diff --git a/src/khoj/search_type/text_search.py b/src/khoj/search_type/text_search.py
index edc735f2..ed3be33c 100644
--- a/src/khoj/search_type/text_search.py
+++ b/src/khoj/search_type/text_search.py
@@ -62,36 +62,32 @@ def compute_embeddings(
 ):
     "Compute (and Save) Embeddings or Load Pre-Computed Embeddings"
     new_entries = []
+    create_index_msg = ""
     # Load pre-computed embeddings from file if exists and update them if required
     if embeddings_file.exists() and not regenerate:
         corpus_embeddings: torch.Tensor = torch.load(get_absolute_path(embeddings_file), map_location=state.device)
         logger.debug(f"Loaded {len(corpus_embeddings)} text embeddings from {embeddings_file}")
-
-        # Encode any new entries in the corpus and update corpus embeddings
-        new_entries = [entry.compiled for id, entry in entries_with_ids if id == -1]
-        if new_entries:
-            logger.info(f"📩 Indexing {len(new_entries)} text entries.")
-            new_embeddings = bi_encoder.encode(
-                new_entries, convert_to_tensor=True, device=state.device, show_progress_bar=True
-            )
-            existing_entry_ids = [id for id, _ in entries_with_ids if id != -1]
-            if existing_entry_ids:
-                existing_embeddings = torch.index_select(
-                    corpus_embeddings, 0, torch.tensor(existing_entry_ids, device=state.device)
-                )
-            else:
-                existing_embeddings = torch.tensor([], device=state.device)
-            corpus_embeddings = torch.cat([existing_embeddings, new_embeddings], dim=0)
-    # Else compute the corpus embeddings from scratch
     else:
-        new_entries = [entry.compiled for _, entry in entries_with_ids]
-        logger.info(f"📩 Indexing {len(new_entries)} text entries. Creating index from scratch.")
-        corpus_embeddings = bi_encoder.encode(
+        corpus_embeddings = torch.tensor([], device=state.device)
+        create_index_msg = " Creating index from scratch."
+
+    # Encode any new entries in the corpus and update corpus embeddings
+    new_entries = [entry.compiled for id, entry in entries_with_ids if id == -1]
+    if new_entries:
+        logger.info(f"📩 Indexing {len(new_entries)} text entries.{create_index_msg}")
+        new_embeddings = bi_encoder.encode(
             new_entries, convert_to_tensor=True, device=state.device, show_progress_bar=True
         )
+        existing_entry_ids = [id for id, _ in entries_with_ids if id != -1]
+        if existing_entry_ids:
+            existing_embeddings = torch.index_select(
+                corpus_embeddings, 0, torch.tensor(existing_entry_ids, device=state.device)
+            )
+        else:
+            existing_embeddings = torch.tensor([], device=state.device)
+        corpus_embeddings = torch.cat([existing_embeddings, new_embeddings], dim=0)
 
-    # Save regenerated or updated embeddings to file
-    if new_entries:
+        # Save regenerated or updated embeddings to file
         corpus_embeddings = util.normalize_embeddings(corpus_embeddings)
         torch.save(corpus_embeddings, embeddings_file)
         logger.info(f"📩 Saved computed text embeddings to {embeddings_file}")