feat - add aretrieve_batch

litellm/batches/main.py

@@ -47,7 +47,7 @@ async def acreate_file(
     **kwargs,
 ) -> Coroutine[Any, Any, FileObject]:
     """
-    Files are used to upload documents that can be used with features like Assistants, Fine-tuning, and Batch API.
+    Async: Files are used to upload documents that can be used with features like Assistants, Fine-tuning, and Batch API.
 
     LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files
     """
@@ -181,7 +181,7 @@ async def acreate_batch(
     **kwargs,
 ) -> Coroutine[Any, Any, Batch]:
     """
-    Creates and executes a batch from an uploaded file of request
+    Async: Creates and executes a batch from an uploaded file of request
 
     LiteLLM Equivalent of POST: https://api.openai.com/v1/batches
     """
@@ -311,6 +311,48 @@ def create_batch(
         raise e
 
 
+async def aretrieve_batch(
+    batch_id: str,
+    custom_llm_provider: Literal["openai"] = "openai",
+    metadata: Optional[Dict[str, str]] = None,
+    extra_headers: Optional[Dict[str, str]] = None,
+    extra_body: Optional[Dict[str, str]] = None,
+    **kwargs,
+) -> Coroutine[Any, Any, Batch]:
+    """
+    Async: Retrieves a batch.
+
+    LiteLLM Equivalent of GET https://api.openai.com/v1/batches/{batch_id}
+    """
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["aretrieve_batch"] = True
+
+        # Use a partial function to pass your keyword arguments
+        func = partial(
+            retrieve_batch,
+            batch_id,
+            custom_llm_provider,
+            metadata,
+            extra_headers,
+            extra_body,
+            **kwargs,
+        )
+
+        # Add the context to the function
+        ctx = contextvars.copy_context()
+        func_with_context = partial(ctx.run, func)
+        init_response = await loop.run_in_executor(None, func_with_context)
+        if asyncio.iscoroutine(init_response):
+            response = await init_response
+        else:
+            response = init_response  # type: ignore
+
+        return response
+    except Exception as e:
+        raise e
+
+
 def retrieve_batch(
     batch_id: str,
     custom_llm_provider: Literal["openai"] = "openai",
@@ -318,7 +360,7 @@ def retrieve_batch(
     extra_headers: Optional[Dict[str, str]] = None,
     extra_body: Optional[Dict[str, str]] = None,
     **kwargs,
-):
+) -> Union[Batch, Coroutine[Any, Any, Batch]]:
     """
     Retrieves a batch.
 
@@ -409,10 +451,6 @@ def list_batch():
     pass
 
 
-async def aretrieve_batch():
-    pass
-
-
 async def acancel_batch():
     pass
 

litellm/llms/openai.py

@@ -1672,6 +1672,14 @@ class OpenAIBatchesAPI(BaseLLM):
         response = openai_client.batches.create(**create_batch_data)
         return response
 
+    async def aretrieve_batch(
+        self,
+        retrieve_batch_data: RetrieveBatchRequest,
+        openai_client: AsyncOpenAI,
+    ) -> Batch:
+        response = await openai_client.batches.retrieve(**retrieve_batch_data)
+        return response
+
     def retrieve_batch(
         self,
         _is_async: bool,
@@ -1696,6 +1704,15 @@ class OpenAIBatchesAPI(BaseLLM):
             raise ValueError(
                 "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
             )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.aretrieve_batch(  # type: ignore
+                retrieve_batch_data=retrieve_batch_data, openai_client=openai_client
+            )
         response = openai_client.batches.retrieve(**retrieve_batch_data)
         return response
 

litellm/tests/test_openai_batches.py

@@ -109,17 +109,15 @@ async def test_async_create_batch():
         create_batch_response.input_file_id == batch_input_file_id
     ), f"Failed to create batch, expected input_file_id to be {batch_input_file_id} but got {create_batch_response.input_file_id}"
 
-    # time.sleep(30)
+    await asyncio.sleep(1)
 
-    # retrieved_batch = litellm.retrieve_batch(
-    #     batch_id=create_batch_response.id, custom_llm_provider="openai"
-    # )
-    # print("retrieved batch=", retrieved_batch)
-    # # just assert that we retrieved a non None batch
+    retrieved_batch = await litellm.aretrieve_batch(
+        batch_id=create_batch_response.id, custom_llm_provider="openai"
+    )
+    print("retrieved batch=", retrieved_batch)
+    # just assert that we retrieved a non None batch
 
-    # assert retrieved_batch.id == create_batch_response.id
-
-    pass
+    assert retrieved_batch.id == create_batch_response.id
 
 
 def test_retrieve_batch():