679 lines
26 KiB
Python
679 lines
26 KiB
Python
from types import SimpleNamespace
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import pytest
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from pydantic import ValidationError
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from app.api import rag_ask_contract
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from app.api.router import api_router
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from app.api.ai_steward_api import router as ai_steward_router
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from app.api.rag_api import routes_query
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from app.core.config import settings
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from app.schemas.rag import RAGAskRequest
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from app.services.adaptive_rag_service.mixin_answer_payload import AdaptiveRAGAnswerPayloadMixin
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def _direct_route_plan(*, goal_kind: str, answer_contract: str) -> dict:
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return {
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"source_strategy": "direct",
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"goal_kind": goal_kind,
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"answer_contract": answer_contract,
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"allowed_executors": ["direct_answer"],
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}
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def test_rag_ask_request_normalizes_contract_fields() -> None:
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assert RAGAskRequest(question="状态怎么样").thinking_mode == "auto"
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req = RAGAskRequest(
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question="状态怎么样",
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asked_via="ai-pmo-process-text",
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message_intent="MIXED",
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mixed_signal_part="新增任务确认流",
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action_mode="read",
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consultant_mode_preference="ADVise",
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)
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assert req.asked_via == "/ai-pmo/process-text"
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assert req.message_intent == "mixed"
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assert req.mixed_signal_part == "新增任务确认流"
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assert req.action_mode == "read"
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assert req.consultant_mode_preference == "advise"
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def test_rag_ask_request_rejects_unknown_asked_via() -> None:
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with pytest.raises(ValidationError):
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RAGAskRequest(
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question="hello",
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asked_via="email",
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)
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def test_build_answer_question_kwargs_maps_fields() -> None:
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request = RAGAskRequest(
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question="请确认任务",
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asked_via="pmflow_manual",
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session_id=33,
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new_session=1,
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thinking_mode="deep",
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action_mode="read_write",
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sync_requirement=0,
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message_intent="mixed",
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mixed_signal_part="新增任务确认流",
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consultant_mode_preference="challenge",
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)
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kwargs = rag_ask_contract.build_answer_question_kwargs(
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request=request,
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current_user=SimpleNamespace(id="77"),
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)
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assert kwargs == {
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"question": "请确认任务",
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"asked_by": 77,
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"tenant_id": None,
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"asked_via": "pmflow_manual",
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"project_id": None,
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"source_chat_id": None,
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"session_id": 33,
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"force_new_session": True,
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"thinking_mode": "deep",
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"action_mode": "read_write",
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"sync_requirement": False,
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"message_intent": "mixed",
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"mixed_signal_part": "新增任务确认流",
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"consultant_mode_preference": "challenge",
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"surface_context": {},
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}
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def test_build_kernel_ingress_kwargs_assigns_unique_source_message_id_for_rag() -> None:
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request = RAGAskRequest(question="现在进展怎么样")
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kwargs_a = rag_ask_contract.build_kernel_ingress_kwargs(
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request=request,
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current_user=SimpleNamespace(id=1, tenant_id=1, username="admin"),
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)
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kwargs_b = rag_ask_contract.build_kernel_ingress_kwargs(
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request=request,
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current_user=SimpleNamespace(id=1, tenant_id=1, username="admin"),
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)
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source_message_id_a = str(kwargs_a["source_message_id"] or "")
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source_message_id_b = str(kwargs_b["source_message_id"] or "")
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assert source_message_id_a.startswith("rag-ask-")
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assert source_message_id_b.startswith("rag-ask-")
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assert source_message_id_a != source_message_id_b
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def test_build_kernel_ingress_kwargs_preserves_explicit_anchor_fields() -> None:
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request = RAGAskRequest(
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question="这个项目怎么样",
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project_id=17,
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source_chat_id="pmflow-project-17",
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)
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kwargs = rag_ask_contract.build_kernel_ingress_kwargs(
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request=request,
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current_user=SimpleNamespace(id=1, tenant_id=1, username="admin"),
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)
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assert kwargs["project_hint_id"] == 17
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assert kwargs["channel_ref"] == "pmflow-project-17"
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assert kwargs["ingress_options"]["rag_request_context"]["project_id"] == 17
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assert kwargs["ingress_options"]["rag_request_context"]["source_chat_id"] == "pmflow-project-17"
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def test_build_kernel_ingress_kwargs_uses_conversation_context_project_binding() -> None:
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request = RAGAskRequest(
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question="把我们的项目产品和宝宝树对比下",
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session_id=524,
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)
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kwargs = rag_ask_contract.build_kernel_ingress_kwargs(
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request=request,
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current_user=SimpleNamespace(id=1, tenant_id=1, username="admin"),
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conversation_context={
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"last_entity_ids": [141],
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"active_workspace": {
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"primary_object_ref": "project:141",
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"primary_object_id": 141,
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"primary_object_title": "童析—学龄前儿童行为特质AI评估助手",
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},
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},
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)
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assert kwargs["ingress_options"]["rag_request_context"]["project_id"] == 141
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assert kwargs["ingress_options"]["rag_request_context"]["active_workspace"]["primary_object_ref"] == "project:141"
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assert kwargs["ingress_options"]["tenant_project_context"]["current_project_id"] == 141
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assert kwargs["ingress_options"]["project_operating_context"]["current_project_id"] == 141
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def test_build_kernel_ingress_kwargs_attaches_project_operating_context() -> None:
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request = RAGAskRequest(
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question="OpenAI Docs 这种能力适不适合我们当前项目？",
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project_id=17,
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source_chat_id="pmflow-project-17",
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asked_via="feishu",
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)
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kwargs = rag_ask_contract.build_kernel_ingress_kwargs(
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request=request,
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current_user=SimpleNamespace(id=1, tenant_id=1, username="admin"),
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)
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packet = kwargs["ingress_options"]["project_operating_context"]
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assert packet["current_project_id"] == 17
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assert packet["current_repo_scope"]
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assert packet["page_anchor"]["project_id"] == 17
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assert packet["fit_scope"] == "current_project_and_repo"
