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快速入门：在 Azure AI 搜索中运行代理检索

2025-06-17

注释

此功能目前处于公开预览状态。此预览版在提供时未附带服务级别协议，建议不要用于生产工作负载。某些功能可能不受支持或者受限。有关详细信息，请参阅 Microsoft Azure 预览版补充使用条款。

在本快速入门中，你将使用代理检索来创建由大型语言模型（LLM）和专有数据提供支持的对话搜索体验。能动检索将复杂的用户查询分解为子查询，并行执行子查询，并从 Azure AI 搜索所索引的文档中提取基础数据。输出适用于与代理和自定义聊天解决方案的集成。

虽然可以提供你自己的数据，但本快速入门使用了美国宇航局“夜间地球”电子书中的示例 JSON 文档。这些文件描述了一般科学主题，以及从太空观测到的夜晚地球的图像。

本快速入门基于 GitHub 上的 Quickstart-Agentic-Retrieval Jupyter 笔记本。

先决条件

拥有有效订阅的 Azure 帐户。免费创建帐户。
Azure AI 搜索服务，位于基本层或更高层级，且已启用语义排序器。
一个 Azure AI Foundry 项目。创建 Azure AI Foundry 项目时，将获得 Azure AI Foundry 资源（模型部署所需的资源）。
具有 Python 扩展和 Jupyter 包的 Visual Studio Code。
使用 Microsoft Entra ID 进行无密钥身份验证的 Azure CLI。

配置基于角色的访问

可以在角色分配中使用搜索服务 API 密钥或Microsoft Entra ID。密钥更容易上手，但角色更安全。本快速入门将使用角色。

若要配置建议的基于角色的访问：

登录到 Azure 门户。
在 Azure AI 搜索服务上启用基于角色的访问。
在 Azure AI 搜索服务中，将以下角色分配给自己。
- 搜索服务参与者
- 搜索索引数据参与者
- 搜索索引数据读取者

若要进行代理检索，Azure AI 搜索还需要访问 Azure OpenAI Foundry 资源。

在 Azure AI 搜索服务上创建系统分配的托管标识。下面介绍如何使用 Azure CLI 执行此作：
```
az search service update --name YOUR-SEARCH-SERVICE-NAME --resource-group YOUR-RESOURCE-GROUP-NAME --identity-type SystemAssigned
```
如果已有托管标识，则可以跳过此步骤。
在 Azure AI Foundry 资源上，将 认知服务用户 分配到为搜索服务创建的托管标识。

部署模型

若要使用代理检索，必须将受支持的 Azure OpenAI 模型之一部署到 Azure AI Foundry 资源：

用于查询规划和答案生成的聊天模型。在本快速入门中，我们将使用 gpt-4.1-mini 。（可选）可以使用不同的模型进行查询规划，也可以使用另一个模型进行答案生成，但本快速入门使用同一模型来简单起见。
矢量查询的嵌入模型。在本快速入门中，我们使用text-embedding-3-large，但你可以使用任何支持text-embedding任务的嵌入模型。

部署 Azure OpenAI 模型：

登录到 Azure AI Foundry 门户并选择 Azure AI Foundry 资源。
在左窗格中，选择 “模型”目录。
选择 gpt-4.1-mini，然后选择“ 使用此模型”。
指定部署名称。为了简化代码，我们建议 使用 gpt-4.1-mini。
保留默认设置。
选择“部署”。
重复之前的步骤，但这次部署嵌入模型 text-embedding-3-large。

获取终结点

在代码中，指定以下终结点来与 Azure AI 搜索服务和 Azure AI Foundry 资源建立连接。这些步骤假定你配置了基于角色的访问，如前所述。

若要获取您的服务终端节点，请执行以下操作：

登录到 Azure 门户。
在 Azure AI 搜索服务上：
1. 在左窗格中，选择“ 概述”。
2. 复制 URL，该 URL 应类似于 https://my-service.search.windows.net。
在 Azure AI Foundry 资源上：
1. 在左窗格中，选择 “资源管理>密钥和终结点”。
2. 选择 “OpenAI ”选项卡，并复制如下所示 https://my-resource.openai.azure.com的 URL。

