Schnellstart: Vektorsuche

In dieser Schnellstartanleitung verwenden Sie ein Jupyter-Notizbuch, um einen Vektorindex zu erstellen, zu laden und abzufragen. Der Code führt diese Vorgänge mithilfe der Azure AI Search-Clientbibliothek für Python aus, die eine Abstraktion über die REST-APIs für den Zugriff auf Indexvorgänge bereitstellt.

In Azure AI Search verfügt ein Vektorindex über ein Indexschema, das Vektor- und Nichtvektorfelder definiert, eine Vektorsuchkonfiguration für Algorithmen, die den Einbettungsraum erstellen, sowie Einstellungen für Vektorfelddefinitionen, die zur Abfragezeit ausgewertet werden. Indizes – Erstellen oder Aktualisieren (REST-API) erstellt den Vektorindex.

Hinweis

In dieser Schnellstartanleitung wird der Vektorisierungsschritt weggelassen und Inlineeinbettungen bereitgestellt. Wenn Sie integriertes Datenchunking und Vektorisierung in Ihren eigenen Inhalt hinzufügen möchten, probieren Sie den Assistenten zum Importieren von Daten (neu) für eine vollständige Schritt-für-Schritt-Anleitung aus.

Voraussetzungen

Ein Azure-Konto mit einem aktiven Abonnement. Kostenlos ein Konto erstellen.
Ein Azure AI Search-Dienst.
- Sie können die Kostenlose Ebene für die meisten dieser Schnellstartanleitungen verwenden, aber wir empfehlen Basic oder höher für größere Datendateien.
- Weisen Sie Für die schlüssellose Authentifizierung mit Microsoft Entra-ID die Rolle "Mitwirkender für Suchindexdaten " Ihrem Benutzerkonto zu.
- Zum Ausführen der semantischen Hybridabfrage müssen Sie den semantischen Rangfolger aktivieren.
Visual Studio Code mit der Python-Erweiterung und dem Jupyter-Paket.
Git , um das Beispiel-Repository zu klonen.

Abrufen von Dienstinformationen

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

Klonen Sie das Repository, das den Code für diese Schnellstartanleitung enthält.
```
git clone https://github.com/Azure-Samples/azure-search-python-samples
```
Öffnen Sie in Visual Studio Code den Quickstart-Vector-Search Ordner.
Benennen Sie die Datei sample.env in .env um.
Setzen Sie AZURE_SEARCH_ENDPOINT auf Ihre Suchdienst-URL, die ähnlich wie https://mydemo.search.windows.net sein sollte.
Weisen Sie AZURE_SEARCH_INDEX_NAME einen eindeutigen Namen für Ihren Index zu. Sie können auch den Standardnamen vector-search-quickstart verwenden.
Wählen Sie "Anzeigen>Neues Terminal" aus, und führen Sie dann die folgenden Befehle aus, um eine virtuelle Umgebung zu erstellen und zu aktivieren.
```
python -m venv .venv
source .venv/scripts/activate
where python
```
Hinweis
- In diesem Schritt wird davon ausgegangen, dass Sie Git Bash in Ihrem Terminal verwenden und in der Windows-Umgebung arbeiten. Wenn Sie eine andere Shell und/oder ein anderes Betriebssystem verwenden, passen Sie diese Anweisungen an Ihre spezifische Umgebung an.
- 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 im Verzeichnis Ihres Computers auflistet.
Wenn Sie dazu aufgefordert werden, lassen Sie Visual Studio Code die Verwendung der neuen Umgebung zu.
Installieren Sie die erforderlichen Bibliotheken.
```
pip install requirements.txt
```
Öffnen Sie die Datei vector-search-quickstart.ipynb .

Führen Sie die Install packages and set variables Codezelle aus, um die Umgebungsvariablen zu laden.

# Load environment variables from .env file
# Rename the samples.env to .env and fill in the values
from azure.identity import DefaultAzureCredential
from dotenv import load_dotenv
import os

load_dotenv(override=True) # take environment variables from .env.

search_endpoint = os.environ["AZURE_SEARCH_ENDPOINT"]
credential = DefaultAzureCredential()
index_name = os.getenv("AZURE_SEARCH_INDEX", "vector-search-quickstart")

print(f"Using Azure Search endpoint: {search_endpoint}")
print(f"Using Azure Search index: {index_name}")
!pip list

Die Ausgabe enthält einige der installierten Pakete.

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
...

Erstellen eines Vektorindexes

vector-search-quickstart.ipynb verwendet mehrere Methoden aus der azure.search.documents Bibliothek, um den Vektorindex und die durchsuchbaren Felder zu erstellen.

So erstellen Sie den Vektorindex:

Führen Sie die Create an index Codezelle aus.

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 wird, wird die folgende Ausgabe unterhalb der Codezelle angezeigt.

vector-search-quickstart created

Wichtige Erkenntnisse:

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)
- Geo-räumliche Suche (Location Feld ist GeographyPoint)
- Filtern, Sortieren (viele Felder, die gefiltert und sortiert werden können)

Hochladen von Dokumenten in den Index

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.

So laden Sie Dokumente in den Index hoch:

Führen Sie die Create documents payload Code-Zelle aus.

   # 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.

Führen Sie die Upload the documents Codezelle aus.
```
# 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 erstellt, indem der SearchClient() Konstruktor aufgerufen und dann die upload_documents() Methode für das Objekt aufgerufen wird.

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 Erkenntnisse:
- 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 bietet, z. B.:
  - Datenaufnahme: upload_documents(), merge_documents(), delete_documents(), usw.
  - Suchvorgänge: search(), autocomplete()suggest()
  - Indexverwaltungsvorgänge: get_index_statistics(), get_document_count()
- Vektorfelder enthalten Gleitkommawerte. Das Dimensions-Attribut weist mindestens 2 und maximal 4096 Gleitkommawerte auf. Diese Schnellstartanleitung legt das Dimensions-Attribut auf 1,536 fest, da dies der Größe der von dem text-embedding-ada-002 Modell generierten Einbettungen entspricht.

Ausführen von Abfragen

Nachdem die Dokumente geladen sind, können Sie search_client.search() aufrufen und dabei ein VectorizedQuery-Objekt, die zurückzugebenden Felder, die Anzahl der Ergebnisse usw. übergeben, um Vektorabfragen gegen sie auszuführen.

Abfragen in diesem Abschnitt:

Einzelvektorsuche
Einzelvektorsuche mit Filter
Hybridsuche
Semantische Hybridsuche

Erstellen der Vektorabfragezeichenfolge

Die Beispielvektorabfragen basieren auf zwei Zeichenfolgen:

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

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.

Führen Sie die Create the vector query string-Codezelle 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.

Führen Sie die Single vector search Codezelle 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 quintessential lodging near running trails, eateries, retail besteht aus 7 Ergebnissen, der Code gibt jedoch an top=5 , sodass nur die ersten 5 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.

So erstellen Sie eine einzelne Vektorsuche mit einem Filter:

Führen Sie die Single vector search with filter Codezelle 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 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.

Im nächsten Filterbeispiel wird ein Geofilter verwendet. Führen Sie die Vector query with a geo filter Codezelle 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: quintessential lodging near running trails, eateries, retail (in eine mathematische Darstellung vektorisiert)

So erstellen Sie eine Hybridsuche:

Führen Sie die Hybrid search Codezelle 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 Ausgabe unterhalb der Zelle.

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 top-Ergebnisse sind Sublime Palace Hotel und Luxury 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"
},

Bei der reinen Vektorabfrage, die HNSW zur Suche nach Übereinstimmungen verwendet, rutscht das Sublime Palace Hotel auf den vierten Platz ab. Luxury Lion, das bei der Volltextsuche an zweiter Stelle und bei der Vektorsuche an dritter Stelle stand, weist nicht die gleichen Schwankungen auf und erscheint daher in einer homogenisierten Ergebnisliste als Top-Treffer.

"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

Dies ist die letzte Abfrage in der Sammlung. Diese Hybridabfrage gibt den semantischen Abfragetyp und eine semantische Konfiguration an, die veranschaulicht, dass Sie eine Hybridabfrage erstellen können, die semantische Reranking verwendet.

So erstellen Sie eine semantische Hybridsuche:

Führen Sie die Semantic hybrid search Codezelle 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.

Mit der semantischen Rangfolge bewegt sich das Swirling Currents Hotel jetzt an die Spitze.

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

Sie können sich die semantische Rangfolge als Eine Möglichkeit vorstellen, um die Relevanz von Suchergebnissen zu verbessern, indem Sie die Bedeutung hinter den Wörtern in der Abfrage und dem Inhalt der Dokumente verstehen. In diesem Fall hilft die semantische Rangfolge, Hotels zu identifizieren, die nicht nur für die Schlüsselwörter relevant sind, sondern auch mit der Absicht der Abfrage übereinstimmen:

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.

Bereinigen von Ressourcen

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.

Alternativ können Sie die Clean up Codezelle ausführen, um den in dieser Schnellstartanleitung erstellten Vektorindex zu löschen.

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

Nächste Schritte

Überprüfen Sie das Repository 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 Java zum Erstellen, Laden und Abfragen eines Vektorindexes. Der Code führt diese Vorgänge mithilfe der Azure AI Search-Clientbibliothek für Java aus, die eine Abstraktion über die REST-APIs für den Zugriff auf Indexvorgänge bereitstellt.

Hinweis

Voraussetzungen

Ein Azure-Konto mit einem aktiven Abonnement. Kostenlos ein Konto erstellen.
Ein Azure AI Search-Dienst.
- Sie können die Kostenlose Ebene für die meisten dieser Schnellstartanleitungen verwenden, aber wir empfehlen Basic oder höher für größere Datendateien.
- Weisen Sie Für die schlüssellose Authentifizierung mit Microsoft Entra-ID die Rolle "Mitwirkender für Suchindexdaten " Ihrem Benutzerkonto zu.
- Zum Ausführen der semantischen Hybridabfrage müssen Sie den semantischen Rangfolger aktivieren.
Java 21 (LTS).
Maven.

Abrufen von Dienstinformationen

In den verbleibenden Abschnitten richten Sie API-Aufrufe für Azure OpenAI und die Azure KI-Suche ein. Rufen Sie die Dienstendpunkte ab, damit Sie sie als Variablen im Code bereitstellen können.

So rufen Sie den Dienstendpunkt ab:

Melden Sie sich beim Azure-Portal an.
Suchen Sie Ihren Suchdienst.
Sie finden die URL auf der Startseite Übersicht. Ein Beispiel für einen Endpunkt ist https://example.search.windows.net.

Einrichten des Java-Projekts

Erstellen Sie ein neues Maven-Projektverzeichnis.

mkdir vector-quickstart && cd vector-quickstart

Erstellen Sie eine pom.xml-Datei mit erforderlichen Abhängigkeiten.

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

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

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

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

Kompilieren Sie das Projekt, um die Abhängigkeiten aufzulösen.
```
mvn compile
```

Erstellen Sie die Quellverzeichnisstruktur.

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

Erstellen Sie eine application.properties Datei im src/main/resources Verzeichnis, und stellen Sie Ihren Suchdienstendpunkt bereit.
```
azure.search.endpoint=<YOUR AZURE AI SEARCH ENDPOINT>
azure.search.index-name=hotels-vector-quickstart
```

Sie verwenden Microsoft Entra ID und Rollenzuweisungen für die Verbindung. Stellen Sie sicher, dass Sie bei demselben Mandanten und Abonnement wie Azure KI-Suche und Azure OpenAI angemeldet sind. Sie können die Azure CLI in der Befehlszeile verwenden, um aktuelle Eigenschaften anzuzeigen, sie zu ändern und um sich anzumelden. Weitere Informationen finden Sie unter Herstellen einer Verbindung ohne Schlüssel.

Führen Sie die folgenden Befehle nacheinander aus.

az account show

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

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

Sie sollten jetzt von Ihrem lokalen Gerät aus bei Azure angemeldet sein.

Erstellen eines Vektorindexes

So erstellen Sie einen Vektorindex:

Erstellen Sie eine CreateIndex.java Datei im src/main/java/com/example/search Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

package com.example.search;

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

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

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

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

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

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

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

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

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

        var searchIndex = new SearchIndex(indexName, fields);

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

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

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

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

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

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

        System.exit(0);
    }
}

Die Codedatei erstellt den Index und definiert das Indexschema, einschließlich des DescriptionVector Vektorfelds.

Führen Sie die Datei aus.
```
mvn compile exec:java -Dexec.mainClass="com.example.search.CreateIndex"
```
Die Ausgabe dieses Codes zeigt, dass der Index erfolgreich erstellt wird.
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Creating index...
hotels-vector-quickstart created
```
Wichtige Erkenntnisse:
- Sie definieren einen Index, indem Sie eine Liste von Feldern erstellen.
- Dieser spezielle Index unterstützt mehrere Suchfunktionen, z. B.:
  - Volltext-Schlüsselwortsuche (setSearchable)
  - Vektorsuche (ermöglicht die Hybridsuche durch Die Zuordnung von Vektor- und Nichtvektorfeldern) (DescriptionVector mit setVectorSearchProfileName)
  - Semantische Suche
  - Facettierte Suche (SearchSuggester)
  - Geo-räumliche Suche (Location Feld mit SearchFieldDataType.GEOGRAPHY_POINT)
  - Filtern, Sortieren (viele Felder, die gefiltert und sortiert werden können)

Hochladen von Dokumenten in den Index

Das Erstellen und Laden des Indexes sind separate Schritte. Sie haben das Indexschema im vorherigen Schritt erstellt. Jetzt müssen Sie Dokumente in den Index mit SearchClient laden. uploadDocuments. Die Dokumente enthalten die vektorisierte Version der Beschreibung des Artikels, wodurch die Ähnlichkeitssuche basierend auf bedeutungsbasierten und nicht exakten Schlüsselwörtern ermöglicht wird.

