Guia de início rápido: pesquisa vetorial

Neste guia de início rápido, você usa um bloco de anotações Jupyter para criar, carregar e consultar vetores. Os exemplos de código executam essas operações usando a biblioteca de cliente do Azure AI Search. A biblioteca fornece uma abstração sobre a API REST para acesso a operações de índice, como ingestão de dados, operações de pesquisa e operações de gerenciamento de índice.

No Azure AI Search, um repositório de vetores tem um esquema de índice que define campos vetoriais e não vetoriais, uma configuração de pesquisa vetorial para algoritmos que criam o espaço de incorporação e configurações em definições de campo vetorial que são avaliadas no momento da consulta. A API REST Create Index cria o repositório vetorial.

Nota

Este guia de início rápido omite a etapa de vetorização e fornece incorporações em linha. Se quiser adicionar fragmentação e vetorização de dados integrada ao seu próprio conteúdo, experimente o assistente Importar e vetorizar dados para um guia completo passo a passo.

Pré-requisitos

• Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.

• Um serviço Azure AI Search. Crie um serviço ou encontre um serviço existente na sua subscrição atual.

  • Você pode usar um serviço de pesquisa gratuito para a maior parte desse início rápido, mas recomendamos a camada Básica ou superior para arquivos de dados maiores.
  • Para executar o exemplo de consulta que invoca a reclassificação semântica, seu serviço de pesquisa deve estar na camada Básica ou superior com o classificador semântico habilitado.

• Visual Studio Code com a extensão Python e o pacote Jupyter.

• Git para clonar o repositório que contém o notebook Jupyter e outros arquivos relacionados.

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Recuperar informações do recurso

As solicitações para o ponto de extremidade de pesquisa devem ser autenticadas e autorizadas. Embora seja possível usar chaves ou funções de API para essa tarefa, recomendamos o uso de uma conexão sem chave via ID do Microsoft Entra.

Este guia rápido de início usa DefaultAzureCredential, que simplifica a autenticação em cenários de desenvolvimento e produção. No entanto, para cenários de produção, você pode ter requisitos mais avançados que exigem uma abordagem diferente. Consulte Autenticar aplicativos Python nos serviços do Azure usando o SDK do Azure para Python para entender todas as suas opções.

Clone o código e o ambiente de configuração

1. Clone o repositório que contém o código para este início rápido.

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

   Este repositório tem exemplos de código Python para vários artigos, cada um em uma subpasta separada.

2. Abra a subpasta Quickstart-Vector-Search no Visual Studio Code.

   Há três arquivos nesta pasta:

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

3. Renomeie o ficheiro sample.env para .env e modifique os valores no ficheiro .env.

   Use o URL do serviço de pesquisa como o AZURE_SEARCH_ENDPOINT. Você pode encontrar a URL no portal do Azure. Vá para o seu serviço Azure AI Search, na página Visão geral , procure o campo URL. Um ponto final de exemplo poderá ser parecido com https://mydemo.search.windows.net.

   Finalmente, escolha um novo AZURE_SEARCH_INDEX_NAME nome ou use o fornecido no arquivo.

4. No Visual Studio Code, trabalhe em um ambiente. No menu Exibir , selecione Terminal..., ou selecione Ctrl+`.

5. Execute os seguintes comandos no terminal:

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

   Nota

   Isso pressupõe que você esteja usando o Git Bash no seu Terminal e esteja executando no Windows. Se você estiver usando um shell diferente e/ou um sistema operacional diferente, ajuste estas instruções para seu ambiente específico.

   Se solicitado, permita que o Visual Studio Code use o novo ambiente.

   O where python comando valida que você está trabalhando no ambiente virtual listando python.exe na Quickstart-Vector-Search\.venv\ pasta e em outros locais do diretório da sua máquina.

6. Instale as bibliotecas necessárias executando o seguinte comando:

   pip install requirements.txt
   

7. No Visual Studio Code, abra o arquivo vector-search-quickstart.ipynb.

   Nota

   Se esta for a primeira vez que você usa um Jupyter Notebook (.ipynb) no Visual Studio Code, você será solicitado a instalar o kernel do Jupyter Notebook e possivelmente outras ferramentas. Opte por instalar as ferramentas sugeridas para continuar com este início rápido.

8. Encontre a célula abaixo da seção intitulada "Instalar pacotes e definir variáveis" e selecione o botão Executar célula (Ctrl + Alt + Enter) (que se parece com um botão de execução típico) à esquerda da célula. A execução da célula carrega as variáveis de ambiente, cria o DefaultAzureCredential e imprime valores na saída para confirmar se as dependências do notebook e .env estão configuradas corretamente.

   # 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 
   

   A execução desta célula produz a seguinte saída.

   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
   ...
   

   Há muitos mais pacotes que você pode visualizar em um elemento rolável (veja a mensagem abaixo dos resultados da célula).

Criar o índice de vetores

O código no vector-search-quickstart.ipynb usa vários métodos da biblioteca para criar o índice vetorial azure.search.documents e campos pesquisáveis.

1. Encontre a seção da célula abaixo intitulada "Criar um índice" e execute a célula para criar o 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')
   

   Se o índice for criado com êxito, você verá o seguinte resultado abaixo da célula:

   vector-search-quickstart created
   

   Principais conclusões ao criar índice vetorial com o azure.search.documents:

   • Você define um índice criando uma lista de campos. Cada campo é criado usando um método auxiliar que define o tipo de campo e suas configurações.

   • Este índice específico suporta várias capacidades de pesquisa, tais como:

     • Pesquisa de palavras-chave de texto completo (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))
     • Pesquisa vetorial (permite a pesquisa híbrida ao colocar campos vetoriais e não vetoriais em conjunto) (DescriptionVector)
     • Pesquisa semântica (semantic_search=SemanticSearch(configurations=[semantic_config]))
     • Pesquisa facetada (facetable=True)
     • Pesquisa semântica (semantic_search=SemanticSearch(configurations=[semantic_config]))
     • Pesquisa geoespacial (Location campo é GeographyPoint)
     • Filtragem, classificação (Muitos campos marcados como filtráveis e classificáveis)

Carregar documentos

Criar e carregar o índice são etapas separadas. Você criou o esquema de índice na etapa anterior. Agora você precisa carregar documentos no índice.

No Azure AI Search, o índice armazena todo o conteúdo pesquisável, enquanto o mecanismo de pesquisa executa consultas nesse índice.

1. Encontre a célula abaixo da seção intitulada "Criar carga útil de documentos" e execute-a. Esta célula contém o seguinte código (truncado para brevidade):

      # 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>
      ]
   

   Essa célula carrega uma variável nomeada documents com um objeto JSON que descreve cada documento, juntamente com a versão vetorizada da descrição do artigo. Este vetor permite a pesquisa de semelhança, onde a correspondência é baseada no significado e não em palavras-chave exatas.

   Importante

   O código neste exemplo não pode ser executado. Vários caracteres ou linhas são removidos para maior brevidade. Em vez disso, execute o código no bloco de anotações Jupyter.

2. Encontre a célula abaixo da seção intitulada "Carregar os documentos" e execute a célula. Esta célula contém o seguinte código (truncado para brevidade):

   # 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)
   

   Isso cria uma instância do cliente de pesquisa ao chamar o construtor SearchClient(), e em seguida chama o método upload_documents() no objeto.

   Depois de executar a célula, o status de cada documento é impresso abaixo dela:

   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
   

   Principais conclusões sobre o upload_documents() método e este exemplo:

   • Seu código interage com um índice de pesquisa específico hospedado em seu serviço Azure AI Search por meio do SearchClient, que é o objeto principal fornecido pelo azure-search-documents pacote. O SearchClient fornece acesso a operações de índice, tais como:

     • Ingestão de - upload_documents() dados, merge_documents(), delete_documents(), etc.
     • Operações de pesquisa - , search(), autocomplete(), suggest()
     • Operações de gestão de índices

   • Os campos vetoriais contêm valores de ponto flutuante. O atributo dimensions tem um mínimo de 2 e um máximo de 4096 valores de ponto flutuante cada. Este guia de início rápido define o atributo dimensions como 1.536 porque esse é o tamanho das incorporações geradas pelo modelo text-embedding-ada-002 do Azure OpenAI.

Executar consultas

Agora que os documentos estão carregados, você pode emitir consultas vetoriais contra eles chamando search_client.search() e passando um objeto VectorizedQuery, os campos que deseja retornar, o número de resultados e assim por diante.

Nas próximas seções, executamos consultas no hotels-vector-quickstart índice. As consultas incluem:

• Pesquisa de vetor único
• Pesquisa vetorial única com filtro
• Pesquisa híbrida
• Pesquisa híbrida semântica

Criar a cadeia de caracteres de consulta vetorial

As consultas vetoriais de exemplo são baseadas em duas cadeias de caracteres:

• Cadeia de pesquisa: historic hotel walk to restaurants and shopping
• Seqüência de consulta vetorial (vetorizada em uma representação matemática): quintessential lodging near running trails, eateries, retail

A cadeia de caracteres de consulta vetorial é semanticamente semelhante à cadeia de pesquisa, mas inclui termos que não existem no índice de pesquisa. Se você fizer uma pesquisa por palavra-chave para quintessential lodging near running trails, eateries, retail, os resultados serão zero. Usamos este exemplo para mostrar como você pode obter resultados relevantes, mesmo que não haja termos correspondentes.

• Encontre a célula abaixo da seção intitulada "Criar a cadeia de caracteres de consulta vetorial" e execute a célula. Isso carrega a vector variável com os dados de consulta vetorizados necessários para executar todas as pesquisas nas próximas seções.

Pesquisa de vetor único

O primeiro exemplo demonstra um cenário básico onde você deseja encontrar descrições de documentos que correspondam à cadeia de caracteres de pesquisa.

• Encontre a célula abaixo da seção intitulada "Pesquisa de vetor único" e execute a célula. Este bloco contém o pedido para consultar o índice de pesquisa.

  # 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 vetorial é encurtada para brevidade. O vectorQueries.vector contém o texto vetorizado da entrada de consulta, fields determina quais campos vetoriais são pesquisados e k especifica o número de vizinhos mais próximos a serem retornados.

  A cadeia de caracteres de consulta vetorial é quintessential lodging near running trails, eateries, retail, que é vetorizada em 1.536 incorporações para esta consulta.

  A resposta para o equivalente vetorial de quintessential lodging near running trails, eateries, retail consiste em 7 resultados, mas o código especifica top=5 para que apenas os primeiros 5 resultados sejam retornados. Além disso, apenas os campos especificados pelo select são retornados.

  search_client.search() Retorna um objeto semelhante a um dicionário. Cada resultado fornece uma pontuação de pesquisa, que pode ser acessada usando score = result.get("@search.score", "N/A"). Embora não seja exibida neste exemplo, em uma pesquisa de similaridade, a resposta sempre inclui k resultados ordenados pela pontuação de similaridade de valor.

  Depois de executar a célula, o status de cada documento é impresso abaixo dela:

  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
  

Pesquisa vetorial única com filtro

Você pode adicionar filtros, mas os filtros são aplicados ao conteúdo não vetorial em seu índice. Neste exemplo, o filtro se aplica ao campo para filtrar todos os Tags hotéis que não oferecem Wi-Fi gratuito.

1. Encontre a célula abaixo da seção intitulada "Pesquisa vetorial única com filtro" e execute a célula. Esta célula contém o pedido para consultar o índice de pesquisa.

   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.")
   

   Depois de executar a célula, o status de cada documento é impresso abaixo dela:

   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']
   

   A consulta era a mesma do exemplo anterior de pesquisa de vetor único, mas inclui um filtro de exclusão de pós-processamento e retorna apenas os dois hotéis que têm Wi-Fi gratuito.

2. O próximo exemplo de filtro usa um filtro geográfico. Execute a célula na seção intitulada "Consulta vetorial com um filtro geográfico". Este bloco contém o pedido para consultar o índice de pesquisa.

   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.")
   

   A consulta era a mesma do exemplo anterior de pesquisa de vetor único, mas inclui um filtro de exclusão de pós-processamento e retorna apenas os dois hotéis dentro de 300 quilômetros.

   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.
   

Pesquisa híbrida

A pesquisa híbrida consiste em consultas de palavras-chave e consultas vetoriais em uma única solicitação de pesquisa. Este exemplo executa a consulta vetorial e a pesquisa de texto completo simultaneamente:

• Cadeia de pesquisa: historic hotel walk to restaurants and shopping

• Seqüência de consulta vetorial (vetorizada em uma representação matemática): quintessential lodging near running trails, eateries, retail

• Encontre a célula abaixo seção intitulada "Pesquisa híbrida" e execute a célula. Este bloco contém o pedido para consultar o índice de pesquisa.

  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.")    
  

  Reveja a resposta:

  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']
  

  Como o RRF (Reciprocal Rank Fusion) mescla resultados, ele ajuda a revisar as entradas. Os resultados a seguir são somente da consulta de texto completo. Os dois primeiros resultados são Sublime Palace Hotel e History Lion Resort. O Sublime Palace Hotel tem uma pontuação de relevância BM25 mais forte.

  {
       "@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"
  },
  

  Na consulta somente vetorial, que usa HNSW para encontrar correspondências, o Sublime Palace Hotel cai para a quarta posição. O Historic Lion, que ficou em segundo lugar na pesquisa de texto completo e terceiro na pesquisa vetorial, não experimenta a mesma faixa de flutuação, por isso aparece como uma correspondência superior em um 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"
       }
  ]
  

Pesquisa híbrida semântica

Aqui está a última consulta na coleção. Esta consulta híbrida especifica o tipo de consulta semântica e uma configuração semântica, demonstrando que você pode criar uma consulta híbrida que usa reclassificação semântica.

