Avvio rapido: Ricerca vettoriale

In questa guida introduttiva si usa un notebook Jupyter per creare, caricare e interrogare vettori. Gli esempi di codice eseguono queste operazioni usando la libreria client di Ricerca intelligenza artificiale di Azure. La libreria fornisce un'astrazione sull'API REST per l'accesso alle operazioni sugli indici, ad esempio l'inserimento di dati, le operazioni di ricerca e le operazioni di gestione degli indici.

In Azure AI Search, un archivio vettoriale ha uno schema di indice che definisce campi vettoriali e non vettoriali, una configurazione di ricerca vettoriale per algoritmi che creano lo spazio di incorporamento e le impostazioni sulle definizioni di campo vettoriale valutate al momento di query. L'API REST Crea indice crea l'archivio vettoriale.

Note

Questo avvio rapido omette il passaggio di vettorializzazione e fornisce incorporamenti inline. Se si desidera aggiungere la suddivisione in blocchi e la vettorizzazione dei dati predefiniti sui propri contenuti, provare la procedura guidata Importare e vettorializzare i dati per una procedura dettagliata end-to-end.

Prerequisiti

• Un account Azure con una sottoscrizione attiva. Creare un account gratuito.

• Un servizio di Azure AI Search. Creare un servizio o trovare un servizio esistente nella sottoscrizione corrente.

  • È possibile usare un servizio di ricerca gratuito per la maggior parte di questa guida introduttiva, ma è consigliabile usare il livello Basic o superiore per i file di dati di dimensioni maggiori.
  • Per eseguire l'esempio di query che richiama il reranking semantico, il servizio di ricerca deve essere al livello Basic o superiore con semantic ranker abilitato.

• Visual Studio Code con l'estensione Python e il pacchetto Jupyter.

• Git per clonare il repository contenente il notebook jupyter e altri file correlati.

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Recuperare informazioni sulla risorsa

Le richieste all'endpoint di ricerca devono essere autenticate e autorizzate. Sebbene sia possibile usare chiavi API o ruoli per questa attività, è consigliabile usare una connessione senza chiave tramite Microsoft Entra ID.

Questo quickstart utilizza DefaultAzureCredential, che semplifica l'autenticazione sia in scenari di sviluppo che in quelli di produzione. Tuttavia, per gli scenari di produzione, potrebbero essere presenti requisiti più avanzati che richiedono un approccio diverso. Per informazioni su tutte le opzioni, vedere Autenticare le app Python nei servizi di Azure usando Azure SDK per Python .

Clonare il codice e l'ambiente di installazione

1. Clonare il repository contenente il codice per questa guida introduttiva.

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

   Questo repository include esempi di codice Python per diversi articoli ognuno in una sottocartella separata.

2. In Visual Studio Code aprire la sottocartella Quickstart-Vector-Search.

   In questa cartella sono presenti tre file:

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

3. Rinominare il sample.env file .env in e modificare i valori nel .env file.

   Usare l'URL del servizio di ricerca come AZURE_SEARCH_ENDPOINT. È possibile trovare l'URL nel portale di Azure. Vai al servizio Ricerca intelligenza artificiale di Azure, nella pagina Panoramica, e cerca il campo URL. Un endpoint di esempio potrebbe essere simile a https://mydemo.search.windows.net.

   Infine, scegliere un nuovo AZURE_SEARCH_INDEX_NAME nome o usare quello fornito nel file.

4. In Visual Studio Code, lavori in un ambiente. Scegliere Terminale dal menu Visualizza oppure premere CTRL+`.

5. Eseguire i comandi seguenti nel terminale:

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

   Note

   Si presuppone che si usi Git Bash nel terminale e che sia in esecuzione in Windows. Se si usa una shell diversa e/o un sistema operativo diverso, modificare queste istruzioni per l'ambiente specifico.

   Se richiesto, consentire a Visual Studio Code di usare il nuovo ambiente.

   Il where python comando verifica che si stia lavorando dall'ambiente virtuale elencando python.exe la Quickstart-Vector-Search\.venv\ cartella e altri percorsi dalla directory del computer.

6. Installare le librerie necessarie eseguendo il comando seguente:

   pip install requirements.txt
   

7. In Visual Studio Code, apri il vector-search-quickstart.ipynb.

   Note

   Se è la prima volta che è stato usato jupyter Notebook (con estensione ipynb) in Visual Studio Code, verrà richiesto di installare il kernel jupyter Notebook ed eventualmente altri strumenti. Scegliere di installare gli strumenti suggeriti per continuare con questa guida introduttiva.

8. Trovare la cella seguente intitolata "Install packages and set variables" (Installa pacchetti e impostare variabili) e selezionare il pulsante Esegui cella (CTRL + ALT + INVIO) (simile a un tipico pulsante di esecuzione) a sinistra della cella. L'esecuzione della cella carica le variabili di ambiente, crea DefaultAzureCredential e stampa i valori nell'output per verificare che le dipendenze del notebook e .env siano configurati correttamente.

   # 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 
   

   L'esecuzione di questa cella produce l'output seguente.

   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
   ...
   

