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Responses API 完整使用文档

概述​

Response API 是一个轻量、灵活的对话接口，支持多模态输入、多种内置工具调用、推理模式等高级特性。

完整的请求协议见：https://platform.openai.com/docs/api-reference/responses/create

完整的响应协议见：https://platform.openai.com/docs/api-reference/responses/object

全部流式事件见：https://platform.openai.com/docs/api-reference/responses-streaming

文件输入以及file_search工具需要的file-id，以 purpose为assistants的方式，上传到file-api，见 文件上传

为什么选择Responses API?​

更好地理解Responses API能为你做什么

如何判断Responses API是否适合你？

核心端点​

创建响应​

端点: POST /v1/responses
认证: Bearer Token

获取响应详情​

端点: GET /v1/responses/{response_id}
认证: Bearer Token

核心功能详解​

1. 基础对话​

简单文本对话​

{
  "model": "gpt-5-nano",
  "input": "How much gold would it take to coat the Statue of Liberty in a 1mm layer?",
  "stream": true
}

带推理模式的对话​

{
  "model": "gpt-5-nano",
  "input": [{
    "role": "user",
    "content": "复杂问题..."
  }],
  "store": true,
  "reasoning": {
    "effort": "medium",
    "summary": "auto"
  },
  "stream": true
}

2. 会话管理​

续接上次对话​

{
  "model": "gpt-5-nano",
  "previous_response_id": "resp_1000012001",
  "input": "继续上面的话题...",
  "store": true,
  "stream": true
}

基于历史会话​

{
  "model": "gpt-5-nano",
  "conversation": "conv_xxx",
  "input": "基于之前的讨论...",
  "store": true
}

3. 多模态输入​

Responses API支持多种输入模态的组合使用：

  • 文本（input_text）
  • 图片（input_image）
  • 音频（input_audio）
  • 文件（input_file）

文件输入​

{
  "model": "gpt-5-nano",
  "input": [{
    "role": "user",
    "content": [
      {
        "type": "input_file",
        "file_id": "file-2509221659180022489862-1989906366"
      },
      {
        "type": "input_text",
        "text": "他的学历是什么？"
      }
    ]
  }],
  "store": false,
  "stream": true
}

图片输入（Base64）​

{
  "model": "gpt-5-nano",
  "input": [{
    "role": "user",
    "content": [
      {
        "type": "input_image",
        "image_url": "data:image;base64,/9j/4AAQSkZJRgAB..."
      },
      {
        "type": "input_text",
        "text": "描述这张图片"
      }
    ]
  }],
  "stream": true
}

音频输入​

{
  "model": "gpt-5-nano",
  "input": [{
    "role": "user",
    "content": [
      {
        "type": "input_audio",
        "input_audio": {
          "format": "wav",
          "data": "UklGRv////9XQVZFZm10..."
        }
      },
      {
        "type": "input_text",
        "text": "这段语音是什么内容？"
      }
    ]
  }],
  "reasoning": {
    "effort": "medium",
    "summary": "auto"
  },
  "stream": true
}

4. 工具调用​

函数工具（Function Tools）​

基础函数定义​
{
  "model": "gpt-5",
  "input": [{
    "type": "message",
    "role": "user",
    "content": "北京和上海现在的天气怎么样？"
  }],
  "tools": [{
    "type": "function",
    "name": "get_weather",
    "description": "Retrieves current weather for the given location.",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {
          "type": "object",
          "properties": {
            "x": {"type": "string"},
            "y": {"type": "string"}
          },
          "required": ["x", "y"]
        },
        "units": {
          "type": "string",
          "enum": ["celsius", "fahrenheit"]
        }
      },
      "required": ["location", "units"]
    },
    "strict": true
  }]
}
包含函数调用历史的对话​
{
  "model": "gpt-5-nano",
  "input": [
    {
      "type": "message",
      "role": "user",
      "content": "北京现在的天气怎么样？"
    },
    {
      "type": "function_call",
      "status": "completed",
      "call_id": "call_weather456",
      "name": "get_weather",
      "arguments": "{\"city\": \"北京\", \"unit\": \"celsius\"}"
    },
    {
      "status": "completed",
      "type": "function_call_output",
      "call_id": "call_weather456",
      "output": "{\"temperature\": 15, \"condition\": \"晴朗\"}"
    },
    {
      "type": "message",
      "role": "user",
      "content": "比较一下上海的天气"
    }
  ],
  "tools": [{
    "type": "function",
    "name": "get_weather",
    "description": "Retrieves current weather for the given location.",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {
          "type": "object",
          "properties": {
            "x": {"type": "string"},
            "y": {"type": "string"}
          },
          "required": ["x", "y"]
        },
        "units": {
          "type": "string",
          "enum": ["celsius", "fahrenheit"]
        }
      },
      "required": ["location", "units"]
    },
    "strict": true
  }]
}
{
  "model": "gpt-5-nano",
  "input": [{
    "type": "message",
    "role": "user",
    "content": "使用网络搜索分别查询今年的房价和贝壳的股价"
  }],
  "tools": [{
    "type": "web_search_preview"
  }],
  "reasoning": {
    "effort": "medium"
  },
  "store": true,
  "stream": true
}

