steven_li
9d6cde2d23
- 将所有环境变量前缀从NANO_改为BEAVER_ - 更新README.md文档内容，包括项目介绍、组件说明和快速开始指南 - 修改.gitignore文件，添加auth-portal运行时路径排除规则 - 更新app-instance镜像标签从nano/app-instance改为beaver/app-instance - 增强技能安全检查器，支持工具前缀白名单功能 - 添加技能草稿重新检查安全性API端点 - 扩展证据选择器，收集工具调用名称用于技能学习 - 改进技能合成器，基于实际调用的工具生成工具提示 - 优化路由超时处理机制，增加重试逻辑 - 更新后端架构文档，添加可视化入口和基础概念说明 - 实现在WebSocket消息中传递工具迭代次数信息
120 lines
4.9 KiB
Python
120 lines
4.9 KiB
Python
"""Evidence selection for skill learning."""
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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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from beaver.engine.session.manager import SessionManager
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from beaver.memory.runs.store import RunMemoryStore
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@dataclass(slots=True)
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class EvidencePacket:
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run_ids: list[str]
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session_ids: list[str]
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task_summaries: list[str]
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session_excerpts: list[str]
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metadata: dict[str, Any] = field(default_factory=dict)
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class EvidenceSelector:
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def __init__(self, run_store: RunMemoryStore, session_manager: SessionManager | None = None) -> None:
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self.run_store = run_store
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self.session_manager = session_manager
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def select_runs_for_revision(self, skill_name: str, version: str, limit: int = 5) -> list[str]:
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runs = self.run_store.list_runs_by_skill(skill_name, version=version, limit=limit)
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return [record.run_id for record in runs]
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def select_runs_for_new_skill(self, theme: str, limit: int = 5) -> list[str]:
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lowered = theme.lower().strip()
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matches = []
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for record in self.run_store.list_runs():
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if lowered and lowered not in record.task_text.lower():
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continue
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matches.append(record.run_id)
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return matches[-limit:]
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def build_evidence_packet(self, run_ids: list[str], session_ids: list[str] | None = None) -> EvidencePacket:
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runs_by_id = {record.run_id: record for record in self.run_store.list_runs()}
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resolved_run_ids: list[str] = []
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resolved_session_ids: list[str] = list(dict.fromkeys(session_ids or []))
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task_summaries: list[str] = []
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session_excerpts: list[str] = []
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tool_names: list[str] = []
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selected_tool_names: list[str] = []
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for run_id in run_ids:
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record = runs_by_id.get(run_id)
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if record is None:
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continue
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resolved_run_ids.append(run_id)
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if record.session_id not in resolved_session_ids:
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resolved_session_ids.append(record.session_id)
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summary = record.task_text.strip()
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if summary:
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task_summaries.append(summary[:400])
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if self.session_manager is not None:
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excerpt = self._session_excerpt(record.session_id, run_id)
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if excerpt:
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session_excerpts.append(excerpt)
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run_tool_names, run_selected_tool_names = self._run_tool_names(record.session_id, run_id)
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tool_names.extend(run_tool_names)
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selected_tool_names.extend(run_selected_tool_names)
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return EvidencePacket(
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run_ids=resolved_run_ids,
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session_ids=resolved_session_ids,
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task_summaries=task_summaries[:8],
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session_excerpts=session_excerpts[:6],
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metadata={
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"bounded": True,
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"tool_names": _unique_strings(tool_names),
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"selected_tool_names": _unique_strings(selected_tool_names),
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},
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)
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def _session_excerpt(self, session_id: str, run_id: str) -> str:
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if self.session_manager is None:
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return ""
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events = self.session_manager.get_run_event_records(session_id, run_id)
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visible: list[str] = []
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for event in events:
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if not event.context_visible or not event.content:
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continue
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visible.append(f"{event.role}: {event.content.strip()}")
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return "\n".join(visible[:12])[:2000]
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def _run_tool_names(self, session_id: str, run_id: str) -> tuple[list[str], list[str]]:
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if self.session_manager is None:
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return [], []
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names: list[str] = []
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selected_names: list[str] = []
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for event in self.session_manager.get_run_event_records(session_id, run_id):
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if event.tool_name:
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names.append(event.tool_name)
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if event.tool_calls:
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for call in event.tool_calls:
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if not isinstance(call, dict):
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continue
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name = call.get("name")
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function = call.get("function")
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if not name and isinstance(function, dict):
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name = function.get("name")
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if name:
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names.append(str(name))
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if event.event_type == "tool_selection_snapshotted" and isinstance(event.event_payload, dict):
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selected = event.event_payload.get("tool_names")
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if isinstance(selected, list):
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selected_names.extend(str(item) for item in selected if str(item).strip())
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return _unique_strings(names), _unique_strings(selected_names)
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def _unique_strings(values: list[str]) -> list[str]:
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result: list[str] = []
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for value in values:
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cleaned = str(value).strip()
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if cleaned and cleaned not in result:
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result.append(cleaned)
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return result
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