응답 - 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": {}
}

Responses API는 상태 유지 대화, 내장 도구, 멀티모달 파일 입력, 추론 제어를 통해 채팅 완성을 확장합니다. 이 엔드포인트는 OpenAI o-series 추론 모델, GPT-5 시리즈, Codex 모델에 권장됩니다.

서로 다른 모델 제공업체는 서로 다른 요청 파라미터를 지원하며, 반환하는 응답 필드도 다를 수 있습니다. 위 플레이그라운드에 나열된 모든 파라미터가 CometAPI의 모든 모델에서 동작하는 것은 아닙니다.

상태 유지 대화 사용

직접 메시지 기록을 관리하는 대신 previous_response_id를 사용해 응답을 서로 연결하세요:

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)

내장 도구 사용

Responses API에는 별도 설정이 필요 없는 플랫폼 제공 도구가 포함되어 있습니다:

Tool	용도
`web_search_preview`	실시간 정보를 위해 웹 검색
`file_search`	업로드된 파일 내 검색
`code_interpreter`	샌드박스에서 Python 코드 실행

내장 도구를 활성화하려면 tools 배열에 추가하세요:

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

print(response.output_text)

사용자 정의 함수 호출

모델이 구조화된 인수와 함께 호출할 수 있는 함수를 정의하세요:

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"]
        }
    }],
)

모델이 함수를 호출하면 응답의 output 배열에는 함수 이름과 파싱된 인수가 포함된 function_call 항목이 들어 있습니다. 함수를 실행한 뒤 후속 요청에서 그 결과를 다시 보내세요.

구조화된 출력 요청

특정 스키마와 일치하는 JSON 출력을 강제하려면 text.format 파라미터를 사용하세요:

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
            }
        }
    },
)

추론 구성

o-series 및 GPT-5 모델의 경우, reasoning.effort로 추론 깊이를 제어할 수 있습니다:

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)

더 높은 추론 effort는 더 철저한 답변을 생성하지만 더 많은 토큰(Token)을 사용합니다. 간단한 질의에는 "low"를, 복잡한 다단계 문제에는 "high"를 사용하세요.

응답 스트리밍(Streaming)

점진적으로 출력 결과를 받으려면 stream을 true로 설정하세요. API는 다음 순서로 server-sent events (SSE)를 전송합니다:

response.created — 응답 객체 초기화됨
response.in_progress — 생성 시작됨
response.output_item.added — 새 출력 항목(메시지 또는 tool call)
response.content_part.added — 콘텐츠 파트 시작됨
response.output_text.delta — 텍스트 청크(delta 필드 포함)
response.output_text.done — 이 콘텐츠 파트의 텍스트 생성 완료
response.content_part.done — 콘텐츠 파트 종료됨
response.output_item.done — 출력 항목 종료됨
response.completed — usage 데이터가 포함된 전체 응답

Python SDK로 응답을 스트리밍하세요:

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="")

각 기능에 대한 심층 가이드는 OpenAI 문서를 참고하세요: Text · Images · PDF files · Structured Outputs · Function Calling · Conversation State · Built-in Tools · Reasoning

인증

Authorization

string

header

필수

Bearer token authentication. Use your CometAPI key.

본문

application/json

model

string

필수

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

예시:

"gpt-5.4"

input

필수

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

기본값: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.

사용 가능한 옵션:

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

기본값: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.

사용 가능한 옵션:

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.

사용 가능한 옵션:

auto,

default,

flex,

priority

store

boolean

기본값:true

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

stream

boolean

기본값: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

기본값: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.

필수 범위: 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

기본값: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.

필수 범위: 0 <= x <= 20

top_p

number

기본값: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.

필수 범위: 0 <= x <= 1

truncation

enum<string>

기본값: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.

사용 가능한 옵션:

auto,

disabled

user

string

지원 중단

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

응답

200 - application/json

The generated Response object.

string

Unique identifier for the response.

예시:

"resp_0a153ae8201f73bc0069a7e8044cc481"

object

enum<string>

The object type, always response.

사용 가능한 옵션:

response

예시:

"response"

created_at

integer

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

예시:

1772611588

status

enum<string>

The status of the response.

사용 가능한 옵션:

completed,

in_progress,

failed,

cancelled,

queued

예시:

"completed"

background

boolean

Whether the response was run in the background.

예시:

false

completed_at

integer | null

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

예시:

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.

예시:

"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.

예시:

"default"

store

boolean

Whether the response was stored.

temperature

number

The temperature value used.

예시:

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.

예시:

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

기본값:0

The frequency penalty applied to the request.

max_tool_calls

integer | null

Maximum number of tool calls allowed, if set.

presence_penalty

number

기본값: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

기본값:0

Number of top log probabilities returned per token position.

채팅 완성

Anthropic 메시지

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

​상태 유지 대화 사용

​내장 도구 사용

​사용자 정의 함수 호출

​구조화된 출력 요청

​추론 구성

​응답 스트리밍(Streaming)

인증

본문

응답

상태 유지 대화 사용

내장 도구 사용

사용자 정의 함수 호출

구조화된 출력 요청

추론 구성

응답 스트리밍(Streaming)