Snelle start: Vectorzoekopdracht

In deze quickstart gebruikt u een Jupyter-notebook om een vectorindex te maken, laden en er query's op uit te voeren. De code voert deze bewerkingen uit met behulp van de Azure AI Search-clientbibliotheek voor Python, die een abstractie biedt van de REST API's voor toegang tot indexbewerkingen.

In Azure AI Search heeft een vectorindex een indexschema dat vectorvelden en niet-vectorvelden definieert, een vectorzoekconfiguratie voor algoritmen die de insluitruimte maken en instellingen voor vectorvelddefinities die tijdens de query worden geëvalueerd. Indexen - Maken of bijwerken (REST API) maakt de vectorindex.

Vereisten

• Een Azure-account met een actief abonnement. Gratis een account maken

• Een Azure AI Search-service.

  • U kunt de gratis laag voor de meeste van deze quickstart gebruiken, maar we raden Basic of hoger aan voor grotere gegevensbestanden.

  • Wijs voor sleutelloze verificatie met Microsoft Entra-id de rol Inzender voor zoekindexgegevens toe aan uw gebruikersaccount.

  • Als u de semantische hybride query wilt uitvoeren, moet u de semantische rangschikking inschakelen.

• Visual Studio Code met de Python-extensie en het Jupyter-pakket.

• Git om de voorbeeldopslagplaats te klonen.

Servicegegevens ophalen

Aanvragen voor het zoekeindpunt moeten worden geverifieerd en geautoriseerd. Hoewel het mogelijk is om API-sleutels voor deze taak te gebruiken, raden we u aan een sleutelloze verbinding via Microsoft Entra-id te gebruiken.

In deze quickstart wordt gebruikgemaakt van DefaultAzureCredential, dat authenticatie vereenvoudigt in zowel ontwikkelings- als productiescenario's. Voor productiescenario's hebt u echter mogelijk meer geavanceerde vereisten waarvoor een andere benadering is vereist. Zie Python-apps verifiëren voor Azure-services met behulp van de Azure SDK voor Python voor meer informatie over al uw opties.

De code en de installatieomgeving klonen

1. Kloon de repository met de code voor deze quickstart.

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

2. Open de Quickstart-Vector-Search map in Visual Studio Code.

3. Wijzig de naam van het sample.env bestand in .env.

4. Stel AZURE_SEARCH_ENDPOINT in als de URL van uw zoekservice, die vergelijkbaar moet zijn met https://mydemo.search.windows.net.

5. Stel AZURE_SEARCH_INDEX_NAME in op een unieke naam voor uw index. U kunt ook de standaardnaam vector-search-quickstart gebruiken.

6. SelecteerNieuwe terminal> en voer vervolgens de volgende opdrachten uit om een virtuele omgeving te maken en te activeren.

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

   Notitie

   • In deze stap wordt ervan uitgegaan dat u Git Bash gebruikt in uw terminal en wordt uitgevoerd in Windows. Als u een andere shell en/of een ander besturingssysteem gebruikt, past u deze instructies aan uw specifieke omgeving aan.

   • Met de where python opdracht valideert u dat u vanuit de virtuele omgeving werkt door python.exe te vermelden in de Quickstart-Vector-Search\.venv\ map en andere locaties in de directory van uw computer.

7. Als u hierom wordt gevraagd, staat u Visual Studio Code toe om de nieuwe omgeving te gebruiken.

8. Installeer de vereiste bibliotheken.

   pip install requirements.txt
   

9. Open het bestand vector-search-quickstart.ipynb.

10. Voer de Install packages and set variables codecel uit om de omgevingsvariabelen te laden.

    # 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 
    

    De uitvoer bevat enkele van de geïnstalleerde pakketten.

    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
    ...
    

Een vectorindex maken

De code in de vector-search-quickstart.ipynb maakt gebruik van verschillende methoden uit de azure.search.documents bibliotheek om de vectorindex en doorzoekbare velden te maken.

De vectorindex maken:

1. Voer de Create an index codecel uit.

   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')
   

   Als de index is gemaakt, wordt de volgende uitvoer weergegeven onder de codecel.

   vector-search-quickstart created
   

   Belangrijkste conclusies:

   • U definieert een index door een lijst met velden te maken. Elk veld wordt gemaakt met behulp van een helpermethode waarmee het veldtype en de bijbehorende instellingen worden gedefinieerd.

   • Deze specifieke index ondersteunt meerdere zoekmogelijkheden, zoals:

     • Zoeken naar trefwoorden in volledige tekst (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))

     • Vectorzoekopdrachten (maakt hybride zoekopdracht mogelijk door vectorvelden en niet-vectorvelden te collocateren) (DescriptionVector)

     • Semantisch zoeken (semantic_search=SemanticSearch(configurations=[semantic_config]))

     • Facet-zoeken (facetable=True)

     • Georuimtelijke zoekopdracht (Location veld is GeographyPoint)

     • Filteren, sorteren (veel velden gemarkeerd als filterbaar en sorteerbaar)

Documenten uploaden naar de index

Het maken en laden van de index zijn afzonderlijke stappen. U hebt het indexschema in de vorige stap gemaakt. U moet nu documenten in de index laden.

In Azure AI Search slaat de index alle doorzoekbare inhoud op, terwijl de zoekmachine query's uitvoert op die index.

Documenten uploaden naar de index:

1. Voer de Create documents payload codecel uit.

      # 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>
      ]
   

   Deze cel laadt een variabele met de naam documents met een JSON-object waarin elk document wordt beschreven, samen met de vectorversie van de beschrijving van het artikel. Deze vector maakt overeenkomsten zoeken mogelijk, waarbij overeenkomende waarden zijn gebaseerd op betekenis in plaats van exacte trefwoorden.

   Belangrijk

   De code in dit voorbeeld kan niet worden uitgevoerd. Er worden verschillende tekens of regels verwijderd om kortheid te krijgen. Voer in plaats daarvan de code uit in het Jupyter-notebook.

2. Voer de Upload the documents codecel uit.

   # 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)
   

   Hiermee maakt u een exemplaar van de zoekclient door de SearchClient() constructor aan te roepen en vervolgens de upload_documents() methode voor het object aan te roepen.

   Nadat u de cel hebt uitgevoerd, wordt de status van elk document eronder weergegeven.

   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
   

   Belangrijkste conclusies:

   • Uw code communiceert met een specifieke zoekindex die wordt gehost in uw Azure AI Search-service via het SearchClienthoofdobject dat door het azure-search-documents pakket wordt geleverd. De SearchClient service biedt toegang tot indexbewerkingen, zoals:

     • Gegevensopname: upload_documents(), merge_documents(), delete_documents(), enzovoort.

     • Zoekbewerkingen: search(), autocomplete()suggest()

     • Indexbeheerbewerkingen: get_index_statistics(), get_document_count()

   • Vectorvelden bevatten drijvendekommawaarden. Het kenmerk dimensies heeft minimaal 2 en een maximum van 4096 drijvendekommawaarden. In deze quickstart wordt het dimensiekenmerk ingesteld op 1536, omdat dit de grootte is van insluitingen die door het text-embedding-ada-002 model worden gegenereerd.

Query's uitvoeren

Nu documenten zijn geladen, kunt u vectorquery's hierop uitvoeren door een search_client.search() object aan te roepen VectorizedQuery en door te geven, de velden die u wilt retourneren, het aantal resultaten, enzovoort.

Query's in deze sectie:

• Zoeken met één vector
• Zoeken met één vector met filter
• Hybride zoekopdracht
• Semantische hybride zoekopdracht

De vector query tekenreeks maken

De voorbeeldvectorquery's zijn gebaseerd op twee tekenreeksen:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

De vectorquerytekenreeks is semantisch vergelijkbaar met de zoekreeks, maar bevat termen die niet bestaan in de zoekindex. Als u een trefwoord zoekt quintessential lodging near running trails, eateries, retail, zijn de resultaten nul. We gebruiken dit voorbeeld om te laten zien hoe u relevante resultaten kunt krijgen, zelfs als er geen overeenkomende termen zijn.

1. Voer de Create the vector query string codecel uit. Hiermee wordt de vector variabele geladen met de gevectoriseerde querygegevens die nodig zijn om alle zoekopdrachten in de volgende secties uit te voeren.

Zoeken met één vector

In het eerste voorbeeld ziet u een basisscenario waarin u documentbeschrijvingen wilt vinden die nauw overeenkomen met de zoekreeks.

1. Voer de Single vector search codecel uit. Dit blok bevat de aanvraag om een query uit te voeren op de zoekindex.

   # 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.")
   

   Deze vectorquery wordt ingekort voor kortheid. De vectorQueries.vector bevat de vectortekst van de query-invoer, fields bepaalt welke vectorvelden worden doorzocht en k geeft het aantal dichtstbijzijnde buren op dat moet worden geretourneerd.

   De vectorquerytekenreeks is quintessential lodging near running trails, eateries, retail, die wordt gevectoriseerd in 1536 insluitingen voor deze query.

   Het antwoord voor het vectorequivalent bestaat quintessential lodging near running trails, eateries, retail uit 7 resultaten, maar de code geeft aan top=5 dat alleen de eerste vijf resultaten worden geretourneerd. Bovendien worden alleen de velden geretourneerd die door de select zijn opgegeven.

   search_client.search() retourneert een dict-achtig object. Elk resultaat biedt een zoekscore, die kan worden geopend met behulp van score = result.get("@search.score", "N/A"). Hoewel dit niet in dit voorbeeld wordt weergegeven, bevat k het antwoord in een zoekopdracht naar overeenkomsten altijd resultaten die zijn gerangschikt op de score voor waarde-overeenkomsten.

   Nadat u de cel hebt uitgevoerd, wordt de status van elk document eronder weergegeven.

   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
   

Zoeken met één vector met filter

U kunt filters toevoegen, maar de filters worden toegepast op de niet-ctorinhoud in uw index. In dit voorbeeld is het filter van toepassing op het Tags veld om alle hotels te filteren die geen gratis Wi-Fi bieden.

Een enkele vectorzoekopdracht maken met een filter:

1. Voer de Single vector search with filter codecel uit. Deze cel bevat de aanvraag om een query uit te voeren op de zoekindex.

   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.")
   

   Nadat u de cel heeft uitgevoerd, wordt de status van elk document eronder weergegeven.

   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']
   

   De query was hetzelfde als het vorige voorbeeld van een enkele vectorzoekopdracht, maar bevat een uitsluitingsfilter na verwerking en retourneert alleen de twee hotels met gratis Wi-Fi.

2. In het volgende filtervoorbeeld wordt een geofilter gebruikt. Voer de Vector query with a geo filter codecel uit. Dit blok bevat de aanvraag om een query uit te voeren op de zoekindex.

   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.")
   

   De query was hetzelfde als het vorige voorbeeld van een enkele vectorzoekopdracht, maar bevat een uitsluitingsfilter na verwerking en retourneert alleen de twee hotels binnen 300 kilometer.

   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.
   

