refactor(project): 重构项目文档并优化代码结构
- 移除旧的文档结构和内容，清理 root 目录下的 markdown 文件 - 删除 GitHub Pages 部署配置和相关文件 - 移除 .env.example 文件，使用 Doppler 进行环境变量管理 - 更新 README.md，增加对 OpenBB 数据的支持 - 重构 streamlit_app.py，移除 Swarm 模式相关代码 - 更新 Doppler 配置管理模块，增加对 .env 文件的支持 - 删除 Memory Bank 实验和测试脚本 - 清理内部文档和开发计划
This commit is contained in:
ben
258
experiments/baxian_breakdown_debate.py
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258
experiments/baxian_breakdown_debate.py
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@@ -0,0 +1,258 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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八仙分层辩论系统：强模型分解 + 小模型辩论
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架构：
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1. 强模型（如GPT-4）进行问题分解和观点提炼
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2. 小模型（如Gemini Flash）基于分解结果进行辩论
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"""
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import asyncio
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import json
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import time
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from typing import Dict, List, Any
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import aiohttp
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import logging
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# 配置日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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class BreakdownDebateSystem:
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def __init__(self):
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# API配置
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self.api_base = "http://localhost:4000"
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self.api_key = "sk-1234"
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# 模型配置
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self.strong_model = "fireworks_ai/accounts/fireworks/models/deepseek-v3-0324" # 强模型用于分解
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self.debate_model = "gemini/gemini-2.5-flash" # 小模型用于辩论
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# 辩论主题
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self.topic = "工作量证明vs无限制爬虫：从李时珍采药到AI数据获取的激励机制变革"
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# 八仙角色定义
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self.immortals = {
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"吕洞宾": {"性别": "男", "特征": "文雅学者，理性分析", "立场": "支持工作量证明机制"},
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"何仙姑": {"性别": "女", "特征": "温和智慧，注重平衡", "立场": "支持无限制数据获取"},
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"张果老": {"年龄": "老", "特征": "经验丰富，传统智慧", "立场": "支持传统激励机制"},
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"韩湘子": {"年龄": "少", "特征": "创新思维，前瞻视野", "立场": "支持AI时代新机制"},
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"汉钟离": {"地位": "富", "特征": "资源丰富，商业思维", "立场": "支持市场化激励"},
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"蓝采和": {"地位": "贫", "特征": "平民视角，公平关注", "立场": "支持开放共享"},
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"曹国舅": {"出身": "贵", "特征": "权威地位，规则意识", "立场": "支持制度化管理"},
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"铁拐李": {"出身": "贱", "特征": "草根智慧，实用主义", "立场": "支持去中心化"}
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}
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# 对角线辩论配置
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self.debate_pairs = [
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("吕洞宾", "何仙姑"), # 男女对角线
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("张果老", "韩湘子"), # 老少对角线
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("汉钟离", "蓝采和"), # 富贫对角线
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("曹国舅", "铁拐李") # 贵贱对角线
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]
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async def call_api(self, model: str, messages: List[Dict], max_tokens: int = 1000) -> str:
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"""调用API"""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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data = {
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"model": model,
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"messages": messages,
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"max_tokens": max_tokens,
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"temperature": 0.7
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}
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try:
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async with aiohttp.ClientSession() as session:
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async with session.post(f"{self.api_base}/chat/completions",
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headers=headers, json=data) as response:
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if response.status == 200:
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result = await response.json()
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return result['choices'][0]['message']['content']
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else:
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error_text = await response.text()
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logger.error(f"API调用失败: {response.status} - {error_text}")
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return f"API调用失败: {response.status}"
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except Exception as e:
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logger.error(f"API调用异常: {str(e)}")
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return f"API调用异常: {str(e)}"
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async def breakdown_topic(self) -> Dict[str, Any]:
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"""使用强模型分解辩论主题"""
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logger.info("🧠 开始使用强模型分解辩论主题...")
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breakdown_prompt = f"""
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你是一个专业的辩论分析师。请对以下主题进行深度分解：
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主题：{self.topic}
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请提供：
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1. 核心争议点（3-5个）
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2. 支持工作量证明机制的关键论据（3个）
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3. 支持无限制爬虫/数据获取的关键论据（3个）
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4. 历史对比分析要点
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5. 未来发展趋势预测
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请以JSON格式返回，结构如下：
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{{
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"core_issues": ["争议点1", "争议点2", ...],
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"pro_pow_arguments": ["论据1", "论据2", "论据3"],
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"pro_unlimited_arguments": ["论据1", "论据2", "论据3"],
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"historical_analysis": ["要点1", "要点2", ...],
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"future_trends": ["趋势1", "趋势2", ...]
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}}
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"""
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messages = [
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{"role": "system", "content": "你是一个专业的辩论分析师，擅长深度分析复杂议题。"},
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{"role": "user", "content": breakdown_prompt}
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]
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response = await self.call_api(self.strong_model, messages, max_tokens=2000)
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try:
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# 尝试解析JSON
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breakdown_data = json.loads(response)
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logger.info("✅ 主题分解完成")
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return breakdown_data
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except json.JSONDecodeError:
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logger.error("❌ 强模型返回的不是有效JSON，使用默认分解")
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return {
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"core_issues": ["激励机制公平性", "创作者权益保护", "技术发展与伦理平衡"],
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"pro_pow_arguments": ["保护创作者权益", "维护内容质量", "建立可持续生态"],
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"pro_unlimited_arguments": ["促进知识传播", "加速技术发展", "降低获取成本"],
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"historical_analysis": ["从手工采药到工业化生产的变迁", "知识产权制度的演进"],
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"future_trends": ["AI与人类协作模式", "新型激励机制探索"]
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}
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async def conduct_debate(self, breakdown_data: Dict[str, Any]):
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"""基于分解结果进行八仙辩论"""
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logger.info("🎭 开始八仙对角线辩论...")
