Vector Search

TidesDB supports approximate nearest-neighbor search through MariaDB’s built-in MHNSW vector index. The server handles graph construction and search, and TidesDB provides the storage for both the table data and the hidden MHNSW graph.

CREATE TABLE embeddings (
  id INT NOT NULL PRIMARY KEY, title VARCHAR(200),
  v VECTOR(384) NOT NULL, VECTOR INDEX (v)
) ENGINE=TIDESDB;

INSERT INTO embeddings VALUES (1, 'cat picture', Vec_FromText('[0.1, 0.9, ...]'));

Searching

Vector search uses ORDER BY VEC_DISTANCE_EUCLIDEAN() or VEC_DISTANCE_COSINE() with a LIMIT. The MHNSW index returns approximate neighbors without scanning the whole table:

SELECT id, title, VEC_DISTANCE_EUCLIDEAN(v, Vec_FromText('[0.15, 0.85, ...]')) AS dist
FROM embeddings ORDER BY dist LIMIT 5;

SELECT id, title, VEC_DISTANCE_COSINE(v, Vec_FromText('[0.15, 0.85, ...]')) AS dist
FROM embeddings ORDER BY dist LIMIT 5;

Index options

The MHNSW index accepts two optional parameters, both handled by the server:

CREATE TABLE docs (
  id INT PRIMARY KEY, v VECTOR(128) NOT NULL,
  VECTOR INDEX (v) M=12 DISTANCE='cosine'
) ENGINE=TIDESDB;

M is the number of neighbors per graph node, default 6, range 3 to 200. Higher values improve recall at the cost of slower inserts and more memory. DISTANCE selects the metric, euclidean (default) or cosine.

DML and limitations

All DML works on vector-indexed tables. INSERT adds the vector to the graph, DELETE removes it, and UPDATE on the vector column invalidates the old graph node and inserts a new one. The engine handles the interleaved record[0]/record[1] access pattern that the MHNSW maintenance uses for BLOB-backed vector data.

A partitioned table cannot carry a vector index, and MariaDB’s MHNSW implementation supports one vector index per table.