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Rychlý start: Vektorové vyhledávání

V tomto rychlém startu použijete poznámkový blok Jupyter k vytváření, načítání a dotazování vektorů. Příklady kódu provádějí tyto operace pomocí klientské knihovny Azure AI Search. Knihovna poskytuje abstrakci rozhraní REST API pro přístup k operacím indexu, jako je příjem dat, operace vyhledávání a operace správy indexů.

Ve službě Azure AI Search má úložiště vektorů schéma indexu, které definuje vektorová a nevectorová pole, konfiguraci vektorového vyhledávání algoritmů, které vytvářejí vložený prostor a nastavení definic vektorových polí, které se vyhodnocují v době dotazu. Rozhraní REST API pro vytvoření indexu vytvoří vektorové úložiště.

Poznámka:

Tento rychlý start vynechá krok vektorizace a poskytuje vložené vkládání. Pokud chcete přidat předdefinované bloky dat a vektorizaci nad vlastním obsahem, vyzkoušejte průvodce importem dat (nový) pro kompletní názorný postup.

Požadavky

Získání informací o službě

Požadavky na koncový bod vyhledávání musí být ověřeny a autorizované. I když je možné pro tuto úlohu použít klíče rozhraní API nebo role, doporučujeme použít připojení bez klíčů prostřednictvím ID Microsoft Entra.

V tomto rychlém startu se používá DefaultAzureCredential, což zjednodušuje ověřování ve vývojových i produkčních scénářích. V případě produkčních scénářů ale můžete mít pokročilejší požadavky, které vyžadují jiný přístup. Informace o všech možnostech najdete v tématu Ověřování aplikací Pythonu ve službách Azure pomocí sady Azure SDK pro Python .

Klonování kódu a nastavení prostředí

  1. Naklonujte úložiště obsahující kód pro účely tohoto rychlého startu.

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

    Toto úložiště obsahuje příklady kódu Pythonu pro několik článků v samostatné podsložce.

  2. V editoru Visual Studio Code otevřete podsložku Quickstart-Vector-Search.

    V této složce jsou tři soubory:

    • vector-search-quickstart.ipynb
    • requirements.txt
    • sample.env
  3. Přejmenujte soubor sample.env na .env a upravte hodnoty v souboru .env.

    Použijte adresu URL vyhledávací služby jako AZURE_SEARCH_ENDPOINT. Adresu URL najdete na webu Azure Portal. Přejděte do služby Azure AI Search na stránce Přehled a vyhledejte pole ADRESY URL. Příkladem koncového bodu může být https://mydemo.search.windows.net.

    Nakonec zvolte nový AZURE_SEARCH_INDEX_NAME název nebo použijte název zadaný v souboru.

  4. V editoru Visual Studio Code můžete pracovat v prostředí. V nabídce Zobrazit vyberte Terminál... nebo stiskněte Ctrl+`.

  5. V terminálu spusťte následující příkazy:

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

    Poznámka:

    Předpokládá se, že v terminálu používáte Git Bash a používáte Windows. Pokud používáte jiné prostředí nebo jiný operační systém, upravte tyto pokyny pro konkrétní prostředí.

    Pokud se zobrazí výzva, povolte editoru Visual Studio Code používat nové prostředí.

    Příkaz where python ověří, že pracujete z virtuálního prostředí, tím, že uvede python.exe ve složce Quickstart-Vector-Search\.venv\ a na dalších umístěních ve vašem adresářovém systému.

  6. Nainstalujte požadované knihovny spuštěním následujícího příkazu:

    pip install requirements.txt
    
  7. V editoru Visual Studio Code otevřete soubor vector-search-quickstart.ipynb.

    Poznámka:

    Pokud jste v editoru Visual Studio Code použili jupyter Notebook (.ipynb) poprvé, zobrazí se výzva k instalaci jádra Jupyter Notebook a případně dalších nástrojů. Zvolte instalaci navrhovaných nástrojů, abyste mohli pokračovat v tomto rychlém startu.

  8. Najděte buňku pod názvem Instalovat balíčky a nastavte proměnné a vyberte tlačítko Spustit buňku (Ctrl + Alt + Enter) (které vypadá jako typické tlačítko spuštění) nalevo od buňky. Spuštění buňky načte proměnné prostředí, vytvoří DefaultAzureCredential a vypíše hodnoty na výstup, aby se ověřilo, že jsou závislosti poznámkového bloku a .env správně nastavené.

    # 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 
    

    Provedení této buňky vytvoří následující výsledek.

    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
    ...
    

    Existuje mnoho dalších balíčků, které můžete zobrazit v posouvatelném prvku (viz zpráva pod výsledky buňky).

Vytvoření vektorového indexu

Kód v vector-search-quickstart.ipynb knihovně azure.search.documents používá několik metod k vytvoření vektorového indexu a prohledávatelných polí.

  1. Najděte buňku pod názvem Vytvořit index a spusťte buňku, abyste vytvořili index.

    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')
    

    Pokud se index úspěšně vytvoří, zobrazí se pod buňkou následující výsledek:

    vector-search-quickstart created
    

    Klíčové poznatky při vytváření vektorového indexu pomocí azure.search.documents:

    • Index definujete vytvořením seznamu polí. Každé pole se vytvoří pomocí pomocné metody, která definuje typ pole a jeho nastavení.

    • Tento konkrétní index podporuje více možností vyhledávání, například:

      • Fulltextové vyhledávání klíčových slov (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))
      • Vektorové vyhledávání (umožňuje hybridní vyhledávání umístěním vektorových a nevektorových polí vedle sebe) (DescriptionVector)
      • Sémantické vyhledávání (semantic_search=SemanticSearch(configurations=[semantic_config]))
      • Fasetové vyhledávání (facetable=True)
      • Sémantické vyhledávání (semantic_search=SemanticSearch(configurations=[semantic_config]))
      • Geografické prostorové vyhledávání (Location pole je GeographyPoint)
      • Filtrování, řazení (mnoho polí označených jako filtrovatelné a řaditelné)

Nahrát dokumenty

Vytvoření a načtení indexu jsou samostatné kroky. Schéma indexu jste vytvořili v předchozím kroku. Teď potřebujete načíst dokumenty do indexu.

Index ve službě Azure AI Search ukládá veškerý prohledávatelný obsah, zatímco vyhledávací web provádí dotazy na tento index.

  1. Najděte buňku pod sekcí s názvem "Create documents payload" (Vytvořit datovou část dokumentů) a spusťte ji. Tato buňka obsahuje následující kód (zkrácený kvůli stručnosti):

       # 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>
       ]
    

    Tato buňka načte proměnnou s documents objektem JSON popisujícím každý dokument spolu s vektorizovanou verzí popisu článku. Tento vektor umožňuje vyhledávání podobnosti, kde porovnávání je založeno na významu, nikoli na přesných klíčových slovech.

    Důležité

    Kód v tomto příkladu nejde spustit. Kvůli stručnosti se odebere několik znaků nebo řádků. Místo toho spusťte kód v poznámkovém bloku Jupyter.

  2. Najděte buňku pod sekcí s názvem "Nahrát dokumenty" a spusťte tu buňku. Tato buňka obsahuje následující kód (zkrácený kvůli stručnosti):

    # 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)
    

    Tím se vytvoří instance vyhledávacího klienta voláním SearchClient() konstruktoru a pak zavolá metodu upload_documents() objektu.

    Po spuštění buňky se pod ní zobrazí stav každého dokumentu.

    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
    

    Klíčové poznatky o metodě a tomto příkladu upload_documents() :

    • Váš kód komunikuje s konkrétním indexem vyhledávání hostovaným ve službě Azure AI Search prostřednictvím SearchClienthlavního objektu poskytovaného balíčkem azure-search-documents . Poskytuje SearchClient přístup k operacím indexu, například:

      • Příjem dat - upload_documents(), merge_documents(), delete_documents(), atd.
      • Vyhledávací operace - search(), autocomplete(), suggest()
      • Operace správy indexů
    • Vektorová pole obsahují hodnoty s plovoucí desetinou čárkou. Atribut dimenzí má minimálně 2 a maximálně 4096 hodnoty s plovoucí desetinnou čárkou. Tento rychlý start nastaví atribut dimenzí na 1 536, protože to je velikost vkládání vygenerovaných modelem Azure OpenAI pro vkládání textu-ada-002 .

Spouštění dotazů

Teď, když jsou dokumenty načtené, můžete vůči nim vydat vektorové dotazy voláním search_client.search() a předáním objektu VectorizedQuery, požadovaným polím, počtem výsledků atd.

V dalších částech spustíme dotazy na hotels-vector-quickstart index. Mezi dotazy patří:

Vytvoření řetězce vektorového dotazu

Ukázkové vektorové dotazy jsou založené na dvou řetězcích:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping
  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): quintessential lodging near running trails, eateries, retail

Řetězec vektorového dotazu je sémanticky podobný hledanému řetězci, ale obsahuje výrazy, které v indexu vyhledávání neexistují. Pokud hledáte quintessential lodging near running trails, eateries, retailklíčové slovo, výsledky jsou nulové. Tento příklad používáme k zobrazení toho, jak můžete získat relevantní výsledky, i když neexistují žádné odpovídající termíny.

  • Najděte buňku pod názvem "Create the vector query string" (Vytvořit řetězec vektorového dotazu) a spusťte buňku. Tím se vector načte proměnná s vektorizovanými daty dotazu potřebnými ke spuštění všech hledání v dalších částech.

První příklad ukazuje základní scénář, ve kterém chcete najít popisy dokumentů, které přesně odpovídají hledanému řetězci.

  • Najděte buňku pod sekcí s názvem "Jednoduché vektorové vyhledávání" a spusťte ji. Tento blok obsahuje požadavek na dotazování indexu vyhledávání.

    # 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.")
    

    Tento vektorový dotaz je zkrácen pro stručnost. Obsahuje vectorQueries.vector vektorizovaný text vstupu dotazu, určuje, fields která vektorová pole se prohledávají, a k určuje počet nejbližších sousedů, které se mají vrátit.

    Řetězec vektorového dotazu je quintessential lodging near running trails, eateries, retail, který je vektorizován do 1 536 vkládání pro tento dotaz.

    Odpověď pro vektorový ekvivalent quintessential lodging near running trails, eateries, retail se skládá ze 7 výsledků, ale kód určuje top=5 , takže se vrátí pouze prvních 5 výsledků. Navíc se vrátí pouze pole zadaná v poli select .

    search_client.search() vrátí objekt podobný dictu. Každý výsledek poskytuje skóre hledání, ke kterému lze získat přístup pomocí score = result.get("@search.score", "N/A"). Ačkoli se v tomto příkladu nezobrazuje, odpověď vždy obsahuje k výsledky seřazené podle skóre podobnosti.

    Po spuštění buňky se pod ní zobrazí stav každého dokumentu.

    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
    

Jednoduché vektorové vyhledávání s filtrem

Můžete přidat filtry, ale filtry se použijí na obsah nevectoru ve vašem indexu. V tomto příkladu Tags se filtr vztahuje na pole a vyfiltruje všechny hotely, které neposkytují bezplatné Wi-Fi.

  1. Najděte buňku pod sekcí s názvem "Jednoduché vektorové hledání s filtrem" a spusťte tuto buňku. Tato buňka obsahuje požadavek na dotazování indexu vyhledávání.

    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 spuštění buňky se pod ní zobrazí stav každého dokumentu.

    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']
    

    Dotaz byl stejný jako předchozí příklad jednoúčelového vyhledávání, ale obsahuje filtr vyloučení po zpracování a vrátí pouze dva hotely, které mají bezplatné Wi-Fi.

  2. Následující příklad filtru používá geografický filtr. Spusťte buňku v části s názvem Vektorový dotaz s geografickým filtrem. Tento blok obsahuje požadavek na dotazování indexu vyhledávání.

    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.")
    

    Dotaz byl stejný jako v předchozím příkladu jednoúčelového vyhledávání, ale zahrnuje filtr vyloučení po zpracování a vrátí pouze dva hotely v okruhu 300 kilometrů.

    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.
    

Hybridní vyhledávání se skládá z dotazů klíčových slov a vektorových dotazů v jediné žádosti o vyhledávání. Tento příklad spustí vektorový dotaz a fulltextové vyhledávání souběžně:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping

  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): quintessential lodging near running trails, eateries, retail

  • Najděte buňku s názvem "Hybridní hledání" a spusťte ji. Tento blok obsahuje požadavek na dotazování indexu vyhledávání.

    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.")    
    

    Zkontrolujte odpověď:

    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']
    

    Vzhledem k tomu, že reciproční Rank Fusion (RRF) sloučí výsledky, pomáhá zkontrolovat vstupy. Následující výsledky pocházejí pouze z fulltextového dotazu. Nejlépe hodnocenými výsledky jsou dva hotely: Sublime Palace Hotel a Luxury Lion Resort. Sublime Palace Hotel má silnější skóre relevance 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"
    },
    

    V dotazu pouze vektoru, který používá HNSW k hledání shody, Sublime Palace Hotel klesne na čtvrtou pozici. Luxury Lion, který byl druhý v fulltextovém vyhledávání a třetí ve vektorovém vyhledávání, nemá stejný rozsah kolísání, takže se zobrazuje jako nejlepší shoda v homogenizované sadě výsledků.

    "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"
         }
    ]
    

Tady je poslední dotaz v kolekci. Tento hybridní dotaz určuje sémantický typ dotazu a sémantickou konfiguraci, což demonstruje, že můžete vytvořit hybridní dotaz, který používá sémantické reranking.

  • Najděte buňku pod sekcí s názvem "Sémantické hybridní vyhledávání" a spusťte ji. Tento blok kódu obsahuje požadavek na dotazování indexu vyhledávání.

    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.")
    

    Prohlédněte si výstup pod buňkou.

