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https://github.com/openai/openai-python

The official Python library for the OpenAI API
https://github.com/openai/openai-python

openai python

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The official Python library for the OpenAI API

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README 

        
# OpenAI Python API library



[![PyPI version](https://img.shields.io/pypi/v/openai.svg)](https://pypi.org/project/openai/)



The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.8+

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

# install from PyPI

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(

    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted

)



chat_completion = client.chat.completions.create(

    messages=[

        {

            "role": "user",

            "content": "Say this is a test",

        }

    ],

    model="gpt-4o",

)

```



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.



### Vision



With a hosted image:



```python

response = client.chat.completions.create(

    model="gpt-4o-mini",

    messages=[

        {

            "role": "user",

            "content": [

                {"type": "text", "text": prompt},

                {

                    "type": "image_url",

                    "image_url": {"url": f"{img_url}"},

                },

            ],

        }

    ],

)

```



With the image as a base64 encoded string:



```python

response = client.chat.completions.create(

    model="gpt-4o-mini",

    messages=[

        {

            "role": "user",

            "content": [

                {"type": "text", "text": prompt},

                {

                    "type": "image_url",

                    "image_url": {"url": f"data:{img_type};base64,{img_b64_str}"},

                },

            ],

        }

    ],

)

```



### 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 that 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 and interacting with vector stores, you can use 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 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](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(

    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted

)



async def main() -> None:

    chat_completion = await client.chat.completions.create(

        messages=[

            {

                "role": "user",

                "content": "Say this is a test",

            }

        ],

        model="gpt-4o",

    )



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(

    messages=[

        {

            "role": "user",

            "content": "Say this is a test",

        }

    ],

    model="gpt-4o",

    stream=True,

)

for chunk in stream:

    print(chunk.choices[0].delta.content or "", end="")

```



The async client uses the exact same interface.



```python

import asyncio

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-4o",

    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

from openai 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

from openai import AsyncOpenAI



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-4o",

    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-4o",

        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`       |



## Request IDs



> For more information on debugging requests, see [these docs](https://platform.openai.com/docs/api-reference/debugging-requests)



All object responses in the SDK provide a `_request_id` property which is added from the `x-request-id` response header so that you can quickly log failing requests and report them back to OpenAI.



```python

completion = await client.chat.completions.create(

    messages=[{"role": "user", "content": "Say this is a test"}], model="gpt-4"

)

print(completion._request_id)  # req_123

```



Note that unlike other properties that use an `_` prefix, the `_request_id` property

*is* public. Unless documented otherwise, *all* other `_` prefix properties,

methods and modules are *private*.



### 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 JavaScript?",

        }

    ],

    model="gpt-4o",

)

```



### 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=5.0).chat.completions.create(

    messages=[

        {

            "role": "user",

            "content": "How can I list all files in a directory using Python?",

        }

    ],

    model="gpt-4o",

)

```



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 `info`.



```shell

$ export OPENAI_LOG=info

```



Or to `debug` for more verbose logging.



### 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-4o",

)

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-4o",

) 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/clients/) 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/v1",

    http_client=DefaultHttpxClient(

        proxies="http://my.test.proxy.example.com",

        transport=httpx.HTTPTransport(local_address="0.0.0.0"),

    ),

)

```



You can also customize the client on a per-request basis by using `with_options()`:



```python

client.with_options(http_client=DefaultHttpxClient(...))

```



### 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/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/azure/ai-services/openai/reference#rest-api-versioning

    api_version="2023-07-01-preview",

    # https://learn.microsoft.com/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 Microsoft Entra ID (formerly known as 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.



### Determining the installed version



If you've upgraded to the latest version but aren't seeing any new features you were expecting then your python environment is likely still using an older version.



You can determine the version that is being used at runtime with:



```py

import openai

print(openai.__version__)

```



## Requirements



Python 3.8 or higher.



## Contributing



See [the contributing documentation](./CONTRIBUTING.md).