99041f0117
Architecture 3 niveaux implémentée et testée (137 tests unitaires + 21 visuels) : MÉSO (acteur intelligent) : - P0 Critic : vérification sémantique post-action via gemma4 (replay_verifier.py) - P1 Observer : pré-analyse écran avant chaque action (api_stream.py /pre_analyze) - P2 Grounding/Policy : séparation localisation (grounding.py) et décision (policy.py) - P3 Recovery : rollback automatique Ctrl+Z/Escape/Alt+F4 (recovery.py) - P4 Learning : apprentissage runtime avec boucle de consolidation (replay_learner.py) MACRO (planificateur) : - TaskPlanner : comprend les ordres en langage naturel via gemma4 (task_planner.py) - Contexte métier TIM/CIM-10 pour les hôpitaux (domain_context.py) - Endpoint POST /api/v1/task pour l'exécution par instruction Traçabilité : - Audit trail complet avec 18 champs par action (audit_trail.py) - Endpoints GET /audit/history, /audit/summary, /audit/export (CSV) Grounding : - Fix parsing bbox_2d qwen2.5vl (pixels relatifs, pas grille 1000x1000) - Benchmarks visuels sur captures réelles (3 approches : baseline, zoom, Citrix) - Reproductibilité validée : variance < 0.008 sur 10 itérations Sécurité : - Tokens de production retirés du code source → .env.local - Secret key aléatoire si non configuré - Suppression logs qui leakent les tokens Résultats : 80% de replay (vs 12.5% avant), 100% détection visuelle Citrix JPEG Q20 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
531 lines
21 KiB
Python
531 lines
21 KiB
Python
"""
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Tests fonctionnels pour P2 (Policy/Grounding), P3 (Recovery), P4 (Learning).
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Vérifie que chaque module fait bien son travail :
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- Grounding : localise ou retourne NOT_FOUND (pas de décision)
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- Policy : décide RETRY/SKIP/ABORT/SUPERVISE (pas de localisation)
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- Recovery : exécute Ctrl+Z / Escape / Alt+F4 selon le contexte
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- Learning : enregistre et requête les résultats structurés
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"""
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import json
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import shutil
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import sys
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import tempfile
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from pathlib import Path
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from unittest.mock import MagicMock, patch, PropertyMock
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import pytest
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_ROOT = str(Path(__file__).resolve().parents[2])
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if _ROOT not in sys.path:
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sys.path.insert(0, _ROOT)
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# =========================================================================
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# P2 : Grounding — localisation pure
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# =========================================================================
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class TestGroundingEngine:
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def _make_engine(self):
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from agent_v0.agent_v1.core.grounding import GroundingEngine
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executor = MagicMock()
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executor._capture_screenshot_b64.return_value = "fake_b64_data"
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return GroundingEngine(executor), executor
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def test_server_found_retourne_coordonnees(self):
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"""Si le serveur trouve l'élément, retourne ses coordonnées."""
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engine, executor = self._make_engine()
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executor._server_resolve_target.return_value = {
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"resolved": True, "x_pct": 0.5, "y_pct": 0.3,
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"method": "som_text", "score": 0.95,
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"matched_element": {"label": "Enregistrer"},
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}
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result = engine.locate("http://server", {"by_text": "Enregistrer"}, 0.5, 0.3, 1920, 1080)
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assert result.found is True
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assert result.x_pct == 0.5
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assert result.y_pct == 0.3
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assert result.method == "som_text"
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def test_server_not_found_cascade_template(self):
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"""Si serveur échoue, cascade vers template matching."""
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engine, executor = self._make_engine()
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executor._server_resolve_target.return_value = None
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executor._template_match_anchor.return_value = {
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"resolved": True, "x_pct": 0.4, "y_pct": 0.6,
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"score": 0.85,
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}
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result = engine.locate(
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"http://server",
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{"by_text": "OK", "anchor_image_base64": "abc123"},
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0.5, 0.3, 1920, 1080,
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)
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assert result.found is True
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assert result.method == "anchor_template"
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def test_toutes_strategies_echouent_retourne_not_found(self):
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"""Si toutes les stratégies échouent, retourne NOT_FOUND."""
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engine, executor = self._make_engine()
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executor._server_resolve_target.return_value = None
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executor._template_match_anchor.return_value = None
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executor._hybrid_vlm_resolve.return_value = None
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result = engine.locate(
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"http://server",
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{"by_text": "Inexistant", "anchor_image_base64": "abc", "vlm_description": "bouton"},
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0.5, 0.3, 1920, 1080,
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)
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assert result.found is False
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assert "échoué" in result.detail
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def test_screenshot_echoue_retourne_not_found(self):
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"""Si la capture screenshot échoue, NOT_FOUND immédiat."""
