kx_guo
1d58bb8913
- 统一供应商异常统计为近3年数据口径 - 以近3年异常记录数作为合作频次代理值并明确提示 - 实现 A/B 缺陷占比与 S/A/B/C 层级判定 - 按供应商层级输出正常合作、加强巡检、驻厂及备选供应商等管理建议 - 完善风险卡片和供应商卡片的频次、层级、建议展示 - 补充供应商画像接口、测试和历史会话卡片兼容处理
317 lines
11 KiB
Python
317 lines
11 KiB
Python
from datetime import UTC, datetime
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import pytest
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from app.models.entities import Conversation, ConversationMessage
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from app.schemas.risk_prediction import SupplierFrequency
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from app.services.conversations import ConversationService
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def test_conversation_message_data_reads_persisted_risk_result() -> None:
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message = ConversationMessage(
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id=10,
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conversation_id=3,
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role="assistant",
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content="已完成风险预估。",
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structured_data={
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"riskResult": {
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"subjectId": "软管新品",
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"score": 82,
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"level": "low",
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"modelVersion": "baseline-safety-v2",
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"factors": [],
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}
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},
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created_at=datetime.now(UTC),
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)
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result = ConversationService._message(message)
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assert result.role == "assistant"
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assert result.risk_result is not None
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assert result.risk_result.score == 82
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assert result.risk_result.level.value == "low"
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def test_conversation_message_data_reads_persisted_semantic_and_response_card() -> None:
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message = ConversationMessage(
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id=11,
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conversation_id=3,
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role="assistant",
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content="已完成分析。",
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structured_data={
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"semantic": {"intent": "solution", "category": "洗发水"},
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"responseCard": {
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"type": "solution",
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"title": "AI 案例推理生成",
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"similarCases": [{"case_id": 101, "similarity": 0.82}],
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"suggestions": ["完成包材相容性测试"],
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},
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},
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created_at=datetime.now(UTC),
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)
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result = ConversationService._message(message)
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assert result.semantic == {"intent": "solution", "category": "洗发水"}
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assert result.response_card is not None
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assert result.response_card["similarCases"][0]["case_id"] == 101
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assert result.response_card["suggestions"] == ["完成包材相容性测试"]
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def test_conversation_data_includes_all_messages() -> None:
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conversation = Conversation(
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id=3,
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user_id=1,
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title="软管新品评估",
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created_at=datetime.now(UTC),
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updated_at=datetime.now(UTC),
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messages=[
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ConversationMessage(
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id=1,
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conversation_id=3,
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role="user",
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content="评估软管新品",
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created_at=datetime.now(UTC),
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),
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ConversationMessage(
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id=2,
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conversation_id=3,
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role="assistant",
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content="已完成分析",
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created_at=datetime.now(UTC),
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),
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],
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)
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result = ConversationService._data(conversation)
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assert result.message_count == 2
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assert [message.role for message in result.messages] == ["user", "assistant"]
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def test_only_explicit_risk_requests_generate_risk_cards() -> None:
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assert ConversationService._is_risk_request(
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"你好",
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{"intent": "greet", "risk_requested": False},
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) is False
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assert ConversationService._is_risk_request(
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"请只回复：AI 连通成功。",
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{"intent": "general", "risk_requested": False},
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) is False
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assert ConversationService._is_risk_request(
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"请评估这个产品的风险",
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{"intent": "assessment", "risk_requested": True},
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) is True
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def test_legacy_general_semantics_hide_old_risk_card() -> None:
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messages = [
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ConversationMessage(
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id=1,
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conversation_id=1,
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role="user",
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content="你好",
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created_at=datetime.now(UTC),
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),
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ConversationMessage(
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id=2,
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conversation_id=1,
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role="assistant",
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content="你好，我可以帮你做什么？",
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structured_data={
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"semantic": {"intent": "general"},
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"riskResult": {
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"subjectId": "误判",
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"score": 61,
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"level": "moderately_low",
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"modelVersion": "baseline-safety-v2",
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"factors": [],
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},
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},
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created_at=datetime.now(UTC),
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),
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]
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assert ConversationService._risk_result_for_message(messages, 1) is None
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assert ConversationService._is_risk_request(
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"我要做一款脱毛膏新品，100g，PE软管包装，加工厂准备使用瑞沣",
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{"intent": "general", "risk_requested": False},
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) is True
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def test_product_assessment_semantics_override_nlu_miss() -> None:
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normalized = ConversationService._normalize_semantics(
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"我要做一款脱毛膏新品，100g，PE软管包装，加工厂准备使用瑞沣",
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{"intent": "general", "risk_requested": False, "category": "脱毛膏"},
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)
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assert normalized["intent"] == "assessment"
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assert normalized["risk_requested"] is True
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def test_short_known_supplier_follow_up_is_routed_to_supplier_card() -> None:
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assert ConversationService._infer_supplier_follow_up(
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"佳亿",
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["佳亿", "祥印", "瑞沣"],
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) == "佳亿"
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assert ConversationService._infer_supplier_follow_up(
