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POST
/
embeddings
Create embeddings
curl --request POST \
  --url https://api.fireworks.ai/inference/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "input": "The quick brown fox jumped over the lazy dog",
  "model": "nomic-ai/nomic-embed-text-v1.5",
  "prompt_template": "Embed this text: {text}",
  "dimensions": 768,
  "return_logits": [
    0,
    1,
    2
  ],
  "normalize": false
}'
{
  "data": [
    {
      "index": 123,
      "embedding": [
        123
      ],
      "object": "embedding"
    }
  ],
  "model": "<string>",
  "object": "list",
  "usage": {
    "prompt_tokens": 123,
    "total_tokens": 123
  }
}

Authorizations

​
Authorization
string
header
required

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

Body

application/json
​
input
default:""
required

Input text to embed, encoded as a string. To embed multiple inputs in a single request, pass an array of strings. You can pass structured object(s) to use along with the prompt_template. The input must not exceed the max input tokens for the model (8192 tokens for nomic-ai/nomic-embed-text-v1.5), cannot be an empty string, and any array must be 2048 dimensions or less.

The string that will be turned into an embedding.

Example:

"This is a test."

​
model
string
required

The model to use for generating embeddings.

Example:

"nomic-ai/nomic-embed-text-v1.5"

​
prompt_template
string

Template string for processing input data before embedding. When provided, fields from the input object are substituted using Jinja2. For example, simple substitution is done using {field_name} syntax. The resulting string(s) are then embedded. For array inputs, each object generates a separate string.

Additionally, we expose truncate_tokens(string) function to the template that allows to truncate the string based on token lengths instead of characters

Example:

"Embed this text: {text}"

​
dimensions
integer

The number of dimensions the resulting output embeddings should have. Only supported in nomic-ai/nomic-embed-text-v1.5 and later models.

Required range: x >= 1
Example:

768

​
return_logits
integer[]

If provided, returns raw model logits (pre-softmax scores) for specified token or class indices. If an empty list is provided, returns logits for all available tokens/classes. Otherwise, only the specified indices are returned.

When used with normalize=true, softmax is applied to create probability distributions. Softmax is applied only to the selected tokens, so output probabilities will always add up to 1.

Example:
[0, 1, 2]
​
normalize
boolean
default:false

Controls normalization of the output. When return_logits is not provided, embeddings are L2 normalized (unit vectors). When return_logits is provided, softmax is applied to the selected logits to create probability distributions.

Example:

false

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.