两种基于标记的计费模型：

用于代理检索的Azure AI Search计费。
Azure OpenAI 的计费，用于查询规划和答案合成的服务。

有关详细信息，请参阅代理检索的可用性和定价。

获取终结点

若要获取本快速入门的终结点，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 在左窗格中，选择“ 概述”。
记下终结点，该终结点应类似于 https://my-service.search.azure.cn。
在“Foundry”资源中：
1. 在左窗格中，选择 “资源管理>密钥和终结点”。
2. 复制 OpenAI 选项卡上的 URL，如下所示 https://my-resource.openai.azure.com/。

设置环境

使用 Git 克隆示例存储库。

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

导航到快速入门文件夹，并在 Visual Studio Code 中打开它。
```
cd azure-search-python-samples/Quickstart-Agentic-Retrieval
code .
```
在sample.env中，将SEARCH_ENDPOINT和AOAI_ENDPOINT这两个占位符值替换为从获取终结点中得到的URL。
将 sample.env 重命名为 .env。
```
mv sample.env .env
```
打开 quickstart-agentic-retrieval.ipynb。
按 Ctrl+Shift+P，选择 笔记本：选择笔记本内核，然后按照提示创建虚拟环境。为依赖项选择 requirements.txt 。

完成后，应该会在项目目录中看到一个 .venv 文件夹。
若要使用 Microsoft Entra ID 进行无密钥身份验证，请登录到Azure帐户。如果有多个订阅，请选择包含 Azure AI 搜索和Microsoft Foundry 资源的订阅。
```
az login
```

运行代码

Load connections运行单元以安装所需的包并加载环境变量。
按顺序运行剩余单元格以创建索引、上传文档、配置知识库和运行代理检索查询。

输出

每个代码单元将输出打印到笔记本。以下示例是运行所有单元格后的输出：

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.

了解代码

注释

本部分中的代码片段可能已修改为可读性。有关完整的工作示例，请参阅源代码。

运行代码后，让我们分解关键步骤：

创建搜索索引
将文档上传到索引
创建知识来源
创建 knowledge base
设置消息
运行检索管道
继续对话

创建搜索索引

在Azure AI Search中，索引是结构化数据集合。以下代码定义名为的 earth-at-night索引。

# Create an index
azure_openai_token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.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.")

Reference：SearchField， VectorSearch、SemanticSearch、SearchIndex、SearchIndexClient

将文档上传到索引

# 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.")

Reference：SearchIndexingBufferedSender

创建知识来源

知识源是对源数据的可重用引用。以下代码定义了一个名为 earth-knowledge-source 的知识源，目标是 earth-at-night 索引。

source_data_fields 指定引文引用中包含哪些索引字段。此示例仅包含人类可读字段，以避免在响应中产生冗长、难以解释的嵌入。

# 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.")

Reference：SearchIndexKnowledgeSource

创建知识库

若要在查询时以 earth-knowledge-source 和 gpt-5-mini 部署为目标，需要一个知识库。以下代码定义名为 earth-knowledge-base 的knowledge base。

output_mode 设置为 ANSWER_SYNTHESIS，可启用以检索到的文档为依据并遵循所提供的 answer_instructions 的自然语言答复。

# 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=KnowledgeRetrievalOutputMode.ANSWER_SYNTHESIS,
    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.")

Reference：KnowledgeBase

设置消息

以下代码创建一条系统消息，指示 earth-knowledge-base 在夜间回答有关地球的问题，并在答案不可用时使用“我不知道”进行回答。

# 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
    }
]

运行检索管道

你已准备好运行智能体检索。以下代码将由两个部分组成的用户查询发送到 earth-knowledge-base：

分析整个对话以推断用户的信息需求。
将复合查询分解为有针对性的子查询。
并发地针对知识来源运行子查询。
使用语义排序器对结果进行重新排序和筛选。
将排名靠前的结果合成为自然语言答案。

