快速入门：矢量搜索

2025-10-16

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。如果想在自己的内容之上添加内置数据分块和矢量化处理，请试用导入数据（新）向导，以体验端到端演练。

先决条件

拥有有效订阅的 Azure 帐户。创建试用版订阅。
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

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。
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
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.azure.cn。

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。
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+`。
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 python.exe 文件夹中的 Quickstart-Vector-Search\.venv\ 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。
运行以下命令安装所需的库：
```
pip install requirements.txt
```
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。

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.azure.cn
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。

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。

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 Beijing. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make Beijing 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": "Beijing",
               "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。

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 SearchClient 包提供的主要对象 azure-search-documents 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，我们将针对 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

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 quintessential lodging near running trails, eateries, retail 矢量的响应由 7 个结果组成，但代码指定 top=5 ，因此只返回前 5 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，每个文档的状态将会显示在其下方。

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。

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，每个文档的状态将会显示在其下方。

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。

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 Beijing.",
         "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"
     }
]

语义混合搜索

下面是集合中的最后一个查询。此混合查询指定语义查询类型和语义配置，演示如何生成使用语义重新调整的混合查询。

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。

借助语义排名，Swirling Currents Hotel 成功登顶。 W

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 Beijing. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make Beijing 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

可以通过了解查询中的字词和文档内容背后的含义，将语义排名视为提高搜索结果相关性的方法。在这种情况下，语义排名有助于识别不仅与关键字相关的酒店，还有助于匹配查询的意图：

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 Java 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。

注意

先决条件

拥有有效订阅的 Azure 帐户。创建试用版订阅。
Azure AI 搜索服务。创建服务或在当前订阅中查找现有服务。
- 在服务范围内分配的 Search Index Data Contributor 角色。
- 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。
Java 21 （LTS）。
Maven。

获取服务终结点

在其余部分中，设置对 Azure OpenAI 和 Azure AI 搜索的 API 调用。获取服务终结点，以便可以在代码中将其作为变量提供。

登录到 Azure 门户。
查找搜索服务
在“概述”主页上，复制 URL。示例终结点可能类似于 https://example.search.azure.cn。

设置 Java 项目

创建新的 Maven 项目目录。

mkdir vector-quickstart && cd vector-quickstart

创建具有所需依赖项的 pom.xml 文件。

<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
                             https://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.example.search</groupId>
    <artifactId>vector-search-quickstart</artifactId>
    <version>1.0.0-SNAPSHOT</version>

    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <maven.compiler.source>21</maven.compiler.source>
        <maven.compiler.target>21</maven.compiler.target>
    </properties>

    <dependencies>
        <dependency>
            <groupId>com.azure</groupId>
            <artifactId>azure-identity</artifactId>
            <version>1.17.0</version>
        </dependency>
        <dependency>
            <groupId>com.azure</groupId>
            <artifactId>azure-search-documents</artifactId>
            <version>11.7.9</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-nop</artifactId>
            <version>2.0.17</version>
            <scope>runtime</scope>
        </dependency>
    </dependencies>
</project>

编译项目以解析依赖项：
```
mvn compile
```

创建源目录结构。

mkdir -p src/main/java/com/example/search
mkdir -p src/main/resources

创建 src/main/resources/application.properties 并提供搜索服务终结点。

azure.search.endpoint=<YOUR AZURE AI SEARCH ENDPOINT>
azure.search.index-name=hotels-vector-quickstart

你正在使用 Microsoft Entra ID 和角色分配进行连接。请确保登录到与 Azure AI 搜索和 Azure OpenAI 相同的租户和订阅。可以在命令行上使用 Azure CLI 来显示当前属性、更改属性和登录。有关详细信息，请参阅在没有密钥的情况下连接。

按顺序运行下面每个命令。

az account show

az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>

az cloud set -n AzureChinaCloud

az login --tenant <PUT YOUR TENANT ID HERE>

# az cloud set -n AzureCloud   //means return to Public Azure.

现在，你应该被本地设备登录到了 Azure。

创建矢量索引

在本部分中，你将使用 SearchIndexClient 在 Azure AI 搜索中创建矢量索引。createOrUpdateIndex。索引架构定义字段，包括向量字段 DescriptionVector。

在CreateIndex.java目录中创建一个src/main/java/com/azure/search文件。

将以下代码复制到文件中。

package com.example.search;

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.search.documents.indexes.SearchIndexClient;
import com.azure.search.documents.indexes.SearchIndexClientBuilder;
import com.azure.search.documents.indexes.models.SearchIndex;
import com.azure.search.documents.indexes.models.SearchField;
import com.azure.search.documents.indexes.models.SearchFieldDataType;
import com.azure.search.documents.indexes.models.SearchSuggester;
import com.azure.search.documents.indexes.models.HnswAlgorithmConfiguration;
import com.azure.search.documents.indexes.models.HnswParameters;
import com.azure.search.documents.indexes.models.ExhaustiveKnnAlgorithmConfiguration;
import com.azure.search.documents.indexes.models.ExhaustiveKnnParameters;
import com.azure.search.documents.indexes.models.VectorSearchProfile;
import com.azure.search.documents.indexes.models.VectorSearch;
import com.azure.search.documents.indexes.models.SemanticPrioritizedFields;
import com.azure.search.documents.indexes.models.SemanticField;
import com.azure.search.documents.indexes.models.SemanticSearch;
import com.azure.search.documents.indexes.models.SemanticConfiguration;

import java.io.IOException;
import java.util.Arrays;
import java.util.List;
import java.util.Properties;

public class CreateIndex {
    private static final String searchEndpoint;
    private static final String indexName;

    static {
        var properties = new Properties();
        try (var in = CreateIndex.class.getClassLoader()
            .getResourceAsStream("application.properties")) {
            if (in != null) properties.load(in);
        } catch (IOException e) {
            System.out.println("Warning: could not read application.properties.");
        }

        searchEndpoint = properties.getProperty("azure.search.endpoint");
        indexName = properties.getProperty("azure.search.index-name");
    }

    public static void main(String[] args) {
        System.out.println("Using Azure Search endpoint: " + searchEndpoint);
        System.out.println("Using index name: " + indexName);

        // Create a SearchIndexClient using DefaultAzureCredential.
        SearchIndexClient searchIndexClient = new SearchIndexClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(searchEndpoint)
            .buildClient();

        System.out.println("Creating index...");

        // Define fields
        List<SearchField> fields = Arrays.asList(
            new SearchField("HotelId", SearchFieldDataType.STRING)
                .setKey(true)
                .setFilterable(true),
            new SearchField("HotelName", SearchFieldDataType.STRING)
                .setSortable(true)
                .setSearchable(true),
            new SearchField("Description", SearchFieldDataType.STRING)
                .setSearchable(true),
            new SearchField("DescriptionVector",
                SearchFieldDataType.collection(SearchFieldDataType.SINGLE))
                .setSearchable(true)
                .setVectorSearchDimensions(1536)
                .setVectorSearchProfileName("my-vector-profile"),
            new SearchField("Category", SearchFieldDataType.STRING)
                .setSortable(true)
                .setFilterable(true)
                .setFacetable(true)
                .setSearchable(true),
            new SearchField("Tags", SearchFieldDataType.collection(
                SearchFieldDataType.STRING))
                .setSearchable(true)
                .setFilterable(true)
                .setFacetable(true),
            new SearchField("ParkingIncluded", SearchFieldDataType.BOOLEAN)
                .setFilterable(true)
                .setSortable(true)
                .setFacetable(true),
            new SearchField("LastRenovationDate",
                SearchFieldDataType.DATE_TIME_OFFSET)
                .setFilterable(true)
                .setSortable(true)
                .setFacetable(true),
            new SearchField("Rating", SearchFieldDataType.DOUBLE)
                .setFilterable(true)
                .setSortable(true)
                .setFacetable(true),
            new SearchField("Address", SearchFieldDataType.COMPLEX)
                .setFields(Arrays.asList(
                    new SearchField("StreetAddress", SearchFieldDataType.STRING),
                    new SearchField("City", SearchFieldDataType.STRING)
                        .setFilterable(true)
                        .setSortable(true)
                        .setFacetable(true),
                    new SearchField("StateProvince", SearchFieldDataType.STRING)
                        .setFilterable(true)
                        .setFacetable(true),
                    new SearchField("PostalCode", SearchFieldDataType.STRING),
                    new SearchField("Country", SearchFieldDataType.STRING)
                        .setFilterable(true)
                        .setFacetable(true)
                )),
            new SearchField("Location", SearchFieldDataType.GEOGRAPHY_POINT)
                .setFilterable(true)
                .setSortable(true)
        );

        var searchIndex = new SearchIndex(indexName, fields);

        // Define vector search configuration
        var hnswParams = new HnswParameters()
            .setM(16)
            .setEfConstruction(200)
            .setEfSearch(128);
        var hnsw = new HnswAlgorithmConfiguration("hnsw-vector-config");
        hnsw.setParameters(hnswParams);

        var eknnParams = new ExhaustiveKnnParameters();
        var eknn = new ExhaustiveKnnAlgorithmConfiguration("eknn-vector-config");
        eknn.setParameters(eknnParams);

        var vectorProfile = new VectorSearchProfile(
            "my-vector-profile",
            "hnsw-vector-config");
        var vectorSearch = new VectorSearch()
            .setAlgorithms(Arrays.asList(hnsw, eknn))
            .setProfiles(Arrays.asList(vectorProfile));
        searchIndex.setVectorSearch(vectorSearch);

        // Define semantic configuration
        var prioritizedFields = new SemanticPrioritizedFields()
            .setTitleField(new SemanticField("HotelName"))
            .setContentFields(Arrays.asList(new SemanticField("Description")))
            .setKeywordsFields(Arrays.asList(new SemanticField("Category")));
        var semanticConfig = new SemanticConfiguration(
            "semantic-config",
            prioritizedFields);
        var semanticSearch = new SemanticSearch()
            .setConfigurations(Arrays.asList(semanticConfig));
        searchIndex.setSemanticSearch(semanticSearch);

        // Define suggesters
        var suggester = new SearchSuggester("sg", Arrays.asList("HotelName"));
        searchIndex.setSuggesters(Arrays.asList(suggester));

        // Create the search index with the semantic settings.
        SearchIndex result = searchIndexClient.createOrUpdateIndex(searchIndex);
        System.out.println("%s created".formatted(result.getName()));

        System.exit(0);
    }
}

代码文件创建索引并定义索引架构，包括向量字段 DescriptionVector。

运行文件：

mvn compile exec:java -Dexec.mainClass="com.example.search.CreateIndex"

