修改了nanobot，往Hermes agent的风格走，进度1/3
This commit is contained in:
17
app-instance/backend/beaver/tools/builtins/__init__.py
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17
app-instance/backend/beaver/tools/builtins/__init__.py
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"""Built-in Beaver tools."""
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from .echo import EchoTool, echo_tool
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from .memory import MemoryTool, memory_tool
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from .skill_view import SkillViewTool, skill_view
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from .session_search import SessionSearchTool, session_search
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__all__ = [
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"EchoTool",
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"MemoryTool",
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"SkillViewTool",
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"SessionSearchTool",
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"echo_tool",
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"memory_tool",
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"skill_view",
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"session_search",
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]
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43
app-instance/backend/beaver/tools/builtins/echo.py
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43
app-instance/backend/beaver/tools/builtins/echo.py
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"""最小调试工具：把输入原样回显。
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它的价值不是业务能力，而是运行时验证：
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当你只想确认 tool loop 是否能走通时，`echo` 是最便宜、最确定的测试工具。
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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ECHO_TOOL_DESCRIPTION = "Echo the provided text back to the agent. Useful for verifying tool calling."
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ECHO_TOOL_PARAMETERS: dict[str, Any] = {
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"type": "object",
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"properties": {
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"text": {
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"type": "string",
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"description": "The text to echo back.",
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}
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},
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"required": ["text"],
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}
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def echo_tool(*, text: str) -> str:
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return text
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@dataclass(slots=True)
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class EchoTool:
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"""面向 runtime 的最小内建工具。"""
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name: str = "echo"
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description: str = ECHO_TOOL_DESCRIPTION
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parameters: dict[str, Any] = field(default_factory=lambda: dict(ECHO_TOOL_PARAMETERS))
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async def execute(self, **kwargs: Any) -> str:
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text = kwargs.get("text")
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if not isinstance(text, str):
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raise ValueError("echo tool requires a string field 'text'")
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return echo_tool(text=text)
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129
app-instance/backend/beaver/tools/builtins/memory.py
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129
app-instance/backend/beaver/tools/builtins/memory.py
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"""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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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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418
app-instance/backend/beaver/tools/builtins/session_search.py
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418
app-instance/backend/beaver/tools/builtins/session_search.py
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"""Beaver 内置 session_search tool。
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这个工具对应 Hermes-agent 的跨会话检索能力，目标不是把所有历史内容塞回主上下文，
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而是按需从过去的 session 中找回“之前发生过什么”。
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当前实现保留了几个关键行为：
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1. query 为空时进入 recent/browse 模式，只列最近会话，不走 LLM，总成本很低
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2. query 不为空时走 transcript DB 的搜索接口，预期底层是 FTS 风格检索
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3. 自动排除当前 session lineage，避免把当前上下文又搜出来一遍
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4. 对长会话做 match-centered truncation，而不是无脑截前 N 字符
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5. summarizer 是可选依赖；没有时降级返回 raw preview，而不是整条工具失败
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import re
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Any, Awaitable, Callable, Protocol
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MAX_SESSION_CHARS = 100_000
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class SessionSearchDB(Protocol):
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"""session_search 依赖的最小数据库契约。
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这里没有直接绑定某个具体 SQLite 实现，而是先定义行为接口。
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这样后面无论你接的是 Hermes 风格 state DB、还是 Beaver 自己的 transcript store，
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只要满足这些方法就能工作。
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"""
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def list_sessions_rich(
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self,
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*,
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limit: int,
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exclude_sources: list[str] | None = None,
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) -> list[dict[str, Any]]: ...
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def get_session(self, session_id: str) -> dict[str, Any] | None: ...
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def get_messages_as_conversation(self, session_id: str) -> list[dict[str, Any]]: ...
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def search_messages(
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self,
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*,
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query: str,
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role_filter: list[str] | None = None,
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exclude_sources: list[str] | None = None,
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limit: int,
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offset: int = 0,
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) -> list[dict[str, Any]]: ...
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SessionSummarizer = Callable[[str, str, dict[str, Any]], Awaitable[str | None]]
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_HIDDEN_SESSION_SOURCES = ("tool",)
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SESSION_SEARCH_TOOL_DESCRIPTION = (
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"Search prior sessions for historical context, or browse recent sessions when "
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"query is omitted. Use this when the user references past work, prior fixes, "
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"or earlier decisions instead of asking them to repeat themselves."
