WIN-JHFT4D3SIVT\caoxiaozhu
a3fe4d24fc
Phase 7: Built-in Hooks (audit_log, dangerous_confirmation, security_scan) Phase 8: Plugin system (PluginManager, PluginSandbox, PluginManifest) Phase 9: Skills registry (SkillRegistry, local/plugin/MCP loaders) Phase 10: TeamLeader, RemoteTransport, BackgroundTaskManager
625 lines
18 KiB
Markdown
625 lines
18 KiB
Markdown
# Phase 5：高级特性（Advanced Features）
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日期：2026-04-03
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状态：规划中
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---
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## 1. 阶段目标
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Phase 5 包含一系列**高级特性**，在完成 Phase 1-4 后根据实际需求选择性实施。
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这些特性不直接影响核心功能，但可以显著提升系统的**可用性、安全性和可扩展性**。
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---
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## 2. 特性清单
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### 2.1 Full Sandbox 隔离
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**目标**：实现完整的Docker级隔离
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**Phase 4已做**：Worktree隔离
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**Phase 5补充**：
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- 完整的容器生命周期管理
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- 资源限制(CPU/内存/网络)
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- 文件系统配额
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- 安全策略配置
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```python
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class FullSandbox:
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"""完整沙箱隔离"""
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async def execute(
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self,
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task: Task,
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config: SandboxConfig
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) -> TaskResult:
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"""
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在完整沙箱中执行
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特点：
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- 完全隔离的网络
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- 资源限制
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- 持久化存储(可选)
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- 安全策略
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"""
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# 1. 创建容器
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container = await self.client.containers.run(
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image=config.image,
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detach=True,
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mem_limit=config.memory_limit,
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cpu_period=config.cpu_period,
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network_mode="isolated", # 完全隔离网络
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volumes=config.volumes,
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)
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try:
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# 2. 执行任务
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result = await self._execute_in_container(container, task)
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return result
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finally:
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# 3. 清理
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await container.remove(force=True)
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```
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### 2.2 Persistence 层
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**目标**：将Event/Message持久化到数据库
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**Phase 1-3已做**：内存trace
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**Phase 5补充**：
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- Event持久化存储
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- Message持久化存储
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- 支持历史查询
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- 数据导出/归档
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```python
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class EventPersistence:
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"""事件持久化"""
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async def save_event(self, event: Event):
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"""保存事件到数据库"""
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...
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async def query_events(
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self,
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conversation_id: str,
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event_types: list[str] | None = None,
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start_time: datetime | None = None,
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end_time: datetime | None = None,
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limit: int = 100
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) -> list[Event]:
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"""查询历史事件"""
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...
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class MessagePersistence:
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"""消息持久化"""
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async def save_message(self, message: AgentMessage):
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"""保存消息到数据库"""
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...
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async def get_thread_history(
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self,
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thread_id: str,
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limit: int = 100
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) -> list[AgentMessage]:
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"""获取线程历史"""
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...
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```
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### 2.3 Multi-turn Memory
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**目标**：支持跨会话的长期记忆
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**当前**：每个会话独立memory
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**Phase 5补充**：
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- 重要信息提取
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- 跨会话上下文复用
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- 知识更新机制
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- 遗忘策略
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```python
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class MultiTurnMemory:
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"""跨会话记忆"""
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def extract_important_info(
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self,
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conversation_summary: str,
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user_profile: UserProfile
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) -> list[MemoryEntry]:
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"""从对话中提取重要信息"""
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...
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async def get_relevant_context(
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self,
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current_request: str,
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user_id: str
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) -> list[MemoryEntry]:
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"""获取与当前请求相关的记忆"""
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...
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def update_memory(
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self,
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user_id: str,
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new_info: MemoryEntry
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):
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"""更新记忆"""
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...
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def decay_old_memories(self, user_id: str):
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"""遗忘旧记忆"""
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...
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```
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### 2.4 Cost Monitoring
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**目标**：实时监控Token成本
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**Phase 3已有**：Budget模型
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**Phase 5补充**：
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- 实时Token计数
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- 成本估算
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- 告警机制
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- 使用报告
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```python
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class CostMonitor:
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"""成本监控"""
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async def track_usage(
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self,
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conversation_id: str,
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model: str,
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input_tokens: int,
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output_tokens: int
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):
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"""跟踪使用量"""
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...
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async def estimate_cost(
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self,
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conversation_id: str
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) -> CostEstimate:
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"""估算当前会话成本"""
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...
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async def check_budget(
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self,
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user_id: str,
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expected_tokens: int
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) -> bool:
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"""检查预算是否足够"""
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...