此代码的输出显示已成功创建索引：
```
Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>
Creating index...
hotels-vector-quickstart created
```
使用 azure-search-documents 库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。
- 此特定索引支持多种搜索功能，例如：
  - 全文关键字搜索（setSearchable）
  - 矢量搜索（通过并置矢量和非矢量字段实现混合搜索）字段 (使用 DescriptionVector 的 setVectorSearchProfileName)
  - 语义搜索
  - 分面搜索 (SearchSuggester)
  - 地理空间搜索（使用 Location 的 SearchFieldDataType.GEOGRAPHY_POINT 字段）
  - 筛选、排序（许多标记为可筛选和可排序的字段）

将文档上传到索引

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，需要使用 SearchClient 将文档加载到索引中。uploadDocuments。文档包含文章说明的矢量化版本，可基于含义而不是确切关键字进行相似性搜索。

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。

在 UploadDocuments.java 目录中创建 src/main/java/com/azure/search 文件。

将以下代码复制到文件中。

package com.example.search;

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.search.documents.SearchClient;
import com.azure.search.documents.SearchClientBuilder;
import com.azure.search.documents.models.IndexDocumentsResult;
import com.azure.search.documents.models.IndexingResult;

import java.io.IOException;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;

public class UploadDocuments {
    private static final String searchEndpoint;
    private static final String indexName;

    static {
        var properties = new Properties();
        try (var in = UploadDocuments.class.getClassLoader()
            .getResourceAsStream("application.properties")) {
            if (in != null) properties.load(in);
        } catch (IOException e) {
            System.out.println("Warning: could not read application.properties.");
        }

        searchEndpoint = properties.getProperty("azure.search.endpoint");
        indexName = properties.getProperty("azure.search.index-name");
    }

    private static final List<Map<String, Object>> DOCUMENTS = Arrays.asList(
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "1");
            put("HotelName", "Stay-Kay City Hotel");
            put("Description", "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of Beijing. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make Beijing one of America's most attractive and cosmopolitan cities.");
            put("DescriptionVector", 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            put("Category", "Boutique");
            put("Tags", Arrays.asList("view", "air conditioning", "concierge"));
            put("ParkingIncluded", false);
            put("LastRenovationDate", "2022-01-18T00:00:00Z");
            put("Rating", 3.6);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "677 5th Ave");
                put("City", "Beijing");
                put("StateProvince", "NY");
                put("PostalCode", "10022");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-73.975403, 40.760586));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "2");
            put("HotelName", "Old Century Hotel");
            put("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.");
            put("DescriptionVector", 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            put("Category", "Boutique");
            put("Tags", Arrays.asList("pool", "free wifi", "air conditioning", "concierge"));
            put("ParkingIncluded", false);
            put("LastRenovationDate", "2019-02-18T00:00:00Z");
            put("Rating", 3.6);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "140 University Town Center Dr");
                put("City", "Sarasota");
                put("StateProvince", "FL");
                put("PostalCode", "34243");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-82.452843, 27.384417));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "3");
            put("HotelName", "Gastronomic Landscape Hotel");
            put("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.");
            put("DescriptionVector", 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            put("Category", "Suite");
            put("Tags", Arrays.asList("restaurant", "bar", "continental breakfast"));
            put("ParkingIncluded", true);
            put("LastRenovationDate", "2015-09-20T00:00:00Z");
            put("Rating", 4.8);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "3393 Peachtree Rd");
                put("City", "Atlanta");
                put("StateProvince", "GA");
                put("PostalCode", "30326");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(33.847848, -84.363602));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "4");
            put("HotelName", "Sublime Palace Hotel");
            put("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.");
            put("DescriptionVector", 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            put("Category", "Boutique");
            put("Tags", Arrays.asList("view", "concierge", "air conditioning"));
            put("ParkingIncluded", true);
            put("Rating", 4.6);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "7400 San Pedro Ave");
                put("City", "San Antonio");
                put("StateProvince", "TX");
                put("PostalCode", "78216");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-98.498923, 29.518029));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "48");
            put("HotelName", "Nordick's Valley Motel");
            put("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.");
            put("DescriptionVector", 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            put("Category", "Boutique");
            put("Tags", Arrays.asList("continental breakfast", "air conditioning", "free wifi"));
            put("ParkingIncluded", false);
            put("LastRenovationDate", "2018-02-19T00:00:00Z");
            put("Rating", 4.5);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "1401 I St NW");
                put("City", "Washington D.C.");
                put("StateProvince", "");
                put("PostalCode", "20005");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-77.03241, 38.90166));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "49");
            put("HotelName", "Swirling Currents Hotel");
            put("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 W-Fi and flat-screen TVs.");
            put("DescriptionVector", 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            put("Category", "Suite");
            put("Tags", Arrays.asList("air conditioning", "laundry service", "24-hour front desk service"));
            put("ParkingIncluded", true);
            put("LastRenovationDate", "2018-01-27T00:00:00Z");
            put("Rating", 2.7);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "1100 S Hayes St");
                put("City", "Arlington");
                put("StateProvince", "VA");
                put("PostalCode", "22202");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-77.060066, 38.863659));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "13");
            put("HotelName", "Luxury Lion Resort");
            put("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.");
            put("DescriptionVector", 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            put("Category", "Luxury");
            put("Tags", Arrays.asList("bar", "concierge", "restaurant"));
            put("ParkingIncluded", false);
            put("LastRenovationDate", "2020-03-18T00:00:00Z");
            put("Rating", 4.1);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "3 Cityplace Dr");
                put("City", "St. Louis");
                put("StateProvince", "MO");
                put("PostalCode", "63141");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-90.44081, 38.67219));
            }});
        }}
    );

    private static void waitUntilIndexed(SearchClient searchClient, int expected)
        throws InterruptedException {
        do {
            try {
                var count = searchClient.getDocumentCount();
                if (count == expected) {
                    System.out.println("All documents indexed successfully.");
                    break;
                }
                System.out.println("Waiting for indexing... Current count: %s".formatted(count));
            } catch (RuntimeException ex) {
                System.out.println("Warning: failed to get document count: " + ex.getMessage());
            }
            Thread.sleep(10_000);
        } while (true);
    }

    public static void main(String[] args) {
        System.out.println("Using Azure Search endpoint: " + searchEndpoint);
        System.out.println("Using index name: " + indexName);

        // Create a SearchClient using DefaultAzureCredential.
        var searchClient = new SearchClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(searchEndpoint)
            .indexName(indexName)
            .buildClient();

        try {
            // Upload documents to the index.
            System.out.println("Uploading documents...");
            IndexDocumentsResult result = searchClient.uploadDocuments(DOCUMENTS);
            for (IndexingResult r : result.getResults()) {
                String err = r.getErrorMessage() == null ? "none" : r.getErrorMessage();
                System.out.println("Key: %s, Succeeded: %s, ErrorMessage: %s"
                    .formatted(r.getKey(), r.isSucceeded(), err));
            }

            // Wait until the documents appear in the index. This is a simple
            // polling loop suitable for samples. For production, consider a
            // more robust approach with retries and timeouts.
            waitUntilIndexed(searchClient, DOCUMENTS.size());
        } catch (Exception ex) {
            System.err.println("Failed to upload documents: " + ex);
        }

        System.exit(0);
    }
}

此代码加载文档集合。每个文档都包含文章的 Description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。

由于本快速入门文章会立即搜索索引，因此 waitUntilIndexed 方法会等到索引准备好进行搜索操作。这一点很重要，因为索引需要完全填充文档，然后才能执行搜索。

生成并运行文件：

mvn compile exec:java -Dexec.mainClass="com.example.search.UploadDocuments"

此代码的输出显示文档已编制索引并可供搜索：
```
Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>
Uploading documents...
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
Waiting for indexing... Current count: 0
All documents indexed successfully.
```
uploadDocuments 方法和此示例的关键要点：
- 代码通过 SearchClient 与 Azure AI 搜索服务中托管的特定搜索索引进行交互。 SearchClient 提供对操作的访问权限，例如：
  - 数据引入 - uploadDocuments、mergeDocuments 和 deleteDocuments 等等。
  - 搜索操作 - search、autocomplete、suggest
- 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。

将查询创建为矢量

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

QueryVector.java在src/main/java/com/azure/search目录中创建文件，并添加代码以创建查询向量。

package com.example.search;

import java.util.ArrayList;

// Query is 1536-dimensional vector of "quintessential lodging near running trails, eateries, retail".
// Note: this same vector can be used for vector search and semantic search.
// Replace with your own vector.
public class QueryVector {
    // The vector data is stored as a private static final double[].
    // Use getVectorList() to obtain a List<Float> suitable for Azure SDK vector search APIs.
    // This approach avoids exposing a mutable array and provides the format required by the SDK.
    private static final double[] VECTOR = 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    public static ArrayList<Float> getVectorList() {
        var vectorList = new ArrayList<Float>(VECTOR.length);
        for (double v : VECTOR) {
            vectorList.add((float) v);
        }
        return vectorList;
    }
}

以下部分中使用此代码来执行矢量搜索。查询向量是使用 Azure OpenAI 中的嵌入模型创建的。

创建单个矢量搜索

第一个示例演示了一个基本方案，其中你想要使用 SearchClient 查找与搜索字符串紧密匹配的文档说明。search 和 VectorQuery 和 SearchOptions。

在SearchSingle.java目录中创建一个src/main/java/com/azure/search文件。

将以下代码复制到文件中。

package com.example.search;

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.search.documents.SearchClientBuilder;
import com.azure.search.documents.models.SearchOptions;
import com.azure.search.documents.models.SearchResult;
import com.azure.core.util.Context;
import com.azure.search.documents.SearchDocument;
import com.azure.search.documents.models.VectorizedQuery;
import com.azure.search.documents.models.VectorSearchOptions;
import com.azure.search.documents.models.VectorFilterMode;

import java.io.IOException;
import java.util.Properties;

public class SearchSingle {
    private static final String searchEndpoint;
    private static final String indexName;

    static {
        var properties = new Properties();
        try (var in = SearchSingle.class.getClassLoader()
            .getResourceAsStream("application.properties")) {
            if (in != null) properties.load(in);
        } catch (IOException e) {
            System.out.println("Warning: could not read application.properties.");
        }

        searchEndpoint = properties.getProperty("azure.search.endpoint");
        indexName = properties.getProperty("azure.search.index-name");
    }

    public static void main(String[] args) {
        System.out.println("Using Azure Search endpoint: " + searchEndpoint);
        System.out.println("Using index name: " + indexName);

        // Create a SearchClient using DefaultAzureCredential.
        var searchClient = new SearchClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(searchEndpoint)
            .indexName(indexName)
            .buildClient();

        try {
            var vectorQuery = new VectorizedQuery(QueryVector.getVectorList())
                .setKNearestNeighborsCount(5)
                .setFields("DescriptionVector")
                .setExhaustive(true);