    Swirling Currents Hotel se nyní díky sémantickému hodnocení přesune na nejvyšší místo. 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
    

Sémantické řazení si můžete představit jako způsob, jak zlepšit význam výsledků hledání pochopením významu slov v dotazu a obsahu dokumentů. V tomto případě sémantické hodnocení pomáhá identifikovat hotely, které nejsou relevantní jenom pro klíčová slova, ale také odpovídat záměru dotazu:

Hlavní klíčové body:

  • Vektorové vyhledávání je určeno prostřednictvím vectors.value vlastnosti. Vyhledávání klíčových slov je určeno prostřednictvím search vlastnosti.

  • V hybridním vyhledávání můžete integrovat vektorové vyhledávání s fulltextovým vyhledáváním pomocí klíčových slov. Filtry, kontrola pravopisu a sémantické řazení se vztahují pouze na textový obsah, nikoli na vektory. V tomto posledním dotazu není žádný sémantický answer , protože systém nevytvořil takový, který by byl dostatečně silný.

  • Skutečné výsledky obsahují více podrobností, včetně sémantických titulků a zvýraznění. Výsledky byly upraveny tak, aby byly čitelné. Pokud chcete získat úplnou strukturu odpovědi, spusťte požadavek v klientovi REST.

Vyčištění

Pokud pracujete s vlastním předplatným, je vhodné vždy na konci projektu zkontrolovat, jestli budete vytvořené prostředky ještě potřebovat. Prostředky, které necháte spuštěné, vás stojí peníze. Prostředky můžete odstraňovat jednotlivě nebo můžete odstranit skupinu prostředků, a odstranit tak celou sadu prostředků najednou.

Prostředky můžete najít a spravovat na webu Azure Portal pomocí odkazu Všechny prostředky nebo skupiny prostředků v levém podokně.

Pokud chcete zachovat vyhledávací službu, ale odstranit index a dokumenty, můžete použít metodu objektu SearchIndexClientdelete_index() . Najděte buňku pod názvem „Vyčistit“ a spusťte tuto buňku, pokud chcete odstranit index hotels-vector-quickstart.

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

Další kroky

  • Projděte si úložiště ukázek kódu pro možnosti vektorového vyhledávání ve službě Azure AI Search pro Python.
  • Projděte si další ukázky kódu Pythonu a Azure AI Search v úložišti azure-search-python-samples.

V tomto rychlém startu použijete Javu k vytváření, načítání a dotazování vektorů. Příklady kódu provádějí tyto operace pomocí klientské knihovny Azure AI Search. Knihovna poskytuje abstrakci rozhraní REST API pro přístup k operacím indexu, jako je příjem dat, operace vyhledávání a operace správy indexů.

Ve službě Azure AI Search má úložiště vektorů schéma indexu, které definuje vektorová a nevectorová pole, konfiguraci vektorového vyhledávání algoritmů, které vytvářejí vložený prostor a nastavení definic vektorových polí, které se vyhodnocují v době dotazu. Rozhraní REST API pro vytvoření indexu vytvoří vektorové úložiště.

Poznámka:

Tento rychlý start vynechá krok vektorizace a poskytuje vložené vkládání. Pokud chcete přidat předdefinované bloky dat a vektorizaci nad vlastním obsahem, vyzkoušejte průvodce importem dat (nový) pro kompletní názorný postup.

Požadavky

Získání koncových bodů služby

Ve zbývajících částech nastavíte volání rozhraní API pro Azure OpenAI a Azure AI Search. Získejte koncové body služby, abyste je mohli zadat jako proměnné v kódu.

  1. Přihlaste se do Azure Portalu.

  2. Vyhledejte vyhledávací službu.

  3. Na domovské stránce Přehled zkopírujte adresu URL. Příkladem koncového bodu může být https://example.search.windows.net.

Nastavení projektu Java

  1. Vytvořte nový adresář projektu Maven.

    mkdir vector-quickstart && cd vector-quickstart
    
  2. Vytvořte pom.xml soubor s požadovanými závislostmi.

    <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>
    
  3. Zkompilujte projekt pro vyřešení závislostí.

    mvn compile
    
  4. Vytvořte zdrojovou adresářovou strukturu.

    mkdir -p src/main/java/com/example/search
    mkdir -p src/main/resources
    
  5. Vytvořte application.properties soubor v src/main/resources adresáři a zadejte koncový bod vyhledávací služby.

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

Přihlásit se do Azure

Pro připojení používáte ID Microsoft Entra a přiřazení rolí. Ujistěte se, že jste přihlášeni ke stejnému tenantovi a předplatnému jako Azure AI Search a Azure OpenAI. Pomocí Azure CLI na příkazovém řádku můžete zobrazit aktuální vlastnosti, změnit vlastnosti a přihlásit se. Další informace najdete v tématu Připojení bez klíčů.

Postupně spusťte každý z následujících příkazů.

az account show

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

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

Teď byste se měli přihlásit k Azure z místního zařízení.

Vytvoření vektorového indexu

V této části vytvoříte vektorový index ve službě Azure AI Search pomocí SearchIndexClient. createOrUpdateIndex. Schéma indexu definuje pole, včetně vektorového pole DescriptionVector.

  1. Vytvořte soubor CreateIndex.java v adresáři src/main/java/com/example/search.

  2. Zkopírujte do souboru následující kód.

    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);
        }
    }
    

    Soubor kódu vytvoří index a definuje schéma indexu, včetně vektorového pole DescriptionVector.

  3. Spusťte soubor.

    mvn compile exec:java -Dexec.mainClass="com.example.search.CreateIndex"
    
  4. Výstup tohoto kódu ukazuje, že se index úspěšně vytvořil:

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

    Klíčové poznatky při vytváření vektorového indexu s knihovnou azure-search-documents :

    • Index definujete vytvořením seznamu polí.

    • Tento konkrétní index podporuje více možností vyhledávání, například:

      • Fulltextové vyhledávání klíčových slov (setSearchable)
      • Vektorové vyhledávání (umožňuje hybridní vyhledávání kombinováním vektorových a nevektorových polí) (DescriptionVector s setVectorSearchProfileName)
      • Sémantické vyhledávání
      • Fasetové vyhledávání (SearchSuggester)
      • Geografické prostorové vyhledávání (Location pole s SearchFieldDataType.GEOGRAPHY_POINT)
      • Filtrování, řazení (mnoho polí označených jako filtrovatelné a řaditelné)

Nahrání dokumentů do indexu

Vytvoření a načtení indexu jsou samostatné kroky. Schéma indexu jste vytvořili v předchozím kroku. Teď potřebujete načíst dokumenty do indexu pomocí SearchClient. uploadDocuments. Dokumenty obsahují vektorizovanou verzi popisu článku, která umožňuje vyhledávání podobnosti na základě významu, nikoli přesných klíčových slov.

Index ve službě Azure AI Search ukládá veškerý prohledávatelný obsah, zatímco vyhledávací web provádí dotazy na tento index.

  1. Vytvořte UploadDocuments.java soubor v adresáři src/main/java/com/example/search .

  2. Zkopírujte do souboru následující kód.

    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);
        }
    }
    

    Tento kód načte kolekci dokumentů. Každý dokument obsahuje vektorizovanou verzi článku Description. Tento vektor umožňuje vyhledávání podobnosti, kde porovnávání je založeno na významu, nikoli na přesných klíčových slovech.

    Vzhledem k tomu, že tento článek rychlého startu prohledá index okamžitě, waitUntilIndexed metoda čeká, až bude index připravený pro operace vyhledávání. To je důležité, protože index musí být plně naplněn dokumenty před provedením hledání.

  3. Sestavte a spusťte soubor.

    mvn compile exec:java -Dexec.mainClass="com.example.search.UploadDocuments"
    
  4. Výstup tohoto kódu ukazuje, že dokumenty jsou indexované a připravené k hledání:

    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.
    

    Klíčové poznatky o metodě a tomto příkladu uploadDocuments :

    • Váš kód komunikuje s konkrétním indexem vyhledávání hostovaným ve službě Azure AI Search prostřednictvím SearchClientslužby . Poskytuje SearchClient přístup k operacím, jako jsou:
      • Příjem dat – uploadDocuments, mergeDocumentsdeleteDocumentsatd.
      • Vyhledávací operace - search, autocompletesuggest
    • Vektorová pole obsahují hodnoty s plovoucí desetinou čárkou. Atribut dimenzí má minimálně 2 a maximálně 4096 hodnoty s plovoucí desetinnou čárkou. Tento rychlý start nastaví atribut dimenzí na 1 536, protože to je velikost vkládání generovaných modelem Azure OpenAI text-embedding-ada-002 .

Vytvoření dotazu jako vektoru

Ukázkové vektorové dotazy jsou založené na dvou řetězcích:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping
  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): quintessential lodging near running trails, eateries, retail

Řetězec vektorového dotazu je sémanticky podobný hledanému řetězci, ale obsahuje výrazy, které v indexu vyhledávání neexistují. Pokud hledáte quintessential lodging near running trails, eateries, retailklíčové slovo, výsledky jsou nulové. Tento příklad používáme k zobrazení toho, jak můžete získat relevantní výsledky, i když neexistují žádné odpovídající termíny.

Vytvořte QueryVector.java v src/main/java/com/example/search adresáři soubor a přidejte kód pro vytvoření vektoru dotazu.

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;
    }
}

Tento kód se používá v následujících částech k provádění vektorových vyhledávání. Vektor dotazu se vytvoří pomocí modelu vkládání z Azure OpenAI.

První příklad ukazuje základní scénář, ve kterém chcete najít popisy dokumentů, které úzce odpovídají hledanému řetězci pomocí SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte SearchSingle.java soubor v adresáři src/main/java/com/example/search .

  2. Zkopírujte do souboru následující kód.

    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);
        }
    }
    

    Obsahuje vectorQuery konfiguraci vektorizovaného dotazu včetně vektorizovaného textu vstupu dotazu, jak je načteno .QueryVector.getVectorList setFields určuje, která vektorová pole se prohledávají, a setKNearestNeighborsCount určuje počet nejbližších sousedů, které se mají vrátit.

    Řetězec vektorového dotazu je quintessential lodging near running trails, eateries, retail, který je vektorizován do 1 536 vkládání pro tento dotaz.

  3. Sestavte a spusťte soubor.

    mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingle"
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro dotaz 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
    

    Odpověď pro vektorový ekvivalent quintessential lodging near running trails, eateries, retail se skládá z 5 výsledků, které obsahují pouze pole určená výběrem.

Vytvoření jednoduchého vektorového vyhledávání pomocí filtru

Můžete přidat filtry, ale filtry se použijí na obsah nevectoru ve vašem indexu. V tomto příkladu Tags se filtr vztahuje na pole a vyfiltruje všechny hotely, které neposkytují bezplatné Wi-Fi. Toto hledání používá SearchClient. SearchClient a SearchOptions.

  1. Vytvořte SearchSingleWithFilter.java soubor v adresáři src/main/java/com/example/search .

  2. Zkopírujte do souboru následující kód.

    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);
        }
    }
    

    Tento kód má stejné funkce vyhledávání jako předchozí vyhledávání, ale přidá filtr vyloučení po zpracování pro hotely s free wifi.

  3. Sestavte a spusťte soubor.

    mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilter"
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro dotaz s použitým filtrem free wifi :

    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
    

Vytvoření hledání s geoprostorovým filtrem

Můžete zadat geoprostorový filtr, který omezí výsledky na konkrétní geografickou oblast. V tomto příkladu filtr omezuje výsledky na hotely v okruhu 300 kilometrů od WashingtonU D.C. (souřadnice: -77.03241 38.90166). Geoprostorové vzdálenosti jsou vždy v kilometrech. Toto hledání používá SearchClient. SearchClient a SearchOptions.

  1. Vytvořte SearchSingleWithFilterGeo.java soubor v adresáři src/main/java/com/example/search .

  2. Zkopírujte do souboru následující kód.

    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);
        }
    }
    
  3. Sestavte a spusťte soubor.

    mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSingleWithFilterGeo"
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro dotaz s použitým geoprostorovým filtrem vyloučení po zpracování.

    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
    

Hybridní vyhledávání se skládá z dotazů klíčových slov a vektorových dotazů v jediné žádosti o vyhledávání. Tento příklad spustí vektorový dotaz a fulltextové vyhledávání souběžně:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping
  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): quintessential lodging near running trails, eateries, retail

Toto hledání používá SearchClient. SearchClient a SearchOptions.

  1. Vytvořte SearchHybrid.java soubor v adresáři src/main/java/com/example/search .

  2. Zkopírujte do souboru následující kód.

    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);
        }
    }
    
  3. Sestavte a spusťte soubor.

    mvn compile exec:java -Dexec.mainClass="com.example.search.SearchHybrid"
    
  4. Výstup tohoto kódu zobrazuje relevantní dokumenty pro hybridní vyhledávání.

    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"]
    

    Vzhledem k tomu, že reciproční Rank Fusion (RRF) sloučí výsledky, pomáhá zkontrolovat vstupy. Následující výsledky pocházejí pouze z fulltextového dotazu. Dva nejlepší výsledky jsou Sublime Palace Hotel a Luxury Lion Resort. Sublime Palace Hotel má silnější skóre relevance 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"
       },
    

    V dotazu pouze vektoru, který používá HNSW k hledání shody, Sublime Palace Hotel klesne na čtvrtou pozici. Luxury Lion, který byl druhý v fulltextovém vyhledávání a třetí ve vektorovém vyhledávání, nemá stejný rozsah kolísání, takže se zobrazuje jako nejlepší shoda v homogenizované sadě výsledků.

    "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"
        }
    ]
    

Tady je poslední dotaz v kolekci, který rozšiřuje sémantické hybridní vyhledávání o další hledaný text historic hotel walk to restaurants and shopping.

Toto hledání používá SearchClient. SearchClient a SearchOptions.

