"""LLM JSON 输出校验（memory 富化）。"""

from __future__ import annotations

import json
from typing import Any, TypeVar

from pydantic import BaseModel, Field, field_validator

TModel = TypeVar("TModel", bound=BaseModel)


class ExtractedFactItem(BaseModel):
    fact_type: str = "event"
    subject: str | None = None
    predicate: str | None = None
    object_json: Any = None
    confidence: float = Field(default=0.75, ge=0.0, le=1.0)
    source_chunk_id: str | None = None

    @field_validator("fact_type", mode="before")
    @classmethod
    def _coerce_fact_type(cls, v: object) -> str:
        ft = str(v or "event").strip() or "event"
        if ft not in ("person", "event", "relation", "place", "milestone"):
            return "event"
        return ft


class FactsExtractionPayload(BaseModel):
    facts: list[ExtractedFactItem] = Field(default_factory=list)


class SessionSummaryPayload(BaseModel):
    summary: str = ""


class RollingSummaryPayload(BaseModel):
    rolling_summary: str = ""


class TimelineEventItem(BaseModel):
    event_year: int | None = None
    event_date: str | None = None
    title: str = ""
    description: str | None = None
    source_fact_ids: list[str] = Field(default_factory=list)

    @field_validator("source_fact_ids", mode="before")
    @classmethod
    def _coerce_sf(cls, v: object) -> list[str]:
        if v is None:
            return []
        if isinstance(v, str):
            return [v] if v else []
        if isinstance(v, list):
            return [str(x) for x in v if x]
        return []


class TimelineEventsPayload(BaseModel):
    events: list[TimelineEventItem] = Field(default_factory=list)


def parse_json_payload(raw: str, model: type[TModel]) -> TModel | None:
    """解析 invoke_json_object 返回的 JSON 字符串。"""
    from app.core.json_utils import extract_json_payload

    try:
        cleaned = extract_json_payload(raw)
        data = json.loads(cleaned)
        return model.model_validate(data)
    except (json.JSONDecodeError, ValueError, TypeError):
        return None


def facts_payload_to_dicts(payload: FactsExtractionPayload) -> list[dict]:
    out: list[dict] = []
    for item in payload.facts:
        d = item.model_dump()
        scid = d.get("source_chunk_id")
        if scid is not None and not isinstance(scid, str):
            d["source_chunk_id"] = str(scid)
        out.append(d)
    return out


def timeline_payload_to_dicts(payload: TimelineEventsPayload) -> list[dict]:
    out: list[dict] = []
    for ev in payload.events:
        title = (ev.title or "").strip()
        if not title:
            continue
        out.append(
            {
                "event_year": ev.event_year,
                "event_date": ev.event_date,
                "title": title,
                "description": ev.description,
                "source_fact_ids": ev.source_fact_ids or [],
            }
        )
    return out[:20]