RoyirinRosmontis
a10442d78c
- 全链路：多源JD接入/清洗交叉验证/结构化抽取(星火+离线双通道)/时序知识图谱/新岗位发现/演化分析/人岗匹配/学习路径 - WebUI 五视图：驾驶舱/图谱工作台/岗位演化/匹配(单人+批量)/学习路径 - 设置中心(config.json)/Token鉴权/SQLite状态库/语义向量通道(BGE-M3本地或联网API) - 71 个 pytest 用例；数据目录仅保留结构(.gitkeep)，数据由 scripts/generate_dataset.py 复现
132 lines
5.2 KiB
Python
132 lines
5.2 KiB
Python
"""抽取层测试：Schema 校验、坏 JSON 重试与降级、多源投票、离线抽取与别名归一。"""
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from __future__ import annotations
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import pytest
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from pydantic import ValidationError
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from app.extract.base import SkillMatcher, load_lexicon, vote_field
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from app.extract.llm import LLMExtractor
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from app.extract.offline import OfflineExtractor
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from app.models import ExtractionResult, JDRecord
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SAMPLE_TEXT = """岗位名称：Java开发工程师
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某科技公司诚邀优秀人才加入。
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岗位职责：
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1、负责后端服务的设计与开发；
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2、参与核心系统的性能优化；
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任职要求：
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1、3年以上相关工作经验；
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2、熟练掌握Java、Spring Boot、MySQL、Redis；
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3、具有电商或金融行业经验者优先；
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加分项：
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1、了解Docker、Kubernetes者优先；
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"""
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def _record(text: str = SAMPLE_TEXT) -> JDRecord:
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return JDRecord(jid="X1", job_title="Java开发工程师", company="某科技",
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publish_date="2024-05-01", text=text)
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def test_offline_extract_fields_and_evidence():
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ext = OfflineExtractor(load_lexicon())
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r = ext.extract(_record())
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assert r.job_title == "Java开发工程师"
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assert set(r.required_skills) == {"Java", "Spring Boot", "MySQL", "Redis"}
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assert set(r.preferred_skills) == {"Docker", "Kubernetes"}
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assert r.responsibilities == ["负责后端服务的设计与开发；", "参与核心系统的性能优化；"]
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assert set(r.scenarios) == {"电商", "金融"}
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assert r.evidence["required_skills"] # 每字段附证据片段
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assert r.confidence == 1.0
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def test_alias_normalization():
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matcher = SkillMatcher(load_lexicon())
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text = "要求LLM应用开发经验，熟悉K8s与SpringBoot，了解检索增强生成"
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names = matcher.find_names(text)
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assert "大模型应用开发" in names # LLM应用开发 归一化
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assert "Kubernetes" in names # K8s 归一化
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assert "Spring Boot" in names # SpringBoot 归一化
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assert "RAG" in names # 检索增强生成 归一化
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def test_tail_section_not_mixed_into_preferred():
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"""尾部“团队与福利/福利待遇”段落不属于加分区：其中的技能词不得混入加分技能。"""
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text = SAMPLE_TEXT + "团队与福利：\n团队标配Kubernetes与Terraform，提供外部培训预算。\n"
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ext = OfflineExtractor(load_lexicon())
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r = ext.extract(_record(text))
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assert set(r.preferred_skills) == {"Docker", "Kubernetes"}
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assert "Terraform" not in r.preferred_skills
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def test_java_not_confused_with_javascript():
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matcher = SkillMatcher(load_lexicon())
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names = matcher.find_names("精通JavaScript与Vue，不写Java后端")
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assert "JavaScript" in names
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assert "Java" in names # “Java后端”中的 Java 是独立词
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only_js = matcher.find_names("精通JavaScript与Vue")
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assert "Java" not in only_js
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def test_schema_validation_rejects_bad_payload():
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with pytest.raises(ValidationError):
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ExtractionResult(job_title="X", required_skills="不是列表")
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with pytest.raises(ValidationError):
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ExtractionResult(job_title="X", confidence="不是数字")
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def _make_llm() -> LLMExtractor:
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return LLMExtractor(api_key="fake", base_url="http://localhost:1", model="m",
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timeout=0.1, fallback=OfflineExtractor(load_lexicon()))
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def test_llm_bad_json_retries_then_success(monkeypatch):
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"""坏 JSON 触发重试，第 3 次返回合法 JSON 则成功。"""
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llm = _make_llm()
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calls = {"n": 0}
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good = ('{"job_title": "Java开发工程师", "responsibilities": [], '
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'"required_skills": ["Java"], "preferred_skills": [], "scenarios": []}')
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def fake_call(text):
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calls["n"] += 1
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return "{坏JSON" if calls["n"] < 3 else good
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monkeypatch.setattr(llm, "_call_api", fake_call)
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r = llm.extract(_record())
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assert calls["n"] == 3
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assert r.extractor == "llm"
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assert r.required_skills == ["Java"]
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def test_llm_all_fail_fallback_offline(monkeypatch):
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"""重试耗尽（含超时/坏 JSON）降级到离线抽取，链路不断。"""
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llm = _make_llm()
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monkeypatch.setattr(llm, "_call_api", lambda text: (_ for _ in ()).throw(TimeoutError("超时")))
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r = llm.extract(_record())
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assert r.extractor == "offline"
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assert "Java" in r.required_skills
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def test_llm_schema_invalid_retries(monkeypatch):
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"""返回 JSON 合法但不符合 Schema（类型错误）同样重试并降级。"""
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llm = _make_llm()
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monkeypatch.setattr(llm, "_call_api",
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lambda text: '{"job_title": 123, "required_skills": "wrong"}')
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r = llm.extract(_record())
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assert r.extractor == "offline"
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def test_vote_field_requires_agreement():
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"""关键字段多源一致才入选：A 三源全票，B 两票，C 一票出局。"""
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chosen, conf = vote_field([
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(["A", "B", "C"], 1.0),
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(["A", "B"], 1.0),
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(["A"], 1.0),
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], min_agreement=0.5)
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assert chosen == ["A", "B"]
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assert conf == 1.0
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chosen2, _ = vote_field([(["A"], 1.0), (["B"], 1.0)], min_agreement=0.5)
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assert chosen2 == ["A", "B"] # 平票 0.5 恰好达到门槛
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chosen3, conf3 = vote_field([(["A"], 1.0), (["B"], 1.0), (["C"], 1.0)], min_agreement=0.5)
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assert chosen3 == []
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