Inicio rápido: Búsqueda de vectores

En este inicio rápido, usará un cuaderno de Jupyter Notebook para crear, cargar y consultar vectores. Los ejemplos de código realizan estas operaciones mediante la biblioteca cliente de Azure AI Search. La biblioteca proporciona una abstracción sobre la API REST para acceder a operaciones de índice, como la ingesta de datos, las operaciones de búsqueda y las operaciones de administración de índices.

En la Búsqueda de Azure AI, un almacén de vectores tiene un esquema de índice que define campos vectoriales y no vectores, una configuración de vector de búsqueda para algoritmos que crean el espacio de inserción y la configuración en las definiciones de campo vectorial que se evalúan en el momento de la consulta. La API de REST Crear índice crea el almacén de vectores.

Nota

En este inicio rápido se omite el paso de vectorización y se proporcionan instrucciones insertadas. Si quiere agregar fragmentación y vectorización de datos integrados a través de su propio contenido, pruebe el Asistente para importar y vectorizar datos para ver un tutorial completo.

Requisitos previos

• Una cuenta de Azure con una suscripción activa. Cree una cuenta gratuita.

• Un servicio de Búsqueda de Azure AI. Cree un servicio o busque un servicio existente en la suscripción actual.

  • Puede usar un servicio de búsqueda gratuito para la mayoría de estos inicios rápidos, pero se recomienda el nivel Básico o superior para archivos de datos más grandes.
  • Para ejecutar el ejemplo de consulta que invoca el reranking semántico, el servicio de búsqueda debe estar en el nivel Básico o superior con el clasificador semántico habilitado.

• Visual Studio Code con la extensión de Python y el paquete de Jupyter.

• Git para clonar el repositorio que contiene el cuaderno de Jupyter Notebook y otros archivos relacionados.

---

Recuperación de información de recursos

Las solicitudes al punto de conexión de búsqueda deben autenticarse y autorizarse. Aunque es posible usar claves o roles de API para esta tarea, se recomienda usar una conexión sin clave a través de Microsoft Entra ID.

En este inicio rápido se usa DefaultAzureCredential, que simplifica la autenticación en escenarios de desarrollo y producción. Sin embargo, en escenarios de producción, es posible que tenga requisitos más avanzados que requieran un enfoque diferente. Consulte Autenticación de aplicaciones de Python en servicios de Azure mediante el SDK de Azure para Python para comprender todas las opciones.

Clonación del código y el entorno de instalación

1. Clone el repositorio que contiene el código de este inicio rápido.

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

   Este repositorio tiene ejemplos de código de Python para varios artículos cada uno en una subcarpeta independiente.

2. En Visual Studio Code, abra la subcarpeta Quickstart-Vector-Search.

   Hay tres archivos en esta carpeta:

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

3. Cambie el nombre del sample.env archivo a .env y modifique los valores del .env archivo.

   Use la dirección URL del servicio de búsqueda como AZURE_SEARCH_ENDPOINT. Puede encontrar la dirección URL en Azure Portal. Vaya al servicio de Azure AI Search, en la página Resumen, busque el campo URL. Un punto de conexión de ejemplo podría ser similar a https://mydemo.search.windows.net.

   Por último, elija un nombre nuevo AZURE_SEARCH_INDEX_NAME o use el proporcionado en el archivo.

4. En Visual Studio Code, trabaje en un entorno. En el menú Ver , seleccione Terminal... o presione Ctrl+`.

5. Ejecute los siguientes comandos en el terminal:

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

   Nota

   Esto supone que usa Git Bash en el terminal y se ejecuta en Windows. Si usa un shell diferente o un sistema operativo diferente, ajuste estas instrucciones para su entorno específico.

   Si se le solicita, permita que Visual Studio Code use el nuevo entorno.

   El where python comando valida que está trabajando desde el entorno virtual enumerando python.exe en la Quickstart-Vector-Search\.venv\ carpeta y otras ubicaciones del directorio de la máquina.

6. Instale las bibliotecas necesarias mediante la ejecución del comando siguiente:

   pip install requirements.txt
   

7. En Visual Studio Code, abra vector-search-quickstart.ipynb.

   Nota

   Si es la primera vez que ha usado jupyter Notebook (.ipynb) en Visual Studio Code, se le pedirá que instale el kernel de Jupyter Notebook y, posiblemente, otras herramientas. Elija instalar las herramientas sugeridas para continuar con este inicio rápido.

