| Name | openai JSON |
| Version |
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| Summary | The official Python library for the openai API |
| upload_time | 2024-05-02 15:52:56 |
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| requires_python | >=3.7.1 |
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# 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](https://github.com/openai/openai-python/tree/main/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
# install from PyPI
pip install openai
```
## Usage
The full API of this library can be found in [api.md](https://github.com/openai/openai-python/tree/main/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.
### Polling Helpers
When interacting with the API some actions such as starting a Run and adding files to vector stores are asynchronous and take time to complete. The SDK includes
helper functions which will poll the status until it reaches a terminal state and then return the resulting object.
If an API method results in an action which could benefit from polling there will be a corresponding version of the
method ending in '\_and_poll'.
For instance to create a Run and poll until it reaches a terminal state you can run:
```python
run = client.beta.threads.runs.create_and_poll(
thread_id=thread.id,
assistant_id=assistant.id,
)
```
More information on the lifecycle of a Run can be found in the [Run Lifecycle Documentation](https://platform.openai.com/docs/assistants/how-it-works/run-lifecycle)
### Bulk Upload Helpers
When creating an interacting with vector stores, you can use the polling helpers to monitor the status of operations.
For convenience, we also provide a bulk upload helper to allow you to simultaneously upload several files at once.
```python
sample_files = [Path("sample-paper.pdf"), ...]
batch = await client.vector_stores.file_batches.upload_and_poll(
store.id,
files=sample_files,
)
```
### Streaming Helpers
The SDK also includes helpers to process streams and handle the incoming events.
```python
with client.beta.threads.runs.stream(
thread_id=thread.id,
assistant_id=assistant.id,
instructions="Please address the user as Jane Doe. The user has a premium account.",
) as stream:
for event in stream:
# Print the text from text delta events
if event.type == "thread.message.delta" and event.data.delta.content:
print(event.data.delta.content[0].text)
```
More information on streaming helpers can be found in the dedicated documentation: [helpers.md](https://github.com/openai/openai-python/tree/main/helpers.md)
## 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 also provide helper methods for things like:
- Serializing back into JSON, `model.to_json()`
- Converting to a dictionary, `model.to_dict()`
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](https://github.com/openai/openai-python/tree/main/#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.
### Making custom/undocumented requests
This library is typed for convenient access to the documented API.
If you need to access undocumented endpoints, params, or response properties, the library can still be used.
#### Undocumented endpoints
To make requests to undocumented endpoints, you can make requests using `client.get`, `client.post`, and other
http verbs. Options on the client will be respected (such as retries) will be respected when making this
request.
```py
import httpx
response = client.post(
"/foo",
cast_to=httpx.Response,
body={"my_param": True},
)
print(response.headers.get("x-foo"))
```
#### Undocumented request params
If you want to explicitly send an extra param, you can do so with the `extra_query`, `extra_body`, and `extra_headers` request
options.
#### Undocumented response properties
To access undocumented response properties, you can access the extra fields like `response.unknown_prop`. You
can also get all the extra fields on the Pydantic model as a dict with
[`response.model_extra`](https://docs.pydantic.dev/latest/api/base_model/#pydantic.BaseModel.model_extra).
### 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
from openai import OpenAI, DefaultHttpxClient
client = OpenAI(
# Or use the `OPENAI_BASE_URL` env var
base_url="http://my.test.server.example.com:8083",
http_client=DefaultHttpxClient(
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.to_json())
```
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.
