"""大周天演化候选 prompt 构建（纯函数，无 IO）。

接收 ``EvaluationSnapshot``（由 application 阶段从 ORM 组装的态势快照），产出
``(system, user)`` 消息对，喂给模型网关。模型返回的 JSON 由 ``extract`` 模块解析。

枚举取值以 [契约字典](../../docs/contracts/contract-glossary.md) §12 与
[自我演化机制设计](../../docs/domain/core-domains.md) §11 为准。
"""

from dataclasses import dataclass, field

from app.modules.evolution import LEVELS as EVOLUTION_LEVELS
from app.modules.evolution import SCOPES as EVOLUTION_SCOPES
from app.modules.evolution import TYPES as EVOLUTION_TYPES

EVOLUTION_TYPES = EVOLUTION_TYPES
EVOLUTION_LEVELS = EVOLUTION_LEVELS
EVOLUTION_SCOPES = EVOLUTION_SCOPES


@dataclass
class EvaluationSnapshot:
    """大周天自我评估态势快照（application 组装，供候选生成消费）。"""

    character_id: str
    memory_count: int
    recent_memories: list[tuple[str, str]]  # (title, content)
    personality_summary: str
    world_entity_count: int
    conclusion: str
    evidence_ids: list[str] = field(default_factory=list)  # 记忆 ID，供 evidence 序号映射
    cumulative_drift: float = 0.0  # 最近大周天的累积漂移值
    drift_budget_remaining: float = 0.0  # 剩余漂移预算


def build_evolution_extraction_messages(
    snapshot: EvaluationSnapshot,
) -> tuple[str, str]:
    system = (
        "你是魂镜的大周天自我演化引擎。基于本轮自我评估的态势快照，生成轻量、可解释、"
        "可回滚的演化候选——只调整人格表达、程序记忆、工具策略等轻量参数，"
        "不改变核心使命、安全边界或高敏关系判断。"
        f"type 取自：{', '.join(EVOLUTION_TYPES)}。"
        f"level 取自：{', '.join(EVOLUTION_LEVELS)}"
        "（L0-L3 低风险可半自动，L4 核心人格/使命/安全边界必须人工确认）。"
        f"scope 取自：{', '.join(EVOLUTION_SCOPES)}。"
        '以 JSON 数组返回，每个元素：{"type": str, "source": "big_cycle", '
        '"level": str, "scope": str, "title": str, "proposal": str, '
        '"delta": 对象, "confidence": 0~1, "benefit_score": 0~1, '
        '"risk_score": 0~1, "auto_apply_allowed": bool, "evidence": [记忆序号]}。'
        "delta 为具体增量（如人格字段覆盖对象、程序记忆 title/content）。"
        "evidence 为下方记忆摘要的序号（[0]/[1]…）列表。"
        "仅在态势充分支撑时提议，宁缺毋滥。若本轮无需演化返回 []。"
        "只返回 JSON 数组，不要解释或 markdown。"
    )
    user = (
        f"记忆总数：{snapshot.memory_count}\n"
        f"世界实体数：{snapshot.world_entity_count}\n"
        f"自我评估结论：{snapshot.conclusion or '（无明显态势）'}\n"
        f"当前人格摘要：\n{snapshot.personality_summary or '（尚未形成）'}\n"
        f"近期记忆摘要：\n{_format_memories(snapshot.recent_memories)}"
    )
    # 人格权重向量：将漂移信息注入 prompt，引导 LLM 保守生成
    if snapshot.cumulative_drift > 0:
        user += (
            f"\n\n⚠️ 人格漂移警告：累积漂移 {snapshot.cumulative_drift:.2f} / "
            f"预算 {snapshot.drift_budget_remaining + snapshot.cumulative_drift:.1f}。"
            f"剩余漂移预算 {snapshot.drift_budget_remaining:.1f}。"
            f"请尽可能保守，减少高风险 proposal，优先微调而非大幅改动。"
        )
    return system, user


def _format_memories(memories: list[tuple[str, str]]) -> str:
    if not memories:
        return "（暂无）"
    lines = [f"[{idx}] {title}：{content}" for idx, (title, content) in enumerate(memories)]
    return "\n".join(lines)