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

So laden Sie Dokumente in den Index hoch:

Erstellen Sie eine UploadDocuments.java-Datei im Verzeichnis src/main/java/com/example/search.

Kopieren Sie den folgenden Code in die Datei.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Dieser Code lädt eine Sammlung von Dokumenten. Jedes Dokument enthält die vektorisierte Version des Artikels Description. Dieser Vektor ermöglicht die Ähnlichkeitssuche, bei der der Abgleich auf Bedeutung und nicht auf exakten Schlüsselwörtern basiert.

Da dieser Schnellstartartikel den Index sofort durchsucht, wartet die Methode waitUntilIndexed, bis der Index für Suchvorgänge bereit ist. Dies ist wichtig, da der Index vollständig mit Dokumenten ausgefüllt werden muss, bevor Sie Suchvorgänge ausführen können.

Erstellen Sie die Datei, und führen Sie sie aus.
```
mvn compile exec:java -Dexec.mainClass="com.example.search.UploadDocuments"
```
Die Ausgabe dieses Codes zeigt, dass die Dokumente indiziert und für die Suche bereit sind.
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Uploading documents...
Key: 1, Succeeded: true, ErrorMessage: none
Key: 2, Succeeded: true, ErrorMessage: none
Key: 3, Succeeded: true, ErrorMessage: none
Key: 4, Succeeded: true, ErrorMessage: none
Key: 48, Succeeded: true, ErrorMessage: none
Key: 49, Succeeded: true, ErrorMessage: none
Key: 13, Succeeded: true, ErrorMessage: none
Waiting for indexing... Current count: 0
All documents indexed successfully.
```
Wichtige Erkenntnisse:
- Ihr Code interagiert mit einem bestimmten Suchindex, der über SearchClient in Ihrem Azure KI-Suche-Dienst gehostet wird. SearchClient bietet Zugriff auf Vorgänge wie:
  - Datenaufnahme: uploadDocuments, mergeDocuments, deleteDocuments, usw.
  - Suchvorgänge: search, autocompletesuggest
- Vektorfelder enthalten Gleitkommawerte. Das Dimensions-Attribut weist mindestens 2 und maximal 4096 Gleitkommawerte auf. Diese Schnellstartanleitung legt das Dimensions-Attribut auf 1,536 fest, da dies der Größe der von dem text-embedding-ada-002 Modell generierten Einbettungen entspricht.

Erstellen der Abfrage als Vektor

Die Beispielvektorabfragen basieren auf zwei Zeichenfolgen:

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

Erstellen Sie eine QueryVector.java Datei im src/main/java/com/example/search Verzeichnis, und fügen Sie den Code hinzu, um den Abfragevektor zu erstellen.

package com.example.search;

import java.util.ArrayList;

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

Dieser Code wird in den folgenden Abschnitten zum Ausführen von Vektorsuchen verwendet. Der Abfragevektor wird mithilfe eines Einbettungsmodells aus Azure OpenAI erstellt.

Erstellen einer einzelnen Vektorsuche

Im ersten Beispiel wird ein einfaches Szenario veranschaulicht, in dem Sie Dokumentbeschreibungen finden möchten, die der Suchzeichenfolge mithilfe des SearchClient, der Funktion search sowie VectorQuery und SearchOptions genau entsprechen.

So erstellen Sie eine einzelne Vektorsuche:

Erstellen Sie eine SearchSingle.java Datei im src/main/java/com/example/search Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

vectorQuery enthält die Konfiguration der vektorisierten Abfrage, einschließlich des vektorisierten Texts der Abfrageeingabe, wie von QueryVector.getVectorList abgerufen. setFields bestimmt, welche Vektorfelder durchsucht werden, und setKNearestNeighborsCount gibt die Anzahl der nächsten Nachbarn 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.

Erstellen Sie die Datei, und führen Sie sie aus.

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

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Abfrage quintessential lodging near running trails, eateries, retailan.

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

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

Die Antwort für das Vektoräquivalent quintessential lodging near running trails, eateries, retail besteht aus 5 Ergebnissen mit nur den von der zurückgegebenen Auswahl angegebenen Feldern.

Erstellen einer einzelnen Vektorsuche mit einem Filter

So erstellen Sie eine einzelne Vektorsuche mit einem Filter:

Erstellen Sie eine SearchSingleWithFilter.java Datei im src/main/java/com/example/search Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Dieser Code verfügt über die gleiche Suchfunktion wie die vorherige Suche, fügt jedoch einen Ausschlussfilter nach der Verarbeitung für Hotels hinzu.free wifi

Erstellen Sie die Datei, und führen Sie sie aus.

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

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Abfrage mit dem angewendeten free wifi Filter an.

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

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

Erstellen einer Suche mit einem Geospatialfilter

Sie können einen Geospatialfilter angeben, um Die Ergebnisse auf einen bestimmten geografischen Bereich zu beschränken. In diesem Beispiel beschränkt der Filter ergebnisse auf Hotels innerhalb von 300 Kilometern von Washington D.C. (Koordinaten: -77.03241 38.90166). Geospatialdistanzen befinden sich immer in Kilometern. Diese Suche verwendet SearchClient.SearchClient und SearchOptions.

So erstellen Sie eine einzelne Vektorsuche mit einem Geospatialfilter:

Erstellen Sie eine SearchSingleWithFilterGeo.java Datei im src/main/java/com/example/search Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Erstellen Sie die Datei, und führen Sie sie aus.

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

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Abfrage mit dem angewendeten Ausschlussfilter für geospatiale Nachbearbeitung an.

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

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

Erstellen einer Hybridsuche

Die Hybridsuche kombiniert Schlüsselwort- und Vektorabfragen in einer Anforderung. In diesem Beispiel werden die folgenden Volltext- und Vektorabfragezeichenfolgen gleichzeitig ausgeführt:

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

Diese Suche verwendet SearchClient.SearchClient und SearchOptions.

Tp erstellt eine Hybridsuche:

Erstellen Sie eine SearchHybrid.java Datei im src/main/java/com/example/search Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Erstellen Sie die Datei, und führen Sie sie aus.

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

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Hybridsuche an.

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

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

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

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

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

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

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

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

Erstellen einer semantischen Hybridsuche

Hier ist die letzte Abfrage in der Sammlung, um die semantische Hybridsuche mit dem zusätzlichen Suchtext historic hotel walk to restaurants and shopping zu erweitern.

Diese Suche verwendet SearchClient.SearchClient und SearchOptions.

So erstellen Sie eine semantische Hybridsuche:

Erstellen Sie eine SearchSemanticHybrid.java Datei im src/main/java/com/example/search Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Erstellen Sie die Datei, und führen Sie sie aus.

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

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die semantische Hybridsuche an.

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

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

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

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

- Score: 0.016393441706895828
  Re-ranker Score: 2.2718777656555176
  HotelId: 1
  HotelName: Stay-Kay City Hotel
  Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
  Category: Boutique

- Score: 0.01515151560306549
  Re-ranker Score: 2.0582215785980225
  HotelId: 3
  HotelName: Gastronomic Landscape Hotel
  Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel's restaurant services.
  Category: Suite

Wichtige Erkenntnisse:

Die Vektorsuche wird über die Klasse VectorSearchOptions angegeben. Die Schlüsselwortsuche wird über die Klasse SemanticSearchOptions 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.

Bereinigen von Ressourcen

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

Alternativ können Sie den in dieser Schnellstartanleitung erstellten Vektorindex programmgesteuert löschen:

Erstellen Sie eine DeleteIndex.java Datei im src/main/java/com/example/search Verzeichnis.

Fügen Sie der Datei den folgenden Code hinzu.

package com.example.search;

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Erstellen Sie die Datei, und führen Sie sie aus.

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

Nächste Schritte

Überprüfen Sie das Repository von Codebeispielen für Vektorsuchfunktionen in Azure AI Search für Java.

In dieser Schnellstartanleitung verwenden Sie JavaScript zum Erstellen, Laden und Abfragen eines Vektorindexes. Der Code führt diese Vorgänge mithilfe der Azure AI Search-Clientbibliothek für JavaScript aus, die eine Abstraktion über die REST-APIs für den Zugriff auf Indexvorgänge bereitstellt.

Hinweis

Voraussetzungen

Ein Azure-Konto mit einem aktiven Abonnement. Kostenlos ein Konto erstellen.
Ein Azure AI Search-Dienst.
- Sie können die Kostenlose Ebene für die meisten dieser Schnellstartanleitungen verwenden, aber wir empfehlen Basic oder höher für größere Datendateien.
- Weisen Sie Für die schlüssellose Authentifizierung mit Microsoft Entra-ID die Rolle "Mitwirkender für Suchindexdaten " Ihrem Benutzerkonto zu.
- Zum Ausführen der semantischen Hybridabfrage müssen Sie den semantischen Rangfolger aktivieren.
Visual Studio Code.
Node.JS mit LTS.

Abrufen von Dienstendpunkten

In den verbleibenden Abschnitten richten Sie API-Aufrufe für Azure OpenAI und die Azure KI-Suche ein. Rufen Sie die Dienstendpunkte ab, damit Sie sie als Variablen im Code bereitstellen können.

Erhalten des Dienstendpunkts:

Melden Sie sich beim Azure-Portal an.
Suchen Sie Ihren Suchdienst.
Sie finden die URL auf der Startseite Übersicht. Ein Beispiel für einen Endpunkt ist https://example.search.windows.net.

Einrichten des Node.JS Projekts

Starten Sie Visual Studio Code in einem neuen Verzeichnis.
```
mkdir vector-quickstart && cd vector-quickstart
code .
```
Erstellen Sie ein neues Paket für ESM-Module in Ihrem Projektverzeichnis.
```
npm init -y
npm pkg set type=module
```
Dadurch wird eine package.json Datei mit Standardwerten erstellt.

Installieren Sie die folgenden npm Pakete.

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

Erstellen Sie ein src Verzeichnis in Ihrem Projektverzeichnis.
```
mkdir src
```

Einrichten von Umgebungsvariablen für die lokale Entwicklung

Erstellen Sie eine .env Datei in Ihrem vector-quickstart Projektverzeichnis.
Fügen Sie der .env Datei die folgenden Umgebungsvariablen hinzu, und ersetzen Sie die Werte durch Ihre eigenen Dienstendpunkte und Schlüssel.
```
AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
```

Führen Sie die folgenden Befehle nacheinander aus.

az account show

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

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

Sie sollten jetzt von Ihrem lokalen Gerät aus bei Azure angemeldet sein.

Erstellen eines Vektorindexes

So erstellen Sie einen Vektorindex:

Erstellen Sie eine createIndex.js Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const indexClient = new SearchIndexClient(searchEndpoint, credential);
console.log('Creating index...');

// Define fields
const fields = [
    {
        name: "HotelId",
        type: "Edm.String",
        key: true,
        filterable: true
    },
    {
        name: "HotelName",
        type: "Edm.String",
        sortable: true,
        searchable: true,
        hidden: false,
    },
    {
        name: "Description",
        type: "Edm.String",
        searchable: true,
        hidden: false
    },
    {
        name: "DescriptionVector",
        type: "Collection(Edm.Single)",
        searchable: true,
        vectorSearchDimensions: 1536,
        vectorSearchProfileName: "my-vector-profile",
        hidden: true
    },
    {
        name: "Category",
        type: "Edm.String",
        sortable: true,
        filterable: true,
        facetable: true,
        searchable: true,
        hidden: false
    },
    {
        name: "Tags",
        type: "Collection(Edm.String)",
        searchable: true,
        filterable: true,
        facetable: true,
        hidden: false
    },
    {
        name: "ParkingIncluded",
        type: "Edm.Boolean",
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "LastRenovationDate",
        type: "Edm.DateTimeOffset",
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Rating",
        type: "Edm.Double",
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Address",
        type: "Edm.ComplexType",
        fields: [
            {
                name: "StreetAddress",
                type: "Edm.String",
                searchable: true,
            },
            {
                name: "City",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "StateProvince",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "PostalCode",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true
            },
            {
                name: "Country",
                type: "Edm.String",
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true,
            }
        ]
    },
    {
        name: "Location",
        type: "Edm.GeographyPoint",
        filterable: true,
        sortable: true
    }
];

// Define vector search configuration
const vectorSearch = {
    algorithms: [
        {
            name: "hnsw-vector-config",
            kind: "hnsw"
        },
        {
            name: "eknn-vector-config",
            kind: "exhaustiveKnn"
        }
    ],
    profiles: [
        {
            name: "my-vector-profile",
            algorithmConfigurationName: "hnsw-vector-config"
        }
    ]
};

// Define semantic configuration
const semanticConfig = {
    name: "semantic-config",
    prioritizedFields: {
        titleField: {
            name: "HotelName"
        },
        contentFields: [
            {
                name: "Description"
            }
        ],
        keywordsFields: [
            {
                name: "Category"
            }
        ]
    }
};

// Create the semantic settings with the configuration
const semanticSearch = {
    configurations: [semanticConfig]
};

// Define suggesters
const suggesters = [];

// Create the search index with the semantic settings
const indexDefinition = {
    name: indexName,
    fields: fields,
    vectorSearch: vectorSearch,
    semanticSearch: semanticSearch,
    suggesters: suggesters
};

const result = await indexClient.createOrUpdateIndex(indexDefinition);
console.log(`${result.name} created`);

Die Codedatei fügt die Abhängigkeiten, Umgebungsvariablen und JavaScript-Typ HotelDocument am Anfang der Datei hinzu. Fügen Sie die createIndex Funktion hinzu, um den Index zu erstellen. Die Funktion definiert das Indexschema, einschließlich des Vektorfelds DescriptionVector.

Führen Sie die Datei aus.
```
node -r dotenv/config src/createIndex.js
```
Die Ausgabe dieses Codes zeigt, dass der Index erfolgreich erstellt wird.
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Creating index...
hotels-vector-quickstart created
```
Wichtige Erkenntnisse:
- Sie definieren einen Index, indem Sie eine Liste von Feldern erstellen.
- Dieser spezielle Index unterstützt mehrere Suchfunktionen, z. B.:
  - Volltext-Schlüsselwortsuche (searchable)
  - Vektorsuche (ermöglicht die Hybridsuche durch Die Zuordnung von Vektor- und Nichtvektorfeldern) (DescriptionVector mit vectorSearchProfileName)
  - Semantische Suche
  - Facettierte Suche (searchSuggester)
  - Geo-räumliche Suche (Location Feld mit geo.distance)
  - Filtern, Sortieren (viele Felder, die gefiltert und sortiert werden können)