8. Busque la siguiente sección titulada "Instalar paquetes y establecer variables" y seleccione el botón Ejecutar celda (Ctrl + Alt + Entrar) (que parece un botón de ejecución típico) a la izquierda de la celda. Al ejecutar la celda se cargan las variables de entorno, se crea DefaultAzureCredential y se imprimen los valores en la salida para confirmar que las dependencias del cuaderno y .env están configuradas correctamente.

   # 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 
   

   La ejecución de esta celda genera la salida siguiente.

   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
   ...
   

   Hay muchos más paquetes que puede ver en un elemento desplazable (vea el mensaje debajo de los resultados de la celda).

Creación del índice de vectores

El código de vector-search-quickstart.ipynb usa varios métodos de la azure.search.documents biblioteca para crear el índice vectorial y los campos que se pueden buscar.

1. Busque la siguiente sección titulada "Crear un índice" y ejecute la celda para crear el índice.

   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')
   

   Si el índice se crea correctamente, verá el siguiente resultado debajo de la celda:

   vector-search-quickstart created
   

   Conclusiones clave al crear un índice vectorial con azure.search.documents:

   • Para definir un índice, cree una lista de campos. Cada campo se crea mediante un método auxiliar que define el tipo de campo y su configuración.

   • Este índice en particular admite varias funcionalidades de búsqueda, como:

     • Búsqueda de palabras clave de texto completo (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))
     • Búsqueda vectorial (habilita la búsqueda híbrida mediante la colocación de campos vectoriales y no vectores) (DescriptionVector)
     • Búsqueda semántica (semantic_search=SemanticSearch(configurations=[semantic_config]))
     • Búsqueda por facetas (facetable=True)
     • Búsqueda semántica (semantic_search=SemanticSearch(configurations=[semantic_config]))
     • Búsqueda geoespacial (Location el campo es GeographyPoint)
     • Filtrado, ordenación (Muchos campos marcados como filtrables y ordenables)

Cargar documentos

La creación y carga del índice son pasos independientes. Ha creado el esquema de índice en el paso anterior. Ahora debe cargar documentos en el índice.

En Azure AI Search, el índice almacena todo el contenido que se puede buscar, mientras que el motor de búsqueda ejecuta consultas en ese índice.

1. Busque la siguiente sección titulada "Crear carga de documentos" y ejecute la celda. Esta celda contiene el código siguiente (truncado para mayor brevedad):

      # 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>
      ]
   

   Esta celda carga una variable denominada documents con un objeto JSON que describe cada documento, junto con la versión vectorizada de la descripción del artículo. Este vector permite la búsqueda de similitud, donde la coincidencia se basa en el significado en lugar de en palabras clave exactas.

   Importante

   El código de este ejemplo no se puede ejecutar. Se quitan varios caracteres o líneas para mayor brevedad. En su lugar, ejecute el código en el cuaderno de Jupyter Notebook.

2. Busque la siguiente sección titulada "Cargar los documentos" y ejecute la celda. Esta celda contiene el código siguiente (truncado para mayor brevedad):

   # 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)
   

   Esto crea una instancia del cliente de búsqueda llamando al SearchClient() constructor y, a continuación, llama al upload_documents() método en el objeto .

   Después de ejecutar la celda, el estado de cada documento se imprime debajo:

   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
   

   Conclusiones clave sobre el upload_documents() método y este ejemplo:

   • El código interactúa con un índice de búsqueda específico hospedado en el servicio Azure AI Search a través de SearchClient, que es el objeto principal proporcionado por el azure-search-documents paquete. SearchClient proporciona acceso a operaciones de índice como:

     • - upload_documents()Ingesta de datos, merge_documents(), delete_documents(), etc.
     • Operaciones - search() de búsqueda, autocomplete(), suggest()
     • Operaciones de administración de índices

   • Los campos vectoriales contienen valores de punto flotante. El atributo dimensions tiene un mínimo de 2 y un máximo de 4096 valores de punto flotante cada uno. En este inicio rápido se establece el atributo de dimensiones en 1 536, ya que ese es el tamaño de las incrustaciones generadas por el modelo de inserción de texto de Azure OpenAI text-embeding-ada-002.

