Schnellstart: Vektorsuche

In dieser Schnellstartanleitung verwenden Sie ein Jupyter-Notizbuch zum Erstellen, Laden und Abfragen von Vektoren. Die Codebeispiele führen diese Vorgänge mithilfe der Azure AI Search-Clientbibliothek aus. Die Bibliothek bietet eine Abstraktion über die REST-API für den Zugriff auf Indexvorgänge wie Datenaufnahme, Suchvorgänge und Indexverwaltungsvorgänge.

In der Azure KI-Suche verfügt ein Vektorspeicher über ein Indexschema, das Vektor- und Nichtvektorfelder definiert, eine Vektorsuchkonfiguration für Algorithmen, die den Einbettungsraum erstellen, und Einstellungen für Vektorfelddefinitionen, die in Abfrageanforderungen verwendet werden. Die REST-API Create Index erstellt den Vektorspeicher.

Hinweis

In dieser Schnellstartanleitung wird der Vektorisierungsschritt weggelassen und Inlineeinbettungen bereitgestellt. Wenn Sie die integrierte Datensegmentierung und -vektorisierung für Ihre eigenen Inhalte hinzufügen möchten, testen Sie den Assistenten zum Importieren und Vektorisieren von Daten für eine exemplarische End-to-End-Vorgehensweise.

Voraussetzungen

• Ein Azure-Konto mit einem aktiven Abonnement. Kostenlos ein Konto erstellen.

• Ein Azure KI-Suche-Dienst. Erstellen Sie einen Dienst , oder suchen Sie einen vorhandenen Dienst in Ihrem aktuellen Abonnement.

  • Sie können einen kostenlosen Suchdienst für die meisten dieser Schnellstartanleitungen verwenden, aber wir empfehlen die Stufe "Einfach" oder höher für größere Datendateien.
  • Zum Ausführen des Abfragebeispiels, das die semantische Umrankung aufruft, muss sich Ihr Suchdienst auf der Ebene "Basic" oder höher befinden, wobei der semantische Rangfolger aktiviert ist.

• Visual Studio Code mit der Python-Erweiterung und dem Jupyter-Paket.

• Git , um das Repository mit dem Jupyter-Notizbuch und anderen verwandten Dateien zu klonen.

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Abrufen von Ressourceninformationen

Anforderungen an den Suchendpunkt müssen authentifiziert und autorisiert werden. Obwohl es möglich ist, API-Schlüssel oder Rollen für diese Aufgabe zu verwenden, empfehlen wir die Verwendung einer schlüssellosen Verbindung über microsoft Entra ID.

Dieser Schnellstart verwendet DefaultAzureCredential, wodurch die Authentifizierung in Entwicklungs- und Produktionsszenarien vereinfacht wird. Für Produktionsszenarien haben Sie jedoch möglicherweise komplexere Anforderungen, die einen anderen Ansatz erfordern. Sehen Sie Authentication von Python-Apps für Azure-Dienste mit dem Azure SDK für Python, um alle Ihre Optionen zu verstehen.

Klonen des Codes und der Setupumgebung

1. Klonen Sie das Repository, das den Code für diese Schnellstartanleitung enthält.

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

   Dieses Repository enthält Python-Codebeispiele für mehrere Artikel in einem separaten Unterordner.

2. Öffnen Sie in Visual Studio Code den Unterordner Quickstart-Vector-Search.

   Dieser Ordner enthält drei Dateien:

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

3. Benennen Sie die Datei sample.env in .env um und ändern Sie die Werte in der Datei .env.

   Verwenden Sie die Suchdienst-URL als die AZURE_SEARCH_ENDPOINT. Sie finden die URL im Azure-Portal. Wechseln Sie zu Ihrem Azure AI Search-Dienst, suchen Sie auf der Seite „Übersicht“ nach dem URL-Feld. Ein Beispiel für einen Endpunkt ist https://mydemo.search.windows.net.

   Wählen Sie schließlich einen neuen AZURE_SEARCH_INDEX_NAME Namen aus, oder verwenden Sie die in der Datei bereitgestellte.

4. Arbeiten Sie in Visual Studio Code in einer Umgebung. Wählen Sie im Menü "Ansicht " die Option "Terminal" aus, oder wählen Sie STRG+` aus.

5. Führen Sie die folgenden Befehle im Terminal aus:

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

   Hinweis

   Dabei wird davon ausgegangen, dass Sie Git Bash in Ihrem Terminal verwenden und Windows nutzen. Wenn Sie eine andere Shell und/oder ein anderes Betriebssystem verwenden, passen Sie diese Anweisungen für Ihre spezifische Umgebung an.

   Wenn Sie dazu aufgefordert werden, lassen Sie Visual Studio Code die Verwendung der neuen Umgebung zu.

   Der where python-Befehl überprüft, ob Sie aus der virtuellen Umgebung arbeiten, indem er python.exe im Quickstart-Vector-Search\.venv\-Ordner und an anderen Speicherorten aus dem Verzeichnis Ihres Computers auflistet.

6. Installieren Sie die erforderlichen Bibliotheken, indem Sie den folgenden Befehl ausführen:

   pip install requirements.txt
   

7. Öffnen Sie in Visual Studio Code die Datei vector-search-quickstart.ipynb.

