499 lines
19 KiB
Python
499 lines
19 KiB
Python
import json
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from types import SimpleNamespace
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import pytest
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from app.api import tasks as tasks_api
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from app.api.sstc_api import tasks_ai_breakdown as sstc_tasks_ai_breakdown_api
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from app.api.sstc_api import tasks_recommend_optimize as sstc_tasks_recommend_optimize_api
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from app.services.llm_service import StructuredExtractionResult
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from app.services.sstc.charter_data import (
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_build_task_breakdown_context,
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_build_task_breakdown_prompt,
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)
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from app.schemas import TaskBreakdownRequest
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def test_task_breakdown_context_flattens_charter_modules() -> None:
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project = SimpleNamespace(
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id=6,
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title="内部知识库升级",
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name="内部知识库升级",
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description="围绕内部知识库问答能力升级",
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status="active",
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assigned_role="backend",
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)
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charter = SimpleNamespace(
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id=55,
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title="内部知识库升级宪章",
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version=2,
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status="aligning",
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content={
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"modules": {
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"module_1": {
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"problem_statement": "减少重复检索",
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"goal_statement": "提升命中率 / 降低重复提问",
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"kpis": "首响 < 3s",
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},
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"module_2": {
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"in_scope": "问答检索，知识补充",
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"out_of_scope": "权限模型重构",
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},
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"module_4": {
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"milestones": "需求冻结\n开发完成",
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},
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"module_5": {
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"risk_register": "需求膨胀; 依赖不清",
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},
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"module_6": {
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"governance_rule": "附件仅作证据引用",
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},
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}
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},
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)
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context = _build_task_breakdown_context(
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description="支持导出知识库内容",
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project=project,
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charter=charter,
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)
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assert context["project"]["title"] == "内部知识库升级"
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assert context["project"]["status"] == "active"
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assert context["charter"]["summary"] == "减少重复检索"
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assert context["charter"]["objectives"] == ["提升命中率", "降低重复提问"]
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assert context["charter"]["in_scope"] == ["问答检索", "知识补充"]
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assert context["charter"]["out_of_scope"] == ["权限模型重构"]
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assert context["charter"]["milestones"] == ["需求冻结", "开发完成"]
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assert "附件仅作证据引用" in context["charter"]["constraints"][0]
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def test_task_breakdown_prompt_embeds_charter_context() -> None:
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prompt = _build_task_breakdown_prompt(
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project_id=6,
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context={
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"project": {
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"id": 6,
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"title": "内部知识库升级",
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"description": "围绕内部知识库问答能力升级",
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"status": "active",
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"assigned_role": "backend",
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},
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"charter": {
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"id": 55,
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"title": "内部知识库升级宪章",
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"version": 2,
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"status": "aligning",
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"summary": "减少重复检索",
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"objectives": ["提升命中率", "降低重复提问"],
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"scope": "知识库检索与问答",
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"in_scope": ["问答检索", "知识补充"],
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"out_of_scope": ["权限模型重构"],
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"success_criteria": ["首响 < 3s"],
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"constraints": ["附件仅作证据引用"],
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"milestones": ["需求冻结", "开发完成"],
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"risks": ["需求膨胀"],
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"signal_ids": [12],
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},
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},
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available_skills="FastAPI, React, LangChain",
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)
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assert "需求宪章与项目上下文" in prompt
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assert "内部知识库升级宪章" in prompt
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assert "FastAPI, React, LangChain" in prompt
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assert "objectives" in prompt
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assert "constraints" in prompt
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@pytest.mark.asyncio
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async def test_general_task_breakdown_route_uses_project_and_charter_context(monkeypatch) -> None:
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captured: dict[str, object] = {}
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async def _fake_load_context(db, *, tenant_id: int, description: str, project_id=None, charter_id=None):
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_ = (db, tenant_id, project_id, charter_id)
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captured["description"] = description
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return {
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"request": {"description": description},
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"project": {
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"id": 6,
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"title": "内部知识库升级",
