Vector multiplication is a core linear algebra operation used in ML for computing projections and model scores. In this task, you’ll implement the **dot product** of two vectors.

The dot product is defined as:
$$
x \cdot y = \sum_{i=1}^{n} x_i y_i
$$

## Requirements

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

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

    Returns:
        float: The dot product of x and y.
    \"""
    # Your solution below
```

**Rules:**
- Compute and return a single float value equal to the dot product of `x` and `y`.
- Do not use prebuilt dot-product helpers (e.g., `numpy.dot`, `@`, `numpy.matmul`).
- You may use NumPy for basic elementwise multiplication (`*`) and summation (`sum`).
- Aim for clean, vectorized code.

## 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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Vector multiplication

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701. Vector multiplication

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Argument	Type
x	np.ndarray
y	np.ndarray