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assert packet["decision_strategy"] == "answer_then_offer_plan"
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def test_build_kernel_ingress_kwargs_attaches_surface_and_tenant_project_context() -> None:
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request = RAGAskRequest(
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question="我今天应该先处理什么？",
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surface_context={
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"surface": "personal_space",
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"workbench": {
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"counts": {"nextActions": 2, "pendingConfirmations": 1, "aiProgress": 1},
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"top_next_actions": [
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{
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"title": "确认项目草案",
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"project": "AI 周报助手",
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"priority": "高",
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"href": "/change-review?projectId=17&changeSetId=cs-1&itemId=ci-1",
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"object_ref": "change_item:ci-1",
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"object_anchor": {
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"object_ref": "change_item:ci-1",
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"object_type": "change_item",
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"object_id": "ci-1",
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"surface": "change_review",
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"project_id": "17",
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"project_title": "AI 周报助手",
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"href": "/change-review?projectId=17&changeSetId=cs-1&itemId=ci-1",
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"parent_object_ref": "project:17",
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},
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}
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],
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},
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"ai_readiness": {"status": "ready", "can_generate_project_draft": True, "can_manage": False},
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},
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)
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kwargs = rag_ask_contract.build_kernel_ingress_kwargs(
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request=request,
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current_user=SimpleNamespace(id=7, tenant_id=3, username="owner"),
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)
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options = kwargs["ingress_options"]
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assert options["surface_context"]["surface"] == "personal_space"
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assert options["tenant_project_context"]["tenant_id"] == 3
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assert options["tenant_project_context"]["surface_context"]["workbench"]["counts"]["nextActions"] == 2
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action = options["tenant_project_context"]["surface_context"]["workbench"]["top_next_actions"][0]
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assert action["object_ref"] == "change_item:ci-1"
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assert action["object_anchor"]["parent_object_ref"] == "project:17"
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assert options["rag_request_context"]["surface_context"]["workbench"]["counts"]["pendingConfirmations"] == 1
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@pytest.mark.asyncio
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async def test_rag_ask_route_forwards_contract_fields(monkeypatch) -> None:
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captured: dict[str, object] = {}
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class FakeKernel:
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def __init__(self, db, tenant_id: int):
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_ = db
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captured["tenant_id"] = tenant_id
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async def process_ingress(self, **kwargs):
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captured["ingress_kwargs"] = kwargs
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return {
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"handled": True,
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"status": "answered",
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"intent": "query.read_only",
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"direct_reply_required": True,
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"direct_reply_text": "ok",
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"rag_result": {"answer": "ok", "session_id": 12, "processing_stage": "qa"},
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}
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monkeypatch.setattr(routes_query, "AIPMOKernelService", FakeKernel)
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request = RAGAskRequest(
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question="现在进展怎么样",
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asked_via="pmflow_manual",
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action_mode="read",
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message_intent="mixed",
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mixed_signal_part="新增任务确认流",
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consultant_mode_preference="clarify",
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)