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"description": "围绕内部知识库问答能力升级",
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"status": "active",
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"assigned_role": "backend",
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},
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"charter": {
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"id": 55,
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"title": "内部知识库升级宪章",
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"version": 2,
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"status": "aligning",
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"summary": "减少重复检索",
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"objectives": ["提升命中率", "降低重复提问"],
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"scope": "知识库检索与问答",
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"in_scope": ["问答检索", "知识补充"],
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"out_of_scope": ["权限模型重构"],
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"success_criteria": ["首响 < 3s"],
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"constraints": ["附件仅作证据引用"],
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"milestones": ["需求冻结", "开发完成"],
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"risks": ["需求膨胀"],
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"signal_ids": [12],
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},
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}
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async def _fake_load_skills(db, *, tenant_id: int):
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_ = (db, tenant_id)
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return "FastAPI, React, LangChain"
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class _FakeLLM:
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async def generate_structured_object(self, prompt: str, **kwargs):
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captured["prompt"] = prompt
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captured["llm_kwargs"] = kwargs
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payload = {
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"tasks": [
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{
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"task_key": "task-1",
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"title": "需求澄清与范围确认",
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"description": "确认导出知识库的范围",
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"type": "ai",
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"estimated_hours": 4,
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"required_skill_names": ["FastAPI"],
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"required_skill_tag_ids": [101],
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"depends_on_task_keys": [],
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"risk_level": "low",
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"risk_notes": "边界明确",
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"confidence": 0.94,
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"evidence_refs": ["charter.objectives.0"],
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},
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{
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"task_key": "task-2",
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"title": "核心方案落地",
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"description": "实现导出与验证流程",
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"type": "backend",
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"estimated_hours": 12,
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"required_skill_names": ["React", "LangChain"],
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"required_skill_tag_ids": [202, 303],
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"depends_on_task_keys": ["task-1"],
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"risk_level": "medium",
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"risk_notes": "依赖范围确认结果",
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"confidence": 0.87,
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"evidence_refs": ["charter.success_criteria.0"],
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},
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],
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"detected_skills": ["FastAPI", "React", "LangChain"],
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}
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return StructuredExtractionResult(
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payload=payload,
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source_field="message.content",
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provider="glm",
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model="qwen-plus",
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raw_text=json.dumps(payload, ensure_ascii=False),
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validation="ok",
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)
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class _FakePlane:
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async def sync_tasks(self, tasks, is_urgent: bool = False):
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_ = is_urgent
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return {"synced_count": len(tasks)}
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monkeypatch.setattr(tasks_api, "_load_task_breakdown_context", _fake_load_context)
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monkeypatch.setattr(tasks_api, "_load_available_skill_names", _fake_load_skills)
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monkeypatch.setattr(tasks_api, "llm_service", _FakeLLM())
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monkeypatch.setattr(tasks_api, "plane_service", _FakePlane())
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result = await tasks_api.breakdown_tasks(
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TaskBreakdownRequest(
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description="支持导出知识库内容",
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project_id=6,
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charter_id=55,
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sync_to_plane=True,
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),
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db=SimpleNamespace(),
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current_user=SimpleNamespace(tenant_id=1, id=1),
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)
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assert "内部知识库升级宪章" in str(captured["prompt"])
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assert "FastAPI, React, LangChain" in str(captured["prompt"])
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assert result["sync_result"]["synced_count"] == 2
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assert result["tasks"][0]["task_key"] == "task-1"
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assert result["tasks"][0]["required_skill_tag_ids"] == [101]
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assert result["tasks"][1]["depends_on_task_keys"] == ["task-1"]
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@pytest.mark.asyncio
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async def test_sstc_task_breakdown_route_persists_business_object_and_payload(monkeypatch) -> None:
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captured: dict[str, object] = {"drafts": []}
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async def _fake_load_context(db, *, tenant_id: int, description: str, project_id=None, charter_id=None):
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_ = (db, tenant_id, project_id, charter_id)
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captured["description"] = description
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return {
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"request": {"description": description},
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"project": {
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"id": 6,
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"title": "内部知识库升级",
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"description": "围绕内部知识库问答能力升级",
