# Vector Overview

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{% hint style="info" %}
Vectors are available from MariaDB 11.7.
{% endhint %}

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](../../../sql-statements/transactions/set-transaction#isolation-level) are supported.

MariaDB uses `int16` for indexes, which gives 15 bits to store the value, rather than 10 bits for float16.

## Creating

Vectors can be defined using `VECTOR INDEX` for the index definition, and using the [VECTOR data type](../../data-types/numeric-data-types/vector) in the [CREATE TABLE](create-table-with-vectors) statement.

```sql
CREATE TABLE v (
     id INT PRIMARY KEY,
     v VECTOR(5) NOT NULL,
     VECTOR INDEX (v)
);
```

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`.

```sql
CREATE TABLE embeddings (
        doc_id BIGINT UNSIGNED PRIMARY KEY,
        embedding VECTOR(1536) NOT NULL,
        VECTOR INDEX (embedding) M=8 DISTANCE=cosine
);
```

## Inserting

Vector columns store [32-bit IEEE 754 floating point numbers](https://en.wikipedia.org/wiki/Single-precision_floating-point_format).

```sql
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');
```

Alternatively, you can use `VEC_FromText()` function:

```sql
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]'));
```

## Querying

For vector indexes built with the `euclidean` function, [VEC\_DISTANCE\_EUCLIDEAN](vector-functions/vec_distance_euclidean) can be used. It calculates a Euclidean (L2) distance between two points:

```sql
SELECT id FROM v ORDER BY 
  VEC_DISTANCE_EUCLIDEAN(v, x'6ca1d43e9df91b3fe580da3e1c247d3f147cf33e');
+----+
| id |
+----+
| 10 |
|  7 |
|  3 |
|  9 |
|  2 |
|  1 |
|  5 |
|  4 |
|  6 |
|  8 |
+----+
```

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:

```sql
SELECT id FROM v 
  ORDER BY VEC_DISTANCE_EUCLIDEAN(v, x'6ca1d43e9df91b3fe580da3e1c247d3f147cf33e') 
  LIMIT 2;
+----+
| id |
+----+
| 10 |
|  7 |
+----+
```

For vector indexes built with the `cosine` function, [VEC\_DISTANCE\_COSINE](vector-functions/vec_distance_cosine) can be used. It calculates a [Cosine distance](https://en.wikipedia.org/wiki/Cosine_similarity#Cosine_distance) between two vectors:

```sql
SELECT VEC_DISTANCE_COSINE(VEC_FROMTEXT('[1,2,3]'), VEC_FROMTEXT('[3,5,7]'));
```

The [VEC\_DISTANCE](vector-functions/vector-functions-vec_distance) function is a generic function that behaves either as [VEC\_DISTANCE\_EUCLIDEAN](vector-functions/vec_distance_euclidean) or [VEC\_DISTANCE\_COSINE](vector-functions/vec_distance_cosine), depending on the underlying index type:

```sql
SELECT id FROM v 
  ORDER BY VEC_DISTANCE(v, x'6ca1d43e9df91b3fe580da3e1c247d3f147cf33e');
+----+
| id |
+----+
| 10 |
|  7 |
|  3 |
|  9 |
|  2 |
|  1 |
|  5 |
|  4 |
|  6 |
|  8 |
+----+
```

## System Variables

There are a number of system variables used for vectors. See [Vector System Variables](vector-system-variables).

## Vector Framework Integrations

MariaDB Vector is integrated in several frameworks, see [Vector Framework Integrations](vector-framework-integrations).

## See Also

* [Get to know MariaDB’s Rocket-Fast Native Vector Search - Sergei Golubchyk](https://www.youtube.com/watch?v=gNyzcy_6qJM) (video)
* [MariaDB Vector, a new Open Source vector database that you are already familiar by Sergei Golubchik](https://www.youtube.com/watch?v=r9af4bvF7jI) (video)
* [AI first applications with MariaDB Vector - Vicentiu Ciorbaru](https://www.youtube.com/watch?v=vp126N1QOws) (video)
* [MariaDB Vector: A storage engine for LLMs - Kaj Arnö and Jonah Harris](https://www.youtube.com/watch?v=3y-yWoH-CF8) (video)
* [Try RAG with MariaDB Vector on your own MariaDB data!](https://mariadb.org/rag-with-mariadb-vector/)

<sub>_This page is licensed: CC BY-SA / Gnu FDL_</sub>

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