Szybki start: wyszukiwanie wektorów

W tym szybkim wprowadzeniu użyjesz notatnika Jupyter do tworzenia, ładowania i wysyłania zapytań wektorów. Przykłady kodu wykonują te operacje przy użyciu biblioteki klienta usługi Azure AI Search. Biblioteka udostępnia abstrakcję interfejsu API REST na potrzeby dostępu do operacji indeksowania, takich jak pozyskiwanie danych, operacje wyszukiwania i operacje zarządzania indeksami.

W usłudze Azure AI Search magazyn wektorów ma schemat indeksu, który definiuje pola wektorów i niewektorów, konfigurację wyszukiwania wektorów dla algorytmów tworzących miejsce osadzania i ustawienia definicji pól wektorów, które są oceniane w czasie zapytania. Interfejs API REST tworzenia indeksu tworzy magazyn wektorów.

Uwaga

Ten przewodnik szybkiego startu pomija krok wektoryzacji i udostępnia osadzenia wbudowane w tekście. Jeśli chcesz dodać wbudowane fragmentowanie i wektoryzację danych do własnej zawartości, wypróbuj kreatora Importuj dane (nowe) w celu uzyskania kompleksowego przewodnika.

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz konto bezpłatnie.
Usługa wyszukiwania sztucznej inteligencji platformy Azure. Utwórz usługę lub znajdź istniejącą usługę w bieżącej subskrypcji.
- W większości tego szybkiego przewodnika można użyć bezpłatnej usługi wyszukiwania, ale zalecamy użycie poziomu Podstawowy lub wyższego dla większych zbiorów danych.
- Aby uruchomić przykład zapytania, który wywołuje semantyczne ponowne ocenianie, usługa wyszukiwania musi mieć włączony semantyczny ranker.
Program Visual Studio Code z rozszerzeniem języka Python i pakietem Jupyter.
Aby sklonować repozytorium zawierające notes Jupyter i inne powiązane pliki, użyj narzędzia Git.

Uzyskiwanie informacji o usłudze

Żądania do punktu końcowego wyszukiwania muszą być uwierzytelnione i autoryzowane. Chociaż w tym zadaniu można używać kluczy interfejsu API lub ról, zalecamy użycie połączenia bez klucza za pośrednictwem identyfikatora Entra firmy Microsoft.

Ten szybki start używa DefaultAzureCredential, który upraszcza uwierzytelnianie zarówno w scenariuszach programistycznych, jak i produkcyjnych. Jednak w przypadku scenariuszy produkcyjnych może istnieć bardziej zaawansowane wymagania, które wymagają innego podejścia. Zobacz Uwierzytelnianie aplikacji języka Python w usługach platformy Azure przy użyciu zestawu Azure SDK dla języka Python , aby poznać wszystkie opcje.

Klonowanie kodu i środowiska konfiguracji

Sklonuj repozytorium zawierające kod dla tego szybkiego startu.
```
git clone https://github.com/Azure-Samples/azure-search-python-samples
```
To repozytorium zawiera przykłady kodu języka Python dla kilku artykułów w osobnym podfolderze.
W programie Visual Studio Code otwórz podfolder Quickstart-Vector-Search.

W tym folderze znajdują się trzy pliki:
- vector-search-quickstart.ipynb
- requirements.txt
- sample.env
Zmień nazwę pliku sample.env na .env i zmodyfikuj wartości w pliku .env.

Użyj adresu URL usługi wyszukiwania jako .AZURE_SEARCH_ENDPOINT Adres URL można znaleźć w witrynie Azure Portal. Przejdź do usługi Azure AI Search, na stronie Przegląd wyszukaj pole adresu URL. Przykładowy punkt końcowy może wyglądać podobnie jak https://mydemo.search.windows.net.

Na koniec wybierz nową AZURE_SEARCH_INDEX_NAME nazwę lub użyj nazwy podanej w pliku.
W programie Visual Studio Code pracuj w środowisku. W menu Widok wybierz pozycję Terminal..., lub naciśnij Ctrl+`.
Uruchom następujące polecenia w terminalu:
```
python -m venv .venv
source .venv/scripts/activate
where python
```
Uwaga

Założono, że używasz powłoki Git Bash w swoim terminalu i korzystasz z systemu Windows. Jeśli używasz innej powłoki i/lub innego systemu operacyjnego, dostosuj te instrukcje dla określonego środowiska.

Jeśli zostanie wyświetlony monit, zezwól programowi Visual Studio Code na korzystanie z nowego środowiska.

Polecenie where python sprawdza, czy pracujesz w środowisku wirtualnym, poprzez wymienienie python.exe w katalogu Quickstart-Vector-Search\.venv\ oraz innych lokalizacji w katalogu twojego komputera.
Zainstaluj wymagane biblioteki, uruchamiając następujące polecenie:
```
pip install requirements.txt
```
W programie Visual Studio Code otwórz plik vector-search-quickstart.ipynb.

Uwaga

Jeśli po raz pierwszy używasz pliku Jupyter Notebook (.ipynb) w programie Visual Studio Code, zostanie wyświetlony monit o zainstalowanie jądra notatnika Jupyter Notebook i ewentualnie innych narzędzi. Wybierz zainstalowanie sugerowanych narzędzi, aby kontynuować ten szybki start.

Znajdź komórkę poniżej zatytułowaną "Zainstaluj pakiety i ustaw zmienne" i wybierz przycisk Wykonaj komórkę (Ctrl + Alt + Enter) ( który wygląda jak typowy przycisk uruchamiania) po lewej stronie komórki. Wykonanie komórki ładuje zmienne środowiskowe, tworzy obiekt DefaultAzureCredential i wyświetla je w danych wyjściowych, aby potwierdzić, że zależności notesu i .env zostały prawidłowo skonfigurowane.

# 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

Uruchomienie tej komórki generuje następujące dane wyjściowe.

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
...

Istnieje wiele pakietów, które można wyświetlić w elemecie przewijanym (zobacz komunikat poniżej wyników komórki).

Tworzenie indeksu wektora

Kod w pliku vector-search-quickstart.ipynb używa kilku metod z azure.search.documents biblioteki, aby utworzyć indeks wektorowy i pola z możliwością wyszukiwania.

Znajdź komórkę poniżej sekcji zatytułowanej "Utwórz indeks" i wykonaj tę komórkę, aby utworzyć indeks.

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')

Jeśli indeks został utworzony pomyślnie, poniżej komórki zostanie wyświetlony następujący wynik:

vector-search-quickstart created

Najważniejsze wnioski podczas tworzenia indeksu wektora za pomocą polecenia azure.search.documents:

Indeks można zdefiniować, tworząc listę pól. Każde pole jest tworzone przy użyciu metody pomocniczej, która definiuje typ pola i jego ustawienia.
Ten konkretny indeks obsługuje wiele funkcji wyszukiwania, takich jak:
- Wyszukiwanie słów kluczowych pełnotekstowych (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))
- Wyszukiwanie wektorowe (umożliwia wyszukiwanie hybrydowe przez sortowanie pól wektorów i pól niewektorów) (DescriptionVector)
- Wyszukiwanie semantyczne (semantic_search=SemanticSearch(configurations=[semantic_config]))
- Wyszukiwanie aspektowe (facetable=True)
- Wyszukiwanie semantyczne (semantic_search=SemanticSearch(configurations=[semantic_config]))
- Wyszukiwanie geograficzne (Location pole to GeographyPoint)
- Filtrowanie, sortowanie (wiele pól oznaczonych jako możliwe do filtrowania i sortowania)

Przekaż dokumenty

Tworzenie i ładowanie indeksu to oddzielne kroki. Schemat indeksu został utworzony w poprzednim kroku. Teraz musisz załadować dokumenty do indeksu.

W usłudze Azure AI Search indeks przechowuje całą zawartość z możliwością wyszukiwania, a aparat wyszukiwania wykonuje zapytania względem tego indeksu.

Znajdź komórkę w sekcji zatytułowanej "Tworzenie ładunku dokumentów" i uruchom komórkę. Ta komórka zawiera następujący kod (obcięty w celu zwięzłości):

   # 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>
   ]

Ta komórka ładuje zmienną o nazwie documents z obiektem JSON opisującym każdy dokument wraz z wektoryzowaną wersją opisu artykułu. Ten wektor umożliwia wyszukiwanie podobieństwa, gdzie dopasowanie jest oparte na znaczeniu, a nie na dokładnych słowach kluczowych.

Ważne

Kod w tym przykładzie nie jest uruchamiany. W celu zwięzłości usunięto kilka znaków lub wierszy. Zamiast tego uruchom kod w notesie Jupyter.

Znajdź komórkę poniżej sekcji zatytułowanej "Przekaż dokumenty" i wykonaj komórkę. Ta komórka zawiera następujący kod (obcięty w celu zwięzłości):
```
# 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)
```
To spowoduje utworzenie wystąpienia klienta wyszukiwania przez wywołanie konstruktora SearchClient() na obiekcie, a następnie wywołanie metody upload_documents().

Po uruchomieniu komórki stan każdego dokumentu zostanie wyświetlony poniżej:
```
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
```
Najważniejsze wnioski dotyczące metody i tego przykładu upload_documents() :
- Kod współdziała z określonym indeksem wyszukiwania hostowanym w usłudze Azure AI Search za pośrednictwem SearchClient obiektu, który jest głównym obiektem udostępnianym przez pakiet azure-search-documents. Zapewnia SearchClient dostęp do operacji indeksowania, takich jak:
  - Pozyskiwanie - upload_documents() danych, merge_documents(), delete_documents()itp.
  - Operacje wyszukiwania - search(), autocomplete(), suggest()
  - Operacje zarządzania indeksami
- Pola wektorowe zawierają wartości zmiennoprzecinkowe. Atrybut wymiarów ma co najmniej 2 i maksymalnie 4096 wartości zmiennoprzecinkowych każdy. Ten przewodnik Szybki start ustawia atrybut wymiarów na 1536, ponieważ jest to rozmiar osadzania wygenerowany przez model osadzania w usłudze Azure OpenAI text-embedding-ada-002 .

Uruchamianie zapytań

Teraz, gdy dokumenty są ładowane, można wysyłać zapytania wektorowe do nich, wywołując search_client.search() i przekazując obiekt VectorizedQuery, pola, które mają zostać zwrócone, liczbę wyników itd.

W następnych sekcjach uruchamiamy zapytania względem indeksu hotels-vector-quickstart . Zapytania obejmują:

Wyszukiwanie jednowektorowe
Wyszukiwanie jednowektorowe z filtrem
Wyszukiwanie hybrydowe
Wyszukiwanie hybrydowe semantyczne

Tworzenie ciągu zapytania wektorowego

Przykładowe zapytania wektorowe są oparte na dwóch ciągach:

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): quintessential lodging near running trails, eateries, retail

Ciąg zapytania wektorowego jest semantycznie podobny do ciągu wyszukiwania, ale zawiera terminy, które nie istnieją w indeksie wyszukiwania. Jeśli wyszukujesz quintessential lodging near running trails, eateries, retailsłowo kluczowe , wyniki są zerowe. W tym przykładzie pokazano, jak można uzyskać odpowiednie wyniki, nawet jeśli nie ma pasujących terminów.

Znajdź komórkę w sekcji zatytułowanej "Utwórz ciąg zapytania wektorowego" i wykonaj ją. Spowoduje to załadowanie zmiennej vector z wektoryzowanymi danymi zapytania wymaganymi do uruchomienia wszystkich wyszukiwań w następnych sekcjach.

Wyszukiwanie jednowektorowe

Pierwszy przykład przedstawia podstawowy scenariusz, w którym chcesz znaleźć opisy dokumentów, które są ściśle zgodne z ciągiem wyszukiwania.

Znajdź komórkę poniżej sekcji zatytułowanej "Wyszukiwanie pojedynczego wektora" i wykonaj tę komórkę. Ten blok zawiera żądanie zapytania względem indeksu wyszukiwania.

# 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.")

To zapytanie wektorowe jest skracane w celu zwięzłości. Zawiera vectorQueries.vector wektoryzowany tekst danych wejściowych zapytania, fields określa, które pola wektorowe są przeszukiwane i k określa liczbę najbliższych sąsiadów do zwrócenia.

