Tune Vamana Search

Vamana is an ANN index for L2-distance workloads.

This example uses vector_l2_ops and configures the build-time parameter indexing.vamana.l_search separately from the search-time parameter vectoron.vamana_l_search.

DROP TABLE IF EXISTS akasicdb_example_research_notes;

CREATE TABLE akasicdb_example_research_notes (
  id integer PRIMARY KEY,
  topic text NOT NULL,
  embedding vector(4) NOT NULL
);

INSERT INTO akasicdb_example_research_notes (id, topic, embedding) VALUES
  (1, 'vector index overview', ARRAY[0.10,0.80,0.05,0.00]::real[]::vector),
  (2, 'ann search tuning', ARRAY[0.12,0.78,0.06,0.01]::real[]::vector),
  (3, 'vamana graph notes', ARRAY[0.09,0.82,0.04,0.00]::real[]::vector),
  (4, 'query latency review', ARRAY[0.15,0.74,0.08,0.02]::real[]::vector),
  (5, 'recall measurement', ARRAY[0.18,0.70,0.07,0.02]::real[]::vector),
  (6, 'filter selectivity', ARRAY[0.20,0.65,0.10,0.03]::real[]::vector),
  (7, 'index operations', ARRAY[0.24,0.60,0.12,0.03]::real[]::vector),
  (8, 'tenant search design', ARRAY[0.26,0.56,0.16,0.04]::real[]::vector),
  (9, 'product taxonomy', ARRAY[0.30,0.50,0.20,0.05]::real[]::vector),
  (10, 'document ranking', ARRAY[0.32,0.48,0.22,0.05]::real[]::vector),
  (11, 'graph traversal', ARRAY[0.50,0.30,0.35,0.08]::real[]::vector),
  (12, 'storage checkpoint', ARRAY[0.55,0.25,0.40,0.10]::real[]::vector),
  (13, 'backup planning', ARRAY[0.60,0.20,0.42,0.12]::real[]::vector),
  (14, 'access control', ARRAY[0.65,0.18,0.44,0.14]::real[]::vector),
  (15, 'event ingestion', ARRAY[0.70,0.16,0.46,0.16]::real[]::vector),
  (16, 'analytics refresh', ARRAY[0.74,0.14,0.48,0.18]::real[]::vector),
  (17, 'search api design', ARRAY[0.16,0.76,0.10,0.02]::real[]::vector),
  (18, 'ranking eval', ARRAY[0.14,0.79,0.09,0.01]::real[]::vector),
  (19, 'vector schema', ARRAY[0.11,0.81,0.03,0.00]::real[]::vector),
  (20, 'index memory review', ARRAY[0.22,0.62,0.13,0.03]::real[]::vector),
  (21, 'content moderation', ARRAY[0.40,0.40,0.28,0.07]::real[]::vector),
  (22, 'stream processing', ARRAY[0.78,0.12,0.50,0.20]::real[]::vector),
  (23, 'catalog sync', ARRAY[0.34,0.46,0.24,0.06]::real[]::vector),
  (24, 'observability plan', ARRAY[0.28,0.58,0.18,0.04]::real[]::vector),
  (25, 'sql migration', ARRAY[0.62,0.22,0.43,0.11]::real[]::vector),
  (26, 'read replica notes', ARRAY[0.66,0.19,0.45,0.13]::real[]::vector),
  (27, 'feature flags', ARRAY[0.72,0.15,0.47,0.17]::real[]::vector),
  (28, 'dashboard refresh', ARRAY[0.76,0.13,0.49,0.19]::real[]::vector),
  (29, 'metadata filters', ARRAY[0.21,0.64,0.11,0.03]::real[]::vector),
  (30, 'distance operators', ARRAY[0.13,0.77,0.05,0.01]::real[]::vector),
  (31, 'index build queue', ARRAY[0.25,0.59,0.14,0.03]::real[]::vector),
  (32, 'service limits', ARRAY[0.68,0.17,0.46,0.15]::real[]::vector);

CREATE INDEX akasicdb_research_notes_vamana_idx
ON akasicdb_example_research_notes
USING vectoron (embedding vector_l2_ops)
WITH (options = $$
[indexing.vamana]
alpha = 1.2
r = 16
l_search = 32
$$);

SELECT fence_vector_index('akasicdb_research_notes_vamana_idx'::regclass);

BEGIN;
SET LOCAL vectoron.vamana_l_search = 80;

SELECT id, topic
FROM akasicdb_example_research_notes
ORDER BY embedding <-> ARRAY[0.10, 0.80, 0.05, 0.00]::real[]::vector
LIMIT 5;

COMMIT;

Settings

Setting	Applies at	Notes
indexing.vamana.alpha	Index build	Controls graph pruning. Its effect on recall, build time, and index size depends on the dataset.
indexing.vamana.r	Index build	Neighbor count retained for each node. Higher values may add graph edges and may increase memory use and build cost.
indexing.vamana.l_search	Index build	Candidate search width during index construction. Higher values may increase build cost.
vectoron.vamana_l_search	Query execution	Candidate exploration width during Vamana search. Higher values evaluate more candidates and may increase CPU work and latency.

Changing build-time options requires rebuilding the index. SET LOCAL applies only inside the surrounding transaction.

When tuning vectoron.vamana_l_search, compare recall and latency before choosing a value.