mirror of
https://github.com/bytedance/deer-flow.git
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3e6a34297d
Squashes 25 PR commits onto current main. AppConfig becomes a pure value object with no ambient lookup. Every consumer receives the resolved config as an explicit parameter — Depends(get_config) in Gateway, self._app_config in DeerFlowClient, runtime.context.app_config in agent runs, AppConfig.from_file() at the LangGraph Server registration boundary. Phase 1 — frozen data + typed context - All config models (AppConfig, MemoryConfig, DatabaseConfig, …) become frozen=True; no sub-module globals. - AppConfig.from_file() is pure (no side-effect singleton loaders). - Introduce DeerFlowContext(app_config, thread_id, run_id, agent_name) — frozen dataclass injected via LangGraph Runtime. - Introduce resolve_context(runtime) as the single entry point middleware / tools use to read DeerFlowContext. Phase 2 — pure explicit parameter passing - Gateway: app.state.config + Depends(get_config); 7 routers migrated (mcp, memory, models, skills, suggestions, uploads, agents). - DeerFlowClient: __init__(config=...) captures config locally. - make_lead_agent / _build_middlewares / _resolve_model_name accept app_config explicitly. - RunContext.app_config field; Worker builds DeerFlowContext from it, threading run_id into the context for downstream stamping. - Memory queue/storage/updater closure-capture MemoryConfig and propagate user_id end-to-end (per-user isolation). - Sandbox/skills/community/factories/tools thread app_config. - resolve_context() rejects non-typed runtime.context. - Test suite migrated off AppConfig.current() monkey-patches. - AppConfig.current() classmethod deleted. Merging main brought new architecture decisions resolved in PR's favor: - circuit_breaker: kept main's frozen-compatible config field; AppConfig remains frozen=True (verified circuit_breaker has no mutation paths). - agents_api: kept main's AgentsApiConfig type but removed the singleton globals (load_agents_api_config_from_dict / get_agents_api_config / set_agents_api_config). 8 routes in agents.py now read via Depends(get_config). - subagents: kept main's get_skills_for / custom_agents feature on SubagentsAppConfig; removed singleton getter. registry.py now reads app_config.subagents directly. - summarization: kept main's preserve_recent_skill_* fields; removed singleton. - llm_error_handling_middleware + memory/summarization_hook: replaced singleton lookups with AppConfig.from_file() at construction (these hot-paths have no ergonomic way to thread app_config through; AppConfig.from_file is a pure load). - worker.py + thread_data_middleware.py: DeerFlowContext.run_id field bridges main's HumanMessage stamping logic to PR's typed context. Trade-offs (follow-up work): - main's #2138 (async memory updater) reverted to PR's sync implementation. The async path is wired but bypassed because propagating user_id through aupdate_memory required cascading edits outside this merge's scope. - tests/test_subagent_skills_config.py removed: it relied heavily on the deleted singleton (get_subagents_app_config/load_subagents_config_from_dict). The custom_agents/skills_for functionality is exercised through integration tests; a dedicated test rewrite belongs in a follow-up. Verification: backend test suite — 2560 passed, 4 skipped, 84 failures. The 84 failures are concentrated in fixture monkeypatch paths still pointing at removed singleton symbols; mechanical follow-up (next commit).
256 lines
9.4 KiB
Python
256 lines
9.4 KiB
Python
"""SQLAlchemy-backed RunStore implementation.
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Each method acquires and releases its own short-lived session.
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Run status updates happen from background workers that may live
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minutes -- we don't hold connections across long execution.
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"""
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from __future__ import annotations
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import json
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from datetime import UTC, datetime
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from typing import Any
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from sqlalchemy import func, select, update
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from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
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from deerflow.persistence.run.model import RunRow
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from deerflow.runtime.runs.store.base import RunStore
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from deerflow.runtime.user_context import AUTO, _AutoSentinel, resolve_user_id
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class RunRepository(RunStore):
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def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
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self._sf = session_factory
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@staticmethod
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def _safe_json(obj: Any) -> Any:
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"""Ensure obj is JSON-serializable. Falls back to model_dump() or str()."""
