The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.7+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.
It is generated from our OpenAPI specification with Stainless.
The API documentation can be found here.
pip install openaiThe full API of this library can be found in api.md.
from openai import OpenAI
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key="My API Key",
)
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Say this is a test",
}
],
model="gpt-3.5-turbo",
)While you can provide an api_key keyword argument,
we recommend using python-dotenv
to add OPENAI_API_KEY="My API Key" to your .env file
so that your API Key is not stored in source control.
Simply import AsyncOpenAI instead of OpenAI and use await with each API call:
import asyncio
from openai import AsyncOpenAI
client = AsyncOpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key="My API Key",
)
async def main() -> None:
chat_completion = await client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Say this is a test",
}
],
model="gpt-3.5-turbo",
)
asyncio.run(main())Functionality between the synchronous and asynchronous clients is otherwise identical.
We provide support for streaming responses using Server Side Events (SSE).
from openai import OpenAI
client = OpenAI()
stream = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Say this is a test"}],
stream=True,
)
for part in stream:
print(part.choices[0].delta.content or "")The async client uses the exact same interface.
from openai import AsyncOpenAI
client = AsyncOpenAI()
stream = await client.chat.completions.create(
prompt="Say this is a test",
messages=[{"role": "user", "content": "Say this is a test"}],
stream=True,
)
async for part in stream:
print(part.choices[0].delta.content or "")Important
We highly recommend instantiating client instances instead of relying on the global client.
We also expose a global client instance that is accessible in a similar fashion to versions prior to v1.
import openai
# optional; defaults to `os.environ['OPENAI_API_KEY']`
openai.api_key = '...'
# all client options can be configured just like the `OpenAI` instantiation counterpart
openai.base_url = "https://..."
openai.default_headers = {"x-foo": "true"}
completion = openai.chat.completions.create(
model="gpt-4",
messages=[
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
],
)
print(completion.choices[0].message.content)The API is the exact same as the standard client instance based API.
This is intended to be used within REPLs or notebooks for faster iteration, not in application code.
We recommend that you always instantiate a client (e.g., with client = OpenAI()) in application code because:
- It can be difficult to reason about where client options are configured
- It's not possible to change certain client options without potentially causing race conditions
- It's harder to mock for testing purposes
- It's not possible to control cleanup of network connections
Nested request parameters are TypedDicts. Responses are Pydantic models, which provide helper methods for things like serializing back into JSON (v1, v2). To get a dictionary, call model.model_dump().
Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set python.analysis.typeCheckingMode to basic.
List methods in the OpenAI API are paginated.
This library provides auto-paginating iterators with each list response, so you do not have to request successive pages manually:
import openai
client = OpenAI()
all_jobs = []
# Automatically fetches more pages as needed.
for job in client.fine_tuning.jobs.list(
limit=20,
):
# Do something with job here
all_jobs.append(job)
print(all_jobs)Or, asynchronously:
import asyncio
import openai
client = AsyncOpenAI()
async def main() -> None:
all_jobs = []
# Iterate through items across all pages, issuing requests as needed.
async for job in client.fine_tuning.jobs.list(
limit=20,
):
all_jobs.append(job)
print(all_jobs)
asyncio.run(main())Alternatively, you can use the .has_next_page(), .next_page_info(), or .get_next_page() methods for more granular control working with pages:
first_page = await client.fine_tuning.jobs.list(
limit=20,
)
if first_page.has_next_page():
print(f"will fetch next page using these details: {first_page.next_page_info()}")
next_page = await first_page.get_next_page()
print(f"number of items we just fetched: {len(next_page.data)}")
# Remove `await` for non-async usage.Or just work directly with the returned data:
first_page = await client.fine_tuning.jobs.list(
limit=20,
)
print(f"next page cursor: {first_page.after}") # => "next page cursor: ..."
for job in first_page.data:
print(job.id)
# Remove `await` for non-async usage.Nested parameters are dictionaries, typed using TypedDict, for example:
from openai import OpenAI
client = OpenAI()
page = client.files.list()Request parameters that correspond to file uploads can be passed as bytes, a PathLike instance or a tuple of (filename, contents, media type).
from pathlib import Path
from openai import OpenAI
client = OpenAI()
client.files.create(
file=Path("input.jsonl"),
purpose="fine-tune",
)The async client uses the exact same interface. If you pass a PathLike instance, the file contents will be read asynchronously automatically.