Hochladen von Dokumenten in den Index

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

So laden Sie Dokumente in den Index hoch:

Erstellen Sie eine uploadDocuments.js-Datei im Verzeichnis src.

Kopieren Sie den folgenden Code in die Datei.

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const DOCUMENTS =  [
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
        "DescriptionVector": 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        "Category": "Boutique",
        "Tags": [
            "view",
            "air conditioning",
            "concierge"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2022-01-18T00:00:00Z",
        "Rating": 3.60,
        "Address": {
            "StreetAddress": "677 5th Ave",
            "City": "New York",
            "StateProvince": "NY",
            "PostalCode": "10022",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                -73.975403,
                40.760586
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "2",
        "HotelName": "Old Century Hotel",
        "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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        "Category": "Boutique",
        "Tags": [
            "pool",
            "free wifi",
            "air conditioning",
            "concierge"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2019-02-18T00:00:00Z",
        "Rating": 3.60,
        "Address": {
            "StreetAddress": "140 University Town Center Dr",
            "City": "Sarasota",
            "StateProvince": "FL",
            "PostalCode": "34243",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
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            ]
        }
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        "HotelId": "3",
        "HotelName": "Gastronomic Landscape Hotel",
        "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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        "Category": "Suite",
        "Tags": [
            "restaurant",
            "bar",
            "continental breakfast"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2015-09-20T00:00:00Z",
        "Rating": 4.80,
        "Address": {
            "StreetAddress": "3393 Peachtree Rd",
            "City": "Atlanta",
            "StateProvince": "GA",
            "PostalCode": "30326",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                33.847848,
                -84.363602
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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        "Category": "Boutique",
        "Tags": [
            "concierge",
            "view",
            "air conditioning"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2020-02-06T00:00:00Z",
        "Rating": 4.60,
        "Address": {
            "StreetAddress": "7400 San Pedro Ave",
            "City": "San Antonio",
            "StateProvince": "TX",
            "PostalCode": "78216",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                -98.498923,
                29.518029
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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        "Category": "Boutique",
        "Tags": [
            "continental breakfast",
            "air conditioning",
            "free wifi"
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        "ParkingIncluded": "false",
        "LastRenovationDate": "2018-02-19T00:00:00Z",
        "Rating": 4.5,
        "Address": {
            "StreetAddress": "1401 I St NW",
            "City": "Washington D.C.",
            "StateProvince": "null",
            "PostalCode": "20005",
            "Country": "USA"
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            "properties": {
                "name": "EPSG:4326"
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        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.",
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        "Category": "Suite",
        "Tags": [
            "air conditioning",
            "laundry service",
            "24-hour front desk service"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2018-01-27T00:00:00Z",
        "Rating": 2.7,
        "Address": {
            "StreetAddress": "1100 S Hayes St",
            "City": "Arlington",
            "StateProvince": "VA",
            "PostalCode": "22202",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -77.060066,
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            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
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        }
    },
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        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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        "Category": "Luxury",
        "Tags": [
            "bar",
            "concierge",
            "restaurant"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2020-03-18T00:00:00Z",
        "Rating": 4.1,
        "Address": {
            "StreetAddress": "3 Cityplace Dr",
            "City": "St. Louis",
            "StateProvince": "MO",
            "PostalCode": "63141",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -90.44081,
            38.67219
            ],
            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
                }
            }
        }
    }
];

async function waitUntilIndexed() {
    try {
        const searchClient = new SearchClient(searchEndpoint, indexName, credential);
        do {
            const count = await searchClient.getDocumentsCount();
            if (count == DOCUMENTS.length) {
                console.log("All documents indexed successfully.");
                break;
            }
            console.log(`Waiting for indexing... Current count: ${count}`);
            await new Promise((resolve) => setTimeout(resolve, 10000)); // Wait for 10 seconds
        } while (true);
    } catch (ex) {
        console.error("Failed to wait until indexed:", ex);
    }
}

const searchClient = new SearchClient(searchEndpoint, indexName, credential);

try {
    console.log("Uploading documents...");
    const result = await searchClient.uploadDocuments(DOCUMENTS);
    for (const r of result.results) {
        console.log(`Key: ${r.key}, Succeeded: ${r.succeeded}, ErrorMessage: ${r.errorMessage || 'none'}`);
    }
    await waitUntilIndexed();
} catch (ex) {
    console.error("Failed to upload documents:", ex);
}

Dieser Code lädt ein Array von JSON-Objekten. Jedes Dokument enthält die vektorisierte Version des Artikels description. Dieser Vektor ermöglicht die Ähnlichkeitssuche, bei der der Abgleich auf Bedeutung und nicht auf exakten Schlüsselwörtern basiert.

Da dieser Schnellstartartikel den Index sofort durchsucht, wartet die waitUntilIndexed Funktion, bis der Index für Suchvorgänge bereit ist. Dies ist wichtig, da der Index vollständig mit Dokumenten ausgefüllt werden muss, bevor Sie Suchvorgänge ausführen können.

