Files
breakpilot-compliance/backend-compliance/compliance/onboarding/__init__.py
T
Benjamin Admin 3ba90f49cf feat: Smart Onboarding Advisor — make the knowledge usable in onboarding (ADR-012)
The user-named "right next runtime step": stop building knowledge, start using it automatically in
onboarding — no sales training, no regulation picking. compliance/onboarding/ is an ORCHESTRATOR (not
a new engine) wiring Company 2A -> RS-005 -> optimization -> completeness:

  advisor_start(input, cert_hypotheses, target_requirements, ...) -> AdvisorResult

From (company + products + certifications + target) it returns inferred_assumptions, rejected_
assumptions, next_best_questions (<=5, ranked by information_gain + leverage + unknown_high_risk +
evidence_missing, each self-explaining), capability_delta, top_measures, evidence_requests,
unsupported_domains, completeness_summary. apply_answer() updates the profile (delta shrinks).

Welt-1 throughout: certificates REDUCE questions but satisfy nothing automatically (verification_
required); relevance(evidence,target) keeps ISO 14001 out of the CRA result. Certificate->capability
hypotheses + target requirements are INJECTED (curated knowledge, outsourced; not in code).

All 7 acceptance criteria pass; mypy --strict clean. First app-caller wiring the engines into a
product flow — still no endpoint/persistence, so 0 runtime effect -> no deploy yet (deploys when
POST /onboarding/advisor-start + frontend are wired). check-loc 0.
2026-06-28 12:45:49 +02:00

31 lines
932 B
Python

"""Smart Onboarding Advisor — the onboarding runtime step (orchestration over existing engines).
Turns (company + products + certifications + target) into inferred assumptions, the next best questions
(<=5, each self-explaining), the capability delta, top measures, evidence requests and completeness —
with NO sales interpretation and NO regulation picking. Orchestrator only: no new engine/registry/
meta-model; certificate->capability hypotheses and target requirements are INJECTED.
"""
from __future__ import annotations
from .engine import advisor_start, apply_answer
from .schemas import (
AdvisorMeasure,
AdvisorQuestion,
AdvisorResult,
InferredAssumption,
OnboardingInput,
RejectedAssumption,
)
__all__ = [
"advisor_start",
"apply_answer",
"OnboardingInput",
"AdvisorResult",
"AdvisorQuestion",
"AdvisorMeasure",
"InferredAssumption",
"RejectedAssumption",
]