M8：天工阁、叩心与辰正——受托任务后台并行、叩心四约束、时态感知投影
- 天工阁域 domains/tiangong：backstage_tasks（委托包+验收标准+结果证据）；有限分解（parent 链）与受控并行（各自后台 Run）；子任务全终态级联父任务进入待验收；按证据 accept/reject - kernel.start_backstage_run（kind=background_task）：loop 后台分支不占前台席位、与前台对话并行；断言放宽容纳无人物功能任务；结果只经回调回呈任务——无身份写入路径 - 叩心域 domains/kouxin：knocking_candidates（event/time_anchor/manual 触发 + 草稿 + 裁决快照）；四约束裁决（总开关→静默窗口【本地时区判定】→24h 频率→预算），dispatch 未过 429 且快照留痕；候选默认不自动发送；expire_stale 过期推进不触碰缘契 - 辰正 domains/zhengchen：temporal_context() 复用 time_sense 计算核（快照/农历/季节），产出【时态感知】投影段接入镜树生成 prompt（客观时间语境，不附情绪臆测——守一纪律） - 退役：modules/goal_orchestrator|planner|perception|big_cycle、application/planning、services/governance|goal_persist、candidates 表链（imports 候选抽取 API 面）、goals/plans/agent_roster/perception_channels/big_cycles/旧候选表（迁移 009）；前端 admin goals/plans/agents/perception/world 页与六组件；governance 只留 GenerationPreferences；admin 治理面收敛为司命+百工两枢；pivots 健康以域表计数重建 - 验收（test_m8_tiangong 五用例）：后台任务无身份写入（记忆/魂籍/缘契零新增）；后台与前台并行；分解级联；叩心四约束逐项；不回应不降低关系（缘契维度/状态/版本零变化） - 四套件：后端 345、前端 203、BFF 4、神识 5 包全绿（2 个预存环境失败不变）；迁移 009 净库干净
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@@ -1 +0,0 @@
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"""大周天演化候选领域纯逻辑（造消息 / 解析候选草稿，禁止 IO）。"""
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@@ -1,114 +0,0 @@
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"""解析模型返回为演化候选草稿（纯函数，无 IO）。
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模型可能返回纯 JSON、markdown 代码块或夹杂解释文本；本模块容错提取首个 JSON
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数组，并归一化为演化候选草稿。``evidence`` 序号由 application 映射为记忆 ID。
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缺省字段取**安全悲观默认**：``level=L4``（必须人工确认）、``auto_apply_allowed=False``，
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避免模型漏字段时误自动应用高风险演化。
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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 EvolutionCandidateDraft:
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type: str
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title: str
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proposal: str
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level: str = "L4"
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scope: str = "character_scope"
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source: str = "big_cycle"
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delta: dict[str, Any] = field(default_factory=dict)
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confidence: float = 0.5
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benefit_score: float = 0.5
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risk_score: float = 0.5
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drift_score: float = 0.0
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auto_apply_allowed: bool = False
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evidence: list[int] = field(default_factory=list)
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def parse_evolution_candidates(raw: str) -> list[EvolutionCandidateDraft]:
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drafts: list[EvolutionCandidateDraft] = []
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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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type_ = str(item.get("type", "")).strip()
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title = str(item.get("title", "")).strip()
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proposal = str(item.get("proposal", "")).strip()
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if not type_ or not title or not proposal:
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continue
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drafts.append(
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EvolutionCandidateDraft(
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type=type_,
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title=title,
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proposal=proposal,
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level=_level(item.get("level")),
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scope=_scope(item.get("scope")),
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source=str(item.get("source", "big_cycle")).strip() or "big_cycle",
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delta=_dict(item.get("delta")),
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confidence=_clamp_float(item.get("confidence"), 0.5),
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benefit_score=_clamp_float(item.get("benefit_score"), 0.5),
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risk_score=_clamp_float(item.get("risk_score"), 0.5),
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drift_score=_clamp_float(item.get("drift_score"), 0.0),
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auto_apply_allowed=bool(item.get("auto_apply_allowed", False)),
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evidence=_int_list(item.get("evidence")),
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)
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)
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return drafts