Hybride zoekopdracht

Hybride zoekopdrachten bestaan uit trefwoordquery's en vectorquery's in één zoekaanvraag. In dit voorbeeld wordt de vectorquery en zoekopdracht in volledige tekst gelijktijdig uitgevoerd:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

Een hybride zoekopdracht maken:

1. Voer de Hybrid search codecel uit. Dit blok bevat de aanvraag om een query uit te voeren op de zoekindex.

   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.")    
   

2. Controleer de uitvoer onder de cel.

   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']
   

   Omdat Recursive Rank Fusion (RRF) resultaten samenvoegt, is het nuttig om de invoer te controleren. De volgende resultaten zijn alleen afkomstig van de volledige-tekstquery. De top twee resultaten zijn Subliem Palace Hotel en Luxury Lion Resort. Het Sublieme Palace Hotel heeft een sterkere BM25 relevantie score.

   {
       "@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"
   },
   

   In de vectorquery, die HNSW gebruikt voor het vinden van overeenkomsten, daalt het Sublieme Palace Hotel naar de vierde positie. Luxe Leeuw, dat tweede was in de volledige-tekstzoekopdracht en derde in de vectorzoek, ondervindt niet hetzelfde bereik van fluctuatie, dus het verschijnt als een topmatch in een gehomogeniseerde resultatenset.

   "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"
       }
   ]
   

Semantische hybride zoekopdracht

Dit is de laatste query in de verzameling. Deze hybride query specificeert het semantische querytype en een semantische configuratie, waarmee wordt gedemonstreerd dat u een hybride query kunt bouwen die gebruikmaakt van semantische herrankering.

Een semantische hybride zoekopdracht maken:

1. Voer de Semantic hybrid search codecel uit. Dit codeblok bevat de aanvraag om een query uit te voeren op de zoekindex.

   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.")
   

2. Controleer de uitvoer onder de cel.

   Met semantische rangschikking gaat het Swirling Currents Hotel nu naar de toppositie.

   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
   

   U kunt de semantische rangschikking beschouwen als een manier om de relevantie van zoekresultaten te verbeteren door inzicht te krijgen in de betekenis achter de woorden in de query en de inhoud van de documenten. In dit geval helpt de semantische classificatie om hotels te identificeren die niet alleen relevant zijn voor de trefwoorden, maar ook overeenkomen met de intentie van de query:

   Belangrijkste conclusies:

   • Vectorzoekopdrachten worden opgegeven via de vectors.value eigenschap. Trefwoorden zoeken wordt opgegeven via de search eigenschap.

   • In een hybride zoekopdracht kunt u vectorzoekopdrachten integreren met zoeken in volledige tekst over trefwoorden. Filters, spellingcontrole en semantische classificatie zijn alleen van toepassing op tekstuele inhoud en niet op vectoren. In deze laatste query is er geen semantisch answer omdat het systeem er geen heeft geproduceerd die voldoende sterk was.

   • Werkelijke resultaten bevatten meer details, inclusief semantische bijschriften en markeringen. De resultaten zijn gewijzigd voor leesbaarheid. Als u de volledige structuur van het antwoord wilt ophalen, voert u de aanvraag uit in de REST-client.

De hulpbronnen opschonen

Wanneer u in uw eigen abonnement werkt, is het een goed idee om aan het einde van een project te bepalen of u de gemaakte resources nog nodig hebt. Resources die actief blijven, kunnen u geld kosten. U kunt resources afzonderlijk verwijderen, maar u kunt ook de resourcegroep verwijderen als u de volledige resourceset wilt verwijderen.

U kunt resources vinden en beheren in Azure Portal met behulp van de koppeling Alle resources of resourcegroepen in het meest linkse deelvenster.

U kunt ook de Clean up codecel uitvoeren om de vectorindex te verwijderen die in deze quickstart is gemaakt.

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

Volgende stappen

• Bekijk de opslagplaats met codevoorbeelden voor vectorzoekmogelijkheden in Azure AI Search voor Python.
• Bekijk de andere Python- en Azure AI Search-codevoorbeelden in de opslagplaats azure-search-python-samples.

In deze quickstart gebruikt u Java om een vectorindex te maken, laden en er query's op uit te voeren. De code voert deze bewerkingen uit met behulp van de Azure AI Search-clientbibliotheek voor Java, die een abstractie biedt van de REST API's voor toegang tot indexbewerkingen.

In Azure AI Search heeft een vectorindex een indexschema dat vectorvelden en niet-vectorvelden definieert, een vectorzoekconfiguratie voor algoritmen die de insluitruimte maken en instellingen voor vectorvelddefinities die tijdens de query worden geëvalueerd. Indexen - Maken of bijwerken (REST API) maakt de vectorindex.

Vereisten

• Een Azure-account met een actief abonnement. Gratis een account maken

• Een Azure AI Search-service.

  • U kunt de gratis laag voor de meeste van deze quickstart gebruiken, maar we raden Basic of hoger aan voor grotere gegevensbestanden.

  • Wijs voor sleutelloze verificatie met Microsoft Entra-id de rol Inzender voor zoekindexgegevens toe aan uw gebruikersaccount.

  • Als u de semantische hybride query wilt uitvoeren, moet u de semantische rangschikking inschakelen.

• Java 21 (LTS).

• Maven.

Servicegegevens ophalen

In de resterende secties stelt u API-aanroepen in op Azure OpenAI en Azure AI Search. Haal de service-eindpunten op, zodat u ze kunt opgeven als variabelen in uw code.

Om het service-eindpunt te verkrijgen:

1. Meld u aan bij het Azure-portaal.

2. Zoek uw zoekservice.

3. Kopieer de URL op de startpagina Overzicht . Een eindpunt ziet er bijvoorbeeld uit als https://example.search.windows.net.

Het Java-project instellen

1. Maak een nieuwe Maven-projectmap.

   mkdir vector-quickstart && cd vector-quickstart
   

2. Maak een pom.xml bestand met vereiste afhankelijkheden.

   <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. Compileer het project om de afhankelijkheden op te lossen.

   mvn compile
   

4. Maak de bronmapstructuur.

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

5. Maak een application.properties bestand in de src/main/resources map en geef uw zoekservice-eindpunt op.

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

Aanmelden bij Azure

U gebruikt Microsoft Entra-id en roltoewijzingen voor de verbinding. Zorg ervoor dat u bent aangemeld bij dezelfde tenant en hetzelfde abonnement als Azure AI Search en Azure OpenAI. U kunt de Azure CLI op de opdrachtregel gebruiken om de huidige eigenschappen weer te geven, eigenschappen te wijzigen en u aan te melden. Zie Verbinding maken zonder sleutels voor meer informatie.

Voer elk van de volgende opdrachten op volgorde uit.

az account show

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

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

U moet nu vanaf uw lokale apparaat zijn aangemeld bij Azure.

Een vectorindex maken

In deze sectie maakt u een vectorindex in Azure AI Search met SearchIndexClient. createOrUpdateIndex. Het indexschema definieert de velden, inclusief het vectorveld DescriptionVector.

Een vectorindex maken:

1. Maak een CreateIndex.java bestand in de src/main/java/com/example/search map.

2. Kopieer de volgende code naar het bestand.

   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);
       }
   }
   

   Het codebestand maakt de index en definieert het indexschema, inclusief het DescriptionVector vectorveld.

3. Voer het bestand uit.

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

   In de uitvoer van deze code ziet u dat de index succesvol is aangemaakt.

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

   Belangrijkste conclusies:

   • U definieert een index door een lijst met velden te maken.

   • Deze specifieke index ondersteunt meerdere zoekmogelijkheden, zoals:

     • Zoeken naar trefwoorden in volledige tekst (setSearchable)

     • Vectorzoekopdrachten (maakt hybride zoekopdrachten mogelijk door vector- en niet-vectorvelden samen te voegen) (DescriptionVector met setVectorSearchProfileName)

     • Semantische zoekopdracht

     • Facet-zoeken (SearchSuggester)

     • Geo-ruimtelijke zoekopdracht (Location veld met SearchFieldDataType.GEOGRAPHY_POINT)

     • Filteren, sorteren (veel velden gemarkeerd als filterbaar en sorteerbaar)

Documenten uploaden naar de index

Het maken en laden van de index zijn afzonderlijke stappen. U hebt het indexschema in de vorige stap gemaakt. U moet nu documenten in de index laden met SearchClient. uploadDocuments. De documenten bevatten de vectorversie van de beschrijving van het artikel, waardoor overeenkomsten kunnen worden gezocht op basis van betekenis in plaats van exacte trefwoorden.

In Azure AI Search slaat de index alle doorzoekbare inhoud op, terwijl de zoekmachine query's uitvoert op die index.

Documenten uploaden naar de index:

1. Maak een UploadDocuments.java bestand in de src/main/java/com/example/search map.

2. Kopieer de volgende code naar het bestand.

   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);
       }
   }
   

   Met deze code wordt een verzameling documenten geladen. Elk document bevat de vectorversie van het artikel Description. Deze vector maakt overeenkomsten zoeken mogelijk, waarbij overeenkomende waarden zijn gebaseerd op betekenis in plaats van exacte trefwoorden.

   Omdat in dit quickstart-artikel de index onmiddellijk wordt doorzocht, wacht de waitUntilIndexed methode totdat de index gereed is voor zoekbewerkingen. Dit is belangrijk omdat de index volledig moet worden gevuld met documenten voordat u zoekopdrachten kunt uitvoeren.

3. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code ziet u dat de documenten zijn geïndexeerd en klaar zijn voor zoekopdrachten.

   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.
   

   Belangrijkste conclusies:

   • Uw code communiceert met een specifieke zoekindex die wordt gehost in uw Azure AI Search-service via de SearchClient. De SearchClient service biedt toegang tot bewerkingen zoals:

     • Gegevensopname: uploadDocuments, mergeDocuments, deleteDocumentsenzovoort.

     • Zoekbewerkingen: search, autocompletesuggest

   • Vectorvelden bevatten drijvendekommawaarden. Het kenmerk dimensies heeft minimaal 2 en een maximum van 4096 drijvendekommawaarden. In deze quickstart wordt het dimensiekenmerk ingesteld op 1536, omdat dit de grootte is van insluitingen die door het text-embedding-ada-002 model worden gegenereerd.

De query als vector maken

De voorbeeldvectorquery's zijn gebaseerd op twee tekenreeksen:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

De vectorquerytekenreeks is semantisch vergelijkbaar met de zoekreeks, maar bevat termen die niet bestaan in de zoekindex. Als u een trefwoord zoekt quintessential lodging near running trails, eateries, retail, zijn de resultaten nul. We gebruiken dit voorbeeld om te laten zien hoe u relevante resultaten kunt krijgen, zelfs als er geen overeenkomende termen zijn.

Maak een QueryVector.java bestand in de src/main/java/com/example/search map en voeg de code toe om de queryvector te maken.

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;
    }
}

Deze code wordt gebruikt in de volgende secties om vectorzoekopdrachten uit te voeren. De queryvector wordt gemaakt met behulp van een insluitmodel van Azure OpenAI.

Een enkele vectorzoekopdracht maken

In het eerste voorbeeld ziet u een basisscenario waarin u documentbeschrijvingen wilt vinden die nauw overeenkomen met de zoekreeks met behulp van SearchClient. zoeken en VectorQuery en SearchOptions.