  1. Vytvořte SearchSemanticHybrid.java soubor v adresáři src/main/java/com/example/search .

  2. Zkopírujte do souboru následující kód.

    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);
        }
    }
    
  3. Sestavte a spusťte soubor.

    mvn compile exec:java -Dexec.mainClass="com.example.search.SearchSemanticHybrid"
    
  4. Výstup tohoto kódu zobrazuje relevantní dokumenty pro sémantické hybridní vyhledávání.

    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
    

    Sémantické řazení si můžete představit jako způsob, jak zlepšit význam výsledků hledání pochopením významu slov v dotazu a obsahu dokumentů. V tomto případě sémantické hodnocení pomáhá identifikovat hotely, které nejsou relevantní jenom pro klíčová slova, ale také odpovídat záměru dotazu:

    Hlavní klíčové body:

    • Vektorové vyhledávání je určeno prostřednictvím VectorSearchOptions třídy. Hledání klíčových slov je určeno prostřednictvím SemanticSearchOptions třídy.

    • V hybridním vyhledávání můžete integrovat vektorové vyhledávání s fulltextovým vyhledáváním pomocí klíčových slov. Filtry, kontrola pravopisu a sémantické řazení se vztahují pouze na textový obsah, nikoli na vektory. V tomto posledním dotazu není žádný sémantický answer , protože systém nevytvořil takový, který by byl dostatečně silný.

    • Skutečné výsledky obsahují více podrobností, včetně sémantických titulků a zvýraznění. Výsledky byly upraveny tak, aby byly čitelné. Pokud chcete získat úplnou strukturu odpovědi, spusťte požadavek v klientovi REST.

Vyčištění

Pokud pracujete s vlastním předplatným, je vhodné vždy na konci projektu zkontrolovat, jestli budete vytvořené prostředky ještě potřebovat. Prostředky, které necháte spuštěné, vás stojí peníze. Prostředky můžete odstraňovat jednotlivě nebo můžete odstranit skupinu prostředků, a odstranit tak celou sadu prostředků najednou.

Prostředky můžete najít a spravovat na webu Azure Portal pomocí odkazu Všechny prostředky nebo skupiny prostředků v levém podokně.

Pokud chcete zachovat vyhledávací službu, ale odstranit index a jeho dokumenty, můžete index odstranit programově.

  1. Vytvořte DeleteIndex.java soubor v adresáři src/main/java/com/example/search .

  2. Do souboru přidejte následující kód.

    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);
        }
    }
    
  3. Sestavte a spusťte soubor.

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

Další kroky

  • Projděte si úložiště ukázek kódu pro možnosti vektorového vyhledávání ve službě Azure AI Search pro Javu.

V tomto rychlém startu použijete JavaScript k vytváření, načítání a dotazování vektorů. Příklady kódu provádějí tyto operace pomocí klientské knihovny Azure AI Search. Knihovna poskytuje abstrakci rozhraní REST API pro přístup k operacím indexu, jako je příjem dat, operace vyhledávání a operace správy indexů.

Ve službě Azure AI Search má úložiště vektorů schéma indexu, které definuje vektorová a nevectorová pole, konfiguraci vektorového vyhledávání algoritmů, které vytvářejí vložený prostor a nastavení definic vektorových polí, které se vyhodnocují v době dotazu. Rozhraní REST API pro vytvoření indexu vytvoří vektorové úložiště.

Poznámka:

Tento rychlý start vynechá krok vektorizace a poskytuje vložené vkládání. Pokud chcete přidat předdefinované bloky dat a vektorizaci nad vlastním obsahem, vyzkoušejte průvodce importem dat (nový) pro kompletní názorný postup.

Požadavky

Získání koncových bodů služby

Ve zbývajících částech nastavíte volání rozhraní API pro Azure OpenAI a Azure AI Search. Získejte koncové body služby, abyste je mohli zadat jako proměnné v kódu.

  1. Přihlaste se do Azure Portalu.

  2. Vyhledejte vyhledávací službu.

  3. Na domovské stránce Přehled zkopírujte adresu URL. Příkladem koncového bodu může být https://example.search.windows.net.

Nastavení projektu Node.JS

Nastavení projektu pomocí editoru Visual Studio Code a JavaScriptu

  1. Spusťte Visual Studio Code v novém adresáři.

    mkdir vector-quickstart && cd vector-quickstart
    code .
    
  2. Vytvořte nový balíček pro moduly ESM v adresáři projektu.

    npm init -y
    npm pkg set type=module
    

    Tím se vytvoří package.json soubor s výchozími hodnotami.

  3. Nainstalujte následující balíčky npm.

    npm install @azure/identity @azure/search-documents dotenv
    
  4. Vytvořte src adresář v adresáři projektu.

    mkdir src
    

Nastavte proměnné prostředí pro místní vývoj

  1. Vytvořte .env soubor v vector-quickstart adresáři projektu.

  2. Přidejte do .env souboru následující proměnné prostředí a nahraďte hodnoty vlastními koncovými body služby a klíči.

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

Přihlásit se do Azure

Pro připojení používáte ID Microsoft Entra a přiřazení rolí. Ujistěte se, že jste přihlášeni ke stejnému tenantovi a předplatnému jako Azure AI Search a Azure OpenAI. Pomocí Azure CLI na příkazovém řádku můžete zobrazit aktuální vlastnosti, změnit vlastnosti a přihlásit se. Další informace najdete v tématu Připojení bez klíčů.

Postupně spusťte každý z následujících příkazů.

az account show

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

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

Teď byste se měli přihlásit k Azure z místního zařízení.

Vytvoření vektorového indexu

V této části vytvoříte vektorový index ve službě Azure AI Search pomocí SearchIndexClient. createOrUpdateIndex. Schéma indexu definuje pole, včetně vektorového pole DescriptionVector.

  1. Vytvořte createIndex.js soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    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`);
    

    Soubor kódu přidá závislosti, proměnné prostředí a typ JavaScriptu do HotelDocument horní části souboru. createIndex Přidejte funkci pro vytvoření indexu. Funkce definuje schéma indexu, včetně vektorového pole DescriptionVector.

  3. Spusťte soubor:

    node -r dotenv/config src/createIndex.js
    
  4. Výstup tohoto kódu ukazuje, že se index úspěšně vytvořil:

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

    Klíčové poznatky při vytváření vektorového indexu s knihovnou @azure/search-documents :

    • Index definujete vytvořením seznamu polí.

    • Tento konkrétní index podporuje více možností vyhledávání, například:

      • Fulltextové vyhledávání klíčových slov (searchable)
      • Vektorové vyhledávání (umožňuje hybridní vyhledávání kombinováním vektorových a nevektorových polí) (DescriptionVector s vectorSearchProfileName)
      • Sémantické vyhledávání
      • Fasetové vyhledávání (searchSuggester)
      • Geografické prostorové vyhledávání (Location pole s geo.distance)
      • Filtrování, řazení (mnoho polí označených jako filtrovatelné a řaditelné)

Nahrání dokumentů do indexu

Vytvoření a načtení indexu jsou samostatné kroky. Schéma indexu jste vytvořili v předchozím kroku. Teď potřebujete načíst dokumenty do indexu pomocí SearchClient. uploadDocuments. Dokumenty obsahují vektorizovanou verzi popisu článku, která umožňuje vyhledávání podobnosti na základě významu, nikoli přesných klíčových slov.

Index ve službě Azure AI Search ukládá veškerý prohledávatelný obsah, zatímco vyhledávací web provádí dotazy na tento index.

  1. Vytvořte uploadDocuments.js soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    import { DefaultAzureCredential } from "@azure/identity";
    import {
        SearchClient
    } from "@azure/search-documents";
    
    const credential = new DefaultAzureCredential();
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    const DOCUMENTS =  [
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "1",
            "HotelName": "Stay-Kay City Hotel",
            "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
            "DescriptionVector": 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            "Category": "Boutique",
            "Tags": [
                "view",
                "air conditioning",
                "concierge"
            ],
            "ParkingIncluded": "false",
            "LastRenovationDate": "2022-01-18T00:00:00Z",
            "Rating": 3.60,
            "Address": {
                "StreetAddress": "677 5th Ave",
                "City": "New York",
                "StateProvince": "NY",
                "PostalCode": "10022",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    -73.975403,
                    40.760586
                ]
            }
        },
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "2",
            "HotelName": "Old Century Hotel",
            "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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            "Category": "Boutique",
            "Tags": [
                "pool",
                "free wifi",
                "air conditioning",
                "concierge"
            ],
            "ParkingIncluded": "false",
            "LastRenovationDate": "2019-02-18T00:00:00Z",
            "Rating": 3.60,
            "Address": {
                "StreetAddress": "140 University Town Center Dr",
                "City": "Sarasota",
                "StateProvince": "FL",
                "PostalCode": "34243",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    -82.452843,
                    27.384417
                ]
            }
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            "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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            }
        },
        {
            "@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"
                    }
                }
            }
        }
    ];
    
    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);
    }
    

    Tento kód načte pole objektů JSON. Každý dokument obsahuje vektorizovanou verzi článku description. Tento vektor umožňuje vyhledávání podobnosti, kde porovnávání je založeno na významu, nikoli na přesných klíčových slovech.

    Vzhledem k tomu, že tento článek rychlého startu prohledá index okamžitě, funkce počká, waitUntilIndexed až bude index připravený na operace vyhledávání. To je důležité, protože index musí být plně naplněn dokumenty před provedením hledání.

  3. Sestavte a spusťte soubor:

    node -r dotenv/config src/uploadDocuments.js
    
  4. Výstup tohoto kódu ukazuje, že dokumenty jsou indexované a připravené k hledání:

    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.
    

    Klíčové poznatky o metodě uploadDocuments() a tomto příkladu:

    • Váš kód komunikuje s konkrétním indexem vyhledávání hostovaným ve službě Azure AI Search prostřednictvím SearchClient, což je hlavní objekt poskytnutý balíčkem @azure/search-documents . SearchClient poskytuje přístup k operacím indexu, například:
      • Příjem dat – uploadDocuments(), mergeDocuments(), deleteDocuments() atd.
      • Vyhledávací operace – search(), autocomplete(), suggest()
      • Operace správy indexů, jako jsou createIndex(), deleteIndex(), getIndex() atd.
    • Vektorová pole obsahují hodnoty s plovoucí desetinou čárkou. Atribut dimenzí má minimálně 2 a maximálně 4096 hodnoty s plovoucí desetinnou čárkou. Tento rychlý start nastaví atribut dimenzí na 1 536, protože to je velikost vkládání vygenerovaných modelem Azure OpenAI pro vkládání textu-ada-002.

Vytvoření dotazu jako vektoru

Ukázkové vektorové dotazy jsou založené na dvou řetězcích:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping
  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): quintessential lodging near running trails, eateries, retail

Řetězec vektorového dotazu je sémanticky podobný hledanému řetězci, ale obsahuje výrazy, které v indexu vyhledávání neexistují. Pokud hledáte quintessential lodging near running trails, eateries, retailklíčové slovo, výsledky jsou nulové. Tento příklad používáme k zobrazení toho, jak můžete získat relevantní výsledky, i když neexistují žádné odpovídající termíny.

  1. Vytvořte queryVector.js v src adresáři soubor a přidejte kód pro vytvoření vektoru dotazu.

    // Query is 1536-dimensional vector of "quintessential lodging near running trails, eateries, retail"
    // Note: this same vector can be used for vector search and semantic search
    // Replace with your own vector
    
    export const vector = 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   
  2. Tento kód se používá v následujících částech k provádění vektorových vyhledávání. Vektor dotazu se vytvoří pomocí modelu vkládání z Azure OpenAI.

První příklad ukazuje základní scénář, ve kterém chcete najít popisy dokumentů, které úzce odpovídají hledanému řetězci pomocí SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte searchSingle.js soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    import { SearchClient } from "@azure/search-documents";
    import { vector } from "./queryVector.js";
    import { DefaultAzureCredential } from "@azure/identity";
    
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    // const key = process.env.AZURE_SEARCH_KEY || "";
    // const searchClient = new SearchClient(
    //     searchEndpoint!,
    //     indexName,
    //     new AzureKeyCredential(key)
    // );
    
    const searchClient = new SearchClient(
        searchEndpoint,
        indexName,
        new DefaultAzureCredential()
    );
    
    try {
    
        const vectorQuery = {
            vector: vector,
            kNearestNeighborsCount: 5,
            fields: ["DescriptionVector"],
            kind: "vector",
            exhaustive: true
        };
    
        // Create a vector search options with the vector query and filter
        const searchOptions = {
            top: 7,
            select: ["HotelId", "HotelName", "Description", "Category", "Tags"] ,
            includeTotalCount: true,
            vectorSearchOptions: {
                queries: [vectorQuery],
                filterMode: "postFilter" // Apply filter after vector similarity is calculated
            }
        };
        const results = await searchClient.search("*", searchOptions);
    
        console.log(`\n\nSingle Vector search found ${results.count}`);
    
        for await (const result of results.results) {
            // Log each result
            const doc = result.document;
            console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
        }
    
    } catch (ex) {
        console.error("Vector search failed:", ex);
        throw ex;
    }
    

    VectorQuery obsahuje konfiguraci vektorizovaného dotazu včetně vektorizovaného textu vstupu dotazu jako vector, fields určuje, která vektorová pole se prohledávají, a kNearestNeighborsCount určuje počet nejbližších sousedů, které se mají vrátit.

    Řetězec vektorového dotazu je quintessential lodging near running trails, eateries, retail, který je vektorizován do 1 536 vkládání pro tento dotaz.

  3. Sestavte a spusťte soubor:

    node -r dotenv/config src/searchSingle.js
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro dotaz 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
    

    Odpověď pro vektorový ekvivalent quintessential lodging near running trails, eateries, retail se skládá z 5 výsledků, které obsahují pouze pole určená výběrem.

Vytvoření jednoduchého vektorového vyhledávání pomocí filtru

Můžete přidat filtry, ale filtry se použijí na obsah nevectoru ve vašem indexu. V tomto příkladu Tags se filtr vztahuje na pole a vyfiltruje všechny hotely, které neposkytují bezplatné Wi-Fi. Toto hledání používá SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte searchSingleWithFilter.js soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    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;
    }
    

    Přidejte závislosti, proměnné prostředí a funkci vyhledávání stejnou jako v předchozím vyhledávání s filtrem vyloučení po zpracování přidaným pro hotely s free wifi.

