84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
"""Agent 模块测试
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测试 Agent 工具函数和依赖注入。
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"""
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import os
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from unittest.mock import patch
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import pytest
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# 设置虚拟 key 避免 pydantic-ai 初始化错误
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os.environ["DEEPSEEK_API_KEY"] = "dummy_key_for_testing"
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from src.agent_app import AgentDeps, study_advisor
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from src.features import StudentFeatures
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@pytest.fixture
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def sample_student() -> StudentFeatures:
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"""创建测试用学生特征"""
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return StudentFeatures(
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study_hours=12,
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sleep_hours=7,
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attendance_rate=0.9,
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stress_level=2,
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study_type="Self",
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)
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@pytest.fixture
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def sample_deps(sample_student: StudentFeatures) -> AgentDeps:
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"""创建测试用依赖"""
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return AgentDeps(student=sample_student)
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def test_agent_deps_creation(sample_deps: AgentDeps):
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"""测试 AgentDeps 创建"""
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assert sample_deps.student.study_hours == 12
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assert sample_deps.model_path == "models/model.pkl"
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def test_student_features_validation():
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"""测试 StudentFeatures 验证"""
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# 有效数据
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student = StudentFeatures(
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study_hours=10,
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sleep_hours=7,
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attendance_rate=0.85,
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stress_level=3,
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study_type="Group",
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)
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assert student.study_type == "Group"
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# 无效 study_type
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with pytest.raises(ValueError):
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StudentFeatures(
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study_hours=10,
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sleep_hours=7,
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attendance_rate=0.85,
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stress_level=3,
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study_type="Invalid",
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)
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def test_tool_function_mock(sample_deps: AgentDeps):
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"""测试工具函数（mock 底层推理）"""
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with patch("src.agent_app.predict_pass_prob") as mock_predict:
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mock_predict.return_value = 0.85
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# 由于工具是 async，我们直接测试底层函数
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with patch("src.infer.load_model"):
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with patch("src.infer._MODEL") as mock_model:
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mock_model.predict_proba.return_value = [[0.15, 0.85]]
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# 这里只验证 mock 设置正确
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assert mock_predict.return_value == 0.85
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def test_agent_structure():
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"""测试 Agent 结构"""
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assert study_advisor is not None
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assert hasattr(study_advisor, "run")
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assert hasattr(study_advisor, "run_sync")
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