Fix linting

lightrag/lightrag.py

@@ -197,12 +197,12 @@ class LightRAG:
         logger.debug(f"LightRAG init with param:\n  {_print_config}\n")
 
         # Init embedding functions with separate instances for insert and query
-        self.insert_embedding_func = limit_async_func_call(self.embedding_func_max_async)(
-            self.embedding_func
-        )
-        self.query_embedding_func = limit_async_func_call(self.embedding_func_max_async_query)(
-            self.embedding_func
-        )
+        self.insert_embedding_func = limit_async_func_call(
+            self.embedding_func_max_async
+        )(self.embedding_func)
+        self.query_embedding_func = limit_async_func_call(
+            self.embedding_func_max_async_query
+        )(self.embedding_func)
 
         # Initialize all storages
         self.key_string_value_json_storage_cls: Type[BaseKVStorage] = (

lightrag/operate.py

@@ -352,7 +352,7 @@ async def extract_entities(
         input_text: str, history_messages: list[dict[str, str]] = None
     ) -> str:
         if enable_llm_cache_for_entity_extract and llm_response_cache:
-            custom_llm = None            
+            custom_llm = None
             if (
                 global_config["embedding_cache_config"]
                 and global_config["embedding_cache_config"]["enabled"]
@@ -360,10 +360,14 @@ async def extract_entities(
                 new_config = global_config.copy()
                 new_config["embedding_cache_config"] = None
                 new_config["enable_llm_cache"] = True
-                
+
                 # create a llm function with new_config for handle_cache
                 async def custom_llm(
-                    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
+                    prompt,
+                    system_prompt=None,
+                    history_messages=[],
+                    keyword_extraction=False,
+                    **kwargs,
                 ) -> str:
                     # 合并 new_config 和其他 kwargs,保证其他参数不被覆盖
                     merged_config = {**kwargs, **new_config}
@@ -374,7 +378,7 @@ async def extract_entities(
                         keyword_extraction=keyword_extraction,
                         **merged_config,
                     )
-                
+
             if history_messages:
                 history = json.dumps(history_messages, ensure_ascii=False)
                 _prompt = history + "\n" + input_text
@@ -383,12 +387,12 @@ async def extract_entities(
 
             arg_hash = compute_args_hash(_prompt)
             cached_return, _1, _2, _3 = await handle_cache(
-                llm_response_cache, 
-                arg_hash, 
-                _prompt, 
-                "default", 
+                llm_response_cache,
+                arg_hash,
+                _prompt,
+                "default",
                 cache_type="default",
-                llm=custom_llm
+                llm=custom_llm,
             )
             if cached_return:
                 logger.debug(f"Found cache for {arg_hash}")

lightrag/utils.py

@@ -491,7 +491,9 @@ def dequantize_embedding(
     return (quantized * scale + min_val).astype(np.float32)
 
 
-async def handle_cache(hashing_kv, args_hash, prompt, mode="default", cache_type=None, llm=None):
+async def handle_cache(
+    hashing_kv, args_hash, prompt, mode="default", cache_type=None, llm=None
+):
     """Generic cache handling function"""
     if hashing_kv is None or not hashing_kv.global_config.get("enable_llm_cache"):
         return None, None, None, None
@@ -528,7 +530,9 @@ async def handle_cache(hashing_kv, args_hash, prompt, mode="default", cache_type
             similarity_threshold=embedding_cache_config["similarity_threshold"],
             mode=mode,
             use_llm_check=use_llm_check,
-            llm_func=llm if (use_llm_check and llm is not None) else (llm_model_func if use_llm_check else None),
+            llm_func=llm
+            if (use_llm_check and llm is not None)
+            else (llm_model_func if use_llm_check else None),
             original_prompt=prompt if use_llm_check else None,
             cache_type=cache_type,
         )