Feat/pgvector numpy support

[DavidHein96]

This addresses #61

The default format is a a numpy float 32 to match pgvector

numpy was chosen over pytorch tensor for the lighter dependencies

This might need a special section in the docs though that to actually be able to do this you may need to coerce first to actually ensure the correct form.

def _coerce_embedding(x: NDArray[np.floating]) -> NDArray[np.float32]:
    # pgvector elements are single-precision floats; enforce dtype + 1D
    arr = np.asarray(x, dtype=np.float32)
    if arr.ndim != 1:
        raise ValueError(f"embedding must be 1-D, got shape {arr.shape}")
    return arr

As well as some kind of way of registering pgvector

from sqlalchemy import event
from pgvector.psycopg import register_vector

@event.listens_for(engine, "connect")
def _pgvector_connect(dbapi_connection, _):
    register_vector(dbapi_connection)