Use Sigmoid to normalize cross-encoder score between 0-1

- While sigmoid normalization isn't required for reranking.
  Normalizing score to distance metrics for both encoder and cross
  encoder scores is useful to reason about them
- Softmax wasn't required as don't need probabilities, sigmoid is good
  enough to get distance metric

src/database/adapters/__init__.py

@@ -11,10 +11,6 @@ from pgvector.django import CosineDistance
 from django.db.models.manager import BaseManager
 from django.db.models import Q
 from torch import Tensor
-from pgvector.django import CosineDistance
-from django.db.models.manager import BaseManager
-from django.db.models import Q
-from torch import Tensor
 
 # Import sync_to_async from Django Channels
 from asgiref.sync import sync_to_async