146 lines
5.7 KiB
Python
146 lines
5.7 KiB
Python
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# -*- coding: utf-8 -*-
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"""知识库自动迭代 真实验证（P2-13）
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真实验证点（来自功能规格说明书 + 状态看板验收标准）：
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- 能基于标注(feedback=useless)生成建议行 status=pending
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- 管理员 approve 后写入 knowledge_base（状态变为 applied）
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- reject 正常（状态变为 rejected，且不写入知识库）
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- get_suggestion_stats 统计正确
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- 关键证据: _generate_update_suggestion / _generate_new_faq_suggestion 内是 TODO 桩，
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返回的 title/content 是 "[待AI生成] ..." 占位符 —— 证实 AI 内容生成未实现，
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数据管道(分析→建建议行→审核应用)是真实的，但 AI 生成是桩。
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"""
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import pytest
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from sqlalchemy import select
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from app.models.conversation_annotation import ConversationAnnotation
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from app.models.knowledge_base import KnowledgeBase
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from app.models.knowledge_suggestion import KnowledgeSuggestion
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from app.services.knowledge_iteration_service import KnowledgeIterationService
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def _seed_useless_annotations(
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db,
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msg_id: str,
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n: int,
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conv_id: str = "conv-1",
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agent_id: str = "agent-1",
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):
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"""播种 n 条 feedback=useless 的标注（同一 message_id 用于触发高频错误判定）。"""
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for _ in range(n):
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db.add(
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ConversationAnnotation(
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conversation_id=conv_id,
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agent_id=agent_id,
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message_id=msg_id,
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feedback="useless",
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)
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)
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# 再播种一条不同 message_id 的（仅 1 次，不构成高频，用于对照）
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db.add(
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ConversationAnnotation(
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conversation_id="conv-2",
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agent_id=agent_id,
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message_id="msg-other",
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feedback="useless",
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)
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)
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@pytest.mark.asyncio
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async def test_analyze_generates_pending_suggestion_with_stub_content(db_session):
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"""分析标注生成 pending 建议；且内容是 [待AI生成] 占位符（证明 AI 生成是桩）。"""
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_seed_useless_annotations(db_session, msg_id="msg-x", n=3)
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await db_session.flush()
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service = KnowledgeIterationService()
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result = await service.analyze_and_generate_suggestions(db_session, days=30)
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# 高频错误(msg-x 被标注 3 次)应生成 >=1 条建议
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assert result["suggestions_generated"] >= 1
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assert result["annotations_analyzed"] >= 4 # 3(msg-x) + 1(msg-other)
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# 查询生成的建议
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stmt = select(KnowledgeSuggestion).where(KnowledgeSuggestion.status == "pending")
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suggestions = (await db_session.execute(stmt)).scalars().all()
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assert len(suggestions) >= 1
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# 关键证据: AI 内容生成是桩 —— title/content 含占位符
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titles = [s.title for s in suggestions]
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contents = [s.content for s in suggestions]
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assert any("[待AI生成]" in t for t in titles)
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assert any("请通过AI分析" in c for c in contents)
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# 仅高频的 msg-x 生成建议，msg-other(仅1次)不应生成
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generated_source = [sd for s in suggestions for sd in (s.source_data or [])]
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assert "msg-x" in generated_source
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assert "msg-other" not in generated_source
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@pytest.mark.asyncio
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async def test_approve_writes_knowledge_base(db_session):
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"""approve 后写入 knowledge_base，建议状态变为 applied。"""
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_seed_useless_annotations(db_session, msg_id="msg-x", n=3)
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await db_session.flush()
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service = KnowledgeIterationService()
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await service.analyze_and_generate_suggestions(db_session, days=30)
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stmt = select(KnowledgeSuggestion).where(KnowledgeSuggestion.status == "pending")
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suggestion = (await db_session.execute(stmt)).scalars().first()
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assert suggestion is not None
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approved = await service.approve_suggestion(db_session, suggestion.id, "reviewer-1")
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assert approved is not None
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assert approved.status == "applied"
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assert approved.reviewer_id == "reviewer-1"
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# knowledge_base 应新增一行（内容仍是桩占位符）
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kb_rows = (await db_session.execute(select(KnowledgeBase))).scalars().all()
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assert len(kb_rows) == 1
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assert "[待AI生成]" in kb_rows[0].title
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@pytest.mark.asyncio
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async def test_reject_marks_rejected(db_session):
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"""reject 将建议标记为 rejected，且不写入知识库。"""
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_seed_useless_annotations(db_session, msg_id="msg-x", n=3)
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await db_session.flush()
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service = KnowledgeIterationService()
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await service.analyze_and_generate_suggestions(db_session, days=30)
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stmt = select(KnowledgeSuggestion).where(KnowledgeSuggestion.status == "pending")
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suggestion = (await db_session.execute(stmt)).scalars().first()
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rejected = await service.reject_suggestion(
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db_session, suggestion.id, "reviewer-2", "内容无意义"
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)
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assert rejected is not None
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assert rejected.status == "rejected"
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assert rejected.reject_reason == "内容无意义"
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# 拒绝不写入知识库
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kb_rows = (await db_session.execute(select(KnowledgeBase))).scalars().all()
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assert len(kb_rows) == 0
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@pytest.mark.asyncio
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async def test_stats_counts_correctly(db_session):
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"""get_suggestion_stats 统计正确。"""
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_seed_useless_annotations(db_session, msg_id="msg-x", n=3)
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await db_session.flush()
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service = KnowledgeIterationService()
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await service.analyze_and_generate_suggestions(db_session, days=30)
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stats = await service.get_suggestion_stats(db_session)
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assert stats["total"] >= 1
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assert stats["pending"] >= 1
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# approve 一条后 applied +1
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stmt = select(KnowledgeSuggestion).where(KnowledgeSuggestion.status == "pending")
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s = (await db_session.execute(stmt)).scalars().first()
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await service.approve_suggestion(db_session, s.id, "reviewer-1")
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stats2 = await service.get_suggestion_stats(db_session)
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assert stats2["applied"] >= 1
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