Pandas dataframe rolling sum column with groupby

I'm trying to create a new column that gives a rolling sum of values in the Values column. The rolling sum includes 4 rows i.e. the current row and the next three rows. I want to do this for each type in the 'Type' column.

However, if there are fewer than 4 rows before the next type starts, I want the rolling sum to use only the remaining rows. For example, if there are 2 rows after the current row for the current type, a total of 3 rows is used for the rolling sum. See the table below showing what I'm currently getting and what I expect.

The following line of code is what I'm currently using to get the rolling sum.

rolling_sum = df.groupby('Type', sort=False)['Value'].rolling(4, min_periods = 3).sum().shift(-3).reset_index()
rolling_sum = rolling_sum.rename(columns={'Value': 'Rolling Sum'})

extracted_col = rolling_sum['Rolling Sum']
df = df.join(extracted_col)

I would really appreciate your help.

You can try running the rolling sum on the reversed values for each group and then reverse back afterward, using a min_periods of 1:

df['Rolling Sum'] = df.groupby('Type', sort=False)['Value'].apply(lambda x: x[::-1].rolling(4, min_periods=1).sum()[::-1])

Result:

   Index        Type    Value   Rolling Sum
0      1        left        5          22.0
1      2        left        9          34.0
2      3        left        0          25.0
3      4        left        8          25.0
4      5        left       17          17.0
5      6    straight        7          61.0
6      7    straight        4          77.0
7      8    straight        0          86.0
8      9    straight       50          97.0
9     10    straight       23          47.0
10    11    straight       13          24.0
11    12    straight       11          11.0