Files
beaver_project/app-instance/backend/tests/unit/test_skill_assembler.py
steven_li 30ab74ffb2 feat(engine): 添加MCP连接管理和工具集成功能
- 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
2026-05-14 09:43:48 +08:00

158 lines
5.0 KiB
Python

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