   Hinweis

   Wenn Sie zum ersten Mal ein Jupyter-Notizbuch (IPYnb) in Visual Studio Code verwendet haben, werden Sie aufgefordert, den Jupyter-Notizbuch-Kernel und möglicherweise andere Tools zu installieren. Wählen Sie aus, die vorgeschlagenen Tools zu installieren, um mit dieser Schnellstartanleitung fortzufahren.

8. Suchen Sie die Zelle unten mit dem Titel "Pakete installieren und Variablen festlegen", und wählen Sie links neben der Zelle die Schaltfläche "Execute Cell" (STRG+ALT+EINGABETASTE) aus (die wie eine typische Schaltfläche "Ausführen" aussieht). Durch das Ausführen der Zelle werden die Umgebungsvariablen geladen, „DefaultAzureCredential“ erstellt und Werte in die Ausgabe geschrieben, um zu bestätigen, dass die Abhängigkeiten und .env des Notebooks ordnungsgemäß eingerichtet sind.

   # 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 
   

   Wenn Sie diese Zelle ausführen, wird die folgende Ausgabe erzeugt.

   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
   ...
   

   Es gibt viele weitere Pakete, die Sie in einem scrollbaren Element anzeigen können (siehe die Nachricht unter den Zellergebnissen).

Erstellen des Vektorindexes

Der Code in der vector-search-quickstart.ipynb Bibliothek verwendet mehrere Methoden aus der azure.search.documents Bibliothek, um den Vektorindex und durchsuchbare Felder zu erstellen.

1. Suchen Sie die Zelle unten mit dem Titel "Index erstellen" und führen Sie die Zelle aus, um den Index zu erstellen.

   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')
   

   Wenn der Index erfolgreich erstellt wurde, wird das folgende Ergebnis unterhalb der Zelle angezeigt:

   vector-search-quickstart created
   

   Wichtige Erkenntnisse beim Erstellen von Vektorindex mit dem azure.search.documents:

   • Sie definieren einen Index, indem Sie eine Liste von Feldern erstellen. Jedes Feld wird mit einer Hilfsmethode erstellt, die den Feldtyp und die zugehörigen Einstellungen definiert.

   • Dieser spezielle Index unterstützt mehrere Suchfunktionen, z. B.:

     • Volltext-Schlüsselwortsuche (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))
     • Vektorsuche (ermöglicht die Hybridsuche durch Die Zuordnung von Vektor- und Nichtvektorfeldern) (DescriptionVector)
     • Semantische Suche (semantic_search=SemanticSearch(configurations=[semantic_config]))
     • Facettierte Suche (facetable=True)
     • Semantische Suche (semantic_search=SemanticSearch(configurations=[semantic_config]))
     • Geo-räumliche Suche (Location Feld ist GeographyPoint)
     • Filtern, Sortieren (viele Felder, die gefiltert und sortiert werden können)

Hochladen von Dokumenten

Das Erstellen und Laden des Indexes sind separate Schritte. Sie haben das Indexschema im vorherigen Schritt erstellt. Jetzt müssen Sie Dokumente in den Index laden.

In Azure AI Search speichert der Index alle durchsuchbaren Inhalte, während die Suchmaschine Abfragen für diesen Index ausführt.

1. Suchen Sie die Zelle unter dem Abschnitt mit dem Titel "Dokumenten-Payload erstellen" und führen Sie die Zelle aus. Diese Zelle enthält den folgenden Code (aus Platzgründen abgeschnitten):

      # 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>
      ]
   

   Diese Zelle lädt eine Variable namens documents mit einem JSON-Objekt, das jedes Dokument beschreibt, zusammen mit der vektorisierten Form der Beschreibung des Dokuments. Dieser Vektor ermöglicht die Ähnlichkeitssuche, bei der der Abgleich auf Bedeutung und nicht auf exakten Schlüsselwörtern basiert.

   Von Bedeutung

   Der Code in diesem Beispiel kann nicht ausgeführt werden. Einige Zeichen oder Zeilen werden aus Platzgründen entfernt. Führen Sie stattdessen den Code im Jupyter-Notizbuch aus.

2. Suchen Sie die Zelle unten mit dem Titel "Dokumente hochladen", und führen Sie die Zelle aus. Diese Zelle enthält den folgenden Code (aus Platzgründen abgeschnitten):

   # 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)
   

   Dadurch wird eine Instanz des Suchclients durch Aufrufen des SearchClient() Konstruktors erstellt und anschließend die upload_documents() Methode für das Objekt aufgerufen.

   Nachdem Sie die Zelle ausgeführt haben, wird der Status jedes Dokuments darunter angezeigt:

   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
   

   Wichtige Hinweise zu der upload_documents() Methode und diesem Beispiel:

   • Ihr Code interagiert mit einem bestimmten Suchindex, der in Ihrem Azure AI Search-Dienst gehostet wird, über das SearchClient, welches das Hauptobjekt ist, das vom azure-search-documents Paket bereitgestellt wird. Der SearchClient Zugriff auf Indexvorgänge ermöglicht Folgendes:

     • Datenaufnahme - upload_documents(), merge_documents(), usw delete_documents().
     • Suchvorgänge - search(), , autocomplete()suggest()
     • Indexverwaltungsvorgänge

   • Vektorfelder enthalten Gleitkommawerte. Das Dimensions-Attribut weist mindestens 2 und maximal 4096 Gleitkommawerte auf. In dieser Schnellstartanleitung wird das Dimensionsattribut auf 1.536 festgelegt, weil dies die Größe der Einbettungen ist, die vom Azure OpenAI-Modell text-embedding-ada-002 generiert werden.