Ejecutar consultas

Ahora que los documentos están cargados, puede realizar consultas vectoriales sobre ellos llamando a search_client.search() y pasando un objeto VectorizedQuery, los campos que desea que se devuelvan, el número de resultados, etc.

En las secciones siguientes, ejecutamos consultas en el índice hotels-vector-quickstart. Entre las consultas se incluyen:

• Vector de búsqueda único
• Vector de búsqueda único con filtro
• Búsqueda híbrida
• Búsqueda híbrida semántica con filtro

Creación de la cadena de consulta vectorial

Las consultas vectoriales de ejemplo se basan en dos cadenas:

• Buscar cadena: historic hotel walk to restaurants and shopping
• Cadena de consulta vectorial (vectorizado en una representación matemática): quintessential lodging near running trails, eateries, retail

La cadena de consulta vectorial es semánticamente similar a la cadena de búsqueda, pero incluye términos que no existen en el índice de búsqueda. Si realiza una búsqueda de palabra clave para quintessential lodging near running trails, eateries, retail, los resultados son cero. Usamos este ejemplo para mostrar cómo puede obtener resultados relevantes incluso si no hay términos coincidentes.

• Encuentre la celda debajo de la sección titulada "Crear la cadena de consulta vectorial" y ejecute la celda. Esto carga la vector variable con los datos de consulta vectorizados necesarios para ejecutar todas las búsquedas en las secciones siguientes.

Búsqueda de vector único

En el primer ejemplo se muestra un escenario básico en el que desea buscar descripciones de documentos que coincidan estrechamente con la cadena de búsqueda.

• Encuentre la celda debajo de la sección titulada "Búsqueda de vector único" y ejecute la celda. Este bloque contiene la solicitud para consultar el índice de búsqueda.

  # 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.")
  

  Esta consulta vectorial se abrevia para mayor brevedad. vectorQueries.vector contiene el texto vectorizado de la entrada de consulta, fields determina qué campos vectoriales se buscan y k especifica el número de vecinos más próximos que se van a devolver.

  La cadena de consulta vectorial es quintessential lodging near running trails, eateries, retail, que se vectoriza en 1 536 incrustaciones para esta consulta.

  La respuesta del vector equivalente de quintessential lodging near running trails, eateries, retail incluye siete resultados, pero el código especifica top=5 , por lo que solo se devuelven los cinco primeros resultados. Además, solo se devuelven los campos especificados por .select

  search_client.search() devuelve un objeto de tipo dict.like. Cada resultado proporciona una puntuación de búsqueda, a la que se puede acceder mediante score = result.get("@search.score", "N/A"). Aunque no se muestra en este ejemplo, en una búsqueda de similitud, la respuesta siempre incluye k resultados ordenados por la puntuación de similitud de valor.

  Después de ejecutar la celda, el estado de cada documento se imprime debajo:

  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
  

Búsqueda de vector único con filtro

Puede agregar filtros, pero los filtros se aplican al contenido no vectorial del índice. En este ejemplo, el filtro se aplica al campo Tags, para filtrar los hoteles que no proporcionan Wi-Fi gratis.

1. Busque la celda debajo de la sección titulada "Búsqueda de vector único con filtro" y ejecute la celda. Esta celda contiene la solicitud para consultar el índice de búsqueda.

   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.")
   

   Después de ejecutar la celda, el estado de cada documento se imprime debajo:

   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 consulta era la misma que en el anterior ejemplo de búsqueda de vector único, pero incluye un filtro de exclusión posterior al procesamiento y devuelve solo los tres hoteles que tienen Wi-Fi gratis.

2. En el ejemplo de filtro siguiente se usa un filtro geográfico. Ejecute la celda en la sección titulada "Consulta vectorial con un filtro geográfico". Este bloque contiene la solicitud para consultar el índice de búsqueda.

   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 consulta era la misma que el ejemplo anterior de búsqueda de vectores únicos, pero incluye un filtro de exclusión posterior al procesamiento y devuelve solo los dos hoteles dentro de 300 kilómetros.

   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.
   

