Snabbstart: Vektorsökning

I den här snabbstarten använder du en Jupyter-notebook-fil för att skapa, läsa in och fråga vektorer. Kodexemplen utför dessa åtgärder med hjälp av Azure AI Search-klientbiblioteket. Biblioteket tillhandahåller en abstraktion över REST-API:et för åtkomst till indexåtgärder som datainmatning, sökåtgärder och indexhanteringsåtgärder.

I Azure AI Search har ett vektorlager ett indexschema som definierar vektor- och icke-vektorfält, en vektorsökningskonfiguration för algoritmer som skapar inbäddningsutrymmet och inställningar för vektorfältdefinitioner som utvärderas vid frågetillfället. REST-API:et Skapa index skapar vektorarkivet.

Kommentar

Den här snabbstarten utelämnar vektoriseringssteget och tillhandahåller infogade inbäddningar. Om du vill lägga till inbyggd datasegmentering och vektorisering över ditt eget innehåll kan du prova guiden Importera och vektorisera data för en genomgång från slutpunkt till slutpunkt.

Förutsättningar

• Ett Azure-konto med en aktiv prenumeration. Skapa ett konto kostnadsfritt.

• En Azure AI-tjänsten Search. Skapa en tjänst eller hitta en befintlig tjänst i din aktuella prenumeration.

  • Du kan använda en kostnadsfri söktjänst för det mesta av den här snabbstarten, men vi rekommenderar Basic-nivån eller högre för större datafiler.
  • Om du vill köra frågeexemplet som anropar semantisk omrankning måste söktjänsten vara på basic-nivån eller högre med semantisk rankning aktiverat.

• Visual Studio Code med Python-tillägget och Jupyter-paketet.

• Git för att klona lagringsplatsen som innehåller Jupyter Notebook och andra relaterade filer.

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Hämta resursinformation

Begäranden till sökslutpunkten måste autentiseras och auktoriseras. Även om det är möjligt att använda API-nycklar eller roller för den här uppgiften rekommenderar vi att du använder en nyckellös anslutning via Microsoft Entra-ID.

Den här snabbstarten använder DefaultAzureCredential, vilket förenklar autentiseringen i både utvecklings- och produktionsscenarier. För produktionsscenarier kan du dock ha mer avancerade krav som kräver en annan metod. Se Autentisera Python-appar till Azure-tjänster med hjälp av Azure SDK för Python för att förstå alla dina alternativ.

Klona koden och installationsmiljön

1. Klona lagringsplatsen som innehåller koden för den här snabbstarten.

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

   Den här lagringsplatsen har Python-kodexempel för flera artiklar var och en i en separat undermapp.

2. Öppna undermappen Quickstart-Vector-Searchi Visual Studio Code.

   Det finns tre filer i den här mappen:

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

3. Byt namn på sample.env filen till .env och ändra värdena i .env filen.

   Använd söktjänstens URL som AZURE_SEARCH_ENDPOINT. Du hittar url:en i Azure-portalen. Gå till din Azure AI Search-tjänst och leta efter URL-fältet på sidan Översikt . Här följer ett exempel på hur en slutpunkt kan se ut: https://mydemo.search.windows.net.

   Välj slutligen ett nytt AZURE_SEARCH_INDEX_NAME namn eller använd det som anges i filen.

4. Arbeta i en miljö i Visual Studio Code. På menyn Visa väljer du Terminal..., eller ctrl+`.

5. Kör följande kommandon i terminalen:

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

   Kommentar

   Detta förutsätter att du använder Git Bash i terminalen och att du kör i Windows. Om du använder ett annat gränssnitt och/eller ett annat operativsystem justerar du dessa instruktioner för din specifika miljö.

   Om du uppmanas att göra det kan du tillåta att Visual Studio Code använder den nya miljön.

   Kommandot where python verifierar att du arbetar från den virtuella miljön genom att lista python.exe i Quickstart-Vector-Search\.venv\ mappen och andra platser från datorns katalog.

6. Installera de bibliotek som krävs genom att köra följande kommando:

   pip install requirements.txt
   

7. I Visual Studio Code öppnar du vector-search-quickstart.ipynb.

   Kommentar

   Om det är första gången du använder en Jupyter Notebook (.ipynb) i Visual Studio Code uppmanas du att installera Jupyter Notebook-kerneln och eventuellt andra verktyg. Välj att installera de föreslagna verktygen för att fortsätta med den här snabbstarten.

8. Leta upp cellen nedan med rubriken "Installera paket och ange variabler" och välj knappen Kör cell (Ctrl + Alt + Retur) (som ser ut som en typisk körningsknapp) till vänster om cellen. När cellen körs läses miljövariablerna in, DefaultAzureCredential skapas och värden skrivs till utdata för att bekräfta att notebook-filens beroenden och .env har konfigurerats korrekt.

   # 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 
   

   Om du kör den här cellen genereras följande utdata.

   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
   ...
   

   Det finns många fler paket som du kan visa i ett rullningsbart element (se meddelandet under cellresultatet).

Skapa vektorindexet

Koden i vector-search-quickstart.ipynb använder flera metoder från azure.search.documents biblioteket för att skapa vektorindexet och sökbara fält.

1. Leta upp cellen nedan med rubriken "Skapa ett index" och kör cellen för att skapa indexet.

   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')
   

   Om indexet har skapats visas följande resultat under cellen:

   vector-search-quickstart created
   

   Viktiga lärdomar när du skapar vektorindex med azure.search.documents:

   • Du definierar ett index genom att skapa en lista med fält. Varje fält skapas med hjälpmetoden som definierar fälttypen och dess inställningar.

   • Det här specifika indexet stöder flera sökfunktioner, till exempel:

     • Nyckelordssökning i fulltext (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))
     • Vektorsökning (möjliggör hybridsökning genom att samanvända fält för vektor- och icke-vektorfält) (DescriptionVector)
     • Semantisk sökning (semantic_search=SemanticSearch(configurations=[semantic_config]))
     • Fasetterad sökning (facetable=True)
     • Semantisk sökning (semantic_search=SemanticSearch(configurations=[semantic_config]))
     • Geosökning (Location är fältet GeographyPoint)
     • Filtrering, sortering (Många fält markerade som filterbara och sorterbara)

Ladda upp dokument

Att skapa och läsa in indexet är separata steg. Du skapade indexschemat i föregående steg. Nu måste du läsa in dokument i indexet.

I Azure AI Search lagrar indexet allt sökbart innehåll, medan sökmotorn kör frågor mot det indexet.

1. Leta upp cellen nedan med rubriken "Skapa dokumentnyttolast" och kör cellen. Den här cellen innehåller följande kod (trunkerad för korthet):

      # 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>
      ]
   

   Den här cellen läser in en variabel med namnet documents med ett JSON-objekt som beskriver varje dokument, tillsammans med den vektoriserade versionen av artikelns beskrivning. Den här vektorn möjliggör likhetssökning, där matchning baseras på betydelse snarare än exakta nyckelord.

   Viktigt!

   Koden i det här exemplet kan inte köras. Flera tecken eller rader tas bort för korthet. Kör i stället koden i Jupyter Notebook.

2. Leta upp cellen nedan med rubriken "Ladda upp dokumenten" och kör cellen. Den här cellen innehåller följande kod (trunkerad för korthet):

   # 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)
   

   Detta skapar en instans av sökklienten genom att anropa SearchClient()-konstruktorn och anropar sedan upload_documents()-metoden på objektet.

   När du har kört cellen skrivs statusen för varje dokument ut under den:

   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
   

   Viktiga lärdomar om upload_documents() metoden och det här exemplet:

   • Koden interagerar med ett specifikt sökindex som finns i Din Azure AI Search-tjänst via SearchClient, vilket är huvudobjektet som tillhandahålls av azure-search-documents paketet. SearchClient Ger åtkomst till indexåtgärder som:

     • Dataintag - upload_documents(), merge_documents(), delete_documents() osv.
     • Sökåtgärder - search(), autocomplete(), suggest()
     • Indexhanteringsåtgärder

   • Vektorfält innehåller flyttalsvärden. Dimensionsattributet har minst 2 och högst 4 096 flyttalsvärden vardera. Den här snabbstarten anger dimensionsattributet till 1 536 eftersom det är storleken på inbäddningar som genereras av Azure OpenAI-modellen textinbäddning-ada-002 .

Köra frågor

Nu när dokument har lästs in kan du skicka vektorfrågor mot dem genom att anropa search_client.search() och skicka in ett VectorizedQuery-objekt, de fält som du vill returnera, antalet resultat och så vidare.

I nästa avsnitt kör vi frågor mot indexet hotels-vector-quickstart . Frågorna omfattar:

• Enkel vektorsökning
• Enkel vektorsökning med filter
• Hybridsökning
• Semantisk hybridsökning

Skapa vektorfrågesträngen

Exempelvektorfrågorna baseras på två strängar:

• Söksträng: historic hotel walk to restaurants and shopping
• Vektorfrågesträng (vektoriserad i en matematisk representation): quintessential lodging near running trails, eateries, retail

Vektorfrågesträngen liknar söksträngen semantiskt, men den innehåller termer som inte finns i sökindexet. Om du gör en nyckelordssökning för quintessential lodging near running trails, eateries, retailär resultatet noll. Vi använder det här exemplet för att visa hur du kan få relevanta resultat även om det inte finns några matchande termer.

• Leta upp cellen nedan med rubriken "Skapa vektorfrågesträngen" och kör cellen. Då läses variabeln vector in med de vektoriserade frågedata som krävs för att köra alla sökningar i nästa avsnitt.

Enkel vektorsökning

Det första exemplet visar ett grundläggande scenario där du vill hitta dokumentbeskrivningar som matchar söksträngen.

• Leta upp cellen nedan med rubriken "Enkel vektorsökning" och kör cellen. Det här blocket innehåller begäran om att köra frågor mot sökindexet.

