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Ejecuta una query de búsqueda vectorial en MongoDB

Overview

En esta guía, puedes aprender cómo realizar búsquedas en tus documentos utilizando la MongoDB Vector Search funcionalidad. El controlador de Ruby permite realizar consultas de MongoDB Vector Search utilizando el marco de agregación. Para obtener más información sobre cómo realizar agregaciones, consulte la Transforme sus datos con la guía de agregación.

Nota

Compatibilidad de implementación

Solo puede usar la función de búsqueda vectorial de MongoDB al conectarse a clústeres de MongoDB Atlas. Esta función no está disponible para implementaciones autogestionadas.

Para obtener más información sobre MongoDB Vector Search, consulta la Descripción general de MongoDB Vector Search en la documentación de Atlas.

Nota

MongoDB búsqueda

Para realizar búsquedas avanzadas de texto completo en tus documentos, puedes utilizar la funcionalidad MongoDB Search. Para aprender sobre esta funcionalidad, consulta la Descripción general de MongoDB Search.

Índice de búsqueda vectorial de MongoDB

Antes de poder realizar consultas de MongoDB Vector Search, debes crear un índice de MongoDB Vector Search en tu colección. Para obtener más información sobre cómo crear este índice, consulta la guía MongoDB Search e índices Vector Search de MongoDB.

Etapa de agregación de búsqueda vectorial

Para crear una $vectorSearch etapa en tu pipeline de agregación, realiza las siguientes acciones:

Cree un vector para almacenar las etapas de la pipeline.
Especifique el operador $vectorSearch y proporcione detalles sobre la consulta de búsqueda vectorial.

Luego, incluya la etapa en un pipeline de agregación y pase el pipeline al método aggregate.

Debes definir los siguientes campos en tu etapa $vectorSearch:

Parameter	Tipo	Descripción
`index`	string	Nombre del índice de búsqueda vectorial
`path`	string	Campo que contiene incrustaciones vectoriales
`queryVector`	`BSON::Binary` subtipo de vector, `BSON::Vector`, o arreglo de números	Representación vectorial de su query
`limit`	Número	Número de resultados a retornar

Ejemplos de consultas de búsqueda de MongoDB

En esta sección, puede aprender cómo realizar consultas de MongoDB Vector Search. Los ejemplos de esta sección utilizan datos de muestra de la colección sample_mflix.embedded_movies. Para aprender cómo cargar estos datos de muestra, consulta el tutorial Cargar datos en Atlas en la documentación de Atlas.

Consulta de búsqueda básica de vectores

El siguiente código realiza una MongoDB Vector Search en el campo vectorial plot_embedding utilizando un vector de consulta que es una incrustación vectorial de la frase "viajes en el tiempo":

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, 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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"}

Tip

Query Vector Type

El ejemplo anterior crea una instancia del BSON::Binary subtipo vector del tipo, introducido en la bson gema v,5.1 que actúa como vector de consulta. También puede usar un array de números como vector de consulta, pero recomendamos usar el BSON::Binary tipo de vector para optimizar el almacenamiento. Para obtener más información sobre este tipo, consulte la sección BSON::Vector de la guía de BSON.

Puntaje de búsqueda vectorial

El siguiente código realiza la misma query que en el ejemplo anterior, pero sólo muestra el campo title y el campo meta vectorSearchScore, que describe qué tan bien se ajusta el documento al vector 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}

Opciones de búsqueda de vectores

Puede utilizar una etapa $vectorSearch para realizar muchos tipos de consultas de Búsqueda Vectorial de MongoDB. Dependiendo de su query deseada, puede especificar las siguientes opciones en la definición de etapa:

Parámetro opcional	Tipo	Descripción	Valor por defecto
`exact`	booleano	Especifica si se ejecutar una búsqueda de Vecino Más Cercano Exacto (`true`) o de ANN (`false`)	`false`
`filter`	Documento	Especifica un pre-filtro para los documentos a buscar.	Sin filtrado
`numCandidates`	Número	Especifica el número de vecinos más cercanos que se usarán durante la búsqueda	No limit

Para obtener más información sobre estas opciones, consulta la sección Campos de la referencia del operador $vectorSearch en la documentación de Atlas.

Información Adicional

Para aprender más sobre los conceptos mencionados en esta guía, consulta las siguientes entradas del manual del servidor:

Para obtener más información sobre el comportamiento del aggregate método, consulte la guía Transforme sus datos con agregación.

Documentación de la API

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

Agregar

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MongoDB búsqueda

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