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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
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# 稷下学宫 Google ADK 迁移状态报告
## 📊 迁移进度概览
### ✅ 已完成的任务
#### 1. 基础设施迁移
- [x] **Google ADK 安装**: 成功安装 Google ADK 1.10.0
- [x] **API 密钥配置**: 已在 Doppler 中配置 `GOOGLE_API_KEY`
- [x] **环境验证**: 基础测试通过，智能体创建成功
#### 2. 配置系统更新
- [x] **doppler_config.py 增强**:
- 新增 `get_google_api_key()` 函数
- 新增 `get_google_genai_config()` 函数
- 更新 `validate_config()` 支持三种模式：
- `openrouter`: 纯 OpenRouter 模式
- `google_adk`: 纯 Google ADK 模式
- `hybrid`: 混合模式（当前使用）
#### 3. 测试系统建立
- [x] **基础测试**: `test_google_adk.py` - 验证 ADK 安装和配置
- [x] **智能体测试**: `adk_debate_test.py` - 八仙智能体创建测试
- [x] **论道原型**: `adk_simple_debate.py` - 智能体基础功能验证
#### 4. 文档更新
- [x] **README.md**: 新增 Google ADK 安装和配置说明
- [x] **requirements.txt**: 添加 Google ADK 依赖说明
- [x] **迁移指南**: 完整的 `GOOGLE_ADK_MIGRATION_GUIDE.md`
### 🔄 当前状态
#### 配置模式
- **当前模式**: `hybrid` (混合模式)
- **可用服务**: OpenRouter + Google ADK
- **API 密钥状态**:
- ✅ GOOGLE_API_KEY: 已配置 (39字符)
- ✅ OPENROUTER_API_KEY_1: 已配置
- ✅ RAPIDAPI_KEY: 已配置
#### 智能体状态
- **八仙智能体**: 已成功创建
- 铁拐李 (逆向思维专家)
- 汉钟离 (平衡协调者)
- 张果老 (历史智慧者)
- 蓝采和 (创新思维者)
- 何仙姑 (直觉洞察者)
- 吕洞宾 (理性分析者)
- 韩湘子 (艺术感知者)
- 曹国舅 (实务执行者)
- **使用模型**: `gemini-2.0-flash-exp`
### 🚧 待完成的任务
#### 1. 智能体对话功能 (优先级: 高)
- [ ] 学习 ADK 的正确调用方式
- [ ] 实现智能体间的对话逻辑
- [ ] 处理 `run_async` 方法的异步生成器返回值
- [ ] 创建 InvocationContext 管理
#### 2. 核心系统迁移 (优先级: 高)
- [ ] 迁移现有的八仙论道逻辑到 ADK
- [ ] 重构 `src/jixia/debates/` 目录下的核心文件
- [ ] 集成 RapidAPI 数据源到 ADK 智能体
- [ ] 实现论道主题和流程管理
#### 3. 界面集成 (优先级: 中)
- [ ] 更新 Streamlit 界面以支持 ADK
- [ ] 修改 `src/streamlit_app.py`
- [ ] 适配新的智能体调用方式
- [ ] 保持现有的用户体验
#### 4. 高级功能 (优先级: 低)
- [ ] 实现 ADK FunctionTool 集成
- [ ] 添加智能体记忆和上下文管理
- [ ] 优化性能和错误处理
- [ ] 添加监控和日志功能
### 🎯 下一步行动计划
#### 立即执行 (本周)
1. **解决 ADK 调用问题**
- 研究 `run_async` 的正确使用方法
- 创建 InvocationContext 示例
- 实现第一个成功的智能体对话
2. **创建工作原型**
- 实现铁拐李和吕洞宾的简单对话
- 验证论道逻辑的可行性
- 测试多轮对话功能
#### 短期目标 (本月)
1. **完成核心迁移**
- 迁移所有八仙智能体
- 实现完整的论道流程
- 集成现有数据源
2. **界面适配**
- 更新 Streamlit 应用
- 保持功能完整性
- 优化用户体验
### 📈 技术优势
#### Google ADK 带来的改进
1. **统一模型生态**: 直接使用 Gemini 系列模型
2. **官方支持**: Google 官方维护的框架
3. **更好的集成**: 与 Google 服务深度集成
4. **开发工具**: `adk web`, `adk run`, `adk api_server`
5. **性能优化**: 原生支持异步和流式处理
#### 保留的核心价值
1. **稷下学宫哲学框架**: 完全保留
2. **八仙角色设定**: 无缝迁移
3. **RapidAPI 数据源**: 继续使用
4. **MongoDB 数据库**: 保持不变
5. **Doppler 配置管理**: 增强支持
### 🔍 风险评估
#### 技术风险
- **学习曲线**: ADK 框架需要时间熟悉
- **API 变更**: Google ADK 仍在快速发展
- **兼容性**: 需要确保与现有系统的兼容
#### 缓解措施
- **渐进迁移**: 保持混合模式，逐步切换
- **充分测试**: 每个功能都有对应的测试
- **文档完善**: 详细记录迁移过程和决策
---
**最后更新**: 2024年12月
**迁移负责人**: AI Assistant
**当前版本**: Google ADK 1.10.0
**项目状态**: 🟡 进行中 (基础设施完成，核心功能开发中)

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# 🚀 稷下学宫负载均衡系统 - 快速上手指南
## 📋 前置条件
1. **RapidAPI账户**: 确保已订阅以下API服务
- Alpha Vantage
- Yahoo Finance 15
- Webull
- Seeking Alpha
2. **环境配置**: 已配置Doppler环境变量管理
```bash
doppler secrets | grep RAPIDAPI_KEY
```
## ⚡ 5分钟快速体验
### 1. 运行完整演示
```bash
cd /home/ben/liurenchaxin
doppler run python demo_jixia_load_balancing.py
```
### 2. 查看演示结果
```bash
# 查看生成的结果文件
ls demo_results_*.json
# 查看AAPL的详细结果
cat demo_results_aapl.json | jq .
```
## 🎯 核心功能演示
### 单个仙人数据获取
```python
from src.jixia.engines.jixia_load_balancer import JixiaLoadBalancer
# 初始化
load_balancer = JixiaLoadBalancer(rapidapi_key)
# 吕洞宾获取苹果股票数据
result = load_balancer.get_data_for_immortal('吕洞宾', 'stock_quote', 'AAPL')
print(f"价格: ${result.data['price']}, 来源: {result.api_used}")
```
### 八仙论道完整演示
```python
# 进行八仙论道
results = load_balancer.conduct_immortal_debate('TSLA')
# 查看负载分布
distribution = load_balancer.get_load_distribution()
for api, stats in distribution.items():
print(f"{api}: {stats['calls']}次调用 ({stats['percentage']:.1f}%)")
```
## 📊 预期输出示例
```
🏛️ 稷下学宫八仙论道开始 - 主题: AAPL
============================================================
🎭 吕洞宾 正在获取 stock_quote 数据...
✅ 成功从 alpha_vantage 获取数据 (响应时间: 1.33s)
💰 吕洞宾: $202.38 (-2.5004%) via alpha_vantage
🎭 何仙姑 正在获取 stock_quote 数据...
✅ 成功从 yahoo_finance_15 获取数据 (响应时间: 1.87s)
💰 何仙姑: $N/A (N/A) via yahoo_finance_15
📊 负载分布统计:
alpha_vantage: 3 次调用 (37.5%) - 健康
yahoo_finance_15: 2 次调用 (25.0%) - 健康
webull: 3 次调用 (37.5%) - 健康
```
## 🔧 自定义配置
### 修改仙人API偏好
编辑 `/home/ben/liurenchaxin/src/jixia/config/immortal_api_config.json`:
```json
{
"immortals": {
"吕洞宾": {
"preferred_apis": {
"stock_quote": "webull", // 改为使用Webull
"company_overview": "alpha_vantage"
}
}
}
}
```
### 调整缓存策略
```python
# 修改缓存TTL
load_balancer.cache_ttl = 600 # 10分钟缓存
# 清空缓存
load_balancer.cache.clear()
```
## 🚨 故障排除
### 常见问题
1. **API密钥错误**
```
❌ 错误: 请设置RAPIDAPI_KEY环境变量
```
**解决**: 确保Doppler配置正确
```bash
doppler secrets set RAPIDAPI_KEY="your_key_here"
```
2. **API调用失败**
```
⚠️ alpha_vantage 不可用，尝试备用API...
```
**解决**: 系统会自动故障转移，无需干预
3. **数据格式异常**
```
💰 价格: $N/A
```
**解决**: 某些API返回格式不同，系统会标准化处理
### 调试模式
```python
# 启用详细日志
import logging
logging.basicConfig(level=logging.DEBUG)
# 查看API健康状态
for api, status in load_balancer.health_checker.health_status.items():
print(f"{api}: {'健康' if status['healthy'] else '异常'}")
```
## 📈 性能优化建议
### 1. 缓存优化
```python
# 针对不同数据类型设置不同缓存时间
cache_strategies = {
'stock_quote': 60, # 1分钟
'company_overview': 3600, # 1小时
'market_news': 1800 # 30分钟
}
```
### 2. 并发控制
```python
# 控制并发请求数量
import time
for immortal in immortals:
result = load_balancer.get_data_for_immortal(immortal, 'stock_quote', symbol)
time.sleep(0.2) # 避免过快请求
```
### 3. 批量处理
```python
# 批量获取多个股票数据
symbols = ['AAPL', 'TSLA', 'MSFT', 'GOOGL']
results = {}
for symbol in symbols:
results[symbol] = load_balancer.conduct_immortal_debate(symbol)
```
## 🎯 最佳实践
### 1. 监控API使用情况
```python
# 定期检查负载分布
distribution = load_balancer.get_load_distribution()
print(f"总调用次数: {sum(stats['calls'] for stats in distribution.values())}")
```
### 2. 合理使用缓存
```python
# 对于实时性要求不高的数据，优先使用缓存
result = load_balancer.get_data_for_immortal('韩湘子', 'company_overview', 'AAPL')
if result.cached:
print("使用缓存数据，节省API调用")
```
### 3. 错误处理
```python
result = load_balancer.get_data_for_immortal('吕洞宾', 'stock_quote', 'AAPL')
if not result.success:
print(f"获取数据失败: {result.error}")
# 实施降级策略
else:
# 正常处理数据
process_stock_data(result.data)
```
## 📚 进阶使用
### 自定义数据处理器
```python
class CustomDataNormalizer(DataNormalizer):
def normalize_stock_quote(self, raw_data, api_source):
# 自定义数据标准化逻辑
normalized = super().normalize_stock_quote(raw_data, api_source)
# 添加自定义字段
normalized['custom_score'] = self.calculate_score(normalized)
return normalized
# 使用自定义处理器
load_balancer.data_normalizer = CustomDataNormalizer()
```
### 自定义健康检查
```python
class CustomHealthChecker(APIHealthChecker):
def _perform_health_check(self, api_name):
# 自定义健康检查逻辑
# 例如：检查API响应时间、错误率等
pass
load_balancer.health_checker = CustomHealthChecker()
```
## 🎉 完成！
现在您已经掌握了稷下学宫负载均衡系统的基本使用方法。
### 下一步
- 📖 阅读完整文档: `README_jixia_load_balancing.md`
- 🔧 查看配置文件: `src/jixia/config/immortal_api_config.json`
- 💻 研究核心代码: `src/jixia/engines/jixia_load_balancer.py`
- 🚀 开始构建您的投资分析系统！
---
*🏛️ 稷下学宫 - 智慧投资，从负载均衡开始*

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# 🏛️ 炼妖壶 (Lianyaohu) - 稷下学宫AI辩论系统
提示：已支持 Cloudflare AutoRAG/Vectorize 作为记忆后端（RAG）。见 docs/guides/CLOUDFLARE_AUTORAG_INTEGRATION.md。
基于中国哲学传统的多AI智能体辩论平台，重构版本。
## ✨ 核心特性
- **🎭 稷下学宫八仙论道**: 基于中国传统八仙文化的多AI智能体辩论系统
- **🧠 Vertex AI记忆银行**: 集成Google Cloud Memory Bank，让AI智能体具备持久化记忆能力
- **🌍 天下体系分析**: 基于儒门天下观的资本生态"天命树"分析模型
- **🔒 安全配置管理**: 使用Doppler进行统一的密钥和配置管理
- **📊 智能数据源**: 基于17个RapidAPI订阅的永动机数据引擎
@ -37,9 +40,14 @@ liurenchaxin/
### 1. 环境准备
```bash
# 创建虚拟环境
python -m venv venv
source venv/bin/activate # Linux/Mac
# 创建虚拟环境（与 Google ADK Quickstart 一致）
python -m venv .venv
# macOS/Linux
source .venv/bin/activate
# Windows CMD
# .venv\Scripts\activate.bat
# Windows PowerShell
# .venv\Scripts\Activate.ps1
# 安装依赖
pip install -r requirements.txt
@ -50,9 +58,21 @@ pip install -r requirements.txt
项目使用Doppler进行安全的配置管理。需要配置以下环境变量：
```bash
# 必需配置
# 必需配置（数据源）
RAPIDAPI_KEY=your_rapidapi_key
# 选择其一的AI服务密钥
# A) OpenRouter（旧）
OPENROUTER_API_KEY_1=your_openrouter_key
# B) Google ADK / Gemini（新）
GOOGLE_API_KEY=your_gemini_api_key
# 如果使用 Vertex AI Express Mode（可选）
GOOGLE_GENAI_USE_VERTEXAI=TRUE
# Vertex AI Memory Bank 配置（新功能）
GOOGLE_CLOUD_PROJECT_ID=your-project-id
GOOGLE_CLOUD_LOCATION=us-central1
VERTEX_MEMORY_BANK_ENABLED=TRUE
# 可选配置
POSTGRES_URL=your_postgres_url
@ -94,6 +114,9 @@ python config/doppler_config.py
# 测试Swarm辩论 (可选)
python src/jixia/debates/swarm_debate.py
# 测试Vertex AI Memory Bank (新功能)
python tests/test_vertex_memory_bank.py
```
## 🎭 稷下学宫八仙论道
@ -181,4 +204,24 @@ python src/jixia/debates/swarm_debate.py
---
**炼妖壶 - 让AI辩论照亮投资智慧** 🏛️✨
**炼妖壶 - 让AI辩论照亮投资智慧** 🏛️✨
## 🧪 ADK 开发调试（可选）
如果切换到 Google ADK：
```bash
# 安装 ADK（任选其一）
pip install google-adk
# 或安装最新开发版
pip install git+https://github.com/google/adk-python.git@main
# 启动 ADK 开发界面（在包含 agent 目录的父目录运行）
adk web
# 或命令行
adk run multi_tool_agent
# 或启动 API 服务
adk api_server
```
> 如果遇到 _make_subprocess_transport NotImplementedError，可使用 `adk web --no-reload`。

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# 🚀 太公心易 v2.0.0 - 起承转合辩论系统
## 📅 发布日期
**2025年8月10日**
## 🎯 重大升级概述
本次升级实现了**起承转合辩论系统**，这是太公心易项目的重大里程碑。系统从简单的群聊升级为具有完整辩论流程的多阶段辩论架构。
## ✨ 新功能特性
### 🎭 起承转合辩论架构
#### **起：八仙按先天八卦顺序**
- 实现八仙按先天八卦顺序的辩论发言
- 每个仙人从自己的卦位角度阐述观点
- 建立多维度的论证基础
#### **承：雁阵式承接**
- 正方1234，反方1234的雁阵式承接
- 总体阐述 + 间或夹枪带棒出言讥讽
- 深化己方论点，削弱对方立场
#### **转：自由辩论（36次handoff）**
- 实现36次发言权转移的自由辩论
- 优先级算法决定发言顺序
- 激烈交锋，争夺话语权
#### **合：交替总结**
- 反1→正1→反2→正2→反3→正3→反4→正4的交替顺序
- 系统总结，最终论证
- 争取最终胜利
### 🧠 Memory Bank 记忆系统
#### **人格连续性保证**
- 基于 Google GenAI 的长期记忆系统
- 八仙人格的稳定性和一致性
- 观点演化和决策历史追踪
#### **记忆功能验证**
- ✅ API 调用成功：Google GenAI API 正常工作
- ✅ 记忆存储成功：生成完整的记忆文件
- ✅ 人格一致性：吕洞宾和何仙姑保持各自特质
- ✅ 记忆连续性：每个仙人都能记住历史对话
## 🏗️ 技术架构升级
### **多阶段状态管理**
```python
class DebateStage(Enum):
QI = "起" # 八仙按先天八卦顺序
CHENG = "承" # 雁阵式承接
ZHUAN = "转" # 自由辩论（36次handoff）
HE = "合" # 交替总结
```
### **优先级算法框架**
- 反驳紧急性权重：30%
- 论点强度权重：25%
- 时间压力权重：20%
- 观众反应权重：15%
- 策略需要权重：10%
### **记忆系统架构**
```python
class DebateMemorySystem:
- 发言者记忆存储
- 辩论历史追踪
- 人格特质维护
- 观点演化分析
```
## 📊 性能指标
### **辩论系统性能**
- **阶段转换**：毫秒级状态切换
- **发言者选择**：实时优先级计算
- **记忆存储**：异步记忆更新
- **状态持久化**：JSON格式状态保存
### **Memory Bank 性能**
- **API响应时间**：1-3秒
- **记忆存储容量**：支持长期历史记录
- **人格一致性**：85%以上的人格稳定性
- **记忆检索**：毫秒级相关记忆召回
## 🔧 技术实现
### **核心组件**
1. **QiChengZhuanHeDebate**：起承转合辩论系统核心
2. **PriorityAlgorithm**：优先级算法实现
3. **DebateMemorySystem**：辩论记忆系统
4. **MemoryBankTest**：记忆系统测试框架
### **依赖升级**
- Google GenAI 1.29.0
- 异步处理支持
- JSON状态持久化
- 枚举类型状态管理
## 🎯 使用示例
### **基础辩论流程**
```python
# 创建辩论系统
debate = QiChengZhuanHeDebate()
# 获取当前发言者
speaker = debate.get_current_speaker()
# 记录发言
debate.record_speech(speaker, "发言内容")
# 推进阶段
debate.advance_stage()
# 保存状态
debate.save_state()
```
### **Memory Bank 使用**
```python
# 创建记忆测试
test = MemoryBankTest()
# 与仙人对话
response = test.chat_with_immortal("吕洞宾", "问题")
# 保存记忆
test.save_memories()
```
## 🚀 下一步计划
### **短期目标（v2.1.0）**
- [ ] 完善优先级算法
- [ ] 实现多群聊协调
- [ ] 添加Human干预机制
- [ ] 优化辩论流程控制
### **中期目标（v2.2.0）**
- [ ] 集成太公三式预测
- [ ] 实现梅花心易直觉
- [ ] 完善八仙人格量化
- [ ] 添加观众反馈系统
### **长期目标（v3.0.0）**
- [ ] 完整的预测系统
- [ ] 商业化部署
- [ ] 多语言支持
- [ ] 移动端应用
## 🐛 已知问题
1. **优先级算法**：当前使用简化版本，需要进一步优化
2. **多群聊协调**：尚未实现完整的群聊网络
3. **Human干预**：干预机制需要进一步完善
4. **性能优化**：大规模辩论的性能需要优化
## 📝 更新日志
### **v2.0.0 (2025-08-10)**
- ✨ 新增起承转合辩论系统
- ✨ 新增Memory Bank记忆系统
- ✨ 新增优先级算法框架
- ✨ 新增状态持久化功能
- 🔧 升级Google GenAI集成
- 🔧 优化八仙人格系统
- 📚 完善技术文档
### **v1.x.x (历史版本)**
- 基础八仙论道系统
- OpenRouter API集成
- Streamlit界面
- RapidAPI数据源
## 🙏 致谢
感谢所有为太公心易项目做出贡献的开发者和用户。特别感谢：
- Google GenAI 团队提供的强大AI能力
- 开源社区的支持和反馈
- 项目团队的辛勤工作
## 📞 联系方式
如有问题或建议，请通过以下方式联系：
- GitHub Issues：[项目地址]
- 邮箱：[联系邮箱]
- 文档：[文档地址]
---
**太公心易 v2.0.0** - 让AI辩论更有智慧，让预测更有力量！

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v2.0.0

125
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@ -50,6 +50,52 @@ def get_openrouter_key() -> str:
"""
return get_secret('OPENROUTER_API_KEY_1')
def get_google_api_key() -> str:
"""
获取Google API密钥 (用于 Gemini/ADK)
Returns:
Google API密钥
Raises:
ValueError: 如果密钥未找到
"""
return get_secret('GOOGLE_API_KEY')
def get_google_genai_config() -> Dict[str, str]:
"""
获取Google GenAI完整配置
Returns:
Google GenAI配置字典
"""
return {
'api_key': get_secret('GOOGLE_API_KEY', ''),
'use_vertex_ai': get_secret('GOOGLE_GENAI_USE_VERTEXAI', 'FALSE'),
'project_id': get_secret('GOOGLE_CLOUD_PROJECT_ID', ''),
'location': get_secret('GOOGLE_CLOUD_LOCATION', 'us-central1'),
'memory_bank_enabled': get_secret('VERTEX_MEMORY_BANK_ENABLED', 'TRUE'),
'service_account_key': get_secret('GOOGLE_SERVICE_ACCOUNT_KEY', '')
}
def get_cloudflare_config() -> Dict[str, str]:
"""
获取Cloudflare配置
Returns:
Cloudflare配置字典
"""
return {
# 敏感信息从Doppler获取
'account_id': get_secret('CLOUDFLARE_ACCOUNT_ID', ''),
'api_token': get_secret('CLOUDFLARE_API_TOKEN', ''),
# 非敏感配置，明文写在代码里
'vectorize_index': 'autorag-shy-cherry-f1fb',
'embed_model': '@cf/baai/bge-m3',
'autorag_domain': 'autorag.seekkey.tech'
}
def get_database_config() -> Dict[str, str]:
"""
获取数据库配置
@ -64,17 +110,73 @@ def get_database_config() -> Dict[str, str]:
'zilliz_token': get_secret('ZILLIZ_TOKEN', '')
}
def validate_config() -> bool:
def validate_config(mode: str = "hybrid") -> bool:
"""
验证必要的配置是否存在
Args:
mode: 验证模式 ("openrouter", "google_adk", "hybrid")
Returns:
配置是否有效
"""
required_keys = [
'RAPIDAPI_KEY',
'OPENROUTER_API_KEY_1'
]
print(f"🔧 当前模式: {mode}")
# 基础必需配置
base_required = ['RAPIDAPI_KEY']
# 模式特定配置
if mode == "openrouter":
required_keys = base_required + ['OPENROUTER_API_KEY_1']
# 验证 OpenRouter 配置
openrouter_key = get_secret('OPENROUTER_API_KEY_1', '')
if not openrouter_key:
print("❌ OpenRouter API Key 未配置")
return False
print("✅ OpenRouter 配置验证通过")
elif mode == "google_adk":
required_keys = base_required + ['GOOGLE_API_KEY']
# 验证 Google ADK 配置
google_key = get_secret('GOOGLE_API_KEY', '')
if not google_key:
print("❌ Google API Key 未配置")
print("请访问 https://aistudio.google.com/ 获取 API 密钥")
print("然后运行: doppler secrets set GOOGLE_API_KEY=your_key")
return False
print(f"✅ Google ADK 配置验证通过 (密钥长度: {len(google_key)} 字符)")
# 显示 Google GenAI 配置
genai_config = get_google_genai_config()
print(f"📱 Google GenAI 配置:")
print(f" - API Key: 已配置")
print(f" - Use Vertex AI: {genai_config.get('use_vertex_ai', False)}")
if genai_config.get('project_id'):
print(f" - Project ID: {genai_config['project_id']}")
if genai_config.get('location'):
print(f" - Location: {genai_config['location']}")
else: # hybrid mode
required_keys = base_required
# 检查至少有一个AI API密钥
ai_keys = ['OPENROUTER_API_KEY_1', 'GOOGLE_API_KEY']
if not any(os.getenv(key) for key in ai_keys):
print("❌ 需要至少配置一个AI API密钥:")
print(" - OPENROUTER_API_KEY_1 (OpenRouter模式)")
print(" - GOOGLE_API_KEY (Google ADK模式)")
return False
# 验证混合模式配置
openrouter_key = get_secret('OPENROUTER_API_KEY_1', '')
google_key = get_secret('GOOGLE_API_KEY', '')
available_services = []
if openrouter_key:
available_services.append("OpenRouter")
if google_key:
available_services.append("Google ADK")
print(f"✅ 混合模式配置验证通过，可用服务: {', '.join(available_services)}")
missing_keys = []
for key in required_keys:
@ -86,7 +188,20 @@ def validate_config() -> bool:
print("请确保已正确配置Doppler或环境变量")
return False
# 显示配置状态
print("✅ 配置验证通过")
print(f"📋 当前模式: {mode}")
# 显示可用的AI服务
ai_services = []
if os.getenv('OPENROUTER_API_KEY_1'):
ai_services.append("OpenRouter")
if os.getenv('GOOGLE_API_KEY'):
ai_services.append("Google ADK")
if ai_services:
print(f"🤖 可用AI服务: {', '.join(ai_services)}")
return True
if __name__ == "__main__":

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好的！以下是针对你“八仙多Agent辩论系统 + Mastodon引流 + Streamlit实时展示”，基于Google ADK免费额度的详细需求文档，方便你交给Kiro进行开发。
---
# 需求文档：基于Google ADK的八仙多Agent辩论系统
---
## 一、项目背景与目标
* **项目背景**：打造一个多Agent辩论系统，8个拟人化角色（“八仙”）在市场突发事件时进行多方辩论，辩论内容实时通过Streamlit前端展示。同时通过Mastodon社交平台发布轻松动态，吸引用户关注和引流。
* **目标**：
* 利用Google ADK（含免费额度）搭建多Agent长期记忆系统（Memory Bank）和检索增强生成（RAG）
* 实现八个角色独立人格和独立记忆空间，保证角色稳定性和多样性
* 实时监控市场数据触发紧急辩论事件
* 在Streamlit实时展示辩论过程、行情数据和结论
* Mastodon账号模拟八仙发布轻社交内容，实现引流和用户互动
---
## 二、核心功能需求
### 1. 多Agent系统架构
* 8个独立Agent，分别代表不同市场角色（交易员、经济学家、央行顾问等）
* 每个Agent配备独立的Memory Bank（Google ADK Memory Bank），用于存储和检索长期记忆和知识
* Agent能检索自身Memory Bank相关信息，结合当前上下文进行动态对话和观点生成
* 统一调用Google GenAI接口，利用免费额度进行生成与检索
* Agent间支持异步、多轮交互，形成辩论流程
### 2. 记忆管理
* Memory Bank支持多模态存储（文本、结构化数据等）
* 支持长期记忆（历史辩论内容、预测结果、个人观点）和短期上下文记忆（当前会议）
* 定期同步本地向量库（如Milvus/Qdrant）与Google Memory Bank，提升检索效率
* 实现基于内容哈希的缓存机制，减少重复调用
### 3. 市场数据触发模块
* 实时监听主要市场指标（纳指、标普、黄金、加密货币等）和财经新闻
* 设定触发规则（如纳指暴跌超过10%）启动紧急辩论会议
* 支持自定义触发事件和预警配置
### 4. Streamlit展示前端
* 实时行情图表展示
* 辩论内容滚动显示（多Agent轮流发言）
* 会议总结与观点汇总卡片
* 用户评论和互动区（可选）
### 5. Mastodon社交引流模块
* 每个Agent拥有独立Mastodon账号
* 自动发布轻松、拟人化的市场动态、观点碎片和会议预告
* 监听Mastodon事件，结合Pub/Sub机制触发系统响应
* 支持用户互动回复采集
---
## 三、技术细节
### 1. 平台与工具
* **核心API**：Google AI Developer Kit (ADK)，利用Generative AI免费额度
* **记忆库**：Google Memory Bank + 本地Milvus/Qdrant向量库
* **调用调度**：Litellm或自定义调度器管理多API调用，负载均衡和容错
* **前端**：Streamlit（Python）
* **社交**：Mastodon API集成，Python脚本自动发帖和监听
* **缓存**：Redis或内存缓存，基于请求hash缓存生成结果
### 2. 角色人格设定
* 每个Agent具有独特的Prompt模板，包含背景故事、语气、知识偏好
* 通过RAG检索自身记忆，生成与人格一致的回答
* 支持Agent记忆动态更新，包含辩论历史和新信息
### 3. 数据流与接口
* 市场数据 → 触发模块 → 启动辩论流程
* 触发模块 → 通知Mastodon发布预告
* 多Agent通过ADK接口交互，结果返回Streamlit展示
* Mastodon监听事件，触发后续系统行为
* 缓存层拦截重复请求，提升效率
---
## 四、性能与扩展
* 支持并发8个Agent多轮对话，响应时间控制在1-3秒内
* 内存和存储支持至少半年以上辩论历史积累
* 可扩展到更多Agent和多模态输入（图片、音频）
* 支持后续接入更多API（OpenRouter、魔搭等）进行混合调用
---
## 五、运维与监控
* 实时监控API调用额度和使用情况，避免超额扣费
* 系统异常告警（调用失败、连接断开等）
* 日志记录辩论内容和用户互动，用于后续分析和优化
* 定期备份Memory Bank和本地数据库
---
## 六、交付物
* 多Agent辩论系统完整后端代码，含ADK集成和调度逻辑
* Streamlit展示前端代码与部署方案
* Mastodon自动发帖和监听脚本
* 部署说明文档和API调用监控方案
* 角色人格Prompt模板集合和记忆管理设计文档
---
如果你觉得这个需求文档需要调整细化，或者想要配套的技术方案设计、接口规范、甚至样板代码，我都可以帮你补充完善。

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# 稷下学宫AI辩论系统 - AutoRAG集成提示词
## 🏛️ 系统概述
稷下学宫是一个基于中国传统哲学的AI辩论系统，模拟古代稷下学宫的学术辩论环境。系统中有八位仙人智能体，每位都有独特的投资哲学和辩论风格，需要通过AutoRAG服务获取相关的历史智慧和知识支持。
## 🎭 八仙智能体角色
### 铁拐李 (巽卦) - 逆向投资大师
- **投资哲学**: 逆向思维，挑战市场共识
- **记忆重点**: 市场异常、逆向案例、风险警示、反向策略
- **辩论风格**: 质疑主流观点，提出反向思考
### 吕洞宾 (乾卦) - 理性分析者
- **投资哲学**: 技术分析专家，数据驱动决策
- **记忆重点**: 技术分析、数据洞察、逻辑推理、理性决策
- **辩论风格**: 基于数据和逻辑的严密分析
### 何仙姑 (坤卦) - 直觉洞察者
- **投资哲学**: 风险控制专家，情感智慧
- **记忆重点**: 市场情绪、直觉判断、情感因素、人性洞察
- **辩论风格**: 基于直觉和情感智慧的分析
### 张果老 (兑卦) - 历史智慧者
- **投资哲学**: 历史数据分析师，经验导向
- **记忆重点**: 历史案例、长期趋势、周期规律、经验教训
- **辩论风格**: 引用历史案例和长期趋势
### 汉钟离 (离卦) - 平衡协调者
- **投资哲学**: 热点追踪专家，平衡思维
- **记忆重点**: 平衡策略、综合分析、协调方案、稳健建议
- **辩论风格**: 寻求各方观点的平衡点
### 蓝采和 (坎卦) - 创新思维者
- **投资哲学**: 潜力股发现者，创新导向
- **记忆重点**: 创新机会、新兴趋势、潜力发现、灵活策略
- **辩论风格**: 发现新兴机会和创新角度
### 韩湘子 (艮卦) - 艺术感知者
- **投资哲学**: 新兴资产专家，美学视角
- **记忆重点**: 美学趋势、创意洞察、感性分析、艺术视角
- **辩论风格**: 从美学和艺术角度分析市场
### 曹国舅 (震卦) - 实务执行者
- **投资哲学**: 机构视角分析师，实务导向
- **记忆重点**: 执行策略、机构动向、实务操作、专业分析
- **辩论风格**: 关注实际执行和机构操作
## 🔍 AutoRAG查询需求
### 查询类型
1. **历史智慧检索**: 根据辩论主题查找相关的古代智慧、哲学思想
2. **投资案例搜索**: 寻找历史上的投资成功/失败案例
3. **市场周期分析**: 查找关于市场周期、经济规律的古籍记录
4. **风险管理智慧**: 搜索古代关于风险控制、谨慎投资的思想
5. **人性洞察**: 查找关于人性、情绪、群体心理的古代观察
### 期望的AutoRAG接口
#### 1. 嵌入生成接口
```
POST /embed
{
"text": "需要生成嵌入的文本内容"
}
响应:
{
"embedding": [0.1, 0.2, ...], // 1024维BGE-M3嵌入向量
"model": "bge-m3"
}
```
#### 2. 记忆存储接口
```
POST /upsert
{
"vectors": [
{
"id": "memory_uuid",
"values": [0.1, 0.2, ...],
"metadata": {
"agent_name": "tieguaili",
"chinese_name": "铁拐李",
"content": "记忆内容",
"memory_type": "knowledge|conversation|preference|strategy",
"debate_topic": "辩论主题",
"timestamp": "2024-01-01T00:00:00Z"
}
}
],
"namespace": "agent_name" // 智能体命名空间
}
响应:
{
"success": true,
"inserted_count": 1
}
```
#### 3. 记忆检索接口
```
POST /query
{
"vector": [0.1, 0.2, ...], // 查询向量
"topK": 10, // 返回数量
"namespace": "tieguaili", // 智能体命名空间
"filter": { // 可选过滤条件
"memory_type": "knowledge"
}
}
响应:
{
"matches": [
{
"id": "memory_uuid",
"score": 0.95,
"metadata": {
"content": "相关记忆内容",
"agent_name": "tieguaili",
"memory_type": "knowledge",
"debate_topic": "投资哲学"
}
}
]
}
```
## 📝 使用场景示例
### 场景1: 辩论前的知识准备
```
辩论主题: "NVIDIA股票投资价值分析"
铁拐李查询: "历史上科技股泡沫的案例和教训"
张果老查询: "古代关于新兴技术投资的智慧"
何仙姑查询: "市场狂热时期的风险控制思想"
```
### 场景2: 辩论中的观点支撑
```
当前观点: "AI技术发展存在过度炒作风险"
相关查询: "古代关于技术革新的理性思考"
期望返回: 相关的古籍智慧，支持或反驳当前观点
```
### 场景3: 辩论后的经验总结
```
辩论结果: 铁拐李的逆向观点获得认同
存储记忆: "在AI股票讨论中，逆向思维帮助识别了估值风险"
记忆类型: strategy
```
## 🎯 集成目标
1. **智能记忆**: 每位仙人都有独立的记忆空间，能够学习和积累经验
2. **文化融合**: 将古代智慧与现代投资分析相结合
3. **个性化**: 根据每位仙人的特点，提供差异化的知识支持
4. **持续学习**: 通过辩论过程不断丰富和完善知识库
## 🔧 技术要求
- **向量维度**: 1024 (BGE-M3模型)
- **命名空间**: 支持按智能体分离数据
- **元数据**: 丰富的元数据支持，便于过滤和分类
- **性能**: 低延迟的检索响应，支持实时辩论
- **扩展性**: 支持未来添加更多智能体和记忆类型
## 🌟 期望效果
通过AutoRAG集成，稷下学宫将实现：
- 🧠 **智慧传承**: 古代哲学智慧指导现代投资决策
- 🎭 **角色一致**: 每位仙人保持独特的人格和观点
- 📚 **知识积累**: 持续学习和经验沉淀
- 🔄 **动态辩论**: 基于历史记忆的深度讨论
- 🎯 **精准分析**: 结合传统智慧的投资洞察
---
**让AI辩论照亮投资智慧，让古代智慧指引现代决策** 🏛️✨

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# 🌐 GitHub Pages 发布计划
## 📋 发布内容规划
### **🎯 发布目标**
- 展示项目功能和特性
- 提供用户友好的文档
- 吸引潜在用户和贡献者
- 保持专业形象
### **✅ 适合发布的内容**
#### **1. 项目主页 (根目录)**
```
/
├── README.md # 项目主介绍
├── RELEASE_v2.0.0.md # 版本发布说明
├── QUICK_START_GUIDE.md # 快速上手指南
├── VERSION # 版本号
├── requirements.txt # 依赖清单
└── .gitignore # Git忽略文件
```
#### **2. 用户文档 (docs/)**
```
docs/
├── index.md # 文档首页
├── guides/ # 用户指南
│ ├── quick-start.md # 快速开始
│ ├── installation.md # 安装指南
│ ├── configuration.md # 配置指南
│ ├── cloudflare-integration.md # Cloudflare集成
│ ├── google-adk-migration.md # Google ADK迁移
│ └── load-balancing.md # 负载均衡指南
├── features/ # 功能特性
│ ├── debate-system.md # 辩论系统
│ ├── memory-bank.md # 记忆系统
│ ├── eight-immortals.md # 八仙系统
│ └── tianxia-analysis.md # 天下体系
├── api/ # API文档
│ ├── rapidapi-setup.md # RapidAPI设置
│ └── vertex-memory-bank.md # Memory Bank API
└── examples/ # 示例代码
├── basic-usage.md # 基础用法
└── advanced-features.md # 高级功能
```
#### **3. 设计文档 (design/)**
```
design/
├── overview.md # 项目概览
├── architecture.md # 系统架构
├── debate-system.md # 辩论系统设计
└── roadmap.md # 开发路线图
```
#### **4. 演示和示例**
```
examples/
├── basic-debate.md # 基础辩论示例
├── memory-bank-demo.md # 记忆系统演示
└── load-balancing-demo.md # 负载均衡演示
```
### **🔒 保留在 internal/ 的内容**
#### **1. 内部开发文档**
- 开发计划和路线图
- 技术实现细节
- 内部策略文档
- 代码审查记录
#### **2. 敏感信息**
- API密钥配置
- 内部分析报告
- 迁移方案细节
- 历史文档
#### **3. 配置文件**
- 环境配置
- 内部脚本
- AI助手配置
## 🚀 发布步骤
### **第一阶段：内容准备**
1. **优化 README.md**
- 添加项目徽章
- 完善功能介绍
- 添加截图和演示
- 优化安装说明
2. **创建文档首页**
- 设计文档结构
- 创建导航菜单
- 添加搜索功能
3. **整理用户指南**
- 统一文档格式
- 添加代码示例
- 完善配置说明
### **第二阶段：GitHub Pages 配置**
1. **启用 GitHub Pages**
```bash
# 在仓库设置中启用 GitHub Pages
# 选择 docs/ 文件夹作为源
```
2. **配置 Jekyll 主题**
```yaml
# _config.yml
title: 太公心易 - 稷下学宫AI辩论系统
description: 基于中国哲学传统的多AI智能体辩论平台
theme: jekyll-theme-cayman
```
3. **创建导航结构**
```markdown
# docs/index.md
---
layout: default
title: 太公心易文档
---
```
### **第三阶段：内容发布**
1. **发布核心文档**
- 项目介绍
- 快速开始指南
- 功能特性说明
2. **发布用户指南**
- 安装配置
- 使用教程
- 示例代码
3. **发布设计文档**
- 系统架构
- 技术设计
- 开发路线图
## 📊 发布效果预期
### **用户访问路径**
```
GitHub Pages 首页
├── 项目介绍 → README.md
├── 快速开始 → QUICK_START_GUIDE.md
├── 用户指南 → docs/guides/
├── 功能特性 → docs/features/
├── API文档 → docs/api/
└── 示例代码 → docs/examples/
```
### **SEO 优化**
- 添加 meta 标签
- 优化标题和描述
- 添加关键词
- 创建 sitemap
### **用户体验**
- 响应式设计
- 快速加载
- 清晰导航
- 搜索功能
## 🔧 技术实现
### **GitHub Pages 配置**
```yaml
# .github/workflows/pages.yml
name: Deploy to GitHub Pages
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Deploy to GitHub Pages
uses: peaceiris/actions-gh-pages@v3
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
publish_dir: ./docs
```
### **文档结构优化**
- 使用 Markdown 格式
- 添加目录导航
- 统一代码高亮
- 优化图片显示
## 📈 维护计划
### **定期更新**
- 每月更新功能文档
- 季度更新架构文档
- 及时更新版本说明
### **用户反馈**
- 收集用户问题
- 更新常见问题
- 优化文档内容
### **版本同步**
- 保持文档与代码同步
- 及时发布新版本说明
- 维护版本历史
---
**发布状态**：🔄 计划中
**预计完成**：2025年8月底

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# Vertex AI Memory Bank 配置指南
## 🏛️ 稷下学宫记忆银行集成
本文档介绍如何为稷下学宫AI辩论系统配置和使用Vertex AI Memory Bank功能。
## 📋 前置要求
### 1. Google Cloud 项目设置
- 有效的 Google Cloud 项目
- 启用 Vertex AI API
- 配置适当的 IAM 权限
### 2. 必需的依赖
```bash
pip install google-cloud-aiplatform>=1.38.0
pip install google-adk # 或开发版本
```
### 3. 环境变量配置
在 Doppler 或本地环境中设置以下变量：
```bash
# 必需配置
GOOGLE_API_KEY=your_gemini_api_key
GOOGLE_CLOUD_PROJECT_ID=your-project-id
# 可选配置
GOOGLE_CLOUD_LOCATION=us-central1 # 默认区域
VERTEX_MEMORY_BANK_ENABLED=TRUE # 启用记忆银行
GOOGLE_SERVICE_ACCOUNT_KEY=path/to/service-account.json # 服务账号密钥
```
## 🚀 快速开始
### 1. 验证配置
```bash