            // Create vector search options with the vector query and filter.
            // Use POST_FILTER to apply filter after vector similarity is calculated.
            var vectorSearchOptions = new VectorSearchOptions()
                .setQueries(vectorQuery)
                .setFilterMode(VectorFilterMode.POST_FILTER);

            var searchOptions = new SearchOptions()
                .setTop(7)
                .setIncludeTotalCount(true)
                .setSelect("HotelId", "HotelName", "Description", "Category", "Tags")
                .setVectorSearchOptions(vectorSearchOptions);

            var results = searchClient.search("*", searchOptions, Context.NONE);

            System.out.println();
            Long count = results.getTotalCount();
            System.out.println("Single Vector search found %s"
                .formatted(count == null ? 0 : count));

            for (SearchResult result : results) {
                // Log each result.
                SearchDocument document = result.getDocument(SearchDocument.class);
                Object tags = document.get("Tags");
                var tagsString = (tags == null ? "N/A" : tags.toString());
                System.out.println(
                    "- HotelId: %s, HotelName: %s, Tags: %s, Score: %s".formatted(
                        document.get("HotelId"), document.get("HotelName"),
                        tagsString, result.getScore()));
            }
        } catch (Exception ex) {
            System.err.println("Search failed: " + ex);
        }

        System.exit(0);
    }
}

vectorQuery 包含矢量化查询的配置，其中包括通过QueryVector.getVectorList 检索到的查询输入的矢量化文本。 setFields 确定搜索哪些向量字段，而 setKNearestNeighborsCount 指定要返回的最近领域的数量。

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。

生成并运行文件：

mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingle"

此代码的输出显示查询 quintessential lodging near running trails, eateries, retail的相关文档。

Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>

Single Vector search found 5
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
- HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
- HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366

等效 quintessential lodging near running trails, eateries, retail 矢量的响应由 5 个结果组成，仅返回了按选择指定的字段。

使用筛选器创建单个矢量搜索

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。此搜索使用 SearchClient.SearchClient 和 SearchOptions。

在SearchSingleWithFilter.java目录中创建一个src/main/java/com/azure/search文件。

将以下代码复制到文件中。

package com.example.search;

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.search.documents.SearchClientBuilder;
import com.azure.search.documents.models.SearchOptions;
import com.azure.search.documents.models.SearchResult;
import com.azure.core.util.Context;
import com.azure.search.documents.SearchDocument;
import com.azure.search.documents.models.VectorizedQuery;
import com.azure.search.documents.models.VectorSearchOptions;
import com.azure.search.documents.models.VectorFilterMode;

import java.io.IOException;
import java.util.Properties;

public class SearchSingleWithFilter {
    private static final String searchEndpoint;
    private static final String indexName;

    static {
        var properties = new Properties();
        try (var in = SearchSingleWithFilter.class.getClassLoader()
            .getResourceAsStream("application.properties")) {
            if (in != null) properties.load(in);
        } catch (IOException e) {
            System.out.println("Warning: could not read application.properties.");
        }

        searchEndpoint = properties.getProperty("azure.search.endpoint");
        indexName = properties.getProperty("azure.search.index-name");
    }

    public static void main(String[] args) {
        System.out.println("Using Azure Search endpoint: " + searchEndpoint);
        System.out.println("Using index name: " + indexName);

        // Create a SearchClient using DefaultAzureCredential.
        var searchClient = new SearchClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(searchEndpoint)
            .indexName(indexName)
            .buildClient();

        try {
            var vectorQuery = new VectorizedQuery(QueryVector.getVectorList())
                .setKNearestNeighborsCount(5)
                .setFields("DescriptionVector")
                .setExhaustive(true);

            // Create vector search options with the vector query and filter.
            // Use POST_FILTER to apply filter after vector similarity is calculated.
            var vectorSearchOptions = new VectorSearchOptions()
                .setQueries(vectorQuery)
                .setFilterMode(VectorFilterMode.POST_FILTER);

            // Add filter for "free wifi" tag.
            var searchOptions = new SearchOptions()
                .setTop(7)
                .setIncludeTotalCount(true)
                .setSelect("HotelId", "HotelName", "Description", "Category", "Tags")
                .setFilter("Tags/any(tag: tag eq 'free wifi')")
                .setVectorSearchOptions(vectorSearchOptions);

            var results = searchClient.search("*", searchOptions, Context.NONE);

            System.out.println();
            Long count = results.getTotalCount();
            System.out.println("Single Vector search found %s"
                .formatted(count == null ? 0 : count));

            for (SearchResult result : results) {
                // Log each result.
                SearchDocument document = result.getDocument(SearchDocument.class);
                Object tags = document.get("Tags");
                var tagsString = (tags == null ? "N/A" : tags.toString());
                System.out.println(
                    "- HotelId: %s, HotelName: %s, Tags: %s, Score: %s".formatted(
                        document.get("HotelId"), document.get("HotelName"),
                        tagsString, result.getScore()));
            }
        } catch (Exception ex) {
            System.err.println("Vector search failed: " + ex);
        }

        System.exit(0);
    }
}

此代码的搜索功能与上一个搜索相同，并为带 free wifi 的酒店添加了后处理排除筛选器。

生成并运行文件：

mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilter"

此代码的输出列出了应用筛选器 free wifi 后与查询相关的文档。

Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>

Single Vector search found 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366

使用地理空间筛选器创建搜索

可以指定地理空间筛选器，以将结果限制为特定的地理区域。在此示例中，筛选器将结果限制为华盛顿特区 300 公里范围内的酒店（坐标： -77.03241 38.90166）。地理空间距离始终以公里为单位。此搜索使用 SearchClient.SearchClient 和 SearchOptions。

在SearchSingleWithFilterGeo.java目录中创建一个src/main/java/com/azure/search文件。

将以下代码复制到文件中。

package com.example.search;

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.search.documents.SearchClientBuilder;
import com.azure.search.documents.models.SearchOptions;
import com.azure.search.documents.models.SearchResult;
import com.azure.core.util.Context;
import com.azure.search.documents.SearchDocument;
import com.azure.search.documents.models.VectorizedQuery;
import com.azure.search.documents.models.VectorSearchOptions;
import com.azure.search.documents.models.VectorFilterMode;

import java.io.IOException;
import java.util.Properties;

public class SearchSingleWithFilterGeo {
    private static final String searchEndpoint;
    private static final String indexName;

    static {
        var properties = new Properties();
        try (var in = SearchSingleWithFilterGeo.class.getClassLoader()
            .getResourceAsStream("application.properties")) {
            if (in != null) properties.load(in);
        } catch (IOException e) {
            System.out.println("Warning: could not read application.properties.");
        }

        searchEndpoint = properties.getProperty("azure.search.endpoint");
        indexName = properties.getProperty("azure.search.index-name");
    }

    public static void main(String[] args) {
        System.out.println("Using Azure Search endpoint: " + searchEndpoint);
        System.out.println("Using index name: " + indexName);

        // Create a SearchClient using DefaultAzureCredential.
        var searchClient = new SearchClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(searchEndpoint)
            .indexName(indexName)
            .buildClient();

        try {
            var vectorQuery = new VectorizedQuery(QueryVector.getVectorList())
                .setKNearestNeighborsCount(5)
                .setFields("DescriptionVector")
                .setExhaustive(true);

            // Create vector search options with the vector query and filter.
            // Use POST_FILTER to apply filter after vector similarity is calculated.
            var vectorSearchOptions = new VectorSearchOptions()
                .setQueries(vectorQuery)
                .setFilterMode(VectorFilterMode.POST_FILTER);

            var searchOptions = new SearchOptions()
                .setTop(5)
                .setIncludeTotalCount(true)
                .setSelect("HotelId", "HotelName", "Category", "Description",
                           "Address/City", "Address/StateProvince")
                .setFacets("Address/StateProvince")
                .setFilter("geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300")
                .setVectorSearchOptions(vectorSearchOptions);

            var results = searchClient.search("*", searchOptions, Context.NONE);

            System.out.println();
            Long count = results.getTotalCount();
            System.out.println("Single Vector search found %s"
                .formatted(count == null ? 0 : count));

            for (SearchResult result : results) {
                // Log each result.
                SearchDocument document = result.getDocument(SearchDocument.class);
                Object tags = document.get("Tags");
                var tagsString = (tags == null ? "N/A" : tags.toString());
                System.out.println(
                    "- HotelId: %s, HotelName: %s, Tags: %s, Score: %s".formatted(
                        document.get("HotelId"), document.get("HotelName"),
                        tagsString, result.getScore()));
            }
        } catch (Exception ex) {
            System.err.println("Vector search failed: " + ex);
        }

        System.exit(0);
    }
}

生成并运行文件：

mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilterGeo"

此代码的输出显示应用了地理空间后处理排除筛选器的查询的相关文档：

Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>

Single Vector search found 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: N/A, Score 0.6605852246284485
- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: N/A, Score 0.602634072303772

创建混合搜索

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

此搜索使用 SearchClient.SearchClient 和 SearchOptions。

在SearchHybrid.java目录中创建一个src/main/java/com/azure/search文件。

将以下代码复制到文件中。

package com.example.search;

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.search.documents.SearchClientBuilder;
import com.azure.search.documents.models.SearchOptions;
import com.azure.search.documents.models.SearchResult;
import com.azure.core.util.Context;
import com.azure.search.documents.SearchDocument;
import com.azure.search.documents.models.VectorizedQuery;
import com.azure.search.documents.models.VectorSearchOptions;
import com.azure.search.documents.models.VectorFilterMode;

import java.io.IOException;
import java.util.Properties;

public class SearchHybrid {
    private static final String searchEndpoint;
    private static final String indexName;

    static {
        var properties = new Properties();
        try (var in = SearchHybrid.class.getClassLoader()
            .getResourceAsStream("application.properties")) {
            if (in != null) properties.load(in);
        } catch (IOException e) {
            System.out.println("Warning: could not read application.properties.");
        }

        searchEndpoint = properties.getProperty("azure.search.endpoint");
        indexName = properties.getProperty("azure.search.index-name");
    }

    public static void main(String[] args) {
        System.out.println("Using Azure Search endpoint: " + searchEndpoint);
        System.out.println("Using index name: " + indexName);

        // Create a SearchClient using DefaultAzureCredential.
        var searchClient = new SearchClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(searchEndpoint)
            .indexName(indexName)
            .buildClient();

        try {
            var vectorQuery = new VectorizedQuery(QueryVector.getVectorList())
                .setKNearestNeighborsCount(5)
                .setFields("DescriptionVector")
                .setExhaustive(true);