Een enkele vectorzoekopdracht maken:

1. Maak een SearchSingle.java bestand in de src/main/java/com/example/search map.

2. Kopieer de volgende code naar het bestand.

   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);
       }
   }
   

   De vectorQuery bevat de configuratie van de gevectoriseerde query, inclusief de gevectoriseerde tekst van de query-invoer zoals opgehaald via QueryVector.getVectorList. setFields bepaalt welke vectorvelden worden doorzocht en setKNearestNeighborsCount geeft het aantal dichtstbijzijnde buren op dat moet worden geretourneerd.

   De vectorquerytekenreeks is quintessential lodging near running trails, eateries, retail, die wordt gevectoriseerd in 1536 insluitingen voor deze query.

3. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code worden de relevante documenten voor de query quintessential lodging near running trails, eateries, retailweergegeven.

   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
   

   Het antwoord voor het vectorequivalent van quintessential lodging near running trails, eateries, retail bestaat uit 5 resultaten, met alleen de velden die worden geretourneerd zoals gespecificeerd door de selectie.

Een zoekopdracht met één vector maken met een filter

U kunt filters toevoegen, maar de filters worden toegepast op de niet-ctorinhoud in uw index. In dit voorbeeld is het filter van toepassing op het Tags veld om alle hotels te filteren die geen gratis Wi-Fi bieden. Deze zoekopdracht maakt gebruik van SearchClient. SearchClient en SearchOptions.

Een enkele vectorzoekopdracht maken met een filter:

1. Maak een SearchSingleWithFilter.java bestand in de src/main/java/com/example/search map.

2. Kopieer de volgende code naar het bestand.

   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);
       }
   }
   

   Deze code heeft dezelfde zoekfunctionaliteit als de vorige zoekopdracht, maar voegt een uitsluitingsfilter voor naverwerking toe voor hotels met free wifi.

3. Bouw het bestand en voer het uit.

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

   De uitvoer van deze code toont de relevante documenten voor de query met het filter voor free wifi toegepast.

   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
   

Een zoekopdracht maken met een georuimtelijk filter

U kunt een georuimtelijk filter opgeven om de resultaten te beperken tot een specifiek geografisch gebied. In dit voorbeeld beperkt het filter de resultaten tot hotels binnen 300 kilometer van Washington D.C. (coördinaten: -77.03241 38.90166). Georuimtelijke afstanden zijn altijd in kilometers. Deze zoekopdracht maakt gebruik van SearchClient. SearchClient en SearchOptions.

Een enkele vectorzoekopdracht maken met een georuimtelijk filter:

1. Maak een SearchSingleWithFilterGeo.java bestand in de src/main/java/com/example/search map.

2. Kopieer de volgende code naar het bestand.

   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. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code ziet u de relevante documenten voor de query met het toegepaste uitsluitingsfilter voor georuimtelijke naverwerking.

   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
   

Een hybride zoekopdracht maken

Hybride zoekopdrachten combineren trefwoord- en vectorquery's in één aanvraag. In dit voorbeeld worden de volgende query-strings voor volledige-tekst en vectorquery's gelijktijdig uitgevoerd:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

Deze zoekopdracht maakt gebruik van SearchClient. SearchClient en SearchOptions.

Tp maakt een hybride zoekopdracht:

1. Maak een SearchHybrid.java bestand in de src/main/java/com/example/search map.

2. Kopieer de volgende code naar het bestand.

   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. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code ziet u de relevante documenten voor de hybride zoekopdracht.

   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"]
   

   Omdat Recursive Rank Fusion (RRF) resultaten samenvoegt, is het nuttig om de invoer te controleren. De volgende resultaten zijn alleen afkomstig van de volledige-tekstquery. De top twee resultaten zijn Subliem Palace Hotel en Luxury Lion Resort. Het Sublieme Palace Hotel heeft een sterkere BM25 relevantie score.

      {
          "@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"
      },
   

   In de vectorquery, die HNSW gebruikt voor het vinden van overeenkomsten, daalt het Sublieme Palace Hotel naar de vierde positie. Luxe Leeuw, dat tweede was in de volledige-tekstzoekopdracht en derde in de vectorzoek, ondervindt niet hetzelfde bereik van fluctuatie, dus het verschijnt als een topmatch in een gehomogeniseerde resultatenset.

   "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"
       }
   ]
   

Een semantische hybride zoekopdracht maken

Hier volgt de laatste query in de verzameling om de semantische hybride zoekopdracht uit te breiden met de extra zoektekst historic hotel walk to restaurants and shopping.

Deze zoekopdracht maakt gebruik van SearchClient. SearchClient en SearchOptions.

Een semantische hybride zoekopdracht maken:

1. Maak een SearchSemanticHybrid.java bestand in de src/main/java/com/example/search map.

2. Kopieer de volgende code naar het bestand.

   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. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code ziet u de relevante documenten voor de semantische hybride zoekopdracht.

   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
   

   U kunt de semantische rangschikking beschouwen als een manier om de relevantie van zoekresultaten te verbeteren door inzicht te krijgen in de betekenis achter de woorden in de query en de inhoud van de documenten. In dit geval helpt de semantische classificatie om hotels te identificeren die niet alleen relevant zijn voor de trefwoorden, maar ook overeenkomen met de intentie van de query:

   Belangrijkste conclusies:

   • Vectorzoekopdrachten worden opgegeven via de VectorSearchOptions klasse. Het zoeken naar trefwoorden wordt opgegeven via de SemanticSearchOptions klasse.

   • In een hybride zoekopdracht kunt u vectorzoekopdrachten integreren met zoeken in volledige tekst over trefwoorden. Filters, spellingcontrole en semantische classificatie zijn alleen van toepassing op tekstuele inhoud en niet op vectoren. In deze laatste query is er geen semantisch answer omdat het systeem er geen heeft geproduceerd die voldoende sterk was.

   • Werkelijke resultaten bevatten meer details, inclusief semantische bijschriften en markeringen. De resultaten zijn gewijzigd voor leesbaarheid. Als u de volledige structuur van het antwoord wilt ophalen, voert u de aanvraag uit in de REST-client.

De hulpbronnen opschonen

Wanneer u in uw eigen abonnement werkt, is het een goed idee om aan het einde van een project te bepalen of u de gemaakte resources nog nodig hebt. Resources die actief blijven, kunnen u geld kosten. U kunt resources afzonderlijk verwijderen, maar u kunt ook de resourcegroep verwijderen als u de volledige resourceset wilt verwijderen.

U kunt resources vinden en beheren in Azure Portal met behulp van de koppeling Alle resources of resourcegroepen in het meest linkse deelvenster.

U kunt ook programmatisch de vectorindex verwijderen die u in deze quickstart hebt gemaakt:

1. Maak een DeleteIndex.java bestand in de src/main/java/com/example/search map.

2. Voeg de volgende code toe aan het bestand.

   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. Bouw het bestand en voer het uit.

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

Volgende stappen

• Bekijk de opslagplaats met codevoorbeelden voor vectorzoekmogelijkheden in Azure AI Search voor Java.

In deze quickstart gebruikt u JavaScript om een vectorindex te maken, laden en er query's op uit te voeren. De code voert deze bewerkingen uit met behulp van de Azure AI Search-clientbibliotheek voor JavaScript, die een abstractie biedt van de REST API's voor toegang tot indexbewerkingen.

In Azure AI Search heeft een vectorindex een indexschema dat vectorvelden en niet-vectorvelden definieert, een vectorzoekconfiguratie voor algoritmen die de insluitruimte maken en instellingen voor vectorvelddefinities die tijdens de query worden geëvalueerd. Indexen - Maken of bijwerken (REST API) maakt de vectorindex.

Vereisten

• Een Azure-account met een actief abonnement. Gratis een account maken

• Een Azure AI Search-service.

  • U kunt de gratis laag voor de meeste van deze quickstart gebruiken, maar we raden Basic of hoger aan voor grotere gegevensbestanden.

  • Wijs voor sleutelloze verificatie met Microsoft Entra-id de rol Inzender voor zoekindexgegevens toe aan uw gebruikersaccount.

  • Als u de semantische hybride query wilt uitvoeren, moet u de semantische rangschikking inschakelen.

• Visual Studio Code.

• Node.JS met LTS.

Service-eindpunten ophalen

In de resterende secties stelt u API-aanroepen in op Azure OpenAI en Azure AI Search. Haal de service-eindpunten op, zodat u ze kunt opgeven als variabelen in uw code.

Om het service-eindpunt te verkrijgen:

1. Meld u aan bij het Azure-portaal.

2. Zoek uw zoekservice.

3. Kopieer de URL op de startpagina Overzicht . Een eindpunt ziet er bijvoorbeeld uit als https://example.search.windows.net.

Het Node.JS-project instellen

1. Start Visual Studio Code in een nieuwe map.

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

2. Maak een nieuw pakket voor ESM-modules in uw projectmap.

   npm init -y
   npm pkg set type=module
   

   Hiermee maakt u een package.json bestand met standaardwaarden.

3. Installeer de volgende npm pakketten.

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

4. Maak een src map in uw projectmap.

   mkdir src
   

Omgevingsvariabelen instellen voor lokale ontwikkeling

1. Maak een .env bestand in de vector-quickstart projectmap.

2. Voeg de volgende omgevingsvariabelen toe aan het .env bestand en vervang de waarden door uw eigen service-eindpunten en -sleutels.

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

Aanmelden bij Azure

U gebruikt Microsoft Entra-id en roltoewijzingen voor de verbinding. Zorg ervoor dat u bent aangemeld bij dezelfde tenant en hetzelfde abonnement als Azure AI Search en Azure OpenAI. U kunt de Azure CLI op de opdrachtregel gebruiken om de huidige eigenschappen weer te geven, eigenschappen te wijzigen en u aan te melden. Zie Verbinding maken zonder sleutels voor meer informatie.

Voer elk van de volgende opdrachten op volgorde uit.

az account show

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

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

U moet nu vanaf uw lokale apparaat zijn aangemeld bij Azure.

Een vectorindex maken

In deze sectie maakt u een vectorindex in Azure AI Search met SearchIndexClient. createOrUpdateIndex. Het indexschema definieert de velden, inclusief het vectorveld DescriptionVector.

Een vectorindex maken:

1. Maak een createIndex.js bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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`);
   

   Het codebestand voegt de afhankelijkheden, omgevingsvariabelen en het JavaScript-type toe aan het begin van het bestand HotelDocument. Voeg de createIndex functie toe om de index te maken. De functie definieert het indexschema, inclusief het vectorveld DescriptionVector.

3. Voer het bestand uit.

   node -r dotenv/config src/createIndex.js
   

   In de uitvoer van deze code ziet u dat de index succesvol is aangemaakt.

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

   Belangrijkste conclusies:

   • U definieert een index door een lijst met velden te maken.