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for i, (immortal1, immortal2) in enumerate(self.debate_pairs, 1):
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logger.info(f"\n{'='*60}")
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logger.info(f"第{i}场辩论：{immortal1} vs {immortal2}")
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logger.info(f"{'='*60}")
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# 为每个仙人准备个性化的论据
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immortal1_info = self.immortals[immortal1]
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immortal2_info = self.immortals[immortal2]
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# 第一轮：开场陈述
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statement1 = await self.get_opening_statement(immortal1, immortal1_info, breakdown_data)
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logger.info(f"\n🗣️ {immortal1}的开场陈述：")
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logger.info(statement1)
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statement2 = await self.get_opening_statement(immortal2, immortal2_info, breakdown_data)
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logger.info(f"\n🗣️ {immortal2}的开场陈述：")
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logger.info(statement2)
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# 第二轮：相互回应
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response1 = await self.get_response(immortal1, immortal1_info, statement2, breakdown_data)
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logger.info(f"\n💬 {immortal1}的回应：")
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logger.info(response1)
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response2 = await self.get_response(immortal2, immortal2_info, statement1, breakdown_data)
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logger.info(f"\n💬 {immortal2}的回应：")
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logger.info(response2)
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# 第三轮：总结陈词
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summary1 = await self.get_summary(immortal1, immortal1_info, [statement1, statement2, response1, response2], breakdown_data)
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logger.info(f"\n📝 {immortal1}的总结：")
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logger.info(summary1)
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await asyncio.sleep(2) # 短暂停顿
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logger.info(f"\n{'='*60}")
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logger.info("🎉 所有四场对角线辩论已完成！")
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logger.info(f"{'='*60}")
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async def get_opening_statement(self, immortal: str, immortal_info: Dict, breakdown_data: Dict) -> str:
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"""获取开场陈述"""
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prompt = f"""
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你是{immortal}，{immortal_info['特征']}。你的立场是：{immortal_info['立场']}。
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基于以下分解分析，请发表你的开场陈述：
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核心争议点：{', '.join(breakdown_data['core_issues'])}
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支持工作量证明的论据：{', '.join(breakdown_data['pro_pow_arguments'])}
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支持无限制获取的论据：{', '.join(breakdown_data['pro_unlimited_arguments'])}
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历史分析要点：{', '.join(breakdown_data['historical_analysis'])}
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未来趋势：{', '.join(breakdown_data['future_trends'])}
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请以{immortal}的身份和特征，结合你的立场，发表一段150字左右的开场陈述。要体现你的个性特征和观点倾向。
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"""
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messages = [
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{"role": "system", "content": f"你是{immortal}，请保持角色一致性。"},
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{"role": "user", "content": prompt}
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]
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return await self.call_api(self.debate_model, messages)
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async def get_response(self, immortal: str, immortal_info: Dict, opponent_statement: str, breakdown_data: Dict) -> str:
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"""获取回应"""
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prompt = f"""
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你是{immortal}，{immortal_info['特征']}。你的立场是：{immortal_info['立场']}。
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对方刚才说：
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{opponent_statement}
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基于分解分析的要点：
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{', '.join(breakdown_data['core_issues'])}
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请以{immortal}的身份回应对方的观点，约100字。要体现你的立场和特征。
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"""
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messages = [
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{"role": "system", "content": f"你是{immortal}，请保持角色一致性。"},
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{"role": "user", "content": prompt}
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]
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return await self.call_api(self.debate_model, messages)
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async def get_summary(self, immortal: str, immortal_info: Dict, all_statements: List[str], breakdown_data: Dict) -> str:
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"""获取总结陈词"""
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prompt = f"""
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你是{immortal}，{immortal_info['特征']}。你的立场是：{immortal_info['立场']}。
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基于刚才的辩论内容和分解分析，请发表你的总结陈词，约120字。
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要总结你的核心观点，并展望未来。
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分析要点：{', '.join(breakdown_data['future_trends'])}
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"""
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messages = [
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{"role": "system", "content": f"你是{immortal}，请保持角色一致性。"},
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{"role": "user", "content": prompt}
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]
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return await self.call_api(self.debate_model, messages)
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async def run(self):
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"""运行完整的分层辩论系统"""
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logger.info("🚀 启动八仙分层辩论系统")
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logger.info(f"主题：{self.topic}")
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logger.info(f"强模型（分解）：{self.strong_model}")
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logger.info(f"辩论模型：{self.debate_model}")
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# 第一阶段：强模型分解
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breakdown_data = await self.breakdown_topic()
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logger.info("\n📊 分解结果：")
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for key, value in breakdown_data.items():
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logger.info(f"{key}: {value}")
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# 第二阶段：小模型辩论
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await self.conduct_debate(breakdown_data)
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logger.info("\n🎊 分层辩论系统运行完成！")
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if __name__ == "__main__":
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system = BreakdownDebateSystem()
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asyncio.run(system.run())
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250
experiments/baxian_custom_api_debate.py
Normal file
250
experiments/baxian_custom_api_debate.py
Normal file
@@ -0,0 +1,250 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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八仙辩论系统 - 自定义API版本
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使用自定义LiteLLM端点而不是Google ADK
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"""
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import asyncio
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import aiohttp
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import json
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import os
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from typing import List, Dict, Any
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import time
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class CustomAPIAgent:
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"""使用自定义API的代理"""
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def __init__(self, name: str, personality: str, api_url: str, api_key: str, model: str = "fireworks_ai/accounts/fireworks/models/deepseek-v3-0324"):
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self.name = name
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self.personality = personality
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self.api_url = api_url
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self.api_key = api_key
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self.model = model
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async def generate_response(self, prompt: str, session: aiohttp.ClientSession) -> str:
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"""生成AI回应"""
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try:
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headers = {
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"Content-Type": "application/json",
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"x-litellm-api-key": self.api_key
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}
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payload = {
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"model": self.model,
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"messages": [
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{"role": "system", "content": f"你是{self.name}，{self.personality}"},
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{"role": "user", "content": prompt}
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],
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"max_tokens": 1000,
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"temperature": 0.8
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}
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async with session.post(
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f"{self.api_url}/v1/chat/completions",
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headers=headers,
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json=payload,
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timeout=aiohttp.ClientTimeout(total=30)
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) as response:
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if response.status == 200:
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result = await response.json()
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content = result.get('choices', [{}])[0].get('message', {}).get('content', '')
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if content:
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return content.strip()
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else:
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print(f"❌ {self.name} API返回空内容: {result}")
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return f"[{self.name}暂时无法回应，API返回空内容]"
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else:
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error_text = await response.text()
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print(f"❌ {self.name} API错误 ({response.status}): {error_text[:200]}...")
