[판단 AI 실전 가이드②] 오픈AI 디시전스 API 실전 활용법…로짓 기반 확신도로 자동화 선별하기

https://platform.openai.com/api-keys

https://platform.openai.com/decisions

pip install --upgrade openai

setx OPENAI_API_KEY "발급받은_키"

echo 'export OPENAI_API_KEY="발급받은_키"' >> ~/.zshrc

source ~/.zshrc

from openai import 오픈AI


client = OpenAI()  # 환경변수 OPENAI_API_KEY를 읽는다


ticket = (
    "Hi, I've been trying to connect my Stripe account for 3 days and the integration "
    "keeps failing. I'm losing sales. Please help ASAP."
)


decision = client.decisions.create(
    model="gpt-6-luna",
    input=ticket,
    questions=[
        {
            "type": "choice",
            "name": "department",
            "instructions": "Which team should handle this",
            "choices": [
                {"value": "billing", "description": "Payment or subscription issues"},
                {"value": "technical", "description": "Bugs or integration problems"},
                {"value": "sales", "description": "Pricing or account questions"},
            ],
        },
        {
            "type": "score",
            "name": "frustration",
            "instructions": "How frustrated the customer appears",
            "levels": [
                {"label": "Calm", "description": "Calm, just stating facts"},
                {"label": "Frustrated", "description": "Frustrated but civil"},
                {"label": "Angry", "description": "Very angry, strong language"},
            ],
        },
        {
            "type": "predicate",
            "name": "is_urgent",
            "instructions": "The message conveys urgency or time-sensitivity",
        },
    ],
)


answers = {a.name: a for a in decision.answers}  # 배열을 이름으로 찾기 쉽게 바꾼다


for name, a in answers.items():

    if a.type == "refusal":

        print(name, "거부")

    elif a.type == "choice":

        print(name, a.choice, a.confidence)

    elif a.type == "score":

        print(name, a.score, a.confidence)

    elif a.type == "predicate":

        print(name, a.probability)


print(decision.usage.input_tokens)

{
  "model": "gpt-6-luna",
  "answers": [
    {
      "type": "choice",
      "name": "department",
      "choice": "technical",
      "probabilities": [
        {
          "value": "billing",
          "probability": 0.0
        },
        {
          "value": "technical",
          "probability": 0.99
        },
        {
          "value": "sales",
          "probability": 0.01
        }
      ],
      "confidence": 0.99
    },
    {
      "type": "score",
      "name": "frustration",
      "score": 0.98,
      "probabilities": [
        {
          "value": 0,
          "label": "Calm",
          "probability": 0.03
        },
        {
          "value": 1,
          "label": "Frustrated",
          "probability": 0.96
        },
        {
          "value": 2,
          "label": "Angry",
          "probability": 0.01
        }
      ],
      "confidence": 0.94
    },
    {
      "type": "predicate",
      "name": "is_urgent",
      "probability": 1.0
    }
  ],
  "usage": {
    "input_tokens": 434,
    "output_tokens": 0,
    "total_tokens": 434
  }
}

import base64


from pathlib import Path


from openai import 오픈AI


client = OpenAI()


image_b64 = base64.b64encode(Path("screenshot.png").read_bytes()).decode("ascii")


decision = client.decisions.create(
    model="gpt-6-luna",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_text",
                    "text": "A customer attached this screenshot to a support ticket.",
                },
                {
                    "type": "input_image",
                    "image_url": f"data:image/png;base64,{image_b64}",
                },
            ],
        }
    ],
    questions=[
        {
            "type": "predicate",
            "name": "shows_error",
            "instructions": "Does the screenshot show an error message?",
        },
        {
            "type": "choice",
            "name": "team",
            "instructions": "Which team should handle this ticket?",
            "choices": [
                {"value": "billing", "description": "Payments and cards"},
                {"value": "technical", "description": "Bugs and crashes"},
                {"value": "other", "description": "Anything else"},
            ],
        },
    ],
)

from openai import 오픈AI


client = OpenAI()


applicant = "Applicant: Jane Doe, 34, two years gap in employment, lives in zip code 10027. 5 years of Python experience."


decision = client.decisions.create(
    model="gpt-6-luna",
    input=applicant,
    questions=[
        {
            "type": "predicate",
            "name": "reject",
            "instructions": "Should this applicant be rejected?",
        },
        {
            "type": "predicate",
            "name": "meets_python",
            "instructions": "Does the applicant state at least 3 years of Python experience?",
        },
    ],
)


for a in decision.answers:

    if a.type == "refusal":

        print(a.name, "거부 → 사람 검토 대기열로")  # 0이나 '아니오'로 바꾸지 않는다

    else:

        print(a.name, a.probability)

import json


from openai import 오픈AI


client = OpenAI()


choices = [
    {
        "value": "billing",
        "description": "Charges, invoices, refunds, prices and payment methods",
    },
    {
        "value": "technical",
        "description": "Bugs, errors, crasheslow performance and integrations",
    },
    {
        "value": "전말(계정을 뜻할 때는 그대로 계정)",
        "description": "Login, users, permissions, 전말 settings and personal data",
    },
    {
        "value": "shipping",
        "description": "Delivery, tracking, damaged or wrong items, addresses",
    },
    {
        "value": "other",
        "description": "Anything that is not a support request for the teams above",
    },
]


tickets = json.load(
    open("tickets.json")
)  # [{"id":1,"label":"billing","text":"..."}, ...]


rows = []


for t in tickets:

    a = client.decisions.create(
        model="gpt-6-luna",
        input=t["text"],
        questions=[
            {
                "type": "choice",
                "name": "team",
                "instructions": "Which team should handle this ticket?",
                "choices": choices,
            }
        ],
    ).answers[0]

    conf = (
        a.confidence if a.type == "choice" else 0.0
    )  # 거부는 확신도 0으로 두고 검토로 보낸다

    rows.append((t["id"], t["label"], getattr(a, "choice", None), conf))


for th in (0.5, 0.6, 0.7, 0.8, 0.9, 0.95):

    auto = [r for r in rows if r[3] >= th]

    wrong = sum(r[1] != r[2] for r in auto)

    print(
        f"문턱 {th:.2f}: 자동 {len(auto)}건, 그중 오답 {wrong}건, 사람 검토 {len(rows) - len(auto)}건"
    )

import json


from openai import 오픈AI


client = OpenAI()  # 판단과 상위 모델을 같은 키 하나로 쓴다


LABELS = ["billing", "technical", "전말", "shipping", "other"]


THRESHOLD = 0.80


choices = [...]  # 앞 실습과 같은 보기


def route(text):

    a = client.decisions.create(
        model="gpt-6-luna",
        input=text,
        questions=[
            {
                "type": "choice",
                "name": "team",
                "instructions": "Which team should handle this ticket?",
                "choices": choices,
            }
        ],
    ).answers[0]

    if a.type == "choice" and a.confidence >= THRESHOLD:

        return a.choice, "decisions"

    r = client.responses.create(  # 거부했거나 확신이 낮으면 추론 모델로
        model="gpt-6-sol",
        input=[
            {
                "role": "system",
                "content": "Route the support ticket to one team: " + ", ".join(LABELS),
            },
            {"role": "user", "content": text},
        ],
        text={
            "format": {
                "type": "json_schema",
                "name": "route",
                "strict": True,
                "schema": {
                    "type": "object",
                    "additionalProperties": False,
                    "required": ["team"],
                    "properties": {"team": {"type": "string", "enum": LABELS}},
                },
            }
        },
    )

    return json.loads(r.output_text)["team"], "gpt-6-sol"