# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from __future__ import annotations
import asyncio
from typing import Dict, List, Iterable, Optional
from typing_extensions import Union, Literal
from concurrent.futures import Future, ThreadPoolExecutor, as_completed
import httpx
import sniffio
from ... import _legacy_response
from ...types import FileChunkingStrategyParam
from ..._types import NOT_GIVEN, Body, Query, Headers, NotGiven, FileTypes
from ..._utils import (
is_given,
maybe_transform,
async_maybe_transform,
)
from ..._compat import cached_property
from ..._resource import SyncAPIResource, AsyncAPIResource
from ..._response import to_streamed_response_wrapper, async_to_streamed_response_wrapper
from ...pagination import SyncCursorPage, AsyncCursorPage
from ..._base_client import AsyncPaginator, make_request_options
from ...types.file_object import FileObject
from ...types.vector_stores import file_batch_create_params, file_batch_list_files_params
from ...types.file_chunking_strategy_param import FileChunkingStrategyParam
from ...types.vector_stores.vector_store_file import VectorStoreFile
from ...types.vector_stores.vector_store_file_batch import VectorStoreFileBatch
__all__ = ["FileBatches", "AsyncFileBatches"]
class FileBatches(SyncAPIResource):
@cached_property
def with_raw_response(self) -> FileBatchesWithRawResponse:
"""
This property can be used as a prefix for any HTTP method call to return
the raw response object instead of the parsed content.
For more information, see https://www.github.com/openai/openai-python#accessing-raw-response-data-eg-headers
"""
return FileBatchesWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> FileBatchesWithStreamingResponse:
"""
An alternative to `.with_raw_response` that doesn't eagerly read the response body.
For more information, see https://www.github.com/openai/openai-python#with_streaming_response
"""
return FileBatchesWithStreamingResponse(self)
def create(
self,
vector_store_id: str,
*,
file_ids: List[str],
attributes: Optional[Dict[str, Union[str, float, bool]]] | NotGiven = NOT_GIVEN,
chunking_strategy: FileChunkingStrategyParam | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""
Create a vector store file batch.
Args:
file_ids: A list of [File](https://platform.openai.com/docs/api-reference/files) IDs that
the vector store should use. Useful for tools like `file_search` that can access
files.
attributes: Set of 16 key-value pairs that can be attached to an object. This can be useful
for storing additional information about the object in a structured format, and
querying for objects via API or the dashboard. Keys are strings with a maximum
length of 64 characters. Values are strings with a maximum length of 512
characters, booleans, or numbers.
chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will use the `auto`
strategy. Only applicable if `file_ids` is non-empty.
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
if not vector_store_id:
raise ValueError(f"Expected a non-empty value for `vector_store_id` but received {vector_store_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._post(
f"/vector_stores/{vector_store_id}/file_batches",
body=maybe_transform(
{
"file_ids": file_ids,
"attributes": attributes,
"chunking_strategy": chunking_strategy,
},
file_batch_create_params.FileBatchCreateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=VectorStoreFileBatch,
)
def retrieve(
self,
batch_id: str,
*,
vector_store_id: str,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""
Retrieves a vector store file batch.
Args:
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
if not vector_store_id:
raise ValueError(f"Expected a non-empty value for `vector_store_id` but received {vector_store_id!r}")
if not batch_id:
raise ValueError(f"Expected a non-empty value for `batch_id` but received {batch_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._get(
f"/vector_stores/{vector_store_id}/file_batches/{batch_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=VectorStoreFileBatch,
)
def cancel(
self,
batch_id: str,
*,
vector_store_id: str,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""Cancel a vector store file batch.
This attempts to cancel the processing of
files in this batch as soon as possible.
