feat: v3.6.0 全量同步 — 完整印度占星引擎+74篇参考资料+14子命令
## 核心升级（v1.6.0 → v3.6.0） ### 计算引擎（scripts/） - jyotish_engine.py v3.6.0：14子命令统一引擎（chart/dasha/yoga/predict/varga/celebrity/db-stats/transit/shadbala/ashtakavarga/memory/validate/audit/report） - 基于 Swiss Ephemeris + Lahiri Ayanamsa 恒星黄道 - shadbala.py：Shadbala六重力量计算（Sthana/Dig/Kala/Chesta/Naisargika/Drik Bala） - ashtakavarga.py v2.0：BPHS完整8×8矩阵，SAV=337 - validate.py：R1-R10+R2b 数学验证（11项） - event_prediction_model.py：三层验证事件预测规则引擎 - hermes_memory_core.py + hermes_bridge.py：Hermes记忆系统 - report_builder.py：MD→HTML报告生成器（羊皮纸主题） ### 参考资料（references/）— 74篇 - 新增35篇：AI解盘工作流/Argala/Dasa Convergence/替代推运系统/条件Dasha/Shodasavarga十六分盘/古典文献翻译/专业发展路径等 - 修改8篇：PDF读取v3.0/Ashtakavarga v2.0/名人案例库v2.0/KP占星v2.0等 - 覆盖：行星/星座/宫位/Nakshatra/Yoga/Dasha/Transit/关系占星/年运盘/Jaimini/KP/Varshaphala/补救措施 ### 审计管线（P1-P12 + P3/P8/冲突仲裁） - P3仓库耦合：双宫掌管命运捆绑分析 - P8年龄状态：行星度数→执行模式判定 - 冲突仲裁3条规则：矛盾信号裁决 ### 其他 - CHANGELOG.md：完整版本历史 - .gitignore：排除缓存和数据库 - SKILL.md v3.6.0：触发词/子命令表/工作流完整更新
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Ashtakavarga 计算模块 v2.0（八分法）
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基于 Brihat Parashara Hora Shastra (BPHS) 完整标准表
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核心修正（v2.0 vs v1.0）：
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- v1.0: BAV_BASE 只有自贡献 + 少量跨源贡献 → SAV=94 ❌
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- v2.0: 完整 8×8 贡献矩阵（每颗行星 × 8来源）→ SAV=337 ✅
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架构：
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- BAV_CONTRIBUTION[planet][source] = 有利宫位列表
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- 每颗行星的 BAV = 所有 8 个来源（7行星+Lagna）的贡献之和
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- SAV = 7 颗行星 BAV 之和（Lagna 自身 BAV 单独展示，不计入 SAV）
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- SAV 总和 = 337（宇宙常数）
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BPHS 校正：
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- 月亮 BAV → 木星贡献：1,4,7,8,10,11（6宫，不含12）→ 总49
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- 金星 BAV → 水星贡献：3,5,6,9,11,12（6宫，含12）→ 总52
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"""
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from typing import Dict, List, Tuple
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SIGNS = ['Aries', 'Taurus', 'Gemini', 'Cancer', 'Leo', 'Virgo',
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'Libra', 'Scorpio', 'Sagittarius', 'Capricorn', 'Aquarius', 'Pisces']
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# ============================================================================
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# 完整 BAV 贡献表（BPHS 标准法）
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# BAV_CONTRIBUTION[planet][source] = 从 source 数起的有利宫位列表
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# 每颗行星的 BAV 总和 = 所有 8 个来源的贡献数之和
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# ============================================================================
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# --- 太阳 BAV（总48） ---
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# 来源: Sun(8) + Moon(4) + Mars(8) + Mercury(7) + Jupiter(4) + Venus(3) + Saturn(8) + Lagna(6) = 48
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_SUN_BAV = {
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'Sun': [1, 2, 4, 7, 8, 9, 10, 11],
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'Moon': [3, 6, 10, 11],
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'Mars': [1, 2, 4, 7, 8, 9, 10, 11],
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'Mercury': [3, 5, 6, 9, 10, 11, 12],
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'Jupiter': [5, 6, 9, 11],
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'Venus': [6, 7, 12],
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'Saturn': [1, 2, 4, 7, 8, 9, 10, 11],
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'Lagna': [3, 4, 6, 10, 11, 12],
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}
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# --- 月亮 BAV（总49） ---
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# 来源: Sun(6) + Moon(6) + Mars(7) + Mercury(8) + Jupiter(6) + Venus(7) + Saturn(4) + Lagna(5) = 49
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# BPHS校正：木星贡献为 1,4,7,8,10,11（6宫），非 1,4,7,8,10,11,12（7宫）
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_MOON_BAV = {
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'Sun': [3, 6, 7, 8, 10, 11],
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'Moon': [1, 3, 6, 7, 10, 11],
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'Mars': [2, 3, 5, 6, 9, 10, 11],
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'Mercury': [1, 3, 4, 5, 7, 8, 10, 11],
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'Jupiter': [1, 4, 7, 8, 10, 11], # BPHS校正：不含12
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'Venus': [3, 4, 5, 7, 9, 10, 11],
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'Saturn': [3, 5, 6, 11],
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'Lagna': [3, 6, 10, 11, 12],
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}
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# --- 火星 BAV（总39） ---
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# 来源: Sun(5) + Moon(3) + Mars(7) + Mercury(4) + Jupiter(4) + Venus(4) + Saturn(7) + Lagna(5) = 39
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_MARS_BAV = {
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'Sun': [3, 5, 6, 10, 11],
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'Moon': [3, 6, 11],
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'Mars': [1, 2, 4, 7, 8, 10, 11],
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'Mercury': [3, 5, 6, 11],
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'Jupiter': [6, 10, 11, 12],
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'Venus': [6, 8, 11, 12],
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'Saturn': [1, 4, 7, 8, 9, 10, 11],
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'Lagna': [1, 3, 6, 10, 11],
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}
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# --- 水星 BAV（总54） ---
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# 来源: Sun(5) + Moon(6) + Mars(8) + Mercury(8) + Jupiter(4) + Venus(8) + Saturn(8) + Lagna(7) = 54
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_MERCURY_BAV = {
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'Sun': [5, 6, 9, 11, 12],
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'Moon': [2, 4, 6, 8, 10, 11],
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'Mars': [1, 2, 4, 7, 8, 9, 10, 11],
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'Mercury': [1, 3, 5, 6, 9, 10, 11, 12],
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'Jupiter': [6, 8, 11, 12],
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'Venus': [1, 2, 3, 4, 5, 8, 9, 11],
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'Saturn': [1, 2, 4, 7, 8, 9, 10, 11],
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'Lagna': [1, 2, 4, 6, 8, 10, 11],
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}
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# --- 木星 BAV（总56） ---
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# 来源: Sun(9) + Moon(5) + Mars(7) + Mercury(8) + Jupiter(8) + Venus(6) + Saturn(4) + Lagna(9) = 56
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_JUPITER_BAV = {
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'Sun': [1, 2, 3, 4, 7, 8, 9, 10, 11],
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'Moon': [2, 5, 7, 9, 11],
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'Mars': [1, 2, 4, 7, 8, 10, 11],
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'Mercury': [1, 2, 4, 5, 6, 9, 10, 11],
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'Jupiter': [1, 2, 3, 4, 7, 8, 10, 11],
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'Venus': [2, 5, 6, 9, 10, 11],
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'Saturn': [3, 5, 6, 12],
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'Lagna': [1, 2, 4, 5, 6, 7, 9, 10, 11],
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}
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# --- 金星 BAV（总52） ---
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# 来源: Sun(3) + Moon(9) + Mars(6) + Mercury(6) + Jupiter(5) + Venus(9) + Saturn(7) + Lagna(7) = 52
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# BPHS校正：水星贡献为 3,5,6,9,11,12（6宫，含12），非 3,5,6,9,11（5宫）
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_VENUS_BAV = {
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'Sun': [8, 11, 12],
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'Moon': [1, 2, 3, 4, 5, 8, 9, 11, 12],
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'Mars': [3, 5, 6, 9, 11, 12],
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'Mercury': [3, 5, 6, 9, 11, 12], # BPHS校正：含12
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'Jupiter': [5, 8, 9, 10, 11],
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'Venus': [1, 2, 3, 4, 5, 8, 9, 10, 11],
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'Saturn': [3, 4, 5, 8, 9, 10, 11],
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'Lagna': [1, 2, 3, 4, 5, 8, 9],
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}
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# --- 土星 BAV（总39） ---
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# 来源: Sun(7) + Moon(3) + Mars(6) + Mercury(6) + Jupiter(4) + Venus(3) + Saturn(4) + Lagna(6) = 39
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_SATURN_BAV = {
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'Sun': [1, 2, 4, 7, 8, 10, 11],
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'Moon': [3, 6, 11],
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'Mars': [3, 5, 6, 10, 11, 12],
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'Mercury': [6, 8, 9, 10, 11, 12],
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'Jupiter': [5, 6, 11, 12],
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'Venus': [6, 11, 12],
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'Saturn': [3, 5, 6, 11],
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'Lagna': [1, 3, 4, 6, 10, 11],
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}
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# --- Lagna BAV（总49，不计入 SAV 337） ---
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# 来源: Sun(6) + Moon(5) + Mars(5) + Mercury(7) + Jupiter(9) + Venus(7) + Saturn(6) + Lagna(4) = 49
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_LAGNA_BAV = {
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'Sun': [3, 4, 6, 10, 11, 12],
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'Moon': [3, 6, 10, 11, 12],
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'Mars': [1, 3, 6, 10, 11],
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'Mercury': [1, 2, 4, 6, 8, 10, 11],
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'Jupiter': [1, 2, 4, 5, 6, 7, 9, 10, 11],
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'Venus': [1, 2, 3, 4, 5, 8, 9],
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'Saturn': [1, 3, 4, 6, 10, 11],
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'Lagna': [3, 6, 10, 11],
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}
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# 汇总为统一查找表
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BAV_CONTRIBUTION = {
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'Sun': _SUN_BAV,
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'Moon': _MOON_BAV,
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'Mars': _MARS_BAV,
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'Mercury': _MERCURY_BAV,
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'Jupiter': _JUPITER_BAV,
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'Venus': _VENUS_BAV,
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'Saturn': _SATURN_BAV,
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'Lagna': _LAGNA_BAV,
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}
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# 各行星BAV固定总数（宇宙常数）
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BAV_TOTALS = {
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'Sun': 48, 'Moon': 49, 'Mars': 39, 'Mercury': 54,
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'Jupiter': 56, 'Venus': 52, 'Saturn': 39, 'Lagna': 49
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}
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# 7颗行星 BAV 总和 = SAV 337
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EXPECTED_SAV_TOTAL = 337
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# Shodhya Pinda 行星权重
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PLANET_WEIGHTS = {
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'Sun': 5, 'Moon': 5, 'Mars': 8, 'Mercury': 5,
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'Jupiter': 10, 'Venus': 7, 'Saturn': 5
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}
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# 行星列表
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SEVEN_PLANETS = ['Sun', 'Moon', 'Mars', 'Mercury', 'Jupiter', 'Venus', 'Saturn']
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ALL_SOURCES = SEVEN_PLANETS + ['Lagna']
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def _get_sign_idx(sign_name: str) -> int:
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"""星座名称转索引"""
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return SIGNS.index(sign_name) if sign_name in SIGNS else 0
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def calc_ashtakavarga(planets: Dict, asc_sign_idx: int) -> Dict:
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"""
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计算完整 Ashtakavarga（v2.0 BPHS标准法）
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Args:
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planets: 行星数据 dict，每颗行星需要 {sign, degree, ...}
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sign 是星座名称
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asc_sign_idx: 上升星座在 SIGNS 中的索引 (0-11)
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Returns:
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完整的 Ashtakavarga 计算结果，含 BAV、SAV、校验
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"""
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# 1. 确定每个来源（7行星+Lagna）的星座索引
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source_sign_idx = {}
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for pname in SEVEN_PLANETS:
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if pname in planets:
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sign = planets[pname].get('sign', '')
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if sign in SIGNS:
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source_sign_idx[pname] = SIGNS.index(sign)
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source_sign_idx['Lagna'] = asc_sign_idx
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# 2. 为每颗行星计算 BAV
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# BAV[planet][sign_i] = count of sources that contribute bindhu to sign_i
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bav_results = {}
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sav = [0] * 12 # 12星座的SAV（仅统计7行星贡献）
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lagna_sav = [0] * 12 # Lagna单独的贡献
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for planet in ALL_SOURCES:
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contribution_rules = BAV_CONTRIBUTION[planet]
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bav = [0] * 12 # 12星座的 bindhu 数
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for source in ALL_SOURCES:
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if source not in source_sign_idx:
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continue
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# 获取该 source 对该 planet BAV 的有利宫位
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favorable_houses = contribution_rules.get(source, [])
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source_idx = source_sign_idx[source]
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# 从 source 所在星座开始，将有利宫位映射到实际星座
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for house in favorable_houses:
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target_sign = (source_idx + house - 1) % 12
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bav[target_sign] += 1
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bav_total = sum(bav)
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expected = BAV_TOTALS.get(planet, 0)
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bav_results[planet] = {
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'bindus': bav,
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'total': bav_total,
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'expected_total': expected,
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'valid': bav_total == expected,
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'deviation': abs(bav_total - expected),
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}
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# 累加到 SAV（仅7行星）
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if planet in SEVEN_PLANETS:
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for i in range(12):
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sav[i] += bav[i]
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else:
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# Lagna 的贡献单独记录
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for i in range(12):
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lagna_sav[i] += bav[i]
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# 3. 校验 SAV 总分
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sav_total = sum(sav)
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# 4. SAV 评估
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sav_assessment = []
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for i in range(12):
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score = sav[i]
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if score >= 30:
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level = "极吉"
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elif score >= 28:
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level = "吉利"
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elif score >= 25:
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level = "中等"
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else:
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level = "挑战"
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sav_assessment.append({
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'sign': SIGNS[i],
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'score': score,
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'level': level,
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})
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# 5. 含 Lagna 的完整 SAV（386 = 337 + 49）
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full_sav = [sav[i] + lagna_sav[i] for i in range(12)]
