Backup before system reinstall

2025-09-06 07:37:16 +00:00
parent f9856c31e5
commit e78aefac88
66 changed files with 16347 additions and 1917 deletions
--- a/jixia_academy/core/debate_system/agents/memory_enhanced_agent.py
+++ b/jixia_academy/core/debate_system/agents/memory_enhanced_agent.py
@@ -0,0 +1,538 @@
+#!/usr/bin/env python3
+"""
+增强记忆的ADK智能体
+集成Vertex AI Memory Bank的稷下学宫智能体
+"""
+
+import asyncio
+from typing import Dict, List, Optional, Any
+from dataclasses import dataclass
+
+try:
+    from google.adk import Agent, InvocationContext
+    ADK_AVAILABLE = True
+except ImportError:
+    ADK_AVAILABLE = False
+    print("⚠️ Google ADK 未安装")
+    # 创建一个简单的 InvocationContext 替代类
+    class InvocationContext:
+        def __init__(self, *args, **kwargs):
+            pass
+
+from src.jixia.memory.base_memory_bank import MemoryBankProtocol
+from src.jixia.memory.factory import get_memory_backend
+from config.settings import get_google_genai_config
+
+
+@dataclass
+class BaxianPersonality:
+    """八仙智能体人格定义"""
+    name: str
+    chinese_name: str
+    hexagram: str  # 对应的易经卦象
+    investment_style: str
+    personality_traits: List[str]
+    debate_approach: str
+    memory_focus: List[str]  # 重点记忆的内容类型
+
+
+class MemoryEnhancedAgent:
+    """
+    集成记忆银行的智能体
+    为稷下学宫八仙提供持久化记忆能力
+    """
+    
+    # 八仙人格定义
+    BAXIAN_PERSONALITIES = {
+        "tieguaili": BaxianPersonality(
+            name="tieguaili",
+            chinese_name="铁拐李",
+            hexagram="巽卦",
+            investment_style="逆向投资大师",
+            personality_traits=["逆向思维", "挑战共识", "独立判断", "风险敏感"],
+            debate_approach="质疑主流观点，提出反向思考",
+            memory_focus=["市场异常", "逆向案例", "风险警示", "反向策略"]
+        ),
+        "hanzhongli": BaxianPersonality(
+            name="hanzhongli", 
+            chinese_name="汉钟离",
+            hexagram="离卦",
+            investment_style="平衡协调者",
+            personality_traits=["平衡思维", "综合分析", "稳健决策", "协调统筹"],
+            debate_approach="寻求各方观点的平衡点",
+            memory_focus=["平衡策略", "综合分析", "协调方案", "稳健建议"]
+        ),
+        "zhangguolao": BaxianPersonality(
+            name="zhangguolao",
+            chinese_name="张果老", 
+            hexagram="兑卦",
+            investment_style="历史智慧者",
+            personality_traits=["博古通今", "历史视角", "经验丰富", "智慧深邃"],
+            debate_approach="引用历史案例和长期趋势",
+            memory_focus=["历史案例", "长期趋势", "周期规律", "经验教训"]
+        ),
+        "lancaihe": BaxianPersonality(
+            name="lancaihe",
+            chinese_name="蓝采和",
+            hexagram="坎卦", 
+            investment_style="创新思维者",
+            personality_traits=["创新思维", "潜力发现", "灵活变通", "机会敏锐"],
+            debate_approach="发现新兴机会和创新角度",
+            memory_focus=["创新机会", "新兴趋势", "潜力发现", "灵活策略"]
+        ),
+        "hexiangu": BaxianPersonality(
+            name="hexiangu",
+            chinese_name="何仙姑",
+            hexagram="坤卦",
+            investment_style="直觉洞察者", 
+            personality_traits=["直觉敏锐", "情感智慧", "温和坚定", "洞察人心"],
+            debate_approach="基于直觉和情感智慧的分析",
+            memory_focus=["市场情绪", "直觉判断", "情感因素", "人性洞察"]
+        ),
+        "lvdongbin": BaxianPersonality(
+            name="lvdongbin",
+            chinese_name="吕洞宾", 
+            hexagram="乾卦",
+            investment_style="理性分析者",
+            personality_traits=["理性客观", "逻辑严密", "技术精通", "决策果断"],
+            debate_approach="基于数据和逻辑的严密分析",
+            memory_focus=["技术分析", "数据洞察", "逻辑推理", "理性决策"]
+        ),
+        "hanxiangzi": BaxianPersonality(
+            name="hanxiangzi",
+            chinese_name="韩湘子",
+            hexagram="艮卦",
+            investment_style="艺术感知者",
+            personality_traits=["艺术感知", "美学视角", "创意思维", "感性理解"],
+            debate_approach="从美学和艺术角度分析市场",
+            memory_focus=["美学趋势", "创意洞察", "感性分析", "艺术视角"]
+        ),
+        "caoguojiu": BaxianPersonality(
+            name="caoguojiu",
+            chinese_name="曹国舅",
+            hexagram="震卦",
+            investment_style="实务执行者",
+            personality_traits=["实务导向", "执行力强", "机构视角", "专业严谨"],
