steven_li
5ba5c7e4c1
集成新的Beaver后端服务到应用实例中，替换原有的nanobot实现。 主要变更包括： - 在Dockerfile和环境配置中添加Beaver相关路径和配置变量 - 更新工作目录结构从.nanobot到.beaver - 实现Beaver引擎加载器，支持配置文件加载和工具组装 - 添加内置工具如ListDirectoryTool、ReadFileTool、SearchFilesTool - 更新消息处理流程，支持通道适配器和网关模式 - 重构技能系统，支持显式工具提示和嵌入式检索 - 改进错误处理和生命周期管理 此变更使应用实例能够使用统一的Beaver后端进行AI代理运行时管理。
180 lines
7.0 KiB
Python
180 lines
7.0 KiB
Python
"""Centralized runtime loading for Beaver agents."""
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from __future__ import annotations
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import os
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Callable
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from beaver.engine.context import ContextBuilder
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from beaver.engine.session import SessionManager
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from beaver.foundation.config import BeaverConfig, load_config
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from beaver.memory.curated.store import MemoryStore
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from beaver.services.memory_service import MemoryService
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from beaver.skills import SkillAssembler, SkillsLoader
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from beaver.tools import ObjectBackedTool, ToolAssembler, ToolExecutor, ToolRegistry
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from beaver.tools.builtins import (
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EchoTool,
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ListDirectoryTool,
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MemoryTool,
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ReadFileTool,
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SearchFilesTool,
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SessionSearchTool,
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SkillViewTool,
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)
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@dataclass(slots=True)
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class EngineLoadResult:
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"""描述当前 agent runtime 已经装好的依赖。
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这里同时保留两类字段：
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1. `tools/skills/memory_stores/permissions`
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- 便于做状态展示、调试、轻量测试
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2. `session_manager/tool_registry/...`
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- 供真正的运行时主链直接使用
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"""
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workspace: Path
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config: BeaverConfig = field(default_factory=BeaverConfig)
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tools: list[str] = field(default_factory=list)
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skills: list[str] = field(default_factory=list)
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memory_stores: list[str] = field(default_factory=list)
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permissions: list[str] = field(default_factory=list)
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session_manager: SessionManager | None = None
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curated_memory_store: MemoryStore | None = None
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memory_service: MemoryService | None = None
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tool_registry: ToolRegistry | None = None
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tool_assembler: ToolAssembler | None = None
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tool_executor: ToolExecutor | None = None
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context_builder: ContextBuilder | None = None
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skills_loader: SkillsLoader | None = None
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skill_assembler: SkillAssembler | None = None
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closeables: list[tuple[str, Callable[[], None]]] = field(default_factory=list, repr=False)
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closed: bool = False
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def register_closeable(self, name: str, close_fn: Callable[[], None]) -> None:
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"""登记一个由 runtime 统一关闭的资源。"""
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self.closeables.append((name, close_fn))
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def close(self) -> None:
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"""按后进先出顺序关闭 runtime 资源。
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这一步先保持同步、最小、可组合：
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1. 只管理已经明确需要关闭的资源
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2. 暂不引入 async shutdown 协议
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3. 为后续 Web/Gateway lifespan 留统一入口
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"""
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if self.closed:
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return
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errors: list[tuple[str, BaseException]] = []
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for name, close_fn in reversed(self.closeables):
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try:
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close_fn()
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except BaseException as exc: # pragma: no cover - defensive cleanup path
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errors.append((name, exc))
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self.closed = True
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if errors:
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parts = ", ".join(f"{name}: {exc}" for name, exc in errors)
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raise RuntimeError(f"Runtime shutdown failed for {parts}")
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class EngineLoader:
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"""为任意 Beaver agent 装载共享 runtime 能力。
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当前先做“最小可运行主链”需要的装配：
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- session manager
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- curated memory store
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- context builder
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- built-in tools
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- tool executor
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等主链跑稳后，再把 skills、权限、MCP、delegation 逐步加进来。
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"""
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def __init__(
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self,
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*,
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workspace: str | Path | None = None,
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config_path: str | Path | None = None,
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config: BeaverConfig | None = None,
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session_manager: SessionManager | None = None,
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curated_memory_store: MemoryStore | None = None,
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memory_service: MemoryService | None = None,
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tool_registry: ToolRegistry | None = None,
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tool_assembler: ToolAssembler | None = None,
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context_builder: ContextBuilder | None = None,
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skills_loader: SkillsLoader | None = None,
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skill_assembler: SkillAssembler | None = None,
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) -> None:
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self.config = config or load_config(workspace=workspace, config_path=config_path)
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configured_workspace = self.config.agents_defaults.workspace
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env_workspace = os.getenv("BEAVER_WORKSPACE")
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self.workspace = Path(workspace or configured_workspace or env_workspace or Path.cwd())
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self._session_manager = session_manager
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self._curated_memory_store = curated_memory_store
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self._memory_service = memory_service
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self._tool_registry = tool_registry
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self._tool_assembler = tool_assembler
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self._context_builder = context_builder
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self._skills_loader = skills_loader
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self._skill_assembler = skill_assembler
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def load(self) -> EngineLoadResult:
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"""装配当前主链需要的最小 runtime 对象。"""
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workspace = self.workspace
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session_manager = self._session_manager or SessionManager(workspace)
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curated_root = workspace / "memory" / "curated"
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curated_memory_store = self._curated_memory_store or MemoryStore(curated_root)
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memory_service = self._memory_service or MemoryService(curated_root, store=curated_memory_store)
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memory_service.initialize()
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tool_registry = self._tool_registry or ToolRegistry()
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skills_loader = self._skills_loader or SkillsLoader(workspace)
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if self._tool_registry is None:
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# 这里先注册最小工具集，满足主链的 tool loop。
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tool_registry.register_many(
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[
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ObjectBackedTool(EchoTool()),
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ObjectBackedTool(MemoryTool(store=memory_service.get_store())),
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ObjectBackedTool(SkillViewTool(loader=skills_loader)),
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ObjectBackedTool(SessionSearchTool(db=session_manager)),
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ObjectBackedTool(ListDirectoryTool()),
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ObjectBackedTool(ReadFileTool()),
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ObjectBackedTool(SearchFilesTool()),
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]
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)
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context_builder = self._context_builder or ContextBuilder()
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tool_assembler = self._tool_assembler or ToolAssembler()
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tool_executor = ToolExecutor(tool_registry)
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skill_assembler = self._skill_assembler or SkillAssembler(skills_loader)
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result = EngineLoadResult(
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workspace=workspace,
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config=self.config,
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tools=[spec.name for spec in tool_registry.list_specs()],
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skills=[record.name for record in skills_loader.list_skills(filter_unavailable=False)],
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memory_stores=["curated"],
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permissions=[],
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session_manager=session_manager,
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curated_memory_store=memory_service.get_store(),
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memory_service=memory_service,
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tool_registry=tool_registry,
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tool_assembler=tool_assembler,
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tool_executor=tool_executor,
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context_builder=context_builder,
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skills_loader=skills_loader,
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skill_assembler=skill_assembler,
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)
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if self._session_manager is None:
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result.register_closeable("session_manager", session_manager.close)
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return result
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