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"description": "# OpenAI Python API library\n\n[](https://pypi.org/project/openai/)\n\nThe OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.7+\napplication. The library includes type definitions for all request params and response fields,\nand offers both synchronous and asynchronous clients powered by [httpx](https://github.com/encode/httpx).\n\nIt is generated from our [OpenAPI specification](https://github.com/openai/openai-openapi) with [Stainless](https://stainlessapi.com/).\n\n## Documentation\n\nThe 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](https://github.com/openai/openai-python/tree/main/api.md).\n\n## Installation\n\n> [!IMPORTANT]\n> 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.\n\n```sh\n# install from PyPI\npip install openai\n```\n\n## Usage\n\nThe full API of this library can be found in [api.md](https://github.com/openai/openai-python/tree/main/api.md).\n\n```python\nimport os\nfrom openai import OpenAI\n\nclient = OpenAI(\n # This is the default and can be omitted\n api_key=os.environ.get(\"OPENAI_API_KEY\"),\n)\n\nchat_completion = client.chat.completions.create(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Say this is a test\",\n }\n ],\n model=\"gpt-3.5-turbo\",\n)\n```\n\nWhile you can provide an `api_key` keyword argument,\nwe recommend using [python-dotenv](https://pypi.org/project/python-dotenv/)\nto add `OPENAI_API_KEY=\"My API Key\"` to your `.env` file\nso that your API Key is not stored in source control.\n\n### Polling Helpers\n\nWhen interacting with the API some actions such as starting a Run and adding files to vector stores are asynchronous and take time to complete. The SDK includes\nhelper functions which will poll the status until it reaches a terminal state and then return the resulting object.\nIf an API method results in an action which could benefit from polling there will be a corresponding version of the\nmethod ending in '\\_and_poll'.\n\nFor instance to create a Run and poll until it reaches a terminal state you can run:\n\n```python\nrun = client.beta.threads.runs.create_and_poll(\n thread_id=thread.id,\n assistant_id=assistant.id,\n)\n```\n\nMore information on the lifecycle of a Run can be found in the [Run Lifecycle Documentation](https://platform.openai.com/docs/assistants/how-it-works/run-lifecycle)\n\n### Bulk Upload Helpers\n\nWhen creating an interacting with vector stores, you can use the polling helpers to monitor the status of operations.\nFor convenience, we also provide a bulk upload helper to allow you to simultaneously upload several files at once.\n\n```python\nsample_files = [Path(\"sample-paper.pdf\"), ...]\n\nbatch = await client.vector_stores.file_batches.upload_and_poll(\n store.id,\n files=sample_files,\n)\n```\n\n### Streaming Helpers\n\nThe SDK also includes helpers to process streams and handle the incoming events.\n\n```python\nwith client.beta.threads.runs.stream(\n thread_id=thread.id,\n assistant_id=assistant.id,\n instructions=\"Please address the user as Jane Doe. The user has a premium account.\",\n) as stream:\n for event in stream:\n # Print the text from text delta events\n if event.type == \"thread.message.delta\" and event.data.delta.content:\n print(event.data.delta.content[0].text)\n```\n\nMore information on streaming helpers can be found in the dedicated documentation: [helpers.md](https://github.com/openai/openai-python/tree/main/helpers.md)\n\n## Async usage\n\nSimply import `AsyncOpenAI` instead of `OpenAI` and use `await` with each API call:\n\n```python\nimport os\nimport asyncio\nfrom openai import AsyncOpenAI\n\nclient = AsyncOpenAI(\n # This is the default and can be omitted\n api_key=os.environ.get(\"OPENAI_API_KEY\"),\n)\n\n\nasync def main() -> None:\n chat_completion = await client.chat.completions.create(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Say this is a test\",\n }\n ],\n model=\"gpt-3.5-turbo\",\n )\n\n\nasyncio.run(main())\n```\n\nFunctionality between the synchronous and asynchronous clients is otherwise identical.\n\n## Streaming responses\n\nWe provide support for streaming responses using Server Side Events (SSE).\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI()\n\nstream = client.chat.completions.create(\n model=\"gpt-4\",\n messages=[{\"role\": \"user\", \"content\": \"Say this is a test\"}],\n stream=True,\n)\nfor chunk in stream:\n print(chunk.choices[0].delta.content or \"\", end=\"\")\n```\n\nThe async client uses the exact same interface.