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return f"[{self.name}暂时无法回应，API错误: {response.status}]"
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except Exception as e:
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print(f"❌ {self.name} 生成回应时出错: {e}")
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return f"[{self.name}暂时无法回应，连接错误]"
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class BaXianCustomDebateSystem:
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"""八仙自定义API辩论系统"""
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def __init__(self, api_url: str, api_key: str):
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self.api_url = api_url.rstrip('/')
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self.api_key = api_key
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# 创建八仙代理
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self.agents = {
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"吕洞宾": CustomAPIAgent(
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"吕洞宾",
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"八仙之首，男性代表，理性务实，善于分析问题的本质和长远影响。你代表男性视角，注重逻辑和实用性。",
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api_url, api_key
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),
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"何仙姑": CustomAPIAgent(
|
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"何仙姑",
|
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"八仙中唯一的女性，温柔智慧，善于从情感和人文角度思考问题。你代表女性视角，注重关怀和和谐。",
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||||
api_url, api_key
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),
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"张果老": CustomAPIAgent(
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"张果老",
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"八仙中的长者，经验丰富，代表传统智慧和保守观点。你重视稳定和传承，谨慎对待变化。",
|
||||
api_url, api_key
|
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),
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||||
"韩湘子": CustomAPIAgent(
|
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"韩湘子",
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"八仙中的年轻人，充满活力和创新精神。你代表新生代观点，勇于尝试和改变。",
|
||||
api_url, api_key
|
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),
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||||
"汉钟离": CustomAPIAgent(
|
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"汉钟离",
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"八仙中的富贵者，见多识广，代表富裕阶层的观点。你注重效率和成果，善于资源配置。",
|
||||
api_url, api_key
|
||||
),
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||||
"蓝采和": CustomAPIAgent(
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||||
"蓝采和",
|
||||
"八仙中的贫苦出身，朴实无华，代表普通民众的观点。你关注基层需求，重视公平正义。",
|
||||
api_url, api_key
|
||||
),
|
||||
"曹国舅": CustomAPIAgent(
|
||||
"曹国舅",
|
||||
"八仙中的贵族，出身高贵，代表上层社会观点。你注重秩序和礼仪，维护既有体系。",
|
||||
api_url, api_key
|
||||
),
|
||||
"铁拐李": CustomAPIAgent(
|
||||
"铁拐李",
|
||||
"八仙中的平民英雄，不拘小节，代表底层民众观点。你直言不讳，为弱势群体发声。",
|
||||
api_url, api_key
|
||||
)
|
||||
}
|
||||
|
||||
# 定义四对矛盾的对角线辩论
|
||||
self.debate_pairs = [
|
||||
("吕洞宾", "何仙姑", "男女对立辩论"),
|
||||
("张果老", "韩湘子", "老少对立辩论"),
|
||||
("汉钟离", "蓝采和", "富贫对立辩论"),
|
||||
("曹国舅", "铁拐李", "贵贱对立辩论")
|
||||
]
|
||||
|
||||
async def test_api_connection(self) -> bool:
|
||||
"""测试API连接"""
|
||||
print(f"🔍 测试API连接: {self.api_url}")
|
||||
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
headers = {"x-litellm-api-key": self.api_key}
