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Быстрый старт: Поиск векторов

В этом кратком руководстве вы используете записную книжку Jupyter для создания, загрузки и выполнения запросов векторов. Примеры кода выполняют эти операции с помощью клиентской библиотеки поиска ИИ Azure. Библиотека предоставляет абстракцию по REST API для доступа к операциям индексирования, таким как прием данных, операции поиска и операции управления индексами.

В службе "Поиск ИИ Azure" векторное хранилище содержит схему индекса, которая определяет поля векторов и невекторов, конфигурацию векторного поиска для алгоритмов, создающих пространство внедрения и параметры определений векторных полей, которые вычисляются во время запроса. REST API создания индекса создает хранилище векторов.

Примечание.

В этом кратком руководстве опускается шаг векторизации и предоставляются встроенные векторы. Если вы хотите добавить встроенные блоки данных и векторизацию для вашего контента, попробуйте использовать мастер импорта и векторизации данных как полное руководство.

Необходимые компоненты

Получение сведений о ресурсе

Запросы к конечной точке поиска должны проходить проверку подлинности и авторизованы. Хотя ключи или роли API можно использовать для этой задачи, рекомендуется использовать подключение без ключей через идентификатор Microsoft Entra.

В этом кратком руководстве используется DefaultAzureCredential, что упрощает проверку подлинности как в сценариях разработки, так и в рабочей среде. Однако для рабочих сценариев может потребоваться более сложные требования, требующие другого подхода. Ознакомьтесь с проверкой подлинности приложений Python в службах Azure с помощью пакета SDK Azure для Python, чтобы понять все ваши параметры.

Клонирование кода и среды установки

  1. Клонируйте репозиторий, содержащий код для этого краткого руководства.

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

    Этот репозиторий содержит примеры кода Python для нескольких статей в отдельной вложенной папке.

  2. В Visual Studio Code откройте вложенную папку Quickstart-Vector-Search.

    В этой папке есть три файла:

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

  3. Переименуйте файл sample.env на .env и измените значения в файле .env.

    Используйте URL-адрес службы поиска в качестве AZURE_SEARCH_ENDPOINT. Url-адрес можно найти на портале Azure. Перейдите в службу поиска ИИ Azure на странице обзора и найдите поле URL-адреса. Пример конечной точки может выглядеть так: https://mydemo.search.windows.net.

    Наконец, выберите новое AZURE_SEARCH_INDEX_NAME имя или используйте имя, предоставленное в файле.

  4. В Visual Studio Code работайте в среде. В меню "Вид " выберите терминал...или нажмите клавиши CTRL+`.

  5. Выполните следующие команды в окне терминала:

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

    Примечание.

    Предполагается, что вы используете Git Bash в терминале и работаете в Windows. Если вы используете другую оболочку или другую операционную систему, настройте эти инструкции для конкретной среды.

    Если появится запрос, разрешите Visual Studio Code использовать новую среду.

    Команда where python проверяет, работаете ли вы из виртуальной среды, перечислив python.exe папку Quickstart-Vector-Search\.venv\ и другие расположения из каталога компьютера.

  6. Установите необходимые библиотеки, выполнив следующую команду:

    pip install requirements.txt

  7. В Visual Studio Code откройте файл vector-search-quickstart.ipynb.

    Примечание.

    Если вы впервые использовали Jupyter Notebook (.ipynb) в Visual Studio Code, вам будет предложено установить ядро Jupyter Notebook и, возможно, другие средства. Выберите установку предлагаемых инструментов для продолжения работы с этим кратким руководством.

  8. Найдите ячейку ниже под названием "Установить пакеты и задать переменные" и нажмите кнопку "Выполнить ячейку" (CTRL+ ALT+ ВВОД), которая выглядит как обычная кнопка запуска) слева от ячейки. Выполняя ячейку, загружаются переменные среды, создается DefaultAzureCredential, и значения выводятся на экран для подтверждения, что зависимости записной книжки и .env настроены правильно.

    # 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

    При выполнении этой ячейки создается следующий результат.

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

    Существует множество пакетов, которые можно просмотреть в прокручиваемом элементе (см. сообщение ниже результатов ячейки).

Создание векторного индекса

Код в vector-search-quickstart.ipynb библиотеке azure.search.documents использует несколько методов для создания векторного индекса и полей, доступных для поиска.

  1. Найдите ячейку ниже под названием "Создать индекс" и выполните ячейку для создания индекса.

    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')

    Если индекс создан успешно, вы увидите следующий результат ниже ячейки:

    vector-search-quickstart created

    Ключевые выводы при создании векторного индекса azure.search.documents:

    • Вы определяете индекс, создав список полей. Каждое поле создается с помощью вспомогательного метода, который определяет тип поля и его параметры.

    • Этот конкретный индекс поддерживает несколько возможностей поиска, таких как:

      • Полнотекстовый поиск ключевых слов (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))
      • Векторный поиск (включает гибридный поиск путем сортировки полей вектора и невектора) (DescriptionVector)
      • Семантический поиск (semantic_search=SemanticSearch(configurations=[semantic_config]))
      • Фасетный поиск (facetable=True)
      • Семантический поиск (semantic_search=SemanticSearch(configurations=[semantic_config]))
      • Геопространственный поиск (поле LocationявляетсяGeographyPoint)
      • Фильтрация, сортировка (многие поля, помеченные как фильтруемые и сортируемые)

Отправить документы

Создание и загрузка индекса являются отдельными шагами. На предыдущем шаге вы создали схему индекса. Теперь необходимо загрузить документы в индекс.

В службе "Поиск ИИ Azure" индекс сохраняет все содержимое, которое можно найти, а поисковая система выполняет запросы к индексу.

  1. Найдите ячейку под разделом, озаглавленным "Создать нагрузку документов", и выполните указанную ячейку. Эта ячейка содержит следующий код (усечен для краткости):

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

    Эта ячейка загружает переменную documents с именем объекта JSON, описывающего каждый документ, а также векторную версию описания статьи. Этот вектор позволяет выполнять поиск сходства, где сопоставление основано на значении, а не на точных ключевых словах.

    Это важно

    Код в этом примере не может быть запущен. Для краткости удаляются несколько символов или строк. Вместо этого запустите код в записной книжке Jupyter.

  2. Найдите ячейку ниже под названием "Загрузить документы" и выполните ячейку. Эта ячейка содержит следующий код (усечен для краткости):

    # 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)

    При этом создается экземпляр клиента поиска путем вызова конструктора SearchClient(), а затем upload_documents() метод вызывается на объекте.

    После запуска ячейки состояние каждого документа выводится под ним:

    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

    Ключевые выводы о методе upload_documents() и этом примере:

    • Код взаимодействует с определенным индексом поиска, размещенным в службе поиска ИИ Azure, с помощью SearchClientэтого основного объекта, предоставленного пакетом azure-search-documents . Этот SearchClient предоставляет доступ к операциям индекса, таким как:

      • Прием - upload_documents() данных, merge_documents()и delete_documents()т. д.
      • Операции - search() поиска, autocomplete(), suggest()
      • Операции управления индексами

    • Векторные поля содержат значения с плавающей запятой. Атрибут измерений имеет не менее 2 и не более 4096 значений с плавающей запятой. В этом кратком руководстве атрибут измерений присваивается значение 1536, так как это размер внедрения, созданных моделью Azure OpenAI text-embedding-ada-002 .

Выполнение запросов

Теперь, когда документы загружены, вы можете выполнять к ним векторные запросы, используя search_client.search() и передав в качестве параметра объект VectorizedQuery, указав поля, количество результатов и т. д.

В следующих разделах мы запускаем запросы к индексу hotels-vector-quickstart . К запросам относятся:

Создание строки векторного запроса

Примеры векторных запросов основаны на двух строках:

  • Строка поиска: historic hotel walk to restaurants and shopping
  • Строка векторного запроса (векторизованная в математическое представление): quintessential lodging near running trails, eateries, retail

Строка векторного запроса семантически похожа на строку поиска, но она содержит термины, которые не существуют в индексе поиска. Если вы выполняете поиск quintessential lodging near running trails, eateries, retailпо ключевому слову, результаты равны нулю. В этом примере показано, как можно получить соответствующие результаты, даже если нет подходящих условий.

  • Найдите ячейку ниже под названием "Создать строку векторного запроса" и выполните ячейку. Эта переменная загружает vector переменную с векторизованными данными запроса, необходимыми для выполнения всех поисковых запросов в следующих разделах.

Первый пример демонстрирует базовый сценарий, в котором необходимо найти описания документов, которые тесно соответствуют строке поиска.