\n\n```python\nfrom openai import AsyncOpenAI\n\nclient = AsyncOpenAI()\n\n\nasync def main():\n stream = await client.chat.completions.create(\n model=\"gpt-4\",\n messages=[{\"role\": \"user\", \"content\": \"Say this is a test\"}],\n stream=True,\n )\n async for chunk in stream:\n print(chunk.choices[0].delta.content or \"\", end=\"\")\n\n\nasyncio.run(main())\n```\n\n## Module-level client\n\n> [!IMPORTANT]\n> We highly recommend instantiating client instances instead of relying on the global client.\n\nWe also expose a global client instance that is accessible in a similar fashion to versions prior to v1.\n\n```py\nimport openai\n\n# optional; defaults to `os.environ['OPENAI_API_KEY']`\nopenai.api_key = '...'\n\n# all client options can be configured just like the `OpenAI` instantiation counterpart\nopenai.base_url = \"https://...\"\nopenai.default_headers = {\"x-foo\": \"true\"}\n\ncompletion = openai.chat.completions.create(\n model=\"gpt-4\",\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"How do I output all files in a directory using Python?\",\n },\n ],\n)\nprint(completion.choices[0].message.content)\n```\n\nThe API is the exact same as the standard client instance based API.\n\nThis is intended to be used within REPLs or notebooks for faster iteration, **not** in application code.\n\nWe recommend that you always instantiate a client (e.g., with `client = OpenAI()`) in application code because:\n\n- It can be difficult to reason about where client options are configured\n- It's not possible to change certain client options without potentially causing race conditions\n- It's harder to mock for testing purposes\n- It's not possible to control cleanup of network connections\n\n## Using types\n\nNested request parameters are [TypedDicts](https://docs.python.org/3/library/typing.html#typing.TypedDict). Responses are [Pydantic models](https://docs.pydantic.dev) which also provide helper methods for things like:\n\n- Serializing back into JSON, `model.to_json()`\n- Converting to a dictionary, `model.to_dict()`\n\nTyped 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`.\n\n## Pagination\n\nList methods in the OpenAI API are paginated.\n\nThis library provides auto-paginating iterators with each list response, so you do not have to request successive pages manually:\n\n```python\nimport openai\n\nclient = OpenAI()\n\nall_jobs = []\n# Automatically fetches more pages as needed.\nfor job in client.fine_tuning.jobs.list(\n limit=20,\n):\n # Do something with job here\n all_jobs.append(job)\nprint(all_jobs)\n```\n\nOr, asynchronously:\n\n```python\nimport asyncio\nimport openai\n\nclient = AsyncOpenAI()\n\n\nasync def main() -> None:\n all_jobs = []\n # Iterate through items across all pages, issuing requests as needed.\n async for job in client.fine_tuning.jobs.list(\n limit=20,\n ):\n all_jobs.append(job)\n print(all_jobs)\n\n\nasyncio.run(main())\n```\n\nAlternatively, you can use the `.has_next_page()`, `.next_page_info()`, or `.get_next_page()` methods for more granular control working with pages:\n\n```python\nfirst_page = await client.fine_tuning.jobs.list(\n limit=20,\n)\nif first_page.has_next_page():\n print(f\"will fetch next page using these details: {first_page.next_page_info()}\")\n next_page = await first_page.get_next_page()\n print(f\"number of items we just fetched: {len(next_page.data)}\")\n\n# Remove `await` for non-async usage.\n```\n\nOr just work directly with the returned data:\n\n```python\nfirst_page = await client.fine_tuning.jobs.list(\n limit=20,\n)\n\nprint(f\"next page cursor: {first_page.after}\") # => \"next page cursor: ...\"\nfor job in first_page.data:\n print(job.id)\n\n# Remove `await` for non-async usage.\n```\n\n## Nested params\n\nNested parameters are dictionaries, typed using `TypedDict`, for example:\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI()\n\ncompletion = client.chat.completions.create(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Can you generate an example json object describing a fruit?\",\n }\n ],\n model=\"gpt-3.5-turbo-1106\",\n response_format={\"type\": \"json_object\"},\n)\n```\n\n## File uploads\n\nRequest 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)`.\n\n```python\nfrom pathlib import Path\nfrom openai import OpenAI\n\nclient = OpenAI()\n\nclient.files.create(\n file=Path(\"input.jsonl\"),\n purpose=\"fine-tune\",\n)\n```\n\nThe 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.\n\n## Handling errors\n\nWhen 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.