Args:
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
if not vector_store_id:
raise ValueError(f"Expected a non-empty value for `vector_store_id` but received {vector_store_id!r}")
if not batch_id:
raise ValueError(f"Expected a non-empty value for `batch_id` but received {batch_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._post(
f"/vector_stores/{vector_store_id}/file_batches/{batch_id}/cancel",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=VectorStoreFileBatch,
)
def create_and_poll(
self,
vector_store_id: str,
*,
file_ids: List[str],
poll_interval_ms: int | NotGiven = NOT_GIVEN,
chunking_strategy: FileChunkingStrategyParam | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""Create a vector store batch and poll until all files have been processed."""
batch = self.create(
vector_store_id=vector_store_id,
file_ids=file_ids,
chunking_strategy=chunking_strategy,
)
# TODO: don't poll unless necessary??
return self.poll(
batch.id,
vector_store_id=vector_store_id,
poll_interval_ms=poll_interval_ms,
)
def list_files(
self,
batch_id: str,
*,
vector_store_id: str,
after: str | NotGiven = NOT_GIVEN,
before: str | NotGiven = NOT_GIVEN,
filter: Literal["in_progress", "completed", "failed", "cancelled"] | NotGiven = NOT_GIVEN,
limit: int | NotGiven = NOT_GIVEN,
order: Literal["asc", "desc"] | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> SyncCursorPage[VectorStoreFile]:
"""
Returns a list of vector store files in a batch.
Args:
after: A cursor for use in pagination. `after` is an object ID that defines your place
in the list. For instance, if you make a list request and receive 100 objects,
ending with obj_foo, your subsequent call can include after=obj_foo in order to
fetch the next page of the list.
before: A cursor for use in pagination. `before` is an object ID that defines your place
in the list. For instance, if you make a list request and receive 100 objects,
starting with obj_foo, your subsequent call can include before=obj_foo in order
to fetch the previous page of the list.
filter: Filter by file status. One of `in_progress`, `completed`, `failed`, `cancelled`.
limit: A limit on the number of objects to be returned. Limit can range between 1 and
100, and the default is 20.
order: Sort order by the `created_at` timestamp of the objects. `asc` for ascending
order and `desc` for descending order.
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
if not vector_store_id:
raise ValueError(f"Expected a non-empty value for `vector_store_id` but received {vector_store_id!r}")
if not batch_id:
raise ValueError(f"Expected a non-empty value for `batch_id` but received {batch_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._get_api_list(
f"/vector_stores/{vector_store_id}/file_batches/{batch_id}/files",
page=SyncCursorPage[VectorStoreFile],
options=make_request_options(
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
query=maybe_transform(
{
"after": after,
"before": before,
"filter": filter,
"limit": limit,
"order": order,
},
file_batch_list_files_params.FileBatchListFilesParams,
),
),
model=VectorStoreFile,
)
def poll(
self,
batch_id: str,
*,
vector_store_id: str,
poll_interval_ms: int | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""Wait for the given file batch to be processed.
Note: this will return even if one of the files failed to process, you need to
check batch.file_counts.failed_count to handle this case.
"""
headers: dict[str, str] = {"X-Stainless-Poll-Helper": "true"}
if is_given(poll_interval_ms):
headers["X-Stainless-Custom-Poll-Interval"] = str(poll_interval_ms)
while True:
response = self.with_raw_response.retrieve(
batch_id,
vector_store_id=vector_store_id,
extra_headers=headers,
)
batch = response.parse()
if batch.file_counts.in_progress > 0:
if not is_given(poll_interval_ms):
from_header = response.headers.get("openai-poll-after-ms")
if from_header is not None:
poll_interval_ms = int(from_header)
else:
poll_interval_ms = 1000
self._sleep(poll_interval_ms / 1000)
continue
return batch
def upload_and_poll(
self,
vector_store_id: str,
*,
files: Iterable[FileTypes],
max_concurrency: int = 5,
file_ids: List[str] = [],
poll_interval_ms: int | NotGiven = NOT_GIVEN,
chunking_strategy: FileChunkingStrategyParam | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""Uploads the given files concurrently and then creates a vector store file batch.
If you've already uploaded certain files that you want to include in this batch
then you can pass their IDs through the `file_ids` argument.
By default, if any file upload fails then an exception will be eagerly raised.
The number of concurrency uploads is configurable using the `max_concurrency`
parameter.