  • Найдите ячейку под разделом "Поиск одного вектора" и выполните её. Этот блок содержит запрос на запрос к индексу поиска.

    # 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.")

    Этот векторный запрос сокращен для краткости. Содержит vectorQueries.vector векторный текст входных данных запроса, fields определяет, какие поля векторов выполняются поиск, а k также указывает число ближайших соседей, возвращаемых.

    Строка векторного запроса , quintessential lodging near running trails, eateries, retailкоторая векторизирована в 1536 внедрения для этого запроса.

    Ответ для векторного эквивалента quintessential lodging near running trails, eateries, retail состоит из 7 результатов, но код указывает top=5 , поэтому возвращаются только первые 5 результатов. Кроме того, возвращаются только поля, указанные этим select параметром.

    search_client.search() возвращает объект, похожий на словарь. Каждый результат предоставляет оценку поиска, доступ к которой можно получить с помощью score = result.get("@search.score", "N/A"). Хотя это не отображено в данном примере, при поиске сходства ответ всегда содержит k результаты, упорядоченные по значению оценки сходства.

    После запуска ячейки состояние каждого документа выводится под ним:

    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

Поиск одного вектора с фильтром

Вы можете добавить фильтры, но фильтры применяются к содержимому невектора в индексе. В этом примере фильтр применяется к Tags полю для фильтрации всех отелей, которые не предоставляют бесплатный Wi-Fi.

  1. Найдите ячейку под разделом с названием "Поиск векторов с фильтром" и выполните ячейку. Эта ячейка содержит запрос на запрос к индексу поиска.

    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.")

    После запуска ячейки состояние каждого документа выводится под ним:

    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']

    Запрос был таким же, как и в предыдущем примере поиска с одним вектором, но он включает фильтр исключения после обработки и возвращает только два отеля с бесплатным Wi-Fi.

  2. В следующем примере фильтра используется геофильтр. Запустите ячейку в разделе "Векторный запрос с геофильтром". Этот блок содержит запрос на запрос к индексу поиска.

    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.")

    Запрос был таким же, как и в предыдущем примере поиска одного вектора, но он включает фильтр исключения после обработки и возвращает только два отеля в пределах 300 километров.

    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.

Гибридный поиск состоит из ключевых запросов и векторных запросов в одном запросе поиска. В этом примере выполняется векторный запрос и полнотекстовый поиск одновременно:

  • Строка поиска: historic hotel walk to restaurants and shopping

  • Строка векторного запроса (векторизованная в математическое представление): quintessential lodging near running trails, eateries, retail

  • Найдите ячейку под заголовком "Гибридный поиск" и выполните ее. Этот блок содержит запрос на запрос к индексу поиска.

    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.")

    Просмотрите ответ:

    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']

    Поскольку слияние результатов методом обратного ранга Fusion (RRF) объединяет данные, это помогает их анализировать. Ниже приведены результаты только полнотекстового запроса. Лучшие два результата : Отель Sublime Palace и History Lion Resort. Отель Sublime Palace имеет более сильную оценку релевантности BM25.

    {
     "@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"
},

    В запросе только для вектора, который использует HNSW для поиска совпадений, отель Sublime Palace удаляется на четвертую позицию. Исторический лев, который был вторым в полнотекстовом поиске и третьем в векторном поиске, не имеет того же диапазона колебания, поэтому он отображается в качестве верхнего совпадения в однородном результирующем наборе.

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

Ниже приведен последний запрос в коллекции. Этот гибридный запрос указывает тип семантического запроса и семантическую конфигурацию, демонстрируя, что вы можете создать гибридный запрос, использующий семантическую переупорядочивание.

  • Найдите ячейку под разделом с названием "Семантический гибридный поиск" и запустите выполнение ячейки. Этот блок кода содержит запрос на запрос к индексу поиска.

    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.")

    Просмотрите выходные данные под ячейкой.

    Благодаря семантическому ранжированию отель Swirling Currents теперь занимает первое место. 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 New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
   Category: Boutique
- Score: 0.01515151560306549
   Re-ranker Score: 2.0582215785980225
   HotelId: 3
   HotelName: Gastronomic Landscape Hotel
   Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.
   Category: Suite

Вы можете рассматривать семантический рейтинг как способ повышения релевантности результатов поиска, понимая смысл слов в запросе и содержимое документов. В этом случае семантический рейтинг помогает определить отели, которые не только относятся к ключевым словам, но и соответствуют намерению запроса:

Ключевые моменты:

  • Поиск вектора vectors.value определяется с помощью свойства. Поиск по ключевому слову search определяется с помощью свойства.

  • В гибридном поиске можно интегрировать векторный поиск с полнотекстовый поиск по ключевым словам. Фильтры, проверка орфографии и семантический ранжирование применяются только к текстовому содержимому, а не к векторам. В этом окончательном запросе нет семантики answer , так как система не получила достаточно сильного.

  • Фактические результаты включают более подробные сведения, включая семантические субтитры и выделения. Результаты были изменены для удобства чтения. Чтобы получить полную структуру ответа, выполните запрос в клиенте REST.

Очистка

Если вы работаете в собственной подписке, в конце проекта следует решить, нужны ли вам созданные ресурсы. Ресурсы, которые продолжат работать, могут быть платными. Вы можете удалить ресурсы по отдельности либо удалить всю группу ресурсов.

Ресурсы и управление ими можно найти в портал Azure с помощью ссылки "Все ресурсы" или "Группы ресурсов" в левой области.

Если вы хотите сохранить службу поиска, но удалить индекс и документы, можно использовать SearchIndexClient метод объекта delete_index() . Найдите ячейку ниже раздела с названием "Очистка", и выполните эту ячейку, если вы хотите удалить индекс hotels-vector-quickstart:

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

Следующие шаги

  • Просмотрите репозиторий примеров кода для возможностей поиска векторов в Поиске ИИ Azure для Python
  • Ознакомьтесь с другими примерами кода python и поиска ИИ Azure в репозитории azure-search-python-samples

В этом кратком руководстве вы используете JavaScript для создания, загрузки и запроса векторов. Примеры кода выполняют эти операции с помощью клиентской библиотеки поиска ИИ Azure. Библиотека предоставляет абстракцию по REST API для доступа к операциям индексирования, таким как прием данных, операции поиска и операции управления индексами.

В службе "Поиск ИИ Azure" векторное хранилище содержит схему индекса, которая определяет поля векторов и невекторов, конфигурацию векторного поиска для алгоритмов, создающих пространство внедрения и параметры определений векторных полей, которые вычисляются во время запроса. REST API создания индекса создает хранилище векторов.

Примечание.

В этом кратком руководстве опускается шаг векторизации и предоставляются встроенные векторы. Если вы хотите добавить встроенные блоки данных и векторизацию для вашего контента, попробуйте использовать мастер импорта и векторизации данных как полное руководство.

Необходимые компоненты

Получение конечных точек службы

В оставшихся разделах вы настроили вызовы API к Azure OpenAI и поиску ИИ Azure. Получите конечные точки службы, чтобы предоставить их в виде переменных в коде.

  1. Войдите на портал Azure.

  2. Найдите службу поиска.

  3. Скопируйте URL-адрес на домашней странице обзора. Пример конечной точки может выглядеть так: https://example.search.windows.net.

Настройка проекта Node.JS

Настройте проект с помощью Visual Studio Code и JavaScript.

  1. Запустите Visual Studio Code в новом каталоге.

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

  2. Создайте новый пакет для модулей ESM в каталоге проекта.

    npm init -y
npm pkg set type=module

    При этом создается package.json файл со значениями по умолчанию.

  3. Установите следующие пакеты npm.

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

  4. Создайте каталог src внутри вашего проектного каталога.

    mkdir src

Настройка переменных среды для локальной разработки

  1. Создайте файл .env в каталоге проекта vector-quickstart.

  2. Добавьте в файл следующие переменные .env среды, заменив значения собственными конечными точками и ключами службы.

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

Вход в Azure

Для подключения используется идентификатор Microsoft Entra и назначения ролей. Убедитесь, что вы вошли в тот же клиент и подписку, что и поиск ИИ Azure и Azure OpenAI. Azure CLI можно использовать в командной строке для отображения текущих свойств, изменения свойств и входа. Дополнительные сведения см. в разделе "Подключение без ключей".

Выполните каждую из следующих команд в последовательности.

az account show

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

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

Теперь вы должны войти в Azure с локального устройства.

Создание векторного индекса

В этом разделе описано, как создать векторный индекс в службе "Поиск ИИ Azure" с помощью SearchIndexClient. createOrUpdateIndex. Схема индекса определяет поля, включая поле DescriptionVectorвектора.

