通过一些例子，介绍 OpenAI chat/response 与 Anthropic messages 协议，不陷于枯燥的接口参数介绍。
在正式介绍 OpenAI Chat / Responses 和 Anthropic Messages 协议之前，有一个概念需要先明确：大模型本身是无状态的。
从模型的视角看，一次调用就是一次独立的输入与输出过程：模型接收当前请求中的上下文，根据概率分布生成结果，然后这次推理就结束了。下一次请求到来时，模型并不会天然记得上一轮发生过什么。因此，无论是 OpenAI 还是 Anthropic，底层的一次次模型调用，本质上都是相互独立、无状态的。
但我们日常使用 ChatGPT、Claude 时，却明显会感觉对话是“有记忆”的。例如：
user：我叫张三。
assistant：你好，张三。
user：我叫什么？
assistant：张三。
模型能够回答“张三”，并不是因为模型在上一次请求结束后保存了某种会话状态，而是因为应用在发起新一轮请求时，把前面的对话历史一起重新提交给了模型。本质上通常是应用层模拟出来的“有状态”。模型调用是无状态的，而多轮对话是在应用层通过重复携带历史上下文，实现出来的“逻辑有状态”。
这种设计也带来了一个直接代价：历史对话会持续占用上下文窗口，逐渐挤占模型可用于新输入、工具结果和输出内容的上下文空间。
理解这一点之后，再去看 OpenAI Chat / Responses 和 Anthropic Messages 协议就会容易很多：这些协议很大一部分工作，其实都围绕着“如何描述当前这一次模型调用所需要的完整上下文”展开。
OpenAI Chat #
endpoint #
POST /v1/chat/completions
examples #
关键点：每次请求携带之前对话的所有内容
请求：
{
"model": "deepseek-v4-pro",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "我叫张三"
},
{
"role": "assistant",
"content": "你好，张三！"
},
{
"role": "user",
"content": "我叫什么？"
}
]
}
响应：
{
"id": "chatcmpl-d3cd1541-e7f0-95e4-9b9b-53aaef0ad77a",
"model": "deepseek-v4-pro",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "你刚才说你叫张三。😊",
"role": "assistant",
"reasoning_content": "我们被问到：“我叫什么？”在前面的对话中，用户说“我叫张三”。所以答案是“张三”。需要直接回复。"
},
}
],
"usage": {
"completion_tokens": 36,
"prompt_tokens": 24,
"total_tokens": 60
}
}
关键点：模型产生调用工具意图，由客户端进行调用获取其他数据。MCP 和 Skill 都建立在 Tool Call 之上，可以自行想象下。
请求：
{
"model": "deepseek-v4-pro",
"messages": [
{
"role": "user",
"content": "What is my horoscope? I am an Aquarius."
}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_horoscope",
"description": "Get today's horoscope for an astrological sign.",
"parameters": {
"type": "object",
"properties": {
"sign": {
"type": "string",
"description": "An astrological sign like Taurus or Aquarius"
}
},
"required": ["sign"]
}
}
}
],
"tool_choice": "auto"
}
响应：
{
"id": "chatcmpl-962bc15f-9701-9e3b-a0cf-bddb6afa16cd",
"model": "deepseek-v4-pro",
"choices": [
{
"finish_reason": "tool_calls",
"index": 0,
"message": {
"content": "",
"role": "assistant",
"tool_calls": [
{
"index": 0,
"function": {
"arguments": "{\"sign\": \"Aquarius\"}",
"name": "get_horoscope"
},
"id": "call_1aea02ba05b84673a6f81aa9",
"type": "function"
}
],
"reasoning_content": "The user is asking for their horoscope and they mention they are an Aquarius. I'll use the get_horoscope function to fetch their horoscope."
}
}
],
"usage": {
"completion_tokens": 79,
"prompt_tokens": 305,
"total_tokens": 384
}
}
第二次请求
{
"model": "deepseek-v4-pro",
"messages": [
{
"role": "user",
"content": "What is my horoscope? I am an Aquarius."
