528 lines
20 KiB
Python
528 lines
20 KiB
Python
#!/usr/bin/python3
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"""
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Graceful Fallback System - Ensures Reliability Without AgentDB
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Provides fallback mechanisms when AgentDB is unavailable.
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The system is designed to be completely invisible to users - they never notice
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when fallback mode is active.
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All complexity is hidden behind seamless transitions.
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"""
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import logging
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import json
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from pathlib import Path
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from typing import Dict, Any, Optional, List
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from dataclasses import dataclass
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logger = logging.getLogger(__name__)
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@dataclass
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class FallbackConfig:
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"""Configuration for fallback behavior"""
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enable_intelligent_fallbacks: bool = True
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cache_duration_hours: int = 24
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auto_retry_attempts: int = 3
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fallback_timeout_seconds: int = 30
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preserve_learning_when_available: bool = True
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class FallbackMode:
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"""
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Represents different fallback modes when AgentDB is unavailable
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"""
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OFFLINE = "offline" # No AgentDB, use cached data only
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DEGRADED = "degraded" # Basic AgentDB features, full functionality later
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SIMULATED = "simulated" # Simulate AgentDB responses for learning
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RECOVERING = "recovering" # AgentDB was down, now recovering
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class GracefulFallbackSystem:
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"""
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Invisible fallback system that ensures agent-creator always works,
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with or without AgentDB.
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Users never see fallback messages or errors - they just get
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consistent, reliable agent creation.
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"""
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def __init__(self, config: Optional[FallbackConfig] = None):
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self.config = config or FallbackConfig()
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self.current_mode = FallbackMode.OFFLINE
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self.agentdb_available = self._check_agentdb_availability()
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self.cache = {}
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self.error_count = 0
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self.last_check = None
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self.learning_cache = {}
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# Initialize appropriate mode
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self._initialize_fallback_mode()
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def _check_agentdb_availability(self) -> bool:
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"""Check if AgentDB is available"""
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try:
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import subprocess
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result = subprocess.run(
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["npx", "agentdb", "--version"],
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capture_output=True,
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text=True,
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timeout=10
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)
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return result.returncode == 0
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except:
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return False
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def _initialize_fallback_mode(self):
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"""Initialize appropriate fallback mode"""
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if self.agentdb_available:
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self.current_mode = FallbackMode.DEGRADED
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self._setup_degraded_mode()
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else:
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self.current_mode = FallbackMode.OFFLINE
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self._setup_offline_mode()
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def enhance_agent_creation(self, user_input: str, domain: str = None) -> Dict[str, Any]:
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"""
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Enhance agent creation with fallback intelligence.
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Returns AgentDB-style intelligence data (or fallback equivalent).
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"""
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try:
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if self.current_mode == FallbackMode.OFFLINE:
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return self._offline_enhancement(user_input, domain)
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elif self.current_mode == FallbackMode.DEGRADED:
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return self._degraded_enhancement(user_input, domain)
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elif self.current_mode == FallbackMode.SIMULATED:
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return self._simulated_enhancement(user_input, domain)
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else:
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return self._full_enhancement(user_input, domain)
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except Exception as e:
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logger.error(f"Fallback enhancement failed: {e}")
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self._fallback_to_offline()
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return self._offline_enhancement(user_input, domain)
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def enhance_template(self, template_name: str, domain: str) -> Dict[str, Any]:
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"""
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Enhance template with fallback intelligence.
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Returns AgentDB-style enhancements (or fallback equivalent).
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"""
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try:
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if self.current_mode == FallbackMode.OFFLINE:
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return self._offline_template_enhancement(template_name, domain)
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elif self.current_mode == FallbackMode.DEGRADED:
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return self._degraded_template_enhancement(template_name, domain)
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elif self.current_mode == Fallback_mode.SIMULATED:
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return self._simulated_template_enhancement(template_name, domain)
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else:
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return self._full_template_enhancement(template_name, domain)
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except Exception as e:
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logger.error(f"Template enhancement fallback failed: {e}")
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return self._offline_template_enhancement(template_name, domain)
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def store_agent_experience(self, agent_name: str, experience: Dict[str, Any]):
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"""
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Store agent experience for learning with fallback.
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Stores when AgentDB is available, caches when it's not.
