Benjamin Admin
3ba90f49cf
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.
91 lines
3.8 KiB
Python
91 lines
3.8 KiB
Python
"""Smart Onboarding Advisor — acceptance tests (the 7 criteria).
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A synthetic multi-certified company (ISO 9001 + ISO 27001 + ISO 14001 + TISAX) onboards toward the CRA.
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The Advisor orchestrates the existing engines and must satisfy: multi-cert works; ISO 14001 is not
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falsely relevant; certs reduce questions but satisfy nothing automatically (Welt-1); <=5 self-explaining
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next-best questions; answers update the profile (delta shrinks); sales selects/interprets nothing.
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"""
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from __future__ import annotations
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import os
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import yaml
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from compliance.onboarding import OnboardingInput, advisor_start, apply_answer
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from compliance.transition_reasoning import TargetRequirement
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_CRA = yaml.safe_load(open(os.path.join(
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os.path.dirname(__file__), "..", "knowledge", "transition_patterns",
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"transition_pattern_iso27001_to_cra_maschinenvo_v1.yaml"), encoding="utf-8"))
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_INFOSEC = [a["capability"] for a in _CRA["likely_covered"]]
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_REQ = [TargetRequirement(capability_id=a["capability"]) for a in _CRA["likely_covered"]]
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_REQ += [TargetRequirement(capability_id=d["capability"], question_intent=d.get("needed_information", "verify_existence"),
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expected_evidence=d.get("expected_evidence", []))
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for d in _CRA["delta_requirements"]]
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_COVERS = {d["capability"]: d.get("covers_targets", []) for d in _CRA["delta_requirements"]}
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_HYP = {
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"ISO27001": _INFOSEC,
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"TISAX": _INFOSEC,
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"ISO9001": ["ce_conformity_assessment_and_technical_documentation"], # a CRA delta cap (relevant)
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"ISO14001": ["environmental_management_documentation"], # NOT in the CRA required set
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}
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_INPUT = OnboardingInput(
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company="synthetic", industry="machine_builder", products=["parking payment system"],
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markets=["EU"], certifications=["ISO9001", "ISO27001", "ISO14001", "TISAX"],
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known_evidence=["CE process"], target=["CRA"])
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def _run(inp=_INPUT, hyp=_HYP):
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return advisor_start(inp, hyp, _REQ, target_id="CRA", covers_targets=_COVERS,
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corpus_status={"CRA": "validated"})
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def test_1_multi_certification_works():
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res = _run()
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certs = {a.certification for a in res.inferred_assumptions}
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assert {"ISO27001", "ISO9001"} <= certs # several certs contribute inferred capabilities
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def test_2_iso14001_not_falsely_relevant_for_cra():
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res = _run()
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assert any(r.certification == "ISO14001" for r in res.rejected_assumptions)
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assert all(a.certification != "ISO14001" for a in res.inferred_assumptions)
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def test_3_certs_reduce_questions_but_satisfy_nothing_automatically():
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res = _run()
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for a in res.inferred_assumptions:
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assert a.verification_required is True
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assert "nicht automatisch erfüllt" in a.statement
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def test_4_at_most_five_next_best_questions():
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res = _run()
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assert 0 < len(res.next_best_questions) <= 5
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def test_5_every_question_explains_why():
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res = _run()
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assert all(q.why.strip() for q in res.next_best_questions)
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def test_6_each_answer_updates_the_profile():
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res = _run()
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open_cap = res.capability_delta[0]
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# the answer "confirmed" adds the capability; re-running shrinks the delta
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confirmed = apply_answer([], open_cap, "confirmed")
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assert confirmed == [open_cap]
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hyp2 = {**_HYP, "ANSWERED": confirmed}
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inp2 = _INPUT.model_copy(update={"certifications": _INPUT.certifications + ["ANSWERED"]})
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res2 = advisor_start(inp2, hyp2, _REQ, target_id="CRA", covers_targets=_COVERS, corpus_status={"CRA": "validated"})
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assert len(res2.capability_delta) < len(res.capability_delta)
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def test_7_sales_selects_nothing_engine_produces_everything():
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res = _run()
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# from plain inputs the engine produced the whole advisory payload
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assert res.headline and res.capability_delta and res.top_measures and res.evidence_requests
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assert res.completeness_summary
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