feat: 重构项目结构并添加新功能
- 新增Cloudflare AutoRAG/Vectorize集成文档 - 实现Vertex AI记忆银行功能 - 重构项目目录结构，清理无用文件 - 更新README以反映最新架构 - 添加Google ADK集成测试脚本 - 完善需求文档和设计规范
This commit is contained in:
ben
275
experiments/memory_bank_experiment.py
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275
experiments/memory_bank_experiment.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Memory Bank 实验脚本
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测试八仙人格的长期记忆功能
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"""
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import os
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import asyncio
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from datetime import datetime
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from typing import Dict, List, Any
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import json
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# Google GenAI 导入
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try:
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import google.genai as genai
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from google.genai import types
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except ImportError:
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print("❌ 请安装 google-genai: pip install google-genai")
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exit(1)
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class MemoryBankExperiment:
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"""Memory Bank 实验类"""
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def __init__(self):
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self.api_key = os.getenv('GOOGLE_API_KEY')
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if not self.api_key:
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raise ValueError("请设置 GOOGLE_API_KEY 环境变量")
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# 初始化 GenAI
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genai.configure(api_key=self.api_key)
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# 八仙人格基线
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self.immortal_baselines = {
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"吕洞宾": {
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"mbti_type": "ENTJ",
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"core_traits": {
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"assertiveness": 0.9,
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"analytical": 0.8,
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"risk_tolerance": 0.8,
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"optimism": 0.7
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},
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"personality_description": "剑仙投资顾问，主动进取，敢于冒险，技术分析专家"
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},
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"何仙姑": {
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"mbti_type": "ISFJ",
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"core_traits": {
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"empathy": 0.9,
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"caution": 0.8,
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"loyalty": 0.8,
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"optimism": 0.4
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},
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"personality_description": "慈悲风控专家，谨慎小心，保护意识强，风险厌恶"
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},
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"张果老": {
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"mbti_type": "INTP",
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"core_traits": {
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"analytical": 0.9,
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"curiosity": 0.8,
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"traditional": 0.7,
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"caution": 0.6
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},
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"personality_description": "历史数据分析师，深度思考，逆向思维，传统智慧"
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}
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}
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# 记忆存储（模拟 Memory Bank）
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self.memory_bank = {}
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def initialize_immortal_memory(self, immortal_name: str):
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"""初始化仙人的记忆空间"""
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if immortal_name not in self.memory_bank:
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self.memory_bank[immortal_name] = {
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"personality_baseline": self.immortal_baselines[immortal_name],
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"conversation_history": [],
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"viewpoint_evolution": [],
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"decision_history": [],
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"created_at": datetime.now().isoformat(),
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"last_updated": datetime.now().isoformat()
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}
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print(f"🎭 初始化 {immortal_name} 的记忆空间")
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def store_memory(self, immortal_name: str, memory_type: str, content: Dict[str, Any]):
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"""存储记忆到 Memory Bank"""
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self.initialize_immortal_memory(immortal_name)
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memory_entry = {
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"type": memory_type,
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"content": content,
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"timestamp": datetime.now().isoformat(),
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"session_id": f"session_{len(self.memory_bank[immortal_name]['conversation_history'])}"
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}
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if memory_type == "conversation":
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self.memory_bank[immortal_name]["conversation_history"].append(memory_entry)
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elif memory_type == "viewpoint":
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self.memory_bank[immortal_name]["viewpoint_evolution"].append(memory_entry)
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elif memory_type == "decision":
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self.memory_bank[immortal_name]["decision_history"].append(memory_entry)
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self.memory_bank[immortal_name]["last_updated"] = datetime.now().isoformat()
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print(f"💾 {immortal_name} 存储了 {memory_type} 记忆")
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def retrieve_relevant_memories(self, immortal_name: str, query: str) -> List[Dict]:
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"""检索相关记忆"""
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if immortal_name not in self.memory_bank:
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return []
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# 简单的关键词匹配（实际应该使用向量相似度搜索）
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relevant_memories = []
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query_lower = query.lower()
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for memory in self.memory_bank[immortal_name]["conversation_history"]:
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if any(keyword in memory["content"].get("message", "").lower()
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for keyword in query_lower.split()):
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relevant_memories.append(memory)
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return relevant_memories[-5:] # 返回最近5条相关记忆
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async def generate_immortal_response(self, immortal_name: str, query: str) -> str:
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"""生成仙人的回应，基于记忆和人格基线"""
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# 检索相关记忆
