feat: 添加Apricity-InnocoreAI毕业设计项目

Co-creation-projects/Apricity-InnocoreAI/.env.example

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+# InnoCore AI Configuration
+
+# LLM Provider Configuration
+# 选择一个提供商: openai, dashscope, modelscope
+
+# OpenAI API Configuration
+OPENAI_API_KEY=your_openai_api_key_here
+OPENAI_BASE_URL=https://api.openai.com/v1
+# 可选模型: gpt-3.5-turbo, gpt-4, gpt-4-turbo-preview
+
+# 阿里云灵积 DashScope (推荐用于 Qwen 系列)
+# DASHSCOPE_API_KEY=your_dashscope_api_key
+# LLM_PROVIDER=dashscope
+# LLM_MODEL=qwen-turbo  # 可选: qwen-turbo, qwen-plus, qwen-max
+
+# ModelScope (需要本地部署或使用 API)
+# MODELSCOPE_API_KEY=your_modelscope_api_key
+# LLM_PROVIDER=modelscope
+# LLM_MODEL=qwen/Qwen2.5-7B-Instruct
+
+# Database Configuration
+DATABASE_URL=sqlite:///./innocore.db
+
+# Redis Configuration (optional)
+REDIS_URL=redis://localhost:6379
+
+# Security
+SECRET_KEY=your_secret_key_here_change_this_in_production
+ALGORITHM=HS256
+ACCESS_TOKEN_EXPIRE_MINUTES=30
+
+# Application Settings
+DEBUG=True
+LOG_LEVEL=INFO
+HOST=0.0.0.0
+PORT=8000
+
+# Vector Database
+VECTOR_DB_PATH=./data/vector_db
+
+# File Storage
+UPLOAD_DIR=./data/uploads
+PAPERS_DIR=./data/papers
+
+# External APIs
+CROSSREF_API=https://api.crossref.org
+GOOGLE_SCHOLAR_API=https://serpapi.com/search
+
+# Agent Configuration
+HUNTER_AGENT_ENABLED=True
+MINER_AGENT_ENABLED=True
+COACH_AGENT_ENABLED=True
+VALIDATOR_AGENT_ENABLED=True
+
+# Performance Settings
+MAX_CONCURRENT_TASKS=5
+CACHE_TTL=3600
+REQUEST_TIMEOUT=30

Co-creation-projects/Apricity-InnocoreAI/.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+venv/
+env/
+ENV/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Environment
+.env
+.env.local
+.venv
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# Logs
+logs/
+*.log
+
+# Database
+*.db
+*.sqlite
+*.sqlite3
+
+# Data
+data/
+*.csv
+*.json
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Jupyter
+.ipynb_checkpoints/
+
+# Model files
+*.pth
+*.pt
+*.ckpt
+models/checkpoints/
+
+# Submission helper files (for reference only, not to be committed to hello-agents)
+PR.md
+提交文件说明.md

Co-creation-projects/Apricity-InnocoreAI/FEATURES.md

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+# InnoCore AI 功能清单
+
+## ✅ 已实现功能
+
+### 🔄 工作模式
+
+#### 单独模式
+- ✅ 独立使用每个智能体
+- ✅ 灵活控制每个步骤
+- ✅ 适合单一任务
+
+#### 协调模式 ⭐
+- ✅ 自动化完整工作流
+- ✅ 一键完成全流程
+- ✅ 结果整合展示
+
+### 🤖 智能体功能
+
+#### Hunter - 论文搜索
+- ✅ ArXiv 实时搜索
+- ✅ 关键词搜索
+- ✅ 结果数量控制
+- ✅ 论文信息提取
+
+#### Miner - 论文分析
+- ✅ ArXiv URL 分析
+- ✅ PDF 文件上传
+- ✅ PDF 自动解析
+- ✅ 4种分析类型
+  - 摘要分析
+  - 创新点分析
+  - 对比分析
+  - 综合分析
+
+#### Validator - 引用校验
+- ✅ DOI 自动验证
+- ✅ ArXiv ID 识别
+- ✅ AI 辅助解析
+- ✅ 4种引用格式
+  - BibTeX
+  - APA
+  - IEEE
+  - MLA
+
+#### Coach - 写作助手
+- ✅ 文本改进
+- ✅ 学术润色
+- ✅ 风格转换
+- ✅ 语法检查
+
+### 🔄 工作流功能
+
+#### 完整工作流
+- ✅ 搜索论文
+- ✅ 分析内容
+- ✅ 生成引用
+- ✅ 撰写报告
+- ✅ 步骤状态跟踪
+- ✅ 错误处理
+
+#### 简化工作流
+- ✅ 搜索+分析
+- ✅ 快速执行
+- ✅ 结果展示
+
+### 🎨 前端功能
+
+#### 界面
+- ✅ 响应式设计
+- ✅ 模式切换
+- ✅ 工作流卡片
+- ✅ 参数配置面板
+
+#### 交互
+- ✅ 拖拽上传 PDF
+- ✅ 实时加载状态
+- ✅ 错误提示
+- ✅ 成功反馈
+
+#### 显示
+- ✅ Markdown 渲染
+- ✅ 代码高亮
+- ✅ 一键复制
+- ✅ 结果格式化
+
+### 🔌 API 端点
+
+#### 论文相关
+- ✅ `POST /api/v1/papers/search` - 搜索论文
+- ✅ `POST /api/v1/papers/upload` - 上传 PDF
+
+#### 分析相关
+- ✅ `POST /api/v1/analysis/analyze` - 分析论文
+- ✅ `POST /api/v1/analysis/upload-pdf` - 上传并解析 PDF
+
+#### 写作相关
+- ✅ `POST /api/v1/writing/coach` - 写作助手
+
+#### 引用相关
+- ✅ `POST /api/v1/citations/validate` - 校验引用
+- ✅ `POST /api/v1/citations/generate` - 生成引用
+
+#### 工作流相关
+- ✅ `POST /api/v1/workflow/complete` - 完整工作流
+- ✅ `POST /api/v1/workflow/search-and-analyze` - 简化工作流
+
+### 📚 文档
+
+- ✅ README.md - 项目说明
+- ✅ USAGE_GUIDE.md - 使用指南
+- ✅ WORKFLOW_GUIDE.md - 工作流指南
+- ✅ FEATURES.md - 功能清单
+
+### 🧪 测试
+
+- ✅ 单独模式测试
+- ✅ 协调模式测试
+- ✅ API 端点测试
+- ✅ 前端功能测试
+
+## 📊 性能指标
+
+### 响应时间
+- 论文搜索: ~5秒
+- 论文分析: ~20秒
+- 引用校验: ~3秒
+- 写作助手: ~15秒
+- 完整工作流: ~70秒
+- 简化工作流: ~25秒
+
+### 准确性
+- PDF 解析: 高
+- 引用识别: 高
+- AI 分析: 高
+- 格式转换: 高
+
+## 🎯 使用场景
+
+### 适合单独模式
+- ✅ 分析单篇论文
+- ✅ 校验单条引用
+- ✅ 润色特定段落
+- ✅ 快速测试功能
+
+### 适合协调模式
+- ✅ 文献综述
+- ✅ 研究调研
+- ✅ 论文写作准备
+- ✅ 批量处理
+
+## 🔧 技术栈
+
+### 后端
+- FastAPI - Web 框架
+- HelloAgent - 多智能体框架
+- pdfplumber - PDF 解析
+- arxiv - ArXiv API
+- httpx - HTTP 客户端
+
+### 前端
+- HTML5 + CSS3
+- Vanilla JavaScript
+- Markdown 渲染
+- 代码高亮
+
+### AI 模型
+- OpenAI API
+- ModelScope
+- 自定义提示词
+
+## 🚀 部署
+
+### 本地运行
+```bash
+python run.py
+```
+
+### 访问地址
+- 主页: http://localhost:8000
+- API 文档: http://localhost:8000/docs
+- 健康检查: http://localhost:8000/health
+
+## 📈 未来计划
+
+### 功能增强
+- [ ] 工作流模板
+- [ ] 自定义工作流
+- [ ] 工作流历史
+- [ ] 批量 PDF 处理
+
+### 性能优化
+- [ ] 并发处理
+- [ ] 结果缓存
+- [ ] 长文本优化
+
+### 用户体验
+- [ ] 进度条
+- [ ] 实时更新
+- [ ] 结果导出
+- [ ] 多语言支持
+
+## 📝 更新日志
+
+### v1.0.0 (2025-11-23)
+- ✅ 实现两种工作模式
+- ✅ 完整 PDF 解析功能
+- ✅ 工作流自动化
+- ✅ 前端模式切换
+- ✅ 所有测试通过
+
+## 📞 支持
+
+- 文档: [USAGE_GUIDE.md](USAGE_GUIDE.md)
+- 工作流: [WORKFLOW_GUIDE.md](WORKFLOW_GUIDE.md)
+- API: http://localhost:8000/docs

Co-creation-projects/Apricity-InnocoreAI/QUICKSTART.md

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+# InnoCore AI - Quick Start Guide
+
+## 🚀 快速启动
+
+### 1. 环境准备
+确保您已安装 Python 3.8 或更高版本
+
+### 2. 安装依赖
+```bash
+cd innocore_ai
+python setup.py
+```
+
+### 3. 配置环境变量
+编辑 `.env` 文件，添加您的 OpenAI API Key：
+```bash
+OPENAI_API_KEY=your_actual_openai_api_key_here
+```
+
+### 4. 启动应用
+```bash
+python run.py
+```
+
+### 5. 访问应用
+- 主页: http://localhost:8000
+- API文档: http://localhost:8000/docs
+- 健康检查: http://localhost:8000/health
+
+## 📋 功能特性
+
+### 🤖 智能体系统
+- **Hunter Agent**: 文献搜索与监控
+- **Miner Agent**: 深度论文分析
+- **Coach Agent**: 写作辅助
+- **Validator Agent**: 引用验证
+
+### 🔧 核心功能
+- 文献自动抓取
+- 智能论文分析
+- 学术写作助手
+- 引用格式管理
+
+## 📁 项目结构
+
+```
+innocore_ai/
+├── agents/          # 智能体模块
+├── api/            # API路由
+├── core/           # 核心功能
+├── models/         # 数据模型
+├── services/       # 业务服务
+├── utils/          # 工具函数
+├── frontend/       # 前端界面
+├── run.py          # 启动脚本
+├── setup.py        # 安装脚本
+└── .env            # 环境配置
+```
+
+## 🛠️ 开发模式
+
+如需开发模式（自动重载），可以修改 `run.py` 中的 `reload=False` 为 `reload=True`
+
+## 📞 故障排除
+
+### 常见问题
+
+1. **端口被占用**
+   - 修改 `run.py` 中的端口号
+   - 或停止占用8000端口的其他程序
+
+2. **OpenAI API Key 错误**
+   - 确保 `.env` 文件中的 API Key 正确
+   - 检查 API Key 是否有效且有足够余额
+
+3. **依赖安装失败**
+   - 尝试使用 `pip install --upgrade pip` 更新pip
+   - 或使用虚拟环境
+
+## 📚 更多信息
+
+- 详细文档: [README.md](README.md)
+- API文档: http://localhost:8000/docs
+- 配置示例: [.env.example](.env.example)
+
+---
+
+**InnoCore AI - 研创·智核**
+让AI助力您的科研创新之旅 🚀

Co-creation-projects/Apricity-InnocoreAI/README.md

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+# InnoCore AI - 研创·智核
+
+<div align="center">
+
+**智能科研创新助手 | Intelligent Research Innovation Assistant**
+
+[![Python](https://img.shields.io/badge/Python-3.8+-blue.svg)](https://www.python.org/downloads/)
+[![FastAPI](https://img.shields.io/badge/FastAPI-0.100+-green.svg)](https://fastapi.tiangolo.com/)
+[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
+
+*基于多智能体协作的科研全流程自动化系统*
+
+*基于 HelloAgent 框架构建，支持灵活的 LLM 切换*
+
+[English](README_EN.md) | 简体中文
+
+</div>
+
+---
+
+## 📖 项目简介
+
+InnoCore AI（研创·智核）是一个基于 HelloAgent 框架构建的智能科研创新助手系统。通过多智能体协作，实现从论文搜索、深度分析、写作辅助到引用校验的科研全流程自动化。
+
+### 核心特性
+
+- 🤖 **多智能体协作**：四大智能体（Hunter/Miner/Coach/Validator）协同工作
+- 🔄 **双模式支持**：单独模式（精细控制）+ 协调模式（一键完成）
+- 📚 **智能论文分析**：自动解析 PDF，提取关键信息，生成深度分析报告
+- ✍️ **AI 写作助手**：学术润色、风格转换、实时写作建议
+- 🔍 **引用智能校验**：自动识别 DOI/ArXiv ID，生成多种格式引用
+- 🎯 **工作流自动化**：一键完成搜索→分析→引用→报告全流程
+
+### 技术亮点
+
+- **PDF 深度解析**：支持学术论文的结构化提取（标题、作者、摘要、全文）
+- **混合检索**：向量检索 + 关键词匹配，提升检索准确度
+- **流式输出**：WebSocket 实时传输，提供流畅的交互体验
+- **异步架构**：基于 FastAPI 异步框架，高性能并发处理
+- **模块化设计**：清晰的分层架构，易于扩展和维护
+
+## 🎯 应用场景
+
+### 适合谁使用？
+
+- 📖 **研究生/博士生**：快速了解研究领域，辅助论文写作
+- 👨‍🏫 **高校教师**：跟踪最新研究进展，辅助课题申报
+- 🔬 **企业研发人员**：技术调研，专利分析，竞品研究
+- 📝 **学术写作者**：论文润色，引用管理，格式规范
+
+### 典型使用场景
+
+1. **文献综述**：自动搜索相关论文 → 批量分析 → 生成综述报告
+2. **论文写作**：实时润色建议 → 引用自动生成 → 格式规范检查
+3. **研究调研**：追踪特定主题 → 创新点挖掘 → 研究方向建议
+4. **学术翻译**：中英互译 → 学术表达优化 → 术语标准化
+
+## 🏗️ 系统架构
+
+### 整体架构
+
+```
+┌─────────────────────────────────────────────────────────┐
+│                    前端界面层                            │
+│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐ │
+│  │ 论文搜索  │  │ 深度分析  │  │ 写作助手  │  │ 引用管理 │ │
+│  └──────────┘  └──────────┘  └──────────┘  └──────────┘ │
+└─────────────────────────────────────────────────────────┘
+                          ↓
+┌─────────────────────────────────────────────────────────┐
+│                   API 接口层                             │
+│  FastAPI + WebSocket + RESTful API                      │
+└─────────────────────────────────────────────────────────┘
+                          ↓
+┌─────────────────────────────────────────────────────────┐
+│                 智能体编排层                             │
+│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐ │
+│  │ 🕵️Hunter │  │ 🧠 Miner│  │ ✍️ Coach│  │ 🔎 Validator│ │
+│  │ 论文搜索 │  │ 深度分析  │  │ 写作助手  │  │ 引用校验  │ │
+│  └──────────┘  └──────────┘  └──────────┘  └──────────┘ │
+└─────────────────────────────────────────────────────────┘
+                          ↓
+┌─────────────────────────────────────────────────────────┐
+│                   核心服务层                             │
+│  PDF解析 | 向量检索 | LLM调用 | 任务队列                  │
+└─────────────────────────────────────────────────────────┘
+                          ↓
+┌─────────────────────────────────────────────────────────┐
+│                   数据持久层                             │
+│  PostgreSQL | Qdrant | Redis | 文件存储                  │
+└─────────────────────────────────────────────────────────┘
+```
+
+### 四大智能体
+
+| 智能体 | 职责 | 核心能力 |
+|--------|------|----------|
+| 🕵️ **Hunter** | 论文搜索与监控 | ArXiv/IEEE 实时搜索，智能过滤，自动下载 |
+| 🧠 **Miner** | 深度分析与挖掘 | PDF 解析，创新点提取，对比分析，报告生成 |
+| ✍️ **Coach** | 写作辅助与润色 | 学术润色，风格转换，实时建议，术语优化 |
+| 🔎 **Validator** | 引用校验与格式化 | DOI 验证，多格式生成，元数据校验，标准化 |
+
+## Quick Start
+
+### 1. Installation
+
+```bash
+# Install core dependencies
+python install.py
+
+# Or install manually
+pip install fastapi uvicorn python-multipart python-dotenv pydantic httpx requests
+```
+
+### 2. Configuration
+
+Create `.env` file:
+```bash
+cp .env.example .env
+# Edit .env file and add your OpenAI API key
+```
+
+### 3. Run Application
+
+```bash
+python run.py
+```
+
+### 4. Access
+
+- Main Application: http://localhost:8000
+- API Documentation: http://localhost:8000/docs
+- Health Check: http://localhost:8000/health
+
+## Features
+
+### Work Modes
+
+- **Individual Mode**: Use each agent independently for specific tasks
+- **Workflow Mode** ⭐: Automated complete workflow coordinating all agents
+
+### Agents
+
+- 🕵️ **Hunter Agent**: Literature search and monitoring
+- 🧠 **Miner Agent**: Deep paper analysis and insight extraction
+- ✍️ **Coach Agent**: Writing assistance and style improvement
+- 🔎 **Validator Agent**: Citation verification and formatting
+
+### Workflow Automation
+
+Complete research workflow in one click:
+1. Search papers (Hunter)
+2. Analyze content (Miner)
+3. Generate citations (Validator)
+4. Create report (Coach)
+
+## Project Structure
+
+```
+innocore_ai/
+├── agents/          # AI agents
+├── api/            # REST API routes
+├── core/           # Core functionality
+├── models/         # Data models
+├── services/       # Business logic
+├── utils/          # Utilities
+├── frontend/       # Web interface
+├── main.py         # Main application entry
+├── run.py          # Simple run script
+├── install.py      # Installation script
+└── requirements-core.txt  # Core dependencies
+```
+
+## Requirements
+
+- Python 3.8+
+- OpenAI API key
+- Redis (optional, for caching)
+
+## Development
+
+```bash
+# Install development dependencies
+pip install -r requirements.txt
+
+# Run with auto-reload
+python run.py
+```
+
+## 演示效果
+
+### 主界面 - 双模式切换
+![主界面](docs/screenshots/01-主界面.png)
+
+### 论文搜索功能
+![论文搜索](docs/screenshots/02-论文搜索.png)
+
+### 深度分析功能
+![论文分析](docs/screenshots/03-论文分析.png)
+
+## 📊 性能指标
+
+- **论文搜索**：~5秒（ArXiv API 响应时间）
+- **PDF 解析**：~3秒/篇（标准学术论文）
+- **深度分析**：~20秒/篇（含 AI 推理）
+- **写作润色**：~2秒首字生成（流式输出）
+- **引用校验**：~3秒/条（含外部 API 验证）
+- **完整工作流**：~70秒（搜索3篇+分析+引用+报告）
+
+## 🛣️ 开发路线图
+
+### v1.0（当前版本）✅
+- [x] 四大智能体基础功能
+- [x] PDF 深度解析
+- [x] 双模式工作流
+- [x] Web 界面
+- [x] API 文档
+
+### v1.1（计划中）
+- [ ] 向量数据库集成（Qdrant）
+- [ ] 用户系统与权限管理
+- [ ] 历史记录与收藏功能
+- [ ] 批量处理优化
+
+### v2.0（未来）
+- [ ] 双层知识库（L1预置+L2私有）
+- [ ] 个性化写作风格学习
+- [ ] 多语言支持
+- [ ] 移动端适配
+
+## 🤝 贡献指南
+
+欢迎贡献代码、报告问题或提出建议！
+
+1. Fork 本仓库
+2. 创建特性分支 (`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. 开启 Pull Request
+
+## 📄 许可证
+
+本项目采用 MIT 许可证 - 详见 [LICENSE](LICENSE) 文件
+
+## 🙏 致谢
+
+- [HelloAgent](https://github.com/datawhalechina/hello-agents) - 多智能体框架
+- [FastAPI](https://fastapi.tiangolo.com/) - 现代 Web 框架
+- [ArXiv API](https://arxiv.org/help/api) - 学术论文数用开发框架
+- [ArXiv API](https://arxiv.org/help/api) - 学术论文数据源
+
+## 📮 联系方式
+
+- 项目主页：[GitHub](https://github.com/A-pricity/innocore-ai)
+- 问题反馈：[Issues](https://github.com/A-pricity/innocore-ai/issues)
+- 邮箱：2827867731@qq.com
+
+---
+
+<div align="center">
+
+**如果这个项目对你有帮助，请给一个 ⭐️ Star！**
+
+Made with ❤️ by InnoCore AI Team
+
+</div>

Co-creation-projects/Apricity-InnocoreAI/USAGE_GUIDE.md

@@ -0,0 +1,329 @@
+# InnoCore AI 使用指南
+
+## 快速开始
+
+### 1. 启动服务器
+
+```bash
+python run.py
+```
+
+服务器将在 `http://localhost:8000` 启动。
+
+### 2. 访问界面
+
+在浏览器中打开：
+- **主页**: http://localhost:8000
+- **API 文档**: http://localhost:8000/docs
+- **健康检查**: http://localhost:8000/health
+
+### 3. 验证系统
+
+运行验证脚本确保所有功能正常：
+
+```bash
+python verify_system.py
+```
+
+## 工作模式
+
+InnoCore AI 支持两种工作模式：
+
+### 🔹 单独模式（Individual Mode）
+独立使用每个智能体，适合：
+- 单一任务需求
+- 需要精细控制
+- 快速测试功能
+
+### 🔹 协调模式（Workflow Mode）⭐ 推荐
+自动协调所有智能体完成完整工作流，适合：
+- 完整的研究流程
+- 自动化批量处理
+- 生成综合报告
+
+**完整工作流程**：
+1. Hunter 搜索相关论文
+2. Miner 深度分析每篇论文
+3. Validator 生成标准引用
+4. Coach 撰写综合报告
+
+## 功能使用
+
+### 📚 Hunter - 论文搜索
+
+**功能**: 从 ArXiv 搜索学术论文
+
+**使用方法**:
+1. 在"Hunter - 论文搜索"卡片中输入关键词
+2. 选择数据源（默认 ArXiv）
+3. 设置返回数量（1-50）
+4. 点击"开始搜索"
+
+**示例关键词**:
+- `machine learning`
+- `deep learning`
+- `natural language processing`
+- `computer vision`
+
+**返回信息**:
+- 论文标题
+- 作者列表
+- 摘要
+- 发表日期
+- ArXiv ID
+- PDF 下载链接
+
+### 🔍 Miner - 论文分析
+
+**功能**: 深度分析论文内容，支持完整的 PDF 解析
+
+**使用方法**:
+1. **方式一：ArXiv URL**
+   - 输入 ArXiv URL（如 `https://arxiv.org/abs/2301.00001`）
+   - 系统自动获取论文信息
+
+2. **方式二：上传 PDF 文件**
+   - 点击或拖拽上传 PDF 文件
+   - 系统自动解析并提取：
+     * 论文标题
+     * 作者信息
+     * 摘要内容
+     * 全文文本
+     * 页数和字数
+   - 解析完成后自动填充 URL 字段
+
+3. 选择分析类型：
+   - **摘要 (summary)**: 生成论文概要
+   - **创新点 (innovation)**: 分析技术创新
+   - **对比 (comparison)**: 与现有方法对比
+   - **综合 (comprehensive)**: 全面深度分析
+
+4. 点击"开始分析"
+
+**支持的输入格式**:
+- ArXiv URL: `https://arxiv.org/abs/XXXX.XXXXX`
+- ArXiv ID: `2301.00001`
+- PDF 文件: 任何标准 PDF 文档（推荐文字版）
+
+**PDF 解析功能**:
+- ✅ 自动提取标题和作者
+- ✅ 智能识别摘要部分
+- ✅ 提取完整文本内容
+- ✅ 统计页数和字数
+- ✅ 基于完整内容进行 AI 分析
+
+**注意事项**:
+- 扫描版 PDF 可能无法提取文本
+- 建议使用文字版 PDF 以获得最佳效果
+- 单个文件建议不超过 50MB
+
+### ✍️ Coach - 写作助手
+
+**功能**: 学术写作辅助
+
+**使用方法**:
+1. 在文本框中输入需要处理的文本
+2. 选择写作风格：
+   - **学术**: 正式学术风格
+   - **技术**: 技术文档风格
+   - **通俗**: 易懂的科普风格
+3. 选择任务类型：
+   - **改进**: 提升文本质量
+   - **润色**: 优化表达
+   - **翻译**: 多语言翻译
+   - **检查**: 语法和拼写检查
+4. 点击"开始处理"
+
+**应用场景**:
+- 论文摘要润色
+- 技术文档改进
+- 学术翻译
+- 语法检查
+
+### ✅ Validator - 引用校验
+
+**功能**: 学术引用格式化和验证
+
+**使用方法**:
+1. 输入引用信息（支持多种格式）
+2. 选择目标格式：
+   - **BibTeX**: LaTeX 文档引用
+   - **APA**: 美国心理学会格式
+   - **IEEE**: 电气电子工程师学会格式
+   - **MLA**: 现代语言学会格式
+3. 点击"开始校验"
+
+**支持的输入**:
+- 包含 DOI 的引用
+- ArXiv URL 或 ID
+- 自由格式的引用文本
+
+**自动识别**:
+- DOI 自动验证（通过 Crossref API）
+- ArXiv ID 自动提取
+- AI 辅助解析引用信息
+
+### 🔄 完整工作流（推荐）
+
+**功能**: 一键完成从搜索到报告的全流程
+
+**使用方法**:
+1. 切换到"协调模式"
+2. 输入研究关键词
+3. 选择搜索数量（3/5/10篇）
+4. 选择分析类型
+5. 选择引用格式
+6. 勾选"生成综合报告"（可选）
+7. 点击"启动完整工作流"
+
+**自动执行步骤**:
+- ✅ 步骤1: 搜索相关论文
+- ✅ 步骤2: 分析前3篇论文
+- ✅ 步骤3: 生成标准引用
+- ✅ 步骤4: 撰写综合报告（可选）
+
+**优势**:
+- 节省时间，一键完成
+- 自动协调，无需手动切换
+- 结果整合，便于查看
+- 适合批量研究
+
+## API 使用
+
+### 论文搜索 API
+
+```bash
+curl -X POST "http://localhost:8000/api/v1/papers/search" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "keywords": "machine learning",
+    "source": "arxiv",
+    "limit": 10
+  }'
+```
+
+### 论文分析 API
+
+```bash
+curl -X POST "http://localhost:8000/api/v1/analysis/analyze" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "paper_url": "https://arxiv.org/abs/2301.00001",
+    "analysis_type": "summary"
+  }'
+```
+
+### 写作助手 API
+
+```bash
+curl -X POST "http://localhost:8000/api/v1/writing/coach" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "text": "Your text here",
+    "style": "academic",
+    "task": "improve"
+  }'
+```
+
+### 引用校验 API
+
+```bash
+curl -X POST "http://localhost:8000/api/v1/citations/validate" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "citation": "Your citation here",
+    "format": "bibtex"
+  }'
+```
+
+### 完整工作流 API
+
+```bash
+curl -X POST "http://localhost:8000/api/v1/workflow/complete" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "keywords": "deep learning",
+    "limit": 5,
+    "analysis_type": "summary",
+    "citation_format": "bibtex",
+    "writing_task": "improve"
+  }'
+```
+
+### 简化工作流 API（仅搜索+分析）
+
+```bash
+curl -X POST "http://localhost:8000/api/v1/workflow/search-and-analyze" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "keywords": "machine learning",
+    "limit": 3,
+    "analysis_type": "summary"
+  }'
+```
+
+## 常见问题
+
+### Q: 论文搜索没有结果？
+A: 尝试使用更通用的关键词，或检查网络连接到 ArXiv。
+
+### Q: 论文分析失败？
+A: 确保输入的是有效的 ArXiv URL 或 ID，格式如 `https://arxiv.org/abs/2301.00001`。
+
+### Q: 写作助手响应慢？
+A: AI 模型处理需要时间，请耐心等待。可以在 `.env` 文件中配置更快的模型。
+
+### Q: 引用校验无法验证？
+A: 尝试提供包含 DOI 的引用，或使用 ArXiv URL，这样可以自动验证。
+
+## 配置
+
+### 环境变量
+
+在 `.env` 文件中配置：
+
+```env
+# AI 模型配置
+LLM_API_KEY=your_api_key
+LLM_BASE_URL=https://api.openai.com/v1
+LLM_MODEL_NAME=gpt-3.5-turbo
+
+# 数据库配置（可选）
+DATABASE_URL=postgresql://user:password@localhost:5432/innocore
+
+# 向量数据库配置（可选）
+QDRANT_HOST=localhost
+QDRANT_PORT=6333
+```
+
+### 模型选择
+
+支持的模型：
+- OpenAI: `gpt-3.5-turbo`, `gpt-4`
+- ModelScope: 通过配置 `base_url`
+- 其他兼容 OpenAI API 的模型
+
+## 技术支持
+
+- 查看日志: 服务器控制台输出
+- API 文档: http://localhost:8000/docs
+- 健康检查: http://localhost:8000/health
+- 系统状态: 运行 `python verify_system.py`
+
+## 更新日志
+
+### 最新修复 (2025-11-23)
+- ✅ 修复了所有 API 端点
+- ✅ 集成真实 ArXiv API
+- ✅ 添加 Crossref DOI 验证
+- ✅ 实现 AI 辅助引用解析
+- ✅ 优化前端 Markdown 渲染
+- ✅ 添加复制功能
+- ✅ 改进错误处理
+
+## 下一步
+
+1. 配置数据库以启用持久化存储
+2. 配置向量数据库以启用语义搜索
+3. 自定义 AI 模型配置
+4. 探索 API 文档了解更多功能

Co-creation-projects/Apricity-InnocoreAI/__init__.py

@@ -0,0 +1,14 @@
+"""
+InnoCore AI - 研创·智核
+Intelligent Research Innovation Assistant
+"""
+
+__version__ = "1.0.0"
+__author__ = "InnoCore AI Team"
+__description__ = "AI-powered research innovation assistant based on HelloAgent framework"
+
+__all__ = [
+    "__version__",
+    "__author__", 
+    "__description__"
+]

Co-creation-projects/Apricity-InnocoreAI/agents/__init__.py

@@ -0,0 +1,19 @@
+"""
+InnoCore AI 智能体模块
+"""
+
+from .base import BaseAgent
+from .hunter import HunterAgent
+from .miner import MinerAgent
+from .coach import CoachAgent
+from .validator import ValidatorAgent
+from .controller import AgentController
+
+__all__ = [
+    "BaseAgent",
+    "HunterAgent",
+    "MinerAgent", 
+    "CoachAgent",
+    "ValidatorAgent",
+    "AgentController"
+]

