From 2e6d2877ade3b2701e93f02f8ca89cfe0ea649eb Mon Sep 17 00:00:00 2001
From: "open-swe[bot]" <open-swe@users.noreply.github.com>
Date: Mon, 11 Aug 2025 20:32:45 +0000
Subject: [PATCH] Apply patch [skip ci]

---
 .../chat_models/_batch_methods.py             | 135 ------------------
 1 file changed, 135 deletions(-)
 delete mode 100644 libs/partners/openai/langchain_openai/chat_models/_batch_methods.py

diff --git a/libs/partners/openai/langchain_openai/chat_models/_batch_methods.py b/libs/partners/openai/langchain_openai/chat_models/_batch_methods.py
deleted file mode 100644
index 7f6d98c3e4f..00000000000
--- a/libs/partners/openai/langchain_openai/chat_models/_batch_methods.py
+++ /dev/null
@@ -1,135 +0,0 @@
-"""Batch API methods for BaseChatOpenAI class."""
-
-from typing import Any, Dict, List, Optional
-
-from langchain_core.messages import BaseMessage
-from langchain_core.outputs import ChatResult
-
-
-def batch_create(
-    self,
-    messages_list: List[List[BaseMessage]],
-    *,
-    description: Optional[str] = None,
-    metadata: Optional[Dict[str, str]] = None,
-    poll_interval: float = 10.0,
-    timeout: Optional[float] = None,
-    **kwargs: Any,
-) -> str:
-    """
-    Create a batch job using OpenAI's Batch API for asynchronous processing.
-    
-    This method provides 50% cost savings compared to the standard API in exchange
-    for asynchronous processing with polling for results.
-    
-    Args:
-        messages_list: List of message sequences to process in batch.
-        description: Optional description for the batch job.
-        metadata: Optional metadata to attach to the batch job.
-        poll_interval: Default time in seconds between status checks when polling.
-        timeout: Default maximum time in seconds to wait for completion.
-        **kwargs: Additional parameters to pass to chat completions.
-        
-    Returns:
-        The batch ID for tracking the asynchronous job.
-        
-    Raises:
-        BatchError: If batch creation fails.
-        
-    Example:
-        .. code-block:: python
-        
-            from langchain_openai import ChatOpenAI
-            from langchain_core.messages import HumanMessage
-            
-            llm = ChatOpenAI()
-            messages_list = [
-                [HumanMessage(content="What is 2+2?")],
-                [HumanMessage(content="What is the capital of France?")],
-            ]
-            
-            # Create batch job (50% cost savings)
-            batch_id = llm.batch_create(messages_list)
-            
-            # Later, retrieve results
-            results = llm.batch_retrieve(batch_id)
-    """
-    # Import here to avoid circular imports
-    from langchain_openai.chat_models.batch import OpenAIBatchProcessor
-    
-    # Create batch processor with current model settings
-    processor = OpenAIBatchProcessor(
-        client=self.root_client,
-        model=self.model_name,
-        poll_interval=poll_interval,
-        timeout=timeout,
-    )
-    
-    # Filter and prepare kwargs for batch processing
-    batch_kwargs = self._get_invocation_params(**kwargs)
-    # Remove model from kwargs since it's handled by the processor
-    batch_kwargs.pop("model", None)
-    
-    return processor.create_batch(
-        messages_list=messages_list,
-        description=description,
-        metadata=metadata,
-        **batch_kwargs,
-    )
-
-
-def batch_retrieve(
-    self,
-    batch_id: str,
-    *,
-    poll_interval: Optional[float] = None,
-    timeout: Optional[float] = None,
-) -> List[ChatResult]:
-    """
-    Retrieve results from a batch job, polling until completion if necessary.
-    
-    This method will poll the batch status until completion and return the results
-    converted to LangChain ChatResult format.
-    
-    Args:
-        batch_id: The batch ID returned from batch_create().
-        poll_interval: Time in seconds between status checks. Uses default if None.
-        timeout: Maximum time in seconds to wait. Uses default if None.
-        
-    Returns:
-        List of ChatResult objects corresponding to the original message sequences.
-        
-    Raises:
-        BatchError: If batch retrieval fails, times out, or batch job failed.
-        
-    Example:
-        .. code-block:: python
-        
-            # After creating a batch job
-            batch_id = llm.batch_create(messages_list)
-            
-            # Retrieve results (will poll until completion)
-            results = llm.batch_retrieve(batch_id)
-            
-            for result in results:
-                print(result.generations[0].message.content)
-    """
-    # Import here to avoid circular imports
-    from langchain_openai.chat_models.batch import OpenAIBatchProcessor
-    
-    # Create batch processor with current model settings
-    processor = OpenAIBatchProcessor(
-        client=self.root_client,
-        model=self.model_name,
-        poll_interval=poll_interval or 10.0,
-        timeout=timeout,
-    )
-    
-    # Poll for completion and retrieve results
-    processor.poll_batch_status(
-        batch_id=batch_id,
-        poll_interval=poll_interval,
-        timeout=timeout,
-    )
-    
-    return processor.retrieve_batch_results(batch_id)