响应 - CometAPI Documentation

POST

responses

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cometapi.com/v1",
    api_key="<COMETAPI_KEY>",
)

response = client.responses.create(
    model="gpt-5.4",
    input="Tell me a three sentence bedtime story about a unicorn.",
)

print(response.output_text)

{
  "id": "resp_0a153ae8201f73bc0069a7e8044cc481",
  "object": "response",
  "created_at": 1772611588,
  "status": "completed",
  "background": false,
  "completed_at": 1772611589,
  "error": null,
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": null,
  "model": "gpt-4.1-nano",
  "output": [
    {
      "id": "msg_0a153ae8201f73bc0069a7e8049a8881",
      "type": "message",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "annotations": [],
          "text": "Four."
        }
      ],
      "role": "assistant"
    }
  ],
  "parallel_tool_calls": true,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "prompt_cache_retention": null,
  "reasoning": {
    "effort": null,
    "summary": null
  },
  "safety_identifier": null,
  "service_tier": "auto",
  "store": true,
  "temperature": 1,
  "text": {
    "format": {
      "type": "text"
    },
    "verbosity": "medium"
  },
  "tool_choice": "auto",
  "tools": [],
  "top_p": 1,
  "truncation": "disabled",
  "usage": {
    "input_tokens": 19,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 9,
    "output_tokens_details": {
      "reasoning_tokens": 0
    },
    "total_tokens": 28
  },
  "user": null,
  "metadata": {}
}

响应 API 在聊天补全的基础上扩展了有状态对话、内置工具、多模态文件输入以及推理控制。它是 OpenAI o-series 推理模型、GPT-5 系列和 Codex 模型的推荐端点。

不同的模型提供商支持不同的请求参数，并返回不同的响应字段。上方 playground 中列出的参数并非都适用于 CometAPI 上的每个模型。

使用有状态对话

使用 previous_response_id 将响应串联起来，而不是自行管理消息历史：

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cometapi.com/v1",
    api_key="<COMETAPI_KEY>",
)

# First turn
response = client.responses.create(
    model="gpt-5.4",
    input="What is quantum computing?",
)

# Second turn — previous context is included automatically
follow_up = client.responses.create(
    model="gpt-5.4",
    input="Can you explain that more simply?",
    previous_response_id=response.id,
)

print(follow_up.output_text)

使用内置工具

响应 API 包含平台提供的工具，无需任何配置：

工具	用途
`web_search_preview`	搜索网络以获取实时信息
`file_search`	在上传的文件中搜索
`code_interpreter`	在沙箱中执行 Python 代码

要启用内置工具，请将其添加到 tools 数组中：

response = client.responses.create(
    model="gpt-5.4",
    input="Find the current price of Bitcoin",
    tools=[{"type": "web_search_preview"}],
)

print(response.output_text)

调用自定义函数

定义模型可以使用结构化参数调用的函数：

response = client.responses.create(
    model="gpt-5.4",
    input="What's the weather in Tokyo?",
    tools=[{
        "type": "function",
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }],
)

当模型调用函数时，响应中的 output 数组会包含一个 function_call 项，其中带有函数名称和解析后的参数。执行该函数，并在后续请求中将结果发回。

请求结构化输出

如果要强制输出与特定 schema 匹配的 JSON，请使用 text.format 参数：

response = client.responses.create(
    model="gpt-5.4",
    input="List 3 programming languages with their main use cases",
    text={
        "format": {
            "type": "json_schema",
            "name": "languages",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "languages": {
                        "type": "array",
                        "items": {
                            "type": "object",
                            "properties": {
                                "name": {"type": "string"},
                                "use_case": {"type": "string"}
                            },
                            "required": ["name", "use_case"],
                            "additionalProperties": False
                        }
                    }
                },
                "required": ["languages"],
                "additionalProperties": False
            }
        }
    },
)

配置推理

对于 o 系列和 GPT-5 模型，可通过 reasoning.effort 控制推理深度：

response = client.responses.create(
    model="o3",
    input="Solve this step by step: if f(x) = x^3 - 6x^2 + 11x - 6, find all roots.",
    reasoning={"effort": "high"},  # "low", "medium", or "high"
)

print(response.output_text)

更高的推理强度会生成更详尽的回答，但也会消耗更多 tokens。对于简单查询使用 "low"，对于复杂的多步骤问题使用 "high"。

流式输出响应

如需接收增量输出，请将 stream 设为 true。API 会按以下顺序发送服务器推送事件（SSE）：

response.created — 响应对象已初始化
response.in_progress — 生成已开始
response.output_item.added — 新的输出项（消息或工具调用）
response.content_part.added — 内容部分已开始
response.output_text.delta — 文本分块（包含 delta 字段）
response.output_text.done — 此内容部分的文本生成完成
response.content_part.done — 内容部分已结束
response.output_item.done — 输出项已结束
response.completed — 完整响应，包含 usage 数据

使用 Python SDK 流式输出响应：

stream = client.responses.create(
    model="gpt-5.4",
    input="Write a haiku about coding",
    stream=True,
)

for event in stream:
    if event.type == "response.output_text.delta":
        print(event.delta, end="")

有关各项能力的深入指南，请参阅 OpenAI 文档：文本 · 图像 · PDF 文件 · 结构化输出 · 函数调用 · 会话状态 · 内置工具 · 推理

授权

Authorization

string

header

必填

Bearer token authentication. Use your CometAPI key.

