CareerNexus 职联：岗位能力图谱构建与动态演化分析平台（挑战杯 XH-202621）
- 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
This commit is contained in:
RoyirinRosmontis
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"""API 测试：TestClient 覆盖 health / jobs / graph / resume / match / learning-path / review。"""
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from __future__ import annotations
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import json
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import pytest
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from fastapi.testclient import TestClient
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from app.api.routes import app
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from app.config import ROOT
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@pytest.fixture(scope="module")
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def client(graph_path):
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with TestClient(app) as c: # 触发 startup 加载图谱
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yield c
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def test_health(client):
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r = client.get("/api/health")
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assert r.status_code == 200
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body = r.json()
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assert body["code"] == 0 and body["request_id"]
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assert body["data"]["graph"]["jobs"] >= 15
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def test_graph_endpoint(client):
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r = client.get("/api/graph")
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assert r.status_code == 200
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data = r.json()["data"]
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assert len(data["nodes"]) > 100 and len(data["edges"]) > 100
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types = {n["type"] for n in data["nodes"]}
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assert "Job" in types and "Skill" in types
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# 行业维度：Industry 节点与 (Job)-[:BELONGS_TO]->(Industry) 边
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assert "Industry" in types
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industries = {n["title"] for n in data["nodes"] if n["type"] == "Industry"}
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assert {"人工智能", "互联网/软件", "金融科技", "智能制造"} <= industries
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assert any(e.get("rel") == "BELONGS_TO" for e in data["edges"])
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def test_jobs_list_and_filter(client):
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r = client.get("/api/jobs")
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assert r.status_code == 200
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jobs = r.json()["data"]
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assert len(jobs) >= 15
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r2 = client.get("/api/jobs", params={"keyword": "Java"})
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titles = [j["title"] for j in r2.json()["data"]]
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assert titles == ["Java开发工程师"]
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def test_job_detail_and_evolution(client):
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r = client.get("/api/jobs/job_Java开发工程师")
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assert r.status_code == 200
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data = r.json()["data"]
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assert data["definition"]["title"] == "Java开发工程师"
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assert data["snapshot"]["skills"]
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r2 = client.get("/api/jobs/job_Java开发工程师/evolution")
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events = r2.json()["data"]["events"]
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assert events and {"新增", "退出"} & {e["change_type"] for e in events}
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assert all("数据源" in e["description"] for e in events)
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assert client.get("/api/jobs/不存在").status_code == 404
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def test_resume_parse_masks_sensitive(client):
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text = "姓名：陈晓\n手机：13900001111\n邮箱：chenx@example.com\n4年经验，熟悉Java与MySQL"
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r = client.post("/api/resume/parse", json={"text": text})
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assert r.status_code == 200
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p = r.json()["data"]
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assert "13900001111" not in json.dumps(p, ensure_ascii=False)
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assert "陈晓" not in json.dumps(p, ensure_ascii=False)
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assert set(p["skills"]) == {"Java", "MySQL"}
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def test_match_and_learning_path(client):