Búsqueda híbrida

La búsqueda híbrida consta de consultas de palabras clave y consultas vectoriales en una sola solicitud de búsqueda. En este ejemplo se ejecuta la consulta vectorial y la búsqueda de texto completo simultáneamente:

• Buscar cadena: historic hotel walk to restaurants and shopping

• Cadena de consulta vectorial (vectorizado en una representación matemática): quintessential lodging near running trails, eateries, retail

• Busque la siguiente sección titulada "Búsqueda híbrida" y ejecute la celda. Este bloque contiene la solicitud para consultar el índice de búsqueda.

  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.")    
  

  Revise la respuesta:

  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']
  

  Dado que la Fusión por Rango Recíproco (RRF) combina resultados, ayuda a revisar las entradas. Los siguientes resultados son solo de la consulta de texto completo. Los dos mejores resultados son Sublime Palace Hotel y History Lion Resort. El Sublime Palace Hotel tiene una puntuación de relevancia BM25 más fuerte.

  {
       "@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"
  },
  

  En la consulta de solo vector, que usa HNSW para buscar coincidencias, el Sublime Palace Hotel cae a la cuarta posición. Historic Lion, que fue el segundo en la búsqueda de texto completo y el tercero en vector de búsqueda, no experimenta el mismo intervalo de fluctuaciones, así que aparece como una coincidencia superior en un conjunto de resultados homogeneizado.

  "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"
       }
  ]
  

Búsqueda híbrida semántica con un filtro

Esta es la última consulta de la colección. Esta consulta híbrida con clasificación semántica se filtra para mostrar solo los hoteles dentro de un radio de 500 kilómetros de Washington D.C. Puede establecer vectorFilterMode en null, que es equivalente al valor predeterminado (preFilter para índices más recientes y postFilter para los más antiguos).

• Busque la celda debajo de la sección titulada "Búsqueda híbrida semántica" y ejecute la celda. Este bloque de código contiene la solicitud para consultar el índice de búsqueda.

  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.")
  

  Revise la salida debajo de la celda. La respuesta es tres hoteles, que se filtran por ubicación y por facetas por StateProvince y se vuelven a clasificar semánticamente para promover resultados más cercanos a la consulta de cadena de búsqueda (historic hotel walk to restaurants and shopping).

  El Swirling Currents Hotel ahora se encuentra en el lugar superior. Sin clasificación semántica, el Valley Motel de Nordick es el número uno. Con la clasificación semántica, los modelos de comprensión de la máquina reconocen que historic se aplica al "hotel", a poca distancia a pie de los restaurantes (para cenar) y las compras."

  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
  

  Puntos clave:

  • El vector de búsqueda se especifica mediante la propiedad vectors.value. La búsqueda de palabra clave se especifica mediante la propiedad search.

  • En una búsqueda híbrida, puede integrar el vector de búsqueda con búsqueda de texto completo sobre palabras clave. Los filtros, la revisión ortográfica y la clasificación semántica solo se aplican al contenido textual y no a los vectores. En esta consulta final, no hay ningún answer semántico porque el sistema no produjo uno suficientemente fuerte.

  • Entre los resultados reales se incluyen más detalles, incluidos los títulos semánticos y los resaltados. Los resultados se modificaron para mejorar la legibilidad. Para obtener la estructura completa de la respuesta, ejecute la solicitud en el cliente REST.

Limpieza

Cuando esté trabajando en su propia suscripción, es una buena idea al final de un proyecto para identificar si todavía necesita los recursos que ha creado. Los recursos que quedan en ejecución pueden costar dinero. Puede eliminar recursos individualmente o eliminar el grupo de recursos para eliminar todo el conjunto de recursos.

Puede encontrar y administrar recursos en Azure Portal mediante el vínculo Todos los recursos o Grupos de recursos en el panel izquierdo.

Si desea mantener el servicio de búsqueda, pero eliminar el índice y los documentos, puede usar el SearchIndexClient método del delete_index() objeto. Busque la siguiente sección titulada "Limpiar" y ejecute la celda si desea eliminar el hotels-vector-quickstart índice:

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

Pasos siguientes

• Revisión del repositorio de ejemplos de código para las funcionalidades de búsqueda de vectores en Azure AI Search para Python
• Revise los otros ejemplos de código de Python y Azure AI Search en el repositorio azure-search-python-samples.

