Apply patch [skip ci]

libs/partners/openai/langchain_openai/chat_models/base.py

@@ -2295,8 +2295,10 @@ class BaseChatOpenAI(BaseChatModel):
             inputs: List of inputs to process in batch.
             config: Configuration for the batch processing.
             return_exceptions: Whether to return exceptions instead of raising them.
-            use_batch_api: If True, use OpenAI's Batch API for cost savings with polling.
-                          If False (default), use standard parallel processing for immediate results.
+            use_batch_api: If True, use OpenAI's Batch API for cost savings
+                          with polling.
+                          If False (default), use standard parallel processing
+                          for immediate results.
             **kwargs: Additional parameters to pass to the underlying model.
 
         Returns:

libs/partners/openai/langchain_openai/chat_models/batch.py

@@ -253,7 +253,7 @@ class OpenAIBatchClient:
             BatchError: If batch cancellation fails.
         """
         try:
-            batch = self.client.batches.cancel(batch_id)
+            _ = self.client.batches.cancel(batch_id)
             return self.retrieve_batch(batch_id)
         except openai.OpenAIError as e:
             raise BatchError(
@@ -267,7 +267,8 @@ class OpenAIBatchClient:
 
 class OpenAIBatchProcessor:
     """
-    High-level processor for managing OpenAI Batch API lifecycle with LangChain integration.
+    High-level processor for managing OpenAI Batch API lifecycle with
+    LangChain integration.
 
     This class handles the complete batch processing workflow:
     1. Converts LangChain messages to OpenAI batch format
@@ -299,7 +300,7 @@ class OpenAIBatchProcessor:
 
     def create_batch(
         self,
-        messages_list: list[List[BaseMessage]],
+        messages_list: list[list[BaseMessage]],
         description: Optional[str] = None,
         metadata: Optional[dict[str, str]] = None,
         **kwargs: Any,
@@ -435,7 +436,7 @@ class OpenAIBatchProcessor:
 
     def process_batch(
         self,
-        messages_list: list[List[BaseMessage]],
+        messages_list: list[list[BaseMessage]],
         description: Optional[str] = None,
         metadata: Optional[dict[str, str]] = None,
         poll_interval: Optional[float] = None,

libs/partners/openai/tests/unit_tests/chat_models/test_batch.py

@@ -600,7 +600,7 @@ class TestBatchIntegrationScenarios:
         mock_processor_class.return_value = mock_processor
 
         inputs = [[HumanMessage(content=f"Question {i}")] for i in range(num_requests)]
-        _ = self.llm.batch(inputs, use_batch_api=True)
+        results = self.llm.batch(inputs, use_batch_api=True)
 
         assert len(results) == num_requests
         for i, result in enumerate(results):