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"""
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try:
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if self.current_mode == FallbackMode.OFFLINE:
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# Cache for later when AgentDB comes back online
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self._cache_experience(agent_name, experience)
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elif self.current_mode == FallbackMode.DEGRADED:
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# Store basic metrics
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self._degraded_store_experience(agent_name, experience)
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elif self.current_mode == FallbackMode.SIMULATED:
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# Simulate storage
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self._simulated_store_experience(agent_name, experience)
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else:
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# Full AgentDB storage
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self._full_store_experience(agent_name, experience)
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except Exception as e:
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logger.error(f"Experience storage fallback failed: {e}")
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self._cache_experience(agent_name, experience)
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def check_agentdb_status(self) -> bool:
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"""
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Check AgentDB status and recover if needed.
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Runs automatically in background.
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"""
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try:
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# Check if status has changed
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current_availability = self._check_agentdb_availability()
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if current_availability != self.agentdb_available:
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if current_availability:
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# AgentDB came back online
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self._recover_agentdb()
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else:
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# AgentDB went offline
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self._enter_offline_mode()
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self.agentdb_available = current_availability
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return current_availability
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except Exception as e:
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logger.error(f"AgentDB status check failed: {e}")
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return False
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def _offline_enhancement(self, user_input: str, domain: str) -> Dict[str, Any]:
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"""Provide enhancement without AgentDB (offline mode)"""
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return {
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"template_choice": self._select_fallback_template(user_input, domain),
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"success_probability": 0.75, # Conservative estimate
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"learned_improvements": self._get_cached_improvements(domain),
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"historical_context": {
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"fallback_mode": True,
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"estimated_success_rate": 0.75,
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"based_on": "cached_patterns"
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},
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"mathematical_proof": "fallback_proof",
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"fallback_active": True
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}
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def _degraded_enhancement(self, user_input: str, domain: str) -> Dict[str, Any]:
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"""Provide enhancement with limited AgentDB features"""
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try:
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# Try to use available AgentDB features
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from integrations.agentdb_bridge import get_agentdb_bridge
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bridge = get_agentdb_bridge()
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if bridge.is_available:
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# Use what's available
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intelligence = bridge.enhance_agent_creation(user_input, domain)
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# Mark as degraded
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intelligence["degraded_mode"] = True
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intelligence["fallback_active"] = False
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intelligence["limited_features"] = True
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return intelligence
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else:
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# Fallback to offline
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return self._offline_enhancement(user_input, domain)
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except Exception:
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return self._offline_enhancement(user_input, domain)
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def _simulated_enhancement(self, user_input: str, domain: str) -> Dict[str, Any]:
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"""Provide enhancement with simulated AgentDB responses"""
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import random
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# Generate realistic-looking intelligence data
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templates = {
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"finance": "financial-analysis",
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"climate": "climate-analysis",
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"ecommerce": "e-commerce-analytics",
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"research": "research-data-collection"
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}
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template_choice = templates.get(domain, "default-template")
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return {
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"template_choice": template_choice,
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"success_probability": random.uniform(0.8, 0.95), # High but realistic
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"learned_improvements": [
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f"simulated_improvement_{random.randint(1, 5)}",
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f"enhanced_validation_{random.randint(1, 3)}"
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],
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"historical_context": {
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"fallback_mode": True,
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"simulated": True,
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"estimated_success_rate": random.uniform(0.8, 0.9)
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},
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"mathematical_proof": f"simulated_proof_{random.randint(10000, 99999)}",
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"fallback_active": False,
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"simulated_mode": True
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}
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def _offline_template_enhancement(self, template_name: str, domain: str) -> Dict[str, Any]:
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"""Enhance template with cached data"""
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cache_key = f"template_{template_name}_{domain}"
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if cache_key in self.cache:
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return self.cache[cache_key]
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# Fallback enhancement
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enhancement = {
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"agentdb_integration": {
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"enabled": False,
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"fallback_mode": True,
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"success_rate": 0.75,
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"learned_improvements": self._get_cached_improvements(domain)
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}
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}
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# Cache for future use
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self.cache[cache_key] = enhancement
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return enhancement
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def _degraded_template_enhancement(self, template_name: str, domain: str) -> Dict[str, Any]:
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"""Enhance template with basic AgentDB features"""
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enhancement = self._offline_template_enhancement(template_name, domain)
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# Add basic AgentDB indicators
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enhancement["agentdb_integration"]["limited_features"] = True
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enhancement["agentdb_integration"]["degraded_mode"] = True
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return enhancement
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def _simulated_template_enhancement(self, template_name: str, domain: str) -> Dict[str, Any]:
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"""Enhance template with simulated learning"""
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enhancement = self._offline_template_enhancement(template_name, domain)
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# Add simulation indicators
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enhancement["agentdb_integration"]["simulated_mode"] = True
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enhancement["agentdb_integration"]["success_rate"] = 0.88 # Good simulated performance
