# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.

from __future__ import annotations

from typing import Dict, Union, Iterable, Optional
from typing_extensions import Literal

import httpx

from ... import _legacy_response
from ...types import FileChunkingStrategyParam
from ..._types import Body, Omit, Query, Headers, NotGiven, SequenceNotStr, omit, not_given
from ..._utils import path_template, 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.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,
        *,
        attributes: Optional[Dict[str, Union[str, float, bool]]] | Omit = omit,
        chunking_strategy: FileChunkingStrategyParam | Omit = omit,
        file_ids: SequenceNotStr[str] | Omit = omit,
        files: Iterable[file_batch_create_params.File] | Omit = omit,
        # 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:
          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.

          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. If `attributes` or `chunking_strategy` are provided, they will be applied
              to all files in the batch. The maximum batch size is 2000 files. This endpoint
              is recommended for multi-file ingestion and helps reduce per-vector-store write
              request pressure. Mutually exclusive with `files`.

          files: A list of objects that each include a `file_id` plus optional `attributes` or
              `chunking_strategy`. Use this when you need to override metadata for specific
              files. The global `attributes` or `chunking_strategy` will be ignored and must
              be specified for each file. The maximum batch size is 2000 files. This endpoint
              is recommended for multi-file ingestion and helps reduce per-vector-store write
              request pressure. Mutually exclusive with `file_ids`.

          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(
            path_template("/vector_stores/{vector_store_id}/file_batches", vector_store_id=vector_store_id),
            body=maybe_transform(
                {
                    "attributes": attributes,
                    "chunking_strategy": chunking_strategy,
                    "file_ids": file_ids,
                    "files": files,
                },
                file_batch_create_params.FileBatchCreateParams,
            ),
            options=make_request_options(
                extra_headers=extra_headers,
                extra_query=extra_query,
                extra_body=extra_body,
                timeout=timeout,
                security={"bearer_auth": True},
            ),
            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(
            path_template(
                "/vector_stores/{vector_store_id}/file_batches/{batch_id}",
                vector_store_id=vector_store_id,
                batch_id=batch_id,
            ),
            options=make_request_options(
                extra_headers=extra_headers,
                extra_query=extra_query,
                extra_body=extra_body,
                timeout=timeout,
                security={"bearer_auth": True},
            ),
            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(
            path_template(
                "/vector_stores/{vector_store_id}/file_batches/{batch_id}/cancel",
                vector_store_id=vector_store_id,
                batch_id=batch_id,
            ),
            options=make_request_options(
                extra_headers=extra_headers,
                extra_query=extra_query,
                extra_body=extra_body,
                timeout=timeout,
                security={"bearer_auth": True},
            ),
            cast_to=VectorStoreFileBatch,
        )

    def list_files(
        self,
        batch_id: str,
        *,
        vector_store_id: str,
        after: str | Omit = omit,
        before: str | Omit = omit,
        filter: Literal["in_progress", "completed", "failed", "cancelled"] | Omit = omit,
        limit: int | Omit = omit,
        order: Literal["asc", "desc"] | Omit = omit,
        # 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(
            path_template(
                "/vector_stores/{vector_store_id}/file_batches/{batch_id}/files",
                vector_store_id=vector_store_id,
                batch_id=batch_id,
            ),
            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,
                ),
                security={"bearer_auth": True},
            ),
            model=VectorStoreFile,
        )


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,
        *,
        attributes: Optional[Dict[str, Union[str, float, bool]]] | Omit = omit,
        chunking_strategy: FileChunkingStrategyParam | Omit = omit,
        file_ids: SequenceNotStr[str] | Omit = omit,
        files: Iterable[file_batch_create_params.File] | Omit = omit,
        # 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:
          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.

          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. If `attributes` or `chunking_strategy` are provided, they will be applied
              to all files in the batch. The maximum batch size is 2000 files. This endpoint
              is recommended for multi-file ingestion and helps reduce per-vector-store write
              request pressure. Mutually exclusive with `files`.

          files: A list of objects that each include a `file_id` plus optional `attributes` or
              `chunking_strategy`. Use this when you need to override metadata for specific
              files. The global `attributes` or `chunking_strategy` will be ignored and must
              be specified for each file. The maximum batch size is 2000 files. This endpoint
              is recommended for multi-file ingestion and helps reduce per-vector-store write
              request pressure. Mutually exclusive with `file_ids`.

          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(
            path_template("/vector_stores/{vector_store_id}/file_batches", vector_store_id=vector_store_id),
            body=await async_maybe_transform(
                {
                    "attributes": attributes,
                    "chunking_strategy": chunking_strategy,
                    "file_ids": file_ids,
                    "files": files,
                },
                file_batch_create_params.FileBatchCreateParams,
            ),
            options=make_request_options(
                extra_headers=extra_headers,
                extra_query=extra_query,
                extra_body=extra_body,
                timeout=timeout,
                security={"bearer_auth": True},
            ),
            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(
            path_template(
                "/vector_stores/{vector_store_id}/file_batches/{batch_id}",
                vector_store_id=vector_store_id,
                batch_id=batch_id,
            ),
            options=make_request_options(
                extra_headers=extra_headers,
                extra_query=extra_query,
                extra_body=extra_body,
                timeout=timeout,
                security={"bearer_auth": True},
            ),
            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(
            path_template(
                "/vector_stores/{vector_store_id}/file_batches/{batch_id}/cancel",
                vector_store_id=vector_store_id,
                batch_id=batch_id,
            ),
            options=make_request_options(
                extra_headers=extra_headers,
                extra_query=extra_query,
                extra_body=extra_body,
                timeout=timeout,
                security={"bearer_auth": True},
            ),
            cast_to=VectorStoreFileBatch,
        )

    def list_files(
        self,
        batch_id: str,
        *,
        vector_store_id: str,
        after: str | Omit = omit,
        before: str | Omit = omit,
        filter: Literal["in_progress", "completed", "failed", "cancelled"] | Omit = omit,
        limit: int | Omit = omit,
        order: Literal["asc", "desc"] | Omit = omit,
        # 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(
            path_template(
                "/vector_stores/{vector_store_id}/file_batches/{batch_id}/files",
                vector_store_id=vector_store_id,
                batch_id=batch_id,
            ),
            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,
                ),
                security={"bearer_auth": True},
            ),
            model=VectorStoreFile,
        )


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,
        )
