快速入门：矢量搜索

在本快速入门中，你将使用 Jupyter 笔记本创建、加载和查询向量。 代码示例使用 Azure AI 搜索客户端库执行这些作。 该库通过 REST API 提供对索引操作的抽象，例如数据摄取、搜索操作和索引管理操作。

在 Azure AI 搜索中，矢量存储具有定义矢量和非矢量字段的索引架构、用于创建嵌入空间的算法的矢量配置，以及查询请求中使用的矢量字段定义的设置。 创建索引 REST API 将创建矢量存储。

注意

本快速入门省略矢量化步骤，并提供内联嵌入。 如果您想在自己的内容上添加内置数据分块和向量化，可以尝试使用导入和向量化数据向导，以获得端到端的演练。

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。 创建服务或在当前订阅中查找现有服务。

  • 对于本快速入门的大部分内容，可以使用免费搜索服务，但对于较大的数据文件，建议使用基本层或更高层级。
  • 若要运行调用 语义重新调用的查询示例，搜索服务必须位于 启用了语义排名器的“基本”层或更高层。

• 具有 Python 扩展和 Jupyter 包的 Visual Studio Code。

• 使用 Git 克隆包含 Jupyter 笔记本和其他相关文件的存储库。

---

检索资源信息

搜索终结点的请求必须经过身份验证和授权。 尽管可以为此任务使用 API 密钥或角色，但建议 通过 Microsoft Entra ID 使用无键连接。

本快速入门使用DefaultAzureCredential，这简化了开发和生产环境中的身份验证。 但是，对于生产环境，您可能有更高级的需求，需要采用不同的方法。 请参阅 使用用于 Python 的 Azure SDK 向 Azure 服务验证 Python 应用 ，了解所有选项。

克隆代码和设置环境

1. 克隆包含本快速入门代码的存储库。

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

   此存储库包含多个文章的 Python 代码示例，每个文章位于单独的子文件夹中。

2. 在 Visual Studio Code 中，打开子文件夹 Quickstart-Vector-Search。

   此文件夹中有三个文件：

   • vector-search-quickstart.ipynb
   • requirements.txt
   • sample.env

3. 将 sample.env 文件重命名为 .env 并修改 .env 文件中的值。

   使用搜索服务 URL 作为 AZURE_SEARCH_ENDPOINT。 可以在 Azure 门户中找到 URL。 转到 Azure AI 搜索服务，在 “概述 ”页上查找 URL 字段。 示例终结点可能类似于 https://mydemo.search.windows.net。

   最后，选择一个新 AZURE_SEARCH_INDEX_NAME 名称，或使用该文件中提供的名称。

4. 在 Visual Studio Code 中，在环境中工作。 在 “视图 ”菜单上，选择“ 终端...”，或选择 Ctrl+`。

5. 在终端中运行以下命令：

   python -m venv .venv
   source .venv/scripts/activate
   where python
   

   注意

   这假定你在终端中使用 Git Bash，并且你在 Windows 上运行。 如果您使用的是其他 shell 或不同的操作系统，请根据您的特定环境调整这些说明。

   如果出现提示，则允许 Visual Studio Code 使用新环境。

   where python 命令通过列出 Quickstart-Vector-Search\.venv\ 文件夹中的 python.exe 以及计算机目录中的其他位置来验证你是否正在从虚拟环境中工作。

6. 运行以下命令安装所需的库：

   pip install requirements.txt
   

7. 在 Visual Studio Code 中，打开vector-search-quickstart.ipynb。

   注意

   如果这是你第一次在 Visual Studio Code 中使用 Jupyter Notebook （.ipynb），系统会提示你安装 Jupyter Notebook 内核和其他工具。 选择安装建议的工具以继续学习本快速入门。

8. 找到标题为“安装包并设置变量”的以下单元格，然后选择单元格左侧的 “执行单元格”（Ctrl + Alt + Enter） 按钮（类似于典型的运行按钮）。 执行该单元会加载环境变量，创建 DefaultAzureCredential，并将值呈现到输出，以确认笔记本的依赖项和 .env 已正确设置。

   # Load environment variables from .env file
   # Rename the samples.env to .env and fill in the values
   from azure.identity import DefaultAzureCredential
   from dotenv import load_dotenv
   import os
   
   load_dotenv(override=True) # take environment variables from .env.
   
   search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
   credential = DefaultAzureCredential()
   index_name = os.getenv("AZURE_SEARCH_INDEX", "vector-search-quickstart")
   
   print(f"Using Azure Search endpoint: {search_endpoint}")
   print(f"Using Azure Search index: {index_name}")
   !pip list 
   

   执行此单元格将生成以下输出。

   Using Azure Search endpoint: https://<search-service-name>.search.windows.net
   Using Azure Search index: <vector-index-name>
   Package                 Version
   ----------------------- -----------
   aiohappyeyeballs        2.6.1
   aiohttp                 3.12.13
   aiosignal               1.3.2
   asttokens               3.0.0
   attrs                   25.3.0
   azure-ai-agents         1.0.0
   azure-ai-projects       1.0.0b11
   azure-common            1.1.28
   azure-core              1.34.0
   azure-identity          1.23.0
   azure-search-documents  11.6.0b12
   azure-storage-blob      12.25.1
   ...
   

   可以在可滚动元素中查看更多包（请参阅单元格结果下方的消息）。

创建矢量索引

vector-search-quickstart.ipynb 中的代码使用 azure.search.documents 库中的多种方法来创建矢量索引和可搜索字段。

1. 找到标题为“创建索引”的以下单元格，并执行单元格来创建索引。

   from azure.search.documents.indexes import SearchIndexClient
   from azure.search.documents import SearchClient
   from azure.search.documents.models import VectorizedQuery
   from azure.search.documents.indexes.models import (
       SimpleField,
       ComplexField,
       SearchField,
       SearchFieldDataType,    
       SearchableField,
       SearchIndex,
       SemanticConfiguration,
       SemanticField,
       SemanticPrioritizedFields,
       SemanticSearch,
       VectorSearch, 
       VectorSearchProfile,
       HnswAlgorithmConfiguration,
       ExhaustiveKnnAlgorithmConfiguration    
   )
   
   # Create a search schema
   index_client = SearchIndexClient(
       endpoint=search_endpoint, credential=credential)
   fields = [
       SimpleField(name="HotelId", type=SearchFieldDataType.String, key=True, filterable=True),
       SearchableField(name="HotelName", type=SearchFieldDataType.String, sortable=True),
       SearchableField(name="Description", type=SearchFieldDataType.String),
       SearchField(
           name="DescriptionVector",
           type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
           searchable=True,
           vector_search_dimensions=1536,
           vector_search_profile_name="my-vector-profile"
       ),
       SearchableField(name="Category", type=SearchFieldDataType.String, sortable=True, filterable=True, facetable=True),
       SearchField(name="Tags", type=SearchFieldDataType.Collection(SearchFieldDataType.String), searchable=True, filterable=True, facetable=True),
       SimpleField(name="ParkingIncluded", type=SearchFieldDataType.Boolean, filterable=True, sortable=True, facetable=True),
       SimpleField(name="LastRenovationDate", type=SearchFieldDataType.DateTimeOffset, filterable=True, sortable=True, facetable=True),
       SimpleField(name="Rating", type=SearchFieldDataType.Double, filterable=True, sortable=True, facetable=True),
       ComplexField(name="Address", fields=[
           SearchableField(name="StreetAddress", type=SearchFieldDataType.String),
           SearchableField(name="City", type=SearchFieldDataType.String, filterable=True, sortable=True, facetable=True),
           SearchableField(name="StateProvince", type=SearchFieldDataType.String, filterable=True, sortable=True, facetable=True),
           SearchableField(name="PostalCode", type=SearchFieldDataType.String, filterable=True, sortable=True, facetable=True),
           SearchableField(name="Country", type=SearchFieldDataType.String, filterable=True, sortable=True, facetable=True),
       ]),
       SimpleField(name="Location", type=SearchFieldDataType.GeographyPoint, filterable=True, sortable=True),
   ]
   
   vector_search = VectorSearch(
           algorithms=[
               HnswAlgorithmConfiguration(name="my-hnsw-vector-config-1", kind="hnsw"),
               ExhaustiveKnnAlgorithmConfiguration(name="my-eknn-vector-config", kind="exhaustiveKnn")
           ],
           profiles=[
               VectorSearchProfile(name="my-vector-profile", algorithm_configuration_name="my-hnsw-vector-config-1")
           ]
       )
   
   semantic_config = SemanticConfiguration(
           name="my-semantic-config",
           prioritized_fields=SemanticPrioritizedFields(
              title_field=SemanticField(field_name="HotelName"), 
              content_fields=[SemanticField(field_name="Description")], 
              keywords_fields=[SemanticField(field_name="Category")]
           )
       )
   
