> ## Documentation Index
> Fetch the complete documentation index at: https://apidoc.cometapi.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Opprett et modellsvar
> Bruk CometAPI POST /v1/responses til å opprette Multimodal-modellsvar og samtalehistorikk med innebygde verktøy og function calling.
Bruk Responses API til å sende tekst, bilder, filer og samtalehistorikk med `gpt-6-astra`. Eksemplene viser også nettsøk, egendefinerte function calls, strukturert utdata, resonnering og streaming.
Den [OpenAI Responses-referansen](https://developers.openai.com/api/reference/typescript/resources/responses/methods/create) beskriver hele API-formatet; eksemplene her konfigurerer dette formatet for CometAPI.
***
## Fortsett en samtale
For en samtale som kun består av tekst, behold brukermeldingene og assistentens tekstsvar, og legg deretter til den neste brukermeldingen:
```python theme={null}
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.cometapi.com/v1",
api_key=os.environ["COMETAPI_KEY"],
)
input_items = [
{"role": "user", "content": "My project is named Cedar and uses Python. Remember these two facts."}
]
response = client.responses.create(
model="gpt-6-astra",
input=input_items,
reasoning={"effort": "low"},
)
input_items.append({"role": "assistant", "content": response.output_text})
input_items.append({
"role": "user",
"content": "What is my project's name and programming language?",
})
follow_up = client.responses.create(
model="gpt-6-astra",
input=input_items,
reasoning={"effort": "low"},
)
print(follow_up.output_text)
```
`response.output_text` er en praktisk SDK-egenskap som kombinerer assistenttekst fra svaret. Samtaler med function calling trenger også elementene for verktøykall og resultater, som beskrevet nedenfor.
***
## Bruk nettsøk
Legg til et `web_search`-verktøy for å søke etter informasjon og inkludere kildehenvisninger:
```python theme={null}
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-6-astra",
input="Search the web for the NASA Artemis II mission overview and cite the official NASA page.",
tools=[
{
"type": "web_search",
},
],
reasoning={
"effort": "low",
},
)
print(response.output_text)
```
Undersøk de returnerte `web_search_call`-elementene og URL-merknadene i meldingen når applikasjonen din trenger søkeresultater eller kildehenvisninger. Andre innebygde verktøy krever egen konfigurasjon; se [OpenAI-verktøyguiden](https://developers.openai.com/api/docs/guides/tools).
***
## Kall egendefinerte funksjoner
Definer funksjonsargumentene, og la modellen be om funksjonen:
```python theme={null}
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-6-astra",
input="What is the weather in Boston, MA, in celsius? Use the weather tool.",
tools=[
{
"type": "function",
"name": "get_current_weather",
"description": "Get the current weather for a city.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state, for example Boston, MA.",
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit",
],
"description": "Temperature unit.",
},
},
"required": [
"location",
"unit",
],
"additionalProperties": False,
},
"strict": True,
},
],
tool_choice="auto",
reasoning={
"effort": "low",
},
)
print(response.output)
```
Et funksjonskall vises som et `function_call`-utdataelement. Feltet `arguments` er en JSON-kodet streng. Kjør funksjonen din, og bygg deretter den neste forespørselen fra den opprinnelige inndataen og returnerte utdataelementer uten respons-elementenes `id`-felt. Behold funksjonens `name`, `arguments` og `call_id`, og legg til et `function_call_output`-element med den samsvarende `call_id` og resultatet ditt i `output`.
***
## Be om strukturert utdata
Bruk `text.format` til å be om JSON som følger et skjema:
```python theme={null}
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-6-astra",
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",
"description": "Programming language name.",
},
"use_case": {
"type": "string",
"description": "Main use case for the language.",
},
},
"required": [
"name",
"use_case",
],
"additionalProperties": False,
},
"description": "Three programming languages and their main uses.",
},
},
"required": [
"languages",
],
"additionalProperties": False,
},
},
},
reasoning={
"effort": "low",
},
)
print(response.output_text)
```
Kontroller at responsen ble fullført og ikke avslo forespørselen før du tolker JSON-en. En respons som når grensen for output-Tokens, kan være ufullstendig.