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)
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SESSION_SEARCH_TOOL_PARAMETERS: dict[str, Any] = {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "Keyword, phrase, or boolean FTS query. Omit to browse recent sessions.",
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},
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"role_filter": {
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"type": "string",
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"description": "Optional comma-separated roles to search, for example 'user,assistant'.",
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},
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"limit": {
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"type": "integer",
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"default": 3,
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"minimum": 1,
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"maximum": 5,
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"description": "Maximum number of sessions to return.",
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},
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},
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"required": [],
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}
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def _format_timestamp(value: int | float | str | None) -> str:
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"""把时间戳或字符串格式化成更可读的展示文本。"""
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if value is None:
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return "unknown"
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try:
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if isinstance(value, (int, float)):
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return datetime.fromtimestamp(value).strftime("%B %d, %Y at %I:%M %p")
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if isinstance(value, str):
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if value.replace(".", "").replace("-", "").isdigit():
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return datetime.fromtimestamp(float(value)).strftime("%B %d, %Y at %I:%M %p")
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return value
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except (OSError, OverflowError, ValueError):
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pass
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return str(value)
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def _format_conversation(messages: list[dict[str, Any]]) -> str:
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"""把消息列表整理成适合摘要模型消费的 transcript 文本。
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这里会保留：
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- role
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- assistant 的 tool calls 名称
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- tool 输出的简短内容
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但不会原样塞入超长工具输出，否则摘要成本会被单个工具结果拉爆。
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"""
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parts: list[str] = []
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for message in messages:
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role = str(message.get("role", "unknown")).upper()
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content = message.get("content") or ""
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tool_name = message.get("tool_name")
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if role == "TOOL" and tool_name:
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if len(content) > 500:
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content = content[:250] + "\n...[truncated]...\n" + content[-250:]
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parts.append(f"[TOOL:{tool_name}]: {content}")
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continue
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if role == "ASSISTANT":
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tool_calls = message.get("tool_calls")
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if isinstance(tool_calls, list) and tool_calls:
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names: list[str] = []
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for tool_call in tool_calls:
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if isinstance(tool_call, dict):
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names.append(
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tool_call.get("name")
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or tool_call.get("function", {}).get("name", "?")
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)
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if names:
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parts.append(f"[ASSISTANT]: [Called: {', '.join(names)}]")
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parts.append(f"[ASSISTANT]: {content}")
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continue
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parts.append(f"[{role}]: {content}")
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return "\n\n".join(parts)
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def _truncate_around_matches(full_text: str, query: str, *, max_chars: int = MAX_SESSION_CHARS) -> str:
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"""围绕匹配位置截取上下文，而不是固定截头。
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优先级：
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1. 先找整句 query
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2. 找不到再找多词近邻共现
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3. 再退化到逐词匹配
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这样做的目的，是尽量把与 query 最相关的对话片段保留下来，提高 summarizer 的命中率。
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"""
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if len(full_text) <= max_chars:
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return full_text
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text_lower = full_text.lower()
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query_lower = query.lower().strip()
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match_positions = [match.start() for match in re.finditer(re.escape(query_lower), text_lower)]
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if not match_positions:
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terms = query_lower.split()
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if len(terms) > 1:
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positions: dict[str, list[int]] = {
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term: [match.start() for match in re.finditer(re.escape(term), text_lower)]
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for term in terms
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}
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rarest = min(terms, key=lambda term: len(positions.get(term, [])))
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for position in positions.get(rarest, []):
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if all(
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any(abs(candidate - position) < 200 for candidate in positions.get(term, []))
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for term in terms
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if term != rarest
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):
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match_positions.append(position)
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if not match_positions:
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for term in query_lower.split():
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match_positions.extend(match.start() for match in re.finditer(re.escape(term), text_lower))
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if not match_positions:
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head = full_text[:max_chars]
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suffix = "\n\n...[later conversation truncated]..." if max_chars < len(full_text) else ""
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return head + suffix
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best_start = 0
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best_count = 0
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for candidate in sorted(match_positions):
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window_start = max(0, candidate - max_chars // 4)
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window_end = window_start + max_chars
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if window_end > len(full_text):
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window_start = max(0, len(full_text) - max_chars)
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window_end = len(full_text)
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count = sum(1 for position in match_positions if window_start <= position < window_end)
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if count > best_count:
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best_count = count
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best_start = window_start
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start = best_start
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end = min(len(full_text), start + max_chars)
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prefix = "...[earlier conversation truncated]...\n\n" if start > 0 else ""
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suffix = "\n\n...[later conversation truncated]..." if end < len(full_text) else ""
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return prefix + full_text[start:end] + suffix
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def _resolve_to_parent(db: SessionSearchDB, session_id: str | None) -> str | None:
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"""沿 parent_session_id 向上追溯到 lineage root。
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这样可以把 delegation/compression 形成的子 session 归并回同一条主会话链，
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避免检索结果里出现多个其实属于同一轮上下文的碎片 session。
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"""
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visited: set[str] = set()
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current = session_id
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while current and current not in visited:
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visited.add(current)
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session = db.get_session(current)
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if not session:
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break