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current_user = SimpleNamespace(id=101, tenant_id=9)
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response = await routes_query.rag_ask(
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request=request,
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current_user=current_user,
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db=object(),
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tenant_id=9,
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)
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assert response["code"] == 0
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data = response["data"]
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assert data["answer"] == "ok"
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assert data["session_id"] == 12
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ingress_kwargs = captured["ingress_kwargs"]
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assert isinstance(ingress_kwargs, dict)
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assert ingress_kwargs["source_system"] == "pmflow_manual"
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assert ingress_kwargs["message_text"] == "现在进展怎么样"
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rag_request_context = ingress_kwargs["ingress_options"]["rag_request_context"]
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assert rag_request_context["asked_via"] == "pmflow_manual"
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assert rag_request_context["action_mode"] == "read"
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assert rag_request_context["message_intent"] == "mixed"
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assert rag_request_context["mixed_signal_part"] == "新增任务确认流"
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assert rag_request_context["consultant_mode_preference"] == "clarify"
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project_operating_context = ingress_kwargs["ingress_options"]["project_operating_context"]
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assert project_operating_context["current_repo_scope"]
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assert project_operating_context["fit_scope"] == "current_project_and_repo"
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assert project_operating_context["decision_strategy"] == "answer_then_offer_plan"
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def test_ai_steward_router_does_not_expose_legacy_rag_ask_route() -> None:
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route_paths = {str(getattr(route, "path", "")).strip() for route in ai_steward_router.routes}
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assert "/ai-steward/rag/ask" not in route_paths
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def test_api_router_uses_single_prefix_for_rag_and_ai_steward() -> None:
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route_paths = {str(getattr(route, "path", "")).strip() for route in api_router.routes}
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if settings.api_route_profile == "lean" and "knowledge" not in settings.api_optional_route_groups:
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assert "/rag/ask" not in route_paths
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else:
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assert "/rag/ask" in route_paths
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assert "/rag/rag/ask" not in route_paths
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if settings.api_route_profile == "lean" and "ai_control" not in settings.api_optional_route_groups:
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assert "/ai-steward/feedback-center/summary" not in route_paths
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else:
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assert "/ai-steward/feedback-center/summary" in route_paths
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assert "/ai-steward/ai-steward/feedback-center/summary" not in route_paths
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@pytest.mark.asyncio
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async def test_rag_ask_route_uses_shared_contract_builder(monkeypatch) -> None:
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captured_kwargs: list[dict[str, object]] = []
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captured_tenants: list[int] = []
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def fake_build_answer_kwargs(request: RAGAskRequest, current_user) -> dict[str, object]:
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return {
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"question": request.question,
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"asked_by": int(current_user.id),
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"asked_via": "shared-builder",
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}
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monkeypatch.setattr(rag_ask_contract, "build_answer_question_kwargs", fake_build_answer_kwargs)
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monkeypatch.setattr(rag_ask_contract, "resolve_effective_tenant_id", lambda *_args, **_kwargs: 88)
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class FakeKernel:
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def __init__(self, db, tenant_id: int):
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_ = db
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captured_tenants.append(int(tenant_id))
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async def process_ingress(self, **kwargs):
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captured_kwargs.append(dict(kwargs))
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return {"handled": True, "status": "answered", "direct_reply_text": "ok", "rag_result": {"answer": "ok"}}