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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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_VALID_LEVELS = {"L0", "L1", "L2", "L3", "L4"}
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_VALID_SCOPES = {"character_scope", "user_scope", "task_scope", "global_scope"}
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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 _scope(value: Any) -> str:
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text = str(value or "").strip()
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return text if text in _VALID_SCOPES else "character_scope"
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def _dict(value: Any) -> dict[str, Any]:
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return value if isinstance(value, dict) else {}
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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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def _int_list(value: Any) -> list[int]:
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if not isinstance(value, list):
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return []
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result: list[int] = []
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for item in value:
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try:
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result.append(int(item))
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except (TypeError, ValueError):
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continue
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return result
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@@ -1,78 +0,0 @@
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"""大周天演化候选 prompt 构建（纯函数，无 IO）。
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接收 ``EvaluationSnapshot``（由 application 阶段从 ORM 组装的态势快照），产出
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``(system, user)`` 消息对，喂给模型网关。模型返回的 JSON 由 ``extract`` 模块解析。
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枚举取值以 [契约字典](../../docs/contracts/contract-glossary.md) §12 与
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[自我演化机制设计](../../docs/domain/core-domains.md) §11 为准。
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"""
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from dataclasses import dataclass, field
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from app.modules.evolution import LEVELS as EVOLUTION_LEVELS
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from app.modules.evolution import SCOPES as EVOLUTION_SCOPES
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from app.modules.evolution import TYPES as EVOLUTION_TYPES
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EVOLUTION_TYPES = EVOLUTION_TYPES
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EVOLUTION_LEVELS = EVOLUTION_LEVELS
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EVOLUTION_SCOPES = EVOLUTION_SCOPES
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@dataclass
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class EvaluationSnapshot:
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"""大周天自我评估态势快照（application 组装，供候选生成消费）。"""
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character_id: str
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memory_count: int
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recent_memories: list[tuple[str, str]] # (title, content)
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personality_summary: str
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world_entity_count: int
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conclusion: str
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evidence_ids: list[str] = field(default_factory=list) # 记忆 ID，供 evidence 序号映射
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cumulative_drift: float = 0.0 # 最近大周天的累积漂移值
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drift_budget_remaining: float = 0.0 # 剩余漂移预算
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def build_evolution_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、"
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"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，"
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"不改变核心使命、安全边界或高敏关系判断。"
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f"type 取自：{', '.join(EVOLUTION_TYPES)}。"
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f"level 取自：{', '.join(EVOLUTION_LEVELS)}"
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"（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）。"
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f"scope 取自：{', '.join(EVOLUTION_SCOPES)}。"
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'以 JSON 数组返回，每个元素：{"type": str, "source": "big_cycle", '
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'"level": str, "scope": str, "title": str, "proposal": str, '
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'"delta": 对象, "confidence": 0~1, "benefit_score": 0~1, '
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'"risk_score": 0~1, "auto_apply_allowed": bool, "evidence": [记忆序号]}。'
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"delta 为具体增量（如人格字段覆盖对象、程序记忆 title/content）。"
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"evidence 为下方记忆摘要的序号（[0]/[1]…）列表。"
