Compute the **dot product** between two vectors, a core linear algebra operation used in ML for measuring similarity and building linear models. In this task, you’ll implement it effectively using NumPy.

$$
\mathbf{x} \cdot \mathbf{y} = \sum_{i=1}^{n} x_i y_i
$$

## Requirements

Implement the function:
```python
def dot_product(x, y):
    \"""
    Computes the dot product between two vectors.

    Args:
        x (np.ndarray): First vector of length n.
        y (np.ndarray): Second vector of length n.

    Returns:
        float: The dot product sum_{i=1..n} x[i] * y[i].
    \"""
    # Your solution below
```

**Rules:**
- Compute the dot product defined by: $$x \cdot y = \sum_{i=1}^{n} x_i y_i$$
- Return a single float value.
- You can use any valid NumPy method (e.g., `np.dot`, `@` operator, or `sum(x * y)`).
- Input vectors are NumPy arrays.

## Example

```python
x = np.array([1.0, 2.0, 3.0])
y = np.array([4.0, 5.0, 6.0])

dot_product(x, y)
```

Output:
```python
32.0
```

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1372. Dot product

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Argument	Type
x	np.ndarray
y	np.ndarray