   Esistono molti altri pacchetti che è possibile visualizzare in un elemento scorrevole (vedere il messaggio sotto i risultati della cella).

Creare l'indice vettoriale

Il codice in vector-search-quickstart.ipynb usa diversi metodi della azure.search.documents libreria per creare l'indice vettoriale e i campi ricercabili.

1. Trovare la cella seguente intitolata "Creare un indice" ed eseguire la cella per creare l'indice.

   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')
   

   Se l'indice viene creato correttamente, viene visualizzato il risultato seguente sotto la cella:

   vector-search-quickstart created
   

   Considerazioni chiave durante la creazione dell'indice vettoriale con :azure.search.documents

   • Per definire un indice, creare un elenco di campi. Ogni campo viene creato usando un metodo helper che definisce il tipo di campo e le relative impostazioni.

   • Questo particolare indice supporta più funzionalità di ricerca, ad esempio:

     • Ricerca di parole chiave full-text (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))
     • Ricerca vettoriale (abilita la ricerca ibrida collocando campi vettoriali e non vettoriali) (DescriptionVector)
     • Ricerca semantica (semantic_search=SemanticSearch(configurations=[semantic_config]))
     • Ricerca in base a facet (facetable=True)
     • Ricerca semantica (semantic_search=SemanticSearch(configurations=[semantic_config]))
     • Ricerca spaziale geografica (Location campo è GeographyPoint)
     • Filtro, ordinamento (molti campi contrassegnati come filtrabili e ordinabili)

Caricamento di documenti

La creazione e il caricamento dell'indice sono passaggi separati. Lo schema dell'indice è stato creato nel passaggio precedente. È ora necessario caricare i documenti nell'indice.

In Ricerca di intelligenza artificiale di Azure l'indice archivia tutto il contenuto ricercabile, mentre il motore di ricerca esegue query su tale indice.

1. Trovare la cella nella sezione intitolata "Creare il payload dei documenti" ed eseguirla. Questa cella contiene il codice seguente (troncato per brevità):

      # 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>
      ]
   

   Questa cella carica una variabile denominata documents con un oggetto JSON che descrive ogni documento, insieme alla versione vettorializzata della descrizione dell'articolo. Questo vettore abilita la ricerca di somiglianza, in cui la corrispondenza si basa sul significato anziché sulle parole chiave esatte.

   Importante

   Il codice in questo esempio non è eseguibile. Diversi caratteri o righe vengono rimossi a fini di brevità. Eseguire invece il codice nel notebook di Jupyter.

2. Trovare la cella nella sezione intitolata "Caricare i documenti" ed eseguirla. Questa cella contiene il codice seguente (troncato per brevità):

   # 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)
   

   In questo modo viene creata un'istanza del client di ricerca chiamando il SearchClient() costruttore , quindi viene chiamato il upload_documents() metodo sull'oggetto .

   Dopo aver eseguito la cella, lo stato di ogni documento viene stampato sotto di essa:

   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
   

   Considerazioni chiave sul upload_documents() metodo e questo esempio:

   • Il codice interagisce con un indice di ricerca specifico ospitato nel servizio Ricerca intelligenza artificiale di Azure tramite SearchClient, che è l'oggetto principale fornito dal azure-search-documents pacchetto. SearchClient fornisce l'accesso alle operazioni sugli indici, ad esempio:

     • Inserimento - upload_documents() dati, merge_documents(), delete_documents()e così via.
     • Operazioni - search() di ricerca, autocomplete(), suggest()
     • Operazioni di gestione degli indici

   • I campi vettoriali contengono valori a virgola mobile. L'attributo dimensions ha un minimo di 2 e un massimo di 4096 valori a virgola mobile ciascuno. Questo argomento di avvio rapido imposta l'attributo dimensions su 1.536 perché è la dimensione degli incorporamenti generati dal modello text-embedding-ada-002 di Azure OpenAI.

Eseguire le query

Ora che i documenti vengono caricati, è possibile eseguire query vettoriali su di esse chiamando search_client.search() e passando un oggetto VectorizedQuery, i campi restituiti, il numero di risultati e così via.

Nelle sezioni successive vengono eseguite query sull'indice hotels-vector-quickstart. Le query includono:

• Ricerca a vettore singolo
• Ricerca a vettore singolo con filtro
• Ricerca ibrida
• Ricerca semantica ibrida con filtro

Creare la stringa di query vettoriale

Le query vettoriali di esempio si basano su due stringhe:

• Stringa di ricerca: historic hotel walk to restaurants and shopping
• Stringa di query vettoriale (vettorializzata in una rappresentazione matematica): quintessential lodging near running trails, eateries, retail

La stringa di query vettoriale è semanticamente simile alla stringa di ricerca, ma include termini che non esistono nell'indice di ricerca. Se si esegue una ricerca di parole chiave per quintessential lodging near running trails, eateries, retail, i risultati sono zero. Questo esempio viene usato per mostrare come ottenere risultati pertinenti anche se non sono presenti termini corrispondenti.

