Package com.arcadedb.index.vector

Class HnswVectorIndexRAM.Builder<TId,​TVector,​TItem extends com.github.jelmerk.knn.Item<TId,​TVector>,​TDistance>

  • Type Parameters:
    TVector - Type of the vector to perform distance calculation on
    TDistance - Type of distance between items (expect any numeric type: float, double, int, ..)
    Enclosing class:
    HnswVectorIndexRAM<TId,​TVector,​TItem extends com.github.jelmerk.knn.Item<TId,​TVector>,​TDistance>
    public static class HnswVectorIndexRAM.Builder<TId,​TVector,​TItem extends com.github.jelmerk.knn.Item<TId,​TVector>,​TDistance>
extends Object
    Builder for initializing an HnswVectorIndexRAM instance.

    • Method Detail

      • withM

        public HnswVectorIndexRAM.Builder<TId,​TVector,​TItem,​TDistance> withM​(int m)
        Sets the number of bi-directional links created for every new element during construction. Reasonable range for m is 2-100. Higher m work better on datasets with high intrinsic dimensionality and/or high recall, while low m work better for datasets with low intrinsic dimensionality and/or low recalls. The parameter also determines the algorithm's memory consumption. As an example for d = 4 random vectors optimal m for search is somewhere around 6, while for high dimensional datasets (word embeddings, good face descriptors), higher M are required (e.g. m = 48, 64) for optimal performance at high recall. The range mM = 12-48 is ok for the most of the use cases. When m is changed one has to update the other parameters. Nonetheless, ef and efConstruction parameters can be roughly estimated by assuming that m efConstruction is a constant.
        Parameters:
        m - the number of bi-directional links created for every new element during construction
        Returns:
        the builder.

      • withEfConstruction

        public HnswVectorIndexRAM.Builder<TId,​TVector,​TItem,​TDistance> withEfConstruction​(int efConstruction)
        ` The parameter has the same meaning as ef, but controls the index time / index precision. Bigger efConstruction leads to longer construction, but better index quality. At some point, increasing efConstruction does not improve the quality of the index. One way to check if the selection of ef_construction was ok is to measure a recall for M nearest neighbor search when ef = efConstruction: if the recall is lower than 0.9, then there is room for improvement.
        Parameters:
        efConstruction - controls the index time / index precision
        Returns:
        the builder

      • withEf

        public HnswVectorIndexRAM.Builder<TId,​TVector,​TItem,​TDistance> withEf​(int ef)
        The size of the dynamic list for the nearest neighbors (used during the search). Higher ef leads to more accurate but slower search. The value ef of can be anything between k and the size of the dataset.
        Parameters:
        ef - size of the dynamic list for the nearest neighbors
        Returns:
        the builder