GPT 6 Luna

import os
import requests

# Structured decision: POST /v1/systemone — every question is evaluated in parallel and in
# isolation against the same state, and comes back typed. No text generation, nothing to parse.
# The answers{} map is keyed by your own question names; each answer holds its value under a
# key named after its type:
#   noul   -> { type, noul }                                      probability of "yes", 0..1
#   choice -> { type, choice, probabilities, confidence }          choice is one of your criteria keys
#   score  -> { type, score, legend, probabilities, confidence }   legend maps level index -> description
# usage carries input_tokens / output_tokens.
url = "https://aihubmix.com/v1/systemone"
headers = {
    "Authorization": "Bearer " + os.environ["AIHUBMIX_API_KEY"],
    "Content-Type": "application/json",
}
payload = {
      "model": "gpt-6-luna",
      "state": "Hi, I have been trying to connect my Stripe account for 3 days and it keeps failing. I am losing sales. Please help ASAP.",
      "questions": {
        "department": {
          "type": "choice",
          "instructions": "Which team should handle this",
          "criteria": {
            "billing": "Payment or subscription issues",
            "technical": "Bugs or integration problems",
            "sales": "Pricing or account questions"
          }
        },
        "frustration": {
          "type": "score",
          "instructions": "How frustrated the customer appears",
          "criteria": [
            "Calm, just stating facts",
            "Frustrated but civil",
            "Very angry, strong language"
          ]
        },
        "is_urgent": {
          "type": "noul",
          "instructions": "The message conveys urgency or time-sensitivity"
        }
      }
    }

response = requests.post(url, headers=headers, json=payload)
response.raise_for_status()

data = response.json()
for name, answer in data["answers"].items():
    kind = answer["type"]
    print(name, kind, answer[kind], answer.get("confidence"))
print("usage:", data.get("usage"))