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "call_1aea02ba05b84673a6f81aa9",
"type": "function",
"function": {
"name": "get_horoscope",
"arguments": "{\"sign\":\"Aquarius\"}"
}
}
]
},
{
"role": "tool",
"tool_call_id": "call_1aea02ba05b84673a6f81aa9",
"content": "Aquarius: Next Tuesday you will befriend a baby otter."
}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_horoscope",
"description": "Get today's horoscope for an astrological sign.",
"parameters": {
"type": "object",
"properties": {
"sign": {
"type": "string"
}
},
"required": [
"sign"
]
}
}
}
]
}
第二次响应
{
"id": "chatcmpl-0e73e44a-a423-9847-959f-8628b068cc81",
"model": "deepseek-v4-pro",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "Here's your horoscope for today, Aquarius! ♒\n\n> **\"Next Tuesday you will befriend a baby otter.\"**\n\nHow delightful! It sounds like an adorable and unexpected encounter is on the horizon for you. Keep your eyes open next Tuesday — perhaps near a river, zoo, or aquarium? Either way, that's one charming friendship in the making! 🦦",
"role": "assistant",
"reasoning_content": "The tool returned the horoscope for Aquarius. Let me present this to the user."
}
}
],
"usage": {
"completion_tokens": 98,
"prompt_tokens": 363,
"total_tokens": 461
}
}
关键点：通过json_schema约束模型输出
请求：
{
"model": "glm-5.2",
"messages": [
{
"role": "user",
"content": "张三今年 28 岁，邮箱是 zhangsan@example.com。请提取用户信息。"
}
],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "user_info",
"strict": true,
"schema": {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"age": {
"type": "integer"
},
"email": {
"type": "string"
}
},
"required": [
"name",
"age",
"email"
],
"additionalProperties": false
}
}
}
}
响应：
{
"id": "chatcmpl-8071dea7-3ad1-93ef-8600-46159b0a28cc",
"model": "glm-5.2",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "{\n \"age\": 28,\n \"email\": \"zhangsan@example.com\",\n \"name\": \"张三\"\n}",
"role": "assistant",
"reasoning_content": "1. **分析请求：**\n * 输入文本：“张三今年 28 岁，邮箱是 zhangsan@example.com。请提取用户信息。”（张三今年 28 岁，他的邮箱是 zhangsan@example.com。请提取用户信息。）\n * 任务：从输入文本中提取用户信息。\n\n2. **识别实体：**\n * 姓名：张三（张三）\n * 年龄：28（28）\n * 邮箱：zhangsan@example.com\n\n3. **格式化输出：**\n * 清晰地呈现提取的信息，可以使用 JSON 格式或结构化列表。结构化列表通常具有很好的可读性，但 JSON 格式在数据提取中非常标准。我们两者都提供，或者只提供一个清晰的列表。采用具有清晰标签的结构化列表：\n * 姓名：张三\n * 年龄：28岁\n * 邮箱：zhangsan@example.com\n\n4. **起草最终回复：**\n “提取到的用户信息如下：\n - 姓名：张三\n - 年龄：28 岁\n - 邮箱：zhangsan@example.com”\n （也可以为了完整性添加一个 JSON 代码块，但我们保持简单直接）。\n\n5. **对照约束条件审查：**\n * 我提取所有信息了吗？是的。\n * 准确吗？是的。\n\n6. **最终输出生成。**"
}
}
],
"usage": {
"completion_tokens": 344,
"prompt_tokens": 34,
"total_tokens": 378
}
}
随后客户端可以提取结构化信息
user = json.loads(response.choices[0].message.content)
print(user["name"])
print(user["age"])
print(user["email"])
关键点：服务端不会一次性返回完整 JSON，而是通过 SSE text/event-stream 持续发送多个 chunk
请求：
{
"model": "glm-5.2",
"stream": true,
"messages": [
{
"role": "user",
"content": "用一句话介绍北京。"
}
]
}
响应：
{
"id": "chatcmpl-2c979863-1421-9629-95bb-03aee6314f35",
"model": "glm-5.2",
"choices": [
{
"index": 0,
"delta": {
"reasoning_content": "",
"content": "",
"role": "assistant"
}
}
]
}
{
"id": "chatcmpl-2c979863-1421-9629-95bb-03aee6314f35",
"model": "glm-5.2",
