Embeddings
curl --request POST \
--url https://openp.ai/v1/embeddingsimport requests
url = "https://openp.ai/v1/embeddings"
response = requests.post(url)
print(response.text)const options = {method: 'POST'};
fetch('https://openp.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://openp.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",
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"net/http"
"io"
)
func main() {
url := "https://openp.ai/v1/embeddings"
req, _ := http.NewRequest("POST", url, nil)
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://openp.ai/v1/embeddings")
.asString();require 'uri'
require 'net/http'
url = URI("https://openp.ai/v1/embeddings")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
response = http.request(request)
puts response.read_bodyOpenAI-compatible
Embeddings
POST /v1/embeddings — text embedding vectors
POST
/
v1
/
embeddings
Embeddings
curl --request POST \
--url https://openp.ai/v1/embeddingsimport requests
url = "https://openp.ai/v1/embeddings"
response = requests.post(url)
print(response.text)const options = {method: 'POST'};
fetch('https://openp.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://openp.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",
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"net/http"
"io"
)
func main() {
url := "https://openp.ai/v1/embeddings"
req, _ := http.NewRequest("POST", url, nil)
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://openp.ai/v1/embeddings")
.asString();require 'uri'
require 'net/http'
url = URI("https://openp.ai/v1/embeddings")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
response = http.request(request)
puts response.read_bodyConvert text into high-dimensional vectors for semantic retrieval, clustering, recommendation and more.
Truncated vectors preserve semantics while using less memory / compute.
Request
curl https://openp.ai/v1/embeddings \
-H "Authorization: Bearer $OPENPAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "text-embedding-3-small",
"input": ["hello world", "你好世界"]
}'
Parameters
| Field | Type | Required | Description |
|---|---|---|---|
model | string | ✅ | Embedding model ID |
input | string | array | ✅ | A single string or an array |
encoding_format | string | float (default) or base64 | |
dimensions | integer | Truncate to a specified dimension (text-embedding-3-* only) | |
user | string | End-user identifier |
Recommended models
| Model ID | Dimensions | Notes |
|---|---|---|
text-embedding-3-small | 1536 (reducible ≥ 512) | Default recommendation |
text-embedding-3-large | 3072 (reducible ≥ 256) | High quality |
text-embedding-ada-002 | 1536 | Older |
text-embedding-004 | 768 | |
bge-m3 | 1024 | Multilingual open-source |
text-embedding-v3 | 1024 | Tongyi Qianwen |
Response
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [0.0023, -0.018, ...]
},
{
"object": "embedding",
"index": 1,
"embedding": [0.0021, -0.015, ...]
}
],
"model": "text-embedding-3-small",
"usage": {"prompt_tokens": 5, "total_tokens": 5}
}
Python example
from openai import OpenAI
client = OpenAI(api_key="sk-...", base_url="https://openp.ai/v1")
resp = client.embeddings.create(
model="text-embedding-3-small",
input=["hello", "world"],
)
import numpy as np
vecs = np.array([e.embedding for e in resp.data])
# cosine similarity
sim = vecs @ vecs.T / (np.linalg.norm(vecs, axis=1)[:, None] * np.linalg.norm(vecs, axis=1)[None, :])
Dimension truncation
{
"model": "text-embedding-3-large",
"input": "hello",
"dimensions": 256
}
Billing
Charged byprompt_tokens only — no output tokens. See Billing.