   # Create the semantic settings with the configuration
   semantic_search = SemanticSearch(configurations=[semantic_config])
   
   semantic_settings = SemanticSearch(configurations=[semantic_config])
   scoring_profiles = []
   suggester = [{'name': 'sg', 'source_fields': ['Tags', 'Address/City', 'Address/Country']}]
   
   # Create the search index with the semantic settings
   index = SearchIndex(name=index_name, fields=fields, vector_search=vector_search, semantic_search=semantic_search)
   result = index_client.create_or_update_index(index)
   print(f' {result.name} created')
   

   如果已成功创建索引，则单元格下方会显示以下结果：

   vector-search-quickstart created
   

   使用 azure.search.documents 创建矢量索引时的关键要点：

   • 通过创建字段列表来定义索引。 每个字段都是使用定义字段类型和设置的帮助程序方法创建的。

   • 此特定索引支持多种搜索功能，例如：

     • 全文关键字搜索 （SearchableField(name="HotelName", ...)， SearchableField(name="Description", ...)）
     • 矢量搜索（通过并置矢量和非矢量字段实现混合搜索）字段 (DescriptionVector)
     • 语义搜索 （semantic_search=SemanticSearch(configurations=[semantic_config])）
     • 分面搜索 (facetable=True)
     • 语义搜索 （semantic_search=SemanticSearch(configurations=[semantic_config])）
     • 地理空间搜索（Location 字段为 GeographyPoint）
     • 筛选、排序（许多标记为可筛选和可排序的字段）

上传文档

创建和加载索引是两个独立的步骤。 在上一步中创建了索引架构。 现在需要将文档加载到索引中。

在 Azure AI 搜索中，索引存储所有可搜索内容，而搜索引擎对该索引执行查询。

1. 找到名为“创建文档有效负载”的部分下方的单元，并执行该单元。 此单元格包含以下代码（为简洁起见截断）：

      # Create a documents payload
      documents = [
          {
              "@search.action": "mergeOrUpload",
              "HotelId": "1",
              "HotelName": "Stay-Kay City Hotel",
              "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
              "DescriptionVector": [-0.048865054,-0.020307425,
              # <truncated>
              -0.018120624,-0.012772904],
              "Category": "Boutique",
              "Tags": [
                  "view",
                  "air conditioning",
                  "concierge"
              ],
              "ParkingIncluded": "false",
              "LastRenovationDate": "2022-01-18T00:00:00Z",
              "Rating": 3.60,
              "Address": {
                  "StreetAddress": "677 5th Ave",
                  "City": "New York",
                  "StateProvince": "NY",
                  "PostalCode": "10022",
                  "Country": "USA"
              },
              "Location": {
                  "type": "Point",
                  "coordinates": [
                      -73.975403,
                      40.760586
                  ]
              }
          },
          # <truncated>
      ]
   

   此单元格加载一个变量，其中包含 documents 描述每个文档的 JSON 对象以及文章说明的矢量化版本。 此向量可实现相似性搜索，其中匹配基于含义而不是确切关键字。

   重要

   此示例中的代码不可运行。 为了简洁起见，删除了多个字符或行。 而是在 Jupyter 笔记本中运行代码。

2. 找到名为“上传文档”的部分下方的单元，并执行该单元。 此单元格包含以下代码（为简洁起见截断）：

   # Upload documents to the index
   search_client = SearchClient(endpoint=search_endpoint,
                         index_name=index_name,
                         credential=credential)
   try:
       result = search_client.upload_documents(documents=documents)
       for r in result:
           print(f"Key: {r.key}, Succeeded: {r.succeeded}, ErrorMessage: {r.error_message}")
   except Exception as ex:
       print("Failed to upload documents:", ex)
   
   # Create the index client which will be used later in the query examples
   index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
   

   这会通过调用 SearchClient() 构造函数创建搜索客户端的实例，然后对对象调用 upload_documents() 该方法。

   运行单元格后，每个文档的状态将会显示在其下方。

   Key: 1, Succeeded: True, ErrorMessage: None
   Key: 2, Succeeded: True, ErrorMessage: None
   Key: 3, Succeeded: True, ErrorMessage: None
   Key: 4, Succeeded: True, ErrorMessage: None
   Key: 48, Succeeded: True, ErrorMessage: None
   Key: 49, Succeeded: True, ErrorMessage: None
   Key: 13, Succeeded: True, ErrorMessage: None
   

   upload_documents() 方法和此示例的关键要点：

   • 您的代码通过 azure-search-documents 包提供的主要对象 SearchClient 与托管在 Azure AI 搜索服务中的特定搜索索引进行交互。 该 SearchClient 提供对索引操作的访问权限，例如：

     • 数据引入 - upload_documents()、merge_documents()delete_documents()等
     • 搜索操作 - search()， autocomplete()suggest()
     • 索引管理操作

   • 矢量字段包含浮点值。 维度属性至少有 2 个，每个浮点值最多为 4096 个。 本快速入门将维度属性设置为 1,536，因为这是 Azure OpenAI text-embedding-ada-002 模型生成的嵌入大小。

运行查询

加载文档后，可以通过调用 search_client.search() 和传入 VectorizedQuery 对象、要返回的字段、结果数等来对其发出向量查询。

在接下来的部分中，我们针对 hotels-vector-quickstart 索引运行查询。 查询包含：

• 单矢量搜索
• 使用筛选器的单矢量搜索
• 混合搜索
• 使用筛选器的语义混合搜索

创建矢量查询字符串

示例矢量查询基于两个字符串：

• 搜索字符串：historic hotel walk to restaurants and shopping
• 矢量查询字符串（矢量化为数学表示形式）：quintessential lodging near running trails, eateries, retail

矢量查询字符串在语义上类似于搜索字符串，但包含搜索索引中不存在的术语。 如果对 quintessential lodging near running trails, eateries, retail 执行关键字搜索，则结果为零。 此示例演示了如何在没有匹配字词的情况下获得相关结果。

• 找到在标题为“创建向量查询字符串”的部分下方的单元格，并执行此单元格。 这会加载变量 vector ，其中包含运行后续部分中所有搜索所需的矢量化查询数据。

单矢量搜索

第一个示例演示了一个基本场景，旨在查找与搜索字符串紧密匹配的文档说明。

• 找到“单向量搜索”标题下的单元格并执行该单元格。 此块包含查询搜索索引的请求。

  # IMPORTANT: Before you run this code, make sure the documents were successfully
  # created in the previous step. Sometimes it may take a few seconds for the index to be ready.
  # Check the "Document count" for the index in the Azure portal.
  
