初始化提交

backend/app/modules/big_cycle/__init__.py

@@ -0,0 +1 @@
+"""大周天演化候选领域纯逻辑（造消息 / 解析候选草稿，禁止 IO）。"""

backend/app/modules/big_cycle/extract.py

@@ -0,0 +1,114 @@
+"""解析模型返回为演化候选草稿（纯函数，无 IO）。
+
+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
+数组，并归一化为演化候选草稿。``evidence`` 序号由 application 映射为记忆 ID。
+
+缺省字段取**安全悲观默认**：``level=L4``（必须人工确认）、``auto_apply_allowed=False``，
+避免模型漏字段时误自动应用高风险演化。
+"""
+
+import json
+import re
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class EvolutionCandidateDraft:
+    type: str
+    title: str
+    proposal: str
+    level: str = "L4"
+    scope: str = "character_scope"
+    source: str = "big_cycle"
+    delta: dict[str, Any] = field(default_factory=dict)
+    confidence: float = 0.5
+    benefit_score: float = 0.5
+    risk_score: float = 0.5
+    drift_score: float = 0.0
+    auto_apply_allowed: bool = False
+    evidence: list[int] = field(default_factory=list)
+
+
+def parse_evolution_candidates(raw: str) -> list[EvolutionCandidateDraft]:
+    drafts: list[EvolutionCandidateDraft] = []
+    for item in _extract_json_array(raw):
+        if not isinstance(item, dict):
+            continue
+        type_ = str(item.get("type", "")).strip()
+        title = str(item.get("title", "")).strip()
+        proposal = str(item.get("proposal", "")).strip()
+        if not type_ or not title or not proposal:
+            continue
+        drafts.append(
+            EvolutionCandidateDraft(
+                type=type_,
+                title=title,
+                proposal=proposal,
+                level=_level(item.get("level")),
+                scope=_scope(item.get("scope")),
+                source=str(item.get("source", "big_cycle")).strip() or "big_cycle",
+                delta=_dict(item.get("delta")),
+                confidence=_clamp_float(item.get("confidence"), 0.5),
+                benefit_score=_clamp_float(item.get("benefit_score"), 0.5),
+                risk_score=_clamp_float(item.get("risk_score"), 0.5),
+                drift_score=_clamp_float(item.get("drift_score"), 0.0),
+                auto_apply_allowed=bool(item.get("auto_apply_allowed", False)),
+                evidence=_int_list(item.get("evidence")),
+            )
+        )
+    return drafts
+
+
+def _extract_json_array(raw: str) -> list[Any]:
+    text = raw.strip()
+    matched = re.search(r"```(?:json)?\s*(\[.*?\])\s*```", text, re.DOTALL)
+    if matched:
+        text = matched.group(1)
+    else:
+        start, end = text.find("["), text.rfind("]")
+        if start != -1 and end > start:
+            text = text[start : end + 1]
+    try:
+        data = json.loads(text)
+    except json.JSONDecodeError:
+        return []
+    return data if isinstance(data, list) else []
+
+
+_VALID_LEVELS = {"L0", "L1", "L2", "L3", "L4"}
+_VALID_SCOPES = {"character_scope", "user_scope", "task_scope", "global_scope"}
+
+
+def _level(value: Any) -> str:
+    text = str(value or "").strip().upper()
+    return text if text in _VALID_LEVELS else "L4"
+
+
+def _scope(value: Any) -> str:
+    text = str(value or "").strip()
+    return text if text in _VALID_SCOPES else "character_scope"
+
+
+def _dict(value: Any) -> dict[str, Any]:
+    return value if isinstance(value, dict) else {}
+
+
+def _clamp_float(value: Any, default: float) -> float:
+    try:
+        num = float(value)
+    except (TypeError, ValueError):
+        return default
+    return max(0.0, min(1.0, num))
+
+
+def _int_list(value: Any) -> list[int]:
+    if not isinstance(value, list):
+        return []
+    result: list[int] = []
+    for item in value:
+        try:
+            result.append(int(item))
+        except (TypeError, ValueError):
+            continue
+    return result

backend/app/modules/big_cycle/prompts.py

@@ -0,0 +1,78 @@
+"""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)