steven_li
8a12c30141
新增内部Task系统，包括验证、反馈门控机制，实现自动质量验证 (通过率>=0.75)和用户反馈闭环(satisfied/revise/abandon)。 实现Agent Team v1协调器，支持sequence/parallel/dag执行策略， sub-agent复用主AgentLoop，每个run使用独立memory snapshot。 建立Skill学习pipeline，包含draft/审核/发布/回滚完整生命周期， 通过Task验证通过且用户满意才生成学习候选。 重构目录结构，移除third_party依赖，建立统一engine内核， 所有agent共享运行时基础组件。 更新ContextBuilder清理provider消息字段，增强SkillContext版本管理， 集成TaskExecutionPlanner和TaskSkillResolver实现技能解析机制。
108 lines
3.8 KiB
Python
108 lines
3.8 KiB
Python
"""Task-driven tool assembler.
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这层和 SkillAssembler 的位置类似：它不执行工具，只决定本轮 run 应该把哪些
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tool schema 暴露给模型。
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"""
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from __future__ import annotations
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from collections.abc import Sequence
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from typing import TYPE_CHECKING
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from beaver.engine.context import SkillContext
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from beaver.foundation.embedding import EmbeddingRetriever
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from beaver.tools.base import ToolSpec
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from beaver.tools.registry import ToolRegistry
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if TYPE_CHECKING:
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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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class ToolAssembler:
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"""Use skill hints and embedding retrieval to select run-scoped tools."""
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def __init__(
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self,
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*,
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retriever: EmbeddingRetriever | None = None,
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always_tool_names: Sequence[str] | None = None,
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) -> None:
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self.retriever = retriever or EmbeddingRetriever()
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self.always_tool_names = tuple(always_tool_names or ("memory", "session_search", "skill_view"))
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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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registry: ToolRegistry,
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skills_loader: SkillsLoader | None = None,
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activated_skills: Sequence[SkillContext] | None = None,
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embedding_runtime: ProviderRuntime | None = None,
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top_k: int = 10,
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) -> list[ToolSpec]:
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"""Return selected tool specs for the current run.
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Selection order is intentionally deterministic:
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1. always tools from config/spec
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2. tools explicitly declared by activated skills
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3. embedding top-k tools for the task
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"""
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selected: list[ToolSpec] = []
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selected_names: set[str] = set()
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def add_specs(specs: Sequence[ToolSpec]) -> None:
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for spec in specs:
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if spec.name in selected_names:
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continue
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selected.append(spec)
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selected_names.add(spec.name)
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add_specs(registry.list_always_specs())
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add_specs(registry.get_specs(self.always_tool_names))
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skill_tool_names = self._collect_skill_tool_names(
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skills_loader=skills_loader,
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activated_skills=activated_skills or (),
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)
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add_specs(registry.get_specs(skill_tool_names))
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candidates = [
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spec.to_embedding_candidate()
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for spec in registry.list_specs()
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if spec.name not in selected_names
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]
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retrieved = 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=top_k,
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)
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add_specs(registry.get_specs([item["name"] for item in retrieved]))
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return selected
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@staticmethod
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def _collect_skill_tool_names(
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*,
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skills_loader: SkillsLoader | None,
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activated_skills: Sequence[SkillContext],
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) -> list[str]:
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if skills_loader is None or not activated_skills:
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return []
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result: list[str] = []
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for skill in activated_skills:
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names = list(skill.tool_hints) if getattr(skill, "tool_hints", None) else skills_loader.get_skill_tool_hints(skill.name)
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for name in names:
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if name not in result:
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result.append(name)
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return result
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