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async def send_alert(
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self,
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user_id: str,
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threshold: float
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):
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"""发送告警"""
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...
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# 使用示例
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@dataclass
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class CostEstimate:
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total_tokens: int
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estimated_cost: float
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breakdown: dict[str, int] # per-model
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threshold_percent: float # 相对于用户限额
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```
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### 2.5 Advanced UI
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**目标**：完整的前端协作面板
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**Phase 4已有**：API
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**Phase 5补充**：
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- 实时协作拓扑图
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- Agent对话界面
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- 任务看板
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- 成本仪表盘
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```
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┌────────────────────────────────────────────────────────────────────┐
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│ Jarvis 协作面板 │
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├────────────────────────────────────────────────────────────────────┤
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│ │
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│ ┌─────────────┐ ┌─────────────────────────────────────────────┐ │
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│ │ 拓扑图 │ │ 当前会话 │ │
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│ │ │ │ │ │
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│ │ [Master] │ │ User: 帮我分析这个项目... │ │
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│ │ │ │ │ │ │
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│ │ [Coord] │ │ [Coordinator]: 拆分为3个子任务 │ │
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│ │ ┌─┴─┐ │ │ - Task 1: 检索相关知识 │ │
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│ │ │ │ │ │ - Task 2: 执行分析 │ │
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│ │ [W1] [W2] │ │ - Task 3: 汇总报告 │ │
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│ │ │ │ │ │
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│ │ 点击查看详情 │ │ [Worker-1]: 正在检索... │ │
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│ └─────────────┘ └─────────────────────────────────────────────┘ │
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│ │
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│ ┌─────────────┐ ┌─────────────────────────────────────────────┐ │
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│ │ 任务列表 │ │ 成本监控 │ │
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│ │ │ │ │ │
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│ │ ☑ Task 1 │ │ Token: 12,345 / 50,000 │ │
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│ │ ◐ Task 2 │ │ 成本: $0.23 / $5.00 │ │
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│ │ ○ Task 3 │ │ [████████████████░░░░░] 24% │ │
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│ └─────────────┘ └─────────────────────────────────────────────┘ │
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│ │
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└────────────────────────────────────────────────────────────────────┘
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```
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### 2.6 Plugin System
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**目标**：支持第三方插件扩展
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**设计参考**：Claude Code CLI的插件系统
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```python
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class PluginSystem:
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"""插件系统"""
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async def load_plugin(self, plugin_path: str) -> Plugin:
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"""加载插件"""
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...
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async def execute_plugin(
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self,
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plugin_id: str,
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context: dict
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) -> Any:
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"""执行插件"""
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...
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@dataclass
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class Plugin:
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"""插件定义"""
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plugin_id: str
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name: str
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version: str
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capabilities: list[str] # 提供的工具/能力
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hooks: list[str] # 生命周期钩子
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async def execute(self, context: dict) -> Any:
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...
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@dataclass
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class PluginManifest:
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"""插件清单"""
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tools: list[ToolManifest]
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commands: list[CommandManifest]
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hooks: list[str]
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```
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### 2.7 TagMemo — 仿生记忆系统
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**目标**：实现基于遗忘曲线的智能记忆系统
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**设计参考**：VCPToolBox的TagMemo V6/V7 RAG系统
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**核心概念**：
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1. **LIF神经元模型** — 脉冲传播机制
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- 记忆不是静态存储，而是动态激活
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- 重要记忆获得更高的激活频率
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2. **Core Tags vs Normal Tags** — 核心记忆
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- Core Tags：获得1.2-1.4x权重加成
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- 核心记忆有虚拟召回能力
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3. **遗忘曲线** — 不是无限存储
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- 模拟生物遗忘，不是简单删除
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- 基于重要性动态计算衰减率
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```python
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class MemoryImportance(str, Enum):
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"""记忆重要性等级"""
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CORE = "core" # 核心记忆，1.2-1.4x权重
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HIGH = "high" # 高重要性
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MEDIUM = "medium" # 中等重要性
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LOW = "low" # 低重要性，会自然遗忘
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@dataclass
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class TagMemoEntry:
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"""TagMemo记忆条目"""
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entry_id: str
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content: str
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# VCPToolBox借鉴
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importance: MemoryImportance = MemoryImportance.MEDIUM
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decay_rate: float = 0.1 # 遗忘率
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last_activated: datetime = field(default_factory=datetime.now)
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activation_count: int = 0 # 激活次数
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# EPA模块（可选）
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logic_depth: int = 0 # 逻辑深度
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resonance_score: float = 0.0 # 共振分数
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class TagMemoMemory:
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"""
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仿生记忆系统
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特点：
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- 遗忘曲线模拟
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- 重要性权重
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- 动态激活
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"""
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async def add_memory(
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self,
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content: str,
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importance: MemoryImportance = MemoryImportance.MEDIUM,
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tags: list[str] | None = None
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) -> TagMemoEntry:
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"""添加记忆"""
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entry = TagMemoEntry(
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entry_id=generate_id(),
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content=content,
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importance=importance,
