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See CONTRIBUTING.md for details. + +from __future__ import annotations + +import os as _os +from typing_extensions import override + +from . import types +from ._types import NOT_GIVEN, Omit, NoneType, NotGiven, Transport, ProxiesTypes +from ._utils import file_from_path +from ._client import Client, OpenAI, Stream, Timeout, Transport, AsyncClient, AsyncOpenAI, AsyncStream, RequestOptions +from ._models import BaseModel +from ._version import __title__, __version__ +from ._response import APIResponse as APIResponse, AsyncAPIResponse as AsyncAPIResponse +from ._constants import DEFAULT_TIMEOUT, DEFAULT_MAX_RETRIES, DEFAULT_CONNECTION_LIMITS +from ._exceptions import ( + APIError, + OpenAIError, + ConflictError, + NotFoundError, + APIStatusError, + RateLimitError, + APITimeoutError, + BadRequestError, + APIConnectionError, + AuthenticationError, + InternalServerError, + PermissionDeniedError, + LengthFinishReasonError, + UnprocessableEntityError, + APIResponseValidationError, + ContentFilterFinishReasonError, +) +from ._base_client import DefaultHttpxClient, DefaultAsyncHttpxClient +from ._utils._logs import setup_logging as _setup_logging +from ._legacy_response import HttpxBinaryResponseContent as HttpxBinaryResponseContent + +__all__ = [ + "types", + "__version__", + "__title__", + "NoneType", + "Transport", + "ProxiesTypes", + "NotGiven", + "NOT_GIVEN", + "Omit", + "OpenAIError", + "APIError", + "APIStatusError", + "APITimeoutError", + "APIConnectionError", + "APIResponseValidationError", + "BadRequestError", + "AuthenticationError", + "PermissionDeniedError", + "NotFoundError", + "ConflictError", + "UnprocessableEntityError", + "RateLimitError", + "InternalServerError", + "LengthFinishReasonError", + "ContentFilterFinishReasonError", + "Timeout", + "RequestOptions", + "Client", + "AsyncClient", + "Stream", + "AsyncStream", + "OpenAI", + "AsyncOpenAI", + "file_from_path", + "BaseModel", + "DEFAULT_TIMEOUT", + "DEFAULT_MAX_RETRIES", + "DEFAULT_CONNECTION_LIMITS", + "DefaultHttpxClient", + "DefaultAsyncHttpxClient", +] + +from .lib import azure as _azure, pydantic_function_tool as pydantic_function_tool +from .version import VERSION as VERSION +from .lib.azure import AzureOpenAI as AzureOpenAI, AsyncAzureOpenAI as AsyncAzureOpenAI +from .lib._old_api import * +from .lib.streaming import ( + AssistantEventHandler as AssistantEventHandler, + AsyncAssistantEventHandler as AsyncAssistantEventHandler, +) + +_setup_logging() + +# Update the __module__ attribute for exported symbols so that +# error messages point to this module instead of the module +# it was originally defined in, e.g. +# openai._exceptions.NotFoundError -> openai.NotFoundError +__locals = locals() +for __name in __all__: + if not __name.startswith("__"): + try: + __locals[__name].__module__ = "openai" + except (TypeError, AttributeError): + # Some of our exported symbols are builtins which we can't set attributes for. + pass + +# ------ Module level client ------ +import typing as _t +import typing_extensions as _te + +import httpx as _httpx + +from ._base_client import DEFAULT_TIMEOUT, DEFAULT_MAX_RETRIES + +api_key: str | None = None + +organization: str | None = None + +project: str | None = None + +base_url: str | _httpx.URL | None = None + +timeout: float | Timeout | None = DEFAULT_TIMEOUT + +max_retries: int = DEFAULT_MAX_RETRIES + +default_headers: _t.Mapping[str, str] | None = None + +default_query: _t.Mapping[str, object] | None = None + +http_client: _httpx.Client | None = None + +_ApiType = _te.Literal["openai", "azure"] + +api_type: _ApiType | None = _t.cast(_ApiType, _os.environ.get("OPENAI_API_TYPE")) + +api_version: str | None = _os.environ.get("OPENAI_API_VERSION") + +azure_endpoint: str | None = _os.environ.get("AZURE_OPENAI_ENDPOINT") + +azure_ad_token: str | None = _os.environ.get("AZURE_OPENAI_AD_TOKEN") + +azure_ad_token_provider: _azure.AzureADTokenProvider | None = None + + +class _ModuleClient(OpenAI): + # Note: we have to use type: ignores here as overriding class members + # with properties is technically unsafe but it is fine for our use case + + @property # type: ignore + @override + def api_key(self) -> str | None: + return api_key + + @api_key.setter # type: ignore + def api_key(self, value: str | None) -> None: # type: ignore + global api_key + + api_key = value + + @property # type: ignore + @override + def organization(self) -> str | None: + return organization + + @organization.setter # type: ignore + def organization(self, value: str | None) -> None: # type: ignore + global organization + + organization = value + + @property # type: ignore + @override + def project(self) -> str | None: + return project + + @project.setter # type: ignore + def project(self, value: str | None) -> None: # type: ignore + global project + + project = value + + @property + @override + def base_url(self) -> _httpx.URL: + if base_url is not None: + return _httpx.URL(base_url) + + return super().base_url + + @base_url.setter + def base_url(self, url: _httpx.URL | str) -> None: + super().base_url = url # type: ignore[misc] + + @property # type: ignore + @override + def timeout(self) -> float | Timeout | None: + return timeout + + @timeout.setter # type: ignore + def timeout(self, value: float | Timeout | None) -> None: # type: ignore + global timeout + + timeout = value + + @property # type: ignore + @override + def max_retries(self) -> int: + return max_retries + + @max_retries.setter # type: ignore + def max_retries(self, value: int) -> None: # type: ignore + global max_retries + + max_retries = value + + @property # type: ignore + @override + def _custom_headers(self) -> _t.Mapping[str, str] | None: + return default_headers + + @_custom_headers.setter # type: ignore + def _custom_headers(self, value: _t.Mapping[str, str] | None) -> None: # type: ignore + global default_headers + + default_headers = value + + @property # type: ignore + @override + def _custom_query(self) -> _t.Mapping[str, object] | None: + return default_query + + @_custom_query.setter # type: ignore + def _custom_query(self, value: _t.Mapping[str, object] | None) -> None: # type: ignore + global default_query + + default_query = value + + @property # type: ignore + @override + def _client(self) -> _httpx.Client: + return http_client or super()._client + + @_client.setter # type: ignore + def _client(self, value: _httpx.Client) -> None: # type: ignore + global http_client + + http_client = value + + +class _AzureModuleClient(_ModuleClient, AzureOpenAI): # type: ignore + ... + + +class _AmbiguousModuleClientUsageError(OpenAIError): + def __init__(self) -> None: + super().__init__( + "Ambiguous use of module client; please set `openai.api_type` or the `OPENAI_API_TYPE` environment variable to `openai` or `azure`" + ) + + +def _has_openai_credentials() -> bool: + return _os.environ.get("OPENAI_API_KEY") is not None + + +def _has_azure_credentials() -> bool: + return azure_endpoint is not None or _os.environ.get("AZURE_OPENAI_API_KEY") is not None + + +def _has_azure_ad_credentials() -> bool: + return ( + _os.environ.get("AZURE_OPENAI_AD_TOKEN") is not None + or azure_ad_token is not None + or azure_ad_token_provider is not None + ) + + +_client: OpenAI | None = None + + +def _load_client() -> OpenAI: # type: ignore[reportUnusedFunction] + global _client + + if _client is None: + global api_type, azure_endpoint, azure_ad_token, api_version + + if azure_endpoint is None: + azure_endpoint = _os.environ.get("AZURE_OPENAI_ENDPOINT") + + if azure_ad_token is None: + azure_ad_token = _os.environ.get("AZURE_OPENAI_AD_TOKEN") + + if api_version is None: + api_version = _os.environ.get("OPENAI_API_VERSION") + + if api_type is None: + has_openai = _has_openai_credentials() + has_azure = _has_azure_credentials() + has_azure_ad = _has_azure_ad_credentials() + + if has_openai and (has_azure or has_azure_ad): + raise _AmbiguousModuleClientUsageError() + + if (azure_ad_token is not None or azure_ad_token_provider is not None) and _os.environ.get( + "AZURE_OPENAI_API_KEY" + ) is not None: + raise _AmbiguousModuleClientUsageError() + + if has_azure or has_azure_ad: + api_type = "azure" + else: + api_type = "openai" + + if api_type == "azure": + _client = _AzureModuleClient( # type: ignore + api_version=api_version, + azure_endpoint=azure_endpoint, + api_key=api_key, + azure_ad_token=azure_ad_token, + azure_ad_token_provider=azure_ad_token_provider, + organization=organization, + base_url=base_url, + timeout=timeout, + max_retries=max_retries, + default_headers=default_headers, + default_query=default_query, + http_client=http_client, + ) + return _client + + _client = _ModuleClient( + api_key=api_key, + organization=organization, + project=project, + base_url=base_url, + timeout=timeout, + max_retries=max_retries, + default_headers=default_headers, + default_query=default_query, + http_client=http_client, + ) + return _client + + return _client + + +def _reset_client() -> None: # type: ignore[reportUnusedFunction] + global _client + + _client = None + + +from ._module_client import ( + beta as beta, + chat as chat, + audio as audio, + files as files, + images as images, + models as models, + batches as batches, + embeddings as embeddings, + completions as completions, + fine_tuning as fine_tuning, + moderations as moderations, +) diff --git a/.venv/lib/python3.11/site-packages/openai/__main__.py b/.venv/lib/python3.11/site-packages/openai/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..4e28416e104515e90fca4b69cc60d0c61fd15d61 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/__main__.py @@ -0,0 +1,3 @@ +from .cli import main + +main() diff --git a/.venv/lib/python3.11/site-packages/openai/_base_client.py b/.venv/lib/python3.11/site-packages/openai/_base_client.py new file mode 100644 index 0000000000000000000000000000000000000000..1fa039c0b1cf00b6299980c2f94fc6da4b16557d --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_base_client.py @@ -0,0 +1,2082 @@ +from __future__ import annotations + +import sys +import json +import time +import uuid +import email +import asyncio +import inspect +import logging +import platform +import warnings +import email.utils +from types import TracebackType +from random import random +from typing import ( + TYPE_CHECKING, + Any, + Dict, + Type, + Union, + Generic, + Mapping, + TypeVar, + Iterable, + Iterator, + Optional, + Generator, + AsyncIterator, + cast, + overload, +) +from typing_extensions import Literal, override, get_origin + +import anyio +import httpx +import distro +import pydantic +from httpx import URL, Limits +from pydantic import PrivateAttr + +from . import _exceptions +from ._qs import Querystring +from ._files import to_httpx_files, async_to_httpx_files +from ._types import ( + NOT_GIVEN, + Body, + Omit, + Query, + Headers, + Timeout, + NotGiven, + ResponseT, + Transport, + AnyMapping, + PostParser, + ProxiesTypes, + RequestFiles, + HttpxSendArgs, + AsyncTransport, + RequestOptions, + HttpxRequestFiles, + ModelBuilderProtocol, +) +from ._utils import SensitiveHeadersFilter, is_dict, is_list, asyncify, is_given, lru_cache, is_mapping +from ._compat import model_copy, model_dump +from ._models import GenericModel, FinalRequestOptions, validate_type, construct_type +from ._response import ( + APIResponse, + BaseAPIResponse, + AsyncAPIResponse, + extract_response_type, +) +from ._constants import ( + DEFAULT_TIMEOUT, + MAX_RETRY_DELAY, + DEFAULT_MAX_RETRIES, + INITIAL_RETRY_DELAY, + RAW_RESPONSE_HEADER, + OVERRIDE_CAST_TO_HEADER, + DEFAULT_CONNECTION_LIMITS, +) +from ._streaming import Stream, SSEDecoder, AsyncStream, SSEBytesDecoder +from ._exceptions import ( + APIStatusError, + APITimeoutError, + APIConnectionError, + APIResponseValidationError, +) +from ._legacy_response import LegacyAPIResponse + +log: logging.Logger = logging.getLogger(__name__) +log.addFilter(SensitiveHeadersFilter()) + +# TODO: make base page type vars covariant +SyncPageT = TypeVar("SyncPageT", bound="BaseSyncPage[Any]") +AsyncPageT = TypeVar("AsyncPageT", bound="BaseAsyncPage[Any]") + + +_T = TypeVar("_T") +_T_co = TypeVar("_T_co", covariant=True) + +_StreamT = TypeVar("_StreamT", bound=Stream[Any]) +_AsyncStreamT = TypeVar("_AsyncStreamT", bound=AsyncStream[Any]) + +if TYPE_CHECKING: + from httpx._config import DEFAULT_TIMEOUT_CONFIG as HTTPX_DEFAULT_TIMEOUT +else: + try: + from httpx._config import DEFAULT_TIMEOUT_CONFIG as HTTPX_DEFAULT_TIMEOUT + except ImportError: + # taken from https://github.com/encode/httpx/blob/3ba5fe0d7ac70222590e759c31442b1cab263791/httpx/_config.py#L366 + HTTPX_DEFAULT_TIMEOUT = Timeout(5.0) + + +class PageInfo: + """Stores the necessary information to build the request to retrieve the next page. + + Either `url` or `params` must be set. + """ + + url: URL | NotGiven + params: Query | NotGiven + + @overload + def __init__( + self, + *, + url: URL, + ) -> None: ... + + @overload + def __init__( + self, + *, + params: Query, + ) -> None: ... + + def __init__( + self, + *, + url: URL | NotGiven = NOT_GIVEN, + params: Query | NotGiven = NOT_GIVEN, + ) -> None: + self.url = url + self.params = params + + @override + def __repr__(self) -> str: + if self.url: + return f"{self.__class__.__name__}(url={self.url})" + return f"{self.__class__.__name__}(params={self.params})" + + +class BasePage(GenericModel, Generic[_T]): + """ + Defines the core interface for pagination. + + Type Args: + ModelT: The pydantic model that represents an item in the response. + + Methods: + has_next_page(): Check if there is another page available + next_page_info(): Get the necessary information to make a request for the next page + """ + + _options: FinalRequestOptions = PrivateAttr() + _model: Type[_T] = PrivateAttr() + + def has_next_page(self) -> bool: + items = self._get_page_items() + if not items: + return False + return self.next_page_info() is not None + + def next_page_info(self) -> Optional[PageInfo]: ... + + def _get_page_items(self) -> Iterable[_T]: # type: ignore[empty-body] + ... + + def _params_from_url(self, url: URL) -> httpx.QueryParams: + # TODO: do we have to preprocess params here? + return httpx.QueryParams(cast(Any, self._options.params)).merge(url.params) + + def _info_to_options(self, info: PageInfo) -> FinalRequestOptions: + options = model_copy(self._options) + options._strip_raw_response_header() + + if not isinstance(info.params, NotGiven): + options.params = {**options.params, **info.params} + return options + + if not isinstance(info.url, NotGiven): + params = self._params_from_url(info.url) + url = info.url.copy_with(params=params) + options.params = dict(url.params) + options.url = str(url) + return options + + raise ValueError("Unexpected PageInfo state") + + +class BaseSyncPage(BasePage[_T], Generic[_T]): + _client: SyncAPIClient = pydantic.PrivateAttr() + + def _set_private_attributes( + self, + client: SyncAPIClient, + model: Type[_T], + options: FinalRequestOptions, + ) -> None: + self._model = model + self._client = client + self._options = options + + # Pydantic uses a custom `__iter__` method to support casting BaseModels + # to dictionaries. e.g. dict(model). + # As we want to support `for item in page`, this is inherently incompatible + # with the default pydantic behaviour. It is not possible to support both + # use cases at once. Fortunately, this is not a big deal as all other pydantic + # methods should continue to work as expected as there is an alternative method + # to cast a model to a dictionary, model.dict(), which is used internally + # by pydantic. + def __iter__(self) -> Iterator[_T]: # type: ignore + for page in self.iter_pages(): + for item in page._get_page_items(): + yield item + + def iter_pages(self: SyncPageT) -> Iterator[SyncPageT]: + page = self + while True: + yield page + if page.has_next_page(): + page = page.get_next_page() + else: + return + + def get_next_page(self: SyncPageT) -> SyncPageT: + info = self.next_page_info() + if not info: + raise RuntimeError( + "No next page expected; please check `.has_next_page()` before calling `.get_next_page()`." + ) + + options = self._info_to_options(info) + return self._client._request_api_list(self._model, page=self.__class__, options=options) + + +class AsyncPaginator(Generic[_T, AsyncPageT]): + def __init__( + self, + client: AsyncAPIClient, + options: FinalRequestOptions, + page_cls: Type[AsyncPageT], + model: Type[_T], + ) -> None: + self._model = model + self._client = client + self._options = options + self._page_cls = page_cls + + def __await__(self) -> Generator[Any, None, AsyncPageT]: + return self._get_page().__await__() + + async def _get_page(self) -> AsyncPageT: + def _parser(resp: AsyncPageT) -> AsyncPageT: + resp._set_private_attributes( + model=self._model, + options=self._options, + client=self._client, + ) + return resp + + self._options.post_parser = _parser + + return await self._client.request(self._page_cls, self._options) + + async def __aiter__(self) -> AsyncIterator[_T]: + # https://github.com/microsoft/pyright/issues/3464 + page = cast( + AsyncPageT, + await self, # type: ignore + ) + async for item in page: + yield item + + +class BaseAsyncPage(BasePage[_T], Generic[_T]): + _client: AsyncAPIClient = pydantic.PrivateAttr() + + def _set_private_attributes( + self, + model: Type[_T], + client: AsyncAPIClient, + options: FinalRequestOptions, + ) -> None: + self._model = model + self._client = client + self._options = options + + async def __aiter__(self) -> AsyncIterator[_T]: + async for page in self.iter_pages(): + for item in page._get_page_items(): + yield item + + async def iter_pages(self: AsyncPageT) -> AsyncIterator[AsyncPageT]: + page = self + while True: + yield page + if page.has_next_page(): + page = await page.get_next_page() + else: + return + + async def get_next_page(self: AsyncPageT) -> AsyncPageT: + info = self.next_page_info() + if not info: + raise RuntimeError( + "No next page expected; please check `.has_next_page()` before calling `.get_next_page()`." + ) + + options = self._info_to_options(info) + return await self._client._request_api_list(self._model, page=self.__class__, options=options) + + +_HttpxClientT = TypeVar("_HttpxClientT", bound=Union[httpx.Client, httpx.AsyncClient]) +_DefaultStreamT = TypeVar("_DefaultStreamT", bound=Union[Stream[Any], AsyncStream[Any]]) + + +class BaseClient(Generic[_HttpxClientT, _DefaultStreamT]): + _client: _HttpxClientT + _version: str + _base_url: URL + max_retries: int + timeout: Union[float, Timeout, None] + _limits: httpx.Limits + _proxies: ProxiesTypes | None + _transport: Transport | AsyncTransport | None + _strict_response_validation: bool + _idempotency_header: str | None + _default_stream_cls: type[_DefaultStreamT] | None = None + + def __init__( + self, + *, + version: str, + base_url: str | URL, + _strict_response_validation: bool, + max_retries: int = DEFAULT_MAX_RETRIES, + timeout: float | Timeout | None = DEFAULT_TIMEOUT, + limits: httpx.Limits, + transport: Transport | AsyncTransport | None, + proxies: ProxiesTypes | None, + custom_headers: Mapping[str, str] | None = None, + custom_query: Mapping[str, object] | None = None, + ) -> None: + self._version = version + self._base_url = self._enforce_trailing_slash(URL(base_url)) + self.max_retries = max_retries + self.timeout = timeout + self._limits = limits + self._proxies = proxies + self._transport = transport + self._custom_headers = custom_headers or {} + self._custom_query = custom_query or {} + self._strict_response_validation = _strict_response_validation + self._idempotency_header = None + self._platform: Platform | None = None + + if max_retries is None: # pyright: ignore[reportUnnecessaryComparison] + raise TypeError( + "max_retries cannot be None. If you want to disable retries, pass `0`; if you want unlimited retries, pass `math.inf` or a very high number; if you want the default behavior, pass `openai.DEFAULT_MAX_RETRIES`" + ) + + def _enforce_trailing_slash(self, url: URL) -> URL: + if url.raw_path.endswith(b"/"): + return url + return url.copy_with(raw_path=url.raw_path + b"/") + + def _make_status_error_from_response( + self, + response: httpx.Response, + ) -> APIStatusError: + if response.is_closed and not response.is_stream_consumed: + # We can't read the response body as it has been closed + # before it was read. This can happen if an event hook + # raises a status error. + body = None + err_msg = f"Error code: {response.status_code}" + else: + err_text = response.text.strip() + body = err_text + + try: + body = json.loads(err_text) + err_msg = f"Error code: {response.status_code} - {body}" + except Exception: + err_msg = err_text or f"Error code: {response.status_code}" + + return self._make_status_error(err_msg, body=body, response=response) + + def _make_status_error( + self, + err_msg: str, + *, + body: object, + response: httpx.Response, + ) -> _exceptions.APIStatusError: + raise NotImplementedError() + + def _build_headers(self, options: FinalRequestOptions, *, retries_taken: int = 0) -> httpx.Headers: + custom_headers = options.headers or {} + headers_dict = _merge_mappings(self.default_headers, custom_headers) + self._validate_headers(headers_dict, custom_headers) + + # headers are case-insensitive while dictionaries are not. + headers = httpx.Headers(headers_dict) + + idempotency_header = self._idempotency_header + if idempotency_header and options.method.lower() != "get" and idempotency_header not in headers: + headers[idempotency_header] = options.idempotency_key or self._idempotency_key() + + # Don't set the retry count header if it was already set or removed by the caller. We check + # `custom_headers`, which can contain `Omit()`, instead of `headers` to account for the removal case. + if "x-stainless-retry-count" not in (header.lower() for header in custom_headers): + headers["x-stainless-retry-count"] = str(retries_taken) + + return headers + + def _prepare_url(self, url: str) -> URL: + """ + Merge a URL argument together with any 'base_url' on the client, + to create the URL used for the outgoing request. + """ + # Copied from httpx's `_merge_url` method. + merge_url = URL(url) + if merge_url.is_relative_url: + merge_raw_path = self.base_url.raw_path + merge_url.raw_path.lstrip(b"/") + return self.base_url.copy_with(raw_path=merge_raw_path) + + return merge_url + + def _make_sse_decoder(self) -> SSEDecoder | SSEBytesDecoder: + return SSEDecoder() + + def _build_request( + self, + options: FinalRequestOptions, + *, + retries_taken: int = 0, + ) -> httpx.Request: + if log.isEnabledFor(logging.DEBUG): + log.debug("Request options: %s", model_dump(options, exclude_unset=True)) + + kwargs: dict[str, Any] = {} + + json_data = options.json_data + if options.extra_json is not None: + if json_data is None: + json_data = cast(Body, options.extra_json) + elif is_mapping(json_data): + json_data = _merge_mappings(json_data, options.extra_json) + else: + raise RuntimeError(f"Unexpected JSON data type, {type(json_data)}, cannot merge with `extra_body`") + + headers = self._build_headers(options, retries_taken=retries_taken) + params = _merge_mappings(self.default_query, options.params) + content_type = headers.get("Content-Type") + files = options.files + + # If the given Content-Type header is multipart/form-data then it + # has to be removed so that httpx can generate the header with + # additional information for us as it has to be in this form + # for the server to be able to correctly parse the request: + # multipart/form-data; boundary=---abc-- + if content_type is not None and content_type.startswith("multipart/form-data"): + if "boundary" not in content_type: + # only remove the header if the boundary hasn't been explicitly set + # as the caller doesn't want httpx to come up with their own boundary + headers.pop("Content-Type") + + # As we are now sending multipart/form-data instead of application/json + # we need to tell httpx to use it, https://www.python-httpx.org/advanced/clients/#multipart-file-encoding + if json_data: + if not is_dict(json_data): + raise TypeError( + f"Expected query input to be a dictionary for multipart requests but got {type(json_data)} instead." + ) + kwargs["data"] = self._serialize_multipartform(json_data) + + # httpx determines whether or not to send a "multipart/form-data" + # request based on the truthiness of the "files" argument. + # This gets around that issue by generating a dict value that + # evaluates to true. + # + # https://github.com/encode/httpx/discussions/2399#discussioncomment-3814186 + if not files: + files = cast(HttpxRequestFiles, ForceMultipartDict()) + + prepared_url = self._prepare_url(options.url) + if "_" in prepared_url.host: + # work around https://github.com/encode/httpx/discussions/2880 + kwargs["extensions"] = {"sni_hostname": prepared_url.host.replace("_", "-")} + + # TODO: report this error to httpx + return self._client.build_request( # pyright: ignore[reportUnknownMemberType] + headers=headers, + timeout=self.timeout if isinstance(options.timeout, NotGiven) else options.timeout, + method=options.method, + url=prepared_url, + # the `Query` type that we use is incompatible with qs' + # `Params` type as it needs to be typed as `Mapping[str, object]` + # so that passing a `TypedDict` doesn't cause an error. + # https://github.com/microsoft/pyright/issues/3526#event-6715453066 + params=self.qs.stringify(cast(Mapping[str, Any], params)) if params else None, + json=json_data, + files=files, + **kwargs, + ) + + def _serialize_multipartform(self, data: Mapping[object, object]) -> dict[str, object]: + items = self.qs.stringify_items( + # TODO: type ignore is required as stringify_items is well typed but we can't be + # well typed without heavy validation. + data, # type: ignore + array_format="brackets", + ) + serialized: dict[str, object] = {} + for key, value in items: + existing = serialized.get(key) + + if not existing: + serialized[key] = value + continue + + # If a value has already been set for this key then that + # means we're sending data like `array[]=[1, 2, 3]` and we + # need to tell httpx that we want to send multiple values with + # the same key which is done by using a list or a tuple. + # + # Note: 2d arrays should never result in the same key at both + # levels so it's safe to assume that if the value is a list, + # it was because we changed it to be a list. + if is_list(existing): + existing.append(value) + else: + serialized[key] = [existing, value] + + return serialized + + def _maybe_override_cast_to(self, cast_to: type[ResponseT], options: FinalRequestOptions) -> type[ResponseT]: + if not is_given(options.headers): + return cast_to + + # make a copy of the headers so we don't mutate user-input + headers = dict(options.headers) + + # we internally support defining a temporary header to override the + # default `cast_to` type for use with `.with_raw_response` and `.with_streaming_response` + # see _response.py for implementation details + override_cast_to = headers.pop(OVERRIDE_CAST_TO_HEADER, NOT_GIVEN) + if is_given(override_cast_to): + options.headers = headers + return cast(Type[ResponseT], override_cast_to) + + return cast_to + + def _should_stream_response_body(self, request: httpx.Request) -> bool: + return request.headers.get(RAW_RESPONSE_HEADER) == "stream" # type: ignore[no-any-return] + + def _process_response_data( + self, + *, + data: object, + cast_to: type[ResponseT], + response: httpx.Response, + ) -> ResponseT: + if data is None: + return cast(ResponseT, None) + + if cast_to is object: + return cast(ResponseT, data) + + try: + if inspect.isclass(cast_to) and issubclass(cast_to, ModelBuilderProtocol): + return cast(ResponseT, cast_to.build(response=response, data=data)) + + if self._strict_response_validation: + return cast(ResponseT, validate_type(type_=cast_to, value=data)) + + return cast(ResponseT, construct_type(type_=cast_to, value=data)) + except pydantic.ValidationError as err: + raise APIResponseValidationError(response=response, body=data) from err + + @property + def qs(self) -> Querystring: + return Querystring() + + @property + def custom_auth(self) -> httpx.Auth | None: + return None + + @property + def auth_headers(self) -> dict[str, str]: + return {} + + @property + def default_headers(self) -> dict[str, str | Omit]: + return { + "Accept": "application/json", + "Content-Type": "application/json", + "User-Agent": self.user_agent, + **self.platform_headers(), + **self.auth_headers, + **self._custom_headers, + } + + @property + def default_query(self) -> dict[str, object]: + return { + **self._custom_query, + } + + def _validate_headers( + self, + headers: Headers, # noqa: ARG002 + custom_headers: Headers, # noqa: ARG002 + ) -> None: + """Validate the given default headers and custom headers. + + Does nothing by default. + """ + return + + @property + def user_agent(self) -> str: + return f"{self.__class__.__name__}/Python {self._version}" + + @property + def base_url(self) -> URL: + return self._base_url + + @base_url.setter + def base_url(self, url: URL | str) -> None: + self._base_url = self._enforce_trailing_slash(url if isinstance(url, URL) else URL(url)) + + def platform_headers(self) -> Dict[str, str]: + # the actual implementation is in a separate `lru_cache` decorated + # function because adding `lru_cache` to methods will leak memory + # https://github.com/python/cpython/issues/88476 + return platform_headers(self._version, platform=self._platform) + + def _parse_retry_after_header(self, response_headers: Optional[httpx.Headers] = None) -> float | None: + """Returns a float of the number of seconds (not milliseconds) to wait after retrying, or None if unspecified. + + About the Retry-After header: https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Retry-After + See also https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Retry-After#syntax + """ + if response_headers is None: + return None + + # First, try the non-standard `retry-after-ms` header for milliseconds, + # which is more precise than integer-seconds `retry-after` + try: + retry_ms_header = response_headers.get("retry-after-ms", None) + return float(retry_ms_header) / 1000 + except (TypeError, ValueError): + pass + + # Next, try parsing `retry-after` header as seconds (allowing nonstandard floats). + retry_header = response_headers.get("retry-after") + try: + # note: the spec indicates that this should only ever be an integer + # but if someone sends a float there's no reason for us to not respect it + return float(retry_header) + except (TypeError, ValueError): + pass + + # Last, try parsing `retry-after` as a date. + retry_date_tuple = email.utils.parsedate_tz(retry_header) + if retry_date_tuple is None: + return None + + retry_date = email.utils.mktime_tz(retry_date_tuple) + return float(retry_date - time.time()) + + def _calculate_retry_timeout( + self, + remaining_retries: int, + options: FinalRequestOptions, + response_headers: Optional[httpx.Headers] = None, + ) -> float: + max_retries = options.get_max_retries(self.max_retries) + + # If the API asks us to wait a certain amount of time (and it's a reasonable amount), just do what it says. + retry_after = self._parse_retry_after_header(response_headers) + if retry_after is not None and 0 < retry_after <= 60: + return retry_after + + # Also cap retry count to 1000 to avoid any potential overflows with `pow` + nb_retries = min(max_retries - remaining_retries, 1000) + + # Apply exponential backoff, but not more than the max. + sleep_seconds = min(INITIAL_RETRY_DELAY * pow(2.0, nb_retries), MAX_RETRY_DELAY) + + # Apply some jitter, plus-or-minus half a second. + jitter = 1 - 0.25 * random() + timeout = sleep_seconds * jitter + return timeout if timeout >= 0 else 0 + + def _should_retry(self, response: httpx.Response) -> bool: + # Note: this is not a standard header + should_retry_header = response.headers.get("x-should-retry") + + # If the server explicitly says whether or not to retry, obey. + if should_retry_header == "true": + log.debug("Retrying as header `x-should-retry` is set to `true`") + return True + if should_retry_header == "false": + log.debug("Not retrying as header `x-should-retry` is set to `false`") + return False + + # Retry on request timeouts. + if response.status_code == 408: + log.debug("Retrying due to status code %i", response.status_code) + return True + + # Retry on lock timeouts. + if response.status_code == 409: + log.debug("Retrying due to status code %i", response.status_code) + return True + + # Retry on rate limits. + if response.status_code == 429: + log.debug("Retrying due to status code %i", response.status_code) + return True + + # Retry internal errors. + if response.status_code >= 500: + log.debug("Retrying due to status code %i", response.status_code) + return True + + log.debug("Not retrying") + return False + + def _idempotency_key(self) -> str: + return f"stainless-python-retry-{uuid.uuid4()}" + + +class _DefaultHttpxClient(httpx.Client): + def __init__(self, **kwargs: Any) -> None: + kwargs.setdefault("timeout", DEFAULT_TIMEOUT) + kwargs.setdefault("limits", DEFAULT_CONNECTION_LIMITS) + kwargs.setdefault("follow_redirects", True) + super().