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Búsqueda vectorial de MongoDB

Overview

En esta guía, aprenderá a usar la función de búsqueda vectorial de MongoDB en el controlador Java Reactive Streams. La Aggregates clase de constructores proporciona el vectorSearch() método auxiliar que puede usar para crear una etapa de canalización $vectorSearch. Esta etapa de canalización le permite realizar una búsqueda semántica en sus 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 proporcionados.

Importante

Compatibilidad de características

MongoDB Vector Search admite la búsqueda aproximada del vecino más cercano (ANN) en clústeres de Atlas que ejecutan MongoDB6.0.11 v, v7.0.2 o posterior, y la búsqueda exacta del vecino más cercano (ENN) en clústeres que ejecutan MongoDB v,6.0.16 v..,7 0v..10 732o posterior. También puede usar MongoDB Vector Search con implementaciones de Atlas autogestionadas o locales que cree con la CLI de Atlas.

Datos de muestra

Los ejemplos en esta guía utilizan la colección embedded_movies en la base de datos sample_mflix de los conjuntos de datos de muestra de Atlas. Para aprender a crear un clúster gratuito de MongoDB Atlas y cargar los conjuntos de datos de muestra, consulte el tutorial Primeros pasos .

Realiza una búsqueda vectorial

Para ejecutar consultas de búsqueda vectorial, debe crear un índice de búsqueda vectorial e indexar sus incrustaciones vectoriales. Para obtener información sobre cómo crear un índice de búsqueda vectorial mediante programación, consulte la sección «Administración de índices de búsqueda de MongoDB» en la guía «Índices». Para obtener más información sobre las incrustaciones vectoriales, consulte «Cómo indexar incrustaciones vectoriales para la búsqueda vectorial» en la documentación de Atlas.

Una vez que hayas creado un índice de búsqueda vectorial en tus incrustaciones vectoriales, puedes hacer referencia a este índice en la etapa de tu pipeline, como se muestra en la siguiente sección.

Ejemplo de búsqueda vectorial

Este ejemplo crea una canalización de agregación que realiza las siguientes acciones:

  • Realiza una búsqueda ENN en el campo plot_embedding.

  • Limita los resultados al documento 1

  • Coincide con los documentos donde el valor year es mayor o igual que 2016.

  • Proyecta la puntuación de búsqueda vectorial en un campo llamado vectorSearchScore

// Create an instance of the BinaryVector class as the query vector
float[] rawVector = new float[]{ -0.0016261312f, -0.028070757f, -0.011342932f, -0.012775794f, -0.0027440966f, 0.008683807f, -0.02575152f, -0.02020668f, -0.010283281f, -0.0041719596f, 0.021392956f, 0.028657231f, -0.006634482f, 0.007490867f, 0.018593878f, 0.0038187427f, 0.029590257f, -0.01451522f, 0.016061379f, 0.00008528442f, -0.008943722f, 0.01627464f, 0.024311995f, -0.025911469f, 0.00022596726f, -0.008863748f, 0.008823762f, -0.034921836f, 0.007910728f, -0.01515501f, 0.035801545f, -0.0035688248f, -0.020299982f, -0.03145631f, -0.032256044f, -0.028763862f, -0.0071576433f, -0.012769129f, 0.012322609f, -0.006621153f, 0.010583182f, 0.024085402f, -0.001623632f, 0.007864078f, -0.021406285f, 0.002554159f, 0.012229307f, -0.011762793f, 0.0051682983f, 0.0048484034f, 0.018087378f, 0.024325324f, -0.037694257f, -0.026537929f, -0.008803768f, -0.017767483f, -0.012642504f, -0.0062712682f, 0.0009771782f, -0.010409906f, 0.017754154f, -0.004671795f, -0.030469967f, 0.008477209f, -0.005218282f, 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BinaryVector queryVector = BinaryVector.floatVector(rawVector);
// Specify the index name for the vector embedding index
String indexName = "vector_index";
// Specify the path of the field to search on
FieldSearchPath fieldSearchPath = SearchPath.fieldPath("plot_embedding");
// Limit the number of matches to 1
long limit = 1;
// Create a pre-filter to only search within a subset of documents
VectorSearchOptions options = VectorSearchOptions.exactVectorSearchOptions()
    .filter(Filters.gte("year", 2016));
// Create the vectorSearch pipeline stage
Publisher<Document> publisher = collection.aggregate(
    Arrays.asList(
        Aggregates.vectorSearch(
            fieldSearchPath,
            queryVector,
            indexName,
            limit,
            options),
        Aggregates.project(
            Projections.metaVectorSearchScore("vectorSearchScore"))
    )
);

Tip

Query Vector Type

El ejemplo anterior crea una instancia de BinaryVector para que sirva como vector de query, pero también puedes crear un List de instancias de Double. Sin embargo, se recomienda usar el tipo BinaryVector para mejorar la eficiencia del almacenamiento.

El siguiente ejemplo muestra cómo ejecutar la agregación e imprimir la meta-puntuación de búsqueda vectorial a partir del resultado de la anterior pipeline de agregación:

Document found = Flux.from(publisher).blockFirst();
if (found != null) {
    double score = found.getDouble("vectorSearchScore");
    System.out.println("vectorSearch score: " + score);
}
vectorSearch score: 0.8821365833282471

Documentación de la API

Para obtener más información sobre los métodos y tipos mencionados en esta guía, vea la siguiente documentación de la API:

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