diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py
index 5ae0a47f..d77af71d 100644
--- a/lightrag/lightrag.py
+++ b/lightrag/lightrag.py
@@ -916,7 +916,7 @@ class LightRAG:
                 else self.key_string_value_json_storage_cls(
                     namespace="llm_response_cache",
                     global_config=asdict(self),
-                    embedding_func=self.embedding_func,  
+                    embedding_func=self.embedding_func,
                 ),
                 prompt=prompt,
             )
diff --git a/lightrag/utils.py b/lightrag/utils.py
index 2ae067ba..3a69513b 100644
--- a/lightrag/utils.py
+++ b/lightrag/utils.py
@@ -368,7 +368,9 @@ async def get_best_cached_response(
     original_prompt=None,
     cache_type=None,
 ) -> Union[str, None]:
-    logger.debug(f"get_best_cached_response:  mode={mode} cache_type={cache_type} use_llm_check={use_llm_check}")
+    logger.debug(
+        f"get_best_cached_response:  mode={mode} cache_type={cache_type} use_llm_check={use_llm_check}"
+    )
     mode_cache = await hashing_kv.get_by_id(mode)
     if not mode_cache:
         return None
@@ -511,7 +513,7 @@ async def handle_cache(
         if is_embedding_cache_enabled:
             # Use embedding cache
             current_embedding = await hashing_kv.embedding_func([prompt])
-            llm_model_func = hashing_kv.global_config.get('llm_model_func')
+            llm_model_func = hashing_kv.global_config.get("llm_model_func")
             quantized, min_val, max_val = quantize_embedding(current_embedding[0])
             best_cached_response = await get_best_cached_response(
                 hashing_kv,