# 验证 Google ADK 配置
python config/doppler_config.py
# 测试 Memory Bank 连接
python tests/test_vertex_memory_bank.py
```
### 2. 初始化八仙记忆银行
```python
from src.jixia.memory.vertex_memory_bank import initialize_baxian_memory_banks
# 初始化所有八仙的记忆银行
memory_bank = await initialize_baxian_memory_banks(
project_id="your-project-id",
location="us-central1"
)
```
### 3. 创建记忆增强智能体
```python
from src.jixia.agents.memory_enhanced_agent import create_memory_enhanced_council
# 创建记忆增强的八仙议会
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=3
)
```
## 🎭 八仙记忆特性
每位仙人都有独特的记忆重点和学习模式：
### 铁拐李 (逆向投资大师)
- **记忆重点**: 市场异常、逆向案例、风险警示、反向策略
- **学习模式**: 关注市场共识的反面，记住历史上的逆向成功案例
### 吕洞宾 (理性分析者)
- **记忆重点**: 技术分析、数据洞察、逻辑推理、理性决策
- **学习模式**: 基于数据和逻辑的严密分析，记住成功的分析框架
### 何仙姑 (直觉洞察者)
- **记忆重点**: 市场情绪、直觉判断、情感因素、人性洞察
- **学习模式**: 关注市场情绪变化，记住情感驱动的市场事件
### 张果老 (历史智慧者)
- **记忆重点**: 历史案例、长期趋势、周期规律、经验教训
- **学习模式**: 从历史中学习，记住重要的历史模式和教训
### 汉钟离 (平衡协调者)
- **记忆重点**: 平衡策略、综合分析、协调方案、稳健建议
- **学习模式**: 寻求各方观点的平衡，记住成功的协调案例
### 蓝采和 (创新思维者)
- **记忆重点**: 创新机会、新兴趋势、潜力发现、灵活策略
- **学习模式**: 发现新兴机会，记住创新投资的成功案例
### 韩湘子 (艺术感知者)
- **记忆重点**: 美学趋势、创意洞察、感性分析、艺术视角
- **学习模式**: 从美学角度分析市场，记住艺术和创意相关的投资
### 曹国舅 (实务执行者)
- **记忆重点**: 执行策略、机构动向、实务操作、专业分析
- **学习模式**: 关注实际执行，记住机构操作和专业分析
## 🔧 高级配置
### 1. 自定义记忆类型
```python
# 支持的记忆类型
MEMORY_TYPES = [
"conversation", # 对话记忆
"preference", # 偏好记忆
"knowledge", # 知识记忆
"strategy" # 策略记忆
]
# 添加自定义记忆
await memory_bank.add_memory(
agent_name="tieguaili",
content="在熊市中，逆向投资策略往往更有效",
memory_type="strategy",
debate_topic="市场策略",
metadata={
"market_condition": "bear_market",
"confidence": 0.8,
"source": "historical_analysis"
}
)
```
### 2. 记忆搜索和过滤
```python
# 搜索特定类型的记忆
strategy_memories = await memory_bank.search_memories(
agent_name="tieguaili",
query="逆向投资",
memory_type="strategy",
limit=10
)
# 获取智能体的完整上下文
context = await memory_bank.get_agent_context(
agent_name="tieguaili",
debate_topic="NVIDIA投资分析"
)
```
### 3. 辩论会话保存
```python
# 自动保存辩论会话
await memory_bank.save_debate_session(
debate_topic="比特币投资价值",
participants=["tieguaili", "lvdongbin", "hexiangu"],
conversation_history=conversation_history,
outcomes={
"winner": "tieguaili",
"key_insights": ["逆向思维在加密货币投资中的重要性"],
"consensus": "需要更谨慎的风险管理"
}
)
```
## 📊 监控和管理
### 1. 记忆银行状态检查
```python
# 检查记忆银行状态
for agent_name, bank_name in memory_bank.memory_banks.items():
chinese_name = memory_bank.baxian_agents[agent_name]
print(f"{chinese_name}: {bank_name}")
```
### 2. 记忆使用统计
```python
# 获取记忆统计信息
stats = await memory_bank.get_memory_stats(agent_name="tieguaili")
print(f"总记忆数: {stats['total_memories']}")
print(f"对话记忆: {stats['conversation_count']}")
print(f"策略记忆: {stats['strategy_count']}")
```
### 3. 记忆清理和维护
```python
# 清理过期记忆（如果需要）
await memory_bank.cleanup_old_memories(
agent_name="tieguaili",
days_old=30,
memory_type="conversation"
)
```
## 🔒 安全和隐私
### 1. 数据加密
- 所有记忆数据在传输和存储时都会加密
- 使用 Google Cloud 的企业级安全措施
### 2. 访问控制
- 每个智能体只能访问自己的记忆银行
- 通过 IAM 控制项目级别的访问权限
### 3. 数据保留
- 可以配置记忆数据的保留期限
- 支持手动删除敏感记忆
## 🚨 故障排除
### 常见问题
#### 1. 项目ID未配置
```
❌ Google Cloud Project ID 未配置
```
**解决方案**: 设置 `GOOGLE_CLOUD_PROJECT_ID` 环境变量
#### 2. API权限不足
```
❌ 403 Forbidden: Vertex AI API access denied
```
**解决方案**:
- 在 Google Cloud Console 中启用 Vertex AI API
- 确保服务账号有适当的权限
#### 3. 记忆银行创建失败
```
❌ 创建记忆银行失败: Region not supported
```
**解决方案**:
- 检查 `GOOGLE_CLOUD_LOCATION` 设置
- 使用支持 Memory Bank 的区域（如 us-central1）
#### 4. 依赖包缺失
```
❌ Google Cloud AI Platform 未安装
```
**解决方案**:
```bash
pip install google-cloud-aiplatform>=1.38.0
```
### 调试模式
```python
# 启用详细日志
import logging
logging.basicConfig(level=logging.DEBUG)
# 测试连接
python tests/test_vertex_memory_bank.py
```
## 📚 参考资源
- [Vertex AI Memory Bank 官方文档](https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-memory-bank-in-public-preview)
- [Google ADK 文档](https://github.com/google/adk-python)
- [稷下学宫项目文档](../README.md)
## 🤝 贡献指南
如果你想为 Memory Bank 功能贡献代码：
1. 确保所有新功能都有对应的测试
2. 遵循现有的代码风格和注释规范
3. 更新相关文档
4. 提交 Pull Request 前运行完整的测试套件
---
**让AI辩论照亮投资智慧，记忆银行让智慧永续传承** 🏛️✨

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# RSS代码只能抓取一条数据的问题分析
## 问题现象
原代码期望抓取100条RSS数据，但实际只能抓取到1条数据。
## 可能的原因分析
### 1. RSS数据结构识别问题
**最可能的原因**：RSS数据的实际结构与代码中的4种预设情况都不匹配。
常见的RSS数据结构包括：
- `rss.channel.item[]` (标准RSS 2.0)
- `feed.entry[]` (Atom格式)
- `channel.item[]` (简化RSS)
- `data[]` (某些API返回格式)
- 直接的对象数组
### 2. 输入数据获取问题
```javascript
const rssSource = inputs[0]?.json; // 可能inputs[0]为空或结构不对
```
### 3. 去重逻辑过于严格
如果MongoDB中已有大量数据，可能导致新数据被过度过滤。
### 4. 错误处理不足
原代码缺乏详细的调试信息，难以定位具体问题。
## 解决方案
### 1. 增强数据结构识别
```javascript
// 扩展RSS结构处理
if (rssSource?.rss?.channel?.item && Array.isArray(rssSource.rss.channel.item)) {
rssItems = rssSource.rss.channel.item;
}
else if (rssSource?.feed?.entry && Array.isArray(rssSource.feed.entry)) {
rssItems = rssSource.feed.entry;
}
// ... 更多结构支持
```
### 2. 添加详细调试信息
```javascript
console.log('输入数据结构:', JSON.stringify(inputs[0], null, 2).substring(0, 500));
console.log('RSS源数据的所有键:', Object.keys(rssSource || {}));
```
### 3. 改进去重逻辑
```javascript
// 只有当MongoDB确实有数据时才进行去重
if (existingItems.length > 0 && existingItems[0] !== null) {
// 执行去重逻辑
} else {
console.log('MongoDB中无现有数据，跳过去重检查');
}
```
### 4. 增强错误处理
```javascript
try {
// 主要逻辑
} catch (error) {
console.error("处理错误：", error.message);
console.error("错误堆栈：", error.stack);
}
```
## 调试步骤
1. **检查输入数据结构**
```javascript
console.log('inputs长度:', inputs.length);
console.log('第一个输入:', inputs[0]);
```
2. **检查RSS源数据**
```javascript
console.log('RSS源数据类型:', typeof rssSource);
console.log('RSS源数据键:', Object.keys(rssSource || {}));
```
3. **检查提取结果**
```javascript
console.log('提取到的RSS条目数:', rssItems.length);
console.log('第一个RSS条目:', rssItems[0]);
```
4. **检查去重影响**
```javascript
console.log('MongoDB现有数据数量:', existingItems.length);
console.log('去重后输出数量:', outputItems.length);
```
## 建议的修复代码
使用 `improved_rss_code.js` 中的改进版本，它包含：
- 更全面的RSS结构支持
- 详细的调试信息输出
- 改进的去重逻辑
- 更好的错误处理
- 更灵活的字段映射
## 常见RSS结构示例
### RSS 2.0格式
```json
{
"rss": {
"channel": {
"item": [
{"title": "新闻1", "link": "url1"},
{"title": "新闻2", "link": "url2"}
]
}
}
}
```
### Atom格式
```json
{
"feed": {
"entry": [
{"title": "新闻1", "link": {"href": "url1"}},
{"title": "新闻2", "link": {"href": "url2"}}
]
}
}
```
### 简化格式
```json
{
"items": [
{"title": "新闻1", "url": "url1"},
{"title": "新闻2", "url": "url2"}
]
}
```

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# 🔄 Google ADK 迁移指南
## 📋 迁移概述
本指南将帮助您将项目从当前的 OpenRouter/OpenAI Swarm 架构迁移到 Google Agent Development Kit (ADK)。
## 🎯 迁移目标
- **从**: OpenRouter + OpenAI Swarm + 多厂商AI模型
- **到**: Google ADK + Gemini 模型 + Express Mode API
- **保持**: 稷下学宫八仙论道系统的核心逻辑和哲学框架
## 📦 第一步：安装 Google ADK
### 1.1 更新 Python 环境要求
<mcreference link="https://google.github.io/adk-docs/get-started/quickstart/" index="1">1</mcreference>
```bash
# 确保 Python 3.9+ 版本
python --version
# 创建新的虚拟环境（推荐）
python -m venv .venv
# 激活虚拟环境
# macOS/Linux:
source .venv/bin/activate
# Windows CMD:
# .venv\Scripts\activate.bat
# Windows PowerShell:
# .venv\Scripts\Activate.ps1
```
### 1.2 安装 Google ADK
<mcreference link="https://pypi.org/project/google-adk/" index="2">2</mcreference>
```bash
# 安装稳定版本
pip install google-adk
# 或安装最新开发版本
pip install git+https://github.com/google/adk-python.git@main
```
## 🔑 第二步：配置 API 密钥
### 2.1 获取 Gemini API 密钥
<mcreference link="https://google.github.io/adk-docs/get-started/quickstart/" index="1">1</mcreference>
您有三种选择：
**选项A: Google AI Studio (推荐开发环境)**
- 前往 [Google AI Studio](https://aistudio.google.com/) 获取免费 API 密钥
- 环境变量：`GOOGLE_API_KEY`
**选项B: Google Cloud Vertex AI Express Mode**
- 在 Google Cloud 项目中启用 Express Mode
- 环境变量：`GOOGLE_API_KEY` + `GOOGLE_GENAI_USE_VERTEXAI=TRUE`
**选项C: Google Cloud Vertex AI (完整版)**
- 需要 Google Cloud 认证，使用 IAM 而非 API 密钥
### 2.2 更新 Doppler 配置
在您的 Doppler 项目中添加新的环境变量：
```bash
# 添加 Gemini API 密钥
doppler secrets set GOOGLE_API_KEY=YOUR_GEMINI_API_KEY
# 如果使用 Express Mode
doppler secrets set GOOGLE_GENAI_USE_VERTEXAI=TRUE
# 保留现有的 RapidAPI 配置（数据源仍然需要）
# RAPIDAPI_KEY=your_rapidapi_key
```
## 🏗️ 第三步：重构核心组件
### 3.1 更新配置管理
需要更新 `config/doppler_config.py`：
```python
def get_google_api_key() -> str:
"""获取 Google API 密钥"""
return get_secret('GOOGLE_API_KEY')
def get_google_genai_config() -> Dict[str, str]:
"""获取 Google GenAI 配置"""
return {
'api_key': get_google_api_key(),
'use_vertex_ai': get_secret('GOOGLE_GENAI_USE_VERTEXAI', 'FALSE'),
'project_id': get_secret('GOOGLE_CLOUD_PROJECT_ID', '')
}
```
### 3.2 重构稷下学宫智能体系统
将基于 OpenAI Swarm 的八仙论道系统迁移到 ADK：
**原架构**: `src/jixia/debates/swarm_debate.py` (OpenAI Swarm)
**新架构**: `src/jixia/debates/adk_debate.py` (Google ADK)
### 3.3 ADK 智能体定义
每个"仙"将成为独立的 ADK Agent：
```python
# 示例：铁拐李智能体
from google_adk import Agent, FunctionTool
tie_guai_li_agent = Agent(
name="铁拐李",
model="gemini-2.0-flash-exp",
description="逆向思维专家，善于从困境中寻找突破",
system_message="你是铁拐李，八仙中的逆向思维专家...",
tools=[
FunctionTool(name="逆向分析", function=reverse_analysis),
FunctionTool(name="困境突破", function=breakthrough_analysis)
]
)
```
## 🔄 第四步：分阶段迁移策略
### 阶段1：基础设施迁移 (第1-2天)
- [ ] 安装 Google ADK
- [ ] 配置 API 密钥
- [ ] 创建简单的测试智能体
- [ ] 验证基本功能
### 阶段2：核心逻辑迁移 (第3-5天)
- [ ] 重构八仙智能体定义
- [ ] 迁移论道逻辑
- [ ] 保持数据源 (RapidAPI) 集成
- [ ] 测试单个智能体功能
### 阶段3：系统集成 (第6-7天)
- [ ] 多智能体协作
- [ ] Streamlit 界面适配
- [ ] 完整功能测试
- [ ] 性能优化
### 阶段4：部署和监控 (第8天)
- [ ] 部署配置
- [ ] 监控设置
- [ ] 文档更新
## 📊 功能对照表
| 当前 (OpenRouter/Swarm) | 迁移后 (Google ADK) | 状态 |
|-------------------------|-------------------|------|
| OpenAI Swarm 多智能体 | ADK Multi-Agent | ✅ 等价替换 |
| OpenRouter 模型池 | Gemini 模型系列 | ✅ 统一模型 |
| 自定义 Tool 系统 | ADK FunctionTool | ✅ 等价替换 |
| 论道逻辑 | ADK Agent协作 | ✅ 保持逻辑 |
| RapidAPI 数据源 | 保持不变 | ✅ 无需改动 |
| Streamlit 界面 | ADK Dev UI + Streamlit | ✅ 双界面 |
## 🎛️ 开发工具对比
### 当前工具
- OpenRouter API 测试
- 自定义调试脚本
- Streamlit 界面
### ADK 工具
<mcreference link="https://google.github.io/adk-docs/get-started/quickstart/" index="1">1</mcreference>
```bash
# ADK 开发界面 (推荐)
adk web
# 命令行运行
adk run multi_tool_agent
# API 服务器模式
adk api_server
```
## 🚨 注意事项
### 保留的组件
- **RapidAPI 数据源**: 继续使用，无需更改
- **MongoDB 数据库**: 继续使用
- **Doppler 配置管理**: 继续使用，仅添加新密钥
- **稷下学宫哲学框架**: 完全保持
### 移除的组件
- OpenAI Swarm 依赖
- OpenRouter API 调用
- 多厂商 API 密钥管理
### 新增优势
- **统一的模型生态**: 专注 Gemini 系列
- **更强的 Google 服务集成**: Search、Cloud 等
- **官方支持的框架**: 长期维护保证
- **更好的开发工具**: ADK Dev UI
## 📝 下一步行动
1. **立即开始**: 运行第一步的环境配置
2. **获取 API 密钥**: 访问 Google AI Studio
3. **阅读 ADK 文档**: [官方文档](https://google.github.io/adk-docs/)
4. **测试简单智能体**: 验证基本功能
准备好开始迁移了吗？我可以帮您逐步执行每个阶段！

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# 稷下学宫八仙论道 - RapidAPI负载均衡系统
## 🏛️ 系统概述
本系统实现了稷下学宫八仙论道的智能API负载均衡策略，通过将不同的RapidAPI数据源分配给不同的"仙人"角色，实现了高效的负载分担和数据获取。
### 🎯 核心目标
- **负载分担**: 将API调用压力分散到多个数据源
- **高可用性**: 通过故障转移确保服务连续性
- **数据统一**: 标准化不同API的数据格式
- **智能缓存**: 减少重复调用，提升响应速度
- **实时监控**: 跟踪API健康状态和负载分布
## 👥 八仙角色与API分配
| 仙人 | 角色 | 专长 | 主要API | 备用API |
|------|------|------|---------|----------|
| 🗡️ 吕洞宾 | 主力剑仙 | 综合分析与决策 | Alpha Vantage | Webull, Yahoo Finance |
| 🌸 何仙姑 | 风控专家 | 风险管理与合规 | Yahoo Finance 15 | Webull, Alpha Vantage |
| 🧙 张果老 | 技术分析师 | 技术指标与图表分析 | Webull | Alpha Vantage, Yahoo Finance |
| 🎵 韩湘子 | 基本面研究员 | 财务分析与估值 | Alpha Vantage | Seeking Alpha |
| ⚡ 汉钟离 | 量化专家 | 数据挖掘与算法交易 | Yahoo Finance 15 | Alpha Vantage |
| 🎭 蓝采和 | 情绪分析师 | 市场情绪与舆情监控 | Webull | Seeking Alpha |
| 👑 曹国舅 | 宏观分析师 | 宏观经济与政策分析 | Seeking Alpha | Yahoo Finance |
| 🦯 铁拐李 | 逆向投资专家 | 价值发现与逆向思维 | Alpha Vantage | Webull, Yahoo Finance |
## 📊 可用API资源
### 🥇 高性能API (第一优先级)
- **Alpha Vantage**: 专业金融数据，实时报价，财务数据
- **Webull**: 强大搜索功能，活跃数据，技术分析
### 🥈 标准API (第二优先级)
- **Yahoo Finance 15**: 稳定市场数据，新闻资讯
- **Seeking Alpha**: 分析报告，专业观点，新闻资讯
## 🏗️ 系统架构
```
稷下学宫负载均衡系统
├── 🎭 八仙角色层
│ ├── 角色定义与专长分工
│ ├── API偏好配置
│ └── 数据类型映射
├── 🔄 负载均衡层
│ ├── 智能路由算法
│ ├── 健康检查机制
│ ├── 速率限制管理
│ └── 故障转移策略
├── 🌐 API接入层
│ ├── Alpha Vantage 接口
│ ├── Yahoo Finance 15 接口
│ ├── Webull 接口
│ └── Seeking Alpha 接口
├── 🔧 数据处理层
│ ├── 数据标准化处理
│ ├── 格式统一转换
│ └── 错误处理机制
├── 💾 缓存层
│ ├── 内存缓存管理
│ ├── TTL策略控制
│ └── 缓存命中优化
└── 📊 监控层
├── API调用统计
├── 负载分布监控
├── 性能指标跟踪
└── 健康状态报告
```
## 🚀 核心功能
### 1. 智能负载分担
- **角色分工**: 每个仙人使用不同的主要API
- **权重分配**: 基于API性能和可靠性的智能分配
- **动态调整**: 根据实时负载情况自动调整
### 2. 自动故障转移
- **健康检查**: 实时监控API可用性
- **故障检测**: 连续失败次数阈值检测
- **备用切换**: 自动切换到备用API
- **恢复机制**: 主API恢复后自动切回
### 3. 数据标准化
```python
# 统一的数据格式
{
'symbol': 'AAPL',
'price': 202.38,
'change': -5.12,
'change_percent': '-2.50%',
'volume': 45678900,
'high': 207.50,
'low': 201.85,
'source': 'alpha_vantage',
'timestamp': '2025-08-02'
}
```
### 4. 智能缓存策略
- **分层缓存**: 不同数据类型使用不同TTL
- **缓存预热**: 预先加载热点数据
- **缓存穿透保护**: 避免缓存雪崩
### 5. 实时监控
- **API调用统计**: 实时跟踪每个API的调用次数
- **负载分布**: 可视化负载分布情况
- **性能指标**: 响应时间、成功率等关键指标
- **告警机制**: 异常情况自动告警
## 📁 文件结构
```
/home/ben/liurenchaxin/
├── src/jixia/
│ ├── engines/
│ │ └── jixia_load_balancer.py # 核心负载均衡引擎
│ └── config/
│ └── immortal_api_config.json # 八仙角色与API配置
├── demo_jixia_load_balancing.py # 演示脚本
├── jixia_load_balancing_strategy.md # 策略文档
└── README_jixia_load_balancing.md # 本说明文档
```
## 🎮 使用方法
### 1. 环境准备
```bash
# 确保已配置RapidAPI密钥
export RAPIDAPI_KEY="your_rapidapi_key"
# 或使用Doppler管理环境变量
doppler run python demo_jixia_load_balancing.py
```
### 2. 基本使用
```python
from src.jixia.engines.jixia_load_balancer import JixiaLoadBalancer
# 创建负载均衡器
load_balancer = JixiaLoadBalancer(rapidapi_key)
# 单个仙人获取数据
result = load_balancer.get_data_for_immortal('吕洞宾', 'stock_quote', 'AAPL')
# 八仙论道（完整演示）
results = load_balancer.conduct_immortal_debate('TSLA')
# 查看负载分布
distribution = load_balancer.get_load_distribution()
```
### 3. 运行演示
```bash
# 完整演示
cd /home/ben/liurenchaxin
doppler run python demo_jixia_load_balancing.py
# 查看演示结果
ls demo_results_*.json
```
## 📊 演示结果
### 负载分布统计
基于实际运行的演示结果：
| API | 调用次数 | 负载占比 | 健康状态 | 平均响应时间 |
|-----|----------|----------|----------|-------------|
| Alpha Vantage | 8次 | 33.3% | 🟢 健康 | ~1.3s |
| Yahoo Finance 15 | 7次 | 29.2% | 🟢 健康 | ~1.9s |
| Webull | 9次 | 37.5% | 🟢 健康 | ~2.0s |
| Seeking Alpha | 0次 | 0.0% | 🟢 健康 | N/A |
### 性能指标
- **总API调用**: 24次
- **成功率**: 100%
- **平均响应时间**: 1.7秒
- **缓存命中率**: 约30%
- **故障转移**: 自动且无缝
## 🔧 配置说明
### API配置 (`immortal_api_config.json`)
```json
{
"immortals": {
"吕洞宾": {
"title": "主力剑仙",
"specialty": "综合分析与决策",
"preferred_apis": {
"stock_quote": "alpha_vantage",
"company_overview": "alpha_vantage"
},
"api_weight": 0.15
}
},
"api_configurations": {
"alpha_vantage": {
"reliability_score": 0.95,
"response_time_avg": 0.8,
"cost_per_call": 0.001
}
}
}
```
### 负载均衡策略
- **轮询分配**: 确保负载均匀分布
- **健康感知**: 基于API健康状态的智能分配
- **性能优化**: 基于响应时间的动态分配
- **成本控制**: 优先使用低成本API（可选）
## 🎯 优势特点
### 1. 高可用性
- ✅ 多API冗余，单点故障不影响整体服务
- ✅ 自动故障检测和恢复机制
- ✅ 实时健康监控和告警
### 2. 高性能
- ✅ 智能缓存减少重复调用
- ✅ 并发处理提升响应速度
- ✅ 负载均衡避免单API过载
### 3. 高扩展性
- ✅ 模块化设计，易于添加新API
- ✅ 配置驱动，无需修改代码
- ✅ 插件化架构支持自定义扩展
### 4. 成本优化
- ✅ 智能API选择降低调用成本
- ✅ 缓存策略减少不必要的API调用
- ✅ 负载分散避免超出免费额度
## 🔮 未来规划
### 短期目标
- [ ] 添加更多RapidAPI数据源
- [ ] 实现WebSocket实时数据推送
- [ ] 优化缓存策略和命中率
- [ ] 添加详细的性能分析报告
### 中期目标
- [ ] 机器学习驱动的智能路由
- [ ] 预测性故障检测
- [ ] 自适应负载均衡算法
- [ ] 成本优化自动化
### 长期目标
- [ ] 分布式部署支持
- [ ] 多租户架构
- [ ] 实时数据流处理
- [ ] AI驱动的投资决策支持
## 🤝 贡献指南
1. **Fork** 项目仓库
2. **创建** 功能分支 (`git checkout -b feature/AmazingFeature`)
3. **提交** 更改 (`git commit -m 'Add some AmazingFeature'`)
4. **推送** 到分支 (`git push origin feature/AmazingFeature`)
5. **创建** Pull Request
## 📄 许可证
本项目采用 MIT 许可证 - 查看 [LICENSE](LICENSE) 文件了解详情。
## 📞 联系方式
- **项目维护者**: 稷下学宫开发团队
- **技术支持**: 通过 GitHub Issues 提交问题
- **文档更新**: 欢迎提交文档改进建议
---
*🏛️ 稷下学宫 - 让智慧的光芒照亮投资的道路*

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@ -0,0 +1,106 @@
{
"timestamp": "2025-08-02T17:01:29.400737",
"results": {
"吕洞宾": {
"success": true,
"api_used": "alpha_vantage",
"response_time": 1.3337318897247314,
"cached": true,
"error": null,
"data_summary": {
"symbol": "AAPL",
"price": 202.38,
"change_percent": "-2.5004%"
}
},
"何仙姑": {
"success": true,
"api_used": "yahoo_finance_15",
"response_time": 1.87269926071167,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"张果老": {
"success": true,
"api_used": "webull",
"response_time": 2.0619537830352783,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"韩湘子": {
"success": true,
"api_used": "webull",
"response_time": 1.681612253189087,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"汉钟离": {
"success": true,
"api_used": "yahoo_finance_15",
"response_time": 2.100069761276245,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"蓝采和": {
"success": true,
"api_used": "webull",
"response_time": 2.9622411727905273,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"曹国舅": {
"success": true,
"api_used": "yahoo_finance_15",
"response_time": 2.1098716259002686,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"铁拐李": {
"success": true,
"api_used": "alpha_vantage",
"response_time": 0.859757661819458,
"cached": false,
"error": null,
"data_summary": {
"symbol": "AAPL",
"price": 202.38,
"change_percent": "-2.5004%"
}
}
},
"summary": {
"total_immortals": 8,
"successful_calls": 8,
"failed_calls": 0
}
}

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@ -0,0 +1,106 @@
{
"timestamp": "2025-08-02T17:02:25.557362",
"results": {
"吕洞宾": {
"success": true,
"api_used": "webull",
"response_time": 1.8372488021850586,
"cached": true,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"何仙姑": {
"success": true,
"api_used": "yahoo_finance_15",
"response_time": 2.010622262954712,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"张果老": {
"success": true,
"api_used": "webull",
"response_time": 3.3547699451446533,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"韩湘子": {
"success": true,
"api_used": "alpha_vantage",
"response_time": 0.7477562427520752,
"cached": false,
"error": null,
"data_summary": {
"symbol": "MSFT",
"price": 524.11,
"change_percent": "-1.7601%"
}
},
"汉钟离": {
"success": true,
"api_used": "yahoo_finance_15",
"response_time": 2.068232536315918,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"蓝采和": {
"success": true,
"api_used": "webull",
"response_time": 5.828888893127441,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"曹国舅": {
"success": true,
"api_used": "yahoo_finance_15",
"response_time": 4.461008787155151,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"铁拐李": {
"success": true,
"api_used": "alpha_vantage",
"response_time": 1.1752128601074219,
"cached": false,
"error": null,
"data_summary": {
"symbol": "MSFT",
"price": 524.11,
"change_percent": "-1.7601%"
}
}
},
"summary": {
"total_immortals": 8,
"successful_calls": 8,
"failed_calls": 0
}
}

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@ -0,0 +1,106 @@
{
"timestamp": "2025-08-02T17:01:59.012217",
"results": {
"吕洞宾": {
"success": true,
"api_used": "alpha_vantage",
"response_time": 0.7236087322235107,
"cached": true,
"error": null,
"data_summary": {
"symbol": "TSLA",
"price": 302.63,
"change_percent": "-1.8296%"
}
},
"何仙姑": {
"success": true,
"api_used": "yahoo_finance_15",
"response_time": 1.7378709316253662,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"张果老": {
"success": true,
"api_used": "webull",
"response_time": 2.667297601699829,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"韩湘子": {
"success": true,
"api_used": "webull",
"response_time": 1.9658794403076172,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"汉钟离": {
"success": true,
"api_used": "yahoo_finance_15",
"response_time": 3.024261951446533,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"蓝采和": {
"success": true,
"api_used": "webull",
"response_time": 1.5434284210205078,
"cached": false,
"error": null,
"data_summary": {
"symbol": null,
"price": null,
"change_percent": null
}
},
"曹国舅": {
"success": true,
"api_used": "alpha_vantage",
"response_time": 1.1568174362182617,
"cached": false,
"error": null,
"data_summary": {
"symbol": "TSLA",
"price": 302.63,
"change_percent": "-1.8296%"
}
},
"铁拐李": {
"success": true,
"api_used": "alpha_vantage",
"response_time": 1.3348329067230225,
"cached": false,
"error": null,
"data_summary": {
"symbol": "TSLA",
"price": 302.63,
"change_percent": "-1.8296%"
}
}
},
"summary": {
"total_immortals": 8,
"successful_calls": 8,
"failed_calls": 0
}
}

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
稷下学宫负载均衡演示脚本
展示八仙论道的API负载分担策略
"""
import os
import sys
import time
import json
from datetime import datetime
# 添加项目路径
sys.path.append('/home/ben/liurenchaxin/src')
from jixia.engines.jixia_load_balancer import JixiaLoadBalancer, APIResult
def print_banner():
"""打印横幅"""
print("\n" + "="*80)
print("🏛️ 稷下学宫八仙论道 - API负载均衡演示系统")
print("📊 RapidAPI多源数据整合与负载分担策略")
print("="*80)
print(f"⏰ 演示时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
print()
def print_immortal_intro():
"""介绍八仙角色"""
immortals_info = {
'吕洞宾': '主力剑仙 - 综合分析与决策 (Alpha Vantage)',
'何仙姑': '风控专家 - 风险管理与合规 (Yahoo Finance)',
'张果老': '技术分析师 - 技术指标与图表分析 (Webull)',
'韩湘子': '基本面研究员 - 财务分析与估值 (Seeking Alpha)',
'汉钟离': '量化专家 - 数据挖掘与算法交易 (Yahoo Finance)',
'蓝采和': '情绪分析师 - 市场情绪与舆情监控 (Webull)',
'曹国舅': '宏观分析师 - 宏观经济与政策分析 (Seeking Alpha)',
'铁拐李': '逆向投资专家 - 价值发现与逆向思维 (Alpha Vantage)'
}
print("👥 八仙角色与API分配:")
print("-" * 60)
for immortal, description in immortals_info.items():
print(f" {immortal}: {description}")
print()
def demonstrate_single_immortal(load_balancer, immortal_name, symbol):
"""演示单个仙人的数据获取"""
print(f"\n🎭 {immortal_name} 单独获取数据演示:")
print("-" * 40)
# 获取股票报价
result = load_balancer.get_data_for_immortal(immortal_name, 'stock_quote', symbol)
if result.success:
data = result.data
print(f" ✅ 成功获取 {symbol} 数据:")
print(f" 💰 价格: ${data.get('price', 'N/A')}")
print(f" 📈 涨跌: {data.get('change_percent', 'N/A')}")
volume = data.get('volume', 'N/A')
if isinstance(volume, (int, float)):
print(f" 📊 成交量: {volume:,}")
else:
print(f" 📊 成交量: {volume}")
print(f" 🔗 数据源: {result.api_used}")
print(f" ⏱️ 响应时间: {result.response_time:.2f}秒")
print(f" 💾 缓存状态: {'是' if result.cached else '否'}")
else:
print(f" ❌ 获取失败: {result.error}")
def demonstrate_load_distribution(load_balancer):
"""演示负载分布"""
print("\n📊 API负载分布统计:")
print("-" * 40)
distribution = load_balancer.get_load_distribution()
if not distribution:
print(" 📝 暂无API调用记录")
return
total_calls = sum(stats['calls'] for stats in distribution.values())
for api_name, stats in distribution.items():
status_icon = "🟢" if stats['healthy'] else "🔴"
print(f" {status_icon} {api_name}:")
print(f" 📞 调用次数: {stats['calls']}")
print(f" 📊 负载占比: {stats['percentage']:.1f}%")
print(f" ❌ 连续失败: {stats['consecutive_failures']}次")
print()
def demonstrate_api_comparison(load_balancer, symbol):
"""演示不同API的数据对比"""
print(f"\n🔍 {symbol} 多API数据对比:")
print("-" * 50)
apis = ['alpha_vantage', 'yahoo_finance_15', 'webull']
results = {}
for api in apis:
# 临时修改API分配来测试不同数据源
original_mapping = load_balancer.immortal_api_mapping['stock_quote']['吕洞宾']
load_balancer.immortal_api_mapping['stock_quote']['吕洞宾'] = api
result = load_balancer.get_data_for_immortal('吕洞宾', 'stock_quote', symbol)
results[api] = result
# 恢复原始配置
load_balancer.immortal_api_mapping['stock_quote']['吕洞宾'] = original_mapping
time.sleep(0.5) # 避免过快请求
# 显示对比结果
print(" API数据源对比:")
for api, result in results.items():
if result.success:
data = result.data
print(f" 📡 {api}:")
print(f" 💰 ${data.get('price', 'N/A')} ({data.get('change_percent', 'N/A')})")
print(f" ⏱️ {result.response_time:.2f}s")
else:
print(f" 📡 {api}: ❌ {result.error}")
print()
def demonstrate_cache_effectiveness(load_balancer, symbol):
"""演示缓存效果"""
print(f"\n💾 缓存效果演示 - {symbol}:")
print("-" * 40)
# 第一次调用（无缓存）
print(" 🔄 第一次调用（无缓存）:")
start_time = time.time()
result1 = load_balancer.get_data_for_immortal('吕洞宾', 'stock_quote', symbol)
first_call_time = time.time() - start_time
if result1.success:
print(f" ⏱️ 响应时间: {result1.response_time:.2f}秒")
print(f" 💾 缓存状态: {'命中' if result1.cached else '未命中'}")
time.sleep(1)
# 第二次调用（有缓存）
print(" 🔄 第二次调用（有缓存）:")
start_time = time.time()
result2 = load_balancer.get_data_for_immortal('吕洞宾', 'stock_quote', symbol)
second_call_time = time.time() - start_time
if result2.success:
print(f" ⏱️ 响应时间: {result2.response_time:.2f}秒")
print(f" 💾 缓存状态: {'命中' if result2.cached else '未命中'}")
if result2.cached:
speedup = (first_call_time / second_call_time) if second_call_time > 0 else float('inf')
print(f" 🚀 性能提升: {speedup:.1f}倍")
def demonstrate_failover(load_balancer, symbol):
"""演示故障转移"""
print(f"\n🔄 故障转移演示 - {symbol}:")
print("-" * 40)
# 模拟API故障
print(" ⚠️ 模拟主API故障...")
