linting errors

examples/lightrag_multi_model_all_modes_demo.py

@@ -9,6 +9,7 @@ WORKING_DIR = "./lightrag_demo"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -21,6 +22,7 @@ async def initialize_rag():
 
     return rag
 
+
 def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
@@ -33,8 +35,7 @@ def main():
     print("--- NAIVE mode ---")
     print(
         rag.query(
-            "What are the main themes in this story?", 
-            param=QueryParam(mode="naive")
+            "What are the main themes in this story?", param=QueryParam(mode="naive")
         )
     )
 
@@ -42,8 +43,7 @@ def main():
     print("\n--- LOCAL mode ---")
     print(
         rag.query(
-            "What are the main themes in this story?", 
-            param=QueryParam(mode="local")
+            "What are the main themes in this story?", param=QueryParam(mode="local")
         )
     )
 
@@ -51,8 +51,7 @@ def main():
     print("\n--- GLOBAL mode ---")
     print(
         rag.query(
-            "What are the main themes in this story?", 
-            param=QueryParam(mode="global")
+            "What are the main themes in this story?", param=QueryParam(mode="global")
         )
     )
 
@@ -60,8 +59,7 @@ def main():
     print("\n--- HYBRID mode ---")
     print(
         rag.query(
-            "What are the main themes in this story?", 
-            param=QueryParam(mode="hybrid")
+            "What are the main themes in this story?", param=QueryParam(mode="hybrid")
         )
     )
 
@@ -69,8 +67,7 @@ def main():
     print("\n--- MIX mode ---")
     print(
         rag.query(
-            "What are the main themes in this story?", 
-            param=QueryParam(mode="mix")
+            "What are the main themes in this story?", param=QueryParam(mode="mix")
         )
     )
 
@@ -81,10 +78,11 @@ def main():
             "How does the character development reflect Victorian-era attitudes?",
             param=QueryParam(
                 mode="global",
-                model_func=gpt_4o_complete  # Override default model with more capable one
-            )
+                model_func=gpt_4o_complete,  # Override default model with more capable one
+            ),
         )
     )
 
+
 if __name__ == "__main__":
-    main() 
+    main()

lightrag/base.py

@@ -84,7 +84,7 @@ class QueryParam:
 
     ids: list[str] | None = None
     """List of ids to filter the results."""
-    
+
     model_func: Callable[..., object] | None = None
     """Optional override for the LLM model function to use for this specific query.
     If provided, this will be used instead of the global model function.

lightrag/lightrag.py

@@ -1338,7 +1338,7 @@ class LightRAG:
         """
         # If a custom model is provided in param, temporarily update global config
         global_config = asdict(self)
-        
+
         if param.mode in ["local", "global", "hybrid"]:
             response = await kg_query(
                 query.strip(),

lightrag/operate.py

@@ -705,7 +705,11 @@ async def kg_query(
     system_prompt: str | None = None,
 ) -> str | AsyncIterator[str]:
     # Handle cache
-    use_model_func = query_param.model_func if query_param.model_func else global_config["llm_model_func"]
+    use_model_func = (
+        query_param.model_func
+        if query_param.model_func
+        else global_config["llm_model_func"]
+    )
     args_hash = compute_args_hash(query_param.mode, query, cache_type="query")
     cached_response, quantized, min_val, max_val = await handle_cache(
         hashing_kv, args_hash, query, query_param.mode, cache_type="query"
@@ -866,7 +870,9 @@ async def extract_keywords_only(
     logger.debug(f"[kg_query]Prompt Tokens: {len_of_prompts}")
 
     # 5. Call the LLM for keyword extraction
-    use_model_func = param.model_func if param.model_func else global_config["llm_model_func"]
+    use_model_func = (
+        param.model_func if param.model_func else global_config["llm_model_func"]
+    )
     result = await use_model_func(kw_prompt, keyword_extraction=True)
 
     # 6. Parse out JSON from the LLM response
@@ -926,7 +932,11 @@ async def mix_kg_vector_query(
     3. Combining both results for comprehensive answer generation
     """
     # 1. Cache handling
-    use_model_func = query_param.model_func if query_param.model_func else global_config["llm_model_func"]
+    use_model_func = (
+        query_param.model_func
+        if query_param.model_func
+        else global_config["llm_model_func"]
+    )
     args_hash = compute_args_hash("mix", query, cache_type="query")
     cached_response, quantized, min_val, max_val = await handle_cache(
         hashing_kv, args_hash, query, "mix", cache_type="query"
@@ -1731,7 +1741,11 @@ async def naive_query(
     system_prompt: str | None = None,
 ) -> str | AsyncIterator[str]:
     # Handle cache
-    use_model_func = query_param.model_func if query_param.model_func else global_config["llm_model_func"]
+    use_model_func = (
+        query_param.model_func
+        if query_param.model_func
+        else global_config["llm_model_func"]
+    )
     args_hash = compute_args_hash(query_param.mode, query, cache_type="query")
     cached_response, quantized, min_val, max_val = await handle_cache(
         hashing_kv, args_hash, query, query_param.mode, cache_type="query"
@@ -1850,7 +1864,11 @@ async def kg_query_with_keywords(
     # ---------------------------
     # 1) Handle potential cache for query results
     # ---------------------------
-    use_model_func = query_param.model_func if query_param.model_func else global_config["llm_model_func"]
+    use_model_func = (
+        query_param.model_func
+        if query_param.model_func
+        else global_config["llm_model_func"]
+    )
     args_hash = compute_args_hash(query_param.mode, query, cache_type="query")
     cached_response, quantized, min_val, max_val = await handle_cache(
         hashing_kv, args_hash, query, query_param.mode, cache_type="query"