Benjamin Admin
97575cc9c0
Kanonisches Compliance-Datenmodell, Impressum-Agent als Referenz: - CheckStatus-Enum + Finding.status GETRENNT von severity (Verdikt ≠ Risiko) - Unbestimmte Rechtsform (weder Text noch Wizard) → INSUFFICIENT_EVIDENCE (INFO) statt hartem HIGH-FAIL; legal_form_dependent-Gate + detect_legal_form_present - §18-MStV-Graubereich (Corporate-Blog via has_editorial_content) → POSSIBLY_APPLICABLE (LOW Prüf-Hinweis); 3-stufig via scope_disposition - Recommendations nur aus echten FAILs; mc_insufficient/mc_possibly-Aggregate - Frontend: Verdikt-Pill + Coverage-Vokabular - 19 neue Tests (test_four_status.py, AgentFindingCard); CI-Suite 204 grün, v3 25 / GT 13 unverändert Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
110 lines
4.2 KiB
Python
110 lines
4.2 KiB
Python
"""Run registered v3 specialist agents and surface their structured
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AgentOutput per topic for the standardized result tabs.
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Additive to the legacy B-wiring HTML (`_b18_wiring`): this does NOT
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replace it — it puts a clean, typed `AgentOutput` into
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`state["agent_outputs"][topic]`, which `_phase_f_persist` forwards into
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the API result so the frontend can render a per-topic tab.
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Phase 1 ships only impressum; the topic map extends to cookie / vendor /
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… as those agents get wired (same contract, no code change here beyond
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the map). Once the tabs are the source of truth, B18's v1 path retires.
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"""
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from __future__ import annotations
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import logging
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from compliance.services.specialist_agents import REGISTRY, AgentInput
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from compliance.services.specialist_agents.impressum._classification import (
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scan_context_to_scope,
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)
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from ._sse import emit
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logger = logging.getLogger(__name__)
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# topic key (matches state["doc_texts"]) -> registered agent_id
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_TOPIC_AGENTS: dict[str, str] = {
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"impressum": "impressum",
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}
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_MIN_TEXT = 100
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def _derive_scope(profile_dict: dict) -> list[str]:
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"""Business-scope aus dem erkannten Profil — identisch zu B18, damit
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der Tab denselben Scope sieht wie die bestehende Auswertung. Das
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Rechtsform-Gate kommt in einer späteren Phase (eigene Klassifizierung)."""
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scope: set[str] = set()
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if profile_dict.get("has_online_shop"):
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scope.add("ecommerce")
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if profile_dict.get("is_regulated_profession"):
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scope.add("regulated_profession")
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if profile_dict.get("industry") in ("insurance", "Finance", "finance"):
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scope.add("insurance")
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# §18 MStV — 3-stufig: Medienunternehmen (Verlag/Presse) = harte Pflicht;
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# nur Blog/News-Inhalte (has_editorial_content) = Graubereich → der Agent
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# wertet 'editorial_possible' als POSSIBLY_APPLICABLE (Pruef-Hinweis).
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if profile_dict.get("industry") == "media":
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scope.add("editorial")
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elif profile_dict.get("has_editorial_content"):
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scope.add("editorial_possible")
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return sorted(scope)
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async def run_agent_outputs(state: dict) -> None:
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"""Für jedes Topic mit registriertem v3-Agent + ausreichend Text:
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Agent laufen lassen, AgentOutput ablegen + als SSE topic-Event
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emittieren (Tab füllt sich progressiv)."""
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check_id = state.get("check_id", "")
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doc_texts = state.get("doc_texts") or {}
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profile_dict = state.get("profile_dict") or {}
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req = state.get("req")
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company_name = (
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(getattr(req, "company_name", None) or "")
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or (state.get("extracted_profile") or {}).get("company_name", "")
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or state.get("site_name", "")
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)
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origin_domain = (
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getattr(req, "origin_domain", None) or ""
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) or state.get("domain", "")
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# Phase 3: die 8 Wizard-Felder (scan_context) sind der primäre
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# Scope-Treiber; das LLM-Profil ergänzt nur (v.a. regulated_profession,
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# das die 8 Felder nicht ausdrücken können).
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scan_context = getattr(req, "scan_context", None)
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scope = sorted(
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set(scan_context_to_scope(scan_context))
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| set(_derive_scope(profile_dict))
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)
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outputs: dict[str, dict] = state.get("agent_outputs") or {}
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for topic, agent_id in _TOPIC_AGENTS.items():
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text = (doc_texts.get(topic) or "").strip()
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if len(text) < _MIN_TEXT:
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continue
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agent = REGISTRY.get(agent_id)
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if agent is None:
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logger.warning("agent_outputs: agent '%s' not registered", agent_id)
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continue
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try:
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out = await agent.evaluate(AgentInput(
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doc_type=topic,
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text=text,
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business_scope=scope,
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company_name=company_name,
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origin_domain=origin_domain,
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))
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outputs[topic] = out.model_dump(mode="json")
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emit(check_id, {"type": "topic", "topic": topic,
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"output": outputs[topic]})
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logger.info(
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"agent_outputs[%s]: %d findings, confidence %.2f",
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topic, len(out.findings), out.confidence,
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)
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except Exception as e: # noqa: BLE001 — best-effort, never break the run
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logger.warning("agent_outputs[%s] failed: %s", topic, e)
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if outputs:
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state["agent_outputs"] = outputs
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