图像生成工具​

{
  "model": "gpt-5-nano",
  "input": [{
    "type": "message",
    "role": "user",
    "content": "生成一张夏日午后森林的图片和一张冬天雪后森林的图片"
  }],
  "tools": [{
    "type": "image_generation",
    "model": "doubao-seedream-3-0-t2i"
  }],
  "stream": true
}

文件搜索工具​

{
  "model": "gpt-5-nano",
  "input": [{
    "type": "message",
    "role": "user",
    "content": "他的技能有哪些？"
  }],
  "tools": [{
    "type": "file_search",
    "vector_store_ids": ["file-2509221659180022489862-1989906366"]
  }],
  "stream": true
}

本地Shell工具​

{
  "model": "gpt-5-nano",
  "input": "使用local_shell工具，列出当前目录下所有以'test_'开头的txt文件",
  "tools": [{
    "type": "local_shell"
  }],
  "store": false,
  "stream": true
}

MCP工具（Model Context Protocol）​

MCP (Model Context Protocol) 是一个开放协议，允许AI模型通过标准化接口与外部工具和数据源交互。Response API支持动态连接MCP服务器，自动发现和调用服务器提供的工具。

基础MCP配置​
{
  "model": "gpt-5-nano",
  "input": [{
    "type": "message",
    "role": "user",
    "content": "使用Context7查询React文档"
  }],
  "tools": [{
    "type": "mcp",
    "server_label": "context7",
    "server_url": "https://mcp.context7.com/mcp",
    "server_description": "Context7 documentation search service"
  }],
  "stream": true
}

核心字段说明：

  • type: 固定为 "mcp"
  • server_label: MCP服务器的唯一标识符（必需）
  • server_url: MCP服务器的HTTP端点URL（与connector_id二选一）
  • connector_id: 预定义的连接器ID（与server_url二选一）
  • server_description: 服务器描述，帮助模型理解服务器用途
  • authorization: OAuth令牌（可选，格式："Bearer token123"）
  • headers: 自定义HTTP请求头（可选）
带认证的MCP服务器​
{
  "model": "gpt-5-nano",
  "input": "查询私有仓库的代码文档",
  "tools": [{
    "type": "mcp",
    "server_label": "github_private",
    "server_url": "https://api.github.com/mcp",
    "authorization": "Bearer ghp_xxxxxxxxxxxx",
    "headers": {
      "X-GitHub-Api-Version": "2022-11-28"
    },
    "server_description": "GitHub private repository access"
  }]
}
工具过滤和访问控制​

允许特定工具

{
  "tools": [{
    "type": "mcp",
    "server_label": "db_server",
    "server_url": "https://db.example.com/mcp",
    "server_description": "Database query service",
    "allowed_tools": ["query_users", "query_orders"]
  }]
}
工具审批机制​

所有工具都需要审批

{
  "tools": [{
    "type": "mcp",
    "server_label": "admin_tools",
    "server_url": "https://admin.example.com/mcp",
    "require_approval": "always"
  }]
}

特定工具需要审批

{
  "tools": [{
    "type": "mcp",
    "server_label": "system_tools",
    "server_url": "https://system.example.com/mcp",
    "require_approval": {
      "always": {
        "tool_names": ["delete_file", "restart_service"]
      }
    }
  }]
}

审批请求流式事件

{
  "type": "mcp_approval_request",
  "id": "mcp_approve_req_123",
  "server_label": "system_tools",
  "name": "delete_file",
  "arguments": "{\"path\": \"/data/temp.txt\"}"
}

审批响应输入

{
  "input": [{
    "type": "mcp_approval_response",
    "approval_request_id": "mcp_approve_req_123",
    "approve": true,
    "reason": "Temporary file deletion approved"
  }]
}
MCP工具调用流程​

