POST
/
embeddings
curl --request POST \
  --url https://api.venice.ai/api/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "encoding_format": "float",
  "input": "The quick brown fox jumped over the lazy dog",
  "model": "text-embedding-bge-m3"
}'
{
  "data": [
    {
      "embedding": [
        0.0023064255,
        -0.009327292,
        0.015797377
      ],
      "index": 0,
      "object": "embedding"
    }
  ],
  "model": "text-embedding-bge-m3",
  "object": "list",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Headers

​
Accept-Encoding
string

Supported compression encodings (gzip, br)

Example:

"gzip, br"

Body

application/json

Create embeddings for the supplied input.

​
input
required

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.

Minimum length: 1
Example:

"The quick brown fox jumped over the lazy dog"

​
model
required

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.

Example:

"text-embedding-bge-m3"

​
dimensions
integer

The number of dimensions the resulting output embeddings should have.

Required range: x >= 1
​
encoding_format
enum<string>
default:float

The format to return the embeddings in. Can be either float or base64.

Available options:
float,
base64
Example:

"float"

​
user
string

This is an unused parameter and is discarded by Venice. It is supported solely for API compatibility with OpenAI.

Response

200
application/json
OK
​
data
object[]
required

The list of embeddings generated by the model.

​
model
string
required

The name of the model used to generate the embedding.

​
object
enum<string>
required

The object type, which is always "list"

Available options:
list
​
usage
object
required

The usage information for the request.