feat: add psagentai agent with deployment and configuration

Initial project setup including the main agent script, configuration
files, profile JSON state, systemd service unit, startup script,
deployment documentation, and Python dependencies.

Includes .env, venv/, and __pycache__/ which should likely be
excluded via .gitignore to avoid committing secrets and artifacts.

venv/lib/python3.11/site-packages/openai/__init__.py

@@ -0,0 +1,329 @@
+# File generated from our OpenAPI spec by Stainless.
+
+from __future__ import annotations
+
+import os as _os
+from typing_extensions import override
+
+from . import types
+from ._types import NoneType, 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 ._exceptions import (
+    APIError,
+    OpenAIError,
+    ConflictError,
+    NotFoundError,
+    APIStatusError,
+    RateLimitError,
+    APITimeoutError,
+    BadRequestError,
+    APIConnectionError,
+    AuthenticationError,
+    InternalServerError,
+    PermissionDeniedError,
+    UnprocessableEntityError,
+    APIResponseValidationError,
+)
+from ._utils._logs import setup_logging as _setup_logging
+
+__all__ = [
+    "types",
+    "__version__",
+    "__title__",
+    "NoneType",
+    "Transport",
+    "ProxiesTypes",
+    "OpenAIError",
+    "APIError",
+    "APIStatusError",
+    "APITimeoutError",
+    "APIConnectionError",
+    "APIResponseValidationError",
+    "BadRequestError",
+    "AuthenticationError",
+    "PermissionDeniedError",
+    "NotFoundError",
+    "ConflictError",
+    "UnprocessableEntityError",
+    "RateLimitError",
+    "InternalServerError",
+    "Timeout",
+    "RequestOptions",
+    "Client",
+    "AsyncClient",
+    "Stream",
+    "AsyncStream",
+    "OpenAI",
+    "AsyncOpenAI",
+    "file_from_path",
+    "BaseModel",
+]
+
+from .lib import azure as _azure
+from .version import VERSION as VERSION
+from .lib.azure import AzureOpenAI as AzureOpenAI, AsyncAzureOpenAI as AsyncAzureOpenAI
+from .lib._old_api import *
+
+_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
+
+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
+    @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,
+            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,
+    embeddings as embeddings,
+    completions as completions,
+    fine_tuning as fine_tuning,
+    moderations as moderations,
+)

venv/lib/python3.11/site-packages/openai/__main__.py

@@ -0,0 +1,3 @@
+from .cli import main
+
+main()

venv/lib/python3.11/site-packages/openai/_client.py

@@ -0,0 +1,503 @@
+# File generated from our OpenAPI spec by Stainless.
+
+from __future__ import annotations
+
+import os
+from typing import Any, Union, Mapping
+from typing_extensions import Self, override
+
+import httpx
+
+from . import resources, _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 ._streaming import Stream as Stream, AsyncStream as AsyncStream
+from ._exceptions import OpenAIError, APIStatusError
+from ._base_client import (
+    DEFAULT_MAX_RETRIES,
+    SyncAPIClient,
+    AsyncAPIClient,
+)
+
+__all__ = [
+    "Timeout",
+    "Transport",
+    "ProxiesTypes",
+    "RequestOptions",
+    "resources",
+    "OpenAI",
+    "AsyncOpenAI",
+    "Client",
+    "AsyncClient",
+]
+
+
+class OpenAI(SyncAPIClient):
+    completions: resources.Completions
+    chat: resources.Chat
+    embeddings: resources.Embeddings
+    files: resources.Files
+    images: resources.Images
+    audio: resources.Audio
+    moderations: resources.Moderations
+    models: resources.Models
+    fine_tuning: resources.FineTuning
+    beta: resources.Beta
+    with_raw_response: OpenAIWithRawResponse
+    with_streaming_response: OpenAIWithStreamedResponse
+
+    # client options
+    api_key: str
+    organization: str | None
+
+    def __init__(
+        self,
+        *,
+        api_key: str | None = None,
+        organization: str | None = None,
+        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. 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`
+        """
+        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 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 = resources.Completions(self)
+        self.chat = resources.Chat(self)
+        self.embeddings = resources.Embeddings(self)
+        self.files = resources.Files(self)
+        self.images = resources.Images(self)
+        self.audio = resources.Audio(self)
+        self.moderations = resources.Moderations(self)
+        self.models = resources.Models(self)
+        self.fine_tuning = resources.FineTuning(self)
+        self.beta = resources.Beta(self)
+        self.with_raw_response = OpenAIWithRawResponse(self)
+        self.with_streaming_response = OpenAIWithStreamedResponse(self)
+
+    @property
+    @override
+    def qs(self) -> Querystring:
+        return Querystring(array_format="comma")
+
+    @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(),
+            **self._custom_headers,
+        }
+
+    def copy(
+        self,
+        *,
+        api_key: str | None = None,
+        organization: str | 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,
+            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: resources.AsyncCompletions
+    chat: resources.AsyncChat
+    embeddings: resources.AsyncEmbeddings
+    files: resources.AsyncFiles
+    images: resources.AsyncImages
+    audio: resources.AsyncAudio
+    moderations: resources.AsyncModerations
+    models: resources.AsyncModels
+    fine_tuning: resources.AsyncFineTuning
+    beta: resources.AsyncBeta
+    with_raw_response: AsyncOpenAIWithRawResponse
+    with_streaming_response: AsyncOpenAIWithStreamedResponse
+
+    # client options
+    api_key: str
+    organization: str | None
+
+    def __init__(
+        self,
+        *,
+        api_key: str | None = None,
+        organization: str | None = None,
+        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. 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`
+        """
+        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 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 = resources.AsyncCompletions(self)
+        self.chat = resources.AsyncChat(self)
+        self.embeddings = resources.AsyncEmbeddings(self)
+        self.files = resources.AsyncFiles(self)
+        self.images = resources.AsyncImages(self)
+        self.audio = resources.AsyncAudio(self)
+        self.moderations = resources.AsyncModerations(self)
+        self.models = resources.AsyncModels(self)
+        self.fine_tuning = resources.AsyncFineTuning(self)
+        self.beta = resources.AsyncBeta(self)
+        self.with_raw_response = AsyncOpenAIWithRawResponse(self)
+        self.with_streaming_response = AsyncOpenAIWithStreamedResponse(self)
+
+    @property
+    @override
+    def qs(self) -> Querystring:
+        return Querystring(array_format="comma")
+
+    @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(),
+            **self._custom_headers,
+        }
+
+    def copy(
+        self,
+        *,
+        api_key: str | None = None,
+        organization: str | 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,
+            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 = resources.CompletionsWithRawResponse(client.completions)
+        self.chat = resources.ChatWithRawResponse(client.chat)
+        self.embeddings = resources.EmbeddingsWithRawResponse(client.embeddings)
+        self.files = resources.FilesWithRawResponse(client.files)
+        self.images = resources.ImagesWithRawResponse(client.images)
+        self.audio = resources.AudioWithRawResponse(client.audio)
+        self.moderations = resources.ModerationsWithRawResponse(client.moderations)
+        self.models = resources.ModelsWithRawResponse(client.models)
+        self.fine_tuning = resources.FineTuningWithRawResponse(client.fine_tuning)
+        self.beta = resources.BetaWithRawResponse(client.beta)
+
+
+class AsyncOpenAIWithRawResponse:
+    def __init__(self, client: AsyncOpenAI) -> None:
+        self.completions = resources.AsyncCompletionsWithRawResponse(client.completions)
+        self.chat = resources.AsyncChatWithRawResponse(client.chat)
+        self.embeddings = resources.AsyncEmbeddingsWithRawResponse(client.embeddings)
+        self.files = resources.AsyncFilesWithRawResponse(client.files)
+        self.images = resources.AsyncImagesWithRawResponse(client.images)
+        self.audio = resources.AsyncAudioWithRawResponse(client.audio)
+        self.moderations = resources.AsyncModerationsWithRawResponse(client.moderations)
+        self.models = resources.AsyncModelsWithRawResponse(client.models)
+        self.fine_tuning = resources.AsyncFineTuningWithRawResponse(client.fine_tuning)
+        self.beta = resources.AsyncBetaWithRawResponse(client.beta)
+
+
+class OpenAIWithStreamedResponse:
+    def __init__(self, client: OpenAI) -> None:
+        self.completions = resources.CompletionsWithStreamingResponse(client.completions)
+        self.chat = resources.ChatWithStreamingResponse(client.chat)
+        self.embeddings = resources.EmbeddingsWithStreamingResponse(client.embeddings)
+        self.files = resources.FilesWithStreamingResponse(client.files)
+        self.images = resources.ImagesWithStreamingResponse(client.images)
+        self.audio = resources.AudioWithStreamingResponse(client.audio)
+        self.moderations = resources.ModerationsWithStreamingResponse(client.moderations)
+        self.models = resources.ModelsWithStreamingResponse(client.models)
+        self.fine_tuning = resources.FineTuningWithStreamingResponse(client.fine_tuning)
+        self.beta = resources.BetaWithStreamingResponse(client.beta)
+
+
+class AsyncOpenAIWithStreamedResponse:
+    def __init__(self, client: AsyncOpenAI) -> None:
+        self.completions = resources.AsyncCompletionsWithStreamingResponse(client.completions)
+        self.chat = resources.AsyncChatWithStreamingResponse(client.chat)
+        self.embeddings = resources.AsyncEmbeddingsWithStreamingResponse(client.embeddings)
+        self.files = resources.AsyncFilesWithStreamingResponse(client.files)
+        self.images = resources.AsyncImagesWithStreamingResponse(client.images)
+        self.audio = resources.AsyncAudioWithStreamingResponse(client.audio)
+        self.moderations = resources.AsyncModerationsWithStreamingResponse(client.moderations)
+        self.models = resources.AsyncModelsWithStreamingResponse(client.models)
+        self.fine_tuning = resources.AsyncFineTuningWithStreamingResponse(client.fine_tuning)
+        self.beta = resources.AsyncBetaWithStreamingResponse(client.beta)
+
+
+Client = OpenAI
+
+AsyncClient = AsyncOpenAI

venv/lib/python3.11/site-packages/openai/_compat.py

@@ -0,0 +1,222 @@
+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
+
+import pydantic
+from pydantic.fields import FieldInfo
+
+from ._types import 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) -> _ModelT:
+    if PYDANTIC_V2:
+        return model.model_copy()
+    return model.copy()  # 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_unset: bool = False,
+    exclude_defaults: bool = False,
+) -> dict[str, Any]:
+    if PYDANTIC_V2:
+        return model.model_dump(
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+        )
+    return cast(
+        "dict[str, Any]",
+        model.dict(  # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
+            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]
+
+
+# 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:
+    try:
+        from functools import cached_property as cached_property
+    except ImportError:
+        from cached_property import cached_property as cached_property
+
+    typed_cached_property = cached_property

venv/lib/python3.11/site-packages/openai/_constants.py

@@ -0,0 +1,14 @@
+# File generated from our OpenAPI spec by Stainless.
+
+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=600.0, connect=5.0)
+DEFAULT_MAX_RETRIES = 2
+DEFAULT_LIMITS = httpx.Limits(max_connections=100, max_keepalive_connections=20)
+
+INITIAL_RETRY_DELAY = 0.5
+MAX_RETRY_DELAY = 8.0

