Responses API 基础调用

Responses API 是一个统一的模型推理接口，支持多种AI模型的对话生成。本示例展示基础的文本对话调用方法。

快速开始

只需要替换 <API-KEY> 为你的实际API密钥即可运行。

curl -X POST "https://api.tokenops.ai/v1/responses" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <API-KEY>" \
  -d '{
    "model": "gpt-5-2025-08-07",
    "input": [
      {
        "role": "user",
        "content": "你好，我叫张三，我今年25岁。",
        "type": "message"
      }
    ]
  }'

import requests
import json

# 配置API密钥和基础URL
API_KEY = "<API-KEY>"
BASE_URL = "https://api.tokenops.ai/v1"

def create_basic_response():
    """创建基础对话响应"""
    url = f"{BASE_URL}/responses"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}"
    }

    payload = {
        "model": "gpt-5-2025-08-07",
        "input": [
            {
                "role": "user",
                "content": "你好，我叫张三，我今年25岁。",
                "type": "message"
            }
        ]
    }

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

    if response.status_code == 200:
        result = response.json()
        
        # 从output数组中提取消息内容
        if result.get("output") and len(result["output"]) > 0:
            for output_item in result["output"]:
                if output_item.get("type") == "message":
                    content_list = output_item.get("content", [])
                    for content in content_list:
                        if content.get("type") == "output_text":
                            print(f"AI回复: {content.get('text', '')}")

        # 显示使用统计
        usage = result.get("usage", {})
        print(f"Token使用: {usage.get('total_tokens', 0)} (输入: {usage.get('input_tokens', 0)}, 输出: {usage.get('output_tokens', 0)})")

        return result
    else:
        print(f"HTTP错误: {response.status_code}")
        print(response.text)
        return None

# 使用示例
if __name__ == "__main__":
    response = create_basic_response()

const axios = require('axios');

// 配置API密钥和基础URL
const API_KEY = '<API-KEY>';
const BASE_URL = 'https://api.tokenops.ai/v1';

async function createBasicResponse() {
    const url = `${BASE_URL}/responses`;
    const headers = {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${API_KEY}`
    };

    const payload = {
        model: 'gpt-5-2025-08-07',
        input: [
            {
                role: 'user',
                content: '你好，我叫张三，我今年25岁。',
                type: 'message'
            }
        ]
    };

    try {
        const response = await axios.post(url, payload, { headers });

        // 从output数组中提取消息内容
        if (response.data.output && response.data.output.length > 0) {
            response.data.output.forEach(outputItem => {
                if (outputItem.type === 'message') {
                    const contentList = outputItem.content || [];
                    contentList.forEach(content => {
                        if (content.type === 'output_text') {
                            console.log(`AI回复: ${content.text}`);
                        }
                    });
                }
            });
        }

        // 显示使用统计
        const usage = response.data.usage || {};
        console.log(`Token使用: ${usage.total_tokens || 0} (输入: ${usage.input_tokens || 0}, 输出: ${usage.output_tokens || 0})`);

        return response.data;
    } catch (error) {
        console.error('请求出错:', error.response?.data || error.message);
        return null;
    }
}

// 使用示例
createBasicResponse();

package main

import (
    "bytes"
    "encoding/json"
    "fmt"
    "net/http"
)

const (
    API_KEY = "<API-KEY>"
    BASE_URL = "https://api.tokenops.ai/v1"
)

type InputMessage struct {
    Role    string `json:"role"`
    Content string `json:"content"`
    Type    string `json:"type"`
}

type ResponseRequest struct {
    Model string         `json:"model"`
    Input []InputMessage `json:"input"`
}

type OutputContent struct {
    Type        string   `json:"type"`
    Annotations []string `json:"annotations"`
    Logprobs    []string `json:"logprobs"`
    Text        string   `json:"text"`
}

type OutputMessage struct {
    ID      string          `json:"id"`
    Type    string          `json:"type"`
    Status  string          `json:"status"`
    Content []OutputContent `json:"content"`
    Role    string          `json:"role"`
}

type Usage struct {
    InputTokens  int `json:"input_tokens"`
    OutputTokens int `json:"output_tokens"`
    TotalTokens  int `json:"total_tokens"`
}

type ResponseResult struct {
    ID       string          `json:"id"`
    Object   string          `json:"object"`
    Created  int64           `json:"created_at"`
    Status   string          `json:"status"`
    Model    string          `json:"model"`
    Output   []OutputMessage `json:"output"`
    Usage    Usage           `json:"usage"`
}

func createBasicResponse() (*ResponseResult, error) {
    url := BASE_URL + "/responses"

    request := ResponseRequest{
        Model: "gpt-5-2025-08-07",
        Input: []InputMessage{
            {
                Role:    "user",
                Content: "你好，我叫张三，我今年25岁。",
                Type:    "message",
            },
        },
    }

    jsonData, err := json.Marshal(request)
    if err != nil {
        return nil, err
    }

    httpReq, err := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))
    if err != nil {
        return nil, err
    }

    httpReq.Header.Set("Content-Type", "application/json")
    httpReq.Header.Set("Authorization", "Bearer "+API_KEY)

    client := &http.Client{}
    resp, err := client.Do(httpReq)
    if err != nil {
        return nil, err
    }
    defer resp.Body.Close()

    var result ResponseResult
    err = json.NewDecoder(resp.Body).Decode(&result)
    if err != nil {
        return nil, err
    }