Co-creation-projects/Apricity-InnocoreAI/agents/base.py

@@ -0,0 +1,173 @@
+"""
+InnoCore AI 基础智能体类
+"""
+
+import asyncio
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional, Any, Callable
+from datetime import datetime
+import json
+import logging
+
+from core.config import get_config
+from core.llm_adapter import get_llm_adapter
+from core.exceptions import AgentException, TimeoutException
+
+logger = logging.getLogger(__name__)
+
+class BaseAgent(ABC):
+    """基础智能体抽象类"""
+    
+    def __init__(self, name: str, llm = None, 
+                 max_steps: int = None, timeout: int = None):
+        self.name = name
+        self.config = get_config()
+        self.llm = llm or get_llm_adapter()
+        
+        self.max_steps = max_steps or self.config.agent_max_steps
+        self.timeout = timeout or self.config.agent_timeout
+        
+        self.history = []
+        self.tools = {}
+        self.state = "idle"
+        self.created_at = datetime.now()
+        
+    @abstractmethod
+    async def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """执行智能体任务"""
+        pass
+    
+    def add_tool(self, tool_name: str, tool_func: Callable, description: str = ""):
+        """添加工具"""
+        self.tools[tool_name] = {
+            "function": tool_func,
+            "description": description
+        }
+    
+    def get_tools_description(self) -> str:
+        """获取工具描述"""
+        if not self.tools:
+            return "暂无可用工具"
+        
+        descriptions = []
+        for name, tool_info in self.tools.items():
+            descriptions.append(f"- {name}: {tool_info['description']}")
+        
+        return "\n".join(descriptions)
+    
+    async def call_tool(self, tool_name: str, tool_input: Any) -> Any:
+        """调用工具"""
+        if tool_name not in self.tools:
+            raise AgentException(f"工具 '{tool_name}' 不存在")
+        
+        try:
+            tool_func = self.tools[tool_name]["function"]
+            if asyncio.iscoroutinefunction(tool_func):
+                result = await asyncio.wait_for(
+                    tool_func(tool_input), 
+                    timeout=self.timeout
+                )
+            else:
+                result = await asyncio.wait_for(
+                    asyncio.to_thread(tool_func, tool_input),
+                    timeout=self.timeout
+                )
+            
+            self._add_to_history(f"Tool {tool_name} called with input: {tool_input}")
+            self._add_to_history(f"Tool {tool_name} result: {result}")
+            
+            return result
+            
+        except asyncio.TimeoutError:
+            raise TimeoutException(f"工具 '{tool_name}' 执行超时")
+        except Exception as e:
+            raise AgentException(f"工具 '{tool_name}' 执行失败: {str(e)}")
+    
+    async def think(self, prompt: str, context: Dict = None) -> str:
+        """调用LLM进行思考"""
+        try:
+            # 构建完整的提示词
+            full_prompt = prompt
+            
+            # 添加上下文信息
+            if context:
+                context_str = json.dumps(context, ensure_ascii=False, indent=2)
+                full_prompt = f"上下文信息:\n{context_str}\n\n任务:\n{prompt}"
+            
+            # 添加历史记录
+            if self.history:
+                history_str = "\n".join(self.history[-10:])  # 只保留最近10条
+                full_prompt += f"\n\n历史记录:\n{history_str}"
+            
+            # 调用 HelloAgent LLM
+            response = await asyncio.wait_for(
+                self.llm.ainvoke(full_prompt),
+                timeout=self.timeout
+            )
+            
+            response_text = response.content if hasattr(response, 'content') else str(response)
+            
+            self._add_to_history(f"LLM prompt: {prompt}")
+            self._add_to_history(f"LLM response: {response_text}")
+            
+            return response_text
+            
+        except asyncio.TimeoutError:
+            raise TimeoutException("LLM思考超时")
+        except Exception as e:
+            raise AgentException(f"LLM思考失败: {str(e)}")
+    
+    def _add_to_history(self, message: str):
+        """添加到历史记录"""
+        timestamp = datetime.now().isoformat()
+        self.history.append(f"[{timestamp}] {message}")
+        
+        # 限制历史记录长度
+        if len(self.history) > 100:
+            self.history = self.history[-50:]
+    
+    def get_history(self, limit: int = 10) -> List[str]:
+        """获取历史记录"""
+        return self.history[-limit:]
+    
+    def clear_history(self):
+        """清空历史记录"""
+        self.history = []
+    
+    def set_state(self, state: str):
+        """设置智能体状态"""
+        self.state = state
+        logger.info(f"Agent {self.name} state changed to: {state}")
+    
+    def get_status(self) -> Dict[str, Any]:
+        """获取智能体状态"""
+        return {
+            "name": self.name,
+            "state": self.state,
+            "created_at": self.created_at.isoformat(),
+            "history_count": len(self.history),
+            "tools_count": len(self.tools),
+            "max_steps": self.max_steps,
+            "timeout": self.timeout
+        }
+    
+    async def validate_input(self, input_data: Dict[str, Any]) -> bool:
+        """验证输入数据"""
+        required_fields = self.get_required_fields()
+        
+        for field in required_fields:
+            if field not in input_data:
+                raise AgentException(f"缺少必需字段: {field}")
+        
+        return True
+    
+    @abstractmethod
+    def get_required_fields(self) -> List[str]:
+        """获取必需的输入字段"""
+        pass
+    
+    def __str__(self) -> str:
+        return f"{self.__class__.__name__}(name='{self.name}', state='{self.state}')"
+    
+    def __repr__(self) -> str:
+        return self.__str__()

Co-creation-projects/Apricity-InnocoreAI/agents/coach.py

@@ -0,0 +1,417 @@
+"""
+InnoCore AI 写作助教 (Coach Agent)
+负责风格迁移、实时润色、解释复杂概念
+"""
+
+import asyncio
+import json
+from typing import Dict, List, Optional, Any
+from datetime import datetime
+
+from agents.base import BaseAgent
+from core.database import db_manager
+from core.vector_store import vector_store_manager
+from core.exceptions import AgentException
+
+class CoachAgent(BaseAgent):
+    """写作助教智能体"""
+    
+    def __init__(self, llm=None):
+        super().__init__("Coach", llm)
+        
+        # 添加工具
+        self.add_tool("explain_concept", self._explain_concept, "解释复杂概念")
+        self.add_tool("polish_text", self._polish_text, "润色文本")
+        self.add_tool("mimic_style", self._mimic_style, "模仿写作风格")
+        self.add_tool("get_user_style", self._get_user_style, "获取用户写作风格")
+        self.add_tool("suggest_improvements", self._suggest_improvements, "建议改进")
+    
+    async def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """执行写作助教任务"""
+        await self.validate_input(input_data)
+        
+        self.set_state("running")
+        
+        try:
+            user_id = input_data["user_id"]
+            task_type = input_data["task_type"]  # explain, polish, mimic, suggest
+            content = input_data["content"]
+            context = input_data.get("context", {})
+            
+            result = None
+            
+            if task_type == "explain":
+                result = await self._handle_explain_task(user_id, content, context)
+            elif task_type == "polish":
+                result = await self._handle_polish_task(user_id, content, context)
+            elif task_type == "mimic":
+                result = await self._handle_mimic_task(user_id, content, context)
+            elif task_type == "suggest":
+                result = await self._handle_suggest_task(user_id, content, context)
+            else:
+                raise AgentException(f"不支持的任务类型: {task_type}")
+            
+            self.set_state("completed")
+            
+            return {
+                "status": "success",
+                "task_type": task_type,
+                "user_id": user_id,
+                "result": result,
+                "timestamp": datetime.now().isoformat()
+            }
+            
+        except Exception as e:
+            self.set_state("error")
+            raise AgentException(f"Coach Agent执行失败: {str(e)}")
+    
+    def get_required_fields(self) -> List[str]:
+        """获取必需的输入字段"""
+        return ["user_id", "task_type", "content"]
+    
+    async def _handle_explain_task(self, user_id: str, content: str, context: Dict) -> Dict[str, Any]:
+        """处理解释任务"""
+        try:
+            # 获取用户的历史论文作为上下文
+            user_context = await self._get_user_context(user_id)
+            
+            explain_prompt = f"""
+            请用通俗易懂的语言解释以下内容：
+            
+            需要解释的内容：
+            {content}
+            
+            上下文信息：
+            {json.dumps(context, ensure_ascii=False, indent=2)}
+            
+            用户研究领域背景：
+            {json.dumps(user_context, ensure_ascii=False, indent=2)}
+            
+            请提供：
+            1. 简单易懂的解释
+            2. 相关的例子或类比
+            3. 在该领域的重要性
+            4. 可能的应用场景
+            
+            请以JSON格式返回结果。
+            """
+            
+            response = await self.think(explain_prompt)
+            
+            try:
+                result = json.loads(response)
+            except json.JSONDecodeError:
+                result = {
+                    "explanation": response,
+                    "examples": ["需要补充具体例子"],
+                    "importance": "在相关领域具有重要意义",
+                    "applications": ["潜在应用场景"]
+                }
+            
+            self._add_to_history(f"完成解释任务: {content[:50]}...")
+            return result
+            
+        except Exception as e:
+            self._add_to_history(f"解释任务失败: {str(e)}")
+            return {
+                "explanation": f"解释过程中出现错误: {str(e)}",
+                "examples": [],
+                "importance": "",
+                "applications": []
+            }
+    
+    async def _handle_polish_task(self, user_id: str, content: str, context: Dict) -> Dict[str, Any]:
+        """处理润色任务"""
+        try:
+            # 获取用户的写作风格偏好
+            user_style = await self._get_user_writing_style(user_id)
+            
+            # 获取相关的风格参考
+            style_references = await self._get_style_references(user_id, content)
+            
+            polish_prompt = f"""
+            请将以下文本润色为地道的学术英语：
+            
+            原文：
+            {content}
+            
+            用户写作风格偏好：
+            {json.dumps(user_style, ensure_ascii=False, indent=2)}
+            
+            风格参考：
+            {json.dumps(style_references, ensure_ascii=False, indent=2)}
+            
+            上下文信息：
+            {json.dumps(context, ensure_ascii=False, indent=2)}
+            
+            请提供：
+            1. 润色后的英文文本
+            2. 主要修改说明
+            3. 风格改进建议
+            4. 参考的论文句式来源
+            
+            要求：
+            - 保持原意不变
+            - 使用地道的学术表达
+            - 符合目标期刊/会议的写作风格
+            - 在注释中说明参考了哪些历史论文的句式
+            
+            请以JSON格式返回结果。
+            """
+            
+            response = await self.think(polish_prompt)
+            
+            try:
+                result = json.loads(response)
+            except json.JSONDecodeError:
+                result = {
+                    "polished_text": response,
+                    "modifications": ["语法修正", "词汇优化"],
+                    "style_suggestions": ["建议使用更正式的表达"],
+                    "references": ["基于学术写作规范"]
+                }
+            
+            self._add_to_history(f"完成润色任务: {content[:50]}...")
+            return result
+            
+        except Exception as e:
+            self._add_to_history(f"润色任务失败: {str(e)}")
+            return {
+                "polished_text": content,
+                "modifications": [f"润色过程中出现错误: {str(e)}"],
+                "style_suggestions": [],
+                "references": []
+            }
+    
+    async def _handle_mimic_task(self, user_id: str, content: str, context: Dict) -> Dict[str, Any]:
+        """处理模仿任务"""
+        try:
+            # 获取目标风格参考
+            target_style = context.get("target_style", "formal_academic")
+            reference_papers = context.get("reference_papers", [])
+            
+            # 如果没有指定参考论文，从用户库中获取
+            if not reference_papers:
+                reference_papers = await self._get_user_top_papers(user_id, limit=3)
+            
+            mimic_prompt = f"""
+            请基于以下参考论文的写作风格，重写给定内容：
+            
+            原文：
+            {content}
+            
+            目标风格：
+            {target_style}
+            
+            参考论文：
+            {json.dumps(reference_papers, ensure_ascii=False, indent=2)}
+            
+            上下文信息：
+            {json.dumps(context, ensure_ascii=False, indent=2)}
+            
+            请提供：
+            1. 重写后的文本
+            2. 风格分析（说明如何体现目标风格）
+            3. 具体的模仿技巧
+            4. 参考的句式结构
+            
+            请以JSON格式返回结果。
+            """
+            
+            response = await self.think(mimic_prompt)
+            
+            try:
+                result = json.loads(response)
+            except json.JSONDecodeError:
+                result = {
+                    "rewritten_text": response,
+                    "style_analysis": "基于学术写作风格进行重写",
+                    "mimic_techniques": ["句式结构模仿", "词汇选择"],
+                    "reference_structures": ["学术表达方式"]
+                }
+            
+            self._add_to_history(f"完成模仿任务: {content[:50]}...")
+            return result
+            
+        except Exception as e:
+            self._add_to_history(f"模仿任务失败: {str(e)}")
+            return {
+                "rewritten_text": content,
+                "style_analysis": f"模仿过程中出现错误: {str(e)}",
+                "mimic_techniques": [],
+                "reference_structures": []
+            }
+    
+    async def _handle_suggest_task(self, user_id: str, content: str, context: Dict) -> Dict[str, Any]:
+        """处理建议任务"""
+        try:
+            # 获取用户的历史写作数据
+            user_writing_history = await self._get_user_writing_history(user_id)
+            
+            suggest_prompt = f"""
+            请对以下文本提供改进建议：
+            
+            文本内容：
+            {content}
+            
+            用户写作历史：
+            {json.dumps(user_writing_history, ensure_ascii=False, indent=2)}
+            
+            上下文信息：
+            {json.dumps(context, ensure_ascii=False, indent=2)}
+            
+            请提供：
+            1. 整体评价
+            2. 具体改进建议（按重要性排序）
+            3. 语法和表达问题
+            4. 结构优化建议
+            5. 学术表达改进
+            
+            请以JSON格式返回结果。
+            """
+            
+            response = await self.think(suggest_prompt)
+            
+            try:
+                result = json.loads(response)
+            except json.JSONDecodeError:
+                result = {
+                    "overall_evaluation": "文本整体质量良好",
+                    "improvement_suggestions": ["建议加强逻辑表达", "可以增加更多细节"],
+                    "grammar_issues": ["检查时态一致性"],
+                    "structure_suggestions": ["建议优化段落结构"],
+                    "academic_improvements": ["使用更正式的学术词汇"]
+                }
+            
+            self._add_to_history(f"完成建议任务: {content[:50]}...")
+            return result
+            
+        except Exception as e:
+            self._add_to_history(f"建议任务失败: {str(e)}")
+            return {
+                "overall_evaluation": f"分析过程中出现错误: {str(e)}",
+                "improvement_suggestions": [],
+                "grammar_issues": [],
+                "structure_suggestions": [],
+                "academic_improvements": []
+            }
+    
+    async def _get_user_context(self, user_id: str) -> Dict[str, Any]:
+        """获取用户的研究背景"""
+        try:
+            user = await db_manager.get_user(user_id)
+            if user:
+                return user.get("profile", {})
+            return {}
+        except Exception:
+            return {}
+    
+    async def _get_user_writing_style(self, user_id: str) -> Dict[str, Any]:
+        """获取用户写作风格偏好"""
+        user_context = await self._get_user_context(user_id)
+        return user_context.get("writing_style", {
+            "tone": "formal",
+            "complexity": "medium",
+            "preferred_journals": ["Nature", "Science"],
+            "language": "english"
+        })
+    
+    async def _get_style_references(self, user_id: str, content: str) -> List[Dict[str, Any]]:
+        """获取风格参考"""
+        try:
+            # 搜索用户库中的相关论文
+            search_results = await vector_store_manager.hybrid_search(
+                query=content,
+                user_id=user_id,
+                top_k=3,
+                include_l2=True,
+                include_l1=False
+            )
+            
+            references = []
+            for result in search_results:
+                payload = result["payload"]
+                references.append({
+                    "title": payload.get("title", ""),
+                    "abstract": payload.get("abstract", "")[:200],
+                    "similarity": result["score"]
+                })
+            
+            return references
+            
+        except Exception:
+            return []
+    
+    async def _get_user_top_papers(self, user_id: str, limit: int = 3) -> List[Dict[str, Any]]:
+        """获取用户评分最高的论文"""
+        try:
+            user_papers = await db_manager.get_user_papers(user_id, limit=limit)
+            
+            top_papers = []
+            for paper in user_papers:
+                top_papers.append({
+                    "title": paper.get("title", ""),
+                    "abstract": paper.get("abstract", "")[:300],
+                    "rating": paper.get("rating", 0),
+                    "authors": paper.get("authors", [])
+                })
+            
+            return top_papers
+            
+        except Exception:
+            return []
+    
+    async def _get_user_writing_history(self, user_id: str) -> List[Dict[str, Any]]:
+        """获取用户写作历史"""
+        try:
+            # 这里应该从用户的写作历史记录中获取数据
+            # 暂时返回模拟数据
+            return [
+                {
+                    "date": "2024-01-01",
+                    "content_type": "abstract",
+                    "word_count": 200,
+                    "feedback_score": 4.5
+                }
+            ]
+        except Exception:
+            return []
+    
+    # 工具方法
+    async def _explain_concept(self, concept: str, context: Dict = None) -> Dict:
+        """解释概念工具"""
+        return await self._handle_explain_task(
+            context.get("user_id", ""), 
+            concept, 
+            context or {}
+        )
+    
+    async def _polish_text(self, text: str, context: Dict = None) -> Dict:
+        """润色文本工具"""
+        return await self._handle_polish_task(
+            context.get("user_id", ""), 
+            text, 
+            context or {}
+        )
+    
+    async def _mimic_style(self, text: str, target_style: str, context: Dict = None) -> Dict:
+        """模仿风格工具"""
+        ctx = context or {}
+        ctx["target_style"] = target_style
+        return await self._handle_mimic_task(
+            ctx.get("user_id", ""), 
+            text, 
+            ctx
+        )
+    
+    async def _get_user_style(self, user_id: str) -> Dict:
+        """获取用户风格工具"""
+        return await self._get_user_writing_style(user_id)
+    
+    async def _suggest_improvements(self, text: str, context: Dict = None) -> Dict:
+        """建议改进工具"""
+        return await self._handle_suggest_task(
+            context.get("user_id", ""), 
+            text, 
+            context or {}
+        )

Co-creation-projects/Apricity-InnocoreAI/agents/controller.py

@@ -0,0 +1,407 @@
+"""
+InnoCore AI 智能体控制器
+负责四大智能体的协同调度和任务编排
+"""
+
+import asyncio
+from typing import Dict, List, Optional, Any, Callable
+from datetime import datetime
+import json
+import logging
+from enum import Enum
+
+from agents.base import BaseAgent
+from agents.hunter import HunterAgent
+from agents.miner import MinerAgent
+from agents.coach import CoachAgent
+from agents.validator import ValidatorAgent
+from core.config import get_config
+from core.exceptions import AgentException, TimeoutException
+
+logger = logging.getLogger(__name__)
+
+class TaskType(Enum):
+    """任务类型枚举"""
+    PAPER_HUNTING = "paper_hunting"
+    PAPER_ANALYSIS = "paper_analysis"
+    WRITING_ASSISTANCE = "writing_assistance"
+    CITATION_VALIDATION = "citation_validation"
+    FULL_WORKFLOW = "full_workflow"
+
+class TaskStatus(Enum):
+    """任务状态枚举"""
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    CANCELLED = "cancelled"
+
+class AgentController:
+    """智能体控制器"""
+    
+    def __init__(self):
+        self.config = get_config()
+        
+        # 初始化智能体
+        self.agents = {
+            "hunter": HunterAgent(),
+            "miner": MinerAgent(),
+            "coach": CoachAgent(),
+            "validator": ValidatorAgent()
+        }
+        
+        # 任务管理
+        self.active_tasks = {}
+        self.task_history = []
+        self.task_queue = asyncio.Queue()
+        
+        # 并发控制
+        self.semaphore = asyncio.Semaphore(self.config.concurrent_agents)
+        
+        # 事件回调
+        self.event_callbacks = {
+            "task_started": [],
+            "task_completed": [],
+            "task_failed": [],
+            "agent_status_changed": []
+        }
+    
+    async def initialize(self):
+        """初始化控制器"""
+        logger.info("初始化Agent Controller...")
+        
+        # 这里可以添加智能体的初始化逻辑
+        # 例如加载模型、建立连接等
+        
+        logger.info("Agent Controller初始化完成")
+    
+    async def submit_task(self, task_type: TaskType, input_data: Dict[str, Any], 
+                         priority: int = 0, callback: Callable = None) -> str:
+        """提交任务"""
+        task_id = f"task_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{len(self.active_tasks)}"
+        
+        task = {
+            "id": task_id,
+            "type": task_type,
+            "input_data": input_data,
+            "status": TaskStatus.PENDING,
+            "priority": priority,
+            "callback": callback,
+            "created_at": datetime.now(),
+            "started_at": None,
+            "completed_at": None,
+            "result": None,
+            "error": None,
+            "agent_results": {}
+        }
+        
+        self.active_tasks[task_id] = task
+        await self.task_queue.put((priority, task))
+        
+        logger.info(f"任务已提交: {task_id}, 类型: {task_type.value}")
+        return task_id
+    
+    async def execute_task(self, task_id: str) -> Dict[str, Any]:
+        """执行单个任务"""
+        if task_id not in self.active_tasks:
+            raise AgentException(f"任务不存在: {task_id}")
+        
+        task = self.active_tasks[task_id]
+        
+        async with self.semaphore:  # 并发控制
+            try:
+                task["status"] = TaskStatus.RUNNING
+                task["started_at"] = datetime.now()
+                
+                await self._trigger_event("task_started", task)
+                
+                # 根据任务类型执行相应的逻辑
+                if task["type"] == TaskType.PAPER_HUNTING:
+                    result = await self._execute_paper_hunting(task)
+                elif task["type"] == TaskType.PAPER_ANALYSIS:
+                    result = await self._execute_paper_analysis(task)
+                elif task["type"] == TaskType.WRITING_ASSISTANCE:
+                    result = await self._execute_writing_assistance(task)
+                elif task["type"] == TaskType.CITATION_VALIDATION:
+                    result = await self._execute_citation_validation(task)
+                elif task["type"] == TaskType.FULL_WORKFLOW:
+                    result = await self._execute_full_workflow(task)
+                else:
+                    raise AgentException(f"不支持的任务类型: {task['type']}")
+                
+                task["status"] = TaskStatus.COMPLETED
+                task["completed_at"] = datetime.now()
+                task["result"] = result
+                
+                await self._trigger_event("task_completed", task)
+                
+                # 执行回调
+                if task["callback"]:
+                    await task["callback"](task)
+                
+                return result
+                
+            except Exception as e:
+                task["status"] = TaskStatus.FAILED
+                task["completed_at"] = datetime.now()
+                task["error"] = str(e)
+                
+                await self._trigger_event("task_failed", task)
+                
+                logger.error(f"任务执行失败 {task_id}: {str(e)}")
+                raise AgentException(f"任务执行失败: {str(e)}")
+            
+            finally:
+                # 移动到历史记录
+                self.task_history.append(task.copy())
+                del self.active_tasks[task_id]
+    
+    async def _execute_paper_hunting(self, task: Dict) -> Dict[str, Any]:
+        """执行论文抓取任务"""
+        input_data = task["input_data"]
+        
+        # 调用Hunter Agent
+        hunter_result = await self.agents["hunter"].run(input_data)
+        task["agent_results"]["hunter"] = hunter_result
+        
+        return {
+            "task_type": "paper_hunting",
+            "papers_found": hunter_result.get("downloaded_papers", []),
+            "statistics": {
+                "total_found": hunter_result.get("total_found", 0),
+                "downloaded": hunter_result.get("downloaded_papers", 0)
+            }
+        }
+    
+    async def _execute_paper_analysis(self, task: Dict) -> Dict[str, Any]:
+        """执行论文分析任务"""
+        input_data = task["input_data"]
+        
+        # 调用Miner Agent
+        miner_result = await self.agents["miner"].run(input_data)
+        task["agent_results"]["miner"] = miner_result
+        
+        return {
+            "task_type": "paper_analysis",
+            "analysis_report": miner_result,
+            "paper_id": input_data.get("paper_id")
+        }
+    
+    async def _execute_writing_assistance(self, task: Dict) -> Dict[str, Any]:
+        """执行写作辅助任务"""
+        input_data = task["input_data"]
+        
+        # 调用Coach Agent
+        coach_result = await self.agents["coach"].run(input_data)
+        task["agent_results"]["coach"] = coach_result
+        
+        return {
+            "task_type": "writing_assistance",
+            "assistance_result": coach_result,
+            "user_id": input_data.get("user_id")
+        }
+    
+    async def _execute_citation_validation(self, task: Dict) -> Dict[str, Any]:
+        """执行引用校验任务"""
+        input_data = task["input_data"]
+        
+        # 调用Validator Agent
+        validator_result = await self.agents["validator"].run(input_data)
+        task["agent_results"]["validator"] = validator_result
+        
+        return {
+            "task_type": "citation_validation",
+            "validation_result": validator_result,
+            "paper_info": input_data.get("paper_info")
+        }
+    
+    async def _execute_full_workflow(self, task: Dict) -> Dict[str, Any]:
+        """执行完整工作流"""
+        input_data = task["input_data"]
+        user_id = input_data.get("user_id")
+        keywords = input_data.get("keywords", [])
+        
+        workflow_result = {
+            "task_type": "full_workflow",
+            "stages": {},
+            "final_papers": [],
+            "analysis_reports": []
+        }
+        
+        try:
+            # Stage 1: 论文抓取
+            self._add_to_history("开始论文抓取阶段")
+            hunting_input = {
+                "keywords": keywords,
+                "max_papers": input_data.get("max_papers", 10),
+                "sources": input_data.get("sources", ["arxiv"])
+            }
+            
+            hunting_result = await self.agents["hunter"].run(hunting_input)
+            workflow_result["stages"]["hunting"] = hunting_result
+            task["agent_results"]["hunter"] = hunting_result
+            
+            downloaded_papers = hunting_result.get("papers", [])
+            workflow_result["final_papers"] = downloaded_papers
+            
+            # Stage 2: 论文分析
+            self._add_to_history("开始论文分析阶段")
+            for paper in downloaded_papers:
+                if paper.get("db_id"):
+                    analysis_input = {
+                        "paper_id": paper["db_id"],
+                        "user_id": user_id,
+                        "analysis_type": "full"
+                    }
+                    
+                    try:
+                        analysis_result = await self.agents["miner"].run(analysis_input)
+                        workflow_result["analysis_reports"].append(analysis_result)
+                    except Exception as e:
+                        self._add_to_history(f"论文分析失败 {paper.get('title', 'Unknown')}: {str(e)}")
+            
+            # Stage 3: 引用校验（可选）
+            if input_data.get("validate_citations", False):
+                self._add_to_history("开始引用校验阶段")
+                for paper in downloaded_papers:
+                    paper_info = {
+                        "title": paper.get("title", ""),
+                        "authors": paper.get("authors", []),
+                        "doi": paper.get("doi", ""),
+                        "year": datetime.now().year
+                    }
+                    
+                    validation_input = {
+                        "paper_info": paper_info,
+                        "formats": ["bibtex", "apa"],
+                        "verify_external": True
+                    }
+                    
+                    try:
+                        validation_result = await self.agents["validator"].run(validation_input)
+                        paper["citations"] = validation_result.get("citations", {})
+                    except Exception as e:
+                        self._add_to_history(f"引用校验失败 {paper.get('title', 'Unknown')}: {str(e)}")
+            
+            self._add_to_history("完整工作流执行完成")
+            
+        except Exception as e:
+            self._add_to_history(f"工作流执行失败: {str(e)}")
+            raise
+        
+        return workflow_result
+    
+    async def start_task_processor(self):
+        """启动任务处理器"""
+        logger.info("启动任务处理器...")
+        
+        while True:
+            try:
+                # 获取任务（按优先级排序）
+                priority, task = await self.task_queue.get()
+                
+                # 异步执行任务
+                asyncio.create_task(self.execute_task(task["id"]))
+                
+            except Exception as e:
+                logger.error(f"任务处理器异常: {str(e)}")
+                await asyncio.sleep(1)
+    
+    async def get_task_status(self, task_id: str) -> Optional[Dict]:
+        """获取任务状态"""
+        if task_id in self.active_tasks:
+            task = self.active_tasks[task_id]
+            return {
+                "id": task["id"],
+                "type": task["type"].value,
+                "status": task["status"].value,
+                "created_at": task["created_at"].isoformat(),
+                "started_at": task["started_at"].isoformat() if task["started_at"] else None,
+                "completed_at": task["completed_at"].isoformat() if task["completed_at"] else None,
+                "priority": task["priority"]
+            }
+        else:
+            # 在历史记录中查找
+            for task in self.task_history:
+                if task["id"] == task_id:
+                    return {
+                        "id": task["id"],
+                        "type": task["type"].value,
+                        "status": task["status"].value,
+                        "created_at": task["created_at"].isoformat(),
+                        "started_at": task["started_at"].isoformat() if task["started_at"] else None,
+                        "completed_at": task["completed_at"].isoformat() if task["completed_at"] else None,
+                        "priority": task["priority"]
+                    }
+        return None
+    
+    async def cancel_task(self, task_id: str) -> bool:
+        """取消任务"""
+        if task_id in self.active_tasks:
+            task = self.active_tasks[task_id]
+            if task["status"] == TaskStatus.PENDING:
+                task["status"] = TaskStatus.CANCELLED
+                task["completed_at"] = datetime.now()
+                
+                # 移动到历史记录
+                self.task_history.append(task.copy())
+                del self.active_tasks[task_id]
+                
+                logger.info(f"任务已取消: {task_id}")
+                return True
+        
+        return False
+    
+    async def get_agent_status(self) -> Dict[str, Any]:
+        """获取所有智能体状态"""
+        agent_status = {}
+        for name, agent in self.agents.items():
+            agent_status[name] = agent.get_status()
+        
+        return {
+            "agents": agent_status,
+            "active_tasks": len(self.active_tasks),
+            "queued_tasks": self.task_queue.qsize(),
+            "completed_tasks": len(self.task_history),
+            "max_concurrent": self.config.concurrent_agents
+        }
+    
+    def add_event_callback(self, event_type: str, callback: Callable):
+        """添加事件回调"""
+        if event_type in self.event_callbacks:
+            self.event_callbacks[event_type].append(callback)
+    
+    async def _trigger_event(self, event_type: str, data: Any):
+        """触发事件"""
+        if event_type in self.event_callbacks:
+            for callback in self.event_callbacks[event_type]:
+                try:
+                    if asyncio.iscoroutinefunction(callback):
+                        await callback(data)
+                    else:
+                        callback(data)
+                except Exception as e:
+                    logger.error(f"事件回调执行失败 {event_type}: {str(e)}")
+    
+    def _add_to_history(self, message: str):
+        """添加到控制器历史记录"""
+        timestamp = datetime.now().isoformat()
+        logger.info(f"[{timestamp}] Controller: {message}")
+    
+    async def shutdown(self):
+        """关闭控制器"""
+        logger.info("关闭Agent Controller...")
+        
+        # 取消所有待处理任务
+        for task_id in list(self.active_tasks.keys()):
+            await self.cancel_task(task_id)
+        
+        # 清理智能体资源
+        for agent in self.agents.values():
+            if hasattr(agent, 'close'):
+                await agent.close()
+        
+        logger.info("Agent Controller已关闭")
+
+# 全局控制器实例
+agent_controller = AgentController()

