Início Rápido: Pesquisa de vetor

Neste início rápido, você usará um jupyter notebook para criar, carregar e consultar um índice de vetor. O código executa essas operações usando a biblioteca cliente do Azure AI Search para Python, que fornece uma abstração sobre as APIs REST para acessar operações em índices.

No Azure AI Search, um índice de vetor tem um esquema de índice que define campos vetoriais e não vetoriais, uma configuração de pesquisa de vetor para algoritmos que criam o espaço de inserção e configurações em definições de campo de vetor que são avaliadas no momento da consulta. Índices – Criar ou atualizar (API REST) cria o índice de vetor.

Observação

Este início rápido omite a etapa de vetorização e fornece inserções embutidas. Se você quiser adicionar agrupamento e vetorização de dados integrados em seu próprio conteúdo, experimente o assistente Importar dados (novo) para um passo a passo completo.

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search.
- Você pode usar a camada Gratuita durante a maior parte deste início rápido, mas recomendamos Basic ou superior para arquivos de dados maiores.
- Para autenticação sem chave com a ID do Microsoft Entra, atribua a função Colaborador de Dados do Índice de Pesquisa à sua conta de usuário.
- Para executar a consulta híbrida semântica, você deve habilitar o classificador semântico.
Visual Studio Code com a extensão python e o pacote Jupyter.
Git para clonar o repositório de exemplo.

Obter informações de serviço

As solicitações para o ponto de extremidade de pesquisa precisam ser autenticadas e autorizadas. Embora seja possível usar chaves de API para essa tarefa, recomendamos usar uma conexão sem chave por meio da ID do Microsoft Entra.

Este início rápido usa DefaultAzureCredential, o 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.

Clonar o código e o ambiente de instalação

Clone o repositório que contém o código deste início rápido.

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

No Visual Studio Code, abra a pasta Quickstart-Vector-Search.
Renomeie o sample.env arquivo para .env.
Defina AZURE_SEARCH_ENDPOINT como a URL do serviço de pesquisa, que deve ser semelhante a https://mydemo.search.windows.net.
Defina AZURE_SEARCH_INDEX_NAME como um nome exclusivo para o índice. Você também pode usar o nome padrão vector-search-quickstart .
Selecione Exibir>Novo Terminal e execute os comandos a seguir para criar e ativar um ambiente virtual.
```
python -m venv .venv
source .venv/scripts/activate
where python
```
Observação
- Esta etapa pressupõe que você esteja usando o Git Bash em seu terminal e em execução no Windows. Se você estiver usando um shell e/ou sistema operacional diferente, ajuste essas instruções para seu ambiente específico.
- 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 do computador.
Se solicitado, permita que o Visual Studio Code use o novo ambiente.
Instale as bibliotecas necessárias.
```
pip install requirements.txt
```
Abra o arquivo vector-search-quickstart.ipynb .

Execute a célula de código Install packages and set variables para carregar as variáveis de ambiente.

# 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 saída inclui alguns dos pacotes instalados.

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
...

Criar um índice de vetor

O código em vector-search-quickstart.ipynb usa vários métodos da azure.search.documents biblioteca para criar o índice de vetor e campos pesquisáveis.

Para criar o índice de vetor:

Execute a célula de código Create an index.

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, a saída a seguir será exibida abaixo da célula de código.

vector-search-quickstart created

Principais conclusões:

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.
Esse índice específico dá suporte a vários recursos de pesquisa, como:
- Pesquisa de palavra-chave de texto completo (SearchableField(name="HotelName", ...), SearchableField(name="Description", ...))
- Pesquisa de vetor (habilita a pesquisa híbrida agrupando campos vetoriais e não vetoriais) (DescriptionVector)
- Pesquisa semântica (semantic_search=SemanticSearch(configurations=[semantic_config]))
- Pesquisa facetada (facetable=True)
- Pesquisa geoespacial (Location campo é GeographyPoint)
- Filtragem, ordenação (muitos campos marcados como filtráveis e classificáveis)

Carregar documentos no índice

A criação e o carregamento do 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.

Para carregar documentos no índice:

Execute a célula de código Create documents payload.

   # 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. Esse vetor permite a pesquisa de similaridade, em que a correspondência é baseada no significado em vez de palavras-chave exatas.

Importante

O código neste exemplo não é executável. Vários caracteres ou linhas foram removidos para fins de brevidade. Em vez disso, execute o código no jupyter notebook.

