Initial commit
This commit is contained in:
Zhongwei Li
233
commands/ctx-dashboard.py
Executable file
233
commands/ctx-dashboard.py
Executable file
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#!/usr/bin/env -S uv run --script
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# /// script
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# requires-python = ">=3.10"
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# dependencies = []
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# ///
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"""
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Contextune Observability Dashboard
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Beautiful, real-time dashboard showing:
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- Detection statistics
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- Performance metrics (P50/P95/P99)
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- Matcher efficiency
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- Recent errors
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- System health
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"""
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import sys
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from pathlib import Path
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# Add lib directory to path
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PLUGIN_ROOT = Path(__file__).parent.parent
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sys.path.insert(0, str(PLUGIN_ROOT / "lib"))
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from observability_db import ObservabilityDB
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import json
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from datetime import datetime
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def format_duration(seconds: float) -> str:
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"""Format duration in human-readable format."""
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if seconds < 60:
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return f"{seconds:.0f}s ago"
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elif seconds < 3600:
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return f"{seconds/60:.0f}m ago"
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elif seconds < 86400:
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return f"{seconds/3600:.1f}h ago"
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else:
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return f"{seconds/86400:.1f}d ago"
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def render_dashboard():
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"""Render comprehensive observability dashboard."""
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db = ObservabilityDB(".contextune/observability.db")
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stats = db.get_stats()
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print("=" * 70)
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print("🎯 CONTEXTUNE OBSERVABILITY DASHBOARD".center(70))
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print("=" * 70)
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print()
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# === DETECTION STATISTICS ===
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det_stats = stats["detections"]
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print("📊 DETECTION STATISTICS")
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print("-" * 70)
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print(f" Total Detections: {det_stats['total']}")
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print()
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if det_stats["by_method"]:
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print(" By Detection Method:")
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for method, count in sorted(det_stats["by_method"].items(), key=lambda x: x[1], reverse=True):
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pct = (count / det_stats['total'] * 100) if det_stats['total'] > 0 else 0
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bar = "█" * int(pct / 5)
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print(f" {method:15s} {count:4d} ({pct:5.1f}%) {bar}")
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print()
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if det_stats["by_command"]:
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print(" Top Commands:")
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for cmd, count in list(det_stats["by_command"].items())[:5]:
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pct = (count / det_stats['total'] * 100) if det_stats['total'] > 0 else 0
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print(f" {cmd:20s} {count:4d} ({pct:5.1f}%)")
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print()
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# === MATCHER PERFORMANCE ===
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matcher_stats = stats["matchers"]
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if matcher_stats:
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print("⚡ MATCHER PERFORMANCE")
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print("-" * 70)
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print(f" {'Method':<15s} {'Avg Latency':>12s} {'Success Rate':>12s}")
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print(f" {'-'*15} {'-'*12} {'-'*12}")
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# Sort by latency
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for method in ["keyword", "model2vec", "semantic"]:
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if method in matcher_stats:
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m = matcher_stats[method]
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latency = m["avg_latency_ms"]
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success = m["success_rate"]
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# Color code latency
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if latency < 1:
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latency_str = f"✓ {latency:.3f}ms"
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elif latency < 10:
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latency_str = f"→ {latency:.2f}ms"
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else:
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latency_str = f"⚠ {latency:.1f}ms"
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print(f" {method:<15s} {latency_str:>12s} {success:>11.1f}%")
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print()
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# === PERFORMANCE METRICS ===
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perf_stats = stats["performance"]
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if perf_stats:
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print("📈 SYSTEM PERFORMANCE")
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print("-" * 70)
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print(f" {'Component':<20s} {'P50':>8s} {'P95':>8s} {'P99':>8s} {'Count':>8s}")
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print(f" {'-'*20} {'-'*8} {'-'*8} {'-'*8} {'-'*8}")
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for component, metrics in perf_stats.items():
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p50 = metrics["p50"]
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p95 = metrics["p95"]
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p99 = metrics["p99"]
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count = metrics["count"]
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print(f" {component:<20s} {p50:>7.2f}ms {p95:>7.2f}ms {p99:>7.2f}ms {count:>8d}")
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print()
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# === RECENT DETECTIONS ===
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recent = db.get_recent_detections(5)