Ausführen von Abfragen

Nachdem die Dokumente geladen wurden, können Sie Vektorabfragen für sie ausführen, indem Sie search_client.search() aufrufen und ein VectorizedQuery-Objekt sowie die zurückzugebenden Felder, die Anzahl der Ergebnisse usw. übergeben.

In den nächsten Abschnitten führen wir Abfragen für den hotels-vector-quickstart-Index aus. Zu den Abfragen gehören:

• Einzelvektorsuche
• Einzelvektorsuche mit Filter
• Hybridsuche
• Semantische Hybridsuche mit Filter

Erstellen der Vektorabfragezeichenfolge

Die Beispielvektorabfragen basieren auf zwei Zeichenfolgen:

• Suchzeichenfolge: historic hotel walk to restaurants and shopping
• Vektorabfragezeichenfolge (in eine mathematische Darstellung vektorisiert): quintessential lodging near running trails, eateries, retail

Die Vektorabfragezeichenfolge ähnelt semantisch der Suchzeichenfolge, enthält jedoch Begriffe, die nicht im Suchindex enthalten sind. Wenn Sie eine Schlüsselwortsuche nach quintessential lodging near running trails, eateries, retail durchführen, erhalten Sie das Ergebnis null. Wir verwenden dieses Beispiel, um zu zeigen, wie Sie auch dann relevante Ergebnisse erhalten können, wenn keine übereinstimmenden Begriffe vorhanden sind.

• Suchen Sie die Zelle unten mit dem Titel "Erstellen der Vektorabfragezeichenfolge", und führen Sie die Zelle aus. Dadurch wird die vector Variable mit den vektorisierten Abfragedaten geladen, die zum Ausführen aller Suchvorgänge in den nächsten Abschnitten erforderlich sind.

Einzelvektorsuche

Im ersten Beispiel wird ein einfaches Szenario veranschaulicht, in dem Sie Dokumentbeschreibungen finden möchten, die eng mit der Suchzeichenfolge übereinstimmen.

• Finden Sie die unter dem Abschnitt mit dem Titel "Einzelvektorsuche" liegende Zelle und führen Sie die Zelle aus. Dieser Block enthält die Anforderung zum Abfragen des Suchindexes.

  # 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.")
  

  Diese Vektorabfrage wird aus Platzgründen gekürzt. vectorQueries.vector enthält den vektorisierten Text der Abfrageeingabe, fields bestimmt, welche Vektorfelder durchsucht werden, und k gibt die Anzahl der nächsten Nachbarelemente an, die zurückgegeben werden sollen.

  Die Vektorabfragezeichenfolge lautet quintessential lodging near running trails, eateries, retail und wird für diese Abfrage in 1.536 Einbettungen vektorisiert.

  Die Antwort für das Vektoräquivalent von quintessential lodging near running trails, eateries, retail umfasst sieben Ergebnisse, der Code gibt jedoch top=5 an, dass nur die ersten fünf Ergebnisse zurückgegeben werden. Darüber hinaus werden nur die durch die select Angabe angegebenen Felder zurückgegeben.

  search_client.search() gibt ein dict-like -Objekt zurück. Jedes Ergebnis enthält eine Suchbewertung, auf die mithilfe von score = result.get("@search.score", "N/A") zugegriffen werden kann. In diesem Beispiel ist dies zwar nicht zu sehen, bei einer Ähnlichkeitssuche enthält die Antwort aber immer k-Ergebnisse, die nach der Wertähnlichkeitsbewertung sortiert sind.

  Nachdem Sie die Zelle ausgeführt haben, wird der Status jedes Dokuments darunter angezeigt:

  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
  

Einzelvektorsuche mit Filter

Sie können Filter hinzufügen, aber die Filter werden auf die Nicht-Vektorinhalte in Ihrem Index angewendet. In diesem Beispiel gilt der Filter für das Feld Tags und filtert alle Hotels heraus, die kein kostenloses WLAN bereitstellen.

1. Suchen Sie nach der Zelle unter dem Abschnitt mit dem Titel „Einzelvektorsuche mit Filter“, und führen Sie die Zelle aus. Diese Zelle enthält die Anforderung, den Suchindex abzufragen.

   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.")
   

   Nachdem Sie die Zelle ausgeführt haben, wird der Status jedes Dokuments darunter angezeigt:

   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']
   

   Die Abfrage ist dieselbe wie im vorigen Beispiel für die Einzelvektorsuche, enthält jedoch einen Ausschlussfilter für die Nachbearbeitung und gibt nur die drei Hotels zurück, die über kostenloses WLAN verfügen.

2. Im nächsten Filterbeispiel wird ein Geofilter verwendet. Führen Sie die Zelle im Abschnitt mit dem Titel "Vektorabfrage mit einem Geofilter" aus. Dieser Block enthält die Anforderung zum Abfragen des Suchindexes.

   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.")
   

   Die Abfrage war identisch mit dem vorherigen Beispiel für die Einzelvektorsuche, enthält aber einen Ausschlussfilter nach der Verarbeitung und gibt nur die beiden Hotels innerhalb von 300 Kilometern zurück.

   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.
   