Execute a célula de código Upload the documents.
```
# 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 chamando o construtor SearchClient() e, em seguida, chamando 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:
- Seu código interage com um índice de pesquisa específico hospedado em seu serviço do Azure AI Search por meio do SearchClient, que é o objeto principal fornecido pelo azure-search-documents pacote. Ele SearchClient fornece acesso a operações de índice, como:
  - Ingestão de dados: upload_documents(), , merge_documents(), delete_documents()etc.
  - Operações de pesquisa: search(), autocomplete(), suggest()
  - Operações de gerenciamento de índice: get_index_statistics(), get_document_count()
- Os campos de vetor contêm valores de ponto flutuante. O atributo de dimensões tem um mínimo de 2 e um máximo de 4.096 valores de ponto flutuante cada. Este início rápido define o atributo de dimensões como 1.536 porque esse é o tamanho das inserções geradas pelo text-embedding-ada-002 modelo.

Executar consultas

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

Consultas nesta seção:

Busca em vetores única
Busca em vetores única com filtro
Pesquisa híbrida
Pesquisa híbrida semântica

Cria a cadeia de caracteres de consulta de vetor

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

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

A cadeia de consulta de vetor é semanticamente semelhante à cadeia de caracteres 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. Utilizamos esse exemplo para mostrar como você pode obter resultados relevantes mesmo que não existam termos correspondentes.

Execute a célula de código Create the vector query string. Isso carrega a vector variável com os dados de consulta vetorizados necessários para executar todas as pesquisas nas próximas seções.

Busca em vetores única

O primeiro exemplo demonstra um cenário básico em que você deseja encontrar descrições de documento que correspondam de perto à cadeia de caracteres de pesquisa.

Execute a célula de código Single vector search. Esse bloco contém a solicitação 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.")

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

A cadeia de consulta de vetor é quintessential lodging near running trails, eateries, retail, que é vetorizada em 1.536 inserções para essa consulta.

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

Busca em vetores única com filtro

É possível adicionar filtros, mas eles serão aplicados ao conteúdo não vetorial do seu índice. Neste exemplo, o filtro se aplica ao campo Tags para filtrar todos os hotéis que não oferecem Wi-Fi gratuito.

Para criar uma única pesquisa de vetor com um filtro:

Execute a célula de código Single vector search with filter. Essa célula contém a solicitação 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:

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 que o exemplo de pesquisa de vetor único anterior, mas inclui um filtro de exclusão pós-processamento e retorna apenas os dois hotéis que têm Wi-Fi gratuito.

O próximo exemplo de filtro usa um filtro geográfico. Execute a célula de código Vector query with a geo filter. Esse bloco contém a solicitação 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 que o exemplo de pesquisa de vetor único anterior, mas inclui um filtro de exclusão 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 palavra-chave e consultas vetoriais em uma só solicitação de pesquisa. Este exemplo executa a consulta vetor e a pesquisa de texto completo simultaneamente:

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

Para criar uma pesquisa híbrida:

Execute a célula de código Hybrid search. Esse bloco contém a solicitação 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.")

Analise a saída abaixo da célula.

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 os resultados, ele ajuda a examinar as entradas. Os resultados a seguir são somente da consulta de texto completo. Os dois principais resultados são Sublime Palace Hotel e Luxury 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 apenas por vetor, que usa HNSW para encontrar correspondências, o Sublime Palace Hotel cai para a quarta posição. O Luxury Lion, que foi o segundo na pesquisa de texto completo e terceiro na pesquisa de vetor, não experimenta o mesmo intervalo de flutuação, por isso aparece como uma correspondência superior em um conjunto de resultados homogêneo.

"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 semântica híbrida

Esta é a última consulta na coleção. Essa consulta híbrida especifica o tipo de consulta semântica e uma configuração semântica, demonstrando que você pode criar uma consulta híbrida que usa o reclassificado semântico.

Para criar uma pesquisa híbrida semântica:

Execute a célula de código Semantic hybrid search. Esse 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 a classificação semântica, o Hotel Swirling Currents agora se move para o primeiro lugar.

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 forma de melhorar a relevância dos resultados da pesquisa entendendo o significado por trás das palavras na consulta e no conteúdo dos documentos. Nesse caso, a classificação semântica ajuda a identificar hotéis que não são apenas relevantes para as palavras-chave, mas também correspondem à intenção da consulta:

Principais conclusões:

A busca em vetores é especificada por meio da propriedade vectors.value. A pesquisa por palavra-chave é especificada por meio da propriedade search.
Em uma pesquisa híbrida, é possível integrar a busca em vetores com a pesquisa de texto completo por palavras-chave. Filtros, verificação ortográfica e classificação semântica se aplicam apenas a conteúdo textual e não a vetores. Nessa consulta final, não existe a semântica answer porque o sistema não produziu uma semântica suficientemente forte.
Os resultados reais incluem mais detalhes, inclusive legendas semânticas e destaques. Os resultados foram modificados para facilitar a leitura. Para obter a estrutura completa da resposta, execute a solicitação no cliente REST.

Limpar os recursos

Quando você está trabalhando na sua assinatura, é uma boa ideia identificar, no final de um projeto, se você ainda precisa dos recursos criados. Recursos deixados em execução podem custar dinheiro. Você pode excluir os recursos individualmente ou excluir o grupo de recursos para excluir todo o conjunto de recursos.

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

Como alternativa, você pode executar a célula de código Clean up para excluir o índice de vetor criado neste início rápido.

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

Próximas etapas

Examine o repositório de exemplos de código para recursos de pesquisa de vetor no Azure AI Search para Python.
Examine os outros exemplos de código do Python e do Azure AI Search no repositório azure-search-python-samples.

Neste início rápido, você usará Java para criar, carregar e consultar um índice de vetor. O código executa essas operações usando a biblioteca cliente do Azure AI Search para Java, que fornece uma abstração sobre as APIs REST para acessar operações de índice.

Observação

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search.
- Você pode usar a camada Gratuita durante a maior parte deste início rápido, mas recomendamos Basic ou superior para arquivos de dados maiores.
- Para autenticação sem chave com a ID do Microsoft Entra, atribua a função Colaborador de Dados do Índice de Pesquisa à sua conta de usuário.
- Para executar a consulta híbrida semântica, você deve habilitar o classificador semântico.
Java 21 (LTS).
Maven.

Obter informações de serviço

Nas seções restantes, você configurará chamadas de API para o OpenAI do Azure e para a Pesquisa de IA do Azure. Obtenha os pontos de extremidade de serviço para que você possa fornecê-los como variáveis em seu código.

Para obter o endpoint de serviço:

Entre no portal do Azure.
Encontre o seu serviço de pesquisa.
Na página inicial Visão Geral, copie a URL. Um ponto de extremidade de exemplo pode se parecer com https://example.search.windows.net.

Configurar o projeto Java

Crie um novo diretório de projeto do Maven.

mkdir vector-quickstart && cd vector-quickstart

Crie um arquivo pom.xml com as dependências necessárias.

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

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

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

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

Compile o projeto para resolver as dependências.
```
mvn compile
```

Crie a estrutura do diretório de origem.

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

Crie um arquivo no diretório application.properties e forneça o ponto de extremidade do serviço de pesquisa src/main/resources.
```
azure.search.endpoint=<YOUR AZURE AI SEARCH ENDPOINT>
azure.search.index-name=hotels-vector-quickstart
```

Você está usando o Microsoft Entra ID e as atribuições de função para a conexão. Verifique se você está conectado ao mesmo locatário e assinatura da Pesquisa de IA do Azure e do OpenAI do Azure. Você pode usar a CLI do Azure na linha de comando para mostrar as propriedades atuais, alterar propriedades e entrar. Para obter mais informações, confira Conectar-se sem chaves.

Execute cada um dos comandos a seguir na 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 em seu dispositivo local.

Criar um índice de vetor

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

Para criar um índice de vetor:

Crie um CreateIndex.java arquivo no src/main/java/com/example/search diretório.

Copie o código a seguir no arquivo.

package com.example.search;

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

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

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

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

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

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

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

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

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

        var searchIndex = new SearchIndex(indexName, fields);

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

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

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

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

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

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

        System.exit(0);
    }
}

O arquivo de código cria o índice e define o esquema de índice, incluindo o campo de DescriptionVector vetor.

Execute o arquivo.
```
mvn compile exec:java -Dexec.mainClass="com.example.search.CreateIndex"
```
A saída desse código mostra que o índice foi criado com êxito.
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Creating index...
hotels-vector-quickstart created
```
Principais conclusões:
- Você define um índice criando uma lista de campos.
- Esse índice específico dá suporte a vários recursos de pesquisa, como:
  - Pesquisa de palavra-chave de texto completo (setSearchable)
  - Pesquisa de vetor (habilita a pesquisa híbrida agrupando campos vetoriais e não vetoriais) (DescriptionVector com setVectorSearchProfileName)
  - Pesquisa semântica
  - Pesquisa facetada (SearchSuggester)
  - Pesquisa geoespacial (Location campo com SearchFieldDataType.GEOGRAPHY_POINT)
  - Filtragem, ordenação (muitos campos marcados como filtráveis e classificáveis)

Carregar documentos no índice

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

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

Para carregar documentos no índice:

Crie um arquivo UploadDocuments.java no diretório src/main/java/com/example/search.

Copie o código a seguir no arquivo.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Esse código carrega uma coleção de documentos. Cada documento inclui a versão vetorizada do artigo.Description Esse vetor permite a pesquisa de similaridade, em que a correspondência é baseada no significado em vez de palavras-chave exatas.

