File size: 8,261 Bytes
8f05ad1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 | """
Performance Monitoring System
Monitors and tracks model performance metrics.
"""
from typing import Dict, List, Optional, Any
from datetime import datetime, timedelta
from collections import defaultdict
import json
from pathlib import Path
class PerformanceMetric:
"""Represents a single performance metric."""
def __init__(
self,
metric_type: str,
value: float,
unit: str = "",
metadata: Optional[Dict[str, Any]] = None,
):
self.metric_type = metric_type
self.value = value
self.unit = unit
self.metadata = metadata or {}
self.timestamp = datetime.now()
def to_dict(self) -> Dict[str, Any]:
return {
"metric_type": self.metric_type,
"value": self.value,
"unit": self.unit,
"metadata": self.metadata,
"timestamp": self.timestamp.isoformat(),
}
class PerformanceMonitor:
"""Monitors model performance over time."""
def __init__(
self,
storage_path: str = "data/performance",
):
"""
Initialize the performance monitor.
Args:
storage_path: Path to store performance data
"""
self.storage_path = Path(storage_path)
self.storage_path.mkdir(parents=True, exist_ok=True)
self.metrics: List[PerformanceMetric] = []
self._session_stats: Dict[str, Any] = {
"total_sessions": 0,
"total_messages": 0,
"total_conversations": 0,
}
def record_metric(
self,
metric_type: str,
value: float,
unit: str = "",
metadata: Optional[Dict[str, Any]] = None,
) -> None:
"""Record a performance metric."""
metric = PerformanceMetric(metric_type, value, unit, metadata)
self.metrics.append(metric)
def record_response_time(self, seconds: float) -> None:
"""Record response time."""
self.record_metric("response_time", seconds, "seconds")
def record_token_count(self, prompt_tokens: int, completion_tokens: int) -> None:
"""Record token count."""
self.record_metric(
"prompt_tokens",
prompt_tokens,
"tokens",
{"completion_tokens": completion_tokens},
)
def record_successful_interaction(self) -> None:
"""Record a successful interaction."""
self.record_metric("successful_interaction", 1, "count")
def record_failed_interaction(self, error_type: str) -> None:
"""Record a failed interaction."""
self.record_metric(
"failed_interaction",
1,
"count",
{"error_type": error_type},
)
def record_user_rating(self, rating: int) -> None:
"""Record user rating."""
self.record_metric("user_rating", rating, "stars")
def get_metrics(
self,
metric_type: Optional[str] = None,
since: Optional[datetime] = None,
limit: int = 100,
) -> List[PerformanceMetric]:
"""Get recorded metrics."""
results = self.metrics
if metric_type:
results = [m for m in results if m.metric_type == metric_type]
if since:
results = [m for m in results if m.timestamp >= since]
return results[-limit:]
def get_average_response_time(
self,
since: Optional[datetime] = None,
) -> float:
"""Get average response time."""
metrics = self.get_metrics("response_time", since=since)
if not metrics:
return 0.0
return sum(m.value for m in metrics) / len(metrics)
def get_success_rate(
self,
since: Optional[datetime] = None,
) -> float:
"""Get interaction success rate."""
successful = len(self.get_metrics("successful_interaction", since=since))
failed = len(self.get_metrics("failed_interaction", since=since))
total = successful + failed
if total == 0:
return 0.0
return successful / total
def get_average_rating(
self,
since: Optional[datetime] = None,
) -> float:
"""Get average user rating."""
ratings = self.get_metrics("user_rating", since=since)
if not ratings:
return 0.0
return sum(m.value for m in ratings) / len(ratings)
def get_summary(
self,
since: Optional[datetime] = None,
) -> Dict[str, Any]:
"""Get performance summary."""
since = since or (datetime.now() - timedelta(hours=24))
return {
"period": "last_24_hours" if since == datetime.now() - timedelta(hours=24) else "custom",
"average_response_time": self.get_average_response_time(since),
"success_rate": self.get_success_rate(since),
"average_rating": self.get_average_rating(since),
"total_interactions": len(self.get_metrics("successful_interaction", since=since)) +
len(self.get_metrics("failed_interaction", since=since)),
"total_tokens": sum(
m.value for m in self.get_metrics("prompt_tokens", since=since)
),
}
def increment_session_count(self) -> None:
"""Increment session count."""
self._session_stats["total_sessions"] += 1
def increment_message_count(self) -> None:
"""Increment message count."""
self._session_stats["total_messages"] += 1
def get_session_stats(self) -> Dict[str, Any]:
"""Get session statistics."""
return self._session_stats.copy()
def export_metrics(
self,
filepath: Optional[str] = None,
) -> str:
"""Export metrics to JSON file."""
filepath = filepath or str(self.storage_path / f"metrics_{datetime.now().strftime('%Y%m%d')}.json")
data = {
"exported_at": datetime.now().isoformat(),
"metrics": [m.to_dict() for m in self.metrics],
"session_stats": self._session_stats,
}
Path(filepath).write_text(json.dumps(data, indent=2))
return filepath
def load_metrics(
self,
filepath: str,
) -> None:
"""Load metrics from JSON file."""
data = json.loads(Path(filepath).read_text())
for metric_data in data.get("metrics", []):
metric = PerformanceMetric(
metric_type=metric_data["metric_type"],
value=metric_data["value"],
unit=metric_data.get("unit", ""),
metadata=metric_data.get("metadata", {}),
)
metric.timestamp = datetime.fromisoformat(metric_data["timestamp"])
self.metrics.append(metric)
if "session_stats" in data:
self._session_stats.update(data["session_stats"])
def clear_old_metrics(self, days: int = 30) -> int:
"""Clear metrics older than specified days."""
cutoff = datetime.now() - timedelta(days=days)
original_count = len(self.metrics)
self.metrics = [
m for m in self.metrics
if m.timestamp > cutoff
]
return original_count - len(self.metrics)
def get_trend(
self,
metric_type: str,
hours: int = 24,
) -> List[Dict[str, Any]]:
"""Get trend data for a metric."""
since = datetime.now() - timedelta(hours=hours)
metrics = self.get_metrics(metric_type, since=since)
# Group by hour
hourly_data: Dict[str, List[float]] = defaultdict(list)
for m in metrics:
hour_key = m.timestamp.strftime("%Y-%m-%d %H:00")
hourly_data[hour_key].append(m.value)
# Calculate hourly averages
trend = []
for hour, values in sorted(hourly_data.items()):
avg = sum(values) / len(values) if values else 0
trend.append({
"hour": hour,
"average": avg,
"count": len(values),
})
return trend
def __repr__(self) -> str:
return f"PerformanceMonitor(metrics={len(self.metrics)}, sessions={self._session_stats['total_sessions']})" |