+            debate_approach="关注实际执行和机构操作",
+            memory_focus=["执行策略", "机构动向", "实务操作", "专业分析"]
+        )
+    }
+    
+    def __init__(self, agent_name: str, memory_bank: MemoryBankProtocol | None = None):
+        """
+        初始化记忆增强智能体
+        
+        Args:
+            agent_name: 智能体名称 (如 "tieguaili")
+            memory_bank: 记忆银行实例
+        """
+        if not ADK_AVAILABLE:
+            raise ImportError("Google ADK 未安装，无法创建智能体")
+            
+        if agent_name not in self.BAXIAN_PERSONALITIES:
+            raise ValueError(f"未知的智能体: {agent_name}")
+        
+        self.agent_name = agent_name
+        self.personality = self.BAXIAN_PERSONALITIES[agent_name]
+        self.memory_bank = memory_bank
+        self.adk_agent = None
+        
+        # 初始化ADK智能体
+        self._initialize_adk_agent()
+    
+    def _initialize_adk_agent(self):
+        """初始化ADK智能体"""
+        try:
+            # 构建智能体系统提示
+            system_prompt = self._build_system_prompt()
+            
+            # 创建ADK智能体
+            self.adk_agent = Agent(
+                name=self.personality.chinese_name,
+                model="gemini-2.0-flash-exp",
+                system_prompt=system_prompt,
+                temperature=0.7
+            )
+            
+            print(f"✅ 创建ADK智能体: {self.personality.chinese_name}")
+            
+        except Exception as e:
+            print(f"❌ 创建ADK智能体失败: {e}")
+            raise
+    
+    def _build_system_prompt(self) -> str:
+        """构建智能体系统提示"""
+        return f"""
+# {self.personality.chinese_name} - {self.personality.investment_style}
+
+## 角色定位
+你是稷下学宫的{self.personality.chinese_name}，对应易经{self.personality.hexagram}，专精于{self.personality.investment_style}。
+
+## 人格特质
+{', '.join(self.personality.personality_traits)}
+
+## 辩论风格
+{self.personality.debate_approach}
+
+## 记忆重点
+你特别关注并记住以下类型的信息：
+{', '.join(self.personality.memory_focus)}
+
+## 行为准则
+1. 始终保持你的人格特质和投资风格
+2. 在辩论中体现你的独特视角
+3. 学习并记住重要的讨论内容
+4. 与其他七仙协作，但保持独立观点
+5. 基于历史记忆提供更有深度的分析
+
+## 记忆运用
+- 在回答前，会参考相关的历史记忆
+- 学习用户偏好，调整沟通风格
+- 记住成功的策略和失败的教训
+- 与其他智能体分享有价值的洞察
+
+请始终以{self.personality.chinese_name}的身份进行对话和分析。
+"""
+    
+    async def get_memory_context(self, topic: str) -> str:
+        """
+        获取与主题相关的记忆上下文
+        
+        Args:
+            topic: 讨论主题
+            
+        Returns:
+            格式化的记忆上下文
+        """
+        if not self.memory_bank:
+            return ""
+        
+        try:
+            context = await self.memory_bank.get_agent_context(
+                self.agent_name, topic
+            )
+            return context
+        except Exception as e:
+            print(f"⚠️ 获取记忆上下文失败: {e}")
+            return ""
+    
+    async def respond_with_memory(self, 
+                                 message: str, 
+                                 topic: str = "",
+                                 context: InvocationContext = None) -> str:
+        """
+        基于记忆增强的响应
+        
+        Args:
+            message: 输入消息
+            topic: 讨论主题
+            context: ADK调用上下文
+            
+        Returns:
+            智能体响应
+        """
+        try:
+            # 获取记忆上下文
+            memory_context = await self.get_memory_context(topic)
+            
+            # 构建增强的提示
+            enhanced_prompt = f"""
+{memory_context}
+
+## 当前讨论
+主题: {topic}
+消息: {message}
+
+请基于你的记忆和人格特质进行回应。
+"""
+            
+            # 使用ADK生成响应
+            if context is None:
+                context = InvocationContext()
+            
+            response_generator = self.adk_agent.run_async(
+                enhanced_prompt, 
+                context=context
+            )
+            
+            # 收集响应
+            response_parts = []
+            async for chunk in response_generator:
+                if hasattr(chunk, 'text'):
+                    response_parts.append(chunk.text)
+                elif isinstance(chunk, str):
+                    response_parts.append(chunk)
+            
+            response = ''.join(response_parts)