# 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.")

Reference：KnowledgeBaseRetrievalClient，KnowledgeBaseRetrievalRequest

查看响应、活动和引用

以下代码显示检索管道中的响应、活动和引用，其中：

response_contents 会根据检索到的文档，生成一个由大型语言模型 (LLM) 合成的答案。如果未启用答案合成功能，则此部分将直接显示从文档中提取的内容。
activity_contents 会追踪检索过程中的所有步骤，包括 gpt-5-mini 部署在检索过程中生成的子查询，以及用于语义排序、查询规划和答案合成所使用的标记。
references_contents 列出构成该响应的文档，每个文档都由其 doc_key标识。

# 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)

继续对话

以下代码继续与 earth-knowledge-base 对话。发送此用户查询后，knowledge base从 earth-knowledge-source 提取相关内容，并将响应追加到消息列表。

# 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.")

查看新的响应、活动和引用

以下代码显示检索管道中的新响应、活动和引用。

# 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)

清理资源

在您自己的订阅计划中工作时，最好通过删除不再需要的资源来完成项目。持续运行的资源可能会产生费用。

在Azure portal中，从左窗格中选择“所有资源或resource 组以查找和管理资源。可以单独删除资源，也可以删除资源组以一次性删除所有资源。

否则，以下代码 quickstart-agentic-retrieval.ipynb 将删除您在本快速入门中创建的对象。

删除知识库

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.")

删除知识来源

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.")

删除搜索索引

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

注释

虽然可以使用自己的数据，但在本快速入门中，我们使用来自美国宇航局《地球夜间观测》电子书中的样本 JSON 文档。

小窍门

此快速入门指南的 TypeScript 版本的源代码不可用。可以直接从本文复制代码。

先决条件

具有活动订阅的Azure帐户。免费创建帐户。
在任何提供代理检索的区域中设置的 Azure AI 搜索服务。本快速入门需要基本层或更高级别来支持托管身份。
Node.js 20 LTS 或更高版本。
Visual Studio Code。
使用 Microsoft Entra ID 进行免密身份验证的 Azure CLI。

配置访问

若要为本快速入门配置访问权限，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 基于角色的访问。
2. 创建系统分配的托管标识。
3. 将以下角色分配给用户帐户： 搜索服务参与者、 搜索索引数据参与者和 搜索索引数据读取者。
在您的资源上，将 认知服务用户 分配给您的搜索服务的托管标识。

重要

代理检索具有两种基于标记的计费模型：

用于代理检索的Azure AI Search计费。
Azure OpenAI 的计费，用于查询规划和答案合成的服务。

有关详细信息，请参阅代理检索的可用性和定价。

获取终结点

若要获取本快速入门的终结点，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 在左窗格中，选择“ 概述”。
记下终结点，该终结点应类似于 https://my-service.search.azure.cn。
在“Foundry”资源中：
1. 在左窗格中，选择 “资源管理>密钥和终结点”。
2. 复制 OpenAI 选项卡上的 URL，如下所示 https://my-resource.openai.azure.com/。

设置环境

若要为本快速入门设置控制台应用程序，请执行以下作：

创建一个名为 quickstart-agentic-retrieval 包含应用程序的文件夹。
在 Visual Studio Code 中打开文件夹。
选择终端>新终端，然后运行以下命令以初始化 package.json。
```
npm init -y
npm pkg set type=module
```

将 TypeScript 安装为开发依赖项。

npm install --save-dev typescript @types/node

安装适用于 JavaScript 的 Azure AI Search 客户端库。
```
npm install @azure/search-documents@12.3.0-beta.1
```
若要使用 Microsoft Entra ID 进行无密钥身份验证，请安装 Azure Identity 客户端库用于 JavaScript。
```
npm install @azure/identity
```
若要使用 Microsoft Entra ID 进行无密钥身份验证，请登录到Azure帐户。如果有多个订阅，请选择包含 Azure AI 搜索和Microsoft Foundry 资源的订阅。
```
az login
```

运行代码

创建文件夹中命名.envquickstart-agentic-retrieval的文件，然后粘贴以下内容。将占位符值替换为在获取端点中获得的 URL。

AZURE_SEARCH_ENDPOINT = https://<your-search-service-name>.search.azure.cn
AZURE_OPENAI_GPT_DEPLOYMENT = gpt-5-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT = text-embedding-3-large

