Fix linting

lightrag/llm/openai.py

@@ -211,15 +211,17 @@ async def openai_complete_if_cache(
             # Track if we've started iterating
             iteration_started = False
             final_chunk_usage = None
-            
+
             try:
                 iteration_started = True
                 async for chunk in response:
                     # Check if this chunk has usage information (final chunk)
                     if hasattr(chunk, "usage") and chunk.usage:
                         final_chunk_usage = chunk.usage
-                        logger.debug(f"Received usage info in streaming chunk: {chunk.usage}")
-                    
+                        logger.debug(
+                            f"Received usage info in streaming chunk: {chunk.usage}"
+                        )
+
                     # Check if choices exists and is not empty
                     if not hasattr(chunk, "choices") or not chunk.choices:
                         logger.warning(f"Received chunk without choices: {chunk}")
@@ -231,21 +233,23 @@ async def openai_complete_if_cache(
                     ):
                         # This might be the final chunk, continue to check for usage
                         continue
-                        
+
                     content = chunk.choices[0].delta.content
                     if content is None:
                         continue
                     if r"\u" in content:
                         content = safe_unicode_decode(content.encode("utf-8"))
-                    
+
                     yield content
-                    
+
                 # After streaming is complete, track token usage
                 if token_tracker and final_chunk_usage:
                     # Use actual usage from the API
                     token_counts = {
                         "prompt_tokens": getattr(final_chunk_usage, "prompt_tokens", 0),
-                        "completion_tokens": getattr(final_chunk_usage, "completion_tokens", 0),
+                        "completion_tokens": getattr(
+                            final_chunk_usage, "completion_tokens", 0
+                        ),
                         "total_tokens": getattr(final_chunk_usage, "total_tokens", 0),
                     }
                     token_tracker.add_usage(token_counts)
@@ -471,4 +475,4 @@ async def openai_embed(
         response = await openai_async_client.embeddings.create(
             model=model, input=texts, encoding_format="float"
         )
-        return np.array([dp.embedding for dp in response.data])
+        return np.array([dp.embedding for dp in response.data])