嵌入接口 (Embeddings)

curl https://aiapi.services/v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "人工智能正在改变世界"
  }'

import requests
 
response = requests.post(
  'https://aiapi.services/v1/embeddings',
  headers={
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  json={
    'model': 'text-embedding-3-small',
    'input': '人工智能正在改变世界'
  }
)
 
data = response.json()
embedding = data['data'][0]['embedding']
print(f'向量维度: {len(embedding)}')
print(f'前5个值: {embedding[:5]}')

const response = await fetch('https://aiapi.services/v1/embeddings', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'text-embedding-3-small',
    input: '人工智能正在改变世界'
  })
});
 
const data = await response.json();
console.log(data.data[0].embedding); // 向量数组

package main
 
import (
  "bytes"
  "encoding/json"
  "fmt"
  "io"
  "net/http"
)
 
func main() {
  url := "https://aiapi.services/v1/embeddings"
 
  payload := map[string]interface{}{
    "model": "text-embedding-3-small",
    "input": "人工智能正在改变世界",
  }
 
  jsonData, _ := json.Marshal(payload)
 
  req, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))
  req.Header.Set("Authorization", "Bearer YOUR_API_KEY")
  req.Header.Set("Content-Type", "application/json")
 
  client := &http.Client{}
  resp, _ := client.Do(req)
  defer resp.Body.Close()
 
  body, _ := io.ReadAll(resp.Body)
  fmt.Println(string(body))
}

use reqwest::header::{HeaderMap, HeaderValue, AUTHORIZATION, CONTENT_TYPE};
use serde_json::json;
 
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = reqwest::Client::new();
 
    let mut headers = HeaderMap::new();
    headers.insert(AUTHORIZATION, HeaderValue::from_static("Bearer YOUR_API_KEY"));
    headers.insert(CONTENT_TYPE, HeaderValue::from_static("application/json"));
 
    let payload = json!({
        "model": "text-embedding-3-small",
        "input": "人工智能正在改变世界"
    });
 
    let response = client
        .post("https://aiapi.services/v1/embeddings")
        .headers(headers)
        .json(&payload)
        .send()
        .await?;
 
    let data: serde_json::Value = response.json().await?;
    let embedding = &data["data"][0]["embedding"];
 
    println!("向量维度: {}", embedding.as_array().unwrap().len());
    println!("向量数据: {:?}", embedding);
 
    Ok(())
}

<?php
 
$ch = curl_init();
curl_setopt($ch, CURLOPT_URL, "https://aiapi.services/v1/embeddings");
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, [
    "Authorization: Bearer YOUR_API_KEY",
    "Content-Type: application/json"
]);
 
$data = [
    "model" => "text-embedding-3-small",
    "input" => "人工智能正在改变世界"
];
 
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));
$response = curl_exec($ch);
curl_close($ch);
 
$result = json_decode($response, true);
$embedding = $result['data'][0]['embedding'];
 
echo "向量维度: " . count($embedding) . "\n";
echo "前5个值: " . implode(", ", array_slice($embedding, 0, 5)) . "\n";
?>

require 'net/http'
require 'json'
 
uri = URI('https://aiapi.services/v1/embeddings')
request = Net::HTTP::Post.new(uri)
request['Authorization'] = 'Bearer YOUR_API_KEY'
request['Content-Type'] = 'application/json'
 
request.body = {
  model: 'text-embedding-3-small',
  input: '人工智能正在改变世界'
}.to_json
 
response = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) do |http|
  http.request(request)
end
 
data = JSON.parse(response.body)
embedding = data['data'][0]['embedding']
 
puts "向量维度: #{embedding.length}"
puts "前5个值: #{embedding[0..4].join(', ')}"

const response = await fetch('https://aiapi.services/v1/embeddings', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'text-embedding-3-small',
    input: [
      '人工智能正在改变世界',
      '机器学习是AI的重要分支',
      '深度学习推动了AI的发展'
    ]
  })
});
 
const data = await response.json();
data.data.forEach((item, index) => {
  console.log(`文本${index + 1}的向量维度: ${item.embedding.length}`);
});

import requests
 
response = requests.post(
  'https://aiapi.services/v1/embeddings',
  headers={'Authorization': 'Bearer YOUR_API_KEY'},
  json={
    'model': 'text-embedding-3-small',
    'input': [
      '人工智能正在改变世界',
      '机器学习是AI的重要分支',
      '深度学习推动了AI的发展'
    ]
  }
)
 
data = response.json()
for i, item in enumerate(data['data']):
  print(f'文本{i+1}的向量维度: {len(item["embedding"])}')