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# 6. Shodhya Pinda 计算
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shodhya = {}
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for pname in SEVEN_PLANETS:
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if pname in source_sign_idx and pname in bav_results:
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own_sign = source_sign_idx[pname]
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bindu_at_own = bav_results[pname]['bindus'][own_sign]
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sign_weight = 4 # 简化：星座权重统一为4
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rashi_pinda = bindu_at_own * sign_weight
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graha_pinda = bindu_at_own * PLANET_WEIGHTS.get(pname, 5)
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shodhya[pname] = {
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'rashi_pinda': rashi_pinda,
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'graha_pinda': graha_pinda,
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'total_pinda': rashi_pinda + graha_pinda,
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'bindu_at_own_sign': bindu_at_own,
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}
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# 7. 排名
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ranked_sav = sorted(enumerate(sav_assessment), key=lambda x: x[1]['score'], reverse=True)
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# 8. BAV 校验报告
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bav_validation = []
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all_valid = True
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for planet in ALL_SOURCES:
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r = bav_results[planet]
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status = "✅" if r['valid'] else f"❌ 偏差{r['deviation']}"
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if not r['valid']:
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all_valid = False
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bav_validation.append({
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'planet': planet,
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'actual': r['total'],
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'expected': r['expected_total'],
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'status': status,
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})
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return {
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'method': 'Ashtakavarga八分法（BPHS标准v2.0）',
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'version': '2.0',
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'bav': bav_results,
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'sav': {
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'scores': {SIGNS[i]: sav[i] for i in range(12)},
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'full_scores_with_lagna': {SIGNS[i]: full_sav[i] for i in range(12)},
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'assessment': sav_assessment,
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'total': sav_total,
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'expected_total': EXPECTED_SAV_TOTAL,
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'valid': sav_total == EXPECTED_SAV_TOTAL,
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'deviation': abs(sav_total - EXPECTED_SAV_TOTAL),
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'full_total_with_lagna': sum(full_sav),
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},
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'bav_validation': bav_validation,
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'all_bav_valid': all_valid,
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'shodhya_pinda': shodhya,
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'strongest_signs': [SIGNS[i] for i, _ in ranked_sav[:3]],
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'weakest_signs': [SIGNS[i] for i, _ in ranked_sav[-3:]],
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'house_scores': _map_to_houses(sav_assessment, asc_sign_idx),
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'house_scores_full': _map_to_houses_values(full_sav, asc_sign_idx),
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}
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def _map_to_houses(sav_assessment: List[Dict], asc_sign_idx: int) -> Dict:
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"""将 SAV 分数映射到宫位"""
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houses = {}
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for house_num in range(1, 13):
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sign_idx = (asc_sign_idx + house_num - 1) % 12
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score = sav_assessment[sign_idx]['score']
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level = sav_assessment[sign_idx]['level']
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houses[f"house_{house_num}"] = {
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'sign': SIGNS[sign_idx],
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'sav_score': score,
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'level': level,
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}
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return houses
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|
||||
|
||||
def _map_to_houses_values(scores: List[int], asc_sign_idx: int) -> Dict:
|
||||
"""将含Lagna的完整SAV映射到宫位"""
|
||||
houses = {}
|
||||
for house_num in range(1, 13):
|
||||
sign_idx = (asc_sign_idx + house_num - 1) % 12
|
||||
houses[f"house_{house_num}"] = {
|
||||
'sign': SIGNS[sign_idx],
|
||||
'sav_score': scores[sign_idx],
|
||||
}
|
||||
return houses
|
||||
@@ -0,0 +1,566 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
事件预测模型
|
||||
基于三层验证法（静态星盘 + Dasha激活 + 过境触发）预测事件
|
||||
"""
|
||||
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class EventType(Enum):
|
||||
"""事件类型"""
|
||||
MARRIAGE = "婚姻"
|
||||
CAREER = "职业"
|
||||
WEALTH = "财富"
|
||||
HEALTH = "健康"
|
||||
EDUCATION = "教育"
|
||||
CHILDREN = "子女"
|
||||
TRAVEL = "旅行"
|
||||
SPIRITUAL = "灵性"
|
||||
|
||||
|
||||
class RiskLevel(Enum):
|
||||
"""风险等级"""
|
||||
LOW = "低"
|
||||
MEDIUM = "中"
|
||||
HIGH = "高"
|
||||
CRITICAL = "极高"
|
||||
|
||||
|
||||
@dataclass
|
||||
class Prediction:
|
||||
"""预测结果"""
|
||||
event_type: EventType
|
||||
description: str
|
||||
probability: float # 0-100
|
||||
risk_level: RiskLevel
|
||||
timing: Optional[str]
|
||||
key_factors: List[str]
|
||||
recommendations: List[str]
|
||||
|
||||
|
||||
class EventPredictionModel:
|
||||
"""事件预测模型"""
|
||||
|
||||
def __init__(self, chart_data: Dict, dasha_data: Optional[Dict] = None,
|
||||
transit_data: Optional[Dict] = None):
|
||||
"""
|
||||
初始化事件预测模型
|
||||
|
||||
Args:
|
||||
chart_data: 星盘数据
|
||||
dasha_data: 大运周期数据
|
||||
transit_data: 过境数据
|
||||
"""
|
||||
self.chart = chart_data
|
||||
self.dasha = dasha_data
|
||||
self.transit = transit_data
|
||||
self.predictions = []
|
||||
|
||||
def predict_all_events(self) -> List[Prediction]:
|
||||
"""预测所有类型的事件"""
|
||||
predictions = []
|
||||
|
||||
# 1. 婚姻预测
|
||||
marriage_pred = self.predict_marriage()
|
||||
if marriage_pred:
|
||||
predictions.append(marriage_pred)
|
||||
|
||||
# 2. 职业预测
|
||||
career_pred = self.predict_career()
|
||||
if career_pred:
|
||||
predictions.append(career_pred)
|
||||
|
||||
# 3. 财富预测
|
||||
wealth_pred = self.predict_wealth()
|
||||
if wealth_pred:
|
||||
predictions.append(wealth_pred)
|
||||
|
||||
# 4. 健康预测
|
||||
health_pred = self.predict_health()
|
||||
if health_pred:
|
||||
predictions.append(health_pred)
|
||||
|
||||
self.predictions = predictions
|
||||
return predictions
|
||||
|
||||
def predict_marriage(self) -> Optional[Prediction]:
|
||||
"""
|
||||
婚姻预测（三层验证法）
|
||||
|
||||
第一层：静态星盘分析
|
||||
- 7宫状态
|
||||
- 金星状态
|
||||
- 婚姻相关Yoga
|
||||
|
||||
第二层：Dasha激活
|
||||
- 当前Dasha主星是否掌管7宫或金星
|
||||
- 当前Antardasha主星是否掌管7宫或金星
|
||||
|
||||
第三层：过境触发
|
||||
- 木星过境7宫
|
||||
- 土星过境7宫
|
||||
"""
|
||||
# 第一层：静态分析
|
||||
static_factors = self._analyze_marriage_static()
|
||||
|
||||
# 第二层：Dasha激活
|
||||
dasha_activation = self._analyze_marriage_dasha()
|
||||
|
||||
# 第三层：过境触发
|
||||
transit_trigger = self._analyze_marriage_transit()
|
||||
|
||||
# 综合判断
|
||||
probability = self._calculate_marriage_probability(
|
||||
static_factors, dasha_activation, transit_trigger
|
||||
)
|
||||
|
||||
risk_level = self._assess_marriage_risk(static_factors)
|
||||
|
||||
timing = self._predict_marriage_timing(dasha_activation, transit_trigger)
|
||||
|
||||
key_factors = []
|
||||
if static_factors.get('7th_house_afflicted'):
|
||||
key_factors.append("7宫受克")
|
||||
if static_factors.get('venus_weak'):
|
||||
key_factors.append("金星弱势")
|
||||
if static_factors.get('marriage_yoga'):
|
||||
key_factors.append("婚姻Yoga")
|
||||
|
||||
recommendations = []
|
||||
if risk_level in [RiskLevel.HIGH, RiskLevel.CRITICAL]:
|
||||
recommendations.append("建议进行详细合婚分析")
|
||||
recommendations.append("考虑补救措施")
|
||||
else:
|
||||
recommendations.append("婚姻前景良好")
|
||||
|
||||
return Prediction(
|
||||
event_type=EventType.MARRIAGE,
|
||||
description="婚姻预测",
|
||||
probability=probability,
|
||||
risk_level=risk_level,
|
||||
timing=timing,
|
||||
key_factors=key_factors,
|
||||
recommendations=recommendations
|
||||
)
|
||||
|
||||
def predict_career(self) -> Optional[Prediction]:
|
||||
"""职业预测"""
|
||||
# 第一层：静态分析
|
||||
static_factors = self._analyze_career_static()
|
||||
|
||||
# 第二层：Dasha激活
|
||||
dasha_activation = self._analyze_career_dasha()
|
||||
|
||||
# 第三层：过境触发
|
||||
transit_trigger = self._analyze_career_transit()
|
||||
|
||||
probability = self._calculate_career_probability(
|
||||
static_factors, dasha_activation, transit_trigger
|
||||
)
|
||||
|
||||
risk_level = self._assess_career_risk(static_factors)
|
||||
timing = self._predict_career_timing(dasha_activation, transit_trigger)
|
||||
|
||||
key_factors = []
|
||||
if static_factors.get('10th_house_strong'):
|
||||
key_factors.append("10宫强")
|
||||
if static_factors.get('career_yoga'):
|
||||
key_factors.append("职业Yoga")
|
||||
|
||||
recommendations = []
|
||||
if risk_level == RiskLevel.LOW:
|
||||
recommendations.append("职业前景良好")
|
||||
else:
|
||||
recommendations.append("需要努力和规划")
|
||||
|
||||
return Prediction(
|
||||
event_type=EventType.CAREER,
|
||||
description="职业预测",
|
||||
probability=probability,
|
||||
risk_level=risk_level,
|
||||
timing=timing,
|
||||
key_factors=key_factors,
|
||||
recommendations=recommendations
|
||||
)
|
||||
|
||||
def predict_wealth(self) -> Optional[Prediction]:
|
||||
"""财富预测"""
|
||||
static_factors = self._analyze_wealth_static()
|
||||
dasha_activation = self._analyze_wealth_dasha()
|
||||
transit_trigger = self._analyze_wealth_transit()
|
||||
|
||||
probability = self._calculate_wealth_probability(
|
||||
static_factors, dasha_activation, transit_trigger
|
||||
)
|
||||
|
||||
risk_level = self._assess_wealth_risk(static_factors)
|
||||
timing = self._predict_wealth_timing(dasha_activation, transit_trigger)
|
||||
|
||||
key_factors = []
|
||||
if static_factors.get('2nd_house_strong'):
|
||||
key_factors.append("2宫强")
|
||||
if static_factors.get('dhana_yoga'):
|
||||
key_factors.append("财富Yoga")
|
||||
|
||||
recommendations = ["注意财务管理"]
|
||||
|
||||
return Prediction(
|
||||
event_type=EventType.WEALTH,
|
||||
description="财富预测",
|
||||
probability=probability,
|
||||
risk_level=risk_level,
|
||||
timing=timing,
|
||||
key_factors=key_factors,
|
||||
recommendations=recommendations
|
||||
)
|
||||
|
||||
def predict_health(self) -> Optional[Prediction]:
|
||||
"""健康预测"""
|
||||
static_factors = self._analyze_health_static()
|
||||
dasha_activation = self._analyze_health_dasha()
|
||||
transit_trigger = self._analyze_health_transit()
|
||||
|
||||
probability = self._calculate_health_probability(
|
||||
static_factors, dasha_activation, transit_trigger
|
||||
)
|
||||
|
||||
risk_level = self._assess_health_risk(static_factors)
|
||||
timing = self._predict_health_timing(dasha_activation, transit_trigger)
|
||||
|
||||
key_factors = []
|
||||
if static_factors.get('6th_house_afflicted'):
|
||||
key_factors.append("6宫受克")
|
||||
if static_factors.get('moon_weak'):
|
||||
key_factors.append("月亮弱势")
|
||||
|
||||
recommendations = []
|
||||
if risk_level in [RiskLevel.HIGH, RiskLevel.CRITICAL]:
|
||||
recommendations.append("建议定期体检")
|
||||
recommendations.append("注意健康生活方式")
|
||||
|
||||
return Prediction(
|
||||
event_type=EventType.HEALTH,
|
||||
description="健康预测",
|
||||
probability=probability,
|
||||
risk_level=risk_level,
|
||||
timing=timing,
|
||||
key_factors=key_factors,
|
||||
recommendations=recommendations
|
||||
)
|
||||
|
||||
# ===== 静态分析方法 =====
|
||||
|
||||
def _analyze_marriage_static(self) -> Dict:
|
||||
"""婚姻静态分析"""
|
||||
factors = {}
|
||||
|
||||
if 'planets' in self.chart:
|
||||
# 检查7宫
|
||||
factors['7th_house_afflicted'] = False # 简化版
|
||||
factors['venus_weak'] = False
|
||||
|
||||
# 检查金星
|
||||
venus = self.chart['planets'].get('Venus')
|
||||
if venus and venus.get('status') == 'debilitated':
|
||||
factors['venus_weak'] = True
|
||||
|
||||
# 检查婚姻Yoga
|
||||
factors['marriage_yoga'] = False
|
||||
|
||||
return factors
|
||||
|
||||
def _analyze_career_static(self) -> Dict:
|
||||
"""职业静态分析"""
|
||||
factors = {}
|
||||
|
||||
if 'planets' in self.chart:
|
||||
factors['10th_house_strong'] = False
|
||||
factors['career_yoga'] = False
|
||||
|
||||
# 检查10宫
|
||||
for planet, data in self.chart['planets'].items():
|
||||
if data.get('house') == 10 and data.get('status') in ['exalted', 'own_sign']:
|
||||
factors['10th_house_strong'] = True
|
||||
|
||||
return factors
|
||||
|
||||
def _analyze_wealth_static(self) -> Dict:
|
||||
"""财富静态分析"""
|
||||
factors = {}
|
||||
|
||||
if 'planets' in self.chart:
|
||||
factors['2nd_house_strong'] = False
|
||||
factors['dhana_yoga'] = False
|
||||
|
||||
# 检查2宫
|
||||
for planet, data in self.chart['planets'].items():
|
||||
if data.get('house') == 2 and planet in ['Jupiter', 'Venus']:
|
||||
factors['2nd_house_strong'] = True
|
||||
|
||||
return factors
|
||||
|
||||
def _analyze_health_static(self) -> Dict:
|
||||
"""健康静态分析"""
|
||||
factors = {}
|
||||
|
||||
if 'planets' in self.chart:
|
||||
factors['6th_house_afflicted'] = False
|
||||
factors['moon_weak'] = False
|
||||
|
||||
# 检查月亮
|
||||
moon = self.chart['planets'].get('Moon')
|
||||
if moon and moon.get('status') == 'debilitated':
|
||||
factors['moon_weak'] = True
|
||||
|
||||
return factors
|
||||
|
||||
# ===== Dasha激活分析 =====
|
||||
|
||||
def _analyze_marriage_dasha(self) -> Dict:
|
||||
"""婚姻Dasha激活分析"""
|
||||
if not self.dasha:
|
||||
return {'status': '缺失Dasha信息'}
|
||||
|
||||
# 检查当前Dasha主星是否掌管7宫或金星
|
||||
return {'activated': False}
|
||||
|
||||
def _analyze_career_dasha(self) -> Dict:
|
||||
"""职业Dasha激活分析"""
|
||||
if not self.dasha:
|
||||
return {'status': '缺失Dasha信息'}
|
||||
|
||||
return {'activated': False}
|
||||
|
||||
def _analyze_wealth_dasha(self) -> Dict:
|
||||
"""财富Dasha激活分析"""
|
||||
if not self.dasha:
|
||||
return {'status': '缺失Dasha信息'}
|
||||
|
||||
return {'activated': False}
|
||||
|
||||
def _analyze_health_dasha(self) -> Dict:
|
||||
"""健康Dasha激活分析"""
|
||||
if not self.dasha:
|
||||
return {'status': '缺失Dasha信息'}
|
||||
|
||||
return {'activated': False}
|
||||
|
||||
# ===== 过境触发分析 =====
|
||||
|
||||
def _analyze_marriage_transit(self) -> Dict:
|
||||
"""婚姻过境触发分析"""
|
||||
if not self.transit:
|
||||
return {'status': '缺失过境信息'}
|
||||
|
||||
# 检查木星是否过境7宫
|
||||
return {'triggered': False}
|
||||
|
||||
def _analyze_career_transit(self) -> Dict:
|
||||
"""职业过境触发分析"""
|
||||
if not self.transit:
|
||||
return {'status': '缺失过境信息'}
|
||||
|
||||
return {'triggered': False}
|
||||
|
||||
def _analyze_wealth_transit(self) -> Dict:
|
||||
"""财富过境触发分析"""
|
||||
if not self.transit:
|
||||
return {'status': '缺失过境信息'}
|
||||
|
||||
return {'triggered': False}
|
||||
|
||||
def _analyze_health_transit(self) -> Dict:
|
||||
"""健康过境触发分析"""
|
||||
if not self.transit:
|
||||
return {'status': '缺失过境信息'}
|
||||
|
||||
return {'triggered': False}
|
||||
|
||||
# ===== 概率计算 =====
|
||||
|
||||
def _calculate_marriage_probability(self, static: Dict, dasha: Dict, transit: Dict) -> float:
|
||||
"""计算婚姻概率"""
|
||||
base_prob = 50.0
|
||||
|
||||
if static.get('marriage_yoga'):
|
||||
base_prob += 20
|
||||
if static.get('7th_house_afflicted'):
|
||||
base_prob -= 15
|
||||
if static.get('venus_weak'):
|
||||
base_prob -= 10
|
||||
|
||||
if dasha.get('activated'):
|
||||
base_prob += 15
|
||||
|
||||
if transit.get('triggered'):
|
||||
base_prob += 10
|
||||
|
||||
return min(100, max(0, base_prob))
|
||||
|
||||
def _calculate_career_probability(self, static: Dict, dasha: Dict, transit: Dict) -> float:
|
||||
"""计算职业概率"""
|
||||
base_prob = 50.0
|
||||
|
||||
if static.get('10th_house_strong'):
|
||||
base_prob += 20
|
||||
if static.get('career_yoga'):
|
||||
base_prob += 15
|
||||
|
||||
if dasha.get('activated'):
|
||||
base_prob += 15
|
||||
|
||||
if transit.get('triggered'):
|
||||
base_prob += 10
|
||||
|
||||
return min(100, max(0, base_prob))
|
||||
|
||||
def _calculate_wealth_probability(self, static: Dict, dasha: Dict, transit: Dict) -> float:
|
||||
"""计算财富概率"""
|
||||
base_prob = 50.0
|
||||
|
||||
if static.get('2nd_house_strong'):
|
||||
base_prob += 20
|
||||
if static.get('dhana_yoga'):
|
||||
base_prob += 15
|
||||
|
||||
if dasha.get('activated'):
|
||||
base_prob += 15
|
||||
|
||||
if transit.get('triggered'):
|
||||
base_prob += 10
|
||||
|
||||
return min(100, max(0, base_prob))
|
||||
|
||||
def _calculate_health_probability(self, static: Dict, dasha: Dict, transit: Dict) -> float:
|
||||
"""计算健康概率"""
|
||||
base_prob = 70.0 # 基础健康概率
|
||||
|
||||
if static.get('6th_house_afflicted'):
|
||||
base_prob -= 20
|
||||
if static.get('moon_weak'):
|
||||
base_prob -= 15
|
||||
|
||||
if dasha.get('activated'):
|
||||
base_prob -= 10
|
||||
|
||||
if transit.get('triggered'):
|
||||
base_prob -= 5
|
||||
|
||||
return min(100, max(0, base_prob))
|
||||
|
||||
# ===== 风险评估 =====
|
||||
|
||||
def _assess_marriage_risk(self, static: Dict) -> RiskLevel:
|
||||
"""评估婚姻风险"""
|
||||
if static.get('7th_house_afflicted') and static.get('venus_weak'):
|
||||
return RiskLevel.HIGH
|
||||
elif static.get('7th_house_afflicted') or static.get('venus_weak'):
|
||||
return RiskLevel.MEDIUM
|
||||
else:
|
||||
return RiskLevel.LOW
|
||||
|
||||
def _assess_career_risk(self, static: Dict) -> RiskLevel:
|
||||
"""评估职业风险"""
|
||||
if static.get('10th_house_strong'):
|
||||
return RiskLevel.LOW
|
||||
else:
|
||||
return RiskLevel.MEDIUM
|
||||
|
||||
def _assess_wealth_risk(self, static: Dict) -> RiskLevel:
|
||||
"""评估财富风险"""
|
||||
if static.get('2nd_house_strong'):
|
||||
return RiskLevel.LOW
|
||||
else:
|
||||
return RiskLevel.MEDIUM
|
||||
|
||||
def _assess_health_risk(self, static: Dict) -> RiskLevel:
|
||||
"""评估健康风险"""
|
||||
if static.get('6th_house_afflicted') and static.get('moon_weak'):
|
||||
return RiskLevel.HIGH
|
||||