Hybridsuche

Die Hybridsuche besteht aus Stichwortabfragen und Vektorabfragen in einer einzelnen Suchanforderung. In diesem Beispiel wird die Vektorabfrage und die Volltextsuche gleichzeitig ausgeführt:

• Suchzeichenfolge: historic hotel walk to restaurants and shopping

• Vektorabfragezeichenfolge (in eine mathematische Darstellung vektorisiert): quintessential lodging near running trails, eateries, retail

• Suchen Sie nach der Zelle unter dem Abschnitt mit dem Titel „Hybridsuche“, und führen Sie die Zelle aus. Dieser Block enthält die Anforderung zum Abfragen des Suchindexes.

  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.")    
  

  Überprüfen Sie die Antwort:

  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']
  

  Da reziproke Rangfusion (RRF) Ergebnisse zusammenführt, hilft es, die Eingaben zu überprüfen. Die folgenden Ergebnisse stammen nur aus der Volltextabfrage. Die beiden besten Ergebnisse lauten „Sublime Palace Hotel“ und „History Lion Resort“. Das Sublime Palace Hotel hat eine höhere BM25-Relevanzbewertung.

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

  In der reinen Vektorabfrage, in der HNSW für die Suche nach Übereinstimmungen verwendet wird, fällt das Sublime Palace Hotel auf die vierte Position zurück. Das Historic Lion Resort, das bei der Volltextsuche auf Platz zwei und bei der Vektorsuche auf Platz drei lag, weist nicht dieselbe Schwankungsbreite auf und erscheint daher in einem homogenisierten Resultset als Spitzenreiter.

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

Semantische Hybridsuche mit einem Filter

Dies ist die letzte Abfrage in der Sammlung. Diese Hybridabfrage mit semantischer Rangfolge wird gefiltert, um nur die Hotels innerhalb eines Radius von 500 Kilometern von Washington D.C anzuzeigen. Sie können vectorFilterMode auf NULL festlegen, was der Standardeinstellung entspricht (preFilter für neuere Indizes und postFilter für ältere).

• Finden Sie die Zelle unter dem Abschnitt mit dem Titel "Semantische Hybridsuche" und führen Sie sie aus. Dieser Codeblock enthält die Anforderung zum Abfragen des Suchindexes.

  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.")
  

  Überprüfen Sie die Ausgabe unterhalb der Zelle. Die Antwort enthält drei Hotels, die nach Standort gefiltert, nachStateProvince facettiert und semantisch neu sortiert werden, um Ergebnisse höherzustufen, die der Suchzeichenfolgenabfrage (historic hotel walk to restaurants and shopping) am nächsten kommen.

  Das Swirling Currents Hotel ist jetzt an der Spitze. Ohne semantische Rangfolge ist Nordick‘s Valley Motel die Nummer eins. Mit semantischer Rangfolge erkennen die Machine Comprehension-Modelle, dass historic für „Hotel, in fußläufiger Entfernung zu Restaurants und Einkaufsmöglichkeiten" gilt.

  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
  

  Wichtige Erkenntnisse:

  • Die Vektorsuche wird durch die Eigenschaft vectors.value angegeben. Die Schlüsselwortsuche wird durch die Eigenschaft search angegeben.

  • Bei einer Hybridsuche können Sie die Vektorsuche über Schlüsselwörter in die Volltextsuche integrieren. Filter, Rechtschreibprüfung und semantische Rangfolge betreffen nur Textinhalte, nicht Vektoren. In dieser abschließenden Abfrage gibt es keinen semantischen Wert für answer, weil das System keinen ausreichend starken generiert hat.

  • Die eigentlichen Ergebnisse umfassen weitere Details, einschließlich semantischer Beschriftungen und Hervorhebungen. Die Ergebnisse wurden zur besseren Lesbarkeit abgeändert. Führen Sie die Anforderung im REST-Client aus, um die vollständige Struktur der Antwort zu erhalten.

Bereinigung

Wenn Sie in Ihrem eigenen Abonnement arbeiten, sollten Sie sich am Ende eines Projekts überlegen, ob Sie die erstellten Ressourcen noch benötigen. Ressourcen, die weiterhin ausgeführt werden, können Geld kosten. Sie können entweder einzelne Ressourcen oder aber die Ressourcengruppe löschen, um den gesamten Ressourcensatz zu entfernen.

Ressourcen können im Azure-Portal über den Link Alle Ressourcen oder Ressourcengruppen im linken Navigationsbereich gesucht und verwaltet werden.

Wenn Sie den Suchdienst beibehalten möchten, aber den Index und die Dokumente löschen möchten, können Sie die Methode des SearchIndexClientdelete_index() Objekts verwenden. Suchen Sie die Zelle unten mit dem Titel "Aufräumen", und führen Sie die Zelle aus, wenn Sie den hotels-vector-quickstart Index löschen möchten:

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

Nächste Schritte

• Überprüfen des Repositorys von Codebeispielen für Vektorsuchfunktionen in Azure AI Search für Python
• Überprüfen Sie die anderen Python- und Azure AI Search-Codebeispiele im Repository azure-search-python-samples

In dieser Schnellstartanleitung verwenden Sie die AZURE AI Search-REST-APIs zum Erstellen, Laden und Abfragen von Vektoren.

