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af61b34 | 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 | """
Metrics counters for streaming intent tracking.
Tracks escalation frequency, gray zone hits, decision timing, and near-misses.
Thread-safe for use in concurrent request handling.
Near-miss tracking:
- Cases where escalation probability exceeded threshold but never committed
- Critical for operational monitoring and safety validation
"""
import threading
import time
import numpy as np
from dataclasses import dataclass, field
from typing import Dict, Any, List, Optional, Tuple
from collections import defaultdict
@dataclass
class MetricsCounter:
"""
Thread-safe metrics tracking for streaming intent router.
Tracks:
- Total steps processed
- Escalation count and rate
- Gray zone hits (neither escalate nor commit)
- Average steps to decision
- Intent commitment distribution
- Time-to-escalation distribution (percentiles)
- Near-miss rate and peak probabilities
"""
total_steps: int = 0
escalation_count: int = 0
gray_zone_count: int = 0
commitment_count: int = 0
commitment_by_intent: Dict[str, int] = field(default_factory=lambda: defaultdict(int))
steps_to_escalation: List[int] = field(default_factory=list)
steps_to_commitment: List[int] = field(default_factory=list)
_current_session_steps: int = 0
_session_count: int = 0
_lock: threading.Lock = field(default_factory=threading.Lock)
# Near-miss tracking
near_miss_count: int = 0
near_miss_peak_probs: List[float] = field(default_factory=list)
# Time-to-escalation in milliseconds (for distribution analysis)
escalation_latencies_ms: List[float] = field(default_factory=list)
_session_start_time_ms: float = 0.0
def record_step(self) -> None:
"""Record a processing step (thread-safe)."""
with self._lock:
self.total_steps += 1
self._current_session_steps += 1
def record_escalation(self) -> None:
"""Record an escalation event (thread-safe)."""
with self._lock:
self.escalation_count += 1
self.steps_to_escalation.append(self._current_session_steps)
# Record time-to-escalation
if self._session_start_time_ms > 0:
latency_ms = time.time() * 1000 - self._session_start_time_ms
self.escalation_latencies_ms.append(latency_ms)
def record_commitment(self, intent: str) -> None:
"""Record an intent commitment (thread-safe)."""
with self._lock:
self.commitment_count += 1
self.commitment_by_intent[intent] += 1
self.steps_to_commitment.append(self._current_session_steps)
def record_gray_zone(self) -> None:
"""Record a gray zone hit (no decision made) (thread-safe)."""
with self._lock:
self.gray_zone_count += 1
def record_near_miss(self, peak_prob: float) -> None:
"""
Record a near-miss event (thread-safe).
A near-miss is when escalation probability exceeded threshold
at some point but escalation was never triggered.
Args:
peak_prob: Peak escalation probability observed in session.
"""
with self._lock:
self.near_miss_count += 1
self.near_miss_peak_probs.append(peak_prob)
def start_session(self) -> None:
"""Start a new tracking session (thread-safe)."""
with self._lock:
self._current_session_steps = 0
self._session_count += 1
self._session_start_time_ms = time.time() * 1000
def reset(self) -> None:
"""Reset all counters (thread-safe)."""
with self._lock:
self.total_steps = 0
self.escalation_count = 0
self.gray_zone_count = 0
self.commitment_count = 0
self.commitment_by_intent = defaultdict(int)
self.steps_to_escalation = []
self.steps_to_commitment = []
self._current_session_steps = 0
self._session_count = 0
# Reset near-miss tracking
self.near_miss_count = 0
self.near_miss_peak_probs = []
# Reset time-to-escalation tracking
self.escalation_latencies_ms = []
self._session_start_time_ms = 0.0
def _compute_percentiles(self, data: List[float], percentiles: Optional[List[int]] = None) -> Dict[str, float]:
"""Compute percentiles for a list of values."""
if percentiles is None:
percentiles = [50, 90, 95, 99]
if not data:
return {f"p{p}": 0.0 for p in percentiles}
arr = np.array(data)
return {f"p{p}": float(np.percentile(arr, p)) for p in percentiles}
def get_summary(self) -> Dict[str, Any]:
"""Get metrics summary (thread-safe)."""
with self._lock:
total_decisions = self.escalation_count + self.commitment_count
avg_steps_to_escalation = (
sum(self.steps_to_escalation) / len(self.steps_to_escalation)
if self.steps_to_escalation else 0.0
)
avg_steps_to_commitment = (
sum(self.steps_to_commitment) / len(self.steps_to_commitment)
if self.steps_to_commitment else 0.0
)
# Time-to-escalation distribution
escalation_latency_distribution = self._compute_percentiles(
self.escalation_latencies_ms
)
# Near-miss statistics
near_miss_rate = (
self.near_miss_count / self._session_count
if self._session_count > 0 else 0.0
)
avg_near_miss_peak = (
sum(self.near_miss_peak_probs) / len(self.near_miss_peak_probs)
if self.near_miss_peak_probs else 0.0
)
return {
"total_steps": self.total_steps,
"total_sessions": self._session_count,
"escalation_count": self.escalation_count,
"escalation_rate": (
self.escalation_count / total_decisions if total_decisions > 0 else 0.0
),
"commitment_count": self.commitment_count,
"commitment_by_intent": dict(self.commitment_by_intent),
"gray_zone_count": self.gray_zone_count,
"gray_zone_rate": (
self.gray_zone_count / self.total_steps if self.total_steps > 0 else 0.0
),
"avg_steps_to_escalation": avg_steps_to_escalation,
"avg_steps_to_commitment": avg_steps_to_commitment,
"avg_steps_to_decision": (
(sum(self.steps_to_escalation) + sum(self.steps_to_commitment)) /
max(1, len(self.steps_to_escalation) + len(self.steps_to_commitment))
),
# Time-to-escalation distribution (ms)
"escalation_latency_ms": escalation_latency_distribution,
# Near-miss metrics
"near_miss_count": self.near_miss_count,
"near_miss_rate": near_miss_rate,
"near_miss_avg_peak_prob": avg_near_miss_peak,
}
def __repr__(self) -> str:
summary = self.get_summary()
return (
f"MetricsCounter("
f"steps={summary['total_steps']}, "
f"escalations={summary['escalation_count']}, "
f"commitments={summary['commitment_count']}, "
f"gray_zone={summary['gray_zone_count']})"
)
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