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Ejecuta una query de búsqueda vectorial en MongoDB

Overview

En esta guía, puedes aprender cómo utilizar el Función de búsqueda vectorial deMongoDB en el controlador Go mediante la etapa de canalización $vectorSearch. Esta etapa permite realizar una búsqueda semántica en los documentos. Una búsqueda semántica localiza información con significado similar, pero no necesariamente idéntico, al término o frase de búsqueda proporcionados.

Importante

Compatibilidad de características

Para saber qué versiones de MongoDB Atlas admiten esta función, consulte Limitaciones en la documentación de MongoDB Atlas.

Datos de muestra

El ejemplo en esta página query el plot_embedding campo de la embedded_movies colección, que se encuentra en la base de datos sample_mflix de los conjuntos de datos de muestra de Atlas.

El campo plot_embedding contiene incrustaciones vectoriales con 1536 dimensiones, creadas utilizando el modelo de incrustación text-embedding-ada-002 de OpenAI.

Para aprender a crear un clúster gratuito de MongoDB Atlas y cargar los conjuntos de datos de ejemplo, consulte la sección Primeros pasos con Atlas.

Ejecuta una query de búsqueda vectorial en MongoDB

Para utilizar esta funcionalidad, debes crear un índice de búsqueda vectorial e indexar tus representaciones vectoriales. Para aprender cómo crear un índice de búsqueda vectorial mediante programación, consulte MongoDB Search and MongoDB Vector Search Indexes. Para aprender más sobre incrustaciones vectoriales, revisa Cómo indexar vector embeddings para búsqueda vectorial en la documentación de Atlas.

Después de crear un índice de búsqueda vectorial en tus incrustaciones vectoriales, puedes hacer referencia a este índice en tu pipeline de agregación para ejecutar tu query de búsqueda vectorial.

Las siguientes secciones demuestran cómo crear un vector binario BSON para tu vector de query y cómo utilizar tu índice de búsqueda vectorial para ejecutar una query de búsqueda vectorial utilizando el campo plot_embedding.

Crear un Vector Binario BSON

En este ejemplo, puedes crear un vector dimensional 1536 para utilizar como vector de query para tu búsqueda vectorial en el campo plot_embedding. La query busca el campo plot_embedding usando una incrustación vectorial para el string "time travel".

El siguiente ejemplo muestra cómo traducir esta incrustación vectorial a un vector binario BSON que puede utilizar como vector de consulta:

// Converts a float32 array to a BSON vector
vectorFloat := bson.NewVector([]float32{-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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-0.013968734, 0.042732596, -0.011649498, -0.011982721, -0.016967745, -0.0060913274, -0.007130985, -0.013109017, -0.009710136})
// Converts the BSON vector to a BSON binary vector
queryVector := vectorFloat.Binary()

Si necesitas acceder a una parte del vector original, también puedes deserializar tu vector de query de vuelta a un vector BSON.

El siguiente ejemplo muestra cómo convertir el vector de consulta de un vector binario BSON en un vector BSON utilizando el método NewVectorFromBinary():

// Converts the BSON binary vector back to a BSON vector
backToVectorFloat, err := bson.NewVectorFromBinary(queryVector)
if err != nil {
	log.Fatal(err)
}

Tip

Query Vector Type

El ejemplo anterior crea una instancia de un vector binario BSON que sirve como vector de consulta, pero también puede usar una matriz de valores BSON double. Sin embargo, recomendamos usar un vector binario BSON para mejorar la eficiencia del almacenamiento.

Ejecutar la consulta de búsqueda de vectores

El siguiente ejemplo muestra cómo crear una canalización de agregación que utiliza los métodos $vectorSearch y $project para realizar una búsqueda vectorial de vecino más cercano aproximado (ANN) con las siguientes especificaciones:

  • Consulta el campo plot_embedding con el binario BSON queryVector

  • Establece el número de vecinos más cercanos utilizados en la búsqueda a 150 mediante la opción numCandidates

  • Se usa el índice vector_search creado en el campo plot_embedding

  • Devuelve 5 documentos con los campos plot, title y score especificados

// Creates the aggregation pipeline
vectorSearchStage := bson.D{
	{"$vectorSearch", bson.D{
		{"index", "vector_index"},
		{"path", "plot_embedding"},
		{"queryVector", queryVector},
		{"numCandidates", 150},
		{"limit", 5},
	}}}
projectStage := bson.D{
	{"$project", bson.D{
		{"_id", 0},
		{"plot", 1},
		{"title", 1},
		{"score", bson.D{{"$meta", "vectorSearchScore"}}},
	}}}
// Runs the aggregation pipeline
cursor, err := coll.Aggregate(ctx, mongo.Pipeline{vectorSearchStage, projectStage})
if err != nil {
	log.Fatalf("failed to retrieve data from the server: %v", err)
}
// Displays the results
type ProjectedMovieResult struct {
	Title string  `bson:"title"`
	Plot  string  `bson:"plot"`
	Score float64 `bson:"score"`
}
var results []ProjectedMovieResult
if err = cursor.All(ctx, &results); err != nil {
	log.Fatalf("failed to unmarshal retrieved docs to ProjectedMovieResult objects: %v", err)
}
for _, result := range results {
	fmt.Printf("Title: %v \nPlot: %v \nScore: %v \n\n", result.Title, result.Plot, result.Score)
}
Title: Thrill Seekers
Plot: A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.
Score: 0.92730712890625
Title: About Time
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.
Score: 0.926605224609375
Title: The Time Machine
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.
Score: 0.9239959716796875
Title: Timecop
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.
Score: 0.923583984375
Title: Crusade in Jeans
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...
Score: 0.9222412109375

Información Adicional

Para obtener más información sobre MongoDB búsqueda vectorial, consulta las guías de MongoDB búsqueda vectorial en la documentación de MongoDB Atlas.

Para obtener más información sobre la sintaxis de la etapa del pipeline $vectorSearch, consulta las secciones de sintaxis y campos de la guía Crear y ejecutar consultas en la sección Búsqueda vectorial de MongoDB de la documentación de MongoDB Atlas.

Documentación de la API

Para obtener más información sobre cualquiera de los métodos o tipos discutidos en esta guía, consultar la siguiente documentación de la API:

Volver

MongoDB búsqueda

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