    // 从output数组中提取消息内容
    for _, output := range result.Output {
        if output.Type == "message" {
            for _, content := range output.Content {
                if content.Type == "output_text" {
                    fmt.Printf("AI回复: %s\n", content.Text)
                }
            }
        }
    }

    fmt.Printf("Token使用: %d (输入: %d, 输出: %d)\n",
        result.Usage.TotalTokens,
        result.Usage.InputTokens,
        result.Usage.OutputTokens)

    return &result, nil
}

func main() {
    response, err := createBasicResponse()
    if err != nil {
        fmt.Printf("错误: %v\n", err)
        return
    }

    fmt.Printf("响应ID: %s\n", response.ID)
}

import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.net.URI;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.JsonNode;

public class ResponsesBasic {
    private static final String API_KEY = "<API-KEY>";
    private static final String BASE_URL = "https://api.tokenops.ai/v1";

    private final HttpClient client;
    private final ObjectMapper mapper;

    public ResponsesBasic() {
        this.client = HttpClient.newHttpClient();
        this.mapper = new ObjectMapper();
    }

    public JsonNode createBasicResponse() throws Exception {
        String url = BASE_URL + "/responses";

        String payload = """
            {
                "model": "gpt-5-2025-08-07",
                "input": [
                    {
                        "role": "user",
                        "content": "你好，我叫张三，我今年25岁。",
                        "type": "message"
                    }
                ]
            }
            """;

        HttpRequest request = HttpRequest.newBuilder()
                .uri(URI.create(url))
                .header("Content-Type", "application/json")
                .header("Authorization", "Bearer " + API_KEY)
                .POST(HttpRequest.BodyPublishers.ofString(payload))
                .build();

        HttpResponse<String> response = client.send(request,
            HttpResponse.BodyHandlers.ofString());

        JsonNode result = mapper.readTree(response.body());

        // 从output数组中提取消息内容
        if (result.has("output")) {
            JsonNode outputArray = result.get("output");
            for (JsonNode outputItem : outputArray) {
                if ("message".equals(outputItem.get("type").asText())) {
                    JsonNode contentArray = outputItem.get("content");
                    for (JsonNode content : contentArray) {
                        if ("output_text".equals(content.get("type").asText())) {
                            String message = content.get("text").asText();
                            System.out.println("AI回复: " + message);
                        }
                    }
                }
            }
        }

        if (result.has("usage")) {
            JsonNode usage = result.get("usage");
            System.out.println("Token使用: " + usage.get("total_tokens").asInt() +
                " (输入: " + usage.get("input_tokens").asInt() +
                ", 输出: " + usage.get("output_tokens").asInt() + ")");
        }

        return result;
    }

    public static void main(String[] args) {
        ResponsesBasic api = new ResponsesBasic();

        try {
            JsonNode response = api.createBasicResponse();
            System.out.println("响应ID: " + response.get("id").asText());
        } catch (Exception e) {
            System.out.println("执行出错: " + e.getMessage());
        }
    }
}

<?php

class ResponsesBasic {
    private $apiKey;
    private $baseUrl;

    public function __construct($apiKey) {
        $this->apiKey = $apiKey;
        $this->baseUrl = 'https://api.tokenops.ai/v1';
    }

    public function createBasicResponse() {
        $url = $this->baseUrl . '/responses';

        $payload = [
            'model' => 'gpt-5-2025-08-07',
            'input' => [
                [
                    'role' => 'user',
                    'content' => '你好，我叫张三，我今年25岁。',
                    'type' => 'message'
                ]
            ]
        ];

        $headers = [
            'Content-Type: application/json',
            'Authorization: Bearer ' . $this->apiKey
        ];

        $ch = curl_init();
        curl_setopt($ch, CURLOPT_URL, $url);
        curl_setopt($ch, CURLOPT_POST, true);
        curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
        curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
        curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);

        $response = curl_exec($ch);
        $httpCode = curl_getinfo($ch, CURLINFO_HTTP_CODE);
        curl_close($ch);

        if ($httpCode === 200) {
            $result = json_decode($response, true);