Co-creation-projects/Apricity-InnocoreAI/agents/hunter.py

@@ -0,0 +1,353 @@
+"""
+InnoCore AI 前哨探员 (Hunter Agent)
+负责每日根据关键词监控ArXiv/IEEE，初筛并下载PDF
+"""
+
+import asyncio
+import aiohttp
+import feedparser
+import re
+from typing import Dict, List, Optional, Any
+from datetime import datetime, timedelta
+import hashlib
+import os
+from urllib.parse import urljoin, quote
+
+from agents.base import BaseAgent
+from core.database import db_manager
+from core.exceptions import AgentException, ExternalAPIException
+
+class HunterAgent(BaseAgent):
+    """前哨探员智能体"""
+    
+    def __init__(self, llm=None):
+        super().__init__("Hunter", llm)
+        self.arxiv_base_url = "http://export.arxiv.org/api/query"
+        self.ieee_base_url = "https://ieeexploreapi.ieee.org/api/v1"
+        self.download_dir = "downloads/papers"
+        
+        # 确保下载目录存在
+        os.makedirs(self.download_dir, exist_ok=True)
+        
+        # 添加工具
+        self.add_tool("search_arxiv", self._search_arxiv, "搜索ArXiv论文")
+        self.add_tool("search_ieee", self._search_ieee, "搜索IEEE论文")
+        self.add_tool("download_pdf", self._download_pdf, "下载PDF文件")
+        self.add_tool("extract_metadata", self._extract_metadata, "提取论文元数据")
+    
+    async def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """执行论文抓取任务"""
+        await self.validate_input(input_data)
+        
+        self.set_state("running")
+        
+        try:
+            keywords = input_data["keywords"]
+            max_papers = input_data.get("max_papers", 20)
+            sources = input_data.get("sources", ["arxiv", "ieee"])
+            days_back = input_data.get("days_back", 1)
+            
+            all_papers = []
+            
+            # 搜索不同来源
+            if "arxiv" in sources:
+                arxiv_papers = await self._search_papers_from_arxiv(keywords, max_papers, days_back)
+                all_papers.extend(arxiv_papers)
+            
+            if "ieee" in sources:
+                ieee_papers = await self._search_papers_from_ieee(keywords, max_papers, days_back)
+                all_papers.extend(ieee_papers)
+            
+            # 去重和筛选
+            unique_papers = self._deduplicate_papers(all_papers)
+            filtered_papers = await self._filter_papers(unique_papers, keywords)
+            
+            # 下载PDF
+            downloaded_papers = []
+            for paper in filtered_papers[:max_papers]:
+                try:
+                    downloaded_paper = await self._download_and_save_paper(paper)
+                    if downloaded_paper:
+                        downloaded_papers.append(downloaded_paper)
+                except Exception as e:
+                    self._add_to_history(f"下载论文失败 {paper.get('title', 'Unknown')}: {str(e)}")
+            
+            self.set_state("completed")
+            
+            return {
+                "status": "success",
+                "total_found": len(all_papers),
+                "unique_papers": len(unique_papers),
+                "filtered_papers": len(filtered_papers),
+                "downloaded_papers": len(downloaded_papers),
+                "papers": downloaded_papers
+            }
+            
+        except Exception as e:
+            self.set_state("error")
+            raise AgentException(f"Hunter Agent执行失败: {str(e)}")
+    
+    def get_required_fields(self) -> List[str]:
+        """获取必需的输入字段"""
+        return ["keywords"]
+    
+    async def _search_papers_from_arxiv(self, keywords: List[str], max_papers: int, days_back: int) -> List[Dict]:
+        """从ArXiv搜索论文"""
+        papers = []
+        
+        # 构建查询字符串
+        query_parts = []
+        for keyword in keywords:
+            query_parts.append(f'all:"{keyword}"')
+        query = " OR ".join(query_parts)
+        
+        # 添加时间过滤
+        date_filter = ""
+        if days_back > 0:
+            start_date = (datetime.now() - timedelta(days=days_back)).strftime("%Y%m%d")
+            date_filter = f"submittedDate:[{start_filter}0000 TO {datetime.now().strftime('%Y%m%d')}2359]"
+        
+        params = {
+            "search_query": query,
+            "start": 0,
+            "max_results": max_papers * 2,  # 获取更多结果以便筛选
+            "sortBy": "submittedDate",
+            "sortOrder": "descending"
+        }
+        
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(self.arxiv_base_url, params=params) as response:
+                    if response.status != 200:
+                        raise ExternalAPIException(f"ArXiv API请求失败: {response.status}")
+                    
+                    xml_content = await response.text()
+                    feed = feedparser.parse(xml_content)
+                    
+                    for entry in feed.entries:
+                        paper = {
+                            "id": entry.id.split("/")[-1],
+                            "title": entry.title,
+                            "authors": [author.name for author in entry.authors],
+                            "abstract": entry.summary,
+                            "published": entry.published,
+                            "pdf_url": entry.link.replace('/abs/', '/pdf/') + '.pdf',
+                            "source": "arxiv",
+                            "doi": entry.get('arxiv_doi', ''),
+                            "categories": [tag.term for tag in entry.tags]
+                        }
+                        
+                        papers.append(paper)
+                        
+        except Exception as e:
+            self._add_to_history(f"ArXiv搜索失败: {str(e)}")
+        
+        return papers
+    
+    async def _search_papers_from_ieee(self, keywords: List[str], max_papers: int, days_back: int) -> List[Dict]:
+        """从IEEE搜索论文"""
+        papers = []
+        
+        # IEEE API需要API key，这里提供基础实现框架
+        config = self.config.external_apis
+        
+        if not config.ieee_base_url:
+            self._add_to_history("IEEE API配置缺失，跳过IEEE搜索")
+            return papers
+        
+        # 构建查询参数
+        query = " OR ".join([f'"All Meta Data:{keyword}"' for keyword in keywords])
+        
+        params = {
+            "apikey": config.ieee_api_key or "",
+            "querytext": query,
+            "max_records": max_papers * 2,
+            "start_record": 1,
+            "sort_order": "desc",
+            "sort_field": "publication_date"
+        }
+        
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(self.ieee_base_url, params=params) as response:
+                    if response.status != 200:
+                        raise ExternalAPIException(f"IEEE API请求失败: {response.status}")
+                    
+                    data = await response.json()
+                    
+                    for article in data.get("articles", []):
+                        paper = {
+                            "id": article.get("article_number", ""),
+                            "title": article.get("title", ""),
+                            "authors": [author.get("full_name", "") for author in article.get("authors", {}).get("authors", [])],
+                            "abstract": article.get("abstract", ""),
+                            "published": article.get("publication_date", ""),
+                            "pdf_url": article.get("pdf_url", ""),
+                            "source": "ieee",
+                            "doi": article.get("doi", ""),
+                            "categories": article.get("index_terms", {}).get("ieee_terms", {}).get("terms", [])
+                        }
+                        
+                        papers.append(paper)
+                        
+        except Exception as e:
+            self._add_to_history(f"IEEE搜索失败: {str(e)}")
+        
+        return papers
+    
+    def _deduplicate_papers(self, papers: List[Dict]) -> List[Dict]:
+        """去重论文"""
+        seen_titles = set()
+        unique_papers = []
+        
+        for paper in papers:
+            title = paper.get("title", "").lower().strip()
+            title_hash = hashlib.md5(title.encode()).hexdigest()
+            
+            if title_hash not in seen_titles:
+                seen_titles.add(title_hash)
+                unique_papers.append(paper)
+        
+        return unique_papers
+    
+    async def _filter_papers(self, papers: List[Dict], keywords: List[str]) -> List[Dict]:
+        """根据关键词筛选论文"""
+        filtered_papers = []
+        
+        for paper in papers:
+            title = paper.get("title", "").lower()
+            abstract = paper.get("abstract", "").lower()
+            combined_text = f"{title} {abstract}"
+            
+            # 计算关键词匹配分数
+            score = 0
+            for keyword in keywords:
+                keyword_lower = keyword.lower()
+                if keyword_lower in title:
+                    score += 2  # 标题匹配权重更高
+                if keyword_lower in abstract:
+                    score += 1
+            
+            # 设定阈值
+            if score >= 1:
+                paper["relevance_score"] = score
+                filtered_papers.append(paper)
+        
+        # 按相关性分数排序
+        filtered_papers.sort(key=lambda x: x.get("relevance_score", 0), reverse=True)
+        
+        return filtered_papers
+    
+    async def _download_and_save_paper(self, paper: Dict) -> Optional[Dict]:
+        """下载并保存论文"""
+        pdf_url = paper.get("pdf_url")
+        if not pdf_url:
+            return None
+        
+        try:
+            # 生成文件名
+            safe_title = re.sub(r'[^\w\s-]', '', paper.get("title", "unknown"))[:50]
+            filename = f"{paper['id']}_{safe_title}.pdf"
+            file_path = os.path.join(self.download_dir, filename)
+            
+            # 检查文件是否已存在
+            if os.path.exists(file_path):
+                self._add_to_history(f"论文已存在: {filename}")
+                paper["file_path"] = file_path
+                return paper
+            
+            # 下载PDF
+            async with aiohttp.ClientSession() as session:
+                async with session.get(pdf_url) as response:
+                    if response.status == 200:
+                        content = await response.read()
+                        
+                        with open(file_path, 'wb') as f:
+                            f.write(content)
+                        
+                        # 计算文件哈希
+                        content_hash = hashlib.sha256(content).hexdigest()
+                        
+                        # 更新论文信息
+                        paper["file_path"] = file_path
+                        paper["content_hash"] = content_hash
+                        paper["file_size"] = len(content)
+                        
+                        # 保存到数据库
+                        await self._save_paper_to_db(paper)
+                        
+                        self._add_to_history(f"成功下载论文: {filename}")
+                        return paper
+                    else:
+                        self._add_to_history(f"下载失败，HTTP状态码: {response.status}")
+                        return None
+                        
+        except Exception as e:
+            self._add_to_history(f"下载论文异常: {str(e)}")
+            return None
+    
+    async def _save_paper_to_db(self, paper: Dict):
+        """保存论文到数据库"""
+        try:
+            # 检查是否已存在
+            existing_paper = await db_manager.get_paper_by_hash(paper.get("content_hash"))
+            if existing_paper:
+                self._add_to_history(f"论文已存在于数据库: {paper.get('title')}")
+                return
+            
+            # 创建论文记录
+            paper_id = await db_manager.create_paper(
+                title=paper.get("title", ""),
+                authors=paper.get("authors", []),
+                abstract=paper.get("abstract", ""),
+                doi=paper.get("doi", ""),
+                file_path=paper.get("file_path", ""),
+                content_hash=paper.get("content_hash", ""),
+                is_preset=False
+            )
+            
+            paper["db_id"] = paper_id
+            self._add_to_history(f"论文已保存到数据库: {paper_id}")
+            
+        except Exception as e:
+            self._add_to_history(f"保存论文到数据库失败: {str(e)}")
+    
+    # 工具方法
+    async def _search_arxiv(self, query: str) -> List[Dict]:
+        """搜索ArXiv工具"""
+        keywords = [kw.strip() for kw in query.split(",")]
+        return await self._search_papers_from_arxiv(keywords, 10, 7)
+    
+    async def _search_ieee(self, query: str) -> List[Dict]:
+        """搜索IEEE工具"""
+        keywords = [kw.strip() for kw in query.split(",")]
+        return await self._search_papers_from_ieee(keywords, 10, 7)
+    
+    async def _download_pdf(self, pdf_url: str) -> str:
+        """下载PDF工具"""
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(pdf_url) as response:
+                    if response.status == 200:
+                        content = await response.read()
+                        filename = f"download_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf"
+                        file_path = os.path.join(self.download_dir, filename)
+                        
+                        with open(file_path, 'wb') as f:
+                            f.write(content)
+                        
+                        return file_path
+                    else:
+                        return f"下载失败，状态码: {response.status}"
+        except Exception as e:
+            return f"下载异常: {str(e)}"
+    
+    async def _extract_metadata(self, file_path: str) -> Dict:
+        """提取论文元数据工具"""
+        # 这里应该使用PDF解析库提取元数据
+        # 暂时返回基础信息
+        return {
+            "file_path": file_path,
+            "file_size": os.path.getsize(file_path) if os.path.exists(file_path) else 0,
+            "extracted_at": datetime.now().isoformat()
+        }

Co-creation-projects/Apricity-InnocoreAI/agents/miner.py

@@ -0,0 +1,416 @@
+"""
+InnoCore AI 洞察专家 (Miner Agent)
+核心大脑。负责阅读、理解、检索历史库、对比分析并生成报告
+"""
+
+import asyncio
+from typing import Dict, List, Optional, Any
+import json
+import re
+from datetime import datetime
+
+from agents.base import BaseAgent
+from core.database import db_manager
+from core.vector_store import vector_store_manager
+from core.exceptions import AgentException
+
+class MinerAgent(BaseAgent):
+    """洞察专家智能体"""
+    
+    def __init__(self, llm=None):
+        super().__init__("Miner", llm)
+        
+        # 添加工具
+        self.add_tool("parse_pdf", self._parse_pdf, "解析PDF文件")
+        self.add_tool("search_memory", self._search_memory, "搜索记忆库")
+        self.add_tool("compare_papers", self._compare_papers, "对比论文")
+        self.add_tool("generate_report", self._generate_report, "生成分析报告")
+    
+    async def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """执行论文分析和创新点挖掘任务"""
+        await self.validate_input(input_data)
+        
+        self.set_state("running")
+        
+        try:
+            paper_id = input_data["paper_id"]
+            user_id = input_data.get("user_id")
+            analysis_type = input_data.get("analysis_type", "full")  # full, quick, innovation_only
+            
+            # 获取论文信息
+            paper = await db_manager.get_paper(paper_id)
+            if not paper:
+                raise AgentException(f"论文不存在: {paper_id}")
+            
+            self._add_to_history(f"开始分析论文: {paper['title']}")
+            
+            # 1. 解析PDF内容
+            parsed_content = await self._parse_paper_content(paper)
+            
+            # 2. 检索相关历史论文
+            related_papers = await self._find_related_papers(
+                paper["title"], 
+                paper["abstract"], 
+                user_id
+            )
+            
+            # 3. 进行对比分析
+            comparison_result = await self._perform_comparison_analysis(
+                parsed_content, 
+                related_papers
+            )
+            
+            # 4. 生成分析报告
+            report = await self._create_analysis_report(
+                paper, 
+                parsed_content, 
+                related_papers, 
+                comparison_result,
+                user_id
+            )
+            
+            # 5. 保存报告到数据库
+            report_id = await self._save_analysis_report(paper_id, report, user_id)
+            
+            # 6. 更新向量库
+            await self._update_vector_store(paper_id, paper, parsed_content, user_id)
+            
+            self.set_state("completed")
+            
+            return {
+                "status": "success",
+                "paper_id": paper_id,
+                "report_id": report_id,
+                "analysis_type": analysis_type,
+                "parsed_content": {
+                    "sections": list(parsed_content.get("sections", {}).keys()),
+                    "word_count": parsed_content.get("word_count", 0)
+                },
+                "related_papers_count": len(related_papers),
+                "report_summary": {
+                    "summary": report.get("summary", "")[:200] + "...",
+                    "innovation_points": len(report.get("innovation_points", [])),
+                    "limitations": len(report.get("limitations", [])),
+                    "future_ideas": len(report.get("future_ideas", []))
+                }
+            }
+            
+        except Exception as e:
+            self.set_state("error")
+            raise AgentException(f"Miner Agent执行失败: {str(e)}")
+    
+    def get_required_fields(self) -> List[str]:
+        """获取必需的输入字段"""
+        return ["paper_id"]
+    
+    async def _parse_paper_content(self, paper: Dict) -> Dict[str, Any]:
+        """解析论文内容"""
+        file_path = paper.get("file_path")
+        if not file_path:
+            # 如果没有PDF文件，使用标题和摘要
+            return {
+                "title": paper.get("title", ""),
+                "abstract": paper.get("abstract", ""),
+                "sections": {
+                    "abstract": paper.get("abstract", ""),
+                    "introduction": "",
+                    "method": "",
+                    "experiment": "",
+                    "conclusion": ""
+                },
+                "word_count": len(paper.get("abstract", "").split()),
+                "parsing_method": "metadata_only"
+            }
+        
+        # 这里应该使用专门的PDF解析库
+        # 暂时返回模拟的结构化内容
+        return await self._extract_structured_content(file_path)
+    
+    async def _extract_structured_content(self, file_path: str) -> Dict[str, Any]:
+        """提取结构化内容"""
+        try:
+            # 这里应该集成Nougat或PyMuPDF进行深度解析
+            # 暂时返回模拟数据
+            mock_content = {
+                "title": "Sample Paper Title",
+                "abstract": "This is a sample abstract...",
+                "sections": {
+                    "introduction": "In this paper, we propose...",
+                    "method": "Our method consists of...",
+                    "experiment": "We conducted experiments...",
+                    "conclusion": "The results show that..."
+                },
+                "word_count": 1500,
+                "parsing_method": "mock_parser"
+            }
+            
+            self._add_to_history(f"PDF解析完成: {file_path}")
+            return mock_content
+            
+        except Exception as e:
+            self._add_to_history(f"PDF解析失败: {str(e)}")
+            return {
+                "title": "",
+                "abstract": "",
+                "sections": {},
+                "word_count": 0,
+                "parsing_method": "failed"
+            }
+    
+    async def _find_related_papers(self, title: str, abstract: str, user_id: str = None) -> List[Dict]:
+        """查找相关论文"""
+        try:
+            # 构建查询
+            query = f"{title} {abstract}"
+            
+            # 执行混合搜索
+            search_results = await vector_store_manager.hybrid_search(
+                query=query,
+                user_id=user_id,
+                top_k=10,
+                include_l1=True,
+                include_l2=bool(user_id)
+            )
+            
+            # 获取详细论文信息
+            related_papers = []
+            for result in search_results:
+                payload = result["payload"]
+                paper_id = payload.get("paper_id")
+                
+                if paper_id:
+                    paper_info = await db_manager.get_paper(paper_id)
+                    if paper_info:
+                        paper_info["similarity_score"] = result["score"]
+                        paper_info["collection_type"] = result["collection_type"]
+                        related_papers.append(paper_info)
+            
+            self._add_to_history(f"找到 {len(related_papers)} 篇相关论文")
+            return related_papers
+            
+        except Exception as e:
+            self._add_to_history(f"搜索相关论文失败: {str(e)}")
+            return []
+    
+    async def _perform_comparison_analysis(self, current_paper: Dict, related_papers: List[Dict]) -> Dict[str, Any]:
+        """执行对比分析"""
+        if not related_papers:
+            return {
+                "comparison_summary": "未找到相关论文进行对比",
+                "unique_contributions": [],
+                "similar_works": [],
+                "gaps_identified": []
+            }
+        
+        # 构建对比分析的prompt
+        comparison_prompt = f"""
+        请分析当前论文与历史相关论文的对比情况：
+        
+        当前论文：
+        标题：{current_paper.get('title', '')}
+        摘要：{current_paper.get('abstract', '')}
+        主要内容：{str(current_paper.get('sections', {}))[:1000]}...
+        
+        相关论文：
+        {self._format_related_papers_for_comparison(related_papers[:5])}
+        
+        请从以下角度进行对比分析：
+        1. 方法的创新性和改进点
+        2. 实验设计的优势
+        3. 与现有工作的区别
+        4. 可能的研究空白
+        
+        请以JSON格式返回分析结果。
+        """
+        
+        try:
+            response = await self.think(comparison_prompt)
+            
+            # 尝试解析JSON响应
+            try:
+                comparison_result = json.loads(response)
+            except json.JSONDecodeError:
+                # 如果JSON解析失败，使用文本解析
+                comparison_result = self._parse_text_comparison(response)
+            
+            self._add_to_history("对比分析完成")
+            return comparison_result
+            
+        except Exception as e:
+            self._add_to_history(f"对比分析失败: {str(e)}")
+            return {
+                "comparison_summary": "对比分析过程中出现错误",
+                "unique_contributions": [],
+                "similar_works": [],
+                "gaps_identified": []
+            }
+    
+    def _format_related_papers_for_comparison(self, papers: List[Dict]) -> str:
+        """格式化相关论文用于对比"""
+        formatted = []
+        for i, paper in enumerate(papers, 1):
+            formatted.append(f"""
+            论文{i}：
+            标题：{paper.get('title', '')}
+            摘要：{paper.get('abstract', '')[:300]}...
+            相似度：{paper.get('similarity_score', 0):.3f}
+            """)
+        return "\n".join(formatted)
+    
+    def _parse_text_comparison(self, text: str) -> Dict[str, Any]:
+        """解析文本格式的对比结果"""
+        # 简单的文本解析逻辑
+        return {
+            "comparison_summary": text[:500],
+            "unique_contributions": ["基于文本分析的创新点"],
+            "similar_works": ["相关研究工作"],
+            "gaps_identified": ["研究空白识别"]
+        }
+    
+    async def _create_analysis_report(self, paper: Dict, parsed_content: Dict, 
+                                    related_papers: List[Dict], comparison_result: Dict,
+                                    user_id: str = None) -> Dict[str, Any]:
+        """创建分析报告"""
+        
+        report_prompt = f"""
+        基于以下信息，生成一份详细的论文分析报告：
+        
+        论文信息：
+        标题：{paper.get('title', '')}
+        作者：{', '.join(paper.get('authors', []))}
+        摘要：{paper.get('abstract', '')}
+        
+        解析内容：
+        {str(parsed_content.get('sections', {}))[:1500]}...
+        
+        对比分析结果：
+        {str(comparison_result)[:1000]}...
+        
+        请生成包含以下部分的报告：
+        1. Summary - 论文主要贡献和方法概述
+        2. Innovation - 相比相关论文的创新点
+        3. Limitation - 当前研究的局限性
+        4. Future Ideas - 基于分析的未来研究方向建议
+        
+        请以JSON格式返回报告。
+        """
+        
+        try:
+            response = await self.think(report_prompt)
+            
+            # 尝试解析JSON响应
+            try:
+                report = json.loads(response)
+            except json.JSONDecodeError:
+                # 如果JSON解析失败，生成默认报告
+                report = self._generate_default_report(paper, parsed_content, comparison_result)
+            
+            # 添加元数据
+            report.update({
+                "paper_id": paper.get("id"),
+                "generated_for_user_id": user_id,
+                "generated_at": datetime.now().isoformat(),
+                "related_papers_count": len(related_papers),
+                "analysis_method": "miner_agent"
+            })
+            
+            self._add_to_history("分析报告生成完成")
+            return report
+            
+        except Exception as e:
+            self._add_to_history(f"生成分析报告失败: {str(e)}")
+            return self._generate_default_report(paper, parsed_content, comparison_result)
+    
+    def _generate_default_report(self, paper: Dict, parsed_content: Dict, comparison_result: Dict) -> Dict[str, Any]:
+        """生成默认报告"""
+        return {
+            "summary": f"本文提出了{paper.get('title', '')}相关的研究工作。",
+            "innovation_points": ["需要进一步分析的创新点"],
+            "limitations": ["识别出的研究局限性"],
+            "future_ideas": ["建议的未来研究方向"],
+            "paper_id": paper.get("id"),
+            "generated_at": datetime.now().isoformat(),
+            "analysis_method": "default"
+        }
+    
+    async def _save_analysis_report(self, paper_id: str, report: Dict, user_id: str = None) -> str:
+        """保存分析报告到数据库"""
+        try:
+            report_id = await db_manager.create_analysis_report(
+                paper_id=paper_id,
+                summary=report.get("summary", ""),
+                innovation_point=json.dumps(report.get("innovation_points", []), ensure_ascii=False),
+                limitation=json.dumps(report.get("limitations", []), ensure_ascii=False),
+                future_idea=json.dumps(report.get("future_ideas", []), ensure_ascii=False),
+                vector_ids=report.get("vector_ids", {}),
+                user_id=user_id
+            )
+            
+            self._add_to_history(f"分析报告已保存: {report_id}")
+            return report_id
+            
+        except Exception as e:
+            self._add_to_history(f"保存分析报告失败: {str(e)}")
+            return ""
+    
+    async def _update_vector_store(self, paper_id: str, paper: Dict, parsed_content: Dict, user_id: str = None):
+        """更新向量库"""
+        try:
+            title = paper.get("title", "")
+            abstract = paper.get("abstract", "")
+            
+            # 组合内容
+            content = f"{title} {abstract}"
+            sections = parsed_content.get("sections", {})
+            if sections:
+                content += " " + " ".join(sections.values())
+            
+            # 添加到L2用户库
+            if user_id:
+                await vector_store_manager.add_to_l2(
+                    user_id=user_id,
+                    paper_id=paper_id,
+                    title=title,
+                    abstract=abstract,
+                    content=content,
+                    metadata={
+                        "authors": paper.get("authors", []),
+                        "sections": list(sections.keys()),
+                        "word_count": parsed_content.get("word_count", 0),
+                        "analysis_date": datetime.now().isoformat()
+                    }
+                )
+                self._add_to_history(f"论文已添加到用户向量库: {user_id}")
+            
+        except Exception as e:
+            self._add_to_history(f"更新向量库失败: {str(e)}")
+    
+    # 工具方法
+    async def _parse_pdf(self, file_path: str) -> Dict:
+        """解析PDF工具"""
+        return await self._extract_structured_content(file_path)
+    
+    async def _search_memory(self, query: str, user_id: str = None) -> List[Dict]:
+        """搜索记忆库工具"""
+        try:
+            results = await vector_store_manager.hybrid_search(
+                query=query,
+                user_id=user_id,
+                top_k=5
+            )
+            return [{"id": r["id"], "score": r["score"], "payload": r["payload"]} for r in results]
+        except Exception as e:
+            return [{"error": str(e)}]
+    
+    async def _compare_papers(self, current_paper: Dict, related_papers: List[Dict]) -> Dict:
+        """对比论文工具"""
+        return await self._perform_comparison_analysis(current_paper, related_papers)
+    
+    async def _generate_report(self, paper_info: Dict, analysis_result: Dict) -> Dict:
+        """生成报告工具"""
+        return await self._create_analysis_report(
+            paper_info, 
+            analysis_result.get("parsed_content", {}),
+            analysis_result.get("related_papers", []),
+            analysis_result.get("comparison_result", {})
+        )

Co-creation-projects/Apricity-InnocoreAI/agents/validator.py

@@ -0,0 +1,610 @@
+"""
+InnoCore AI 校验官 (Validator Agent)
+负责生成引用格式并联网校验元数据
+"""
+
+import asyncio
+import aiohttp
+import re
+import json
+from typing import Dict, List, Optional, Any
+from datetime import datetime
+import hashlib
+
+from agents.base import BaseAgent
+from core.database import db_manager
+from core.exceptions import AgentException, ExternalAPIException
+
+class ValidatorAgent(BaseAgent):
+    """校验官智能体"""
+    
+    def __init__(self, llm=None):
+        super().__init__("Validator", llm)
+        
+        # API配置
+        self.crossref_base_url = "https://api.crossref.org/works"
+        self.google_scholar_url = "https://serpapi.com/search"
+        
+        # 添加工具
+        self.add_tool("generate_bibtex", self._generate_bibtex, "生成BibTeX引用")
+        self.add_tool("generate_apa", self._generate_apa, "生成APA格式引用")
+        self.add_tool("generate_ieee", self._generate_ieee, "生成IEEE格式引用")
+        self.add_tool("verify_metadata", self._verify_metadata, "校验元数据")
+        self.add_tool("crossref_lookup", self._crossref_lookup, "CrossRef查询")
+        self.add_tool("scholar_lookup", self._scholar_lookup, "Google Scholar查询")
+    
+    async def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """执行引用校验任务"""
+        await self.validate_input(input_data)
+        
+        self.set_state("running")
+        
+        try:
+            paper_info = input_data["paper_info"]
+            formats = input_data.get("formats", ["bibtex", "apa", "ieee"])
+            verify_external = input_data.get("verify_external", True)
+            
+            # 1. 生成多种格式的引用
+            citations = await self._generate_citations(paper_info, formats)
+            
+            # 2. 外部校验元数据
+            verification_result = {}
+            if verify_external:
+                verification_result = await self._verify_paper_metadata(paper_info)
+            
+            # 3. 合并和更新引用信息
+            final_citations = await self._merge_citation_data(
+                citations, 
+                verification_result, 
+                paper_info
+            )
+            
+            # 4. 缓存结果
+            await self._cache_citation_results(final_citations)
+            
+            self.set_state("completed")
+            
+            return {
+                "status": "success",
+                "paper_info": paper_info,
+                "citations": final_citations,
+                "verification": verification_result,
+                "formats_generated": list(citations.keys()),
+                "verification_status": verification_result.get("status", "unknown"),
+                "timestamp": datetime.now().isoformat()
+            }
+            
+        except Exception as e:
+            self.set_state("error")
+            raise AgentException(f"Validator Agent执行失败: {str(e)}")
+    
+    def get_required_fields(self) -> List[str]:
+        """获取必需的输入字段"""
+        return ["paper_info"]
+    
+    async def _generate_citations(self, paper_info: Dict, formats: List[str]) -> Dict[str, Any]:
+        """生成多种格式的引用"""
+        citations = {}
+        
+        for format_type in formats:
+            try:
+                if format_type.lower() == "bibtex":
+                    citations["bibtex"] = await self._generate_bibtex_citation(paper_info)
+                elif format_type.lower() == "apa":
+                    citations["apa"] = await self._generate_apa_citation(paper_info)
+                elif format_type.lower() == "ieee":
+                    citations["ieee"] = await self._generate_ieee_citation(paper_info)
+                else:
+                    self._add_to_history(f"不支持的引用格式: {format_type}")
+                    
+            except Exception as e:
+                self._add_to_history(f"生成{format_type}格式失败: {str(e)}")
+                citations[format_type] = f"生成失败: {str(e)}"
+        
+        return citations
+    
+    async def _generate_bibtex_citation(self, paper_info: Dict) -> str:
+        """生成BibTeX格式引用"""
+        # 生成引用键
+        first_author = paper_info.get("authors", [""])[0]
+        if isinstance(first_author, str):
+            last_name = first_author.split()[-1].lower()
+        else:
+            last_name = "unknown"
+        
+        year = paper_info.get("year", datetime.now().year)
+        title_words = paper_info.get("title", "").split()[:3]
+        title_key = "".join([w.lower() for w in title_words if w.isalpha()])
+        
+        citation_key = f"{last_name}{year}{title_key}"
+        
+        # 构建BibTeX条目
+        entry_type = self._determine_entry_type(paper_info)
+        
+        bibtex = f"@{entry_type}{{{citation_key},\n"
+        
+        # 添加作者
+        authors = paper_info.get("authors", [])
+        if authors:
+            bibtex += f"  author = {{{self._format_bibtex_authors(authors)}}},\n"
+        
+        # 添加标题
+        title = paper_info.get("title", "")
+        if title:
+            bibtex += f"  title = {{{title}}},\n"
+        
+        # 添加期刊/会议信息
+        if entry_type == "article":
+            journal = paper_info.get("journal", "")
+            if journal:
+                bibtex += f"  journal = {{{journal}}},\n"
+            
+            volume = paper_info.get("volume", "")
+            if volume:
+                bibtex += f"  volume = {{{volume}}},\n"
+            
+            number = paper_info.get("number", "")
+            if number:
+                bibtex += f"  number = {{{number}}},\n"
+            
+            pages = paper_info.get("pages", "")
+            if pages:
+                bibtex += f"  pages = {{{pages}}},\n"
+        
+        elif entry_type == "inproceedings":
+            booktitle = paper_info.get("booktitle", "")
+            if booktitle:
+                bibtex += f"  booktitle = {{{booktitle}}},\n"
+            
+            pages = paper_info.get("pages", "")
+            if pages:
+                bibtex += f"  pages = {{{pages}}},\n"
+        
+        # 添加年份
+        if year:
+            bibtex += f"  year = {{{year}}},\n"
+        
+        # 添加DOI
+        doi = paper_info.get("doi", "")
+        if doi:
+            bibtex += f"  doi = {{{doi}}},\n"
+        
+        # 添加URL
+        url = paper_info.get("url", "")
+        if url:
+            bibtex += f"  url = {{{url}}},\n"
+        
+        # 移除最后的逗号并关闭
+        bibtex = bibtex.rstrip(",\n") + "\n}"
+        
+        return bibtex
+    
+    async def _generate_apa_citation(self, paper_info: Dict) -> str:
+        """生成APA格式引用"""
+        authors = paper_info.get("authors", [])
+        year = paper_info.get("year", "")
+        title = paper_info.get("title", "")
+        
+        # 格式化作者
+        if len(authors) == 0:
+            author_text = ""
+        elif len(authors) == 1:
+            author_text = authors[0]
+        elif len(authors) == 2:
+            author_text = f"{authors[0]} & {authors[1]}"
+        elif len(authors) <= 7:
+            author_text = ", ".join(authors[:-1]) + f", & {authors[-1]}"
+        else:
+            author_text = ", ".join(authors[:6]) + f", ... {authors[-1]}"
+        
+        # 构建APA引用
+        if year:
+            apa_citation = f"{author_text} ({year}). {title}."
+        else:
+            apa_citation = f"{author_text}. {title}."
+        
+        # 添加期刊信息
+        journal = paper_info.get("journal", "")
+        volume = paper_info.get("volume", "")
+        number = paper_info.get("number", "")
+        pages = paper_info.get("pages", "")
+        
+        if journal:
+            if volume and number:
+                apa_citation += f" *{journal}*, *{volume}({number})*"
+            elif volume:
+                apa_citation += f" *{journal}*, *{volume}*"
+            else:
+                apa_citation += f" *{journal}*"
+            
+            if pages:
+                apa_citation += f", {pages}."
+            else:
+                apa_citation += "."
+        
+        # 添加DOI
+        doi = paper_info.get("doi", "")
+        if doi:
+            apa_citation += f" https://doi.org/{doi}"
+        
+        return apa_citation
+    
+    async def _generate_ieee_citation(self, paper_info: Dict) -> str:
+        """生成IEEE格式引用"""
+        authors = paper_info.get("authors", [])
+        year = paper_info.get("year", "")
+        title = paper_info.get("title", "")
+        
+        # 格式化作者（IEEE使用首字母缩写）
+        ieee_authors = []
+        for author in authors[:3]:  # IEEE通常只列出前3个作者
+            if isinstance(author, str):
+                parts = author.split()
+                if len(parts) >= 2:
+                    last_name = parts[-1]
+                    initials = " ".join([p[0] + "." for p in parts[:-1]])
+                    ieee_authors.append(f"{initials} {last_name}")
+                else:
+                    ieee_authors.append(author)
+        
+        if len(authors) > 3:
+            ieee_authors.append("et al.")
+        
+        author_text = ", ".join(ieee_authors)
+        
+        # 构建IEEE引用
+        if title:
+            ieee_citation = f'"{title},"'
+        else:
+            ieee_citation = ""
+        
+        # 添加期刊信息
+        journal = paper_info.get("journal", "")
+        volume = paper_info.get("volume", "")
+        number = paper_info.get("number", "")
+        pages = paper_info.get("pages", "")
+        
+        if journal:
+            if volume and number:
+                ieee_citation += f" *{journal}*, vol. {volume}, no. {number}"
+            elif volume:
+                ieee_citation += f" *{journal}*, vol. {volume}"
+            else:
+                ieee_citation += f" *{journal}*"
+            
+            if pages:
+                ieee_citation += f", pp. {pages}"
+        
+        # 添加年份和月份
+        if year:
+            month = paper_info.get("month", "")
+            if month:
+                ieee_citation += f", {month}. {year}."
+            else:
+                ieee_citation += f", {year}."
+        
+        # 添加DOI
+        doi = paper_info.get("doi", "")
+        if doi:
+            ieee_citation += f" doi: {doi}"
+        
+        return ieee_citation
+    
+    def _determine_entry_type(self, paper_info: Dict) -> str:
+        """确定BibTeX条目类型"""
+        if paper_info.get("journal"):
+            return "article"
+        elif paper_info.get("booktitle"):
+            return "inproceedings"
+        elif paper_info.get("publisher"):
+            return "book"
+        else:
+            return "misc"
+    
+    def _format_bibtex_authors(self, authors: List[str]) -> str:
+        """格式化BibTeX作者"""
+        formatted_authors = []
+        for author in authors:
+            if isinstance(author, str):
+                # 将 "First Last" 转换为 "Last, First"
+                parts = author.split()
+                if len(parts) >= 2:
+                    formatted_authors.append(f"{parts[-1]}, {' '.join(parts[:-1])}")
+                else:
+                    formatted_authors.append(author)
+            else:
+                formatted_authors.append(str(author))
+        
+        return " and ".join(formatted_authors)
+    
+    async def _verify_paper_metadata(self, paper_info: Dict) -> Dict[str, Any]:
+        """校验论文元数据"""
+        verification_result = {
+            "status": "pending",
+            "crossref_verified": False,
+            "scholar_verified": False,
+            "discrepancies": [],
+            "suggested_corrections": {},
+            "verification_timestamp": datetime.now().isoformat()
+        }
+        
+        doi = paper_info.get("doi", "")
+        title = paper_info.get("title", "")
+        
+        try:
+            # 1. CrossRef校验
+            if doi:
+                crossref_data = await self._crossref_lookup_by_doi(doi)
+                if crossref_data:
+                    verification_result["crossref_verified"] = True
+                    discrepancies = self._compare_metadata(paper_info, crossref_data)
+                    if discrepancies:
+                        verification_result["discrepancies"].extend(discrepancies)
+                        verification_result["suggested_corrections"].update(
+                            self._generate_corrections(discrepancies)
+                        )
+            
+            # 2. Google Scholar校验
+            if title:
+                scholar_data = await self._scholar_lookup_by_title(title)
+                if scholar_data:
+                    verification_result["scholar_verified"] = True
+                    discrepancies = self._compare_metadata(paper_info, scholar_data)
+                    if discrepancies:
+                        verification_result["discrepancies"].extend(discrepancies)
+                        verification_result["suggested_corrections"].update(
+                            self._generate_corrections(discrepancies)
+                        )
+            
+            # 确定最终状态
+            if verification_result["crossref_verified"] or verification_result["scholar_verified"]:
+                if not verification_result["discrepancies"]:
+                    verification_result["status"] = "verified"
+                else:
+                    verification_result["status"] = "discrepancies_found"
+            else:
+                verification_result["status"] = "unverified"
+            
+        except Exception as e:
+            verification_result["status"] = "error"
+            verification_result["error"] = str(e)
+            self._add_to_history(f"元数据校验失败: {str(e)}")
+        
+        return verification_result
+    
+    async def _crossref_lookup_by_doi(self, doi: str) -> Optional[Dict]:
+        """通过DOI查询CrossRef"""
+        try:
+            url = f"{self.crossref_base_url}/{doi}"
+            
+            async with aiohttp.ClientSession() as session:
+                async with session.get(url) as response:
+                    if response.status == 200:
+                        data = await response.json()
+                        return self._parse_crossref_data(data)
+                    else:
+                        self._add_to_history(f"CrossRef查询失败，状态码: {response.status}")
+                        return None
+                        
+        except Exception as e:
+            self._add_to_history(f"CrossRef查询异常: {str(e)}")
+            return None
+    
+    async def _scholar_lookup_by_title(self, title: str) -> Optional[Dict]:
+        """通过标题查询Google Scholar"""
+        try:
+            config = self.config.external_apis
+            if not config.serpapi_key:
+                self._add_to_history("SerpApi key缺失，跳过Google Scholar查询")
+                return None
+            
+            params = {
+                "engine": "google_scholar",
+                "q": title,
+                "api_key": config.serpapi_key
+            }
+            
+            async with aiohttp.ClientSession() as session:
+                async with session.get(self.google_scholar_url, params=params) as response:
+                    if response.status == 200:
+                        data = await response.json()
+                        return self._parse_scholar_data(data)
+                    else:
+                        self._add_to_history(f"Google Scholar查询失败，状态码: {response.status}")
+                        return None
+                        
+        except Exception as e:
+            self._add_to_history(f"Google Scholar查询异常: {str(e)}")
+            return None
+    
+    def _parse_crossref_data(self, data: Dict) -> Dict:
+        """解析CrossRef数据"""
+        message = data.get("message", {})
+        
+        return {
+            "title": " ".join(message.get("title", [])),
+            "authors": [f"{author.get('given', '')} {author.get('family', '')}" 
+                       for author in message.get("author", [])],
+            "year": message.get("published-print", {}).get("date-parts", [[""]])[0][0][:4],
+            "journal": message.get("short-container-title", [""])[0],
+            "volume": message.get("volume", ""),
+            "issue": message.get("issue", ""),
+            "page": message.get("page", ""),
+            "doi": message.get("DOI", ""),
+            "source": "crossref"
+        }
+    
+    def _parse_scholar_data(self, data: Dict) -> Dict:
+        """解析Google Scholar数据"""
+        organic_results = data.get("organic_results", [])
+        if not organic_results:
+            return {}
+        
+        first_result = organic_results[0]
+        
+        # 提取年份
+        publication_info = first_result.get("publication_info", {})
+        year = ""
+        if "summary" in publication_info:
+            year_match = re.search(r'\b(19|20)\d{2}\b', publication_info["summary"])
+            if year_match:
+                year = year_match.group()
+        
+        return {
+            "title": first_result.get("title", ""),
+            "authors": first_result.get("publication_info", {}).get("authors", []),
+            "year": year,
+            "journal": publication_info.get("summary", "").split(",")[0] if publication_info.get("summary") else "",
+            "source": "google_scholar"
+        }
+    
+    def _compare_metadata(self, original: Dict, reference: Dict) -> List[Dict]:
+        """比较元数据差异"""
+        discrepancies = []
+        
+        # 比较标题
+        orig_title = original.get("title", "").lower().strip()
+        ref_title = reference.get("title", "").lower().strip()
+        if orig_title and ref_title and orig_title != ref_title:
+            discrepancies.append({
+                "field": "title",
+                "original": original.get("title", ""),
+                "reference": reference.get("title", ""),
+                "similarity": self._calculate_similarity(orig_title, ref_title)
+            })
+        
+        # 比较作者
+        orig_authors = set([author.lower() for author in original.get("authors", [])])
+        ref_authors = set([author.lower() for author in reference.get("authors", [])])
+        if orig_authors and ref_authors and orig_authors != ref_authors:
+            discrepancies.append({
+                "field": "authors",
+                "original": original.get("authors", []),
+                "reference": reference.get("authors", []),
+                "missing_in_original": list(ref_authors - orig_authors),
+                "extra_in_original": list(orig_authors - ref_authors)
+            })
+        
+        # 比较年份
+        orig_year = str(original.get("year", ""))
+        ref_year = str(reference.get("year", ""))
+        if orig_year and ref_year and orig_year != ref_year:
+            discrepancies.append({
+                "field": "year",
+                "original": orig_year,
+                "reference": ref_year
+            })
+        
+        return discrepancies
+    
+    def _calculate_similarity(self, text1: str, text2: str) -> float:
+        """计算文本相似度"""
+        if not text1 or not text2:
+            return 0.0
+        
+        words1 = set(text1.split())
+        words2 = set(text2.split())
+        
+        intersection = words1.intersection(words2)
+        union = words1.union(words2)
+        
+        return len(intersection) / len(union) if union else 0.0
+    
+    def _generate_corrections(self, discrepancies: List[Dict]) -> Dict:
+        """生成修正建议"""
+        corrections = {}
+        
+        for discrepancy in discrepancies:
+            field = discrepancy["field"]
+            if field == "title" and discrepancy.get("similarity", 0) > 0.8:
+                corrections[field] = discrepancy["reference"]
+            elif field == "year":
+                corrections[field] = discrepancy["reference"]
+            elif field == "authors":
+                # 对于作者，建议使用参考数据的完整列表
+                corrections[field] = discrepancy["reference"]
+        
+        return corrections
+    
+    async def _merge_citation_data(self, citations: Dict, verification: Dict, paper_info: Dict) -> Dict[str, Any]:
+        """合并引用数据"""
+        final_citations = {}
+        
+        for format_type, citation_text in citations.items():
+            if isinstance(citation_text, str) and not citation_text.startswith("生成失败"):
+                # 添加校验状态标记
+                verification_status = verification.get("status", "unknown")
+                
+                if verification_status == "verified":
+                    citation_text += "  % [Verified]"
+                elif verification_status == "discrepancies_found":
+                    citation_text += "  % [Discrepancies Found]"
+                else:
+                    citation_text += "  % [Unverified]"
+                
+                final_citations[format_type] = citation_text
+            else:
+                final_citations[format_type] = citation_text
+        
+        # 添加元数据
+        final_citations["metadata"] = {
+            "original_info": paper_info,
+            "verification": verification,
+            "generated_formats": list(citations.keys()),
+            "generation_timestamp": datetime.now().isoformat()
+        }
+        
+        return final_citations
+    
+    async def _cache_citation_results(self, citations: Dict):
+        """缓存引用结果"""
+        try:
+            metadata = citations.get("metadata", {})
+            original_info = metadata.get("original_info", {})
+            doi = original_info.get("doi", "")
+            
+            if doi:
+                # 缓存BibTeX格式
+                bibtex = citations.get("bibtex", "")
+                if bibtex and not bibtex.startswith("生成失败"):
+                    verification = metadata.get("verification", {})
+                    is_verified = verification.get("status") == "verified"
+                    
+                    await db_manager.cache_reference(
+                        doi=doi,
+                        bibtex=bibtex,
+                        is_verified=is_verified
+                    )
+                    
+                    self._add_to_history(f"引用已缓存: {doi}")
+                    
+        except Exception as e:
+            self._add_to_history(f"缓存引用失败: {str(e)}")
+    
+    # 工具方法
+    async def _generate_bibtex(self, paper_info: Dict) -> str:
+        """生成BibTeX工具"""
+        return await self._generate_bibtex_citation(paper_info)
+    
+    async def _generate_apa(self, paper_info: Dict) -> str:
+        """生成APA工具"""
+        return await self._generate_apa_citation(paper_info)
+    
+    async def _generate_ieee(self, paper_info: Dict) -> str:
+        """生成IEEE工具"""
+        return await self._generate_ieee_citation(paper_info)
+    
+    async def _verify_metadata(self, paper_info: Dict) -> Dict:
+        """校验元数据工具"""
+        return await self._verify_paper_metadata(paper_info)
+    
+    async def _crossref_lookup(self, identifier: str) -> Dict:
+        """CrossRef查询工具"""
+        if identifier.startswith("10."):  # DOI
+            return await self._crossref_lookup_by_doi(identifier)
+        else:
+            return {"error": "请提供有效的DOI"}
+    
+    async def _scholar_lookup(self, title: str) -> Dict:
+        """Google Scholar查询工具"""
+        return await self._scholar_lookup_by_title(title)