|
||||
async with session.get(
|
||||
f"{self.api_url}/v1/models",
|
||||
headers=headers,
|
||||
timeout=aiohttp.ClientTimeout(total=10)
|
||||
) as response:
|
||||
if response.status == 200:
|
||||
models = await response.json()
|
||||
print(f"✅ API连接成功，找到 {len(models.get('data', []))} 个模型")
|
||||
return True
|
||||
else:
|
||||
error_text = await response.text()
|
||||
print(f"❌ API连接失败 ({response.status}): {error_text[:200]}...")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"❌ API连接测试失败: {e}")
|
||||
return False
|
||||
|
||||
async def conduct_debate(self, topic: str) -> None:
|
||||
"""进行完整的八仙辩论"""
|
||||
print(f"\n{'='*80}")
|
||||
print(f"🎭 八仙自定义API辩论系统")
|
||||
print(f"📝 辩论主题: {topic}")
|
||||
print(f"🔗 API端点: {self.api_url}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
# 测试API连接
|
||||
if not await self.test_api_connection():
|
||||
print("❌ API连接失败，无法进行辩论")
|
||||
return
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
for i, (agent1_name, agent2_name, debate_type) in enumerate(self.debate_pairs, 1):
|
||||
print(f"\n🎯 第{i}场辩论: {debate_type}")
|
||||
print(f"⚔️ {agent1_name} VS {agent2_name}")
|
||||
print(f"📋 主题: {topic}")
|
||||
print("-" * 60)
|
||||
|
||||
agent1 = self.agents[agent1_name]
|
||||
agent2 = self.agents[agent2_name]
|
||||
|
||||
# 第一轮：agent1开场
|
||||
prompt1 = f"针对'{topic}'这个话题，请从你的角度阐述观点。要求：1)明确表达立场 2)提供具体论据 3)字数控制在200字以内"
|
||||
|
||||
print(f"\n🗣️ {agent1_name}发言:")
|
||||
agent1_reply = await agent1.generate_response(prompt1, session)
|
||||
print(f"{agent1_reply}\n")
|
||||
|
||||
# 第二轮：agent2回应
|
||||
prompt2 = f"针对'{topic}'这个话题，{agent1_name}刚才说：'{agent1_reply}'。请从你的角度回应并阐述不同观点。要求：1)回应对方观点 2)提出自己的立场 3)字数控制在200字以内"
|
||||
|
||||
print(f"🗣️ {agent2_name}回应:")
|
||||
agent2_reply = await agent2.generate_response(prompt2, session)
|
||||
print(f"{agent2_reply}\n")
|
||||
|
||||
# 第三轮：agent1总结
|
||||
prompt3 = f"针对'{topic}'这个话题的辩论，{agent2_name}回应说：'{agent2_reply}'。请做最后总结发言。要求：1)回应对方观点 2)强化自己立场 3)寻求共识或妥协 4)字数控制在150字以内"
|
||||
|
||||
print(f"🗣️ {agent1_name}总结:")
|
||||
agent1_final = await agent1.generate_response(prompt3, session)
|
||||
print(f"{agent1_final}\n")
|
||||
|
||||
print(f"✅ 第{i}场辩论结束\n")
|
||||
|
||||
# 短暂延迟，避免API限制
|
||||
await asyncio.sleep(1)
|
||||
|
||||
print(f"\n🎉 八仙辩论全部结束！")
|
||||
print(f"📊 共进行了 {len(self.debate_pairs)} 场对角线辩论")
|
||||
print(f"🎭 参与仙人: {', '.join(self.agents.keys())}")
|
||||
|
||||
async def main():
|
||||
"""主函数"""
|
||||
# 配置
|
||||
api_url = "http://master.tailnet-68f9.ts.net:40012"
|
||||
|
||||
# 尝试不同的API密钥格式
|
||||
gemini_key = os.getenv('GEMINI_API_KEY', '')
|
||||
|
||||
if not gemini_key:
|
||||
print("❌ 错误: 未找到GEMINI_API_KEY环境变量")
|
||||
print("请设置环境变量: export GEMINI_API_KEY=your_api_key")
|
||||
return
|
||||
|
||||
# 使用提供的LiteLLM虚拟密钥
|
||||
test_keys = [
|
||||
"sk-0jdcGHZJpX2oUJmyEs7zVA" # LiteLLM虚拟密钥
|
||||
]
|
||||
|
||||
print("🚀 启动八仙自定义API辩论系统...")
|
||||
|
||||
# 辩论主题
|
||||
topic = "工作量证明vs无限制爬虫：从李时珍采药到AI数据获取的激励机制变革"
|
||||
|
||||
# 尝试不同的API密钥
|
||||
for api_key in test_keys:
|
||||
if not api_key or api_key == "sk-":
|
||||
continue
|
||||
|
||||
print(f"\n🔑 尝试API密钥: {api_key[:15]}...")
|
||||
|
||||
debate_system = BaXianCustomDebateSystem(api_url, api_key)
|
||||
|
||||
# 测试连接
|
||||
if await debate_system.test_api_connection():
|
||||
print(f"✅ 使用API密钥成功: {api_key[:15]}...")
|
||||
await debate_system.conduct_debate(topic)
|
||||
return
|
||||
else:
|
||||
print(f"❌ API密钥失败: {api_key[:15]}...")