\n\nWhen the API returns a non-success status code (that is, 4xx or 5xx\nresponse), a subclass of `openai.APIStatusError` is raised, containing `status_code` and `response` properties.\n\nAll errors inherit from `openai.APIError`.\n\n```python\nimport openai\nfrom openai import OpenAI\n\nclient = OpenAI()\n\ntry:\n client.fine_tuning.jobs.create(\n model=\"gpt-3.5-turbo\",\n training_file=\"file-abc123\",\n )\nexcept openai.APIConnectionError as e:\n print(\"The server could not be reached\")\n print(e.__cause__) # an underlying Exception, likely raised within httpx.\nexcept openai.RateLimitError as e:\n print(\"A 429 status code was received; we should back off a bit.\")\nexcept openai.APIStatusError as e:\n print(\"Another non-200-range status code was received\")\n print(e.status_code)\n print(e.response)\n```\n\nError codes are as followed:\n\n| Status Code | Error Type |\n| ----------- | -------------------------- |\n| 400 | `BadRequestError` |\n| 401 | `AuthenticationError` |\n| 403 | `PermissionDeniedError` |\n| 404 | `NotFoundError` |\n| 422 | `UnprocessableEntityError` |\n| 429 | `RateLimitError` |\n| >=500 | `InternalServerError` |\n| N/A | `APIConnectionError` |\n\n### Retries\n\nCertain errors are automatically retried 2 times by default, with a short exponential backoff.\nConnection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict,\n429 Rate Limit, and >=500 Internal errors are all retried by default.\n\nYou can use the `max_retries` option to configure or disable retry settings:\n\n```python\nfrom openai import OpenAI\n\n# Configure the default for all requests:\nclient = OpenAI(\n # default is 2\n max_retries=0,\n)\n\n# Or, configure per-request:\nclient.with_options(max_retries=5).chat.completions.create(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"How can I get the name of the current day in Node.js?\",\n }\n ],\n model=\"gpt-3.5-turbo\",\n)\n```\n\n### Timeouts\n\nBy default requests time out after 10 minutes. You can configure this with a `timeout` option,\nwhich accepts a float or an [`httpx.Timeout`](https://www.python-httpx.org/advanced/#fine-tuning-the-configuration) object:\n\n```python\nfrom openai import OpenAI\n\n# Configure the default for all requests:\nclient = OpenAI(\n # 20 seconds (default is 10 minutes)\n timeout=20.0,\n)\n\n# More granular control:\nclient = OpenAI(\n timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),\n)\n\n# Override per-request:\nclient.with_options(timeout=5 * 1000).chat.completions.create(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"How can I list all files in a directory using Python?\",\n }\n ],\n model=\"gpt-3.5-turbo\",\n)\n```\n\nOn timeout, an `APITimeoutError` is thrown.\n\nNote that requests that time out are [retried twice by default](https://github.com/openai/openai-python/tree/main/#retries).\n\n## Advanced\n\n### Logging\n\nWe use the standard library [`logging`](https://docs.python.org/3/library/logging.html) module.\n\nYou can enable logging by setting the environment variable `OPENAI_LOG` to `debug`.\n\n```shell\n$ export OPENAI_LOG=debug\n```\n\n### How to tell whether `None` means `null` or missing\n\nIn 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`:\n\n```py\nif response.my_field is None:\n if 'my_field' not in response.model_fields_set:\n print('Got json like {}, without a \"my_field\" key present at all.')\n else:\n print('Got json like {\"my_field\": null}.')\n```\n\n### Accessing raw response data (e.g. headers)\n\nThe \"raw\" Response object can be accessed by prefixing `.with_raw_response.` to any HTTP method call, e.g.,\n\n```py\nfrom openai import OpenAI\n\nclient = OpenAI()\nresponse = client.chat.completions.with_raw_response.create(\n messages=[{\n \"role\": \"user\",\n \"content\": \"Say this is a test\",\n }],\n model=\"gpt-3.5-turbo\",\n)\nprint(response.headers.get('X-My-Header'))\n\ncompletion = response.parse() # get the object that `chat.completions.create()` would have returned\nprint(completion)\n```\n\nThese 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.\n\nFor the sync client this will mostly be the same with the exception\nof `content` & `text` will be methods instead of properties. In the\nasync client, all methods will be async.\n\nA migration script will be provided & the migration in general should\nbe smooth.\n\n#### `.with_streaming_response`\n\nThe above interface eagerly reads the full response body when you make the request, which may not always be what you want.