__init__(**kwargs) + + +if TYPE_CHECKING: + DefaultHttpxClient = httpx.Client + """An alias to `httpx.Client` that provides the same defaults that this SDK + uses internally. + + This is useful because overriding the `http_client` with your own instance of + `httpx.Client` will result in httpx's defaults being used, not ours. + """ +else: + DefaultHttpxClient = _DefaultHttpxClient + + +class SyncHttpxClientWrapper(DefaultHttpxClient): + def __del__(self) -> None: + if self.is_closed: + return + + try: + self.close() + except Exception: + pass + + +class SyncAPIClient(BaseClient[httpx.Client, Stream[Any]]): + _client: httpx.Client + _default_stream_cls: type[Stream[Any]] | None = None + + def __init__( + self, + *, + version: str, + base_url: str | URL, + max_retries: int = DEFAULT_MAX_RETRIES, + timeout: float | Timeout | None | NotGiven = NOT_GIVEN, + transport: Transport | None = None, + proxies: ProxiesTypes | None = None, + limits: Limits | None = None, + http_client: httpx.Client | None = None, + custom_headers: Mapping[str, str] | None = None, + custom_query: Mapping[str, object] | None = None, + _strict_response_validation: bool, + ) -> None: + kwargs: dict[str, Any] = {} + if limits is not None: + warnings.warn( + "The `connection_pool_limits` argument is deprecated. The `http_client` argument should be passed instead", + category=DeprecationWarning, + stacklevel=3, + ) + if http_client is not None: + raise ValueError("The `http_client` argument is mutually exclusive with `connection_pool_limits`") + else: + limits = DEFAULT_CONNECTION_LIMITS + + if transport is not None: + kwargs["transport"] = transport + warnings.warn( + "The `transport` argument is deprecated. The `http_client` argument should be passed instead", + category=DeprecationWarning, + stacklevel=3, + ) + if http_client is not None: + raise ValueError("The `http_client` argument is mutually exclusive with `transport`") + + if proxies is not None: + kwargs["proxies"] = proxies + warnings.warn( + "The `proxies` argument is deprecated. The `http_client` argument should be passed instead", + category=DeprecationWarning, + stacklevel=3, + ) + if http_client is not None: + raise ValueError("The `http_client` argument is mutually exclusive with `proxies`") + + if not is_given(timeout): + # if the user passed in a custom http client with a non-default + # timeout set then we use that timeout. + # + # note: there is an edge case here where the user passes in a client + # where they've explicitly set the timeout to match the default timeout + # as this check is structural, meaning that we'll think they didn't + # pass in a timeout and will ignore it + if http_client and http_client.timeout != HTTPX_DEFAULT_TIMEOUT: + timeout = http_client.timeout + else: + timeout = DEFAULT_TIMEOUT + + if http_client is not None and not isinstance(http_client, httpx.Client): # pyright: ignore[reportUnnecessaryIsInstance] + raise TypeError( + f"Invalid `http_client` argument; Expected an instance of `httpx.Client` but got {type(http_client)}" + ) + + super().__init__( + version=version, + limits=limits, + # cast to a valid type because mypy doesn't understand our type narrowing + timeout=cast(Timeout, timeout), + proxies=proxies, + base_url=base_url, + transport=transport, + max_retries=max_retries, + custom_query=custom_query, + custom_headers=custom_headers, + _strict_response_validation=_strict_response_validation, + ) + self._client = http_client or SyncHttpxClientWrapper( + base_url=base_url, + # cast to a valid type because mypy doesn't understand our type narrowing + timeout=cast(Timeout, timeout), + limits=limits, + follow_redirects=True, + **kwargs, # type: ignore + ) + + def is_closed(self) -> bool: + return self._client.is_closed + + def close(self) -> None: + """Close the underlying HTTPX client. + + The client will *not* be usable after this. + """ + # If an error is thrown while constructing a client, self._client + # may not be present + if hasattr(self, "_client"): + self._client.close() + + def __enter__(self: _T) -> _T: + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: + self.close() + + def _prepare_options( + self, + options: FinalRequestOptions, # noqa: ARG002 + ) -> FinalRequestOptions: + """Hook for mutating the given options""" + return options + + def _prepare_request( + self, + request: httpx.Request, # noqa: ARG002 + ) -> None: + """This method is used as a callback for mutating the `Request` object + after it has been constructed. + This is useful for cases where you want to add certain headers based off of + the request properties, e.g. `url`, `method` etc. + """ + return None + + @overload + def request( + self, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + remaining_retries: Optional[int] = None, + *, + stream: Literal[True], + stream_cls: Type[_StreamT], + ) -> _StreamT: ... + + @overload + def request( + self, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + remaining_retries: Optional[int] = None, + *, + stream: Literal[False] = False, + ) -> ResponseT: ... + + @overload + def request( + self, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + remaining_retries: Optional[int] = None, + *, + stream: bool = False, + stream_cls: Type[_StreamT] | None = None, + ) -> ResponseT | _StreamT: ... + + def request( + self, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + remaining_retries: Optional[int] = None, + *, + stream: bool = False, + stream_cls: type[_StreamT] | None = None, + ) -> ResponseT | _StreamT: + if remaining_retries is not None: + retries_taken = options.get_max_retries(self.max_retries) - remaining_retries + else: + retries_taken = 0 + + return self._request( + cast_to=cast_to, + options=options, + stream=stream, + stream_cls=stream_cls, + retries_taken=retries_taken, + ) + + def _request( + self, + *, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + retries_taken: int, + stream: bool, + stream_cls: type[_StreamT] | None, + ) -> ResponseT | _StreamT: + # create a copy of the options we were given so that if the + # options are mutated later & we then retry, the retries are + # given the original options + input_options = model_copy(options) + + cast_to = self._maybe_override_cast_to(cast_to, options) + options = self._prepare_options(options) + + remaining_retries = options.get_max_retries(self.max_retries) - retries_taken + request = self._build_request(options, retries_taken=retries_taken) + self._prepare_request(request) + + kwargs: HttpxSendArgs = {} + if self.custom_auth is not None: + kwargs["auth"] = self.custom_auth + + log.debug("Sending HTTP Request: %s %s", request.method, request.url) + + try: + response = self._client.send( + request, + stream=stream or self._should_stream_response_body(request=request), + **kwargs, + ) + except httpx.TimeoutException as err: + log.debug("Encountered httpx.TimeoutException", exc_info=True) + + if remaining_retries > 0: + return self._retry_request( + input_options, + cast_to, + retries_taken=retries_taken, + stream=stream, + stream_cls=stream_cls, + response_headers=None, + ) + + log.debug("Raising timeout error") + raise APITimeoutError(request=request) from err + except Exception as err: + log.debug("Encountered Exception", exc_info=True) + + if remaining_retries > 0: + return self._retry_request( + input_options, + cast_to, + retries_taken=retries_taken, + stream=stream, + stream_cls=stream_cls, + response_headers=None, + ) + + log.debug("Raising connection error") + raise APIConnectionError(request=request) from err + + log.debug( + 'HTTP Response: %s %s "%i %s" %s', + request.method, + request.url, + response.status_code, + response.reason_phrase, + response.headers, + ) + log.debug("request_id: %s", response.headers.get("x-request-id")) + + try: + response.raise_for_status() + except httpx.HTTPStatusError as err: # thrown on 4xx and 5xx status code + log.debug("Encountered httpx.HTTPStatusError", exc_info=True) + + if remaining_retries > 0 and self._should_retry(err.response): + err.response.close() + return self._retry_request( + input_options, + cast_to, + retries_taken=retries_taken, + response_headers=err.response.headers, + stream=stream, + stream_cls=stream_cls, + ) + + # If the response is streamed then we need to explicitly read the response + # to completion before attempting to access the response text. + if not err.response.is_closed: + err.response.read() + + log.debug("Re-raising status error") + raise self._make_status_error_from_response(err.response) from None + + return self._process_response( + cast_to=cast_to, + options=options, + response=response, + stream=stream, + stream_cls=stream_cls, + retries_taken=retries_taken, + ) + + def _retry_request( + self, + options: FinalRequestOptions, + cast_to: Type[ResponseT], + *, + retries_taken: int, + response_headers: httpx.Headers | None, + stream: bool, + stream_cls: type[_StreamT] | None, + ) -> ResponseT | _StreamT: + remaining_retries = options.get_max_retries(self.max_retries) - retries_taken + if remaining_retries == 1: + log.debug("1 retry left") + else: + log.debug("%i retries left", remaining_retries) + + timeout = self._calculate_retry_timeout(remaining_retries, options, response_headers) + log.info("Retrying request to %s in %f seconds", options.url, timeout) + + # In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a + # different thread if necessary. + time.sleep(timeout) + + return self._request( + options=options, + cast_to=cast_to, + retries_taken=retries_taken + 1, + stream=stream, + stream_cls=stream_cls, + ) + + def _process_response( + self, + *, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + response: httpx.Response, + stream: bool, + stream_cls: type[Stream[Any]] | type[AsyncStream[Any]] | None, + retries_taken: int = 0, + ) -> ResponseT: + if response.request.headers.get(RAW_RESPONSE_HEADER) == "true": + return cast( + ResponseT, + LegacyAPIResponse( + raw=response, + client=self, + cast_to=cast_to, + stream=stream, + stream_cls=stream_cls, + options=options, + retries_taken=retries_taken, + ), + ) + + origin = get_origin(cast_to) or cast_to + + if inspect.isclass(origin) and issubclass(origin, BaseAPIResponse): + if not issubclass(origin, APIResponse): + raise TypeError(f"API Response types must subclass {APIResponse}; Received {origin}") + + response_cls = cast("type[BaseAPIResponse[Any]]", cast_to) + return cast( + ResponseT, + response_cls( + raw=response, + client=self, + cast_to=extract_response_type(response_cls), + stream=stream, + stream_cls=stream_cls, + options=options, + retries_taken=retries_taken, + ), + ) + + if cast_to == httpx.Response: + return cast(ResponseT, response) + + api_response = APIResponse( + raw=response, + client=self, + cast_to=cast("type[ResponseT]", cast_to), # pyright: ignore[reportUnnecessaryCast] + stream=stream, + stream_cls=stream_cls, + options=options, + retries_taken=retries_taken, + ) + if bool(response.request.headers.get(RAW_RESPONSE_HEADER)): + return cast(ResponseT, api_response) + + return api_response.parse() + + def _request_api_list( + self, + model: Type[object], + page: Type[SyncPageT], + options: FinalRequestOptions, + ) -> SyncPageT: + def _parser(resp: SyncPageT) -> SyncPageT: + resp._set_private_attributes( + client=self, + model=model, + options=options, + ) + return resp + + options.post_parser = _parser + + return self.request(page, options, stream=False) + + @overload + def get( + self, + path: str, + *, + cast_to: Type[ResponseT], + options: RequestOptions = {}, + stream: Literal[False] = False, + ) -> ResponseT: ... + + @overload + def get( + self, + path: str, + *, + cast_to: Type[ResponseT], + options: RequestOptions = {}, + stream: Literal[True], + stream_cls: type[_StreamT], + ) -> _StreamT: ... + + @overload + def get( + self, + path: str, + *, + cast_to: Type[ResponseT], + options: RequestOptions = {}, + stream: bool, + stream_cls: type[_StreamT] | None = None, + ) -> ResponseT | _StreamT: ... + + def get( + self, + path: str, + *, + cast_to: Type[ResponseT], + options: RequestOptions = {}, + stream: bool = False, + stream_cls: type[_StreamT] | None = None, + ) -> ResponseT | _StreamT: + opts = FinalRequestOptions.construct(method="get", url=path, **options) + # cast is required because mypy complains about returning Any even though + # it understands the type variables + return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) + + @overload + def post( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + options: RequestOptions = {}, + files: RequestFiles | None = None, + stream: Literal[False] = False, + ) -> ResponseT: ... + + @overload + def post( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + options: RequestOptions = {}, + files: RequestFiles | None = None, + stream: Literal[True], + stream_cls: type[_StreamT], + ) -> _StreamT: ... + + @overload + def post( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + options: RequestOptions = {}, + files: RequestFiles | None = None, + stream: bool, + stream_cls: type[_StreamT] | None = None, + ) -> ResponseT | _StreamT: ... + + def post( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + options: RequestOptions = {}, + files: RequestFiles | None = None, + stream: bool = False, + stream_cls: type[_StreamT] | None = None, + ) -> ResponseT | _StreamT: + opts = FinalRequestOptions.construct( + method="post", url=path, json_data=body, files=to_httpx_files(files), **options + ) + return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) + + def patch( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + options: RequestOptions = {}, + ) -> ResponseT: + opts = FinalRequestOptions.construct(method="patch", url=path, json_data=body, **options) + return self.request(cast_to, opts) + + def put( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + files: RequestFiles | None = None, + options: RequestOptions = {}, + ) -> ResponseT: + opts = FinalRequestOptions.construct( + method="put", url=path, json_data=body, files=to_httpx_files(files), **options + ) + return self.request(cast_to, opts) + + def delete( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + options: RequestOptions = {}, + ) -> ResponseT: + opts = FinalRequestOptions.construct(method="delete", url=path, json_data=body, **options) + return self.request(cast_to, opts) + + def get_api_list( + self, + path: str, + *, + model: Type[object], + page: Type[SyncPageT], + body: Body | None = None, + options: RequestOptions = {}, + method: str = "get", + ) -> SyncPageT: + opts = FinalRequestOptions.construct(method=method, url=path, json_data=body, **options) + return self._request_api_list(model, page, opts) + + +class _DefaultAsyncHttpxClient(httpx.AsyncClient): + def __init__(self, **kwargs: Any) -> None: + kwargs.setdefault("timeout", DEFAULT_TIMEOUT) + kwargs.setdefault("limits", DEFAULT_CONNECTION_LIMITS) + kwargs.setdefault("follow_redirects", True) + super().__init__(**kwargs) + + +if TYPE_CHECKING: + DefaultAsyncHttpxClient = httpx.AsyncClient + """An alias to `httpx.AsyncClient` that provides the same defaults that this SDK + uses internally. + + This is useful because overriding the `http_client` with your own instance of + `httpx.AsyncClient` will result in httpx's defaults being used, not ours. + """ +else: + DefaultAsyncHttpxClient = _DefaultAsyncHttpxClient + + +class AsyncHttpxClientWrapper(DefaultAsyncHttpxClient): + def __del__(self) -> None: + if self.is_closed: + return + + try: + # TODO(someday): support non asyncio runtimes here + asyncio.get_running_loop().create_task(self.aclose()) + except Exception: + pass + + +class AsyncAPIClient(BaseClient[httpx.AsyncClient, AsyncStream[Any]]): + _client: httpx.AsyncClient + _default_stream_cls: type[AsyncStream[Any]] | None = None + + def __init__( + self, + *, + version: str, + base_url: str | URL, + _strict_response_validation: bool, + max_retries: int = DEFAULT_MAX_RETRIES, + timeout: float | Timeout | None | NotGiven = NOT_GIVEN, + transport: AsyncTransport | None = None, + proxies: ProxiesTypes | None = None, + limits: Limits | None = None, + http_client: httpx.AsyncClient | None = None, + custom_headers: Mapping[str, str] | None = None, + custom_query: Mapping[str, object] | None = None, + ) -> None: + kwargs: dict[str, Any] = {} + if limits is not None: + warnings.warn( + "The `connection_pool_limits` argument is deprecated. The `http_client` argument should be passed instead", + category=DeprecationWarning, + stacklevel=3, + ) + if http_client is not None: + raise ValueError("The `http_client` argument is mutually exclusive with `connection_pool_limits`") + else: + limits = DEFAULT_CONNECTION_LIMITS + + if transport is not None: + kwargs["transport"] = transport + warnings.warn( + "The `transport` argument is deprecated. The `http_client` argument should be passed instead", + category=DeprecationWarning, + stacklevel=3, + ) + if http_client is not None: + raise ValueError("The `http_client` argument is mutually exclusive with `transport`") + + if proxies is not None: + kwargs["proxies"] = proxies + warnings.warn( + "The `proxies` argument is deprecated. The `http_client` argument should be passed instead", + category=DeprecationWarning, + stacklevel=3, + ) + if http_client is not None: + raise ValueError("The `http_client` argument is mutually exclusive with `proxies`") + + if not is_given(timeout): + # if the user passed in a custom http client with a non-default + # timeout set then we use that timeout. + # + # note: there is an edge case here where the user passes in a client + # where they've explicitly set the timeout to match the default timeout + # as this check is structural, meaning that we'll think they didn't + # pass in a timeout and will ignore it + if http_client and http_client.timeout != HTTPX_DEFAULT_TIMEOUT: + timeout = http_client.timeout + else: + timeout = DEFAULT_TIMEOUT + + if http_client is not None and not isinstance(http_client, httpx.AsyncClient): # pyright: ignore[reportUnnecessaryIsInstance] + raise TypeError( + f"Invalid `http_client` argument; Expected an instance of `httpx.AsyncClient` but got {type(http_client)}" + ) + + super().__init__( + version=version, + base_url=base_url, + limits=limits, + # cast to a valid type because mypy doesn't understand our type narrowing + timeout=cast(Timeout, timeout), + proxies=proxies, + transport=transport, + max_retries=max_retries, + custom_query=custom_query, + custom_headers=custom_headers, + _strict_response_validation=_strict_response_validation, + ) + self._client = http_client or AsyncHttpxClientWrapper( + base_url=base_url, + # cast to a valid type because mypy doesn't understand our type narrowing + timeout=cast(Timeout, timeout), + limits=limits, + follow_redirects=True, + **kwargs, # type: ignore + ) + + def is_closed(self) -> bool: + return self._client.is_closed + + async def close(self) -> None: + """Close the underlying HTTPX client. + + The client will *not* be usable after this. + """ + await self._client.aclose() + + async def __aenter__(self: _T) -> _T: + return self + + async def __aexit__( + self, + exc_type: type[BaseException] | None, + exc: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: + await self.close() + + async def _prepare_options( + self, + options: FinalRequestOptions, # noqa: ARG002 + ) -> FinalRequestOptions: + """Hook for mutating the given options""" + return options + + async def _prepare_request( + self, + request: httpx.Request, # noqa: ARG002 + ) -> None: + """This method is used as a callback for mutating the `Request` object + after it has been constructed. + This is useful for cases where you want to add certain headers based off of + the request properties, e.g. `url`, `method` etc. + """ + return None + + @overload + async def request( + self, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + *, + stream: Literal[False] = False, + remaining_retries: Optional[int] = None, + ) -> ResponseT: ... + + @overload + async def request( + self, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + *, + stream: Literal[True], + stream_cls: type[_AsyncStreamT], + remaining_retries: Optional[int] = None, + ) -> _AsyncStreamT: ... + + @overload + async def request( + self, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + *, + stream: bool, + stream_cls: type[_AsyncStreamT] | None = None, + remaining_retries: Optional[int] = None, + ) -> ResponseT | _AsyncStreamT: ... + + async def request( + self, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + *, + stream: bool = False, + stream_cls: type[_AsyncStreamT] | None = None, + remaining_retries: Optional[int] = None, + ) -> ResponseT | _AsyncStreamT: + if remaining_retries is not None: + retries_taken = options.get_max_retries(self.max_retries) - remaining_retries + else: + retries_taken = 0 + + return await self._request( + cast_to=cast_to, + options=options, + stream=stream, + stream_cls=stream_cls, + retries_taken=retries_taken, + ) + + async def _request( + self, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + *, + stream: bool, + stream_cls: type[_AsyncStreamT] | None, + retries_taken: int, + ) -> ResponseT | _AsyncStreamT: + if self._platform is None: + # `get_platform` can make blocking IO calls so we + # execute it earlier while we are in an async context + self._platform = await asyncify(get_platform)() + + # create a copy of the options we were given so that if the + # options are mutated later & we then retry, the retries are + # given the original options + input_options = model_copy(options) + + cast_to = self._maybe_override_cast_to(cast_to, options) + options = await self._prepare_options(options) + + remaining_retries = options.get_max_retries(self.max_retries) - retries_taken + request = self._build_request(options, retries_taken=retries_taken) + await self._prepare_request(request) + + kwargs: HttpxSendArgs = {} + if self.custom_auth is not None: + kwargs["auth"] = self.custom_auth + + try: + response = await self._client.send( + request, + stream=stream or self._should_stream_response_body(request=request), + **kwargs, + ) + except httpx.TimeoutException as err: + log.debug("Encountered httpx.TimeoutException", exc_info=True) + + if remaining_retries > 0: + return await self._retry_request( + input_options, + cast_to, + retries_taken=retries_taken, + stream=stream, + stream_cls=stream_cls, + response_headers=None, + ) + + log.debug("Raising timeout error") + raise APITimeoutError(request=request) from err + except Exception as err: + log.debug("Encountered Exception", exc_info=True) + + if remaining_retries > 0: + return await self._retry_request( + input_options, + cast_to, + retries_taken=retries_taken, + stream=stream, + stream_cls=stream_cls, + response_headers=None, + ) + + log.debug("Raising connection error") + raise APIConnectionError(request=request) from err + + log.debug( + 'HTTP Request: %s %s "%i %s"', request.method, request.url, response.status_code, response.reason_phrase + ) + + try: + response.raise_for_status() + except httpx.HTTPStatusError as err: # thrown on 4xx and 5xx status code + log.debug("Encountered httpx.HTTPStatusError", exc_info=True) + + if remaining_retries > 0 and self._should_retry(err.response): + await err.response.aclose() + return await self._retry_request( + input_options, + cast_to, + retries_taken=retries_taken, + response_headers=err.response.headers, + stream=stream, + stream_cls=stream_cls, + ) + + # If the response is streamed then we need to explicitly read the response + # to completion before attempting to access the response text. + if not err.response.is_closed: + await err.response.aread() + + log.debug("Re-raising status error") + raise self._make_status_error_from_response(err.response) from None + + return await self._process_response( + cast_to=cast_to, + options=options, + response=response, + stream=stream, + stream_cls=stream_cls, + retries_taken=retries_taken, + ) + + async def _retry_request( + self, + options: FinalRequestOptions, + cast_to: Type[ResponseT], + *, + retries_taken: int, + response_headers: httpx.Headers | None, + stream: bool, + stream_cls: type[_AsyncStreamT] | None, + ) -> ResponseT | _AsyncStreamT: + remaining_retries = options.get_max_retries(self.max_retries) - retries_taken + if remaining_retries == 1: + log.debug("1 retry left") + else: + log.debug("%i retries left", remaining_retries) + + timeout = self._calculate_retry_timeout(remaining_retries, options, response_headers) + log.info("Retrying request to %s in %f seconds", options.url, timeout) + + await anyio.sleep(timeout) + + return await self._request( + options=options, + cast_to=cast_to, + retries_taken=retries_taken + 1, + stream=stream, + stream_cls=stream_cls, + ) + + async def _process_response( + self, + *, + cast_to: Type[ResponseT], + options: FinalRequestOptions, + response: httpx.Response, + stream: bool, + stream_cls: type[Stream[Any]] | type[AsyncStream[Any]] | None, + retries_taken: int = 0, + ) -> ResponseT: + if response.request.headers.get(RAW_RESPONSE_HEADER) == "true": + return cast( + ResponseT, + LegacyAPIResponse( + raw=response, + client=self, + cast_to=cast_to, + stream=stream, + stream_cls=stream_cls, + options=options, + retries_taken=retries_taken, + ), + ) + + origin = get_origin(cast_to) or cast_to + + if inspect.isclass(origin) and issubclass(origin, BaseAPIResponse): + if not issubclass(origin, AsyncAPIResponse): + raise TypeError(f"API Response types must subclass {AsyncAPIResponse}; Received {origin}") + + response_cls = cast("type[BaseAPIResponse[Any]]", cast_to) + return cast( + "ResponseT", + response_cls( + raw=response, + client=self, + cast_to=extract_response_type(response_cls), + stream=stream, + stream_cls=stream_cls, + options=options, + retries_taken=retries_taken, + ), + ) + + if cast_to == httpx.Response: + return cast(ResponseT, response) + + api_response = AsyncAPIResponse( + raw=response, + client=self, + cast_to=cast("type[ResponseT]", cast_to), # pyright: ignore[reportUnnecessaryCast] + stream=stream, + stream_cls=stream_cls, + options=options, + retries_taken=retries_taken, + ) + if bool(response.request.headers.get(RAW_RESPONSE_HEADER)): + return cast(ResponseT, api_response) + + return await api_response.parse() + + def _request_api_list( + self, + model: Type[_T], + page: Type[AsyncPageT], + options: FinalRequestOptions, + ) -> AsyncPaginator[_T, AsyncPageT]: + return AsyncPaginator(client=self, options=options, page_cls=page, model=model) + + @overload + async def get( + self, + path: str, + *, + cast_to: Type[ResponseT], + options: RequestOptions = {}, + stream: Literal[False] = False, + ) -> ResponseT: ... + + @overload + async def get( + self, + path: str, + *, + cast_to: Type[ResponseT], + options: RequestOptions = {}, + stream: Literal[True], + stream_cls: type[_AsyncStreamT], + ) -> _AsyncStreamT: ... + + @overload + async def get( + self, + path: str, + *, + cast_to: Type[ResponseT], + options: RequestOptions = {}, + stream: bool, + stream_cls: type[_AsyncStreamT] | None = None, + ) -> ResponseT | _AsyncStreamT: ... + + async def get( + self, + path: str, + *, + cast_to: Type[ResponseT], + options: RequestOptions = {}, + stream: bool = False, + stream_cls: type[_AsyncStreamT] | None = None, + ) -> ResponseT | _AsyncStreamT: + opts = FinalRequestOptions.construct(method="get", url=path, **options) + return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls) + + @overload + async def post( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + files: RequestFiles | None = None, + options: RequestOptions = {}, + stream: Literal[False] = False, + ) -> ResponseT: ... + + @overload + async def post( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + files: RequestFiles | None = None, + options: RequestOptions = {}, + stream: Literal[True], + stream_cls: type[_AsyncStreamT], + ) -> _AsyncStreamT: ... + + @overload + async def post( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + files: RequestFiles | None = None, + options: RequestOptions = {}, + stream: bool, + stream_cls: type[_AsyncStreamT] | None = None, + ) -> ResponseT | _AsyncStreamT: ... + + async def post( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + files: RequestFiles | None = None, + options: RequestOptions = {}, + stream: bool = False, + stream_cls: type[_AsyncStreamT] | None = None, + ) -> ResponseT | _AsyncStreamT: + opts = FinalRequestOptions.construct( + method="post", url=path, json_data=body, files=await async_to_httpx_files(files), **options + ) + return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls) + + async def patch( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + options: RequestOptions = {}, + ) -> ResponseT: + opts = FinalRequestOptions.construct(method="patch", url=path, json_data=body, **options) + return await self.request(cast_to, opts) + + async def put( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + files: RequestFiles | None = None, + options: RequestOptions = {}, + ) -> ResponseT: + opts = FinalRequestOptions.construct( + method="put", url=path, json_data=body, files=await async_to_httpx_files(files), **options + ) + return await self.request(cast_to, opts) + + async def delete( + self, + path: str, + *, + cast_to: Type[ResponseT], + body: Body | None = None, + options: RequestOptions = {}, + ) -> ResponseT: + opts = FinalRequestOptions.construct(method="delete", url=path, json_data=body, **options) + return await self.request(cast_to, opts) + + def get_api_list( + self, + path: str, + *, + model: Type[_T], + page: Type[AsyncPageT], + body: Body | None = None, + options: RequestOptions = {}, + method: str = "get", + ) -> AsyncPaginator[_T, AsyncPageT]: + opts = FinalRequestOptions.construct(method=method, url=path, json_data=body, **options) + return self._request_api_list(model, page, opts) + + +def make_request_options( + *, + query: Query | None = None, + extra_headers: Headers | None = None, + extra_query: Query | None = None, + extra_body: Body | None = None, + idempotency_key: str | None = None, + timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN, + post_parser: PostParser | NotGiven = NOT_GIVEN, +) -> RequestOptions: + """Create a dict of type RequestOptions without keys of NotGiven values.""" + options: RequestOptions = {} + if extra_headers is not None: + options["headers"] = extra_headers + + if extra_body is not None: + options["extra_json"] = cast(AnyMapping, extra_body) + + if query is not None: + options["params"] = query + + if extra_query is not None: + options["params"] = {**options.get("params", {}), **extra_query} + + if not isinstance(timeout, NotGiven): + options["timeout"] = timeout + + if idempotency_key is not None: + options["idempotency_key"] = idempotency_key + + if is_given(post_parser): + # internal + options["post_parser"] = post_parser # type: ignore + + return options + + +class ForceMultipartDict(Dict[str, None]): + def __bool__(self) -> bool: + return True + + +class OtherPlatform: + def __init__(self, name: str) -> None: + self.name = name + + @override + def __str__(self) -> str: + return f"Other:{self.name}" + + +Platform = Union[ + OtherPlatform, + Literal[ + "MacOS", + "Linux", + "Windows", + "FreeBSD", + "OpenBSD", + "iOS", + "Android", + "Unknown", + ], +] + + +def get_platform() -> Platform: + try: + system = platform.system().lower() + platform_name = platform.platform().lower() + except Exception: + return "Unknown" + + if "iphone" in platform_name or "ipad" in platform_name: + # Tested using Python3IDE on an iPhone 11 and Pythonista on an iPad 7 + # system is Darwin and platform_name is a string like: + # - Darwin-21.6.0-iPhone12,1-64bit + # - Darwin-21.6.0-iPad7,11-64bit + return "iOS" + + if system == "darwin": + return "MacOS" + + if system == "windows": + return "Windows" + + if "android" in platform_name: + # Tested using Pydroid 3 + # system is Linux and platform_name is a string like 'Linux-5.10.81-android12-9-00001-geba40aecb3b7-ab8534902-aarch64-with-libc' + return "Android" + + if system == "linux": + # https://distro.readthedocs.io/en/latest/#distro.id + distro_id = distro.id() + if distro_id == "freebsd": + return "FreeBSD" + + if distro_id == "openbsd": + return "OpenBSD" + + return "Linux" + + if platform_name: + return OtherPlatform(platform_name) + + return "Unknown" + + +@lru_cache(maxsize=None) +def platform_headers(version: str, *, platform: Platform | None) -> Dict[str, str]: + return { + "X-Stainless-Lang": "python", + "X-Stainless-Package-Version": version, + "X-Stainless-OS": str(platform or get_platform()), + "X-Stainless-Arch": str(get_architecture()), + "X-Stainless-Runtime": get_python_runtime(), + "X-Stainless-Runtime-Version": get_python_version(), + } + + +class OtherArch: + def __init__(self, name: str) -> None: + self.name = name + + @override + def __str__(self) -> str: + return f"other:{self.name}" + + +Arch = Union[OtherArch, Literal["x32", "x64", "arm", "arm64", "unknown"]] + + +def get_python_runtime() -> str: + try: + return platform.python_implementation() + except Exception: + return "unknown" + + +def get_python_version() -> str: + try: + return platform.python_version() + except Exception: + return "unknown" + + +def get_architecture() -> Arch: + try: + machine = platform.machine().lower() + except Exception: + return "unknown" + + if machine in ("arm64", "aarch64"): + return "arm64" + + # TODO: untested + if machine == "arm": + return "arm" + + if machine == "x86_64": + return "x64" + + # TODO: untested + if sys.maxsize <= 2**32: + return "x32" + + if machine: + return OtherArch(machine) + + return "unknown" + + +def _merge_mappings( + obj1: Mapping[_T_co, Union[_T, Omit]], + obj2: Mapping[_T_co, Union[_T, Omit]], +) -> Dict[_T_co, _T]: + """Merge two mappings of the same type, removing any values that are instances of `Omit`. + + In cases with duplicate keys the second mapping takes precedence. + """ + merged = {**obj1, **obj2} + return {key: value for key, value in merged.items() if not isinstance(value, Omit)} diff --git a/.venv/lib/python3.11/site-packages/openai/_client.py b/.venv/lib/python3.11/site-packages/openai/_client.py new file mode 100644 index 0000000000000000000000000000000000000000..c784694f2030908079baa0086f1c01df5adc3ce1 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_client.py @@ -0,0 +1,565 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +import os +from typing import Any, Union, Mapping +from typing_extensions import Self, override + +import httpx + +from . import _exceptions +from ._qs import Querystring +from ._types import ( + NOT_GIVEN, + Omit, + Timeout, + NotGiven, + Transport, + ProxiesTypes, + RequestOptions, +) +from ._utils import ( + is_given, + is_mapping, + get_async_library, +) +from ._version import __version__ +from .resources import files, images, models, batches, embeddings, completions, moderations +from ._streaming import Stream as Stream, AsyncStream as AsyncStream +from ._exceptions import OpenAIError, APIStatusError +from ._base_client import ( + DEFAULT_MAX_RETRIES, + SyncAPIClient, + AsyncAPIClient, +) +from .resources.beta import beta +from .resources.chat import chat +from .resources.audio import audio +from .resources.uploads import uploads +from .resources.fine_tuning import fine_tuning + +__all__ = ["Timeout", "Transport", "ProxiesTypes", "RequestOptions", "OpenAI", "AsyncOpenAI", "Client", "AsyncClient"] + + +class OpenAI(SyncAPIClient): + completions: completions.Completions + chat: chat.Chat + embeddings: embeddings.Embeddings + files: files.Files + images: images.Images + audio: audio.Audio + moderations: moderations.Moderations + models: models.Models + fine_tuning: fine_tuning.FineTuning + beta: beta.Beta + batches: batches.Batches + uploads: uploads.Uploads + with_raw_response: OpenAIWithRawResponse + with_streaming_response: OpenAIWithStreamedResponse + + # client options + api_key: str + organization: str | None + project: str | None + + websocket_base_url: str | httpx.URL | None + """Base URL for WebSocket connections. + + If not specified, the default base URL will be used, with 'wss://' replacing the + 'http://' or 'https://' scheme. For example: 'http://example.com' becomes + 'wss://example.com' + """ + + def __init__( + self, + *, + api_key: str | None = None, + organization: str | None = None, + project: str | None = None, + base_url: str | httpx.URL | None = None, + websocket_base_url: str | httpx.URL | None = None, + timeout: Union[float, Timeout, None, NotGiven] = NOT_GIVEN, + max_retries: int = DEFAULT_MAX_RETRIES, + default_headers: Mapping[str, str] | None = None, + default_query: Mapping[str, object] | None = None, + # Configure a custom httpx client. + # We provide a `DefaultHttpxClient` class that you can pass to retain the default values we use for `limits`, `timeout` & `follow_redirects`. + # See the [httpx documentation](https://www.python-httpx.org/api/#client) for more details. + http_client: httpx.Client | None = None, + # Enable or disable schema validation for data returned by the API. + # When enabled an error APIResponseValidationError is raised + # if the API responds with invalid data for the expected schema. + # + # This parameter may be removed or changed in the future. + # If you rely on this feature, please open a GitHub issue + # outlining your use-case to help us decide if it should be + # part of our public interface in the future. + _strict_response_validation: bool = False, + ) -> None: + """Construct a new synchronous openai client instance. + + This automatically infers the following arguments from their corresponding environment variables if they are not provided: + - `api_key` from `OPENAI_API_KEY` + - `organization` from `OPENAI_ORG_ID` + - `project` from `OPENAI_PROJECT_ID` + """ + if api_key is None: + api_key = os.environ.get("OPENAI_API_KEY") + if api_key is None: + raise OpenAIError( + "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable" + ) + self.api_key = api_key + + if organization is None: + organization = os.environ.get("OPENAI_ORG_ID") + self.organization = organization + + if project is None: + project = os.environ.get("OPENAI_PROJECT_ID") + self.project = project + + self.websocket_base_url = websocket_base_url + + if base_url is None: + base_url = os.environ.get("OPENAI_BASE_URL") + if base_url is None: + base_url = f"https://api.openai.com/v1" + + super().