Vector Similarity
Compare a record's embedding against a reference vector with cosine, L2, or dot-product distance.
The vector_distance operator builds a per-row vector out of named component fields, measures its distance to a constant reference vector, and compares that distance with an op and value. Use it to flag records whose embedding is close to (or far from) a known point.
The vector_distance leaf
vector_distance leaf- vector_distance:
dims: [embedding_0, embedding_1, embedding_2]
metric: cosine
reference: [0.1, 0.2, 0.3]
op: gt
value: 0.8Field reference
| Field | Purpose |
|---|---|
dims | The list of per-component field names that form the vector — one field per dimension, not a single array field. |
metric | The distance metric: cosine, l2, or dot. |
reference | The constant reference vector. Its length must match the number of dims. |
op | Comparison operator applied to the computed distance. |
value | The threshold to compare against. |
Metrics
cosine— cosine distance between the row vector and the reference.l2— Euclidean (straight-line) distance.dot— dot product of the two vectors.
The kernels are vectorized with NEON (the ARM SIMD instruction set; SIMD = single instruction, multiple data) on Apple Silicon. Unlike a Window, vector_distance is stateless — it depends only on the current row, with no cross-batch history.
This is not a nearest-neighbor index
vector_distancemeasures each row's distance to one constant reference vector. It is not an ANN (approximate nearest neighbor) index and does not search a corpus of vectors. For nearest-neighbor search over a large embedding collection, use a dedicated vector database.