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 pesquisa vetorial. O driver C permite que você execute queries de pesquisa do MongoDB Vector Search usando o framework de agregação. Para aprender 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 na documentação do Atlas.

Índice de Vector Search do MongoDB

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

Estágio de agregação do Vector Search

Para criar um estágio $vectorSearch em seu pipeline de agregação , inclua a definição de estágio de pipeline $vectorSearch na estrutura de pipeline bson_t e, em seguida, passe o pipeline para a função mongoc_collection_aggregate() .

Defina 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 BSON de valores duplos	Representação vetorial da sua query
`limit`	número	Número de resultados a retornar

Exemplos de query de pesquisa do MongoDB

Dica

Dados de amostra

O exemplo nesta seção utiliza dados de amostra da coleção sample_mflix.embedded_movies. Para aprender como carregar esses dados de amostra, consulte o guia Introdução ao MongoDB.

O código a seguir executa uma pesquisa vetorial MongoDB no campo de vetor plot_embedding usando um vetor query que é uma incorporação vetorial da frase "viagem no tempo". Em seguida, ele usa o estágio $project para gerar somente os campos title e plot e adicionar um campo score para mostrar a pontuação de relevância de cada documento:

// 2048-dimensional vector embedding representation of the phrase "time travel"
double embeddings[2048] = 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// Build the query vector array for the vector embedding representation of the phrase "time travel".
bson_t query_vector = BSON_INITIALIZER;
{
    bson_array_builder_t *builder = bson_array_builder_new();
    for (int i = 0; i < 2048; i++) {
        bson_array_builder_append_double(builder, embeddings[i]);
    }
    bson_array_builder_build(builder, &query_vector);
    bson_array_builder_destroy(builder);
}
// Create aggregation pipeline for Vector Search
bson_t *pipeline = BCON_NEW(
    "pipeline", "[",
        "{",
            "$vectorSearch", "{",
                "index", "vector_index",
                "path", "plot_embedding_voyage_3_large",
                "queryVector", BCON_ARRAY(&query_vector),
                "numCandidates", BCON_INT32(150),
                "limit", BCON_INT32(10),
            "}",
        "}",
        "{",
            "$project", "{",
                "title", BCON_BOOL(true),
                "plot", BCON_BOOL(true),
                "_id", BCON_BOOL(false),
                "score", "{",
                    "$meta", "vectorSearchScore",
                "}",
            "}",
        "}",
    "]"
);
// Execute the collection aggregation command
mongoc_cursor_t *cursor = mongoc_collection_aggregate(collection, MONGOC_QUERY_NONE, pipeline, NULL, NULL );
// Print results
const bson_t *doc;
while (mongoc_cursor_next(cursor, &doc)) {
    char *str = bson_as_canonical_extended_json(doc, NULL);
    printf("%s\n", str);
    bson_free(str);
}

{ "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", "score" : { "$numberDouble" : "0.77101063728332519531" } }
{ "plot" : "A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.", "title" : "Retroactive", "score" : { "$numberDouble" : "0.76004797220230102539" } }
{ "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.", "score" : { "$numberDouble" : "0.75768613815307617188" } }
{ "plot" : "An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.", "title" : "Timecop", "score" : { "$numberDouble" : "0.75765615701675415039" } }
{ "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", "score" : { "$numberDouble" : "0.7521393895149230957" } }
{ "plot" : "A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.", "title" : "Thrill Seekers", "score" : { "$numberDouble" : "0.75099325180053710938" } }
{ "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", "score" : { "$numberDouble" : "0.75026428699493408203" } }
{ "plot" : "Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.", "title" : "The Time Machine", "score" : { "$numberDouble" : "0.75025033950805664062" } }
{ "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", "score" : { "$numberDouble" : "0.74949634075164794922" } }
{ "plot" : "A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.", "title" : "The Final Countdown", "score" : { "$numberDouble" : "0.74691337347030639648" } }

Opções de Vector Search

Você pode modificar suas queries de pesquisa do MongoDB Vector Search especificando as seguintes opções na definição do estágio:

Opção	Tipo	Descrição	Valor padrão
`exact`	booleano	Especifica se a 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 para pesquisa. Você pode filtrar booleanos, data, objectId, numéricos, string, valores UUID e arrays desses tipos.	Sem filtragem
`numCandidates`	número	Especifica o número de vizinhos mais próximos a serem usados durante a pesquisa.	No limit

To learn more about these options, see the Fields section of the $vectorSearch operator reference in the Atlas documentation.

Informações adicionais

Para saber mais sobre os conceitos mencionados neste guia, consulte as seguintes entradas de manual do MongoDB Server :

Para saber mais sobre pipelines de agregação no driver C, consulte o guia C de agregação.

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

Documentação da API

Para saber mais sobre as funções mencionadas neste guia, consulte a seguinte documentação da API:

mongoc_collection_aggregate()

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