WEBINAR
The Next Generation of MariaDB: Powered by Vector Search
MariaDB Vector is a feature that allows MariaDB Server to perform as a relational vector database. Vectors generated by an AI model can be stored and searched in MariaDB.
The initial implementation uses the modified HNSW algorithm for searching in the vector index (to solve the so-called Approximate Nearest Neighbor problem), and defaults to Euclidean distance. Concurrent reads/writes and all transaction isolation levels are supported.
MariaDB uses int16 for indexes, which gives 15 bits to store the value, rather than 10 bits for float16.
Vectors can be defined using VECTOR INDEX for the index definition, and using the in the statement.
The distance function used to build the vector index can be euclidean (the default) or cosine. An additional option, M, can be used to configure the vector index. Larger values mean slower SELECT and INSERT statements, larger index size and higher memory consumption but more accurate results. The valid range is from 3 to 200.
Vector columns store .
Alternatively, you can use VEC_FromText() function:
For vector indexes built with the euclidean function, can be used. It calculates a Euclidean (L2) distance between two points:
Most commonly, this kind of query is done with a limit, for example to return vectors that are closest to a given vector, such as from a user search query, image or a song fragment:
For vector indexes built with the cosine function, can be used. It calculates a between two vectors:
The function is a generic function that behaves either as or , depending on the underlying index type:
There are a number of system variables used for vectors. See .
MariaDB Vector is integrated in several frameworks, see .
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This page is licensed: CC BY-SA / Gnu FDL
CREATE TABLE v (
id INT PRIMARY KEY,
v VECTOR(5) NOT NULL,
VECTOR INDEX (v)
);CREATE TABLE embeddings (
doc_id BIGINT UNSIGNED PRIMARY KEY,
embedding VECTOR(1536) NOT NULL,
VECTOR INDEX (embedding) M=8 DISTANCE=cosine
);INSERT INTO v VALUES
(1, x'e360d63ebe554f3fcdbc523f4522193f5236083d'),
(2, x'f511303f72224a3fdd05fe3eb22a133ffae86a3f'),
(3,x'f09baa3ea172763f123def3e0c7fe53e288bf33e'),
(4,x'b97a523f2a193e3eb4f62e3f2d23583e9dd60d3f'),
(5,x'f7c5df3e984b2b3e65e59d3d7376db3eac63773e'),
(6,x'de01453ffa486d3f10aa4d3fdd66813c71cb163f'),
(7,x'76edfc3e4b57243f10f8423fb158713f020bda3e'),
(8,x'56926c3fdf098d3e2c8c5e3d1ad4953daa9d0b3e'),
(9,x'7b713f3e5258323f80d1113d673b2b3f66e3583f'),
(10,x'6ca1d43e9df91b3fe580da3e1c247d3f147cf33e');INSERT INTO v VALUES
(1,Vec_FromText('[0.418708,0.809902,0.823193,0.598179,0.0332549]')),
(2,Vec_FromText('[0.687774,0.789588,0.496138,0.57487,0.917617]')),
(3,Vec_FromText('[0.333221,0.962687,0.467263,0.448235,0.475671]')),
(4,Vec_FromText('[0.822185,0.185643,0.683452,0.211072,0.554056]')),
(5,Vec_FromText('[0.437057,0.167281,0.0770977,0.428638,0.241591]')),
(6,Vec_FromText('[0.76956,0.926895,0.803376,0.0157961,0.589042]')),
(7,Vec_FromText('[0.493999,0.641957,0.761598,0.94276,0.425865]')),
(8,Vec_FromText('[0.924108,0.275466,0.0543329,0.0731585,0.136344]')),
(9,Vec_FromText('[0.186956,0.69666,0.0356002,0.668875,0.84722]')),
(10,Vec_FromText('[0.415294,0.609278,0.426765,0.988832,0.475556]'));SELECT id FROM v ORDER BY
VEC_DISTANCE_EUCLIDEAN(v, x'6ca1d43e9df91b3fe580da3e1c247d3f147cf33e');
+----+
| id |
+----+
| 10 |
| 7 |
| 3 |
| 9 |
| 2 |
| 1 |
| 5 |
| 4 |
| 6 |
| 8 |
+----+SELECT id FROM v
ORDER BY VEC_DISTANCE_EUCLIDEAN(v, x'6ca1d43e9df91b3fe580da3e1c247d3f147cf33e')
LIMIT 2;
+----+
| id |
+----+
| 10 |
| 7 |
+----+SELECT VEC_DISTANCE_COSINE(VEC_FROMTEXT('[1,2,3]'), VEC_FROMTEXT('[3,5,7]'));SELECT id FROM v
ORDER BY VEC_DISTANCE(v, x'6ca1d43e9df91b3fe580da3e1c247d3f147cf33e');
+----+
| id |
+----+
| 10 |
| 7 |
| 3 |
| 9 |
| 2 |
| 1 |
| 5 |
| 4 |
| 6 |
| 8 |
+----+WEBINAR
The Next Generation of MariaDB: Powered by Vector Search