  3. Sestavte a spusťte soubor:

    node -r dotenv/config src/searchSingleWithFilter.js
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro dotaz s použitým filtrem free wifi :

    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
    

Vytvoření hledání s geoprostorovým filtrem

Můžete zadat geoprostorový filtr, který omezí výsledky na konkrétní geografickou oblast. V tomto příkladu filtr omezuje výsledky na hotely v okruhu 300 kilometrů od WashingtonU D.C. (souřadnice: -77.03241 38.90166). Geoprostorové vzdálenosti jsou vždy v kilometrech. Toto hledání používá SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte searchSingleWithFilterGeo.js soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    import { SearchClient } from "@azure/search-documents";
    import { vector } from "./queryVector.js";
    import { DefaultAzureCredential } from "@azure/identity";
    
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    // const key = process.env.AZURE_SEARCH_KEY || "";
    // const searchClient = new SearchClient(
    //     searchEndpoint!,
    //     indexName,
    //     new AzureKeyCredential(key)
    // );
    
    const searchClient = new SearchClient(
        searchEndpoint,
        indexName,
        new DefaultAzureCredential()
    );
    
    try {
    
        const vectorQuery = {
            vector: vector,
            kNearestNeighborsCount: 5,
            fields: ["DescriptionVector"],
            kind: "vector",
            exhaustive: true
        };
    
        // Create a vector search options with the vector query and filter
        const searchOptions = {
            top: 5,
            includeTotalCount: true,
            select: [
                "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
            ],
            facets: ["Address/StateProvince"],
            vectorSearchOptions: {
                queries: [vectorQuery],
                filterMode: "postFilter" // Apply filter after vector similarity is calculated
            },
            filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
        };
        const results = await searchClient.search("*", searchOptions);
    
        console.log(`\n\nSingle Vector search found ${results.count}`);
    
        for await (const result of results.results) {
            // Log each result
            const doc = result.document;
            console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
        }
    
    } catch (ex) {
        console.error("Vector search failed:", ex);
        throw ex;
    }
    
  3. Sestavte a spusťte soubor:

    node -r dotenv/config src/searchSingleWithFilterGeo.js
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro dotaz s použitým geoprostorovým filtrem vyloučení po zpracování:

    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
    

Hybridní vyhledávání se skládá z dotazů klíčových slov a vektorových dotazů v jediné žádosti o vyhledávání. Tento příklad spustí vektorový dotaz a fulltextové vyhledávání souběžně:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping
  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): quintessential lodging near running trails, eateries, retail

Toto hledání používá SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte searchHybrid.js soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    import { SearchClient } from "@azure/search-documents";
    import { vector } from "./queryVector.js";
    import { DefaultAzureCredential } from "@azure/identity";
    
    // const key = process.env.AZURE_SEARCH_KEY || "";
    // const searchClient = new SearchClient(
    //     searchEndpoint!,
    //     indexName,
    //     new AzureKeyCredential(key)
    // );
    
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    const searchClient = new SearchClient(
        searchEndpoint,
        indexName,
        new DefaultAzureCredential()
    );
    
    try {
    
        const vectorQuery = {
            vector: vector,
            kNearestNeighborsCount: 5,
            fields: ["DescriptionVector"],
            kind: "vector",
            exhaustive: true
    
        };
    
        // Create hybrid search options with both vector query and search text
        const searchOptions = {
            top: 5,
            includeTotalCount: true,
            select: ["HotelId", "HotelName", "Description", "Category", "Tags"],
            vectorSearchOptions: {
                queries: [vectorQuery],
                filterMode: "postFilter" // Apply filter after vector similarity is calculated
            }
        };
    
        // Use search_text for keyword search (hybrid search = vector + keyword)
        const searchText = "historic hotel walk to restaurants and shopping";
        const results = await searchClient.search(searchText, searchOptions);            // Convert results to typed format and log for debugging
    
        console.log(`\n\nHybrid search found ${results.count} then limited to top ${searchOptions.top}`);
    
        for await (const result of results.results) {
    
            // Log each result
            const doc = result.document;
    
            console.log(`- Score: ${result.score}`);
            console.log(`  HotelId: ${doc.HotelId}`);
            console.log(`  HotelName: ${doc.HotelName}`);
            console.log(`  Description: ${doc.Description || 'N/A'}`);
            console.log(`  Category: ${doc.Category || 'N/A'}`);
            console.log(`  Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}\n`);
        }
    
    } catch (ex) {
        console.error("Hybrid search failed:", ex);
        throw ex;
    }
    
  3. Sestavte a spusťte soubor:

    node -r dotenv/config src/searchHybrid.js
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro hybridní vyhledávání:

    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"]
    

    Vzhledem k tomu, že reciproční Rank Fusion (RRF) sloučí výsledky, pomáhá zkontrolovat vstupy. Následující výsledky pocházejí pouze z fulltextového dotazu. Dva nejlepší výsledky jsou Sublime Palace Hotel a Luxury Lion Resort. Sublime Palace Hotel má silnější skóre relevance 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"
       },
    

    V dotazu pouze vektoru, který používá HNSW k hledání shody, Sublime Palace Hotel klesne na čtvrtou pozici. Luxury Lion, který byl druhý v plnotextovém vyhledávání a třetí ve vektorovém vyhledávání, nemá stejný rozsah kolísání, takže se zobrazuje jako nejlepší shoda v homogenizované sadě výsledků.

    "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"
        }
    ]
    

Tady je poslední dotaz v kolekci, který rozšiřuje sémantické hybridní vyhledávání o další hledaný text historic hotel walk to restaurants and shopping.

Toto hledání používá SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte searchSemanticHybrid.js soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    import { SearchClient } from "@azure/search-documents";
    import { vector } from "./queryVector.js";
    import { DefaultAzureCredential } from "@azure/identity";
    
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    // const key = process.env.AZURE_SEARCH_KEY || "";
    // const searchClient = new SearchClient(
    //     searchEndpoint!,
    //     indexName,
    //     new AzureKeyCredential(key)
    // );
    
    const searchClient = new SearchClient(
        searchEndpoint,
        indexName,
        new DefaultAzureCredential()
    );
    
    try {
    
        const vectorQuery = {
            vector: vector,
            kNearestNeighborsCount: 5,
            fields: ["DescriptionVector"],
            kind: "vector",
            exhaustive: true
    
        };
    
        // Create semantic hybrid search options with vector query and semantic configuration
        const searchOptions = {
            top: 5,
            includeTotalCount: true,
            select: ["HotelId", "HotelName", "Category", "Description"],
            queryType: "semantic",
            semanticSearchOptions: {
                configurationName: "semantic-config"
            },
            vectorSearchOptions: {
                queries: [vectorQuery],
                filterMode: "postFilter" // Apply filter after vector similarity is calculated
            },
        };
    
        // Use search_text for semantic search
        const searchText = "historic hotel walk to restaurants and shopping";
        const results = await searchClient.search(searchText, searchOptions);
    
        console.log(`\n\nSemantic hybrid search found ${results.count} then limited to top ${searchOptions.top}`);
    
        for await (const result of results.results) {
    
            // Log each result
            const doc = result.document;
            const score = result.score;
            const rerankerScoreDisplay = result.rerankerScore;
    
            console.log(`- Score: ${score}`);
            console.log(`  Re-ranker Score: ${rerankerScoreDisplay}`);
            console.log(`  HotelId: ${doc.HotelId}`);
            console.log(`  HotelName: ${doc.HotelName}`);
            console.log(`  Description: ${doc.Description || 'N/A'}`);
            console.log(`  Category: ${doc.Category || 'N/A'}`);
            console.log('');
        }
    
    } catch (ex) {
        console.error("Semantic hybrid search failed:", ex);
        throw ex;
    }
    
  3. Sestavte a spusťte soubor:

    node -r dotenv/config src/searchSemanticHybrid.js
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro sémantické hybridní vyhledávání:

    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
    

    Sémantické řazení si můžete představit jako způsob, jak zlepšit význam výsledků hledání pochopením významu slov v dotazu a obsahu dokumentů. V tomto případě sémantické hodnocení pomáhá identifikovat hotely, které nejsou relevantní jenom pro klíčová slova, ale také odpovídat záměru dotazu:

    Hlavní klíčové body:

    • Vektorové vyhledávání je určeno prostřednictvím vectorSearchOptions vlastnosti. Vyhledávání klíčových slov je určeno prostřednictvím semanticSearchOptions vlastnosti.

    • V hybridním vyhledávání můžete integrovat vektorové vyhledávání s fulltextovým vyhledáváním pomocí klíčových slov. Filtry, kontrola pravopisu a sémantické řazení se vztahují pouze na textový obsah, nikoli na vektory. V tomto posledním dotazu není žádný sémantický answer , protože systém nevytvořil takový, který by byl dostatečně silný.

    • Skutečné výsledky obsahují více podrobností, včetně sémantických titulků a zvýraznění. Výsledky byly upraveny tak, aby byly čitelné. Pokud chcete získat úplnou strukturu odpovědi, spusťte požadavek v klientovi REST.

Vyčištění

Pokud pracujete s vlastním předplatným, je vhodné vždy na konci projektu zkontrolovat, jestli budete vytvořené prostředky ještě potřebovat. Prostředky, které necháte spuštěné, vás stojí peníze. Prostředky můžete odstraňovat jednotlivě nebo můžete odstranit skupinu prostředků, a odstranit tak celou sadu prostředků najednou.

Prostředky můžete najít a spravovat na webu Azure Portal pomocí odkazu Všechny prostředky nebo skupiny prostředků v levém podokně.

Pokud chcete zachovat vyhledávací službu, ale odstranit index a jeho dokumenty, můžete index odstranit programově.

  1. Vytvořte deleteIndex.js soubor v adresáři src .

  2. Přidejte závislosti, proměnné prostředí a kód pro odstranění indexu.

    import { DefaultAzureCredential } from "@azure/identity";
    import {
        SearchIndexClient
    } from "@azure/search-documents";
    
    const credential = new DefaultAzureCredential();
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    const searchIndexClient = new SearchIndexClient(searchEndpoint, credential);
    
    try {
        console.log("Deleting index...");
        await searchIndexClient.deleteIndex(indexName);
        console.log(`Index ${indexName} deleted`);
    } catch (ex) {
        console.error("Failed to delete index:", ex);
    }
    
  3. Sestavte a spusťte soubor:

    node -r dotenv/config src/deleteIndex.js
    

Další kroky

  • Projděte si úložiště ukázek kódu pro funkce vektorového vyhledávání ve službě Azure AI Search pro JavaScript.

V tomto rychlém startu použijete TypeScript k vytváření, načítání a dotazování vektorů. Příklady kódu provádějí tyto operace pomocí klientské knihovny Azure AI Search. Knihovna poskytuje abstrakci rozhraní REST API pro přístup k operacím indexu, jako je příjem dat, operace vyhledávání a operace správy indexů.

Ve službě Azure AI Search má úložiště vektorů schéma indexu, které definuje vektorová a nevectorová pole, konfiguraci vektorového vyhledávání algoritmů, které vytvářejí vložený prostor a nastavení definic vektorových polí, které se vyhodnocují v době dotazu. Rozhraní REST API pro vytvoření indexu vytvoří vektorové úložiště.

Poznámka:

Tento rychlý start vynechá krok vektorizace a poskytuje vložené vkládání. Pokud chcete přidat předdefinované bloky dat a vektorizaci nad vlastním obsahem, vyzkoušejte průvodce importem dat (nový) pro kompletní názorný postup.

Požadavky

Získání koncových bodů služby

Ve zbývajících částech nastavíte volání rozhraní API pro Azure OpenAI a Azure AI Search. Získejte koncové body služby, abyste je mohli zadat jako proměnné v kódu.

  1. Přihlaste se do Azure Portalu.

  2. Vyhledejte vyhledávací službu.

  3. Na domovské stránce Přehled zkopírujte adresu URL. Příkladem koncového bodu může být https://example.search.windows.net.

Nastavení projektu Node.JS

Nastavení projektu pomocí editoru Visual Studio Code a TypeScriptu

  1. Spusťte Visual Studio Code v novém adresáři.

    mkdir vector-quickstart && cd vector-quickstart
    code .
    
  2. Vytvořte nový balíček pro moduly ESM v adresáři projektu.

    npm init -y
    npm pkg set type=module
    

    Tím se vytvoří package.json soubor s výchozími hodnotami.

  3. Upravit package.json tak, aby zahrnoval skript sestavení. Do oddílu scripts přidejte následující řádek:

    "build": "tsc"
    
  4. Nainstalujte následující balíčky npm.

    npm install @azure/identity @azure/search-documents dotenv @types/node
    
  5. Vytvořte src adresář v adresáři projektu.

    mkdir src
    
  6. V adresáři projektu pro ESM vytvořte tsconfig.json soubor s následujícím obsahem.

    {
      "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"
      ]
    }
    

Nastavte proměnné prostředí pro místní vývoj

  1. Vytvořte .env soubor v vector-quickstart adresáři projektu.

  2. Přidejte do .env souboru následující proměnné prostředí a nahraďte hodnoty vlastními koncovými body služby a klíči.

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

Přihlásit se do Azure

Pro připojení používáte ID Microsoft Entra a přiřazení rolí. Ujistěte se, že jste přihlášeni ke stejnému tenantovi a předplatnému jako Azure AI Search a Azure OpenAI. Pomocí Azure CLI na příkazovém řádku můžete zobrazit aktuální vlastnosti, změnit vlastnosti a přihlásit se. Další informace najdete v tématu Připojení bez klíčů.

Postupně spusťte každý z následujících příkazů.

az account show

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

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

Teď byste se měli přihlásit k Azure z místního zařízení.

Vytvoření vektorového indexu

V této části vytvoříte vektorový index ve službě Azure AI Search pomocí SearchIndexClient. createOrUpdateIndex. Schéma indexu definuje pole, včetně vektorového pole DescriptionVector. Po vytvoření indexu nahrajete dokumenty do indexu. Dokumenty obsahují vektorizovanou verzi popisu článku, která umožňuje vyhledávání podobnosti na základě významu, nikoli přesných klíčových slov.

