embedding_create_params

Classes:

Name	Description
EmbeddingCreateParams

EmbeddingCreateParams

Attributes:

Name	Type	Description
dimensions	int	The number of dimensions the resulting output embeddings should have.
encoding_format	Literal['float', 'base64']	The format to return the embeddings in.
input	Required[Union[str, List[str], Iterable[int], Iterable[Iterable[int]]]]	Input text to embed, encoded as a string or array of tokens.
model	Required[Union[str, Literal['text-embedding-ada-002', 'text-embedding-3-small', 'text-embedding-3-large']]]	ID of the model to use.
user	str	A unique identifier representing your end-user, which can help OpenAI to monitor

dimensions instance-attribute

dimensions: int

The number of dimensions the resulting output embeddings should have.

Only supported in text-embedding-3 and later models.

encoding_format instance-attribute

encoding_format: Literal['float', 'base64']

The format to return the embeddings in.

Can be either float or base64.

input instance-attribute

input: Required[
    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.

model instance-attribute

model: Required[
    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.

user instance-attribute

user: str

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.