Responses 支持

功能概览

本页用于说明 Responses 接口 的核心能力、调用入口和接入要点，帮助开发者快速判断适用场景并完成集成。

适用场景

• 产品原型验证：快速接入模型能力，验证内容生成、理解或编辑流程。
• 生产业务接入：用于批量任务、自动化工作流和多模型组合调用。
• 能力迁移适配：适合从原有模型或 SDK 平滑切换到亿速云AI统一接口。

接入建议

• 优先确认模型名称、请求路径和响应字段，再接入具体业务流程。
• 对异步任务、媒体生成和长耗时请求，建议在业务侧加入重试与状态轮询。
• 上线前建议准备日志追踪、错误兜底和内容安全校验，便于稳定运行。

🚀 核心特性

📋 支持的模型

推理模型（推荐）

• O3 系列：o3, o3-pro, o4-mini
• 特色：推理令牌跨请求保持，提供更智能的上下文理解

对话模型

• GPT-4.1 系列：gpt-4.1, gpt-4.1-mini
• 特色：强大的工具调用和多模态能力

🔧 基础用法

简单对话

  curl https://xxx.yisu.com/v1/responses \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -d '{
      "model": "gpt-4.1",
      "input": "Hello! How can you help me today?",
      "instructions": "You are a helpful assistant."
    }'

  from openai import OpenAI

  client = OpenAI(
      api_key="YOUR_API_KEY",
      base_url="https://xxx.yisu.com/v1"
  )

  response = client.responses.create(
      model="gpt-4.1",
      input="Hello! How can you help me today?",
      instructions="You are a helpful assistant."
  )

  print(response.output[0].content[0].text)

  import OpenAI from 'openai';

  const openai = new OpenAI({
    apiKey: 'YOUR_API_KEY',
    baseURL: 'https://xxx.yisu.com/v1'
  });

  const response = await openai.responses.create({
    model: 'gpt-4.1',
    input: 'Hello! How can you help me today?',
    instructions: 'You are a helpful assistant.'
  });

  console.log(response.output[0].content[0].text);

实际响应示例

基于您的测试结果，以下是完整的响应格式：

{
  "id": "resp_6884fcab4930819dbbc02f15cbe63f6c0a92c38ff214d10a",
  "object": "response",
  "created_at": 1753545899,
  "status": "completed",
  "background": false,
  "error": null,
  "incomplete_details": null,
  "instructions": "You are a helpful assistant.",
  "max_output_tokens": null,
  "max_tool_calls": null,
  "model": "gpt-4.1-2025-04-14",
  "output": [
    {
      "id": "msg_6884fcab8f18819dbcdf349f01b424f80a92c38ff214d10a",
      "type": "message",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "annotations": [],
          "logprobs": [],
          "text": "Hello! How can I assist you today?"
        }
      ],
      "role": "assistant"
    }
  ],
  "parallel_tool_calls": true,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "reasoning": {
    "effort": null,
    "summary": null
  },
  "safety_identifier": null,
  "service_tier": "default",
  "store": true,
  "temperature": 1.0,
  "text": {
    "format": {
      "type": "text"
    }
  },
  "tool_choice": "auto",
  "tools": [],
  "top_logprobs": 0,
  "top_p": 1.0,
  "truncation": "disabled",
  "usage": {
    "input_tokens": 19,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 10,
    "output_tokens_details": {
      "reasoning_tokens": 0
    },
    "total_tokens": 29
  },
  "user": null,
  "metadata": {}
}

📊 请求参数详解

必需参数

可选参数

🛠️ 内置工具支持

1. 函数调用

response = client.responses.create(
    model="gpt-4.1",
    input="What's the weather like in Beijing?",
    instructions="You are a helpful weather assistant.",
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get current weather for a city",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "city": {
                            "type": "string",
                            "description": "City name"
                        }
                    },
                    "required": ["city"]
                }
            }
        }
    ]
)

2. 代码解释器

response = client.responses.create(
    model="gpt-4.1", 
    input="Create a chart showing sales data: Jan:100, Feb:150, Mar:120",
    instructions="You are a data analyst. Use code interpreter to create visualizations.",
    tools=[{"type": "code_interpreter"}]
)

3. 文件搜索

response = client.responses.create(
    model="gpt-4.1",
    input="Search for information about quarterly reports",
    instructions="You are a document analyst.",
    tools=[{"type": "file_search"}]
)

🔄 状态管理

维护对话上下文

# 第一轮对话
response1 = client.responses.create(
    model="gpt-4.1",
    input="My name is Alice. Please remember this.",
    instructions="You are a helpful assistant with good memory."
)

# 第二轮对话 - 使用 previous_response_id 维护上下文
response2 = client.responses.create(
    model="gpt-4.1", 
    input="What's my name?",
    instructions="You are a helpful assistant with good memory.",
    previous_response_id=response1.id
)

print(response2.output[0].content[0].text)  # 应该回答 "Alice"

多轮工具调用

def multi_turn_conversation:
    response_id = None
    
    for user_input in ["What's 2+2?", "Now multiply that by 3", "And divide by 2"]:
        response = client.responses.create(
            model="o3",
            input=user_input,
            instructions="You are a math tutor. Show your reasoning.",
            previous_response_id=response_id,
            tools=[{"type": "code_interpreter"}]
        )
        
        print(f"User: {user_input}")
        print(f"Assistant: {response.output[0].content[0].text}")
        
        response_id = response.id  # 保持上下文

📈 推理模型特性

O3/O4-mini 推理保持

推理模型在 Responses API 中具有特殊优势：

# 使用 O3 进行复杂推理
response = client.responses.create(
    model="o3",
    input="Solve this step by step: If a train travels 120km in 2 hours, then speeds up 20% for the next hour, how far did it travel in total?",
    instructions="Think through this problem step by step, showing all reasoning."
)