  # Execute vector search
  if vector:
       try:
           vector_query = VectorizedQuery(
               vector=vector,
               k_nearest_neighbors=5,
               fields="DescriptionVector",
               kind="vector",
               exhaustive=True
           )
  
           results = search_client.search(
               vector_queries=[vector_query],
               select=["HotelId", "HotelName", "Description", "Category", "Tags"],
               top=5,
               include_total_count=True
           )
  
           print(f"Total results: {results.get_count()}")
           for result in results:
               doc = result  # result is a dict-like object
               print(f"- HotelId: {doc['HotelId']}, HotelName: {doc['HotelName']}, Category: {doc.get('Category')}")
       except Exception as ex:
           print("Vector search failed:", ex)
  else:
      print("No vector loaded, skipping search.")
  

  为简洁起见，缩短了此矢量查询。 vectorQueries.vector 包含查询输入的矢量化文本，fields 确定要搜索的矢量字段，k 指定要返回的最近邻域的数目。

  矢量查询字符串为 quintessential lodging near running trails, eateries, retail，该字符串将矢量化为此查询的 1536 个嵌入项。

  等效于矢量的 quintessential lodging near running trails, eateries, retail 响应包括七个结果，但代码指定 top=5 只返回前五个结果。 此外，仅返回由 select 该字段指定的字段。

  search_client.search() 返回一个类似字典的对象。 每个结果都提供一个搜索评分，可以使用 score = result.get("@search.score", "N/A") 访问该评分。 虽然在此示例中未显示相似性搜索，但响应始终包括 k 按值相似性分数排序的结果。

  运行单元格后，每个文档的状态将打印在其下方。

  Total results: 5
  - HotelId: 48, HotelName: Nordick's Valley Motel, Category: Boutique
  - HotelId: 13, HotelName: Luxury Lion Resort, Category: Luxury
  - HotelId: 4, HotelName: Sublime Palace Hotel, Category: Boutique
  - HotelId: 49, HotelName: Swirling Currents Hotel, Category: Suite
  - HotelId: 2, HotelName: Old Century Hotel, Category: Boutique
  

使用筛选器的单矢量搜索

可以添加筛选器，但筛选器将应用于索引中的非矢量内容。 在此示例中，筛选器适用于 Tags 字段，可筛选出任何不提供免费 Wi-Fi 的酒店。

1. 找到标题为“单向量搜索（带过滤器）”的部分下方的单元格，并执行该单元格。 此单元格包含查询搜索索引的请求。

   if vector:
      try:
          vector_query = VectorizedQuery(
              vector=vector,
              k_nearest_neighbors=5,
              fields="DescriptionVector",
              kind="vector",
              exhaustive=True
          )
   
          results = search_client.search(
              vector_queries=[vector_query],
              filter="Tags/any(tag: tag eq 'free wifi')",  # <--- NOTICE THE FILTER IS ADDED HERE
              select=["HotelId", "HotelName", "Description", "Category", "Tags"],
              top=7,
              include_total_count=True
          )
   
          print(f"Total filtered results: {results.get_count()}")
          for result in results:
              doc = result
              print(f"- HotelId: {doc['HotelId']}, HotelName: {doc['HotelName']}, Tags: {doc.get('Tags')}")
      except Exception as ex:
          print("Vector search with filter failed:", ex)
   else:
      print("No vector loaded, skipping search.")
   

   运行单元格后，每个文档的状态将显示在下面。

   Total filtered results: 2
   - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ['continental breakfast', 'air conditioning', 'free wifi']
   - HotelId: 2, HotelName: Old Century Hotel, Tags: ['pool', 'free wifi', 'air conditioning', 'concierge']
   

   该查询与前面的单矢量搜索示例相同，但包含后处理排除筛选器，仅返回具有免费 Wi-Fi 的三家酒店。

2. 下一个筛选器示例使用 地理筛选器。 运行标题为“具有地理筛选器的矢量查询”部分中的单元格。 此块包含查询搜索索引的请求。

   if vector:
      try:
         vector_query = VectorizedQuery(
         vector=vector,
         k_nearest_neighbors=5,
         fields="DescriptionVector",
         kind="vector",
         exhaustive=True
      )
   
      results = search_client.search(
         include_total_count=True,
         top=5,
         select=[
             "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
         ],
         facets=["Address/StateProvince"],
         filter="geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
         vector_filter_mode="postFilter",
         vector_queries=[vector_query]
      )
   
      print(f"Total semantic hybrid results: {results.get_count()}")
      for result in results:
         doc = result
         score = result.get("@search.score", "N/A")
         print(f"- HotelId: {doc['HotelId']}")
         print(f"  HotelName: {doc['HotelName']}")
         print(f"  Score: {score}")
         print(f"  City/State: {doc['Address']['City']}, {doc['Address']['StateProvince']}")
         print(f"  Description: {doc.get('Description')}\n")
   
      except Exception as ex:
         print("Semantic hybrid search failed:", ex)
   else:
      print("No vector loaded, skipping search.")
   

   该查询与上 一个矢量搜索示例相同，但它包含后处理排除筛选器，仅返回 300 公里内的两家酒店。

   Total semantic hybrid results: 2
   - HotelId: 48
     HotelName: Nordick's Valley Motel
     Score: 0.6605852246284485
     City/State: Washington D.C., null
     Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
   
   - HotelId: 49
     HotelName: Swirling Currents Hotel
     Score: 0.602634072303772
     City/State: Arlington, VA
     Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
   

混合搜索

混合搜索由单个搜索请求中的关键字查询和矢量查询组成。 此示例并发运行矢量查询和全文搜索：

• 搜索字符串：historic hotel walk to restaurants and shopping

• 矢量查询字符串（矢量化为数学表示形式）：quintessential lodging near running trails, eateries, retail

• 找到“混合搜索”标题下的单元格并执行它。 此块包含查询搜索索引的请求。

  if vector:
       try:
           vector_query = VectorizedQuery(
               vector=vector,
               k_nearest_neighbors=5,
               fields="DescriptionVector",
               kind="vector",
               exhaustive=True
           )
  
           results = search_client.search(
               include_total_count=True,
               search_text="historic hotel walk to restaurants and shopping",  # keyword part
               select=["HotelId", "HotelName", "Description", "Category", "Tags"],
               top=5,
               vector_queries=[vector_query]
           )
  
           print(f"Total hybrid results: {results.get_count()}")
           for result in results:
               doc = result
               score = result.get("@search.score", "N/A")
               print(f"- Score: {score}")
               print(f"  HotelId: {doc['HotelId']}")            
               print(f"  HotelName: {doc['HotelName']}")
               print(f"  Description: {doc.get('Description')}")
               print(f"  Category: {doc.get('Category')}")
               print(f"  Tags: {doc.get('Tags', 'N/A')}\n")
  
      except Exception as ex:
         print("Hybrid search failed:", ex)
  else:
      print("No vector loaded, skipping search.")    
  