Ciąg zapytania wektorowego to quintessential lodging near running trails, eateries, retail, który jest wektoryzowany do 1536 osadzania dla tego zapytania.

Odpowiedź dla wektora równoważnego quintessential lodging near running trails, eateries, retail składa się z 7 wyników, ale kod określa top=5 , więc zwracane są tylko pierwsze 5 wyników. Ponadto zwracane są tylko pola określone przez obiekt select .

search_client.search() Zwraca obiekt przypominający dykt. Każdy wynik zawiera wynik wyszukiwania, do którego można uzyskać dostęp przy użyciu polecenia score = result.get("@search.score", "N/A"). Chociaż nie jest wyświetlany w tym przykładzie, w wyszukiwaniu podobieństwa odpowiedź zawsze zawiera k wyniki uporządkowane według wyniku podobieństwa wartości.

Po uruchomieniu komórki stan każdego dokumentu zostanie wyświetlony poniżej:

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

Wyszukiwanie jednowektorowe z filtrem

Filtry można dodawać, ale filtry są stosowane do zawartości niewektorowej w indeksie. W tym przykładzie filtr dotyczy Tags pola, aby odfiltrować wszystkie hotele, które nie zapewniają bezpłatnej sieci Wi-Fi.

Znajdź komórkę poniżej sekcji zatytułowanej "Wyszukiwanie pojedynczego wektora z filtrem" i wykonaj ją. Ta komórka zawiera żądanie zapytania względem indeksu wyszukiwania.

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.")

Po uruchomieniu komórki stan każdego dokumentu zostanie wyświetlony poniżej:

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']

Zapytanie było takie samo jak w poprzednim przykładzie wyszukiwania pojedynczego wektora, ale zawiera filtr wykluczeń po przetworzeniu i zwraca tylko dwa hotele, które mają bezpłatny Wi-Fi.

W następnym przykładzie filtru użyto filtru geograficznego. Uruchom komórkę w sekcji zatytułowanej "Zapytanie wektorowe z filtrem geograficznym". Ten blok zawiera żądanie zapytania względem indeksu wyszukiwania.

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.")

Zapytanie było takie samo jak w poprzednim przykładzie wyszukiwania pojedynczego wektora, ale zawiera filtr wykluczeń po przetworzeniu i zwraca tylko dwa hotele w ciągu 300 kilometrów.

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.

Wyszukiwanie hybrydowe

Wyszukiwanie hybrydowe składa się z zapytań słów kluczowych i zapytań wektorowych w jednym żądaniu wyszukiwania. W tym przykładzie zapytanie wektorowe i wyszukiwanie pełnotekstowe są uruchamiane współbieżnie:

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): quintessential lodging near running trails, eateries, retail

Znajdź komórkę znajdującą się poniżej sekcji zatytułowanej "Wyszukiwanie hybrydowe" i wykonaj ją. Ten blok zawiera żądanie zapytania względem indeksu wyszukiwania.

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.")

Przejrzyj odpowiedź:

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']

Ponieważ metoda wzajemnego łączenia rang (RRF) scala wyniki, pomaga w analizie danych wejściowych. Poniższe wyniki pochodzą tylko z zapytania pełnotekstowego. Dwa najlepsze wyniki to Sublime Palace Hotel i Luxury Lion Resort. Hotel Sublime Palace ma silniejszy wynik istotności 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"
},

W zapytaniu tylko wektorów, które używa HNSW do znajdowania dopasowań, Hotel Sublime Palace spada na czwartą pozycję. Luxury Lion, który był drugi w wyszukiwaniu pełnotekstowym i trzeci w wyszukiwaniu wektorowym, nie doświadcza tego samego zakresu wahań, więc wydaje się być najlepszym dopasowaniem w zestawie wyników homogenizowanych.

"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"
     }
]

Wyszukiwanie hybrydowe semantyczne

Oto ostatnie zapytanie w kolekcji. To zapytanie hybrydowe określa typ zapytania semantycznego i konfigurację semantyczną, co pokazuje, że można utworzyć zapytanie hybrydowe korzystające z semantycznego ponownego korbowania.

Znajdź komórkę znajdującą się pod sekcją zatytułowaną "Wyszukiwanie hybrydowe semantyczne" i wykonaj tę komórkę. Ten blok kodu zawiera żądanie zapytania względem indeksu wyszukiwania.

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.")

Przejrzyj dane wyjściowe poniżej komórki.

Dzięki semantycznemu rankingowi, Swirling Currents Hotel przenosi się teraz na pierwsze miejsce. 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

Klasyfikację semantyczną można traktować jako sposób na poprawę istotności wyników wyszukiwania, rozumiejąc znaczenie słów w zapytaniu i zawartości dokumentów. W takim przypadku semantyczny ranking pomaga zidentyfikować hotele, które nie tylko są istotne dla słów kluczowych, ale także pasują do intencji zapytania:

Kluczowe informacje:

Wyszukiwanie wektorowe vectors.value jest określane za pośrednictwem właściwości . Wyszukiwanie słów kluczowych jest określane za pośrednictwem search właściwości .
W wyszukiwaniu hybrydowym można zintegrować wyszukiwanie wektorów z wyszukiwaniem pełnotekstowym za pomocą słów kluczowych. Filtry, sprawdzanie pisowni i klasyfikacja semantyczna mają zastosowanie tylko do zawartości tekstowej, a nie do wektorów. W tym ostatnim zapytaniu nie ma semantyki answer , ponieważ system nie wyprodukował takiego, który był wystarczająco silny.
Rzeczywiste wyniki zawierają więcej szczegółów, w tym semantyczne podpisy i wyróżnienia. Wyniki zostały zmodyfikowane pod kątem czytelności. Aby uzyskać pełną strukturę odpowiedzi, uruchom żądanie w kliencie REST.

Czyszczenie

Jeśli pracujesz w ramach własnej subskrypcji, dobrym pomysłem po zakończeniu projektu jest sprawdzenie, czy dalej potrzebujesz utworzonych zasobów. Uruchomione zasoby mogą generować koszty. Zasoby możesz usuwać pojedynczo lub jako grupę zasobów, usuwając cały zestaw zasobów.

Zasoby można znaleźć w witrynie Azure Portal i zarządzać nimi, korzystając z linku Wszystkie zasoby lub Grupy zasobów w okienku po lewej stronie.

Jeśli chcesz zachować usługę wyszukiwania, ale usunąć indeks i dokumenty, możesz użyć SearchIndexClient metody obiektu delete_index() . Znajdź komórkę poniżej sekcji zatytułowanej "Clean up" i wykonaj ją, jeśli chcesz usunąć hotels-vector-quickstart indeks:

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

Następne kroki

Przejrzyj repozytorium przykładów kodu pod kątem możliwości wyszukiwania wektorów w usłudze Azure AI Search dla języka Python
Przejrzyj inne przykłady kodu języka Python i usługi Azure AI Search w repozytorium azure-search-python-samples

W tym przewodniku Szybki start użyjesz języka Java do tworzenia, ładowania i wektorów zapytań. Przykłady kodu wykonują te operacje przy użyciu biblioteki klienta usługi Azure AI Search. Biblioteka udostępnia abstrakcję interfejsu API REST na potrzeby dostępu do operacji indeksowania, takich jak pozyskiwanie danych, operacje wyszukiwania i operacje zarządzania indeksami.

Uwaga

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz konto bezpłatnie.
Usługa wyszukiwania sztucznej inteligencji platformy Azure. Utwórz usługę lub znajdź istniejącą usługę w bieżącej subskrypcji.
- Rola przypisana Search Index Data Contributor w zakresie usługi.
- W większości tego szybkiego przewodnika można użyć bezpłatnej usługi wyszukiwania, ale zalecamy użycie poziomu Podstawowy lub wyższego dla większych zbiorów danych.
- Aby uruchomić przykład zapytania, który wywołuje semantyczne ponowne ocenianie, usługa wyszukiwania musi mieć włączony semantyczny ranker.
Java 21 (LTS).
Maven.

Uzyskiwanie punktów końcowych usługi

W pozostałych sekcjach skonfigurujesz wywołania interfejsu API do usług Azure OpenAI i Azure AI Search. Pobierz punkty końcowe usługi, aby udostępnić je jako zmienne w kodzie.

Zaloguj się do witryny Azure Portal.
Znajdź usługę wyszukiwania.
Na stronie głównej Przegląd skopiuj adres URL. Przykładowy punkt końcowy może wyglądać podobnie jak https://example.search.windows.net.

Konfigurowanie projektu Java

Utwórz nowy katalog projektu Maven.

mkdir vector-quickstart && cd vector-quickstart

pom.xml Utwórz plik z wymaganymi zależnościami.

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

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

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

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

Skompiluj projekt, aby rozwiązać problemy z zależnościami.
```
mvn compile
```

Utwórz strukturę katalogu źródłowego.

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

application.properties Utwórz plik w src/main/resources katalogu i podaj punkt końcowy usługi wyszukiwania.
```
azure.search.endpoint=<YOUR AZURE AI SEARCH ENDPOINT>
azure.search.index-name=hotels-vector-quickstart
```

Używasz identyfikatora Entra firmy Microsoft i przypisań ról dla połączenia. Upewnij się, że zalogowano się do tej samej dzierżawy i subskrypcji co usługa Azure AI Search i Azure OpenAI. Interfejs wiersza polecenia platformy Azure można użyć w wierszu polecenia, aby wyświetlić bieżące właściwości, zmienić właściwości i zalogować się. Aby uzyskać więcej informacji, zobacz Łączenie bez kluczy.

Uruchom każde z poniższych poleceń w sekwencji.

az account show

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

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

Teraz powinno nastąpić zalogowanie do platformy Azure z urządzenia lokalnego.

Tworzenie indeksu wektora

W tej sekcji utworzysz indeks wektorowy w usłudze Azure AI Search za pomocą elementu SearchIndexClient. createOrUpdateIndex. Schemat indeksu definiuje pola, w tym pole wektora DescriptionVector.

Utwórz plik CreateIndex.java w katalogu src/main/java/com/example/search.

Skopiuj następujący kod do pliku.

package com.example.search;

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

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

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

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

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

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

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

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

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

        var searchIndex = new SearchIndex(indexName, fields);

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

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

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

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

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

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

        System.exit(0);
    }
}

Plik kodu tworzy indeks i definiuje schemat indeksu, w tym pole DescriptionVectorwektora .

Uruchom plik.

