Vector Search Queries

Nearest Neighbor

SEARCH articles USING VECTOR(embedding, ARRAY[0.1, 0.3, -0.2, ...], 10);

Returns the 10 nearest neighbors by the metric configured on the index (l2, cosine, inner_product, manhattan, chebyshev, hamming, jaccard, or pearson).

Filtered Vector Search

SELECT title, vector_distance(embedding, ARRAY[0.1, 0.3, ...]) AS score
FROM articles
WHERE category = 'machine-learning'
  AND id IN (SEARCH articles USING VECTOR(embedding, ARRAY[0.1, 0.3, ...], 10));

The engine builds a Roaring Bitmap of matching IDs and selects the optimal strategy: pre-filter (selective filters), post-filter (broad filters), or brute-force (very selective).

Distance Function

SELECT id, vector_distance(embedding, $query_vec) AS dist
FROM articles
ORDER BY dist LIMIT 10;

Hybrid Vector + Text (RRF)

SELECT title, rrf_score(
    vector_distance(embedding, $query_vec),
    bm25_score(body, 'transformer attention')
) AS score
FROM articles
LIMIT 10;

Reciprocal Rank Fusion merges BM25 text results with vector similarity in a single pass.

Multi-Vector

Collections can have multiple vector indexes on different embedding columns:

CREATE VECTOR INDEX idx_text ON products METRIC cosine DIM 384;
CREATE VECTOR INDEX idx_image ON products METRIC cosine DIM 512;