"choices": [
{
"index": 0,
"delta": {
"reasoning_content": "1",
"content": ""
}
}
]
}
{
"id": "chatcmpl-2c979863-1421-9629-95bb-03aee6314f35",
"model": "glm-5.2",
"choices": [
{
"index": 0,
"delta": {
"reasoning_content": "",
"content": "历史与蓬勃现代活力完美交融的国际"
}
}
]
}
{
"id": "chatcmpl-2c979863-1421-9629-95bb-03aee6314f35",
"model": "glm-5.2",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"delta": {}
}
]
}
{
"id": "chatcmpl-2c979863-1421-9629-95bb-03aee6314f35",
"model": "glm-5.2",
"choices": [
{
"index": 0,
"delta": {}
}
],
"usage": {
"completion_tokens": 607,
"prompt_tokens": 17,
"total_tokens": 624
}
}
[DONE]
客户端对应的处理代码：
content = ""
reasoning_content = ""
for chunk in stream:
delta = chunk.choices[0].delta
if delta.reasoning_content:
reasoning_content += delta.reasoning_content
if delta.content:
content += delta.content
关键点：不同模型对 reasoning.effort 的支持和语义可能不一样
请求：
{
"model": "glm-5.3",
"messages": [
{
"role": "system",
"content": "hello"
},
{
"role": "user",
"content": "hello"
}
],
"thinking": {
"type": "enabled"
},
"reasoning_effort": "none",
"max_tokens": 65536,
"temperature": 1.0
}
响应：
{
"error": {
"code": "1210",
"message": "该模型始终思考，不支持关闭思考；请使用 low、high 或 max。"
}
}
请求：
{
"model": "glm-5.3",
"messages": [
{
"role": "system",
"content": "hello"
},
{
"role": "user",
"content": "hello"
}
],
"thinking": {
"type": "enabled"
},
"reasoning_effort": "low",
"max_tokens": 65536,
"temperature": 1.0
}
响应：
{
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "Hello! I'm the GLM language model trained by Z.ai. How can I help you today? Whether you have questions or need assistance, I'm happy to talk with you.",
"reasoning_content": "Let me consider how to respond to this greeting effectively.\n\nThe user has started with a simple \"hello\" - this is likely the beginning of a conversation. I should respond in a way that's both welcoming and open-ended.\n\nFirst, I'll acknowledge their greeting warmly. Then I should introduce myself briefly to establish context and trust. Since this is an initial interaction, I should also express my willingness to help while inviting them to share what's on their mind.\n\nI need to make sure my response is natural and conversational, not overly formal or robotic. It would be good to keep it concise yet friendly, allowing the conversation to flow naturally from there.\n\nThe key is to create a positive first impression while being helpful and approachable. I'll make sure to convey that I'm here to assist while leaving the door open for them to direct the conversation.\n\nLet me craft a response that's welcoming, professional, and encourages further interaction.",
"role": "assistant"
}
}
],
"id": "20260913170714c399c37d74e74a79",
"model": "glm-5.3",
"usage": {
"completion_tokens": 226,
"prompt_tokens": 15,
"total_tokens": 241
}
}
Anthropic Messages #
endpoint #
POST /v1/messages
examples #
和OpenAI chat很相似
请求：
{
"model": "glm-5.2-anthropic",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "我叫张三"
},
{
"role": "assistant",
"content": "你好，张三！"
},
{
"role": "user",
"content": "我叫什么？"
}
]
}
响应：
{
"id": "chatcmpl-838d4b77-8f0c-9cc4-b479-eae65acfedb3",
"type": "message",
"role": "assistant",