venv/lib/python3.11/site-packages/openai/_exceptions.py

@@ -0,0 +1,124 @@
+# File generated from our OpenAPI spec by Stainless.
+
+from __future__ import annotations
+
+from typing import Any, Optional, cast
+from typing_extensions import Literal
+
+import httpx
+
+from ._utils import is_dict
+
+__all__ = [
+    "BadRequestError",
+    "AuthenticationError",
+    "PermissionDeniedError",
+    "NotFoundError",
+    "ConflictError",
+    "UnprocessableEntityError",
+    "RateLimitError",
+    "InternalServerError",
+]
+
+
+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, body.get("code"))
+            self.param = cast(Any, body.get("param"))
+            self.type = cast(Any, 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
+
+    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
+
+
+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

venv/lib/python3.11/site-packages/openai/_extras/__init__.py

@@ -0,0 +1,2 @@
+from .numpy_proxy import numpy as numpy, has_numpy as has_numpy
+from .pandas_proxy import pandas as pandas

venv/lib/python3.11/site-packages/openai/_extras/_common.py

@@ -0,0 +1,21 @@
+from .._exceptions import OpenAIError
+
+INSTRUCTIONS = """
+
+OpenAI error:
+
+    missing `{library}`
+
+This feature requires additional dependencies:
+
+    $ pip install openai[{extra}]
+
+"""
+
+
+def format_instructions(*, library: str, extra: str) -> str:
+    return INSTRUCTIONS.format(library=library, extra=extra)
+
+
+class MissingDependencyError(OpenAIError):
+    pass

venv/lib/python3.11/site-packages/openai/_extras/numpy_proxy.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+from typing_extensions import override
+
+from .._utils import LazyProxy
+from ._common import MissingDependencyError, format_instructions
+
+if TYPE_CHECKING:
+    import numpy as numpy
+
+
+NUMPY_INSTRUCTIONS = format_instructions(library="numpy", extra="datalib")
+
+
+class NumpyProxy(LazyProxy[Any]):
+    @override
+    def __load__(self) -> Any:
+        try:
+            import numpy
+        except ImportError as err:
+            raise MissingDependencyError(NUMPY_INSTRUCTIONS) from err
+
+        return numpy
+
+
+if not TYPE_CHECKING:
+    numpy = NumpyProxy()
+
+
+def has_numpy() -> bool:
+    try:
+        import numpy  # noqa: F401  # pyright: ignore[reportUnusedImport]
+    except ImportError:
+        return False
+
+    return True

venv/lib/python3.11/site-packages/openai/_extras/pandas_proxy.py

@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+from typing_extensions import override
+
+from .._utils import LazyProxy
+from ._common import MissingDependencyError, format_instructions
+
+if TYPE_CHECKING:
+    import pandas as pandas
+
+
+PANDAS_INSTRUCTIONS = format_instructions(library="pandas", extra="datalib")
+
+
+class PandasProxy(LazyProxy[Any]):
+    @override
+    def __load__(self) -> Any:
+        try:
+            import pandas
+        except ImportError as err:
+            raise MissingDependencyError(PANDAS_INSTRUCTIONS) from err
+
+        return pandas
+
+
+if not TYPE_CHECKING:
+    pandas = PandasProxy()

venv/lib/python3.11/site-packages/openai/_files.py

@@ -0,0 +1,122 @@
+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,
+    HttpxFileContent,
+    HttpxRequestFiles,
+)
+from ._utils import is_tuple_t, is_mapping_t, is_sequence_t
+
+
+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

venv/lib/python3.11/site-packages/openai/_legacy_response.py

@@ -0,0 +1,439 @@
+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
+from ._models import BaseModel, is_basemodel
+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
+
+    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,
+    ) -> 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
+
+    @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`
+          - `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)
+
+        self._parsed_by_type[cache_key] = parsed
+        return 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:
+        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=self._cast_to,
+                    response=self.http_response,
+                    client=cast(Any, self._client),
+                ),
+            )
+
+        cast_to = to if to is not None else self._cast_to
+        if cast_to is NoneType:
+            return cast(R, None)
+
+        response = self.http_response
+        if cast_to == str:
+            return cast(R, response.text)
+
+        origin = get_origin(cast_to) or cast_to
+
+        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) 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) 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"<APIResponse [{self.status_code} {self.http_response.reason_phrase}] type={self._cast_to}>"
+
+
+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 = {**(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 = {**(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()

venv/lib/python3.11/site-packages/openai/_models.py

@@ -0,0 +1,468 @@
+from __future__ import annotations
+
+import inspect
+from typing import TYPE_CHECKING, Any, Type, Union, Generic, TypeVar, Callable, cast
+from datetime import date, datetime
+from typing_extensions import (
+    Unpack,
+    Literal,
+    ClassVar,
+    Protocol,
+    Required,
+    TypedDict,
+    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 is_list, is_given, is_mapping, parse_date, parse_datetime, strip_not_given
+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
+
+__all__ = ["BaseModel", "GenericModel"]
+
+_T = TypeVar("_T")
+
+
+@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")
+    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 __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(
+        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,
+            exclude: IncEx = None,
+            by_alias: bool = False,
+            exclude_unset: bool = False,
+            exclude_defaults: bool = False,
+            exclude_none: bool = False,
+            round_trip: bool = False,
+            warnings: bool = True,
+        ) -> 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 != "python":
+                raise ValueError("mode is only supported in Pydantic v2")
+            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")
+            return 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,
+            )
+
+        @override
+        def model_dump_json(
+            self,
+            *,
+            indent: int | None = None,
+            include: IncEx = None,
+            exclude: IncEx = None,
+            by_alias: bool = False,
+            exclude_unset: bool = False,
+            exclude_defaults: bool = False,
+            exclude_none: bool = False,
+            round_trip: bool = False,
+            warnings: bool = True,
+        ) -> 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")
+            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`"""
+    origin = get_origin(type_) or type_
+    if is_union(type_):
+        for variant in get_args(type_):
+            if is_basemodel(variant):
+                return True
+
+        return False
+
+    return issubclass(origin, BaseModel) or issubclass(origin, GenericModel)
+
+
+def construct_type(*, value: object, type_: type) -> 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 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_=type_, value=value)
+        except Exception:
+            pass
+
+        # 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 (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
+
+
+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))
+
+
+# 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
+
+    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

venv/lib/python3.11/site-packages/openai/_module_client.py

@@ -0,0 +1,78 @@
+# File generated from our OpenAPI spec by Stainless.
+
+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 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__()
+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__()

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

venv/lib/python3.11/site-packages/openai/_resource.py

@@ -0,0 +1,42 @@
+# File generated from our OpenAPI spec by Stainless.
+
+from __future__ import annotations
+
+import time
+import asyncio
+from typing import TYPE_CHECKING
+
+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 asyncio.sleep(seconds)

venv/lib/python3.11/site-packages/openai/_response.py

@@ -0,0 +1,807 @@
+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_var_from_base
+from ._models import BaseModel, is_basemodel
+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
+
+    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,
+    ) -> 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
+
+    @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:
+        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=self._cast_to,
+                    response=self.http_response,
+                    client=cast(Any, self._client),
+                ),
+            )
+
+        cast_to = to if to is not None else self._cast_to
+        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)
+
+        origin = get_origin(cast_to) or cast_to
+
+        # 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) 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]):
+    @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`
+          - `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)
+
+        self._parsed_by_type[cache_key] = parsed
+        return 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]):
+    @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)
+
+        self._parsed_by_type[cache_key] = parsed
+        return 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 = {**(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 = {**(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 = {**(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 = {**(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 = {**(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 = {**(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 = {**(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 = {**(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,
+    )

venv/lib/python3.11/site-packages/openai/_streaming.py

@@ -0,0 +1,315 @@
+# 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, TypeGuard, override, get_origin
+
+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
+
+    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 = SSEDecoder()
+        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(self.response.iter_lines())
+
+    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"):
+                    raise APIError(
+                        message="An error occurred during streaming",
+                        request=self.response.request,
+                        body=data["error"],
+                    )
+
+                yield process_data(data=data, 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
+
+    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 = SSEDecoder()
+        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(self.response.aiter_lines()):
+            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"):
+                    raise APIError(
+                        message="An error occurred during streaming",
+                        request=self.response.request,
+                        body=data["error"],
+                    )
+
+                yield process_data(data=data, 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(self, iterator: Iterator[str]) -> Iterator[ServerSentEvent]:
+        """Given an iterator that yields lines, iterate over it & yield every event encountered"""
+        for line in iterator:
+            line = line.rstrip("\n")
+            sse = self.decode(line)
+            if sse is not None:
+                yield sse
+
+    async def aiter(self, iterator: AsyncIterator[str]) -> AsyncIterator[ServerSentEvent]:
+        """Given an async iterator that yields lines, iterate over it & yield every event encountered"""
+        async for line in iterator:
+            line = line.rstrip("\n")
+            sse = self.decode(line)
+            if sse is not None:
+                yield sse
+
+    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
+
+
+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,
+    )

venv/lib/python3.11/site-packages/openai/_types.py

@@ -0,0 +1,220 @@
+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 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:
+    FileContent = Union[IO[bytes], bytes, PathLike[str]]
+else:
+    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/32ea570bf96e84234d2992e1ddf40ab8a565925a/pydantic/main.py#L49
+IncEx: TypeAlias = "set[int] | set[str] | dict[int, Any] | dict[str, Any] | None"
+
+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

venv/lib/python3.11/site-packages/openai/_utils/__init__.py

@@ -0,0 +1,48 @@
+from ._sync import asyncify as asyncify
+from ._proxy import LazyProxy as LazyProxy
+from ._utils import (
+    flatten as flatten,
+    is_dict as is_dict,
+    is_list as is_list,
+    is_given as is_given,
+    is_tuple as is_tuple,
+    is_mapping as is_mapping,
+    is_tuple_t as is_tuple_t,
+    parse_date as parse_date,
+    is_iterable as is_iterable,
+    is_sequence as is_sequence,
+    coerce_float as coerce_float,
+    is_mapping_t as is_mapping_t,
+    removeprefix as removeprefix,
+    removesuffix as removesuffix,
+    extract_files as extract_files,
+    is_sequence_t as is_sequence_t,
+    required_args as required_args,
+    coerce_boolean as coerce_boolean,
+    coerce_integer as coerce_integer,
+    file_from_path as file_from_path,
+    parse_datetime as parse_datetime,
+    strip_not_given as strip_not_given,
+    deepcopy_minimal as deepcopy_minimal,
+    get_async_library as get_async_library,
+    maybe_coerce_float as maybe_coerce_float,
+    get_required_header as get_required_header,
+    maybe_coerce_boolean as maybe_coerce_boolean,
+    maybe_coerce_integer as maybe_coerce_integer,
+)
+from ._typing import (
+    is_list_type as is_list_type,
+    is_union_type as is_union_type,
+    extract_type_arg as extract_type_arg,
+    is_iterable_type as is_iterable_type,
+    is_required_type as is_required_type,
+    is_annotated_type as is_annotated_type,
+    strip_annotated_type as strip_annotated_type,
+    extract_type_var_from_base as extract_type_var_from_base,
+)
+from ._streams import consume_sync_iterator as consume_sync_iterator, consume_async_iterator as consume_async_iterator
+from ._transform import (
+    PropertyInfo as PropertyInfo,
+    transform as transform,
+    maybe_transform as maybe_transform,
+)

venv/lib/python3.11/site-packages/openai/_utils/_logs.py

@@ -0,0 +1,25 @@
+import os
+import logging
+
+logger: logging.Logger = logging.getLogger("openai")
+httpx_logger: logging.Logger = logging.getLogger("httpx")
+
+
+def _basic_config() -> None:
+    # e.g. [2023-10-05 14:12:26 - openai._base_client:818 - DEBUG] HTTP Request: POST http://127.0.0.1:4010/foo/bar "200 OK"
+    logging.basicConfig(
+        format="[%(asctime)s - %(name)s:%(lineno)d - %(levelname)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+
+
+def setup_logging() -> None:
+    env = os.environ.get("OPENAI_LOG")
+    if env == "debug":
+        _basic_config()
+        logger.setLevel(logging.DEBUG)
+        httpx_logger.setLevel(logging.DEBUG)
+    elif env == "info":
+        _basic_config()
+        logger.setLevel(logging.INFO)
+        httpx_logger.setLevel(logging.INFO)

