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
The format to return the embeddings in.
Can be either float
or base64
.
input
instance-attribute
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.