• Trovare la cella nella sezione intitolata "Creare la stringa di query vettoriale" ed eseguirla. Questa carica la variabile vector con i dati di query vettorializzati necessari per eseguire tutte le ricerche nelle sezioni successive.

Ricerca vettoriale singola

Il primo esempio illustra uno scenario di base in cui si desidera trovare descrizioni di documenti che corrispondano strettamente alla stringa di ricerca.

• Trova la cella sotto la sezione intitolata "Ricerca vettore singolo" ed esegui la cella. Questo blocco contiene la richiesta di eseguire query sull'indice di ricerca.

  # 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.")
  

  Questa query vettoriale viene abbreviata per motivi di brevità. Il vectorQueries.vector contiene il testo vettorializzato dell'input della query, fields determina quali campi vettoriali vengono ricercati e k specifica il numero di vicini più prossimi da restituire.

  La stringa di query vettoriale è quintessential lodging near running trails, eateries, retail, che viene vettorializzata in 1.536 incorporamenti per questa query.

  La risposta per l'equivalente vettore di quintessential lodging near running trails, eateries, retail include sette risultati, ma il codice specifica in modo che vengano restituiti top=5 solo i primi cinque risultati. Inoltre, vengono restituiti solo i campi specificati da select .

  search_client.search() restituisce un oggetto dict-like. Ogni risultato fornisce un punteggio di ricerca accessibile tramite score = result.get("@search.score", "N/A"). Anche se non viene visualizzato in questo esempio, in una ricerca di somiglianza, la risposta include k sempre i risultati ordinati in base al punteggio di somiglianza del valore.

  Dopo aver eseguito la cella, lo stato di ogni documento viene stampato sotto di essa:

  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
  

Ricerca vettoriale singola con filtro

È possibile aggiungere filtri, ma i filtri vengono applicati al contenuto non di filtro nell'indice. In questo esempio, il filtro si applica al campo Tags per filtrare tutti gli hotel che non forniscono il Wi-Fi gratuito.

1. Trovare la cella seguente intitolata "Ricerca a vettore singolo con filtro" ed eseguire la cella. Questa cella contiene la richiesta di eseguire una query sull'indice di ricerca.

   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.")
   

   Dopo aver eseguito la cella, lo stato di ogni documento viene stampato sotto di essa:

   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']
   

   La query è la stessa dell'esempio di ricerca vettoriale singola precedente, ma include un filtro di esclusione post-elaborazione e restituisce solo i tre hotel che dispongono di Wi-Fi gratuito.

2. Nell'esempio di filtro successivo viene usato un filtro geografico. Eseguire la cella nella sezione intitolata "Query vettoriale con un filtro geografico". Questo blocco contiene la richiesta di eseguire query sull'indice di ricerca.

   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.")
   

   La query corrisponde all'esempio di ricerca a vettore singolo precedente, ma include un filtro di esclusione post-elaborazione e restituisce solo i due hotel entro 300 chilometri.

   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.
   

Ricerca ibrida

La ricerca ibrida è costituita da query con parole chiave e query vettoriali in una singola richiesta di ricerca. Questo esempio esegue simultaneamente la query vettoriale e la ricerca full-text:

• Stringa di ricerca: historic hotel walk to restaurants and shopping

• Stringa di query vettoriale (vettorializzata in una rappresentazione matematica): quintessential lodging near running trails, eateries, retail

• Trova la cella sotto la sezione intitolata "Ricerca ibrida" ed esegui la cella. Questo blocco contiene la richiesta di eseguire query sull'indice di ricerca.

  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.")    
  

  Esaminare la risposta:

  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']
  

  Poiché la Fusione del Rank Reciproco (RRF) unisce i risultati, aiuta a esaminare gli input. I risultati seguenti provengono solo dalla query full-text. I primi due risultati sono Sublime Palace Hotel e History Lion Resort. Il Sublime Palace Hotel ha un punteggio di pertinenza BM25 più elevato.

  {
       "@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"
  },
  

  Nella query di solo vettore, che usa HNSW per trovare corrispondenze, sublime Palace Hotel scende alla quarta posizione. Historic Lion, secondo nella ricerca full-text e terzo nella ricerca vettoriale, non riscontra lo stesso intervallo di fluttuazione, quindi appare come una corrispondenza principale in un set di risultati omogeneizzato.

  "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"
       }
  ]
  

Ricerca semantica ibrida con un filtro

Ecco l'ultima query della raccolta. Questa query ibrida con classificazione semantica viene filtrata per mostrare solo gli hotel entro un raggio di 500 chilometri da Washington D.C. È possibile impostare vectorFilterMode su Null, che equivale al valore predefinito (preFilter per gli indici più recenti e postFilter per quelli meno recenti).

• Trovare la cella seguente intitolata "Ricerca ibrida semantica" ed eseguire la cella. Questo blocco di codice contiene la richiesta di eseguire una query sull'indice di ricerca.

  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.")
  

  Esaminare l'output sotto la cella. La risposta è costituita da tre hotel, filtrati in base alla posizione e facet in base a StateProvince, e classificati in modo semantico per alzare di livello i risultati più vicini alla query della stringa di ricerca (historic hotel walk to restaurants and shopping).

