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Mock ARF components for demo purposes
In production, these would use the real agentic-reliability-framework package
"""
import time
import json
from typing import Dict, Any, List
import random
def simulate_arf_analysis(scenario: Dict[str, Any]) -> Dict[str, Any]:
"""Simulate ARF analysis pipeline"""
return {
"analysis_complete": True,
"anomaly_detected": True,
"severity": "critical",
"root_cause": scenario.get('root_cause', 'unknown'),
"pattern_detected": True,
"pattern_confidence": random.uniform(0.8, 0.95),
"analysis_timestamp": time.time(),
"processing_time_ms": random.randint(200, 500)
}
def run_rag_similarity_search(scenario: Dict[str, Any]) -> List[Dict[str, Any]]:
"""Simulate RAG similarity search"""
component = scenario.get('component', 'redis_cache')
# Mock similar incidents based on scenario
similar_incidents = []
# Generate 3-5 similar incidents
for i in range(random.randint(3, 5)):
similarity = random.uniform(0.7, 0.95)
success = similarity > 0.8
incident = {
"incident_id": f"inc_{int(time.time())}_{i}",
"component": component,
"similarity_score": similarity,
"success": success,
"resolution": "scale_out" if component == "redis_cache" else "restart",
"actions_taken": ["scale_out", "adjust_cache_ttl"] if component == "redis_cache" else ["restart_container"],
"resolution_time_minutes": random.uniform(5, 15),
"timestamp": time.time() - random.randint(86400, 2592000) # 1-30 days ago
}
if success:
incident["cost_savings"] = random.randint(1000, 10000)
similar_incidents.append(incident)
# Sort by similarity
similar_incidents.sort(key=lambda x: x['similarity_score'], reverse=True)
return similar_incidents
def calculate_pattern_confidence(scenario: Dict[str, Any], similar_incidents: List[Dict[str, Any]]) -> float:
"""Calculate pattern detection confidence"""
if not similar_incidents:
return 0.7
# Base confidence
base_confidence = 0.75
# Boost based on number of similar incidents
incident_boost = min(0.15, len(similar_incidents) * 0.03)
# Boost based on average similarity
avg_similarity = sum(i['similarity_score'] for i in similar_incidents) / len(similar_incidents)
similarity_boost = avg_similarity * 0.1
# Boost based on success rate
success_rate = sum(1 for i in similar_incidents if i['success']) / len(similar_incidents)
success_boost = success_rate * 0.1
total_confidence = base_confidence + incident_boost + similarity_boost + success_boost
return min(0.98, total_confidence)
def create_mock_healing_intent(scenario: Dict[str, Any],
similar_incidents: List[Dict[str, Any]],
confidence: float = 0.85) -> Dict[str, Any]:
"""Create a mock HealingIntent object"""
# Determine action based on scenario
component = scenario.get('component', 'redis_cache')
if component == 'redis_cache':
action = 'scale_out'
parameters = {'scale_factor': 2, 'cache_ttl': 300}
justification = "Scale Redis cluster and adjust cache TTL based on historical pattern"
elif component == 'database':
action = 'optimize_connections'
parameters = {'max_connections': 200, 'connection_timeout': 30}
justification = "Optimize database connection pool settings"
else:
action = 'restart_container'
parameters = {}
justification = "Restart container to resolve memory issues"
# Calculate RAG similarity score
rag_score = None
if similar_incidents:
rag_score = sum(i['similarity_score'] for i in similar_incidents[:3]) / min(3, len(similar_incidents))
return {
"action": action,
"component": component,
"parameters": parameters,
"justification": justification,
"confidence": confidence,
"incident_id": scenario.get('incident_id', f"inc_{int(time.time())}"),
"detected_at": time.time(),
"similar_incidents": similar_incidents,
"rag_similarity_score": rag_score,
"source": "oss_analysis",
"intent_id": f"intent_{int(time.time())}",
"created_at": time.time(),
"status": "created",
"oss_edition": "community",
"requires_enterprise": True,
"execution_allowed": False,
"deterministic_id": f"intent_{hash(json.dumps(parameters, sort_keys=True)) % 10000:04d}"
} |