Fix linting

lightrag/operate.py

@@ -466,12 +466,12 @@ async def extract_entities(
         """
         maybe_nodes = defaultdict(list)
         maybe_edges = defaultdict(list)
-        
+
         records = split_string_by_multi_markers(
             result,
             [context_base["record_delimiter"], context_base["completion_delimiter"]],
         )
-        
+
         for record in records:
             record = re.search(r"\((.*)\)", record)
             if record is None:
@@ -480,14 +480,14 @@ async def extract_entities(
             record_attributes = split_string_by_multi_markers(
                 record, [context_base["tuple_delimiter"]]
             )
-            
+
             if_entities = await _handle_single_entity_extraction(
                 record_attributes, chunk_key
             )
             if if_entities is not None:
                 maybe_nodes[if_entities["entity_name"]].append(if_entities)
                 continue
-                
+
             if_relation = await _handle_single_relationship_extraction(
                 record_attributes, chunk_key
             )
@@ -495,7 +495,7 @@ async def extract_entities(
                 maybe_edges[(if_relation["src_id"], if_relation["tgt_id"])].append(
                     if_relation
                 )
-                
+
         return maybe_nodes, maybe_edges
 
     async def _process_single_content(chunk_key_dp: tuple[str, TextChunkSchema]):
@@ -508,45 +508,49 @@ async def extract_entities(
         chunk_key = chunk_key_dp[0]
         chunk_dp = chunk_key_dp[1]
         content = chunk_dp["content"]
-        
+
         # Get initial extraction
         hint_prompt = entity_extract_prompt.format(
             **context_base, input_text="{input_text}"
         ).format(**context_base, input_text=content)
-        
+
         final_result = await _user_llm_func_with_cache(hint_prompt)
         history = pack_user_ass_to_openai_messages(hint_prompt, final_result)
-        
+
         # Process initial extraction
-        maybe_nodes, maybe_edges = await _process_extraction_result(final_result, chunk_key)
+        maybe_nodes, maybe_edges = await _process_extraction_result(
-        
+            final_result, chunk_key
+        )
+
         # Process additional gleaning results
         for now_glean_index in range(entity_extract_max_gleaning):
             glean_result = await _user_llm_func_with_cache(
                 continue_prompt, history_messages=history
             )
-            
+
             history += pack_user_ass_to_openai_messages(continue_prompt, glean_result)
-            
+
             # Process gleaning result separately
-            glean_nodes, glean_edges = await _process_extraction_result(glean_result, chunk_key)
+            glean_nodes, glean_edges = await _process_extraction_result(
-            
+                glean_result, chunk_key
+            )
+
             # Merge results
             for entity_name, entities in glean_nodes.items():
                 maybe_nodes[entity_name].extend(entities)
             for edge_key, edges in glean_edges.items():
                 maybe_edges[edge_key].extend(edges)
-                
+
             if now_glean_index == entity_extract_max_gleaning - 1:
                 break
-                
+
             if_loop_result: str = await _user_llm_func_with_cache(
                 if_loop_prompt, history_messages=history
             )
             if_loop_result = if_loop_result.strip().strip('"').strip("'").lower()
             if if_loop_result != "yes":
                 break
-                
+
         processed_chunks += 1
         entities_count = len(maybe_nodes)
         relations_count = len(maybe_edges)