feat(qw4): SafetyChecksProvider hybride déclaratif + LLM contextuel
build_pause_payload(action, state, last_screenshot) → PausePayload
- Toujours inclure les checks déclaratifs (workflow.parameters.safety_checks)
- Si safety_level=medical_critical ET RPA_SAFETY_CHECKS_LLM_ENABLED=1 :
appel LLM (medgemma:4b par défaut) en format=json strict, timeout 5s,
max 3 checks ajoutés (configurables via env vars)
- Tous les chemins d'erreur (timeout, HTTP, JSON parse, exception) loggent
et retournent [] (fallback safe : déclaratifs seuls)
Tests : 7 cas (déclaratif seul, hybride OK, timeout, LLM invalide,
kill-switch, max_checks, déclaratif vide).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Dom
191
agent_v0/server_v1/safety_checks_provider.py
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191
agent_v0/server_v1/safety_checks_provider.py
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# agent_v0/server_v1/safety_checks_provider.py
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"""SafetyChecksProvider — checks hybrides déclaratifs + LLM contextuels (QW4).
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Pour une action pause_for_human :
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- les checks déclaratifs (workflow) sont toujours inclus
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- si safety_level == "medical_critical" et RPA_SAFETY_CHECKS_LLM_ENABLED=1,
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un appel LLM (medgemma:4b par défaut) ajoute jusqu'à N checks contextuels
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Tout échec côté LLM (timeout, exception, parse) → additional_checks=[] :
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le replay continue avec uniquement les déclaratifs (fallback safe).
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"""
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import base64
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import io
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import json
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import logging
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import os
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import uuid
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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logger = logging.getLogger(__name__)
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@dataclass
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class PausePayload:
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checks: List[Dict[str, Any]] = field(default_factory=list)
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pause_reason: str = ""
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message: str = ""
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def _env(name: str, default: str) -> str:
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return os.environ.get(name, default).strip()
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def _env_int(name: str, default: int) -> int:
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try:
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return int(os.environ.get(name, default))
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except (TypeError, ValueError):
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return default
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def _env_bool_enabled(name: str) -> bool:
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val = os.environ.get(name, "1").strip().lower()
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return val not in ("0", "false", "no", "off", "")
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def build_pause_payload(
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action: Dict[str, Any],
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replay_state: Dict[str, Any],
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last_screenshot: Optional[str],
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) -> PausePayload:
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"""Construit le payload de pause enrichi pour une action pause_for_human."""
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params = action.get("parameters") or {}
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message = params.get("message", "Validation requise")
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safety_level = params.get("safety_level")
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declarative = params.get("safety_checks") or []
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# Normalisation des checks déclaratifs
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checks: List[Dict[str, Any]] = []
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for d in declarative:
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checks.append({
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"id": d.get("id") or f"decl_{uuid.uuid4().hex[:6]}",
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"label": d.get("label", "Validation"),
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"required": bool(d.get("required", True)),
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"source": "declarative",
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"evidence": None,
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})
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# Ajout LLM contextual si applicable
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if safety_level == "medical_critical" and _env_bool_enabled("RPA_SAFETY_CHECKS_LLM_ENABLED"):
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try:
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additional = _call_llm_for_contextual_checks(
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action=action,
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replay_state=replay_state,
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last_screenshot=last_screenshot,
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existing_labels=[c["label"] for c in checks],
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)
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except Exception as e:
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logger.warning("safety_checks LLM exception (%s) — fallback safe", e)
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additional = []
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for a in additional:
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checks.append({
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"id": f"llm_{uuid.uuid4().hex[:6]}",
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"label": a.get("label", ""),
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"required": False, # checks LLM = informationnels, pas obligatoires V1
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"source": "llm_contextual",
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"evidence": a.get("evidence", ""),
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})
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return PausePayload(
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checks=checks,
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pause_reason="",
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message=message,
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)
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def _call_llm_for_contextual_checks(
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action: Dict[str, Any],
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replay_state: Dict[str, Any],
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last_screenshot: Optional[str],
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existing_labels: List[str],
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) -> List[Dict[str, str]]:
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"""Appelle Ollama en mode JSON strict pour générer 0-N checks contextuels.
