Fix linting

lightrag/operate.py

@@ -352,7 +352,6 @@ 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:
-
             if history_messages:
                 history = json.dumps(history_messages, ensure_ascii=False)
                 _prompt = history + "\n" + input_text
@@ -381,7 +380,12 @@ async def extract_entities(
                 res: str = await use_llm_func(input_text)
             await save_to_cache(
                 llm_response_cache,
-                CacheData(args_hash=arg_hash, content=res, prompt=_prompt, cache_type="extract"),
+                CacheData(
+                    args_hash=arg_hash,
+                    content=res,
+                    prompt=_prompt,
+                    cache_type="extract",
+                ),
             )
             return res
 
@@ -747,7 +751,10 @@ async def extract_keywords_only(
 
     # 7. Cache only the processed keywords with cache type
     if hl_keywords or ll_keywords:
-        cache_data = {"high_level_keywords": hl_keywords, "low_level_keywords": ll_keywords}
+        cache_data = {
+            "high_level_keywords": hl_keywords,
+            "low_level_keywords": ll_keywords,
+        }
         await save_to_cache(
             hashing_kv,
             CacheData(

lightrag/utils.py

@@ -484,10 +484,17 @@ def dequantize_embedding(
 
 
 async def handle_cache(
-    hashing_kv, args_hash, prompt, mode="default", cache_type=None, force_llm_cache=False
+    hashing_kv,
+    args_hash,
+    prompt,
+    mode="default",
+    cache_type=None,
+    force_llm_cache=False,
 ):
     """Generic cache handling function"""
-    if hashing_kv is None or not (force_llm_cache or hashing_kv.global_config.get("enable_llm_cache")):
+    if hashing_kv is None or not (
+        force_llm_cache or hashing_kv.global_config.get("enable_llm_cache")
+    ):
         return None, None, None, None
 
     if mode != "default":
@@ -504,7 +511,9 @@ async def handle_cache(
             # Use embedding cache
             current_embedding = await hashing_kv.embedding_func([prompt])
             llm_model_func = (
-                hashing_kv.llm_model_func if hasattr(hashing_kv, "llm_model_func") else None
+                hashing_kv.llm_model_func
+                if hasattr(hashing_kv, "llm_model_func")
+                else None
             )
             quantized, min_val, max_val = quantize_embedding(current_embedding[0])
             best_cached_response = await get_best_cached_response(