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

@@ -2149,6 +2149,114 @@ class BaseChatOpenAI(BaseChatModel):
         )
         
         return processor.retrieve_batch_results(batch_id)
+    @override
+    def batch(
+        self,
+        inputs: List[LanguageModelInput],
+        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
+        *,
+        return_exceptions: bool = False,
+        use_batch_api: bool = False,
+        **kwargs: Any,
+    ) -> List[BaseMessage]:
+        """
+        Batch process multiple inputs using either standard API or OpenAI Batch API.
+        
+        This method provides two processing modes:
+        1. Standard mode (use_batch_api=False): Uses parallel invoke for immediate results
+        2. Batch API mode (use_batch_api=True): Uses OpenAI's Batch API for 50% cost savings
+        
+        Args:
+            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.
+            **kwargs: Additional parameters to pass to the underlying model.
+            
+        Returns:
+            List of BaseMessage objects corresponding to the inputs.
+            
+        Raises:
+            BatchError: If batch processing fails (when use_batch_api=True).
+            
+        Note:
+            **Cost vs Latency Tradeoff:**
+            - use_batch_api=False: Immediate results, standard API pricing
+            - use_batch_api=True: 50% cost savings, asynchronous processing with polling
+            
+        Example:
+            .. code-block:: python
+            
+                from langchain_openai import ChatOpenAI
+                from langchain_core.messages import HumanMessage
+                
+                llm = ChatOpenAI()
+                inputs = [
+                    [HumanMessage(content="What is 2+2?")],
+                    [HumanMessage(content="What is the capital of France?")],
+                ]
+                
+                # Standard processing (immediate results)
+                results = llm.batch(inputs)
+                
+                # Batch API processing (50% cost savings, polling required)
+                results = llm.batch(inputs, use_batch_api=True)
+        """
+        if use_batch_api:
+            # Convert inputs to messages_list format expected by batch_create
+            messages_list = []
+            for input_item in inputs:
+                if isinstance(input_item, list):
+                    # Already a list of messages
+                    messages_list.append(input_item)
+                else:
+                    # Convert single input to list of messages
+                    messages = self._convert_input_to_messages(input_item)
+                    messages_list.append(messages)
+            
+            # Create batch job and poll for results
+            batch_id = self.batch_create(messages_list, **kwargs)
+            chat_results = self.batch_retrieve(batch_id)
+            
+            # Convert ChatResult objects to BaseMessage objects
+            return [result.generations[0].message for result in chat_results]
+        else:
+            # Use the parent class's standard batch implementation
+            return super().batch(
+                inputs=inputs,
+                config=config,
+                return_exceptions=return_exceptions,
+                **kwargs,
+            )
+
+    def _convert_input_to_messages(self, input_item: LanguageModelInput) -> List[BaseMessage]:
+        """Convert various input formats to a list of BaseMessage objects."""
+        if isinstance(input_item, list):
+            # Already a list of messages
+            return input_item
+        elif isinstance(input_item, BaseMessage):
+            # Single message
+            return [input_item]
+        elif isinstance(input_item, str):
+            # String input - convert to HumanMessage
+            from langchain_core.messages import HumanMessage
+            return [HumanMessage(content=input_item)]
+        elif hasattr(input_item, 'to_messages'):
+            # PromptValue or similar
+            return input_item.to_messages()
+        else:
+            # Try to convert to string and then to HumanMessage
+            from langchain_core.messages import HumanMessage
+            return [HumanMessage(content=str(input_item))]
+
+
+
+
+
+    
+
+