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快速入门:智能体检索

注意

Azure AI 搜索可通过Azure门户、REST API 和Azure SDK获取。 它也是 Foundry IQ 的基础;Foundry IQ 是一个托管式知识层,可将企业内容转化为供 Microsoft Foundry 门户中的智能体使用的、可复用且具备权限感知能力的知识库。

重要

标记为“预览”的特性、功能或属性不受服务级别协议 (SLA) 保障,不建议用于生产工作负载,并且在正式发布之前可能会更改或受到限制。 Azure AI 搜索预览条款适用于所有预览功能,无论是独立功能还是正式版功能的一部分。

在此快速入门中,使用代理检索创建由 Azure AI 搜索索引的文档和 Azure OpenAI Foundry 模型的大型语言模型 (LLM) 驱动的会话式搜索体验。

知识库使用基于 LLM 的查询规划(预览版)将复杂查询分解为子查询。 然后,它针对一个或多个 知识源 运行子查询,并使用元数据返回结果。 默认情况下,知识库从其源返回原始内容,但本快速入门使用答案合成(预览版)生成自然语言答案。

虽然可以使用自己的数据,但本快速入门使用 NASA 的《Earth at Night》电子书中的样本 JSON 文档。

提示

想要立即开始? 在 GitHub 上下载 source code。

先决条件

配置访问权限

在开始之前,请确保你有权访问内容和操作。 本快速入门使用 Microsoft Entra ID 进行身份验证,并通过基于角色的访问控制进行授权。 你必须是 所有者 或 用户访问管理员 才能分配角色。 如果角色不可行,请改用 基于密钥的身份验证 。

若要为本快速入门配置访问权限,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 启用基于角色的访问。

    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。

  3. 在 Microsoft Foundry 资源上,将 Cognitive Services User 分配给搜索服务的托管标识。

重要

代理检索具有两种基于令牌的计费模型:

  • 从 Azure AI 搜索中进行智能体检索的计费。
  • Azure OpenAI 的查询规划和答案生成功能收费标准。

有关详细信息,请参阅 区域可用性、限制和计费。

获取终结点

每个Azure AI 搜索服务和 Microsoft Foundry 资源都有一个 endpoint,这是一个唯一的 URL,用于标识并提供对资源的网络访问。 在后面的部分中,指定这些端点以通过编程操作连接到资源。

若要获取本快速入门的终结点,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 在左窗格中,选择“ 概述”。

    2. 复制 URL,其外观应如下所示 https://my-service.search.windows.net。

  3. 在您的 Microsoft Foundry 资源上:

    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

  1. 使用 Git 克隆示例存储库。

    git clone https://github.com/Azure-Samples/azure-search-dotnet-samples
    
  2. 转到快速入门文件夹。

    cd azure-search-dotnet-samples/quickstart-agentic-retrieval
    
  3. 在 sample.env 中,将 SEARCH_ENDPOINT 和 AOAI_ENDPOINT 的占位符值替换为你在 获取终结点 中获取的 URL。

  4. 重命名 sample.env 为 .env.

    mv sample.env .env
    
  5. 安装依赖项。

    dotnet restore AgenticRetrievalQuickstart.csproj
    

    还原完成后,请确保输出中未显示任何错误。

  6. 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

注意

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:

  1. 创建搜索索引
  2. 将文档上传到索引
  3. 创建知识源
  4. 创建知识库
  5. 设置消息
  6. 运行检索管道
  7. 继续对话

创建搜索索引

在Azure AI 搜索中,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。 该架构还包括语义排名和矢量搜索的配置,该配置使用部署 text-embedding-3-large 根据语义或概念相似性向量化文本和匹配文档。

// 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 搜索的要求,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,并以 earth-at-night 索引为目标。

SourceDataFields 指定引文引用中包含哪些索引字段。 此示例仅包含易于人类阅读的字段,以避免在响应中出现冗长且难以解释的嵌入内容。

// 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

若要在查询时定位 earth-knowledge-source 和 gpt-5-mini 部署,需要一个知识库。 以下代码定义名为 earth-knowledge-base 的知识库。

