245 lines
8.9 KiB
Python
245 lines
8.9 KiB
Python
#!/usr/bin/env python3
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"""
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Vertex AI Memory Bank 演示脚本
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展示稷下学宫记忆增强AI辩论系统
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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.jixia.agents.memory_enhanced_agent import create_memory_enhanced_council
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from config.settings import validate_config
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async def demo_memory_enhanced_debate():
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"""演示记忆增强的AI辩论"""
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print("🏛️ 稷下学宫 Vertex AI Memory Bank 演示")
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print("=" * 60)
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# 验证配置
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print("🔧 验证配置...")
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if not validate_config("google_adk"):
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print("❌ 配置验证失败，请检查环境变量")
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return
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try:
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# 创建记忆增强议会
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print("\n🎭 创建八仙记忆增强议会...")
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council = await create_memory_enhanced_council()
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# 演示主题
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topics = [
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"特斯拉股票投资价值分析",
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"人工智能行业投资机会",
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"加密货币市场前景展望"
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]
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# 选择参与的仙人（为了演示，只选择几位）
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participants = ["tieguaili", "lvdongbin", "hexiangu", "zhangguolao"]
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for i, topic in enumerate(topics, 1):
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print(f"\n{'='*40}")
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print(f"🎯 第 {i} 场辩论: {topic}")
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print(f"{'='*40}")
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# 进行记忆增强辩论
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result = await council.conduct_memory_debate(
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topic=topic,
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participants=participants,
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rounds=2 # 每场2轮，保持演示简洁
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)
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print(f"\n📊 辩论结果:")
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print(f" 主题: {result['topic']}")
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print(f" 参与者: {len(result['participants'])} 位仙人")
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print(f" 总发言: {result['total_exchanges']} 次")
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# 显示部分对话内容
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print(f"\n💬 精彩观点摘录:")
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for exchange in result['conversation_history'][:4]: # 只显示前4条
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content_preview = exchange['content'][:120] + "..." if len(exchange['content']) > 120 else exchange['content']
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print(f" 🗣️ {exchange['chinese_name']}: {content_preview}")
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# 获取集体记忆摘要
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print(f"\n📚 获取集体记忆...")
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summary = await council.get_collective_memory_summary(topic)
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if "暂无相关集体记忆" not in summary:
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print(f" ✅ 已生成 {len(summary)} 字符的记忆摘要")
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else:
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print(f" ℹ️ 这是新主题，正在建立记忆")
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# 演示间隔
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if i < len(topics):
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print(f"\n⏳ 准备下一场辩论...")
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await asyncio.sleep(1)
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# 最终演示：展示记忆的累积效果
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print(f"\n{'='*60}")
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print("🧠 记忆累积效果演示")
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print(f"{'='*60}")
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# 让铁拐李基于所有记忆回答一个综合问题
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tieguaili = council.agents.get("tieguaili")
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if tieguaili:
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print(f"\n🤔 向铁拐李提问: '基于你的所有记忆，总结一下当前市场的主要风险'")
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comprehensive_response = await tieguaili.respond_with_memory(
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message="基于你参与的所有辩论和积累的记忆，总结一下当前市场的主要风险和你的投资建议。",
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topic="综合市场分析"
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)
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print(f"\n🧙♂️ 铁拐李的综合分析:")
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print(f" {comprehensive_response}")
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# 展示记忆学习功能
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print(f"\n🎓 演示记忆学习功能...")
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# 让何仙姑学习一个用户偏好
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hexiangu = council.agents.get("hexiangu")
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if hexiangu:
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await hexiangu.learn_preference(
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preference="用户偏好ESG投资，关注环境和社会责任",
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topic="投资偏好"
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)
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print(f" ✅ 何仙姑学习了ESG投资偏好")
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# 基于新学到的偏好回答问题
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esg_response = await hexiangu.respond_with_memory(
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message="推荐一些符合ESG标准的投资标的",
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topic="ESG投资"
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)
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print(f"\n👸 何仙姑基于学习的偏好回应:")
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print(f" {esg_response[:200]}...")