"content": [
{
"type": "thinking",
"text": "",
"thinking": "1. **分析用户的输入**：用户正在询问“我叫什么？”（What is my name?）。\n2. **回顾对话历史**：\n * 用户：“我叫张三”（我的名字是张三）。\n * 机器人：“你好，张三！”（你好，张三！）。\n * 用户：“我叫什么？”（我叫什么？）。\n3. **识别核心实体**：用户在第一轮对话中已经说明了他们的名字叫“张三”（Zhang San）。\n4. **构建回复**：清楚地说明用户的名字叫张三，并保持礼貌和乐于助人的语气。\n5. **起草回复（内心独白/草稿）**：\n * *草稿1*：你叫张三。（太生硬）\n * *草稿2*：你刚才告诉我，你叫张三。（更好，符合语境）\n * *草稿3*：你叫张三呀。（友好，口语化）\n6. **选择最佳回复**：草稿2或3。让我们采用简单、直接的回答。“你叫张三呀，刚才你自己说的。”或者简单地说“你叫张三。”让我们使其友好些：“你叫张三呀！”\n\n*起草过程中的自我纠正*：用户可能在测试记忆。一个简单直接的“你叫张三。”就很完美。添加一个礼貌的短语：“你叫张三。”\n\n最终选择：“你叫张三。刚才你告诉我了。”（你是张三。你刚才告诉我了。） -> 缩短为“你叫张三呀。”（你是张三。） -> 保持简单：“你叫张三。”"
},
{
"type": "text",
"text": "你叫张三。刚才你告诉我了。"
}
],
"model": "glm-5.2-anthropic",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 34,
"output_tokens": 360,
"total_tokens": 394
},
"error": null
}
第一次请求：
{
"model": "glm-5.2-anthropic",
"messages": [
{
"role": "user",
"content": "What is my horoscope? I am an Aquarius."
}
],
"tools": [
{
"name": "get_horoscope",
"description": "Get today's horoscope for an astrological sign.",
"input_schema": {
"type": "object",
"properties": {
"sign": {
"type": "string",
"description": "An astrological sign like Taurus or Aquarius"
}
},
"required": [
"sign"
]
}
}
],
"tool_choice": {
"type": "auto"
}
}
第一次响应：
{
"id": "chatcmpl-0092bb86-85eb-9114-ac64-7e63c9ccf444",
"type": "message",
"role": "assistant",
"content": [
{
"type": "thinking",
"text": "",
"thinking": "The user wants their horoscope and they are an Aquarius. Let me call the get_horoscope function with the sign \"Aquarius\"."
},
{
"type": "text",
"text": "Let me fetch your horoscope for Aquarius!"
},
{
"type": "tool_use",
"text": "",
"id": "call_0cad738fedeb4701b75335ff",
"name": "get_horoscope",
"input": {
"sign": "Aquarius"
}
}
],
"model": "glm-5.2-anthropic",
"stop_reason": "tool_use",
"stop_sequence": null,
"usage": {
"input_tokens": 186,
"output_tokens": 51,
"total_tokens": 237
},
"error": null
}
第二次请求：
{
"model": "glm-5.2-anthropic",
"messages": [
{
"role": "user",
"content": "What is my horoscope? I am an Aquarius."
},
{
"role": "assistant",
"content": [
{
"type": "thinking",
"text": "",
"thinking": "The user wants their horoscope and they are an Aquarius. Let me call the get_horoscope function with the sign \"Aquarius\"."
},
{
"type": "text",
"text": "Let me fetch your horoscope for Aquarius!"
},
{
"type": "tool_use",
"text": "",
"id": "call_0cad738fedeb4701b75335ff",
"name": "get_horoscope",
"input": {
"sign": "Aquarius"
}
}
]
},
{
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": "call_0cad738fedeb4701b75335ff",
"content": "Aquarius: Next Tuesday you will befriend a baby otter."
}
]
}
],
"tools": [
{
"name": "get_horoscope",
"description": "Get today's horoscope for an astrological sign.",
"input_schema": {
"type": "object",
"properties": {
"sign": {
"type": "string",
"description": "An astrological sign like Taurus or Aquarius"
}
},
"required": [
"sign"
]
}
}
]
}
第二次响应：
{
"id": "chatcmpl-adbfb112-5723-9800-9b6b-c1ced58d6953",
"type": "message",
"role": "assistant",
"content": [
{
"type": "thinking",
"text": "",
"thinking": "The function returned a horoscope for Aquarius. Let me share this with the user."