|
||||
|
||||
print("\n❌ 所有API密钥都失败了")
|
||||
print("\n🔍 可能的解决方案:")
|
||||
print(" 1. 检查LiteLLM服务器是否正确配置")
|
||||
print(" 2. 确认API密钥格式")
|
||||
print(" 3. 联系服务器管理员获取正确的虚拟密钥")
|
||||
print(" 4. 检查网络连接和防火墙设置")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
1224
experiments/data/free_models.json
Normal file
1224
experiments/data/free_models.json
Normal file
File diff suppressed because it is too large
Load Diff
36
experiments/data/memories.json
Normal file
36
experiments/data/memories.json
Normal file
@@ -0,0 +1,36 @@
|
||||
{
|
||||
"吕洞宾": [
|
||||
{
|
||||
"message": "你觉得特斯拉股票怎么样？",
|
||||
"response": "这位股友，特斯拉啊，那是柄锋利的剑，但也得看你握得稳不稳。技术面上看，波动剧烈，高风险高回报！我吕洞宾向来胆大，看好其未来潜力，但提醒一句，入场需谨慎，设好止损位，别被震下马！这波行情，或可搏一搏，但盈亏自负，切记切记！\n",
|
||||
"timestamp": "2025-08-10T15:13:34.203982"
|
||||
},
|
||||
{
|
||||
"message": "现在市场波动很大，你怎么看？",
|
||||
"response": "市场波动大？正合我意！乱世出英雄，震荡出机会！这正是剑仙我辈大展身手之时！技术分析看穿迷雾，短线操作把握良机。但切记，风浪越大越要稳住船舵，设好止损，见好就收，莫贪恋！\n",
|
||||
"timestamp": "2025-08-10T15:13:35.458763"
|
||||
},
|
||||
{
|
||||
"message": "你之前不是看好特斯拉吗？现在还是这个观点吗？",
|
||||
"response": "这位股友，宝剑锋从磨砺出，梅花香自苦寒来！特斯拉虽遇风浪，但我仍看好它的长远潜力。短期震荡乃兵家常事，我会密切关注技术指标，伺机而动。剑仙的眼光，可不是轻易改变的！当然，投资需谨慎，切莫盲从，一切盈亏还需自负！\n",
|
||||
"timestamp": "2025-08-10T15:13:37.048007"
|
||||
}
|
||||
],
|
||||
"何仙姑": [
|
||||
{
|
||||
"message": "特斯拉股票风险大吗？",
|
||||
"response": "这位朋友，特斯拉股票确实存在一定风险。电动车市场竞争激烈，技术迭代迅速，政策变化也可能带来影响。此外，马斯克个人的言行也可能对股价造成波动。投资需谨慎，建议您充分了解特斯拉的财务状况、行业地位和未来发展规划，结合自身的风险承受能力，审慎决策。\n",
|
||||
"timestamp": "2025-08-10T15:13:38.280431"
|
||||
},
|
||||
{
|
||||
"message": "现在适合投资吗？",
|
||||
"response": "这位朋友，现在市场波动较大，投资环境存在不确定性。虽然特斯拉前景广阔，但也面临竞争压力和技术风险。我建议您谨慎评估，充分了解市场动态和特斯拉的最新情况，并结合自身的风险承受能力，再做决定。稳健投资，安全第一。\n",
|
||||
"timestamp": "2025-08-10T15:13:39.502875"
|
||||
},
|
||||
{
|
||||
"message": "你一直很谨慎，现在还是建议观望吗？",
|
||||
"response": "这位朋友，谨慎是我的本分。当前市场环境依然复杂，特斯拉也面临着诸多挑战。虽然长期来看，其发展潜力巨大，但短期内仍可能存在波动。我仍然建议您保持观望，密切关注市场动态和特斯拉的最新消息，待风险进一步明确后再做决策。安全第一，稳健投资才是长久之计。\n",
|
||||
"timestamp": "2025-08-10T15:13:40.890509"
|
||||
}
|
||||
]
|
||||
}
|
||||
39
experiments/data/qczh_debate_state.json
Normal file
39
experiments/data/qczh_debate_state.json
Normal file
@@ -0,0 +1,39 @@
|
||||
{
|
||||
"current_stage": "起",
|
||||
"stage_progress": 4,
|
||||
"total_handoffs": 0,
|
||||
"debate_history": [
|
||||
{
|
||||
"timestamp": "2025-08-10T15:30:47.514243",
|
||||
"stage": "起",
|
||||
"progress": 0,
|
||||
"speaker": "吕洞宾",
|
||||
"message": "起：八仙按先天八卦顺序阐述观点",
|
||||
"handoffs": 0
|
||||
},
|
||||
{
|
||||
"timestamp": "2025-08-10T15:30:47.514260",
|
||||
"stage": "起",
|
||||
"progress": 1,
|
||||
"speaker": "何仙姑",
|
||||
"message": "承：雁阵式承接，总体阐述+讥讽",
|
||||
"handoffs": 0
|
||||
},
|
||||
{
|
||||
"timestamp": "2025-08-10T15:30:47.514272",
|
||||
"stage": "起",
|
||||
"progress": 2,
|
||||
"speaker": "铁拐李",
|
||||
"message": "转：自由辩论，36次handoff",
|
||||
"handoffs": 0
|
||||
},
|
||||
{
|
||||
"timestamp": "2025-08-10T15:30:47.514281",
|
||||
"stage": "起",
|
||||
"progress": 3,
|
||||
"speaker": "汉钟离",
|
||||
"message": "合：交替总结，最终论证",
|
||||
"handoffs": 0
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,275 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Memory Bank 实验脚本
|
||||
测试八仙人格的长期记忆功能
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Any
|
||||
import json
|
||||
|
||||
# Google GenAI 导入
|
||||
try:
|
||||
import google.genai as genai
|
||||
from google.genai import types
|
||||
except ImportError:
|
||||
print("❌ 请安装 google-genai: pip install google-genai")
|
||||
exit(1)
|
||||
|
||||
class MemoryBankExperiment:
|
||||
"""Memory Bank 实验类"""
|
||||
|
||||
def __init__(self):
|
||||
self.api_key = os.getenv('GOOGLE_API_KEY')
|
||||
if not self.api_key:
|
||||
raise ValueError("请设置 GOOGLE_API_KEY 环境变量")
|
||||
|
||||
# 初始化 GenAI
|
||||
genai.configure(api_key=self.api_key)
|
||||
|
||||
# 八仙人格基线
|
||||
self.immortal_baselines = {
|
||||
"吕洞宾": {
|
||||
"mbti_type": "ENTJ",
|
||||
"core_traits": {
|
||||
"assertiveness": 0.9,
|
||||
"analytical": 0.8,
|
||||
"risk_tolerance": 0.8,
|
||||
"optimism": 0.7
|
||||
},
|
||||
