Create embeddings
curl --request POST \
--url https://ai.liara.ir/api/{workspaceID}/v1/embeddings \
--header 'Authorization: <api-key>' \
--header 'Content-Type: application/json' \
--data '
{
"input": "<string>",
"model": "openai/text-embedding-3-small",
"dimensions": 123,
"encoding_format": "<string>",
"user": "<string>"
}
'import requests
url = "https://ai.liara.ir/api/{workspaceID}/v1/embeddings"
payload = {
"input": "<string>",
"model": "openai/text-embedding-3-small",
"dimensions": 123,
"encoding_format": "<string>",
"user": "<string>"
}
headers = {
"Authorization": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
input: '<string>',
model: 'openai/text-embedding-3-small',
dimensions: 123,
encoding_format: '<string>',
user: '<string>'
})
};
fetch('https://ai.liara.ir/api/{workspaceID}/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://ai.liara.ir/api/{workspaceID}/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' => '<string>',
'model' => 'openai/text-embedding-3-small',
'dimensions' => 123,
'encoding_format' => '<string>',
'user' => '<string>'
]),
CURLOPT_HTTPHEADER => [
"Authorization: <api-key>",
"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://ai.liara.ir/api/{workspaceID}/v1/embeddings"
payload := strings.NewReader("{\n \"input\": \"<string>\",\n \"model\": \"openai/text-embedding-3-small\",\n \"dimensions\": 123,\n \"encoding_format\": \"<string>\",\n \"user\": \"<string>\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "<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://ai.liara.ir/api/{workspaceID}/v1/embeddings")
.header("Authorization", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"input\": \"<string>\",\n \"model\": \"openai/text-embedding-3-small\",\n \"dimensions\": 123,\n \"encoding_format\": \"<string>\",\n \"user\": \"<string>\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://ai.liara.ir/api/{workspaceID}/v1/embeddings")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"input\": \"<string>\",\n \"model\": \"openai/text-embedding-3-small\",\n \"dimensions\": 123,\n \"encoding_format\": \"<string>\",\n \"user\": \"<string>\"\n}"
response = http.request(request)
puts response.read_body{
"object": "list",
"data": [
{
"object": "embedding",
"embedding": [
123
],
"index": 123
}
],
"model": "<string>",
"usage": {
"prompt_tokens": 123,
"total_tokens": 123,
"total_cost_toman": 123,
"total_cost": 123
}
}This response has no body data.This response has no body data.This response has no body data.This response has no body data.Embeddings
Create embeddings
Generates embeddings for the given input text. Supports single strings or arrays of strings. Response includes token usage and cost information.
POST
/
api
/
{workspaceID}
/
v1
/
embeddings
Create embeddings
curl --request POST \
--url https://ai.liara.ir/api/{workspaceID}/v1/embeddings \
--header 'Authorization: <api-key>' \
--header 'Content-Type: application/json' \
--data '
{
"input": "<string>",
"model": "openai/text-embedding-3-small",
"dimensions": 123,
"encoding_format": "<string>",
"user": "<string>"
}
'import requests
url = "https://ai.liara.ir/api/{workspaceID}/v1/embeddings"
payload = {
"input": "<string>",
"model": "openai/text-embedding-3-small",
"dimensions": 123,
"encoding_format": "<string>",
"user": "<string>"
}
headers = {
"Authorization": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
input: '<string>',
model: 'openai/text-embedding-3-small',
dimensions: 123,
encoding_format: '<string>',
user: '<string>'
})
};
fetch('https://ai.liara.ir/api/{workspaceID}/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://ai.liara.ir/api/{workspaceID}/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' => '<string>',
'model' => 'openai/text-embedding-3-small',
'dimensions' => 123,
'encoding_format' => '<string>',
'user' => '<string>'
]),
CURLOPT_HTTPHEADER => [
"Authorization: <api-key>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://ai.liara.ir/api/{workspaceID}/v1/embeddings"
payload := strings.NewReader("{\n \"input\": \"<string>\",\n \"model\": \"openai/text-embedding-3-small\",\n \"dimensions\": 123,\n \"encoding_format\": \"<string>\",\n \"user\": \"<string>\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://ai.liara.ir/api/{workspaceID}/v1/embeddings")
.header("Authorization", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"input\": \"<string>\",\n \"model\": \"openai/text-embedding-3-small\",\n \"dimensions\": 123,\n \"encoding_format\": \"<string>\",\n \"user\": \"<string>\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://ai.liara.ir/api/{workspaceID}/v1/embeddings")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"input\": \"<string>\",\n \"model\": \"openai/text-embedding-3-small\",\n \"dimensions\": 123,\n \"encoding_format\": \"<string>\",\n \"user\": \"<string>\"\n}"
response = http.request(request)
puts response.read_body{
"object": "list",
"data": [
{
"object": "embedding",
"embedding": [
123
],
"index": 123
}
],
"model": "<string>",
"usage": {
"prompt_tokens": 123,
"total_tokens": 123,
"total_cost_toman": 123,
"total_cost": 123
}
}This response has no body data.This response has no body data.This response has no body data.This response has no body data.Authorizations
Enter the API key with the Bearer: prefix, e.g. "Bearer "
Path Parameters
The workspace ID
Pattern:
^[a-f0-9]{24}$Body
application/json
Text or array of texts to embed
Embedding model ID. Supported models:
google/gemini-embedding-2intfloat/multilingual-e5-largegoogle/gemini-embedding-001openai/text-embedding-3-smallopenai/text-embedding-3-largeopenai/text-embedding-ada-002
Example:
"openai/text-embedding-3-small"
Output dimensions (model-dependent)
Encoding format for embeddings
End-user identifier
⌘I