***
## Konfigurer resonnering
Angi `reasoning.effort` for å velge modellens resonneringsinnsats:
```python theme={null}
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-6-astra",
input="Solve this step by step: what is 15% of 240?",
reasoning={
"effort": "high",
},
)
print(response.output_text)
```
Eksemplene bruker `low` for generelle oppgaver og `high` for resonneringseksempelet. `max_output_tokens` inkluderer både reasoning og synlige output Tokens.
***
## Strøm Responses
Angi `stream` til `true` for å motta server-sendte hendelser:
```python theme={null}
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.cometapi.com/v1",
api_key=os.environ["COMETAPI_KEY"],
)
stream = client.responses.create(
model="gpt-6-astra",
input="Tell me a three sentence bedtime story about a unicorn.",
reasoning={
"effort": "low",
},
stream=True,
)
for event in stream:
if event.type == "response.output_text.delta":
print(event.delta, end="")
```
Et tekstsvar inkluderer livssyklushendelser som `response.created`, `response.in_progress` og `response.completed`. Tekst kommer i `response.output_text.delta`-hendelser. Verktøy- og resonneringshendelser avhenger av forespørselen, så håndter hendelser etter `type` i stedet for å anta at alle Responses har samme sekvens.
***
Se OpenAI-guidene for [bildeinndata](https://developers.openai.com/api/docs/guides/images-vision), [filinndata](https://developers.openai.com/api/docs/guides/file-inputs), [strukturerte utdata](https://developers.openai.com/api/docs/guides/structured-outputs), [Function Calling](https://developers.openai.com/api/docs/guides/function-calling), [samtaletilstand](https://developers.openai.com/api/docs/guides/conversation-state), og [resonnering](https://developers.openai.com/api/docs/guides/reasoning).
## OpenAPI
````yaml api/openapi/text/post-responses.openapi.json POST /v1/responses
openapi: 3.1.0
info:
title: Responses API
version: 1.0.0
servers:
- url: https://api.cometapi.com
security:
- bearerAuth: []
paths:
/v1/responses:
post:
summary: Create Response
description: >-
Send text, images, files, or conversation history to a model with the
Responses API.
operationId: createResponse
requestBody:
required: true
content:
application/json:
schema:
type: object
required:
- model
- input
properties:
model:
type: string
description: >-
Model ID to use for this request. See the [Models
page](/overview/models) for current options.
example: gpt-6-astra
default: gpt-6-astra
input:
oneOf:
- type: string
description: A plain text string as input.
- type: array
description: >-
An array of input items with roles and multimodal
content.
items:
type: object
description: >-
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:
type: string
description: >-
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:
type: boolean
description: >-
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:
type: array
description: >-
Context management configuration for this request. Controls
how the model manages context when the conversation exceeds
the context window.
items:
type: object
properties:
type:
type: string
description: The type of context management.
compact_threshold:
type: number
description: >-
The threshold at which context compaction is
triggered.
conversation:
type:
- string
- object
- 'null'
description: >-
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:
type: array
description: >-
Additional output data to include in the response. Use this
to request extra information that is not included by
default.
items:
type: string
enum:
- 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:
type: integer
description: >-
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:
type: integer
description: >-
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:
type: object
description: >-
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.
additionalProperties:
type: string
parallel_tool_calls:
type: boolean
description: Whether to allow the model to run tool calls in parallel.
previous_response_id:
type: string
description: >-
An ID for a stored response that is accessible to the
selected model and request context. Cannot be combined with
conversation. The conversation-history example sends prior
input and output items explicitly.
prompt:
type: object
description: Reference to a prompt template and its variables.
properties:
id:
type: string
description: The ID of the prompt template.
variables:
type: object
description: Key-value pairs for template variables.
additionalProperties:
type: string
version:
type: string
description: The version of the prompt template to use.
prompt_cache_key:
type: string
description: >-
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:
type: string
description: >-
Legacy prompt-cache retention setting. Refer to the selected
model documentation for prompt_cache_options and supported
retention settings.
enum:
- in_memory
- 24h
deprecated: true
reasoning:
type: object
description: Reasoning configuration for the selected model.
properties:
effort:
type: string
description: >-
Model-dependent reasoning effort. The examples use low
for general tasks and high for the reasoning example.
