Create embeddings

curl --request POST \
  --url https://api.aurous-labs.com/v1/embeddings \
  --header 'Content-Type: application/json' \
  --header 'X-Api-Key: <api-key>' \
  --data '
{
  "model": "aurous-embed-vision-1.0",
  "input": "<string>",
  "dimensions": 1024,
  "encoding_format": "float",
  "user": "<string>"
}
'

import requests

url = "https://api.aurous-labs.com/v1/embeddings"

payload = {
    "model": "aurous-embed-vision-1.0",
    "input": "<string>",
    "dimensions": 1024,
    "encoding_format": "float",
    "user": "<string>"
}
headers = {
    "X-Api-Key": "<api-key>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {'X-Api-Key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    model: 'aurous-embed-vision-1.0',
    input: '<string>',
    dimensions: 1024,
    encoding_format: 'float',
    user: '<string>'
  })
};

fetch('https://api.aurous-labs.com/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.aurous-labs.com/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([
    'model' => 'aurous-embed-vision-1.0',
    'input' => '<string>',
    'dimensions' => 1024,
    'encoding_format' => 'float',
    'user' => '<string>'
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "X-Api-Key: <api-key>"
  ],
]);

$response = curl_exec($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.aurous-labs.com/v1/embeddings"

	payload := strings.NewReader("{\n  \"model\": \"aurous-embed-vision-1.0\",\n  \"input\": \"<string>\",\n  \"dimensions\": 1024,\n  \"encoding_format\": \"float\",\n  \"user\": \"<string>\"\n}")

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

	req.Header.Add("X-Api-Key", "<api-key>")
	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.aurous-labs.com/v1/embeddings")
  .header("X-Api-Key", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"aurous-embed-vision-1.0\",\n  \"input\": \"<string>\",\n  \"dimensions\": 1024,\n  \"encoding_format\": \"float\",\n  \"user\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.aurous-labs.com/v1/embeddings")

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

request = Net::HTTP::Post.new(url)
request["X-Api-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"model\": \"aurous-embed-vision-1.0\",\n  \"input\": \"<string>\",\n  \"dimensions\": 1024,\n  \"encoding_format\": \"float\",\n  \"user\": \"<string>\"\n}"

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

{
  "object": "list",
  "data": [
    {
      "index": 0,
      "object": "embedding",
      "embedding": [
        0.0123,
        -0.0456,
        0.0789
      ]
    }
  ],
  "model": "aurous-embed-vision-1.0",
  "usage": {
    "prompt_tokens": 7000,
    "total_tokens": 7000,
    "credits_charged": 0.19125,
    "breakdown": {
      "input": {
        "text": 0.09375,
        "visual": 0.0975,
        "video": 0
      },
      "model": "aurous-embed-vision-1.0"
    }
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

Embeddings

Create embeddings

Create embeddings from text and/or visual content (images, video). Multimodal input is combined into a SINGLE embedding (the underlying model concatenates parts into one document representation). For OpenAI-style N→N batch embedding, loop client-side: send one request per item.

Returns the OpenAI-compatible envelope (object: "list", data: [{ embedding, index, object }], model, usage) plus the Aurous usage extension carrying credits_charged and a per-modality breakdown (input: { text, visual, video }) so you can correlate charge to input. credits_charged is authoritative. Idempotency-Key is always honored.

POST

embeddings

Create embeddings

curl --request POST \
  --url https://api.aurous-labs.com/v1/embeddings \
  --header 'Content-Type: application/json' \
  --header 'X-Api-Key: <api-key>' \
  --data '
{
  "model": "aurous-embed-vision-1.0",
  "input": "<string>",
  "dimensions": 1024,
  "encoding_format": "float",
  "user": "<string>"
}
'

import requests

url = "https://api.aurous-labs.com/v1/embeddings"

payload = {
    "model": "aurous-embed-vision-1.0",
    "input": "<string>",
    "dimensions": 1024,
    "encoding_format": "float",
    "user": "<string>"
}
headers = {
    "X-Api-Key": "<api-key>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {'X-Api-Key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    model: 'aurous-embed-vision-1.0',
    input: '<string>',
    dimensions: 1024,
    encoding_format: 'float',
    user: '<string>'
  })
};

fetch('https://api.aurous-labs.com/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.aurous-labs.com/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([
    'model' => 'aurous-embed-vision-1.0',
    'input' => '<string>',
    'dimensions' => 1024,
    'encoding_format' => 'float',
    'user' => '<string>'
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "X-Api-Key: <api-key>"
  ],
]);

