curl -X POST https://api-llm.sunra.ai/v1/responses \
-H "Authorization: Bearer <SUNRA_KEY>" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-4o",
"input": [
{
"type": "message",
"role": "user",
"content": "Hello, how are you?"
}
]
}'
import requests
response = requests.post(
"https://api-llm.sunra.ai/v1/responses",
headers={
"Authorization": "Bearer <SUNRA_KEY>",
"Content-Type": "application/json"
},
json={
"model": "openai/gpt-4o",
"input": [
{
"type": "message",
"role": "user",
"content": "Hello, how are you?"
}
]
}
)
print(response.json())
const response = await fetch("https://api-llm.sunra.ai/v1/responses", {
method: "POST",
headers: {
"Authorization": "Bearer <SUNRA_KEY>",
"Content-Type": "application/json"
},
body: JSON.stringify({
model: "openai/gpt-4o",
input: [
{
type: "message",
role: "user",
content: "Hello, how are you?"
}
]
})
});
const data = await response.json();
console.log(data);
{
"id": "resp-abc123",
"object": "response",
"created_at": 1704067200,
"status": "completed",
"model": "openai/gpt-4o",
"output": [
{
"type": "message",
"id": "msg_abc123",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "Hello! I'm doing well, thank you for asking."
}
]
}
],
"frequency_penalty": 0,
"presence_penalty": 0,
"temperature": 1.0,
"top_p": 1.0,
"usage": {
"input_tokens": 15,
"output_tokens": 12,
"total_tokens": 27
}
}
LLM
Create a response
POST
/
v1
/
responses
curl -X POST https://api-llm.sunra.ai/v1/responses \
-H "Authorization: Bearer <SUNRA_KEY>" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-4o",
"input": [
{
"type": "message",
"role": "user",
"content": "Hello, how are you?"
}
]
}'
import requests
response = requests.post(
"https://api-llm.sunra.ai/v1/responses",
headers={
"Authorization": "Bearer <SUNRA_KEY>",
"Content-Type": "application/json"
},
json={
"model": "openai/gpt-4o",
"input": [
{
"type": "message",
"role": "user",
"content": "Hello, how are you?"
}
]
}
)
print(response.json())
const response = await fetch("https://api-llm.sunra.ai/v1/responses", {
method: "POST",
headers: {
"Authorization": "Bearer <SUNRA_KEY>",
"Content-Type": "application/json"
},
body: JSON.stringify({
model: "openai/gpt-4o",
input: [
{
type: "message",
role: "user",
content: "Hello, how are you?"
}
]
})
});
const data = await response.json();
console.log(data);
{
"id": "resp-abc123",
"object": "response",
"created_at": 1704067200,
"status": "completed",
"model": "openai/gpt-4o",
"output": [
{
"type": "message",
"id": "msg_abc123",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "Hello! I'm doing well, thank you for asking."
}
]
}
],
"frequency_penalty": 0,
"presence_penalty": 0,
"temperature": 1.0,
"top_p": 1.0,
"usage": {
"input_tokens": 15,
"output_tokens": 12,
"total_tokens": 27
}
}
Erstellt eine Streaming- oder Nicht-Streaming-Antwort im OpenAI Responses API-Format.
Authentifizierung
string
erforderlich
Bearer-Token. Verwenden Sie Ihren API-Schlüssel als Bearer-Token im Authorization-Header.Format:
Bearer <SUNRA_KEY>Anfrage
Dieser Endpunkt erwartet ein JSON-Objekt.string
erforderlich
Das Modell, das für die Generierung der Antwort verwendet werden soll. Verfügbare Modelle finden Sie unter sunra.ai/models.
string | object[]
string
Anweisungen auf Systemebene für das Modell. Entspricht einer Systemnachricht.
boolean
Standard:false
Wenn auf
true gesetzt, wird die Antwort mittels Server-Sent Events (SSE) gestreamt.integer
Die maximale Anzahl der zu generierenden Ausgabe-Tokens.
number
Sampling-Temperatur zwischen 0 und 2. Höhere Werte erhöhen die Zufälligkeit.
number
Nucleus-Sampling-Parameter (0-1).
number
Zahl zwischen -2.0 und 2.0. Bestraft Tokens basierend auf ihrer bestehenden Häufigkeit.
number
Zahl zwischen -2.0 und 2.0. Bestraft Tokens basierend darauf, ob sie bereits vorkommen.
boolean
Standard:true
Ob die generierte Antwort für späteren Abruf gespeichert werden soll.
Antwort
Erfolgreiches Antwortobjekt.string
Eindeutiger Antwortbezeichner.
string
Objekttyp. Immer
response.number
Unix-Zeitstempel der Erstellung.
string
Der Status der Antwort. Kann
completed, failed, in_progress oder cancelled sein.string
Das für die Antwort verwendete Modell.
object[]
Liste der vom Modell generierten Ausgabeelemente.
object
curl -X POST https://api-llm.sunra.ai/v1/responses \
-H "Authorization: Bearer <SUNRA_KEY>" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-4o",
"input": [
{
"type": "message",
"role": "user",
"content": "Hello, how are you?"
}
]
}'
import requests
response = requests.post(
"https://api-llm.sunra.ai/v1/responses",
headers={
"Authorization": "Bearer <SUNRA_KEY>",
"Content-Type": "application/json"
},
json={
"model": "openai/gpt-4o",
"input": [
{
"type": "message",
"role": "user",
"content": "Hello, how are you?"
}
]
}
)
print(response.json())
const response = await fetch("https://api-llm.sunra.ai/v1/responses", {
method: "POST",
headers: {
"Authorization": "Bearer <SUNRA_KEY>",
"Content-Type": "application/json"
},
body: JSON.stringify({
model: "openai/gpt-4o",
input: [
{
type: "message",
role: "user",
content: "Hello, how are you?"
}
]
})
});
const data = await response.json();
console.log(data);
{
"id": "resp-abc123",
"object": "response",
"created_at": 1704067200,
"status": "completed",
"model": "openai/gpt-4o",
"output": [
{
"type": "message",
"id": "msg_abc123",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "Hello! I'm doing well, thank you for asking."
}
]
}
],
"frequency_penalty": 0,
"presence_penalty": 0,
"temperature": 1.0,
"top_p": 1.0,
"usage": {
"input_tokens": 15,
"output_tokens": 12,
"total_tokens": 27
}
}
⌘I