1. 工具列表获取

当模型首次访问MCP服务器时，会自动获取可用工具列表：

流式事件：

{
  "type": "mcp_list_tools",
  "event": "in_progress",
  "server_label": "context7"
}
{
  "type": "mcp_list_tools",
  "event": "completed",
  "server_label": "context7",
  "tools": [
    {
      "name": "resolve-library-id",
      "description": "Resolve library name to Context7 ID",
      "input_schema": {
        "type": "object",
        "properties": {
          "libraryName": {"type": "string"}
        },
        "required": ["libraryName"]
      }
    },
    {
      "name": "get-library-docs",
      "description": "Fetch library documentation",
      "input_schema": {
        "type": "object",
        "properties": {
          "context7CompatibleLibraryID": {"type": "string"},
          "topic": {"type": "string"},
          "tokens": {"type": "number"}
        },
        "required": ["context7CompatibleLibraryID"]
      }
    }
  ]
}

2. 工具调用执行

模型自动调用MCP服务器上的工具：

流式事件序列：

{
  "type": "mcp_call",
  "event": "in_progress",
  "id": "call_mcp_001",
  "server_label": "context7",
  "name": "resolve-library-id"
}
{
  "type": "mcp_call",
  "event": "arguments_delta",
  "id": "call_mcp_001",
  "delta": "{\"libraryName\": "
}
{
  "type": "mcp_call",
  "event": "arguments_done",
  "id": "call_mcp_001",
  "arguments": "{\"libraryName\": \"bella-openapi\"}"
}
{
  "type": "mcp_call",
  "event": "completed",
  "id": "call_mcp_001",
  "server_label": "context7",
  "name": "resolve-library-id",
  "arguments": "{\"libraryName\": \"bella-openapi\"}",
  "output": "[{\"id\": \"/ke/bella-openapi\", \"name\": \"bella-openapi\", \"description\": \"...\"}]"
}

3. 对话中引用MCP调用历史

{
  "model": "gpt-5-nano",
  "input": [
    {
      "type": "message",
      "role": "user",
      "content": "查询React hooks文档"
    },
    {
      "type": "mcp_call",
      "id": "call_001",
      "server_label": "context7",
      "name": "resolve-library-id",
      "arguments": "{\"libraryName\": \"react\"}",
      "output": "[{\"id\": \"/facebook/react\"}]"
    },
    {
      "type": "mcp_call",
      "id": "call_002",
      "server_label": "context7",
      "name": "get-library-docs",
      "arguments": "{\"context7CompatibleLibraryID\": \"/facebook/react\", \"topic\": \"hooks\"}",
      "output": "# React Hooks\n\nHooks are functions that..."
    },
    {
      "type": "message",
      "role": "user",
      "content": "useState和useEffect的区别是什么？"
    }
  ],
  "tools": [{
    "type": "mcp",
    "server_label": "context7",
    "server_url": "https://mcp.context7.com/mcp"
  }]
}
实际应用示例​

文档查询助手

{
  "model": "gpt-5-nano",
  "input": "如何在Next.js中使用服务端组件？",
  "tools": [{
    "type": "mcp",
    "server_label": "context7",
    "server_url": "https://mcp.context7.com/mcp",
    "server_description": "Technical documentation search"
  }],
  "stream": true
}

数据库查询工具

{
  "model": "gpt-5",
  "input": "查询销售额最高的前10个商品",
  "tools": [{
    "type": "mcp",
    "server_label": "analytics_db",
    "connector_id": "postgres_readonly",
    "server_description": "Analytics database (read-only)",
    "allowed_tools": {
      "read_only": true
    }
  }]
}

文件系统操作（需审批）

{
  "model": "gpt-5-nano",
  "input": "清理/tmp目录下的过期日志文件",
  "tools": [{
    "type": "mcp",
    "server_label": "filesystem",
    "server_url": "https://fs.internal.com/mcp",
    "authorization": "Bearer internal_token",
    "require_approval": {
      "always": {
        "tool_names": ["delete_file", "delete_directory"]
      }
    }
  }]
}
MCP最佳实践​
  1. 服务器标识：使用清晰的server_label，便于在日志和审计中追踪
  2. 安全认证：敏感服务器必须配置authorization或headers进行身份验证
  3. 工具白名单：使用allowed_tools限制模型只能访问必要的工具
  4. 审批机制：对危险操作（删除、修改、执行）启用require_approval
  5. 错误处理：MCP调用失败会在流式响应中包含详细错误信息
  6. 并行调用：多个MCP服务器工具可自动并行执行，提升效率
  7. 描述准确：server_description应准确描述服务器能力，帮助模型正确选择工具