  L'Hotel Swirling Currents ora si sposta al primo posto. Senza classifica semantica, Il Nordick's Valley Hotel è il numero uno. Con la classificazione semantica, i modelli di comprensione della macchina riconoscono che historic si applica a "hotel, a pochi passi da ristoranti e negozi".

  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
  

  Aspetti chiave:

  • La ricerca vettoriale viene specificata tramite la proprietà vectors.value. La ricerca di parole chiave viene specificata tramite la proprietà search.

  • In una ricerca ibrida è possibile integrare la ricerca vettoriale con la ricerca full-text sulle parole chiave. I filtri, il controllo ortografico e la classificazione semantica si applicano solo ai contenuti testuali e non ai vettori. In questa query finale non esiste un answer semantico perché il sistema non ha prodotto una query sufficientemente forte.

  • I risultati effettivi includono più dettagli, tra cui didascalie semantiche ed evidenziazioni. I risultati sono stati modificati per la leggibilità. Per ottenere la struttura completa della risposta, eseguire la richiesta nel client REST.

Eseguire la pulizia

Quando si lavora nella propria sottoscrizione, alla fine di un progetto è opportuno verificare se le risorse create sono ancora necessarie. L'esecuzione continua delle risorse può avere un costo. È possibile eliminare le singole risorse oppure il gruppo di risorse per eliminare l'intero set di risorse.

È possibile trovare e gestire le risorse nel portale di Azure usando il collegamento Tutte le risorse o Gruppi di risorse nel riquadro più a sinistra.

Se si desidera mantenere il servizio di ricerca, ma eliminare l'indice e i documenti, è possibile usare il SearchIndexClient metodo dell'oggetto delete_index() . Trovare la cella sotto la sezione intitolata "Pulizia" ed eseguire la cella se si vuole eliminare l'indice hotels-vector-quickstart.

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

Passaggi successivi

• Esamina il repository di esempi di codice per le funzionalità di ricerca vettoriale in Azure AI Search per Python
• Esamina gli altri esempi di codice Python e Ricerca AI di Azure nel repository azure-search-python-samples

In questa guida introduttiva si usano le API REST di Ricerca Intelligenza Artificiale di Azure per creare, caricare ed eseguire query sui vettori.

In Azure AI Search, un archivio vettoriale ha uno schema di indice che definisce campi vettoriali e non vettoriali, una configurazione di ricerca vettoriale per algoritmi che creano lo spazio di incorporamento e le impostazioni sulle definizioni di campo vettoriale valutate al momento di query. L'API REST Crea indice crea l'archivio vettoriale.

Note

Questo avvio rapido omette il passaggio di vettorializzazione e fornisce incorporamenti inline. Se si desidera aggiungere la suddivisione in blocchi e la vettorizzazione dei dati predefiniti sui propri contenuti, provare la procedura guidata Importare e vettorializzare i dati per una procedura dettagliata end-to-end.

Prerequisiti

• Un account Azure con una sottoscrizione attiva. Creare un account gratuito.

• Un servizio di Azure AI Search. Creare un servizio o trovare un servizio esistente nella sottoscrizione corrente.

  • È possibile usare un servizio di ricerca gratuito per la maggior parte di questa guida introduttiva, ma è consigliabile usare il livello Basic o superiore per i file di dati di dimensioni maggiori.
  • Per eseguire l'esempio di query che richiama il reranking semantico, il servizio di ricerca deve essere al livello Basic o superiore con semantic ranker abilitato.

• Visual Studio Code con un client REST.

---

Recuperare informazioni sulla risorsa

Le richieste all'endpoint di ricerca devono essere autenticate e autorizzate. È possibile usare chiavi API o ruoli per questa attività. È consigliabile usare una connessione senza chiave tramite Microsoft Entra ID.

Selezionare la scheda corrispondente al metodo di autenticazione preferito. Usare lo stesso metodo per tutte le richieste in questo argomento di avvio rapido.

Microsoft Entra ID

1. Accedere al portale di Azure e trovare il servizio di ricerca.

2. Nella home page Panoramica trovare l'URL. Un endpoint di esempio potrebbe essere simile a https://mydemo.search.windows.net.

   

3. Seguire i passaggi dell'avvio rapido senza chiave per ottenere il token Microsoft Entra.

   Il token viene ottenuto quando si esegue il comando az account get-access-token nel passaggio 3 dell'avvio rapido precedente.

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

Chiave API

1. Accedere al portale di Azure e trovare il servizio di ricerca.

2. Nella home page Panoramica trovare l'URL. Un endpoint di esempio potrebbe essere simile a https://mydemo.search.windows.net.

   

3. Selezionare Impostazioni>Chiavi. L'opzione Chiavi API o Entrambe deve essere abilitata. Le chiavi API di amministrazione vengono usate per aggiungere, modificare ed eliminare oggetti. Sono disponibili due chiavi amministratore intercambiabili. Copiarne una.

   

Creare o scaricare il file di codice

Si usa un file .rest o .http per eseguire tutte le richieste in questo avvio rapido. È possibile scaricare il file REST contenente il codice per questo avvio rapido oppure creare un nuovo file in Visual Studio Code e copiarvi il codice.