Erstellen Sie die Datei, und führen Sie sie aus.
```
node -r dotenv/config src/uploadDocuments.js
```
Die Ausgabe dieses Codes zeigt, dass die Dokumente indiziert und für die Suche bereit sind.
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Uploading documents...
Key: 1, Succeeded: true, ErrorMessage: none
Key: 2, Succeeded: true, ErrorMessage: none
Key: 3, Succeeded: true, ErrorMessage: none
Key: 4, Succeeded: true, ErrorMessage: none
Key: 48, Succeeded: true, ErrorMessage: none
Key: 49, Succeeded: true, ErrorMessage: none
Key: 13, Succeeded: true, ErrorMessage: none
Waiting for indexing... Current count: 0
All documents indexed successfully.
```
Wichtige Erkenntnisse:
- 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 bietet, z. B.:
  - Datenaufnahme: uploadDocuments, mergeDocuments, deleteDocuments, usw.
  - Suchvorgänge: search, autocompletesuggest
  - Indexverwaltungsvorgänge: createIndex, deleteIndex, getIndex, usw.
- Vektorfelder enthalten Gleitkommawerte. Das Dimensions-Attribut weist mindestens 2 und maximal 4096 Gleitkommawerte auf. n dieser Schnellstartanleitung wird das Attribut „Dimensionen“ auf 1.536 gesetzt, da dies die Größe der vom text-embedding-ada-002-Modell generierten Einbettungen ist.

Erstellen der Abfrage als Vektor

Die Beispielvektorabfragen basieren auf zwei Zeichenfolgen:

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

Erstellen Sie eine queryVector.js Datei im src Verzeichnis, und fügen Sie den Code hinzu, um den Abfragevektor zu erstellen.

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

export const vector = 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Dieser Code wird in den folgenden Abschnitten zum Ausführen von Vektorsuchen verwendet. Der Abfragevektor wird mithilfe eines Einbettungsmodells aus Azure OpenAI erstellt.

Erstellen einer einzelnen Vektorsuche

So erstellen Sie eine einzelne Vektorsuche:

Erstellen Sie eine searchSingle.js Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] ,
        includeTotalCount: true,
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

Der vectorQuery enthält die Konfiguration der vektorisierten Abfrage, einschließlich des vektorisierten Textes der Abfrageeingabe als vector, fields bestimmt, welche Vektorfelder durchsucht werden, und kNearestNeighborsCount gibt an, wie viele der nächstgelegenen Nachbarn zurückgegeben werden sollen.

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

Erstellen Sie die Datei, und führen Sie sie aus.

node -r dotenv/config src/searchSingle.js

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Abfrage quintessential lodging near running trails, eateries, retailan.

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


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

Die Antwort für das Vektoräquivalent quintessential lodging near running trails, eateries, retail besteht aus 5 Ergebnissen mit nur den von der zurückgegebenen Auswahl angegebenen Feldern.

Erstellen einer einzelnen Vektorsuche mit einem Filter

So erstellen Sie eine einzelne Vektorsuche mit einem Filter:

Erstellen Sie eine searchSingleWithFilter.js Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] ,
        includeTotalCount: true,
        filter: "Tags/any(tag: tag eq 'free wifi')", // Adding filter for "free wifi" tag
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

Fügen Sie die Abhängigkeiten, die Umgebungsvariablen und dieselbe Suchfunktion wie bei der vorherigen Suche hinzu, wobei ein Nachbearbeitungsausschlussfilter für Hotels hinzugefügt wurde.free wifi

Erstellen Sie die Datei, und führen Sie sie aus.

node -r dotenv/config src/searchSingleWithFilter.js

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Abfrage mit dem angewendeten free wifi Filter an.

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


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

Erstellen einer Suche mit einem Geospatialfilter

So erstellen Sie eine Suche mit einem Geospatialfilter:

Erstellen Sie eine searchSingleWithFilterGeo.js Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions = {
        top: 5,
        includeTotalCount: true,
        select: [
            "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
        ],
        facets: ["Address/StateProvince"],
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
        filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
    };
    const results = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

Erstellen Sie die Datei, und führen Sie sie aus.

node -r dotenv/config src/searchSingleWithFilterGeo.js

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Abfrage mit dem angewendeten Ausschlussfilter für geospatiale Nachbearbeitung an.

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


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

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

Diese Suche verwendet SearchClient.search sowie VectorQuery und SearchOptions.

So erstellen Sie eine Hybridsuche:

Erstellen Sie eine searchHybrid.js Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create hybrid search options with both vector query and search text
    const searchOptions = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"],
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };

    // Use search_text for keyword search (hybrid search = vector + keyword)
    const searchText = "historic hotel walk to restaurants and shopping";
    const results = await searchClient.search(searchText, searchOptions);            // Convert results to typed format and log for debugging

    console.log(`\n\nHybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;

        console.log(`- Score: ${result.score}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log(`  Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}\n`);
    }

} catch (ex) {
    console.error("Hybrid search failed:", ex);
    throw ex;
}

Erstellen Sie die Datei, und führen Sie sie aus.

node -r dotenv/config src/searchHybrid.js

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Hybridsuche an.

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


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

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

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

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

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

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

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

Erstellen einer semantischen Hybridsuche

Hier ist die letzte Abfrage in der Sammlung, um die semantische Hybridsuche mit dem zusätzlichen Suchtext historic hotel walk to restaurants and shopping zu erweitern.

Diese Suche verwendet SearchClient.search sowie VectorQuery und SearchOptions.

So erstellen Sie eine semantische Hybridsuche:

Erstellen Sie eine searchSemanticHybrid.js Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { SearchClient } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create semantic hybrid search options with vector query and semantic configuration
    const searchOptions = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Category", "Description"],
        queryType: "semantic",
        semanticSearchOptions: {
            configurationName: "semantic-config"
        },
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
    };

    // Use search_text for semantic ranking
    const searchText = "historic hotel walk to restaurants and shopping";
    const results = await searchClient.search(searchText, searchOptions);

    console.log(`\n\nSemantic hybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;
        const score = result.score;
        const rerankerScoreDisplay = result.rerankerScore;

        console.log(`- Score: ${score}`);
        console.log(`  Re-ranker Score: ${rerankerScoreDisplay}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log('');
    }

} catch (ex) {
    console.error("Semantic hybrid search failed:", ex);
    throw ex;
}

Erstellen Sie die Datei, und führen Sie sie aus.

node -r dotenv/config src/searchSemanticHybrid.js

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die semantische Hybridsuche an.

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


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

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

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

- Score: 0.016393441706895828
  Re-ranker Score: 2.2718777656555176
  HotelId: 1
  HotelName: Stay-Kay City Hotel
  Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
  Category: Boutique

- Score: 0.01515151560306549
  Re-ranker Score: 2.0582215785980225
  HotelId: 3
  HotelName: Gastronomic Landscape Hotel
  Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel's restaurant services.
  Category: Suite

Wichtige Erkenntnisse:

Die Vektorsuche wird durch die Eigenschaft vectorSearchOptions angegeben. Die Schlüsselwortsuche wird durch die Eigenschaft semanticSearchOptions 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.

Bereinigen von Ressourcen

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

Alternativ können Sie den in dieser Schnellstartanleitung erstellten Vektorindex programmgesteuert löschen:

Erstellen Sie eine deleteIndex.js Datei im src Verzeichnis.

Fügen Sie die Abhängigkeiten, Umgebungsvariablen und Code hinzu, um den Index zu löschen.

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const searchIndexClient = new SearchIndexClient(searchEndpoint, credential);

try {
    console.log("Deleting index...");
    await searchIndexClient.deleteIndex(indexName);
    console.log(`Index ${indexName} deleted`);
} catch (ex) {
    console.error("Failed to delete index:", ex);
}

Erstellen Sie die Datei, und führen Sie sie aus.
```
node -r dotenv/config src/deleteIndex.js
```

Nächste Schritte

Überprüfen Sie das Repository von Codebeispielen für Vektorsuchfunktionen in Azure AI Search für JavaScript.

In dieser Schnellstartanleitung verwenden Sie TypeScript zum Erstellen, Laden und Abfragen eines Vektorindexes. Der Code führt diese Vorgänge mithilfe der Azure AI Search-Clientbibliothek für TypeScript aus, die eine Abstraktion über die REST-APIs für den Zugriff auf Indexvorgänge bereitstellt.

Hinweis

Voraussetzungen

Ein Azure-Konto mit einem aktiven Abonnement. Kostenlos ein Konto erstellen.
Ein Azure AI Search-Dienst.
- Sie können die Kostenlose Ebene für die meisten dieser Schnellstartanleitungen verwenden, aber wir empfehlen Basic oder höher für größere Datendateien.
- Weisen Sie Für die schlüssellose Authentifizierung mit Microsoft Entra-ID die Rolle "Mitwirkender für Suchindexdaten " Ihrem Benutzerkonto zu.
- Zum Ausführen der semantischen Hybridabfrage müssen Sie den semantischen Rangfolger aktivieren.
Visual Studio Code.
Node.JS mit LTS.
TypeScript. Sie können TypeScript global mithilfe von npm installieren:
```
npm install -g typescript
```

Abrufen von Dienstinformationen

In den verbleibenden Abschnitten richten Sie API-Aufrufe für Azure OpenAI und die Azure KI-Suche ein. Rufen Sie die Dienstendpunkte ab, damit Sie sie als Variablen im Code bereitstellen können.

So rufen Sie den Dienstendpunkt ab:

Melden Sie sich beim Azure-Portal an.
Suchen Sie Ihren Suchdienst.
Sie finden die URL auf der Startseite Übersicht. Ein Beispiel für einen Endpunkt ist https://example.search.windows.net.

Einrichten des Node.JS Projekts

Starten Sie Visual Studio Code in einem neuen Verzeichnis.
```
mkdir vector-quickstart && cd vector-quickstart
code .
```
Erstellen Sie ein neues Paket für ESM-Module in Ihrem Projektverzeichnis.
```
npm init -y
npm pkg set type=module
```
Dadurch wird eine package.json Datei mit Standardwerten erstellt.
Bearbeiten Sie package.json und fügen Sie ein Build-Skript hinzu. Fügen Sie die folgende Zeile zum scripts Abschnitt hinzu.
```
"build": "tsc"
```

Installieren Sie die folgenden npm-Pakete.

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

Erstellen Sie ein src Verzeichnis in Ihrem Projektverzeichnis.
```
mkdir src
```

Erstellen Sie eine tsconfig.json Datei im Projektverzeichnis für ESM mit dem folgenden Inhalt.

{
  "compilerOptions": {
    "target": "esnext",
    "module": "NodeNext",
    "moduleResolution": "nodenext",
    "rootDir": "./src",
    "outDir": "./dist/",
    "esModuleInterop": true,
    "forceConsistentCasingInFileNames": true,
    "strict": true,
    "skipLibCheck": true,
    "declaration": true,
    "sourceMap": true,
    "resolveJsonModule": true,
    "moduleDetection": "force", // Add this for ESM
    "allowSyntheticDefaultImports": true // Helpful for ESM interop
  },
  "include": [
    "src/**/*.ts"
  ]
}

Einrichten von Umgebungsvariablen für die lokale Entwicklung

Erstellen Sie eine .env Datei in Ihrem vector-quickstart Projektverzeichnis.
Fügen Sie der .env Datei die folgenden Umgebungsvariablen hinzu, und ersetzen Sie die Werte durch Ihre eigenen Dienstendpunkte und Schlüssel.
```
AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
```

Führen Sie die folgenden Befehle nacheinander aus.

az account show

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

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

Sie sollten jetzt von Ihrem lokalen Gerät aus bei Azure angemeldet sein.

Erstellen eines Vektorindexes

In diesem Abschnitt erstellen Sie einen Vektorindex in Azure AI Search mit SearchIndexClient. createOrUpdateIndex. Das Indexschema definiert die Felder, einschließlich des Vektorfelds DescriptionVector. Nachdem der Index erstellt wurde, laden Sie Dokumente in den Index hoch. Die Dokumente enthalten die vektorisierte Version der Beschreibung des Artikels, wodurch die Ähnlichkeitssuche basierend auf bedeutungsbasierten und nicht exakten Schlüsselwörtern ermöglicht wird.

So erstellen Sie den Vektorindex:

Erstellen Sie eine createIndex.ts Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient,
    SearchIndex,
    SearchField,
    VectorSearch,
    SemanticSearch,
    SearchSuggester
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}


const indexClient = new SearchIndexClient(searchEndpoint, credential);
console.log('Creating index...');

// Define fields
const fields: SearchField[] = [
    {
        name: "HotelId",
        type: "Edm.String" as const,
        key: true,
        filterable: true
    },
    {
        name: "HotelName",
        type: "Edm.String" as const,
        sortable: true,
        searchable: true,
        hidden: false,
    },
    {
        name: "Description",
        type: "Edm.String" as const,
        searchable: true,
        hidden: false
    },
    {
        name: "DescriptionVector",
        type: "Collection(Edm.Single)" as const,
        searchable: true,
        vectorSearchDimensions: 1536,
        vectorSearchProfileName: "my-vector-profile",
        hidden: true
    },
    {
        name: "Category",
        type: "Edm.String" as const,
        sortable: true,
        filterable: true,
        facetable: true,
        searchable: true,
        hidden: false
    },
    {
        name: "Tags",
        type: "Collection(Edm.String)" as const,
        searchable: true,
        filterable: true,
        facetable: true,
        hidden: false
    },
    {
        name: "ParkingIncluded",
        type: "Edm.Boolean" as const,
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "LastRenovationDate",
        type: "Edm.DateTimeOffset" as const,
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Rating",
        type: "Edm.Double" as const,
        filterable: true,
        sortable: true,
        facetable: true
    },
    {
        name: "Address",
        type: "Edm.ComplexType" as const,
        fields: [
            {
                name: "StreetAddress",
                type: "Edm.String" as const,
                searchable: true,
            },
            {
                name: "City",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "StateProvince",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                hidden: false,
                searchable: true
            },
            {
                name: "PostalCode",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true
            },
            {
                name: "Country",
                type: "Edm.String" as const,
                filterable: true,
                sortable: true,
                facetable: true,
                searchable: true,
            }
        ]
    },
    {
        name: "Location",
        type: "Edm.GeographyPoint" as const,
        filterable: true,
        sortable: true
    }
];

// Define vector search configuration
const vectorSearch: VectorSearch = {
    algorithms: [
        {
            name: "hnsw-vector-config",
            kind: "hnsw" as const
        },
        {
            name: "eknn-vector-config",
            kind: "exhaustiveKnn" as const
        }
    ],
    profiles: [
        {
            name: "my-vector-profile",
            algorithmConfigurationName: "hnsw-vector-config"
        }
    ]
};

// Define semantic configuration
const semanticConfig = {
    name: "semantic-config",
    prioritizedFields: {
        titleField: {
            name: "HotelName"
        },
        contentFields: [
            {
                name: "Description"
            }
        ],
        keywordsFields: [
            {
                name: "Category"
            }
        ]
    }
};

// Create the semantic settings with the configuration
const semanticSearch: SemanticSearch = {
    configurations: [semanticConfig]
};

// Define suggesters
const suggesters: SearchSuggester[] = [];

// Create the search index with the semantic settings
const indexDefinition: SearchIndex = {
    name: indexName,
    fields: fields,
    vectorSearch: vectorSearch,
    semanticSearch: semanticSearch,
    suggesters: suggesters
};

const result = await indexClient.createOrUpdateIndex(indexDefinition);
console.log(`${result.name} created`);

Erstellen Sie die Datei, und führen Sie sie aus.
```
npm run build && node -r dotenv/config dist/createIndex.js
```
Die Ausgabe dieses Codes zeigt, dass der Index erfolgreich erstellt wird.
```
Using Azure Search endpoint: https://my-service.search.windows.net
Using index name: hotels-vector-quickstart
Creating index...
hotels-vector-quickstart created
```
Wichtige Erkenntnisse:
- Sie definieren einen Index, indem Sie eine Liste von Feldern erstellen.
- Dieser spezielle Index unterstützt mehrere Suchfunktionen, z. B.:
  - Volltext-Schlüsselwortsuche (searchable)
  - Vektorsuche (ermöglicht die Hybridsuche durch Die Zuordnung von Vektor- und Nichtvektorfeldern) (DescriptionVector mit vectorSearchProfileName)
  - Semantische Suche
  - Facettierte Suche (searchSuggester)
  - Geo-räumliche Suche (Location Feld mit geo.distance)