从本地系统进行连接

你配置了基于角色的访问，以便与 Azure AI 搜索和 Azure OpenAI 交互。

从本地系统进行连接：

在 Visual Studio Code 中打开一个新终端，并更改为要保存文件的目录。
运行以下 Azure CLI 命令并使用 Azure 帐户登录。如果有多个订阅，请选择包含 Azure AI 搜索服务和 Azure AI Foundry 项目的订阅。
```
az login
```

有关详细信息，请参阅快速入门：在没有密钥的情况下连接。

安装软件包并加载连接

在运行任何代码之前，请安装 Python 包并定义连接到 Azure AI 搜索和 Azure OpenAI 的凭据、终结点和部署详细信息。这些值用于后续的操作。

安装包并加载连接：

在 Visual Studio Code 中，创建一个 .ipynb 文件。例如，可以命名该文件 quickstart-agentic-retrieval.ipynb。

在第一个代码单元中，粘贴以下代码以安装所需的包。

! pip install azure-search-documents==11.6.0b12 --quiet
! pip install azure-identity --quiet
! pip install openai --quiet
! pip install aiohttp --quiet
! pip install ipykernel --quiet
! pip install requests --quiet

可以通过选择 “运行单元格 ”按钮或按键 Shift+Enter来运行此单元格。

添加另一个代码单元并粘贴以下 import 语句和变量。

from azure.identity import DefaultAzureCredential, get_bearer_token_provider
import os

endpoint = "PUT YOUR SEARCH SERVICE ENDPOINT HERE"
credential = DefaultAzureCredential()
token_provider = get_bearer_token_provider(credential, "https://search.azure.com/.default")
azure_openai_endpoint = "PUT YOUR AZURE AI FOUNDRY ENDPOINT HERE"
azure_openai_gpt_deployment = "gpt-4.1-mini"
azure_openai_gpt_model = "gpt-4.1-mini"
azure_openai_api_version = "2025-03-01-preview"
azure_openai_embedding_deployment = "text-embedding-3-large"
azure_openai_embedding_model = "text-embedding-3-large"
index_name = "earth_at_night"
agent_name = "earth-search-agent"
answer_model = "gpt-4.1-mini"
api_version = "2025-05-01-Preview"

将 endpoint 设置为 Azure AI 搜索终结点，这类似于 https://<your-search-service-name>.search.windows.net.。将 azure_openai_endpoint 设置为 Azure AI Foundry 终结点，这类似于 https://<your-foundry-resource-name>.openai.azure.com.。你在获取终结点部分获得这两个值。
若要验证变量，请运行代码单元。

创建搜索索引

在 Azure AI 搜索中，索引是结构化数据集合。以下代码定义了一个名为 earth_at_night 的索引，该索引由上一节中的 index_name 变量指定。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

from azure.search.documents.indexes.models import SearchIndex, SearchField, VectorSearch, VectorSearchProfile, HnswAlgorithmConfiguration, AzureOpenAIVectorizer, AzureOpenAIVectorizerParameters, SemanticSearch, SemanticConfiguration, SemanticPrioritizedFields, SemanticField
from azure.search.documents.indexes import SearchIndexClient

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=azure_openai_endpoint,
                    deployment_name=azure_openai_embedding_deployment,
                    model_name=azure_openai_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=endpoint, credential=credential)
index_client.create_or_update_index(index)
print(f"Index '{index_name}' created or updated successfully")

索引架构包含文档标识和页面内容、嵌入和数字的字段。它还包括语义排名和矢量查询的配置，这些配置使用之前部署的 text-embedding-3-large 模型。

重要

代理检索具有两种基于标记的计费模型：

Azure OpenAI 的查询规划计费。
Azure AI 搜索的查询执行计费（语义排序）。

语义排名在初始公共预览版中是免费的。预览版后，将应用标准标记计费。有关详细信息，请参阅代理检索的可用性和定价。

将文档上传到索引

目前，索引 earth_at_night 为空。运行以下代码，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。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

from azure.search.documents import SearchIndexingBufferedSender
import requests

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

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

创建知识代理

要在查询时将 Azure AI 搜索连接到 gpt-4.1-mini 部署并面向 earth_at_night 索引，需要使用知识代理。以下代码定义一个名为 earth-search-agent的知识代理，该代理使用上一部分中的 agent_name 变量指定。