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r = client.post("/api/match", json={
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"job_id": "job_Java开发工程师",
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"skills": ["Java", "Spring Boot", "Spring Cloud", "MySQL", "Redis", "分布式系统"],
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"years": 5, "scenarios": ["电商", "金融"]})
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assert r.status_code == 200
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m = r.json()["data"]
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assert m["is_match"] and m["total_score"] >= 60
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assert m["dimension_scores"]["required"] == 100.0
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r2 = client.post("/api/learning-path", json={
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"job_id": "job_Java开发工程师", "known_skills": ["Java"]})
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assert r2.status_code == 200
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path = r2.json()["data"]["learning_path"]
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assert path and "missing_required" in r2.json()["data"]
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def test_review_flow(client, tmp_path, monkeypatch):
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"""审核流：先造一个待审核候选，再 accept 写入图谱并留痕（SQLite 状态库重定向到临时目录）。"""
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import app.api.routes as routes
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from app.discover.cluster import save_pending
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from app.models import JobDefinition
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from app.storage import db as state_db
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# 把状态库/迁移种子/图谱路径重定向到 tmp，避免污染工程内已提交数据
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db_path = tmp_path / "state.db"
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monkeypatch.setitem(routes._cfg, "state_db_path", str(db_path))
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monkeypatch.setitem(routes._cfg, "audit_path", str(tmp_path / "audit.jsonl")) # 旧留痕迁移种子（不存在则跳过）
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monkeypatch.setitem(routes._cfg, "graph_path", str(tmp_path / "graph.json"))
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cand = JobDefinition(job_id="cand_测试新岗位", title="测试新岗位",
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required_skills=["Python"], status="pending",
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source_count=10, company_count=6)
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save_pending([cand], db_path)
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r = client.post("/api/review", json={
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"candidate_id": "cand_测试新岗位", "action": "accept",
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"operator": "张老师", "reason": "证据充分"})
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assert r.status_code == 200
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data = r.json()["data"]
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assert data["accepted"]["status"] == "active"
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assert data["review"]["before"]["status"] == "pending"
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assert data["review"]["operator"] == "张老师"
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# 审核后能在岗位列表查到
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assert client.get("/api/jobs/job_测试新岗位").status_code == 200
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# 留痕落在 SQLite（结构与旧 jsonl 行一致），候选已从待审核分区移除
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recs = state_db.get_audit(db_path)
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assert recs and recs[0]["operator"] == "张老师" and recs[0]["action"] == "accept"
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assert state_db.get_candidate(db_path, "cand_测试新岗位") is None
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# /api/audit 兼容查询
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r2 = client.get("/api/audit", params={"node_id": "cand_测试新岗位"})
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assert r2.status_code == 200 and r2.json()["data"][0]["candidate_id"] == "cand_测试新岗位"
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# 不存在的候选返回 404
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assert client.post("/api/review", json={
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"candidate_id": "cand_不存在", "action": "accept", "operator": "x"}).status_code == 404
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def test_node_update_flow(client, tmp_path, monkeypatch):
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"""节点编辑：白名单字段更新 + 图谱落盘 + 审核留痕（SQLite 状态库重定向到临时目录）。"""
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import app.api.routes as routes
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monkeypatch.setitem(routes._cfg, "graph_path", str(tmp_path / "graph.json"))
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monkeypatch.setitem(routes._cfg, "state_db_path", str(tmp_path / "state.db"))
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prev_nodes = client.get("/api/graph").json()["data"]["nodes"]