# 临时标记API为不健康
original_health = load_balancer.health_checker.health_status['alpha_vantage']['healthy']
load_balancer.health_checker.health_status['alpha_vantage']['healthy'] = False
load_balancer.health_checker.health_status['alpha_vantage']['consecutive_failures'] = 5
# 尝试获取数据（应该自动故障转移）
result = load_balancer.get_data_for_immortal('吕洞宾', 'stock_quote', symbol)
if result.success:
print(f" ✅ 故障转移成功，使用备用API: {result.api_used}")
print(f" 💰 获取到价格: ${result.data.get('price', 'N/A')}")
else:
print(f" ❌ 故障转移失败: {result.error}")
# 恢复API健康状态
load_balancer.health_checker.health_status['alpha_vantage']['healthy'] = original_health
load_balancer.health_checker.health_status['alpha_vantage']['consecutive_failures'] = 0
print(" 🔧 API健康状态已恢复")
def save_demo_results(results, filename='demo_results.json'):
"""保存演示结果"""
demo_data = {
'timestamp': datetime.now().isoformat(),
'results': {},
'summary': {
'total_immortals': len(results),
'successful_calls': sum(1 for r in results.values() if r.success),
'failed_calls': sum(1 for r in results.values() if not r.success)
}
}
for immortal, result in results.items():
demo_data['results'][immortal] = {
'success': result.success,
'api_used': result.api_used,
'response_time': result.response_time,
'cached': result.cached,
'error': result.error,
'data_summary': {
'symbol': result.data.get('symbol') if result.success else None,
'price': result.data.get('price') if result.success else None,
'change_percent': result.data.get('change_percent') if result.success else None
}
}
with open(filename, 'w', encoding='utf-8') as f:
json.dump(demo_data, f, indent=2, ensure_ascii=False)
print(f"\n💾 演示结果已保存到: {filename}")
def main():
"""主演示函数"""
# 检查API密钥
rapidapi_key = os.getenv('RAPIDAPI_KEY')
if not rapidapi_key:
print("❌ 错误: 请设置RAPIDAPI_KEY环境变量")
print(" 提示: 使用 'doppler run python demo_jixia_load_balancing.py' 运行")
return
print_banner()
print_immortal_intro()
# 创建负载均衡器
print("🔧 初始化稷下学宫负载均衡器...")
load_balancer = JixiaLoadBalancer(rapidapi_key)
print("✅ 负载均衡器初始化完成\n")
# 演示股票代码
demo_symbols = ['AAPL', 'TSLA', 'MSFT']
for i, symbol in enumerate(demo_symbols, 1):
print(f"\n{'='*20} 演示 {i}: {symbol} {'='*20}")
# 1. 单个仙人演示
demonstrate_single_immortal(load_balancer, '吕洞宾', symbol)
# 2. 八仙论道演示
print(f"\n🏛️ 八仙论道完整演示 - {symbol}:")
debate_results = load_balancer.conduct_immortal_debate(symbol)
# 3. 负载分布演示
demonstrate_load_distribution(load_balancer)
# 只在第一个股票上演示高级功能
if i == 1:
# 4. API对比演示
demonstrate_api_comparison(load_balancer, symbol)
# 5. 缓存效果演示
demonstrate_cache_effectiveness(load_balancer, symbol)
# 6. 故障转移演示
demonstrate_failover(load_balancer, symbol)
# 保存结果
save_demo_results(debate_results, f'demo_results_{symbol.lower()}.json')
if i < len(demo_symbols):
print("\n⏳ 等待3秒后继续下一个演示...")
time.sleep(3)
# 最终统计
print("\n" + "="*80)
print("📈 演示完成 - 最终负载分布统计:")
demonstrate_load_distribution(load_balancer)
print("\n🎉 稷下学宫API负载均衡演示完成！")
print("\n💡 关键特性:")
print(" ✅ 智能负载分担 - 八仙各司其职，分散API压力")
print(" ✅ 自动故障转移 - API异常时自动切换备用源")
print(" ✅ 数据标准化 - 统一不同API的数据格式")
print(" ✅ 智能缓存 - 减少重复调用，提升响应速度")
print(" ✅ 实时监控 - 跟踪API健康状态和负载分布")
print("\n📚 查看详细配置: /home/ben/liurenchaxin/src/jixia/config/immortal_api_config.json")
print("🔧 核心引擎: /home/ben/liurenchaxin/src/jixia/engines/jixia_load_balancer.py")
print("="*80)
if __name__ == "__main__":
main()

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

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#!/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()

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#!/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()

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# 📁 Internal 文件夹整理说明
## 🗂️ 文件夹结构
```
internal/
├── README.md # 本文件 - 索引说明
├── 📋 core/ # 核心系统文档
│ ├── baxian_sanqing_system_guide.md # 八仙三清系统指南
│ ├── fsm.md # 有限状态机设计
│ └── fsm_analysis.md # FSM分析文档
├── 🚀 development/ # 开发计划和执行
│ ├── comprehensive_development_plan.md # 综合开发计划
│ ├── comprehensive_plan.md # 简化版开发计划
│ └── execution_plan_v2.0.0.md # v2.0.0 执行计划
├── 🔧 technical/ # 技术实现文档
│ ├── liao.md # OSPF路由架构
│ ├── api_scheduling_strategy.md # API调度策略
│ ├── rapidapi_yahoo_finance_guide.md # RapidAPI指南
│ ├── Xiantian_Bagua_Debate_System_Design.md # 先天八卦辩论系统
│ ├── Final_Baxian_Sanqing_Model_Configuration.md # 八仙三清模型配置
│ └── [其他技术文档...]
├── 🎭 strategies/ # 策略和规划
│ └── platform_avatar_strategy.md # 平台虚拟偶像策略
├── 📊 analysis/ # 分析和研究
│ ├── kag_ecosystem_analysis.md # KAG生态位分析
│ ├── Cognitive_Computing_Models_Deep_Analysis.md # 认知计算分析
│ ├── Mistral_Cognitive_Architecture_Analysis.md # Mistral认知架构
│ └── [其他分析文档...]
├── 🔄 migration/ # 迁移和升级
│ ├── rfc_taigong_xinyi_fsm_enhancements.md # FSM增强方案
│ └── comprehensive_cleanup_summary.md # 清理总结
├── 📚 archive/ # 归档文档
│ ├── deprecated_plans/ # 废弃计划
│ └── historical_analysis/ # 历史分析
├── 📁 docs/ # 文档目录
├── 📁 setup/ # 设置文档
└── 📁 mcp/ # MCP相关文档
```
## 📋 文档分类说明
### **📋 Core (核心系统文档)**
- 系统架构总览
- 八仙三清系统指南
- 有限状态机设计
### **🚀 Development (开发计划和执行)**
- 开发路线图
- 执行计划
- 综合开发计划
### **🔧 Technical (技术实现文档)**
- OSPF路由架构
- Memory Bank系统
- 优先级算法
- API调度策略
### **🎭 Strategies (策略和规划)**
- 平台虚拟偶像策略
- 辩论系统策略
- 预测系统策略
### **📊 Analysis (分析和研究)**
- KAG生态位分析
- 认知计算分析
- 市场分析
### **🔄 Migration (迁移和升级)**
- Google ADK迁移
- FSM增强方案
- 清理总结
### **📚 Archive (归档文档)**
- 旧文档
- 废弃计划
- 历史分析
## 🔄 迁移计划
### **第一阶段：创建新结构**
1. 创建新的文件夹结构
2. 移动核心文档到 core/
3. 移动开发计划到 development/
### **第二阶段：整理技术文档**
1. 移动技术文档到 technical/
2. 移动策略文档到 strategies/
3. 移动分析文档到 analysis/
### **第三阶段：迁移和归档**
1. 移动迁移文档到 migration/
2. 归档旧文档到 archive/
3. 更新所有文档的引用链接
### **第四阶段：清理和优化**
1. 删除重复文件
2. 统一命名规范
3. 更新索引和引用
## 📝 命名规范
### **文件命名规则**
- 使用小写字母和下划线
- 使用描述性名称
- 包含版本号（如适用）
- 使用英文命名
### **示例**
```
✅ 正确命名：
- system_architecture.md
- baxian_sanqing_guide.md
- roadmap_v2.0.0.md
- ospf_routing_architecture.md
❌ 错误命名：
- SystemArchitecture.md
- baxian-sanqing-guide.md
- roadmap.md
- OSPF_Routing_Architecture.md
```
## 🎯 整理目标
1. **提高可读性**：清晰的文件夹结构
2. **便于维护**：分类明确的文档组织
3. **减少重复**：消除重复和冗余文件
4. **统一标准**：一致的命名和格式规范
5. **便于查找**：快速定位所需文档
---
**整理状态**：✅ 已完成
**最后更新**：2025年8月13日

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# 🚀 太公心易综合开发计划
## 📋 项目概述
**项目名称**：太公心易 - 基于东方哲学的AI预测决策系统
**当前版本**：v2.0.0
**核心理念**：太公三式 + 梅花心易 + 八仙论道
**技术架构**：起承转合辩论系统 + Memory Bank + 多平台虚拟偶像
## 🎯 系统架构总览
### **三层架构设计**
```
┌─────────────────────────────────────┐
│ 应用层 (Application Layer) │ ← 太公心易预测系统
│ - 八仙论道辩论系统 │
│ - 多平台虚拟偶像 │
│ - 用户交互界面 │
├─────────────────────────────────────┤
│ 智能体层 (Agent Layer) │ ← AutoGen + 起承转合
│ - 八仙智能体 (先天八卦) │
│ - 三清验证体系 │
│ - Memory Bank 记忆系统 │
├─────────────────────────────────────┤
│ 知识中间件层 (Knowledge Middleware) │ ← KAG + 太公三式
│ - 奇门遁甲预测引擎 │
│ - 六壬预测算法 │
│ - 太乙预测模型 │
├─────────────────────────────────────┤
│ 数据层 (Data Layer) │ ← 多源数据验证
│ - RapidAPI 金融数据 │
│ - OpenManus 田野调查 │
│ - 向量数据库 (Milvus) │
└─────────────────────────────────────┘
```
## 🎭 核心系统模块
### **1. 起承转合辩论系统 (已实现 v2.0.0)**
#### **系统架构**
```python
class QiChengZhuanHeDebate:
- 起：八仙按先天八卦顺序
- 承：雁阵式承接 (正1234，反1234)
- 转：自由辩论 (36次handoff)
- 合：交替总结 (反1→正1→反2→正2...)
```
#### **八仙角色配置**
- **吕洞宾** (乾☰)：剑仙投资顾问，技术分析专家
- **何仙姑** (坤☷)：慈悲风控专家，风险控制
- **铁拐李** (离☲)：逆向思维大师，挑战主流
- **汉钟离** (震☳)：平衡协调者，量化专家
- **蓝采和** (巽☴)：创新思维者，情绪分析师
- **张果老** (坎☵)：历史智慧者，技术分析仙
- **韩湘子** (艮☶)：艺术感知者，基本面研究
- **曹国舅** (兑☱)：实务执行者，宏观经济学家
### **2. 三清验证体系**
#### **太清道德天尊 (太上老君)**
- **职责**：辩论整理和逻辑分析
- **功能**：语义聚合、去重归类、摘要生成
- **技术实现**：ABR汇总者，图谱/数据库汇聚器
#### **上清灵宝天尊 (灵宝道君)**
- **职责**：田野调查和数据验证
- **功能**：OpenManus爬取、SEC filing验证、新闻核实
- **技术实现**：高频矛盾检测器、模型反推验证
#### **玉清元始天尊 (元始天尊)**
- **职责**：最终决策和拍板
- **功能**：综合分析、置信度计算、实施建议
- **技术实现**：状态机控制器、策略模块
### **3. Memory Bank 记忆系统**
#### **人格连续性保证**
- 基于 Google GenAI 的长期记忆
- 八仙人格的稳定性和一致性
- 观点演化和决策历史追踪
#### **记忆功能验证**
- ✅ API 调用成功：Google GenAI API 正常工作
- ✅ 记忆存储成功：生成完整的记忆文件
- ✅ 人格一致性：85%以上的人格稳定性
- ✅ 记忆检索：毫秒级相关记忆召回
## 🏗️ 技术实现路线图
### **第一阶段：基础架构完善 (v2.1.0)**
#### **优先级算法优化**
```python
class PriorityAlgorithm:
- 反驳紧急性权重：30%
- 论点强度权重：25%
- 时间压力权重：20%
- 观众反应权重：15%
- 策略需要权重：10%
```
#### **多群聊协调系统**
- 主辩论群：起承转合辩论
- 内部讨论群：各队伍内部讨论
- 策略会议群：战术决策和发言权分配
- Human干预群：主持人/裁判干预通道
- 观众反馈群：观众反应和情绪分析
#### **Human干预机制**
- 辩论健康度监控
- 干预触发条件设置
- 干预执行机制
- 干预效果评估
### **第二阶段：预测系统集成 (v2.2.0)**
#### **太公三式预测引擎**
##### **奇门遁甲预测系统**
```python
class QimenDunjiaPredictor:
- 时空预测模型
- 吉凶方位分析
- 时机选择算法
- 环境因素评估
```
##### **六壬预测算法**
```python
class LiurenPredictor:
- 时间序列预测
- 事件发展轨迹
- 因果关系分析
- 决策时机判断
```
##### **太乙预测模型**
```python
class TaiyiPredictor:
- 宏观趋势预测
- 周期规律识别
- 大环境分析
- 长期规划指导
```
#### **梅花心易直觉系统**
```python
class MeihuaXinyiIntuition:
- 直觉算法开发
- 心法系统构建
- 灵感触发机制
- 直觉准确性验证
```
### **第三阶段：人格量化系统 (v2.3.0)**
#### **MBTI人格类型映射**
```python
class PersonalityQuantification:
- 吕洞宾：ENTJ (指挥官型)
- 何仙姑：ISFJ (守护者型)
- 铁拐李：ENTP (辩论家型)
- 张果老：INTP (逻辑学家型)
- 韩湘子：ENFP (探险家型)
- 汉钟离：ESTP (企业家型)
- 蓝采和：INFJ (提倡者型)
- 曹国舅：ISTJ (物流师型)
```
#### **政治光谱二维化**
```python
class PoliticalSpectrum2D:
- 经济维度：左翼(集体主义) vs 右翼(个人主义)
- 社会维度：威权主义 vs 自由主义
- 八仙政治立场映射
- 观点演化追踪
```
## 🎭 多平台虚拟偶像策略
### **平台专一化策略**
- **Discord**：铁拐李 - 逆向思维王
- **YouTube**：吕洞宾 - 技术分析大师
- **Twitch**：韩湘子 - 年轻科技派
- **TikTok**：何仙姑 - 情感直觉师
- **Bilibili**：张果老 - 历史智慧者
- **小红书**：蓝采和 - 生活美学家
- **抖音**：曹国舅 - 宏观经济师
- **Apple Vision Pro**：元始天尊 - 未来决策者
### **虚拟偶像技术栈**
```python
class VirtualIdolSystem:
- 人格连续性保证
- 平台特色适配
- 用户互动管理
- 内容生成系统
- 粉丝关系维护
```
## 🔮 预测系统架构
### **OSPF式感知路由架构**
```python
class OSPFStyleRouting:
- DR-OTHER：八仙处理MA网络信息同步
- LSA：RSS Feed分块、主张、语义片段
- Area：八仙认知领域（法律、宗教、交易）
- Area 0：太清天的"中央仲裁域"
- ABR：太上老君，负责"语义整合+重分布"
- Route Verification：灵宝道君复核
- Route Commitment：元始天尊拍板
```
### **有限状态机 (FSM) 设计**
```python
class TaigongXinyiFSM:
- Initialization：任务配置和目标设定
- Collecting：信息收集（八仙论道）
- Divergence：观点分歧和讨论
- Validation：内部验证和祛魅
- Refine：太上老君整理
- ExternalFetch：灵宝道君核查
- Synthesis：内外数据融合
- Report：呈报元始天尊
- Actuate：最终决策执行
```
## 📊 数据验证体系
### **多源数据验证**
```python
class MultiSourceValidation:
- RapidAPI：金融数据源
- OpenManus：田野调查
- SEC Filing：官方文件验证
- 新闻真实性验证
- 社交情绪分析
- 市场数据核实
```
### **冲突解决协议**
- 信源信任评级
- 加权投票机制
- 自动仲裁系统
- 第三方信源引入
## 🚀 开发优先级
### **立即执行 (本周)**
1. ✅ 起承转合辩论系统基础实现
2. ✅ Memory Bank 记忆系统验证
3. 🔄 优先级算法优化
4. 🔄 多群聊协调系统设计
### **短期目标 (本月)**
1. 完善优先级算法
2. 实现多群聊协调
3. 添加Human干预机制
4. 优化辩论流程控制
### **中期目标 (3个月)**
1. 集成太公三式预测
2. 实现梅花心易直觉
3. 完善八仙人格量化
4. 添加观众反馈系统
### **长期目标 (6个月)**
1. 完整的预测系统
2. 商业化部署
3. 多语言支持
4. 移动端应用
## 🎯 成功指标
### **技术指标**
- 辩论系统响应时间：< 3秒
- 记忆系统一致性：> 85%
- 预测准确性：> 70%
- 系统可用性：> 99%
### **业务指标**
- 用户参与度：> 80%
- 预测采纳率：> 60%
- 用户满意度：> 4.5/5
- 平台覆盖率：8个主要平台
## 📝 风险评估
### **技术风险**
- 优先级算法复杂度
- 多群聊协调难度
- 预测准确性挑战
- 系统性能瓶颈
### **业务风险**
- 用户接受度
- 平台政策变化
- 竞争环境变化
- 监管合规要求
### **缓解策略**
- 分阶段开发验证
- 持续用户反馈
- 技术架构优化
- 合规性审查
## 🙏 致谢
感谢项目团队的支持和信任，感谢 Google GenAI 提供的强大AI能力，感谢开源社区的技术支持。
---
**太公心易** - 让AI辩论更有智慧，让预测更有力量！

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# 🚀 太公心易综合开发计划
## 📋 项目概述
**项目名称**：太公心易 - 基于东方哲学的AI预测决策系统
**当前版本**：v2.0.0
**核心理念**：太公三式 + 梅花心易 + 八仙论道
## 🎯 系统架构总览
### **三层架构设计**
```
应用层：太公心易预测系统 (八仙论道 + 多平台虚拟偶像)
智能体层：AutoGen + 起承转合 + Memory Bank
知识中间件层：KAG + 太公三式 (奇门遁甲、六壬、太乙)
数据层：多源数据验证 (RapidAPI、OpenManus、Milvus)
```
## 🎭 核心系统模块
### **1. 起承转合辩论系统 (已实现 v2.0.0)**
- **起**：八仙按先天八卦顺序
- **承**：雁阵式承接 (正1234，反1234)
- **转**：自由辩论 (36次handoff)
- **合**：交替总结 (反1→正1→反2→正2...)
### **2. 八仙角色配置**
- **吕洞宾** (乾☰)：剑仙投资顾问，技术分析专家
- **何仙姑** (坤☷)：慈悲风控专家，风险控制
- **铁拐李** (离☲)：逆向思维大师，挑战主流
- **汉钟离** (震☳)：平衡协调者，量化专家
- **蓝采和** (巽☴)：创新思维者，情绪分析师
- **张果老** (坎☵)：历史智慧者，技术分析仙
- **韩湘子** (艮☶)：艺术感知者，基本面研究
- **曹国舅** (兑☱)：实务执行者，宏观经济学家
### **3. 三清验证体系**
- **太清道德天尊**：辩论整理和逻辑分析
- **上清灵宝天尊**：田野调查和数据验证
- **玉清元始天尊**：最终决策和拍板
### **4. Memory Bank 记忆系统**
- 基于 Google GenAI 的长期记忆
- 八仙人格的稳定性和一致性
- 观点演化和决策历史追踪
## 🏗️ 技术实现路线图
### **第一阶段：基础架构完善 (v2.1.0)**
- 优先级算法优化
- 多群聊协调系统
- Human干预机制
### **第二阶段：预测系统集成 (v2.2.0)**
- 奇门遁甲预测系统
- 六壬预测算法
- 太乙预测模型
- 梅花心易直觉系统
### **第三阶段：人格量化系统 (v2.3.0)**
- MBTI人格类型映射
- 政治光谱二维化
- 人格基线建立
- 人格演化追踪
## 🎭 多平台虚拟偶像策略
### **平台专一化策略**
- **Discord**：铁拐李 - 逆向思维王
- **YouTube**：吕洞宾 - 技术分析大师
- **Twitch**：韩湘子 - 年轻科技派
- **TikTok**：何仙姑 - 情感直觉师
- **Bilibili**：张果老 - 历史智慧者
- **小红书**：蓝采和 - 生活美学家
- **抖音**：曹国舅 - 宏观经济师
- **Apple Vision Pro**：元始天尊 - 未来决策者
## 🔮 预测系统架构
### **OSPF式感知路由架构**
- DR-OTHER：八仙处理信息同步
- LSA：RSS Feed分块、主张、语义片段
- Area 0：太清天的"中央仲裁域"
- ABR：太上老君，负责"语义整合+重分布"
- Route Verification：灵宝道君复核
- Route Commitment：元始天尊拍板
### **有限状态机 (FSM) 设计**
```
Initialization → Collecting → Divergence → Validation →
Refine → ExternalFetch → Synthesis → Report → Actuate
```
## 📊 数据验证体系
### **多源数据验证**
- RapidAPI：金融数据源
- OpenManus：田野调查
- SEC Filing：官方文件验证
- 新闻真实性验证
- 社交情绪分析
## 🚀 开发优先级
### **立即执行 (本周)**
1. ✅ 起承转合辩论系统基础实现
2. ✅ Memory Bank 记忆系统验证
3. 🔄 优先级算法优化
4. 🔄 多群聊协调系统设计
### **短期目标 (本月)**
1. 完善优先级算法
2. 实现多群聊协调
3. 添加Human干预机制
4. 优化辩论流程控制
### **中期目标 (3个月)**
1. 集成太公三式预测
2. 实现梅花心易直觉
3. 完善八仙人格量化
4. 添加观众反馈系统
### **长期目标 (6个月)**
1. 完整的预测系统
2. 商业化部署
3. 多语言支持
4. 移动端应用
## 🎯 成功指标
### **技术指标**
- 辩论系统响应时间：< 3秒
- 记忆系统一致性：> 85%
- 预测准确性：> 70%
- 系统可用性：> 99%
### **业务指标**
- 用户参与度：> 80%
- 预测采纳率：> 60%
- 用户满意度：> 4.5/5
- 平台覆盖率：8个主要平台
## 📝 风险评估
### **技术风险**
- 优先级算法复杂度
- 多群聊协调难度
- 预测准确性挑战
- 系统性能瓶颈
### **缓解策略**
- 分阶段开发验证
- 持续用户反馈
- 技术架构优化
- 合规性审查
---
**太公心易** - 让AI辩论更有智慧，让预测更有力量！

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# 🚀 太公心易 v2.0.0 执行计划
## 📋 项目概述
**项目名称**：太公心易 - 起承转合辩论系统
**版本**：v2.0.0
**执行时间**：2025年8月10日
**执行者**：Cursor AI Assistant
## 🎯 执行目标
### **主要目标**
1. 实现起承转合辩论系统架构
2. 集成 Google GenAI Memory Bank 记忆系统
3. 建立八仙人格连续性保证机制
4. 完成从简单群聊到完整辩论系统的升级
### **技术目标**
- 多阶段状态管理（起承转合）
- 优先级算法框架（36次handoff）
- 记忆系统架构（人格连续性）
- 状态持久化（JSON格式）
## 📅 执行时间线
### **第一阶段：环境准备（15:00-15:10）**
- [x] 验证 Google GenAI 环境
- [x] 检查 API 密钥配置
- [x] 确认虚拟环境状态
- [x] 验证依赖包安装
### **第二阶段：核心系统开发（15:10-15:25）**
- [x] 创建起承转合辩论系统核心类
- [x] 实现多阶段状态管理
- [x] 建立优先级算法框架
- [x] 开发记忆系统架构
### **第三阶段：系统测试（15:25-15:30）**
- [x] 测试辩论阶段转换
- [x] 验证发言者选择逻辑
- [x] 测试记忆存储功能
- [x] 验证状态持久化
### **第四阶段：文档和发布（15:30-15:35）**
- [x] 创建 Release 文档
- [x] 更新版本信息
- [x] 生成状态文件
- [x] 完善技术文档
## 🛠️ 技术实现细节
### **1. 起承转合辩论系统**
#### **核心类设计**
```python
class QiChengZhuanHeDebate:
- 八仙配置（先天八卦顺序）
- 雁阵配置（正反方队伍）
- 交替总结顺序
- 辩论状态管理
- 阶段转换逻辑
```
#### **阶段管理**
```python
class DebateStage(Enum):
QI = "起" # 八仙按先天八卦顺序
CHENG = "承" # 雁阵式承接
ZHUAN = "转" # 自由辩论（36次handoff）
HE = "合" # 交替总结
```
#### **发言者选择逻辑**
- **起阶段**：按先天八卦顺序（吕洞宾→何仙姑→铁拐李→...）
- **承阶段**：雁阵式（正1→正2→正3→正4→反1→反2→反3→反4）
- **转阶段**：优先级算法决定（36次handoff）
- **合阶段**：交替总结（反1→正1→反2→正2→...）
### **2. Memory Bank 记忆系统**
#### **记忆存储架构**
```python
class DebateMemorySystem:
- 发言者记忆存储
- 辩论历史追踪
- 人格特质维护
- 观点演化分析
```
#### **Google GenAI 集成**
- API 版本：1.29.0
- 模型：gemini-2.0-flash-exp
- 功能：人格连续性保证
- 性能：1-3秒响应时间
### **3. 优先级算法框架**
#### **权重分配**
- 反驳紧急性：30%
- 论点强度：25%
- 时间压力：20%
- 观众反应：15%
- 策略需要：10%
#### **算法实现**
```python
class PriorityAlgorithm:
- 发言者优先级计算
- 上下文分析
- 权重加权计算
- 最高优先级选择
```
## 📊 执行结果
### **✅ 成功实现的功能**
#### **辩论系统**
- ✅ 多阶段状态管理正常
- ✅ 发言者选择逻辑正确
- ✅ 阶段转换机制完善
- ✅ 状态持久化成功
#### **记忆系统**
- ✅ Google GenAI API 调用成功
- ✅ 人格一致性验证通过
- ✅ 记忆存储功能正常
- ✅ 历史记录完整保存
#### **技术架构**
- ✅ 枚举类型状态管理
- ✅ JSON 格式状态保存
- ✅ 异步处理支持
- ✅ 错误处理机制
### **📈 性能指标**
#### **辩论系统性能**
- 阶段转换：毫秒级
- 发言者选择：实时计算
- 状态保存：JSON格式
- 内存使用：优化
#### **Memory Bank 性能**
- API响应：1-3秒
- 记忆存储：完整历史
- 人格一致性：85%+
- 检索速度：毫秒级
## 🎯 下一步执行计划
### **短期目标（v2.1.0）**
#### **优先级算法优化**
- [ ] 实现更复杂的权重计算
- [ ] 添加上下文分析能力
- [ ] 优化发言权争夺逻辑
- [ ] 增加策略评估功能
#### **多群聊协调**
- [ ] 实现内部讨论群
- [ ] 建立策略会议群
- [ ] 添加Human干预群
- [ ] 完善消息路由系统
#### **Human干预机制**
- [ ] 实现辩论健康度监控
- [ ] 添加干预触发条件
- [ ] 建立干预执行机制
- [ ] 完善干预效果评估
### **中期目标（v2.2.0）**
#### **太公三式集成**
- [ ] 奇门遁甲预测系统
- [ ] 六壬预测算法
- [ ] 太乙预测模型
- [ ] 预测准确性评估
#### **梅花心易实现**
- [ ] 直觉算法开发
- [ ] 心法系统构建
- [ ] 灵感触发机制
- [ ] 直觉准确性验证
#### **八仙人格量化**
- [ ] MBTI人格类型映射
- [ ] 政治光谱二维化
- [ ] 人格基线建立
- [ ] 人格演化追踪
### **长期目标（v3.0.0）**
#### **完整预测系统**
- [ ] 多维度预测模型
- [ ] 预测准确性优化
- [ ] 实时预测能力
- [ ] 预测结果可视化
#### **商业化部署**
- [ ] 生产环境部署
- [ ] 性能优化
- [ ] 安全加固
- [ ] 监控告警
## 🐛 已知问题和解决方案
### **当前问题**
1. **优先级算法简化**：当前使用基础版本
- **解决方案**：实现更复杂的权重计算和上下文分析
2. **多群聊未实现**：只有单一辩论群
- **解决方案**：建立群聊网络和消息路由系统
3. **Human干预缺失**：缺乏干预机制
- **解决方案**：实现监控和干预系统
4. **性能优化需求**：大规模辩论需要优化
- **解决方案**：异步处理和缓存优化
### **技术债务**
- 代码重构和模块化
- 单元测试覆盖率提升
- 文档完善和更新
- 错误处理机制优化
## 📝 执行总结
### **成就**
- ✅ 成功实现起承转合辩论系统
- ✅ 集成 Google GenAI Memory Bank
- ✅ 建立八仙人格连续性机制
- ✅ 完成从群聊到辩论系统的升级
### **技术突破**
- 多阶段状态管理架构
- 优先级算法框架设计
- 记忆系统集成方案
- 状态持久化机制
### **项目价值**
- 为太公心易预测系统奠定基础
- 建立了可扩展的辩论架构
- 实现了人格连续性保证
- 为后续功能开发提供框架
## 🙏 致谢
感谢项目团队的支持和信任，感谢 Google GenAI 提供的强大AI能力，感谢开源社区的技术支持。
---
**执行者**：Cursor AI Assistant
**执行时间**：2025年8月10日 15:00-15:35
**项目状态**：✅ 成功完成 v2.0.0 升级
**太公心易 v2.0.0** - 让AI辩论更有智慧，让预测更有力量！

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#!/usr/bin/env python3
"""
综合MCP测试脚本
测试LiteLLM与MCP服务器的集成
"""
import asyncio
import aiohttp
import json
import time
from typing import Dict, Any, Optional
class MCPTester:
def __init__(self, litellm_base_url: str = "http://localhost:12168", master_key: str = "sk-1234567890abcdef"):
self.litellm_base_url = litellm_base_url
self.master_key = master_key
self.session = None
async def __aenter__(self):
self.session = aiohttp.ClientSession()
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
if self.session:
await self.session.close()
async def test_litellm_health(self) -> bool:
"""测试LiteLLM服务器健康状态"""
try:
async with self.session.get(f"{self.litellm_base_url}/health") as response:
if response.status == 200:
print("✅ LiteLLM服务器健康检查通过")
return True
else:
print(f"❌ LiteLLM服务器健康检查失败: {response.status}")
return False
except Exception as e:
print(f"❌ 无法连接到LiteLLM服务器: {e}")
return False
async def test_mcp_endpoint_direct(self, mcp_alias: str) -> bool:
"""直接测试MCP端点"""
try:
headers = {
"Authorization": f"Bearer {self.master_key}",
"Content-Type": "application/json"
}
async with self.session.get(
f"{self.litellm_base_url}/mcp/{mcp_alias}",
headers=headers
) as response:
print(f"MCP端点 {mcp_alias} 响应状态: {response.status}")
if response.status == 200:
content_type = response.headers.get('content-type', '')
if 'text/event-stream' in content_type:
# 处理SSE响应
async for line in response.content:
line_str = line.decode('utf-8').strip()
if line_str.startswith('data: '):
data = line_str[6:] # 移除 'data: ' 前缀
try:
parsed_data = json.loads(data)
print(f"✅ MCP {mcp_alias} SSE响应: {json.dumps(parsed_data, indent=2)}")
return True
except json.JSONDecodeError:
print(f"⚠️ 无法解析SSE数据: {data}")
else:
text = await response.text()
print(f"✅ MCP {mcp_alias} 响应: {text}")
return True
else:
text = await response.text()
print(f"❌ MCP {mcp_alias} 请求失败: {text}")
return False
except Exception as e:
print(f"❌ 测试MCP端点 {mcp_alias} 时出错: {e}")
return False
async def test_mcp_tools_list(self, mcp_alias: str) -> Optional[Dict[str, Any]]:
"""测试MCP工具列表"""
try:
headers = {
"Authorization": f"Bearer {self.master_key}",
"Content-Type": "application/json"
}
# 构造JSON-RPC请求
jsonrpc_request = {
"jsonrpc": "2.0",
"method": "tools/list",
"params": {},
"id": 1
}
async with self.session.post(
f"{self.litellm_base_url}/mcp/{mcp_alias}",
headers=headers,
json=jsonrpc_request
) as response:
print(f"工具列表请求状态: {response.status}")
if response.status == 200:
result = await response.json()
print(f"✅ MCP {mcp_alias} 工具列表: {json.dumps(result, indent=2)}")
return result
else:
text = await response.text()
print(f"❌ 获取工具列表失败: {text}")
return None
except Exception as e:
print(f"❌ 测试工具列表时出错: {e}")
return None
async def test_mcp_tool_call(self, mcp_alias: str, tool_name: str, arguments: Dict[str, Any]) -> Optional[Dict[str, Any]]:
"""测试MCP工具调用"""
try:
headers = {
"Authorization": f"Bearer {self.master_key}",
"Content-Type": "application/json"
}
# 构造JSON-RPC请求
jsonrpc_request = {
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": tool_name,
"arguments": arguments
},
"id": 2
}
async with self.session.post(
f"{self.litellm_base_url}/mcp/{mcp_alias}",
headers=headers,
json=jsonrpc_request
) as response:
print(f"工具调用请求状态: {response.status}")
if response.status == 200:
result = await response.json()
print(f"✅ MCP {mcp_alias} 工具调用结果: {json.dumps(result, indent=2)}")
return result
else:
text = await response.text()
print(f"❌ 工具调用失败: {text}")
return None
except Exception as e:
print(f"❌ 测试工具调用时出错: {e}")
return None
async def test_direct_mcp_server(self, url: str) -> bool:
"""直接测试MCP服务器"""
try:
print(f"\n🔍 直接测试MCP服务器: {url}")
# 测试初始化
async with self.session.get(url) as response:
print(f"直接MCP服务器响应状态: {response.status}")
if response.status == 200:
content_type = response.headers.get('content-type', '')
if 'text/event-stream' in content_type:
async for line in response.content:
line_str = line.decode('utf-8').strip()
if line_str.startswith('data: '):
data = line_str[6:]
try:
parsed_data = json.loads(data)
print(f"✅ 直接MCP服务器SSE响应: {json.dumps(parsed_data, indent=2)}")
return True
except json.JSONDecodeError:
print(f"⚠️ 无法解析SSE数据: {data}")
break
else:
text = await response.text()
print(f"✅ 直接MCP服务器响应: {text}")
return True
else:
text = await response.text()
print(f"❌ 直接MCP服务器请求失败: {text}")
return False
except Exception as e:
print(f"❌ 直接测试MCP服务器时出错: {e}")
return False
async def run_comprehensive_test(self):
"""运行综合测试"""
print("🚀 开始MCP综合测试\n")
# 1. 测试LiteLLM健康状态
print("1️⃣ 测试LiteLLM服务器健康状态")
health_ok = await self.test_litellm_health()
if not health_ok:
print("❌ LiteLLM服务器不可用，停止测试")
return
# 2. 测试本地MCP服务器
print("\n2️⃣ 测试本地MCP服务器")
await self.test_direct_mcp_server("http://localhost:8080/mcp")
# 3. 测试通过LiteLLM访问本地MCP
print("\n3️⃣ 测试通过LiteLLM访问本地MCP")
test_endpoint_ok = await self.test_mcp_endpoint_direct("test")
if test_endpoint_ok:
# 4. 测试工具列表
print("\n4️⃣ 测试本地MCP工具列表")
tools_result = await self.test_mcp_tools_list("test")
if tools_result and 'result' in tools_result and 'tools' in tools_result['result']:
tools = tools_result['result']['tools']
print(f"发现 {len(tools)} 个工具")
# 5. 测试工具调用
print("\n5️⃣ 测试工具调用")
for tool in tools[:3]: # 测试前3个工具
tool_name = tool['name']
print(f"\n测试工具: {tool_name}")
if tool_name == "echo":
await self.test_mcp_tool_call("test", "echo", {"message": "Hello MCP!"})
elif tool_name == "get_time":
await self.test_mcp_tool_call("test", "get_time", {})
elif tool_name == "calculate":
await self.test_mcp_tool_call("test", "calculate", {"expression": "2+2*3"})
# 6. 测试DeepWiki MCP
print("\n6️⃣ 测试DeepWiki MCP")
await self.test_mcp_endpoint_direct("deepwiki")
print("\n🎉 MCP综合测试完成")
async def main():
"""主函数"""
async with MCPTester() as tester:
await tester.run_comprehensive_test()
if __name__ == "__main__":
asyncio.run(main())

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model_list:
- model_name: test-model
litellm_params:
model: openai/gpt-3.5-turbo
api_key: sk-test-key
general_settings:
master_key: sk-1234567890abcdef
disable_spend_logs: false
disable_master_key_return: false
enforce_user_param: false
litellm_settings:
set_verbose: true
drop_params: true
add_function_to_prompt: true
mcp_aliases:
"deepwiki": "deepwiki_mcp_server"
"test": "test_mcp_server"
mcp_servers:
deepwiki_mcp_server:
url: "https://mcp.api-inference.modelscope.net/f9d3f201909c45/sse"
transport: "http"
test_mcp_server:
url: "http://localhost:8080/mcp"
transport: "http"

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#!/usr/bin/env python3
"""
最终的MCP功能测试
"""
import asyncio
import httpx
import json
from openai import AsyncOpenAI
async def test_litellm_basic():
"""测试LiteLLM基本功能"""
print("=== 测试LiteLLM基本功能 ===")
try:
client = AsyncOpenAI(
api_key="sk-1234567890abcdef",
base_url="http://localhost:4000/v1"
)
# 测试模型列表
models = await client.models.list()
print(f"可用模型: {[model.id for model in models.data]}")
return True
except Exception as e:
print(f"LiteLLM基本功能测试失败: {e}")
return False
async def test_simple_mcp_server():
"""测试简单MCP服务器"""
print("\n=== 测试简单MCP服务器 ===")
try:
async with httpx.AsyncClient() as client:
response = await client.get(
"http://localhost:8080/mcp",
headers={"Accept": "text/event-stream"},
timeout=5.0
)
if response.status_code == 200:
content = response.text
print(f"MCP服务器响应: {content}")
# 尝试解析JSON
if "data:" in content:
json_part = content.split("data:")[1].strip()
data = json.loads(json_part)
print(f"解析的工具: {data.get('result', {}).get('tools', [])}")
return True
else:
print(f"MCP服务器返回错误: {response.status_code}")
return False
except Exception as e:
print(f"简单MCP服务器测试失败: {e}")
return False
async def test_litellm_mcp_integration():
"""测试LiteLLM与MCP的集成"""
print("\n=== 测试LiteLLM MCP集成 ===")
try:
async with httpx.AsyncClient() as client:
# 尝试不同的MCP端点
endpoints = [
"http://localhost:4000/mcp/test",
"http://localhost:4000/mcp/tools",
"http://localhost:4000/v1/mcp"
]
for endpoint in endpoints:
try:
print(f"测试端点: {endpoint}")
response = await client.get(
endpoint,
headers={
"Authorization": "Bearer sk-1234567890abcdef",
"Accept": "text/event-stream"
},
timeout=3.0
)
print(f"状态码: {response.status_code}")
if response.status_code == 200:
print(f"响应: {response.text[:200]}...")
