Create Embeddings

curl --request POST \
  --url https://api.meshapi.ai/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "openai/text-embedding-3-small",
  "input": "The quick brown fox jumps over the lazy dog."
}
'

import requests

url = "https://api.meshapi.ai/v1/embeddings"

payload = {
    "model": "openai/text-embedding-3-small",
    "input": "The quick brown fox jumps over the lazy dog."
}
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({
    model: 'openai/text-embedding-3-small',
    input: 'The quick brown fox jumps over the lazy dog.'
  })
};

fetch('https://api.meshapi.ai/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.meshapi.ai/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' => 'openai/text-embedding-3-small',
    'input' => 'The quick brown fox jumps over the lazy dog.'
  ]),
  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.meshapi.ai/v1/embeddings"

	payload := strings.NewReader("{\n  \"model\": \"openai/text-embedding-3-small\",\n  \"input\": \"The quick brown fox jumps over the lazy dog.\"\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.meshapi.ai/v1/embeddings")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"openai/text-embedding-3-small\",\n  \"input\": \"The quick brown fox jumps over the lazy dog.\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.meshapi.ai/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  \"model\": \"openai/text-embedding-3-small\",\n  \"input\": \"The quick brown fox jumps over the lazy dog.\"\n}"

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

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        -0.03264831379055977,
        -0.03786936774849892,
        0.007462255656719208
      ]
    }
  ],
  "model": "amazon/titan-embed-text-v2",
  "usage": {
    "prompt_tokens": 3,
    "total_tokens": 3
  }
}

Embeddings

Create Embeddings

Turn text into embedding vectors.

OpenAI-compatible: point the OpenAI SDK’s embeddings client at this gateway and it works unchanged. input takes a single string or a batch, and the response keeps the input order so you can zip results back to your records.

model is required and must name a specific embedding model — auto is rejected, because a vector is only comparable to other vectors from the same model, so silently choosing one for you would corrupt an index. Provider failover and BYOK keys apply as they do to chat.

POST

embeddings

Create Embeddings

curl --request POST \
  --url https://api.meshapi.ai/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "openai/text-embedding-3-small",
  "input": "The quick brown fox jumps over the lazy dog."
}
'

import requests

url = "https://api.meshapi.ai/v1/embeddings"

payload = {
    "model": "openai/text-embedding-3-small",
    "input": "The quick brown fox jumps over the lazy dog."
}
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({
    model: 'openai/text-embedding-3-small',
    input: 'The quick brown fox jumps over the lazy dog.'
  })
};

fetch('https://api.meshapi.ai/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.meshapi.ai/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' => 'openai/text-embedding-3-small',
    'input' => 'The quick brown fox jumps over the lazy dog.'
  ]),
  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.meshapi.ai/v1/embeddings"

	payload := strings.NewReader("{\n  \"model\": \"openai/text-embedding-3-small\",\n  \"input\": \"The quick brown fox jumps over the lazy dog.\"\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.meshapi.ai/v1/embeddings")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"openai/text-embedding-3-small\",\n  \"input\": \"The quick brown fox jumps over the lazy dog.\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.meshapi.ai/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  \"model\": \"openai/text-embedding-3-small\",\n  \"input\": \"The quick brown fox jumps over the lazy dog.\"\n}"

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

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        -0.03264831379055977,
        -0.03786936774849892,
        0.007462255656719208
      ]
    }
  ],
  "model": "amazon/titan-embed-text-v2",
  "usage": {
    "prompt_tokens": 3,
    "total_tokens": 3
  }
}

Authorizations

Authorization

string

header

required

Enter your MeshAPI key (rsk_...) — sent as Authorization: Bearer <key>.

Headers

X-Mesh-Version

enum<string>

default:2026-08

Dated version of the API contract to pin this request to. Omit it and the request is served under 2026-08 — the oldest supported version, so an existing integration is never moved by a release. A malformed or unsupported value is rejected with 400 invalid_api_version rather than falling back silently. The version actually served is echoed as X-Mesh-Version on every response, including errors.

Available options:

2026-08

Body

application/json

Request body for POST /v1/embeddings.

Mirrors the OpenAI embeddings API. The input field accepts four shapes:

string — a single text to embed
list[string] — a batch of texts
list[int] — a single pre-tokenised input (token IDs)
list[list[int]] — a batch of pre-tokenised inputs

input

required

Text(s) to embed. Accepts a string, list of strings, list of token IDs, list of token ID lists, or a list of MultimodalEmbeddingInput objects (for BytePlus multimodal embedding models).

model

string | null

Model ID to use for embedding, e.g. perplexity/pplx-embed-v1-4b.

Example:

"perplexity/pplx-embed-v1-4b"

dimensions

integer | null

Number of dimensions for the output embedding vector (model-dependent).

Required range: x >= 1

encoding_format

enum<string> | null

Format of the returned embedding. Defaults to float.

Available options:

float,

base64

input_type

string | null

Intended use of the embedding, e.g. query or document. Some models use this to apply asymmetric embedding.

provider

Provider routing preferences. Pass a provider slug string (e.g. 'perplexity') or a ProviderPreferences object to control fallback and ordering behaviour.

user

string | null

End-user identifier for abuse monitoring.

Maximum string length: 256

instructions

string | null

Inference prompt for BytePlus multimodal embeddings. A default is generated from the input modality if not set.

sparse_embedding

Sparse Embedding · object | null

Sparse vector switch for BytePlus text embeddings. Pass {"type": "enabled"} to return both dense and sparse vectors, or {"type": "disabled"} for dense only.

Response

Embedding response

Overview

Endpoints

Create Embeddings

Authorizations

Headers

Body

Response