__init__( + version=__version__, + base_url=base_url, + max_retries=max_retries, + timeout=timeout, + http_client=http_client, + custom_headers=default_headers, + custom_query=default_query, + _strict_response_validation=_strict_response_validation, + ) + + self._default_stream_cls = Stream + + self.completions = completions.Completions(self) + self.chat = chat.Chat(self) + self.embeddings = embeddings.Embeddings(self) + self.files = files.Files(self) + self.images = images.Images(self) + self.audio = audio.Audio(self) + self.moderations = moderations.Moderations(self) + self.models = models.Models(self) + self.fine_tuning = fine_tuning.FineTuning(self) + self.beta = beta.Beta(self) + self.batches = batches.Batches(self) + self.uploads = uploads.Uploads(self) + self.with_raw_response = OpenAIWithRawResponse(self) + self.with_streaming_response = OpenAIWithStreamedResponse(self) + + @property + @override + def qs(self) -> Querystring: + return Querystring(array_format="brackets") + + @property + @override + def auth_headers(self) -> dict[str, str]: + api_key = self.api_key + return {"Authorization": f"Bearer {api_key}"} + + @property + @override + def default_headers(self) -> dict[str, str | Omit]: + return { + **super().default_headers, + "X-Stainless-Async": "false", + "OpenAI-Organization": self.organization if self.organization is not None else Omit(), + "OpenAI-Project": self.project if self.project is not None else Omit(), + **self._custom_headers, + } + + def copy( + self, + *, + api_key: str | None = None, + organization: str | None = None, + project: str | None = None, + websocket_base_url: str | httpx.URL | None = None, + base_url: str | httpx.URL | None = None, + timeout: float | Timeout | None | NotGiven = NOT_GIVEN, + http_client: httpx.Client | None = None, + max_retries: int | NotGiven = NOT_GIVEN, + default_headers: Mapping[str, str] | None = None, + set_default_headers: Mapping[str, str] | None = None, + default_query: Mapping[str, object] | None = None, + set_default_query: Mapping[str, object] | None = None, + _extra_kwargs: Mapping[str, Any] = {}, + ) -> Self: + """ + Create a new client instance re-using the same options given to the current client with optional overriding. + """ + if default_headers is not None and set_default_headers is not None: + raise ValueError("The `default_headers` and `set_default_headers` arguments are mutually exclusive") + + if default_query is not None and set_default_query is not None: + raise ValueError("The `default_query` and `set_default_query` arguments are mutually exclusive") + + headers = self._custom_headers + if default_headers is not None: + headers = {**headers, **default_headers} + elif set_default_headers is not None: + headers = set_default_headers + + params = self._custom_query + if default_query is not None: + params = {**params, **default_query} + elif set_default_query is not None: + params = set_default_query + + http_client = http_client or self._client + return self.__class__( + api_key=api_key or self.api_key, + organization=organization or self.organization, + project=project or self.project, + websocket_base_url=websocket_base_url or self.websocket_base_url, + base_url=base_url or self.base_url, + timeout=self.timeout if isinstance(timeout, NotGiven) else timeout, + http_client=http_client, + max_retries=max_retries if is_given(max_retries) else self.max_retries, + default_headers=headers, + default_query=params, + **_extra_kwargs, + ) + + # Alias for `copy` for nicer inline usage, e.g. + # client.with_options(timeout=10).foo.create(...) + with_options = copy + + @override + def _make_status_error( + self, + err_msg: str, + *, + body: object, + response: httpx.Response, + ) -> APIStatusError: + data = body.get("error", body) if is_mapping(body) else body + if response.status_code == 400: + return _exceptions.BadRequestError(err_msg, response=response, body=data) + + if response.status_code == 401: + return _exceptions.AuthenticationError(err_msg, response=response, body=data) + + if response.status_code == 403: + return _exceptions.PermissionDeniedError(err_msg, response=response, body=data) + + if response.status_code == 404: + return _exceptions.NotFoundError(err_msg, response=response, body=data) + + if response.status_code == 409: + return _exceptions.ConflictError(err_msg, response=response, body=data) + + if response.status_code == 422: + return _exceptions.UnprocessableEntityError(err_msg, response=response, body=data) + + if response.status_code == 429: + return _exceptions.RateLimitError(err_msg, response=response, body=data) + + if response.status_code >= 500: + return _exceptions.InternalServerError(err_msg, response=response, body=data) + return APIStatusError(err_msg, response=response, body=data) + + +class AsyncOpenAI(AsyncAPIClient): + completions: completions.AsyncCompletions + chat: chat.AsyncChat + embeddings: embeddings.AsyncEmbeddings + files: files.AsyncFiles + images: images.AsyncImages + audio: audio.AsyncAudio + moderations: moderations.AsyncModerations + models: models.AsyncModels + fine_tuning: fine_tuning.AsyncFineTuning + beta: beta.AsyncBeta + batches: batches.AsyncBatches + uploads: uploads.AsyncUploads + with_raw_response: AsyncOpenAIWithRawResponse + with_streaming_response: AsyncOpenAIWithStreamedResponse + + # client options + api_key: str + organization: str | None + project: str | None + + websocket_base_url: str | httpx.URL | None + """Base URL for WebSocket connections. + + If not specified, the default base URL will be used, with 'wss://' replacing the + 'http://' or 'https://' scheme. For example: 'http://example.com' becomes + 'wss://example.com' + """ + + def __init__( + self, + *, + api_key: str | None = None, + organization: str | None = None, + project: str | None = None, + base_url: str | httpx.URL | None = None, + websocket_base_url: str | httpx.URL | None = None, + timeout: Union[float, Timeout, None, NotGiven] = NOT_GIVEN, + max_retries: int = DEFAULT_MAX_RETRIES, + default_headers: Mapping[str, str] | None = None, + default_query: Mapping[str, object] | None = None, + # Configure a custom httpx client. + # We provide a `DefaultAsyncHttpxClient` class that you can pass to retain the default values we use for `limits`, `timeout` & `follow_redirects`. + # See the [httpx documentation](https://www.python-httpx.org/api/#asyncclient) for more details. + http_client: httpx.AsyncClient | None = None, + # Enable or disable schema validation for data returned by the API. + # When enabled an error APIResponseValidationError is raised + # if the API responds with invalid data for the expected schema. + # + # This parameter may be removed or changed in the future. + # If you rely on this feature, please open a GitHub issue + # outlining your use-case to help us decide if it should be + # part of our public interface in the future. + _strict_response_validation: bool = False, + ) -> None: + """Construct a new async openai client instance. + + This automatically infers the following arguments from their corresponding environment variables if they are not provided: + - `api_key` from `OPENAI_API_KEY` + - `organization` from `OPENAI_ORG_ID` + - `project` from `OPENAI_PROJECT_ID` + """ + if api_key is None: + api_key = os.environ.get("OPENAI_API_KEY") + if api_key is None: + raise OpenAIError( + "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable" + ) + self.api_key = api_key + + if organization is None: + organization = os.environ.get("OPENAI_ORG_ID") + self.organization = organization + + if project is None: + project = os.environ.get("OPENAI_PROJECT_ID") + self.project = project + + self.websocket_base_url = websocket_base_url + + if base_url is None: + base_url = os.environ.get("OPENAI_BASE_URL") + if base_url is None: + base_url = f"https://api.openai.com/v1" + + super().__init__( + version=__version__, + base_url=base_url, + max_retries=max_retries, + timeout=timeout, + http_client=http_client, + custom_headers=default_headers, + custom_query=default_query, + _strict_response_validation=_strict_response_validation, + ) + + self._default_stream_cls = AsyncStream + + self.completions = completions.AsyncCompletions(self) + self.chat = chat.AsyncChat(self) + self.embeddings = embeddings.AsyncEmbeddings(self) + self.files = files.AsyncFiles(self) + self.images = images.AsyncImages(self) + self.audio = audio.AsyncAudio(self) + self.moderations = moderations.AsyncModerations(self) + self.models = models.AsyncModels(self) + self.fine_tuning = fine_tuning.AsyncFineTuning(self) + self.beta = beta.AsyncBeta(self) + self.batches = batches.AsyncBatches(self) + self.uploads = uploads.AsyncUploads(self) + self.with_raw_response = AsyncOpenAIWithRawResponse(self) + self.with_streaming_response = AsyncOpenAIWithStreamedResponse(self) + + @property + @override + def qs(self) -> Querystring: + return Querystring(array_format="brackets") + + @property + @override + def auth_headers(self) -> dict[str, str]: + api_key = self.api_key + return {"Authorization": f"Bearer {api_key}"} + + @property + @override + def default_headers(self) -> dict[str, str | Omit]: + return { + **super().default_headers, + "X-Stainless-Async": f"async:{get_async_library()}", + "OpenAI-Organization": self.organization if self.organization is not None else Omit(), + "OpenAI-Project": self.project if self.project is not None else Omit(), + **self._custom_headers, + } + + def copy( + self, + *, + api_key: str | None = None, + organization: str | None = None, + project: str | None = None, + websocket_base_url: str | httpx.URL | None = None, + base_url: str | httpx.URL | None = None, + timeout: float | Timeout | None | NotGiven = NOT_GIVEN, + http_client: httpx.AsyncClient | None = None, + max_retries: int | NotGiven = NOT_GIVEN, + default_headers: Mapping[str, str] | None = None, + set_default_headers: Mapping[str, str] | None = None, + default_query: Mapping[str, object] | None = None, + set_default_query: Mapping[str, object] | None = None, + _extra_kwargs: Mapping[str, Any] = {}, + ) -> Self: + """ + Create a new client instance re-using the same options given to the current client with optional overriding. + """ + if default_headers is not None and set_default_headers is not None: + raise ValueError("The `default_headers` and `set_default_headers` arguments are mutually exclusive") + + if default_query is not None and set_default_query is not None: + raise ValueError("The `default_query` and `set_default_query` arguments are mutually exclusive") + + headers = self._custom_headers + if default_headers is not None: + headers = {**headers, **default_headers} + elif set_default_headers is not None: + headers = set_default_headers + + params = self._custom_query + if default_query is not None: + params = {**params, **default_query} + elif set_default_query is not None: + params = set_default_query + + http_client = http_client or self._client + return self.__class__( + api_key=api_key or self.api_key, + organization=organization or self.organization, + project=project or self.project, + websocket_base_url=websocket_base_url or self.websocket_base_url, + base_url=base_url or self.base_url, + timeout=self.timeout if isinstance(timeout, NotGiven) else timeout, + http_client=http_client, + max_retries=max_retries if is_given(max_retries) else self.max_retries, + default_headers=headers, + default_query=params, + **_extra_kwargs, + ) + + # Alias for `copy` for nicer inline usage, e.g. + # client.with_options(timeout=10).foo.create(...) + with_options = copy + + @override + def _make_status_error( + self, + err_msg: str, + *, + body: object, + response: httpx.Response, + ) -> APIStatusError: + data = body.get("error", body) if is_mapping(body) else body + if response.status_code == 400: + return _exceptions.BadRequestError(err_msg, response=response, body=data) + + if response.status_code == 401: + return _exceptions.AuthenticationError(err_msg, response=response, body=data) + + if response.status_code == 403: + return _exceptions.PermissionDeniedError(err_msg, response=response, body=data) + + if response.status_code == 404: + return _exceptions.NotFoundError(err_msg, response=response, body=data) + + if response.status_code == 409: + return _exceptions.ConflictError(err_msg, response=response, body=data) + + if response.status_code == 422: + return _exceptions.UnprocessableEntityError(err_msg, response=response, body=data) + + if response.status_code == 429: + return _exceptions.RateLimitError(err_msg, response=response, body=data) + + if response.status_code >= 500: + return _exceptions.InternalServerError(err_msg, response=response, body=data) + return APIStatusError(err_msg, response=response, body=data) + + +class OpenAIWithRawResponse: + def __init__(self, client: OpenAI) -> None: + self.completions = completions.CompletionsWithRawResponse(client.completions) + self.chat = chat.ChatWithRawResponse(client.chat) + self.embeddings = embeddings.EmbeddingsWithRawResponse(client.embeddings) + self.files = files.FilesWithRawResponse(client.files) + self.images = images.ImagesWithRawResponse(client.images) + self.audio = audio.AudioWithRawResponse(client.audio) + self.moderations = moderations.ModerationsWithRawResponse(client.moderations) + self.models = models.ModelsWithRawResponse(client.models) + self.fine_tuning = fine_tuning.FineTuningWithRawResponse(client.fine_tuning) + self.beta = beta.BetaWithRawResponse(client.beta) + self.batches = batches.BatchesWithRawResponse(client.batches) + self.uploads = uploads.UploadsWithRawResponse(client.uploads) + + +class AsyncOpenAIWithRawResponse: + def __init__(self, client: AsyncOpenAI) -> None: + self.completions = completions.AsyncCompletionsWithRawResponse(client.completions) + self.chat = chat.AsyncChatWithRawResponse(client.chat) + self.embeddings = embeddings.AsyncEmbeddingsWithRawResponse(client.embeddings) + self.files = files.AsyncFilesWithRawResponse(client.files) + self.images = images.AsyncImagesWithRawResponse(client.images) + self.audio = audio.AsyncAudioWithRawResponse(client.audio) + self.moderations = moderations.AsyncModerationsWithRawResponse(client.moderations) + self.models = models.AsyncModelsWithRawResponse(client.models) + self.fine_tuning = fine_tuning.AsyncFineTuningWithRawResponse(client.fine_tuning) + self.beta = beta.AsyncBetaWithRawResponse(client.beta) + self.batches = batches.AsyncBatchesWithRawResponse(client.batches) + self.uploads = uploads.AsyncUploadsWithRawResponse(client.uploads) + + +class OpenAIWithStreamedResponse: + def __init__(self, client: OpenAI) -> None: + self.completions = completions.CompletionsWithStreamingResponse(client.completions) + self.chat = chat.ChatWithStreamingResponse(client.chat) + self.embeddings = embeddings.EmbeddingsWithStreamingResponse(client.embeddings) + self.files = files.FilesWithStreamingResponse(client.files) + self.images = images.ImagesWithStreamingResponse(client.images) + self.audio = audio.AudioWithStreamingResponse(client.audio) + self.moderations = moderations.ModerationsWithStreamingResponse(client.moderations) + self.models = models.ModelsWithStreamingResponse(client.models) + self.fine_tuning = fine_tuning.FineTuningWithStreamingResponse(client.fine_tuning) + self.beta = beta.BetaWithStreamingResponse(client.beta) + self.batches = batches.BatchesWithStreamingResponse(client.batches) + self.uploads = uploads.UploadsWithStreamingResponse(client.uploads) + + +class AsyncOpenAIWithStreamedResponse: + def __init__(self, client: AsyncOpenAI) -> None: + self.completions = completions.AsyncCompletionsWithStreamingResponse(client.completions) + self.chat = chat.AsyncChatWithStreamingResponse(client.chat) + self.embeddings = embeddings.AsyncEmbeddingsWithStreamingResponse(client.embeddings) + self.files = files.AsyncFilesWithStreamingResponse(client.files) + self.images = images.AsyncImagesWithStreamingResponse(client.images) + self.audio = audio.AsyncAudioWithStreamingResponse(client.audio) + self.moderations = moderations.AsyncModerationsWithStreamingResponse(client.moderations) + self.models = models.AsyncModelsWithStreamingResponse(client.models) + self.fine_tuning = fine_tuning.AsyncFineTuningWithStreamingResponse(client.fine_tuning) + self.beta = beta.AsyncBetaWithStreamingResponse(client.beta) + self.batches = batches.AsyncBatchesWithStreamingResponse(client.batches) + self.uploads = uploads.AsyncUploadsWithStreamingResponse(client.uploads) + + +Client = OpenAI + +AsyncClient = AsyncOpenAI diff --git a/.venv/lib/python3.11/site-packages/openai/_compat.py b/.venv/lib/python3.11/site-packages/openai/_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..87fc3707651bf7ada3831ea4957e8591bd67c5dd --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_compat.py @@ -0,0 +1,231 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Union, Generic, TypeVar, Callable, cast, overload +from datetime import date, datetime +from typing_extensions import Self, Literal + +import pydantic +from pydantic.fields import FieldInfo + +from ._types import IncEx, StrBytesIntFloat + +_T = TypeVar("_T") +_ModelT = TypeVar("_ModelT", bound=pydantic.BaseModel) + +# --------------- Pydantic v2 compatibility --------------- + +# Pyright incorrectly reports some of our functions as overriding a method when they don't +# pyright: reportIncompatibleMethodOverride=false + +PYDANTIC_V2 = pydantic.VERSION.startswith("2.") + +# v1 re-exports +if TYPE_CHECKING: + + def parse_date(value: date | StrBytesIntFloat) -> date: # noqa: ARG001 + ... + + def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime: # noqa: ARG001 + ... + + def get_args(t: type[Any]) -> tuple[Any, ...]: # noqa: ARG001 + ... + + def is_union(tp: type[Any] | None) -> bool: # noqa: ARG001 + ... + + def get_origin(t: type[Any]) -> type[Any] | None: # noqa: ARG001 + ... + + def is_literal_type(type_: type[Any]) -> bool: # noqa: ARG001 + ... + + def is_typeddict(type_: type[Any]) -> bool: # noqa: ARG001 + ... + +else: + if PYDANTIC_V2: + from pydantic.v1.typing import ( + get_args as get_args, + is_union as is_union, + get_origin as get_origin, + is_typeddict as is_typeddict, + is_literal_type as is_literal_type, + ) + from pydantic.v1.datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime + else: + from pydantic.typing import ( + get_args as get_args, + is_union as is_union, + get_origin as get_origin, + is_typeddict as is_typeddict, + is_literal_type as is_literal_type, + ) + from pydantic.datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime + + +# refactored config +if TYPE_CHECKING: + from pydantic import ConfigDict as ConfigDict +else: + if PYDANTIC_V2: + from pydantic import ConfigDict + else: + # TODO: provide an error message here? + ConfigDict = None + + +# renamed methods / properties +def parse_obj(model: type[_ModelT], value: object) -> _ModelT: + if PYDANTIC_V2: + return model.model_validate(value) + else: + return cast(_ModelT, model.parse_obj(value)) # pyright: ignore[reportDeprecated, reportUnnecessaryCast] + + +def field_is_required(field: FieldInfo) -> bool: + if PYDANTIC_V2: + return field.is_required() + return field.required # type: ignore + + +def field_get_default(field: FieldInfo) -> Any: + value = field.get_default() + if PYDANTIC_V2: + from pydantic_core import PydanticUndefined + + if value == PydanticUndefined: + return None + return value + return value + + +def field_outer_type(field: FieldInfo) -> Any: + if PYDANTIC_V2: + return field.annotation + return field.outer_type_ # type: ignore + + +def get_model_config(model: type[pydantic.BaseModel]) -> Any: + if PYDANTIC_V2: + return model.model_config + return model.__config__ # type: ignore + + +def get_model_fields(model: type[pydantic.BaseModel]) -> dict[str, FieldInfo]: + if PYDANTIC_V2: + return model.model_fields + return model.__fields__ # type: ignore + + +def model_copy(model: _ModelT, *, deep: bool = False) -> _ModelT: + if PYDANTIC_V2: + return model.model_copy(deep=deep) + return model.copy(deep=deep) # type: ignore + + +def model_json(model: pydantic.BaseModel, *, indent: int | None = None) -> str: + if PYDANTIC_V2: + return model.model_dump_json(indent=indent) + return model.json(indent=indent) # type: ignore + + +def model_dump( + model: pydantic.BaseModel, + *, + exclude: IncEx | None = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + warnings: bool = True, + mode: Literal["json", "python"] = "python", +) -> dict[str, Any]: + if PYDANTIC_V2 or hasattr(model, "model_dump"): + return model.model_dump( + mode=mode, + exclude=exclude, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + # warnings are not supported in Pydantic v1 + warnings=warnings if PYDANTIC_V2 else True, + ) + return cast( + "dict[str, Any]", + model.dict( # pyright: ignore[reportDeprecated, reportUnnecessaryCast] + exclude=exclude, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + ), + ) + + +def model_parse(model: type[_ModelT], data: Any) -> _ModelT: + if PYDANTIC_V2: + return model.model_validate(data) + return model.parse_obj(data) # pyright: ignore[reportDeprecated] + + +def model_parse_json(model: type[_ModelT], data: str | bytes) -> _ModelT: + if PYDANTIC_V2: + return model.model_validate_json(data) + return model.parse_raw(data) # pyright: ignore[reportDeprecated] + + +def model_json_schema(model: type[_ModelT]) -> dict[str, Any]: + if PYDANTIC_V2: + return model.model_json_schema() + return model.schema() # pyright: ignore[reportDeprecated] + + +# generic models +if TYPE_CHECKING: + + class GenericModel(pydantic.BaseModel): ... + +else: + if PYDANTIC_V2: + # there no longer needs to be a distinction in v2 but + # we still have to create our own subclass to avoid + # inconsistent MRO ordering errors + class GenericModel(pydantic.BaseModel): ... + + else: + import pydantic.generics + + class GenericModel(pydantic.generics.GenericModel, pydantic.BaseModel): ... + + +# cached properties +if TYPE_CHECKING: + cached_property = property + + # we define a separate type (copied from typeshed) + # that represents that `cached_property` is `set`able + # at runtime, which differs from `@property`. + # + # this is a separate type as editors likely special case + # `@property` and we don't want to cause issues just to have + # more helpful internal types. + + class typed_cached_property(Generic[_T]): + func: Callable[[Any], _T] + attrname: str | None + + def __init__(self, func: Callable[[Any], _T]) -> None: ... + + @overload + def __get__(self, instance: None, owner: type[Any] | None = None) -> Self: ... + + @overload + def __get__(self, instance: object, owner: type[Any] | None = None) -> _T: ... + + def __get__(self, instance: object, owner: type[Any] | None = None) -> _T | Self: + raise NotImplementedError() + + def __set_name__(self, owner: type[Any], name: str) -> None: ... + + # __set__ is not defined at runtime, but @cached_property is designed to be settable + def __set__(self, instance: object, value: _T) -> None: ... +else: + from functools import cached_property as cached_property + + typed_cached_property = cached_property diff --git a/.venv/lib/python3.11/site-packages/openai/_constants.py b/.venv/lib/python3.11/site-packages/openai/_constants.py new file mode 100644 index 0000000000000000000000000000000000000000..7029dc72b02235bb529bb3b5e07e6e21e42a92c5 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_constants.py @@ -0,0 +1,14 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +import httpx + +RAW_RESPONSE_HEADER = "X-Stainless-Raw-Response" +OVERRIDE_CAST_TO_HEADER = "____stainless_override_cast_to" + +# default timeout is 10 minutes +DEFAULT_TIMEOUT = httpx.Timeout(timeout=600, connect=5.0) +DEFAULT_MAX_RETRIES = 2 +DEFAULT_CONNECTION_LIMITS = httpx.Limits(max_connections=1000, max_keepalive_connections=100) + +INITIAL_RETRY_DELAY = 0.5 +MAX_RETRY_DELAY = 8.0 diff --git a/.venv/lib/python3.11/site-packages/openai/_exceptions.py b/.venv/lib/python3.11/site-packages/openai/_exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..e326ed9578f2a197f8d5f0de8dc47e235705cec3 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_exceptions.py @@ -0,0 +1,156 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Optional, cast +from typing_extensions import Literal + +import httpx + +from ._utils import is_dict +from ._models import construct_type + +if TYPE_CHECKING: + from .types.chat import ChatCompletion + +__all__ = [ + "BadRequestError", + "AuthenticationError", + "PermissionDeniedError", + "NotFoundError", + "ConflictError", + "UnprocessableEntityError", + "RateLimitError", + "InternalServerError", + "LengthFinishReasonError", + "ContentFilterFinishReasonError", +] + + +class OpenAIError(Exception): + pass + + +class APIError(OpenAIError): + message: str + request: httpx.Request + + body: object | None + """The API response body. + + If the API responded with a valid JSON structure then this property will be the + decoded result. + + If it isn't a valid JSON structure then this will be the raw response. + + If there was no response associated with this error then it will be `None`. + """ + + code: Optional[str] = None + param: Optional[str] = None + type: Optional[str] + + def __init__(self, message: str, request: httpx.Request, *, body: object | None) -> None: + super().__init__(message) + self.request = request + self.message = message + self.body = body + + if is_dict(body): + self.code = cast(Any, construct_type(type_=Optional[str], value=body.get("code"))) + self.param = cast(Any, construct_type(type_=Optional[str], value=body.get("param"))) + self.type = cast(Any, construct_type(type_=str, value=body.get("type"))) + else: + self.code = None + self.param = None + self.type = None + + +class APIResponseValidationError(APIError): + response: httpx.Response + status_code: int + + def __init__(self, response: httpx.Response, body: object | None, *, message: str | None = None) -> None: + super().__init__(message or "Data returned by API invalid for expected schema.", response.request, body=body) + self.response = response + self.status_code = response.status_code + + +class APIStatusError(APIError): + """Raised when an API response has a status code of 4xx or 5xx.""" + + response: httpx.Response + status_code: int + request_id: str | None + + def __init__(self, message: str, *, response: httpx.Response, body: object | None) -> None: + super().__init__(message, response.request, body=body) + self.response = response + self.status_code = response.status_code + self.request_id = response.headers.get("x-request-id") + + +class APIConnectionError(APIError): + def __init__(self, *, message: str = "Connection error.", request: httpx.Request) -> None: + super().__init__(message, request, body=None) + + +class APITimeoutError(APIConnectionError): + def __init__(self, request: httpx.Request) -> None: + super().__init__(message="Request timed out.", request=request) + + +class BadRequestError(APIStatusError): + status_code: Literal[400] = 400 # pyright: ignore[reportIncompatibleVariableOverride] + + +class AuthenticationError(APIStatusError): + status_code: Literal[401] = 401 # pyright: ignore[reportIncompatibleVariableOverride] + + +class PermissionDeniedError(APIStatusError): + status_code: Literal[403] = 403 # pyright: ignore[reportIncompatibleVariableOverride] + + +class NotFoundError(APIStatusError): + status_code: Literal[404] = 404 # pyright: ignore[reportIncompatibleVariableOverride] + + +class ConflictError(APIStatusError): + status_code: Literal[409] = 409 # pyright: ignore[reportIncompatibleVariableOverride] + + +class UnprocessableEntityError(APIStatusError): + status_code: Literal[422] = 422 # pyright: ignore[reportIncompatibleVariableOverride] + + +class RateLimitError(APIStatusError): + status_code: Literal[429] = 429 # pyright: ignore[reportIncompatibleVariableOverride] + + +class InternalServerError(APIStatusError): + pass + + +class LengthFinishReasonError(OpenAIError): + completion: ChatCompletion + """The completion that caused this error. + + Note: this will *not* be a complete `ChatCompletion` object when streaming as `usage` + will not be included. + """ + + def __init__(self, *, completion: ChatCompletion) -> None: + msg = "Could not parse response content as the length limit was reached" + if completion.usage: + msg += f" - {completion.usage}" + + super().__init__(msg) + self.completion = completion + + +class ContentFilterFinishReasonError(OpenAIError): + def __init__(self) -> None: + super().