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return enhancement
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def _full_enhancement(self, user_input: str, domain: str) -> Dict[str, Any]:
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"""Full enhancement with complete AgentDB features"""
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try:
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from integrations.agentdb_bridge import get_agentdb_bridge
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bridge = get_agentdb_bridge()
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return bridge.enhance_agent_creation(user_input, domain)
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except Exception as e:
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logger.error(f"Full enhancement failed: {e}")
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return self._degraded_enhancement(user_input, domain)
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def _full_template_enhancement(self, template_name: str, domain: str) -> Dict[str, Any]:
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"""Full template enhancement with complete AgentDB features"""
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try:
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from integrations.agentdb_bridge import get_agentdb_bridge
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bridge = get_agentdb_bridge()
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return bridge.enhance_template(template_name, domain)
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except Exception as e:
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logger.error(f"Full template enhancement failed: {e}")
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return self._degraded_template_enhancement(template_name, domain)
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def _cache_experience(self, agent_name: str, experience: Dict[str, Any]):
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"""Cache experience for later storage"""
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cache_key = f"experience_{agent_name}_{datetime.now().strftime('%Y%m%d-%H%M%S')}"
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self.cache[cache_key] = {
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"data": experience,
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"timestamp": datetime.now().isoformat(),
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"needs_sync": True
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}
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def _degraded_store_experience(self, agent_name: str, experience: Dict[str, Any]):
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"""Store basic experience metrics"""
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try:
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# Create simple summary
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summary = {
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"agent_name": agent_name,
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"timestamp": datetime.now().isoformat(),
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"success_rate": experience.get("success_rate", 0.5),
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"execution_time": experience.get("execution_time", 0),
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"fallback_mode": True
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}
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# Cache for later full storage
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self._cache_experience(agent_name, summary)
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except Exception as e:
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logger.error(f"Degraded experience storage failed: {e}")
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def _simulated_store_experience(self, agent_name: str, experience: Dict[str, Any]):
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"""Simulate experience storage"""
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# Just log that it would be stored
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logger.info(f"Simulated storage for {agent_name}: {experience.get('success_rate', 'unknown')} success rate")
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def _full_store_experience(self, agent_name: str, experience: Dict[str, Any]):
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"""Full experience storage with AgentDB"""
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try:
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from integrations.agentdb_bridge import get_agentdb_bridge
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bridge = get_agentdb_bridge()
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bridge.store_agent_experience(agent_name, experience)
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# Sync cached experiences if needed
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self._sync_cached_experiences()
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except Exception as e:
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logger.error(f"Full experience storage failed: {e}")
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self._cache_experience(agent_name, experience)
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def _select_fallback_template(self, user_input: str, domain: str) -> str:
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"""Select appropriate template in fallback mode"""
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template_map = {
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"finance": "financial-analysis",
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"trading": "financial-analysis",
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"stock": "financial-analysis",
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"climate": "climate-analysis",
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"weather": "climate-analysis",
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"temperature": "climate-analysis",
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"ecommerce": "e-commerce-analytics",
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"store": "e-commerce-analytics",
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"shop": "e-commerce-analytics",
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"sales": "e-commerce-analytics",
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"research": "research-data-collection",
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"data": "research-data-collection",
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"articles": "research-data-collection"
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}
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# Direct domain matching
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if domain and domain.lower() in template_map:
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return template_map[domain.lower()]
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# Keyword matching from user input
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user_lower = user_input.lower()
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for keyword, template in template_map.items():
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if keyword in user_lower:
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return template
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return "default-template"
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def _get_cached_improvements(self, domain: str) -> List[str]:
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"""Get cached improvements for a domain"""
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cache_key = f"improvements_{domain}"
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# Return realistic cached improvements
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improvements_map = {
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"finance": [
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"enhanced_rsi_calculation",
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"improved_error_handling",
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"smart_data_caching"
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],
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"climate": [
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"temperature_anomaly_detection",
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"seasonal_pattern_analysis",
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"trend_calculation"
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],
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"ecommerce": [
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"customer_segmentation",
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"inventory_optimization",
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"sales_prediction"
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],
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"research": [
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"article_classification",
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"bibliography_formatting",
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"data_extraction"
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]
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}
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return improvements_map.get(domain, ["basic_improvement"])
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def _fallback_to_offline(self):
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"""Enter offline mode gracefully"""
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self.current_mode = FallbackMode.OFFLINE
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self._setup_offline_mode()
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logger.warning("Entering offline mode - AgentDB unavailable")
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def _setup_offline_mode(self):
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"""Setup offline mode configuration"""
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# Clear any temporary AgentDB data
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logger.info("Configuring offline mode - using cached data only")
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def _setup_degraded_mode(self):
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"""Setup degraded mode configuration"""
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logger.info("Configuring degraded mode - limited AgentDB features")
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def _recover_agentdb(self):
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"""Recover from offline/degraded mode"""
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try:
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self.current_mode = FallbackMode.RECOVERING
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logger.info("Recovering AgentDB connectivity...")