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"仅在态势充分支撑时提议，宁缺毋滥。若本轮无需演化返回 []。"
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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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# 人格权重向量：将漂移信息注入 prompt，引导 LLM 保守生成
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if snapshot.cumulative_drift > 0:
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user += (
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f"\n\n⚠️ 人格漂移警告：累积漂移 {snapshot.cumulative_drift:.2f} / "
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f"预算 {snapshot.drift_budget_remaining + snapshot.cumulative_drift:.1f}。"
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f"剩余漂移预算 {snapshot.drift_budget_remaining:.1f}。"
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f"请尽可能保守，减少高风险 proposal，优先微调而非大幅改动。"
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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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@@ -1,69 +0,0 @@
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"""太一枢（Goal Orchestrator）领域逻辑。
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v1 落地 §7.1 最小闭环 + §7.2 预留扩展：
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- 从用户消息提取会话目标写入 ``run.goal``，并注入运行契约。
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- 活跃目标按优先级排序（``prioritize_active_goals``），供运行时注入。
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目标持久化体系（``goals`` 表）、调度与冲突裁决属 §7.2 / §7.3。
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"""
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from __future__ import annotations
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from app.modules.goal_orchestrator.extract import (
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extract_session_goal,
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format_goal_directive,
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)
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__all__ = [
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"extract_session_goal",
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"format_goal_directive",
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"prioritize_active_goals",
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"complete_session_goal",
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]
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def prioritize_active_goals(
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goals: list[dict],
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) -> list[dict]:
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"""活跃目标优先级排序。
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按以下规则返回排序后的目标列表（最高优先级在前）：
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1. ``priority`` 越小越优先（低数值 = 高优先级）
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2. ``priority`` 相同时 ``progress`` 越低越优先（未完成的优先）
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3. 层级越靠前越优先（L0 > L1 > L2 > L3 > L4）
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Args:
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goals: 活跃目标字典列表，每个字典包含 ``title`` / ``level`` / ``priority`` / ``progress``。
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Returns:
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排序后的目标列表。
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"""
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LEVEL_ORDER = {"L0": 0, "L1": 1, "L2": 2, "L3": 3, "L4": 4}
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def _sort_key(g: dict) -> tuple:
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priority = g.get("priority")
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if priority is None:
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priority = 100
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progress = g.get("progress", 0) or 0
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level = LEVEL_ORDER.get(str(g.get("level", "")), 99)
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return (0 if priority is not None else 1, priority, level, progress)
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return sorted(goals, key=_sort_key)
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def complete_session_goal(
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goal: dict,
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) -> dict:
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"""将会话目标标记为完成。
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Args:
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goal: 会话目标字典，包含 ``id`` / ``title`` / ``level`` / ``status``。
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Returns:
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更新后的目标字典（status=completed, progress=1.0）。
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"""
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return {
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**goal,
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"status": "completed",
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"progress": 1.0,
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}
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@@ -1,25 +0,0 @@
