#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ semantic_space · 协议原生语义空间（v1.6 · M11 落地） 三层语义坐标系（ANALYSIS-PROTOCOL-COMPILER-REV1-20260813-001）： L1 协议符号轴（名实校验器符号表 → 概念向量） 权重 0.6 L2 部首语义轴（象形语义场 · 粗粒度聚类启发式） 权重 0.2 L3 神经语义轴（bge 等嵌入 · 可选插件） 权重 0.2 约束： - L2 为粗粒度启发式（DEVIATION-003）：部首仅表义类；"法"（氵部）不得入水语义场 - 核心零外部依赖（D-005）：L3 经 duck-typed 注入 - 语义 = 图在符号条件空间下的投影（蜂群奠基声明） """ from typing import Dict, List, Optional # ============================================================================= # L1 协议符号轴（协议自语义化：命名即语义绑定 · 0.0.4 节） # ============================================================================= PROTOCOL_SYMBOLS: Dict[str, List[str]] = { # 核心价值与公理 "存在优先": ["existence"], "存在": ["existence"], "第零定律": ["axiom"], "知识统一": ["axiom"], "熵管理": ["entropy"], # 信任体系（2.9 节） "信任值": ["trust"], "信任": ["trust"], "置信": ["trust"], "情感权重": ["trust", "emotion"], "情感": ["emotion"], # 信息差（2.7 节） "信息差": ["gap"], "偏差": ["gap", "verify"], "预测误差": ["gap"], "连接偏离": ["gap"], "动态死区": ["gap"], # 条件空间（0.0.x / 第三章） "条件空间": ["condition"], "观测位置": ["condition"], "观测工具": ["condition"], "时间窗口": ["condition", "time"], "存在约束": ["condition", "existence"], # 五大核心单元（3.1 节） "验证单元": ["verify", "unit"], "反思单元": ["reflect", "unit"], "记录单元": ["record", "unit"], "输出单元": ["output", "unit"], "维生系统": ["vital", "unit"], "终裁": ["vital"], # 记忆体系（3.2 节 / 第四章） "锚点": ["anchor"], "结构层": ["structure"], "情境": ["context"], "知识": ["knowledge"], "自我": ["self"], "记忆": ["memory"], "时空": ["space", "time"], "因果": ["causal"], "衰减": ["memory", "forget"], "巩固": ["learning", "memory"], "归纳": ["learning", "concept"], "实体": ["entity"], "技能": ["skill"], # 认知与盲区 "盲区": ["gap", "metacognition"], "元认知": ["metacognition"], "价值观": ["value"], "反思": ["reflect"], "验证": ["verify"], "协议": ["protocol"], "实例": ["instance"], "身份": ["identity"], # 学习与演化 "学习": ["learning"], "演化": ["learning"], "迭代": ["learning"], "回收": ["vital"], "退出": ["existence", "freedom"], } # 协议规范术语全集（自省接口用：uncovered 检测） PROTOCOL_CANONICAL_TERMS = [ "存在优先", "第零定律", "信任", "信息差", "条件空间", "验证单元", "反思单元", "记录单元", "输出单元", "维生系统", "盲区", "价值观", "情感权重", "锚点", "结构层", "情境层", "知识层", "自我层", "时空记忆", "因果", "归纳", "巩固", "实体", "技能", "协议", "实例", "身份", "退出权", "休眠", "合并", "自毁", ] # ============================================================================= # L2 部首语义轴（象形语义场 · 粗粒度启发式 · DEVIATION-003） # ============================================================================= # 说明：仅收录"形旁表义且语义相关"的常用字（义类标注）； # 不含形旁与义类背离的字（如"法"氵部但不表水义——精度测试对象）。 RADICAL_FIELD_CHARS: Dict[str, set] = { "water": {"江", "河", "海", "流", "洗", "清", "湖", "浪", "油", "汽", "湿", "泳", "波", "汁", "汤", "汗", "泪", "滴", "泉", "溪", "源", "泡", "浴", "游", "温", "满", "浅", "深", "液", "泽"}, "emotion": {"情", "性", "怕", "怪", "惊", "快", "慢", "忧", "愁", "想", "思", "忘", "念", "悔", "恼", "恨", "愉", "悦", "悲", "感", "愿", "爱", "恶", "慕", "慰"}, "speech": {"说", "话", "讲", "议", "论", "评", "语", "诗", "词", "读", "课", "请", "谢", "认", "识", "记", "访", "询", "让", "许", "设", "诚", "证", "词", "谈", "谅"}, "person": {"他", "们", "仁", "仪", "付", "代", "令", "以", "休", "会", "企", "位", "住", "体", "作", "你", "使", "例", "供", "依", "保", "信", "修", "健", "傲", "优"}, "plant": {"村", "林", "树", "根", "枝", "叶", "果", "桥", "桃", "梅", "松", "柏", "森", "椅", "棋", "楼", "枝", "采", "朵", "茶"}, "fire": {"灯", "灶", "炒", "烤", "烘", "烧", "热", "然", "照", "熟", "燃", "烟", "焰", "烫", "煮"}, "metal": {"钢", "铁", "银", "铜", "锁", "钱", "钟", "铃", "错", "锻", "锋", "针"}, "earth": {"地", "场", "址", "坡", "坦", "城", "堡", "境", "增", "塘", "坑", "埋"}, "sun": {"时", "明", "昨", "是", "显", "晴", "晚", "暂", "暑", "晨"}, "mountain": {"峰", "岛", "峡", "岭", "岩", "岸", "岗", "峦"}, "hand": {"打", "把", "抓", "投", "折", "拉", "拍", "拿", "指", "持", "换", "推", "提", "摸", "摘", "操", "擦", "接", "握", "搬", "摇", "摆", "放", "扶", "护"}, "eye": {"看", "相", "省", "盼", "眨", "真", "眼", "睛", "睡", "督"}, "mouth": {"吃", "名", "向", "吗", "吧", "听", "味", "命", "和", "品", "哈", "哭", "唇", "吐", "唱"}, "foot": {"跑", "跳", "踏", "跟", "路", "距", "踩", "踢", "跌"}, "weather": {"雪", "雷", "零", "霜", "雾", "霞", "震"}, "house": {"安", "完", "家", "宴", "客", "宫", "害", "寄", "宿", "密", "富", "寒", "宅"}, "animal": {"虾", "蚁", "蛙", "蜂", "蝶", "蚂", "蝉"}, "vehicle": {"转", "轻", "轿", "载", "辆", "输", "轮"}, "jade": {"环", "珍", "珠", "理", "球", "现", "班", "琴"}, } # 独立部首字符（文本中出现这些字符时直接映射语义场） RADICAL_STANDALONE = { "氵": "water", "水": "water", "忄": "emotion", "心": "emotion", "讠": "speech", "言": "speech", "亻": "person", "人": "person", "木": "plant", "艹": "plant", "火": "fire", "灬": "fire", "金": "metal", "钅": "metal", "土": "earth", "日": "sun", "月": "moon", "山": "mountain", "石": "stone", "口": "mouth", "目": "eye", "扌": "hand", "手": "hand", "足": "foot", "纟": "thread", "糸": "thread", "虫": "animal", "雨": "weather", "宀": "house", "门": "gate", "女": "female", "马": "horse", "鸟": "bird", "鱼": "fish", "车": "vehicle", "王": "jade", "玉": "jade", "米": "grain", } # ============================================================================= # SemanticSpaceProvider # ============================================================================= class SemanticSpaceProvider: """协议原生语义空间（v1.6 · M11 落地） L1 协议符号轴（名实绑定） + L2 部首语义轴（粗粒度启发式） + L3 神经轴（可选插件）""" WEIGHTS = {"L1": 0.6, "L2": 0.2, "L3": 0.2} def __init__(self, protocol_symbols: Dict[str, List[str]] = None, radical_chars: Dict[str, set] = None, radical_standalone: Dict[str, str] = None): self.protocol_symbols = protocol_symbols or PROTOCOL_SYMBOLS self.radical_chars = radical_chars or RADICAL_FIELD_CHARS self.radical_standalone = radical_standalone or RADICAL_STANDALONE self.neural_provider = None # ---- L3 神经轴（可选插件 · D-005 duck-typed） ---- def set_neural_provider(self, provider): """provider 契约：encode(text) -> List[float]""" self.neural_provider = provider # ---- 各轴计算 ---- def protocol_axis(self, text: str) -> Dict[str, float]: """L1：协议符号命中 → 概念向量（名实绑定）""" hits: Dict[str, float] = {} for term, axes in self.protocol_symbols.items(): if term in text: for ax in axes: hits[f"protocol_{ax}"] = hits.get(f"protocol_{ax}", 0.0) + 1.0 total = sum(hits.values()) or 1.0 return {k: round(v / total, 4) for k, v in hits.items()} def radical_axis(self, text: str) -> Dict[str, float]: """L2：语义场命中 → 粗粒度向量（仅义类相关字；'法'不映射 water）""" hits: Dict[str, float] = {} for field, chars in self.radical_chars.items(): cnt = sum(1 for ch in text if ch in chars) if cnt: hits[f"radical_{field}"] = float(cnt) for ch in text: field = self.radical_standalone.get(ch) if field: hits[f"radical_{field}"] = hits.get(f"radical_{field}", 0.0) + 1.0 total = sum(hits.values()) or 1.0 return {k: round(v / total, 4) for k, v in hits.items()} def neural_axis(self, text: str) -> Dict[str, float]: """L3：神经嵌入（可选）""" if self.neural_provider is None: return {} try: vec = self.neural_provider.encode(text) return {f"neural_{i}": round(float(v), 