• Encontre a célula abaixo da seção intitulada "Pesquisa híbrida semântica" e execute a célula. Este bloco de código contém a solicitação para consultar o índice de pesquisa.

  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.")
  

  Analise a saída abaixo da célula.

  Com ranking semântico, o Swirling Currents Hotel passa agora para o primeiro lugar. Q

  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
  

Você pode pensar na classificação semântica como uma maneira de melhorar a relevância dos resultados da pesquisa, entendendo o significado por trás das palavras na consulta e o conteúdo dos documentos. Neste caso, a classificação semântica ajuda a identificar hotéis que não só são relevantes para as palavras-chave, mas também correspondem à intenção da consulta:

Conclusões principais:

• A pesquisa vetorial é especificada através da vectors.value propriedade. A pesquisa por palavra-chave é especificada através da search propriedade.

• Em uma pesquisa híbrida, você pode integrar a pesquisa vetorial com a pesquisa de texto completo sobre palavras-chave. Os filtros, a verificação ortográfica e a classificação semântica aplicam-se apenas ao conteúdo textual e não aos vetores. Nesta consulta final, não há semântica answer porque o sistema não produziu uma que fosse suficientemente forte.

• Os resultados reais incluem mais detalhes, incluindo legendas semânticas e destaques. Os resultados foram modificados quanto à legibilidade. Para obter a estrutura completa da resposta, execute a solicitação no cliente REST.

Limpeza

Ao trabalhar na sua própria subscrição, recomendamos que verifique, depois de concluir um projeto, se ainda vai precisar dos recursos que criou. Os recursos que deixar em execução podem custar dinheiro. Pode eliminar recursos individualmente ou eliminar o grupo de recursos para eliminar todo o conjunto de recursos.

Você pode localizar e gerenciar recursos no portal do Azure usando o link Todos os recursos ou Grupos de recursos no painel mais à esquerda.

Se você quiser manter o serviço de pesquisa, mas excluir o índice e os documentos, poderá usar o SearchIndexClient método do delete_index() objeto. Localize a célula abaixo da secção intitulada "Limpar" e execute-a se quiser apagar o índice hotels-vector-quickstart.

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

Próximos passos

• Revise o repositório de exemplos de código para recursos de pesquisa vetorial no Azure AI Search for Python
• Analise os outros exemplos de código Python e Azure AI Search no repositório azure-search-python-samples

Neste guia de início rápido, você usa JavaScript para criar, carregar e consultar vetores. Os exemplos de código executam essas operações usando a biblioteca de cliente do Azure AI Search. A biblioteca fornece uma abstração sobre a API REST para acesso a operações de índice, como ingestão de dados, operações de pesquisa e operações de gerenciamento de índice.

No Azure AI Search, um repositório de vetores tem um esquema de índice que define campos vetoriais e não vetoriais, uma configuração de pesquisa vetorial para algoritmos que criam o espaço de incorporação e configurações em definições de campo vetorial que são avaliadas no momento da consulta. A API REST Create Index cria o repositório vetorial.

Nota

Este guia de início rápido omite a etapa de vetorização e fornece incorporações em linha. Se quiser adicionar fragmentação e vetorização de dados integrada ao seu próprio conteúdo, experimente o assistente Importar e vetorizar dados para um guia completo passo a passo.

Pré-requisitos

• Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.

• Um serviço Azure AI Search. Crie um serviço ou encontre um serviço existente na sua subscrição atual.

  • Você pode usar um serviço de pesquisa gratuito para a maior parte desse início rápido, mas recomendamos a camada Básica ou superior para arquivos de dados maiores.
  • Para executar o exemplo de consulta que invoca a reclassificação semântica, seu serviço de pesquisa deve estar na camada Básica ou superior com o classificador semântico habilitado.

• Código do Visual Studio.

• Node.JS com LTS.

---

Obter pontos de extremidade de serviço

Nas seções restantes, você configura chamadas de API para o Azure OpenAI e o Azure AI Search. Obtenha os pontos de extremidade de serviço para que você possa fornecê-los como variáveis em seu código.

1. Inicie sessão no portal Azure.

2. Encontre o seu serviço de pesquisa.

3. Na página inicial Visão geral , copie o URL. Um ponto final de exemplo poderá ser parecido com https://example.search.windows.net.

Configurar o projeto Node.JS

Configure o projeto com Visual Studio Code e JavaScript.

1. Inicie o Visual Studio Code em um novo diretório.

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

2. Crie um novo pacote para módulos ESM no diretório do projeto.

   npm init -y
   npm pkg set type=module
   

   Isso cria um package.json arquivo com valores padrão.

3. Instale os seguintes pacotes npm.

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

4. Crie um src diretório no diretório do projeto.

   mkdir src
   

Configurar variáveis de ambiente para desenvolvimento local

1. Crie um .env arquivo no vector-quickstart diretório do projeto.

2. Adicione as seguintes variáveis de ambiente ao arquivo .env, substituindo os valores por seus próprios pontos de extremidade e chaves de serviço.

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

Iniciar sessão no Azure

Você está usando a ID do Microsoft Entra e atribuições de função para a conexão. Certifique-se de que tem sessão iniciada no mesmo inquilino e subscrição que o Azure AI Search e o Azure OpenAI. Você pode usar a CLI do Azure na linha de comando para mostrar as propriedades atuais, alterar as propriedades e entrar. Para obter mais informações, consulte Conectar-se sem chaves.

Execute cada um dos seguintes comandos em sequência.

az account show

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

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

Agora você deve estar conectado ao Azure a partir do seu dispositivo local.

Criar o índice de vetores

Nesta seção, você cria um índice de vetor no Azure AI Search com SearchIndexClient. createOrUpdateIndex. O esquema de índice define os campos, incluindo o campo DescriptionVectorvetorial.

1. Crie um createIndex.js arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

   import { DefaultAzureCredential } from "@azure/identity";
   import {
       SearchIndexClient
   } from "@azure/search-documents";
   
   const credential = new DefaultAzureCredential();
   export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
   export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;
   
   console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
   console.log(`Using index name: ${indexName}`);
   
   const indexClient = new SearchIndexClient(searchEndpoint, credential);
   console.log('Creating index...');
   
   // Define fields
   const fields = [
       {
           name: "HotelId",
           type: "Edm.String",
           key: true,
           filterable: true
       },
       {
           name: "HotelName",
           type: "Edm.String",
           sortable: true,
           searchable: true,
           hidden: false,
       },
       {
           name: "Description",
           type: "Edm.String",
           searchable: true,
           hidden: false
       },
       {
           name: "DescriptionVector",
           type: "Collection(Edm.Single)",
           searchable: true,
           vectorSearchDimensions: 1536,
           vectorSearchProfileName: "my-vector-profile",
           hidden: true
       },
       {
           name: "Category",
           type: "Edm.String",
           sortable: true,
           filterable: true,
           facetable: true,
           searchable: true,
           hidden: false
       },
       {
           name: "Tags",
           type: "Collection(Edm.String)",
           searchable: true,
           filterable: true,
           facetable: true,
           hidden: false
       },
       {
           name: "ParkingIncluded",
           type: "Edm.Boolean",
           filterable: true,
           sortable: true,
           facetable: true
       },
       {
           name: "LastRenovationDate",
           type: "Edm.DateTimeOffset",
           filterable: true,
           sortable: true,
           facetable: true
       },
       {
           name: "Rating",
           type: "Edm.Double",
           filterable: true,
           sortable: true,
           facetable: true
       },
       {
           name: "Address",
           type: "Edm.ComplexType",
           fields: [
               {
                   name: "StreetAddress",
                   type: "Edm.String",
                   searchable: true,
               },
               {
                   name: "City",
                   type: "Edm.String",
                   filterable: true,
                   sortable: true,
                   facetable: true,
                   hidden: false,
                   searchable: true
               },
               {
                   name: "StateProvince",
                   type: "Edm.String",
                   filterable: true,
                   sortable: true,
                   facetable: true,
                   hidden: false,
                   searchable: true
               },
               {
                   name: "PostalCode",
                   type: "Edm.String",
                   filterable: true,
                   sortable: true,
                   facetable: true,
                   searchable: true
               },
               {
                   name: "Country",
                   type: "Edm.String",
                   filterable: true,
                   sortable: true,
                   facetable: true,
                   searchable: true,
               }
           ]
       },
       {
           name: "Location",
           type: "Edm.GeographyPoint",
           filterable: true,
           sortable: true
       }
   ];
   
   // Define vector search configuration
   const vectorSearch = {
       algorithms: [
           {
               name: "hnsw-vector-config",
               kind: "hnsw"
           },
           {
               name: "eknn-vector-config",
               kind: "exhaustiveKnn"
           }
       ],
       profiles: [
           {
               name: "my-vector-profile",
               algorithmConfigurationName: "hnsw-vector-config"
           }
       ]
   };
   
   // Define semantic configuration
   const semanticConfig = {
       name: "semantic-config",
       prioritizedFields: {
           titleField: {
               name: "HotelName"
           },
           contentFields: [
               {
                   name: "Description"
               }
           ],
           keywordsFields: [
               {
                   name: "Category"
               }
           ]
       }
   };
   
   // Create the semantic settings with the configuration
   const semanticSearch = {
       configurations: [semanticConfig]
   };
   
   // Define suggesters
   const suggesters = [];
   
   // Create the search index with the semantic settings
   const indexDefinition = {
       name: indexName,
       fields: fields,
       vectorSearch: vectorSearch,
       semanticSearch: semanticSearch,
       suggesters: suggesters
   };
   
   const result = await indexClient.createOrUpdateIndex(indexDefinition);
   console.log(`${result.name} created`);
   

   O arquivo de código adiciona as dependências, as variáveis de ambiente e o tipo JavaScript para HotelDocument na parte superior do arquivo. Adicione a createIndex função para criar o índice. A função define o esquema de índice, incluindo o campo DescriptionVectorvetorial.

3. Execute o arquivo:

   node -r dotenv/config src/createIndex.js
   

4. A saída deste código mostra que o índice é criado com êxito:

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

   Principais conclusões ao criar índice vetorial com a @azure/search-documents biblioteca:

   • Você define um índice criando uma lista de campos.

   • Este índice específico suporta várias capacidades de pesquisa, tais como:

     • Pesquisa de palavras-chave de texto completo (searchable)
     • Pesquisa vetorial (permite a pesquisa híbrida colocando campos vetoriais e não vetoriais) campos (DescriptionVector com vectorSearchProfileName)
     • Pesquisa semântica
     • Pesquisa facetada (searchSuggester)
     • Pesquisa geoespacial (Location campo com geo.distance)
     • Filtragem, classificação (Muitos campos marcados como filtráveis e classificáveis)

Carregar documentos para o índice

Criar e carregar o índice são etapas separadas. Você criou o esquema de índice na etapa anterior. Agora você precisa carregar documentos no índice com SearchClient. carregarDocumentos. Os documentos contêm a versão vetorizada da descrição do artigo, o que permite a pesquisa de semelhança com base no significado e não em palavras-chave exatas.

No Azure AI Search, o índice armazena todo o conteúdo pesquisável, enquanto o mecanismo de pesquisa executa consultas nesse índice.

1. Crie um uploadDocuments.js arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

   import { DefaultAzureCredential } from "@azure/identity";
   import {
       SearchClient
   } from "@azure/search-documents";
   
   const credential = new DefaultAzureCredential();
   export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
   export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;
   
   console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
   console.log(`Using index name: ${indexName}`);
   
