179 lines
5.9 KiB
Python
179 lines
5.9 KiB
Python
#!/usr/bin/env python3
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"""
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LangGraph 工作流测试
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"""
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import asyncio
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import sys
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import os
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# 添加项目根目录到 Python 路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from src.workflows.chat_workflow import get_chat_workflow, create_chat_session
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from src.models.chat_models import ChatMessage, MessageRole
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from datetime import datetime
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async def test_workflow_simple():
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"""测试工作流简单模式"""
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print("🧪 测试 LangGraph 工作流 (简单模式)")
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print("=" * 50)
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try:
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# 获取工作流
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workflow = get_chat_workflow()
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# 测试用例
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test_cases = [
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"你好，我是新用户",
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"查询订单 ORD12345",
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"我的订单 ORD12346 有什么问题吗？",
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"今天天气怎么样？",
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"谢谢你的帮助"
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]
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for i, user_input in enumerate(test_cases, 1):
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print(f"\n📝 测试 {i}: {user_input}")
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# 使用简单模式
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result = await workflow.run_simple(user_input)
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print(f"🤖 AI: {result['response']}")
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print(f"✅ 会话ID: {result['session_id'][:8]}...")
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print(f"📊 处理步骤: {len(result['results'])} 个")
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print("\n🎉 工作流简单模式测试完成！")
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except Exception as e:
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print(f"❌ 测试失败: {e}")
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raise
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async def test_workflow_streaming():
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"""测试工作流流式模式"""
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print("\n🧪 测试 LangGraph 工作流 (流式模式)")
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print("=" * 50)
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try:
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# 获取工作流
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workflow = get_chat_workflow()
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session_id = await create_chat_session()
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# 测试订单查询
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print(f"\n📝 测试订单查询 (会话: {session_id[:8]}...)")
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print("用户: 帮我查询订单 ORD12345 的状态")
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print("🤖 AI: ", end="", flush=True)
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async for result in workflow.run_streaming("帮我查询订单 ORD12345 的状态", session_id):
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result_type = result.get("type", "")
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content = result.get("content", "")
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if result_type == "workflow_start":
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print(f"\n🔄 {content}")
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elif result_type == "intent_analysis":
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print(f"\n🎯 意图分析完成")
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elif result_type == "route_decision":
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print(f"\n🚦 {content}")
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elif result_type == "order_info":
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print(f"\n📦 订单信息已获取")
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elif result_type == "thinking_start":
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print(f"\n🤔 {content}")
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elif result_type == "thinking":
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print(content, end="", flush=True)
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elif result_type == "diagnosis_result":
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print(f"\n🔍 诊断完成")
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elif result_type == "final_response":
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print(f"\n💬 最终回复: {content[:100]}...")
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elif result_type == "error":
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print(f"\n❌ {content}")
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print("\n✅ 订单查询测试完成")
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# 测试自然对话
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print(f"\n📝 测试自然对话")
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print("用户: 今天天气真不错")
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print("🤖 AI: ", end="", flush=True)
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async for result in workflow.run_streaming("今天天气真不错", session_id):
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result_type = result.get("type", "")
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content = result.get("content", "")
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if result_type == "chat_chunk":
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print(content, end="", flush=True)
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elif result_type == "chat_complete":
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if not content: # 如果没有通过 chunk 获取到内容
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print(content, end="", flush=True)
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print("\n✅ 自然对话测试完成")
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print("\n🎉 工作流流式模式测试完成！")
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except Exception as e:
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print(f"❌ 测试失败: {e}")
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raise
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async def test_intent_analysis():
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"""测试意图分析"""
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print("\n🧪 测试意图分析")
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print("=" * 50)
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try:
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from src.workflows.nodes.intent_analysis import intent_analysis_node
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from src.models.chat_models import WorkflowState
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# 测试用例
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test_cases = [
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("查询订单 ORD12345", "订单查询"),
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("我的订单什么时候到？", "订单查询"),
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("今天天气怎么样？", "一般对话"),
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("你好，很高兴认识你", "一般对话"),
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("订单 ORD12346 有问题", "订单查询")
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]
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for user_input, expected_type in test_cases:
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print(f"\n📝 测试: {user_input}")
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# 创建工作流状态
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state = WorkflowState(
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session_id="test_intent",
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user_input=user_input,
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chat_history=[],
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timestamp=datetime.now(),
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current_node="intent_analysis"
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)
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# 运行意图分析
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result = await intent_analysis_node(state)
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intent_result = result.get("intent_result", {})
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intent_type = intent_result.get("intent_type", "unknown")
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confidence = intent_result.get("confidence", 0.0)
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print(f"🎯 预期: {expected_type}")
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print(f"🎯 结果: {intent_type} (置信度: {confidence:.2f})")
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print("\n🎉 意图分析测试完成！")
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except Exception as e:
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print(f"❌ 测试失败: {e}")
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raise
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async def main():
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"""主测试函数"""
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print("🚀 开始测试 LangGraph 工作流")
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try:
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await test_workflow_simple()
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await test_workflow_streaming()
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await test_intent_analysis()
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print("\n🎊 所有工作流测试通过！")
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except Exception as e:
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print(f"\n💥 测试过程中出现错误: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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asyncio.run(main()) |