En este inicio rápido, usará las API REST de Azure AI Search para crear, cargar y consultar vectores.

En la Búsqueda de Azure AI, un almacén de vectores tiene un esquema de índice que define campos vectoriales y no vectores, una configuración de vector de búsqueda para algoritmos que crean el espacio de inserción y la configuración en las definiciones de campo vectorial que se evalúan en el momento de la consulta. La API de REST Crear índice crea el almacén de vectores.

Nota

En este inicio rápido se omite el paso de vectorización y se proporcionan instrucciones insertadas. Si quiere agregar fragmentación y vectorización de datos integrados a través de su propio contenido, pruebe el Asistente para importar y vectorizar datos para ver un tutorial completo.

Requisitos previos

• Una cuenta de Azure con una suscripción activa. Cree una cuenta gratuita.

• Un servicio de Búsqueda de Azure AI. Cree un servicio o busque un servicio existente en la suscripción actual.

  • Puede usar un servicio de búsqueda gratuito para la mayoría de estos inicios rápidos, pero se recomienda el nivel Básico o superior para archivos de datos más grandes.
  • Para ejecutar el ejemplo de consulta que invoca el reranking semántico, el servicio de búsqueda debe estar en el nivel Básico o superior con el clasificador semántico habilitado.

• Visual Studio Code con un cliente REST.

---

Recuperación de información de recursos

Las solicitudes al punto de conexión de búsqueda deben autenticarse y autorizarse. Puede usar claves de API o roles para esta tarea. Se recomienda usar una conexión sin claves a través de Microsoft Entra ID.

Seleccione la pestaña que se corresponde con su método de autenticación preferido. Use el mismo método para todas las solicitudes de este inicio rápido.

Microsoft Entra ID

1. Inicie sesión en Azure Portal y busque su servicio de búsqueda.

2. En la página principal Información general, busque la dirección URL. Un punto de conexión de ejemplo podría ser similar a https://mydemo.search.windows.net.

   

3. Siga los pasos del inicio rápido sin claves para obtener el token de Microsoft Entra.

   Obtendrá el token al ejecutar el comando az account get-access-token en el paso 3 del inicio rápido anterior.

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

Clave de API

1. Inicie sesión en Azure Portal y busque su servicio de búsqueda.

2. En la página principal Información general, busque la dirección URL. Un punto de conexión de ejemplo podría ser similar a https://mydemo.search.windows.net.

   

3. Seleccione Configuración>Claves. Las claves de API o Ambos deben estar habilitados. Claves de API de administrador se usan para agregar, modificar y eliminar objetos. Hay dos claves de administrador intercambiables. Copie cualquiera de ellos.

   

Crear o descargar el archivo de código

Use un archivo .rest o .http para ejecutar todas las solicitudes de este inicio rápido. Puede descargar el archivo REST que contiene el código de este inicio rápido, o bien puede crear un nuevo archivo en Visual Studio Code y copiar el código en él.

1. En Visual Studio Code, cree un nuevo archivo con una extensión de archivo .rest o .http. Por ejemplo, az-search-vector-quickstart.rest. Copie y pegue el contenido sin procesar del archivo Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest en este nuevo archivo.

2. En la parte superior del archivo, reemplace el valor de marcador de posición de @baseUrl por la dirección URL del servicio de búsqueda. Consulte la sección Recuperar información de recursos para obtener instrucciones sobre cómo encontrar la dirección URL del servicio de búsqueda.

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

3. En la parte superior del archivo, reemplace el valor del marcador de posición por la autenticación. Consulte la sección Recuperar información de recursos para obtener instrucciones sobre cómo obtener el token de Microsoft Entra o la clave de API.

   Para la autenticación sin clave recomendada mediante Microsoft Entra ID, debe reemplazar @apiKey por la variable @token.

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

   Si prefiere usar una clave de API, reemplace @apiKey por la clave que copió de Azure Portal.

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

4. Para la autenticación sin clave recomendada mediante Microsoft Entra ID, debe reemplazar api-key: {{apiKey}} por Authorization: Bearer {{token}} en los encabezados de solicitud. Reemplace todas las instancias de api-key: {{apiKey}} que encuentre en el archivo.