   const DOCUMENTS =  [
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "1",
           "HotelName": "Stay-Kay City Hotel",
           "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
           "DescriptionVector": 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           "Category": "Boutique",
           "Tags": [
               "view",
               "air conditioning",
               "concierge"
           ],
           "ParkingIncluded": "false",
           "LastRenovationDate": "2022-01-18T00:00:00Z",
           "Rating": 3.60,
           "Address": {
               "StreetAddress": "677 5th Ave",
               "City": "New York",
               "StateProvince": "NY",
               "PostalCode": "10022",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
                   -73.975403,
                   40.760586
               ]
           }
       },
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "2",
           "HotelName": "Old Century Hotel",
           "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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           "Category": "Boutique",
           "Tags": [
               "pool",
               "free wifi",
               "air conditioning",
               "concierge"
           ],
           "ParkingIncluded": "false",
           "LastRenovationDate": "2019-02-18T00:00:00Z",
           "Rating": 3.60,
           "Address": {
               "StreetAddress": "140 University Town Center Dr",
               "City": "Sarasota",
               "StateProvince": "FL",
               "PostalCode": "34243",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
                   -82.452843,
                   27.384417
               ]
           }
       },
       {
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           "HotelId": "3",
           "HotelName": "Gastronomic Landscape Hotel",
           "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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           "Category": "Suite",
           "Tags": [
               "restaurant",
               "bar",
               "continental breakfast"
           ],
           "ParkingIncluded": "true",
           "LastRenovationDate": "2015-09-20T00:00:00Z",
           "Rating": 4.80,
           "Address": {
               "StreetAddress": "3393 Peachtree Rd",
               "City": "Atlanta",
               "StateProvince": "GA",
               "PostalCode": "30326",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
                   33.847848,
                   -84.363602
               ]
           }
       },
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "4",
           "HotelName": "Sublime Palace Hotel",
           "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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           "Category": "Boutique",
           "Tags": [
               "concierge",
               "view",
               "air conditioning"
           ],
           "ParkingIncluded": "true",
           "LastRenovationDate": "2020-02-06T00:00:00Z",
           "Rating": 4.60,
           "Address": {
               "StreetAddress": "7400 San Pedro Ave",
               "City": "San Antonio",
               "StateProvince": "TX",
               "PostalCode": "78216",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
                   -98.498923,
                   29.518029
               ]
           }
       },
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "48",
           "HotelName": "Nordick's Valley Motel",
           "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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           "Category": "Boutique",
           "Tags": [
               "continental breakfast",
               "air conditioning",
               "free wifi"
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           "ParkingIncluded": "false",
           "LastRenovationDate": "2018-02-19T00:00:00Z",
           "Rating": 4.5,
           "Address": {
               "StreetAddress": "1401 I St NW",
               "City": "Washington D.C.",
               "StateProvince": "null",
               "PostalCode": "20005",
               "Country": "USA"
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               "properties": {
                   "name": "EPSG:4326"
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           "HotelId": "49",
           "HotelName": "Swirling Currents Hotel",
           "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.",
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           "Category": "Suite",
           "Tags": [
               "air conditioning",
               "laundry service",
               "24-hour front desk service"
           ],
           "ParkingIncluded": "true",
           "LastRenovationDate": "2018-01-27T00:00:00Z",
           "Rating": 2.7,
           "Address": {
               "StreetAddress": "1100 S Hayes St",
               "City": "Arlington",
               "StateProvince": "VA",
               "PostalCode": "22202",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
               -77.060066,
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               ],
               "crs": {
               "type": "name",
               "properties": {
                   "name": "EPSG:4326"
                   }
               }
           }
       },
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "13",
           "HotelName": "Luxury Lion Resort",
           "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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           "Category": "Luxury",
           "Tags": [
               "bar",
               "concierge",
               "restaurant"
           ],
           "ParkingIncluded": "false",
           "LastRenovationDate": "2020-03-18T00:00:00Z",
           "Rating": 4.1,
           "Address": {
               "StreetAddress": "3 Cityplace Dr",
               "City": "St. Louis",
               "StateProvince": "MO",
               "PostalCode": "63141",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
               -90.44081,
               38.67219
               ],
               "crs": {
               "type": "name",
               "properties": {
                   "name": "EPSG:4326"
                   }
               }
           }
       }
   ];
   
   async function waitUntilIndexed() {
       try {
           const searchClient = new SearchClient(searchEndpoint, indexName, credential);
           do {
               const count = await searchClient.getDocumentsCount();
               if (count == DOCUMENTS.length) {
                   console.log("All documents indexed successfully.");
                   break;
               }
               console.log(`Waiting for indexing... Current count: ${count}`);
               await new Promise((resolve) => setTimeout(resolve, 10000)); // Wait for 10 seconds
           } while (true);
       } catch (ex) {
           console.error("Failed to wait until indexed:", ex);
       }
   }
   
   const searchClient = new SearchClient(searchEndpoint, indexName, credential);
   
   try {
       console.log("Uploading documents...");
       const result = await searchClient.uploadDocuments(DOCUMENTS);
       for (const r of result.results) {
           console.log(`Key: ${r.key}, Succeeded: ${r.succeeded}, ErrorMessage: ${r.errorMessage || 'none'}`);
       }
       await waitUntilIndexed();
   } catch (ex) {
       console.error("Failed to upload documents:", ex);
   }
   

   Esse código carrega uma matriz de objetos JSON. Cada documento inclui a versão vetorizada do artigo description. Este vetor permite a pesquisa de semelhança, onde a correspondência é baseada no significado e não em palavras-chave exatas.

   Como este artigo de início rápido pesquisa o índice imediatamente, a waitUntilIndexed função aguarda até que o índice esteja pronto para operações de pesquisa. Isso é importante porque o índice precisa estar totalmente preenchido com documentos antes que você possa realizar pesquisas.

3. Compile e execute o arquivo:

   node -r dotenv/config src/uploadDocuments.js
   

4. A saída deste código mostra que os documentos estão indexados e prontos para pesquisa:

   Uploading documents...
   Key: 1, Succeeded: true, ErrorMessage: none
   Key: 2, Succeeded: true, ErrorMessage: none
   Key: 3, Succeeded: true, ErrorMessage: none
   Key: 4, Succeeded: true, ErrorMessage: none
   Key: 48, Succeeded: true, ErrorMessage: none
   Key: 49, Succeeded: true, ErrorMessage: none
   Key: 13, Succeeded: true, ErrorMessage: none
   Waiting for indexing... Current count: 0
   All documents indexed successfully.
   

   Principais conclusões sobre o método upload_documents() e este exemplo:

   • Seu código interage com um índice de pesquisa específico hospedado em seu serviço Azure AI Search por meio do SearchClient, que é o objeto principal fornecido pelo @azure/search-documents pacote. O SearchClient fornece acesso a operações de índice, tais como:
     • Ingestão de dados - "uploadDocuments()", "mergeDocuments()", "deleteDocuments()", etc.
     • Operações de pesquisa - search(), autocompletar(), sugerir()
     • Operações de gerenciamento de índice, como createIndex(), deleteIndex(), getIndex(), etc.
   • Os campos vetoriais contêm valores de ponto flutuante. O atributo dimensions tem um mínimo de 2 e um máximo de 4096 valores de ponto flutuante cada. Este guia de início rápido define o atributo dimensions como 1.536 porque esse é o tamanho das incorporações geradas pelo modelo text-embedding-ada-002 do Azure OpenAI.

Criar a consulta como um vetor

As consultas vetoriais de exemplo são baseadas em duas cadeias de caracteres:

• Cadeia de pesquisa: historic hotel walk to restaurants and shopping
• Seqüência de consulta vetorial (vetorizada em uma representação matemática): quintessential lodging near running trails, eateries, retail

A cadeia de caracteres de consulta vetorial é semanticamente semelhante à cadeia de pesquisa, mas inclui termos que não existem no índice de pesquisa. Se você fizer uma pesquisa por palavra-chave para quintessential lodging near running trails, eateries, retail, os resultados serão zero. Usamos este exemplo para mostrar como você pode obter resultados relevantes, mesmo que não haja termos correspondentes.

1. Crie um queryVector.js arquivo no src diretório e adicione o código para criar o vetor de consulta.

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

2. Esse código é usado nas seções a seguir para executar pesquisas vetoriais. O vetor de consulta é criado usando um modelo de incorporação do Azure OpenAI.

Criar uma única pesquisa vetorial

O primeiro exemplo demonstra um cenário básico onde se pretende encontrar descrições de documentos que correspondam estreitamente à cadeia de caracteres de pesquisa usando o SearchClient.search e o VectorQuery e SearchOptions.

1. Crie um searchSingle.js arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

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

   O vectorQuery contém a configuração da consulta vetorizada, incluindo o texto vetorizado da entrada de consulta como vector, fields determina quais campos vetoriais são pesquisados e kNearestNeighborsCount especifica o número de vizinhos mais próximos a serem retornados.

   A cadeia de caracteres de consulta vetorial é quintessential lodging near running trails, eateries, retail, que é vetorizada em 1.536 incorporações para esta consulta.

3. Compile e execute o arquivo:

   node -r dotenv/config src/searchSingle.js
   

4. A saída deste código mostra os documentos relevantes para a consulta quintessential lodging near running trails, eateries, retail.

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

   A resposta para o equivalente vetorial de quintessential lodging near running trails, eateries, retail consiste em 5 resultados com apenas os campos especificados pela seleção retornada.

Criar uma pesquisa vetorial única com um filtro

Você pode adicionar filtros, mas os filtros são aplicados ao conteúdo não vetorial em seu índice. Neste exemplo, o filtro se aplica ao campo para filtrar todos os Tags hotéis que não oferecem Wi-Fi gratuito. Esta pesquisa usa SearchClient.search e o VectorQuery e SearchOptions.

1. Crie um searchSingleWithFilter.js arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

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

   Adicione as dependências, as variáveis de ambiente e a mesma funcionalidade de pesquisa da anterior, com um filtro de exclusão aplicado após o processamento para hotéis com free wifi.

3. Compile e execute o arquivo:

   node -r dotenv/config src/searchSingleWithFilter.js
   

4. A saída deste código mostra os documentos relevantes para a consulta com o filtro para free wifi aplicado:

   Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
   Using index name: hotels-vector-quickstart-0627-4
   
   
   Single Vector search found 2
   - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
   - HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
   

Criar uma pesquisa com um filtro geoespacial

Você pode especificar um filtro geoespacial para limitar os resultados a uma área geográfica específica. Neste exemplo, o filtro limita os resultados a hotéis a menos de 300 quilômetros de Washington D.C. (coordenadas: -77.03241 38.90166). As distâncias geoespaciais são sempre em quilómetros. Esta pesquisa usa SearchClient.search e o VectorQuery e SearchOptions.

1. Crie um searchSingleWithFilterGeo.js arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

   import { SearchClient } from "@azure/search-documents";
   import { vector } from "./queryVector.js";
   import { DefaultAzureCredential } from "@azure/identity";
   
   export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
   export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;
   
   console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
   console.log(`Using index name: ${indexName}`);
   
   // const key = process.env.AZURE_SEARCH_KEY || "";
   // const searchClient = new SearchClient(
   //     searchEndpoint!,
   //     indexName,
   //     new AzureKeyCredential(key)
   // );
   
   const searchClient = new SearchClient(
       searchEndpoint,
       indexName,
       new DefaultAzureCredential()
   );
   
   try {
   
       const vectorQuery = {
           vector: vector,
           kNearestNeighborsCount: 5,
           fields: ["DescriptionVector"],
           kind: "vector",
           exhaustive: true
       };
   
       // Create a vector search options with the vector query and filter
       const searchOptions = {
           top: 5,
           includeTotalCount: true,
           select: [
               "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
           ],
           facets: ["Address/StateProvince"],
           vectorSearchOptions: {
               queries: [vectorQuery],
               filterMode: "postFilter" // Apply filter after vector similarity is calculated
           },
           filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
       };
       const results = await searchClient.search("*", searchOptions);
   
       console.log(`\n\nSingle Vector search found ${results.count}`);
   
       for await (const result of results.results) {
           // Log each result
           const doc = result.document;
           console.log(`- HotelId: ${doc.HotelId}, HotelName: ${doc.HotelName}, Tags: ${doc.Tags ? JSON.stringify(doc.Tags) : 'N/A'}, Score ${result.score}`);
       }
   
   } catch (ex) {
       console.error("Vector search failed:", ex);
       throw ex;
   }
   

3. Compile e execute o arquivo:

   node -r dotenv/config src/searchSingleWithFilterGeo.js
   

4. A saída deste código mostra os documentos relevantes para a consulta com o filtro de exclusão de pós-processamento geoespacial aplicado:

   Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
   Using index name: hotels-vector-quickstart-0627-4
   
   
   Single Vector search found 2
   - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: N/A, Score 0.6605852246284485
   - HotelId: 49, HotelName: Swirling Currents Hotel, Tags: N/A, Score 0.602634072303772
   

Criar uma pesquisa híbrida

A pesquisa híbrida consiste em consultas de palavras-chave e consultas vetoriais em uma única solicitação de pesquisa. Este exemplo executa a consulta vetorial e a pesquisa de texto completo simultaneamente:

• Cadeia de pesquisa: historic hotel walk to restaurants and shopping
• Seqüência de consulta vetorial (vetorizada em uma representação matemática): quintessential lodging near running trails, eateries, retail

Esta pesquisa usa SearchClient.search e o VectorQuery e SearchOptions.

1. Crie um searchHybrid.js arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

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

3. Compile e execute o arquivo:

   node -r dotenv/config src/searchHybrid.js
   

4. A saída deste código mostra os documentos relevantes para a pesquisa híbrida:

   Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
   Using index name: hotels-vector-quickstart-0627-4
   
   
   Hybrid search found 7 then limited to top 5
   - Score: 0.03279569745063782
     HotelId: 4
     HotelName: Sublime Palace Hotel
     Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
     Category: Boutique
     Tags: ["concierge","view","air conditioning"]
   
   - Score: 0.032522473484277725
     HotelId: 13
     HotelName: Luxury Lion Resort
     Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
     Category: Luxury
     Tags: ["bar","concierge","restaurant"]
   
   - Score: 0.03205128386616707
     HotelId: 48
     HotelName: Nordick's Valley Motel
     Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
     Category: Boutique
     Tags: ["continental breakfast","air conditioning","free wifi"]
   
   - Score: 0.0317460335791111
     HotelId: 49
     HotelName: Swirling Currents Hotel
     Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
     Category: Suite
     Tags: ["air conditioning","laundry service","24-hour front desk service"]
   
   - Score: 0.03125
     HotelId: 2
     HotelName: Old Century Hotel
     Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
     Category: Boutique
     Tags: ["pool","free wifi","air conditioning","concierge"]
   

   Como o RRF (Reciprocal Rank Fusion) mescla resultados, ele ajuda a revisar as entradas. Os resultados a seguir são somente da consulta de texto completo. Os dois primeiros resultados são Sublime Palace Hotel e History Lion Resort. O Sublime Palace Hotel tem uma pontuação de relevância BM25 mais forte.

      {
          "@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"
      },
   

   Na consulta somente vetorial, que usa HNSW para encontrar correspondências, o Sublime Palace Hotel cai para a quarta posição. O Historic Lion, que ficou em segundo lugar na pesquisa de texto completo e terceiro na pesquisa vetorial, não experimenta a mesma faixa de flutuação, por isso aparece como uma correspondência superior em um 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"
       }
   ]
   

Criar uma pesquisa híbrida semântica

Aqui está a última consulta na coleção para criar e estender a pesquisa semântica híbrida com o texto de pesquisa adicional historic hotel walk to restaurants and shopping.

