How to subtract two great vectors?

I want to calculate the vector difference between two large vectors. I am able to subtract a list of arrays to an array and put it to the power of 2

(train["quest_emb"][0] - train["sent_emb"][0])**2

but do not generalize it with a column of the array's data table to a matrix data frame:

train["quest_emb"] - train["sent_emb"]

How it crashes my computer.

Array analysis

Here is an example of its content.

>>> print((train["quest_emb"][2]))
[[0.03949683 0.04509903 0.01808935 ... 0.04610749 0.0416535  0.02240689]]

>>> print((train["sent_emb"][2]))
[array([0.03037658, 0.04433101, 0.08135635, ..., 0.06764812, 0.04971079,
       0.02240689], dtype=float32), array([0.05260669, 0.04548098, 0.0382337 , ..., 0.04823414, 0.07656007,
       0.03501297], dtype=float32), array([0.0502927 , 0.04480611, 0.02038252, ..., 0.03942193, 0.03132772,
       0.04595207], dtype=float32), array([0.06769167, 0.03393815, 0.0625218 , ..., 0.05555448, 0.03059104,
       0.03422254], dtype=float32)]

It seems like there is a difference in size:

>>> print(len(train["quest_emb"][0]))
1
>>> print(len(train["sent_emb"][0]))
4

Here's what the first array looks like:

>>> print((train["quest_emb"][2][0]))
[0.03949683 0.04509903 0.01808935 ... 0.04610749 0.0416535  0.02240689]

>>> print((train["sent_emb"][2][0]))
[0.03037658 0.04433101 0.08135635 ... 0.06764812 0.04971079 0.02240689]

The size of the vector 'train ["quest_emb"] e o mesmo do vetor 'train [" sent_emb "] : 130318

Here are the types of arrays

>>> print(type(train["quest_emb"][2]))
<class 'numpy.ndarray'>

>>> print(type(train["sent_emb"][2]))
<class 'list'>

** Is there any way to make this computable difference for a computer with 8 G of RAM? Or if it is not an approximate way? **

Attempt with theano

I tried to subtract with Theano:

import theano.tensor as T
from theano import function
x = T.dscalar('x')
y = T.dscalar('y')
z = x - y
f = function([x, y], z)   
f(train["quest_emb"],train["sent_emb"])

But he gave the error:

ValueError: Bad input argument with name "quest_emb" to theano function with name "<ipython-input-41-c53eb459cbc4>:6" at index 0 (0-based).