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"status": "active",
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"assigned_role": "backend",
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},
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"charter": {
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"id": 55,
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"title": "内部知识库升级宪章",
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"version": 2,
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"status": "aligning",
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"summary": "减少重复检索",
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"objectives": ["提升命中率", "降低重复提问"],
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"scope": "知识库检索与问答",
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"in_scope": ["问答检索", "知识补充"],
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"out_of_scope": ["权限模型重构"],
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"success_criteria": ["首响 < 3s"],
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"constraints": ["附件仅作证据引用"],
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"milestones": ["需求冻结", "开发完成"],
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"risks": ["需求膨胀"],
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"signal_ids": [12],
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},
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}
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async def _fake_load_skills(db, *, tenant_id: int):
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_ = (db, tenant_id)
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return "FastAPI, React, LangChain"
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class _FakeLLM:
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async def generate_structured_object(self, prompt: str, **kwargs):
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captured["prompt"] = prompt
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captured["llm_kwargs"] = kwargs
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payload = {
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"tasks": [
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{
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"task_key": "task-1",
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"title": "需求澄清与范围确认",
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"description": "确认导出知识库的范围",
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"type": "ai",
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"estimated_hours": 4,
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"required_skill_names": ["FastAPI"],
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"required_skill_tag_ids": [101],
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"depends_on_task_keys": [],
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"risk_level": "low",
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"risk_notes": "边界明确",
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"confidence": 0.94,
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"evidence_refs": ["charter.objectives.0"],
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},
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{
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"task_key": "task-2",
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"title": "核心方案落地",
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"description": "实现导出与验证流程",
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"type": "backend",
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"estimated_hours": 12,
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"required_skill_names": ["React", "LangChain"],
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"required_skill_tag_ids": [202, 303],
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"depends_on_task_keys": ["task-1"],
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"risk_level": "medium",
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"risk_notes": "依赖范围确认结果",
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"confidence": 0.87,
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"evidence_refs": ["charter.success_criteria.0"],
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},
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],
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"detected_skills": ["FastAPI", "React", "LangChain"],
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}
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return StructuredExtractionResult(
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payload=payload,
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source_field="message.content",
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provider="glm",
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model="qwen-plus",
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raw_text=json.dumps(payload, ensure_ascii=False),
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validation="ok",
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)
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class _FakeResult:
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def __init__(self, titles=None):
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self._titles = titles or []
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def scalars(self):
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return self
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def all(self):
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return self._titles
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def first(self):
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return self._titles[0] if self._titles else None
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class _FakeDB:
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def __init__(self):
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self.added = []
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self.committed = False
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async def execute(self, stmt):
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_ = stmt
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return _FakeResult([])
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def add(self, value):
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self.added.append(value)
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async def commit(self):
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self.committed = True
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async def refresh(self, obj):
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_ = obj
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return None
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async def _fake_persist_task(db, **kwargs):
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_ = db
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draft = kwargs["draft"]
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captured["drafts"].append(draft)
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return SimpleNamespace(
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id=len(captured["drafts"]),
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title=draft.title,
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description=draft.description,
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priority=draft.priority,
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status=draft.status,
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assignee_id=draft.assignee_id,
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required_skill_tag_ids=list(draft.required_skill_tag_ids or []),
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ai_breakdown_payload=draft.ai_breakdown_payload,
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manual_overrides=draft.manual_overrides,
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)
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async def _fake_ensure_project(service, project_id):
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_ = (service, project_id)
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return SimpleNamespace(
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id=6,
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title="内部知识库升级",
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name="内部知识库升级",
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description="围绕内部知识库问答能力升级",