请求体

application/json

model

string

必填

Model ID to use for this request. See the Models page for current options.

示例:

"gpt-5.4"

input

必填

Text, image, or file inputs to the model, used to generate a response. Can be a simple string for text-only input, or an array of input items for multimodal content (images, files) and multi-turn conversations.

instructions

string

A system (or developer) message inserted into the model's context. When used with previous_response_id, instructions from the previous response are not carried over — this makes it easy to swap system messages between turns.

background

boolean

默认值:false

Whether to run the model response in the background. Background responses do not return output directly — you retrieve the result later via the response ID.

context_management

object[]

Context management configuration for this request. Controls how the model manages context when the conversation exceeds the context window.

Show child attributes

conversation

The conversation this response belongs to. Items from the conversation are prepended to input for context. Input and output items are automatically added to the conversation after the response completes. Cannot be used with previous_response_id.

include

enum<string>[]

Additional output data to include in the response. Use this to request extra information that is not included by default.

可用选项:

web_search_call.action.sources,

code_interpreter_call.outputs,

computer_call_output.output.image_url,

file_search_call.results,

message.input_image.image_url,

message.output_text.logprobs,

reasoning.encrypted_content

max_output_tokens

integer

An upper bound for the number of tokens that can be generated for a response, including visible output tokens and reasoning tokens.

max_tool_calls

integer

The maximum number of total calls to built-in tools that can be processed in a response. This limit applies across all built-in tool calls, not per individual tool. Any further tool call attempts by the model will be ignored.

metadata

object

Set of up to 16 key-value pairs that can be attached to the response. Useful for storing additional information in a structured format. Keys have a maximum length of 64 characters; values have a maximum length of 512 characters.

Show child attributes

parallel_tool_calls

boolean

默认值:true

Whether to allow the model to run tool calls in parallel.

previous_response_id

string

The unique ID of a previous response. Use this to create multi-turn conversations without manually managing conversation state. Cannot be used with conversation.

prompt

object

Reference to a prompt template and its variables.

Show child attributes

prompt_cache_key

string

A key used to cache responses for similar requests, helping optimize cache hit rates. Replaces the deprecated user field for caching purposes.

prompt_cache_retention

enum<string>

The retention policy for the prompt cache. Set to 24h to keep cached prefixes active for up to 24 hours.

可用选项:

in-memory,

24h

reasoning

object

Configuration options for reasoning models (o-series and gpt-5). Controls the depth of reasoning before generating a response.

Show child attributes

safety_identifier

string

A stable identifier for your end-users, used to help detect policy violations. Should be a hashed username or email — do not send identifying information directly.

Maximum string length: 64

service_tier

enum<string>

Specifies the processing tier for the request. When set, the response will include the actual service_tier used.

auto: Uses the tier configured in project settings (default behavior).
default: Standard pricing and performance.
flex: Flexible processing with potential cost savings.
priority: Priority processing with faster response times.

可用选项:

auto,

default,

flex,

priority

store

boolean

默认值:true

Whether to store the generated response for later retrieval via API.

stream

boolean

默认值:false

If set to true, the response data will be streamed to the client as it is generated using server-sent events (SSE). Events include response.created, response.output_text.delta, response.completed, and more.

stream_options

object

Options for streaming responses. Only set this when stream is true.

Show child attributes

temperature

number

默认值:1

Sampling temperature between 0 and 2. Higher values (e.g., 0.8) increase randomness; lower values (e.g., 0.2) make output more focused and deterministic. We recommend adjusting either this or top_p, but not both.

必填范围: 0 <= x <= 2

text

object

Configuration for text output. Use this to request structured JSON output via JSON mode or JSON Schema.

Show child attributes

tool_choice

默认值:auto

Controls how the model selects which tool(s) to call.

auto (default): The model decides whether and which tools to call.
none: The model will not call any tools.
required: The model must call at least one tool.
An object specifying a particular tool to use.

tools

object[]

An array of tools the model may call while generating a response. CometAPI supports three categories:

Built-in tools: Platform-provided tools like web_search_preview and file_search.
Function calls: Custom functions you define, enabling the model to call your own code with structured arguments.
MCP tools: Integrations with third-party systems via MCP servers.

top_logprobs

integer

Number of most likely tokens to return at each position (0–20), each with an associated log probability. Must include message.output_text.logprobs in the include parameter to receive logprobs.