Esta pesquisa usa SearchClient.search e o VectorQuery e SearchOptions.

1. Crie um searchSemanticHybrid.js arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

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

3. Compile e execute o arquivo:

   node -r dotenv/config src/searchSemanticHybrid.js
   

4. A saída deste código mostra os documentos relevantes para a pesquisa híbrida semântica:

   Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
   Using index name: hotels-vector-quickstart-0627-4
   
   
   Semantic hybrid search found 7 then limited to top 5
   - Score: 0.0317460335791111
     Re-ranker Score: 2.6550590991973877
     HotelId: 49
     HotelName: Swirling Currents Hotel
     Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
     Category: Suite
   
   - Score: 0.03279569745063782
     Re-ranker Score: 2.599761724472046
     HotelId: 4
     HotelName: Sublime Palace Hotel
     Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
     Category: Boutique
   
   - Score: 0.03125
     Re-ranker Score: 2.3480887413024902
     HotelId: 2
     HotelName: Old Century Hotel
     Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
     Category: Boutique
   
   - Score: 0.016393441706895828
     Re-ranker Score: 2.2718777656555176
     HotelId: 1
     HotelName: Stay-Kay City Hotel
     Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
     Category: Boutique
   
   - Score: 0.01515151560306549
     Re-ranker Score: 2.0582215785980225
     HotelId: 3
     HotelName: Gastronomic Landscape Hotel
     Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.
     Category: Suite
   

   Você pode pensar na classificação semântica como uma maneira de melhorar a relevância dos resultados da pesquisa, entendendo o significado por trás das palavras na consulta e o conteúdo dos documentos. Neste caso, a classificação semântica ajuda a identificar hotéis que não só são relevantes para as palavras-chave, mas também correspondem à intenção da consulta:

   Conclusões principais:

   • A pesquisa vetorial é especificada através da vectorSearchOptions propriedade. A pesquisa por palavra-chave é especificada através da semanticSearchOptions propriedade.

   • Em uma pesquisa híbrida, você pode integrar a pesquisa vetorial com a pesquisa de texto completo sobre palavras-chave. Os filtros, a verificação ortográfica e a classificação semântica aplicam-se apenas ao conteúdo textual e não aos vetores. Nesta consulta final, não há semântica answer porque o sistema não produziu uma que fosse suficientemente forte.

   • Os resultados reais incluem mais detalhes, incluindo legendas semânticas e destaques. Os resultados foram modificados quanto à legibilidade. Para obter a estrutura completa da resposta, execute a solicitação no cliente REST.

Limpeza

Ao trabalhar na sua própria subscrição, recomendamos que verifique, depois de concluir um projeto, se ainda vai precisar dos recursos que criou. Os recursos que deixar em execução podem custar dinheiro. Pode eliminar recursos individualmente ou eliminar o grupo de recursos para eliminar todo o conjunto de recursos.

Você pode localizar e gerenciar recursos no portal do Azure usando o link Todos os recursos ou Grupos de recursos no painel mais à esquerda.

Se quiser manter o serviço de pesquisa, mas excluir o índice e os documentos, você pode excluir o índice programaticamente.

1. Crie um deleteIndex.js arquivo no src diretório.

2. Adicione as dependências, as variáveis de ambiente e o código para excluir o índice.

   import { DefaultAzureCredential } from "@azure/identity";
   import {
       SearchIndexClient
   } from "@azure/search-documents";
   
   const credential = new DefaultAzureCredential();
   export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
   export const indexName = process.env.AZURE_SEARCH_INDEX_NAME;
   
   console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
   console.log(`Using index name: ${indexName}`);
   
   const searchIndexClient = new SearchIndexClient(searchEndpoint, credential);
   
   try {
       console.log("Deleting index...");
       await searchIndexClient.deleteIndex(indexName);
       console.log(`Index ${indexName} deleted`);
   } catch (ex) {
       console.error("Failed to delete index:", ex);
   }
   

3. Compile e execute o arquivo:

   node -r dotenv/config src/deleteIndex.js
   

Próximos passos

• Revise o repositório de exemplos de código para recursos de pesquisa vetorial no Azure AI Search for JavaScript

Neste início rápido, você usa TypeScript para criar, carregar e consultar vetores. Os exemplos de código executam essas operações usando a biblioteca de cliente do Azure AI Search. A biblioteca fornece uma abstração sobre a API REST para acesso a operações de índice, como ingestão de dados, operações de pesquisa e operações de gerenciamento de índice.

No Azure AI Search, um repositório de vetores tem um esquema de índice que define campos vetoriais e não vetoriais, uma configuração de pesquisa vetorial para algoritmos que criam o espaço de incorporação e configurações em definições de campo vetorial que são avaliadas no momento da consulta. A API REST Create Index cria o repositório vetorial.

Nota

Este guia de início rápido omite a etapa de vetorização e fornece incorporações em linha. Se quiser adicionar fragmentação e vetorização de dados integrada ao seu próprio conteúdo, experimente o assistente Importar e vetorizar dados para um guia completo passo a passo.

Pré-requisitos

• Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.

• Um serviço Azure AI Search. Crie um serviço ou encontre um serviço existente na sua subscrição atual.

  • Você pode usar um serviço de pesquisa gratuito para a maior parte desse início rápido, mas recomendamos a camada Básica ou superior para arquivos de dados maiores.
  • Para executar o exemplo de consulta que invoca a reclassificação semântica, seu serviço de pesquisa deve estar na camada Básica ou superior com o classificador semântico habilitado.

• Código do Visual Studio.

• Node.JS com LTS.

• TypeScript. Você pode instalar globalmente o TypeScript usando npm:

  npm install -g typescript
  

---

Obter pontos de extremidade de serviço

Nas seções restantes, você configura chamadas de API para o Azure OpenAI e o Azure AI Search. Obtenha os pontos de extremidade de serviço para que você possa fornecê-los como variáveis em seu código.

1. Inicie sessão no portal Azure.

2. Encontre o seu serviço de pesquisa.

3. Na página inicial Visão geral , copie o URL. Um ponto final de exemplo poderá ser parecido com https://example.search.windows.net.

Configurar o projeto Node.JS

Configure o projeto com Visual Studio Code e TypeScript.

1. Inicie o Visual Studio Code em um novo diretório.

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

2. Crie um novo pacote para módulos ESM no diretório do projeto.

   npm init -y
   npm pkg set type=module
   

   Isso cria um package.json arquivo com valores padrão.

3. Edite package.json para incluir um script de construção. Adicione a seguinte linha à scripts secção:

   "build": "tsc"
   

4. Instale os seguintes pacotes npm.

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

5. Crie um src diretório no diretório do projeto.

   mkdir src
   

6. Crie um tsconfig.json arquivo no diretório do projeto para o ESM com o seguinte conteúdo.

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

Configurar variáveis de ambiente para desenvolvimento local

1. Crie um .env arquivo no vector-quickstart diretório do projeto.

2. Adicione as seguintes variáveis de ambiente ao arquivo .env, substituindo os valores por seus próprios pontos de extremidade e chaves de serviço.

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

Iniciar sessão no Azure

Você está usando a ID do Microsoft Entra e atribuições de função para a conexão. Certifique-se de que tem sessão iniciada no mesmo inquilino e subscrição que o Azure AI Search e o Azure OpenAI. Você pode usar a CLI do Azure na linha de comando para mostrar as propriedades atuais, alterar as propriedades e entrar. Para obter mais informações, consulte Conectar-se sem chaves.

Execute cada um dos seguintes comandos em sequência.

az account show

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

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

Agora você deve estar conectado ao Azure a partir do seu dispositivo local.

Criar o índice de vetores

Nesta seção, você cria um índice de vetor no Azure AI Search com SearchIndexClient. createOrUpdateIndex. O esquema de índice define os campos, incluindo o campo DescriptionVectorvetorial. Depois que o índice é criado, você carrega documentos para o índice. Os documentos contêm a versão vetorizada da descrição do artigo, o que permite a pesquisa de semelhança com base no significado e não em palavras-chave exatas.

1. Crie um createIndex.ts arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

   import { DefaultAzureCredential } from "@azure/identity";
   import {
       SearchIndexClient,
       SearchIndex,
       SearchField,
       VectorSearch,
       SemanticSearch,
       SearchSuggester
   } from "@azure/search-documents";
   
   const credential = new DefaultAzureCredential();
   export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
   export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
   
   console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
   console.log(`Using index name: ${indexName}`);
   
   // Define an interface for the hotel document
   export interface HotelDocument {
       HotelId: string;
       HotelName: string;
       Description: string;
       DescriptionVector?: number[];
       Category?: string;
       Tags?: string[] | string;
       Address?: {
           City: string;
           StateProvince: string;
       };
       Location?: {
           type: string;
           coordinates: [number, number]; // [longitude, latitude]
       };
       "@search.score"?: number;
       "@search.reranker_score"?: number;
   }
   
   
   const indexClient = new SearchIndexClient(searchEndpoint, credential);
   console.log('Creating index...');
   
   // Define fields
   const fields: SearchField[] = [
       {
           name: "HotelId",
           type: "Edm.String" as const,
           key: true,
           filterable: true
       },
       {
           name: "HotelName",
           type: "Edm.String" as const,
           sortable: true,
           searchable: true,
           hidden: false,
       },
       {
           name: "Description",
           type: "Edm.String" as const,
           searchable: true,
           hidden: false
       },
       {
           name: "DescriptionVector",
           type: "Collection(Edm.Single)" as const,
           searchable: true,
           vectorSearchDimensions: 1536,
           vectorSearchProfileName: "my-vector-profile",
           hidden: true
       },
       {
           name: "Category",
           type: "Edm.String" as const,
           sortable: true,
           filterable: true,
           facetable: true,
           searchable: true,
           hidden: false
       },
       {
           name: "Tags",
           type: "Collection(Edm.String)" as const,
           searchable: true,
           filterable: true,
           facetable: true,
           hidden: false
       },
       {
           name: "ParkingIncluded",
           type: "Edm.Boolean" as const,
           filterable: true,
           sortable: true,
           facetable: true
       },
       {
           name: "LastRenovationDate",
           type: "Edm.DateTimeOffset" as const,
           filterable: true,
           sortable: true,
           facetable: true
       },
       {
           name: "Rating",
           type: "Edm.Double" as const,
           filterable: true,
           sortable: true,
           facetable: true
       },
       {
           name: "Address",
           type: "Edm.ComplexType" as const,
           fields: [
               {
                   name: "StreetAddress",
                   type: "Edm.String" as const,
                   searchable: true,
               },
               {
                   name: "City",
                   type: "Edm.String" as const,
                   filterable: true,
                   sortable: true,
                   facetable: true,
                   hidden: false,
                   searchable: true
               },
               {
                   name: "StateProvince",
                   type: "Edm.String" as const,
                   filterable: true,
                   sortable: true,
                   facetable: true,
                   hidden: false,
                   searchable: true
               },
               {
                   name: "PostalCode",
                   type: "Edm.String" as const,
                   filterable: true,
                   sortable: true,
                   facetable: true,
                   searchable: true
               },
               {
                   name: "Country",
                   type: "Edm.String" as const,
                   filterable: true,
                   sortable: true,
                   facetable: true,
                   searchable: true,
               }
           ]
       },
       {
           name: "Location",
           type: "Edm.GeographyPoint" as const,
           filterable: true,
           sortable: true
       }
   ];
   
   // Define vector search configuration
   const vectorSearch: VectorSearch = {
       algorithms: [
           {
               name: "hnsw-vector-config",
               kind: "hnsw" as const
           },
           {
               name: "eknn-vector-config",
               kind: "exhaustiveKnn" as const
           }
       ],
       profiles: [
           {
               name: "my-vector-profile",
               algorithmConfigurationName: "hnsw-vector-config"
           }
       ]
   };
   
   // Define semantic configuration
   const semanticConfig = {
       name: "semantic-config",
       prioritizedFields: {
           titleField: {
               name: "HotelName"
           },
           contentFields: [
               {
                   name: "Description"
               }
           ],
           keywordsFields: [
               {
                   name: "Category"
               }
           ]
       }
   };
   
   // Create the semantic settings with the configuration
   const semanticSearch: SemanticSearch = {
       configurations: [semanticConfig]
   };
   
   // Define suggesters
   const suggesters: SearchSuggester[] = [];
   
   // Create the search index with the semantic settings
   const indexDefinition: SearchIndex = {
       name: indexName,
       fields: fields,
       vectorSearch: vectorSearch,
       semanticSearch: semanticSearch,
       suggesters: suggesters
   };
   
   const result = await indexClient.createOrUpdateIndex(indexDefinition);
   console.log(`${result.name} created`);
   

   O arquivo de código adiciona as dependências, as variáveis de ambiente e o tipo JavaScript para HotelDocument na parte superior do arquivo. Adicione a createIndex função para criar o índice. A função define o esquema de índice, incluindo o campo DescriptionVectorvetorial.

3. Compile e execute o arquivo:

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

4. A saída deste código mostra que o índice é criado com êxito:

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

   Principais conclusões ao criar índice vetorial com a @azure/search-documents biblioteca:

   • Você define um índice criando uma lista de campos.

