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+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+import array
+import base64
+from typing import List, Union, Iterable, cast
+from typing_extensions import Literal
+
+import httpx
+
+from .. import _legacy_response
+from ..types import embedding_create_params
+from .._types import NOT_GIVEN, Body, Query, Headers, NotGiven
+from .._utils import is_given, maybe_transform
+from .._compat import cached_property
+from .._extras import numpy as np, has_numpy
+from .._resource import SyncAPIResource, AsyncAPIResource
+from .._response import to_streamed_response_wrapper, async_to_streamed_response_wrapper
+from .._base_client import make_request_options
+from ..types.embedding_model import EmbeddingModel
+from ..types.create_embedding_response import CreateEmbeddingResponse
+
+__all__ = ["Embeddings", "AsyncEmbeddings"]
+
+
+class Embeddings(SyncAPIResource):
+ @cached_property
+ def with_raw_response(self) -> EmbeddingsWithRawResponse:
+ """
+ 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 EmbeddingsWithRawResponse(self)
+
+ @cached_property
+ def with_streaming_response(self) -> EmbeddingsWithStreamingResponse:
+ """
+ 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 EmbeddingsWithStreamingResponse(self)
+
+ def create(
+ self,
+ *,
+ input: Union[str, List[str], Iterable[int], Iterable[Iterable[int]]],
+ model: Union[str, EmbeddingModel],
+ dimensions: int | NotGiven = NOT_GIVEN,
+ encoding_format: Literal["float", "base64"] | NotGiven = NOT_GIVEN,
+ user: str | 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,
+ ) -> CreateEmbeddingResponse:
+ """
+ Creates an embedding vector representing the input text.
+
+ Args:
+ input: Input text to embed, encoded as a string or array of tokens. To embed multiple
+ inputs in a single request, pass an array of strings or array of token arrays.
+ The input must not exceed the max input tokens for the model (8192 tokens for
+ `text-embedding-ada-002`), cannot be an empty string, and any array must be 2048
+ dimensions or less.
+ [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
+ for counting tokens. Some models may also impose a limit on total number of
+ tokens summed across inputs.
+
+ model: ID of the model to use. You can use the
+ [List models](https://platform.openai.com/docs/api-reference/models/list) API to
+ see all of your available models, or see our
+ [Model overview](https://platform.openai.com/docs/models) for descriptions of
+ them.
+
+ dimensions: The number of dimensions the resulting output embeddings should have. Only
+ supported in `text-embedding-3` and later models.
+
+ encoding_format: The format to return the embeddings in. Can be either `float` or
+ [`base64`](https://pypi.org/project/pybase64/).
+
+ user: A unique identifier representing your end-user, which can help OpenAI to monitor
+ and detect abuse.
+ [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids).
+
+ 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
+ """
+ params = {
+ "input": input,
+ "model": model,
+ "user": user,
+ "dimensions": dimensions,
+ "encoding_format": encoding_format,
+ }
+ if not is_given(encoding_format):
+ params["encoding_format"] = "base64"
+
+ def parser(obj: CreateEmbeddingResponse) -> CreateEmbeddingResponse:
+ if is_given(encoding_format):
+ # don't modify the response object if a user explicitly asked for a format
+ return obj
+
+ for embedding in obj.data:
+ data = cast(object, embedding.embedding)
+ if not isinstance(data, str):
+ continue
+ if not has_numpy():
+ # use array for base64 optimisation
+ embedding.embedding = array.array("f", base64.b64decode(data)).tolist()
+ else:
+ embedding.embedding = np.frombuffer( # type: ignore[no-untyped-call]
+ base64.b64decode(data), dtype="float32"
+ ).tolist()
+
+ return obj
+
+ return self._post(
+ "/embeddings",
+ body=maybe_transform(params, embedding_create_params.EmbeddingCreateParams),
+ options=make_request_options(
+ extra_headers=extra_headers,
+ extra_query=extra_query,
+ extra_body=extra_body,
+ timeout=timeout,
+ post_parser=parser,
+ ),
+ cast_to=CreateEmbeddingResponse,
+ )
+
+
+class AsyncEmbeddings(AsyncAPIResource):
+ @cached_property
+ def with_raw_response(self) -> AsyncEmbeddingsWithRawResponse:
+ """
+ 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 AsyncEmbeddingsWithRawResponse(self)
+
+ @cached_property
+ def with_streaming_response(self) -> AsyncEmbeddingsWithStreamingResponse:
+ """
+ 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 AsyncEmbeddingsWithStreamingResponse(self)
+
+ async def create(
+ self,
+ *,
+ input: Union[str, List[str], Iterable[int], Iterable[Iterable[int]]],
+ model: Union[str, EmbeddingModel],
+ dimensions: int | NotGiven = NOT_GIVEN,
+ encoding_format: Literal["float", "base64"] | NotGiven = NOT_GIVEN,
+ user: str | 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,
+ ) -> CreateEmbeddingResponse:
+ """
+ Creates an embedding vector representing the input text.