Crear un índice vectorial

Use la API de REST Crear índice para crear un índice vectorial y configurar las estructuras de datos físicas en el servicio de búsqueda.

El esquema de índice de este ejemplo se organiza en torno al contenido del hotel. Los datos de muestra constan de descripciones vectoriales y no vectoriales de hoteles ficticios. En este esquema se incluyen configuraciones para la indexación de vectores y las consultas, así como para la clasificación semántica.

1. En Visual Studio Code, abra el archivo az-search-vector-quickstart.rest que creó anteriormente.

2. Busque el bloque de código ### Create a new index en el archivo. Este bloque contiene la solicitud para crear el índice hotels-vector-quickstart en el servicio de búsqueda.

   ### 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. Seleccione Enviar solicitud. Debe tener una respuesta HTTP/1.1 201 Created.

   El cuerpo de la respuesta debe incluir la representación JSON del esquema de índice.

   {
       "@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"
               }
               ]
           }
           }
       ]
       }
   }
   

Conclusiones clave sobre la API de REST de Crear índice:

• La colección fields incluye un campo de clave obligatorio y campos de texto y vector (como Description y DescriptionVector) para la el texto y el vector de búsqueda. La colocación de campos de vectoriales y no vectoriales en el mismo índice permite consultas híbridas. Por ejemplo, puede combinar filtros, búsqueda de texto con clasificación semántica y vectores en una sola operación de consulta.

• Los campos vectoriales deben ser uno de los tipos de datos EDM usados para vectores, como type: Collection(Edm.Single). Los campos vectoriales también tienen dimensions propiedades y vectorSearchProfile .

• La sección vectorSearch es una matriz de configuraciones y perfiles de algoritmos vecinos más cercanos aproximados. Los algoritmos compatibles incluyen un mundo pequeño navegable jerárquico y el vecinos más próximo k exhaustivo. Para más información, consulte Relevancia y puntuación en el vector de búsqueda.

• La configuración (opcional) de semantic permite reordenar los resultados de búsqueda. Puede volver a generar resultados en consultas de tipo semantic para los campos de cadena que se especifican en la configuración. Para saber más información, consulte Introducción a la clasificación semántica.

Cargar documentos

La creación y carga del índice son pasos independientes. Ha creado el esquema de índice en el paso anterior. Ahora debe cargar documentos en el índice.

En Búsqueda de Azure AI, el índice contiene todos los datos que se pueden buscar y las consultas se ejecutan en el servicio de búsqueda. En las llamadas de REST, los datos se proporcionan como documentos JSON. Use Documentación: Indexar API de REST para esta tarea. El URI se extiende para incluir la colección docs y la operación index.

1. Formule una solicitud de carga para subir documentos al índice hotels-vector-quickstart de su servicio de búsqueda.

   ### 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. Seleccione Enviar solicitud. Debe tener una respuesta HTTP/1.1 200 OK. El cuerpo de la respuesta debe incluir la representación JSON de los documentos de búsqueda.

Conclusiones clave sobre la solicitud de Documentos: Indexar API de REST:

• Los documentos de la carga constan de campos definidos en el esquema de índice.

• Los campos vectoriales contienen valores de punto flotante. El atributo dimensiones tiene un mínimo de 2 y un máximo de 4096 valores de punto flotante cada uno. En esta guía de inicio rápido se establece el atributo de dimensiones en 1536 debido a que ese es el tamaño de las incrustaciones generadas por el modelo text-embedding-3-small de Azure OpenAI.

Ejecutar consultas

Ahora que los documentos se cargan, puede ejecutar consultas vectoriales en ellos mediante Documentos - Buscar post (REST).

En las secciones siguientes, ejecutamos consultas en el índice hotels-vector-quickstart. Entre las consultas se incluyen:

• Vector de búsqueda único
• Vector de búsqueda único con filtro
• Búsqueda híbrida
• Búsqueda híbrida semántica

Las consultas de ejemplo se basan en dos cadenas:

• Buscar cadena: historic hotel walk to restaurants and shopping
• Cadena de consulta vectorial (vectorizado en una representación matemática): quintessential lodging near running trails, eateries, retail

La cadena de consulta vectorial es semánticamente similar a la cadena de búsqueda, pero incluye términos que no existen en el índice de búsqueda. Si realiza una búsqueda de palabras clave para quintessential lodging near running trails, eateries, retail, los resultados son cero en una búsqueda de palabras clave pura sin clasificación semántica. Usamos este ejemplo para mostrar cómo puede obtener resultados relevantes mediante vectores, incluso si no hay términos coincidentes.