   • Deze specifieke index ondersteunt meerdere zoekmogelijkheden, zoals:

     • Zoeken naar trefwoorden in volledige tekst (searchable)

     • Vectorzoekopdrachten (maakt hybride zoekopdrachten mogelijk door vector- en niet-vectorvelden samen te voegen) (DescriptionVector met vectorSearchProfileName)

     • Semantische zoekopdracht

     • Facet-zoeken (searchSuggester)

     • Geo-ruimtelijke zoekopdracht (Location veld met geo.distance)

     • Filteren, sorteren (veel velden gemarkeerd als filterbaar en sorteerbaar)

Documenten uploaden naar de index

Het maken en laden van de index zijn afzonderlijke stappen. U hebt het indexschema in de vorige stap gemaakt. U moet nu documenten in de index laden met SearchClient. uploadDocuments. De documenten bevatten de vectorversie van de beschrijving van het artikel, waardoor overeenkomsten kunnen worden gezocht op basis van betekenis in plaats van exacte trefwoorden.

In Azure AI Search slaat de index alle doorzoekbare inhoud op, terwijl de zoekmachine query's uitvoert op die index.

Documenten uploaden naar de index:

1. Maak een uploadDocuments.js bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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"
                   }
               }
           }
       },
       {
           "@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);
   }
   

   Met deze code wordt een matrix van JSON-objecten geladen. Elk document bevat de vectorversie van het artikel description. Deze vector maakt overeenkomsten zoeken mogelijk, waarbij overeenkomende waarden zijn gebaseerd op betekenis in plaats van exacte trefwoorden.

   Omdat in dit quickstart-artikel direct in de index wordt gezocht, wacht de waitUntilIndexed functie totdat de index gereed is voor zoekbewerkingen. Dit is belangrijk omdat de index volledig moet worden gevuld met documenten voordat u zoekopdrachten kunt uitvoeren.

3. Bouw het bestand en voer het uit.

   node -r dotenv/config src/uploadDocuments.js
   

   In de uitvoer van deze code ziet u dat de documenten zijn geïndexeerd en klaar zijn voor zoekopdrachten.

   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.
   

   Belangrijkste conclusies:

   • Uw code communiceert met een specifieke zoekindex die wordt gehost in uw Azure AI Search-service via het SearchClienthoofdobject dat door het @azure/search-documents pakket wordt geleverd. De SearchClient service biedt toegang tot indexbewerkingen, zoals:

     • Gegevensopname: uploadDocuments, mergeDocuments, deleteDocumentsenzovoort.

     • Zoekbewerkingen: search, autocompletesuggest

     • Indexbeheerbewerkingen: createIndex, deleteIndex, getIndexenzovoort.

   • Vectorvelden bevatten drijvendekommawaarden. Het kenmerk dimensies heeft minimaal 2 en een maximum van 4096 drijvendekommawaarden. In deze quickstart wordt het dimensiekenmerk ingesteld op 1536, omdat dit de grootte is van insluitingen die door het text-embedding-ada-002 model worden gegenereerd.

De query als vector maken

De voorbeeldvectorquery's zijn gebaseerd op twee tekenreeksen:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

De vectorquerytekenreeks is semantisch vergelijkbaar met de zoekreeks, maar bevat termen die niet bestaan in de zoekindex. Als u een trefwoord zoekt quintessential lodging near running trails, eateries, retail, zijn de resultaten nul. We gebruiken dit voorbeeld om te laten zien hoe u relevante resultaten kunt krijgen, zelfs als er geen overeenkomende termen zijn.

Maak een queryVector.js bestand in de src map en voeg de code toe om de queryvector te maken.

// 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 ranking
// Replace with your own vector

export const vector = 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Deze code wordt gebruikt in de volgende secties om vectorzoekopdrachten uit te voeren. De queryvector wordt gemaakt met behulp van een insluitmodel van Azure OpenAI.

Een enkele vectorzoekopdracht maken

In het eerste voorbeeld ziet u een basisscenario waarin u documentbeschrijvingen wilt vinden die nauw overeenkomen met de zoekreeks met behulp van SearchClient. zoeken en VectorQuery en SearchOptions.

Een enkele vectorzoekopdracht maken:

1. Maak een searchSingle.js bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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;
   }
   

   De vectorQuery bevat de configuratie van de gevectoriseerde query, inclusief de gevectoriseerde tekst van de queryinvoer, vectorfields bepaalt welke vectorvelden worden doorzocht en kNearestNeighborsCount geeft het aantal dichtstbijzijnde buren op dat moet worden geretourneerd.

   De vectorquerytekenreeks is quintessential lodging near running trails, eateries, retail, die wordt gevectoriseerd in 1536 insluitingen voor deze query.

3. Bouw het bestand en voer het uit.

   node -r dotenv/config src/searchSingle.js
   

   In de uitvoer van deze code worden de relevante documenten voor de query quintessential lodging near running trails, eateries, retailweergegeven.

   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
   

   Het antwoord voor het vectorequivalent van quintessential lodging near running trails, eateries, retail bestaat uit 5 resultaten, met alleen de velden die worden geretourneerd zoals gespecificeerd door de selectie.

Een zoekopdracht met één vector maken met een filter

U kunt filters toevoegen, maar de filters worden toegepast op de niet-ctorinhoud in uw index. In dit voorbeeld is het filter van toepassing op het Tags veld om alle hotels te filteren die geen gratis Wi-Fi bieden. Deze zoekopdracht maakt gebruik van SearchClient. zoeken en VectorQuery en SearchOptions.

Een enkele vectorzoekopdracht maken met een filter:

1. Maak een searchSingleWithFilter.js bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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;
   }
   

   Voeg de afhankelijkheden, omgevingsvariabelen en dezelfde zoekfunctionaliteit toe als de vorige zoekopdracht met een uitsluitingsfilter na verwerking toegevoegd voor hotels met free wifi.

3. Bouw het bestand en voer het uit.

   node -r dotenv/config src/searchSingleWithFilter.js
   

   De uitvoer van deze code toont de relevante documenten voor de query met het filter voor free wifi toegepast.

   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
   

Een zoekopdracht maken met een georuimtelijk filter

U kunt een georuimtelijk filter opgeven om de resultaten te beperken tot een specifiek geografisch gebied. In dit voorbeeld beperkt het filter de resultaten tot hotels binnen 300 kilometer van Washington D.C. (coördinaten: -77.03241 38.90166). Georuimtelijke afstanden zijn altijd in kilometers. Deze zoekopdracht maakt gebruik van SearchClient. zoeken en VectorQuery en SearchOptions.

Een zoekopdracht maken met een georuimtelijk filter:

1. Maak een searchSingleWithFilterGeo.js bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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. Bouw het bestand en voer het uit.

   node -r dotenv/config src/searchSingleWithFilterGeo.js
   

   In de uitvoer van deze code ziet u de relevante documenten voor de query met het toegepaste uitsluitingsfilter voor georuimtelijke naverwerking.

   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
   

Een hybride zoekopdracht maken

Hybride zoekopdrachten bestaan uit trefwoordquery's en vectorquery's in één zoekaanvraag. In dit voorbeeld wordt de vectorquery en zoekopdracht in volledige tekst gelijktijdig uitgevoerd:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

Deze zoekopdracht maakt gebruik van SearchClient. zoeken en VectorQuery en SearchOptions.

Een hybride zoekopdracht maken:

1. Maak een searchHybrid.js bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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. Bouw het bestand en voer het uit.

   node -r dotenv/config src/searchHybrid.js
   

   In de uitvoer van deze code ziet u de relevante documenten voor de hybride zoekopdracht.

   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"]
   

   Omdat Recursive Rank Fusion (RRF) resultaten samenvoegt, is het nuttig om de invoer te controleren. De volgende resultaten zijn alleen afkomstig van de volledige-tekstquery. De top twee resultaten zijn Subliem Palace Hotel en Luxury Lion Resort. Het Sublieme Palace Hotel heeft een sterkere BM25 relevantie score.

      {
          "@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"
      },
   

   In de vectorquery, die HNSW gebruikt voor het vinden van overeenkomsten, daalt het Sublieme Palace Hotel naar de vierde positie. Luxe Leeuw, dat tweede was in de volledige-tekstzoekopdracht en derde in de vectorzoek, ondervindt niet hetzelfde bereik van fluctuatie, dus het verschijnt als een topmatch in een gehomogeniseerde resultatenset.

   "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"
       }
   ]
   

Een semantische hybride zoekopdracht maken

Hier volgt de laatste query in de verzameling om de semantische hybride zoekopdracht uit te breiden met de extra zoektekst historic hotel walk to restaurants and shopping.

Deze zoekopdracht maakt gebruik van SearchClient. zoeken en VectorQuery en SearchOptions.

Een semantische hybride zoekopdracht maken:

1. Maak een searchSemanticHybrid.js bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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 ranking
       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. Bouw het bestand en voer het uit.

   node -r dotenv/config src/searchSemanticHybrid.js
   

   In de uitvoer van deze code ziet u de relevante documenten voor de semantische hybride zoekopdracht.

   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
   

   U kunt de semantische rangschikking beschouwen als een manier om de relevantie van zoekresultaten te verbeteren door inzicht te krijgen in de betekenis achter de woorden in de query en de inhoud van de documenten. In dit geval helpt de semantische classificatie om hotels te identificeren die niet alleen relevant zijn voor de trefwoorden, maar ook overeenkomen met de intentie van de query:

   Belangrijkste conclusies:

   • Vectorzoekopdrachten worden opgegeven via de vectorSearchOptions eigenschap. Trefwoorden zoeken wordt opgegeven via de semanticSearchOptions eigenschap.

   • In een hybride zoekopdracht kunt u vectorzoekopdrachten integreren met zoeken in volledige tekst over trefwoorden. Filters, spellingcontrole en semantische classificatie zijn alleen van toepassing op tekstuele inhoud en niet op vectoren. In deze laatste query is er geen semantisch answer omdat het systeem er geen heeft geproduceerd die voldoende sterk was.

   • Werkelijke resultaten bevatten meer details, inclusief semantische bijschriften en markeringen. De resultaten zijn gewijzigd voor leesbaarheid. Als u de volledige structuur van het antwoord wilt ophalen, voert u de aanvraag uit in de REST-client.

De hulpbronnen opschonen

Wanneer u in uw eigen abonnement werkt, is het een goed idee om aan het einde van een project te bepalen of u de gemaakte resources nog nodig hebt. Resources die actief blijven, kunnen u geld kosten. U kunt resources afzonderlijk verwijderen, maar u kunt ook de resourcegroep verwijderen als u de volledige resourceset wilt verwijderen.

U kunt resources vinden en beheren in Azure Portal met behulp van de koppeling Alle resources of resourcegroepen in het meest linkse deelvenster.

U kunt ook programmatisch de vectorindex verwijderen die u in deze quickstart hebt gemaakt:

1. Maak een deleteIndex.js bestand in de src map.

2. Voeg de afhankelijkheden, omgevingsvariabelen en code toe om de index te verwijderen.

   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. Bouw het bestand en voer het uit.

   node -r dotenv/config src/deleteIndex.js
   

Volgende stappen

• Bekijk de opslagplaats met codevoorbeelden voor vectorzoekmogelijkheden in Azure AI Search voor JavaScript.

