examples/ask.py

"""Ask Jegue questions without the benchmark harness.

Needs one CUDA GPU (about 20 GB), the Decision Index kit installed (it provides decision_index.engines.base) and the base model
Qwen/Qwen3.5-9B in the Hugging Face cache. From the repo root:

    PYTHONPATH=code python examples/ask.py

The engine takes a shared context ("state") and a dict of questions. A "choice" question has option keys and descriptions and
returns one probability per option; a "noul" question is a yes/no and returns the probability of yes.
"""
import os
import sys

HERE = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.dirname(HERE)
sys.path.insert(0, os.path.join(ROOT, "code"))

from decision_engine_dense import DenseSlotEngine  # noqa: E402

WEIGHTS = os.path.join(ROOT, "weights")
engine = DenseSlotEngine(model="Qwen/Qwen3.5-9B", run=WEIGHTS, ckpt=WEIGHTS)

state = "Email from a customer: 'My parcel was due on Monday and it is Friday. Tracking has not changed since Tuesday. I want my money back.'"
questions = {
    "intent": {
        "type": "choice",
        "instructions": "What does the customer mainly want?",
        "criteria": {"refund": "A refund", "status": "An update on the delivery", "complaint": "To complain about the service", "other": "Something else"},
    },
    "urgent": {
        "type": "noul",
        "instructions": "Should this email be escalated to a human agent today?",
    },
}

response, _ = engine(state, questions)
for qid, answer in response["answers"].items():
    if answer["type"] == "choice":
        ranked = sorted(answer["probabilities"].items(), key=lambda kv: -kv[1])
        print(f"{qid}: {answer['choice']}  " + "  ".join(f"{k}={v:.2f}" for k, v in ranked))
    else:
        print(f"{qid}: P(yes)={answer['noul']:.2f}")