mirror of
https://github.com/bytedance/deer-flow.git
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rayhpeng
5cb0471af5
Phase 2-C: feedback and follow-up tracking. - FeedbackRow ORM model (rating +1/-1, optional message_id, comment) - FeedbackRepository with CRUD, list_by_run/thread, aggregate stats - Feedback API endpoints: create, list, stats, delete - follow_up_to_run_id in RunCreateRequest (explicit or auto-detected from latest successful run on the thread) - Worker writes follow_up_to_run_id into human_message event metadata - Gateway deps: feedback_repo factory + getter - 17 new tests (14 FeedbackRepository + 3 follow-up association) - 109 total tests pass, zero regressions Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
"""SQLAlchemy-backed feedback storage.
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Each method acquires its own short-lived session.
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"""
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from __future__ import annotations
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import logging
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import uuid
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from datetime import UTC, datetime
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
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from deerflow.persistence.models.feedback import FeedbackRow
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logger = logging.getLogger(__name__)
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class FeedbackRepository:
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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 _row_to_dict(row: FeedbackRow) -> dict:
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d = row.to_dict()
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val = d.get("created_at")
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if isinstance(val, datetime):
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d["created_at"] = val.isoformat()
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return d
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async def create(
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self,
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*,
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run_id: str,
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thread_id: str,
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rating: int,
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owner_id: str | None = None,
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message_id: str | None = None,
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comment: str | None = None,
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) -> dict:
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"""Create a feedback record. rating must be +1 or -1."""
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if rating not in (1, -1):
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raise ValueError(f"rating must be +1 or -1, got {rating}")
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row = FeedbackRow(
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feedback_id=str(uuid.uuid4()),
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run_id=run_id,
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thread_id=thread_id,
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owner_id=owner_id,
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message_id=message_id,
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rating=rating,
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comment=comment,
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created_at=datetime.now(UTC),
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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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await session.refresh(row)
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return self._row_to_dict(row)
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async def get(self, feedback_id: str) -> dict | None:
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async with self._sf() as session:
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row = await session.get(FeedbackRow, feedback_id)
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return self._row_to_dict(row) if row else None
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async def list_by_run(self, thread_id: str, run_id: str, *, limit: int = 100) -> list[dict]:
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stmt = select(FeedbackRow).where(FeedbackRow.thread_id == thread_id, FeedbackRow.run_id == run_id).order_by(FeedbackRow.created_at.asc()).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 list_by_thread(self, thread_id: str, *, limit: int = 100) -> list[dict]:
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stmt = select(FeedbackRow).where(FeedbackRow.thread_id == thread_id).order_by(FeedbackRow.created_at.asc()).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 delete(self, feedback_id: str) -> bool:
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async with self._sf() as session:
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row = await session.get(FeedbackRow, feedback_id)
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if row is None:
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return False
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await session.delete(row)
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await session.commit()
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return True
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async def aggregate_by_run(self, thread_id: str, run_id: str) -> dict:
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"""Aggregate feedback stats for a run."""
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items = await self.list_by_run(thread_id, run_id, limit=10000)
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positive = sum(1 for i in items if i["rating"] == 1)
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negative = sum(1 for i in items if i["rating"] == -1)
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return {
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"run_id": run_id,
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"total": len(items),
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"positive": positive,
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"negative": negative,
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}
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