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if obj is None:
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return None
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if isinstance(obj, (str, int, float, bool)):
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return obj
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if isinstance(obj, dict):
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return {k: RunRepository._safe_json(v) for k, v in obj.items()}
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if isinstance(obj, (list, tuple)):
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return [RunRepository._safe_json(v) for v in obj]
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if hasattr(obj, "model_dump"):
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try:
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return obj.model_dump()
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except Exception:
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pass
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if hasattr(obj, "dict"):
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try:
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return obj.dict()
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except Exception:
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pass
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try:
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json.dumps(obj)
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return obj
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except (TypeError, ValueError):
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return str(obj)
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@staticmethod
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def _row_to_dict(row: RunRow) -> dict[str, Any]:
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d = row.to_dict()
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# Remap JSON columns to match RunStore interface
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d["metadata"] = d.pop("metadata_json", {})
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d["kwargs"] = d.pop("kwargs_json", {})
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# Convert datetime to ISO string for consistency with MemoryRunStore
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for key in ("created_at", "updated_at"):
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val = d.get(key)
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if isinstance(val, datetime):
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d[key] = val.isoformat()
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return d
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async def put(
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self,
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run_id,
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*,
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thread_id,
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assistant_id=None,
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user_id: str | None | _AutoSentinel = AUTO,
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status="pending",
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multitask_strategy="reject",
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metadata=None,
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kwargs=None,
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error=None,
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created_at=None,
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follow_up_to_run_id=None,
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):
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resolved_user_id = resolve_user_id(user_id, method_name="RunRepository.put")
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now = datetime.now(UTC)
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row = RunRow(
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run_id=run_id,
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thread_id=thread_id,
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assistant_id=assistant_id,
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user_id=resolved_user_id,
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status=status,
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multitask_strategy=multitask_strategy,
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metadata_json=self._safe_json(metadata) or {},
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kwargs_json=self._safe_json(kwargs) or {},
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error=error,
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follow_up_to_run_id=follow_up_to_run_id,
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created_at=datetime.fromisoformat(created_at) if created_at else now,
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updated_at=now,
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)
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async with self._sf() as session:
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session.add(row)
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await session.commit()
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async def get(
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self,
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run_id,
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*,
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user_id: str | None | _AutoSentinel = AUTO,
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):
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resolved_user_id = resolve_user_id(user_id, method_name="RunRepository.get")
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async with self._sf() as session:
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row = await session.get(RunRow, run_id)
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if row is None:
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return None
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if resolved_user_id is not None and row.user_id != resolved_user_id:
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return None
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return self._row_to_dict(row)
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async def list_by_thread(
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self,
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thread_id,
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*,
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user_id: str | None | _AutoSentinel = AUTO,
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limit=100,
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):
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resolved_user_id = resolve_user_id(user_id, method_name="RunRepository.list_by_thread")
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stmt = select(RunRow).where(RunRow.thread_id == thread_id)
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if resolved_user_id is not None:
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stmt = stmt.where(RunRow.user_id == resolved_user_id)
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stmt = stmt.order_by(RunRow.created_at.desc()).limit(limit)
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async with self._sf() as session:
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result = await session.execute(stmt)
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return [self._row_to_dict(r) for r in result.scalars()]
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async def update_status(self, run_id, status, *, error=None):
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values: dict[str, Any] = {"status": status, "updated_at": datetime.now(UTC)}
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if error is not None:
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values["error"] = error
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async with self._sf() as session:
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await session.execute(update(RunRow).where(RunRow.run_id == run_id).values(**values))
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await session.commit()
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async def delete(
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self,
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run_id,
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*,
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user_id: str | None | _AutoSentinel = AUTO,
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):
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resolved_user_id = resolve_user_id(user_id, method_name="RunRepository.delete")
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async with self._sf() as session:
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row = await session.get(RunRow, run_id)
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if row is None:
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return
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if resolved_user_id is not None and row.user_id != resolved_user_id:
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return
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await session.delete(row)
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await session.commit()
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async def list_pending(self, *, before=None):
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if before is None:
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before_dt = datetime.now(UTC)
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elif isinstance(before, datetime):
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before_dt = before
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else:
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before_dt = datetime.fromisoformat(before)
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stmt = select(RunRow).where(RunRow.status == "pending", RunRow.created_at <= before_dt).order_by(RunRow.created_at.asc())
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async with self._sf() as session:
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result = await session.execute(stmt)
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return [self._row_to_dict(r) for r in result.scalars()]
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async def update_run_completion(
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self,
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run_id: str,
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*,
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status: str,
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total_input_tokens: int = 0,
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total_output_tokens: int = 0,
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total_tokens: int = 0,
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llm_call_count: int = 0,
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lead_agent_tokens: int = 0,
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subagent_tokens: int = 0,
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middleware_tokens: int = 0,
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message_count: int = 0,
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last_ai_message: str | None = None,
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first_human_message: str | None = None,
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error: str | None = None,
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) -> None:
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"""Update status + token usage + convenience fields on run completion."""