OutputMode(预览版)设置为 AnswerSynthesis,从而生成引用检索到的文档并遵循所提供的 AnswerInstructions 的自然语言回答。 RetrievalReasoningEffort(预览版)设置为 low,以控制查询规划所用的推理量。

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

var model = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: openAiParameters);

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

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

参考: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 确定哪些角色有效。

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。 以下代码将用户的两部分查询发送到earth-knowledge-base:

  1. 分析整个对话,以推断用户的信息需求。
  2. 将复合查询分解为重点子查询。
  3. 针对知识源并发运行子查询。
  4. 使用语义排名器重新排序并筛选结果。
  5. 将顶级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 }
});

参考: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 为引用检索的文档的查询提供合成的 LLM 生成的答案。 如果未启用答案合成,此部分将包含直接从文档中提取的内容。

  • Activity 跟踪在检索过程中执行的步骤,包括由您的 gpt-5-mini 部署生成的子查询,以及用于语义排名、查询规划和答案合成的令牌。

  • References 列出了有助于生成响应的文档,每个文档由其唯一的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,知识库将从 earth-knowledge-source 消息列表中提取相关内容并将响应追加到消息列表中。

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

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

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

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

当你在自己的订阅中工作时,项目结束后删除不再需要的资源是一个好习惯。 让资源保持运行状态会耗费成本。

在Azure门户中,从左窗格中选择“所有资源或资源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,下面来自 program.cs 的代码会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.");     

在此快速入门中,使用代理检索创建由 Azure AI 搜索索引的文档和 Azure OpenAI Foundry 模型的大型语言模型 (LLM) 驱动的会话式搜索体验。

知识库使用基于 LLM 的查询规划(预览版)将复杂查询分解为子查询。 然后,它针对一个或多个 知识源 运行子查询,并使用元数据返回结果。 默认情况下,知识库从其源返回原始内容,但本快速入门使用答案合成(预览版)生成自然语言答案。

虽然可以使用自己的数据,但本快速入门使用 NASA 的《Earth at Night》电子书中的样本 JSON 文档。

提示

想要立即开始? 在 GitHub 上下载 source code。

先决条件

配置访问权限

在开始之前,请确保你有权访问内容和操作。 本快速入门使用 Microsoft Entra ID 进行身份验证,并通过基于角色的访问控制进行授权。 你必须是 所有者 或 用户访问管理员 才能分配角色。 如果角色不可行,请改用 基于密钥的身份验证 。

若要为本快速入门配置访问权限,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 启用基于角色的访问。

    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。

  3. 在 Microsoft Foundry 资源上,将 Cognitive Services User 分配给搜索服务的托管标识。

重要

代理检索具有两种基于令牌的计费模型:

  • 从 Azure AI 搜索中进行智能体检索的计费。
  • Azure OpenAI 的查询规划和答案生成功能收费标准。

有关详细信息,请参阅 区域可用性、限制和计费。

获取终结点

每个Azure AI 搜索服务和 Microsoft Foundry 资源都有一个 endpoint,这是一个唯一的 URL,用于标识并提供对资源的网络访问。 在后面的部分中,指定这些端点以通过编程操作连接到资源。

若要获取本快速入门的终结点,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 在左窗格中,选择“ 概述”。

    2. 复制 URL,其外观应如下所示 https://my-service.search.windows.net。

  3. 在您的 Microsoft Foundry 资源上:

    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

  1. 使用 Git 克隆示例存储库。

    git clone https://github.com/Azure-Samples/azure-search-java-samples
    
  2. 转到快速入门文件夹。

    cd azure-search-java-samples/quickstart-agentic-retrieval
    
  3. 在 sample.env 中,将 SEARCH_ENDPOINT 和 AOAI_ENDPOINT 的占位符值替换为你在 获取终结点 中获取的 URL。

  4. 重命名 sample.env 为 .env.

    mv sample.env .env
    
  5. 安装依赖项。

    mvn clean dependency:copy-dependencies
    
  6. 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

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

注意

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:

