Cosine distance between a stored vector attribute and a query embedding — the ash_ai-compatible counterpart to AshNeo4j.Functions.VectorSimilarity.

Returns a float in [0.0, 2.0] where 0.0 is identical direction and larger is further apart (lower = closer). This matches pgvector's <=> operator (vector_cosine_distance in ash_ai), so the same read action expression —

Ash.Query.filter(query, vector_cosine_distance(embedding, ^q) < 0.5)
|> Ash.Query.sort({calc(vector_cosine_distance(embedding, ^q), type: :float), :asc})
|> Ash.Query.limit(10)

— works against AshNeo4j and AshPostgres alike.

Neo4j's vector.similarity.cosine/2 returns a normalised similarity in [0.0, 1.0] (1.0 identical, 0.5 orthogonal, 0.0 opposite). The data layer maps it back to pgvector-style distance as 2 * (1 - similarity).

Requires Cypher 25 (Neo4j ≥ 2025.06). The data layer pushes this down to 2 * (1 - vector.similarity.cosine(s.embedding, $q)). evaluate/1 mirrors that exact maths in Elixir (1 - raw_cosine) so the data layer's in-memory correctness re-filter agrees with the pushdown.