Crear una respuesta de modelo

POST

responses

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.cometapi.com/v1",
    api_key=os.environ["COMETAPI_KEY"],
)

response = client.responses.create(
    model="gpt-5.6-sol",
    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": {}
}

La API de Responses amplía Chat Completions con conversaciones con estado, herramientas integradas, entradas de archivos multimodal y control de razonamiento. Es el endpoint recomendado para los modelos de razonamiento de OpenAI o-series, la serie GPT-5 y los modelos Codex.

Los distintos proveedores de modelos admiten diferentes parámetros de solicitud y devuelven campos de respuesta variables. No todos los parámetros enumerados en el playground anterior funcionan con todos los modelos de CometAPI.

Usar conversaciones con estado

Encadena respuestas mediante previous_response_id en lugar de administrar tú mismo el historial de mensajes:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.cometapi.com/v1",
    api_key=os.environ["COMETAPI_KEY"],
)

# First turn
response = client.responses.create(
    model="gpt-5.6-sol",
    input="What is quantum computing?",
)

# Second turn — previous context is included automatically
follow_up = client.responses.create(
    model="gpt-5.6-sol",
    input="Can you explain that more simply?",
    previous_response_id=response.id,
)

print(follow_up.output_text)

Usar herramientas integradas

La API de Responses incluye herramientas proporcionadas por la plataforma que no requieren configuración:

Herramienta	Propósito
`web_search_preview`	Busca información en tiempo real en la web
`file_search`	Busca en los archivos cargados
`code_interpreter`	Ejecuta código Python en un sandbox

Para habilitar una herramienta integrada, agrégala al array tools:

response = client.responses.create(
    model="gpt-5.6-sol",
    input="Find the current price of Bitcoin",
    tools=[{"type": "web_search_preview"}],
)

print(response.output_text)

Llamar a funciones personalizadas

Define funciones que el modelo puede invocar con argumentos estructurados:

response = client.responses.create(
    model="gpt-5.6-sol",
    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"]
        }
    }],
)

Cuando el modelo llama a una función, el array output de la respuesta contiene un elemento function_call con el nombre de la función y los argumentos analizados. Ejecuta la función y envía el resultado en una solicitud de seguimiento.

Solicitar salida estructurada

Para forzar una salida JSON que coincida con un esquema específico, usa el parámetro text.format:

response = client.responses.create(
    model="gpt-5.6-sol",
    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
            }
        }
    },
)

Configurar el razonamiento

Para los modelos o-series y GPT-5, controla la profundidad de razonamiento con reasoning.effort:

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)

Un mayor esfuerzo de razonamiento produce respuestas más exhaustivas, pero usa más tokens. Usa "low" para consultas simples y "high" para problemas complejos de varios pasos.

Transmitir respuestas

Para recibir salida incremental, establece stream en true. La API envía eventos enviados por el servidor (SSE) en este orden:

response.created — Objeto de respuesta inicializado
response.in_progress — Generación iniciada
response.output_item.added — Nuevo elemento de salida (mensaje o llamada a herramienta)
response.content_part.added — Parte de contenido iniciada
response.output_text.delta — Fragmento de texto (contiene el campo delta)
response.output_text.done — Generación de texto completada para esta parte de contenido
response.content_part.done — Parte de contenido finalizada
response.output_item.done — Elemento de salida finalizado
response.completed — Respuesta completa con datos de usage

Transmite una respuesta con el SDK de Python:

stream = client.responses.create(
    model="gpt-5.6-sol",
    input="Write a haiku about coding",
    stream=True,
)

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

Para obtener guías detalladas sobre cada capacidad, consulta la documentación de OpenAI: Texto · Imágenes · Archivos PDF · Salidas estructuradas · Function Calling · Estado de la conversación · Herramientas integradas · Razonamiento

Autorizaciones

Authorization

string

header

requerido

Bearer token authentication. Use your CometAPI key.

Cuerpo

application/json

model

string

requerido

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

Ejemplo:

"gpt-5.6-sol"

input

requerido

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

predeterminado: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.

Opciones disponibles:

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

predeterminado: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.

Opciones disponibles:

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.

Opciones disponibles:

auto,

default,

flex,

priority

store

boolean

predeterminado:true

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

stream

boolean

predeterminado: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

predeterminado: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.

Rango requerido: 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

predeterminado: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.

Rango requerido: 0 <= x <= 20

top_p

number

predeterminado: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.

Rango requerido: 0 <= x <= 1

truncation

enum<string>

predeterminado: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.

Opciones disponibles:

auto,

disabled

user

string

obsoleto

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

Respuesta

200 - application/json

The generated Response object.

string

Unique identifier for the response.

Ejemplo:

"resp_0a153ae8201f73bc0069a7e8044cc481"

object

enum<string>

The object type, always response.

Opciones disponibles:

response

Ejemplo:

"response"

created_at

integer

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

Ejemplo:

1772611588

status

enum<string>

The status of the response.

Opciones disponibles:

completed,

in_progress,

failed,

cancelled,

queued

Ejemplo:

"completed"

background

boolean

Whether the response was run in the background.

Ejemplo:

false

completed_at

integer | null

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

Ejemplo:

1772611589

error

object | null

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

Show child attributes

incomplete_details

object | null

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.

Ejemplo:

"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.

Ejemplo:

"default"

store

boolean

Whether the response was stored.

temperature

number

The temperature value used.

Ejemplo:

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.

Ejemplo:

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

predeterminado:0

The frequency penalty applied to the request.

max_tool_calls

integer | null

Maximum number of tool calls allowed, if set.

presence_penalty

number

predeterminado: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

predeterminado:0

Number of top log probabilities returned per token position.

Crear una finalización de chat

Crear un mensaje

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.cometapi.com/v1",
    api_key=os.environ["COMETAPI_KEY"],
)

response = client.responses.create(
    model="gpt-5.6-sol",
    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": {}
}

Moderación de contenido

Claves API

Usar conversaciones con estado

Usar herramientas integradas

Llamar a funciones personalizadas

Solicitar salida estructurada

Configurar el razonamiento

Transmitir respuestas

Autorizaciones

Cuerpo

Respuesta

​Usar conversaciones con estado

​Usar herramientas integradas

​Llamar a funciones personalizadas

​Solicitar salida estructurada

​Configurar el razonamiento

​Transmitir respuestas

Autorizaciones

Cuerpo

Respuesta

Usar conversaciones con estado

Usar herramientas integradas

Llamar a funciones personalizadas

Solicitar salida estructurada

Configurar el razonamiento

Transmitir respuestas