},
{
"type": "text",
"text": "Here is your horoscope for today, Aquarius! 🌟\n\n**Aquarius:** Next Tuesday you will befriend a baby otter.\n\nThat sounds like a wonderful and unexpected encounter! Baby otters are absolutely adorable. Keep an eye out for any opportunities to connect with nature or animals early next week. 🦦✨\n\nIs there anything else you'd like to know?"
}
],
"model": "glm-5.2-anthropic",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 228,
"output_tokens": 98,
"total_tokens": 326
},
"error": null
}
请求：
{
"model": "glm-5.2-anthropic",
"messages": [
{
"role": "user",
"content": "张三今年 28 岁，邮箱是 zhangsan@example.com。请提取用户信息。"
}
],
"output_config": {
"format": {
"type": "json_schema",
"name": "user_info",
"schema": {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"age": {
"type": "integer"
},
"email": {
"type": "string"
}
},
"required": [
"name",
"age",
"email"
],
"additionalProperties": false
}
}
}
}
响应：
{
"id": "chatcmpl-9781df6b-f970-9085-89f9-eaa0315df8a0",
"type": "message",
"role": "assistant",
"content": [
{
"type": "thinking",
"text": "",
"thinking": "1. **分析请求：**\n * 输入文本：“张三今年 28 岁，邮箱是 zhangsan@example.com。请提取用户信息。”（张三今年 28 岁，他的邮箱是 zhangsan@example.com。请提取用户信息。）\n * 任务：提取用户信息（姓名、年龄、邮箱）。\n * 输出格式：结构化信息（例如：JSON、键值对）。\n\n2. **识别实体：**\n * 姓名 (姓名)：张三\n * 年龄 (年龄)：28\n * 邮箱 (邮箱)：zhangsan@example.com\n\n3. **格式化输出：**\n * 提供清晰、易读的提取信息格式。键值对或 JSON 格式效果最好。\n\n *草稿 1 (键值对)：*\n 姓名：张三\n 年龄：28\n 邮箱：zhangsan@example.com\n\n *草稿 2 (JSON)：*\n ```json\n {\n \"name\": \"张三\",\n \"age\": 28,\n \"email\": \"zhangsan@example.com\"\n }\n ```\n\n4. **选择最佳输出：** 提供键值对通常是最直接且易读的，但添加 JSON 格式会显得非常专业。我们同时提供两者，或者只提供结构化的文本列表。为了清晰起见，我将使用简洁的文本列表。\n\n5. **最终确定回复：**\n 提取到的用户信息如下：\n - 姓名：张三\n - 年龄：28岁\n - 邮箱：zhangsan@example.com\n （同时也可以选择以 JSON 格式输出，这通常在“提取”类任务中很受欢迎）。我们直接输出结构化数据即可。"
},
{
"type": "text",
"text": "{\"name\":\"张三\",\"age\":28,\"email\":\"zhangsan@example.com\"}"
}
],
"model": "glm-5.2-anthropic",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 32,
"output_tokens": 467,
"total_tokens": 499
},
"error": null
}
请求：
{
"model": "qwen3.7-max-anthropic",
"stream": true,
"messages": [
{
"role": "user",
"content": "用一句话介绍北京。"
}
]
}
响应：
// message_start
{
"type": "message_start",
"message": {
"id": "chatcmpl-284e68c1-9908-982c-b2bb-366a70fab6cb",
"type": "message",
"role": "assistant",
"content": [],
"model": "qwen3.7-max",
"stop_reason": null,
"stop_sequence": null,
"usage": {
"input_tokens": 0
},
"error": null
}
}
// content_block_start - thinking
{
"type": "content_block_start",
"index": 0,
"content_block": {
"type": "thinking",
"text": ""
}
}