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

Dane wyjściowe tego kodu pokazują, że indeks został utworzony pomyślnie:
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Creating index...
hotels-vector-quickstart created
```
Najważniejsze wnioski podczas tworzenia indeksu wektorów za pomocą azure-search-documents biblioteki:
- Indeks można zdefiniować, tworząc listę pól.
- Ten konkretny indeks obsługuje wiele funkcji wyszukiwania, takich jak:
  - Wyszukiwanie słów kluczowych pełnotekstowych (setSearchable)
  - Wyszukiwanie wektorowe (umożliwia wyszukiwanie hybrydowe przez sortowanie pól wektorów i pól niewektorów) (DescriptionVector z setVectorSearchProfileName)
  - Wyszukiwanie semantyczne
  - Wyszukiwanie aspektowe (SearchSuggester)
  - Wyszukiwanie przestrzenne (Location pole z SearchFieldDataType.GEOGRAPHY_POINT)
  - Filtrowanie, sortowanie (wiele pól oznaczonych jako możliwe do filtrowania i sortowania)

Przekazywanie dokumentów do indeksu

Tworzenie i ładowanie indeksu to oddzielne kroki. Schemat indeksu został utworzony w poprzednim kroku. Teraz musisz załadować dokumenty do indeksu za pomocą elementu SearchClient. uploadDocuments. Dokumenty zawierają wektoryzowaną wersję opisu artykułu, która umożliwia wyszukiwanie podobieństw na podstawie znaczenia, a nie dokładnych słów kluczowych.

W usłudze Azure AI Search indeks przechowuje całą zawartość z możliwością wyszukiwania, a aparat wyszukiwania wykonuje zapytania względem tego indeksu.

UploadDocuments.java Utwórz plik w src/main/java/com/example/search katalogu.

Skopiuj następujący kod do pliku.

package com.example.search;

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

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

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

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

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

    private static final List<Map<String, Object>> DOCUMENTS = Arrays.asList(
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "1");
            put("HotelName", "Stay-Kay City Hotel");
            put("Description", "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of 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.");
            put("DescriptionVector", 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            put("Category", "Boutique");
            put("Tags", Arrays.asList("view", "air conditioning", "concierge"));
            put("ParkingIncluded", false);
            put("LastRenovationDate", "2022-01-18T00:00:00Z");
            put("Rating", 3.6);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "677 5th Ave");
                put("City", "New York");
                put("StateProvince", "NY");
                put("PostalCode", "10022");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-73.975403, 40.760586));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "2");
            put("HotelName", "Old Century Hotel");
            put("Description", "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.");
            put("DescriptionVector", 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            put("Category", "Boutique");
            put("Tags", Arrays.asList("pool", "free wifi", "air conditioning", "concierge"));
            put("ParkingIncluded", false);
            put("LastRenovationDate", "2019-02-18T00:00:00Z");
            put("Rating", 3.6);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "140 University Town Center Dr");
                put("City", "Sarasota");
                put("StateProvince", "FL");
                put("PostalCode", "34243");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-82.452843, 27.384417));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "3");
            put("HotelName", "Gastronomic Landscape Hotel");
            put("Description", "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.");
            put("DescriptionVector", 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            put("Category", "Suite");
            put("Tags", Arrays.asList("restaurant", "bar", "continental breakfast"));
            put("ParkingIncluded", true);
            put("LastRenovationDate", "2015-09-20T00:00:00Z");
            put("Rating", 4.8);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "3393 Peachtree Rd");
                put("City", "Atlanta");
                put("StateProvince", "GA");
                put("PostalCode", "30326");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(33.847848, -84.363602));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "4");
            put("HotelName", "Sublime Palace Hotel");
            put("Description", "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.");
            put("DescriptionVector", 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            put("Category", "Boutique");
            put("Tags", Arrays.asList("view", "concierge", "air conditioning"));
            put("ParkingIncluded", true);
            put("Rating", 4.6);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "7400 San Pedro Ave");
                put("City", "San Antonio");
                put("StateProvince", "TX");
                put("PostalCode", "78216");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-98.498923, 29.518029));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "48");
            put("HotelName", "Nordick's Valley Motel");
            put("Description", "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.");
            put("DescriptionVector", 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            put("Category", "Boutique");
            put("Tags", Arrays.asList("continental breakfast", "air conditioning", "free wifi"));
            put("ParkingIncluded", false);
            put("LastRenovationDate", "2018-02-19T00:00:00Z");
            put("Rating", 4.5);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "1401 I St NW");
                put("City", "Washington D.C.");
                put("StateProvince", "");
                put("PostalCode", "20005");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-77.03241, 38.90166));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "49");
            put("HotelName", "Swirling Currents Hotel");
            put("Description", "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.");
            put("DescriptionVector", 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            put("Category", "Suite");
            put("Tags", Arrays.asList("air conditioning", "laundry service", "24-hour front desk service"));
            put("ParkingIncluded", true);
            put("LastRenovationDate", "2018-01-27T00:00:00Z");
            put("Rating", 2.7);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "1100 S Hayes St");
                put("City", "Arlington");
                put("StateProvince", "VA");
                put("PostalCode", "22202");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-77.060066, 38.863659));
            }});
        }},
        new HashMap<>() {{
            put("@search.action", "mergeOrUpload");
            put("HotelId", "13");
            put("HotelName", "Luxury Lion Resort");
            put("Description", "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.");
            put("DescriptionVector", 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            put("Category", "Luxury");
            put("Tags", Arrays.asList("bar", "concierge", "restaurant"));
            put("ParkingIncluded", false);
            put("LastRenovationDate", "2020-03-18T00:00:00Z");
            put("Rating", 4.1);
            put("Address", new HashMap<String, Object>() {{
                put("StreetAddress", "3 Cityplace Dr");
                put("City", "St. Louis");
                put("StateProvince", "MO");
                put("PostalCode", "63141");
                put("Country", "USA");
            }});
            put("Location", new HashMap<String, Object>() {{
                put("type", "Point");
                put("coordinates", Arrays.asList(-90.44081, 38.67219));
            }});
        }}
    );

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

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

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

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

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

        System.exit(0);
    }
}

Ten kod ładuje kolekcję dokumentów. Każdy dokument zawiera wektoryzowaną wersję artykułu Description. Ten wektor umożliwia wyszukiwanie podobieństwa, gdzie dopasowanie jest oparte na znaczeniu, a nie na dokładnych słowach kluczowych.

Ponieważ ten artykuł z przewodnikiem Szybki start natychmiast przeszukuje indeks, waitUntilIndexed metoda czeka, aż indeks będzie gotowy do wykonywania operacji wyszukiwania. Jest to ważne, ponieważ indeks musi być w pełni wypełniony dokumentami przed rozpoczęciem wyszukiwania.

Skompiluj i uruchom plik.

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

Dane wyjściowe tego kodu pokazują, że dokumenty są indeksowane i gotowe do wyszukiwania:
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Uploading documents...
Key: 1, Succeeded: true, ErrorMessage: none
Key: 2, Succeeded: true, ErrorMessage: none
Key: 3, Succeeded: true, ErrorMessage: none
Key: 4, Succeeded: true, ErrorMessage: none
Key: 48, Succeeded: true, ErrorMessage: none
Key: 49, Succeeded: true, ErrorMessage: none
Key: 13, Succeeded: true, ErrorMessage: none
Waiting for indexing... Current count: 0
All documents indexed successfully.
```
Najważniejsze wnioski dotyczące metody i tego przykładu uploadDocuments :
- Kod współdziała z określonym indeksem wyszukiwania hostowanym w usłudze Azure AI Search za pośrednictwem .SearchClient Zapewnia SearchClient dostęp do operacji, takich jak:
  - Pozyskiwanie danych — uploadDocuments, mergeDocuments, deleteDocumentsi tak dalej.
  - Operacje wyszukiwania — search, , autocompletesuggest
- Pola wektorowe zawierają wartości zmiennoprzecinkowe. Atrybut wymiarów ma co najmniej 2 i maksymalnie 4096 wartości zmiennoprzecinkowych każdy. Ten przewodnik Szybki start ustawia atrybut wymiarów na 1536, ponieważ jest to rozmiar osadzania wygenerowany przez model Azure OpenAI text-embedding-ada-002 .

Tworzenie zapytania jako wektora

Przykładowe zapytania wektorowe są oparte na dwóch ciągach:

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): quintessential lodging near running trails, eateries, retail

QueryVector.java Utwórz plik w src/main/java/com/example/search katalogu i dodaj kod, aby utworzyć wektor zapytania.

package com.example.search;

import java.util.ArrayList;

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

Ten kod jest używany w poniższych sekcjach do wykonywania wyszukiwania wektorów. Wektor zapytania jest tworzony przy użyciu modelu osadzania z poziomu usługi Azure OpenAI.

Tworzenie pojedynczego wyszukiwania wektorowego

W pierwszym przykładzie przedstawiono podstawowy scenariusz, w którym chcesz znaleźć opisy dokumentów, które są ściśle zgodne z ciągiem wyszukiwania przy użyciu SearchClient i wyszukaj oraz VectorQuery i SearchOptions.

SearchSingle.java Utwórz plik w src/main/java/com/example/search katalogu.

Skopiuj następujący kod do pliku.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Zawiera vectorQuery konfigurację wektoryzowanego zapytania, w tym wektoryzowany tekst danych wejściowych zapytania, który jest pobierany za pośrednictwem QueryVector.getVectorListelementu . setFields określa, które pola wektorowe są przeszukiwane i setKNearestNeighborsCount określa liczbę najbliższych sąsiadów do zwrócenia.

Ciąg zapytania wektorowego to quintessential lodging near running trails, eateries, retail, który jest wektoryzowany do 1536 osadzania dla tego zapytania.

Skompiluj i uruchom plik.

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

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dla zapytania quintessential lodging near running trails, eateries, retail.

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

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

Odpowiedź dla odpowiednika wektora quintessential lodging near running trails, eateries, retail składa się z 5 wyników, zawierających tylko pola określone przez wykonany wybór.

Tworzenie pojedynczego wyszukiwania wektorowego z filtrem

SearchSingleWithFilter.java Utwórz plik w src/main/java/com/example/search katalogu.

Skopiuj następujący kod do pliku.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Ten kod ma tę samą funkcję wyszukiwania co poprzednie wyszukiwanie, ale dodaje filtr wykluczający po przetwarzaniu dla hoteli z free wifi.

Skompiluj i uruchom plik.

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

Wynik tego kodu pokazuje odpowiednie dokumenty dla zapytania z filtrem free wifi zastosowanym.

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

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

Tworzenie wyszukiwania za pomocą filtru geoprzestrzennego

Możesz określić filtr geoprzestrzenny, aby ograniczyć wyniki do określonego obszaru geograficznego. W tym przykładzie filtr ogranicza wyniki do hoteli w odległości 300 kilometrów od Waszyngtonu (współrzędne: -77.03241 38.90166). Odległości geoprzestrzenne są zawsze w kilometrach. To wyszukiwanie używa elementu SearchClient. SearchClient i SearchOptions.

SearchSingleWithFilterGeo.java Utwórz plik w src/main/java/com/example/search katalogu.

Skopiuj następujący kod do pliku.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Skompiluj i uruchom plik.

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

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dla zapytania z zastosowanym filtrem wykluczeń po przetwarzaniu geoprzestrzennym.

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

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

Tworzenie wyszukiwania hybrydowego

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): quintessential lodging near running trails, eateries, retail

To wyszukiwanie używa elementu SearchClient. SearchClient i SearchOptions.

SearchHybrid.java Utwórz plik w src/main/java/com/example/search katalogu.

Skopiuj następujący kod do pliku.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Skompiluj i uruchom plik.

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

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dotyczące wyszukiwania hybrydowego.

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

Hybrid search found 7 then limited to top 5
- Score: 0.03279569745063782
  HotelId: 4
  HotelName: Sublime Palace Hotel
  Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
  Category: Boutique
  Tags: ["concierge","view","air conditioning"]

- Score: 0.032522473484277725
  HotelId: 13
  HotelName: Luxury Lion Resort
  Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
  Category: Luxury
  Tags: ["bar","concierge","restaurant"]

- Score: 0.03205128386616707
  HotelId: 48
  HotelName: Nordick's Valley Motel
  Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
  Category: Boutique
  Tags: ["continental breakfast","air conditioning","free wifi"]

- Score: 0.0317460335791111
  HotelId: 49
  HotelName: Swirling Currents Hotel
  Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
  Category: Suite
  Tags: ["air conditioning","laundry service","24-hour front desk service"]

- Score: 0.03125
  HotelId: 2
  HotelName: Old Century Hotel
  Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
  Category: Boutique
  Tags: ["pool","free wifi","air conditioning","concierge"]

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

"value": [
    {
        "@search.score": 0.857736,
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.8399129,
        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
        "Category": "Luxury"
    },
    {
        "@search.score": 0.8383954,
        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
        "Category": "Resort and Spa"
    },
    {
        "@search.score": 0.8254346,
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.82380056,
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of 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"
    }
]

Tworzenie semantycznego wyszukiwania hybrydowego

Oto ostatnie zapytanie z kolekcji do rozszerzenia hybrydowego wyszukiwania semantycznego za pomocą dodatkowego tekstu wyszukiwania historic hotel walk to restaurants and shopping.

To wyszukiwanie używa elementu SearchClient. SearchClient i SearchOptions.

SearchSemanticHybrid.java Utwórz plik w src/main/java/com/example/search katalogu.

Skopiuj następujący kod do pliku.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Skompiluj i uruchom plik.

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

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dla semantycznego wyszukiwania hybrydowego.