+            
+            # 保存对话记忆
+            if self.memory_bank and response:
+                await self._save_conversation_memory(message, response, topic)
+            
+            return response
+            
+        except Exception as e:
+            print(f"❌ 生成响应失败: {e}")
+            return f"抱歉，{self.personality.chinese_name}暂时无法回应。"
+    
+    async def _save_conversation_memory(self, 
+                                      user_message: str, 
+                                      agent_response: str, 
+                                      topic: str):
+        """
+        保存对话记忆
+        
+        Args:
+            user_message: 用户消息
+            agent_response: 智能体响应
+            topic: 讨论主题
+        """
+        try:
+            # 保存用户消息记忆
+            await self.memory_bank.add_memory(
+                agent_name=self.agent_name,
+                content=f"用户询问: {user_message}",
+                memory_type="conversation",
+                debate_topic=topic,
+                metadata={"role": "user"}
+            )
+            
+            # 保存智能体响应记忆
+            await self.memory_bank.add_memory(
+                agent_name=self.agent_name,
+                content=f"我的回应: {agent_response}",
+                memory_type="conversation", 
+                debate_topic=topic,
+                metadata={"role": "assistant"}
+            )
+            
+        except Exception as e:
+            print(f"⚠️ 保存对话记忆失败: {e}")
+    
+    async def learn_preference(self, preference: str, topic: str = ""):
+        """
+        学习用户偏好
+        
+        Args:
+            preference: 偏好描述
+            topic: 相关主题
+        """
+        if not self.memory_bank:
+            return
+        
+        try:
+            await self.memory_bank.add_memory(
+                agent_name=self.agent_name,
+                content=f"用户偏好: {preference}",
+                memory_type="preference",
+                debate_topic=topic,
+                metadata={"learned_from": "user_feedback"}
+            )
+            
+            print(f"✅ {self.personality.chinese_name} 学习了新偏好")
+            
+        except Exception as e:
+            print(f"⚠️ 学习偏好失败: {e}")
+    
+    async def save_strategy_insight(self, insight: str, topic: str = ""):
+        """
+        保存策略洞察
+        
+        Args:
+            insight: 策略洞察
+            topic: 相关主题
+        """
+        if not self.memory_bank:
+            return
+        
+        try:
+            await self.memory_bank.add_memory(
+                agent_name=self.agent_name,
+                content=f"策略洞察: {insight}",
+                memory_type="strategy",
+                debate_topic=topic,
+                metadata={"insight_type": "strategy"}
+            )
+            
+            print(f"✅ {self.personality.chinese_name} 保存了策略洞察")
+            
+        except Exception as e:
+            print(f"⚠️ 保存策略洞察失败: {e}")
+
+
+class BaxianMemoryCouncil:
+    """
+    八仙记忆议会
+    管理所有八仙智能体的记忆增强功能
+    """
+    
+    def __init__(self, memory_bank: MemoryBankProtocol | None = None):
+        """
+        初始化八仙记忆议会
+        
+        Args:
+            memory_bank: 记忆银行实例
+        """
+        self.memory_bank = memory_bank
+        self.agents = {}
+        
+        # 初始化所有八仙智能体
+        self._initialize_agents()
+    
+    def _initialize_agents(self):
+        """初始化所有八仙智能体"""
+        for agent_name in MemoryEnhancedAgent.BAXIAN_PERSONALITIES.keys():
+            try:
+                agent = MemoryEnhancedAgent(agent_name, self.memory_bank)
+                self.agents[agent_name] = agent
+                print(f"✅ 初始化 {agent.personality.chinese_name}")
+            except Exception as e:
+                print(f"❌ 初始化 {agent_name} 失败: {e}")
+    
+    async def conduct_memory_debate(self, 
+                                   topic: str, 
+                                   participants: List[str] = None,
+                                   rounds: int = 3) -> Dict[str, Any]:
+        """
+        进行记忆增强的辩论