Búsqueda de vector único

1. Formule la solicitud. La consulta es una representación flotante de 1536 alojamiento de quintaessential cerca de senderos para correr, restaurantes, minorista. La consulta busca DescriptionVector y devuelve resultados 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
               }
           ]
       }
   

   Conclusiones clave sobre la API REST Documents - Search Post :

   • vectorQueries.vector es la cadena de consulta vectorial. Es una representación vectorial de alojamiento esencial cerca de senderos para correr, restaurantes, tiendas, que se vectoriza en 1.536 incrustaciones para esta consulta.

   • fields determina qué campos vectoriales se buscan.

   • kind establecido en vector significa que la cadena de consulta es un vector. Si kind se estableciera a text, necesitaría funcionalidad adicional (un vectorizador) para codificar una cadena de texto legible para humanos en un vector en el momento de la consulta. Los vectorizadores se omiten en este inicio rápido para simplificar el ejercicio.

   • k especifica el número de vecinos más cercanos que se devolverán en la respuesta. Un count parámetro especifica el número de coincidencias encontradas en el índice. Incluir recuento es un procedimiento recomendado para las consultas, pero es menos útil para la búsqueda de similitud donde el algoritmo puede encontrar algún grado de similitud en casi cualquier documento.

2. Seleccione Enviar solicitud. Debe tener una respuesta HTTP/1.1 200 OK. El cuerpo de la respuesta debe incluir la representación JSON de los resultados de la búsqueda.

   La respuesta del vector equivalente de quintessential lodging near running trails, eateries, retail incluye resultados k-5, aunque el motor de búsqueda encontró 7 coincidencias. Los resultados principales se consideran más semánticamente similares a la consulta. Cada resultado proporciona una puntuación de búsqueda y los campos enumerados en select. En una búsqueda de similitud, la respuesta siempre incluirá los k resultados ordenados por la puntuación de similitud de valores.

   {
     "@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"
         ]
       }
     ]
   }
   

Búsqueda de vector único con filtro

Puede agregar filtros, pero los filtros se aplican al contenido no vectorial del índice. En este ejemplo, el filtro se aplica al campo Tags, para filtrar los hoteles que no proporcionan Wi-Fi gratis.

1. Formule la solicitud. Esta es la misma solicitud que la anterior, con un parámetro de filtro y modo de filtro adicional.

   ### 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. Seleccione Enviar solicitud. Debe tener una respuesta HTTP/1.1 200 OK. El cuerpo de la respuesta debe incluir la representación JSON de los resultados de la búsqueda.

   La consulta era la misma que el ejemplo anterior de búsqueda de vectores únicos, pero incluye un filtro de exclusión posterior al procesamiento y devuelve solo los dos hoteles que tienen conexión Wi-Fi gratuita.

   {
     "@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. Esta es otra consulta que usa un filtro geográfico, limitando los resultados a hoteles dentro de 300 kilómetros alrededor de 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. Revise la respuesta. Se limita a dos hoteles cerca de Washington D.C, con una estructura de facetas que proporciona la agregación.

   "@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"
       }
     }
   ]
   

Búsqueda híbrida

La búsqueda híbrida consta de consultas de palabras clave y consultas vectoriales en una sola solicitud de búsqueda. En este ejemplo se ejecuta la consulta vectorial y la búsqueda de texto completo simultáneamente:

• Buscar cadena: historic hotel walk to restaurants and shopping
• Cadena de consulta vectorial (vectorizado en una representación matemática): quintessential lodging near running trails, eateries, retail

1. Formulación de la consulta híbrida.

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

2. Seleccione Enviar solicitud. Debe tener una respuesta HTTP/1.1 200 OK. El cuerpo de la respuesta debe incluir la representación JSON de los resultados de la búsqueda.