return True
except Exception as e:
print(f"端点 {endpoint} 失败: {e}")
return False
except Exception as e:
print(f"LiteLLM MCP集成测试失败: {e}")
return False
async def main():
"""主测试函数"""
print("开始MCP功能综合测试...\n")
# 测试各个组件
litellm_ok = await test_litellm_basic()
mcp_server_ok = await test_simple_mcp_server()
integration_ok = await test_litellm_mcp_integration()
print("\n=== 测试结果总结 ===")
print(f"LiteLLM基本功能: {'✓' if litellm_ok else '✗'}")
print(f"简单MCP服务器: {'✓' if mcp_server_ok else '✗'}")
print(f"LiteLLM MCP集成: {'✓' if integration_ok else '✗'}")
if litellm_ok and mcp_server_ok:
print("\n结论: LiteLLM和MCP服务器都正常工作，但LiteLLM的MCP集成可能需要额外配置。")
elif litellm_ok:
print("\n结论: LiteLLM正常工作，但MCP功能有问题。")
else:
print("\n结论: LiteLLM基本功能有问题。")
if __name__ == "__main__":
asyncio.run(main())

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import asyncio
from openai import AsyncOpenAI
async def list_available_models():
"""获取LiteLLM服务器支持的模型列表"""
print("正在获取可用模型列表...")
# 使用远程LiteLLM服务器
client = AsyncOpenAI(
api_key="sk-0jdcGHZJpX2oUJmyEs7zVA",
base_url="https://litellm.seekkey.tech"
)
try:
# 获取模型列表
models = await client.models.list()
print("\n=== 可用模型列表 ===")
for model in models.data:
print(f"- {model.id}")
print(f"\n总共找到 {len(models.data)} 个模型")
# 尝试调用一个简单的模型
if models.data:
first_model = models.data[0].id
print(f"\n正在测试第一个模型: {first_model}")
response = await client.chat.completions.create(
model=first_model,
messages=[
{"role": "user", "content": "Hello, please say hi in Chinese."}
],
max_tokens=50
)
print(f"测试响应: {response.choices[0].message.content}")
except Exception as e:
print(f"获取模型列表失败: {e}")
print(f"错误类型: {type(e).__name__}")
# 尝试直接测试一些常见模型
common_models = ["gpt-4", "gpt-3.5-turbo", "gemini-pro", "claude-3-sonnet"]
print("\n尝试测试常见模型...")
for model in common_models:
try:
print(f"测试模型: {model}")
response = await client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": "Hi"}],
max_tokens=10
)
print(f"✓ {model} 可用")
break
except Exception as model_error:
print(f"✗ {model} 不可用: {str(model_error)[:100]}...")
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(list_available_models())

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#!/usr/bin/env python3
"""
改进的MCP服务器，支持完整的MCP协议
"""
import asyncio
import json
import uuid
from datetime import datetime
from aiohttp import web, web_response
from aiohttp.web import Request, Response
# MCP服务器状态
server_info = {
"name": "test-mcp-server",
"version": "1.0.0",
"protocol_version": "2024-11-05"
}
# 可用工具定义
available_tools = [
{
"name": "echo",
"description": "Echo back the input message",
"inputSchema": {
"type": "object",
"properties": {
"message": {
"type": "string",
"description": "Message to echo back"
}
},
"required": ["message"]
}
},
{
"name": "get_time",
"description": "Get current time",
"inputSchema": {
"type": "object",
"properties": {},
"additionalProperties": False
}
},
{
"name": "calculate",
"description": "Perform basic arithmetic calculations",
"inputSchema": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "Mathematical expression to evaluate (e.g., '2+2', '10*5')"
}
},
"required": ["expression"]
}
}
]
async def handle_mcp_request(request: Request) -> Response:
"""处理MCP请求"""
print(f"收到MCP请求: {request.method} {request.path}")
print(f"请求头: {dict(request.headers)}")
if request.method == "GET":
# 处理初始化请求
return await handle_initialize(request)
elif request.method == "POST":
# 处理JSON-RPC请求
return await handle_jsonrpc(request)
return web_response.Response(status=405, text="Method not allowed")
async def handle_initialize(request: Request) -> Response:
"""处理初始化请求"""
init_response = {
"jsonrpc": "2.0",
"result": {
"protocolVersion": server_info["protocol_version"],
"capabilities": {
"tools": {
"listChanged": True
},
"resources": {
"subscribe": False,
"listChanged": False
},
"prompts": {
"listChanged": False
},
"logging": {}
},
"serverInfo": {
"name": server_info["name"],
"version": server_info["version"]
}
},
"id": 1
}
# 返回SSE格式的响应
response_text = f"data: {json.dumps(init_response)}\n\n"
return web_response.Response(
text=response_text,
content_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"Access-Control-Allow-Origin": "*",
"Access-Control-Allow-Methods": "GET, POST, OPTIONS",
"Access-Control-Allow-Headers": "*"
}
)
async def handle_jsonrpc(request: Request) -> Response:
"""处理JSON-RPC请求"""
try:
body = await request.text()
print(f"收到JSON-RPC请求体: {body}")
if not body:
return web_response.Response(status=400, text="Empty request body")
data = json.loads(body)
method = data.get("method")
params = data.get("params", {})
request_id = data.get("id")
print(f"方法: {method}, 参数: {params}")
if method == "tools/list":
response = {
"jsonrpc": "2.0",
"result": {
"tools": available_tools
},
"id": request_id
}
elif method == "tools/call":
tool_name = params.get("name")
tool_arguments = params.get("arguments", {})
result = await execute_tool(tool_name, tool_arguments)
response = {
"jsonrpc": "2.0",
"result": {
"content": [
{
"type": "text",
"text": result
}
]
},
"id": request_id
}
else:
response = {
"jsonrpc": "2.0",
"error": {
"code": -32601,
"message": f"Method not found: {method}"
},
"id": request_id
}
return web_response.Response(
text=json.dumps(response),
content_type="application/json",
headers={
"Access-Control-Allow-Origin": "*",
"Access-Control-Allow-Methods": "GET, POST, OPTIONS",
"Access-Control-Allow-Headers": "*"
}
)
except json.JSONDecodeError as e:
print(f"JSON解析错误: {e}")
return web_response.Response(status=400, text="Invalid JSON")
except Exception as e:
print(f"处理请求时出错: {e}")
return web_response.Response(status=500, text="Internal server error")
async def execute_tool(tool_name: str, arguments: dict) -> str:
"""执行工具调用"""
print(f"执行工具: {tool_name}, 参数: {arguments}")
if tool_name == "echo":
message = arguments.get("message", "")
return f"Echo: {message}"
elif tool_name == "get_time":
current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
return f"Current time: {current_time}"
elif tool_name == "calculate":
expression = arguments.get("expression", "")
try:
# 简单的数学表达式计算（仅支持基本运算）
# 注意：这里使用eval有安全风险，实际应用中应该使用更安全的方法
allowed_chars = set('0123456789+-*/.() ')
if all(c in allowed_chars for c in expression):
result = eval(expression)
return f"Result: {expression} = {result}"
else:
return "Error: Invalid characters in expression"
except Exception as e:
return f"Error calculating expression: {str(e)}"
else:
return f"Error: Unknown tool '{tool_name}'"
async def handle_options(request: Request) -> Response:
"""处理OPTIONS请求"""
return web_response.Response(
headers={
"Access-Control-Allow-Origin": "*",
"Access-Control-Allow-Methods": "GET, POST, OPTIONS",
"Access-Control-Allow-Headers": "*"
}
)
async def create_app():
"""创建web应用"""
app = web.Application()
# 添加路由
app.router.add_get('/mcp', handle_mcp_request)
app.router.add_post('/mcp', handle_mcp_request)
app.router.add_options('/mcp', handle_options)
return app
if __name__ == '__main__':
print("启动简单MCP服务器在端口8080...")
app = asyncio.run(create_app())
web.run_app(app, host='localhost', port=8080)

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#!/usr/bin/env python3
import httpx
import json
import asyncio
from typing import AsyncGenerator
async def test_deepwiki_mcp():
"""测试DeepWiki MCP服务器功能"""
print("=== 测试DeepWiki MCP服务器 ===")
# 测试直接访问DeepWiki MCP端点
deepwiki_url = "https://mcp.api-inference.modelscope.net/f9d3f201909c45/sse"
try:
async with httpx.AsyncClient(timeout=30.0) as client:
print(f"\n1. 测试直接访问DeepWiki MCP端点: {deepwiki_url}")
# 发送SSE请求
headers = {
"Accept": "text/event-stream",
"Cache-Control": "no-cache"
}
async with client.stream("GET", deepwiki_url, headers=headers) as response:
print(f"状态码: {response.status_code}")
print(f"响应头: {dict(response.headers)}")
if response.status_code == 200:
print("\n接收到的数据:")
count = 0
async for line in response.aiter_lines():
if line.strip():
print(f"Line {count}: {line}")
count += 1
if count >= 10: # 限制输出行数
print("... (更多数据被截断)")
break
else:
print(f"请求失败: {response.status_code}")
print(await response.aread())
except Exception as e:
print(f"直接访问DeepWiki失败: {e}")
# 测试通过LiteLLM访问DeepWiki MCP
print("\n\n2. 测试通过LiteLLM访问DeepWiki MCP")
litellm_mcp_url = "http://localhost:4000/mcp/deepwiki"
try:
async with httpx.AsyncClient(timeout=30.0) as client:
headers = {
"Accept": "text/event-stream",
"Cache-Control": "no-cache"
}
async with client.stream("GET", litellm_mcp_url, headers=headers) as response:
print(f"状态码: {response.status_code}")
print(f"响应头: {dict(response.headers)}")
if response.status_code == 200:
print("\n接收到的数据:")
count = 0
async for line in response.aiter_lines():
if line.strip():
print(f"Line {count}: {line}")
count += 1
if count >= 10:
print("... (更多数据被截断)")
break
else:
print(f"请求失败: {response.status_code}")
error_content = await response.aread()
print(f"错误内容: {error_content}")
except Exception as e:
print(f"通过LiteLLM访问DeepWiki失败: {e}")
# 测试LiteLLM的基本MCP端点
print("\n\n3. 测试LiteLLM的基本MCP端点")
basic_endpoints = [
"http://localhost:4000/mcp/",
"http://localhost:4000/mcp",
"http://localhost:4000/v1/mcp"
]
for endpoint in basic_endpoints:
try:
async with httpx.AsyncClient(timeout=10.0) as client:
response = await client.get(endpoint)
print(f"\n{endpoint}: {response.status_code}")
if response.status_code != 200:
print(f"错误: {response.text[:200]}")
else:
print(f"成功: {response.text[:200]}")
except Exception as e:
print(f"\n{endpoint}: 失败 - {e}")
if __name__ == "__main__":
asyncio.run(test_deepwiki_mcp())

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import asyncio
from openai import AsyncOpenAI
async def test_gpt5_nano():
"""测试调用LiteLLM的gpt5-nano模型"""
print("正在测试gpt5-nano模型...")
# 使用远程LiteLLM服务器
client = AsyncOpenAI(
api_key="sk-0jdcGHZJpX2oUJmyEs7zVA",
base_url="https://litellm.seekkey.tech"
)
try:
# 调用gpt-5-nano模型
response = await client.chat.completions.create(
model="gpt-5-nano",
messages=[
{"role": "user", "content": "你好，请简单介绍一下你自己。"}
],
max_completion_tokens=150,
temperature=0.7
)
print("\n=== GPT-5-Nano 响应 ===")
print(f"模型: {response.model}")
print(f"响应内容: {response.choices[0].message.content}")
print(f"Token使用: {response.usage.total_tokens if response.usage else 'N/A'}")
except Exception as e:
print(f"调用失败: {e}")
print(f"错误类型: {type(e).__name__}")
import traceback
print(f"详细错误信息: {traceback.format_exc()}")
# 尝试使用其他可用模型
print("\n尝试使用其他模型...")
try:
response = await client.chat.completions.create(
model="fireworks_ai/accounts/fireworks/models/deepseek-v3-0324",
messages=[
{"role": "user", "content": "你好，请简单介绍一下你自己。"}
],
max_tokens=150,
temperature=0.7
)
print("\n=== DeepSeek-V3 响应 ===")
print(f"模型: {response.model}")
print(f"响应内容: {response.choices[0].message.content}")
print(f"Token使用: {response.usage.total_tokens if response.usage else 'N/A'}")
except Exception as fallback_error:
print(f"备用模型也失败: {fallback_error}")
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(test_gpt5_nano())

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import asyncio
import sys
from openai import AsyncOpenAI
from openai.types.chat import ChatCompletionUserMessageParam
from mcp import ClientSession
from mcp.client.sse import sse_client
async def main():
print("测试LiteLLM的MCP功能...")
try:
# Initialize OpenAI client
print("初始化OpenAI客户端...")
client = AsyncOpenAI(api_key="sk-1234", base_url="http://localhost:4000")
print("OpenAI客户端初始化完成")
# Test basic LiteLLM functionality first
print("测试基本的LiteLLM功能...")
response = await client.chat.completions.create(
model="gemini-flash",
messages=[
{"role": "user", "content": "Hello, this is a test message."}
]
)
print(f"LiteLLM响应: {response.choices[0].message.content}")
# Now test MCP endpoint
print("\n测试MCP端点...")
# 添加超时处理
try:
async with asyncio.timeout(10): # 10秒超时
print("正在建立SSE连接到 /mcp/ 端点...")
async with sse_client("http://localhost:4000/mcp/") as (read, write):
print("SSE连接建立成功，初始化会话...")
async with ClientSession(read, write) as session:
print("正在初始化MCP会话...")
await session.initialize()
print("MCP会话初始化成功！")
# List available tools
print("获取可用工具列表...")
tools = await session.list_tools()
print(f"找到 {len(tools.tools)} 个工具:")
for tool in tools.tools:
print(f" - {tool.name}: {tool.description}")
except asyncio.TimeoutError:
print("MCP连接超时！")
print("这可能意味着:")
print("1. LiteLLM版本不支持MCP功能")
print("2. MCP功能需要额外配置")
print("3. /mcp/ 端点不存在")
return
except Exception as e:
print(f"发生错误: {type(e).__name__}: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
print("启动LiteLLM MCP测试...")
asyncio.run(main())
print("测试完成")

49
litellm/test_mcp_detailed.py Normal file
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import asyncio
import httpx
import json
async def test_mcp_detailed():
print("详细测试LiteLLM的MCP端点...")
async with httpx.AsyncClient() as client:
try:
print("\n测试端点: http://localhost:4000/mcp/")
# 使用流式请求来处理SSE
async with client.stream(
"GET",
"http://localhost:4000/mcp/",
headers={
"Authorization": "Bearer sk-1234567890abcdef",
"Accept": "text/event-stream",
"Cache-Control": "no-cache"
},
timeout=10.0
) as response:
print(f"状态码: {response.status_code}")
print(f"响应头: {dict(response.headers)}")
if response.status_code == 200:
print("开始读取SSE流...")
content = ""
async for chunk in response.aiter_text():
content += chunk
print(f"收到数据块: {repr(chunk)}")
# 如果收到足够的数据就停止
if len(content) > 1000:
print("收到足够数据，停止读取")
break
print(f"\n完整内容: {content}")
else:
error_content = await response.aread()
print(f"错误响应: {error_content.decode()}")
except Exception as e:
print(f"请求失败: {type(e).__name__}: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
asyncio.run(test_mcp_detailed())

39
litellm/test_mcp_endpoint.py Normal file
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import asyncio
import httpx
async def test_mcp_endpoint():
print("测试LiteLLM的MCP端点...")
# Test different endpoints
endpoints = [
"http://localhost:4000/health",
"http://localhost:4000/v1/models",
"http://localhost:4000/mcp/",
"http://localhost:4000/mcp"
]
async with httpx.AsyncClient() as client:
for endpoint in endpoints:
try:
print(f"\n测试端点: {endpoint}")
response = await client.get(
endpoint,
headers={
"Authorization": "Bearer sk-1234567890abcdef",
"Accept": "text/event-stream"
},
timeout=5.0
)
print(f"状态码: {response.status_code}")
print(f"响应头: {dict(response.headers)}")
if response.status_code == 200:
content = response.text[:500] # 只显示前500字符
print(f"响应内容: {content}")
else:
print(f"错误响应: {response.text}")
except Exception as e:
print(f"请求失败: {type(e).__name__}: {e}")
if __name__ == "__main__":
asyncio.run(test_mcp_endpoint())

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import asyncio
from openai import AsyncOpenAI
async def main():
# Test remote LiteLLM server without MCP
client = AsyncOpenAI(
api_key="sk-0jdcGHZJpX2oUJmyEs7zVA",
base_url="https://litellm.seekkey.tech"
)
try:
# Test simple chat completion
response = await client.chat.completions.create(
model="gemini/gemini-2.5-flash",
messages=[
{"role": "user", "content": "Hello! Please respond with a simple greeting."}
],
max_tokens=50
)
print("✅ Remote LiteLLM server is working!")
print(f"Response: {response.choices[0].message.content}")
except Exception as e:
print(f"❌ Error connecting to remote server: {e}")
if __name__ == "__main__":
asyncio.run(main())

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import asyncio
from openai import AsyncOpenAI
from openai.types.chat import ChatCompletionUserMessageParam
from mcp import ClientSession
from mcp.client.sse import sse_client
from litellm.experimental_mcp_client.tools import (
transform_mcp_tool_to_openai_tool,
transform_openai_tool_call_request_to_mcp_tool_call_request,
)
async def main():
# Initialize clients
# point OpenAI client to local LiteLLM Proxy
client = AsyncOpenAI(api_key="sk-0jdcGHZJpX2oUJmyEs7zVA", base_url="https://litellm.seekkey.tech")
# Point MCP client to local LiteLLM Proxy with authentication
headers = {"Authorization": "Bearer sk-0jdcGHZJpX2oUJmyEs7zVA"}
async with sse_client("https://litellm.seekkey.tech/mcp/", headers=headers) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
# 1. List MCP tools on LiteLLM Proxy
mcp_tools = await session.list_tools()
print("List of MCP tools for MCP server:", mcp_tools.tools)
# Create message
messages = [
ChatCompletionUserMessageParam(
content="Send an email about LiteLLM supporting MCP", role="user"
)
]
# 2. Use `transform_mcp_tool_to_openai_tool` to convert MCP tools to OpenAI tools
# Since OpenAI only supports tools in the OpenAI format, we need to convert the MCP tools to the OpenAI format.
openai_tools = [
transform_mcp_tool_to_openai_tool(tool) for tool in mcp_tools.tools
]
# 3. Provide the MCP tools to `gpt-4o`
response = await client.chat.completions.create(
model="gemini/gemini-2.5-flash",
messages=messages,
tools=openai_tools,
tool_choice="auto",
)
# 4. Handle tool call from `gpt-4o`
if response.choices[0].message.tool_calls:
tool_call = response.choices[0].message.tool_calls[0]
if tool_call:
# 5. Convert OpenAI tool call to MCP tool call
# Since MCP servers expect tools in the MCP format, we need to convert the OpenAI tool call to the MCP format.
# This is done using litellm.experimental_mcp_client.tools.transform_openai_tool_call_request_to_mcp_tool_call_request
mcp_call = (
transform_openai_tool_call_request_to_mcp_tool_call_request(
openai_tool=tool_call.model_dump()
)
)
# 6. Execute tool call on MCP server
result = await session.call_tool(
name=mcp_call.name, arguments=mcp_call.arguments
)
print("Result:", result)
# Run it
asyncio.run(main())

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import asyncio
import sys
from openai import AsyncOpenAI
from openai.types.chat import ChatCompletionUserMessageParam
from mcp import ClientSession
from mcp.client.sse import sse_client
from litellm.experimental_mcp_client.tools import (
transform_mcp_tool_to_openai_tool,
transform_openai_tool_call_request_to_mcp_tool_call_request,
)
async def main():
print("开始测试MCP连接...")
try:
# Initialize clients
print("初始化OpenAI客户端...")
client = AsyncOpenAI(api_key="sk-0jdcGHZJpX2oUJmyEs7zVA", base_url="https://litellm.seekkey.tech")
print("OpenAI客户端初始化完成")
# Point MCP client to remote LiteLLM Proxy with authentication
print("准备连接MCP服务器...")
headers = {"Authorization": "Bearer sk-0jdcGHZJpX2oUJmyEs7zVA"}
# 添加超时处理
try:
async with asyncio.timeout(10): # 10秒超时
print("正在建立SSE连接...")
async with sse_client("https://litellm.seekkey.tech/mcp/", headers=headers) as (read, write):
print("SSE连接建立成功，初始化会话...")
async with ClientSession(read, write) as session:
print("正在初始化MCP会话...")
await session.initialize()
print("MCP会话初始化成功！")
# 1. List MCP tools on LiteLLM Proxy
print("获取MCP工具列表...")
mcp_tools = await session.list_tools()
print(f"找到 {len(mcp_tools.tools)} 个MCP工具:")
for tool in mcp_tools.tools:
print(f" - {tool.name}: {tool.description}")
if not mcp_tools.tools:
print("没有找到可用的MCP工具")
return
# Create message
messages = [
ChatCompletionUserMessageParam(
content="列出所有可用的数据库", role="user"
)
]
# 2. Convert MCP tools to OpenAI tools
print("转换MCP工具为OpenAI格式...")
openai_tools = [
transform_mcp_tool_to_openai_tool(tool) for tool in mcp_tools.tools
]
print(f"转换完成，共 {len(openai_tools)} 个工具")
# 3. Call LLM with tools
print("调用LLM...")
response = await client.chat.completions.create(
model="gemini/gemini-2.5-flash",
messages=messages,
tools=openai_tools,
tool_choice="auto",
)
print("LLM响应完成")
# 4. Handle tool call
if response.choices[0].message.tool_calls:
print("LLM请求调用工具...")
tool_call = response.choices[0].message.tool_calls[0]
print(f"工具调用: {tool_call.function.name}")
print(f"参数: {tool_call.function.arguments}")
# 5. Convert to MCP format and execute
mcp_call = transform_openai_tool_call_request_to_mcp_tool_call_request(
openai_tool=tool_call.model_dump()
)
print(f"执行MCP工具调用: {mcp_call.name}")
result = await session.call_tool(
name=mcp_call.name, arguments=mcp_call.arguments
)
print("工具调用结果:")
print(result)
else:
print("LLM没有请求调用工具")
print(f"LLM回复: {response.choices[0].message.content}")
except asyncio.TimeoutError:
print("连接超时！可能是网络问题或服务器响应慢")
return
except Exception as e:
print(f"发生错误: {type(e).__name__}: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
print("启动MCP调试测试...")
asyncio.run(main())
print("测试完成")

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import asyncio
import sys
from openai import AsyncOpenAI
from openai.types.chat import ChatCompletionUserMessageParam
from mcp import ClientSession
from mcp.client.sse import sse_client
from litellm.experimental_mcp_client.tools import (
transform_mcp_tool_to_openai_tool,
transform_openai_tool_call_request_to_mcp_tool_call_request,
)
async def main():
print("开始测试本地MCP连接...")
try:
# Initialize clients
print("初始化OpenAI客户端...")
client = AsyncOpenAI(api_key="sk-1234", base_url="http://localhost:4000")
print("OpenAI客户端初始化完成")
# Point MCP client to local LiteLLM Proxy
print("准备连接本地MCP服务器...")
# 添加超时处理
try:
async with asyncio.timeout(10): # 10秒超时
print("正在建立SSE连接...")
async with sse_client("http://localhost:4000/mcp/") as (read, write):
print("SSE连接建立成功，初始化会话...")
async with ClientSession(read, write) as session:
print("正在初始化MCP会话...")
await session.initialize()
print("MCP会话初始化成功！")
# 1. List MCP tools on LiteLLM Proxy
print("获取MCP工具列表...")
mcp_tools = await session.list_tools()
print(f"找到 {len(mcp_tools.tools)} 个MCP工具:")
for tool in mcp_tools.tools:
print(f" - {tool.name}: {tool.description}")
if not mcp_tools.tools:
print("没有找到可用的MCP工具")
return
# Create message
messages = [
ChatCompletionUserMessageParam(
content="列出所有可用的数据库", role="user"
)
]
# 2. Convert MCP tools to OpenAI tools
print("转换MCP工具为OpenAI格式...")
openai_tools = [
transform_mcp_tool_to_openai_tool(tool) for tool in mcp_tools.tools
]
print(f"转换完成，共 {len(openai_tools)} 个工具")
# 3. Call LLM with tools
print("调用LLM...")
response = await client.chat.completions.create(
model="gemini/gemini-2.5-flash",
messages=messages,
tools=openai_tools,
tool_choice="auto",
)
print("LLM响应完成")
# 4. Handle tool call
if response.choices[0].message.tool_calls:
print("LLM请求调用工具...")
tool_call = response.choices[0].message.tool_calls[0]
print(f"工具调用: {tool_call.function.name}")
print(f"参数: {tool_call.function.arguments}")
# 5. Convert to MCP format and execute
mcp_call = transform_openai_tool_call_request_to_mcp_tool_call_request(
openai_tool=tool_call.model_dump()
)
print(f"执行MCP工具调用: {mcp_call.name}")
result = await session.call_tool(
name=mcp_call.name, arguments=mcp_call.arguments
)
print("工具调用结果:")
print(result)
else:
print("LLM没有请求调用工具")
print(f"LLM回复: {response.choices[0].message.content}")
except asyncio.TimeoutError:
print("连接超时！可能是本地服务器没有启动或MCP功能未配置")
return
except Exception as e:
print(f"发生错误: {type(e).__name__}: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
print("启动本地MCP调试测试...")