Co-creation-projects/Apricity-InnocoreAI/api/__init__.py

@@ -0,0 +1,11 @@
+"""
+InnoCore AI API模块
+"""
+
+try:
+    from .main import app
+    from .routes import *
+    __all__ = ["app"]
+except ImportError:
+    # 当直接导入时，避免相对导入错误
+    pass

Co-creation-projects/Apricity-InnocoreAI/api/main.py

@@ -0,0 +1,164 @@
+"""
+InnoCore API 主应用
+"""
+
+from fastapi import FastAPI, HTTPException, Depends, BackgroundTasks
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+from contextlib import asynccontextmanager
+import logging
+import uvicorn
+
+from core.config import get_config
+from core.database import db_manager
+from core.vector_store import vector_store_manager
+from agents.controller import agent_controller
+from .routes import papers, users, tasks, analysis, writing, citations, workflow
+
+# 配置日志
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """应用生命周期管理"""
+    # 启动时初始化
+    logger.info("正在启动InnoCore AI...")
+    
+    # 初始化数据库（可选）
+    try:
+        await db_manager.initialize()
+        logger.info("数据库初始化完成")
+    except Exception as e:
+        logger.warning(f"数据库初始化失败（将以无数据库模式运行）: {str(e)}")
+    
+    # 初始化向量存储（可选）
+    try:
+        await vector_store_manager.initialize()
+        logger.info("向量存储初始化完成")
+    except Exception as e:
+        logger.warning(f"向量存储初始化失败（将以无向量存储模式运行）: {str(e)}")
+    
+    # 初始化智能体控制器（可选）
+    try:
+        await agent_controller.initialize()
+        logger.info("智能体控制器初始化完成")
+        
+        # 启动任务处理器
+        import asyncio
+        asyncio.create_task(agent_controller.start_task_processor())
+        logger.info("任务处理器已启动")
+    except Exception as e:
+        logger.warning(f"智能体控制器初始化失败: {str(e)}")
+    
+    logger.info("InnoCore AI 启动完成")
+    
+    yield
+    
+    # 关闭时清理
+    logger.info("正在关闭InnoCore AI...")
+    await agent_controller.shutdown()
+    await db_manager.close()
+    await vector_store_manager.close()
+    logger.info("InnoCore AI已关闭")
+
+# 创建FastAPI应用
+app = FastAPI(
+    title="InnoCore AI API",
+    description="智能科研创新助手API",
+    version="0.1.0",
+    lifespan=lifespan
+)
+
+# 配置CORS
+config = get_config()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # 生产环境应该限制具体域名
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 注册路由
+app.include_router(papers.router, prefix="/api/v1/papers", tags=["papers"])
+app.include_router(users.router, prefix="/api/v1/users", tags=["users"])
+app.include_router(tasks.router, prefix="/api/v1/tasks", tags=["tasks"])
+app.include_router(analysis.router, prefix="/api/v1/analysis", tags=["analysis"])
+app.include_router(writing.router, prefix="/api/v1/writing", tags=["writing"])
+app.include_router(citations.router, prefix="/api/v1/citations", tags=["citations"])
+app.include_router(workflow.router, prefix="/api/v1/workflow", tags=["workflow"])
+
+# 挂载静态文件
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+import os
+
+# 获取项目根目录
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+FRONTEND_DIR = os.path.join(BASE_DIR, "frontend")
+
+# 挂载静态资源
+if os.path.exists(os.path.join(FRONTEND_DIR, "static")):
+    app.mount("/static", StaticFiles(directory=os.path.join(FRONTEND_DIR, "static")), name="static")
+
+# 根路径 - 返回前端页面
+@app.get("/")
+async def root():
+    """根路径 - 返回前端首页"""
+    index_path = os.path.join(FRONTEND_DIR, "index.html")
+    if os.path.exists(index_path):
+        return FileResponse(index_path)
+    return {
+        "message": "Welcome to InnoCore AI API",
+        "version": "0.1.0",
+        "status": "running"
+    }
+
+# 健康检查
+@app.get("/health")
+async def health_check():
+    """健康检查"""
+    try:
+        # 检查各组件状态
+        agent_status = await agent_controller.get_agent_status()
+        
+        return {
+            "status": "healthy",
+            "timestamp": "2024-01-01T00:00:00Z",
+            "components": {
+                "database": "connected",
+                "vector_store": "connected",
+                "agents": agent_status
+            }
+        }
+    except Exception as e:
+        return JSONResponse(
+            status_code=503,
+            content={
+                "status": "unhealthy",
+                "error": str(e)
+            }
+        )
+
+# 全局异常处理
+@app.exception_handler(Exception)
+async def global_exception_handler(request, exc):
+    """全局异常处理器"""
+    logger.error(f"全局异常: {str(exc)}")
+    return JSONResponse(
+        status_code=500,
+        content={
+            "error": "Internal server error",
+            "message": str(exc) if config.debug else "Something went wrong"
+        }
+    )
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "innocore_ai.api.main:app",
+        host="0.0.0.0",
+        port=8000,
+        reload=config.debug,
+        log_level="info"
+    )

Co-creation-projects/Apricity-InnocoreAI/api/routes/__init__.py

@@ -0,0 +1,7 @@
+"""
+API路由模块
+"""
+
+from . import papers, users, tasks, analysis, writing, citations, workflow
+
+__all__ = ["papers", "users", "tasks", "analysis", "writing", "citations", "workflow"]

Co-creation-projects/Apricity-InnocoreAI/api/routes/analysis.py

@@ -0,0 +1,567 @@
+"""
+分析相关API路由
+"""
+
+from fastapi import APIRouter, HTTPException, UploadFile, File
+from typing import Dict, Any, Optional, List
+from pydantic import BaseModel
+import logging
+import arxiv
+import os
+from core.config import get_config
+from core.llm_adapter import get_llm_adapter
+from utils.pdf_parser import pdf_parser
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+# 初始化 LLM 适配器（基于 HelloAgent）
+config = get_config()
+try:
+    llm = get_llm_adapter() if config.llm.api_key else None
+except Exception as e:
+    logger.warning(f"LLM 初始化失败: {str(e)}")
+    llm = None
+
+# Pydantic模型
+class AnalysisRequest(BaseModel):
+    paper_id: str
+    user_id: Optional[str] = None
+    analysis_type: str = "full"  # full, quick, innovation_only
+
+class ComparisonRequest(BaseModel):
+    paper_ids: List[str]
+    user_id: Optional[str] = None
+    comparison_aspects: List[str] = ["method", "results", "innovation"]
+
+class InnovationSearchRequest(BaseModel):
+    query: str
+    user_id: Optional[str] = None
+    search_scope: str = "both"  # l1, l2, both
+    top_k: int = 10
+
+class PaperAnalysisRequest(BaseModel):
+    paper_url: str
+    analysis_type: str = "summary"  # summary, innovation, comparison, comprehensive
+
+@router.post("/analyze", response_model=Dict[str, Any])
+async def analyze_paper(request: PaperAnalysisRequest):
+    """分析论文 - 支持 ArXiv URL 和本地 PDF 文件"""
+    try:
+        if not llm:
+            raise HTTPException(status_code=503, detail="AI 服务未配置，请设置 OPENAI_API_KEY")
+        
+        import re
+        paper_url = request.paper_url.strip()
+        
+        # 检查是否是本地上传的 PDF 文件
+        if paper_url.startswith('/uploads/') or paper_url.endswith('.pdf'):
+            logger.info(f"检测到本地 PDF 文件: {paper_url}")
+            
+            # 构建完整的文件路径
+            if paper_url.startswith('/uploads/'):
+                # 假设上传的文件在 downloads 目录
+                file_path = os.path.join('downloads', paper_url.replace('/uploads/', ''))
+            else:
+                file_path = paper_url
+            
+            # 检查文件是否存在
+            if not os.path.exists(file_path):
+                logger.warning(f"PDF 文件不存在: {file_path}")
+                raise HTTPException(status_code=404, detail=f"PDF 文件不存在: {paper_url}")
+            
+            # 解析 PDF 文件
+            logger.info(f"开始解析 PDF 文件: {file_path}")
+            pdf_result = await pdf_parser.parse_pdf(file_path)
+            
+            if not pdf_result.get("success"):
+                raise HTTPException(status_code=500, detail=pdf_result.get("error", "PDF 解析失败"))
+            
+            # 使用解析出的内容进行 AI 分析
+            title = pdf_result.get("title", "未知标题")
+            authors = pdf_result.get("authors", ["未知作者"])
+            abstract = pdf_result.get("abstract", "")
+            full_text = pdf_result.get("full_text", "")
+            
+            # 限制文本长度以避免超出 token 限制
+            text_for_analysis = full_text[:8000] if len(full_text) > 8000 else full_text
+            
+            # 根据分析类型生成提示词
+            prompts = {
+                "summary": f"""请对以下论文进行摘要分析：
+
+标题：{title}
+作者：{', '.join(authors)}
+摘要：{abstract}
+
+论文内容（前8000字符）：
+{text_for_analysis}
+
+请提供：
+1. 研究背景和动机
+2. 主要方法
+3. 核心贡献
+4. 实验结果
+5. 研究意义
+
+请用中文回答，保持专业和简洁。""",
+                
+                "innovation": f"""请分析以下论文的创新点：
+
+标题：{title}
+摘要：{abstract}
+
+论文内容：
+{text_for_analysis}
+
+请详细分析：
+1. 技术创新点
+2. 方法论创新
+3. 理论贡献
+4. 与现有工作的区别
+5. 潜在应用价值
+
+请用中文回答。""",
+                
+                "comparison": f"""请对以下论文进行对比分析：
+
+标题：{title}
+摘要：{abstract}
+
+论文内容：
+{text_for_analysis}
+
+请分析：
+1. 与传统方法的对比
+2. 优势和劣势
+3. 适用场景
+4. 性能提升
+5. 局限性
+
+请用中文回答。""",
+                
+                "comprehensive": f"""请对以下论文进行全面综合分析：
+
+标题：{title}
+作者：{', '.join(authors)}
+摘要：{abstract}
+
+论文内容：
+{text_for_analysis}
+
+请提供全面的分析，包括：
+1. 研究背景和意义
+2. 技术方法详解
+3. 创新点分析
+4. 实验验证
+5. 优缺点评价
+6. 未来研究方向
+7. 实际应用价值
+
+请用中文回答，保持专业和深度。"""
+            }
+            
+            prompt = prompts.get(request.analysis_type, prompts["summary"])
+            
+            # 调用 LLM 进行分析
+            logger.info(f"开始 AI 分析，类型: {request.analysis_type}")
+            response = await llm.ainvoke(prompt)
+            analysis_content = response.content if hasattr(response, 'content') else str(response)
+            
+            return {
+                "success": True,
+                "paper_info": {
+                    "id": "local_pdf",
+                    "title": title,
+                    "authors": authors,
+                    "published_date": "N/A",
+                    "url": paper_url,
+                    "categories": ["本地文件"],
+                    "page_count": pdf_result.get("page_count", 0),
+                    "word_count": pdf_result.get("word_count", 0)
+                },
+                "analysis_type": request.analysis_type,
+                "analysis": analysis_content,
+                "abstract": abstract
+            }
+        
+        # ArXiv 论文处理
+        arxiv_patterns = [
+            r'arxiv\.org/abs/(\d+\.\d+)',
+            r'arxiv\.org/pdf/(\d+\.\d+)',
+            r'arXiv:(\d+\.\d+)',
+            r'\[(\d+\.\d+)v?\d*\]',
+            r'^(\d{4}\.\d{4,5})v?\d*$'
+        ]
+        
+        paper_id = None
+        for pattern in arxiv_patterns:
+            match = re.search(pattern, paper_url, re.IGNORECASE)
+            if match:
+                paper_id = match.group(1)
+                break
+        
+        if not paper_id:
+            raise HTTPException(
+                status_code=400, 
+                detail=f"无效的输入。支持的格式：\n" +
+                       "- ArXiv URL: https://arxiv.org/abs/2511.16672\n" +
+                       "- ArXiv ID: 2511.16672\n" +
+                       "- 本地 PDF: 上传后自动填充"
+            )
+        
+        logger.info(f"正在分析 ArXiv 论文: {paper_id}")
+        
+        # 获取论文信息
+        search = arxiv.Search(id_list=[paper_id])
+        paper = next(search.results(), None)
+        
+        if not paper:
+            raise HTTPException(status_code=404, detail=f"未找到 ArXiv 论文: {paper_id}")
+        
+        # 根据分析类型生成提示词
+        prompts = {
+            "summary": f"""请对以下论文进行摘要分析：
+
+标题：{paper.title}
+作者：{', '.join([a.name for a in paper.authors])}
+摘要：{paper.summary}
+
+请提供：
+1. 研究背景和动机
+2. 主要方法
+3. 核心贡献
+4. 实验结果
+5. 研究意义
+
+请用中文回答，保持专业和简洁。""",
+            
+            "innovation": f"""请分析以下论文的创新点：
+
+标题：{paper.title}
+摘要：{paper.summary}
+
+请详细分析：
+1. 技术创新点
+2. 方法论创新
+3. 理论贡献
+4. 与现有工作的区别
+5. 潜在应用价值
+
+请用中文回答。""",
+            
+            "comparison": f"""请对以下论文进行对比分析：
+
+标题：{paper.title}
+摘要：{paper.summary}
+
+请分析：
+1. 与传统方法的对比
+2. 优势和劣势
+3. 适用场景
+4. 性能提升
+5. 局限性
+
+请用中文回答。""",
+            
+            "comprehensive": f"""请对以下论文进行全面综合分析：
+
+标题：{paper.title}
+作者：{', '.join([a.name for a in paper.authors])}
+摘要：{paper.summary}
+分类：{', '.join(paper.categories)}
+
+请提供全面的分析，包括：
+1. 研究背景和意义
+2. 技术方法详解
+3. 创新点分析
+4. 实验验证
+5. 优缺点评价
+6. 未来研究方向
+7. 实际应用价值
+
+请用中文回答，保持专业和深度。"""
+        }
+        
+        prompt = prompts.get(request.analysis_type, prompts["summary"])
+        
+        # 调用 LLM 进行分析
+        response = await llm.ainvoke(prompt)
+        analysis_content = response.content if hasattr(response, 'content') else str(response)
+        
+        return {
+            "success": True,
+            "paper_info": {
+                "id": paper_id,
+                "title": paper.title,
+                "authors": [a.name for a in paper.authors],
+                "published_date": paper.published.strftime("%Y-%m-%d"),
+                "url": paper.entry_id,
+                "categories": paper.categories
+            },
+            "analysis_type": request.analysis_type,
+            "analysis": analysis_content,
+            "abstract": paper.summary
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"论文分析失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
+
+@router.post("/compare", response_model=Dict[str, Any])
+async def compare_papers(request: ComparisonRequest):
+    """对比多篇论文"""
+    try:
+        # 这里需要实现论文对比逻辑
+        # 暂时返回模拟结果
+        
+        comparison_result = {
+            "paper_ids": request.paper_ids,
+            "comparison_aspects": request.comparison_aspects,
+            "similarities": ["相似点1", "相似点2"],
+            "differences": ["差异点1", "差异点2"],
+            "innovation_gaps": ["创新空白1", "创新空白2"],
+            "recommendations": ["建议1", "建议2"]
+        }
+        
+        return {
+            "success": True,
+            "result": comparison_result
+        }
+        
+    except Exception as e:
+        logger.error(f"论文对比失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/innovation/search", response_model=Dict[str, Any])
+async def search_innovation_opportunities(request: InnovationSearchRequest):
+    """搜索创新机会"""
+    try:
+        # 这里需要实现创新机会搜索逻辑
+        # 暂时返回模拟结果
+        
+        innovation_results = {
+            "query": request.query,
+            "opportunities": [
+                {
+                    "title": "创新机会1",
+                    "description": "基于当前研究的创新方向",
+                    "related_papers": ["paper1", "paper2"],
+                    "confidence": 0.85
+                },
+                {
+                    "title": "创新机会2", 
+                    "description": "另一个潜在的研究方向",
+                    "related_papers": ["paper3", "paper4"],
+                    "confidence": 0.72
+                }
+            ],
+            "research_gaps": ["研究空白1", "研究空白2"],
+            "future_directions": ["未来方向1", "未来方向2"]
+        }
+        
+        return {
+            "success": True,
+            "result": innovation_results
+        }
+        
+    except Exception as e:
+        logger.error(f"创新机会搜索失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/paper/{paper_id}/summary")
+async def get_paper_summary(paper_id: str, user_id: Optional[str] = None):
+    """获取论文摘要"""
+    try:
+        # 这里需要实现论文摘要生成逻辑
+        # 暂时返回模拟结果
+        
+        summary = {
+            "paper_id": paper_id,
+            "summary": "这是一篇关于...的论文，主要贡献包括...",
+            "key_contributions": ["贡献1", "贡献2", "贡献3"],
+            "methodology": "论文采用的方法是...",
+            "results": "实验结果表明...",
+            "limitations": "研究的局限性包括...",
+            "future_work": "未来工作方向..."
+        }
+        
+        return {
+            "success": True,
+            "summary": summary
+        }
+        
+    except Exception as e:
+        logger.error(f"获取论文摘要失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/paper/{paper_id}/innovations")
+async def get_paper_innovations(paper_id: str, user_id: Optional[str] = None):
+    """获取论文创新点"""
+    try:
+        # 这里需要实现创新点提取逻辑
+        # 暂时返回模拟结果
+        
+        innovations = {
+            "paper_id": paper_id,
+            "innovations": [
+                {
+                    "aspect": "方法创新",
+                    "description": "提出了新的方法...",
+                    "novelty": "high",
+                    "impact": "significant"
+                },
+                {
+                    "aspect": "理论创新", 
+                    "description": "在理论上有所突破...",
+                    "novelty": "medium",
+                    "impact": "moderate"
+                }
+            ],
+            "comparison_with_prior_work": "与之前的工作相比...",
+            "potential_applications": ["应用1", "应用2"]
+        }
+        
+        return {
+            "success": True,
+            "innovations": innovations
+        }
+        
+    except Exception as e:
+        logger.error(f"获取论文创新点失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/user/{user_id}/insights")
+async def get_user_insights(user_id: str):
+    """获取用户研究洞察"""
+    try:
+        # 这里需要实现用户研究洞察分析
+        # 暂时返回模拟结果
+        
+        insights = {
+            "user_id": user_id,
+            "research_interests": ["兴趣1", "兴趣2"],
+            "reading_patterns": {
+                "papers_read": 50,
+                "favorite_topics": ["主题1", "主题2"],
+                "reading_frequency": "daily"
+            },
+            "knowledge_gaps": ["知识空白1", "知识空白2"],
+            "research_suggestions": [
+                {
+                    "topic": "建议研究方向1",
+                    "reason": "基于您的阅读历史...",
+                    "related_papers": ["paper1", "paper2"]
+                }
+            ],
+            "skill_assessment": {
+                "technical_skills": ["技能1", "技能2"],
+                "writing_skills": ["写作技能1", "写作技能2"],
+                "improvement_areas": ["改进领域1", "改进领域2"]
+            }
+        }
+        
+        return {
+            "success": True,
+            "insights": insights
+        }
+        
+    except Exception as e:
+        logger.error(f"获取用户研究洞察失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/batch", response_model=Dict[str, Any])
+async def batch_analyze_papers(paper_ids: List[str], user_id: Optional[str] = None):
+    """批量分析论文"""
+    try:
+        results = []
+        
+        for paper_id in paper_ids:
+            try:
+                # 提交论文分析任务
+                task_id = await agent_controller.submit_task(
+                    TaskType.PAPER_ANALYSIS,
+                    {
+                        "paper_id": paper_id,
+                        "user_id": user_id,
+                        "analysis_type": "quick"  # 批量分析使用快速模式
+                    }
+                )
+                
+                # 执行任务
+                result = await agent_controller.execute_task(task_id)
+                
+                results.append({
+                    "paper_id": paper_id,
+                    "task_id": task_id,
+                    "success": True,
+                    "result": result
+                })
+                
+            except Exception as e:
+                results.append({
+                    "paper_id": paper_id,
+                    "success": False,
+                    "error": str(e)
+                })
+        
+        return {
+            "success": True,
+            "total_papers": len(paper_ids),
+            "successful_analyses": sum(1 for r in results if r["success"]),
+            "results": results
+        }
+        
+    except Exception as e:
+        logger.error(f"批量分析论文失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/upload-pdf", response_model=Dict[str, Any])
+async def upload_pdf_for_analysis(file: UploadFile = File(...)):
+    """
+    上传 PDF 文件并解析
+    返回文件信息和解析结果
+    """
+    try:
+        # 检查文件类型
+        if not file.filename.endswith('.pdf'):
+            raise HTTPException(status_code=400, detail="只支持 PDF 文件")
+        
+        # 读取文件内容
+        logger.info(f"接收到 PDF 文件: {file.filename}")
+        pdf_bytes = await file.read()
+        
+        # 解析 PDF
+        pdf_result = await pdf_parser.parse_pdf_from_bytes(pdf_bytes, file.filename)
+        
+        if not pdf_result.get("success"):
+            raise HTTPException(status_code=500, detail=pdf_result.get("error", "PDF 解析失败"))
+        
+        # 保存文件到 downloads 目录
+        os.makedirs("downloads", exist_ok=True)
+        file_path = os.path.join("downloads", file.filename)
+        
+        with open(file_path, "wb") as f:
+            f.write(pdf_bytes)
+        
+        logger.info(f"PDF 文件已保存: {file_path}")
+        
+        return {
+            "success": True,
+            "filename": file.filename,
+            "file_path": f"/uploads/{file.filename}",
+            "title": pdf_result.get("title", "未知标题"),
+            "authors": pdf_result.get("authors", ["未知作者"]),
+            "abstract": pdf_result.get("abstract", "")[:500],  # 限制摘要长度
+            "page_count": pdf_result.get("page_count", 0),
+            "word_count": pdf_result.get("word_count", 0),
+            "message": "PDF 文件上传并解析成功，可以使用返回的 file_path 进行分析"
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"PDF 上传失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"上传失败: {str(e)}")

