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。
132 lines
4.1 KiB
Python
132 lines
4.1 KiB
Python
"""Beaver 内置 memory tool。
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这个文件的职责很单纯：把 `MemoryStore` 暴露成一个 agent runtime 可以调用的统一工具。
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设计边界：
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1. `store.py` 负责底层数据与并发安全
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2. 本文件负责工具接口、参数校验分发、JSON 响应
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3. 更高层的 engine / loader 之后再决定如何把这个工具注册进 runtime
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换句话说，本文件是“memory 能力的工具化外壳”，不是记忆实现本身。
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field
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from typing import Any
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from beaver.memory.curated.store import MemoryStore
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MEMORY_TOOL_DESCRIPTION = (
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"Save durable information to persistent memory that survives across sessions. "
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"Use this proactively for user corrections, preferences, environment facts, "
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"project conventions, and stable tool quirks. Do not store temporary task "
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"progress or raw session logs here; use session search for historical detail."
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)
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MEMORY_TOOL_PARAMETERS: dict[str, Any] = {
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"type": "object",
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"properties": {
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"action": {
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"type": "string",
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"enum": ["add", "replace", "remove"],
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"description": "The memory operation to perform.",
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},
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"target": {
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"type": "string",
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"enum": ["memory", "user"],
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"description": "Which curated store to update.",
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},
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"content": {
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"type": "string",
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"description": "The new entry content. Required for add and replace.",
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},
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"old_text": {
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"type": "string",
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"description": "A short unique substring identifying the entry to replace or remove.",
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},
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},
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"required": ["action", "target"],
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}
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def memory_tool(
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*,
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action: str,
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target: str = "memory",
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content: str | None = None,
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old_text: str | None = None,
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store: MemoryStore | None = None,
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) -> str:
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"""分发 Hermes 风格的 CRUD memory API，并返回 JSON 字符串。
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这里统一采用 JSON 返回，是为了兼容常见 tool-calling 场景：
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- LLM 更容易消费结构化结果
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- Web/API/日志层也更容易透传和记录
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"""
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if store is None:
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return json.dumps(
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{
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"success": False,
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"error": "Memory store is not available for this runtime.",
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},
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ensure_ascii=False,
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)
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if target not in {"memory", "user"}:
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return json.dumps(
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{
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"success": False,
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"error": f"Invalid target '{target}'. Use 'memory' or 'user'.",
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},
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ensure_ascii=False,
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)
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if action == "add":
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if not content:
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result = {"success": False, "error": "content is required for add."}
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else:
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result = store.add(target, content)
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elif action == "replace":
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if not old_text:
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result = {"success": False, "error": "old_text is required for replace."}
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elif not content:
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result = {"success": False, "error": "content is required for replace."}
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else:
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result = store.replace(target, old_text, content)
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elif action == "remove":
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if not old_text:
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result = {"success": False, "error": "old_text is required for remove."}
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else:
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result = store.remove(target, old_text)
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else:
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result = {
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"success": False,
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"error": f"Unknown action '{action}'. Use add, replace, or remove.",
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}
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return json.dumps(result, ensure_ascii=False)
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@dataclass(slots=True)
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class MemoryTool:
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"""面向 runtime 的轻量工具封装。
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这里故意保持很薄：
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1. 不重复实现业务逻辑
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2. 不重复维护 schema
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3. 只做 `execute()` 到 `memory_tool()` 的桥接
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"""
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store: MemoryStore
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name: str = "memory"
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description: str = MEMORY_TOOL_DESCRIPTION
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toolset: str = "memory"
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always_available: bool = True
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parameters: dict[str, Any] = field(default_factory=lambda: dict(MEMORY_TOOL_PARAMETERS))
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async def execute(self, **kwargs: Any) -> str:
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return memory_tool(store=self.store, **kwargs)
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