Note: this method only supports `asyncio` or `trio` as the backing async
runtime.
"""
results: list[FileObject] = []
with ThreadPoolExecutor(max_workers=max_concurrency) as executor:
futures: list[Future[FileObject]] = [
executor.submit(
self._client.files.create,
file=file,
purpose="assistants",
)
for file in files
]
for future in as_completed(futures):
exc = future.exception()
if exc:
raise exc
results.append(future.result())
batch = self.create_and_poll(
vector_store_id=vector_store_id,
file_ids=[*file_ids, *(f.id for f in results)],
poll_interval_ms=poll_interval_ms,
chunking_strategy=chunking_strategy,
)
return batch
class AsyncFileBatches(AsyncAPIResource):
@cached_property
def with_raw_response(self) -> AsyncFileBatchesWithRawResponse:
"""
This property can be used as a prefix for any HTTP method call to return
the raw response object instead of the parsed content.
For more information, see https://www.github.com/openai/openai-python#accessing-raw-response-data-eg-headers
"""
return AsyncFileBatchesWithRawResponse(self)
@cached_property
def with_streaming_response(self) -> AsyncFileBatchesWithStreamingResponse:
"""
An alternative to `.with_raw_response` that doesn't eagerly read the response body.
For more information, see https://www.github.com/openai/openai-python#with_streaming_response
"""
return AsyncFileBatchesWithStreamingResponse(self)
async def create(
self,
vector_store_id: str,
*,
file_ids: List[str],
attributes: Optional[Dict[str, Union[str, float, bool]]] | NotGiven = NOT_GIVEN,
chunking_strategy: FileChunkingStrategyParam | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""
Create a vector store file batch.
Args:
file_ids: A list of [File](https://platform.openai.com/docs/api-reference/files) IDs that
the vector store should use. Useful for tools like `file_search` that can access
files.
attributes: Set of 16 key-value pairs that can be attached to an object. This can be useful
for storing additional information about the object in a structured format, and
querying for objects via API or the dashboard. Keys are strings with a maximum
length of 64 characters. Values are strings with a maximum length of 512
characters, booleans, or numbers.
chunking_strategy: The chunking strategy used to chunk the file(s). If not set, will use the `auto`
strategy. Only applicable if `file_ids` is non-empty.
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
if not vector_store_id:
raise ValueError(f"Expected a non-empty value for `vector_store_id` but received {vector_store_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._post(
f"/vector_stores/{vector_store_id}/file_batches",
body=await async_maybe_transform(
{
"file_ids": file_ids,
"attributes": attributes,
"chunking_strategy": chunking_strategy,
},
file_batch_create_params.FileBatchCreateParams,
),
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=VectorStoreFileBatch,
)
async def retrieve(
self,
batch_id: str,
*,
vector_store_id: str,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""
Retrieves a vector store file batch.
Args:
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
if not vector_store_id:
raise ValueError(f"Expected a non-empty value for `vector_store_id` but received {vector_store_id!r}")
if not batch_id:
raise ValueError(f"Expected a non-empty value for `batch_id` but received {batch_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._get(
f"/vector_stores/{vector_store_id}/file_batches/{batch_id}",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=VectorStoreFileBatch,
)
async def cancel(
self,
batch_id: str,
*,
vector_store_id: str,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""Cancel a vector store file batch.
This attempts to cancel the processing of
files in this batch as soon as possible.
Args:
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
if not vector_store_id:
raise ValueError(f"Expected a non-empty value for `vector_store_id` but received {vector_store_id!r}")
if not batch_id:
raise ValueError(f"Expected a non-empty value for `batch_id` but received {batch_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return await self._post(
f"/vector_stores/{vector_store_id}/file_batches/{batch_id}/cancel",
options=make_request_options(
extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
),
cast_to=VectorStoreFileBatch,
)
async def create_and_poll(
self,
vector_store_id: str,
*,
file_ids: List[str],
poll_interval_ms: int | NotGiven = NOT_GIVEN,
chunking_strategy: FileChunkingStrategyParam | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""Create a vector store batch and poll until all files have been processed."""