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prev_level = next(n for n in prev_nodes if n["id"] == "job_Java开发工程师").get("level")
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r = client.post("/api/graph/node/update", json={
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"node_id": "job_Java开发工程师",
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"updates": {"level": "高级", "weight": 0.9, "hack": "ignored"},
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"operator": "张老师", "reason": "webui 校正"})
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assert r.status_code == 200
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data = r.json()["data"]
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assert data["node"]["level"] == "高级" and data["node"]["weight"] == 0.9
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assert "hack" not in data["node"]
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assert data["audit"]["before"]["level"] == prev_level
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# Job 节点编辑同步 JobDefinition：级别同步回期望年限，/api/jobs 一致
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assert data["node"]["expected_years"] == 6
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d = client.get("/api/jobs/job_Java开发工程师").json()["data"]["definition"]
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assert d["expected_years"] == 6
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assert (tmp_path / "graph.json").exists()
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# 留痕可查
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r2 = client.get("/api/audit", params={"node_id": "job_Java开发工程师"})
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assert r2.status_code == 200
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recs = r2.json()["data"]
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assert recs and recs[0]["operator"] == "张老师"
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# 不存在的节点 / 空更新
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assert client.post("/api/graph/node/update", json={
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"node_id": "不存在", "updates": {"level": "x"}, "operator": "x"}).status_code == 404
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assert client.post("/api/graph/node/update", json={
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"node_id": "job_Java开发工程师", "updates": {"hack": 1}, "operator": "x"}).status_code == 400
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def test_jd_lookup(client):
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"""JD 原文回查：合法 source_id 返回记录，非法输入 400/404。"""
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g = client.get("/api/graph").json()["data"]
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sid = next(s for e in g["edges"] for s in e.get("sources", []))
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r = client.get("/api/jd", params={"sid": sid})
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assert r.status_code == 200
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rec = r.json()["data"]
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assert rec["source_id"] == sid and rec["text"] and rec["job_title"]
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assert client.get("/api/jd", params={"sid": "无行号"}).status_code == 400
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assert client.get("/api/jd", params={"sid": "../evil.jsonl#1"}).status_code == 400
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assert client.get("/api/jd", params={"sid": "batch_2019.jsonl#99999"}).status_code == 404
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def _minimal_pdf(text: str) -> bytes:
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"""构造含可提取文本的最小合法 PDF（ASCII 内容）。"""
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stream = f"BT /F1 12 Tf 72 720 Td ({text}) Tj ET".encode()
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objs = [
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b"<< /Type /Catalog /Pages 2 0 R >>",
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b"<< /Type /Pages /Kids [3 0 R] /Count 1 >>",
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b"<< /Type /Page /Parent 2 0 R /MediaBox [0 0 612 792] "
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b"/Resources << /Font << /F1 4 0 R >> >> /Contents 5 0 R >>",
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b"<< /Type /Font /Subtype /Type1 /BaseFont /Helvetica >>",
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b"<< /Length " + str(len(stream)).encode() + b" >>\nstream\n" + stream + b"\nendstream",
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]
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out = b"%PDF-1.4\n"
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offsets = []
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for i, body in enumerate(objs, 1):
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offsets.append(len(out))
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out += f"{i} 0 obj\n".encode() + body + b"\nendobj\n"
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xref_pos = len(out)
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out += f"xref\n0 {len(objs)+1}\n".encode() + b"0000000000 65535 f \n"
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for off in offsets:
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out += f"{off:010d} 00000 n \n".encode()
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out += (f"trailer\n<< /Size {len(objs)+1} /Root 1 0 R >>\n"
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f"startxref\n{xref_pos}\n%%EOF").encode()
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return out
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def test_resume_file_upload_docx_and_pdf(client, tmp_path):
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"""简历文件上传：docx/pdf 均解析出技能并脱敏，非法格式与超限被拒。"""
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from docx import Document
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# docx：python-docx 真实生成
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doc = Document()
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doc.add_paragraph("姓名：李晓峰 手机：13912345678")
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doc.add_paragraph("6年 Java 后端经验，熟悉 Java、Spring Boot、MySQL、Redis")
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p = tmp_path / "r.docx"
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doc.save(str(p))
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r = client.post("/api/resume/parse/file",
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files={"file": ("r.docx", p.read_bytes(), "application/octet-stream")})
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assert r.status_code == 200
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data = r.json()["data"]
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assert "Java" in data["skills"] and "MySQL" in data["skills"]
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assert "13912345678" not in data["raw_masked_text"] # 已脱敏
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assert data["name"].startswith("李") and "晓峰" not in data["name"]
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# pdf：最小合法 PDF（ASCII 技能名，验证 pypdf 链路）
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pdf = _minimal_pdf("Java Spring Boot MySQL Redis Docker Kubernetes")
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r2 = client.post("/api/resume/parse/file",
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files={"file": ("r.pdf", pdf, "application/pdf")})
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assert r2.status_code == 200
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skills2 = r2.json()["data"]["skills"]
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assert "Java" in skills2 and "Kubernetes" in skills2
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# txt 也走同一入口
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r3 = client.post("/api/resume/parse/file",
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files={"file": ("r.txt", "熟悉 Python 与 TensorFlow".encode(), "text/plain")})
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assert r3.status_code == 200 and "Python" in r3.json()["data"]["skills"]
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# 非法格式 / 空文件
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bad = client.post("/api/resume/parse/file",
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files={"file": ("r.exe", b"MZ", "application/octet-stream")})
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assert bad.status_code == 400
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empty = client.post("/api/resume/parse/file",
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files={"file": ("r.pdf", b"", "application/pdf")})
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assert empty.status_code == 400
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def test_match_normalizes_manual_skills(client):
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"""手动录入技能经大小写/别名归一化后再匹配：redis→Redis、go→Go、K8s→Kubernetes。"""
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r = client.post("/api/match", json={
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"job_id": "job_Go开发工程师",
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"skills": ["go", "redis", "grpc", "K8s", "kafka", "微服务"],
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"years": 5})
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assert r.status_code == 200
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m = r.json()["data"]
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hit = set(m["matched_required"]) | set(m["matched_preferred"])
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assert {"Go", "Redis", "gRPC", "Kubernetes", "Kafka", "微服务"} <= hit
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assert "redis" not in hit and "K8s" not in hit # 返回的是标准名
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def test_skill_normalize_semantic_fallback():
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"""normalize 三级容错：别名→包含匹配（取最长）→原样返回。"""
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from app.extract.base import SkillMatcher, load_lexicon
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m = SkillMatcher(load_lexicon())
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assert m.normalize("redis") == "Redis" # 大小写
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assert m.normalize("K8s") == "Kubernetes" # 别名
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assert m.normalize("微服务架构") == "微服务" # 中文包含
