Metadata-Version: 2.3 Name: openai Version: 1.25.2 Summary: The official Python library for the openai API Project-URL: Homepage, https://github.com/openai/openai-python Project-URL: Repository, https://github.com/openai/openai-python Author-email: OpenAI License-Expression: Apache-2.0 License-File: LICENSE Classifier: Intended Audience :: Developers Classifier: License :: OSI Approved :: Apache Software License Classifier: Operating System :: MacOS Classifier: Operating System :: Microsoft :: Windows Classifier: Operating System :: OS Independent Classifier: Operating System :: POSIX Classifier: Operating System :: POSIX :: Linux Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 Classifier: Topic :: Software Development :: Libraries :: Python Modules Classifier: Typing :: Typed Requires-Python: >=3.7.1 Requires-Dist: anyio<5,>=3.5.0 Requires-Dist: cached-property; python_version < '3.8' Requires-Dist: distro<2,>=1.7.0 Requires-Dist: httpx<1,>=0.23.0 Requires-Dist: pydantic<3,>=1.9.0 Requires-Dist: sniffio Requires-Dist: tqdm>4 Requires-Dist: typing-extensions<5,>=4.7 Provides-Extra: datalib Requires-Dist: numpy>=1; extra == 'datalib' Requires-Dist: pandas-stubs>=1.1.0.11; extra == 'datalib' Requires-Dist: pandas>=1.2.3; extra == 'datalib' Description-Content-Type: text/markdown # 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 3.7+ 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=5.0).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.