AdministratorandClaude Opus 4.8
e7c745a512
- 排行榜改为按天预计算快照(admin_leaderboard_daily_stats),请求只读取合并,重统计移至后台调度;过去日不可变、当天 TTL 刷新,避免在线聚合长时间占用数据库 - 为 runs/tool_call_metrics/llm_call_metrics/threads_meta 增加面向时间段+用户聚合的复合索引(model __table_args__ + migration 20260608_01) - 连接池可调:DatabaseConfig 新增 max_overflow / pool_timeout,接入 postgres/mysql 引擎;config.yaml 上调 pool_size=20/max_overflow=20 - 新增手动执行的索引脚本 scripts/sql/leaderboard_indexes_mysql.sql(幂等,适用于已存在数据、create_all 不补索引且 alembic 关闭的部署) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
68 lines
2.8 KiB
Python
68 lines
2.8 KiB
Python
"""ORM model for per-LLM-call metrics."""
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from __future__ import annotations
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from datetime import UTC, datetime
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from sqlalchemy import Float, Index, Integer, 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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from deerflow.persistence.types import BeijingDateTime
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class LlmCallMetricRow(Base):
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"""One row per completed (or failed) LLM provider invocation."""
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__tablename__ = "llm_call_metrics"
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__table_args__ = (
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Index("ix_llm_call_metrics_created_user", "created_at", "user_id"),
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Index("ix_llm_call_metrics_run_created", "run_id", "created_at"),
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)
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id: Mapped[str] = mapped_column(String(64), primary_key=True)
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# Wall-clock time the LLM call ended. Indexed because admin views always
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# sort/filter by recency.
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created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=lambda: datetime.now(UTC), index=True)
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user_id: Mapped[str | None] = mapped_column(String(64), index=True)
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thread_id: Mapped[str | None] = mapped_column(String(64), index=True)
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run_id: Mapped[str | None] = mapped_column(String(64), index=True)
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agent_name: Mapped[str | None] = mapped_column(String(128), index=True)
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model_name: Mapped[str | None] = mapped_column(String(128), index=True)
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# Wall-clock duration of the provider invocation in milliseconds. Always set.
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duration_ms: Mapped[int] = mapped_column(Integer, default=0)
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input_tokens: Mapped[int | None] = mapped_column(Integer)
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output_tokens: Mapped[int | None] = mapped_column(Integer)
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total_tokens: Mapped[int | None] = mapped_column(Integer)
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# Output tokens / seconds; null when there is no completion (errors).
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tokens_per_sec: Mapped[float | None] = mapped_column(Float)
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# "success" or "error". Errors store the classification and original message
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# so admins can scan for transient vs auth/quota issues.
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status: Mapped[str] = mapped_column(String(16), default="success", index=True)
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error_type: Mapped[str | None] = mapped_column(String(64))
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error_message: Mapped[str | None] = mapped_column(Text)
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def to_dict(self) -> dict:
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return {
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"id": self.id,
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"created_at": self.created_at.isoformat() if isinstance(self.created_at, datetime) else self.created_at,
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"user_id": self.user_id,
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"thread_id": self.thread_id,
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"run_id": self.run_id,
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"agent_name": self.agent_name,
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"model_name": self.model_name,
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"duration_ms": self.duration_ms,
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"input_tokens": self.input_tokens,
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"output_tokens": self.output_tokens,
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"total_tokens": self.total_tokens,
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"tokens_per_sec": self.tokens_per_sec,
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"status": self.status,
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"error_type": self.error_type,
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"error_message": self.error_message,
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}
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