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

Compile e execute o arquivo.
```
mvn compile exec:java -Dexec.mainClass="com.example.search.UploadDocuments"
```
A saída desse código mostra que os documentos estão indexados e prontos para pesquisa.
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Uploading documents...
Key: 1, Succeeded: true, ErrorMessage: none
Key: 2, Succeeded: true, ErrorMessage: none
Key: 3, Succeeded: true, ErrorMessage: none
Key: 4, Succeeded: true, ErrorMessage: none
Key: 48, Succeeded: true, ErrorMessage: none
Key: 49, Succeeded: true, ErrorMessage: none
Key: 13, Succeeded: true, ErrorMessage: none
Waiting for indexing... Current count: 0
All documents indexed successfully.
```
Principais conclusões:
- Seu código interage com um índice de pesquisa específico hospedado no serviço Pesquisa de IA do Azure por meio do SearchClient. O SearchClient fornece acesso a operações como:
  - Ingestão de dados: uploadDocuments, , mergeDocumentse deleteDocumentsassim por diante.
  - Operações de pesquisa: search, autocomplete, suggest
- Os campos de vetor contêm valores de ponto flutuante. O atributo de dimensões tem um mínimo de 2 e um máximo de 4.096 valores de ponto flutuante cada. Este início rápido define o atributo de dimensões como 1.536 porque esse é o tamanho das inserções geradas pelo text-embedding-ada-002 modelo.

Criar a consulta como um vetor

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

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

Crie um QueryVector.java arquivo no src/main/java/com/example/search diretório e adicione o código para criar o vetor de consulta.

package com.example.search;

import java.util.ArrayList;

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

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

Criar uma única pesquisa de vetor

O primeiro exemplo demonstra um cenário básico em que você deseja encontrar descrições de documento que correspondam de perto à cadeia de caracteres de pesquisa usando o SearchClient. pesquisar e o VectorQuery e SearchOptions.

Para criar uma única pesquisa de vetor:

Crie um SearchSingle.java arquivo no src/main/java/com/example/search diretório.

Copie o código a seguir no arquivo.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

O vectorQuery contém a configuração da consulta vetorizada, incluindo o texto vetorizado da entrada da consulta, conforme recuperado por meio de QueryVector.getVectorList. setFields determina quais campos de vetor são pesquisados e setKNearestNeighborsCount especifica o número de vizinhos mais próximos a serem retornados.

A cadeia de consulta de vetor é quintessential lodging near running trails, eateries, retail, que é vetorizada em 1.536 inserções para essa consulta.

Compile e execute o arquivo.

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

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

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

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

A resposta para o vetor equivalente a quintessential lodging near running trails, eateries, retail consiste em 5 resultados, com apenas os campos especificados pelo campo selecionado retornados.

Criar uma única pesquisa de vetor com um filtro

Para criar uma única pesquisa de vetor com um filtro:

Crie um SearchSingleWithFilter.java arquivo no src/main/java/com/example/search diretório.

Copie o código a seguir no arquivo.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Esse código tem a mesma funcionalidade de pesquisa da pesquisa anterior, mas adiciona um filtro de exclusão pós-processamento para hotéis com free wifi.

Compile e execute o arquivo.

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

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

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

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

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 para hotéis dentro de 300 quilômetros de Washington D.C. (coordenadas: -77.03241 38.90166). As distâncias geoespaciais estão sempre em quilômetros. Essa pesquisa usa SearchClient. SearchClient e SearchOptions.

Para criar uma única pesquisa de vetor com um filtro geoespacial:

Crie um SearchSingleWithFilterGeo.java arquivo no src/main/java/com/example/search diretório.

Copie o código a seguir no arquivo.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Compile e execute o arquivo.

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

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

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

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

Criar uma pesquisa híbrida

A pesquisa híbrida combina consultas de palavra-chave e vetor em uma solicitação. Este exemplo executa as seguintes cadeias de caracteres de consulta de vetor e texto completo simultaneamente:

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

Essa pesquisa usa SearchClient. SearchClient e SearchOptions.

O TP cria uma pesquisa híbrida:

Crie um SearchHybrid.java arquivo no src/main/java/com/example/search diretório.

Copie o código a seguir no arquivo.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Compile e execute o arquivo.

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

A saída desse código mostra os documentos relevantes para a pesquisa híbrida.

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

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

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

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

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

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

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

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

Criar uma pesquisa híbrida semântica

Esta é a última consulta da coleção para estender a pesquisa híbrida semântica com o texto de pesquisa adicional historic hotel walk to restaurants and shopping.

Essa pesquisa usa SearchClient. SearchClient e SearchOptions.

Para criar uma pesquisa híbrida semântica:

Crie um SearchSemanticHybrid.java arquivo no src/main/java/com/example/search diretório.

Copie o código a seguir no arquivo.

package com.example.search;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Compile e execute o arquivo.

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

A saída desse código mostra os documentos relevantes para a pesquisa híbrida semântica.

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

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

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

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

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

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

Principais conclusões:

A busca em vetores é especificada por meio da classe VectorSearchOptions. A pesquisa de palavra-chave é especificada por meio da classe SemanticSearchOptions.
Em uma pesquisa híbrida, é possível integrar a busca em vetores com a pesquisa de texto completo por palavras-chave. Filtros, verificação ortográfica e classificação semântica se aplicam apenas a conteúdo textual e não a vetores. Nessa consulta final, não existe a semântica answer porque o sistema não produziu uma semântica suficientemente forte.
Os resultados reais incluem mais detalhes, inclusive legendas semânticas e destaques. Os resultados foram modificados para facilitar a leitura. Para obter a estrutura completa da resposta, execute a solicitação no cliente REST.

Limpar os recursos

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

Como alternativa, para excluir o índice de vetor que você criou neste início rápido programaticamente:

Crie um DeleteIndex.java arquivo no src/main/java/com/example/search diretório.

Adicione o código a seguir ao arquivo.

package com.example.search;

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

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

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

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

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

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

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

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

        System.exit(0);
    }
}

Compile e execute o arquivo.

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

Próximas etapas

Examine o repositório de exemplos de código para recursos de pesquisa de vetor no Azure AI Search para Java.

Neste início rápido, você usará JavaScript para criar, carregar e consultar um índice de vetor. O código executa essas operações usando a biblioteca cliente do Azure AI Search para JavaScript, que fornece uma abstração sobre as APIs REST para acessar operações de índice.

Observação

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search.
- Você pode usar a camada Gratuita durante a maior parte deste início rápido, mas recomendamos Basic ou superior para arquivos de dados maiores.
- Para autenticação sem chave com a ID do Microsoft Entra, atribua a função Colaborador de Dados do Índice de Pesquisa à sua conta de usuário.
- Para executar a consulta híbrida semântica, você deve habilitar o classificador semântico.
Visual Studio Code.
Node.JS com LTS.

Obter pontos de extremidade de serviço

Para obter o endpoint de serviço:

Entre no portal do Azure.
Encontre o seu serviço de pesquisa.
Na página inicial Visão Geral, copie a URL. Um ponto de extremidade de exemplo pode se parecer com https://example.search.windows.net.

Configurar o projeto de Node.JS

Inicie o Visual Studio Code em um novo diretório.

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

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.

Instale os pacotes a seguir npm .

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

Crie um src diretório no diretório do projeto.
```
mkdir src
```

Configurar variáveis de ambiente para desenvolvimento local

Crie um .env arquivo no diretório vector-quickstart do projeto.
Adicione as seguintes variáveis de ambiente ao arquivo .env, substituindo os valores por seus próprios pontos de extremidade de serviço e chaves.
```
AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
```

Execute cada um dos comandos a seguir na 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 em seu dispositivo local.

Criar um índice de vetor

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

Para criar um índice de vetor:

Crie um createIndex.js arquivo no src diretório.

Copie o código a seguir no 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 a parte superior do arquivo. Adicione a createIndex função para criar o índice. A função define o esquema de índice, incluindo o campo DescriptionVectorde vetor.

Execute o arquivo.
```
node -r dotenv/config src/createIndex.js
```
A saída desse código mostra que o índice foi criado com êxito.
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Creating index...
hotels-vector-quickstart created
```
Principais conclusões:
- Você define um índice criando uma lista de campos.
- Esse índice específico dá suporte a vários recursos de pesquisa, como:
  - Pesquisa de palavra-chave de texto completo (searchable)
  - Pesquisa de vetor (habilita a pesquisa híbrida agrupando campos vetoriais e não vetoriais) (DescriptionVector com vectorSearchProfileName)
  - Pesquisa semântica
  - Pesquisa facetada (searchSuggester)
  - Pesquisa geoespacial (Location campo com geo.distance)
  - Filtragem, ordenação (muitos campos marcados como filtráveis e classificáveis)

Carregar documentos no índice

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

Para carregar documentos no índice:

Crie um arquivo uploadDocuments.js no diretório src.