  # 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.")
  

  Den här vektorfrågan förkortas för korthet. Innehåller vectorQueries.vector den vektoriserade texten i frågeindata, fields avgör vilka vektorfält som söks och k anger antalet närmaste grannar som ska returneras.

  Vektorfrågesträngen är quintessential lodging near running trails, eateries, retail, som är vektoriserad i 1 536 inbäddningar för den här frågan.

  Svaret för vektorekvivalenten består av quintessential lodging near running trails, eateries, retail 7 resultat, men koden anger top=5 så att endast de första 5 resultaten returneras. Dessutom returneras endast de fält som anges av select.

  search_client.search() returnerar ett diktalikt objekt. Varje resultat ger en sökpoäng som kan nås med hjälp av score = result.get("@search.score", "N/A"). Även om det inte visas i det här exemplet innehåller k svaret alltid resultat ordnade efter värdelikhetspoängen i en likhetssökning.

  När du har kört cellen skrivs statusen för varje dokument ut under den:

  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
  

Enkel vektorsökning med filter

Du kan lägga till filter, men filtren tillämpas på icke-bevektorinnehållet i ditt index. I det här exemplet gäller filtret för fältet Tags för att filtrera bort alla hotell som inte tillhandahåller kostnadsfritt Wi-Fi.

1. Leta upp cellen nedan med rubriken "Enkel vektorsökning med filter" och kör cellen. Den här cellen innehåller begäran om att köra frågor mot sökindexet.

   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.")
   

   När du har kört cellen skrivs statusen för varje dokument ut under den:

   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']
   

   Frågan var densamma som i föregående exempel på enkel vektorsökning, men den innehåller ett exkluderingsfilter efter bearbetning och returnerar endast de två hotell som har kostnadsfritt Wi-Fi.

2. I nästa filterexempel används ett geo-filter. Kör cellen i avsnittet "Vektorfråga med ett geofilter". Det här blocket innehåller begäran om att köra frågor mot sökindexet.

   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.")
   

   Frågan var densamma som i föregående exempel på enkel vektorsökning, men den innehåller ett exkluderingsfilter efter bearbetning och returnerar endast de två hotellen inom 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.
   

Hybridsökning

Hybridsökning består av nyckelordsfrågor och vektorfrågor i en enda sökbegäran. I det här exemplet körs vektorfrågan och fulltextsökning samtidigt:

• Söksträng: historic hotel walk to restaurants and shopping

• Vektorfrågesträng (vektoriserad i en matematisk representation): quintessential lodging near running trails, eateries, retail

• Leta upp cellen nedan med rubriken "Hybridsökning" och kör cellen. Det här blocket innehåller begäran om att köra frågor mot sökindexet.

  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.")    
  

  Granska svaret:

  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']
  

  Eftersom Reciprocal Rank Fusion (RRF) sammanfogar resultat hjälper det till att granska indata. Följande resultat kommer endast från fulltextfrågan. De två främsta resultaten är Sublime Palace Hotel och History Lion Resort. Sublime Palace Hotel har en starkare BM25 relevanspoäng.

  {
       "@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"
  },
  

  I frågan med endast vektorer, som använder HNSW för att hitta matchningar, sjunker Sublime Palace Hotel till fjärde plats. Historic Lion, som var tvåa i fulltextsökningen och tredje i vektorsökningen, upplever inte samma fluktuationsintervall, så det visas som en toppmatchning i en homogeniserad resultatuppsättning.

  "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"
       }
  ]
  

Semantisk hybridsökning

Här är den sista frågan i samlingen. Den här hybridfrågan anger den semantiska frågetypen och en semantisk konfiguration, vilket visar att du kan skapa en hybridfråga som använder semantisk reranking.

• Leta upp cellen nedan med titeln "Semantisk hybridsökning" och kör cellen. Det här kodblocket innehåller begäran om att köra frågor mot sökindexet.

  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.")
  

  Granska utdata under cellen.

  Med semantisk rankning går Swirling Currents Hotel nu in på topplaceringen. W

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

Du kan se den semantiska rangordningen som ett sätt att förbättra sökresultatens relevans genom att förstå innebörden bakom orden i frågan och innehållet i dokumenten. I det här fallet hjälper den semantiska rangordningen till att identifiera hotell som inte bara är relevanta för nyckelorden utan även matchar frågans avsikt:

Nyckelinsikter:

• Vektorsökning anges via egenskapen vectors.value . Nyckelordssökning anges via egenskapen search .

• I en hybridsökning kan du integrera vektorsökning med fulltextsökning över nyckelord. Filter, stavningskontroll och semantisk rangordning gäller endast för textinnehåll och inte vektorer. I den här sista frågan finns det ingen semantisk answer eftersom systemet inte har skapat en som var tillräckligt stark.

• Faktiska resultat innehåller mer information, inklusive semantiska bildtexter och markeringar. Resultaten ändrades för läsbarhet. Kör begäran i REST-klienten för att få hela strukturen för svaret.

Rensa

När du arbetar i din egen prenumeration kan det dock vara klokt att i slutet av ett projekt kontrollera om du fortfarande behöver de resurser som du skapade. Resurser som fortsätter att köras kostar pengar. Du kan ta bort enstaka resurser eller hela resursgruppen om du vill ta bort alla resurser.

Du kan hitta och hantera resurser i Azure Portal med hjälp av länken Alla resurser eller Resursgrupper i den vänstra rutan.

Om du vill behålla söktjänsten, men ta bort indexet och dokumenten, kan du använda SearchIndexClient objektets delete_index() metod. Leta upp cellen nedan med rubriken "Rensa" och kör cellen om du vill ta bort indexet hotels-vector-quickstart :

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

Nästa steg

• Granska lagringsplatsen med kodexempel för vektorsökningsfunktioner i Azure AI Search for Python
• Granska de andra Kodexemplen för Python- och Azure AI Search i lagringsplatsen azure-search-python-samples

I den här snabbstarten använder du JavaScript för att skapa, läsa in och fråga vektorer. Kodexemplen utför dessa åtgärder med hjälp av Azure AI Search-klientbiblioteket. Biblioteket tillhandahåller en abstraktion över REST-API:et för åtkomst till indexåtgärder som datainmatning, sökåtgärder och indexhanteringsåtgärder.

I Azure AI Search har ett vektorlager ett indexschema som definierar vektor- och icke-vektorfält, en vektorsökningskonfiguration för algoritmer som skapar inbäddningsutrymmet och inställningar för vektorfältdefinitioner som utvärderas vid frågetillfället. REST-API:et Skapa index skapar vektorarkivet.

Kommentar

Den här snabbstarten utelämnar vektoriseringssteget och tillhandahåller infogade inbäddningar. Om du vill lägga till inbyggd datasegmentering och vektorisering över ditt eget innehåll kan du prova guiden Importera och vektorisera data för en genomgång från slutpunkt till slutpunkt.

Förutsättningar

• Ett Azure-konto med en aktiv prenumeration. Skapa ett konto kostnadsfritt.

• En Azure AI-tjänsten Search. Skapa en tjänst eller hitta en befintlig tjänst i din aktuella prenumeration.

  • Du kan använda en kostnadsfri söktjänst för det mesta av den här snabbstarten, men vi rekommenderar Basic-nivån eller högre för större datafiler.
  • Om du vill köra frågeexemplet som anropar semantisk omrankning måste söktjänsten vara på basic-nivån eller högre med semantisk rankning aktiverat.

• Visual Studio Code.

• Node.JS med LTS.

---

Hämta tjänstslutpunkter

I de återstående avsnitten konfigurerar du API-anrop till Azure OpenAI och Azure AI Search. Hämta tjänstslutpunkterna så att du kan ange dem som variabler i koden.

1. Logga in på Azure-portalen.

2. Hitta söktjänsten.

3. På startsidan Översikt kopierar du URL:en. Här följer ett exempel på hur en slutpunkt kan se ut: https://example.search.windows.net.

Konfigurera Node.JS-projektet

Konfigurera projekt med Visual Studio Code och JavaScript.

1. Starta Visual Studio Code i en ny katalog.

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

2. Skapa ett nytt paket för ESM-moduler i projektkatalogen.

   npm init -y
   npm pkg set type=module
   

   Då skapas en package.json fil med standardvärden.

3. Installera följande npm-paket.

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

4. Skapa en src katalog i projektkatalogen.

   mkdir src
   

Konfigurera miljövariabler för lokal utveckling

1. Skapa en .env fil i projektkatalogen vector-quickstart .

2. Lägg till följande miljövariabler i .env filen och ersätt värdena med dina egna tjänstslutpunkter och nycklar.

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

Logga in på Azure

Du använder Microsoft Entra-ID och rolltilldelningar för anslutningen. Kontrollera att du är inloggad på samma klientorganisation och prenumeration som Azure AI Search och Azure OpenAI. Du kan använda Azure CLI på kommandoraden för att visa aktuella egenskaper, ändra egenskaper och logga in. Mer information finns i Ansluta utan nycklar.

Kör vart och ett av följande kommandon i följd.

az account show

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

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

Du bör nu vara inloggad på Azure från din lokala enhet.

Skapa vektorindexet

I det här avsnittet skapar du ett vektorindex i Azure AI Search med SearchIndexClient. createOrUpdateIndex. Indexschemat definierar fälten, inklusive vektorfältet DescriptionVector.

1. Skapa en createIndex.js fil i src katalogen.

2. Kopiera följande kod till filen.

   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`);
   

   Kodfilen lägger till beroenden, miljövariabler och JavaScript-typ för HotelDocument överst i filen. createIndex Lägg till funktionen för att skapa indexet. Funktionen definierar indexschemat, inklusive vektorfältet DescriptionVector.

3. Kör filen:

   node -r dotenv/config src/createIndex.js
   

4. Utdata från den här koden visar att indexet har skapats:

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

   Viktiga lärdomar när du skapar vektorindex med @azure/search-documents biblioteket:

   • Du definierar ett index genom att skapa en lista med fält.

