Files
llm/scripts/report_quality_metrics.py

180 lines
7.0 KiB
Python

from __future__ import annotations
import argparse
import json
from collections import Counter, defaultdict
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_REPORT_DIR = ROOT / "reports" / "observability"
DEFAULT_OUTPUT_DIR = DEFAULT_REPORT_DIR / "quality"
def parse_iso_datetime(value: str | None) -> datetime | None:
if not value:
return None
text = str(value).strip()
if not text:
return None
if text.endswith("Z"):
text = text[:-1] + "+00:00"
try:
dt = datetime.fromisoformat(text)
except ValueError:
return None
if dt.tzinfo is None:
return dt.replace(tzinfo=timezone.utc)
return dt.astimezone(timezone.utc)
def iter_records(report_dir: Path) -> list[dict[str, Any]]:
records: list[dict[str, Any]] = []
if not report_dir.exists():
return records
for path in sorted(report_dir.rglob("*.jsonl")):
with path.open("r", encoding="utf-8") as handle:
for line_number, line in enumerate(handle, start=1):
line = line.strip()
if not line:
continue
try:
record = json.loads(line)
except json.JSONDecodeError as exc:
raise ValueError(f"{path}:{line_number}: invalid JSONL: {exc}") from exc
if isinstance(record, dict):
records.append(record)
return records
def filter_recent(records: list[dict[str, Any]], *, days: int | None) -> list[dict[str, Any]]:
if not days or days <= 0:
return records
threshold = datetime.now(timezone.utc) - timedelta(days=days)
result = []
for record in records:
dt = parse_iso_datetime(record.get("timestamp") or record.get("logged_at"))
if dt is None or dt >= threshold:
result.append(record)
return result
def week_bucket(dt: datetime | None) -> str:
if dt is None:
return "unknown"
iso_year, iso_week, _ = dt.isocalendar()
return f"{iso_year}-W{iso_week:02d}"
def top_counter(counter: Counter[str], *, limit: int) -> list[dict[str, Any]]:
return [{"key": key, "count": count} for key, count in counter.most_common(limit)]
def summarize(records: list[dict[str, Any]], *, limit: int = 10) -> dict[str, Any]:
weekly_turns: dict[str, dict[str, int]] = defaultdict(lambda: {"turns": 0, "success": 0, "partial": 0, "failure": 0})
failure_types = Counter()
repeated_failed_prompts = Counter()
model_breakdown: dict[str, dict[str, Any]] = defaultdict(lambda: {"calls": 0, "_latencies": [], "avg_latency_ms": None})
tool_breakdown: dict[str, dict[str, Any]] = defaultdict(lambda: {"calls": 0, "_durations": [], "avg_duration_ms": None})
rag_breakdown: dict[str, dict[str, int]] = defaultdict(lambda: {"retrievals": 0, "sources": 0})
for record in records:
event_type = str(record.get("event_type") or "")
if event_type == "turn_audit":
dt = parse_iso_datetime(record.get("timestamp"))
week = week_bucket(dt)
outcome = str(record.get("outcome") or "")
weekly_turns[week]["turns"] += 1
if outcome in {"success", "partial", "failure"}:
weekly_turns[week][outcome] += 1
failure_type = str(record.get("failure_type") or "")
if failure_type and failure_type != "none":
failure_types[failure_type] += 1
prompt = " ".join(str(record.get("user_text") or "").split())
if prompt:
repeated_failed_prompts[prompt] += 1
elif event_type == "model_calls":
key = f"{record.get('route_name') or ''} :: {record.get('served_model_name') or ''}"
model_breakdown[key]["calls"] += 1
latency = record.get("latency_ms")
if isinstance(latency, int | float):
model_breakdown[key]["_latencies"].append(float(latency))
elif event_type == "tool_calls":
key = str(record.get("tool_name") or "")
tool_breakdown[key]["calls"] += 1
duration = record.get("duration_ms")
if isinstance(duration, int | float):
tool_breakdown[key]["_durations"].append(float(duration))
elif event_type == "retrieval_events":
key = f"{record.get('plugin') or ''} :: {record.get('profile') or ''}"
rag_breakdown[key]["retrievals"] += 1
sources = record.get("sources_json") or []
if isinstance(sources, list):
rag_breakdown[key]["sources"] += len(sources)
model_rows = []
for key, item in sorted(model_breakdown.items()):
latencies = item.pop("_latencies")
item["key"] = key
item["avg_latency_ms"] = round(sum(latencies) / len(latencies)) if latencies else None
model_rows.append(item)
tool_rows = []
for key, item in sorted(tool_breakdown.items()):
durations = item.pop("_durations")
item["key"] = key
item["avg_duration_ms"] = round(sum(durations) / len(durations)) if durations else None
tool_rows.append(item)
rag_rows = []
for key, item in sorted(rag_breakdown.items()):
sources = item["sources"]
retrievals = item["retrievals"]
rag_rows.append(
{
"key": key,
"retrievals": retrievals,
"avg_sources": round(sources / retrievals, 2) if retrievals else None,
}
)
weekly_rows = [{"week": week, **stats} for week, stats in sorted(weekly_turns.items())]
return {
"records": len(records),
"weekly_turns": weekly_rows,
"top_failure_types": top_counter(failure_types, limit=limit),
"top_repeated_failed_prompts": top_counter(repeated_failed_prompts, limit=limit),
"model_breakdown": model_rows[:limit],
"tool_breakdown": tool_rows[:limit],
"rag_breakdown": rag_rows[:limit],
}
def write_report(path: Path, payload: dict[str, Any]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
def main() -> int:
parser = argparse.ArgumentParser(description="Build a quality report from observability logs.")
parser.add_argument("--report-dir", type=Path, default=DEFAULT_REPORT_DIR)
parser.add_argument("--days", type=int, default=7, help="Include only the latest N days. Use 0 to include all records.")
parser.add_argument("--limit", type=int, default=10, help="Top-N rows per section.")
parser.add_argument("--output", type=Path, help="Optional JSON output path.")
args = parser.parse_args()
records = iter_records(args.report_dir)
filtered = filter_recent(records, days=args.days)
payload = summarize(filtered, limit=args.limit)
print(json.dumps(payload, ensure_ascii=False, indent=2))
if args.output:
write_report(args.output, payload)
return 0
if __name__ == "__main__":
raise SystemExit(main())