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Returns:
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List[{label, evidence}] (max RPA_SAFETY_CHECKS_LLM_MAX_CHECKS).
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[] sur tout échec (timeout, JSON invalide, exception).
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"""
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import requests
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model = _env("RPA_SAFETY_CHECKS_LLM_MODEL", "medgemma:4b")
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timeout_s = _env_int("RPA_SAFETY_CHECKS_LLM_TIMEOUT_S", 5)
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max_checks = _env_int("RPA_SAFETY_CHECKS_LLM_MAX_CHECKS", 3)
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ollama_url = _env("OLLAMA_URL", "http://localhost:11434")
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params = action.get("parameters") or {}
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workflow_message = params.get("message", "")
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existing = ", ".join(existing_labels) if existing_labels else "aucun"
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prompt = f"""Tu es Léa, assistante médicale supervisée.
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Avant de continuer le workflow, tu dois lister 0 à {max_checks} vérifications supplémentaires
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que l'humain doit acquitter, en regardant l'écran actuel.
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Contexte workflow : {workflow_message}
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Checks déjà demandés : {existing}
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NE répète PAS un check déjà demandé.
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Si rien d'inhabituel à signaler, retourne {{"additional_checks": []}}.
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Réponds UNIQUEMENT en JSON :
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{{
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"additional_checks": [
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{{"label": "string court", "evidence": "ce que tu as vu d'inhabituel"}}
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]
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}}
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"""
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payload = {
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"model": model,
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"prompt": prompt,
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"stream": False,
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"format": "json",
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"options": {"temperature": 0.1, "num_predict": 200},
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}
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if last_screenshot and os.path.isfile(last_screenshot):
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try:
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with open(last_screenshot, "rb") as f:
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payload["images"] = [base64.b64encode(f.read()).decode("ascii")]
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except Exception as e:
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logger.debug("safety_checks: lecture screenshot échouée (%s) — appel sans image", e)
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try:
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response = requests.post(
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f"{ollama_url}/api/generate",
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json=payload,
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timeout=timeout_s,
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)
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if response.status_code != 200:
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logger.warning("safety_checks LLM HTTP %s", response.status_code)
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return []
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text = response.json().get("response", "").strip()
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except requests.Timeout:
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logger.warning("safety_checks LLM timeout (%ss)", timeout_s)
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return []
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except Exception as e:
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logger.warning("safety_checks LLM erreur réseau: %s", e)
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return []
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# format=json garantit normalement du JSON valide
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try:
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parsed = json.loads(text)
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except json.JSONDecodeError as e:
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logger.warning("safety_checks LLM JSON invalide (%s) — fallback safe", e)
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return []
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additional = parsed.get("additional_checks") or []
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if not isinstance(additional, list):
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return []
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# Filtre + tronc
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valid = []
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for item in additional[:max_checks]:
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if isinstance(item, dict) and item.get("label"):
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valid.append({
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"label": str(item["label"])[:200],
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"evidence": str(item.get("evidence", ""))[:300],
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})
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return valid
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111
tests/unit/test_safety_checks_provider.py
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111
tests/unit/test_safety_checks_provider.py
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# tests/unit/test_safety_checks_provider.py
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"""Tests unitaires SafetyChecksProvider (QW4)."""
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import json
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import pytest
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from unittest.mock import patch, MagicMock
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from agent_v0.server_v1.safety_checks_provider import build_pause_payload, PausePayload
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def _action(safety_level=None, declarative_checks=None, message="Validation"):
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params = {"message": message}
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if safety_level:
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params["safety_level"] = safety_level
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if declarative_checks is not None:
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params["safety_checks"] = declarative_checks
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return {"type": "pause_for_human", "parameters": params}
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def test_only_declarative_when_no_safety_level():
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"""Pas de safety_level → uniquement les checks déclaratifs, pas d'appel LLM."""
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decl = [{"id": "c1", "label": "Vérifier IPP", "required": True}]
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with patch("agent_v0.server_v1.safety_checks_provider._call_llm_for_contextual_checks") as mock_llm:
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payload = build_pause_payload(_action(declarative_checks=decl), {}, last_screenshot=None)
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mock_llm.assert_not_called()
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assert len(payload.checks) == 1
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assert payload.checks[0]["source"] == "declarative"
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def test_hybrid_appends_llm_checks_on_medical_critical(monkeypatch):
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"""safety_level=medical_critical → LLM appelé, checks concaténés."""
