MongoDB Vector Search

// 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"))
    )
);