batch = await self.create(
vector_store_id=vector_store_id,
file_ids=file_ids,
chunking_strategy=chunking_strategy,
)
# TODO: don't poll unless necessary??
return await self.poll(
batch.id,
vector_store_id=vector_store_id,
poll_interval_ms=poll_interval_ms,
)
def list_files(
self,
batch_id: str,
*,
vector_store_id: str,
after: str | NotGiven = NOT_GIVEN,
before: str | NotGiven = NOT_GIVEN,
filter: Literal["in_progress", "completed", "failed", "cancelled"] | NotGiven = NOT_GIVEN,
limit: int | NotGiven = NOT_GIVEN,
order: Literal["asc", "desc"] | NotGiven = NOT_GIVEN,
# Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
# The extra values given here take precedence over values defined on the client or passed to this method.
extra_headers: Headers | None = None,
extra_query: Query | None = None,
extra_body: Body | None = None,
timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
) -> AsyncPaginator[VectorStoreFile, AsyncCursorPage[VectorStoreFile]]:
"""
Returns a list of vector store files in a batch.
Args:
after: A cursor for use in pagination. `after` is an object ID that defines your place
in the list. For instance, if you make a list request and receive 100 objects,
ending with obj_foo, your subsequent call can include after=obj_foo in order to
fetch the next page of the list.
before: A cursor for use in pagination. `before` is an object ID that defines your place
in the list. For instance, if you make a list request and receive 100 objects,
starting with obj_foo, your subsequent call can include before=obj_foo in order
to fetch the previous page of the list.
filter: Filter by file status. One of `in_progress`, `completed`, `failed`, `cancelled`.
limit: A limit on the number of objects to be returned. Limit can range between 1 and
100, and the default is 20.
order: Sort order by the `created_at` timestamp of the objects. `asc` for ascending
order and `desc` for descending order.
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
"""
if not vector_store_id:
raise ValueError(f"Expected a non-empty value for `vector_store_id` but received {vector_store_id!r}")
if not batch_id:
raise ValueError(f"Expected a non-empty value for `batch_id` but received {batch_id!r}")
extra_headers = {"OpenAI-Beta": "assistants=v2", **(extra_headers or {})}
return self._get_api_list(
f"/vector_stores/{vector_store_id}/file_batches/{batch_id}/files",
page=AsyncCursorPage[VectorStoreFile],
options=make_request_options(
extra_headers=extra_headers,
extra_query=extra_query,
extra_body=extra_body,
timeout=timeout,
query=maybe_transform(
{
"after": after,
"before": before,
"filter": filter,
"limit": limit,
"order": order,
},
file_batch_list_files_params.FileBatchListFilesParams,
),
),
model=VectorStoreFile,
)
async def poll(
self,
batch_id: str,
*,
vector_store_id: str,
poll_interval_ms: int | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""Wait for the given file batch to be processed.
Note: this will return even if one of the files failed to process, you need to
check batch.file_counts.failed_count to handle this case.
"""
headers: dict[str, str] = {"X-Stainless-Poll-Helper": "true"}
if is_given(poll_interval_ms):
headers["X-Stainless-Custom-Poll-Interval"] = str(poll_interval_ms)
while True:
response = await self.with_raw_response.retrieve(
batch_id,
vector_store_id=vector_store_id,
extra_headers=headers,
)
batch = response.parse()
if batch.file_counts.in_progress > 0:
if not is_given(poll_interval_ms):
from_header = response.headers.get("openai-poll-after-ms")
if from_header is not None:
poll_interval_ms = int(from_header)
else:
poll_interval_ms = 1000
await self._sleep(poll_interval_ms / 1000)
continue
return batch
async def upload_and_poll(
self,
vector_store_id: str,
*,
files: Iterable[FileTypes],
max_concurrency: int = 5,
file_ids: List[str] = [],
poll_interval_ms: int | NotGiven = NOT_GIVEN,
chunking_strategy: FileChunkingStrategyParam | NotGiven = NOT_GIVEN,
) -> VectorStoreFileBatch:
"""Uploads the given files concurrently and then creates a vector store file batch.
