Logging and debugging
We use the standard library logging
module.
You can enable logging by setting the environment variable OPENAI_LOG
to debug
.
How to tell whether None
means null
or missing
In an API response, a field may be explicitly null
, or missing entirely; in either case, its value is None
in this library. You can differentiate the two cases with .model_fields_set
:
if response.my_field is None:
if 'my_field' not in response.model_fields_set:
print('Got json like {}, without a "my_field" key present at all.')
else:
print('Got json like {"my_field": null}.')
Accessing raw response data (headers)
The "raw" Response object can be accessed by prefixing .with_raw_response.
to any HTTP method call, for example:
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.with_raw_response.create(
messages=[{
"role": "user",
"content": "Say this is a test",
}],
model="gpt-3.5-turbo",
)
print(response.headers.get('X-My-Header'))
completion = response.parse() # get the object that `chat.completions.create()` would have returned
print(completion)
These methods return an LegacyAPIResponse
object. This is a legacy class as we're changing it slightly in the next major version.
For the sync client this will mostly be the same with the exception
of content
& text
will be methods instead of properties. In the
async client, all methods will be async.
A migration script will be provided & the migration in general should be smooth.
.with_streaming_response
The above interface eagerly reads the full response body when you make the request, which may not always be what you want.
To stream the response body, use .with_streaming_response
instead, which requires a context manager and only reads the response body once you call .read()
, .text()
, .json()
, .iter_bytes()
, .iter_text()
, .iter_lines()
or .parse()
. In the async client, these are async methods.
As such, .with_streaming_response
methods return a different APIResponse
object, and the async client returns an AsyncAPIResponse
object.
with client.chat.completions.with_streaming_response.create(
messages=[
{
"role": "user",
"content": "Say this is a test",
}
],
model="gpt-3.5-turbo",
) as response:
print(response.headers.get("X-My-Header"))
for line in response.iter_lines():
print(line)
The context manager is required so that the response will reliably be closed.