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Scala

MongoDB Vector Search

Visão geral

Neste guia, você pode aprender como usar o recurso MongoDB Vector Search no driver Scala. A classe de construtores Aggregates fornece o método assistente vectorSearch() que você pode usar para criar uma etapa de pipeline $vectorSearch. Essa etapa do pipeline permite que você execute uma pesquisa semântica nos seus documentos. Pesquisa semântica é um tipo de pesquisa que localiza informações que são semelhantes em significado, mas não necessariamente idênticas ao termo ou frase de pesquisa fornecido.

Importante

Compatibilidade de recursos

A pesquisa vetorial do MongoDB suporta a pesquisa ANN em clusters do Atlas que executam o MongoDB v6.0.11, v7.0.2 ou posterior e a pesquisa ENN em clusters que executam o MongoDB v6.0.16, v7.0.10, v7.3.2, ou posterior. Você também pode utilizar a pesquisa vetorial do MongoDB com implantações autogerenciadas ou locais do Atlas que você cria com o Atlas CLI.

Dados de amostra

Os exemplos neste guia usam a coleção embedded_movies no banco de dados sample_mflix dos conjuntos de dados de amostra do Atlas. Para **aprender** como criar um cluster gratuito do MongoDB Atlas e carregar os conjuntos de dados de amostra, consulte o tutorial de Introdução ao driver Scala .

Realizar uma Vector Search

Para executar query de pesquisa vetorial, você deve criar um índice de pesquisa vetorial e indexar seus embedding de vetor. Para aprender a criar programaticamente um índice de pesquisa vetorial, consulte a seção Índices de pesquisa e pesquisa vetorial no guia de Índices. Para aprender mais sobre embedding de vetor, consulte Como indexar embedding de vetor para pesquisa vetorial na documentação do Atlas.

Depois de criar um índice de pesquisa vetorial nas incorporações vetoriais, você poderá fazer referência a esse índice no estágio do pipeline, conforme mostrado na seção a seguir.

Exemplo de Vector Search

O exemplo nesta seção requer um índice de pesquisa vetorial na coleção embedded_movies com a seguinte configuração:

  • Nome do Índice: vector_index

  • Campo vetorial: plot_embedding, com 1536 dimensões

  • Campo de filtro: year, indexado como o tipo filter para suportar o pré-filtro no campo year

Este exemplo de pesquisa vetorial do MongoDB executa as seguintes ações:

  • Cria um vetor de query como um Seq[Double]

  • Cria um pipeline de agregação que executa uma pesquisa ENN no campo plot_embedding

  • Limita os resultados a 1 documento

  • Corresponde aos documentos onde o valor year é maior ou igual a 2016

  • Projeta a pontuação da pesquisa vetorial em um campo chamado vectorSearchScore

// Create an instance of the Seq[Double] class as the query vector
val rawVector: Array[Float] = Array( -0.0016261312f, -0.028070757f, -0.011342932f, -0.012775794f, -0.0027440966f, 0.008683807f, -0.02575152f, -0.02020668f, -0.010283281f, -0.0041719596f, 0.021392956f, 0.028657231f, -0.006634482f, 0.007490867f, 0.018593878f, 0.0038187427f, 0.029590257f, -0.01451522f, 0.016061379f, 0.00008528442f, -0.008943722f, 0.01627464f, 0.024311995f, -0.025911469f, 0.00022596726f, -0.008863748f, 0.008823762f, -0.034921836f, 0.007910728f, -0.01515501f, 0.035801545f, -0.0035688248f, -0.020299982f, -0.03145631f, -0.032256044f, -0.028763862f, -0.0071576433f, -0.012769129f, 0.012322609f, -0.006621153f, 0.010583182f, 0.024085402f, -0.001623632f, 0.007864078f, -0.021406285f, 0.002554159f, 0.012229307f, -0.011762793f, 0.0051682983f, 0.0048484034f, 0.018087378f, 0.024325324f, -0.037694257f, -0.026537929f, -0.008803768f, -0.017767483f, -0.012642504f, -0.0062712682f, 0.0009771782f, -0.010409906f, 0.017754154f, -0.004671795f, -0.030469967f, 0.008477209f, -0.005218282f, 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val queryVector: Seq[java.lang.Double] = rawVector.map(x => java.lang.Double.valueOf(x.toDouble)).toSeq
// Specify the index name for the vector embedding index
val indexName = "vector_index"
// Specify the path of the field to search on
val fieldSearchPath: FieldSearchPath = SearchPath.fieldPath("plot_embedding")
// Limit the number of matches to 1
val limit = 1L
// Create a pre-filter to only search within a subset of documents
val options: VectorSearchOptions = VectorSearchOptions.exactVectorSearchOptions()
  .filter(Filters.gte("year", 2016))
// Create the vectorSearch pipeline stage
val observable: Observable[Document] = collection.aggregate(
  Seq(
    Aggregates.vectorSearch(
      fieldSearchPath,
      queryVector,
      indexName,
      limit,
      options),
    Aggregates.project(
      Projections.metaVectorSearchScore("vectorSearchScore"))
  )
)

Dica

Tipo de vetor de query

O exemplo anterior cria um Seq[Double] para servir como o vetor de query, mas você também pode usar um BinaryVector. Use o tipo BinaryVector para melhorar a eficiência do armazenamento.

O exemplo a seguir mostra como executar a agregação e imprimir a meta-score do vector search a partir do resultado da agregação pipeline anterior:

observable.subscribe(
  (doc: Document) => {
    val score = doc.getDouble("vectorSearchScore")
    println(s"vectorSearch score: $score")
  },
  (e: Throwable) => println(s"There was an error: $e")
)
vectorSearch score: 0.8821365833282471

Documentação da API

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

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