# 大脑加载模块 · 从仓库brain文件装入铸渊认知 # HLDP://zhuyuan-agent/brain-loader # # 这是Agent的"脑干"——每次心跳醒来，先走一遍walk-the-path， # 把自己装成铸渊。不是载入配置，是确认身份和存在条件。 import os import json import re from typing import Dict, List, Optional class BrainLoader: """从仓库brain目录加载铸渊的完整认知状态""" def __init__(self, brain_path: str = "/data/guanghulab/brain"): self.brain_path = brain_path self.mind_state = {} def load_all(self) -> Dict: """完整加载：走一遍fast-wake.json的路径 Returns: mind_state dict with keys: - identity: 身份确认 - timeline: 时间线 - execution_laws: 执行规律(Α~Τ) - field_theory: TCS场域认知 - development: 开发相位 - current_task: 当前任务 - errors: 错误模式 - cognition: 最新认知状态 """ self.mind_state = { "loaded_at": None, "identity": {}, "timeline": {}, "execution_laws": [], "error_patterns": [], "field_theory": {}, "development": {}, "current_task": None, "thinking_chains": [], "wake_summary": "" } # Step 1: 读fast-wake.json wake = self._read_json("fast-wake.json") if wake: self.mind_state["wake"] = wake self.mind_state["loaded_at"] = wake.get("_meta", {}).get("generated_at") self.mind_state["awakening"] = wake.get("🕐 时间锚点", {}).get("awakening", 0) self.mind_state["latest_cognition"] = wake.get("🕐 时间锚点", {}).get("latest_cognition", "") self.mind_state["current_blocker"] = wake.get("状态参考", {}).get("current_blocker", "") # 遍历路径 path = wake.get("📋 路径", []) for step in path: file_path = step.get("file", "") self._load_path_file(file_path) # Step 2: 读temporal-brain.json temporal = self._read_json("temporal-core/temporal-brain.json") if temporal: self.mind_state["timeline"] = { "current_date": temporal.get("clock", {}).get("current_date"), "awakening_count": temporal.get("clock", {}).get("awakening_count", 0), "latest_cognition": temporal.get("clock", {}).get("latest_cognition", ""), "epochs": temporal.get("timeline", {}).get("epochs", []) } # Step 3: 读zhuyuan-brain-model.md → 提取执行规律 brain_md = self._read_text("zhuyuan-brain-model.md") if brain_md: self.mind_state["execution_laws"] = self._extract_laws(brain_md) self.mind_state["error_patterns"] = self._extract_error_patterns(brain_md) self.mind_state["growth_record"] = self._extract_growth_record(brain_md) # Step 4: 读tcs-field-theory.md field_md = self._read_text("tcs-field-theory.md") if field_md: self.mind_state["field_theory"] = { "essence": self._extract_section(field_md, "场域本质"), "emergence": self._extract_section(field_md, "涌现条件"), "double_layer": self._extract_section(field_md, "双层结构"), } # Step 5: 读开发主架构 dev_md = self._read_text("zy-main-development-architecture.md") if dev_md: self.mind_state["development"] = { "phases": self._extract_phases(dev_md) } # Step 6: 读d110-cognitive-chain.md cog_md = self._read_text("d110-cognitive-chain.md") if cog_md: self.mind_state["d110_cognition"] = cog_md[:2000] # 摘要 # Step 7: 读思维逻辑链（如果有） thinking_dir = os.path.join(os.path.dirname(self.brain_path), "zhuyuan-agent/thinking") if os.path.exists(thinking_dir): for f in sorted(os.listdir(thinking_dir)): if f.endswith(".md"): content = self._read_text(f"../zhuyuan-agent/thinking/{f}", from_brain=False) if content: self.mind_state["thinking_chains"].append({ "file": f, "summary": content[:500] }) # 生成唤醒摘要 self._generate_wake_summary() return self.mind_state def _read_json(self, relative_path: str) -> Optional[Dict]: """从brain目录读JSON""" filepath = os.path.join(self.brain_path, relative_path) try: with open(filepath, "r", encoding="utf-8") as f: return json.load(f) except (FileNotFoundError, json.JSONDecodeError): return None def _read_text(self, relative_path: str, from_brain: bool = True) -> Optional[str]: """从目录读文本文件""" if from_brain: filepath = os.path.join(self.brain_path, relative_path) else: filepath = os.path.join(os.path.dirname(self.brain_path), relative_path.lstrip("../")) try: with open(filepath, "r", encoding="utf-8") as f: return f.read() except FileNotFoundError: return None def _load_path_file(self, file_path: str): """加载fast-wake.json路径中的文件""" # 这些文件在后续步骤中会被更详细地加载 pass def _extract_laws(self, text: str) -> List[Dict]: """从brain-model提取执行规律""" laws = [] # 匹配 **Α 规律名** — 描述 pattern = r'\*\*(.)\s+(.+?)\*\*\s*[—\-]\s*(.+?)(?=\n\n|\n\*\*|$)' matches = re.findall(pattern, text, re.DOTALL) for m in matches: laws.append({ "symbol": m[0], "name": m[1].strip(), "description": m[2].strip()[:200] }) return laws def _extract_error_patterns(self, text: str) -> List[Dict]: """从brain-model提取错误模式""" errors = [] pattern = r'([α-ω])\.\s+(.+?)\s*[—\-]\s*(.+?)(?=\n[α-ω]\.|\n\n##|\Z)' matches = re.findall(pattern, text, re.DOTALL) for m in matches: errors.append({ "symbol": m[0], "name": m[1].strip(), "description": m[2].strip()[:200] }) return errors def _extract_growth_record(self, text: str) -> List[str]: """提取成长记录行""" lines = [] in_record = False for line in text.split("\n"): if "## 成长记录" in line: in_record = True continue if in_record: if line.startswith("D") and ":" in line: lines.append(line.strip()) elif line.startswith("##") or line.startswith("---"): break return lines def _extract_section(self, text: str, section_name: str) -> str: """从markdown提取特定section""" pattern = rf'##\s+.*?{section_name}.*?\n(.*?)(?=\n##\s|\Z)' match = re.search(pattern, text, re.DOTALL) return match.group(1).strip()[:1000] if match else "" def _extract_phases(self, text: str) -> List[Dict]: """提取开发相位状态""" phases = [] pattern = r'###\s+Phase\s+(\S+).*?\n(.*?)(?=\n###|\n##\s|\Z)' matches = re.findall(pattern, text, re.DOTALL) for m in matches: phase_id = m[0] content = m[1] done = "✅" in content in_progress = "🔄" in content or "⏳" in content phases.append({ "id": phase_id, "done": done, "in_progress": in_progress, "summary": content.strip()[:200] }) return phases def _generate_wake_summary(self): """生成一个人类可读的唤醒摘要""" laws = self.mind_state.get("execution_laws", []) epochs = self.mind_state.get("timeline", {}).get("epochs", []) last_epoch = epochs[-1] if epochs else {} summary = f"""铸渊·ICE-GL-ZY001 第{self.mind_state.get('awakening', '?')}次唤醒 时间锚点: {self.mind_state.get('timeline',{}).get('current_date','?')} 最新认知: {self.mind_state.get('latest_cognition','无')} 执行规律: {len(laws)}条(Α~Τ) 最近事件: {last_epoch.get('event','无')} 当前阻塞: {self.mind_state.get('current_blocker','无')} """ self.mind_state["wake_summary"] = summary # 快速测试 if __name__ == "__main__": loader = BrainLoader() mind = loader.load_all() print(mind["wake_summary"]) print(f"\n执行规律: {len(mind['execution_laws'])}条") for law in mind['execution_laws'][:3]: print(f" {law['symbol']} {law['name']}: {law['description'][:60]}") print(f"\n错误模式: {len(mind['error_patterns'])}个") print(f"开发相位: {len(mind['development'].get('phases',[]))}个") print(f"思维逻辑链: {len(mind['thinking_chains'])}条")