  1. 创建搜索索引
  2. 将文档上传到索引
  3. 创建知识源
  4. 创建知识库
  5. 设置消息
  6. 运行检索管道
  7. 继续对话

创建搜索索引

在Azure AI 搜索中,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。 该架构还包括语义排名和矢量搜索的配置,该配置使用部署 text-embedding-3-large 根据语义或概念相似性向量化文本和匹配文档。

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

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 搜索的要求,每个文档都符合索引架构中定义的字段和数据类型。

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,并以 earth-at-night 索引为目标。

sourceDataFields 指定引文引用中包含哪些索引字段。 此示例仅包含易于人类阅读的字段,以避免在响应中出现冗长且难以解释的嵌入内容。

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

若要在查询时定位 earth-knowledge-source 和 gpt-5-mini 部署,需要一个知识库。 以下代码定义名为 earth-knowledge-base 的知识库。

OutputMode(预览版)设置为 ANSWER_SYNTHESIS,以启用能够引用检索到的文档并遵循所提供的 AnswerInstructions 的自然语言答案。 RetrievalReasoningEffort(预览版)设置为 low,以控制用于查询规划的推理量。

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

KnowledgeBaseAzureOpenAIModel model =
    new KnowledgeBaseAzureOpenAIModel(openAiParameters);

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

indexClient.createOrUpdateKnowledgeBase(knowledgeBase);

参考: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 确定哪些角色有效。

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。

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。 以下代码将用户的两部分查询发送到earth-knowledge-base:

  1. 分析整个对话,以推断用户的信息需求。
  2. 将复合查询分解为重点子查询。
  3. 针对知识源并发运行子查询。
  4. 使用语义排名器重新排序并筛选结果。
  5. 将顶级结果合成为自然语言答案。
KnowledgeBaseRetrievalClient baseClient =
    new KnowledgeBaseRetrievalClientBuilder()
        .endpoint(searchEndpoint)
        .knowledgeBaseName(knowledgeBaseName)
        .credential(
            new DefaultAzureCredentialBuilder().build())
        .buildClient();

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

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

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

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

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

retrieve 辅助函数根据对话历史构建一个KnowledgeBaseRetrievalOptions,设置检索推理强度,附加知识源参数,并返回一个KnowledgeBaseRetrievalResult:

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

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

    return client.retrieve(request);
}

参考:KnowledgeBaseRetrievalClient、 KnowledgeBaseRetrievalOptions

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 为引用检索的文档的查询提供合成的 LLM 生成的答案。 如果未启用答案合成,此部分将包含直接从文档中提取的内容。

  • Activity 跟踪在检索过程中执行的步骤,包括由您的 gpt-5-mini 部署生成的子查询,以及用于语义排名、查询规划和答案合成的令牌。

  • References 列出了有助于生成响应的文档,每个文档由其唯一的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,知识库将从 earth-knowledge-source 消息列表中提取相关内容并将响应追加到消息列表中。

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

retrievalResult = retrieve(baseClient, messages, knowledgeSourceName);

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

当你在自己的订阅中工作时,项目结束后删除不再需要的资源是一个好习惯。 让资源保持运行状态会耗费成本。

在Azure门户中,从左窗格中选择“所有资源或资源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,下面来自 AgenticRetrievalQuickstart.java 的代码会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.");

在此快速入门中,使用代理检索创建由 Azure AI 搜索索引的文档和 Azure OpenAI Foundry 模型的大型语言模型 (LLM) 驱动的会话式搜索体验。

知识库使用基于 LLM 的查询规划(预览版)将复杂查询分解为子查询。 然后,它针对一个或多个 知识源 运行子查询,并使用元数据返回结果。 默认情况下,知识库从其源返回原始内容,但本快速入门使用答案合成(预览版)生成自然语言答案。