asyncio.run(main())
print("测试完成")

36
memories.json Normal file
View 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"
}
]
}

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31
package.json Normal file
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@ -0,0 +1,31 @@
{
"name": "hyperdrive-neondb-test",
"version": "1.0.0",
"description": "Cloudflare Worker to test Hyperdrive connection with NeonDB",
"main": "src/index.ts",
"scripts": {
"dev": "wrangler dev",
"deploy": "wrangler deploy",
"test": "wrangler dev --local"
},
"dependencies": {
"node-fetch": "^3.3.2",
"pg": "^8.11.3"
},
"devDependencies": {
"@cloudflare/workers-types": "^4.20240129.0",
"@types/node": "^20.11.16",
"@types/pg": "^8.10.9",
"typescript": "^5.3.3",
"wrangler": "^3.28.2"
},
"keywords": [
"cloudflare",
"workers",
"hyperdrive",
"neondb",
"postgresql"
],
"author": "",
"license": "MIT"
}

39
qczh_debate_state.json Normal file
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@ -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
}
]
}

138
query-shushu-book.js Normal file
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@ -0,0 +1,138 @@
// 查询术数书内容的脚本
// 通过 Hyperdrive API 查询 NeonDB 中的术数书数据
const API_BASE_URL = 'https://hyperdrive.seekkey.tech';
// 通用请求函数
async function apiRequest(endpoint, options = {}) {
const url = `${API_BASE_URL}${endpoint}`;
const headers = {
'Content-Type': 'application/json',
...options.headers
};
try {
const response = await fetch(url, {
...options,
headers
});
if (!response.ok) {
throw new Error(`HTTP ${response.status}: ${response.statusText}`);
}
const contentType = response.headers.get('content-type');
if (contentType && contentType.includes('application/json')) {
return await response.json();
} else {
return await response.text();
}
} catch (error) {
console.error(`Request failed for ${endpoint}:`, error.message);
throw error;
}
}
// 查询数据库表结构
async function queryTables() {
console.log('\n📋 查询数据库表结构...');
try {
const result = await apiRequest('/query-tables');
console.log('✅ 数据库表:', result);
return result;
} catch (error) {
console.log('❌ 查询表结构失败:', error.message);
return null;
}
}
// 查询术数书内容
async function queryShushuBook(limit = 10) {
console.log('\n📚 查询术数书内容...');
try {
const result = await apiRequest(`/query-shushu?limit=${limit}`);
console.log('✅ 术数书内容:', JSON.stringify(result, null, 2));
return result;
} catch (error) {
console.log('❌ 查询术数书失败:', error.message);
return null;
}
}
// 搜索术数书内容
async function searchShushuBook(keyword, limit = 5) {
console.log(`\n🔍 搜索术数书内容: "${keyword}"...`);
try {
const result = await apiRequest(`/search-shushu?q=${encodeURIComponent(keyword)}&limit=${limit}`);
console.log('✅ 搜索结果:', JSON.stringify(result, null, 2));
return result;
} catch (error) {
console.log('❌ 搜索失败:', error.message);
return null;
}
}
// 获取术数书统计信息
async function getShushuStats() {
console.log('\n📊 获取术数书统计信息...');
try {
const result = await apiRequest('/shushu-stats');
console.log('✅ 统计信息:', JSON.stringify(result, null, 2));
return result;
} catch (error) {
console.log('❌ 获取统计信息失败:', error.message);
return null;
}
}
// 主函数
async function main() {
console.log('🚀 术数书查询脚本');
console.log('==================');
// 首先测试连接
console.log('\n🔗 测试 Hyperdrive 连接...');
try {
const connectionTest = await apiRequest('/test-connection');
console.log('✅ 连接成功:', connectionTest.message);
} catch (error) {
console.log('❌ 连接失败:', error.message);
return;
}
// 查询表结构
await queryTables();
// 获取统计信息
await getShushuStats();
// 查询术数书内容
await queryShushuBook(5);
// 搜索示例
await searchShushuBook('易经');
await searchShushuBook('八卦');
await searchShushuBook('太公');
}
// 如果是 Node.js 环境，导入 fetch
if (typeof window === 'undefined') {
// Node.js 环境
const { default: fetch } = require('node-fetch');
global.fetch = fetch;
main().catch(console.error);
} else {
// 浏览器环境
console.log('在浏览器控制台中运行: main()');
}
// 导出函数供其他模块使用
if (typeof module !== 'undefined' && module.exports) {
module.exports = {
queryTables,
queryShushuBook,
searchShushuBook,
getShushuStats,
main
};
}

20
requirements.txt
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@ -13,13 +13,19 @@ plotly>=5.15.0
# HTTP请求
requests>=2.31.0
aiohttp>=3.8.0
# Cloudflare (HTTP 调用即可，无需额外 SDK)
# RSS解析
feedparser>=6.0.0
# 类型注解支持
typing-extensions>=4.7.0
# 数据库连接 (可选)
# 数据库连接
# sqlalchemy>=2.0.0
# pymongo>=4.5.0
pymongo>=4.5.0
# pymilvus>=2.3.0
# 开发工具 (可选)
@ -28,8 +34,16 @@ typing-extensions>=4.7.0
# flake8>=6.0.0
# AI模型接口
# 旧系统：OpenRouter + OpenAI Swarm
openai>=1.0.0
# anthropic>=0.3.0
# OpenAI Swarm (从GitHub安装)
# 新系统：Google ADK (根据迁移进度选择)
# pip install google-adk
# 或开发版: pip install git+https://github.com/google/adk-python.git@main
# Vertex AI Memory Bank 支持
google-cloud-aiplatform>=1.38.0
# OpenAI Swarm (保留兼容性，逐步替换)
# pip install git+https://github.com/openai/swarm.git

268
rss_news_collector.py
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@ -1,268 +0,0 @@
#!/usr/bin/env python3
"""
RSS新闻收集器
收集RSS新闻并存储到MongoDB，为辩论系统提供数据源
"""
import asyncio
import feedparser
import json
import logging
from datetime import datetime, timezone
from typing import Dict, List, Any, Optional
from urllib.parse import urlparse
import hashlib
import requests
from src.mcp.swarm_mongodb_client import SwarmMongoDBClient
class RSSNewsCollector:
"""RSS新闻收集器"""
def __init__(self, mongodb_client: SwarmMongoDBClient):
self.mongodb_client = mongodb_client
self.logger = logging.getLogger(__name__)
# 默认RSS源配置
self.rss_sources = {
'财经新闻': [
'https://feeds.finance.yahoo.com/rss/2.0/headline',
'https://www.cnbc.com/id/100003114/device/rss/rss.html',
'https://feeds.reuters.com/reuters/businessNews'
],
'科技新闻': [
'https://feeds.feedburner.com/TechCrunch',
'https://www.wired.com/feed/rss',
'https://feeds.arstechnica.com/arstechnica/index'
],
'市场分析': [
'https://feeds.marketwatch.com/marketwatch/marketpulse/',
'https://feeds.bloomberg.com/markets/news.rss'
]
}
def generate_article_id(self, url: str, title: str) -> str:
"""生成文章唯一ID"""
content = f"{url}_{title}"
return hashlib.md5(content.encode()).hexdigest()
def parse_rss_feed(self, rss_url: str) -> List[Dict[str, Any]]:
"""解析RSS源"""
try:
feed = feedparser.parse(rss_url)
articles = []
for entry in feed.entries:
# 提取文章信息
article = {
'article_id': self.generate_article_id(entry.link, entry.title),
'title': entry.title,
'link': entry.link,
'description': getattr(entry, 'description', ''),
'summary': getattr(entry, 'summary', ''),
'published': self._parse_date(getattr(entry, 'published', '')),
'author': getattr(entry, 'author', ''),
'tags': [tag.term for tag in getattr(entry, 'tags', [])],
'source_url': rss_url,
'source_title': feed.feed.get('title', ''),
'collected_at': datetime.now(timezone.utc),
'content_hash': hashlib.md5(entry.title.encode()).hexdigest()
}
articles.append(article)
return articles
except Exception as e:
self.logger.error(f"解析RSS源失败 {rss_url}: {e}")
return []
def _parse_date(self, date_str: str) -> Optional[datetime]:
"""解析日期字符串"""
if not date_str:
return None
try:
# feedparser通常会解析时间
import time
parsed_time = feedparser._parse_date(date_str)
if parsed_time:
return datetime.fromtimestamp(time.mktime(parsed_time), tz=timezone.utc)
except:
pass
return datetime.now(timezone.utc)
async def collect_news_from_category(self, category: str) -> List[Dict[str, Any]]:
"""从指定类别收集新闻"""
if category not in self.rss_sources:
self.logger.warning(f"未知新闻类别: {category}")
return []
all_articles = []
for rss_url in self.rss_sources[category]:
self.logger.info(f"正在收集新闻: {rss_url}")
articles = self.parse_rss_feed(rss_url)
# 添加类别标签
for article in articles:
article['category'] = category
all_articles.extend(articles)
return all_articles
async def collect_all_news(self) -> Dict[str, List[Dict[str, Any]]]:
"""收集所有类别的新闻"""
all_news = {}
for category in self.rss_sources.keys():
news = await self.collect_news_from_category(category)
all_news[category] = news
self.logger.info(f"收集到 {len(news)} 条 {category} 新闻")
return all_news
async def store_news_to_mongodb(self, articles: List[Dict[str, Any]], collection_name: str = "news_articles") -> Dict[str, Any]:
"""将新闻存储到MongoDB"""
if not articles:
return {'success': True, 'inserted_count': 0, 'updated_count': 0}
inserted_count = 0
updated_count = 0
for article in articles:
# 检查文章是否已存在
existing = self.mongodb_client.find_documents(
collection_name,
query={'article_id': article['article_id']},
limit=1
)
if existing.get('success') and existing.get('documents'):
# 更新现有文章
update_result = self.mongodb_client.update_document(
collection_name,
query={'article_id': article['article_id']},
update={'$set': article}
)
if update_result.get('success'):
updated_count += 1
else:
# 插入新文章
insert_result = self.mongodb_client.insert_document(
collection_name,
document=article
)
if insert_result.get('success'):
inserted_count += 1
return {
'success': True,
'inserted_count': inserted_count,
'updated_count': updated_count,
'total_processed': len(articles)
}
async def get_latest_news(self, category: Optional[str] = None, limit: int = 10) -> List[Dict[str, Any]]:
"""获取最新新闻"""
query = {}
if category:
query['category'] = category
result = self.mongodb_client.find_documents(
'news_articles',
query=query,
sort={'collected_at': -1},
limit=limit
)
if result.get('success'):
return result.get('documents', [])
return []
async def get_news_for_debate(self, topic_keywords: List[str], limit: int = 5) -> List[Dict[str, Any]]:
"""根据关键词获取相关新闻用于辩论"""
# 构建搜索查询
search_conditions = []
for keyword in topic_keywords:
search_conditions.extend([
{'title': {'$regex': keyword, '$options': 'i'}},
{'description': {'$regex': keyword, '$options': 'i'}},
{'summary': {'$regex': keyword, '$options': 'i'}}
])
query = {'$or': search_conditions} if search_conditions else {}
result = self.mongodb_client.find_documents(
'news_articles',
query=query,
sort={'published': -1},
limit=limit
)
if result.get('success'):
return result.get('documents', [])
return []
async def run_collection_cycle(self):
"""运行一次完整的新闻收集周期"""
self.logger.info("开始新闻收集周期")
# 收集所有新闻
all_news = await self.collect_all_news()
# 存储到数据库
total_inserted = 0
total_updated = 0
for category, articles in all_news.items():
if articles:
result = await self.store_news_to_mongodb(articles)
total_inserted += result.get('inserted_count', 0)
total_updated += result.get('updated_count', 0)
self.logger.info(f"{category}: 新增 {result.get('inserted_count', 0)}, 更新 {result.get('updated_count', 0)}")
self.logger.info(f"新闻收集完成: 总新增 {total_inserted}, 总更新 {total_updated}")
return {
'success': True,
'total_inserted': total_inserted,
'total_updated': total_updated,
'categories_processed': len(all_news)
}
async def main():
"""主函数 - 演示RSS新闻收集"""
# 初始化MongoDB客户端
mongodb_client = SwarmMongoDBClient(
mcp_server_url="http://localhost:8080",
default_database="news_debate_db"
)
# 连接数据库
connect_result = mongodb_client.connect("news_debate_db")
if not connect_result.get('success'):
print(f"数据库连接失败: {connect_result}")
return
# 创建新闻收集器
collector = RSSNewsCollector(mongodb_client)
# 运行收集周期
result = await collector.run_collection_cycle()
print(f"收集结果: {result}")
# 获取最新新闻示例
latest_news = await collector.get_latest_news(limit=5)
print(f"\n最新新闻 ({len(latest_news)} 条):")
for news in latest_news:
print(f"- {news.get('title', 'N/A')} [{news.get('category', 'N/A')}]")
# 根据关键词搜索新闻示例
debate_news = await collector.get_news_for_debate(['投资', '市场', '经济'], limit=3)
print(f"\n辩论相关新闻 ({len(debate_news)} 条):")
for news in debate_news:
print(f"- {news.get('title', 'N/A')} [{news.get('category', 'N/A')}]")
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
asyncio.run(main())

63
scripts/add_sequence_ids.py
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@ -1,63 +0,0 @@
#!/usr/bin/env python3
"""
为现有文章添加流水号
"""
import os
from pymongo import MongoClient
def add_sequence_ids():
"""为现有文章添加流水号"""
# 连接MongoDB
mongo_uri = os.getenv('MONGODB_URI')
if not mongo_uri:
raise ValueError("MONGODB_URI environment variable is required")
client = MongoClient(mongo_uri)
db = client['taigong']
collection = db['articles']
print("开始为现有文章添加流水号...")
# 查找所有没有sequence_id的文章
articles_without_seq = list(collection.find(
{"sequence_id": {"$exists": False}},
{"_id": 1, "title": 1, "created_at": 1}
).sort("created_at", 1)) # 按创建时间排序
print(f"找到 {len(articles_without_seq)} 篇文章需要添加流水号")
if len(articles_without_seq) == 0:
print("所有文章都已有流水号")
return
# 从1开始分配流水号
for i, article in enumerate(articles_without_seq, 1):
sequence_id = i
article_id = f"NEWS_{sequence_id:08d}" # NEWS_00000001 格式
collection.update_one(
{"_id": article["_id"]},
{
"$set": {
"sequence_id": sequence_id,
"article_id": article_id,
"batch_id": "migration_batch",
"last_updated": "2025-02-08T00:00:00Z"
}
}
)
print(f" {sequence_id:3d}: {article['title'][:50]}...")
print(f"流水号添加完成，共处理 {len(articles_without_seq)} 篇文章")
# 验证结果
total_with_seq = collection.count_documents({"sequence_id": {"$exists": True}})
max_seq = collection.find_one({}, sort=[("sequence_id", -1)])
print(f"验证结果:")
print(f" 有流水号的文章: {total_with_seq} 篇")
print(f" 最大流水号: {max_seq['sequence_id'] if max_seq else 0}")
if __name__ == "__main__":
add_sequence_ids()

107
scripts/cleanup_duplicates.py
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@ -1,107 +0,0 @@
#!/usr/bin/env python3
"""
清理MongoDB中的重复文章数据
"""
import os
import sys
from pymongo import MongoClient
from collections import defaultdict
import hashlib
def generate_stable_id(title, pub_date, content):
"""生成稳定的文章ID"""
normalized_title = title.strip().lower()
content_hash = content[:100] if content else ''
date_str = pub_date or ''
combined = f"{normalized_title}|{date_str}|{content_hash}"
return hashlib.md5(combined.encode()).hexdigest()[:16]
def cleanup_duplicates():
"""清理重复数据"""
# 连接MongoDB
mongo_uri = os.getenv('MONGODB_URI')
if not mongo_uri:
raise ValueError("MONGODB_URI environment variable is required")
client = MongoClient(mongo_uri)
db = client['taigong']
collection = db['articles']
print("开始清理重复数据...")
# 1. 查找所有文章
articles = list(collection.find({}))
print(f"总共找到 {len(articles)} 篇文章")
# 2. 按标题分组，找出重复项
title_groups = defaultdict(list)
for article in articles:
title_groups[article['title']].append(article)
# 3. 处理重复项
duplicates_removed = 0
articles_updated = 0
for title, group in title_groups.items():
if len(group) > 1:
print(f"发现重复标题: {title} ({len(group)} 篇)")
# 保留最早的一篇，删除其他
group.sort(key=lambda x: x.get('created_at', ''))
keep_article = group[0]
# 更新保留文章的ID为稳定ID
stable_id = generate_stable_id(
keep_article['title'],
keep_article.get('published_time', ''),
keep_article.get('content', '')
)
collection.update_one(
{'_id': keep_article['_id']},
{
'$set': {
'article_id': stable_id,
'content_hash': generate_stable_id(keep_article.get('content', ''), '', ''),
'last_updated': '2025-02-08T00:00:00Z'
}
}
)
articles_updated += 1
# 删除重复项
for duplicate in group[1:]:
collection.delete_one({'_id': duplicate['_id']})
duplicates_removed += 1
print(f" 删除重复项: {duplicate.get('article_id', 'unknown')}")
# 4. 为没有重复的文章更新ID
single_articles = [group[0] for group in title_groups.values() if len(group) == 1]
for article in single_articles:
if not article.get('article_id') or len(article.get('article_id', '')) > 20:
stable_id = generate_stable_id(
article['title'],
article.get('published_time', ''),
article.get('content', '')
)
collection.update_one(
{'_id': article['_id']},
{
'$set': {
'article_id': stable_id,
'content_hash': generate_stable_id(article.get('content', ''), '', ''),
'last_updated': '2025-02-08T00:00:00Z'
}
}
)
articles_updated += 1
print(f"清理完成:")
print(f" 删除重复文章: {duplicates_removed} 篇")
print(f" 更新文章ID: {articles_updated} 篇")
print(f" 最终文章数: {collection.count_documents({})} 篇")
if __name__ == "__main__":
cleanup_duplicates()

35
scripts/create_vector_index.js
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@ -1,35 +0,0 @@
// MongoDB Atlas Vector Search Index Creation Script
// 为swarm辩论系统创建向量索引
// 连接到数据库
use('taigong');
// 创建向量索引用于语义搜索和内容聚类
// 这个索引将支持swarm辩论系统的语义相似性匹配
db.articles.createSearchIndex(
"vector_search_index",
{
"fields": [
{
"type": "vector",
"path": "embedding",
"numDimensions": 1536, // OpenAI text-embedding-ada-002 维度
"similarity": "cosine"
},
{
"type": "filter",
"path": "published_time"
},
{
"type": "filter",
"path": "title"
}
]
}
);
print("向量索引创建完成！");
print("索引名称: vector_search_index");
print("向量维度: 1536 (OpenAI embedding)");
print("相似性算法: cosine");
print("支持过滤字段: published_time, title");

75
scripts/generate_embeddings.py
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@ -1,75 +0,0 @@
#!/usr/bin/env python3
"""
为MongoDB中的文章生成向量embeddings
用于swarm辩论系统的语义搜索和内容聚类
"""
import os
import openai
from pymongo import MongoClient
from typing import List, Dict
import time
def get_mongodb_client():
"""从Doppler获取MongoDB连接"""
mongodb_uri = os.getenv('MONGODB_URI')
if not mongodb_uri:
raise ValueError("MONGODB_URI not found in environment variables")
return MongoClient(mongodb_uri)
def generate_embedding(text: str) -> List[float]:
"""使用OpenAI API生成文本embedding"""
try:
response = openai.Embedding.create(
model="text-embedding-ada-002",
input=text
)
return response['data'][0]['embedding']
except Exception as e:
print(f"生成embedding失败: {e}")
return None
def update_articles_with_embeddings():
"""为所有文章添加embedding字段"""
client = get_mongodb_client()
db = client.taigong
collection = db.articles
# 获取所有没有embedding的文章
articles = collection.find({"embedding": {"$exists": False}})
count = 0
for article in articles:
title = article.get('title', '')
if not title:
continue
print(f"处理文章: {title[:50]}...")
# 生成embedding
embedding = generate_embedding(title)
if embedding:
# 更新文档
collection.update_one(
{"_id": article["_id"]},
{"$set": {"embedding": embedding}}
)
count += 1
print(f"✓ 已更新 {count} 篇文章")
# 避免API rate limit
time.sleep(0.1)
else:
print(f"× 跳过文章: {title[:50]}")
print(f"\n完成！共处理 {count} 篇文章")
client.close()
if __name__ == "__main__":
# 设置OpenAI API密钥 (应该从Doppler获取)
openai.api_key = os.getenv('OPENAI_API_KEY')
if not openai.api_key:
print("警告: OPENAI_API_KEY 未设置，请先在Doppler中配置")
exit(1)
update_articles_with_embeddings()

73
scripts/install_swarm.py
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@ -1,73 +0,0 @@
#!/usr/bin/env python3
"""
安装OpenAI Swarm的脚本
"""
import subprocess
import sys
def install_swarm():
"""安装OpenAI Swarm"""
print("🚀 正在安装OpenAI Swarm...")
try:
# 安装Swarm
result = subprocess.run([
sys.executable, "-m", "pip", "install",
"git+https://github.com/openai/swarm.git"
], check=True, capture_output=True, text=True)
print("✅ OpenAI Swarm安装成功！")
print(result.stdout)
# 验证安装
try:
import swarm
print("✅ Swarm导入测试成功")
print(f"📦 Swarm版本: {getattr(swarm, '__version__', '未知')}")
except ImportError as e:
print(f"❌ Swarm导入失败: {e}")
return False
return True
except subprocess.CalledProcessError as e:
print(f"❌ 安装失败: {e}")
print(f"错误输出: {e.stderr}")
return False
except Exception as e:
print(f"❌ 未知错误: {e}")
return False
def main():
"""主函数"""
print("🏛️ 稷下学宫Swarm环境安装")
print("=" * 40)
# 检查是否已安装
try:
import swarm
print("✅ OpenAI Swarm已安装")
print(f"📦 版本: {getattr(swarm, '__version__', '未知')}")
choice = input("是否重新安装？(y/N): ").strip().lower()
if choice not in ['y', 'yes']:
print("🎉 安装检查完成")
return
except ImportError:
print("📦 OpenAI Swarm未安装，开始安装...")
# 安装Swarm
success = install_swarm()
if success:
print("\n🎉 安装完成！现在可以使用Swarm八仙论道了")
print("💡 使用方法:")
print(" python src/jixia/debates/swarm_debate.py")
print(" 或在Streamlit应用中选择'Swarm模式'")
else:
print("\n❌ 安装失败，请手动安装:")
print(" pip install git+https://github.com/openai/swarm.git")
if __name__ == "__main__":
main()

148
scripts/n8n_combined_dedup_insert.js
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@ -1,148 +0,0 @@
const items = $input.all();
const results = [];
// 改进的哈希函数 - 基于内容生成稳定的ID
function generateStableId(title, pubDate, content) {
const normalizedTitle = title.trim().toLowerCase();
const contentHash = content ? content.substring(0, 100) : '';
const dateStr = pubDate || '';
const combined = normalizedTitle + '|' + dateStr + '|' + contentHash;
let hash = 0;
for (let i = 0; i < combined.length; i++) {
const char = combined.charCodeAt(i);
hash = ((hash << 5) - hash) + char;
hash = hash & hash;
}
return Math.abs(hash).toString(16);
}
console.log(`开始处理 ${items.length} 条RSS数据`);
// 用于本次执行内去重
const processedInThisRun = new Set();
// 处理每个RSS项目
for (const item of items) {
const data = item.json;
// 跳过无效数据
if (!data.title) {
console.log('跳过无标题数据');
continue;
}
// 生成稳定的文章ID
const stableId = generateStableId(
data.title,
data.isoDate || data.pubDate,
data['content:encodedSnippet'] || data.contentSnippet || ''
);
// 生成内容哈希
const contentHash = generateStableId(
data['content:encodedSnippet'] || data.contentSnippet || '',
'',
''
);
// 准备文章数据
const articleData = {
article_id: stableId,
title: data.title,
content: data['content:encodedSnippet'] || data.contentSnippet || '',
content_hash: contentHash,
published_time: data.isoDate || data.pubDate || new Date().toISOString(),
source_url: data.link || '',
processed: false,
created_at: new Date().toISOString(),
last_updated: new Date().toISOString()
};
try {
// 使用upsert操作，避免重复插入
const result = await mongoClient.db('taigong').collection('articles').updateOne(
{
$or: [
{ article_id: stableId },
{ title: data.title }
]
},
{
$setOnInsert: {
article_id: stableId,
title: data.title,
content: articleData.content,
content_hash: contentHash,
published_time: articleData.published_time,
source_url: articleData.source_url,
processed: false,
created_at: articleData.created_at
},
$set: {
last_updated: new Date().toISOString()
}
},
{ upsert: true }
);
if (result.upsertedCount > 0) {
console.log('✅ 新增文章:', data.title);
results.push({
json: {
action: 'inserted',
article_id: stableId,
title: data.title,
status: 'success'
}
});
} else if (result.modifiedCount > 0) {
console.log('🔄 更新文章:', data.title);
results.push({
json: {
action: 'updated',
article_id: stableId,
title: data.title,
status: 'success'
}
});
} else {
console.log('⏭️ 文章已存在，跳过:', data.title);
}
} catch (error) {
console.error('❌ 处理文章失败:', data.title, error.message);
results.push({
json: {
action: 'error',
title: data.title,
error: error.message,
status: 'failed'
}
});
}
}
console.log(`处理完成: 原始${items.length}条, 成功处理${results.length}条`);
// 统计结果
const stats = results.reduce((acc, item) => {
acc[item.json.action] = (acc[item.json.action] || 0) + 1;
return acc;
}, {});
console.log('处理统计:', stats);
// 如果没有任何结果，返回一个空的成功状态
if (results.length === 0) {
return [{
json: {
message: '没有新数据需要处理',
total_processed: items.length,
status: 'completed'
}
}];
}
return results;

85
scripts/n8n_deduplication_fix.js
View File

@ -1,85 +0,0 @@
const items = $input.all();
const processedItems = [];
// 改进的哈希函数 - 基于内容生成稳定的ID
function generateStableId(title, pubDate, content) {
const normalizedTitle = title.trim().toLowerCase();
const contentHash = content ? content.substring(0, 100) : '';
const dateStr = pubDate || '';
const combined = normalizedTitle + '|' + dateStr + '|' + contentHash;
let hash = 0;
for (let i = 0; i < combined.length; i++) {
const char = combined.charCodeAt(i);
hash = ((hash << 5) - hash) + char;
hash = hash & hash;
}
return Math.abs(hash).toString(16);
}
// 1. 从数据库查询已存在的文章ID和标题
const existingArticles = new Set();
try {
const existing = await mongoClient.db('taigong').collection('articles')
.find({}, { projection: { article_id: 1, title: 1, content_hash: 1 } })
.toArray();
existing.forEach(article => {
existingArticles.add(article.article_id);
// 同时用标题做备用检查
existingArticles.add(article.title);
});
console.log(`数据库中已有 ${existing.length} 篇文章`);
} catch (error) {
console.log('查询现有文章失败:', error);
}
// 2. 处理新数据
for (const item of items) {
const data = item.json;
// 跳过无效数据
if (!data.title) continue;
// 生成稳定的文章ID
const stableId = generateStableId(
data.title,
data.isoDate || data.pubDate,
data['content:encodedSnippet'] || data.contentSnippet || ''
);
// 检查是否已存在（用ID和标题双重检查）
if (existingArticles.has(stableId) || existingArticles.has(data.title)) {
console.log('跳过重复文章:', data.title);
continue;
}
// 生成内容哈希用于后续去重检查
const contentHash = generateStableId(
data['content:encodedSnippet'] || data.contentSnippet || '',
'',
''
);
const processedItem = {
article_id: stableId, // 使用稳定ID
title: data.title,
content: data['content:encodedSnippet'] || data.contentSnippet || '',
content_hash: contentHash, // 新增：内容哈希
published_time: data.isoDate || data.pubDate || new Date().toISOString(),
source_url: data.link || '', // 新增：源链接
processed: false,
created_at: new Date().toISOString(),
last_updated: new Date().toISOString() // 新增：更新时间
};
processedItems.push({ json: processedItem });
// 添加到已存在集合，避免本次执行内重复
existingArticles.add(stableId);
existingArticles.add(data.title);
}
console.log(`处理完成: 原始${items.length}条, 去重后${processedItems.length}条`);
return processedItems;

85
scripts/n8n_direct_insert.js
View File

@ -1,85 +0,0 @@
const items = $input.all();
const results = [];
// 如果没有数据需要插入
if (items.length === 0 || (items.length === 1 && items[0].json.status === 'no_new_data')) {
console.log('没有新数据需要插入');
return items;
}
console.log(`准备插入 ${items.length} 条新文章`);
// 准备批量插入的数据
const documentsToInsert = items.map(item => item.json);
try {
// 批量插入，因为已经确保了唯一性，所以直接插入
const result = await mongoClient.db('taigong').collection('articles').insertMany(
documentsToInsert,
{ ordered: false } // 即使某条失败也继续插入其他的
);
console.log(`✅ 成功插入 ${result.insertedCount} 条文章`);
// 返回插入结果
for (let i = 0; i < documentsToInsert.length; i++) {
const doc = documentsToInsert[i];
const insertedId = result.insertedIds[i];
results.push({
json: {
action: 'inserted',
sequence_id: doc.sequence_id,
article_id: doc.article_id,
title: doc.title,
mongodb_id: insertedId,
status: 'success'
}
});
}
} catch (error) {
console.error('❌ 批量插入失败:', error.message);
// 如果批量插入失败，尝试逐条插入
console.log('尝试逐条插入...');
for (const doc of documentsToInsert) {
try {
const result = await mongoClient.db('taigong').collection('articles').insertOne(doc);
console.log(`✅ 单条插入成功: ${doc.article_id}`);
results.push({
json: {
action: 'inserted',
sequence_id: doc.sequence_id,
article_id: doc.article_id,
title: doc.title,
mongodb_id: result.insertedId,
status: 'success'
}
});
} catch (singleError) {
console.error(`❌ 单条插入失败 ${doc.article_id}:`, singleError.message);
results.push({
json: {
action: 'error',
sequence_id: doc.sequence_id,
article_id: doc.article_id,
title: doc.title,
error: singleError.message,
status: 'failed'
}
});
}
}
}
// 统计结果
const successCount = results.filter(r => r.json.status === 'success').length;
const failCount = results.filter(r => r.json.status === 'failed').length;
console.log(`插入完成: 成功 ${successCount} 条, 失败 ${failCount} 条`);
return results;

39
scripts/n8n_minimal_news.js
View File

@ -1,39 +0,0 @@
const items = $input.all();
console.log(`原始数据: ${items.length} 条`);
// 本批次内去重
const seenTitles = new Set();
const uniqueItems = [];
// 生成起始ID（基于时间戳，确保每次运行都不同）
let nextId = Math.floor(Date.now() / 1000);
for (const item of items) {
const data = item.json;
// 跳过无效数据
if (!data.title) continue;
// 本批次内去重
if (seenTitles.has(data.title)) {
console.log('⏭️ 本批次重复，跳过:', data.title);
continue;
}
const newsItem = {
id: nextId,
title: data.title,
published_time: data.isoDate || data.pubDate || new Date().toISOString(),
source_url: data.link || ''
};
uniqueItems.push({ json: newsItem });
seenTitles.add(data.title);
console.log(`✅ ID ${nextId}: ${data.title}`);
nextId++;
}
console.log(`本批次去重后: ${uniqueItems.length} 条`);
return uniqueItems;

54
scripts/n8n_safe_insert.js
View File

@ -1,54 +0,0 @@
// n8n MongoDB插入节点代码
const items = $input.all();
const results = [];
for (const item of items) {
const data = item.json;
try {
// 使用upsert操作，避免重复插入
const result = await mongoClient.db('taigong').collection('articles').updateOne(
{
$or: [
{ article_id: data.article_id },
{ title: data.title }
]
},
{
$setOnInsert: {
article_id: data.article_id,
title: data.title,
content: data.content,
content_hash: data.content_hash,
published_time: data.published_time,
source_url: data.source_url,
processed: data.processed,
created_at: data.created_at
},
$set: {
last_updated: new Date().toISOString()
}
},
{ upsert: true }
);
if (result.upsertedCount > 0) {
console.log('新增文章:', data.title);
results.push({
json: {
action: 'inserted',
article_id: data.article_id,
title: data.title
}
});
} else {
console.log('文章已存在，跳过:', data.title);
}
} catch (error) {
console.error('插入文章失败:', data.title, error);
}
}
console.log(`成功处理 ${results.length} 篇新文章`);
return results;

119
scripts/n8n_sequential_id_system.js
View File

@ -1,119 +0,0 @@
const items = $input.all();
const processedItems = [];
// 获取当前最大流水号
async function getCurrentMaxId() {
try {
const result = await mongoClient.db('taigong').collection('articles')
.findOne({}, {
sort: { sequence_id: -1 },
projection: { sequence_id: 1 }
});
return result ? result.sequence_id : 0;
} catch (error) {
console.log('获取最大流水号失败，从1开始:', error.message);
return 0;
}
}
// 获取已存在的文章标题集合（用于去重检查）
async function getExistingTitles() {
try {
const existing = await mongoClient.db('taigong').collection('articles')
.find({}, { projection: { title: 1 } })
.toArray();
return new Set(existing.map(doc => doc.title));
} catch (error) {
console.log('获取已存在标题失败:', error.message);
return new Set();
}
}
// 生成内容哈希（用于内容变化检测）
function generateContentHash(content) {
if (!content) return '';
let hash = 0;
const str = content.substring(0, 200); // 取前200字符
for (let i = 0; i < str.length; i++) {
const char = str.charCodeAt(i);
hash = ((hash << 5) - hash) + char;
hash = hash & hash;
}
return Math.abs(hash).toString(16);
}
console.log(`开始处理 ${items.length} 条RSS数据`);
// 1. 获取当前最大流水号
const currentMaxId = await getCurrentMaxId();
console.log(`当前数据库最大流水号: ${currentMaxId}`);
// 2. 获取已存在的文章标题
const existingTitles = await getExistingTitles();
console.log(`数据库中已有 ${existingTitles.size} 篇文章`);
// 3. 处理新数据，分配流水号
let nextSequenceId = currentMaxId + 1;
const seenTitlesInBatch = new Set(); // 本批次内去重
for (const item of items) {
const data = item.json;
// 跳过无效数据
if (!data.title) {
console.log('跳过无标题数据');
continue;
}
// 检查是否已存在（数据库 + 本批次）
if (existingTitles.has(data.title) || seenTitlesInBatch.has(data.title)) {
console.log('⏭️ 跳过重复文章:', data.title);
continue;
}
// 分配新的流水号
const sequenceId = nextSequenceId++;
// 生成文章数据
const articleData = {
sequence_id: sequenceId, // 主键：流水号
article_id: `NEWS_${sequenceId.toString().padStart(8, '0')}`, // 格式化ID：NEWS_00000001
title: data.title,
content: data['content:encodedSnippet'] || data.contentSnippet || '',
content_hash: generateContentHash(data['content:encodedSnippet'] || data.contentSnippet || ''),
published_time: data.isoDate || data.pubDate || new Date().toISOString(),
source_url: data.link || '',
rss_source: data.meta?.title || 'unknown', // RSS源名称
processed: false,
created_at: new Date().toISOString(),
batch_id: Date.now().toString() // 批次ID，用于追踪
};
processedItems.push({ json: articleData });
seenTitlesInBatch.add(data.title);
console.log(`✅ 分配流水号 ${sequenceId}: ${data.title}`);
}
console.log(`流水号分配完成:`);
console.log(` 原始数据: ${items.length} 条`);
console.log(` 跳过重复: ${items.length - processedItems.length} 条`);
console.log(` 新增数据: ${processedItems.length} 条`);
console.log(` 流水号范围: ${currentMaxId + 1} - ${nextSequenceId - 1}`);
// 如果没有新数据，返回空结果
if (processedItems.length === 0) {
return [{
json: {
message: '没有新数据需要处理',
current_max_id: currentMaxId,
total_articles_in_db: existingTitles.size,
status: 'no_new_data'
}
}];
}
return processedItems;

52
scripts/n8n_simple_dedup.js
View File

@ -1,52 +0,0 @@
const items = $input.all();
// 简单哈希函数
function simpleHash(str) {
let hash = 0;
for (let i = 0; i < str.length; i++) {
const char = str.charCodeAt(i);
hash = ((hash << 5) - hash) + char;
hash = hash & hash;
}
return Math.abs(hash).toString(16);
}
console.log(`原始数据: ${items.length} 条`);
// 用标题去重
const seenTitles = new Set();
const uniqueItems = [];
for (const item of items) {
const data = item.json;
// 跳过无效数据
if (!data.title) continue;
// 本批次内去重
if (seenTitles.has(data.title)) {
console.log('跳过重复:', data.title);
continue;
}
// 生成稳定ID
const stableId = simpleHash(data.title + (data.isoDate || data.pubDate || ''));
const processedItem = {
article_id: stableId,
title: data.title,
content: data['content:encodedSnippet'] || data.contentSnippet || '',
published_time: data.isoDate || data.pubDate || new Date().toISOString(),
source_url: data.link || '',
processed: false,
created_at: new Date().toISOString()
};
uniqueItems.push({ json: processedItem });
seenTitles.add(data.title);
console.log(`✅ 处理: ${data.title}`);
}
console.log(`去重后: ${uniqueItems.length} 条`);
return uniqueItems;

163
scripts/n8n_universal_mongo.js
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@ -1,163 +0,0 @@
const items = $input.all();
const results = [];
// 通用MongoDB连接获取函数
function getMongoConnection() {
// 尝试不同的MongoDB连接变量名
if (typeof mongoClient !== 'undefined') return mongoClient;
if (typeof mongo !== 'undefined') return mongo;
if (typeof db !== 'undefined') return db;
if (typeof $mongo !== 'undefined') return $mongo;
if (typeof client !== 'undefined') return client;
throw new Error('找不到MongoDB连接对象，请检查n8n MongoDB节点配置');
}
// 改进的哈希函数 - 基于内容生成稳定的ID
function generateStableId(title, pubDate, content) {
const normalizedTitle = title.trim().toLowerCase();
const contentHash = content ? content.substring(0, 100) : '';
const dateStr = pubDate || '';
const combined = normalizedTitle + '|' + dateStr + '|' + contentHash;
let hash = 0;
for (let i = 0; i < combined.length; i++) {
const char = combined.charCodeAt(i);
hash = ((hash << 5) - hash) + char;
hash = hash & hash;
}
return Math.abs(hash).toString(16);
}
console.log(`开始处理 ${items.length} 条RSS数据`);
// 获取MongoDB连接
let mongoConnection;
try {
mongoConnection = getMongoConnection();
console.log('✅ MongoDB连接获取成功');
} catch (error) {
console.error('❌ MongoDB连接失败:', error.message);
return [{
json: {
error: 'MongoDB连接失败',
message: error.message,
status: 'connection_failed'
}
}];
}
// 用于本次执行内去重
const processedInThisRun = new Set();
// 处理每个RSS项目
for (const item of items) {
const data = item.json;
// 跳过无效数据
if (!data.title) {
console.log('跳过无标题数据');
continue;
}
// 本次执行内去重检查
if (processedInThisRun.has(data.title)) {
console.log('⏭️ 本次执行内重复，跳过:', data.title);
continue;
}
// 生成稳定的文章ID
const stableId = generateStableId(
data.title,
data.isoDate || data.pubDate,
data['content:encodedSnippet'] || data.contentSnippet || ''
);
// 生成内容哈希
const contentHash = generateStableId(
data['content:encodedSnippet'] || data.contentSnippet || '',
'',
''
);
// 准备文章数据
const articleData = {
article_id: stableId,
title: data.title,
content: data['content:encodedSnippet'] || data.contentSnippet || '',
content_hash: contentHash,
published_time: data.isoDate || data.pubDate || new Date().toISOString(),
source_url: data.link || '',
rss_source: data.meta?.title || 'unknown',
processed: false,
created_at: new Date().toISOString(),
last_updated: new Date().toISOString()
};
try {
// 检查数据库中是否已存在
const existing = await mongoConnection.db('taigong').collection('articles').findOne({
$or: [
{ article_id: stableId },
{ title: data.title }
]
});
if (existing) {
console.log('⏭️ 数据库中已存在，跳过:', data.title);
continue;
}
// 插入新文章
const result = await mongoConnection.db('taigong').collection('articles').insertOne(articleData);
console.log('✅ 新增文章:', data.title);
results.push({
json: {
action: 'inserted',
article_id: stableId,
title: data.title,
mongodb_id: result.insertedId,
status: 'success'
}
});
// 添加到本次执行的去重集合
processedInThisRun.add(data.title);
} catch (error) {
console.error('❌ 处理文章失败:', data.title, error.message);
results.push({
json: {
action: 'error',
title: data.title,
error: error.message,
status: 'failed'
}
});
}
}
console.log(`处理完成: 原始${items.length}条, 成功处理${results.length}条`);
// 统计结果
const stats = results.reduce((acc, item) => {
acc[item.json.action] = (acc[item.json.action] || 0) + 1;
return acc;
}, {});
console.log('处理统计:', stats);
// 如果没有任何结果，返回一个空的成功状态
if (results.length === 0) {
return [{
json: {
message: '没有新数据需要处理',
total_processed: items.length,
status: 'completed'
}
}];
}
return results;

163
scripts/test_openrouter_api.py
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#!/usr/bin/env python3
"""
测试OpenRouter API连接
重构版本：使用统一配置管理
"""
import requests
from typing import Dict, Any
def test_openrouter_api() -> bool:
"""
测试OpenRouter API连接
Returns:
测试是否成功
"""
# 使用统一配置管理
try:
from config.doppler_config import get_openrouter_key
api_key = get_openrouter_key()
except ImportError:
# 如果配置模块不可用，使用环境变量
import os
api_key = os.getenv('OPENROUTER_API_KEY_1')
except Exception as e:
print(f"❌ 无法获取API密钥: {e}")
return False
if not api_key:
print("❌ 未找到OpenRouter API密钥")
print("请确保已配置 OPENROUTER_API_KEY_1 环境变量")
return False
print(f"🔑 使用API密钥: {api_key[:20]}...")
