AnoobFeng e7a03e5243
fix(gateway): attribute token usage to actual models (#3658)
* fix(gateway): attribute token usage to actual models

Capture per-call model names from LLM response metadata for lead, middleware, and subagent calls.

Persist a per-run token_usage_by_model breakdown and aggregate by that map in both SQL and memory stores, with legacy fallback to the run-level model_name for older rows.

Add regression coverage for by_model totals, caller consistency, active progress snapshots, store parity, and SubagentTokenCollector model propagation.

* fix(gateway): harden by-model token aggregation

Use usage.get("total_tokens", 0) when reducing per-model token usage maps so aggregation tolerates partially written or manually edited JSON blobs without changing behavior for journal-written rows.

* docs(gateway): clarify by-model run count semantics

Document that by_model[*].runs counts the number of runs in which a model appeared, so multi-model runs can increment multiple model buckets.
2026-06-19 21:42:42 +08:00

379 lines
15 KiB
Python

"""SQLAlchemy-backed RunStore implementation.
Each method acquires and releases its own short-lived session.
Run status updates happen from background workers that may live
minutes -- we don't hold connections across long execution.
"""
from __future__ import annotations
import json
from datetime import UTC, datetime
from typing import Any
from sqlalchemy import select, update
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
from deerflow.persistence.run.model import RunRow
from deerflow.runtime.runs.store.base import RunStore
from deerflow.runtime.user_context import AUTO, _AutoSentinel, resolve_user_id
from deerflow.utils.time import coerce_iso
class RunRepository(RunStore):
def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
self._sf = session_factory
@staticmethod
def _normalize_model_name(model_name: str | None) -> str | None:
"""Normalize model_name for storage: strip whitespace, truncate to 128 chars."""
if model_name is None:
return None
if not isinstance(model_name, str):
model_name = str(model_name)
normalized = model_name.strip()
if len(normalized) > 128:
normalized = normalized[:128]
return normalized
@staticmethod
def _safe_json(obj: Any) -> Any:
"""Ensure obj is JSON-serializable. Falls back to model_dump() or str()."""
if obj is None:
return None
if isinstance(obj, (str, int, float, bool)):
return obj
if isinstance(obj, dict):
return {k: RunRepository._safe_json(v) for k, v in obj.items()}
if isinstance(obj, (list, tuple)):
return [RunRepository._safe_json(v) for v in obj]
if hasattr(obj, "model_dump"):
try:
return obj.model_dump()
except Exception:
pass
if hasattr(obj, "dict"):
try:
return obj.dict()
except Exception:
pass
try:
json.dumps(obj)
return obj
except (TypeError, ValueError):
return str(obj)
@staticmethod
def _row_to_dict(row: RunRow) -> dict[str, Any]:
d = row.to_dict()
# Remap JSON columns to match RunStore interface
d["metadata"] = d.pop("metadata_json", {})
d["kwargs"] = d.pop("kwargs_json", {})
# Convert datetime to ISO string for consistency with MemoryRunStore.
# SQLite drops tzinfo on read despite ``DateTime(timezone=True)`` —
# ``coerce_iso`` normalizes naive datetimes as UTC.
for key in ("created_at", "updated_at"):
val = d.get(key)
if isinstance(val, datetime):
d[key] = coerce_iso(val)
return d
async def put(
self,
run_id,
*,
thread_id,
assistant_id=None,
user_id: str | None | _AutoSentinel = AUTO,
model_name: str | None = None,
status="pending",
multitask_strategy="reject",
metadata=None,
kwargs=None,
error=None,
created_at=None,
follow_up_to_run_id=None,
):
"""Insert or update a run row.
``RunManager`` retries ``put`` after transient SQLite failures. Making
this operation idempotent prevents a successful-but-unacknowledged first
commit from turning the retry into a primary-key failure.
"""
resolved_user_id = resolve_user_id(user_id, method_name="RunRepository.put")
now = datetime.now(UTC)
created = datetime.fromisoformat(created_at) if created_at else now
values = {
"thread_id": thread_id,
"assistant_id": assistant_id,
"user_id": resolved_user_id,
"model_name": self._normalize_model_name(model_name),
"status": status,
"multitask_strategy": multitask_strategy,
"metadata_json": self._safe_json(metadata) or {},
