{% hint style="info" %}
[Vectors](https://mariadb.com/docs/server/reference/sql-structure/vectors) are available from [MariaDB 11.7](https://app.gitbook.com/s/aEnK0ZXmUbJzqQrTjFyb/community-server/old-releases/11.7/what-is-mariadb-117).
{% endhint %}
MariaDB has a dedicated [VECTOR(N)](https://mariadb.com/docs/server/reference/sql-structure/vectors/vector) data type with a built-in data validation. `N` is the number of dimensions that all vector values in the column have.
{% hint style="info" %}
* Vector indexes are dimensionality-specific.
* All vectors inserted into an indexed column must match the index's target dimensionality.
* Inserting vectors with different dimensionalities will result in an error.
{% endhint %}
Consider the following table:
```sql
CREATE TABLE embeddings (
doc_id BIGINT UNSIGNED PRIMARY KEY,
embedding VECTOR(1536)
);
```
To have a fast vector search, you have to index the vector column, creating a `VECTOR` index:
```sql
CREATE TABLE embeddings (
doc_id BIGINT UNSIGNED PRIMARY KEY,
embedding VECTOR(1536) NOT NULL,
VECTOR INDEX (embedding)
);
```
{% hint style="warning" %}
Note that there can be only one vector index in the table, and the indexed vector column must be `NOT NULL`.
{% endhint %}
There are two options that can be used to configure the vector index:
* `M` — 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`.
* `DISTANCE` — Distance function to build the vector index for. Searches using a different distance function will not be able to use a vector index. Valid values are `cosine` and `euclidean` (the default).
```sql
CREATE TABLE embeddings (
doc_id BIGINT UNSIGNED PRIMARY KEY,
embedding VECTOR(1536) NOT NULL,
VECTOR INDEX (embedding) M=8 DISTANCE=cosine
);
```
*This page is licensed: CC BY-SA / Gnu FDL*
{% @marketo/form formId="4316" %}
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