- 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
158 lines
5.0 KiB
Python
158 lines
5.0 KiB
Python
from __future__ import annotations
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import asyncio
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from types import SimpleNamespace
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from beaver.engine.providers.base import LLMProvider, LLMResponse
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from beaver.skills.assembler.task_assembler import SkillAssembler
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class RecordingProvider(LLMProvider):
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def __init__(self) -> None:
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super().__init__()
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self.thinking_enabled: bool | None = None
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async def chat(
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self,
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messages: list[dict],
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tools: list[dict] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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thinking_enabled: bool | None = None,
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) -> LLMResponse:
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self.thinking_enabled = thinking_enabled
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return LLMResponse(content='["daily-news"]', provider_name="stub", model="stub-model")
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def get_default_model(self) -> str:
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return "stub-model"
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class SequencedProvider(LLMProvider):
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def __init__(self, responses: list[str]) -> None:
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super().__init__()
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self.responses = list(responses)
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self.messages: list[list[dict]] = []
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async def chat(
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self,
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messages: list[dict],
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tools: list[dict] | None = None,
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model: str | None = None,
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max_tokens: int = 4096,
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temperature: float = 0.7,
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thinking_enabled: bool | None = None,
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) -> LLMResponse:
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self.messages.append(messages)
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content = self.responses.pop(0)
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return LLMResponse(content=content, provider_name="stub", model="stub-model")
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def get_default_model(self) -> str:
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return "stub-model"
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class StaticRetriever:
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async def retrieve(self, **kwargs):
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return kwargs["candidates"][: kwargs["top_k"]]
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class LoaderWithFullSkill:
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def build_selection_candidates(self) -> list[dict[str, str]]:
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return [
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{
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"name": "docker-debug",
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"description": "General container tips.",
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"version": "v1",
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"content_hash": "abc",
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}
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]
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def load_published_skill(self, name: str) -> str | None:
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if name != "docker-debug":
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return None
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return """---
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description: General container tips.
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tools:
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- search_files
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---
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# Docker Debug
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Use this skill when doing Docker log triage and container failure analysis.
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"""
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def get_skill_record(self, name: str):
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return SimpleNamespace(version="v1", content_hash="abc", tool_hints=["search_files"])
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def test_skill_selection_receives_thinking_mode() -> None:
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provider = RecordingProvider()
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assembler = SkillAssembler(loader=SimpleNamespace())
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selected = asyncio.run(
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assembler._select_skill_names(
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task_description="summarize daily news",
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candidates=[{"name": "daily-news", "description": "Summarize news"}],
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provider=provider,
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model="Qwen3.6-35B",
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thinking_enabled=False,
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)
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)
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assert selected == ["daily-news"]
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assert provider.thinking_enabled is False
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def test_skill_assembler_loads_detail_directly_for_small_candidate_sets() -> None:
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provider = SequencedProvider(['["docker-debug"]'])
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assembler = SkillAssembler(loader=LoaderWithFullSkill(), retriever=StaticRetriever())
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result = asyncio.run(
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assembler.assemble(
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task_description="debug a failing Docker container",
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provider=provider,
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model="stub-model",
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)
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)
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assert [skill.name for skill in result.activated_skills] == ["docker-debug"]
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assert result.activated_skills[0].tool_hints == ["search_files"]
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assert [item["stage"] for item in result.llm_interactions] == ["final"]
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assert len(provider.messages) == 1
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first_user_prompt = provider.messages[0][1]["content"]
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assert "Use this skill when doing Docker log triage" in first_user_prompt
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def test_skill_assembler_shortlists_before_loading_detail_for_large_candidate_sets() -> None:
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provider = SequencedProvider(['["docker-debug"]', '["docker-debug"]'])
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loader = LoaderWithFullSkill()
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original_candidates = loader.build_selection_candidates
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loader.build_selection_candidates = lambda: [
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*original_candidates(),
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{
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"name": "other-skill",
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"description": "Other workflow.",
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"version": "v1",
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"content_hash": "def",
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},
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]
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assembler = SkillAssembler(
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loader=loader,
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retriever=StaticRetriever(),
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max_detailed_candidates=1,
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)
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result = asyncio.run(
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assembler.assemble(
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task_description="debug a failing Docker container",
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provider=provider,
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model="stub-model",
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)
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)
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assert [skill.name for skill in result.activated_skills] == ["docker-debug"]
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assert [item["stage"] for item in result.llm_interactions] == ["shortlist", "final"]
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assert len(provider.messages) == 2
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assert "Use this skill when doing Docker log triage" not in provider.messages[0][1]["content"]
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assert "Use this skill when doing Docker log triage" in provider.messages[1][1]["content"]
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