5. 自定义工具（Custom Tools）​

文本格式工具​

{
  "model": "gpt-5-nano",
  "input": [{
    "type": "message",
    "role": "user",
    "content": "人工智能技术在过去十年中取得了显著进展..."
  }],
  "instructions": "使用text_summarizer对用户信息进行摘要",
  "tools": [{
    "name": "text_summarizer",
    "type": "custom",
    "description": "对输入的长文本进行智能摘要",
    "format": {
      "type": "text"
    }
  }]
}

正则表达式格式工具​

{
  "model": "gpt-5-nano",
  "input": [{
    "type": "message",
    "role": "user",
    "content": "张三，电话：13800138000，国家：中国，邮箱：zhangsan@ke.com"
  }],
  "instructions": "根据提供的信息，使用contact_info_generator工具生成特定格式的联系方式。",
  "tools": [{
    "name": "contact_info_generator",
    "type": "custom",
    "description": "生成符合特定格式的联系方式信息",
    "format": {
      "type": "grammar",
      "syntax": "regex",
      "definition": "^[A-Za-z\\s]{2,30}\\s*\\|\\s*\\+\\d{1,3}[-\\s]?\\d{3,4}[-\\s]?\\d{3,4}[-\\s]?\\d{3,4}\\s*\\|\\s*[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,4}$"
    }
  }]
}

Lark语法格式工具​

{
  "model": "gpt-5-nano",
  "input": [{
    "type": "message",
    "role": "user",
    "content": "使用api_response_generator工具，随机生成一个符合要求的示例"
  }],
  "tools": [{
    "name": "api_response_generator",
    "type": "custom",
    "description": "生成符合特定JSON结构的API响应数据",
    "format": {
      "type": "grammar",
      "syntax": "lark",
      "definition": "start: response\n\nresponse: \"{\" \"\\\"status\\\":\" status \",\" \"\\\"code\\\":\" number \",\" \"\\\"data\\\":\" data_object \",\" \"\\\"message\\\":\" string \"}\"\n\nstatus: \"\\\"success\\\"\" | \"\\\"error\\\"\"\n\ndata_object: \"{\" \"\\\"id\\\":\" number \",\" \"\\\"name\\\":\" string \",\" \"\\\"items\\\":\" array \"}\"\n\narray: \"[\" [item (\",\" item)*] \"]\"\n\nitem: \"{\" \"\\\"title\\\":\" string \",\" \"\\\"count\\\":\" number \"}\"\n\nstring: \"\\\"\" /[^\"]*/ \"\\\"\"\n\nnumber: /\\d+/\n\n%import common.WS\n%ignore WS"
    }
  }]
}

6. 高级特性​

推理链（Reasoning）​

{
  "model": "gpt-5-nano",
  "input": "复杂推理问题...",
  "reasoning": {
    "effort": "high",
    "summary": "auto"
  }
}

响应格式控制​

{
  "model": "gpt-5-nano",
  "input": "生成JSON格式的数据",
  "text": {
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "name": "response",
        "schema": {
          "type": "object",
          "properties": {
            "result": {"type": "string"},
            "confidence": {"type": "number"}
          },
          "required": ["result", "confidence"]
        }
      }
    }
  }
}

流式响应​

启用流式响应后，响应以Server-Sent Events (SSE)格式返回：

{
  "stream": true,
  "background": false
}

流式事件类型：

  • message_start: 响应开始
  • content_block_start: 内容块开始
  • content_block_delta: 内容增量
  • content_block_stop: 内容块结束
  • message_delta: 消息元数据更新
  • message_stop: 响应结束

存储控制​

基本配置​

{
  "store": false
}
  • store: true: 持久化存储对话（默认）
  • store: false: 一次性对话，不保存

Store模式详解​

1. Store模式（store: true）​

功能特性：

  • 对话历史会持久化到数据库（Thread和Message表）
  • 响应中会返回conversation字段，包含thread_id
  • 支持使用previous_response_id或conversation续接对话
  • 可以通过GET /v1/responses/{response_id}查询历史响应

适用场景：

  • 多轮对话应用：需要保持对话上下文的聊天机器人
  • 会话管理：需要查看和管理历史对话记录
  • 长期交互：用户可能在不同时间段继续同一话题
  • 合规审计：需要保留完整的对话记录用于审计
  • 对话分析：需要分析用户对话模式和行为

示例：

{
  "model": "gpt-5-nano",
  "input": "北京天气怎么样？",
  "store": true
}

响应包含conversation：

{
  "id": "resp_xxx",
  "conversation": "thread_abc123",
  "output": []
}

2. 非Store模式（store: false）​

功能特性：

  • 对话不会持久化到数据库
  • 响应中不会包含conversation字段
  • 不支持使用previous_response_id或conversation续接（会抛出异常）
  • 执行完成后不保留任何对话记录
  • 无法通过GET /v1/responses/{response_id}查询（response_id映射会存储，但message不存储）

