Vector Utilities

Inspect, transform, and perform arithmetic operations on vectors, and manage vector indexes.

Inspection

ai.dimension()

Returns the number of dimensions in a vector.

GQL
LET v = ai.vector([3.0, 4.0])
RETURN ai.dimension(v)

Result: 2

ai.magnitude()

Returns the magnitude (L2 norm) of a vector.

GQL
LET v = ai.vector([3.0, 4.0])
RETURN ai.magnitude(v)

Result: 5

ai.normalize()

Normalizes a vector to a unit vector (magnitude of 1).

GQL
LET v = ai.vector([3.0, 4.0])
RETURN ai.normalize(v)

Result:

JSON
{
  "values": [
    0.6000000238418579,
    0.800000011920929
  ]
}

ai.toList()

Converts a vector to a list of numbers.

GQL
LET embedding = ai.embed("Introduction to graph databases")
RETURN ai.toList(embedding)

Result: [-0.0258026123046875, -0.0126800537109375, …, 0.0162200927734375, -0.017486572265625]

Vector Arithmetic

ai.add()

Adds two vectors element-wise.

GQL
LET v1 = ai.vector([1.0, 2.0])
LET v2 = ai.vector([3.0, 4.0])
RETURN ai.toList(ai.add(v1, v2))

Result: [4, 6]

ai.subtract()

Subtracts the second vector from the first element-wise.

GQL
LET v1 = ai.vector([5.0, 3.0])
LET v2 = ai.vector([1.0, 2.0])
RETURN ai.toList(ai.subtract(v1, v2))

Result: [4, 1]

ai.scale()

Multiplies a vector by a scalar value.

GQL
LET v = ai.vector([1.0, 2.0, 3.0])
RETURN ai.toList(ai.scale(v, 2))

Result: [2, 4, 6]

Vector Index Management

ai.rebuildIndex()

Rebuilds an HNSW vector index from the underlying data. Use this to recover a STALE index (e.g., after a crash) or to apply new m/efConstruction parameters.

GQL
RETURN ai.rebuildIndex('summary_embedding')

ai.setIndexOption()

Updates a runtime-mutable vector index option. Currently only efSearch can be changed at runtime; changing m or efConstruction requires a rebuild.

GQL
RETURN ai.setIndexOption('summary_embedding', 'efSearch', 200)