Filtered Search with VBase

This example combines relational filters and vector search in one PostgreSQL query.

DROP TABLE IF EXISTS akasicdb_example_tenant_articles;

CREATE TABLE akasicdb_example_tenant_articles (
  id integer PRIMARY KEY,
  tenant_id integer NOT NULL,
  category_id integer NOT NULL,
  status text NOT NULL,
  title text NOT NULL,
  created_at timestamptz NOT NULL,
  embedding vector(3) NOT NULL
);

INSERT INTO akasicdb_example_tenant_articles
  (id, tenant_id, category_id, status, title, created_at, embedding)
VALUES
  (101, 1, 10, 'published', 'close article from another tenant', '2026-05-01', ARRAY[0.10, 0.10, 0.00]::real[]::vector),
  (102, 2, 10, 'draft', 'close draft article', '2026-05-02', ARRAY[0.11, 0.10, 0.00]::real[]::vector),
  (201, 2, 10, 'published', 'tenant guide overview', '2026-05-03', ARRAY[0.12, 0.10, 0.00]::real[]::vector),
  (202, 2, 10, 'published', 'tenant guide indexing', '2026-05-04', ARRAY[0.20, 0.10, 0.00]::real[]::vector),
  (203, 2, 10, 'published', 'tenant guide operations', '2026-05-05', ARRAY[0.28, 0.10, 0.00]::real[]::vector),
  (204, 2, 20, 'published', 'different category', '2026-05-06', ARRAY[0.13, 0.10, 0.00]::real[]::vector),
  (205, 2, 10, 'archived', 'archived close article', '2026-05-07', ARRAY[0.14, 0.10, 0.00]::real[]::vector),
  (301, 3, 10, 'published', 'close article from tenant three', '2026-05-08', ARRAY[0.15, 0.10, 0.00]::real[]::vector);

CREATE INDEX akasicdb_tenant_articles_meta_idx
ON akasicdb_example_tenant_articles (tenant_id, category_id, status, created_at);

CREATE INDEX akasicdb_tenant_articles_embedding_idx
ON akasicdb_example_tenant_articles
USING vectoron (embedding vector_l2_ops)
WITH (options = $$
[indexing.hnsw]
$$);

SELECT fence_vector_index('akasicdb_tenant_articles_embedding_idx'::regclass);

BEGIN;
SET LOCAL vectoron.search_mode = 'vbase';

SELECT id, title
FROM akasicdb_example_tenant_articles
WHERE tenant_id = 2
  AND category_id = 10
  AND status = 'published'
  AND created_at >= TIMESTAMPTZ '2026-05-01'
ORDER BY embedding <-> ARRAY[0.10, 0.10, 0.00]::real[]::vector
LIMIT 3;

COMMIT;

Query Shape

Part	Role
B-tree index	Supports scalar predicates such as tenant_id, category_id, status, and created_at
AkasicDB vector index	Supports vector ordering with ORDER BY embedding <-> ...
vectoron.search_mode = 'vbase'	Explicitly pins the default search mode for the transaction

The query result contains rows that satisfy the scalar predicates. The matching rows are ordered by vector distance inside the same SQL statement.