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parent = session.get("parent_session_id")
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if not parent:
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break
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current = parent
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return current
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def _list_recent_sessions(
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db: SessionSearchDB,
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*,
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limit: int,
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current_session_id: str | None = None,
|
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) -> str:
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"""recent mode：仅列出最近 session 的元数据，不做摘要调用。"""
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sessions = db.list_sessions_rich(
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limit=limit + 5,
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exclude_sources=list(_HIDDEN_SESSION_SOURCES),
|
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)
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current_root = _resolve_to_parent(db, current_session_id) if current_session_id else None
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results: list[dict[str, Any]] = []
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for session in sessions:
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session_id = session.get("id", "")
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if current_root and session_id == current_root:
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continue
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if current_session_id and session_id == current_session_id:
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continue
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if session.get("parent_session_id"):
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continue
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results.append(
|
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{
|
||||
"session_id": session_id,
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||||
"title": session.get("title") or None,
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||||
"source": session.get("source", ""),
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"started_at": session.get("started_at", ""),
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"last_active": session.get("last_active", ""),
|
||||
"message_count": session.get("message_count", 0),
|
||||
"preview": session.get("preview", ""),
|
||||
}
|
||||
)
|
||||
if len(results) >= limit:
|
||||
break
|
||||
|
||||
return json.dumps(
|
||||
{
|
||||
"success": True,
|
||||
"mode": "recent",
|
||||
"results": results,
|
||||
"count": len(results),
|
||||
"message": f"Showing {len(results)} most recent sessions.",
|
||||
},
|
||||
ensure_ascii=False,
|
||||
)
|
||||
|
||||
|
||||
async def session_search(
|
||||
*,
|
||||
query: str = "",
|
||||
role_filter: str | None = None,
|
||||
limit: int = 3,
|
||||
db: SessionSearchDB | None = None,
|
||||
current_session_id: str | None = None,
|
||||
summarizer: SessionSummarizer | None = None,
|
||||
) -> str:
|
||||
"""搜索过去的会话并返回结构化 JSON 结果。
|
||||
|
||||
运行流程：
|
||||
1. 空 query -> recent mode
|
||||
2. 有 query -> 调 transcript DB 搜索
|
||||
3. 去掉当前会话链
|
||||
4. 拉取命中的 session transcript
|
||||
5. 对 transcript 做 match-centered truncation
|
||||
6. 如果提供 summarizer，就并发摘要；否则回退 raw preview
|
||||
"""
|
||||
|
||||
if db is None:
|
||||
return json.dumps({"success": False, "error": "Session database is not available."}, ensure_ascii=False)
|
||||
|
||||
limit = max(1, min(limit, 5))
|
||||
if not query or not query.strip():
|
||||
return _list_recent_sessions(db, limit=limit, current_session_id=current_session_id)
|
||||
|
||||
role_list = [item.strip() for item in (role_filter or "").split(",") if item.strip()] or None
|
||||
try:
|
||||
raw_results = db.search_messages(
|
||||
query=query.strip(),
|
||||
role_filter=role_list,
|
||||
exclude_sources=list(_HIDDEN_SESSION_SOURCES),
|
||||
limit=50,
|
||||
offset=0,
|
||||
)
|
||||
except Exception as exc:
|
||||
logging.error("Session search failed during FTS lookup: %s", exc, exc_info=True)
|
||||
return json.dumps({"success": False, "error": f"Search failed: {exc}"}, ensure_ascii=False)
|
||||
|
||||
if not raw_results:
|
||||
return json.dumps(
|
||||
{
|
||||
"success": True,
|
||||
"query": query.strip(),
|
||||
"results": [],
|
||||
"count": 0,
|
||||
"message": "No matching sessions found.",
|
||||
},
|
||||
ensure_ascii=False,
|
||||
)
|
||||
|
||||
current_root = _resolve_to_parent(db, current_session_id) if current_session_id else None
|
||||
seen_sessions: dict[str, dict[str, Any]] = {}
|
||||
for result in raw_results:
|
||||
raw_session_id = result["session_id"]
|
||||
resolved_session_id = _resolve_to_parent(db, raw_session_id) or raw_session_id
|
||||
if current_root and resolved_session_id == current_root:
|
||||
continue
|
||||
if current_session_id and raw_session_id == current_session_id:
|
||||
continue
|
||||
if resolved_session_id not in seen_sessions:
|
||||
entry = dict(result)
|
||||
entry["session_id"] = resolved_session_id
|
||||
seen_sessions[resolved_session_id] = entry
|
||||
if len(seen_sessions) >= limit:
|
||||
break
|
||||
|
||||
