M6：缘契、人格与世界——契约版本链、铸魂分灵、河图境界与三界分流
- 缘契域 domains/yuanqi：covenants（当前版）+ covenant_events（不可变历史）；提议—问心—铭契—撤回全链；边界变更立即生效；多维关系并存，服务层拒绝 affinity/好感度单一键；浪漫维度成人资格标记位 - 道体铸魂与分灵 domains/jingsheng/soul.py：soul_forgings 确认流（锚点变更 pending→confirmed 应用并签发新魂籍版本，确认前人物锚点不变）；分灵创建新人物（继承资料与谱系 lineage 链、不继承原身份，魂籍从 v1 起） - 河图域 domains/hetu：persistent_worlds 境界 + world_entities/world_relations 境界作用域重建 + 世谱挂接；境界正史视图（scope=persistent_world 归真集合）；canonize 后置推进正史计数 - 三界分流：reality→息壤提取（不变）；persistent_world→境界实录+正史推进；simulation→只记试演实录；factual_records 增 scope 列——境界/幻境实录与真界本相隔离 - 正影打通：引文 target_type=memory 时被引记忆 superseded → 返回 successor_memory_id 提示，不静默替换 - 前端：人物详情页缘契面板（提议维度选择/铭契/撤回/事件史）+ 世界页重建（境界目录/实体/正史概览）+ covenants/worlds api 库 - 退役：旧 personality/drive/evolution/world 模块、服务、端点、仓储、schema、前端五页与组件样式；迁移 007 退役九张旧表与 characters.current_personality_version_id - 验收（test_m6_domains 六用例）：多维无单一好感度/铭契版本链/事件不可抹除/撤回立即生效/边界即时/铸魂确认流+魂籍版本/分灵谱系/境界正史不进真界（实录 scope 隔离+真界候选=0）/引文正影后继 - 四套件：后端 379、前端 199、BFF 4、神识 5 包全绿（2 个预存环境失败不变）；迁移 007 净库+±1 干净
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"""大周天内驱力领域纯逻辑（造消息 / 解析好奇问题与次生目标草稿，禁止 IO）。"""
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@@ -1,127 +0,0 @@
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"""解析模型返回为内驱力草稿（纯函数，无 IO）。
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好奇问题可内嵌次生目标（[内驱力生成器设计] ../../docs/domain/core-domains.md
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§6：好奇问题→转化次生目标）。缺省取**安全悲观默认**：``risk_level=L4``、
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``source=reflection``，避免模型漏字段时生成高风险探索。
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"""
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import json
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import re
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from dataclasses import dataclass, field
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from typing import Any
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@dataclass
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class SubgoalDraft:
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title: str
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description: str
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expected_information_gain: float = 0.5
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interruption_cost: float = 0.5
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risk_level: str = "L4"
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@dataclass
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class CuriosityDraft:
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question: str
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source: str = "reflection"
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related_entities: list[str] = field(default_factory=list)
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expected_value: float = 0.5
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urgency: float = 0.5
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risk_level: str = "L4"
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subgoals: list[SubgoalDraft] = field(default_factory=list)
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_VALID_SOURCES = {
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"world_uncertainty",
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"memory_gap",
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"perception_change",
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"goal_tension",
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"reflection",
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"time_rhythm",
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}
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_VALID_LEVELS = {"L0", "L1", "L2", "L3", "L4"}
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def parse_curiosities(raw: str) -> list[CuriosityDraft]:
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drafts: list[CuriosityDraft] = []
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for item in _extract_json_array(raw):
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if not isinstance(item, dict):
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continue
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question = str(item.get("question", "")).strip()
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if not question:
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continue
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drafts.append(
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CuriosityDraft(
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question=question,
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source=_source(item.get("source")),
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related_entities=_str_list(item.get("related_entities")),
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expected_value=_clamp_float(item.get("expected_value"), 0.5),
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urgency=_clamp_float(item.get("urgency"), 0.5),
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risk_level=_level(item.get("risk_level")),
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subgoals=_parse_subgoals(item.get("subgoals")),
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)
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)
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return drafts
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def _parse_subgoals(value: Any) -> list[SubgoalDraft]:
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if not isinstance(value, list):
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return []
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result: list[SubgoalDraft] = []
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for item in value:
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if not isinstance(item, dict):
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continue
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title = str(item.get("title", "")).strip()
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description = str(item.get("description", "")).strip()
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if not title or not description:
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continue
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result.append(
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SubgoalDraft(
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title=title,
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description=description,
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expected_information_gain=_clamp_float(item.get("expected_information_gain"), 0.5),
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interruption_cost=_clamp_float(item.get("interruption_cost"), 0.5),
