embeddings

The embeddings module provides classes for creating embeddings from text inputs using OpenAI's models and supports both synchronous and asynchronous operations as well as the handling of raw responses and streaming response capabilities.

The module is appropriate for use in applications that require semantic analysis of text, like similarity searches, text clustering, and other natural language processing tasks that can benefit from high-dimensional vector representations of text.

Classes:

Name	Description
AsyncEmbeddings
AsyncEmbeddingsWithRawResponse
AsyncEmbeddingsWithStreamingResponse
Embeddings
EmbeddingsWithRawResponse
EmbeddingsWithStreamingResponse

AsyncEmbeddings

AsyncEmbeddings(client: AsyncOpenAI)

Methods:

Name	Description
create	Creates an embedding vector representing the input text.
with_raw_response
with_streaming_response

create async

create(
    *,
    input: Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
    ],
    model: Union[
        str,
        Literal[
            "text-embedding-ada-002",
            "text-embedding-3-small",
            "text-embedding-3-large",
        ],
    ],
    dimensions: int | NotGiven = NOT_GIVEN,
    encoding_format: (
        Literal["float", "base64"] | NotGiven
    ) = NOT_GIVEN,
    user: str | NotGiven = NOT_GIVEN,
    extra_headers: Headers | None = None,
    extra_query: Query | None = None,
    extra_body: Body | None = None,
    timeout: float | Timeout | None | NotGiven = NOT_GIVEN
) -> CreateEmbeddingResponse

Creates an embedding vector representing the input text.

Parameters:

Name	Type	Description	Default
input	Union[str, List[str], Iterable[int], Iterable[Iterable[int]]]	Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for text-embedding-ada-002), cannot be an empty string, and any array must be 2048 dimensions or less. Example Python code for counting tokens.	required
model	Union[str, Literal['text-embedding-ada-002', 'text-embedding-3-small', 'text-embedding-3-large']]	ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.	required
dimensions	int | NotGiven	The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.	NOT_GIVEN
encoding_format	Literal['float', 'base64'] | NotGiven	The format to return the embeddings in. Can be either float or base64.	NOT_GIVEN
user	str | NotGiven	A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.	NOT_GIVEN
extra_headers	Headers | None	Send extra headers	None
extra_query	Query | None	Add additional query parameters to the request	None
extra_body	Body | None	Add additional JSON properties to the request	None
timeout	float | Timeout | None | NotGiven	Override the client-level default timeout for this request, in seconds	NOT_GIVEN

with_raw_response

with_raw_response() -> AsyncEmbeddingsWithRawResponse

with_streaming_response

with_streaming_response() -> (
    AsyncEmbeddingsWithStreamingResponse
)

AsyncEmbeddingsWithRawResponse

AsyncEmbeddingsWithRawResponse(embeddings: AsyncEmbeddings)

Attributes:

Name	Type	Description
create

create instance-attribute

create = async_to_raw_response_wrapper(create)

AsyncEmbeddingsWithStreamingResponse

AsyncEmbeddingsWithStreamingResponse(
    embeddings: AsyncEmbeddings,
)

Attributes:

Name	Type	Description
create

create instance-attribute

create = async_to_streamed_response_wrapper(create)

Embeddings

Embeddings(client: OpenAI)

Methods:

Name	Description
create	Creates an embedding vector representing the input text.
with_raw_response
with_streaming_response

create

create(
    *,
    input: Union[
        str,
        List[str],
        Iterable[int],
        Iterable[Iterable[int]],
    ],
    model: Union[
        str,
        Literal[
            "text-embedding-ada-002",
            "text-embedding-3-small",
            "text-embedding-3-large",
        ],
    ],
    dimensions: int | NotGiven = NOT_GIVEN,
    encoding_format: (
        Literal["float", "base64"] | NotGiven
    ) = NOT_GIVEN,
    user: str | NotGiven = NOT_GIVEN,
    extra_headers: Headers | None = None,
    extra_query: Query | None = None,
    extra_body: Body | None = None,
    timeout: float | Timeout | None | NotGiven = NOT_GIVEN
) -> CreateEmbeddingResponse

Creates an embedding vector representing the input text.

Parameters:

Name	Type	Description	Default
input	Union[str, List[str], Iterable[int], Iterable[Iterable[int]]]	Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for text-embedding-ada-002), cannot be an empty string, and any array must be 2048 dimensions or less. Example Python code for counting tokens.	required
model	Union[str, Literal['text-embedding-ada-002', 'text-embedding-3-small', 'text-embedding-3-large']]	ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.	required
dimensions	int | NotGiven	The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.	NOT_GIVEN
encoding_format	Literal['float', 'base64'] | NotGiven	The format to return the embeddings in. Can be either float or base64.	NOT_GIVEN
user	str | NotGiven	A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.	NOT_GIVEN
extra_headers	Headers | None	Send extra headers	None
extra_query	Query | None	Add additional query parameters to the request	None
extra_body	Body | None	Add additional JSON properties to the request	None
timeout	float | Timeout | None | NotGiven	Override the client-level default timeout for this request, in seconds	NOT_GIVEN

with_raw_response

with_raw_response() -> EmbeddingsWithRawResponse

with_streaming_response

with_streaming_response() -> (
    EmbeddingsWithStreamingResponse
)

EmbeddingsWithRawResponse

EmbeddingsWithRawResponse(embeddings: Embeddings)

Attributes:

Name	Type	Description
create

create instance-attribute

create = to_raw_response_wrapper(create)

EmbeddingsWithStreamingResponse

EmbeddingsWithStreamingResponse(embeddings: Embeddings)

Attributes:

Name	Type	Description
create

create instance-attribute

create = to_streamed_response_wrapper(create)