  - Filtern, Sortieren (viele Felder, die gefiltert und sortiert werden können)

Hochladen von Dokumenten in den Index

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

So laden Sie Dokumente in den Index hoch:

Erstellen Sie eine uploadDocuments.ts Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const DOCUMENTS =  [
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "1",
        "HotelName": "Stay-Kay City Hotel",
        "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
        "DescriptionVector": 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        "Category": "Boutique",
        "Tags": [
            "view",
            "air conditioning",
            "concierge"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2022-01-18T00:00:00Z",
        "Rating": 3.60,
        "Address": {
            "StreetAddress": "677 5th Ave",
            "City": "New York",
            "StateProvince": "NY",
            "PostalCode": "10022",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                -73.975403,
                40.760586
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "2",
        "HotelName": "Old Century Hotel",
        "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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        "Category": "Boutique",
        "Tags": [
            "pool",
            "free wifi",
            "air conditioning",
            "concierge"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2019-02-18T00:00:00Z",
        "Rating": 3.60,
        "Address": {
            "StreetAddress": "140 University Town Center Dr",
            "City": "Sarasota",
            "StateProvince": "FL",
            "PostalCode": "34243",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
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            ]
        }
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        "HotelId": "3",
        "HotelName": "Gastronomic Landscape Hotel",
        "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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        "Category": "Suite",
        "Tags": [
            "restaurant",
            "bar",
            "continental breakfast"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2015-09-20T00:00:00Z",
        "Rating": 4.80,
        "Address": {
            "StreetAddress": "3393 Peachtree Rd",
            "City": "Atlanta",
            "StateProvince": "GA",
            "PostalCode": "30326",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                33.847848,
                -84.363602
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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        "Category": "Boutique",
        "Tags": [
            "concierge",
            "view",
            "air conditioning"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2020-02-06T00:00:00Z",
        "Rating": 4.60,
        "Address": {
            "StreetAddress": "7400 San Pedro Ave",
            "City": "San Antonio",
            "StateProvince": "TX",
            "PostalCode": "78216",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                -98.498923,
                29.518029
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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        "Category": "Boutique",
        "Tags": [
            "continental breakfast",
            "air conditioning",
            "free wifi"
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        "ParkingIncluded": "false",
        "LastRenovationDate": "2018-02-19T00:00:00Z",
        "Rating": 4.5,
        "Address": {
            "StreetAddress": "1401 I St NW",
            "City": "Washington D.C.",
            "StateProvince": "null",
            "PostalCode": "20005",
            "Country": "USA"
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            "properties": {
                "name": "EPSG:4326"
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        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.",
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        "Category": "Suite",
        "Tags": [
            "air conditioning",
            "laundry service",
            "24-hour front desk service"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2018-01-27T00:00:00Z",
        "Rating": 2.7,
        "Address": {
            "StreetAddress": "1100 S Hayes St",
            "City": "Arlington",
            "StateProvince": "VA",
            "PostalCode": "22202",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -77.060066,
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            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
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        }
    },
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        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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        "Category": "Luxury",
        "Tags": [
            "bar",
            "concierge",
            "restaurant"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2020-03-18T00:00:00Z",
        "Rating": 4.1,
        "Address": {
            "StreetAddress": "3 Cityplace Dr",
            "City": "St. Louis",
            "StateProvince": "MO",
            "PostalCode": "63141",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -90.44081,
            38.67219
            ],
            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
                }
            }
        }
    }
];

async function waitUntilIndexed(): Promise<void> {
    try {
        const searchClient = new SearchClient(searchEndpoint, indexName, credential);
        do {
            const count = await searchClient.getDocumentsCount();
            if (count == DOCUMENTS.length) {
                console.log("All documents indexed successfully.");
                break;
            }
            console.log(`Waiting for indexing... Current count: ${count}`);
            await new Promise((resolve) => setTimeout(resolve, 10000)); // Wait for 10 seconds
        } while (true);
    } catch (ex) {
        console.error("Failed to wait until indexed:", ex);
    }
}

const searchClient = new SearchClient(searchEndpoint, indexName, credential);

try {
    console.log("Uploading documents...");
    const result = await searchClient.uploadDocuments(DOCUMENTS);
    for (const r of result.results) {
        console.log(`Key: ${r.key}, Succeeded: ${r.succeeded}, ErrorMessage: ${r.errorMessage || 'none'}`);
    }
    await waitUntilIndexed();
} catch (ex) {
    console.error("Failed to upload documents:", ex);
}

Dieser Code lädt variable Dokumente mit einem JSON-Objekt, das jedes Dokument beschreibt, zusammen mit der vektorisierten Version der Beschreibung des Artikels. Dieser Vektor ermöglicht die Ähnlichkeitssuche, bei der der Abgleich auf Bedeutung und nicht auf exakten Schlüsselwörtern basiert.

Erstellen Sie die Datei, und führen Sie sie aus.
```
npm run build && node -r dotenv/config dist/uploadDocuments.js
```
Die Ausgabe dieses Codes zeigt, dass die Dokumente indiziert und für die Suche bereit sind.
```
Uploading documents...
Key: 1, Succeeded: true, ErrorMessage: none
Key: 2, Succeeded: true, ErrorMessage: none
Key: 3, Succeeded: true, ErrorMessage: none
Key: 4, Succeeded: true, ErrorMessage: none
Key: 48, Succeeded: true, ErrorMessage: none
Key: 49, Succeeded: true, ErrorMessage: none
Key: 13, Succeeded: true, ErrorMessage: none
Waiting for indexing... Current count: 0
All documents indexed successfully.
```
Wichtige Erkenntnisse:
- 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 bietet, z. B.:
  - Datenaufnahme: uploadDocuments, mergeDocuments, deleteDocuments, usw.
  - Suchvorgänge: search, autocompletesuggest
  - Indexverwaltungsvorgänge: createIndex, deleteIndex, getIndex, usw.
- Vektorfelder enthalten Gleitkommawerte. Das Dimensions-Attribut weist mindestens 2 und maximal 4096 Gleitkommawerte auf. n dieser Schnellstartanleitung wird das Attribut „Dimensionen“ auf 1.536 gesetzt, da dies die Größe der vom text-embedding-ada-002-Modell generierten Einbettungen ist.

Erstellen der Abfrage als Vektor

Die Beispielvektorabfragen basieren auf zwei Zeichenfolgen:

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

Erstellen Sie eine queryVector.ts Datei im src Verzeichnis, und fügen Sie den Code hinzu, um den Abfragevektor zu erstellen.

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

export const vector = 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Dieser Code wird in den folgenden Abschnitten zum Ausführen von Vektorsuchen verwendet. Der Abfragevektor wird mithilfe eines Einbettungsmodells aus Azure OpenAI erstellt.

Erstellen einer einzelnen Vektorsuche

So erstellen Sie eine einzelne Vektorsuche:

Erstellen Sie eine searchSingle.ts Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
        includeTotalCount: true,
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search failed:", ex);
    throw ex;
}

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

Erstellen Sie die Datei, und führen Sie sie aus.

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

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Abfrage quintessential lodging near running trails, eateries, retailan.

Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


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

Die Antwort für das Vektoräquivalent quintessential lodging near running trails, eateries, retail besteht aus 5 Ergebnissen mit nur den von der zurückgegebenen Auswahl angegebenen Feldern.

Erstellen einer einzelnen Vektorsuche mit einem Filter

So erstellen Sie eine einzelne Vektorsuche mit einem Filter:

Erstellen Sie eine searchSingleWithFilter.ts Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    // Create a vector search options with the vector query and filter
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 7,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
        includeTotalCount: true,
        filter: "Tags/any(tag: tag eq 'free wifi')", // Adding filter for "free wifi" tag
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);

    console.log(`\n\nSingle Vector search with filter found ${results.count}`);

    for await (const result of results.results) {
        // Log each result
        const doc = result.document;
        console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
    }

} catch (ex) {
    console.error("Vector search with filter failed:", ex);
    throw ex;
}

Erstellen Sie die Datei, und führen Sie sie aus.

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

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Abfrage mit dem angewendeten free wifi Filter an:

Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


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

Erstellen einer Suche mit einem Geospatialfilter

So erstellen Sie eine Suche mit einem Geospatialfilter:

Erstellen Sie eine searchSingleWithFilterGeo.ts Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);


try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true
    };

    const searchOptions: SearchOptions<HotelDocument> = {
        top: 5,
        includeTotalCount: true,
        select: [
            "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
        ] as const,
        facets: ["Address/StateProvince"],
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
        filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
    };
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);

    console.log(`\n\nVector search with geo filter found ${results.count}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;

        console.log(`- HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Score: ${result.score}`);

        if (doc.Address) {
            console.log(`  City/State: ${doc.Address.City}, ${doc.Address.StateProvince}`);
        }

        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Score: ${result.score}\n`);
    }


} catch (ex) {
    console.error("Vector search with geo filter failed:", ex);
    throw ex;
}

Erstellen Sie die Datei, und führen Sie sie aus.

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

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Abfrage mit dem angewendeten Ausschlussfilter für geospatiale Nachbearbeitung an.

Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


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

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

Diese Suche verwendet SearchClient.search sowie VectorQuery und SearchOptions.

So erstellen Sie eine Hybridsuche:

Erstellen Sie eine searchHybrid.ts Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create hybrid search options with both vector query and search text
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Description", "Category", "Tags"] as const,
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        }
    };

    // Use search_text for keyword search (hybrid search = vector + keyword)
    const searchText = "historic hotel walk to restaurants and shopping";
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search(searchText, searchOptions);            // Convert results to typed format and log for debugging

    console.log(`\n\nHybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;

        console.log(`- Score: ${result.score}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log(`  Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}\n`);
    }

} catch (ex) {
    console.error("Hybrid search failed:", ex);
    throw ex;
}

Erstellen Sie die Datei, und führen Sie sie aus.

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

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die Hybridsuche an:

Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


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

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

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

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

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

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

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

Erstellen einer semantischen Hybridsuche

Hier ist die letzte Abfrage in der Sammlung, um die semantische Hybridsuche mit dem zusätzlichen Suchtext historic hotel walk to restaurants and shopping zu erweitern.

Diese Suche verwendet SearchClient.search sowie VectorQuery und SearchOptions.

So erstellen Sie eine semantische Hybridsuche:

Erstellen Sie eine searchSemanticHybrid.ts Datei im src Verzeichnis.

Kopieren Sie den folgenden Code in die Datei.

import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
import { vector } from "./queryVector.js";
import { DefaultAzureCredential } from "@azure/identity";

export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

// Define an interface for the hotel document
export interface HotelDocument {
    HotelId: string;
    HotelName: string;
    Description: string;
    DescriptionVector?: number[];
    Category?: string;
    Tags?: string[] | string;
    Address?: {
        City: string;
        StateProvince: string;
    };
    Location?: {
        type: string;
        coordinates: [number, number]; // [longitude, latitude]
    };
    "@search.score"?: number;
    "@search.reranker_score"?: number;
}

// const key = process.env.AZURE_SEARCH_KEY || "";
// const searchClient = new SearchClient<HotelDocument>(
//     searchEndpoint!,
//     indexName,
//     new AzureKeyCredential(key)
// );

const searchClient = new SearchClient<HotelDocument>(
    searchEndpoint,
    indexName,
    new DefaultAzureCredential()
);

try {

    const vectorQuery: VectorQuery<HotelDocument> = {
        vector: vector,
        kNearestNeighborsCount: 5,
        fields: ["DescriptionVector"],
        kind: "vector",
        exhaustive: true

    };

    // Create semantic hybrid search options with vector query and semantic configuration
    const searchOptions: SearchOptions<HotelDocument> = {
        top: 5,
        includeTotalCount: true,
        select: ["HotelId", "HotelName", "Category", "Description"] as const,
        queryType: "semantic" as const,
        semanticSearchOptions: {
            configurationName: "semantic-config"
        },
        vectorSearchOptions: {
            queries: [vectorQuery],
            filterMode: "postFilter" // Apply filter after vector similarity is calculated
        },
    };

    // Use search_text for semantic ranking
    const searchText = "historic hotel walk to restaurants and shopping";
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search(searchText, searchOptions);

    console.log(`\n\nSemantic hybrid search found ${results.count} then limited to top ${searchOptions.top}`);

    for await (const result of results.results) {

        // Log each result
        const doc = result.document;
        const score = result.score;
        const rerankerScoreDisplay = result.rerankerScore;

        console.log(`- Score: ${score}`);
        console.log(`  Re-ranker Score: ${rerankerScoreDisplay}`);
        console.log(`  HotelId: ${doc.HotelId}`);
        console.log(`  HotelName: ${doc.HotelName}`);
        console.log(`  Description: ${doc.Description || 'N/A'}`);
        console.log(`  Category: ${doc.Category || 'N/A'}`);
        console.log('');
    }

} catch (ex) {
    console.error("Semantic hybrid search failed:", ex);
    throw ex;
}

Erstellen Sie die Datei, und führen Sie sie aus.

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

Die Ausgabe dieses Codes zeigt die relevanten Dokumente für die semantische Hybridsuche an.

Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
Using index name: hotels-vector-quickstart-0627-4


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

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

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

- Score: 0.016393441706895828
  Re-ranker Score: 2.2718777656555176
  HotelId: 1
  HotelName: Stay-Kay City Hotel
  Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
  Category: Boutique

- Score: 0.01515151560306549
  Re-ranker Score: 2.0582215785980225
  HotelId: 3
  HotelName: Gastronomic Landscape Hotel
  Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel's restaurant services.
  Category: Suite

Wichtige Erkenntnisse:

Die Vektorsuche wird durch die Eigenschaft vectorSearchOptions angegeben. Die Schlüsselwortsuche wird durch die Eigenschaft semanticSearchOptions 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.

Bereinigen von Ressourcen

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

Alternativ können Sie den in dieser Schnellstartanleitung erstellten Vektorindex programmgesteuert löschen:

Erstellen Sie eine deleteIndex.ts Datei im src Verzeichnis.