虽然可以使用自己的数据,但本快速入门使用 NASA 的《Earth at Night》电子书中的样本 JSON 文档。

提示

想要立即开始? 在 GitHub 上下载 source code。

先决条件

配置访问权限

在开始之前,请确保你有权访问内容和操作。 本快速入门使用 Microsoft Entra ID 进行身份验证,并通过基于角色的访问控制进行授权。 你必须是 所有者 或 用户访问管理员 才能分配角色。 如果角色不可行,请改用 基于密钥的身份验证 。

若要为本快速入门配置访问权限,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 启用基于角色的访问。

    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。

  3. 在 Microsoft Foundry 资源上,将 Cognitive Services User 分配给搜索服务的托管标识。

重要

代理检索具有两种基于令牌的计费模型:

  • 从 Azure AI 搜索中进行智能体检索的计费。
  • Azure OpenAI 的查询规划和答案生成功能收费标准。

有关详细信息,请参阅 区域可用性、限制和计费。

获取终结点

每个Azure AI 搜索服务和 Microsoft Foundry 资源都有一个 endpoint,这是一个唯一的 URL,用于标识并提供对资源的网络访问。 在后面的部分中,指定这些端点以通过编程操作连接到资源。

若要获取本快速入门的终结点,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 在左窗格中,选择“ 概述”。

    2. 复制 URL,其外观应如下所示 https://my-service.search.windows.net。

  3. 在您的 Microsoft Foundry 资源上:

    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

  1. 使用 Git 克隆示例存储库。

    git clone https://github.com/Azure-Samples/azure-search-javascript-samples
    
  2. 转到快速入门文件夹。

    cd azure-search-javascript-samples/quickstart-agentic-retrieval-js
    
  3. 在 sample.env 中,将 AZURE_SEARCH_ENDPOINT 和 AZURE_OPENAI_ENDPOINT 的占位符值替换为你在 获取终结点 中获取的 URL。

  4. 重命名 sample.env 为 .env.

    mv sample.env .env
    
  5. 安装依赖项。

    npm install
    

    安装完成后,你将在项目目录中看到一个 node_modules 文件夹。

  6. 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。

npm start

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:

  1. 创建搜索索引
  2. 将文档上传到索引
  3. 创建知识源
  4. 创建知识库
  5. 运行检索管道
  6. 查看响应、活动和引用
  7. 继续对话

创建搜索索引

在Azure AI 搜索中,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。 该架构还包括语义排名和矢量搜索的配置,该配置使用部署 text-embedding-3-large 根据语义相似性向量化文本和匹配文档。

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

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 搜索的要求,每个文档都符合索引架构中定义的字段和数据类型。

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,并以 earth-at-night 索引为目标。

sourceDataFields 指定引文引用中包含哪些索引字段。 此示例仅包含易于人类阅读的字段,以避免在响应中出现冗长且难以解释的嵌入内容。

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

若要在查询时定位 earth-knowledge-source 和 gpt-5-mini 部署,需要一个知识库。 以下代码定义名为 earth-knowledge-base 的知识库。

outputMode(预览版)设置为 answerSynthesis,启用引用检索到的文档并遵循所提供 answerInstructions 的自然语言答案。

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.`);

参考:KnowledgeBase

运行检索管道

你已准备好运行智能体检索。 以下代码将用户的两部分查询发送到earth-knowledge-base:

  1. 分析整个对话,以推断用户的信息需求。
  2. 将复合查询分解为重点子查询。
  3. 针对知识源并发运行子查询。
  4. 使用语义排名器重新排序并筛选结果。
  5. 将顶级结果合成为自然语言答案。

将 retrievalReasoningEffort(预览)设置为 low,以控制用于查询规划的推理量级。

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

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

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

const result = await knowledgeRetrievalClient.retrieve(retrievalRequest);

参考: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 为引用检索的文档的查询提供合成的 LLM 生成的答案。 如果未启用答案合成,此部分将包含直接从文档中提取的内容。

  • Activities 跟踪在检索过程中执行的步骤,包括由您的 gpt-5-mini 部署生成的子查询,以及用于语义排名、查询规划和答案合成的令牌。

  • References 列出了有助于生成响应的文档,每个文档由其唯一的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,知识库将从 earth-knowledge-source 消息列表中提取相关内容并将响应追加到消息列表中。

