Vector

Use Vector for a fixed-width numeric representation supplied by another system, such as an embedding or sensor window. Use Text when relflow should encode strings, or Branch for repeated objects.

embedding: [0.12, -0.03, 0.44, 0.08]

import relflow as rf

embedding = rf.Vector(n_dim=4, objective="l2", jitter=rf.Jitter(add=0.02))

Input and options

Each valued input must be an Arrow list or fixed-size list with exactly n_dim numeric, non-null elements. Values convert to float32. A null vector is a separate field state; null elements inside a valued vector are invalid. relflow does not fit a vector normalizer: prepare coordinate scales upstream.

Option	Default	Meaning
n_dim	required	Positive vector width.
jitter	rf.Jitter()	Training noise in supplied coordinate units.
objective	"l2"	Reconstruction loss: "l1" or "l2".

Jitter changes finite coordinates of valued training inputs before projection. Targets remain pristine. Both settings of jitter.normalize use the same coordinate space; see Jitter.

Learning and output

Reconstruction combines state cross entropy with content loss on valued targets. MAE and RMSE metrics use the supplied coordinate scale.

Prediction content is fixed_size_list<float32>[n_dim]. When the most likely predicted state is non-valued, the writer emits a zero vector. Zeros therefore do not establish whether a vector was observed; read the shared state and inferred members in the prediction envelope.

Use shared leaf options to make the vector a target or export a contextual embedding. That exported embedding has the model width and is distinct from the supplied vector.