  1. Vytvořte createIndex.ts soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    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`);
    

    Soubor kódu přidá závislosti, proměnné prostředí a typ JavaScriptu do HotelDocument horní části souboru. createIndex Přidejte funkci pro vytvoření indexu. Funkce definuje schéma indexu, včetně vektorového pole DescriptionVector.

  3. Sestavte a spusťte soubor:

    npm run build && node -r dotenv/config dist/createIndex.js
    
  4. Výstup tohoto kódu ukazuje, že se index úspěšně vytvořil:

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

    Klíčové poznatky při vytváření vektorového indexu s knihovnou @azure/search-documents :

    • Index definujete vytvořením seznamu polí.

    • Tento konkrétní index podporuje více možností vyhledávání, například:

      • Fulltextové vyhledávání klíčových slov (searchable)
      • Vektorové vyhledávání (umožňuje hybridní vyhledávání kombinováním vektorových a nevektorových polí) (DescriptionVector s vectorSearchProfileName)
      • Sémantické vyhledávání
      • Fasetové vyhledávání (searchSuggester)
      • Geografické prostorové vyhledávání (Location pole s geo.distance)
      • Filtrování, řazení (mnoho polí označených jako filtrovatelné a řaditelné)

Nahrání dokumentů do indexu

Vytvoření a načtení indexu jsou samostatné kroky. Schéma indexu jste vytvořili v předchozím kroku. Teď potřebujete načíst dokumenty do indexu pomocí SearchClient. uploadDocuments.

Index ve službě Azure AI Search ukládá veškerý prohledávatelný obsah, zatímco vyhledávací web provádí dotazy na tento index.

  1. Vytvořte uploadDocuments.ts soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    import { DefaultAzureCredential } from "@azure/identity";
    import {
        SearchClient
    } from "@azure/search-documents";
    
    const credential = new DefaultAzureCredential();
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    const DOCUMENTS =  [
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "1",
            "HotelName": "Stay-Kay City Hotel",
            "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
            "DescriptionVector": 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            "Category": "Boutique",
            "Tags": [
                "view",
                "air conditioning",
                "concierge"
            ],
            "ParkingIncluded": "false",
            "LastRenovationDate": "2022-01-18T00:00:00Z",
            "Rating": 3.60,
            "Address": {
                "StreetAddress": "677 5th Ave",
                "City": "New York",
                "StateProvince": "NY",
                "PostalCode": "10022",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    -73.975403,
                    40.760586
                ]
            }
        },
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "2",
            "HotelName": "Old Century Hotel",
            "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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            "Category": "Boutique",
            "Tags": [
                "pool",
                "free wifi",
                "air conditioning",
                "concierge"
            ],
            "ParkingIncluded": "false",
            "LastRenovationDate": "2019-02-18T00:00:00Z",
            "Rating": 3.60,
            "Address": {
                "StreetAddress": "140 University Town Center Dr",
                "City": "Sarasota",
                "StateProvince": "FL",
                "PostalCode": "34243",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    -82.452843,
                    27.384417
                ]
            }
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            "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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            }
        },
        {
            "@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"
                    }
                }
            }
        }
    ];
    
    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);
    }
    

    Tento kód načte dokumenty proměnných s objektem JSON popisujícím každý dokument spolu s vektorizovanou verzí popisu článku. Tento vektor umožňuje vyhledávání podobnosti, kde porovnávání je založeno na významu, nikoli na přesných klíčových slovech.

    Vzhledem k tomu, že tento článek rychlého startu prohledá index okamžitě, funkce počká, waitUntilIndexed až bude index připravený na operace vyhledávání. To je důležité, protože index musí být plně naplněn dokumenty před provedením hledání.

  3. Sestavte a spusťte soubor:

    npm run build && node -r dotenv/config dist/uploadDocuments.js
    
  4. Výstup tohoto kódu ukazuje, že dokumenty jsou indexované a připravené k hledání:

    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.
    

    Klíčové poznatky o metodě upload_documents() a tomto příkladu:

    • Váš kód komunikuje s konkrétním indexem vyhledávání hostovaným ve službě Azure AI Search prostřednictvím SearchClient, což je hlavní objekt poskytnutý balíčkem @azure/search-documents . SearchClient poskytuje přístup k operacím indexu, například:
      • Příjem dat – uploadDocuments(), mergeDocuments(), deleteDocuments() atd.
      • Vyhledávací operace – search(), autocomplete(), suggest()
      • Operace správy indexů, jako jsou createIndex(), deleteIndex(), getIndex() atd.
    • Vektorová pole obsahují hodnoty s plovoucí desetinou čárkou. Atribut dimenzí má minimálně 2 a maximálně 4096 hodnoty s plovoucí desetinnou čárkou. Tento rychlý start nastaví atribut dimenzí na 1 536, protože to je velikost vkládání vygenerovaných modelem Azure OpenAI pro vkládání textu-ada-002.

Vytvoření dotazu jako vektoru

Ukázkové vektorové dotazy jsou založené na dvou řetězcích:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping
  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): quintessential lodging near running trails, eateries, retail

Řetězec vektorového dotazu je sémanticky podobný hledanému řetězci, ale obsahuje výrazy, které v indexu vyhledávání neexistují. Pokud hledáte quintessential lodging near running trails, eateries, retailklíčové slovo, výsledky jsou nulové. Tento příklad používáme k zobrazení toho, jak můžete získat relevantní výsledky, i když neexistují žádné odpovídající termíny.

  1. Vytvořte queryVector.ts v src adresáři soubor a přidejte kód pro vytvoření vektoru dotazu.

    // Query is 1536-dimensional vector of "quintessential lodging near running trails, eateries, retail"
    // Note: this same vector can be used for vector search and semantic search
    // Replace with your own vector
    
    export const vector = 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   
  2. Tento kód se používá v následujících částech k provádění vektorových vyhledávání. Vektor dotazu se vytvoří pomocí modelu vkládání z Azure OpenAI.

První příklad ukazuje základní scénář, ve kterém chcete najít popisy dokumentů, které úzce odpovídají hledanému řetězci pomocí SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte searchSingle.ts soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
    import { vector } from "./queryVector.js";
    import { DefaultAzureCredential } from "@azure/identity";
    
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    // Define an interface for the hotel document
    export interface HotelDocument {
        HotelId: string;
        HotelName: string;
        Description: string;
        DescriptionVector?: number[];
        Category?: string;
        Tags?: string[] | string;
        Address?: {
            City: string;
            StateProvince: string;
        };
        Location?: {
            type: string;
            coordinates: [number, number]; // [longitude, latitude]
        };
        "@search.score"?: number;
        "@search.reranker_score"?: number;
    }
    
    // const key = process.env.AZURE_SEARCH_KEY || "";
    // const searchClient = new SearchClient<HotelDocument>(
    //     searchEndpoint!,
    //     indexName,
    //     new AzureKeyCredential(key)
    // );
    
    const searchClient = new SearchClient<HotelDocument>(
        searchEndpoint,
        indexName,
        new DefaultAzureCredential()
    );
    
    try {
    
        const vectorQuery: VectorQuery<HotelDocument> = {
            vector: vector,
            kNearestNeighborsCount: 5,
            fields: ["DescriptionVector"],
            kind: "vector",
            exhaustive: true
        };
    
        // Create a vector search options with the vector query and filter
        const searchOptions: SearchOptions<HotelDocument> = {
            top: 7,
            select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
            includeTotalCount: true,
            vectorSearchOptions: {
                queries: [vectorQuery],
                filterMode: "postFilter" // Apply filter after vector similarity is calculated
            }
        };
        const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);
    
        console.log(`\n\nSingle Vector search found ${results.count}`);
    
        for await (const result of results.results) {
            // Log each result
            const doc = result.document;
            console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
        }
    
    } catch (ex) {
        console.error("Vector search failed:", ex);
        throw ex;
    }
    

    VectorQuery obsahuje konfiguraci vektorizovaného dotazu včetně vektorizovaného textu vstupu dotazu jako vector, fields určuje, která vektorová pole se prohledávají, a kNearestNeighborsCount určuje počet nejbližších sousedů, které se mají vrátit.

    Řetězec vektorového dotazu je quintessential lodging near running trails, eateries, retail, který je vektorizován do 1 536 vkládání pro tento dotaz.

  3. Sestavte a spusťte soubor:

    npm run build && node -r dotenv/config dist/searchSingle.js
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro dotaz 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
    

    Odpověď pro vektorový ekvivalent quintessential lodging near running trails, eateries, retail se skládá z 5 výsledků, které obsahují pouze pole určená výběrem.

Vytvoření jednoduchého vektorového vyhledávání pomocí filtru

Můžete přidat filtry, ale filtry se použijí na obsah nevectoru ve vašem indexu. V tomto příkladu Tags se filtr vztahuje na pole a vyfiltruje všechny hotely, které neposkytují bezplatné Wi-Fi. Toto hledání používá SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte searchSingleWithFilter.ts soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    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;
    }
    

    Přidejte závislosti, proměnné prostředí a funkci vyhledávání stejnou jako v předchozím vyhledávání s filtrem vyloučení po zpracování přidaným pro hotely s free wifi.

  3. Sestavte a spusťte soubor:

    npm run build && node -r dotenv/config dist/searchSingleWithFilter.js
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro dotaz s použitým filtrem free wifi :

    Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
    Using index name: hotels-vector-quickstart-0627-4
    
    
    Single Vector search found 2
    - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
    - HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
    

Vytvoření hledání s geoprostorovým filtrem

Můžete zadat geoprostorový filtr, který omezí výsledky na konkrétní geografickou oblast. V tomto příkladu filtr omezuje výsledky na hotely v okruhu 300 kilometrů od WashingtonU D.C. (souřadnice: -77.03241 38.90166). Geoprostorové vzdálenosti jsou vždy v kilometrech. Toto hledání používá SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte searchSingleWithFilterGeo.ts soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
    import { vector } from "./queryVector.js";
    import { DefaultAzureCredential } from "@azure/identity";
    
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    // Define an interface for the hotel document
    export interface HotelDocument {
        HotelId: string;
        HotelName: string;
        Description: string;
        DescriptionVector?: number[];
        Category?: string;
        Tags?: string[] | string;
        Address?: {
            City: string;
            StateProvince: string;
        };
        Location?: {
            type: string;
            coordinates: [number, number]; // [longitude, latitude]
        };
        "@search.score"?: number;
        "@search.reranker_score"?: number;
    }
    
    // const key = process.env.AZURE_SEARCH_KEY || "";
    // const searchClient = new SearchClient<HotelDocument>(
    //     searchEndpoint!,
    //     indexName,
    //     new AzureKeyCredential(key)
    // );
    
    const searchClient = new SearchClient<HotelDocument>(
        searchEndpoint,
        indexName,
        new DefaultAzureCredential()
    );
    
    
    try {
    
        const vectorQuery: VectorQuery<HotelDocument> = {
            vector: vector,
            kNearestNeighborsCount: 5,
            fields: ["DescriptionVector"],
            kind: "vector",
            exhaustive: true
        };
    
        const searchOptions: SearchOptions<HotelDocument> = {
            top: 5,
            includeTotalCount: true,
            select: [
                "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
            ] as const,
            facets: ["Address/StateProvince"],
            vectorSearchOptions: {
                queries: [vectorQuery],
                filterMode: "postFilter" // Apply filter after vector similarity is calculated
            },
            filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
        };
        const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);
    
        console.log(`\n\nVector search with geo filter found ${results.count}`);
    
        for await (const result of results.results) {
    
            // Log each result
            const doc = result.document;
    
            console.log(`- HotelId: ${doc.HotelId}`);
            console.log(`  HotelName: ${doc.HotelName}`);
            console.log(`  Score: ${result.score}`);
    
            if (doc.Address) {
                console.log(`  City/State: ${doc.Address.City}, ${doc.Address.StateProvince}`);
            }
    
            console.log(`  Description: ${doc.Description || 'N/A'}`);
            console.log(`  Score: ${result.score}\n`);
        }
    
    
    } catch (ex) {
        console.error("Vector search with geo filter failed:", ex);
        throw ex;
    }
    
  3. Sestavte a spusťte soubor:

    npm run build && node -r dotenv/config dist/searchSingleWithFilterGeo.js
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro dotaz s použitým geoprostorovým filtrem vyloučení po zpracování:

    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
    

Hybridní vyhledávání se skládá z dotazů klíčových slov a vektorových dotazů v jediné žádosti o vyhledávání. Tento příklad spustí vektorový dotaz a fulltextové vyhledávání souběžně:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping
  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): quintessential lodging near running trails, eateries, retail

Toto hledání používá SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte searchHybrid.ts soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
    import { vector } from "./queryVector.js";
    import { DefaultAzureCredential } from "@azure/identity";
    
    // const key = process.env.AZURE_SEARCH_KEY || "";
    // const searchClient = new SearchClient<HotelDocument>(
    //     searchEndpoint!,
    //     indexName,
    //     new AzureKeyCredential(key)
    // );
    
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    // Define an interface for the hotel document
    export interface HotelDocument {
        HotelId: string;
        HotelName: string;
        Description: string;
        DescriptionVector?: number[];
        Category?: string;
        Tags?: string[] | string;
        Address?: {
            City: string;
            StateProvince: string;
        };
        Location?: {
            type: string;
            coordinates: [number, number]; // [longitude, latitude]
        };
        "@search.score"?: number;
        "@search.reranker_score"?: number;
    }
    
    const searchClient = new SearchClient<HotelDocument>(
        searchEndpoint,
        indexName,
        new DefaultAzureCredential()
    );
    
    try {
    
        const vectorQuery: VectorQuery<HotelDocument> = {
            vector: vector,
            kNearestNeighborsCount: 5,
            fields: ["DescriptionVector"],
            kind: "vector",
            exhaustive: true
    
        };
    
        // Create hybrid search options with both vector query and search text
        const searchOptions: SearchOptions<HotelDocument> = {
            top: 5,
            includeTotalCount: true,
            select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
            vectorSearchOptions: {
                queries: [vectorQuery],
                filterMode: "postFilter" // Apply filter after vector similarity is calculated
            }
        };
    
        // Use search_text for keyword search (hybrid search = vector + keyword)
        const searchText = "historic hotel walk to restaurants and shopping";
        const results: SearchDocumentsResult<HotelDocument> = await searchClient.search(searchText, searchOptions);            // Convert results to typed format and log for debugging
    
        console.log(`\n\nHybrid search found ${results.count} then limited to top ${searchOptions.top}`);
    
        for await (const result of results.results) {
    
            // Log each result
            const doc = result.document;
    
            console.log(`- Score: ${result.score}`);
            console.log(`  HotelId: ${doc.HotelId}`);
            console.log(`  HotelName: ${doc.HotelName}`);
            console.log(`  Description: ${doc.Description || 'N/A'}`);
            console.log(`  Category: ${doc.Category || 'N/A'}`);
            console.log(`  Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}\n`);
        }
    
    } catch (ex) {
        console.error("Hybrid search failed:", ex);
        throw ex;
    }
    
  3. Sestavte a spusťte soubor:

    npm run build && node -r dotenv/config dist/searchHybrid.js
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro hybridní vyhledávání:

    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"]
    

    Vzhledem k tomu, že reciproční Rank Fusion (RRF) sloučí výsledky, pomáhá zkontrolovat vstupy. Následující výsledky pocházejí pouze z fulltextového dotazu. Nejlepšími dvěma výsledky jsou Sublime Palace Hotel a Luxury Lion Resort. Sublime Palace Hotel má silnější skóre relevance 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"
       },
    

    V dotazu pouze vektoru, který používá HNSW k hledání shody, Sublime Palace Hotel klesne na čtvrtou pozici. Luxury Lion, který byl druhý v fulltextovém vyhledávání a třetí ve vektorovém vyhledávání, nemá stejný rozsah kolísání, takže se zobrazuje jako nejlepší shoda v homogenizované sadě výsledků.