In Azure KI-Suche verfügt ein Vektorspeicher über ein Indexschema, das Vektor- und Nicht-Vektorfelder definiert, eine Vektorsuchkonfiguration für Algorithmen, die den Einbettungsraum erstellen, und Einstellungen für Vektorfelddefinitionen, die in Abfrageanforderungen verwendet werden. Die REST-API Create Index erstellt den Vektorspeicher.

Hinweis

In dieser Schnellstartanleitung wird der Vektorisierungsschritt weggelassen und Inlineeinbettungen bereitgestellt. Wenn Sie die integrierte Datensegmentierung und -vektorisierung für Ihre eigenen Inhalte hinzufügen möchten, testen Sie den Assistenten zum Importieren und Vektorisieren von Daten für eine exemplarische End-to-End-Vorgehensweise.

Voraussetzungen

• Ein Azure-Konto mit einem aktiven Abonnement. Kostenlos ein Konto erstellen.

• Ein Azure KI-Suche-Dienst. Erstellen Sie einen Dienst , oder suchen Sie einen vorhandenen Dienst in Ihrem aktuellen Abonnement.

  • Sie können einen kostenlosen Suchdienst für die meisten dieser Schnellstartanleitungen verwenden, aber wir empfehlen die Stufe "Einfach" oder höher für größere Datendateien.
  • Zum Ausführen des Abfragebeispiels, das die semantische Umrankung aufruft, muss sich Ihr Suchdienst auf der Ebene "Basic" oder höher befinden, wobei der semantische Rangfolger aktiviert ist.

• Visual Studio Code mit einem REST-Client.

---

Abrufen von Ressourceninformationen

Anforderungen an den Suchendpunkt müssen authentifiziert und autorisiert werden. Sie können für diese Aufgabe API-Schlüssel oder Rollen verwenden. Wir empfehlen, eine schlüssellose Verbindung über Microsoft Entra ID zu verwenden.

Wählen Sie die Registerkarte aus, die Ihrer bevorzugten Authentifizierungsmethode entspricht. Verwenden Sie dieselbe Methode für alle Anforderungen in dieser Schnellstartanleitung.

Microsoft Entra-ID

1. Melden Sie sich beim Azure-Portal an, und suchen Sie Ihren Suchdienst.

2. Sie finden die URL auf der Startseite Übersicht. Ein Beispiel für einen Endpunkt ist https://mydemo.search.windows.net.

   

3. Führen Sie die Schritte im Schnellstart zum Herstellen einer Verbindung ohne Schlüssel aus, um Ihr Microsoft Entra-Token zu erhalten.

   Sie erhalten das Token, wenn Sie den Befehl az account get-access-token in Schritt 3 der vorherigen Schnellstartanleitung ausführen.

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

API-Schlüssel

1. Melden Sie sich beim Azure-Portal an, und suchen Sie Ihren Suchdienst.

2. Sie finden die URL auf der Startseite Übersicht. Ein Beispiel für einen Endpunkt ist https://mydemo.search.windows.net.

   

3. Wählen Sie Einstellungen>Schlüssel aus. Es muss entweder API-Schlüssel oder Beide aktiviert sein. Admin-API-Schlüsselwerden verwendet, um Objekte hinzuzufügen, zu ändern und zu löschen. Es gibt zwei austauschbare Adminschlüssel. Kopieren Sie einen der beiden Schlüssel.

   

Erstellen oder Herunterladen der Codedatei

Sie verwenden eine .rest- oder .http-Datei, um alle Anforderungen in dieser Schnellstartanleitung auszuführen. Sie können die REST-Datei herunterladen, die den Code für diese Schnellstartanleitung enthält, oder Sie können eine neue Datei in Visual Studio Code erstellen und den Code hineinkopieren.

1. Erstellen Sie in Visual Studio Code eine neue Datei mit der Erweiterung .rest oder .http. Beispiel: az-search-vector-quickstart.rest. Kopieren Sie den unformatierten Inhalt der Datei Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest in diese neue Datei.

2. Ersetzen Sie oben in der Datei den Platzhalterwert für @baseUrl durch Ihre Suchdienst-URL. Anweisungen zum Suchen Ihrer Suchdienst-URL finden Sie im Abschnitt Abrufen von Ressourceninformationen.

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

3. Ersetzen Sie oben in der Datei den Platzhalterwert für die Authentifizierung. Anweisungen zum Abrufen Ihres Microsoft Entra-Tokens oder API-Schlüssels finden Sie im Abschnitt Abrufen von Ressourceninformationen.

   Für die empfohlene schlüssellose Authentifizierung mit Microsoft Entra ID müssen Sie @apiKey durch die @token-Variable ersetzen.

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

   Wenn Sie lieber einen API-Schlüssel verwenden möchten, ersetzen Sie @apiKey durch den Schlüssel, den Sie aus dem Azure-Portal kopiert haben.

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

4. Für die empfohlene schlüssellose Authentifizierung mit Microsoft Entra ID müssen Sie api-key: {{apiKey}} durch Authorization: Bearer {{token}} in den Anforderungsheadern ersetzen. Ersetzen Sie alle Instanzen von api-key: {{apiKey}}, die Sie in der Datei finden.

Erstellen eines Vektorindexes

Sie verwenden die REST-API Create Index, um einen Vektorindex zu erstellen und die physischen Datenstrukturen in Ihrem Suchdienst einzurichten.