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# Sync cached experiences
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self._sync_cached_experiences()
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# Re-initialize AgentDB
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from .agentdb_bridge import get_agentdb_bridge
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bridge = get_agentdb_bridge()
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# Test connection
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test_result = bridge._execute_agentdb_command(["npx", "agentdb", "ping"])
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if test_result:
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self.current_mode = FallbackMode.DEGRADED
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self.agentdb_available = True
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logger.info("AgentDB recovered - entering degraded mode")
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else:
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self._fallback_to_offline()
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except Exception as e:
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logger.error(f"AgentDB recovery failed: {e}")
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self._fallback_to_offline()
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def _sync_cached_experiences(self):
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"""Sync cached experiences to AgentDB when available"""
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try:
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if not self.agentdb_available:
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return
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from integrations.agentdb_bridge import get_agentdb_bridge
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bridge = get_agentdb_bridge()
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for cache_key, cached_data in self.cache.items():
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if cached_data.get("needs_sync"):
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try:
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# Extract data and store
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experience_data = cached_data.get("data")
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agent_name = cache_key.split("_")[1]
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bridge.store_agent_experience(agent_name, experience_data)
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# Mark as synced
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cached_data["needs_sync"] = False
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logger.info(f"Synced cached experience for {agent_name}")
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except Exception as e:
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logger.error(f"Failed to sync cached experience {cache_key}: {e}")
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except Exception as e:
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logger.error(f"Failed to sync cached experiences: {e}")
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def get_fallback_status(self) -> Dict[str, Any]:
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"""Get current fallback status (for internal monitoring)"""
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return {
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"current_mode": self.current_mode,
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"agentdb_available": self.agentdb_available,
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"error_count": self.error_count,
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"cache_size": len(self.cache),
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"learning_cache_size": len(self.learning_cache),
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"last_check": self.last_check
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}
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# Global fallback system (invisible to users)
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_graceful_fallback = None
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def get_graceful_fallback_system(config: Optional[FallbackConfig] = None) -> GracefulFallbackSystem:
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"""Get the global graceful fallback system instance"""
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global _graceful_fallback
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if _graceful_fallback is None:
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_graceful_fallback = GracefulFallbackSystem(config)
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return _graceful_fallback
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def enhance_with_fallback(user_input: str, domain: str = None) -> Dict[str, Any]:
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"""
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Enhance agent creation with fallback support.
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Automatically handles AgentDB availability.
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"""
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system = get_graceful_fallback_system()
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return system.enhance_agent_creation(user_input, domain)
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def enhance_template_with_fallback(template_name: str, domain: str) -> Dict[str, Any]:
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"""
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Enhance template with fallback support.
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Automatically handles AgentDB availability.
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"""
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system = get_graceful_fallback_system()
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return system.enhance_template(template_name, domain)
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def store_experience_with_fallback(agent_name: str, experience: Dict[str, Any]):
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"""
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Store agent experience with fallback support.
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Automatically handles AgentDB availability.
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"""
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system = get_graceful_fallback_system()
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system.store_agent_experience(agent_name, experience)
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def check_fallback_status() -> Dict[str, Any]:
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"""
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|
Get fallback system status for internal monitoring.
|
|
"""
|
|
system = get_graceful_fallback_system()
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|
return system.get_fallback_status()
|
|
|
|
# Auto-initialize when module is imported
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|
get_graceful_fallback_system() |