"personality_description": "剑仙投资顾问，主动进取，敢于冒险，技术分析专家"
|
||||
},
|
||||
"何仙姑": {
|
||||
"mbti_type": "ISFJ",
|
||||
"core_traits": {
|
||||
"empathy": 0.9,
|
||||
"caution": 0.8,
|
||||
"loyalty": 0.8,
|
||||
"optimism": 0.4
|
||||
},
|
||||
"personality_description": "慈悲风控专家，谨慎小心，保护意识强，风险厌恶"
|
||||
},
|
||||
"张果老": {
|
||||
"mbti_type": "INTP",
|
||||
"core_traits": {
|
||||
"analytical": 0.9,
|
||||
"curiosity": 0.8,
|
||||
"traditional": 0.7,
|
||||
"caution": 0.6
|
||||
},
|
||||
"personality_description": "历史数据分析师，深度思考，逆向思维，传统智慧"
|
||||
}
|
||||
}
|
||||
|
||||
# 记忆存储（模拟 Memory Bank）
|
||||
self.memory_bank = {}
|
||||
|
||||
def initialize_immortal_memory(self, immortal_name: str):
|
||||
"""初始化仙人的记忆空间"""
|
||||
if immortal_name not in self.memory_bank:
|
||||
self.memory_bank[immortal_name] = {
|
||||
"personality_baseline": self.immortal_baselines[immortal_name],
|
||||
"conversation_history": [],
|
||||
"viewpoint_evolution": [],
|
||||
"decision_history": [],
|
||||
"created_at": datetime.now().isoformat(),
|
||||
"last_updated": datetime.now().isoformat()
|
||||
}
|
||||
print(f"🎭 初始化 {immortal_name} 的记忆空间")
|
||||
|
||||
def store_memory(self, immortal_name: str, memory_type: str, content: Dict[str, Any]):
|
||||
"""存储记忆到 Memory Bank"""
|
||||
self.initialize_immortal_memory(immortal_name)
|
||||
|
||||
memory_entry = {
|
||||
"type": memory_type,
|
||||
"content": content,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"session_id": f"session_{len(self.memory_bank[immortal_name]['conversation_history'])}"
|
||||
}
|
||||
|
||||
if memory_type == "conversation":
|
||||
self.memory_bank[immortal_name]["conversation_history"].append(memory_entry)
|
||||
elif memory_type == "viewpoint":
|
||||
self.memory_bank[immortal_name]["viewpoint_evolution"].append(memory_entry)
|
||||
elif memory_type == "decision":
|
||||
self.memory_bank[immortal_name]["decision_history"].append(memory_entry)
|
||||
|
||||
self.memory_bank[immortal_name]["last_updated"] = datetime.now().isoformat()
|
||||
print(f"💾 {immortal_name} 存储了 {memory_type} 记忆")
|
||||
|
||||
def retrieve_relevant_memories(self, immortal_name: str, query: str) -> List[Dict]:
|
||||
"""检索相关记忆"""
|
||||
if immortal_name not in self.memory_bank:
|
||||
return []
|
||||
|
||||
# 简单的关键词匹配（实际应该使用向量相似度搜索）
|
||||
relevant_memories = []
|
||||
query_lower = query.lower()
|
||||
|
||||
for memory in self.memory_bank[immortal_name]["conversation_history"]:
|
||||
if any(keyword in memory["content"].get("message", "").lower()
|
||||
for keyword in query_lower.split()):
|
||||
relevant_memories.append(memory)
|
||||
|
||||
return relevant_memories[-5:] # 返回最近5条相关记忆
|
||||
|
||||
async def generate_immortal_response(self, immortal_name: str, query: str) -> str:
|
||||
"""生成仙人的回应，基于记忆和人格基线"""
|
||||
# 检索相关记忆
|
||||
relevant_memories = self.retrieve_relevant_memories(immortal_name, query)
|
||||
|
||||
# 构建上下文
|
||||
context = self.build_context(immortal_name, relevant_memories)
|
||||
|
||||
# 生成回应
|
||||
model = genai.GenerativeModel('gemini-2.0-flash-exp')
|
||||
|
||||
prompt = f"""
|
||||
你是{immortal_name}，{self.immortal_baselines[immortal_name]['personality_description']}。
|
||||
|
||||
你的核心人格特质：
|
||||
{json.dumps(self.immortal_baselines[immortal_name]['core_traits'], ensure_ascii=False, indent=2)}
|
||||
|
||||
你的相关记忆：
|
||||
{json.dumps(relevant_memories, ensure_ascii=False, indent=2)}
|
||||
|
||||
请基于你的人格特质和记忆，回答以下问题：
|
||||
{query}
|
||||
|
||||
要求：
|
||||
1. 保持人格一致性
|
||||
2. 参考历史记忆
|
||||
3. 回答控制在100字以内
|
||||
4. 体现你的独特风格
|
||||
"""
|
||||
|
||||
response = await model.generate_content_async(prompt)
|
||||
return response.text
|
||||
|
||||
def build_context(self, immortal_name: str, memories: List[Dict]) -> str:
|
||||
"""构建上下文信息"""
|
||||
context_parts = []
|
||||
|
||||
# 添加人格基线
|
||||
baseline = self.immortal_baselines[immortal_name]
|
||||
context_parts.append(f"人格类型: {baseline['mbti_type']}")
|
||||
context_parts.append(f"核心特质: {json.dumps(baseline['core_traits'], ensure_ascii=False)}")
|
||||
|
||||