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engine, executor = self._make_engine()
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executor._capture_screenshot_b64.return_value = None
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result = engine.locate("http://server", {"by_text": "OK"}, 0.5, 0.3, 1920, 1080)
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assert result.found is False
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assert "screenshot" in result.detail.lower()
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def test_strategies_custom(self):
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"""On peut spécifier les stratégies à utiliser."""
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engine, executor = self._make_engine()
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executor._template_match_anchor.return_value = {
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"resolved": True, "x_pct": 0.2, "y_pct": 0.8, "score": 0.9,
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}
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# Seulement template, pas de serveur
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result = engine.locate(
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"", {"anchor_image_base64": "abc"}, 0.5, 0.3, 1920, 1080,
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strategies=["template"],
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)
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assert result.found is True
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# Le serveur n'a PAS été appelé
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executor._server_resolve_target.assert_not_called()
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def test_grounding_result_to_dict(self):
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"""Le GroundingResult se sérialise correctement."""
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from agent_v0.agent_v1.core.grounding import GroundingResult
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r = GroundingResult(found=True, x_pct=0.5, y_pct=0.3, method="som", score=0.9)
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d = r.to_dict()
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assert d["found"] is True
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assert d["x_pct"] == 0.5
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assert d["method"] == "som"
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# =========================================================================
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# P2 : Policy — décisions quand grounding échoue
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# =========================================================================
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class TestPolicyEngine:
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def _make_engine(self):
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from agent_v0.agent_v1.core.policy import PolicyEngine
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executor = MagicMock()
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return PolicyEngine(executor), executor
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def test_premier_essai_popup_fermee_retry(self):
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"""Premier échec + popup fermée → RETRY."""
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from agent_v0.agent_v1.core.policy import Decision
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engine, executor = self._make_engine()
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executor._handle_popup_vlm.return_value = True # Popup fermée
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decision = engine.decide(
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action={"type": "click"},
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target_spec={"by_text": "OK"},
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retry_count=0,
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)
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assert decision.decision == Decision.RETRY
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assert "popup" in decision.reason.lower()
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def test_premier_essai_pas_de_popup_retry(self):
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"""Premier échec + pas de popup → RETRY quand même (max_retries > 0)."""
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from agent_v0.agent_v1.core.policy import Decision
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engine, executor = self._make_engine()
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executor._handle_popup_vlm.return_value = False
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decision = engine.decide(
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action={"type": "click"},
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target_spec={"by_text": "OK"},
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retry_count=0,
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max_retries=2,
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)
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assert decision.decision == Decision.RETRY
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def test_max_retries_acteur_passer_skip(self):
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"""Max retries atteint + acteur dit PASSER → SKIP."""
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from agent_v0.agent_v1.core.policy import Decision
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engine, executor = self._make_engine()
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executor._actor_decide.return_value = "PASSER"
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decision = engine.decide(
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action={"type": "click"},
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target_spec={"by_text": "Onglet"},
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retry_count=1,
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max_retries=1,
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)
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assert decision.decision == Decision.SKIP
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def test_max_retries_acteur_stopper_abort(self):
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"""Max retries atteint + acteur dit STOPPER → ABORT."""
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from agent_v0.agent_v1.core.policy import Decision
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engine, executor = self._make_engine()
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executor._actor_decide.return_value = "STOPPER"
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decision = engine.decide(
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action={"type": "click"},
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target_spec={"by_text": "X"},
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retry_count=1,
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max_retries=1,
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)
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assert decision.decision == Decision.ABORT
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def test_max_retries_acteur_executer_supervise(self):
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"""Max retries + acteur dit EXECUTER → SUPERVISE (rendre la main)."""
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from agent_v0.agent_v1.core.policy import Decision
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engine, executor = self._make_engine()
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executor._actor_decide.return_value = "EXECUTER"
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decision = engine.decide(
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action={"type": "click"},
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target_spec={"by_text": "X"},
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retry_count=1,
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max_retries=1,
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)
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assert decision.decision == Decision.SUPERVISE
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def test_policy_decision_to_dict(self):
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"""PolicyDecision se sérialise correctement."""