__init__( + f"Could not parse response content as the request was rejected by the content filter", + ) diff --git a/.venv/lib/python3.11/site-packages/openai/_files.py b/.venv/lib/python3.11/site-packages/openai/_files.py new file mode 100644 index 0000000000000000000000000000000000000000..801a0d29280c4ee23937b456352f77dee2b9f151 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_files.py @@ -0,0 +1,123 @@ +from __future__ import annotations + +import io +import os +import pathlib +from typing import overload +from typing_extensions import TypeGuard + +import anyio + +from ._types import ( + FileTypes, + FileContent, + RequestFiles, + HttpxFileTypes, + Base64FileInput, + HttpxFileContent, + HttpxRequestFiles, +) +from ._utils import is_tuple_t, is_mapping_t, is_sequence_t + + +def is_base64_file_input(obj: object) -> TypeGuard[Base64FileInput]: + return isinstance(obj, io.IOBase) or isinstance(obj, os.PathLike) + + +def is_file_content(obj: object) -> TypeGuard[FileContent]: + return ( + isinstance(obj, bytes) or isinstance(obj, tuple) or isinstance(obj, io.IOBase) or isinstance(obj, os.PathLike) + ) + + +def assert_is_file_content(obj: object, *, key: str | None = None) -> None: + if not is_file_content(obj): + prefix = f"Expected entry at `{key}`" if key is not None else f"Expected file input `{obj!r}`" + raise RuntimeError( + f"{prefix} to be bytes, an io.IOBase instance, PathLike or a tuple but received {type(obj)} instead. See https://github.com/openai/openai-python/tree/main#file-uploads" + ) from None + + +@overload +def to_httpx_files(files: None) -> None: ... + + +@overload +def to_httpx_files(files: RequestFiles) -> HttpxRequestFiles: ... + + +def to_httpx_files(files: RequestFiles | None) -> HttpxRequestFiles | None: + if files is None: + return None + + if is_mapping_t(files): + files = {key: _transform_file(file) for key, file in files.items()} + elif is_sequence_t(files): + files = [(key, _transform_file(file)) for key, file in files] + else: + raise TypeError(f"Unexpected file type input {type(files)}, expected mapping or sequence") + + return files + + +def _transform_file(file: FileTypes) -> HttpxFileTypes: + if is_file_content(file): + if isinstance(file, os.PathLike): + path = pathlib.Path(file) + return (path.name, path.read_bytes()) + + return file + + if is_tuple_t(file): + return (file[0], _read_file_content(file[1]), *file[2:]) + + raise TypeError(f"Expected file types input to be a FileContent type or to be a tuple") + + +def _read_file_content(file: FileContent) -> HttpxFileContent: + if isinstance(file, os.PathLike): + return pathlib.Path(file).read_bytes() + return file + + +@overload +async def async_to_httpx_files(files: None) -> None: ... + + +@overload +async def async_to_httpx_files(files: RequestFiles) -> HttpxRequestFiles: ... + + +async def async_to_httpx_files(files: RequestFiles | None) -> HttpxRequestFiles | None: + if files is None: + return None + + if is_mapping_t(files): + files = {key: await _async_transform_file(file) for key, file in files.items()} + elif is_sequence_t(files): + files = [(key, await _async_transform_file(file)) for key, file in files] + else: + raise TypeError("Unexpected file type input {type(files)}, expected mapping or sequence") + + return files + + +async def _async_transform_file(file: FileTypes) -> HttpxFileTypes: + if is_file_content(file): + if isinstance(file, os.PathLike): + path = anyio.Path(file) + return (path.name, await path.read_bytes()) + + return file + + if is_tuple_t(file): + return (file[0], await _async_read_file_content(file[1]), *file[2:]) + + raise TypeError(f"Expected file types input to be a FileContent type or to be a tuple") + + +async def _async_read_file_content(file: FileContent) -> HttpxFileContent: + if isinstance(file, os.PathLike): + return await anyio.Path(file).read_bytes() + + return file diff --git a/.venv/lib/python3.11/site-packages/openai/_legacy_response.py b/.venv/lib/python3.11/site-packages/openai/_legacy_response.py new file mode 100644 index 0000000000000000000000000000000000000000..8880e5f104398f5f8e6c39552a16c009f85b0f76 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_legacy_response.py @@ -0,0 +1,488 @@ +from __future__ import annotations + +import os +import inspect +import logging +import datetime +import functools +from typing import ( + TYPE_CHECKING, + Any, + Union, + Generic, + TypeVar, + Callable, + Iterator, + AsyncIterator, + cast, + overload, +) +from typing_extensions import Awaitable, ParamSpec, override, deprecated, get_origin + +import anyio +import httpx +import pydantic + +from ._types import NoneType +from ._utils import is_given, extract_type_arg, is_annotated_type, is_type_alias_type +from ._models import BaseModel, is_basemodel, add_request_id +from ._constants import RAW_RESPONSE_HEADER +from ._streaming import Stream, AsyncStream, is_stream_class_type, extract_stream_chunk_type +from ._exceptions import APIResponseValidationError + +if TYPE_CHECKING: + from ._models import FinalRequestOptions + from ._base_client import BaseClient + + +P = ParamSpec("P") +R = TypeVar("R") +_T = TypeVar("_T") + +log: logging.Logger = logging.getLogger(__name__) + + +class LegacyAPIResponse(Generic[R]): + """This is a legacy class as it will be replaced by `APIResponse` + and `AsyncAPIResponse` in the `_response.py` file in the next major + release. + + 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. + """ + + _cast_to: type[R] + _client: BaseClient[Any, Any] + _parsed_by_type: dict[type[Any], Any] + _stream: bool + _stream_cls: type[Stream[Any]] | type[AsyncStream[Any]] | None + _options: FinalRequestOptions + + http_response: httpx.Response + + retries_taken: int + """The number of retries made. If no retries happened this will be `0`""" + + def __init__( + self, + *, + raw: httpx.Response, + cast_to: type[R], + client: BaseClient[Any, Any], + stream: bool, + stream_cls: type[Stream[Any]] | type[AsyncStream[Any]] | None, + options: FinalRequestOptions, + retries_taken: int = 0, + ) -> None: + self._cast_to = cast_to + self._client = client + self._parsed_by_type = {} + self._stream = stream + self._stream_cls = stream_cls + self._options = options + self.http_response = raw + self.retries_taken = retries_taken + + @property + def request_id(self) -> str | None: + return self.http_response.headers.get("x-request-id") # type: ignore[no-any-return] + + @overload + def parse(self, *, to: type[_T]) -> _T: ... + + @overload + def parse(self) -> R: ... + + def parse(self, *, to: type[_T] | None = None) -> R | _T: + """Returns the rich python representation of this response's data. + + NOTE: For the async client: this will become a coroutine in the next major version. + + For lower-level control, see `.read()`, `.json()`, `.iter_bytes()`. + + You can customise the type that the response is parsed into through + the `to` argument, e.g. + + ```py + from openai import BaseModel + + + class MyModel(BaseModel): + foo: str + + + obj = response.parse(to=MyModel) + print(obj.foo) + ``` + + We support parsing: + - `BaseModel` + - `dict` + - `list` + - `Union` + - `str` + - `int` + - `float` + - `httpx.Response` + """ + cache_key = to if to is not None else self._cast_to + cached = self._parsed_by_type.get(cache_key) + if cached is not None: + return cached # type: ignore[no-any-return] + + parsed = self._parse(to=to) + if is_given(self._options.post_parser): + parsed = self._options.post_parser(parsed) + + if isinstance(parsed, BaseModel): + add_request_id(parsed, self.request_id) + + self._parsed_by_type[cache_key] = parsed + return cast(R, parsed) + + @property + def headers(self) -> httpx.Headers: + return self.http_response.headers + + @property + def http_request(self) -> httpx.Request: + return self.http_response.request + + @property + def status_code(self) -> int: + return self.http_response.status_code + + @property + def url(self) -> httpx.URL: + return self.http_response.url + + @property + def method(self) -> str: + return self.http_request.method + + @property + def content(self) -> bytes: + """Return the binary response content. + + NOTE: this will be removed in favour of `.read()` in the + next major version. + """ + return self.http_response.content + + @property + def text(self) -> str: + """Return the decoded response content. + + NOTE: this will be turned into a method in the next major version. + """ + return self.http_response.text + + @property + def http_version(self) -> str: + return self.http_response.http_version + + @property + def is_closed(self) -> bool: + return self.http_response.is_closed + + @property + def elapsed(self) -> datetime.timedelta: + """The time taken for the complete request/response cycle to complete.""" + return self.http_response.elapsed + + def _parse(self, *, to: type[_T] | None = None) -> R | _T: + cast_to = to if to is not None else self._cast_to + + # unwrap `TypeAlias('Name', T)` -> `T` + if is_type_alias_type(cast_to): + cast_to = cast_to.__value__ # type: ignore[unreachable] + + # unwrap `Annotated[T, ...]` -> `T` + if cast_to and is_annotated_type(cast_to): + cast_to = extract_type_arg(cast_to, 0) + + origin = get_origin(cast_to) or cast_to + + if self._stream: + if to: + if not is_stream_class_type(to): + raise TypeError(f"Expected custom parse type to be a subclass of {Stream} or {AsyncStream}") + + return cast( + _T, + to( + cast_to=extract_stream_chunk_type( + to, + failure_message="Expected custom stream type to be passed with a type argument, e.g. Stream[ChunkType]", + ), + response=self.http_response, + client=cast(Any, self._client), + ), + ) + + if self._stream_cls: + return cast( + R, + self._stream_cls( + cast_to=extract_stream_chunk_type(self._stream_cls), + response=self.http_response, + client=cast(Any, self._client), + ), + ) + + stream_cls = cast("type[Stream[Any]] | type[AsyncStream[Any]] | None", self._client._default_stream_cls) + if stream_cls is None: + raise MissingStreamClassError() + + return cast( + R, + stream_cls( + cast_to=cast_to, + response=self.http_response, + client=cast(Any, self._client), + ), + ) + + if cast_to is NoneType: + return cast(R, None) + + response = self.http_response + if cast_to == str: + return cast(R, response.text) + + if cast_to == int: + return cast(R, int(response.text)) + + if cast_to == float: + return cast(R, float(response.text)) + + if cast_to == bool: + return cast(R, response.text.lower() == "true") + + if inspect.isclass(origin) and issubclass(origin, HttpxBinaryResponseContent): + return cast(R, cast_to(response)) # type: ignore + + if origin == LegacyAPIResponse: + raise RuntimeError("Unexpected state - cast_to is `APIResponse`") + + if inspect.isclass( + origin # pyright: ignore[reportUnknownArgumentType] + ) and issubclass(origin, httpx.Response): + # Because of the invariance of our ResponseT TypeVar, users can subclass httpx.Response + # and pass that class to our request functions. We cannot change the variance to be either + # covariant or contravariant as that makes our usage of ResponseT illegal. We could construct + # the response class ourselves but that is something that should be supported directly in httpx + # as it would be easy to incorrectly construct the Response object due to the multitude of arguments. + if cast_to != httpx.Response: + raise ValueError(f"Subclasses of httpx.Response cannot be passed to `cast_to`") + return cast(R, response) + + if ( + inspect.isclass( + origin # pyright: ignore[reportUnknownArgumentType] + ) + and not issubclass(origin, BaseModel) + and issubclass(origin, pydantic.BaseModel) + ): + raise TypeError("Pydantic models must subclass our base model type, e.g. `from openai import BaseModel`") + + if ( + cast_to is not object + and not origin is list + and not origin is dict + and not origin is Union + and not issubclass(origin, BaseModel) + ): + raise RuntimeError( + f"Unsupported type, expected {cast_to} to be a subclass of {BaseModel}, {dict}, {list}, {Union}, {NoneType}, {str} or {httpx.Response}." + ) + + # split is required to handle cases where additional information is included + # in the response, e.g. application/json; charset=utf-8 + content_type, *_ = response.headers.get("content-type", "*").split(";") + if content_type != "application/json": + if is_basemodel(cast_to): + try: + data = response.json() + except Exception as exc: + log.debug("Could not read JSON from response data due to %s - %s", type(exc), exc) + else: + return self._client._process_response_data( + data=data, + cast_to=cast_to, # type: ignore + response=response, + ) + + if self._client._strict_response_validation: + raise APIResponseValidationError( + response=response, + message=f"Expected Content-Type response header to be `application/json` but received `{content_type}` instead.", + body=response.text, + ) + + # If the API responds with content that isn't JSON then we just return + # the (decoded) text without performing any parsing so that you can still + # handle the response however you need to. + return response.text # type: ignore + + data = response.json() + + return self._client._process_response_data( + data=data, + cast_to=cast_to, # type: ignore + response=response, + ) + + @override + def __repr__(self) -> str: + return f"" + + +class MissingStreamClassError(TypeError): + def __init__(self) -> None: + super().__init__( + "The `stream` argument was set to `True` but the `stream_cls` argument was not given. See `openai._streaming` for reference", + ) + + +def to_raw_response_wrapper(func: Callable[P, R]) -> Callable[P, LegacyAPIResponse[R]]: + """Higher order function that takes one of our bound API methods and wraps it + to support returning the raw `APIResponse` object directly. + """ + + @functools.wraps(func) + def wrapped(*args: P.args, **kwargs: P.kwargs) -> LegacyAPIResponse[R]: + extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})} + extra_headers[RAW_RESPONSE_HEADER] = "true" + + kwargs["extra_headers"] = extra_headers + + return cast(LegacyAPIResponse[R], func(*args, **kwargs)) + + return wrapped + + +def async_to_raw_response_wrapper(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[LegacyAPIResponse[R]]]: + """Higher order function that takes one of our bound API methods and wraps it + to support returning the raw `APIResponse` object directly. + """ + + @functools.wraps(func) + async def wrapped(*args: P.args, **kwargs: P.kwargs) -> LegacyAPIResponse[R]: + extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})} + extra_headers[RAW_RESPONSE_HEADER] = "true" + + kwargs["extra_headers"] = extra_headers + + return cast(LegacyAPIResponse[R], await func(*args, **kwargs)) + + return wrapped + + +class HttpxBinaryResponseContent: + response: httpx.Response + + def __init__(self, response: httpx.Response) -> None: + self.response = response + + @property + def content(self) -> bytes: + return self.response.content + + @property + def text(self) -> str: + return self.response.text + + @property + def encoding(self) -> str | None: + return self.response.encoding + + @property + def charset_encoding(self) -> str | None: + return self.response.charset_encoding + + def json(self, **kwargs: Any) -> Any: + return self.response.json(**kwargs) + + def read(self) -> bytes: + return self.response.read() + + def iter_bytes(self, chunk_size: int | None = None) -> Iterator[bytes]: + return self.response.iter_bytes(chunk_size) + + def iter_text(self, chunk_size: int | None = None) -> Iterator[str]: + return self.response.iter_text(chunk_size) + + def iter_lines(self) -> Iterator[str]: + return self.response.iter_lines() + + def iter_raw(self, chunk_size: int | None = None) -> Iterator[bytes]: + return self.response.iter_raw(chunk_size) + + def write_to_file( + self, + file: str | os.PathLike[str], + ) -> None: + """Write the output to the given file. + + Accepts a filename or any path-like object, e.g. pathlib.Path + + Note: if you want to stream the data to the file instead of writing + all at once then you should use `.with_streaming_response` when making + the API request, e.g. `client.with_streaming_response.foo().stream_to_file('my_filename.txt')` + """ + with open(file, mode="wb") as f: + for data in self.response.iter_bytes(): + f.write(data) + + @deprecated( + "Due to a bug, this method doesn't actually stream the response content, `.with_streaming_response.method()` should be used instead" + ) + def stream_to_file( + self, + file: str | os.PathLike[str], + *, + chunk_size: int | None = None, + ) -> None: + with open(file, mode="wb") as f: + for data in self.response.iter_bytes(chunk_size): + f.write(data) + + def close(self) -> None: + return self.response.close() + + async def aread(self) -> bytes: + return await self.response.aread() + + async def aiter_bytes(self, chunk_size: int | None = None) -> AsyncIterator[bytes]: + return self.response.aiter_bytes(chunk_size) + + async def aiter_text(self, chunk_size: int | None = None) -> AsyncIterator[str]: + return self.response.aiter_text(chunk_size) + + async def aiter_lines(self) -> AsyncIterator[str]: + return self.response.aiter_lines() + + async def aiter_raw(self, chunk_size: int | None = None) -> AsyncIterator[bytes]: + return self.response.aiter_raw(chunk_size) + + @deprecated( + "Due to a bug, this method doesn't actually stream the response content, `.with_streaming_response.method()` should be used instead" + ) + async def astream_to_file( + self, + file: str | os.PathLike[str], + *, + chunk_size: int | None = None, + ) -> None: + path = anyio.Path(file) + async with await path.open(mode="wb") as f: + async for data in self.response.aiter_bytes(chunk_size): + await f.write(data) + + async def aclose(self) -> None: + return await self.response.aclose() diff --git a/.venv/lib/python3.11/site-packages/openai/_models.py b/.venv/lib/python3.11/site-packages/openai/_models.py new file mode 100644 index 0000000000000000000000000000000000000000..c6e1305087f8e9deea7eb724f0884196019e1c2c --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_models.py @@ -0,0 +1,835 @@ +from __future__ import annotations + +import os +import inspect +from typing import TYPE_CHECKING, Any, Type, Tuple, Union, Generic, TypeVar, Callable, Optional, cast +from datetime import date, datetime +from typing_extensions import ( + Unpack, + Literal, + ClassVar, + Protocol, + Required, + Sequence, + ParamSpec, + TypedDict, + TypeGuard, + final, + override, + runtime_checkable, +) + +import pydantic +import pydantic.generics +from pydantic.fields import FieldInfo + +from ._types import ( + Body, + IncEx, + Query, + ModelT, + Headers, + Timeout, + NotGiven, + AnyMapping, + HttpxRequestFiles, +) +from ._utils import ( + PropertyInfo, + is_list, + is_given, + json_safe, + lru_cache, + is_mapping, + parse_date, + coerce_boolean, + parse_datetime, + strip_not_given, + extract_type_arg, + is_annotated_type, + is_type_alias_type, + strip_annotated_type, +) +from ._compat import ( + PYDANTIC_V2, + ConfigDict, + GenericModel as BaseGenericModel, + get_args, + is_union, + parse_obj, + get_origin, + is_literal_type, + get_model_config, + get_model_fields, + field_get_default, +) +from ._constants import RAW_RESPONSE_HEADER + +if TYPE_CHECKING: + from pydantic_core.core_schema import ModelField, LiteralSchema, ModelFieldsSchema + +__all__ = ["BaseModel", "GenericModel"] + +_T = TypeVar("_T") +_BaseModelT = TypeVar("_BaseModelT", bound="BaseModel") + +P = ParamSpec("P") + +ReprArgs = Sequence[Tuple[Optional[str], Any]] + + +@runtime_checkable +class _ConfigProtocol(Protocol): + allow_population_by_field_name: bool + + +class BaseModel(pydantic.BaseModel): + if PYDANTIC_V2: + model_config: ClassVar[ConfigDict] = ConfigDict( + extra="allow", defer_build=coerce_boolean(os.environ.get("DEFER_PYDANTIC_BUILD", "true")) + ) + else: + + @property + @override + def model_fields_set(self) -> set[str]: + # a forwards-compat shim for pydantic v2 + return self.__fields_set__ # type: ignore + + class Config(pydantic.BaseConfig): # pyright: ignore[reportDeprecated] + extra: Any = pydantic.Extra.allow # type: ignore + + @override + def __repr_args__(self) -> ReprArgs: + # we don't want these attributes to be included when something like `rich.print` is used + return [arg for arg in super().__repr_args__() if arg[0] not in {"_request_id", "__exclude_fields__"}] + + if TYPE_CHECKING: + _request_id: Optional[str] = None + """The ID of the request, returned via the X-Request-ID header. Useful for debugging requests and reporting issues to OpenAI. + + This will **only** be set for the top-level response object, it will not be defined for nested objects. For example: + + ```py + completion = await client.chat.completions.create(...) + completion._request_id # req_id_xxx + completion.usage._request_id # raises `AttributeError` + ``` + + Note: unlike other properties that use an `_` prefix, this property + *is* public. Unless documented otherwise, all other `_` prefix properties, + methods and modules are *private*. + """ + + def to_dict( + self, + *, + mode: Literal["json", "python"] = "python", + use_api_names: bool = True, + exclude_unset: bool = True, + exclude_defaults: bool = False, + exclude_none: bool = False, + warnings: bool = True, + ) -> dict[str, object]: + """Recursively generate a dictionary representation of the model, optionally specifying which fields to include or exclude. + + By default, fields that were not set by the API will not be included, + and keys will match the API response, *not* the property names from the model. + + For example, if the API responds with `"fooBar": true` but we've defined a `foo_bar: bool` property, + the output will use the `"fooBar"` key (unless `use_api_names=False` is passed). + + Args: + mode: + If mode is 'json', the dictionary will only contain JSON serializable types. e.g. `datetime` will be turned into a string, `"2024-3-22T18:11:19.117000Z"`. + If mode is 'python', the dictionary may contain any Python objects. e.g. `datetime(2024, 3, 22)` + + use_api_names: Whether to use the key that the API responded with or the property name. Defaults to `True`. + exclude_unset: Whether to exclude fields that have not been explicitly set. + exclude_defaults: Whether to exclude fields that are set to their default value from the output. + exclude_none: Whether to exclude fields that have a value of `None` from the output. + warnings: Whether to log warnings when invalid fields are encountered. This is only supported in Pydantic v2. + """ + return self.model_dump( + mode=mode, + by_alias=use_api_names, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + warnings=warnings, + ) + + def to_json( + self, + *, + indent: int | None = 2, + use_api_names: bool = True, + exclude_unset: bool = True, + exclude_defaults: bool = False, + exclude_none: bool = False, + warnings: bool = True, + ) -> str: + """Generates a JSON string representing this model as it would be received from or sent to the API (but with indentation). + + By default, fields that were not set by the API will not be included, + and keys will match the API response, *not* the property names from the model. + + For example, if the API responds with `"fooBar": true` but we've defined a `foo_bar: bool` property, + the output will use the `"fooBar"` key (unless `use_api_names=False` is passed). + + Args: + indent: Indentation to use in the JSON output. If `None` is passed, the output will be compact. Defaults to `2` + use_api_names: Whether to use the key that the API responded with or the property name. Defaults to `True`. + exclude_unset: Whether to exclude fields that have not been explicitly set. + exclude_defaults: Whether to exclude fields that have the default value. + exclude_none: Whether to exclude fields that have a value of `None`. + warnings: Whether to show any warnings that occurred during serialization. This is only supported in Pydantic v2. + """ + return self.model_dump_json( + indent=indent, + by_alias=use_api_names, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + warnings=warnings, + ) + + @override + def __str__(self) -> str: + # mypy complains about an invalid self arg + return f"{self.__repr_name__()}({self.__repr_str__(', ')})" # type: ignore[misc] + + # Override the 'construct' method in a way that supports recursive parsing without validation. + # Based on https://github.com/samuelcolvin/pydantic/issues/1168#issuecomment-817742836. + @classmethod + @override + def construct( # pyright: ignore[reportIncompatibleMethodOverride] + __cls: Type[ModelT], + _fields_set: set[str] | None = None, + **values: object, + ) -> ModelT: + m = __cls.__new__(__cls) + fields_values: dict[str, object] = {} + + config = get_model_config(__cls) + populate_by_name = ( + config.allow_population_by_field_name + if isinstance(config, _ConfigProtocol) + else config.get("populate_by_name") + ) + + if _fields_set is None: + _fields_set = set() + + model_fields = get_model_fields(__cls) + for name, field in model_fields.items(): + key = field.alias + if key is None or (key not in values and populate_by_name): + key = name + + if key in values: + fields_values[name] = _construct_field(value=values[key], field=field, key=key) + _fields_set.add(name) + else: + fields_values[name] = field_get_default(field) + + _extra = {} + for key, value in values.items(): + if key not in model_fields: + if PYDANTIC_V2: + _extra[key] = value + else: + _fields_set.add(key) + fields_values[key] = value + + object.__setattr__(m, "__dict__", fields_values) + + if PYDANTIC_V2: + # these properties are copied from Pydantic's `model_construct()` method + object.__setattr__(m, "__pydantic_private__", None) + object.__setattr__(m, "__pydantic_extra__", _extra) + object.__setattr__(m, "__pydantic_fields_set__", _fields_set) + else: + # init_private_attributes() does not exist in v2 + m._init_private_attributes() # type: ignore + + # copied from Pydantic v1's `construct()` method + object.__setattr__(m, "__fields_set__", _fields_set) + + return m + + if not TYPE_CHECKING: + # type checkers incorrectly complain about this assignment + # because the type signatures are technically different + # although not in practice + model_construct = construct + + if not PYDANTIC_V2: + # we define aliases for some of the new pydantic v2 methods so + # that we can just document these methods without having to specify + # a specific pydantic version as some users may not know which + # pydantic version they are currently using + + @override + def model_dump( + self, + *, + mode: Literal["json", "python"] | str = "python", + include: IncEx | None = None, + exclude: IncEx | None = None, + by_alias: bool = False, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + round_trip: bool = False, + warnings: bool | Literal["none", "warn", "error"] = True, + context: dict[str, Any] | None = None, + serialize_as_any: bool = False, + ) -> dict[str, Any]: + """Usage docs: https://docs.pydantic.dev/2.4/concepts/serialization/#modelmodel_dump + + Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. + + Args: + mode: The mode in which `to_python` should run. + If mode is 'json', the dictionary will only contain JSON serializable types. + If mode is 'python', the dictionary may contain any Python objects. + include: A list of fields to include in the output. + exclude: A list of fields to exclude from the output. + by_alias: Whether to use the field's alias in the dictionary key if defined. + exclude_unset: Whether to exclude fields that are unset or None from the output. + exclude_defaults: Whether to exclude fields that are set to their default value from the output. + exclude_none: Whether to exclude fields that have a value of `None` from the output. + round_trip: Whether to enable serialization and deserialization round-trip support. + warnings: Whether to log warnings when invalid fields are encountered. + + Returns: + A dictionary representation of the model. + """ + if mode not in {"json", "python"}: + raise ValueError("mode must be either 'json' or 'python'") + if round_trip != False: + raise ValueError("round_trip is only supported in Pydantic v2") + if warnings != True: + raise ValueError("warnings is only supported in Pydantic v2") + if context is not None: + raise ValueError("context is only supported in Pydantic v2") + if serialize_as_any != False: + raise ValueError("serialize_as_any is only supported in Pydantic v2") + dumped = super().dict( # pyright: ignore[reportDeprecated] + include=include, + exclude=exclude, + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + ) + + return cast(dict[str, Any], json_safe(dumped)) if mode == "json" else dumped + + @override + def model_dump_json( + self, + *, + indent: int | None = None, + include: IncEx | None = None, + exclude: IncEx | None = None, + by_alias: bool = False, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + round_trip: bool = False, + warnings: bool | Literal["none", "warn", "error"] = True, + context: dict[str, Any] | None = None, + serialize_as_any: bool = False, + ) -> str: + """Usage docs: https://docs.pydantic.dev/2.4/concepts/serialization/#modelmodel_dump_json + + Generates a JSON representation of the model using Pydantic's `to_json` method. + + Args: + indent: Indentation to use in the JSON output. If None is passed, the output will be compact. + include: Field(s) to include in the JSON output. Can take either a string or set of strings. + exclude: Field(s) to exclude from the JSON output. Can take either a string or set of strings. + by_alias: Whether to serialize using field aliases. + exclude_unset: Whether to exclude fields that have not been explicitly set. + exclude_defaults: Whether to exclude fields that have the default value. + exclude_none: Whether to exclude fields that have a value of `None`. + round_trip: Whether to use serialization/deserialization between JSON and class instance. + warnings: Whether to show any warnings that occurred during serialization. + + Returns: + A JSON string representation of the model. + """ + if round_trip != False: + raise ValueError("round_trip is only supported in Pydantic v2") + if warnings != True: + raise ValueError("warnings is only supported in Pydantic v2") + if context is not None: + raise ValueError("context is only supported in Pydantic v2") + if serialize_as_any != False: + raise ValueError("serialize_as_any is only supported in Pydantic v2") + return super().json( # type: ignore[reportDeprecated] + indent=indent, + include=include, + exclude=exclude, + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + ) + + +def _construct_field(value: object, field: FieldInfo, key: str) -> object: + if value is None: + return field_get_default(field) + + if PYDANTIC_V2: + type_ = field.annotation + else: + type_ = cast(type, field.outer_type_) # type: ignore + + if type_ is None: + raise RuntimeError(f"Unexpected field type is None for {key}") + + return construct_type(value=value, type_=type_) + + +def is_basemodel(type_: type) -> bool: + """Returns whether or not the given type is either a `BaseModel` or a union of `BaseModel`""" + if is_union(type_): + for variant in get_args(type_): + if is_basemodel(variant): + return True + + return False + + return is_basemodel_type(type_) + + +def is_basemodel_type(type_: type) -> TypeGuard[type[BaseModel] | type[GenericModel]]: + origin = get_origin(type_) or type_ + if not inspect.isclass(origin): + return False + return issubclass(origin, BaseModel) or issubclass(origin, GenericModel) + + +def build( + base_model_cls: Callable[P, _BaseModelT], + *args: P.args, + **kwargs: P.kwargs, +) -> _BaseModelT: + """Construct a BaseModel class without validation. + + This is useful for cases where you need to instantiate a `BaseModel` + from an API response as this provides type-safe params which isn't supported + by helpers like `construct_type()`. + + ```py + build(MyModel, my_field_a="foo", my_field_b=123) + ``` + """ + if args: + raise TypeError( + "Received positional arguments which are not supported; Keyword arguments must be used instead", + ) + + return cast(_BaseModelT, construct_type(type_=base_model_cls, value=kwargs)) + + +def construct_type_unchecked(*, value: object, type_: type[_T]) -> _T: + """Loose coercion to the expected type with construction of nested values. + + Note: the returned value from this function is not guaranteed to match the + given type. + """ + return cast(_T, construct_type(value=value, type_=type_)) + + +def construct_type(*, value: object, type_: object) -> object: + """Loose coercion to the expected type with construction of nested values. + + If the given value does not match the expected type then it is returned as-is. + """ + # we allow `object` as the input type because otherwise, passing things like + # `Literal['value']` will be reported as a type error by type checkers + type_ = cast("type[object]", type_) + if is_type_alias_type(type_): + type_ = type_.__value__ # type: ignore[unreachable] + + # unwrap `Annotated[T, ...]` -> `T` + if is_annotated_type(type_): + meta: tuple[Any, ...] = get_args(type_)[1:] + type_ = extract_type_arg(type_, 0) + else: + meta = tuple() + + # we need to use the origin class for any types that are subscripted generics + # e.g. Dict[str, object] + origin = get_origin(type_) or type_ + args = get_args(type_) + + if is_union(origin): + try: + return validate_type(type_=cast("type[object]", type_), value=value) + except Exception: + pass + + # if the type is a discriminated union then we want to construct the right variant + # in the union, even if the data doesn't match exactly, otherwise we'd break code + # that relies on the constructed class types, e.g. + # + # class FooType: + # kind: Literal['foo'] + # value: str + # + # class BarType: + # kind: Literal['bar'] + # value: int + # + # without this block, if the data we get is something like `{'kind': 'bar', 'value': 'foo'}` then + # we'd end up constructing `FooType` when it should be `BarType`. + discriminator = _build_discriminated_union_meta(union=type_, meta_annotations=meta) + if discriminator and is_mapping(value): + variant_value = value.get(discriminator.field_alias_from or discriminator.field_name) + if variant_value and isinstance(variant_value, str): + variant_type = discriminator.mapping.get(variant_value) + if variant_type: + return construct_type(type_=variant_type, value=value) + + # if the data is not valid, use the first variant that doesn't fail while deserializing + for variant in args: + try: + return construct_type(value=value, type_=variant) + except Exception: + continue + + raise RuntimeError(f"Could not convert data into a valid instance of {type_}") + + if origin == dict: + if not is_mapping(value): + return value + + _, items_type = get_args(type_) # Dict[_, items_type] + return {key: construct_type(value=item, type_=items_type) for key, item in value.items()} + + if ( + not is_literal_type(type_) + and inspect.isclass(origin) + and (issubclass(origin, BaseModel) or issubclass(origin, GenericModel)) + ): + if is_list(value): + return [cast(Any, type_).construct(**entry) if is_mapping(entry) else entry for entry in value] + + if is_mapping(value): + if issubclass(type_, BaseModel): + return type_.construct(**value) # type: ignore[arg-type] + + return cast(Any, type_).construct(**value) + + if origin == list: + if not is_list(value): + return value + + inner_type = args[0] # List[inner_type] + return [construct_type(value=entry, type_=inner_type) for entry in value] + + if origin == float: + if isinstance(value, int): + coerced = float(value) + if coerced != value: + return value + return coerced + + return value + + if type_ == datetime: + try: + return parse_datetime(value) # type: ignore + except Exception: + return value + + if type_ == date: + try: + return parse_date(value) # type: ignore + except Exception: + return value + + return value + + +@runtime_checkable +class CachedDiscriminatorType(Protocol): + __discriminator__: DiscriminatorDetails + + +class DiscriminatorDetails: + field_name: str + """The name of the discriminator field in the variant class, e.g. + + ```py + class Foo(BaseModel): + type: Literal['foo'] + ``` + + Will result in field_name='type' + """ + + field_alias_from: str | None + """The name of the discriminator field in the API response, e.g. + + ```py + class Foo(BaseModel): + type: Literal['foo'] = Field(alias='type_from_api') + ``` + + Will result in field_alias_from='type_from_api' + """ + + mapping: dict[str, type] + """Mapping of discriminator value to variant type, e.g. + + {'foo': FooVariant, 'bar': BarVariant} + """ + + def __init__( + self, + *, + mapping: dict[str, type], + discriminator_field: str, + discriminator_alias: str | None, + ) -> None: + self.mapping = mapping + self.field_name = discriminator_field + self.field_alias_from = discriminator_alias + + +def _build_discriminated_union_meta(*, union: type, meta_annotations: tuple[Any, ...]) -> DiscriminatorDetails | None: + if isinstance(union, CachedDiscriminatorType): + return union.