MongoDB Vector Search

Visão geral

Neste guia, você pode aprender a realizar pesquisas em seus documentos usando o recurso Pesquisa Vetorial do MongoDB. O driver Rust permite que você execute query de pesquisa do MongoDB Vector Search usando a framework de agregação. Para saber mais sobre como realizar agregações, consulte o agregação guia.

Observação

Compatibilidade de implantação

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

Para saber mais sobre a pesquisa vetorial do MongoDB, consulte a Visão geral da pesquisa vetorial do MongoDB.

Observação

MongoDB Search

Para realizar uma pesquisa avançada de texto completo em seus documentos, você pode usar o recurso de pesquisa do MongoDB . Para saber mais sobre esse recurso, consulte a Visão geral da pesquisa do MongoDB.

Índice de Vector Search do MongoDB

Antes de executar queries de pesquisa do MongoDB pesquisa vetorial, você deve criar um índice do MongoDB pesquisa vetorial em sua coleção. Para saber mais sobre como criar esse índice, consulte o guia Índices de pesquisa MongoDB pesquisa e Vector pesquisa.

Estágio de agregação do MongoDB pesquisa vetorial

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.

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`	array de números	Representação vetorial da sua query
`limit`	número	Número de resultados a retornar

Exemplos de query de pesquisa vetorial do MongoDB

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

query básica de pesquisa vetorial do MongoDB

O código a seguir executa uma query do MongoDB pesquisa vetorial no campo de vetor plot_embedding usando um vetor de query que representa o termo viagem no tempo:

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, 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, -0.020473259, 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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" }

Dica

Tipo de vetor de query

O exemplo anterior cria uma instância de bson::binary::Vector, introduzida no bson v2.14, para servir como o vetor de query. Você também pode usar um vec de números como um vetor de consulta, mas recomendamos que você use o tipo Vector para melhorar a eficiência do armazenamento.

Pontuação de Vector Search do MongoDB

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:

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 }

Opções de pesquisa vetorial no MongoDB

Você pode usar um estágio $vectorSearch para executar vários tipos de queries de pesquisa do MongoDB 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 método aggregate() , consulte a seção Operações de Agregação do guia Recuperar Dados.

Documentação da API

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

Voltar

MongoDB Search

Transações