See the selected model reference for supported levels.
generate_summary:
type: string
description: >-
Deprecated alias. Use summary for a reasoning summary
when supported.
enum:
- auto
- concise
- detailed
deprecated: true
summary:
type: string
description: >-
Request a summary of the model reasoning when supported
by the selected model.
enum:
- auto
- concise
- detailed
safety_identifier:
type: string
description: >-
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.
maxLength: 64
service_tier:
type: string
description: >-
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.
enum:
- auto
- default
- flex
- priority
store:
type: boolean
description: >-
Request response storage for later retrieval, when supported
by the selected model and request context.
stream:
type: boolean
description: >-
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:
type: object
description: >-
Options for streaming responses. Only set this when `stream`
is `true`.
properties:
include_obfuscation:
type: boolean
description: Whether to include obfuscation data in streaming events.
temperature:
type: number
description: Sampling temperature. Omit this field for GPT-6 Astra.
minimum: 0
maximum: 2
text:
type: object
description: >-
Configuration for text output. Use this to request
structured JSON output via JSON mode or JSON Schema.
properties:
format:
type: object
description: The format of the text output.
properties:
type:
type: string
description: >-
The output format type. `text` returns plain text,
`json_object` returns valid JSON, `json_schema`
returns JSON conforming to a provided schema.
enum:
- text
- json_object
- json_schema
name:
type: string
description: Name of the output schema when type is json_schema.
schema:
type: object
description: >-
JSON Schema describing the requested output when
type is json_schema.
strict:
type: boolean
description: >-
Require the supported JSON Schema to be followed for
a completed, non-refusal structured response.
verbosity:
type: string
description: Controls the verbosity of the text output.
enum:
- low
- medium
- high
tool_choice:
type:
- string
- object
description: >-
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:
type: array
description: >-
Tools available to the model. This page demonstrates
web_search and custom function tools. Other tool types
require their own configuration; see the official tool
reference.
items:
type: object
top_logprobs:
type: integer
description: >-
Number of token alternatives to include for models
supporting log probabilities. Requires
message.output_text.logprobs in include. Omit for GPT-6
Astra.
minimum: 0
maximum: 20
top_p:
type: number
description: >-
Nucleus sampling threshold. Omit this field for GPT-6 Astra.
For sampling overrides, adjust either top_p or temperature.
minimum: 0
maximum: 1
truncation:
type: string
description: >-
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.
enum:
- auto
- disabled
user:
type: string
description: >-
Deprecated. Use `safety_identifier` and `prompt_cache_key`
instead. A stable identifier for your end-user.
deprecated: true
default:
model: gpt-6-astra
input: Tell me a three sentence bedtime story about a unicorn.
reasoning:
effort: low
examples:
Text Input:
summary: Text Input
value:
model: gpt-6-astra
input: Tell me a three sentence bedtime story about a unicorn.
reasoning:
effort: low
Image Input:
summary: Image Input
value:
model: gpt-6-astra
input:
- role: user
content:
- type: input_text
text: >-
Describe the road and surrounding landscape in this
image.
- type: input_image
image_url: >-
https://images.unsplash.com/photo-1500530855697-b586d89ba3ee?w=1200
reasoning:
effort: low
File Input:
summary: File Input
value:
model: gpt-6-astra
input:
- role: user
content:
- type: input_text
text: >-
Summarize the main topics in this shareholder
letter.
- type: input_file
file_url: >-
https://www.berkshirehathaway.com/letters/2024ltr.pdf
reasoning:
effort: low
Web Search:
summary: Web Search
value:
model: gpt-6-astra
input: >-
Search the web for the NASA Artemis II mission overview and
cite the official NASA page.
tools:
- type: web_search
reasoning:
effort: low
Streaming:
summary: Streaming
value:
model: gpt-6-astra
input: Tell me a three sentence bedtime story about a unicorn.
reasoning:
effort: low
stream: true
Functions:
summary: Functions
value:
model: gpt-6-astra
input: >-
What is the weather in Boston, MA, in celsius? Use the
weather tool.