$response = curl_exec($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.aurous-labs.com/v1/embeddings"

	payload := strings.NewReader("{\n  \"model\": \"aurous-embed-vision-1.0\",\n  \"input\": \"<string>\",\n  \"dimensions\": 1024,\n  \"encoding_format\": \"float\",\n  \"user\": \"<string>\"\n}")

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

	req.Header.Add("X-Api-Key", "<api-key>")
	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.aurous-labs.com/v1/embeddings")
  .header("X-Api-Key", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"aurous-embed-vision-1.0\",\n  \"input\": \"<string>\",\n  \"dimensions\": 1024,\n  \"encoding_format\": \"float\",\n  \"user\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.aurous-labs.com/v1/embeddings")

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

request = Net::HTTP::Post.new(url)
request["X-Api-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"model\": \"aurous-embed-vision-1.0\",\n  \"input\": \"<string>\",\n  \"dimensions\": 1024,\n  \"encoding_format\": \"float\",\n  \"user\": \"<string>\"\n}"

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

{
  "object": "list",
  "data": [
    {
      "index": 0,
      "object": "embedding",
      "embedding": [
        0.0123,
        -0.0456,
        0.0789
      ]
    }
  ],
  "model": "aurous-embed-vision-1.0",
  "usage": {
    "prompt_tokens": 7000,
    "total_tokens": 7000,
    "credits_charged": 0.19125,
    "breakdown": {
      "input": {
        "text": 0.09375,
        "visual": 0.0975,
        "video": 0
      },
      "model": "aurous-embed-vision-1.0"
    }
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

{
  "error": {
    "type": "invalid_request",
    "code": "balance_too_low",
    "message": "Team available balance is 1.5 credits, generation requires 2.0.",
    "doc_url": "https://docs.aurous-labs.com/errors#balance_too_low",
    "request_id": "req_01HXMQ7Z3K8Y2ABCDEFGHJKM",
    "param": "prompt"
  }
}

Authorizations

X-Api-Key

string

header

required

Your team API key (starts with al_live_).

Headers

Idempotency-Key

string

Stripe-style idempotency key. Replays return the cached response with Aurous-Idempotent-Replayed: true. Same key + different canonical body returns 409 idempotency_key_in_use. Replay window is 24 hours.

Aurous-Version

string

Optional API version pin (YYYY-MM-DD). Defaults to your team's pinned version, or the system default 2026-07-16 for unauthenticated requests.

Pattern: ^\d{4}-\d{2}-\d{2}$

Example:

"2026-07-16"

Body

application/json

model

string

required

Public model slug (e.g. "aurous-embed-vision-1.0"). Pass exactly as listed by GET /v1/models.

Example:

"aurous-embed-vision-1.0"

input

required

Input — accepts a string OR an array of content parts ({type: "text"|"image_url"|"video_url"}) for multimodal. String-array (string[]) batch input is NOT accepted on v1: the underlying model concatenates batched text into a single embedding, so a customer expecting OpenAI-style N→N would get one combined vector. Loop client-side or pass a multimodal-parts array (returns one combined embedding).

dimensions

integer

Output vector dimensions. Most models return a fixed dimension and reject this parameter. If the model does not support dimensions, the request returns 400 embeddings_unsupported_dimensions.

Example:

1024

encoding_format

enum<string>

default:float

Vector encoding format. Accepted for OpenAI SDK compatibility (the Node SDK sends base64 by default); v1 always returns float vectors regardless of the value sent. base64 support (returning base64-encoded float buffers) is reserved for a future release.

Available options:

float,

base64

user

string

End-user identifier for your records (optional). Stored on the inference row; no Aurous-side behavior. Mirrors OpenAI compat.

Maximum string length: 256

Response

Embedding created.

object

enum<string>

required

OpenAI envelope discriminator.

Available options:

list

data

object[]

required

Embedding items. Always a single-element array on v1.

Show child attributes

model

string

required

Model slug used for this request (echoed from the request).

Example:

"aurous-embed-vision-1.0"

usage

object

required

Token + credit accounting for this request.

Show child attributes

Estimate Cost Before Embedding Estimate embedding credits

Get started

Guides

Concepts

API Reference

Resources

Create embeddings

Authorizations

Headers

Body

Response