Executar uma query no Atlas Vector Search

Visão geral

Neste guia, você pode aprender como realizar pesquisas em seus documentos usando o recurso Atlas Vector Search . O driver Ruby permite executar queries do Atlas Vector Search usando a estrutura de agregação . Para saber mais sobre como realizar agregações, consulte o guiaTransforme seus dados em agregação.

Observação

Compatibilidade de implantação

Você pode usar a funcionalidade Atlas Vector Search somente quando se conectar a clusters do MongoDB Atlas . Esta funcionalidade não está disponível para implementações autogerenciadas.

Para saber mais sobre o Atlas Vector Search, consulte a Visão geral do Atlas Vector Search na documentação do Atlas .

Observação

Atlas Search

Para executar a pesquisa avançada de texto completo em seus documentos, você pode usar o recurso Atlas Search . Para saber mais sobre esse recurso, consulte a visão geral do Atlas Search.

Índice do Atlas Vector Search

Antes de executar queries do Atlas Vector Search , você deve criar um índice do Atlas Vector Search em sua collection. Para saber mais sobre como criar esse índice, consulte o guia Índices de Atlas Search .

Estágio de agregação do Vector Search

Para criar um estágio $vectorSearch em seu pipeline de agregação , execute as seguintes ações:

Crie um vetor para armazenar os estágios do pipeline.
Especifique o operador $vectorSearch e forneça detalhes sobre a query de pesquisa vetorial.

Em seguida, inclua o estágio em um pipeline de agregação e passe o pipeline para o método aggregate.

Você deve definir os seguintes campos em seu estágio $vectorSearch:

Parâmetro	Tipo	Descrição
`index`	string	Nome do índice de pesquisa vetorial
`path`	string	Campo que contém incorporações vetoriais
`queryVector`	`BSON::Binary` subtipo de vetor, `BSON::Vector` ou array de números	Representação vetorial da sua query
`limit`	número	Número de resultados a retornar

Exemplos de query do Atlas Search

Nesta seção, você pode aprender como executar queries do Atlas Vector Search . Os exemplos nesta seção utilizam dados de amostra da coleção sample_mflix.embedded_movies. Para saber como carregar esses dados de amostra, consulte o tutorial Carregar dados no Atlas na documentação do Atlas.

Query de Vector Search básica

O código a seguir executa uma consulta do Atlas Vector Search no campo de vetor plot_embedding usando um vetor de consulta que é uma incorporação vetorial da frase "viagem no tempo":

query_vector = BSON::Binary.from_vector([ -0.0016261312, -0.028070757, -0.011342932, -0.012775794, -0.0027440966, 0.008683807, -0.02575152, -0.02020668, -0.010283281, -0.0041719596, 0.021392956, 0.028657231, -0.006634482, 0.007490867, 0.018593878, 0.0038187427, 0.029590257, -0.01451522, 0.016061379, 0.00008528442, -0.008943722, 0.01627464, 0.024311995, -0.025911469, 0.00022596726, -0.008863748, 0.008823762, -0.034921836, 0.007910728, -0.01515501, 0.035801545, -0.0035688248, -0.020299982, -0.03145631, -0.032256044, -0.028763862, -0.0071576433, -0.012769129, 0.012322609, -0.006621153, 0.010583182, 0.024085402, -0.001623632, 0.007864078, -0.021406285, 0.002554159, 0.012229307, -0.011762793, 0.0051682983, 0.0048484034, 0.018087378, 0.024325324, -0.037694257, -0.026537929, -0.008803768, -0.017767483, -0.012642504, -0.0062712682, 0.0009771782, -0.010409906, 0.017754154, -0.004671795, -0.030469967, 0.008477209, -0.005218282, -0.0058480743, -0.020153364, -0.0032805866, 0.004248601, 0.0051449724, 0.006791097, 0.007650814, 0.003458861, -0.0031223053, -0.01932697, -0.033615597, 0.00745088, 0.006321252, -0.0038154104, 0.014555207, 0.027697546, -0.02828402, 0.0066711367, 0.0077107945, 0.01794076, 0.011349596, -0.0052715978, 0.014755142, -0.019753495, -0.011156326, 0.011202978, 0.022126047, 0.00846388, 0.030549942, -0.0041386373, 0.018847128, -0.00033655585, 0.024925126, -0.003555496, -0.019300312, 0.010749794, 0.0075308536, -0.018287312, -0.016567878, -0.012869096, -0.015528221, 0.0078107617, -0.011156326, 0.013522214, -0.020646535, -0.01211601, 0.055928253, 0.011596181, -0.017247654, 0.0005939711, -0.026977783, -0.003942035, -0.009583511, -0.0055248477, -0.028737204, 0.023179034, 0.003995351, 0.0219661, -0.008470545, 0.023392297, 0.010469886, -0.015874773, 0.007890735, -0.009690142, -0.00024970944, 0.012775794, 0.0114762215, 0.013422247, 0.010429899, -0.03686786, -0.006717788, -0.027484283, 0.011556195, -0.036068123, -0.013915418, -0.0016327957, 0.0151016945, 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0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136 ], :float32)
pipeline = [
  {
    '$vectorSearch' => {
      'queryVector' => query_vector,
      'index' => 'vs_idx',
      'path' => 'plot_embedding',
      'numCandidates' => 150,
      'limit' => 5
    }
  },
  {
    '$project' => {
      '_id' => 0,
      'plot' => 1,
      'title' => 1,
    }
  }
]
results = collection.aggregate(pipeline)
  