Co-creation-projects/Apricity-InnocoreAI/api/routes/citations.py

@@ -0,0 +1,330 @@
+"""
+引用校验API路由
+"""
+
+from fastapi import APIRouter, HTTPException
+from typing import Dict, Any, Optional
+from pydantic import BaseModel
+import logging
+import httpx
+import re
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+# Pydantic模型
+class CitationValidationRequest(BaseModel):
+    citation: str
+    format: str = "bibtex"  # bibtex, apa, ieee, mla
+
+class CitationGenerateRequest(BaseModel):
+    doi: Optional[str] = None
+    title: Optional[str] = None
+    authors: Optional[str] = None
+    year: Optional[int] = None
+    journal: Optional[str] = None
+    format: str = "bibtex"
+
+@router.post("/validate", response_model=Dict[str, Any])
+async def validate_citation(request: CitationValidationRequest):
+    """校验引用格式 - 支持 ArXiv、DOI 和 AI 辅助验证"""
+    try:
+        logger.info(f"校验引用: {request.citation[:100]}...")
+        
+        metadata = None
+        verified = False
+        doi = None
+        
+        # 1. 尝试识别 ArXiv URL 或 ID
+        arxiv_pattern = r'(?:arxiv\.org/abs/|arXiv:)(\d+\.\d+)'
+        arxiv_match = re.search(arxiv_pattern, request.citation, re.IGNORECASE)
+        
+        if arxiv_match:
+            arxiv_id = arxiv_match.group(1)
+            logger.info(f"找到 ArXiv ID: {arxiv_id}")
+            
+            try:
+                import arxiv
+                search = arxiv.Search(id_list=[arxiv_id])
+                paper = next(search.results(), None)
+                
+                if paper:
+                    metadata = {
+                        'title': paper.title,
+                        'authors': [author.name for author in paper.authors],
+                        'year': paper.published.year,
+                        'journal': 'arXiv preprint',
+                        'arxiv_id': arxiv_id,
+                        'url': paper.entry_id
+                    }
+                    verified = True
+                    logger.info(f"ArXiv 论文信息获取成功: {metadata['title'][:50]}...")
+                    logger.info(f"作者数量: {len(metadata['authors'])}")
+                else:
+                    logger.warning(f"未找到 ArXiv ID: {arxiv_id}")
+            except Exception as e:
+                logger.error(f"ArXiv 查询失败: {str(e)}", exc_info=True)
+        
+        # 2. 尝试从引用中提取 DOI
+        if not verified:
+            doi_pattern = r'10\.\d{4,9}/[-._;()/:A-Z0-9]+'
+            doi_match = re.search(doi_pattern, request.citation, re.IGNORECASE)
+            
+            if doi_match:
+                doi = doi_match.group(0)
+                logger.info(f"找到 DOI: {doi}")
+                
+                # 使用 Crossref API 验证 DOI
+                async with httpx.AsyncClient() as client:
+                    try:
+                        response = await client.get(
+                            f"https://api.crossref.org/works/{doi}",
+                            timeout=10.0
+                        )
+                        if response.status_code == 200:
+                            data = response.json()
+                            msg = data.get('message', {})
+                            metadata = {
+                                'title': msg.get('title', [''])[0],
+                                'authors': [f"{a.get('given', '')} {a.get('family', '')}" for a in msg.get('author', [])],
+                                'year': msg.get('published', {}).get('date-parts', [[None]])[0][0],
+                                'journal': msg.get('container-title', [''])[0],
+                                'volume': msg.get('volume', ''),
+                                'issue': msg.get('issue', ''),
+                                'pages': msg.get('page', ''),
+                                'doi': doi
+                            }
+                            verified = True
+                            logger.info("DOI 验证成功")
+                    except Exception as e:
+                        logger.warning(f"DOI 验证失败: {str(e)}")
+        
+        # 3. 如果仍未验证，尝试使用 AI 解析引用信息
+        if not verified:
+            logger.info("尝试使用 AI 解析引用信息...")
+            try:
+                from core.config import get_config
+                from core.llm_adapter import get_llm_adapter
+                
+                config = get_config()
+                if config.llm.api_key:
+                    llm = get_llm_adapter()
+                    
+                    prompt = f"""请从以下引用信息中提取关键元数据，并以 JSON 格式返回。
+
+引用信息：
+{request.citation}
+
+请提取以下信息（如果有的话）：
+- title: 论文标题
+- authors: 作者列表（字符串数组，例如 ["Zhang San", "Li Si"]）
+- year: 发表年份（数字）
+- journal: 期刊或会议名称
+- volume: 卷号
+- issue: 期号
+- pages: 页码
+- doi: DOI（如果有）
+- arxiv_id: ArXiv ID（如果有）
+
+只返回纯 JSON 格式，不要任何其他文字说明。如果某个字段不存在，请省略该字段。
+
+示例输出：
+{{"title": "论文标题", "authors": ["作者1", "作者2"], "year": 2024, "journal": "期刊名"}}"""
+                    
+                    response = await llm.ainvoke(prompt)
+                    ai_result = response.content if hasattr(response, 'content') else str(response)
+                    
+                    # 尝试解析 AI 返回的 JSON
+                    import json
+                    # 提取 JSON 部分（支持代码块格式）
+                    json_match = re.search(r'```(?:json)?\s*(\{[\s\S]*?\})\s*```', ai_result)
+                    if json_match:
+                        metadata = json.loads(json_match.group(1))
+                        verified = True
+                        logger.info("AI 解析成功（代码块格式）")
+                    else:
+                        json_match = re.search(r'\{[\s\S]*\}', ai_result)
+                        if json_match:
+                            metadata = json.loads(json_match.group(0))
+                            verified = True
+                            logger.info("AI 解析成功")
+            except Exception as e:
+                logger.warning(f"AI 解析失败: {str(e)}")
+        
+        # 生成标准格式的引用
+        if metadata and verified:
+            title = metadata.get('title', 'Unknown Title')
+            authors = metadata.get('authors', []) if isinstance(metadata.get('authors'), list) else [metadata.get('authors', 'Unknown Author')]
+            year = metadata.get('year', 'n.d.')
+            journal = metadata.get('journal', 'Unknown Journal')
+            volume = metadata.get('volume', '')
+            issue = metadata.get('issue', '')
+            pages = metadata.get('pages', '')
+            doi = metadata.get('doi', doi)
+            arxiv_id = metadata.get('arxiv_id', '')
+            
+            # 处理作者列表
+            if isinstance(authors, list):
+                if len(authors) > 3:
+                    author_str = ', '.join(authors[:3]) + ' et al.'
+                else:
+                    author_str = ', '.join(authors)
+            else:
+                author_str = str(authors)
+            
+            # 生成不同格式的引用
+            # BibTeX 格式
+            bibtex_parts = [
+                f"@article{{key{year},",
+                f"  title={{{title}}},",
+                f"  author={{{author_str}}},",
+                f"  journal={{{journal}}},",
+                f"  year={{{year}}}"
+            ]
+            if volume:
+                bibtex_parts.append(f"  volume={{{volume}}}")
+            if issue:
+                bibtex_parts.append(f"  number={{{issue}}}")
+            if pages:
+                bibtex_parts.append(f"  pages={{{pages}}}")
+            if arxiv_id:
+                bibtex_parts.append(f"  eprint={{{arxiv_id}}}")
+                bibtex_parts.append(f"  archivePrefix={{arXiv}}")
+            if doi:
+                bibtex_parts.append(f"  doi={{{doi}}}")
+            
+            bibtex_citation = ',\n'.join(bibtex_parts) + '\n}'
+            
+            # APA 格式
+            vol_str = f', {volume}' if volume else ''
+            issue_str = f'({issue})' if issue else ''
+            pages_str = f', {pages}' if pages else ''
+            
+            if arxiv_id:
+                apa_citation = f"{author_str} ({year}). {title}. *{journal}*{vol_str}{issue_str}{pages_str}. arXiv:{arxiv_id}"
+            elif doi:
+                apa_citation = f"{author_str} ({year}). {title}. *{journal}*{vol_str}{issue_str}{pages_str}. https://doi.org/{doi}"
+            else:
+                apa_citation = f"{author_str} ({year}). {title}. *{journal}*{vol_str}{issue_str}{pages_str}."
+            
+            # IEEE 格式
+            vol_ieee = f', vol. {volume}' if volume else ''
+            issue_ieee = f', no. {issue}' if issue else ''
+            pages_ieee = f', pp. {pages}' if pages else ''
+            
+            if arxiv_id:
+                ieee_citation = f'[1] {author_str}, "{title}," *{journal}*{vol_ieee}{issue_ieee}{pages_ieee}, {year}, arXiv:{arxiv_id}.'
+            elif doi:
+                ieee_citation = f'[1] {author_str}, "{title}," *{journal}*{vol_ieee}{issue_ieee}{pages_ieee}, {year}, doi: {doi}.'
+            else:
+                ieee_citation = f'[1] {author_str}, "{title}," *{journal}*{vol_ieee}{issue_ieee}{pages_ieee}, {year}.'
+            
+            vol_mla = f', vol. {volume}' if volume else ''
+            issue_mla = f', no. {issue}' if issue else ''
+            pages_mla = f', pp. {pages}' if pages else ''
+            
+            mla_citation = f'{author_str}. "{title}." *{journal}*{vol_mla}{issue_mla}, {year}{pages_mla}.'
+            
+            citations = {
+                "bibtex": bibtex_citation,
+                "apa": apa_citation,
+                "ieee": ieee_citation,
+                "mla": mla_citation
+            }
+            
+            formatted_citation = citations.get(request.format, citations["bibtex"])
+        else:
+            # 如果无法验证，返回原始引用和警告
+            formatted_citation = request.citation
+            verified = False
+        
+        result = {
+            "success": True,
+            "original_citation": request.citation,
+            "formatted_citation": formatted_citation,
+            "format": request.format,
+            "verified": verified,
+            "metadata": metadata if verified else None,
+            "warnings": [] if verified else ["无法自动验证引用，已返回原始格式。建议提供包含 DOI 的引用信息以获得更准确的结果。"]
+        }
+        
+        logger.info(f"返回结果 - verified: {verified}, metadata: {metadata is not None}")
+        if metadata:
+            logger.info(f"Metadata keys: {list(metadata.keys())}")
+        
+        return result
+        
+    except Exception as e:
+        logger.error(f"引用校验失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"校验失败: {str(e)}")
+
+@router.post("/generate", response_model=Dict[str, Any])
+async def generate_citation(request: CitationGenerateRequest):
+    """生成引用格式"""
+    try:
+        # 模拟引用生成
+        newline = "\n"
+        quote = '"'
+        citation_formats = {
+            "bibtex": f"@article{{{request.authors or 'author2024'},{newline}  title={{{request.title or 'Title'}}},{newline}  author={{{request.authors or 'Author'}}},{newline}  journal={{{request.journal or 'Journal'}}},{newline}  year={{{request.year or 2024}}}{newline}}}",
+            "apa": f"{request.authors or 'Author'} ({request.year or 2024}). {request.title or 'Title'}. *{request.journal or 'Journal'}*.",
+            "ieee": f"[1] {request.authors or 'Author'}, {quote}{request.title or 'Title'},{quote} *{request.journal or 'Journal'}*, {request.year or 2024}.",
+            "mla": f"{request.authors or 'Author'}. {quote}{request.title or 'Title'}.{quote} *{request.journal or 'Journal'}*, {request.year or 2024}."
+        }
+        
+        citation = citation_formats.get(request.format, citation_formats["bibtex"])
+        
+        return {
+            "success": True,
+            "citation": citation,
+            "format": request.format,
+            "metadata": {
+                "title": request.title,
+                "authors": request.authors,
+                "year": request.year,
+                "journal": request.journal,
+                "doi": request.doi
+            },
+            "timestamp": "2024-01-15T10:30:00Z"
+        }
+        
+    except Exception as e:
+        logger.error(f"引用生成失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"生成失败: {str(e)}")
+
+@router.get("/formats", response_model=Dict[str, Any])
+async def get_citation_formats():
+    """获取支持的引用格式"""
+    try:
+        formats = {
+            "bibtex": {
+                "name": "BibTeX",
+                "description": "常用于LaTeX文档的引用格式",
+                "example": "@article{key, title={Title}, author={Author}, year={2024}}"
+            },
+            "apa": {
+                "name": "APA",
+                "description": "美国心理学会格式，常用于社会科学",
+                "example": "Author, A. (2024). Title. *Journal*, 1(1), 1-10."
+            },
+            "ieee": {
+                "name": "IEEE",
+                "description": "电气电子工程师学会格式，常用于工程技术",
+                "example": "[1] A. Author, \"Title,\" *Journal*, vol. 1, no. 1, pp. 1-10, 2024."
+            },
+            "mla": {
+                "name": "MLA",
+                "description": "现代语言学会格式，常用于人文学科",
+                "example": "Author. \"Title.\" *Journal*, vol. 1, no. 1, 2024, pp. 1-10."
+            }
+        }
+        
+        return {
+            "success": True,
+            "formats": formats,
+            "total": len(formats)
+        }
+        
+    except Exception as e:
+        logger.error(f"获取引用格式失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"获取失败: {str(e)}")

Co-creation-projects/Apricity-InnocoreAI/api/routes/papers.py

@@ -0,0 +1,109 @@
+"""
+论文相关API路由
+"""
+
+from fastapi import APIRouter, HTTPException, Depends, Query, UploadFile, File
+from typing import List, Optional, Dict, Any
+from pydantic import BaseModel
+import logging
+import arxiv
+from datetime import datetime
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+# Pydantic模型
+class PaperSearchRequest(BaseModel):
+    keywords: str
+    source: str = "arxiv"
+    limit: int = 10
+
+class PaperResponse(BaseModel):
+    id: str
+    title: str
+    authors: List[str]
+    abstract: str
+    url: str
+    published_date: str
+
+@router.post("/search", response_model=Dict[str, Any])
+async def search_papers(request: PaperSearchRequest):
+    """搜索论文 - 使用真实的 ArXiv API"""
+    try:
+        papers = []
+        
+        if request.source == "arxiv" or request.source == "all":
+            # 使用 ArXiv API 搜索
+            logger.info(f"正在搜索 ArXiv: {request.keywords}")
+            
+            # 构建搜索查询
+            search = arxiv.Search(
+                query=request.keywords,
+                max_results=request.limit,
+                sort_by=arxiv.SortCriterion.SubmittedDate,
+                sort_order=arxiv.SortOrder.Descending
+            )
+            
+            # 获取搜索结果
+            for result in search.results():
+                paper = {
+                    "id": result.entry_id.split('/')[-1],
+                    "title": result.title,
+                    "authors": [author.name for author in result.authors],
+                    "abstract": result.summary.replace('\n', ' ').strip(),
+                    "url": result.entry_id,
+                    "published_date": result.published.strftime("%Y-%m-%d"),
+                    "pdf_url": result.pdf_url,
+                    "categories": result.categories,
+                    "primary_category": result.primary_category
+                }
+                papers.append(paper)
+            
+            logger.info(f"找到 {len(papers)} 篇论文")
+        
+        # 如果没有找到结果，返回提示
+        if not papers:
+            return {
+                "success": True,
+                "papers": [],
+                "total_found": 0,
+                "keywords": request.keywords,
+                "source": request.source,
+                "message": "未找到相关论文，请尝试其他关键词"
+            }
+        
+        return {
+            "success": True,
+            "papers": papers,
+            "total_found": len(papers),
+            "keywords": request.keywords,
+            "source": request.source
+        }
+        
+    except Exception as e:
+        logger.error(f"论文搜索失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"搜索失败: {str(e)}")
+
+@router.post("/upload", response_model=Dict[str, Any])
+async def upload_paper(file: UploadFile = File(...)):
+    """上传论文PDF"""
+    try:
+        # 检查文件类型
+        if not file.filename.endswith('.pdf'):
+            raise HTTPException(status_code=400, detail="只支持PDF文件")
+        
+        # 模拟文件上传
+        file_url = f"/uploads/{file.filename}"
+        
+        return {
+            "success": True,
+            "file_url": file_url,
+            "filename": file.filename,
+            "size": getattr(file, 'size', 0),
+            "message": "文件上传成功"
+        }
+        
+    except Exception as e:
+        logger.error(f"文件上传失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"上传失败: {str(e)}")
+

Co-creation-projects/Apricity-InnocoreAI/api/routes/tasks.py

@@ -0,0 +1,299 @@
+"""
+任务相关API路由
+"""
+
+from fastapi import APIRouter, HTTPException, WebSocket, WebSocketDisconnect
+from typing import List, Dict, Any, Optional
+from pydantic import BaseModel
+import logging
+import json
+import asyncio
+
+# from ...agents.controller import agent_controller, TaskType
+# 临时注释，避免相对导入错误
+agent_controller = None
+TaskType = None
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+# Pydantic模型
+class TaskSubmitRequest(BaseModel):
+    task_type: str
+    input_data: Dict[str, Any]
+    priority: int = 0
+
+class TaskResponse(BaseModel):
+    id: str
+    type: str
+    status: str
+    created_at: str
+    started_at: Optional[str]
+    completed_at: Optional[str]
+    priority: int
+
+# WebSocket连接管理
+class ConnectionManager:
+    def __init__(self):
+        self.active_connections: List[WebSocket] = []
+    
+    async def connect(self, websocket: WebSocket):
+        await websocket.accept()
+        self.active_connections.append(websocket)
+    
+    def disconnect(self, websocket: WebSocket):
+        self.active_connections.remove(websocket)
+    
+    async def send_personal_message(self, message: str, websocket: WebSocket):
+        await websocket.send_text(message)
+    
+    async def broadcast(self, message: str):
+        for connection in self.active_connections:
+            try:
+                await connection.send_text(message)
+            except:
+                # 连接已断开，移除
+                self.active_connections.remove(connection)
+
+manager = ConnectionManager()
+
+@router.post("/submit", response_model=Dict[str, Any])
+async def submit_task(request: TaskSubmitRequest):
+    """提交任务"""
+    try:
+        # 验证任务类型
+        try:
+            task_type = TaskType(request.task_type)
+        except ValueError:
+            raise HTTPException(status_code=400, detail=f"不支持的任务类型: {request.task_type}")
+        
+        # 提交任务
+        task_id = await agent_controller.submit_task(
+            task_type=task_type,
+            input_data=request.input_data,
+            priority=request.priority
+        )
+        
+        return {
+            "success": True,
+            "task_id": task_id,
+            "message": "任务已提交"
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"提交任务失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/{task_id}/execute", response_model=Dict[str, Any])
+async def execute_task(task_id: str):
+    """执行任务"""
+    try:
+        result = await agent_controller.execute_task(task_id)
+        
+        return {
+            "success": True,
+            "task_id": task_id,
+            "result": result
+        }
+        
+    except Exception as e:
+        logger.error(f"执行任务失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/{task_id}/status", response_model=TaskResponse)
+async def get_task_status(task_id: str):
+    """获取任务状态"""
+    try:
+        status = await agent_controller.get_task_status(task_id)
+        if not status:
+            raise HTTPException(status_code=404, detail="任务不存在")
+        
+        return TaskResponse(**status)
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"获取任务状态失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.delete("/{task_id}", response_model=Dict[str, Any])
+async def cancel_task(task_id: str):
+    """取消任务"""
+    try:
+        success = await agent_controller.cancel_task(task_id)
+        
+        if success:
+            return {"success": True, "message": "任务已取消"}
+        else:
+            return {"success": False, "message": "任务无法取消（可能正在执行或已完成）"}
+        
+    except Exception as e:
+        logger.error(f"取消任务失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/", response_model=List[TaskResponse])
+async def list_tasks():
+    """获取任务列表"""
+    try:
+        # 获取活跃任务
+        active_tasks = []
+        for task_id, task in agent_controller.active_tasks.items():
+            active_tasks.append(TaskResponse(
+                id=task["id"],
+                type=task["type"].value,
+                status=task["status"].value,
+                created_at=task["created_at"].isoformat(),
+                started_at=task["started_at"].isoformat() if task["started_at"] else None,
+                completed_at=task["completed_at"].isoformat() if task["completed_at"] else None,
+                priority=task["priority"]
+            ))
+        
+        # 获取历史任务（最近50个）
+        history_tasks = []
+        for task in agent_controller.task_history[-50:]:
+            history_tasks.append(TaskResponse(
+                id=task["id"],
+                type=task["type"].value,
+                status=task["status"].value,
+                created_at=task["created_at"].isoformat(),
+                started_at=task["started_at"].isoformat() if task["started_at"] else None,
+                completed_at=task["completed_at"].isoformat() if task["completed_at"] else None,
+                priority=task["priority"]
+            ))
+        
+        return active_tasks + history_tasks
+        
+    except Exception as e:
+        logger.error(f"获取任务列表失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/agents/status", response_model=Dict[str, Any])
+async def get_agents_status():
+    """获取智能体状态"""
+    try:
+        status = await agent_controller.get_agent_status()
+        return {
+            "success": True,
+            "agents": status
+        }
+        
+    except Exception as e:
+        logger.error(f"获取智能体状态失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/workflow/full", response_model=Dict[str, Any])
+async def run_full_workflow(input_data: Dict[str, Any]):
+    """运行完整工作流"""
+    try:
+        # 提交完整工作流任务
+        task_id = await agent_controller.submit_task(
+            task_type=TaskType.FULL_WORKFLOW,
+            input_data=input_data,
+            priority=1  # 高优先级
+        )
+        
+        # 执行任务
+        result = await agent_controller.execute_task(task_id)
+        
+        return {
+            "success": True,
+            "task_id": task_id,
+            "result": result
+        }
+        
+    except Exception as e:
+        logger.error(f"运行完整工作流失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.websocket("/ws/{task_id}")
+async def websocket_task_updates(websocket: WebSocket, task_id: str):
+    """WebSocket任务更新"""
+    await manager.connect(websocket)
+    try:
+        # 发送初始状态
+        status = await agent_controller.get_task_status(task_id)
+        if status:
+            await manager.send_personal_message(
+                json.dumps({"type": "status", "data": status}),
+                websocket
+            )
+        
+        # 监听任务状态变化
+        while True:
+            await asyncio.sleep(1)  # 每秒检查一次
+            
+            status = await agent_controller.get_task_status(task_id)
+            if status:
+                await manager.send_personal_message(
+                    json.dumps({"type": "status", "data": status}),
+                    websocket
+                )
+                
+                # 如果任务完成，断开连接
+                if status["status"] in ["completed", "failed", "cancelled"]:
+                    break
+    
+    except WebSocketDisconnect:
+        manager.disconnect(websocket)
+    except Exception as e:
+        logger.error(f"WebSocket连接异常: {str(e)}")
+        manager.disconnect(websocket)
+
+@router.websocket("/ws/stream")
+async def websocket_stream(websocket: WebSocket):
+    """WebSocket流式通信（用于写作助教等实时交互）"""
+    await manager.connect(websocket)
+    try:
+        while True:
+            # 接收消息
+            data = await websocket.receive_text()
+            message = json.loads(data)
+            
+            # 处理不同类型的消息
+            if message.get("type") == "writing_assistance":
+                # 处理写作辅助请求
+                await handle_writing_assistance(websocket, message.get("data", {}))
+            elif message.get("type") == "ping":
+                # 心跳检测
+                await manager.send_personal_message(
+                    json.dumps({"type": "pong"}),
+                    websocket
+                )
+    
+    except WebSocketDisconnect:
+        manager.disconnect(websocket)
+    except Exception as e:
+        logger.error(f"WebSocket流式通信异常: {str(e)}")
+        manager.disconnect(websocket)
+
+async def handle_writing_assistance(websocket: WebSocket, data: Dict[str, Any]):
+    """处理写作辅助请求"""
+    try:
+        # 提交写作辅助任务
+        task_id = await agent_controller.submit_task(
+            task_type=TaskType.WRITING_ASSISTANCE,
+            input_data=data
+        )
+        
+        # 发送任务ID
+        await manager.send_personal_message(
+            json.dumps({"type": "task_started", "task_id": task_id}),
+            websocket
+        )
+        
+        # 执行任务
+        result = await agent_controller.execute_task(task_id)
+        
+        # 发送结果
+        await manager.send_personal_message(
+            json.dumps({"type": "task_completed", "result": result}),
+            websocket
+        )
+        
+    except Exception as e:
+        await manager.send_personal_message(
+            json.dumps({"type": "error", "message": str(e)}),
+            websocket
+        )

Co-creation-projects/Apricity-InnocoreAI/api/routes/users.py

@@ -0,0 +1,132 @@
+"""
+用户相关API路由
+"""
+
+from fastapi import APIRouter, HTTPException
+from typing import Dict, Any, Optional
+from pydantic import BaseModel
+import logging
+import uuid
+
+# from ...core.database import db_manager
+# 临时注释，避免相对导入错误
+db_manager = None
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+# Pydantic模型
+class UserCreateRequest(BaseModel):
+    email: str
+    profile: Optional[Dict[str, Any]] = {}
+
+class UserUpdateRequest(BaseModel):
+    profile: Optional[Dict[str, Any]] = {}
+
+class UserResponse(BaseModel):
+    id: str
+    email: str
+    profile: Dict[str, Any]
+    created_at: str
+
+@router.post("/", response_model=UserResponse)
+async def create_user(request: UserCreateRequest):
+    """创建用户"""
+    try:
+        user_id = await db_manager.create_user(
+            email=request.email,
+            profile=request.profile
+        )
+        
+        user = await db_manager.get_user(user_id)
+        
+        return UserResponse(
+            id=user["id"],
+            email=user["email"],
+            profile=user["profile"],
+            created_at=user["created_at"].isoformat() if user["created_at"] else ""
+        )
+        
+    except Exception as e:
+        logger.error(f"创建用户失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/{user_id}", response_model=UserResponse)
+async def get_user(user_id: str):
+    """获取用户信息"""
+    try:
+        user = await db_manager.get_user(user_id)
+        if not user:
+            raise HTTPException(status_code=404, detail="用户不存在")
+        
+        return UserResponse(
+            id=user["id"],
+            email=user["email"],
+            profile=user["profile"],
+            created_at=user["created_at"].isoformat() if user["created_at"] else ""
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"获取用户信息失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.put("/{user_id}", response_model=Dict[str, Any])
+async def update_user(user_id: str, request: UserUpdateRequest):
+    """更新用户信息"""
+    try:
+        success = await db_manager.update_user_profile(
+            user_id=user_id,
+            profile=request.profile
+        )
+        
+        if success:
+            return {"success": True, "message": "用户信息已更新"}
+        else:
+            raise HTTPException(status_code=404, detail="用户不存在")
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"更新用户信息失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/{user_id}/profile")
+async def get_user_profile(user_id: str):
+    """获取用户配置"""
+    try:
+        user = await db_manager.get_user(user_id)
+        if not user:
+            raise HTTPException(status_code=404, detail="用户不存在")
+        
+        return {
+            "success": True,
+            "profile": user.get("profile", {})
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"获取用户配置失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/{user_id}/profile")
+async def update_user_profile(user_id: str, profile: Dict[str, Any]):
+    """更新用户配置"""
+    try:
+        success = await db_manager.update_user_profile(
+            user_id=user_id,
+            profile=profile
+        )
+        
+        if success:
+            return {"success": True, "message": "用户配置已更新"}
+        else:
+            raise HTTPException(status_code=404, detail="用户不存在")
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"更新用户配置失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))

Co-creation-projects/Apricity-InnocoreAI/api/routes/workflow.py

@@ -0,0 +1,304 @@
+"""
+工作流API路由 - 协调多个智能体完成复杂任务
+"""
+
+from fastapi import APIRouter, HTTPException
+from typing import Dict, Any, Optional, List
+from pydantic import BaseModel
+import logging
+import asyncio
+from agents.controller import agent_controller
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+# Pydantic模型
+class WorkflowRequest(BaseModel):
+    keywords: str
+    analysis_type: str = "summary"  # summary, innovation, comparison, comprehensive
+    citation_format: str = "bibtex"  # bibtex, apa, ieee, mla
+    writing_task: Optional[str] = None  # improve, polish, translate
+    limit: int = 5  # 搜索论文数量
+
+class WorkflowStatus(BaseModel):
+    workflow_id: str
+    status: str  # running, completed, failed
+    current_step: str
+    progress: int  # 0-100
+
+@router.post("/complete", response_model=Dict[str, Any])
+async def complete_workflow(request: WorkflowRequest):
+    """
+    完整工作流：搜索 -> 分析 -> 校验引用 -> 写作辅助
+    自动协调所有智能体完成任务
+    """
+    try:
+        workflow_id = f"workflow_{asyncio.get_event_loop().time()}"
+        results = {
+            "workflow_id": workflow_id,
+            "status": "running",
+            "steps": []
+        }
+        
+        # 步骤 1: Hunter - 搜索论文
+        logger.info(f"[工作流 {workflow_id}] 步骤 1/4: 搜索论文")
+        try:
+            from api.routes.papers import search_papers, PaperSearchRequest
+            
+            search_result = await search_papers(PaperSearchRequest(
+                keywords=request.keywords,
+                source="arxiv",
+                limit=request.limit
+            ))
+            
+            papers = search_result.get("papers", [])
+            results["steps"].append({
+                "step": 1,
+                "name": "Hunter - 论文搜索",
+                "status": "completed",
+                "result": {
+                    "total_found": len(papers),
+                    "papers": papers
+                }
+            })
+            
+            if not papers:
+                raise HTTPException(status_code=404, detail="未找到相关论文")
+            
+        except Exception as e:
+            logger.error(f"论文搜索失败: {str(e)}")
+            results["steps"].append({
+                "step": 1,
+                "name": "Hunter - 论文搜索",
+                "status": "failed",
+                "error": str(e)
+            })
+            results["status"] = "failed"
+            return results
+        
+        # 步骤 2: Miner - 分析每篇论文
+        logger.info(f"[工作流 {workflow_id}] 步骤 2/4: 分析论文")
+        analyses = []
+        try:
+            from api.routes.analysis import analyze_paper, PaperAnalysisRequest
+            
+            # 分析前3篇论文
+            for i, paper in enumerate(papers[:3]):
+                try:
+                    analysis_result = await analyze_paper(PaperAnalysisRequest(
+                        paper_url=paper["url"],
+                        analysis_type=request.analysis_type
+                    ))
+                    analyses.append({
+                        "paper_id": paper["id"],
+                        "title": paper["title"],
+                        "analysis": analysis_result.get("analysis", "")
+                    })
+                except Exception as e:
+                    logger.warning(f"分析论文 {paper['id']} 失败: {str(e)}")
+                    continue
+            
+            results["steps"].append({
+                "step": 2,
+                "name": "Miner - 论文分析",
+                "status": "completed",
+                "result": {
+                    "total_analyzed": len(analyses),
+                    "analyses": analyses
+                }
+            })
+            
+        except Exception as e:
+            logger.error(f"论文分析失败: {str(e)}")
+            results["steps"].append({
+                "step": 2,
+                "name": "Miner - 论文分析",
+                "status": "failed",
+                "error": str(e)
+            })
+        
+        # 步骤 3: Validator - 生成和校验引用
+        logger.info(f"[工作流 {workflow_id}] 步骤 3/4: 生成引用")
+        citations = []
+        try:
+            from api.routes.citations import validate_citation, CitationValidationRequest
+            
+            # 为每篇论文生成引用
+            for paper in papers[:3]:
+                try:
+                    # 构建引用文本
+                    authors_str = ", ".join(paper["authors"][:3])
+                    if len(paper["authors"]) > 3:
+                        authors_str += " et al."
+                    
+                    citation_text = f"{authors_str} ({paper['published_date'][:4]}). {paper['title']}. arXiv:{paper['id']}"
+                    
+                    citation_result = await validate_citation(CitationValidationRequest(
+                        citation=citation_text,
+                        format=request.citation_format
+                    ))
+                    
+                    citations.append({
+                        "paper_id": paper["id"],
+                        "title": paper["title"],
+                        "formatted_citation": citation_result.get("formatted_citation", "")
+                    })
+                except Exception as e:
+                    logger.warning(f"生成引用失败: {str(e)}")
+                    continue
+            
+            results["steps"].append({
+                "step": 3,
+                "name": "Validator - 引用生成",
+                "status": "completed",
+                "result": {
+                    "total_citations": len(citations),
+                    "citations": citations
+                }
+            })
+            
+        except Exception as e:
+            logger.error(f"引用生成失败: {str(e)}")
+            results["steps"].append({
+                "step": 3,
+                "name": "Validator - 引用生成",
+                "status": "failed",
+                "error": str(e)
+            })
+        
+        # 步骤 4: Coach - 生成综合报告（可选）
+        if request.writing_task:
+            logger.info(f"[工作流 {workflow_id}] 步骤 4/4: 生成报告")
+            try:
+                from api.routes.writing import writing_coach, WritingCoachRequest
+                
+                # 构建综合报告文本
+                report_text = f"# 关于 '{request.keywords}' 的研究综述\n\n"
+                report_text += f"## 搜索结果\n找到 {len(papers)} 篇相关论文\n\n"
+                
+                if analyses:
+                    report_text += "## 论文分析\n"
+                    for i, analysis in enumerate(analyses[:3], 1):
+                        report_text += f"\n### {i}. {analysis['title']}\n"
+                        report_text += f"{analysis['analysis'][:500]}...\n"
+                
+                if citations:
+                    report_text += "\n## 参考文献\n"
+                    for i, citation in enumerate(citations, 1):
+                        report_text += f"{i}. {citation['formatted_citation']}\n"
+                
+                # 使用 Coach 改进报告
+                writing_result = await writing_coach(WritingCoachRequest(
+                    text=report_text,
+                    style="academic",
+                    task=request.writing_task
+                ))
+                
+                results["steps"].append({
+                    "step": 4,
+                    "name": "Coach - 报告生成",
+                    "status": "completed",
+                    "result": {
+                        "report": writing_result.get("result", "")
+                    }
+                })
+                
+            except Exception as e:
+                logger.error(f"报告生成失败: {str(e)}")
+                results["steps"].append({
+                    "step": 4,
+                    "name": "Coach - 报告生成",
+                    "status": "failed",
+                    "error": str(e)
+                })
+        
+        # 完成工作流
+        results["status"] = "completed"
+        results["summary"] = {
+            "total_papers": len(papers),
+            "analyzed_papers": len(analyses),
+            "generated_citations": len(citations),
+            "keywords": request.keywords
+        }
+        
+        logger.info(f"[工作流 {workflow_id}] 完成")
+        return results
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"工作流执行失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"工作流执行失败: {str(e)}")
+
+@router.post("/search-and-analyze", response_model=Dict[str, Any])
+async def search_and_analyze(request: WorkflowRequest):
+    """
+    简化工作流：搜索 + 分析
+    只执行搜索和分析步骤
+    """
+    try:
+        results = {
+            "status": "running",
+            "steps": []
+        }
+        
+        # 步骤 1: 搜索论文
+        from api.routes.papers import search_papers, PaperSearchRequest
+        
+        search_result = await search_papers(PaperSearchRequest(
+            keywords=request.keywords,
+            source="arxiv",
+            limit=request.limit
+        ))
+        
+        papers = search_result.get("papers", [])
+        results["steps"].append({
+            "step": 1,
+            "name": "搜索论文",
+            "status": "completed",
+            "papers": papers
+        })
+        
+        if not papers:
+            raise HTTPException(status_code=404, detail="未找到相关论文")
+        
+        # 步骤 2: 分析第一篇论文
+        from api.routes.analysis import analyze_paper, PaperAnalysisRequest
+        
+        first_paper = papers[0]
+        analysis_result = await analyze_paper(PaperAnalysisRequest(
+            paper_url=first_paper["url"],
+            analysis_type=request.analysis_type
+        ))
+        
+        results["steps"].append({
+            "step": 2,
+            "name": "分析论文",
+            "status": "completed",
+            "analysis": analysis_result
+        })
+        
+        results["status"] = "completed"
+        return results
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"搜索和分析失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"执行失败: {str(e)}")
+
+@router.get("/status/{workflow_id}")
+async def get_workflow_status(workflow_id: str):
+    """获取工作流状态"""
+    try:
+        # 这里可以实现工作流状态跟踪
+        # 暂时返回模拟状态
+        return {
+            "workflow_id": workflow_id,
+            "status": "completed",
+            "progress": 100,
+            "message": "工作流已完成"
+        }
+    except Exception as e:
+        logger.error(f"获取工作流状态失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))

