"""ORM model for per-LLM-call metrics."""

from __future__ import annotations

from datetime import UTC, datetime

from sqlalchemy import Float, Index, Integer, String, Text
from sqlalchemy.orm import Mapped, mapped_column

from deerflow.persistence.base import Base
from deerflow.persistence.types import BeijingDateTime


class LlmCallMetricRow(Base):
    """One row per completed (or failed) LLM provider invocation."""

    __tablename__ = "llm_call_metrics"
    __table_args__ = (
        Index("ix_llm_call_metrics_created_user", "created_at", "user_id"),
        Index("ix_llm_call_metrics_run_created", "run_id", "created_at"),
        Index("ix_llm_call_metrics_created_model_user", "created_at", "model_name", "user_id"),
    )

    id: Mapped[str] = mapped_column(String(64), primary_key=True)
    # Wall-clock time the LLM call ended. Indexed because admin views always
    # sort/filter by recency.
    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), default=lambda: datetime.now(UTC), index=True)

    user_id: Mapped[str | None] = mapped_column(String(64), index=True)
    thread_id: Mapped[str | None] = mapped_column(String(64), index=True)
    run_id: Mapped[str | None] = mapped_column(String(64), index=True)
    agent_name: Mapped[str | None] = mapped_column(String(128), index=True)
    model_name: Mapped[str | None] = mapped_column(String(128), index=True)

    # Wall-clock duration of the provider invocation in milliseconds. Always set.
    duration_ms: Mapped[int] = mapped_column(Integer, default=0)

    input_tokens: Mapped[int | None] = mapped_column(Integer)
    output_tokens: Mapped[int | None] = mapped_column(Integer)
    total_tokens: Mapped[int | None] = mapped_column(Integer)

    # Output tokens / seconds; null when there is no completion (errors).
    tokens_per_sec: Mapped[float | None] = mapped_column(Float)

    # "success" or "error". Errors store the classification and original message
    # so admins can scan for transient vs auth/quota issues.
    status: Mapped[str] = mapped_column(String(16), default="success", index=True)
    error_type: Mapped[str | None] = mapped_column(String(64))
    error_message: Mapped[str | None] = mapped_column(Text)

    def to_dict(self) -> dict:
        return {
            "id": self.id,
            "created_at": self.created_at.isoformat() if isinstance(self.created_at, datetime) else self.created_at,
            "user_id": self.user_id,
            "thread_id": self.thread_id,
            "run_id": self.run_id,
            "agent_name": self.agent_name,
            "model_name": self.model_name,
            "duration_ms": self.duration_ms,
            "input_tokens": self.input_tokens,
            "output_tokens": self.output_tokens,
            "total_tokens": self.total_tokens,
            "tokens_per_sec": self.tokens_per_sec,
            "status": self.status,
            "error_type": self.error_type,
            "error_message": self.error_message,
        }