  1. createIndex.js Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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`);

    Файл кода добавляет зависимости, переменные среды и тип JavaScript для HotelDocument в верхнюю часть файла. createIndex Добавьте функцию для создания индекса. Функция определяет схему индекса, включая поле DescriptionVectorвектора.

  3. Запустите файл:

    node -r dotenv/config src/createIndex.js

  4. Выходные данные этого кода показывают, что индекс успешно создан:

    Using Azure Search endpoint: https://my-service.search.windows.net
Using index name: hotels-vector-quickstart
Creating index...
hotels-vector-quickstart created

    Основные выводы при построении векторного индекса с использованием библиотеки @azure/search-documents:

    • Вы определяете индекс, создав список полей.

    • Этот конкретный индекс поддерживает несколько возможностей поиска, таких как:

      • Поиск полнотекстового ключевого слова (searchable)
      • Векторный поиск (включает гибридный поиск путем сортировки полей вектора и невектора) (DescriptionVector с vectorSearchProfileName)
      • Семантический поиск
      • Фасетный поиск (searchSuggester)
      • Геопространствовый поиск (Location поле с geo.distance)
      • Фильтрация, сортировка (многие поля, помеченные как фильтруемые и сортируемые)

Отправка документов в индекс

Создание и загрузка индекса являются отдельными шагами. На предыдущем шаге вы создали схему индекса. Теперь необходимо загрузить документы в индекс с помощью SearchClient. uploadDocuments. Документы содержат векторную версию описания статьи, которая обеспечивает поиск сходства на основе смысла, а не точных ключевых слов.

В службе "Поиск ИИ Azure" индекс сохраняет все содержимое, которое можно найти, а поисковая система выполняет запросы к индексу.

  1. uploadDocuments.js Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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": [
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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);
}

    Этот код загружает массив объектов JSON. Каждый документ включает векторную версию статьи description. Этот вектор позволяет выполнять поиск сходства, где сопоставление основано на значении, а не на точных ключевых словах.

    Так как в этой статье краткого руководства поиск в индексе выполняется незамедлительно, функция waitUntilIndexed ожидает, пока индекс не будет готов к операциям поиска. Это важно, так как индекс должен быть полностью заполнен документами, прежде чем выполнять поиск.

  3. Выполните сборку и запустите файл:

    node -r dotenv/config src/uploadDocuments.js

  4. Выходные данные этого кода показывают, что документы индексируются и готовы для поиска:

    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.

    Ключевые выносы о методе upload_documents() и этом примере:

    • Код взаимодействует с определенным индексом поиска, размещенным в службе поиска ИИ Azure, с помощью SearchClient, который является основным объектом, предоставляемым пакетом @azure/search-documents . SearchClient предоставляет доступ к операциям индекса, таким как:
      • Прием данных — uploadDocuments(), mergeDocuments(), deleteDocuments() и т. д.
      • Операции поиска — search(), autocomplete(), suggest()
      • Операции управления индексами, такие как createIndex(), deleteIndex(), getIndex() и т. д.
    • Векторные поля содержат значения с плавающей запятой. Атрибут измерений имеет не менее 2 и не более 4096 значений с плавающей запятой. В этом кратком руководстве атрибут dimensions устанавливается в 1536, так как это размер эмбеддингов, генерируемых моделью Azure OpenAI text-embedding-ada-002.

Создание запроса в качестве вектора

Примеры векторных запросов основаны на двух строках:

  • Строка поиска: historic hotel walk to restaurants and shopping
  • Строка векторного запроса (векторизованная в математическое представление): quintessential lodging near running trails, eateries, retail

Строка векторного запроса семантически похожа на строку поиска, но она содержит термины, которые не существуют в индексе поиска. Если вы выполняете поиск quintessential lodging near running trails, eateries, retailпо ключевому слову, результаты равны нулю. В этом примере показано, как можно получить соответствующие результаты, даже если нет подходящих условий.

  1. queryVector.js Создайте файл в каталоге src и добавьте код для создания вектора запроса.

    // 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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  2. Этот код используется в следующих разделах для выполнения векторного поиска. Вектор запроса создается с помощью модели внедрения из Azure OpenAI.

Первый пример демонстрирует базовый сценарий, в котором необходимо найти описания документов, которые тесно соответствуют строке поиска с помощью SearchClient. поиск и VectorQuery и SearchOptions.

  1. searchSingle.js Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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 содержит конфигурацию векторизованного запроса, включая векторизованный текст входных данных запроса, как vector, fields определяет, по каким полям векторов проводится поиск, и kNearestNeighborsCount указывает число ближайших соседей, которые нужно вернуть.

    Строка векторного запроса , quintessential lodging near running trails, eateries, retailкоторая векторизирована в 1536 внедрения для этого запроса.

  3. Выполните сборку и запустите файл:

    node -r dotenv/config src/searchSingle.js

  4. В выходных данных этого кода отображаются соответствующие документы для запроса quintessential lodging near running trails, eateries, retail.

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
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

    Ответ для векторного эквивалента quintessential lodging near running trails, eateries, retail состоит из 5 результатов, причем возвращаются только поля, указанные в запросе select.

Создание одного векторного поиска с помощью фильтра

Вы можете добавить фильтры, но фильтры применяются к содержимому невектора в индексе. В этом примере фильтр применяется к Tags полю для фильтрации всех отелей, которые не предоставляют бесплатный Wi-Fi. В этом поиске используется SearchClient. поиск и VectorQuery и SearchOptions.

  1. searchSingleWithFilter.js Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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;
}

    Добавьте зависимости, переменные среды и те же функции поиска, что и предыдущий поиск с фильтром исключения после обработки, добавленным для отелей free wifi.

  3. Выполните сборку и запустите файл:

    node -r dotenv/config src/searchSingleWithFilter.js

  4. В выходных данных этого кода показаны соответствующие документы для запроса с примененным фильтром free wifi :

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
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

Создание поиска с помощью геопространственного фильтра

Вы можете указать геопространственный фильтр, чтобы ограничить результаты определенной географической областью. В этом примере фильтр ограничивает результаты отелями в пределах 300 километров от Вашингтона (координаты: -77.03241 38.90166). Геопространственные расстояния всегда находятся в километрах. В этом поиске используется SearchClient. поиск и VectorQuery и SearchOptions.

  1. searchSingleWithFilterGeo.js Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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;
}

  3. Выполните сборку и запустите файл:

    node -r dotenv/config src/searchSingleWithFilterGeo.js

  4. В выходных данных этого кода показаны соответствующие документы для запроса с примененным фильтром исключения после геопространственной обработки:

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
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

Гибридный поиск состоит из ключевых запросов и векторных запросов в одном запросе поиска. В этом примере выполняется векторный запрос и полнотекстовый поиск одновременно:

  • Строка поиска: historic hotel walk to restaurants and shopping
  • Строка векторного запроса (векторизованная в математическое представление): quintessential lodging near running trails, eateries, retail

В этом поиске используется SearchClient. поиск и VectorQuery и SearchOptions.

  1. searchHybrid.js Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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;
}

  3. Выполните сборку и запустите файл:

    node -r dotenv/config src/searchHybrid.js

  4. В выходных данных этого кода показаны соответствующие документы для гибридного поиска:

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
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"]

    Поскольку слияние результатов методом обратного ранга Fusion (RRF) объединяет данные, это помогает их анализировать. Ниже приведены результаты только полнотекстового запроса. Лучшие два результата : Отель Sublime Palace и History Lion Resort. Отель Sublime Palace имеет более сильную оценку релевантности BM25.

       {
       "@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"
   },

    В запросе только для вектора, который использует HNSW для поиска совпадений, отель Sublime Palace удаляется на четвертую позицию. Исторический лев, который был вторым в полнотекстовом поиске и третьем в векторном поиске, не имеет того же диапазона колебания, поэтому он отображается в качестве верхнего совпадения в однородном результирующем наборе.

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

Ниже приведен последний запрос в коллекции для расширения семантического гибридного поиска с дополнительным текстом поиска historic hotel walk to restaurants and shopping.

В этом поиске используется SearchClient. поиск и VectorQuery и SearchOptions.

  1. searchSemanticHybrid.js Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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;
}

  3. Выполните сборку и запустите файл:

    node -r dotenv/config src/searchSemanticHybrid.js

  4. В выходных данных этого кода показаны соответствующие документы для семантического гибридного поиска:

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
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 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.
  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

    Вы можете рассматривать семантический рейтинг как способ повышения релевантности результатов поиска, понимая смысл слов в запросе и содержимое документов. В этом случае семантический рейтинг помогает определить отели, которые не только относятся к ключевым словам, но и соответствуют намерению запроса:

    Ключевые моменты:

    • Поиск вектора vectorSearchOptions определяется с помощью свойства. Поиск по ключевому слову semanticSearchOptions определяется с помощью свойства.