tools:
- type: function
name: get_current_weather
description: Get the current weather for a city.
parameters:
type: object
properties:
location:
type: string
description: City and state, for example Boston, MA.
unit:
type: string
enum:
- celsius
- fahrenheit
description: Temperature unit.
required:
- location
- unit
additionalProperties: false
strict: true
tool_choice: auto
reasoning:
effort: low
Reasoning:
summary: Reasoning
value:
model: gpt-6-astra
input: 'Solve this step by step: what is 15% of 240?'
reasoning:
effort: high
Structured Output:
summary: Structured Output
value:
model: gpt-6-astra
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
description: Programming language name.
use_case:
type: string
description: Main use case for the language.
required:
- name
- use_case
additionalProperties: false
description: Three programming languages and their main uses.
required:
- languages
additionalProperties: false
reasoning:
effort: low
responses:
'200':
description: The generated Response object.
content:
application/json:
schema:
type: object
properties:
id:
type: string
description: Unique identifier for the response.
example: resp_example
object:
type: string
description: The object type, always `response`.
enum:
- response
example: response
created_at:
type: integer
description: >-
Unix timestamp (in seconds) of when the response was
created.
example: 1788763707
status:
type: string
description: The status of the response.
enum:
- completed
- in_progress
- failed
- cancelled
- queued
- incomplete
example: completed
background:
type: boolean
description: Whether the response was run in the background.
example: false
completed_at:
type:
- integer
- 'null'
description: >-
Unix timestamp of when the response was completed, or
`null` if still in progress.
example: 1788763711
error:
type:
- object
- 'null'
description: >-
Error information if the response failed, or `null` on
success.
properties:
code:
type: string
description: The error code.
message:
type: string
description: A human-readable error message.
incomplete_details:
type:
- object
- 'null'
description: >-
Details about why the response is incomplete, if
applicable.
properties:
reason:
type: string
description: The reason the response is incomplete.
enum:
- max_output_tokens
- content_filter
instructions:
type:
- string
- 'null'
description: The system instructions used for this response.
max_output_tokens:
type:
- integer
- 'null'
description: The maximum output token limit that was applied.
model:
type: string
description: The model used for the response.
example: gpt-6-astra
output:
type: array
description: >-
An array of output items generated by the model. Each item
can be a message, function call, or other output type.
items:
type: object
properties:
id:
type: string
description: Unique identifier for the output item.
type:
type: string
description: The type of output item.
enum:
- message
- function_call
- web_search_call
- file_search_call
- code_interpreter_call
- computer_call
- reasoning
status:
type: string
description: The status of this output item.
enum:
- completed
- in_progress
role:
type: string
description: >-
The role of the message (present when `type` is
`message`).
enum:
- assistant
content:
type: array
description: >-
The content parts of the message (present when
`type` is `message`).
items:
type: object
properties:
type:
type: string
description: The content type.
enum:
- output_text
text:
type: string
description: The generated text content.
annotations:
type: array
description: >-
Annotations such as file citations or URL
citations.
items:
type: object
logprobs:
type: array
description: >-
Log probability information (when requested
via `include`).
items:
type: object
name:
type: string
description: >-
The name of the function being called (present when
`type` is `function_call`).
arguments:
type: string
description: >-
The JSON-encoded arguments for the function call
(present when `type` is `function_call`).
call_id:
type: string
description: >-
The unique call identifier (present when `type` is
`function_call`).
phase:
type: string
description: >-
Message phase reported by the model, such as
final_answer.
parallel_tool_calls:
type: boolean
description: Whether parallel tool calls were enabled.
previous_response_id:
type:
- string
- 'null'
description: >-
The ID of the previous response, if this is a multi-turn
conversation.
reasoning:
type: object
description: The reasoning configuration that was used.
properties:
effort:
type:
- string
- 'null'
description: The reasoning effort level.
summary:
type:
- string
- 'null'
description: The reasoning summary setting.
context:
type:
- string
- 'null'
description: Reasoning context mode reported by the model.
mode:
type:
- string
- 'null'
description: Reasoning execution mode reported by the model.