创建一个名为 index.ts的文件，然后将以下代码粘贴到文件中。

import { DefaultAzureCredential } from '@azure/identity';
import {
    SearchIndexClient,
    SearchClient,
    SearchIndex,
    SearchField,
    VectorSearch,
    VectorSearchProfile,
    HnswAlgorithmConfiguration,
    AzureOpenAIVectorizer,
    AzureOpenAIParameters,
    KnowledgeRetrievalClient,
    SemanticSearch,
    SemanticConfiguration,
    SemanticPrioritizedFields,
    SemanticField,
    SearchIndexingBufferedSender,
    KnowledgeRetrievalOutputMode,
    IndexDocumentsAction
} from '@azure/search-documents';
import type { IndexDocumentsResult } from '@azure/search-documents';

interface EarthAtNightDocument {
    id: string;
    page_chunk: string;
    page_embedding_text_3_large: number[];
    page_number: number;
}

export const documentKeyRetriever: (document: EarthAtNightDocument) => string = (document: EarthAtNightDocument): string => {
  return document.id!;
};

export const WAIT_TIME = 4000;
export function delay(timeInMs: number): Promise<void> {
  return new Promise((resolve) => setTimeout(resolve, timeInMs));
}

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);

// get Documents with vectors
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!`);

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.`);

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.`);

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.retrieveKnowledge(retrievalRequest);

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));
    });
}

// Follow-up query - to demonstrate conversational context
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.retrieveKnowledge(retrievalRequest2);

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!");

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

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

创建名为 tsconfig.json的文件，然后将 ECMAScript 的以下 JSON 粘贴到文件中。

{
  "compilerOptions": {
    "target": "ES2022",
    "module": "ES2022",
    "lib": ["ES2022", "DOM"],
    "moduleResolution": "node",
    "types": ["node"],
    "outDir": "./dist",
    "rootDir": "./",
    "strict": true,
    "esModuleInterop": true,
    "skipLibCheck": true,
    "forceConsistentCasingInFileNames": true,
    "resolveJsonModule": true,
    "declaration": true,
    "declarationMap": true,
    "sourceMap": true
  },
  "include": ["*.ts"],
  "exclude": ["node_modules", "dist"]
}

将代码从 TypeScript 转译到 JavaScript。
```
npx tsc
```
生成并运行应用程序。
```
node --env-file ./.env dist/index.js
```

输出

应用程序的输出应如下所示：

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.

了解代码

有了代码后，让我们分解关键组件：

创建搜索索引
将文档上传到索引
创建知识来源
创建 knowledge base
运行检索管道
查看响应、活动和引用
继续对话

创建搜索索引

在Azure AI Search中，索引是结构化数据集合。以下代码定义名为的 earth_at_night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);

Reference：SearchField VectorSearch SemanticSearch， SearchIndex、SearchIndexClient、SearchClient、DefaultAzureCredential

将文档上传到索引

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!`);

Reference：SearchIndexingBufferedSender

创建知识来源

知识源是对源数据的可重用引用。以下代码定义了一个名为 earth-knowledge-source 的知识源，目标是 earth-at-night 索引。

sourceDataFields 指定引文引用中包含哪些索引字段。此示例仅包含人类可读字段，以避免在响应中产生冗长、难以解释的嵌入。

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.`);

Reference：SearchIndexKnowledgeSource

创建知识库

若要在查询时以 earth-knowledge-source 和 gpt-5-mini 部署为目标，需要一个知识库。以下代码定义名为 earth-knowledge-base 的knowledge base。

outputMode 设置为 answerSynthesis，可启用以检索到的文档为依据并遵循所提供的 answerInstructions 的自然语言答复。

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.`);

Reference：KnowledgeBase

运行检索管道

你已准备好运行智能体检索。以下代码将由两个部分组成的用户查询发送到 earth-knowledge-base：

分析整个对话以推断用户的信息需求。
将复合查询分解为有针对性的子查询。
并发地针对知识来源运行子查询。
使用语义排序器对结果进行重新排序和筛选。
将排名靠前的结果合成为自然语言答案。

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.retrieveKnowledge(retrievalRequest);