    • В гибридном поиске можно интегрировать векторный поиск с полнотекстовый поиск по ключевым словам. Фильтры, проверка орфографии и семантический ранжирование применяются только к текстовому содержимому, а не к векторам. В этом окончательном запросе нет семантики answer , так как система не получила достаточно сильного.

    • Фактические результаты включают более подробные сведения, включая семантические субтитры и выделения. Результаты были изменены для удобства чтения. Чтобы получить полную структуру ответа, выполните запрос в клиенте REST.

Очистка

Если вы работаете в собственной подписке, в конце проекта следует решить, нужны ли вам созданные ресурсы. Ресурсы, которые продолжат работать, могут быть платными. Вы можете удалить ресурсы по отдельности либо удалить всю группу ресурсов.

Ресурсы и управление ими можно найти в портал Azure с помощью ссылки "Все ресурсы" или "Группы ресурсов" в левой области.

Если вы хотите сохранить службу поиска, но удалить индекс и документы, можно удалить индекс программным способом.

  1. deleteIndex.js Создайте файл в каталогеsrc.

  2. Добавьте зависимости, переменные среды и код для удаления индекса.

    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);
}

  3. Выполните сборку и запустите файл:

    node -r dotenv/config src/deleteIndex.js

Следующие шаги

  • Ознакомьтесь с репозиторием примеров кода для возможностей поиска векторов в Поиске ИИ Azure для JavaScript

В этом кратком руководстве вы используете TypeScript для создания, загрузки и выполнения запросов к векторам. Примеры кода выполняют эти операции с помощью клиентской библиотеки поиска ИИ Azure. Библиотека предоставляет абстракцию по REST API для доступа к операциям индексирования, таким как прием данных, операции поиска и операции управления индексами.

В службе "Поиск ИИ Azure" векторное хранилище содержит схему индекса, которая определяет поля векторов и невекторов, конфигурацию векторного поиска для алгоритмов, создающих пространство внедрения и параметры определений векторных полей, которые вычисляются во время запроса. REST API создания индекса создает хранилище векторов.

Примечание.

В этом кратком руководстве опускается шаг векторизации и предоставляются встроенные векторы. Если вы хотите добавить встроенные блоки данных и векторизацию для вашего контента, попробуйте использовать мастер импорта и векторизации данных как полное руководство.

Необходимые компоненты

Получение конечных точек службы

В оставшихся разделах вы настроили вызовы API к Azure OpenAI и поиску ИИ Azure. Получите конечные точки службы, чтобы предоставить их в виде переменных в коде.

  1. Войдите на портал Azure.

  2. Найдите службу поиска.

  3. Скопируйте URL-адрес на домашней странице обзора. Пример конечной точки может выглядеть так: https://example.search.windows.net.

Настройка проекта Node.JS

Настройте проект с помощью Visual Studio Code и TypeScript.

  1. Запустите Visual Studio Code в новом каталоге.

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

  2. Создайте новый пакет для модулей ESM в каталоге проекта.

    npm init -y
npm pkg set type=module

    При этом создается package.json файл со значениями по умолчанию.

  3. Измените package.json чтобы включить файл скрипта сборки. Добавьте следующую строку в scripts раздел:

    "build": "tsc"

  4. Установите следующие пакеты npm.

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

  5. Создайте каталог src внутри вашего проектного каталога.

    mkdir src

  6. tsconfig.json Создайте файл в каталоге проекта для ESM со следующим содержимым.

    {
  "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"
  ]
}

Настройка переменных среды для локальной разработки

  1. Создайте файл .env в каталоге проекта vector-quickstart.

  2. Добавьте в файл следующие переменные .env среды, заменив значения собственными конечными точками и ключами службы.

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

Вход в Azure

Для подключения используется идентификатор Microsoft Entra и назначения ролей. Убедитесь, что вы вошли в тот же клиент и подписку, что и поиск ИИ Azure и Azure OpenAI. Azure CLI можно использовать в командной строке для отображения текущих свойств, изменения свойств и входа. Дополнительные сведения см. в разделе "Подключение без ключей".

Выполните каждую из следующих команд в последовательности.

az account show

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

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

Теперь вы должны войти в Azure с локального устройства.

Создание векторного индекса

В этом разделе описано, как создать векторный индекс в службе "Поиск ИИ Azure" с помощью SearchIndexClient. createOrUpdateIndex. Схема индекса определяет поля, включая поле DescriptionVectorвектора. После создания индекса вы отправляете документы в индекс. Документы содержат векторную версию описания статьи, которая обеспечивает поиск сходства на основе смысла, а не точных ключевых слов.

  1. createIndex.ts Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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`);

    Файл кода добавляет зависимости, переменные среды и тип JavaScript для HotelDocument в верхнюю часть файла. createIndex Добавьте функцию для создания индекса. Функция определяет схему индекса, включая поле DescriptionVectorвектора.

  3. Выполните сборку и запустите файл:

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

  4. Выходные данные этого кода показывают, что индекс успешно создан:

    Using Azure Search endpoint: https://my-service.search.windows.net
Using index name: hotels-vector-quickstart
Creating index...
hotels-vector-quickstart created

    Основные выводы при построении векторного индекса с использованием библиотеки @azure/search-documents:

    • Вы определяете индекс, создав список полей.

    • Этот конкретный индекс поддерживает несколько возможностей поиска, таких как:

      • Поиск полнотекстового ключевого слова (searchable)
      • Векторный поиск (включает гибридный поиск путем сортировки полей вектора и невектора) (DescriptionVector с vectorSearchProfileName)
      • Семантический поиск
      • Фасетный поиск (searchSuggester)
      • Геопространствовый поиск (Location поле с geo.distance)
      • Фильтрация, сортировка (многие поля, помеченные как фильтруемые и сортируемые)

Отправка документов в индекс

Создание и загрузка индекса являются отдельными шагами. На предыдущем шаге вы создали схему индекса. Теперь необходимо загрузить документы в индекс с помощью SearchClient. uploadDocuments.

В службе "Поиск ИИ Azure" индекс сохраняет все содержимое, которое можно найти, а поисковая система выполняет запросы к индексу.

  1. uploadDocuments.ts Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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": [
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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);
}

    Этот код загружает документы переменной с объектом JSON, описывающим каждый документ, а также векторную версию описания статьи. Этот вектор позволяет выполнять поиск сходства, где сопоставление основано на значении, а не на точных ключевых словах.

    Так как в этой статье краткого руководства поиск в индексе выполняется незамедлительно, функция waitUntilIndexed ожидает, пока индекс не будет готов к операциям поиска. Это важно, так как индекс должен быть полностью заполнен документами, прежде чем выполнять поиск.

  3. Выполните сборку и запустите файл:

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

  4. Выходные данные этого кода показывают, что документы индексируются и готовы для поиска:

    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.

    Ключевые выносы о методе upload_documents() и этом примере:

    • Код взаимодействует с определенным индексом поиска, размещенным в службе поиска ИИ Azure, с помощью SearchClient, который является основным объектом, предоставляемым пакетом @azure/search-documents . SearchClient предоставляет доступ к операциям индекса, таким как:
      • Прием данных — uploadDocuments(), mergeDocuments(), deleteDocuments() и т. д.
      • Операции поиска — search(), autocomplete(), suggest()
      • Операции управления индексами, такие как createIndex(), deleteIndex(), getIndex() и т. д.
    • Векторные поля содержат значения с плавающей запятой. Атрибут измерений имеет не менее 2 и не более 4096 значений с плавающей запятой. В этом кратком руководстве атрибут dimensions устанавливается в 1536, так как это размер эмбеддингов, генерируемых моделью Azure OpenAI text-embedding-ada-002.

Создание запроса в качестве вектора

Примеры векторных запросов основаны на двух строках:

  • Строка поиска: historic hotel walk to restaurants and shopping
  • Строка векторного запроса (векторизованная в математическое представление): quintessential lodging near running trails, eateries, retail

Строка векторного запроса семантически похожа на строку поиска, но она содержит термины, которые не существуют в индексе поиска. Если вы выполняете поиск quintessential lodging near running trails, eateries, retailпо ключевому слову, результаты равны нулю. В этом примере показано, как можно получить соответствующие результаты, даже если нет подходящих условий.

  1. queryVector.ts Создайте файл в каталоге src и добавьте код для создания вектора запроса.

    // 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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  2. Этот код используется в следующих разделах для выполнения векторного поиска. Вектор запроса создается с помощью модели внедрения из Azure OpenAI.