1. In Visual Studio Code, creare un nuovo file con un'estensione .rest o .http. Ad esempio: az-search-vector-quickstart.rest. Copiare e incollare il contenuto non elaborato del file Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest in questo nuovo file.

2. Nella parte superiore del file, sostituire il valore segnaposto per @baseUrl con l'URL del servizio di ricerca. Per istruzioni su come trovare l'URL del servizio di ricerca, vedere la sezione Recuperare le informazioni sulla risorsa.

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

3. Nella parte superiore del file, sostituire il valore segnaposto per l'autenticazione. Vedere la sezione Recuperare informazioni sulla risorsa per istruzioni su come ottenere il token o la chiave API di Microsoft Entra.

   Per l'autenticazione senza chiave tramite Microsoft Entra ID consigliata, è necessario sostituire @apiKey con la variabile @token.

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

   Se si preferisce usare una chiave API, sostituire @apiKey con la chiave copiata dal portale di Azure.

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

4. Per l'autenticazione senza chiave tramite Microsoft Entra ID consigliata, è necessario sostituire api-key: {{apiKey}} con Authorization: Bearer {{token}} nelle intestazioni della richiesta. Sostituire tutte le istanze di api-key: {{apiKey}} che si trovano nel file.

Creare un indice vettoriale

Usare l'API REST Crea indice per creare un indice vettoriale e configurare le strutture di dati fisiche nel servizio di ricerca.

Lo schema dell’indice in questo esempio è organizzato attorno al contenuto su hotel. I dati di esempio sono costituiti da descrizioni vettoriali e non vettoriali di hotel fittizi. Questo schema include configurazioni per l'indicizzazione vettoriale e le query e per la classificazione semantica.

1. In Visual Studio Code, aprire il file az-search-vector-quickstart.rest creato in precedenza.

2. Trovare il blocco di codice ### Create a new index nel file. Questo blocco contiene la richiesta di creare l'indice hotels-vector-quickstart nel servizio di ricerca.

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

3. Selezionare Invia richiesta. Si dovrebbe avere una risposta HTTP/1.1 201 Created.

   Il corpo della risposta deve includere la rappresentazione JSON dello schema dell'indice.

   {
       "@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"
               }
               ]
           }
           }
       ]
       }
   }
   

Considerazioni chiave sull'API REST Crea indice:

• La raccolta fields include un campo chiave obbligatorio e campi di testo e vettore (ad esempio Description e DescriptionVector) per la ricerca di testo e vettoriale. Il colocating di campi vettoriali e non vettoriali nello stesso indice consente query ibride. Ad esempio, è possibile combinare filtri, ricerca di testo con classificazione semantica e vettori in una singola operazione di query.

• I campi vettoriali devono essere uno dei tipi di dati EDM usati per i vettori, ad esempio type: Collection(Edm.Single). I campi vettoriali hanno anche le proprietà dimensions e vectorSearchProfile.

• La sezione vectorSearch è una matrice di configurazioni e profili approssimativi dell'algoritmo adiacente più vicino. Gli algoritmi supportati includono piccoli mondi navigabili gerarchici e vicini più prossimi k esaustivi. Per altre informazioni, vedere Assegnazione dei punteggi per pertinenza nella ricerca vettoriale.

• La configurazione semantic (facoltativa) consente di eseguire il reranking dei risultati della ricerca. È possibile eseguire il reranking dei risultati nelle query di tipo semantic per i campi stringa specificati nella configurazione. Per altre informazioni, vedere Panoramica della classificazione semantica.

Caricamento di documenti

La creazione e il caricamento dell'indice sono passaggi separati. Lo schema dell'indice è stato creato nel passaggio precedente. È ora necessario caricare i documenti nell'indice.

In Ricerca di intelligenza artificiale di Azure l'indice contiene tutti i dati e le query ricercabili eseguiti nel servizio di ricerca. Per le chiamate REST, i dati vengono forniti come documenti JSON. Usare l'API REST Documents- Index per questa attività. L'URI viene esteso per includere la raccolta docs e l'operazione index.

1. Formulare una richiesta di caricamento di documenti per caricare documenti sull'indice del hotels-vector-quickstart servizio di ricerca.

