Executar uma query de pesquisa vetorial do MongoDB

Visão geral

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

Observação

Compatibilidade de implantação

Você pode utilizar a funcionalidade MongoDB Vector Search somente em clusters do MongoDB Atlas . Esta funcionalidade não está disponível para implementações autogerenciadas.

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

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 de índices de Vector Search do MongoDB .

Estágio de agregação do 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 de Dados	Descrição
`index`	`string`	Nome do índice de pesquisa vetorial
`path`	`string`	Campo que contém incorporações vetoriais
`queryVector`	`BSON::Binary` subtipo de vetor, `BSON::Vector` ou array de números	Representação vetorial da sua query
`limit`	`number`	Número de resultados a retornar

Em seguida, inclua o estágio em um pipeline de agregação e passe o pipeline para o método aggregate.

Exemplos de query de pesquisa 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 de Vector Search básica

O código a seguir executa uma query de pesquisa vetorial do MongoDB no campo plot_embedding usando um vetor de consulta que é uma incorporação vetorial da frase "viagem no tempo":

#include <bsoncxx/builder/basic/document.hpp>
#include <bsoncxx/builder/list.hpp>
#include <bsoncxx/json.hpp>
#include <mongocxx/client.hpp>
#include <mongocxx/instance.hpp>
#include <mongocxx/uri.hpp>
using bsoncxx::builder::basic::kvp;
using bsoncxx::builder::basic::make_document;
int main() {
  try {
    mongocxx::instance inst{};
    // Replace the placeholder with your Atlas connection string
    const auto uri = mongocxx::uri{"<connection-string>"};
    // Connect to your Atlas cluster
    auto client = mongocxx::client{uri};
    auto db = client["sample_mflix"];
    auto collection = db["embedded_movies"];
    // Define vector embeddings to search
    auto vectors = bsoncxx::builder::list{
      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024262359,0.054507218,-0.009721561,0.005816319,-0.00206687,-0.008766023,0.017365867,-0.000737426,0.018362951,-0.023597637,-0.019110762,0.021935832,0.041545134,-0.020357117,-0.017615138,0.044868745,-0.018030589,-0.032405205,-0.050186522,-0.014540797,0.005213914,-0.006688767,0.047527634,0.040714234
    };
    // Define the pipeline with vectorSearch query options
    mongocxx::pipeline stages;
    stages
        .append_stage(make_document(
            kvp("$vectorSearch",
                make_document(kvp("index", "vector_index"),
                              kvp("path", "plot_embedding_voyage_3_large"),
                              kvp("queryVector", vectors),
                              kvp("numCandidates", 150), kvp("limit", 10)))))
        .project(make_document(
            kvp("_id", 0), kvp("plot", 1), kvp("title", 1)));
    auto cursor = collection.aggregate(stages);
    for (auto&& doc : cursor) {
      std::cout << bsoncxx::to_json(doc) << std::endl;
    }
  } catch (const std::exception& e) {
    std::cout << "Exception: " << e.what() << std::endl;
  }
  return 0;
}

{ "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" : "A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.", "title" : "Retroactive" }
{ "plot" : "A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...", "title" : "A.P.E.X." }
{ "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 visiting 2015, Marty McFly must repeat his visit to 1955 to prevent disastrous changes to 1985... without interfering with his first trip.", "title" : "Back to the Future Part II" }
{ "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" : "Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...", "title" : "Timerider: The Adventure of Lyle Swann" }
{ "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" : "A romantic drama about a Chicago librarian with a gene that causes him to involuntarily time travel, and the complications it creates for his marriage.", "title" : "The Time Traveler's Wife" }
{ "plot" : "A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.", "title" : "The Final Countdown" }

Esta query usa a fase $vectorSearch para:

Realizar uma pesquisa de vetor de vizinho mais próximo aproximado (ANN)
Pesquise o termo especificado no campo plot_embedding_voyage_3_large.
Defina o número de vizinhos mais próximos utilizados na pesquisa para 150 através da opção numCandidates
Retorne no máximo 10 documentos da consulta usando a opção limit

Ele usa o estágio $project para:

Inclua apenas os campos de filme plot e title nos resultados

Pontuação de pesquisa vetorial

O código a seguir executa a mesma query do exemplo anterior , mas usa o estágio $project para gerar apenas o campo title e o campo vectorSearchScore , que descreve quão bem o documento corresponde ao vetor de query:

// Define the pipeline with vectorSearch and project stages
mongocxx::pipeline score_pipeline;
score_pipeline
    .append_stage(make_document(
        kvp("$vectorSearch",
            make_document(kvp("queryVector", vectors),
                          kvp("index", "vector_index"),
                          kvp("path", "plot_embedding_voyage_3_large"),
                          kvp("numCandidates", 150),
                          kvp("limit", 10)))))
    .project(make_document(
        kvp("_id", 0),
        kvp("title", 1),
        kvp("score", make_document(kvp("$meta", "vectorSearchScore")))));
auto cursor = collection.aggregate(score_pipeline);
for (auto&& doc : cursor) {
  std::cout << bsoncxx::to_json(doc) << std::endl;
}

{ "title" : "About Time", "score" : 0.77101051807403564453 }
{ "title" : "Retroactive", "score" : 0.76004779338836669922 }
{ "title" : "A.P.E.X.", "score" : 0.7576859593391418457 }
{ "title" : "Timecop", "score" : 0.757656097412109375 }
{ "title" : "Back to the Future Part II", "score" : 0.75213932991027832031 }
{ "title" : "Thrill Seekers", "score" : 0.75099325180053710938 }
{ "title" : "Timerider: The Adventure of Lyle Swann", "score" : 0.75026416778564453125 }
{ "title" : "The Time Machine", "score" : 0.75025022029876708984 }
{ "title" : "The Time Traveler's Wife", "score" : 0.74949628114700317383 }
{ "title" : "The Final Countdown", "score" : 0.7469132542610168457 }

Opções de Vector Search

Você pode utilizar um estágio $vectorSearch para executar muitos tipos de queries do Atlas Vector Search . Dependendo da query desejada, você pode especificar as seguintes opções na definição do estágio:

Parâmetro opcional	Tipo de Dados	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 aggregate método, consulte o Guia de agregação.

Documentação da API

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

Aggregate()

Voltar

MongoDB Search

Exploração madeireira