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risk_level=_level(item.get("risk_level")),
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)
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)
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return result
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def _extract_json_array(raw: str) -> list[Any]:
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text = raw.strip()
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matched = re.search(r"```(?:json)?\s*(\[.*?\])\s*```", text, re.DOTALL)
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if matched:
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text = matched.group(1)
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else:
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start, end = text.find("["), text.rfind("]")
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if start != -1 and end > start:
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text = text[start : end + 1]
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try:
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data = json.loads(text)
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except json.JSONDecodeError:
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return []
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return data if isinstance(data, list) else []
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def _source(value: Any) -> str:
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text = str(value or "").strip()
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return text if text in _VALID_SOURCES else "reflection"
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def _level(value: Any) -> str:
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text = str(value or "").strip().upper()
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return text if text in _VALID_LEVELS else "L4"
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def _str_list(value: Any) -> list[str]:
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if not isinstance(value, list):
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return []
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return [str(v) for v in value if v is not None]
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def _clamp_float(value: Any, default: float) -> float:
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try:
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num = float(value)
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except (TypeError, ValueError):
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return default
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return max(0.0, min(1.0, num))
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@@ -1,55 +0,0 @@
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"""大周天内驱力 prompt 构建（纯函数，无 IO）。
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复用 ``EvaluationSnapshot``（态势快照），产出 ``(system, user)`` 消息对，喂给
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模型网关。模型返回的 JSON 由 ``extract`` 模块解析。
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枚举取值以 [内驱力生成器设计](../../docs/domain/core-domains.md) §5 与
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[数据库 schema](../../docs/contracts/contract-glossary.md) §22 为准。
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"""
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from app.modules.big_cycle.prompts import EvaluationSnapshot
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DRIVE_SOURCES = (
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"world_uncertainty",
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"memory_gap",
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"perception_change",
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"goal_tension",
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"reflection",
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"time_rhythm",
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)
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DRIVE_LEVELS = ("L0", "L1", "L2", "L3", "L4")
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def build_drive_extraction_messages(
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snapshot: EvaluationSnapshot,
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) -> tuple[str, str]:
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system = (
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"你是魂镜的内驱力生成器。基于本轮自我评估的态势快照，识别「匮乏感」——"
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"世界模型不确定性、记忆缺口、目标张力等——生成好奇问题与可审核的探索性次生目标。"
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"不生成已有确信答案的问题；不为满足好奇而越权（摄像头 / 麦克风 / 敏感读取仍需授权）。"
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f"source 取自：{', '.join(DRIVE_SOURCES)}。"
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f"risk_level 取自：{', '.join(DRIVE_LEVELS)}"
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"（探索风险等级，L4 必须人工确认）。"
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'以 JSON 数组返回，每个元素是一个好奇问题：{"question": str, "source": str, '
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'"expected_value": 0~1, "urgency": 0~1, "risk_level": str, '
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'"related_entities": [实体名], "subgoals": [{"title": str, "description": str, '
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'"expected_information_gain": 0~1, "interruption_cost": 0~1, "risk_level": str}]}。'
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"subgoals 为该好奇问题转化的可执行探索目标，可空数组。"
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"仅在态势确有缺口时生成，宁缺毋滥；无缺口返回 []。"
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"只返回 JSON 数组，不要解释或 markdown。"
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)
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user = (
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f"记忆总数：{snapshot.memory_count}\n"
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f"世界实体数：{snapshot.world_entity_count}\n"
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f"自我评估结论：{snapshot.conclusion or '（无明显态势）'}\n"
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f"当前人格摘要：\n{snapshot.personality_summary or '（尚未形成）'}\n"
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f"近期记忆摘要：\n{_format_memories(snapshot.recent_memories)}"
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)
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return system, user
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def _format_memories(memories: list[tuple[str, str]]) -> str:
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if not memories:
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return "（暂无）"
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lines = [f"[{idx}] {title}：{content}" for idx, (title, content) in enumerate(memories)]