results.each do |doc|
  puts doc
end

{"plot"=>"A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title"=>"Thrill Seekers"}
{"plot"=>"At the age of 21, Tim discovers he can travel in time and change what happens and has happened in his own life. His decision to make his world a better place by getting a girlfriend turns out not to be as easy as you might think.", "title"=>"About Time"}
{"plot"=>"Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title"=>"The Time Machine"}
{"plot"=>"An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title"=>"Timecop"}
{"plot"=>"After using his mother's newly built time machine, Dolf gets stuck involuntary in the year 1212. He ends up in a children's crusade where he confronts his new friends with modern techniques...", "title"=>"Crusade in Jeans"}

Dica

Tipo de vetor de query

O exemplo anterior cria uma instância do BSON::Binary subtipo de vetor do tipo, introduzido no bson gem5.1 v, para servir como o vetor de consulta. Você também pode usar uma array de números como vetor de consulta, mas recomendamos usar o BSON::Binary tipo de vetor para melhorar a eficiência do armazenamento. Para saber mais sobre esse tipo, consulte a seção BSON::Vector do guia BSON.

Pontuação de pesquisa vetorial

O código a seguir executa a mesma query do exemplo anterior , mas gera apenas o campo title e o campo vectorSearchScore , que descreve até que ponto o documento corresponde ao vetor de query:

score_pipeline = [
  {
    '$vectorSearch' => {
      'queryVector' => query_vector,
      'index' => 'vs_idx',
      'path' => 'plot_embedding',
      'numCandidates' => 150,
      'limit' => 5
    }
  },
  {
    '$project' => {
      '_id' => 0,
      'title' => 1,
      'score' => { '$meta' => 'vectorSearchScore' }
    }
  }
]
results = collection.aggregate(score_pipeline)
results.each do |document|
  puts document
end

{"title"=>"Thrill Seekers", "score"=>0.9253387451171875}
{"title"=>"About Time", "score"=>0.9246978759765625}
{"title"=>"The Time Machine", "score"=>0.9229583740234375}
{"title"=>"Timecop", "score"=>0.9228057861328125}
{"title"=>"Crusade in Jeans", "score"=>0.9222259521484375}

Opções de Vector Search

Você pode utilizar um estágio $vectorSearch para executar muitos tipos de queries do Atlas Vector Search . Dependendo da query desejada, você pode especificar as seguintes opções na definição do estágio:

Parâmetro opcional	Tipo	Descrição	Valor padrão
`exact`	booleano	Especifica se uma pesquisa deve ser executada no vizinho mais próximo exato (`true`) ou no vizinho mais próximo aproximado (`false`)	`false`
`filter`	documento	Especifica um pré-filtro para documentos a serem pesquisados	Sem filtragem
`numCandidates`	número	Especifica o número de vizinhos mais próximos a serem usados durante a pesquisa	No limit

Para saber mais sobre essas opções, consulte a seção Campos da referência do operador $vectorSearch na documentação do Atlas.

Informações adicionais

Para saber mais sobre os conceitos mencionados neste guia, consulte as seguintes entradas manuais do servidor:

Para saber mais sobre o comportamento do aggregate método, consulte o guia Transforme seus dados com agregação.

Documentação da API

Para saber mais sobre os métodos e tipos mencionados neste guia, consulte a documentação da API abaixo:

Agregação

Voltar

aggregation de dados

Segurança