            // Create vector search options with the vector query and filter.
            // Use POST_FILTER to apply filter after vector similarity is calculated.
            var vectorSearchOptions = new VectorSearchOptions()
                .setQueries(vectorQuery)
                .setFilterMode(VectorFilterMode.POST_FILTER);

            var searchOptions = new SearchOptions()
                .setTop(5)
                .setIncludeTotalCount(true)
                .setSelect("HotelId", "HotelName", "Description", "Category", "Tags")
                .setVectorSearchOptions(vectorSearchOptions);

            var results = searchClient.search(
                "historic hotel walk to restaurants and shopping",
                searchOptions, Context.NONE);

            System.out.println();
            Long count = results.getTotalCount();
            System.out.println("Hybrid search found %s then limited to top %s"
                .formatted(count == null ? 0 : count,
                    searchOptions.getTop() == null ? 0 : searchOptions.getTop()));

            for (SearchResult result : results) {
                // Log each result.
                SearchDocument document = result.getDocument(SearchDocument.class);
                Object description = document.get("Description");
                Object category = document.get("Category");
                Object tags = document.get("Tags");

                System.out.println(
                    "- Score: %s\n".formatted(result.getScore()) +
                    "  HotelId: %s\n".formatted(document.get("HotelId")) +
                    "  HotelName: %s\n".formatted(document.get("HotelName")) +
                    "  Description: %s\n".formatted(description == null ? "N/A" : description) +
                    "  Category: %s\n".formatted(category == null ? "N/A" : category) +
                    "  Tags: %s\n".formatted(tags == null ? "N/A" : tags.toString())
                );
            }
        } catch (Exception ex) {
            System.err.println("Hybrid search failed: " + ex);
        }

        System.exit(0);
    }
}

生成并运行文件：

mvn compile exec:java -Dexec.mainClass="com.example.search.SearchHybrid"

此代码的输出显示混合搜索的相关文档：

Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>

Hybrid search found 7 then limited to top 5
- 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 W-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"]

   {
       "@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"
   },

"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 Beijing.",
        "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"
    }
]

创建语义混合搜索

这是集合中的最后一个查询，可使用附加搜索文本 historic hotel walk to restaurants and shopping 来创建/扩展语义混合搜索。

此搜索使用 SearchClient.SearchClient 和 SearchOptions。

在SearchSemanticHybrid.java目录中创建一个src/main/java/com/azure/search文件。

将以下代码复制到文件中。

package com.example.search;

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.search.documents.SearchClientBuilder;
import com.azure.search.documents.models.SearchOptions;
import com.azure.search.documents.models.SearchResult;
import com.azure.core.util.Context;
import com.azure.search.documents.SearchDocument;
import com.azure.search.documents.models.VectorizedQuery;
import com.azure.search.documents.models.VectorSearchOptions;
import com.azure.search.documents.models.VectorFilterMode;
import com.azure.search.documents.models.QueryType;
import com.azure.search.documents.models.SemanticSearchOptions;

import java.io.IOException;
import java.util.Properties;

public class SearchSemanticHybrid {
    private static final String searchEndpoint;
    private static final String indexName;

    static {
        var properties = new Properties();
        try (var in = SearchSemanticHybrid.class.getClassLoader()
            .getResourceAsStream("application.properties")) {
            if (in != null) properties.load(in);
        } catch (IOException e) {
            System.out.println("Warning: could not read application.properties.");
        }

        searchEndpoint = properties.getProperty("azure.search.endpoint");
        indexName = properties.getProperty("azure.search.index-name");
    }

    public static void main(String[] args) {
        System.out.println("Using Azure Search endpoint: " + searchEndpoint);
        System.out.println("Using index name: " + indexName);

        // Create a SearchClient using DefaultAzureCredential.
        var searchClient = new SearchClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(searchEndpoint)
            .indexName(indexName)
            .buildClient();

        try {
            var vectorQuery = new VectorizedQuery(QueryVector.getVectorList())
                .setKNearestNeighborsCount(5)
                .setFields("DescriptionVector")
                .setExhaustive(true);

            // Create vector search options with the vector query and filter.
            // Use POST_FILTER to apply filter after vector similarity is calculated.
            var vectorSearchOptions = new VectorSearchOptions()
                .setQueries(vectorQuery)
                .setFilterMode(VectorFilterMode.POST_FILTER);

            SemanticSearchOptions semanticSearchOptions = new SemanticSearchOptions()
                .setSemanticConfigurationName("semantic-config");

            var searchOptions = new SearchOptions()
                .setTop(5)
                .setIncludeTotalCount(true)
                .setSelect("HotelId", "HotelName", "Category", "Description")
                .setQueryType(QueryType.SEMANTIC)
                .setSemanticSearchOptions(semanticSearchOptions)
                .setVectorSearchOptions(vectorSearchOptions);

            var results = searchClient.search(
                "historic hotel walk to restaurants and shopping",
                searchOptions, Context.NONE);

            System.out.println();
            Long count = results.getTotalCount();
            System.out.println("Semantic hybrid search found %s then limited to top %s"
                .formatted(count == null ? 0 : count,
                    searchOptions.getTop() == null ? 0 : searchOptions.getTop()));

            for (SearchResult result : results) {
                // Log each result.
                SearchDocument document = result.getDocument(SearchDocument.class);
                var semanticResult = result.getSemanticSearch();
                Double rerankerScore = semanticResult == null ?
                    null : semanticResult.getRerankerScore();
                Object desc = document.get("Description");
                Object category = document.get("Category");

                System.out.println(
                    "- Score: %s\n".formatted(result.getScore()) +
                    "  Re-ranker Score: %s\n".formatted(rerankerScore == null ? "N/A" : rerankerScore) +
                    "  HotelId: %s\n".formatted(document.get("HotelId")) +
                    "  HotelName: %s\n".formatted(document.get("HotelName")) +
                    "  Description: %s\n".formatted(desc == null ? "N/A" : desc) +
                    "  Category: %s\n".formatted(category == null ? "N/A" : category)
                );
            }
        } catch (Exception ex) {
            System.err.println("Semantic hybrid search failed: " + ex);
        }

        System.exit(0);
    }
}

生成并运行文件：

mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSemanticHybrid"

此代码的输出显示语义混合搜索的相关文档：

Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>

Semantic hybrid search found 7 then limited to top 5
- 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 W-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 Beijing. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make Beijing 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 VectorSearchOptions 类指定。关键字搜索通过 SemanticSearchOptions 类指定。
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

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，则可以以编程方式删除索引。

在DeleteIndex.java目录中创建一个src/main/java/com/azure/search文件。

添加代码以删除索引。

package com.example.search;

import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.search.documents.indexes.SearchIndexClient;
import com.azure.search.documents.indexes.SearchIndexClientBuilder;

import java.io.IOException;
import java.util.Properties;

public class DeleteIndex {
    private static final String searchEndpoint;
    private static final String indexName;

    static {
        var properties = new Properties();
        try (var in = DeleteIndex.class.getClassLoader()
            .getResourceAsStream("application.properties")) {
            if (in != null) properties.load(in);
        } catch (IOException e) {
            System.out.println("Warning: could not read application.properties.");
        }

        searchEndpoint = properties.getProperty("azure.search.endpoint");
        indexName = properties.getProperty("azure.search.index-name");
    }

    public static void main(String[] args) {
        System.out.println("Using Azure Search endpoint: " + searchEndpoint);
        System.out.println("Using index name: " + indexName);

        // Create a SearchIndexClient using DefaultAzureCredential.
        SearchIndexClient searchIndexClient = new SearchIndexClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(searchEndpoint)
            .buildClient();

        try {
            System.out.println("Deleting index...");
            searchIndexClient.deleteIndex(indexName);
            System.out.println("Index %s deleted".formatted(indexName));
        } catch (Exception ex) {
            System.err.println("Failed to delete index: " + ex);
        }

        System.exit(0);
    }
}

生成并运行文件：

mvn compile exec:java -Dexec.mainClass="com.example.search.DeleteIndex"

后续步骤

查看适用于 Java 的 Azure AI 搜索中矢量搜索功能的代码示例存储库

在本快速入门中，你将使用 JavaScript 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。

注意

先决条件

拥有有效订阅的 Azure 帐户。创建试用版订阅。
Azure AI 搜索服务。创建服务或在当前订阅中查找现有服务。
- 在服务范围内分配的 Search Index Data Contributor 角色。
- 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。
Visual Studio Code。
Node.JS 之 LTS 版本。

获取服务终结点

在其余部分中，设置对 Azure OpenAI 和 Azure AI 搜索的 API 调用。获取服务终结点，以便可以在代码中将其作为变量提供。

登录到 Azure 门户。
查找搜索服务
在“概述”主页上，复制 URL。示例终结点可能类似于 https://example.search.azure.cn。

设置Node.JS项目

使用 Visual Studio Code 和 JavaScript 设置项目。

在新目录中启动 Visual Studio Code。

mkdir vector-quickstart && cd vector-quickstart
code .

在项目目录中为 ESM 模块创建新包。
```
npm init -y
npm pkg set type=module
```
这会创建一个 package.json 具有默认值的文件。

安装以下 npm 包。

npm install @azure/identity @azure/search-documents dotenv

在您的项目目录中创建一个名为src的目录。
```
mkdir src
```

设置用于本地开发的环境变量

在.env项目目录中创建一个vector-quickstart文件。

将以下环境变量添加到 .env 文件中，将值替换为自己的服务终结点和密钥。

AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart

按顺序运行下面每个命令。

az account show

az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>

az cloud set -n AzureChinaCloud

az login --tenant <PUT YOUR TENANT ID HERE>

# az cloud set -n AzureCloud   //means return to Public Azure.

现在，你应该被本地设备登录到了 Azure。

创建矢量索引

在本部分中，你将使用 SearchIndexClient 在 Azure AI 搜索中创建矢量索引。createOrUpdateIndex。索引架构定义字段，包括向量字段 DescriptionVector。

在createIndex.js目录中创建一个src文件。

将以下代码复制到文件中。

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const indexClient = new SearchIndexClient(searchEndpoint, credential);
console.log('Creating index...');