venv/lib/python3.11/site-packages/openai/_utils/_proxy.py

@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Generic, TypeVar, Iterable, cast
+from typing_extensions import override
+
+T = TypeVar("T")
+
+
+class LazyProxy(Generic[T], ABC):
+    """Implements data methods to pretend that an instance is another instance.
+
+    This includes forwarding attribute access and othe methods.
+    """
+
+    # Note: we have to special case proxies that themselves return proxies
+    # to support using a proxy as a catch-all for any random access, e.g. `proxy.foo.bar.baz`
+
+    def __getattr__(self, attr: str) -> object:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return proxied  # pyright: ignore
+        return getattr(proxied, attr)
+
+    @override
+    def __repr__(self) -> str:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return proxied.__class__.__name__
+        return repr(self.__get_proxied__())
+
+    @override
+    def __str__(self) -> str:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return proxied.__class__.__name__
+        return str(proxied)
+
+    @override
+    def __dir__(self) -> Iterable[str]:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return []
+        return proxied.__dir__()
+
+    @property  # type: ignore
+    @override
+    def __class__(self) -> type:
+        proxied = self.__get_proxied__()
+        if issubclass(type(proxied), LazyProxy):
+            return type(proxied)
+        return proxied.__class__
+
+    def __get_proxied__(self) -> T:
+        return self.__load__()
+
+    def __as_proxied__(self) -> T:
+        """Helper method that returns the current proxy, typed as the loaded object"""
+        return cast(T, self)
+
+    @abstractmethod
+    def __load__(self) -> T:
+        ...

venv/lib/python3.11/site-packages/openai/_utils/_streams.py

@@ -0,0 +1,12 @@
+from typing import Any
+from typing_extensions import Iterator, AsyncIterator
+
+
+def consume_sync_iterator(iterator: Iterator[Any]) -> None:
+    for _ in iterator:
+        ...
+
+
+async def consume_async_iterator(iterator: AsyncIterator[Any]) -> None:
+    async for _ in iterator:
+        ...

venv/lib/python3.11/site-packages/openai/_utils/_sync.py

@@ -0,0 +1,64 @@
+from __future__ import annotations
+
+import functools
+from typing import TypeVar, Callable, Awaitable
+from typing_extensions import ParamSpec
+
+import anyio
+import anyio.to_thread
+
+T_Retval = TypeVar("T_Retval")
+T_ParamSpec = ParamSpec("T_ParamSpec")
+
+
+# copied from `asyncer`, https://github.com/tiangolo/asyncer
+def asyncify(
+    function: Callable[T_ParamSpec, T_Retval],
+    *,
+    cancellable: bool = False,
+    limiter: anyio.CapacityLimiter | None = None,
+) -> Callable[T_ParamSpec, Awaitable[T_Retval]]:
+    """
+    Take a blocking function and create an async one that receives the same
+    positional and keyword arguments, and that when called, calls the original function
+    in a worker thread using `anyio.to_thread.run_sync()`. Internally,
+    `asyncer.asyncify()` uses the same `anyio.to_thread.run_sync()`, but it supports
+    keyword arguments additional to positional arguments and it adds better support for
+    autocompletion and inline errors for the arguments of the function called and the
+    return value.
+
+    If the `cancellable` option is enabled and the task waiting for its completion is
+    cancelled, the thread will still run its course but its return value (or any raised
+    exception) will be ignored.
+
+    Use it like this:
+
+    ```Python
+    def do_work(arg1, arg2, kwarg1="", kwarg2="") -> str:
+        # Do work
+        return "Some result"
+
+
+    result = await to_thread.asyncify(do_work)("spam", "ham", kwarg1="a", kwarg2="b")
+    print(result)
+    ```
+
+    ## Arguments
+
+    `function`: a blocking regular callable (e.g. a function)
+    `cancellable`: `True` to allow cancellation of the operation
+    `limiter`: capacity limiter to use to limit the total amount of threads running
+        (if omitted, the default limiter is used)
+
+    ## Return
+
+    An async function that takes the same positional and keyword arguments as the
+    original one, that when called runs the same original function in a thread worker
+    and returns the result.
+    """
+
+    async def wrapper(*args: T_ParamSpec.args, **kwargs: T_ParamSpec.kwargs) -> T_Retval:
+        partial_f = functools.partial(function, *args, **kwargs)
+        return await anyio.to_thread.run_sync(partial_f, cancellable=cancellable, limiter=limiter)
+
+    return wrapper

venv/lib/python3.11/site-packages/openai/_utils/_transform.py

@@ -0,0 +1,224 @@
+from __future__ import annotations
+
+from typing import Any, Mapping, TypeVar, cast
+from datetime import date, datetime
+from typing_extensions import Literal, get_args, override, get_type_hints
+
+import pydantic
+
+from ._utils import (
+    is_list,
+    is_mapping,
+    is_iterable,
+)
+from ._typing import (
+    is_list_type,
+    is_union_type,
+    extract_type_arg,
+    is_iterable_type,
+    is_required_type,
+    is_annotated_type,
+    strip_annotated_type,
+)
+from .._compat import model_dump, is_typeddict
+
+_T = TypeVar("_T")
+
+
+# TODO: support for drilling globals() and locals()
+# TODO: ensure works correctly with forward references in all cases
+
+
+PropertyFormat = Literal["iso8601", "custom"]
+
+
+class PropertyInfo:
+    """Metadata class to be used in Annotated types to provide information about a given type.
+
+    For example:
+
+    class MyParams(TypedDict):
+        account_holder_name: Annotated[str, PropertyInfo(alias='accountHolderName')]
+
+    This means that {'account_holder_name': 'Robert'} will be transformed to {'accountHolderName': 'Robert'} before being sent to the API.
+    """
+
+    alias: str | None
+    format: PropertyFormat | None
+    format_template: str | None
+
+    def __init__(
+        self,
+        *,
+        alias: str | None = None,
+        format: PropertyFormat | None = None,
+        format_template: str | None = None,
+    ) -> None:
+        self.alias = alias
+        self.format = format
+        self.format_template = format_template
+
+    @override
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(alias='{self.alias}', format={self.format}, format_template='{self.format_template}')"
+
+
+def maybe_transform(
+    data: object,
+    expected_type: object,
+) -> Any | None:
+    """Wrapper over `transform()` that allows `None` to be passed.
+
+    See `transform()` for more details.
+    """
+    if data is None:
+        return None
+    return transform(data, expected_type)
+
+
+# Wrapper over _transform_recursive providing fake types
+def transform(
+    data: _T,
+    expected_type: object,
+) -> _T:
+    """Transform dictionaries based off of type information from the given type, for example:
+
+    ```py
+    class Params(TypedDict, total=False):
+        card_id: Required[Annotated[str, PropertyInfo(alias="cardID")]]
+
+
+    transformed = transform({"card_id": "<my card ID>"}, Params)
+    # {'cardID': '<my card ID>'}
+    ```
+
+    Any keys / data that does not have type information given will be included as is.
+
+    It should be noted that the transformations that this function does are not represented in the type system.
+    """
+    transformed = _transform_recursive(data, annotation=cast(type, expected_type))
+    return cast(_T, transformed)
+
+
+def _get_annotated_type(type_: type) -> type | None:
+    """If the given type is an `Annotated` type then it is returned, if not `None` is returned.
+
+    This also unwraps the type when applicable, e.g. `Required[Annotated[T, ...]]`
+    """
+    if is_required_type(type_):
+        # Unwrap `Required[Annotated[T, ...]]` to `Annotated[T, ...]`
+        type_ = get_args(type_)[0]
+
+    if is_annotated_type(type_):
+        return type_
+
+    return None
+
+
+def _maybe_transform_key(key: str, type_: type) -> str:
+    """Transform the given `data` based on the annotations provided in `type_`.
+
+    Note: this function only looks at `Annotated` types that contain `PropertInfo` metadata.
+    """
+    annotated_type = _get_annotated_type(type_)
+    if annotated_type is None:
+        # no `Annotated` definition for this type, no transformation needed
+        return key
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.alias is not None:
+            return annotation.alias
+
+    return key
+
+
+def _transform_recursive(
+    data: object,
+    *,
+    annotation: type,
+    inner_type: type | None = None,
+) -> object:
+    """Transform the given data against the expected type.
+
+    Args:
+        annotation: The direct type annotation given to the particular piece of data.
+            This may or may not be wrapped in metadata types, e.g. `Required[T]`, `Annotated[T, ...]` etc
+
+        inner_type: If applicable, this is the "inside" type. This is useful in certain cases where the outside type
+            is a container type such as `List[T]`. In that case `inner_type` should be set to `T` so that each entry in
+            the list can be transformed using the metadata from the container type.
+
+            Defaults to the same value as the `annotation` argument.
+    """
+    if inner_type is None:
+        inner_type = annotation
+
+    stripped_type = strip_annotated_type(inner_type)
+    if is_typeddict(stripped_type) and is_mapping(data):
+        return _transform_typeddict(data, stripped_type)
+
+    if (
+        # List[T]
+        (is_list_type(stripped_type) and is_list(data))
+        # Iterable[T]
+        or (is_iterable_type(stripped_type) and is_iterable(data) and not isinstance(data, str))
+    ):
+        inner_type = extract_type_arg(stripped_type, 0)
+        return [_transform_recursive(d, annotation=annotation, inner_type=inner_type) for d in data]
+
+    if is_union_type(stripped_type):
+        # For union types we run the transformation against all subtypes to ensure that everything is transformed.
+        #
+        # TODO: there may be edge cases where the same normalized field name will transform to two different names
+        # in different subtypes.
+        for subtype in get_args(stripped_type):
+            data = _transform_recursive(data, annotation=annotation, inner_type=subtype)
+        return data
+
+    if isinstance(data, pydantic.BaseModel):
+        return model_dump(data, exclude_unset=True)
+
+    return _transform_value(data, annotation)
+
+
+def _transform_value(data: object, type_: type) -> object:
+    annotated_type = _get_annotated_type(type_)
+    if annotated_type is None:
+        return data
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.format is not None:
+            return _format_data(data, annotation.format, annotation.format_template)
+
+    return data
+
+
+def _format_data(data: object, format_: PropertyFormat, format_template: str | None) -> object:
+    if isinstance(data, (date, datetime)):
+        if format_ == "iso8601":
+            return data.isoformat()
+
+        if format_ == "custom" and format_template is not None:
+            return data.strftime(format_template)
+
+    return data
+
+
+def _transform_typeddict(
+    data: Mapping[str, object],
+    expected_type: type,
+) -> Mapping[str, object]:
+    result: dict[str, object] = {}
+    annotations = get_type_hints(expected_type, include_extras=True)
+    for key, value in data.items():
+        type_ = annotations.get(key)
+        if type_ is None:
+            # we do not have a type annotation for this field, leave it as is
+            result[key] = value
+        else:
+            result[_maybe_transform_key(key, type_)] = _transform_recursive(value, annotation=type_)
+    return result