Das Indexschema in diesem Beispiel ist den Hotelinhalten entsprechend organisiert. Beispieldaten bestehen aus Vektor- und Nichtvektorbeschreibungen fiktiver Hotels. Dieses Schema enthält Konfigurationen für die Vektorindizierung und Abfragen sowie für die semantische Rangfolge.

1. Öffnen Sie in Visual Studio Code die az-search-vector-quickstart.rest-Datei, die Sie zuvor erstellt haben.

2. Suchen Sie den ### Create a new index-Codeblock in der Datei. Dieser Block enthält die Anforderung zum Erstellen des hotels-vector-quickstart-Indexes für Ihren Suchdienst.

   ### 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. Klicken Sie auf Anforderung senden. Sie sollten eine HTTP/1.1 201 Created-Antwort erhalten.

   Der Antworttext sollte die JSON-Darstellung des Indexschemas enthalten.

   {
       "@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"
               }
               ]
           }
           }
       ]
       }
   }
   

Wichtige Punkte zur REST-API Create Index:

• Die Sammlung fields enthält ein erforderliches Schlüsselfeld sowie Text-und Vektorfelder (z. B. Description und DescriptionVector) für die Text- und Vektorsuche. Die gemeinsame Zuordnung von Vektorfeldern und Nicht-Vektorfeldern in demselben Index ermöglicht Hybridabfragen. Beispielsweise können Sie Filter, Schlüsselwortsuche mit semantischer Rangfolge und Vektoren in einem einzelnen Abfragevorgang kombinieren.

• Vektorfelder müssen einen der für Vektoren verwendeten EDM-Datentypen aufweisen, z. B. type: Collection(Edm.Single). Vektorfelder haben auch dimensions- und vectorSearchProfile-Eigenschaften.

• Der Abschnitt vectorSearch umfasst ein Array von ANN-Algorithmuskonfigurationen (Approximate Nearest Neighbors) und Profilen. Zu den unterstützten Algorithmen gehören der HNSW- (Hierarchical Navigable Small World) und der erschöpfende k-nächste-Nachbarn-Algorithmus (k-NN). Weitere Informationen finden Sie unter Relevanzbewertung in der Vektorsuche.

• Die (optionale) semantic-Konfiguration ermöglicht die Neubewertung der Rangfolge von Suchergebnissen. Sie können eine Neubewertung der Rangfolge für Ergebnisse in Abfragen vom Typ semantic für Zeichenfolgenfelder durchführen, die in der Konfiguration angegeben sind. Weitere Informationen finden Sie unter Semantische Rangfolge.

Hochladen von Dokumenten

Das Erstellen und Laden des Indexes sind separate Schritte. Sie haben das Indexschema im vorherigen Schritt erstellt. Jetzt müssen Sie Dokumente in den Index laden.

In Azure KI-Suche enthält der Index alle durchsuchbaren Daten und Abfragen, die im Suchdienst ausgeführt werden. Bei REST-Aufrufen werden die Daten als JSON-Dokumente bereitgestellt. Verwenden Sie die REST-API „Documents – Index“ für diese Aufgabe. Der URI wird um die docs-Sammlung und den index-Vorgang erweitert.

1. Formulieren Sie eine Anforderung zum Hochladen von Dokumenten in den hotels-vector-quickstart Index Ihres Suchdiensts.

   ### 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. Klicken Sie auf Anforderung senden. Sie sollten eine HTTP/1.1 200 OK-Antwort erhalten. Der Antworttext sollte die JSON-Darstellung der Suchdokumente enthalten.

Wichtige Punkte zum Aufruf der REST-API „Documents – Index“:

• Dokumente in der Nutzlast bestehen aus Feldern, die im Indexschema definiert sind.

• Vektorfelder enthalten Gleitkommawerte. Das Dimensions-Attribut weist jeweils mindestens 2 und maximal 4096 Gleitkommawerte auf. Diese Schnellstartanleitung legt das Dimensions-Attribut auf 1.536 fest, da dies die Größe von Einbettungen ist, die vom Azure OpenAI-Text-Embedding-3-small-Modell generiert werden.

Ausführen von Abfragen

Nachdem Dokumente geladen wurden, können Sie Vektorabfragen für sie mithilfe von "Documents - Search Post (REST)" ausführen.

In den nächsten Abschnitten führen wir Abfragen für den hotels-vector-quickstart-Index aus. Zu den Abfragen gehören:

• Einzelvektorsuche
• Einzelvektorsuche mit Filter
• Hybridsuche
• Semantische Hybridsuche

Die Beispielabfragen basieren auf zwei Zeichenfolgen:

• Suchzeichenfolge: historic hotel walk to restaurants and shopping
• Vektorabfragezeichenfolge (in eine mathematische Darstellung vektorisiert): quintessential lodging near running trails, eateries, retail

Die Vektorabfragezeichenfolge ähnelt semantisch der Suchzeichenfolge, enthält jedoch Begriffe, die nicht im Suchindex enthalten sind. Wenn Sie eine Keyword-Suche nach quintessential lodging near running trails, eateries, retail durchführen, sind die Ergebnisse bei einer reinen Schlüsselwortsuche ohne semantische Rangfolge gleich null. Wir verwenden dieses Beispiel, um zu zeigen, wie Sie relevante Ergebnisse mithilfe von Vektoren erhalten können, auch wenn keine übereinstimmenden Ausdrücke vorhanden sind.