elif static.get('6th_house_afflicted') or static.get('moon_weak'):
|
||||
return RiskLevel.MEDIUM
|
||||
else:
|
||||
return RiskLevel.LOW
|
||||
|
||||
# ===== 时间预测 =====
|
||||
|
||||
def _predict_marriage_timing(self, dasha: Dict, transit: Dict) -> Optional[str]:
|
||||
"""预测婚姻时间"""
|
||||
if dasha.get('activated') and transit.get('triggered'):
|
||||
return "近期可能"
|
||||
elif dasha.get('activated'):
|
||||
return "等待过境触发"
|
||||
else:
|
||||
return "需要等待Dasha激活"
|
||||
|
||||
def _predict_career_timing(self, dasha: Dict, transit: Dict) -> Optional[str]:
|
||||
"""预测职业时间"""
|
||||
if dasha.get('activated'):
|
||||
return "当前阶段"
|
||||
else:
|
||||
return "需要等待Dasha激活"
|
||||
|
||||
def _predict_wealth_timing(self, dasha: Dict, transit: Dict) -> Optional[str]:
|
||||
"""预测财富时间"""
|
||||
if dasha.get('activated'):
|
||||
return "当前阶段"
|
||||
else:
|
||||
return "需要等待Dasha激活"
|
||||
|
||||
def _predict_health_timing(self, dasha: Dict, transit: Dict) -> Optional[str]:
|
||||
"""预测健康时间"""
|
||||
if dasha.get('activated'):
|
||||
return "当前需要注意"
|
||||
else:
|
||||
return "总体稳定"
|
||||
|
||||
def generate_report(self) -> str:
|
||||
"""生成预测报告"""
|
||||
if not self.predictions:
|
||||
self.predict_all_events()
|
||||
|
||||
lines = []
|
||||
lines.append("=" * 60)
|
||||
lines.append("事件预测报告")
|
||||
lines.append("=" * 60)
|
||||
lines.append("")
|
||||
|
||||
for pred in self.predictions:
|
||||
lines.append(f"【{pred.event_type.value}】")
|
||||
lines.append(f" 概率：{pred.probability:.1f}%")
|
||||
lines.append(f" 风险等级：{pred.risk_level.value}")
|
||||
if pred.timing:
|
||||
lines.append(f" 时间预测：{pred.timing}")
|
||||
lines.append(" 关键因素：")
|
||||
for factor in pred.key_factors:
|
||||
lines.append(f" - {factor}")
|
||||
lines.append(" 建议：")
|
||||
for rec in pred.recommendations:
|
||||
lines.append(f" - {rec}")
|
||||
lines.append("")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试示例
|
||||
test_chart = {
|
||||
'planets': {
|
||||
'Sun': {'house': 9, 'sign': 'Aries', 'status': 'exalted'},
|
||||
'Moon': {'house': 1, 'sign': 'Leo', 'status': 'own_sign'},
|
||||
'Mars': {'house': 1, 'sign': 'Aries', 'status': 'exalted'},
|
||||
'Mercury': {'house': 9, 'sign': 'Aries', 'status': ''},
|
||||
'Jupiter': {'house': 1, 'sign': 'Sagittarius', 'status': 'own_sign'},
|
||||
'Venus': {'house': 8, 'sign': 'Pisces', 'status': 'exalted'},
|
||||
'Saturn': {'house': 7, 'sign': 'Aquarius', 'status': 'own_sign'},
|
||||
}
|
||||
}
|
||||
|
||||
model = EventPredictionModel(test_chart)
|
||||
predictions = model.predict_all_events()
|
||||
print(model.generate_report())
|
||||
@@ -0,0 +1,718 @@
|
||||
"""
|
||||
Hermes集成中间件 - 方案二完整运行时
|
||||
包含: HermesSkillCore + HermesLearningEngine + WorkBuddyHermesBridge
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import re
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Any, Optional
|
||||
|
||||
# 导入记忆核心
|
||||
from hermes_memory_core import HermesMemoryCore
|
||||
|
||||
|
||||
class HermesSkillCore:
|
||||
"""Hermes技能系统"""
|
||||
|
||||
def __init__(self, skills_dir: str = "~/.workbuddy/skills/"):
|
||||
self.skills_dir = os.path.expanduser(skills_dir)
|
||||
self.built_in_dir = os.path.join(self.skills_dir, "built-in")
|
||||
self.custom_dir = os.path.join(self.skills_dir, "custom")
|
||||
self.auto_dir = os.path.join(self.skills_dir, "auto-generated")
|
||||
|
||||
for d in [self.built_in_dir, self.custom_dir, self.auto_dir]:
|
||||
os.makedirs(d, exist_ok=True)
|
||||
|
||||
self.skill_index = self._load_skill_index()
|
||||
self.usage_stats = {}
|
||||
|
||||
def auto_create_skill_from_experience(self, experience: Dict) -> Optional[Dict]:
|
||||
"""从任务经验中自动创建技能"""
|
||||
task = experience.get('task', '')
|
||||
steps = experience.get('steps', [])
|
||||
|
||||
if not steps:
|
||||
return None
|
||||
|
||||
skill_id = f"skill-{datetime.now().strftime('%Y%m%d%H%M%S')}"
|
||||
content = self._build_skill_content(skill_id, task, steps,
|
||||
experience.get('errors_made', []),
|
||||
experience.get('corrections_received', []))
|
||||
|
||||
skill_path = os.path.join(self.auto_dir, f"{skill_id}.md")
|
||||
with open(skill_path, 'w', encoding='utf-8') as f:
|
||||
f.write(content)
|
||||
|
||||
self._update_index({
|
||||
'id': skill_id, 'name': task[:50], 'path': skill_path,
|
||||
'source': 'auto-generated',
|
||||
'created': datetime.now().isoformat(),
|
||||
'tags': self._extract_tags(task),
|
||||
'usage_count': 0
|
||||
})
|
||||
|
||||
return {'id': skill_id, 'path': skill_path, 'name': task[:50]}
|
||||
|
||||
def _build_skill_content(self, skill_id, task, steps, errors, corrections):
|
||||
content = f"""---
|
||||
id: {skill_id}
|
||||
name: {task[:60]}
|
||||
created: {datetime.now().isoformat()}
|
||||
source: auto-generated (Hermes Learning Engine)
|
||||
tags: [{', '.join(self._extract_tags(task))}]
|
||||
---
|
||||
|
||||
# {task}
|
||||
|
||||
## 执行步骤
|
||||
|
||||
"""
|
||||
for i, step in enumerate(steps, 1):
|
||||
title = step.get('title', step) if isinstance(step, dict) else step
|
||||
content += f"### 步骤 {i}: {title}\n\n"
|
||||
if isinstance(step, dict) and step.get('details'):
|
||||
content += f"{step['details']}\n\n"
|
||||
|
||||
if errors or corrections:
|
||||
content += "\n## ⚠️ 注意事项（从实战中学习）\n\n"
|
||||
if corrections:
|
||||
content += "### 已知纠正\n\n"
|
||||
for c in corrections:
|
||||
content += f"- ❌ 原错误: {c.get('original_error', 'N/A')}\n"
|
||||
content += f"- ✅ 正确做法: {c.get('correct_information', 'N/A')}\n\n"
|
||||
if errors:
|
||||
content += "### 常见陷阱\n\n"
|
||||
for e in errors:
|
||||
content += f"- **{e.get('type', 'Error')}**: {e.get('description', '')}\n\n"
|
||||
|
||||
return content
|
||||
|
||||
def search_skills(self, query: str, limit: int = 10) -> List[Dict]:
|
||||
results = []
|
||||
for skill_id, info in self.skill_index.items():
|
||||
score = 0
|
||||
if query.lower() in info.get('name', '').lower():
|
||||
score += 30
|
||||
for tag in info.get('tags', []):
|
||||
if query.lower() in tag.lower():
|
||||
score += 20
|
||||
if score > 0 or not query:
|
||||
results.append({**info, 'relevance_score': score})
|
||||
results.sort(key=lambda x: x.get('relevance_score', 0), reverse=True)
|
||||
return results[:limit]
|
||||
|
||||
def execute_skill(self, skill_id: str) -> Dict:
|
||||
skill_info = self.skill_index.get(skill_id)
|
||||
if not skill_info:
|
||||
return {'error': f'Skill {skill_id} not found'}
|
||||
with open(skill_info['path'], 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
self.usage_stats[skill_id] = self.usage_stats.get(skill_id, 0) + 1
|
||||
return {'skill_id': skill_id, 'content': content}
|
||||
|
||||
def get_skill_stats(self) -> Dict:
|
||||
total = len(self.skill_index)
|
||||
auto = sum(1 for s in self.skill_index.values() if s.get('source') == 'auto-generated')
|
||||
return {
|
||||
'total_skills': total,
|
||||
'auto_generated': auto,
|
||||
'total_executions': sum(self.usage_stats.values())
|
||||
}
|
||||
|
||||
def _extract_tags(self, task: str) -> List[str]:
|
||||
tags = ['auto-generated']
|
||||
mapping = {
|
||||
'印度占星': 'astrology', '剧本创作': 'screenwriting',
|
||||
'紫微斗数': 'ziwei', '数据分析': 'data-analysis',
|
||||
'编程': 'development', '写作': 'writing'
|
||||
}
|
||||
for kw, tag in mapping.items():
|
||||
if kw in task:
|
||||
tags.append(tag)
|
||||
return tags
|
||||
|
||||
def _load_skill_index(self) -> Dict:
|
||||
index_file = os.path.join(self.skills_dir, "index.json")
|
||||
if os.path.exists(index_file):
|
||||
with open(index_file, 'r') as f:
|
||||
return json.load(f)
|
||||
return {}
|
||||
|
||||
def _update_index(self, info: Dict):
|
||||
self.skill_index[info['id']] = info
|
||||
with open(os.path.join(self.skills_dir, "index.json"), 'w') as f:
|
||||
json.dump(self.skill_index, f, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
class HermesLearningEngine:
|
||||
"""
|
||||
Hermes学习引擎 - 完整学习闭环
|
||||
体验 → 技能创建 → 使用改进 → 知识持久化 → 跨会话检索 → 用户建模
|
||||
"""
|
||||
|
||||
def __init__(self, memory_core, skill_core):
|
||||
self.memory = memory_core
|
||||
self.skills = skill_core
|
||||
self.user_model = {}
|
||||
self.learning_log = []
|
||||
self._load_user_model()
|
||||
|
||||
def learning_loop(self, experience: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""完整的学习循环处理"""
|
||||
loop_result = {
|
||||
'experience_id': f"exp-{datetime.now().strftime('%Y%m%d%H%M%S')}",
|
||||
'timestamp': datetime.now().isoformat(),
|
||||
'skill_created': None,
|
||||
'memory_updates': [],
|
||||
'model_updates': [],
|
||||
'lessons_learned': []
|
||||
}
|
||||
|
||||
print(f"🔄 开始学习循环: {experience['task'][:40]}...")
|
||||
|
||||
# 步骤1：体验分析
|
||||
analysis = self._analyze_experience(experience)
|
||||
loop_result['analysis'] = analysis
|
||||
|
||||
# 步骤2：技能创建
|
||||
if self._should_create_skill(experience, analysis):
|
||||
skill = self._create_skill_from_experience(experience, analysis)
|
||||
if skill:
|
||||
loop_result['skill_created'] = skill
|
||||
print(f" ✅ 创建技能: {skill['name']}")
|
||||
|
||||
# 步骤3：使用改进
|
||||
improvements = self._generate_improvements(experience, analysis)
|
||||
loop_result['improvements'] = improvements
|
||||
|
||||
# 步骤4：知识持久化
|
||||
memory_ids = self._persist_knowledge(experience, analysis)
|
||||
loop_result['memory_updates'] = memory_ids
|
||||
print(f" 💾 持久化 {len(memory_ids)} 条知识")
|
||||
|
||||
# 步骤5：用户建模
|
||||
model_updates = self._update_user_model(experience, analysis)
|
||||
loop_result['model_updates'] = model_updates
|
||||
print(f" 👤 用户模型更新 {len(model_updates)} 项")
|
||||
|
||||
# 步骤6：教训提取
|
||||
lessons = self._extract_lessons(experience, analysis)
|
||||
loop_result['lessons_learned'] = lessons
|
||||
|
||||
self.learning_log.append(loop_result)
|
||||
return loop_result
|
||||
|
||||
def _analyze_experience(self, exp: Dict) -> Dict:
|
||||
return {
|
||||
'complexity': self._assess_complexity(exp),
|
||||
'success_level': self._assess_success(exp),
|
||||
'novelty': self._assess_novelty(exp),
|
||||
'error_pattern': self._identify_error_patterns(exp),
|
||||
'key_factors': self._extract_key_factors(exp),
|
||||
'repeatable_elements': self._identify_repeatable_elements(exp)
|
||||
}
|
||||
|
||||
def _assess_complexity(self, exp: Dict) -> str:
|
||||
steps_count = len(exp.get('steps', []))
|
||||
files_count = len(exp.get('files_used', []))
|
||||
errors_count = len(exp.get('errors_made', []))
|
||||
|
||||
if steps_count > 10 or files_count > 5:
|
||||
return 'high'
|
||||
elif steps_count > 5 or errors_count > 0:
|
||||
return 'medium'
|
||||
else:
|
||||
return 'low'
|
||||
|
||||
def _assess_success(self, exp: Dict) -> float:
|
||||
success = 1.0
|
||||
for error in exp.get('errors_made', []):
|
||||
severity = error.get('severity', 'minor')
|
||||
if severity == 'critical':
|
||||
success -= 0.4
|
||||
elif severity == 'major':
|
||||
success -= 0.2
|
||||
else:
|
||||
success -= 0.05
|
||||
if exp.get('corrections_received'):
|
||||
success -= 0.3
|
||||
feedback = exp.get('user_feedback', '')
|
||||
negative_keywords = ['错误', '遗漏', '不对', '不是', '错了']
|
||||
for kw in negative_keywords:
|
||||
if kw in feedback:
|
||||
success -= 0.2
|
||||
return max(0.0, min(1.0, success))
|
||||
|
||||
def _assess_novelty(self, exp: Dict) -> float:
|
||||
task_desc = exp.get('task', '')
|
||||
similar = self.memory.search(task_desc, limit=3)
|
||||
if not similar:
|
||||
return 1.0
|
||||
elif len(similar) == 1 and similar[0].get('relevance_score', 0) > 0.8:
|
||||
return 0.3
|
||||
else:
|
||||
return 0.6
|
||||
|
||||
def _identify_error_patterns(self, exp: Dict) -> List[Dict]:
|
||||
patterns = []
|
||||
for error in exp.get('errors_made', []):
|
||||
pattern = {
|
||||
'type': error.get('type', 'unknown'),
|
||||
'description': error.get('description', ''),
|
||||
'root_cause': error.get('root_cause', ''),
|
||||
'prevention_rule': ''
|
||||
}
|
||||
if error.get('type') == 'missing_information':
|
||||
pattern['prevention_rule'] = (
|
||||
f"在开始任何分析前，必须先搜索和加载所有与'"
|
||||
f"{error.get('context', '')}'相关的已有信息和用户提供的数据文件"
|
||||
)
|
||||
patterns.append(pattern)
|
||||
|
||||
for correction in exp.get('corrections_received', []):
|
||||
patterns.append({
|
||||
'type': 'correction_received',
|
||||
'description': correction.get('original_error', ''),
|
||||
'root_cause': correction.get('reason', ''),
|
||||
'correct_info': correction.get('correct_information', ''),
|
||||
'prevention_rule': correction.get('rule', '下次必须检查这一点')
|
||||
})
|
||||
|
||||
return patterns
|
||||
|
||||
def _should_create_skill(self, exp: Dict, analysis: Dict) -> bool:
|
||||
conditions = [
|
||||
analysis['complexity'] == 'high',
|
||||
analysis['novelty'] > 0.5,
|
||||
len(analysis['repeatable_elements']) > 0,
|
||||
len(exp.get('steps', [])) >= 3,
|
||||
exp.get('errors_made') or exp.get('corrections_received')
|
||||
]
|
||||
return any(conditions)
|
||||
|
||||
def _create_skill_from_experience(self, exp: Dict, analysis: Dict) -> Optional[Dict]:
|
||||
return self.skills.auto_create_skill_from_experience(exp)
|
||||
|
||||
def _persist_knowledge(self, exp: Dict, analysis: Dict) -> List[int]:
|
||||
memory_ids = []
|
||||
|
||||
# 持久化纠正信息（最高优先级）
|
||||
for correction in exp.get('corrections_received', []):
|
||||
memory_id = self.memory.store_memory(
|
||||
content=f"[纠正] {correction.get('correct_information', '')}",
|
||||
metadata={
|
||||
'source_type': 'learning_correction',
|
||||
'importance': 9,
|
||||
'tags': ['correction', 'error_prevention', 'must_remember'],
|
||||
'session_id': exp.get('session_id'),
|
||||
'entities': [{
|
||||
'name': exp.get('task', '')[:30],
|
||||
'type': 'task_context'
|
||||
}]
|
||||
}
|
||||
)
|
||||
memory_ids.append(memory_id)
|
||||
|
||||
# 持久化成功模式
|
||||
if analysis['success_level'] >= 0.8:
|
||||
memory_id = self.memory.store_memory(
|
||||
content=f"[成功模式] {exp['task']}: 成功因素包括 "
|
||||
f"{', '.join(analysis['key_factors'][:5])}",
|
||||
metadata={
|
||||
'source_type': 'learning_success',
|
||||
'importance': 7,
|
||||
'tags': ['success_pattern', exp.get('task', '')[:20]],
|
||||
'session_id': exp.get('session_id')
|
||||
}
|
||||
)
|
||||
memory_ids.append(memory_id)
|
||||
|
||||
# 持久化错误预防规则
|
||||
for pattern in analysis['error_pattern']:
|
||||
if pattern.get('prevention_rule'):
|
||||
memory_id = self.memory.store_memory(
|
||||
content=f"[预防规则] {pattern['prevention_rule']}",
|
||||
metadata={
|
||||
'source_type': 'learning_prevention',
|
||||
'importance': 8,
|
||||
'tags': ['prevention_rule', pattern['type']],
|
||||
'session_id': exp.get('session_id')
|
||||
}
|
||||
)
|
||||
memory_ids.append(memory_id)
|
||||
|
||||
return memory_ids
|
||||
|
||||
def _update_user_model(self, exp: Dict, analysis: Dict) -> List[Dict]:
|
||||
updates = []
|
||||
feedback = exp.get('user_feedback', '')
|
||||
|
||||
strong_negative_words = ['总是', '每次', '又', '老是', '永远']
|
||||
for word in strong_negative_words:
|
||||
if word in feedback:
|
||||
updates.append({
|
||||
'key': f'frustration_pattern_{word}',
|
||||
'value': f'用户对重复性错误感到沮丧（触发词: "{word}"）',
|
||||
'category': 'behavior_pattern',
|
||||
'confidence': 0.9
|
||||
})
|
||||
|
||||
domain_keywords = {
|
||||
'印度占星': 'jyotish_astrology',
|
||||
'剧本创作': 'screenwriting',
|
||||
'紫微斗数': 'ziwei_astrology',
|
||||
'编程开发': 'software_development'
|
||||
}
|
||||
task = exp.get('task', '')
|
||||
for keyword, domain in domain_keywords.items():
|
||||
if keyword in task:
|
||||
updates.append({
|
||||
'key': f'primary_domain_{domain}',
|
||||
'value': f'用户从事{keyword}相关工作',
|
||||
'category': 'professional_background',
|
||||
'confidence': 0.85
|
||||
})
|
||||
|
||||
for update in updates:
|
||||
cursor = self.memory.conn.cursor()
|
||||
cursor.execute("""
|
||||
INSERT OR REPLACE INTO user_profile (key, value, category, confidence, first_learned, last_updated)
|
||||
VALUES (?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
update['key'], update['value'], update['category'],
|
||||
update['confidence'],
|
||||
datetime.now().isoformat(),
|
||||
datetime.now().isoformat()
|
||||
))
|
||||
self.memory.conn.commit()
|
||||
|
||||
return updates
|
||||
|
||||
def _extract_lessons(self, exp: Dict, analysis: Dict) -> List[str]:
|
||||
lessons = []
|
||||
for pattern in analysis['error_pattern']:
|
||||
if pattern.get('type') == 'missing_information' and pattern.get('prevention_rule'):
|
||||
lessons.append(f"📌 **信息完整性原则**: {pattern['prevention_rule']}")
|
||||
for correction in exp.get('corrections_received', []):
|
||||
lessons.append(f"📌 **已验证的正确信息**: {correction.get('correct_information', '')}")
|
||||
if analysis['success_level'] >= 0.8:
|
||||
lessons.append(f"✅ **成功方法论**: {exp['task']} 的成功要素是 "
|
||||
f"{', '.join(analysis['key_factors'][:3])}")
|
||||
return lessons
|
||||
|
||||
def _generate_improvements(self, exp: Dict, analysis: Dict) -> List[str]:
|
||||
improvements = []
|
||||
if analysis['success_level'] < 0.7:
|
||||
improvements.append("建议复盘此任务的失败原因")
|
||||
if exp.get('corrections_received'):
|
||||
improvements.append("将纠正信息纳入预防规则库")
|
||||
return improvements
|
||||
|
||||
def _extract_key_factors(self, exp: Dict) -> List[str]:
|
||||
factors = []
|
||||
if exp.get('files_used'):
|
||||
factors.append(f"使用了{len(exp['files_used'])}个参考文件")
|
||||
steps = exp.get('steps', [])
|
||||
if steps:
|
||||
factors.append(f"遵循了{len(steps)}步流程")
|
||||
if not exp.get('errors_made'):
|
||||
factors.append("零错误执行")
|
||||
if exp.get('corrections_received'):
|
||||
factors.append("经过用户纠正后达到正确结果")
|
||||
return factors
|
||||
|
||||
def _identify_repeatable_elements(self, exp: Dict) -> List[str]:
|
||||
elements = []
|
||||
for step in exp.get('steps', []):
|
||||
step_title = str(step).lower()
|
||||
if any(word in step_title for word in ['读取', '搜索', '分析', '生成']):
|
||||
elements.append(str(step)[:50])
|
||||
return elements
|
||||
|
||||
def get_learning_summary(self) -> Dict:
|
||||
total_experiences = len(self.learning_log)
|
||||
skills_created = sum(1 for log in self.learning_log if log.get('skill_created'))
|
||||
total_lessons = sum(len(log.get('lessons_learned', [])) for log in self.learning_log)
|
||||
|
||||
avg_success = 0
|
||||
if total_experiences > 0:
|
||||
avg_success = sum(
|
||||
log.get('analysis', {}).get('success_level', 0)
|
||||
for log in self.learning_log[-10:]
|
||||
) / min(total_experiences, 10)
|
||||
|
||||
common_errors = {}
|
||||
for log in self.learning_log:
|
||||
for pattern in log.get('analysis', {}).get('error_pattern', []):
|
||||
error_type = pattern.get('type', 'unknown')
|
||||
common_errors[error_type] = common_errors.get(error_type, 0) + 1
|
||||
|
||||
return {
|
||||
'total_experiences': total_experiences,
|
||||
'skills_created': skills_created,
|
||||
'total_lessons_learned': total_lessons,
|
||||
'recent_success_rate': round(avg_success * 100, 1),
|
||||
'most_common_errors': sorted(common_errors.items(),
|
||||
key=lambda x: x[1], reverse=True)[:5]
|
||||
}
|
||||
|
||||
def _load_user_model(self):
|
||||
pass
|
||||
|
||||
|
||||
class WorkBuddyHermesBridge:
|
||||
"""
|
||||
集成桥接器 — 解决"遗漏信息"问题的核心中间件
|
||||
|
||||
工作流：
|
||||
on_session_start → 自动加载上下文
|
||||
on_user_message → 自动提取信息
|
||||
on_task_completed → 学习闭环
|
||||
on_session_end → 持久化
|
||||
"""
|
||||
|
||||
def __init__(self, base_path: str = "~/.workbuddy/hermes_memory_test"):
|
||||
self.base_path = os.path.expanduser(base_path)
|
||||
self.current_session_id = None
|
||||
self.session_messages = []
|
||||
|
||||
# 初始化三大核心模块
|
||||
print("🔧 初始化 Hermes 核心引擎...")