为确保响应的相关性和语义意义明确，已设置 default_reranker_threshold，以排除 reranker 分数为 2.5 或更低的响应。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

from azure.search.documents.indexes.models import KnowledgeAgent, KnowledgeAgentAzureOpenAIModel, KnowledgeAgentTargetIndex, KnowledgeAgentRequestLimits, AzureOpenAIVectorizerParameters

agent = KnowledgeAgent(
    name=agent_name,
    models=[
        KnowledgeAgentAzureOpenAIModel(
            azure_open_ai_parameters=AzureOpenAIVectorizerParameters(
                resource_url=azure_openai_endpoint,
                deployment_name=azure_openai_gpt_deployment,
                model_name=azure_openai_gpt_model
            )
        )
    ],
    target_indexes=[
        KnowledgeAgentTargetIndex(
            index_name=index_name,
            default_reranker_threshold=2.5
        )
    ],
)

index_client.create_or_update_agent(agent)
print(f"Knowledge agent '{agent_name}' created or updated successfully")

设置消息

下一步是使用 messages 数组定义知识代理指令和聊天上下文。每条消息包含一个 role（例如 user 或 assistant）以及自然语言的 content。用户消息表示要处理的查询，而助理消息指导知识代理如何响应。在检索过程中，这些消息将发送到 LLM，以便从索引文档中提取相关响应。

现在，请创建以下助手消息，该消息指示 earth-search-agent 回答有关夜间地球的问题，使用 ref_id 引用来源，并在答案不可用时回答“我不知道”。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

instructions = """
An Q&A agent that can answer questions about the Earth at night.
Sources have a JSON format with a ref_id that must be cited in the answer.
If you do not have the answer, respond with "I don't know".
"""

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

运行检索管道

你已准备好启动代理检索管道。此管道的输入是 messages 数组，其对话历史记录包括之前提供的说明和用户查询。此外， target_index_params 指定要查询的索引和其他配置，例如语义排名器阈值。

以下代码将一个由两部分构成的用户查询发送到 earth-search-agent，该代码会将查询解构为子查询，并在 earth_at_night 索引中的文本字段和向量嵌入上运行这些子查询，随后对结果进行排名和合并。然后，响应将追加到 messages 数组中。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

from azure.search.documents.agent import KnowledgeAgentRetrievalClient
from azure.search.documents.agent.models import KnowledgeAgentRetrievalRequest, KnowledgeAgentMessage, KnowledgeAgentMessageTextContent, KnowledgeAgentIndexParams

agent_client = KnowledgeAgentRetrievalClient(endpoint=endpoint, agent_name=agent_name, credential=credential)

messages.append({
    "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?
    """
})

retrieval_result = agent_client.retrieve(
    retrieval_request=KnowledgeAgentRetrievalRequest(
        messages=[KnowledgeAgentMessage(role=msg["role"], content=[KnowledgeAgentMessageTextContent(text=msg["content"])]) for msg in messages if msg["role"] != "system"],
        target_index_params=[KnowledgeAgentIndexParams(index_name=index_name, reranker_threshold=2.5)]
    )
)
messages.append({
    "role": "assistant",
    "content": retrieval_result.response[0].content[0].text
})

查看响应、活动和结果

现在，你想要显示检索管道的响应、活动和结果。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

import textwrap
import json

print("Response")
print(textwrap.fill(retrieval_result.response[0].content[0].text, width=120))

print("Activity")
print(json.dumps([a.as_dict() for a in retrieval_result.activity], indent=2))

print("Results")
print(json.dumps([r.as_dict() for r in retrieval_result.references], indent=2))

