VectorDistanceType enum
The vector distance algorithm used by an HnswIndex (vector search).
Values
- euclidean → const VectorDistanceType
-
The default; typically "Euclidean squared" internally.
- cosine → const VectorDistanceType
-
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)
- dotProduct → const VectorDistanceType
-
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)
- dotProductNonNormalized → const VectorDistanceType
-
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)
Properties
Methods
-
noSuchMethod(
Invocation invocation) → dynamic -
Invoked when a nonexistent method or property is accessed.
inherited
-
toString(
) → String -
A string representation of this object.
inherited
Operators
-
operator ==(
Object other) → bool -
The equality operator.
inherited
Constants
-
values
→ const List<
VectorDistanceType> - A constant List of the values in this enum, in order of their declaration.