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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"""
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Hermes集成中间件 - 方案二完整运行时
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包含: HermesSkillCore + HermesLearningEngine + WorkBuddyHermesBridge
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"""
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import os
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import json
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import re
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from datetime import datetime
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from typing import Dict, List, Any, Optional
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# 导入记忆核心
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from hermes_memory_core import HermesMemoryCore
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class HermesSkillCore:
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"""Hermes技能系统"""
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def __init__(self, skills_dir: str = "~/.workbuddy/skills/"):
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self.skills_dir = os.path.expanduser(skills_dir)
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self.built_in_dir = os.path.join(self.skills_dir, "built-in")
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self.custom_dir = os.path.join(self.skills_dir, "custom")
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self.auto_dir = os.path.join(self.skills_dir, "auto-generated")
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for d in [self.built_in_dir, self.custom_dir, self.auto_dir]:
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os.makedirs(d, exist_ok=True)
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self.skill_index = self._load_skill_index()
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self.usage_stats = {}
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def auto_create_skill_from_experience(self, experience: Dict) -> Optional[Dict]:
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"""从任务经验中自动创建技能"""
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task = experience.get('task', '')
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steps = experience.get('steps', [])
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if not steps:
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return None
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skill_id = f"skill-{datetime.now().strftime('%Y%m%d%H%M%S')}"
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content = self._build_skill_content(skill_id, task, steps,
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experience.get('errors_made', []),
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experience.get('corrections_received', []))
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skill_path = os.path.join(self.auto_dir, f"{skill_id}.md")
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with open(skill_path, 'w', encoding='utf-8') as f:
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f.write(content)
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self._update_index({
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'id': skill_id, 'name': task[:50], 'path': skill_path,
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'source': 'auto-generated',
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'created': datetime.now().isoformat(),
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'tags': self._extract_tags(task),
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'usage_count': 0
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})
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return {'id': skill_id, 'path': skill_path, 'name': task[:50]}
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def _build_skill_content(self, skill_id, task, steps, errors, corrections):
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content = f"""---
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id: {skill_id}
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name: {task[:60]}
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created: {datetime.now().isoformat()}
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source: auto-generated (Hermes Learning Engine)
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tags: [{', '.join(self._extract_tags(task))}]
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---
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# {task}
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## 执行步骤
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"""
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for i, step in enumerate(steps, 1):
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title = step.get('title', step) if isinstance(step, dict) else step
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content += f"### 步骤 {i}: {title}\n\n"
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if isinstance(step, dict) and step.get('details'):
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content += f"{step['details']}\n\n"
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if errors or corrections:
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content += "\n## ⚠️ 注意事项（从实战中学习）\n\n"
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if corrections:
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content += "### 已知纠正\n\n"
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for c in corrections:
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content += f"- ❌ 原错误: {c.get('original_error', 'N/A')}\n"
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content += f"- ✅ 正确做法: {c.get('correct_information', 'N/A')}\n\n"
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if errors:
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content += "### 常见陷阱\n\n"
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for e in errors:
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content += f"- **{e.get('type', 'Error')}**: {e.get('description', '')}\n\n"
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return content
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def search_skills(self, query: str, limit: int = 10) -> List[Dict]:
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results = []
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for skill_id, info in self.skill_index.items():
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score = 0
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if query.lower() in info.get('name', '').lower():
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score += 30
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for tag in info.get('tags', []):
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if query.lower() in tag.lower():
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score += 20
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if score > 0 or not query:
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results.append({**info, 'relevance_score': score})
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results.sort(key=lambda x: x.get('relevance_score', 0), reverse=True)
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return results[:limit]
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def execute_skill(self, skill_id: str) -> Dict:
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skill_info = self.skill_index.get(skill_id)
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if not skill_info:
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return {'error': f'Skill {skill_id} not found'}
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with open(skill_info['path'], 'r', encoding='utf-8') as f:
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content = f.read()
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self.usage_stats[skill_id] = self.usage_stats.get(skill_id, 0) + 1
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return {'skill_id': skill_id, 'content': content}
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def get_skill_stats(self) -> Dict:
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total = len(self.skill_index)
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auto = sum(1 for s in self.skill_index.values() if s.get('source') == 'auto-generated')
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return {
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'total_skills': total,
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'auto_generated': auto,
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'total_executions': sum(self.usage_stats.values())
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}
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def _extract_tags(self, task: str) -> List[str]:
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tags = ['auto-generated']
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mapping = {
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'印度占星': 'astrology', '剧本创作': 'screenwriting',
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'紫微斗数': 'ziwei', '数据分析': 'data-analysis',
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'编程': 'development', '写作': 'writing'
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}
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for kw, tag in mapping.items():
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if kw in task:
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tags.append(tag)
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return tags
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def _load_skill_index(self) -> Dict:
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index_file = os.path.join(self.skills_dir, "index.json")
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if os.path.exists(index_file):
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with open(index_file, 'r') as f:
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return json.load(f)
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return {}
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def _update_index(self, info: Dict):
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self.skill_index[info['id']] = info
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with open(os.path.join(self.skills_dir, "index.json"), 'w') as f:
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json.dump(self.skill_index, f, ensure_ascii=False, indent=2)
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class HermesLearningEngine:
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"""
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Hermes学习引擎 - 完整学习闭环
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体验 → 技能创建 → 使用改进 → 知识持久化 → 跨会话检索 → 用户建模
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"""
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def __init__(self, memory_core, skill_core):
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self.memory = memory_core
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self.skills = skill_core
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self.user_model = {}
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self.learning_log = []
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self._load_user_model()
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def learning_loop(self, experience: Dict[str, Any]) -> Dict[str, Any]:
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"""完整的学习循环处理"""
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loop_result = {
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'experience_id': f"exp-{datetime.now().strftime('%Y%m%d%H%M%S')}",
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'timestamp': datetime.now().isoformat(),
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'skill_created': None,
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'memory_updates': [],
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'model_updates': [],
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'lessons_learned': []
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}
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print(f"🔄 开始学习循环: {experience['task'][:40]}...")
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# 步骤1：体验分析
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analysis = self._analyze_experience(experience)
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loop_result['analysis'] = analysis
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# 步骤2：技能创建
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if self._should_create_skill(experience, analysis):
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skill = self._create_skill_from_experience(experience, analysis)
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if skill:
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loop_result['skill_created'] = skill
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print(f" ✅ 创建技能: {skill['name']}")
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# 步骤3：使用改进
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improvements = self._generate_improvements(experience, analysis)
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loop_result['improvements'] = improvements
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# 步骤4：知识持久化
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memory_ids = self._persist_knowledge(experience, analysis)
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loop_result['memory_updates'] = memory_ids
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print(f" 💾 持久化 {len(memory_ids)} 条知识")
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# 步骤5：用户建模
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model_updates = self._update_user_model(experience, analysis)
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loop_result['model_updates'] = model_updates
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print(f" 👤 用户模型更新 {len(model_updates)} 项")
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# 步骤6：教训提取
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lessons = self._extract_lessons(experience, analysis)
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loop_result['lessons_learned'] = lessons
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self.learning_log.append(loop_result)
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return loop_result
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def _analyze_experience(self, exp: Dict) -> Dict:
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return {
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'complexity': self._assess_complexity(exp),
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'success_level': self._assess_success(exp),
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'novelty': self._assess_novelty(exp),
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'error_pattern': self._identify_error_patterns(exp),
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'key_factors': self._extract_key_factors(exp),
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'repeatable_elements': self._identify_repeatable_elements(exp)
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}
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def _assess_complexity(self, exp: Dict) -> str:
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steps_count = len(exp.get('steps', []))
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files_count = len(exp.get('files_used', []))
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errors_count = len(exp.get('errors_made', []))
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if steps_count > 10 or files_count > 5:
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return 'high'
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elif steps_count > 5 or errors_count > 0:
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return 'medium'
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else:
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return 'low'
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def _assess_success(self, exp: Dict) -> float:
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success = 1.0
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for error in exp.get('errors_made', []):
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severity = error.get('severity', 'minor')
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if severity == 'critical':
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success -= 0.4
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elif severity == 'major':
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success -= 0.2
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else:
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success -= 0.05
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if exp.get('corrections_received'):
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success -= 0.3
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feedback = exp.get('user_feedback', '')
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negative_keywords = ['错误', '遗漏', '不对', '不是', '错了']
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for kw in negative_keywords:
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if kw in feedback:
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success -= 0.2
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return max(0.0, min(1.0, success))
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def _assess_novelty(self, exp: Dict) -> float:
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task_desc = exp.get('task', '')
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similar = self.memory.search(task_desc, limit=3)
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if not similar:
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return 1.0
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elif len(similar) == 1 and similar[0].get('relevance_score', 0) > 0.8:
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return 0.3
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else:
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return 0.6
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def _identify_error_patterns(self, exp: Dict) -> List[Dict]:
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patterns = []
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for error in exp.get('errors_made', []):
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pattern = {
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'type': error.get('type', 'unknown'),
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'description': error.get('description', ''),
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'root_cause': error.get('root_cause', ''),
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'prevention_rule': ''