\n\nTo 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.\n\nAs 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.\n\n```python\nwith client.chat.completions.with_streaming_response.create(\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"Say this is a test\",\n }\n ],\n model=\"gpt-3.5-turbo\",\n) as response:\n print(response.headers.get(\"X-My-Header\"))\n\n for line in response.iter_lines():\n print(line)\n```\n\nThe context manager is required so that the response will reliably be closed.\n\n### Making custom/undocumented requests\n\nThis library is typed for convenient access to the documented API.\n\nIf you need to access undocumented endpoints, params, or response properties, the library can still be used.\n\n#### Undocumented endpoints\n\nTo make requests to undocumented endpoints, you can make requests using `client.get`, `client.post`, and other\nhttp verbs. Options on the client will be respected (such as retries) will be respected when making this\nrequest.\n\n```py\nimport httpx\n\nresponse = client.post(\n \"/foo\",\n cast_to=httpx.Response,\n body={\"my_param\": True},\n)\n\nprint(response.headers.get(\"x-foo\"))\n```\n\n#### Undocumented request params\n\nIf you want to explicitly send an extra param, you can do so with the `extra_query`, `extra_body`, and `extra_headers` request\noptions.\n\n#### Undocumented response properties\n\nTo access undocumented response properties, you can access the extra fields like `response.unknown_prop`. You\ncan also get all the extra fields on the Pydantic model as a dict with\n[`response.model_extra`](https://docs.pydantic.dev/latest/api/base_model/#pydantic.BaseModel.model_extra).\n\n### Configuring the HTTP client\n\nYou can directly override the [httpx client](https://www.python-httpx.org/api/#client) to customize it for your use case, including:\n\n- Support for proxies\n- Custom transports\n- Additional [advanced](https://www.python-httpx.org/advanced/#client-instances) functionality\n\n```python\nfrom openai import OpenAI, DefaultHttpxClient\n\nclient = OpenAI(\n # Or use the `OPENAI_BASE_URL` env var\n base_url=\"http://my.test.server.example.com:8083\",\n http_client=DefaultHttpxClient(\n proxies=\"http://my.test.proxy.example.com\",\n transport=httpx.HTTPTransport(local_address=\"0.0.0.0\"),\n ),\n)\n```\n\n### Managing HTTP resources\n\nBy 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.\n\n## Microsoft Azure OpenAI\n\nTo use this library with [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview), use the `AzureOpenAI`\nclass instead of the `OpenAI` class.\n\n> [!IMPORTANT]\n> The Azure API shape differs from the core API shape which means that the static types for responses / params\n> won't always be correct.\n\n```py\nfrom openai import AzureOpenAI\n\n# gets the API Key from environment variable AZURE_OPENAI_API_KEY\nclient = AzureOpenAI(\n # https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning\n api_version=\"2023-07-01-preview\",\n # https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource\n azure_endpoint=\"https://example-endpoint.openai.azure.com\",\n)\n\ncompletion = client.chat.completions.create(\n model=\"deployment-name\", # e.g. gpt-35-instant\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"How do I output all files in a directory using Python?\",\n },\n ],\n)\nprint(completion.to_json())\n```\n\nIn addition to the options provided in the base `OpenAI` client, the following options are provided:\n\n- `azure_endpoint` (or the `AZURE_OPENAI_ENDPOINT` environment variable)\n- `azure_deployment`\n- `api_version` (or the `OPENAI_API_VERSION` environment variable)\n- `azure_ad_token` (or the `AZURE_OPENAI_AD_TOKEN` environment variable)\n- `azure_ad_token_provider`\n\nAn 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).\n\n## Versioning\n\nThis package generally follows [SemVer](https://semver.org/spec/v2.0.0.html) conventions, though certain backwards-incompatible changes may be released as minor versions:\n\n1. Changes that only affect static types, without breaking runtime behavior.\n2. 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)_.\n3. Changes that we do not expect to impact the vast majority of users in practice.\n\nWe take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.\n\nWe are keen for your feedback; please open an [issue](https://www.github.com/openai/openai-python/issues) with questions, bugs, or suggestions.\n\n## Requirements\n\nPython 3.7 or higher.\n",
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