In deze quickstart gebruikt u TypeScript om een vectorindex te maken, laden en er query's op uit te voeren. De code voert deze bewerkingen uit met behulp van de Azure AI Search-clientbibliotheek voor TypeScript, die een abstractie biedt van de REST API's voor toegang tot indexbewerkingen.

In Azure AI Search heeft een vectorindex een indexschema dat vectorvelden en niet-vectorvelden definieert, een vectorzoekconfiguratie voor algoritmen die de insluitruimte maken en instellingen voor vectorvelddefinities die tijdens de query worden geëvalueerd. Indexen - Maken of bijwerken (REST API) maakt de vectorindex.

Vereisten

• Een Azure-account met een actief abonnement. Gratis een account maken

• Een Azure AI Search-service.

  • U kunt de gratis laag voor de meeste van deze quickstart gebruiken, maar we raden Basic of hoger aan voor grotere gegevensbestanden.

  • Wijs voor sleutelloze verificatie met Microsoft Entra-id de rol Inzender voor zoekindexgegevens toe aan uw gebruikersaccount.

  • Als u de semantische hybride query wilt uitvoeren, moet u de semantische rangschikking inschakelen.

• Visual Studio Code.

• Node.JS met LTS.

• TypeScript. U kunt TypeScript globaal installeren met behulp van npm:

  npm install -g typescript
  

Servicegegevens ophalen

In de resterende secties stelt u API-aanroepen in op Azure OpenAI en Azure AI Search. Haal de service-eindpunten op, zodat u ze kunt opgeven als variabelen in uw code.

Om het service-eindpunt te verkrijgen:

1. Meld u aan bij het Azure-portaal.

2. Zoek uw zoekservice.

3. Kopieer de URL op de startpagina Overzicht . Een eindpunt ziet er bijvoorbeeld uit als https://example.search.windows.net.

Het Node.JS-project instellen

1. Start Visual Studio Code in een nieuwe map.

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

2. Maak een nieuw pakket voor ESM-modules in uw projectmap.

   npm init -y
   npm pkg set type=module
   

   Hiermee maakt u een package.json bestand met standaardwaarden.

3. Bewerken package.json om een buildscript op te nemen. Voeg de volgende regel toe aan de scripts sectie.

   "build": "tsc"
   

4. Installeer de volgende npm-pakketten.

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

5. Maak een src map in uw projectmap.

   mkdir src
   

6. Maak een tsconfig.json bestand in de projectmap voor ESM met de volgende inhoud.

   {
     "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"
     ]
   }
   

Omgevingsvariabelen instellen voor lokale ontwikkeling

1. Maak een .env bestand in de vector-quickstart projectmap.

2. Voeg de volgende omgevingsvariabelen toe aan het .env bestand en vervang de waarden door uw eigen service-eindpunten en -sleutels.

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

Aanmelden bij Azure

U gebruikt Microsoft Entra-id en roltoewijzingen voor de verbinding. Zorg ervoor dat u bent aangemeld bij dezelfde tenant en hetzelfde abonnement als Azure AI Search en Azure OpenAI. U kunt de Azure CLI op de opdrachtregel gebruiken om de huidige eigenschappen weer te geven, eigenschappen te wijzigen en u aan te melden. Zie Verbinding maken zonder sleutels voor meer informatie.

Voer elk van de volgende opdrachten op volgorde uit.

az account show

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

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

U moet nu vanaf uw lokale apparaat zijn aangemeld bij Azure.

Een vectorindex maken

In deze sectie maakt u een vectorindex in Azure AI Search met SearchIndexClient. createOrUpdateIndex. Het indexschema definieert de velden, inclusief het vectorveld DescriptionVector. Zodra de index is gemaakt, uploadt u documenten naar de index. De documenten bevatten de vectorversie van de beschrijving van het artikel, waardoor overeenkomsten kunnen worden gezocht op basis van betekenis in plaats van exacte trefwoorden.

De vectorindex maken:

1. Maak een createIndex.ts bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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`);
   

   Het codebestand voegt de afhankelijkheden, omgevingsvariabelen en het JavaScript-type toe aan het begin van het bestand HotelDocument. Voeg de createIndex functie toe om de index te maken. De functie definieert het indexschema, inclusief het vectorveld DescriptionVector.

3. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code ziet u dat de index succesvol is aangemaakt.

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

   Belangrijkste conclusies:

   • U definieert een index door een lijst met velden te maken.

   • Deze specifieke index ondersteunt meerdere zoekmogelijkheden, zoals:

     • Zoeken naar trefwoorden in volledige tekst (searchable)

     • Vectorzoekopdrachten (maakt hybride zoekopdrachten mogelijk door vector- en niet-vectorvelden samen te voegen) (DescriptionVector met vectorSearchProfileName)

     • Semantische zoekopdracht

     • Facet-zoeken (searchSuggester)

     • Geo-ruimtelijke zoekopdracht (Location veld met geo.distance)

     • Filteren, sorteren (veel velden gemarkeerd als filterbaar en sorteerbaar)

Documenten uploaden naar de index

Het maken en laden van de index zijn afzonderlijke stappen. U hebt het indexschema in de vorige stap gemaakt. U moet nu documenten in de index laden met SearchClient. uploadDocuments. De documenten bevatten de vectorversie van de beschrijving van het artikel, waardoor overeenkomsten kunnen worden gezocht op basis van betekenis in plaats van exacte trefwoorden.

In Azure AI Search slaat de index alle doorzoekbare inhoud op, terwijl de zoekmachine query's uitvoert op die index.

Documenten uploaden naar de index:

1. Maak een uploadDocuments.ts bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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"
                   }
               }
           }
       },
       {
           "@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);
   }
   

   Met deze code worden variabele documenten geladen met een JSON-object dat elk document beschrijft, samen met de vectorversie van de beschrijving van het artikel. Deze vector maakt overeenkomsten zoeken mogelijk, waarbij overeenkomende waarden zijn gebaseerd op betekenis in plaats van exacte trefwoorden.

   Omdat in dit quickstart-artikel direct in de index wordt gezocht, wacht de waitUntilIndexed functie totdat de index gereed is voor zoekbewerkingen. Dit is belangrijk omdat de index volledig moet worden gevuld met documenten voordat u zoekopdrachten kunt uitvoeren.

3. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code ziet u dat de documenten zijn geïndexeerd en klaar zijn voor zoekopdrachten.

   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.
   

   Belangrijkste conclusies:

   • Uw code communiceert met een specifieke zoekindex die wordt gehost in uw Azure AI Search-service via het SearchClienthoofdobject dat door het @azure/search-documents pakket wordt geleverd. De SearchClient service biedt toegang tot indexbewerkingen, zoals:

     • Gegevensopname: uploadDocuments, mergeDocuments, deleteDocumentsenzovoort.

     • Zoekbewerkingen: search, autocompletesuggest

     • Indexbeheerbewerkingen: createIndex, deleteIndex, getIndexenzovoort.

   • Vectorvelden bevatten drijvendekommawaarden. Het kenmerk dimensies heeft minimaal 2 en een maximum van 4096 drijvendekommawaarden. In deze quickstart wordt het dimensiekenmerk ingesteld op 1536, omdat dit de grootte is van insluitingen die door het text-embedding-ada-002 model worden gegenereerd.

De query als vector maken

De voorbeeldvectorquery's zijn gebaseerd op twee tekenreeksen:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

De vectorquerytekenreeks is semantisch vergelijkbaar met de zoekreeks, maar bevat termen die niet bestaan in de zoekindex. Als u een trefwoord zoekt quintessential lodging near running trails, eateries, retail, zijn de resultaten nul. We gebruiken dit voorbeeld om te laten zien hoe u relevante resultaten kunt krijgen, zelfs als er geen overeenkomende termen zijn.

Maak een queryVector.ts bestand in de src map en voeg de code toe om de queryvector te maken.

// 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 ranking
// Replace with your own vector

export const vector = 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Deze code wordt gebruikt in de volgende secties om vectorzoekopdrachten uit te voeren. De queryvector wordt gemaakt met behulp van een insluitmodel van Azure OpenAI.

Een enkele vectorzoekopdracht maken

In het eerste voorbeeld ziet u een basisscenario waarin u documentbeschrijvingen wilt vinden die nauw overeenkomen met de zoekreeks met behulp van SearchClient. zoeken en VectorQuery en SearchOptions.

Een enkele vectorzoekopdracht maken:

1. Maak een searchSingle.ts bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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;
   }
   

   De vectorQuery bevat de configuratie van de gevectoriseerde query, inclusief de gevectoriseerde tekst van de queryinvoer, vectorfields bepaalt welke vectorvelden worden doorzocht en kNearestNeighborsCount geeft het aantal dichtstbijzijnde buren op dat moet worden geretourneerd.

   De vectorquerytekenreeks is quintessential lodging near running trails, eateries, retail, die wordt gevectoriseerd in 1536 insluitingen voor deze query.

3. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code worden de relevante documenten voor de query quintessential lodging near running trails, eateries, retailweergegeven.

   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
   

   Het antwoord voor het vectorequivalent van quintessential lodging near running trails, eateries, retail bestaat uit 5 resultaten, met alleen de velden die worden geretourneerd zoals gespecificeerd door de selectie.

Een zoekopdracht met één vector maken met een filter

U kunt filters toevoegen, maar de filters worden toegepast op de niet-ctorinhoud in uw index. In dit voorbeeld is het filter van toepassing op het Tags veld om alle hotels te filteren die geen gratis Wi-Fi bieden. Deze zoekopdracht maakt gebruik van SearchClient. zoeken en VectorQuery en SearchOptions.

Een enkele vectorzoekopdracht maken met een filter:

1. Maak een searchSingleWithFilter.ts bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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;
   }
   

   Voeg de afhankelijkheden, omgevingsvariabelen en dezelfde zoekfunctionaliteit toe als de vorige zoekopdracht met een uitsluitingsfilter na verwerking toegevoegd voor hotels met free wifi.

3. Bouw het bestand en voer het uit.

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

   De uitvoer van deze code toont de relevante documenten voor de query met het filter voor free wifi toegepast:

   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
   

Een zoekopdracht maken met een georuimtelijk filter

U kunt een georuimtelijk filter opgeven om de resultaten te beperken tot een specifiek geografisch gebied. In dit voorbeeld beperkt het filter de resultaten tot hotels binnen 300 kilometer van Washington D.C. (coördinaten: -77.03241 38.90166). Georuimtelijke afstanden zijn altijd in kilometers. Deze zoekopdracht maakt gebruik van SearchClient. zoeken en VectorQuery en SearchOptions.

Een zoekopdracht maken met een georuimtelijk filter:

1. Maak een searchSingleWithFilterGeo.ts bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code ziet u de relevante documenten voor de query met het toegepaste uitsluitingsfilter voor georuimtelijke naverwerking.

   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
   

Een hybride zoekopdracht maken

Hybride zoekopdrachten bestaan uit trefwoordquery's en vectorquery's in één zoekaanvraag. In dit voorbeeld wordt de vectorquery en zoekopdracht in volledige tekst gelijktijdig uitgevoerd:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

Deze zoekopdracht maakt gebruik van SearchClient. zoeken en VectorQuery en SearchOptions.