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

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

const result2 = await knowledgeRetrievalClient.retrieve(retrievalRequest2);

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

当你在自己的订阅中工作时,项目结束后删除不再需要的资源是一个好习惯。 让资源保持运行状态会耗费成本。

在Azure门户中,从左窗格中选择“所有资源或资源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,下面来自 index.js 的代码会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.`);

在此快速入门中,使用代理检索创建由 Azure AI 搜索索引的文档和 Azure OpenAI Foundry 模型的大型语言模型 (LLM) 驱动的会话式搜索体验。

知识库使用基于 LLM 的查询规划(预览版)将复杂查询分解为子查询。 然后,它针对一个或多个 知识源 运行子查询,并使用元数据返回结果。 默认情况下,知识库从其源返回原始内容,但本快速入门使用答案合成(预览版)生成自然语言答案。

虽然可以使用自己的数据,但本快速入门使用 NASA 的《Earth at Night》电子书中的样本 JSON 文档。

提示

想要立即开始? 在 GitHub 上下载 source code。

先决条件

配置访问权限

在开始之前,请确保你有权访问内容和操作。 本快速入门使用 Microsoft Entra ID 进行身份验证,并通过基于角色的访问控制进行授权。 你必须是 所有者 或 用户访问管理员 才能分配角色。 如果角色不可行,请改用 基于密钥的身份验证 。

若要为本快速入门配置访问权限,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 启用基于角色的访问。

    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。

  3. 在 Microsoft Foundry 资源上,将 Cognitive Services User 分配给搜索服务的托管标识。

重要

代理检索具有两种基于令牌的计费模型:

  • 从 Azure AI 搜索中进行智能体检索的计费。
  • Azure OpenAI 的查询规划和答案生成功能收费标准。

有关详细信息,请参阅 区域可用性、限制和计费。

获取终结点

每个Azure AI 搜索服务和 Microsoft Foundry 资源都有一个 endpoint,这是一个唯一的 URL,用于标识并提供对资源的网络访问。 在后面的部分中,指定这些端点以通过编程操作连接到资源。

若要获取本快速入门的终结点,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 在左窗格中,选择“ 概述”。

    2. 复制 URL,其外观应如下所示 https://my-service.search.windows.net。

  3. 在您的 Microsoft Foundry 资源上:

    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

  1. 使用 Git 克隆示例存储库。

    git clone https://github.com/Azure-Samples/azure-search-python-samples
    
  2. 转到快速入门文件夹,并在Visual Studio Code中将其打开。

    cd azure-search-python-samples/Quickstart-Agentic-Retrieval
    code .
    
  3. 在 sample.env 中,将 SEARCH_ENDPOINT 和 AOAI_ENDPOINT 的占位符值替换为你在 获取终结点 中获取的 URL。

  4. 重命名 sample.env 为 .env.

    mv sample.env .env
    
  5. 打开 quickstart-agentic-retrieval.ipynb。

  6. 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。

  7. 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

  1. Load connections运行单元以安装所需的包并加载环境变量。

  2. 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。 以下示例显示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

注意

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:

  1. 创建搜索索引
  2. 将文档上传到索引
  3. 创建知识源
  4. 创建知识库
  5. 设置消息
  6. 运行检索管道
  7. 继续对话

创建搜索索引

在Azure AI 搜索中,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。 该架构还包括语义排名和矢量搜索的配置,该配置使用部署 text-embedding-3-large 根据语义相似性向量化文本和匹配文档。

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

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

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

Reference:SearchField、 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 搜索的要求,每个文档都符合索引架构中定义的字段和数据类型。

# Upload documents
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,并以 earth-at-night 索引为目标。

source_data_fields 指定引文引用中包含哪些索引字段。 此示例仅包含易于人类阅读的字段,以避免在响应中出现冗长且难以解释的嵌入内容。

# 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

若要在查询时定位 earth-knowledge-source 和 gpt-5-mini 部署,需要一个知识库。 以下代码定义名为 earth-knowledge-base 的知识库。

output_mode(预览版)设置为 answerSynthesis,从而启用可引用检索到的文档并遵循所提供的 answer_instructions 的自然语言回答。