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monkeypatch.setattr(routes_query, "AIPMOKernelService", FakeKernel)
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response_rag = await routes_query.rag_ask(
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request=RAGAskRequest(question="q1"),
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current_user=SimpleNamespace(id=1, tenant_id=1),
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db=object(),
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tenant_id=1,
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)
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assert response_rag["code"] == 0
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assert captured_tenants == [88]
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assert len(captured_kwargs) == 1
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forwarded = captured_kwargs[0]
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assert str(forwarded["source_message_id"]).startswith("rag-ask-")
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assert forwarded["source_system"] == "pmflow"
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assert forwarded["message_text"] == "q1"
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assert forwarded["sender"] == "PMFlow用户"
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assert forwarded["channel_ref"] is None
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assert forwarded["source_sender_open_id"] is None
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assert forwarded["captured_at"] is None
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assert forwarded["chat_type"] == "manual"
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assert forwarded["project_hint_id"] is None
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assert forwarded["ingress_options"]["rag_request_context"] == {
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"question": "q1",
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"asked_by": 1,
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"asked_via": "shared-builder",
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}
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assert forwarded["ingress_options"]["project_operating_context"]["current_project_id"] is None
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assert forwarded["ingress_options"]["project_operating_context"]["current_repo_scope"]
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assert forwarded["ingress_options"]["project_operating_context"]["fit_scope"] == "current_project_and_repo"
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assert forwarded["ingress_options"]["project_operating_context"]["decision_strategy"] == "answer_then_offer_plan"
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def test_build_rag_response_payload_preserves_rag_fields() -> None:
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payload = rag_ask_contract.build_rag_response_payload(
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ingress_result={
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"status": "answered",
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"intent": "query.read_only",
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"work_item_id": 321,
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"ai_trace_id": "ai-pmo-321",
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"direct_reply_required": True,
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"direct_reply_text": "fallback",
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"rag_result": {
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"answer": "final answer",
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"session_id": 18,
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"sources": [{"source": "snapshot"}],
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"answer_mode": "retrieval_deep",
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"grounding_status": "grounded",
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"policy_version": "gr-v2.1|routing-v3|prompt:feishu_direct",
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"citations": [{"source_type": "doc", "source_id": "doc-1"}],
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"sources_used": [{"source_type": "doc", "source_id": "doc-1"}],
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"evidence_pack": {
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"summary": {
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"conclusion": "final answer",
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"evidence": ["doc-1"],
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"uncertainties": [],
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},
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"tool_trace": [{"tool": "vector_search"}],
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},
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"processing_stage": "qa",
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},
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}
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)
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assert payload["answer"] == "final answer"
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assert payload["session_id"] == 18
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assert payload["work_item_id"] == 321
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assert payload["trace_id"] == "ai-pmo-321"
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assert payload["reply_required"] is True
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assert payload["reply_text"] == "fallback"
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assert payload["answer_mode"] == "retrieval_deep"
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assert payload["grounding_status"] == "grounded"
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assert payload["policy_version"] == "gr-v2.1|routing-v3|prompt:feishu_direct"