输出应类似于以下示例，其中：

Response 根据用户查询在搜索索引中提供最相关的文档（或区块）的文本字符串。如本快速入门稍后所示，可以将此字符串传递给 LLM 进行答案生成。
Activity 跟踪检索过程中采取的步骤，其中包括由您的 gpt-4.1-mini 部署生成的子查询，以及用于查询规划和执行的令牌。
Results 列出构成该响应的文档，每个文档都由其 doc_key标识。

Response
[{"ref_id":1,"content":"# 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"},{"ref_id":0,"content":"<!-- 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, ... 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\" -->"}]
Activity
[
  {
    "id": 0,
    "type": "ModelQueryPlanning",
    "input_tokens": 1407,
    "output_tokens": 309
  },
  {
    "id": 1,
    "type": "AzureSearchQuery",
    "target_index": "earth_at_night",
    "query": {
      "search": "suburban belts December brightening urban cores light levels"
    },
    "query_time": "2025-05-06T20:47:01.814Z",
    "elapsed_ms": 714
  },
  {
    "id": 2,
    "type": "AzureSearchQuery",
    "target_index": "earth_at_night",
    "query": {
      "search": "Phoenix nighttime street grid visibility from space"
    },
    "query_time": "2025-05-06T20:47:02.230Z",
    "count": 2,
    "elapsed_ms": 416
  }
]
Results
[
  {
    "type": "AzureSearchDoc",
    "id": "0",
    "activity_source": 2,
    "doc_key": "earth_at_night_508_page_104_verbalized"
  },
  {
    "type": "AzureSearchDoc",
    "id": "1",
    "activity_source": 2,
    "doc_key": "earth_at_night_508_page_105_verbalized"
  }
]

创建 Azure OpenAI 客户端

若要将检索管道从应答提取扩展到应答生成，请设置 Azure OpenAI 客户端以与 gpt-4.1-mini 部署进行交互，该部署是使用上一部分中的 answer_model 变量指定的。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

from openai import AzureOpenAI
from azure.identity import get_bearer_token_provider

azure_openai_token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")
client = AzureOpenAI(
    azure_endpoint=azure_openai_endpoint,
    azure_ad_token_provider=azure_openai_token_provider,
    api_version=azure_openai_api_version
)

使用响应 API 生成答案

现在，可以使用响应 API 基于索引文档生成详细答案。以下代码将 messages 包含会话历史记录的数组发送到 gpt-4.1-mini 部署。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

response = client.responses.create(
    model=answer_model,
    input=messages
)

wrapped = textwrap.fill(response.output_text, width=100)
print(wrapped)

输出应类似于以下示例，该示例使用推理功能 gpt-4.1-mini 提供上下文相关的答案。

Suburban belts often exhibit larger December brightening than urban cores primarily because of the type of development and light distribution in those areas. Suburbs tend to have more uniform and expansive lighting, making them more visible in nighttime satellite images. In contrast, urban cores, although having higher absolute light levels, often contain dense building clusters that can cause light to be obscured or concentrated in smaller areas, leading to less visible brightening when viewed from space.  Regarding the visibility of the Phoenix nighttime street grid from space, it is attributed to the city's grid layout and the intensity of its street lighting. The grid pattern of the streets and the significant development around them create a stark contrast against less developed areas. Conversely, large stretches of interstate in the Midwest may remain dimmer due to fewer densely populated structures and less intensive street lighting, resulting in less illumination overall.  For more detailed insights, you can refer to the sources: [0] and [1].

使用聊天补全 API 生成答案

或者，可以使用聊天完成 API 生成答案。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

response = client.chat.completions.create(
    model=answer_model,
    messages=messages
)

wrapped = textwrap.fill(response.choices[0].message.content, width=100)
print(wrapped)

输出应如以下示例所示。

Suburban belts tend to display larger December brightening than urban cores, despite the absolute light levels being higher in downtown areas, due to the differing density of light sources and how light scatters. In urban cores, the intense concentration of lights may result in a more uniform light distribution that can obscure the brightening effect, whereas suburban areas, with their lower density of lights and more open spaces, allow for clearer visibility of atmospheric light scattering, thus enhancing the brightening effect in those regions.  As for why the Phoenix nighttime street grid is sharply visible from space compared to the dim stretches of the interstate between Midwestern cities, it primarily relates to urban planning and development patterns. The Phoenix metropolitan area is laid out along a regular grid of city blocks that include extensive street lighting, making the urban structure distinctly visible from space. In contrast, the interstates between Midwestern cities often traverse areas with less concentrated development and fewer bright lighting sources, leading to these sections appearing dimmer in nighttime imagery [1; ref_id:1].

