133 lines
5.2 KiB
Python
133 lines
5.2 KiB
Python
"""T21-1 embedding 服务单测：embed_texts / embed_text（全部 mock HTTP，不依赖 Ollama）。
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覆盖四类：
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1. 成功语义：批量顺序一一对应、维度校验通过、空列表短路不发请求；
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2. 失败语义：连接拒绝 / 非 200（模型未拉取 404）/ 响应结构异常 → EmbeddingError；
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3. 维度防线：维度不符（8 维假数据）必须报错——防静默污染检索（拍板口径）；
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4. 便捷方法：embed_text 等价 embed_texts([text])[0]。
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httpx.MockTransport 注入：_client() 是 monkeypatch 点（settings 超时透传）。
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"""
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from __future__ import annotations
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import httpx
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import pytest
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from app.config.settings import settings
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from app.service import embedding as emb
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def _mock_client(handler) -> httpx.Client:
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"""构造 mock 传输层的 httpx.Client（替换真实网络）。"""
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return httpx.Client(transport=httpx.MockTransport(handler), timeout=5)
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@pytest.fixture(autouse=True)
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def _patch_client(monkeypatch):
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"""默认给一个「合法 1024 维响应」的 mock 客户端，各用例按需覆盖。"""
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def handler(request: httpx.Request) -> httpx.Response:
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body = request.read()
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import json
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payload = json.loads(body)
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vectors = [[0.1] * settings.embed_dim for _ in payload["input"]]
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return httpx.Response(200, json={"model": payload["model"], "embeddings": vectors})
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monkeypatch.setattr(emb, "_client", lambda: _mock_client(handler))
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class TestSuccess:
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"""成功语义。"""
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def test_batch_order_preserved(self, monkeypatch):
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def handler(request: httpx.Request) -> httpx.Response:
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import json
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payload = json.loads(request.read())
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# 每条向量以首字符标记，断言顺序一一对应
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vectors = [[float(ord(t[0]))] + [0.0] * (settings.embed_dim - 1) for t in payload["input"]]
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return httpx.Response(200, json={"embeddings": vectors})
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monkeypatch.setattr(emb, "_client", lambda: _mock_client(handler))
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out = emb.embed_texts(["甲文本", "乙文本", "丙文本"])
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assert len(out) == 3
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assert out[0][0] == float(ord("甲"))
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assert out[2][0] == float(ord("丙"))
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def test_empty_input_short_circuits(self, monkeypatch):
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# 空列表不发请求：handler 一旦被调用就 fail（返回 500 触发错误）
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def handler(request: httpx.Request) -> httpx.Response:
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raise AssertionError("空输入不应发起 HTTP 请求")
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monkeypatch.setattr(emb, "_client", lambda: _mock_client(handler))
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assert emb.embed_texts([]) == []
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def test_request_payload_uses_settings_model(self, monkeypatch):
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captured: dict = {}
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def handler(request: httpx.Request) -> httpx.Response:
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import json
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captured.update(json.loads(request.read()))
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payload = json.loads(request.read())
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vectors = [[0.0] * settings.embed_dim for _ in payload["input"]]
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return httpx.Response(200, json={"embeddings": vectors})
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monkeypatch.setattr(emb, "_client", lambda: _mock_client(handler))
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emb.embed_texts(["测试"])
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assert captured["model"] == settings.embed_model
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assert captured["input"] == ["测试"]
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class TestFailure:
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"""失败语义：一律 EmbeddingError，不静默降级。"""
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def test_connection_refused(self, monkeypatch):
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def handler(request: httpx.Request) -> httpx.Response:
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raise httpx.ConnectError("connection refused")
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monkeypatch.setattr(emb, "_client", lambda: _mock_client(handler))
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with pytest.raises(emb.EmbeddingError, match="连接失败"):
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emb.embed_texts(["任意"])
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def test_model_not_found_404(self, monkeypatch):
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def handler(request: httpx.Request) -> httpx.Response:
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return httpx.Response(404, json={"error": "model 'bge-m3' not found"})
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monkeypatch.setattr(emb, "_client", lambda: _mock_client(handler))
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with pytest.raises(emb.EmbeddingError, match="HTTP 404"):
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emb.embed_texts(["任意"])
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def test_malformed_response(self, monkeypatch):
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def handler(request: httpx.Request) -> httpx.Response:
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# embeddings 缺失 / 数量不匹配均属结构异常
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return httpx.Response(200, json={"embeddings": [[0.0] * settings.embed_dim]})
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monkeypatch.setattr(emb, "_client", lambda: _mock_client(handler))
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with pytest.raises(emb.EmbeddingError, match="数量与输入不匹配"):
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emb.embed_texts(["一", "二"])
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class TestDimensionGuard:
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"""维度防线（拍板：维度不符必须报错）。"""
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def test_wrong_dimension_rejected(self, monkeypatch):
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def handler(request: httpx.Request) -> httpx.Response:
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import json
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payload = json.loads(request.read())
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vectors = [[0.1] * 8 for _ in payload["input"]] # 假 8 维
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return httpx.Response(200, json={"embeddings": vectors})
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monkeypatch.setattr(emb, "_client", lambda: _mock_client(handler))
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with pytest.raises(emb.EmbeddingError, match="维度不符"):
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emb.embed_texts(["任意"])
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class TestConvenience:
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def test_embed_text_single(self):
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out = emb.embed_text("单条")
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assert len(out) == settings.embed_dim
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