When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of openai.APIConnectionError is raised.
When the API returns a non-success status code (that is, 4xx or 5xx
response), a subclass of openai.APIStatusError is raised, containing status_code and response properties.
All errors inherit from openai.APIError.
import openai
from openai import OpenAI
client = OpenAI()
try:
client.fine_tunes.create(
training_file="file-XGinujblHPwGLSztz8cPS8XY",
)
except openai.APIConnectionError as e:
print("The server could not be reached")
print(e.__cause__) # an underlying Exception, likely raised within httpx.
except openai.RateLimitError as e:
print("A 429 status code was received; we should back off a bit.")
except openai.APIStatusError as e:
print("Another non-200-range status code was received")
print(e.status_code)
print(e.response)Error codes are as followed:
| Status Code | Error Type |
|---|---|
| 400 | BadRequestError |
| 401 | AuthenticationError |
| 403 | PermissionDeniedError |
| 404 | NotFoundError |
| 422 | UnprocessableEntityError |
| 429 | RateLimitError |
| >=500 | InternalServerError |
| N/A | APIConnectionError |
Certain errors are automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors are all retried by default.
You can use the max_retries option to configure or disable retry settings:
from openai import OpenAI
# Configure the default for all requests:
client = OpenAI(
# default is 2
max_retries=0,
)
# Or, configure per-request:
client.with_options(max_retries=5).chat.completions.create(
messages=[
{
"role": "user",
"content": "How can I get the name of the current day in Node.js?",
}
],
model="gpt-3.5-turbo",
)By default requests time out after 10 minutes. You can configure this with a timeout option,
which accepts a float or an httpx.Timeout object:
from openai import OpenAI
# Configure the default for all requests:
client = OpenAI(
# default is 60s
timeout=20.0,
)
# More granular control:
client = OpenAI(
timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)
# Override per-request:
client.with_options(timeout=5 * 1000).chat.completions.create(
messages=[
{
"role": "user",
"content": "How can I list all files in a directory using Python?",
}
],
model="gpt-3.5-turbo",
)On timeout, an APITimeoutError is thrown.
Note that requests that time out are retried twice by default.
We use the standard library logging module.
You can enable logging by setting the environment variable OPENAI_LOG to debug.
$ export OPENAI_LOG=debugIn 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}.')The "raw" Response object can be accessed by prefixing .with_raw_response. to any HTTP method call.
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 APIResponse object.
You can directly override the httpx client to customize it for your use case, including:
- Support for proxies
- Custom transports
- Additional advanced functionality
import httpx
from openai import OpenAI
client = OpenAI(
base_url="http://my.test.server.example.com:8083",
http_client=httpx.Client(
proxies="http://my.test.proxy.example.com",
transport=httpx.HTTPTransport(local_address="0.0.0.0"),
),
)By default the library closes underlying HTTP connections whenever the client is garbage collected. You can manually close the client using the .close() method if desired, or with a context manager that closes when exiting.
To use this library with Azure OpenAI, use the AzureOpenAI
class instead of the OpenAI class.
Important
The Azure API shape differs from the core API shape which means that the static types for responses / params won't always be correct.
from openai import AzureOpenAI
# gets the API Key from environment variable AZURE_OPENAI_API_KEY
client = AzureOpenAI(
# https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning
api_version="2023-07-01-preview"
# https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource
azure_endpoint="https://example-endpoint.openai.azure.com",
)
completion = client.chat.completions.create(
model="deployment-name", # e.g. gpt-35-instant
messages=[
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
],
)
print(completion.model_dump_json(indent=2))In addition to the options provided in the base OpenAI client, the following options are provided:
azure_endpointazure_deploymentapi_versionazure_ad_tokenazure_ad_token_provider
An example of using the client with Azure Active Directory can be found here.
This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:
- Changes that only affect static types, without breaking runtime behavior.
- Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals).
- Changes that we do not expect to impact the vast majority of users in practice.
We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.
We are keen for your feedback; please open an issue with questions, bugs, or suggestions.
Python 3.7 or higher.