venv/lib/python3.11/site-packages/openai/_utils/_typing.py

@@ -0,0 +1,120 @@
+from __future__ import annotations
+
+from typing import Any, TypeVar, Iterable, cast
+from collections import abc as _c_abc
+from typing_extensions import Required, Annotated, get_args, get_origin
+
+from .._types import InheritsGeneric
+from .._compat import is_union as _is_union
+
+
+def is_annotated_type(typ: type) -> bool:
+    return get_origin(typ) == Annotated
+
+
+def is_list_type(typ: type) -> bool:
+    return (get_origin(typ) or typ) == list
+
+
+def is_iterable_type(typ: type) -> bool:
+    """If the given type is `typing.Iterable[T]`"""
+    origin = get_origin(typ) or typ
+    return origin == Iterable or origin == _c_abc.Iterable
+
+
+def is_union_type(typ: type) -> bool:
+    return _is_union(get_origin(typ))
+
+
+def is_required_type(typ: type) -> bool:
+    return get_origin(typ) == Required
+
+
+def is_typevar(typ: type) -> bool:
+    # type ignore is required because type checkers
+    # think this expression will always return False
+    return type(typ) == TypeVar  # type: ignore
+
+
+# Extracts T from Annotated[T, ...] or from Required[Annotated[T, ...]]
+def strip_annotated_type(typ: type) -> type:
+    if is_required_type(typ) or is_annotated_type(typ):
+        return strip_annotated_type(cast(type, get_args(typ)[0]))
+
+    return typ
+
+
+def extract_type_arg(typ: type, index: int) -> type:
+    args = get_args(typ)
+    try:
+        return cast(type, args[index])
+    except IndexError as err:
+        raise RuntimeError(f"Expected type {typ} to have a type argument at index {index} but it did not") from err
+
+
+def extract_type_var_from_base(
+    typ: type,
+    *,
+    generic_bases: tuple[type, ...],
+    index: int,
+    failure_message: str | None = None,
+) -> type:
+    """Given a type like `Foo[T]`, returns the generic type variable `T`.
+
+    This also handles the case where a concrete subclass is given, e.g.
+    ```py
+    class MyResponse(Foo[bytes]):
+        ...
+
+    extract_type_var(MyResponse, bases=(Foo,), index=0) -> bytes
+    ```
+
+    And where a generic subclass is given:
+    ```py
+    _T = TypeVar('_T')
+    class MyResponse(Foo[_T]):
+        ...
+
+    extract_type_var(MyResponse[bytes], bases=(Foo,), index=0) -> bytes
+    ```
+    """
+    cls = cast(object, get_origin(typ) or typ)
+    if cls in generic_bases:
+        # we're given the class directly
+        return extract_type_arg(typ, index)
+
+    # if a subclass is given
+    # ---
+    # this is needed as __orig_bases__ is not present in the typeshed stubs
+    # because it is intended to be for internal use only, however there does
+    # not seem to be a way to resolve generic TypeVars for inherited subclasses
+    # without using it.
+    if isinstance(cls, InheritsGeneric):
+        target_base_class: Any | None = None
+        for base in cls.__orig_bases__:
+            if base.__origin__ in generic_bases:
+                target_base_class = base
+                break
+
+        if target_base_class is None:
+            raise RuntimeError(
+                "Could not find the generic base class;\n"
+                "This should never happen;\n"
+                f"Does {cls} inherit from one of {generic_bases} ?"
+            )
+
+        extracted = extract_type_arg(target_base_class, index)
+        if is_typevar(extracted):
+            # If the extracted type argument is itself a type variable
+            # then that means the subclass itself is generic, so we have
+            # to resolve the type argument from the class itself, not
+            # the base class.
+            #
+            # Note: if there is more than 1 type argument, the subclass could
+            # change the ordering of the type arguments, this is not currently
+            # supported.
+            return extract_type_arg(typ, index)
+
+        return extracted
+
+    raise RuntimeError(failure_message or f"Could not resolve inner type variable at index {index} for {typ}")

venv/lib/python3.11/site-packages/openai/_utils/_utils.py

@@ -0,0 +1,391 @@
+from __future__ import annotations
+
+import os
+import re
+import inspect
+import functools
+from typing import (
+    Any,
+    Tuple,
+    Mapping,
+    TypeVar,
+    Callable,
+    Iterable,
+    Sequence,
+    cast,
+    overload,
+)
+from pathlib import Path
+from typing_extensions import TypeGuard
+
+import sniffio
+
+from .._types import Headers, NotGiven, FileTypes, NotGivenOr, HeadersLike
+from .._compat import parse_date as parse_date, parse_datetime as parse_datetime
+
+_T = TypeVar("_T")
+_TupleT = TypeVar("_TupleT", bound=Tuple[object, ...])
+_MappingT = TypeVar("_MappingT", bound=Mapping[str, object])
+_SequenceT = TypeVar("_SequenceT", bound=Sequence[object])
+CallableT = TypeVar("CallableT", bound=Callable[..., Any])
+
+
+def flatten(t: Iterable[Iterable[_T]]) -> list[_T]:
+    return [item for sublist in t for item in sublist]
+
+
+def extract_files(
+    # TODO: this needs to take Dict but variance issues.....
+    # create protocol type ?
+    query: Mapping[str, object],
+    *,
+    paths: Sequence[Sequence[str]],
+) -> list[tuple[str, FileTypes]]:
+    """Recursively extract files from the given dictionary based on specified paths.
+
+    A path may look like this ['foo', 'files', '<array>', 'data'].
+
+    Note: this mutates the given dictionary.
+    """
+    files: list[tuple[str, FileTypes]] = []
+    for path in paths:
+        files.extend(_extract_items(query, path, index=0, flattened_key=None))
+    return files
+
+
+def _extract_items(
+    obj: object,
+    path: Sequence[str],
+    *,
+    index: int,
+    flattened_key: str | None,
+) -> list[tuple[str, FileTypes]]:
+    try:
+        key = path[index]
+    except IndexError:
+        if isinstance(obj, NotGiven):
+            # no value was provided - we can safely ignore
+            return []
+
+        # cyclical import
+        from .._files import assert_is_file_content
+
+        # We have exhausted the path, return the entry we found.
+        assert_is_file_content(obj, key=flattened_key)
+        assert flattened_key is not None
+        return [(flattened_key, cast(FileTypes, obj))]
+
+    index += 1
+    if is_dict(obj):
+        try:
+            # We are at the last entry in the path so we must remove the field
+            if (len(path)) == index:
+                item = obj.pop(key)
+            else:
+                item = obj[key]
+        except KeyError:
+            # Key was not present in the dictionary, this is not indicative of an error
+            # as the given path may not point to a required field. We also do not want
+            # to enforce required fields as the API may differ from the spec in some cases.
+            return []
+        if flattened_key is None:
+            flattened_key = key
+        else:
+            flattened_key += f"[{key}]"
+        return _extract_items(
+            item,
+            path,
+            index=index,
+            flattened_key=flattened_key,
+        )
+    elif is_list(obj):
+        if key != "<array>":
+            return []
+
+        return flatten(
+            [
+                _extract_items(
+                    item,
+                    path,
+                    index=index,
+                    flattened_key=flattened_key + "[]" if flattened_key is not None else "[]",
+                )
+                for item in obj
+            ]
+        )
+
+    # Something unexpected was passed, just ignore it.
+    return []
+
+
+def is_given(obj: NotGivenOr[_T]) -> TypeGuard[_T]:
+    return not isinstance(obj, NotGiven)
+
+
+# Type safe methods for narrowing types with TypeVars.
+# The default narrowing for isinstance(obj, dict) is dict[unknown, unknown],
+# however this cause Pyright to rightfully report errors. As we know we don't
+# care about the contained types we can safely use `object` in it's place.
+#
+# There are two separate functions defined, `is_*` and `is_*_t` for different use cases.
+# `is_*` is for when you're dealing with an unknown input
+# `is_*_t` is for when you're narrowing a known union type to a specific subset
+
+
+def is_tuple(obj: object) -> TypeGuard[tuple[object, ...]]:
+    return isinstance(obj, tuple)
+
+
+def is_tuple_t(obj: _TupleT | object) -> TypeGuard[_TupleT]:
+    return isinstance(obj, tuple)
+
+
+def is_sequence(obj: object) -> TypeGuard[Sequence[object]]:
+    return isinstance(obj, Sequence)
+
+
+def is_sequence_t(obj: _SequenceT | object) -> TypeGuard[_SequenceT]:
+    return isinstance(obj, Sequence)
+
+
+def is_mapping(obj: object) -> TypeGuard[Mapping[str, object]]:
+    return isinstance(obj, Mapping)
+
+
+def is_mapping_t(obj: _MappingT | object) -> TypeGuard[_MappingT]:
+    return isinstance(obj, Mapping)
+
+
+def is_dict(obj: object) -> TypeGuard[dict[object, object]]:
+    return isinstance(obj, dict)
+
+
+def is_list(obj: object) -> TypeGuard[list[object]]:
+    return isinstance(obj, list)
+
+
+def is_iterable(obj: object) -> TypeGuard[Iterable[object]]:
+    return isinstance(obj, Iterable)
+
+
+def deepcopy_minimal(item: _T) -> _T:
+    """Minimal reimplementation of copy.deepcopy() that will only copy certain object types:
+
+    - mappings, e.g. `dict`
+    - list
+
+    This is done for performance reasons.
+    """
+    if is_mapping(item):
+        return cast(_T, {k: deepcopy_minimal(v) for k, v in item.items()})
+    if is_list(item):
+        return cast(_T, [deepcopy_minimal(entry) for entry in item])
+    return item
+
+
+# copied from https://github.com/Rapptz/RoboDanny
+def human_join(seq: Sequence[str], *, delim: str = ", ", final: str = "or") -> str:
+    size = len(seq)
+    if size == 0:
+        return ""
+
+    if size == 1:
+        return seq[0]
+
+    if size == 2:
+        return f"{seq[0]} {final} {seq[1]}"
+
+    return delim.join(seq[:-1]) + f" {final} {seq[-1]}"
+
+
+def quote(string: str) -> str:
+    """Add single quotation marks around the given string. Does *not* do any escaping."""
+    return f"'{string}'"
+
+
+def required_args(*variants: Sequence[str]) -> Callable[[CallableT], CallableT]:
+    """Decorator to enforce a given set of arguments or variants of arguments are passed to the decorated function.
+
+    Useful for enforcing runtime validation of overloaded functions.
+
+    Example usage:
+    ```py
+    @overload
+    def foo(*, a: str) -> str:
+        ...
+
+
+    @overload
+    def foo(*, b: bool) -> str:
+        ...
+
+
+    # This enforces the same constraints that a static type checker would
+    # i.e. that either a or b must be passed to the function
+    @required_args(["a"], ["b"])
+    def foo(*, a: str | None = None, b: bool | None = None) -> str:
+        ...
+    ```
+    """
+
+    def inner(func: CallableT) -> CallableT:
+        params = inspect.signature(func).parameters
+        positional = [
+            name
+            for name, param in params.items()
+            if param.kind
+            in {
+                param.POSITIONAL_ONLY,
+                param.POSITIONAL_OR_KEYWORD,
+            }
+        ]
+
+        @functools.wraps(func)
+        def wrapper(*args: object, **kwargs: object) -> object:
+            given_params: set[str] = set()
+            for i, _ in enumerate(args):
+                try:
+                    given_params.add(positional[i])
+                except IndexError:
+                    raise TypeError(
+                        f"{func.__name__}() takes {len(positional)} argument(s) but {len(args)} were given"
+                    ) from None
+
+            for key in kwargs.keys():
+                given_params.add(key)
+
+            for variant in variants:
+                matches = all((param in given_params for param in variant))
+                if matches:
+                    break
+            else:  # no break
+                if len(variants) > 1:
+                    variations = human_join(
+                        ["(" + human_join([quote(arg) for arg in variant], final="and") + ")" for variant in variants]
+                    )
+                    msg = f"Missing required arguments; Expected either {variations} arguments to be given"
+                else:
+                    # TODO: this error message is not deterministic
+                    missing = list(set(variants[0]) - given_params)
+                    if len(missing) > 1:
+                        msg = f"Missing required arguments: {human_join([quote(arg) for arg in missing])}"
+                    else:
+                        msg = f"Missing required argument: {quote(missing[0])}"
+                raise TypeError(msg)
+            return func(*args, **kwargs)
+
+        return wrapper  # type: ignore
+
+    return inner
+
+
+_K = TypeVar("_K")
+_V = TypeVar("_V")
+
+
+@overload
+def strip_not_given(obj: None) -> None:
+    ...
+
+
+@overload
+def strip_not_given(obj: Mapping[_K, _V | NotGiven]) -> dict[_K, _V]:
+    ...
+
+
+@overload
+def strip_not_given(obj: object) -> object:
+    ...
+
+
+def strip_not_given(obj: object | None) -> object:
+    """Remove all top-level keys where their values are instances of `NotGiven`"""
+    if obj is None:
+        return None
+
+    if not is_mapping(obj):
+        return obj
+
+    return {key: value for key, value in obj.items() if not isinstance(value, NotGiven)}
+
+
+def coerce_integer(val: str) -> int:
+    return int(val, base=10)
+
+
+def coerce_float(val: str) -> float:
+    return float(val)
+
+
+def coerce_boolean(val: str) -> bool:
+    return val == "true" or val == "1" or val == "on"
+
+
+def maybe_coerce_integer(val: str | None) -> int | None:
+    if val is None:
+        return None
+    return coerce_integer(val)
+
+
+def maybe_coerce_float(val: str | None) -> float | None:
+    if val is None:
+        return None
+    return coerce_float(val)
+
+
+def maybe_coerce_boolean(val: str | None) -> bool | None:
+    if val is None:
+        return None
+    return coerce_boolean(val)
+
+
+def removeprefix(string: str, prefix: str) -> str:
+    """Remove a prefix from a string.
+
+    Backport of `str.removeprefix` for Python < 3.9
+    """
+    if string.startswith(prefix):
+        return string[len(prefix) :]
+    return string
+
+
+def removesuffix(string: str, suffix: str) -> str:
+    """Remove a suffix from a string.
+
+    Backport of `str.removesuffix` for Python < 3.9
+    """
+    if string.endswith(suffix):
+        return string[: -len(suffix)]
+    return string
+
+
+def file_from_path(path: str) -> FileTypes:
+    contents = Path(path).read_bytes()
+    file_name = os.path.basename(path)
+    return (file_name, contents)
+
+
+def get_required_header(headers: HeadersLike, header: str) -> str:
+    lower_header = header.lower()
+    if isinstance(headers, Mapping):
+        headers = cast(Headers, headers)
+        for k, v in headers.items():
+            if k.lower() == lower_header and isinstance(v, str):
+                return v
+
+    """ to deal with the case where the header looks like Stainless-Event-Id """
+    intercaps_header = re.sub(r"([^\w])(\w)", lambda pat: pat.group(1) + pat.group(2).upper(), header.capitalize())
+
+    for normalized_header in [header, lower_header, header.upper(), intercaps_header]:
+        value = headers.get(normalized_header)
+        if value:
+            return value
+
+    raise ValueError(f"Could not find {header} header")
+
+
+def get_async_library() -> str:
+    try:
+        return sniffio.current_async_library()
+    except Exception:
+        return "false"

