diff --git a/backend/app/database/falkor/code-graph-backend/api/graph.py b/backend/app/database/falkor/code-graph-backend/api/graph.py
index 7c91405b..6b112c9a 100644
--- a/backend/app/database/falkor/code-graph-backend/api/graph.py
+++ b/backend/app/database/falkor/code-graph-backend/api/graph.py
@@ -8,6 +8,7 @@
 import logging
 logging.basicConfig(level=logging.DEBUG,
                     format='%(filename)s - %(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
 
 def graph_exists(name: str):
     db = FalkorDB(host=os.getenv('FALKORDB_HOST', 'localhost'),
@@ -53,13 +54,19 @@ def __init__(self, name: str) -> None:
         try:
             self.g.create_node_range_index("File", "name", "ext")
         except Exception:
-            pass
+            logger.exception(
+                "Failed to create node range index for File(name, ext)"
+            )
+
 
         # index Function using full-text search
         try:
             self.g.create_node_fulltext_index("Searchable", "name")
         except Exception:
-            pass
+            logger.exception(
+                "Failed to create fulltext index for Searchable(name)"
+            )
+
 
     def clone(self, clone: str) -> "Graph":
         """
diff --git a/backend/app/services/embedding_service/service.py b/backend/app/services/embedding_service/service.py
index 4527c4f9..98a59c68 100644
--- a/backend/app/services/embedding_service/service.py
+++ b/backend/app/services/embedding_service/service.py
@@ -1,3 +1,4 @@
+import asyncio
 import logging
 import config
 from typing import List, Dict, Any, Optional
@@ -66,6 +67,10 @@ def llm(self) -> ChatGoogleGenerativeAI:
                 raise
         return self._llm
 
+
+    def _encode_sync(self, *args, **kwargs):
+        return self.model.encode(*args, **kwargs)
+
     async def get_embedding(self, text: str) -> List[float]:
         """Generate embedding for a single text input"""
         try:
@@ -74,12 +79,14 @@ async def get_embedding(self, text: str) -> List[float]:
                 text = [text]
 
             # Generate embeddings
-            embeddings = self.model.encode(
+            embeddings = await asyncio.to_thread(
+                self._encode_sync,
                 text,
                 convert_to_tensor=True,
                 show_progress_bar=False
             )
 
+
             # Convert to standard Python list and return
             embedding_list = embeddings[0].cpu().tolist()
             logger.debug(f"Generated embedding with dimension: {len(embedding_list)}")
@@ -92,13 +99,15 @@ async def get_embeddings(self, texts: List[str]) -> List[List[float]]:
         """Generate embeddings for multiple text inputs in batches"""
         try:
             # Generate embeddings
-            embeddings = self.model.encode(
+            embeddings = await asyncio.to_thread(
+                self._encode_sync,
                 texts,
                 convert_to_tensor=True,
                 batch_size=MAX_BATCH_SIZE,
                 show_progress_bar=len(texts) > 10
             )
 
+
             # Convert to standard Python list
             embedding_list = embeddings.cpu().tolist()
             logger.info(f"Generated {len(embedding_list)} embeddings")