If you've already uploaded certain files that you want to include in this batch
then you can pass their IDs through the `file_ids` argument.
By default, if any file upload fails then an exception will be eagerly raised.
The number of concurrency uploads is configurable using the `max_concurrency`
parameter.
Note: this method only supports `asyncio` or `trio` as the backing async
runtime.
"""
uploaded_files: list[FileObject] = []
async_library = sniffio.current_async_library()
if async_library == "asyncio":
async def asyncio_upload_file(semaphore: asyncio.Semaphore, file: FileTypes) -> None:
async with semaphore:
file_obj = await self._client.files.create(
file=file,
purpose="assistants",
)
uploaded_files.append(file_obj)
semaphore = asyncio.Semaphore(max_concurrency)
tasks = [asyncio_upload_file(semaphore, file) for file in files]
await asyncio.gather(*tasks)
elif async_library == "trio":
# We only import if the library is being used.
# We support Python 3.7 so are using an older version of trio that does not have type information
import trio # type: ignore # pyright: ignore[reportMissingTypeStubs]
async def trio_upload_file(limiter: trio.CapacityLimiter, file: FileTypes) -> None:
async with limiter:
file_obj = await self._client.files.create(
file=file,
purpose="assistants",
)
uploaded_files.append(file_obj)
limiter = trio.CapacityLimiter(max_concurrency)
async with trio.open_nursery() as nursery:
for file in files:
nursery.start_soon(trio_upload_file, limiter, file) # pyright: ignore [reportUnknownMemberType]
else:
raise RuntimeError(
f"Async runtime {async_library} is not supported yet. Only asyncio or trio is supported",
)
batch = await self.create_and_poll(
vector_store_id=vector_store_id,
file_ids=[*file_ids, *(f.id for f in uploaded_files)],
poll_interval_ms=poll_interval_ms,
chunking_strategy=chunking_strategy,
)
return batch
class FileBatchesWithRawResponse:
def __init__(self, file_batches: FileBatches) -> None:
self._file_batches = file_batches
self.create = _legacy_response.to_raw_response_wrapper(
file_batches.create,
)
self.retrieve = _legacy_response.to_raw_response_wrapper(
file_batches.retrieve,
)
self.cancel = _legacy_response.to_raw_response_wrapper(
file_batches.cancel,
)
self.list_files = _legacy_response.to_raw_response_wrapper(
file_batches.list_files,
)
class AsyncFileBatchesWithRawResponse:
def __init__(self, file_batches: AsyncFileBatches) -> None:
self._file_batches = file_batches
self.create = _legacy_response.async_to_raw_response_wrapper(
file_batches.create,
)
self.retrieve = _legacy_response.async_to_raw_response_wrapper(
file_batches.retrieve,
)
self.cancel = _legacy_response.async_to_raw_response_wrapper(
file_batches.cancel,
)
self.list_files = _legacy_response.async_to_raw_response_wrapper(
file_batches.list_files,
)
class FileBatchesWithStreamingResponse:
def __init__(self, file_batches: FileBatches) -> None:
self._file_batches = file_batches
self.create = to_streamed_response_wrapper(
file_batches.create,
)
self.retrieve = to_streamed_response_wrapper(
file_batches.retrieve,
)
self.cancel = to_streamed_response_wrapper(
file_batches.cancel,
)
self.list_files = to_streamed_response_wrapper(
file_batches.list_files,
)
class AsyncFileBatchesWithStreamingResponse:
def __init__(self, file_batches: AsyncFileBatches) -> None:
self._file_batches = file_batches
self.create = async_to_streamed_response_wrapper(
file_batches.create,
)
self.retrieve = async_to_streamed_response_wrapper(
file_batches.retrieve,
)
self.cancel = async_to_streamed_response_wrapper(
file_batches.cancel,
)
self.list_files = async_to_streamed_response_wrapper(
file_batches.list_files,
)
