1445043649
9ff790554d
通过 SQLAlchemy 的 comment= 给 5 张持久化表（users / threads_meta / runs / run_events / feedback）的所有字段以及表本身加上中文注释，便于读代码、 生成文档与未来切到 Postgres 时直接落库为 COMMENT ON。 SQLite 引擎本身不支持 COMMENT ON，运行时不会改变 .schema 输出。 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
56 lines
3.6 KiB
Python
56 lines
3.6 KiB
Python
"""ORM model for run metadata."""
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from __future__ import annotations
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from datetime import UTC, datetime
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from sqlalchemy import JSON, DateTime, Index, String, Text
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from sqlalchemy.orm import Mapped, mapped_column
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from deerflow.persistence.base import Base
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class RunRow(Base):
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__tablename__ = "runs"
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run_id: Mapped[str] = mapped_column(String(64), primary_key=True, comment="运行主键")
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thread_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True, comment="所属会话 ID（threads_meta.thread_id）")
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assistant_id: Mapped[str | None] = mapped_column(String(128), comment="使用的 Assistant ID（自定义智能体名）；为 NULL 表示默认 lead agent")
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user_id: Mapped[str | None] = mapped_column(String(64), index=True, comment="发起本次运行的用户 ID")
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status: Mapped[str] = mapped_column(
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String(20),
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default="pending",
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comment='运行状态："pending" / "running" / "success" / "error" / "timeout" / "interrupted"',
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)
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model_name: Mapped[str | None] = mapped_column(String(128), comment="本次运行的主模型名（来自 config.yaml.models[*].name）")
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multitask_strategy: Mapped[str] = mapped_column(
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String(20),
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default="reject",
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comment='并发策略：同一 thread 已有运行时怎么处理（"reject" / "interrupt" / "rollback" / "enqueue"）',
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)
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metadata_json: Mapped[dict] = mapped_column(JSON, default=dict, comment="运行级元数据（JSON），如 channel/source 等")
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kwargs_json: Mapped[dict] = mapped_column(JSON, default=dict, comment="提交运行时的额外参数（JSON），如 thinking_enabled、tool 配置等")
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error: Mapped[str | None] = mapped_column(Text, comment="运行失败时的错误文本；成功时为 NULL")
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message_count: Mapped[int] = mapped_column(default=0, comment="本次运行产生的消息总数（便利字段，避免列表页查 RunEventStore）")
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first_human_message: Mapped[str | None] = mapped_column(Text, comment="首条用户消息文本预览（用于列表展示）")
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last_ai_message: Mapped[str | None] = mapped_column(Text, comment="末条 AI 消息文本预览（用于列表展示）")
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total_input_tokens: Mapped[int] = mapped_column(default=0, comment="累计输入 token 数（运行结束时由 RunJournal 落盘）")
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total_output_tokens: Mapped[int] = mapped_column(default=0, comment="累计输出 token 数")
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total_tokens: Mapped[int] = mapped_column(default=0, comment="累计 token 总数 = input + output")
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llm_call_count: Mapped[int] = mapped_column(default=0, comment="累计 LLM 调用次数")
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lead_agent_tokens: Mapped[int] = mapped_column(default=0, comment="主 agent 自身消耗的 token 数")
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subagent_tokens: Mapped[int] = mapped_column(default=0, comment="子 agent（task 工具委派）消耗的 token 数")
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middleware_tokens: Mapped[int] = mapped_column(default=0, comment="中间件（如 summarization、title）消耗的 token 数")
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follow_up_to_run_id: Mapped[str | None] = mapped_column(String(64), comment="续接的上一次运行 ID（用于'重新生成'/'继续'等链式调用）")
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created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=lambda: datetime.now(UTC), comment="创建时间（UTC）")
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updated_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=lambda: datetime.now(UTC), onupdate=lambda: datetime.now(UTC), comment="最近更新时间（UTC，写入时自动更新）")
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__table_args__ = (
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Index("ix_runs_thread_status", "thread_id", "status"),
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{"comment": "运行（一次完整 agent 执行）的元数据 + 累计 token 指标"},
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)
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