e4bf0710c7
数据库 - 新增迁移 0003：timeline_events.memory_source_id 外键 → memory_sources，便于按 ingest 源做时间线幂等 后端 - 记忆 - 新增 ingest 后 LLM 富化（摘要/事实/时间线），可配置开关与最大字符数 - 新增证据包组装：合并 chunk、摘要、事实、时间线、故事等检索结果；支持空 query 时是否仍带 rolling 等开关 - repo/retriever/service/router/schemas/summarizer/timeline/extractor 等扩展；文档 memory-retrieval.md 更新 后端 - 对话 WS - 增加 PING/PONG；分段 ASR 日志与空音频处理；转写失败与「无助手回复」错误提示更明确 - 助手多段回复持久化使用统一分隔符，与分段逻辑一致 后端 - Agent - reply_limits：按 [SPLIT] 与段落拆段，并保证非空 fallback，供 WS 与 TTS 多段下发 后端 - 回忆录任务 - transcript ingest 记录 source_id；任务成功结?
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
"""ClassificationAgent：零散档案启发式与 none→summary 兜底（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_maps_birth_year_fragment_to_summary_without_llm() -> None:
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agent = ClassificationAgent()
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assert (
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agent.classify("1999年出生", fallback_stage="childhood", llm=None) == "summary"
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)
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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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