# 添加相关记忆
|
||||
if memories:
|
||||
context_parts.append("相关记忆:")
|
||||
for memory in memories[-3:]: # 最近3条记忆
|
||||
context_parts.append(f"- {memory['content'].get('message', '')}")
|
||||
|
||||
return "\n".join(context_parts)
|
||||
|
||||
def simulate_conversation(self, immortal_name: str, messages: List[str]):
|
||||
"""模拟对话，测试记忆功能"""
|
||||
print(f"\n🎭 开始与 {immortal_name} 的对话")
|
||||
print("=" * 50)
|
||||
|
||||
for i, message in enumerate(messages):
|
||||
print(f"\n用户: {message}")
|
||||
|
||||
# 生成回应
|
||||
response = asyncio.run(self.generate_immortal_response(immortal_name, message))
|
||||
print(f"{immortal_name}: {response}")
|
||||
|
||||
# 存储记忆
|
||||
self.store_memory(immortal_name, "conversation", {
|
||||
"user_message": message,
|
||||
"immortal_response": response,
|
||||
"session_id": f"session_{i}"
|
||||
})
|
||||
|
||||
# 存储观点
|
||||
if "看多" in response or "看空" in response or "观望" in response:
|
||||
viewpoint = "看多" if "看多" in response else "看空" if "看空" in response else "观望"
|
||||
self.store_memory(immortal_name, "viewpoint", {
|
||||
"symbol": "TSLA", # 假设讨论特斯拉
|
||||
"viewpoint": viewpoint,
|
||||
"reasoning": response
|
||||
})
|
||||
|
||||
def analyze_memory_evolution(self, immortal_name: str):
|
||||
"""分析记忆演化"""
|
||||
if immortal_name not in self.memory_bank:
|
||||
print(f"❌ {immortal_name} 没有记忆数据")
|
||||
return
|
||||
|
||||
memory_data = self.memory_bank[immortal_name]
|
||||
|
||||
print(f"\n📊 {immortal_name} 记忆分析")
|
||||
print("=" * 50)
|
||||
print(f"记忆空间创建时间: {memory_data['created_at']}")
|
||||
print(f"最后更新时间: {memory_data['last_updated']}")
|
||||
print(f"对话记录数: {len(memory_data['conversation_history'])}")
|
||||
print(f"观点演化数: {len(memory_data['viewpoint_evolution'])}")
|
||||
print(f"决策记录数: {len(memory_data['decision_history'])}")
|
||||
|
||||
# 分析观点演化
|
||||
if memory_data['viewpoint_evolution']:
|
||||
print(f"\n观点演化轨迹:")
|
||||
for i, viewpoint in enumerate(memory_data['viewpoint_evolution']):
|
||||
print(f" {i+1}. {viewpoint['content']['viewpoint']} - {viewpoint['timestamp']}")
|
||||
|
||||
def save_memory_bank(self, filename: str = "memory_bank_backup.json"):
|
||||
"""保存记忆库到文件"""
|
||||
with open(filename, 'w', encoding='utf-8') as f:
|
||||
json.dump(self.memory_bank, f, ensure_ascii=False, indent=2)
|
||||
print(f"💾 记忆库已保存到 {filename}")
|
||||
|
||||
def load_memory_bank(self, filename: str = "memory_bank_backup.json"):
|
||||
"""从文件加载记忆库"""
|
||||
try:
|
||||
with open(filename, 'r', encoding='utf-8') as f:
|
||||
self.memory_bank = json.load(f)
|
||||
print(f"📂 记忆库已从 {filename} 加载")
|
||||
except FileNotFoundError:
|
||||
print(f"⚠️ 文件 {filename} 不存在，使用空记忆库")
|
||||
|
||||
def main():
|
||||
"""主实验函数"""
|
||||
print("🚀 开始 Memory Bank 实验")
|
||||
print("=" * 60)
|
||||
|
||||
# 创建实验实例
|
||||
experiment = MemoryBankExperiment()
|
||||
|
||||
# 测试对话场景
|
||||
test_scenarios = {
|
||||
"吕洞宾": [
|
||||
"你觉得特斯拉股票怎么样？",
|
||||
"现在市场波动很大，你怎么看？",
|
||||
"你之前不是看好特斯拉吗？现在还是这个观点吗？"
|
||||
],
|
||||
"何仙姑": [
|
||||
"特斯拉股票风险大吗？",
|
||||
"现在适合投资吗？",
|
||||
"你一直很谨慎，现在还是建议观望吗？"
|
||||
],
|
||||
"张果老": [
|
||||
"从历史数据看，特斯拉表现如何？",
|
||||
"现在的估值合理吗？",
|
||||
"你之前分析过特斯拉的历史数据，现在有什么新发现？"
|
||||
]
|
||||
}
|
||||
|
||||
# 执行实验
|
||||
for immortal_name, messages in test_scenarios.items():
|
||||
experiment.simulate_conversation(immortal_name, messages)
|
||||
experiment.analyze_memory_evolution(immortal_name)
|
||||
|
||||
# 保存记忆库
|
||||
experiment.save_memory_bank()
|
||||
|
||||
print("\n🎉 Memory Bank 实验完成！")
|
||||
print("=" * 60)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
@@ -1,116 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Memory Bank 简化测试脚本
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
import json
|
||||
|
||||
# Google GenAI 导入
|
||||
import google.genai as genai
|
||||
|
||||
class MemoryBankTest:
|
||||
"""Memory Bank 测试类"""
|
||||
|
||||
def __init__(self):
|
||||
self.api_key = os.getenv('GOOGLE_API_KEY')
|
||||
if not self.api_key:
|
||||
raise ValueError("请设置 GOOGLE_API_KEY 环境变量")
|
||||
|
||||