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from agent_v0.agent_v1.core.policy import PolicyDecision, Decision
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d = PolicyDecision(decision=Decision.SKIP, reason="État atteint").to_dict()
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assert d["decision"] == "skip"
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assert d["reason"] == "État atteint"
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# =========================================================================
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# P3 : Recovery — rollback après échec
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# =========================================================================
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class TestRecoveryEngine:
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def _make_engine(self):
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from agent_v0.agent_v1.core.recovery import RecoveryEngine
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executor = MagicMock()
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executor.keyboard = MagicMock()
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executor.sct = MagicMock()
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executor.sct.monitors = [{}, {"width": 1920, "height": 1080}]
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executor._click = MagicMock()
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return RecoveryEngine(executor), executor
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def test_popup_detectee_escape(self):
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"""Critic dit "popup" → Recovery fait Escape."""
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from agent_v0.agent_v1.core.recovery import RecoveryAction
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engine, executor = self._make_engine()
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result = engine.attempt(
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failed_action={"type": "click"},
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critic_detail="Une popup d'erreur est apparue",
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)
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assert result.action_taken == RecoveryAction.ESCAPE
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assert result.success is True
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# Vérifie que Escape a été pressé
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executor.keyboard.press.assert_called()
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def test_frappe_incorrecte_undo(self):
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"""Frappe incorrecte → Recovery fait Ctrl+Z."""
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from agent_v0.agent_v1.core.recovery import RecoveryAction
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engine, executor = self._make_engine()
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result = engine.attempt(
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failed_action={"type": "type"},
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critic_detail="Le texte a été tapé au mauvais endroit",
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)
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assert result.action_taken == RecoveryAction.UNDO
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assert result.success is True
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def test_mauvaise_fenetre_close(self):
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"""Mauvaise fenêtre → Recovery fait Alt+F4."""
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from agent_v0.agent_v1.core.recovery import RecoveryAction
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engine, executor = self._make_engine()
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result = engine.attempt(
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failed_action={"type": "click"},
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critic_detail="Mauvaise fenêtre ouverte au lieu du bloc-notes",
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)
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assert result.action_taken == RecoveryAction.CLOSE_WINDOW
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assert result.success is True
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def test_menu_ouvert_escape(self):
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"""Menu déroulant ouvert → Recovery fait Escape."""
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from agent_v0.agent_v1.core.recovery import RecoveryAction
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engine, executor = self._make_engine()
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result = engine.attempt(
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failed_action={"type": "click"},
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critic_detail="Un menu déroulant s'est ouvert",
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)
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assert result.action_taken == RecoveryAction.ESCAPE
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assert result.success is True
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def test_aucune_strategie_applicable(self):
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"""Pas de pattern reconnu → NONE."""
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from agent_v0.agent_v1.core.recovery import RecoveryAction
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engine, executor = self._make_engine()
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result = engine.attempt(
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failed_action={"type": "wait"},
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critic_detail="Quelque chose d'inattendu",
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)
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assert result.action_taken == RecoveryAction.NONE
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assert result.success is False
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def test_recovery_result_to_dict(self):
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"""RecoveryResult se sérialise correctement."""
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from agent_v0.agent_v1.core.recovery import RecoveryResult, RecoveryAction
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d = RecoveryResult(
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action_taken=RecoveryAction.UNDO, success=True, detail="Ctrl+Z"
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).to_dict()
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assert d["action_taken"] == "undo"
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assert d["success"] is True
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# =========================================================================
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# P4 : Learning — apprentissage runtime
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# =========================================================================
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class TestReplayLearner:
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@pytest.fixture
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def learner(self):
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tmpdir = tempfile.mkdtemp(prefix="test_learning_")
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from agent_v0.server_v1.replay_learner import ReplayLearner
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l = ReplayLearner(learning_dir=tmpdir)
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yield l
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shutil.rmtree(tmpdir, ignore_errors=True)
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def test_record_et_load_session(self, learner):
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"""Enregistrer un résultat et le relire depuis le fichier."""
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from agent_v0.server_v1.replay_learner import ActionOutcome
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outcome = ActionOutcome(
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session_id="test_session",
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action_id="act_001",
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action_type="click",
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target_description="Bouton Enregistrer",
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resolution_method="som_text",
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resolution_score=0.95,
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success=True,
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)
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learner.record(outcome)
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# Relire
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loaded = learner.load_session("test_session")
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assert len(loaded) == 1
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assert loaded[0].action_id == "act_001"
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assert loaded[0].success is True
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assert loaded[0].resolution_method == "som_text"
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def test_record_from_replay_result(self, learner):
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"""Convertir le format replay en ActionOutcome."""
