# Vector Operations

PostgresML adds optimized vector operations that can be used inside SQL queries. Vector operations are particularly useful for dealing with embeddings that have been generated from other machine learning algorithms, and can provide functions like nearest neighbor calculations using various distance functions.

Embeddings can be a relatively efficient mechanism to leverage the power of deep learning, without the runtime inference costs. These functions are fast with the most expensive distance functions computing upwards of ~100k per second for a memory resident dataset on modern hardware.

The PostgreSQL planner will also automatically parallelize evaluation on larger datasets, if configured to take advantage of multiple CPU cores when available.

Vector operations are implemented in Rust using `ndarray`

and BLAS, for maximum performance.

## Element-wise Arithmetic with Constants

#### Addition

```
pgml.add(a REAL[], b REAL) -> REAL[]
```

```
SELECT pgml.add(ARRAY[1.0, 2.0, 3.0], 3);
```

```
pgml=# SELECT pgml.add(ARRAY[1.0, 2.0, 3.0], 3);
add
---------
{4,5,6}
(1 row)
```

#### Subtraction

```
pgml.subtract(minuend REAL[], subtrahend REAL) -> REAL[]
```

#### Multiplication

```
pgml.multiply(multiplicand REAL[], multiplier REAL) -> REAL[]
```

#### Division

```
pgml.divide(dividend REAL[], divisor REAL) -> REAL[]
```

## Pairwise arithmetic with Vectors

#### Addition

```
pgml.add(a REAL[], b REAL[]) -> REAL[]
```

#### Subtraction

```
pgml.subtract(minuend REAL[], subtrahend REAL[]) -> REAL[]
```

#### Multiplication

```
pgml.multiply(multiplicand REAL[], multiplier REAL[]) -> REAL[]
```

#### Division

```
pgml.divide(dividend REAL[], divisor REAL[]) -> REAL[]
```

## Norms

#### Dimensions not at origin

```
pgml.norm_l0(vector REAL[]) -> REAL
```

#### Manhattan distance from origin

```
pgml.norm_l1(vector REAL[]) -> REAL
```

#### Euclidean distance from origin

```
pgml.norm_l2(vector REAL[]) -> REAL
```

#### Absolute value of largest element

```
pgml.norm_max(vector REAL[]) -> REAL
```

## Normalization

#### Unit Vector

```
pgml.normalize_l1(vector REAL[]) -> REAL[]
```

#### Squared Unit Vector

```
pgml.normalize_l2(vector REAL[]) -> REAL[]
```

#### -1:1 values

```
pgml.normalize_max(vector REAL[]) -> REAL[]
```

## Distances

#### Manhattan

```
pgml.distance_l1(a REAL[], b REAL[]) -> REAL
```

#### Euclidean

```
pgml.distance_l2(a REAL[], b REAL[]) -> REAL
```

#### Projection

```
pgml.dot_product(a REAL[], b REAL[]) -> REAL
```

#### Direction

```
pgml.cosine_similarity(a REAL[], b REAL[]) -> REAL
```

## Nearest Neighbor Example

If we had precalculated the embeddings for a set of user and product data, we could find the 100 best products for a user with a similarity search.

```
SELECT
products.id,
pgml.cosine_similarity(
users.embedding,
products.embedding
) AS distance
FROM users
JOIN products
WHERE users.id = 123
ORDER BY distance ASC
LIMIT 100;
```

## Have Questions?

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## Try It Out

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