   ### Upload documents
   POST {{baseUrl}}/indexes/hotels-quickstart-vectors/docs/index?api-version=2024-07-01  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   {
       "value": [
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "1",
               "HotelName": "Stay-Kay City Hotel",
               "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
               "DescriptionVector": 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               "Category": "Boutique",
               "Tags": [
                   "view",
                   "air conditioning",
                   "concierge"
               ],
               "ParkingIncluded": false,
               "LastRenovationDate": "2022-01-18T00:00:00Z",
               "Rating": 3.60,
               "Address": {
                   "StreetAddress": "677 5th Ave",
                   "City": "New York",
                   "StateProvince": "NY",
                   "PostalCode": "10022",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       -73.975403,
                       40.760586
                   ]
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "2",
               "HotelName": "Old Century Hotel",
               "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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               "Category": "Boutique",
               "Tags": [
                   "pool",
                   "free wifi",
                   "air conditioning",
                   "concierge"
               ],
               "ParkingIncluded": false,
               "LastRenovationDate": "2019-02-18T00:00:00Z",
               "Rating": 3.60,
               "Address": {
                   "StreetAddress": "140 University Town Center Dr",
                   "City": "Sarasota",
                   "StateProvince": "FL",
                   "PostalCode": "34243",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       -82.452843,
                       27.384417
                   ]
               }
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           {
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               "HotelId": "3",
               "HotelName": "Gastronomic Landscape Hotel",
               "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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               "Category": "Suite",
               "Tags": [
                   "restaurant",
                   "bar",
                   "continental breakfast"
               ],
               "ParkingIncluded": true,
               "LastRenovationDate": "2015-09-20T00:00:00Z",
               "Rating": 4.80,
               "Address": {
                   "StreetAddress": "3393 Peachtree Rd",
                   "City": "Atlanta",
                   "StateProvince": "GA",
                   "PostalCode": "30326",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       33.847848,
                       -84.363602
                   ]
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "4",
               "HotelName": "Sublime Palace Hotel",
               "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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               "Category": "Boutique",
               "Tags": [
                   "concierge",
                   "view",
                   "air conditioning"
               ],
               "ParkingIncluded": true,
               "LastRenovationDate": "2020-02-06T00:00:00Z",
               "Rating": 4.60,
               "Address": {
                   "StreetAddress": "7400 San Pedro Ave",
                   "City": "San Antonio",
                   "StateProvince": "TX",
                   "PostalCode": "78216",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                       -98.498923,
                       29.518029
                   ]
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "48",
               "HotelName": "Nordick's Valley Motel",
               "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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               "Category": "Boutique",
               "Tags": [
                   "continental breakfast",
                   "air conditioning",
                   "free wifi"
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               "ParkingIncluded": false,
               "LastRenovationDate": "2018-02-19T00:00:00Z",
               "Rating": 4.5,
               "Address": {
                   "StreetAddress": "1401 I St NW",
                   "City": "Washington D.C.",
                   "StateProvince": null,
                   "PostalCode": "20005",
                   "Country": "USA"
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                   "properties": {
                       "name": "EPSG:4326"
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               "HotelId": "49",
               "HotelName": "Swirling Currents Hotel",
               "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
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              "Category": "Suite",
               "Tags": [
                   "air conditioning",
                   "laundry service",
                   "24-hour front desk service"
               ],
               "ParkingIncluded": true,
               "LastRenovationDate": "2018-01-27T00:00:00Z",
               "Rating": 2.7,
               "Address": {
                   "StreetAddress": "1100 S Hayes St",
                   "City": "Arlington",
                   "StateProvince": "VA",
                   "PostalCode": "22202",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                   -77.060066,
                   38.863659
                   ],
                   "crs": {
                   "type": "name",
                   "properties": {
                       "name": "EPSG:4326"
                       }
                   }
               }
           },
           {
               "@search.action": "mergeOrUpload",
               "HotelId": "13",
               "HotelName": "Luxury Lion Resort",
               "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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               "Category": "Luxury",
               "Tags": [
                   "bar",
                   "concierge",
                   "restaurant"
               ],
               "ParkingIncluded": false,
               "LastRenovationDate": "2020-03-18T00:00:00Z",
               "Rating": 4.1,
               "Address": {
                   "StreetAddress": "3 Cityplace Dr",
                   "City": "St. Louis",
                   "StateProvince": "MO",
                   "PostalCode": "63141",
                   "Country": "USA"
               },
               "Location": {
                   "type": "Point",
                   "coordinates": [
                   -90.44081,
                   38.67219
                   ],
                   "crs": {
                   "type": "name",
                   "properties": {
                       "name": "EPSG:4326"
                       }
                   }
               }
           }
       ]
   }
   

2. Selezionare Invia richiesta. Si dovrebbe avere una risposta HTTP/1.1 200 OK. Il corpo della risposta deve includere la rappresentazione JSON dei documenti di ricerca.

Aspetti chiave sulla richiesta Documenti - API REST Indice:

• I documenti nel payload sono costituiti da campi definiti nello schema dell'indice.

• I campi vettoriali contengono valori a virgola mobile. L'attributo dimensions ha un minimo di 2 e un massimo di 4096 valori a virgola mobile ciascuno. Questo avvio rapido imposta l'attributo dimensioni su 1.536, poiché si tratta delle dimensioni degli incorporamenti generati dal modello text-embedding-3-small di Azure OpenAI.

Eseguire le query

Ora che i documenti vengono caricati, è possibile eseguire query vettoriali su di esse usando Documenti - Post di ricerca (REST).