   • Det här specifika indexet stöder flera sökfunktioner, till exempel:

     • Nyckelordssökning i fulltext (searchable)
     • Vektorsökning (möjliggör hybridsökning genom att kombinera vektor- och icke-vektorfält) fält (DescriptionVector med vectorSearchProfileName)
     • Semantisk sökning
     • Fasetterad sökning (searchSuggester)
     • Geospatial sökning (Location fält med geo.distance)
     • Filtrering, sortering (Många fält markerade som filterbara och sorterbara)

Ladda upp dokument till indexet

Att skapa och läsa in indexet är separata steg. Du skapade indexschemat i föregående steg. Nu måste du läsa in dokument i indexet med SearchClient. uploadDocuments. Dokumenten innehåller den vektoriserade versionen av artikelns beskrivning, vilket möjliggör likhetssökning baserat på innebörd snarare än exakta nyckelord.

I Azure AI Search lagrar indexet allt sökbart innehåll, medan sökmotorn kör frågor mot det indexet.

1. Skapa en uploadDocuments.js fil i src katalogen.

2. Kopiera följande kod till filen.

   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);
   }
   

   Den här koden läser in en matris med JSON-objekt. Varje dokument innehåller den vektoriserade versionen av artikelns description. Den här vektorn möjliggör likhetssökning, där matchning baseras på betydelse snarare än exakta nyckelord.

   Eftersom den här snabbstartsartikeln söker i indexet direkt waitUntilIndexed väntar funktionen tills indexet är redo för sökåtgärder. Detta är viktigt eftersom indexet måste fyllas i fullständigt med dokument innan du kan utföra sökningar.

3. Skapa och kör filen:

   node -r dotenv/config src/uploadDocuments.js
   

4. Utdata från den här koden visar att dokumenten är indexerade och redo för sökning:

   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.
   

   Viktiga lärdomar om metoden upload_documents() och det här exemplet:

   • Koden interagerar med ett specifikt sökindex som finns i Azure AI Search-tjänsten via SearchClient, som är huvudobjektet som tillhandahålls av @azure/search-documents paketet. SearchClient ger åtkomst till indexåtgärder som:
     • Datainmatning – uploadDocuments(), mergeDocuments(), deleteDocuments() osv.
     • Sökfunktioner – search(), autocomplete(), suggest()
     • Indexhanteringsåtgärder som createIndex(), deleteIndex(), getIndex() osv.
   • Vektorfält innehåller flyttalsvärden. Dimensionsattributet har minst 2 och högst 4 096 flyttalsvärden vardera. Den här snabbstarten anger dimensionsattributet till 1 536 eftersom det är storleken på inbäddningar som genereras av Azure OpenAI-modellen textinbäddning-ada-002.

Skapa frågan som en vektor

Exempelvektorfrågorna baseras på två strängar:

• Söksträng: historic hotel walk to restaurants and shopping
• Vektorfrågesträng (vektoriserad i en matematisk representation): quintessential lodging near running trails, eateries, retail

Vektorfrågesträngen liknar söksträngen semantiskt, men den innehåller termer som inte finns i sökindexet. Om du gör en nyckelordssökning för quintessential lodging near running trails, eateries, retailär resultatet noll. Vi använder det här exemplet för att visa hur du kan få relevanta resultat även om det inte finns några matchande termer.

1. Skapa en queryVector.js fil i src katalogen och lägg till koden för att skapa frågevektorn.

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

2. Den här koden används i följande avsnitt för att utföra vektorsökningar. Frågevektorn skapas med hjälp av en inbäddningsmodell från Azure OpenAI.

Skapa en enda vektorsökning

Det första exemplet visar ett grundläggande scenario där du vill hitta dokumentbeskrivningar som nära matchar söksträngen med hjälp av SearchClient.search, VectorQuery och SearchOptions.

1. Skapa en searchSingle.js fil i src katalogen.

2. Kopiera följande kod till filen.

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

   VectorQuery innehåller konfigurationen av den vektoriserade frågan, inklusive den vektoriserade texten i frågeindata som vector, fields avgör vilka vektorfält som söks och kNearestNeighborsCount anger antalet närmaste grannar som ska returneras.

   Vektorfrågesträngen är quintessential lodging near running trails, eateries, retail, som är vektoriserad i 1 536 inbäddningar för den här frågan.

3. Skapa och kör filen:

   node -r dotenv/config src/searchSingle.js
   

4. Utdata från den här koden visar relevanta dokument för frågan quintessential lodging near running trails, eateries, retail.

   Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
   Using index name: hotels-vector-quickstart-0627-4
   
   
   Single Vector search found 5
   - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
   - HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
   - HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
   - HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
   - HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
   

   Svaret för vektorekvivalenten av quintessential lodging near running trails, eateries, retail består av 5 resultat med endast de fält som anges av urvalet som returneras.

Skapa en enskild vektorsökning med ett filter

Du kan lägga till filter, men filtren tillämpas på icke-bevektorinnehållet i ditt index. I det här exemplet gäller filtret för fältet Tags för att filtrera bort alla hotell som inte tillhandahåller kostnadsfritt Wi-Fi. Den här sökningen använder SearchClient.search samt VectorQuery och SearchOptions.

1. Skapa en searchSingleWithFilter.js fil i src katalogen.

2. Kopiera följande kod till filen.

   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;
   }
   

   Lägg till beroenden, miljövariabler och samma sökfunktion som den tidigare sökningen med ett exkluderingsfilter efter bearbetning som lagts till för hotell med free wifi.

3. Skapa och kör filen:

   node -r dotenv/config src/searchSingleWithFilter.js
   

4. Utdata från den här koden visar relevanta dokument för frågan med filtret för free wifi tillämpat:

   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
   

Skapa en sökning med ett geospatialt filter

Du kan ange ett geospatialt filter för att begränsa resultatet till ett visst geografiskt område. I det här exemplet begränsar filtret resultat till hotell inom 300 kilometer från Washington D.C. (koordinater: -77.03241 38.90166). Geospatiala avstånd är alltid i kilometer. Den här sökningen använder SearchClient.search samt VectorQuery och SearchOptions.

1. Skapa en searchSingleWithFilterGeo.js fil i src katalogen.

2. Kopiera följande kod till filen.

   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. Skapa och kör filen:

   node -r dotenv/config src/searchSingleWithFilterGeo.js
   

4. Utdata från den här koden visar relevanta dokument för frågan med det geospatiala exkluderingsfiltret efter bearbetning:

   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
   

Skapa en hybridsökning

Hybridsökning består av nyckelordsfrågor och vektorfrågor i en enda sökbegäran. I det här exemplet körs vektorfrågan och fulltextsökning samtidigt:

• Söksträng: historic hotel walk to restaurants and shopping
• Vektorfrågesträng (vektoriserad i en matematisk representation): quintessential lodging near running trails, eateries, retail

Den här sökningen använder SearchClient.search samt VectorQuery och SearchOptions.

1. Skapa en searchHybrid.js fil i src katalogen.

2. Kopiera följande kod till filen.

   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. Skapa och kör filen:

   node -r dotenv/config src/searchHybrid.js
   

4. Utdata från den här koden visar relevanta dokument för hybridsökningen:

   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"]
   

   Eftersom Reciprocal Rank Fusion (RRF) sammanfogar resultat hjälper det till att granska indata. Följande resultat kommer endast från fulltextfrågan. De två främsta resultaten är Sublime Palace Hotel och History Lion Resort. Sublime Palace Hotel har en starkare BM25 relevanspoäng.

      {
          "@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"
      },
   

   I frågan med endast vektorer, som använder HNSW för att hitta matchningar, sjunker Sublime Palace Hotel till fjärde plats. Historic Lion, som var tvåa i fulltextsökningen och tredje i vektorsökningen, upplever inte samma fluktuationsintervall, så det visas som en toppmatchning i en homogeniserad resultatuppsättning.

   "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"
       }
   ]
   

Skapa en semantisk hybridsökning

Här är den sista frågan i samlingen som ska skapas för att utöka den semantiska hybridsökningen med den ytterligare söktexten historic hotel walk to restaurants and shopping.

Den här sökningen använder SearchClient.search samt VectorQuery och SearchOptions.

1. Skapa en searchSemanticHybrid.js fil i src katalogen.

2. Kopiera följande kod till filen.

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

3. Skapa och kör filen:

   node -r dotenv/config src/searchSemanticHybrid.js
   

4. Utdata från den här koden visar relevanta dokument för den semantiska hybridsökningen:

   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
   

   Du kan se den semantiska rangordningen som ett sätt att förbättra sökresultatens relevans genom att förstå innebörden bakom orden i frågan och innehållet i dokumenten. I det här fallet hjälper den semantiska rangordningen till att identifiera hotell som inte bara är relevanta för nyckelorden utan även matchar frågans avsikt:

   Nyckelinsikter:

   • Vektorsökning anges via egenskapen vectorSearchOptions . Nyckelordssökning anges via egenskapen semanticSearchOptions .

   • I en hybridsökning kan du integrera vektorsökning med fulltextsökning över nyckelord. Filter, stavningskontroll och semantisk rangordning gäller endast för textinnehåll och inte vektorer. I den här sista frågan finns det ingen semantisk answer eftersom systemet inte har skapat en som var tillräckligt stark.

   • Faktiska resultat innehåller mer information, inklusive semantiska bildtexter och markeringar. Resultaten ändrades för läsbarhet. Kör begäran i REST-klienten för att få hela strukturen för svaret.

Rensa

När du arbetar i din egen prenumeration kan det dock vara klokt att i slutet av ett projekt kontrollera om du fortfarande behöver de resurser som du skapade. Resurser som fortsätter att köras kostar pengar. Du kan ta bort enstaka resurser eller hela resursgruppen om du vill ta bort alla resurser.