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"这个供应商",
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["佳亿", "祥印"],
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) is None
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def test_unknown_standalone_name_is_treated_as_new_supplier() -> None:
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assert ConversationService._infer_unknown_supplier_follow_up("德盛") == "德盛"
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assert ConversationService._infer_unknown_supplier_follow_up("新供应商A") is None
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assert ConversationService._infer_unknown_supplier_follow_up("测试供应商") is None
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assert ConversationService._infer_unknown_supplier_follow_up("新供应商B") is None
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assert ConversationService._infer_unknown_supplier_follow_up("洗发水") is None
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assert ConversationService._infer_unknown_supplier_follow_up("帮我查德盛") is None
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assert ConversationService._response_type("测试供应商", {"intent": "supplier"}) == "general"
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assert ConversationService._response_type("新供应商A", {"intent": "supplier"}) == "general"
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@pytest.mark.parametrize(
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("count", "a_count", "b_count", "expected"),
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[
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(0, 0, 0, "新供应商"),
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(12, 0, 1, "S级 · 战略"),
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(40, 0, 4, "A级 · 优质"),
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(70, 0, 10, "B级 · 关注"),
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(40, 0, 16, "B级 · 关注"),
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(10, 1, 0, "C级 · 高风险"),
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(121, 0, 0, "C级 · 高风险"),
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],
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)
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def test_supplier_tier_uses_prd_frequency_and_defect_rules(
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count: int,
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a_count: int,
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b_count: int,
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expected: str,
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) -> None:
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assert ConversationService._supplier_tier(
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count,
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a_count=a_count,
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b_count=b_count,
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) == expected
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def test_supplier_management_suggestions_follow_prd_actions() -> None:
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assert ConversationService._supplier_management_suggestions("B级 · 关注") == [
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"加强巡检",
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"新品打样加严",
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]
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assert ConversationService._supplier_management_suggestions("新供应商") == [
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"无历史数据",
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"首单全检",
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"安排驻厂巡检",
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]
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@pytest.mark.asyncio
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async def test_supplier_card_uses_prd_proxy_frequency_and_tier_advice(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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service = object.__new__(ConversationService)
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async def history_stats(*_args, **_kwargs):
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return 70, 0, 10
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async def top_anomaly_types(*_args, **_kwargs):
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return [{"name": "泵头异常", "count": 12}]
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monkeypatch.setattr(service, "_supplier_history_stats", history_stats)
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monkeypatch.setattr(service, "_top_anomaly_types", top_anomaly_types)
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result = await service._supplier_card(
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"祥印供应商风险怎么样",
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{"supplier": "祥印"},
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)
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assert result["anomalyCount"] == 70
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assert result["cooperationCount"] == 70
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assert result["frequencyCoefficient"] == 1.21
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assert result["tier"] == "B级 · 关注"
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assert result["suggestions"] == ["加强巡检", "新品打样加严"]
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assert result["frequencyBasis"] == "近3年异常记录数"
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assert result["dataAvailable"] is True
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def test_new_supplier_frequency_serializes_zero_proxy_count() -> None:
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result = SupplierFrequency(
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supplier_name="新供应商",
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cooperation_count=0,
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anomaly_count=0,
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tier="新供应商",
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suggestion="无历史数据；首单全检；安排驻厂巡检",
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note="合作次数为近3年异常记录数代理值，不代表真实采购订单合作次数。",
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).model_dump(by_alias=True)
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assert result["cooperationCount"] == 0
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assert result["anomalyCount"] == 0
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assert result["frequencyBasis"] == "近3年异常记录数"
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def test_unspecified_supplier_returns_displayable_no_data_frequency_card() -> None:
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result = SupplierFrequency(
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note="未指定供应商，暂无供应商频次分析。",
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frequency_basis="未提供供应商",
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).model_dump(by_alias=True)
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assert result["dataAvailable"] is False
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assert result["alpha"] is None
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assert result["note"] == "未指定供应商，暂无供应商频次分析。"
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def test_similar_cases_are_typed_before_risk_result_serialization() -> None:
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from app.schemas.risk_prediction import RiskAssessmentResult, SimilarCase
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result = RiskAssessmentResult(
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subject_id="test",
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score=52,
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level="medium",
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model_version="test",
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factors=[],
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similar_cases=[SimilarCase(case_id=1, similarity=0.75)],
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)
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payload = result.model_dump(mode="json")
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assert payload["similar_cases"][0]["case_id"] == 1
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@pytest.mark.asyncio
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async def test_any_product_without_history_uses_solution_card(monkeypatch: pytest.MonkeyPatch) -> None:
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service = object.__new__(ConversationService)
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async def no_history(_: str) -> int:
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return 0
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monkeypatch.setattr(service, "_count_category_anomalies", no_history)
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response_type = ConversationService._response_type(
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"评估防晒霜新品，50g，软管包装",
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{"intent": "assessment", "risk_requested": True, "category": "防晒霜"},
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)
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result = await service._use_solution_card_without_history(
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response_type,
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"评估防晒霜新品，50g，软管包装",
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{"intent": "assessment", "risk_requested": True, "category": "防晒霜"},
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)
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assert response_type == "risk_assessment"
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assert result == "solution"
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@pytest.mark.asyncio
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async def test_unknown_product_without_category_uses_solution_card() -> None:
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service = object.__new__(ConversationService)
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result = await service._use_solution_card_without_history(
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"risk_assessment",
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"评估这个新产品的风险",
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{"intent": "assessment", "risk_requested": True},
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)
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assert result == "solution"
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