Een hybride zoekopdracht maken:

1. Maak een searchHybrid.ts bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code ziet u de relevante documenten voor de hybride zoekopdracht:

   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"]
   

   Omdat Recursive Rank Fusion (RRF) resultaten samenvoegt, is het nuttig om de invoer te controleren. De volgende resultaten zijn alleen afkomstig van de volledige-tekstquery. De top twee resultaten zijn Subliem Palace Hotel en Luxury Lion Resort. Het Sublieme Palace Hotel heeft een sterkere BM25 relevantie score.

      {
          "@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"
      },
   

   In de vectorquery, die HNSW gebruikt voor het vinden van overeenkomsten, daalt het Sublieme Palace Hotel naar de vierde positie. Luxe Leeuw, dat tweede was in de volledige-tekstzoekopdracht en derde in de vectorzoek, ondervindt niet hetzelfde bereik van fluctuatie, dus het verschijnt als een topmatch in een gehomogeniseerde resultatenset.

   "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"
       }
   ]
   

Een semantische hybride zoekopdracht maken

Hier volgt de laatste query in de verzameling om de semantische hybride zoekopdracht uit te breiden met de extra zoektekst historic hotel walk to restaurants and shopping.

Deze zoekopdracht maakt gebruik van SearchClient. zoeken en VectorQuery en SearchOptions.

Een semantische hybride zoekopdracht maken:

1. Maak een searchSemanticHybrid.ts bestand in de src map.

2. Kopieer de volgende code naar het bestand.

   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 ranking
       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. Bouw het bestand en voer het uit.

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

   In de uitvoer van deze code ziet u de relevante documenten voor de semantische hybride zoekopdracht.

   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
   

   U kunt de semantische rangschikking beschouwen als een manier om de relevantie van zoekresultaten te verbeteren door inzicht te krijgen in de betekenis achter de woorden in de query en de inhoud van de documenten. In dit geval helpt de semantische classificatie om hotels te identificeren die niet alleen relevant zijn voor de trefwoorden, maar ook overeenkomen met de intentie van de query:

   Belangrijkste conclusies:

   • Vectorzoekopdrachten worden opgegeven via de vectorSearchOptions eigenschap. Trefwoorden zoeken wordt opgegeven via de semanticSearchOptions eigenschap.

   • In een hybride zoekopdracht kunt u vectorzoekopdrachten integreren met zoeken in volledige tekst over trefwoorden. Filters, spellingcontrole en semantische classificatie zijn alleen van toepassing op tekstuele inhoud en niet op vectoren. In deze laatste query is er geen semantisch answer omdat het systeem er geen heeft geproduceerd die voldoende sterk was.

   • Werkelijke resultaten bevatten meer details, inclusief semantische bijschriften en markeringen. De resultaten zijn gewijzigd voor leesbaarheid. Als u de volledige structuur van het antwoord wilt ophalen, voert u de aanvraag uit in de REST-client.

De hulpbronnen opschonen

Wanneer u in uw eigen abonnement werkt, is het een goed idee om aan het einde van een project te bepalen of u de gemaakte resources nog nodig hebt. Resources die actief blijven, kunnen u geld kosten. U kunt resources afzonderlijk verwijderen, maar u kunt ook de resourcegroep verwijderen als u de volledige resourceset wilt verwijderen.

U kunt resources vinden en beheren in Azure Portal met behulp van de koppeling Alle resources of resourcegroepen in het meest linkse deelvenster.

U kunt ook programmatisch de vectorindex verwijderen die u in deze quickstart hebt gemaakt:

1. Maak een deleteIndex.ts bestand in de src map.

2. Voeg de afhankelijkheden, omgevingsvariabelen en code toe om de index te verwijderen.

   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. Bouw het bestand en voer het uit.

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

Volgende stappen

• Bekijk de opslagplaats met codevoorbeelden voor vectorzoekmogelijkheden in Azure AI Search voor JavaScript.

In deze quickstart gebruikt u een .NET-app om een vectorindex te maken, laden en er query's op uit te voeren. De code voert deze bewerkingen uit met behulp van de Azure AI Search-clientbibliotheek voor .NET, die een abstractie biedt van de REST API's voor toegang tot indexbewerkingen.

In Azure AI Search heeft een vectorindex een indexschema dat vectorvelden en niet-vectorvelden definieert, een vectorzoekconfiguratie voor algoritmen die de insluitruimte maken en instellingen voor vectorvelddefinities die tijdens de query worden geëvalueerd. Indexen - Maken of bijwerken (REST API) maakt de vectorindex.

Vereisten

• Een Azure-account met een actief abonnement. Gratis een account maken

• Een Azure AI Search-service.

  • U kunt de gratis laag voor de meeste van deze quickstart gebruiken, maar we raden Basic of hoger aan voor grotere gegevensbestanden.

  • Wijs voor sleutelloze verificatie met Microsoft Entra-id de rol Inzender voor zoekindexgegevens toe aan uw gebruikersaccount.

  • Als u de semantische hybride query wilt uitvoeren, moet u de semantische rangschikking inschakelen.

• Visual Studio Code of Visual Studio om de code uit te voeren.

• Git om de voorbeeldopslagplaats te klonen.

Servicegegevens ophalen

Aanvragen voor het zoekeindpunt moeten worden geverifieerd en geautoriseerd. Hoewel het mogelijk is om API-sleutels voor deze taak te gebruiken, raden we u aan een sleutelloze verbinding via Microsoft Entra-id te gebruiken.

In deze quickstart wordt gebruikgemaakt van DefaultAzureCredential, dat authenticatie vereenvoudigt in zowel ontwikkelings- als productiescenario's. Voor productiescenario's hebt u echter mogelijk meer geavanceerde vereisten waarvoor een andere benadering is vereist. Zie .NET-apps verifiëren bij Azure-services met behulp van de Azure SDK voor .NET voor meer informatie over al uw opties.

De code en de installatieomgeving klonen

1. Kloon de repository met de code voor deze quickstart.

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

2. Open de quickstart-vector-search map in Visual Studio Code of dubbelklik op het VectorSearchQuickstart.sln bestand om de oplossing in Visual Studio te openen.

3. Open appsettings.json in zowel de VectorSearchCreatePopulateIndex als de VectorSearchExamples mappen.

4. Stel Endpoint in als de URL van uw zoekservice, die vergelijkbaar moet zijn met https://mydemo.search.windows.net.

5. Stel IndexName in op een unieke naam voor uw index. U kunt ook de standaardnaam hotels-vector-quickstart gebruiken.

Een vectorindex maken en documenten uploaden

Als u zoekquery's wilt uitvoeren voor uw Azure AI Search-service, moet u eerst een zoekindex maken en documenten uploaden naar de index.

De index maken en vullen:

1. Open een terminal in de VectorSearchCreatePopulateIndex map.

2. Gebruik de dotnet run opdracht om het project uit te voeren.

   dotnet run
   

   De volgende code wordt uitgevoerd om een index te maken:

   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();
   }
   

   Met de volgende code worden JSON-geformatteerde documenten in het hotel-samples.json bestand geüpload naar uw zoekservice.

   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);
       }
   }
   

   Nadat u het project hebt uitgevoerd, wordt de volgende uitvoer afgedrukt.

   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
   

   Belangrijkste conclusies:

   • Uw code communiceert met een specifieke zoekindex die wordt gehost in uw Azure AI Search-service via SearchClient. Dit is het hoofdobject dat wordt geleverd door het pakket azure-search-documents. SearchClient biedt toegang tot indexbewerkingen, zoals:

     • Gegevensopname: UploadDocuments(), MergeDocuments()DeleteDocuments()

     • Zoekbewerkingen: Search(), Autocomplete()Suggest()

     • Indexbeheerbewerkingen: CreateOrUpdateIndex()

   • Vectorvelden bevatten drijvendekommawaarden. Het kenmerk dimensies heeft minimaal 2 en een maximum van 4096 drijvendekommawaarden. In deze quickstart wordt het dimensiekenmerk ingesteld op 1536, omdat dit de grootte is van insluitingen die door het text-embedding-ada-002 model worden gegenereerd.

Query's uitvoeren

Als u de vectorquery's in deze sectie wilt uitvoeren, opent u Program.cs in de VectorSearchExamples map en opent u vervolgens een terminal in de VectorSearchExamples map.

Query's in deze sectie:

• Zoeken met één vector
• Zoeken met één vector met filter
• Hybride zoekopdracht
• Semantische hybride zoekopdracht met filter

Zoeken met één vector

De eerste query laat een basisscenario zien waarin u documentbeschrijvingen wilt vinden die nauw overeenkomen met de zoekreeks.

Een enkele vectorzoekopdracht maken:

1. Maak in het Program.cs bestand van de VectorSearchExamples map de commentaar bij de SearchExamples.SearchSingleVector(searchClient, vectorizedResult); methodeaanroep ongedaan.

   Met deze methode wordt de volgende zoekfunctie in de SearchExamples.cs klasse uitgevoerd.

   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. Voer dotnet run uit om de query uit te voeren, die de volgende resultaten retourneert.

   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
   

Zoeken met één vector met filter

In Azure AI Search zijn filters van toepassing op niet-ctorvelden in een index. Dit voorbeeld filtert op het Tags veld om alle hotels te filteren die geen gratis Wi-Fi bieden.

Een enkele vectorzoekopdracht maken met een filter:

1. Maak in het Program.cs bestand van de VectorSearchExamples map de commentaar bij de SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult); methodeaanroep ongedaan.

   Met deze methode wordt de volgende zoekfunctie in de SearchExamples.cs klasse uitgevoerd.

   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. Voer dotnet run uit om de query uit te voeren, die alleen hotels retourneert die gratis Wi-Fi bieden.

    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
   

3. Voor een geofilter verwijdert u de commentaarregels bij SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult); in Program.cs.

   Met deze methode wordt de volgende zoekfunctie in de SearchExamples.cs klasse uitgevoerd.

   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();
   }
   

4. Voer dotnet run uit om de query uit te voeren, die alleen hotels binnen 300 kilometer retourneert.

   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.
   

Hybride zoekopdracht

Hybride zoekopdrachten combineren trefwoord- en vectorquery's in één aanvraag. In dit voorbeeld worden de volgende query-strings voor volledige-tekst en vectorquery's gelijktijdig uitgevoerd:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

Een hybride zoekopdracht maken:

1. Verwijder in het Program.cs bestand commentaar bij de SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult); methode-aanroep.

   Met deze methode wordt de volgende zoekfunctie in de SearchExamples.cs klasse uitgevoerd.

   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. Voer dotnet run uit om de query uit te voeren, die de volgende resultaten retourneert.

   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
   

   Omdat Recursive Rank Fusion (RRF) resultaten samenvoegt, is het nuttig om de invoer te controleren. De volgende resultaten zijn alleen afkomstig van de volledige-tekstquery. De top twee resultaten zijn Subliem Palace Hotel en Luxury Lion Resort. Het Sublieme Palace Hotel heeft een sterkere BM25 relevantie score.