Copie o código a seguir no 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": [
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            ]
        }
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        "HotelId": "3",
        "HotelName": "Gastronomic Landscape Hotel",
        "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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        "Category": "Suite",
        "Tags": [
            "restaurant",
            "bar",
            "continental breakfast"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2015-09-20T00:00:00Z",
        "Rating": 4.80,
        "Address": {
            "StreetAddress": "3393 Peachtree Rd",
            "City": "Atlanta",
            "StateProvince": "GA",
            "PostalCode": "30326",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                33.847848,
                -84.363602
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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        "Category": "Boutique",
        "Tags": [
            "concierge",
            "view",
            "air conditioning"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2020-02-06T00:00:00Z",
        "Rating": 4.60,
        "Address": {
            "StreetAddress": "7400 San Pedro Ave",
            "City": "San Antonio",
            "StateProvince": "TX",
            "PostalCode": "78216",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                -98.498923,
                29.518029
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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        "Category": "Boutique",
        "Tags": [
            "continental breakfast",
            "air conditioning",
            "free wifi"
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        "ParkingIncluded": "false",
        "LastRenovationDate": "2018-02-19T00:00:00Z",
        "Rating": 4.5,
        "Address": {
            "StreetAddress": "1401 I St NW",
            "City": "Washington D.C.",
            "StateProvince": "null",
            "PostalCode": "20005",
            "Country": "USA"
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            "properties": {
                "name": "EPSG:4326"
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        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.",
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        "Category": "Suite",
        "Tags": [
            "air conditioning",
            "laundry service",
            "24-hour front desk service"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2018-01-27T00:00:00Z",
        "Rating": 2.7,
        "Address": {
            "StreetAddress": "1100 S Hayes St",
            "City": "Arlington",
            "StateProvince": "VA",
            "PostalCode": "22202",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -77.060066,
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            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
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        }
    },
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        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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        "Category": "Luxury",
        "Tags": [
            "bar",
            "concierge",
            "restaurant"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2020-03-18T00:00:00Z",
        "Rating": 4.1,
        "Address": {
            "StreetAddress": "3 Cityplace Dr",
            "City": "St. Louis",
            "StateProvince": "MO",
            "PostalCode": "63141",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -90.44081,
            38.67219
            ],
            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
                }
            }
        }
    }
];

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

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

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

Esse código carrega uma matriz de objetos JSON. Cada documento inclui a versão vetorizada do artigo.description Esse vetor permite a pesquisa de similaridade, em que a correspondência é baseada no significado em vez de 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 ser totalmente preenchido com documentos antes que você possa executar pesquisas.

Compile e execute o arquivo.
```
node -r dotenv/config src/uploadDocuments.js
```
A saída desse código mostra que os documentos estão indexados e prontos para pesquisa.
```
Using Azure Search endpoint: https://<search-service-name>.search.windows.net
Using Azure Search index: <vector-index-name>
Uploading documents...
Key: 1, Succeeded: true, ErrorMessage: none
Key: 2, Succeeded: true, ErrorMessage: none
Key: 3, Succeeded: true, ErrorMessage: none
Key: 4, Succeeded: true, ErrorMessage: none
Key: 48, Succeeded: true, ErrorMessage: none
Key: 49, Succeeded: true, ErrorMessage: none
Key: 13, Succeeded: true, ErrorMessage: none
Waiting for indexing... Current count: 0
All documents indexed successfully.
```
Principais conclusões:
- Seu código interage com um índice de pesquisa específico hospedado em seu serviço do Azure AI Search por meio do SearchClient, que é o objeto principal fornecido pelo @azure/search-documents pacote. Ele SearchClient fornece acesso a operações de índice, como:
  - Ingestão de dados: uploadDocuments, , mergeDocumentse deleteDocumentsassim por diante.
  - Operações de pesquisa: search, autocomplete, suggest
  - Operações de gerenciamento de índice: createIndex, deleteIndex, getIndexe assim por diante.
- Os campos de vetor contêm valores de ponto flutuante. O atributo de dimensões tem um mínimo de 2 e um máximo de 4.096 valores de ponto flutuante cada. Este início rápido define o atributo de dimensões como 1.536 porque esse é o tamanho das inserções geradas pelo text-embedding-ada-002 modelo.

Criar a consulta como um vetor

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

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

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 ranking
// Replace with your own vector

export const vector = 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Esse código é usado nas seções a seguir para executar pesquisas de vetor. O vetor de consulta é criado usando um modelo de inserção do Azure OpenAI.

Criar uma única pesquisa de vetor

Para criar uma única pesquisa de vetor:

Crie um searchSingle.js arquivo no src diretório.

Copie o código a seguir no 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 da consulta, vectorfields determina quais campos de vetor são pesquisados e kNearestNeighborsCount especifica o número de vizinhos mais próximos a serem retornados.

A cadeia de consulta de vetor é quintessential lodging near running trails, eateries, retail, que é vetorizada em 1.536 inserções para essa consulta.

Compile e execute o arquivo.

node -r dotenv/config src/searchSingle.js

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

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


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

A resposta para o vetor equivalente a quintessential lodging near running trails, eateries, retail consiste em 5 resultados, com apenas os campos especificados pelo campo selecionado retornados.

Criar uma única pesquisa de vetor com um filtro

Para criar uma única pesquisa de vetor com um filtro:

Crie um searchSingleWithFilter.js arquivo no src diretório.

Copie o código a seguir no 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 as mesmas funcionalidades de busca da pesquisa anterior, com um filtro de exclusão adicionado no pós-processamento para hotéis com free wifi.

Compile e execute o arquivo.

node -r dotenv/config src/searchSingleWithFilter.js

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

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


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

Criar uma pesquisa com um filtro geoespacial

Para criar uma pesquisa com um filtro geoespacial:

Crie um searchSingleWithFilterGeo.js arquivo no src diretório.

Copie o código a seguir no 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;
}

Compile e execute o arquivo.

node -r dotenv/config src/searchSingleWithFilterGeo.js

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

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


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

Criar uma pesquisa híbrida

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

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

Para criar uma pesquisa híbrida:

Crie um searchHybrid.js arquivo no src diretório.

Copie o código a seguir no 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;
}

Compile e execute o arquivo.

node -r dotenv/config src/searchHybrid.js

A saída desse código mostra os documentos relevantes para a pesquisa híbrida.

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


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

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

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

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

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

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

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

Criar uma pesquisa híbrida semântica

Esta é a última consulta da coleção para estender a pesquisa híbrida semântica com o texto de pesquisa adicional historic hotel walk to restaurants and shopping.

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

Para criar uma pesquisa híbrida semântica:

Crie um searchSemanticHybrid.js arquivo no src diretório.

Copie o código a seguir no 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 ranking
    const searchText = "historic hotel walk to restaurants and shopping";
    const results = await searchClient.search(searchText, searchOptions);

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

    for await (const result of results.results) {

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

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

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

Compile e execute o arquivo.

node -r dotenv/config src/searchSemanticHybrid.js

A saída desse código mostra os documentos relevantes para a pesquisa híbrida semântica.

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


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

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

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

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

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

Principais conclusões:

A busca em vetores é especificada por meio da propriedade vectorSearchOptions. A pesquisa por palavra-chave é especificada por meio da propriedade semanticSearchOptions.
Em uma pesquisa híbrida, é possível integrar a busca em vetores com a pesquisa de texto completo por palavras-chave. Filtros, verificação ortográfica e classificação semântica se aplicam apenas a conteúdo textual e não a vetores. Nessa consulta final, não existe a semântica answer porque o sistema não produziu uma semântica suficientemente forte.
Os resultados reais incluem mais detalhes, inclusive legendas semânticas e destaques. Os resultados foram modificados para facilitar a leitura. Para obter a estrutura completa da resposta, execute a solicitação no cliente REST.

Limpar os recursos

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

Como alternativa, para excluir o índice de vetor que você criou neste início rápido programaticamente:

Crie um deleteIndex.js arquivo no src diretório.

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);
}

Compile e execute o arquivo.

node -r dotenv/config src/deleteIndex.js

Próximas etapas

Examine o repositório de exemplos de código para recursos de pesquisa de vetor no Azure AI Search para JavaScript.

Neste início rápido, você usará TypeScript para criar, carregar e consultar um índice de vetor. O código executa essas operações usando a biblioteca cliente do Azure AI Search para TypeScript, que fornece uma abstração sobre as APIs REST para acessar operações nos índices.

Observação

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search.
- Você pode usar a camada Gratuita durante a maior parte deste início rápido, mas recomendamos Basic ou superior para arquivos de dados maiores.
- Para autenticação sem chave com a ID do Microsoft Entra, atribua a função Colaborador de Dados do Índice de Pesquisa à sua conta de usuário.
- Para executar a consulta híbrida semântica, você deve habilitar o classificador semântico.
Visual Studio Code.
Node.JS com LTS.
TypeScript. Você pode instalar o TypeScript globalmente usando o npm:
```
npm install -g typescript
```

Obter informações de serviço

Para obter o endpoint de serviço:

Entre no portal do Azure.
Encontre o seu serviço de pesquisa.
Na página inicial Visão Geral, copie a URL. Um ponto de extremidade de exemplo pode se parecer com https://example.search.windows.net.