    "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"
        }
    ]
    

Tady je poslední dotaz v kolekci, který rozšiřuje sémantické hybridní vyhledávání o další hledaný text historic hotel walk to restaurants and shopping.

Toto hledání používá SearchClient, search a VectorQuery a SearchOptions.

  1. Vytvořte searchSemanticHybrid.ts soubor v adresáři src .

  2. Zkopírujte do souboru následující kód.

    import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
    import { vector } from "./queryVector.js";
    import { DefaultAzureCredential } from "@azure/identity";
    
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    // Define an interface for the hotel document
    export interface HotelDocument {
        HotelId: string;
        HotelName: string;
        Description: string;
        DescriptionVector?: number[];
        Category?: string;
        Tags?: string[] | string;
        Address?: {
            City: string;
            StateProvince: string;
        };
        Location?: {
            type: string;
            coordinates: [number, number]; // [longitude, latitude]
        };
        "@search.score"?: number;
        "@search.reranker_score"?: number;
    }
    
    // const key = process.env.AZURE_SEARCH_KEY || "";
    // const searchClient = new SearchClient<HotelDocument>(
    //     searchEndpoint!,
    //     indexName,
    //     new AzureKeyCredential(key)
    // );
    
    const searchClient = new SearchClient<HotelDocument>(
        searchEndpoint,
        indexName,
        new DefaultAzureCredential()
    );
    
    try {
    
        const vectorQuery: VectorQuery<HotelDocument> = {
            vector: vector,
            kNearestNeighborsCount: 5,
            fields: ["DescriptionVector"],
            kind: "vector",
            exhaustive: true
    
        };
    
        // Create semantic hybrid search options with vector query and semantic configuration
        const searchOptions: SearchOptions<HotelDocument> = {
            top: 5,
            includeTotalCount: true,
            select: ["HotelId", "HotelName", "Category", "Description"] as const,
            queryType: "semantic" as const,
            semanticSearchOptions: {
                configurationName: "semantic-config"
            },
            vectorSearchOptions: {
                queries: [vectorQuery],
                filterMode: "postFilter" // Apply filter after vector similarity is calculated
            },
        };
    
        // Use search_text for semantic search
        const searchText = "historic hotel walk to restaurants and shopping";
        const results: SearchDocumentsResult<HotelDocument> = await searchClient.search(searchText, searchOptions);
    
        console.log(`\n\nSemantic hybrid search found ${results.count} then limited to top ${searchOptions.top}`);
    
        for await (const result of results.results) {
    
            // Log each result
            const doc = result.document;
            const score = result.score;
            const rerankerScoreDisplay = result.rerankerScore;
    
            console.log(`- Score: ${score}`);
            console.log(`  Re-ranker Score: ${rerankerScoreDisplay}`);
            console.log(`  HotelId: ${doc.HotelId}`);
            console.log(`  HotelName: ${doc.HotelName}`);
            console.log(`  Description: ${doc.Description || 'N/A'}`);
            console.log(`  Category: ${doc.Category || 'N/A'}`);
            console.log('');
        }
    
    } catch (ex) {
        console.error("Semantic hybrid search failed:", ex);
        throw ex;
    }
    
  3. Sestavte a spusťte soubor:

    npm run build && node -r dotenv/config dist/searchSemanticHybrid.js
    
  4. Výstup tohoto kódu ukazuje relevantní dokumenty pro sémantické hybridní vyhledávání:

    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
    

    Sémantické řazení si můžete představit jako způsob, jak zlepšit význam výsledků hledání pochopením významu slov v dotazu a obsahu dokumentů. V tomto případě sémantické hodnocení pomáhá identifikovat hotely, které nejsou relevantní jenom pro klíčová slova, ale také odpovídat záměru dotazu:

    Hlavní klíčové body:

    • Vektorové vyhledávání je určeno prostřednictvím vectorSearchOptions vlastnosti. Vyhledávání klíčových slov je určeno prostřednictvím semanticSearchOptions vlastnosti.

    • V hybridním vyhledávání můžete integrovat vektorové vyhledávání s fulltextovým vyhledáváním pomocí klíčových slov. Filtry, kontrola pravopisu a sémantické řazení se vztahují pouze na textový obsah, nikoli na vektory. V tomto posledním dotazu není žádný sémantický answer , protože systém nevytvořil takový, který by byl dostatečně silný.

    • Skutečné výsledky obsahují více podrobností, včetně sémantických titulků a zvýraznění. Výsledky byly upraveny tak, aby byly čitelné. Pokud chcete získat úplnou strukturu odpovědi, spusťte požadavek v klientovi REST.

Vyčištění

Pokud pracujete s vlastním předplatným, je vhodné vždy na konci projektu zkontrolovat, jestli budete vytvořené prostředky ještě potřebovat. Prostředky, které necháte spuštěné, vás stojí peníze. Prostředky můžete odstraňovat jednotlivě nebo můžete odstranit skupinu prostředků, a odstranit tak celou sadu prostředků najednou.

Prostředky můžete najít a spravovat na webu Azure Portal pomocí odkazu Všechny prostředky nebo skupiny prostředků v levém podokně.

Pokud chcete zachovat vyhledávací službu, ale odstranit index a jeho dokumenty, můžete index odstranit programově.

  1. Vytvořte deleteIndex.ts soubor v adresáři src .

  2. Přidejte závislosti, proměnné prostředí a kód pro odstranění indexu.

    import { DefaultAzureCredential } from "@azure/identity";
    import {
        SearchIndexClient
    } from "@azure/search-documents";
    
    const credential = new DefaultAzureCredential();
    export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
    export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
    
    console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
    console.log(`Using index name: ${indexName}`);
    
    const searchIndexClient = new SearchIndexClient(searchEndpoint, credential);
    
    try {
        console.log("Deleting index...");
        await searchIndexClient.deleteIndex(indexName);
        console.log(`Index ${indexName} deleted`);
    } catch (ex) {
        console.error("Failed to delete index:", ex);
    }
    
  3. Sestavte a spusťte soubor:

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

Další kroky

  • Projděte si úložiště ukázek kódu pro funkce vektorového vyhledávání ve službě Azure AI Search pro JavaScript.

V tomto rychlém průvodci pracujete s aplikací .NET na vytváření, naplnění a dotazování vektorů. Příklady kódu provádějí tyto operace pomocí klientské knihovny Azure AI Search. Knihovna poskytuje abstrakci rozhraní REST API pro přístup k operacím indexu, jako je příjem dat, operace vyhledávání a operace správy indexů.

Ve službě Azure AI Search má úložiště vektorů schéma indexu, které definuje vektorová a nevectorová pole, konfiguraci vektorového vyhledávání algoritmů, které vytvářejí vložený prostor a nastavení definic vektorových polí, které se vyhodnocují v době dotazu. Rozhraní REST API pro vytvoření indexu vytvoří vektorové úložiště.

Poznámka:

Tento rychlý start vynechá krok vektorizace a poskytuje vložené vkládání. Pokud chcete přidat předdefinované bloky dat a vektorizaci nad vlastním obsahem, vyzkoušejte průvodce importem dat (nový) pro kompletní názorný postup.

Požadavky

Získání informací o službě

Požadavky na koncový bod vyhledávání musí být ověřeny a autorizované. I když je možné pro tuto úlohu použít klíče rozhraní API nebo role, doporučujeme použít připojení bez klíčů prostřednictvím ID Microsoft Entra.

V tomto rychlém startu se používá DefaultAzureCredential, což zjednodušuje ověřování ve vývojových i produkčních scénářích. V případě produkčních scénářů ale můžete mít pokročilejší požadavky, které vyžadují jiný přístup. Informace o všech možnostech najdete v tématu Ověřování aplikací .NET ve službách Azure pomocí sady Azure SDK pro .NET .

Klonování kódu a nastavení prostředí

  1. Naklonujte úložiště obsahující kód pro účely tohoto rychlého startu.

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

    Toto úložiště obsahuje příklady kódu .NET pro několik článků v samostatné podsložce.

  2. Otevřete podsložku quickstart-Vector-Search v editoru Visual Studio Code, nebo poklepejte na soubor .sln, aby se otevřelo řešení v aplikaci Visual Studio.

  3. Otevřete soubory ve složkách appsettings.json a VectorSearchExamples. Aktualizujte následující hodnoty:

    • AZURE_SEARCH_ENDPOINT: Na webu Azure Portal najděte adresu URL vaší služby Azure AI Search. Na stránce Přehled vašeho vyhledávacího prostředku vyhledejte pole adresy URL. Příkladem koncového bodu může být https://mydemo.search.windows.net.
    • AZURE_SEARCH_INDEX_NAME: Ponechte výchozí hodnotu zadanou v souboru nebo zadejte vlastní název indexu.

Vytvoření vektorového indexu a nahrání dokumentů

Pokud chcete spouštět vyhledávací dotazy ve službě Azure AI Search, musíte nejprve vytvořit index vyhledávání a nahrát dokumenty do služby.

  1. Ve složce otevřete nový terminál VectorSearchCreatePopulateIndex .

  2. Spusťte projekt pomocí dotnet run příkazu:

    dotnet run
    

Následující kód se spustí pro vytvoření indexu:

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();
}

Následující kód nahraje do služby Azure AI Search dokumenty ve formátu JSON v hotel-samples.json souboru:

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 spuštění projektu se vytiskne následující výstup:

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

Hlavní klíčové body:

  • Váš kód komunikuje s konkrétním indexem vyhledávání hostovaným ve službě Azure AI Search prostřednictvím SearchClienthlavního objektu poskytovaného balíčkem azure-search-documents . Poskytuje SearchClient přístup k operacím indexu, například:

    • Příjem dat: UploadDocuments(), MergeDocuments()DeleteDocuments()
    • Vyhledávací operace: Search(), Autocomplete()Suggest()
    • Operace správy indexů: CreateOrUpdateIndex()
  • Vektorová pole obsahují hodnoty s plovoucí desetinou čárkou. Atribut dimenzí má minimálně 2 a maximálně 4096 hodnoty s plovoucí desetinnou čárkou. Velikost embeddingů, které generuje model Azure OpenAI text-embedding-3-small pro tento rychlý start, je 1536.

Spouštění vyhledávacích dotazů

Po vytvoření indexu a načtení dokumentů můžete vydat vektorové dotazy na ně voláním SearchAsync() s různými parametry.

  1. VectorSearchExamples Ve složce otevřete Program.cs soubor.
  2. Ve složce otevřete nový terminál VectorSearchExamples .

V následujících částech spustíte dotazy na hotels-vector-quickstart index. Mezi dotazy patří:

První příklad ukazuje základní scénář, ve kterém chcete najít popisy dokumentů, které přesně odpovídají hledanému řetězci.

  1. Ve složce Program.cs v souboru VectorSearchExamples zrušte komentář volání metody SearchExamples.SearchSingleVector(searchClient, vectorizedResult);. Tato metoda spustí následující vyhledávací funkci ve SearchExamples.cs třídě:

    public static async Task SearchSingleVector(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
    {
        SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
            new SearchOptions
            {
                VectorSearch = new()
                {
                    Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
                },
                Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
            });
    
        Console.WriteLine($"Single Vector Search Results:");
        await foreach (SearchResult<Hotel> result in response.GetResultsAsync())
        {
            Hotel doc = result.Document;
            Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}");
        }
        Console.WriteLine();
    }
    
  2. Spusťte projekt pomocí dotnet run příkazu:

    dotnet run
    

    Po spuštění projektu se výsledky hledání vytisknou v okně výstupu:

    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
    

Jednoduché vektorové vyhledávání s filtrem

Můžete přidat filtry, ale filtry se použijí na obsah nevectoru ve vašem indexu. V tomto příkladu Tags se filtr vztahuje na pole a vyfiltruje všechny hotely, které neposkytují bezplatné Wi-Fi.

