获取可用模型

返回指定类型的可用模型。用它查找可配置到知识库的 text-embedding 和 rerank 模型。

GET /workspaces/current/models/model-types/{model_type}

获取可用模型

cURL

curl --request GET \
  --url https://{api_base_url}/workspaces/current/models/model-types/{model_type} \
  --header 'Authorization: Bearer <token>'

import requests

url = "https://{api_base_url}/workspaces/current/models/model-types/{model_type}"

headers = {"Authorization": "Bearer <token>"}

response = requests.get(url, headers=headers)

print(response.text)

const options = {method: 'GET', headers: {Authorization: 'Bearer <token>'}};

fetch('https://{api_base_url}/workspaces/current/models/model-types/{model_type}', 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_base_url}/workspaces/current/models/model-types/{model_type}",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "GET",
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>"
  ],
]);

$response = curl_exec($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://{api_base_url}/workspaces/current/models/model-types/{model_type}"

	req, _ := http.NewRequest("GET", url, nil)

	req.Header.Add("Authorization", "Bearer <token>")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.get("https://{api_base_url}/workspaces/current/models/model-types/{model_type}")
  .header("Authorization", "Bearer <token>")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://{api_base_url}/workspaces/current/models/model-types/{model_type}")

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

request = Net::HTTP::Get.new(url)
request["Authorization"] = 'Bearer <token>'

response = http.request(request)
puts response.read_body

200

{
  "data": [
    {
      "provider": "langgenius/openai/openai",
      "label": {
        "en_US": "OpenAI",
        "zh_Hans": "OpenAI"
      },
      "icon_small": {
        "en_US": "https://example.com/openai-small.svg",
        "zh_Hans": "https://example.com/openai-small.svg"
      },
      "status": "active",
      "models": [
        {
          "model": "text-embedding-3-small",
          "label": {
            "en_US": "text-embedding-3-small",
            "zh_Hans": "text-embedding-3-small"
          },
          "model_type": "text-embedding",
          "features": null,
          "fetch_from": "predefined-model",
          "model_properties": {
            "context_size": 8191
          },
          "status": "active",
          "deprecated": false,
          "load_balancing_enabled": false,
          "has_invalid_load_balancing_configs": false
        }
      ],
      "tenant_id": "11223344-5566-7788-99aa-bbccddeeff00",
      "icon_small_dark": null
    }
  ]
}

Authorizations

Authorization · string · header · required

每个请求都通过 API Key 认证：Authorization: Bearer {API_KEY}。应用接口使用应用 API Key，知识库接口使用知识库 API Key（快速开始）。

API Key 应保存在服务端，切勿嵌入客户端代码。缺失或无效的 Key 会返回 HTTP 401（unauthorized）。

Path Parameters

model_type · enum<string> · required

要获取的模型类型。对于知识库配置，使用 text-embedding 获取嵌入模型，使用 rerank 获取重排序模型。

Available options: text-embedding, rerank, llm, tts, speech2text, moderation

Response

200 - application/json

指定类型的可用模型。

data · object[]

模型供应商及其可用模型的列表。

Show child attributes