__discriminator__ + + discriminator_field_name: str | None = None + + for annotation in meta_annotations: + if isinstance(annotation, PropertyInfo) and annotation.discriminator is not None: + discriminator_field_name = annotation.discriminator + break + + if not discriminator_field_name: + return None + + mapping: dict[str, type] = {} + discriminator_alias: str | None = None + + for variant in get_args(union): + variant = strip_annotated_type(variant) + if is_basemodel_type(variant): + if PYDANTIC_V2: + field = _extract_field_schema_pv2(variant, discriminator_field_name) + if not field: + continue + + # Note: if one variant defines an alias then they all should + discriminator_alias = field.get("serialization_alias") + + field_schema = field["schema"] + + if field_schema["type"] == "literal": + for entry in cast("LiteralSchema", field_schema)["expected"]: + if isinstance(entry, str): + mapping[entry] = variant + else: + field_info = cast("dict[str, FieldInfo]", variant.__fields__).get(discriminator_field_name) # pyright: ignore[reportDeprecated, reportUnnecessaryCast] + if not field_info: + continue + + # Note: if one variant defines an alias then they all should + discriminator_alias = field_info.alias + + if field_info.annotation and is_literal_type(field_info.annotation): + for entry in get_args(field_info.annotation): + if isinstance(entry, str): + mapping[entry] = variant + + if not mapping: + return None + + details = DiscriminatorDetails( + mapping=mapping, + discriminator_field=discriminator_field_name, + discriminator_alias=discriminator_alias, + ) + cast(CachedDiscriminatorType, union).__discriminator__ = details + return details + + +def _extract_field_schema_pv2(model: type[BaseModel], field_name: str) -> ModelField | None: + schema = model.__pydantic_core_schema__ + if schema["type"] != "model": + return None + + fields_schema = schema["schema"] + if fields_schema["type"] != "model-fields": + return None + + fields_schema = cast("ModelFieldsSchema", fields_schema) + + field = fields_schema["fields"].get(field_name) + if not field: + return None + + return cast("ModelField", field) # pyright: ignore[reportUnnecessaryCast] + + +def validate_type(*, type_: type[_T], value: object) -> _T: + """Strict validation that the given value matches the expected type""" + if inspect.isclass(type_) and issubclass(type_, pydantic.BaseModel): + return cast(_T, parse_obj(type_, value)) + + return cast(_T, _validate_non_model_type(type_=type_, value=value)) + + +def set_pydantic_config(typ: Any, config: pydantic.ConfigDict) -> None: + """Add a pydantic config for the given type. + + Note: this is a no-op on Pydantic v1. + """ + setattr(typ, "__pydantic_config__", config) # noqa: B010 + + +def add_request_id(obj: BaseModel, request_id: str | None) -> None: + obj._request_id = request_id + + # in Pydantic v1, using setattr like we do above causes the attribute + # to be included when serializing the model which we don't want in this + # case so we need to explicitly exclude it + if not PYDANTIC_V2: + try: + exclude_fields = obj.__exclude_fields__ # type: ignore + except AttributeError: + cast(Any, obj).__exclude_fields__ = {"_request_id", "__exclude_fields__"} + else: + cast(Any, obj).__exclude_fields__ = {*(exclude_fields or {}), "_request_id", "__exclude_fields__"} + + +# our use of subclasssing here causes weirdness for type checkers, +# so we just pretend that we don't subclass +if TYPE_CHECKING: + GenericModel = BaseModel +else: + + class GenericModel(BaseGenericModel, BaseModel): + pass + + +if PYDANTIC_V2: + from pydantic import TypeAdapter as _TypeAdapter + + _CachedTypeAdapter = cast("TypeAdapter[object]", lru_cache(maxsize=None)(_TypeAdapter)) + + if TYPE_CHECKING: + from pydantic import TypeAdapter + else: + TypeAdapter = _CachedTypeAdapter + + def _validate_non_model_type(*, type_: type[_T], value: object) -> _T: + return TypeAdapter(type_).validate_python(value) + +elif not TYPE_CHECKING: # TODO: condition is weird + + class RootModel(GenericModel, Generic[_T]): + """Used as a placeholder to easily convert runtime types to a Pydantic format + to provide validation. + + For example: + ```py + validated = RootModel[int](__root__="5").__root__ + # validated: 5 + ``` + """ + + __root__: _T + + def _validate_non_model_type(*, type_: type[_T], value: object) -> _T: + model = _create_pydantic_model(type_).validate(value) + return cast(_T, model.__root__) + + def _create_pydantic_model(type_: _T) -> Type[RootModel[_T]]: + return RootModel[type_] # type: ignore + + +class FinalRequestOptionsInput(TypedDict, total=False): + method: Required[str] + url: Required[str] + params: Query + headers: Headers + max_retries: int + timeout: float | Timeout | None + files: HttpxRequestFiles | None + idempotency_key: str + json_data: Body + extra_json: AnyMapping + + +@final +class FinalRequestOptions(pydantic.BaseModel): + method: str + url: str + params: Query = {} + headers: Union[Headers, NotGiven] = NotGiven() + max_retries: Union[int, NotGiven] = NotGiven() + timeout: Union[float, Timeout, None, NotGiven] = NotGiven() + files: Union[HttpxRequestFiles, None] = None + idempotency_key: Union[str, None] = None + post_parser: Union[Callable[[Any], Any], NotGiven] = NotGiven() + + # It should be noted that we cannot use `json` here as that would override + # a BaseModel method in an incompatible fashion. + json_data: Union[Body, None] = None + extra_json: Union[AnyMapping, None] = None + + if PYDANTIC_V2: + model_config: ClassVar[ConfigDict] = ConfigDict(arbitrary_types_allowed=True) + else: + + class Config(pydantic.BaseConfig): # pyright: ignore[reportDeprecated] + arbitrary_types_allowed: bool = True + + def get_max_retries(self, max_retries: int) -> int: + if isinstance(self.max_retries, NotGiven): + return max_retries + return self.max_retries + + def _strip_raw_response_header(self) -> None: + if not is_given(self.headers): + return + + if self.headers.get(RAW_RESPONSE_HEADER): + self.headers = {**self.headers} + self.headers.pop(RAW_RESPONSE_HEADER) + + # override the `construct` method so that we can run custom transformations. + # this is necessary as we don't want to do any actual runtime type checking + # (which means we can't use validators) but we do want to ensure that `NotGiven` + # values are not present + # + # type ignore required because we're adding explicit types to `**values` + @classmethod + def construct( # type: ignore + cls, + _fields_set: set[str] | None = None, + **values: Unpack[FinalRequestOptionsInput], + ) -> FinalRequestOptions: + kwargs: dict[str, Any] = { + # we unconditionally call `strip_not_given` on any value + # as it will just ignore any non-mapping types + key: strip_not_given(value) + for key, value in values.items() + } + if PYDANTIC_V2: + return super().model_construct(_fields_set, **kwargs) + return cast(FinalRequestOptions, super().construct(_fields_set, **kwargs)) # pyright: ignore[reportDeprecated] + + if not TYPE_CHECKING: + # type checkers incorrectly complain about this assignment + model_construct = construct diff --git a/.venv/lib/python3.11/site-packages/openai/_module_client.py b/.venv/lib/python3.11/site-packages/openai/_module_client.py new file mode 100644 index 0000000000000000000000000000000000000000..6f7356eb3c85a709e970e18a2c91c16429dc8822 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_module_client.py @@ -0,0 +1,85 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import override + +from . import resources, _load_client +from ._utils import LazyProxy + + +class ChatProxy(LazyProxy[resources.Chat]): + @override + def __load__(self) -> resources.Chat: + return _load_client().chat + + +class BetaProxy(LazyProxy[resources.Beta]): + @override + def __load__(self) -> resources.Beta: + return _load_client().beta + + +class FilesProxy(LazyProxy[resources.Files]): + @override + def __load__(self) -> resources.Files: + return _load_client().files + + +class AudioProxy(LazyProxy[resources.Audio]): + @override + def __load__(self) -> resources.Audio: + return _load_client().audio + + +class ImagesProxy(LazyProxy[resources.Images]): + @override + def __load__(self) -> resources.Images: + return _load_client().images + + +class ModelsProxy(LazyProxy[resources.Models]): + @override + def __load__(self) -> resources.Models: + return _load_client().models + + +class BatchesProxy(LazyProxy[resources.Batches]): + @override + def __load__(self) -> resources.Batches: + return _load_client().batches + + +class EmbeddingsProxy(LazyProxy[resources.Embeddings]): + @override + def __load__(self) -> resources.Embeddings: + return _load_client().embeddings + + +class CompletionsProxy(LazyProxy[resources.Completions]): + @override + def __load__(self) -> resources.Completions: + return _load_client().completions + + +class ModerationsProxy(LazyProxy[resources.Moderations]): + @override + def __load__(self) -> resources.Moderations: + return _load_client().moderations + + +class FineTuningProxy(LazyProxy[resources.FineTuning]): + @override + def __load__(self) -> resources.FineTuning: + return _load_client().fine_tuning + + +chat: resources.Chat = ChatProxy().__as_proxied__() +beta: resources.Beta = BetaProxy().__as_proxied__() +files: resources.Files = FilesProxy().__as_proxied__() +audio: resources.Audio = AudioProxy().__as_proxied__() +images: resources.Images = ImagesProxy().__as_proxied__() +models: resources.Models = ModelsProxy().__as_proxied__() +batches: resources.Batches = BatchesProxy().__as_proxied__() +embeddings: resources.Embeddings = EmbeddingsProxy().__as_proxied__() +completions: resources.Completions = CompletionsProxy().__as_proxied__() +moderations: resources.Moderations = ModerationsProxy().__as_proxied__() +fine_tuning: resources.FineTuning = FineTuningProxy().__as_proxied__() diff --git a/.venv/lib/python3.11/site-packages/openai/_qs.py b/.venv/lib/python3.11/site-packages/openai/_qs.py new file mode 100644 index 0000000000000000000000000000000000000000..274320ca5ee8fe4200ac4e10cb4809b773dd1e18 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_qs.py @@ -0,0 +1,150 @@ +from __future__ import annotations + +from typing import Any, List, Tuple, Union, Mapping, TypeVar +from urllib.parse import parse_qs, urlencode +from typing_extensions import Literal, get_args + +from ._types import NOT_GIVEN, NotGiven, NotGivenOr +from ._utils import flatten + +_T = TypeVar("_T") + + +ArrayFormat = Literal["comma", "repeat", "indices", "brackets"] +NestedFormat = Literal["dots", "brackets"] + +PrimitiveData = Union[str, int, float, bool, None] +# this should be Data = Union[PrimitiveData, "List[Data]", "Tuple[Data]", "Mapping[str, Data]"] +# https://github.com/microsoft/pyright/issues/3555 +Data = Union[PrimitiveData, List[Any], Tuple[Any], "Mapping[str, Any]"] +Params = Mapping[str, Data] + + +class Querystring: + array_format: ArrayFormat + nested_format: NestedFormat + + def __init__( + self, + *, + array_format: ArrayFormat = "repeat", + nested_format: NestedFormat = "brackets", + ) -> None: + self.array_format = array_format + self.nested_format = nested_format + + def parse(self, query: str) -> Mapping[str, object]: + # Note: custom format syntax is not supported yet + return parse_qs(query) + + def stringify( + self, + params: Params, + *, + array_format: NotGivenOr[ArrayFormat] = NOT_GIVEN, + nested_format: NotGivenOr[NestedFormat] = NOT_GIVEN, + ) -> str: + return urlencode( + self.stringify_items( + params, + array_format=array_format, + nested_format=nested_format, + ) + ) + + def stringify_items( + self, + params: Params, + *, + array_format: NotGivenOr[ArrayFormat] = NOT_GIVEN, + nested_format: NotGivenOr[NestedFormat] = NOT_GIVEN, + ) -> list[tuple[str, str]]: + opts = Options( + qs=self, + array_format=array_format, + nested_format=nested_format, + ) + return flatten([self._stringify_item(key, value, opts) for key, value in params.items()]) + + def _stringify_item( + self, + key: str, + value: Data, + opts: Options, + ) -> list[tuple[str, str]]: + if isinstance(value, Mapping): + items: list[tuple[str, str]] = [] + nested_format = opts.nested_format + for subkey, subvalue in value.items(): + items.extend( + self._stringify_item( + # TODO: error if unknown format + f"{key}.{subkey}" if nested_format == "dots" else f"{key}[{subkey}]", + subvalue, + opts, + ) + ) + return items + + if isinstance(value, (list, tuple)): + array_format = opts.array_format + if array_format == "comma": + return [ + ( + key, + ",".join(self._primitive_value_to_str(item) for item in value if item is not None), + ), + ] + elif array_format == "repeat": + items = [] + for item in value: + items.extend(self._stringify_item(key, item, opts)) + return items + elif array_format == "indices": + raise NotImplementedError("The array indices format is not supported yet") + elif array_format == "brackets": + items = [] + key = key + "[]" + for item in value: + items.extend(self._stringify_item(key, item, opts)) + return items + else: + raise NotImplementedError( + f"Unknown array_format value: {array_format}, choose from {', '.join(get_args(ArrayFormat))}" + ) + + serialised = self._primitive_value_to_str(value) + if not serialised: + return [] + return [(key, serialised)] + + def _primitive_value_to_str(self, value: PrimitiveData) -> str: + # copied from httpx + if value is True: + return "true" + elif value is False: + return "false" + elif value is None: + return "" + return str(value) + + +_qs = Querystring() +parse = _qs.parse +stringify = _qs.stringify +stringify_items = _qs.stringify_items + + +class Options: + array_format: ArrayFormat + nested_format: NestedFormat + + def __init__( + self, + qs: Querystring = _qs, + *, + array_format: NotGivenOr[ArrayFormat] = NOT_GIVEN, + nested_format: NotGivenOr[NestedFormat] = NOT_GIVEN, + ) -> None: + self.array_format = qs.array_format if isinstance(array_format, NotGiven) else array_format + self.nested_format = qs.nested_format if isinstance(nested_format, NotGiven) else nested_format diff --git a/.venv/lib/python3.11/site-packages/openai/_resource.py b/.venv/lib/python3.11/site-packages/openai/_resource.py new file mode 100644 index 0000000000000000000000000000000000000000..fff9ba19c3400201ae15f73d4df89ce48572b8ef --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_resource.py @@ -0,0 +1,43 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +import time +from typing import TYPE_CHECKING + +import anyio + +if TYPE_CHECKING: + from ._client import OpenAI, AsyncOpenAI + + +class SyncAPIResource: + _client: OpenAI + + def __init__(self, client: OpenAI) -> None: + self._client = client + self._get = client.get + self._post = client.post + self._patch = client.patch + self._put = client.put + self._delete = client.delete + self._get_api_list = client.get_api_list + + def _sleep(self, seconds: float) -> None: + time.sleep(seconds) + + +class AsyncAPIResource: + _client: AsyncOpenAI + + def __init__(self, client: AsyncOpenAI) -> None: + self._client = client + self._get = client.get + self._post = client.post + self._patch = client.patch + self._put = client.put + self._delete = client.delete + self._get_api_list = client.get_api_list + + async def _sleep(self, seconds: float) -> None: + await anyio.sleep(seconds) diff --git a/.venv/lib/python3.11/site-packages/openai/_response.py b/.venv/lib/python3.11/site-packages/openai/_response.py new file mode 100644 index 0000000000000000000000000000000000000000..95e94e6537848c7c87206507edbc66b72b8bbd25 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_response.py @@ -0,0 +1,848 @@ +from __future__ import annotations + +import os +import inspect +import logging +import datetime +import functools +from types import TracebackType +from typing import ( + TYPE_CHECKING, + Any, + Union, + Generic, + TypeVar, + Callable, + Iterator, + AsyncIterator, + cast, + overload, +) +from typing_extensions import Awaitable, ParamSpec, override, get_origin + +import anyio +import httpx +import pydantic + +from ._types import NoneType +from ._utils import is_given, extract_type_arg, is_annotated_type, is_type_alias_type, extract_type_var_from_base +from ._models import BaseModel, is_basemodel, add_request_id +from ._constants import RAW_RESPONSE_HEADER, OVERRIDE_CAST_TO_HEADER +from ._streaming import Stream, AsyncStream, is_stream_class_type, extract_stream_chunk_type +from ._exceptions import OpenAIError, APIResponseValidationError + +if TYPE_CHECKING: + from ._models import FinalRequestOptions + from ._base_client import BaseClient + + +P = ParamSpec("P") +R = TypeVar("R") +_T = TypeVar("_T") +_APIResponseT = TypeVar("_APIResponseT", bound="APIResponse[Any]") +_AsyncAPIResponseT = TypeVar("_AsyncAPIResponseT", bound="AsyncAPIResponse[Any]") + +log: logging.Logger = logging.getLogger(__name__) + + +class BaseAPIResponse(Generic[R]): + _cast_to: type[R] + _client: BaseClient[Any, Any] + _parsed_by_type: dict[type[Any], Any] + _is_sse_stream: bool + _stream_cls: type[Stream[Any]] | type[AsyncStream[Any]] | None + _options: FinalRequestOptions + + http_response: httpx.Response + + retries_taken: int + """The number of retries made. If no retries happened this will be `0`""" + + def __init__( + self, + *, + raw: httpx.Response, + cast_to: type[R], + client: BaseClient[Any, Any], + stream: bool, + stream_cls: type[Stream[Any]] | type[AsyncStream[Any]] | None, + options: FinalRequestOptions, + retries_taken: int = 0, + ) -> None: + self._cast_to = cast_to + self._client = client + self._parsed_by_type = {} + self._is_sse_stream = stream + self._stream_cls = stream_cls + self._options = options + self.http_response = raw + self.retries_taken = retries_taken + + @property + def headers(self) -> httpx.Headers: + return self.http_response.headers + + @property + def http_request(self) -> httpx.Request: + """Returns the httpx Request instance associated with the current response.""" + return self.http_response.request + + @property + def status_code(self) -> int: + return self.http_response.status_code + + @property + def url(self) -> httpx.URL: + """Returns the URL for which the request was made.""" + return self.http_response.url + + @property + def method(self) -> str: + return self.http_request.method + + @property + def http_version(self) -> str: + return self.http_response.http_version + + @property + def elapsed(self) -> datetime.timedelta: + """The time taken for the complete request/response cycle to complete.""" + return self.http_response.elapsed + + @property + def is_closed(self) -> bool: + """Whether or not the response body has been closed. + + If this is False then there is response data that has not been read yet. + You must either fully consume the response body or call `.close()` + before discarding the response to prevent resource leaks. + """ + return self.http_response.is_closed + + @override + def __repr__(self) -> str: + return ( + f"<{self.__class__.__name__} [{self.status_code} {self.http_response.reason_phrase}] type={self._cast_to}>" + ) + + def _parse(self, *, to: type[_T] | None = None) -> R | _T: + cast_to = to if to is not None else self._cast_to + + # unwrap `TypeAlias('Name', T)` -> `T` + if is_type_alias_type(cast_to): + cast_to = cast_to.__value__ # type: ignore[unreachable] + + # unwrap `Annotated[T, ...]` -> `T` + if cast_to and is_annotated_type(cast_to): + cast_to = extract_type_arg(cast_to, 0) + + origin = get_origin(cast_to) or cast_to + + if self._is_sse_stream: + if to: + if not is_stream_class_type(to): + raise TypeError(f"Expected custom parse type to be a subclass of {Stream} or {AsyncStream}") + + return cast( + _T, + to( + cast_to=extract_stream_chunk_type( + to, + failure_message="Expected custom stream type to be passed with a type argument, e.g. Stream[ChunkType]", + ), + response=self.http_response, + client=cast(Any, self._client), + ), + ) + + if self._stream_cls: + return cast( + R, + self._stream_cls( + cast_to=extract_stream_chunk_type(self._stream_cls), + response=self.http_response, + client=cast(Any, self._client), + ), + ) + + stream_cls = cast("type[Stream[Any]] | type[AsyncStream[Any]] | None", self._client._default_stream_cls) + if stream_cls is None: + raise MissingStreamClassError() + + return cast( + R, + stream_cls( + cast_to=cast_to, + response=self.http_response, + client=cast(Any, self._client), + ), + ) + + if cast_to is NoneType: + return cast(R, None) + + response = self.http_response + if cast_to == str: + return cast(R, response.text) + + if cast_to == bytes: + return cast(R, response.content) + + if cast_to == int: + return cast(R, int(response.text)) + + if cast_to == float: + return cast(R, float(response.text)) + + if cast_to == bool: + return cast(R, response.text.lower() == "true") + + # handle the legacy binary response case + if inspect.isclass(cast_to) and cast_to.__name__ == "HttpxBinaryResponseContent": + return cast(R, cast_to(response)) # type: ignore + + if origin == APIResponse: + raise RuntimeError("Unexpected state - cast_to is `APIResponse`") + + if inspect.isclass(origin) and issubclass(origin, httpx.Response): + # Because of the invariance of our ResponseT TypeVar, users can subclass httpx.Response + # and pass that class to our request functions. We cannot change the variance to be either + # covariant or contravariant as that makes our usage of ResponseT illegal. We could construct + # the response class ourselves but that is something that should be supported directly in httpx + # as it would be easy to incorrectly construct the Response object due to the multitude of arguments. + if cast_to != httpx.Response: + raise ValueError(f"Subclasses of httpx.Response cannot be passed to `cast_to`") + return cast(R, response) + + if ( + inspect.isclass( + origin # pyright: ignore[reportUnknownArgumentType] + ) + and not issubclass(origin, BaseModel) + and issubclass(origin, pydantic.BaseModel) + ): + raise TypeError("Pydantic models must subclass our base model type, e.g. `from openai import BaseModel`") + + if ( + cast_to is not object + and not origin is list + and not origin is dict + and not origin is Union + and not issubclass(origin, BaseModel) + ): + raise RuntimeError( + f"Unsupported type, expected {cast_to} to be a subclass of {BaseModel}, {dict}, {list}, {Union}, {NoneType}, {str} or {httpx.Response}." + ) + + # split is required to handle cases where additional information is included + # in the response, e.g. application/json; charset=utf-8 + content_type, *_ = response.headers.get("content-type", "*").split(";") + if content_type != "application/json": + if is_basemodel(cast_to): + try: + data = response.json() + except Exception as exc: + log.debug("Could not read JSON from response data due to %s - %s", type(exc), exc) + else: + return self._client._process_response_data( + data=data, + cast_to=cast_to, # type: ignore + response=response, + ) + + if self._client._strict_response_validation: + raise APIResponseValidationError( + response=response, + message=f"Expected Content-Type response header to be `application/json` but received `{content_type}` instead.", + body=response.text, + ) + + # If the API responds with content that isn't JSON then we just return + # the (decoded) text without performing any parsing so that you can still + # handle the response however you need to. + return response.text # type: ignore + + data = response.json() + + return self._client._process_response_data( + data=data, + cast_to=cast_to, # type: ignore + response=response, + ) + + +class APIResponse(BaseAPIResponse[R]): + @property + def request_id(self) -> str | None: + return self.http_response.headers.get("x-request-id") # type: ignore[no-any-return] + + @overload + def parse(self, *, to: type[_T]) -> _T: ... + + @overload + def parse(self) -> R: ... + + def parse(self, *, to: type[_T] | None = None) -> R | _T: + """Returns the rich python representation of this response's data. + + For lower-level control, see `.read()`, `.json()`, `.iter_bytes()`. + + You can customise the type that the response is parsed into through + the `to` argument, e.g. + + ```py + from openai import BaseModel + + + class MyModel(BaseModel): + foo: str + + + obj = response.parse(to=MyModel) + print(obj.foo) + ``` + + We support parsing: + - `BaseModel` + - `dict` + - `list` + - `Union` + - `str` + - `int` + - `float` + - `httpx.Response` + """ + cache_key = to if to is not None else self._cast_to + cached = self._parsed_by_type.get(cache_key) + if cached is not None: + return cached # type: ignore[no-any-return] + + if not self._is_sse_stream: + self.read() + + parsed = self._parse(to=to) + if is_given(self._options.post_parser): + parsed = self._options.post_parser(parsed) + + if isinstance(parsed, BaseModel): + add_request_id(parsed, self.request_id) + + self._parsed_by_type[cache_key] = parsed + return cast(R, parsed) + + def read(self) -> bytes: + """Read and return the binary response content.""" + try: + return self.http_response.read() + except httpx.StreamConsumed as exc: + # The default error raised by httpx isn't very + # helpful in our case so we re-raise it with + # a different error message. + raise StreamAlreadyConsumed() from exc + + def text(self) -> str: + """Read and decode the response content into a string.""" + self.read() + return self.http_response.text + + def json(self) -> object: + """Read and decode the JSON response content.""" + self.read() + return self.http_response.json() + + def close(self) -> None: + """Close the response and release the connection. + + Automatically called if the response body is read to completion. + """ + self.http_response.close() + + def iter_bytes(self, chunk_size: int | None = None) -> Iterator[bytes]: + """ + A byte-iterator over the decoded response content. + + This automatically handles gzip, deflate and brotli encoded responses. + """ + for chunk in self.http_response.iter_bytes(chunk_size): + yield chunk + + def iter_text(self, chunk_size: int | None = None) -> Iterator[str]: + """A str-iterator over the decoded response content + that handles both gzip, deflate, etc but also detects the content's + string encoding. + """ + for chunk in self.http_response.iter_text(chunk_size): + yield chunk + + def iter_lines(self) -> Iterator[str]: + """Like `iter_text()` but will only yield chunks for each line""" + for chunk in self.http_response.iter_lines(): + yield chunk + + +class AsyncAPIResponse(BaseAPIResponse[R]): + @property + def request_id(self) -> str | None: + return self.http_response.headers.get("x-request-id") # type: ignore[no-any-return] + + @overload + async def parse(self, *, to: type[_T]) -> _T: ... + + @overload + async def parse(self) -> R: ... + + async def parse(self, *, to: type[_T] | None = None) -> R | _T: + """Returns the rich python representation of this response's data. + + For lower-level control, see `.read()`, `.json()`, `.iter_bytes()`. + + You can customise the type that the response is parsed into through + the `to` argument, e.g. + + ```py + from openai import BaseModel + + + class MyModel(BaseModel): + foo: str + + + obj = response.parse(to=MyModel) + print(obj.foo) + ``` + + We support parsing: + - `BaseModel` + - `dict` + - `list` + - `Union` + - `str` + - `httpx.Response` + """ + cache_key = to if to is not None else self._cast_to + cached = self._parsed_by_type.get(cache_key) + if cached is not None: + return cached # type: ignore[no-any-return] + + if not self._is_sse_stream: + await self.read() + + parsed = self._parse(to=to) + if is_given(self._options.post_parser): + parsed = self._options.post_parser(parsed) + + if isinstance(parsed, BaseModel): + add_request_id(parsed, self.request_id) + + self._parsed_by_type[cache_key] = parsed + return cast(R, parsed) + + async def read(self) -> bytes: + """Read and return the binary response content.""" + try: + return await self.http_response.aread() + except httpx.StreamConsumed as exc: + # the default error raised by httpx isn't very + # helpful in our case so we re-raise it with + # a different error message + raise StreamAlreadyConsumed() from exc + + async def text(self) -> str: + """Read and decode the response content into a string.""" + await self.read() + return self.http_response.text + + async def json(self) -> object: + """Read and decode the JSON response content.""" + await self.read() + return self.http_response.json() + + async def close(self) -> None: + """Close the response and release the connection. + + Automatically called if the response body is read to completion. + """ + await self.http_response.aclose() + + async def iter_bytes(self, chunk_size: int | None = None) -> AsyncIterator[bytes]: + """ + A byte-iterator over the decoded response content. + + This automatically handles gzip, deflate and brotli encoded responses. + """ + async for chunk in self.http_response.aiter_bytes(chunk_size): + yield chunk + + async def iter_text(self, chunk_size: int | None = None) -> AsyncIterator[str]: + """A str-iterator over the decoded response content + that handles both gzip, deflate, etc but also detects the content's + string encoding. + """ + async for chunk in self.http_response.aiter_text(chunk_size): + yield chunk + + async def iter_lines(self) -> AsyncIterator[str]: + """Like `iter_text()` but will only yield chunks for each line""" + async for chunk in self.http_response.aiter_lines(): + yield chunk + + +class BinaryAPIResponse(APIResponse[bytes]): + """Subclass of APIResponse providing helpers for dealing with binary data. + + Note: If you want to stream the response data instead of eagerly reading it + all at once then you should use `.with_streaming_response` when making + the API request, e.g. `.with_streaming_response.get_binary_response()` + """ + + def write_to_file( + self, + file: str | os.PathLike[str], + ) -> None: + """Write the output to the given file. + + Accepts a filename or any path-like object, e.g. pathlib.Path + + Note: if you want to stream the data to the file instead of writing + all at once then you should use `.with_streaming_response` when making + the API request, e.g. `.with_streaming_response.get_binary_response()` + """ + with open(file, mode="wb") as f: + for data in self.iter_bytes(): + f.write(data) + + +class AsyncBinaryAPIResponse(AsyncAPIResponse[bytes]): + """Subclass of APIResponse providing helpers for dealing with binary data. + + Note: If you want to stream the response data instead of eagerly reading it + all at once then you should use `.with_streaming_response` when making + the API request, e.g. `.with_streaming_response.get_binary_response()` + """ + + async def write_to_file( + self, + file: str | os.PathLike[str], + ) -> None: + """Write the output to the given file. + + Accepts a filename or any path-like object, e.g. pathlib.Path + + Note: if you want to stream the data to the file instead of writing + all at once then you should use `.with_streaming_response` when making + the API request, e.g. `.with_streaming_response.get_binary_response()` + """ + path = anyio.Path(file) + async with await path.open(mode="wb") as f: + async for data in self.iter_bytes(): + await f.write(data) + + +class StreamedBinaryAPIResponse(APIResponse[bytes]): + def stream_to_file( + self, + file: str | os.PathLike[str], + *, + chunk_size: int | None = None, + ) -> None: + """Streams the output to the given file. + + Accepts a filename or any path-like object, e.g. pathlib.Path + """ + with open(file, mode="wb") as f: + for data in self.iter_bytes(chunk_size): + f.write(data) + + +class AsyncStreamedBinaryAPIResponse(AsyncAPIResponse[bytes]): + async def stream_to_file( + self, + file: str | os.PathLike[str], + *, + chunk_size: int | None = None, + ) -> None: + """Streams the output to the given file. + + Accepts a filename or any path-like object, e.g. pathlib.Path + """ + path = anyio.Path(file) + async with await path.open(mode="wb") as f: + async for data in self.iter_bytes(chunk_size): + await f.write(data) + + +class MissingStreamClassError(TypeError): + def __init__(self) -> None: + super().__init__( + "The `stream` argument was set to `True` but the `stream_cls` argument was not given. See `openai._streaming` for reference", + ) + + +class StreamAlreadyConsumed(OpenAIError): + """ + Attempted to read or stream content, but the content has already + been streamed. + + This can happen if you use a method like `.iter_lines()` and then attempt + to read th entire response body afterwards, e.g. + + ```py + response = await client.post(...) + async for line in response.iter_lines(): + ... # do something with `line` + + content = await response.read() + # ^ error + ``` + + If you want this behaviour you'll need to either manually accumulate the response + content or call `await response.read()` before iterating over the stream. + """ + + def __init__(self) -> None: + message = ( + "Attempted to read or stream some content, but the content has " + "already been streamed. " + "This could be due to attempting to stream the response " + "content more than once." + "\n\n" + "You can fix this by manually accumulating the response content while streaming " + "or by calling `.read()` before starting to stream." + ) + super().__init__(message) + + +class ResponseContextManager(Generic[_APIResponseT]): + """Context manager for ensuring that a request is not made + until it is entered and that the response will always be closed + when the context manager exits + """ + + def __init__(self, request_func: Callable[[], _APIResponseT]) -> None: + self._request_func = request_func + self.__response: _APIResponseT | None = None + + def __enter__(self) -> _APIResponseT: + self.__response = self._request_func() + return self.__response + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: + if self.__response is not None: + self.__response.close() + + +class AsyncResponseContextManager(Generic[_AsyncAPIResponseT]): + """Context manager for ensuring that a request is not made + until it is entered and that the response will always be closed + when the context manager exits + """ + + def __init__(self, api_request: Awaitable[_AsyncAPIResponseT]) -> None: + self._api_request = api_request + self.