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

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

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

参考: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 确定哪些角色有效。

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。 以下代码将用户的两部分查询发送到earth-knowledge-base:

  1. 分析整个对话,以推断用户的信息需求。
  2. 将复合查询分解为重点子查询。
  3. 针对知识源并发运行子查询。
  4. 使用语义排名器重新排序并筛选结果。
  5. 将顶级结果合成为自然语言答案。

retrieval_reasoning_effort(预览版)被设置为 low,以控制查询规划中使用的推理量。

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

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

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

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

参考: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 为引用检索的文档的查询提供合成的 LLM 生成的答案。 如果未启用答案合成,此部分将包含直接从文档中提取的内容。

  • activity_contents 跟踪在检索过程中执行的步骤,包括由您的 gpt-5-mini 部署生成的子查询,以及用于语义排名、查询规划和答案合成的令牌。

  • references_contents 列出了有助于生成响应的文档,每个文档由其唯一的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,知识库将从 earth-knowledge-source 消息列表中提取相关内容并将响应追加到消息列表中。

# 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

当你在自己的订阅中工作时,项目结束后删除不再需要的资源是一个好习惯。 让资源保持运行状态会耗费成本。

在Azure门户中,从左窗格中选择“所有资源或资源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,下面来自 quickstart-agentic-retrieval.ipynb 的代码会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.")

在此快速入门中,使用代理检索创建由 Azure AI 搜索索引的文档和 Azure OpenAI Foundry 模型的大型语言模型 (LLM) 驱动的会话式搜索体验。

知识库使用基于 LLM 的查询规划(预览版)将复杂查询分解为子查询。 然后,它针对一个或多个 知识源 运行子查询,并使用元数据返回结果。 默认情况下,知识库从其源返回原始内容,但本快速入门使用答案合成(预览版)生成自然语言答案。

虽然可以使用自己的数据,但本快速入门使用 NASA 的《Earth at Night》电子书中的样本 JSON 文档。

提示

想要立即开始? 在 GitHub 上下载 source code。

先决条件

配置访问权限

在开始之前,请确保你有权访问内容和操作。 本快速入门使用 Microsoft Entra ID 进行身份验证,并通过基于角色的访问控制进行授权。 你必须是 所有者 或 用户访问管理员 才能分配角色。 如果角色不可行,请改用 基于密钥的身份验证 。

若要为本快速入门配置访问权限,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 启用基于角色的访问。

    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。

  3. 在 Microsoft Foundry 资源上,将 Cognitive Services User 分配给搜索服务的托管标识。

重要

代理检索具有两种基于令牌的计费模型:

  • 从 Azure AI 搜索中进行智能体检索的计费。
  • Azure OpenAI 的查询规划和答案生成功能收费标准。

有关详细信息,请参阅 区域可用性、限制和计费。

获取终结点

每个Azure AI 搜索服务和 Microsoft Foundry 资源都有一个 endpoint,这是一个唯一的 URL,用于标识并提供对资源的网络访问。 在后面的部分中,指定这些端点以通过编程操作连接到资源。

若要获取本快速入门的终结点,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 在左窗格中,选择“ 概述”。

    2. 复制 URL,其外观应如下所示 https://my-service.search.windows.net。

  3. 在您的 Microsoft Foundry 资源上:

    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

  1. 使用 Git 克隆示例存储库。

    git clone https://github.com/Azure-Samples/azure-search-javascript-samples
    
  2. 转到快速入门文件夹。

    cd azure-search-javascript-samples/quickstart-agentic-retrieval-ts
    
  3. 在 sample.env 中,将 AZURE_SEARCH_ENDPOINT 和 AZURE_OPENAI_ENDPOINT 的占位符值替换为你在 获取终结点 中获取的 URL。

  4. 重命名 sample.env 为 .env.