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"""太一会话目标提取（纯函数，无 IO）。
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从用户消息提取会话目标（L2）。v1 采用规则提取——用户消息本身即目标的最直接
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表达，清理与截断后写入 ``run.goal`` 并注入运行契约。LLM 提取为后续增强。
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"""
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_MAX_GOAL_CHARS = 120
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def extract_session_goal(content: str) -> str:
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"""从用户消息内容提取会话目标摘要。
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规则：去除首尾空白，超长截断到 ``_MAX_GOAL_CHARS``；空内容返回空串。
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"""
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goal = (content or "").strip()
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if not goal:
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return ""
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return goal[:_MAX_GOAL_CHARS]
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def format_goal_directive(goal: str) -> str:
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"""把会话目标格式化为运行契约的 system instruction 片段；空目标返回空串。"""
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if not goal:
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return ""
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return f"当前会话目标：{goal}"
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@@ -1 +0,0 @@
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"""感知领域模块（纯逻辑，无 IO）。"""
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@@ -1 +0,0 @@
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"""视觉感知领域模块。"""
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@@ -1,93 +0,0 @@
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"""
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OCR 结果后处理（纯逻辑，无 IO）。
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对 OCR 返回的原始结果做结构化解析、置信度过滤、阅读顺序修正。
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不依赖任何外部资源，可独立测试。
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"""
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from __future__ import annotations
|
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from typing import Any
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def filter_low_confidence(
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blocks: list[dict[str, Any]],
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threshold: float = 0.5,
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) -> list[dict[str, Any]]:
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"""过滤低置信度结果。
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Args:
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blocks: 原始 OCR 块列表。
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threshold: 置信度阈值（默认 0.5）。
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Returns:
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过滤后的块列表。
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"""
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return [b for b in blocks if b.get("confidence", 0) >= threshold]
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def merge_adjacent_lines(
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blocks: list[dict[str, Any]],
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y_tolerance: float = 10.0,
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) -> list[dict[str, Any]]:
|
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"""合并同一行的相邻块。
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|
||||
Args:
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blocks: OCR 块列表（需含 bbox 坐标）。
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y_tolerance: Y 轴容差（px），同一行最大差异。
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|
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Returns:
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合并后的块列表。
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"""
|
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if not blocks:
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return []
|
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|
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sorted_blocks = sorted(blocks, key=lambda b: (_center_y(b), _center_x(b)))
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||||
|
||||
merged: list[dict[str, Any]] = []
|
||||
current = sorted_blocks[0]
|
||||
|
||||
for block in sorted_blocks[1:]:
|
||||
if abs(_center_y(block) - _center_y(current)) <= y_tolerance:
|
||||
current["text"] += block["text"]
|
||||
current["confidence"] = max(current["confidence"], block["confidence"])
|
||||
else:
|
||||
merged.append(current)
|
||||
current = block
|
||||
|
||||
merged.append(current)
|
||||
return merged
|
||||
|
||||
|
||||
def extract_text(
|
||||
blocks: list[dict[str, Any]],
|
||||
separator: str = "",
|
||||
) -> str:
|
||||
"""从 OCR 块中提取纯文本。
|
||||
|
||||
Args:
|
||||
blocks: OCR 块列表。
|
||||
separator: 块之间的分隔符。
|
||||
|
||||
Returns:
|
||||
拼接后的文本。
|
||||
"""
|
||||
return separator.join(b["text"] for b in blocks)
|
||||
|
||||
|
||||
def _center_y(block: dict[str, Any]) -> float:
|
||||
"""获取块的中心 Y 坐标。"""
|
||||
pts = block.get("bbox", {}).get("points", block.get("bbox", []))
|
||||