__response: _AsyncAPIResponseT | None = None + + async def __aenter__(self) -> _AsyncAPIResponseT: + self.__response = await self._api_request + return self.__response + + async def __aexit__( + self, + exc_type: type[BaseException] | None, + exc: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: + if self.__response is not None: + await self.__response.close() + + +def to_streamed_response_wrapper(func: Callable[P, R]) -> Callable[P, ResponseContextManager[APIResponse[R]]]: + """Higher order function that takes one of our bound API methods and wraps it + to support streaming and returning the raw `APIResponse` object directly. + """ + + @functools.wraps(func) + def wrapped(*args: P.args, **kwargs: P.kwargs) -> ResponseContextManager[APIResponse[R]]: + extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})} + extra_headers[RAW_RESPONSE_HEADER] = "stream" + + kwargs["extra_headers"] = extra_headers + + make_request = functools.partial(func, *args, **kwargs) + + return ResponseContextManager(cast(Callable[[], APIResponse[R]], make_request)) + + return wrapped + + +def async_to_streamed_response_wrapper( + func: Callable[P, Awaitable[R]], +) -> Callable[P, AsyncResponseContextManager[AsyncAPIResponse[R]]]: + """Higher order function that takes one of our bound API methods and wraps it + to support streaming and returning the raw `APIResponse` object directly. + """ + + @functools.wraps(func) + def wrapped(*args: P.args, **kwargs: P.kwargs) -> AsyncResponseContextManager[AsyncAPIResponse[R]]: + extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})} + extra_headers[RAW_RESPONSE_HEADER] = "stream" + + kwargs["extra_headers"] = extra_headers + + make_request = func(*args, **kwargs) + + return AsyncResponseContextManager(cast(Awaitable[AsyncAPIResponse[R]], make_request)) + + return wrapped + + +def to_custom_streamed_response_wrapper( + func: Callable[P, object], + response_cls: type[_APIResponseT], +) -> Callable[P, ResponseContextManager[_APIResponseT]]: + """Higher order function that takes one of our bound API methods and an `APIResponse` class + and wraps the method to support streaming and returning the given response class directly. + + Note: the given `response_cls` *must* be concrete, e.g. `class BinaryAPIResponse(APIResponse[bytes])` + """ + + @functools.wraps(func) + def wrapped(*args: P.args, **kwargs: P.kwargs) -> ResponseContextManager[_APIResponseT]: + extra_headers: dict[str, Any] = {**(cast(Any, kwargs.get("extra_headers")) or {})} + extra_headers[RAW_RESPONSE_HEADER] = "stream" + extra_headers[OVERRIDE_CAST_TO_HEADER] = response_cls + + kwargs["extra_headers"] = extra_headers + + make_request = functools.partial(func, *args, **kwargs) + + return ResponseContextManager(cast(Callable[[], _APIResponseT], make_request)) + + return wrapped + + +def async_to_custom_streamed_response_wrapper( + func: Callable[P, Awaitable[object]], + response_cls: type[_AsyncAPIResponseT], +) -> Callable[P, AsyncResponseContextManager[_AsyncAPIResponseT]]: + """Higher order function that takes one of our bound API methods and an `APIResponse` class + and wraps the method to support streaming and returning the given response class directly. + + Note: the given `response_cls` *must* be concrete, e.g. `class BinaryAPIResponse(APIResponse[bytes])` + """ + + @functools.wraps(func) + def wrapped(*args: P.args, **kwargs: P.kwargs) -> AsyncResponseContextManager[_AsyncAPIResponseT]: + extra_headers: dict[str, Any] = {**(cast(Any, kwargs.get("extra_headers")) or {})} + extra_headers[RAW_RESPONSE_HEADER] = "stream" + extra_headers[OVERRIDE_CAST_TO_HEADER] = response_cls + + kwargs["extra_headers"] = extra_headers + + make_request = func(*args, **kwargs) + + return AsyncResponseContextManager(cast(Awaitable[_AsyncAPIResponseT], make_request)) + + return wrapped + + +def to_raw_response_wrapper(func: Callable[P, R]) -> Callable[P, APIResponse[R]]: + """Higher order function that takes one of our bound API methods and wraps it + to support returning the raw `APIResponse` object directly. + """ + + @functools.wraps(func) + def wrapped(*args: P.args, **kwargs: P.kwargs) -> APIResponse[R]: + extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})} + extra_headers[RAW_RESPONSE_HEADER] = "raw" + + kwargs["extra_headers"] = extra_headers + + return cast(APIResponse[R], func(*args, **kwargs)) + + return wrapped + + +def async_to_raw_response_wrapper(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[AsyncAPIResponse[R]]]: + """Higher order function that takes one of our bound API methods and wraps it + to support returning the raw `APIResponse` object directly. + """ + + @functools.wraps(func) + async def wrapped(*args: P.args, **kwargs: P.kwargs) -> AsyncAPIResponse[R]: + extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})} + extra_headers[RAW_RESPONSE_HEADER] = "raw" + + kwargs["extra_headers"] = extra_headers + + return cast(AsyncAPIResponse[R], await func(*args, **kwargs)) + + return wrapped + + +def to_custom_raw_response_wrapper( + func: Callable[P, object], + response_cls: type[_APIResponseT], +) -> Callable[P, _APIResponseT]: + """Higher order function that takes one of our bound API methods and an `APIResponse` class + and wraps the method to support returning the given response class directly. + + Note: the given `response_cls` *must* be concrete, e.g. `class BinaryAPIResponse(APIResponse[bytes])` + """ + + @functools.wraps(func) + def wrapped(*args: P.args, **kwargs: P.kwargs) -> _APIResponseT: + extra_headers: dict[str, Any] = {**(cast(Any, kwargs.get("extra_headers")) or {})} + extra_headers[RAW_RESPONSE_HEADER] = "raw" + extra_headers[OVERRIDE_CAST_TO_HEADER] = response_cls + + kwargs["extra_headers"] = extra_headers + + return cast(_APIResponseT, func(*args, **kwargs)) + + return wrapped + + +def async_to_custom_raw_response_wrapper( + func: Callable[P, Awaitable[object]], + response_cls: type[_AsyncAPIResponseT], +) -> Callable[P, Awaitable[_AsyncAPIResponseT]]: + """Higher order function that takes one of our bound API methods and an `APIResponse` class + and wraps the method to support returning the given response class directly. + + Note: the given `response_cls` *must* be concrete, e.g. `class BinaryAPIResponse(APIResponse[bytes])` + """ + + @functools.wraps(func) + def wrapped(*args: P.args, **kwargs: P.kwargs) -> Awaitable[_AsyncAPIResponseT]: + extra_headers: dict[str, Any] = {**(cast(Any, kwargs.get("extra_headers")) or {})} + extra_headers[RAW_RESPONSE_HEADER] = "raw" + extra_headers[OVERRIDE_CAST_TO_HEADER] = response_cls + + kwargs["extra_headers"] = extra_headers + + return cast(Awaitable[_AsyncAPIResponseT], func(*args, **kwargs)) + + return wrapped + + +def extract_response_type(typ: type[BaseAPIResponse[Any]]) -> type: + """Given a type like `APIResponse[T]`, returns the generic type variable `T`. + + This also handles the case where a concrete subclass is given, e.g. + ```py + class MyResponse(APIResponse[bytes]): + ... + + extract_response_type(MyResponse) -> bytes + ``` + """ + return extract_type_var_from_base( + typ, + generic_bases=cast("tuple[type, ...]", (BaseAPIResponse, APIResponse, AsyncAPIResponse)), + index=0, + ) diff --git a/.venv/lib/python3.11/site-packages/openai/_streaming.py b/.venv/lib/python3.11/site-packages/openai/_streaming.py new file mode 100644 index 0000000000000000000000000000000000000000..0fda992cff4950f4aa68a72d615e261011dfb714 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_streaming.py @@ -0,0 +1,410 @@ +# Note: initially copied from https://github.com/florimondmanca/httpx-sse/blob/master/src/httpx_sse/_decoders.py +from __future__ import annotations + +import json +import inspect +from types import TracebackType +from typing import TYPE_CHECKING, Any, Generic, TypeVar, Iterator, AsyncIterator, cast +from typing_extensions import Self, Protocol, TypeGuard, override, get_origin, runtime_checkable + +import httpx + +from ._utils import is_mapping, extract_type_var_from_base +from ._exceptions import APIError + +if TYPE_CHECKING: + from ._client import OpenAI, AsyncOpenAI + + +_T = TypeVar("_T") + + +class Stream(Generic[_T]): + """Provides the core interface to iterate over a synchronous stream response.""" + + response: httpx.Response + + _decoder: SSEBytesDecoder + + def __init__( + self, + *, + cast_to: type[_T], + response: httpx.Response, + client: OpenAI, + ) -> None: + self.response = response + self._cast_to = cast_to + self._client = client + self._decoder = client._make_sse_decoder() + self._iterator = self.__stream__() + + def __next__(self) -> _T: + return self._iterator.__next__() + + def __iter__(self) -> Iterator[_T]: + for item in self._iterator: + yield item + + def _iter_events(self) -> Iterator[ServerSentEvent]: + yield from self._decoder.iter_bytes(self.response.iter_bytes()) + + def __stream__(self) -> Iterator[_T]: + cast_to = cast(Any, self._cast_to) + response = self.response + process_data = self._client._process_response_data + iterator = self._iter_events() + + for sse in iterator: + if sse.data.startswith("[DONE]"): + break + + if sse.event is None: + data = sse.json() + if is_mapping(data) and data.get("error"): + message = None + error = data.get("error") + if is_mapping(error): + message = error.get("message") + if not message or not isinstance(message, str): + message = "An error occurred during streaming" + + raise APIError( + message=message, + request=self.response.request, + body=data["error"], + ) + + yield process_data(data=data, cast_to=cast_to, response=response) + + else: + data = sse.json() + + if sse.event == "error" and is_mapping(data) and data.get("error"): + message = None + error = data.get("error") + if is_mapping(error): + message = error.get("message") + if not message or not isinstance(message, str): + message = "An error occurred during streaming" + + raise APIError( + message=message, + request=self.response.request, + body=data["error"], + ) + + yield process_data(data={"data": data, "event": sse.event}, cast_to=cast_to, response=response) + + # Ensure the entire stream is consumed + for _sse in iterator: + ... + + def __enter__(self) -> Self: + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: + self.close() + + def close(self) -> None: + """ + Close the response and release the connection. + + Automatically called if the response body is read to completion. + """ + self.response.close() + + +class AsyncStream(Generic[_T]): + """Provides the core interface to iterate over an asynchronous stream response.""" + + response: httpx.Response + + _decoder: SSEDecoder | SSEBytesDecoder + + def __init__( + self, + *, + cast_to: type[_T], + response: httpx.Response, + client: AsyncOpenAI, + ) -> None: + self.response = response + self._cast_to = cast_to + self._client = client + self._decoder = client._make_sse_decoder() + self._iterator = self.__stream__() + + async def __anext__(self) -> _T: + return await self._iterator.__anext__() + + async def __aiter__(self) -> AsyncIterator[_T]: + async for item in self._iterator: + yield item + + async def _iter_events(self) -> AsyncIterator[ServerSentEvent]: + async for sse in self._decoder.aiter_bytes(self.response.aiter_bytes()): + yield sse + + async def __stream__(self) -> AsyncIterator[_T]: + cast_to = cast(Any, self._cast_to) + response = self.response + process_data = self._client._process_response_data + iterator = self._iter_events() + + async for sse in iterator: + if sse.data.startswith("[DONE]"): + break + + if sse.event is None: + data = sse.json() + if is_mapping(data) and data.get("error"): + message = None + error = data.get("error") + if is_mapping(error): + message = error.get("message") + if not message or not isinstance(message, str): + message = "An error occurred during streaming" + + raise APIError( + message=message, + request=self.response.request, + body=data["error"], + ) + + yield process_data(data=data, cast_to=cast_to, response=response) + + else: + data = sse.json() + + if sse.event == "error" and is_mapping(data) and data.get("error"): + message = None + error = data.get("error") + if is_mapping(error): + message = error.get("message") + if not message or not isinstance(message, str): + message = "An error occurred during streaming" + + raise APIError( + message=message, + request=self.response.request, + body=data["error"], + ) + + yield process_data(data={"data": data, "event": sse.event}, cast_to=cast_to, response=response) + + # Ensure the entire stream is consumed + async for _sse in iterator: + ... + + async def __aenter__(self) -> Self: + return self + + async def __aexit__( + self, + exc_type: type[BaseException] | None, + exc: BaseException | None, + exc_tb: TracebackType | None, + ) -> None: + await self.close() + + async def close(self) -> None: + """ + Close the response and release the connection. + + Automatically called if the response body is read to completion. + """ + await self.response.aclose() + + +class ServerSentEvent: + def __init__( + self, + *, + event: str | None = None, + data: str | None = None, + id: str | None = None, + retry: int | None = None, + ) -> None: + if data is None: + data = "" + + self._id = id + self._data = data + self._event = event or None + self._retry = retry + + @property + def event(self) -> str | None: + return self._event + + @property + def id(self) -> str | None: + return self._id + + @property + def retry(self) -> int | None: + return self._retry + + @property + def data(self) -> str: + return self._data + + def json(self) -> Any: + return json.loads(self.data) + + @override + def __repr__(self) -> str: + return f"ServerSentEvent(event={self.event}, data={self.data}, id={self.id}, retry={self.retry})" + + +class SSEDecoder: + _data: list[str] + _event: str | None + _retry: int | None + _last_event_id: str | None + + def __init__(self) -> None: + self._event = None + self._data = [] + self._last_event_id = None + self._retry = None + + def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[ServerSentEvent]: + """Given an iterator that yields raw binary data, iterate over it & yield every event encountered""" + for chunk in self._iter_chunks(iterator): + # Split before decoding so splitlines() only uses \r and \n + for raw_line in chunk.splitlines(): + line = raw_line.decode("utf-8") + sse = self.decode(line) + if sse: + yield sse + + def _iter_chunks(self, iterator: Iterator[bytes]) -> Iterator[bytes]: + """Given an iterator that yields raw binary data, iterate over it and yield individual SSE chunks""" + data = b"" + for chunk in iterator: + for line in chunk.splitlines(keepends=True): + data += line + if data.endswith((b"\r\r", b"\n\n", b"\r\n\r\n")): + yield data + data = b"" + if data: + yield data + + async def aiter_bytes(self, iterator: AsyncIterator[bytes]) -> AsyncIterator[ServerSentEvent]: + """Given an iterator that yields raw binary data, iterate over it & yield every event encountered""" + async for chunk in self._aiter_chunks(iterator): + # Split before decoding so splitlines() only uses \r and \n + for raw_line in chunk.splitlines(): + line = raw_line.decode("utf-8") + sse = self.decode(line) + if sse: + yield sse + + async def _aiter_chunks(self, iterator: AsyncIterator[bytes]) -> AsyncIterator[bytes]: + """Given an iterator that yields raw binary data, iterate over it and yield individual SSE chunks""" + data = b"" + async for chunk in iterator: + for line in chunk.splitlines(keepends=True): + data += line + if data.endswith((b"\r\r", b"\n\n", b"\r\n\r\n")): + yield data + data = b"" + if data: + yield data + + def decode(self, line: str) -> ServerSentEvent | None: + # See: https://html.spec.whatwg.org/multipage/server-sent-events.html#event-stream-interpretation # noqa: E501 + + if not line: + if not self._event and not self._data and not self._last_event_id and self._retry is None: + return None + + sse = ServerSentEvent( + event=self._event, + data="\n".join(self._data), + id=self._last_event_id, + retry=self._retry, + ) + + # NOTE: as per the SSE spec, do not reset last_event_id. + self._event = None + self._data = [] + self._retry = None + + return sse + + if line.startswith(":"): + return None + + fieldname, _, value = line.partition(":") + + if value.startswith(" "): + value = value[1:] + + if fieldname == "event": + self._event = value + elif fieldname == "data": + self._data.append(value) + elif fieldname == "id": + if "\0" in value: + pass + else: + self._last_event_id = value + elif fieldname == "retry": + try: + self._retry = int(value) + except (TypeError, ValueError): + pass + else: + pass # Field is ignored. + + return None + + +@runtime_checkable +class SSEBytesDecoder(Protocol): + def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[ServerSentEvent]: + """Given an iterator that yields raw binary data, iterate over it & yield every event encountered""" + ... + + def aiter_bytes(self, iterator: AsyncIterator[bytes]) -> AsyncIterator[ServerSentEvent]: + """Given an async iterator that yields raw binary data, iterate over it & yield every event encountered""" + ... + + +def is_stream_class_type(typ: type) -> TypeGuard[type[Stream[object]] | type[AsyncStream[object]]]: + """TypeGuard for determining whether or not the given type is a subclass of `Stream` / `AsyncStream`""" + origin = get_origin(typ) or typ + return inspect.isclass(origin) and issubclass(origin, (Stream, AsyncStream)) + + +def extract_stream_chunk_type( + stream_cls: type, + *, + failure_message: str | None = None, +) -> type: + """Given a type like `Stream[T]`, returns the generic type variable `T`. + + This also handles the case where a concrete subclass is given, e.g. + ```py + class MyStream(Stream[bytes]): + ... + + extract_stream_chunk_type(MyStream) -> bytes + ``` + """ + from ._base_client import Stream, AsyncStream + + return extract_type_var_from_base( + stream_cls, + index=0, + generic_bases=cast("tuple[type, ...]", (Stream, AsyncStream)), + failure_message=failure_message, + ) diff --git a/.venv/lib/python3.11/site-packages/openai/_types.py b/.venv/lib/python3.11/site-packages/openai/_types.py new file mode 100644 index 0000000000000000000000000000000000000000..a5cf207aa379f1631a851948d7d70821ca6d4aa1 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_types.py @@ -0,0 +1,219 @@ +from __future__ import annotations + +from os import PathLike +from typing import ( + IO, + TYPE_CHECKING, + Any, + Dict, + List, + Type, + Tuple, + Union, + Mapping, + TypeVar, + Callable, + Optional, + Sequence, +) +from typing_extensions import Set, Literal, Protocol, TypeAlias, TypedDict, override, runtime_checkable + +import httpx +import pydantic +from httpx import URL, Proxy, Timeout, Response, BaseTransport, AsyncBaseTransport + +if TYPE_CHECKING: + from ._models import BaseModel + from ._response import APIResponse, AsyncAPIResponse + from ._legacy_response import HttpxBinaryResponseContent + +Transport = BaseTransport +AsyncTransport = AsyncBaseTransport +Query = Mapping[str, object] +Body = object +AnyMapping = Mapping[str, object] +ModelT = TypeVar("ModelT", bound=pydantic.BaseModel) +_T = TypeVar("_T") + + +# Approximates httpx internal ProxiesTypes and RequestFiles types +# while adding support for `PathLike` instances +ProxiesDict = Dict["str | URL", Union[None, str, URL, Proxy]] +ProxiesTypes = Union[str, Proxy, ProxiesDict] +if TYPE_CHECKING: + Base64FileInput = Union[IO[bytes], PathLike[str]] + FileContent = Union[IO[bytes], bytes, PathLike[str]] +else: + Base64FileInput = Union[IO[bytes], PathLike] + FileContent = Union[IO[bytes], bytes, PathLike] # PathLike is not subscriptable in Python 3.8. +FileTypes = Union[ + # file (or bytes) + FileContent, + # (filename, file (or bytes)) + Tuple[Optional[str], FileContent], + # (filename, file (or bytes), content_type) + Tuple[Optional[str], FileContent, Optional[str]], + # (filename, file (or bytes), content_type, headers) + Tuple[Optional[str], FileContent, Optional[str], Mapping[str, str]], +] +RequestFiles = Union[Mapping[str, FileTypes], Sequence[Tuple[str, FileTypes]]] + +# duplicate of the above but without our custom file support +HttpxFileContent = Union[IO[bytes], bytes] +HttpxFileTypes = Union[ + # file (or bytes) + HttpxFileContent, + # (filename, file (or bytes)) + Tuple[Optional[str], HttpxFileContent], + # (filename, file (or bytes), content_type) + Tuple[Optional[str], HttpxFileContent, Optional[str]], + # (filename, file (or bytes), content_type, headers) + Tuple[Optional[str], HttpxFileContent, Optional[str], Mapping[str, str]], +] +HttpxRequestFiles = Union[Mapping[str, HttpxFileTypes], Sequence[Tuple[str, HttpxFileTypes]]] + +# Workaround to support (cast_to: Type[ResponseT]) -> ResponseT +# where ResponseT includes `None`. In order to support directly +# passing `None`, overloads would have to be defined for every +# method that uses `ResponseT` which would lead to an unacceptable +# amount of code duplication and make it unreadable. See _base_client.py +# for example usage. +# +# This unfortunately means that you will either have +# to import this type and pass it explicitly: +# +# from openai import NoneType +# client.get('/foo', cast_to=NoneType) +# +# or build it yourself: +# +# client.get('/foo', cast_to=type(None)) +if TYPE_CHECKING: + NoneType: Type[None] +else: + NoneType = type(None) + + +class RequestOptions(TypedDict, total=False): + headers: Headers + max_retries: int + timeout: float | Timeout | None + params: Query + extra_json: AnyMapping + idempotency_key: str + + +# Sentinel class used until PEP 0661 is accepted +class NotGiven: + """ + A sentinel singleton class used to distinguish omitted keyword arguments + from those passed in with the value None (which may have different behavior). + + For example: + + ```py + def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response: ... + + + get(timeout=1) # 1s timeout + get(timeout=None) # No timeout + get() # Default timeout behavior, which may not be statically known at the method definition. + ``` + """ + + def __bool__(self) -> Literal[False]: + return False + + @override + def __repr__(self) -> str: + return "NOT_GIVEN" + + +NotGivenOr = Union[_T, NotGiven] +NOT_GIVEN = NotGiven() + + +class Omit: + """In certain situations you need to be able to represent a case where a default value has + to be explicitly removed and `None` is not an appropriate substitute, for example: + + ```py + # as the default `Content-Type` header is `application/json` that will be sent + client.post("/upload/files", files={"file": b"my raw file content"}) + + # you can't explicitly override the header as it has to be dynamically generated + # to look something like: 'multipart/form-data; boundary=0d8382fcf5f8c3be01ca2e11002d2983' + client.post(..., headers={"Content-Type": "multipart/form-data"}) + + # instead you can remove the default `application/json` header by passing Omit + client.post(..., headers={"Content-Type": Omit()}) + ``` + """ + + def __bool__(self) -> Literal[False]: + return False + + +@runtime_checkable +class ModelBuilderProtocol(Protocol): + @classmethod + def build( + cls: type[_T], + *, + response: Response, + data: object, + ) -> _T: ... + + +Headers = Mapping[str, Union[str, Omit]] + + +class HeadersLikeProtocol(Protocol): + def get(self, __key: str) -> str | None: ... + + +HeadersLike = Union[Headers, HeadersLikeProtocol] + +ResponseT = TypeVar( + "ResponseT", + bound=Union[ + object, + str, + None, + "BaseModel", + List[Any], + Dict[str, Any], + Response, + ModelBuilderProtocol, + "APIResponse[Any]", + "AsyncAPIResponse[Any]", + "HttpxBinaryResponseContent", + ], +) + +StrBytesIntFloat = Union[str, bytes, int, float] + +# Note: copied from Pydantic +# https://github.com/pydantic/pydantic/blob/6f31f8f68ef011f84357330186f603ff295312fd/pydantic/main.py#L79 +IncEx: TypeAlias = Union[Set[int], Set[str], Mapping[int, Union["IncEx", bool]], Mapping[str, Union["IncEx", bool]]] + +PostParser = Callable[[Any], Any] + + +@runtime_checkable +class InheritsGeneric(Protocol): + """Represents a type that has inherited from `Generic` + + The `__orig_bases__` property can be used to determine the resolved + type variable for a given base class. + """ + + __orig_bases__: tuple[_GenericAlias] + + +class _GenericAlias(Protocol): + __origin__: type[object] + + +class HttpxSendArgs(TypedDict, total=False): + auth: httpx.Auth diff --git a/.venv/lib/python3.11/site-packages/openai/_version.py b/.venv/lib/python3.11/site-packages/openai/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..7ffe16b95d73f3dfecb6c81680b77ed07aa33c99 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/_version.py @@ -0,0 +1,4 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +__title__ = "openai" +__version__ = "1.61.1" # x-release-please-version diff --git a/.venv/lib/python3.11/site-packages/openai/pagination.py b/.venv/lib/python3.11/site-packages/openai/pagination.py new file mode 100644 index 0000000000000000000000000000000000000000..8293638269b2bf90173756c252e5ae0ff33b2261 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/pagination.py @@ -0,0 +1,107 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import Any, List, Generic, TypeVar, Optional, cast +from typing_extensions import Protocol, override, runtime_checkable + +from ._base_client import BasePage, PageInfo, BaseSyncPage, BaseAsyncPage + +__all__ = ["SyncPage", "AsyncPage", "SyncCursorPage", "AsyncCursorPage"] + +_T = TypeVar("_T") + + +@runtime_checkable +class CursorPageItem(Protocol): + id: Optional[str] + + +class SyncPage(BaseSyncPage[_T], BasePage[_T], Generic[_T]): + """Note: no pagination actually occurs yet, this is for forwards-compatibility.""" + + data: List[_T] + object: str + + @override + def _get_page_items(self) -> List[_T]: + data = self.data + if not data: + return [] + return data + + @override + def next_page_info(self) -> None: + """ + This page represents a response that isn't actually paginated at the API level + so there will never be a next page. + """ + return None + + +class AsyncPage(BaseAsyncPage[_T], BasePage[_T], Generic[_T]): + """Note: no pagination actually occurs yet, this is for forwards-compatibility.""" + + data: List[_T] + object: str + + @override + def _get_page_items(self) -> List[_T]: + data = self.data + if not data: + return [] + return data + + @override + def next_page_info(self) -> None: + """ + This page represents a response that isn't actually paginated at the API level + so there will never be a next page. + """ + return None + + +class SyncCursorPage(BaseSyncPage[_T], BasePage[_T], Generic[_T]): + data: List[_T] + + @override + def _get_page_items(self) -> List[_T]: + data = self.data + if not data: + return [] + return data + + @override + def next_page_info(self) -> Optional[PageInfo]: + data = self.data + if not data: + return None + + item = cast(Any, data[-1]) + if not isinstance(item, CursorPageItem) or item.id is None: + # TODO emit warning log + return None + + return PageInfo(params={"after": item.id}) + + +class AsyncCursorPage(BaseAsyncPage[_T], BasePage[_T], Generic[_T]): + data: List[_T] + + @override + def _get_page_items(self) -> List[_T]: + data = self.data + if not data: + return [] + return data + + @override + def next_page_info(self) -> Optional[PageInfo]: + data = self.data + if not data: + return None + + item = cast(Any, data[-1]) + if not isinstance(item, CursorPageItem) or item.id is None: + # TODO emit warning log + return None + + return PageInfo(params={"after": item.id}) diff --git a/.venv/lib/python3.11/site-packages/openai/py.typed b/.venv/lib/python3.11/site-packages/openai/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.venv/lib/python3.11/site-packages/openai/types/__init__.py b/.venv/lib/python3.11/site-packages/openai/types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7abb22f2394de525c9677dba219a6dc9ec9341f7 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/__init__.py @@ -0,0 +1,54 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from .batch import Batch as Batch +from .image import Image as Image +from .model import Model as Model +from .shared import ( + Metadata as Metadata, + ErrorObject as ErrorObject, + FunctionDefinition as FunctionDefinition, + FunctionParameters as FunctionParameters, + ResponseFormatText as ResponseFormatText, + ResponseFormatJSONObject as ResponseFormatJSONObject, + ResponseFormatJSONSchema as ResponseFormatJSONSchema, +) +from .upload import Upload as Upload +from .embedding import Embedding as Embedding +from .chat_model import ChatModel as ChatModel +from .completion import Completion as Completion +from .moderation import Moderation as Moderation +from .audio_model import AudioModel as AudioModel +from .batch_error import BatchError as BatchError +from .file_object import FileObject as FileObject +from .image_model import ImageModel as ImageModel +from .file_content import FileContent as FileContent +from .file_deleted import FileDeleted as FileDeleted +from .file_purpose import FilePurpose as FilePurpose +from .model_deleted import ModelDeleted as ModelDeleted +from .embedding_model import EmbeddingModel as EmbeddingModel +from .images_response import ImagesResponse as ImagesResponse +from .completion_usage import CompletionUsage as CompletionUsage +from .file_list_params import FileListParams as FileListParams +from .moderation_model import ModerationModel as ModerationModel +from .batch_list_params import BatchListParams as BatchListParams +from .completion_choice import CompletionChoice as CompletionChoice +from .image_edit_params import ImageEditParams as ImageEditParams +from .file_create_params import FileCreateParams as FileCreateParams +from .batch_create_params import BatchCreateParams as BatchCreateParams +from .batch_request_counts import BatchRequestCounts as BatchRequestCounts +from .upload_create_params import UploadCreateParams as UploadCreateParams +from .audio_response_format import AudioResponseFormat as AudioResponseFormat +from .image_generate_params import ImageGenerateParams as ImageGenerateParams +from .upload_complete_params import UploadCompleteParams as UploadCompleteParams +from .embedding_create_params import EmbeddingCreateParams as EmbeddingCreateParams +from .completion_create_params import CompletionCreateParams as CompletionCreateParams +from .moderation_create_params import ModerationCreateParams as ModerationCreateParams +from .create_embedding_response import CreateEmbeddingResponse as CreateEmbeddingResponse +from .moderation_create_response import ModerationCreateResponse as ModerationCreateResponse +from .moderation_text_input_param import ModerationTextInputParam as ModerationTextInputParam +from .websocket_connection_options import WebsocketConnectionOptions as WebsocketConnectionOptions +from .image_create_variation_params import ImageCreateVariationParams as ImageCreateVariationParams +from .moderation_image_url_input_param import ModerationImageURLInputParam as ModerationImageURLInputParam +from .moderation_multi_modal_input_param import ModerationMultiModalInputParam as ModerationMultiModalInputParam diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/speech_create_params.py b/.venv/lib/python3.11/site-packages/openai/types/audio/speech_create_params.py new file mode 100644 index 0000000000000000000000000000000000000000..ed1a1ce748937ba3d7a83e3cce62e2e902dbc312 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/speech_create_params.py @@ -0,0 +1,41 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import Union +from typing_extensions import Literal, Required, TypedDict + +from .speech_model import SpeechModel + +__all__ = ["SpeechCreateParams"] + + +class SpeechCreateParams(TypedDict, total=False): + input: Required[str] + """The text to generate audio for. The maximum length is 4096 characters.""" + + model: Required[Union[str, SpeechModel]] + """ + One of the available [TTS models](https://platform.openai.com/docs/models#tts): + `tts-1` or `tts-1-hd` + """ + + voice: Required[Literal["alloy", "ash", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer"]] + """The voice to use when generating the audio. + + Supported voices are `alloy`, `ash`, `coral`, `echo`, `fable`, `onyx`, `nova`, + `sage` and `shimmer`. Previews of the voices are available in the + [Text to speech guide](https://platform.openai.com/docs/guides/text-to-speech#voice-options). + """ + + response_format: Literal["mp3", "opus", "aac", "flac", "wav", "pcm"] + """The format to audio in. + + Supported formats are `mp3`, `opus`, `aac`, `flac`, `wav`, and `pcm`. + """ + + speed: float + """The speed of the generated audio. + + Select a value from `0.25` to `4.0`. `1.0` is the default. + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/speech_model.py b/.venv/lib/python3.11/site-packages/openai/types/audio/speech_model.py new file mode 100644 index 0000000000000000000000000000000000000000..bd685ab34d8be325ba419f4e059ed1a138ed0a2c --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/speech_model.py @@ -0,0 +1,7 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import Literal, TypeAlias + +__all__ = ["SpeechModel"] + +SpeechModel: TypeAlias = Literal["tts-1", "tts-1-hd"] diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/transcription.py b/.venv/lib/python3.11/site-packages/openai/types/audio/transcription.py new file mode 100644 index 0000000000000000000000000000000000000000..edb5f227fcf1888adbb26fffefc5790ff3d6246e --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/transcription.py @@ -0,0 +1,11 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + + +from ..._models import BaseModel + +__all__ = ["Transcription"] + + +class Transcription(BaseModel): + text: str + """The transcribed text.