prepared: list[tuple[str, dict[str, Any], str, dict[str, Any]]] = []
|
||||
for session_id, match_info in seen_sessions.items():
|
||||
try:
|
||||
messages = db.get_messages_as_conversation(session_id)
|
||||
if not messages:
|
||||
continue
|
||||
session_meta = db.get_session(session_id) or {}
|
||||
transcript = _truncate_around_matches(_format_conversation(messages), query.strip())
|
||||
prepared.append((session_id, match_info, transcript, session_meta))
|
||||
except Exception as exc:
|
||||
logging.warning("Failed to prepare session %s: %s", session_id, exc, exc_info=True)
|
||||
|
||||
if summarizer is not None:
|
||||
summaries = await asyncio.gather(
|
||||
*(summarizer(transcript, query.strip(), session_meta) for _, _, transcript, session_meta in prepared),
|
||||
return_exceptions=True,
|
||||
)
|
||||
else:
|
||||
summaries = [None] * len(prepared)
|
||||
|
||||
results: list[dict[str, Any]] = []
|
||||
for (session_id, match_info, transcript, _), summary in zip(prepared, summaries):
|
||||
resolved_summary: str | None
|
||||
if isinstance(summary, Exception):
|
||||
logging.warning("Failed to summarize session %s: %s", session_id, summary, exc_info=True)
|
||||
resolved_summary = None
|
||||
else:
|
||||
resolved_summary = summary
|
||||
|
||||
if not resolved_summary:
|
||||
preview = transcript[:500] + ("\n…[truncated]" if len(transcript) > 500 else "")
|
||||
resolved_summary = f"[Raw preview — summarization unavailable]\n{preview}"
|
||||
|
||||
results.append(
|
||||
{
|
||||
"session_id": session_id,
|
||||
"when": _format_timestamp(match_info.get("session_started")),
|
||||
"source": match_info.get("source", "unknown"),
|
||||
"model": match_info.get("model"),
|
||||
"summary": resolved_summary,
|
||||
}
|
||||
)
|
||||
|
||||
return json.dumps(
|
||||
{
|
||||
"success": True,
|
||||
"query": query.strip(),
|
||||
"results": results,
|
||||
"count": len(results),
|
||||
"sessions_searched": len(seen_sessions),
|
||||
},
|
||||
ensure_ascii=False,
|
||||
)
|
||||
|
||||
|
||||
@dataclass(slots=True)
|
||||
class SessionSearchTool:
|
||||
"""面向 runtime 的轻量 session_search 工具封装。"""
|
||||
|
||||
db: SessionSearchDB
|
||||
current_session_id: str | None = None
|
||||
summarizer: SessionSummarizer | None = None
|
||||
name: str = "session_search"
|
||||
description: str = SESSION_SEARCH_TOOL_DESCRIPTION
|
||||
parameters: dict[str, Any] = field(default_factory=lambda: dict(SESSION_SEARCH_TOOL_PARAMETERS))
|
||||
|
||||
async def execute(self, **kwargs: Any) -> str:
|
||||
current_session_id = kwargs.pop("current_session_id", None)
|
||||
return await session_search(
|
||||
db=self.db,
|
||||
current_session_id=current_session_id if current_session_id is not None else self.current_session_id,
|
||||
summarizer=self.summarizer,
|
||||
**kwargs,
|
||||
)
|
||||
82
app-instance/backend/beaver/tools/builtins/skill_view.py
Normal file
82
app-instance/backend/beaver/tools/builtins/skill_view.py
Normal file
@ -0,0 +1,82 @@
|
||||
"""Beaver 内置 skill_view tool。
|
||||
|
||||
这个工具对应 Hermes 风格的显式 skill loading path：
|
||||
1. skill 正文默认不会长期塞进 system prompt
|
||||
2. 模型若想查看某个 skill 的完整正文或支持文件，必须显式调用 `skill_view`
|
||||
|
||||
这样 skill 的按需展开路径会保持显式，而不是依赖 prompt 里长期堆目录信息。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
|
||||
from beaver.skills.catalog.loader import SkillsLoader
|
||||
|
||||
SKILL_VIEW_TOOL_DESCRIPTION = (
|
||||
"Load the full content of a skill or one of its supporting files. "
|
||||
"Use this when you want to inspect a skill in detail."
|
||||
)
|
||||
|
||||
SKILL_VIEW_TOOL_PARAMETERS: dict[str, Any] = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "The skill name to inspect.",
|
||||
},
|
||||
"file_path": {
|
||||
"type": "string",
|
||||
"description": (
|
||||
"Optional relative path to a supporting file inside the skill directory, "
|
||||
"for example 'references/usage.md'. Omit to load SKILL.md itself."
|
||||
),
|
||||
},
|
||||
},
|
||||
"required": ["name"],
|
||||
}
|
||||
|
||||
|
||||
def skill_view(*, name: str, file_path: str | None = None, loader: SkillsLoader | None = None) -> str:
|
||||
"""读取 skill 正文或支持文件，并返回结构化 JSON。"""
|
||||
|
||||
if loader is None:
|
||||
return json.dumps({"success": False, "error": "Skills loader is not available."}, ensure_ascii=False)
|
||||
|
||||
try:
|
||||
viewed = loader.view_skill(name, file_path=file_path)
|
||||
except FileNotFoundError as exc:
|
||||
return json.dumps({"success": False, "error": str(exc)}, ensure_ascii=False)
|
||||
except ValueError as exc:
|
||||
return json.dumps({"success": False, "error": str(exc)}, ensure_ascii=False)
|
||||
|
||||
if viewed is None:
|
||||
return json.dumps({"success": False, "error": f"Unknown skill '{name}'."}, ensure_ascii=False)
|
||||
|
||||
display_name, content = viewed
|
||||
support_files = loader.list_skill_supporting_files(name)
|
||||
return json.dumps(
|
||||
{
|
||||
"success": True,
|
||||
"name": name,
|
||||
"file": display_name,
|
||||
"content": content,
|
||||
"supporting_files": support_files,
|
||||
},
|
||||
ensure_ascii=False,
|
||||
)
|
||||
|
||||
|
||||
@dataclass(slots=True)
|
||||
class SkillViewTool:
|
||||
"""面向 runtime 的 skill_view 工具封装。"""
|
||||
|
||||
loader: SkillsLoader
|
||||
name: str = "skill_view"
|
||||
description: str = SKILL_VIEW_TOOL_DESCRIPTION
|
||||
parameters: dict[str, Any] = field(default_factory=lambda: dict(SKILL_VIEW_TOOL_PARAMETERS))
|
||||
|
||||
async def execute(self, **kwargs: Any) -> str:
|
||||
return skill_view(loader=self.loader, **kwargs)
|
||||
Reference in New Issue
Block a user