继续对话

通过向earth-search-agent发送另一个用户查询继续对话。以下代码重新运行检索管道，从 earth_at_night 索引中提取相关内容，并将响应追加到 messages 数组。但是，与以前不同，现在可以使用 Azure OpenAI 客户端基于检索的内容生成答案。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

messages.append({
    "role": "user",
    "content": "How do I find lava at night?"
})

retrieval_result = agent_client.retrieve(
    retrieval_request=KnowledgeAgentRetrievalRequest(
        messages=[KnowledgeAgentMessage(role=msg["role"], content=[KnowledgeAgentMessageTextContent(text=msg["content"])]) for msg in messages if msg["role"] != "system"],
        target_index_params=[KnowledgeAgentIndexParams(index_name=index_name, reranker_threshold=2.5)]
    )
)
messages.append({
    "role": "assistant",
    "content": retrieval_result.response[0].content[0].text
})

查看新的响应、活动和结果

现在，你想要显示检索管道的响应、活动和结果。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

import textwrap
import json

print("Response")
print(textwrap.fill(retrieval_result.response[0].content[0].text, width=120))

print("Activity")
print(json.dumps([a.as_dict() for a in retrieval_result.activity], indent=2))

print("Results")
print(json.dumps([r.as_dict() for r in retrieval_result.references], indent=2))

生成 LLM 支持的答案

现在您已经发送了多个用户查询，请使用响应 API 根据messages数组中捕获的索引文档和对话历史生成答案。

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

response = client.responses.create(
    model=answer_model,
    input=messages
)

wrapped = textwrap.fill(response.output_text, width=100)
print(wrapped)

输出应如以下示例所示。

To find lava at night, you can look for the following signs:  1. **Active Volcanoes**: Research volcanoes that are currently active. Notable examples include Mount Etna in Italy and Kilauea in Hawaii. Both have had significant eruptions that can be observed at night due to the glow of lava. 2. **Satellite Imagery**: Use satellite imagery, especially those from sources like VIIRS (Visible Infrared Imaging Radiometer Suite) on the Suomi NPP satellite, which captures nighttime images of active lava flows. During eruptions, lava glows brightly in thermal infrared images, making it detectable from space.  3. **Safe Viewing Locations**: If you’re near an active volcano, find designated viewing areas for safety. Many national parks with volcanoes offer nighttime lava viewing experiences.  4. **Moonlight**: The presence of moonlight can enhance visibility, allowing you to spot lava flows more easily against the backdrop of the dark landscape.  5. **Monitoring Reports**: Follow updates from geological services or local authorities that monitor volcanic activity, which often provide real-time information about eruptions and visible lava flows at night.  6. **Photography**: If you're an enthusiast, consider using long-exposure photography techniques to capture the glow of lava flows at night.  For more information on observing volcanic activity, satellite imagery can provide vital data for detecting lava flows and volcanic eruptions.

清理资源

在你自己的订阅中工作时，最好在结束项目时确定你是否仍需要创建的资源。持续运行的资源可能会产生费用。可以单独删除资源，也可以删除资源组以删除整个资源集。

在 Azure 门户中，可以通过从左窗格中选择 “所有资源 ”或 “资源组 ”来查找和管理资源。还可以运行以下代码来删除在本快速入门中创建的对象。

删除知识代理

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

index_client = SearchIndexClient(endpoint=endpoint, credential=credential)
index_client.delete_agent(agent_name)
print(f"Knowledge agent '{agent_name}' deleted successfully")