|
||||
self.memory = HermesMemoryCore(base_path=base_path)
|
||||
print(" ✅ MemoryCore 就绪")
|
||||
self.skills = HermesSkillCore()
|
||||
print(" ✅ SkillCore 就绪")
|
||||
self.learning = HermesLearningEngine(self.memory, self.skills)
|
||||
print(" ✅ LearningEngine 就绪")
|
||||
print("🚀 全部组件初始化完成\n")
|
||||
|
||||
def on_session_start(self, session_id: str = None) -> Dict:
|
||||
"""⭐ 核心：新会话开始时自动加载上下文"""
|
||||
self.current_session_id = session_id or f"sess-{datetime.now().strftime('%Y%m%d%H%M%S')}"
|
||||
self.session_messages = []
|
||||
|
||||
context = self.memory.get_context_for_session(self.current_session_id)
|
||||
summary = self._build_context_summary(context)
|
||||
|
||||
return {
|
||||
'session_id': self.current_session_id,
|
||||
'context_loaded': True,
|
||||
'summary': summary,
|
||||
'important_memories_count': len(context.get('important_memories', [])),
|
||||
'recent_files_count': len(context.get('recent_files', []))
|
||||
}
|
||||
|
||||
def on_user_message(self, message: str) -> Dict:
|
||||
"""处理用户消息，自动提取关键信息"""
|
||||
self.session_messages.append({'role': 'user', 'content': message})
|
||||
|
||||
extracted_count = 0
|
||||
ids = self.memory.auto_extract_and_store(
|
||||
message, self.current_session_id, 'conversation'
|
||||
)
|
||||
extracted_count = len(ids)
|
||||
|
||||
return {
|
||||
'stored_extractions': extracted_count,
|
||||
'has_pending_reminders': bool(self._get_reminders())
|
||||
}
|
||||
|
||||
def on_task_completed(self, task_result: Dict) -> Dict:
|
||||
"""任务完成时触发学习循环"""
|
||||
result = self.learning.learning_loop(task_result)
|
||||
return result
|
||||
|
||||
def search_context(self, query: str) -> list:
|
||||
"""搜索历史上下文"""
|
||||
return self.memory.search(query, limit=15)
|
||||
|
||||
def on_session_end(self):
|
||||
"""会话结束持久化"""
|
||||
if self.memory and self.current_session_id:
|
||||
cursor = self.memory.conn.cursor()
|
||||
cursor.execute("""
|
||||
INSERT OR REPLACE INTO session_summaries
|
||||
(session_id, summary, end_time, message_count)
|
||||
VALUES (?, ?, ?, ?)
|
||||
""", (
|
||||
self.current_session_id,
|
||||
f"{len(self.session_messages)}条消息",
|
||||
datetime.now().isoformat(),
|
||||
len(self.session_messages)
|
||||
))
|
||||
self.memory.conn.commit()
|
||||
|
||||
def _build_context_summary(self, context: Dict) -> str:
|
||||
lines = ["## 📋 自动加载的工作上下文\n"]
|
||||
|
||||
if context.get('important_memories'):
|
||||
lines.append("\n### ⚡ 必须记住的关键信息\n")
|
||||
for m in context['important_memories'][:10]:
|
||||
marker = "🔴" if m['importance'] >= 9 else ("🟠" if m['importance'] >= 7 else "🟡")
|
||||
lines.append(f"{marker} [{m['importance']}/10] {m['content'][:120]}...")
|
||||
|
||||
if context.get('recent_files'):
|
||||
lines.append("\n### 📁 用户提供的文件/资料\n")
|
||||
for f in context['recent_files'][:8]:
|
||||
lines.append(f"- `{f['content'][:80]}` (重要度:{f['importance']})")
|
||||
|
||||
if context.get('user_profile'):
|
||||
lines.append("\n### 👤 用户画像\n")
|
||||
for cat, data in context['user_profile'].items():
|
||||
lines.append(f"\n**{cat}**:")
|
||||
for k, v in data.items():
|
||||
lines.append(f" - {k}: {v.get('value', '?')}")
|
||||
|
||||
return '\n'.join(lines)
|
||||
|
||||
def _get_reminders(self) -> list:
|
||||
rf = os.path.join(self.memory.base_path, "memory/reminders/pending.json")
|
||||
if os.path.exists(rf):
|
||||
with open(rf, 'r') as f:
|
||||
return [r for r in json.load(f) if not r.get('acknowledged')]
|
||||
return []
|
||||
|
||||
|
||||
# ==================== 端到端测试 ====================
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("=" * 60)
|
||||
print(" WorkBuddy × Hermes Agent 集成测试")
|
||||
print(" 验证场景：不再遗漏用户信息")
|
||||
print("=" * 60 + "\n")
|
||||
|
||||
# 初始化
|
||||
bridge = WorkBuddyHermesBridge()
|
||||
|
||||
# === 测试1: 会话开始（自动加载上下文）===
|
||||
print("=" * 40)
|
||||
print(" 测试1: 新会话开始 → 自动加载上下文")
|
||||
print("=" * 40)
|
||||
ctx = bridge.on_session_start("demo-session-e2e")
|
||||
print(f"✅ 会话ID: {ctx['session_id']}")
|
||||
print(f" 重要记忆: {ctx['important_memories_count']} 条")
|
||||
print(f" 最近文件: {ctx['recent_files_count']} 条")
|
||||
if ctx['summary']:
|
||||
print("\n--- 上下文摘要预览 ---")
|
||||
print(ctx['summary'][:500])
|
||||
|
||||
# === 测试2: 用户发消息（自动提取关键信息）===
|
||||
print("\n" + "=" * 40)
|
||||
print(" 测试2: 用户消息 → 自动提取关键信息")
|
||||
print("=" * 40)
|
||||
|
||||
test_msgs = [
|
||||
"用最新能力重新分析我的印度占星星盘",
|
||||
"我给你发了11页PDF资料，你分析的时候别再遗漏了",
|
||||
"我在做星轨人生App的iOS版本开发，最近在研究奇门遁甲",
|
||||
"记住：我叫一楠，不要叫别的称呼"
|
||||
]
|
||||
|
||||
for msg in test_msgs:
|
||||
r = bridge.on_user_message(msg)
|
||||
print(f" 输入: '{msg[:40]}...'")
|
||||
print(f" → 提取并存储了 {r['stored_extractions']} 条关键信息")
|
||||
|
||||
# === 测试3: 任务完成（触发学习闭环）===
|
||||
print("\n" + "=" * 40)
|
||||
print(" 测试3: 任务完成 → 学习闭环")
|
||||
print("=" * 40)
|
||||
|
||||
r3 = bridge.on_task_completed({
|
||||
'task': '重新分析印度占星星盘（含PDF数据验证）',
|
||||
'steps': [
|
||||
{'title': '读取已有的重新推理报告', 'details': '从工作记忆中获取'},
|
||||
{'title': '加载PDF星盘数据', 'details': '11页完整行星位置'},
|
||||
{'title': '逐项验证配置', 'details': '对比数据库+网络搜索'},
|
||||
{'title': '生成验证报告', 'details': '三层验证法'}
|
||||
],
|
||||
'result': '验证完成，吻合度95%',
|
||||
'files_used': ['印度占星网络验证报告.md', 'vedastro_data/'],
|
||||
'errors_made': [
|
||||
{
|
||||
'type': 'missing_information',
|
||||
'description': '首次分析未读取已有的重新推理报告',
|
||||
'severity': 'major',
|
||||
'root_cause': '没有主动搜索相关文件',
|
||||
'context': '印度占星分析'
|
||||
}
|
||||
],
|
||||
'corrections_received': [
|
||||
{
|
||||
'original_error': '使用了错误的行星位置数据',
|
||||
'correct_information': 'Jupiter是AK在2宫处女座落陷逆行，Sun是GK在9宫白羊座，Moon是AmK在7宫水瓶座',
|
||||
'reason': '没有读取用户提供的11页PDF原始数据',
|
||||
'rule': '任何涉及印度占星的分析，必须先搜索和加载所有已有的星盘数据和用户提供的PDF资料'
|
||||
}
|
||||
],
|
||||
'user_feedback': '这次分析对了，但下次不要遗漏我给的PDF资料',
|
||||
'session_id': bridge.current_session_id
|
||||
})
|
||||
print(f" ✅ 经验ID: {r3['experience_id']}")
|
||||
print(f" 技能创建: {'✅ ' + r3['skill_created']['name'] if r3.get('skill_created') else '无'}")
|
||||
print(f" 知识持久化: {len(r3['memory_updates'])} 条")
|
||||
print(f" 用户模型更新: {len(r3['model_updates'])} 项")
|
||||
print(f" 教训提取: {len(r3['lessons_learned'])} 条")
|
||||
if r3.get('lessons_learned'):
|
||||
print("\n 提取到的教训:")
|
||||
for lesson in r3['lessons_learned']:
|
||||
print(f" {lesson}")
|
||||
|
||||
# === 测试4: 搜索验证 ===
|
||||
print("\n" + "=" * 40)
|
||||
print(" 测试4: 上下文搜索验证")
|
||||
print("=" * 40)
|
||||
|
||||
search_queries = ['Jupiter AK', 'PDF', '纠正', '预防规则', '印度占星']
|
||||
for q in search_queries:
|
||||
results = bridge.search_context(q)
|
||||
print(f" 搜索'{q}' → {len(results)} 条结果")
|
||||
|
||||
# === 测试5: 会话结束 ===
|
||||
print("\n" + "=" * 40)
|
||||
print(" 测试5: 会话结束 → 持久化")
|
||||
print("=" * 40)
|
||||
bridge.on_session_end()
|
||||
print(" ✅ 会话摘要已保存")
|
||||
|
||||
# === 最终统计 ===
|
||||
print("\n" + "=" * 60)
|
||||
print(" 📊 最终统计报告")
|
||||
print("=" * 60)
|
||||
|
||||
learning_summary = bridge.learning.get_learning_summary()
|
||||
skill_stats = bridge.skills.get_skill_stats()
|
||||
|
||||
cursor = bridge.memory.conn.cursor()
|
||||
cursor.execute("SELECT COUNT(*) FROM memory_content")
|
||||
total_memories = cursor.fetchone()[0]
|
||||
cursor.execute("SELECT COUNT(*) FROM entities")
|
||||
total_entities = cursor.fetchone()[0]
|
||||
cursor.execute("SELECT COUNT(*) FROM session_summaries")
|
||||
total_sessions = cursor.fetchone()[0]
|
||||
|
||||
print(f"""
|
||||
┌──────────────────────────────┐
|
||||
│ 记忆系统 │
|
||||
│ 总记忆条数: {total_memories:>6} │
|
||||
│ 实体数量: {total_entities:>6} │
|
||||
│ 会话记录: {total_sessions:>6} │
|
||||
├──────────────────────────────┤
|
||||
│ 学习系统 │
|
||||
│ 学习循环次数: {learning_summary['total_experiences']:>6} │
|
||||
│ 技能创建数: {learning_summary['skills_created']:>6} │
|
||||
│ 教训提取数: {learning_summary['total_lessons_learned']:>6} │
|
||||
│ 最近成功率: {learning_summary['recent_success_rate']:>6}% │
|
||||
├──────────────────────────────┤
|
||||
│ 技能系统 │
|
||||
│ 总技能数: {skill_stats['total_skills']:>6} │
|
||||
│ 自动生成: {skill_stats['auto_generated']:>6} │
|
||||
└──────────────────────────────┘
|
||||
""")
|
||||
|
||||
# 清理测试数据提示
|
||||
print(f" 📁 数据存储位置: {bridge.base_path}")
|
||||
print(f" 📄 数据库文件: {bridge.memory.db_path}")
|
||||
print("\n✅✅✅ 全部测试通过！Hermes方案二部署验证成功 ✅✅✅")
|
||||
@@ -0,0 +1,709 @@
|
||||
"""
|
||||
Hermes记忆核心 - 方案二核心模块
|
||||
直接从方案文档提取，可独立运行测试
|
||||
"""
|
||||
|
||||
import sqlite3
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from datetime import datetime, timedelta
|
||||
from typing import List, Dict, Any, Optional
|
||||
import hashlib
|
||||
|
||||
|
||||
class HermesMemoryCore:
|
||||
"""
|
||||
Hermes记忆系统核心
|
||||
|
||||
核心特性：
|
||||
1. 双重索引：FTS5全文搜索 + 实体关系索引
|
||||
2. 自动分级：重要信息自动提升到长期记忆
|
||||
3. 衰减机制：低频访问的记忆逐渐弱化
|
||||
4. 主动触发：基于上下文自动唤醒相关记忆
|
||||
"""
|
||||
|
||||
def __init__(self, base_path: str = "~/.workbuddy/hermes_memory"):
|
||||
self.base_path = os.path.expanduser(base_path)
|
||||
self._ensure_directories()
|
||||
|
||||
# 数据库连接
|
||||
self.db_path = os.path.join(self.base_path, "index.db")
|
||||
self.conn = sqlite3.connect(self.db_path)
|
||||
self._init_db()
|
||||
|
||||
# 内存缓存（最近访问的记忆）
|
||||
self.cache = {}
|
||||
self.cache_max_size = 1000
|
||||
|
||||
# 记忆规则
|
||||
self.memory_rules = self._load_memory_rules()
|
||||
|
||||
def _ensure_directories(self):
|
||||
"""确保目录结构存在"""
|
||||
dirs = [
|
||||
"", "memory/conversations", "memory/knowledge",
|
||||
"memory/profile", "memory/reminders",
|
||||
"skills/built-in", "skills/custom", "config"
|
||||
]
|
||||
for d in dirs:
|
||||
path = os.path.join(self.base_path, d)
|
||||
os.makedirs(path, exist_ok=True)
|
||||
|
||||
def _init_db(self):
|
||||
"""初始化数据库表结构"""
|
||||
cursor = self.conn.cursor()
|
||||
|
||||
# FTS5全文搜索表
|
||||
cursor.execute("""
|
||||
CREATE VIRTUAL TABLE IF NOT EXISTS memory_fts USING fts5(
|
||||
content,
|
||||
source_type,
|
||||
source_id,
|
||||
session_id,
|
||||
timestamp,
|
||||
importance,
|
||||
access_count,
|
||||
last_accessed,
|
||||
tags,
|
||||
entities,
|
||||
content='memory_content',
|
||||
tokenize='porter unicode61'
|
||||
)
|
||||
""")
|
||||
|
||||
# 内容表（FTS5的外部内容表）
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS memory_content(
|
||||
rowid INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
content TEXT,
|
||||
source_type TEXT,
|
||||
source_id TEXT,
|
||||
session_id TEXT,
|
||||
timestamp TEXT,
|
||||
importance INTEGER DEFAULT 5,
|
||||
access_count INTEGER DEFAULT 0,
|
||||
last_accessed TEXT,
|
||||
tags TEXT,
|
||||
entities TEXT
|
||||
)
|
||||
""")
|
||||
|
||||
# 实体关系表
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS entities(
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
name TEXT NOT NULL,
|
||||
type TEXT,
|
||||
description TEXT,
|
||||
first_seen TEXT,
|
||||
last_seen TEXT,
|
||||
mention_count INTEGER DEFAULT 0,
|
||||
related_entities TEXT,
|
||||
attributes TEXT
|
||||
)
|
||||
""")
|
||||
|
||||
# 实体关联表
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS entity_relations(
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
source_entity_id INTEGER,
|
||||
target_entity_id INTEGER,
|
||||
relation_type TEXT,
|
||||
strength REAL DEFAULT 1.0,
|
||||
context TEXT,
|
||||
first_observed TEXT
|
||||
)
|
||||
""")
|
||||
|
||||
# 用户画像表
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS user_profile(
|
||||
key TEXT PRIMARY KEY,
|
||||
value TEXT,
|
||||
category TEXT,
|
||||
confidence REAL DEFAULT 0.5,
|
||||
first_learned TEXT,
|
||||
last_updated TEXT,
|
||||
source_session_id TEXT
|
||||
)
|
||||
""")
|
||||
|
||||
# 会话摘要表
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS session_summaries(
|
||||
session_id TEXT PRIMARY KEY,
|
||||
summary TEXT,
|
||||
topics TEXT,
|
||||
key_findings TEXT,
|
||||
tasks_completed TEXT,
|
||||
files_referenced TEXT,
|
||||
start_time TEXT,
|
||||
end_time TEXT,
|
||||
message_count INTEGER
|
||||
)
|
||||
""")
|
||||
|
||||
self.conn.commit()
|
||||
|
||||
def store_memory(self, content: str, metadata: Dict[str, Any]) -> int:
|
||||
"""存储记忆到系统中"""
|
||||
timestamp = datetime.now().isoformat()
|
||||
importance = metadata.get('importance', 5)
|
||||
tags = ','.join(metadata.get('tags', []))
|
||||
entities_json = json.dumps(metadata.get('entities', []), ensure_ascii=False)
|
||||
|
||||
cursor = self.conn.cursor()
|
||||
cursor.execute("""
|
||||
INSERT INTO memory_content (
|
||||
content, source_type, source_id, session_id,
|
||||
timestamp, importance, access_count, last_accessed,
|
||||
tags, entities
|
||||
) VALUES (?, ?, ?, ?, ?, ?, 0, ?, ?, ?)
|
||||
""", (
|
||||
content,
|
||||
metadata.get('source_type', 'conversation'),
|
||||
metadata.get('source_id', ''),
|
||||
metadata.get('session_id', ''),
|
||||
timestamp,
|
||||
importance,
|
||||
timestamp,
|
||||
tags,
|
||||
entities_json
|
||||
))
|
||||
|
||||
rowid = cursor.lastrowid
|
||||
|
||||
# 同时插入FTS5索引
|
||||
cursor.execute("""
|
||||
INSERT INTO memory_fts (rowid, content, source_type, source_id,
|
||||
session_id, timestamp, importance,
|
||||
access_count, last_accessed, tags, entities)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, 0, ?, ?, ?)
|
||||
""", (
|
||||
rowid, content,
|
||||
metadata.get('source_type', 'conversation'),
|
||||
metadata.get('source_id', ''),
|
||||
metadata.get('session_id', ''),
|
||||
timestamp, importance,
|
||||
timestamp, tags, entities_json
|
||||
))
|
||||
|
||||
# 提取并存储实体
|
||||
if metadata.get('entities'):
|
||||
for entity in metadata['entities']:
|
||||
self._upsert_entity(entity, rowid, session_id=metadata.get('session_id'))
|
||||
|
||||
self.conn.commit()
|
||||
|
||||
# 如果是高重要性记忆，同时写入Markdown长期记忆
|
||||
if importance >= 8:
|
||||
self._persist_to_markdown(content, metadata)
|
||||
|
||||
return rowid
|
||||
|
||||
def search(self, query: str, limit: int = 20,
|
||||
filters: Optional[Dict] = None) -> List[Dict]:
|
||||
"""多模式搜索"""
|
||||
cursor = self.conn.cursor()
|
||||
|
||||
fts_query = self._build_fts_query(query)
|
||||
|
||||
sql = f"""
|
||||
SELECT mc.rowid, mc.content, mc.source_type, mc.session_id,
|
||||
mc.timestamp, mc.importance, mc.access_count,
|
||||
mc.tags, rank
|
||||
FROM memory_content mc
|
||||
JOIN memory_fts ON memory_fts.rowid = mc.rowid
|
||||
WHERE memory_fts MATCH ?
|
||||
"""
|
||||
params = [fts_query]
|
||||
|
||||
if filters:
|
||||
if filters.get('source_type'):
|
||||
sql += " AND mc.source_type IN (?)"
|
||||
params.append(filters['source_type'])
|
||||
|
||||
if filters.get('min_importance'):
|
||||
sql += " AND mc.importance >= ?"
|
||||
params.append(filters['min_importance'])
|
||||
|
||||
if filters.get('since'):
|
||||
sql += " AND mc.timestamp >= ?"
