Fix linting errors

lightrag/lightrag.py

@@ -756,10 +756,7 @@ class LightRAG:
         return response
 
     def query_with_separate_keyword_extraction(
-        self,
-        query: str,
-        prompt: str,    
-        param: QueryParam = QueryParam()
+        self, query: str, prompt: str, param: QueryParam = QueryParam()
     ):
         """
         1. Extract keywords from the 'query' using new function in operate.py.
@@ -767,13 +764,12 @@ class LightRAG:
         """
 
         loop = always_get_an_event_loop()
-        return loop.run_until_complete(self.aquery_with_separate_keyword_extraction(query, prompt,  param))
+        return loop.run_until_complete(
+            self.aquery_with_separate_keyword_extraction(query, prompt, param)
+        )
 
     async def aquery_with_separate_keyword_extraction(
-        self,
-        query: str,
-        prompt: str, 
-        param: QueryParam = QueryParam()
+        self, query: str, prompt: str, param: QueryParam = QueryParam()
     ):
         """
         1. Calls extract_keywords_only to get HL/LL keywords from 'query'.
@@ -788,15 +784,16 @@ class LightRAG:
             text=query,
             param=param,
             global_config=asdict(self),
-            hashing_kv=self.llm_response_cache or self.key_string_value_json_storage_cls(
+            hashing_kv=self.llm_response_cache
+            or self.key_string_value_json_storage_cls(
                 namespace="llm_response_cache",
                 global_config=asdict(self),
                 embedding_func=None,
-            )
+            ),
         )
 
-        param.hl_keywords=hl_keywords,
-        param.ll_keywords=ll_keywords,
+        param.hl_keywords = (hl_keywords,)
+        param.ll_keywords = (ll_keywords,)
 
         # ---------------------
         # STEP 2: Final Query Logic
@@ -817,7 +814,8 @@ class LightRAG:
                 param,
                 asdict(self),
                 hashing_kv=self.llm_response_cache
-                if self.llm_response_cache and hasattr(self.llm_response_cache, "global_config")
+                if self.llm_response_cache
+                and hasattr(self.llm_response_cache, "global_config")
                 else self.key_string_value_json_storage_cls(
                     namespace="llm_response_cache",
                     global_config=asdict(self),
@@ -832,7 +830,8 @@ class LightRAG:
                 param,
                 asdict(self),
                 hashing_kv=self.llm_response_cache
-                if self.llm_response_cache and hasattr(self.llm_response_cache, "global_config")
+                if self.llm_response_cache
+                and hasattr(self.llm_response_cache, "global_config")
                 else self.key_string_value_json_storage_cls(
                     namespace="llm_response_cache",
                     global_config=asdict(self),
@@ -850,7 +849,8 @@ class LightRAG:
                 param,
                 asdict(self),
                 hashing_kv=self.llm_response_cache
-                if self.llm_response_cache and hasattr(self.llm_response_cache, "global_config")
+                if self.llm_response_cache
+                and hasattr(self.llm_response_cache, "global_config")
                 else self.key_string_value_json_storage_cls(
                     namespace="llm_response_cache",
                     global_config=asdict(self),

lightrag/operate.py

@@ -680,6 +680,7 @@ async def kg_query(
     )
     return response
 
+
 async def kg_query_with_keywords(
     query: str,
     knowledge_graph_inst: BaseGraphStorage,
@@ -717,7 +718,9 @@ async def kg_query_with_keywords(
 
     # If neither has any keywords, you could handle that logic here.
     if not hl_keywords and not ll_keywords:
-        logger.warning("No keywords found in query_param. Could default to global mode or fail.")
+        logger.warning(
+            "No keywords found in query_param. Could default to global mode or fail."
+        )
         return PROMPTS["fail_response"]
     if not ll_keywords and query_param.mode in ["local", "hybrid"]:
         logger.warning("low_level_keywords is empty, switching to global mode.")
@@ -727,8 +730,16 @@ async def kg_query_with_keywords(
         query_param.mode = "local"
 
     # Flatten low-level and high-level keywords if needed
-    ll_keywords_flat = [item for sublist in ll_keywords for item in sublist] if any(isinstance(i, list) for i in ll_keywords) else ll_keywords
-    hl_keywords_flat = [item for sublist in hl_keywords for item in sublist] if any(isinstance(i, list) for i in hl_keywords) else hl_keywords
+    ll_keywords_flat = (
+        [item for sublist in ll_keywords for item in sublist]
+        if any(isinstance(i, list) for i in ll_keywords)
+        else ll_keywords
+    )
+    hl_keywords_flat = (
+        [item for sublist in hl_keywords for item in sublist]
+        if any(isinstance(i, list) for i in hl_keywords)
+        else hl_keywords
+    )
 
     # Join the flattened lists
     ll_keywords_str = ", ".join(ll_keywords_flat) if ll_keywords_flat else ""
@@ -803,6 +814,7 @@ async def kg_query_with_keywords(
     )
     return response
 
+
 async def extract_keywords_only(
     text: str,
     param: QueryParam,
@@ -881,6 +893,7 @@ async def extract_keywords_only(
     )
     return hl_keywords, ll_keywords
 
+
 async def _build_query_context(
     query: list,
     knowledge_graph_inst: BaseGraphStorage,