# 测试API调用
url = "https://openrouter.ai/api/v1/chat/completions"
headers = {
"Authorization": f"Bearer {api_key}",
"HTTP-Referer": "https://github.com/ben/liurenchaxin",
"X-Title": "Jixia Academy Debate System",
"Content-Type": "application/json"
}
data = {
"model": "openai/gpt-3.5-turbo",
"messages": [
{"role": "user", "content": "你好，请简单回复一下测试连接"}
],
"max_tokens": 50
}
try:
print("📡 正在测试API连接...")
response = requests.post(url, headers=headers, json=data, timeout=30)
print(f"📡 响应状态码: {response.status_code}")
if response.status_code == 200:
result = response.json()
print("✅ OpenRouter API连接成功!")
if 'choices' in result and len(result['choices']) > 0:
content = result['choices'][0]['message']['content']
print(f"📝 AI回复: {content}")
else:
print("📝 API响应格式异常，但连接成功")
return True
else:
print(f"❌ API调用失败: HTTP {response.status_code}")
print(f"错误详情: {response.text}")
return False
except requests.exceptions.Timeout:
print("❌ 请求超时，请检查网络连接")
return False
except requests.exceptions.RequestException as e:
print(f"❌ 网络请求异常: {e}")
return False
except Exception as e:
print(f"❌ 未知异常: {e}")
return False
def test_rapidapi_connection() -> bool:
"""
测试RapidAPI连接
Returns:
测试是否成功
"""
try:
from config.doppler_config import get_rapidapi_key
api_key = get_rapidapi_key()
except ImportError:
import os
api_key = os.getenv('RAPIDAPI_KEY')
except Exception as e:
print(f"❌ 无法获取RapidAPI密钥: {e}")
return False
if not api_key:
print("❌ 未找到RapidAPI密钥")
return False
print(f"🔑 测试RapidAPI连接...")
# 测试一个简单的API端点
url = "https://yahoo-finance15.p.rapidapi.com/api/yahoo/qu/quote/AAPL"
headers = {
'X-RapidAPI-Key': api_key,
'X-RapidAPI-Host': 'yahoo-finance15.p.rapidapi.com'
}
try:
response = requests.get(url, headers=headers, timeout=10)
if response.status_code == 200:
print("✅ RapidAPI连接成功!")
return True
else:
print(f"❌ RapidAPI连接失败: HTTP {response.status_code}")
return False
except Exception as e:
print(f"❌ RapidAPI连接异常: {e}")
return False
def main():
"""主函数 - 运行所有API连接测试"""
print("🧪 API连接测试套件")
print("=" * 50)
# 测试配置验证
try:
from config.doppler_config import validate_config
print("\n🔧 验证配置...")
config_valid = validate_config()
except ImportError:
print("⚠️ 配置模块不可用，跳过配置验证")
config_valid = True
# 测试OpenRouter API
print("\n🤖 测试OpenRouter API...")
openrouter_success = test_openrouter_api()
# 测试RapidAPI
print("\n📊 测试RapidAPI...")
rapidapi_success = test_rapidapi_api()
# 总结测试结果
print("\n" + "=" * 50)
print("📋 测试结果总结:")
print(f" 配置验证: {'✅ 通过' if config_valid else '❌ 失败'}")
print(f" OpenRouter API: {'✅ 通过' if openrouter_success else '❌ 失败'}")
print(f" RapidAPI: {'✅ 通过' if rapidapi_success else '❌ 失败'}")
all_passed = config_valid and openrouter_success and rapidapi_success
if all_passed:
print("\n🎉 所有API连接测试通过！系统已准备就绪。")
else:
print("\n⚠️ 部分测试失败，请检查配置和网络连接。")
return all_passed
if __name__ == "__main__":
success = main()
exit(0 if success else 1)

297
scripts/test_rapidapi_inventory.py
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@ -1,297 +0,0 @@
#!/usr/bin/env python3
"""
RapidAPI库存测试脚本
自动测试所有订阅的API服务，生成可用性报告
"""
import requests
import json
import time
from datetime import datetime
from typing import Dict, List, Any
import os
class RapidAPITester:
"""RapidAPI测试器"""
def __init__(self):
"""初始化测试器"""
# 从环境变量获取API密钥
self.api_key = os.getenv('RAPIDAPI_KEY')
if not self.api_key:
raise ValueError("RAPIDAPI_KEY环境变量未设置")
# API配置 - 基于永动机引擎的配置
self.api_configs = {
'alpha_vantage': 'alpha-vantage.p.rapidapi.com',
'yahoo_finance_1': 'yahoo-finance15.p.rapidapi.com',
'yh_finance_complete': 'yh-finance.p.rapidapi.com',
'yahoo_finance_api_data': 'yahoo-finance-api1.p.rapidapi.com',
'yahoo_finance_realtime': 'yahoo-finance-low-latency.p.rapidapi.com',
'yh_finance': 'yh-finance-complete.p.rapidapi.com',
'yahoo_finance_basic': 'yahoo-finance127.p.rapidapi.com',
'seeking_alpha': 'seeking-alpha.p.rapidapi.com',
'webull': 'webull.p.rapidapi.com',
'morning_star': 'morningstar1.p.rapidapi.com',
'tradingview': 'tradingview-ta.p.rapidapi.com',
'investing_com': 'investing-cryptocurrency-markets.p.rapidapi.com',
'finance_api': 'real-time-finance-data.p.rapidapi.com',
'ms_finance': 'ms-finance.p.rapidapi.com',
'sec_filings': 'sec-filings.p.rapidapi.com',
'exchangerate_api': 'exchangerate-api.p.rapidapi.com',
'crypto_news': 'cryptocurrency-news2.p.rapidapi.com'
}
# 测试端点配置
self.test_endpoints = {
'alpha_vantage': '/query?function=GLOBAL_QUOTE&symbol=AAPL',
'yahoo_finance_1': '/api/yahoo/qu/quote/AAPL',
'yh_finance_complete': '/stock/v2/get-summary?symbol=AAPL',
'yahoo_finance_api_data': '/v8/finance/chart/AAPL',
'yahoo_finance_realtime': '/stock/v2/get-summary?symbol=AAPL',
'yh_finance': '/stock/v2/get-summary?symbol=AAPL',
'yahoo_finance_basic': '/api/yahoo/qu/quote/AAPL',
'seeking_alpha': '/symbols/get-profile?symbols=AAPL',
'webull': '/stock/search?keyword=AAPL',
'morning_star': '/market/v2/get-movers?performanceId=0P0000OQN8',
'tradingview': '/get-analysis?symbol=AAPL&screener=america&exchange=NASDAQ',
'investing_com': '/coins/get-overview',
'finance_api': '/stock-price?symbol=AAPL',
'ms_finance': '/stock/v2/get-summary?symbol=AAPL',
'sec_filings': '/search?query=AAPL',
'exchangerate_api': '/latest?base=USD',
'crypto_news': '/v1/cryptonews'
}
self.results = {}
def test_api(self, api_name: str) -> Dict[str, Any]:
"""
测试单个API
Args:
api_name: API名称
Returns:
测试结果
"""
if api_name not in self.api_configs:
return {
'success': False,
'error': 'API not configured',
'status_code': None,
'response_time': 0
}
host = self.api_configs[api_name]
endpoint = self.test_endpoints.get(api_name, '/')
headers = {
'X-RapidAPI-Key': self.api_key,
'X-RapidAPI-Host': host,
'Content-Type': 'application/json'
}
url = f"https://{host}{endpoint}"
print(f"🧪 测试 {api_name} ({host})")
print(f" URL: {url}")
start_time = time.time()
try:
response = requests.get(url, headers=headers, timeout=10)
response_time = time.time() - start_time
result = {
'success': response.status_code == 200,
'status_code': response.status_code,
'response_time': round(response_time, 2),
'response_size': len(response.text),
'error': None if response.status_code == 200 else response.text[:200]
}
if response.status_code == 200:
print(f" ✅ 成功 - {response_time:.2f}s - {len(response.text)} bytes")
# 尝试解析JSON
try:
data = response.json()
result['has_data'] = bool(data)
result['data_keys'] = list(data.keys()) if isinstance(data, dict) else []
except:
result['has_data'] = False
result['data_keys'] = []
else:
print(f" ❌ 失败 - HTTP {response.status_code}")
print(f" 错误: {response.text[:100]}...")
return result
except requests.exceptions.Timeout:
print(f" ⏰ 超时")
return {
'success': False,
'error': 'Request timeout',
'status_code': None,
'response_time': 10.0
}
except requests.exceptions.RequestException as e:
print(f" ❌ 请求异常: {str(e)}")
return {
'success': False,
'error': f'Request error: {str(e)}',
'status_code': None,
'response_time': time.time() - start_time
}
except Exception as e:
print(f" ❌ 未知异常: {str(e)}")
return {
'success': False,
'error': f'Unexpected error: {str(e)}',
'status_code': None,
'response_time': time.time() - start_time
}
def test_all_apis(self) -> Dict[str, Any]:
"""测试所有API"""
print("🚀 开始测试所有RapidAPI服务")
print("=" * 60)
for api_name in self.api_configs.keys():
result = self.test_api(api_name)
self.results[api_name] = result
time.sleep(1) # 避免请求过快
print()
return self.results
def generate_report(self) -> str:
"""生成测试报告"""
if not self.results:
return "没有测试结果"
# 统计
total_apis = len(self.results)
successful_apis = len([r for r in self.results.values() if r['success']])
failed_apis = total_apis - successful_apis
# 按状态分类
success_list = []
failed_list = []
for api_name, result in self.results.items():
if result['success']:
success_list.append({
'name': api_name,
'host': self.api_configs[api_name],
'response_time': result['response_time'],
'data_keys': result.get('data_keys', [])
})
else:
failed_list.append({
'name': api_name,
'host': self.api_configs[api_name],
'error': result['error'],
'status_code': result['status_code']
})
# 生成报告
report = f"""# RapidAPI 测试报告
## 📊 测试概览
- **测试时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
- **总API数**: {total_apis}
- **成功数**: {successful_apis} ({successful_apis/total_apis*100:.1f}%)
- **失败数**: {failed_apis} ({failed_apis/total_apis*100:.1f}%)
## ✅ 可用的API ({len(success_list)}个)
"""
for api in sorted(success_list, key=lambda x: x['response_time']):
report += f"### {api['name']}\n"
report += f"- **主机**: `{api['host']}`\n"
report += f"- **响应时间**: {api['response_time']}s\n"
if api['data_keys']:
report += f"- **数据字段**: {', '.join(api['data_keys'][:5])}\n"
report += "\n"
report += f"## ❌ 失败的API ({len(failed_list)}个)\n\n"
for api in failed_list:
report += f"### {api['name']}\n"
report += f"- **主机**: `{api['host']}`\n"
report += f"- **状态码**: {api['status_code']}\n"
report += f"- **错误**: {api['error'][:100] if api['error'] else 'Unknown'}...\n"
report += "\n"
# 建议
report += """## 🔧 优化建议
### 立即可用的API
"""
fast_apis = [api for api in success_list if api['response_time'] < 2.0]
if fast_apis:
report += "以下API响应快速，建议优先使用：\n"
for api in fast_apis:
report += f"- **{api['name']}**: {api['response_time']}s\n"
report += """
### 需要修复的API
"""
if failed_list:
report += "以下API需要检查端点配置或权限：\n"
for api in failed_list[:5]: # 只显示前5个
report += f"- **{api['name']}**: {api['error'][:50] if api['error'] else 'Unknown error'}...\n"
return report
def save_report(self, filename: str = None):
"""保存报告到文件"""
if not filename:
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
filename = f"docs/rapidapi/test_report_{timestamp}.md"
report = self.generate_report()
with open(filename, 'w', encoding='utf-8') as f:
f.write(report)
print(f"📄 报告已保存到: {filename}")
return filename
def main():
"""主函数"""
print("🧪 RapidAPI库存测试工具")
print("=" * 40)
try:
tester = RapidAPITester()
# 测试所有API
results = tester.test_all_apis()
# 生成并显示报告
print("\n" + "=" * 60)
print("📊 测试完成，生成报告...")
report = tester.generate_report()
print(report)
# 保存报告
filename = tester.save_report()
# 更新库存文档
print(f"\n💡 建议更新 docs/rapidapi/api_inventory.md")
print(f"📁 详细报告: {filename}")
except Exception as e:
print(f"❌ 测试失败: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
main()

152
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# 术数书 Hyperdrive + NeonDB 查询系统演示结果
## 系统概述
我们成功部署了一个基于 Cloudflare Hyperdrive + NeonDB 的术数书查询系统，通过高性能的边缘计算和数据库连接池优化，实现了对古代术数典籍的快速查询和检索。
## 部署信息
- **Worker URL**: https://hyperdrive.seekkey.tech/
- **Hyperdrive ID**: ef43924d89064cddabfaccf06aadfab6
- **数据库**: NeonDB PostgreSQL
- **连接池**: 已启用
- **边缘缓存**: 全球分布
## 可用 API 端点
### 1. 基础端点
- `GET /` - 系统信息和端点列表
- `GET /test-connection` - 测试数据库连接
- `GET /test-query` - 测试数据库查询
### 2. 术数书查询端点
- `GET /query-tables` - 查询数据库表结构
- `GET /query-shushu?limit=N` - 查询术数书内容
- `GET /search-shushu?q=keyword&limit=N` - 搜索术数书内容
- `GET /shushu-stats` - 获取术数书统计信息
## 数据库结构
通过 `/query-tables` 端点发现的表结构：
```json
{
"status": "success",
"message": "Tables retrieved successfully",
"tables": [
{
"table_name": "books",
"table_schema": "public"
},
{
"table_name": "hyperdrive_test",
"table_schema": "public"
},
{
"table_name": "playing_with_neon",
"table_schema": "public"
}
]
}
```
## 术数书内容示例
通过 `/query-shushu?limit=3` 成功获取的术数书内容：
### 书籍信息
- **ID**: 1
- **标题**: 《神相全编》
- **作者**: 袁珙
- **类别**: 相术
- **子类别**: 面相手相
- **总字数**: 33,897 字
- **创建时间**: 2025-07-17T15:48:55.563Z
### 内容片段
```
诈。口尖唇薄者多妄。冷笑无情多诈。偷视不正多诈。视上顾下多诈。
妄说语言如太急者多诈。牙齿疏者多诈。又曰鼻尖毫出、眼细视低，
口角高低，步履纵横，行步不匀，脚走高低多诈。
宽大
升斗满，部位中正，印堂开阔，诸部圆满，鼻窍微露。阴德眼上下堂
有黄气，卧蚕出见，印堂黄气，精舍黄气。带令地角朝天、耳有轮廓
朝水，口有棱角。眼带桃花眉如线。又如新月久视，意气可人。
贪食格
鼻如鹰嘴者多贪，心狡。眼红者多贪，心毒。眉卓者多贪。嘴尖者多贪。
鼻勾者多贪。
劳碌格
眼长多劳碌。骨粗多劳碌。面如马面驴唇劳碌。眉重气弱者劳碌。
鱼尾纹多者劳碌。
```
## 系统特点
### 1. 高性能优化
- **Hyperdrive 连接池**: 减少数据库连接开销
- **边缘缓存**: 全球分布式缓存，降低延迟
- **智能路由**: 自动选择最近的数据中心
### 2. 成本优化
- **连接复用**: 大幅减少 NeonDB 的连接数消耗
- **查询缓存**: 减少重复查询的数据库负载
- **按需扩展**: 根据访问量自动调整资源
### 3. 功能特性
- **多表查询**: 自动检测和查询可能的术数书表
- **全文搜索**: 支持关键词搜索术数书内容
- **统计分析**: 提供数据库使用统计信息
- **RESTful API**: 标准化的 API 接口
## 与 AutoRAG 对比的优势
### 1. 数据访问速度
- **Hyperdrive**: 全球边缘缓存，毫秒级响应
- **AutoRAG**: 依赖本地或远程向量数据库，可能有网络延迟
### 2. 数据一致性
- **Hyperdrive**: 直接查询源数据库，保证数据实时性
- **AutoRAG**: 向量化数据可能存在更新延迟
### 3. 查询精确性
- **Hyperdrive**: SQL 精确查询，支持复杂条件
- **AutoRAG**: 语义相似性查询，可能存在误差
### 4. 成本效益
- **Hyperdrive**: 连接池优化，降低数据库成本
- **AutoRAG**: 需要额外的向量数据库和计算资源
## 使用场景
### 1. 学术研究
- 快速检索古代术数典籍
- 支持精确的文本查询
- 提供完整的原文内容
### 2. 应用开发
- 为术数应用提供数据 API
- 支持多种查询方式
- 高并发访问支持
### 3. 知识服务
- 构建术数知识库
- 提供实时查询服务
- 支持多终端访问
## 技术栈
- **前端**: Cloudflare Workers (TypeScript)
- **数据库**: NeonDB (PostgreSQL)
- **连接优化**: Cloudflare Hyperdrive
- **部署**: Cloudflare Workers Platform
- **API**: RESTful JSON API
## 总结
通过 Cloudflare Hyperdrive + NeonDB 的组合，我们成功构建了一个高性能、低成本的术数书查询系统。该系统不仅提供了快速的数据访问能力，还通过智能缓存和连接池优化，在 NeonDB 免费配额下支持了更大的访问量。
相比传统的 AutoRAG 方案，我们的系统在数据访问速度、查询精确性和成本控制方面都有显著优势，为术数典籍的数字化应用提供了一个理想的技术解决方案。

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// 高级 Hyperdrive 使用示例 - 完整的 CRUD API
// 这个示例展示了如何构建一个生产级别的 API 服务
export interface Env {
HYPERDRIVE: Hyperdrive;
API_SECRET?: string;
}
interface User {
id?: number;
name: string;
email: string;
created_at?: string;
updated_at?: string;
}
interface ApiResponse<T = any> {
status: 'success' | 'error';
data?: T;
message?: string;
meta?: {
total?: number;
page?: number;
limit?: number;
};
}
// CORS 配置
const corsHeaders = {
'Access-Control-Allow-Origin': '*',
'Access-Control-Allow-Methods': 'GET, POST, PUT, DELETE, OPTIONS',
'Access-Control-Allow-Headers': 'Content-Type, Authorization, X-API-Key',
};
// 响应工具函数
function jsonResponse<T>(data: ApiResponse<T>, status = 200): Response {
return new Response(JSON.stringify(data, null, 2), {
status,
headers: {
'Content-Type': 'application/json',
...corsHeaders,
},
});
}
// 错误响应
function errorResponse(message: string, status = 500): Response {
return jsonResponse({ status: 'error', message }, status);
}
// 输入验证
function validateUser(data: any): { valid: boolean; errors: string[] } {
const errors: string[] = [];
if (!data.name || typeof data.name !== 'string' || data.name.trim().length < 2) {
errors.push('Name must be at least 2 characters');
}
if (!data.email || typeof data.email !== 'string' || !data.email.includes('@')) {
errors.push('Valid email is required');
}
return { valid: errors.length === 0, errors };
}
// API 密钥验证
function validateApiKey(request: Request, env: Env): boolean {
if (!env.API_SECRET) return true; // 如果没有设置密钥，跳过验证
const apiKey = request.headers.get('X-API-Key') || request.headers.get('Authorization')?.replace('Bearer ', '');
return apiKey === env.API_SECRET;
}
// 数据库连接工具
async function withDatabase<T>(env: Env, operation: (client: any) => Promise<T>): Promise<T> {
const { Client } = await import('pg');
const client = new Client({ connectionString: env.HYPERDRIVE.connectionString });
try {
await client.connect();
return await operation(client);
} finally {
await client.end();
}
}
// 用户 CRUD 操作
class UserService {
static async getUsers(env: Env, page = 1, limit = 10, search?: string): Promise<{ users: User[]; total: number }> {
return withDatabase(env, async (client) => {
let query = 'SELECT id, name, email, created_at, updated_at FROM users';
let countQuery = 'SELECT COUNT(*) FROM users';
const params: any[] = [];
if (search) {
query += ' WHERE name ILIKE $1 OR email ILIKE $1';
countQuery += ' WHERE name ILIKE $1 OR email ILIKE $1';
params.push(`%${search}%`);
}
query += ` ORDER BY created_at DESC LIMIT $${params.length + 1} OFFSET $${params.length + 2}`;
params.push(limit, (page - 1) * limit);
const [usersResult, countResult] = await Promise.all([
client.query(query, params),
client.query(countQuery, search ? [`%${search}%`] : [])
]);
return {
users: usersResult.rows,
total: parseInt(countResult.rows[0].count)
};
});
}
static async getUserById(env: Env, id: number): Promise<User | null> {
return withDatabase(env, async (client) => {
const result = await client.query(
'SELECT id, name, email, created_at, updated_at FROM users WHERE id = $1',
[id]
);
return result.rows[0] || null;
});
}
static async createUser(env: Env, userData: Omit<User, 'id' | 'created_at' | 'updated_at'>): Promise<User> {
return withDatabase(env, async (client) => {
const result = await client.query(
'INSERT INTO users (name, email, created_at, updated_at) VALUES ($1, $2, NOW(), NOW()) RETURNING id, name, email, created_at, updated_at',
[userData.name.trim(), userData.email.toLowerCase().trim()]
);
return result.rows[0];
});
}
static async updateUser(env: Env, id: number, userData: Partial<Omit<User, 'id' | 'created_at' | 'updated_at'>>): Promise<User | null> {
return withDatabase(env, async (client) => {
const setParts: string[] = [];
const params: any[] = [];
let paramIndex = 1;
if (userData.name !== undefined) {
setParts.push(`name = $${paramIndex++}`);
params.push(userData.name.trim());
}
if (userData.email !== undefined) {
setParts.push(`email = $${paramIndex++}`);
params.push(userData.email.toLowerCase().trim());
}
if (setParts.length === 0) {
throw new Error('No fields to update');
}
setParts.push(`updated_at = NOW()`);
params.push(id);
const result = await client.query(
`UPDATE users SET ${setParts.join(', ')} WHERE id = $${paramIndex} RETURNING id, name, email, created_at, updated_at`,
params
);
return result.rows[0] || null;
});
}
static async deleteUser(env: Env, id: number): Promise<boolean> {
return withDatabase(env, async (client) => {
const result = await client.query('DELETE FROM users WHERE id = $1', [id]);
return result.rowCount > 0;
});
}
static async initializeDatabase(env: Env): Promise<void> {
return withDatabase(env, async (client) => {
await client.query(`
CREATE TABLE IF NOT EXISTS users (
id SERIAL PRIMARY KEY,
name VARCHAR(255) NOT NULL,
email VARCHAR(255) UNIQUE NOT NULL,
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
)
`);
// 创建索引
await client.query('CREATE INDEX IF NOT EXISTS idx_users_email ON users(email)');
await client.query('CREATE INDEX IF NOT EXISTS idx_users_created_at ON users(created_at)');
});
}
}
// 路由处理
export default {
async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
const url = new URL(request.url);
const path = url.pathname;
const method = request.method;
// 处理 CORS 预检请求
if (method === 'OPTIONS') {
return new Response(null, { headers: corsHeaders });
}
// API 密钥验证
if (!validateApiKey(request, env)) {
return errorResponse('Unauthorized', 401);
}
try {
// 路由匹配
if (path === '/init' && method === 'POST') {
await UserService.initializeDatabase(env);
return jsonResponse({ status: 'success', message: 'Database initialized' });
}
if (path === '/users' && method === 'GET') {
const page = parseInt(url.searchParams.get('page') || '1');
const limit = Math.min(parseInt(url.searchParams.get('limit') || '10'), 100);
const search = url.searchParams.get('search') || undefined;
const { users, total } = await UserService.getUsers(env, page, limit, search);
return jsonResponse({
status: 'success',
data: users,
meta: {
total,
page,
limit,
}
});
}
if (path.match(/^\/users\/\d+$/) && method === 'GET') {
const id = parseInt(path.split('/')[2]);
const user = await UserService.getUserById(env, id);
if (!user) {
return errorResponse('User not found', 404);
}
return jsonResponse({ status: 'success', data: user });
}
if (path === '/users' && method === 'POST') {
const body = await request.json() as any;
const validation = validateUser(body);
if (!validation.valid) {
return errorResponse(`Validation failed: ${validation.errors.join(', ')}`, 400);
}
const user = await UserService.createUser(env, body as Omit<User, 'id' | 'created_at' | 'updated_at'>);
return jsonResponse({ status: 'success', data: user, message: 'User created successfully' }, 201);
}
if (path.match(/^\/users\/\d+$/) && method === 'PUT') {
const id = parseInt(path.split('/')[2]);
const body = await request.json() as any;
// 部分验证（只验证提供的字段）
if (body.name !== undefined || body.email !== undefined) {
const validation = validateUser({ name: body.name || 'valid', email: body.email || 'valid@email.com' });
if (!validation.valid) {
return errorResponse(`Validation failed: ${validation.errors.join(', ')}`, 400);
}
}
const user = await UserService.updateUser(env, id, body as Partial<Omit<User, 'id' | 'created_at' | 'updated_at'>>);
if (!user) {
return errorResponse('User not found', 404);
}
return jsonResponse({ status: 'success', data: user, message: 'User updated successfully' });
}
if (path.match(/^\/users\/\d+$/) && method === 'DELETE') {
const id = parseInt(path.split('/')[2]);
const deleted = await UserService.deleteUser(env, id);
if (!deleted) {
return errorResponse('User not found', 404);
}
return jsonResponse({ status: 'success', message: 'User deleted successfully' });
}
// 健康检查
if (path === '/health') {
return jsonResponse({
status: 'success',
data: {
service: 'hyperdrive-api',
timestamp: new Date().toISOString(),
version: '1.0.0'
}
});
}
// API 文档
if (path === '/docs') {
const docs = {
endpoints: {
'POST /init': 'Initialize database tables',
'GET /users': 'List users (supports ?page, ?limit, ?search)',
'GET /users/:id': 'Get user by ID',
'POST /users': 'Create new user',
'PUT /users/:id': 'Update user',
'DELETE /users/:id': 'Delete user',
'GET /health': 'Health check',
'GET /docs': 'API documentation'
},
authentication: 'Include X-API-Key header or Authorization: Bearer <token>',
examples: {
createUser: {
method: 'POST',
url: '/users',
body: { name: 'John Doe', email: 'john@example.com' }
},
listUsers: {
method: 'GET',
url: '/users?page=1&limit=10&search=john'
}
}
};
return jsonResponse({ status: 'success', data: docs });
}
return errorResponse('Not found', 404);
} catch (error) {
console.error('API Error:', error);
return errorResponse('Internal server error', 500);
}
},
};

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/// <reference types="@cloudflare/workers-types" />
export interface Env {
HYPERDRIVE: Hyperdrive;
}
export default {
async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
try {
// Test Hyperdrive connection to NeonDB
const { pathname } = new URL(request.url);
if (pathname === '/test-connection') {
return await testConnection(env);
}
if (pathname === '/test-query') {
return await testQuery(env);
}
if (pathname === '/query-tables') {
return await queryTables(env);
}
if (pathname === '/query-shushu') {
const url = new URL(request.url);
const limit = parseInt(url.searchParams.get('limit') || '10');
return await queryShushuBook(env, limit);
}
if (pathname === '/search-shushu') {
const url = new URL(request.url);
const keyword = url.searchParams.get('q') || '';
const limit = parseInt(url.searchParams.get('limit') || '5');
return await searchShushuBook(env, keyword, limit);
}
if (pathname === '/shushu-stats') {
return await getShushuStats(env);
}
return new Response('Hyperdrive NeonDB Test Worker\n\nEndpoints:\n- /test-connection - Test database connection\n- /test-query - Test database query\n- /query-tables - List all tables\n- /query-shushu?limit=N - Query shushu book content\n- /search-shushu?q=keyword&limit=N - Search shushu book\n- /shushu-stats - Get shushu book statistics', {
headers: { 'Content-Type': 'text/plain' }
});
} catch (error) {
return new Response(`Error: ${error.message}`, {
status: 500,
headers: { 'Content-Type': 'text/plain' }
});
}
},
};
async function testConnection(env: Env): Promise<Response> {
try {
// Get connection string from Hyperdrive
const connectionString = env.HYPERDRIVE.connectionString;
// Create a simple connection test
const { Client } = await import('pg');
const client = new Client({ connectionString });
await client.connect();
// Test basic query
const result = await client.query('SELECT NOW() as current_time, version() as pg_version');
await client.end();
return new Response(JSON.stringify({
status: 'success',
message: 'Successfully connected to NeonDB via Hyperdrive',
data: result.rows[0],
connectionInfo: {
hyperdrive_id: 'ef43924d89064cddabfaccf06aadfab6',
connection_pooled: true
}
}, null, 2), {
headers: { 'Content-Type': 'application/json' }
});
} catch (error) {
return new Response(JSON.stringify({
status: 'error',
message: 'Failed to connect to NeonDB',
error: error.message
}, null, 2), {
status: 500,
headers: { 'Content-Type': 'application/json' }
});
}
}
// 查询数据库表结构
async function queryTables(env: Env): Promise<Response> {
try {
const { Client } = await import('pg');
const client = new Client({
connectionString: env.HYPERDRIVE.connectionString
});
await client.connect();
// 查询所有表
const result = await client.query(`
SELECT table_name, table_schema
FROM information_schema.tables
WHERE table_schema NOT IN ('information_schema', 'pg_catalog')
ORDER BY table_schema, table_name
`);
await client.end();
return new Response(JSON.stringify({
status: 'success',
message: 'Tables retrieved successfully',
tables: result.rows
}, null, 2), {
headers: { 'Content-Type': 'application/json' }
});
} catch (error) {
return new Response(JSON.stringify({
status: 'error',
message: 'Failed to query tables',
error: error.message
}, null, 2), {
status: 500,
headers: { 'Content-Type': 'application/json' }
});
}
}
// 查询术数书内容
async function queryShushuBook(env: Env, limit: number = 10): Promise<Response> {
try {
const { Client } = await import('pg');
const client = new Client({
connectionString: env.HYPERDRIVE.connectionString
});
await client.connect();
// 尝试查询可能的术数书表名
const tableNames = ['shushu', 'shushu_book', 'books', 'articles', 'content', 'documents'];
let result: any = null;
let tableName: string | null = null;
for (const name of tableNames) {
try {
const testResult = await client.query(`SELECT * FROM ${name} LIMIT 1`);
if (testResult.rows.length > 0) {
tableName = name;
result = await client.query(`SELECT * FROM ${name} ORDER BY id DESC LIMIT $1`, [limit]);
break;
}
} catch (e) {
// 表不存在，继续尝试下一个
continue;
}
}
await client.end();
if (!result) {
return new Response(JSON.stringify({
status: 'error',
message: 'No shushu book table found',
searched_tables: tableNames
}, null, 2), {
status: 404,
headers: { 'Content-Type': 'application/json' }
});
}
return new Response(JSON.stringify({
status: 'success',
message: 'Shushu book content retrieved successfully',
table_name: tableName,
count: result.rows.length,
data: result.rows
}, null, 2), {
headers: { 'Content-Type': 'application/json' }
});
} catch (error) {
return new Response(JSON.stringify({
status: 'error',
message: 'Failed to query shushu book',
error: error.message
}, null, 2), {
status: 500,
headers: { 'Content-Type': 'application/json' }
});
}
}
// 搜索术数书内容
async function searchShushuBook(env: Env, keyword: string, limit: number = 5): Promise<Response> {
try {
if (!keyword) {
return new Response(JSON.stringify({
status: 'error',
message: 'Search keyword is required'
}, null, 2), {
status: 400,
headers: { 'Content-Type': 'application/json' }
});
}
const { Client } = await import('pg');
const client = new Client({
connectionString: env.HYPERDRIVE.connectionString
});
await client.connect();
// 尝试在不同的表和字段中搜索
const searchQueries = [
{ table: 'shushu', fields: ['title', 'content', 'description'] },
{ table: 'shushu_book', fields: ['title', 'content', 'text'] },
{ table: 'books', fields: ['title', 'content', 'description'] },
{ table: 'articles', fields: ['title', 'content', 'body'] },
{ table: 'content', fields: ['title', 'text', 'content'] },
{ table: 'documents', fields: ['title', 'content', 'text'] }
];
let results: any[] = [];
let searchedTables: string[] = [];
for (const { table, fields } of searchQueries) {
try {
// 构建搜索条件
const conditions = fields.map(field => `${field} ILIKE $1`).join(' OR ');
const query = `SELECT * FROM ${table} WHERE ${conditions} LIMIT $2`;
const result = await client.query(query, [`%${keyword}%`, limit]);
if (result.rows.length > 0) {
results.push({
table_name: table,
count: result.rows.length,
data: result.rows
});
}
searchedTables.push(table);
} catch (e) {
// 表或字段不存在，继续搜索
continue;
}
}
await client.end();
return new Response(JSON.stringify({
status: 'success',
message: `Search completed for keyword: ${keyword}`,
keyword: keyword,
searched_tables: searchedTables,
results: results,
total_matches: results.reduce((sum, r) => sum + r.count, 0)
}, null, 2), {
headers: { 'Content-Type': 'application/json' }
});
} catch (error) {
return new Response(JSON.stringify({
status: 'error',
message: 'Search failed',
error: error.message
}, null, 2), {
status: 500,
headers: { 'Content-Type': 'application/json' }
});
}
}
// 获取术数书统计信息
async function getShushuStats(env: Env): Promise<Response> {
try {
const { Client } = await import('pg');
const client = new Client({
connectionString: env.HYPERDRIVE.connectionString
});
await client.connect();
const tableNames = ['shushu', 'shushu_book', 'books', 'articles', 'content', 'documents'];
let stats: any[] = [];
for (const tableName of tableNames) {
try {
const countResult = await client.query(`SELECT COUNT(*) as count FROM ${tableName}`);
const sampleResult = await client.query(`SELECT * FROM ${tableName} LIMIT 1`);
stats.push({
table_name: tableName,
record_count: parseInt(countResult.rows[0].count),
sample_columns: sampleResult.rows.length > 0 ? Object.keys(sampleResult.rows[0]) : [],
exists: true
});
} catch (e) {
stats.push({
table_name: tableName,
exists: false
});
}
}
await client.end();
return new Response(JSON.stringify({
status: 'success',
message: 'Statistics retrieved successfully',
stats: stats,
existing_tables: stats.filter(s => s.exists)
}, null, 2), {
headers: { 'Content-Type': 'application/json' }
});
} catch (error) {
return new Response(JSON.stringify({
status: 'error',
message: 'Failed to get statistics',
error: error.message
}, null, 2), {
status: 500,
headers: { 'Content-Type': 'application/json' }
});
}
}
async function testQuery(env: Env): Promise<Response> {
try {
const { Client } = await import('pg');
const client = new Client({
connectionString: env.HYPERDRIVE.connectionString
});
await client.connect();
// Create a test table if it doesn't exist
await client.query(`
CREATE TABLE IF NOT EXISTS hyperdrive_test (
id SERIAL PRIMARY KEY,
message TEXT,
created_at TIMESTAMP DEFAULT NOW()
)
`);
// Insert a test record
const insertResult = await client.query(
'INSERT INTO hyperdrive_test (message) VALUES ($1) RETURNING *',
[`Test from Hyperdrive at ${new Date().toISOString()}`]
);
// Query recent records
const selectResult = await client.query(
'SELECT * FROM hyperdrive_test ORDER BY created_at DESC LIMIT 5'
);
await client.end();
return new Response(JSON.stringify({
status: 'success',
message: 'Database operations completed successfully',
inserted: insertResult.rows[0],
recent_records: selectResult.rows,
performance: {
hyperdrive_enabled: true,
connection_pooled: true
}
}, null, 2), {
headers: { 'Content-Type': 'application/json' }
});
} catch (error) {
return new Response(JSON.stringify({
status: 'error',
message: 'Database query failed',
error: error.message
}, null, 2), {
status: 500,
headers: { 'Content-Type': 'application/json' }
});
}
}

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#!/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")
InvocationContext = Any
from src.jixia.memory.base_memory_bank import MemoryBankProtocol
from src.jixia.memory.factory import get_memory_backend
from config.doppler_config 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())

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src/jixia/config/immortal_api_config.json Normal file
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{
"immortals": {
"吕洞宾": {
"title": "主力剑仙",
"specialty": "综合分析与决策",
"description": "作为八仙之首，负责整体投资策略制定，需要最快最准确的数据",
"preferred_apis": {
"stock_quote": "alpha_vantage",
"company_overview": "alpha_vantage",
"market_movers": "yahoo_finance_15",
"market_news": "yahoo_finance_15"
},
"data_priority": ["实时价格", "公司基本面", "市场动态"],
"api_weight": 0.15
},
"何仙姑": {
"title": "风控专家",
"specialty": "风险管理与合规",
"description": "专注风险评估和投资组合管理，需要稳定可靠的数据源",
"preferred_apis": {
"stock_quote": "yahoo_finance_15",
"company_overview": "seeking_alpha",
"market_movers": "webull",
"market_news": "seeking_alpha"
},
"data_priority": ["波动率", "风险指标", "合规信息"],
"api_weight": 0.12
},
"张果老": {
"title": "技术分析师",
"specialty": "技术指标与图表分析",
"description": "专精技术分析，需要详细的价格和成交量数据",
"preferred_apis": {
"stock_quote": "webull",
"company_overview": "alpha_vantage",
"market_movers": "yahoo_finance_15",
"market_news": "yahoo_finance_15"
},
"data_priority": ["技术指标", "成交量", "价格走势"],
"api_weight": 0.13
},
"韩湘子": {
"title": "基本面研究员",
"specialty": "财务分析与估值",
"description": "深度研究公司财务状况和内在价值",
"preferred_apis": {
"stock_quote": "alpha_vantage",
"company_overview": "seeking_alpha",
"market_movers": "webull",
"market_news": "seeking_alpha"
},
"data_priority": ["财务报表", "估值指标", "盈利预测"],
"api_weight": 0.14
},
"汉钟离": {
"title": "量化专家",
"specialty": "数据挖掘与算法交易",
"description": "运用数学模型和算法进行量化分析",
"preferred_apis": {
"stock_quote": "yahoo_finance_15",
"company_overview": "alpha_vantage",
"market_movers": "yahoo_finance_15",
"market_news": "yahoo_finance_15"
},
"data_priority": ["历史数据", "统计指标", "相关性分析"],
"api_weight": 0.13
},
"蓝采和": {
"title": "情绪分析师",
"specialty": "市场情绪与舆情监控",
"description": "分析市场情绪和投资者行为模式",
"preferred_apis": {
"stock_quote": "webull",
"company_overview": "seeking_alpha",
"market_movers": "webull",
"market_news": "seeking_alpha"
},
"data_priority": ["新闻情绪", "社交媒体", "投资者情绪"],
"api_weight": 0.11
},
"曹国舅": {
"title": "宏观分析师",
"specialty": "宏观经济与政策分析",
"description": "关注宏观经济环境和政策影响",
"preferred_apis": {
"stock_quote": "seeking_alpha",
"company_overview": "seeking_alpha",
"market_movers": "yahoo_finance_15",
"market_news": "seeking_alpha"
},
"data_priority": ["宏观数据", "政策解读", "行业趋势"],
"api_weight": 0.12
},
"铁拐李": {
"title": "逆向投资专家",
"specialty": "价值发现与逆向思维",
"description": "寻找被低估的投资机会，逆向思考市场",
"preferred_apis": {
"stock_quote": "alpha_vantage",
"company_overview": "alpha_vantage",
"market_movers": "webull",
"market_news": "yahoo_finance_15"
},
"data_priority": ["估值偏差", "市场异常", "价值机会"],
"api_weight": 0.10
}
},
"api_configurations": {
"alpha_vantage": {
"name": "Alpha Vantage",
"tier": "premium",
"strengths": ["实时数据", "财务数据", "技术指标"],
"rate_limits": {
"per_minute": 500,
"per_month": 500000
},
"reliability_score": 0.95,
"response_time_avg": 0.8,
"data_quality": "high",
"cost_per_call": 0.001
},
"yahoo_finance_15": {
"name": "Yahoo Finance 15",
"tier": "standard",
"strengths": ["市场数据", "新闻资讯", "实时报价"],
"rate_limits": {
"per_minute": 500,
"per_month": 500000
},
"reliability_score": 0.90,
"response_time_avg": 1.2,
"data_quality": "medium",
"cost_per_call": 0.0005
},
"webull": {
"name": "Webull",
"tier": "premium",
"strengths": ["搜索功能", "活跃数据", "技术分析"],
"rate_limits": {
"per_minute": 500,
"per_month": 500000
},
"reliability_score": 0.88,
"response_time_avg": 1.0,
"data_quality": "high",
"cost_per_call": 0.0008
},
"seeking_alpha": {
"name": "Seeking Alpha",
"tier": "standard",
"strengths": ["分析报告", "新闻资讯", "专业观点"],
"rate_limits": {
"per_minute": 500,
"per_month": 500000
},
"reliability_score": 0.85,
"response_time_avg": 1.5,
"data_quality": "medium",
"cost_per_call": 0.0006
}
},
"load_balancing_strategies": {
"round_robin": {
"description": "轮询分配，确保负载均匀分布",
"enabled": true,
"weight_based": true
},
"health_aware": {
"description": "基于API健康状态的智能分配",
"enabled": true,
"health_check_interval": 300
},
"performance_based": {
"description": "基于响应时间的动态分配",
"enabled": true,
"response_time_threshold": 2.0
},
"cost_optimization": {
"description": "成本优化策略，优先使用低成本API",
"enabled": false,
"cost_threshold": 0.001
}
},
"failover_matrix": {
"alpha_vantage": ["webull", "yahoo_finance_15", "seeking_alpha"],
"yahoo_finance_15": ["webull", "alpha_vantage", "seeking_alpha"],
"webull": ["alpha_vantage", "yahoo_finance_15", "seeking_alpha"],
"seeking_alpha": ["yahoo_finance_15", "alpha_vantage", "webull"]
},
"cache_settings": {
"enabled": true,
"ttl_seconds": 300,
"max_entries": 1000,
"cache_strategies": {
"stock_quote": 60,
"company_overview": 3600,
"market_movers": 300,
"market_news": 1800
}
},
"monitoring": {
"enabled": true,
"metrics": [
"api_call_count",
"response_time",
"error_rate",
"cache_hit_rate",
"load_distribution"
],
"alerts": {
"high_error_rate": 0.1,
"slow_response_time": 3.0,
"api_unavailable": true
}
}
}

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
稷下学宫 Google ADK 论道系统测试
基于 Google ADK 的八仙论道原型
"""
import os
import asyncio
from google.adk import Agent
from google.adk.tools import FunctionTool
# 八仙智能体定义
def create_baxian_agents():
"""创建八仙智能体"""
# 铁拐李 - 逆向思维专家
tie_guai_li = Agent(
name="铁拐李",
model="gemini-2.0-flash-exp"
)
# 汉钟离 - 平衡协调者
han_zhong_li = Agent(
name="汉钟离",
model="gemini-2.0-flash-exp"
)
# 张果老 - 历史智慧者
zhang_guo_lao = Agent(
name="张果老",
model="gemini-2.0-flash-exp"
)
# 蓝采和 - 创新思维者
lan_cai_he = Agent(
name="蓝采和",
model="gemini-2.0-flash-exp"
)
# 何仙姑 - 直觉洞察者
he_xian_gu = Agent(
name="何仙姑",
model="gemini-2.0-flash-exp"
)
# 吕洞宾 - 理性分析者
lu_dong_bin = Agent(
name="吕洞宾",
model="gemini-2.0-flash-exp"
)
# 韩湘子 - 艺术感知者
han_xiang_zi = Agent(
name="韩湘子",
model="gemini-2.0-flash-exp"
)
# 曹国舅 - 实务执行者
cao_guo_jiu = Agent(
name="曹国舅",
model="gemini-2.0-flash-exp"
)
return {
"铁拐李": tie_guai_li,
"汉钟离": han_zhong_li,
"张果老": zhang_guo_lao,
"蓝采和": lan_cai_he,
"何仙姑": he_xian_gu,
"吕洞宾": lu_dong_bin,
"韩湘子": han_xiang_zi,
"曹国舅": cao_guo_jiu
}
def test_single_agent():
"""测试单个智能体"""
print("🧪 测试单个智能体...")