   {
       "@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"
   },
   

   In de vectorquery, die HNSW gebruikt voor het vinden van overeenkomsten, daalt het Sublieme Palace Hotel naar de vierde positie. Luxe Leeuw, dat tweede was in de volledige-tekstzoekopdracht en derde in de vectorzoek, ondervindt niet hetzelfde bereik van fluctuatie, dus het verschijnt als een topmatch in een gehomogeniseerde resultatenset.

   "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"
       }
   ]
   

Semantische hybride zoekopdracht met een filter

Voeg semantische rangschikking toe om resultaten opnieuw te rangschikkingen op basis van taalkennis.

Een semantische hybride zoekopdracht maken met een filter:

1. Verwijder in het Program.cs bestand de aanroep van de commentaarmethode SearchExamples.SearchHybridVectorAndSemantic(searchClient, vectorizedResult); .

   Met deze methode wordt de volgende zoekfunctie in de SearchExamples.cs klasse uitgevoerd.

   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. Voer dotnet run uit om de query uit te voeren, die de volgende hotels retourneert. De hotels worden gefilterd op locatie, gefaceteerd door StateProvinceen semantisch opnieuw gerangschikt om resultaten te promoten die het dichtst bij de zoektekenreeksquery (historic hotel walk to restaurants and shopping) liggen.

   Het Swirling Currents Hotel gaat nu naar de bovenste plek. Zonder semantische rangschikking is Nordick's Valley Motel nummer één. Met semantische rangschikking herkennen de machinebegripmodellen die historic van toepassing zijn op "hotel op loopafstand van diner (restaurants) en winkelen."

   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
   

   Belangrijkste conclusies:

   • In een hybride zoekopdracht kunt u vectorzoekopdrachten integreren met zoeken in volledige tekst over trefwoorden. Filters, spellingcontrole en semantische classificatie zijn alleen van toepassing op tekstuele inhoud en niet op vectoren. In deze laatste query is er geen semantisch answer omdat het systeem er geen heeft geproduceerd die voldoende sterk was.

   • Werkelijke resultaten bevatten meer details, inclusief semantische bijschriften en markeringen. De resultaten zijn gewijzigd voor leesbaarheid. Als u de volledige structuur van het antwoord wilt ophalen, voert u de aanvraag uit in de REST-client.

De hulpbronnen opschonen

Wanneer u in uw eigen abonnement werkt, is het een goed idee om aan het einde van een project te bepalen of u de gemaakte resources nog nodig hebt. Resources die actief blijven, kunnen u geld kosten. U kunt resources afzonderlijk verwijderen, maar u kunt ook de resourcegroep verwijderen als u de volledige resourceset wilt verwijderen.

U kunt resources vinden en beheren in Azure Portal met behulp van de koppeling Alle resources of resourcegroepen in het meest linkse deelvenster.

Volgende stappen

• Bekijk de opslagplaats met codevoorbeelden voor vectorzoekmogelijkheden in Azure AI Search voor .NET.
• Bekijk de andere .NET- en Azure AI Search-codevoorbeelden in de opslagplaats azure-search-dotnet-samples.

In deze quickstart gebruikt u de AZURE AI Search REST API's om een vectorindex te maken, laden en er query's op uit te voeren.

In Azure AI Search heeft een vectorindex een indexschema dat vectorvelden en niet-vectorvelden definieert, een vectorzoekconfiguratie voor algoritmen die de insluitruimte maken en instellingen voor vectorvelddefinities die tijdens de query worden geëvalueerd. Indexen - Maken of bijwerken (REST API) maakt de vectorindex.

Vereisten

• Een Azure-account met een actief abonnement. Gratis een account maken

• Een Azure AI Search-service.

  • U kunt de gratis laag voor de meeste van deze quickstart gebruiken, maar we raden Basic of hoger aan voor grotere gegevensbestanden.

  • Als u de semantische hybride query wilt uitvoeren, moet u de semantische rangschikking inschakelen.

• Visual Studio Code met een REST-client.

Servicegegevens ophalen

Aanvragen voor het zoekeindpunt moeten worden geverifieerd en geautoriseerd. U kunt API-sleutels of -rollen voor deze taak gebruiken. U wordt aangeraden een sleutelloze verbinding te gebruiken via Microsoft Entra ID.

Selecteer het tabblad dat overeenkomt met de verificatiemethode van uw voorkeur. Gebruik dezelfde methode voor alle aanvragen in deze quickstart.

Het codebestand maken of downloaden

U gebruikt een .rest of .http bestand om alle aanvragen in deze quickstart uit te voeren. U kunt het REST-bestand met de code voor deze quickstart downloaden of u kunt een nieuw bestand maken in Visual Studio Code en de code ernaar kopiëren.

Het codebestand maken:

1. Maak in Visual Studio Code een bestand met een .rest of .http bestandsextensie, zoals az-search-vector-quickstart.rest.

2. Kopieer en plak de onbewerkte inhoud van het bestand az-search-vector-quickstart.rest in het nieuwe bestand.

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

3. Stel @baseUrl als het eindpunt dat u hebt verkregen in Servicegegevens ophalen.

4. Stel de sleutel of het token in voor verificatie. U hebt deze waarde verkregen in servicegegevens ophalen.

   • Voor de aanbevolen sleutelloze verificatie met Microsoft Entra ID, vervang @apiKey door de @token variabele.

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

   • Als u liever een API-sleutel gebruikt, vervangt u deze door @apiKey de sleutel die u hebt gekopieerd uit Azure Portal.

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

5. Voor de aanbevolen sleutelloze verificatie met Microsoft Entra ID vervangt u alle exemplaren van api-key: {{apiKey}} door Authorization: Bearer {{token}} in de aanvraagheaders.

Een vectorindex maken

U gebruikt Indexen - Maken (REST API) om een vectorindex te maken en de fysieke gegevensstructuren in uw zoekservice in te stellen.

Het indexschema in dit voorbeeld is georganiseerd rond hotelinhoud. Voorbeeldgegevens bestaan uit vector- en nietvectorbeschrijvingen van fictieve hotels. Dit schema bevat configuraties voor vectorindexering en query's en voor semantische classificatie.

Een vectorindex maken:

1. Open in Visual Studio Code het az-search-vector-quickstart.rest bestand dat u eerder hebt gemaakt.

2. Zoek het ### Create a new index codeblok in het bestand. Dit blok bevat de aanvraag voor het maken van de hotels-vector-quickstart index in uw zoekservice.

   ### 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. Selecteer Aanvraag Verzenden. U moet een HTTP/1.1 201 Created antwoord hebben.

   De hoofdtekst van het antwoord moet de JSON-weergave van het indexschema bevatten.

   {
       "@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"
               }
               ]
           }
           }
       ]
       }
   }
   

   Belangrijkste conclusies:

   • De fields verzameling bevat een vereist sleutelveld en tekst- en vectorvelden (zoals Description en DescriptionVector) voor tekst- en vectorzoekopdrachten. Door vectorvelden en niet-vectorvelden in dezelfde index samen te stellen, kunnen hybride query's worden uitgevoerd. U kunt bijvoorbeeld filters, tekstzoekopdrachten combineren met semantische rangschikking en vectoren in één querybewerking.

   • Vectorvelden moeten een van de EDM-gegevenstypen zijn die worden gebruikt voor vectoren, zoals type: Collection(Edm.Single). Vectorvelden hebben ook dimensions en vectorSearchProfile eigenschappen.

   • De vectorSearch sectie is een array van configuraties en profielen van de benaderde dichtstbijzijnde buur (ANN) algoritme. Ondersteunde algoritmen zijn hiërarchische navigeerbare Small World en exhaustieve K-Nearest Neighbor. Zie Relevantiescore in vectorzoekopdrachten voor meer informatie.

   • Met de (optionele) semantic configuratie kunnen zoekresultaten opnieuw worden gerankt. U kunt resultaten opnieuw rangverkennen in query's van het type semantic voor tekenreeksvelden die zijn opgegeven in de configuratie. Zie het overzicht van Semantische classificaties voor meer informatie.

Documenten uploaden naar de index

Het maken en laden van de index zijn afzonderlijke stappen. U hebt het indexschema in de vorige stap gemaakt. U moet nu documenten in de index laden.

In Azure AI Search bevat de index alle doorzoekbare gegevens en query's die worden uitgevoerd op de zoekservice. Voor REST-aanroepen worden de gegevens verstrekt als JSON-documenten. Gebruik Documenten - Index (REST API) voor deze taak. De URI wordt uitgebreid om de docs verzameling en de index bewerking op te nemen.

Documenten uploaden naar de index:

1. Formuleer een aanvraag voor uploaddocumenten om documenten te uploaden naar de hotels-vector-quickstart index in uw zoekservice.

   ### 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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           {
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               "HotelId": "3",
               "HotelName": "Gastronomic Landscape Hotel",
               "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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               "Category": "Suite",
               "Tags": [
                   "restaurant",
                   "bar",
                   "continental breakfast"
               ],
               "ParkingIncluded": true,
               "LastRenovationDate": "2015-09-20T00:00:00Z",
               "Rating": 4.80,
               "Address": {
                   "StreetAddress": "3393 Peachtree Rd",
                   "City": "Atlanta",
                   "StateProvince": "GA",
                   "PostalCode": "30326",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       33.847848,
                       -84.363602
                   ]
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "4",
               "HotelName": "Sublime Palace Hotel",
               "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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               "Category": "Boutique",
               "Tags": [
                   "concierge",
                   "view",
                   "air conditioning"
               ],
               "ParkingIncluded": true,
               "LastRenovationDate": "2020-02-06T00:00:00Z",
               "Rating": 4.60,
               "Address": {
                   "StreetAddress": "7400 San Pedro Ave",
                   "City": "San Antonio",
                   "StateProvince": "TX",
                   "PostalCode": "78216",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       -98.498923,
                       29.518029
                   ]
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "48",
               "HotelName": "Nordick's Valley Motel",
               "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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               "Category": "Boutique",
               "Tags": [
                   "continental breakfast",
                   "air conditioning",
                   "free wifi"
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               "ParkingIncluded": false,
               "LastRenovationDate": "2018-02-19T00:00:00Z",
               "Rating": 4.5,
               "Address": {
                   "StreetAddress": "1401 I St NW",
                   "City": "Washington D.C.",
                   "StateProvince": null,
                   "PostalCode": "20005",
                   "Country": "USA"
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                   "properties": {
                       "name": "EPSG:4326"
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               "HotelId": "49",
               "HotelName": "Swirling Currents Hotel",
               "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
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              "Category": "Suite",
               "Tags": [
                   "air conditioning",
                   "laundry service",
                   "24-hour front desk service"
               ],
               "ParkingIncluded": true,
               "LastRenovationDate": "2018-01-27T00:00:00Z",
               "Rating": 2.7,
               "Address": {
                   "StreetAddress": "1100 S Hayes St",
                   "City": "Arlington",
                   "StateProvince": "VA",
                   "PostalCode": "22202",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                   -77.060066,
                   38.863659
                   ],
                   "crs": {
                   "type": "name",
                   "properties": {
                       "name": "EPSG:4326"
                       }
                   }
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "13",
               "HotelName": "Luxury Lion Resort",
               "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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               "Category": "Luxury",
               "Tags": [
                   "bar",
                   "concierge",
                   "restaurant"
               ],
               "ParkingIncluded": false,
               "LastRenovationDate": "2020-03-18T00:00:00Z",
               "Rating": 4.1,
               "Address": {
                   "StreetAddress": "3 Cityplace Dr",
                   "City": "St. Louis",
                   "StateProvince": "MO",
                   "PostalCode": "63141",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                   -90.44081,
                   38.67219
                   ],
                   "crs": {
                   "type": "name",
                   "properties": {
                       "name": "EPSG:4326"
                       }
                   }
               }
           }
       ]
   }
   

2. Selecteer Aanvraag Verzenden. U moet een HTTP/1.1 200 OK antwoord hebben. De hoofdtekst van het antwoord moet de JSON-weergave van de zoekdocumenten bevatten.