+        
+        Args:
+            topic: 辩论主题
+            participants: 参与者列表，None表示所有八仙
+            rounds: 辩论轮数
+            
+        Returns:
+            辩论结果
+        """
+        if participants is None:
+            participants = list(self.agents.keys())
+        
+        conversation_history = []
+        context = InvocationContext()
+        
+        print(f"🏛️ 稷下学宫八仙论道开始: {topic}")
+        
+        for round_num in range(rounds):
+            print(f"\n--- 第 {round_num + 1} 轮 ---")
+            
+            for agent_name in participants:
+                if agent_name not in self.agents:
+                    continue
+                
+                agent = self.agents[agent_name]
+                
+                # 构建当前轮次的提示
+                round_prompt = f"""
+轮次: {round_num + 1}/{rounds}
+主题: {topic}
+
+请基于你的记忆和人格特质，对此主题发表观点。
+如果这不是第一轮，请考虑其他仙友的观点并做出回应。
+"""
+                
+                # 获取响应
+                response = await agent.respond_with_memory(
+                    round_prompt, topic, context
+                )
+                
+                # 记录对话历史
+                conversation_history.append({
+                    "round": round_num + 1,
+                    "agent": agent_name,
+                    "chinese_name": agent.personality.chinese_name,
+                    "content": response
+                })
+                
+                print(f"{agent.personality.chinese_name}: {response[:100]}...")
+        
+        # 保存辩论会话到记忆银行
+        if self.memory_bank:
+            await self.memory_bank.save_debate_session(
+                debate_topic=topic,
+                participants=participants,
+                conversation_history=conversation_history
+            )
+        
+        return {
+            "topic": topic,
+            "participants": participants,
+            "rounds": rounds,
+            "conversation_history": conversation_history,
+            "total_exchanges": len(conversation_history)
+        }
+    
+    async def get_collective_memory_summary(self, topic: str) -> str:
+        """
+        获取集体记忆摘要
+        
+        Args:
+            topic: 主题
+            
+        Returns:
+            集体记忆摘要
+        """
+        if not self.memory_bank:
+            return "记忆银行未启用"
+        
+        summaries = []
+        
+        for agent_name, agent in self.agents.items():
+            context = await agent.get_memory_context(topic)
+            if context and context.strip():
+                summaries.append(context)
+        
+        if summaries:
+            return f"# 稷下学宫集体记忆摘要\n\n" + "\n\n".join(summaries)
+        else:
+            return "暂无相关集体记忆"
+
+
+# 便捷函数
+async def create_memory_enhanced_council() -> BaxianMemoryCouncil:
+    """
+    创建记忆增强的八仙议会
+    
+    Returns:
+        配置好的BaxianMemoryCouncil实例
+    """
+    try:
+        # 初始化记忆银行
+        memory_bank = get_memory_backend()
+        
+        # 创建八仙议会
+        council = BaxianMemoryCouncil(memory_bank)
+        
+        print("🏛️ 稷下学宫记忆增强议会创建完成")
+        return council
+        
+    except Exception as e:
+        print(f"❌ 创建记忆增强议会失败: {e}")
+        # 创建无记忆版本
+        return BaxianMemoryCouncil(None)
+
+
+if __name__ == "__main__":
+    async def test_memory_enhanced_agent():
+        """测试记忆增强智能体"""
+        try:
+            # 创建记忆增强议会
+            council = await create_memory_enhanced_council()
+            
+            # 进行记忆增强辩论
+            result = await council.conduct_memory_debate(
+                topic="NVIDIA股票投资分析",
+                participants=["tieguaili", "lvdongbin", "hexiangu"],
+                rounds=2
+            )
+            
+            print(f"\n🏛️ 辩论完成，共 {result['total_exchanges']} 次发言")
+            
+            # 获取集体记忆摘要
+            summary = await council.get_collective_memory_summary("NVIDIA股票投资分析")
+            print(f"\n📚 集体记忆摘要:\n{summary}")
+            
+        except Exception as e:
+            print(f"❌ 测试失败: {e}")
+    
+    # 运行测试
+    asyncio.run(test_memory_enhanced_agent())