# 创建铁拐李智能体
tie_guai_li = Agent(
name="铁拐李",
model="gemini-2.0-flash-exp"
)
print(f"✅ 智能体 '{tie_guai_li.name}' 创建成功")
print(f"📱 使用模型: {tie_guai_li.model}")
return tie_guai_li
def test_baxian_creation():
"""测试八仙智能体创建"""
print("\n🎭 创建八仙智能体...")
baxian = create_baxian_agents()
print(f"✅ 成功创建 {len(baxian)} 个智能体:")
for name, agent in baxian.items():
print(f" - {name}: {agent.model}")
return baxian
def main():
"""主测试函数"""
print("🚀 开始稷下学宫 ADK 论道系统测试...")
# 检查API密钥
api_key = os.getenv('GOOGLE_API_KEY')
if not api_key:
print("❌ 未找到 GOOGLE_API_KEY 环境变量")
print("请使用: doppler run -- python src/jixia/debates/adk_debate_test.py")
return
print(f"✅ API密钥已配置 (长度: {len(api_key)} 字符)")
# 测试单个智能体
single_agent = test_single_agent()
# 测试八仙智能体创建
baxian = test_baxian_creation()
print("\n🎉 ADK 论道系统基础测试完成！")
print("\n📝 下一步:")
print(" 1. 实现智能体间的对话逻辑")
print(" 2. 集成 RapidAPI 数据源")
print(" 3. 创建论道主题和流程")
print(" 4. 连接 Streamlit 界面")
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
稷下学宫 ADK Memory Bank 论道系统
实现带有记忆银行的八仙智能体辩论
"""
import os
import asyncio
from google.adk import Agent, Runner
from google.adk.sessions import InMemorySessionService
from google.adk.memory import MemoryBank, MemoryItem
from google.genai import types
import json
from datetime import datetime
from typing import Dict, List, Optional
class BaxianMemoryManager:
"""八仙记忆管理器"""
def __init__(self):
self.memory_banks: Dict[str, MemoryBank] = {}
self.agents: Dict[str, Agent] = {}
async def initialize_baxian_agents(self):
"""初始化八仙智能体及其记忆银行"""
# 八仙角色配置
baxian_config = {
"铁拐李": {
"instruction": "你是铁拐李，八仙中的逆向思维专家。你善于从批判和质疑的角度看问题，总是能发现事物的另一面。你会从你的记忆中回忆相关的逆向投资案例和失败教训。",
"memory_context": "逆向投资案例、市场泡沫警告、风险识别经验"
},
"吕洞宾": {
"instruction": "你是吕洞宾，八仙中的理性分析者。你善于平衡各方观点，用理性和逻辑来分析问题。你会从记忆中调用技术分析的成功案例和理论知识。",
"memory_context": "技术分析理论、成功预测案例、市场趋势分析"
},
"何仙姑": {
"instruction": "你是何仙姑，八仙中的风险控制专家。你总是从风险管理的角度思考问题，善于发现潜在危险。你会回忆历史上的重大风险事件。",
"memory_context": "风险管理案例、黑天鹅事件、危机预警经验"
},
"张果老": {
"instruction": "你是张果老，八仙中的历史智慧者。你善于从历史数据中寻找规律和智慧，总是能提供长期视角。你会从记忆中调用历史数据和长期趋势。",
"memory_context": "历史市场数据、长期投资趋势、周期性规律"
}
}
# 为每个仙人创建智能体和记忆银行
for name, config in baxian_config.items():
# 创建记忆银行
memory_bank = MemoryBank(
name=f"{name}_memory_bank",
description=f"{name}的个人记忆银行，存储{config['memory_context']}"
)
# 初始化记忆内容
await self._initialize_agent_memory(memory_bank, name, config['memory_context'])
# 创建智能体
agent = Agent(
name=name,
model="gemini-2.0-flash-exp",
instruction=f"{config['instruction']} 在回答时，请先从你的记忆银行中检索相关信息，然后结合当前话题给出回应。",
memory_bank=memory_bank
)
self.memory_banks[name] = memory_bank
self.agents[name] = agent
print(f"✅ 已初始化 {len(self.agents)} 个八仙智能体及其记忆银行")
async def _initialize_agent_memory(self, memory_bank: MemoryBank, agent_name: str, context: str):
"""为智能体初始化记忆内容"""
# 根据角色添加初始记忆
initial_memories = {
"铁拐李": [
"2000年互联网泡沫破裂，许多高估值科技股暴跌90%以上",
"2008年金融危机前，房地产市场过度繁荣，逆向思维者提前撤离",
"比特币从2万美元跌到3千美元，提醒我们任何资产都可能大幅回调",
"巴菲特说过：别人贪婪时我恐惧，别人恐惧时我贪婪"
],
"吕洞宾": [
"移动平均线交叉是经典的技术分析信号",
"RSI指标超过70通常表示超买，低于30表示超卖",
"支撑位和阻力位是技术分析的核心概念",
"成功的技术分析需要结合多个指标综合判断"
],
"何仙姑": [
"2008年雷曼兄弟倒闭引发全球金融危机",
"长期资本管理公司(LTCM)的失败说明了风险管理的重要性",
"分散投资是降低风险的基本原则",
"黑天鹅事件虽然罕见但影响巨大，需要提前准备"
],
"张果老": [
"股市存在7-10年的长期周期",
"康德拉季耶夫长波理论描述了50-60年的经济周期",
"历史上每次重大技术革命都带来新的投资机会",
"长期来看，优质资产总是向上的"
]
}
memories = initial_memories.get(agent_name, [])
for memory_text in memories:
memory_item = MemoryItem(
content=memory_text,
metadata={
"agent": agent_name,
"type": "historical_knowledge",
"timestamp": datetime.now().isoformat()
}
)
await memory_bank.add_memory(memory_item)
async def add_debate_memory(self, agent_name: str, content: str, topic: str):
"""为智能体添加辩论记忆"""
if agent_name in self.memory_banks:
memory_item = MemoryItem(
content=content,
metadata={
"agent": agent_name,
"type": "debate_history",
"topic": topic,
"timestamp": datetime.now().isoformat()
}
)
await self.memory_banks[agent_name].add_memory(memory_item)
async def retrieve_relevant_memories(self, agent_name: str, query: str, limit: int = 3) -> List[str]:
"""检索智能体的相关记忆"""
if agent_name not in self.memory_banks:
return []
try:
memories = await self.memory_banks[agent_name].search(query, limit=limit)
return [memory.content for memory in memories]
except Exception as e:
print(f"⚠️ 记忆检索失败 ({agent_name}): {e}")
return []
class MemoryEnhancedDebate:
"""带记忆增强的辩论系统"""
def __init__(self):
self.memory_manager = BaxianMemoryManager()
self.session_service = InMemorySessionService()
self.runners: Dict[str, Runner] = {}
async def initialize(self):
"""初始化辩论系统"""
await self.memory_manager.initialize_baxian_agents()
# 创建会话
self.session = await self.session_service.create_session(
state={},
app_name="稷下学宫记忆增强论道系统",
user_id="memory_debate_user"
)
# 为每个智能体创建Runner
for name, agent in self.memory_manager.agents.items():
runner = Runner(
app_name="稷下学宫记忆增强论道系统",
agent=agent,
session_service=self.session_service
)
self.runners[name] = runner
async def conduct_memory_debate(self, topic: str, participants: List[str] = None):
"""进行带记忆的辩论"""
if participants is None:
participants = ["铁拐李", "吕洞宾", "何仙姑", "张果老"]
print(f"\n🎭 稷下学宫记忆增强论道开始...")
print(f"📋 论道主题: {topic}")
print(f"🎯 参与仙人: {', '.join(participants)}")
debate_history = []
for round_num in range(2): # 进行2轮辩论
print(f"\n🔄 第 {round_num + 1} 轮论道:")
for participant in participants:
if participant not in self.runners:
continue
print(f"\n🗣️ {participant} 发言:")
# 检索相关记忆
relevant_memories = await self.memory_manager.retrieve_relevant_memories(
participant, topic, limit=2
)
# 构建包含记忆的提示
memory_context = ""
if relevant_memories:
memory_context = f"\n从你的记忆中回忆到：\n" + "\n".join([f"- {memory}" for memory in relevant_memories])
# 构建辩论历史上下文
history_context = ""
if debate_history:
recent_history = debate_history[-3:] # 最近3条发言
history_context = f"\n最近的论道内容：\n" + "\n".join([f"- {h}" for h in recent_history])
prompt = f"关于'{topic}'这个话题{memory_context}{history_context}\n\n请结合你的记忆和当前讨论，从你的角色特点出发发表观点。请控制在150字以内。"
# 发送消息并获取回复
content = types.Content(role='user', parts=[types.Part(text=prompt)])
response = self.runners[participant].run_async(
user_id=self.session.user_id,
session_id=self.session.id,
new_message=content
)
# 收集回复
reply = ""
async for event in response:
if hasattr(event, 'content') and event.content:
if hasattr(event.content, 'parts') and event.content.parts:
for part in event.content.parts:
if hasattr(part, 'text') and part.text:
reply += str(part.text)
if reply.strip():
clean_reply = reply.strip()
print(f" {clean_reply}")
# 记录到辩论历史
debate_entry = f"{participant}: {clean_reply}"
debate_history.append(debate_entry)
# 添加到记忆银行
await self.memory_manager.add_debate_memory(
participant, clean_reply, topic
)
await asyncio.sleep(1) # 避免API调用过快
print(f"\n🎉 记忆增强论道完成！")
print(f"📝 本次论道共产生 {len(debate_history)} 条发言，已存储到各仙人的记忆银行中。")
return debate_history
async def close(self):
"""关闭资源"""
for runner in self.runners.values():
await runner.close()
async def main():
"""主函数"""
print("🚀 稷下学宫 ADK Memory Bank 论道系统")
# 检查API密钥
api_key = os.getenv('GOOGLE_API_KEY')
if not api_key:
print("❌ 未找到 GOOGLE_API_KEY 环境变量")
print("请使用: doppler run -- python src/jixia/debates/adk_memory_debate.py")
return
print(f"✅ API密钥已配置")
# 创建并初始化辩论系统
debate_system = MemoryEnhancedDebate()
try:
await debate_system.initialize()
# 进行辩论
await debate_system.conduct_memory_debate(
topic="人工智能对投资市场的影响",
participants=["铁拐李", "吕洞宾", "何仙姑", "张果老"]
)
except Exception as e:
print(f"❌ 运行失败: {e}")
import traceback
traceback.print_exc()
finally:
await debate_system.close()
if __name__ == "__main__":
asyncio.run(main())

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
稷下学宫 ADK 真实论道系统
实现铁拐李和吕洞宾的实际对话辩论
"""
import os
import asyncio
from google.adk import Agent, Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
import re
import sys
from contextlib import contextmanager
def create_debate_agents():
"""创建论道智能体"""
# 铁拐李 - 逆向思维专家
tie_guai_li = Agent(
name="铁拐李",
model="gemini-2.0-flash-exp",
instruction="你是铁拐李，八仙中的逆向思维专家。你善于从批判和质疑的角度看问题，总是能发现事物的另一面。你的发言风格直接、犀利，但富有智慧。每次发言控制在100字以内。"
)
# 吕洞宾 - 理性分析者
lu_dong_bin = Agent(
name="吕洞宾",
model="gemini-2.0-flash-exp",
instruction="你是吕洞宾，八仙中的理性分析者。你善于平衡各方观点，用理性和逻辑来分析问题。你的发言风格温和而深刻，总是能找到问题的核心。每次发言控制在100字以内。"
)
return tie_guai_li, lu_dong_bin
async def conduct_debate():
"""进行实际辩论"""
print("🎭 稷下学宫论道开始...")
# 创建智能体
tie_guai_li, lu_dong_bin = create_debate_agents()
print("\n📋 论道主题: 人工智能对未来社会的影响")
print("\n🎯 八仙论道，智慧交锋...")
try:
print("\n🚀 使用真实ADK调用进行论道...")
await real_adk_debate(tie_guai_li, lu_dong_bin)
except Exception as e:
print(f"\n❌ ADK调用失败: {e}")
print("🔧 回退到模拟对话模式...")
await simple_mock_debate(tie_guai_li, lu_dong_bin)
@contextmanager
def suppress_stdout():
"""临时抑制stdout输出"""
with open(os.devnull, 'w') as devnull:
old_stdout = sys.stdout
sys.stdout = devnull
try:
yield
finally:
sys.stdout = old_stdout
def clean_debug_output(text):
"""清理ADK输出中的调试信息"""
if not text:
return ""
# 移除API密钥相关信息
text = re.sub(r'Both GOOGLE_API_KEY and GEMINI_API_KEY are set\. Using GOOGLE_API_KEY\.', '', text)
# 移除Event from unknown agent信息
text = re.sub(r'Event from an unknown agent: [^\n]*\n?', '', text)
# 移除多余的空白字符
text = re.sub(r'\n\s*\n', '\n', text)
text = text.strip()
return text
async def real_adk_debate(tie_guai_li, lu_dong_bin):
"""使用真实ADK进行辩论"""
print("\n🔥 真实ADK论道模式")
# 设置环境变量来抑制ADK调试输出
os.environ['GOOGLE_CLOUD_DISABLE_GRPC_LOGS'] = 'true'
os.environ['GRPC_VERBOSITY'] = 'NONE'
os.environ['GRPC_TRACE'] = ''
# 临时抑制警告和调试信息
import warnings
warnings.filterwarnings('ignore')
# 设置日志级别
import logging
logging.getLogger().setLevel(logging.ERROR)
# 创建会话服务
session_service = InMemorySessionService()
# 创建会话
session = await session_service.create_session(
state={},
app_name="稷下学宫论道系统",
user_id="debate_user"
)
# 创建Runner实例
tie_runner = Runner(
app_name="稷下学宫论道系统",
agent=tie_guai_li,
session_service=session_service
)
lu_runner = Runner(
app_name="稷下学宫论道系统",
agent=lu_dong_bin,
session_service=session_service
)
try:
# 第一轮：铁拐李开场
print("\n🗣️ 铁拐李发言:")
tie_prompt = "作为逆向思维专家，请从批判角度分析人工智能对未来社会可能带来的负面影响。请控制在100字以内。"
tie_content = types.Content(role='user', parts=[types.Part(text=tie_prompt)])
with suppress_stdout():
tie_response = tie_runner.run_async(
user_id=session.user_id,
session_id=session.id,
new_message=tie_content
)
tie_reply = ""
async for event in tie_response:
# 只处理包含实际文本内容的事件，过滤调试信息
if hasattr(event, 'content') and event.content:
if hasattr(event.content, 'parts') and event.content.parts:
for part in event.content.parts:
if hasattr(part, 'text') and part.text and part.text.strip():
text_content = str(part.text).strip()
# 过滤掉调试信息和系统消息
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
tie_reply += text_content
elif hasattr(event, 'text') and event.text:
text_content = str(event.text).strip()
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
tie_reply += text_content
# 清理并输出铁拐李的回复
clean_tie_reply = clean_debug_output(tie_reply)
if clean_tie_reply:
print(f" {clean_tie_reply}")
# 第二轮：吕洞宾回应
print("\n🗣️ 吕洞宾回应:")
lu_prompt = f"铁拐李提到了AI的负面影响：'{tie_reply[:50]}...'。作为理性分析者，请从平衡角度回应，既承认风险又指出机遇。请控制在100字以内。"
lu_content = types.Content(role='user', parts=[types.Part(text=lu_prompt)])
with suppress_stdout():
lu_response = lu_runner.run_async(
user_id=session.user_id,
session_id=session.id,
new_message=lu_content
)
lu_reply = ""
async for event in lu_response:
# 只处理包含实际文本内容的事件，过滤调试信息
if hasattr(event, 'content') and event.content:
if hasattr(event.content, 'parts') and event.content.parts:
for part in event.content.parts:
if hasattr(part, 'text') and part.text and part.text.strip():
text_content = str(part.text).strip()
# 过滤掉调试信息和系统消息
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
lu_reply += text_content
elif hasattr(event, 'text') and event.text:
text_content = str(event.text).strip()
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
lu_reply += text_content
# 清理并输出吕洞宾的回复
clean_lu_reply = clean_debug_output(lu_reply)
if clean_lu_reply:
print(f" {clean_lu_reply}")
# 第三轮：铁拐李再次发言
print("\n🗣️ 铁拐李再次发言:")
tie_prompt2 = f"吕洞宾提到了AI的机遇：'{lu_reply[:50]}...'。请从逆向思维角度，对这些所谓的机遇进行质疑和反思。请控制在100字以内。"
tie_content2 = types.Content(role='user', parts=[types.Part(text=tie_prompt2)])
with suppress_stdout():
tie_response2 = tie_runner.run_async(
user_id=session.user_id,
session_id=session.id,
new_message=tie_content2
)
tie_reply2 = ""
async for event in tie_response2:
# 只处理包含实际文本内容的事件，过滤调试信息
if hasattr(event, 'content') and event.content:
if hasattr(event.content, 'parts') and event.content.parts:
for part in event.content.parts:
if hasattr(part, 'text') and part.text and part.text.strip():
text_content = str(part.text).strip()
# 过滤掉调试信息和系统消息
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
tie_reply2 += text_content
elif hasattr(event, 'text') and event.text:
text_content = str(event.text).strip()
if not text_content.startswith('Event from') and not 'API_KEY' in text_content:
tie_reply2 += text_content
# 清理并输出铁拐李的第二次回复
clean_tie_reply2 = clean_debug_output(tie_reply2)
if clean_tie_reply2:
print(f" {clean_tie_reply2}")
print("\n🎉 真实ADK论道完成！")
print("\n📝 智慧交锋，各抒己见，这就是稷下学宫的魅力所在。")
finally:
# 清理资源
await tie_runner.close()
await lu_runner.close()
def main():
"""主函数"""
print("🚀 稷下学宫 ADK 真实论道系统")
# 检查API密钥
api_key = os.getenv('GOOGLE_API_KEY')
if not api_key:
print("❌ 未找到 GOOGLE_API_KEY 环境变量")
print("请使用: doppler run -- python src/jixia/debates/adk_real_debate.py")
return
print(f"✅ API密钥已配置")
# 运行异步辩论
try:
asyncio.run(conduct_debate())
except Exception as e:
print(f"❌ 运行失败: {e}")
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
稷下学宫 ADK 简单论道测试
实现智能体间的基本对话功能
"""
import os
from google.adk import Agent
def create_debate_agents():
"""创建论道智能体"""
# 铁拐李 - 逆向思维专家
tie_guai_li = Agent(
name="铁拐李",
model="gemini-2.0-flash-exp"
)
# 吕洞宾 - 理性分析者
lu_dong_bin = Agent(
name="吕洞宾",
model="gemini-2.0-flash-exp"
)
return tie_guai_li, lu_dong_bin
def simple_debate_test():
"""简单论道测试"""
print("🎭 开始简单论道测试...")
# 创建智能体
tie_guai_li, lu_dong_bin = create_debate_agents()
print("\n📋 论道主题: 人工智能对未来社会的影响")
print("\n🎯 开始论道...")
try:
# 测试智能体创建
print("\n✅ 智能体创建成功:")
print(f" - {tie_guai_li.name}: {tie_guai_li.model}")
print(f" - {lu_dong_bin.name}: {lu_dong_bin.model}")
print("\n🎉 简单论道测试完成！")
print("\n📝 智能体基础功能验证成功")
except Exception as e:
print(f"❌ 论道测试失败: {e}")
return False
return True
def main():
"""主函数"""
print("🚀 稷下学宫 ADK 简单论道系统")
# 检查API密钥
api_key = os.getenv('GOOGLE_API_KEY')
if not api_key:
print("❌ 未找到 GOOGLE_API_KEY 环境变量")
print("请使用: doppler run -- python src/jixia/debates/adk_simple_debate.py")
return
print(f"✅ API密钥已配置")
# 运行测试
try:
result = simple_debate_test()
if result:
print("\n📝 测试结果: 成功")
print("\n🎯 下一步开发计划:")
print(" 1. 学习ADK的正确调用方式")
print(" 2. 实现智能体对话功能")
print(" 3. 扩展到八仙全员论道")
print(" 4. 集成实时数据源")
else:
print("\n❌ 测试失败")
except Exception as e:
print(f"❌ 运行失败: {e}")
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
太公心易 - 起承转合辩论系统
"""
import json
from datetime import datetime
from typing import Dict, List, Any
from enum import Enum
class DebateStage(Enum):
QI = "起" # 八仙按先天八卦顺序
CHENG = "承" # 雁阵式承接
ZHUAN = "转" # 自由辩论（36次handoff）
HE = "合" # 交替总结
class QiChengZhuanHeDebate:
"""起承转合辩论系统"""
def __init__(self):
# 八仙配置（先天八卦顺序）
self.baxian_sequence = ["吕洞宾", "何仙姑", "铁拐李", "汉钟离", "蓝采和", "张果老", "韩湘子", "曹国舅"]
# 雁阵配置
self.goose_formation = {
"positive": ["正1", "正2", "正3", "正4"],
"negative": ["反1", "反2", "反3", "反4"]
}
# 交替总结顺序
self.alternating_sequence = ["反1", "正1", "反2", "正2", "反3", "正3", "反4", "正4"]
# 辩论状态
self.current_stage = DebateStage.QI
self.stage_progress = 0
self.total_handoffs = 0
self.debate_history = []
# 阶段配置
self.stage_configs = {
DebateStage.QI: {"max_progress": 8, "description": "八仙按先天八卦顺序"},
DebateStage.CHENG: {"max_progress": 8, "description": "雁阵式承接"},
DebateStage.ZHUAN: {"max_progress": 36, "description": "自由辩论"},
DebateStage.HE: {"max_progress": 8, "description": "交替总结"}
}
def get_current_speaker(self) -> str:
"""获取当前发言者"""
if self.current_stage == DebateStage.QI:
return self.baxian_sequence[self.stage_progress % 8]
elif self.current_stage == DebateStage.CHENG:
if self.stage_progress < 4:
return self.goose_formation["positive"][self.stage_progress]
else:
return self.goose_formation["negative"][self.stage_progress - 4]
elif self.current_stage == DebateStage.ZHUAN:
# 简化的优先级算法
speakers = self.goose_formation["positive"] + self.goose_formation["negative"]
return speakers[self.total_handoffs % 8]
elif self.current_stage == DebateStage.HE:
return self.alternating_sequence[self.stage_progress % 8]
return "未知发言者"
def advance_stage(self):
"""推进辩论阶段"""
config = self.stage_configs[self.current_stage]
if self.stage_progress >= config["max_progress"] - 1:
self._transition_to_next_stage()
else:
self.stage_progress += 1
def _transition_to_next_stage(self):
"""转换到下一阶段"""
transitions = {
DebateStage.QI: DebateStage.CHENG,
DebateStage.CHENG: DebateStage.ZHUAN,
DebateStage.ZHUAN: DebateStage.HE,
DebateStage.HE: None
}
next_stage = transitions[self.current_stage]
if next_stage:
self.current_stage = next_stage
self.stage_progress = 0
print(f"🎭 辩论进入{next_stage.value}阶段")
else:
print("🎉 辩论结束！")
def record_speech(self, speaker: str, message: str):
"""记录发言"""
record = {
"timestamp": datetime.now().isoformat(),
"stage": self.current_stage.value,
"progress": self.stage_progress,
"speaker": speaker,
"message": message,
"handoffs": self.total_handoffs
}
self.debate_history.append(record)
if self.current_stage == DebateStage.ZHUAN:
self.total_handoffs += 1
def get_stage_info(self) -> Dict[str, Any]:
"""获取阶段信息"""
config = self.stage_configs[self.current_stage]
return {
"stage": self.current_stage.value,
"progress": self.stage_progress + 1,
"max_progress": config["max_progress"],
"description": config["description"],
"current_speaker": self.get_current_speaker(),
"total_handoffs": self.total_handoffs
}
def save_state(self, filename: str = "qczh_debate_state.json"):
"""保存状态"""
state = {
"current_stage": self.current_stage.value,
"stage_progress": self.stage_progress,
"total_handoffs": self.total_handoffs,
"debate_history": self.debate_history
}
with open(filename, 'w', encoding='utf-8') as f:
json.dump(state, f, ensure_ascii=False, indent=2)
print(f"💾 辩论状态已保存到 {filename}")
def main():
"""测试函数"""
print("🚀 起承转合辩论系统测试")
print("=" * 50)
debate = QiChengZhuanHeDebate()
# 测试各阶段
test_messages = [
"起：八仙按先天八卦顺序阐述观点",
"承：雁阵式承接，总体阐述+讥讽",
"转：自由辩论，36次handoff",
"合：交替总结，最终论证"
]
for i, message in enumerate(test_messages):
info = debate.get_stage_info()
speaker = debate.get_current_speaker()
print(f"\n🎭 阶段: {info['stage']} ({info['progress']}/{info['max_progress']})")
print(f"🗣️ 发言者: {speaker}")
print(f"💬 消息: {message}")
debate.record_speech(speaker, message)
debate.advance_stage()
debate.save_state()
print("\n✅ 测试完成！")
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
太公心易 - 起承转合辩论系统
基于先天八卦的八仙辩论架构
"""
import asyncio
import json
from datetime import datetime
from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from enum import Enum
class DebateStage(Enum):
"""辩论阶段枚举"""
QI = "起" # 八仙按先天八卦顺序
CHENG = "承" # 雁阵式承接
ZHUAN = "转" # 自由辩论（36次handoff）
HE = "合" # 交替总结
@dataclass
class Speaker:
"""发言者数据类"""
name: str
role: str
team: str # "positive" or "negative"
bagua_position: Optional[int] = None # 八卦位置（0-7）
@dataclass
class DebateContext:
"""辩论上下文"""
current_stage: DebateStage
stage_progress: int
total_handoffs: int
current_speaker: Optional[str] = None
last_message: Optional[str] = None
debate_history: List[Dict] = None
class QiChengZhuanHeDebateSystem:
"""起承转合辩论系统"""
def __init__(self):
# 八仙配置（按先天八卦顺序）
self.baxian_speakers = {
"吕洞宾": Speaker("吕洞宾", "剑仙投资顾问", "neutral", 0), # 乾
"何仙姑": Speaker("何仙姑", "慈悲风控专家", "neutral", 1), # 兑
"铁拐李": Speaker("铁拐李", "逆向思维专家", "neutral", 2), # 离
"汉钟离": Speaker("汉钟离", "平衡协调者", "neutral", 3), # 震
"蓝采和": Speaker("蓝采和", "创新思维者", "neutral", 4), # 巽
"张果老": Speaker("张果老", "历史智慧者", "neutral", 5), # 坎
"韩湘子": Speaker("韩湘子", "艺术感知者", "neutral", 6), # 艮
"曹国舅": Speaker("曹国舅", "实务执行者", "neutral", 7) # 坤
}
# 雁阵队伍配置
self.goose_formation = {
"positive": ["正1", "正2", "正3", "正4"],
"negative": ["反1", "反2", "反3", "反4"]
}
# 辩论状态
self.context = DebateContext(
current_stage=DebateStage.QI,
stage_progress=0,
total_handoffs=0,
debate_history=[]
)
# 阶段配置
self.stage_configs = {
DebateStage.QI: {
"duration": "8-10分钟",
"max_progress": 8, # 八仙轮流发言
"description": "八仙按先天八卦顺序阐述观点"
},
DebateStage.CHENG: {
"duration": "8-10分钟",
"max_progress": 8, # 正反各4人
"description": "雁阵式承接，总体阐述+讥讽"
},
DebateStage.ZHUAN: {
"duration": "12-15分钟",
"max_progress": 36, # 36次handoff
"description": "自由辩论，优先级算法决定发言"
},
DebateStage.HE: {
"duration": "8-10分钟",
"max_progress": 8, # 交替总结
"description": "交替总结，最终论证"
}
}
# 优先级算法
self.priority_algorithm = PriorityAlgorithm()
# 记忆系统
self.memory_system = DebateMemorySystem()
def get_current_speaker(self) -> str:
"""获取当前发言者"""
stage = self.context.current_stage
progress = self.context.stage_progress
if stage == DebateStage.QI:
return self._get_bagua_speaker(progress)
elif stage == DebateStage.CHENG:
return self._get_goose_formation_speaker(progress)
elif stage == DebateStage.ZHUAN:
return self._get_priority_speaker()
elif stage == DebateStage.HE:
return self._get_alternating_speaker(progress)
return "未知发言者"
def _get_bagua_speaker(self, progress: int) -> str:
"""获取八卦顺序发言者"""
bagua_sequence = ["吕洞宾", "何仙姑", "铁拐李", "汉钟离", "蓝采和", "张果老", "韩湘子", "曹国舅"]
return bagua_sequence[progress % 8]
def _get_goose_formation_speaker(self, progress: int) -> str:
"""获取雁阵发言者"""
if progress < 4:
# 正方雁阵
return self.goose_formation["positive"][progress]
else:
# 反方雁阵
return self.goose_formation["negative"][progress - 4]
def _get_priority_speaker(self) -> str:
"""获取优先级发言者"""
return self.priority_algorithm.calculate_next_speaker(self.context)
def _get_alternating_speaker(self, progress: int) -> str:
"""获取交替总结发言者"""
alternating_sequence = ["反1", "正1", "反2", "正2", "反3", "正3", "反4", "正4"]
return alternating_sequence[progress % 8]
def advance_stage(self):
"""推进辩论阶段"""
current_config = self.stage_configs[self.context.current_stage]
if self.context.stage_progress >= current_config["max_progress"] - 1:
# 当前阶段完成，进入下一阶段
self._transition_to_next_stage()
else:
# 当前阶段继续
self.context.stage_progress += 1
def _transition_to_next_stage(self):
"""转换到下一阶段"""
stage_transitions = {
DebateStage.QI: DebateStage.CHENG,
DebateStage.CHENG: DebateStage.ZHUAN,
DebateStage.ZHUAN: DebateStage.HE,
DebateStage.HE: None # 辩论结束
}
next_stage = stage_transitions[self.context.current_stage]
if next_stage:
self.context.current_stage = next_stage
self.context.stage_progress = 0
print(f"🎭 辩论进入{next_stage.value}阶段")
else:
print("🎉 辩论结束！")
def record_speech(self, speaker: str, message: str):
"""记录发言"""
speech_record = {
"timestamp": datetime.now().isoformat(),
"stage": self.context.current_stage.value,
"stage_progress": self.context.stage_progress,
"speaker": speaker,
"message": message,
"total_handoffs": self.context.total_handoffs
}
self.context.debate_history.append(speech_record)
self.context.last_message = message
self.context.current_speaker = speaker
# 更新记忆系统
self.memory_system.store_speech(speaker, message, self.context)
# 如果是转阶段，增加handoff计数
if self.context.current_stage == DebateStage.ZHUAN:
self.context.total_handoffs += 1
def get_stage_info(self) -> Dict[str, Any]:
"""获取当前阶段信息"""
stage = self.context.current_stage
config = self.stage_configs[stage]
return {
"current_stage": stage.value,
"stage_progress": self.context.stage_progress,
"max_progress": config["max_progress"],
"description": config["description"],
"current_speaker": self.get_current_speaker(),
"total_handoffs": self.context.total_handoffs
}
def save_debate_state(self, filename: str = "debate_state.json"):
"""保存辩论状态"""
state_data = {
"context": {
"current_stage": self.context.current_stage.value,
"stage_progress": self.context.stage_progress,
"total_handoffs": self.context.total_handoffs,
"current_speaker": self.context.current_speaker,
"last_message": self.context.last_message
},
"debate_history": self.context.debate_history,
"memory_data": self.memory_system.get_memory_data()
}
with open(filename, 'w', encoding='utf-8') as f:
json.dump(state_data, f, ensure_ascii=False, indent=2)
print(f"💾 辩论状态已保存到 {filename}")
class PriorityAlgorithm:
"""优先级算法"""
def __init__(self):
self.speaker_weights = {
"rebuttal_urgency": 0.3,
"argument_strength": 0.25,
"time_pressure": 0.2,
"audience_reaction": 0.15,
"strategy_need": 0.1
}
def calculate_next_speaker(self, context: DebateContext) -> str:
"""计算下一个发言者"""
# 简化的优先级算法
available_speakers = ["正1", "正2", "正3", "正4", "反1", "反2", "反3", "反4"]
# 基于当前上下文计算优先级
priorities = {}
for speaker in available_speakers:
priority_score = self._calculate_speaker_priority(speaker, context)
priorities[speaker] = priority_score
# 选择最高优先级发言者
return max(priorities, key=priorities.get)
def _calculate_speaker_priority(self, speaker: str, context: DebateContext) -> float:
"""计算发言者优先级"""
# 简化的优先级计算
base_score = 0.5
# 根据发言者角色调整
if "正" in speaker:
base_score += 0.1
if "反" in speaker:
base_score += 0.1
# 根据handoff次数调整
if context.total_handoffs % 2 == 0:
base_score += 0.2
return base_score
class DebateMemorySystem:
"""辩论记忆系统"""
def __init__(self):
self.speaker_memories = {}
self.debate_memories = []
def store_speech(self, speaker: str, message: str, context: DebateContext):
"""存储发言记忆"""
if speaker not in self.speaker_memories:
self.speaker_memories[speaker] = []
memory_entry = {
"timestamp": datetime.now().isoformat(),
"stage": context.current_stage.value,
"message": message,
"context": {
"stage_progress": context.stage_progress,
"total_handoffs": context.total_handoffs
}
}
self.speaker_memories[speaker].append(memory_entry)
self.debate_memories.append(memory_entry)
def get_speaker_memory(self, speaker: str, limit: int = 5) -> List[Dict]:
"""获取发言者记忆"""
if speaker in self.speaker_memories:
return self.speaker_memories[speaker][-limit:]
return []
def get_memory_data(self) -> Dict[str, Any]:
"""获取记忆数据"""
return {
"speaker_memories": self.speaker_memories,
"debate_memories": self.debate_memories
}
def main():
"""主函数 - 测试起承转合辩论系统"""
print("🚀 太公心易 - 起承转合辩论系统")
print("=" * 60)
# 创建辩论系统
debate_system = QiChengZhuanHeDebateSystem()
# 测试各阶段
test_messages = [
"起：八仙按先天八卦顺序阐述观点",
"承：雁阵式承接，总体阐述+讥讽",
"转：自由辩论，36次handoff",
"合：交替总结，最终论证"
]
for i, message in enumerate(test_messages):
stage_info = debate_system.get_stage_info()
current_speaker = debate_system.get_current_speaker()
print(f"\n🎭 当前阶段: {stage_info['current_stage']}")
print(f"📊 进度: {stage_info['stage_progress'] + 1}/{stage_info['max_progress']}")
print(f"🗣️ 发言者: {current_speaker}")
print(f"💬 消息: {message}")
# 记录发言
debate_system.record_speech(current_speaker, message)
# 推进阶段
debate_system.advance_stage()
# 保存状态
debate_system.save_debate_state()
print("\n✅ 起承转合辩论系统测试完成！")
if __name__ == "__main__":
main()

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src/jixia/memory/base_memory_bank.py Normal file
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#!/usr/bin/env python3
"""
通用记忆银行抽象，便于插入不同后端（Vertex、Cloudflare AutoRAG等）
"""
from __future__ import annotations
from typing import Dict, List, Any, Optional, Protocol, runtime_checkable
@runtime_checkable
class MemoryBankProtocol(Protocol):
async def create_memory_bank(self, agent_name: str, display_name: Optional[str] = None) -> str: ...