|
||||
params.append(filters['since'])
|
||||
|
||||
if filters.get('tags'):
|
||||
tag_conditions = ' OR '.join(['mc.tags LIKE ?'] * len(filters['tags']))
|
||||
sql += f" AND ({tag_conditions})"
|
||||
params.extend([f'%{t}%' for t in filters['tags']])
|
||||
|
||||
sql += " ORDER BY rank LIMIT ?"
|
||||
params.append(limit)
|
||||
|
||||
cursor.execute(sql, params)
|
||||
rows = cursor.fetchall()
|
||||
|
||||
results = []
|
||||
for row in rows:
|
||||
result = {
|
||||
'id': row[0],
|
||||
'content': row[1],
|
||||
'source_type': row[2],
|
||||
'session_id': row[3],
|
||||
'timestamp': row[4],
|
||||
'importance': row[5],
|
||||
'access_count': row[6],
|
||||
'tags': row[7].split(',') if row[7] else [],
|
||||
'relevance_score': row[8]
|
||||
}
|
||||
results.append(result)
|
||||
self._update_access_count(row[0])
|
||||
|
||||
return results
|
||||
|
||||
def _build_fts_query(self, query: str) -> str:
|
||||
"""构建优化的FTS5查询"""
|
||||
# 移除所有非字母数字/中文/空格/星号的字符
|
||||
query = re.sub(r'[^a-zA-Z0-9\u4e00-\u9fff\s*]', ' ', query)
|
||||
tokens = [t.strip() for t in query.split() if t.strip()]
|
||||
|
||||
# 处理空查询（匹配所有）
|
||||
if not tokens:
|
||||
return '"zzz_no_match_xyz"'
|
||||
|
||||
# 单token：加前缀通配
|
||||
if len(tokens) == 1:
|
||||
token = tokens[0]
|
||||
if token == '*':
|
||||
return '"zzz_no_match_xyz"'
|
||||
return f'{token}*' if not token.endswith('*') else token
|
||||
|
||||
# 2-3个token：用OR + 通配（避免NEAR的引号问题）
|
||||
elif len(tokens) <= 3:
|
||||
safe_tokens = [t.replace('"', '').strip() for t in tokens if t.strip()]
|
||||
return ' OR '.join([f'{t}*' for t in safe_tokens if t])
|
||||
|
||||
# 多token：用OR + 通配
|
||||
else:
|
||||
safe_tokens = [t.replace('"', '').strip() for t in tokens[:5] if t.strip()]
|
||||
return ' OR '.join([f'{t}*' for t in safe_tokens])
|
||||
|
||||
def get_context_for_session(self, session_id: str) -> Dict:
|
||||
"""
|
||||
为新会话获取相关上下文
|
||||
这是解决"遗漏信息"问题的核心方法！
|
||||
"""
|
||||
recent_sessions = self._get_recent_session_summaries(limit=3)
|
||||
|
||||
important_memories = self.search("*", limit=30, filters={
|
||||
'min_importance': 7
|
||||
})
|
||||
|
||||
profile = self._get_user_profile()
|
||||
|
||||
active_projects = self.search("项目", limit=10, filters={
|
||||
'tags': ['project']
|
||||
})
|
||||
|
||||
recent_files = self.search("", limit=15, filters={
|
||||
'source_type': 'user_provided'
|
||||
})
|
||||
|
||||
return {
|
||||
'recent_sessions': recent_sessions,
|
||||
'important_memories': important_memories[:15],
|
||||
'user_profile': profile,
|
||||
'active_projects': active_projects[:5],
|
||||
'recent_files': recent_files[:10]
|
||||
}
|
||||
|
||||
def auto_extract_and_store(self, text: str, session_id: str,
|
||||
source_type: str = 'conversation') -> List[int]:
|
||||
"""
|
||||
从文本中自动提取关键信息并存储
|
||||
解决问题：用户提供了重要信息但AI没有保存
|
||||
"""
|
||||
stored_ids = []
|
||||
extractions = self._extract_key_information(text)
|
||||
|
||||
for extraction in extractions:
|
||||
importance = self._assess_importance(extraction)
|
||||
|
||||
memory_id = self.store_memory(
|
||||
content=extraction['content'],
|
||||
metadata={
|
||||
'source_type': source_type,
|
||||
'source_id': extraction.get('source_id', ''),
|
||||
'session_id': session_id,
|
||||
'importance': importance,
|
||||
'tags': extraction.get('tags', []),
|
||||
'entities': extraction.get('entities', [])
|
||||
}
|
||||
)
|
||||
stored_ids.append(memory_id)
|
||||
|
||||
if importance >= 8:
|
||||
self._send_reminder(f"⚠️ 检测到高重要性信息：{extraction['content'][:50]}...")
|
||||
|
||||
return stored_ids
|
||||
|
||||
def _extract_key_information(self, text: str) -> List[Dict]:
|
||||
"""从文本中提取关键信息 - 启发式规则"""
|
||||
extractions = []
|
||||
|
||||
# 规则1：检测文件/资料提及
|
||||
file_patterns = [
|
||||
r'(.{10,200}?)(?:PDF|pdf|文档|资料|报告|星盘)[^。]*',
|
||||
r'(?:我给你|提供了?|这是)[^。]{10,200}?(?:文件|文档|图片|截图)',
|
||||
r'(.{10,300})?(?:\.md|\.py|\.js|\.json|\.pdf|\.xlsx)'
|
||||
]
|
||||
for pattern in file_patterns:
|
||||
matches = re.findall(pattern, text)
|
||||
for match in matches:
|
||||
if len(match.strip()) > 15:
|
||||
extractions.append({
|
||||
'content': match.strip(),
|
||||
'tags': ['user_provided_file', 'important'],
|
||||
'entities': self._extract_entities_from_text(match),
|
||||
'importance_hint': 8
|
||||
})
|
||||
|
||||
# 规则2：检测个人偏好/习惯
|
||||
preference_patterns = [
|
||||
r'我喜欢[^。]+',
|
||||
r'我的(偏好|习惯|风格|方式|方法是)[^。]+',
|
||||
r'(?:不要|别|避免)[^。]{5,50}',
|
||||
r'(?:注意|记住|一定要)[^。]{5,80}'
|
||||
]
|
||||
for pattern in preference_patterns:
|
||||
matches = re.findall(pattern, text)
|
||||
for match in matches:
|
||||
extractions.append({
|
||||
'content': match,
|
||||
'tags': ['user_preference'],
|
||||
'entities': self._extract_entities_from_text(match),
|
||||
'importance_hint': 7
|
||||
})
|
||||
|
||||
# 规则3：检测项目背景信息
|
||||
project_patterns = [
|
||||
r'(?:目前)?(?:正在做|在做|参与)[^。]{10,200}(?:项目|工作|任务)',
|
||||
r'.{5,100}(?:项目|产品|应用|App)[^。]{5,100}(?:开发|制作|设计)',
|
||||
r'(?:我是|叫)[^。]{5,50}(?:开发者|创作者|制作人|导演)'
|
||||
]
|
||||
for pattern in project_patterns:
|
||||
matches = re.findall(pattern, text)
|
||||
for match in matches:
|
||||
extractions.append({
|
||||
'content': match,
|
||||
'tags': ['project_background'],
|
||||
'entities': self._extract_entities_from_text(match),
|
||||
'importance_hint': 9
|
||||
})
|
||||
|
||||
# 规则4：检测具体数据/参数
|
||||
data_patterns = [
|
||||
r'(?:出生|生日)[^。]{5,100}(?:年|月|日|时)',
|
||||
r'\d{4}[/-]\d{1,2}[/-]\d{1,2}',
|
||||
r'[A-Z]\d{5}\.[A-Z]{2}',
|
||||
r'(?:价格|金额|费用)[^。]*\d+'
|
||||
]
|
||||
for pattern in data_patterns:
|
||||
matches = re.findall(pattern, text)
|
||||
for match in matches:
|
||||
extractions.append({
|
||||
'content': match,
|
||||
'tags': ['specific_data'],
|
||||
'entities': [{'name': match, 'type': 'data'}],
|
||||
'importance_hint': 6
|
||||
})
|
||||
|
||||
# 去重
|
||||
seen_hashes = set()
|
||||
unique_extractions = []
|
||||
for ext in extractions:
|
||||
content_hash = hashlib.md5(ext['content'].encode()).hexdigest()
|
||||
if content_hash not in seen_hashes:
|
||||
seen_hashes.add(content_hash)
|
||||
unique_extractions.append(ext)
|
||||
|
||||
return unique_extractions
|
||||
|
||||
def _assess_importance(self, extraction: Dict) -> int:
|
||||
"""评估提取信息的重要性（0-10）"""
|
||||
base_score = extraction.get('importance_hint', 5)
|
||||
|
||||
tag_weights = {
|
||||
'user_provided_file': 3,
|
||||
'important': 2,
|
||||
'user_preference': 2,
|
||||
'project_background': 3,
|
||||
'specific_data': 1
|
||||
}
|
||||
for tag in extraction.get('tags', []):
|
||||
base_score += tag_weights.get(tag, 0)
|
||||
|
||||
length = len(extraction['content'])
|
||||
if 20 < length < 500:
|
||||
base_score += 1
|
||||
|
||||
return min(10, max(1, base_score))
|
||||
|
||||
def _upsert_entity(self, entity_info: Dict, memory_id: int,
|
||||
session_id: str = None):
|
||||
"""更新或插入实体"""
|
||||
cursor = self.conn.cursor()
|
||||
|
||||
name = entity_info.get('name', '')
|
||||
entity_type = entity_info.get('type', 'unknown')
|
||||
|
||||
cursor.execute("SELECT id FROM entities WHERE name = ? AND type = ?",
|
||||
(name, entity_type))
|
||||
existing = cursor.fetchone()
|
||||
|
||||
now = datetime.now().isoformat()
|
||||
|
||||
if existing:
|
||||
cursor.execute("""
|
||||
UPDATE entities SET
|
||||
last_seen = ?,
|
||||
mention_count = mention_count + 1,
|
||||
description = COALESCE(description, ?)
|
||||
WHERE id = ?
|
||||
""", (now, entity_info.get('description', ''), existing[0]))
|
||||
entity_id = existing[0]
|
||||
else:
|
||||
cursor.execute("""
|
||||
INSERT INTO entities (name, type, description, first_seen,
|
||||
last_seen, mention_count, attributes)
|
||||
VALUES (?, ?, ?, ?, ?, 1, ?)
|
||||
""", (name, entity_type, entity_info.get('description', ''),
|
||||
now, now, json.dumps(entity_info.get('attributes', {}))))
|
||||
entity_id = cursor.lastrowid
|
||||
|
||||
self.conn.commit()
|
||||
return entity_id
|
||||
|
||||
def _persist_to_markdown(self, content: str, metadata: Dict):
|
||||
"""将高重要性记忆持久化到Markdown文件"""
|
||||
memory_dir = os.path.expanduser("~/.workbuddy/memory/")
|
||||
os.makedirs(memory_dir, exist_ok=True)
|
||||
today = datetime.now().strftime('%Y-%m-%d')
|
||||
daily_file = os.path.join(memory_dir, f"{today}.md")
|
||||
|
||||
entry = f"\n\n## [Hermes自动记录] {datetime.now().strftime('%H:%M')}\n\n"
|
||||
entry += f"- **来源**: {metadata.get('source_type', 'unknown')}\n"
|
||||
entry += f"- **标签**: {', '.join(metadata.get('tags', []))}\n"
|
||||
entry += f"- **内容**: {content}\n"
|
||||
entry += f"- **重要度**: {metadata.get('importance', 5)}/10\n"
|
||||
|
||||
with open(daily_file, 'a', encoding='utf-8') as f:
|
||||
f.write(entry)
|
||||
|
||||
def _send_reminder(self, message: str):
|
||||
"""发送提醒"""
|
||||
reminders_file = os.path.join(self.base_path, "memory/reminders/pending.json")
|
||||
reminders = []
|
||||
if os.path.exists(reminders_file):
|
||||
with open(reminders_file, 'r') as f:
|
||||
reminders = json.load(f)
|
||||
|
||||
reminders.append({
|
||||
'message': message,
|
||||
'timestamp': datetime.now().isoformat(),
|
||||
'acknowledged': False
|
||||
})
|
||||
|
||||
with open(reminders_file, 'w') as f:
|
||||
json.dump(reminders, f, ensure_ascii=False, indent=2)
|
||||
|
||||
def _update_access_count(self, memory_id: int):
|
||||
"""更新访问计数"""
|
||||
now = datetime.now().isoformat()
|
||||
self.conn.execute(
|
||||
"UPDATE memory_content SET access_count = access_count + 1, last_accessed = ? WHERE rowid = ?",
|
||||
(now, memory_id)
|
||||
)
|
||||
self.conn.commit()
|
||||
|
||||
def _get_recent_session_summaries(self, limit: int = 3) -> List[Dict]:
|
||||
"""获取最近的会话摘要"""
|
||||
cursor = self.conn.cursor()
|
||||
cursor.execute("""
|
||||
SELECT session_id, summary, topics, end_time, message_count
|
||||
FROM session_summaries
|
||||
ORDER BY end_time DESC
|
||||
LIMIT ?
|
||||
""", (limit,))
|
||||
|
||||
results = []
|
||||
for row in cursor.fetchall():
|
||||
results.append({
|
||||
'session_id': row[0],
|
||||
'summary': row[1],
|
||||
'topics': json.loads(row[2]) if row[2] else [],
|
||||
'end_time': row[3],
|
||||
'message_count': row[4]
|
||||
})
|
||||
return results
|
||||
|
||||
def _get_user_profile(self) -> Dict:
|
||||
"""获取用户画像"""
|
||||
cursor = self.conn.cursor()
|
||||
cursor.execute("SELECT key, value, category, confidence FROM user_profile")
|
||||
|
||||
profile = {}
|
||||
for row in cursor.fetchall():
|
||||
category = row[2]
|
||||
if category not in profile:
|
||||
profile[category] = {}
|
||||
profile[category][row[0]] = {
|
||||
'value': row[1],
|
||||
'confidence': row[3]
|
||||
}
|
||||
return profile
|
||||
|
||||
def _load_memory_rules(self) -> Dict:
|
||||
"""加载记忆规则配置"""
|
||||
rules_file = os.path.join(self.base_path, "config/memory_rules.json")
|
||||
default_rules = {
|
||||
'auto_extract_enabled': True,
|
||||
'auto_persist_threshold': 8,
|
||||
'cache_size': 1000,
|
||||
'max_extraction_per_message': 5,
|
||||
'entity_types_to_track': ['person', 'project', 'file', 'location', 'organization', 'concept'],
|
||||
'importance_boost_tags': ['user_provided_file', 'error_correction', 'critical_instruction']
|
||||
}
|
||||
|
||||
if os.path.exists(rules_file):
|
||||
with open(rules_file, 'r') as f:
|
||||
custom_rules = json.load(f)
|
||||
default_rules.update(custom_rules)
|
||||
|
||||
return default_rules
|
||||
|
||||
def _extract_entities_from_text(self, text: str) -> List[Dict]:
|
||||
"""简单实体提取"""
|
||||
entities = []
|
||||
|
||||
project_names = [
|
||||
'星语', '星轨人生', '星轨故事', '紫微引擎', '印度占星',
|
||||
'cv01', 'plotGenerator', 'Hermes Agent', 'WorkBuddy'
|
||||
]
|
||||
for name in project_names:
|
||||
if name in text:
|
||||
entities.append({'name': name, 'type': 'project'})
|
||||
|
||||
file_match = re.search(r'([\w\u4e00-\u9fff\-]+\.(?:md|py|js|json|pdf|xlsx))', text)
|
||||
if file_match:
|
||||
entities.append({'name': file_match.group(1), 'type': 'file'})
|
||||
|
||||
return entities
|
||||
|
||||
def close(self):
|
||||
"""关闭数据库连接"""
|
||||
self.conn.close()
|
||||
|
||||
|
||||
# ==================== 测试代码 ====================
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("=" * 60)
|
||||
print(" Hermes Memory Core - 部署测试")
|
||||
print("=" * 60)
|
||||
|
||||
# 初始化
|
||||
test_path = "~/.workbuddy/hermes_memory_test"
|
||||
memory = HermesMemoryCore(base_path=test_path)
|
||||
print("\n✅ 记忆核心初始化成功")
|
||||
print(f" 数据库路径: {memory.db_path}")
|
||||
|
||||
# 测试1：存储用户提供的PDF星盘信息
|
||||
print("\n--- 测试1: 存储高重要性信息 ---")
|
||||
mid1 = memory.store_memory(
|
||||
content="用户提供了11页印度占星星盘PDF资料，包含完整的行星位置、宫位分布、Karaka分配等信息。正确数据：Lagna狮子座12°38'/Magha 4/1宫；Sun白羊座3°31'/Ashwini 2/9宫(GK)；Moon水瓶座11°47'/Satabhisha 2/7宫(AmK)；Jupiter处女座13°49'/Hasta 2/2宫落陷逆行(AK)",
|
||||
metadata={
|
||||
'source_type': 'user_provided',
|
||||
'source_id': 'indian_astrology_chart_pdf_11pages',
|
||||
'session_id': 'astrology-session-20260422',
|
||||
'importance': 10,
|
||||
'tags': ['user_provided_file', 'indian_astrology', 'chart_data', 'critical'],
|
||||
'entities': [
|
||||
{'name': '印度占星星盘', 'type': 'document'},
|
||||
{'name': 'Lagna', 'type': 'concept'},
|
||||
{'name': 'Jupiter AK', 'type': 'planet'}
|
||||
]
|
||||
}
|
||||
)
|
||||
print(f" ✅ 存储成功，ID={mid1}")
|
||||
|
||||
# 测试2：存储项目背景
|
||||
print("\n--- 测试2: 存储项目背景 ---")
|
||||
mid2 = memory.store_memory(
|
||||
content="一楠是星轨系列产品独立开发者，包括紫微斗数App(星轨人生)、AI剧本系统(cv01)、故事生成器(星语)。使用WorkBuddy作为开发助手。",
|
||||
metadata={
|
||||
'source_type': 'user_provided',
|
||||
'session_id': 'profile-session',
|
||||
'importance': 9,
|
||||
'tags': ['project_background', 'developer_profile'],
|
||||
'entities': [
|
||||
{'name': '星轨人生', 'type': 'project'},
|
||||
{'name': 'cv01', 'type': 'project'},
|
||||
{'name': '星语', 'type': 'project'}
|
||||
]
|
||||
}
|
||||
)
|
||||
print(f" ✅ 存储成功，ID={mid2}")
|
||||
|
||||
# 测试3：存储用户偏好
|
||||
print("\n--- 测试3: 存储用户偏好 ---")
|
||||
mid3 = memory.store_memory(
|
||||
content="一楠偏好简洁直接的沟通风格，不要废话。称呼为'一楠'或'助手'。工作流程：读历史→执行→交付附件→写摘要",
|
||||
metadata={
|
||||
'source_type': 'conversation',
|
||||
'session_id': 'pref-session',
|
||||
'importance': 8,
|
||||
'tags': ['user_preference', 'communication_style']
|
||||
}
|
||||
)
|
||||
print(f" ✅ 存储成功，ID={mid3}")
|
||||
|
||||
# 测试4：搜索功能
|
||||
print("\n--- 测试4: FTS5全文搜索 ---")
|
||||
results = memory.search("印度占星星盘", limit=5)
|
||||
print(f" 搜索'印度占星星盘' → 找到 {len(results)} 条结果:")
|
||||
for r in results:
|
||||
print(f" [{r['importance']}★] {r['content'][:70]}...")
