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POST
/
openai
/
v1
/
embeddings
创建嵌入请求
curl --request POST \
  --url https://api.ppio.com/openai/v1/embeddings \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: <content-type>' \
  --data '
{
  "input": {},
  "model": {},
  "encoding_format": "<string>"
}
'
import requests

url = "https://api.ppio.com/openai/v1/embeddings"

payload = {
"input": {},
"model": {},
"encoding_format": "<string>"
}
headers = {
"Content-Type": "<content-type>",
"Authorization": "<authorization>"
}

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

print(response.text)
const options = {
method: 'POST',
headers: {'Content-Type': '<content-type>', Authorization: '<authorization>'},
body: JSON.stringify({input: {}, model: {}, encoding_format: '<string>'})
};

fetch('https://api.ppio.com/openai/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.ppio.com/openai/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' => [

],
'model' => [

],
'encoding_format' => '<string>'
]),
CURLOPT_HTTPHEADER => [
"Authorization: <authorization>",
"Content-Type: <content-type>"
],
]);

$response = curl_exec($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.ppio.com/openai/v1/embeddings"

payload := strings.NewReader("{\n \"input\": {},\n \"model\": {},\n \"encoding_format\": \"<string>\"\n}")

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

req.Header.Add("Content-Type", "<content-type>")
req.Header.Add("Authorization", "<authorization>")

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.ppio.com/openai/v1/embeddings")
.header("Content-Type", "<content-type>")
.header("Authorization", "<authorization>")
.body("{\n \"input\": {},\n \"model\": {},\n \"encoding_format\": \"<string>\"\n}")
.asString();
require 'uri'
require 'net/http'

url = URI("https://api.ppio.com/openai/v1/embeddings")

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

request = Net::HTTP::Post.new(url)
request["Content-Type"] = '<content-type>'
request["Authorization"] = '<authorization>'
request.body = "{\n \"input\": {},\n \"model\": {},\n \"encoding_format\": \"<string>\"\n}"

response = http.request(request)
puts response.read_body
{
  "object": "<string>",
  "data": [
    {
      "index": 123,
      "embedding": [
        123
      ],
      "object": "<string>"
    }
  ],
  "model": "<string>",
  "usage": {
    "prompt_tokens": 123,
    "total_tokens": 123
  }
}
创建一个表示输入文本的嵌入向量。

请求头

Content-Type
string
必填
枚举值: application/json
Authorization
string
必填
Bearer 身份验证格式,例如:Bearer {{API 密钥}}。

请求体

input
string | arrary
必填
要嵌入的输入文本,编码为字符串或 tokens 数组。要在单个请求中嵌入多个输入,请传入字符串数组或 tokens 数组的数组。输入不得超过模型的最大输入 tokens(text-embedding-ada-002 的最大输入为 8192 个 tokens),不能是空字符串,且任何数组的维度不得超过 2048。
model
enum<string>
必填
要使用的模型 ID。Enum: baai/bge-m3
encoding_format
string
返回嵌入的格式。可以为 float 或 base64。

响应参数

object
string
必填
固定为 list
data
object[]
必填
模型生成的嵌入列表。
model
string
必填
使用的模型 ID。
usage
object
必填
使用情况信息。
最后修改于 2026年7月3日