Reporting metrics and costs (Phase 2.4.b)

Repository report generation now captures per-run operational metrics and exposes aggregate period snapshots for operators.

Each generated repository Report now stores nullable metrics fields:

• model_latency_ms
• prompt_tokens
• completion_tokens
• total_tokens

Latency is measured by the reporting service (time.monotonic) and token usage comes from the selected status model adapter when available.

Reporting runs emit structured lifecycle events:

• reporting.report.started
• reporting.report.completed
• reporting.report.failed

Completion events include model identifier, latency, and token counts.

Use ReportingMetricsService to compute totals and latency profile for a time window:

import asyncio
import datetime as dt

from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine

from ghillie.reporting.metrics_service import ReportingMetricsService


async def main() -> None:
    engine = create_async_engine("postgresql+asyncpg://user:pass@host:5432/ghillie")
    session_factory = async_sessionmaker(engine, expire_on_commit=False)
    service = ReportingMetricsService(session_factory)

    snapshot = await service.get_metrics_for_period(
        period_start=dt.datetime(2026, 2, 1, tzinfo=dt.UTC),
        period_end=dt.datetime(2026, 3, 1, tzinfo=dt.UTC),
    )
    print(snapshot.total_reports)
    print(snapshot.avg_latency_ms)
    print(snapshot.total_tokens)


asyncio.run(main())

For estate-scoped queries, call get_metrics_for_estate(estate_id, start, end).

ReportingMetricsService returns token totals. Convert those totals to currency using pricing configured for the active model (for example, prompt and completion token rates from the provider contract).