Du kan hitta och hantera resurser i Azure Portal med hjälp av länken Alla resurser eller Resursgrupper i den vänstra rutan.

Om du vill behålla söktjänsten, men ta bort indexet och dokumenten, kan du ta bort indexet programmatiskt.

1. Skapa en deleteIndex.js fil i src katalogen.

2. Lägg till beroenden, miljövariabler och kod för att ta bort indexet.

   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. Skapa och kör filen:

   node -r dotenv/config src/deleteIndex.js
   

Nästa steg

• Granska lagringsplatsen med kodexempel för vektorsökningsfunktioner i Azure AI Search for JavaScript

I den här snabbstarten använder du TypeScript för att skapa, läsa in och fråga vektorer. Kodexemplen utför dessa åtgärder med hjälp av Azure AI Search-klientbiblioteket. Biblioteket tillhandahåller en abstraktion över REST-API:et för åtkomst till indexåtgärder som datainmatning, sökåtgärder och indexhanteringsåtgärder.

I Azure AI Search har ett vektorlager ett indexschema som definierar vektor- och icke-vektorfält, en vektorsökningskonfiguration för algoritmer som skapar inbäddningsutrymmet och inställningar för vektorfältdefinitioner som utvärderas vid frågetillfället. REST-API:et Skapa index skapar vektorarkivet.

Kommentar

Den här snabbstarten utelämnar vektoriseringssteget och tillhandahåller infogade inbäddningar. Om du vill lägga till inbyggd datasegmentering och vektorisering över ditt eget innehåll kan du prova guiden Importera och vektorisera data för en genomgång från slutpunkt till slutpunkt.

Förutsättningar

• Ett Azure-konto med en aktiv prenumeration. Skapa ett konto kostnadsfritt.

• En Azure AI-tjänsten Search. Skapa en tjänst eller hitta en befintlig tjänst i din aktuella prenumeration.

  • Du kan använda en kostnadsfri söktjänst för det mesta av den här snabbstarten, men vi rekommenderar Basic-nivån eller högre för större datafiler.
  • Om du vill köra frågeexemplet som anropar semantisk omrankning måste söktjänsten vara på basic-nivån eller högre med semantisk rankning aktiverat.

• Visual Studio Code.

• Node.JS med LTS.

• TypeScript. Du kan installera TypeScript globalt med npm:

  npm install -g typescript
  

---

Hämta tjänstslutpunkter

I de återstående avsnitten konfigurerar du API-anrop till Azure OpenAI och Azure AI Search. Hämta tjänstslutpunkterna så att du kan ange dem som variabler i koden.

1. Logga in på Azure-portalen.

2. Hitta söktjänsten.

3. På startsidan Översikt kopierar du URL:en. Här följer ett exempel på hur en slutpunkt kan se ut: https://example.search.windows.net.

Konfigurera Node.JS-projektet

Konfigurera projekt med Visual Studio Code och TypeScript.

1. Starta Visual Studio Code i en ny katalog.

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

2. Skapa ett nytt paket för ESM-moduler i projektkatalogen.

   npm init -y
   npm pkg set type=module
   

   Då skapas en package.json fil med standardvärden.

3. Redigera package.json för att inkludera ett byggskript. Lägg till följande rad i avsnittet scripts :

   "build": "tsc"
   

4. Installera följande npm-paket.

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

5. Skapa en src katalog i projektkatalogen.

   mkdir src
   

6. Skapa en tsconfig.json fil i projektkatalogen för ESM med följande innehåll.

   {
     "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"
     ]
   }
   

Konfigurera miljövariabler för lokal utveckling

1. Skapa en .env fil i projektkatalogen vector-quickstart .

2. Lägg till följande miljövariabler i .env filen och ersätt värdena med dina egna tjänstslutpunkter och nycklar.

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

Logga in på Azure

Du använder Microsoft Entra-ID och rolltilldelningar för anslutningen. Kontrollera att du är inloggad på samma klientorganisation och prenumeration som Azure AI Search och Azure OpenAI. Du kan använda Azure CLI på kommandoraden för att visa aktuella egenskaper, ändra egenskaper och logga in. Mer information finns i Ansluta utan nycklar.

Kör vart och ett av följande kommandon i följd.

az account show

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

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

Du bör nu vara inloggad på Azure från din lokala enhet.

Skapa vektorindexet

I det här avsnittet skapar du ett vektorindex i Azure AI Search med SearchIndexClient. createOrUpdateIndex. Indexschemat definierar fälten, inklusive vektorfältet DescriptionVector. När indexet har skapats laddar du upp dokument till indexet. Dokumenten innehåller den vektoriserade versionen av artikelns beskrivning, vilket möjliggör likhetssökning baserat på innebörd snarare än exakta nyckelord.

1. Skapa en createIndex.ts fil i src katalogen.

2. Kopiera följande kod till filen.

   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`);
   

   Kodfilen lägger till beroenden, miljövariabler och JavaScript-typ för HotelDocument överst i filen. createIndex Lägg till funktionen för att skapa indexet. Funktionen definierar indexschemat, inklusive vektorfältet DescriptionVector.

3. Skapa och kör filen:

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

4. Utdata från den här koden visar att indexet har skapats:

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

   Viktiga lärdomar när du skapar vektorindex med @azure/search-documents biblioteket:

   • Du definierar ett index genom att skapa en lista med fält.

   • Det här specifika indexet stöder flera sökfunktioner, till exempel:

     • Nyckelordssökning i fulltext (searchable)
     • Vektorsökning (möjliggör hybridsökning genom att kombinera vektor- och icke-vektorfält) fält (DescriptionVector med vectorSearchProfileName)
     • Semantisk sökning
     • Fasetterad sökning (searchSuggester)
     • Geospatial sökning (Location fält med geo.distance)
     • Filtrering, sortering (Många fält markerade som filterbara och sorterbara)

Ladda upp dokument till indexet

Att skapa och läsa in indexet är separata steg. Du skapade indexschemat i föregående steg. Nu måste du läsa in dokument i indexet med SearchClient. uploadDocuments.

I Azure AI Search lagrar indexet allt sökbart innehåll, medan sökmotorn kör frågor mot det indexet.

1. Skapa en uploadDocuments.ts fil i src katalogen.

2. Kopiera följande kod till filen.

   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);
   }
   

   Den här koden läser in ett variabeldokument med ett JSON-objekt som beskriver varje dokument, tillsammans med den vektoriserade versionen av artikelns beskrivning. Den här vektorn möjliggör likhetssökning, där matchning baseras på betydelse snarare än exakta nyckelord.

   Eftersom den här snabbstartsartikeln söker i indexet direkt waitUntilIndexed väntar funktionen tills indexet är redo för sökåtgärder. Detta är viktigt eftersom indexet måste fyllas i fullständigt med dokument innan du kan utföra sökningar.

3. Skapa och kör filen:

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

4. Utdata från den här koden visar att dokumenten är indexerade och redo för sökning:

   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.
   

   Viktiga lärdomar om metoden upload_documents() och det här exemplet:

   • Koden interagerar med ett specifikt sökindex som finns i Azure AI Search-tjänsten via SearchClient, som är huvudobjektet som tillhandahålls av @azure/search-documents paketet. SearchClient ger åtkomst till indexåtgärder som:
     • Datainmatning – uploadDocuments(), mergeDocuments(), deleteDocuments() osv.
     • Sökfunktioner – search(), autocomplete(), suggest()
     • Indexhanteringsåtgärder som createIndex(), deleteIndex(), getIndex() osv.
   • Vektorfält innehåller flyttalsvärden. Dimensionsattributet har minst 2 och högst 4 096 flyttalsvärden vardera. Den här snabbstarten anger dimensionsattributet till 1 536 eftersom det är storleken på inbäddningar som genereras av Azure OpenAI-modellen textinbäddning-ada-002.

Skapa frågan som en vektor

Exempelvektorfrågorna baseras på två strängar:

• Söksträng: historic hotel walk to restaurants and shopping
• Vektorfrågesträng (vektoriserad i en matematisk representation): quintessential lodging near running trails, eateries, retail

Vektorfrågesträngen liknar söksträngen semantiskt, men den innehåller termer som inte finns i sökindexet. Om du gör en nyckelordssökning för quintessential lodging near running trails, eateries, retailär resultatet noll. Vi använder det här exemplet för att visa hur du kan få relevanta resultat även om det inte finns några matchande termer.

1. Skapa en queryVector.ts fil i src katalogen och lägg till koden för att skapa frågevektorn.

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

2. Den här koden används i följande avsnitt för att utföra vektorsökningar. Frågevektorn skapas med hjälp av en inbäddningsmodell från Azure OpenAI.

Skapa en enda vektorsökning

Det första exemplet visar ett grundläggande scenario där du vill hitta dokumentbeskrivningar som nära matchar söksträngen med hjälp av SearchClient.search, VectorQuery och SearchOptions.

1. Skapa en searchSingle.ts fil i src katalogen.

2. Kopiera följande kod till filen.

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

   VectorQuery innehåller konfigurationen av den vektoriserade frågan, inklusive den vektoriserade texten i frågeindata som vector, fields avgör vilka vektorfält som söks och kNearestNeighborsCount anger antalet närmaste grannar som ska returneras.

   Vektorfrågesträngen är quintessential lodging near running trails, eateries, retail, som är vektoriserad i 1 536 inbäddningar för den här frågan.

3. Skapa och kör filen:

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

4. Utdata från den här koden visar relevanta dokument för frågan quintessential lodging near running trails, eateries, retail.

   Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
   Using index name: hotels-vector-quickstart-0627-4
   
   
   Single Vector search found 5
   - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
   - HotelId: 13, HotelName: Luxury Lion Resort, Tags: ["bar","concierge","restaurant"], Score 0.6333684
   - HotelId: 4, HotelName: Sublime Palace Hotel, Tags: ["concierge","view","air conditioning"], Score 0.605672
   - HotelId: 49, HotelName: Swirling Currents Hotel, Tags: ["air conditioning","laundry service","24-hour front desk service"], Score 0.6026341
   - HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
   

   Svaret för vektorekvivalenten av quintessential lodging near running trails, eateries, retail består av 5 resultat med endast de fält som anges av urvalet som returneras.

