Confirm this is an issue with the Python library and not an underlying OpenAI API
Describe the bug
A weird issue that I am facing in production, after the code is deployed and it has been 24-26 hours the any calls made using the above client start timing out. Usually taking out a redeployment helps.
This pattern makes it feel like the underlying HTTP connections are not being released or refreshed correctly, which may be causing a connection pool exhaustion.
Any best practices or recommendations on how to tackle this ?
My configuration
openai.api_key = os.environ.get("OPENAI_API_KEY")
os.environ.setdefault("OPENAI_LOG", "debug")
client = AsyncOpenAI(
http_client=DefaultAioHttpClient(
limits=httpx.Limits(max_connections=1000, max_keepalive_connections=100)
),
)
I am streaming and once i am done with streaming I am closing the stream as well
response = return await chat_client.chat.completions.create(**params)
## Later on
if response:
await response.close()
name = "openai"
version = "1.99.9"
To Reproduce
- NA since it it not easily replicable in local.
Code snippets
OS
Debian Linux
Python version
3.10.6
Library version
1.99.9
Confirm this is an issue with the Python library and not an underlying OpenAI API
Describe the bug
A weird issue that I am facing in production, after the code is deployed and it has been 24-26 hours the any calls made using the above client start timing out. Usually taking out a redeployment helps.
This pattern makes it feel like the underlying HTTP connections are not being released or refreshed correctly, which may be causing a connection pool exhaustion.
Any best practices or recommendations on how to tackle this ?
My configuration
I am streaming and once i am done with streaming I am closing the stream as well
To Reproduce
Code snippets
OS
Debian Linux
Python version
3.10.6
Library version
1.99.9