   Belangrijkste conclusies:

   • Documenten in de nettolading bestaan uit velden die zijn gedefinieerd in het indexschema.

   • Vectorvelden bevatten drijvendekommawaarden. Het kenmerk dimensies heeft minimaal 2 en een maximum van 4096 drijvendekommawaarden. In deze quickstart wordt het dimensiekenmerk ingesteld op 1536, omdat dit de grootte is van insluitingen die door het text-embedding-ada-002 model worden gegenereerd.

Query's uitvoeren

Nu documenten zijn geladen, kunt u er vectorquery's op uitvoeren met behulp van Documenten - Zoekpost (REST API).

Query's in deze sectie:

• Zoeken met één vector
• Zoeken met één vector met filter
• Hybride zoekopdracht
• Semantische hybride zoekopdracht

De voorbeeldquery's zijn gebaseerd op twee tekenreeksen:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

De vectorquerytekenreeks is semantisch vergelijkbaar met de zoekreeks, maar bevat termen die niet bestaan in de zoekindex. Als u een trefwoordzoekopdracht uitvoert quintessential lodging near running trails, eateries, retail, zijn de resultaten nul in een pure trefwoordzoekopdracht zonder semantische rangschikking. We gebruiken dit voorbeeld om te laten zien hoe u relevante resultaten kunt krijgen met behulp van vectoren, zelfs als er geen overeenkomende termen zijn.

Zoeken met één vector

Een enkele vectorzoekopdracht maken:

1. Formuleer de aanvraag. De query is een 1536 float vertegenwoordiging van klassiek verblijf nabij hardlooppaden, eetgelegenheden, winkels. De query zoekt DescriptionVector en retourneert k-5 resultaten. Het gebruikt de "uitputtende" parameter om een volledige scan van de index uit te voeren in plaats van ANN. Een uitgebreide zoekopdracht is handig voor kleine indexen.

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

   Belangrijke punten over documenten - Zoekbericht (REST API):

   • Dit vectorQueries.vector is de vectorquerytekenreeks. Het is een vectorweergave van typische accommodatie in de buurt van hardlooppaden, eetgelegenheden, detailhandel, die wordt gevectoriseerd in 1536 vectorrepresentaties voor deze query.

   • fields bepaalt welke vectorvelden worden doorzocht.

   • kind ingesteld op vector betekent dat de querytekenreeks een vector is. Als kind zou worden ingesteld op text, zou u extra mogelijkheden (zoals een vectorizer) nodig hebben om een leesbare tekstreeks om te zetten in een vector bij het uitvoeren van een query. Vectorizers worden weggelaten uit deze starthandleiding om de oefening eenvoudig te houden.

   • k geeft het aantal overeenkomsten op dat moet worden geretourneerd in het antwoord. Een count parameter geeft het aantal overeenkomsten op dat in de index is gevonden. Het opnemen van aantallen is een best practice voor query's, maar het is minder handig voor overeenkomsten zoeken waarbij het algoritme een zekere mate van gelijkenis kan vinden in vrijwel elk document.

2. Selecteer Aanvraag Verzenden. U moet een HTTP/1.1 200 OK antwoord hebben. De hoofdtekst van het antwoord moet de JSON-weergave van de zoekresultaten bevatten.

   Het antwoord voor het vectorequivalent van quintessential lodging near running trails, eateries, retail bevat k-5 resultaten, hoewel de zoekmachine 7 overeenkomsten heeft gevonden. De belangrijkste resultaten worden beschouwd als de meest semantisch vergelijkbaar met de query. Elk resultaat bevat een zoekscore en de velden die worden vermeld in select. In een zoekopdracht naar overeenkomsten bevat k het antwoord altijd resultaten die zijn gerangschikt op de waarde-overeenkomstscore.

   {
     "@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"
         ]
       }
     ]
   }
   

Zoeken met één vector met filter

U kunt filters toevoegen, maar de filters worden toegepast op de niet-ctorinhoud in uw index. In dit voorbeeld is het filter van toepassing op het Tags veld om alle hotels te filteren die geen gratis Wi-Fi bieden.

Een enkele vectorzoekopdracht maken met een filter:

1. Formuleer de aanvraag. Dit is dezelfde aanvraag als de vorige, met een extra filter- en filtermodusparameter.

   ### 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. Selecteer Aanvraag Verzenden. U moet een HTTP/1.1 200 OK antwoord hebben. De hoofdtekst van het antwoord moet de JSON-weergave van de zoekresultaten bevatten.

   De query was hetzelfde als het vorige voorbeeld van een enkele vectorzoekopdracht, maar bevat een uitsluitingsfilter na verwerking en retourneert alleen de twee hotels met gratis 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. Hier volgt een andere query die gebruikmaakt van een geofilter, waardoor de resultaten worden beperkt tot hotels binnen 300 kilometer rond Washington 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. Bekijk het antwoord. Het is beperkt tot twee hotels in de omgeving van Washington D.C, waarbij een facetstructuur de aggregatie verzorgt.

   "@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"
       }
     }
   ]
   

Hybride zoekopdracht

Hybride zoekopdrachten bestaan uit trefwoordquery's en vectorquery's in één zoekaanvraag. In dit voorbeeld wordt de vectorquery en de zoekopdracht in volledige tekst gelijktijdig uitgevoerd:

• Zoekreeks: historic hotel walk to restaurants and shopping
• Vectorquerytekenreeks: quintessential lodging near running trails, eateries, retail (gevectoriseerd in een wiskundige weergave)

Een hybride zoekopdracht maken:

1. Formuleer de hybride query.

   ### 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. Selecteer Aanvraag Verzenden. U moet een HTTP/1.1 200 OK antwoord hebben. De hoofdtekst van het antwoord moet de JSON-weergave van de zoekresultaten bevatten.

   Omdat dit een hybride query is, worden de resultaten gerangschikt op Wederzijdse Rank Fusion (RRF). U ziet dat @search.score waarden een andere basis hebben en uniform kleinere waarden zijn. RRF evalueert zoekscores van meerdere zoekresultaten, neemt de inverse en voegt vervolgens de gecombineerde resultaten samen en sorteert deze. Het top aantal resultaten wordt geretourneerd.

   Bekijk het antwoord, bestaande uit de top k-5 overeenkomsten van de 7 overeenkomende documenten in de index.

   {
     "@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."
       }
     ]
   }
   

   Omdat RRF resultaten samenvoegt, is het handig om de invoer afzonderlijk te controleren.

   De volgende resultaten zijn afkomstig uit het volledige tekstgedeelte van de query: historische hotelwandeling naar restaurants en winkelen. In de volledige tekstquery zijn de top drie resultaten Subliem Palace Hotel, Stay-Kay City Hotel en Luxury Lion Resort. Het Sublieme Palace Hotel heeft een sterkere BM25 relevantie score. In de gefuseerde query staan slechts twee van deze overeenkomsten in de top 3 en wordt de tweede overeenkomst (Stay-Kay City Hotel) helemaal niet weergegeven in de top 5.

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

   In de vectorquery (ideaal verblijf in de buurt van hardlooppaden, eetgelegenheden, winkels) is een andere tekenreeks, dus we moeten verschillende resultaten verwachten. Deze query retourneert Nordick's Valley Motel, Luxury Lion Resort en Sublieme Palace Hotel als de top drie wedstrijden. In de gefuseerde query bevinden deze resultaten zich in de top 3, maar in omgekeerde volgorde.

     "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."
       },
      ...
     ]
   

Semantische hybride zoekopdracht

Dit is de laatste query in de verzameling. Deze hybride query voegt L2 semantische classificatie toe waarmee leesbegrip van machines wordt toegepast op de L1-gerangschikte resultaten, waardoor relevantere overeenkomsten naar de top worden gepromoot.

Een semantische hybride zoekopdracht maken:

1. Formuleer de aanvraag.

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

2. Selecteer Aanvraag Verzenden. U moet een HTTP/1.1 200 OK antwoord hebben. De hoofdtekst van het antwoord moet de JSON-weergave van de zoekresultaten bevatten.

   Bekijk het antwoord, bestaande uit een semantische herrankering van de RRF-gerangschikte resultaten van de hybride query. Semantische classificatie werkt van tekstinvoer. In een tekst- of hybride query is deze invoer het tekstgedeelte van de query (historische hotelwandeling naar restaurants en winkelen). Als u semantische classificatie wilt gebruiken voor een pure vectorquery, zoals het eerste voorbeeld, of om expliciet de tekst te bepalen die wordt gebruikt voor semantische classificatie, geeft u een semanticQuery-tekenreeks op.

   Na semantische rerankering, Swirling Currents Hotel met zijn verwijzing naar wandeltoegang tot winkelen, dineren, entertainment en het stadscentrum gaat naar de top plek. Het model voor machinebegrip stimuleert toegang voor voetgangers tot winkels en restaurants als een dichterbij zijnde overeenkomst met lopen naar restaurants en winkels.

   Voor de semantische herordening was het Sublieme Paleis, met zijn verwijzing naar de loopafstand van de sites en bezienswaardigheden van de stad, en omgeven door de buitengewone schoonheid van kerken, gebouwen, winkels en monumenten, nummer één.

   {
     "@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"
       }
     ]
   }
   

   Aanbeveling

   Semantisch gerangschikte resultaten kunnen meer details bevatten, waaronder semantische antwoorden, bijschriften en markeringen. Door meer parameters aan de aanvraag toe te voegen, worden de extra details gegenereerd. Zie Een semantische query instellen voor meer informatie.

De hulpbronnen opschonen

Wanneer u in uw eigen abonnement werkt, is het een goed idee om aan het einde van een project te bepalen of u de gemaakte resources nog nodig hebt. Resources die actief blijven, kunnen u geld kosten. U kunt resources afzonderlijk verwijderen, maar u kunt ook de resourcegroep verwijderen als u de volledige resourceset wilt verwijderen.

U kunt resources vinden en beheren in Azure Portal met behulp van de koppeling Alle resources of resourcegroepen in het meest linkse deelvenster.

U kunt ook de volgende aanvraag verzenden om de vectorindex te verwijderen die in deze quickstart is gemaakt.

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

Volgende stappen

• Meer informatie over het aanroepen van REST API-aanroepen zonder API-sleutels.