Configurar o projeto de Node.JS

Inicie o Visual Studio Code em um novo diretório.

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

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.
Edite package.json para incluir um script de build. Adicione a seguinte linha à seção scripts.
```
"build": "tsc"
```

Instale os seguintes pacotes npm.

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

Crie um src diretório no diretório do projeto.
```
mkdir src
```

Crie um tsconfig.json arquivo no diretório do projeto para ESM com o conteúdo a seguir.

{
  "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

Crie um .env arquivo no diretório vector-quickstart do projeto.
Adicione as seguintes variáveis de ambiente ao arquivo .env, substituindo os valores por seus próprios pontos de extremidade de serviço e chaves.
```
AZURE_SEARCH_ENDPOINT=<YOUR AZURE AI SEARCH ENDPOINT>
AZURE_SEARCH_INDEX_NAME=hotels-vector-quickstart
```

Execute cada um dos comandos a seguir na 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 em seu dispositivo local.

Criar um índice de vetor

Nesta seção, você criará um índice de vetor no Azure AI Search com SearchIndexClient. createOrUpdateIndex. O esquema de índice define os campos, incluindo o campo DescriptionVectorde vetor. Depois que o índice for criado, você carregará documentos no índice. Os documentos contêm a versão vetorizada da descrição do artigo, que permite a pesquisa de similaridade com base no significado em vez de palavras-chave exatas.

Para criar o índice de vetor:

Crie um createIndex.ts arquivo no src diretório.

Copie o código a seguir no 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`);

Compile e execute o arquivo.
```
npm run build && node -r dotenv/config dist/createIndex.js
```
A saída desse código mostra que o índice foi 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:
- Você define um índice criando uma lista de campos.
- Esse índice específico dá suporte a vários recursos de pesquisa, como:
  - Pesquisa de palavra-chave de texto completo (searchable)
  - Pesquisa de vetor (habilita a pesquisa híbrida agrupando campos vetoriais e não vetoriais) (DescriptionVector com vectorSearchProfileName)
  - Pesquisa semântica
  - Pesquisa facetada (searchSuggester)
  - Pesquisa geoespacial (Location campo com geo.distance)
  - Filtragem, ordenação (muitos campos marcados como filtráveis e classificáveis)

Carregar documentos no índice

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

Para carregar documentos no índice:

Crie um uploadDocuments.ts arquivo no src diretório.

Copie o código a seguir no 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": [
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            ]
        }
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        "HotelId": "3",
        "HotelName": "Gastronomic Landscape Hotel",
        "Description": "The Gastronomic Hotel stands out for its culinary excellence under the management of William Dough, who advises on and oversees all of the Hotel’s restaurant services.",
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        "Category": "Suite",
        "Tags": [
            "restaurant",
            "bar",
            "continental breakfast"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2015-09-20T00:00:00Z",
        "Rating": 4.80,
        "Address": {
            "StreetAddress": "3393 Peachtree Rd",
            "City": "Atlanta",
            "StateProvince": "GA",
            "PostalCode": "30326",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                33.847848,
                -84.363602
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "4",
        "HotelName": "Sublime Palace Hotel",
        "Description": "Sublime Palace Hotel is located in the heart of the historic center of Sublime in an extremely vibrant and lively area within short walking distance to the sites and landmarks of the city and is surrounded by the extraordinary beauty of churches, buildings, shops and monuments. Sublime Cliff is part of a lovingly restored 19th century resort, updated for every modern convenience.",
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        "Category": "Boutique",
        "Tags": [
            "concierge",
            "view",
            "air conditioning"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2020-02-06T00:00:00Z",
        "Rating": 4.60,
        "Address": {
            "StreetAddress": "7400 San Pedro Ave",
            "City": "San Antonio",
            "StateProvince": "TX",
            "PostalCode": "78216",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
                -98.498923,
                29.518029
            ]
        }
    },
    {
        "@search.action": "mergeOrUpload",
        "HotelId": "48",
        "HotelName": "Nordick's Valley Motel",
        "Description": "Only 90 miles (about 2 hours) from the nation's capital and nearby most everything the historic valley has to offer. Hiking? Wine Tasting? Exploring the caverns? It's all nearby and we have specially priced packages to help make our B&B your home base for fun while visiting the valley.",
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        "Category": "Boutique",
        "Tags": [
            "continental breakfast",
            "air conditioning",
            "free wifi"
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        "ParkingIncluded": "false",
        "LastRenovationDate": "2018-02-19T00:00:00Z",
        "Rating": 4.5,
        "Address": {
            "StreetAddress": "1401 I St NW",
            "City": "Washington D.C.",
            "StateProvince": "null",
            "PostalCode": "20005",
            "Country": "USA"
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            "properties": {
                "name": "EPSG:4326"
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        "HotelId": "49",
        "HotelName": "Swirling Currents Hotel",
        "Description": "Spacious rooms, glamorous suites and residences, rooftop pool, walking access to shopping, dining, entertainment and the city center. Each room comes equipped with a microwave, a coffee maker and a minifridge. In-room entertainment includes complimentary W-Fi and flat-screen TVs.",
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        "Category": "Suite",
        "Tags": [
            "air conditioning",
            "laundry service",
            "24-hour front desk service"
        ],
        "ParkingIncluded": "true",
        "LastRenovationDate": "2018-01-27T00:00:00Z",
        "Rating": 2.7,
        "Address": {
            "StreetAddress": "1100 S Hayes St",
            "City": "Arlington",
            "StateProvince": "VA",
            "PostalCode": "22202",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -77.060066,
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            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
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        }
    },
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        "HotelId": "13",
        "HotelName": "Luxury Lion Resort",
        "Description": "Unmatched Luxury. Visit our downtown hotel to indulge in luxury accommodations. Moments from the stadium and transportation hubs, we feature the best in convenience and comfort.",
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        "Category": "Luxury",
        "Tags": [
            "bar",
            "concierge",
            "restaurant"
        ],
        "ParkingIncluded": "false",
        "LastRenovationDate": "2020-03-18T00:00:00Z",
        "Rating": 4.1,
        "Address": {
            "StreetAddress": "3 Cityplace Dr",
            "City": "St. Louis",
            "StateProvince": "MO",
            "PostalCode": "63141",
            "Country": "USA"
        },
        "Location": {
            "type": "Point",
            "coordinates": [
            -90.44081,
            38.67219
            ],
            "crs": {
            "type": "name",
            "properties": {
                "name": "EPSG:4326"
                }
            }
        }
    }
];

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

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

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

Esse código carrega documentos variáveis com um objeto JSON que descreve cada documento, juntamente com a versão vetorizada da descrição do artigo. Esse vetor permite a pesquisa de similaridade, em que a correspondência é baseada no significado em vez de palavras-chave exatas.

Compile e execute o arquivo.
```
npm run build && node -r dotenv/config dist/uploadDocuments.js
```
A saída desse 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:
- Seu código interage com um índice de pesquisa específico hospedado em seu serviço do Azure AI Search por meio do SearchClient, que é o objeto principal fornecido pelo @azure/search-documents pacote. Ele SearchClient fornece acesso a operações de índice, como:
  - Ingestão de dados: uploadDocuments, , mergeDocumentse deleteDocumentsassim por diante.
  - Operações de pesquisa: search, autocomplete, suggest
  - Operações de gerenciamento de índice: createIndex, deleteIndex, getIndexe assim por diante.
- Os campos de vetor contêm valores de ponto flutuante. O atributo de dimensões tem um mínimo de 2 e um máximo de 4.096 valores de ponto flutuante cada. Este início rápido define o atributo de dimensões como 1.536 porque esse é o tamanho das inserções geradas pelo text-embedding-ada-002 modelo.