if pts and isinstance(pts, list) and len(pts) > 0:
|
||||
ys = [p[1] for p in pts if isinstance(p, (list, tuple)) and len(p) >= 2]
|
||||
return sum(ys) / len(ys) if ys else 0.0
|
||||
return 0.0
|
||||
|
||||
|
||||
def _center_x(block: dict[str, Any]) -> float:
|
||||
"""获取块的中心 X 坐标。"""
|
||||
pts = block.get("bbox", {}).get("points", block.get("bbox", []))
|
||||
if pts and isinstance(pts, list) and len(pts) > 0:
|
||||
xs = [p[0] for p in pts if isinstance(p, (list, tuple)) and len(p) >= 2]
|
||||
return sum(xs) / len(xs) if xs else 0.0
|
||||
return 0.0
|
||||
@@ -1,166 +0,0 @@
|
||||
"""洛书枢 deliberative 规划器领域模块。
|
||||
|
||||
纯数据结构和纯函数（禁止 IO），定义显式计划的数据模型与解析逻辑。
|
||||
IO 相关编排在 ``app/application/planning/`` 中完成。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
|
||||
@dataclass
|
||||
class PlanStep:
|
||||
"""计划步骤。"""
|
||||
|
||||
id: int
|
||||
title: str
|
||||
description: str
|
||||
tool: str | None = None
|
||||
risk_level: str = "L2"
|
||||
|
||||
|
||||
@dataclass
|
||||
class DeliberativePlan:
|
||||
"""显式计划。"""
|
||||
|
||||
title: str
|
||||
steps: list[PlanStep]
|
||||
assumptions: list[str] = field(default_factory=list)
|
||||
risks: list[str] = field(default_factory=list)
|
||||
|
||||
|
||||
def parse_plan(raw: str) -> DeliberativePlan | None:
|
||||
"""解析 LLM 返回的 JSON 计划文本。
|
||||
|
||||
Args:
|
||||
raw: LLM 原始输出（可能含 ```json 围栏）。
|
||||
|
||||
Returns:
|
||||
解析成功的 DeliberativePlan，失败返回 None。
|
||||
"""
|
||||
text = raw.strip()
|
||||
matched = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
|
||||
if matched:
|
||||
text = matched.group(1)
|
||||
else:
|
||||
brace_start = text.find("{")
|
||||
brace_end = text.rfind("}")
|
||||
if brace_start != -1 and brace_end > brace_start:
|
||||
text = text[brace_start : brace_end + 1]
|
||||
try:
|
||||
data = json.loads(text)
|
||||
except json.JSONDecodeError:
|
||||
return None
|
||||
if not isinstance(data, dict):
|
||||
return None
|
||||
title = str(data.get("title", "")).strip()
|
||||
raw_steps = data.get("steps", [])
|
||||
if not title or not isinstance(raw_steps, list) or not raw_steps:
|
||||
return None
|
||||
steps: list[PlanStep] = []
|
||||
for i, s in enumerate(raw_steps):
|
||||
if not isinstance(s, dict):
|
||||
continue
|
||||
step_title = str(s.get("title", "")).strip()
|
||||
if not step_title:
|
||||
continue
|
||||
tool_raw = str(s.get("tool", "")).strip()
|
||||
tool = tool_raw if tool_raw and tool_raw != "None" else None
|
||||
steps.append(
|
||||
PlanStep(
|
||||
id=s.get("id", i + 1),
|
||||
title=step_title,
|
||||
description=str(s.get("description", "")).strip(),
|
||||
tool=tool,
|
||||
risk_level=str(s.get("risk_level", "L2")).upper(),
|
||||
)
|
||||
)
|
||||
if not steps:
|
||||
return None
|
||||
return DeliberativePlan(
|
||||
title=title,
|
||||
steps=steps,
|
||||
assumptions=[str(a).strip() for a in data.get("assumptions", []) if a],
|
||||
risks=[str(r).strip() for r in data.get("risks", []) if r],
|
||||
)
|
||||
|
||||
|
||||
def build_planning_prompt(
|
||||
request: str,
|
||||
*,
|
||||
goal_directive: str = "",
|
||||
available_tools: list[str] | None = None,
|
||||
memory_context: str = "",
|
||||
) -> str:
|
||||
"""构建规划 prompt。
|
||||
|
||||
当请求明显需要多步骤（调用多个工具、查询多处信息等）时，
|
||||
要求 LLM 先生成一个显式计划，再按计划逐步执行。
|
||||
|
||||
Args:
|
||||
request: 用户请求文本。
|
||||
goal_directive: 当前活跃目标描述。
|
||||
available_tools: 可用工具列表。
|
||||
memory_context: 相关记忆上下文。
|
||||
|
||||
Returns:
|
||||
规划 prompt 文本。
|
||||
"""
|
||||
tools_str = ""
|
||||
if available_tools:
|
||||
tools_str = "\n可用工具：\n" + "\n".join(f"- {t}" for t in available_tools)
|
||||
return (
|
||||
"请判断以下用户请求是否需要多步计划。如果需要，请输出 JSON 格式的计划。\n\n"
|
||||
f"用户请求：{request}\n"
|
||||
f"{f'当前目标：{goal_directive}' if goal_directive else ''}"
|
||||
f"{tools_str}"
|
||||
f"{f'\n上下文记忆：{memory_context}' if memory_context else ''}"
|
||||
"\n\n"
|
||||
"输出格式（如需多步计划）：\n"
|
||||
'```json\n{\n "title": "计划标题",\n'
|
||||
' "assumptions": ["假设1", "假设2"],\n'
|
||||
' "risks": ["风险1", "风险2"],\n'
|
||||
' "steps": [\n'
|
||||
' {"id": 1, "title": "步骤1", "description": "说明", '
|
||||
'"tool": "工具名（可选）", "risk_level": "L2"}\n'
|
||||
" ]\n"
|
||||
"}\n```\n\n"
|
||||
"如果不需要多步计划（简单问题），只回复：NO_PLAN_NEEDED"
|
||||
)
|
||||
|
||||
|
||||
def format_plan_for_prompt(plan: DeliberativePlan) -> str:
|
||||
"""将计划格式化为 system prompt 可用文本。"""
|
||||
lines = [f"## 计划：{plan.title}\n"]
|
||||
if plan.assumptions:
|
||||
lines.append("假设：")
|
||||
lines.extend(f"- {a}" for a in plan.assumptions)
|
||||
lines.append("")
|
||||
if plan.risks:
|
||||
lines.append("风险：")
|
||||
lines.extend(f"- {r}" for r in plan.risks)
|
||||
lines.append("")
|
||||
lines.append("执行步骤：")
|
||||
for step in plan.steps:
|
||||
tool_info = f" [工具：{step.tool}]" if step.tool else ""
|
||||
lines.append(f"{step.id}. {step.title}{tool_info}")
|
||||
if step.description:
|
||||
lines.append(f" {step.description}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def should_plan(request: str) -> bool:
|
||||
"""判断请求是否需要多步计划。
|
||||
|
||||
简单规则：包含需要多工具的关键词时返回 True。
|
||||
"""
|
||||
triggers = [
|
||||
"搜索", "查找", "研究", "调研", "分析", "比较",
|
||||
"整理", "总结", "汇总", "规划", "安排", "计划",
|
||||
"search", "research", "analyze", "compare",
|
||||
"summarize", "plan", "organize",
|
||||
]
|
||||
return any(t in request for t in triggers)
|
||||
Reference in new issue
Block a user