Первый пример демонстрирует базовый сценарий, в котором необходимо найти описания документов, которые тесно соответствуют строке поиска с помощью SearchClient. поиск и VectorQuery и SearchOptions.

  1. searchSingle.ts Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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;
}

    VectorQuery содержит конфигурацию векторизованного запроса, включая векторизованный текст входных данных запроса, как vector, fields определяет, по каким полям векторов проводится поиск, и kNearestNeighborsCount указывает число ближайших соседей, которые нужно вернуть.

    Строка векторного запроса , quintessential lodging near running trails, eateries, retailкоторая векторизирована в 1536 внедрения для этого запроса.

  3. Выполните сборку и запустите файл:

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

  4. В выходных данных этого кода отображаются соответствующие документы для запроса quintessential lodging near running trails, eateries, retail.

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
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

    Ответ для векторного эквивалента quintessential lodging near running trails, eateries, retail состоит из 5 результатов, причем возвращаются только поля, указанные в запросе select.

Создание одного векторного поиска с помощью фильтра

Вы можете добавить фильтры, но фильтры применяются к содержимому невектора в индексе. В этом примере фильтр применяется к Tags полю для фильтрации всех отелей, которые не предоставляют бесплатный Wi-Fi. В этом поиске используется SearchClient. поиск и VectorQuery и SearchOptions.

  1. searchSingleWithFilter.ts Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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;
}

    Добавьте зависимости, переменные среды и те же функции поиска, что и предыдущий поиск с фильтром исключения после обработки, добавленным для отелей free wifi.

  3. Выполните сборку и запустите файл:

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

  4. В выходных данных этого кода показаны соответствующие документы для запроса с примененным фильтром free wifi :

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
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

Создание поиска с помощью геопространственного фильтра

Вы можете указать геопространственный фильтр, чтобы ограничить результаты определенной географической областью. В этом примере фильтр ограничивает результаты отелями в пределах 300 километров от Вашингтона (координаты: -77.03241 38.90166). Геопространственные расстояния всегда находятся в километрах. В этом поиске используется SearchClient. поиск и VectorQuery и SearchOptions.

  1. searchSingleWithFilterGeo.ts Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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;
}

  3. Выполните сборку и запустите файл:

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

  4. В выходных данных этого кода показаны соответствующие документы для запроса с примененным фильтром исключения после геопространственной обработки:

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
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

Гибридный поиск состоит из ключевых запросов и векторных запросов в одном запросе поиска. В этом примере выполняется векторный запрос и полнотекстовый поиск одновременно:

  • Строка поиска: historic hotel walk to restaurants and shopping
  • Строка векторного запроса (векторизованная в математическое представление): quintessential lodging near running trails, eateries, retail

В этом поиске используется SearchClient. поиск и VectorQuery и SearchOptions.

  1. searchHybrid.ts Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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;
}

  3. Выполните сборку и запустите файл:

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

  4. В выходных данных этого кода показаны соответствующие документы для гибридного поиска:

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
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"]

    Поскольку слияние результатов методом обратного ранга Fusion (RRF) объединяет данные, это помогает их анализировать. Ниже приведены результаты только полнотекстового запроса. Лучшие два результата : Отель Sublime Palace и History Lion Resort. Отель Sublime Palace имеет более сильную оценку релевантности BM25.

       {
       "@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"
   },

    В запросе только для вектора, который использует HNSW для поиска совпадений, отель Sublime Palace удаляется на четвертую позицию. Исторический лев, который был вторым в полнотекстовом поиске и третьем в векторном поиске, не имеет того же диапазона колебания, поэтому он отображается в качестве верхнего совпадения в однородном результирующем наборе.

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

Ниже приведен последний запрос в коллекции для расширения семантического гибридного поиска с дополнительным текстом поиска historic hotel walk to restaurants and shopping.

В этом поиске используется SearchClient. поиск и VectorQuery и SearchOptions.

  1. searchSemanticHybrid.ts Создайте файл в каталогеsrc.

  2. Скопируйте следующий код в файл.

    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;
}

  3. Выполните сборку и запустите файл:

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

  4. В выходных данных этого кода показаны соответствующие документы для семантического гибридного поиска:

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
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 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.
  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

    Вы можете рассматривать семантический рейтинг как способ повышения релевантности результатов поиска, понимая смысл слов в запросе и содержимое документов. В этом случае семантический рейтинг помогает определить отели, которые не только относятся к ключевым словам, но и соответствуют намерению запроса:

    Ключевые моменты:

    • Поиск вектора vectorSearchOptions определяется с помощью свойства. Поиск по ключевому слову semanticSearchOptions определяется с помощью свойства.

    • В гибридном поиске можно интегрировать векторный поиск с полнотекстовый поиск по ключевым словам. Фильтры, проверка орфографии и семантический ранжирование применяются только к текстовому содержимому, а не к векторам. В этом окончательном запросе нет семантики answer , так как система не получила достаточно сильного.

    • Фактические результаты включают более подробные сведения, включая семантические субтитры и выделения. Результаты были изменены для удобства чтения. Чтобы получить полную структуру ответа, выполните запрос в клиенте REST.

Очистка

Если вы работаете в собственной подписке, в конце проекта следует решить, нужны ли вам созданные ресурсы. Ресурсы, которые продолжат работать, могут быть платными. Вы можете удалить ресурсы по отдельности либо удалить всю группу ресурсов.

Ресурсы и управление ими можно найти в портал Azure с помощью ссылки "Все ресурсы" или "Группы ресурсов" в левой области.

Если вы хотите сохранить службу поиска, но удалить индекс и документы, можно удалить индекс программным способом.

  1. deleteIndex.ts Создайте файл в каталогеsrc.

  2. Добавьте зависимости, переменные среды и код для удаления индекса.

    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);
}

  3. Выполните сборку и запустите файл:

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

Следующие шаги

  • Ознакомьтесь с репозиторием примеров кода для возможностей поиска векторов в Поиске ИИ Azure для JavaScript

В этом кратком руководстве вы работаете с приложением .NET для создания, заполнения и запроса векторов. Примеры кода выполняют эти операции с помощью клиентской библиотеки поиска ИИ Azure. Библиотека предоставляет абстракцию по REST API для доступа к операциям индексирования, таким как прием данных, операции поиска и операции управления индексами.

В службе "Поиск ИИ Azure" векторное хранилище содержит схему индекса, которая определяет поля векторов и невекторов, конфигурацию векторного поиска для алгоритмов, создающих пространство внедрения и параметры определений векторных полей, которые вычисляются во время запроса. REST API создания индекса создает хранилище векторов.

Примечание.

В этом кратком руководстве опускается шаг векторизации и предоставляются встроенные векторы. Если вы хотите добавить встроенные блоки данных и векторизацию для вашего контента, попробуйте использовать мастер импорта и векторизации данных как полное руководство.

Необходимые компоненты

Получение сведений о ресурсе

Запросы к конечной точке поиска должны проходить проверку подлинности и авторизованы. Хотя ключи или роли API можно использовать для этой задачи, рекомендуется использовать подключение без ключей через идентификатор Microsoft Entra.

В этом кратком руководстве используется DefaultAzureCredential, что упрощает проверку подлинности как в сценариях разработки, так и в рабочей среде. Однако для рабочих сценариев может потребоваться более сложные требования, требующие другого подхода. Ознакомьтесь с проверкой подлинности приложений .NET в службах Azure с помощью пакета SDK Azure для .NET, чтобы понять все ваши параметры.

Клонирование кода и среды установки

  1. Клонируйте репозиторий, содержащий код для этого краткого руководства.

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

    Этот репозиторий содержит примеры кода .NET для нескольких статей в отдельной вложенной папке.

  2. Откройте вложенную папку quickstart-Vector-Search в Visual Studio Code или дважды щелкните .sln файл, чтобы открыть решение в Visual Studio.

  3. appsettings.json Откройте файлы в папках VectorSearchExamples и VectorSearchCreatePopulateIndex. Измените следующие значения:

    • AZURE_SEARCH_ENDPOINT: найдите URL-адрес службы поиска ИИ Azure на портале Azure. На странице обзора ресурса поиска найдите поле URL-адреса. Пример конечной точки может выглядеть так: https://mydemo.search.windows.net.
    • AZURE_SEARCH_INDEX_NAME: оставьте значение по умолчанию, указанное в файле, или введите собственное имя индекса.

Создание векторного индекса и отправка документов

Чтобы выполнить поисковые запросы к службе поиска Azure AI, сначала необходимо создать индекс поиска и отправить документы в службу.

  1. Откройте новый терминал в папке VectorSearchCreatePopulateIndex .