Criar a consulta como um vetor

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

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

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 ranking
// Replace with your own vector

export const vector = 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Esse código é usado nas seções a seguir para executar pesquisas de vetor. O vetor de consulta é criado usando um modelo de inserção do Azure OpenAI.

Criar uma única pesquisa de vetor

Para criar uma única pesquisa de vetor:

Crie um searchSingle.ts arquivo no src diretório.

Copie o código a seguir no 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;
}

A cadeia de consulta de vetor é quintessential lodging near running trails, eateries, retail, que é vetorizada em 1.536 inserções para essa consulta.

Compile e execute o arquivo.

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

A saída desse 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 vetor equivalente a quintessential lodging near running trails, eateries, retail consiste em 5 resultados, com apenas os campos especificados pelo campo selecionado retornados.

Criar uma única pesquisa de vetor com um filtro

Para criar uma única pesquisa de vetor com um filtro:

Crie um searchSingleWithFilter.ts arquivo no src diretório.

Copie o código a seguir no 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;
}

Compile e execute o arquivo.

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

A saída desse 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

Para criar uma pesquisa com um filtro geoespacial:

Crie um searchSingleWithFilterGeo.ts arquivo no src diretório.

Copie o código a seguir no 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;
}

Compile e execute o arquivo.

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

A saída desse código mostra os documentos relevantes para a consulta com o filtro de exclusão 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

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

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

Para criar uma pesquisa híbrida:

Crie um searchHybrid.ts arquivo no src diretório.

Copie o código a seguir no 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;
}

Compile e execute o arquivo.

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

A saída desse 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"]

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

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

Criar uma pesquisa híbrida semântica

Esta é a última consulta da coleção para estender a pesquisa híbrida semântica com o texto de pesquisa adicional historic hotel walk to restaurants and shopping.

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

Para criar uma pesquisa híbrida semântica:

Crie um searchSemanticHybrid.ts arquivo no src diretório.

Copie o código a seguir no 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 ranking
    const searchText = "historic hotel walk to restaurants and shopping";
    const results: SearchDocumentsResult<HotelDocument> = await searchClient.search(searchText, searchOptions);

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

    for await (const result of results.results) {

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

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

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

Compile e execute o arquivo.

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

A saída desse 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

Principais conclusões:

A busca em vetores é especificada por meio da propriedade vectorSearchOptions. A pesquisa por palavra-chave é especificada por meio da propriedade semanticSearchOptions.
Em uma pesquisa híbrida, é possível integrar a busca em vetores com a pesquisa de texto completo por palavras-chave. Filtros, verificação ortográfica e classificação semântica se aplicam apenas a conteúdo textual e não a vetores. Nessa consulta final, não existe a semântica answer porque o sistema não produziu uma semântica suficientemente forte.
Os resultados reais incluem mais detalhes, inclusive legendas semânticas e destaques. Os resultados foram modificados para facilitar a leitura. Para obter a estrutura completa da resposta, execute a solicitação no cliente REST.

Limpar os recursos

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

Como alternativa, para excluir o índice de vetor que você criou neste início rápido programaticamente:

Crie um deleteIndex.ts arquivo no src diretório.

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);
}

Compile e execute o arquivo.

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

Próximas etapas

Examine o repositório de exemplos de código para recursos de pesquisa de vetor no Azure AI Search para JavaScript.

Neste início rápido, você usará um aplicativo .NET para criar, carregar e consultar um índice de vetor. O código executa essas operações usando a biblioteca cliente do Azure AI Search para .NET, que fornece uma abstração sobre as APIs REST para acessar operações em índices.

Observação

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search.
- Você pode usar a camada Gratuita durante a maior parte deste início rápido, mas recomendamos Basic ou superior para arquivos de dados maiores.
- Para autenticação sem chave com a ID do Microsoft Entra, atribua a função Colaborador de Dados do Índice de Pesquisa à sua conta de usuário.
- Para executar a consulta híbrida semântica, você deve habilitar o classificador semântico.
Visual Studio Code ou Visual Studio para executar o código.
Git para clonar o repositório de exemplo.

Obter informações de serviço

Este início rápido usa DefaultAzureCredential, o 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. Para entender todas as opções, consulte Autenticar aplicativos .NET nos serviços do Azure usando o SDK do Azure para .NET.

Clonar o código e o ambiente de instalação

Clone o repositório que contém o código deste início rápido.

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

Abra a quickstart-vector-search pasta no Visual Studio Code ou clique duas vezes no VectorSearchQuickstart.sln arquivo para abrir a solução no Visual Studio.
Abra appsettings.json em ambas as pastas VectorSearchCreatePopulateIndex e VectorSearchExamples.
Defina Endpoint como a URL do serviço de pesquisa, que deve ser semelhante a https://mydemo.search.windows.net.
Defina IndexName como um nome exclusivo para o índice. Você também pode usar o nome padrão hotels-vector-quickstart .

Criar um índice de vetor e carregar documentos

Para executar consultas de pesquisa em seu serviço do Azure AI Search, primeiro você deve criar um índice de pesquisa e carregar documentos no índice.

Para criar e preencher o índice:

Abra um terminal na VectorSearchCreatePopulateIndex pasta.

Use o dotnet run comando para executar o projeto.

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 faz o upload de documentos formatados em JSON no arquivo hotel-samples.json para o seu serviço de pesquisa.

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 que você executar o projeto, a seguinte saída será impressa.

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

Principais conclusões:

Seu código interage com um índice de pesquisa específico hospedado em seu serviço de Pesquisa de IA do Azure por meio do SearchClient, que é o objeto principal fornecido pelo pacote azure-search-documents. O SearchClient fornece acesso a operações de índice, como:
- Ingestão de dados: UploadDocuments(), , MergeDocuments()DeleteDocuments()
- Operações de pesquisa: Search(), Autocomplete(), Suggest()
- Operações de gerenciamento de índice: CreateOrUpdateIndex()
Os campos de vetor contêm valores de ponto flutuante. O atributo de dimensões tem um mínimo de 2 e um máximo de 4.096 valores de ponto flutuante cada. Este início rápido define o atributo de dimensões como 1.536 porque esse é o tamanho das inserções geradas pelo text-embedding-ada-002 modelo.

Executar consultas

Para emitir as consultas de vetor nesta seção, abra Program.cs na VectorSearchExamples pasta e abra um terminal na VectorSearchExamples pasta.

Consultas nesta seção:

Busca em vetores única
Busca em vetores única com filtro
Pesquisa híbrida
Pesquisa semântica híbrida com filtro

Busca em vetores única

A primeira consulta demonstra um cenário básico em que você deseja encontrar descrições de documento que correspondam de perto à cadeia de caracteres de pesquisa.

Para criar uma única pesquisa de vetor:

No arquivo Program.cs da pasta VectorSearchExamples, remova a marca de comentário da chamada do método SearchExamples.SearchSingleVector(searchClient, vectorizedResult);.

Esse 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();
}

Execute dotnet run para executar a consulta, que retorna os seguintes resultados.

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

Busca em vetores única com filtro

No Azure AI Search, os filtros se aplicam a campos não vetores em um índice. Este exemplo filtra no campo Tags para remover quaisquer hotéis que não oferecem Wi-Fi gratuito.

Para criar uma única pesquisa de vetor com um filtro:

No arquivo Program.cs da pasta VectorSearchExamples, remova a marca de comentário da chamada do método SearchExamples.SearchSingleVectorWithFilter(searchClient, vectorizedResult);.

Esse 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();
}

Execute dotnet run para executar a consulta, que retorna apenas hotéis que fornecem Wi-Fi gratuito.

 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

Para um filtro geográfico, descompacte SearchExamples.SingleSearchWithGeoFilter(searchClient, vectorizedResult); em Program.cs.

Esse 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();
}

Execute dotnet run para executar a consulta, que retorna apenas hotéis dentro de 300 quilômetros.

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 combina consultas de palavra-chave e vetor em uma solicitação. Este exemplo executa as seguintes cadeias de caracteres de consulta de vetor e texto completo simultaneamente:

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

Para criar uma pesquisa híbrida:

No arquivo Program.cs, remova a marca de comentário da chamada do método SearchExamples.SearchHybridVectorAndText(searchClient, vectorizedResult);.