  1. Ve složce Program.cs v souboru VectorSearchExamples zrušte komentář volání metody SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);. Tato metoda spustí následující vyhledávací funkci ve SearchExamples.cs třídě:

    public static async Task SearchSingleVectorWithFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
    {
        SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
            new SearchOptions
            {
                VectorSearch = new()
                {
                    Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
                },
                Filter = "Tags/any(tag: tag eq 'free wifi')",
                Select = { "HotelId", "HotelName", "Description", "Category", "Tags" }
            });
    
        Console.WriteLine($"Single Vector Search With Filter Results:");
        await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
        {
            Hotel doc = result.Document;
            Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}, Tags: {string.Join(String.Empty, doc.Tags)}");
        }
        Console.WriteLine();
    }
    
  2. Spusťte projekt znovu a stav každého dokumentu se vytiskne pod ním:

     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
    

    Dotaz byl stejný jako předchozí příklad jednoúčelového vyhledávání, ale obsahuje filtr vyloučení po zpracování a vrátí pouze dva hotely, které mají bezplatné Wi-Fi.

  3. Následující příklad filtru používá geografický filtr. Ve složce Program.cs v souboru VectorSearchExamples zrušte komentář volání metody SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);. Tato metoda spustí následující vyhledávací funkci ve SearchExamples.cs třídě:

    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();
    }
    

    Dotaz byl stejný jako v předchozím příkladu jednoúčelového vyhledávání, ale zahrnuje filtr vyloučení po zpracování a vrátí pouze dva hotely v okruhu 300 kilometrů.

  4. Spusťte projekt znovu a stav každého dokumentu se vytiskne pod ním:

    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.
    

Hybridní vyhledávání se skládá z dotazů klíčových slov a vektorových dotazů v jediné žádosti o vyhledávání. Tento příklad spustí vektorový dotaz a fulltextové vyhledávání souběžně:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping
  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): Quintessential lodging near running trails, eateries, retail
  1. Ve složce Program.cs v souboru VectorSearchExamples zrušte komentář volání metody SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);. Tato metoda spustí následující vyhledávací funkci ve SearchExamples.cs třídě:

    public static async Task<SearchResults<Hotel>> SearchHybridVectorAndText(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
    {
        SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
            "historic hotel walk to restaurants and shopping",
            new SearchOptions
            {
                VectorSearch = new()
                {
                    Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
                },
                Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
                Size = 5,
            });
    
        Console.WriteLine($"Hybrid search results:");
        await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
        {
            Hotel doc = result.Document;
            Console.WriteLine($"Score: {result.Score}");
            Console.WriteLine($"HotelId: {doc.HotelId}");
            Console.WriteLine($"HotelName: {doc.HotelName}");
            Console.WriteLine($"Description: {doc.Description}");
            Console.WriteLine($"Category: {doc.Category}");
            Console.WriteLine($"Tags: {string.Join(String.Empty, doc.Tags)}");
            Console.WriteLine();
        }
        Console.WriteLine();
        return responseWithFilter;
    }
    
  2. Spusťte projekt znovu a stav každého dokumentu se vytiskne pod ním:

    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
    

Vzhledem k tomu, že reciproční Rank Fusion (RRF) sloučí výsledky, pomáhá zkontrolovat vstupy. Následující výsledky pocházejí pouze z fulltextového dotazu. Nejlepšími dvěma výsledky jsou Sublime Palace Hotel a Luxury Lion Resort. Sublime Palace Hotel má silnější skóre relevance 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"
},

V dotazu pouze vektoru, který používá HNSW k hledání shody, Sublime Palace Hotel klesne na čtvrtou pozici. Luxury Lion, který byl druhý ve fulltextovém vyhledávání a třetí ve vyhledávání pomocí vektorů, nemá stejný rozsah kolísání; proto se zobrazuje jako nejlepší shoda v homogenizované sadě výsledků.

"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"
    }
]

Sémantické hybridní vyhledávání s filtrem

Hybridní dotaz s sémantickým řazením se filtruje tak, aby zobrazoval pouze hotely v okruhu 500 kilometrů od WashingtonU D.C.

  1. Ve složce Program.cs v souboru VectorSearchExamples zrušte komentář volání metody SearchExamples.SearchHybridVectoryAndSemantic(searchClient, vectorizedResult);. Tato metoda spustí následující vyhledávací funkci ve SearchExamples.cs třídě:

    public static async Task SearchHybridVectorAndSemantic(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
    {
        SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
            "historic hotel walk to restaurants and shopping",
            new SearchOptions
            {
                IncludeTotalCount = true,
                VectorSearch = new()
                {
                    Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
                },
                Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
                SemanticSearch = new SemanticSearchOptions
                {
                    SemanticConfigurationName = "semantic-config"
                },
                QueryType = SearchQueryType.Semantic,
                Size = 5
            });
    
        Console.WriteLine($"Hybrid search results:");
        await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
        {
            Hotel doc = result.Document;
            Console.WriteLine($"Score: {result.Score}");
            Console.WriteLine($"HotelId: {doc.HotelId}");
            Console.WriteLine($"HotelName: {doc.HotelName}");
            Console.WriteLine($"Description: {doc.Description}");
            Console.WriteLine($"Category: {doc.Category}");
            Console.WriteLine();
        }
        Console.WriteLine();
    }
    
  2. Spusťte projekt znovu a zkontrolujte výstup pod buňkou. Odpověď je tři hotely, které jsou filtrované podle umístění a omezující StateProvince vlastnosti a sémanticky se přeřadí tak, aby podporovaly výsledky, které jsou nejblíže dotazu vyhledávacího řetězce (historic hotel walk to restaurants and shopping).

    Swirling Currents Hotel se nyní přesune do horního místa. Bez sémantického hodnocení je Nordick's Valley Motel číslo jedna. Díky sémantickému řazení modely strojového porozumění rozpoznávají, že historic se vztahuje na "hotel, v pěší vzdálenosti od restaurace a nakupování".

    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
    

Hlavní klíčové body:

  • V hybridním vyhledávání můžete integrovat vektorové vyhledávání s fulltextovým vyhledáváním pomocí klíčových slov. Filtry, kontrola pravopisu a sémantické řazení se vztahují pouze na textový obsah, nikoli na vektory. V tomto posledním dotazu není žádný sémantický answer , protože systém nevytvořil takový, který by byl dostatečně silný.

  • Skutečné výsledky obsahují více podrobností, včetně sémantických titulků a zvýraznění. Výsledky byly upraveny tak, aby byly čitelné. Pokud chcete získat úplnou strukturu odpovědi, spusťte požadavek v klientovi REST.

Vyčištění

Pokud pracujete s vlastním předplatným, je vhodné vždy na konci projektu zkontrolovat, jestli budete vytvořené prostředky ještě potřebovat. Prostředky, které necháte spuštěné, vás stojí peníze. Prostředky můžete odstraňovat jednotlivě nebo můžete odstranit skupinu prostředků, a odstranit tak celou sadu prostředků najednou.

Prostředky můžete najít a spravovat na webu Azure Portal pomocí odkazu Všechny prostředky nebo skupiny prostředků v levém podokně.

Další kroky

  • Projděte si úložiště ukázek kódu pro možnosti vektorového vyhledávání ve službě Azure AI Search pro .NET.
  • Projděte si další ukázky kódu .NET a Azure AI Search v úložišti azure-search-dotnet-samples.

V tomto rychlém startu použijete rozhraní REST API služby Azure AI Search k vytváření, načítání a dotazování vektorů.

Ve službě Azure AI Search má úložiště vektorů schéma indexu, které definuje vektorová a nevectorová pole, konfiguraci vektorového vyhledávání algoritmů, které vytvářejí vložený prostor a nastavení definic vektorových polí, které se vyhodnocují v době dotazu. Rozhraní REST API pro vytvoření indexu vytvoří vektorové úložiště.

Poznámka:

Tento rychlý start vynechá krok vektorizace a poskytuje vložené vkládání. Pokud chcete přidat předdefinované bloky dat a vektorizaci nad vlastním obsahem, vyzkoušejte průvodce importem dat (nový) pro kompletní názorný postup.

Požadavky

Získání informací o službě

Požadavky na koncový bod vyhledávání musí být ověřeny a autorizované. Pro tuto úlohu můžete použít klíče rozhraní API nebo role. Doporučujeme použít bezklíčové připojení prostřednictvím ID Microsoft Entra.

Vyberte kartu odpovídající preferované metodě ověřování. Stejnou metodu použijte pro všechny požadavky v tomto rychlém startu.

  1. Přihlaste se k webu Azure Portal a vyhledejte vyhledávací službu.

  2. Na domovské stránce Přehled vyhledejte adresu URL. Příkladem koncového bodu může být https://mydemo.search.windows.net.

    Snímek obrazovky s vlastností URL na stránce přehledu

  3. Pomocí kroků v rychlém startu bez klíčů získejte token Microsoft Entra.

    Token získáte při spuštění az account get-access-token příkazu v kroku 3 předchozího rychlého startu.

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

Vytvoření nebo stažení souboru kódu

Ke spuštění všech požadavků v tomto rychlém startu použijete jeden .rest nebo .http soubor. Můžete si stáhnout soubor REST, který obsahuje kód pro tento rychlý start, nebo můžete vytvořit nový soubor v editoru Visual Studio Code a zkopírovat do něj kód.

  1. V editoru Visual Studio Code vytvořte nový soubor s příponou nebo .rest příponou.http. Například az-search-vector-quickstart.rest. Zkopírujte a vložte nezpracovaný obsah souboru Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest do tohoto nového souboru.

  2. V horní části souboru nahraďte zástupnou hodnotu adresou @baseUrl URL vyhledávací služby. Pokyny k vyhledání adresy URL vyhledávací služby najdete v části Získání informací o službě .

    @baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE
    
  3. V horní části souboru nahraďte zástupnou hodnotu pro ověřování. Pokyny k získání tokenu Microsoft Entra nebo klíče rozhraní API najdete v části Získání informací o službě .

    Pro doporučené ověřování bez klíčů prostřednictvím ID Microsoft Entra je nutné nahradit @apiKey proměnnou@token.

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

    Pokud raději používáte klíč rozhraní API, nahraďte @apiKey ho klíčem, který jste zkopírovali z webu Azure Portal.

    @apiKey = PUT-YOUR-ADMIN-KEY-HERE
    
  4. Pro doporučené ověřování bez klíčů prostřednictvím ID Microsoft Entra je nutné nahradit api-key: {{apiKey}}Authorization: Bearer {{token}} v hlavičce požadavku. Nahraďte všechny nalezené api-key: {{apiKey}} instance v souboru.

Vytvoření vektorového indexu

Pomocí rozhraní REST API pro vytvoření indexu vytvoříte vektorový index a nastavíte fyzické datové struktury ve vyhledávací službě.

Schéma indexu v tomto příkladu je uspořádané kolem obsahu hotelu. Ukázková data se skládají z vektorových a nevectorových popisů fiktivních hotelů. Toto schéma zahrnuje konfigurace pro indexování vektorů a dotazy a sémantické řazení.

  1. V editoru az-search-vector-quickstart.rest Visual Studio Code otevřete soubor, který jste vytvořili dříve.

  2. ### Create a new index Vyhledejte blok kódu v souboru. Tento blok obsahuje požadavek na vytvoření indexu hotels-vector-quickstart ve vyhledávací službě.

    ### 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" }
                        ]
                    }
                }
            ]
        }
    }
    
  3. Vyberte Odeslat žádost. Měli byste mít HTTP/1.1 201 Created odpověď.

    Text odpovědi by měl obsahovat reprezentaci schématu indexu JSON.

    {
        "@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"
                }
                ]
            }
            }
        ]
        }
    }
    

Klíčové poznatky o rozhraní REST API pro vytvoření indexu:

  • Kolekce fields obsahuje povinné klíčové pole a textová a vektorová pole (například Description a DescriptionVector) pro textové a vektorové vyhledávání. Společné přidělení vektorových a nevectorových polí ve stejném indexu umožňuje hybridní dotazy. Můžete například zkombinovat filtry, vyhledávání textu pomocí sémantického řazení a vektorů do jedné operace dotazu.

  • Vektorová pole musí být jedním z datových typů EDM používaných pro vektory, například type: Collection(Edm.Single). Vektorová pole mají také vlastnosti dimensions a vectorSearchProfile.

  • Tato vectorSearch část je pole konfigurací a profilů algoritmu Přibližný nejbližší soused (ANN). Mezi podporované algoritmy patří Hierarchical Navigable Small World a vyčerpávající K-Nearest Neighbor. Další informace najdete v tématu Bodování relevance při hledání vektorů.

  • Konfigurace (volitelné) semantic umožňuje přeřadit výsledky hledání. Výsledky můžete přeřadit v dotazech typu semantic pro pole řetězců, která jsou zadaná v konfiguraci. Další informace najdete v přehledu sémantického řazení.

Nahrát dokumenty

Vytvoření a načtení indexu jsou samostatné kroky. Schéma indexu jste vytvořili v předchozím kroku. Teď potřebujete načíst dokumenty do indexu.

Index ve službě Azure AI Search obsahuje všechna prohledávatelná data a dotazy spuštěné ve vyhledávací službě. Pro volání REST jsou data poskytována jako dokumenty JSON. Pro tuto úlohu použijte rozhraní REST API indexu dokumentů. Identifikátor URI je rozšířen tak, aby zahrnoval kolekci docsindex a operaci.

