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

src/khoj/processor/embeddings.py

@@ -1,6 +1,7 @@
 from typing import List
 
 from sentence_transformers import SentenceTransformer, CrossEncoder
+from torch import nn
 
 from khoj.utils.helpers import get_device
 from khoj.utils.rawconfig import SearchResponse
@@ -26,6 +27,6 @@ class CrossEncoderModel:
         self.cross_encoder_model = CrossEncoder(model_name=self.model_name, device=get_device())
 
     def predict(self, query, hits: List[SearchResponse], key: str = "compiled"):
-        cross__inp = [[query, hit.additional[key]] for hit in hits]
+        cross_inp = [[query, hit.additional[key]] for hit in hits]
-        cross_scores = self.cross_encoder_model.predict(cross__inp, apply_softmax=True)
+        cross_scores = self.cross_encoder_model.predict(cross_inp, activation_fct=nn.Sigmoid())
         return cross_scores