# Sparse Vector: `svector`

since v0.3.0

Unlike dense vectors, sparse vectors are very high-dimensional but contain few non-zero values.

Typically, sparse vectors can be created from:

- Word co-occurrence matrices
- Term frequency-inverse document frequency (TF-IDF) vectors
- User-item interaction matrices
- Network adjacency matrices

Sparse vectors in `pgvecto.rs`

are called `svector`

.

Here's an example of creating a table with a svector column and inserting values:

sql

```
CREATE TABLE items (
id bigserial PRIMARY KEY,
embedding svector(10) NOT NULL
);
INSERT INTO items (embedding) VALUES ('[0.1,0,0,0,0,0,0,0,0,0]'), ('[0,0,0,0,0,0,0,0,0,0.5]');
```

Index can be created on `svector`

type as well.

sql

```
CREATE INDEX your_index_name ON items USING vectors (embedding svector_l2_ops);
SELECT * FROM items ORDER BY embedding <-> '[0.3,0,0,0,0,0,0,0,0,0]' LIMIT 1;
```

We support three operators to calculate the distance between two `svector`

values.

`<->`

(`svector_l2_ops`

): squared Euclidean distance, defined as. `<#>`

(`svector_dot_ops`

): negative dot product, defined as. `<=>`

(`svector_cos_ops`

): cosine distance, defined as.

There is also a function `to_svector`

to create a `svector`

. It will set the value at the specified position.

sql

```
-- to_svector(dim: INTEGER, position: ARRAY, value: ARRAY) -> svector
SELECT to_svector(5, '{0, 4}', '{0.3, 0.5}');
-- [0.3, 0, 0, 0, 0.5]
```

Sparse vectors are