// Define fields
const fields = [
    {
        name: "HotelId",
        type: "Edm.String",
        key: true,
        filterable: true
    },
    {
        name: "HotelName",
        type: "Edm.String",
        sortable: true,
        searchable: true,
        hidden: false,
    },
    {
        name: "Description",
        type: "Edm.String",
        searchable: true,
        hidden: false
    },
    {
        name: "DescriptionVector",
        type: "Collection(Edm.Single)",
        searchable: true,
        vectorSearchDimensions: 1536,
        vectorSearchProfileName: "my-vector-profile",
        hidden: true
    },
    {
        name: "Category",
        type: "Edm.String",
        sortable: true,
        filterable: true,
        facetable: true,
        searchable: true,
        hidden: false
    },
    {
        name: "Tags",
        type: "Collection(Edm.String)",
        searchable: true,
        filterable: true,
        facetable: true,
        hidden: false
    },
    {
        name: "ParkingIncluded",
        type: "Edm.Boolean",
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "LastRenovationDate",
        type: "Edm.DateTimeOffset",
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Rating",
        type: "Edm.Double",
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Address",
        type: "Edm.ComplexType",
        fields: [
            {
                name: "StreetAddress",
                type: "Edm.String",
                searchable: true,
            },
            {
                name: "City",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "StateProvince",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "PostalCode",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true
            },
            {
                name: "Country",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true,
            }
        ]
    },
    {
        name: "Location",
        type: "Edm.GeographyPoint",
        filterable: true,
        sortable: true
    }
];

// Define vector search configuration
const vectorSearch = {
    algorithms: [
        {
            name: "hnsw-vector-config",
            kind: "hnsw"
        },
        {
            name: "eknn-vector-config",
            kind: "exhaustiveKnn"
        }
    ],
    profiles: [
        {
            name: "my-vector-profile",
            algorithmConfigurationName: "hnsw-vector-config"
        }
    ]
};

// Define semantic configuration
const semanticConfig = {
    name: "semantic-config",
    prioritizedFields: {
        titleField: {
            name: "HotelName"
        },
        contentFields: [
            {
                name: "Description"
            }
        ],
        keywordsFields: [
            {
                name: "Category"
            }
        ]
    }
};

// Create the semantic settings with the configuration
const semanticSearch = {
    configurations: [semanticConfig]
};

// Define suggesters
const suggesters = [];

// Create the search index with the semantic settings
const indexDefinition = {
    name: indexName,
    fields: fields,
    vectorSearch: vectorSearch,
    semanticSearch: semanticSearch,
    suggesters: suggesters
};

const result = await indexClient.createOrUpdateIndex(indexDefinition);
console.log(`${result.name} created`);

代码文件将依赖项、环境变量和 JavaScript 类型 HotelDocument 添加到文件顶部。添加函数 createIndex 以创建索引。该函数定义索引架构，包括向量字段 DescriptionVector。

运行文件：

node -r dotenv/config src/createIndex.js

此代码的输出显示已成功创建索引：
```
Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>
Creating index...
hotels-vector-quickstart created
```
使用 @azure/search-documents 库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。
- 此特定索引支持多种搜索功能，例如：
  - 全文关键字搜索（searchable）
  - 矢量搜索（通过并置矢量和非矢量字段实现混合搜索）字段 (使用 DescriptionVector 的 vectorSearchProfileName)
  - 语义搜索
  - 分面搜索 (searchSuggester)
  - 地理空间搜索（使用 Location 的 geo.distance 字段）
  - 筛选、排序（许多标记为可筛选和可排序的字段）

将文档上传到索引

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。

在 uploadDocuments.js 目录中创建 src 文件。

将以下代码复制到文件中。

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const 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 centre 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 W-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,
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            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
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        }
    },
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        "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"
                }
            }
        }
    }
];

async function waitUntilIndexed() {
    try {
        const searchClient = new SearchClient(searchEndpoint, indexName, credential);
        do {
            const count = await searchClient.getDocumentsCount();
            if (count == DOCUMENTS.length) {
                console.log("All documents indexed successfully.");
                break;
            }
            console.log(`Waiting for indexing... Current count: ${count}`);
            await new Promise((resolve) => setTimeout(resolve, 10000)); // Wait for 10 seconds
        } while (true);
    } catch (ex) {
        console.error("Failed to wait until indexed:", ex);
    }
}

const searchClient = new SearchClient(searchEndpoint, indexName, credential);

try {
    console.log("Uploading documents...");
    const result = await searchClient.uploadDocuments(DOCUMENTS);
    for (const r of result.results) {
        console.log(`Key: ${r.key}, Succeeded: ${r.succeeded}, ErrorMessage: ${r.errorMessage || 'none'}`);
    }
    await waitUntilIndexed();
} catch (ex) {
    console.error("Failed to upload documents:", ex);
}

此代码加载 JSON 对象的数组。每个文档都包含文章的 description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。

由于此快速入门文章会立即搜索索引，因此 waitUntilIndexed 函数将等待索引准备好进行搜索操作。这一点很重要，因为索引需要完全填充文档，然后才能执行搜索。

生成并运行文件：

node -r dotenv/config src/uploadDocuments.js

此代码的输出显示文档已编制索引并可供搜索：
```
Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>
Uploading documents...
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
Waiting for indexing... Current count: 0
All documents indexed successfully.
```
有关 uploadDocuments() 方法的关键要点和以下示例：
- 代码通过 SearchClient（包提供 @azure/search-documents 的主要对象）与 Azure AI 搜索服务中托管的特定搜索索引进行交互。 SearchClient 提供对索引操作的访问权限，例如：
  - 数据引入 - uploadDocuments（）、mergeDocuments（）、deleteDocuments（）等。
  - 搜索操作 - search()、autocomplete()、suggest()
  - 索引管理作，如 createIndex（）、deleteIndex（）、getIndex（）等。
- 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。

将查询创建为矢量

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

queryVector.js在src目录中创建文件，并添加代码以创建查询向量。

// Query is 1536-dimensional vector of "quintessential lodging near running trails, eateries, retail"
// Note: this same vector can be used for vector search and semantic search
// Replace with your own vector

export const vector = 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以下部分中使用此代码来执行矢量搜索。查询向量是使用 Azure OpenAI 中的嵌入模型创建的。

创建单个矢量搜索

第一个示例演示了一个基本方案，其中你想要使用 SearchClient 查找与搜索字符串紧密匹配的文档说明。search 和 VectorQuery 和 SearchOptions。

在searchSingle.js目录中创建一个src文件。

将以下代码复制到文件中。

import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] ,
        includeTotalCount: true,
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

vectorQuery 包含矢量化查询的配置，包括查询输入 vector的向量化文本， fields 确定要搜索的向量字段，并 kNearestNeighborsCount 指定要返回的最近邻居数。

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。

生成并运行文件：

node -r dotenv/config src/searchSingle.js

此代码的输出显示查询 quintessential lodging near running trails, eateries, retail的相关文档。

Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>

Single Vector search found 5
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
- HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
- HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366

等效 quintessential lodging near running trails, eateries, retail 矢量的响应由 5 个结果组成，仅返回了按选择指定的字段。

使用筛选器创建单个矢量搜索

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。此搜索使用 SearchClient。search 和 VectorQuery 和 SearchOptions。

在searchSingleWithFilter.js目录中创建一个src文件。

将以下代码复制到文件中。

import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] ,
        includeTotalCount: true,
        filter: "Tags/any(tag: tag eq 'free wifi')", // Adding filter for "free wifi" tag
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

添加依赖项、环境变量以及与上一次搜索相同的搜索功能，并为带 free wifi 的酒店添加后处理排除筛选器。

生成并运行文件：

node -r dotenv/config src/searchSingleWithFilter.js

此代码的输出列出了应用筛选器 free wifi 后与查询相关的文档。

Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>

Single Vector search found 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366

使用地理空间筛选器创建搜索

可以指定地理空间筛选器，以将结果限制为特定的地理区域。在此示例中，筛选器将结果限制为华盛顿特区 300 公里范围内的酒店（坐标： -77.03241 38.90166）。地理空间距离始终以公里为单位。此搜索使用 SearchClient。search 和 VectorQuery 和 SearchOptions。

在searchSingleWithFilterGeo.js目录中创建一个src文件。

将以下代码复制到文件中。

import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions = {
        top: 5,
        includeTotalCount: true,
        select: [
            "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
        ],
        facets: ["Address/StateProvince"],
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
        filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
    };
    const results = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

生成并运行文件：

node -r dotenv/config src/searchSingleWithFilterGeo.js

此代码的输出显示应用了地理空间后处理排除筛选器的查询的相关文档：

Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>

Single Vector search found 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: N/A, Score 0.6605852246284485
- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: N/A, Score 0.602634072303772

创建混合搜索

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

此搜索使用 SearchClient。search 和 VectorQuery 和 SearchOptions。

在searchHybrid.js目录中创建一个src文件。

将以下代码复制到文件中。

import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create hybrid search options with both vector query and search text
    const searchOptions = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"],
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };

    // Use search_text for keyword search (hybrid search = vector + keyword)
    const searchText = "historic hotel walk to restaurants and shopping";
    const results = await searchClient.search(searchText, searchOptions);            // Convert results to typed format and log for debugging

    console.log(`\n\nHybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;

        console.log(`- Score: ${result.score}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log(`  Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}\n`);
    }

} catch (ex) {
    console.error("Hybrid search failed:", ex);
    throw ex;
}

生成并运行文件：

node -r dotenv/config src/searchHybrid.js

此代码的输出显示混合搜索的相关文档：

Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>

Hybrid search found 7 then limited to top 5
- 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 W-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"]

   {
       "@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"
   },

"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 Beijing.",
        "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"
    }
]

创建语义混合搜索

这是集合中的最后一个查询，可使用附加搜索文本 historic hotel walk to restaurants and shopping 来创建/扩展语义混合搜索。

此搜索使用 SearchClient。search 和 VectorQuery 和 SearchOptions。

在searchSemanticHybrid.js目录中创建一个src文件。

将以下代码复制到文件中。

import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create semantic hybrid search options with vector query and semantic configuration
    const searchOptions = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Category", "Description"],
        queryType: "semantic",
        semanticSearchOptions: {
            configurationName: "semantic-config"
        },
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
    };

    // Use search_text for semantic search
    const searchText = "historic hotel walk to restaurants and shopping";
    const results = await searchClient.search(searchText, searchOptions);

    console.log(`\n\nSemantic hybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;
        const score = result.score;
        const rerankerScoreDisplay = result.rerankerScore;

        console.log(`- Score: ${score}`);
        console.log(`  Re-ranker Score: ${rerankerScoreDisplay}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log('');
    }

} catch (ex) {
    console.error("Semantic hybrid search failed:", ex);
    throw ex;
}

生成并运行文件：

node -r dotenv/config src/searchSemanticHybrid.js

此代码的输出显示语义混合搜索的相关文档：

Using Azure Search endpoint: https://<search-service-name>.search.azure.cn
Using Azure Search index: <vector-index-name>

Semantic hybrid search found 7 then limited to top 5
- 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 W-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 Beijing. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make Beijing 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 vectorSearchOptions 属性指定。关键字搜索通过 semanticSearchOptions 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

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，则可以以编程方式删除索引。

在deleteIndex.js目录中创建一个src文件。

添加依赖项、环境变量和代码以删除索引。

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const searchIndexClient = new SearchIndexClient(searchEndpoint, credential);

try {
    console.log("Deleting index...");
    await searchIndexClient.deleteIndex(indexName);
    console.log(`Index ${indexName} deleted`);
} catch (ex) {
    console.error("Failed to delete index:", ex);
}

生成并运行文件：

node -r dotenv/config src/deleteIndex.js

后续步骤

查看 Azure AI Search for JavaScript 中矢量搜索功能的代码示例存储库

在本快速入门中，你将使用 TypeScript 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。

注意

先决条件

拥有有效订阅的 Azure 帐户。创建试用版订阅。
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。
Visual Studio Code。
Node.JS 之 LTS 版本。
TypeScript。可以使用 npm 全局安装 TypeScript：
```
npm install -g typescript
```

获取服务终结点

在其余部分中，设置对 Azure OpenAI 和 Azure AI 搜索的 API 调用。获取服务终结点，以便可以在代码中将其作为变量提供。

登录到 Azure 门户。
查找搜索服务
在“概述”主页上，复制 URL。示例终结点可能类似于 https://example.search.azure.cn。

设置Node.JS项目

使用 Visual Studio Code 和 TypeScript 设置项目。

在新目录中启动 Visual Studio Code。

mkdir vector-quickstart && cd vector-quickstart
code .

