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POST
/
v1
/
embeddings
Embeddings
curl --request POST \
  --url https://openp.ai/v1/embeddings
import 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_body
把文本转换为高维向量,用于语义检索、聚类、推荐等场景。

​
请求

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"]
  }'

​
参数

字段类型必填说明
modelstring✅嵌入模型 ID
inputstring | array✅单条字符串或数组
encoding_formatstringfloat(默认)或 base64
dimensionsinteger截断到指定维度(仅 text-embedding-3-*)
userstring终端用户标识

​
推荐模型

模型 ID维度备注
text-embedding-3-small1536(可缩 ≥ 512)默认推荐
text-embedding-3-large3072(可缩 ≥ 256)高质量
text-embedding-ada-0021536旧版
text-embedding-004768Google
bge-m31024多语种开源
text-embedding-v31024通义千问

​
响应

{
  "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 示例

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])
# 余弦相似度
sim = vecs @ vecs.T / (np.linalg.norm(vecs, axis=1)[:, None] * np.linalg.norm(vecs, axis=1)[None, :])

​
维度截断

{
  "model": "text-embedding-3-large",
  "input": "hello",
  "dimensions": 256
}
截断后向量在保留语义的同时更省内存 / 计算。

​
计费

仅按 prompt_tokens 计费,无输出 token。详见 计费机制。