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Ejecuta una query de búsqueda vectorial en MongoDB

Overview

En esta guía, puedes aprender cómo usar la funcionalidad MongoDB Vector Search en el driver de Go utilizando la etapa de pipeline $vectorSearch. Esta etapa de la pipeline te permite realizar una búsqueda semántica en tus documentos. Una búsqueda semántica es un tipo de búsqueda que localiza información similar en significado, pero no necesariamente idéntica, al término o frase de búsqueda que has proporcionado.

Importante

Compatibilidad de características

Para aprender qué versiones de MongoDB Atlas admiten esta funcionalidad, consulte Limitaciones en la documentación de MongoDB Atlas.

Datos de muestra

El ejemplo en esta página consulta el campo plot_embedding de la colección embedded_movies, 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 muestra, consulta Comienza 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 acerca de cómo crear programáticamente un índice de búsqueda vectorial, consulta MongoDB Search y MongoDB Vector Search Indexes. Para aprender más acerca de incrustaciones vectoriales, consulte Cómo indexar incrustaciones vectoriales para la 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 este embedding de vector a un vector binario BSON que puede utilizar como vector de query:

// 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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// 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 se puede utilizar un arreglo de valores BSON double. Sin embargo, recomendamos el uso de un vector binario BSON para mejorar la eficiencia del almacenamiento.

Ejecutar la query vectorial de búsqueda

El siguiente ejemplo muestra cómo construir una pipeline de agregación que utiliza los métodos $vectorSearch y $project para realizar una búsqueda vectorial de 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)
}

Consulta un índice de auto-embeddings

Puede automatizar la generación de vectores para búsquedas de texto consultando un índice de búsqueda vectorial auto-incrustación de MongoDB. Para obtener información sobre cómo crear un índice de autoincrustación utilizando el controlador Go, consulte la sección Modelo de Índice de Búsqueda de Auto-Incrustación de MongoDB en la guía Índices.

El siguiente ejemplo crea una canalización de agregación que busca la semejanza semántica con la frase "time travel" en el campo plot. La consulta utiliza un índice de búsqueda vectorial auto-incrustación de MongoDB en el campo plot llamado auto_embedding_index:

// Creates the $vectorSearch stage that queries the
// auto-embedding index named "auto_embedding_index"
vectorSearchStage := bson.D{
	{"$vectorSearch", bson.D{
		{"index", "auto_embedding_index"},
		{"path", "plot"},
		{"query", bson.D{{"text", "time travel"}}},
		{"model", "voyage-4"},
		{"numCandidates", 150},
		{"limit", 10},
	}},
}
// Projects only the title and plot fields
projectStage := bson.D{
	{"$project", bson.D{
		{"_id", 0},
		{"title", 1},
		{"plot", 1},
	}},
}
// 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)
}
defer cursor.Close(ctx)
type ProjectedMovieResult struct {
	Title string `bson:"title"`
	Plot  string `bson:"plot"`
}
var results []ProjectedMovieResult
if err := cursor.All(ctx, &results); err != nil {
	log.Fatalf("failed to unmarshal retrieved docs: %v", err)
}
for _, result := range results {
	fmt.Printf("Title: %v\nPlot: %v\n---\n\n", result.Title, result.Plot)
}

Nota

Al query un índice de búsqueda vectorial MongoDB auto-embedding, proporcione el texto que se desea buscar en el campo query (por ejemplo, {"text", "time travel"}) y un nombre de modelo compatible en el campo model. Los modelos compatibles son voyage-4-lite, voyage-4, voyage-4-large y voyage-code-3. No necesitas calcular ni pasar un valor queryVector.

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:

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