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
}
'

import requests

url = "https://api.fireworks.ai/inference/v1/embeddings"

payload = {
    "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
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    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
  })
};

fetch('https://api.fireworks.ai/inference/v1/embeddings', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.fireworks.ai/inference/v1/embeddings",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    '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
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.fireworks.ai/inference/v1/embeddings"

	payload := strings.NewReader("{\n  \"input\": \"The quick brown fox jumped over the lazy dog\",\n  \"model\": \"nomic-ai/nomic-embed-text-v1.5\",\n  \"prompt_template\": \"Embed this text: {text}\",\n  \"dimensions\": 768,\n  \"return_logits\": [\n    0,\n    1,\n    2\n  ],\n  \"normalize\": false\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.fireworks.ai/inference/v1/embeddings")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"input\": \"The quick brown fox jumped over the lazy dog\",\n  \"model\": \"nomic-ai/nomic-embed-text-v1.5\",\n  \"prompt_template\": \"Embed this text: {text}\",\n  \"dimensions\": 768,\n  \"return_logits\": [\n    0,\n    1,\n    2\n  ],\n  \"normalize\": false\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.fireworks.ai/inference/v1/embeddings")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"input\": \"The quick brown fox jumped over the lazy dog\",\n  \"model\": \"nomic-ai/nomic-embed-text-v1.5\",\n  \"prompt_template\": \"Embed this text: {text}\",\n  \"dimensions\": 768,\n  \"return_logits\": [\n    0,\n    1,\n    2\n  ],\n  \"normalize\": false\n}"

response = http.request(request)
puts response.read_body

{
  "data": [
    {
      "index": 123,
      "embedding": [
        123
      ],
      "object": "embedding"
    }
  ],
  "model": "<string>",
  "object": "list",
  "usage": {
    "prompt_tokens": 123,
    "total_tokens": 123
  }
}

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
}
'

import requests

url = "https://api.fireworks.ai/inference/v1/embeddings"

payload = {
    "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
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    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
  })
};

fetch('https://api.fireworks.ai/inference/v1/embeddings', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.fireworks.ai/inference/v1/embeddings",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    '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
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.fireworks.ai/inference/v1/embeddings"

	payload := strings.NewReader("{\n  \"input\": \"The quick brown fox jumped over the lazy dog\",\n  \"model\": \"nomic-ai/nomic-embed-text-v1.5\",\n  \"prompt_template\": \"Embed this text: {text}\",\n  \"dimensions\": 768,\n  \"return_logits\": [\n    0,\n    1,\n    2\n  ],\n  \"normalize\": false\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.fireworks.ai/inference/v1/embeddings")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"input\": \"The quick brown fox jumped over the lazy dog\",\n  \"model\": \"nomic-ai/nomic-embed-text-v1.5\",\n  \"prompt_template\": \"Embed this text: {text}\",\n  \"dimensions\": 768,\n  \"return_logits\": [\n    0,\n    1,\n    2\n  ],\n  \"normalize\": false\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.fireworks.ai/inference/v1/embeddings")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"input\": \"The quick brown fox jumped over the lazy dog\",\n  \"model\": \"nomic-ai/nomic-embed-text-v1.5\",\n  \"prompt_template\": \"Embed this text: {text}\",\n  \"dimensions\": 768,\n  \"return_logits\": [\n    0,\n    1,\n    2\n  ],\n  \"normalize\": false\n}"

response = http.request(request)
puts response.read_body

{
  "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.

Example:

"The quick brown fox jumped over the lazy dog"

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

data

object[]

required

The list of embeddings generated by the model.

Show child attributes

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.

Show child attributes

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