Nelle sezioni successive vengono eseguite query sull'indice hotels-vector-quickstart. Le query includono:

• Ricerca vettoriale singola
• Ricerca vettoriale singola con filtro
• Ricerca ibrida
• Ricerca ibrida semantica

Le query di esempio sono basate su due stringhe:

• Stringa di ricerca: historic hotel walk to restaurants and shopping
• Stringa di query vettoriale (vettorializzata in una rappresentazione matematica): quintessential lodging near running trails, eateries, retail

La stringa di query vettoriale è semanticamente simile alla stringa di ricerca, ma include termini che non esistono nell'indice di ricerca. Se si esegue una ricerca di parole chiave per quintessential lodging near running trails, eateries, retail, i risultati sono zero in una ricerca di parole chiave pura senza classificazione semantica. Questo esempio viene usato per mostrare come ottenere risultati pertinenti usando vettori, anche se non sono presenti termini corrispondenti.

Ricerca vettoriale singola

1. Formulare la richiesta. La query è una rappresentazione in virgola mobile a 1536 dimensioni di alloggio ideale vicino a sentieri per correre, ristoranti, vendita al dettaglio. La query esegue la ricerca in DescriptionVector e restituisce i risultati k-5.

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

   Considerazioni chiave sull'API REST Documenti - Cerca post:

   • vectorQueries.vector è la stringa di query vettoriale. Si tratta di una rappresentazione vettoriale di alloggi tipici vicino a sentieri per correre, ristoranti, negozi, che è vettorizzato in 1.536 incorporamenti per questa query.

   • fields determina i campi vettoriali in cui viene eseguita la ricerca.

   • kind impostato su vector significa che la stringa di query è un vettore. Se kind è stato impostato su text, è necessaria una funzionalità aggiuntiva (un vettore) per codificare una stringa di testo leggibile in un vettore in fase di query. I vettorizzatori sono stati omessi da questa guida introduttiva per semplificare l'esercizio.

   • k specifica il numero di vicini più vicini da restituire nella risposta. Un count parametro specifica il numero di corrispondenze trovate nell'indice. L'inclusione del conteggio è una procedura consigliata per le query, ma è meno utile per la ricerca di somiglianza in cui l'algoritmo può trovare un certo grado di somiglianza in quasi tutti i documenti.

2. Selezionare Invia richiesta. Si dovrebbe avere una risposta HTTP/1.1 200 OK. Il corpo della risposta deve includere la rappresentazione JSON dei risultati della ricerca.

   La risposta per il vettore equivalente di quintessential lodging near running trails, eateries, retail include i risultati k-5, anche se il motore di ricerca ha trovato 7 corrispondenze. I risultati principali sono considerati i più semanticamente simili alla query. Ogni risultato fornisce un punteggio di ricerca e i campi elencati in select. In una ricerca di somiglianza, la risposta include sempre risultati k ordinati in base al punteggio di somiglianza del valore.

   {
     "@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"
         ]
       }
     ]
   }
   

Ricerca a vettore singolo con filtro

È possibile aggiungere filtri, ma i filtri vengono applicati al contenuto non di filtro nell'indice. In questo esempio, il filtro si applica al campo Tags per filtrare tutti gli hotel che non forniscono il Wi-Fi gratuito.

1. Formulare la richiesta. Questa è la stessa richiesta di quella precedente, con un filtro aggiuntivo e un parametro della modalità filtro.

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

2. Selezionare Invia richiesta. Si dovrebbe avere una risposta HTTP/1.1 200 OK. Il corpo della risposta deve includere la rappresentazione JSON dei risultati della ricerca.

   La query corrisponde all'esempio di ricerca a vettore singolo precedente, ma include un filtro di esclusione post-elaborazione e restituisce solo i due hotel che dispongono di Wi-Fi gratuito.

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

3. Ecco un'altra query che usa un filtro geografico, limitando i risultati agli hotel entro 300 chilometri intorno a Washington D.C.

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

4. Esaminare la risposta. È limitata a due hotel vicino a Washington D.C, con una struttura facet che fornisce l'aggregazione.

   "@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"
       }
     }
   ]
   

Ricerca ibrida

La ricerca ibrida è costituita da query con parole chiave e query vettoriali in una singola richiesta di ricerca. Questo esempio esegue simultaneamente la query vettoriale e la ricerca full-text:

• Stringa di ricerca: historic hotel walk to restaurants and shopping
• Stringa di query vettoriale (vettorializzata in una rappresentazione matematica): quintessential lodging near running trails, eateries, retail

1. Formulare la query ibrida.

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

2. Selezionare Invia richiesta. Si dovrebbe avere una risposta HTTP/1.1 200 OK. Il corpo della risposta deve includere la rappresentazione JSON dei risultati della ricerca.

Poiché si tratta di una query ibrida, i risultati vengono classificati in base al valore RRF (Reciprocal Rank Fusion). Si noti che @search.score i valori hanno una base diversa e sono valori uniformemente più piccoli. RRF valuta i punteggi di ricerca di più risultati di ricerca, accetta l'inverso e quindi unisce e ordina i risultati combinati. Viene restituito il numero top di risultati.

Esaminare la risposta, costituita dalle prime 5 corrispondenze di 7 documenti corrispondenti nell'indice:

{
  "@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."
    }
  ]
}

Poiché RRF unisce i risultati, consente di esaminare gli input singolarmente.

