steven_li
520a21a027
添加 DEFAULT_TEAM_NODE_MAX_TOOL_ITERATIONS 配置项以控制团队节点的最大工具迭代次数， 并修改 LocalAgentRunner 中的逻辑来使用此默认值当 envelope 中未指定时。 fix(runtime): 修复团队节点运行成功判断逻辑 更新运行成功判断条件，将 finish_reason 为 "max_tool_iterations_finalized" 的情况 视为运行失败，并添加对原始工具调用输出的检测，避免将其误判为成功完成。 feat(mcp): 添加团队工作流MCP工具类别支持 增加新的本地MCP工具类别 "team_workflow" 及其对应的工具创建功能， 为团队工作流提供本地工具支持。 refactor(engine): 调整AgentLoop最大工具迭代次数设置 将 AgentProfile 中的默认 max_tool_iterations 从 30 增加到 100， 同时移除 TaskExecutionPlanner 构造函数中的重复参数传递。 perf(mcp): 优化MCP连接管理避免重复连接 添加 mcp_connected 标志来跟踪MCP连接状态，确保 connect_all 只执行一次， 提高性能并避免不必要的重复连接。 refactor(skills): 移除技能团队模板相关功能 移除与技能团队模板相关的代码，包括解析、存储和处理逻辑， 简化技能记录结构和加载流程。 feat(process): 增强会话过程投影器功能 添加技能激活快照事件处理，改进团队运行完成消息显示， 并增强技能激活事件的时间戳记录功能。 refactor(tasks): 简化任务尝试编排器团队执行逻辑 移除团队执行相关代码，将所有任务统一按单步执行处理， 简化任务编排器的复杂度并提升执行效率。 fix(evidence): 修复节点证据评估中需求验证逻辑 更新节点证据评估逻辑，跳过自然语言证据需求的确定性验证， 只执行机器可读的需求验证，避免因自然语言需求导致的节点失败。
286 lines
11 KiB
Python
286 lines
11 KiB
Python
"""LLM-driven skill assembler.
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这层现在不再自己做规则打分，而是分两步把：
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1. task description
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2. embedding 召回后的候选 skill 摘要
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3. 粗选候选的完整 skill 正文
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交给一个模型来决定本轮要激活哪些 skill。
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当前目标非常克制：
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- 主 agent 不拿 skill_view，也不动态探索技能库
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- SkillAssembler 可以在系统侧内部读取候选 skill 正文
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- 输出只要 skill 名称
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- 没有命中就返回空 skills
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"""
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from __future__ import annotations
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import asyncio
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from dataclasses import dataclass, field
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import json
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from typing import Any
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from beaver.engine.context import SkillContext
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from beaver.engine.providers.base import LLMProvider
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from beaver.engine.providers.runtime import ProviderRuntime
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from beaver.skills.catalog.loader import SkillsLoader
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from beaver.skills.catalog.utils import strip_frontmatter
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from .embedding_retriever import SkillEmbeddingRetriever
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@dataclass(slots=True)
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class SkillAssemblyResult:
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"""一次装配后真正要注入当前 run 的 skills。"""
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activated_skills: list[SkillContext] = field(default_factory=list)
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llm_interactions: list[dict[str, Any]] = field(default_factory=list)
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class SkillAssembler:
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"""用 LLM 根据 task description 选择当前 run 的 skills。"""
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def __init__(
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self,
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loader: SkillsLoader,
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retriever: SkillEmbeddingRetriever | None = None,
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*,
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max_detailed_candidates: int = 5,
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max_candidate_content_chars: int = 6000,
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) -> None:
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self.loader = loader
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self.retriever = retriever or SkillEmbeddingRetriever()
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self.max_detailed_candidates = max(1, max_detailed_candidates)
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self.max_candidate_content_chars = max(1000, max_candidate_content_chars)
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async def assemble(
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self,
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*,
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task_description: str,
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provider: LLMProvider,
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model: str,
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embedding_runtime: ProviderRuntime | None = None,
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thinking_enabled: bool | None = None,
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top_k: int = 12,
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) -> SkillAssemblyResult:
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candidates = self.loader.build_selection_candidates()
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if not candidates:
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return SkillAssemblyResult()
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candidates = await self.retriever.retrieve(
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query=task_description,
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candidates=candidates,
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top_k=top_k,
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api_key=embedding_runtime.api_key if embedding_runtime is not None else None,
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api_base=embedding_runtime.api_base if embedding_runtime is not None else None,
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model=embedding_runtime.model if embedding_runtime is not None else None,
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extra_headers=embedding_runtime.extra_headers if embedding_runtime is not None else None,
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timeout_seconds=(
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embedding_runtime.request_timeout_seconds if embedding_runtime is not None else None
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),
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fallback_top_k=None,
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)
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if not candidates:
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return SkillAssemblyResult()
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llm_interactions: list[dict[str, Any]] = []
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if len(candidates) == 1:
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return SkillAssemblyResult(
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activated_skills=self._activate_skill_contexts([candidates[0]["name"]]),
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llm_interactions=llm_interactions,
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)
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if len(candidates) <= self.max_detailed_candidates:
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shortlisted_names = [item["name"] for item in candidates]
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else:
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shortlisted_names = await self._select_skill_names(
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task_description=task_description,
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candidates=candidates,
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provider=provider,
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model=model,
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thinking_enabled=thinking_enabled,
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max_selected=self.max_detailed_candidates,
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selection_stage="shortlist",
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llm_interactions=llm_interactions,
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)
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if not shortlisted_names:
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return SkillAssemblyResult(llm_interactions=llm_interactions)
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detailed_candidates = self._build_detailed_candidates(
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candidates=candidates,
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selected_names=shortlisted_names,
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)
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selected_names = await self._select_skill_names(
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task_description=task_description,
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candidates=detailed_candidates,
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provider=provider,
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model=model,
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thinking_enabled=thinking_enabled,
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selection_stage="final",
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llm_interactions=llm_interactions,
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)
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if not selected_names:
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return SkillAssemblyResult(llm_interactions=llm_interactions)
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activated_skills = self._activate_skill_contexts(selected_names)
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return SkillAssemblyResult(activated_skills=activated_skills, llm_interactions=llm_interactions)
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def _activate_skill_contexts(self, selected_names: list[str]) -> list[SkillContext]:
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activated_skills: list[SkillContext] = []
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for name in selected_names:
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record = self.loader.get_skill_record(name)
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raw_content = self.loader.load_published_skill(name)
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content = strip_frontmatter(raw_content).strip() if raw_content else ""
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if not content:
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continue
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activated_skills.append(
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SkillContext(
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name=name,
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content=content,
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version=record.version if record is not None else "legacy",