+
+ Args:
+ input: Input text to embed, encoded as a string or array of tokens. To embed multiple
+ inputs in a single request, pass an array of strings or array of token arrays.
+ The input must not exceed the max input tokens for the model (8192 tokens for
+ `text-embedding-ada-002`), cannot be an empty string, and any array must be 2048
+ dimensions or less.
+ [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
+ for counting tokens. Some models may also impose a limit on total number of
+ tokens summed across inputs.
+
+ model: ID of the model to use. You can use the
+ [List models](https://platform.openai.com/docs/api-reference/models/list) API to
+ see all of your available models, or see our
+ [Model overview](https://platform.openai.com/docs/models) for descriptions of
+ them.
+
+ dimensions: The number of dimensions the resulting output embeddings should have. Only
+ supported in `text-embedding-3` and later models.
+
+ encoding_format: The format to return the embeddings in. Can be either `float` or
+ [`base64`](https://pypi.org/project/pybase64/).
+
+ user: A unique identifier representing your end-user, which can help OpenAI to monitor
+ and detect abuse.
+ [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids).
+
+ 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
+ """
+ params = {
+ "input": input,
+ "model": model,
+ "user": user,
+ "dimensions": dimensions,
+ "encoding_format": encoding_format,
+ }
+ if not is_given(encoding_format):
+ params["encoding_format"] = "base64"
+
+ def parser(obj: CreateEmbeddingResponse) -> CreateEmbeddingResponse:
+ if is_given(encoding_format):
+ # don't modify the response object if a user explicitly asked for a format
+ return obj
+
+ for embedding in obj.data:
+ data = cast(object, embedding.embedding)
+ if not isinstance(data, str):
+ continue
+ if not has_numpy():
+ # use array for base64 optimisation
+ embedding.embedding = array.array("f", base64.b64decode(data)).tolist()
+ else:
+ embedding.embedding = np.frombuffer( # type: ignore[no-untyped-call]
+ base64.b64decode(data), dtype="float32"
+ ).tolist()
+
+ return obj
+
+ return await self._post(
+ "/embeddings",
+ body=maybe_transform(params, embedding_create_params.EmbeddingCreateParams),
+ options=make_request_options(
+ extra_headers=extra_headers,
+ extra_query=extra_query,
+ extra_body=extra_body,
+ timeout=timeout,
+ post_parser=parser,
+ ),
+ cast_to=CreateEmbeddingResponse,
+ )
+
+
+class EmbeddingsWithRawResponse:
+ def __init__(self, embeddings: Embeddings) -> None:
+ self._embeddings = embeddings
+
+ self.create = _legacy_response.to_raw_response_wrapper(
+ embeddings.create,
+ )
+
+
+class AsyncEmbeddingsWithRawResponse:
+ def __init__(self, embeddings: AsyncEmbeddings) -> None:
+ self._embeddings = embeddings
+
+ self.create = _legacy_response.async_to_raw_response_wrapper(
+ embeddings.create,
+ )
+
+
+class EmbeddingsWithStreamingResponse:
+ def __init__(self, embeddings: Embeddings) -> None:
+ self._embeddings = embeddings
+
+ self.create = to_streamed_response_wrapper(
+ embeddings.create,
+ )
+
+
+class AsyncEmbeddingsWithStreamingResponse:
+ def __init__(self, embeddings: AsyncEmbeddings) -> None:
+ self._embeddings = embeddings
+
+ self.create = async_to_streamed_response_wrapper(
+ embeddings.create,
+ )