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arc53--docsgpt/scripts/db/backfill_token_usage_model_id.py
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Python

"""Backfill ``token_usage.model_id`` for rows written before the column.
New rows get ``model_id`` stamped at write time (see
``application.llm.llm_creator`` / ``application.usage``). This script
fills the historical NULLs by deriving the model from data we already
trust, in priority order. A row is only ever filled by the
highest-priority tier that matches it; tiers run in one transaction so
each later tier sees only the rows still NULL.
Tiers (both touch only ``source='agent_stream'`` rows)
-----
1. ``request_id`` join (high confidence). The route stamps the same
``request_id`` on the token_usage row and the assistant message, so
``conversation_messages.model_id`` is authoritative for the call.
2. ``agent_id`` + nearest message (medium confidence). For primary rows
with no usable ``request_id`` (legacy), copy ``model_id`` from the
closest-in-time message of any conversation belonging to the same
agent, within ``--window-minutes`` (ties broken toward the later
message so re-runs are reproducible).
Side-channel rows (``fallback`` / ``compression`` / ``title`` /
``rag_condense`` / ``schedule``) are left NULL: they share the primary
turn's ``request_id`` or agent but often ran a *different* model (a
backup, a compression override), so copying the primary turn's model
onto them would mis-attribute spend. New rows already get the correct
per-call model stamped at write time, so this only concerns history.
Rows that match neither tier are left NULL on purpose — the partial
index ``token_usage_model_ts_idx`` excludes them, and a model we can't
tie to the specific call (e.g. the agent's configured default) would
poison the analytics it feeds.
Both ``model_id`` columns store the canonical id (catalog name for
built-ins, UUID for BYOM), so BYOM rows backfill to the UUID unchanged.
Usage::
# Dry-run (default): runs the fills in a rolled-back transaction and
# reports exactly how many rows each tier would touch.
python scripts/db/backfill_token_usage_model_id.py
# Commit the backfill.
python scripts/db/backfill_token_usage_model_id.py --apply
# Widen the tier-2 match window (default 5 minutes).
python scripts/db/backfill_token_usage_model_id.py --window-minutes 10 --apply
Exit codes:
0 — success (dry-run or apply)
1 — bad arguments
"""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
from sqlalchemy import text # noqa: E402
from application.storage.db.engine import get_engine # noqa: E402
# Tier 1: same request -> same model, primary (agent_stream) rows only.
# conversation_messages.model_id is authoritative for that turn; fallback
# / compression rows share the request_id but ran a different model.
_TIER1 = text(
"""
UPDATE token_usage tu
SET model_id = cm.model_id
FROM conversation_messages cm
WHERE cm.request_id = tu.request_id
AND cm.model_id IS NOT NULL
AND tu.model_id IS NULL
AND tu.request_id IS NOT NULL
AND tu.source = 'agent_stream'
"""
)
# Tier 2: nearest message of the same agent within the window, primary
# (agent_stream) rows only. The EXISTS mirror skips rows with no match
# (else the subquery would set NULL); the ORDER BY tiebreak (later message
# wins) keeps the pick reproducible across re-runs.
_TIER2 = text(
"""
UPDATE token_usage tu
SET model_id = (
SELECT cm.model_id
FROM conversation_messages cm
JOIN conversations c ON c.id = cm.conversation_id
WHERE c.agent_id = tu.agent_id
AND cm.model_id IS NOT NULL
AND cm.timestamp BETWEEN tu.timestamp - make_interval(mins => :win)
AND tu.timestamp + make_interval(mins => :win)
ORDER BY abs(extract(epoch FROM (cm.timestamp - tu.timestamp))), cm.timestamp DESC
LIMIT 1
)
WHERE tu.model_id IS NULL
AND tu.agent_id IS NOT NULL
AND tu.source = 'agent_stream'
AND EXISTS (
SELECT 1
FROM conversation_messages cm
JOIN conversations c ON c.id = cm.conversation_id
WHERE c.agent_id = tu.agent_id
AND cm.model_id IS NOT NULL
AND cm.timestamp BETWEEN tu.timestamp - make_interval(mins => :win)
AND tu.timestamp + make_interval(mins => :win)
)
"""
)
_COUNT_NULL = text("SELECT count(*) FROM token_usage WHERE model_id IS NULL")
def main() -> int:
parser = argparse.ArgumentParser(
description="Backfill token_usage.model_id from existing data.",
)
parser.add_argument(
"--apply",
action="store_true",
help="Commit the backfill. Default is a rolled-back dry-run.",
)
parser.add_argument(
"--window-minutes",
type=int,
default=5,
metavar="N",
help="Tier-2 nearest-message match window, in minutes (default 5).",
)
args = parser.parse_args()
if args.window_minutes < 0:
print("--window-minutes must be >= 0", file=sys.stderr)
return 1
engine = get_engine()
with engine.connect() as conn:
trans = conn.begin()
try:
# A one-shot maintenance UPDATE can run well past the engine's
# 30s per-statement guardrail; lift it for this transaction.
conn.execute(text("SET LOCAL statement_timeout = 0"))
before = conn.execute(_COUNT_NULL).scalar_one()
t1 = conn.execute(_TIER1).rowcount or 0
t2 = conn.execute(_TIER2, {"win": args.window_minutes}).rowcount or 0
after = conn.execute(_COUNT_NULL).scalar_one()
print(f"NULL model_id rows before: {before}")
print(f" tier 1 (request_id): {t1}")
print(f" tier 2 (agent + nearest msg): {t2}")
print(f"NULL model_id rows remaining: {after}")
if args.apply:
trans.commit()
print("\nCommitted.")
else:
trans.rollback()
print("\nDry run — rolled back. Re-run with --apply to commit.")
except Exception:
trans.rollback()
raise
return 0
if __name__ == "__main__":
sys.exit(main())