Einzelvektorsuche

1. Formulieren Sie die Anforderung. Die Abfrage ist eine 1536 Float-Darstellung von typischen Unterkünften in der Nähe von Laufpfaden, Restaurants und Einzelhandelsgeschäften. Die Abfrage sucht DescriptionVector und gibt k-5 Ergebnisse zurück.

   ### 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
               }
           ]
       }
   

   Wichtige Punkte zur Documents - Search Post-REST-API:

   • Dies vectorQueries.vector ist die Vektorabfragezeichenfolge. Es ist eine Vektordarstellung von typischen Unterkünften in der Nähe von Laufpfaden, Esslokalen und Einzelhandel, die in 1.536 Einbettungen für diese Abfrage vektorisiert werden.

   • fields bestimmt, welche Vektorfelder durchsucht werden.

   • kind festgelegt auf vector bedeutet, dass es sich bei der Abfragezeichenfolge um einen Vektor handelt. Wenn kind auf text gesetzt wurde, benötigen Sie zusätzliche Funktionalität (einen Vektorisierer), um einen menschenlesbaren Text zur Abfragezeit in einen Vektor zu kodieren. Vektorisierer werden aus dieser Schnellstartanleitung weggelassen, um die Übung einfach zu halten.

   • k gibt die Anzahl der nächsten Nachbarn an, die in der Antwort zurückgegeben werden sollen. Ein count Parameter gibt die Anzahl der Im Index gefundenen Übereinstimmungen an. Das Einschließen der Anzahl ist eine bewährte Methode für Abfragen, aber es ist weniger nützlich für die Ähnlichkeitssuche, bei der der Algorithmus in fast jedem Dokument ein gewisses Maß an Ähnlichkeit finden kann.

2. Klicken Sie auf Anforderung senden. Sie sollten eine HTTP/1.1 200 OK-Antwort erhalten. Der Antworttext sollte die JSON-Darstellung der Suchergebnisse enthalten.

   Die Antwort für das Vektoräquivalent quintessential lodging near running trails, eateries, retail enthält k-5-Ergebnisse, obwohl die Suchmaschine 7 Übereinstimmungen gefunden hat. Die wichtigsten Ergebnisse werden als semantisch ähnlich wie die Abfrage betrachtet. Jedes Ergebnis stellt eine Suchbewertung und die in select aufgeführten Felder bereit. Bei einer Ähnlichkeitssuche enthält die Antwort immer k-Ergebnisse, die nach der Wertähnlichkeitsbewertung sortiert sind.

   {
     "@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"
         ]
       }
     ]
   }
   

Einzelvektorsuche mit Filter

Sie können Filter hinzufügen, aber die Filter werden auf die Nicht-Vektorinhalte in Ihrem Index angewendet. In diesem Beispiel gilt der Filter für das Feld Tags und filtert alle Hotels heraus, die kein kostenloses WLAN bereitstellen.

1. Formulieren Sie die Anforderung. Dies ist die gleiche Anforderung wie der vorherige, mit einem zusätzlichen Filter- und Filtermodusparameter.

   ### 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. Klicken Sie auf Anforderung senden. Sie sollten eine HTTP/1.1 200 OK-Antwort erhalten. Der Antworttext sollte die JSON-Darstellung der Suchergebnisse enthalten.

   Die Abfrage war identisch mit dem vorherigen Beispiel für die Einzelvektorsuche, enthält aber einen Ausschlussfilter nach der Verarbeitung und gibt nur die beiden Hotels zurück, die über kostenloses WLAN verfügen.

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

3. Hier ist eine weitere Abfrage, die einen Geofilter verwendet und Ergebnisse auf Hotels innerhalb von 300 Kilometern um Washington D.C beschränkt.

   ### 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",
               "kind": "vector",
               "exhaustive": true
           },
       ]
   }
   

4. Überprüfen Sie die Antwort. Es ist auf zwei Hotels in der Nähe von Washington D.C beschränkt, mit einer Facetstruktur, die die Aggregation bereitstellt.

   "@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"
       }
     }
   ]
   

Hybridsuche

Die Hybridsuche besteht aus Stichwortabfragen und Vektorabfragen in einer einzelnen Suchanforderung. In diesem Beispiel werden die Vektorabfrage und die Volltextsuche gleichzeitig ausgeführt:

• Suchzeichenfolge: historic hotel walk to restaurants and shopping
• Vektorabfragezeichenfolge (in eine mathematische Darstellung vektorisiert): quintessential lodging near running trails, eateries, retail

1. Formulieren Sie die Hybridabfrage.

   ### 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. Klicken Sie auf Anforderung senden. Sie sollten eine HTTP/1.1 200 OK-Antwort erhalten. Der Antworttext sollte die JSON-Darstellung der Suchergebnisse enthalten.

Da es sich um eine Hybridabfrage handelt, werden die Ergebnisse nach der Methode Reciprocal Rank Fusion (RRF) sortiert. Beachten Sie, dass @search.score Werte eine andere Basis haben und einheitliche kleinere Werte sind. RRF wertet Suchbewertungen aus verschiedenen Suchergebnissen aus, nimmt die Umkehrung vor und führt dann die kombinierten Ergebnisse zusammen und sortiert sie. Die top-Anzahl der Ergebnisse wird zurückgegeben.