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relevant_memories = self.retrieve_relevant_memories(immortal_name, query)
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# 构建上下文
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context = self.build_context(immortal_name, relevant_memories)
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# 生成回应
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model = genai.GenerativeModel('gemini-2.0-flash-exp')
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prompt = f"""
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你是{immortal_name}，{self.immortal_baselines[immortal_name]['personality_description']}。
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你的核心人格特质：
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{json.dumps(self.immortal_baselines[immortal_name]['core_traits'], ensure_ascii=False, indent=2)}
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你的相关记忆：
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{json.dumps(relevant_memories, ensure_ascii=False, indent=2)}
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请基于你的人格特质和记忆，回答以下问题：
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{query}
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要求：
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1. 保持人格一致性
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2. 参考历史记忆
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3. 回答控制在100字以内
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4. 体现你的独特风格
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"""
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response = await model.generate_content_async(prompt)
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return response.text
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def build_context(self, immortal_name: str, memories: List[Dict]) -> str:
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"""构建上下文信息"""
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context_parts = []
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# 添加人格基线
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baseline = self.immortal_baselines[immortal_name]
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context_parts.append(f"人格类型: {baseline['mbti_type']}")
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context_parts.append(f"核心特质: {json.dumps(baseline['core_traits'], ensure_ascii=False)}")
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# 添加相关记忆
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if memories:
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context_parts.append("相关记忆:")
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for memory in memories[-3:]: # 最近3条记忆
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context_parts.append(f"- {memory['content'].get('message', '')}")
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return "\n".join(context_parts)
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def simulate_conversation(self, immortal_name: str, messages: List[str]):
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"""模拟对话，测试记忆功能"""
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print(f"\n🎭 开始与 {immortal_name} 的对话")
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print("=" * 50)
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for i, message in enumerate(messages):
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print(f"\n用户: {message}")
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# 生成回应
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response = asyncio.run(self.generate_immortal_response(immortal_name, message))
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print(f"{immortal_name}: {response}")
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# 存储记忆
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self.store_memory(immortal_name, "conversation", {
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"user_message": message,
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"immortal_response": response,
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"session_id": f"session_{i}"
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})
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# 存储观点
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if "看多" in response or "看空" in response or "观望" in response:
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viewpoint = "看多" if "看多" in response else "看空" if "看空" in response else "观望"
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self.store_memory(immortal_name, "viewpoint", {
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"symbol": "TSLA", # 假设讨论特斯拉
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"viewpoint": viewpoint,
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"reasoning": response
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})
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def analyze_memory_evolution(self, immortal_name: str):
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"""分析记忆演化"""
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if immortal_name not in self.memory_bank:
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print(f"❌ {immortal_name} 没有记忆数据")
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return
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memory_data = self.memory_bank[immortal_name]
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print(f"\n📊 {immortal_name} 记忆分析")
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print("=" * 50)
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print(f"记忆空间创建时间: {memory_data['created_at']}")
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print(f"最后更新时间: {memory_data['last_updated']}")
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print(f"对话记录数: {len(memory_data['conversation_history'])}")
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print(f"观点演化数: {len(memory_data['viewpoint_evolution'])}")
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print(f"决策记录数: {len(memory_data['decision_history'])}")
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# 分析观点演化
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if memory_data['viewpoint_evolution']:
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print(f"\n观点演化轨迹:")
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for i, viewpoint in enumerate(memory_data['viewpoint_evolution']):
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print(f" {i+1}. {viewpoint['content']['viewpoint']} - {viewpoint['timestamp']}")
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def save_memory_bank(self, filename: str = "memory_bank_backup.json"):
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"""保存记忆库到文件"""
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with open(filename, 'w', encoding='utf-8') as f:
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json.dump(self.memory_bank, f, ensure_ascii=False, indent=2)
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print(f"💾 记忆库已保存到 {filename}")
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def load_memory_bank(self, filename: str = "memory_bank_backup.json"):
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"""从文件加载记忆库"""
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try:
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with open(filename, 'r', encoding='utf-8') as f:
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self.memory_bank = json.load(f)
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print(f"📂 记忆库已从 {filename} 加载")
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except FileNotFoundError:
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print(f"⚠️ 文件 {filename} 不存在，使用空记忆库")
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def main():
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"""主实验函数"""
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print("🚀 开始 Memory Bank 实验")
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print("=" * 60)
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# 创建实验实例
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experiment = MemoryBankExperiment()
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# 测试对话场景
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test_scenarios = {
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"吕洞宾": [
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"你觉得特斯拉股票怎么样？",
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"现在市场波动很大，你怎么看？",
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"你之前不是看好特斯拉吗？现在还是这个观点吗？"
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],
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"何仙姑": [