"kwargs_json": self._safe_json(kwargs) or {},
"error": error,
"follow_up_to_run_id": follow_up_to_run_id,
"updated_at": now,
}
async with self._sf() as session:
row = await session.get(RunRow, run_id)
if row is None:
session.add(RunRow(run_id=run_id, created_at=created, **values))
else:
for key, value in values.items():
setattr(row, key, value)
await session.commit()
async def get(
self,
run_id,
*,
user_id: str | None | _AutoSentinel = AUTO,
):
resolved_user_id = resolve_user_id(user_id, method_name="RunRepository.get")
async with self._sf() as session:
row = await session.get(RunRow, run_id)
if row is None:
return None
if resolved_user_id is not None and row.user_id != resolved_user_id:
return None
return self._row_to_dict(row)
async def list_by_thread(
self,
thread_id,
*,
user_id: str | None | _AutoSentinel = AUTO,
limit=100,
):
resolved_user_id = resolve_user_id(user_id, method_name="RunRepository.list_by_thread")
stmt = select(RunRow).where(RunRow.thread_id == thread_id)
if resolved_user_id is not None:
stmt = stmt.where(RunRow.user_id == resolved_user_id)
stmt = stmt.order_by(RunRow.created_at.desc()).limit(limit)
async with self._sf() as session:
result = await session.execute(stmt)
return [self._row_to_dict(r) for r in result.scalars()]
async def update_status(self, run_id, status, *, error=None) -> bool:
values: dict[str, Any] = {"status": status, "updated_at": datetime.now(UTC)}
if error is not None:
values["error"] = error
async with self._sf() as session:
result = await session.execute(update(RunRow).where(RunRow.run_id == run_id).values(**values))
await session.commit()
return result.rowcount != 0
async def update_model_name(self, run_id, model_name):
async with self._sf() as session:
await session.execute(update(RunRow).where(RunRow.run_id == run_id).values(model_name=self._normalize_model_name(model_name), updated_at=datetime.now(UTC)))
await session.commit()
async def delete(
self,
run_id,
*,
user_id: str | None | _AutoSentinel = AUTO,
):
resolved_user_id = resolve_user_id(user_id, method_name="RunRepository.delete")
async with self._sf() as session:
row = await session.get(RunRow, run_id)
if row is None:
return
if resolved_user_id is not None and row.user_id != resolved_user_id:
return
await session.delete(row)
await session.commit()
async def list_pending(self, *, before=None):
if before is None:
before_dt = datetime.now(UTC)
elif isinstance(before, datetime):
before_dt = before
else:
before_dt = datetime.fromisoformat(before)
stmt = select(RunRow).where(RunRow.status == "pending", RunRow.created_at <= before_dt).order_by(RunRow.created_at.asc())
async with self._sf() as session:
result = await session.execute(stmt)
return [self._row_to_dict(r) for r in result.scalars()]
async def list_inflight(self, *, before=None):
"""Return persisted active runs for startup recovery."""
if before is None:
before_dt = datetime.now(UTC)
elif isinstance(before, datetime):
before_dt = before
else:
before_dt = datetime.fromisoformat(before)
stmt = (
select(RunRow)
.where(
RunRow.status.in_(("pending", "running")),
RunRow.created_at <= before_dt,
)
.order_by(RunRow.created_at.asc())
)
async with self._sf() as session:
result = await session.execute(stmt)
return [self._row_to_dict(r) for r in result.scalars()]
async def update_run_completion(
self,
run_id: str,
*,
status: str,
total_input_tokens: int = 0,
total_output_tokens: int = 0,
total_tokens: int = 0,
llm_call_count: int = 0,
lead_agent_tokens: int = 0,
subagent_tokens: int = 0,
middleware_tokens: int = 0,
token_usage_by_model: dict[str, dict[str, int]] | None = None,
message_count: int = 0,
last_ai_message: str | None = None,
first_human_message: str | None = None,
error: str | None = None,
) -> bool:
"""Update status + token usage + convenience fields on run completion.
Returns ``False`` when no run row matched the requested ``run_id``.
"""
values: dict[str, Any] = {
"status": status,
"total_input_tokens": total_input_tokens,
"total_output_tokens": total_output_tokens,
"total_tokens": total_tokens,
"llm_call_count": llm_call_count,
"lead_agent_tokens": lead_agent_tokens,
"subagent_tokens": subagent_tokens,
"middleware_tokens": middleware_tokens,
"token_usage_by_model": self._safe_json(token_usage_by_model) or {},
"message_count": message_count,
"updated_at": datetime.now(UTC),
}
