Ejecuta una query de búsqueda vectorial en MongoDB

Overview

En esta guía, puedes aprender cómo realizar búsquedas en tus documentos utilizando la MongoDB Vector Search funcionalidad. El driver de C te permite realizar consultas 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 en la documentación de Atlas.

Índice de búsqueda vectorial de MongoDB

Antes de realizar queries de MongoDB Vector Search, debes crear un índice de MongoDB Vector Search en tu colección. Para obtener más información sobre cómo crear este índice, consulta la guía C MongoDB Search Indexes.

Etapa de agregación de búsqueda vectorial

Para crear una $vectorSearch etapa en tu pipeline de agregación, incluye la definición de la etapa de pipeline $vectorSearch en la estructura de pipeline bson_t, luego pasa el pipeline a la función mongoc_collection_aggregate().

Define 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`	Matriz BSON de dobles	Representación vectorial de su query
`limit`	Número	Número de resultados a retornar

Ejemplos de consultas de búsqueda de MongoDB

Tip

Datos de muestra

El ejemplo en esta sección usa datos de muestra de la colección sample_mflix.embedded_movies. Para aprender cómo cargar estos datos de muestra, consulta la guía Cómo empezar con MongoDB.

El siguiente código realiza una consulta de búsqueda vectorial de MongoDB en el campo vectorial plot_embedding utilizando un vector de consulta que es una incrustación vectorial de la frase "viaje en el tiempo". A continuación, utiliza la etapa $project para generar solo los campos title y plot y añade un campo score para mostrar la puntuación de relevancia 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" } }

Opciones de búsqueda de vectores

Puedes modificar tus consultas de búsqueda de vectores de MongoDB especificando las opciones siguientes en la definición de etapa:

Opción	Tipo	Descripción	Valor por defecto
`exact`	booleano	Especifica si se debe ejecutar una búsqueda Exacta de Vecino Más Cercano (`true`) o ANN (`false`).	`false`
`filter`	Documento	Especifica un prefiltro para la búsqueda de documentos. Puede filtrar por valores booleanos, de fecha, objectId, numéricos, de cadena, UUID y matrices de estos tipos.	Sin filtrado
`numCandidates`	Número	Especifica el número de vecinos más cercanos a utilizar 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 conocer más sobre los conceptos mencionados en esta guía, consulta las siguientes entradas del manual de servidor de MongoDB:

Para obtener más información sobre las canalizaciones de agregación en el controlador C, consulte la guía de agregación de C.

Para realizar búsquedas avanzadas de texto completo en tus documentos, puedes utilizar la funcionalidad MongoDB Search. Para obtener más información, consulta la Visión general de MongoDB Search.

Documentación de la API

Para obtener más información sobre las funciones mencionadas en esta guía, consulta la siguiente documentación de la API:

mongoc_collection_aggregate()

Volver

MongoDB búsqueda

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