Skapa en enskild vektorsökning med ett filter

Du kan lägga till filter, men filtren tillämpas på icke-bevektorinnehållet i ditt index. I det här exemplet gäller filtret för fältet Tags för att filtrera bort alla hotell som inte tillhandahåller kostnadsfritt Wi-Fi. Den här sökningen använder SearchClient.search samt VectorQuery och SearchOptions.

1. Skapa en searchSingleWithFilter.ts fil i src katalogen.

2. Kopiera följande kod till filen.

   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;
   }
   

   Lägg till beroenden, miljövariabler och samma sökfunktion som den tidigare sökningen med ett exkluderingsfilter efter bearbetning som lagts till för hotell med free wifi.

3. Skapa och kör filen:

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

4. Utdata från den här koden visar relevanta dokument för frågan med filtret för free wifi tillämpat:

   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
   

Skapa en sökning med ett geospatialt filter

Du kan ange ett geospatialt filter för att begränsa resultatet till ett visst geografiskt område. I det här exemplet begränsar filtret resultat till hotell inom 300 kilometer från Washington D.C. (koordinater: -77.03241 38.90166). Geospatiala avstånd är alltid i kilometer. Den här sökningen använder SearchClient.search samt VectorQuery och SearchOptions.

1. Skapa en searchSingleWithFilterGeo.ts fil i src katalogen.

2. Kopiera följande kod till filen.

   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. Skapa och kör filen:

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

4. Utdata från den här koden visar relevanta dokument för frågan med det geospatiala exkluderingsfiltret efter bearbetning:

   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
   

Skapa en hybridsökning

Hybridsökning består av nyckelordsfrågor och vektorfrågor i en enda sökbegäran. I det här exemplet körs vektorfrågan och fulltextsökning samtidigt:

• Söksträng: historic hotel walk to restaurants and shopping
• Vektorfrågesträng (vektoriserad i en matematisk representation): quintessential lodging near running trails, eateries, retail

Den här sökningen använder SearchClient.search samt VectorQuery och SearchOptions.

1. Skapa en searchHybrid.ts fil i src katalogen.

2. Kopiera följande kod till filen.

   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. Skapa och kör filen:

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

4. Utdata från den här koden visar relevanta dokument för hybridsökningen:

   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"]
   

   Eftersom Reciprocal Rank Fusion (RRF) sammanfogar resultat hjälper det till att granska indata. Följande resultat kommer endast från fulltextfrågan. De två främsta resultaten är Sublime Palace Hotel och History Lion Resort. Sublime Palace Hotel har en starkare BM25 relevanspoäng.

      {
          "@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"
      },
   

   I frågan med endast vektorer, som använder HNSW för att hitta matchningar, sjunker Sublime Palace Hotel till fjärde plats. Historic Lion, som var tvåa i fulltextsökningen och tredje i vektorsökningen, upplever inte samma fluktuationsintervall, så det visas som en toppmatchning i en homogeniserad resultatuppsättning.

   "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"
       }
   ]
   

Skapa en semantisk hybridsökning

Här är den sista frågan i samlingen som ska skapas för att utöka den semantiska hybridsökningen med den ytterligare söktexten historic hotel walk to restaurants and shopping.

Den här sökningen använder SearchClient.search samt VectorQuery och SearchOptions.

1. Skapa en searchSemanticHybrid.ts fil i src katalogen.

2. Kopiera följande kod till filen.

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

3. Skapa och kör filen:

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

4. Utdata från den här koden visar relevanta dokument för den semantiska hybridsökningen:

   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
   

   Du kan se den semantiska rangordningen som ett sätt att förbättra sökresultatens relevans genom att förstå innebörden bakom orden i frågan och innehållet i dokumenten. I det här fallet hjälper den semantiska rangordningen till att identifiera hotell som inte bara är relevanta för nyckelorden utan även matchar frågans avsikt:

   Nyckelinsikter:

   • Vektorsökning anges via egenskapen vectorSearchOptions . Nyckelordssökning anges via egenskapen semanticSearchOptions .

   • I en hybridsökning kan du integrera vektorsökning med fulltextsökning över nyckelord. Filter, stavningskontroll och semantisk rangordning gäller endast för textinnehåll och inte vektorer. I den här sista frågan finns det ingen semantisk answer eftersom systemet inte har skapat en som var tillräckligt stark.

   • Faktiska resultat innehåller mer information, inklusive semantiska bildtexter och markeringar. Resultaten ändrades för läsbarhet. Kör begäran i REST-klienten för att få hela strukturen för svaret.

Rensa

När du arbetar i din egen prenumeration kan det dock vara klokt att i slutet av ett projekt kontrollera om du fortfarande behöver de resurser som du skapade. Resurser som fortsätter att köras kostar pengar. Du kan ta bort enstaka resurser eller hela resursgruppen om du vill ta bort alla resurser.

Du kan hitta och hantera resurser i Azure Portal med hjälp av länken Alla resurser eller Resursgrupper i den vänstra rutan.

Om du vill behålla söktjänsten, men ta bort indexet och dokumenten, kan du ta bort indexet programmatiskt.

1. Skapa en deleteIndex.ts fil i src katalogen.

2. Lägg till beroenden, miljövariabler och kod för att ta bort indexet.

   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. Skapa och kör filen:

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

Nästa steg

• Granska lagringsplatsen med kodexempel för vektorsökningsfunktioner i Azure AI Search for JavaScript

I den här snabbstarten arbetar du med en .NET-app för att skapa, fylla i och fråga vektorer. Kodexemplen utför dessa åtgärder med hjälp av Azure AI Search-klientbiblioteket. Biblioteket tillhandahåller en abstraktion över REST-API:et för åtkomst till indexåtgärder som datainmatning, sökåtgärder och indexhanteringsåtgärder.

I Azure AI Search har ett vektorlager ett indexschema som definierar vektor- och icke-vektorfält, en vektorsökningskonfiguration för algoritmer som skapar inbäddningsutrymmet och inställningar för vektorfältdefinitioner som utvärderas vid frågetillfället. REST-API:et Skapa index skapar vektorarkivet.

Kommentar

Den här snabbstarten utelämnar vektoriseringssteget och tillhandahåller infogade inbäddningar. Om du vill lägga till inbyggd datasegmentering och vektorisering över ditt eget innehåll kan du prova guiden Importera och vektorisera data för en genomgång från slutpunkt till slutpunkt.

Förutsättningar

• Ett Azure-konto med en aktiv prenumeration. Skapa ett konto kostnadsfritt.

• En Azure AI-tjänsten Search. Skapa en tjänst eller hitta en befintlig tjänst i din aktuella prenumeration.

  • Du kan använda en kostnadsfri söktjänst för det mesta av den här snabbstarten, men vi rekommenderar Basic-nivån eller högre för större datafiler.
  • Om du vill köra frågeexemplet som anropar semantisk omrankning måste söktjänsten vara på basic-nivån eller högre med semantisk rankning aktiverat.

• Visual Studio Code eller Visual Studio.

• Git för att klona exempelrepo.

---

Hämta resursinformation

Begäranden till sökslutpunkten måste autentiseras och auktoriseras. Även om det är möjligt att använda API-nycklar eller roller för den här uppgiften rekommenderar vi att du använder en nyckellös anslutning via Microsoft Entra-ID.

Den här snabbstarten använder DefaultAzureCredential, vilket förenklar autentiseringen i både utvecklings- och produktionsscenarier. För produktionsscenarier kan du dock ha mer avancerade krav som kräver en annan metod. Se Autentisera .NET-appar till Azure-tjänster med hjälp av Azure SDK för .NET för att förstå alla dina alternativ.

Klona koden och installationsmiljön

1. Klona lagringsplatsen som innehåller koden för den här snabbstarten.

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

   Den här lagringsplatsen har .NET-kodexempel för flera artiklar var och en i en separat undermapp.

2. Öppna undermappen quickstart-Vector-Search i Visual Studio Code eller dubbelklicka på .sln filen för att öppna lösningen i Visual Studio.

3. appsettings.json Öppna filerna i mapparna VectorSearchExamples ochVectorSearchCreatePopulateIndex. Uppdatera följande värden:

   • AZURE_SEARCH_ENDPOINT: Hitta URL:en för din Azure AI Search-tjänst i Azure-portalen. Leta efter URL-fältet på sidan Översikt för sökresursen. Här följer ett exempel på hur en slutpunkt kan se ut: https://mydemo.search.windows.net.
   • AZURE_SEARCH_INDEX_NAME: Lämna standardvärdet i filen eller ange ditt eget indexnamn.

Skapa vektorindexet och ladda upp dokument

Om du vill köra sökfrågor mot Azure AI Search-tjänsten måste du först skapa ett sökindex och ladda upp dokument till tjänsten.

1. Öppna en ny terminal i VectorSearchCreatePopulateIndex mappen.

2. Kör projektet med dotnet run kommandot :

   dotnet run
   

Följande kod körs för att skapa ett index:

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();
}

Följande kod laddar upp JSON-formaterade dokument i hotel-samples.json filen till Azure AI Search-tjänsten:

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);
    }
}

När du har kört projektet skrivs följande utdata ut:

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

Nyckelinsikter:

• Koden interagerar med ett specifikt sökindex som finns i Din Azure AI Search-tjänst via SearchClient, vilket är huvudobjektet som tillhandahålls av azure-search-documents paketet. SearchClient Ger åtkomst till indexåtgärder som:

  • Datainmatning: UploadDocuments(), MergeDocuments(), DeleteDocuments()
  • Sökåtgärder: Search(), Autocomplete(), Suggest()
  • Indexhanteringsåtgärder: CreateOrUpdateIndex()

• Vektorfält innehåller flyttalsvärden. Dimensionsattributet har minst 2 och högst 4 096 flyttalsvärden vardera. Den här storleken på inbäddningar som genereras av Azure OpenAI-modellen textinbäddning-3-small för den här snabbstarten är 1536.