Fügen Sie die Abhängigkeiten, Umgebungsvariablen und Code hinzu, um den Index zu löschen.

import { DefaultAzureCredential } from "@azure/identity";
import {
    SearchIndexClient
} from "@azure/search-documents";

const credential = new DefaultAzureCredential();
export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;

console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
console.log(`Using index name: ${indexName}`);

const searchIndexClient = new SearchIndexClient(searchEndpoint, credential);

try {
    console.log("Deleting index...");
    await searchIndexClient.deleteIndex(indexName);
    console.log(`Index ${indexName} deleted`);
} catch (ex) {
    console.error("Failed to delete index:", ex);
}

Erstellen Sie die Datei, und führen Sie sie aus.

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

Nächste Schritte

Überprüfen Sie das Repository von Codebeispielen für Vektorsuchfunktionen in Azure AI Search für JavaScript.

In dieser Schnellstartanleitung verwenden Sie eine .NET-App zum Erstellen, Laden und Abfragen eines Vektorindexes. Der Code führt diese Vorgänge mithilfe der Azure AI Search-Clientbibliothek für .NET aus, die eine Abstraktion über die REST-APIs für den Zugriff auf Indexvorgänge bereitstellt.

Hinweis

Voraussetzungen

Ein Azure-Konto mit einem aktiven Abonnement. Kostenlos ein Konto erstellen.
Ein Azure AI Search-Dienst.
- Sie können die Kostenlose Ebene für die meisten dieser Schnellstartanleitungen verwenden, aber wir empfehlen Basic oder höher für größere Datendateien.
- Weisen Sie Für die schlüssellose Authentifizierung mit Microsoft Entra-ID die Rolle "Mitwirkender für Suchindexdaten " Ihrem Benutzerkonto zu.
- Zum Ausführen der semantischen Hybridabfrage müssen Sie den semantischen Rangfolger aktivieren.
Visual Studio Code oder Visual Studio zum Ausführen des Codes.
Git , um das Beispiel-Repository zu klonen.

Abrufen von Dienstinformationen

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. Informationen zu allen Optionen finden Sie unter Authentifizieren von .NET-Apps für Azure-Dienste mithilfe des Azure SDK für .NET.

Klonen des Codes und der Setupumgebung

Klonen Sie das Repository, das den Code für diese Schnellstartanleitung enthält.
```
git clone https://github.com/Azure-Samples/azure-search-dotnet-samples
```
Öffnen Sie den quickstart-vector-search Ordner in Visual Studio Code, oder doppelklicken Sie auf die VectorSearchQuickstart.sln Datei, um die Projektmappe in Visual Studio zu öffnen.
Öffnen Sie appsettings.json in beiden Ordnern VectorSearchCreatePopulateIndex und VectorSearchExamples.
Legen Sie Endpoint auf Ihre Suchdienst-URL fest, die ähnlich wie https://mydemo.search.windows.net sein sollte.
Legen Sie IndexName einen eindeutigen Namen für Ihren Index fest. Sie können auch den Standardnamen hotels-vector-quickstart verwenden.

Erstellen eines Vektorindexes und Hochladen von Dokumenten

Um Suchabfragen für Ihren Azure AI Search-Dienst auszuführen, müssen Sie zuerst einen Suchindex erstellen und Dokumente in den Index hochladen.

So erstellen und füllen Sie den Index auf:

Öffnen Sie ein Terminal im VectorSearchCreatePopulateIndex Ordner.

Verwenden Sie den dotnet run Befehl, um das Projekt auszuführen.

dotnet run

Der folgende Code wird ausgeführt, um einen Index zu erstellen:

static async Task CreateSearchIndex(string indexName, SearchIndexClient indexClient)
{
    var addressField = new ComplexField("Address");
    addressField.Fields.Add(new SearchableField("StreetAddress") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft });
    addressField.Fields.Add(new SearchableField("City") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
    addressField.Fields.Add(new SearchableField("StateProvince") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
    addressField.Fields.Add(new SearchableField("PostalCode") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });
    addressField.Fields.Add(new SearchableField("Country") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true });

    var allFields = new List<SearchField>()
{
    new SimpleField("HotelId", SearchFieldDataType.String) { IsKey = true, IsFacetable = true, IsFilterable = true },
    new SearchableField("HotelName") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft },
    new SearchableField("Description") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft },
    new VectorSearchField("DescriptionVector", 1536, "my-vector-profile"),
    new SearchableField("Category") { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true },
    new SearchableField("Tags", collection: true) { AnalyzerName = LexicalAnalyzerName.EnMicrosoft, IsFacetable = true, IsFilterable = true },
    new SimpleField("ParkingIncluded", SearchFieldDataType.Boolean) { IsFacetable = true, IsFilterable = true },
    new SimpleField("LastRenovationDate", SearchFieldDataType.DateTimeOffset) { IsSortable = true },
    new SimpleField("Rating", SearchFieldDataType.Double) { IsFacetable = true, IsFilterable = true, IsSortable = true },
    addressField,
    new SimpleField("Location", SearchFieldDataType.GeographyPoint) { IsFilterable = true, IsSortable = true },
};

    // Create the suggester configuration
    var suggester = new SearchSuggester("sg", new[] { "Address/City", "Address/Country" });

    // Create the semantic search
    var semanticSearch = new SemanticSearch()
    {
        Configurations =
        {
            new SemanticConfiguration(
            name: "semantic-config",
            prioritizedFields: new SemanticPrioritizedFields
            {
                TitleField = new SemanticField("HotelName"),
                KeywordsFields = { new SemanticField("Category") },
                ContentFields = { new SemanticField("Description") }
            })
    }
    };

    // Add vector search configuration
    var vectorSearch = new VectorSearch();
    vectorSearch.Algorithms.Add(new HnswAlgorithmConfiguration(name: "my-hnsw-vector-config-1"));
    vectorSearch.Profiles.Add(new VectorSearchProfile(name: "my-vector-profile", algorithmConfigurationName: "my-hnsw-vector-config-1"));

    var definition = new SearchIndex(indexName)
    {
        Fields = allFields,
        Suggesters = { suggester },
        VectorSearch = vectorSearch,
        SemanticSearch = semanticSearch
    };

    // Create or update the index
    Console.WriteLine($"Creating or updating index '{indexName}'...");
    var result = await indexClient.CreateOrUpdateIndexAsync(definition);
    Console.WriteLine($"Index '{result.Value.Name}' updated.");
    Console.WriteLine();
}

Der folgende Code lädt JSON-formatierte Dokumente in die hotel-samples.json Datei in Ihren Suchdienst hoch.

static async Task UploadDocs(SearchClient searchClient)
{
    var jsonPath = Path.Combine(Directory.GetCurrentDirectory(), "HotelData.json");

    // Read and parse hotel data
    var json = await File.ReadAllTextAsync(jsonPath);
    List<Hotel> hotels = new List<Hotel>();
    try
    {
        using var doc = JsonDocument.Parse(json);
        if (doc.RootElement.ValueKind != JsonValueKind.Array)
        {
            Console.WriteLine("HotelData.json root is not a JSON array.");
        }
        // Deserialize all hotel objects
        hotels = doc.RootElement.EnumerateArray()
            .Select(e => JsonSerializer.Deserialize<Hotel>(e.GetRawText()))
            .Where(h => h != null)
            .ToList();
    }
    catch (JsonException ex)
    {
        Console.WriteLine($"Failed to parse HotelData.json: {ex.Message}");
    }

    try
    {
        // Upload hotel documents to Azure Search
        var result = await searchClient.UploadDocumentsAsync(hotels);
        foreach (var r in result.Value.Results)
        {
            Console.WriteLine($"Key: {r.Key}, Succeeded: {r.Succeeded}");
        }
    }
    catch (Exception ex)
    {
        Console.WriteLine("Failed to upload documents: " + ex);
    }
}

Nachdem Sie das Projekt ausgeführt haben, wird die folgende Ausgabe gedruckt.

Key: 1, Succeeded: True
Key: 2, Succeeded: True
Key: 3, Succeeded: True
Key: 4, Succeeded: True
Key: 48, Succeeded: True
Key: 49, Succeeded: True
Key: 13, Succeeded: True

Wichtige Erkenntnisse:

Ihr Code interagiert mit einem bestimmten Suchindex, der in Ihrem Azure AI Search-Dienst über den SearchClient gehostet wird. Dabei handelt es sich um das Hauptobjekt, das vom Azure-search-documents-Paket bereitgestellt wird. Der SearchClient bietet Zugriff auf Indexvorgänge, z. B.:
- Datenaufnahme: UploadDocuments(), MergeDocuments()DeleteDocuments()
- Suchvorgänge: Search(), Autocomplete()Suggest()
- Indexverwaltungsvorgänge: CreateOrUpdateIndex()
Vektorfelder enthalten Gleitkommawerte. Das Dimensions-Attribut weist mindestens 2 und maximal 4096 Gleitkommawerte auf. n dieser Schnellstartanleitung wird das Attribut „Dimensionen“ auf 1.536 gesetzt, da dies die Größe der vom text-embedding-ada-002-Modell generierten Einbettungen ist.

Ausführen von Abfragen

Um die Vektorabfragen in diesem Abschnitt ausstellen zu können, öffnen Sie Program.cs den VectorSearchExamples Ordner, und öffnen Sie dann ein Terminal im VectorSearchExamples Ordner.

Abfragen in diesem Abschnitt:

Einzelvektorsuche
Einzelvektorsuche mit Filter
Hybridsuche
Semantische Hybridsuche mit Filter

Einzelvektorsuche

Die erste Abfrage veranschaulicht ein einfaches Szenario, in dem Sie Dokumentbeschreibungen finden möchten, die eng mit der Suchzeichenfolge übereinstimmen.

So erstellen Sie eine einzelne Vektorsuche:

Entkommentieren Sie in der Datei Program.cs des Ordners VectorSearchExamples den Methodenaufruf SearchExamples.SearchSingleVector(searchClient, vectorizedResult);.

Diese Methode führt die folgende Suchfunktion in der SearchExamples.cs Klasse aus.

public static async Task SearchSingleVector(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
        });

    Console.WriteLine($"Single Vector Search Results:");
    await foreach (SearchResult<Hotel> result in response.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}");
    }
    Console.WriteLine();
}

Führen Sie den Befehl dotnet run aus, um die Abfrage auszuführen, die die folgenden Ergebnisse zurückgibt.

Single Vector Search Results:
Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel
Score: 0.6333684, HotelId: 13, HotelName: Luxury Lion Resort
Score: 0.605672, HotelId: 4, HotelName: Sublime Palace Hotel
Score: 0.6026341, HotelId: 49, HotelName: Swirling Currents Hotel
Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel

Einzelvektorsuche mit Filter

In Azure AI Search gelten Filter für Nichtvektorfelder in einem Index. In diesem Beispiel wird das Tags Feld gefiltert, um Hotels herauszufiltern, die kein kostenloses WLAN bereitstellen.

So erstellen Sie eine einzelne Vektorsuche mit einem Filter:

Entkommentieren Sie in der Datei Program.cs des Ordners VectorSearchExamples den Methodenaufruf SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);.

Diese Methode führt die folgende Suchfunktion in der SearchExamples.cs Klasse aus.

public static async Task SearchSingleVectorWithFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Filter = "Tags/any(tag: tag eq 'free wifi')",
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" }
        });

    Console.WriteLine($"Single Vector Search With Filter Results:");
    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}, Tags: {string.Join(String.Empty, doc.Tags)}");
    }
    Console.WriteLine();
}

Führen Sie dotnet run die Abfrage aus, die nur Hotels zurückgibt, die kostenloses WLAN bereitstellen.

 Single Vector Search With Filter Results:
 Score: 0.6605852, HotelId: 48, HotelName: Nordick's Valley Motel, Tags: continental breakfastair conditioningfree wifi
 Score: 0.57902366, HotelId: 2, HotelName: Old Century Hotel, Tags: poolfree wifiair conditioningconcierge

Um einen Geofilter zu verwenden, entfernen Sie die Kommentare in SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult); in Program.cs.

Diese Methode führt die folgende Suchfunktion in der SearchExamples.cs Klasse aus.

public static async Task SingleSearchWithGeoFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithGeoFilter = await searchClient.SearchAsync<Hotel>(
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Filter = "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
            Select = { "HotelId", "HotelName", "Description", "Address", "Category", "Tags" },
            Facets = { "Address/StateProvince" },
        });

    Console.WriteLine($"Vector query with a geo filter:");
    await foreach (SearchResult<Hotel> result in responseWithGeoFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"HotelId: {doc.HotelId}");
        Console.WriteLine($"HotelName: {doc.HotelName}");
        Console.WriteLine($"Score: {result.Score}");
        Console.WriteLine($"City/State: {doc.Address.City}/{doc.Address.StateProvince}");
        Console.WriteLine($"Description: {doc.Description}");
        Console.WriteLine();
    }
    Console.WriteLine();
}

Führen Sie dotnet run die Abfrage aus, die nur Hotels innerhalb von 300 Kilometern zurückgibt.

Vector query with a geo filter:
-HotelId: 48
HotelName: Nordick's Valley Motel
Score: 0.6605852246284485
City/State: Washington D.C./null
Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.

-HotelId: 49
HotelName: Swirling Currents Hotel
Score: 0.602634072303772
City/State: Arlington/VA
Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.

Hybridsuche

Die Hybridsuche kombiniert Schlüsselwort- und Vektorabfragen in einer Anforderung. In diesem Beispiel werden die folgenden Volltext- und Vektorabfragezeichenfolgen gleichzeitig ausgeführt:

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

So erstellen Sie eine Hybridsuche:

In der Datei Program.cs muss der Kommentar des Methodenaufrufs SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult); entfernt werden.

Diese Methode führt die folgende Suchfunktion in der SearchExamples.cs Klasse aus.

public static async Task<SearchResults<Hotel>> SearchHybridVectorAndText(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
        "historic hotel walk to restaurants and shopping",
        new SearchOptions
        {
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
            Size = 5,
        });

    Console.WriteLine($"Hybrid search results:");
    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}");
        Console.WriteLine($"HotelId: {doc.HotelId}");
        Console.WriteLine($"HotelName: {doc.HotelName}");
        Console.WriteLine($"Description: {doc.Description}");
        Console.WriteLine($"Category: {doc.Category}");
        Console.WriteLine($"Tags: {string.Join(String.Empty, doc.Tags)}");
        Console.WriteLine();
    }
    Console.WriteLine();
    return responseWithFilter;
}

Führen Sie den Befehl dotnet run aus, um die Abfrage auszuführen, die die folgenden Ergebnisse zurückgibt.

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

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

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

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

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

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

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

Semantische Hybridsuche mit einem Filter

Fügen Sie die semantische Rangfolge hinzu, um Ergebnisse basierend auf dem Sprachverständnis neu zu reranken.

So erstellen Sie eine semantische Hybridsuche mit einem Filter:

Löschen Sie in der Datei Program.cs den Kommentar für den SearchExamples.SearchHybridVectorAndSemantic(searchClient, vectorizedResult);-Methodenaufruf.

Diese Methode führt die folgende Suchfunktion in der SearchExamples.cs Klasse aus.

public static async Task SearchHybridVectorAndSemantic(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
{
    SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
        "historic hotel walk to restaurants and shopping",
        new SearchOptions
        {
            IncludeTotalCount = true,
            VectorSearch = new()
            {
                Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
            },
            Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
            SemanticSearch = new SemanticSearchOptions
            {
                SemanticConfigurationName = "semantic-config"
            },
            QueryType = SearchQueryType.Semantic,
            Size = 5
        });

    Console.WriteLine($"Hybrid search results:");
    await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
    {
        Hotel doc = result.Document;
        Console.WriteLine($"Score: {result.Score}");
        Console.WriteLine($"HotelId: {doc.HotelId}");
        Console.WriteLine($"HotelName: {doc.HotelName}");
        Console.WriteLine($"Description: {doc.Description}");
        Console.WriteLine($"Category: {doc.Category}");
        Console.WriteLine();
    }
    Console.WriteLine();
}

Führen Sie dotnet run die Abfrage aus, die die folgenden Hotels zurückgibt. Die Hotels werden nach Standort gefiltert, nach StateProvince facettiert und semantisch neu geordnet, um Ergebnisse zu fördern, die der Suchanfrage am nächsten kommen (historic hotel walk to restaurants and shopping).

Das Swirling Currents Hotel rückt nun an die Spitze vor. Ohne semantische Rangfolge ist Nordick‘s Valley Motel die Nummer eins. Mit semantischem Ranking erkennen die Maschinenverständnismodelle, dass historic sich auf "Hotel in fußläufiger Entfernung zu Restaurants und Einkaufsmöglichkeiten" bezieht.

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:

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.

Bereinigen von Ressourcen

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

Nächste Schritte

Überprüfen Sie das Repository von Codebeispielen für Vektorsuchfunktionen in Azure AI Search für .NET.
Überprüfen Sie die anderen .NET- und Azure AI Search-Codebeispiele im Repository azure-search-dotnet-samples.

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

Hinweis

Voraussetzungen

Ein Azure-Konto mit einem aktiven Abonnement. Kostenlos ein Konto erstellen.
Ein Azure AI Search-Dienst.
- Sie können die Kostenlose Ebene für die meisten dieser Schnellstartanleitungen verwenden, aber wir empfehlen Basic oder höher für größere Datendateien.
- Zum Ausführen der semantischen Hybridabfrage müssen Sie den semantischen Rangfolger aktivieren.
Visual Studio Code mit einem REST-Client.

Abrufen von Dienstinformationen

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
API-Schlüssel

Melden Sie sich beim Azure-Portal an, und suchen Sie Ihren Suchdienst.
Sie finden die URL auf der Startseite Übersicht. Ein Beispiel für einen Endpunkt ist https://mydemo.search.windows.net.
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
```