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return "\n".join(lines)
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@@ -1,18 +0,0 @@
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"""演化候选领域纯逻辑。
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本模块是演化系统（EvolutionCandidate）的纯函数域，禁止 IO。
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IO 操作由 ``services/evolution.py`` 或 ``application/big_cycle.py`` 负责编排。
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当前内容：
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- ``policies`` — level 分类、自动应用策略、类型/作用域枚举
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"""
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from app.modules.evolution.policies import AUTO_LEVELS, LEVELS, SCOPES, TYPES, should_auto_apply
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__all__ = [
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"AUTO_LEVELS",
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"LEVELS",
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"SCOPES",
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"TYPES",
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"should_auto_apply",
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]
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@@ -1,57 +0,0 @@
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"""演化候选领域纯逻辑（level 分类、自动应用策略、类型/作用域枚举）。
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本模块是演化候选相关常量与纯谓词的单一源，禁止 IO。
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枚举取值以 [契约字典](../../docs/contracts/contract-glossary.md) §12 为准。
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"""
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from app.models.evolution import EvolutionCandidate
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# ---------------------------------------------------------------------------
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# Level 常量与自动应用策略
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# ---------------------------------------------------------------------------
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# 半自动应用的 level 范围（L0-L3 写入程序记忆/人格权重等轻量参数，L4 核心变更需人工审核）
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AUTO_LEVELS: set[str] = {"L0", "L1", "L2", "L3"}
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# 所有可选 level，按风险升序排列
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LEVELS: tuple[str, ...] = ("L0", "L1", "L2", "L3", "L4")
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# ---------------------------------------------------------------------------
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# Type / Scope 枚举
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# ---------------------------------------------------------------------------
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# 演化候选类型
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TYPES: tuple[str, ...] = (
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"personality",
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"personality_weights",
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"retrieval_strategy",
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"tool_affinity",
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"memory",
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"tool_strategy",
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"safety_rule",
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"world_model",
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"procedural",
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"mood",
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)
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# 演化候选作用域
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SCOPES: tuple[str, ...] = (
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"character_scope",
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"user_scope",
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"task_scope",
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"global_scope",
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)
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# ---------------------------------------------------------------------------
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# 策略谓词
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# ---------------------------------------------------------------------------
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def should_auto_apply(candidate: EvolutionCandidate) -> bool:
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"""判断演化候选是否可半自动应用。
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满足任一条件即可自动应用：
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1. level 在 AUTO_LEVELS 范围内（L0-L3 低风险调整）
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2. auto_apply_allowed 为 True（模型+系统共同判定允许自动应用）
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"""
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return candidate.level in AUTO_LEVELS or candidate.auto_apply_allowed
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Whitespace-only changes.
@@ -1,149 +0,0 @@
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"""灵核情绪状态机：纯计算（无 IO）。
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消费辰正 ``time.*`` 事件（time.reunion / time.user_care / time.sentiment），
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派生 ``EmotionState``（mood + intensity）。intensity 向人格版本 ``emotion_baseline`` 指数衰减回归。
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设计见 ``docs/domain/core-domains.md`` §11。
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"""
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from __future__ import annotations
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import json
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from typing import Any
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from pydantic import BaseModel
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# v1 mood 词表（七种）。v2 路线图见设计 §11.6。
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MOODS: tuple[str, ...] = (
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"neutral",
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"pleased",
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"touched",
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"wistful",
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"concerned",
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"festive",
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"melancholy",
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)
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MOOD_LABELS_ZH: dict[str, str] = {
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"neutral": "平静",
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"pleased": "愉悦",