在项目目录中为 ESM 模块创建新包。
```
npm init -y
npm pkg set type=module
```
这会创建一个 package.json 具有默认值的文件。
编辑 package.json 以包含生成脚本。将以下行添加到 scripts 该节：
```
"build": "tsc"
```

安装以下 npm 包。

npm install @azure/identity @azure/search-documents dotenv @types/node

在您的项目目录中创建一个名为src的目录。
```
mkdir src
```

在项目目录中为 ESM 创建一个tsconfig.json文件，并使用以下内容。

{
  "compilerOptions": {
    "target": "esnext",
    "module": "NodeNext",
    "moduleResolution": "nodenext",
    "rootDir": "./src",
    "outDir": "./dist/",
    "esModuleInterop": true,
    "forceConsistentCasingInFileNames": true,
    "strict": true,
    "skipLibCheck": true,
    "declaration": true,
    "sourceMap": true,
    "resolveJsonModule": true,
    "moduleDetection": "force", // Add this for ESM
    "allowSyntheticDefaultImports": true // Helpful for ESM interop
  },
  "include": [
    "src/**/*.ts"
  ]
}

设置用于本地开发的环境变量

在.env项目目录中创建一个vector-quickstart文件。

将以下环境变量添加到 .env 文件中，将值替换为自己的服务终结点和密钥。

AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart

按顺序运行下面每个命令。

az account show

az account set --subscription <PUT YOUR SUBSCRIPTION ID HERE>

az cloud set -n AzureChinaCloud

az login --tenant <PUT YOUR TENANT ID HERE>

# az cloud set -n AzureCloud   //means return to Public Azure.

现在，你应该被本地设备登录到了 Azure。

创建矢量索引

在本部分中，你将使用 SearchIndexClient 在 Azure AI 搜索中创建矢量索引。createOrUpdateIndex。索引架构定义字段，包括向量字段 DescriptionVector。创建索引后，将文档上传到索引。文档包含文章说明的矢量化版本，可基于含义而不是确切关键字进行相似性搜索。

在createIndex.ts目录中创建一个src文件。

将以下代码复制到文件中。

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient,
    SearchIndex,
    SearchField,
    VectorSearch,
    SemanticSearch,
    SearchSuggester
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}


const indexClient = new SearchIndexClient(searchEndpoint, credential);
console.log('Creating index...');

// Define fields
const fields: SearchField[] = [
    {
        name: "HotelId",
        type: "Edm.String" as const,
        key: true,
        filterable: true
    },
    {
        name: "HotelName",
        type: "Edm.String" as const,
        sortable: true,
        searchable: true,
        hidden: false,
    },
    {
        name: "Description",
        type: "Edm.String" as const,
        searchable: true,
        hidden: false
    },
    {
        name: "DescriptionVector",
        type: "Collection(Edm.Single)" as const,
        searchable: true,
        vectorSearchDimensions: 1536,
        vectorSearchProfileName: "my-vector-profile",
        hidden: true
    },
    {
        name: "Category",
        type: "Edm.String" as const,
        sortable: true,
        filterable: true,
        facetable: true,
        searchable: true,
        hidden: false
    },
    {
        name: "Tags",
        type: "Collection(Edm.String)" as const,
        searchable: true,
        filterable: true,
        facetable: true,
        hidden: false
    },
    {
        name: "ParkingIncluded",
        type: "Edm.Boolean" as const,
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "LastRenovationDate",
        type: "Edm.DateTimeOffset" as const,
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Rating",
        type: "Edm.Double" as const,
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Address",
        type: "Edm.ComplexType" as const,
        fields: [
            {
                name: "StreetAddress",
                type: "Edm.String" as const,
                searchable: true,
            },
            {
                name: "City",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "StateProvince",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "PostalCode",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true
            },
            {
                name: "Country",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true,
            }
        ]
    },
    {
        name: "Location",
        type: "Edm.GeographyPoint" as const,
        filterable: true,
        sortable: true
    }
];

// Define vector search configuration
const vectorSearch: VectorSearch = {
    algorithms: [
        {
            name: "hnsw-vector-config",
            kind: "hnsw" as const
        },
        {
            name: "eknn-vector-config",
            kind: "exhaustiveKnn" as const
        }
    ],
    profiles: [
        {
            name: "my-vector-profile",
            algorithmConfigurationName: "hnsw-vector-config"
        }
    ]
};

// Define semantic configuration
const semanticConfig = {
    name: "semantic-config",
    prioritizedFields: {
        titleField: {
            name: "HotelName"
        },
        contentFields: [
            {
                name: "Description"
            }
        ],
        keywordsFields: [
            {
                name: "Category"
            }
        ]
    }
};

// Create the semantic settings with the configuration
const semanticSearch: SemanticSearch = {
    configurations: [semanticConfig]
};

// Define suggesters
const suggesters: SearchSuggester[] = [];

// Create the search index with the semantic settings
const indexDefinition: SearchIndex = {
    name: indexName,
    fields: fields,
    vectorSearch: vectorSearch,
    semanticSearch: semanticSearch,
    suggesters: suggesters
};

const result = await indexClient.createOrUpdateIndex(indexDefinition);
console.log(`${result.name} created`);

生成并运行文件：

npm run build && node -r dotenv/config dist/createIndex.js

此代码的输出显示已成功创建索引：
```
Using Azure Search endpoint: https://my-service.search.azure.cn
Using index name: hotels-vector-quickstart
Creating index...
hotels-vector-quickstart created
```
使用 @azure/search-documents 库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。
- 此特定索引支持多种搜索功能，例如：
  - 全文关键字搜索（searchable）
  - 矢量搜索（通过并置矢量和非矢量字段实现混合搜索）字段 (使用 DescriptionVector 的 vectorSearchProfileName)
  - 语义搜索
  - 分面搜索 (searchSuggester)
  - 地理空间搜索（使用 Location 的 geo.distance 字段）
  - 筛选、排序（许多标记为可筛选和可排序的字段）

将文档上传到索引

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，需要使用 SearchClient 将文档加载到索引中。uploadDocuments。

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。

在uploadDocuments.ts目录中创建一个src文件。

将以下代码复制到文件中。

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const 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 centre 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 W-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,
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            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
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        }
    },
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        "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"
                }
            }
        }
    }
];

async function waitUntilIndexed(): Promise<void> {
    try {
        const searchClient = new SearchClient(searchEndpoint, indexName, credential);
        do {
            const count = await searchClient.getDocumentsCount();
            if (count == DOCUMENTS.length) {
                console.log("All documents indexed successfully.");
                break;
            }
            console.log(`Waiting for indexing... Current count: ${count}`);
            await new Promise((resolve) => setTimeout(resolve, 10000)); // Wait for 10 seconds
        } while (true);
    } catch (ex) {
        console.error("Failed to wait until indexed:", ex);
    }
}

const searchClient = new SearchClient(searchEndpoint, indexName, credential);

try {
    console.log("Uploading documents...");
    const result = await searchClient.uploadDocuments(DOCUMENTS);
    for (const r of result.results) {
        console.log(`Key: ${r.key}, Succeeded: ${r.succeeded}, ErrorMessage: ${r.errorMessage || 'none'}`);
    }
    await waitUntilIndexed();
} catch (ex) {
    console.error("Failed to upload documents:", ex);
}

此代码加载一个变量文档，其中包含描述每个文档的 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。

生成并运行文件：

npm run build && node -r dotenv/config dist/uploadDocuments.js

此代码的输出显示文档已编制索引并可供搜索：
```
Uploading documents...
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
Waiting for indexing... Current count: 0
All documents indexed successfully.
```
有关 upload_documents（）方法的关键要点和以下示例：
- 代码通过 SearchClient（包提供 @azure/search-documents 的主要对象）与 Azure AI 搜索服务中托管的特定搜索索引进行交互。 SearchClient 提供对索引操作的访问权限，例如：
  - 数据引入 - uploadDocuments（）、mergeDocuments（）、deleteDocuments（）等。
  - 搜索操作 - search()、autocomplete()、suggest()
  - 索引管理作，如 createIndex（）、deleteIndex（）、getIndex（）等。
- 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。

将查询创建为矢量

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

queryVector.ts在src目录中创建文件，并添加代码以创建查询向量。

// Query is 1536-dimensional vector of "quintessential lodging near running trails, eateries, retail"
// Note: this same vector can be used for vector search and semantic search
// Replace with your own vector

export const vector = 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以下部分中使用此代码来执行矢量搜索。查询向量是使用 Azure OpenAI 中的嵌入模型创建的。

创建单个矢量搜索

第一个示例演示了一个基本方案，其中你想要使用 SearchClient 查找与搜索字符串紧密匹配的文档说明。search 和 VectorQuery 和 SearchOptions。

在searchSingle.ts目录中创建一个src文件。

将以下代码复制到文件中。

import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
        includeTotalCount: true,
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

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。

生成并运行文件：

npm run build && node -r dotenv/config dist/searchSingle.js

此代码的输出显示查询 quintessential lodging near running trails, eateries, retail的相关文档。

Using Azure Search endpoint: https://ai-search-dib-2.search.azure.cn
Using index name: hotels-vector-quickstart-0627-4

Single Vector search found 5
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
- HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
- HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366

等效 quintessential lodging near running trails, eateries, retail 矢量的响应由 5 个结果组成，仅返回了按选择指定的字段。