if last_ai_message is not None:
values["last_ai_message"] = last_ai_message[:2000]
if first_human_message is not None:
values["first_human_message"] = first_human_message[:2000]
if error is not None:
values["error"] = error
async with self._sf() as session:
result = await session.execute(update(RunRow).where(RunRow.run_id == run_id).values(**values))
await session.commit()
return result.rowcount != 0
async def update_run_progress(
self,
run_id: str,
*,
total_input_tokens: int | None = None,
total_output_tokens: int | None = None,
total_tokens: int | None = None,
llm_call_count: int | None = None,
lead_agent_tokens: int | None = None,
subagent_tokens: int | None = None,
middleware_tokens: int | None = None,
token_usage_by_model: dict[str, dict[str, int]] | None = None,
message_count: int | None = None,
last_ai_message: str | None = None,
first_human_message: str | None = None,
) -> None:
"""Update token usage + convenience fields while a run is still active."""
values: dict[str, Any] = {"updated_at": datetime.now(UTC)}
optional_counters = {
"total_input_tokens": total_input_tokens,
"total_output_tokens": total_output_tokens,
"total_tokens": total_tokens,
"llm_call_count": llm_call_count,
"lead_agent_tokens": lead_agent_tokens,
"subagent_tokens": subagent_tokens,
"middleware_tokens": middleware_tokens,
"message_count": message_count,
}
for key, value in optional_counters.items():
if value is not None:
values[key] = value
if token_usage_by_model is not None:
values["token_usage_by_model"] = self._safe_json(token_usage_by_model) or {}
if last_ai_message is not None:
values["last_ai_message"] = last_ai_message[:2000]
if first_human_message is not None:
values["first_human_message"] = first_human_message[:2000]
async with self._sf() as session:
await session.execute(update(RunRow).where(RunRow.run_id == run_id, RunRow.status == "running").values(**values))
await session.commit()
async def aggregate_tokens_by_thread(self, thread_id: str, *, include_active: bool = False) -> dict[str, Any]:
"""Aggregate token usage for a thread.
``by_model`` is reduced in Python from each row's ``token_usage_by_model``
JSON column so subagent / middleware tokens land on the model that
actually produced them (issue #3645). Rows written before that column
existed fall back to ``RunRow.model_name`` + ``RunRow.total_tokens``,
preserving the legacy lead-only behavior instead of dropping the data.
Headline totals (``total_tokens``, ``total_input_tokens``,
``total_output_tokens``) and the ``by_caller`` bucket are summed from
their own columns and are therefore unaffected by the JSON column being
empty.
"""
statuses = ("success", "error", "running") if include_active else ("success", "error")
_completed = RunRow.status.in_(statuses)
_thread = RunRow.thread_id == thread_id
stmt = select(
RunRow.model_name,
RunRow.total_tokens,
RunRow.total_input_tokens,
RunRow.total_output_tokens,
RunRow.lead_agent_tokens,
RunRow.subagent_tokens,
RunRow.middleware_tokens,
RunRow.token_usage_by_model,
).where(_thread, _completed)
async with self._sf() as session:
rows = (await session.execute(stmt)).all()
total_tokens = total_input = total_output = total_runs = 0
lead_agent = subagent = middleware = 0
by_model: dict[str, dict] = {}
for r in rows:
total_runs += 1
total_tokens += r.total_tokens
total_input += r.total_input_tokens
total_output += r.total_output_tokens
lead_agent += r.lead_agent_tokens
subagent += r.subagent_tokens
middleware += r.middleware_tokens
# ``or {}`` covers rows written before ``token_usage_by_model``
# existed (the column is NULL on a manual ALTER ADD COLUMN without
# backfill); fresh rows always carry the journal-produced dict.
usage_by_model = r.token_usage_by_model or {}
if usage_by_model:
for model, usage in usage_by_model.items():
entry = by_model.setdefault(model, {"tokens": 0, "runs": 0})
entry["tokens"] += usage.get("total_tokens", 0)
entry["runs"] += 1
else:
model = r.model_name or "unknown"
entry = by_model.setdefault(model, {"tokens": 0, "runs": 0})
entry["tokens"] += r.total_tokens
entry["runs"] += 1
return {
"total_tokens": total_tokens,
"total_input_tokens": total_input,
"total_output_tokens": total_output,
"total_runs": total_runs,
"by_model": by_model,
"by_caller": {
"lead_agent": lead_agent,
"subagent": subagent,
"middleware": middleware,
},
}