Dado que se trata de una consulta híbrida, los resultados se clasifican por la fusión de clasificación recíproca (RRF). Observe que @search.score los valores tienen una base diferente y son valores uniformemente más pequeños. RRF evalúa las puntuaciones de búsqueda de varios resultados de búsqueda, toma el inverso y, después, combina y ordena los resultados combinados. Se devuelve el número de resultados top.

Revise la respuesta, que consta de las coincidencias k-5 principales de 7 documentos coincidentes en el índice:

{
  "@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."
    }
  ]
}

Dado que RRF combina los resultados, ayuda a revisar las entradas individualmente.

Los siguientes resultados proceden de la parte de texto completo de la consulta: hotel histórico a poca distancia de restaurantes y tiendas. En la consulta de texto completo, los tres primeros resultados son Sublime Palace Hotel, Stay-Kay City Hotel y Luxury Lion Resort. El Sublime Palace Hotel tiene una puntuación de relevancia BM25 más fuerte. En la consulta fusionada, solo dos de estas coincidencias están entre los 3 primeros, y la segunda coincidencia (Stay-Kay City Hotel) no aparece entre los 5 primeros.

  "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."
    },
    ...
  ]

En la consulta de la parte vectorial (alojamiento esencial cerca de senderos para correr, restaurantes, tiendas) es una cadena diferente, por lo que deberíamos esperar resultados diferentes. Esta consulta devuelve el Valley Motel de Nordick, Luxury Lion Resort y Sublime Palace Hotel como las tres primeras coincidencias. En la consulta fusionada, estos resultados están entre los 3 primeros, pero en orden 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."
    },
   ...
  ]

Búsqueda híbrida semántica

Esta es la última consulta de la colección. Esta consulta híbrida agrega una clasificación semántica L2 que aplica la comprensión de lectura automática a través de los resultados con clasificación L1, lo que promueve coincidencias más relevantes en la parte superior.

1. Formule la solicitud.

   ### 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. Seleccione Enviar solicitud. Debe tener una respuesta HTTP/1.1 200 OK. El cuerpo de la respuesta debe incluir la representación JSON de los resultados de la búsqueda.

Revise la respuesta, que consta de un reordenamiento semántico de los resultados clasificados por RRF de la consulta híbrida. La clasificación semántica funciona fuera de las entradas de texto. En una consulta híbrida o de texto, esta entrada es la parte de texto de la consulta (paseo histórico del hotel a restaurantes y compras). Para usar la clasificación semántica en una consulta vectorial pura, como el primer ejemplo, o para controlar explícitamente el texto usado para la clasificación semántica, proporcione una cadena semanticQuery.

Después del reranking semántico, Swirling Currents Hotel con su referencia a acceso a pie a tiendas, restaurantes, entretenimiento y el centro de la ciudad se coloca en el primer lugar. El modelo de comprensión automática promueve el acceso a pie a las tiendas y los restaurantes como una coincidencia más cercana para caminar hacia los restaurantes y las tiendas.

Antes de la reordenación semántica, Sublime Palace, que destaca por estar a poca distancia a pie de los sitios y lugares de interés de la ciudad y por estar rodeado por la extraordinaria belleza de iglesias, edificios, tiendas y monumentos, era el número uno.

{
  "@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"
    }
  ]
}

Sugerencia

Los resultados clasificados semánticamente pueden incluir más detalles, como respuestas semánticas, subtítulos y resaltados. Agregar más parámetros a la solicitud genera el detalle adicional. Para obtener más información, consulte Configuración de una consulta semántica.

Limpieza

Cuando esté trabajando en su propia suscripción, es una buena idea al final de un proyecto para identificar si todavía necesita los recursos que ha creado. Los recursos que quedan en ejecución pueden costar dinero. Puede eliminar recursos individualmente o eliminar el grupo de recursos para eliminar todo el conjunto de recursos.

Puede encontrar y administrar recursos en Azure Portal mediante el vínculo Todos los recursos o Grupos de recursos en el panel izquierdo.

Si desea mantener el servicio de búsqueda, pero quiere eliminar el índice y los documentos, puede usar el comando DELETE en el cliente de REST. Este comando (al final del archivo az-search-vector-quickstart.rest) elimina el índice 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}}

Pasos siguientes

Como paso siguiente, se recomienda aprender a invocar llamadas a la API de REST sin claves de API.

Quizás también quiera revisar el código de demostración para Python, C# o JavaScript.