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Asking questions

Reading the response

Python
r = c.ask(state, questions)

r["team"]                 # one answer
r["team"].choice          # fields by name; r["team"].raw is the JSON object
r.values                  # {"team": "billing", "amount": 89.0, "done": True, ...}  every answer as a plain value
r.certainty               # {"team": 0.94, "amount": 0.99, "done": 0.97, ...}      how sure, per answer
r.cost_usd                # what this request cost, in US dollars
r.balance_usd             # your balance after it
r.usage                   # tokens: input_tokens, output_tokens, cost
r.timing                  # Timing(client_ms=..., server_ms=...)
r.model                   # "simo-1-pro"
r.request_id              # quote it to support

Fields per answer type

Every answer has .result (its value in the natural type) and .certainty (in [0, 1]), so code can treat all types alike:

Type Fields .result .certainty
noul noul noul >= 0.5 max(noul, 1 - noul)
choice choice, probabilities, confidence, args the option its probability
score score, probabilities, legend, confidence the expected level confidence
fill value, parsed, valid, confidence, check, tokens parsed, else value confidence
extract value / values, valid, confidence, check, tokens values or value confidence
locate coordinates, center, image_size, valid, confidence center confidence

To group answers by type, use r.nouls, r.choices, r.scores, r.fills and r.extracts.

Act on probabilities

Decisions are calibrated: when one says 0.8 it is right about 80% of the time. Act above a high threshold, escalate in the middle, refuse or ask a person below:

Python
p = r["refund"].noul
if p > 0.95:   auto_refund()
elif p > 0.6:  ask_a_person()
else:          reply_no_refund()

For a Choice, look at the probability of the chosen option, or at the whole distribution when two options are close:

Python
a = r["team"]
if a.probabilities[a.choice] < 0.8:
    route_to_triage_queue()

The raw JSON

Over HTTP, answers sit under answers, keyed by question id. See Response format for the complete shape.

JSON
{
  "model": "simo-1-pro",
  "answers": {
    "refund": {"type": "noul", "noul": 0.991},
    "team": {"type": "choice", "choice": "billing",
             "probabilities": {"billing": 0.962, "shipping": 0.021, "tech": 0.017}, "confidence": 0.962}
  },
  "usage": {"input_tokens": 212, "output_tokens": 0, "cost": 0.00000636}
}