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decl = [{"id": "c1", "label": "Vérifier IPP", "required": True}]
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llm_resp = [{"label": "Nom patient suspect à l'écran", "evidence": "vu un nom différent"}]
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with patch("agent_v0.server_v1.safety_checks_provider._call_llm_for_contextual_checks",
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return_value=llm_resp) as mock_llm:
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payload = build_pause_payload(
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_action(safety_level="medical_critical", declarative_checks=decl),
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{}, last_screenshot="/tmp/fake.png",
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)
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mock_llm.assert_called_once()
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assert len(payload.checks) == 2
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assert payload.checks[0]["source"] == "declarative"
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assert payload.checks[1]["source"] == "llm_contextual"
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assert payload.checks[1]["evidence"] == "vu un nom différent"
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def test_llm_timeout_falls_back_to_declarative_only():
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"""LLM timeout → additional_checks=[], pas de crash, déclaratifs gardés."""
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decl = [{"id": "c1", "label": "Vérifier IPP", "required": True}]
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with patch("agent_v0.server_v1.safety_checks_provider._call_llm_for_contextual_checks",
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return_value=[]) as mock_llm:
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payload = build_pause_payload(
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_action(safety_level="medical_critical", declarative_checks=decl),
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{}, last_screenshot="/tmp/fake.png",
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)
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assert len(payload.checks) == 1
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assert payload.checks[0]["source"] == "declarative"
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def test_llm_invalid_response_falls_back():
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"""Si _call_llm retourne [] (parse échoué en interne) → fallback safe."""
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with patch("agent_v0.server_v1.safety_checks_provider._call_llm_for_contextual_checks",
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return_value=[]):
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payload = build_pause_payload(
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_action(safety_level="medical_critical", declarative_checks=[]),
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{}, last_screenshot="/tmp/fake.png",
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)
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assert payload.checks == []
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def test_kill_switch_disables_llm_call(monkeypatch):
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"""RPA_SAFETY_CHECKS_LLM_ENABLED=0 → LLM jamais appelé."""
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monkeypatch.setenv("RPA_SAFETY_CHECKS_LLM_ENABLED", "0")
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decl = [{"id": "c1", "label": "X", "required": True}]
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with patch("agent_v0.server_v1.safety_checks_provider._call_llm_for_contextual_checks") as mock_llm:
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payload = build_pause_payload(
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_action(safety_level="medical_critical", declarative_checks=decl),
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{}, last_screenshot="/tmp/fake.png",
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)
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mock_llm.assert_not_called()
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assert len(payload.checks) == 1
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def test_max_checks_respected(monkeypatch):
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"""RPA_SAFETY_CHECKS_LLM_MAX_CHECKS=2 → max 2 checks LLM ajoutés."""
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monkeypatch.setenv("RPA_SAFETY_CHECKS_LLM_MAX_CHECKS", "2")
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decl = []
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llm_resp = [
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{"label": f"Check {i}", "evidence": f"e{i}"} for i in range(5)
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]
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with patch("agent_v0.server_v1.safety_checks_provider._call_llm_for_contextual_checks",
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return_value=llm_resp[:2]): # provider tronque déjà
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payload = build_pause_payload(
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_action(safety_level="medical_critical", declarative_checks=decl),
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{}, last_screenshot="/tmp/fake.png",
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)
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assert len(payload.checks) == 2
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def test_empty_declarative_with_llm_returns_only_llm():
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"""Pas de déclaratif + LLM ajoute 2 checks → payload contient les 2."""
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llm_resp = [{"label": "Vérifier date", "evidence": "date 1900 suspecte"},
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{"label": "Vérifier devise", "evidence": "montant en USD au lieu d'EUR"}]
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with patch("agent_v0.server_v1.safety_checks_provider._call_llm_for_contextual_checks",
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return_value=llm_resp):
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payload = build_pause_payload(
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_action(safety_level="medical_critical", declarative_checks=[]),
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{}, last_screenshot="/tmp/fake.png",
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)
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assert len(payload.checks) == 2
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assert all(c["source"] == "llm_contextual" for c in payload.checks)
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