    mv sample.env .env
    
  5. 安装依赖项。

    npm install
    

    安装完成后,你将在项目目录中看到一个 node_modules 文件夹。

  6. 将 TypeScript 文件编译为 JavaScript。

    npm run build
    
  7. 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。

npm start

注意

此命令运行来自.js文件夹的已编译dist文件。 Node.js 要求 TypeScript 代码在可以执行之前转译到 JavaScript,这就是为什么你以前运行 npm run build的原因。

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:

  1. 创建搜索索引
  2. 将文档上传到索引
  3. 创建知识源
  4. 创建知识库
  5. 运行检索管道
  6. 查看响应、活动和引用
  7. 继续对话

创建搜索索引

在Azure AI 搜索中,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。 该架构还包括语义排名和矢量搜索的配置,该配置使用部署 text-embedding-3-large 根据语义相似性向量化文本和匹配文档。

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

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 搜索的要求,每个文档都符合索引架构中定义的字段和数据类型。

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,并以 earth-at-night 索引为目标。

sourceDataFields 指定引文引用中包含哪些索引字段。 此示例仅包含易于人类阅读的字段,以避免在响应中出现冗长且难以解释的嵌入内容。

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

若要在查询时定位 earth-knowledge-source 和 gpt-5-mini 部署,需要一个知识库。 以下代码定义名为 earth-knowledge-base 的知识库。

outputMode(预览版)设置为 answerSynthesis,从而启用能够引用检索到的文档并遵循所提供的 answerInstructions 的自然语言答案。

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.`); 

参考:KnowledgeBase

运行检索管道

你已准备好运行智能体检索。 以下代码将用户的两部分查询发送到earth-knowledge-base:

  1. 分析整个对话,以推断用户的信息需求。
  2. 将复合查询分解为重点子查询。
  3. 针对知识源并发运行子查询。
  4. 使用语义排名器重新排序并筛选结果。
  5. 将顶级结果合成为自然语言答案。

retrievalReasoningEffort(预览版)被设置为 low,以控制查询规划所使用的推理量。

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

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

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

const result = await knowledgeRetrievalClient.retrieve(retrievalRequest);

参考: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 为引用检索的文档的查询提供合成的 LLM 生成的答案。 如果未启用答案合成,此部分将包含直接从文档中提取的内容。

  • Activities 跟踪在检索过程中执行的步骤,包括由您的 gpt-5-mini 部署生成的子查询,以及用于语义排名、查询规划和答案合成的令牌。

  • References 列出了有助于生成响应的文档,每个文档由其唯一的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,知识库将从 earth-knowledge-source 消息列表中提取相关内容并将响应追加到消息列表中。

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

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

const result2 = await knowledgeRetrievalClient.retrieve(retrievalRequest2);

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

当你在自己的订阅中工作时,项目结束后删除不再需要的资源是一个好习惯。 让资源保持运行状态会耗费成本。

在Azure门户中,从左窗格中选择“所有资源或资源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,下面来自 index.ts 的代码会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.`);

在此快速入门中,使用代理检索创建由 Azure AI 搜索索引的文档和 Azure OpenAI Foundry 模型的大型语言模型 (LLM) 驱动的会话式搜索体验。

知识库使用基于 LLM 的查询规划(预览版)将复杂查询分解为子查询。 然后,它针对一个或多个 知识源 运行子查询,并使用元数据返回结果。 默认情况下,知识库从其源返回原始内容,但本快速入门使用答案合成(预览版)生成自然语言答案。

虽然可以使用自己的数据,但本快速入门使用 NASA 的《Earth at Night》电子书中的样本 JSON 文档。

提示

想要立即开始? 在 GitHub 上下载 source code。

先决条件

配置访问权限

在开始之前,请确保你有权访问内容和操作。 本快速入门使用 Microsoft Entra ID 进行身份验证,并通过基于角色的访问控制进行授权。 你必须是 所有者 或 用户访问管理员 才能分配角色。 如果角色不可行,请改用 基于密钥的身份验证 。