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assert m.normalize("Java语言") == "Java" # 后缀容错
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assert m.normalize("JavaScript") == "JavaScript" # 最长优先，不被 Java 截胡
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assert m.normalize("222") == "222" # 词表外原样返回
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def test_resume_structured_and_export(client):
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"""结构化简历：上传 docx 提取照片/基本信息/分节；导出 DOCX 合法且含关键词。"""
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from app.config import ROOT as _ROOT
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p = _ROOT / "data" / "resumes" / "resume_full.docx"
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r = client.post("/api/resume/parse/file",
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files={"file": ("resume_full.docx", p.read_bytes(), "application/octet-stream")})
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assert r.status_code == 200
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prof = r.json()["data"]
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assert prof["photo_b64"].startswith("data:image/") # 照片识别
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assert prof["gender"] == "男" and prof["education"] == "本科"
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assert prof["address"].endswith("***") # 住址脱敏
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titles = [s["title"] for s in prof["sections"]]
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assert "教育经历" in titles and "项目经历" in titles # 分节
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assert "13812345678" not in prof["raw_masked_text"] # 手机脱敏
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# 匹配后导出 DOCX
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m = client.post("/api/match", json={
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"job_id": "job_Java开发工程师",
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"skills": prof["skills"], "years": prof["years"]}).json()["data"]
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r2 = client.post("/api/resume/export", json={"profile": prof, "match": m})
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assert r2.status_code == 200
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assert r2.headers["content-type"].startswith("application/vnd.openxmlformats")
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import io, zipfile
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z = zipfile.ZipFile(io.BytesIO(r2.content))
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assert "word/document.xml" in z.namelist() # 合法 docx
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assert any(n.startswith("word/media/") for n in z.namelist()) # 照片已嵌入
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def test_runtime_settings_and_token_guard(client, monkeypatch, tmp_path):
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"""设置读写 config.json（白名单+校验）、LLM 热生效判定、访问令牌鉴权。"""
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import app.config as cfgmod
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from app.api import routes
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monkeypatch.setattr(cfgmod, "CONFIG_JSON", tmp_path / "config.json")
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monkeypatch.setattr(routes, "load_runtime_settings", cfgmod.load_runtime_settings)
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# 保存设置：host/port 校验
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assert client.post("/api/config", json={"host": "8.8.8.8"}).status_code == 400
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assert client.post("/api/config", json={"port": 80}).status_code == 400
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r = client.post("/api/config", json={
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"host": "0.0.0.0", "port": 9012, "llm_api_key": "sk-test-1234", "mode": "demo"})
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assert r.status_code == 200
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data = r.json()["data"]
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assert data["restart_required"] is True # 保存的监听与运行中不同
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assert data["llm"]["configured"] is True # LLM 热生效
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assert data["llm"]["api_key_masked"].endswith("1234")
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assert "sk-test-1234" not in r.text # 密钥不落明文
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saved = cfgmod.load_runtime_settings()
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assert saved["llm_api_key"] == "sk-test-1234" and saved["port"] == 9012
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# 非白名单键不写入
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client.post("/api/config", json={})
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assert "hack" not in cfgmod.load_runtime_settings()
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# 令牌鉴权：设置令牌后 /api/* 需带 X-Access-Token，health 与 GET config 豁免
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monkeypatch.setitem(routes._cfg, "access_token", "tok-abc")
|
||||
assert client.get("/api/jobs").status_code == 401
|
||||
assert client.get("/api/jobs", headers={"X-Access-Token": "tok-abc"}).status_code == 200
|
