Files
beaver_project/app-instance/backend/tests/unit/test_task_skill_resolver.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

177 lines
6.1 KiB
Python

from __future__ import annotations
import asyncio
from pathlib import Path
from types import SimpleNamespace
from beaver.coordinator import AgentDescriptor, ExecutionGraph, ExecutionNode
from beaver.engine.context import SkillContext
from beaver.engine.providers.base import LLMProvider, LLMResponse
from beaver.engine.providers.factory import ProviderBundle
from beaver.skills.drafts import DraftService
from beaver.skills.learning import EphemeralGuidanceSynthesizer
from beaver.skills.publisher import SkillPublisher
from beaver.skills.reviews import ReviewService
from beaver.skills.specs import SkillSpecStore
from beaver.skills import SkillsLoader
from beaver.tasks import TaskRecord, TaskSkillResolver
class RecordingProvider(LLMProvider):
def __init__(self, responses: list[str]) -> None:
super().__init__()
self.responses = list(responses)
self.calls: 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,
) -> LLMResponse:
self.calls.append(messages)
content = self.responses.pop(0) if self.responses else "[]"
return LLMResponse(content=content, finish_reason="stop", provider_name="stub", model="stub-model")
def get_default_model(self) -> str:
return "stub-model"
def _bundle(provider: RecordingProvider) -> ProviderBundle:
return ProviderBundle(
main_runtime=SimpleNamespace(model="stub-model", provider_name="stub"),
main_provider=provider,
)
def _task() -> TaskRecord:
return TaskRecord(
task_id="task-1",
session_id="session-1",
description="review api compatibility",
goal="review api compatibility",
constraints=[],
priority=0,
status="open",
creator="test",
created_at="now",
updated_at="now",
)
def _publish_skill(workspace: Path, *, skill_name: str) -> None:
store = SkillSpecStore(workspace)
draft = DraftService(store).create_new_skill_draft(
skill_name=skill_name,
proposed_content="# API Contract Review\n\nCheck schema compatibility and breaking changes.",
proposed_frontmatter={"description": "API contract compatibility review", "tools": []},
created_by="tester",
reason="test",
)
ReviewService(store).approve(skill_name, draft.draft_id, reviewer="tester")
SkillPublisher(store).publish(skill_name, draft.draft_id, publisher="tester")
def test_task_skill_resolver_pins_matching_published_skill(tmp_path: Path) -> None:
_publish_skill(tmp_path, skill_name="api-contract-review")
provider = RecordingProvider(['["api-contract-review"]'])
resolver = TaskSkillResolver(
skills_loader=SkillsLoader(tmp_path),
draft_service=DraftService(SkillSpecStore(tmp_path)),
)
graph = ExecutionGraph(
strategy="sequence",
nodes=[
ExecutionNode(
"api_review",
"review API compatibility",
AgentDescriptor(
name="api_review",
metadata={
"skill_query": "API contract compatibility review",
"required_capabilities": ["schema compatibility"],
},
),
)
],
)
resolved, reports = asyncio.run(
resolver.resolve_graph(
graph,
task=_task(),
user_message="review api",
attempt_index=1,
provider_bundle=_bundle(provider),
)
)
assert resolved.nodes[0].agent.name == "api_review"
assert resolved.nodes[0].agent.role == ""
assert resolved.nodes[0].inherited_pinned_skills == ["api-contract-review"]
assert resolved.nodes[0].inherited_pinned_skill_contexts == []
assert reports[0].selected_skill_names == ["api-contract-review"]
assert reports[0].ephemeral_used is False
def test_task_skill_resolver_generates_ephemeral_guidance_when_missing(tmp_path: Path) -> None:
provider = RecordingProvider(
[
"""
{
"guidance_name": "api-compatibility-review",
"description": "Review API compatibility",
"content": "# API Compatibility Review\\n\\nCheck schema compatibility.",
"tags": ["api", "review"]
}
"""
]
)
store = SkillSpecStore(tmp_path)
resolver = TaskSkillResolver(
skills_loader=SkillsLoader(tmp_path),
draft_service=DraftService(store),
missing_skill_synthesizer=EphemeralGuidanceSynthesizer(),
)
graph = ExecutionGraph(
strategy="sequence",
nodes=[
ExecutionNode(
"api_review",
"review API compatibility",
AgentDescriptor(
name="api_review",
metadata={
"skill_query": "API compatibility review",
"required_capabilities": ["schema compatibility"],
},
),
)
],
)
resolved, reports = asyncio.run(
resolver.resolve_graph(
graph,
task=_task(),
user_message="review api",
attempt_index=1,
provider_bundle=_bundle(provider),
)
)
drafts = store.list_drafts("api-compatibility-review")
assert drafts == []
assert store.list_published_skill_names() == []
assert resolved.nodes[0].inherited_pinned_skills == []
assert len(resolved.nodes[0].inherited_pinned_skill_contexts) == 1
context: SkillContext = resolved.nodes[0].inherited_pinned_skill_contexts[0]
assert context.name == "ephemeral:api-compatibility-review"
assert context.version.startswith("ephemeral:eg_")
assert context.activation_reason == "ephemeral_guidance"
assert reports[0].ephemeral_guidance_id is not None
assert reports[0].ephemeral_guidance_name == "api-compatibility-review"
assert reports[0].ephemeral_used is True