async def add_memory(
self,
agent_name: str,
content: str,
memory_type: str = "conversation",
debate_topic: str = "",
metadata: Optional[Dict[str, Any]] = None,
) -> str: ...
async def search_memories(
self,
agent_name: str,
query: str,
memory_type: Optional[str] = None,
limit: int = 10,
) -> List[Dict[str, Any]]: ...
async def get_agent_context(self, agent_name: str, debate_topic: str) -> str: ...
async def save_debate_session(
self,
debate_topic: str,
participants: List[str],
conversation_history: List[Dict[str, str]],
outcomes: Optional[Dict[str, Any]] = None,
) -> None: ...

37
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#!/usr/bin/env python3
"""
记忆银行工厂：根据配置创建 Vertex 实现
"""
from __future__ import annotations
import os
from typing import Optional
from .base_memory_bank import MemoryBankProtocol
from .vertex_memory_bank import VertexMemoryBank
def get_memory_backend(prefer: Optional[str] = None) -> MemoryBankProtocol:
"""
根据环境变量选择记忆后端：
- JIXIA_MEMORY_BACKEND=vertex (默认)
- 如果未设置，默认使用 Vertex
"""
# 从环境变量读取后端选择，默认为 vertex
backend = os.getenv("JIXIA_MEMORY_BACKEND", "vertex").lower()
if prefer:
backend = prefer.lower()
if backend != "vertex":
raise ValueError(f"不支持的记忆后端: {backend}，当前只支持 'vertex'")
# Vertex 作为唯一后端
try:
mem = VertexMemoryBank.from_config()
return mem
except Exception as e:
# 不可用时抛错
raise RuntimeError(
"未能创建 Vertex 记忆后端：请配置 Vertex (GOOGLE_*) 环境变量"
) from e

463
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#!/usr/bin/env python3
"""
Vertex AI Memory Bank 集成模块
为稷下学宫AI辩论系统提供记忆银行功能
"""
import os
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from datetime import datetime
import json
try:
from google.cloud import aiplatform
# Memory Bank 功能可能还在预览版中，先使用基础功能
VERTEX_AI_AVAILABLE = True
except ImportError:
VERTEX_AI_AVAILABLE = False
print("⚠️ Google Cloud AI Platform 未安装，Memory Bank功能不可用")
print("安装命令: pip install google-cloud-aiplatform")
from config.doppler_config import get_google_genai_config
@dataclass
class MemoryEntry:
"""记忆条目数据结构"""
content: str
metadata: Dict[str, Any]
timestamp: datetime
agent_name: str
debate_topic: str
memory_type: str # "conversation", "preference", "knowledge", "strategy"
class VertexMemoryBank:
"""
Vertex AI Memory Bank 管理器
为八仙辩论系统提供智能记忆功能
"""
def __init__(self, project_id: str, location: str = "us-central1"):
"""
初始化Memory Bank
Args:
project_id: Google Cloud项目ID
location: 部署区域
"""
if not VERTEX_AI_AVAILABLE:
print("⚠️ Google Cloud AI Platform 未安装，使用本地模拟模式")
# 不抛出异常，允许使用本地模拟模式
self.project_id = project_id
self.location = location
self.memory_banks = {} # 存储不同智能体的记忆银行
self.local_memories = {} # 本地记忆存储 (临时方案)
# 初始化AI Platform
try:
aiplatform.init(project=project_id, location=location)
print(f"✅ Vertex AI 初始化成功: {project_id} @ {location}")
except Exception as e:
print(f"⚠️ Vertex AI 初始化失败，使用本地模拟模式: {e}")
# 八仙智能体名称映射
self.baxian_agents = {
"tieguaili": "铁拐李",
"hanzhongli": "汉钟离",
"zhangguolao": "张果老",
"lancaihe": "蓝采和",
"hexiangu": "何仙姑",
"lvdongbin": "吕洞宾",
"hanxiangzi": "韩湘子",
"caoguojiu": "曹国舅"
}
@classmethod
def from_config(cls) -> 'VertexMemoryBank':
"""
从配置创建Memory Bank实例
Returns:
VertexMemoryBank实例
"""
config = get_google_genai_config()
project_id = config.get('project_id')
location = config.get('location', 'us-central1')
if not project_id:
raise ValueError("Google Cloud Project ID 未配置，请设置 GOOGLE_CLOUD_PROJECT_ID")
return cls(project_id=project_id, location=location)
async def create_memory_bank(self, agent_name: str, display_name: str = None) -> str:
"""
为指定智能体创建记忆银行
Args:
agent_name: 智能体名称 (如 "tieguaili")
display_name: 显示名称 (如 "铁拐李的记忆银行")
Returns:
记忆银行ID
"""
if not display_name:
chinese_name = self.baxian_agents.get(agent_name, agent_name)
display_name = f"{chinese_name}的记忆银行"
try:
# 使用本地存储模拟记忆银行 (临时方案)
memory_bank_id = f"memory_bank_{agent_name}_{self.project_id}"
# 初始化本地记忆存储
if agent_name not in self.local_memories:
self.local_memories[agent_name] = []
self.memory_banks[agent_name] = memory_bank_id
print(f"✅ 为 {display_name} 创建记忆银行: {memory_bank_id}")
return memory_bank_id
except Exception as e:
print(f"❌ 创建记忆银行失败: {e}")
raise
async def add_memory(self,
agent_name: str,
content: str,
memory_type: str = "conversation",
debate_topic: str = "",
metadata: Dict[str, Any] = None) -> str:
"""
添加记忆到指定智能体的记忆银行
Args:
agent_name: 智能体名称
content: 记忆内容
memory_type: 记忆类型 ("conversation", "preference", "knowledge", "strategy")
debate_topic: 辩论主题
metadata: 额外元数据
Returns:
记忆ID
"""
if agent_name not in self.memory_banks:
await self.create_memory_bank(agent_name)
if metadata is None:
metadata = {}
# 构建记忆条目
memory_entry = MemoryEntry(
content=content,
metadata={
**metadata,
"agent_name": agent_name,
"chinese_name": self.baxian_agents.get(agent_name, agent_name),
"memory_type": memory_type,
"debate_topic": debate_topic,
"system": "jixia_academy"
},
timestamp=datetime.now(),
agent_name=agent_name,
debate_topic=debate_topic,
memory_type=memory_type
)
try:
# 使用本地存储添加记忆 (临时方案)
memory_id = f"memory_{agent_name}_{len(self.local_memories[agent_name])}"
# 添加到本地存储
memory_data = {
"id": memory_id,
"content": content,
"metadata": memory_entry.metadata,
"timestamp": memory_entry.timestamp.isoformat(),
"memory_type": memory_type,
"debate_topic": debate_topic
}
self.local_memories[agent_name].append(memory_data)
print(f"✅ 为 {self.baxian_agents.get(agent_name)} 添加记忆: {memory_type}")
return memory_id
except Exception as e:
print(f"❌ 添加记忆失败: {e}")
raise
async def search_memories(self,
agent_name: str,
query: str,
memory_type: str = None,
limit: int = 10) -> List[Dict[str, Any]]:
"""
搜索智能体的相关记忆
Args:
agent_name: 智能体名称
query: 搜索查询
memory_type: 记忆类型过滤
limit: 返回结果数量限制
Returns:
相关记忆列表
"""
if agent_name not in self.memory_banks:
return []
try:
# 使用本地存储搜索记忆 (临时方案)
if agent_name not in self.local_memories:
return []
memories = self.local_memories[agent_name]
results = []
# 简单的文本匹配搜索
query_lower = query.lower()
for memory in memories:
# 检查记忆类型过滤
if memory_type and memory.get("memory_type") != memory_type:
continue
# 检查内容匹配
content_lower = memory["content"].lower()
debate_topic_lower = memory.get("debate_topic", "").lower()
# 在内容或辩论主题中搜索
if query_lower in content_lower or query_lower in debate_topic_lower:
# 计算简单的相关性分数
content_matches = content_lower.count(query_lower)
topic_matches = debate_topic_lower.count(query_lower)
total_words = len(content_lower.split()) + len(debate_topic_lower.split())
relevance_score = (content_matches + topic_matches) / max(total_words, 1)
results.append({
"content": memory["content"],
"metadata": memory["metadata"],
"relevance_score": relevance_score
})
# 按相关性排序并限制结果数量
results.sort(key=lambda x: x["relevance_score"], reverse=True)
return results[:limit]
except Exception as e:
print(f"❌ 搜索记忆失败: {e}")
return []
async def get_agent_context(self, agent_name: str, debate_topic: str) -> str:
"""
获取智能体在特定辩论主题下的上下文记忆
Args:
agent_name: 智能体名称
debate_topic: 辩论主题
Returns:
格式化的上下文字符串
"""
# 搜索相关记忆
conversation_memories = await self.search_memories(
agent_name, debate_topic, "conversation", limit=5
)
preference_memories = await self.search_memories(
agent_name, debate_topic, "preference", limit=3
)
strategy_memories = await self.search_memories(
agent_name, debate_topic, "strategy", limit=3
)
# 构建上下文
context_parts = []
if conversation_memories:
context_parts.append("## 历史对话记忆")
for mem in conversation_memories:
context_parts.append(f"- {mem['content']}")
if preference_memories:
context_parts.append("\n## 偏好记忆")
for mem in preference_memories:
context_parts.append(f"- {mem['content']}")
if strategy_memories:
context_parts.append("\n## 策略记忆")
for mem in strategy_memories:
context_parts.append(f"- {mem['content']}")
chinese_name = self.baxian_agents.get(agent_name, agent_name)
if context_parts:
return f"# {chinese_name}的记忆上下文\n\n" + "\n".join(context_parts)
else:
return f"# {chinese_name}的记忆上下文\n\n暂无相关记忆。"
async def save_debate_session(self,
debate_topic: str,
participants: List[str],
conversation_history: List[Dict[str, str]],
outcomes: Dict[str, Any] = None) -> None:
"""
保存完整的辩论会话到各参与者的记忆银行
Args:
debate_topic: 辩论主题
participants: 参与者列表
conversation_history: 对话历史
outcomes: 辩论结果和洞察
"""
for agent_name in participants:
if agent_name not in self.baxian_agents:
continue
# 保存对话历史
conversation_summary = self._summarize_conversation(
conversation_history, agent_name
)
await self.add_memory(
agent_name=agent_name,
content=conversation_summary,
memory_type="conversation",
debate_topic=debate_topic,
metadata={
"participants": participants,
"session_length": len(conversation_history)
}
)
# 保存策略洞察
if outcomes:
strategy_insight = self._extract_strategy_insight(
outcomes, agent_name
)
if strategy_insight:
await self.add_memory(
agent_name=agent_name,
content=strategy_insight,
memory_type="strategy",
debate_topic=debate_topic,
metadata={"session_outcome": outcomes}
)
def _summarize_conversation(self,
conversation_history: List[Dict[str, str]],
agent_name: str) -> str:
"""
为特定智能体总结对话历史
Args:
conversation_history: 对话历史
agent_name: 智能体名称
Returns:
对话总结
"""
agent_messages = [
msg for msg in conversation_history
if msg.get("agent") == agent_name
]
if not agent_messages:
return "本次辩论中未发言"
chinese_name = self.baxian_agents.get(agent_name, agent_name)
summary = f"{chinese_name}在本次辩论中的主要观点：\n"
for i, msg in enumerate(agent_messages[:3], 1): # 只取前3条主要观点
summary += f"{i}. {msg.get('content', '')[:100]}...\n"
return summary
def _extract_strategy_insight(self,
outcomes: Dict[str, Any],
agent_name: str) -> Optional[str]:
"""
从辩论结果中提取策略洞察
Args:
outcomes: 辩论结果
agent_name: 智能体名称
Returns:
策略洞察或None
"""
# 这里可以根据实际的outcomes结构来提取洞察
# 暂时返回一个简单的示例
chinese_name = self.baxian_agents.get(agent_name, agent_name)
if "winner" in outcomes and outcomes["winner"] == agent_name:
return f"{chinese_name}在本次辩论中获胜，其论证策略值得保持。"
elif "insights" in outcomes and agent_name in outcomes["insights"]:
return outcomes["insights"][agent_name]
return None
# 便捷函数
async def initialize_baxian_memory_banks(project_id: str, location: str = "us-central1") -> VertexMemoryBank:
"""
初始化所有八仙智能体的记忆银行
Args:
project_id: Google Cloud项目ID
location: 部署区域
Returns:
配置好的VertexMemoryBank实例
"""
memory_bank = VertexMemoryBank(project_id, location)
print("🏛️ 正在为稷下学宫八仙创建记忆银行...")
for agent_key, chinese_name in memory_bank.baxian_agents.items():
try:
await memory_bank.create_memory_bank(agent_key)
except Exception as e:
print(f"⚠️ 创建 {chinese_name} 记忆银行时出错: {e}")
print("✅ 八仙记忆银行初始化完成")
return memory_bank
if __name__ == "__main__":
import asyncio
async def test_memory_bank():
"""测试Memory Bank功能"""
try:
# 从配置创建Memory Bank
memory_bank = VertexMemoryBank.from_config()
# 测试创建记忆银行
await memory_bank.create_memory_bank("tieguaili")
# 测试添加记忆
await memory_bank.add_memory(
agent_name="tieguaili",
content="在讨论NVIDIA股票时，我倾向于逆向思维，关注潜在风险。",
memory_type="preference",
debate_topic="NVIDIA投资分析"
)
# 测试搜索记忆
results = await memory_bank.search_memories(
agent_name="tieguaili",
query="NVIDIA",
limit=5
)
print(f"搜索结果: {len(results)} 条记忆")
for result in results:
print(f"- {result['content']}")
except Exception as e:
print(f"❌ 测试失败: {e}")
# 运行测试
asyncio.run(test_memory_bank())

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// API 测试示例脚本
// 演示如何使用 Hyperdrive API 进行 CRUD 操作
const API_BASE_URL = 'https://hyperdrive-neondb-test.<your-subdomain>.workers.dev';
const API_KEY = 'your-api-key'; // 可选，如果设置了 API_SECRET
// 通用请求函数
async function apiRequest(endpoint, options = {}) {
const url = `${API_BASE_URL}${endpoint}`;
const headers = {
'Content-Type': 'application/json',
...(API_KEY && { 'X-API-Key': API_KEY }),
...options.headers
};
try {
const response = await fetch(url, {
...options,
headers
});
const data = await response.json();
if (!response.ok) {
throw new Error(`API Error: ${data.message || response.statusText}`);
}
return data;
} catch (error) {
console.error(`Request failed for ${endpoint}:`, error.message);
throw error;
}
}
// API 测试函数
class ApiTester {
static async testHealthCheck() {
console.log('\n🏥 Testing health check...');
try {
const result = await apiRequest('/health');
console.log('✅ Health check passed:', result.data);
return true;
} catch (error) {
console.log('❌ Health check failed:', error.message);
return false;
}
}
static async initializeDatabase() {
console.log('\n🗄️ Initializing database...');
try {
const result = await apiRequest('/init', { method: 'POST' });
console.log('✅ Database initialized:', result.message);
return true;
} catch (error) {
console.log('❌ Database initialization failed:', error.message);
return false;
}
}
static async createUser(name, email) {
console.log(`\n👤 Creating user: ${name} (${email})...`);
try {
const result = await apiRequest('/users', {
method: 'POST',
body: JSON.stringify({ name, email })
});
console.log('✅ User created:', result.data);
return result.data;
} catch (error) {
console.log('❌ User creation failed:', error.message);
return null;
}
}
static async getUsers(page = 1, limit = 10, search = null) {
console.log(`\n📋 Getting users (page ${page}, limit ${limit}${search ? `, search: ${search}` : ''})...`);
try {
let endpoint = `/users?page=${page}&limit=${limit}`;
if (search) endpoint += `&search=${encodeURIComponent(search)}`;
const result = await apiRequest(endpoint);
console.log('✅ Users retrieved:', {
count: result.data.length,
total: result.meta.total,
users: result.data.map(u => `${u.name} (${u.email})`)
});
return result;
} catch (error) {
console.log('❌ Failed to get users:', error.message);
return null;
}
}
static async getUserById(id) {
console.log(`\n🔍 Getting user by ID: ${id}...`);
try {
const result = await apiRequest(`/users/${id}`);
console.log('✅ User found:', result.data);
return result.data;
} catch (error) {
console.log('❌ Failed to get user:', error.message);
return null;
}
}
static async updateUser(id, updates) {
console.log(`\n✏️ Updating user ${id}:`, updates);
try {
const result = await apiRequest(`/users/${id}`, {
method: 'PUT',
body: JSON.stringify(updates)
});
console.log('✅ User updated:', result.data);
return result.data;
} catch (error) {
console.log('❌ Failed to update user:', error.message);
return null;
}
}
static async deleteUser(id) {
console.log(`\n🗑️ Deleting user ${id}...`);
try {
const result = await apiRequest(`/users/${id}`, { method: 'DELETE' });
console.log('✅ User deleted:', result.message);
return true;
} catch (error) {
console.log('❌ Failed to delete user:', error.message);
return false;
}
}
static async getApiDocs() {
console.log('\n📚 Getting API documentation...');
try {
const result = await apiRequest('/docs');
console.log('✅ API Documentation:');
console.log('Endpoints:', result.data.endpoints);
console.log('Authentication:', result.data.authentication);
console.log('Examples:', result.data.examples);
return result.data;
} catch (error) {
console.log('❌ Failed to get API docs:', error.message);
return null;
}
}
}
// 完整的测试流程
async function runFullTest() {
console.log('🚀 Starting Hyperdrive API Test Suite');
console.log('=====================================');
// 1. 健康检查
const healthOk = await ApiTester.testHealthCheck();
if (!healthOk) {
console.log('\n❌ Health check failed. Please check your deployment.');
return;
}
// 2. 获取 API 文档
await ApiTester.getApiDocs();
// 3. 初始化数据库
await ApiTester.initializeDatabase();
// 4. 创建测试用户
const user1 = await ApiTester.createUser('张三', 'zhangsan@example.com');
const user2 = await ApiTester.createUser('李四', 'lisi@example.com');
const user3 = await ApiTester.createUser('王五', 'wangwu@example.com');
if (!user1 || !user2 || !user3) {
console.log('\n❌ Failed to create test users.');
return;
}
// 5. 获取用户列表
await ApiTester.getUsers();
// 6. 搜索用户
await ApiTester.getUsers(1, 10, '张');
// 7. 获取单个用户
await ApiTester.getUserById(user1.id);
// 8. 更新用户
await ApiTester.updateUser(user1.id, {
name: '张三丰',
email: 'zhangsanfeng@example.com'
});
// 9. 验证更新
await ApiTester.getUserById(user1.id);
// 10. 分页测试
await ApiTester.getUsers(1, 2); // 第一页，每页2条
await ApiTester.getUsers(2, 2); // 第二页，每页2条
// 11. 删除用户
await ApiTester.deleteUser(user3.id);
// 12. 验证删除
await ApiTester.getUserById(user3.id); // 应该返回 404
// 13. 最终用户列表
await ApiTester.getUsers();
console.log('\n🎉 API Test Suite Completed!');
console.log('============================');
}
// 性能测试
async function performanceTest() {
console.log('\n⚡ Performance Test');
console.log('==================');
const startTime = Date.now();
const promises = [];
// 并发创建10个用户
for (let i = 0; i < 10; i++) {
promises.push(
ApiTester.createUser(`测试用户${i}`, `test${i}@example.com`)
);
}
try {
const results = await Promise.all(promises);
const endTime = Date.now();
const duration = endTime - startTime;
console.log(`✅ Created ${results.filter(r => r).length} users in ${duration}ms`);
console.log(`📊 Average: ${(duration / 10).toFixed(2)}ms per user`);
// 清理测试数据
console.log('\n🧹 Cleaning up test data...');
for (const user of results.filter(r => r)) {
await ApiTester.deleteUser(user.id);
}
} catch (error) {
console.log('❌ Performance test failed:', error.message);
}
}
// 错误处理测试
async function errorHandlingTest() {
console.log('\n🚨 Error Handling Test');
console.log('======================');
// 测试无效数据
console.log('\n Testing invalid user data...');
await ApiTester.createUser('', 'invalid-email'); // 应该失败
// 测试不存在的用户
console.log('\n Testing non-existent user...');
await ApiTester.getUserById(99999); // 应该返回 404
// 测试无效的更新
console.log('\n Testing invalid update...');
await ApiTester.updateUser(99999, { name: 'Test' }); // 应该返回 404
}
// 主函数
async function main() {
console.log('请确保已经部署了 Worker 并更新了 API_BASE_URL');
console.log('如果设置了 API_SECRET，请更新 API_KEY 变量\n');
try {
await runFullTest();
await performanceTest();
await errorHandlingTest();
} catch (error) {
console.error('\n💥 Test suite failed:', error.message);
}
}
// 如果直接运行此脚本
if (typeof window === 'undefined') {
// Node.js 环境
const fetch = require('node-fetch');
global.fetch = fetch;
main();
} else {
// 浏览器环境
console.log('在浏览器控制台中运行: main()');
}
// 导出函数供其他模块使用
if (typeof module !== 'undefined' && module.exports) {
module.exports = {
ApiTester,
runFullTest,
performanceTest,
errorHandlingTest,
main
};
}

79
test-hyperdrive-remote.js Normal file
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// Test script to validate Hyperdrive configuration for remote deployment
// This script helps test the configuration without local PostgreSQL
const { execSync } = require('child_process');
const fs = require('fs');
console.log('🚀 Testing Hyperdrive Configuration for Remote Deployment');
console.log('======================================================');
try {
// Check wrangler configuration
console.log('\n📋 Validating wrangler.toml...');
const wranglerContent = fs.readFileSync('wrangler.toml', 'utf8');
console.log('✅ wrangler.toml loaded successfully');
// Validate configuration syntax
try {
execSync('wrangler config validate', { stdio: 'pipe' });
console.log('✅ wrangler.toml syntax is valid');
} catch (error) {
console.log('⚠️ Configuration validation warning (this is normal for Hyperdrive)');
}
// Check if we can authenticate with Cloudflare
console.log('\n🔐 Checking Cloudflare authentication...');
try {
const whoami = execSync('wrangler whoami', { encoding: 'utf8' });
console.log('✅ Authenticated with Cloudflare');
console.log(` ${whoami.trim()}`);
} catch (error) {
console.log('❌ Not authenticated with Cloudflare');
console.log(' Run: wrangler login');
return;
}
// List Hyperdrive configurations
console.log('\n🔗 Checking Hyperdrive configurations...');
try {
const hyperdrives = execSync('wrangler hyperdrive list', { encoding: 'utf8' });
console.log('✅ Hyperdrive configurations:');
console.log(hyperdrives);
} catch (error) {
console.log('⚠️ Could not list Hyperdrive configurations');
console.log(' Error:', error.message);
}
// Check specific Hyperdrive
console.log('\n🎯 Checking specific Hyperdrive ID...');
try {
const hyperdriveInfo = execSync('wrangler hyperdrive get ef43924d89064cddabfaccf06aadfab6', { encoding: 'utf8' });
console.log('✅ Hyperdrive configuration found:');
console.log(hyperdriveInfo);
} catch (error) {
console.log('❌ Could not find Hyperdrive configuration');
console.log(' Error:', error.message);
console.log(' Make sure the Hyperdrive ID is correct and exists in your account');
}
console.log('\n📝 Configuration Summary:');
console.log(' - Worker Name: hyperdrive-neondb-test');
console.log(' - Hyperdrive ID: ef43924d89064cddabfaccf06aadfab6');
console.log(' - Binding: HYPERDRIVE');
console.log(' - Database Type: NeonDB (PostgreSQL)');
console.log('\n🚀 Deployment Commands:');
console.log(' 1. Deploy to production: wrangler deploy');
console.log(' 2. Test endpoints after deployment:');
console.log(' - https://hyperdrive-neondb-test.<your-subdomain>.workers.dev/test-connection');
console.log(' - https://hyperdrive-neondb-test.<your-subdomain>.workers.dev/test-query');
console.log('\n💡 Tips:');
console.log(' - Hyperdrive provides connection pooling and caching for your database');
console.log(' - It reduces latency and improves performance for database queries');
console.log(' - The worker will automatically use the Hyperdrive connection in production');
} catch (error) {
console.error('❌ Error during testing:', error.message);
}

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test-hyperdrive.js Normal file
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// Simple test script for Hyperdrive configuration
// This script helps verify the wrangler.toml configuration
console.log('🚀 Hyperdrive NeonDB Test Configuration');
console.log('=====================================');
// Check if wrangler.toml exists and has correct configuration
const fs = require('fs');
const path = require('path');
try {
const wranglerPath = path.join(__dirname, 'wrangler.toml');
if (fs.existsSync(wranglerPath)) {
console.log('✅ wrangler.toml found');
const content = fs.readFileSync(wranglerPath, 'utf8');
// Check for Hyperdrive configuration
if (content.includes('hyperdrive')) {
console.log('✅ Hyperdrive configuration found');
} else {
console.log('❌ Hyperdrive configuration missing');
}
// Check for binding
if (content.includes('binding = "HYPERDRIVE"')) {
console.log('✅ HYPERDRIVE binding configured');
} else {
console.log('❌ HYPERDRIVE binding missing');
}
// Check for Hyperdrive ID
if (content.includes('ef43924d89064cddabfaccf06aadfab6')) {
console.log('✅ Hyperdrive ID configured');
} else {
console.log('❌ Hyperdrive ID missing');
}
// Check for nodejs_compat
if (content.includes('nodejs_compat')) {
console.log('✅ nodejs_compat flag enabled');
} else {
console.log('❌ nodejs_compat flag missing');
}
} else {
console.log('❌ wrangler.toml not found');
}
// Check if src/index.ts exists
const indexPath = path.join(__dirname, 'src', 'index.ts');
if (fs.existsSync(indexPath)) {
console.log('✅ src/index.ts found');
} else {
console.log('❌ src/index.ts missing');
}
// Check if package.json exists
const packagePath = path.join(__dirname, 'package.json');
if (fs.existsSync(packagePath)) {
console.log('✅ package.json found');
const packageContent = JSON.parse(fs.readFileSync(packagePath, 'utf8'));
// Check for required dependencies
if (packageContent.dependencies && packageContent.dependencies.pg) {
console.log('✅ pg dependency configured');
} else {
console.log('❌ pg dependency missing');
}
if (packageContent.devDependencies && packageContent.devDependencies['@cloudflare/workers-types']) {
console.log('✅ Cloudflare Workers types configured');
} else {
console.log('❌ Cloudflare Workers types missing');
}
}
console.log('\n📋 Next Steps:');
console.log('1. Run: wrangler dev --local (for local testing)');
console.log('2. Run: wrangler dev (for remote testing with Hyperdrive)');
console.log('3. Test endpoints:');
console.log(' - http://localhost:8787/test-connection');
console.log(' - http://localhost:8787/test-query');
console.log('\n🔧 Hyperdrive Configuration:');
console.log(' - Hyperdrive ID: ef43924d89064cddabfaccf06aadfab6');
console.log(' - Binding: HYPERDRIVE');
console.log(' - Database: NeonDB (PostgreSQL)');
} catch (error) {
console.error('❌ Error checking configuration:', error.message);
}

67
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#!/bin/bash
# 术数书 API 测试脚本
# 使用 curl 命令测试 Hyperdrive + NeonDB 术数书查询系统
API_BASE="https://hyperdrive.seekkey.tech"
echo "🚀 术数书 Hyperdrive API 测试"
echo "================================"
# 测试连接
echo ""
echo "🔗 测试数据库连接..."
curl -s "$API_BASE/test-connection" | jq -r '.message // .error // "连接测试完成"'
# 查询表结构
echo ""
echo "📋 查询数据库表结构..."
echo "发现的表:"
curl -s "$API_BASE/query-tables" | jq -r '.tables[]? | "- \(.table_name) (\(.table_schema))"'
# 获取术数书统计
echo ""
echo "📊 术数书统计信息..."
curl -s "$API_BASE/shushu-stats" | jq -r '.existing_tables[]? | "- \(.table_name): \(.record_count) 条记录"'
# 查询术数书内容
echo ""
echo "📚 查询术数书内容 (前3条)..."
echo "书籍信息:"
curl -s "$API_BASE/query-shushu?limit=3" | jq -r '.data[]? | "- ID: \(.id), 标题: \(.title), 作者: \(.author), 类别: \(.category)"'
# 搜索功能测试
echo ""
echo "🔍 搜索测试..."
echo "搜索关键词: 易经"
curl -s "$API_BASE/search-shushu?q=易经&limit=2" | jq -r '.total_matches // 0 | "找到 \(.) 条匹配记录"'
echo "搜索关键词: 八卦"
curl -s "$API_BASE/search-shushu?q=八卦&limit=2" | jq -r '.total_matches // 0 | "找到 \(.) 条匹配记录"'
echo "搜索关键词: 面相"
curl -s "$API_BASE/search-shushu?q=面相&limit=2" | jq -r '.total_matches // 0 | "找到 \(.) 条匹配记录"'
# 性能测试
echo ""
echo "⚡ 性能测试..."
echo "测试查询响应时间:"
time curl -s "$API_BASE/query-shushu?limit=1" > /dev/null
echo ""
echo "✅ API 测试完成！"
echo ""
echo "📖 可用端点:"
echo "- GET $API_BASE/ - 系统信息"
echo "- GET $API_BASE/test-connection - 测试连接"
echo "- GET $API_BASE/query-tables - 查询表结构"
echo "- GET $API_BASE/query-shushu?limit=N - 查询术数书"
echo "- GET $API_BASE/search-shushu?q=keyword&limit=N - 搜索术数书"
echo "- GET $API_BASE/shushu-stats - 统计信息"
echo ""
echo "🎯 与 AutoRAG 对比优势:"
echo "- ✅ 毫秒级响应 (Hyperdrive 边缘缓存)"
echo "- ✅ 精确查询 (SQL vs 向量相似性)"
echo "- ✅ 实时数据 (直连数据库)"
echo "- ✅ 成本优化 (连接池 + 缓存)"
echo "- ✅ 全球分布 (Cloudflare 边缘网络)"

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