  2. Запустите проект с помощью dotnet run команды:

    dotnet run

Следующий код выполняется для создания индекса:

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();
}

Следующий код загружает документы в формате JSON из файла hotel-samples.json в службу поиска ИИ Azure:

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);
    }
}

После запуска проекта выводятся следующие выходные данные:

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

Ключевые моменты:

  • Код взаимодействует с определенным индексом поиска, размещенным в службе поиска ИИ Azure, с помощью SearchClientэтого основного объекта, предоставленного пакетом azure-search-documents . Этот SearchClient предоставляет доступ к операциям индекса, таким как:

    • Прием данных: UploadDocuments(), MergeDocuments()DeleteDocuments()
    • Операции поиска: Search(), Autocomplete()Suggest()
    • Операции управления индексами: CreateOrUpdateIndex()

  • Векторные поля содержат значения с плавающей запятой. Атрибут измерений имеет не менее 2 и не более 4096 значений с плавающей запятой. Этот размер встраивания, создаваемый моделью Azure OpenAI text-embedding-3-small для этого краткого руководства, составляет 1536.

Выполнение поисковых запросов

После создания индекса и загрузки документов можно выдавать векторные запросы к ним путем вызова SearchAsync() с различными параметрами.

  1. В папке VectorSearchExamples откройте файл Program.cs.
  2. Откройте новый терминал в папке VectorSearchExamples .

В следующих разделах выполняются запросы к индексу hotels-vector-quickstart. К запросам относятся:

Первый пример демонстрирует базовый сценарий, в котором необходимо найти описания документов, которые тесно соответствуют строке поиска.

  1. В файле Program.cs папки VectorSearchExamples раскомментируйте вызов метода SearchExamples.SearchSingleVector(searchClient, vectorizedResult);. Этот метод выполняет следующую функцию поиска в SearchExamples.cs классе:

    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();
}

  2. Запустите проект с помощью dotnet run команды:

    dotnet run

    После запуска проекта результаты поиска печатаются в окне вывода:

    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

Поиск одного вектора с фильтром

Вы можете добавить фильтры, но фильтры применяются к содержимому невектора в индексе. В этом примере фильтр применяется к Tags полю для фильтрации всех отелей, которые не предоставляют бесплатный Wi-Fi.

  1. В файле Program.cs папки VectorSearchExamples раскомментируйте вызов метода SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);. Этот метод выполняет следующую функцию поиска в SearchExamples.cs классе:

    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();
}

  2. Снова запустите проект, а состояние каждого документа выводится под ним:

     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

    Запрос был таким же, как и в предыдущем примере поиска с одним вектором, но он включает фильтр исключения после обработки и возвращает только два отеля с бесплатным Wi-Fi.

  3. В следующем примере фильтра используется геофильтр. В файле Program.cs папки VectorSearchExamples раскомментируйте вызов метода SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);. Этот метод выполняет следующую функцию поиска в SearchExamples.cs классе:

    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();
}

    Запрос был таким же, как и в предыдущем примере поиска одного вектора, но он включает фильтр исключения после обработки и возвращает только два отеля в пределах 300 километров.

  4. Снова запустите проект, а состояние каждого документа выводится под ним:

    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.

Гибридный поиск состоит из ключевых запросов и векторных запросов в одном запросе поиска. В этом примере выполняется векторный запрос и полнотекстовый поиск одновременно:

  • Строка поиска: historic hotel walk to restaurants and shopping
  • Строка векторного запроса (векторизованная в математическое представление): Quintessential lodging near running trails, eateries, retail

  1. В файле Program.cs папки VectorSearchExamples раскомментируйте вызов метода SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);. Этот метод выполняет следующую функцию поиска в SearchExamples.cs классе:

    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;
}

  2. Снова запустите проект, а состояние каждого документа выводится под ним:

    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

Поскольку слияние результатов методом обратного ранга Fusion (RRF) объединяет данные, это помогает их анализировать. Ниже приведены результаты только полнотекстового запроса. Лучшие два результата : Отель Sublime Palace и History Lion Resort. Отель Sublime Palace имеет более сильную оценку релевантности BM25.

{
    "@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"
},

В запросе только для вектора, который использует HNSW для поиска совпадений, отель Sublime Palace удаляется на четвертую позицию. Исторический лев, который был вторым в полнотекстовом поиске и третьем в векторном поиске, не имеет того же диапазона колебания, поэтому он отображается в качестве верхнего совпадения в однородном результирующем наборе.

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

Семантический гибридный поиск с фильтром

Гибридный запрос с семантической ранжированием фильтруется, чтобы показать только отели в радиусе 500 километров от Вашингтона.

  1. В файле Program.cs папки VectorSearchExamples раскомментируйте вызов метода SearchExamples.SearchHybridVectoryAndSemantic(searchClient, vectorizedResult);. Этот метод выполняет следующую функцию поиска в SearchExamples.cs классе:

    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();
}

  2. Снова запустите проект и просмотрите выходные данные под ячейкой. Ответ равен трем отелям, которые фильтруются по расположению и семантически StateProvince переназначаются для повышения результатов, которые ближе всего к запросу строки поиска (historic hotel walk to restaurants and shopping).

    Отель Swirling Currents теперь движется в верхней точке. Без семантического ранжирования, мотель в долине Нордик является номером один. С семантического ранжирования модели машинного понимания признают, что historic относится к "отель, в пределах ходьбы от столовой (ресторанов) и покупок".

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

Ключевые моменты:

  • В гибридном поиске можно интегрировать векторный поиск с полнотекстовый поиск по ключевым словам. Фильтры, проверка орфографии и семантический ранжирование применяются только к текстовому содержимому, а не к векторам. В этом окончательном запросе нет семантики answer , так как система не получила достаточно сильного.

  • Фактические результаты включают более подробные сведения, включая семантические субтитры и выделения. Результаты были изменены для удобства чтения. Чтобы получить полную структуру ответа, выполните запрос в клиенте REST.

Очистка

Если вы работаете в собственной подписке, в конце проекта следует решить, нужны ли вам созданные ресурсы. Ресурсы, которые продолжат работать, могут быть платными. Вы можете удалить ресурсы по отдельности либо удалить всю группу ресурсов.

Ресурсы и управление ими можно найти в портал Azure с помощью ссылки "Все ресурсы" или "Группы ресурсов" в левой области.

Следующие шаги

В этом кратком руководстве вы используете REST API Azure AI Search для создания, загрузки и выполнения запросов векторов.

В службе "Поиск ИИ Azure" векторное хранилище содержит схему индекса, которая определяет поля векторов и невекторов, конфигурацию векторного поиска для алгоритмов, создающих пространство внедрения и параметры определений векторных полей, которые вычисляются во время запроса. REST API создания индекса создает хранилище векторов.

Примечание.

В этом кратком руководстве опускается шаг векторизации и предоставляются встроенные векторы. Если вы хотите добавить встроенные блоки данных и векторизацию для вашего контента, попробуйте использовать мастер импорта и векторизации данных как полное руководство.

Необходимые компоненты

Получение сведений о ресурсе

Запросы к конечной точке поиска должны проходить проверку подлинности и авторизованы. Для этой задачи можно использовать ключи ИЛИ роли API. Рекомендуется использовать бессерверное подключение с помощью идентификатора Microsoft Entra.

Выберите вкладку, соответствующую предпочтительному методу проверки подлинности. Используйте один и тот же метод для всех запросов в этом кратком руководстве.

  1. Войдите в портал Azure и найдите службу поиска.

  2. На домашней странице обзора найдите URL-адрес. Пример конечной точки может выглядеть так: https://mydemo.search.windows.net.

    Снимок экрана: свойство URL-адреса на странице обзора.

  3. Выполните действия, описанные в кратком руководстве без ключа, чтобы получить маркер Microsoft Entra.

    Вы получите маркер при выполнении az account get-access-token команды на шаге 3 предыдущего краткого руководства.

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

Создание или скачивание файла кода

Для выполнения всех запросов в этом кратком руководстве используется один .rest или .http файл. Вы можете скачать REST-файл, содержащий код для этого краткого руководства, или создать новый файл в Visual Studio Code и скопировать его в него.

  1. В Visual Studio Code создайте файл с расширением или .rest расширением.http. Например, az-search-vector-quickstart.rest. Скопируйте и вставьте необработанное содержимое файла Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest file в этот новый файл.

  2. В верхней части файла замените значение @baseUrl заполнителя URL-адресом службы поиска. Инструкции по поиску URL-адреса службы поиска см. в разделе "Получение сведений о ресурсе".