  查看响应：

  Total hybrid results: 7
  - Score: 0.03279569745063782
     HotelId: 4
     HotelName: Sublime Palace Hotel
     Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
     Category: Boutique
     Tags: ['concierge', 'view', 'air conditioning']
  
  - Score: 0.032522473484277725
     HotelId: 13
     HotelName: Luxury Lion Resort
     Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
     Category: Luxury
     Tags: ['bar', 'concierge', 'restaurant']
  
  - Score: 0.03205128386616707
     HotelId: 48
     HotelName: Nordick's Valley Motel
     Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
     Category: Boutique
     Tags: ['continental breakfast', 'air conditioning', 'free wifi']
  
  - Score: 0.0317460335791111
     HotelId: 49
     HotelName: Swirling Currents Hotel
     Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
     Category: Suite
     Tags: ['air conditioning', 'laundry service', '24-hour front desk service']
  
  - Score: 0.03125
     HotelId: 2
     HotelName: Old Century Hotel
     Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
     Category: Boutique
     Tags: ['pool', 'free wifi', 'air conditioning', 'concierge']
  

  由于倒数排序融合 (RRF) 合并了结果，因此这有助于查看输入。 以下结果仅来自全文查询。 前两个结果是 Sublime Palace Hotel 和 History Lion Resort。 Sublime Palace Hotel 具有更强的 BM25 相关性分数。

  {
       "@search.score": 2.2626662,
       "HotelName": "Sublime Palace Hotel",
       "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace."
  },
  {
       "@search.score": 0.86421645,
       "HotelName": "Luxury Lion Resort",
       "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort"
  },
  

  在使用 HNSW 查找匹配项的纯矢量查询中，Sublime Palace Hotel 跌至第四位。 History Lion 在全文搜索中排第二位，在矢量搜索中排第三位，并没有经历相同的波动范围，因此在均匀化结果集中显示为顶级匹配。

  "value": [
       {
           "@search.score": 0.857736,
           "HotelId": "48",
           "HotelName": "Nordick's Valley Motel",
           "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
           "Category": "Boutique"
       },
       {
           "@search.score": 0.8399129,
           "HotelId": "49",
           "HotelName": "Swirling Currents Hotel",
           "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
           "Category": "Luxury"
       },
       {
           "@search.score": 0.8383954,
           "HotelId": "13",
           "HotelName": "Luxury Lion Resort",
           "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
           "Category": "Resort and Spa"
       },
       {
           "@search.score": 0.8254346,
           "HotelId": "4",
           "HotelName": "Sublime Palace Hotel",
           "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
           "Category": "Boutique"
       },
       {
           "@search.score": 0.82380056,
           "HotelId": "1",
           "HotelName": "Stay-Kay City Hotel",
           "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of New York.",
           "Category": "Boutique"
       },
       {
           "@search.score": 0.81514084,
           "HotelId": "2",
           "HotelName": "Old Century Hotel",
           "Description": "The hotel is situated in a  nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts.",
           "Category": "Boutique"
       },
       {
           "@search.score": 0.8133763,
           "HotelId": "3",
           "HotelName": "Gastronomic Landscape Hotel",
           "Description": "The Hotel stands out for its gastronomic excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
           "Category": "Resort and Spa"
       }
  ]
  

使用筛选器的语义混合搜索

下面是集合中的最后一个查询。 具有语义排名的此混合查询经过筛选，仅显示华盛顿特区半径 500 公里内的酒店。可以将 vectorFilterMode 设置为 null，这相当于默认值（较新的索引为 preFilter，较旧的索引为 postFilter）。

• 找到标题为“语义混合搜索”的区块下面的单元格，并执行它。 此代码块包含查询搜索索引的请求。

  if semantic_hybrid_query_vector:
      try:
         vector_query = VectorizedQuery(
            vector=vector,
            k_nearest_neighbors=5,
            fields="DescriptionVector",
            kind="vector",
            exhaustive=True
         )
  
         results = search_client.search(
            include_total_count=True,
            search_text="historic hotel walk to restaurants and shopping",
            select=[
               "HotelId", "HotelName", "Category", "Description"
            ],
            query_type="semantic",
            semantic_configuration_name="my-semantic-config",
            top=7,            
            vector_queries=[vector_query]
         )
  
         print(f"Total semantic hybrid results: {results.get_count()}")
         for result in results:
            doc = result
            score = result.get("@search.score", "N/A")
            reranker_score = result.get("@search.reranker_score", "N/A")
            print(f"- Score: {score}")
            print(f"  Re-ranker Score: {reranker_score}")
            print(f"  HotelId: {doc['HotelId']}")
            print(f"  HotelName: {doc['HotelName']}")
            print(f"  Description: {doc.get('Description')}")
            print(f"  Category: {doc.get('Category')}")
  
  except Exception as ex:
      print("Semantic hybrid search failed:", ex)
  else:
      print("No vector loaded, skipping search.")
  

  查看单元格下方的输出。 响应返回三家酒店，它们按位置筛选，按 StateProvince 划分，按语义重新排名以提升结果从而显示最接近搜索字符串查询的结果 (historic hotel walk to restaurants and shopping)。

  现在，Swirling Currents Hotel 跃居榜首。 如果没有语义排名，Nordick's Valley Motel 就是第一名。 借助语义排名，机器理解模型理解 historic 适用于“酒店，并且步行可达餐饮（餐馆）和购物场所”。

  Total semantic hybrid results: 7
  - Score: 0.0317460335791111
     Re-ranker Score: 2.6550590991973877
     HotelId: 49
     HotelName: Swirling Currents Hotel
     Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
     Category: Suite
  - Score: 0.03279569745063782
     Re-ranker Score: 2.599761724472046
     HotelId: 4
     HotelName: Sublime Palace Hotel
     Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
     Category: Boutique
  - Score: 0.03125
     Re-ranker Score: 2.3480887413024902
     HotelId: 2
     HotelName: Old Century Hotel
     Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
     Category: Boutique
  - Score: 0.016393441706895828
     Re-ranker Score: 2.2718777656555176
     HotelId: 1
     HotelName: Stay-Kay City Hotel
     Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
     Category: Boutique
  - Score: 0.01515151560306549
     Re-ranker Score: 2.0582215785980225
     HotelId: 3
     HotelName: Gastronomic Landscape Hotel
     Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.
     Category: Suite
  

  要点：

  • 矢量搜索通过 vectors.value 属性指定。 关键字搜索通过 search 属性指定。

  • 在混合搜索中，可以将矢量搜索与关键字的全文搜索相集成。 筛选器、拼写检查和语义排名仅适用于文本内容，不适用于矢量。 在此最终查询中，不存在语义 answer，因为系统未生成足够可靠的答案。

  • 实际结果包含更多详细信息，包括语义说明和突出显示。 为便于阅读，对结果进行了修改。 若要获取响应的完整结构，请在 REST 客户端中运行请求。

清理

在自己的订阅中操作时，最好在项目结束时确定是否仍需要已创建的资源。 持续运行资源可能会产生费用。 可以逐个删除资源，也可以删除资源组以删除整个资源集。

可以在 Azure 门户中使用最左侧窗格中的“所有资源”或“资源组”链接来查找和管理资源。

如果要保留搜索服务，但删除索引和文档，可以使用 SearchIndexClient 对象的 delete_index() 方法。 若要删除 hotels-vector-quickstart 索引，请查找位于标题“清理”下方的单元格，并执行该单元格中的步骤：

index_client.delete_index(index_name)
print(f"Index '{index_name}' deleted successfully.")

