07e392913f
Phase A1. The real knowledge production is not writing — it is TARGETED UPDATING: when 20 documents arrive, which 5 change our knowledge and which 15 are ignorable? Before the parser, Knowledge Intake classifies a new document (no content extraction) and intersects its signals with an index of the existing knowledge to emit a Knowledge Package (an impact analysis). - compliance/knowledge_intake/: build_knowledge_index(patterns, playbooks, reference_scenarios, obligation_index) + assess_document_impact(descriptor, index) -> KnowledgePackage. Deterministic, NO content extraction, NO LLM. Surfaces affected capabilities / playbooks / transition patterns / reference scenarios / (injected) obligations, whether it is a new domain, and a triage level (HIGH / LOW / NONE / NEW_DOMAIN) with a recommendation. - ADR-006: Knowledge Intake = classify + impact before extraction; full factory Intake -> Package -> Parser -> Draft -> Review -> Published; phase order A1 Intake / A2 Draft / A3 Review. - reference suite: "Knowledge Intake" section triages 3 example documents (CRA SBOM-FAQ -> high, 14C/2PB/3RTS/2Obl; environmental guidance -> new_domain; marketing blog -> ignorable). Section lives in _helpers.py to keep generate.py under the 500-LOC budget. - Honest known refinement surfaced by intake: regulation-ID normalization (CRA vs Cyber Resilience Act). 10 intake tests (60 with the adjacent modules), mypy --strict clean (16 files), check-loc 0. Product code with no app caller + ADR/reference = non-runtime -> no deploy (ADR-001). Freeze-safe. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
107 lines
4.6 KiB
Python
107 lines
4.6 KiB
Python
# ruff: noqa
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# mypy: ignore-errors
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"""Rendering helpers for the Reference Scenario Suite generator.
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Holds the shared mutable output buffers (OUT, ROLLUP) and the small markdown helpers so the
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generator script (`generate.py`) stays under the LOC budget. Not product code; not imported by
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the app — only by the generator (run via `PYTHONPATH=. python3 reference_scenarios/generate.py`).
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"""
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from __future__ import annotations
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from typing import List, Tuple
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Row = Tuple[str, str, str]
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OUT: List[str] = []
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ROLLUP: List[str] = []
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def w(s: str = "") -> None:
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OUT.append(s)
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def coverage_table(rows: List[Row]) -> None:
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w("**Architecture Coverage**")
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w("")
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w("| Layer | Status | Hinweis |")
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w("|---|---|---|")
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for layer, status, note in rows:
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w("| %s | **%s** | %s |" % (layer, status, note))
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ROLLUP.append(status)
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w("")
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def reg_map_block(rmap) -> None:
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w("**Expected Regulatory Map**")
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w("")
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w("> " + rmap.executive_summary)
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w("")
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for v in rmap.applicable_regulations:
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obs = ", ".join(o.obligation_id for o in v.obligations) or v.obligations_note
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w("- **%s** (%s) — Pflichten: %s" % (v.regulation_id, v.name, obs))
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for u in rmap.uncertain_regulations:
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w("- _unsicher_ %s — fehlt: %s" % (u.regulation_id, ", ".join(u.missing_facts) or "-"))
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for ov in rmap.overlaps:
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w("- Overlap %s: %s" % (ov.overlap_group_id, ", ".join(ov.shared_obligations)))
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for ev, ids in rmap.shared_evidence.items():
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w("- 1 Nachweis `%s` => %d Pflichten" % (ev, len(ids)))
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w("")
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def unsupported_block(rmap) -> None:
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w("**Expected Unsupported Domains**")
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w("")
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if not rmap.unsupported_domains:
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w("- keine — alle getriggerten Domaenen sind im Korpus")
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for d in rmap.unsupported_domains:
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w("- `%s` (Trigger: %s) -> %s" % (d.domain, d.trigger, d.note))
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w("")
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def interp_status(verdict_value: str) -> str:
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return "PARTIAL" if verdict_value in ("uncertain", "unsupported") else "PASS"
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def knowledge_intake_section(base_dir) -> None:
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"""Render the Knowledge Intake section (kept here so generate.py stays under the LOC budget)."""
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import os
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import yaml
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from compliance.knowledge_intake import (
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DocumentDescriptor, assess_document_impact, build_knowledge_index,
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)
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def _load(sub):
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d = os.path.join(base_dir, "..", "knowledge", sub)
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return [yaml.safe_load(open(os.path.join(d, f), encoding="utf-8"))
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for f in sorted(os.listdir(d)) if f.endswith(".yaml")]
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idx = build_knowledge_index(
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_load("transition_patterns"), _load("implementation_playbooks"),
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_load("reference_transition_scenarios"), obligation_index={"CRA": ["cra_obl_1", "cra_obl_2"]})
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docs = [
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DocumentDescriptor(document_id="ENISA CRA SBOM-FAQ", regulations=["CRA"], keywords=["sbom", "vulnerability"], document_type="faq"),
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DocumentDescriptor(document_id="EU Umwelt-Leitfaden", regulations=["UmweltVO"], keywords=["wastewater"], document_type="guidance"),
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DocumentDescriptor(document_id="Marketing-Blog", keywords=["newsletter"], document_type="blog"),
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]
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w("## Knowledge Intake — Impact zuerst, Extraktion später")
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w("")
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w('_Vor dem Parser: ein neues Dokument NUR einordnen und seinen Impact auf den bestehenden Wissensbestand bestimmen. „Von N Dokumenten verändern wenige tatsächlich unser Wissen." Deterministisch, keine Extraktion, kein LLM._')
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w("")
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w("| Dokument | Impact | betrifft | Empfehlung |")
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w("|---|---|---|---|")
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for d in docs:
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kp = assess_document_impact(d, idx)
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touch = "neue Domäne" if kp.new_domain else "%dC·%dPB·%dRTS·%dObl" % (
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len(kp.affected_capabilities), len(kp.affected_playbooks),
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len(kp.affected_reference_scenarios), len(kp.affected_obligations))
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w("| %s | **%s** | %s | %s |" % (d.document_id, kp.impact_level.value, touch, kp.recommendation.split(" —")[0]))
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w("")
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w("**Beispiel-Knowledge-Package** (`%s`): %s" % (docs[0].document_id, assess_document_impact(docs[0], idx).impact_summary))
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w("")
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w('_So entsteht bei jedem neuen Dokument eine Impact-Analyse statt „200 Seiten PDF" — Targeted Updating statt Schreiben._')
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w("")
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coverage_table([
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("Knowledge Intake (Klassifikation+Impact)", "PASS", "%d Regelwerke / %d Capabilities im Index" % (len(idx.regulations), len(idx.capability_regulations))),
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("Impact-Triage (HIGH/LOW/NONE/new_domain)", "PASS", "3 Beispiel-Dokumente korrekt eingeordnet"),
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("Regelwerk-ID-Normalisierung", "TODO", "CRA vs Cyber Resilience Act vereinheitlichen"),
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])
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