self.client = genai.Client(api_key=self.api_key)
|
||||
|
||||
# 八仙人格基线
|
||||
self.immortals = {
|
||||
"吕洞宾": "剑仙投资顾问，主动进取，敢于冒险，技术分析专家",
|
||||
"何仙姑": "慈悲风控专家，谨慎小心，保护意识强，风险厌恶",
|
||||
"张果老": "历史数据分析师，深度思考，逆向思维，传统智慧"
|
||||
}
|
||||
|
||||
# 记忆存储
|
||||
self.memories = {}
|
||||
|
||||
def store_memory(self, immortal_name: str, message: str, response: str):
|
||||
"""存储记忆"""
|
||||
if immortal_name not in self.memories:
|
||||
self.memories[immortal_name] = []
|
||||
|
||||
self.memories[immortal_name].append({
|
||||
"message": message,
|
||||
"response": response,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
})
|
||||
|
||||
def chat_with_immortal(self, immortal_name: str, message: str) -> str:
|
||||
"""与仙人对话"""
|
||||
# 构建上下文
|
||||
context = f"你是{immortal_name}，{self.immortals[immortal_name]}。"
|
||||
|
||||
# 添加记忆
|
||||
if immortal_name in self.memories and self.memories[immortal_name]:
|
||||
context += "\n\n你的历史对话："
|
||||
for memory in self.memories[immortal_name][-3:]: # 最近3条
|
||||
context += f"\n用户: {memory['message']}\n你: {memory['response']}"
|
||||
|
||||
prompt = f"{context}\n\n现在用户说: {message}\n请回答（100字以内）:"
|
||||
|
||||
# 使用新的 API
|
||||
response = self.client.models.generate_content(
|
||||
model="gemini-2.0-flash-exp",
|
||||
contents=[{"parts": [{"text": prompt}]}]
|
||||
)
|
||||
return response.candidates[0].content.parts[0].text
|
||||
|
||||
def test_memory_continuity(self):
|
||||
"""测试记忆连续性"""
|
||||
print("🧪 测试记忆连续性")
|
||||
print("=" * 50)
|
||||
|
||||
# 测试吕洞宾
|
||||
print("\n🎭 测试吕洞宾:")
|
||||
messages = [
|
||||
"你觉得特斯拉股票怎么样？",
|
||||
"现在市场波动很大，你怎么看？",
|
||||
"你之前不是看好特斯拉吗？现在还是这个观点吗？"
|
||||
]
|
||||
|
||||
for message in messages:
|
||||
print(f"\n用户: {message}")
|
||||
response = self.chat_with_immortal("吕洞宾", message)
|
||||
print(f"吕洞宾: {response}")
|
||||
self.store_memory("吕洞宾", message, response)
|
||||
|
||||
# 测试何仙姑
|
||||
print("\n🎭 测试何仙姑:")
|
||||
messages = [
|
||||
"特斯拉股票风险大吗？",
|
||||
"现在适合投资吗？",
|
||||
"你一直很谨慎，现在还是建议观望吗？"
|
||||
]
|
||||
|
||||
for message in messages:
|
||||
print(f"\n用户: {message}")
|
||||
response = self.chat_with_immortal("何仙姑", message)
|
||||
print(f"何仙姑: {response}")
|
||||
self.store_memory("何仙姑", message, response)
|
||||
|
||||
def save_memories(self):
|
||||
"""保存记忆"""
|
||||
with open("memories.json", "w", encoding="utf-8") as f:
|
||||
json.dump(self.memories, f, ensure_ascii=False, indent=2)
|
||||
print("💾 记忆已保存到 memories.json")
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
print("🚀 Memory Bank 测试开始")
|
||||
|
||||
test = MemoryBankTest()
|
||||
test.test_memory_continuity()
|
||||
test.save_memories()
|
||||
|
||||
print("\n✅ 测试完成！")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
37
experiments/v2_1_comprehensive_test_report.json
Normal file
37
experiments/v2_1_comprehensive_test_report.json
Normal file
@@ -0,0 +1,37 @@
|
||||
{
|
||||
"timestamp": "2025-08-16T15:17:54.175476",
|
||||
"version": "v2.1.0",
|
||||
"test_results": {
|
||||
"priority_algorithm_integration": true,
|
||||
"flow_controller_integration": true,
|
||||
"health_monitor_integration": true,
|
||||
"performance_under_load": true,
|
||||
"data_consistency": true,
|
||||
"chat_coordinator_integration": true,
|
||||
"cross_component_integration": true
|
||||
},
|
||||
"performance_metrics": {
|
||||
"total_operations": 400,
|
||||
"duration": 0.006308555603027344,
|
||||
"ops_per_second": 63405.956160241876,
|
||||
"avg_operation_time": 0.01577138900756836,
|
||||
"concurrent_threads": 5,
|
||||
"errors": 0
|
||||
},
|
||||
"error_log": [],
|
||||
"summary": {
|
||||
"pass_rate": 100.0,
|
||||
"total_tests": 7,
|
||||
"passed_tests": 7,
|
||||
"failed_tests": 0,
|
||||
"performance_metrics": {
|
||||
"total_operations": 400,
|
||||
"duration": 0.006308555603027344,
|
||||
"ops_per_second": 63405.956160241876,
|
||||
"avg_operation_time": 0.01577138900756836,
|
||||
"concurrent_threads": 5,
|
||||
"errors": 0
|
||||
},
|
||||
"error_count": 0
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user