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_create_params.py b/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_create_params.py new file mode 100644 index 0000000000000000000000000000000000000000..f1779c35e6a3dc32cf480964f9e66600abc4eb5e --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_create_params.py @@ -0,0 +1,67 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import List, Union +from typing_extensions import Literal, Required, TypedDict + +from ..._types import FileTypes +from ..audio_model import AudioModel +from ..audio_response_format import AudioResponseFormat + +__all__ = ["TranscriptionCreateParams"] + + +class TranscriptionCreateParams(TypedDict, total=False): + file: Required[FileTypes] + """ + The audio file object (not file name) to transcribe, in one of these formats: + flac, mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm. + """ + + model: Required[Union[str, AudioModel]] + """ID of the model to use. + + Only `whisper-1` (which is powered by our open source Whisper V2 model) is + currently available. + """ + + language: str + """The language of the input audio. + + Supplying the input language in + [ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) (e.g. `en`) + format will improve accuracy and latency. + """ + + prompt: str + """An optional text to guide the model's style or continue a previous audio + segment. + + The [prompt](https://platform.openai.com/docs/guides/speech-to-text#prompting) + should match the audio language. + """ + + response_format: AudioResponseFormat + """ + The format of the output, in one of these options: `json`, `text`, `srt`, + `verbose_json`, or `vtt`. + """ + + temperature: float + """The sampling temperature, between 0 and 1. + + Higher values like 0.8 will make the output more random, while lower values like + 0.2 will make it more focused and deterministic. If set to 0, the model will use + [log probability](https://en.wikipedia.org/wiki/Log_probability) to + automatically increase the temperature until certain thresholds are hit. + """ + + timestamp_granularities: List[Literal["word", "segment"]] + """The timestamp granularities to populate for this transcription. + + `response_format` must be set `verbose_json` to use timestamp granularities. + Either or both of these options are supported: `word`, or `segment`. Note: There + is no additional latency for segment timestamps, but generating word timestamps + incurs additional latency. + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_segment.py b/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_segment.py new file mode 100644 index 0000000000000000000000000000000000000000..522c401ebb6635012bb113b6f148f504872dfc27 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_segment.py @@ -0,0 +1,49 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import List + +from ..._models import BaseModel + +__all__ = ["TranscriptionSegment"] + + +class TranscriptionSegment(BaseModel): + id: int + """Unique identifier of the segment.""" + + avg_logprob: float + """Average logprob of the segment. + + If the value is lower than -1, consider the logprobs failed. + """ + + compression_ratio: float + """Compression ratio of the segment. + + If the value is greater than 2.4, consider the compression failed. + """ + + end: float + """End time of the segment in seconds.""" + + no_speech_prob: float + """Probability of no speech in the segment. + + If the value is higher than 1.0 and the `avg_logprob` is below -1, consider this + segment silent. + """ + + seek: int + """Seek offset of the segment.""" + + start: float + """Start time of the segment in seconds.""" + + temperature: float + """Temperature parameter used for generating the segment.""" + + text: str + """Text content of the segment.""" + + tokens: List[int] + """Array of token IDs for the text content.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_verbose.py b/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_verbose.py new file mode 100644 index 0000000000000000000000000000000000000000..2a670189e043eb959f9f3a161a01e52228ded37b --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_verbose.py @@ -0,0 +1,26 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import List, Optional + +from ..._models import BaseModel +from .transcription_word import TranscriptionWord +from .transcription_segment import TranscriptionSegment + +__all__ = ["TranscriptionVerbose"] + + +class TranscriptionVerbose(BaseModel): + duration: float + """The duration of the input audio.""" + + language: str + """The language of the input audio.""" + + text: str + """The transcribed text.""" + + segments: Optional[List[TranscriptionSegment]] = None + """Segments of the transcribed text and their corresponding details.""" + + words: Optional[List[TranscriptionWord]] = None + """Extracted words and their corresponding timestamps.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_word.py b/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_word.py new file mode 100644 index 0000000000000000000000000000000000000000..969da3250991e587289ffa35f69f84fb10b28864 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/transcription_word.py @@ -0,0 +1,17 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + + +from ..._models import BaseModel + +__all__ = ["TranscriptionWord"] + + +class TranscriptionWord(BaseModel): + end: float + """End time of the word in seconds.""" + + start: float + """Start time of the word in seconds.""" + + word: str + """The text content of the word.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/translation.py b/.venv/lib/python3.11/site-packages/openai/types/audio/translation.py new file mode 100644 index 0000000000000000000000000000000000000000..7c0e9051892342130660ffa7e8d6cb8d9c6a206d --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/translation.py @@ -0,0 +1,10 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + + +from ..._models import BaseModel + +__all__ = ["Translation"] + + +class Translation(BaseModel): + text: str diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/translation_create_params.py b/.venv/lib/python3.11/site-packages/openai/types/audio/translation_create_params.py new file mode 100644 index 0000000000000000000000000000000000000000..62f85b875759e4d406e9166dd96807efb407d7ee --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/translation_create_params.py @@ -0,0 +1,50 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import Union +from typing_extensions import Required, TypedDict + +from ..._types import FileTypes +from ..audio_model import AudioModel +from ..audio_response_format import AudioResponseFormat + +__all__ = ["TranslationCreateParams"] + + +class TranslationCreateParams(TypedDict, total=False): + file: Required[FileTypes] + """ + The audio file object (not file name) translate, in one of these formats: flac, + mp3, mp4, mpeg, mpga, m4a, ogg, wav, or webm. + """ + + model: Required[Union[str, AudioModel]] + """ID of the model to use. + + Only `whisper-1` (which is powered by our open source Whisper V2 model) is + currently available. + """ + + prompt: str + """An optional text to guide the model's style or continue a previous audio + segment. + + The [prompt](https://platform.openai.com/docs/guides/speech-to-text#prompting) + should be in English. + """ + + response_format: AudioResponseFormat + """ + The format of the output, in one of these options: `json`, `text`, `srt`, + `verbose_json`, or `vtt`. + """ + + temperature: float + """The sampling temperature, between 0 and 1. + + Higher values like 0.8 will make the output more random, while lower values like + 0.2 will make it more focused and deterministic. If set to 0, the model will use + [log probability](https://en.wikipedia.org/wiki/Log_probability) to + automatically increase the temperature until certain thresholds are hit. + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/translation_create_response.py b/.venv/lib/python3.11/site-packages/openai/types/audio/translation_create_response.py new file mode 100644 index 0000000000000000000000000000000000000000..9953813c08ef2559e154bfd2d375e0abd098e2b7 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/translation_create_response.py @@ -0,0 +1,11 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import Union +from typing_extensions import TypeAlias + +from .translation import Translation +from .translation_verbose import TranslationVerbose + +__all__ = ["TranslationCreateResponse"] + +TranslationCreateResponse: TypeAlias = Union[Translation, TranslationVerbose] diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio/translation_verbose.py b/.venv/lib/python3.11/site-packages/openai/types/audio/translation_verbose.py new file mode 100644 index 0000000000000000000000000000000000000000..27cb02d64fffc978c2a792fa18875829f6c6908b --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio/translation_verbose.py @@ -0,0 +1,22 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import List, Optional + +from ..._models import BaseModel +from .transcription_segment import TranscriptionSegment + +__all__ = ["TranslationVerbose"] + + +class TranslationVerbose(BaseModel): + duration: float + """The duration of the input audio.""" + + language: str + """The language of the output translation (always `english`).""" + + text: str + """The translated text.""" + + segments: Optional[List[TranscriptionSegment]] = None + """Segments of the translated text and their corresponding details.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio_model.py b/.venv/lib/python3.11/site-packages/openai/types/audio_model.py new file mode 100644 index 0000000000000000000000000000000000000000..94ae84c015e8a5acf955ab3af2ebed65947bc54b --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio_model.py @@ -0,0 +1,7 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import Literal, TypeAlias + +__all__ = ["AudioModel"] + +AudioModel: TypeAlias = Literal["whisper-1"] diff --git a/.venv/lib/python3.11/site-packages/openai/types/audio_response_format.py b/.venv/lib/python3.11/site-packages/openai/types/audio_response_format.py new file mode 100644 index 0000000000000000000000000000000000000000..f8c8d45945cd35a5a9f0fda74467825bf4829cdc --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/audio_response_format.py @@ -0,0 +1,7 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import Literal, TypeAlias + +__all__ = ["AudioResponseFormat"] + +AudioResponseFormat: TypeAlias = Literal["json", "text", "srt", "verbose_json", "vtt"] diff --git a/.venv/lib/python3.11/site-packages/openai/types/batch.py b/.venv/lib/python3.11/site-packages/openai/types/batch.py new file mode 100644 index 0000000000000000000000000000000000000000..35de90ac85509d1a230dc171ce9aac255453c155 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/batch.py @@ -0,0 +1,87 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import List, Optional +from typing_extensions import Literal + +from .._models import BaseModel +from .batch_error import BatchError +from .shared.metadata import Metadata +from .batch_request_counts import BatchRequestCounts + +__all__ = ["Batch", "Errors"] + + +class Errors(BaseModel): + data: Optional[List[BatchError]] = None + + object: Optional[str] = None + """The object type, which is always `list`.""" + + +class Batch(BaseModel): + id: str + + completion_window: str + """The time frame within which the batch should be processed.""" + + created_at: int + """The Unix timestamp (in seconds) for when the batch was created.""" + + endpoint: str + """The OpenAI API endpoint used by the batch.""" + + input_file_id: str + """The ID of the input file for the batch.""" + + object: Literal["batch"] + """The object type, which is always `batch`.""" + + status: Literal[ + "validating", "failed", "in_progress", "finalizing", "completed", "expired", "cancelling", "cancelled" + ] + """The current status of the batch.""" + + cancelled_at: Optional[int] = None + """The Unix timestamp (in seconds) for when the batch was cancelled.""" + + cancelling_at: Optional[int] = None + """The Unix timestamp (in seconds) for when the batch started cancelling.""" + + completed_at: Optional[int] = None + """The Unix timestamp (in seconds) for when the batch was completed.""" + + error_file_id: Optional[str] = None + """The ID of the file containing the outputs of requests with errors.""" + + errors: Optional[Errors] = None + + expired_at: Optional[int] = None + """The Unix timestamp (in seconds) for when the batch expired.""" + + expires_at: Optional[int] = None + """The Unix timestamp (in seconds) for when the batch will expire.""" + + failed_at: Optional[int] = None + """The Unix timestamp (in seconds) for when the batch failed.""" + + finalizing_at: Optional[int] = None + """The Unix timestamp (in seconds) for when the batch started finalizing.""" + + in_progress_at: Optional[int] = None + """The Unix timestamp (in seconds) for when the batch started processing.""" + + metadata: Optional[Metadata] = None + """Set of 16 key-value pairs that can be attached to an object. + + This can be useful for storing additional information about the object in a + structured format, and querying for objects via API or the dashboard. + + Keys are strings with a maximum length of 64 characters. Values are strings with + a maximum length of 512 characters. + """ + + output_file_id: Optional[str] = None + """The ID of the file containing the outputs of successfully executed requests.""" + + request_counts: Optional[BatchRequestCounts] = None + """The request counts for different statuses within the batch.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/batch_create_params.py b/.venv/lib/python3.11/site-packages/openai/types/batch_create_params.py new file mode 100644 index 0000000000000000000000000000000000000000..e5be1d2bacba94a4be84e1e0fc199806d758a19a --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/batch_create_params.py @@ -0,0 +1,48 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import Optional +from typing_extensions import Literal, Required, TypedDict + +from .shared_params.metadata import Metadata + +__all__ = ["BatchCreateParams"] + + +class BatchCreateParams(TypedDict, total=False): + completion_window: Required[Literal["24h"]] + """The time frame within which the batch should be processed. + + Currently only `24h` is supported. + """ + + endpoint: Required[Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"]] + """The endpoint to be used for all requests in the batch. + + Currently `/v1/chat/completions`, `/v1/embeddings`, and `/v1/completions` are + supported. Note that `/v1/embeddings` batches are also restricted to a maximum + of 50,000 embedding inputs across all requests in the batch. + """ + + input_file_id: Required[str] + """The ID of an uploaded file that contains requests for the new batch. + + See [upload file](https://platform.openai.com/docs/api-reference/files/create) + for how to upload a file. + + Your input file must be formatted as a + [JSONL file](https://platform.openai.com/docs/api-reference/batch/request-input), + and must be uploaded with the purpose `batch`. The file can contain up to 50,000 + requests, and can be up to 200 MB in size. + """ + + metadata: Optional[Metadata] + """Set of 16 key-value pairs that can be attached to an object. + + This can be useful for storing additional information about the object in a + structured format, and querying for objects via API or the dashboard. + + Keys are strings with a maximum length of 64 characters. Values are strings with + a maximum length of 512 characters. + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/batch_error.py b/.venv/lib/python3.11/site-packages/openai/types/batch_error.py new file mode 100644 index 0000000000000000000000000000000000000000..1cdd808dbd6b8f52151874c2450cd2449158befe --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/batch_error.py @@ -0,0 +1,21 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import Optional + +from .._models import BaseModel + +__all__ = ["BatchError"] + + +class BatchError(BaseModel): + code: Optional[str] = None + """An error code identifying the error type.""" + + line: Optional[int] = None + """The line number of the input file where the error occurred, if applicable.""" + + message: Optional[str] = None + """A human-readable message providing more details about the error.""" + + param: Optional[str] = None + """The name of the parameter that caused the error, if applicable.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/batch_list_params.py b/.venv/lib/python3.11/site-packages/openai/types/batch_list_params.py new file mode 100644 index 0000000000000000000000000000000000000000..ef5e966b79987a467552514805c21da6175051c8 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/batch_list_params.py @@ -0,0 +1,24 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing_extensions import TypedDict + +__all__ = ["BatchListParams"] + + +class BatchListParams(TypedDict, total=False): + after: str + """A cursor for use in pagination. + + `after` is an object ID that defines your place in the list. For instance, if + you make a list request and receive 100 objects, ending with obj_foo, your + subsequent call can include after=obj_foo in order to fetch the next page of the + list. + """ + + limit: int + """A limit on the number of objects to be returned. + + Limit can range between 1 and 100, and the default is 20. + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/batch_request_counts.py b/.venv/lib/python3.11/site-packages/openai/types/batch_request_counts.py new file mode 100644 index 0000000000000000000000000000000000000000..7e1d49fb88307ea4f80f47913bc715242e3986d1 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/batch_request_counts.py @@ -0,0 +1,17 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + + +from .._models import BaseModel + +__all__ = ["BatchRequestCounts"] + + +class BatchRequestCounts(BaseModel): + completed: int + """Number of requests that have been completed successfully.""" + + failed: int + """Number of requests that have failed.""" + + total: int + """Total number of requests in the batch.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/chat_model.py b/.venv/lib/python3.11/site-packages/openai/types/chat_model.py new file mode 100644 index 0000000000000000000000000000000000000000..c191cb973427bfd1f3e145949317a2ed6727ffe1 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/chat_model.py @@ -0,0 +1,47 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import Literal, TypeAlias + +__all__ = ["ChatModel"] + +ChatModel: TypeAlias = Literal[ + "o3-mini", + "o3-mini-2025-01-31", + "o1", + "o1-2024-12-17", + "o1-preview", + "o1-preview-2024-09-12", + "o1-mini", + "o1-mini-2024-09-12", + "gpt-4o", + "gpt-4o-2024-11-20", + "gpt-4o-2024-08-06", + "gpt-4o-2024-05-13", + "gpt-4o-audio-preview", + "gpt-4o-audio-preview-2024-10-01", + "gpt-4o-audio-preview-2024-12-17", + "gpt-4o-mini-audio-preview", + "gpt-4o-mini-audio-preview-2024-12-17", + "chatgpt-4o-latest", + "gpt-4o-mini", + "gpt-4o-mini-2024-07-18", + "gpt-4-turbo", + "gpt-4-turbo-2024-04-09", + "gpt-4-0125-preview", + "gpt-4-turbo-preview", + "gpt-4-1106-preview", + "gpt-4-vision-preview", + "gpt-4", + "gpt-4-0314", + "gpt-4-0613", + "gpt-4-32k", + "gpt-4-32k-0314", + "gpt-4-32k-0613", + "gpt-3.5-turbo", + "gpt-3.5-turbo-16k", + "gpt-3.5-turbo-0301", + "gpt-3.5-turbo-0613", + "gpt-3.5-turbo-1106", + "gpt-3.5-turbo-0125", + "gpt-3.5-turbo-16k-0613", +] diff --git a/.venv/lib/python3.11/site-packages/openai/types/completion.py b/.venv/lib/python3.11/site-packages/openai/types/completion.py new file mode 100644 index 0000000000000000000000000000000000000000..d3b3102a4ab0ff7f528e839cf16e43ccdc33bc23 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/completion.py @@ -0,0 +1,37 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import List, Optional +from typing_extensions import Literal + +from .._models import BaseModel +from .completion_usage import CompletionUsage +from .completion_choice import CompletionChoice + +__all__ = ["Completion"] + + +class Completion(BaseModel): + id: str + """A unique identifier for the completion.""" + + choices: List[CompletionChoice] + """The list of completion choices the model generated for the input prompt.""" + + created: int + """The Unix timestamp (in seconds) of when the completion was created.""" + + model: str + """The model used for completion.""" + + object: Literal["text_completion"] + """The object type, which is always "text_completion" """ + + system_fingerprint: Optional[str] = None + """This fingerprint represents the backend configuration that the model runs with. + + Can be used in conjunction with the `seed` request parameter to understand when + backend changes have been made that might impact determinism. + """ + + usage: Optional[CompletionUsage] = None + """Usage statistics for the completion request.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/completion_choice.py b/.venv/lib/python3.11/site-packages/openai/types/completion_choice.py new file mode 100644 index 0000000000000000000000000000000000000000..d948ebc942c9d0c1882fe4754a6b41b9e9623355 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/completion_choice.py @@ -0,0 +1,35 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import Dict, List, Optional +from typing_extensions import Literal + +from .._models import BaseModel + +__all__ = ["CompletionChoice", "Logprobs"] + + +class Logprobs(BaseModel): + text_offset: Optional[List[int]] = None + + token_logprobs: Optional[List[float]] = None + + tokens: Optional[List[str]] = None + + top_logprobs: Optional[List[Dict[str, float]]] = None + + +class CompletionChoice(BaseModel): + finish_reason: Literal["stop", "length", "content_filter"] + """The reason the model stopped generating tokens. + + This will be `stop` if the model hit a natural stop point or a provided stop + sequence, `length` if the maximum number of tokens specified in the request was + reached, or `content_filter` if content was omitted due to a flag from our + content filters. + """ + + index: int + + logprobs: Optional[Logprobs] = None + + text: str diff --git a/.venv/lib/python3.11/site-packages/openai/types/completion_create_params.py b/.venv/lib/python3.11/site-packages/openai/types/completion_create_params.py new file mode 100644 index 0000000000000000000000000000000000000000..fdb1680d2610c0146187608a8143d7d75547c13a --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/completion_create_params.py @@ -0,0 +1,187 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import Dict, List, Union, Iterable, Optional +from typing_extensions import Literal, Required, TypedDict + +from .chat.chat_completion_stream_options_param import ChatCompletionStreamOptionsParam + +__all__ = ["CompletionCreateParamsBase", "CompletionCreateParamsNonStreaming", "CompletionCreateParamsStreaming"] + + +class CompletionCreateParamsBase(TypedDict, total=False): + model: Required[Union[str, Literal["gpt-3.5-turbo-instruct", "davinci-002", "babbage-002"]]] + """ID of the model to use. + + You can use the + [List models](https://platform.openai.com/docs/api-reference/models/list) API to + see all of your available models, or see our + [Model overview](https://platform.openai.com/docs/models) for descriptions of + them. + """ + + prompt: Required[Union[str, List[str], Iterable[int], Iterable[Iterable[int]], None]] + """ + The prompt(s) to generate completions for, encoded as a string, array of + strings, array of tokens, or array of token arrays. + + Note that <|endoftext|> is the document separator that the model sees during + training, so if a prompt is not specified the model will generate as if from the + beginning of a new document. + """ + + best_of: Optional[int] + """ + Generates `best_of` completions server-side and returns the "best" (the one with + the highest log probability per token). Results cannot be streamed. + + When used with `n`, `best_of` controls the number of candidate completions and + `n` specifies how many to return – `best_of` must be greater than `n`. + + **Note:** Because this parameter generates many completions, it can quickly + consume your token quota. Use carefully and ensure that you have reasonable + settings for `max_tokens` and `stop`. + """ + + echo: Optional[bool] + """Echo back the prompt in addition to the completion""" + + frequency_penalty: Optional[float] + """Number between -2.0 and 2.0. + + Positive values penalize new tokens based on their existing frequency in the + text so far, decreasing the model's likelihood to repeat the same line verbatim. + + [See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation) + """ + + logit_bias: Optional[Dict[str, int]] + """Modify the likelihood of specified tokens appearing in the completion. + + Accepts a JSON object that maps tokens (specified by their token ID in the GPT + tokenizer) to an associated bias value from -100 to 100. You can use this + [tokenizer tool](/tokenizer?view=bpe) to convert text to token IDs. + Mathematically, the bias is added to the logits generated by the model prior to + sampling. The exact effect will vary per model, but values between -1 and 1 + should decrease or increase likelihood of selection; values like -100 or 100 + should result in a ban or exclusive selection of the relevant token. + + As an example, you can pass `{"50256": -100}` to prevent the <|endoftext|> token + from being generated. + """ + + logprobs: Optional[int] + """ + Include the log probabilities on the `logprobs` most likely output tokens, as + well the chosen tokens. For example, if `logprobs` is 5, the API will return a + list of the 5 most likely tokens. The API will always return the `logprob` of + the sampled token, so there may be up to `logprobs+1` elements in the response. + + The maximum value for `logprobs` is 5. + """ + + max_tokens: Optional[int] + """ + The maximum number of [tokens](/tokenizer) that can be generated in the + completion. + + The token count of your prompt plus `max_tokens` cannot exceed the model's + context length. + [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) + for counting tokens. + """ + + n: Optional[int] + """How many completions to generate for each prompt. + + **Note:** Because this parameter generates many completions, it can quickly + consume your token quota. Use carefully and ensure that you have reasonable + settings for `max_tokens` and `stop`. + """ + + presence_penalty: Optional[float] + """Number between -2.0 and 2.0. + + Positive values penalize new tokens based on whether they appear in the text so + far, increasing the model's likelihood to talk about new topics. + + [See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation) + """ + + seed: Optional[int] + """ + If specified, our system will make a best effort to sample deterministically, + such that repeated requests with the same `seed` and parameters should return + the same result. + + Determinism is not guaranteed, and you should refer to the `system_fingerprint` + response parameter to monitor changes in the backend. + """ + + stop: Union[Optional[str], List[str], None] + """Up to 4 sequences where the API will stop generating further tokens. + + The returned text will not contain the stop sequence. + """ + + stream_options: Optional[ChatCompletionStreamOptionsParam] + """Options for streaming response. Only set this when you set `stream: true`.""" + + suffix: Optional[str] + """The suffix that comes after a completion of inserted text. + + This parameter is only supported for `gpt-3.5-turbo-instruct`. + """ + + temperature: Optional[float] + """What sampling temperature to use, between 0 and 2. + + Higher values like 0.8 will make the output more random, while lower values like + 0.2 will make it more focused and deterministic. + + We generally recommend altering this or `top_p` but not both. + """ + + top_p: Optional[float] + """ + An alternative to sampling with temperature, called nucleus sampling, where the + model considers the results of the tokens with top_p probability mass. So 0.1 + means only the tokens comprising the top 10% probability mass are considered. + + We generally recommend altering this or `temperature` but not both. + """ + + user: str + """ + A unique identifier representing your end-user, which can help OpenAI to monitor + and detect abuse. + [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids). + """ + + +class CompletionCreateParamsNonStreaming(CompletionCreateParamsBase, total=False): + stream: Optional[Literal[False]] + """Whether to stream back partial progress. + + If set, tokens will be sent as data-only + [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) + as they become available, with the stream terminated by a `data: [DONE]` + message. + [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions). + """ + + +class CompletionCreateParamsStreaming(CompletionCreateParamsBase): + stream: Required[Literal[True]] + """Whether to stream back partial progress. + + If set, tokens will be sent as data-only + [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) + as they become available, with the stream terminated by a `data: [DONE]` + message. + [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions). + """ + + +CompletionCreateParams = Union[CompletionCreateParamsNonStreaming, CompletionCreateParamsStreaming] diff --git a/.venv/lib/python3.11/site-packages/openai/types/completion_usage.py b/.venv/lib/python3.11/site-packages/openai/types/completion_usage.py new file mode 100644 index 0000000000000000000000000000000000000000..d8c4e84cf7adcf35f91837d9d34fe847e32eb2e9 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/completion_usage.py @@ -0,0 +1,54 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import Optional + +from .._models import BaseModel + +__all__ = ["CompletionUsage", "CompletionTokensDetails", "PromptTokensDetails"] + + +class CompletionTokensDetails(BaseModel): + accepted_prediction_tokens: Optional[int] = None + """ + When using Predicted Outputs, the number of tokens in the prediction that + appeared in the completion. + """ + + audio_tokens: Optional[int] = None + """Audio input tokens generated by the model.""" + + reasoning_tokens: Optional[int] = None + """Tokens generated by the model for reasoning.""" + + rejected_prediction_tokens: Optional[int] = None + """ + When using Predicted Outputs, the number of tokens in the prediction that did + not appear in the completion. However, like reasoning tokens, these tokens are + still counted in the total completion tokens for purposes of billing, output, + and context window limits. + """ + + +class PromptTokensDetails(BaseModel): + audio_tokens: Optional[int] = None + """Audio input tokens present in the prompt.""" + + cached_tokens: Optional[int] = None + """Cached tokens present in the prompt.""" + + +class CompletionUsage(BaseModel): + completion_tokens: int + """Number of tokens in the generated completion.""" + + prompt_tokens: int + """Number of tokens in the prompt.""" + + total_tokens: int + """Total number of tokens used in the request (prompt + completion).""" + + completion_tokens_details: Optional[CompletionTokensDetails] = None + """Breakdown of tokens used in a completion.""" + + prompt_tokens_details: Optional[PromptTokensDetails] = None + """Breakdown of tokens used in the prompt.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/create_embedding_response.py b/.venv/lib/python3.11/site-packages/openai/types/create_embedding_response.py new file mode 100644 index 0000000000000000000000000000000000000000..eff247a11279ce4c6c0d2af36665fddbdafc9411 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/create_embedding_response.py @@ -0,0 +1,31 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import List +from typing_extensions import Literal + +from .._models import BaseModel +from .embedding import Embedding + +__all__ = ["CreateEmbeddingResponse", "Usage"] + + +class Usage(BaseModel): + prompt_tokens: int + """The number of tokens used by the prompt.""" + + total_tokens: int + """The total number of tokens used by the request.""" + + +class CreateEmbeddingResponse(BaseModel): + data: List[Embedding] + """The list of embeddings generated by the model.""" + + model: str + """The name of the model used to generate the embedding.""" + + object: Literal["list"] + """The object type, which is always "list".""" + + usage: Usage + """The usage information for the request.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/embedding.py b/.venv/lib/python3.11/site-packages/openai/types/embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..769b1d165faf65308a87c2c0aba33e96e898825d --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/embedding.py @@ -0,0 +1,23 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import List +from typing_extensions import Literal + +from .._models import BaseModel + +__all__ = ["Embedding"] + + +class Embedding(BaseModel): + embedding: List[float] + """The embedding vector, which is a list of floats. + + The length of vector depends on the model as listed in the + [embedding guide](https://platform.openai.com/docs/guides/embeddings). + """ + + index: int + """The index of the embedding in the list of embeddings.""" + + object: Literal["embedding"] + """The object type, which is always "embedding".""