Confirm this is an issue with the Python library and not an underlying OpenAI API
Describe the bug
Describe the bug
When using the Batch API to download large result files (specifically when the output .jsonl exceeds ~200-300MB, e.g., 50k embedding rows), the download connection is prematurely closed by the peer.
The error typically manifests as:
httpcore.RemoteProtocolError: peer closed connection without sending complete message body (received X bytes, expected Y bytes)
To Reproduce
Create a Batch job with 50,000 embedding requests (text-embedding-3-small).
Wait for the batch to complete (status: completed).
Attempt to download the result file using the SDK:
Python
Standard SDK approach that fails
content = client.batches.retrieve_content(batch.output_file_id)
content.with_streaming_response.content()
Or using the requests library directly with the file URL.
The download will consistently fail after receiving a few hundred megabytes.
Expected behavior
The SDK should handle large file streaming robustly, or provide a built-in chunked download/retry mechanism for massive Batch outputs (1GB+).
Environment
OS: macOS 15.x
Python Version: 3.12.x
Additional context
Workaround: I've confirmed that splitting the 50k requests into smaller batches of 10k (resulting in ~200MB files) allows for stable downloads. This suggests a potential timeout or buffer limitation on the server-side proxy or within the SDK's streaming implementation for payloads exceeding a certain threshold.
I am currently implementing a manual chunked-download helper with exponential backoff to bypass this. but i dont think it;s optimal
To Reproduce
To Reproduce:
- Prepare a batch with ~50,000 rows (near the OpenAI limit)
- Submit to Batch API with endpoint /v1/embeddings
- Wait for status = completed
- Attempt to download result file via Files API Code Snippets:
sample code：
from openai import OpenAI
client = OpenAI()
Result file is ~1GB when batch has 50,000 embeddings
result_content = client.files.content(result_file_id)
result_text = result_content.text # fails silently or raises
#failed on streaming ：
with client.files.with_streaming_response.content(result_file_id) as resp:
with open("result.jsonl", "wb") as f:
for chunk in resp.iter_bytes(chunk_size=1024 * 1024):
f.write(chunk)
raises: peer closed connection without sending complete message body
(received 940216419 bytes, expected 1056098116)
#same for requests：
import requests
response = requests.get(
f"https://api.openai.com/v1/files/{result_file_id}/content",
headers={"Authorization": f"Bearer {api_key}"},
stream=True,
timeout=(10, 300)
)
same error, connection drops around 500-1000MB
Code snippets
OS
macOS
Python version
Python Version: 3.12.x
Library version
2.24.0
Confirm this is an issue with the Python library and not an underlying OpenAI API
Describe the bug
Describe the bug
When using the Batch API to download large result files (specifically when the output .jsonl exceeds ~200-300MB, e.g., 50k embedding rows), the download connection is prematurely closed by the peer.
The error typically manifests as:
httpcore.RemoteProtocolError: peer closed connection without sending complete message body (received X bytes, expected Y bytes)
To Reproduce
Create a Batch job with 50,000 embedding requests (text-embedding-3-small).
Wait for the batch to complete (status: completed).
Attempt to download the result file using the SDK:
Python
Standard SDK approach that fails
content = client.batches.retrieve_content(batch.output_file_id)
content.with_streaming_response.content()
Or using the requests library directly with the file URL.
The download will consistently fail after receiving a few hundred megabytes.
Expected behavior
The SDK should handle large file streaming robustly, or provide a built-in chunked download/retry mechanism for massive Batch outputs (1GB+).
Environment
OS: macOS 15.x
Python Version: 3.12.x
Additional context
Workaround: I've confirmed that splitting the 50k requests into smaller batches of 10k (resulting in ~200MB files) allows for stable downloads. This suggests a potential timeout or buffer limitation on the server-side proxy or within the SDK's streaming implementation for payloads exceeding a certain threshold.
I am currently implementing a manual chunked-download helper with exponential backoff to bypass this. but i dont think it;s optimal
To Reproduce
To Reproduce:
sample code：
from openai import OpenAI
client = OpenAI()
Result file is ~1GB when batch has 50,000 embeddings
result_content = client.files.content(result_file_id)
result_text = result_content.text # fails silently or raises
#failed on streaming ：
with client.files.with_streaming_response.content(result_file_id) as resp:
with open("result.jsonl", "wb") as f:
for chunk in resp.iter_bytes(chunk_size=1024 * 1024):
f.write(chunk)
raises: peer closed connection without sending complete message body
(received 940216419 bytes, expected 1056098116)
#same for requests：
import requests
response = requests.get(
f"https://api.openai.com/v1/files/{result_file_id}/content",
headers={"Authorization": f"Bearer {api_key}"},
stream=True,
timeout=(10, 300)
)
same error, connection drops around 500-1000MB
Code snippets
OS
macOS
Python version
Python Version: 3.12.x
Library version
2.24.0