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

Semantic hybrid search found 7 then limited to top 5
- Score: 0.0317460335791111
  Re-ranker Score: 2.6550590991973877
  HotelId: 49
  HotelName: Swirling Currents Hotel
  Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
  Category: Suite

- Score: 0.03279569745063782
  Re-ranker Score: 2.599761724472046
  HotelId: 4
  HotelName: Sublime Palace Hotel
  Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
  Category: Boutique

- Score: 0.03125
  Re-ranker Score: 2.3480887413024902
  HotelId: 2
  HotelName: Old Century Hotel
  Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
  Category: Boutique

- Score: 0.016393441706895828
  Re-ranker Score: 2.2718777656555176
  HotelId: 1
  HotelName: Stay-Kay City Hotel
  Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of 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

Kluczowe informacje:

Wyszukiwanie wektorowe VectorSearchOptions jest określane za pośrednictwem klasy . Wyszukiwanie słów kluczowych jest określane za pośrednictwem SemanticSearchOptions klasy .
W wyszukiwaniu hybrydowym można zintegrować wyszukiwanie wektorów z wyszukiwaniem pełnotekstowym za pomocą słów kluczowych. Filtry, sprawdzanie pisowni i klasyfikacja semantyczna mają zastosowanie tylko do zawartości tekstowej, a nie do wektorów. W tym ostatnim zapytaniu nie ma semantyki answer , ponieważ system nie wyprodukował takiego, który był wystarczająco silny.
Rzeczywiste wyniki zawierają więcej szczegółów, w tym semantyczne podpisy i wyróżnienia. Wyniki zostały zmodyfikowane pod kątem czytelności. Aby uzyskać pełną strukturę odpowiedzi, uruchom żądanie w kliencie REST.

Czyszczenie

Zasoby można znaleźć w witrynie Azure Portal i zarządzać nimi, korzystając z linku Wszystkie zasoby lub Grupy zasobów w okienku po lewej stronie.

Jeśli chcesz zachować usługę wyszukiwania, ale usunąć indeks i jego dokumenty, możesz usunąć indeks programowo.

DeleteIndex.java Utwórz plik w src/main/java/com/example/search katalogu.

Dodaj następujący kod do pliku.

package com.example.search;

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Skompiluj i uruchom plik.

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

Następne kroki

Przejrzyj repozytorium przykładów kodu pod kątem możliwości wyszukiwania wektorów w usłudze Azure AI Search for Java

W przewodniku Quickstart użyjesz JavaScript do tworzenia, ładowania i przetwarzania zapytań wektorowych. Przykłady kodu wykonują te operacje przy użyciu biblioteki klienta usługi Azure AI Search. Biblioteka udostępnia abstrakcję interfejsu API REST na potrzeby dostępu do operacji indeksowania, takich jak pozyskiwanie danych, operacje wyszukiwania i operacje zarządzania indeksami.

Uwaga

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz konto bezpłatnie.
Usługa wyszukiwania sztucznej inteligencji platformy Azure. Utwórz usługę lub znajdź istniejącą usługę w bieżącej subskrypcji.
- Rola przypisana Search Index Data Contributor w zakresie usługi.
- W większości tego szybkiego przewodnika można użyć bezpłatnej usługi wyszukiwania, ale zalecamy użycie poziomu Podstawowy lub wyższego dla większych zbiorów danych.
- Aby uruchomić przykład zapytania, który wywołuje semantyczne ponowne ocenianie, usługa wyszukiwania musi mieć włączony semantyczny ranker.
Visual Studio Code.
Node.JS z LTS.

Uzyskiwanie punktów końcowych usługi

W pozostałych sekcjach skonfigurujesz wywołania interfejsu API do usług Azure OpenAI i Azure AI Search. Pobierz punkty końcowe usługi, aby udostępnić je jako zmienne w kodzie.

Zaloguj się do witryny Azure Portal.
Znajdź usługę wyszukiwania.
Na stronie głównej Przegląd skopiuj adres URL. Przykładowy punkt końcowy może wyglądać podobnie jak https://example.search.windows.net.

Konfigurowanie projektu Node.JS

Konfigurowanie projektu przy użyciu programu Visual Studio Code i języka JavaScript.

Uruchom program Visual Studio Code w nowym katalogu.

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

Utwórz nowy pakiet dla modułów ESM w katalogu projektu.
```
npm init -y
npm pkg set type=module
```
Spowoduje to utworzenie package.json pliku z wartościami domyślnymi.

Zainstaluj następujące pakiety npm.

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

Utwórz katalog src w katalogu projektu.
```
mkdir src
```

Konfigurowanie zmiennych środowiskowych na potrzeby programowania lokalnego

.env Utwórz plik w vector-quickstart katalogu projektu.
Dodaj następujące zmienne środowiskowe do .env pliku, zastępując wartości własnymi punktami końcowymi i kluczami usługi.
```
AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
```

Uruchom każde z poniższych poleceń w sekwencji.

az account show

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

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

Teraz powinno nastąpić zalogowanie do platformy Azure z urządzenia lokalnego.

Tworzenie indeksu wektora

W tej sekcji utworzysz indeks wektorowy w usłudze Azure AI Search za pomocą elementu SearchIndexClient. createOrUpdateIndex. Schemat indeksu definiuje pola, w tym pole wektora DescriptionVector.

createIndex.js Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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`);

Plik kodu dodaje zależności, zmienne środowiskowe i typ JavaScript dla HotelDocument na początku pliku. Dodaj funkcję , createIndex aby utworzyć indeks. Funkcja definiuje schemat indeksu, w tym pole wektora DescriptionVector.

Uruchom plik:

node -r dotenv/config src/createIndex.js

Dane wyjściowe tego kodu pokazują, że indeks został utworzony pomyślnie:
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Creating index...
hotels-vector-quickstart created
```
Najważniejsze wnioski podczas tworzenia indeksu wektorów za pomocą @azure/search-documents biblioteki:
- Indeks można zdefiniować, tworząc listę pól.
- Ten konkretny indeks obsługuje wiele funkcji wyszukiwania, takich jak:
  - Wyszukiwanie słów kluczowych pełnotekstowych (searchable)
  - Wyszukiwanie wektorowe (umożliwia wyszukiwanie hybrydowe przez sortowanie pól wektorów i pól niewektorów) (DescriptionVector z vectorSearchProfileName)
  - Wyszukiwanie semantyczne
  - Wyszukiwanie aspektowe (searchSuggester)
  - Wyszukiwanie przestrzenne (Location pole z geo.distance)
  - Filtrowanie, sortowanie (wiele pól oznaczonych jako możliwe do filtrowania i sortowania)

Przekazywanie dokumentów do indeksu

W usłudze Azure AI Search indeks przechowuje całą zawartość z możliwością wyszukiwania, a aparat wyszukiwania wykonuje zapytania względem tego indeksu.

uploadDocuments.js Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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);
}

Ten kod ładuje tablicę obiektów JSON. Każdy dokument zawiera wektoryzowaną wersję artykułu description. Ten wektor umożliwia wyszukiwanie podobieństwa, gdzie dopasowanie jest oparte na znaczeniu, a nie na dokładnych słowach kluczowych.

Ponieważ ten artykuł przewodnika Quickstart natychmiast przeszukuje indeks, funkcja waitUntilIndexed czeka, aż indeks będzie gotowy do operacji wyszukiwania. Jest to ważne, ponieważ indeks musi być w pełni wypełniony dokumentami przed rozpoczęciem wyszukiwania.

Skompiluj i uruchom plik:

node -r dotenv/config src/uploadDocuments.js

Dane wyjściowe tego kodu pokazują, że dokumenty są indeksowane i gotowe do wyszukiwania:
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Uploading documents...
Key: 1, Succeeded: true, ErrorMessage: none
Key: 2, Succeeded: true, ErrorMessage: none
Key: 3, Succeeded: true, ErrorMessage: none
Key: 4, Succeeded: true, ErrorMessage: none
Key: 48, Succeeded: true, ErrorMessage: none
Key: 49, Succeeded: true, ErrorMessage: none
Key: 13, Succeeded: true, ErrorMessage: none
Waiting for indexing... Current count: 0
All documents indexed successfully.
```
Najważniejsze wnioski dotyczące metody uploadDocuments() i tego przykładu:
- Kod współdziała z określonym indeksem wyszukiwania hostowanym w usłudze Azure AI Search za pośrednictwem elementu SearchClient, który jest głównym obiektem udostępnianym @azure/search-documents przez pakiet. Element SearchClient zapewnia dostęp do operacji indeksowania, takich jak:
  - Pozyskiwanie danych — uploadDocuments(), mergeDocuments(), deleteDocuments() itp.
  - Operacje wyszukiwania — search(), autouzupełnianie(), suggest()
  - Operacje zarządzania indeksami, takie jak createIndex(), deleteIndex(), getIndex() itp.
- Pola wektorowe zawierają wartości zmiennoprzecinkowe. Atrybut wymiarów ma co najmniej 2 i maksymalnie 4096 wartości zmiennoprzecinkowych każdy. Ten szybki start ustawia atrybut rozmiarów na 1536, ponieważ jest to rozmiar osadzania generowany przez model text-embedding-ada-002 w usłudze Azure OpenAI.

Tworzenie zapytania jako wektora

Przykładowe zapytania wektorowe są oparte na dwóch ciągach:

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): quintessential lodging near running trails, eateries, retail

queryVector.js Utwórz plik w src katalogu i dodaj kod, aby utworzyć wektor zapytania.

// 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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Ten kod jest używany w poniższych sekcjach do wykonywania wyszukiwania wektorów. Wektor zapytania jest tworzony przy użyciu modelu osadzania z poziomu usługi Azure OpenAI.

Tworzenie pojedynczego wyszukiwania wektorowego

searchSingle.js Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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;
}

WektorQuery zawiera konfigurację zapytania wektoryzowanego, w tym wektoryzowany tekst wejściowy zapytania jako vector, fields określa, które pola wektorowe są przeszukiwane, a kNearestNeighborsCount określa liczbę najbliższych sąsiadów, którzy mają zostać zwróceni.

Ciąg zapytania wektorowego to quintessential lodging near running trails, eateries, retail, który jest wektoryzowany do 1536 osadzania dla tego zapytania.

Skompiluj i uruchom plik:

node -r dotenv/config src/searchSingle.js

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dla zapytania quintessential lodging near running trails, eateries, retail.

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


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

Odpowiedź dla odpowiednika wektora quintessential lodging near running trails, eateries, retail składa się z 5 wyników, zawierających tylko pola określone przez wykonany wybór.

Tworzenie pojedynczego wyszukiwania wektorowego z filtrem

searchSingleWithFilter.js Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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;
}

Dodaj zależności, zmienne środowiskowe i te same funkcje wyszukiwania co poprzednie wyszukiwanie z filtrem wykluczeń po przetworzeniu dodanym dla hoteli za pomocą polecenia free wifi.

Skompiluj i uruchom plik:

node -r dotenv/config src/searchSingleWithFilter.js

Wynik tego kodu pokazuje odpowiednie dokumenty dla zapytania z filtrem free wifi zastosowanym.

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


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

Tworzenie wyszukiwania za pomocą filtru geoprzestrzennego

Możesz określić filtr geoprzestrzenny, aby ograniczyć wyniki do określonego obszaru geograficznego. W tym przykładzie filtr ogranicza wyniki do hoteli w odległości 300 kilometrów od Waszyngtonu (współrzędne: -77.03241 38.90166). Odległości geoprzestrzenne są zawsze w kilometrach. To wyszukiwanie używa SearchClient.search oraz VectorQuery i SearchOptions.

searchSingleWithFilterGeo.js Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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;
}

Skompiluj i uruchom plik:

node -r dotenv/config src/searchSingleWithFilterGeo.js

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dla zapytania z zastosowanym filtrem wykluczeń po przetwarzaniu geoprzestrzennym:

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


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

Tworzenie wyszukiwania hybrydowego

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): quintessential lodging near running trails, eateries, retail

To wyszukiwanie używa SearchClient.search oraz VectorQuery i SearchOptions.

searchHybrid.js Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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;
}

Skompiluj i uruchom plik:

node -r dotenv/config src/searchHybrid.js

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dotyczące wyszukiwania hybrydowego:

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


Hybrid search found 7 then limited to top 5
- Score: 0.03279569745063782
  HotelId: 4
  HotelName: Sublime Palace Hotel
  Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
  Category: Boutique
  Tags: ["concierge","view","air conditioning"]

- Score: 0.032522473484277725
  HotelId: 13
  HotelName: Luxury Lion Resort
  Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
  Category: Luxury
  Tags: ["bar","concierge","restaurant"]

- Score: 0.03205128386616707
  HotelId: 48
  HotelName: Nordick's Valley Motel
  Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
  Category: Boutique
  Tags: ["continental breakfast","air conditioning","free wifi"]

- Score: 0.0317460335791111
  HotelId: 49
  HotelName: Swirling Currents Hotel
  Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
  Category: Suite
  Tags: ["air conditioning","laundry service","24-hour front desk service"]

- Score: 0.03125
  HotelId: 2
  HotelName: Old Century Hotel
  Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
  Category: Boutique
  Tags: ["pool","free wifi","air conditioning","concierge"]

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

"value": [
    {
        "@search.score": 0.857736,
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.8399129,
        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
        "Category": "Luxury"
    },
    {
        "@search.score": 0.8383954,
        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
        "Category": "Resort and Spa"
    },
    {
        "@search.score": 0.8254346,
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.82380056,
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of 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"
    }
]

Tworzenie semantycznego wyszukiwania hybrydowego

Oto ostatnie zapytanie z kolekcji do rozszerzenia hybrydowego wyszukiwania semantycznego za pomocą dodatkowego tekstu wyszukiwania historic hotel walk to restaurants and shopping.