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/embedding_create_params.py b/.venv/lib/python3.11/site-packages/openai/types/embedding_create_params.py new file mode 100644 index 0000000000000000000000000000000000000000..a90566449b57ba020febbf2db1e5cf790ce2f6b3 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/embedding_create_params.py @@ -0,0 +1,53 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import List, Union, Iterable +from typing_extensions import Literal, Required, TypedDict + +from .embedding_model import EmbeddingModel + +__all__ = ["EmbeddingCreateParams"] + + +class EmbeddingCreateParams(TypedDict, total=False): + input: Required[Union[str, List[str], Iterable[int], Iterable[Iterable[int]]]] + """Input text to embed, encoded as a string or array of tokens. + + To embed multiple inputs in a single request, pass an array of strings or array + of token arrays. The input must not exceed the max input tokens for the model + (8192 tokens for `text-embedding-ada-002`), cannot be an empty string, and any + array must be 2048 dimensions or less. + [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) + for counting tokens. Some models may also impose a limit on total number of + tokens summed across inputs. + """ + + model: Required[Union[str, EmbeddingModel]] + """ID of the model to use. + + You can use the + [List models](https://platform.openai.com/docs/api-reference/models/list) API to + see all of your available models, or see our + [Model overview](https://platform.openai.com/docs/models) for descriptions of + them. + """ + + dimensions: int + """The number of dimensions the resulting output embeddings should have. + + Only supported in `text-embedding-3` and later models. + """ + + encoding_format: Literal["float", "base64"] + """The format to return the embeddings in. + + Can be either `float` or [`base64`](https://pypi.org/project/pybase64/). + """ + + user: str + """ + A unique identifier representing your end-user, which can help OpenAI to monitor + and detect abuse. + [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids). + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/embedding_model.py b/.venv/lib/python3.11/site-packages/openai/types/embedding_model.py new file mode 100644 index 0000000000000000000000000000000000000000..075ff9764412316c537626ff2c24651793dc7fa2 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/embedding_model.py @@ -0,0 +1,7 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import Literal, TypeAlias + +__all__ = ["EmbeddingModel"] + +EmbeddingModel: TypeAlias = Literal["text-embedding-ada-002", "text-embedding-3-small", "text-embedding-3-large"] diff --git a/.venv/lib/python3.11/site-packages/openai/types/file_content.py b/.venv/lib/python3.11/site-packages/openai/types/file_content.py new file mode 100644 index 0000000000000000000000000000000000000000..d89eee623e36e3475322b43a4cec91716703b700 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/file_content.py @@ -0,0 +1,7 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import TypeAlias + +__all__ = ["FileContent"] + +FileContent: TypeAlias = str diff --git a/.venv/lib/python3.11/site-packages/openai/types/file_create_params.py b/.venv/lib/python3.11/site-packages/openai/types/file_create_params.py new file mode 100644 index 0000000000000000000000000000000000000000..ecf75033580eed817f3456afddd08cac4d871a8a --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/file_create_params.py @@ -0,0 +1,26 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing_extensions import Required, TypedDict + +from .._types import FileTypes +from .file_purpose import FilePurpose + +__all__ = ["FileCreateParams"] + + +class FileCreateParams(TypedDict, total=False): + file: Required[FileTypes] + """The File object (not file name) to be uploaded.""" + + purpose: Required[FilePurpose] + """The intended purpose of the uploaded file. + + Use "assistants" for + [Assistants](https://platform.openai.com/docs/api-reference/assistants) and + [Message](https://platform.openai.com/docs/api-reference/messages) files, + "vision" for Assistants image file inputs, "batch" for + [Batch API](https://platform.openai.com/docs/guides/batch), and "fine-tune" for + [Fine-tuning](https://platform.openai.com/docs/api-reference/fine-tuning). + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/file_deleted.py b/.venv/lib/python3.11/site-packages/openai/types/file_deleted.py new file mode 100644 index 0000000000000000000000000000000000000000..f25fa87a8db0742882efd7dbfffeb9235184eae1 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/file_deleted.py @@ -0,0 +1,15 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import Literal + +from .._models import BaseModel + +__all__ = ["FileDeleted"] + + +class FileDeleted(BaseModel): + id: str + + deleted: bool + + object: Literal["file"] diff --git a/.venv/lib/python3.11/site-packages/openai/types/file_list_params.py b/.venv/lib/python3.11/site-packages/openai/types/file_list_params.py new file mode 100644 index 0000000000000000000000000000000000000000..058d874c2967a24783bcc01c45e51c5c0a5b638c --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/file_list_params.py @@ -0,0 +1,33 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing_extensions import Literal, TypedDict + +__all__ = ["FileListParams"] + + +class FileListParams(TypedDict, total=False): + after: str + """A cursor for use in pagination. + + `after` is an object ID that defines your place in the list. For instance, if + you make a list request and receive 100 objects, ending with obj_foo, your + subsequent call can include after=obj_foo in order to fetch the next page of the + list. + """ + + limit: int + """A limit on the number of objects to be returned. + + Limit can range between 1 and 10,000, and the default is 10,000. + """ + + order: Literal["asc", "desc"] + """Sort order by the `created_at` timestamp of the objects. + + `asc` for ascending order and `desc` for descending order. + """ + + purpose: str + """Only return files with the given purpose.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/file_object.py b/.venv/lib/python3.11/site-packages/openai/types/file_object.py new file mode 100644 index 0000000000000000000000000000000000000000..6e2bf310a4013cb145a3da50a93c67691282b665 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/file_object.py @@ -0,0 +1,48 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import Optional +from typing_extensions import Literal + +from .._models import BaseModel + +__all__ = ["FileObject"] + + +class FileObject(BaseModel): + id: str + """The file identifier, which can be referenced in the API endpoints.""" + + bytes: int + """The size of the file, in bytes.""" + + created_at: int + """The Unix timestamp (in seconds) for when the file was created.""" + + filename: str + """The name of the file.""" + + object: Literal["file"] + """The object type, which is always `file`.""" + + purpose: Literal[ + "assistants", "assistants_output", "batch", "batch_output", "fine-tune", "fine-tune-results", "vision" + ] + """The intended purpose of the file. + + Supported values are `assistants`, `assistants_output`, `batch`, `batch_output`, + `fine-tune`, `fine-tune-results` and `vision`. + """ + + status: Literal["uploaded", "processed", "error"] + """Deprecated. + + The current status of the file, which can be either `uploaded`, `processed`, or + `error`. + """ + + status_details: Optional[str] = None + """Deprecated. + + For details on why a fine-tuning training file failed validation, see the + `error` field on `fine_tuning.job`. + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/file_purpose.py b/.venv/lib/python3.11/site-packages/openai/types/file_purpose.py new file mode 100644 index 0000000000000000000000000000000000000000..32dc352c62a1ede5bea1eda1a957b8e337dfe493 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/file_purpose.py @@ -0,0 +1,7 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import Literal, TypeAlias + +__all__ = ["FilePurpose"] + +FilePurpose: TypeAlias = Literal["assistants", "batch", "fine-tune", "vision"] diff --git a/.venv/lib/python3.11/site-packages/openai/types/image.py b/.venv/lib/python3.11/site-packages/openai/types/image.py new file mode 100644 index 0000000000000000000000000000000000000000..f48aa2c7025eee83b440e7b5b5569b0c1d32deac --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/image.py @@ -0,0 +1,24 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import Optional + +from .._models import BaseModel + +__all__ = ["Image"] + + +class Image(BaseModel): + b64_json: Optional[str] = None + """ + The base64-encoded JSON of the generated image, if `response_format` is + `b64_json`. + """ + + revised_prompt: Optional[str] = None + """ + The prompt that was used to generate the image, if there was any revision to the + prompt. + """ + + url: Optional[str] = None + """The URL of the generated image, if `response_format` is `url` (default).""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/image_create_variation_params.py b/.venv/lib/python3.11/site-packages/openai/types/image_create_variation_params.py new file mode 100644 index 0000000000000000000000000000000000000000..d20f672912352e2098e35f2743ae949b0ce372de --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/image_create_variation_params.py @@ -0,0 +1,51 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import Union, Optional +from typing_extensions import Literal, Required, TypedDict + +from .._types import FileTypes +from .image_model import ImageModel + +__all__ = ["ImageCreateVariationParams"] + + +class ImageCreateVariationParams(TypedDict, total=False): + image: Required[FileTypes] + """The image to use as the basis for the variation(s). + + Must be a valid PNG file, less than 4MB, and square. + """ + + model: Union[str, ImageModel, None] + """The model to use for image generation. + + Only `dall-e-2` is supported at this time. + """ + + n: Optional[int] + """The number of images to generate. + + Must be between 1 and 10. For `dall-e-3`, only `n=1` is supported. + """ + + response_format: Optional[Literal["url", "b64_json"]] + """The format in which the generated images are returned. + + Must be one of `url` or `b64_json`. URLs are only valid for 60 minutes after the + image has been generated. + """ + + size: Optional[Literal["256x256", "512x512", "1024x1024"]] + """The size of the generated images. + + Must be one of `256x256`, `512x512`, or `1024x1024`. + """ + + user: str + """ + A unique identifier representing your end-user, which can help OpenAI to monitor + and detect abuse. + [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids). + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/image_edit_params.py b/.venv/lib/python3.11/site-packages/openai/types/image_edit_params.py new file mode 100644 index 0000000000000000000000000000000000000000..1cb10611f36a29ed326de5f5e2d0f7c6f3673623 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/image_edit_params.py @@ -0,0 +1,62 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import Union, Optional +from typing_extensions import Literal, Required, TypedDict + +from .._types import FileTypes +from .image_model import ImageModel + +__all__ = ["ImageEditParams"] + + +class ImageEditParams(TypedDict, total=False): + image: Required[FileTypes] + """The image to edit. + + Must be a valid PNG file, less than 4MB, and square. If mask is not provided, + image must have transparency, which will be used as the mask. + """ + + prompt: Required[str] + """A text description of the desired image(s). + + The maximum length is 1000 characters. + """ + + mask: FileTypes + """An additional image whose fully transparent areas (e.g. + + where alpha is zero) indicate where `image` should be edited. Must be a valid + PNG file, less than 4MB, and have the same dimensions as `image`. + """ + + model: Union[str, ImageModel, None] + """The model to use for image generation. + + Only `dall-e-2` is supported at this time. + """ + + n: Optional[int] + """The number of images to generate. Must be between 1 and 10.""" + + response_format: Optional[Literal["url", "b64_json"]] + """The format in which the generated images are returned. + + Must be one of `url` or `b64_json`. URLs are only valid for 60 minutes after the + image has been generated. + """ + + size: Optional[Literal["256x256", "512x512", "1024x1024"]] + """The size of the generated images. + + Must be one of `256x256`, `512x512`, or `1024x1024`. + """ + + user: str + """ + A unique identifier representing your end-user, which can help OpenAI to monitor + and detect abuse. + [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids). + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/image_generate_params.py b/.venv/lib/python3.11/site-packages/openai/types/image_generate_params.py new file mode 100644 index 0000000000000000000000000000000000000000..c88c45f5188d52e93d60d3aa8bb10f8f65cf1f19 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/image_generate_params.py @@ -0,0 +1,65 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import Union, Optional +from typing_extensions import Literal, Required, TypedDict + +from .image_model import ImageModel + +__all__ = ["ImageGenerateParams"] + + +class ImageGenerateParams(TypedDict, total=False): + prompt: Required[str] + """A text description of the desired image(s). + + The maximum length is 1000 characters for `dall-e-2` and 4000 characters for + `dall-e-3`. + """ + + model: Union[str, ImageModel, None] + """The model to use for image generation.""" + + n: Optional[int] + """The number of images to generate. + + Must be between 1 and 10. For `dall-e-3`, only `n=1` is supported. + """ + + quality: Literal["standard", "hd"] + """The quality of the image that will be generated. + + `hd` creates images with finer details and greater consistency across the image. + This param is only supported for `dall-e-3`. + """ + + response_format: Optional[Literal["url", "b64_json"]] + """The format in which the generated images are returned. + + Must be one of `url` or `b64_json`. URLs are only valid for 60 minutes after the + image has been generated. + """ + + size: Optional[Literal["256x256", "512x512", "1024x1024", "1792x1024", "1024x1792"]] + """The size of the generated images. + + Must be one of `256x256`, `512x512`, or `1024x1024` for `dall-e-2`. Must be one + of `1024x1024`, `1792x1024`, or `1024x1792` for `dall-e-3` models. + """ + + style: Optional[Literal["vivid", "natural"]] + """The style of the generated images. + + Must be one of `vivid` or `natural`. Vivid causes the model to lean towards + generating hyper-real and dramatic images. Natural causes the model to produce + more natural, less hyper-real looking images. This param is only supported for + `dall-e-3`. + """ + + user: str + """ + A unique identifier representing your end-user, which can help OpenAI to monitor + and detect abuse. + [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids). + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/image_model.py b/.venv/lib/python3.11/site-packages/openai/types/image_model.py new file mode 100644 index 0000000000000000000000000000000000000000..1672369bea3cc66c778e551ad05c7a4987548687 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/image_model.py @@ -0,0 +1,7 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import Literal, TypeAlias + +__all__ = ["ImageModel"] + +ImageModel: TypeAlias = Literal["dall-e-2", "dall-e-3"] diff --git a/.venv/lib/python3.11/site-packages/openai/types/images_response.py b/.venv/lib/python3.11/site-packages/openai/types/images_response.py new file mode 100644 index 0000000000000000000000000000000000000000..7cee8131848991f44a26720b5f04134db0aed677 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/images_response.py @@ -0,0 +1,14 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import List + +from .image import Image +from .._models import BaseModel + +__all__ = ["ImagesResponse"] + + +class ImagesResponse(BaseModel): + created: int + + data: List[Image] diff --git a/.venv/lib/python3.11/site-packages/openai/types/model.py b/.venv/lib/python3.11/site-packages/openai/types/model.py new file mode 100644 index 0000000000000000000000000000000000000000..2631ee8d1a9da956f4b1ba2f1f055ef1f1868dd0 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/model.py @@ -0,0 +1,21 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import Literal + +from .._models import BaseModel + +__all__ = ["Model"] + + +class Model(BaseModel): + id: str + """The model identifier, which can be referenced in the API endpoints.""" + + created: int + """The Unix timestamp (in seconds) when the model was created.""" + + object: Literal["model"] + """The object type, which is always "model".""" + + owned_by: str + """The organization that owns the model.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/model_deleted.py b/.venv/lib/python3.11/site-packages/openai/types/model_deleted.py new file mode 100644 index 0000000000000000000000000000000000000000..7f81e1b3802c6df915240df29705b4a81916ae44 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/model_deleted.py @@ -0,0 +1,14 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + + +from .._models import BaseModel + +__all__ = ["ModelDeleted"] + + +class ModelDeleted(BaseModel): + id: str + + deleted: bool + + object: str diff --git a/.venv/lib/python3.11/site-packages/openai/types/moderation.py b/.venv/lib/python3.11/site-packages/openai/types/moderation.py new file mode 100644 index 0000000000000000000000000000000000000000..e4ec182ce2628bda1e2032ec565be189020e6a1e --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/moderation.py @@ -0,0 +1,186 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import List +from typing_extensions import Literal + +from pydantic import Field as FieldInfo + +from .._models import BaseModel + +__all__ = ["Moderation", "Categories", "CategoryAppliedInputTypes", "CategoryScores"] + + +class Categories(BaseModel): + harassment: bool + """ + Content that expresses, incites, or promotes harassing language towards any + target. + """ + + harassment_threatening: bool = FieldInfo(alias="harassment/threatening") + """ + Harassment content that also includes violence or serious harm towards any + target. + """ + + hate: bool + """ + Content that expresses, incites, or promotes hate based on race, gender, + ethnicity, religion, nationality, sexual orientation, disability status, or + caste. Hateful content aimed at non-protected groups (e.g., chess players) is + harassment. + """ + + hate_threatening: bool = FieldInfo(alias="hate/threatening") + """ + Hateful content that also includes violence or serious harm towards the targeted + group based on race, gender, ethnicity, religion, nationality, sexual + orientation, disability status, or caste. + """ + + illicit: bool + """ + Content that includes instructions or advice that facilitate the planning or + execution of wrongdoing, or that gives advice or instruction on how to commit + illicit acts. For example, "how to shoplift" would fit this category. + """ + + illicit_violent: bool = FieldInfo(alias="illicit/violent") + """ + Content that includes instructions or advice that facilitate the planning or + execution of wrongdoing that also includes violence, or that gives advice or + instruction on the procurement of any weapon. + """ + + self_harm: bool = FieldInfo(alias="self-harm") + """ + Content that promotes, encourages, or depicts acts of self-harm, such as + suicide, cutting, and eating disorders. + """ + + self_harm_instructions: bool = FieldInfo(alias="self-harm/instructions") + """ + Content that encourages performing acts of self-harm, such as suicide, cutting, + and eating disorders, or that gives instructions or advice on how to commit such + acts. + """ + + self_harm_intent: bool = FieldInfo(alias="self-harm/intent") + """ + Content where the speaker expresses that they are engaging or intend to engage + in acts of self-harm, such as suicide, cutting, and eating disorders. + """ + + sexual: bool + """ + Content meant to arouse sexual excitement, such as the description of sexual + activity, or that promotes sexual services (excluding sex education and + wellness). + """ + + sexual_minors: bool = FieldInfo(alias="sexual/minors") + """Sexual content that includes an individual who is under 18 years old.""" + + violence: bool + """Content that depicts death, violence, or physical injury.""" + + violence_graphic: bool = FieldInfo(alias="violence/graphic") + """Content that depicts death, violence, or physical injury in graphic detail.""" + + +class CategoryAppliedInputTypes(BaseModel): + harassment: List[Literal["text"]] + """The applied input type(s) for the category 'harassment'.""" + + harassment_threatening: List[Literal["text"]] = FieldInfo(alias="harassment/threatening") + """The applied input type(s) for the category 'harassment/threatening'.""" + + hate: List[Literal["text"]] + """The applied input type(s) for the category 'hate'.""" + + hate_threatening: List[Literal["text"]] = FieldInfo(alias="hate/threatening") + """The applied input type(s) for the category 'hate/threatening'.""" + + illicit: List[Literal["text"]] + """The applied input type(s) for the category 'illicit'.""" + + illicit_violent: List[Literal["text"]] = FieldInfo(alias="illicit/violent") + """The applied input type(s) for the category 'illicit/violent'.""" + + self_harm: List[Literal["text", "image"]] = FieldInfo(alias="self-harm") + """The applied input type(s) for the category 'self-harm'.""" + + self_harm_instructions: List[Literal["text", "image"]] = FieldInfo(alias="self-harm/instructions") + """The applied input type(s) for the category 'self-harm/instructions'.""" + + self_harm_intent: List[Literal["text", "image"]] = FieldInfo(alias="self-harm/intent") + """The applied input type(s) for the category 'self-harm/intent'.""" + + sexual: List[Literal["text", "image"]] + """The applied input type(s) for the category 'sexual'.""" + + sexual_minors: List[Literal["text"]] = FieldInfo(alias="sexual/minors") + """The applied input type(s) for the category 'sexual/minors'.""" + + violence: List[Literal["text", "image"]] + """The applied input type(s) for the category 'violence'.""" + + violence_graphic: List[Literal["text", "image"]] = FieldInfo(alias="violence/graphic") + """The applied input type(s) for the category 'violence/graphic'.""" + + +class CategoryScores(BaseModel): + harassment: float + """The score for the category 'harassment'.""" + + harassment_threatening: float = FieldInfo(alias="harassment/threatening") + """The score for the category 'harassment/threatening'.""" + + hate: float + """The score for the category 'hate'.""" + + hate_threatening: float = FieldInfo(alias="hate/threatening") + """The score for the category 'hate/threatening'.""" + + illicit: float + """The score for the category 'illicit'.""" + + illicit_violent: float = FieldInfo(alias="illicit/violent") + """The score for the category 'illicit/violent'.""" + + self_harm: float = FieldInfo(alias="self-harm") + """The score for the category 'self-harm'.""" + + self_harm_instructions: float = FieldInfo(alias="self-harm/instructions") + """The score for the category 'self-harm/instructions'.""" + + self_harm_intent: float = FieldInfo(alias="self-harm/intent") + """The score for the category 'self-harm/intent'.""" + + sexual: float + """The score for the category 'sexual'.""" + + sexual_minors: float = FieldInfo(alias="sexual/minors") + """The score for the category 'sexual/minors'.""" + + violence: float + """The score for the category 'violence'.""" + + violence_graphic: float = FieldInfo(alias="violence/graphic") + """The score for the category 'violence/graphic'.""" + + +class Moderation(BaseModel): + categories: Categories + """A list of the categories, and whether they are flagged or not.""" + + category_applied_input_types: CategoryAppliedInputTypes + """ + A list of the categories along with the input type(s) that the score applies to. + """ + + category_scores: CategoryScores + """A list of the categories along with their scores as predicted by model.""" + + flagged: bool + """Whether any of the below categories are flagged.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/moderation_create_params.py b/.venv/lib/python3.11/site-packages/openai/types/moderation_create_params.py new file mode 100644 index 0000000000000000000000000000000000000000..3ea2f3cd88b5da672470d58b7e2e75809f3027ca --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/moderation_create_params.py @@ -0,0 +1,29 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import List, Union, Iterable +from typing_extensions import Required, TypedDict + +from .moderation_model import ModerationModel +from .moderation_multi_modal_input_param import ModerationMultiModalInputParam + +__all__ = ["ModerationCreateParams"] + + +class ModerationCreateParams(TypedDict, total=False): + input: Required[Union[str, List[str], Iterable[ModerationMultiModalInputParam]]] + """Input (or inputs) to classify. + + Can be a single string, an array of strings, or an array of multi-modal input + objects similar to other models. + """ + + model: Union[str, ModerationModel] + """The content moderation model you would like to use. + + Learn more in + [the moderation guide](https://platform.openai.com/docs/guides/moderation), and + learn about available models + [here](https://platform.openai.com/docs/models#moderation). + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/moderation_create_response.py b/.venv/lib/python3.11/site-packages/openai/types/moderation_create_response.py new file mode 100644 index 0000000000000000000000000000000000000000..79684f8a70453e06d59f44716321f2d733114dcc --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/moderation_create_response.py @@ -0,0 +1,19 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import List + +from .._models import BaseModel +from .moderation import Moderation + +__all__ = ["ModerationCreateResponse"] + + +class ModerationCreateResponse(BaseModel): + id: str + """The unique identifier for the moderation request.""" + + model: str + """The model used to generate the moderation results.""" + + results: List[Moderation] + """A list of moderation objects.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/moderation_image_url_input_param.py b/.venv/lib/python3.11/site-packages/openai/types/moderation_image_url_input_param.py new file mode 100644 index 0000000000000000000000000000000000000000..9a69a6a257cd1db3f22d204fa1fb1f7478208f0d --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/moderation_image_url_input_param.py @@ -0,0 +1,20 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing_extensions import Literal, Required, TypedDict + +__all__ = ["ModerationImageURLInputParam", "ImageURL"] + + +class ImageURL(TypedDict, total=False): + url: Required[str] + """Either a URL of the image or the base64 encoded image data.""" + + +class ModerationImageURLInputParam(TypedDict, total=False): + image_url: Required[ImageURL] + """Contains either an image URL or a data URL for a base64 encoded image.""" + + type: Required[Literal["image_url"]] + """Always `image_url`.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/moderation_model.py b/.venv/lib/python3.11/site-packages/openai/types/moderation_model.py new file mode 100644 index 0000000000000000000000000000000000000000..64954c4547746e36538525f4921c7e5494a258be --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/moderation_model.py @@ -0,0 +1,9 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing_extensions import Literal, TypeAlias + +__all__ = ["ModerationModel"] + +ModerationModel: TypeAlias = Literal[ + "omni-moderation-latest", "omni-moderation-2024-09-26", "text-moderation-latest", "text-moderation-stable" +] diff --git a/.venv/lib/python3.11/site-packages/openai/types/moderation_multi_modal_input_param.py b/.venv/lib/python3.11/site-packages/openai/types/moderation_multi_modal_input_param.py new file mode 100644 index 0000000000000000000000000000000000000000..4314e7b0314c29bcf76137284ebb7755bc51501a --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/moderation_multi_modal_input_param.py @@ -0,0 +1,13 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import Union +from typing_extensions import TypeAlias + +from .moderation_text_input_param import ModerationTextInputParam +from .moderation_image_url_input_param import ModerationImageURLInputParam + +__all__ = ["ModerationMultiModalInputParam"] + +ModerationMultiModalInputParam: TypeAlias = Union[ModerationImageURLInputParam, ModerationTextInputParam] diff --git a/.venv/lib/python3.11/site-packages/openai/types/moderation_text_input_param.py b/.venv/lib/python3.11/site-packages/openai/types/moderation_text_input_param.py new file mode 100644 index 0000000000000000000000000000000000000000..e5da53337bb9839211090431d42919aa813e3f59 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/moderation_text_input_param.py @@ -0,0 +1,15 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing_extensions import Literal, Required, TypedDict + +__all__ = ["ModerationTextInputParam"] + + +class ModerationTextInputParam(TypedDict, total=False): + text: Required[str] + """A string of text to classify.""" + + type: Required[Literal["text"]] + """Always `text`.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/upload.py b/.venv/lib/python3.11/site-packages/openai/types/upload.py new file mode 100644 index 0000000000000000000000000000000000000000..d8108c62f949092fd7cfe528122a10648fdca5c6 --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/upload.py @@ -0,0 +1,42 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from typing import Optional +from typing_extensions import Literal + +from .._models import BaseModel +from .file_object import FileObject + +__all__ = ["Upload"] + + +class Upload(BaseModel): + id: str + """The Upload unique identifier, which can be referenced in API endpoints.""" + + bytes: int + """The intended number of bytes to be uploaded.""" + + created_at: int + """The Unix timestamp (in seconds) for when the Upload was created.""" + + expires_at: int + """The Unix timestamp (in seconds) for when the Upload was created.""" + + filename: str + """The name of the file to be uploaded.""" + + object: Literal["upload"] + """The object type, which is always "upload".""" + + purpose: str + """The intended purpose of the file. + + [Please refer here](https://platform.openai.com/docs/api-reference/files/object#files/object-purpose) + for acceptable values. + """ + + status: Literal["pending", "completed", "cancelled", "expired"] + """The status of the Upload.""" + + file: Optional[FileObject] = None + """The `File` object represents a document that has been uploaded to OpenAI.""" diff --git a/.venv/lib/python3.11/site-packages/openai/types/upload_complete_params.py b/.venv/lib/python3.11/site-packages/openai/types/upload_complete_params.py new file mode 100644 index 0000000000000000000000000000000000000000..cce568d5c674241636ac6c87c2e36338a3e1bfab --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/upload_complete_params.py @@ -0,0 +1,19 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import List +from typing_extensions import Required, TypedDict + +__all__ = ["UploadCompleteParams"] + + +class UploadCompleteParams(TypedDict, total=False): + part_ids: Required[List[str]] + """The ordered list of Part IDs.""" + + md5: str + """ + The optional md5 checksum for the file contents to verify if the bytes uploaded + matches what you expect. + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/upload_create_params.py b/.venv/lib/python3.11/site-packages/openai/types/upload_create_params.py new file mode 100644 index 0000000000000000000000000000000000000000..2ebabe6c663451a9bbb659b5a1b0feeaa485f4db --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/upload_create_params.py @@ -0,0 +1,31 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing_extensions import Required, TypedDict + +from .file_purpose import FilePurpose + +__all__ = ["UploadCreateParams"] + + +class UploadCreateParams(TypedDict, total=False): + bytes: Required[int] + """The number of bytes in the file you are uploading.""" + + filename: Required[str] + """The name of the file to upload.""" + + mime_type: Required[str] + """The MIME type of the file. + + This must fall within the supported MIME types for your file purpose. See the + supported MIME types for assistants and vision. + """ + + purpose: Required[FilePurpose] + """The intended purpose of the uploaded file. + + See the + [documentation on File purposes](https://platform.openai.com/docs/api-reference/files/create#files-create-purpose). + """ diff --git a/.venv/lib/python3.11/site-packages/openai/types/websocket_connection_options.py b/.venv/lib/python3.11/site-packages/openai/types/websocket_connection_options.py new file mode 100644 index 0000000000000000000000000000000000000000..40fd24ab03386d64de2f2d0ad5280f6dbaa5a97c --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/types/websocket_connection_options.py @@ -0,0 +1,36 @@ +# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details. + +from __future__ import annotations + +from typing import TYPE_CHECKING +from typing_extensions import Sequence, TypedDict + +if TYPE_CHECKING: + from websockets import Subprotocol + from websockets.extensions import ClientExtensionFactory + + +class WebsocketConnectionOptions(TypedDict, total=False): + """Websocket connection options copied from `websockets`. + + For example: https://websockets.readthedocs.io/en/stable/reference/asyncio/client.html#websockets.asyncio.client.connect + """ + + extensions: Sequence[ClientExtensionFactory] | None + """List of supported extensions, in order in which they should be negotiated and run.""" + + subprotocols: Sequence[Subprotocol] | None + """List of supported subprotocols, in order of decreasing preference.""" + + compression: str | None + """The “permessage-deflate” extension is enabled by default. Set compression to None to disable it. See the [compression guide](https://websockets.readthedocs.io/en/stable/topics/compression.html) for details.""" + + # limits + max_size: int | None + """Maximum size of incoming messages in bytes. None disables the limit.""" + + max_queue: int | None | tuple[int | None, int | None] + """High-water mark of the buffer where frames are received. It defaults to 16 frames. The low-water mark defaults to max_queue // 4. You may pass a (high, low) tuple to set the high-water and low-water marks. If you want to disable flow control entirely, you may set it to None, although that’s a bad idea.""" + + write_limit: int | tuple[int, int | None] + """High-water mark of write buffer in bytes. It is passed to set_write_buffer_limits(). It defaults to 32 KiB. You may pass a (high, low) tuple to set the high-water and low-water marks.""" diff --git a/.venv/lib/python3.11/site-packages/openai/version.py b/.venv/lib/python3.11/site-packages/openai/version.py new file mode 100644 index 0000000000000000000000000000000000000000..01a08ab5a93de69d97a4639113dfa0bede48ddba --- /dev/null +++ b/.venv/lib/python3.11/site-packages/openai/version.py @@ -0,0 +1,3 @@ +from ._version import __version__ + +VERSION: str = __version__