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LangGraph AI 系统测试与高可用保障体系

LangGraph 应用的测试和高可用保障需要系统化方法，以下从测试策略到生产保障的完整方案：

一、LangGraph 测试金字塔模型

1. 节点单元测试

# 测试工具节点
def test_tool_node():state = {"input": "查询北京天气"}result = tool_node(state)assert "temperature" in resultassert isinstance(result["temperature"], float)# 测试LLM节点
from unittest.mock import MagicMockdef test_llm_node(monkeypatch):mock_llm = MagicMock()mock_llm.invoke.return_value = "晴天，25℃"monkeypatch.setattr("app.llm", mock_llm)state = {"messages": [HumanMessage("北京天气")]}result = llm_node(state)assert "晴天" in result["response"]

2. 工作流集成测试

def test_weather_workflow():# 模拟完整工作流app = build_weather_graph()inputs = {"location": "北京", "unit": "celsius"}result = app.invoke(inputs)# 验证输出结构assert "weather" in resultassert "temperature" in result["weather"]assert "unit" in result["weather"]# 验证执行路径tracer = get_trace_log(result)assert "location_validation" in tracerassert "weather_api_call" in tracer

二、端到端测试策略

1. 真实场景测试框架

# conftest.py
@pytest.fixture
def production_graph():return compile_production_graph()# test_e2e.py
@pytest.mark.parametrize("input,expected", [({"query": "北京天气"}, {"contains": ["温度"]}),({"query": "股票AAPL"}, {"contains": ["Apple"]}),({"query": "无效指令"}, {"error_type": "InvalidInput"})
])
def test_full_workflow(production_graph, input, expected):result = production_graph.invoke(input)