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decay_rate=self._calculate_decay_rate(importance),
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tags=tags or []
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)
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await self._storage.save(entry)
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return entry
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def should_retain(self, entry: TagMemoEntry, days_elapsed: int) -> bool:
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"""
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判断记忆是否应该保留
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基于动态Beta公式：
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β = σ(L·log(1+R) - S·noise_penalty)
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"""
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if entry.importance == MemoryImportance.CORE:
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return True # 核心记忆永远保留
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# 遗忘概率 = 基础衰减 × 时间 × 噪声惩罚
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retention_prob = math.exp(
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-entry.decay_rate * days_elapsed * self._noise_factor
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)
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return random.random() < retention_prob
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async def get_relevant_memories(
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self,
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query: str,
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limit: int = 5
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) -> list[TagMemoEntry]:
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"""获取相关记忆（带权重）"""
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candidates = await self._vector_search(query, limit=limit * 2)
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# 按重要性权重排序
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weighted = []
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for entry in candidates:
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weight = self._calculate_weight(entry)
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weighted.append((entry, weight))
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weighted.sort(key=lambda x: x[1], reverse=True)
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return [e for e, _ in weighted[:limit]]
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def _calculate_weight(self, entry: TagMemoEntry) -> float:
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"""计算记忆权重"""
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base = 1.0
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# 重要性权重
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if entry.importance == MemoryImportance.CORE:
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base *= 1.3
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elif entry.importance == MemoryImportance.HIGH:
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base *= 1.1
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# 激活频率奖励
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base *= (1 + math.log(1 + entry.activation_count))
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# 时间衰减
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days = (datetime.now() - entry.last_activated).days
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base *= math.exp(-0.01 * days)
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return base
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```
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### 2.8 AgentDream — 仿生梦境系统
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**目标**：AI在"睡眠"时自动整理和巩固记忆
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**设计参考**：VCPToolBox的AgentDream bijective morphic system
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**三层时间记忆涟漪**：
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| 时间层 | 范围 | 特点 |
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|--------|------|------|
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| 短期记忆 | 0-7天 | 高频共振，快速激活 |
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| 中期记忆 | 7-90天 | 弱共振，需要触发 |
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| 长期记忆 | >90天 | 遗忘边界，需要特殊唤醒 |
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```python
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class DreamLayer(str, Enum):
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"""梦境记忆层"""
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SHORT_TERM = "short_term" # 0-7天
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MID_TERM = "mid_term" # 7-90天
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LONG_TERM = "long_term" # >90天
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@dataclass
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class DreamMemory:
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"""梦境记忆结构"""
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layer: DreamLayer
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resonance_bridges: list[str] = field(default_factory=list) # 共振桥接
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consolidation_level: float = 0.0 # 巩固程度 0-1
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class AgentDreamEngine:
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"""
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仿生梦境引擎
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功能：
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- 定时触发记忆整理
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- 跨层共振发现
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- 遗忘边界管理
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"""
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async def dream(self, user_id: str) -> DreamReport:
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"""
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执行梦境整理
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流程：
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1. 获取近期记忆（0-7天）
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2. 与中期记忆建立共振桥
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3. 评估哪些记忆应该升级/遗忘
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4. 生成梦境叙事报告
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"""
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short_term = await self._get_memories(
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user_id,
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layer=DreamLayer.SHORT_TERM
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)
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mid_term = await self._get_memories(
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user_id,
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layer=DreamLayer.MID_TERM
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)
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# 发现共振
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bridges = await self._discover_resonance_bridges(
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short_term, mid_term
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)
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# 评估记忆
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to_promote = [] # 升级到中期
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to_forget = [] # 标记遗忘
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to_consolidate = [] # 巩固
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for memory in short_term:
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if memory.activation_count > 10:
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to_promote.append(memory)
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elif memory.activation_count < 2:
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to_forget.append(memory)
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else:
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to_consolidate.append(memory)
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# 执行整理
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await self._promote_memories(to_promote)
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await self._apply_forgetting(to_forget)
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await self._consolidate(to_consolidate, bridges)
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# 生成梦境报告
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return await self._generate_dream_narrative(
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promoted=to_promote,
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forgotten=to_forget,
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bridges=bridges
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)
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async def _discover_resonance_bridges(
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self,
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short_term: list[TagMemoEntry],
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mid_term: list[TagMemoEntry]
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) -> list[tuple[TagMemoEntry, TagMemoEntry, float]]:
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"""发现跨层共振桥接"""
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bridges = []
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for s in short_term:
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for m in mid_term:
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||
similarity = await self._calculate_resonance(s, m)
|
||
if similarity > 0.7:
|
||
bridges.append((s, m, similarity))
|
||
|
||
return bridges
|
||
|
||
async def _generate_dream_narrative(
|
||
self,
|
||
promoted: list,
|
||
forgotten: list,
|
||
bridges: list
|
||
) -> DreamReport:
|
||
"""生成第一人称梦境叙事"""
|
||
narrative = f"""
|
||
【梦境记录】
|
||
|
||
今夜整理了{len(promoted)}段重要记忆，它们已经被巩固：
|
||
{', '.join(m.content for m in promoted[:3])}
|
||
|
||
发现了{len(bridges)}条记忆共振：
|
||
{self._describe_bridges(bridges)}
|
||
|
||
{len(forgotten)}段记忆正在消散...