   • Este índice específico suporta várias capacidades de pesquisa, tais como:

     • Pesquisa de palavras-chave de texto completo (searchable)
     • Pesquisa vetorial (permite a pesquisa híbrida colocando campos vetoriais e não vetoriais) campos (DescriptionVector com vectorSearchProfileName)
     • Pesquisa semântica
     • Pesquisa facetada (searchSuggester)
     • Pesquisa geoespacial (Location campo com geo.distance)
     • Filtragem, classificação (Muitos campos marcados como filtráveis e classificáveis)

Carregar documentos para o índice

Criar e carregar o índice são etapas separadas. Você criou o esquema de índice na etapa anterior. Agora você precisa carregar documentos no índice com SearchClient. carregarDocumentos.

No Azure AI Search, o índice armazena todo o conteúdo pesquisável, enquanto o mecanismo de pesquisa executa consultas nesse índice.

1. Crie um uploadDocuments.ts arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

   import { DefaultAzureCredential } from "@azure/identity";
   import {
       SearchClient
   } from "@azure/search-documents";
   
   const credential = new DefaultAzureCredential();
   export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
   export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
   
   console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
   console.log(`Using index name: ${indexName}`);
   
   const DOCUMENTS =  [
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "1",
           "HotelName": "Stay-Kay City Hotel",
           "Description": "This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.",
           "DescriptionVector": 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           "Category": "Boutique",
           "Tags": [
               "view",
               "air conditioning",
               "concierge"
           ],
           "ParkingIncluded": "false",
           "LastRenovationDate": "2022-01-18T00:00:00Z",
           "Rating": 3.60,
           "Address": {
               "StreetAddress": "677 5th Ave",
               "City": "New York",
               "StateProvince": "NY",
               "PostalCode": "10022",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
                   -73.975403,
                   40.760586
               ]
           }
       },
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "2",
           "HotelName": "Old Century Hotel",
           "Description": "The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.",
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           "Category": "Boutique",
           "Tags": [
               "pool",
               "free wifi",
               "air conditioning",
               "concierge"
           ],
           "ParkingIncluded": "false",
           "LastRenovationDate": "2019-02-18T00:00:00Z",
           "Rating": 3.60,
           "Address": {
               "StreetAddress": "140 University Town Center Dr",
               "City": "Sarasota",
               "StateProvince": "FL",
               "PostalCode": "34243",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
                   -82.452843,
                   27.384417
               ]
           }
       },
       {
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           "HotelId": "3",
           "HotelName": "Gastronomic Landscape Hotel",
           "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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           "Category": "Suite",
           "Tags": [
               "restaurant",
               "bar",
               "continental breakfast"
           ],
           "ParkingIncluded": "true",
           "LastRenovationDate": "2015-09-20T00:00:00Z",
           "Rating": 4.80,
           "Address": {
               "StreetAddress": "3393 Peachtree Rd",
               "City": "Atlanta",
               "StateProvince": "GA",
               "PostalCode": "30326",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
                   33.847848,
                   -84.363602
               ]
           }
       },
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "4",
           "HotelName": "Sublime Palace Hotel",
           "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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           "Category": "Boutique",
           "Tags": [
               "concierge",
               "view",
               "air conditioning"
           ],
           "ParkingIncluded": "true",
           "LastRenovationDate": "2020-02-06T00:00:00Z",
           "Rating": 4.60,
           "Address": {
               "StreetAddress": "7400 San Pedro Ave",
               "City": "San Antonio",
               "StateProvince": "TX",
               "PostalCode": "78216",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
                   -98.498923,
                   29.518029
               ]
           }
       },
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "48",
           "HotelName": "Nordick's Valley Motel",
           "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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           "Category": "Boutique",
           "Tags": [
               "continental breakfast",
               "air conditioning",
               "free wifi"
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           "ParkingIncluded": "false",
           "LastRenovationDate": "2018-02-19T00:00:00Z",
           "Rating": 4.5,
           "Address": {
               "StreetAddress": "1401 I St NW",
               "City": "Washington D.C.",
               "StateProvince": "null",
               "PostalCode": "20005",
               "Country": "USA"
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               "properties": {
                   "name": "EPSG:4326"
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           "HotelId": "49",
           "HotelName": "Swirling Currents Hotel",
           "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.",
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           "Category": "Suite",
           "Tags": [
               "air conditioning",
               "laundry service",
               "24-hour front desk service"
           ],
           "ParkingIncluded": "true",
           "LastRenovationDate": "2018-01-27T00:00:00Z",
           "Rating": 2.7,
           "Address": {
               "StreetAddress": "1100 S Hayes St",
               "City": "Arlington",
               "StateProvince": "VA",
               "PostalCode": "22202",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
               -77.060066,
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               ],
               "crs": {
               "type": "name",
               "properties": {
                   "name": "EPSG:4326"
                   }
               }
           }
       },
       {
           "@search.action": "mergeOrUpload",
           "HotelId": "13",
           "HotelName": "Luxury Lion Resort",
           "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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           "Category": "Luxury",
           "Tags": [
               "bar",
               "concierge",
               "restaurant"
           ],
           "ParkingIncluded": "false",
           "LastRenovationDate": "2020-03-18T00:00:00Z",
           "Rating": 4.1,
           "Address": {
               "StreetAddress": "3 Cityplace Dr",
               "City": "St. Louis",
               "StateProvince": "MO",
               "PostalCode": "63141",
               "Country": "USA"
           },
           "Location": {
               "type": "Point",
               "coordinates": [
               -90.44081,
               38.67219
               ],
               "crs": {
               "type": "name",
               "properties": {
                   "name": "EPSG:4326"
                   }
               }
           }
       }
   ];
   
   async function waitUntilIndexed(): Promise<void> {
       try {
           const searchClient = new SearchClient(searchEndpoint, indexName, credential);
           do {
               const count = await searchClient.getDocumentsCount();
               if (count == DOCUMENTS.length) {
                   console.log("All documents indexed successfully.");
                   break;
               }
               console.log(`Waiting for indexing... Current count: ${count}`);
               await new Promise((resolve) => setTimeout(resolve, 10000)); // Wait for 10 seconds
           } while (true);
       } catch (ex) {
           console.error("Failed to wait until indexed:", ex);
       }
   }
   
   const searchClient = new SearchClient(searchEndpoint, indexName, credential);
   
   try {
       console.log("Uploading documents...");
       const result = await searchClient.uploadDocuments(DOCUMENTS);
       for (const r of result.results) {
           console.log(`Key: ${r.key}, Succeeded: ${r.succeeded}, ErrorMessage: ${r.errorMessage || 'none'}`);
       }
       await waitUntilIndexed();
   } catch (ex) {
       console.error("Failed to upload documents:", ex);
   }
   

   Esse código carrega um documento variável com um objeto JSON descrevendo cada documento, juntamente com a versão vetorizada da descrição do artigo. Este vetor permite a pesquisa de semelhança, onde a correspondência é baseada no significado e não em palavras-chave exatas.

   Como este artigo de início rápido pesquisa o índice imediatamente, a waitUntilIndexed função aguarda até que o índice esteja pronto para operações de pesquisa. Isso é importante porque o índice precisa estar totalmente preenchido com documentos antes que você possa realizar pesquisas.

3. Compile e execute o arquivo:

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

4. A saída deste código mostra que os documentos estão indexados e prontos para pesquisa:

   Uploading documents...
   Key: 1, Succeeded: true, ErrorMessage: none
   Key: 2, Succeeded: true, ErrorMessage: none
   Key: 3, Succeeded: true, ErrorMessage: none
   Key: 4, Succeeded: true, ErrorMessage: none
   Key: 48, Succeeded: true, ErrorMessage: none
   Key: 49, Succeeded: true, ErrorMessage: none
   Key: 13, Succeeded: true, ErrorMessage: none
   Waiting for indexing... Current count: 0
   All documents indexed successfully.
   

   Principais conclusões sobre o método upload_documents() e este exemplo:

   • Seu código interage com um índice de pesquisa específico hospedado em seu serviço Azure AI Search por meio do SearchClient, que é o objeto principal fornecido pelo @azure/search-documents pacote. O SearchClient fornece acesso a operações de índice, tais como:
     • Ingestão de dados - "uploadDocuments()", "mergeDocuments()", "deleteDocuments()", etc.
     • Operações de pesquisa - search(), autocompletar(), sugerir()
     • Operações de gerenciamento de índice, como createIndex(), deleteIndex(), getIndex(), etc.
   • Os campos vetoriais contêm valores de ponto flutuante. O atributo dimensions tem um mínimo de 2 e um máximo de 4096 valores de ponto flutuante cada. Este guia de início rápido define o atributo dimensions como 1.536 porque esse é o tamanho das incorporações geradas pelo modelo text-embedding-ada-002 do Azure OpenAI.

Criar a consulta como um vetor

As consultas vetoriais de exemplo são baseadas em duas cadeias de caracteres:

• Cadeia de pesquisa: historic hotel walk to restaurants and shopping
• Seqüência de consulta vetorial (vetorizada em uma representação matemática): quintessential lodging near running trails, eateries, retail

A cadeia de caracteres de consulta vetorial é semanticamente semelhante à cadeia de pesquisa, mas inclui termos que não existem no índice de pesquisa. Se você fizer uma pesquisa por palavra-chave para quintessential lodging near running trails, eateries, retail, os resultados serão zero. Usamos este exemplo para mostrar como você pode obter resultados relevantes, mesmo que não haja termos correspondentes.

1. Crie um queryVector.ts arquivo no src diretório e adicione o código para criar o vetor de consulta.

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

2. Esse código é usado nas seções a seguir para executar pesquisas vetoriais. O vetor de consulta é criado usando um modelo de incorporação do Azure OpenAI.

Criar uma única pesquisa vetorial

O primeiro exemplo demonstra um cenário básico onde se pretende encontrar descrições de documentos que correspondam estreitamente à cadeia de caracteres de pesquisa usando o SearchClient.search e o VectorQuery e SearchOptions.

1. Crie um searchSingle.ts arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

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

   O vectorQuery contém a configuração da consulta vetorizada, incluindo o texto vetorizado da entrada de consulta como vector, fields determina quais campos vetoriais são pesquisados e kNearestNeighborsCount especifica o número de vizinhos mais próximos a serem retornados.

   A cadeia de caracteres de consulta vetorial é quintessential lodging near running trails, eateries, retail, que é vetorizada em 1.536 incorporações para esta consulta.

3. Compile e execute o arquivo:

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

4. A saída deste código mostra os documentos relevantes para a consulta quintessential lodging near running trails, eateries, retail.

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

   A resposta para o equivalente vetorial de quintessential lodging near running trails, eateries, retail consiste em 5 resultados com apenas os campos especificados pela seleção retornada.

Criar uma pesquisa vetorial única com um filtro

Você pode adicionar filtros, mas os filtros são aplicados ao conteúdo não vetorial em seu índice. Neste exemplo, o filtro se aplica ao campo para filtrar todos os Tags hotéis que não oferecem Wi-Fi gratuito. Esta pesquisa usa SearchClient.search e o VectorQuery e SearchOptions.

1. Crie um searchSingleWithFilter.ts arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

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

   Adicione as dependências, as variáveis de ambiente e a mesma funcionalidade de pesquisa da anterior, com um filtro de exclusão aplicado após o processamento para hotéis com free wifi.

3. Compile e execute o arquivo:

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

4. A saída deste código mostra os documentos relevantes para a consulta com o filtro para free wifi aplicado:

   Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
   Using index name: hotels-vector-quickstart-0627-4
   
   
   Single Vector search found 2
   - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: ["continental breakfast","air conditioning","free wifi"], Score 0.6605852
   - HotelId: 2, HotelName: Old Century Hotel, Tags: ["pool","free wifi","air conditioning","concierge"], Score 0.57902366
   

Criar uma pesquisa com um filtro geoespacial

Você pode especificar um filtro geoespacial para limitar os resultados a uma área geográfica específica. Neste exemplo, o filtro limita os resultados a hotéis a menos de 300 quilômetros de Washington D.C. (coordenadas: -77.03241 38.90166). As distâncias geoespaciais são sempre em quilómetros. Esta pesquisa usa SearchClient.search e o VectorQuery e SearchOptions.