// content_block_delta
{
"type": "content_block_delta",
"index": 0,
"delta": {
"type": "thinking_delta",
"thinking": "用户"
}
}
// content_block_stop
{
"type": "content_block_stop",
"index": 0
}
// content_block_start - text
{
"type": "content_block_start",
"index": 1,
"content_block": {
"type": "text",
"text": ""
}
}
// content_block_delta
{
"type": "content_block_delta",
"index": 1,
"delta": {
"type": "text_delta",
"text": "北京是中国的首都"
}
}
// content_block_stop
{
"type": "content_block_stop",
"index": 1
}
{
"type": "message_stop"
}
{
"type": "message_delta",
"delta": {
"type": "",
"stop_reason": "end_turn"
},
"usage": {
"input_tokens": 15,
"output_tokens": 342,
"total_tokens": 357
}
}
请求：
{
"model": "glm-5.3",
"max_tokens": 1024,
"stream": false,
"messages": [
{
"role": "user",
"content": "Hello, ZHIPU"
}
],
"thinking": {
"type": "adaptive"
},
"output_config": {
"effort": "none"
}
}
响应：
{
"type": "error",
"error": {
"type": "invalid_request_error",
"code": "1210",
"message": "[1210][该模型始终思考，不支持关闭思考；请使用 low、high 或 max。][20260913170635172c7f6d4b74414b]"
},
"request_id": "20260913170635172c7f6d4b74414b"
}
请求：
{
"model": "qwen3.7-max-anthropic",
"stream": false,
"messages": [
{
"role": "user",
"content": "用一句话介绍北京。"
}
],
"thinking": {
"type": "adaptive"
},
"output_config": {
"effort": "low"
}
}
响应：
{
"id": "chatcmpl-0e524038-1489-951e-9eb1-56a3018e6260",
"type": "message",
"role": "assistant",
"content": [
{
"type": "thinking",
"text": "",
"thinking": "用户要求用一句话介绍北京。\n需要涵盖北京的核心特征：首都、历史文化、现代化、政治/文化中心等。\n\n思考几个选项：\n1. 北京是中华人民共和国的首都，也是一座融合了三千多年悠久历史与现代化国际大都市风貌的政治、文化、国际交往和科技创新中心。\n2. 北京是中国的首都，一座将三千多年厚重历史底蕴与现代国际化大都市活力完美交融的政治、文化与科创中心。\n3. 作为中国的首都，北京是一座完美融合了千年古都厚重历史与现代国际大都市繁华活力的政治、文化和科技创新中心。\n\n选择第3个或第2个，稍微精简一下，使其更通顺、大气。\n\n“北京是中国的首都，一座将三千多年厚重历史底蕴与现代国际化大都市活力完美交融的政治、文化、国际交往和科技创新中心。”\n\n再精简一点：\n“北京是中华人民共和国的首都，一座完美融合了千年古都厚重历史与现代国际大都市繁华活力的政治、文化和科技创新中心。”\n\n这句话既点明了政治地位（首都），又概括了历史（千年古都）和现代（国际大都市），以及城市定位（政治、文化、科创中心）。符合“一句话”的要求，且信息全面。\n\n就用这句：\n北京是中国的首都，一座完美融合了千年古都厚重历史与现代国际大都市繁华活力的政治、文化、国际交往和科技创新中心。\n或者更文艺一点：\n北京是中国的首都，一座将三千多年悠久历史底蕴与现代国际化大都市活力完美交融的政治、文化与科创中心。\n\n最终版本：\n北京是中国的首都，一座完美融合了三千多年厚重历史底蕴与现代国际大都市繁华活力的政治、文化、国际交往和科技创新中心。\n字数适中，结构严谨。"
},
{
"type": "text",
"text": "北京是中国的首都，一座完美融合了三千多年厚重历史底蕴与现代国际大都市繁华活力的政治、文化、国际交往和科技创新中心。"
}
],
"model": "qwen3.7-max-anthropic",
"stop_reason": "end_turn",
"stop_sequence": null,
"usage": {
"input_tokens": 15,
"output_tokens": 378,
"total_tokens": 393
},
"error": null
}
OpenAI Responses #
Responses API 是 OpenAI 后来推出的新一代调用接口，主要变化是平台支持 Built-in 工具的调用比如web_search等，支持store: true设置有状态的上下文。
详细查看：https://developers.openai.com/api/docs/guides/migrate-to-responses