Esse 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;
}

Execute dotnet run para executar a consulta, que retorna os seguintes resultados.

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

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

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

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

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

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

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

Pesquisa híbrida semântica com um filtro

Adicione classificação semântica para reclassificar os resultados com base na compreensão da linguagem.

Para criar uma pesquisa híbrida semântica com um filtro:

No arquivo Program.cs, descomente a chamada do método SearchExamples.SearchHybridVectorAndSemantic(searchClient, vectorizedResult);.

Esse 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();
}

Execute dotnet run para executar a consulta, que retorna os seguintes hotéis. Os hotéis são filtrados por localização, facetados por StateProvincee semanticamente reclassificados para promover resultados mais próximos da consulta de cadeia de caracteres de pesquisa (historic hotel walk to restaurants and shopping).

O Hotel Swirling Currents agora se move para o primeiro lugar. Sem a classificação semântica, o Nordick's Valley Motel é o número um. Com a classificação semântica, os modelos de compreensão de máquina reconhecem que historic se aplica ao "hotel a uma curta distância a pé de locais para jantar (restaurantes) e compras".

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

Principais conclusões:

Em uma pesquisa híbrida, é possível integrar a busca em vetores com a pesquisa de texto completo por palavras-chave. Filtros, verificação ortográfica e classificação semântica se aplicam apenas a conteúdo textual e não a vetores. Nessa consulta final, não existe a semântica answer porque o sistema não produziu uma semântica suficientemente forte.
Os resultados reais incluem mais detalhes, inclusive legendas semânticas e destaques. Os resultados foram modificados para facilitar a leitura. Para obter a estrutura completa da resposta, execute a solicitação no cliente REST.

Limpar os recursos

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

Próximas etapas

Examine o repositório de exemplos de código para recursos de pesquisa de vetor no Azure AI Search para .NET.
Examine os outros exemplos de código do .NET e do Azure AI Search no repositório azure-search-dotnet-samples.

Neste início rápido, você usará as APIs REST do Azure AI Search para criar, carregar e consultar um índice de vetor.

Observação

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search.
- Você pode usar a camada Gratuita durante a maior parte deste início rápido, mas recomendamos Basic ou superior para arquivos de dados maiores.
- Para executar a consulta híbrida semântica, você deve habilitar o classificador semântico.
Visual Studio Code com um cliente REST.

Obter informações de serviço

As solicitações para o ponto de extremidade de pesquisa precisam ser autenticadas e autorizadas. Use funções ou chaves de API para essa tarefa. Recomendamos usar uma conexão sem chave por meio do Microsoft Entra ID.

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

Microsoft Entra ID
Chave de API

Entre no portal do Azure e localize seu serviço de pesquisa.
Na página inicial Visão geral, encontre a URL. Um ponto de extremidade de exemplo pode se parecer com https://mydemo.search.windows.net.
Siga as etapas descritas no início rápido sem chave para obter o token do Microsoft Entra.

Você obtém o token quando executa o comando az account get-access-token na etapa 3 do início rápido anterior.
```
az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
```

Entre no portal do Azure e localize seu serviço de pesquisa.
Na página inicial Visão geral, encontre a URL. Um ponto de extremidade de exemplo pode se parecer com https://mydemo.search.windows.net.
Selecione Configurações>Chaves. Uma das chaves de API ou Ambas precisam estar habilitadas. As chaves de API de administrador são usadas para adicionar, modificar e excluir objetos. Há duas chaves de administrador intercambiáveis. Copie uma delas.

Criar ou baixar o arquivo de código

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

Para criar o arquivo de código:

No Visual Studio Code, crie um arquivo com uma .rest ou .http extensão de arquivo, como az-search-vector-quickstart.rest.
Copie e cole o conteúdo bruto do arquivo az-search-vector-quickstart.rest no novo arquivo.
```
@baseUrl = PUT-YOUR-SEARCH-SERVICE-URL-HERE
```
Defina @baseUrl para o ponto de extremidade obtido em Obter informações de serviço.
Defina a chave ou o token para autenticação. Você obteve esse valor em Obter informações de serviço.
- Para a autenticação sem chave recomendada com o Microsoft Entra ID, substitua @apiKey pela variável @token.
```
@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
```
Para a autenticação sem chave recomendada com a ID do Microsoft Entra, substitua todas as instâncias de api-key: {{apiKey}} por Authorization: Bearer {{token}} nos cabeçalhos de solicitação.

Criar um índice de vetor

Você usa Índices – Criar (API REST) para criar um índice de vetor 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 exemplo consistem em descrições vetoriais e não vetoriais de hotéis fictícios. Esse esquema inclui configurações para indexação de vetor e consultas e para classificação semântica.

Para criar um índice de vetor:

No Visual Studio Code, abra o arquivo az-search-vector-quickstart.restcriado anteriormente.

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

### Create a new index
POST  {{baseUrl}}/indexes?api-version=2025-09-01  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

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

Selecione Enviar solicitação. Você terá uma resposta HTTP/1.1 201 Created.

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:

A coleção fields inclui um campo de chave obrigatório e campos de texto e vetor (como Description e DescriptionVector) para busca em vetores e texto. A colocação de campos vetoriais e não vetoriais no mesmo índice permite a realização de consultas híbridas. Por exemplo, você pode combinar filtros, pesquisa de texto com classificação semântica e vetores em uma só operação de consulta.
Os campos de vetor devem ser um dos tipos de dados EDM usados para vetores, como type: Collection(Edm.Single). Campos de vetor também têm dimensions e vectorSearchProfile propriedades.
A seção vectorSearch é uma matriz de perfis e configurações do algoritmo de ANN (vizinho mais próximo aproximado). Os algoritmos aceitos incluem o HNSW (pequeno mundo hierárquico navegável) e o KNN (k vizinhos mais próximos) completo. Para obter mais informações, veja Pontuação de relevância na busca em vetores.
A configuração semantic (opcional) permite reclassificar dos resultados da pesquisa. Você pode reclassificar os resultados em consultas do tipo semantic para os campos de cadeia de caracteres especificados na configuração. Para saber mais, veja Visão geral da classificação semântica.

Carregar documentos no índice

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

Na Pesquisa de IA do Azure, o índice contém todos os dados pesquisáveis e as consultas executadas no serviço de pesquisa. Para chamadas REST, os dados são fornecidos como documentos JSON. Use Documentos – Índice (API REST) para esta tarefa. O URI é estendido para incluir a coleção docs e a operação index.

Para carregar documentos no índice:

Formule uma solicitação de upload de documentos para enviar documentos ao índice hotels-vector-quickstart no seu serviço de pesquisa.

### Upload documents
POST {{baseUrl}}/indexes/hotels-quickstart-vectors/docs/index?api-version=2025-09-01  HTTP/1.1
Content-Type: application/json
Authorization: Bearer {{token}}

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

Selecione Enviar solicitação. Você terá uma resposta HTTP/1.1 200 OK. O corpo da resposta deve incluir a representação JSON dos documentos de pesquisa.

Principais conclusões:
- Os documentos na carga consistem em campos definidos no esquema de índice.
- Os campos de vetor contêm valores de ponto flutuante. O atributo de dimensões tem um mínimo de 2 e um máximo de 4.096 valores de ponto flutuante cada. Este início rápido define o atributo de dimensões como 1.536 porque esse é o tamanho das inserções geradas pelo text-embedding-ada-002 modelo.

Executar consultas

Agora que os documentos são carregados, você pode executar consultas de vetor em relação a eles usando Documentos – Postagem de Pesquisa (API REST).

Consultas nesta seção:

Busca em vetores única
Busca em vetores única com filtro
Pesquisa híbrida
Pesquisa híbrida semântica

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

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

A cadeia de consulta de vetor é semanticamente semelhante à cadeia de caracteres de pesquisa, mas inclui termos que não existem no índice de pesquisa. Se você fizer uma pesquisa por palavra-chave 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.

