Benjamin Admin
98d616d82b
The learning point is not the hypothesis, it is the QUESTION — and confirmed/refuted is too coarse.
"partial, only critical suppliers" or "certified but not lived" are not "wrong", they are valuable
knowledge. So the chain is Hypothesis -> Question -> Observation -> (Review) -> Hypothesis, and the
observation model must be defined cleanly before any store/API (else thousands of too-coarse
observations get migrated later).
compliance/onboarding/observations.py:
- ObservationType: confirmed / partial / refuted / not_applicable / unknown (richer than binary).
- Observation: {hypothesis_id, capability, question, answer (free text), observation_type,
scope_note ("only critical suppliers"), evidence_uploaded, reviewed, reviewed_by}.
- empirical_distribution() -> a DISTRIBUTION (confirmed 61 / partial 31 / refuted 8), not one %.
- empirical_confidence() -> (confirmed + 0.5*partial) / (confirmed+partial+refuted); n.a./unknown
excluded; None until calibrated.
- REVIEW GATE: only reviewed observations calibrate — a raw answer never changes a hypothesis (no
learning from outliers).
Refactor: the hypothesis is now PURE curated knowledge — the binary observations counter and any
confidence are removed from CapabilityHypothesis and the YAML; confidence is COMPUTED from the separate
reviewed observation stream. Pure, mypy --strict clean. Persistence/aggregation/calibration are 59b/c/d.
Non-runtime -> no deploy. 12 tests pass, check-loc 0.
51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
"""Smart Onboarding Advisor — the onboarding runtime step (orchestration over existing engines).
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Turns (company + products + certifications + target) into inferred assumptions, the next best questions
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(<=5, each self-explaining), the capability delta, top measures, evidence requests and completeness —
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with NO sales interpretation and NO regulation picking. Orchestrator only: no new engine/registry/
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meta-model; certificate->capability hypotheses and target requirements are INJECTED.
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"""
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from __future__ import annotations
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from .engine import advisor_start, apply_answer
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from .hypotheses import (
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CapabilityHypothesis,
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inferred_hypotheses,
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resolve_for_certifications,
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)
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from .observations import (
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Observation,
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ObservationType,
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empirical_confidence,
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empirical_distribution,
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reviewed,
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)
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from .schemas import (
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AdvisorMeasure,
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AdvisorQuestion,
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AdvisorResult,
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InferredAssumption,
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OnboardingInput,
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RejectedAssumption,
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)
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__all__ = [
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"advisor_start",
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"apply_answer",
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"OnboardingInput",
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"AdvisorResult",
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"AdvisorQuestion",
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"AdvisorMeasure",
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"InferredAssumption",
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"RejectedAssumption",
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"CapabilityHypothesis",
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"inferred_hypotheses",
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"resolve_for_certifications",
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"Observation",
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"ObservationType",
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"empirical_distribution",
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"empirical_confidence",
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"reviewed",
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]
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