1. Crie um searchSingleWithFilterGeo.ts arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

   import { SearchClient, SearchDocumentsResult, VectorQuery, SearchOptions, SearchResult, AzureKeyCredential } from "@azure/search-documents";
   import { vector } from "./queryVector.js";
   import { DefaultAzureCredential } from "@azure/identity";
   
   export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
   export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
   
   console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
   console.log(`Using index name: ${indexName}`);
   
   // Define an interface for the hotel document
   export interface HotelDocument {
       HotelId: string;
       HotelName: string;
       Description: string;
       DescriptionVector?: number[];
       Category?: string;
       Tags?: string[] | string;
       Address?: {
           City: string;
           StateProvince: string;
       };
       Location?: {
           type: string;
           coordinates: [number, number]; // [longitude, latitude]
       };
       "@search.score"?: number;
       "@search.reranker_score"?: number;
   }
   
   // const key = process.env.AZURE_SEARCH_KEY || "";
   // const searchClient = new SearchClient<HotelDocument>(
   //     searchEndpoint!,
   //     indexName,
   //     new AzureKeyCredential(key)
   // );
   
   const searchClient = new SearchClient<HotelDocument>(
       searchEndpoint,
       indexName,
       new DefaultAzureCredential()
   );
   
   
   try {
   
       const vectorQuery: VectorQuery<HotelDocument> = {
           vector: vector,
           kNearestNeighborsCount: 5,
           fields: ["DescriptionVector"],
           kind: "vector",
           exhaustive: true
       };
   
       const searchOptions: SearchOptions<HotelDocument> = {
           top: 5,
           includeTotalCount: true,
           select: [
               "HotelId", "HotelName", "Category", "Description", "Address/City", "Address/StateProvince"
           ] as const,
           facets: ["Address/StateProvince"],
           vectorSearchOptions: {
               queries: [vectorQuery],
               filterMode: "postFilter" // Apply filter after vector similarity is calculated
           },
           filter: "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
       };
       const results: SearchDocumentsResult<HotelDocument> = await searchClient.search("*", searchOptions);
   
       console.log(`\n\nVector search with geo filter found ${results.count}`);
   
       for await (const result of results.results) {
   
           // Log each result
           const doc = result.document;
   
           console.log(`- HotelId: ${doc.HotelId}`);
           console.log(`  HotelName: ${doc.HotelName}`);
           console.log(`  Score: ${result.score}`);
   
           if (doc.Address) {
               console.log(`  City/State: ${doc.Address.City}, ${doc.Address.StateProvince}`);
           }
   
           console.log(`  Description: ${doc.Description || 'N/A'}`);
           console.log(`  Score: ${result.score}\n`);
       }
   
   
   } catch (ex) {
       console.error("Vector search with geo filter failed:", ex);
       throw ex;
   }
   

3. Compile e execute o arquivo:

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

4. A saída deste código mostra os documentos relevantes para a consulta com o filtro de exclusão de pós-processamento geoespacial aplicado:

   Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
   Using index name: hotels-vector-quickstart-0627-4
   
   
   Single Vector search found 2
   - HotelId: 48, HotelName: Nordick's Valley Motel, Tags: N/A, Score 0.6605852246284485
   - HotelId: 49, HotelName: Swirling Currents Hotel, Tags: N/A, Score 0.602634072303772
   

Criar uma pesquisa híbrida

A pesquisa híbrida consiste em consultas de palavras-chave e consultas vetoriais em uma única solicitação de pesquisa. Este exemplo executa a consulta vetorial e a pesquisa de texto completo simultaneamente:

• Cadeia de pesquisa: historic hotel walk to restaurants and shopping
• Seqüência de consulta vetorial (vetorizada em uma representação matemática): quintessential lodging near running trails, eateries, retail

Esta pesquisa usa SearchClient.search e o VectorQuery e SearchOptions.

1. Crie um searchHybrid.ts arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

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

3. Compile e execute o arquivo:

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

4. A saída deste código mostra os documentos relevantes para a pesquisa híbrida:

   Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
   Using index name: hotels-vector-quickstart-0627-4
   
   
   Hybrid search found 7 then limited to top 5
   - Score: 0.03279569745063782
     HotelId: 4
     HotelName: Sublime Palace Hotel
     Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
     Category: Boutique
     Tags: ["concierge","view","air conditioning"]
   
   - Score: 0.032522473484277725
     HotelId: 13
     HotelName: Luxury Lion Resort
     Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
     Category: Luxury
     Tags: ["bar","concierge","restaurant"]
   
   - Score: 0.03205128386616707
     HotelId: 48
     HotelName: Nordick's Valley Motel
     Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
     Category: Boutique
     Tags: ["continental breakfast","air conditioning","free wifi"]
   
   - Score: 0.0317460335791111
     HotelId: 49
     HotelName: Swirling Currents Hotel
     Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
     Category: Suite
     Tags: ["air conditioning","laundry service","24-hour front desk service"]
   
   - Score: 0.03125
     HotelId: 2
     HotelName: Old Century Hotel
     Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
     Category: Boutique
     Tags: ["pool","free wifi","air conditioning","concierge"]
   

   Como o RRF (Reciprocal Rank Fusion) mescla resultados, ele ajuda a revisar as entradas. Os resultados a seguir são somente da consulta de texto completo. Os dois primeiros resultados são Sublime Palace Hotel e History Lion Resort. O Sublime Palace Hotel tem uma pontuação de relevância BM25 mais forte.

      {
          "@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"
      },
   

   Na consulta somente vetorial, que usa HNSW para encontrar correspondências, o Sublime Palace Hotel cai para a quarta posição. O Historic Lion, que ficou em segundo lugar na pesquisa de texto completo e terceiro na pesquisa vetorial, não experimenta a mesma faixa de flutuação, por isso aparece como uma correspondência superior em um 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"
       }
   ]
   

Criar uma pesquisa híbrida semântica

Aqui está a última consulta na coleção para criar e estender a pesquisa semântica híbrida com o texto de pesquisa adicional historic hotel walk to restaurants and shopping.

Esta pesquisa usa SearchClient.search e o VectorQuery e SearchOptions.

1. Crie um searchSemanticHybrid.ts arquivo no src diretório.

2. Copie o código a seguir para o arquivo.

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

3. Compile e execute o arquivo:

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

4. A saída deste código mostra os documentos relevantes para a pesquisa híbrida semântica:

   Using Azure Search endpoint: https://ai-search-dib-2.search.windows.net
   Using index name: hotels-vector-quickstart-0627-4
   
   
   Semantic hybrid search found 7 then limited to top 5
   - Score: 0.0317460335791111
     Re-ranker Score: 2.6550590991973877
     HotelId: 49
     HotelName: Swirling Currents Hotel
     Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
     Category: Suite
   
   - Score: 0.03279569745063782
     Re-ranker Score: 2.599761724472046
     HotelId: 4
     HotelName: Sublime Palace Hotel
     Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.
     Category: Boutique
   
   - Score: 0.03125
     Re-ranker Score: 2.3480887413024902
     HotelId: 2
     HotelName: Old Century Hotel
     Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer dinners, and live music.
     Category: Boutique
   
   - Score: 0.016393441706895828
     Re-ranker Score: 2.2718777656555176
     HotelId: 1
     HotelName: Stay-Kay City Hotel
     Description: This classic hotel is fully-refurbished and ideally located on the main commercial artery of the city in the heart of New York. A few minutes away is Times Square and the historic centre of the city, as well as other places of interest that make New York one of America's most attractive and cosmopolitan cities.
     Category: Boutique
   
   - Score: 0.01515151560306549
     Re-ranker Score: 2.0582215785980225
     HotelId: 3
     HotelName: Gastronomic Landscape Hotel
     Description: The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.
     Category: Suite
   

   Você pode pensar na classificação semântica como uma maneira de melhorar a relevância dos resultados da pesquisa, entendendo o significado por trás das palavras na consulta e o conteúdo dos documentos. Neste caso, a classificação semântica ajuda a identificar hotéis que não só são relevantes para as palavras-chave, mas também correspondem à intenção da consulta:

   Conclusões principais:

   • A pesquisa vetorial é especificada através da vectorSearchOptions propriedade. A pesquisa por palavra-chave é especificada através da semanticSearchOptions propriedade.

   • Em uma pesquisa híbrida, você pode integrar a pesquisa vetorial com a pesquisa de texto completo sobre palavras-chave. Os filtros, a verificação ortográfica e a classificação semântica aplicam-se apenas ao conteúdo textual e não aos vetores. Nesta consulta final, não há semântica answer porque o sistema não produziu uma que fosse suficientemente forte.

   • Os resultados reais incluem mais detalhes, incluindo legendas semânticas e destaques. Os resultados foram modificados quanto à legibilidade. Para obter a estrutura completa da resposta, execute a solicitação no cliente REST.

Limpeza

Ao trabalhar na sua própria subscrição, recomendamos que verifique, depois de concluir um projeto, se ainda vai precisar dos recursos que criou. Os recursos que deixar em execução podem custar dinheiro. Pode eliminar recursos individualmente ou eliminar o grupo de recursos para eliminar todo o conjunto de recursos.

Você pode localizar e gerenciar recursos no portal do Azure usando o link Todos os recursos ou Grupos de recursos no painel mais à esquerda.

Se quiser manter o serviço de pesquisa, mas excluir o índice e os documentos, você pode excluir o índice programaticamente.

1. Crie um deleteIndex.ts arquivo no src diretório.

2. Adicione as dependências, as variáveis de ambiente e o código para excluir o índice.

   import { DefaultAzureCredential } from "@azure/identity";
   import {
       SearchIndexClient
   } from "@azure/search-documents";
   
   const credential = new DefaultAzureCredential();
   export const searchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
   export const indexName = process.env.AZURE_SEARCH_INDEX_NAME!;
   
   console.log(`Using Azure Search endpoint: ${searchEndpoint}`);
   console.log(`Using index name: ${indexName}`);
   
   const searchIndexClient = new SearchIndexClient(searchEndpoint, credential);
   
   try {
       console.log("Deleting index...");
       await searchIndexClient.deleteIndex(indexName);
       console.log(`Index ${indexName} deleted`);
   } catch (ex) {
       console.error("Failed to delete index:", ex);
   }
   

3. Compile e execute o arquivo:

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

Próximos passos

• Revise o repositório de exemplos de código para recursos de pesquisa vetorial no Azure AI Search for JavaScript

Neste início rápido, você trabalha com um aplicativo .NET para criar, preencher e consultar vetores. Os exemplos de código executam essas operações usando a biblioteca de cliente do Azure AI Search. A biblioteca fornece uma abstração sobre a API REST para acesso a operações de índice, como ingestão de dados, operações de pesquisa e operações de gerenciamento de índice.

No Azure AI Search, um repositório de vetores tem um esquema de índice que define campos vetoriais e não vetoriais, uma configuração de pesquisa vetorial para algoritmos que criam o espaço de incorporação e configurações em definições de campo vetorial que são avaliadas no momento da consulta. A API REST Create Index cria o repositório vetorial.

Nota

Este guia de início rápido omite a etapa de vetorização e fornece incorporações em linha. Se quiser adicionar fragmentação e vetorização de dados integrada ao seu próprio conteúdo, experimente o assistente Importar e vetorizar dados para um guia completo passo a passo.

Pré-requisitos

• Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.

• Um serviço Azure AI Search. Crie um serviço ou encontre um serviço existente na sua subscrição atual.

  • Você pode usar um serviço de pesquisa gratuito para a maior parte desse início rápido, mas recomendamos a camada Básica ou superior para arquivos de dados maiores.
  • Para executar o exemplo de consulta que invoca a reclassificação semântica, seu serviço de pesquisa deve estar na camada Básica ou superior com o classificador semântico habilitado.

• Visual Studio Code ou Visual Studio.

• Git para clonar o repositório de amostra.

---

Recuperar informações do recurso

As solicitações para o ponto de extremidade de pesquisa devem ser autenticadas e autorizadas. Embora seja possível usar chaves ou funções de API para essa tarefa, recomendamos o uso de uma conexão sem chave via ID do Microsoft Entra.

Este guia rápido de início usa DefaultAzureCredential, que simplifica a autenticação em cenários de desenvolvimento e produção. No entanto, para cenários de produção, você pode ter requisitos mais avançados que exigem uma abordagem diferente. Consulte Autenticar aplicativos .NET nos serviços do Azure usando o SDK do Azure para .NET para entender todas as suas opções.

Clone o código e o ambiente de configuração

1. Clone o repositório que contém o código para este início rápido.

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

   Este repositório tem exemplos de código .NET para vários artigos, cada um em uma subpasta separada.

2. Abra a subpasta quickstart-Vector-Search no Visual Studio Code ou clique duas vezes no .sln arquivo para abrir a solução no Visual Studio.

3. Abra os appsettings.json arquivos nas VectorSearchExamples pastas e VectorSearchCreatePopulateIndex . Atualize os seguintes valores:

   • AZURE_SEARCH_ENDPOINT: Encontre a url do seu serviço Azure AI Search no portal do Azure. Na página Visão geral do recurso de pesquisa, procure o campo URL. Um ponto final de exemplo poderá ser parecido com https://mydemo.search.windows.net.
   • AZURE_SEARCH_INDEX_NAME: Deixe o valor padrão fornecido no arquivo ou digite seu próprio nome de índice.

Criar o índice vetorial e carregar documentos

Para executar consultas de pesquisa no serviço Azure AI Search, primeiro você precisa criar um índice de pesquisa e carregar documentos para o serviço.

1. Abra um novo terminal na VectorSearchCreatePopulateIndex pasta.

2. Execute o projeto usando o dotnet run comando:

   dotnet run
   

O código a seguir é executado para criar um índice:

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

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

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

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

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

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

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

O código a seguir carrega os documentos formatados em JSON no hotel-samples.json arquivo para o serviço Azure AI Search:

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

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

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

Depois de executar o projeto, a seguinte saída é mostrada:

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

Conclusões principais:

• Seu código interage com um índice de pesquisa específico hospedado em seu serviço Azure AI Search por meio do SearchClient, que é o objeto principal fornecido pelo azure-search-documents pacote. O SearchClient fornece acesso a operações de índice, tais como:

  • Ingestão de dados: UploadDocuments(), MergeDocuments(), DeleteDocuments()
  • Operações de pesquisa: Search(), Autocomplete(), Suggest()
  • Operações de gerenciamento de índice: CreateOrUpdateIndex()

• Os campos vetoriais contêm valores de ponto flutuante. O atributo dimensions tem um mínimo de 2 e um máximo de 4096 valores de ponto flutuante cada. Esse tamanho de incorporações geradas pelo modelo de texto-incorporação-3-pequeno do Azure OpenAI para este início rápido é 1536.

Executar consultas de pesquisa

Depois que o índice é criado e os documentos são carregados, você pode emitir consultas vetoriais contra eles chamando SearchAsync() com vários parâmetros.

