Anton Palgunov
f936968c5c
- agent/session_stats.py: SessionDB context/compression metrics - agent/skill_stats.py: curator usage.json reader + prune history - agent/system_health.py: gateway uptime, version, cron activity - agent/stats_dashboard.py: Telegram-friendly bullet renderer - cli.py: /stats dispatch + _handle_stats_command method - gateway/run.py: /stats dispatch + _handle_stats_command for messaging platforms - hermes_cli/commands.py: /stats CommandDef registration
77 lines
3.0 KiB
Python
77 lines
3.0 KiB
Python
"""Renderer for the Telegram-friendly /stats dashboard."""
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from __future__ import annotations
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from typing import Any
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from agent.session_stats import collect_context_stats, collect_semantic_rle_stats
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from agent.skill_stats import collect_curator_prunes, collect_skill_stats
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from agent.system_health import collect_system_health
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def _fmt_int(value: Any) -> str:
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try:
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return f"{int(value):,}"
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except (TypeError, ValueError):
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return "0"
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def _fmt_pct(value: Any) -> str:
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if value is None:
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return "unknown"
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try:
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return f"{float(value):.1f}%"
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except (TypeError, ValueError):
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return "unknown"
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def _fallback_text(chain: list[dict]) -> str:
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if not chain:
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return "none"
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return " → ".join(
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f"{item.get('model') or '?'} ({item.get('provider') or '?'})"
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for item in chain
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)
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def format_stats_dashboard(*, agent: Any = None, session_db: Any = None, session_id: str | None = None, started_at: Any = None, start_monotonic: float | None = None) -> str:
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context = collect_context_stats(agent=agent, session_db=session_db, session_id=session_id)
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rle = collect_semantic_rle_stats(session_db=session_db)
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skills = collect_skill_stats(limit=5)
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prunes = collect_curator_prunes(days=7, limit=3)
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health = collect_system_health(started_at=started_at, start_monotonic=start_monotonic)
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cron = health.get("cron") or {}
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lines = [
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"📊 Hermes stats",
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"",
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f"• Model: {context['model']} ({context['provider']})",
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f"• Fallback: {_fallback_text(context.get('fallback_chain') or [])}",
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f"• Context: {_fmt_int(context.get('total_tokens'))}/{_fmt_int(context.get('context_length'))} tokens ({_fmt_pct(context.get('usage_percent'))})",
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f"• Semantic RLE: {rle.get('sessions_compressed', 0)} sessions · ratio {_fmt_pct((rle.get('compression_ratio') or 0) * 100 if rle.get('compression_ratio') is not None else None)} · avg saved {_fmt_int(rle.get('avg_tokens_saved'))} tokens",
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"",
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"• Top skills:",
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]
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top = skills.get("top_skills") or []
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if top:
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for row in top[:5]:
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lines.append(f" - {row['name']}: {row['activity_count']} activity ({row['use_count']} use / {row['view_count']} view / {row['patch_count']} patch)")
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else:
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lines.append(" - no skill usage telemetry yet")
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lines.append("• Gardener prunes (7d):")
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recent_prunes = prunes.get("recent_prunes") or []
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if recent_prunes:
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for row in recent_prunes[:3]:
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stamp = str(row.get("archived_at") or "unknown").split("T", 1)[0]
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lines.append(f" - {row.get('name')}: {stamp}")
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else:
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lines.append(" - none")
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lines.extend([
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f"• Nightly/cron 24h: {cron.get('runs', 0)} runs · {cron.get('ok', 0)} ok · {cron.get('error', 0)} errors · {cron.get('health_checks', 0)} health checks",
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f"• Uptime/version: {health.get('uptime')} · v{health.get('version')} · pid {health.get('pid')}",
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])
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return "\n".join(lines)
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