||||
assert client.get("/api/health").status_code == 200
|
||||
assert client.get("/api/config").status_code == 200
|
||||
assert client.post("/api/config", json={}).status_code == 401
|
||||
monkeypatch.delitem(routes._cfg, "access_token")
|
||||
|
||||
|
||||
def test_get_extractor_llm_key_from_config():
|
||||
"""get_extractor：config.json 提供 llm_api_key 即启用 LLM 通道，无密钥回退离线。"""
|
||||
from app.extract.llm import LLMExtractor, get_extractor
|
||||
from app.extract.offline import OfflineExtractor
|
||||
e1 = get_extractor({"llm_api_key": "sk-x", "llm_base_url": "http://x/v1",
|
||||
"llm_model": "m", "lexicon_path": None})
|
||||
assert isinstance(e1, LLMExtractor)
|
||||
e2 = get_extractor({"lexicon_path": None, "llm_api_key_env": "NO_SUCH_ENV_VAR"})
|
||||
assert isinstance(e2, OfflineExtractor)
|
||||
|
||||
|
||||
def test_embed_model_status_and_config(client, tmp_path, monkeypatch):
|
||||
"""语义权重状态：未配置/假目录/缺依赖时 ready=False 且状态字段正确；设置可保存。"""
|
||||
import app.config as cfgmod
|
||||
monkeypatch.setattr(cfgmod, "CONFIG_JSON", tmp_path / "config.json") # 不写真实 config.json
|
||||
from app.match import semantic
|
||||
assert not semantic.weights_present("") and not semantic.weights_present(tmp_path)
|
||||
# 伪造一个"文件齐全"的权重目录
|
||||
for f in ["config.json", "model.safetensors", "tokenizer.json"]:
|
||||
(tmp_path / f).write_text("{}")
|
||||
assert semantic.weights_present(tmp_path)
|
||||
st = semantic.status({"embed_model_path": str(tmp_path)})
|
||||
assert st["configured"] and st["weights_present"]
|
||||
assert st["ready"] == st["deps_installed"] # 本环境未装 sentence-transformers → False
|
||||
|
||||
r = client.post("/api/config", json={"embed_model_path": str(tmp_path)})
|
||||
assert r.status_code == 200
|
||||
emb = r.json()["data"]["embed"]
|
||||
assert emb["configured"] and emb["weights_present"]
|
||||
# health 同步暴露
|
||||
h = client.get("/api/health").json()["data"]
|
||||
assert h["embed"]["configured"] is True
|
||||
# 清除
|
||||
r2 = client.post("/api/config", json={"embed_model_path": ""})
|
||||
assert r2.json()["data"]["embed"]["configured"] is False
|
||||
|
||||
|
||||
def test_embed_dual_provider(client, tmp_path, monkeypatch):
|
||||
"""语义双模式：api 模式有 Key 即就绪（无需权重）；local 模式需权重+依赖。"""
|
||||
import app.config as cfgmod
|
||||
monkeypatch.setattr(cfgmod, "CONFIG_JSON", tmp_path / "config.json") # 不写真实 config.json
|
||||
from app.match import semantic
|
||||
st_api = semantic.status({"embed_provider": "api", "embed_api_key": "sk-1234"})
|
||||
assert st_api["ready"] is True and st_api["provider"] == "api"
|
||||
st_local = semantic.status({"embed_provider": "local", "embed_model_path": ""})
|
||||
assert st_local["ready"] is False
|
||||
# most_similar 在未就绪时安全返回 None
|
||||
assert semantic.most_similar("微服务", {"embed_provider": "local"}, {"skills": [{"name": "微服务"}]}) is None
|
||||
|
||||
r = client.post("/api/config", json={
|
||||
"embed_provider": "api", "embed_api_key": "sk-dash-9999"})
|
||||
emb = r.json()["data"]["embed"]
|
||||
assert emb["ready"] is True and emb["api_key_masked"].endswith("9999")
|
||||
assert "sk-dash-9999" not in r.text
|
||||
# 还原：回到本地模式并清除 key
|
||||
r2 = client.post("/api/config", json={"embed_provider": "local", "embed_api_key": ""})
|
||||
assert r2.json()["data"]["embed"]["ready"] is False
|
||||
|
||||
|
||||
def test_match_batch(client):
|
||||
"""批量匹配：多份简历打分降序、坏文件记 error 不拖垮整批。"""
|
||||
from app.config import ROOT as _ROOT
|
||||
resumes = _ROOT / "data" / "resumes"
|
||||
files = []
|
||||
for fn in ["resume_full.txt", "resume_partial.txt", "resume_mismatch.txt"]:
|
||||
files.append(("files", (fn, (resumes / fn).read_bytes(), "text/plain")))
|
||||
files.append(("files", ("bad.exe", b"MZ", "application/octet-stream")))
|
||||
r = client.post("/api/match/batch",
|
||||
data={"job_id": "job_Java开发工程师"}, files=files)
|
||||
assert r.status_code == 200
|
||||
data = r.json()["data"]
|
||||
assert data["total"] == 4 and data["ok"] == 3
|
||||
scores = [x["total_score"] for x in data["results"] if "error" not in x]
|
||||
assert scores == sorted(scores, reverse=True) # 按总分降序
|
||||
assert any("error" in x for x in data["results"]) # 坏文件有错误记录
|
||||
top = data["results"][0]
|
||||
assert top["matched"] and top["name"] # 第一名有命中与脱敏姓名
|
||||
assert client.post("/api/match/batch",
|
||||
data={"job_id": "不存在"}, files=files[:1]).status_code == 404
|
||||
|
||||
|
||||
def test_semantic_setup_endpoints(client, monkeypatch):
|
||||
"""一键配置：状态接口默认空闲；已就绪时幂等快速完成；重复启动不并发。"""
|
||||
import app.api.routes as routes
|
||||
from app.config import ROOT as _ROOT
|
||||
# 让运行配置处于"语义已就绪"状态，命中幂等快路径（避免测试里真跑 pip）
|
||||
monkeypatch.setitem(routes._cfg, "embed_provider", "local")
|
||||
monkeypatch.setitem(routes._cfg, "embed_model_path", str(_ROOT / "models" / "bge-m3"))
|
||||
routes._semantic_setup.update({"running": False, "stage": "", "log": [], "error": "", "done": False})
|
||||
st = client.get("/api/semantic/setup/status").json()["data"]
|
||||
assert st["running"] is False and "embed" in st
|
||||
r = client.post("/api/semantic/setup")
|
||||
assert r.status_code == 200
|
||||
import time
|
||||
st2 = st
|
||||
for _ in range(50):
|
||||
st2 = client.get("/api/semantic/setup/status").json()["data"]
|
||||
if not st2["running"]:
|
||||
break
|
||||
time.sleep(0.2)
|
||||
assert st2["done"] is True and st2["error"] == ""
|
||||
assert st2["embed"]["ready"] is True
|
||||
Reference in New Issue
Block a user