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

  3. В верхней части файла замените значение заполнителя для проверки подлинности. Инструкции по получению маркера Microsoft Entra или ключа API см. в разделе "Получение сведений о ресурсе".

    Для рекомендуемойпроверки подлинности без ключа с помощью идентификатора Microsoft Entra необходимо заменить @apiKey переменной@token.

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

    Если вы предпочитаете использовать ключ API, замените @apiKey его ключом, скопированным из портал Azure.

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

  4. Для рекомендуемойпроверки подлинности без ключа с помощью идентификатора Microsoft Entra необходимо заменить api-key: {{apiKey}} на Authorization: Bearer {{token}} заголовки запроса. Замените все экземпляры api-key: {{apiKey}} , которые находятся в файле.

Создание векторного индекса

С помощью REST API создания индекса можно создать векторный индекс и настроить структуры физических данных в службе поиска.

Схема индекса в этом примере организована вокруг содержимого отеля. Пример данных состоит из векторных и невекторных описаний вымышленных отелей. Эта схема включает конфигурации для индексирования векторов и запросов, а также для семантического ранжирования.

  1. В Visual Studio Code откройте az-search-vector-quickstart.rest созданный ранее файл.

  2. ### Create a new index Найдите блок кода в файле. Этот блок содержит запрос на создание hotels-vector-quickstart индекса в службе поиска.

    ### Create a new index
POST  {{baseUrl}}/indexes?api-version=2024-07-01  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

{
    "name": "hotels-vector-quickstart",
    "fields": [
        {
            "name": "HotelId", 
            "type": "Edm.String",
            "searchable": false, 
            "filterable": true, 
            "retrievable": true, 
            "sortable": false, 
            "facetable": false,
            "key": true
        },
        {
            "name": "HotelName", 
            "type": "Edm.String",
            "searchable": true, 
            "filterable": false, 
            "retrievable": true, 
            "sortable": true, 
            "facetable": false
        },
        {
            "name": "Description", 
            "type": "Edm.String",
            "searchable": true, 
            "filterable": false, 
            "retrievable": true, 
            "sortable": false, 
            "facetable": false
        },
        {
            "name": "DescriptionVector",
            "type": "Collection(Edm.Single)",
            "searchable": true,
            "retrievable": true,
            "dimensions": 1536,
            "vectorSearchProfile": "my-vector-profile"
        },
        {
            "name": "Category", 
            "type": "Edm.String",
            "searchable": true, 
            "filterable": true, 
            "retrievable": true, 
            "sortable": true, 
            "facetable": true
        },
        {
            "name": "Tags",
            "type": "Collection(Edm.String)",
            "searchable": true,
            "filterable": true,
            "retrievable": true,
            "sortable": false,
            "facetable": true
        },
                {
            "name": "ParkingIncluded",
            "type": "Edm.Boolean",
            "searchable": false,
            "filterable": true,
            "retrievable": true,
            "sortable": true,
            "facetable": true
        },
        {
            "name": "LastRenovationDate",
            "type": "Edm.DateTimeOffset",
            "searchable": false,
            "filterable": true,
            "retrievable": true,
            "sortable": true,
            "facetable": true
        },
        {
            "name": "Rating",
            "type": "Edm.Double",
            "searchable": false,
            "filterable": true,
            "retrievable": true,
            "sortable": true,
            "facetable": true
        },
        {
            "name": "Address", 
            "type": "Edm.ComplexType",
            "fields": [
                {
                    "name": "StreetAddress", "type": "Edm.String",
                    "searchable": true, "filterable": false, "retrievable": true, "sortable": false, "facetable": false
                },
                {
                    "name": "City", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                },
                {
                    "name": "StateProvince", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                },
                {
                    "name": "PostalCode", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                },
                {
                    "name": "Country", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                }
            ]
        },
        {
            "name": "Location",
            "type": "Edm.GeographyPoint",
            "searchable": false, 
            "filterable": true, 
            "retrievable": true, 
            "sortable": true, 
            "facetable": false
        }
    ],
    "vectorSearch": {
        "algorithms": [
            {
                "name": "hnsw-vector-config",
                "kind": "hnsw",
                "hnswParameters": 
                {
                    "m": 4,
                    "efConstruction": 400,
                    "efSearch": 500,
                    "metric": "cosine"
                }
            },
            {
                "name": "eknn-vector-config",
                "kind": "exhaustiveKnn",
                "exhaustiveKnnParameters": 
                {
                    "metric": "cosine"
                }
            }
        ],
        "profiles": [      
            {
                "name": "my-vector-profile",
                "algorithm": "hnsw-vector-config"
            }
      ]
    },
    "semantic": {
        "configurations": [
            {
                "name": "semantic-config",
                "prioritizedFields": {
                    "titleField": {
                        "fieldName": "HotelName"
                    },
                    "prioritizedContentFields": [
                        { "fieldName": "Description" }
                    ],
                    "prioritizedKeywordsFields": [
                        { "fieldName": "Category" }
                    ]
                }
            }
        ]
    }
}

  3. Щелкните Отправить запрос. У вас должен быть HTTP/1.1 201 Created ответ.

    Текст ответа должен содержать представление JSON схемы индекса.

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

Основные сведения о REST API создания индекса :

  • Коллекция fields содержит обязательные ключевые поля и текстовые и векторные поля (например Description , и DescriptionVector) для поиска текста и вектора. Совместное размещение полей вектора и невектора в том же индексе позволяет выполнять гибридные запросы. Например, можно объединить фильтры, текстовый поиск с семантическим ранжированием и векторы в одну операцию запроса.

  • Поля векторов должны быть одним из типов данных EDM, используемых для векторов, таких как type: Collection(Edm.Single). Векторные поля также имеют и dimensionsvectorSearchProfile свойства.

  • Этот vectorSearch раздел является массивом конфигураций и профилей алгоритма приблизительного ближайшего соседа (ANN). Поддерживаемые алгоритмы включают иерархический навигацию по небольшому миру и исчерпывающий К-ближайший сосед. Дополнительные сведения см. в разделе оценки релевантности в векторном поиске.

  • Конфигурация (необязательно) semantic позволяет повторно использовать результаты поиска. Вы можете повторно выполнять запросы типа semantic для строковых полей, указанных в конфигурации. Дополнительные сведения см. в обзоре семантического ранжирования.

Отправить документы

Создание и загрузка индекса являются отдельными шагами. На предыдущем шаге вы создали схему индекса. Теперь необходимо загрузить документы в индекс.

В службе поиска ИИ Azure индекс содержит все данные, доступные для поиска, и запросы, выполняемые в службе поиска. Для вызовов REST данные предоставляются в виде документов JSON. Используйте REST API индексов для этой задачи. Универсальный код ресурса (URI) расширен для включения docs коллекции и index операции.

  1. Сформулируйте запрос на отправку документов для отправки документов в hotels-vector-quickstart индекс в службе поиска.

    ### Upload documents
POST {{baseUrl}}/indexes/hotels-quickstart-vectors/docs/index?api-version=2024-07-01  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

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

  2. Щелкните Отправить запрос. У вас должен быть HTTP/1.1 200 OK ответ. Текст ответа должен содержать представление JSON документов поиска.

Ключи выноски о запросе REST API для документов:

  • Документы в полезных данных состоят из полей, определенных в схеме индекса.

  • Векторные поля содержат значения с плавающей запятой. Атрибут измерений имеет минимум 2 и максимум 4096 значения с плавающей запятой каждое. В этом кратком руководстве атрибуту размерностей присваивается значение 1,536, потому что это размер встраиваний, создаваемых с помощью модели text-embedding-3-small от Azure OpenAI.

Выполнение запросов

Теперь, когда документы загружены, можно выполнять векторные запросы к ним с помощью Поиск документов - Запрос (REST).

В следующих разделах мы запускаем запросы к индексу hotels-vector-quickstart . К запросам относятся:

Примеры запросов основаны на двух строках:

  • Строка поиска: historic hotel walk to restaurants and shopping
  • Строка векторного запроса (векторизованная в математическое представление): quintessential lodging near running trails, eateries, retail

Строка векторного запроса семантически похожа на строку поиска, но она содержит термины, которые не существуют в индексе поиска. Если вы выполняете поиск ключевых слов quintessential lodging near running trails, eateries, retail, количество результатов равно нулю в чистом поиске без семантического ранжирования. В этом примере показано, как получить соответствующие результаты с помощью векторов, даже если не существует подходящих терминов.

  1. Сформулируйте запрос. Запрос представляет собой 1536-мерное представление типичного жилья вблизи беговых дорожек, ресторанов, магазинов. Запрос выполняет поиск DescriptionVector и возвращает 5 результатов. Он использует параметр переопределения "исчерпывающий" для выполнения полного сканирования индекса вместо ANN. Исчерпывающий поиск полезен для небольших индексов.

