104 lines
5.7 KiB
Python
104 lines
5.7 KiB
Python
import json
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import httpx
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import pytest
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from sqlalchemy import select
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from wisegrade.llm import DeepSeekClient, ModelError, ScoreResult, validate_score
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from wisegrade.models import AppSetting, KnowledgeChunk
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from wisegrade.rag import ingest, local_encoder, retrieve, split_pages
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from wisegrade.settings_store import model_config, save_model_config
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def fake_embed(texts):
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return [[1.0] + [0.0] * 1023 for _ in texts]
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def test_json_validation_and_evidence():
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criterion = {"criterion_id": "stage1.problem", "max_score": 25}
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chunks = [{"chunk_id": 7, "page": 2, "text": "工程需求是实时目标识别"}]
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result = ScoreResult(criterion_id="stage1.problem", score=19, max_score=25, reason="满足需求",
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evidence=[{"chunk_id": 7, "page": 2, "quote": "实时目标识别"}], confidence=.8)
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assert validate_score(result, criterion, chunks).score == 19
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with pytest.raises(ModelError):
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validate_score(result, criterion, [{**chunks[0], "text": "其他内容"}])
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with pytest.raises(ValueError):
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ScoreResult(criterion_id="x", score=5, max_score=10, reason="无依据", evidence=[], confidence=.5)
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def test_evidence_quote_resolves_pdf_line_breaks_and_page():
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criterion = {"criterion_id": "stage1.problem", "max_score": 25}
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chunks = [{"chunk_id": 7, "page": 2, "text": "机器人\n控制精度：2 cm。"},
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{"chunk_id": 8, "page": 3, "text": "其他实验结果"}]
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result = ScoreResult(criterion_id="stage1.problem", score=19, max_score=25, reason="有指标",
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evidence=[{"chunk_id": 8, "page": 3, "quote": "机器人控制精度:2 cm"}], confidence=.8)
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checked = validate_score(result, criterion, chunks)
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assert checked.evidence[0].chunk_id == 7 and checked.evidence[0].page == 2
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assert checked.evidence[0].quote in chunks[0]["text"]
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def test_deepseek_adapter_mock_and_key_not_in_error(tmp_path):
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image = tmp_path / "page.png"
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image.write_bytes(b"png fixture")
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def handler(request):
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assert request.url.path == "/chat/completions"
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assert request.headers["Authorization"] == "Bearer fake-secret"
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content = json.loads(request.content)["messages"][1]["content"]
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assert content[1]["type"] == "image_url" and content[1]["image_url"]["url"].startswith("data:image/png;base64,")
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return httpx.Response(200, json={"choices": [{"message": {"content": json.dumps({"criterion_id": "x", "score": 0,
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"max_score": 10, "reason": "缺失", "evidence": [], "confidence": .2})}}]})
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client = DeepSeekClient(base_url="https://example.test", api_key="fake-secret", retries=0, transport=httpx.MockTransport(handler))
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assert client.json_call("prompt", {}, ScoreResult, images=[str(image)]).score == 0
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bad = DeepSeekClient(base_url="https://example.test", api_key="fake-secret", retries=0,
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transport=httpx.MockTransport(lambda _: httpx.Response(500, text="fake-secret")))
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with pytest.raises(ModelError) as error:
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bad.json_call("prompt", {}, ScoreResult)
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assert "fake-secret" not in str(error.value)
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def test_project_isolation_and_chunk_metadata(db):
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pages = [{"page_number": 3, "section": "实验", "text": "机器人实验数据和分析"}]
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ingest(db, course_version_id=1, project_id=101, submission_file_id=None, title="学生A", category="stage3",
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source_hash="a", pages=pages, embedder=fake_embed)
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ingest(db, course_version_id=1, project_id=102, submission_file_id=None, title="学生B", category="stage3",
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source_hash="b", pages=[{**pages[0], "text": "另一个学生的秘密"}], embedder=fake_embed)
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hits = retrieve(db, course_version_id=1, project_id=101, category="stage3", query="实验数据", embedder=fake_embed)
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assert hits and all(hit["project_id"] == 101 and hit["page"] == 3 for hit in hits)
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assert "秘密" not in str(hits)
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def test_short_paragraphs_share_a_page_chunk():
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text = "\n\n".join(f"实验步骤{i}：采集机器人定位数据。" for i in range(12))
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chunks = list(split_pages([{"page_number": 4, "section": "实验", "text": text}]))
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assert len(chunks) == 1
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assert chunks[0]["page"] == 4
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assert "实验步骤0" in chunks[0]["text"] and "实验步骤11" in chunks[0]["text"]
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def test_stale_knowledge_index_reuses_document_id(db):
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pages = [{"page_number": 1, "text": "实验方案与测量结果"}]
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document = ingest(db, course_version_id=1, project_id=101, submission_file_id=None,
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title="报告", category="stage3", source_hash="same-source", pages=pages, embedder=fake_embed)
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old_chunk = db.scalar(select(KnowledgeChunk).where(KnowledgeChunk.document_id == document.id))
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old_chunk.text = "过时片段"
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document.index_version = 1
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db.commit()
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reindexed = ingest(db, course_version_id=1, project_id=101, submission_file_id=None,
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title="报告", category="stage3", source_hash="same-source", pages=pages, embedder=fake_embed)
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assert reindexed.id == document.id and reindexed.index_version == 2
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assert db.scalar(select(KnowledgeChunk.text).where(KnowledgeChunk.document_id == document.id)) == pages[0]["text"]
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def test_model_key_is_encrypted_and_hidden(db):
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result = save_model_config(db, {"model": "deepseek-flash", "api_key": "test-private-key"})
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assert result["api_key_configured"] is True
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assert "api_key" not in result
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stored = db.get(AppSetting, "api_key")
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assert b"test-private-key" not in stored.encrypted_value
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assert model_config(db)["model"] == "deepseek-flash"
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def test_missing_offline_embedding_package_fails_clearly(tmp_path):
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with pytest.raises(RuntimeError, match="离线嵌入模型不存在"):
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local_encoder(str(tmp_path / "missing-model"))
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