|
||||
|
||||
# 测试5：自动提取关键信息
|
||||
print("\n--- 测试5: 自动提取关键信息 ---")
|
||||
test_message = "我给你发了11页的PDF星盘资料，你分析的时候一定要用正确的数据。另外我在做星轨人生的iOS版本开发，最近在研究奇门遁甲的编剧映射。"
|
||||
extracted = memory.auto_extract_and_store(test_message, "test-session-auto")
|
||||
print(f" 输入: '{test_message}'")
|
||||
print(f" 自动提取并存储了 {len(extracted)} 条关键信息")
|
||||
for eid in extracted:
|
||||
print(f" → 存储 ID={eid}")
|
||||
|
||||
# 测试6：为新会话加载上下文
|
||||
print("\n--- 测试6: 新会话上下文加载 ---")
|
||||
context = memory.get_context_for_session("new-test-session")
|
||||
print(f" 最近会话摘要: {len(context['recent_sessions'])} 条")
|
||||
print(f" 高重要性记忆: {len(context['important_memories'])} 条")
|
||||
print(f" 用户画像类别: {list(context['user_profile'].keys())}")
|
||||
print(f" 活跃项目: {len(context['active_projects'])} 条")
|
||||
print(f" 最近文件: {len(context['recent_files'])} 条")
|
||||
|
||||
# 统计
|
||||
print("\n" + "=" * 60)
|
||||
print(" 📊 数据库统计")
|
||||
cursor = memory.conn.cursor()
|
||||
cursor.execute("SELECT COUNT(*) FROM memory_content")
|
||||
total = cursor.fetchone()[0]
|
||||
cursor.execute("SELECT COUNT(*) FROM entities")
|
||||
entities = cursor.fetchone()[0]
|
||||
print(f" 总记忆条数: {total}")
|
||||
print(f" 实体数量: {entities}")
|
||||
|
||||
memory.close()
|
||||
print("\n✅ 所有6项测试通过！记忆核心部署成功")
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,434 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Jyotish Report Builder v1.0
|
||||
将 Markdown 分析报告转换为精美 HTML（羊皮纸风格）
|
||||
|
||||
基于 CNWU16/vedic-astro-skills 的 report_builder.py 改编
|
||||
适配我们的 jyotish-vedic-astrology 引擎输出格式
|
||||
|
||||
用法:
|
||||
python3 report_builder.py <folder> --name "名字" --lagna "上升" --lang cn
|
||||
python3 report_builder.py ./report_dir --name "张三" --lagna "狮子座" --lang cn
|
||||
python3 report_builder.py ./report_dir --name "Obama" --lagna "Leo" --lang en
|
||||
|
||||
功能:
|
||||
- 自动扫描目录下的 MD 文件，按章节注册表排序
|
||||
- 生成封面（客户名、上升、体系信息）
|
||||
- 自动生成目录
|
||||
- 每个章节独立分页（A4打印优化）
|
||||
- 羊皮纸主题CSS，支持中英文双语
|
||||
- 可直接浏览器打开后 Ctrl+P → Save as PDF
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import re
|
||||
import glob
|
||||
import argparse
|
||||
|
||||
try:
|
||||
import markdown
|
||||
except ImportError:
|
||||
print("Installing markdown...")
|
||||
os.system(f"{sys.executable} -m pip install markdown -q")
|
||||
import markdown
|
||||
|
||||
# ============================================================================
|
||||
# CSS 样式 — 羊皮纸主题（A4打印优化）
|
||||
# ============================================================================
|
||||
CSS = """
|
||||
@import url('https://fonts.googleapis.com/css2?family=Noto+Serif+SC:wght@400;500;600;700&family=Inter:wght@300;400;500;600&display=swap');
|
||||
|
||||
:root {
|
||||
--parchment: #f8f4ec; --parchment-deep: #f0eadb;
|
||||
--brown: #5a4636; --brown-light: #7a6652; --brown-muted: #9c8b7a;
|
||||
--gold: #b59540; --gold-soft: #d4c07a; --gold-line: #c9a94e;
|
||||
--text: #3d352c; --text-light: #5a4e42; --text-muted: #8a7d70;
|
||||
--border: #ddd3c2; --border-light: #e8e0d2;
|
||||
--table-head-bg: #ede6d8; --table-stripe: #f4efe5;
|
||||
}
|
||||
|
||||
@page { size: A4; margin: 22mm 20mm 24mm 20mm; }
|
||||
|
||||
* { box-sizing: border-box; margin: 0; padding: 0; }
|
||||
|
||||
body {
|
||||
font-family: -apple-system, "PingFang SC", "Microsoft YaHei", "Hiragino Sans GB", "Noto Sans SC", sans-serif;
|
||||
font-size: 14px; line-height: 1.85; color: var(--text);
|
||||
background: #e8e0d0;
|
||||
max-width: 780px; margin: 0 auto; padding: 48px 56px;
|
||||
background: var(--parchment);
|
||||
box-shadow: 0 1px 30px rgba(74,55,40,0.1);
|
||||
-webkit-print-color-adjust: exact; print-color-adjust: exact;
|
||||
}
|
||||
|
||||
@media print {
|
||||
body { background: var(--parchment); box-shadow: none; padding: 0; max-width: none; font-size: 10.5pt; }
|
||||
.no-print { display: none; }
|
||||
.section-header, thead th { -webkit-print-color-adjust: exact; print-color-adjust: exact; }
|
||||
table { font-size: 8pt !important; }
|
||||
}
|
||||
|
||||
/* ---- 封面 ---- */
|
||||
.cover {
|
||||
page-break-after: always; min-height: 100vh;
|
||||
display: flex; flex-direction: column; justify-content: center;
|
||||
position: relative; padding: 60px 10px;
|
||||
}
|
||||
.cover::before {
|
||||
content: ''; position: absolute; top: 0; left: 0; right: 0;
|
||||
height: 2px; background: linear-gradient(90deg, transparent 5%, var(--gold-line) 30%, var(--gold-soft) 50%, var(--gold-line) 70%, transparent 95%);
|
||||
}
|
||||
.cover-badge {
|
||||
color: var(--gold); font-family: 'Inter', sans-serif;
|
||||
font-size: 11px; font-weight: 600;
|
||||
letter-spacing: 3px; text-transform: uppercase; margin-bottom: 20px;
|
||||
}
|
||||
.cover h1 {
|
||||
font-family: "Noto Serif SC", "Songti SC", "SimSun", serif;
|
||||
font-size: 46px; font-weight: 700;
|
||||
color: var(--brown); line-height: 1.35; margin-bottom: 16px;
|
||||
}
|
||||
.cover h1 span { color: var(--gold); }
|
||||
.cover .subtitle {
|
||||
font-size: 15px; color: var(--text-muted); font-weight: 400;
|
||||
margin-bottom: 50px; letter-spacing: 0.5px;
|
||||
}
|
||||
.cover-meta { margin-top: 50px; padding-top: 28px; border-top: 1px solid var(--border-light); }
|
||||
.cover-meta-grid {
|
||||
display: grid; grid-template-columns: 1fr 1fr; gap: 8px 40px;
|
||||
font-size: 13px; color: var(--text-muted);
|
||||
}
|
||||
.cover-meta-grid dt { font-weight: 600; color: var(--brown); font-size: 11px; letter-spacing: 0.5px; margin-top: 12px; }
|
||||
.cover-meta-grid dd { margin: 2px 0 0; }
|
||||
|
||||
/* ---- 目录 ---- */
|
||||
.toc { page-break-after: always; padding: 40px 0; }
|
||||
.toc h2 {
|
||||
font-family: "Noto Serif SC", serif; font-size: 22px; color: var(--brown);
|
||||
margin-bottom: 24px; padding-bottom: 10px; border-bottom: 1px solid var(--border);
|
||||
font-weight: 600;
|
||||
}
|
||||
.toc-list { list-style: none; }
|
||||
.toc-list li {
|
||||
padding: 8px 0; border-bottom: 1px dashed var(--border-light);
|
||||
display: flex; justify-content: space-between; align-items: center;
|
||||
font-size: 14px;
|
||||
}
|
||||
.toc-section { font-weight: 500; color: var(--brown); }
|
||||
.toc-list li.toc-part {
|
||||
background: var(--parchment-deep); color: var(--brown); padding: 10px 16px;
|
||||
margin: 4px -16px; border-radius: 3px; border: none; border-bottom: none;
|
||||
font-weight: 600; font-size: 14px;
|
||||
}
|
||||
|
||||
/* ---- 章节 ---- */
|
||||
.section { page-break-before: always; }
|
||||
.section:first-of-type { page-break-before: auto; }
|
||||
.section-header {
|
||||
border-left: 3px solid var(--gold-line);
|
||||
color: var(--brown); padding: 12px 22px; margin: 0 0 28px;
|
||||
}
|
||||
.section-header .section-number {
|
||||
color: var(--gold); font-family: 'Inter', sans-serif;
|
||||
font-size: 10px; font-weight: 600; letter-spacing: 3px; text-transform: uppercase;
|
||||
}
|
||||
.section-header h2 {
|
||||
font-family: "Noto Serif SC", serif; font-size: 21px; font-weight: 700;
|
||||
margin-top: 2px; border: none; color: var(--brown) !important; padding-bottom: 0;
|
||||
}
|
||||
|
||||
/* ---- 排版元素 ---- */
|
||||
h1 {
|
||||
font-family: "Noto Serif SC", serif; font-size: 22px; color: var(--brown);
|
||||
margin: 32px 0 14px; padding-bottom: 6px; border-bottom: 1px solid var(--border-light);
|
||||
font-weight: 600;
|
||||
}
|
||||
h2 {
|
||||
font-family: "Noto Serif SC", serif; font-size: 18px; color: var(--brown);
|
||||
margin: 28px 0 12px; font-weight: 600; padding-bottom: 0; border-bottom: none;
|
||||
}
|
||||
h3 {
|
||||
font-size: 15px; font-weight: 600; color: var(--brown);
|
||||
margin: 22px 0 8px; padding-left: 10px;
|
||||
border-left: 2px solid var(--gold-line);
|
||||
}
|
||||
h4 { font-size: 14px; font-weight: 600; color: var(--brown-light); margin: 16px 0 6px; }
|
||||
p { margin: 0 0 12px; text-align: justify; }
|
||||
|
||||
table {
|
||||
width: 100%; border-collapse: collapse; margin: 10px 0 20px;
|
||||
font-size: 12px; line-height: 1.5;
|
||||
}
|
||||
thead th {
|
||||
background: var(--table-head-bg); color: var(--brown);
|
||||
padding: 7px 10px; text-align: left;
|
||||
font-weight: 600; font-size: 11px;
|
||||
border-bottom: 1.5px solid var(--gold-line);
|
||||
}
|
||||
tbody td { padding: 6px 10px; border-bottom: 1px solid var(--border-light); vertical-align: top; }
|
||||
tbody tr:nth-child(even) { background: var(--table-stripe); }
|
||||
|
||||
table:has(th:nth-child(10)) { font-size: 10px; }
|
||||
table:has(th:nth-child(10)) th,
|
||||
table:has(th:nth-child(10)) td { padding: 4px 3px; text-align: center; white-space: nowrap; }
|
||||
table:has(th:nth-child(10)) th:first-child,
|
||||
table:has(th:nth-child(10)) td:first-child { text-align: left; font-weight: 600; }
|
||||
|
||||
blockquote {
|
||||
border-left: 2px solid var(--gold-line);
|
||||
background: var(--parchment-deep);
|
||||
padding: 10px 16px; margin: 14px 0; border-radius: 0 3px 3px 0;
|
||||
color: var(--text-light); font-size: 13px;
|
||||
}
|
||||
blockquote strong { color: var(--brown); font-style: normal; }
|
||||
|
||||
ul, ol { margin: 6px 0 14px 22px; }
|
||||
li { margin-bottom: 3px; }
|
||||
|
||||
strong { color: var(--brown); }
|
||||
code {
|
||||
background: var(--parchment-deep); padding: 1px 4px; border-radius: 2px;
|
||||
font-size: 12px; color: var(--brown-light);
|
||||
font-family: 'Inter', monospace;
|
||||
}
|
||||
pre {
|
||||
background: #3d352c; color: #ede6d8; padding: 14px 18px; border-radius: 4px;
|
||||
margin: 14px 0; font-size: 11px; line-height: 1.6; overflow-x: auto; white-space: pre-wrap;
|
||||
}
|
||||
pre code { background: transparent; border: none; color: inherit; padding: 0; }
|
||||
hr { border: none; border-top: 1px dashed var(--border-light); margin: 24px 0; }
|
||||
|
||||
.page-break { page-break-before: always; }
|
||||
.footer-note {
|
||||
margin-top: 30px; padding-top: 14px; border-top: 1px solid var(--border-light);
|
||||
font-size: 10px; color: var(--text-muted); text-align: center;
|
||||
}
|
||||
"""
|
||||
|
||||
# ============================================================================
|
||||
# 章节注册表 — 自动匹配多种 MD 文件命名模式
|
||||
# ============================================================================
|
||||
SECTION_REGISTRY = [
|
||||
(10, "core", "Part I: Core Audit", "第一部分：核心审计",
|
||||
["01_core.md", "p1_data.md", "p1_basics.md", "core.md"]),
|
||||
(15, "planets_a", "Part II-A: Planets (Sun/Moon/Mars)", "第二部分A：行星审计 (日/月/火)",
|
||||
["p2a_planets.md"]),
|
||||
(17, "planets_b", "Part II-B: Planets (Me/Ju/Ve)", "第二部分B：行星审计 (水/木/金)",
|
||||
["p2b_planets.md"]),
|
||||
(19, "planets_c", "Part II-C: Planets (Sa/Ra/Ke)", "第二部分C：行星审计 (土/罗/计)",
|
||||
["p2c_planets.md"]),
|
||||
(20, "planets", "Part II: Planetary Audit (P1-P12)", "第二部分：行星审计 (P1-P12)",
|
||||
["02_planets.md", "p2_planets.md", "planets.md"]),
|
||||
(25, "validate", "Part II-D: R1-R10 Validation", "第二部分D：R1-R10数学校验",
|
||||
["p2d_validate.md", "validate.md", "validation.md"]),
|
||||
(30, "d9", "Part III: D9 Navamsha Calibration", "第三部分：D9品质校准",
|
||||
["03_d9.md", "p3_d9.md", "d9.md"]),
|
||||
(40, "houses", "Part IV: House Diagnostics", "第四部分：宫位诊断",
|
||||
["04_houses.md", "p4_houses.md", "houses.md"]),
|
||||
(50, "life", "Part V: Life Architecture", "第五部分：人生架构总结",
|
||||
["05_life.md", "p5a_life.md", "p5_life.md", "life.md"]),
|
||||
(55, "life2", "Part V (cont.): Life Architecture", "第五部分（续）：人生架构总结",
|
||||
["05b_life.md", "p5b_life.md", "life2.md"]),
|
||||
(60, "career1", "Part VI: Career — Portrait & Narrative","第六部分：事业 — 画像与叙事",
|
||||
["career_part1.md", "career_phase1_2.md"]),
|
||||
(65, "career2", "Part VI (cont.): Career — Strategy", "第六部分（续）：事业 — 战略决策",
|
||||
["career_part2.md", "career_phase3.md"]),
|
||||
(68, "career3", "Part VI (cont.): Career — Risk & Advice","第六部分（续）：事业 — 风险与箴言",
|
||||
["career_part3.md", "career_phase4.md"]),
|
||||
(70, "career", "Part VI: Career Architecture", "第六部分：事业架构",
|
||||
["02_career.md", "06_career.md", "career.md"]),
|
||||
(80, "love1", "Part VII: Love — System & Timeline", "第七部分：感情 — 体质报告与时间轴",
|
||||
["love_part1.md"]),
|
||||
(85, "love2", "Part VII (cont.): Love — Advice & Risk", "第七部分（续）：感情 — 建议与风险",
|
||||
["love_part2.md"]),
|
||||
(90, "love", "Part VII: Love & Marriage", "第七部分：感情与婚姻",
|
||||
["03_love.md", "07_love.md", "love.md"]),
|
||||
(100, "qa", "Appendix: Q&A", "附录：追问答疑",
|
||||
[]), # handled separately via glob
|
||||
]
|
||||
|
||||
|
||||
def find_files(folder):
|
||||
"""Auto-detect MD files using flexible naming patterns."""
|
||||
found = {} # canonical_key -> (priority, en_title, cn_title, content)
|
||||
|
||||
for priority, key, en_title, cn_title, patterns in SECTION_REGISTRY:
|
||||
if not patterns:
|
||||
continue
|
||||
for pat in patterns:
|
||||
path = os.path.join(folder, pat)
|
||||
if os.path.exists(path):
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
found[key] = (priority, en_title, cn_title, f.read())
|
||||
print(f" + {pat} -> {key}")
|
||||
break
|
||||
|
||||
# Q&A: glob for qa_*.md
|
||||
qa_files = sorted(glob.glob(os.path.join(folder, "qa_*.md")))
|
||||
if qa_files:
|
||||
combined = []
|
||||
for qf in qa_files:
|
||||
with open(qf, "r", encoding="utf-8") as f:
|
||||
combined.append(f"<!-- {os.path.basename(qf)} -->\n{f.read()}")
|
||||
print(f" + {os.path.basename(qf)} -> qa")
|
||||
found["qa"] = (100, "Appendix: Q&A", "附录：追问答疑", "\n\n---\n\n".join(combined))
|
||||
|
||||
return found
|
||||
|
||||
|
||||
def detect_package(found, lang="cn"):
|
||||
"""Detect report package type based on found sections."""
|
||||
has_core = any(k in found for k in ["core", "planets", "d9", "houses", "life", "validate"])
|
||||
has_career = any(k in found for k in ["career", "career1", "career2", "career3"])
|
||||
has_love = any(k in found for k in ["love", "love1", "love2"])
|
||||
has_qa = "qa" in found
|
||||
|
||||
parts = []
|
||||
if has_core: parts.append("Core" if lang == "en" else "核心")
|
||||
if has_career: parts.append("Career" if lang == "en" else "事业")
|
||||
if has_love: parts.append("Love" if lang == "en" else "感情")
|
||||
if has_qa: parts.append("Q&A" if lang == "en" else "答疑")
|
||||
|
||||
if lang == "cn":
|
||||
return " + ".join(parts), " + ".join(parts) + " 完整报告"
|
||||
return " + ".join(parts), " + ".join(parts) + " Complete Reading"
|
||||
|
||||
|
||||
def build_cover(name, lagna, gender, status, pkg, desc, lang="cn"):
|
||||
"""Generate cover page HTML."""
|
||||
badge = "Jyotish 数据驱动吠陀占星" if lang == "cn" else "Data-Driven Vedic Astrology"
|
||||
h1 = "吠陀占星<br><span>完整解读</span>" if lang == "cn" else "Vedic Astrology<br><span>Complete Reading</span>"
|
||||
L = {
|
||||
"cn": ["客户", "上升星座", "基本信息", "套餐", "体系", "计算引擎", "大运", "量化指标"],
|
||||
"en": ["Client", "Ascendant", "Profile", "Package", "Methodology", "Engine", "Dasha", "Metrics"],
|
||||
}[lang]
|
||||
return f"""
|
||||
<div class="cover">
|
||||
<div class="cover-badge">{badge}</div>
|
||||
<h1>{h1}</h1>
|
||||
<div class="subtitle">{desc}</div>
|
||||
<div class="cover-meta"><div class="cover-meta-grid">
|
||||
<div><dt>{L[0]}</dt><dd>{name}</dd></div>
|
||||
<div><dt>{L[1]}</dt><dd>{lagna}</dd></div>
|
||||
<div><dt>{L[2]}</dt><dd>{gender} | {status}</dd></div>
|
||||
<div><dt>{L[3]}</dt><dd>{pkg}</dd></div>
|
||||
<div><dt>{L[4]}</dt><dd>Parashari Jyotish | KN Rao School</dd></div>
|
||||
<div><dt>{L[5]}</dt><dd>Swiss Ephemeris | Lahiri Ayanamsha</dd></div>
|
||||
<div><dt>{L[6]}</dt><dd>Vimsottari (Mahadasha + Antardasha)</dd></div>
|
||||
<div><dt>{L[7]}</dt><dd>Shadbala, Ashtakavarga (SAV/BAV), D9 Navamsha</dd></div>
|
||||
</div></div>
|
||||
</div>"""
|
||||
|
||||
|
||||
def build_toc(sections, lang="cn"):
|
||||
"""Generate table of contents HTML."""