Utföra sökfrågor

När indexet har skapats och dokument har lästs in kan du skicka vektorfrågor mot dem genom att anropa SearchAsync() med olika parametrar.

1. VectorSearchExamples Öppna filen i Program.cs mappen.
2. Öppna en ny terminal i VectorSearchExamples mappen.

I följande avsnitt kör du frågor mot indexet hotels-vector-quickstart . Frågorna omfattar:

• Enkel vektorsökning
• Enkel vektorsökning med filter
• Hybridsökning
• Semantisk hybridsökning med filter

Enkel vektorsökning

Det första exemplet visar ett grundläggande scenario där du vill hitta dokumentbeskrivningar som matchar söksträngen.

1. Program.cs fil i mappen VectorSearchExamples, avkommentera metodanropet SearchExamples.SearchSingleVector(searchClient, vectorizedResult);. Den här metoden kör följande sökfunktion i SearchExamples.cs klassen:

   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. Kör projektet med dotnet run kommandot :

   dotnet run
   

   När du har kört projektet skrivs sökresultaten ut i utdatafönstret:

   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
   

Enkel vektorsökning med filter

Du kan lägga till filter, men filtren tillämpas på icke-bevektorinnehållet i ditt index. I det här exemplet gäller filtret för fältet Tags för att filtrera bort alla hotell som inte tillhandahåller kostnadsfritt Wi-Fi.

1. Program.cs fil i mappen VectorSearchExamples, avkommentera metodanropet SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);. Den här metoden kör följande sökfunktion i SearchExamples.cs klassen:

   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. Kör projektet igen och statusen för varje dokument skrivs ut under det:

    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
   

   Frågan var densamma som i föregående exempel på enkel vektorsökning, men den innehåller ett exkluderingsfilter efter bearbetning och returnerar endast de två hotell som har kostnadsfritt Wi-Fi.

3. I nästa filterexempel används ett geo-filter. Program.cs fil i mappen VectorSearchExamples, avkommentera metodanropet SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);. Den här metoden kör följande sökfunktion i SearchExamples.cs klassen:

   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();
   }
   

   Frågan var densamma som i föregående exempel på enkel vektorsökning, men den innehåller ett exkluderingsfilter efter bearbetning och returnerar endast de två hotellen inom 300 kilometer.

4. Kör projektet igen och statusen för varje dokument skrivs ut under det:

   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.
   

Hybridsökning

Hybridsökning består av nyckelordsfrågor och vektorfrågor i en enda sökbegäran. I det här exemplet körs vektorfrågan och fulltextsökning samtidigt:

• Söksträng: historic hotel walk to restaurants and shopping
• Vektorfrågesträng (vektoriserad i en matematisk representation): Quintessential lodging near running trails, eateries, retail

1. Program.cs fil i mappen VectorSearchExamples, avkommentera metodanropet SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);. Den här metoden kör följande sökfunktion i SearchExamples.cs klassen:

   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. Kör projektet igen och statusen för varje dokument skrivs ut under det:

   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
   

Eftersom Reciprocal Rank Fusion (RRF) sammanfogar resultat hjälper det till att granska indata. Följande resultat kommer endast från fulltextfrågan. De två främsta resultaten är Sublime Palace Hotel och History Lion Resort. Sublime Palace Hotel har en starkare BM25 relevanspoäng.

{
    "@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"
},

I frågan med endast vektorer, som använder HNSW för att hitta matchningar, sjunker Sublime Palace Hotel till fjärde plats. Historic Lion, som var tvåa i fulltextsökningen och tredje i vektorsökningen, upplever inte samma fluktuationsintervall, så det visas som en toppmatchning i en homogeniserad resultatuppsättning.

"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"
    }
]

Semantisk hybridsökning med ett filter

Hybridfrågan med semantisk rangordning filtreras för att endast visa hotellen inom en radie på 500 kilometer från Washington D.C.

1. Program.cs fil i mappen VectorSearchExamples, avkommentera metodanropet SearchExamples.SearchHybridVectoryAndSemantic(searchClient, vectorizedResult);. Den här metoden kör följande sökfunktion i SearchExamples.cs klassen:

   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. Kör projektet igen och granska utdata under cellen. Svaret är tre hotell, som filtreras efter plats och fasetteras av StateProvince och semantiskt rangordnas om för att höja upp resultat som är närmast söksträngsfrågan (historic hotel walk to restaurants and shopping).

   The Swirling Currents Hotel går nu in på topplaceringen. Utan semantisk ranking är Nordick's Valley Motel nummer ett. Med semantisk rankning känner maskinförståelsemodellerna igen som historic gäller för "hotell, inom gångavstånd till restauranger och shopping.".

   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
   

Nyckelinsikter:

• I en hybridsökning kan du integrera vektorsökning med fulltextsökning över nyckelord. Filter, stavningskontroll och semantisk rangordning gäller endast för textinnehåll och inte vektorer. I den här sista frågan finns det ingen semantisk answer eftersom systemet inte har skapat en som var tillräckligt stark.

• Faktiska resultat innehåller mer information, inklusive semantiska bildtexter och markeringar. Resultaten ändrades för läsbarhet. Kör begäran i REST-klienten för att få hela strukturen för svaret.

Rensa

När du arbetar i din egen prenumeration kan det dock vara klokt att i slutet av ett projekt kontrollera om du fortfarande behöver de resurser som du skapade. Resurser som fortsätter att köras kostar pengar. Du kan ta bort enstaka resurser eller hela resursgruppen om du vill ta bort alla resurser.

Du kan hitta och hantera resurser i Azure Portal med hjälp av länken Alla resurser eller Resursgrupper i den vänstra rutan.

Nästa steg

• Granska lagringsplatsen med kodexempel för funktioner för vektorsökning i Azure AI Search för .NET
• Granska de andra .NET- och Azure AI Search-kodexemplen på lagringsplatsen azure-search-dotnet-samples

I den här snabbstarten använder du REST API:er för Azure AI Search för att skapa, läsa in och fråga vektorer.

I Azure AI Search har ett vektorlager ett indexschema som definierar vektor- och icke-vektorfält, en vektorsökningskonfiguration för algoritmer som skapar inbäddningsutrymmet och inställningar för vektorfältdefinitioner som utvärderas vid frågetillfället. REST-API:et Skapa index skapar vektorarkivet.

Kommentar

Den här snabbstarten utelämnar vektoriseringssteget och tillhandahåller infogade inbäddningar. Om du vill lägga till inbyggd datasegmentering och vektorisering över ditt eget innehåll kan du prova guiden Importera och vektorisera data för en genomgång från slutpunkt till slutpunkt.

Förutsättningar

• Ett Azure-konto med en aktiv prenumeration. Skapa ett konto kostnadsfritt.

• En Azure AI-tjänsten Search. Skapa en tjänst eller hitta en befintlig tjänst i din aktuella prenumeration.

  • Du kan använda en kostnadsfri söktjänst för det mesta av den här snabbstarten, men vi rekommenderar Basic-nivån eller högre för större datafiler.
  • Om du vill köra frågeexemplet som anropar semantisk omrankning måste söktjänsten vara på basic-nivån eller högre med semantisk rankning aktiverat.

• Visual Studio Code med en REST-klient.

---

Hämta resursinformation

Begäranden till sökslutpunkten måste autentiseras och auktoriseras. Du kan använda API-nycklar eller roller för den här uppgiften. Vi rekommenderar att du använder en nyckellös anslutning via Microsoft Entra-ID.

Välj den flik som motsvarar den autentiseringsmetod du föredrar. Använd samma metod för alla begäranden i den här snabbstarten.

Microsoft Entra-ID

1. Logga in på Azure Portal och hitta söktjänsten.

2. På startsidan Översikt hittar du URL:en. Här följer ett exempel på hur en slutpunkt kan se ut: https://mydemo.search.windows.net.

   

3. Följ stegen i den nyckellösa snabbstarten för att hämta din Microsoft Entra-token.

   Du får token när du kör az account get-access-token kommandot i steg 3 i föregående snabbstart.

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

API-nyckel

1. Logga in på Azure Portal och hitta söktjänsten.

2. På startsidan Översikt hittar du URL:en. Här följer ett exempel på hur en slutpunkt kan se ut: https://mydemo.search.windows.net.

   

3. Välj Inställningar>Nycklar. ANTINGEN API-nycklar eller Båda måste vara aktiverade. Administratörs-API-nycklar används för att lägga till, ändra och ta bort objekt. Det finns två utbytbara administratörsnycklar. Kopiera någon av dem.

   

Skapa eller ladda ned kodfilen

Du använder en fil eller .rest en .http fil för att köra alla begäranden i den här snabbstarten. Du kan ladda ned REST-filen som innehåller koden för den här snabbstarten, eller så kan du skapa en ny fil i Visual Studio Code och kopiera koden till den.

1. I Visual Studio Code skapar du en ny fil med filnamnstillägget .rest eller .http filnamnstillägget. Exempel: az-search-vector-quickstart.rest Kopiera och klistra in råinnehållet i filen Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest i den här nya filen.

2. Överst i filen ersätter du platshållarvärdet för @baseUrl med söktjänstens URL. Se avsnittet Hämta resursinformation för anvisningar om hur du hittar söktjänstens URL.

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

3. Överst i filen ersätter du platshållarvärdet för autentisering. Se avsnittet Hämta resursinformation för anvisningar om hur du hämtar din Microsoft Entra-token eller API-nyckel.

   För den rekommenderade nyckellösa autentiseringen via Microsoft Entra-ID måste du ersätta @apiKey med variabeln @token .