适用场景：

  • 单次查询：一次性问答，不需要上下文
  • 隐私敏感：用户不希望对话被记录
  • 高并发场景：减少数据库写入压力，提升性能
  • 临时任务：文本翻译、格式转换等无需保存的任务
  • 成本优化：减少存储成本，避免冗余数据

限制条件：

// 代码限制：非store模式不能使用previous_response_id或conversation
if(Boolean.FALSE == request.getStore() &&
   (request.getPreviousResponseId() != null || request.getConversation() != null)) {
    throw new BizParamCheckException(
        "store can not be set `false` when you request with previous_response_id or conversation"
    );
}

示例：

{
  "model": "gpt-5-nano",
  "input": "翻译成英文：你好世界",
  "store": false
}

响应不包含conversation：

{
  "id": "resp_xxx",
  "output": [{
    "type": "message",
    "role": "assistant",
    "content": "Hello World"
  }]
}

Store模式对比​

特性Store模式（true）非Store模式（false）
数据持久化✅ 完整保存到数据库❌ 不保存对话记录
返回conversation✅ 包含thread_id❌ 不返回
续接对话✅ 支持previous_response_id和conversation❌ 不支持，会报错
历史查询✅ 可通过API查询❌ 不支持
性能较低（需要数据库写入）高（无数据库写入）
存储成本高（保存所有消息）无
隐私保护一般优秀
适用场景多轮对话、会话管理单次对话、客户端维护上下文、隐私敏感

使用建议​

  1. 默认使用Store模式：如果只是想想使用response-api丰富的内置工具，用于chat-completions接口的迁移，可以使用store: false，性能更优。

  2. 客户端管理对话：如果你在客户端（前端/移动端）自己管理对话历史，可以使用store: false：

    {
      "model": "gpt-5-nano",
      "input": [
        {"role": "user", "content": "第一个问题"},
        {"role": "assistant", "content": "第一个回答"},
        {"role": "user", "content": "第二个问题"}
      ],
      "store": false
    }

  3. 混合使用：对于同一应用，可以根据场景灵活选择：

    • 主要对话流程：store: true
    • 快速查询、辅助功能：store: false

  4. 注意previous_response_id限制： ❌ 常见错误：非store模式不能续接:

   {
     "model": "gpt-5-nano",
     "input": "继续上面的话题",
     "previous_response_id": "resp_xxx",
     "store": false
   }

错误处理​

API使用标准HTTP状态码：

  • 200 OK: 成功
  • 400 Bad Request: 请求格式错误
  • 401 Unauthorized: 认证失败
  • 429 Too Many Requests: 速率限制
  • 500 Internal Server Error: 服务器错误

错误响应格式：

{
  "error": {
    "message": "错误描述",
    "type": "error_type",
    "code": "error_code"
  }
}

最佳实践​

  1. 选择合适的模型: 根据任务复杂度选择模型
  2. 使用流式响应: 提升用户体验，特别是长响应
  3. 合理设置推理级别: 简单任务用low，复杂推理用high
  4. 工具并行调用: 提高效率，减少延迟
  5. 更佳的性能: 如在client端实现对话管理，则使用store: false避免数据存储的性能开销
  6. 续接对话: 使用previous_response_id保持上下文

示例场景​

智能客服​

{
  "model": "gpt-5",
  "input": "查询我的订单状态",
  "tools": [{
    "type": "function",
    "name": "query_order",
    "description": "查询订单状态"
  }],
  "store": true,
  "stream": true
}

代码生成助手​

{
  "model": "gpt-5-nano",
  "input": "生成一个Python快速排序算法",
  "reasoning": {
    "effort": "medium"
  },
  "text": {
    "response_format": {
      "type": "code"
    }
  }
}

多模态分析​

{
  "model": "gpt-5",
  "input": [{
    "role": "user",
    "content": [
      {
        "type": "input_image",
        "image_url": "https://example.com/chart.png"
      },
      {
        "type": "input_text",
        "text": "分析这个图表的趋势"
      }
    ]
  }],
  "reasoning": {
    "effort": "high"
  }
}

API vs Assistant API对比​

特性Responses APIAssistant API
使用场景轻量对话、无状态交互复杂智能体、有状态工作流
会话管理灵活（可选持久化）强制Thread管理
配置方式每次请求配置预定义Assistant
工具使用即时定义预配置+运行时覆盖
学习曲线简单直接需要理解Thread概念
性能更轻量额外管理开销