MongoDB Vector Search

Visão geral

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

Observação

Compatibilidade de implantação

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

Para saber mais sobre a Vector Search do MongoDB , consulte a Visão geral da Vector Search 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 do MongoDB Vector Search , você deve criar um índice do MongoDB Vector Search em sua collection. Para saber mais sobre como criar esse índice, consulte o guia Índices de pesquisa MongoDB Search e Vector Search .

Estágio de agregação do MongoDB Vector Search

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 Vector Search do MongoDB

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

Query básica de Vector Search do MongoDB

O código a seguir executa uma query do MongoDB Vector Search no plot_embedding campo de vetor 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, 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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 Vector Search no MongoDB

Você pode usar um estágio $vectorSearch para executar vários tipos de queries 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