Chris Christiansen
9fcb9c354a
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99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""
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ml/telemetry.py — Strukturert telemetri-innsamling for OPAX-agenter.
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Formål:
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Loggfører hvert agent-kall til JSONL-filer.
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Disse filene er rådata for Fase ML-2 (inkrementell læring)
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og Fase ML-3 (XGBoost feature importance).
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Felter per record:
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ts — ISO 8601 UTC tidsstempel
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agent — agent-ID (f.eks. "billing-agent")
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model — LLM-modell brukt (f.eks. "gemini-2.0-flash")
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mode — "standard" eller "heavy"
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duration_s — latens i sekunder
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success — bool
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error — feilmelding eller None
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input_tokens — proxy: len(str(input))
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output_tokens — proxy: len(str(output))
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"""
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import json
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import os
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict, Optional
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# Skriv til Cloud Run-vennlig sti — overstyr med TELEMETRY_DIR env var
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_DEFAULT_DIR = os.environ.get("TELEMETRY_DIR", "/tmp/vauco_telemetry")
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def log_agent_call(
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agent_id: str,
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input_payload: Dict[str, Any],
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output: Any,
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model_used: str,
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mode: str,
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duration_s: float,
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success: bool,
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error: Optional[str] = None,
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telemetry_dir: Optional[str] = None,
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) -> None:
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"""
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Logg ett agent-kall til JSONL-fil (én fil per dato).
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Filnavnformat: YYYY-MM-DD.jsonl
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Trygt for parallell skriving (én linje per kall, atomisk append).
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"""
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base = Path(telemetry_dir or _DEFAULT_DIR)
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base.mkdir(parents=True, exist_ok=True)
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record = {
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"ts": datetime.now(timezone.utc).isoformat(),
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"agent": agent_id,
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"model": model_used,
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"mode": mode,
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"duration_s": round(duration_s, 3),
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"success": success,
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"error": error,
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"input_tokens": len(str(input_payload)),
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"output_tokens": len(str(output)),
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}
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log_file = base / f"{datetime.now(timezone.utc).strftime('%Y-%m-%d')}.jsonl"
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with open(log_file, "a", encoding="utf-8") as fh:
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fh.write(json.dumps(record, ensure_ascii=False) + "\n")
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def log_dag_execution(
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dag_id: str,
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agent_results: list,
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total_duration_s: float,
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telemetry_dir: Optional[str] = None,
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) -> None:
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"""
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Logg ett DAG-kjøring (aggregert over alle agenter i DAGen).
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Brukes av execute_dag() for å gi oversikt over parallelle kjøringer.
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"""
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base = Path(telemetry_dir or _DEFAULT_DIR)
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base.mkdir(parents=True, exist_ok=True)
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record = {
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"ts": datetime.now(timezone.utc).isoformat(),
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"type": "dag_execution",
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"dag_id": dag_id,
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"agent_count": len(agent_results),
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"success_count": sum(1 for r in agent_results if r.get("success")),
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"total_duration_s": round(total_duration_s, 3),
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"agents": [
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{"agent": r.get("agent"), "duration_s": r.get("duration_s"), "success": r.get("success")}
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for r in agent_results
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],
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}
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log_file = base / f"{datetime.now(timezone.utc).strftime('%Y-%m-%d')}.jsonl"
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with open(log_file, "a", encoding="utf-8") as fh:
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fh.write(json.dumps(record, ensure_ascii=False) + "\n")
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