删除搜索索引

使用以下代码在 quickstart-agentic-retrieval.ipynb 笔记本中添加并运行新的代码单元：

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

注释

在本快速入门中，你将使用代理检索来创建由大型语言模型（LLM）和专有数据提供支持的对话搜索体验。能动检索将复杂的用户查询分解为子查询，并行执行子查询，并从 Azure AI 搜索所索引的文档中提取基础数据。输出旨在与自定义聊天解决方案集成。

小窍门

本快速入门的 REST 版本在 Azure AI 搜索中引入了智能检索，这种检索通过提取而非生成答案。有关端到端工作流（包括添加对话轮次的步骤以及将检索的内容传递到 LLM 进行应答生成的步骤），请参阅 Python 版本。

先决条件

拥有有效订阅的 Azure 帐户。免费创建帐户。
Azure AI 搜索服务，位于基本层或更高层级，且已启用语义排序器。
一个 Azure AI Foundry 项目。创建 Azure AI Foundry 项目时，将获得 Azure AI Foundry 资源（模型部署所需的资源）。
带有REST 客户端的Visual Studio Code。
用于与 Microsoft Entra ID 进行无密钥身份验证的 Azure CLI。

配置基于角色的访问

可以在角色分配中使用搜索服务 API 密钥或Microsoft Entra ID。密钥更容易上手，但角色更安全。本快速入门将使用角色。

若要配置建议的基于角色的访问：

登录到 Azure 门户。
在 Azure AI 搜索服务上启用基于角色的访问。
在 Azure AI 搜索服务中，将以下角色分配给自己。
- 搜索服务参与者
- 搜索索引数据参与者
- 搜索索引数据读取者

若要进行代理检索，Azure AI 搜索还需要访问 Azure OpenAI Foundry 资源。

在 Azure AI 搜索服务上创建系统分配的托管标识。下面介绍如何使用 Azure CLI 执行此作：
```
az search service update --name YOUR-SEARCH-SERVICE-NAME --resource-group YOUR-RESOURCE-GROUP-NAME --identity-type SystemAssigned
```
如果已有托管标识，则可以跳过此步骤。
在 Azure AI Foundry 资源上，将 认知服务用户 分配到为搜索服务创建的托管标识。

部署模型

若要使用代理检索，必须将受支持的 Azure OpenAI 模型之一部署到 Azure AI Foundry 资源：

用于查询规划和答案生成的聊天模型。在本快速入门中，我们将使用 gpt-4.1-mini 。（可选）可以使用不同的模型进行查询规划，也可以使用另一个模型进行答案生成，但本快速入门使用同一模型来简单起见。
矢量查询的嵌入模型。在本快速入门中，我们使用text-embedding-3-large，但你可以使用任何支持text-embedding任务的模型嵌入。

部署 Azure OpenAI 模型：

登录到 Azure AI Foundry 门户并选择 Azure AI Foundry 资源。
在左窗格中，选择 “模型”目录。
选择 gpt-4.1-mini，然后选择“ 使用此模型”。
指定部署名称。为了简化代码，我们建议 使用 gpt-4.1-mini。
保留默认设置。
选择“部署”。
重复前面的步骤，但这次部署 text-embedding-3-large 嵌入模型。

获取终结点

在代码中，指定以下终结点来与 Azure AI 搜索服务和 Azure AI Foundry 资源建立连接。这些步骤假定你配置了基于角色的访问，如前所述。

若要获取您的服务终端节点，请执行以下操作：

登录到 Azure 门户。
在 Azure AI 搜索服务上：
1. 在左窗格中，选择“ 概述”。
2. 复制 URL，该 URL 应类似于 https://my-service.search.windows.net。
在 Azure AI Foundry 资源上：
1. 在左窗格中，选择 “资源管理>密钥和终结点”。
2. 选择 “OpenAI ”选项卡，并复制如下所示 https://my-resource.openai.azure.com的 URL。

从本地系统进行连接

你配置了基于角色的访问，以便与 Azure AI 搜索和 Azure OpenAI 交互。在命令行中，使用 Azure CLI 登录到这两个服务的同一订阅和租户。有关详细信息，请参阅快速入门：在没有密钥的情况下连接。

