diff --git a/src/khoj/database/admin.py b/src/khoj/database/admin.py
index d7d61266..160c1544 100644
--- a/src/khoj/database/admin.py
+++ b/src/khoj/database/admin.py
@@ -136,7 +136,7 @@ class KhojUserAdmin(UserAdmin, unfold_admin.ModelAdmin):
     fieldsets = (
         (
             "Personal info",
-            {"fields": ("phone_number", "email_verification_code", "verified_phone_number", "verified_email")},
+            {"fields": ("phone_number", "email_verification_code", "verified_phone_number", "verified_email","email_verification_code_expiry")},
         ),
     ) + UserAdmin.fieldsets
 
diff --git a/src/khoj/processor/conversation/openai/utils.py b/src/khoj/processor/conversation/openai/utils.py
index 8af836f1..e952b767 100644
--- a/src/khoj/processor/conversation/openai/utils.py
+++ b/src/khoj/processor/conversation/openai/utils.py
@@ -100,7 +100,7 @@ def completion_with_backoff(
     # Calculate cost of chat
     input_tokens = chunk.usage.prompt_tokens if hasattr(chunk, "usage") and chunk.usage else 0
     output_tokens = chunk.usage.completion_tokens if hasattr(chunk, "usage") and chunk.usage else 0
-    cost = chunk.usage.model_extra.get("estimated_cost") or 0  # Estimated costs returned by DeepInfra API
+    cost = chunk.usage.model_extra.get("estimated_cost", 0) if hasattr(chunk, "usage") and chunk.usage else 0  # Estimated costs returned by DeepInfra API
     tracer["usage"] = get_chat_usage_metrics(model_name, input_tokens, output_tokens, tracer.get("usage"), cost)
 
     # Save conversation trace
@@ -215,7 +215,7 @@ def llm_thread(
         # Calculate cost of chat
         input_tokens = chunk.usage.prompt_tokens if hasattr(chunk, "usage") and chunk.usage else 0
         output_tokens = chunk.usage.completion_tokens if hasattr(chunk, "usage") and chunk.usage else 0
-        cost = chunk.usage.model_extra.get("estimated_cost") or 0  # Estimated costs returned by DeepInfra API
+        cost = chunk.usage.model_extra.get("estimated_cost", 0) if hasattr(chunk, "usage") and chunk.usage else 0  # Estimated costs returned by DeepInfra API
         tracer["usage"] = get_chat_usage_metrics(model_name, input_tokens, output_tokens, tracer.get("usage"), cost)
 
         # Save conversation trace