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"touched": "感动",
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"wistful": "怅然怀念",
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"concerned": "关切",
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"festive": "喜庆",
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"melancholy": "惆怅",
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}
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# 衰减半衰期（对话回合）：intensity 向 baseline 走一半所需的回合数。
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DECAY_HALFLIFE_TURNS: float = 6.0
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# intensity 衰减至此阈值以下时，mood 回 baseline.mood。
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NEUTRAL_THRESHOLD: float = 0.15
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# time.sentiment 无 payload 强度字段，使用固定中值。
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_SENTIMENT_DEFAULT_DELTA: float = 0.5
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# time.sentiment trigger_type → mood（late_night → melancholy；其余 → wistful）。
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_SENTIMENT_TRIGGER_MOOD: dict[str, str] = {
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"late_night": "melancholy",
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}
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class EmotionState(BaseModel):
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"""情绪状态：离散 mood（``MOODS`` 枚举）+ intensity（0-1 标量）。"""
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mood: str = "neutral"
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intensity: float = 0.2
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|
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|
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def parse_baseline(value: dict[str, Any] | str | None) -> EmotionState:
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"""从人格版本 ``emotion_baseline``（dict / JSON str / None）解析 baseline。
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容错：非法 JSON / 非 dict / 未知 mood / 越界 intensity → 回退默认（neutral 0.2）。
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"""
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if value is None:
|
||||
return EmotionState()
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if isinstance(value, str):
|
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try:
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value = json.loads(value)
|
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except (json.JSONDecodeError, TypeError):
|
||||
return EmotionState()
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if not isinstance(value, dict):
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return EmotionState()
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mood = value.get("mood", "neutral")
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if mood not in MOODS:
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mood = "neutral"
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try:
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intensity = float(value.get("intensity", 0.2))
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except (TypeError, ValueError):
|
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intensity = 0.2
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intensity = max(0.0, min(1.0, intensity))
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return EmotionState(mood=mood, intensity=intensity)
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def map_signal_to_mood(event_type: str, payload: dict[str, Any]) -> str | None:
|
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"""辰正 time.* 事件 → mood 映射。无映射的事件类型返回 None。"""
|
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if event_type == "time.reunion":
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return "touched"
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if event_type == "time.user_care":
|
||||
if payload.get("care_kind") == "festival_lonely":
|
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return "melancholy"
|
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return "concerned"
|
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if event_type == "time.sentiment":
|
||||
trigger = payload.get("trigger_type", "")
|
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return _SENTIMENT_TRIGGER_MOOD.get(trigger, "wistful")
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return None
|
||||
|
||||
|
||||
def signal_intensity_delta(event_type: str, payload: dict[str, Any]) -> float:
|
||||
"""从事件 payload 提取 intensity delta（复用辰正既有强度，不重算）。"""
|
||||
if event_type == "time.reunion":
|
||||
return float(payload.get("reunion_intensity", 0.5))
|
||||
if event_type == "time.user_care":
|
||||
return float(payload.get("care_suggest", 0.5))
|
||||
if event_type == "time.sentiment":
|
||||
return _SENTIMENT_DEFAULT_DELTA
|
||||
return 0.0
|
||||
|
||||
|
||||
def apply_signal(
|
||||
state: EmotionState,
|
||||
event_type: str,
|
||||
payload: dict[str, Any],
|
||||
baseline: EmotionState,
|
||||
) -> EmotionState:
|
||||
"""事件驱动状态转移。
|
||||
|
||||
- mood 变化时：intensity = ``baseline.intensity + delta``（新情绪从基线起跳）。
|
||||
- mood 不变时：intensity = ``state.intensity + delta``（累积）。
|
||||
- clamp 至 [0, 1]。
|
||||
- 无映射的事件（``map_signal_to_mood`` 返回 None）原样返回 state。
|
||||
"""
|
||||
new_mood = map_signal_to_mood(event_type, payload)
|
||||
if new_mood is None:
|
||||
return state
|
||||
delta = signal_intensity_delta(event_type, payload)
|
||||
base = state.intensity if new_mood == state.mood else baseline.intensity
|
||||
intensity = max(0.0, min(1.0, base + delta))
|
||||
return EmotionState(mood=new_mood, intensity=round(intensity, 3))
|
||||
|
||||
|
||||
def decay(state: EmotionState, baseline: EmotionState, elapsed_turns: float) -> EmotionState:
|
||||
"""intensity 向 baseline 指数衰减；与 baseline 的差距收敛到阈值内时 mood 回 baseline.mood。
|
||||
|
||||
``elapsed_turns <= 0`` 时原样返回 state（无衰减）。
|
||||
判断用相对差距（``decayed - baseline.intensity``）而非绝对阈值——否则当
|
||||
baseline.intensity 本身 > 阈值时（如默认 neutral 0.2），mood 永远不会回归。
|
||||
"""
|
||||
if elapsed_turns <= 0:
|
||||
return state
|
||||
factor = 0.5 ** (elapsed_turns / DECAY_HALFLIFE_TURNS)
|
||||
decayed = baseline.intensity + (state.intensity - baseline.intensity) * factor
|
||||
if decayed - baseline.intensity <= NEUTRAL_THRESHOLD:
|
||||
return EmotionState(mood=baseline.mood, intensity=baseline.intensity)
|
||||
return EmotionState(mood=state.mood, intensity=round(decayed, 3))
|
||||
|
||||
|
||||
def to_prompt_text(state: EmotionState) -> str:
|
||||
"""注入对话 system prompt 的情绪摘要（中文 label + 强度百分比）。"""
|
||||
label = MOOD_LABELS_ZH.get(state.mood, state.mood)
|
||||
pct = round(state.intensity * 100)
|
||||
return f"[当前情绪] {label}（强度 {pct}%）"
|
||||
Reference in new issue
Block a user