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learner.record_from_replay_result(
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session_id="s1",
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action={"action_id": "a1", "type": "click", "target_spec": {"by_text": "OK", "window_title": "App"}},
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result={"success": True, "resolution_method": "template", "resolution_score": 0.9},
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verification={"verified": True, "semantic_verified": True, "semantic_detail": "OK"},
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)
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loaded = learner.load_session("s1")
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assert len(loaded) == 1
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assert loaded[0].target_description == "OK"
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assert loaded[0].semantic_verified is True
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def test_query_similar(self, learner):
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"""Requêter des résultats similaires par description."""
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from agent_v0.server_v1.replay_learner import ActionOutcome
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# Enregistrer plusieurs résultats
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for i, (desc, method, success) in enumerate([
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("Bouton Enregistrer", "som_text", True),
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("Bouton Annuler", "template", True),
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("Bouton Enregistrer", "vlm_direct", False),
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("Menu Fichier", "som_text", True),
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]):
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learner.record(ActionOutcome(
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session_id="s1", action_id=f"a{i}",
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action_type="click", target_description=desc,
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resolution_method=method, success=success,
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))
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# Chercher "Enregistrer"
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results = learner.query_similar(target_description="Enregistrer")
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assert len(results) == 2
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# Les deux résultats concernent "Enregistrer"
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for r in results:
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assert "enregistrer" in r["outcome"]["target_description"].lower()
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def test_get_stats(self, learner):
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"""Les statistiques globales sont correctes."""
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from agent_v0.server_v1.replay_learner import ActionOutcome
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for success, method in [(True, "som"), (True, "som"), (False, "template"), (True, "vlm")]:
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learner.record(ActionOutcome(
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session_id="s1", action_id="a",
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action_type="click", success=success,
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resolution_method=method,
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))
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stats = learner.get_stats()
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assert stats["total"] == 4
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assert stats["success_rate"] == 0.75
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assert stats["methods"]["som"]["success_rate"] == 1.0
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assert stats["methods"]["template"]["success_rate"] == 0.0
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def test_gemma4_indisponible_pas_de_crash(self, learner):
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"""Le learning fonctionne même sans VLM."""
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from agent_v0.server_v1.replay_learner import ActionOutcome
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# Pas de crash, juste un record simple
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learner.record(ActionOutcome(
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session_id="s1", action_id="a1", action_type="click",
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success=False, error="target_not_found",
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))
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stats = learner.get_stats()
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assert stats["total"] == 1
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assert stats["success_rate"] == 0.0
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def test_fichier_jsonl_format(self, learner):
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"""Le fichier JSONL contient du JSON valide ligne par ligne."""
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from agent_v0.server_v1.replay_learner import ActionOutcome
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learner.record(ActionOutcome(
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session_id="s1", action_id="a1", action_type="click", success=True,
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))
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learner.record(ActionOutcome(
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session_id="s1", action_id="a2", action_type="type", success=False,
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))
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jsonl_file = learner.learning_dir / "s1.jsonl"
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assert jsonl_file.is_file()
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with open(jsonl_file) as f:
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lines = f.readlines()
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assert len(lines) == 2
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for line in lines:
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data = json.loads(line) # Doit être du JSON valide
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assert "action_id" in data
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assert "success" in data
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|
|
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# =========================================================================
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# Boucle d'apprentissage : consolidation cross-workflow
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# =========================================================================
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|
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class TestLearningLoop:
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|
"""Tests de la boucle d'apprentissage : les replays passés améliorent les suivants."""
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@pytest.fixture
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def learner(self):
|
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tmpdir = tempfile.mkdtemp(prefix="test_learning_loop_")
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from agent_v0.server_v1.replay_learner import ReplayLearner
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l = ReplayLearner(learning_dir=tmpdir)
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yield l
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shutil.rmtree(tmpdir, ignore_errors=True)
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def test_best_strategy_apprend_du_succes(self, learner):
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"""La meilleure stratégie est celle qui a le plus de succès."""