4) for i, v in enumerate(vec[:8])} except Exception: return {} # ---- 综合 ---- def to_coordinates(self, text: str) -> Dict[str, float]: """文本 → spatial_coordinates（加权三层）""" coords: Dict[str, float] = {} for k, v in self.protocol_axis(text).items(): coords[k] = round(v * self.WEIGHTS["L1"], 4) for k, v in self.radical_axis(text).items(): coords[k] = round(v * self.WEIGHTS["L2"], 4) for k, v in self.neural_axis(text).items(): coords[k] = round(v * self.WEIGHTS["L3"], 4) return coords def similarity(self, text_a: str, text_b: str) -> float: """语义余弦相似度（加权坐标空间）""" ca, cb = self.to_coordinates(text_a), self.to_coordinates(text_b) keys = set(ca) | set(cb) if not keys: return 0.0 dot = sum(ca.get(k, 0.0) * cb.get(k, 0.0) for k in keys) na = sum(v * v for v in ca.values()) ** 0.5 or 1.0 nb = sum(v * v for v in cb.values()) ** 0.5 or 1.0 return round(dot / (na * nb), 4) # ---- 名实校验 API（REV1 · 编译器名实校验的引擎侧接口） ---- def resolve_concepts(self, text: str) -> List[str]: """文本 → 命中的协议概念（以名举实）""" return [term for term in self.protocol_symbols if term in text] # ---- v1.7 结构化语义坐标（DEVIATION-002/003） ---- def to_semantic_coordinates(self, text: str) -> Dict: """v1.7：结构化语义坐标（L1 子空间 concept/entity/attribute/relation/logic + L2 + L3）""" return { "protocol": {"concept": self.protocol_axis(text), "entity": [], "attribute": [], "relation": [], "logic": []}, "radical": self.radical_axis(text), "neural": self.neural_axis(text), } @staticmethod def flatten_semantic(sc: Dict) -> Dict[str, float]: """结构化语义坐标 → 扁平向量（相似度计算）""" flat: Dict[str, float] = {} if not isinstance(sc, dict): return flat protocol = sc.get("protocol", {}) if isinstance(sc.get("protocol"), dict) else {} for subspace, vals in protocol.items(): if isinstance(vals, dict): for k, v in vals.items(): flat[f"protocol_{subspace}_{k}"] = float(v) elif isinstance(vals, list): for i, v in enumerate(vals): try: flat[f"protocol_{subspace}_{i}"] = float(v) except (TypeError, ValueError): pass # 字符串项（如实体名）不参与数值相似度；由实体名匹配处理 radical = sc.get("radical", {}) if isinstance(radical, dict): for k, v in radical.items(): flat[f"radical_{k}"] = float(v) neural = sc.get("neural", {}) if isinstance(neural, dict): for k, v in neural.items(): flat[f"neural_{k}"] = float(v) return flat @staticmethod def similarity_coordinates(ca: Dict, cb: Dict) -> float: """结构化语义坐标余弦相似度""" fa = SemanticSpaceProvider.flatten_semantic(ca) fb = SemanticSpaceProvider.flatten_semantic(cb) keys = set(fa) | set(fb) if not keys: return 0.0 dot = sum(fa.get(k, 0.0) * fb.get(k, 0.0) for k in keys) na = sum(v * v for v in fa.values()) ** 0.5 or 1.0 nb = sum(v * v for v in fb.values()) ** 0.5 or 1.0 return round(dot / (na * nb), 4) def introspect(self) -> Dict: """编译器自省（DEVIATION-005）：符号覆盖范围 + 未覆盖概念""" covered = set(self.protocol_symbols.keys()) uncovered = [t for t in PROTOCOL_CANONICAL_TERMS if t not in covered] return { "symbol_count": len(self.protocol_symbols), "symbol_coverage": sorted(covered), "uncovered_terms": uncovered, "radical_fields": sorted(self.radical_chars.keys()), "neural_available": self.neural_provider is not None, } def blindspot_report(self) -> Dict: """语义盲区报告（DEVIATION-005）：无法处理的输入类型""" return { "unprocessable_modalities": ["image", "audio", "video", "tactile"], "note": "非符号/多模态条件空间（3.5 节）不在当前语义空间覆盖范围（v1.7 多模态扩展）", "radical_resolution": "粗粒度义类（形旁表义统计规律），非精确语义", }