Benjamin Admin
9c33582412
Not the endpoint yet — the bigger knowledge lever first. The Advisor can say "I need 5 answers" but does not yet decide what it can find out by ITSELF. The Silent Knowledge Pass runs in front of the Advisor and, from signals existing scanners/parsers already produce (website, repository, documents, product data), deterministically derives capabilities the company demonstrably HAS + product facts that drive scope — so every recognised item shrinks the delta and removes a question. compliance/onboarding/silent_intake.py: silent_intake(signals, signal_map) -> detected_capabilities (+ evidence already in hand) + product_facts. The signal->conclusion map is curated DATA (knowledge/onboarding/intake_signal_map.yaml), signals are injected (scanners are upstream). Pure, deterministic, no LLM. advisor_start gains detected_capabilities (folded into the profile at HIGH confidence -> covered, not asked) and an auto_detected result + headline. The experience flips from a question wall to "we already recognised 4 capabilities, 2 product facts and have 4 pieces of evidence in hand — only these few remain". Order now: Silent Pass -> #58 endpoint/frontend -> #59 empirical loop. NOT new architecture, just an orchestration step in front. Non-runtime (no app caller) -> no deploy. 15 onboarding tests pass, mypy --strict clean, check-loc 0.
94 lines
5.1 KiB
Python
94 lines
5.1 KiB
Python
# ruff: noqa
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# mypy: ignore-errors
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"""Smart Onboarding Advisor demo — what the frontend shows, automatically (no sales interpretation).
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The user types company + products + certifications + target. The Advisor orchestrates the existing
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engines and returns the next best questions, assumptions and measures. Sales sees only the result.
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Synthetic, no real names. Non-runtime demo of a runtime step.
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Run: cd backend-compliance && PYTHONPATH=. python3 reference_scenarios/onboarding_advisor_demo.py
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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 (
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CapabilityHypothesis, IntakeSignal, OnboardingInput, SignalMapping,
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advisor_start, resolve_for_certifications, silent_intake,
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)
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from compliance.transition_reasoning import TargetRequirement
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OUT = []
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def w(s=""):
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OUT.append(s)
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CRA = yaml.safe_load(open(os.path.join(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", [])) 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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# certificate hypotheses come from the CURATED, capability-centric library (multi-cert merges automatically)
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_lib = [CapabilityHypothesis(**h) for h in yaml.safe_load(
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open(os.path.join(os.path.dirname(__file__), "..", "knowledge", "certification_hypotheses", "hypotheses.yaml"), encoding="utf-8"))["hypotheses"]]
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inp = OnboardingInput(company="synthetisch", industry="machine_builder",
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products=["Parkschein-/Schrankensystem"], markets=["EU", "DE"],
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certifications=["ISO9001", "ISO27001", "ISO14001", "TISAX"],
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known_evidence=["CE process"], target=["CRA"])
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hyp = resolve_for_certifications(inp.certifications, _lib)
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# Phase 0 — Silent Knowledge Pass: recognise everything possible from scanner signals BEFORE asking.
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_smap = [SignalMapping(**m) for m in yaml.safe_load(
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open(os.path.join(os.path.dirname(__file__), "..", "knowledge", "onboarding", "intake_signal_map.yaml"), encoding="utf-8"))["mappings"]]
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_signals = [IntakeSignal(source="website", signal="security_txt_or_cvd_policy", detail="/.well-known/security.txt"),
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IntakeSignal(source="repository", signal="sbom_file_found", detail="sbom.cdx.json"),
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IntakeSignal(source="repository", signal="signed_releases"),
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IntakeSignal(source="document", signal="product_risk_assessment_doc"),
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IntakeSignal(source="product", signal="cloud_connectivity"),
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IntakeSignal(source="product", signal="plc_sps")]
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si = silent_intake(_signals, _smap)
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res = advisor_start(inp, hyp, req, target_id="CRA", covers_targets=covers, corpus_status={"CRA": "validated"},
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detected_capabilities=si.capability_ids())
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w("# Smart Onboarding Advisor — was der Nutzer sieht (automatisch, ohne Vertrieb)")
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w("")
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w("_Eingabe: Unternehmen + Produkte + Zertifizierungen + Ziel. Den Rest macht die Orchestrierung über die bestehenden Engines (Company 2A · RS-005 · Optimization · Completeness). Synthetisch, keine echten Namen._")
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w("")
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w("## Eingabe")
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w("> Zertifizierungen: **%s** · Produkt: **%s** · Ziel: **%s**" % (", ".join(inp.certifications), inp.products[0], ", ".join(inp.target)))
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w("")
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w("## Phase 0 — Stille Vorbefüllung (BEVOR eine Frage erscheint)")
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w("> %s" % si.summary)
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w("- **Automatisch erkannte Fähigkeiten:** %s" % ", ".join("`%s`" % d.capability for d in si.detected_capabilities))
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w("- **Produktfakten (steuern den Scope):** %s" % ", ".join("`%s=%s`" % (f.key, f.value) for f in si.product_facts))
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w("- **Nachweise bereits in der Hand (kein Upload nötig):** %s" % ", ".join(si.evidence_found))
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w("")
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w("## Was wir erkannt haben")
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w("> %s" % res.headline)
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w("")
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w("**Aus Ihren Zertifizierungen abgeleitet (zu bestätigen, nicht automatisch erfüllt):**")
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for a in res.inferred_assumptions:
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w("- %s" % a.statement)
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for r in res.rejected_assumptions:
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w("- _%s — %s_" % (r.statement, r.reason))
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w("")
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w("## Die wenigen offenen Punkte — nur die nächsten besten Fragen")
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for n, q in enumerate(res.next_best_questions, 1):
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w("**Frage %d von %d** _(Informationswert %.0f)_" % (n, len(res.next_best_questions), q.information_value))
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w("> %s? — _Warum fragen wir das: %s_" % (q.capability_id.replace("_", " "), q.why))
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w("")
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w("## Womit zuerst anfangen (größter Hebel)")
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for m in res.top_measures[:5]:
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w("- `%s` — schließt %d Anforderung(en): %s" % (m.capability_id, m.leverage, ", ".join(m.closes) or "—"))
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w("")
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w("## Vollständigkeit (ehrlich)")
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w("> %s" % res.completeness_summary)
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w("")
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w("---")
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w("_Der Vertrieb wählt KEIN Regelwerk und interpretiert nichts — er sieht nur dieses Ergebnis. Jede beantwortete Frage aktualisiert das Capability Profile und verkleinert das Delta._")
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print("\n".join(OUT))
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