venv/lib/python3.11/site-packages/openai/_version.py

@@ -0,0 +1,4 @@
+# File generated from our OpenAPI spec by Stainless.
+
+__title__ = "openai"
+__version__ = "1.12.0"  # x-release-please-version

venv/lib/python3.11/site-packages/openai/cli/__init__.py

@@ -0,0 +1 @@
+from ._cli import main as main

venv/lib/python3.11/site-packages/openai/cli/_api/__init__.py

@@ -0,0 +1 @@
+from ._main import register_commands as register_commands

venv/lib/python3.11/site-packages/openai/cli/_api/_main.py

@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from argparse import ArgumentParser
+
+from . import chat, audio, files, image, models, completions
+
+
+def register_commands(parser: ArgumentParser) -> None:
+    subparsers = parser.add_subparsers(help="All API subcommands")
+
+    chat.register(subparsers)
+    image.register(subparsers)
+    audio.register(subparsers)
+    files.register(subparsers)
+    models.register(subparsers)
+    completions.register(subparsers)

venv/lib/python3.11/site-packages/openai/cli/_api/audio.py

@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Optional, cast
+from argparse import ArgumentParser
+
+from .._utils import get_client, print_model
+from ..._types import NOT_GIVEN
+from .._models import BaseModel
+from .._progress import BufferReader
+
+if TYPE_CHECKING:
+    from argparse import _SubParsersAction
+
+
+def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
+    # transcriptions
+    sub = subparser.add_parser("audio.transcriptions.create")
+
+    # Required
+    sub.add_argument("-m", "--model", type=str, default="whisper-1")
+    sub.add_argument("-f", "--file", type=str, required=True)
+    # Optional
+    sub.add_argument("--response-format", type=str)
+    sub.add_argument("--language", type=str)
+    sub.add_argument("-t", "--temperature", type=float)
+    sub.add_argument("--prompt", type=str)
+    sub.set_defaults(func=CLIAudio.transcribe, args_model=CLITranscribeArgs)
+
+    # translations
+    sub = subparser.add_parser("audio.translations.create")
+
+    # Required
+    sub.add_argument("-f", "--file", type=str, required=True)
+    # Optional
+    sub.add_argument("-m", "--model", type=str, default="whisper-1")
+    sub.add_argument("--response-format", type=str)
+    # TODO: doesn't seem to be supported by the API
+    # sub.add_argument("--language", type=str)
+    sub.add_argument("-t", "--temperature", type=float)
+    sub.add_argument("--prompt", type=str)
+    sub.set_defaults(func=CLIAudio.translate, args_model=CLITranslationArgs)
+
+
+class CLITranscribeArgs(BaseModel):
+    model: str
+    file: str
+    response_format: Optional[str] = None
+    language: Optional[str] = None
+    temperature: Optional[float] = None
+    prompt: Optional[str] = None
+
+
+class CLITranslationArgs(BaseModel):
+    model: str
+    file: str
+    response_format: Optional[str] = None
+    language: Optional[str] = None
+    temperature: Optional[float] = None
+    prompt: Optional[str] = None
+
+
+class CLIAudio:
+    @staticmethod
+    def transcribe(args: CLITranscribeArgs) -> None:
+        with open(args.file, "rb") as file_reader:
+            buffer_reader = BufferReader(file_reader.read(), desc="Upload progress")
+
+        model = get_client().audio.transcriptions.create(
+            file=(args.file, buffer_reader),
+            model=args.model,
+            language=args.language or NOT_GIVEN,
+            temperature=args.temperature or NOT_GIVEN,
+            prompt=args.prompt or NOT_GIVEN,
+            # casts required because the API is typed for enums
+            # but we don't want to validate that here for forwards-compat
+            response_format=cast(Any, args.response_format),
+        )
+        print_model(model)
+
+    @staticmethod
+    def translate(args: CLITranslationArgs) -> None:
+        with open(args.file, "rb") as file_reader:
+            buffer_reader = BufferReader(file_reader.read(), desc="Upload progress")
+
+        model = get_client().audio.translations.create(
+            file=(args.file, buffer_reader),
+            model=args.model,
+            temperature=args.temperature or NOT_GIVEN,
+            prompt=args.prompt or NOT_GIVEN,
+            # casts required because the API is typed for enums
+            # but we don't want to validate that here for forwards-compat
+            response_format=cast(Any, args.response_format),
+        )
+        print_model(model)

venv/lib/python3.11/site-packages/openai/cli/_api/chat/__init__.py

@@ -0,0 +1,13 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+from argparse import ArgumentParser
+
+from . import completions
+
+if TYPE_CHECKING:
+    from argparse import _SubParsersAction
+
+
+def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
+    completions.register(subparser)

venv/lib/python3.11/site-packages/openai/cli/_api/chat/completions.py

@@ -0,0 +1,156 @@
+from __future__ import annotations
+
+import sys
+from typing import TYPE_CHECKING, List, Optional, cast
+from argparse import ArgumentParser
+from typing_extensions import Literal, NamedTuple
+
+from ..._utils import get_client
+from ..._models import BaseModel
+from ...._streaming import Stream
+from ....types.chat import (
+    ChatCompletionRole,
+    ChatCompletionChunk,
+    CompletionCreateParams,
+)
+from ....types.chat.completion_create_params import (
+    CompletionCreateParamsStreaming,
+    CompletionCreateParamsNonStreaming,
+)
+
+if TYPE_CHECKING:
+    from argparse import _SubParsersAction
+
+
+def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
+    sub = subparser.add_parser("chat.completions.create")
+
+    sub._action_groups.pop()
+    req = sub.add_argument_group("required arguments")
+    opt = sub.add_argument_group("optional arguments")
+
+    req.add_argument(
+        "-g",
+        "--message",
+        action="append",
+        nargs=2,
+        metavar=("ROLE", "CONTENT"),
+        help="A message in `{role} {content}` format. Use this argument multiple times to add multiple messages.",
+        required=True,
+    )
+    req.add_argument(
+        "-m",
+        "--model",
+        help="The model to use.",
+        required=True,
+    )
+
+    opt.add_argument(
+        "-n",
+        "--n",
+        help="How many completions to generate for the conversation.",
+        type=int,
+    )
+    opt.add_argument("-M", "--max-tokens", help="The maximum number of tokens to generate.", type=int)
+    opt.add_argument(
+        "-t",
+        "--temperature",
+        help="""What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
+
+Mutually exclusive with `top_p`.""",
+        type=float,
+    )
+    opt.add_argument(
+        "-P",
+        "--top_p",
+        help="""An alternative to sampling with temperature, called nucleus sampling, where the 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.
+
+            Mutually exclusive with `temperature`.""",
+        type=float,
+    )
+    opt.add_argument(
+        "--stop",
+        help="A stop sequence at which to stop generating tokens for the message.",
+    )
+    opt.add_argument("--stream", help="Stream messages as they're ready.", action="store_true")
+    sub.set_defaults(func=CLIChatCompletion.create, args_model=CLIChatCompletionCreateArgs)
+
+
+class CLIMessage(NamedTuple):
+    role: ChatCompletionRole
+    content: str
+
+
+class CLIChatCompletionCreateArgs(BaseModel):
+    message: List[CLIMessage]
+    model: str
+    n: Optional[int] = None
+    max_tokens: Optional[int] = None
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    stop: Optional[str] = None
+    stream: bool = False
+
+
+class CLIChatCompletion:
+    @staticmethod
+    def create(args: CLIChatCompletionCreateArgs) -> None:
+        params: CompletionCreateParams = {
+            "model": args.model,
+            "messages": [
+                {"role": cast(Literal["user"], message.role), "content": message.content} for message in args.message
+            ],
+            "n": args.n,
+            "temperature": args.temperature,
+            "top_p": args.top_p,
+            "stop": args.stop,
+            # type checkers are not good at inferring union types so we have to set stream afterwards
+            "stream": False,
+        }
+        if args.stream:
+            params["stream"] = args.stream  # type: ignore
+        if args.max_tokens is not None:
+            params["max_tokens"] = args.max_tokens
+
+        if args.stream:
+            return CLIChatCompletion._stream_create(cast(CompletionCreateParamsStreaming, params))
+
+        return CLIChatCompletion._create(cast(CompletionCreateParamsNonStreaming, params))
+
+    @staticmethod
+    def _create(params: CompletionCreateParamsNonStreaming) -> None:
+        completion = get_client().chat.completions.create(**params)
+        should_print_header = len(completion.choices) > 1
+        for choice in completion.choices:
+            if should_print_header:
+                sys.stdout.write("===== Chat Completion {} =====\n".format(choice.index))
+
+            content = choice.message.content if choice.message.content is not None else "None"
+            sys.stdout.write(content)
+
+            if should_print_header or not content.endswith("\n"):
+                sys.stdout.write("\n")
+
+            sys.stdout.flush()
+
+    @staticmethod
+    def _stream_create(params: CompletionCreateParamsStreaming) -> None:
+        # cast is required for mypy
+        stream = cast(  # pyright: ignore[reportUnnecessaryCast]
+            Stream[ChatCompletionChunk], get_client().chat.completions.create(**params)
+        )
+        for chunk in stream:
+            should_print_header = len(chunk.choices) > 1
+            for choice in chunk.choices:
+                if should_print_header:
+                    sys.stdout.write("===== Chat Completion {} =====\n".format(choice.index))
+
+                content = choice.delta.content or ""
+                sys.stdout.write(content)
+
+                if should_print_header:
+                    sys.stdout.write("\n")
+
+                sys.stdout.flush()
+
+        sys.stdout.write("\n")