1. Na pasta VectorSearchExamples, abra o arquivo Program.cs.
2. Abra um novo terminal na VectorSearchExamples pasta.

Nas seções a seguir, o utilizador realiza consultas no índice hotels-vector-quickstart. As consultas incluem:

• Pesquisa de vetor único
• Pesquisa vetorial única com filtro
• Pesquisa híbrida
• Pesquisa híbrida semântica com filtro

Pesquisa de vetor único

O primeiro exemplo demonstra um cenário básico onde você deseja encontrar descrições de documentos que correspondam à cadeia de caracteres de pesquisa.

1. No arquivo Program.cs da pasta VectorSearchExamples, descomente a chamada de método SearchExamples.SearchSingleVector(searchClient, vectorizedResult);. Este método executa a seguinte função de pesquisa na SearchExamples.cs classe:

   public static async Task SearchSingleVector(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
   {
       SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
           new SearchOptions
           {
               VectorSearch = new()
               {
                   Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
               },
               Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
           });
   
       Console.WriteLine($"Single Vector Search Results:");
       await foreach (SearchResult<Hotel> result in response.GetResultsAsync())
       {
           Hotel doc = result.Document;
           Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}");
       }
       Console.WriteLine();
   }
   

2. Execute o projeto usando o dotnet run comando:

   dotnet run
   

   Depois de executar o projeto, os resultados da pesquisa são impressos na janela de saída:

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

Pesquisa vetorial única com filtro

Você pode adicionar filtros, mas os filtros são aplicados ao conteúdo não vetorial em seu índice. Neste exemplo, o filtro se aplica ao campo para filtrar todos os Tags hotéis que não oferecem Wi-Fi gratuito.

1. No arquivo Program.cs da pasta VectorSearchExamples, descomente a chamada de método SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);. Este método executa a seguinte função de pesquisa na SearchExamples.cs classe:

   public static async Task SearchSingleVectorWithFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
   {
       SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
           new SearchOptions
           {
               VectorSearch = new()
               {
                   Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
               },
               Filter = "Tags/any(tag: tag eq 'free wifi')",
               Select = { "HotelId", "HotelName", "Description", "Category", "Tags" }
           });
   
       Console.WriteLine($"Single Vector Search With Filter Results:");
       await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
       {
           Hotel doc = result.Document;
           Console.WriteLine($"Score: {result.Score}, HotelId: {doc.HotelId}, HotelName: {doc.HotelName}, Tags: {string.Join(String.Empty, doc.Tags)}");
       }
       Console.WriteLine();
   }
   

2. Execute o projeto novamente e o status de cada documento é impresso abaixo dele:

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

   A consulta era a mesma do exemplo anterior de pesquisa de vetor único, mas inclui um filtro de exclusão de pós-processamento e retorna apenas os dois hotéis que têm Wi-Fi gratuito.

3. O próximo exemplo de filtro usa um filtro geográfico. No arquivo Program.cs da pasta VectorSearchExamples, descomente a chamada de método SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult);. Este método executa a seguinte função de pesquisa na SearchExamples.cs classe:

   public static async Task SingleSearchWithGeoFilter(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
   {
       SearchResults<Hotel> responseWithGeoFilter = await searchClient.SearchAsync<Hotel>(
           new SearchOptions
           {
               VectorSearch = new()
               {
                   Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
               },
               Filter = "geo.distance(Location, geography'POINT(-77.03241 38.90166)') le 300",
               Select = { "HotelId", "HotelName", "Description", "Address", "Category", "Tags" },
               Facets = { "Address/StateProvince" },
           });
   
       Console.WriteLine($"Vector query with a geo filter:");
       await foreach (SearchResult<Hotel> result in responseWithGeoFilter.GetResultsAsync())
       {
           Hotel doc = result.Document;
           Console.WriteLine($"HotelId: {doc.HotelId}");
           Console.WriteLine($"HotelName: {doc.HotelName}");
           Console.WriteLine($"Score: {result.Score}");
           Console.WriteLine($"City/State: {doc.Address.City}/{doc.Address.StateProvince}");
           Console.WriteLine($"Description: {doc.Description}");
           Console.WriteLine();
       }
       Console.WriteLine();
   }
   

   A consulta era a mesma do exemplo anterior de pesquisa de vetor único, mas inclui um filtro de exclusão de pós-processamento e retorna apenas os dois hotéis dentro de 300 quilômetros.

4. Execute o projeto novamente e o status de cada documento é impresso abaixo dele:

   Vector query with a geo filter:
   -HotelId: 48
   HotelName: Nordick's Valley Motel
   Score: 0.6605852246284485
   City/State: Washington D.C./null
   Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
   
   -HotelId: 49
   HotelName: Swirling Currents Hotel
   Score: 0.602634072303772
   City/State: Arlington/VA
   Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary Wi-Fi and flat-screen TVs.
   

Pesquisa híbrida

A pesquisa híbrida consiste em consultas de palavras-chave e consultas vetoriais em uma única solicitação de pesquisa. Este exemplo executa a consulta vetorial e a pesquisa de texto completo simultaneamente:

• Cadeia de pesquisa: historic hotel walk to restaurants and shopping
• Seqüência de consulta vetorial (vetorizada em uma representação matemática): Quintessential lodging near running trails, eateries, retail

1. No arquivo Program.cs da pasta VectorSearchExamples, descomente a chamada de método SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);. Este método executa a seguinte função de pesquisa na SearchExamples.cs classe:

   public static async Task<SearchResults<Hotel>> SearchHybridVectorAndText(SearchClient searchClient, ReadOnlyMemory<float> precalculatedVector)
   {
       SearchResults<Hotel> responseWithFilter = await searchClient.SearchAsync<Hotel>(
           "historic hotel walk to restaurants and shopping",
           new SearchOptions
           {
               VectorSearch = new()
               {
                   Queries = { new VectorizedQuery(precalculatedVector) { KNearestNeighborsCount = 5, Fields = { "DescriptionVector" } } }
               },
               Select = { "HotelId", "HotelName", "Description", "Category", "Tags" },
               Size = 5,
           });
   
       Console.WriteLine($"Hybrid search results:");
       await foreach (SearchResult<Hotel> result in responseWithFilter.GetResultsAsync())
       {
           Hotel doc = result.Document;
           Console.WriteLine($"Score: {result.Score}");
           Console.WriteLine($"HotelId: {doc.HotelId}");
           Console.WriteLine($"HotelName: {doc.HotelName}");
           Console.WriteLine($"Description: {doc.Description}");
           Console.WriteLine($"Category: {doc.Category}");
           Console.WriteLine($"Tags: {string.Join(String.Empty, doc.Tags)}");
           Console.WriteLine();
       }
       Console.WriteLine();
       return responseWithFilter;
   }
   

2. Execute o projeto novamente e o status de cada documento é impresso abaixo dele:

   Hybrid search results:
   Score: 0.03279569745063782
   HotelId: 4
   HotelName: Sublime Palace Hotel
   Description: Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th   century resort, updated for every modern convenience.
   Category: Boutique
   Tags: conciergeviewair conditioning
   
   Score: 0.032786883413791656
   HotelId: 13
   HotelName: Luxury Lion Resort
   Description: Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.
   Category: Luxury
   Tags: barconciergerestaurant
   
   Score: 0.03205128386616707
   HotelId: 48
   HotelName: Nordick's Valley Motel
   Description: Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.
   Category: Boutique
   Tags: continental breakfastair conditioningfree wifi
   
   Score: 0.0317460335791111
   HotelId: 49
   HotelName: Swirling Currents Hotel
   Description: Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.
   Category: Suite
   Tags: air conditioninglaundry service24-hour front desk service
   
   Score: 0.03077651560306549
   HotelId: 2
   HotelName: Old Century Hotel
   Description: The hotel is situated in a nineteenth century plaza, which has been expanded and renovated to the highest architectural standards to create a modern, functional and first-class hotel in which art and unique historical elements coexist with the most modern comforts. The hotel also regularly hosts events like wine tastings, beer   dinners, and live music.
   Category: Boutique
   Tags: poolfree wifiair conditioningconcierge
   

Como o RRF (Reciprocal Rank Fusion) mescla resultados, ele ajuda a revisar as entradas. Os resultados a seguir são somente da consulta de texto completo. Os dois primeiros resultados são Sublime Palace Hotel e History Lion Resort. O Sublime Palace Hotel tem uma pontuação de relevância BM25 mais forte.

{
    "@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"
},

Na consulta somente vetorial, que usa HNSW para encontrar correspondências, o Sublime Palace Hotel cai para a quarta posição. O Historic Lion, que ficou em segundo lugar na pesquisa de texto completo e terceiro na pesquisa vetorial, não experimenta a mesma faixa de flutuação, por isso aparece como uma correspondência superior em um 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"
    }
]

Pesquisa híbrida semântica com um filtro

A consulta híbrida com classificação semântica é filtrada para mostrar apenas os hotéis dentro de um raio de 500 km de Washington D.C.

1. No arquivo Program.cs da pasta VectorSearchExamples, descomente a chamada de método SearchExamples.SearchHybridVectoryAndSemantic(searchClient, vectorizedResult);. Este método executa a seguinte função de pesquisa na SearchExamples.cs classe:

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

2. Execute o projeto novamente e revise a saída abaixo da célula. A resposta são três hotéis, que são filtrados por localização e facetados e semanticamente reclassificados para StateProvince promover resultados mais próximos da consulta da cadeia de pesquisa (historic hotel walk to restaurants and shopping).

   O Swirling Currents Hotel agora se move para o primeiro lugar. Sem ranking semântico, Nordick's Valley Motel é o número um. Com classificação semântica, os modelos de compreensão da máquina reconhecem que historic se aplica a "hotel, a uma curta distância de restaurantes e lojas".

   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
   

Conclusões principais:

• Em uma pesquisa híbrida, você pode integrar a pesquisa vetorial com a pesquisa de texto completo sobre palavras-chave. Os filtros, a verificação ortográfica e a classificação semântica aplicam-se apenas ao conteúdo textual e não aos vetores. Nesta consulta final, não há semântica answer porque o sistema não produziu uma que fosse suficientemente forte.

• Os resultados reais incluem mais detalhes, incluindo legendas semânticas e destaques. Os resultados foram modificados quanto à legibilidade. Para obter a estrutura completa da resposta, execute a solicitação no cliente REST.

Limpeza

Ao trabalhar na sua própria subscrição, recomendamos que verifique, depois de concluir um projeto, se ainda vai precisar dos recursos que criou. Os recursos que deixar em execução podem custar dinheiro. Pode eliminar recursos individualmente ou eliminar o grupo de recursos para eliminar todo o conjunto de recursos.

Você pode localizar e gerenciar recursos no portal do Azure usando o link Todos os recursos ou Grupos de recursos no painel mais à esquerda.

Próximos passos

• Revise o repositório de exemplos de código para recursos de pesquisa vetorial no Azure AI Search for .NET
• Analise os outros exemplos de código .NET e Azure AI Search no repositório azure-search-dotnet-samples

Neste início rápido, você usa as APIs REST do Azure AI Search para criar, carregar e consultar vetores.

No Azure AI Search, um repositório de vetores tem um esquema de índice que define campos vetoriais e não vetoriais, uma configuração de pesquisa vetorial para algoritmos que criam o espaço de incorporação e configurações em definições de campo vetorial que são avaliadas no momento da consulta. A API REST Create Index cria o repositório vetorial.

Nota

Este guia de início rápido omite a etapa de vetorização e fornece incorporações em linha. Se quiser adicionar fragmentação e vetorização de dados integrada ao seu próprio conteúdo, experimente o assistente Importar e vetorizar dados para um guia completo passo a passo.

Pré-requisitos

• Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.

• Um serviço Azure AI Search. Crie um serviço ou encontre um serviço existente na sua subscrição atual.

  • Você pode usar um serviço de pesquisa gratuito para a maior parte desse início rápido, mas recomendamos a camada Básica ou superior para arquivos de dados maiores.
  • Para executar o exemplo de consulta que invoca a reclassificação semântica, seu serviço de pesquisa deve estar na camada Básica ou superior com o classificador semântico habilitado.

• Código do Visual Studio com um cliente REST.

---

Recuperar informações do recurso

As solicitações para o ponto de extremidade de pesquisa devem ser autenticadas e autorizadas. Você pode usar chaves ou funções de API para essa tarefa. Recomendamos o uso de uma conexão sem chave via Microsoft Entra ID.

Selecione a guia que corresponde ao seu método de autenticação preferido. Use o mesmo método para todas as solicitações neste início rápido.

Microsoft Entra ID

1. Entre no portal do Azure e encontre seu serviço de pesquisa.

2. Na página inicial Visão geral , localize o URL. Um ponto final de exemplo poderá ser parecido com https://mydemo.search.windows.net.

   

3. Siga as etapas no início rápido sem chave para obter seu token Microsoft Entra.

   Você obtém o token quando executa o az account get-access-token comando na etapa 3 do início rápido anterior.

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

Chave de API

1. Entre no portal do Azure e encontre seu serviço de pesquisa.

2. Na página inicial Visão geral , localize o URL. Um ponto final de exemplo poderá ser parecido com https://mydemo.search.windows.net.

   

3. Selecione Teclas de configurações>. As chaves de API ou ambas devem estar habilitadas. As chaves da API Admin são usadas para adicionar, modificar e excluir objetos. Existem duas chaves de administração intercambiáveis. Copie qualquer uma delas.

   

Criar ou baixar o arquivo de código

Você usa um .rest arquivo ou .http para executar todas as solicitações neste início rápido. Você pode baixar o arquivo REST que contém o código para este início rápido, ou você pode criar um novo arquivo no Visual Studio Code e copiar o código para ele.