使用筛选器创建单个矢量搜索

在searchSingleWithFilter.ts目录中创建一个src文件。

将以下代码复制到文件中。

import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
        includeTotalCount: true,
        filter: "Tags/any(tag: tag eq 'free wifi')", // Adding filter for "free wifi" tag
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search with filter found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search with filter failed:", ex);
    throw ex;
}

添加依赖项、环境变量以及与上一次搜索相同的搜索功能，并为带 free wifi 的酒店添加后处理排除筛选器。

生成并运行文件：

npm run build && node -r dotenv/config dist/searchSingleWithFilter.js

此代码的输出列出了应用筛选器 free wifi 后与查询相关的文档。

Using Azure Search endpoint: https://ai-search-dib-2.search.azure.cn
Using index name: hotels-vector-quickstart-0627-4

Single Vector search found 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
- HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366

使用地理空间筛选器创建搜索

在searchSingleWithFilterGeo.ts目录中创建一个src文件。

将以下代码复制到文件中。

import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);


try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    const searchOptions: SearchOptions<HotelDocument> = {
        top: 5,
        includeTotalCount: true,
        select: [
            "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
        ] as const,
        facets: ["Address/StateProvince"],
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
        filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
    };
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);

    console.log(`\n\nVector search with geo filter found ${results.count}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;

        console.log(`- HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Score: ${result.score}`);

        if (doc.Address) {
            console.log(`  City/State: ${doc.Address.City}, ${doc.Address.StateProvince}`);
        }

        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Score: ${result.score}\n`);
    }


} catch (ex) {
    console.error("Vector search with geo filter failed:", ex);
    throw ex;
}

生成并运行文件：

npm run build && node -r dotenv/config dist/searchSingleWithFilterGeo.js

此代码的输出显示应用了地理空间后处理排除筛选器的查询的相关文档：

Using Azure Search endpoint: https://ai-search-dib-2.search.azure.cn
Using index name: hotels-vector-quickstart-0627-4

Single Vector search found 2
- HotelId: 48, HotelName: Nordick's Valley Motel, Tags: N/A, Score 0.6605852246284485
- HotelId: 49, HotelName: Swirling Currents Hotel, Tags: N/A, Score 0.602634072303772

创建混合搜索

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

此搜索使用 SearchClient。search 和 VectorQuery 和 SearchOptions。

在searchHybrid.ts目录中创建一个src文件。

将以下代码复制到文件中。

import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create hybrid search options with both vector query and search text
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };

    // Use search_text for keyword search (hybrid search = vector + keyword)
    const searchText = "historic hotel walk to restaurants and shopping";
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search(searchText, searchOptions);            // Convert results to typed format and log for debugging

    console.log(`\n\nHybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;

        console.log(`- Score: ${result.score}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log(`  Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}\n`);
    }

} catch (ex) {
    console.error("Hybrid search failed:", ex);
    throw ex;
}

生成并运行文件：

npm run build && node -r dotenv/config dist/searchHybrid.js

此代码的输出显示混合搜索的相关文档：

Using Azure Search endpoint: https://ai-search-dib-2.search.azure.cn
Using index name: hotels-vector-quickstart-0627-4

Hybrid search found 7 then limited to top 5
- 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 W-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"]

   {
       "@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"
   },

"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 Beijing.",
        "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"
    }
]

创建语义混合搜索

这是集合中的最后一个查询，可使用附加搜索文本 historic hotel walk to restaurants and shopping 来创建/扩展语义混合搜索。

此搜索使用 SearchClient。search 和 VectorQuery 和 SearchOptions。

在searchSemanticHybrid.ts目录中创建一个src文件。

将以下代码复制到文件中。

import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create semantic hybrid search options with vector query and semantic configuration
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Category", "Description"] as const,
        queryType: "semantic" as const,
        semanticSearchOptions: {
            configurationName: "semantic-config"
        },
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
    };

    // Use search_text for semantic search
    const searchText = "historic hotel walk to restaurants and shopping";
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search(searchText, searchOptions);

    console.log(`\n\nSemantic hybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;
        const score = result.score;
        const rerankerScoreDisplay = result.rerankerScore;

        console.log(`- Score: ${score}`);
        console.log(`  Re-ranker Score: ${rerankerScoreDisplay}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log('');
    }

} catch (ex) {
    console.error("Semantic hybrid search failed:", ex);
    throw ex;
}

生成并运行文件：

npm run build && node -r dotenv/config dist/searchSemanticHybrid.js

此代码的输出显示语义混合搜索的相关文档：

Using Azure Search endpoint: https://ai-search-dib-2.search.azure.cn
Using index name: hotels-vector-quickstart-0627-4

Semantic hybrid search found 7 then limited to top 5
- 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 W-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 Beijing. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make Beijing 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 vectorSearchOptions 属性指定。关键字搜索通过 semanticSearchOptions 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

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，则可以以编程方式删除索引。

在deleteIndex.ts目录中创建一个src文件。

添加依赖项、环境变量和代码以删除索引。

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const searchIndexClient = new SearchIndexClient(searchEndpoint, credential);

try {
    console.log("Deleting index...");
    await searchIndexClient.deleteIndex(indexName);
    console.log(`Index ${indexName} deleted`);
} catch (ex) {
    console.error("Failed to delete index:", ex);
}

生成并运行文件：

npm run build && node -r dotenv/config dist/deleteIndex.js

后续步骤

查看 Azure AI Search for JavaScript 中矢量搜索功能的代码示例存储库

在本快速入门中，你将使用 .NET 应用来创建、填充和查询向量。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。

注意

先决条件

拥有有效订阅的 Azure 帐户。创建试用版订阅。
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。
Visual Studio Code 或 Visual Studio。
使用Git克隆示例存储库。

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。请参阅使用适用于 .NET 的 Azure SDK 向 Azure 服务验证 .NET 应用，了解所有选项。

克隆代码和设置环境

克隆包含本快速入门代码的存储库。
```
git clone https://github.com/Azure-Samples/azure-search-dotnet-samples
```
此存储库包含多个文章的 .NET 代码示例，每篇文章的代码示例都位于单独的子文件夹中。
在 Visual Studio Code 中打开子文件夹 quickstart-Vector-Search ，或双击 .sln 该文件以在 Visual Studio 中打开解决方案。
打开appsettings.json文件夹和VectorSearchExamples文件夹中的VectorSearchCreatePopulateIndex文件。更新以下值：
- AZURE_SEARCH_ENDPOINT：在 Azure 门户中查找 Azure AI 搜索服务的 URL。在搜索资源的 “概述 ”页上，查找 URL 字段。示例终结点可能类似于 https://mydemo.search.azure.cn。
- AZURE_SEARCH_INDEX_NAME：保留文件中提供的默认值，或输入自己的索引名称。

创建矢量索引并上传文档

若要针对 Azure AI 搜索服务运行搜索查询，首先需要创建搜索索引并将文档上传到服务。

打开 VectorSearchCreatePopulateIndex 文件夹中的新终端。
使用 dotnet run 以下命令运行项目：
```
dotnet run
```

以下代码执行以创建索引：

static async Task CreateSearchIndex(string indexName, SearchIndexClient indexClient)
{
    var addressField = new ComplexField("Address");
    addressField.Fields.Add(new SearchableField("StreetAddress") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft });
    addressField.Fields.Add(new SearchableField("City") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
    addressField.Fields.Add(new SearchableField("StateProvince") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
    addressField.Fields.Add(new SearchableField("PostalCode") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
    addressField.Fields.Add(new SearchableField("Country") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });

    var allFields = new List<SearchField>()
{
    new SimpleField("HotelId", SearchFieldDataType.String) { IsKey = true, IsFacetable = true, IsFilterable = true },
    new SearchableField("HotelName") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft },
    new SearchableField("Description") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft },
    new VectorSearchField("DescriptionVector", 1536, "my-vector-profile"),
    new SearchableField("Category") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true },
    new SearchableField("Tags", collection: true) { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true },
    new SimpleField("ParkingIncluded", SearchFieldDataType.Boolean) { IsFacetable = true, IsFilterable = true },
    new SimpleField("LastRenovationDate", SearchFieldDataType.DateTimeOffset) { IsSortable = true },
    new SimpleField("Rating", SearchFieldDataType.Double) { IsFacetable = true, IsFilterable = true, IsSortable = true },
    addressField,
    new SimpleField("Location", SearchFieldDataType.GeographyPoint) { IsFilterable = true, IsSortable = true },
};

    // Create the suggester configuration
    var suggester = new SearchSuggester("sg", new[] { "Address/City", "Address/Country" });

    // Create the semantic search
    var semanticSearch = new SemanticSearch()
    {
        Configurations =
        {
            new SemanticConfiguration(
            name: "semantic-config",
            prioritizedFields: new SemanticPrioritizedFields
            {
                TitleField = new SemanticField("HotelName"),
                KeywordsFields = { new SemanticField("Category") },
                ContentFields = { new SemanticField("Description") }
            })
    }
    };

    // Add vector search configuration
    var vectorSearch = new VectorSearch();
    vectorSearch.Algorithms.Add(new HnswAlgorithmConfiguration(name: "my-hnsw-vector-config-1"));
    vectorSearch.Profiles.Add(new VectorSearchProfile(name: "my-vector-profile", algorithmConfigurationName: "my-hnsw-vector-config-1"));

    var definition = new SearchIndex(indexName)
    {
        Fields = allFields,
        Suggesters = { suggester },
        VectorSearch = vectorSearch,
        SemanticSearch = semanticSearch
    };

    // Create or update the index
    Console.WriteLine($"Creating or updating index '{indexName}'...");
    var result = await indexClient.CreateOrUpdateIndexAsync(definition);
    Console.WriteLine($"Index '{result.Value.Name}' updated.");
    Console.WriteLine();
}

以下代码将文件中的 JSON 格式文档 hotel-samples.json 上传到 Azure AI 搜索服务：

static async Task UploadDocs(SearchClient searchClient)
{
    var jsonPath = Path.Combine(Directory.GetCurrentDirectory(), "HotelData.json");

    // Read and parse hotel data
    var json = await File.ReadAllTextAsync(jsonPath);
    List<Hotel> hotels = new List<Hotel>();
    try
    {
        using var doc = JsonDocument.Parse(json);
        if (doc.RootElement.ValueKind != JsonValueKind.Array)
        {
            Console.WriteLine("HotelData.json root is not a JSON array.");
        }
        // Deserialize all hotel objects
        hotels = doc.RootElement.EnumerateArray()
            .Select(e => JsonSerializer.Deserialize<Hotel>(e.GetRawText()))
            .Where(h => h != null)
            .ToList();
    }
    catch (JsonException ex)
    {
        Console.WriteLine($"Failed to parse HotelData.json: {ex.Message}");
    }

    try
    {
        // Upload hotel documents to Azure Search
        var result = await searchClient.UploadDocumentsAsync(hotels);
        foreach (var r in result.Value.Results)
        {
            Console.WriteLine($"Key: {r.Key}, Succeeded: {r.Succeeded}");
        }
    }
    catch (Exception ex)
    {
        Console.WriteLine("Failed to upload documents: " + ex);
    }
}