  1. Formulujte žádost o nahrání dokumentů pro nahrání dokumentů do indexu hotels-vector-quickstart ve vyhledávací službě.

    ### 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": [
                        -82.452843,
                        27.384417
                    ]
                }
            },
            {
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                "HotelId": "3",
                "HotelName": "Gastronomic Landscape Hotel",
                "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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                "Category": "Suite",
                "Tags": [
                    "restaurant",
                    "bar",
                    "continental breakfast"
                ],
                "ParkingIncluded": true,
                "LastRenovationDate": "2015-09-20T00:00:00Z",
                "Rating": 4.80,
                "Address": {
                    "StreetAddress": "3393 Peachtree Rd",
                    "City": "Atlanta",
                    "StateProvince": "GA",
                    "PostalCode": "30326",
                    "Country": "USA"
                },
                "Location": {
                    "type": "Point",
                    "coordinates": [
                        33.847848,
                        -84.363602
                    ]
                }
            },
            {
                "@search.action": "mergeOrUpload",
                "HotelId": "4",
                "HotelName": "Sublime Palace Hotel",
                "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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                "Category": "Boutique",
                "Tags": [
                    "concierge",
                    "view",
                    "air conditioning"
                ],
                "ParkingIncluded": true,
                "LastRenovationDate": "2020-02-06T00:00:00Z",
                "Rating": 4.60,
                "Address": {
                    "StreetAddress": "7400 San Pedro Ave",
                    "City": "San Antonio",
                    "StateProvince": "TX",
                    "PostalCode": "78216",
                    "Country": "USA"
                },
                "Location": {
                    "type": "Point",
                    "coordinates": [
                        -98.498923,
                        29.518029
                    ]
                }
            },
            {
                "@search.action": "mergeOrUpload",
                "HotelId": "48",
                "HotelName": "Nordick's Valley Motel",
                "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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                "Category": "Boutique",
                "Tags": [
                    "continental breakfast",
                    "air conditioning",
                    "free wifi"
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                "ParkingIncluded": false,
                "LastRenovationDate": "2018-02-19T00:00:00Z",
                "Rating": 4.5,
                "Address": {
                    "StreetAddress": "1401 I St NW",
                    "City": "Washington D.C.",
                    "StateProvince": null,
                    "PostalCode": "20005",
                    "Country": "USA"
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                    "properties": {
                        "name": "EPSG:4326"
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                "HotelId": "49",
                "HotelName": "Swirling Currents Hotel",
                "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
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               "Category": "Suite",
                "Tags": [
                    "air conditioning",
                    "laundry service",
                    "24-hour front desk service"
                ],
                "ParkingIncluded": true,
                "LastRenovationDate": "2018-01-27T00:00:00Z",
                "Rating": 2.7,
                "Address": {
                    "StreetAddress": "1100 S Hayes St",
                    "City": "Arlington",
                    "StateProvince": "VA",
                    "PostalCode": "22202",
                    "Country": "USA"
                },
                "Location": {
                    "type": "Point",
                    "coordinates": [
                    -77.060066,
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                    ],
                    "crs": {
                    "type": "name",
                    "properties": {
                        "name": "EPSG:4326"
                        }
                    }
                }
            },
            {
                "@search.action": "mergeOrUpload",
                "HotelId": "13",
                "HotelName": "Luxury Lion Resort",
                "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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                "Category": "Luxury",
                "Tags": [
                    "bar",
                    "concierge",
                    "restaurant"
                ],
                "ParkingIncluded": false,
                "LastRenovationDate": "2020-03-18T00:00:00Z",
                "Rating": 4.1,
                "Address": {
                    "StreetAddress": "3 Cityplace Dr",
                    "City": "St. Louis",
                    "StateProvince": "MO",
                    "PostalCode": "63141",
                    "Country": "USA"
                },
                "Location": {
                    "type": "Point",
                    "coordinates": [
                    -90.44081,
                    38.67219
                    ],
                    "crs": {
                    "type": "name",
                    "properties": {
                        "name": "EPSG:4326"
                        }
                    }
                }
            }
        ]
    }
    
  2. Vyberte Odeslat žádost. Měli byste mít HTTP/1.1 200 OK odpověď. Text odpovědi by měl obsahovat reprezentaci JSON vyhledávacích dokumentů.

Klíčové poznatky o požadavku na Documents - Index REST API:

  • Dokumenty v datové části se skládají z polí definovaných ve schématu indexu.

  • Vektorová pole obsahují hodnoty s plovoucí desetinou čárkou. Atribut dimenzí má minimálně 2 a maximálně 4096 hodnoty v plovoucím bodu každé. Tento rychlý návod nastaví atribut dimenzí na 1 536, protože to je velikost embeddingů generovaných modelem Azure OpenAI text-embedding-3-small.

Spouštění dotazů

Teď, když jsou načítány dokumenty, můžete na ně spouštět vektorové dotazy pomocí funkce Documents – Search Post (REST).

V dalších částech spustíme dotazy na hotels-vector-quickstart index. Mezi dotazy patří:

Ukázkové dotazy jsou založené na dvou řetězcích:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping
  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): quintessential lodging near running trails, eateries, retail

Řetězec vektorového dotazu je sémanticky podobný hledanému řetězci, ale obsahuje výrazy, které v indexu vyhledávání neexistují. Pokud hledáte klíčové slovo quintessential lodging near running trails, eateries, retail, nezobrazí se žádné výsledky při čistém vyhledávání bez sémantického řazení klíčových slov. Tento příklad používáme k zobrazení toho, jak můžete získat relevantní výsledky pomocí vektorů, i když neexistují žádné odpovídající termíny.

  1. Formulujte požadavek. Dotaz je 1536bodová reprezentace plovoucí řádové hodnoty výjimečného ubytování v blízkosti běžeckých tras, restaurací, obchodů. Dotaz hledá DescriptionVector a vrací 5 výsledků. Používá se 'exhaustive override' parametr k provedení úplného prohledávání indexu místo ANN. Podrobné vyhledávání je užitečné pro malé indexy.

    ### 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
                }
            ]
        }
    

    Klíčové poznatky o rozhraní REST API pro vyhledávání příspěvků Documents - Search Post:

    • Jedná se vectorQueries.vector o řetězec vektorového dotazu. Je to vektorové znázornění typického ubytování v blízkosti běžeckých tras, restaurací, obchodů, které je vektorizováno do 1 536 vložených prvků pro tento dotaz.

    • fields určuje, která vektorová pole se prohledávají.

    • kind nastavená na vector hodnotu znamená, že řetězec dotazu je vektor. Pokud byla kind nastavena na text, budete potřebovat další schopnosti ( vektorizátor) pro kódování lidmi čitelného textového řetězce do vektoru při dotazu. Vektorizátory jsou z tohoto rychlého startu vynechány, aby cvičení bylo jednoduché.

    • k určuje počet shod, které mají být vráceny v odpovědi. Parametr count určuje počet shod nalezených v indexu. Zahrnutí počtu je osvědčeným postupem pro dotazy, ale je méně užitečné pro vyhledávání podobnosti, kde algoritmus dokáže najít určitou míru podobnosti téměř v jakémkoli dokumentu.

  2. Vyberte Odeslat žádost. Měli byste mít HTTP/1.1 200 OK odpověď. Text odpovědi by měl obsahovat reprezentaci výsledků hledání ve formátu JSON.

    Odpověď na vektorový ekvivalent quintessential lodging near running trails, eateries, retail zahrnuje výsledky k-5, i když vyhledávací web našel 7 shod. Nejvyšší výsledky se považují za sémanticky podobné dotazu. Každý výsledek poskytuje skóre hledání a pole uvedená v selectseznamu . Ve vyhledávání podobnosti odpověď vždy obsahuje k výsledky seřazené podle skóre podobnosti hodnoty.

    {
      "@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"
          ]
        }
      ]
    }
    

Jednoduché vektorové vyhledávání s filtrem

Můžete přidat filtry, ale filtry se použijí na obsah nevectoru ve vašem indexu. V tomto příkladu Tags se filtr vztahuje na pole a vyfiltruje všechny hotely, které neposkytují bezplatné Wi-Fi.

  1. Formulujte požadavek. Jedná se o stejný požadavek jako předchozí, ale s dalším parametrem filtru a režimu filtru.

    ### 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
            },
        ]
    }
    
  2. Vyberte Odeslat žádost. Měli byste mít HTTP/1.1 200 OK odpověď. Text odpovědi by měl obsahovat reprezentaci výsledků hledání ve formátu JSON.

    Dotaz byl stejný jako předchozí příklad jednoúčelového vyhledávání, ale obsahuje filtr vyloučení po zpracování a vrátí pouze dva hotely, které mají bezplatné Wi-Fi.

    {
      "@odata.context": "https://my-demo-search.search.windows.net/indexes('hotels-vector-quickstart')/$metadata#docs(*)",
      "@odata.count": 2,
      "value": [
        {
          "@search.score": 0.6605852,
          "HotelId": "48",
          "HotelName": "Nordick's Valley Motel",
          "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
          "Category": "Boutique",
          "Tags": [
            "continental breakfast",
            "air conditioning",
            "free wifi"
          ]
        },
        {
          "@search.score": 0.57902366,
          "HotelId": "2",
          "HotelName": "Old Century Hotel",
          "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
          "Category": "Boutique",
          "Tags": [
            "pool",
            "free wifi",
            "air conditioning",
            "concierge"
          ]
        }
      ]
    }
    
  3. Tady je další dotaz, který používá geografický filtr, který omezuje výsledky na hotely v okruhu 300 kilometrů kolem WashingtonU D.C.

    ### 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
            },
        ]
    }
    
  4. Prohlédněte si odpověď. Je omezeno na dva hotely poblíž Washingtonu D.C, přičemž fasetová struktura poskytuje agregaci.

    "@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"
        }
      }
    ]
    

Hybridní vyhledávání se skládá z dotazů klíčových slov a vektorových dotazů v jediné žádosti o vyhledávání. Tento příklad spustí vektorový dotaz a fulltextové vyhledávání souběžně:

  • Hledaný řetězec: historic hotel walk to restaurants and shopping
  • Vektorový řetězec dotazu (vektorizovaný do matematické reprezentace): quintessential lodging near running trails, eateries, retail
  1. Zformulujte hybridní dotaz.

    ### 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": [
            {
                "vector": 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                "k": 5,
                "fields": "DescriptionVector",
                "kind": "vector",
                "exhaustive": true
            }
        ]
    }
    
  2. Vyberte Odeslat žádost. Měli byste mít HTTP/1.1 200 OK odpověď. Text odpovědi by měl obsahovat reprezentaci výsledků hledání ve formátu JSON.

Vzhledem k tomu, že se jedná o hybridní dotaz, výsledky jsou seřazené podle reciproční Rank Fusion (RRF). Všimněte si, že @search.score hodnoty mají jiný základ a jsou jednotně menší. RRF vyhodnocuje skóre hledání více výsledků hledání, vezme inverzní funkci a pak sloučí a seřadí kombinované výsledky. Vrátí top se počet výsledků.

Zkontrolujte odpověď, která se skládá z nejlepších k-5 shod ze 7 dostupných odpovídajících dokumentů v indexu.

{
  "@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."
    }
  ]
}

Vzhledem k tomu, že RRF sloučí výsledky, pomáhá kontrolovat vstupy jednotlivě.

Následující výsledky pocházejí z celé textové části dotazu: historický hotel v pěší vzdálenosti od restaurací a obchodů. Ve výsledcích plnotextového dotazu jsou tři první výsledky Sublime Palace Hotel, Stay-Kay City Hotel a Luxury Lion Resort. Sublime Palace Hotel má silnější skóre relevance BM25. Ve sloučeném dotazu jsou v první trojici pouze dvě z těchto shod a druhá shoda (Stay-Kay City Hotel) se vůbec nezobrazuje v první pětici.

  "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."
    },
    ...
  ]

V části dotazu o vektorové části (typické ubytování poblíž běžeckých tras, restaurací a obchodů) je jiný řetězec, takže bychom měli očekávat odlišné výsledky. Tento dotaz vrátí nordick's Valley Motel, Luxury Lion Resort a Sublime Palace Hotel jako nejlepší tři zápasy. Ve sloučeném dotazu jsou tyto výsledky v prvních 3, ale v obráceném pořadí.

  "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."
    },
   ...
  ]

Tady je poslední dotaz v kolekci. Tento hybridní dotaz přidává L2 sémantické řazení, které uplatňuje porozumění strojového čtení na výsledky seřazené podle L1, čímž posouvá relevantnější shody na vrchol.

  1. Formulujte požadavek.

    ### 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
            }
        ]
    }
    
  2. Vyberte Odeslat žádost. Měli byste mít HTTP/1.1 200 OK odpověď. Text odpovědi by měl obsahovat reprezentaci výsledků hledání ve formátu JSON.

Zkontrolujte odpověď skládající se z sémantického řazení výsledků RRF seřazených v hybridním dotazu. Sémantické řazení funguje mimo textové vstupy. V textovém nebo hybridním dotazu je tento vstup textovou částí dotazu (historická procházka po restauracích a nákupech). Pokud chcete použít sémantické řazení na čistě vektorovém dotazu, například v prvním příkladu, nebo explicitně řídit text použitý pro sémantické řazení, zadejte sémantický řetězecQuery.

Po sémantickém přerovnání se Swirling Currents Hotel s odkazem na procházky k nakupování, stravování, zábavě a centru města posouvá na první místo. Model strojového porozumění podporuje pěší přístup k nakupování a stravování jako bližší shodu s procházkou k restauracím a obchodům.

Před sémantickým rerankingem byl Sublime Palace, s odkazem na svou polohu v docházkové vzdálenosti k místům a památkám města a je obklopen mimořádnou krásou kostelů, budov, obchodů a památek, číslo jedna.

{
  "@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"
    }
  ]
}

Návod

Sémantické výsledky můžou obsahovat více podrobností, včetně sémantických odpovědí, titulků a zvýraznění. Přidání dalších parametrů do požadavku vytvoří další podrobnosti. Další informace naleznete v tématu Nastavení sémantického dotazu.

Vyčištění

Pokud pracujete s vlastním předplatným, je vhodné vždy na konci projektu zkontrolovat, jestli budete vytvořené prostředky ještě potřebovat. Prostředky, které necháte spuštěné, vás stojí peníze. Prostředky můžete odstraňovat jednotlivě nebo můžete odstranit skupinu prostředků, a odstranit tak celou sadu prostředků najednou.

Prostředky můžete najít a spravovat na webu Azure Portal pomocí odkazu Všechny prostředky nebo skupiny prostředků v levém podokně.

Pokud chcete vyhledávací službu zachovat, ale odstranit index a dokumenty, můžete použít DELETE příkaz v klientovi REST. Tento příkaz (na konci az-search-vector-quickstart.rest souboru) odstraní hotels-vector-quickstart index:

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

Další kroky

V dalším kroku doporučujeme zjistit, jak volat volání rozhraní REST API bez klíčů rozhraní API.

Můžete si také projít ukázkový kód pro Python, C# nebo JavaScript.