Busca em vetores única

Para criar uma única pesquisa de vetor:

Formular a solicitação. A consulta é uma representação de ponto flutuante 1536 de hospedagem ideal perto de trilhas para corrida, restaurantes, lojas. A consulta está pesquisando DescriptionVector e retornando resultados k-5. Ele está usando o parâmetro de substituição "completo" para executar uma verificação completa do índice, em vez de um ANN. Uma pesquisa exaustiva é útil para índices pequenos.

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

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

Principais pontos sobre Documentos - Post de Pesquisa (API REST):

A cadeia de caracteres de consulta de vetor é vectorQueries.vector. É uma representação vetorial de uma hospedagem típica próxima a trilhas para corrida, restaurantes e lojas, que é vetorizada em 1.536 inserções para essa consulta.
fields determina quais campos de vetor são pesquisados.
kind definido como vector significa que a cadeia de caracteres de consulta é um vetor. Se kind estiver definido como text, você precisaria de capacidade extra (um vetor) para codificar uma cadeia de caracteres de texto legível humana em um vetor no momento da consulta. Os vetorizadores são omitidos deste início rápido para manter o exercício simples.
k especifica o número de correspondências a serem retornadas na resposta. Um count parâmetro especifica o número de correspondências encontradas no índice. Incluir a contagem é uma prática recomendada para consultas, mas é menos útil para a pesquisa de similaridade em que o algoritmo pode encontrar algum grau de similaridade em quase qualquer documento.

Selecione Enviar solicitação. Você terá uma resposta HTTP/1.1 200 OK. 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 mecanismo de pesquisa tenha encontrado 7 correspondências. Os principais resultados são considerados os mais semanticamente 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 do 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"
      ]
    }
  ]
}

Busca em vetores única com filtro

Para criar uma única pesquisa de vetor com um filtro:

Formular a solicitação. Essa é a mesma solicitação que a anterior, com um filtro extra e um parâmetro de modo de filtro.

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

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

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

A consulta era a mesma que o exemplo de pesquisa de vetor único anterior, mas inclui um filtro de exclusão 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"
      ]
    }
  ]
}

Aqui está outra consulta que usa um filtro geográfico, limitando os resultados a hotéis dentro de 300 quilômetros ao redor de Washington D.C.

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

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

Analise a resposta. Está limitado a dois hotéis próximos a Washington D.C., com uma estrutura de facetas que fornece 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 palavra-chave e consultas vetoriais em uma só solicitação de pesquisa. Este exemplo executa simultaneamente a busca em vetores e a pesquisa de texto completo:

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

Para criar uma pesquisa híbrida:

Formular a consulta híbrida.

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

{
    "count": true,
    "search": "historic hotel walk to restaurants and shopping",
    "select": "HotelName, Description",
    "top": 7,
    "vectorQueries": [
        {
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}

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

Por ser uma consulta híbrida, os resultados são classificados por RRF (Fusão de Reciprocidade de Classificação). Observe que @search.score os valores têm uma base diferente e são valores uniformemente menores. A RRF avalia as pontuações de pesquisa de vários resultados da pesquisa, usa o inverso e mescla e classifica os resultados combinados. O número top de resultados é retornado.

Analise a resposta, que consiste nas 5 principais correspondências entre 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, ajuda a examinar as entradas individualmente.

Os seguintes resultados são da parte de texto completo da consulta: passeio histórico de hotel até restaurantes e compras. Na consulta de texto completo, os três principais 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 parte vetorial da consulta (hospedagem típica próxima a trilhas para corrida, restaurantes e lojas), a cadeia de caracteres é diferente, então devemos esperar resultados diferentes. Essa consulta retorna Nordick's Valley Motel, Luxury Lion Resort e Sublime Palace Hotel como os três principais resultados. Na consulta fundida, esses resultados estão entre os três primeiros, 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 semântica híbrida

Esta é a última consulta na coleção. Essa consulta híbrida adiciona a classificação semântica L2, que aplica compreensão de leitura por máquina sobre os resultados classificados no L1, promovendo as correspondências mais relevantes para o topo.

Para criar uma pesquisa híbrida semântica:

Formular a solicitação.

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

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

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

Revise a resposta, que consiste em uma reclassificação semântica dos resultados ranqueados por RRF da consulta híbrida. A classificação semântica funciona fora das entradas de texto. Em um texto ou consulta híbrida, essa entrada é a parte de texto da consulta (passeio histórico de hotel até restaurantes e compras). Para usar a classificação semântica em uma consulta de vetor puro, como o primeiro exemplo, ou para controlar explicitamente o texto usado para classificação semântica, forneça uma cadeia de caracteres semântica.

Após a reclassificação semântica, o Swirling Currents Hotel, com sua referência à acessibilidade a pé a compras, restaurantes, entretenimento e ao centro da cidade, se move para o primeiro lugar. O modelo de compreensão de máquina promove acesso a pé a lojas e restaurantes como uma correspondência mais próxima de caminhar até restaurantes e lojas.

Antes do reclassificado semântico, o Palácio Sublime, referido como estando a uma distância a pé dos locais e pontos turísticos da cidade e cercado 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"
    }
  ]
}

Dica

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

Limpar os recursos

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

Como alternativa, você pode enviar a solicitação a seguir para excluir o índice de vetor criado neste início rápido.

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

Próximas etapas

Saiba como invocar chamadas à API REST sem chaves de API.

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Last updated on 2026-01-16

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Início Rápido: Pesquisa de vetor

Pré-requisitos

Obter informações de serviço

Clonar o código e o ambiente de instalação

Criar um índice de vetor

Carregar documentos no índice

Executar consultas

Cria a cadeia de caracteres de consulta de vetor

Busca em vetores única

Busca em vetores única com filtro

Pesquisa híbrida

Pesquisa semântica híbrida

Limpar os recursos

Próximas etapas

Pré-requisitos

Obter informações de serviço

Configurar o projeto Java

Entrar no Azure

Criar um índice de vetor

Carregar documentos no índice

Criar a consulta como um vetor

Criar uma única pesquisa de vetor

Criar uma única pesquisa de vetor com um filtro

Criar uma pesquisa com um filtro geoespacial

Criar uma pesquisa híbrida

Criar uma pesquisa híbrida semântica

Limpar os recursos

Próximas etapas

Pré-requisitos

Obter pontos de extremidade de serviço

Configurar o projeto de Node.JS

Configurar variáveis de ambiente para desenvolvimento local

Entrar no Azure

Criar um índice de vetor

Carregar documentos no índice

Criar a consulta como um vetor

Criar uma única pesquisa de vetor

Criar uma única pesquisa de vetor com um filtro

Criar uma pesquisa com um filtro geoespacial

Criar uma pesquisa híbrida

Criar uma pesquisa híbrida semântica

Limpar os recursos

Próximas etapas

Pré-requisitos

Obter informações de serviço

Configurar o projeto de Node.JS

Configurar variáveis de ambiente para desenvolvimento local

Entrar no Azure

Criar um índice de vetor

Carregar documentos no índice

Criar a consulta como um vetor

Criar uma única pesquisa de vetor

Criar uma única pesquisa de vetor com um filtro

Criar uma pesquisa com um filtro geoespacial

Criar uma pesquisa híbrida

Criar uma pesquisa híbrida semântica

Limpar os recursos

Próximas etapas

Pré-requisitos

Obter informações de serviço

Clonar o código e o ambiente de instalação

Criar um índice de vetor e carregar documentos

Executar consultas

Busca em vetores única

Busca em vetores única com filtro

Pesquisa híbrida

Pesquisa híbrida semântica com um filtro

Limpar os recursos

Próximas etapas

Pré-requisitos

Obter informações de serviço

Criar ou baixar o arquivo de código

Criar um índice de vetor

Carregar documentos no índice

Executar consultas

Busca em vetores única

Busca em vetores única com filtro

Pesquisa híbrida

Pesquisa semântica híbrida