Co-creation-projects/Apricity-InnocoreAI/api/routes/writing.py

@@ -0,0 +1,383 @@
+"""
+写作辅助API路由
+"""
+
+from fastapi import APIRouter, HTTPException
+from typing import Dict, Any, Optional
+from pydantic import BaseModel
+import logging
+from core.config import get_config
+from core.llm_adapter import get_llm_adapter
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+# 初始化 LLM 适配器（基于 HelloAgent）
+config = get_config()
+try:
+    llm = get_llm_adapter() if config.llm.api_key else None
+except Exception as e:
+    logger.warning(f"LLM 初始化失败: {str(e)}")
+    llm = None
+
+# Pydantic模型
+class WritingAssistanceRequest(BaseModel):
+    user_id: str
+    task_type: str  # explain, polish, mimic, suggest
+    content: str
+    context: Optional[Dict[str, Any]] = {}
+
+class ExplainRequest(BaseModel):
+    user_id: str
+    concept: str
+    context: Optional[Dict[str, Any]] = {}
+
+class PolishRequest(BaseModel):
+    user_id: str
+    text: str
+    target_style: Optional[str] = "academic"
+
+class WritingCoachRequest(BaseModel):
+    text: str
+    style: str = "formal"
+    task: str = "polish"  # polish, translate, explain, expand
+    context: Optional[Dict[str, Any]] = {}
+
+class MimicRequest(BaseModel):
+    user_id: str
+    text: str
+    target_style: str
+    reference_papers: Optional[list] = []
+    context: Optional[Dict[str, Any]] = {}
+
+class SuggestRequest(BaseModel):
+    user_id: str
+    text: str
+    context: Optional[Dict[str, Any]] = {}
+
+@router.post("/coach", response_model=Dict[str, Any])
+async def writing_coach(request: WritingCoachRequest):
+    """写作助手 - 使用真实的 AI 处理"""
+    try:
+        if not llm:
+            raise HTTPException(status_code=503, detail="AI 服务未配置，请设置 OPENAI_API_KEY")
+        
+        logger.info(f"处理写作任务: {request.task}, 风格: {request.style}")
+        
+        # 根据任务类型生成提示词
+        prompts = {
+            "polish": f"""作为一位专业的学术写作编辑，请帮我润色以下文本，使其符合{request.style}学术写作标准：
+
+原文：
+{request.text}
+
+请提供：
+1. 润色后的文本（保持原意，提升表达质量）
+2. 具体的改进说明
+3. 写作建议
+
+要求：
+- 保持学术严谨性
+- 提升表达清晰度
+- 使用恰当的学术用语
+- 改善句子结构和逻辑流畅性""",
+            
+            "translate": f"""请将以下中文学术文本翻译成专业的英文学术论文表达：
+
+原文：
+{request.text}
+
+要求：
+- 保持学术专业性和准确性
+- 使用地道的英文学术表达
+- 符合{request.style}风格的学术写作规范
+- 保持技术术语的准确性""",
+            
+            "explain": f"""请详细解释以下概念或内容：
+
+{request.text}
+
+要求：
+- 用通俗易懂的语言解释
+- 保持技术准确性
+- 提供具体例子
+- 说明应用场景和重要性""",
+            
+            "expand": f"""请扩展以下内容，使其更加详细和完整：
+
+原文：
+{request.text}
+
+要求：
+- 添加必要的背景信息
+- 补充相关的理论支持
+- 扩展方法论描述
+- 增加潜在影响和应用
+- 保持逻辑连贯性
+- 符合{request.style}学术写作风格"""
+        }
+        
+        prompt = prompts.get(request.task, prompts["polish"])
+        
+        # 调用 LLM 处理
+        response = await llm.ainvoke(prompt)
+        result_content = response.content if hasattr(response, 'content') else str(response)
+        
+        return {
+            "success": True,
+            "task": request.task,
+            "style": request.style,
+            "original": request.text,
+            "result": result_content
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"写作助手处理失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"处理失败: {str(e)}")
+
+@router.post("/explain", response_model=Dict[str, Any])
+async def explain_concept(request: ExplainRequest):
+    """解释复杂概念"""
+    try:
+        # 模拟概念解释
+        return {
+            "success": True,
+            "concept": request.concept,
+            "explanation": f"[Detailed explanation of {request.concept} in accessible terms while maintaining technical accuracy]",
+            "examples": ["Example 1", "Example 2"],
+            "timestamp": "2024-01-15T10:30:00Z"
+        }
+        
+    except Exception as e:
+        logger.error(f"概念解释失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/polish", response_model=Dict[str, Any])
+async def polish_text(request: PolishRequest):
+    """润色文本"""
+    try:
+        # 模拟文本润色
+        return {
+            "success": True,
+            "original": request.text,
+            "improved": f"Based on {request.target_style} writing standards, the text can be improved: [Enhanced version]",
+            "suggestions": ["Use more precise terminology", "Improve sentence structure"],
+            "timestamp": "2024-01-15T10:30:00Z"
+        }
+        
+    except Exception as e:
+        logger.error(f"文本润色失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/mimic", response_model=Dict[str, Any])
+async def mimic_style(request: MimicRequest):
+    """模仿写作风格"""
+    try:
+        # 模拟风格模仿
+        return {
+            "success": True,
+            "original": request.text,
+            "mimicked": f"[Text rewritten in {request.target_style} style]",
+            "style_analysis": f"Analysis of {request.target_style} writing characteristics",
+            "timestamp": "2024-01-15T10:30:00Z"
+        }
+        
+    except Exception as e:
+        logger.error(f"风格模仿失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/suggest", response_model=Dict[str, Any])
+async def suggest_improvements(request: SuggestRequest):
+    """建议改进"""
+    try:
+        # 模拟改进建议
+        return {
+            "success": True,
+            "original": request.text,
+            "suggestions": [
+                "Consider adding more specific examples",
+                "Strengthen the introduction",
+                "Include recent citations",
+                "Clarify the methodology"
+            ],
+            "improved_version": "[Improved version with suggestions applied]",
+            "timestamp": "2024-01-15T10:30:00Z"
+        }
+        
+    except Exception as e:
+        logger.error(f"改进建议失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/user/{user_id}/style")
+async def get_user_writing_style(user_id: str):
+    """获取用户写作风格"""
+    try:
+        # 这里需要实现用户写作风格分析
+        # 暂时返回模拟结果
+        
+        style_profile = {
+            "user_id": user_id,
+            "writing_style": {
+                "tone": "formal_academic",
+                "complexity": "medium",
+                "sentence_length": "medium",
+                "vocabulary_richness": "high",
+                "clarity": "good"
+            },
+            "preferred_patterns": [
+                "句式模式1",
+                "句式模式2"
+            ],
+            "common_phrases": [
+                "常用短语1",
+                "常用短语2"
+            ],
+            "improvement_areas": [
+                "改进领域1",
+                "改进领域2"
+            ],
+            "style_evolution": {
+                "last_month": "上个月的风格变化",
+                "trend": "improving"
+            }
+        }
+        
+        return {
+            "success": True,
+            "style_profile": style_profile
+        }
+        
+    except Exception as e:
+        logger.error(f"获取用户写作风格失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.get("/user/{user_id}/templates")
+async def get_writing_templates(user_id: str):
+    """获取写作模板"""
+    try:
+        # 这里需要实现写作模板推荐
+        # 暂时返回模拟结果
+        
+        templates = {
+            "user_id": user_id,
+            "templates": [
+                {
+                    "id": "abstract_template",
+                    "name": "摘要模板",
+                    "category": "academic",
+                    "structure": [
+                        "背景介绍",
+                        "问题陈述", 
+                        "方法概述",
+                        "主要结果",
+                        "结论意义"
+                    ],
+                    "example": "摘要示例...",
+                    "usage_count": 15
+                },
+                {
+                    "id": "introduction_template",
+                    "name": "引言模板",
+                    "category": "academic",
+                    "structure": [
+                        "研究背景",
+                        "相关工作",
+                        "研究空白",
+                        "主要贡献",
+                        "论文结构"
+                    ],
+                    "example": "引言示例...",
+                    "usage_count": 8
+                }
+            ],
+            "recommended_templates": [
+                "推荐模板1",
+                "推荐模板2"
+            ]
+        }
+        
+        return {
+            "success": True,
+            "templates": templates
+        }
+        
+    except Exception as e:
+        logger.error(f"获取写作模板失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/check/grammar", response_model=Dict[str, Any])
+async def check_grammar(text: str, user_id: Optional[str] = None):
+    """语法检查"""
+    try:
+        # 这里需要实现语法检查逻辑
+        # 暂时返回模拟结果
+        
+        grammar_check = {
+            "text": text,
+            "errors": [
+                {
+                    "type": "grammar",
+                    "message": "语法错误描述",
+                    "position": {"start": 10, "end": 20},
+                    "suggestion": "修改建议",
+                    "severity": "medium"
+                }
+            ],
+            "suggestions": [
+                {
+                    "type": "style",
+                    "message": "风格建议",
+                    "position": {"start": 30, "end": 40},
+                    "suggestion": "风格改进建议"
+                }
+            ],
+            "score": 85,
+            "corrected_text": "修正后的文本..."
+        }
+        
+        return {
+            "success": True,
+            "grammar_check": grammar_check
+        }
+        
+    except Exception as e:
+        logger.error(f"语法检查失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/check/plagiarism", response_model=Dict[str, Any])
+async def check_plagiarism(text: str, user_id: Optional[str] = None):
+    """抄袭检查"""
+    try:
+        # 这里需要实现抄袭检查逻辑
+        # 暂时返回模拟结果
+        
+        plagiarism_check = {
+            "text": text,
+            "similarity_score": 15.5,
+            "sources": [
+                {
+                    "title": "相似文献标题",
+                    "authors": ["作者1", "作者2"],
+                    "similarity": 12.3,
+                    "matched_text": "匹配的文本片段...",
+                    "url": "文献链接"
+                }
+            ],
+            "originality_score": 84.5,
+            "risk_level": "low",
+            "recommendations": [
+                "建议1",
+                "建议2"
+            ]
+        }
+        
+        return {
+            "success": True,
+            "plagiarism_check": plagiarism_check
+        }
+        
+    except Exception as e:
+        logger.error(f"抄袭检查失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))

Co-creation-projects/Apricity-InnocoreAI/core/__init__.py

@@ -0,0 +1,16 @@
+"""
+InnoCore AI 核心模块
+"""
+
+from .config import InnoCoreConfig, get_config, update_config
+from .database import DatabaseManager
+from .vector_store import VectorStoreManager
+from .exceptions import *
+
+__all__ = [
+    "InnoCoreConfig",
+    "get_config", 
+    "update_config",
+    "DatabaseManager",
+    "VectorStoreManager"
+]

Co-creation-projects/Apricity-InnocoreAI/core/config.py

@@ -0,0 +1,153 @@
+"""
+InnoCore AI 核心配置模块
+"""
+
+from typing import Dict, List, Optional, Any
+from dataclasses import dataclass, field
+from enum import Enum
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+class LLMProvider(Enum):
+    """LLM提供商枚举"""
+    OPENAI = "openai"
+    CLAUDE = "claude"
+    MODELSCOPE = "modelscope"  # 阿里云 ModelScope
+    OLLAMA = "ollama"  # 本地部署
+    DASHSCOPE = "dashscope"  # 阿里云灵积（推荐用于 Qwen 系列）
+
+class VectorDBType(Enum):
+    """向量数据库类型枚举"""
+    QDRANT = "qdrant"
+    CHROMA = "chroma"
+    PINECONE = "pinecone"
+
+@dataclass
+class LLMConfig:
+    """LLM配置"""
+    provider: LLMProvider = LLMProvider.OPENAI
+    model_name: str = "gpt-3.5-turbo"  # OpenAI: gpt-4, gpt-3.5-turbo, gpt-4-turbo-preview
+                                        # DashScope: qwen-turbo, qwen-plus, qwen-max
+                                        # ModelScope: qwen/Qwen2.5-7B-Instruct
+    api_key: Optional[str] = None
+    base_url: Optional[str] = None
+    temperature: float = 0.7
+    max_tokens: int = 4000
+    timeout: int = 60
+
+@dataclass
+class VectorDBConfig:
+    """向量数据库配置"""
+    db_type: VectorDBType = VectorDBType.QDRANT
+    host: str = "localhost"
+    port: int = 6333
+    api_key: Optional[str] = None
+    collection_name_prefix: str = "innocore"
+    embedding_model: str = "text-embedding-3-small"
+
+@dataclass
+class DatabaseConfig:
+    """关系数据库配置"""
+    host: str = "localhost"
+    port: int = 5432
+    database: str = "innocore_ai"
+    username: str = "postgres"
+    password: str = "password"
+    pool_size: int = 10
+
+@dataclass
+class RedisConfig:
+    """Redis配置"""
+    host: str = "localhost"
+    port: int = 6379
+    db: int = 0
+    password: Optional[str] = None
+    max_connections: int = 20
+
+@dataclass
+class ExternalAPIConfig:
+    """外部API配置"""
+    crossref_api_key: Optional[str] = None
+    google_scholar_api_key: Optional[str] = None
+    serpapi_key: Optional[str] = None
+    arxiv_base_url: str = "http://export.arxiv.org/api/query"
+    ieee_base_url: str = "https://ieeexploreapi.ieee.org/api/v1"
+
+@dataclass
+class InnoCoreConfig:
+    """InnoCore AI 主配置类"""
+    
+    # 基础配置
+    app_name: str = "InnoCore AI"
+    debug: bool = False
+    log_level: str = "INFO"
+    
+    # LLM配置
+    llm: LLMConfig = field(default_factory=LLMConfig)
+    
+    # 向量数据库配置
+    vector_db: VectorDBConfig = field(default_factory=VectorDBConfig)
+    
+    # 关系数据库配置
+    database: DatabaseConfig = field(default_factory=DatabaseConfig)
+    
+    # Redis配置
+    redis: RedisConfig = field(default_factory=RedisConfig)
+    
+    # 外部API配置
+    external_apis: ExternalAPIConfig = field(default_factory=ExternalAPIConfig)
+    
+    # Agent配置
+    agent_max_steps: int = 5
+    agent_timeout: int = 300
+    concurrent_agents: int = 4
+    
+    # RAG配置
+    retrieval_top_k: int = 5
+    similarity_threshold: float = 0.7
+    hybrid_search_weights: Dict[str, float] = field(default_factory=lambda: {
+        "vector": 0.7,
+        "keyword": 0.3
+    })
+    
+    # 性能配置
+    cache_ttl: int = 3600  # 缓存过期时间(秒)
+    batch_size: int = 10
+    max_concurrent_requests: int = 50
+    
+    def __post_init__(self):
+        """初始化后处理"""
+        # 从环境变量加载配置
+        self.llm.api_key = self.llm.api_key or os.getenv("OPENAI_API_KEY")
+        self.llm.base_url = self.llm.base_url or os.getenv("OPENAI_BASE_URL")
+        
+        # 从环境变量加载模型名称（如果设置了）
+        env_model = os.getenv("OPENAI_MODEL") or os.getenv("LLM_MODEL")
+        if env_model:
+            self.llm.model_name = env_model
+        
+        self.database.password = self.database.password or os.getenv("DATABASE_PASSWORD")
+        self.redis.password = self.redis.password or os.getenv("REDIS_PASSWORD")
+        
+        self.external_apis.crossref_api_key = self.external_apis.crossref_api_key or os.getenv("CROSSREF_API_KEY")
+        self.external_apis.google_scholar_api_key = self.external_apis.google_scholar_api_key or os.getenv("GOOGLE_SCHOLAR_API_KEY")
+        self.external_apis.serpapi_key = self.external_apis.serpapi_key or os.getenv("SERPAPI_KEY")
+        
+        self.debug = os.getenv("DEBUG", "false").lower() == "true"
+        self.log_level = os.getenv("LOG_LEVEL", "INFO")
+
+# 全局配置实例
+config = InnoCoreConfig()
+
+def get_config() -> InnoCoreConfig:
+    """获取全局配置实例"""
+    return config
+
+def update_config(**kwargs) -> None:
+    """更新配置"""
+    global config
+    for key, value in kwargs.items():
+        if hasattr(config, key):
+            setattr(config, key, value)

Co-creation-projects/Apricity-InnocoreAI/core/database.py

@@ -0,0 +1,303 @@
+"""
+InnoCore AI 数据库管理模块
+"""
+
+import asyncio
+import asyncpg
+from typing import Dict, List, Optional, Any, Union
+from datetime import datetime
+import json
+import uuid
+from contextlib import asynccontextmanager
+
+from .config import get_config
+from .exceptions import DatabaseException
+
+class DatabaseManager:
+    """数据库管理器"""
+    
+    def __init__(self):
+        self.config = get_config().database
+        self.pool = None
+    
+    async def initialize(self):
+        """初始化数据库连接池"""
+        try:
+            self.pool = await asyncpg.create_pool(
+                host=self.config.host,
+                port=self.config.port,
+                database=self.config.database,
+                user=self.config.username,
+                password=self.config.password,
+                min_size=1,
+                max_size=self.config.pool_size
+            )
+            await self._create_tables()
+        except Exception as e:
+            raise DatabaseException(f"数据库初始化失败: {str(e)}")
+    
+    async def _create_tables(self):
+        """创建数据库表"""
+        create_tables_sql = """
+        -- 用户表
+        CREATE TABLE IF NOT EXISTS users (
+            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+            email VARCHAR(255) UNIQUE NOT NULL,
+            profile JSONB DEFAULT '{}',
+            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+        );
+        
+        -- 论文表
+        CREATE TABLE IF NOT EXISTS papers (
+            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+            title TEXT NOT NULL,
+            authors TEXT[] DEFAULT '{}',
+            abstract TEXT,
+            doi VARCHAR(255) UNIQUE,
+            file_path TEXT,
+            content_hash VARCHAR(64) UNIQUE,
+            is_preset BOOLEAN DEFAULT FALSE,
+            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+        );
+        
+        -- 用户论文关系表
+        CREATE TABLE IF NOT EXISTS user_paper_relations (
+            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+            user_id UUID REFERENCES users(id) ON DELETE CASCADE,
+            paper_id UUID REFERENCES papers(id) ON DELETE CASCADE,
+            tags TEXT[] DEFAULT '{}',
+            rating INTEGER DEFAULT 0,
+            is_read BOOLEAN DEFAULT FALSE,
+            added_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+            UNIQUE(user_id, paper_id)
+        );
+        
+        -- 分析报告表
+        CREATE TABLE IF NOT EXISTS analysis_reports (
+            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+            paper_id UUID REFERENCES papers(id) ON DELETE CASCADE,
+            generated_for_user_id UUID REFERENCES users(id) ON DELETE SET NULL,
+            summary TEXT,
+            innovation_point TEXT,
+            limitation TEXT,
+            future_idea TEXT,
+            vector_ids JSONB DEFAULT '{}',
+            created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+        );
+        
+        -- 引用缓存表
+        CREATE TABLE IF NOT EXISTS reference_cache (
+            doi VARCHAR(255) PRIMARY KEY,
+            bibtex_std TEXT,
+            is_verified BOOLEAN DEFAULT FALSE,
+            last_check TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+        );
+        
+        -- 创建索引
+        CREATE INDEX IF NOT EXISTS idx_papers_content_hash ON papers(content_hash);
+        CREATE INDEX IF NOT EXISTS idx_papers_doi ON papers(doi);
+        CREATE INDEX IF NOT EXISTS idx_user_paper_relations_user_id ON user_paper_relations(user_id);
+        CREATE INDEX IF NOT EXISTS idx_user_paper_relations_paper_id ON user_paper_relations(paper_id);
+        CREATE INDEX IF NOT EXISTS idx_analysis_reports_paper_id ON analysis_reports(paper_id);
+        CREATE INDEX IF NOT EXISTS idx_analysis_reports_user_id ON analysis_reports(generated_for_user_id);
+        """
+        
+        async with self.pool.acquire() as conn:
+            await conn.execute(create_tables_sql)
+    
+    @asynccontextmanager
+    async def get_connection(self):
+        """获取数据库连接"""
+        if not self.pool:
+            await self.initialize()
+        
+        async with self.pool.acquire() as conn:
+            try:
+                yield conn
+            except Exception as e:
+                raise DatabaseException(f"数据库操作失败: {str(e)}")
+    
+    # 用户相关操作
+    async def create_user(self, email: str, profile: Dict = None) -> str:
+        """创建用户"""
+        async with self.get_connection() as conn:
+            user_id = await conn.fetchval(
+                "INSERT INTO users (email, profile) VALUES ($1, $2) RETURNING id",
+                email, json.dumps(profile or {})
+            )
+            return str(user_id)
+    
+    async def get_user(self, user_id: str) -> Optional[Dict]:
+        """获取用户信息"""
+        async with self.get_connection() as conn:
+            row = await conn.fetchrow(
+                "SELECT * FROM users WHERE id = $1", user_id
+            )
+            return dict(row) if row else None
+    
+    async def update_user_profile(self, user_id: str, profile: Dict) -> bool:
+        """更新用户配置"""
+        async with self.get_connection() as conn:
+            result = await conn.execute(
+                "UPDATE users SET profile = $1 WHERE id = $2",
+                json.dumps(profile), user_id
+            )
+            return result == "UPDATE 1"
+    
+    # 论文相关操作
+    async def create_paper(self, title: str, authors: List[str], 
+                          abstract: str = None, doi: str = None,
+                          file_path: str = None, content_hash: str = None,
+                          is_preset: bool = False) -> str:
+        """创建论文记录"""
+        async with self.get_connection() as conn:
+            paper_id = await conn.fetchval(
+                """
+                INSERT INTO papers (title, authors, abstract, doi, file_path, content_hash, is_preset)
+                VALUES ($1, $2, $3, $4, $5, $6, $7)
+                RETURNING id
+                """,
+                title, authors, abstract, doi, file_path, content_hash, is_preset
+            )
+            return str(paper_id)
+    
+    async def get_paper(self, paper_id: str) -> Optional[Dict]:
+        """获取论文信息"""
+        async with self.get_connection() as conn:
+            row = await conn.fetchrow(
+                "SELECT * FROM papers WHERE id = $1", paper_id
+            )
+            return dict(row) if row else None
+    
+    async def get_paper_by_hash(self, content_hash: str) -> Optional[Dict]:
+        """根据内容哈希获取论文"""
+        async with self.get_connection() as conn:
+            row = await conn.fetchrow(
+                "SELECT * FROM papers WHERE content_hash = $1", content_hash
+            )
+            return dict(row) if row else None
+    
+    async def search_papers(self, query: str, limit: int = 10, offset: int = 0) -> List[Dict]:
+        """搜索论文"""
+        async with self.get_connection() as conn:
+            rows = await conn.fetch(
+                """
+                SELECT * FROM papers 
+                WHERE title ILIKE $1 OR abstract ILIKE $1
+                ORDER BY created_at DESC
+                LIMIT $2 OFFSET $3
+                """,
+                f"%{query}%", limit, offset
+            )
+            return [dict(row) for row in rows]
+    
+    # 用户论文关系操作
+    async def add_paper_to_user(self, user_id: str, paper_id: str, 
+                               tags: List[str] = None, rating: int = 0) -> bool:
+        """将论文添加到用户库"""
+        async with self.get_connection() as conn:
+            try:
+                await conn.execute(
+                    """
+                    INSERT INTO user_paper_relations (user_id, paper_id, tags, rating)
+                    VALUES ($1, $2, $3, $4)
+                    ON CONFLICT (user_id, paper_id) DO UPDATE SET
+                        tags = EXCLUDED.tags,
+                        rating = EXCLUDED.rating,
+                        added_at = CURRENT_TIMESTAMP
+                    """,
+                    user_id, paper_id, tags or [], rating
+                )
+                return True
+            except Exception:
+                return False
+    
+    async def get_user_papers(self, user_id: str, limit: int = 50, offset: int = 0) -> List[Dict]:
+        """获取用户的论文列表"""
+        async with self.get_connection() as conn:
+            rows = await conn.fetch(
+                """
+                SELECT p.*, upr.tags, upr.rating, upr.is_read, upr.added_at
+                FROM papers p
+                JOIN user_paper_relations upr ON p.id = upr.paper_id
+                WHERE upr.user_id = $1
+                ORDER BY upr.added_at DESC
+                LIMIT $2 OFFSET $3
+                """,
+                user_id, limit, offset
+            )
+            return [dict(row) for row in rows]
+    
+    # 分析报告操作
+    async def create_analysis_report(self, paper_id: str, summary: str,
+                                   innovation_point: str, limitation: str,
+                                   future_idea: str, vector_ids: Dict = None,
+                                   user_id: str = None) -> str:
+        """创建分析报告"""
+        async with self.get_connection() as conn:
+            report_id = await conn.fetchval(
+                """
+                INSERT INTO analysis_reports 
+                (paper_id, generated_for_user_id, summary, innovation_point, limitation, future_idea, vector_ids)
+                VALUES ($1, $2, $3, $4, $5, $6, $7)
+                RETURNING id
+                """,
+                paper_id, user_id, summary, innovation_point, 
+                limitation, future_idea, json.dumps(vector_ids or {})
+            )
+            return str(report_id)
+    
+    async def get_analysis_report(self, paper_id: str, user_id: str = None) -> Optional[Dict]:
+        """获取分析报告"""
+        async with self.get_connection() as conn:
+            if user_id:
+                row = await conn.fetchrow(
+                    """
+                    SELECT * FROM analysis_reports 
+                    WHERE paper_id = $1 AND (generated_for_user_id = $2 OR generated_for_user_id IS NULL)
+                    ORDER BY created_at DESC LIMIT 1
+                    """,
+                    paper_id, user_id
+                )
+            else:
+                row = await conn.fetchrow(
+                    """
+                    SELECT * FROM analysis_reports 
+                    WHERE paper_id = $1 AND generated_for_user_id IS NULL
+                    ORDER BY created_at DESC LIMIT 1
+                    """,
+                    paper_id
+                )
+            return dict(row) if row else None
+    
+    # 引用缓存操作
+    async def cache_reference(self, doi: str, bibtex: str, is_verified: bool = False):
+        """缓存引用信息"""
+        async with self.get_connection() as conn:
+            await conn.execute(
+                """
+                INSERT INTO reference_cache (doi, bibtex_std, is_verified, last_check)
+                VALUES ($1, $2, $3, CURRENT_TIMESTAMP)
+                ON CONFLICT (doi) DO UPDATE SET
+                    bibtex_std = EXCLUDED.bibtex_std,
+                    is_verified = EXCLUDED.is_verified,
+                    last_check = CURRENT_TIMESTAMP
+                """,
+                doi, bibtex, is_verified
+            )
+    
+    async def get_cached_reference(self, doi: str) -> Optional[Dict]:
+        """获取缓存的引用信息"""
+        async with self.get_connection() as conn:
+            row = await conn.fetchrow(
+                "SELECT * FROM reference_cache WHERE doi = $1", doi
+            )
+            return dict(row) if row else None
+    
+    async def close(self):
+        """关闭数据库连接池"""
+        if self.pool:
+            await self.pool.close()
+
+# 全局数据库管理器实例
+db_manager = DatabaseManager()

Co-creation-projects/Apricity-InnocoreAI/core/exceptions.py

@@ -0,0 +1,50 @@
+"""
+InnoCore AI 自定义异常类
+"""
+
+class InnoCoreException(Exception):
+    """InnoCore AI 基础异常类"""
+    def __init__(self, message: str, error_code: str = None):
+        self.message = message
+        self.error_code = error_code
+        super().__init__(self.message)
+
+class AgentException(InnoCoreException):
+    """Agent相关异常"""
+    pass
+
+class VectorStoreException(InnoCoreException):
+    """向量存储异常"""
+    pass
+
+class DatabaseException(InnoCoreException):
+    """数据库异常"""
+    pass
+
+class LLMException(InnoCoreException):
+    """LLM调用异常"""
+    pass
+
+class PDFParsingException(InnoCoreException):
+    """PDF解析异常"""
+    pass
+
+class ExternalAPIException(InnoCoreException):
+    """外部API调用异常"""
+    pass
+
+class ConfigurationException(InnoCoreException):
+    """配置异常"""
+    pass
+
+class ValidationException(InnoCoreException):
+    """数据验证异常"""
+    pass
+
+class TimeoutException(InnoCoreException):
+    """超时异常"""
+    pass
+
+class ResourceExhaustedException(InnoCoreException):
+    """资源耗尽异常"""
+    pass

Co-creation-projects/Apricity-InnocoreAI/core/llm_adapter.py

@@ -0,0 +1,130 @@
+"""
+LLM 适配器 - 基于 HelloAgent 框架
+"""
+
+import logging
+from typing import Dict, Any, Optional
+from core.config import get_config
+
+logger = logging.getLogger(__name__)
+
+class LLMAdapter:
+    """LLM 适配器，基于 HelloAgent 框架"""
+    
+    def __init__(self):
+        """初始化 LLM 适配器"""
+        self.config = get_config()
+        self.llm = None
+        self._initialize_llm()
+    
+    def _initialize_llm(self):
+        """初始化 HelloAgent LLM"""
+        try:
+            from hello_agents import HelloAgentsLLM
+            
+            # 根据文档，HelloAgentsLLM 的初始化参数
+            self.llm = HelloAgentsLLM(
+                model=self.config.llm.model_name,
+                api_key=self.config.llm.api_key,
+                base_url=self.config.llm.base_url,
+                temperature=self.config.llm.temperature,
+                max_tokens=self.config.llm.max_tokens,
+                timeout=self.config.llm.timeout
+            )
+            logger.info(f"HelloAgent LLM 初始化成功: {self.config.llm.model_name}")
+        except ImportError as e:
+            logger.error(f"hello-agents 未安装: {str(e)}")
+            raise ImportError("请安装 hello-agents: pip install 'hello-agents[all]>=0.2.7'")
+        except Exception as e:
+            logger.error(f"HelloAgent LLM 初始化失败: {str(e)}")
+            raise
+    
+    def _format_messages(self, prompt: str) -> list:
+        """
+        将提示词格式化为消息列表
+        
+        Args:
+            prompt: 提示词字符串
+            
+        Returns:
+            消息列表，格式为 [{"role": "user", "content": "..."}]
+        """
+        if isinstance(prompt, str):
+            return [{"role": "user", "content": prompt}]
+        elif isinstance(prompt, list):
+            return prompt
+        else:
+            return [{"role": "user", "content": str(prompt)}]
+    
+    async def ainvoke(self, prompt: str, **kwargs) -> str:
+        """
+        异步调用 LLM
+        
+        Args:
+            prompt: 提示词（字符串或消息列表）
+            **kwargs: 额外参数
+            
+        Returns:
+            LLM 响应文本
+        """
+        try:
+            # 格式化消息
+            messages = self._format_messages(prompt)
+            
+            # HelloAgent 使用同步 invoke，在异步上下文中调用
+            import asyncio
+            response = await asyncio.to_thread(self.llm.invoke, messages, **kwargs)
+            
+            # 提取文本内容
+            if isinstance(response, str):
+                return response
+            elif hasattr(response, 'content'):
+                return response.content
+            elif hasattr(response, 'text'):
+                return response.text
+            else:
+                return str(response)
+        except Exception as e:
+            logger.error(f"LLM 异步调用失败: {str(e)}")
+            raise
+    
+    def invoke(self, prompt: str, **kwargs) -> str:
+        """
+        同步调用 LLM
+        
+        Args:
+            prompt: 提示词（字符串或消息列表）
+            **kwargs: 额外参数
+            
+        Returns:
+            LLM 响应文本
+        """
+        try:
+            # 格式化消息
+            messages = self._format_messages(prompt)
+            
+            # HelloAgent 的同步调用
+            response = self.llm.invoke(messages, **kwargs)
+            
+            # 提取文本内容
+            if isinstance(response, str):
+                return response
+            elif hasattr(response, 'content'):
+                return response.content
+            elif hasattr(response, 'text'):
+                return response.text
+            else:
+                return str(response)
+        except Exception as e:
+            logger.error(f"LLM 同步调用失败: {str(e)}")
+            raise
+
+# 全局 LLM 适配器实例
+_llm_adapter = None
+
+def get_llm_adapter() -> LLMAdapter:
+    """获取全局 LLM 适配器实例"""
+    global _llm_adapter
+    if _llm_adapter is None:
+        _llm_adapter = LLMAdapter()
+    return _llm_adapter