必填范围: 0 <= x <= 20

top_p

number

默认值:1

Nucleus sampling parameter. The model considers tokens with top_p cumulative probability mass. For example, 0.1 means only the top 10% probability tokens are considered. We recommend adjusting either this or temperature, but not both.

必填范围: 0 <= x <= 1

truncation

enum<string>

默认值:disabled

The truncation strategy for handling inputs that exceed the model's context window.

auto: The model truncates the input by dropping items from the beginning of the conversation to fit.
disabled (default): The request fails with a 400 error if the input exceeds the context window.

可用选项:

auto,

disabled

user

string

已弃用

Deprecated. Use safety_identifier and prompt_cache_key instead. A stable identifier for your end-user.

响应

200 - application/json

The generated Response object.

string

Unique identifier for the response.

示例:

"resp_0a153ae8201f73bc0069a7e8044cc481"

object

enum<string>

The object type, always response.

可用选项:

response

示例:

"response"

created_at

integer

Unix timestamp (in seconds) of when the response was created.

示例:

1772611588

status

enum<string>

The status of the response.

可用选项:

completed,

in_progress,

failed,

cancelled,

queued

示例:

"completed"

background

boolean

Whether the response was run in the background.

示例:

false

completed_at

integer | null

Unix timestamp of when the response was completed, or null if still in progress.

示例:

1772611589

error

object

Error information if the response failed, or null on success.

Show child attributes

incomplete_details

object

Details about why the response is incomplete, if applicable.

Show child attributes

instructions

string | null

The system instructions used for this response.

max_output_tokens

integer | null

The maximum output token limit that was applied.

model

string

The model used for the response.

示例:

"gpt-4.1-nano"

output

object[]

An array of output items generated by the model. Each item can be a message, function call, or other output type.

Show child attributes

output_text

string

A convenience field containing the concatenated text output from all output message items.

parallel_tool_calls

boolean

Whether parallel tool calls were enabled.

previous_response_id

string | null

The ID of the previous response, if this is a multi-turn conversation.

reasoning

object

The reasoning configuration that was used.

Show child attributes

service_tier

string

The service tier actually used to process the request.

示例:

"default"

store

boolean

Whether the response was stored.

temperature

number

The temperature value used.

示例:

1

text

object

The text configuration used.

Show child attributes

tool_choice

The tool choice setting used.

tools

object[]

The tools that were available for this response.

top_p

number

The top_p value used.

示例:

1

truncation

string

The truncation strategy used.

usage

object

Token usage statistics for this response.

Show child attributes

user

string | null

The user identifier, if provided.

metadata

object

The metadata attached to this response.

Show child attributes

content_filters

array | null

Content filter results applied to the response, if any.

frequency_penalty

number

默认值:0

The frequency penalty applied to the request.

max_tool_calls

integer | null

Maximum number of tool calls allowed, if set.

presence_penalty

number

默认值:0

The presence penalty applied to the request.

prompt_cache_key

string | null

Cache key for prompt caching, if applicable.

prompt_cache_retention

string | null

Prompt cache retention policy, if applicable.

safety_identifier

string | null

Safety system identifier for the response, if applicable.

top_logprobs

integer

默认值:0

Number of top log probabilities returned per token position.

聊天补全

Anthropic 消息

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cometapi.com/v1",
    api_key="<COMETAPI_KEY>",
)

response = client.responses.create(
    model="gpt-5.4",
    input="Tell me a three sentence bedtime story about a unicorn.",
)

print(response.output_text)

{
  "id": "resp_0a153ae8201f73bc0069a7e8044cc481",
  "object": "response",
  "created_at": 1772611588,
  "status": "completed",
  "background": false,
  "completed_at": 1772611589,
  "error": null,
  "incomplete_details": null,
  "instructions": null,
  "max_output_tokens": null,
  "model": "gpt-4.1-nano",
  "output": [
    {
      "id": "msg_0a153ae8201f73bc0069a7e8049a8881",
      "type": "message",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "annotations": [],
          "text": "Four."
        }
      ],
      "role": "assistant"
    }
  ],
  "parallel_tool_calls": true,
  "previous_response_id": null,
  "prompt_cache_key": null,
  "prompt_cache_retention": null,
  "reasoning": {
    "effort": null,
    "summary": null
  },
  "safety_identifier": null,
  "service_tier": "auto",
  "store": true,
  "temperature": 1,
  "text": {
    "format": {
      "type": "text"
    },
    "verbosity": "medium"
  },
  "tool_choice": "auto",
  "tools": [],
  "top_p": 1,
  "truncation": "disabled",
  "usage": {
    "input_tokens": 19,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 9,
    "output_tokens_details": {
      "reasoning_tokens": 0
    },
    "total_tokens": 28
  },
  "user": null,
  "metadata": {}
}

Documentation Index

​使用有状态对话

​使用内置工具

​调用自定义函数

​请求结构化输出

​配置推理

​流式输出响应

授权

请求体

响应

使用有状态对话

使用内置工具

调用自定义函数

请求结构化输出

配置推理

流式输出响应