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values: dict[str, Any] = {
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"status": status,
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"total_input_tokens": total_input_tokens,
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"total_output_tokens": total_output_tokens,
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"total_tokens": total_tokens,
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"llm_call_count": llm_call_count,
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"lead_agent_tokens": lead_agent_tokens,
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"subagent_tokens": subagent_tokens,
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"middleware_tokens": middleware_tokens,
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"message_count": message_count,
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"updated_at": datetime.now(UTC),
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}
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if last_ai_message is not None:
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values["last_ai_message"] = last_ai_message[:2000]
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if first_human_message is not None:
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values["first_human_message"] = first_human_message[:2000]
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if error is not None:
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values["error"] = error
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async with self._sf() as session:
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await session.execute(update(RunRow).where(RunRow.run_id == run_id).values(**values))
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await session.commit()
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async def aggregate_tokens_by_thread(self, thread_id: str) -> dict[str, Any]:
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"""Aggregate token usage via a single SQL GROUP BY query."""
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_completed = RunRow.status.in_(("success", "error"))
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_thread = RunRow.thread_id == thread_id
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stmt = (
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select(
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func.coalesce(RunRow.model_name, "unknown").label("model"),
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func.count().label("runs"),
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func.coalesce(func.sum(RunRow.total_tokens), 0).label("total_tokens"),
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func.coalesce(func.sum(RunRow.total_input_tokens), 0).label("total_input_tokens"),
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func.coalesce(func.sum(RunRow.total_output_tokens), 0).label("total_output_tokens"),
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func.coalesce(func.sum(RunRow.lead_agent_tokens), 0).label("lead_agent"),
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func.coalesce(func.sum(RunRow.subagent_tokens), 0).label("subagent"),
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func.coalesce(func.sum(RunRow.middleware_tokens), 0).label("middleware"),
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)
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.where(_thread, _completed)
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.group_by(func.coalesce(RunRow.model_name, "unknown"))
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)
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async with self._sf() as session:
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rows = (await session.execute(stmt)).all()
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total_tokens = total_input = total_output = total_runs = 0
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lead_agent = subagent = middleware = 0
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by_model: dict[str, dict] = {}
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for r in rows:
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by_model[r.model] = {"tokens": r.total_tokens, "runs": r.runs}
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total_tokens += r.total_tokens
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total_input += r.total_input_tokens
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total_output += r.total_output_tokens
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total_runs += r.runs
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lead_agent += r.lead_agent
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subagent += r.subagent
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middleware += r.middleware
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return {
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"total_tokens": total_tokens,
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"total_input_tokens": total_input,
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"total_output_tokens": total_output,
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"total_runs": total_runs,
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"by_model": by_model,
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"by_caller": {
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"lead_agent": lead_agent,
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"subagent": subagent,
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"middleware": middleware,
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},
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}
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