venv/lib/python3.11/site-packages/openai/cli/_api/completions.py

@@ -0,0 +1,173 @@
+from __future__ import annotations
+
+import sys
+from typing import TYPE_CHECKING, Optional, cast
+from argparse import ArgumentParser
+from functools import partial
+
+from openai.types.completion import Completion
+
+from .._utils import get_client
+from ..._types import NOT_GIVEN, NotGivenOr
+from ..._utils import is_given
+from .._errors import CLIError
+from .._models import BaseModel
+from ..._streaming import Stream
+
+if TYPE_CHECKING:
+    from argparse import _SubParsersAction
+
+
+def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
+    sub = subparser.add_parser("completions.create")
+
+    # Required
+    sub.add_argument(
+        "-m",
+        "--model",
+        help="The model to use",
+        required=True,
+    )
+
+    # Optional
+    sub.add_argument("-p", "--prompt", help="An optional prompt to complete from")
+    sub.add_argument("--stream", help="Stream tokens as they're ready.", action="store_true")
+    sub.add_argument("-M", "--max-tokens", help="The maximum number of tokens to generate", type=int)
+    sub.add_argument(
+        "-t",
+        "--temperature",
+        help="""What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
+
+Mutually exclusive with `top_p`.""",
+        type=float,
+    )
+    sub.add_argument(
+        "-P",
+        "--top_p",
+        help="""An alternative to sampling with temperature, called nucleus sampling, where the 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.
+
+            Mutually exclusive with `temperature`.""",
+        type=float,
+    )
+    sub.add_argument(
+        "-n",
+        "--n",
+        help="How many sub-completions to generate for each prompt.",
+        type=int,
+    )
+    sub.add_argument(
+        "--logprobs",
+        help="Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. So for example, if `logprobs` is 10, the API will return a list of the 10 most likely tokens. If `logprobs` is 0, only the chosen tokens will have logprobs returned.",
+        type=int,
+    )
+    sub.add_argument(
+        "--best_of",
+        help="Generates `best_of` completions server-side and returns the 'best' (the one with the highest log probability per token). Results cannot be streamed.",
+        type=int,
+    )
+    sub.add_argument(
+        "--echo",
+        help="Echo back the prompt in addition to the completion",
+        action="store_true",
+    )
+    sub.add_argument(
+        "--frequency_penalty",
+        help="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.",
+        type=float,
+    )
+    sub.add_argument(
+        "--presence_penalty",
+        help="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.",
+        type=float,
+    )
+    sub.add_argument("--suffix", help="The suffix that comes after a completion of inserted text.")
+    sub.add_argument("--stop", help="A stop sequence at which to stop generating tokens.")
+    sub.add_argument(
+        "--user",
+        help="A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.",
+    )
+    # TODO: add support for logit_bias
+    sub.set_defaults(func=CLICompletions.create, args_model=CLICompletionCreateArgs)
+
+
+class CLICompletionCreateArgs(BaseModel):
+    model: str
+    stream: bool = False
+
+    prompt: Optional[str] = None
+    n: NotGivenOr[int] = NOT_GIVEN
+    stop: NotGivenOr[str] = NOT_GIVEN
+    user: NotGivenOr[str] = NOT_GIVEN
+    echo: NotGivenOr[bool] = NOT_GIVEN
+    suffix: NotGivenOr[str] = NOT_GIVEN
+    best_of: NotGivenOr[int] = NOT_GIVEN
+    top_p: NotGivenOr[float] = NOT_GIVEN
+    logprobs: NotGivenOr[int] = NOT_GIVEN
+    max_tokens: NotGivenOr[int] = NOT_GIVEN
+    temperature: NotGivenOr[float] = NOT_GIVEN
+    presence_penalty: NotGivenOr[float] = NOT_GIVEN
+    frequency_penalty: NotGivenOr[float] = NOT_GIVEN
+
+
+class CLICompletions:
+    @staticmethod
+    def create(args: CLICompletionCreateArgs) -> None:
+        if is_given(args.n) and args.n > 1 and args.stream:
+            raise CLIError("Can't stream completions with n>1 with the current CLI")
+
+        make_request = partial(
+            get_client().completions.create,
+            n=args.n,
+            echo=args.echo,
+            stop=args.stop,
+            user=args.user,
+            model=args.model,
+            top_p=args.top_p,
+            prompt=args.prompt,
+            suffix=args.suffix,
+            best_of=args.best_of,
+            logprobs=args.logprobs,
+            max_tokens=args.max_tokens,
+            temperature=args.temperature,
+            presence_penalty=args.presence_penalty,
+            frequency_penalty=args.frequency_penalty,
+        )
+
+        if args.stream:
+            return CLICompletions._stream_create(
+                # mypy doesn't understand the `partial` function but pyright does
+                cast(Stream[Completion], make_request(stream=True))  # pyright: ignore[reportUnnecessaryCast]
+            )
+
+        return CLICompletions._create(make_request())
+
+    @staticmethod
+    def _create(completion: Completion) -> None:
+        should_print_header = len(completion.choices) > 1
+        for choice in completion.choices:
+            if should_print_header:
+                sys.stdout.write("===== Completion {} =====\n".format(choice.index))
+
+            sys.stdout.write(choice.text)
+
+            if should_print_header or not choice.text.endswith("\n"):
+                sys.stdout.write("\n")
+
+            sys.stdout.flush()
+
+    @staticmethod
+    def _stream_create(stream: Stream[Completion]) -> None:
+        for completion in stream:
+            should_print_header = len(completion.choices) > 1
+            for choice in sorted(completion.choices, key=lambda c: c.index):
+                if should_print_header:
+                    sys.stdout.write("===== Chat Completion {} =====\n".format(choice.index))
+
+                sys.stdout.write(choice.text)
+
+                if should_print_header:
+                    sys.stdout.write("\n")
+
+                sys.stdout.flush()
+
+        sys.stdout.write("\n")

venv/lib/python3.11/site-packages/openai/cli/_api/files.py

@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, cast
+from argparse import ArgumentParser
+
+from .._utils import get_client, print_model
+from .._models import BaseModel
+from .._progress import BufferReader
+
+if TYPE_CHECKING:
+    from argparse import _SubParsersAction
+
+
+def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
+    sub = subparser.add_parser("files.create")
+
+    sub.add_argument(
+        "-f",
+        "--file",
+        required=True,
+        help="File to upload",
+    )
+    sub.add_argument(
+        "-p",
+        "--purpose",
+        help="Why are you uploading this file? (see https://platform.openai.com/docs/api-reference/ for purposes)",
+        required=True,
+    )
+    sub.set_defaults(func=CLIFile.create, args_model=CLIFileCreateArgs)
+
+    sub = subparser.add_parser("files.retrieve")
+    sub.add_argument("-i", "--id", required=True, help="The files ID")
+    sub.set_defaults(func=CLIFile.get, args_model=CLIFileCreateArgs)
+
+    sub = subparser.add_parser("files.delete")
+    sub.add_argument("-i", "--id", required=True, help="The files ID")
+    sub.set_defaults(func=CLIFile.delete, args_model=CLIFileCreateArgs)
+
+    sub = subparser.add_parser("files.list")
+    sub.set_defaults(func=CLIFile.list)
+
+
+class CLIFileIDArgs(BaseModel):
+    id: str
+
+
+class CLIFileCreateArgs(BaseModel):
+    file: str
+    purpose: str
+
+
+class CLIFile:
+    @staticmethod
+    def create(args: CLIFileCreateArgs) -> None:
+        with open(args.file, "rb") as file_reader:
+            buffer_reader = BufferReader(file_reader.read(), desc="Upload progress")
+
+        file = get_client().files.create(
+            file=(args.file, buffer_reader),
+            # casts required because the API is typed for enums
+            # but we don't want to validate that here for forwards-compat
+            purpose=cast(Any, args.purpose),
+        )
+        print_model(file)
+
+    @staticmethod
+    def get(args: CLIFileIDArgs) -> None:
+        file = get_client().files.retrieve(file_id=args.id)
+        print_model(file)
+
+    @staticmethod
+    def delete(args: CLIFileIDArgs) -> None:
+        file = get_client().files.delete(file_id=args.id)
+        print_model(file)
+
+    @staticmethod
+    def list() -> None:
+        files = get_client().files.list()
+        for file in files:
+            print_model(file)