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assert payload["citations"] == [{"source_type": "doc", "source_id": "doc-1"}]
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assert payload["sources_used"] == [{"source_type": "doc", "source_id": "doc-1"}]
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assert payload["evidence_pack"]["summary"]["conclusion"] == "final answer"
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assert payload["evidence_pack"]["tool_trace"] == [{"tool": "vector_search"}]
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def test_answer_payload_normalizes_source_registry_fields_into_citations() -> None:
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class _FakeService(AdaptiveRAGAnswerPayloadMixin):
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@staticmethod
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def _to_float(value, default: float) -> float:
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try:
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return float(value)
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except Exception:
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return float(default)
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service = _FakeService()
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citations = service._build_citations_from_sources(
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[
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{
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"source": "pmflow",
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"label": "PMFlow[task#88]",
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"score": 0.91,
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"metadata": {
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"record_type": "task",
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"record_id": 88,
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"project_id": 17,
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"target_url": "/projects/17/tasks/88",
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"updated_at": "2026-04-01T09:30:00+08:00",
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},
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},
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{
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"source": "doc",
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"label": "Document[架构说明]",
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"score": 0.73,
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"metadata": {
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"doc_id": 3,
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"chunk_index": 2,
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"file_url": "https://docs.example.com/spec",
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},
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},
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]
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)
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assert citations == [
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{
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"source_type": "pmflow",
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"source_label": "PMFlow[task#88]",
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"source_id": "task:88",
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"chunk_id": None,
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"target_url": "/projects/17/tasks/88",
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"project_id": "17",
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"updated_at": "2026-04-01T09:30:00+08:00",
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"permission_scope": "project",
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"source_priority": 100,
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"confidence": 0.91,
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},
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{
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"source_type": "doc",
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||
"source_label": "Document[架构说明]",
|
||
"source_id": "3",
|
||
"chunk_id": "2",
|
||
"target_url": "https://docs.example.com/spec",
|
||
"project_id": None,
|
||
"updated_at": None,
|
||
"permission_scope": "tenant",
|
||
"source_priority": 70,
|
||
"confidence": 0.73,
|
||
},
|
||
]
|
||
|
||
assert service._build_sources_used_from_citations(citations) == [
|
||
{
|
||
"source_type": "pmflow",
|
||
"source_label": "PMFlow[task#88]",
|
||
"source_id": "task:88",
|
||
"chunk_id": None,
|
||
"target_url": "/projects/17/tasks/88",
|
||
"project_id": "17",
|
||
"updated_at": "2026-04-01T09:30:00+08:00",
|
||
"permission_scope": "project",
|
||
"source_priority": 100,
|
||
},
|
||
{
|
||
"source_type": "doc",
|
||
"source_label": "Document[架构说明]",
|
||
"source_id": "3",
|
||
"chunk_id": "2",
|
||
"target_url": "https://docs.example.com/spec",
|
||
"project_id": None,
|
||
"updated_at": None,
|
||
"permission_scope": "tenant",
|
||
"source_priority": 70,
|
||
},
|
||
]
|
||
|
||
|
||
def test_build_rag_response_payload_adds_grounding_contract_defaults() -> None:
|
||
payload = rag_ask_contract.build_rag_response_payload(
|
||
ingress_result={
|
||
"status": "answered",
|
||
"intent": "query.read_only",
|
||
"direct_reply_text": "fallback",
|
||
"rag_result": {
|
||
"answer": "final answer",
|
||
},
|
||
}
|
||
)
|
||
|
||
assert payload["answer_mode"] == "retrieval_normal"
|
||
assert payload["grounding_status"] == "partially_grounded"
|
||
assert payload["policy_version"] == "retrieval-v2.1|routing:unknown|prompt:unknown"
|
||
assert payload["citations"] == []
|
||
assert payload["sources_used"] == []
|
||
assert payload["evidence_pack"] == {
|
||
"summary": {
|
||
"conclusion": "final answer",
|
||
"evidence": [],
|
||
"uncertainties": [],
