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Rust

Driver Rust

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Rust

Driver Rust

Búsqueda vectorial de MongoDB

Overview

En esta guía, puedes aprender cómo realizar búsquedas en tus documentos utilizando la MongoDB Vector Search funcionalidad. El driver de Rust permite realizar queries de MongoDB Vector Search utilizando el marco de agregación. Para obtener más información sobre cómo realizar agregaciones, consulte la agregación guide.

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.

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 ejecutar queries de Búsqueda Vectorial de MongoDB, debes crear un índice de Búsqueda Vectorial de MongoDB en tu colección. Para obtener más información sobre cómo crear este índice, consulta la guía Índices de Búsqueda y Búsqueda Vectorial de MongoDB.

Etapa de agregación de búsqueda vectorial de MongoDB

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.

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`	arreglo de números	Representación vectorial de su query
`limit`	Número	Número de resultados a retornar

Ejemplos de consultas de MongoDB Vector Search

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 básica de MongoDB Vector Search

El siguiente código realiza una búsqueda vectorial en MongoDB en el campo vectorial plot_embedding utilizando un vector de query que representa el término viaje en el tiempo:

let query_vector = Vector::Float32(vec![-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, 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0.02071318, -0.03156294, -0.030869836, -0.006324584, 0.013795458, -0.00047151142, 0.016874444, 0.00947688, 0.00985009, -0.029883493, 0.024205362, -0.013522214, -0.015075036, -0.030603256, 0.029270362, 0.010503208, 0.021539574, 0.01743426, -0.023898797, 0.022019416, -0.0068777353, 0.027857494, -0.021259667, 0.0025758184, 0.006197959, 0.006447877, -0.00025200035, -0.004941706, -0.021246338, -0.005504854, -0.008390571, -0.0097301295, 0.027244363, -0.04446536, 0.05216949, 0.010243294, -0.016008062, 0.0122493, -0.0199401, 0.009077012, 0.019753495, 0.006431216, -0.037960835, -0.027377652, 0.016381273, -0.0038620618, 0.022512587, -0.010996379, -0.0015211658, -0.0102233, 0.007071005, 0.008230623, -0.009490209, -0.010083347, 0.024431955, 0.002427534, 0.02828402, 0.0035721571, -0.022192692, -0.011882754, 0.010056688, 0.0011904413, -0.01426197, -0.017500903, -0.00010985966, 0.005591492, -0.0077707744, -0.012049366, 0.011869425, 0.00858384, -0.024698535, -0.030283362, 0.020140035, 0.011949399, -0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136]);
let pipeline = vec![
    doc! {
        "$vectorSearch": doc! {
            "queryVector": &query_vector,
            "path": "plot_embedding",
            "numCandidates": 150,
            "index": "vector_index",
            "limit": 5
        }
    },
    doc! {
        "$project": doc! {
            "_id": 0,
            "title": 1,
            "plot": 1,
        }
    },
];
let mut results = my_coll.aggregate(pipeline).await?;
while let Some(result) = results.try_next().await? {
    println!("{}", result);
}

{ "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": "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" }
{ "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" }

Tip

Query Vector Type

El ejemplo anterior crea una instancia de bson::binary::Vector, introducida en bson v2.14, para servir como el vector de query. También puede utilizar un vec de números como vector de query, pero le recomendamos que utilice el tipo Vector para mejorar la eficiencia del almacenamiento.

Puntuación de búsqueda vectorial de MongoDB

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:

let pipeline = vec![
    doc! {
        "$vectorSearch": doc! {
            "queryVector": &query_vector,
            "path": "plot_embedding",
            "numCandidates": 150,
            "index": "vector_index",
            "limit": 5
        }
    },
    doc! {
        "$project": doc! {
            "_id": 0,
            "title": 1,
            "score": doc! { "$meta": "vectorSearchScore" },
        }
    },
];
let mut results = my_coll.aggregate(pipeline).await?;
while let Some(result) = results.try_next().await? {
    println!("{}", result);
}

{ "title": "Thrill Seekers", "score": 0.9332504272460938 }
{ "title": "About Time", "score": 0.9312690496444702 }
{ "title": "The Time Machine", "score": 0.9295310378074646 }
{ "title": "Crusade in Jeans", "score": 0.9290415048599243 }
{ "title": "Timecop", "score": 0.9283164739608765 }

MongoDB Vector Search Options

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 método aggregate(), consulta la sección Operaciones de agregación de la guía Recuperar datos.

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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