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|
from agent_v0.server_v1.replay_learner import ActionOutcome
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# template échoue 3 fois sur "Enregistrer"
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for i in range(3):
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learner.record(ActionOutcome(
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session_id=f"s{i}", action_id=f"a{i}", action_type="click",
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target_description="Enregistrer", resolution_method="anchor_template",
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success=False,
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))
|
|
# som_text réussit 2 fois sur "Enregistrer"
|
|
for i in range(2):
|
|
learner.record(ActionOutcome(
|
|
session_id=f"s{10+i}", action_id=f"a{10+i}", action_type="click",
|
|
target_description="Enregistrer", resolution_method="som_text_match",
|
|
success=True,
|
|
))
|
|
|
|
best = learner.best_strategy_for("Enregistrer")
|
|
assert best == "som_text_match"
|
|
|
|
def test_best_strategy_minimum_2_essais(self, learner):
|
|
"""Il faut au moins 2 essais pour qu'une stratégie soit recommandée."""
|
|
from agent_v0.server_v1.replay_learner import ActionOutcome
|
|
# Un seul succès → pas assez pour recommander
|
|
learner.record(ActionOutcome(
|
|
session_id="s1", action_id="a1", action_type="click",
|
|
target_description="OK", resolution_method="vlm_direct",
|
|
success=True,
|
|
))
|
|
best = learner.best_strategy_for("OK")
|
|
assert best is None
|
|
|
|
def test_best_strategy_rien_si_historique_vide(self, learner):
|
|
"""Pas d'historique → pas de recommandation."""
|
|
best = learner.best_strategy_for("Inexistant")
|
|
assert best is None
|
|
|
|
def test_consolidate_workflow_enrichit_les_actions(self, learner):
|
|
"""La consolidation injecte _learned_strategy dans les target_spec."""
|
|
from agent_v0.server_v1.replay_learner import ActionOutcome
|
|
# Historique : som_text_match marche pour "Fichier"
|
|
for i in range(3):
|
|
learner.record(ActionOutcome(
|
|
session_id=f"s{i}", action_id=f"a{i}", action_type="click",
|
|
target_description="Fichier", resolution_method="som_text_match",
|
|
success=True,
|
|
))
|
|
|
|
# Workflow avec une action "Fichier"
|
|
actions = [
|
|
{"type": "click", "target_spec": {"by_text": "Fichier", "window_title": "Bloc-notes"}},
|
|
{"type": "type", "text": "bonjour"},
|
|
{"type": "click", "target_spec": {"by_text": "Inconnu"}},
|
|
]
|
|
|
|
enriched = learner.consolidate_workflow(actions)
|
|
assert enriched == 1 # Seul "Fichier" a un historique
|
|
assert actions[0]["target_spec"]["_learned_strategy"] == "som_text_match"
|
|
assert "_learned_strategy" not in actions[2].get("target_spec", {})
|
|
|
|
def test_consolidation_cross_workflow(self, learner):
|
|
"""Un succès dans le workflow A améliore le workflow B."""
|
|
from agent_v0.server_v1.replay_learner import ActionOutcome
|
|
# Workflow A : "Enregistrer" réussit avec grounding_vlm
|
|
for i in range(3):
|
|
learner.record(ActionOutcome(
|
|
session_id="workflow_A", action_id=f"a{i}", action_type="click",
|
|
target_description="Enregistrer",
|
|
window_title="Bloc-notes",
|
|
resolution_method="grounding_vlm", success=True,
|
|
))
|
|
|
|
# Workflow B : contient aussi "Enregistrer"
|
|
workflow_b = [
|
|
{"type": "click", "target_spec": {"by_text": "Enregistrer", "window_title": "Bloc-notes"}},
|
|
]
|
|
enriched = learner.consolidate_workflow(workflow_b, "workflow_B")
|
|
assert enriched == 1
|
|
assert workflow_b[0]["target_spec"]["_learned_strategy"] == "grounding_vlm"
|
|
|
|
def test_grounding_reordonne_strategies(self):
|
|
"""Le GroundingEngine réordonne ses stratégies selon _learned_strategy."""
|
|
from agent_v0.agent_v1.core.grounding import GroundingEngine
|
|
executor = MagicMock()
|
|
executor._capture_screenshot_b64.return_value = "fake"
|
|
# Simuler que template marche
|
|
executor._server_resolve_target.return_value = None
|
|
executor._template_match_anchor.return_value = {
|
|
"resolved": True, "x_pct": 0.5, "y_pct": 0.5, "score": 0.9,
|
|
}
|
|
executor._hybrid_vlm_resolve.return_value = None
|
|
|
|
engine = GroundingEngine(executor)
|
|
|
|
# Avec _learned_strategy = anchor_template → template en premier
|
|
result = engine.locate(
|
|
"http://server",
|
|
{"by_text": "OK", "anchor_image_base64": "abc", "_learned_strategy": "anchor_template"},
|
|
0.5, 0.3, 1920, 1080,
|
|
)
|
|
assert result.found is True
|
|
assert result.method == "anchor_template"
|
|
# Le serveur n'a PAS été appelé (template était en premier)
|
|
executor._server_resolve_target.assert_not_called()
|