I seguenti risultati provengono dalla parte full-text della query: hotel storico situato a breve distanza da ristoranti e negozi. Nella query full-text i primi tre risultati sono Sublime Palace Hotel, Stay-Kay City Hotel e Luxury Lion Resort. Il Sublime Palace Hotel ha un punteggio di pertinenza BM25 più elevato. Nella query fusa, solo due di queste corrispondenze vengono visualizzate nei primi 3 risultati mentre la seconda corrispondenza (Stay-Kay City Hotel) non è visualizzata nei primi 5 risultati.

  "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."
    },
    ...
  ]

Nella query della parte vettoriale (sistemazione ideale vicino a percorsi per corsa, ristoranti, negozi) è una stringa diversa, quindi dovremmo aspettarci risultati diversi. Questa query restituisce Nordick's Valley Motel, Luxury Lion Resort e Sublime Palace Hotel come prime tre corrispondenze. Nella query fusa, questi risultati vengono visualizzati nei primi 3, ma in ordine inverso.

  "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."
    },
   ...
  ]

Ricerca semantica ibrida

Ecco l'ultima query della raccolta. Questa query ibrida aggiunge una classificazione semantica di livello L2 che applica la comprensione automatica del testo sui risultati classificati a livello L1, promuovendo corrispondenze più rilevanti in cima.

1. Formulare la richiesta.

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

2. Selezionare Invia richiesta. Si dovrebbe avere una risposta HTTP/1.1 200 OK. Il corpo della risposta deve includere la rappresentazione JSON dei risultati della ricerca.

Esaminare la risposta, costituita da una riclassificazione semantica dei risultati classificati dall'algoritmo RRF della query ibrida. La classificazione semantica si basa sugli input di testo. In una query di testo o ibrida, questo input è la parte di testo della query (passeggiata dell'hotel storico per ristoranti e negozi). Per usare la classificazione semantica in una query vettoriale pura, ad esempio il primo esempio o per controllare in modo esplicito il testo usato per la classificazione semantica, fornire una stringa semanticQuery.

Dopo il reranking semantico, Swirling Currents Hotel con il suo riferimento a accesso a piedi a shopping, ristoranti, intrattenimento e il centro cittadino si posiziona al primo posto. Il modello di comprensione automatico promuove la breve distanza da negozi e ristoranti come corrispondenza più stretta rispetto a raggiungere ristoranti e negozi a piedi.

Prima del riordinamento semantico, Sublime Palace, con il suo riferimento a essere a pochi passi dai siti e dai luoghi d'interesse della città, circondato dalla straordinaria bellezza di chiese, edifici, negozi e monumenti, era al primo posto.

{
  "@odata.count": 7,
  "@search.answers": [],
  "value": [
    {
      "@search.score": 0.0317460335791111,
      "@search.rerankerScore": 2.6550590991973877,
      "HotelId": "49",
      "HotelName": "Swirling Currents Hotel",
      "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.",
      "Category": "Suite"
    },
    {
      "@search.score": 0.03279569745063782,
      "@search.rerankerScore": 2.599761724472046,
      "HotelId": "4",
      "HotelName": "Sublime Palace Hotel",
      "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
      "Category": "Boutique"
    },
    {
      "@search.score": 0.03125,
      "@search.rerankerScore": 2.3480887413024902,
      "HotelId": "2",
      "HotelName": "Old Century Hotel",
      "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
      "Category": "Boutique"
    },
    {
      "@search.score": 0.03154495730996132,
      "@search.rerankerScore": 2.2718777656555176,
      "HotelId": "1",
      "HotelName": "Stay-Kay City Hotel",
      "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic center of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
      "Category": "Boutique"
    },
    {
      "@search.score": 0.03053613007068634,
      "@search.rerankerScore": 2.0582215785980225,
      "HotelId": "3",
      "HotelName": "Gastronomic Landscape Hotel",
      "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel\u2019s restaurant services.",
      "Category": "Suite"
    }
  ]
}

Suggerimento

I risultati classificati semanticamente possono includere più dettagli, tra cui risposte semantiche, sottotitoli ed evidenziazioni. L'aggiunta di altri parametri alla richiesta produce i dettagli aggiuntivi. Per altre informazioni, vedere Configurare una query semantica.

Eseguire la pulizia

Quando si lavora nella propria sottoscrizione, alla fine di un progetto è opportuno verificare se le risorse create sono ancora necessarie. L'esecuzione continua delle risorse può avere un costo. È possibile eliminare le singole risorse oppure il gruppo di risorse per eliminare l'intero set di risorse.

È possibile trovare e gestire le risorse nel portale di Azure usando il collegamento Tutte le risorse o Gruppi di risorse nel riquadro più a sinistra.

Se si vuole conservare il servizio di ricerca, ma eliminare l'indice e i documenti, è possibile usare il comando DELETE nel client REST. Questo comando (alla fine del file az-search-vector-quickstart.rest) elimina l'indice hotels-vector-quickstart:

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

Passaggi successivi

Come passaggio successivo, è consigliabile imparare a richiamare le chiamate API REST senza chiavi API.

È anche possibile esaminare il codice demo per Python, C# o JavaScript.