从本地系统进行连接：

在 Visual Studio Code 中打开一个新终端，并更改为要保存文件的目录。
运行以下命令并使用 Azure 帐户登录。如果有多个订阅，请选择包含 Azure AI 搜索服务和 Azure AI Foundry 项目的订阅。
```
az login
```

若要获取Microsoft Entra 令牌，请运行以下命令。在下一部分中指定此值。

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

加载连接

在发送任何请求之前，请定义连接到 Azure AI 搜索和 Azure OpenAI 的凭据、终结点和部署详细信息。这些值用于后续的操作。

若要加载连接：

在 Visual Studio Code 中创建一个.rest类型或.http类型的文件。例如，可以命名该文件 agentic-retrieval.rest。

将这些占位符粘贴到新文件中：

@baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE
@token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HERE
@aoaiBaseUrl = PUT-YOUR-AI-FOUNDRY-URL-HERE
@aoaiGptModel = gpt-4.1-mini
@aoaiGptDeployment = gpt-4.1-mini
@aoaiEmbeddingModel = text-embedding-3-large
@aoaiEmbeddingDeployment = text-embedding-3-large
@index-name = earth_at_night
@agent-name = earth-search-agent
@api-version = 2025-05-01-Preview

设置 @baseUrl 为您的 Azure AI 搜索终结点，其样式类似于 https://<your-search-service-name>.search.windows.net.。设置 @aoaiBaseUrl 为您的 Azure AI Foundry 终结点，其样式类似于 https://<your-foundry-resource-name>.openai.azure.com.。您已在获取终结点部分中获取了这两个值。
将 @token 替换为你在从本地系统连接中获取的 Microsoft Entra 令牌。
在同一文件中，输入并发送以下 HTTP 请求，以验证是否可以连接到 Azure AI 搜索。请求列出了搜索服务中的现有索引。
```
### List existing indexes by name
GET {{baseUrl}}/indexes?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}
```
相邻窗格中应该会显示响应。如果有现有索引，则会列出它们。否则，列表将为空。如果 HTTP 状态码是 200 OK，则可以继续。

创建搜索索引

在 Azure AI 搜索中，索引是结构化数据集合。使用 “创建索引” 定义一 earth_at_night个名为的索引，该索引使用 @index-name 上一节中的变量指定。

### Create an index
PUT {{baseUrl}}/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": "{{aoaiBaseUrl}}",
            "deploymentId": "{{aoaiEmbeddingDeployment}}",
            "modelName": "{{aoaiEmbeddingModel}}"
            }
        }
        ]
    }
    }

索引架构包含文档标识和页面内容、嵌入和数字的字段。它还包括语义排名和矢量查询的配置，这些配置使用之前部署的 text-embedding-3-large 模型。

重要

代理检索具有两种基于标记的计费模型：

Azure OpenAI 的查询规划计费。
Azure AI 搜索的查询执行计费（语义排序）。

语义排名在初始公共预览版中是免费的。预览版后，将应用标准标记计费。有关详细信息，请参阅代理检索的可用性和定价。

将文档上传到索引

目前，索引 earth_at_night 为空。使用索引文档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。

### Load documents
POST {{baseUrl}}/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
            }
        ]
    }

创建知识代理

要在查询时将 Azure AI 搜索连接到 gpt-4.1-mini 部署并面向 earth_at_night 索引，需要使用知识代理。使用 “创建知识代理 ”定义一个名为 earth-search-agent的代理，该代理使用上一部分中的 @agent-name 变量指定。

为确保响应的相关性和语义意义明确，已设置 defaultRerankerThreshold，以排除 reranker 分数为 2.5 或更低的响应。

### Create an agent
PUT {{baseUrl}}/agents/{{agent-name}}?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "name": "{{agent-name}}",
        "targetIndexes": [
            {
                "indexName": "{{index-name}}",
                "defaultRerankerThreshold": 2.5
            }
        ],
        "models": [
            {
                "kind": "azureOpenAI",
                "azureOpenAIParameters": {
                    "resourceUri": "{{aoaiBaseUrl}}",
                    "deploymentId": "{{aoaiGptDeployment}}",
                    "modelName": "{{aoaiGptModel}}"
                }
            }
        ]
    }