service_tier:
type: string
description: The service tier actually used to process the request.
store:
type: boolean
description: Whether the response was stored.
temperature:
type: number
description: The temperature value used.
text:
type: object
description: The text configuration used.
properties:
format:
type: object
properties:
type:
type: string
description: >-
Format type: `text` (default), `json_object`, or
`json_schema`.
description: Output text format configuration.
verbosity:
type: string
description: The verbosity level used.
tool_choice:
type:
- string
- object
description: The tool choice setting used.
tools:
type: array
description: The tools that were available for this response.
items:
type: object
top_p:
type: number
description: The `top_p` value used.
truncation:
type: string
description: The truncation strategy used.
usage:
type: object
description: Token usage statistics for this response.
properties:
input_tokens:
type: integer
description: Number of input tokens consumed.
example: 17
input_tokens_details:
type: object
description: Breakdown of input token usage.
properties:
cached_tokens:
type: integer
description: Number of input tokens that were cached.
example: 0
cache_write_tokens:
type: integer
description: >-
Input tokens written to the prompt cache, when
reported.
output_tokens:
type: integer
description: Number of output tokens generated.
example: 81
output_tokens_details:
type: object
description: Breakdown of output token usage.
properties:
reasoning_tokens:
type: integer
description: Number of tokens used for reasoning.
example: 0
total_tokens:
type: integer
description: Total number of tokens (input + output).
example: 98
user:
type:
- string
- 'null'
description: The user identifier, if provided.
metadata:
type: object
description: The metadata attached to this response.
additionalProperties:
type: string
content_filters:
type:
- array
- 'null'
description: Content filter results applied to the response, if any.
nullable: true
frequency_penalty:
type: number
description: The frequency penalty applied to the request.
max_tool_calls:
type:
- integer
- 'null'
description: Maximum number of tool calls allowed, if set.
nullable: true
presence_penalty:
type: number
description: The presence penalty applied to the request.
prompt_cache_key:
type:
- string
- 'null'
description: Cache key for prompt caching, if applicable.
nullable: true
prompt_cache_retention:
type:
- string
- 'null'
description: Prompt cache retention policy, if applicable.
nullable: true
safety_identifier:
type:
- string
- 'null'
description: Safety system identifier for the response, if applicable.
nullable: true
top_logprobs:
type: integer
description: >-
Number of top log probabilities returned per token
position.
example:
id: resp_example
object: response
created_at: 1788763707
status: completed
background: false
completed_at: 1788763711
error: null
incomplete_details: null
model: gpt-6-astra
output:
- id: msg_example
type: message
status: completed
content:
- type: output_text
annotations: []
logprobs: []
text: >-
A little unicorn named Luna followed a trail of silver
fireflies to a meadow where the flowers chimed softly
in the breeze. She curled beneath a willow tree,
tucked her hooves into the cool grass, and wished
sweet dreams for every creature in the forest. As the
moon wrapped the meadow in gentle light, Luna closed
her eyes and drifted into dreams of dancing among the
stars.