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"特斯拉股票风险大吗？",
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"现在适合投资吗？",
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"你一直很谨慎，现在还是建议观望吗？"
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],
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"张果老": [
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"从历史数据看，特斯拉表现如何？",
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"现在的估值合理吗？",
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"你之前分析过特斯拉的历史数据，现在有什么新发现？"
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]
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}
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# 执行实验
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for immortal_name, messages in test_scenarios.items():
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experiment.simulate_conversation(immortal_name, messages)
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experiment.analyze_memory_evolution(immortal_name)
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# 保存记忆库
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experiment.save_memory_bank()
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print("\n🎉 Memory Bank 实验完成！")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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116
experiments/memory_bank_test.py
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experiments/memory_bank_test.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Memory Bank 简化测试脚本
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"""
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import os
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import asyncio
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from datetime import datetime
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import json
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# Google GenAI 导入
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import google.genai as genai
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class MemoryBankTest:
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"""Memory Bank 测试类"""
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def __init__(self):
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self.api_key = os.getenv('GOOGLE_API_KEY')
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if not self.api_key:
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raise ValueError("请设置 GOOGLE_API_KEY 环境变量")
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self.client = genai.Client(api_key=self.api_key)
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# 八仙人格基线
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self.immortals = {
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"吕洞宾": "剑仙投资顾问，主动进取，敢于冒险，技术分析专家",
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"何仙姑": "慈悲风控专家，谨慎小心，保护意识强，风险厌恶",
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"张果老": "历史数据分析师，深度思考，逆向思维，传统智慧"
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}
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# 记忆存储
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self.memories = {}
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def store_memory(self, immortal_name: str, message: str, response: str):
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"""存储记忆"""
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if immortal_name not in self.memories:
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self.memories[immortal_name] = []
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self.memories[immortal_name].append({
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"message": message,
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"response": response,
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"timestamp": datetime.now().isoformat()
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})
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def chat_with_immortal(self, immortal_name: str, message: str) -> str:
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"""与仙人对话"""
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# 构建上下文
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context = f"你是{immortal_name}，{self.immortals[immortal_name]}。"
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# 添加记忆
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if immortal_name in self.memories and self.memories[immortal_name]:
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context += "\n\n你的历史对话："
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for memory in self.memories[immortal_name][-3:]: # 最近3条
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context += f"\n用户: {memory['message']}\n你: {memory['response']}"
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prompt = f"{context}\n\n现在用户说: {message}\n请回答（100字以内）:"
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# 使用新的 API
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response = self.client.models.generate_content(
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model="gemini-2.0-flash-exp",
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contents=[{"parts": [{"text": prompt}]}]
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)
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return response.candidates[0].content.parts[0].text
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def test_memory_continuity(self):
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"""测试记忆连续性"""
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print("🧪 测试记忆连续性")
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print("=" * 50)
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# 测试吕洞宾
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print("\n🎭 测试吕洞宾:")
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messages = [
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"你觉得特斯拉股票怎么样？",
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"现在市场波动很大，你怎么看？",
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"你之前不是看好特斯拉吗？现在还是这个观点吗？"
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]
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for message in messages:
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print(f"\n用户: {message}")
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response = self.chat_with_immortal("吕洞宾", message)
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print(f"吕洞宾: {response}")
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self.store_memory("吕洞宾", message, response)
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# 测试何仙姑
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print("\n🎭 测试何仙姑:")
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messages = [
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"特斯拉股票风险大吗？",
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"现在适合投资吗？",
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"你一直很谨慎，现在还是建议观望吗？"
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]
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for message in messages:
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print(f"\n用户: {message}")
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response = self.chat_with_immortal("何仙姑", message)
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print(f"何仙姑: {response}")
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self.store_memory("何仙姑", message, response)
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def save_memories(self):
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"""保存记忆"""
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with open("memories.json", "w", encoding="utf-8") as f:
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json.dump(self.memories, f, ensure_ascii=False, indent=2)
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print("💾 记忆已保存到 memories.json")
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def main():
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"""主函数"""
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print("🚀 Memory Bank 测试开始")
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test = MemoryBankTest()
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test.test_memory_continuity()
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test.save_memories()
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print("\n✅ 测试完成！")
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if __name__ == "__main__":
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main()
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