|
||
},
|
||
"tool_trace": [],
|
||
}
|
||
|
||
|
||
def test_build_rag_response_payload_falls_back_to_top_level_consultant_dispatch() -> None:
|
||
consultant_dispatch = {
|
||
"domain": "project",
|
||
"mode": "challenge",
|
||
"risk_level": "high",
|
||
"missing_fields": [],
|
||
"allow_execute": False,
|
||
"next_action": "请先拆分范围，再确认是否继续推进高风险动作。",
|
||
}
|
||
|
||
payload = rag_ask_contract.build_rag_response_payload(
|
||
ingress_result={
|
||
"status": "answered",
|
||
"intent": "project.create",
|
||
"direct_reply_text": "当前建议先拆分范围。",
|
||
"consultant_dispatch": consultant_dispatch,
|
||
"rag_result": {
|
||
"answer": "当前建议先拆分范围。",
|
||
"processing_stage": "qa",
|
||
},
|
||
}
|
||
)
|
||
|
||
assert payload["consultant_dispatch"] == consultant_dispatch
|
||
|
||
|
||
def test_build_rag_response_payload_does_not_inject_fake_answer_text() -> None:
|
||
payload = rag_ask_contract.build_rag_response_payload(
|
||
ingress_result={
|
||
"status": "answered",
|
||
"intent": "query.read_only",
|
||
"direct_reply_text": "",
|
||
"rag_result": {
|
||
"processing_stage": "qa",
|
||
},
|
||
}
|
||
)
|
||
|
||
assert payload["answer"] == ""
|
||
|
||
|
||
def test_customer_direct_answer_handles_greeting_without_route_clarification() -> None:
|
||
payload = rag_ask_contract.build_customer_direct_answer(
|
||
RAGAskRequest(question="hi"),
|
||
route_plan=_direct_route_plan(goal_kind="casual_chat", answer_contract="pmflow_product_help"),
|
||
)
|
||
|
||
assert payload is not None
|
||
assert "外部公开事实" not in payload["reply_text"]
|
||
assert payload["processing_stage"] == "planner_direct"
|
||
|
||
|
||
def test_customer_direct_answer_handles_identity_question_without_internal_retrieval() -> None:
|
||
payload = rag_ask_contract.build_customer_direct_answer(
|
||
RAGAskRequest(question="你是谁"),
|
||
route_plan=_direct_route_plan(goal_kind="pmflow_product_help", answer_contract="pmflow_product_help"),
|
||
)
|
||
|
||
assert payload is not None
|
||
assert payload["reason_code"] == "planner_product_capability"
|
||
assert "我是 PMFlow AI" in payload["answer"]
|
||
assert "系统代码" in payload["answer"]
|
||
assert payload["processing_stage"] == "planner_direct"
|
||
|
||
|
||
@pytest.mark.parametrize(
|
||
"question",
|
||
[
|
||
"这个系统能干啥",
|
||
"你能干嘛",
|
||
"PMFlow 能解决什么问题？",
|
||
"这个平台有什么功能",
|
||
"你能帮我什么",
|
||
],
|
||
)
|
||
def test_customer_direct_answer_handles_product_capability_questions_without_internal_retrieval(question: str) -> None:
|
||
payload = rag_ask_contract.build_customer_direct_answer(
|
||
RAGAskRequest(question=question),
|
||
route_plan=_direct_route_plan(goal_kind="pmflow_product_help", answer_contract="pmflow_product_help"),
|
||
)
|
||
|
||
assert payload is not None
|
||
assert payload["reason_code"] == "planner_product_capability"
|
||
assert "我是 PMFlow AI" in payload["answer"]
|
||
assert "当前租户下的项目" in payload["answer"]
|
||
assert payload["processing_stage"] == "planner_direct"
|
||
|
||
|
||
def test_customer_payload_sanitizes_internal_source_and_prompt_leaks() -> None:
|
||
payload = rag_ask_contract.sanitize_customer_rag_payload(
|
||
{
|
||
"answer": "模型调用失败：name 'PROMPT_RAG_PENDING_CLARIFICATION_SELECTION_SEMANTICS' is not defined",
|
||
"sources": [{"source": "system", "label": "Session State", "score": 0.8, "metadata": {"trace_id": "t1"}}],
|
||
"sources_used": [{"raw_source_label": "Project database"}],
|
||
"tool_trace": [{"tool": "query_route_gateway"}],
|
||
},
|
||
request=RAGAskRequest(question="hi"),
|
||
current_user=SimpleNamespace(id=7, tenant_id=3),
|
||
)
|
||
|
||
assert payload["reply_text"] == "这次回答生成失败，系统没有拿到可展示的业务结论。你可以换一种问法，或稍后重试。"
|
||
assert payload["sources"][0]["label"] == "当前空间状态"
|
||
assert payload["sources_used"][0]["display_label"] == "项目数据"
|
||
assert "tool_trace" not in payload
|
||
assert "trace_id" not in payload
|
||
assert payload["customer_answer_policy"]["visibility"] == "customer"
|
||
|
||
|
||
def test_packet_ad_customer_payload_keeps_project_operating_source_label() -> None:
|
||
payload = rag_ask_contract.sanitize_customer_rag_payload(
|
||
{
|
||
"answer": "当前项目可以继续推进。",
|
||
"reply_text": "当前项目可以继续推进。",
|
||
"sources": [
|
||
{"source": "project_status", "label": "PMFlow 项目执行态证据", "score": 1},
|
||
{"source": "project_object_resolver", "label": "PMFlow 项目对象解析", "score": 1},
|
||
],
|
||
"sources_used": [
|
||
{"source_type": "project_status", "display_label": "PMFlow 项目执行态证据"},
|
||
],
|
||
"customer_evidence_packet": {"answer_type": "execution_guidance", "facts": [], "source_summary": []},
|
||
},
|
||
apply_presentation_contract=False,
|
||
)
|
||
|
||
assert payload["sources"][0]["label"] == "项目运行态证据"
|
||
assert payload["sources"][1]["label"] == "项目数据"
|
||
assert payload["sources_used"][0]["display_label"] == "项目运行态证据"
|
||
assert not any(item["label"] == "PMFlow 上下文" for item in payload["sources"])
|
||
|
||
|
||
@pytest.mark.asyncio
|
||
async def test_rag_ask_route_returns_customer_safe_failure_for_empty_rag_answer(monkeypatch) -> None:
|
||
class FakeKernel:
|
||
def __init__(self, db, tenant_id: int):
|
||
_ = db
|
||
_ = tenant_id
|
||
|
||
async def process_ingress(self, **kwargs):
|
||
_ = kwargs
|
||
return {
|
||
"handled": True,
|
||
"status": "answered",
|
||
"intent": "query.read_only",
|
||
"direct_reply_text": "",
|
||
"rag_result": {
|
||
"processing_stage": "qa",
|
||
},
|
||
}
|
||
|
||
monkeypatch.setattr(routes_query, "AIPMOKernelService", FakeKernel)
|
||
|
||
response = await routes_query.rag_ask(
|
||
request=RAGAskRequest(question="空回答测试"),
|
||
current_user=SimpleNamespace(id=1, tenant_id=1),
|
||
db=object(),
|
||
tenant_id=1,
|
||
)
|
||
|
||
assert getattr(response, "status_code", None) is None
|
||
assert response["code"] == 0
|
||
assert response["message"] == "success"
|
||
assert response["data"]["answer"] == "我现在没有生成足够明确的可展示回答。你可以换一种问法，或补充项目/对象范围后我再查。"
|
||
assert response["data"]["answer_mode"] == "customer_deterministic"
|
||
assert response["data"]["customer_answer_policy"]["visibility"] == "customer"
|
||
assert response["data"]["answer_process_packet"]["steps"] == []
|