110 lines
4.5 KiB
Python
110 lines
4.5 KiB
Python
from __future__ import annotations
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from types import SimpleNamespace
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import pytest
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from fastapi import FastAPI
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from httpx import ASGITransport, AsyncClient
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from app.api.ai_steward_api import routes_capture
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from app.api.ai_steward_api.schemas import ProjectFromTextResponse
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from app.core.database import get_db
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from app.dependencies.auth import get_current_user
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from app.dependencies.tenant import get_tenant_id
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from app.services.ai_steward_service import AIStewardService
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@pytest.mark.asyncio
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async def test_project_from_text_returns_enveloped_signal_sources(monkeypatch) -> None:
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async def fake_create_project_from_natural_language(self, **kwargs): # noqa: ANN001
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_ = (self, kwargs)
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return {
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"project": {"id": "11", "name": "AI 客服", "title": "AI 客服平台", "description": "demo", "status": "active"},
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"signal": {"id": "21", "content": "main signal", "impact_score": 0.91},
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"charter": {"id": "31", "title": "宪章", "content": {"summary": "demo"}},
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"tasks": [],
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"risks": [],
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"analysis": {
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"project_name": "AI 客服",
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"project_title": "AI 客服平台",
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"tech_stack": ["FastAPI", "React"],
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"timeline": "3 周",
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"primary_role": "backend",
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"requirement_items": [
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{
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"section": "核心痛点",
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"title": "内容本地化",
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"description": "翻译公司贵、慢、不地道",
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}
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],
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},
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"signal_sources": [
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{
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"id": "101",
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"source_kind": "custom",
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"source_name": "核心痛点 - 内容本地化",
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"source_identifier": "custom:core-pain-content-localization",
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"display_source_kind": "自定义来源",
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"technical_identifier": "custom:core-pain-content-localization",
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"description": "翻译公司贵、慢、不地道",
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"section": "核心痛点",
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}
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],
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}
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async def _fake_get_db():
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yield object()
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monkeypatch.setattr(AIStewardService, "create_project_from_natural_language", fake_create_project_from_natural_language)
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app = FastAPI()
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app.include_router(routes_capture.router)
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app.dependency_overrides[get_db] = _fake_get_db
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app.dependency_overrides[get_tenant_id] = lambda: 1
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app.dependency_overrides[get_current_user] = lambda: SimpleNamespace(
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id=9,
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tenant_id=1,
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name="管理员",
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username="admin",
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role="ADMIN",
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)
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transport = ASGITransport(app=app)
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async with AsyncClient(transport=transport, base_url="http://test") as client:
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response = await client.post("/ai-steward/project-from-text", json={"input_text": "我们要做一个 AI 客服工具"})
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assert response.status_code == 200
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payload = response.json()
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assert payload["code"] == 0
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assert payload["message"] == "success"
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assert payload["data"]["project"]["id"] == "11"
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assert payload["data"]["signal_sources"][0]["source_name"] == "核心痛点 - 内容本地化"
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assert payload["data"]["signal_sources"][0]["display_source_kind"] == "自定义来源"
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assert payload["data"]["signal_sources"][0]["technical_identifier"] == "custom:core-pain-content-localization"
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def test_project_from_text_response_schema_exposes_signal_source_display_fields() -> None:
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schema = ProjectFromTextResponse.model_json_schema()
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signal_source = schema["$defs"]["ProjectFromTextSignalSource"]
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assert "display_source_kind" in signal_source["properties"]
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assert "technical_identifier" in signal_source["properties"]
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@pytest.mark.asyncio
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async def test_project_requirement_analysis_uses_canonical_function_name(monkeypatch) -> None:
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service = AIStewardService(object())
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captured: dict[str, object] = {}
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async def fake_generate(prompt: str, **kwargs): # noqa: ANN001
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_ = prompt
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captured["function_name"] = kwargs.get("function_name")
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return '{"project_title":"AI 客服","requirement_items":[]}'
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monkeypatch.setattr(service.llm, "generate", fake_generate)
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analysis = await service._analyze_project_input("请创建一个 AI 客服项目", tenant_id=1)
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assert analysis["project_title"] == "AI 客服"
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assert captured["function_name"] == "project_requirement_extraction"
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