;
};
## Overview
[pgvector](https://github.com/pgvector/pgvector) adds a `vector` column type, distance operators, and approximate nearest-neighbor indexes (HNSW and IVFFlat) to PostgreSQL. Use it to store embeddings alongside relational data and query by similarity.
PlanetScale Postgres also supports [pgvectorscale](https://github.com/timescale/pgvectorscale), a companion extension for higher-performance approximate search. See the [supported extensions](/docs/postgres/extensions#supported-community-extensions) table for available versions.
## Enabling pgvector
pgvector does not require a cluster restart. Install it with a role that has superuser privileges:
```sql theme={null}
CREATE EXTENSION IF NOT EXISTS vector;
```
Confirm the extension is installed:
```sql theme={null}
SELECT extname, extversion
FROM pg_extension
WHERE extname = 'vector';
```
## Usage
Create a table with a vector column, insert embeddings, and query by distance:
```sql theme={null}
CREATE TABLE items (
id bigserial PRIMARY KEY,
content text,
embedding vector(3)
);
INSERT INTO items (content, embedding) VALUES
('red', '[1,0,0]'),
('green', '[0,1,0]'),
('blue', '[0,0,1]');
-- Nearest neighbors by L2 distance
SELECT id, content
FROM items
ORDER BY embedding <-> '[1,0.2,0]'
LIMIT 5;
```
Common distance operators:
| Operator | Meaning |
| -------- | ---------------------- |
| `<->` | L2 distance |
| `<#>` | Negative inner product |
| `<=>` | Cosine distance |
Add an HNSW index when you need approximate search at scale:
```sql theme={null}
CREATE INDEX ON items USING hnsw (embedding vector_l2_ops);
```
## External documentation
For indexing options, half-precision and sparse vectors, and language clients, see the [official pgvector documentation](https://github.com/pgvector/pgvector).