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status="active",
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assigned_role="backend",
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)
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async def _fake_ensure_charter(db, *, tenant_id: int, project_id: int, charter_id: int):
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_ = (db, tenant_id, project_id, charter_id)
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return SimpleNamespace(
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id=55,
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title="内部知识库升级宪章",
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version=2,
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content={"modules": {}},
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status="aligning",
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)
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def _fake_assert_charter_aligned(charter):
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_ = charter
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return None
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async def _fake_log_operation(*args, **kwargs):
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_ = (args, kwargs)
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return None
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async def _fake_invalidate_system_context(*args, **kwargs):
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_ = (args, kwargs)
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return None
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def _fake_sync_pmflow_rag_async(*args, **kwargs):
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_ = (args, kwargs)
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return None
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monkeypatch.setattr(sstc_tasks_ai_breakdown_api, "_ensure_project", _fake_ensure_project)
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monkeypatch.setattr(sstc_tasks_ai_breakdown_api, "_ensure_charter", _fake_ensure_charter)
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monkeypatch.setattr(sstc_tasks_ai_breakdown_api, "_assert_charter_aligned", _fake_assert_charter_aligned)
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monkeypatch.setattr(sstc_tasks_ai_breakdown_api, "_load_available_skill_names", _fake_load_skills)
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monkeypatch.setattr(sstc_tasks_ai_breakdown_api, "llm_service", _FakeLLM())
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monkeypatch.setattr(sstc_tasks_ai_breakdown_api, "persist_task", _fake_persist_task)
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monkeypatch.setattr(sstc_tasks_ai_breakdown_api, "log_operation", _fake_log_operation)
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monkeypatch.setattr(sstc_tasks_ai_breakdown_api, "_sync_pmflow_rag_async", _fake_sync_pmflow_rag_async)
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monkeypatch.setattr(sstc_tasks_ai_breakdown_api, "context_cache_service", SimpleNamespace(invalidate_system_context=_fake_invalidate_system_context))
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result = await sstc_tasks_ai_breakdown_api.ai_breakdown_tasks(
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project_id="6",
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charter_id="55",
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db=_FakeDB(),
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current_user=SimpleNamespace(tenant_id=1, id=1, role="ADMIN"),
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permission_user=SimpleNamespace(),
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)
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assert "内部知识库升级宪章" in str(captured["prompt"])
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assert len(captured["drafts"]) == 2
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assert captured["drafts"][0].required_skill_tag_ids == [101]
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assert captured["drafts"][0].ai_breakdown_payload["tasks"][0]["task_key"] == "task-1"
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assert captured["drafts"][0].manual_overrides == {}
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assert result["data"]["created_count"] == 2
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assert result["data"]["items"][0]["title"] == "需求澄清与范围确认"
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@pytest.mark.asyncio
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async def test_recommend_assignee_route_uses_skill_tag_ids_semantically(monkeypatch) -> None:
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captured: dict[str, object] = {}
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class _FakeTask:
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id = 77
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title = "导出知识库"
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description = "支持导出知识库内容"
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project_id = 6
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required_skill_tag_ids = [101, 202]
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async def _fake_ensure_project(service, project_id):
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_ = (service, project_id)
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return None
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async def _fake_ensure_task(db, *, tenant_id: int, project_id: int, task_id: int):
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_ = (db, tenant_id, project_id, task_id)
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return _FakeTask()
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async def _fake_recommend_personnel(*, db, tenant_id: int, required_skills, task_description=None, task_id=None, limit=5):
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_ = (db, tenant_id, task_description, task_id, limit)
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captured["required_skills"] = list(required_skills)
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return {
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"task_id": task_id,
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"task_description": task_description,
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"required_skills": list(required_skills),
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"recommendations": [
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{
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"user_id": "9",
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"user_name": "Ada",
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"match_score": 91,
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"reason": "strong match",
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"skill_match_count": 2,
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"avg_proficiency": 88,
|
|
"workload_rate": 45,
|
|
"recommended_role": "backend",
|
|
}
|
|
],
|
|
"total_count": 1,
|
|
}
|
|
|
|
class _FakeResult:
|
|
def __init__(self, values):
|
|
self._values = values
|
|
|
|
def scalars(self):
|
|
return self
|
|
|
|
def all(self):
|
|
return self._values
|
|
|
|
async def _fake_ensure_project(service, project_id):
|
|
_ = (service, project_id)
|
|
return SimpleNamespace(id=6)
|
|
|
|
async def _fake_ensure_task(db, *, tenant_id: int, project_id: int, task_id: int):
|
|
_ = (db, tenant_id, project_id, task_id)
|
|
return _FakeTask()
|
|
|
|
class _FakeDB:
|
|
async def execute(self, stmt):
|
|
sql = str(stmt)
|
|
if "user_projects" in sql.lower():
|
|
return _FakeResult([9])
|
|
return _FakeResult([9])
|
|
|
|
monkeypatch.setattr(sstc_tasks_recommend_optimize_api, "_ensure_project", _fake_ensure_project)
|
|
monkeypatch.setattr(sstc_tasks_recommend_optimize_api, "_ensure_task", _fake_ensure_task)
|
|
monkeypatch.setattr(sstc_tasks_recommend_optimize_api, "recommend_personnel", _fake_recommend_personnel)
|
|
|
|
result = await sstc_tasks_recommend_optimize_api.recommend_assignee_for_task(
|
|
project_id="6",
|
|
task_id="77",
|
|
db=_FakeDB(),
|
|
current_user=SimpleNamespace(tenant_id=1, id=1),
|
|
permission_user=SimpleNamespace(),
|
|
)
|
|
|
|
assert captured["required_skills"] == [101, 202]
|
|
assert result["data"]["recommendations"][0]["user_id"] == "9"
|
|
assert result["data"]["total_count"] == 1
|