v1.0 - Version stable: multi-PC, détection UI-DETR-1, 3 modes exécution
- Frontend v4 accessible sur réseau local (192.168.1.40) - Ports ouverts: 3002 (frontend), 5001 (backend), 5004 (dashboard) - Ollama GPU fonctionnel - Self-healing interactif - Dashboard confiance Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Dom
180
core/learning/learning_manager.py
Normal file
180
core/learning/learning_manager.py
Normal file
@@ -0,0 +1,180 @@
|
||||
"""Learning Manager - Manages workflow learning states and transitions"""
|
||||
import logging
|
||||
from typing import Dict, Optional, List
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
from ..models.workflow_graph import LearningState, Workflow
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@dataclass
|
||||
class WorkflowStats:
|
||||
"""Statistics for a workflow"""
|
||||
workflow_id: str
|
||||
learning_state: LearningState
|
||||
observation_count: int = 0
|
||||
execution_count: int = 0
|
||||
success_count: int = 0
|
||||
failure_count: int = 0
|
||||
last_execution: Optional[datetime] = None
|
||||
confidence_scores: List[float] = field(default_factory=list)
|
||||
created_at: datetime = field(default_factory=datetime.now)
|
||||
|
||||
@property
|
||||
def success_rate(self) -> float:
|
||||
"""Calculate success rate"""
|
||||
if self.execution_count == 0:
|
||||
return 0.0
|
||||
return self.success_count / self.execution_count
|
||||
|
||||
@property
|
||||
def avg_confidence(self) -> float:
|
||||
"""Calculate average confidence"""
|
||||
if not self.confidence_scores:
|
||||
return 0.0
|
||||
return sum(self.confidence_scores) / len(self.confidence_scores)
|
||||
|
||||
class LearningManager:
|
||||
"""Manages workflow learning states and transitions"""
|
||||
|
||||
def __init__(self):
|
||||
self.workflows: Dict[str, WorkflowStats] = {}
|
||||
logger.info("LearningManager initialized")
|
||||
|
||||
def register_workflow(self, workflow: Workflow) -> None:
|
||||
"""Register a new workflow for learning"""
|
||||
wf_id = workflow.workflow_id
|
||||
if wf_id not in self.workflows:
|
||||
self.workflows[wf_id] = WorkflowStats(
|
||||
workflow_id=wf_id,
|
||||
learning_state=workflow.learning_state
|
||||
)
|
||||
logger.info(f"Registered workflow: {wf_id} (state={workflow.learning_state})")
|
||||
|
||||
def record_observation(self, workflow_id: str) -> None:
|
||||
"""Record an observation of the workflow"""
|
||||
if workflow_id not in self.workflows:
|
||||
logger.warning(f"Unknown workflow: {workflow_id}")
|
||||
return
|
||||
|
||||
stats = self.workflows[workflow_id]
|
||||
stats.observation_count += 1
|
||||
logger.debug(f"Observation recorded for {workflow_id} (count={stats.observation_count})")
|
||||
|
||||
self._check_state_transition(workflow_id)
|
||||
|
||||
def record_execution(self, workflow_id: str, success: bool, confidence: float) -> None:
|
||||
"""Record an execution result"""
|
||||
if workflow_id not in self.workflows:
|
||||
logger.warning(f"Unknown workflow: {workflow_id}")
|
||||
return
|
||||
|
||||
stats = self.workflows[workflow_id]
|
||||
stats.execution_count += 1
|
||||
stats.last_execution = datetime.now()
|
||||
stats.confidence_scores.append(confidence)
|
||||
|
||||
if success:
|
||||
stats.success_count += 1
|
||||
else:
|
||||
stats.failure_count += 1
|
||||
|
||||
logger.info(
|
||||
f"Execution recorded for {workflow_id}: "
|
||||
f"success={success}, confidence={confidence:.2f}, "
|
||||
f"success_rate={stats.success_rate:.2f}"
|
||||
)
|
||||
|
||||
self._check_state_transition(workflow_id)
|
||||
|
||||
def _check_state_transition(self, workflow_id: str) -> None:
|
||||
"""Check if workflow should transition to next learning state"""
|
||||
stats = self.workflows[workflow_id]
|