phase: final_answer
role: assistant
parallel_tool_calls: true
previous_response_id: null
reasoning:
context: all_turns
effort: low
mode: standard
summary: null
store: true
text:
format:
type: text
verbosity: medium
tool_choice: auto
tools: []
usage:
input_tokens: 17
input_tokens_details:
cache_write_tokens: 0
cached_tokens: 0
output_tokens: 81
output_tokens_details:
reasoning_tokens: 0
total_tokens: 98
x-codeSamples:
- lang: Python
label: Text Input
source: |
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-6-astra",
input="Tell me a three sentence bedtime story about a unicorn.",
reasoning={
"effort": "low",
},
)
print(response.output_text)
- lang: Python
label: Image Input
source: |
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-6-astra",
input=[
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "Describe the road and surrounding landscape in this image.",
},
{
"type": "input_image",
"image_url": "https://images.unsplash.com/photo-1500530855697-b586d89ba3ee?w=1200",
},
],
},
],
reasoning={
"effort": "low",
},
)
print(response.output_text)
- lang: Python
label: File Input
source: |
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-6-astra",
input=[
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "Summarize the main topics in this shareholder letter.",
},
{
"type": "input_file",
"file_url": "https://www.berkshirehathaway.com/letters/2024ltr.pdf",
},
],
},
],
reasoning={
"effort": "low",
},
)
print(response.output_text)
- lang: Python
label: Web Search
source: |
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-6-astra",
input="Search the web for the NASA Artemis II mission overview and cite the official NASA page.",
tools=[
{
"type": "web_search",
},
],
reasoning={
"effort": "low",
},
)
print(response.output_text)
- lang: Python
label: Streaming
source: |
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.cometapi.com/v1",
api_key=os.environ["COMETAPI_KEY"],
)
stream = client.responses.create(
model="gpt-6-astra",
input="Tell me a three sentence bedtime story about a unicorn.",
reasoning={
"effort": "low",
},
stream=True,
)
for event in stream:
if event.type == "response.output_text.delta":
print(event.delta, end="")
- lang: Python
label: Functions
source: |
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-6-astra",
input="What is the weather in Boston, MA, in celsius? Use the weather tool.",
tools=[
{
"type": "function",
"name": "get_current_weather",
"description": "Get the current weather for a city.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state, for example Boston, MA.",
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit",
],
"description": "Temperature unit.",
},
},
"required": [
"location",
"unit",
],
"additionalProperties": False,
},
"strict": True,
},
],
tool_choice="auto",
reasoning={
"effort": "low",
},
)
print(response.output)
- lang: Python
label: Reasoning
source: |
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-6-astra",
input="Solve this step by step: what is 15% of 240?",
reasoning={
"effort": "high",
},
)
print(response.output_text)
- lang: Python
label: Structured Output
source: |
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-6-astra",
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",
"description": "Programming language name.",
},
"use_case": {
"type": "string",
"description": "Main use case for the language.",
},
},
"required": [
"name",
"use_case",
],
"additionalProperties": False,
},
"description": "Three programming languages and their main uses.",
},
},
"required": [
"languages",
],
"additionalProperties": False,
},
},
},
reasoning={
"effort": "low",
},
)
print(response.output_text)
- lang: JavaScript
label: Text Input
source: |
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
const response = await client.responses.create({
"model": "gpt-6-astra",
"input": "Tell me a three sentence bedtime story about a unicorn.",
"reasoning": {
"effort": "low"
}
});
console.log(response.output_text);
- lang: JavaScript
label: Image Input
source: |
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
const response = await client.responses.create({
"model": "gpt-6-astra",
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "Describe the road and surrounding landscape in this image."
},
{
"type": "input_image",
"image_url": "https://images.unsplash.com/photo-1500530855697-b586d89ba3ee?w=1200"
}
]
}
],
"reasoning": {
"effort": "low"
}
});
console.log(response.output_text);
- lang: JavaScript
label: File Input
source: |
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
const response = await client.responses.create({
"model": "gpt-6-astra",
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "Summarize the main topics in this shareholder letter."
},
{
"type": "input_file",
"file_url": "https://www.berkshirehathaway.com/letters/2024ltr.pdf"
}
]
}
],
"reasoning": {
"effort": "low"
}
});
console.log(response.output_text);
- lang: JavaScript
label: Web Search
source: |
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
const response = await client.responses.create({
"model": "gpt-6-astra",
"input": "Search the web for the NASA Artemis II mission overview and cite the official NASA page.",
"tools": [
{
"type": "web_search"
}
],
"reasoning": {
"effort": "low"
}
});
console.log(response.output_text);
- lang: JavaScript
label: Streaming
source: |
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
const stream = await client.responses.create({
"model": "gpt-6-astra",
"input": "Tell me a three sentence bedtime story about a unicorn.",
"reasoning": {
"effort": "low"
},
"stream": true
});
for await (const event of stream) {
if (event.type === "response.output_text.delta") {
process.stdout.write(event.delta);
}
}
- lang: JavaScript
label: Functions
source: |
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
const response = await client.responses.create({
"model": "gpt-6-astra",
"input": "What is the weather in Boston, MA, in celsius? Use the weather tool.",
"tools": [
{
"type": "function",
"name": "get_current_weather",
"description": "Get the current weather for a city.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state, for example Boston, MA."
