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"""AI 讲解/知识点，以及用户 AI 配置。判分相关见 test_grading.py。"""
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import json
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from unittest.mock import patch
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from app.services.llm.client import LLMResult
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SUBJECTIVE = {
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"type": "short_answer",
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"stem_markdown": "简述牛顿第一定律",
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"correct_answer": ["惯性定律"],
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}
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def _fake_text(content: str):
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async def _inner(self, system, user, json_mode=False, model=None):
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return LLMResult(content, "text-model", 10, 20)
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return _inner
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class TestSettings:
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async def test_api_key_never_returned(self, client, auth_headers, configure_ai):
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h = await auth_headers()
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out = await configure_ai(h, api_key="sk-secret-value")
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assert out["has_api_key"] is True
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assert "sk-secret-value" not in json.dumps(out)
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r = await client.get("/api/settings", headers=h)
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assert "sk-secret-value" not in r.text
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assert r.json()["has_api_key"] is True
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async def test_api_key_stored_encrypted(self, client, auth_headers, configure_ai):
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"""库里存的必须是密文，且能正确解密回原值。"""
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from sqlalchemy import select
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from app.db import SessionLocal
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from app.models.user import UserSettings
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from app.services import crypto
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h = await auth_headers()
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await configure_ai(h, api_key="sk-secret-value")
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async with SessionLocal() as db:
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row = (await db.scalars(select(UserSettings))).first()
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assert row.llm_api_key_encrypted != "sk-secret-value"
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assert crypto.decrypt(row.llm_api_key_encrypted) == "sk-secret-value"
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async def test_empty_api_key_leaves_existing(self, client, auth_headers, configure_ai):
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"""留空表示不修改，不该把已配置的 key 清掉。"""
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h = await auth_headers()
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await configure_ai(h)
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r = await client.put(
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"/api/settings", json={"llm_text_model": "new-model"}, headers=h
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)
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assert r.json()["has_api_key"] is True
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assert r.json()["llm_text_model"] == "new-model"
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async def test_settings_isolated_per_user(self, client, auth_headers, configure_ai):
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ha = await auth_headers("alice")
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hb = await auth_headers("bob")
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await configure_ai(ha)
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assert (
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await client.get("/api/settings", headers=hb)
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).json()["has_api_key"] is False
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class TestAiExplain:
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async def test_explain_persists(self, client, auth_headers, configure_ai):
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h = await auth_headers()
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await configure_ai(h)
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qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=h)).json()["id"]
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with patch(
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"app.services.llm.client.LLMClient.chat_text",
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_fake_text("## 讲解\n\n物体在无外力时 $v$ 恒定。"),
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):
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r = await client.post(f"/api/questions/{qid}/ai/explain", headers=h)
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assert r.status_code == 200
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assert "$v$" in r.json()["explanation_markdown"]
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q = (await client.get(f"/api/questions/{qid}", headers=h)).json()
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assert "讲解" in q["explanation_markdown"]
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async def test_explain_never_touches_my_note(self, client, auth_headers, configure_ai):
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"""我自己写的笔记是我的，AI 讲解不得覆盖它。"""
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h = await auth_headers()
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await configure_ai(h)
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qid = (
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await client.post(
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"/api/questions",
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json={**SUBJECTIVE, "my_note_markdown": "我当时错在没考虑摩擦力"},
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headers=h,
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)
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).json()["id"]
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with patch(
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"app.services.llm.client.LLMClient.chat_text", _fake_text("AI 的讲解内容")
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):
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await client.post(f"/api/questions/{qid}/ai/explain", headers=h)
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q = (await client.get(f"/api/questions/{qid}", headers=h)).json()
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assert q["my_note_markdown"] == "我当时错在没考虑摩擦力"
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assert q["explanation_markdown"] == "AI 的讲解内容"
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async def test_unconfigured_ai_returns_400(self, client, auth_headers):
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h = await auth_headers()
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qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=h)).json()["id"]
