TaobaoClient client = new DefaultTaobaoClient(url, appkey, secret);
AgentModelRerankCapacityRequest req = new AgentModelRerankCapacityRequest();
req.setOriginBizParams("{\"a\":\"aaaa\",\"b\",\"bbbbb\"}");
AgentModelRerankCapacityRequest.Document obj1 = new AgentModelRerankCapacityRequest.Document();
obj1.setText("文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序");
req.setDocuments(obj1);
req.setQuery("什么是文本排序模型");
req.setModel("gte-rerank-v2");
req.setTopN(1L);
AgentModelRerankCapacityResponse rsp = client.execute(req);
System.out.println(rsp.getBody());

ITopClient client = new DefaultTopClient(url, appkey, secret);
AgentModelRerankCapacityRequest req = new AgentModelRerankCapacityRequest();
req.OriginBizParams = "{\"a\":\"aaaa\",\"b\",\"bbbbb\"}";
AgentModelRerankCapacityRequest.DocumentDomain obj1 = new AgentModelRerankCapacityRequest.DocumentDomain();
obj1.Text = "文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序";
req.Documents_ = obj1;
req.Query = "什么是文本排序模型";
req.Model = "gte-rerank-v2";
req.TopN = 1L;
AgentModelRerankCapacityResponse rsp = client.Execute(req);
Console.WriteLine(rsp.Body);

$c = new TopClient;
$c->appkey = $appkey;
$c->secretKey = $secret;
$req = new AgentModelRerankCapacityRequest;
$req->setOriginBizParams("{\"a\":\"aaaa\",\"b\",\"bbbbb\"}");
$documents = new Document;
$documents->text="文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序";
$req->setDocuments(json_encode($documents));
$req->setQuery("什么是文本排序模型");
$req->setModel("gte-rerank-v2");
$req->setTopN("1");
$resp = $c->execute($req);

curl -X POST 'http://gw.api.taobao.com/router/rest' \
-H 'Content-Type:application/x-www-form-urlencoded;charset=utf-8' \
-d 'app_key=12129701' \
-d 'format=json' \
-d 'method=taobao.agent.model.rerank.capacity' \
-d 'partner_id=apidoc' \
-d 'sign=2199033D2A603E8356F47A9B65E1603B' \
-d 'sign_method=hmac' \
-d 'timestamp=2026-04-05+17%3A57%3A06' \
-d 'v=2.0' \
-d 'documents=documents' \
-d 'model=gte-rerank-v2' \
-d 'origin_biz_params=%7B%5C%22a%5C%22%3A%5C%22aaaa%5C%22%2C%5C%22b%5C%22%2C%5C%22bbbbb%5C%22%7D' \
-d 'query=%E4%BB%80%E4%B9%88%E6%98%AF%E6%96%87%E6%9C%AC%E6%8E%92%E5%BA%8F%E6%A8%A1%E5%9E%8B' \
-d 'top_n=1'

# -*- coding: utf-8 -*-
import top.api

req=top.api.AgentModelRerankCapacityRequest(url,port)
req.set_app_info(top.appinfo(appkey,secret))

req.origin_biz_params="{\"a\":\"aaaa\",\"b\",\"bbbbb\"}"
req.documents="documents"
req.query="什么是文本排序模型"
req.model="gte-rerank-v2"
req.top_n=1
try:
	resp= req.getResponse()
	print(resp)
except Exception,e:
	print(e)

pTopRequest pRequest = alloc_top_request();
pTopResponse pResponse = NULL;
pTaobaoClient pClient = alloc_taobao_client(url, appkey, appsecret);
set_api_name(pRequest,"taobao.agent.model.rerank.capacity");
add_param(pRequest,"origin_biz_params","{\"a\":\"aaaa\",\"b\",\"bbbbb\"}");
add_param(pRequest,"documents","数据结构JSON示例");
add_param(pRequest,"query","什么是文本排序模型");
add_param(pRequest,"model","gte-rerank-v2");
add_param(pRequest,"top_n","1");
pResponse = top_execute(pClient,pRequest,NULL);
printf("ret code:%d\n",pResponse->code);
if(pResponse->code == 0){
	pTopResponseIterator ite = init_response_iterator(pResponse);
	pResultItem pResultItem = alloc_result_item();
	while(parseNext(ite, pResultItem) == 0){
		printf("%s:%s\n",pResultItem->key,pResultItem->value);
	}
	destroy_response_iterator(ite);
	destroy_result_item(pResultItem);
}
destroy_top_request(pRequest);
destroy_top_response(pResponse);
destroy_taobao_client(pClient);

TopClient = require('./topClient').TopClient;
var client = new TopClient({
	'appkey': 'appkey',
	'appsecret': 'secret',
	'url': 'http://gw.api.taobao.com/router/rest'
});

client.execute('taobao.agent.model.rerank.capacity', {
	'origin_biz_params':'{\"a\":\"aaaa\",\"b\",\"bbbbb\"}',
	'documents':'数据结构JSON示例',
	'query':'什么是文本排序模型',
	'model':'gte-rerank-v2',
	'top_n':'1'
}, function(error, response) {
	if (!error) console.log(response);
	else console.log(error);
})

<agent_model_rerank_capacity_response>
    <documents>
        <document_with_score>
            <score>0.34343</score>
            <text>文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序</text>
        </document_with_score>
    </documents>
</agent_model_rerank_capacity_response>

{
    "agent_model_rerank_capacity_response":{
        "documents":{
            "document_with_score":[
                {
                    "score":"0.34343",
                    "text":"文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序"
                }
            ]
        }
    }
}

<error_response>
    <code>50</code>
    <msg>Remote service error</msg>
    <sub_code>isv.invalid-parameter</sub_code>
    <sub_msg>非法参数</sub_msg>
</error_response>

{
	"error_response":{
		"msg":"Remote service error",
		"code":50,
		"sub_msg":"非法参数",
		"sub_code":"isv.invalid-parameter"
	}
}