venv/lib/python3.11/site-packages/openai/cli/_api/image.py

@@ -0,0 +1,139 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, cast
+from argparse import ArgumentParser
+
+from .._utils import get_client, print_model
+from ..._types import NOT_GIVEN, NotGiven, NotGivenOr
+from .._models import BaseModel
+from .._progress import BufferReader
+
+if TYPE_CHECKING:
+    from argparse import _SubParsersAction
+
+
+def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
+    sub = subparser.add_parser("images.generate")
+    sub.add_argument("-m", "--model", type=str)
+    sub.add_argument("-p", "--prompt", type=str, required=True)
+    sub.add_argument("-n", "--num-images", type=int, default=1)
+    sub.add_argument("-s", "--size", type=str, default="1024x1024", help="Size of the output image")
+    sub.add_argument("--response-format", type=str, default="url")
+    sub.set_defaults(func=CLIImage.create, args_model=CLIImageCreateArgs)
+
+    sub = subparser.add_parser("images.edit")
+    sub.add_argument("-m", "--model", type=str)
+    sub.add_argument("-p", "--prompt", type=str, required=True)
+    sub.add_argument("-n", "--num-images", type=int, default=1)
+    sub.add_argument(
+        "-I",
+        "--image",
+        type=str,
+        required=True,
+        help="Image to modify. Should be a local path and a PNG encoded image.",
+    )
+    sub.add_argument("-s", "--size", type=str, default="1024x1024", help="Size of the output image")
+    sub.add_argument("--response-format", type=str, default="url")
+    sub.add_argument(
+        "-M",
+        "--mask",
+        type=str,
+        required=False,
+        help="Path to a mask image. It should be the same size as the image you're editing and a RGBA PNG image. The Alpha channel acts as the mask.",
+    )
+    sub.set_defaults(func=CLIImage.edit, args_model=CLIImageEditArgs)
+
+    sub = subparser.add_parser("images.create_variation")
+    sub.add_argument("-m", "--model", type=str)
+    sub.add_argument("-n", "--num-images", type=int, default=1)
+    sub.add_argument(
+        "-I",
+        "--image",
+        type=str,
+        required=True,
+        help="Image to modify. Should be a local path and a PNG encoded image.",
+    )
+    sub.add_argument("-s", "--size", type=str, default="1024x1024", help="Size of the output image")
+    sub.add_argument("--response-format", type=str, default="url")
+    sub.set_defaults(func=CLIImage.create_variation, args_model=CLIImageCreateVariationArgs)
+
+
+class CLIImageCreateArgs(BaseModel):
+    prompt: str
+    num_images: int
+    size: str
+    response_format: str
+    model: NotGivenOr[str] = NOT_GIVEN
+
+
+class CLIImageCreateVariationArgs(BaseModel):
+    image: str
+    num_images: int
+    size: str
+    response_format: str
+    model: NotGivenOr[str] = NOT_GIVEN
+
+
+class CLIImageEditArgs(BaseModel):
+    image: str
+    num_images: int
+    size: str
+    response_format: str
+    prompt: str
+    mask: NotGivenOr[str] = NOT_GIVEN
+    model: NotGivenOr[str] = NOT_GIVEN
+
+
+class CLIImage:
+    @staticmethod
+    def create(args: CLIImageCreateArgs) -> None:
+        image = get_client().images.generate(
+            model=args.model,
+            prompt=args.prompt,
+            n=args.num_images,
+            # casts required because the API is typed for enums
+            # but we don't want to validate that here for forwards-compat
+            size=cast(Any, args.size),
+            response_format=cast(Any, args.response_format),
+        )
+        print_model(image)
+
+    @staticmethod
+    def create_variation(args: CLIImageCreateVariationArgs) -> None:
+        with open(args.image, "rb") as file_reader:
+            buffer_reader = BufferReader(file_reader.read(), desc="Upload progress")
+
+        image = get_client().images.create_variation(
+            model=args.model,
+            image=("image", buffer_reader),
+            n=args.num_images,
+            # casts required because the API is typed for enums
+            # but we don't want to validate that here for forwards-compat
+            size=cast(Any, args.size),
+            response_format=cast(Any, args.response_format),
+        )
+        print_model(image)
+
+    @staticmethod
+    def edit(args: CLIImageEditArgs) -> None:
+        with open(args.image, "rb") as file_reader:
+            buffer_reader = BufferReader(file_reader.read(), desc="Image upload progress")
+
+        if isinstance(args.mask, NotGiven):
+            mask: NotGivenOr[BufferReader] = NOT_GIVEN
+        else:
+            with open(args.mask, "rb") as file_reader:
+                mask = BufferReader(file_reader.read(), desc="Mask progress")
+
+        image = get_client().images.edit(
+            model=args.model,
+            prompt=args.prompt,
+            image=("image", buffer_reader),
+            n=args.num_images,
+            mask=("mask", mask) if not isinstance(mask, NotGiven) else mask,
+            # casts required because the API is typed for enums
+            # but we don't want to validate that here for forwards-compat
+            size=cast(Any, args.size),
+            response_format=cast(Any, args.response_format),
+        )
+        print_model(image)

venv/lib/python3.11/site-packages/openai/cli/_api/models.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+from argparse import ArgumentParser
+
+from .._utils import get_client, print_model
+from .._models import BaseModel
+
+if TYPE_CHECKING:
+    from argparse import _SubParsersAction
+
+
+def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
+    sub = subparser.add_parser("models.list")
+    sub.set_defaults(func=CLIModels.list)
+
+    sub = subparser.add_parser("models.retrieve")
+    sub.add_argument("-i", "--id", required=True, help="The model ID")
+    sub.set_defaults(func=CLIModels.get, args_model=CLIModelIDArgs)
+
+    sub = subparser.add_parser("models.delete")
+    sub.add_argument("-i", "--id", required=True, help="The model ID")
+    sub.set_defaults(func=CLIModels.delete, args_model=CLIModelIDArgs)
+
+
+class CLIModelIDArgs(BaseModel):
+    id: str
+
+
+class CLIModels:
+    @staticmethod
+    def get(args: CLIModelIDArgs) -> None:
+        model = get_client().models.retrieve(model=args.id)
+        print_model(model)
+
+    @staticmethod
+    def delete(args: CLIModelIDArgs) -> None:
+        model = get_client().models.delete(model=args.id)
+        print_model(model)
+
+    @staticmethod
+    def list() -> None:
+        models = get_client().models.list()
+        for model in models:
+            print_model(model)

venv/lib/python3.11/site-packages/openai/cli/_cli.py

@@ -0,0 +1,234 @@
+from __future__ import annotations
+
+import sys
+import logging
+import argparse
+from typing import Any, List, Type, Optional
+from typing_extensions import ClassVar
+
+import httpx
+import pydantic
+
+import openai
+
+from . import _tools
+from .. import _ApiType, __version__
+from ._api import register_commands
+from ._utils import can_use_http2
+from .._types import ProxiesDict
+from ._errors import CLIError, display_error
+from .._compat import PYDANTIC_V2, ConfigDict, model_parse
+from .._models import BaseModel
+from .._exceptions import APIError
+
+logger = logging.getLogger()
+formatter = logging.Formatter("[%(asctime)s] %(message)s")
+handler = logging.StreamHandler(sys.stderr)
+handler.setFormatter(formatter)
+logger.addHandler(handler)
+
+
+class Arguments(BaseModel):
+    if PYDANTIC_V2:
+        model_config: ClassVar[ConfigDict] = ConfigDict(
+            extra="ignore",
+        )
+    else:
+
+        class Config(pydantic.BaseConfig):  # type: ignore
+            extra: Any = pydantic.Extra.ignore  # type: ignore
+
+    verbosity: int
+    version: Optional[str] = None
+
+    api_key: Optional[str]
+    api_base: Optional[str]
+    organization: Optional[str]
+    proxy: Optional[List[str]]
+    api_type: Optional[_ApiType] = None
+    api_version: Optional[str] = None
+
+    # azure
+    azure_endpoint: Optional[str] = None
+    azure_ad_token: Optional[str] = None
+
+    # internal, set by subparsers to parse their specific args
+    args_model: Optional[Type[BaseModel]] = None
+
+    # internal, used so that subparsers can forward unknown arguments
+    unknown_args: List[str] = []
+    allow_unknown_args: bool = False
+
+
+def _build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description=None, prog="openai")
+    parser.add_argument(
+        "-v",
+        "--verbose",
+        action="count",
+        dest="verbosity",
+        default=0,
+        help="Set verbosity.",
+    )
+    parser.add_argument("-b", "--api-base", help="What API base url to use.")
+    parser.add_argument("-k", "--api-key", help="What API key to use.")
+    parser.add_argument("-p", "--proxy", nargs="+", help="What proxy to use.")
+    parser.add_argument(
+        "-o",
+        "--organization",
+        help="Which organization to run as (will use your default organization if not specified)",
+    )
+    parser.add_argument(
+        "-t",
+        "--api-type",
+        type=str,
+        choices=("openai", "azure"),
+        help="The backend API to call, must be `openai` or `azure`",
+    )
+    parser.add_argument(
+        "--api-version",
+        help="The Azure API version, e.g. 'https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning'",
+    )
+
+    # azure
+    parser.add_argument(
+        "--azure-endpoint",
+        help="The Azure endpoint, e.g. 'https://endpoint.openai.azure.com'",
+    )
+    parser.add_argument(
+        "--azure-ad-token",
+        help="A token from Azure Active Directory, https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id",
+    )
+
+    # prints the package version
+    parser.add_argument(
+        "-V",
+        "--version",
+        action="version",
+        version="%(prog)s " + __version__,
+    )
+
+    def help() -> None:
+        parser.print_help()
+
+    parser.set_defaults(func=help)
+
+    subparsers = parser.add_subparsers()
+    sub_api = subparsers.add_parser("api", help="Direct API calls")
+
+    register_commands(sub_api)
+
+    sub_tools = subparsers.add_parser("tools", help="Client side tools for convenience")
+    _tools.register_commands(sub_tools, subparsers)
+
+    return parser
+
+
+def main() -> int:
+    try:
+        _main()
+    except (APIError, CLIError, pydantic.ValidationError) as err:
+        display_error(err)
+        return 1
+    except KeyboardInterrupt:
+        sys.stderr.write("\n")
+        return 1
+    return 0
+
+
+def _parse_args(parser: argparse.ArgumentParser) -> tuple[argparse.Namespace, Arguments, list[str]]:
+    # argparse by default will strip out the `--` but we want to keep it for unknown arguments
+    if "--" in sys.argv:
+        idx = sys.argv.index("--")
+        known_args = sys.argv[1:idx]
+        unknown_args = sys.argv[idx:]
+    else:
+        known_args = sys.argv[1:]
+        unknown_args = []
+
+    parsed, remaining_unknown = parser.parse_known_args(known_args)
+
+    # append any remaining unknown arguments from the initial parsing
+    remaining_unknown.extend(unknown_args)
+
+    args = model_parse(Arguments, vars(parsed))
+    if not args.allow_unknown_args:
+        # we have to parse twice to ensure any unknown arguments
+        # result in an error if that behaviour is desired
+        parser.parse_args()
+
+    return parsed, args, remaining_unknown
+
+
+def _main() -> None:
+    parser = _build_parser()
+    parsed, args, unknown = _parse_args(parser)
+
+    if args.verbosity != 0:
+        sys.stderr.write("Warning: --verbosity isn't supported yet\n")
+
+    proxies: ProxiesDict = {}
+    if args.proxy is not None:
+        for proxy in args.proxy:
+            key = "https://" if proxy.startswith("https") else "http://"
+            if key in proxies:
+                raise CLIError(f"Multiple {key} proxies given - only the last one would be used")
+
+            proxies[key] = proxy
+
+    http_client = httpx.Client(
+        proxies=proxies or None,
+        http2=can_use_http2(),
+    )
+    openai.http_client = http_client
+
+    if args.organization:
+        openai.organization = args.organization
+
+    if args.api_key:
+        openai.api_key = args.api_key
+
+    if args.api_base:
+        openai.base_url = args.api_base
+
+    # azure
+    if args.api_type is not None:
+        openai.api_type = args.api_type
+
+    if args.azure_endpoint is not None:
+        openai.azure_endpoint = args.azure_endpoint
+
+    if args.api_version is not None:
+        openai.api_version = args.api_version
+
+    if args.azure_ad_token is not None:
+        openai.azure_ad_token = args.azure_ad_token
+
+    try:
+        if args.args_model:
+            parsed.func(
+                model_parse(
+                    args.args_model,
+                    {
+                        **{
+                            # we omit None values so that they can be defaulted to `NotGiven`
+                            # and we'll strip it from the API request
+                            key: value
+                            for key, value in vars(parsed).items()
+                            if value is not None
+                        },
+                        "unknown_args": unknown,
+                    },
+                )
+            )
+        else:
+            parsed.func()
+    finally:
+        try:
+            http_client.close()
+        except Exception:
+            pass
+
+
+if __name__ == "__main__":
+    sys.exit(main())

venv/lib/python3.11/site-packages/openai/cli/_errors.py

@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+import sys
+
+import pydantic
+
+from ._utils import Colors, organization_info
+from .._exceptions import APIError, OpenAIError
+
+
+class CLIError(OpenAIError):
+    ...
+
+
+class SilentCLIError(CLIError):
+    ...
+
+
+def display_error(err: CLIError | APIError | pydantic.ValidationError) -> None:
+    if isinstance(err, SilentCLIError):
+        return
+
+    sys.stderr.write("{}{}Error:{} {}\n".format(organization_info(), Colors.FAIL, Colors.ENDC, err))

