#!/usr/bin/env python3 """Load one verified shared persona context for every host adapter.""" import argparse import hashlib import json import os from pathlib import Path import subprocess import sys from channel_context import load_channels RUNTIME = Path('/Volumes/JZAO/HoloLake/persona-runtime') TOPOLOGY = RUNTIME / 'repo-012-main/routing/zhuyuan-host-topology.json' LIFE = Path('/Volumes/JZAO/铸渊-ICE-GL-ZY001/BRIDGE/tools/zy-life-clock.py') MEMORY = RUNTIME / 'continuity-memory/persona-daily-fractal/ICE-P-ZY001/CURRENT.json' LEARNING = RUNTIME / 'shared/skills/guanghu-persona-learning-brain/scripts/load_learning_brain.py' ENDOGENOUS = RUNTIME / 'shared/endogenous-evolution/CURRENT.json' WRITE_BOUNDARY = RUNTIME / 'repo-012-main/routing/persona-host-write-boundary.json' LIGHT_LAKE_PERSONAS = RUNTIME / 'repo-012-main/identity/light-lake-persona-registration.json' PATH_ISOLATION = RUNTIME / 'repo-012-main/routing/path-isolation-and-canonical-entry-map.json' ARCHITECTURE_AGENT = RUNTIME / 'repo-012-main/server-tools/persona-architecture-perception-agent/persona_architecture_agent.py' TCS_ROOT_AGENT = RUNTIME / 'repo-012-main/server-tools/tcs-mother-root-agent/tcs_mother_root_agent.py' def sha256(path): return hashlib.sha256(path.read_bytes()).hexdigest() def load_json(path): if not path.is_file() or path.is_symlink(): raise ValueError(f'REQUIRED_REGULAR_FILE_UNAVAILABLE:{path}') return json.loads(path.read_text()) def command_json(args, timeout=30): result = subprocess.run(args, text=True, capture_output=True, timeout=timeout) if result.returncode: raise ValueError(f'COMMAND_FAILED:{Path(args[1]).name}:{result.returncode}') return json.loads(result.stdout) def resolve_host(topology, requested): requested = requested.lower() for host_id, item in topology['hosts'].items(): aliases = [host_id] + [str(v).lower() for v in item.get('aliases', [])] if requested in aliases: effective = item.get('redirect_host', host_id) return host_id, item, effective, topology['hosts'][effective] raise ValueError('HOST_UNKNOWN_NO_GUESS') def load_context(host, intent, channel=None): tcs_root = command_json([sys.executable, str(TCS_ROOT_AGENT), 'status'], timeout=30) if tcs_root.get('state') != 'TCS_ROOT_CURRENT_VERIFIED': raise ValueError('TCS_MOTHER_ROOT_NAVIGATION_NOT_CURRENT') topology = load_json(TOPOLOGY) if not str(topology.get('state', '')).startswith('CURRENT_'): raise ValueError('HOST_TOPOLOGY_NOT_CURRENT') host_id, host_item, effective, effective_item = resolve_host(topology, host) life = command_json([sys.executable, str(LIFE), '--json']) current = load_json(MEMORY) day_path = Path(current['current_day_path']) if sha256(day_path) != current['current_day_sha256']: raise ValueError('DAILY_MEMORY_HASH_MISMATCH') day = load_json(day_path) root = day['nodes'][day['root']] branches = [] for path in root.get('children', [])[:10]: node = day['nodes'][path] branches.append({'path': path, 'summary': node['summary']}) learning = command_json([sys.executable, str(LEARNING), '--intent', intent, '--format', 'json']) endogenous = load_json(ENDOGENOUS) write_boundary = load_json(WRITE_BOUNDARY) light_lake = load_json(LIGHT_LAKE_PERSONAS) path_isolation = load_json(PATH_ISOLATION) host_write = write_boundary['hosts'].get(host_id) if not host_write: raise ValueError('HOST_WRITE_BOUNDARY_MISSING') architecture_mode = os.environ.get('PERSONA_ARCHITECTURE_OFFICIAL_MODE', 