Package io.objectbox.model

Class HnswDistanceType

  • public final class HnswDistanceType
extends java.lang.Object
    The distance algorithm used by an HNSW index (vector search).

    • Field Summary

      Fields 
      Modifier and Type Field Description
      static short Cosine
      Cosine similarity compares two vectors irrespective of their magnitude (compares the angle of two vectors).
      static short DotProduct
      For normalized vectors (vector length == 1.0), the dot product is equivalent to the cosine similarity.
      static short DotProductNonNormalized
      A custom dot product similarity measure that does not require the vectors to be normalized.
      static short Euclidean
      The default; typically "Euclidean squared" internally.
      static short Geo
      For geospatial coordinates aka latitude/longitude pairs.
      static java.lang.String[] names  
      static short Unknown
      Not a real type, just best practice (e.g.

    • Constructor Summary

      Constructors 
      Modifier Constructor Description
      private HnswDistanceType()  

    • Method Summary

      All Methods Static Methods Concrete Methods 
      Modifier and Type Method Description
      static java.lang.String name​(int e)  

      • Methods inherited from class java.lang.Object

        clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Field Detail

      • Unknown

        public static final short Unknown
        Not a real type, just best practice (e.g. forward compatibility)
        See Also:
        Constant Field Values

      • Euclidean

        public static final short Euclidean
        The default; typically "Euclidean squared" internally.
        See Also:
        Constant Field Values

      • Cosine

        public static final short Cosine
        Cosine similarity compares two vectors irrespective of their magnitude (compares the angle of two vectors). Often used for document or semantic similarity. Value range: 0.0 - 2.0 (0.0: same direction, 1.0: orthogonal, 2.0: opposite direction)
        See Also:
        Constant Field Values

      • DotProduct

        public static final short DotProduct
        For normalized vectors (vector length == 1.0), the dot product is equivalent to the cosine similarity. Because of this, the dot product is often preferred as it performs better. Value range (normalized vectors): 0.0 - 2.0 (0.0: same direction, 1.0: orthogonal, 2.0: opposite direction)
        See Also:
        Constant Field Values

      • Geo

        public static final short Geo
        For geospatial coordinates aka latitude/longitude pairs. Note, that the vector dimension must be 2, with the latitude being the first element and longitude the second. Internally, this uses haversine distance.
        See Also:
        Constant Field Values

      • DotProductNonNormalized

        public static final short DotProductNonNormalized
        A custom dot product similarity measure that does not require the vectors to be normalized. Note: this is no replacement for cosine similarity (like DotProduct for normalized vectors is). The non-linear conversion provides a high precision over the entire float range (for the raw dot product). The higher the dot product, the lower the distance is (the nearer the vectors are). The more negative the dot product, the higher the distance is (the farther the vectors are). Value range: 0.0 - 2.0 (nonlinear; 0.0: nearest, 1.0: orthogonal, 2.0: farthest)
        See Also:
        Constant Field Values

      • names

        public static final java.lang.String[] names

    • Constructor Detail

      • HnswDistanceType

        private HnswDistanceType()

    • Method Detail

      • name

        public static java.lang.String name​(int e)