1. No Visual Studio Code, crie um novo arquivo com uma .rest extensão ou .http arquivo. Por exemplo, az-search-vector-quickstart.rest. Copie e cole o conteúdo bruto do arquivo Azure-Samples/azure-search-rest-samples/blob/main/Quickstart-vectors/az-search-vector-quickstart.rest nesse novo arquivo.

2. Na parte superior do arquivo, substitua o valor de espaço reservado por @baseUrl URL do serviço de pesquisa. Consulte a seção Recuperar informações do recurso para obter instruções sobre como encontrar o URL do serviço de pesquisa.

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

3. Na parte superior do arquivo, substitua o valor de espaço reservado para autenticação. Consulte a seção Recuperar informações do recurso para obter instruções sobre como obter seu token ou chave de API do Microsoft Entra.

   Para a autenticação sem chave recomendada via Microsoft Entra ID, você precisa substituir @apiKey pela @token variável.

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

   Se preferir usar uma chave de API, substitua @apiKey pela chave copiada do portal do Azure.

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

4. Para a autenticação sem chave recomendada via ID do Microsoft Entra, você precisa substituir api-key: {{apiKey}} por Authorization: Bearer {{token}} nos cabeçalhos de solicitação. Substitua todas as instâncias que api-key: {{apiKey}} você encontrar no arquivo.

Criar um índice de vetores

Use a API REST Criar índice para criar um índice vetorial e configurar as estruturas de dados físicos em seu serviço de pesquisa.

O esquema de índice neste exemplo é organizado em torno do conteúdo do hotel. Os dados de amostra consistem em descrições vetoriais e não vetoriais de hotéis fictícios. Esse esquema inclui configurações para indexação vetorial e consultas, e para classificação semântica.

1. No Visual Studio Code, abra o az-search-vector-quickstart.rest arquivo criado anteriormente.

2. Localize o bloco de ### Create a new index código no arquivo. Este bloco contém o pedido para criar o hotels-vector-quickstart índice no seu serviço de pesquisa.

   ### 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. Selecione Enviar pedido. Você deve ter uma HTTP/1.1 201 Created resposta.

   O corpo da resposta deve incluir a representação JSON do 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"
               }
               ]
           }
           }
       ]
       }
   }
   

Principais conclusões sobre a API REST de criação de índice :

• A fields coleção inclui um campo de chave obrigatório e campos de texto e vetor (como Description e DescriptionVector) para pesquisa de texto e vetorial. A colocalização de campos vetoriais e não vetoriais no mesmo índice permite consultas híbridas. Por exemplo, você pode combinar filtros, pesquisa de texto com classificação semântica e vetores em uma única operação de consulta.

• Os campos vetoriais devem ser um dos tipos de dados EDM usados para vetores, como type: Collection(Edm.Single). Os campos vetoriais também têm dimensions e vectorSearchProfile propriedades.

• A vectorSearch seção é uma matriz de configurações e perfis de algoritmo ANN (Approximate Nearest Neighbor). Os algoritmos suportados incluem o pequeno mundo navegável hierárquico e o exaustivo K-Nearest Neighbor. Para obter mais informações, consulte Pontuação de relevância na pesquisa vetorial.

• A configuração (opcional) semantic permite a reclassificação dos resultados da pesquisa. Você pode reclassificar os resultados em consultas do tipo semantic para campos de cadeia de caracteres especificados na configuração. Para saber mais, consulte Visão geral da classificação semântica.

Carregar documentos

Criar e carregar o índice são etapas separadas. Você criou o esquema de índice na etapa anterior. Agora você precisa carregar documentos no índice.

No Azure AI Search, o índice contém todos os dados pesquisáveis e consultas executadas no serviço de pesquisa. Para chamadas REST, os dados são fornecidos como documentos JSON. Use Documents- Index REST API para esta tarefa. O URI é estendido para incluir a docs coleta e a index operação.

1. Formule uma solicitação de upload de documentos para carregar documentos para o hotels-vector-quickstart índice em seu serviço de pesquisa.

   ### 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. Selecione Enviar pedido. Você deve ter uma HTTP/1.1 200 OK resposta. O corpo de resposta deve incluir a representação JSON dos documentos de pesquisa.

Principais conclusões sobre o Documents - Index REST API request:

• Os documentos na carga útil consistem em campos definidos no esquema de índice.

• Os campos vetoriais contêm valores de ponto flutuante. O atributo dimensions tem um mínimo de 2 e um máximo de 4096 valores de ponto flutuante cada. Este guia de início rápido define o atributo dimensions como 1.536 porque esse é o tamanho das incorporações geradas pelo modelo text-embedding-3-small do Azure OpenAI.

Executar consultas

Agora que os documentos estão carregados, você pode executar consultas vetoriais em relação a eles usando Documents - Search Post (REST).

Nas próximas seções, executamos consultas no hotels-vector-quickstart índice. As consultas incluem:

• Pesquisa de vetor único
• Pesquisa vetorial única com filtro
• Pesquisa híbrida
• Pesquisa híbrida semântica

As consultas de exemplo são baseadas em duas cadeias de caracteres:

• Cadeia de pesquisa: historic hotel walk to restaurants and shopping
• Seqüência de consulta vetorial (vetorizada em uma representação matemática): quintessential lodging near running trails, eateries, retail

A cadeia de caracteres de consulta vetorial é semanticamente semelhante à cadeia de pesquisa, mas inclui termos que não existem no índice de pesquisa. Se você fizer uma pesquisa por palavra-chave para quintessential lodging near running trails, eateries, retail, os resultados serão zero em uma pesquisa de palavra-chave pura sem classificação semântica. Usamos este exemplo para mostrar como você pode obter resultados relevantes usando vetores, mesmo que não haja termos correspondentes.

Pesquisa de vetor único

1. Formule o pedido. A consulta é uma representação flutuante de 1536 de alojamento por excelência perto de trilhas de corrida, restaurantes, varejo. A consulta está pesquisando DescriptionVector e retornando resultados k-5. Ele está usando o parâmetro de substituição "exaustivo" para executar uma verificação completa do índice em vez de ANN. Uma pesquisa exaustiva é útil para pequenos índices.

   ### 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
               }
           ]
       }
   

   Principais conclusões sobre os Documentos - Search Post REST API:

   • O vectorQueries.vector é a cadeia de caracteres de consulta vetorial. É uma representação vetorial do alojamento por excelência perto de trilhas de corrida, restaurantes, varejo, que é vetorizada em 1.536 incorporações para esta consulta.

   • fields Determina quais campos vetoriais são pesquisados.

   • kind definido como vector significa que a cadeia de caracteres de consulta é um vetor. Se kind fosse definido como text, você precisaria de capacidade extra (um vetorizador) para codificar uma cadeia de texto legível por humanos em um vetor no momento da consulta. Os vetorizadores são omitidos deste início rápido para manter o exercício simples.

   • k especifica o número de resultados a serem retornados na resposta. Um count parâmetro especifica o número de correspondências encontradas no índice. Incluir contagem é uma prática recomendada para consultas, mas é menos útil para pesquisa de semelhança, onde o algoritmo pode encontrar algum grau de semelhança em quase qualquer documento.

2. Selecione Enviar pedido. Você deve ter uma HTTP/1.1 200 OK resposta. O corpo da resposta deve incluir a representação JSON dos resultados da pesquisa.

   A resposta para o equivalente vetorial de quintessential lodging near running trails, eateries, retail inclui resultados k-5, embora o motor de busca tenha encontrado 7 correspondências. Os principais resultados são considerados os mais semânticos semelhantes à consulta. Cada resultado fornece uma pontuação de pesquisa e os campos listados em select. Em uma pesquisa de similaridade, a resposta sempre inclui k resultados ordenados pela pontuação de similaridade de valor.

   {
     "@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"
         ]
       }
     ]
   }
   

Pesquisa vetorial única com filtro

Você pode adicionar filtros, mas os filtros são aplicados ao conteúdo não vetorial em seu índice. Neste exemplo, o filtro se aplica ao campo para filtrar todos os Tags hotéis que não oferecem Wi-Fi gratuito.

1. Formule o pedido. Esta é a mesma solicitação que a anterior, com um filtro extra e parâmetro de modo de filtro.

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

2. Selecione Enviar pedido. Você deve ter uma HTTP/1.1 200 OK resposta. O corpo da resposta deve incluir a representação JSON dos resultados da pesquisa.

   A consulta era a mesma do exemplo anterior de pesquisa de vetor único, mas inclui um filtro de exclusão de pós-processamento e retorna apenas os dois hotéis que têm Wi-Fi gratuito.

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

3. Aqui está outra consulta que usa um filtro geográfico, limitando os resultados a hotéis a menos de 300 quilômetros em torno 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. Veja a resposta. Está limitado a dois hotéis perto de Washington D.C., com uma estrutura de facetas que proporciona a agregação.

   "@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"
       }
     }
   ]
   

Pesquisa híbrida

A pesquisa híbrida consiste em consultas de palavras-chave e consultas vetoriais em uma única solicitação de pesquisa. Este exemplo executa a consulta vetorial e a pesquisa de texto completo simultaneamente:

• Cadeia de pesquisa: historic hotel walk to restaurants and shopping
• Seqüência de consulta vetorial (vetorizada em uma representação matemática): quintessential lodging near running trails, eateries, retail

1. Formule a 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. Selecione Enviar pedido. Você deve ter uma HTTP/1.1 200 OK resposta. O corpo da resposta deve incluir a representação JSON dos resultados da pesquisa.

Como esta é uma consulta híbrida, os resultados são classificados por Reciprocal Rank Fusion (RRF). Observe que @search.score os valores têm uma base diferente e são valores uniformemente menores. O RRF avalia as pontuações de pesquisa de vários resultados de pesquisa, faz o inverso e, em seguida, mescla e classifica os resultados combinados. O top número de resultados é retornado.

Analise a resposta, que consiste nas principais correspondências de k a 5 entre os 7 documentos correspondentes no 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."
    }
  ]
}

Como o RRF mescla resultados, ele ajuda a revisar as entradas individualmente.

Os resultados a seguir são da parte de texto completo da consulta: caminhada histórica do hotel até restaurantes e lojas. Na consulta de texto completo, os três primeiros resultados são Sublime Palace Hotel, Stay-Kay City Hotel e Luxury Lion Resort. O Sublime Palace Hotel tem uma pontuação de relevância BM25 mais forte. Na consulta fundida, apenas duas dessas partidas estão no top 3, e a segunda partida (Stay-Kay City Hotel) não aparece no top 5.

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

Na consulta de porção vetorial (alojamento por excelência perto de trilhas de corrida, restaurantes, varejo) é uma string diferente, então devemos esperar resultados diferentes. Esta consulta retorna Nordick's Valley Motel, Luxury Lion Resort e Sublime Palace Hotel como as três melhores opções. Na consulta fundida, esses resultados estão no top 3, mas em ordem inversa.

  "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."
    },
   ...
  ]

Pesquisa híbrida semântica

Aqui está a última consulta na coleção. Esta consulta híbrida adiciona uma classificação semântica L2 que aplica a compreensão de leitura por máquina aos resultados classificados em L1, promovendo as correspondências mais relevantes para o topo.

1. Formule o pedido.

   ### 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. Selecione Enviar pedido. Você deve ter uma HTTP/1.1 200 OK resposta. O corpo da resposta deve incluir a representação JSON dos resultados da pesquisa.

Analise a resposta, que consiste em uma reclassificação semântica dos resultados classificados pelo RRF da consulta híbrida. A classificação semântica funciona a partir de entradas de texto. Em uma consulta de texto ou híbrida, essa entrada é a parte de texto da consulta (caminhada histórica do hotel até restaurantes e lojas). Para usar a classificação semântica em uma consulta vetorial pura, como o primeiro exemplo, ou para controlar explicitamente o texto usado para classificação semântica, forneça uma cadeia de caracteres semanticQuery.

Após a reclassificação semântica, o Swirling Currents Hotel, com sua referência ao acesso a pé a lojas, restaurantes, entretenimento e ao centro da cidade, passa para o primeiro lugar. O modelo de compreensão de máquinas promove o acesso a pé a lojas e restaurantes como uma combinação mais próxima da caminhada para restaurantes e lojas.

Antes da reclassificação semântica, o Sublime Palace, com a sua referência a a uma curta distância a pé dos locais e marcos da cidade, está rodeado pela extraordinária beleza de igrejas, edifícios, lojas e monumentos, era o número um.

{
  "@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"
    }
  ]
}

Sugestão

Os resultados classificados semanticamente podem incluir mais detalhes, incluindo respostas semânticas, legendas e destaques. Adicionar mais parâmetros à solicitação produz o detalhe extra. Para obter mais informações, consulte Configurar uma consulta semântica.

Limpeza

Ao trabalhar na sua própria subscrição, recomendamos que verifique, depois de concluir um projeto, se ainda vai precisar dos recursos que criou. Os recursos que deixar em execução podem custar dinheiro. Pode eliminar recursos individualmente ou eliminar o grupo de recursos para eliminar todo o conjunto de recursos.

Você pode localizar e gerenciar recursos no portal do Azure usando o link Todos os recursos ou Grupos de recursos no painel mais à esquerda.

Se você quiser manter o serviço de pesquisa, mas excluir o índice e os documentos, você pode usar o DELETE comando no cliente REST. Este comando (no final do ficheiro az-search-vector-quickstart.rest ) elimina o hotels-vector-quickstart índice:

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

Próximos passos

Como próxima etapa, recomendamos aprender a invocar chamadas de API REST sem chaves de API.

Você também pode querer rever o código de demonstração para Python, C# ou JavaScript.