后续步骤

• 在用于 Python 的 Azure AI 搜索中查看矢量搜索功能的代码示例存储库
• 查看 azure-search-python-samples 存储库中的其他 Python 和 Azure AI 搜索代码示例

在本快速入门中，你将使用 Azure AI 搜索 REST API 创建、加载和查询矢量。

在 Azure AI 搜索中，矢量存储具有定义矢量和非矢量字段的索引架构、用于创建嵌入空间的算法的矢量配置，以及在查询时评估的矢量字段定义的设置。 创建索引 REST API 将创建矢量存储。

注意

本快速入门省略矢量化步骤，并提供内联嵌入。 如果您想在自己的内容上添加内置数据分块和向量化，可以尝试使用导入和向量化数据向导，以获得端到端的演练。

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。 创建服务或在当前订阅中查找现有服务。

  • 对于本快速入门的大部分内容，可以使用免费搜索服务，但对于较大的数据文件，建议使用基本层或更高层级。
  • 若要运行调用 语义重新调用的查询示例，搜索服务必须位于 启用了语义排名器的“基本”层或更高层。

• 具有 REST 客户端的 Visual Studio Code。

---

检索资源信息

搜索终结点的请求必须经过身份验证和授权。 你可以使用 API 密钥或角色来完成此任务。 建议通过 Microsoft Entra ID 使用无密钥连接。

选择与首选身份验证方法对应的选项卡。 对于本快速入门中的所有请求，请使用相同的方法。

Microsoft Entra ID

1. 登录到 Azure 门户并查找你的搜索服务。

2. 在“概述”主页上，找到相应 URL。 示例终结点可能类似于 https://mydemo.search.windows.net。

   

3. 按照无密钥快速入门中的步骤获取 Microsoft Entra 令牌。

   在上一快速入门的步骤 3 中运行 az account get-access-token 命令时，将获得令牌。

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

API 密钥

1. 登录到 Azure 门户并查找你的搜索服务。

2. 在“概述”主页上，找到相应 URL。 示例终结点可能类似于 https://mydemo.search.windows.net。

   

3. 选择“设置”>“密钥”。 必须启用“API 密钥”或“两者”。 管理 API 密钥用于添加、修改和删除对象。 有两个可互换的管理密钥。 复制其中任意一个。

   

创建或下载代码文件

使用一个 .rest 或 .http 文件运行本快速入门中的所有请求。 可以下载包含本快速入门代码的 REST 文件，也可以在 Visual Studio Code 中创建新文件，并将代码复制到其中。

1. 在 Visual Studio Code 中，创建包含 .rest 或 .http 文件扩展的新文件。 例如，az-search-vector-quickstart.rest。 将 Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest 文件的原始内容复制并粘贴到此新文件中。

2. 在文件的顶部，将 @baseUrl 的占位符值替换为搜索服务 URL。 有关如何查找搜索服务 URL 的说明，请参阅检索资源信息部分。

   @baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE
   

3. 在文件的顶部，将占位符值替换为身份验证。 有关如何获取 Microsoft Entra 令牌或 API 密钥的说明，请参阅检索资源信息部分。

   若要使用 Microsoft Entra ID 进行推荐的无密钥身份验证，你需要将 @apiKey 替换为 @token 变量。

   @token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HERE
   

   如果想要使用 API 密钥，请将 @apiKey 替换为从 Azure 门户复制的密钥。

   @apiKey = PUT-YOUR-ADMIN-KEY-HERE
   

4. 若要使用 Microsoft Entra ID 进行推荐的无密钥身份验证，你需要将 api-key: {{apiKey}} 替换为请求标头中的 Authorization: Bearer {{token}}。 替换文件中找到的所有 api-key: {{apiKey}} 实例。

创建矢量索引

使用创建索引 REST API 创建矢量索引，并在搜索服务上设置物理数据结构。

此示例中的索引架构围绕酒店内容进行组织。 示例数据由虚构酒店矢量和非矢量描述组成。 该架构包括矢量索引和查询以及语义排名的配置。

1. 在 Visual Studio Code 中，打开先前创建的 az-search-vector-quickstart.rest 文件。

2. 在文件中查找 ### Create a new index 代码块。 此块包含在搜索服务上创建 hotels-vector-quickstart 索引的请求。

   ### Create a new index
   POST  {{baseUrl}}/indexes?api-version=2024-07-01  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   {
       "name": "hotels-vector-quickstart",
       "fields": [
           {
               "name": "HotelId", 
               "type": "Edm.String",
               "searchable": false, 
               "filterable": true, 
               "retrievable": true, 
               "sortable": false, 
               "facetable": false,
               "key": true
           },
           {
               "name": "HotelName", 
               "type": "Edm.String",
               "searchable": true, 
               "filterable": false, 
               "retrievable": true, 
               "sortable": true, 
               "facetable": false
           },
           {
               "name": "Description", 
               "type": "Edm.String",
               "searchable": true, 
               "filterable": false, 
               "retrievable": true, 
               "sortable": false, 
               "facetable": false
           },
           {
               "name": "DescriptionVector",
               "type": "Collection(Edm.Single)",
               "searchable": true,
               "retrievable": true,
               "dimensions": 1536,
               "vectorSearchProfile": "my-vector-profile"
           },
           {
               "name": "Category", 
               "type": "Edm.String",
               "searchable": true, 
               "filterable": true, 
               "retrievable": true, 
               "sortable": true, 
               "facetable": true
           },
           {
               "name": "Tags",
               "type": "Collection(Edm.String)",
               "searchable": true,
               "filterable": true,
               "retrievable": true,
               "sortable": false,
               "facetable": true
           },
                   {
               "name": "ParkingIncluded",
               "type": "Edm.Boolean",
               "searchable": false,
               "filterable": true,
               "retrievable": true,
               "sortable": true,
               "facetable": true
           },
           {
               "name": "LastRenovationDate",
               "type": "Edm.DateTimeOffset",
               "searchable": false,
               "filterable": true,
               "retrievable": true,
               "sortable": true,
               "facetable": true
           },
           {
               "name": "Rating",
               "type": "Edm.Double",
               "searchable": false,
               "filterable": true,
               "retrievable": true,
               "sortable": true,
               "facetable": true
           },
           {
               "name": "Address", 
               "type": "Edm.ComplexType",
               "fields": [
                   {
                       "name": "StreetAddress", "type": "Edm.String",
                       "searchable": true, "filterable": false, "retrievable": true, "sortable": false, "facetable": false
                   },
                   {
                       "name": "City", "type": "Edm.String",
                       "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                   },
                   {
                       "name": "StateProvince", "type": "Edm.String",
                       "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                   },
                   {
                       "name": "PostalCode", "type": "Edm.String",
                       "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                   },
                   {
                       "name": "Country", "type": "Edm.String",
                       "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                   }
               ]
           },
           {
               "name": "Location",
               "type": "Edm.GeographyPoint",
               "searchable": false, 
               "filterable": true, 
               "retrievable": true, 
               "sortable": true, 
               "facetable": false
           }
       ],
       "vectorSearch": {
           "algorithms": [
               {
                   "name": "hnsw-vector-config",
                   "kind": "hnsw",
                   "hnswParameters": 
                   {
                       "m": 4,
                       "efConstruction": 400,
                       "efSearch": 500,
                       "metric": "cosine"
                   }
               },
               {
                   "name": "eknn-vector-config",
                   "kind": "exhaustiveKnn",
                   "exhaustiveKnnParameters": 
                   {
                       "metric": "cosine"
                   }
               }
           ],
           "profiles": [      
               {
                   "name": "my-vector-profile",
                   "algorithm": "hnsw-vector-config"
               }
         ]
       },
       "semantic": {
           "configurations": [
               {
                   "name": "semantic-config",
                   "prioritizedFields": {
                       "titleField": {
                           "fieldName": "HotelName"
                       },
                       "prioritizedContentFields": [
                           { "fieldName": "Description" }
                       ],
                       "prioritizedKeywordsFields": [
                           { "fieldName": "Category" }
                       ]
                   }
               }
           ]
       }
   }
   