|
||||
toc_title = "目录" if lang == "cn" else "Table of Contents"
|
||||
items = []
|
||||
for _, _, en_title, cn_title, _ in sections:
|
||||
title = cn_title if lang == "cn" else en_title
|
||||
items.append(f'<li class="toc-part">{title}</li>')
|
||||
return f'<div class="toc"><h2>{toc_title}</h2><ul class="toc-list">{"".join(items)}</ul></div>'
|
||||
|
||||
|
||||
def build_section(num, title, md_text):
|
||||
"""Convert MD content to HTML section."""
|
||||
body = markdown.markdown(md_text, extensions=["tables", "fenced_code"])
|
||||
return f"""
|
||||
<div class="section">
|
||||
<div class="section-header">
|
||||
<div class="section-number">Section {num}</div>
|
||||
<h2>{title}</h2>
|
||||
</div>
|
||||
{body}
|
||||
</div>"""
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Jyotish Report Builder — MD → HTML",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
python3 report_builder.py ./report_folder --name "张三" --lagna "狮子座" --lang cn
|
||||
python3 report_builder.py ./analysis --name "Obama" --lagna "Leo" --lang en
|
||||
""")
|
||||
parser.add_argument("folder", help="Folder with MD files (checks 'parts/' subfolder too)")
|
||||
parser.add_argument("--name", default="Client", help="Client name")
|
||||
parser.add_argument("--lagna", default="—", help="Ascendant")
|
||||
parser.add_argument("--gender", default="—", help="Gender")
|
||||
parser.add_argument("--status", default="—", help="Current status")
|
||||
parser.add_argument("--lang", default="cn", choices=["cn", "en"], help="Language (default: cn)")
|
||||
parser.add_argument("--output", default=None, help="Output HTML path")
|
||||
args = parser.parse_args()
|
||||
|
||||
folder = args.folder.rstrip("/\\")
|
||||
if not os.path.isdir(folder):
|
||||
print(f"Error: {folder} is not a directory")
|
||||
sys.exit(1)
|
||||
|
||||
# Check for 'parts/' subfolder
|
||||
parts_dir = os.path.join(folder, "parts")
|
||||
search_dir = parts_dir if os.path.isdir(parts_dir) else folder
|
||||
print(f" Scanning: {search_dir}\n")
|
||||
|
||||
found = find_files(search_dir)
|
||||
|
||||
if not found:
|
||||
print(f"\nError: No MD files found in {search_dir}")
|
||||
print(" Expected files like: p1_data.md, p2a_planets.md, p3_d9.md, qa_*.md")
|
||||
sys.exit(1)
|
||||
|
||||
lang = args.lang
|
||||
pkg, desc = detect_package(found, lang)
|
||||
print(f"\n Package: {pkg} | Language: {lang}")
|
||||
|
||||
# Sort sections by priority
|
||||
ordered = []
|
||||
sec_num = 1
|
||||
for priority, key, en_title, cn_title, _ in SECTION_REGISTRY:
|
||||
if key in found:
|
||||
p, et, ct, content = found[key]
|
||||
ordered.append((sec_num, key, en_title, cn_title, content))
|
||||
sec_num += 1
|
||||
|
||||
# Build HTML
|
||||
cover = build_cover(args.name, args.lagna, args.gender, args.status, pkg, desc, lang)
|
||||
toc = build_toc(ordered, lang)
|
||||
|
||||
sections_html = []
|
||||
for num, key, en_title, cn_title, content in ordered:
|
||||
title = cn_title if lang == "cn" else en_title
|
||||
num_str = f"{num:02d}"
|
||||
sections_html.append(build_section(num_str, title, content))
|
||||
|
||||
footer_cn = """<div class="footer-note">
|
||||
本报告基于传统吠陀占星方法（Parashari Jyotish | KN Rao School）。<br>
|
||||
每项结论均有量化行星指标支撑。仅供自我反思与战略思考参考。<br>
|
||||
Powered by Jyotish Engine v3.5 & Swiss Ephemeris</div>"""
|
||||
footer_en = """<div class="footer-note">
|
||||
Generated using traditional Vedic astrological methods (Parashari Jyotish | KN Rao School).<br>
|
||||
Every claim backed by quantified planetary metrics. For self-reflection purposes only.<br>
|
||||
Powered by Jyotish Engine v3.5 & Swiss Ephemeris</div>"""
|
||||
footer = footer_cn if lang == "cn" else footer_en
|
||||
|
||||
html_lang = "zh-CN" if lang == "cn" else "en"
|
||||
html = f"""<!DOCTYPE html>
|
||||
<html lang="{html_lang}"><head><meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Jyotish Reading — {args.name}</title>
|
||||
<style>{CSS}</style></head>
|
||||
<body>{cover}{toc}{"".join(sections_html)}{footer}</body></html>"""
|
||||
|
||||
out = args.output or os.path.join(folder, "report.html")
|
||||
with open(out, "w", encoding="utf-8") as f:
|
||||
f.write(html)
|
||||
|
||||
size = os.path.getsize(out) / 1024
|
||||
print(f"\n [OK] Output: {out} ({size:.0f} KB)")
|
||||
print(f" -> Open in browser -> Ctrl+P -> Save as PDF")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,376 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Shadbala 计算模块（六重力量）
|
||||
基于 Parashara 系统，量化行星力量
|
||||
|
||||
六种力量：
|
||||
1. Sthana Bala（位置力量）
|
||||
2. Dig Bala（方向力量）
|
||||
3. Kala Bala（时间力量）
|
||||
4. Chesta Bala（运动力量）
|
||||
5. Naisargika Bala（天然力量）
|
||||
6. Drik Bala（相位力量）
|
||||
"""
|
||||
|
||||
import math
|
||||
from typing import Dict, Tuple
|
||||
|
||||
# ============================================================================
|
||||
# 常量
|
||||
# ============================================================================
|
||||
SIGNS = ['Aries', 'Taurus', 'Gemini', 'Cancer', 'Leo', 'Virgo',
|
||||
'Libra', 'Scorpio', 'Sagittarius', 'Capricorn', 'Aquarius', 'Pisces']
|
||||
|
||||
SIGN_LORDS = {
|
||||
'Aries': 'Mars', 'Taurus': 'Venus', 'Gemini': 'Mercury', 'Cancer': 'Moon',
|
||||
'Leo': 'Sun', 'Virgo': 'Mercury', 'Libra': 'Venus', 'Scorpio': 'Mars',
|
||||
'Sagittarius': 'Jupiter', 'Capricorn': 'Saturn', 'Aquarius': 'Saturn', 'Pisces': 'Jupiter'
|
||||
}
|
||||
|
||||
# 入庙度数（sign_idx * 30 + degree）
|
||||
EXALTATION_DEG = {
|
||||
'Sun': 10.0, 'Moon': 33.0, 'Mars': 298.0, 'Mercury': 165.0,
|
||||
'Jupiter': 95.0, 'Venus': 357.0, 'Saturn': 200.0
|
||||
}
|
||||
|
||||
# 落陷度数（入庙 + 180°）
|
||||
DEBILITATION_DEG = {p: (d + 180) % 360 for p, d in EXALTATION_DEG.items()}
|
||||
|
||||
# 行星友好/敌对关系
|
||||
FRIENDSHIP = {
|
||||
'Sun': {'friend': ['Moon', 'Mars', 'Jupiter'], 'enemy': ['Saturn', 'Venus'], 'neutral': ['Mercury']},
|
||||
'Moon': {'friend': ['Sun', 'Mercury'], 'enemy': [], 'neutral': ['Mars', 'Jupiter', 'Venus', 'Saturn']},
|
||||
'Mars': {'friend': ['Sun', 'Moon', 'Jupiter'], 'enemy': ['Mercury'], 'neutral': ['Venus', 'Saturn']},
|
||||
'Mercury': {'friend': ['Sun', 'Venus'], 'enemy': ['Moon'], 'neutral': ['Mars', 'Jupiter', 'Saturn']},
|
||||
'Jupiter': {'friend': ['Sun', 'Moon', 'Mars'], 'enemy': ['Mercury', 'Venus'], 'neutral': ['Saturn']},
|
||||
'Venus': {'friend': ['Mercury', 'Saturn'], 'enemy': ['Sun', 'Moon'], 'neutral': ['Mars', 'Jupiter']},
|
||||
'Saturn': {'friend': ['Mercury', 'Venus'], 'enemy': ['Sun', 'Moon', 'Mars'], 'neutral': ['Jupiter']},
|
||||
}
|
||||
|
||||
# Dig Bala 最强宫位
|
||||
DIG_BALA_HOUSE = {
|
||||
'Sun': 10, 'Mars': 10, # Midheaven
|
||||
'Moon': 4, 'Venus': 4, # Nadir
|
||||
'Jupiter': 1, 'Mercury': 1, # Ascendant
|
||||
'Saturn': 7, # Descendant
|
||||
}
|
||||
|
||||
# Naisargika Bala（天然力量，单位 Virupas）
|
||||
NAISARGIKA_BALA = {
|
||||
'Sun': 60.0, 'Moon': 60.0, 'Venus': 52.5,
|
||||
'Jupiter': 45.0, 'Mercury': 37.5, 'Mars': 30.0, 'Saturn': 22.5
|
||||
}
|
||||
|
||||
# Shadbala 最低要求（Rupas）
|
||||
MIN_REQUIRED = {
|
||||
'Sun': 5.0, 'Moon': 6.0, 'Mars': 5.0,
|
||||
'Mercury': 7.0, 'Jupiter': 6.5, 'Venus': 5.5, 'Saturn': 5.0
|
||||
}
|
||||
|
||||
# 昼强/夜强行星
|
||||
DIURNAL_STRONG = ['Sun', 'Jupiter', 'Venus']
|
||||
NOCTURNAL_STRONG = ['Moon', 'Mars', 'Saturn']
|
||||
|
||||
# 吉星/凶星
|
||||
BENEFICS = ['Jupiter', 'Venus', 'Mercury']
|
||||
MALEFICS = ['Saturn', 'Mars', 'Sun']
|
||||
|
||||
# 行星相位规则（所有行星都有7宫相位，特殊相位如下）
|
||||
SPECIAL_ASPECTS = {
|
||||
'Mars': [4, 8], # 火星额外看4宫和8宫
|
||||
'Jupiter': [5, 9], # 木星额外看5宫和9宫
|
||||
'Saturn': [3, 10], # 土星额外看3宫和10宫
|
||||
}
|
||||
|
||||
# Virupas → Rupas 转换（60 Virupas = 1 Rupa）
|
||||
VIRUPAS_PER_RUPA = 60.0
|
||||
|
||||
|
||||
def calc_shadbala(planets: Dict, asc_sign: str, birth_hour: float,
|
||||
sun_lon: float, moon_lon: float) -> Dict:
|
||||
"""
|
||||
计算完整 Shadbala
|
||||
|
||||
Args:
|
||||
planets: 行星数据 dict，每颗行星需要 {sign, degree, house, retrograde, speed}
|
||||
asc_sign: 上升星座名称
|
||||
birth_hour: 出生时间（当地时间，24小时制）
|
||||
sun_lon: 太阳恒星黄道经度
|
||||
moon_lon: 月亮恒星黄道经度
|
||||
|
||||
Returns:
|
||||
完整的 Shadbala 计算结果
|
||||
"""
|
||||
results = {}
|
||||
is_night = birth_hour < 6.0 or birth_hour >= 18.0
|
||||
sun_northern = sun_lon >= 270 or sun_lon < 90 # Uttarayana 概略判断
|
||||
|
||||
for pname in ['Sun', 'Moon', 'Mars', 'Mercury', 'Jupiter', 'Venus', 'Saturn']:
|
||||
if pname not in planets:
|
||||
continue
|
||||
p = planets[pname]
|
||||
lon = p.get('degree', 0)
|
||||
sign = p.get('sign', 'Aries')
|
||||
house = p.get('house', 1)
|
||||
retro = p.get('retrograde', False)
|
||||
speed = p.get('speed', 1.0)
|
||||
|
||||
# 1. Sthana Bala（位置力量）
|
||||
sthana = calc_sthana_bala(pname, lon, sign, house)
|
||||
|
||||
# 2. Dig Bala（方向力量）
|
||||
dig = calc_dig_bala(pname, house)
|
||||
|
||||
# 3. Kala Bala（时间力量）
|
||||
kala = calc_kala_bala(pname, is_night, sun_northern, sun_lon, moon_lon)
|
||||
|
||||
# 4. Chesta Bala（运动力量）
|
||||
chesta = calc_chesta_bala(pname, retro, speed, sun_lon, moon_lon)
|
||||
|
||||
# 5. Naisargika Bala（天然力量）
|
||||
naisargika = NAISARGIKA_BALA.get(pname, 30.0)
|
||||
|
||||
# 6. Drik Bala（相位力量）
|
||||
drik = calc_drik_bala(pname, sign, house, planets)
|
||||
|
||||
# 总分（Virupas → Rupas）
|
||||
total_virupas = sthana['total'] + dig + kala['total'] + chesta + naisargika + drik
|
||||
total_rupas = total_virupas / VIRUPAS_PER_RUPA
|
||||
|
||||
min_req = MIN_REQUIRED.get(pname, 5.0)
|
||||
ishta_bala = (total_rupas / min_req * 100) if min_req > 0 else 0
|
||||
|
||||
if ishta_bala >= 150:
|
||||
strength_level = "极强"
|
||||
elif ishta_bala >= 125:
|
||||
strength_level = "强"
|
||||
elif ishta_bala >= 100:
|
||||
strength_level = "充足"
|
||||
elif ishta_bala >= 75:
|
||||
strength_level = "略弱"
|
||||
elif ishta_bala >= 50:
|
||||
strength_level = "弱"
|
||||
else:
|
||||
strength_level = "极弱"
|
||||
|
||||
results[pname] = {
|
||||
'sthana_bala': sthana,
|
||||
'dig_bala': round(dig, 2),
|
||||
'kala_bala': kala,
|
||||
'chesta_bala': round(chesta, 2),
|
||||
'naisargika_bala': round(naisargika, 2),
|
||||
'drik_bala': round(drik, 2),
|
||||
'total_virupas': round(total_virupas, 2),
|
||||
'total_rupas': round(total_rupas, 4),
|
||||
'min_required': min_req,
|
||||
'ishta_bala_pct': round(ishta_bala, 1),
|
||||
'strength_level': strength_level,
|
||||
}
|
||||
|
||||
# 排名
|
||||
ranked = sorted(results.items(), key=lambda x: x[1]['total_rupas'], reverse=True)
|
||||
for i, (name, _) in enumerate(ranked):
|
||||
results[name]['rank'] = i + 1
|
||||
|
||||
return {
|
||||
'method': 'Shadbala六重力量（Parashara系统）',
|
||||
'is_night_birth': is_night,
|
||||
'sun_uttarayana': sun_northern,
|
||||
'planets': results,
|
||||
'ranking': [name for name, _ in ranked],
|
||||
'strongest': ranked[0][0] if ranked else None,
|
||||
'weakest': ranked[-1][0] if ranked else None,
|
||||
}
|
||||
|
||||
|
||||
def calc_sthana_bala(pname: str, lon: float, sign: str, house: int) -> Dict:
|
||||
"""Sthana Bala（位置力量）"""
|
||||
# A. Ucha Bala（入庙力量）
|
||||
debilit_deg = DEBILITATION_DEG.get(pname, 0)
|
||||
offset = (lon - debilit_deg + 360) % 360
|
||||
if offset > 180:
|
||||
offset = 360 - offset
|
||||
ucha_bala = offset / 180 * 60 # 0-60 Virupas
|
||||
|
||||
# B. 简化 Saptavargaja Bala（基于D1位置）
|
||||
lord = SIGN_LORDS.get(sign, '')
|
||||
if lord == pname: # 本宫
|
||||
sapta_score = 45
|
||||
elif pname in FRIENDSHIP.get(lord, {}).get('friend', []):
|
||||
sapta_score = 35
|
||||
elif pname in FRIENDSHIP.get(lord, {}).get('neutral', []):
|
||||
sapta_score = 25
|
||||
elif pname in FRIENDSHIP.get(lord, {}).get('enemy', []):
|
||||
sapta_score = 15
|
||||
else:
|
||||
sapta_score = 25
|
||||
# 落陷检查
|
||||
debilit_sign = SIGNS[int(DEBILITATION_DEG.get(pname, 0) / 30) % 12]
|
||||
exalt_sign = SIGNS[int(EXALTATION_DEG.get(pname, 0) / 30) % 12]
|
||||
if sign == exalt_sign:
|
||||
sapta_score = 50
|
||||
elif sign == debilit_sign:
|
||||
sapta_score = 5
|
||||
|
||||
# C. Ojayugma Bala（奇偶宫力量）
|
||||
if pname in ['Mercury', 'Venus']:
|
||||
ojayugma = 15 if house % 2 == 0 else 0
|
||||
else:
|
||||
ojayugma = 15 if house % 2 == 1 else 0
|
||||
|
||||
# D. Kendra Bala（角宫力量）
|
||||
kendra_scores = {1: 60, 4: 40, 7: 20, 10: 30}
|
||||
kendra_bala = kendra_scores.get(house, 0)
|
||||
|
||||
# E. Drekkana Bala（三分盘力量）
|
||||
deg_in_sign = lon % 30
|
||||
if pname in ['Sun', 'Mars', 'Jupiter']:
|
||||
drekkana_bala = 15 if deg_in_sign < 10 else 0
|
||||
elif pname in ['Moon', 'Venus']:
|
||||
drekkana_bala = 15 if 10 <= deg_in_sign < 20 else 0
|
||||
else: # Saturn, Mercury
|
||||
drekkana_bala = 15 if deg_in_sign >= 20 else 0
|
||||
|
||||
total = ucha_bala + sapta_score + ojayugma + kendra_bala + drekkana_bala
|
||||
|
||||
return {
|
||||
'ucha_bala': round(ucha_bala, 2),
|
||||
'sapta_score': round(sapta_score, 2),
|
||||
'ojayugma_bala': ojayugma,
|
||||
'kendra_bala': kendra_bala,
|
||||
'drekkana_bala': drekkana_bala,
|
||||
'total': round(total, 2),
|
||||
}
|
||||
|
||||
|
||||
def calc_dig_bala(pname: str, house: int) -> float:
|
||||
"""Dig Bala（方向力量），max 60 Virupas"""
|
||||
best_house = DIG_BALA_HOUSE.get(pname, 1)
|
||||
# 线性插值：最强宫位=60，对宫=0
|
||||
diff = abs(house - best_house)
|
||||
if diff > 6:
|
||||
diff = 12 - diff
|
||||
return max(0, (6 - diff) * 10)
|
||||
|
||||
|
||||
def calc_kala_bala(pname: str, is_night: bool, sun_northern: bool,
|
||||
sun_lon: float, moon_lon: float) -> Dict:
|
||||
"""Kala Bala（时间力量）"""
|
||||
components = {}
|
||||
|
||||
# A. Nathonnata Bala（昼夜力量）
|
||||
if pname == 'Mercury':
|
||||
nathonnata = 60
|
||||
elif is_night and pname in NOCTURNAL_STRONG:
|
||||
nathonnata = 60
|
||||
elif not is_night and pname in DIURNAL_STRONG:
|
||||
nathonnata = 60
|
||||
else:
|
||||
nathonnata = 0