To wyszukiwanie używa SearchClient.search oraz VectorQuery i SearchOptions.

searchSemanticHybrid.js Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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;
}

Skompiluj i uruchom plik:

node -r dotenv/config src/searchSemanticHybrid.js

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dla semantycznego wyszukiwania hybrydowego:

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


Semantic hybrid search found 7 then limited to top 5
- Score: 0.0317460335791111
  Re-ranker Score: 2.6550590991973877
  HotelId: 49
  HotelName: Swirling Currents Hotel
  Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
  Category: Suite

- Score: 0.03279569745063782
  Re-ranker Score: 2.599761724472046
  HotelId: 4
  HotelName: Sublime Palace Hotel
  Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
  Category: Boutique

- Score: 0.03125
  Re-ranker Score: 2.3480887413024902
  HotelId: 2
  HotelName: Old Century Hotel
  Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
  Category: Boutique

- Score: 0.016393441706895828
  Re-ranker Score: 2.2718777656555176
  HotelId: 1
  HotelName: Stay-Kay City Hotel
  Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of 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

Kluczowe informacje:

Wyszukiwanie wektorowe vectorSearchOptions jest określane za pośrednictwem właściwości . Wyszukiwanie słów kluczowych jest określane za pośrednictwem semanticSearchOptions właściwości .
W wyszukiwaniu hybrydowym można zintegrować wyszukiwanie wektorów z wyszukiwaniem pełnotekstowym za pomocą słów kluczowych. Filtry, sprawdzanie pisowni i klasyfikacja semantyczna mają zastosowanie tylko do zawartości tekstowej, a nie do wektorów. W tym ostatnim zapytaniu nie ma semantyki answer , ponieważ system nie wyprodukował takiego, który był wystarczająco silny.
Rzeczywiste wyniki zawierają więcej szczegółów, w tym semantyczne podpisy i wyróżnienia. Wyniki zostały zmodyfikowane pod kątem czytelności. Aby uzyskać pełną strukturę odpowiedzi, uruchom żądanie w kliencie REST.

Czyszczenie

Zasoby można znaleźć w witrynie Azure Portal i zarządzać nimi, korzystając z linku Wszystkie zasoby lub Grupy zasobów w okienku po lewej stronie.

Jeśli chcesz zachować usługę wyszukiwania, ale usunąć indeks i jego dokumenty, możesz usunąć indeks programowo.

deleteIndex.js Utwórz plik w src katalogu.

Dodaj zależności, zmienne środowiskowe i kod, aby usunąć indeks.

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);
}

Skompiluj i uruchom plik:

node -r dotenv/config src/deleteIndex.js

Następne kroki

Przejrzyj repozytorium przykładów kodu pod kątem możliwości wyszukiwania wektorów w usłudze Azure AI Search dla języka JavaScript

W tym szybkim przewodniku użyjesz języka TypeScript do tworzenia, ładowania i zapytań wektorowych. Przykłady kodu wykonują te operacje przy użyciu biblioteki klienta usługi Azure AI Search. Biblioteka udostępnia abstrakcję interfejsu API REST na potrzeby dostępu do operacji indeksowania, takich jak pozyskiwanie danych, operacje wyszukiwania i operacje zarządzania indeksami.

Uwaga

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz konto bezpłatnie.
Usługa wyszukiwania sztucznej inteligencji platformy Azure. Utwórz usługę lub znajdź istniejącą usługę w bieżącej subskrypcji.
- Rola przypisana Search Index Data Contributor w zakresie usługi.
- W większości tego szybkiego przewodnika można użyć bezpłatnej usługi wyszukiwania, ale zalecamy użycie poziomu Podstawowy lub wyższego dla większych zbiorów danych.
- Aby uruchomić przykład zapytania, który wywołuje semantyczne ponowne ocenianie, usługa wyszukiwania musi mieć włączony semantyczny ranker.
Visual Studio Code.
Node.JS z LTS.
TypeScript. Język TypeScript można zainstalować globalnie przy użyciu narzędzia npm:
```
npm install -g typescript
```

Uzyskiwanie punktów końcowych usługi

W pozostałych sekcjach skonfigurujesz wywołania interfejsu API do usług Azure OpenAI i Azure AI Search. Pobierz punkty końcowe usługi, aby udostępnić je jako zmienne w kodzie.

Zaloguj się do witryny Azure Portal.
Znajdź usługę wyszukiwania.
Na stronie głównej Przegląd skopiuj adres URL. Przykładowy punkt końcowy może wyglądać podobnie jak https://example.search.windows.net.

Konfigurowanie projektu Node.JS

Konfigurowanie projektu przy użyciu programu Visual Studio Code i języka TypeScript.

Uruchom program Visual Studio Code w nowym katalogu.

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

Utwórz nowy pakiet dla modułów ESM w katalogu projektu.
```
npm init -y
npm pkg set type=module
```
Spowoduje to utworzenie package.json pliku z wartościami domyślnymi.
Edytuj package.json , aby uwzględnić skrypt kompilacji. Dodaj następujący wiersz do scripts sekcji:
```
"build": "tsc"
```

Zainstaluj następujące pakiety npm.

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

Utwórz katalog src w katalogu projektu.
```
mkdir src
```

tsconfig.json Utwórz plik w katalogu projektu dla platformy ESM z następującą zawartością.

{
  "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"
  ]
}

Konfigurowanie zmiennych środowiskowych na potrzeby programowania lokalnego

.env Utwórz plik w vector-quickstart katalogu projektu.
Dodaj następujące zmienne środowiskowe do .env pliku, zastępując wartości własnymi punktami końcowymi i kluczami usługi.
```
AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
```

Uruchom każde z poniższych poleceń w sekwencji.

az account show

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

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

Teraz powinno nastąpić zalogowanie do platformy Azure z urządzenia lokalnego.

Tworzenie indeksu wektora

W tej sekcji utworzysz indeks wektorowy w usłudze Azure AI Search za pomocą elementu SearchIndexClient. createOrUpdateIndex. Schemat indeksu definiuje pola, w tym pole wektora DescriptionVector. Po utworzeniu indeksu prześlij dokumenty do indeksu. Dokumenty zawierają wektoryzowaną wersję opisu artykułu, która umożliwia wyszukiwanie podobieństw na podstawie znaczenia, a nie dokładnych słów kluczowych.

createIndex.ts Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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`);

Skompiluj i uruchom plik:

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

Dane wyjściowe tego kodu pokazują, że indeks został utworzony pomyślnie:
```
Using Azure Search endpoint: https://my-service.search.windows.net
Using index name: hotels-vector-quickstart
Creating index...
hotels-vector-quickstart created
```
Najważniejsze wnioski podczas tworzenia indeksu wektorów za pomocą @azure/search-documents biblioteki:
- Indeks można zdefiniować, tworząc listę pól.
- Ten konkretny indeks obsługuje wiele funkcji wyszukiwania, takich jak:
  - Wyszukiwanie słów kluczowych pełnotekstowych (searchable)
  - Wyszukiwanie wektorowe (umożliwia wyszukiwanie hybrydowe przez sortowanie pól wektorów i pól niewektorów) (DescriptionVector z vectorSearchProfileName)
  - Wyszukiwanie semantyczne
  - Wyszukiwanie aspektowe (searchSuggester)
  - Wyszukiwanie przestrzenne (Location pole z geo.distance)
  - Filtrowanie, sortowanie (wiele pól oznaczonych jako możliwe do filtrowania i sortowania)

Przekazywanie dokumentów do indeksu

W usłudze Azure AI Search indeks przechowuje całą zawartość z możliwością wyszukiwania, a aparat wyszukiwania wykonuje zapytania względem tego indeksu.

uploadDocuments.ts Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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);
}

Ten kod ładuje dokumenty zmiennych z obiektem JSON opisującym każdy dokument wraz z wektoryzowaną wersją opisu artykułu. Ten wektor umożliwia wyszukiwanie podobieństwa, gdzie dopasowanie jest oparte na znaczeniu, a nie na dokładnych słowach kluczowych.

Skompiluj i uruchom plik:

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

Dane wyjściowe tego kodu pokazują, że dokumenty są indeksowane i gotowe do wyszukiwania:
```
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.
```
Najważniejsze wnioski dotyczące metody upload_documents() i tego przykładu:
- Kod współdziała z określonym indeksem wyszukiwania hostowanym w usłudze Azure AI Search za pośrednictwem elementu SearchClient, który jest głównym obiektem udostępnianym @azure/search-documents przez pakiet. Element SearchClient zapewnia dostęp do operacji indeksowania, takich jak:
  - Pozyskiwanie danych — uploadDocuments(), mergeDocuments(), deleteDocuments() itp.
  - Operacje wyszukiwania — search(), autouzupełnianie(), suggest()
  - Operacje zarządzania indeksami, takie jak createIndex(), deleteIndex(), getIndex() itp.
- Pola wektorowe zawierają wartości zmiennoprzecinkowe. Atrybut wymiarów ma co najmniej 2 i maksymalnie 4096 wartości zmiennoprzecinkowych każdy. Ten szybki start ustawia atrybut rozmiarów na 1536, ponieważ jest to rozmiar osadzania generowany przez model text-embedding-ada-002 w usłudze Azure OpenAI.

Tworzenie zapytania jako wektora

Przykładowe zapytania wektorowe są oparte na dwóch ciągach:

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): quintessential lodging near running trails, eateries, retail

queryVector.ts Utwórz plik w src katalogu i dodaj kod, aby utworzyć wektor zapytania.

// 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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Ten kod jest używany w poniższych sekcjach do wykonywania wyszukiwania wektorów. Wektor zapytania jest tworzony przy użyciu modelu osadzania z poziomu usługi Azure OpenAI.

Tworzenie pojedynczego wyszukiwania wektorowego

searchSingle.ts Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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;
}

Ciąg zapytania wektorowego to quintessential lodging near running trails, eateries, retail, który jest wektoryzowany do 1536 osadzania dla tego zapytania.

Skompiluj i uruchom plik:

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

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dla zapytania 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

Odpowiedź dla odpowiednika wektora quintessential lodging near running trails, eateries, retail składa się z 5 wyników, zawierających tylko pola określone przez wykonany wybór.

Tworzenie pojedynczego wyszukiwania wektorowego z filtrem

searchSingleWithFilter.ts Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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;
}

Dodaj zależności, zmienne środowiskowe i te same funkcje wyszukiwania co poprzednie wyszukiwanie z filtrem wykluczeń po przetworzeniu dodanym dla hoteli za pomocą polecenia free wifi.

Skompiluj i uruchom plik:

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

Wynik tego kodu pokazuje odpowiednie dokumenty dla zapytania z filtrem free wifi zastosowanym.

