steven_li
30ab74ffb2
- 集成MCP连接管理器，支持MCP服务器连接 - 添加多种内置工具：ClarifyTool、CronTool、DelegateTool、ExecuteCodeTool、 PatchFileTool、ProcessTool、SendMessageTool、SpawnTool、TerminalTool、 TodoTool、WebFetchTool、WebSearchTool、WriteFileTool等 - 实现工具注册和装配功能 - 添加技能选择上下文参数 - 支持思考模式控制参数thinking_enabled feat(coordinator): 重构任务执行计划器参数命名 - 将learning_candidate_enabled重命名为allow_candidate_generation - 更新TeamGraphScheduler中的参数传递 - 修改LocalAgentRunner中的相关参数处理 - 更新README文档中的相应描述 refactor(context): 标准化工具调用参数格式 - 添加_json导入用于参数序列化 - 实现_provider_tool_calls方法标准化OpenAI兼容的工具调用载荷 - 修复工具调用中参数非字符串类型的序列化问题 refactor(session): 优化消息历史记录过滤逻辑 - 修改get_messages_as_conversation为基于运行状态过滤消息 - 排除未完成、失败或错误结束的运行记录 - 改进对话历史的可见性控制机制 fix(store): 修复FTS索引重建逻辑 - 添加异常处理防止FTS索引创建失败 - 实现_rebuild_fts_index方法重新构建全文搜索索引 - 优化索引触发器和表的维护流程
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"))
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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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