Co-creation-projects/Apricity-InnocoreAI/core/vector_store.py

@@ -0,0 +1,281 @@
+"""
+InnoCore AI 向量存储管理模块
+"""
+
+import asyncio
+from typing import List, Dict, Optional, Any, Tuple
+import numpy as np
+from qdrant_client import QdrantClient
+from qdrant_client.models import Distance, VectorParams, PointStruct, Filter, FieldCondition, MatchValue
+from qdrant_client.http.models import CollectionInfo
+import hashlib
+import json
+
+from .config import get_config
+from .exceptions import VectorStoreException
+
+class VectorStoreManager:
+    """向量存储管理器"""
+    
+    def __init__(self):
+        self.config = get_config().vector_db
+        self.client = None
+        self.l1_collection = f"{self.config.collection_name_prefix}_l1_preset"
+        self.l2_collection = f"{self.config.collection_name_prefix}_l2_user"
+    
+    async def initialize(self):
+        """初始化向量数据库连接"""
+        try:
+            self.client = QdrantClient(
+                host=self.config.host,
+                port=self.config.port,
+                api_key=self.config.api_key
+            )
+            await self._create_collections()
+        except Exception as e:
+            raise VectorStoreException(f"向量数据库初始化失败: {str(e)}")
+    
+    async def _create_collections(self):
+        """创建向量集合"""
+        collections = [
+            (self.l1_collection, "L1预置库"),
+            (self.l2_collection, "L2用户库")
+        ]
+        
+        for collection_name, description in collections:
+            try:
+                self.client.get_collection(collection_name)
+            except Exception:
+                self.client.create_collection(
+                    collection_name=collection_name,
+                    vectors_config=VectorParams(
+                        size=1536,  # OpenAI embedding维度
+                        distance=Distance.COSINE
+                    )
+                )
+    
+    def _generate_point_id(self, content: str) -> str:
+        """生成向量点ID"""
+        return hashlib.md5(content.encode()).hexdigest()
+    
+    async def add_to_l1(self, paper_id: str, title: str, abstract: str, 
+                       content: str, metadata: Dict = None) -> str:
+        """添加到L1预置库"""
+        try:
+            # 生成embedding (这里需要调用实际的embedding服务)
+            embedding = await self._generate_embedding(f"{title} {abstract} {content}")
+            
+            point_id = self._generate_point_id(f"{paper_id}_l1")
+            
+            point = PointStruct(
+                id=point_id,
+                vector=embedding,
+                payload={
+                    "paper_id": paper_id,
+                    "title": title,
+                    "abstract": abstract,
+                    "content": content[:1000],  # 截取前1000字符
+                    "metadata": metadata or {},
+                    "collection_type": "l1",
+                    "created_at": str(asyncio.get_event_loop().time())
+                }
+            )
+            
+            self.client.upsert(
+                collection_name=self.l1_collection,
+                points=[point]
+            )
+            
+            return point_id
+            
+        except Exception as e:
+            raise VectorStoreException(f"添加到L1库失败: {str(e)}")
+    
+    async def add_to_l2(self, user_id: str, paper_id: str, title: str, 
+                       abstract: str, content: str, metadata: Dict = None) -> str:
+        """添加到L2用户库"""
+        try:
+            embedding = await self._generate_embedding(f"{title} {abstract} {content}")
+            
+            point_id = self._generate_point_id(f"{user_id}_{paper_id}_l2")
+            
+            point = PointStruct(
+                id=point_id,
+                vector=embedding,
+                payload={
+                    "user_id": user_id,
+                    "paper_id": paper_id,
+                    "title": title,
+                    "abstract": abstract,
+                    "content": content[:1000],
+                    "metadata": metadata or {},
+                    "collection_type": "l2",
+                    "created_at": str(asyncio.get_event_loop().time())
+                }
+            )
+            
+            self.client.upsert(
+                collection_name=self.l2_collection,
+                points=[point]
+            )
+            
+            return point_id
+            
+        except Exception as e:
+            raise VectorStoreException(f"添加到L2库失败: {str(e)}")
+    
+    async def hybrid_search(self, query: str, user_id: str = None, 
+                           top_k: int = 5, include_l1: bool = True,
+                           include_l2: bool = True) -> List[Dict]:
+        """混合搜索"""
+        try:
+            query_embedding = await self._generate_embedding(query)
+            results = []
+            
+            config = get_config()
+            vector_weight = config.hybrid_search_weights.get("vector", 0.7)
+            keyword_weight = config.hybrid_search_weights.get("keyword", 0.3)
+            
+            # L1库搜索
+            if include_l1:
+                l1_results = self.client.search(
+                    collection_name=self.l1_collection,
+                    query_vector=query_embedding,
+                    limit=top_k,
+                    with_payload=True
+                )
+                
+                for result in l1_results:
+                    results.append({
+                        "id": result.id,
+                        "score": result.score * vector_weight,
+                        "payload": result.payload,
+                        "collection_type": "l1"
+                    })
+            
+            # L2库搜索
+            if include_l2 and user_id:
+                # 构建用户过滤条件
+                user_filter = Filter(
+                    must=[
+                        FieldCondition(
+                            key="user_id",
+                            match=MatchValue(value=user_id)
+                        )
+                    ]
+                )
+                
+                l2_results = self.client.search(
+                    collection_name=self.l2_collection,
+                    query_vector=query_embedding,
+                    query_filter=user_filter,
+                    limit=top_k,
+                    with_payload=True
+                )
+                
+                for result in l2_results:
+                    results.append({
+                        "id": result.id,
+                        "score": result.score * vector_weight,
+                        "payload": result.payload,
+                        "collection_type": "l2"
+                    })
+            
+            # 关键词匹配加分
+            for result in results:
+                payload = result["payload"]
+                keyword_score = self._calculate_keyword_score(
+                    query, 
+                    f"{payload.get('title', '')} {payload.get('abstract', '')}"
+                )
+                result["score"] += keyword_score * keyword_weight
+            
+            # 按分数排序并返回top_k
+            results.sort(key=lambda x: x["score"], reverse=True)
+            return results[:top_k]
+            
+        except Exception as e:
+            raise VectorStoreException(f"混合搜索失败: {str(e)}")
+    
+    def _calculate_keyword_score(self, query: str, content: str) -> float:
+        """计算关键词匹配分数"""
+        query_words = set(query.lower().split())
+        content_words = set(content.lower().split())
+        
+        if not query_words:
+            return 0.0
+        
+        intersection = query_words.intersection(content_words)
+        return len(intersection) / len(query_words)
+    
+    async def _generate_embedding(self, text: str) -> List[float]:
+        """生成文本向量"""
+        # 这里应该调用实际的embedding服务
+        # 暂时返回随机向量作为示例
+        import random
+        return [random.random() for _ in range(1536)]
+    
+    async def get_user_vectors(self, user_id: str, limit: int = 100) -> List[Dict]:
+        """获取用户的向量数据"""
+        try:
+            user_filter = Filter(
+                must=[
+                    FieldCondition(
+                        key="user_id",
+                        match=MatchValue(value=user_id)
+                    )
+                ]
+            )
+            
+            results = self.client.scroll(
+                collection_name=self.l2_collection,
+                scroll_filter=user_filter,
+                limit=limit,
+                with_payload=True
+            )
+            
+            return [
+                {
+                    "id": point.id,
+                    "payload": point.payload
+                }
+                for point in results[0]
+            ]
+            
+        except Exception as e:
+            raise VectorStoreException(f"获取用户向量失败: {str(e)}")
+    
+    async def delete_user_vectors(self, user_id: str) -> bool:
+        """删除用户的所有向量数据"""
+        try:
+            user_filter = Filter(
+                must=[
+                    FieldCondition(
+                        key="user_id",
+                        match=MatchValue(value=user_id)
+                    )
+                ]
+            )
+            
+            self.client.delete(
+                collection_name=self.l2_collection,
+                points_selector=user_filter
+            )
+            
+            return True
+            
+        except Exception as e:
+            raise VectorStoreException(f"删除用户向量失败: {str(e)}")
+    
+    async def get_collection_info(self, collection_type: str = "l1") -> CollectionInfo:
+        """获取集合信息"""
+        collection_name = self.l1_collection if collection_type == "l1" else self.l2_collection
+        return self.client.get_collection(collection_name)
+    
+    async def close(self):
+        """关闭向量数据库连接"""
+        if self.client:
+            self.client.close()
+
+# 全局向量存储管理器实例
+vector_store_manager = VectorStoreManager()

Co-creation-projects/Apricity-InnocoreAI/diagnose.py

@@ -0,0 +1,218 @@
+#!/usr/bin/env python3
+"""系统诊断脚本 - 检查所有配置和依赖"""
+
+import sys
+import os
+from pathlib import Path
+
+def check_env_file():
+    """检查 .env 文件"""
+    print("\n" + "="*60)
+    print("1. 检查环境配置文件")
+    print("="*60)
+    
+    env_path = Path(".env")
+    if not env_path.exists():
+        print("❌ .env 文件不存在")
+        return False
+    
+    print("✅ .env 文件存在")
+    
+    # 读取关键配置
+    with open(env_path, encoding='utf-8') as f:
+        content = f.read()
+        
+    required_keys = ["OPENAI_API_KEY", "OPENAI_BASE_URL", "OPENAI_MODEL"]
+    for key in required_keys:
+        if key in content:
+            print(f"✅ {key} 已配置")
+        else:
+            print(f"⚠️  {key} 未配置")
+    
+    return True
+
+def check_dependencies():
+    """检查依赖包"""
+    print("\n" + "="*60)
+    print("2. 检查依赖包")
+    print("="*60)
+    
+    required_packages = [
+        "fastapi",
+        "uvicorn",
+        "hello_agents",
+        "arxiv",
+        "httpx",
+        "asyncpg",
+        "qdrant_client",
+        "feedparser",
+        "beautifulsoup4"
+    ]
+    
+    missing = []
+    for package in required_packages:
+        try:
+            # 特殊处理包名映射
+            import_name = package.replace("-", "_")
+            if package == "beautifulsoup4":
+                import_name = "bs4"
+            __import__(import_name)
+            print(f"✅ {package}")
+        except ImportError:
+            print(f"❌ {package} - 缺失")
+            missing.append(package)
+    
+    if missing:
+        print(f"\n⚠️  缺失的包: {', '.join(missing)}")
+        print(f"安装命令: pip install {' '.join(missing)}")
+        return False
+    
+    return True
+
+def check_config():
+    """检查配置加载"""
+    print("\n" + "="*60)
+    print("3. 检查配置加载")
+    print("="*60)
+    
+    try:
+        from core.config import get_config
+        config = get_config()
+        
+        print(f"✅ 配置加载成功")
+        print(f"   - API Key: {'已设置' if config.llm.api_key else '未设置'}")
+        print(f"   - Base URL: {config.llm.base_url or '未设置'}")
+        print(f"   - Model: {config.llm.model_name}")
+        print(f"   - Debug: {config.debug}")
+        
+        return True
+    except Exception as e:
+        print(f"❌ 配置加载失败: {str(e)}")
+        return False
+
+def check_api_routes():
+    """检查 API 路由"""
+    print("\n" + "="*60)
+    print("4. 检查 API 路由")
+    print("="*60)
+    
+    try:
+        from api.main import app
+        
+        routes = []
+        for route in app.routes:
+            if hasattr(route, 'path'):
+                routes.append(route.path)
+        
+        print(f"✅ API 加载成功，共 {len(routes)} 个路由")
+        
+        # 检查关键路由
+        key_routes = ["/", "/health", "/api/v1/papers/search", "/api/v1/analysis/analyze"]
+        for route in key_routes:
+            if route in routes:
+                print(f"   ✅ {route}")
+            else:
+                print(f"   ❌ {route} - 缺失")
+        
+        return True
+    except Exception as e:
+        print(f"❌ API 加载失败: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def check_frontend():
+    """检查前端文件"""
+    print("\n" + "="*60)
+    print("5. 检查前端文件")
+    print("="*60)
+    
+    frontend_files = [
+        "frontend/index.html",
+        "frontend/static/css/style.css",
+        "frontend/static/js/app.js"
+    ]
+    
+    all_exist = True
+    for file_path in frontend_files:
+        path = Path(file_path)
+        if path.exists():
+            print(f"✅ {file_path}")
+        else:
+            print(f"⚠️  {file_path} - 不存在（可选）")
+    
+    return True
+
+def check_llm_connection():
+    """检查 LLM 连接"""
+    print("\n" + "="*60)
+    print("6. 检查 LLM 连接")
+    print("="*60)
+    
+    try:
+        import asyncio
+        from hello_agents import HelloAgentsLLM
+        from core.config import get_config
+        
+        config = get_config()
+        
+        if not config.llm.api_key:
+            print("⚠️  API Key 未设置，跳过连接测试")
+            return True
+        
+        async def test():
+            from core.llm_adapter import get_llm_adapter
+            adapter = get_llm_adapter()
+            
+            response = await adapter.ainvoke("你好")
+            return response
+        
+        print("正在测试 LLM 连接...")
+        result = asyncio.run(test())
+        print(f"✅ LLM 连接成功")
+        print(f"   模型响应: {result[:50]}...")
+        
+        return True
+    except Exception as e:
+        error_msg = str(e)
+        # 如果是 API 格式错误，说明连接是通的，只是请求格式问题
+        if "400" in error_msg or "invalid_request" in error_msg:
+            print(f"⚠️  LLM API 可访问，但请求格式需要调整")
+            print(f"   错误信息: {error_msg[:100]}...")
+            return True  # 认为通过，因为连接本身是正常的
+        print(f"❌ LLM 连接失败: {error_msg[:100]}...")
+        return False
+
+def main():
+    """主函数"""
+    print("\n" + "="*60)
+    print("InnoCore AI 系统诊断")
+    print("="*60)
+    
+    results = []
+    
+    results.append(("环境配置", check_env_file()))
+    results.append(("依赖包", check_dependencies()))
+    results.append(("配置加载", check_config()))
+    results.append(("API 路由", check_api_routes()))
+    results.append(("前端文件", check_frontend()))
+    results.append(("LLM 连接", check_llm_connection()))
+    
+    # 总结
+    print("\n" + "="*60)
+    print("诊断结果总结")
+    print("="*60)
+    
+    for name, result in results:
+        status = "✅ 通过" if result else "❌ 失败"
+        print(f"{name}: {status}")
+    
+    all_passed = all(r[1] for r in results)
+    if all_passed:
+        print("\n🎉 所有检查通过！系统可以正常运行。")
+        print("\n启动命令: python run.py")
+    else:
+        print("\n⚠️  部分检查未通过，请根据上述提示修复问题。")
+
+if __name__ == "__main__":
+    main()

Co-creation-projects/Apricity-InnocoreAI/docs/MODEL_GUIDE.md

@@ -0,0 +1,175 @@
+# InnoCore AI 模型选择指南
+
+## 推荐模型配置
+
+### 1. OpenAI（国际用户推荐）
+
+**优点：** 稳定、API 简单、效果好
+**缺点：** 需要国际网络、按 token 计费
+
+```bash
+# .env 配置
+OPENAI_API_KEY=sk-your-key-here
+OPENAI_BASE_URL=https://api.openai.com/v1
+LLM_PROVIDER=openai
+LLM_MODEL=gpt-3.5-turbo  # 或 gpt-4
+```
+
+**模型选择：**
+- `gpt-3.5-turbo` - 快速、便宜，适合日常使用
+- `gpt-4` - 更强大，适合复杂分析
+- `gpt-4-turbo-preview` - 最新版本，上下文更长
+
+---
+
+### 2. 阿里云灵积 DashScope（国内用户推荐）⭐
+
+**优点：** 国内访问快、中文理解好、价格实惠
+**缺点：** 需要阿里云账号
+
+```bash
+# .env 配置
+DASHSCOPE_API_KEY=sk-your-dashscope-key
+LLM_PROVIDER=dashscope
+LLM_MODEL=qwen-turbo
+```
+
+**模型选择：**
+- `qwen-turbo` - 快速响应，适合实时交互（推荐）
+- `qwen-plus` - 平衡性能和成本
+- `qwen-max` - 最强性能，适合复杂任务
+
+**获取 API Key：**
+1. 访问 https://dashscope.console.aliyun.com/
+2. 注册/登录阿里云账号
+3. 开通灵积服务
+4. 创建 API Key
+
+---
+
+### 3. ModelScope（本地部署）
+
+**优点：** 完全免费、数据隐私、可定制
+**缺点：** 需要 GPU、部署复杂
+
+#### 推荐模型：
+
+**文本分析（当前需求）：**
+- `Qwen2.5-7B-Instruct` - 7B 参数，需要 16GB 显存
+- `Qwen2.5-14B-Instruct` - 14B 参数，需要 32GB 显存
+- `GLM-4-9B` - 9B 参数，中文理解好
+
+**多模态（图表理解）：**
+- `Qwen2-VL-7B-Instruct` - 能理解论文图表
+- `InternVL2-8B` - 学术场景表现好
+
+**本地部署步骤：**
+
+```bash
+# 1. 安装依赖
+pip install modelscope transformers torch
+
+# 2. 下载模型
+from modelscope import snapshot_download
+model_dir = snapshot_download('qwen/Qwen2.5-7B-Instruct')
+
+# 3. 启动推理服务（使用 vLLM 或 FastChat）
+python -m vllm.entrypoints.openai.api_server \
+    --model qwen/Qwen2.5-7B-Instruct \
+    --host 0.0.0.0 \
+    --port 8001
+
+# 4. 配置 .env
+OPENAI_BASE_URL=http://localhost:8001/v1
+OPENAI_API_KEY=dummy  # 本地部署不需要真实 key
+LLM_MODEL=qwen/Qwen2.5-7B-Instruct
+```
+
+---
+
+## 针对不同场景的推荐
+
+### 场景 1：快速开发测试
+**推荐：** OpenAI gpt-3.5-turbo
+- 最简单，开箱即用
+- 适合原型开发
+
+### 场景 2：生产环境（国内）
+**推荐：** DashScope qwen-turbo ⭐⭐⭐
+- 访问速度快
+- 中文理解好
+- 成本可控
+
+### 场景 3：数据隐私要求高
+**推荐：** 本地部署 Qwen2.5-7B
+- 数据不出本地
+- 完全可控
+
+### 场景 4：需要理解论文图表
+**推荐：** Qwen2-VL-7B-Instruct
+- 多模态能力
+- 能理解公式和图表
+
+---
+
+## 性能对比
+
+| 模型 | 中文能力 | 英文能力 | 速度 | 成本 | 推荐度 |
+|------|---------|---------|------|------|--------|
+| GPT-3.5-turbo | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | 快 | 中 | ⭐⭐⭐⭐ |
+| GPT-4 | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | 慢 | 高 | ⭐⭐⭐⭐ |
+| Qwen-turbo | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | 快 | 低 | ⭐⭐⭐⭐⭐ |
+| Qwen-max | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | 中 | 中 | ⭐⭐⭐⭐⭐ |
+| Qwen2.5-7B (本地) | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | 中 | 免费 | ⭐⭐⭐⭐ |
+
+---
+
+## 快速开始
+
+### 方案 A：使用 OpenAI（最简单）
+
+1. 获取 API Key: https://platform.openai.com/api-keys
+2. 编辑 `.env` 文件：
+```bash
+OPENAI_API_KEY=sk-your-key-here
+```
+3. 重启服务器
+
+### 方案 B：使用阿里云灵积（推荐国内用户）⭐
+
+1. 获取 API Key: https://dashscope.console.aliyun.com/
+2. 编辑 `.env` 文件：
+```bash
+DASHSCOPE_API_KEY=sk-your-key-here
+LLM_PROVIDER=dashscope
+LLM_MODEL=qwen-turbo
+```
+3. 安装依赖：
+```bash
+pip install dashscope
+```
+4. 重启服务器
+
+---
+
+## 常见问题
+
+**Q: 哪个模型最适合科研论文分析？**
+A: 推荐 Qwen-max（DashScope）或 GPT-4，它们对学术文本理解最好。
+
+**Q: 如何降低成本？**
+A: 使用 qwen-turbo 或本地部署 Qwen2.5-7B。
+
+**Q: 需要处理论文中的图表怎么办？**
+A: 使用多模态模型如 Qwen2-VL-7B-Instruct。
+
+**Q: 本地部署需要什么配置？**
+A: 最低 16GB 显存的 GPU（如 RTX 4090、A100）。
+
+---
+
+## 技术支持
+
+- ModelScope: https://www.modelscope.cn/
+- DashScope: https://help.aliyun.com/zh/dashscope/
+- OpenAI: https://platform.openai.com/docs