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

Tworzenie wyszukiwania za pomocą filtru geoprzestrzennego

Możesz określić filtr geoprzestrzenny, aby ograniczyć wyniki do określonego obszaru geograficznego. W tym przykładzie filtr ogranicza wyniki do hoteli w odległości 300 kilometrów od Waszyngtonu (współrzędne: -77.03241 38.90166). Odległości geoprzestrzenne są zawsze w kilometrach. To wyszukiwanie używa SearchClient.search oraz VectorQuery i SearchOptions.

searchSingleWithFilterGeo.ts Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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;
}

Skompiluj i uruchom plik:

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

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dla zapytania z zastosowanym filtrem wykluczeń po przetwarzaniu geoprzestrzennym:

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

Tworzenie wyszukiwania hybrydowego

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): quintessential lodging near running trails, eateries, retail

To wyszukiwanie używa SearchClient.search oraz VectorQuery i SearchOptions.

searchHybrid.ts Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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;
}

Skompiluj i uruchom plik:

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

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dotyczące wyszukiwania hybrydowego:

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"]

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

"value": [
    {
        "@search.score": 0.857736,
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.8399129,
        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
        "Category": "Luxury"
    },
    {
        "@search.score": 0.8383954,
        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
        "Category": "Resort and Spa"
    },
    {
        "@search.score": 0.8254346,
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.82380056,
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of 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"
    }
]

Tworzenie semantycznego wyszukiwania hybrydowego

Oto ostatnie zapytanie z kolekcji do rozszerzenia hybrydowego wyszukiwania semantycznego za pomocą dodatkowego tekstu wyszukiwania historic hotel walk to restaurants and shopping.

To wyszukiwanie używa SearchClient.search oraz VectorQuery i SearchOptions.

searchSemanticHybrid.ts Utwórz plik w src katalogu.

Skopiuj następujący kod do pliku.

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;
}

Skompiluj i uruchom plik:

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

Dane wyjściowe tego kodu przedstawiają odpowiednie dokumenty dla semantycznego wyszukiwania hybrydowego:

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

Kluczowe informacje:

Wyszukiwanie wektorowe vectorSearchOptions jest określane za pośrednictwem właściwości . Wyszukiwanie słów kluczowych jest określane za pośrednictwem semanticSearchOptions właściwości .
W wyszukiwaniu hybrydowym można zintegrować wyszukiwanie wektorów z wyszukiwaniem pełnotekstowym za pomocą słów kluczowych. Filtry, sprawdzanie pisowni i klasyfikacja semantyczna mają zastosowanie tylko do zawartości tekstowej, a nie do wektorów. W tym ostatnim zapytaniu nie ma semantyki answer , ponieważ system nie wyprodukował takiego, który był wystarczająco silny.
Rzeczywiste wyniki zawierają więcej szczegółów, w tym semantyczne podpisy i wyróżnienia. Wyniki zostały zmodyfikowane pod kątem czytelności. Aby uzyskać pełną strukturę odpowiedzi, uruchom żądanie w kliencie REST.

Czyszczenie

Zasoby można znaleźć w witrynie Azure Portal i zarządzać nimi, korzystając z linku Wszystkie zasoby lub Grupy zasobów w okienku po lewej stronie.

Jeśli chcesz zachować usługę wyszukiwania, ale usunąć indeks i jego dokumenty, możesz usunąć indeks programowo.

deleteIndex.ts Utwórz plik w src katalogu.

Dodaj zależności, zmienne środowiskowe i kod, aby usunąć indeks.

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);
}

Skompiluj i uruchom plik:

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

Następne kroki

Przejrzyj repozytorium przykładów kodu pod kątem możliwości wyszukiwania wektorów w usłudze Azure AI Search dla języka JavaScript

W tym szybkim starcie pracujesz z aplikacją .NET, aby tworzyć, wypełniać i wykonywać zapytania dotyczące wektorów. Przykłady kodu wykonują te operacje przy użyciu biblioteki klienta usługi Azure AI Search. Biblioteka udostępnia abstrakcję interfejsu API REST na potrzeby dostępu do operacji indeksowania, takich jak pozyskiwanie danych, operacje wyszukiwania i operacje zarządzania indeksami.

Uwaga

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz konto bezpłatnie.
Usługa wyszukiwania sztucznej inteligencji platformy Azure. Utwórz usługę lub znajdź istniejącą usługę w bieżącej subskrypcji.
- W większości tego szybkiego przewodnika można użyć bezpłatnej usługi wyszukiwania, ale zalecamy użycie poziomu Podstawowy lub wyższego dla większych zbiorów danych.
- Aby uruchomić przykład zapytania, który wywołuje semantyczne ponowne ocenianie, usługa wyszukiwania musi mieć włączony semantyczny ranker.
Visual Studio Code lub Visual Studio.
Użyj Git do sklonowania przykładowego repozytorium.

Uzyskiwanie informacji o usłudze

Ten szybki start używa DefaultAzureCredential, który upraszcza uwierzytelnianie zarówno w scenariuszach programistycznych, jak i produkcyjnych. Jednak w przypadku scenariuszy produkcyjnych może istnieć bardziej zaawansowane wymagania, które wymagają innego podejścia. Zobacz Uwierzytelnianie aplikacji platformy .NET w usługach platformy Azure przy użyciu zestawu Azure SDK dla platformy .NET , aby poznać wszystkie opcje.

Klonowanie kodu i środowiska konfiguracji

Sklonuj repozytorium zawierające kod dla tego szybkiego startu.
```
git clone https://github.com/Azure-Samples/azure-search-dotnet-samples
```
To repozytorium zawiera przykłady kodu platformy .NET dla kilku artykułów w osobnym podfolderze.
Otwórz podfolder quickstart-Vector-Search w programie Visual Studio Code lub kliknij .sln dwukrotnie plik, aby otworzyć rozwiązanie w programie Visual Studio.
appsettings.json Otwórz pliki w folderach VectorSearchExamples i VectorSearchCreatePopulateIndex . Zaktualizuj następujące wartości:
- AZURE_SEARCH_ENDPOINT: znajdź adres URL usługi Azure AI Search w witrynie Azure Portal. Na stronie Przegląd zasobu wyszukiwania poszukaj pola Adres URL. Przykładowy punkt końcowy może wyglądać podobnie jak https://mydemo.search.windows.net.
- AZURE_SEARCH_INDEX_NAME: pozostaw wartość domyślną podaną w pliku lub wprowadź własną nazwę indeksu.

Utwórz indeks wektorowy i wyślij dokumenty

Aby uruchomić zapytania wyszukiwania względem usługi Azure AI Search, najpierw należy utworzyć indeks wyszukiwania i przekazać dokumenty do usługi.

Otwórz nowy terminal w folderze VectorSearchCreatePopulateIndex .
Uruchom projekt przy użyciu dotnet run polecenia :
```
dotnet run
```

Poniższy kod jest wykonywany w celu utworzenia indeksu:

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();
}

Poniższy kod przekazuje dokumenty sformatowane w formacie JSON w hotel-samples.json pliku do usługi Azure AI Search:

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);
    }
}

Po uruchomieniu projektu wyświetlane są następujące dane wyjściowe:

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

Kluczowe informacje:

Kod współdziała z określonym indeksem wyszukiwania hostowanym w usłudze Azure AI Search za pośrednictwem SearchClient obiektu, który jest głównym obiektem udostępnianym przez pakiet azure-search-documents. Zapewnia SearchClient dostęp do operacji indeksowania, takich jak:
- Pozyskiwanie danych: UploadDocuments(), , MergeDocuments()DeleteDocuments()
- Operacje wyszukiwania: Search(), , Autocomplete()Suggest()
- Operacje zarządzania indeksami: CreateOrUpdateIndex()
Pola wektorowe zawierają wartości zmiennoprzecinkowe. Atrybut wymiarów ma co najmniej 2 i maksymalnie 4096 wartości zmiennoprzecinkowych każdy. Rozmiar osadzeń generowanych przez model text-embedding-3-small w usłudze Azure OpenAI dla tego szybkiego startu to 1536.

Uruchamianie zapytań wyszukiwania

Po utworzeniu indeksu i załadowaniu dokumentów można wydawać zapytania wektorowe względem nich, wywołując SearchAsync() z różnymi parametrami.

W folderze VectorSearchExamples otwórz Program.cs plik.
Otwórz nowy terminal w folderze VectorSearchExamples .

W poniższych sekcjach uruchamiasz zapytania względem indeksu hotels-vector-quickstart . Zapytania obejmują:

Wyszukiwanie jednowektorowe
Wyszukiwanie jednowektorowe z filtrem
Wyszukiwanie hybrydowe
Wyszukiwanie hybrydowe semantyczne z filtrem

Wyszukiwanie jednowektorowe

Pierwszy przykład przedstawia podstawowy scenariusz, w którym chcesz znaleźć opisy dokumentów, które są ściśle zgodne z ciągiem wyszukiwania.

W pliku Program.cs w folderze VectorSearchExamples odkomentuj wywołanie metody SearchExamples.SearchSingleVector(searchClient, vectorizedResult);. Ta metoda wykonuje następującą funkcję wyszukiwania w SearchExamples.cs klasie:

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();
}

Uruchom projekt przy użyciu dotnet run polecenia :

dotnet run

Po uruchomieniu projektu wyniki wyszukiwania zostaną wyświetlone w oknie danych wyjściowych:

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

Wyszukiwanie jednowektorowe z filtrem

W pliku Program.cs w folderze VectorSearchExamples odkomentuj wywołanie metody SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);. Ta metoda wykonuje następującą funkcję wyszukiwania w SearchExamples.cs klasie:

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();
}

Uruchom ponownie projekt, a stan każdego dokumentu zostanie wyświetlony poniżej:
```
 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
```
Zapytanie było takie samo jak w poprzednim przykładzie wyszukiwania pojedynczego wektora, ale zawiera filtr wykluczeń po przetworzeniu i zwraca tylko dwa hotele, które mają bezpłatny Wi-Fi.

W następnym przykładzie filtru użyto filtru geograficznego. W pliku Program.cs w folderze VectorSearchExamples odkomentuj wywołanie metody SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);. Ta metoda wykonuje następującą funkcję wyszukiwania w SearchExamples.cs klasie:

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();
}

Zapytanie było takie samo jak w poprzednim przykładzie wyszukiwania pojedynczego wektora, ale zawiera filtr wykluczeń po przetworzeniu i zwraca tylko dwa hotele w ciągu 300 kilometrów.

Uruchom ponownie projekt, a stan każdego dokumentu zostanie wyświetlony poniżej:

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.

Wyszukiwanie hybrydowe

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): Quintessential lodging near running trails, eateries, retail

W pliku Program.cs w folderze VectorSearchExamples odkomentuj wywołanie metody SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);. Ta metoda wykonuje następującą funkcję wyszukiwania w SearchExamples.cs klasie:

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;
}

Uruchom ponownie projekt, a stan każdego dokumentu zostanie wyświetlony poniżej:

Hybrid search results:
Score: 0.03279569745063782
HotelId: 4
HotelName: Sublime Palace Hotel
Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th   century resort, updated for every modern convenience.
Category: Boutique
Tags: conciergeviewair conditioning

Score: 0.032786883413791656
HotelId: 13
HotelName: Luxury Lion Resort
Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
Category: Luxury
Tags: barconciergerestaurant

Score: 0.03205128386616707
HotelId: 48
HotelName: Nordick's Valley Motel
Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
Category: Boutique
Tags: continental breakfastair conditioningfree wifi

Score: 0.0317460335791111
HotelId: 49
HotelName: Swirling Currents Hotel
Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
Category: Suite
Tags: air conditioninglaundry service24-hour front desk service

Score: 0.03077651560306549
HotelId: 2
HotelName: Old Century Hotel
Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer   dinners, and live music.
Category: Boutique
Tags: poolfree wifiair conditioningconcierge

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

"value": [
    {
        "@search.score": 0.857736,
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer.  Hiking? Wine Tasting? Exploring the caverns?  It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.8399129,
        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center.",
        "Category": "Luxury"
    },
    {
        "@search.score": 0.8383954,
        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury.  Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium, we feature the best in comfort",
        "Category": "Resort and Spa"
    },
    {
        "@search.score": 0.8254346,
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Palace is part of a lovingly restored 1800 palace.",
        "Category": "Boutique"
    },
    {
        "@search.score": 0.82380056,
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "The hotel is ideally located on the main commercial artery of the city in the heart of 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"
    }
]

Semantyczne wyszukiwanie hybrydowe z filtrem

Zapytanie hybrydowe z klasyfikacją semantyczną jest filtrowane, aby pokazać tylko hotele w promieniu 500 kilometrów od Waszyngtonu.

