life-echo/api/app/agents/state_schema.py

"""
共享状态 Schema（对话 Agent 与后台 Agent 共用）
"""

from __future__ import annotations

from typing import Dict, List, Optional

from pydantic import BaseModel, Field

from app.agents.stage_constants import CHAT_STAGES


class SlotData(BaseModel):
    """Slot 数据结构"""

    snippet: Optional[str] = None
    segment_ids: List[str] = Field(default_factory=list)


class MemoirStateSchema(BaseModel):
    """回忆录状态"""

    stage_order: List[str]
    current_stage: str
    covered_stages: List[str]
    slots: Dict[str, Dict[str, SlotData]]

    def empty_slots_for_current_stage(self) -> List[str]:
        stage_slots = self.slots.get(self.current_stage, {})
        empty_keys: List[str] = []
        for key, value in stage_slots.items():
            if not value.snippet:
                empty_keys.append(key)
        return empty_keys

    def empty_slots_for_stage(self, stage: str) -> List[str]:
        """获取指定阶段的空槽位"""
        stage_slots = self.slots.get(stage, {})
        return [key for key, value in stage_slots.items() if not value.snippet]

    def filled_slots_for_stage(self, stage: str) -> Dict[str, str]:
        """获取指定阶段已填充的槽位及其内容"""
        stage_slots = self.slots.get(stage, {})
        return {
            key: value.snippet for key, value in stage_slots.items() if value.snippet
        }

    def all_stages_coverage(self) -> Dict[str, Dict]:
        """获取所有阶段的覆盖情况摘要"""
        coverage: Dict[str, Dict] = {}
        for stage in self.stage_order:
            stage_slots = self.slots.get(stage, {})
            total = len(stage_slots)
            filled = sum(1 for v in stage_slots.values() if v.snippet)
            coverage[stage] = {
                "total": total,
                "filled": filled,
                "empty": total - filled,
                "ratio": filled / total if total > 0 else 0,
            }
        return coverage


# 与 stage_constants.CHAT_STAGES 同一顺序；list() 避免与元组共享可变别名
DEFAULT_STAGE_ORDER: list[str] = list(CHAT_STAGES)


def default_slots() -> Dict[str, Dict[str, SlotData]]:
    return {
        "childhood": {
            "place": SlotData(),
            "people": SlotData(),
            "daily_life": SlotData(),
            "emotion": SlotData(),
            "turning_event": SlotData(),
        },
        "education": {
            "school": SlotData(),
            "city": SlotData(),
            "motivation": SlotData(),
            "challenge": SlotData(),
            "change": SlotData(),
        },
        "career": {
            "job": SlotData(),
            "environment": SlotData(),
            "decision": SlotData(),
            "pressure": SlotData(),
            "growth": SlotData(),
        },
        "family": {
            "relationship": SlotData(),
            "conflict": SlotData(),
            "support": SlotData(),
            "responsibility": SlotData(),
            "change": SlotData(),
        },
        "belief": {
            "value": SlotData(),
            "regret": SlotData(),
            "pride": SlotData(),
            "lesson": SlotData(),
        },
    }


def default_state() -> MemoirStateSchema:
    return MemoirStateSchema(
        stage_order=DEFAULT_STAGE_ORDER,
        current_stage=DEFAULT_STAGE_ORDER[0],
        covered_stages=[],
        slots=default_slots(),
    )