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if recent:
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print("🔍 RECENT DETECTIONS (Last 5)")
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print("-" * 70)
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for d in recent:
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timestamp = datetime.fromtimestamp(d["timestamp"])
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time_ago = format_duration(datetime.now().timestamp() - d["timestamp"])
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prompt = d.get("prompt_preview", "")[:40]
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latency = d.get("latency_ms", 0)
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print(f" {timestamp.strftime('%H:%M:%S')} ({time_ago})")
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print(f" → {d['command']} ({d['confidence']*100:.0f}% {d['method']}, {latency:.3f}ms)")
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if prompt:
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print(f" Prompt: \"{prompt}\"")
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print()
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# === ERROR TRACKING ===
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error_stats = stats["errors"]
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if error_stats["total"] > 0:
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print("❌ ERROR TRACKING")
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print("-" * 70)
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print(f" Total Errors: {error_stats['total']}")
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print()
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if error_stats["by_component"]:
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print(" By Component:")
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for component, count in sorted(error_stats["by_component"].items(), key=lambda x: x[1], reverse=True):
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print(f" {component:20s} {count:4d}")
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print()
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# Recent errors
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recent_errors = db.get_error_summary(24)
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if recent_errors:
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print(" Recent Errors (Last 24h):")
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for err in recent_errors[:3]:
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timestamp = datetime.fromtimestamp(err["timestamp"])
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time_ago = format_duration(datetime.now().timestamp() - err["timestamp"])
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print(f" [{timestamp.strftime('%H:%M:%S')}] {err['component']}")
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print(f" {err['error_type']}: {err['message']}")
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print(f" ({time_ago})")
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print()
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# === SYSTEM HEALTH ===
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print("🏥 SYSTEM HEALTH")
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print("-" * 70)
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# Calculate health score
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health_score = 100
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# Deduct for errors
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if error_stats["total"] > 0:
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error_penalty = min(30, error_stats["total"] * 5)
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health_score -= error_penalty
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# Deduct for slow performance
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if perf_stats:
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for component, metrics in perf_stats.items():
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if metrics["p95"] > 100: # > 100ms is slow
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health_score -= 10
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# Health indicator
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if health_score >= 90:
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health_icon = "✅"
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health_status = "Excellent"
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elif health_score >= 70:
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health_icon = "✓"
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health_status = "Good"
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elif health_score >= 50:
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health_icon = "⚠"
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health_status = "Fair"
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else:
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health_icon = "❌"
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health_status = "Poor"
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print(f" Overall Health: {health_icon} {health_score}/100 ({health_status})")
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print()
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# === RECOMMENDATIONS ===
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recommendations = []
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if matcher_stats.get("semantic", {}).get("success_rate", 0) < 50:
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recommendations.append("⚠ Semantic router has low success rate - check Cohere API key")
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if error_stats["total"] > 10:
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recommendations.append("⚠ High error count - review error logs")
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if perf_stats.get("hook", {}).get("p95", 0) > 50:
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recommendations.append("⚠ Hook P95 latency >50ms - may impact UX")
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if det_stats["total"] < 10:
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recommendations.append("💡 Try natural language queries like 'research best React libraries'")
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if recommendations:
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print("💡 RECOMMENDATIONS")
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print("-" * 70)
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for rec in recommendations:
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print(f" {rec}")
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print()
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print("=" * 70)
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print()
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print("💡 Commands:")
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print(" /ctx:help Full command reference")
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print(" /ctx:research Fast parallel research")
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print(" /ctx:plan Create parallel development plan")
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print()
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if __name__ == "__main__":
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try:
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render_dashboard()
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except FileNotFoundError:
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print("⚠ No observability data yet. Use Contextune first to collect metrics!")
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sys.exit(0)
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except Exception as e:
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print(f"❌ Error rendering dashboard: {e}", file=sys.stderr)
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sys.exit(1)
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