W pliku Program.cs w folderze VectorSearchExamples odkomentuj wywołanie metody SearchExamples.SearchHybridVectoryAndSemantic(searchClient, vectorizedResult);. Ta metoda wykonuje następującą funkcję wyszukiwania w SearchExamples.cs klasie:

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();
}

Uruchom ponownie projekt i przejrzyj dane wyjściowe poniżej komórki. Odpowiedź to trzy hotele, które są filtrowane według lokalizacji i semantycznie StateProvince ponownie sklasyfikowane w celu podwyższenia poziomu wyników znajdujących się najbliżej zapytania ciągu wyszukiwania (historic hotel walk to restaurants and shopping).

Swirling Currents Hotel przenosi się teraz na pierwsze miejsce. Bez semantycznego rankingu, Nordick's Valley Motel jest numerem jeden. Dzięki semantycznemu rankingowi modele zrozumienia maszyn rozpoznają, że historic dotyczy "hotelu, w odległości spaceru od restauracji i zakupów".

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

Kluczowe informacje:

W wyszukiwaniu hybrydowym można zintegrować wyszukiwanie wektorów z wyszukiwaniem pełnotekstowym za pomocą słów kluczowych. Filtry, sprawdzanie pisowni i klasyfikacja semantyczna mają zastosowanie tylko do zawartości tekstowej, a nie do wektorów. W tym ostatnim zapytaniu nie ma semantyki answer , ponieważ system nie wyprodukował takiego, który był wystarczająco silny.
Rzeczywiste wyniki zawierają więcej szczegółów, w tym semantyczne podpisy i wyróżnienia. Wyniki zostały zmodyfikowane pod kątem czytelności. Aby uzyskać pełną strukturę odpowiedzi, uruchom żądanie w kliencie REST.

Czyszczenie

Zasoby można znaleźć w witrynie Azure Portal i zarządzać nimi, korzystając z linku Wszystkie zasoby lub Grupy zasobów w okienku po lewej stronie.

Następne kroki

Przejrzyj repozytorium przykładów kodu pod kątem możliwości wyszukiwania wektorów w usłudze Azure AI Search dla platformy .NET
Przejrzyj inne przykłady kodu platformy .NET i usługi Azure AI Search w repozytorium azure-search-dotnet-samples

W tym szybkim starcie użyjesz interfejsów API REST usługi Azure AI Search do tworzenia, ładowania i zapytania wektorów.

Uwaga

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz konto bezpłatnie.
Usługa wyszukiwania sztucznej inteligencji platformy Azure. Utwórz usługę lub znajdź istniejącą usługę w bieżącej subskrypcji.
- W większości tego szybkiego przewodnika można użyć bezpłatnej usługi wyszukiwania, ale zalecamy użycie poziomu Podstawowy lub wyższego dla większych zbiorów danych.
- Aby uruchomić przykład zapytania, który wywołuje semantyczne ponowne ocenianie, usługa wyszukiwania musi mieć włączony semantyczny ranker.
Program Visual Studio Code z klientem REST.

Uzyskiwanie informacji o usłudze

Żądania do punktu końcowego wyszukiwania muszą być uwierzytelnione i autoryzowane. W tym zadaniu można użyć kluczy interfejsu API lub ról. Zalecamy używanie połączenia bez klucza za pośrednictwem identyfikatora Entra firmy Microsoft.

Wybierz kartę odpowiadającą preferowanej metodzie uwierzytelniania. Użyj tej samej metody dla wszystkich żądań w tym przewodniku Szybki start.

Tożsamość Microsoft Entra
Klucz interfejsu API

Zaloguj się do witryny Azure Portal i znajdź usługę wyszukiwania.
Na stronie głównej Przegląd znajdź adres URL. Przykładowy punkt końcowy może wyglądać podobnie jak https://mydemo.search.windows.net.
Wykonaj kroki opisane w przewodniku Szybki start bez kluczy, aby uzyskać token entra firmy Microsoft.

Token otrzymasz po uruchomieniu az account get-access-token polecenia w kroku 3 poprzedniego przewodnika Szybki start.
```
az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
```

Zaloguj się do witryny Azure Portal i znajdź usługę wyszukiwania.
Na stronie głównej Przegląd znajdź adres URL. Przykładowy punkt końcowy może wyglądać podobnie jak https://mydemo.search.windows.net.
Wybierz pozycję Klucze ustawień>. Klucze interfejsu API lub Oba muszą być włączone. Klucze interfejsu API administratora służą do dodawania, modyfikowania i usuwania obiektów. Istnieją dwa zamienne klucze administratora. Skopiuj jedną z nich.

Tworzenie lub pobieranie pliku kodu

Użyj jednego .rest pliku lub .http , aby uruchomić wszystkie żądania w tym przewodniku Szybki start. Możesz pobrać plik REST zawierający kod tego przewodnika Szybki start lub utworzyć nowy plik w programie Visual Studio Code i skopiować do niego kod.

W programie Visual Studio Code utwórz nowy plik z .rest rozszerzeniem lub .http . Na przykład az-search-vector-quickstart.rest. Skopiuj i wklej nieprzetworzona zawartość pliku Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest do tego nowego pliku.
W górnej części pliku zastąp wartość symbolu zastępczego adresem @baseUrl URL usługi wyszukiwania. Zobacz sekcję Pobieranie informacji o usłudze , aby uzyskać instrukcje dotyczące znajdowania adresu URL usługi wyszukiwania.
```
@baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE
```
W górnej części pliku zastąp wartość symbolu zastępczego uwierzytelniania. Zobacz sekcję Pobieranie informacji o usłudze , aby uzyskać instrukcje dotyczące uzyskiwania tokenu lub klucza interfejsu API firmy Microsoft.

W przypadku zalecanego uwierzytelniania bez klucza za pośrednictwem identyfikatora Entra firmy Microsoft należy zastąpić @apiKey zmienną @token .
```
@token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HERE
```
Jeśli wolisz użyć klucza interfejsu API, zastąp ciąg @apiKey kluczem skopiowanym z witryny Azure Portal.
```
@apiKey = PUT-YOUR-ADMIN-KEY-HERE
```
W przypadku zalecanego uwierzytelniania bez klucza za pośrednictwem identyfikatora Entra firmy Microsoft należy zastąpić api-key: {{apiKey}} ciąg ciągiem Authorization: Bearer {{token}} w nagłówkach żądania. Zastąp wszystkie wystąpienia api-key: {{apiKey}} , które znajdziesz w pliku.

Tworzenie indeksu wektorowego

Interfejs API REST tworzenia indeksu służy do tworzenia indeksu wektora i konfigurowania fizycznych struktur danych w usłudze wyszukiwania.

Schemat indeksu w tym przykładzie jest zorganizowany wokół zawartości hotelowej. Przykładowe dane składają się z wektorowych i niewektorowych opisów fikcyjnych hoteli. Ten schemat obejmuje konfiguracje indeksowania wektorów i zapytań oraz klasyfikacji semantycznej.

W programie Visual Studio Code otwórz az-search-vector-quickstart.rest utworzony wcześniej plik.

### Create a new index Znajdź blok kodu w pliku. Ten blok zawiera żądanie utworzenia indeksu hotels-vector-quickstart w usłudze wyszukiwania.

### Create a new index
POST  {{baseUrl}}/indexes?api-version=2025-09-01  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" }
                    ]
                }
            }
        ]
    }
}

Wybierz pozycję Wyślij żądanie. Powinna zostać wyświetlona HTTP/1.1 201 Created odpowiedź.

Treść odpowiedzi powinna zawierać reprezentację JSON schematu indeksu.

{
    "@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"
            }
            ]
        }
        }
    ]
    }
}

Najważniejsze wnioski dotyczące interfejsu API REST tworzenia indeksu:

Kolekcja fields zawiera wymagane pole klucza oraz pola tekstowe i wektorowe (takie jak Description i DescriptionVector) do wyszukiwania tekstu i wektora. Kolokowanie wektorów i pól niewektorów w tym samym indeksie umożliwia wykonywanie zapytań hybrydowych. Na przykład można połączyć filtry, wyszukiwanie tekstu z semantycznym rankingiem i wektorami w jedną operację zapytania.
Pola wektorowe muszą być jednym z typów danych EDM używanych dla wektorów, takich jak type: Collection(Edm.Single). Pola wektorowe mają właściwości dimensions i vectorSearchProfile.
Sekcja vectorSearch jest tablicą konfiguracji i profili Algorytmu Przybliżonego Najbliższego Sąsiada (ANN). Obsługiwane algorytmy obejmują hierarchiczny mały świat z możliwością nawigacji i wyczerpujący najbliższy sąsiad K. Aby uzyskać więcej informacji, zobacz Ocenianie istotności w wyszukiwaniu wektorów.
Konfiguracja (opcjonalnie) semantic umożliwia ponowne korbowanie wyników wyszukiwania. Wyniki można ponowić w zapytaniach typu semantic dla pól ciągów określonych w konfiguracji. Aby dowiedzieć się więcej, zobacz Semantic ranking overview (Omówienie klasyfikacji semantycznej).

Przekaż dokumenty

Tworzenie i ładowanie indeksu to oddzielne kroki. Schemat indeksu został utworzony w poprzednim kroku. Teraz musisz załadować dokumenty do indeksu.

W usłudze Azure AI Search indeks zawiera wszystkie dane z możliwością wyszukiwania i zapytania uruchamiane w usłudze wyszukiwania. W przypadku wywołań REST dane są udostępniane jako dokumenty JSON. Użyj interfejsu API REST indeksu dokumentów dla tego zadania. Identyfikator URI został rozszerzony w celu uwzględnienia docs kolekcji i index operacji.

Sformułuj żądanie przekazania dokumentów w celu przekazania dokumentów do indeksu hotels-vector-quickstart w usłudze wyszukiwania.

### Upload documents
POST {{baseUrl}}/indexes/hotels-quickstart-vectors/docs/index?api-version=2025-09-01  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"
                    }
                }
            }
        }
    ]
}

Wybierz pozycję Wyślij żądanie. Powinna zostać wyświetlona HTTP/1.1 200 OK odpowiedź. Treść odpowiedzi powinna zawierać reprezentację JSON dokumentów wyszukiwania.

Najważniejsze wnioski dotyczące żądania interfejsu API REST indeksu :

Dokumenty w ładunku składają się z pól zdefiniowanych w schemacie indeksu.
Pola wektorowe zawierają wartości zmiennoprzecinkowe. Atrybut wymiarów ma co najmniej 2 i maksymalnie 4096 wartości zmiennoprzecinkowych. Ten przewodnik Szybki start ustawia atrybut wymiarów na 1536, ponieważ jest to rozmiar osadzeń wygenerowany przez model Azure OpenAI text-embedding-3-small.

Uruchamianie zapytań

Teraz, gdy dokumenty są ładowane, możesz uruchamiać zapytania wektorowe względem nich przy użyciu funkcji Dokumenty — wyszukiwanie w wpisie (REST).

W następnych sekcjach uruchamiamy zapytania względem indeksu hotels-vector-quickstart . Zapytania obejmują:

Wyszukiwanie jednowektorowe
Wyszukiwanie jednowektorowe z filtrem
Wyszukiwanie hybrydowe
Wyszukiwanie hybrydowe semantyczne

Przykładowe zapytania są oparte na dwóch ciągach:

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): quintessential lodging near running trails, eateries, retail

Ciąg zapytania wektorowego jest semantycznie podobny do ciągu wyszukiwania, ale zawiera terminy, które nie istnieją w indeksie wyszukiwania. Jeśli wyszukujesz quintessential lodging near running trails, eateries, retailsłowo kluczowe , wyniki są zerowe w czystym wyszukiwaniu słów kluczowych bez klasyfikacji semantycznej. W tym przykładzie pokazano, jak uzyskać odpowiednie wyniki przy użyciu wektorów, nawet jeśli nie ma pasujących terminów.

Wyszukiwanie jednowektorowe

Sformułuj żądanie. Zapytanie to 1536-floatowa reprezentacja idealnego zakwaterowania w pobliżu biegowych szlaków, restauracji, sklepów detalicznych. Zapytanie wyszukuje DescriptionVector i zwraca wyniki k-5. Używa parametru nadpisania "kompletny", aby wykonać pełne skanowanie indeksu zamiast ANN. Wyczerpujące wyszukiwanie jest przydatne w przypadku małych indeksów.