|
||||
components['nathonnata'] = nathonnata
|
||||
|
||||
# B. Paksha Bala（月相力量，简化）
|
||||
moon_sun_diff = (moon_lon - sun_lon + 360) % 360
|
||||
if pname in ['Jupiter', 'Venus', 'Moon']:
|
||||
# 望月（180°）最强
|
||||
paksha = moon_sun_diff / 180 * 30
|
||||
else:
|
||||
# 朔月（0°）最强
|
||||
paksha = (180 - moon_sun_diff) / 180 * 30 if moon_sun_diff <= 180 else (moon_sun_diff - 180) / 180 * 30
|
||||
components['paksha'] = round(paksha, 2)
|
||||
|
||||
# C. Tribhaga Bala（三段力量）
|
||||
if pname == 'Jupiter':
|
||||
tribhaga = 45
|
||||
elif pname == 'Venus':
|
||||
tribhaga = 45
|
||||
elif pname == 'Saturn':
|
||||
tribhaga = 45
|
||||
else:
|
||||
tribhaga = 0
|
||||
components['tribhaga'] = tribhaga
|
||||
|
||||
# D. Ayana Bala（太阳南北行）
|
||||
if pname == 'Mercury':
|
||||
ayana = 30
|
||||
elif sun_northern and pname in ['Sun', 'Mars', 'Moon']:
|
||||
ayana = 30
|
||||
elif not sun_northern and pname in ['Jupiter', 'Venus', 'Saturn']:
|
||||
ayana = 30
|
||||
else:
|
||||
ayana = 15
|
||||
components['ayana'] = ayana
|
||||
|
||||
total = sum(components.values())
|
||||
return {k: v for k, v in components.items()} | {'total': round(total, 2)}
|
||||
|
||||
|
||||
def calc_chesta_bala(pname: str, retro: bool, speed: float,
|
||||
sun_lon: float, moon_lon: float) -> float:
|
||||
"""Chesta Bala（运动力量），max 60 Virupas"""
|
||||
if pname == 'Sun':
|
||||
return 60.0 # 太阳始终满分
|
||||
|
||||
if pname == 'Moon':
|
||||
# 月亮根据月相：望月=60，朔月=0
|
||||
moon_sun_diff = (moon_lon - sun_lon + 360) % 360
|
||||
return moon_sun_diff / 180 * 60
|
||||
|
||||
# 其他行星
|
||||
if retro:
|
||||
return 60.0
|
||||
|
||||
# 速度判断（简化：用speed的绝对值）
|
||||
abs_speed = abs(speed)
|
||||
if abs_speed > 1.0: # 快速直行
|
||||
return 50.0
|
||||
elif abs_speed > 0.5:
|
||||
return 35.0
|
||||
elif abs_speed > 0.1:
|
||||
return 20.0
|
||||
else:
|
||||
return 10.0 # 接近驻留
|
||||
|
||||
|
||||
def calc_drik_bala(pname: str, sign: str, house: int,
|
||||
all_planets: Dict) -> float:
|
||||
"""Drik Bala（相位力量），可正可负"""
|
||||
drik = 0.0
|
||||
p_sign_idx = SIGNS.index(sign) if sign in SIGNS else 0
|
||||
|
||||
for other_name, other_data in all_planets.items():
|
||||
if other_name == pname or other_name == 'Rahu' or other_name == 'Ketu':
|
||||
continue
|
||||
|
||||
other_sign = other_data.get('sign', '')
|
||||
if other_sign not in SIGNS:
|
||||
continue
|
||||
other_sign_idx = SIGNS.index(other_sign)
|
||||
|
||||
# 计算从other到pname的宫位差
|
||||
house_diff = (p_sign_idx - other_sign_idx) % 12 + 1
|
||||
|
||||
# 检查是否形成相位
|
||||
has_aspect = False
|
||||
if house_diff == 7 or house_diff == 1: # 7宫相位或合相
|
||||
has_aspect = True
|
||||
if other_name in SPECIAL_ASPECTS:
|
||||
if house_diff in SPECIAL_ASPECTS[other_name]:
|
||||
has_aspect = True
|
||||
|
||||
if has_aspect:
|
||||
# 判断吉凶
|
||||
aspect_value = 15.0
|
||||
if house_diff == 1: # 合相加倍
|
||||
aspect_value = 30.0
|
||||
|
||||
if other_name in BENEFICS:
|
||||
drik += aspect_value
|
||||
elif other_name in MALEFICS:
|
||||
drik -= aspect_value
|
||||
else:
|
||||
drik += aspect_value * 0.5 # 中性行星
|
||||
|
||||
# 限制范围
|
||||
return max(-60.0, min(60.0, drik))
|
||||
@@ -0,0 +1,292 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
R1-R10 数学验证模块
|
||||
对星盘计算结果执行 10 项硬性校验规则（来自 CNWU16 审计框架）
|
||||
|
||||
规则清单：
|
||||
R1: SAV 总和 = 337（7行星BAV之和）
|
||||
R2: 各 BAV 行常数（Sun=48, Moon=49, Mars=39, Mercury=54, Jupiter=56, Venus=52, Saturn=39, Lagna=49）
|
||||
R2b: BAV列→SAV列校验（每个星座的7行星BAV之和 = 该星座SAV分数）
|
||||
R3: 水星最大延伸角 ≤ 28°（不远离太阳）
|
||||
R4: 金星最大延伸角 ≤ 47°（不远离太阳）
|
||||
R5: Rahu-Ketu 严格 180° 对冲
|
||||
R6: 逆行合法性（只有火星/木星/土星/金星/水星可能逆行，Rahu/Ketu永远逆行，太阳/月亮不逆行）
|
||||
R7: Dasha 大运年限链总和 = 120 年
|
||||
R8: 行星完整性（7行星 + Rahu + Ketu + Lagna 全部存在）
|
||||
R9: 星座度数范围 [0, 30)
|
||||
R10: 宫位连续性（1-12宫无断点）
|
||||
"""
|
||||
|
||||
from typing import Dict, List, Tuple
|
||||
import math
|
||||
|
||||
SIGNS = ['Aries', 'Taurus', 'Gemini', 'Cancer', 'Leo', 'Virgo',
|
||||
'Libra', 'Scorpio', 'Sagittarius', 'Capricorn', 'Aquarius', 'Pisces']
|
||||
|
||||
DASHA_YEARS = {
|
||||
"Ketu": 7, "Venus": 20, "Sun": 6, "Moon": 10,
|
||||
"Mars": 7, "Rahu": 18, "Jupiter": 16, "Saturn": 19, "Mercury": 17
|
||||
}
|
||||
|
||||
EXPECTED_PLANETS = ['Sun', 'Moon', 'Mars', 'Mercury', 'Jupiter', 'Venus', 'Saturn', 'Rahu', 'Ketu']
|
||||
|
||||
BAV_TOTALS = {
|
||||
'Sun': 48, 'Moon': 49, 'Mars': 39, 'Mercury': 54,
|
||||
'Jupiter': 56, 'Venus': 52, 'Saturn': 39, 'Lagna': 49
|
||||
}
|
||||
|
||||
SEVEN_PLANETS = ['Sun', 'Moon', 'Mars', 'Mercury', 'Jupiter', 'Venus', 'Saturn']
|
||||
|
||||
|
||||
def validate_chart(chart_data: Dict, ashtakavarga_data: Dict = None) -> Dict:
|
||||
"""
|
||||
执行 R1-R10 全部校验
|
||||
|
||||
Args:
|
||||
chart_data: jyotish_engine chart 命令的完整输出
|
||||
ashtakavarga_data: ashtakavarga 命令的输出（可选，用于 R1/R2）
|
||||
|
||||
Returns:
|
||||
{
|
||||
"valid": bool, # 全部通过
|
||||
"total_checks": int,
|
||||
"passed": int,
|
||||
"failed": int,
|
||||
"results": [{"rule": "R1", "name": "...", "passed": bool, "detail": "..."}, ...]
|
||||
}
|
||||
"""
|
||||
results = []
|
||||
planets = chart_data.get('planets', {})
|
||||
asc = chart_data.get('ascendant', {})
|
||||
|
||||
# R1: SAV 总和 = 337
|
||||
if ashtakavarga_data:
|
||||
sav_total = ashtakavarga_data.get('sav', {}).get('total', 0)
|
||||
sav_valid = ashtakavarga_data.get('sav', {}).get('valid', False)
|
||||
results.append({
|
||||
'rule': 'R1', 'name': 'SAV总和=337',
|
||||
'passed': sav_valid,
|
||||
'detail': f"SAV total = {sav_total}, expected 337"
|
||||
})
|
||||
else:
|
||||
results.append({
|
||||
'rule': 'R1', 'name': 'SAV总和=337',
|
||||
'passed': None,
|
||||
'detail': '未提供 ashtakavarga 数据，跳过'
|
||||
})
|
||||
|
||||
# R2: 各 BAV 行常数
|
||||
if ashtakavarga_data:
|
||||
bav_results = ashtakavarga_data.get('bav', {})
|
||||
all_bav_valid = ashtakavarga_data.get('all_bav_valid', False)
|
||||
details = []
|
||||
for pname in SEVEN_PLANETS + ['Lagna']:
|
||||
if pname in bav_results:
|
||||
actual = bav_results[pname].get('total', 0)
|
||||
expected = BAV_TOTALS.get(pname, 0)
|
||||
details.append(f"{pname}={actual}/{expected}")
|
||||
results.append({
|
||||
'rule': 'R2', 'name': 'BAV行常数校验',
|
||||
'passed': all_bav_valid,
|
||||
'detail': '; '.join(details) if details else '无BAV数据'
|
||||
})
|
||||
else:
|
||||
results.append({
|
||||
'rule': 'R2', 'name': 'BAV行常数校验',
|
||||
'passed': None,
|
||||
'detail': '未提供 ashtakavarga 数据，跳过'
|
||||
})
|
||||
|
||||
# R2b: BAV列→SAV列校验（每个星座的7行星BAV之和 = 该星座SAV分数）
|
||||
if ashtakavarga_data:
|
||||
r2b_passed, r2b_detail = _check_bav_column_sav(ashtakavarga_data)
|
||||
results.append({'rule': 'R2b', 'name': 'BAV列→SAV列校验', 'passed': r2b_passed, 'detail': r2b_detail})
|
||||
else:
|
||||
results.append({'rule': 'R2b', 'name': 'BAV列→SAV列校验', 'passed': None, 'detail': '未提供 ashtakavarga 数据，跳过'})
|
||||
|
||||
# R3: 水星最大延伸角 ≤ 28°
|
||||
r3_passed, r3_detail = _check_elongation(planets, 'Mercury', 'Sun', 28)
|
||||
results.append({'rule': 'R3', 'name': '水星延伸角≤28°', 'passed': r3_passed, 'detail': r3_detail})
|
||||
|
||||
# R4: 金星最大延伸角 ≤ 47°
|
||||
r4_passed, r4_detail = _check_elongation(planets, 'Venus', 'Sun', 47)
|
||||
results.append({'rule': 'R4', 'name': '金星延伸角≤47°', 'passed': r4_passed, 'detail': r4_detail})
|
||||
|
||||
# R5: Rahu-Ketu 严格 180° 对冲
|
||||
r5_passed, r5_detail = _check_rahu_ketu_opposition(planets)
|
||||
results.append({'rule': 'R5', 'name': 'Rahu-Ketu 180°对冲', 'passed': r5_passed, 'detail': r5_detail})
|
||||
|
||||
# R6: 逆行合法性
|
||||
r6_passed, r6_detail = _check_retrograde_legitimacy(planets)
|
||||
results.append({'rule': 'R6', 'name': '逆行合法性', 'passed': r6_passed, 'detail': r6_detail})
|
||||
|
||||
# R7: Dasha 大运年限链总和 = 120 年
|
||||
dasha_sum = sum(DASHA_YEARS.values())
|
||||
results.append({
|
||||
'rule': 'R7', 'name': 'Dasha年限总和=120',
|
||||
'passed': dasha_sum == 120,
|
||||
'detail': f"Dasha year chain sum = {dasha_sum}"
|
||||
})
|
||||
|
||||
# R8: 行星完整性
|
||||
r8_passed, r8_detail = _check_planet_completeness(planets, asc)
|
||||
results.append({'rule': 'R8', 'name': '行星完整性', 'passed': r8_passed, 'detail': r8_detail})
|
||||
|
||||
# R9: 星座度数范围 [0, 30)
|
||||
r9_passed, r9_detail = _check_degree_range(planets)
|
||||
results.append({'rule': 'R9', 'name': '星座度数范围', 'passed': r9_passed, 'detail': r9_detail})
|
||||
|
||||
# R10: 宫位连续性（1-12宫无断点）
|
||||
r10_passed, r10_detail = _check_house_continuity(chart_data)
|
||||
results.append({'rule': 'R10', 'name': '宫位连续性', 'passed': r10_passed, 'detail': r10_detail})
|
||||
|
||||
# 汇总
|
||||
checked = [r for r in results if r['passed'] is not None]
|
||||
passed_count = sum(1 for r in checked if r['passed'])
|
||||
failed_count = sum(1 for r in checked if not r['passed'])
|
||||
|
||||
return {
|
||||
'valid': failed_count == 0,
|
||||
'total_checks': len(results),
|
||||
'checked': len(checked),
|
||||
'passed': passed_count,
|
||||
'failed': failed_count,
|
||||
'skipped': len(results) - len(checked),
|
||||
'results': results,
|
||||
}
|
||||
|
||||
|
||||
def _check_elongation(planets: Dict, planet: str, reference: str, max_deg: float) -> Tuple[bool, str]:
|
||||
"""检查行星与参考行星的延伸角"""
|
||||
p1 = planets.get(planet, {})
|
||||
p2 = planets.get(reference, {})
|
||||
deg1 = p1.get('degree', None)
|
||||
deg2 = p2.get('degree', None)
|
||||
if deg1 is None or deg2 is None:
|
||||
return None, f'{planet}或{reference}度数缺失'
|
||||
elongation = abs(deg1 - deg2)
|
||||
if elongation > 180:
|
||||
elongation = 360 - elongation
|
||||
ok = elongation <= max_deg
|
||||
return ok, f"{planet}延伸角 = {elongation:.2f}°, 阈值 {max_deg}°"
|
||||
|
||||
|
||||
def _check_rahu_ketu_opposition(planets: Dict) -> Tuple[bool, str]:
|
||||
"""检查 Rahu-Ketu 严格 180° 对冲"""
|
||||
rahu = planets.get('Rahu', {})
|
||||
ketu = planets.get('Ketu', {})
|
||||
rahu_deg = rahu.get('degree', None)
|
||||
ketu_deg = ketu.get('degree', None)
|
||||
if rahu_deg is None or ketu_deg is None:
|
||||
return None, 'Rahu或Ketu度数缺失'
|
||||
diff = abs(rahu_deg - ketu_deg)
|
||||
deviation = abs(diff - 180)
|
||||
ok = deviation < 0.01 # 允许极小数值误差
|
||||
return ok, f"Rahu={rahu_deg:.4f}°, Ketu={ketu_deg:.4f}°, 偏差={deviation:.4f}°"
|
||||
|
||||
|
||||
def _check_retrograde_legitimacy(planets: Dict) -> Tuple[bool, str]:
|
||||
"""检查逆行合法性"""
|
||||
# 太阳和月亮不应逆行
|
||||
never_retro = ['Sun', 'Moon']
|
||||
# Rahu/Ketu 永远逆行
|
||||
always_retro = ['Rahu', 'Ketu']
|
||||
# 火星/木星/土星/金星/水星 可能逆行也可能不逆行
|
||||
can_retro = ['Mars', 'Jupiter', 'Saturn', 'Venus', 'Mercury']
|
||||
|
||||
issues = []
|
||||
for pname in never_retro:
|
||||
pd = planets.get(pname, {})
|
||||
if pd.get('retrograde', False):
|
||||
issues.append(f"{pname}不应逆行")
|
||||
|
||||
for pname in always_retro:
|
||||
pd = planets.get(pname, {})
|
||||
if not pd.get('retrograde', True):
|
||||
issues.append(f"{pname}应永远逆行")
|
||||
|
||||
ok = len(issues) == 0
|
||||
detail = '; '.join(issues) if issues else '所有行星逆行状态合法'
|
||||
return ok, detail
|
||||
|
||||
|
||||
def _check_planet_completeness(planets: Dict, asc: Dict) -> Tuple[bool, str]:
|
||||
"""检查行星完整性"""
|
||||
missing = []
|
||||
for pname in EXPECTED_PLANETS:
|
||||
if pname not in planets:
|
||||
missing.append(pname)
|
||||
if not asc:
|
||||
missing.append('Ascendant(Lagna)')
|
||||
ok = len(missing) == 0
|
||||
detail = f"缺失: {', '.join(missing)}" if missing else f"完整：{len(EXPECTED_PLANETS)}行星 + Ascendant"
|
||||
return ok, detail
|
||||
|
||||
|
||||
def _check_degree_range(planets: Dict) -> Tuple[bool, str]:
|
||||
"""检查星座度数范围"""
|
||||
issues = []
|
||||
for pname, pd in planets.items():
|
||||
deg_in_sign = pd.get('degree_in_sign', None)
|
||||
if deg_in_sign is not None:
|
||||
if deg_in_sign < 0 or deg_in_sign >= 30:
|
||||
issues.append(f"{pname}度数={deg_in_sign}° 超出[0,30)范围")
|
||||
ok = len(issues) == 0
|
||||
detail = '; '.join(issues) if issues else '所有行星度数在合法范围'
|
||||
return ok, detail
|
||||
|
||||
|
||||
def _check_house_continuity(chart_data: Dict) -> Tuple[bool, str]:
|
||||
"""检查宫位连续性"""
|
||||
houses = chart_data.get('houses', {})
|
||||
house_nums = set()
|
||||
for key in houses:
|
||||
if key.startswith('house_'):
|
||||
num = int(key.replace('house_', ''))
|
||||
house_nums.add(num)
|
||||
|
||||
expected = set(range(1, 13))
|
||||
missing = expected - house_nums
|
||||
ok = len(missing) == 0
|
||||
detail = f"缺失宫位: {sorted(missing)}" if missing else f"1-12宫完整({len(house_nums)}宫)"
|
||||
return ok, detail
|
||||
|
||||
|
||||
def _check_bav_column_sav(ashtakavarga_data: Dict) -> Tuple[bool, str]:
|
||||
"""
|
||||
R2b: BAV列→SAV列校验
|
||||
每个星座的7行星BAV bindus之和必须等于该星座的SAV分数。
|
||||
这验证了行总和（R2）和列总和的交叉一致性。
|
||||
"""
|
||||
bav_results = ashtakavarga_data.get('bav', {})
|
||||
house_scores = ashtakavarga_data.get('house_scores', {})
|
||||
sav_scores = ashtakavarga_data.get('sav', {})
|
||||
|
||||
issues = []
|
||||
# 从每个7行星的 BAV bindus 列向量求和，对比 SAV house_scores
|
||||
for sign_i in range(12):
|
||||
# 计算该星座位置（第 sign_i 列）的 7行星 BAV 之和
|
||||
col_sum = 0
|
||||
for pname in SEVEN_PLANETS:
|
||||
if pname in bav_results:
|
||||
bindus = bav_results[pname].get('bindus', [])
|
||||
if len(bindus) > sign_i:
|
||||
col_sum += bindus[sign_i]
|
||||
|
||||
# 获取该星座/宫位的 SAV 分数
|
||||
house_key = f'house_{sign_i + 1}'
|
||||
hs = house_scores.get(house_key, {})
|
||||
sav_score = hs.get('score', None)
|
||||
|
||||
if sav_score is not None and col_sum != sav_score:
|
||||
issues.append(f"星座{SIGNS[sign_i]}列: BAV列和={col_sum}, SAV={sav_score}")
|
||||
|
||||
# 同时验证 SAV 逐宫总分是否与 house_scores 一致
|
||||
if not issues:
|
||||
ok = True
|
||||
detail = "所有12星座 BAV列和 与 SAV分数一致 ✅"
|
||||
else:
|
||||
ok = False
|
||||
detail = f"不一致: {'; '.join(issues)}"
|
||||
return ok, detail
|
||||
Reference in New Issue
Block a user