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}
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if error.get('type') == 'missing_information':
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pattern['prevention_rule'] = (
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f"在开始任何分析前，必须先搜索和加载所有与'"
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f"{error.get('context', '')}'相关的已有信息和用户提供的数据文件"
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)
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patterns.append(pattern)
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for correction in exp.get('corrections_received', []):
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patterns.append({
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'type': 'correction_received',
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'description': correction.get('original_error', ''),
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'root_cause': correction.get('reason', ''),
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'correct_info': correction.get('correct_information', ''),
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'prevention_rule': correction.get('rule', '下次必须检查这一点')
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})
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return patterns
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def _should_create_skill(self, exp: Dict, analysis: Dict) -> bool:
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conditions = [
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analysis['complexity'] == 'high',
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analysis['novelty'] > 0.5,
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len(analysis['repeatable_elements']) > 0,
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len(exp.get('steps', [])) >= 3,
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exp.get('errors_made') or exp.get('corrections_received')
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]
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return any(conditions)
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def _create_skill_from_experience(self, exp: Dict, analysis: Dict) -> Optional[Dict]:
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return self.skills.auto_create_skill_from_experience(exp)
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def _persist_knowledge(self, exp: Dict, analysis: Dict) -> List[int]:
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memory_ids = []
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# 持久化纠正信息（最高优先级）
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for correction in exp.get('corrections_received', []):
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memory_id = self.memory.store_memory(
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content=f"[纠正] {correction.get('correct_information', '')}",
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metadata={
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'source_type': 'learning_correction',
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'importance': 9,
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'tags': ['correction', 'error_prevention', 'must_remember'],
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'session_id': exp.get('session_id'),
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'entities': [{
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'name': exp.get('task', '')[:30],
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'type': 'task_context'
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}]
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}
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)
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memory_ids.append(memory_id)
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# 持久化成功模式
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if analysis['success_level'] >= 0.8:
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memory_id = self.memory.store_memory(
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content=f"[成功模式] {exp['task']}: 成功因素包括 "
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f"{', '.join(analysis['key_factors'][:5])}",
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metadata={
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'source_type': 'learning_success',
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'importance': 7,
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'tags': ['success_pattern', exp.get('task', '')[:20]],
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'session_id': exp.get('session_id')
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}
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)
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memory_ids.append(memory_id)
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# 持久化错误预防规则
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for pattern in analysis['error_pattern']:
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if pattern.get('prevention_rule'):
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memory_id = self.memory.store_memory(
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content=f"[预防规则] {pattern['prevention_rule']}",
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metadata={
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'source_type': 'learning_prevention',
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'importance': 8,
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'tags': ['prevention_rule', pattern['type']],
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'session_id': exp.get('session_id')
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}
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)
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memory_ids.append(memory_id)
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return memory_ids
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def _update_user_model(self, exp: Dict, analysis: Dict) -> List[Dict]:
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updates = []
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feedback = exp.get('user_feedback', '')
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strong_negative_words = ['总是', '每次', '又', '老是', '永远']
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for word in strong_negative_words:
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if word in feedback:
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updates.append({
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'key': f'frustration_pattern_{word}',
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'value': f'用户对重复性错误感到沮丧（触发词: "{word}"）',
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'category': 'behavior_pattern',
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'confidence': 0.9
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})
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domain_keywords = {
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'印度占星': 'jyotish_astrology',
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'剧本创作': 'screenwriting',
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'紫微斗数': 'ziwei_astrology',
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'编程开发': 'software_development'
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}
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task = exp.get('task', '')
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for keyword, domain in domain_keywords.items():
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if keyword in task:
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updates.append({
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'key': f'primary_domain_{domain}',
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'value': f'用户从事{keyword}相关工作',
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'category': 'professional_background',
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'confidence': 0.85
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})
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for update in updates:
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cursor = self.memory.conn.cursor()
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cursor.execute("""
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INSERT OR REPLACE INTO user_profile (key, value, category, confidence, first_learned, last_updated)
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VALUES (?, ?, ?, ?, ?, ?)
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""", (
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update['key'], update['value'], update['category'],
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update['confidence'],
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datetime.now().isoformat(),
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datetime.now().isoformat()
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))
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self.memory.conn.commit()
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return updates
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def _extract_lessons(self, exp: Dict, analysis: Dict) -> List[str]:
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lessons = []
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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 ✅✅✅")
|
||||
Reference in New Issue
Block a user