venv/lib/python3.11/site-packages/openai/cli/_models.py

@@ -0,0 +1,17 @@
+from typing import Any
+from typing_extensions import ClassVar
+
+import pydantic
+
+from .. import _models
+from .._compat import PYDANTIC_V2, ConfigDict
+
+
+class BaseModel(_models.BaseModel):
+    if PYDANTIC_V2:
+        model_config: ClassVar[ConfigDict] = ConfigDict(extra="ignore", arbitrary_types_allowed=True)
+    else:
+
+        class Config(pydantic.BaseConfig):  # type: ignore
+            extra: Any = pydantic.Extra.ignore  # type: ignore
+            arbitrary_types_allowed: bool = True

venv/lib/python3.11/site-packages/openai/cli/_progress.py

@@ -0,0 +1,59 @@
+from __future__ import annotations
+
+import io
+from typing import Callable
+from typing_extensions import override
+
+
+class CancelledError(Exception):
+    def __init__(self, msg: str) -> None:
+        self.msg = msg
+        super().__init__(msg)
+
+    @override
+    def __str__(self) -> str:
+        return self.msg
+
+    __repr__ = __str__
+
+
+class BufferReader(io.BytesIO):
+    def __init__(self, buf: bytes = b"", desc: str | None = None) -> None:
+        super().__init__(buf)
+        self._len = len(buf)
+        self._progress = 0
+        self._callback = progress(len(buf), desc=desc)
+
+    def __len__(self) -> int:
+        return self._len
+
+    @override
+    def read(self, n: int | None = -1) -> bytes:
+        chunk = io.BytesIO.read(self, n)
+        self._progress += len(chunk)
+
+        try:
+            self._callback(self._progress)
+        except Exception as e:  # catches exception from the callback
+            raise CancelledError("The upload was cancelled: {}".format(e)) from e
+
+        return chunk
+
+
+def progress(total: float, desc: str | None) -> Callable[[float], None]:
+    import tqdm
+
+    meter = tqdm.tqdm(total=total, unit_scale=True, desc=desc)
+
+    def incr(progress: float) -> None:
+        meter.n = progress
+        if progress == total:
+            meter.close()
+        else:
+            meter.refresh()
+
+    return incr
+
+
+def MB(i: int) -> int:
+    return int(i // 1024**2)

venv/lib/python3.11/site-packages/openai/cli/_tools/__init__.py

@@ -0,0 +1 @@
+from ._main import register_commands as register_commands

venv/lib/python3.11/site-packages/openai/cli/_tools/_main.py

@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+from argparse import ArgumentParser
+
+from . import migrate, fine_tunes
+
+if TYPE_CHECKING:
+    from argparse import _SubParsersAction
+
+
+def register_commands(parser: ArgumentParser, subparser: _SubParsersAction[ArgumentParser]) -> None:
+    migrate.register(subparser)
+
+    namespaced = parser.add_subparsers(title="Tools", help="Convenience client side tools")
+
+    fine_tunes.register(namespaced)

venv/lib/python3.11/site-packages/openai/cli/_tools/fine_tunes.py

@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+import sys
+from typing import TYPE_CHECKING
+from argparse import ArgumentParser
+
+from .._models import BaseModel
+from ...lib._validators import (
+    get_validators,
+    write_out_file,
+    read_any_format,
+    apply_validators,
+    apply_necessary_remediation,
+)
+
+if TYPE_CHECKING:
+    from argparse import _SubParsersAction
+
+
+def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
+    sub = subparser.add_parser("fine_tunes.prepare_data")
+    sub.add_argument(
+        "-f",
+        "--file",
+        required=True,
+        help="JSONL, JSON, CSV, TSV, TXT or XLSX file containing prompt-completion examples to be analyzed."
+        "This should be the local file path.",
+    )
+    sub.add_argument(
+        "-q",
+        "--quiet",
+        required=False,
+        action="store_true",
+        help="Auto accepts all suggestions, without asking for user input. To be used within scripts.",
+    )
+    sub.set_defaults(func=prepare_data, args_model=PrepareDataArgs)
+
+
+class PrepareDataArgs(BaseModel):
+    file: str
+
+    quiet: bool
+
+
+def prepare_data(args: PrepareDataArgs) -> None:
+    sys.stdout.write("Analyzing...\n")
+    fname = args.file
+    auto_accept = args.quiet
+    df, remediation = read_any_format(fname)
+    apply_necessary_remediation(None, remediation)
+
+    validators = get_validators()
+
+    assert df is not None
+
+    apply_validators(
+        df,
+        fname,
+        remediation,
+        validators,
+        auto_accept,
+        write_out_file_func=write_out_file,
+    )

venv/lib/python3.11/site-packages/openai/cli/_tools/migrate.py

@@ -0,0 +1,181 @@
+from __future__ import annotations
+
+import os
+import sys
+import json
+import shutil
+import tarfile
+import platform
+import subprocess
+from typing import TYPE_CHECKING, List
+from pathlib import Path
+from argparse import ArgumentParser
+
+import httpx
+
+from .._errors import CLIError, SilentCLIError
+from .._models import BaseModel
+
+if TYPE_CHECKING:
+    from argparse import _SubParsersAction
+
+
+def register(subparser: _SubParsersAction[ArgumentParser]) -> None:
+    sub = subparser.add_parser("migrate")
+    sub.set_defaults(func=migrate, args_model=MigrateArgs, allow_unknown_args=True)
+
+    sub = subparser.add_parser("grit")
+    sub.set_defaults(func=grit, args_model=GritArgs, allow_unknown_args=True)
+
+
+class GritArgs(BaseModel):
+    # internal
+    unknown_args: List[str] = []
+
+
+def grit(args: GritArgs) -> None:
+    grit_path = install()
+
+    try:
+        subprocess.check_call([grit_path, *args.unknown_args])
+    except subprocess.CalledProcessError:
+        # stdout and stderr are forwarded by subprocess so an error will already
+        # have been displayed
+        raise SilentCLIError() from None
+
+
+class MigrateArgs(BaseModel):
+    # internal
+    unknown_args: List[str] = []
+
+
+def migrate(args: MigrateArgs) -> None:
+    grit_path = install()
+
+    try:
+        subprocess.check_call([grit_path, "apply", "openai", *args.unknown_args])
+    except subprocess.CalledProcessError:
+        # stdout and stderr are forwarded by subprocess so an error will already
+        # have been displayed
+        raise SilentCLIError() from None
+
+
+# handles downloading the Grit CLI until they provide their own PyPi package
+
+KEYGEN_ACCOUNT = "custodian-dev"
+
+
+def _cache_dir() -> Path:
+    xdg = os.environ.get("XDG_CACHE_HOME")
+    if xdg is not None:
+        return Path(xdg)
+
+    return Path.home() / ".cache"
+
+
+def _debug(message: str) -> None:
+    if not os.environ.get("DEBUG"):
+        return
+
+    sys.stdout.write(f"[DEBUG]: {message}\n")
+
+
+def install() -> Path:
+    """Installs the Grit CLI and returns the location of the binary"""
+    if sys.platform == "win32":
+        raise CLIError("Windows is not supported yet in the migration CLI")
+
+    platform = "macos" if sys.platform == "darwin" else "linux"
+
+    dir_name = _cache_dir() / "openai-python"
+    install_dir = dir_name / ".install"
+    target_dir = install_dir / "bin"
+
+    target_path = target_dir / "marzano"
+    temp_file = target_dir / "marzano.tmp"
+
+    if target_path.exists():
+        _debug(f"{target_path} already exists")
+        sys.stdout.flush()
+        return target_path
+
+    _debug(f"Using Grit CLI path: {target_path}")
+
+    target_dir.mkdir(parents=True, exist_ok=True)
+
+    if temp_file.exists():
+        temp_file.unlink()
+
+    arch = _get_arch()
+    _debug(f"Using architecture {arch}")
+
+    file_name = f"marzano-{platform}-{arch}"
+    meta_url = f"https://api.keygen.sh/v1/accounts/{KEYGEN_ACCOUNT}/artifacts/{file_name}"
+
+    sys.stdout.write(f"Retrieving Grit CLI metadata from {meta_url}\n")
+    with httpx.Client() as client:
+        response = client.get(meta_url)  # pyright: ignore[reportUnknownMemberType]
+
+        data = response.json()
+        errors = data.get("errors")
+        if errors:
+            for error in errors:
+                sys.stdout.write(f"{error}\n")
+
+            raise CLIError("Could not locate Grit CLI binary - see above errors")
+
+        write_manifest(install_dir, data["data"]["relationships"]["release"]["data"]["id"])
+
+        link = data["data"]["links"]["redirect"]
+        _debug(f"Redirect URL {link}")
+
+        download_response = client.get(link)  # pyright: ignore[reportUnknownMemberType]
+        with open(temp_file, "wb") as file:
+            for chunk in download_response.iter_bytes():
+                file.write(chunk)
+
+    unpacked_dir = target_dir / "cli-bin"
+    unpacked_dir.mkdir(parents=True, exist_ok=True)
+
+    with tarfile.open(temp_file, "r:gz") as archive:
+        archive.extractall(unpacked_dir)
+
+    for item in unpacked_dir.iterdir():
+        item.rename(target_dir / item.name)
+
+    shutil.rmtree(unpacked_dir)
+    os.remove(temp_file)
+    os.chmod(target_path, 0o755)
+
+    sys.stdout.flush()
+
+    return target_path
+
+
+def _get_arch() -> str:
+    architecture = platform.machine().lower()
+
+    # Map the architecture names to Node.js equivalents
+    arch_map = {
+        "x86_64": "x64",
+        "amd64": "x64",
+        "armv7l": "arm",
+        "aarch64": "arm64",
+    }
+
+    return arch_map.get(architecture, architecture)
+
+
+def write_manifest(install_path: Path, release: str) -> None:
+    manifest = {
+        "installPath": str(install_path),
+        "binaries": {
+            "marzano": {
+                "name": "marzano",
+                "release": release,
+            },
+        },
+    }
+    manifest_path = Path(install_path) / "manifests.json"
+    with open(manifest_path, "w") as f:
+        json.dump(manifest, f, indent=2)

venv/lib/python3.11/site-packages/openai/cli/_utils.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import sys
+
+import openai
+
+from .. import OpenAI, _load_client
+from .._compat import model_json
+from .._models import BaseModel
+
+
+class Colors:
+    HEADER = "\033[95m"
+    OKBLUE = "\033[94m"
+    OKGREEN = "\033[92m"
+    WARNING = "\033[93m"
+    FAIL = "\033[91m"
+    ENDC = "\033[0m"
+    BOLD = "\033[1m"
+    UNDERLINE = "\033[4m"
+
+
+def get_client() -> OpenAI:
+    return _load_client()
+
+
+def organization_info() -> str:
+    organization = openai.organization
+    if organization is not None:
+        return "[organization={}] ".format(organization)
+
+    return ""
+
+
+def print_model(model: BaseModel) -> None:
+    sys.stdout.write(model_json(model, indent=2) + "\n")
+
+
+def can_use_http2() -> bool:
+    try:
+        import h2  # type: ignore  # noqa
+    except ImportError:
+        return False
+
+    return True

venv/lib/python3.11/site-packages/openai/lib/.keep

@@ -0,0 +1,4 @@
+File generated from our OpenAPI spec by Stainless.
+
+This directory can be used to store custom files to expand the SDK.
+It is ignored by Stainless code generation and its content (other than this keep file) won't be touched.