OBXVectorDistanceType class abstract
The vector distance algorithm used by an HNSW index (vector search).
Constructors
Properties
- hashCode → int
-
The hash code for this object.
no setterinherited
- runtimeType → Type
-
A representation of the runtime type of the object.
no setterinherited
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
- Cosine → const int
- 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 int
- 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 int
- 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)
- Euclidean → const int
- The default; typically "Euclidean squared" internally.
- Hamming → const int
- Manhattan → const int
- Unknown → const int
- Not a real type, just best practice (e.g. forward compatibility)