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content_hash=record.content_hash or "" if record is not None else "",
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activation_reason="llm_selected",
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tool_hints=list(record.tool_hints) if record is not None else [],
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)
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)
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return activated_skills
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async def _select_skill_names(
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self,
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*,
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task_description: str,
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candidates: list[dict[str, str]],
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provider: LLMProvider,
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model: str,
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thinking_enabled: bool | None = None,
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max_selected: int | None = None,
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selection_stage: str = "final",
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llm_interactions: list[dict[str, Any]] | None = None,
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timeout_seconds: float = 8.0,
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) -> list[str]:
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candidate_summary = self._render_candidates(candidates)
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candidate_names = {item["name"] for item in candidates}
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selection_instruction = (
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f"Return at most {max_selected} names for detailed inspection. "
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if max_selected is not None
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else "Return the final skill names to activate. "
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)
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messages = [
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{
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"role": "system",
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"content": (
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"You select Beaver skills for a single run. "
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"Given a task description and candidate skill information, "
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"return only a JSON array of skill names to activate. "
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"Do not invent names. If nothing matches, return []. "
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f"Selection stage: {selection_stage}. {selection_instruction}"
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),
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},
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{
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"role": "user",
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"content": (
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f"Task description:\n{task_description}\n\n"
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f"Candidate skills:\n{candidate_summary}\n\n"
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"Return only JSON, for example: [\"skill-a\", \"skill-b\"]"
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),
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},
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]
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chat_kwargs: dict[str, Any] = {
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"messages": messages,
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"tools": None,
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"model": model,
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"max_tokens": 256,
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"temperature": 0,
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}
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if thinking_enabled is not None:
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chat_kwargs["thinking_enabled"] = thinking_enabled
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try:
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response = await asyncio.wait_for(provider.chat(**chat_kwargs), timeout=timeout_seconds)
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except Exception:
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return []
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if llm_interactions is not None:
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llm_interactions.append(
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{
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"stage": selection_stage,
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"model": model,
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"messages": messages,
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"response": {
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"content": response.content,
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"finish_reason": response.finish_reason,
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"provider_name": response.provider_name,
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"model": response.model,
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"usage": response.usage,
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},
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}
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)
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if response.finish_reason == "error" or not response.content:
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return []
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parsed = self._parse_selected_names(response.content)
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if not parsed:
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return []
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# 只保留当前候选集中真实存在的 skill 名称，并维持模型输出顺序。
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filtered: list[str] = []
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for name in parsed:
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if name in candidate_names and name not in filtered:
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filtered.append(name)
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return filtered[:max_selected] if max_selected is not None else filtered
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@staticmethod
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def _render_candidates(candidates: list[dict[str, str]]) -> str:
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lines: list[str] = []
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for item in candidates:
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content = item.get("content")
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if content:
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lines.append(
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f"## {item['name']}\n"
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f"Description: {item['description']}\n"
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f"Skill content:\n{content}"
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)
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else:
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lines.append(f"- {item['name']}: {item['description']}")
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return "\n".join(lines)
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def _build_detailed_candidates(
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self,
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*,
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candidates: list[dict[str, str]],
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selected_names: list[str],
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) -> list[dict[str, str]]:
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by_name = {item["name"]: item for item in candidates}
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detailed: list[dict[str, str]] = []
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for name in selected_names:
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candidate = by_name.get(name)
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if candidate is None:
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continue
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raw_content = self.loader.load_published_skill(name)
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content = strip_frontmatter(raw_content).strip() if raw_content else ""
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if len(content) > self.max_candidate_content_chars:
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content = content[: self.max_candidate_content_chars].rstrip() + "\n...[truncated]"
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detailed.append({**candidate, "content": content})
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return detailed
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@staticmethod
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def _parse_selected_names(content: str) -> list[str]:
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cleaned = content.strip()
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if cleaned.startswith("```"):
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lines = cleaned.splitlines()
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if len(lines) >= 3 and lines[0].startswith("```") and lines[-1].startswith("```"):
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cleaned = "\n".join(lines[1:-1]).strip()
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try:
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payload: Any = json.loads(cleaned)
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except json.JSONDecodeError:
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return []
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if isinstance(payload, dict):
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for key in ("skills", "selected_skills", "activated_skills", "selected"):
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value = payload.get(key)
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if isinstance(value, list):
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payload = value
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break
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if not isinstance(payload, list):
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return []
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return [item.strip() for item in payload if isinstance(item, str) and item.strip()]
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