# 查看推理过程
reasoning_tokens = response.usage.output_tokens_details.reasoning_tokens
print(f"Reasoning tokens used: {reasoning_tokens}")

# 继续对话，推理上下文会保持
follow_up = client.responses.create(
    model="o3",
    input="Now what if the train slowed down 10% in the fourth hour?",
    previous_response_id=response.id
)

🆚 与 Chat Completions 对比

迁移示例

从 Chat Completions 迁移到 Responses API：

  # 旧方式
  response = client.chat.completions.create(
      model="gpt-4.1",
      messages=[
          {"role": "system", "content": "You are a helpful assistant."},
          {"role": "user", "content": "Hello!"}
      ]
  )
  content = response.choices[0].message.content

  # 新方式
  response = client.responses.create(
      model="gpt-4.1",
      input="Hello!",
      instructions="You are a helpful assistant."
  )
  content = response.output[0].content[0].text

🔧 高级功能

并行工具调用

response = client.responses.create(
    model="gpt-4.1",
    input="Get weather for Beijing and Shanghai, then calculate travel time between them",
    instructions="You are a travel assistant.",
    parallel_tool_calls=True,
    tools=[
        {"type": "function", "function": {"name": "get_weather", ...}},
        {"type": "function", "function": {"name": "calculate_distance", ...}}
    ]
)

输出格式控制

response = client.responses.create(
    model="gpt-4.1",
    input="Summarize this data in JSON format",
    instructions="Always respond in valid JSON.",
    text={
        "format": {
            "type": "json_object"
        }
    }
)

推理努力控制（O3系列）

response = client.responses.create(
    model="o3",
    input="Solve this complex physics problem",
    instructions="Think carefully and show detailed reasoning.",
    reasoning={
        "effort": "high"  # low, medium, high
    }
)

📊 响应字段详解

核心字段

输出消息格式

{
  "id": "msg_xxx",
  "type": "message",
  "status": "completed",
  "content": [
    {
      "type": "output_text",
      "text": "响应内容",
      "annotations": [],
      "logprobs": []
    }
  ],
  "role": "assistant"
}

使用统计

{
  "usage": {
    "input_tokens": 19,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 10,
    "output_tokens_details": {
      "reasoning_tokens": 0  // 仅推理模型
    },
    "total_tokens": 29
  }
}

🚨 错误处理

标准错误格式

{
  "error": {
    "type": "invalid_request_error",
    "code": "model_not_supported",
    "message": "The model 'gpt-3.5-turbo' is not supported for the responses endpoint.",
    "param": "model"
  }
}

常见错误

💡 最佳实践

1. 状态管理策略

class ConversationManager:
    def __init__(self, model="gpt-4.1", instructions="You are a helpful assistant."):
        self.model = model
        self.instructions = instructions
        self.last_response_id = None
    
    def send_message(self, input_text, tools=None):
        response = client.responses.create(
            model=self.model,
            input=input_text,
            instructions=self.instructions,
            previous_response_id=self.last_response_id,
            tools=tools or []
        )
        
        self.last_response_id = response.id
        return response.output[0].content[0].text
    
    def reset_conversation(self):
        self.last_response_id = None

# 使用示例
conv = ConversationManager
print(conv.send_message("Hello, I'm Alice"))
print(conv.send_message("What's my name?"))  # 会记住是 Alice

2. 工具调用优化

def smart_tool_calling(user_input):
    # 根据输入智能选择工具
    available_tools = []
    
    if "weather" in user_input.lower:
        available_tools.append(weather_tool)
    if "calculate" in user_input.lower:
        available_tools.append(calculator_tool)
    if "search" in user_input.lower:
        available_tools.append(search_tool)
    
    response = client.responses.create(
        model="gpt-4.1",
        input=user_input,
        instructions="Use the appropriate tools to help the user.",
        tools=available_tools,
        tool_choice="auto"
    )
    
    return response

3. 推理模型优化

def optimized_reasoning(complex_problem):
    response = client.responses.create(
        model="o3",
        input=complex_problem,
        instructions="Think step by step and show your reasoning process.",
        reasoning={
            "effort": "high"  # 对复杂问题使用高推理努力
        },
        temperature=0.1  # 降低随机性以获得一致结果
    )
    
    # 分析推理使用情况
    reasoning_tokens = response.usage.output_tokens_details.reasoning_tokens
    total_cost = calculate_cost(response.usage)
    
    return {
        "answer": response.output[0].content[0].text,
        "reasoning_tokens": reasoning_tokens,
        "cost": total_cost
    }

🔮 未来发展

即将推出的功能

1. 完整的 Assistants API 功能集成（2026年上半年）
2. 更多内置工具：Web搜索、计算机使用等
3. 模型上下文协议 (MCP) 支持
4. 增强的多模态能力

迁移时间线

• 现在：可以开始使用 Responses API
• 2026年上半年：功能对等 Assistants API
• 2026年：Assistants API 弃用公告
• 2027年：完全迁移到 Responses API

需要更多帮助？请访问 亿速云AI官网 或查看 OpenAI Responses API 官方文档。