Reference：KnowledgeRetrievalClient，KnowledgeBaseRetrievalRequest

查看响应、活动和引用

以下代码显示检索管道中的响应、活动和引用，其中：

Answer 会根据检索到的文档，生成一个由大型语言模型 (LLM) 合成的答案。如果未启用答案合成功能，则此部分将直接显示从文档中提取的内容。
Activities 会追踪检索过程中的所有步骤，包括 gpt-5-mini 部署在检索过程中生成的子查询，以及用于语义排序、查询规划和答案合成所使用的标记。
References 列出构成该响应的文档，每个文档都由其 docKey标识。

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));
    });
}

继续对话

以下代码继续与 earth-knowledge-base 对话。发送此用户查询后，knowledge base从 earth-knowledge-source 提取相关内容，并将响应追加到消息列表。

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.retrieveKnowledge(retrievalRequest2);

查看新的响应、活动和引用

以下代码显示检索管道中的新响应、活动和引用。

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!");

清理资源

在您自己的订阅计划中工作时，最好通过删除不再需要的资源来完成项目。持续运行的资源可能会产生费用。

在Azure portal中，从左窗格中选择“所有资源或resource 组以查找和管理资源。可以单独删除资源，也可以删除资源组以一次性删除所有资源。

否则，以下代码 index.ts 将删除您在本快速入门中创建的对象。

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

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

注释

虽然可以使用自己的数据，但在本快速入门中，我们使用来自美国宇航局《地球夜间观测》电子书中的样本 JSON 文档。

小窍门

想立即开始吗？在 GitHub 上下载源代码。

先决条件

具有活动订阅的Azure帐户。创建试用版订阅。
在任何提供代理检索的区域中设置的 Azure AI 搜索服务。本快速入门需要基本层或更高级别来支持托管身份。
使用 Microsoft Entra ID 进行免密身份验证的 Azure CLI。
Visual Studio Code与REST 客户端扩展一起。
Git 克隆示例存储库。
使用 Microsoft Entra ID 进行免密身份验证的 Azure CLI。

配置访问

若要为本快速入门配置访问权限，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 基于角色的访问。
2. 创建系统分配的托管标识。
3. 将以下角色分配给用户帐户： 搜索服务参与者、 搜索索引数据参与者和 搜索索引数据读取者。
在您的资源中，向搜索服务的托管标识赋予 认知服务用户。

重要

代理检索具有两种基于标记的计费模型：

用于代理检索的Azure AI Search计费。
Azure OpenAI 的计费，用于查询规划和答案合成的服务。

有关详细信息，请参阅代理检索的可用性和定价。

获取终结点

若要获取本快速入门的终结点，请执行以下步骤：

登录到 Azure 门户。
在 Azure AI 搜索服务中：
1. 在左窗格中，选择“ 概述”。
记下终结点，该终结点应类似于 https://my-service.search.azure.cn。
在“Foundry”资源中：
1. 在左窗格中，选择 “资源管理>密钥和终结点”。
2. 复制 OpenAI 选项卡上的 URL，如下所示 https://my-resource.openai.azure.com/。

设置环境

使用 Git 克隆示例存储库。

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

导航到快速入门文件夹，并在 Visual Studio Code 中打开它。
```
cd azure-search-rest-samples/Quickstart-agentic-retrieval
code .
```
在agentic-retrieval.rest中，将@search-url和@aoai-url这两个占位符值替换为从获取终结点中得到的URL。
若要使用 Microsoft Entra ID 进行无密钥身份验证，请登录到Azure帐户。如果有多个订阅，请选择包含 Azure AI 搜索和Microsoft Foundry 资源的订阅。
```
az login
```

要使用 Microsoft Entra ID 进行无密钥身份验证，请生成访问令牌。

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

将占位符值 @token 替换为上一步中的标记。

运行代码

依次发送每个请求，从 ### Create an index 开始。

每个请求都应返回一个200 OK或201 Created204 No Content状态代码。如果收到错误，请检查请求中是否有拼写错误，并确保令牌有效。

输出

每个请求根据操作返回不同的 JSON。主要输出来自 ### Run agentic retrieval，应类似于以下结果：

{
  "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"
    }
  ]
}

了解代码

注释

本部分中的代码片段可能已修改为可读性。有关完整的工作示例，请参阅源代码。

运行代码后，让我们分解关键步骤：

创建搜索索引
将文档上传到索引
创建知识来源
创建 knowledge base
运行检索管道

创建搜索索引

在Azure AI Search中，索引是结构化数据集合。以下代码定义名为的 earth-at-night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}}"
                }
            }
        ]
    }
}