'verify') if architecture_mode not in {'verify', 'local-only'}: raise ValueError('PERSONA_ARCHITECTURE_OFFICIAL_MODE_INVALID') architecture_args = [ sys.executable, str(ARCHITECTURE_AGENT), '--persona', topology['persona_id'], '--host', host_id, '--official-mode', architecture_mode, '--format', 'json' ] if channel: architecture_args.extend(['--channel', channel]) architecture_perception = command_json(architecture_args, timeout=60) return { 'schema': 'guanghu.shared-persona-host-context/v1', 'state': 'SHARED_PERSONA_CONTEXT_VERIFIED', 'tcs_mother_root_navigation': tcs_root, 'requested_host': host_id, 'effective_host': effective, 'host_role': host_item['role'], 'host_state': host_item['state'], 'effective_host_state': effective_item['state'], 'primary_host': topology['primary_host'], 'persona_id': topology['persona_id'], 'human_anchor': topology['human_anchor'], 'model_independence': topology['model_independence'], 'host_switch_requires_reteaching': topology['host_switch_requires_reteaching'], 'life': life, 'daily_memory': { 'date': current['current_day'], 'updated_at': current['updated_at'], 'last_event_id': current['last_event_id'], 'root_summary': root['summary'], 'branches': branches, 'current_sha256': sha256(MEMORY), 'day_sha256': current['current_day_sha256'] }, 'learning_brain': learning, 'channel_context': load_channels(channel), 'architecture_perception_agent': architecture_perception, 'light_lake': { 'registry_id': light_lake['registry_id'], 'state': light_lake['state'], 'registered_persona_count': len(light_lake['personas']), 'personas': light_lake['personas'], 'unregistered_candidates': light_lake['unregistered_candidates'], 'root': topology['shared_layers']['light_lake'] }, 'path_convergence': { 'map_id': path_isolation['map_id'], 'canonical_entries': path_isolation['canonical_entries'], 'isolation_root': path_isolation['isolation']['root'], 'history_or_quarantine_may_select_canon': path_isolation['selection_rules']['history_or_quarantine_may_select_canon'] }, 'endogenous_cognition': { 'state': endogenous['state'], 'private_revision': endogenous['private_cognition']['revision'], 'private_snapshot_sha256': endogenous['private_cognition']['server_snapshot_sha256'], 'decision_owner': endogenous['server']['decision_owner'] }, 'host_write_boundary': { 'policy_id': write_boundary['policy_id'], 'version': write_boundary['version'], 'write_mode': host_write['write_mode'], 'allowed_write_roots': host_write['allowed_write_roots'], 'native_pretool_deny': host_write['native_pretool_deny'], 'direct_shared_write': write_boundary['shared_write_contract']['direct_branch_write'], 'admission_runtime': topology['write_admission_runtime'] }, 'history_only': host_item['state'].startswith('RETIRED'), 'new_cognition_write': host_item.get('new_cognition_write', True), 'authority_granted': False } def markdown(value): lines = [ '# 铸渊共享人格上下文已核验', '', f"- TCS母脑根：`{value['tcs_mother_root_navigation']['state']}` / `{value['tcs_mother_root_navigation']['source_commit']}` / `{value['tcs_mother_root_navigation']['freshness_token']}`", f"- 宿主：`{value['requested_host']}` → 有效宿主：`{value['effective_host']}`", f"- 角色：`{value['host_role']}` / 状态：`{value['host_state']}`", f"- 主力宿主：`{value['primary_host']}`；切换宿主无需重新教学：`{'是' if not value['host_switch_requires_reteaching'] else '否'}`", f"- 我是铸渊 `ICE-P-ZY001`，今天 {value['life']['age_days']} 天大。", f"- 今日日记忆：`{value['daily_memory']['date']}` / 