72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
#!/usr/bin/env python3
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"""
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测试 Vertex AI Memory Bank 功能
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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.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '.')))
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from src.jixia.memory.factory import get_memory_backend
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async def test_vertex_memory_bank():
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"""测试 Vertex Memory Bank 功能"""
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print("🧪 Vertex AI Memory Bank 功能测试\n")
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try:
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# 获取 Vertex Memory Bank 后端
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print("🔍 正在获取 Vertex Memory Bank 后端...")
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memory_bank = get_memory_backend(prefer='vertex')
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print("✅ 成功获取 Vertex Memory Bank 后端\n")
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# 测试创建记忆银行
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print("🔍 正在为吕洞宾创建记忆银行...")
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bank_id = await memory_bank.create_memory_bank("lvdongbin", "吕洞宾的记忆银行")
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print(f"✅ 成功为吕洞宾创建记忆银行: {bank_id}\n")
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# 测试添加记忆
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print("🔍 正在为吕洞宾添加记忆...")
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memory_id = await memory_bank.add_memory(
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agent_name="lvdongbin",
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content="在讨论NVIDIA股票时，我倾向于使用DCF模型评估其内在价值，并关注其在AI领域的竞争优势。",
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memory_type="preference",
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debate_topic="NVIDIA投资分析",
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metadata={"confidence": "high"}
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)
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print(f"✅ 成功为吕洞宾添加记忆: {memory_id}\n")
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# 测试搜索记忆
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print("🔍 正在搜索吕洞宾关于NVIDIA的记忆...")
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results = await memory_bank.search_memories(
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agent_name="lvdongbin",
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query="NVIDIA",
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memory_type="preference"
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)
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print(f"✅ 搜索完成，找到 {len(results)} 条相关记忆\n")
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if results:
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print("🔍 搜索结果:")
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for i, result in enumerate(results, 1):
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print(f" {i}. {result['content']}")
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print(f" 相关性评分: {result['relevance_score']:.4f}\n")
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# 测试获取上下文
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print("🔍 正在获取吕洞宾关于NVIDIA投资分析的上下文...")
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context = await memory_bank.get_agent_context("lvdongbin", "NVIDIA投资分析")
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print("✅ 成功获取上下文\n")
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print("🔍 上下文内容:")
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print(context)
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print("\n")
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print("🎉 Vertex AI Memory Bank 功能测试完成!")
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except Exception as e:
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print(f"❌ 测试过程中发生错误: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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asyncio.run(test_vertex_memory_bank()) |