### Run a single vector query
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2025-09-01  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
            }
        ]
    }

Najważniejsze informacje dotyczące Documents - Search Post REST API:

vectorQueries.vector to ciąg zapytania wektorowego. Jest to wektorowa reprezentacja typowego zakwaterowania w pobliżu tras biegowych, restauracji, handlu detalicznego, która jest wektoryzowana do 1536 wektorów dla tego zapytania.
fields określa, które pola wektorowe są przeszukiwane.
kind ustawienie na vector oznacza, że ciąg znaków zapytania jest wektorem. Jeśli kind ustawiono na text, potrzebujesz dodatkowej funkcji (wektoryzatora), aby zakodować tekst czytelny dla człowieka do postaci wektora w czasie zapytania. Wektoryzatory zostaną pominięte w tym przewodniku Szybki start, aby zachować prostotę ćwiczenia.
k określa liczbę dopasowań, które mają być zwracane w odpowiedzi. Parametr count określa liczbę dopasowań znalezionych w indeksie. Uwzględnianie liczby jest najlepszym rozwiązaniem w przypadku zapytań, ale jest mniej przydatne w przypadku wyszukiwania podobieństwa, w którym algorytm może znaleźć jakiś stopień podobieństwa w niemal każdym dokumencie.

Wybierz pozycję Wyślij żądanie. Powinna zostać wyświetlona HTTP/1.1 200 OK odpowiedź. Treść odpowiedzi powinna zawierać reprezentację JSON wyników wyszukiwania.

Odpowiedź na odpowiednik wektorowy quintessential lodging near running trails, eateries, retail obejmuje k-5 wyników, mimo że wyszukiwarka znalazła 7 dopasowań. Najlepsze wyniki są uważane za najbardziej semantycznie podobne do zapytania. Każdy wynik zawiera wynik wyszukiwania i pola wymienione w selectpliku . W wyszukiwaniu podobieństwa odpowiedź zawsze zawiera k wyniki uporządkowane według wyniku podobieństwa wartości.

{
  "@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"
      ]
    }
  ]
}

Wyszukiwanie jednowektorowe z filtrem

Sformułuj żądanie. Jest to to samo żądanie co poprzednie, z dodatkowym parametrem filtru i trybu filtrowania.

### Run a vector query with a filter
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2025-09-01  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
        },
    ]
}

Wybierz pozycję Wyślij żądanie. Powinna zostać wyświetlona HTTP/1.1 200 OK odpowiedź. Treść odpowiedzi powinna zawierać reprezentację JSON wyników wyszukiwania.

Zapytanie było takie samo jak w poprzednim przykładzie wyszukiwania pojedynczego wektora, ale zawiera filtr wykluczeń po przetworzeniu i zwraca tylko dwa hotele, które mają bezpłatny 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"
      ]
    }
  ]
}

Oto kolejne zapytanie, które używa filtru geograficznego, ograniczając wyniki do hoteli w odległości 300 kilometrów wokół Waszyngtonu.

### Run a vector query with a geo filter
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2025-09-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "count": true,
        "select": "HotelId, HotelName, Description, Address/City, Address/StateProvince",
        "top": 5,
        "facets": [ "Address/StateProvince"],
        "filter": "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
        "vectorFilterMode": "postFilter",
        "vectorQueries": [
        {
            "vector": 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            "k": 5,
            "fields": "DescriptionVector",
            "kind": "vector",
            "exhaustive": true
        },
    ]
}

Przejrzyj odpowiedź. Jest ograniczony do dwóch hoteli w pobliżu Waszyngtonu D.C., z strukturą fasetową zapewniającą agregację.

"@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"
    }
  }
]

Wyszukiwanie hybrydowe

Ciąg wyszukiwania: historic hotel walk to restaurants and shopping
Ciąg zapytania wektorowego (wektoryzowany do reprezentacji matematycznej): quintessential lodging near running trails, eateries, retail

Sformułuj zapytanie hybrydowe.

### Run a hybrid query
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2025-09-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

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

Wybierz pozycję Wyślij żądanie. Powinna zostać wyświetlona HTTP/1.1 200 OK odpowiedź. Treść odpowiedzi powinna zawierać reprezentację JSON wyników wyszukiwania.

Ponieważ jest to zapytanie hybrydowe, wyniki są klasyfikowane przez wzajemne łączenie rangi (RRF). Zwróć uwagę, że @search.score wartości mają inną podstawę i są równomiernie mniejsze. Funkcja RRF ocenia wyniki wyszukiwania wielu wyników wyszukiwania, przyjmuje odwrotność, a następnie scala i sortuje połączone wyniki. Zwracana top jest liczba wyników.

Przejrzyj odpowiedź składającą się z najlepszych dopasowań k-5 z 7 pasujących dokumentów w indeksie:

{
  "@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."
    }
  ]
}

Ponieważ funkcja RRF scala wyniki, pomaga przeglądać dane wejściowe indywidualnie.

Następujące wyniki pochodzą z pełnotekstowej części zapytania: zabytkowy hotel w pobliżu restauracji i sklepów. W zapytaniu pełnotekstowym trzy najlepsze wyniki to Sublime Palace Hotel, Stay-Kay City Hotel i Luxury Lion Resort. Hotel Sublime Palace ma silniejszy wynik istotności BM25. W zapytaniu połączonym tylko dwa z tych meczów znajdują się w pierwszej 3, a drugi mecz (Stay-Kay City Hotel) w ogóle nie pojawia się w pierwszej 5.

  "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."
    },
    ...
  ]

W zapytaniu dotyczącym części wektorowej (idealne zakwaterowanie w pobliżu tras biegowych, restauracji, handlu detalicznego) jest innym ciągiem tekstowym, więc powinniśmy oczekiwać różnych rezultatów. To zapytanie zwraca Nordick's Valley Motel, Luxury Lion Resort i Sublime Palace Hotel jako trzy najlepsze mecze. W zapytaniu połączonym te wyniki znajdują się w pierwszej 3, ale w odwrotnej kolejności.

  "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."
    },
   ...
  ]

Wyszukiwanie hybrydowe semantyczne

Oto ostatnie zapytanie w kolekcji. To zapytanie hybrydowe dodaje klasyfikację semantyczną L2, która stosuje zrozumienie tekstu przez maszynę w wynikach rankingu L1, wynosząc bardziej odpowiednie dopasowania na szczyt.

Sformułuj żądanie.

### Run a hybrid query with semantic reranking
POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2025-09-01  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
        }
    ]
}

Wybierz pozycję Wyślij żądanie. Powinna zostać wyświetlona HTTP/1.1 200 OK odpowiedź. Treść odpowiedzi powinna zawierać reprezentację JSON wyników wyszukiwania.

Przejrzyj odpowiedź, składającą się z semantycznego ponownego uszeregowania wyników uszeregowanych przez RRF hybrydowego zapytania. Klasyfikacja semantyczna działa poza wprowadzaniem tekstu. W zapytaniu tekstowym lub hybrydowym dane wejściowe są częścią tekstową zapytania (zabytkowy spacer hotelowy do restauracji i zakupów). Aby użyć klasyfikacji semantycznej dla czystego zapytania wektorowego, takiego jak pierwszy przykład, lub jawnie kontrolować tekst używany do klasyfikacji semantycznej, podaj ciąg semanticQuery.

Po semantycznym rerankingu, Swirling Currents Hotel z odniesieniem do pieszego dostępu do zakupów, restauracji, rozrywki i centrum miasta przesuwa się na najwyższą pozycję. Model rozumienia maszynowego promuje dostęp do sklepów i lokali gastronomicznych w zasięgu spaceru jako bliższe dopasowanie do spacer do restauracji i sklepów.

Przed semantycznym rerankingiem, Pałac Wzniosły, z odniesieniem do odległości spaceru od miejsc i zabytków miasta i jest otoczony niezwykłym pięknem kościołów, budynków, sklepów i zabytków było numerem jeden.

{
  "@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"
    }
  ]
}

Wskazówka

Semantycznie sklasyfikowane wyniki mogą zawierać więcej szczegółów, w tym semantyczne odpowiedzi, podpisy i wyróżnienia. Dodanie kolejnych parametrów do żądania powoduje utworzenie dodatkowych szczegółów. Aby uzyskać więcej informacji, zobacz Konfigurowanie zapytania semantycznego.

Czyszczenie

Zasoby można znaleźć w witrynie Azure Portal i zarządzać nimi, korzystając z linku Wszystkie zasoby lub Grupy zasobów w okienku po lewej stronie.

Jeśli chcesz zachować usługę wyszukiwania, ale usunąć indeks i dokumenty, możesz użyć DELETE polecenia w kliencie REST. To polecenie (na końcu az-search-vector-quickstart.rest pliku) usuwa hotels-vector-quickstart indeks:

### Delete an index
DELETE  {{baseUrl}}/indexes/hotels-vector-quickstart?api-version=2025-09-01 HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

Następne kroki

W następnym kroku zalecamy, aby dowiedzieć się, jak wywoływać wywołania interfejsu API REST bez kluczy interfejsu API.

Możesz również przejrzeć kod demonstracyjny dla języka Python, C# lub JavaScript.

Sprzężenie zwrotne

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Last updated on 2025-11-20

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Szybki start: wyszukiwanie wektorów

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Tworzenie indeksu wektora

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Następne kroki

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Uzyskiwanie punktów końcowych usługi

Konfigurowanie projektu Java

Zaloguj się do Azure

Tworzenie indeksu wektora

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Tworzenie zapytania jako wektora

Tworzenie pojedynczego wyszukiwania wektorowego

Tworzenie pojedynczego wyszukiwania wektorowego z filtrem

Tworzenie wyszukiwania za pomocą filtru geoprzestrzennego

Tworzenie wyszukiwania hybrydowego

Tworzenie semantycznego wyszukiwania hybrydowego

Czyszczenie

Następne kroki

Wymagania wstępne

Uzyskiwanie punktów końcowych usługi

Konfigurowanie projektu Node.JS

Konfigurowanie zmiennych środowiskowych na potrzeby programowania lokalnego

Zaloguj się do Azure

Tworzenie indeksu wektora

Przekazywanie dokumentów do indeksu

Tworzenie zapytania jako wektora

Tworzenie pojedynczego wyszukiwania wektorowego

Tworzenie pojedynczego wyszukiwania wektorowego z filtrem

Tworzenie wyszukiwania za pomocą filtru geoprzestrzennego

Tworzenie wyszukiwania hybrydowego

Tworzenie semantycznego wyszukiwania hybrydowego

Czyszczenie

Następne kroki

Wymagania wstępne

Uzyskiwanie punktów końcowych usługi

Konfigurowanie projektu Node.JS

Konfigurowanie zmiennych środowiskowych na potrzeby programowania lokalnego

Zaloguj się do Azure

Tworzenie indeksu wektora

Przekazywanie dokumentów do indeksu

Tworzenie zapytania jako wektora

Tworzenie pojedynczego wyszukiwania wektorowego

Tworzenie pojedynczego wyszukiwania wektorowego z filtrem

Tworzenie wyszukiwania za pomocą filtru geoprzestrzennego

Tworzenie wyszukiwania hybrydowego

Tworzenie semantycznego wyszukiwania hybrydowego

Czyszczenie

Następne kroki

Wymagania wstępne

Uzyskiwanie informacji o usłudze

Klonowanie kodu i środowiska konfiguracji

Utwórz indeks wektorowy i wyślij dokumenty

Uruchamianie zapytań wyszukiwania

Wyszukiwanie jednowektorowe

Wyszukiwanie jednowektorowe z filtrem

Wyszukiwanie hybrydowe

Semantyczne wyszukiwanie hybrydowe z filtrem

Czyszczenie

Następne kroki

Wymagania wstępne

Uzyskiwanie informacji o usłudze

Tworzenie lub pobieranie pliku kodu

Tworzenie indeksu wektorowego

Przekaż dokumenty

Uruchamianie zapytań

Wyszukiwanie jednowektorowe

Wyszukiwanie jednowektorowe z filtrem

Wyszukiwanie hybrydowe

Wyszukiwanie hybrydowe semantyczne