||||
current_state = stats.learning_state
|
||||
new_state = None
|
||||
reason = ""
|
||||
|
||||
if current_state == LearningState.OBSERVATION:
|
||||
if self._can_transition_to_coaching(stats):
|
||||
new_state = LearningState.COACHING
|
||||
reason = f"5+ observations ({stats.observation_count}), avg confidence > 0.90"
|
||||
|
||||
elif current_state == LearningState.COACHING:
|
||||
if self._can_transition_to_auto_candidate(stats):
|
||||
new_state = LearningState.AUTO_CANDIDATE
|
||||
reason = f"10+ assists ({stats.execution_count}), success rate > 0.90"
|
||||
|
||||
elif current_state == LearningState.AUTO_CANDIDATE:
|
||||
if self._can_transition_to_auto_confirmed(stats):
|
||||
new_state = LearningState.AUTO_CONFIRMED
|
||||
reason = f"20+ executions ({stats.execution_count}), success rate > 0.95"
|
||||
|
||||
elif current_state == LearningState.AUTO_CONFIRMED:
|
||||
if self._should_rollback(stats):
|
||||
new_state = LearningState.COACHING
|
||||
reason = f"Confidence dropped below 0.90 (avg={stats.avg_confidence:.2f})"
|
||||
|
||||
if new_state:
|
||||
self._transition_state(workflow_id, new_state, reason)
|
||||
|
||||
def _can_transition_to_coaching(self, stats: WorkflowStats) -> bool:
|
||||
"""Check if can transition from OBSERVATION to COACHING"""
|
||||
return (
|
||||
stats.observation_count >= 5 and
|
||||
stats.avg_confidence >= 0.90
|
||||
)
|
||||
|
||||
def _can_transition_to_auto_candidate(self, stats: WorkflowStats) -> bool:
|
||||
"""Check if can transition from COACHING to AUTO_CANDIDATE"""
|
||||
return (
|
||||
stats.execution_count >= 10 and
|
||||
stats.success_rate >= 0.90
|
||||
)
|
||||
|
||||
def _can_transition_to_auto_confirmed(self, stats: WorkflowStats) -> bool:
|
||||
"""Check if can transition from AUTO_CANDIDATE to AUTO_CONFIRMED"""
|
||||
return (
|
||||
stats.execution_count >= 20 and
|
||||
stats.success_rate >= 0.95
|
||||
)
|
||||
|
||||
def _should_rollback(self, stats: WorkflowStats) -> bool:
|
||||
"""Check if should rollback from AUTO_CONFIRMED to COACHING"""
|
||||
recent_scores = stats.confidence_scores[-10:] if len(stats.confidence_scores) >= 10 else stats.confidence_scores
|
||||
if not recent_scores:
|
||||
return False
|
||||
|
||||
recent_avg = sum(recent_scores) / len(recent_scores)
|
||||
return recent_avg < 0.90
|
||||
|
||||
def _transition_state(self, workflow_id: str, new_state: LearningState, reason: str) -> None:
|
||||
"""Transition workflow to new learning state"""
|
||||
stats = self.workflows[workflow_id]
|
||||
old_state = stats.learning_state
|
||||
stats.learning_state = new_state
|
||||
|
||||
logger.info(
|
||||
f"State transition for {workflow_id}: "
|
||||
f"{old_state.value} → {new_state.value} "
|
||||
f"(reason: {reason})"
|
||||
)
|
||||
|
||||
def get_workflow_state(self, workflow_id: str) -> Optional[LearningState]:
|
||||
"""Get current learning state of workflow"""
|
||||
if workflow_id in self.workflows:
|
||||
return self.workflows[workflow_id].learning_state
|
||||
return None
|
||||
|
||||
def get_workflow_stats(self, workflow_id: str) -> Optional[WorkflowStats]:
|
||||
"""Get statistics for workflow"""
|
||||
return self.workflows.get(workflow_id)
|
||||
|
||||
def should_execute_automatically(self, workflow_id: str) -> bool:
|
||||
"""Check if workflow should execute automatically"""
|
||||
state = self.get_workflow_state(workflow_id)
|
||||
return state in [LearningState.AUTO_CANDIDATE, LearningState.AUTO_CONFIRMED]
|
||||
|
||||
def should_ask_confirmation(self, workflow_id: str) -> bool:
|
||||
"""Check if should ask user confirmation before execution"""
|
||||
state = self.get_workflow_state(workflow_id)
|
||||
return state == LearningState.COACHING
|
||||
Reference in New Issue
Block a user