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Búsqueda vectorial de MongoDB

Overview

En esta guía, puedes aprender cómo utilizar la funcionalidad MongoDB búsqueda vectorial en el driver Java Reactive Streams. La clase de desarrolladores Aggregates proporciona el método asistente vectorSearch() que puedes usar para crear una etapa del pipeline $vectorSearch. Esta etapa del pipeline permite realizar una búsqueda semántica en los documentos. Una búsqueda semántica es un tipo de búsqueda que localiza información de significado similar, pero no necesariamente idéntica, al término o frase de búsqueda proporcionado.

Importante

Compatibilidad de características

MongoDB Vector Search admite la búsqueda de ANN (ANN) en clústeres de Atlas que ejecutan MongoDB v6.0.11, v7.0.2 o posterior y la búsqueda de vecinos más cercanos exactos (ENN) en clústeres que ejecutan MongoDB v6.0.16, v7.0.10, v7.3.2, o posterior. También puedes usar MongoDB búsqueda vectorial con implementaciones Atlas autogestionadas o locales que crees con la Atlas CLI.

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 Gestión de índices de MongoDB Search 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 compila un pipeline de agregación que realiza las siguientes acciones:

  • Ejecuta una búsqueda ENN en el campo plot_embedding

  • Limita los resultados a 1 documentos

  • 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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