Vector query (kNN)

Vector query (kNN) #

Finds the k nearest neighbors of a query vector — similarity search over dense_vector / sparse_vector fields. The query vector can be supplied directly, or as natural-language text that the serving layer embeds with the field’s configured model (schema-driven semantic search).

How it works #

Execution depends on where the documents live:

• Realtime (not yet flushed) — an exact brute-force scan over the mutable store: every vector is scored, a size-k min-heap keeps the best, and a filter (see below) is tested before any distance is computed.
• Flushed segments — the field’s index_type decides: flat scans the stored f32 vectors (exact, O(N·dims)); the graph types (hnsw_flat, turbo_quant_hnsw, bbq_hnsw) traverse the persisted HNSW graph with distances computed on the flat vectors — scores stay exact and only an ef-bounded neighborhood is evaluated; the quantized scans (turbo_quant, bbq_flat) score compact codes for a k × oversampling candidate pool and rescore it with full precision (approximate). The per-type breakdown is in Index types.

Scores follow the field’s similarity (overridable per query, but it must match the schema): cosine is the cosine similarity in [-1.0, 1.0]; dot_product the raw dot product; l2 scores as the negated squared Euclidean distance — 0 means identical vectors. Higher is always more similar, so one top-k heap serves all three.