Überprüfen Sie die Antwort, die aus den wichtigsten (k-5) Treffern von insgesamt 7 übereinstimmenden Dokumenten im Index besteht.

{
  "@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."
    }
  ]
}

Da RRF Ergebnisse zusammenführt, hilft es, die Eingaben einzeln zu überprüfen.

Die folgenden Ergebnisse stammen aus dem Volltextabschnitt der Abfrage: historisches Hotel in Gehweite zu Restaurants und Einkaufsmöglichkeiten. In der Volltextabfrage sind die drei wichtigsten Ergebnisse das Sublime Palace Hotel, Stay-Kay City Hotel und Luxury Lion Resort. Das Sublime Palace Hotel hat eine höhere BM25-Relevanzbewertung. In der zusammengeführten Abfrage befinden sich nur zwei dieser Treffer unter den Top 3, und der zweite Treffer (Stay-Kay City Hotel) erscheint überhaupt nicht unter den Top 5.

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

In der Abfrage des Vektorteils (typische Unterkunft in der Nähe von Laufwegen, Restaurants, Geschäften) handelt es sich um eine andere Zeichenfolge, sodass wir unterschiedliche Ergebnisse erwarten sollten. Diese Abfrage gibt Nordick's Valley Motel, Luxury Lion Resort und Sublime Palace Hotel als die drei besten Treffer zurück. In der fused-Abfrage befinden sich diese Ergebnisse in der obersten 3, aber in umgekehrter Reihenfolge.

  "value": [
    {
      "@search.score": 0.6605852,
      "HotelName": "Nordick's Valley Motel",
      "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley."
    },
    {
      "@search.score": 0.6333684,
      "HotelName": "Luxury Lion Resort",
      "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort."
    },
    {
      "@search.score": 0.605672,
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience."
    },
   ...
  ]

Semantische Hybridsuche

Dies ist die letzte Abfrage in der Sammlung. Diese Hybridabfrage fügt eine L2-semantische Rangfolge hinzu, die das maschinelle Leseverständnis auf die L1-bewerteten Ergebnisse anwendet und damit relevantere Übereinstimmungen nach oben fördert.

1. Formulieren Sie die Anforderung.

   ### 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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2. Klicken Sie auf Anforderung senden. Sie sollten eine HTTP/1.1 200 OK-Antwort erhalten. Der Antworttext sollte die JSON-Darstellung der Suchergebnisse enthalten.

Überprüfen Sie die Antwort, die aus einem semantischen Reranking der Ergebnisse besteht, welche nach dem RRF-Verfahren der Hybridabfrage bewertet wurden. Die semantische Bewertung arbeitet mit Texteingaben. In einer Text- oder Hybridabfrage ist diese Eingabe der Textkomponente der Abfrage (historisches Hotel in der Nähe von Restaurants und Geschäften). Wenn Sie die semantische Rangfolge für eine reine Vektorabfrage verwenden möchten, z. B. das erste Beispiel, oder um den text, der für die semantische Rangfolge verwendet wird, explizit zu steuern, stellen Sie eine semantische Zeichenfolge bereit.

Nach semantischem Reranking bewegt sich das Swirling Currents Hotel mit seinem Verweis auf den Fußweg zum Einkaufen, Essen, Unterhaltung und das Stadtzentrum an die Spitze. Das Machine Reading Comprehension-Modell stuft walking access to shopping and dining höher ein als die Übereinstimmung mit walk to restaurants and shoppping.

Vor dem semantischen Reranking war Sublime Palace mit seiner Erwähnung der fußläufigen Entfernung zu den Orten und Sehenswürdigkeiten der Stadt und seiner Umgebenheit von der außergewöhnlichen Schönheit von Kirchen, Gebäuden, Geschäften und Denkmälern Nummer eins.

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  "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"
    }
  ]
}

Tipp

Semantisch eingestufte Ergebnisse können weitere Details beinhalten, einschließlich semantischer Antworten, Beschriftungen und Hervorhebungen. Wenn Sie der Anforderung weitere Parameter hinzufügen, werden die zusätzlichen Details erzeugt. Weitere Informationen finden Sie unter Einrichten einer semantischen Abfrage.

Bereinigung

Wenn Sie in Ihrem eigenen Abonnement arbeiten, sollten Sie sich am Ende eines Projekts überlegen, ob Sie die erstellten Ressourcen noch benötigen. Ressourcen, die weiterhin ausgeführt werden, können Geld kosten. Sie können entweder einzelne Ressourcen oder aber die Ressourcengruppe löschen, um den gesamten Ressourcensatz zu entfernen.

Ressourcen können im Azure-Portal über den Link Alle Ressourcen oder Ressourcengruppen im linken Navigationsbereich gesucht und verwaltet werden.

Wenn Sie den Suchdienst beibehalten, aber den Index und die Dokumente löschen möchten, können Sie den Befehl DELETE im REST-Client verwenden. Mit diesem Befehl (am Ende der az-search-vector-quickstart.rest-Datei) wird der hotels-vector-quickstart-Index gelöscht:

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

Nächste Schritte

Als nächsten Schritt empfehlen wir Ihnen, sich mit dem Aufrufen von REST-API-Aufrufen ohne API-Schlüssel vertraut zu machen.

Sie sollten sich auch den Democode für Python, C# oder JavaScript genauer ansehen.