3. 选择“发送请求”。 你应该收到 HTTP/1.1 201 Created 响应。

   响应正文应包含索引架构的 JSON 表示形式。

   {
       "@odata.context": "https://my-demo-search.search.windows.net/$metadata#indexes/$entity",
       "@odata.etag": "\"0x8DD2E70E6C36D8E\"",
       "name": "hotels-vector-quickstart",
       "defaultScoringProfile": null,
       "fields": [
       {
           "name": "HotelId",
           "type": "Edm.String",
           "searchable": false,
           "filterable": true,
           "retrievable": true,
           "sortable": false,
           "facetable": false,
           "key": true,
           "indexAnalyzer": null,
           "searchAnalyzer": null,
           "analyzer": null,
           "dimensions": null,
           "vectorSearchProfile": null,
           "synonymMaps": []
       },
       [MORE FIELD DEFINITIONS OMITTED FOR BREVITY]
       ],
       "scoringProfiles": [],
       "corsOptions": null,
       "suggesters": [],
       "analyzers": [],
       "tokenizers": [],
       "tokenFilters": [],
       "charFilters": [],
       "encryptionKey": null,
       "similarity": {
       "@odata.type": "#Microsoft.Azure.Search.BM25Similarity",
       "k1": null,
       "b": null
       },
       "vectorSearch": {
       "algorithms": [
           {
           "name": "hnsw-vector-config",
           "kind": "hnsw",
           "hnswParameters": {
               "metric": "cosine",
               "m": 4,
               "efConstruction": 400,
               "efSearch": 500
           },
           "exhaustiveKnnParameters": null
           },
           "exhaustiveKnnParameters": null
           },
           {
           "name": "eknn-vector-config",
           "kind": "exhaustiveKnn",
           "hnswParameters": null,
           "exhaustiveKnnParameters": {
               "metric": "cosine"
           }
           }
       ],
       "profiles": [
           {
           "name": "my-vector-profile",
           "algorithm": "hnsw-vector-config-1"
           }
       ]
       },
       "semantic": {
       "defaultConfiguration": null,
       "configurations": [
           {
           "name": "semantic-config",
           "prioritizedFields": {
               "titleField": {
               "fieldName": "HotelName"
               },
               "prioritizedContentFields": [
               {
                   "fieldName": "Description"
               }
               ],
               "prioritizedKeywordsFields": [
               {
                   "fieldName": "Category"
               }
               ]
           }
           }
       ]
       }
   }
   

有关创建索引 REST API 的要点：

• fields 集合包含文本和矢量搜索所需的关键字段、文本字段和矢量字段（例如 Description 和 DescriptionVector）。 将矢量字段和非矢量字段并置在同一索引中可实现混合查询。 例如，可以将筛选器、带语义排名的文本搜索和矢量合并到单个查询操作中。

• 矢量字段必须是 用于矢量的 EDM 数据类型之一，例如 type: Collection(Edm.Single)。 矢量字段也有属性dimensionsvectorSearchProfile。

• vectorSearch 部分是一个数组，由近似最邻近算法配置和配置文件组成。 支持的算法包括分层可导航小世界和穷举 k 最近邻域。 有关详细信息，请参阅矢量搜索中的相关性评分。

• （可选）semantic 配置允许对搜索结果重新排名。 可以对配置中指定字符串字段的 semantic 类型查询的结果进行重新排名。 若要了解详细信息，请参阅语义排名概述。

上传文档

创建和加载索引是两个独立的步骤。 在上一步中创建了索引架构。 现在需要将文档加载到索引中。

在 Azure AI 搜索中，索引包含所有可搜索的数据，并且查询在搜索服务上运行。 对于 REST 调用，数据以 JSON 文档的形式提供。 为此任务使用文档 - 索引 REST API。 会扩展 URI 以包含 docs 集合和 index 操作。

1. 制定上传文档请求，以将文档上传到 hotels-vector-quickstart 搜索服务的索引。

   ### Upload documents
   POST {{baseUrl}}/indexes/hotels-quickstart-vectors/docs/index?api-version=2024-07-01  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   {
       "value": [
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "1",
               "HotelName": "Stay-Kay City Hotel",
               "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
               "DescriptionVector": 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               "Category": "Boutique",
               "Tags": [
                   "view",
                   "air conditioning",
                   "concierge"
               ],
               "ParkingIncluded": false,
               "LastRenovationDate": "2022-01-18T00:00:00Z",
               "Rating": 3.60,
               "Address": {
                   "StreetAddress": "677 5th Ave",
                   "City": "New York",
                   "StateProvince": "NY",
                   "PostalCode": "10022",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       -73.975403,
                       40.760586
                   ]
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "2",
               "HotelName": "Old Century Hotel",
               "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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               "Category": "Boutique",
               "Tags": [
                   "pool",
                   "free wifi",
                   "air conditioning",
                   "concierge"
               ],
               "ParkingIncluded": false,
               "LastRenovationDate": "2019-02-18T00:00:00Z",
               "Rating": 3.60,
               "Address": {
                   "StreetAddress": "140 University Town Center Dr",
                   "City": "Sarasota",
                   "StateProvince": "FL",
                   "PostalCode": "34243",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       -82.452843,
                       27.384417
                   ]
               }
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           {
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               "HotelId": "3",
               "HotelName": "Gastronomic Landscape Hotel",
               "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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               "Category": "Suite",
               "Tags": [
                   "restaurant",
                   "bar",
                   "continental breakfast"
               ],
               "ParkingIncluded": true,
               "LastRenovationDate": "2015-09-20T00:00:00Z",
               "Rating": 4.80,
               "Address": {
                   "StreetAddress": "3393 Peachtree Rd",
                   "City": "Atlanta",
                   "StateProvince": "GA",
                   "PostalCode": "30326",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       33.847848,
                       -84.363602
                   ]
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "4",
               "HotelName": "Sublime Palace Hotel",
               "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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               "Category": "Boutique",
               "Tags": [
                   "concierge",
                   "view",
                   "air conditioning"
               ],
               "ParkingIncluded": true,
               "LastRenovationDate": "2020-02-06T00:00:00Z",
               "Rating": 4.60,
               "Address": {
                   "StreetAddress": "7400 San Pedro Ave",
                   "City": "San Antonio",
                   "StateProvince": "TX",
                   "PostalCode": "78216",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       -98.498923,
                       29.518029
                   ]
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "48",
               "HotelName": "Nordick's Valley Motel",
               "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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               "Category": "Boutique",
               "Tags": [
                   "continental breakfast",
                   "air conditioning",
                   "free wifi"
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               "ParkingIncluded": false,
               "LastRenovationDate": "2018-02-19T00:00:00Z",
               "Rating": 4.5,
               "Address": {
                   "StreetAddress": "1401 I St NW",
                   "City": "Washington D.C.",
                   "StateProvince": null,
                   "PostalCode": "20005",
                   "Country": "USA"
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                   "properties": {
                       "name": "EPSG:4326"
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               "HotelId": "49",
               "HotelName": "Swirling Currents Hotel",
               "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
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              "Category": "Suite",
               "Tags": [
                   "air conditioning",
                   "laundry service",
                   "24-hour front desk service"
               ],
               "ParkingIncluded": true,
               "LastRenovationDate": "2018-01-27T00:00:00Z",
               "Rating": 2.7,
               "Address": {
                   "StreetAddress": "1100 S Hayes St",
                   "City": "Arlington",
                   "StateProvince": "VA",
                   "PostalCode": "22202",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                   -77.060066,
                   38.863659
                   ],
                   "crs": {
                   "type": "name",
                   "properties": {
                       "name": "EPSG:4326"
                       }
                   }
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "13",
               "HotelName": "Luxury Lion Resort",
               "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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               "Category": "Luxury",
               "Tags": [
                   "bar",
                   "concierge",
                   "restaurant"
               ],
               "ParkingIncluded": false,
               "LastRenovationDate": "2020-03-18T00:00:00Z",
               "Rating": 4.1,
               "Address": {
                   "StreetAddress": "3 Cityplace Dr",
                   "City": "St. Louis",
                   "StateProvince": "MO",
                   "PostalCode": "63141",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                   -90.44081,
                   38.67219
                   ],
                   "crs": {
                   "type": "name",
                   "properties": {
                       "name": "EPSG:4326"
                       }
                   }
               }
           }
       ]
   }
   