Melden Sie sich beim Azure-Portal an, und suchen Sie Ihren Suchdienst.
Sie finden die URL auf der Startseite Übersicht. Ein Beispiel für einen Endpunkt ist https://mydemo.search.windows.net.
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.

So erstellen Sie die Codedatei:

Erstellen Sie in Visual Studio Code eine Datei mit einer .rest- oder .http-Dateierweiterung, z. B. az-search-vector-quickstart.rest.
Kopieren und fügen Sie den ursprünglichen Inhalt der az-search-vector-quickstart.rest Datei in die neue Datei ein.
```
@baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE
```
Legen Sie @baseUrl diesen auf den Endpunkt fest, den Sie in "Dienstinformationen abrufen" abgerufen haben.
Legen Sie den Schlüssel oder das Token für die Authentifizierung fest. Sie haben diesen Wert in "Dienstinformationen abrufen" abgerufen.
- Ersetzen Sie für die @apiKey schlüssellose Authentifizierung mit microsoft Entra ID durch die @token Variable.
```
@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
```
Ersetzen Sie für die empfohlene schlüssellose Authentifizierung mit Microsoft Entra ID alle Vorkommen von api-key: {{apiKey}} durch Authorization: Bearer {{token}} in den Anforderungsheadern.

Erstellen eines Vektorindexes

Sie verwenden Indizes – Erstellen (REST-API), 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.

So erstellen Sie einen Vektorindex:

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

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=2025-09-01  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

{
    "name": "hotels-vector-quickstart",
    "fields": [
        {
            "name": "HotelId", 
            "type": "Edm.String",
            "searchable": false, 
            "filterable": true, 
            "retrievable": true, 
            "sortable": false, 
            "facetable": false,
            "key": true
        },
        {
            "name": "HotelName", 
            "type": "Edm.String",
            "searchable": true, 
            "filterable": false, 
            "retrievable": true, 
            "sortable": true, 
            "facetable": false
        },
        {
            "name": "Description", 
            "type": "Edm.String",
            "searchable": true, 
            "filterable": false, 
            "retrievable": true, 
            "sortable": false, 
            "facetable": false
        },
        {
            "name": "DescriptionVector",
            "type": "Collection(Edm.Single)",
            "searchable": true,
            "retrievable": true,
            "dimensions": 1536,
            "vectorSearchProfile": "my-vector-profile"
        },
        {
            "name": "Category", 
            "type": "Edm.String",
            "searchable": true, 
            "filterable": true, 
            "retrievable": true, 
            "sortable": true, 
            "facetable": true
        },
        {
            "name": "Tags",
            "type": "Collection(Edm.String)",
            "searchable": true,
            "filterable": true,
            "retrievable": true,
            "sortable": false,
            "facetable": true
        },
                {
            "name": "ParkingIncluded",
            "type": "Edm.Boolean",
            "searchable": false,
            "filterable": true,
            "retrievable": true,
            "sortable": true,
            "facetable": true
        },
        {
            "name": "LastRenovationDate",
            "type": "Edm.DateTimeOffset",
            "searchable": false,
            "filterable": true,
            "retrievable": true,
            "sortable": true,
            "facetable": true
        },
        {
            "name": "Rating",
            "type": "Edm.Double",
            "searchable": false,
            "filterable": true,
            "retrievable": true,
            "sortable": true,
            "facetable": true
        },
        {
            "name": "Address", 
            "type": "Edm.ComplexType",
            "fields": [
                {
                    "name": "StreetAddress", "type": "Edm.String",
                    "searchable": true, "filterable": false, "retrievable": true, "sortable": false, "facetable": false
                },
                {
                    "name": "City", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                },
                {
                    "name": "StateProvince", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                },
                {
                    "name": "PostalCode", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                },
                {
                    "name": "Country", "type": "Edm.String",
                    "searchable": true, "filterable": true, "retrievable": true, "sortable": true, "facetable": true
                }
            ]
        },
        {
            "name": "Location",
            "type": "Edm.GeographyPoint",
            "searchable": false, 
            "filterable": true, 
            "retrievable": true, 
            "sortable": true, 
            "facetable": false
        }
    ],
    "vectorSearch": {
        "algorithms": [
            {
                "name": "hnsw-vector-config",
                "kind": "hnsw",
                "hnswParameters": 
                {
                    "m": 4,
                    "efConstruction": 400,
                    "efSearch": 500,
                    "metric": "cosine"
                }
            },
            {
                "name": "eknn-vector-config",
                "kind": "exhaustiveKnn",
                "exhaustiveKnnParameters": 
                {
                    "metric": "cosine"
                }
            }
        ],
        "profiles": [      
            {
                "name": "my-vector-profile",
                "algorithm": "hnsw-vector-config"
            }
      ]
    },
    "semantic": {
        "configurations": [
            {
                "name": "semantic-config",
                "prioritizedFields": {
                    "titleField": {
                        "fieldName": "HotelName"
                    },
                    "prioritizedContentFields": [
                        { "fieldName": "Description" }
                    ],
                    "prioritizedKeywordsFields": [
                        { "fieldName": "Category" }
                    ]
                }
            }
        ]
    }
}

