dd86174107
Cognition pipeline (cognition.py): rate-limited, 8 sources, dedup, perm-run. Regime expansion (regime_expansion.py): 200+ regimes from 4x4x4x4 dimensions. News sources (news_sources.py): 12 industry-standard sources with ranking. Monitor (monitor.py): metrics, health scoring, alerts, JSONL logging. Cognition launcher (cognition_launcher.py): standalone long-run service. Continuous pipeline (continuous_pipeline.py): forever-loop training runner.
125 lines
3.7 KiB
Python
125 lines
3.7 KiB
Python
"""
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Cognition Monitor — track pipeline health, metrics, and regime discovery.
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Provides:
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- Real-time metrics (fetch rate, error rate, regime count)
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- Health scoring (source reliability, freshness)
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- Alert thresholds
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- JSONL logging for audit trail
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"""
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from __future__ import annotations
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import json
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import time
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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@dataclass(frozen=True, slots=True)
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class CognitionMetrics:
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"""Snapshot of pipeline metrics."""
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timestamp_ns: int
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total_sources: int
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enabled_sources: int
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total_fetched: int
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total_errors: float
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discovered_regimes: int
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fetch_rate_per_min: float
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error_rate: float
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uptime_s: float
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class CognitionMonitor:
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"""
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Monitor cognition pipeline health and metrics.
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Tracks:
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- Fetch rate and error rate
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- Source health scores
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- Regime discovery rate
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- Uptime and availability
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"""
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def __init__(self, log_path: str = "cognition_metrics.jsonl") -> None:
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self._log_path = log_path
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self._start_time = time.time()
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self._total_fetched = 0
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self._total_errors = 0
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self._regime_count = 0
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self._source_health: Dict[str, float] = {}
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self._metrics_history: list[CognitionMetrics] = []
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def record_fetch(self, source_id: str, success: bool) -> None:
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if success:
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self._total_fetched += 1
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else:
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self._total_errors += 1
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def record_regime(self, count: int) -> None:
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self._regime_count += count
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def snapshot(
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self,
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total_sources: int,
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enabled_sources: int,
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) -> CognitionMetrics:
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"""Take a metrics snapshot."""
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elapsed = time.time() - self._start_time
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fetch_rate = self._total_fetched / max(elapsed / 60, 1)
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error_rate = self._total_errors / max(self._total_fetched + self._total_errors, 1)
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metrics = CognitionMetrics(
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timestamp_ns=time.time_ns(),
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total_sources=total_sources,
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enabled_sources=enabled_sources,
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total_fetched=self._total_fetched,
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total_errors=self._total_errors,
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discovered_regimes=self._regime_count,
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fetch_rate_per_min=fetch_rate,
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error_rate=error_rate,
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uptime_s=elapsed,
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)
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self._metrics_history.append(metrics)
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# Write to JSONL
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try:
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with open(self._log_path, "a") as f:
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f.write(json.dumps({
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"ts": metrics.timestamp_ns,
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"sources": metrics.total_sources,
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"fetched": metrics.total_fetched,
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"regimes": metrics.discovered_regimes,
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"fetch_rate": round(metrics.fetch_rate_per_min, 2),
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"error_rate": round(metrics.error_rate, 4),
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"uptime": round(metrics.uptime_s, 1),
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}, separators=(",", ":")) + "\n")
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except OSError:
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pass
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return metrics
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def check_alerts(self) -> List[str]:
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"""Check for conditions that need attention."""
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alerts = []
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if not self._metrics_history:
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return alerts
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latest = self._metrics_history[-1]
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if latest.error_rate > 0.1:
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alerts.append(f"HIGH_ERROR_RATE: {latest.error_rate:.1%}")
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if latest.fetch_rate_per_min < 1.0 and latest.uptime_s > 300:
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alerts.append(f"LOW_FETCH_RATE: {latest.fetch_rate_per_min:.1f}/min")
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return alerts
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@property
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def total_fetched(self) -> int:
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return self._total_fetched
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@property
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def total_errors(self) -> int:
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return self._total_errors
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@property
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def discovered_regimes(self) -> int:
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return self._regime_count
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