参考：索引 - 创建

将文档上传到索引

目前，索引 earth-at-night 为空。以下代码使用美国宇航局地球在夜间电子书中的 JSON 文档填充索引。根据Azure AI Search的要求，每个文档都符合索引架构中定义的字段和数据类型。

### 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
        }
    ]
}

参考：文档 - 索引

创建知识来源

知识源是对源数据的可重用引用。以下代码定义了一个名为 earth-knowledge-source 的知识源，目标是 earth-at-night 索引。

sourceDataFields 指定引文引用中包含哪些索引字段。此示例仅包含人类可读字段，以避免在响应中产生冗长、难以解释的嵌入。

### 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" }
        ]
    }
}

参考：知识源 - 创建

创建知识库

若要在查询时针对 earth-knowledge-source 和 gpt-5-mini 的部署进行操作，需要一个知识库。以下代码定义一个名为earth-knowledge-base的基类。

outputMode 设置为 answerSynthesis，可启用以检索到的文档为依据并遵循所提供的 answerInstructions 的自然语言答复。

### 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."
}

参考：知识库 - 创建

运行检索管道

你已准备好运行智能体检索。以下代码将由两个部分组成的用户查询发送到 earth-knowledge-base：

分析整个对话以推断用户的信息需求。
将复合查询分解为有针对性的子查询。
并发地针对知识来源运行子查询。
使用语义排序器对结果进行重新排序和筛选。我们的示例将排除那些重排器评分为 2.5 或更低的响应。
将排名靠前的结果合成为自然语言答案。

### 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" }
}

参考：知识检索 - 检索

输出应包含以下组件：

response 会根据检索到的文档，生成一个由大型语言模型 (LLM) 合成的答案。如果未启用答案合成功能，则此部分将直接显示从文档中提取的内容。
activity 会追踪检索过程中的所有步骤，包括 gpt-5-mini 部署在检索过程中生成的子查询，以及用于语义排序、查询规划和答案合成所使用的标记。
references 列出构成该响应的文档，每个文档都由其 docKey标识。

清理资源

在您自己的订阅计划中工作时，最好通过删除不再需要的资源来完成项目。持续运行的资源可能会产生费用。

在Azure portal中，从左窗格中选择“所有资源或resource 组以查找和管理资源。可以单独删除资源，也可以删除资源组以一次性删除所有资源。

否则，来自 agentic-retrieval.rest 的以下请求已删除了您在本快速入门中创建的所有对象。

删除知识库

### 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}}

删除知识来源

### 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}}

删除搜索索引

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

Last updated on 2026-03-16

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快速入门：代理检索

先决条件

配置访问

获取终结点

设置环境

运行代码

输出

了解代码

创建搜索索引

将文档上传到索引

创建知识来源

创建知识库

设置消息

运行检索管道

查看响应、活动和引用

继续对话

查看新的响应、活动和引用

清理资源

删除知识库

删除知识来源

删除搜索索引

先决条件

配置访问

获取终结点

设置环境

运行代码

输出

了解代码

创建搜索索引

将文档上传到索引

创建知识来源

创建知识库

设置消息

运行检索管道

查看响应、活动和引用

继续对话

查看新的响应、活动和引用

清理资源

删除知识库

删除知识来源

删除搜索索引

先决条件

配置访问

获取终结点

设置环境

运行代码

输出

了解代码

创建搜索索引

将文档上传到索引

创建知识来源

创建知识库

运行检索管道

查看响应、活动和引用

继续对话

查看新的响应、活动和引用

清理资源

先决条件

配置访问

获取终结点

设置环境

运行代码

输出

了解代码

创建搜索索引

将文档上传到索引

创建知识来源

创建知识库

设置消息

运行检索管道

查看响应、活动和引用

继续对话

查看新的响应、活动和引用

清理资源

删除知识库

删除知识来源

删除搜索索引

先决条件

配置访问