Wählen Sie Anforderung senden aus. 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 Erkenntnisse:

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 vectorSearch Abschnitt ist ein Array von AnN-Algorithmuskonfigurationen (Approximate Nearest Neighbor) 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 in den Index

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 Dokumente – Index (REST-API) für diese Aufgabe. Der URI wird um die docs-Sammlung und den index-Vorgang erweitert.

So laden Sie Dokumente in den Index hoch:

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=2025-09-01  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

{
    "value": [
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "1",
            "HotelName": "Stay-Kay City Hotel",
            "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
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            "Category": "Boutique",
            "Tags": [
                "view",
                "air conditioning",
                "concierge"
            ],
            "ParkingIncluded": false,
            "LastRenovationDate": "2022-01-18T00:00:00Z",
            "Rating": 3.60,
            "Address": {
                "StreetAddress": "677 5th Ave",
                "City": "New York",
                "StateProvince": "NY",
                "PostalCode": "10022",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    -73.975403,
                    40.760586
                ]
            }
        },
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "2",
            "HotelName": "Old Century Hotel",
            "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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            "Category": "Boutique",
            "Tags": [
                "pool",
                "free wifi",
                "air conditioning",
                "concierge"
            ],
            "ParkingIncluded": false,
            "LastRenovationDate": "2019-02-18T00:00:00Z",
            "Rating": 3.60,
            "Address": {
                "StreetAddress": "140 University Town Center Dr",
                "City": "Sarasota",
                "StateProvince": "FL",
                "PostalCode": "34243",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
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                ]
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            "HotelId": "3",
            "HotelName": "Gastronomic Landscape Hotel",
            "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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            "Category": "Suite",
            "Tags": [
                "restaurant",
                "bar",
                "continental breakfast"
            ],
            "ParkingIncluded": true,
            "LastRenovationDate": "2015-09-20T00:00:00Z",
            "Rating": 4.80,
            "Address": {
                "StreetAddress": "3393 Peachtree Rd",
                "City": "Atlanta",
                "StateProvince": "GA",
                "PostalCode": "30326",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    33.847848,
                    -84.363602
                ]
            }
        },
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "4",
            "HotelName": "Sublime Palace Hotel",
            "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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            "Category": "Boutique",
            "Tags": [
                "concierge",
                "view",
                "air conditioning"
            ],
            "ParkingIncluded": true,
            "LastRenovationDate": "2020-02-06T00:00:00Z",
            "Rating": 4.60,
            "Address": {
                "StreetAddress": "7400 San Pedro Ave",
                "City": "San Antonio",
                "StateProvince": "TX",
                "PostalCode": "78216",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                    -98.498923,
                    29.518029
                ]
            }
        },
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "48",
            "HotelName": "Nordick's Valley Motel",
            "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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            "Category": "Boutique",
            "Tags": [
                "continental breakfast",
                "air conditioning",
                "free wifi"
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            "ParkingIncluded": false,
            "LastRenovationDate": "2018-02-19T00:00:00Z",
            "Rating": 4.5,
            "Address": {
                "StreetAddress": "1401 I St NW",
                "City": "Washington D.C.",
                "StateProvince": null,
                "PostalCode": "20005",
                "Country": "USA"
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                "properties": {
                    "name": "EPSG:4326"
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            "HotelId": "49",
            "HotelName": "Swirling Currents Hotel",
            "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
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           "Category": "Suite",
            "Tags": [
                "air conditioning",
                "laundry service",
                "24-hour front desk service"
            ],
            "ParkingIncluded": true,
            "LastRenovationDate": "2018-01-27T00:00:00Z",
            "Rating": 2.7,
            "Address": {
                "StreetAddress": "1100 S Hayes St",
                "City": "Arlington",
                "StateProvince": "VA",
                "PostalCode": "22202",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                -77.060066,
                38.863659
                ],
                "crs": {
                "type": "name",
                "properties": {
                    "name": "EPSG:4326"
                    }
                }
            }
        },
        {
            "@search.action": "mergeOrUpload",
            "HotelId": "13",
            "HotelName": "Luxury Lion Resort",
            "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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            "Category": "Luxury",
            "Tags": [
                "bar",
                "concierge",
                "restaurant"
            ],
            "ParkingIncluded": false,
            "LastRenovationDate": "2020-03-18T00:00:00Z",
            "Rating": 4.1,
            "Address": {
                "StreetAddress": "3 Cityplace Dr",
                "City": "St. Louis",
                "StateProvince": "MO",
                "PostalCode": "63141",
                "Country": "USA"
            },
            "Location": {
                "type": "Point",
                "coordinates": [
                -90.44081,
                38.67219
                ],
                "crs": {
                "type": "name",
                "properties": {
                    "name": "EPSG:4326"
                    }
                }
            }
        }
    ]
}

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

Wichtige Erkenntnisse:
- Dokumente in der Nutzlast bestehen aus Feldern, die im Indexschema definiert sind.
- Vektorfelder enthalten Gleitkommawerte. Das Dimensions-Attribut weist mindestens 2 und maximal 4096 Gleitkommawerte auf. Diese Schnellstartanleitung legt das Dimensions-Attribut auf 1,536 fest, da dies der Größe der von dem text-embedding-ada-002 Modell generierten Einbettungen entspricht.

Ausführen von Abfragen

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

Abfragen in diesem Abschnitt:

Einzelvektorsuche
Einzelvektorsuche mit Filter
Hybridsuche
Semantische Hybridsuche

Die Beispielabfragen basieren auf zwei Zeichenfolgen:

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

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

So erstellen Sie eine einzelne Vektorsuche:

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. Es wird der Überschreibungsparameter „exhaustive“ verwendet, um einen vollständigen Scan des Index anstelle von ANN durchzuführen. Eine umfassende Suche ist für kleine Indizes nützlich.

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

    {
        "count": true,
        "select": "HotelId, HotelName, Description, Category, Tags",
        "vectorQueries": [
            {
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                "k": 5,
                "fields": "DescriptionVector",
                "kind": "vector",
                "exhaustive": true
            }
        ]
    }

Wichtige Hinweise zu Dokumenten – Suchanfrage (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 Übereinstimmungen 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.

Wählen Sie Anforderung senden aus. 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

So erstellen Sie eine einzelne Vektorsuche mit einem Filter:

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=2025-09-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "count": true,
        "select": "HotelId, HotelName, Category, Tags, Description",
        "filter": "Tags/any(tag: tag eq 'free wifi')",
        "vectorFilterMode": "postFilter",
        "vectorQueries": [
        {
            "vector": 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            "k": 5,
            "fields": "DescriptionVector",
            "kind": "vector",
            "exhaustive": true
        },
    ]
}

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

{
  "@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"
      ]
    }
  ]
}

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=2025-09-01  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

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

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

So erstellen Sie eine Hybridsuche:

Formulieren Sie die Hybridabfrage.

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

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

Wählen Sie Anforderung senden aus. 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.

So erstellen Sie eine semantische Hybridsuche:

Formulieren Sie die Anforderung.

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

{
    "count": true,
    "search": "historic hotel walk to restaurants and shopping",
    "select": "HotelName, Description",
    "queryType": "semantic",
    "semanticConfiguration": "semantic-config",
    "top": 5,
    "vectorQueries": [
        {
            "vector": 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Wählen Sie Anforderung senden aus. 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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    {
      "@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.

Bereinigen von Ressourcen

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

Alternativ können Sie die folgende Anforderung senden, um den in dieser Schnellstartanleitung erstellten Vektorindex zu löschen.

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

Nächste Schritte

Erfahren Sie, wie Sie REST-API-Aufrufe ohne API-Schlüssel aufrufen.

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Last updated on 2026-01-16

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Schnellstart: Vektorsuche

Voraussetzungen

Abrufen von Dienstinformationen

Klonen des Codes und der Setupumgebung

Erstellen eines Vektorindexes

Hochladen von Dokumenten in den Index

Ausführen von Abfragen

Erstellen der Vektorabfragezeichenfolge

Einzelvektorsuche

Einzelvektorsuche mit Filter

Hybridsuche

Semantische Hybridsuche

Bereinigen von Ressourcen

Nächste Schritte

Voraussetzungen

Abrufen von Dienstinformationen

Einrichten des Java-Projekts

Anmelden bei Azure

Erstellen eines Vektorindexes

Hochladen von Dokumenten in den Index

Erstellen der Abfrage als Vektor

Erstellen einer einzelnen Vektorsuche

Erstellen einer einzelnen Vektorsuche mit einem Filter

Erstellen einer Suche mit einem Geospatialfilter

Erstellen einer Hybridsuche

Erstellen einer semantischen Hybridsuche

Bereinigen von Ressourcen

Nächste Schritte

Voraussetzungen

Abrufen von Dienstendpunkten

Einrichten des Node.JS Projekts

Einrichten von Umgebungsvariablen für die lokale Entwicklung

Anmelden bei Azure

Erstellen eines Vektorindexes

Hochladen von Dokumenten in den Index

Erstellen der Abfrage als Vektor

Erstellen einer einzelnen Vektorsuche

Erstellen einer einzelnen Vektorsuche mit einem Filter

Erstellen einer Suche mit einem Geospatialfilter

Erstellen einer Hybridsuche

Erstellen einer semantischen Hybridsuche

Bereinigen von Ressourcen

Nächste Schritte

Voraussetzungen

Abrufen von Dienstinformationen

Einrichten des Node.JS Projekts

Einrichten von Umgebungsvariablen für die lokale Entwicklung

Anmelden bei Azure

Erstellen eines Vektorindexes

Hochladen von Dokumenten in den Index

Erstellen der Abfrage als Vektor

Erstellen einer einzelnen Vektorsuche

Erstellen einer einzelnen Vektorsuche mit einem Filter

Erstellen einer Suche mit einem Geospatialfilter

Erstellen einer Hybridsuche

Erstellen einer semantischen Hybridsuche

Bereinigen von Ressourcen

Nächste Schritte

Voraussetzungen

Abrufen von Dienstinformationen

Klonen des Codes und der Setupumgebung

Erstellen eines Vektorindexes und Hochladen von Dokumenten

Ausführen von Abfragen

Einzelvektorsuche

Einzelvektorsuche mit Filter

Hybridsuche

Semantische Hybridsuche mit einem Filter

Bereinigen von Ressourcen

Nächste Schritte

Voraussetzungen

Abrufen von Dienstinformationen

Erstellen oder Herunterladen der Codedatei

Erstellen eines Vektorindexes

Hochladen von Dokumenten in den Index

Ausführen von Abfragen

Einzelvektorsuche

Einzelvektorsuche mit Filter

Hybridsuche

Semantische Hybridsuche