2. 选择“发送请求”。 你应该收到 HTTP/1.1 200 OK 响应。 响应正文应包含搜索文档的 JSON 表示形式。

有关文档 - 编制 REST API 索引请求的要点：

• 有效负载中的文档由索引架构中定义的字段组成。

• 矢量字段包含浮点值。 维度属性至少包含 2 个浮点数值，上限为 4096 个浮点数值。 本快速入门将维度属性设置为 1,536，因为这是 Azure OpenAI text-embedding-3-small 模型生成的嵌入大小。

运行查询

加载文档后，可以使用 文档 - 搜索帖子（REST）对其运行向量查询。

在接下来的部分中，我们针对 hotels-vector-quickstart 索引运行查询。 查询包含：

• 单矢量搜索
• 使用筛选器的单矢量搜索
• 混合搜索
• 语义混合搜索

示例查询基于两个字符串：

• 搜索字符串：historic hotel walk to restaurants and shopping
• 矢量查询字符串（矢量化为数学表示形式）：quintessential lodging near running trails, eateries, retail

矢量查询字符串在语义上类似于搜索字符串，但包含搜索索引中不存在的术语。 如果执行 quintessential lodging near running trails, eateries, retail关键字搜索，则结果在纯关键字搜索中为零，没有语义排名。 此示例演示如何使用矢量获取相关结果，即使没有匹配的字词也是如此。

单矢量搜索

1. 制定请求。 该查询是“跑步道、餐馆、零售店附近的经典住宿地”的 1536 浮动表示形式。 该查询将搜索 DescriptionVector 并返回 k-5 个结果。

   ### Run a single vector query
   POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
       Content-Type: application/json
       Authorization: Bearer {{token}}
   
       {
           "count": true,
           "select": "HotelId, HotelName, Description, Category, Tags",
           "vectorQueries": [
               {
                   "vector"": 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                   "k": 5,
                   "fields": "DescriptionVector",
                   "kind": "vector",
                   "exhaustive": true
               }
           ]
       }
   

   有关文档 - 搜索帖子 REST API 的关键要点：

   • vectorQueries.vector是矢量查询字符串。 它是“跑步道、餐馆、零售店附近的经典住宿地”的矢量表示形式，已针对此查询将其矢量化为 1,536 个嵌入。

   • fields 确定搜索哪些向量字段。

   • 将kind设置为vector表示查询字符串是一个向量。 如果将 kind 设置为 text，则需要额外的功能（向量化器）在查询时将人类可读的文本字符串编码为向量。 为了使练习简单，本快速入门中省略了矢量化器的相关内容。

   • k 指定要在响应中返回的最接近的邻居数。 参数 count 指定索引中找到的匹配项数。 在查询中包括计数被视为最佳做法，但在相似性搜索中，其意义不大，因为算法几乎可以在任何文档中找到某种程度的相似性。

2. 选择“发送请求”。 你应该收到 HTTP/1.1 200 OK 响应。 响应正文应包含搜索结果的 JSON 表示形式。

   虽然搜索引擎找到了 7 个匹配项，但对 quintessential lodging near running trails, eateries, retail 的矢量等效项的响应包括 k-5 个结果。 排名靠前的结果在语义上与查询最相似。 每个结果都提供一个搜索分数和 select 中列出的字段。 在相似性搜索中，响应总是包含按值相似度分数值排序的 k 个结果。

   {
     "@odata.context": "https://my-demo-search.search.windows.net/indexes('hotels-vector-quickstart')/$metadata#docs(*)",
     "@odata.count": 7,
     "value": [
       {
         "@search.score": 0.6605852,
         "HotelId": "48",
         "HotelName": "Nordick's Valley Motel",
         "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
         "Category": "Boutique",
         "Tags": [
           "continental breakfast",
           "air conditioning",
           "free wifi"
         ]
       },
       {
         "@search.score": 0.6333684,
         "HotelId": "13",
         "HotelName": "Luxury Lion Resort",
         "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
         "Category": "Luxury",
         "Tags": [
           "bar",
           "concierge",
           "restaurant"
         ]
       },
       {
         "@search.score": 0.605672,
         "HotelId": "4",
         "HotelName": "Sublime Palace Hotel",
         "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
         "Category": "Boutique",
         "Tags": [
           "concierge",
           "view",
           "air conditioning"
         ]
       },
       {
         "@search.score": 0.6026341,
         "HotelId": "49",
         "HotelName": "Swirling Currents Hotel",
         "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
         "Category": "Suite",
         "Tags": [
           "air conditioning",
           "laundry service",
           "24-hour front desk service"
         ]
       },
       {
         "@search.score": 0.57902366,
         "HotelId": "2",
         "HotelName": "Old Century Hotel",
         "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
         "Category": "Boutique",
         "Tags": [
           "pool",
           "free wifi",
           "air conditioning",
           "concierge"
         ]
       }
     ]
   }
   

使用筛选器的单矢量搜索

可以添加筛选器，但筛选器将应用于索引中的非矢量内容。 在此示例中，筛选器适用于 Tags 字段，可筛选出任何不提供免费 Wi-Fi 的酒店。

1. 制定请求。 这是与上一个请求相同的请求，具有额外的筛选器和筛选器模式参数。

   ### Run a vector query with a filter
   POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
       Content-Type: application/json
       Authorization: Bearer {{token}}
   
       {
           "count": true,
           "select": "HotelId, HotelName, Category, Tags, Description",
           "filter": "Tags/any(tag: tag eq 'free wifi')",
           "vectorFilterMode": "postFilter",
           "vectorQueries": [
           {
               "vector": 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               "k": 5,
               "fields": "DescriptionVector",
               "kind": "vector",
               "exhaustive": true
           },
       ]
   }
   

2. 选择“发送请求”。 你应该收到 HTTP/1.1 200 OK 响应。 响应正文应包含搜索结果的 JSON 表示形式。

   该查询与上 一个矢量搜索示例相同，但它包含后处理排除筛选器，仅返回具有免费 Wi-Fi 的两家酒店。

   {
     "@odata.context": "https://my-demo-search.search.windows.net/indexes('hotels-vector-quickstart')/$metadata#docs(*)",
     "@odata.count": 2,
     "value": [
       {
         "@search.score": 0.6605852,
         "HotelId": "48",
         "HotelName": "Nordick's Valley Motel",
         "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
         "Category": "Boutique",
         "Tags": [
           "continental breakfast",
           "air conditioning",
           "free wifi"
         ]
       },
       {
         "@search.score": 0.57902366,
         "HotelId": "2",
         "HotelName": "Old Century Hotel",
         "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
         "Category": "Boutique",
         "Tags": [
           "pool",
           "free wifi",
           "air conditioning",
           "concierge"
         ]
       }
     ]
   }
   

3. 下面是另一个使用地理筛选器的查询，将结果限制在华盛顿特区周围 300 公里内的酒店。

   ### Run a vector query with a geo filter
   POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
       Content-Type: application/json
       Authorization: Bearer {{token}}
   
       {
           "count": true,
           "select": "HotelId, HotelName, Description, Address/City, Address/StateProvince",
           "top": 5,
           "facets": [ "Address/StateProvince"],
           "filter": "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
           "vectorFilterMode": "postFilter",
           "vectorQueries": [
           {
               "vector": 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               "k": 5,
               "fields": "DescriptionVector",
               "kind": "vector",
               "exhaustive": true
           },
       ]
   }
   

4. 查看回应。 它仅限于华盛顿特区附近的两家酒店，由一个分面结构提供聚合。

   "@odata.count": 2,
   "@search.facets": {
     "Address/StateProvince": [
       {
         "value": "VA",
         "count": 1
       }
     ]
   },
   "value": [
     {
       "@search.score": 0.6605852246284485,
       "HotelId": "48",
       "HotelName": "Nordick's Valley Motel",
       "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
       "Address": {
         "City": "Washington D.C.",
         "StateProvince": null
       }
     },
     {
       "@search.score": 0.602634072303772,
       "HotelId": "49",
       "HotelName": "Swirling Currents Hotel",
       "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
       "Address": {
         "City": "Arlington",
         "StateProvince": "VA"
       }
     }
   ]
   