若要为本快速入门配置访问权限,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 启用基于角色的访问。

    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。

  3. 在 Microsoft Foundry 资源上,将 Cognitive Services User 分配给搜索服务的托管标识。

重要

代理检索具有两种基于令牌的计费模型:

  • 从 Azure AI 搜索中进行智能体检索的计费。
  • Azure OpenAI 的查询规划和答案生成功能收费标准。

有关详细信息,请参阅 区域可用性、限制和计费。

获取终结点

每个Azure AI 搜索服务和 Microsoft Foundry 资源都有一个 endpoint,这是一个唯一的 URL,用于标识并提供对资源的网络访问。 在后面的部分中,指定这些端点以通过编程操作连接到资源。

若要获取本快速入门的终结点,请执行以下操作:

  1. 登录到 Azure 门户。

  2. 在您的 Azure AI 搜索服务中:

    1. 在左窗格中,选择“ 概述”。

    2. 复制 URL,其外观应如下所示 https://my-service.search.windows.net。

  3. 在您的 Microsoft Foundry 资源上:

    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

  1. 使用 Git 克隆示例存储库。

    git clone https://github.com/Azure-Samples/azure-search-rest-samples
    
  2. 转到快速入门文件夹,并在Visual Studio Code中将其打开。

    cd azure-search-rest-samples/Quickstart-agentic-retrieval
    code .
    
  3. 在 agentic-retrieval.rest 中,将 @search-url 和 @aoai-url 的占位符值替换为你在 获取终结点 中获取的 URL。

  4. 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
    
  5. 请使用 Microsoft Entra ID 进行无密钥身份验证,生成访问令牌。

    az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
    
  6. 将占位符值 @token 替换为上一步中的标记。

运行代码

按顺序发送每个请求,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。 如果出现错误,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。 关键输出来自 ### Run agentic retrieval,其应与以下内容类似:

{
  "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

注意

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:

  1. 创建搜索索引
  2. 将文档上传到索引
  3. 创建知识源
  4. 创建知识库
  5. 运行检索管道

创建搜索索引

在Azure AI 搜索中,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。 该架构还包括语义排名和矢量搜索的配置,该配置使用部署 text-embedding-3-large 根据语义相似性向量化文本和匹配文档。

### 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 搜索的要求,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,并以 earth-at-night 索引为目标。

sourceDataFields 指定引文引用中包含哪些索引字段。 此示例仅包含易于人类阅读的字段,以避免在响应中出现冗长且难以解释的嵌入内容。

### 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

为在查询时将 earth-knowledge-source 和 gpt-5-mini 部署作为目标,需要一个知识库。 以下代码定义一个名为earth-knowledge-base的基类。

outputMode(预览版)设置为 answerSynthesis,启用可引用检索到的文档并遵循所提供的 answerInstructions 的自然语言回答。

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

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

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。 以下代码将用户的两部分查询发送到earth-knowledge-base:

  1. 分析整个对话,以推断用户的信息需求。
  2. 将复合查询分解为重点子查询。
  3. 针对知识源并发运行子查询。
  4. 使用语义排名器重新排序并筛选结果。 此示例排除重排序分数为 2.5 或更低的响应。
  5. 将顶级结果合成为自然语言答案。

retrievalReasoningEffort(预览)被设置为 low,以控制查询规划中使用的推理量。

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

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

参考:知识检索 - 检索

输出包含以下组件:

  • response 为引用检索的文档的查询提供合成的 LLM 生成的答案。 如果未启用答案合成,此部分将包含直接从文档中提取的内容。

  • activity 跟踪在检索过程中执行的步骤,包括由您的 gpt-5-mini 部署生成的子查询,以及用于语义排名、查询规划和答案合成的令牌。

  • references 列出了有助于生成响应的文档,每个文档由其唯一的docKey标识。

清理资源

当你在自己的订阅中工作时,项目结束后删除不再需要的资源是一个好习惯。 让资源保持运行状态会耗费成本。

在Azure门户中,从左窗格中选择“所有资源或资源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 的以下请求会删除你在本快速入门中创建的对象。

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}}