- DB: segments 用户输入文本(Alembic 0002) - Chat: 阶段检测/阶段提示/回复限制,编排与访谈/画像 prompts 调整 - Memoir: 忠实度检查 agent,叙事与分类等链路更新 - Core: agent 日志、Alembic 启动、LangChain/日志/配置等 - Story: time_hints;Memory 检索与相关测试 - Expo: 助手头像、会话页与消息拆分、实时会话与文案/i18n - Docs/scripts/tests: 迁移脚本、LLM JSON/记忆检索文档、新增单测
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
"""ClassificationAgent:零散档案启发式与分类 none 语义(纯函数/无 LLM)。"""
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import pytest
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from app.agents.memoir.classification_agent import (
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ClassificationAgent,
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_looks_like_fragment_only,
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_parse_category_from_llm_response,
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)
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@pytest.mark.parametrize(
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"text,expected_fragment",
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[
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("", True),
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(" ", True),
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("我1999年出生", True),
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("1999年出生。", True),
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("1999年出生!", True),
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("我是云南人", True),
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("我是北京籍。", True),
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("小学二年级那次下雨爷爷背我过河,鞋全湿了。", False),
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("我出生在农村,家里养过一头黄牛。", False),
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("我是北京人,后来去上海读了大学。", False),
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],
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)
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def test_looks_like_fragment_only(text: str, expected_fragment: bool) -> None:
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assert _looks_like_fragment_only(text) is expected_fragment
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def test_classify_skips_story_for_birth_year_without_llm() -> None:
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agent = ClassificationAgent()
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assert agent.classify("1999年出生", fallback_stage="childhood", llm=None) is None
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@pytest.mark.parametrize(
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"raw,expected",
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[
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('{"category": "childhood"}', "childhood"),
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('```json\n{"category": "none"}\n```', "none"),
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("childhood", "childhood"),
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('"education"', "education"),
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],
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)
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def test_parse_category_from_llm_response(raw: str, expected: str) -> None:
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assert _parse_category_from_llm_response(raw) == expected
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def test_classify_fallback_when_no_llm_and_narrative_snippet() -> None:
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agent = ClassificationAgent()
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out = agent.classify(
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"小学二年级的时候我在操场上摔了一跤,膝盖流了很多血,是老师背我去医务室的。",
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fallback_stage="childhood",
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llm=None,
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)
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assert out == "education"
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