            // 从output数组中提取消息内容
            if (isset($result['output'])) {
                foreach ($result['output'] as $outputItem) {
                    if ($outputItem['type'] === 'message') {
                        foreach ($outputItem['content'] as $content) {
                            if ($content['type'] === 'output_text') {
                                echo "AI回复: " . $content['text'] . "\n";
                            }
                        }
                    }
                }
            }

            if (isset($result['usage'])) {
                $usage = $result['usage'];
                echo "Token使用: " . $usage['total_tokens'] .
                     " (输入: " . $usage['input_tokens'] .
                     ", 输出: " . $usage['output_tokens'] . ")\n";
            }

            return $result;
        } else {
            echo "HTTP错误: " . $httpCode . "\n";
            echo $response . "\n";
            return null;
        }
    }
}

// 使用示例
$api = new ResponsesBasic('<API-KEY>');
$response = $api->createBasicResponse();

if ($response) {
    echo "响应ID: " . $response['id'] . "\n";
}

?>

多模态输入支持

Responses API 支持多种输入格式，包括文本、图像和文件：

消息列表输入

const response = await client.responses.create({
    model: "gpt-5-2025-08-07",
    input: [
        {
            role: "user",
            content: "你好，我叫张三，我今年25岁。",
            type: "message"
        }
    ]
});

多模态输入（文本+图像）

const response = await client.responses.create({
    model: "gpt-5-2025-08-07",
    input: [
        {
            role: "user",
            content: [
                {
                    type: "input_text",
                    text: "这张图片里有什么？"
                },
                {
                    type: "input_image",
                    image_url: "https://example.com/image.jpg"
                }
            ],
            type: "message"
        }
    ]
});

参数说明

必需参数

model - 要使用的模型ID（如 gpt-5-2025-08-07, gpt-4o 等）
input - 输入内容，必须是消息列表格式

可选参数

max_output_tokens - 最大输出token数量
temperature - 控制输出随机性（0-2之间）
stream - 是否启用流式响应
tools - 工具列表（如 web_search, file_search 等）

响应格式

成功响应包含以下字段：

{
  "id": "resp_0a7d927460af6c470068fae279feac8195a7ac939eae94a027",
  "object": "response",
  "created_at": 1761272442,
  "status": "completed",
  "background": false,
  "content_filters": null,
  "error": null,
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": 13107,
  "max_tool_calls": null,
  "model": "gpt-5-2025-08-07",
  "output": [
    {
      "id": "rs_0a7d927460af6c470068fae27a60a08195b4b87bd98c1a8e72",
      "type": "reasoning",
      "summary": []
    },
    {
      "id": "msg_0a7d927460af6c470068fae28178108195a865bc81c3fccc31",
      "type": "message",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "annotations": [],
          "logprobs": [],
          "text": "张三你好，很高兴认识你！25岁正是探索和积累的好阶段。今天想聊点什么或需要我帮你做什么？"
        }
      ],
      "role": "assistant"
    }
  ],
  "parallel_tool_calls": true,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "reasoning": {
    "effort": "medium",
    "summary": null
  },
  "safety_identifier": null,
  "service_tier": "default",
  "store": true,
  "temperature": 1.0,
  "text": {
    "format": {
      "type": "text"
    },
    "verbosity": "medium"
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 1.0,
  "truncation": "disabled",
  "usage": {
    "input_tokens": 16,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 606,
    "output_tokens_details": {
      "reasoning_tokens": 512
    },
    "total_tokens": 622
  },
  "user": null,
  "metadata": {}
}

注意事项

模型选择: 不同模型有不同的特点和成本，请根据需求选择
输入格式: 必须使用消息列表格式，每个消息包含 role、content 和 type 字段
响应格式: 使用 output 数组获取生成的内容，通过 type 为 message 的项目获取文本
内容提取: 从 output[].content[] 中找到 type 为 output_text 的项目获取实际文本
Token统计: 使用 usage 字段查看详细的token使用情况，包括推理token
错误处理: 请妥善处理API错误和异常情况
速率限制: 注意API调用频率限制

常见错误

401 Unauthorized: 检查API密钥是否正确
400 Bad Request: 检查请求参数格式和必需字段
429 Too Many Requests: 降低请求频率
Model not found: 检查模型名称是否正确
Invalid input format: 确保输入格式符合API规范

​Responses API 基础调用

​快速开始

​多模态输入支持

​消息列表输入

​多模态输入（文本+图像）

​参数说明

​必需参数

​可选参数

​响应格式

​注意事项

​常见错误

Responses API 基础调用

快速开始

多模态输入支持

消息列表输入

多模态输入（文本+图像）

参数说明

必需参数

可选参数

响应格式

注意事项

常见错误