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

   Om du föredrar att använda en API-nyckel ersätter @apiKey du med nyckeln som du kopierade från Azure Portal.

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

4. För den rekommenderade nyckellösa autentiseringen via Microsoft Entra-ID måste du ersätta api-key: {{apiKey}} med Authorization: Bearer {{token}} i begärandehuvudena. Ersätt alla instanser av api-key: {{apiKey}} som du hittar i filen.

Skapa ett vektorindex

Du använder REST-API:et Skapa index för att skapa ett vektorindex och konfigurera de fysiska datastrukturerna i söktjänsten.

Indexschemat i det här exemplet är organiserat kring hotellinnehåll. Exempeldata består av vektor- och icke-bevektorbeskrivningar av fiktiva hotell. Det här schemat innehåller konfigurationer för vektorindexering och frågor samt för semantisk rangordning.

1. Öppna filen az-search-vector-quickstart.rest som du skapade tidigare i Visual Studio Code.

2. Leta upp kodblocket ### Create a new index i filen. Det här blocket innehåller begäran om att skapa indexet för hotels-vector-quickstart söktjänsten.

   ### Create a new index
   POST  {{baseUrl}}/indexes?api-version=2024-07-01  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   {
       "name": "hotels-vector-quickstart",
       "fields": [
           {
               "name": "HotelId", 
               "type": "Edm.String",
               "searchable": false, 
               "filterable": true, 
               "retrievable": true, 
               "sortable": false, 
               "facetable": false,
               "key": true
           },
           {
               "name": "HotelName", 
               "type": "Edm.String",
               "searchable": true, 
               "filterable": false, 
               "retrievable": true, 
               "sortable": true, 
               "facetable": false
           },
           {
               "name": "Description", 
               "type": "Edm.String",
               "searchable": true, 
               "filterable": false, 
               "retrievable": true, 
               "sortable": false, 
               "facetable": false
           },
           {
               "name": "DescriptionVector",
               "type": "Collection(Edm.Single)",
               "searchable": true,
               "retrievable": true,
               "dimensions": 1536,
               "vectorSearchProfile": "my-vector-profile"
           },
           {
               "name": "Category", 
               "type": "Edm.String",
               "searchable": true, 
               "filterable": true, 
               "retrievable": true, 
               "sortable": true, 
               "facetable": true
           },
           {
               "name": "Tags",
               "type": "Collection(Edm.String)",
               "searchable": true,
               "filterable": true,
               "retrievable": true,
               "sortable": false,
               "facetable": true
           },
                   {
               "name": "ParkingIncluded",
               "type": "Edm.Boolean",
               "searchable": false,
               "filterable": true,
               "retrievable": true,
               "sortable": true,
               "facetable": true
           },
           {
               "name": "LastRenovationDate",
               "type": "Edm.DateTimeOffset",
               "searchable": false,
               "filterable": true,
               "retrievable": true,
               "sortable": true,
               "facetable": true
           },
           {
               "name": "Rating",
               "type": "Edm.Double",
               "searchable": false,
               "filterable": true,
               "retrievable": true,
               "sortable": true,
               "facetable": true
           },
           {
               "name": "Address", 
               "type": "Edm.ComplexType",
               "fields": [
                   {
                       "name": "StreetAddress", "type": "Edm.String",
                       "searchable": true, "filterable": false, "retrievable": true, "sortable": false, "facetable": false
                   },
                   {
                       "name": "City", "type": "Edm.String",
                       "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                   },
                   {
                       "name": "StateProvince", "type": "Edm.String",
                       "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                   },
                   {
                       "name": "PostalCode", "type": "Edm.String",
                       "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                   },
                   {
                       "name": "Country", "type": "Edm.String",
                       "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                   }
               ]
           },
           {
               "name": "Location",
               "type": "Edm.GeographyPoint",
               "searchable": false, 
               "filterable": true, 
               "retrievable": true, 
               "sortable": true, 
               "facetable": false
           }
       ],
       "vectorSearch": {
           "algorithms": [
               {
                   "name": "hnsw-vector-config",
                   "kind": "hnsw",
                   "hnswParameters": 
                   {
                       "m": 4,
                       "efConstruction": 400,
                       "efSearch": 500,
                       "metric": "cosine"
                   }
               },
               {
                   "name": "eknn-vector-config",
                   "kind": "exhaustiveKnn",
                   "exhaustiveKnnParameters": 
                   {
                       "metric": "cosine"
                   }
               }
           ],
           "profiles": [      
               {
                   "name": "my-vector-profile",
                   "algorithm": "hnsw-vector-config"
               }
         ]
       },
       "semantic": {
           "configurations": [
               {
                   "name": "semantic-config",
                   "prioritizedFields": {
                       "titleField": {
                           "fieldName": "HotelName"
                       },
                       "prioritizedContentFields": [
                           { "fieldName": "Description" }
                       ],
                       "prioritizedKeywordsFields": [
                           { "fieldName": "Category" }
                       ]
                   }
               }
           ]
       }
   }
   

3. Välj Skicka begäran. Du bör ha ett HTTP/1.1 201 Created svar.

   Svarstexten bör innehålla JSON-representationen av indexschemat.

   {
       "@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"
               }
               ]
           }
           }
       ]
       }
   }
   

Viktiga lärdomar om REST API för att skapa index :

• Samlingen fields innehåller ett obligatoriskt nyckelfält och text- och vektorfält (till exempel Description och DescriptionVector) för text- och vektorsökning. Genom att samlokalisera vektor- och icke-bevektorfält i samma index kan du använda hybridfrågor. Du kan till exempel kombinera filter, textsökning med semantisk rangordning och vektorer i en enda frågeåtgärd.

• Vektorfält måste vara en av de EDM-datatyper som används för vektorer, till exempel type: Collection(Edm.Single). Vektorfält har också dimensions och vectorSearchProfile de egenskaper.

• Avsnittet vectorSearch är en matris med algoritmkonfigurationer och profiler för ungefärlig närmaste granne (ANN). Algoritmer som stöds är hierarkisk navigeringsbar liten värld och fullständig K-Närmaste granne. Mer information finns i Relevansbedömning i vektorsökning.

• Konfigurationen (valfritt) semantic gör det möjligt att ändra rangordning av sökresultat. Du kan ändra rangordningen av resultat i frågor av typen semantic för strängfält som anges i konfigurationen. Mer information finns i Översikt över semantisk rangordning.

Ladda upp dokument

Att skapa och läsa in indexet är separata steg. Du skapade indexschemat i föregående steg. Nu måste du läsa in dokument i indexet.

I Azure AI Search innehåller indexet alla sökbara data och frågor som körs i söktjänsten. För REST-anrop tillhandahålls data som JSON-dokument. Använd Documents – Index REST API för den här uppgiften. URI:n utökas till att omfatta docs samlingen och åtgärden index .

1. Formulera en begäran om att ladda upp dokument för att ladda upp dokument till indexet hotels-vector-quickstart i söktjänsten.

   ### Upload documents
   POST {{baseUrl}}/indexes/hotels-quickstart-vectors/docs/index?api-version=2024-07-01  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   {
       "value": [
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "1",
               "HotelName": "Stay-Kay City Hotel",
               "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
               "DescriptionVector": 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               "Category": "Boutique",
               "Tags": [
                   "view",
                   "air conditioning",
                   "concierge"
               ],
               "ParkingIncluded": false,
               "LastRenovationDate": "2022-01-18T00:00:00Z",
               "Rating": 3.60,
               "Address": {
                   "StreetAddress": "677 5th Ave",
                   "City": "New York",
                   "StateProvince": "NY",
                   "PostalCode": "10022",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       -73.975403,
                       40.760586
                   ]
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "2",
               "HotelName": "Old Century Hotel",
               "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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               "Category": "Boutique",
               "Tags": [
                   "pool",
                   "free wifi",
                   "air conditioning",
                   "concierge"
               ],
               "ParkingIncluded": false,
               "LastRenovationDate": "2019-02-18T00:00:00Z",
               "Rating": 3.60,
               "Address": {
                   "StreetAddress": "140 University Town Center Dr",
                   "City": "Sarasota",
                   "StateProvince": "FL",
                   "PostalCode": "34243",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       -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. Välj Skicka begäran. Du bör ha ett HTTP/1.1 200 OK svar. Svarstexten bör innehålla JSON-representationen av sökdokumenten.

Viktiga lärdomar om begäran om Dokument – Index-REST API :

• Dokument i nyttolasten består av fält som definierats i indexschemat.

• Vektorfält innehåller flyttalsvärden. Dimensionsattributet har minst 2 och högst 4096 flyttalsvärden vardera. Den här snabbstartsguiden anger dimensionsattributet till 1 536 eftersom det är storleken på embedningar som genereras av Azure, OpenAI-modellen text-embedding-3-small.

Köra frågor

Nu när dokument har lästs in kan du köra vektorfrågor mot dem med hjälp av Dokument - Sök inlägg (REST).

I nästa avsnitt kör vi frågor mot indexet hotels-vector-quickstart . Frågorna omfattar:

• Enkel vektorsökning
• Enkel vektorsökning med filter
• Hybridsökning
• Semantisk hybridsökning

Exempelfrågorna baseras på två strängar:

• Söksträng: historic hotel walk to restaurants and shopping
• Vektorfrågesträng (vektoriserad i en matematisk representation): quintessential lodging near running trails, eateries, retail

Vektorfrågesträngen liknar söksträngen semantiskt, men den innehåller termer som inte finns i sökindexet. Om du gör en nyckelordssökning för quintessential lodging near running trails, eateries, retailär resultatet noll i en ren nyckelordssökning utan semantisk rangordning. Vi använder det här exemplet för att visa hur du kan få relevanta resultat med hjälp av vektorer, även om det inte finns några matchande termer.