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
],
"description": "Temperature unit."
}
},
"required": [
"location",
"unit"
],
"additionalProperties": false
},
"strict": true
}
],
"tool_choice": "auto",
"reasoning": {
"effort": "low"
}
});
console.log(response.output);
- lang: JavaScript
label: Reasoning
source: |
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
const response = await client.responses.create({
"model": "gpt-6-astra",
"input": "Solve this step by step: what is 15% of 240?",
"reasoning": {
"effort": "high"
}
});
console.log(response.output_text);
- lang: JavaScript
label: Structured Output
source: |
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
const response = await client.responses.create({
"model": "gpt-6-astra",
"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",
"description": "Programming language name."
},
"use_case": {
"type": "string",
"description": "Main use case for the language."
}
},
"required": [
"name",
"use_case"
],
"additionalProperties": false
},
"description": "Three programming languages and their main uses."
}
},
"required": [
"languages"
],
"additionalProperties": false
}
}
},
"reasoning": {
"effort": "low"
}
});
console.log(response.output_text);
- lang: Shell
label: Text Input
source: |
curl https://api.cometapi.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
-d '{
"model": "gpt-6-astra",
"input": "Tell me a three sentence bedtime story about a unicorn.",
"reasoning": {
"effort": "low"
}
}'
- lang: Shell
label: Image Input
source: |
curl https://api.cometapi.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
-d '{
"model": "gpt-6-astra",
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "Describe the road and surrounding landscape in this image."
},
{
"type": "input_image",
"image_url": "https://images.unsplash.com/photo-1500530855697-b586d89ba3ee?w=1200"
}
]
}
],
"reasoning": {
"effort": "low"
}
}'
- lang: Shell
label: File Input
source: |
curl https://api.cometapi.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
-d '{
"model": "gpt-6-astra",
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "Summarize the main topics in this shareholder letter."
},
{
"type": "input_file",
"file_url": "https://www.berkshirehathaway.com/letters/2024ltr.pdf"
}
]
}
],
"reasoning": {
"effort": "low"
}
}'
- lang: Shell
label: Web Search
source: |
curl https://api.cometapi.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
-d '{
"model": "gpt-6-astra",
"input": "Search the web for the NASA Artemis II mission overview and cite the official NASA page.",
"tools": [
{
"type": "web_search"
}
],
"reasoning": {
"effort": "low"
}
}'
- lang: Shell
label: Streaming
source: |
curl https://api.cometapi.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
--no-buffer \
-d '{
"model": "gpt-6-astra",
"input": "Tell me a three sentence bedtime story about a unicorn.",
"reasoning": {
"effort": "low"
},
"stream": true
}'
- lang: Shell
label: Functions
source: |
curl https://api.cometapi.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
-d '{
"model": "gpt-6-astra",
"input": "What is the weather in Boston, MA, in celsius? Use the weather tool.",
"tools": [
{
"type": "function",
"name": "get_current_weather",
"description": "Get the current weather for a city.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state, for example Boston, MA."
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
],
"description": "Temperature unit."
}
},
"required": [
"location",
"unit"
],
"additionalProperties": false
},
"strict": true
}
],
"tool_choice": "auto",
"reasoning": {
"effort": "low"
}
}'
- lang: Shell
label: Reasoning
source: |
curl https://api.cometapi.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
-d '{
"model": "gpt-6-astra",
"input": "Solve this step by step: what is 15% of 240?",
"reasoning": {
"effort": "high"
}
}'
- lang: Shell
label: Structured Output
source: |
curl https://api.cometapi.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
-d '{
"model": "gpt-6-astra",
"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",
"description": "Programming language name."
},
"use_case": {
"type": "string",
"description": "Main use case for the language."
}
},
"required": [
"name",
"use_case"
],
"additionalProperties": false
},
"description": "Three programming languages and their main uses."
}
},
"required": [
"languages"
],
"additionalProperties": false
}
}
},
"reasoning": {
"effort": "low"
}
}'
components:
securitySchemes:
bearerAuth:
type: http
scheme: bearer
description: Bearer token authentication. Use your CometAPI key.
````