运行检索管道

你已准备好启动代理检索管道。使用知识检索 - 检索将两部分用户查询发送到 earth-search-agent，该系统将查询解构为子查询，并在 earth_at_night 索引中的文本字段和向量嵌入上运行子查询，然后对结果进行排名和合并。

### Run agentic retrieval
POST {{baseUrl}}/agents/{{agent-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?"
                    }
                ]
            }
        ],
        "targetIndexParams": [
            {
                "indexName": "{{index-name}}",
                "rerankerThreshold": 2.5
            }
        ]
    }

输出应类似于以下 JSON，其中：

response 根据用户查询在搜索索引中提供最相关的文档（或区块）的文本字符串。可以将此字符串传递给 LLM，以便在应答生成中用作基础数据。
activity 跟踪检索过程中采取的步骤，其中包括由您的 gpt-4.1-mini 部署生成的子查询，以及用于查询规划和执行的令牌。
references 列出构成该响应的文档，每个文档都由其 docKey标识。

{
  "response": [
    {
      "role": "assistant",
      "content": [
        {
          "type": "text",
          "text": "[{\"ref_id\":1,\"content\":\"# 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\"},{\"ref_id\":0,\"content\":\"<!-- 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\\\" -->\"}]",
          "image": null
        }
      ]
    }
  ],
  "activity": [
    {
      "type": "ModelQueryPlanning",
      "id": 0,
      "inputTokens": 1355,
      "outputTokens": 423
    },
    {
      "type": "AzureSearchQuery",
      "id": 1,
      "targetIndex": "earth_at_night",
      "query": {
        "search": "suburban belts December brightening urban cores comparison",
        "filter": null
      },
      "queryTime": "2025-05-06T15:57:14.666Z",
      "elapsedMs": 270
    },
    {
      "type": "AzureSearchQuery",
      "id": 2,
      "targetIndex": "earth_at_night",
      "query": {
        "search": "Phoenix nighttime street grid visibility from space",
        "filter": null
      },
      "queryTime": "2025-05-06T15:57:14.858Z",
      "count": 2,
      "elapsedMs": 192
    },
    {
      "type": "AzureSearchQuery",
      "id": 3,
      "targetIndex": "earth_at_night",
      "query": {
        "search": "interstate visibility from space midwestern cities",
        "filter": null
      },
      "queryTime": "2025-05-06T15:57:15.026Z",
      "count": 1,
      "elapsedMs": 167
    }
  ],
  "references": [
    {
      "type": "AzureSearchDoc",
      "id": "0",
      "activitySource": 2,
      "docKey": "earth_at_night_508_page_104_verbalized",
      "sourceData": null
    },
    {
      "type": "AzureSearchDoc",
      "id": "1",
      "activitySource": 2,
      "docKey": "earth_at_night_508_page_105_verbalized",
      "sourceData": null
    }
  ]
}

清理资源

删除知识代理

### Delete the agent
DELETE {{baseUrl}}/agents/{{agent-name}}?api-version={{api-version}}
    Content-Type: application/json
    Authorization: Bearer {{token}}

删除搜索索引

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

通过

快速入门：在 Azure AI 搜索中运行代理检索

先决条件

配置基于角色的访问

部署模型

获取终结点

从本地系统进行连接

安装软件包并加载连接

创建搜索索引

将文档上传到索引

创建知识代理

设置消息

运行检索管道

查看响应、活动和结果

创建 Azure OpenAI 客户端

使用响应 API 生成答案

使用聊天补全 API 生成答案

继续对话

查看新的响应、活动和结果

生成 LLM 支持的答案

清理资源

删除知识代理

删除搜索索引

先决条件

配置基于角色的访问

部署模型

获取终结点

从本地系统进行连接

加载连接

创建搜索索引

将文档上传到索引

创建知识代理

运行检索管道

清理资源

删除知识代理

删除搜索索引

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