# OpenAI Python API library
[](https://pypi.org/project/openai/)
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](https://github.com/encode/httpx).
It is generated from our [OpenAPI specification](https://github.com/openai/openai-openapi) with [Stainless](https://stainlessapi.com/).
## Documentation
The REST API documentation can be found [on platform.openai.com](https://platform.openai.com/docs). The full API of this library can be found in [api.md](api.md).
## Installation
> [!IMPORTANT]
> The SDK was rewritten in v1, which was released November 6th 2023. See the [v1 migration guide](https://github.com/openai/openai-python/discussions/742), which includes scripts to automatically update your code.
```sh
pip install openai
```
## Usage
The full API of this library can be found in [api.md](api.md).
```python
import os
from openai import OpenAI
client = OpenAI(
# This is the default and can be omitted
api_key=os.environ.get("OPENAI_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](https://pypi.org/project/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.
## Async usage
Simply import `AsyncOpenAI` instead of `OpenAI` and use `await` with each API call:
```python
import os
import asyncio
from openai import AsyncOpenAI
client = AsyncOpenAI(
# This is the default and can be omitted
api_key=os.environ.get("OPENAI_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.
## Streaming Responses
We provide support for streaming responses using Server Side Events (SSE).
```python
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 chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
```
The async client uses the exact same interface.
```python
from openai import AsyncOpenAI
client = AsyncOpenAI()
async def main():
stream = await client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Say this is a test"}],
stream=True,
)
async for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
asyncio.run(main())
```
## Module-level client
> [!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.
```py
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
## Using types
Nested request parameters are [TypedDicts](https://docs.python.org/3/library/typing.html#typing.TypedDict). Responses are [Pydantic models](https://docs.pydantic.dev), which provide helper methods for things like:
- Serializing back into JSON, `model.model_dump_json(indent=2, exclude_unset=True)`
- Converting to a dictionary, `model.model_dump(exclude_unset=True)`
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`.
## Pagination
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:
```python
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:
```python
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:
```python
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:
```python
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 params
Nested parameters are dictionaries, typed using `TypedDict`, for example:
```python
from openai import OpenAI
client = OpenAI()
completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Can you generate an example json object describing a fruit?",
}
],
model="gpt-3.5-turbo-1106",
response_format={"type": "json_object"},
)
```
## File Uploads
Request parameters that correspond to file uploads can be passed as `bytes`, a [`PathLike`](https://docs.python.org/3/library/os.html#os.PathLike) instance or a tuple of `(filename, contents, media type)`.
```python
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`](https://docs.python.org/3/library/os.html#os.PathLike) instance, the file contents will be read asynchronously automatically.
## Handling errors
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`.
```python
import openai
from openai import OpenAI
client = OpenAI()
try:
client.fine_tuning.jobs.create(
model="gpt-3.5-turbo",
training_file="file-abc123",
)
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` |
### Retries
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:
```python
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",
)
```
### Timeouts
By default requests time out after 10 minutes. You can configure this with a `timeout` option,
which accepts a float or an [`httpx.Timeout`](https://www.python-httpx.org/advanced/#fine-tuning-the-configuration) object:
```python
from openai import OpenAI
# Configure the default for all requests:
client = OpenAI(
# 20 seconds (default is 10 minutes)
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](#retries).
## Advanced
### Logging
We use the standard library [`logging`](https://docs.python.org/3/library/logging.html) module.
You can enable logging by setting the environment variable `OPENAI_LOG` to `debug`.
```shell
$ export OPENAI_LOG=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`:
```py
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 (e.g. headers)
The "raw" Response object can be accessed by prefixing `.with_raw_response.` to any HTTP method call, e.g.,
```py
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`](https://github.com/openai/openai-python/tree/main/src/openai/_legacy_response.py) 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`](https://github.com/openai/openai-python/tree/main/src/openai/_response.py) object, and the async client returns an [`AsyncAPIResponse`](https://github.com/openai/openai-python/tree/main/src/openai/_response.py) object.
```python
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.
### Configuring the HTTP client
You can directly override the [httpx client](https://www.python-httpx.org/api/#client) to customize it for your use case, including:
- Support for proxies
- Custom transports
- Additional [advanced](https://www.python-httpx.org/advanced/#client-instances) functionality
```python
import httpx
from openai import OpenAI
client = OpenAI(
# Or use the `OPENAI_BASE_URL` env var
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"),
),
)
```
### Managing HTTP resources
By default the library closes underlying HTTP connections whenever the client is [garbage collected](https://docs.python.org/3/reference/datamodel.html#object.__del__). You can manually close the client using the `.close()` method if desired, or with a context manager that closes when exiting.
## Microsoft Azure OpenAI
To use this library with [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview), 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.
```py
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_endpoint` (or the `AZURE_OPENAI_ENDPOINT` environment variable)
- `azure_deployment`
- `api_version` (or the `OPENAI_API_VERSION` environment variable)
- `azure_ad_token` (or the `AZURE_OPENAI_AD_TOKEN` environment variable)
- `azure_ad_token_provider`
An example of using the client with Azure Active Directory can be found [here](https://github.com/openai/openai-python/blob/main/examples/azure_ad.py).
## Versioning
This package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:
1. Changes that only affect static types, without breaking runtime behavior.
2. 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)_.
3. 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](https://www.github.com/openai/openai-python/issues) with questions, bugs, or suggestions.
## Requirements
Python 3.7 or higher.