Explicitly close AsyncClient to avoid getting asyncio event loop is closed issues

src/bespokelabs/curator/request_processor/openai_batch_request_processor.py

@@ -134,6 +134,7 @@ def create_api_specific_request(
         return request
 
     async def asubmit_batch(self, batch_file: str) -> dict:
+        async_client = AsyncOpenAI()
         # Create a list to store API-specific requests
         api_specific_requests = []
 
@@ -147,13 +148,13 @@ async def asubmit_batch(self, batch_file: str) -> dict:
         # Join requests with newlines and encode to bytes for upload
         file_content = "\n".join(api_specific_requests).encode()
 
-        batch_file_upload = await self.async_client.files.create(
+        batch_file_upload = await async_client.files.create(
             file=file_content, purpose="batch"
         )
 
         logger.info(f"File uploaded: {batch_file_upload}")
 
-        batch_object = await self.async_client.batches.create(
+        batch_object = await async_client.batches.create(
             input_file_id=batch_file_upload.id,
             endpoint="/v1/chat/completions",
             completion_window="24h",
@@ -164,6 +165,9 @@ async def asubmit_batch(self, batch_file: str) -> dict:
         logger.info(
             f"Batch request submitted, received batch object: {batch_object}"
         )
+        # Explicitly close the client. Otherwise we get something like
+        # future: <Task finished name='Task-46' coro=<AsyncClient.aclose() done ... >>
+        await async_client.close()
 
         return batch_object
 
@@ -198,8 +202,6 @@ def run(
             )
         else:
             # upload requests files and submit batches
-            self.async_client = AsyncOpenAI()
-
             # asyncio gather preserves order
             async def submit_all_batches():
                 tasks = [
@@ -226,18 +228,21 @@ async def submit_all_batches():
         # TODO(Ryan): This creates responses_0.jsonl, responses_1.jsonl, etc. errors named same way? or errors_0.jsonl, errors_1.jsonl?
         # TODO(Ryan): retries, resubmits on lagging batches - need to study this a little closer
         # TODO(Ryan): likely can add some logic for smarter check_interval based on batch size and if the batch has started or not, fine to do a dumb ping for now
-        batch_watcher = BatchWatcher(
-            working_dir, check_interval=self.check_interval
-        )
-
         # NOTE(Ryan): If we allow for multiple heterogeneous requests per dataset row, we will need to update this.
         total_requests = 1 if dataset is None else len(dataset)
 
-        run_in_event_loop(
-            batch_watcher.watch(
+        async def watch_batches():
+            batch_watcher = BatchWatcher(
+                working_dir, check_interval=self.check_interval
+            )
+            await batch_watcher.watch(
                 prompt_formatter.response_format, total_requests
             )
-        )
+            # Explicitly close the client. Otherwise we get something like
+            # future: <Task finished name='Task-46' coro=<AsyncClient.aclose() done ... >>
+            await batch_watcher.close_client()
+
+        run_in_event_loop(watch_batches())
 
         dataset = self.create_dataset_files(
             working_dir, parse_func_hash, prompt_formatter
@@ -266,6 +271,9 @@ def __init__(self, working_dir: str, check_interval) -> None:
         self.check_interval = check_interval
         self.working_dir = working_dir
 
+    async def close_client(self):
+        await self.client.close()
+
     async def check_batch_status(self, batch_id: str) -> tuple[str, str]:
         """Check the status of a batch by its ID.