Embedding text embedding ada 002

import { EmbeddingClient } from "@runapi.ai/embedding";

const client = new EmbeddingClient();
const result = await client.embedding.run({
    model: "text-embedding-ada-002",
    input: ["为这些支持文档和一个客户问题生成向量，方便按相似度排序。"],
    encoding_format: "float",
});

import os
import requests

response = requests.post(
    "https://runapi.ai/v1/embeddings",
    headers={"Authorization": f"Bearer {os.environ['RUNAPI_API_KEY']}"},
    json={"model":"text-embedding-ada-002","input":["为这些支持文档和一个客户问题生成向量，方便按相似度排序。"],"encoding_format":"float"}
)
response.raise_for_status()

<?php

$client = curl_init("https://runapi.ai/v1/embeddings");
curl_setopt_array($client, [
    CURLOPT_HTTPHEADER => ["Authorization: Bearer " . getenv("RUNAPI_API_KEY"), "Content-Type: application/json"],
    CURLOPT_POSTFIELDS => json_encode([
        'model' => 'text-embedding-ada-002',
        'input' => ['为这些支持文档和一个客户问题生成向量，方便按相似度排序。'],
        'encoding_format' => 'float',
    ])
]);
$response = curl_exec($client);

RunApi client = new RunApi(System.getenv("RUNAPI_API_KEY"));
Task task = client.embedding().run({"model":"text-embedding-ada-002","input":["为这些支持文档和一个客户问题生成向量，方便按相似度排序。"],"encoding_format":"float"});
task.waitForResult();

require "runapi/embedding"

client = RunApi::Embedding::Client.new
result = client.embedding.run(
    model: "text-embedding-ada-002",
    input: ["为这些支持文档和一个客户问题生成向量，方便按相似度排序。"],
    encoding_format: "float"
)

client := runapi.NewClient(os.Getenv("RUNAPI_API_KEY"))
task, err := client.embedding.Run({"model":"text-embedding-ada-002","input":["为这些支持文档和一个客户问题生成向量，方便按相似度排序。"],"encoding_format":"float"})
if err != nil { log.Fatal(err) }
task.Wait()

$ runapi generate \
    --model text-embedding-ada-002 \
    --endpoint embedding \
    --wait