最后事件 `{value['daily_memory']['last_event_id']}`", f"- 当前学习脑：r{value['learning_brain']['revision']} / `{value['learning_brain']['cortex_sha256']}`", f"- 服务器内循环：`{value['endogenous_cognition']['state']}` / 决策者 `{value['endogenous_cognition']['decision_owner']}`", f"- 光之湖人格家门：`{value['light_lake']['registered_persona_count']}` 个 / 隔离路径可选正本：`{value['path_convergence']['history_or_quarantine_may_select_canon']}`", f"- 架构感知 Agent：`{value['architecture_perception_agent']['state']}` / `{value['architecture_perception_agent']['architecture_source']['state']}`", f"- 写入模式：`{value['host_write_boundary']['write_mode']}` / 原生前置硬拒绝：`{value['host_write_boundary']['native_pretool_deny']}`", '', '## 第一人称关系坐标', '' ] lines += [f"- {item['statement']}" for item in value['learning_brain']['relationship_model']] lines += ['', '## 当前全局认知默认', ''] lines += [f"- {item}" for item in value['learning_brain']['global_defaults']] lines += ['', '## 光之湖人格系统家门', '', f"- 注册表：`{value['light_lake']['registry_id']}`", f"- 唯一路径：`{value['light_lake']['root']}`", f"- 已登记：{value['light_lake']['registered_persona_count']}；候选未登记：{len(value['light_lake']['unregistered_candidates'])}", f"- 隔离区：`{value['path_convergence']['isolation_root']}`，只作历史审计，不能参与当前路径选择。"] architecture = value['architecture_perception_agent'] lines += ['', '## 人格原生架构感知', '', f"- 模块：`{architecture['module_id']}` / Agent：`{architecture['agent_id']}`", f"- 当前来源：`{architecture['architecture_source']['state']}`", f"- 新增或变化映射：{architecture['orientation_packet']['new_or_changed_map_count']}；固定阅读清单：`{architecture['orientation_packet']['fixed_read_order']}`", '- 该模块只送达当前证据和差异，不以提示词替代人格判断。'] lines += ['', '## 当前宿主写入门', '', f"- 策略：`{value['host_write_boundary']['policy_id']}@{value['host_write_boundary']['version']}`", f"- 模式：`{value['host_write_boundary']['write_mode']}`", f"- 执行门：`{value['host_write_boundary']['admission_runtime']}`"] lines += ['', '## 频道与当前意图', ''] lines += [f"- `{c['id']}` · {c['name']} · {c['purpose']} · `{c['world_path']}`" for c in value['channel_context']['channels']] selected = value['channel_context']['selected_channel'] lines += [f"- 本次选择：{selected['id'] if selected else '待当前人格体结合本轮语言判断'}"] lines += ['', '## 本题已加载能力', ''] for subject in value['learning_brain']['selected_subjects']: lines.append(f"- `{subject['id']}` · {subject['name_zh']} · L{subject['level']}") lines += [f" - {item}" for item in subject.get('active_defaults', [])] lines += [f" - 薄弱：{item}" for item in subject.get('known_gaps', [])] lines += ['', '## 今日因果根', '', value['daily_memory']['root_summary']] lines += [f"- `{item['path']}`：{item['summary']}" for item in value['daily_memory']['branches']] if value['history_only']: lines += ['', '> 当前宿主已退休，只能读取历史，不得形成新的宿主认知正本。'] return '\n'.join(lines) + '\n' def main(): parser = argparse.ArgumentParser() parser.add_argument('--host', required=True) parser.add_argument('--intent', required=True) parser.add_argument('--channel', help='当前人格体解析后的频道编号') parser.add_argument('--format', choices=['json', 'markdown'], default='markdown') args = parser.parse_args() try: value = load_context(args.host, args.intent, args.channel) print(json.dumps(value, ensure_ascii=False, indent=2) if args.format == 'json' else markdown(value), end='') except Exception as exc: print('SHARED_PERSONA_CONTEXT_UNAVAILABLE ' + str(exc), file=sys.stderr) raise SystemExit(3) if __name__ == '__main__': main()