Enkel vektorsökning

1. Formulera begäran. Förfrågan är en 1536 float-representation av typiska logi nära löparleder, matställen, butiker. Frågan söker efter DescriptionVector och returnerar k-5-resultat. Den använder den "uttömmande" åsidosättningsparametern för att utföra en fullständig genomsökning av indexet i stället för ANN. En fullständig sökning är användbar för små index.

   ### Run a single vector query
   POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
       Content-Type: application/json
       Authorization: Bearer {{token}}
   
       {
           "count": true,
           "select": "HotelId, HotelName, Description, Category, Tags",
           "vectorQueries": [
               {
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                   "k": 5,
                   "fields": "DescriptionVector",
                   "kind": "vector",
                   "exhaustive": true
               }
           ]
       }
   

   Viktiga insikter om Dokument – Sökpost REST API:

   • vectorQueries.vector är vektorfrågesträngen. Det är en vektorrepresentation av det perfekta boendet nära löparleder, restauranger, butiker, som är vektoriserad i 1 536 inbäddningar för denna begäran.

   • fields avgör vilka vektorfält som genomsöks.

   • kind vector inställd på innebär att frågesträngen är en vektor. Om kind har angetts till textskulle du behöva extra kapacitet (en vektoriserare) för att koda en läsbar textsträng till en vektor vid frågetillfället. Vectorizers utelämnas från den här snabbstarten för att hålla övningen enkel.

   • k anger antalet matchningar som ska returneras i svaret. En count parameter anger antalet matchningar som hittas i indexet. Att inkludera antal är bästa praxis för frågor, men det är mindre användbart för likhetssökning där algoritmen kan hitta en viss grad av likhet i nästan alla dokument.

2. Välj Skicka begäran. Du bör ha ett HTTP/1.1 200 OK svar. Svarstexten bör innehålla JSON-representationen av sökresultaten.

   Svaret för vektorekvivalenten av quintessential lodging near running trails, eateries, retail inkluderar k-5 resultat, trots att sökmotorn hittade 7 matchningar. De främsta resultaten anses vara de mest semantiskt lika frågan. Varje resultat ger en sökpoäng och fälten som anges i select. I en likhetssökning innehåller k svaret alltid resultat ordnade efter värdets likhetspoäng.

   {
     "@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"
         ]
       }
     ]
   }
   

Enkel vektorsökning med filter

Du kan lägga till filter, men filtren tillämpas på icke-bevektorinnehållet i ditt index. I det här exemplet gäller filtret för fältet Tags för att filtrera bort alla hotell som inte tillhandahåller kostnadsfritt Wi-Fi.

1. Formulera begäran. Det här är samma begäran som föregående, med en extra filter- och filterlägesparameter.

   ### Run a vector query with a filter
   POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
       Content-Type: application/json
       Authorization: Bearer {{token}}
   
       {
           "count": true,
           "select": "HotelId, HotelName, Category, Tags, Description",
           "filter": "Tags/any(tag: tag eq 'free wifi')",
           "vectorFilterMode": "postFilter",
           "vectorQueries": [
           {
               "vector": 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               "k": 5,
               "fields": "DescriptionVector",
               "kind": "vector",
               "exhaustive": true
           },
       ]
   }
   

2. Välj Skicka begäran. Du bör ha ett HTTP/1.1 200 OK svar. Svarstexten bör innehålla JSON-representationen av sökresultaten.

   Frågan var densamma som i föregående exempel på enkel vektorsökning, men den innehåller ett exkluderingsfilter efter bearbetning och returnerar endast de två hotell som har kostnadsfritt 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. Här är en annan fråga som använder ett geo-filter som begränsar resultaten till hotell inom 300 kilometer runt Washington D.C.

   ### Run a vector query with a geo filter
   POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
       Content-Type: application/json
       Authorization: Bearer {{token}}
   
       {
           "count": true,
           "select": "HotelId, HotelName, Description, Address/City, Address/StateProvince",
           "top": 5,
           "facets": [ "Address/StateProvince"],
           "filter": "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
           "vectorFilterMode": "postFilter",
           "vectorQueries": [
           {
               "vector": 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               "k": 5,
               "fields": "DescriptionVector",
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               "exhaustive": true
           },
       ]
   }
   

4. Läs svaret. Det är begränsat till två hotell nära Washington D.C, med en fasetteringsstruktur som tillhandahåller aggregeringen.

   "@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"
       }
     }
   ]
   

Hybridsökning

Hybridsökning består av nyckelordsfrågor och vektorfrågor i en enda sökbegäran. I det här exemplet körs vektorfrågan och fulltextsökning samtidigt:

• Söksträng: historic hotel walk to restaurants and shopping
• Vektorfrågesträng (vektoriserad i en matematisk representation): quintessential lodging near running trails, eateries, retail

1. Formulera hybridfrågan.

   ### Run a hybrid query
   POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
       Content-Type: application/json
       Authorization: Bearer {{token}}
   
   {
       "count": true,
       "search": "historic hotel walk to restaurants and shopping",
       "select": "HotelName, Description",
       "top": 7,
       "vectorQueries": [
           {
               "vector": 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               "k": 5,
               "fields": "DescriptionVector",
               "kind": "vector",
               "exhaustive": true
           }
       ]
   }
   

2. Välj Skicka begäran. Du bör ha ett HTTP/1.1 200 OK svar. Svarstexten bör innehålla JSON-representationen av sökresultaten.

Eftersom det här är en hybridfråga rangordnas resultaten efter Reciprocal Rank Fusion (RRF). Observera att @search.score värdena har en annan grund och är lika mindre värden. RRF utvärderar sökpoäng för flera sökresultat, tar inversen och sammanfogar och sorterar sedan de kombinerade resultaten. Antalet top resultat returneras.

Granska svaret, som består av de översta k-5 matchar av 7 matchande dokument i indexet:

{
  "@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."
    }
  ]
}

Eftersom RRF sammanfogar resultat hjälper det att granska indata individuellt.

Följande resultat kommer från fulltextdelen av frågan: historisk hotellvandring till restauranger och shopping. I den fullständiga textfrågan är de tre främsta resultaten Sublime Palace Hotel, Stay-Kay City Hotel och Luxury Lion Resort. Sublime Palace Hotel har en starkare BM25 relevanspoäng. I den sammansvetsade frågan är endast två av dessa matchningar i topp 3 och den andra matchen (Stay-Kay City Hotel) visas inte alls i topp 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."
    },
    ...
  ]

I vektordelsfrågan (utmärkt logi nära löparspår, restauranger, detaljhandel) är en annan sträng, därför bör vi förvänta oss olika resultat. Den här frågan returnerar Nordick's Valley Motel, Luxury Lion Resort och Sublime Palace Hotel som de tre bästa matcherna. I den sammansvetsade frågan är dessa resultat i topp 3, men i omvänd ordning.

  "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."
    },
   ...
  ]

Semantisk hybridsökning

Här är den sista frågan i samlingen. Den här hybridfrågan lägger till L2-semantisk rangordning som tillämpar maskinläsningsförståelse över L1-rankade resultat, vilket främjar mer relevanta matchningar högst upp.

1. Formulera begäran.

   ### Run a hybrid query with semantic reranking
   POST {{baseUrl}}/indexes/hotels-vector-quickstart/docs/search?api-version=2024-07-01  HTTP/1.1
       Content-Type: application/json
       Authorization: Bearer {{token}}
   
   {
       "count": true,
       "search": "historic hotel walk to restaurants and shopping",
       "select": "HotelName, Description",
       "queryType": "semantic",
       "semanticConfiguration": "semantic-config",
       "top": 5,
       "vectorQueries": [
           {
               "vector": 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               "k": 5,
               "fields": "DescriptionVector",
               "kind": "vector",
               "exhaustive": true
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       ]
   }
   

2. Välj Skicka begäran. Du bör ha ett HTTP/1.1 200 OK svar. Svarstexten bör innehålla JSON-representationen av sökresultaten.

Granska svaret, som består av en semantisk omrankning av RRF-rankade resultat för hybridfrågan. Semantisk rangordning fungerar utanför textinmatningar. I en text- eller hybridfråga är den här inmatningen textdelen av frågan (historisk hotellvandring till restauranger och shopping). Om du vill använda semantisk rangordning i en ren vektorfråga, till exempel det första exemplet, eller för att uttryckligen styra texten som används för semantisk rangordning, anger du en semanticQuery-sträng.

Efter semantisk reranking flyttar Swirling Currents Hotel med sin referens till gångåtkomst till shopping, restauranger, underhållning och stadens centrum till topplaceringen. Maskinförståelsemodellen främjar gångåtkomst till shopping och mat som en närmare match för att gå till restauranger och shopping.

Innan den semantiska omrankningen var Sublime Palace nummer ett på grund av sin hänvisning till både gångavstånd till platser och landmärken i staden och att vara omgiven av den extraordinära skönheten i kyrkor, byggnader, butiker och monument.

{
  "@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"
    }
  ]
}

Tips/Råd

Semantiskt rankade resultat kan innehålla mer information, inklusive semantiska svar, bildtexter och markeringar. Om du lägger till fler parametrar i begäran skapas den extra informationen. Mer information finns i Konfigurera en semantisk fråga.

Rensa

När du arbetar i din egen prenumeration kan det dock vara klokt att i slutet av ett projekt kontrollera om du fortfarande behöver de resurser som du skapade. Resurser som fortsätter att köras kostar pengar. Du kan ta bort enstaka resurser eller hela resursgruppen om du vill ta bort alla resurser.

Du kan hitta och hantera resurser i Azure Portal med hjälp av länken Alla resurser eller Resursgrupper i den vänstra rutan.

Om du vill behålla söktjänsten, men ta bort indexet och dokumenten, kan du använda DELETE kommandot i REST-klienten. Det här kommandot (i slutet av az-search-vector-quickstart.rest filen) tar bort indexet hotels-vector-quickstart :

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

Nästa steg

Som nästa steg rekommenderar vi att du lär dig hur du anropar REST API-anrop utan API-nycklar.

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