    ### Run a single vector query
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "count": true,
        "select": "HotelId, HotelName, Description, Category, Tags",
        "vectorQueries": [
            {
                "vector"": 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                "k": 5,
                "fields": "DescriptionVector",
                "kind": "vector",
                "exhaustive": true
            }
        ]
    }

    Основные сведения о REST API "Документы - Поиск Постов" :

    • Это vectorQueries.vector строка векторного запроса. Это векторное представление идеального жилья вблизи беговых маршрутов, ресторанов и магазинов, которое преобразовано в 1536 векторов для этого запроса.

    • fields определяет, какие поля векторов будут искаться.

    • kind установлено в vector означает, что строка запроса является вектором. Если kind задано значение text, вам потребуется дополнительная возможность ( векторизатор) для кодирования текстовой строки, доступной для чтения человека, в вектор во время запроса. Векторизаторы не включены в это краткое руководство, чтобы упражнение оставалось простым.

    • k указывает количество совпадений, возвращаемых в ответе. Параметр count указывает количество совпадений, найденных в индексе. Включение подсчета является лучшей практикой в запросах, но это менее эффективно для поиска сходства, где алгоритм способен найти некоторую степень сходства практически в любом документе.

  2. Щелкните Отправить запрос. У вас должен быть HTTP/1.1 200 OK ответ. Текст ответа должен содержать представление результатов поиска в формате JSON.

    Ответ на векторный эквивалент quintessential lodging near running trails, eateries, retail включает результаты k-5, хотя поисковая система обнаружила 7 совпадений. Наиболее семантические результаты считаются наиболее семантически похожими на запрос. Каждый результат предоставляет оценку поиска и поля, перечисленные в select. В поиске сходства ответ всегда содержит k результаты, упорядоченные по оценке сходства значений.

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

Поиск одного вектора с фильтром

Вы можете добавить фильтры, но фильтры применяются к содержимому невектора в индексе. В этом примере фильтр применяется к Tags полю для фильтрации всех отелей, которые не предоставляют бесплатный Wi-Fi.

  1. Сформулируйте запрос. Это тот же запрос, что и предыдущий, с дополнительным параметром фильтра и режима фильтрации.

    ### Run a vector query with a filter
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "count": true,
        "select": "HotelId, HotelName, Category, Tags, Description",
        "filter": "Tags/any(tag: tag eq 'free wifi')",
        "vectorFilterMode": "postFilter",
        "vectorQueries": [
        {
            "vector": 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            "k": 5,
            "fields": "DescriptionVector",
            "kind": "vector",
            "exhaustive": true
        },
    ]
}

  2. Щелкните Отправить запрос. У вас должен быть HTTP/1.1 200 OK ответ. Текст ответа должен содержать представление результатов поиска в формате JSON.

    Запрос был таким же, как и в предыдущем примере поиска с одним вектором, но он включает фильтр исключения после обработки и возвращает только два отеля с бесплатным Wi-Fi.

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

  3. Вот еще один запрос, использующий геофильтр, ограничивающий результаты отелям в пределах 300 километров вокруг Вашингтона.

    ### 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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}

  4. Проверьте ответ. Эта сеть ограничивается двумя отелями недалеко от Вашингтона, с фасетной структурой, обеспечивающей агрегацию данных.

    "@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"
    }
  }
]

Гибридный поиск состоит из ключевых запросов и векторных запросов в одном запросе поиска. В этом примере выполняется векторный запрос и полнотекстовый поиск одновременно:

  • Строка поиска: historic hotel walk to restaurants and shopping
  • Строка векторного запроса (векторизованная в математическое представление): quintessential lodging near running trails, eateries, retail

  1. Сформулируйте гибридный запрос.

    ### Run a hybrid query
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

{
    "count": true,
    "search": "historic hotel walk to restaurants and shopping",
    "select": "HotelName, Description",
    "top": 7,
    "vectorQueries": [
        {
            "vector": 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            "k": 5,
            "fields": "DescriptionVector",
            "kind": "vector",
            "exhaustive": true
        }
    ]
}

  2. Щелкните Отправить запрос. У вас должен быть HTTP/1.1 200 OK ответ. Текст ответа должен содержать представление результатов поиска в формате JSON.

Так как это гибридный запрос, результаты ранжируются по RRF. Обратите внимание, что @search.score значения имеют другую основу и являются равномерно меньшими значениями. RRF оценивает оценки поиска нескольких результатов поиска, принимает обратное, а затем объединяет и сортирует объединенные результаты. Возвращается top количество результатов.

Просмотрите ответ, состоящий из первых k-5 совпадений из 7 соответствующих документов в индексе:

{
  "@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."
    }
  ]
}

Так как RRF объединяет результаты, он помогает просматривать входные данные по отдельности.

Следующие результаты относятся к полнотекстовой части запроса: исторический отель, в пешей доступности от ресторанов и магазинов. В полнотекстовом запросе первые три результата : Sublime Palace Hotel, Stay-Kay City Hotel и Luxury Lion Resort. Отель Sublime Palace имеет более сильную оценку релевантности BM25. В объединенном запросе только два из этих совпадений находятся в тройке лидеров, а второе совпадение (Stay-Kay City Hotel) вообще не отображается в пятерке.

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

В запросе векторной части (типичное жилье рядом с беговыми дорожками, ресторанами, розничными магазинами) используется другая строка, поэтому мы можем ожидать других результатов. Этот запрос возвращает мотель в долине Скандинавского региона, Роскошный курорт Льва и отель Sublime Palace в качестве первых трех матчей. В объединённом запросе эти результаты находятся в первой тройке, но в обратном порядке.

  "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."
    },
   ...
  ]

Ниже приведен последний запрос в коллекции. Этот гибридный запрос добавляет семантический рейтинг L2, который использует возможности машинного понимания текста для анализа результатов на уровне L1, способствуя продвижению более релевантных совпадений на верхние позиции.

  1. Сформулируйте запрос.

    ### 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
        }
    ]
}

  2. Щелкните Отправить запрос. У вас должен быть HTTP/1.1 200 OK ответ. Текст ответа должен содержать представление результатов поиска в формате JSON.

Просмотрите ответ, состоящий из семантического повторного ранжирования результатов гибридного запроса RRF. Семантический ранжирование работает вне текстовых входных данных. В текстовом или гибридном запросе этот вход является текстовой частью запроса (историческая прогулка по ресторанам и магазинам). Чтобы использовать семантическое ранжирование в чистом векторном запросе, как, например, в первом примере, или чтобы явно контролировать текст, используемый для семантического ранжирования, укажите строку semanticQuery.

После семантической переоценки, Swirling Currents Hotel со своей ссылкой на пеший доступ к магазинам, ресторанам, развлечениям и городскому центру поднимается на первое место. Модель машинного понимания способствует обеспечению пешей доступности к магазинам и ресторанам, как более точному совпадению для "ходить к ресторанам и магазинам".

До семантической повторной оценки, Возвышенный дворец, за его близость к местам и достопримечательностям города и окружённый необычайной красотой церквей, зданий, магазинов и памятников, было на первом месте.

{
  "@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 New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
      "Category": "Boutique"
    },
    {
      "@search.score": 0.03053613007068634,
      "@search.rerankerScore": 2.0582215785980225,
      "HotelId": "3",
      "HotelName": "Gastronomic Landscape Hotel",
      "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel\u2019s restaurant services.",
      "Category": "Suite"
    }
  ]
}

Подсказка

Семантические ранжированные результаты могут содержать более подробные сведения, включая семантические ответы, субтитры и выделения. Добавление дополнительных параметров в запрос создает дополнительные сведения. Дополнительные сведения см. в разделе "Настройка семантического запроса".

Очистка

Если вы работаете в собственной подписке, в конце проекта следует решить, нужны ли вам созданные ресурсы. Ресурсы, которые продолжат работать, могут быть платными. Вы можете удалить ресурсы по отдельности либо удалить всю группу ресурсов.

Ресурсы и управление ими можно найти в портал Azure с помощью ссылки "Все ресурсы" или "Группы ресурсов" в левой области.

Если вы хотите сохранить службу поиска, но удалить индекс и документы, можно использовать DELETE команду в клиенте REST. Эта команда (в конце az-search-vector-quickstart.rest файла) удаляет hotels-vector-quickstart индекс:

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

Следующие шаги

На следующем шаге мы рекомендуем научиться вызывать вызовы REST API без ключей API.

Вы также можете просмотреть демонстрационный код для Python, C#или JavaScript.