|
||
"""
|
||
|
||
return DreamReport(
|
||
narrative=narrative,
|
||
promoted_count=len(promoted),
|
||
forgotten_count=len(forgotten),
|
||
bridges_count=len(bridges)
|
||
)
|
||
```
|
||
|
||
**触发机制**：
|
||
|
||
```python
|
||
# 定时任务：每天凌晨3点执行梦境整理
|
||
@scheduler.scheduled(cron="0 3 * * *")
|
||
async def scheduled_agent_dream():
|
||
"""AI睡眠时整理记忆"""
|
||
for user_id in await get_active_users():
|
||
try:
|
||
report = await agent_dream_engine.dream(user_id)
|
||
logger.info(f"Dream complete for {user_id}: {report.summary}")
|
||
except Exception as e:
|
||
logger.error(f"Dream failed for {user_id}: {e}")
|
||
```
|
||
|
||
---
|
||
|
||
## 3. 实施优先级
|
||
|
||
| 特性 | 优先级 | 依赖 | 建议实施时机 |
|
||
|------|--------|------|--------------|
|
||
| Cost Monitoring | 🔴 高 | Phase 3 | 正式上线前 |
|
||
| TagMemo | 🟡 中 | Phase 2 | 用户反馈需要更好记忆时 |
|
||
| AgentDream | 🟢 低 | Phase 5+TagMemo | 凌晨调度资源时 |
|
||
| Persistence | 🟡 中 | Phase 1-3 | 有审计需求时 |
|
||
| Multi-turn Memory | 🟡 中 | Phase 1-2 | 用户反馈需要时 |
|
||
| Advanced UI | 🟡 中 | Phase 4 | 有前端资源时 |
|
||
| Full Sandbox | 🟢 低 | Phase 4 | 有安全需求时 |
|
||
| Plugin System | 🟢 低 | Phase 1 | 有社区需求时 |
|
||
|
||
---
|
||
|
||
## 4. 风险点
|
||
|
||
| 风险 | 缓解措施 |
|
||
|------|----------|
|
||
| 功能蔓延 | 严格控制每个特性的scope |
|
||
| 性能影响 | Persistence要考虑异步和索引优化 |
|
||
| 成本增加 | Full Sandbox资源限制要明确 |
|
||
|
||
---
|
||
|
||
## 5. 验收标准
|
||
|
||
| 特性 | 验收标准 |
|
||
|------|----------|
|
||
| Cost Monitoring | 能实时显示Token使用量和估算成本 |
|
||
| Persistence | 事件和消息可持久化存储和查询 |
|
||
| Multi-turn Memory | 跨会话可复用关键信息 |
|
||
| Advanced UI | 有可用的前端协作面板 |
|
||
| Full Sandbox | 容器隔离完整，资源限制生效 |
|
||
| Plugin System | 插件可加载和执行 |
|
||
|
||
---
|
||
|
||
## 6. 本阶段完成后预期结果
|
||
|
||
完成后，Jarvis 将具备：
|
||
|
||
- ✅ 完整的成本监控能力
|
||
- ✅ 仿生记忆系统（TagMemo）
|
||
- ✅ AI梦境整理（AgentDream）
|
||
- ✅ 历史数据持久化
|
||
- ✅ 跨会话的智能记忆
|
||
- ✅ 完整的协作可视化UI
|
||
- ✅ 高级隔离执行环境
|
||
- ✅ 可扩展的插件系统
|
||
|
||
**Phase 5 为可选特性，根据实际需求选择性实施。**
|