混合搜索

混合搜索由单个搜索请求中的关键字查询和矢量查询组成。 此示例并发运行矢量查询和全文搜索：

• 搜索字符串：historic hotel walk to restaurants and shopping
• 矢量查询字符串（矢量化为数学表示形式）：quintessential lodging near running trails, eateries, retail

1. 构建混合查询。

   ### Run a hybrid query
   POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
       Content-Type: application/json
       Authorization: Bearer {{token}}
   
   {
       "count": true,
       "search": "historic hotel walk to restaurants and shopping",
       "select": "HotelName, Description",
       "top": 7,
       "vectorQueries": [
           {
               "vector": 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               "k": 5,
               "fields": "DescriptionVector",
               "kind": "vector",
               "exhaustive": true
           }
       ]
   }
   

2. 选择“发送请求”。 你应该收到 HTTP/1.1 200 OK 响应。 响应正文应包含搜索结果的 JSON 表示形式。

由于这是混合查询，因此结果按倒数融合排序 (RRF) 排名。 请注意， @search.score 值具有不同的基础，并且是统一较小的值。 RRF 会计算多个搜索结果的搜索分数，取到数，然后进行合并和对合并后的结果进行排序。 将会返回前 top 个结果。

查看响应，其中包含索引中 7 个匹配文档中的前 k-5 个匹配项：

{
  "@odata.count": 7,
  "value": [
    {
      "@search.score": 0.03279569745063782,
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience."
    },
    {
      "@search.score": 0.032522473484277725,
      "HotelName": "Luxury Lion Resort",
      "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort."
    },
    {
      "@search.score": 0.03205128386616707,
      "HotelName": "Nordick's Valley Motel",
      "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley."
    },
    {
      "@search.score": 0.0317460335791111,
      "HotelName": "Swirling Currents Hotel",
      "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs."
    },
    {
      "@search.score": 0.03125,
      "HotelName": "Old Century Hotel",
      "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music."
    }
  ]
}

由于 RRF 合并结果，因此将输入逐一查看会更有帮助。

以下结果来自查询的全文部分：步行可到餐馆和购物区的历史悠久酒店。 在全文查询中，前三个结果是苏美宫酒店、Stay-Kay 城市酒店和豪华狮子度假村。 Sublime Palace Hotel 具有更强的 BM25 相关性分数。 在融合查询中，只有两个匹配结果进入前 3 名，而第二个匹配项（Stay-Kay 城市酒店）未进入前 5 名。

  "value": [
    {
      "@search.score": 1.4868739,
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience."
    },
    {
      "@search.score": 1.2699215,
      "HotelName": "Stay-Kay City Hotel",
      "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities."
    },
    {
      "@search.score": 1.2456272,
      "HotelName": "Luxury Lion Resort",
      "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort."
    },
    ...
  ]

在矢量部分查询中“(跑步道、餐馆、零售店附近的经典住宿地)”是一个不同的字符串，因此我们应该期望不同的结果。 此查询返回的前三个匹配结果是 Nordick's Valley Motel、Luxury Lion Resort 和 Sublime Palace Hotel。 在融合查询中，这些结果处于前 3 位，但顺序相反。

  "value": [
    {
      "@search.score": 0.6605852,
      "HotelName": "Nordick's Valley Motel",
      "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley."
    },
    {
      "@search.score": 0.6333684,
      "HotelName": "Luxury Lion Resort",
      "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort."
    },
    {
      "@search.score": 0.605672,
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience."
    },
   ...
  ]

语义混合搜索

下面是集合中的最后一个查询。 此混合查询添加了 L2 语义排名，该排名对 L1 排名结果应用机器阅读理解，从而将更相关的匹配项推至前列。

1. 制定请求。

   ### Run a hybrid query with semantic reranking
   POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
       Content-Type: application/json
       Authorization: Bearer {{token}}
   
   {
       "count": true,
       "search": "historic hotel walk to restaurants and shopping",
       "select": "HotelName, Description",
       "queryType": "semantic",
       "semanticConfiguration": "semantic-config",
       "top": 5,
       "vectorQueries": [
           {
               "vector": 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               "k": 5,
               "fields": "DescriptionVector",
               "kind": "vector",
               "exhaustive": true
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       ]
   }
   

2. 选择“发送请求”。 你应该收到 HTTP/1.1 200 OK 响应。 响应正文应包含搜索结果的 JSON 表示形式。

查看响应，其中包含对混合查询的 RRF 排名结果的语义重排序。 语义排名基于文本输入工作。 在文本或混合查询中，此输入是查询的文本部分（历史悠久的酒店可步行到餐馆和购物区）。 若要对纯向量查询（例如第一个示例）使用语义排名，或显式控制用于语义排名的文本， 请提供 semanticQuery 字符串。

经过语义重排，斯威林流酒店因其提到步行到购物、餐饮、娱乐和市中心的便利而升至首位。 机器理解模型将“步行前往购物和用餐”提升为与“步行到餐馆和购物场所”更接近的匹配。

在语义重新排序之前，Sublime Palace 因其提到 步行即可到达城市的遗址和地标，并被教堂、建筑、商店和纪念碑的非凡美丽所包围，因此排名第一。

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  "@search.answers": [],
  "value": [
    {
      "@search.score": 0.0317460335791111,
      "@search.rerankerScore": 2.6550590991973877,
      "HotelId": "49",
      "HotelName": "Swirling Currents Hotel",
      "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
      "Category": "Suite"
    },
    {
      "@search.score": 0.03279569745063782,
      "@search.rerankerScore": 2.599761724472046,
      "HotelId": "4",
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
      "Category": "Boutique"
    },
    {
      "@search.score": 0.03125,
      "@search.rerankerScore": 2.3480887413024902,
      "HotelId": "2",
      "HotelName": "Old Century Hotel",
      "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
      "Category": "Boutique"
    },
    {
      "@search.score": 0.03154495730996132,
      "@search.rerankerScore": 2.2718777656555176,
      "HotelId": "1",
      "HotelName": "Stay-Kay City Hotel",
      "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
      "Category": "Boutique"
    },
    {
      "@search.score": 0.03053613007068634,
      "@search.rerankerScore": 2.0582215785980225,
      "HotelId": "3",
      "HotelName": "Gastronomic Landscape Hotel",
      "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel\u2019s restaurant services.",
      "Category": "Suite"
    }
  ]
}

小窍门

语义排名的结果可以包含更多详细信息，包括语义答案、标题和突出显示。 向请求添加更多参数会产生额外的详细信息。 有关详细信息，请参阅 设置语义查询。

清理

在自己的订阅中操作时，最好在项目结束时确定是否仍需要已创建的资源。 持续运行资源可能会产生费用。 可以逐个删除资源，也可以删除资源组以删除整个资源集。

可以在 Azure 门户中使用最左侧窗格中的“所有资源”或“资源组”链接来查找和管理资源。

如果要保留搜索服务，但删除索引和文档，则可以在 REST 客户端中使用 DELETE 命令。 此命令（在 az-search-vector-quickstart.rest 文件末尾）删除 hotels-vector-quickstart 索引：

### Delete an index
DELETE  {{baseUrl}}/indexes/hotels-vector-quickstart?api-version=2024-07-01 HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

后续步骤

接下来，我们建议学习如何在不使用 API 密钥的情况下调用 REST API。

你还可以查看 Python、C# 或 JavaScript 的演示代码。