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r = await client.post(f"/api/questions/{qid}/ai/explain", headers=h)
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assert r.status_code == 400
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assert "设置" in r.json()["detail"]
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async def test_cannot_explain_others_note(self, client, auth_headers, configure_ai):
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ha = await auth_headers("alice")
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hb = await auth_headers("bob")
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await configure_ai(hb)
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qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=ha)).json()["id"]
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r = await client.post(f"/api/questions/{qid}/ai/explain", headers=hb)
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assert r.status_code == 404
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class TestAiTags:
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async def test_summarize_tags_merges(self, client, auth_headers, configure_ai):
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h = await auth_headers()
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await configure_ai(h)
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qid = (
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await client.post(
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"/api/questions", json={**SUBJECTIVE, "tags": ["物理"]}, headers=h
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)
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).json()["id"]
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with patch(
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"app.services.llm.client.LLMClient.chat_text",
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_fake_text(json.dumps({"tags": ["力学", "牛顿定律"]})),
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):
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r = await client.post(f"/api/questions/{qid}/ai/summarize-tags", headers=h)
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assert r.status_code == 200
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assert set(r.json()["tags"]) == {"物理", "力学", "牛顿定律"}
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async def test_malformed_tags_json_yields_no_tags(
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self, client, auth_headers, configure_ai
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):
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h = await auth_headers()
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await configure_ai(h)
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qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=h)).json()["id"]
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with patch("app.services.llm.client.LLMClient.chat_text", _fake_text("not json")):
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r = await client.post(f"/api/questions/{qid}/ai/summarize-tags", headers=h)
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assert r.status_code == 200
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assert r.json()["tags"] == []
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async def test_tags_on_unclassified_note(self, client, auth_headers, configure_ai):
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"""拍照记下的未分类笔记，也能让 AI 归类知识点。"""
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h = await auth_headers()
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await configure_ai(h)
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qid = (
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await client.post(
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"/api/questions",
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json={"type": "unclassified", "stem_markdown": "求导数"},
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headers=h,
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)
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).json()["id"]
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with patch(
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"app.services.llm.client.LLMClient.chat_text",
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_fake_text(json.dumps({"tags": ["导数"]})),
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):
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r = await client.post(f"/api/questions/{qid}/ai/summarize-tags", headers=h)
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assert r.json()["tags"] == ["导数"]
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class TestAiAudit:
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async def test_ai_generation_is_audited(self, client, auth_headers, configure_ai):
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"""每次 AI 调用都要留审计记录，便于观察成本。"""
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from sqlalchemy import select
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from app.db import SessionLocal
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from app.models.practice import AiGeneration
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h = await auth_headers()
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await configure_ai(h)
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qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=h)).json()["id"]
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with patch("app.services.llm.client.LLMClient.chat_text", _fake_text("讲解内容")):
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await client.post(f"/api/questions/{qid}/ai/explain", headers=h)
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async with SessionLocal() as db:
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rows = (await db.scalars(select(AiGeneration))).all()
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assert len(rows) == 1
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assert rows[0].task == "explain"
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assert rows[0].model == "text-model"
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assert rows[0].request_tokens == 10
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assert rows[0].response_tokens == 20
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async def test_recording_attempt_makes_no_ai_call(self, client, auth_headers):
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"""记录作答不该产生任何 AI 调用（也就不消耗额度）。"""
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from sqlalchemy import select
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from app.db import SessionLocal
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from app.models.practice import AiGeneration
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h = await auth_headers()
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qid = (await client.post("/api/questions", json=SUBJECTIVE, headers=h)).json()["id"]
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await client.post(
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f"/api/questions/{qid}/attempts", json={"user_answer": ["x"]}, headers=h
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)
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async with SessionLocal() as db:
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assert (await db.scalars(select(AiGeneration))).all() == []
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