运行项目后，将打印以下输出：

Key: 1, Succeeded: True
Key: 2, Succeeded: True
Key: 3, Succeeded: True
Key: 4, Succeeded: True
Key: 48, Succeeded: True
Key: 49, Succeeded: True
Key: 13, Succeeded: True

要点：

您的代码通过 SearchClient 包提供的主要对象 azure-search-documents 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：UploadDocuments()、、 MergeDocuments()DeleteDocuments()
- 搜索操作： Search()、Autocomplete()、Suggest()
- 索引管理操作： CreateOrUpdateIndex()
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。 Azure OpenAI text-embedding-3-small 模型为本快速入门生成的嵌入的这个大小为 1536。

运行搜索查询

创建索引并加载文档后，可以通过使用各种参数调用 SearchAsync() 来针对它们发出向量查询。

在 VectorSearchExamples 文件夹中，打开 Program.cs 该文件。
打开 VectorSearchExamples 文件夹中的新终端。

在以下部分中，将针对 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

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。

在 Program.cs 文件夹的 VectorSearchExamples 文件中，取消注释方法调用 SearchExamples.SearchSingleVector(searchClient, vectorizedResult);。此方法在类中 SearchExamples.cs 执行以下搜索函数：

public static async Task SearchSingleVector(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
        });

    Console.WriteLine($"Single Vector Search Results:");
    await foreach (SearchResult<Hotel> result in response.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}");
    }
    Console.WriteLine();
}

使用 dotnet run 以下命令运行项目：

dotnet run

运行项目后，搜索结果将打印在输出窗口中：

Single Vector Search Results:
Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel
Score: 0.6333684, HotelId: 13, HotelName: Luxury Lion Resort
Score: 0.605672, HotelId: 4, HotelName: Sublime Palace Hotel
Score: 0.6026341, HotelId: 49, HotelName: Swirling Currents Hotel
Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel

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。

在 Program.cs 文件夹的 VectorSearchExamples 文件中，取消注释方法调用 SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);。此方法在类中 SearchExamples.cs 执行以下搜索函数：

public static async Task SearchSingleVectorWithFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Filter = "Tags/any(tag: tag eq 'free wifi')",
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" }
        });

    Console.WriteLine($"Single Vector Search With Filter Results:");
    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}, Tags: {string.Join(String.Empty, doc.Tags)}");
    }
    Console.WriteLine();
}

再次运行项目，并打印其下方每个文档的状态：

Single Vector Search With Filter Results:
Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel, Tags: continental breakfastair conditioningfree wifi
Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel, Tags: poolfree wifiair conditioningconcierge

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。

下一个筛选器示例使用 地理筛选器。在 Program.cs 文件夹的 VectorSearchExamples 文件中，取消注释方法调用 SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);。此方法在类中 SearchExamples.cs 执行以下搜索函数：

public static async Task SingleSearchWithGeoFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithGeoFilter = await searchClient.SearchAsync<Hotel>(
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Filter = "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
            Select = { "HotelId", "HotelName", "Description", "Address", "Category", "Tags" },
            Facets = { "Address/StateProvince" },
        });

    Console.WriteLine($"Vector query with a geo filter:");
    await foreach (SearchResult<Hotel> result in responseWithGeoFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"HotelId: {doc.HotelId}");
        Console.WriteLine($"HotelName: {doc.HotelName}");
        Console.WriteLine($"Score: {result.Score}");
        Console.WriteLine($"City/State: {doc.Address.City}/{doc.Address.StateProvince}");
        Console.WriteLine($"Description: {doc.Description}");
        Console.WriteLine();
    }
    Console.WriteLine();
}

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。

再次运行项目，并打印其下方每个文档的状态：

Vector query with a geo filter:
-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

在 Program.cs 文件夹的 VectorSearchExamples 文件中，取消注释方法调用 SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);。此方法在类中 SearchExamples.cs 执行以下搜索函数：

public static async Task<SearchResults<Hotel>> SearchHybridVectorAndText(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
        "historic hotel walk to restaurants and shopping",
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
            Size = 5,
        });

    Console.WriteLine($"Hybrid search results:");
    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}");
        Console.WriteLine($"HotelId: {doc.HotelId}");
        Console.WriteLine($"HotelName: {doc.HotelName}");
        Console.WriteLine($"Description: {doc.Description}");
        Console.WriteLine($"Category: {doc.Category}");
        Console.WriteLine($"Tags: {string.Join(String.Empty, doc.Tags)}");
        Console.WriteLine();
    }
    Console.WriteLine();
    return responseWithFilter;
}

再次运行项目，并打印其下方每个文档的状态：

Hybrid search results:
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: conciergeviewair conditioning

Score: 0.032786883413791656
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: barconciergerestaurant

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 breakfastair conditioningfree 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 W-Fi and flat-screen TVs.
Category: Suite
Tags: air conditioninglaundry service24-hour front desk service

Score: 0.03077651560306549
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: poolfree wifiair conditioningconcierge

{
    "@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"
},

"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 Beijing.",
        "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

筛选了具有语义排名的混合查询，以仅显示华盛顿特区 500 公里半径内的酒店。

在 Program.cs 文件夹的 VectorSearchExamples 文件中，取消注释方法调用 SearchExamples.SearchHybridVectoryAndSemantic(searchClient, vectorizedResult);。此方法在类中 SearchExamples.cs 执行以下搜索函数：

public static async Task SearchHybridVectorAndSemantic(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
        "historic hotel walk to restaurants and shopping",
        new SearchOptions
        {
            IncludeTotalCount = true,
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
            SemanticSearch = new SemanticSearchOptions
            {
                SemanticConfigurationName = "semantic-config"
            },
            QueryType = SearchQueryType.Semantic,
            Size = 5
        });

    Console.WriteLine($"Hybrid search results:");
    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}");
        Console.WriteLine($"HotelId: {doc.HotelId}");
        Console.WriteLine($"HotelName: {doc.HotelName}");
        Console.WriteLine($"Description: {doc.Description}");
        Console.WriteLine($"Category: {doc.Category}");
        Console.WriteLine();
    }
    Console.WriteLine();
}

再次运行项目，并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 Beijing. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make Beijing 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：

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

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。

后续步骤

查看 Azure AI Search for .NET 中矢量搜索功能的代码示例存储库
查看 azure-search-dotnet-samples 存储库中的其他 .NET 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。

注意

先决条件

拥有有效订阅的 Azure 帐户。创建试用版订阅。
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。
包含 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 使用无键连接。

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
API 密钥

登录到 Azure 门户并查找你的搜索服务。
在“概述”主页上，找到相应 URL。示例终结点可能类似于 https://mydemo.search.azure.cn。
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
```

登录到 Azure 门户并查找你的搜索服务。
在“概述”主页上，找到相应 URL。示例终结点可能类似于 https://mydemo.search.azure.cn。
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。

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。
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
```
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，你需要将替换为 @apiKey 变量。@token
```
@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
```
若要使用 Microsoft Entra ID 进行推荐的无密钥身份验证，你需要将替换为请求标头中的 api-key: {{apiKey}}。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。

在 Visual Studio Code 中，打开az-search-vector-quickstart.rest的文件。

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" }
                    ]
                }
            }
        ]
    }
}

选择“发送请求”。应具有 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.azure.cn/$metadata#indexes/$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 部分是一组最近的邻域 (ANN) 算法配置和配置文件的数组。支持的算法包括分层可导航小世界算法和穷举 K 最近的邻域算法。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。

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 Beijing. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make Beijing 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": "Beijing",
                "StateProvince": "NY",
                "PostalCode": "10022",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    -73.975403,
                    40.760586
                ]
            }
        },
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            "@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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            "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"
                    }
                }
            }
        }
    ]
}

选择“发送请求”。应具有 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 - 索引 REST API 请求的关键要点：

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，我们将针对 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

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。它是使用“exhaustive”替代参数来执行索引的完整扫描，而不是使用 ANN。详尽的搜索对于小型索引非常有用。

### 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": [
            {
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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匹配项数。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。

选择“发送请求”。应具有 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，响应总是包含按相似性分数值排序的 k 个结果。

{
  "@odata.context": "https://my-demo-search.search.azure.cn/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。

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
        },
    ]
}

选择“发送请求”。应具有 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.azure.cn/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"
      ]
    }
  ]
}

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",
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            "exhaustive": true
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}

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

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": [
        {
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            "k": 5,
            "fields": "DescriptionVector",
            "kind": "vector",
            "exhaustive": true
        }
    ]
}

选择“发送请求”。应具有 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，因此结果按倒数排名融合 (RRF) 进行排名。请注意， @search.score 值具有不同的基础，并且是统一较小的值。 RRF 评估多个搜索结果的搜索分数，采用反函数，然后合并和排序组合的结果。返回 top 结果数。

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 Beijing. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make Beijing 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。

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
        }
    ]
}

选择“发送请求”。应具有 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。

{
  "@odata.count": 7,
  "@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 Beijing. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make Beijing 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

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。

다음을 통해 공유

快速入门：矢量搜索

先决条件

检索资源信息

克隆代码和设置环境

创建矢量索引

上传文档

运行查询

创建矢量查询字符串

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

清理

后续步骤

先决条件

获取服务终结点

设置 Java 项目

登录到 Azure

创建矢量索引

将文档上传到索引

将查询创建为矢量

创建单个矢量搜索

使用筛选器创建单个矢量搜索

使用地理空间筛选器创建搜索

创建混合搜索

创建语义混合搜索

清理

后续步骤

先决条件

获取服务终结点

设置Node.JS项目

设置用于本地开发的环境变量

登录到 Azure

创建矢量索引

将文档上传到索引

将查询创建为矢量

创建单个矢量搜索

使用筛选器创建单个矢量搜索

使用地理空间筛选器创建搜索

创建混合搜索

创建语义混合搜索

清理

后续步骤

先决条件

获取服务终结点

设置Node.JS项目

设置用于本地开发的环境变量

登录到 Azure

创建矢量索引

将文档上传到索引

将查询创建为矢量

创建单个矢量搜索

使用筛选器创建单个矢量搜索

使用地理空间筛选器创建搜索

创建混合搜索

创建语义混合搜索

清理

后续步骤

先决条件

检索资源信息

克隆代码和设置环境

创建矢量索引并上传文档

运行搜索查询

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

清理

后续步骤

先决条件

检索资源信息

创建或下载代码文件

创建矢量索引

上传文档

运行查询

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