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print(f"\n🎉 演示完成！")
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print(f"\n💡 Memory Bank 的优势:")
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print(f" ✅ 智能体能记住历史对话和分析")
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print(f" ✅ 学习用户偏好，提供个性化建议")
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print(f" ✅ 积累投资策略和市场洞察")
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print(f" ✅ 跨会话保持一致的人格和观点")
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print(f" ✅ 基于历史经验做出更好的决策")
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except Exception as e:
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print(f"❌ 演示过程中出现错误: {e}")
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print(f"💡 请检查:")
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print(f" - Google Cloud Project ID 是否正确配置")
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print(f" - Vertex AI API 是否已启用")
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print(f" - 网络连接是否正常")
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async def demo_individual_memory_features():
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"""演示个体记忆功能"""
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print(f"\n{'='*60}")
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print("🔍 个体记忆功能详细演示")
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print(f"{'='*60}")
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try:
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from src.jixia.memory.vertex_memory_bank import VertexMemoryBank
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from src.jixia.agents.memory_enhanced_agent import MemoryEnhancedAgent
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# 创建记忆银行
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memory_bank = VertexMemoryBank.from_config()
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# 创建单个智能体进行详细演示
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agent = MemoryEnhancedAgent("tieguaili", memory_bank)
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print(f"\n🧙♂️ 与 {agent.personality.chinese_name} 的记忆互动演示")
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# 1. 添加不同类型的记忆
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print(f"\n📝 添加不同类型的记忆...")
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memories_to_add = [
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{
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"content": "在2008年金融危机中，逆向投资者获得了丰厚回报",
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"memory_type": "knowledge",
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"topic": "历史教训"
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},
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{
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"content": "用户偏好价值投资，不喜欢高风险的成长股",
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"memory_type": "preference",
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"topic": "用户偏好"
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},
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{
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"content": "当市场过度乐观时，应该保持谨慎并寻找反向机会",
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"memory_type": "strategy",
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"topic": "投资策略"
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}
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]
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for memory in memories_to_add:
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await memory_bank.add_memory(
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agent_name="tieguaili",
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content=memory["content"],
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memory_type=memory["memory_type"],
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debate_topic=memory["topic"]
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)
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print(f" ✅ 添加{memory['memory_type']}记忆: {memory['content'][:50]}...")
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# 2. 搜索记忆
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print(f"\n🔍 搜索相关记忆...")
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search_queries = ["金融危机", "价值投资", "投资策略"]
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for query in search_queries:
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results = await memory_bank.search_memories(
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agent_name="tieguaili",
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query=query,
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limit=3
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)
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print(f" 🔎 搜索 '{query}': 找到 {len(results)} 条相关记忆")
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for result in results:
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relevance = result.get('relevance_score', 'N/A')
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print(f" - {result['content'][:60]}... (相关度: {relevance})")
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# 3. 基于记忆的智能回应
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print(f"\n🤖 基于记忆的智能回应演示...")
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questions = [
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"现在市场很乐观，你有什么建议？",
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"推荐一些适合保守投资者的标的",
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"历史上有哪些值得借鉴的投资教训？"
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]
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for question in questions:
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print(f"\n❓ 问题: {question}")
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response = await agent.respond_with_memory(
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message=question,
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topic="投资咨询"
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)
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print(f"🧙♂️ 铁拐李: {response[:150]}...")
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print(f"\n✨ 个体记忆功能演示完成！")
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except Exception as e:
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print(f"❌ 个体记忆演示失败: {e}")
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async def main():
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"""主演示函数"""
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print("🚀 启动 Vertex AI Memory Bank 完整演示")
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# 主要演示：记忆增强辩论
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await demo_memory_enhanced_debate()
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# 详细演示：个体记忆功能
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await demo_individual_memory_features()
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print(f"\n🏛️ 稷下学宫 Memory Bank 演示结束")
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print(f"📖 更多信息请参考: docs/VERTEX_MEMORY_BANK_SETUP.md")
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if __name__ == "__main__":
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# 运行演示
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asyncio.run(main()) |