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Scala

Búsqueda vectorial de MongoDB

Overview

En esta guía, aprenderá a usar la función de búsqueda vectorial de MongoDB en el controlador de Scala.Aggregates La vectorSearch() clase constructora proporciona el método auxiliar que puede usar para crear una etapa de canalización $vectorSearch. Esta etapa de canalización le permite realizar una búsqueda semántica en sus documentos. Una búsqueda semántica es un tipo de búsqueda que localiza información similar en significado, pero no necesariamente idéntica, al término o frase de búsqueda proporcionados.

Importante

Compatibilidad de características

MongoDB Vector Search admite la búsqueda de ANN (ANN) en clústeres de Atlas que ejecutan MongoDB v6.0.11, v7.0.2 o posterior y la búsqueda de vecinos más cercanos exactos (ENN) en clústeres que ejecutan MongoDB v6.0.16, v7.0.10, v7.3.2, o posterior. También puedes usar MongoDB búsqueda vectorial con implementaciones Atlas autogestionadas o locales que crees con la Atlas CLI.

Datos de muestra

Los ejemplos de esta guía utilizan la embedded_movies colección sample_mflix de la base de datos de los conjuntos de datos de ejemplo de Atlas. Para aprender a crear un clúster gratuito de MongoDB Atlas y cargar los conjuntos de datos de ejemplo, consulte el tutorial «Primeros pasos con el controlador Scala».

Realiza una búsqueda vectorial

Para ejecutar consultas de búsqueda vectorial, debe crear un índice de búsqueda vectorial e indexar sus incrustaciones vectoriales. Para obtener información sobre cómo crear un índice de búsqueda vectorial mediante programación, consulte la sección «Crear índices de búsqueda y de búsqueda vectorial» en la guía de índices. Para obtener más información sobre las incrustaciones vectoriales, consulte «Cómo indexar incrustaciones vectoriales para la búsqueda vectorial» en la documentación de Atlas.

Una vez que hayas creado un índice de búsqueda vectorial en tus incrustaciones vectoriales, puedes hacer referencia a este índice en la etapa de tu pipeline, como se muestra en la siguiente sección.

Ejemplo de búsqueda vectorial

El ejemplo de esta sección requiere un índice de búsqueda vectorial en la colección embedded_movies con la siguiente configuración:

  • Nombre del índice: vector_index

  • Campo vectorial: plot_embedding, con 1536 dimensiones

  • Campo de filtro: year, indexado como el tipo filter para admitir el prefiltro en el campo year.

Este ejemplo de búsqueda vectorial en MongoDB realiza las siguientes acciones:

  • Crea un vector de consulta como un Seq[Double]

  • Crea una canalización de agregación que ejecuta una búsqueda ENN en el campo plot_embedding.

  • Limita los resultados a 1 documentos

  • Coincide con los documentos donde el valor year es mayor o igual que 2016

  • Proyecta la puntuación de búsqueda vectorial en un campo llamado 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"))
  )
)

Tip

Query Vector Type

El ejemplo anterior crea un Seq[Double] que sirve como vector de consulta, pero también se puede usar un BinaryVector. Utilice el tipo BinaryVector para mejorar la eficiencia del almacenamiento.

El siguiente ejemplo muestra cómo ejecutar la agregación e imprimir la meta-puntuación de búsqueda vectorial a partir del resultado de la anterior pipeline de agregación:

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

Documentación de la API

Para obtener más información sobre los métodos y tipos mencionados en esta guía, vea la siguiente documentación de la API:

Volver

MongoDB búsqueda

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