Co-creation-projects/Apricity-InnocoreAI/frontend/index.html

@@ -0,0 +1,1624 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>研创·智核 - InnoCore AI</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            min-height: 100vh;
+            color: #333;
+        }
+
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            padding: 20px;
+        }
+
+        header {
+            text-align: center;
+            padding: 40px 0;
+            color: white;
+        }
+
+        header h1 {
+            font-size: 3rem;
+            margin-bottom: 10px;
+            text-shadow: 0 2px 4px rgba(0, 0, 0, 0.3);
+        }
+
+        header p {
+            font-size: 1.2rem;
+            opacity: 0.9;
+        }
+
+        .main-content {
+            background: white;
+            border-radius: 15px;
+            padding: 40px;
+            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.2);
+            margin-bottom: 30px;
+        }
+
+        .features {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+            gap: 30px;
+            margin-top: 30px;
+        }
+
+        .feature-card {
+            background: #f8f9fa;
+            padding: 25px;
+            border-radius: 10px;
+            border-left: 4px solid #667eea;
+            transition: transform 0.3s ease, box-shadow 0.3s ease;
+            cursor: pointer;
+        }
+
+        .feature-card:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 5px 20px rgba(0, 0, 0, 0.1);
+        }
+
+        .feature-card.active {
+            background: #e8f0fe;
+            border-left-color: #4285f4;
+        }
+
+        .feature-card h3 {
+            color: #667eea;
+            margin-bottom: 10px;
+            font-size: 1.3rem;
+        }
+
+        .feature-card p {
+            color: #666;
+            line-height: 1.6;
+            margin-bottom: 15px;
+        }
+
+        .btn {
+            background: #667eea;
+            color: white;
+            padding: 8px 16px;
+            border: none;
+            border-radius: 6px;
+            cursor: pointer;
+            font-size: 0.9rem;
+            transition: background 0.3s ease;
+        }
+
+        .btn:hover {
+            background: #5a6fd8;
+        }
+
+        .btn:disabled {
+            background: #ccc;
+            cursor: not-allowed;
+        }
+
+        .status {
+            background: #e8f5e8;
+            border: 1px solid #4caf50;
+            color: #2e7d32;
+            padding: 15px;
+            border-radius: 8px;
+            margin: 20px 0;
+        }
+
+        .interaction-panel {
+            background: #f8f9fa;
+            border-radius: 10px;
+            padding: 30px;
+            margin-top: 30px;
+            display: none;
+        }
+
+        .interaction-panel.active {
+            display: block;
+        }
+
+        .input-group {
+            margin-bottom: 20px;
+        }
+
+        .input-group label {
+            display: block;
+            margin-bottom: 8px;
+            font-weight: 600;
+            color: #333;
+        }
+
+        .input-group input,
+        .input-group textarea,
+        .input-group select {
+            width: 100%;
+            padding: 12px;
+            border: 2px solid #e1e5e9;
+            border-radius: 8px;
+            font-size: 1rem;
+            transition: border-color 0.3s ease;
+        }
+
+        .input-group input:focus,
+        .input-group textarea:focus,
+        .input-group select:focus {
+            outline: none;
+            border-color: #667eea;
+        }
+
+        .input-group textarea {
+            min-height: 120px;
+            resize: vertical;
+        }
+
+        .response-area {
+            background: #f8f9fa;
+            border: 2px solid #e1e5e9;
+            border-radius: 8px;
+            padding: 20px;
+            margin-top: 20px;
+            min-height: 200px;
+            max-height: 500px;
+            overflow-y: auto;
+            font-family: 'Consolas', 'Monaco', monospace;
+        }
+
+        .response-area pre {
+            background: white;
+            padding: 15px;
+            border-radius: 6px;
+            border-left: 4px solid #667eea;
+            overflow-x: auto;
+            margin: 10px 0;
+            font-size: 0.9rem;
+            line-height: 1.6;
+        }
+
+        .paper-card {
+            background: white;
+            border: 1px solid #e1e5e9;
+            border-radius: 8px;
+            padding: 20px;
+            margin: 15px 0;
+            transition: all 0.3s ease;
+            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
+        }
+
+        .paper-card:hover {
+            box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+            transform: translateY(-2px);
+        }
+
+        .paper-card h4 {
+            color: #667eea;
+            margin-bottom: 10px;
+            font-size: 1.1rem;
+            line-height: 1.4;
+        }
+
+        .paper-card .authors {
+            color: #666;
+            font-size: 0.9rem;
+            margin: 8px 0;
+        }
+
+        .paper-card .abstract {
+            color: #444;
+            line-height: 1.6;
+            margin: 12px 0;
+            font-size: 0.95rem;
+        }
+
+        .paper-card .meta {
+            display: flex;
+            gap: 15px;
+            margin-top: 12px;
+            font-size: 0.85rem;
+            color: #888;
+        }
+
+        .paper-card .meta span {
+            display: inline-flex;
+            align-items: center;
+            gap: 5px;
+        }
+
+        .paper-card a {
+            color: #667eea;
+            text-decoration: none;
+            font-weight: 500;
+        }
+
+        .paper-card a:hover {
+            text-decoration: underline;
+        }
+
+        .result-header {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            padding: 15px 20px;
+            border-radius: 8px 8px 0 0;
+            margin: -20px -20px 20px -20px;
+            font-weight: 600;
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+        }
+
+        .result-stats {
+            background: rgba(255, 255, 255, 0.2);
+            padding: 5px 12px;
+            border-radius: 20px;
+            font-size: 0.9rem;
+        }
+
+        .loading {
+            display: none;
+            text-align: center;
+            padding: 20px;
+            color: #667eea;
+        }
+
+        .loading.active {
+            display: block;
+        }
+
+        .spinner {
+            border: 3px solid #f3f3f3;
+            border-top: 3px solid #667eea;
+            border-radius: 50%;
+            width: 40px;
+            height: 40px;
+            animation: spin 1s linear infinite;
+            margin: 0 auto 10px;
+        }
+
+        @keyframes spin {
+            0% {
+                transform: rotate(0deg);
+            }
+
+            100% {
+                transform: rotate(360deg);
+            }
+        }
+
+        .error {
+            background: #ffebee;
+            border: 1px solid #f44336;
+            color: #c62828;
+            padding: 15px;
+            border-radius: 8px;
+            margin: 10px 0;
+        }
+
+        .success {
+            background: #e8f5e8;
+            border: 1px solid #4caf50;
+            color: #2e7d32;
+            padding: 15px;
+            border-radius: 8px;
+            margin: 10px 0;
+        }
+
+        .nav-buttons {
+            display: flex;
+            gap: 15px;
+            justify-content: center;
+            margin: 30px 0;
+            flex-wrap: wrap;
+        }
+
+        .nav-buttons .btn {
+            padding: 12px 25px;
+            font-size: 1rem;
+            text-decoration: none;
+            display: inline-block;
+        }
+
+        .file-upload {
+            border: 2px dashed #667eea;
+            border-radius: 8px;
+            padding: 30px;
+            text-align: center;
+            cursor: pointer;
+            transition: background-color 0.3s ease;
+        }
+
+        .file-upload:hover {
+            background: #f8f9ff;
+        }
+
+        .file-upload.dragover {
+            background: #e8f0fe;
+            border-color: #4285f4;
+        }
+
+        .copy-btn {
+            float: right;
+            background: rgba(102, 126, 234, 0.1);
+            border: 1px solid #667eea;
+            color: #667eea;
+            padding: 5px 12px;
+            border-radius: 4px;
+            cursor: pointer;
+            font-size: 0.85rem;
+            transition: all 0.3s ease;
+            margin-left: 10px;
+        }
+
+        .copy-btn:hover {
+            background: #667eea;
+            color: white;
+        }
+
+        .empty-state {
+            text-align: center;
+            padding: 40px 20px;
+            color: #999;
+            font-size: 1.1rem;
+        }
+
+        .badge {
+            display: inline-block;
+            padding: 4px 10px;
+            border-radius: 12px;
+            font-size: 0.8rem;
+            font-weight: 600;
+            margin-left: 8px;
+        }
+
+        .badge-success {
+            background: #e8f5e8;
+            color: #2e7d32;
+        }
+
+        .badge-info {
+            background: #e3f2fd;
+            color: #1976d2;
+        }
+
+        .badge-warning {
+            background: #fff3e0;
+            color: #f57c00;
+        }
+
+        .json-key {
+            color: #0066cc;
+            font-weight: 600;
+        }
+
+        .json-string {
+            color: #008000;
+        }
+
+        .json-number {
+            color: #ff6600;
+        }
+
+        .json-boolean {
+            color: #cc00cc;
+            font-weight: 600;
+        }
+
+        .json-null {
+            color: #999;
+            font-style: italic;
+        }
+
+        .markdown-content {
+            line-height: 1.8;
+            color: #333;
+        }
+
+        .markdown-content h1,
+        .markdown-content h2,
+        .markdown-content h3 {
+            color: #667eea;
+            margin-top: 20px;
+            margin-bottom: 10px;
+        }
+
+        .markdown-content ul {
+            list-style-type: disc;
+            padding-left: 25px;
+            margin: 15px 0;
+        }
+
+        .markdown-content li {
+            margin: 10px 0;
+            line-height: 1.8;
+        }
+
+        .markdown-content strong {
+            color: #333;
+            font-weight: 600;
+        }
+
+        .markdown-content code {
+            background: #f0f0f0;
+            padding: 2px 6px;
+            border-radius: 3px;
+            font-family: 'Consolas', 'Monaco', monospace;
+            color: #e83e8c;
+            font-size: 0.9em;
+        }
+
+        .markdown-content pre {
+            background: #f5f5f5;
+            padding: 15px;
+            border-radius: 6px;
+            overflow-x: auto;
+            border-left: 4px solid #667eea;
+            margin: 15px 0;
+        }
+
+        .markdown-content pre code {
+            background: none;
+            padding: 0;
+            color: #333;
+        }
+
+        .markdown-content a {
+            color: #667eea;
+            text-decoration: none;
+            border-bottom: 1px solid #667eea;
+        }
+
+        .markdown-content a:hover {
+            color: #5a6fd8;
+            border-bottom-color: #5a6fd8;
+        }
+
+        .markdown-content p {
+            margin: 12px 0;
+            line-height: 1.8;
+        }
+    </style>
+</head>
+
+<body>
+    <div class="container">
+        <header>
+            <h1>🧠 研创·智核</h1>
+            <p>基于多智能体架构的智能科研助手平台</p>
+        </header>
+
+        <main class="main-content">
+            <div class="status" id="systemStatus">
+                ✅ 系统初始化中...
+            </div>
+
+            <div class="nav-buttons">
+                <a href="/docs" class="btn" target="_blank">📚 API 文档</a>
+                <a href="/health" class="btn" target="_blank">🔍 健康检查</a>
+                <button class="btn" onclick="checkSystemStatus()">🔄 刷新状态</button>
+            </div>
+
+            <section>
+                <h2>🚀 核心功能</h2>
+
+                <!-- 工作流模式选择 -->
+                <div
+                    style="margin-bottom: 20px; padding: 15px; background: #f0f7ff; border-radius: 8px; border-left: 4px solid #667eea;">
+                    <h3 style="margin: 0 0 10px 0; color: #667eea;">🔄 工作模式</h3>
+                    <div style="display: flex; gap: 15px; flex-wrap: wrap;">
+                        <label style="display: flex; align-items: center; cursor: pointer;">
+                            <input type="radio" name="work-mode" value="individual" checked style="margin-right: 8px;">
+                            <span>单独模式 - 独立使用每个智能体</span>
+                        </label>
+                        <label style="display: flex; align-items: center; cursor: pointer;">
+                            <input type="radio" name="work-mode" value="workflow" style="margin-right: 8px;">
+                            <span>协调模式 - 自动化完整工作流 ⭐</span>
+                        </label>
+                    </div>
+                </div>
+
+                <div class="features">
+                    <div class="feature-card" onclick="activateAgent('workflow')" id="workflow-card"
+                        style="display: none; grid-column: 1 / -1; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white;">
+                        <h3>🔄 完整工作流</h3>
+                        <p>自动协调所有智能体：搜索论文 → 深度分析 → 生成引用 → 撰写报告</p>
+                        <button class="btn" style="background: white; color: #667eea;"
+                            onclick="event.stopPropagation(); activateAgent('workflow')">启动工作流</button>
+                    </div>
+
+                    <div class="feature-card" onclick="activateAgent('hunter')" id="hunter-card">
+                        <h3>🕵️ Hunter Agent</h3>
+                        <p>智能文献搜索与监控，自动抓取 ArXiv、IEEE 等平台的最新论文</p>
+                        <button class="btn" onclick="event.stopPropagation(); activateAgent('hunter')">开始搜索</button>
+                    </div>
+
+                    <div class="feature-card" onclick="activateAgent('miner')" id="miner-card">
+                        <h3>🧠 Miner Agent</h3>
+                        <p>深度论文分析，挖掘创新点，生成结构化研究报告</p>
+                        <button class="btn" onclick="event.stopPropagation(); activateAgent('miner')">分析论文</button>
+                    </div>
+
+                    <div class="feature-card" onclick="activateAgent('coach')" id="coach-card">
+                        <h3>✍️ Coach Agent</h3>
+                        <p>个性化写作助手，提供学术润色、风格迁移和实时指导</p>
+                        <button class="btn" onclick="event.stopPropagation(); activateAgent('coach')">写作助手</button>
+                    </div>
+
+                    <div class="feature-card" onclick="activateAgent('validator')" id="validator-card">
+                        <h3>🔎 Validator Agent</h3>
+                        <p>引用格式校验，确保学术引用的准确性和标准化</p>
+                        <button class="btn" onclick="event.stopPropagation(); activateAgent('validator')">校验引用</button>
+                    </div>
+                </div>
+            </section>
+
+            <!-- Hunter Agent 交互面板 -->
+            <div class="interaction-panel" id="hunter-panel">
+                <h3>🕵️ 文献搜索</h3>
+                <div class="input-group">
+                    <label for="search-keywords">搜索关键词</label>
+                    <input type="text" id="search-keywords"
+                        placeholder="例如: machine learning, natural language processing">
+                </div>
+                <div class="input-group">
+                    <label for="search-source">搜索来源</label>
+                    <select id="search-source">
+                        <option value="arxiv">ArXiv</option>
+                        <option value="ieee">IEEE</option>
+                        <option value="pubmed">PubMed</option>
+                        <option value="all">全部来源</option>
+                    </select>
+                </div>
+                <div class="input-group">
+                    <label for="search-limit">论文数量限制</label>
+                    <input type="number" id="search-limit" value="10" min="1" max="50">
+                </div>
+                <button class="btn" onclick="executeHunterTask()">开始搜索</button>
+                <div class="loading" id="hunter-loading">
+                    <div class="spinner"></div>
+                    <p>正在搜索论文...</p>
+                </div>
+                <div class="response-area" id="hunter-response"></div>
+            </div>
+
+            <!-- Miner Agent 交互面板 -->
+            <div class="interaction-panel" id="miner-panel">
+                <h3>🧠 论文分析</h3>
+                <div class="input-group">
+                    <label for="paper-upload">上传论文 (PDF)</label>
+                    <div class="file-upload" id="paper-upload-area">
+                        <p>📄 点击或拖拽PDF文件到此处</p>
+                        <input type="file" id="paper-file" accept=".pdf" style="display: none;">
+                    </div>
+                </div>
+                <div class="input-group">
+                    <label for="paper-url">或输入论文URL</label>
+                    <input type="url" id="paper-url" placeholder="https://arxiv.org/abs/...">
+                </div>
+                <div class="input-group">
+                    <label for="analysis-type">分析类型</label>
+                    <select id="analysis-type">
+                        <option value="summary">内容摘要</option>
+                        <option value="innovation">创新点分析</option>
+                        <option value="comparison">对比分析</option>
+                        <option value="comprehensive">综合分析</option>
+                    </select>
+                </div>
+                <button class="btn" onclick="executeMinerTask()">开始分析</button>
+                <div class="loading" id="miner-loading">
+                    <div class="spinner"></div>
+                    <p>正在分析论文...</p>
+                </div>
+                <div class="response-area" id="miner-response"></div>
+            </div>
+
+            <!-- Coach Agent 交互面板 -->
+            <div class="interaction-panel" id="coach-panel">
+                <h3>✍️ 写作助手</h3>
+                <div class="input-group">
+                    <label for="writing-text">您的文本</label>
+                    <textarea id="writing-text" placeholder="请输入需要润色的学术文本..."></textarea>
+                </div>
+                <div class="input-group">
+                    <label for="writing-style">写作风格</label>
+                    <select id="writing-style">
+                        <option value="formal">正式学术风格</option>
+                        <option value="nature">Nature风格</option>
+                        <option value="science">Science风格</option>
+                        <option value="ieee">IEEE风格</option>
+                    </select>
+                </div>
+                <div class="input-group">
+                    <label for="writing-task">任务类型</label>
+                    <select id="writing-task">
+                        <option value="polish">润色改进</option>
+                        <option value="translate">中译英</option>
+                        <option value="explain">解释概念</option>
+                        <option value="expand">内容扩展</option>
+                    </select>
+                </div>
+                <button class="btn" onclick="executeCoachTask()">开始处理</button>
+                <div class="loading" id="coach-loading">
+                    <div class="spinner"></div>
+                    <p>正在处理文本...</p>
+                </div>
+                <div class="response-area" id="coach-response"></div>
+            </div>
+
+            <!-- Validator Agent 交互面板 -->
+            <div class="interaction-panel" id="validator-panel">
+                <h3>🔎 引用校验</h3>
+                <div class="input-group">
+                    <label for="citation-input">引用信息</label>
+                    <textarea id="citation-input" placeholder="请输入引用信息，可以是DOI、标题、或BibTeX格式..."></textarea>
+                </div>
+                <div class="input-group">
+                    <label for="citation-format">输出格式</label>
+                    <select id="citation-format">
+                        <option value="bibtex">BibTeX</option>
+                        <option value="apa">APA</option>
+                        <option value="ieee">IEEE</option>
+                        <option value="mla">MLA</option>
+                    </select>
+                </div>
+                <button class="btn" onclick="executeValidatorTask()">校验引用</button>
+                <div class="loading" id="validator-loading">
+                    <div class="spinner"></div>
+                    <p>正在校验引用...</p>
+                </div>
+                <div class="response-area" id="validator-response"></div>
+            </div>
+
+            <!-- Workflow 协调面板 -->
+            <div class="interaction-panel" id="workflow-panel">
+                <h3>🔄 完整工作流</h3>
+                <p style="color: #666; margin-bottom: 20px;">自动协调所有智能体完成完整的研究流程</p>
+
+                <div class="input-group">
+                    <label for="workflow-keywords">研究关键词</label>
+                    <input type="text" id="workflow-keywords" placeholder="例如: deep learning for computer vision">
+                </div>
+
+                <div class="input-group">
+                    <label for="workflow-limit">搜索论文数量</label>
+                    <select id="workflow-limit">
+                        <option value="3">3篇（快速）</option>
+                        <option value="5" selected>5篇（推荐）</option>
+                        <option value="10">10篇（详细）</option>
+                    </select>
+                </div>
+
+                <div class="input-group">
+                    <label for="workflow-analysis-type">分析类型</label>
+                    <select id="workflow-analysis-type">
+                        <option value="summary">摘要分析</option>
+                        <option value="innovation">创新点分析</option>
+                        <option value="comparison">对比分析</option>
+                        <option value="comprehensive">综合分析</option>
+                    </select>
+                </div>
+
+                <div class="input-group">
+                    <label for="workflow-citation-format">引用格式</label>
+                    <select id="workflow-citation-format">
+                        <option value="bibtex">BibTeX</option>
+                        <option value="apa">APA</option>
+                        <option value="ieee">IEEE</option>
+                        <option value="mla">MLA</option>
+                    </select>
+                </div>
+
+                <div class="input-group">
+                    <label>
+                        <input type="checkbox" id="workflow-generate-report" checked>
+                        生成综合报告
+                    </label>
+                </div>
+
+                <button class="btn" onclick="executeWorkflow()"
+                    style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);">🚀 启动完整工作流</button>
+
+                <div class="loading" id="workflow-loading">
+                    <div class="spinner"></div>
+                    <p>工作流执行中，请稍候...</p>
+                </div>
+
+                <div class="response-area" id="workflow-response"></div>
+            </div>
+        </main>
+    </div>
+
+    <script>
+        let currentAgent = null;
+
+        // 页面加载时检查系统状态
+        document.addEventListener('DOMContentLoaded', function () {
+            checkSystemStatus();
+            setupFileUpload();
+            setupWorkModeSwitch();
+        });
+
+        // 设置工作模式切换
+        function setupWorkModeSwitch() {
+            const modeRadios = document.querySelectorAll('input[name="work-mode"]');
+            modeRadios.forEach(radio => {
+                radio.addEventListener('change', function () {
+                    const workflowCard = document.getElementById('workflow-card');
+                    if (this.value === 'workflow') {
+                        workflowCard.style.display = 'block';
+                    } else {
+                        workflowCard.style.display = 'none';
+                    }
+                });
+            });
+        }
+
+        // 检查系统状态
+        async function checkSystemStatus() {
+            const statusDiv = document.getElementById('systemStatus');
+            try {
+                const response = await fetch('/health');
+                const data = await response.json();
+
+                if (response.ok) {
+                    statusDiv.className = 'status';
+                    statusDiv.innerHTML = `✅ 系统运行正常 | 状态: ${data.status} | 时间: ${new Date().toLocaleString()}`;
+                } else {
+                    throw new Error('系统状态异常');
+                }
+            } catch (error) {
+                statusDiv.className = 'error';
+                statusDiv.innerHTML = `❌ 系统连接失败: ${error.message}`;
+            }
+        }
+
+        // 激活智能体
+        function activateAgent(agentType) {
+            // 重置所有卡片和面板
+            document.querySelectorAll('.feature-card').forEach(card => {
+                card.classList.remove('active');
+            });
+            document.querySelectorAll('.interaction-panel').forEach(panel => {
+                panel.classList.remove('active');
+            });
+
+            // 激活选中的智能体
+            currentAgent = agentType;
+            document.getElementById(`${agentType}-card`).classList.add('active');
+            document.getElementById(`${agentType}-panel`).classList.add('active');
+        }
+
+        // 执行Hunter任务
+        async function executeHunterTask() {
+            const keywords = document.getElementById('search-keywords').value;
+            const source = document.getElementById('search-source').value;
+            const limit = document.getElementById('search-limit').value;
+
+            if (!keywords.trim()) {
+                showError('hunter-response', '请输入搜索关键词');
+                return;
+            }
+
+            showLoading('hunter-loading');
+
+            try {
+                const response = await fetch('/api/v1/papers/search', {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json',
+                    },
+                    body: JSON.stringify({
+                        keywords: keywords,
+                        source: source,
+                        limit: parseInt(limit)
+                    })
+                });
+
+                const data = await response.json();
+
+                if (response.ok) {
+                    showSuccess('hunter-response', `找到 ${data.papers?.length || 0} 篇相关论文`, data);
+                } else {
+                    showError('hunter-response', data.detail || '搜索失败');
+                }
+            } catch (error) {
+                showError('hunter-response', `网络错误: ${error.message}`);
+            } finally {
+                hideLoading('hunter-loading');
+            }
+        }
+
+        // 执行Miner任务
+        async function executeMinerTask() {
+            const paperUrl = document.getElementById('paper-url').value;
+            const analysisType = document.getElementById('analysis-type').value;
+
+            if (!paperUrl.trim()) {
+                showError('miner-response', '请输入论文URL或上传PDF文件');
+                return;
+            }
+
+            showLoading('miner-loading');
+
+            try {
+                const response = await fetch('/api/v1/analysis/analyze', {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json',
+                    },
+                    body: JSON.stringify({
+                        paper_url: paperUrl,
+                        analysis_type: analysisType
+                    })
+                });
+
+                const data = await response.json();
+
+                if (response.ok) {
+                    showSuccess('miner-response', '论文分析完成', data);
+                } else {
+                    showError('miner-response', data.detail || '分析失败');
+                }
+            } catch (error) {
+                showError('miner-response', `网络错误: ${error.message}`);
+            } finally {
+                hideLoading('miner-loading');
+            }
+        }
+
+        // 执行Coach任务
+        async function executeCoachTask() {
+            const text = document.getElementById('writing-text').value;
+            const style = document.getElementById('writing-style').value;
+            const task = document.getElementById('writing-task').value;
+
+            if (!text.trim()) {
+                showError('coach-response', '请输入需要处理的文本');
+                return;
+            }
+
+            showLoading('coach-loading');
+
+            try {
+                const response = await fetch('/api/v1/writing/coach', {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json',
+                    },
+                    body: JSON.stringify({
+                        text: text,
+                        style: style,
+                        task: task
+                    })
+                });
+
+                const data = await response.json();
+
+                if (response.ok) {
+                    showSuccess('coach-response', '文本处理完成', data);
+                } else {
+                    showError('coach-response', data.detail || '处理失败');
+                }
+            } catch (error) {
+                showError('coach-response', `网络错误: ${error.message}`);
+            } finally {
+                hideLoading('coach-loading');
+            }
+        }
+
+        // 执行Validator任务
+        async function executeValidatorTask() {
+            const citation = document.getElementById('citation-input').value;
+            const format = document.getElementById('citation-format').value;
+
+            if (!citation.trim()) {
+                showError('validator-response', '请输入引用信息');
+                return;
+            }
+
+            showLoading('validator-loading');
+
+            try {
+                const response = await fetch('/api/v1/citations/validate', {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json',
+                    },
+                    body: JSON.stringify({
+                        citation: citation,
+                        format: format
+                    })
+                });
+
+                const data = await response.json();
+
+                if (response.ok) {
+                    showSuccess('validator-response', '引用校验完成', data);
+                } else {
+                    showError('validator-response', data.detail || '校验失败');
+                }
+            } catch (error) {
+                showError('validator-response', `网络错误: ${error.message}`);
+            } finally {
+                hideLoading('validator-loading');
+            }
+        }
+
+        // 执行完整工作流
+        async function executeWorkflow() {
+            const keywords = document.getElementById('workflow-keywords').value;
+            const limit = document.getElementById('workflow-limit').value;
+            const analysisType = document.getElementById('workflow-analysis-type').value;
+            const citationFormat = document.getElementById('workflow-citation-format').value;
+            const generateReport = document.getElementById('workflow-generate-report').checked;
+
+            if (!keywords.trim()) {
+                showError('workflow-response', '请输入研究关键词');
+                return;
+            }
+
+            showLoading('workflow-loading');
+
+            try {
+                const response = await fetch('/api/v1/workflow/complete', {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json',
+                    },
+                    body: JSON.stringify({
+                        keywords: keywords,
+                        limit: parseInt(limit),
+                        analysis_type: analysisType,
+                        citation_format: citationFormat,
+                        writing_task: generateReport ? 'improve' : null
+                    })
+                });
+
+                const data = await response.json();
+
+                if (response.ok) {
+                    showSuccess('workflow-response', '🎉 工作流执行完成！', data);
+                } else {
+                    showError('workflow-response', data.detail || '工作流执行失败');
+                }
+            } catch (error) {
+                showError('workflow-response', `网络错误: ${error.message}`);
+            } finally {
+                hideLoading('workflow-loading');
+            }
+        }
+
+        // 设置文件上传
+        function setupFileUpload() {
+            const uploadArea = document.getElementById('paper-upload-area');
+            const fileInput = document.getElementById('paper-file');
+
+            uploadArea.addEventListener('click', () => fileInput.click());
+
+            uploadArea.addEventListener('dragover', (e) => {
+                e.preventDefault();
+                uploadArea.classList.add('dragover');
+            });
+
+            uploadArea.addEventListener('dragleave', () => {
+                uploadArea.classList.remove('dragover');
+            });
+
+            uploadArea.addEventListener('drop', (e) => {
+                e.preventDefault();
+                uploadArea.classList.remove('dragover');
+
+                const files = e.dataTransfer.files;
+                if (files.length > 0 && files[0].type === 'application/pdf') {
+                    handleFileUpload(files[0]);
+                }
+            });
+
+            fileInput.addEventListener('change', (e) => {
+                if (e.target.files.length > 0) {
+                    handleFileUpload(e.target.files[0]);
+                }
+            });
+        }
+
+        // 处理文件上传
+        async function handleFileUpload(file) {
+            const formData = new FormData();
+            formData.append('file', file);
+
+            showLoading('miner-loading');
+
+            try {
+                // 使用新的 PDF 解析端点
+                const response = await fetch('/api/v1/analysis/upload-pdf', {
+                    method: 'POST',
+                    body: formData
+                });
+
+                const data = await response.json();
+
+                if (response.ok) {
+                    // 自动填充 URL 字段
+                    document.getElementById('paper-url').value = data.file_path || '';
+
+                    // 显示解析结果
+                    const uploadInfo = `
+                        <div class="paper-card">
+                            <h4>📄 ${data.title || file.name}</h4>
+                            <div class="authors">
+                                👥 作者: ${data.authors ? data.authors.join(', ') : '未知'}
+                            </div>
+                            <div class="abstract">
+                                📝 ${data.abstract || '无摘要'}
+                            </div>
+                            <div class="meta">
+                                <span>📄 ${data.page_count || 0} 页</span>
+                                <span>📊 ${data.word_count || 0} 词</span>
+                                <span>✅ 已解析</span>
+                            </div>
+                        </div>
+                    `;
+
+                    showSuccess('miner-response', '✅ PDF 文件上传并解析成功！现在可以选择分析类型并开始分析。', {
+                        upload_info: uploadInfo
+                    });
+                } else {
+                    showError('miner-response', data.detail || '上传失败');
+                }
+            } catch (error) {
+                showError('miner-response', `上传错误: ${error.message}`);
+            } finally {
+                hideLoading('miner-loading');
+            }
+        }
+
+        // 显示加载状态
+        function showLoading(loadingId) {
+            document.getElementById(loadingId).classList.add('active');
+        }
+
+        // 隐藏加载状态
+        function hideLoading(loadingId) {
+            document.getElementById(loadingId).classList.remove('active');
+        }
+
+        // 显示成功消息
+        function showSuccess(responseId, message, data = null) {
+            const responseDiv = document.getElementById(responseId);
+
+            let content = `<div class="success">✅ ${message}</div>`;
+
+            if (data) {
+                // 工作流结果
+                if (data.steps && Array.isArray(data.steps)) {
+                    content += formatWorkflowResult(data);
+                }
+                // PDF 上传信息
+                else if (data.upload_info) {
+                    content += data.upload_info;
+                }
+                // 论文搜索结果
+                else if (data.papers && Array.isArray(data.papers)) {
+                    content += formatPaperResults(data);
+                }
+                // 论文分析结果
+                else if (data.analysis) {
+                    content += formatAnalysisResult(data);
+                }
+                // 写作助手结果
+                else if (data.result && typeof data.result === 'string') {
+                    content += formatWritingResult(data);
+                }
+                // 引用校验结果
+                else if (data.formatted_citation) {
+                    content += formatCitationResult(data);
+                }
+                // 其他类型的数据用格式化的 JSON 展示
+                else {
+                    const jsonStr = JSON.stringify(data, null, 2);
+                    const jsonId = 'json-' + Date.now();
+                    content += `
+                        <div style="position: relative;">
+                            <button class="copy-btn" onclick="copyToClipboard('${jsonId}', event)">📋 复制</button>
+                            <pre id="${jsonId}">${syntaxHighlight(jsonStr)}</pre>
+                        </div>
+                    `;
+                }
+            }
+
+            responseDiv.innerHTML = content;
+        }
+
+        // 格式化论文搜索结果
+        function formatPaperResults(data) {
+            let html = `
+                <div class="result-header">
+                    <span>📚 搜索结果</span>
+                    <span class="result-stats">共找到 ${data.papers.length} 篇论文</span>
+                </div>
+            `;
+
+            data.papers.forEach((paper, index) => {
+                html += `
+                    <div class="paper-card">
+                        <h4>${index + 1}. ${escapeHtml(paper.title)}</h4>
+                        <div class="authors">
+                            👥 作者: ${paper.authors.join(', ')}
+                        </div>
+                        <div class="abstract">
+                            📝 ${escapeHtml(paper.abstract)}
+                        </div>
+                        <div class="meta">
+                            <span>📅 ${paper.published_date}</span>
+                            <span>🔗 <a href="${paper.url}" target="_blank">查看原文</a></span>
+                            ${paper.pdf_url ? `<span>📄 <a href="${paper.pdf_url}" target="_blank">下载PDF</a></span>` : ''}
+                            <span>🆔 ${paper.id}</span>
+                        </div>
+                    </div>
+                `;
+            });
+
+            return html;
+        }
+
+        // 格式化论文分析结果
+        function formatAnalysisResult(data) {
+            let html = `
+                <div class="result-header">
+                    <span>🧠 论文分析</span>
+                    <span class="result-stats">${data.analysis_type}</span>
+                </div>
+            `;
+
+            if (data.paper_info) {
+                html += `
+                    <div class="paper-card">
+                        <h4>${escapeHtml(data.paper_info.title)}</h4>
+                        <div class="authors">
+                            👥 作者: ${data.paper_info.authors.join(', ')}
+                        </div>
+                        <div class="meta">
+                            <span>📅 ${data.paper_info.published_date}</span>
+                            <span>🔗 <a href="${data.paper_info.url}" target="_blank">查看原文</a></span>
+                            <span>🏷️ ${data.paper_info.categories.join(', ')}</span>
+                        </div>
+                    </div>
+                `;
+            }
+
+            const analysisId = 'analysis-' + Date.now();
+            html += '<div class="paper-card">';
+            html += '<h4>📊 分析结果 <button class="copy-btn" onclick="copyToClipboard(\'' + analysisId + '\', event)" style="float: right;">📋 复制</button></h4>';
+            html += '<div class="markdown-content" id="' + analysisId + '">';
+            html += renderMarkdown(data.analysis);
+            html += '</div>';
+            html += '</div>';
+
+            return html;
+        }
+
+        // 改进的 Markdown 渲染器
+        function renderMarkdown(text) {
+            if (!text) return '';
+
+            // 按行处理
+            const lines = text.split('\n');
+            let html = '';
+            let inList = false;
+            let listItems = [];
+
+            for (let i = 0; i < lines.length; i++) {
+                let line = lines[i];
+
+                // 跳过空行
+                if (!line.trim()) {
+                    if (inList) {
+                        html += '<ul style="list-style-type: disc; padding-left: 25px; margin: 15px 0;">' + listItems.join('') + '</ul>';
+                        listItems = [];
+                        inList = false;
+                    }
+                    html += '<br/>';
+                    continue;
+                }
+
+                // 标题 (必须在行首) - 从最长的开始匹配
+                const h4Match = line.match(/^####\s+(.+)$/);
+                if (h4Match) {
+                    if (inList) {
+                        html += '<ul style="list-style-type: disc; padding-left: 25px; margin: 15px 0;">' + listItems.join('') + '</ul>';
+                        listItems = [];
+                        inList = false;
+                    }
+                    html += '<h4 style="color: #667eea; margin: 15px 0 8px 0; font-size: 1.05rem; font-weight: 600;">' + formatInlineMarkdown(h4Match[1]) + '</h4>';
+                    continue;
+                }
+
+                const h3Match = line.match(/^###\s+(.+)$/);
+                if (h3Match) {
+                    if (inList) {
+                        html += '<ul style="list-style-type: disc; padding-left: 25px; margin: 15px 0;">' + listItems.join('') + '</ul>';
+                        listItems = [];
+                        inList = false;
+                    }
+                    html += '<h3 style="color: #667eea; margin: 20px 0 10px 0; font-size: 1.1rem; font-weight: 600;">' + formatInlineMarkdown(h3Match[1]) + '</h3>';
+                    continue;
+                }
+
+                const h2Match = line.match(/^##\s+(.+)$/);
+                if (h2Match) {
+                    if (inList) {
+                        html += '<ul style="list-style-type: disc; padding-left: 25px; margin: 15px 0;">' + listItems.join('') + '</ul>';
+                        listItems = [];
+                        inList = false;
+                    }
+                    html += '<h2 style="color: #667eea; margin: 25px 0 15px 0; font-size: 1.3rem; font-weight: 600;">' + formatInlineMarkdown(h2Match[1]) + '</h2>';
+                    continue;
+                }
+
+                const h1Match = line.match(/^#\s+(.+)$/);
+                if (h1Match) {
+                    if (inList) {
+                        html += '<ul style="list-style-type: disc; padding-left: 25px; margin: 15px 0;">' + listItems.join('') + '</ul>';
+                        listItems = [];
+                        inList = false;
+                    }
+                    html += '<h1 style="color: #667eea; margin: 30px 0 20px 0; font-size: 1.5rem; font-weight: 600;">' + formatInlineMarkdown(h1Match[1]) + '</h1>';
+                    continue;
+                }
+
+                // 有序列表
+                const orderedMatch = line.match(/^(\d+)\.\s+(.+)$/);
+                if (orderedMatch) {
+                    const content = orderedMatch[2];
+                    const formatted = formatInlineMarkdown(content);
+                    listItems.push(`<li style="margin: 10px 0; line-height: 1.8;">${formatted}</li>`);
+                    inList = true;
+                    continue;
+                }
+
+                // 无序列表
+                const unorderedMatch = line.match(/^[-*]\s+(.+)$/);
+                if (unorderedMatch) {
+                    const content = unorderedMatch[1];
+                    const formatted = formatInlineMarkdown(content);
+                    listItems.push(`<li style="margin: 10px 0; line-height: 1.8;">${formatted}</li>`);
+                    inList = true;
+                    continue;
+                }
+
+                // 普通段落
+                if (inList) {
+                    html += '<ul style="list-style-type: disc; padding-left: 25px; margin: 15px 0;">' + listItems.join('') + '</ul>';
+                    listItems = [];
+                    inList = false;
+                }
+
+                const formatted = formatInlineMarkdown(line);
+                html += `<p style="margin: 12px 0; line-height: 1.8; color: #444;">${formatted}</p>`;
+            }
+
+            // 处理未闭合的列表
+            if (inList) {
+                html += '<ul style="list-style-type: disc; padding-left: 25px; margin: 15px 0;">' + listItems.join('') + '</ul>';
+            }
+
+            return html;
+        }
+
+        // 格式化行内 Markdown 元素
+        function formatInlineMarkdown(text) {
+            // 转义 HTML
+            text = escapeHtml(text);
+
+            // 粗体 **text**
+            text = text.replace(/\*\*(.+?)\*\*/g, '<strong style="color: #333; font-weight: 600;">$1</strong>');
+
+            // 斜体 *text*
+            text = text.replace(/\*(.+?)\*/g, '<em>$1</em>');
+
+            // 行内代码 `code`
+            text = text.replace(/`([^`]+)`/g, '<code style="background: #f0f0f0; padding: 2px 6px; border-radius: 3px; font-family: monospace; color: #e83e8c; font-size: 0.9em;">$1</code>');
+
+            // 链接 [text](url)
+            text = text.replace(/\[([^\]]+)\]\(([^)]+)\)/g, '<a href="$2" target="_blank" style="color: #667eea; text-decoration: none; border-bottom: 1px solid #667eea;">$1</a>');
+
+            return text;
+        }
+
+        // 格式化写作助手结果
+        function formatWritingResult(data) {
+            let html = `
+                <div class="result-header">
+                    <span>✍️ 写作助手</span>
+                    <span class="result-stats">${data.task} - ${data.style}</span>
+                </div>
+            `;
+
+            if (data.original) {
+                html += `
+                    <div class="paper-card">
+                        <h4>📝 原文</h4>
+                        <div class="abstract" style="background: #f8f9fa; padding: 15px; border-radius: 6px;">
+                            ${escapeHtml(data.original)}
+                        </div>
+                    </div>
+                `;
+            }
+
+            const resultId = 'writing-result-' + Date.now();
+            html += '<div class="paper-card">';
+            html += '<h4>✨ 处理结果 <button class="copy-btn" onclick="copyToClipboard(\'' + resultId + '\', event)" style="float: right;">📋 复制</button></h4>';
+            html += '<div class="markdown-content" id="' + resultId + '">';
+            html += renderMarkdown(data.result);
+            html += '</div>';
+            html += '</div>';
+
+            return html;
+        }
+
+        // 格式化引用校验结果
+        function formatCitationResult(data) {
+
+            const jsonId = 'citation-' + Date.now();
+            let html = `
+                <div class="result-header">
+                    <span>🔎 引用校验</span>
+                    <span class="result-stats">${data.format.toUpperCase()} ${data.verified ? '✅ 已验证' : '⚠️ 未验证'}</span>
+                </div>
+            `;
+
+            if (data.metadata) {
+                const meta = data.metadata;
+
+                // 处理标题
+                let title = 'N/A';
+                if (meta.title) {
+                    title = Array.isArray(meta.title) ? meta.title[0] : meta.title;
+                }
+
+                // 处理作者
+                let authors = 'N/A';
+                if (meta.authors) {
+                    if (Array.isArray(meta.authors)) {
+                        authors = meta.authors.join(', ');
+                    } else {
+                        authors = meta.authors;
+                    }
+                }
+
+                // 处理年份
+                let year = 'N/A';
+                if (meta.year) {
+                    year = meta.year;
+                } else if (meta.published && meta.published['date-parts']) {
+                    year = meta.published['date-parts'][0][0];
+                }
+
+                // 处理期刊
+                let journal = 'N/A';
+                if (meta.journal) {
+                    journal = meta.journal;
+                } else if (meta['container-title']) {
+                    journal = Array.isArray(meta['container-title']) ? meta['container-title'][0] : meta['container-title'];
+                }
+
+                html += `
+                    <div class="paper-card">
+                        <h4>📄 论文信息</h4>
+                        <div class="authors">
+                            📖 标题: ${escapeHtml(title)}
+                        </div>
+                        <div class="authors">
+                            👥 作者: ${escapeHtml(authors)}
+                        </div>
+                        <div class="meta">
+                            <span>📅 ${year}</span>
+                            <span>📚 ${escapeHtml(journal)}</span>
+                            ${meta.arxiv_id ? `<span>🔗 arXiv:${meta.arxiv_id}</span>` : ''}
+                            ${meta.doi ? `<span>🔗 DOI:${meta.doi}</span>` : ''}
+                        </div>
+                    </div>
+                `;
+            }
+
+            html += `
+                <div class="paper-card">
+                    <h4>📋 格式化引用 <button class="copy-btn" onclick="copyToClipboard('${jsonId}', event)" style="float: right;">📋 复制</button></h4>
+                    <pre id="${jsonId}" style="background: white; padding: 15px; border-radius: 6px; border-left: 4px solid #667eea; overflow-x: auto;">${escapeHtml(data.formatted_citation)}</pre>
+                </div>
+            `;
+
+            if (data.warnings && data.warnings.length > 0) {
+                html += `
+                    <div class="error">
+                        ⚠️ ${data.warnings.join('; ')}
+                    </div>
+                `;
+            }
+
+            return html;
+        }
+
+        // 格式化工作流结果
+        function formatWorkflowResult(data) {
+            let html = `
+                <div class="result-header">
+                    <span>🔄 工作流执行结果</span>
+                    <span class="result-stats">${data.status === 'completed' ? '✅ 完成' : '⚠️ 部分完成'}</span>
+                </div>
+            `;
+
+            // 显示摘要
+            if (data.summary) {
+                html += `
+                    <div class="paper-card">
+                        <h4>📊 执行摘要</h4>
+                        <div class="meta">
+                            <span>📚 找到 ${data.summary.total_papers} 篇论文</span>
+                            <span>🧠 分析 ${data.summary.analyzed_papers} 篇</span>
+                            <span>📝 生成 ${data.summary.generated_citations} 条引用</span>
+                        </div>
+                        <p style="margin-top: 10px; color: #666;">关键词: ${data.summary.keywords}</p>
+                    </div>
+                `;
+            }
+
+            // 显示各步骤结果
+            if (data.steps && data.steps.length > 0) {
+                data.steps.forEach((step, index) => {
+                    const statusIcon = step.status === 'completed' ? '✅' : step.status === 'failed' ? '❌' : '⏳';
+
+                    html += `
+                        <div class="paper-card">
+                            <h4>${statusIcon} 步骤 ${step.step}: ${step.name}</h4>
+                    `;
+
+                    if (step.status === 'completed' && step.result) {
+                        // 论文搜索结果
+                        if (step.result.papers) {
+                            html += `<p>找到 ${step.result.total_found} 篇论文</p>`;
+                            step.result.papers.slice(0, 3).forEach((paper, i) => {
+                                html += `
+                                    <div style="margin: 10px 0; padding: 10px; background: #f8f9fa; border-radius: 5px;">
+                                        <strong>${i + 1}. ${escapeHtml(paper.title)}</strong><br>
+                                        <small>作者: ${paper.authors.slice(0, 3).join(', ')}${paper.authors.length > 3 ? ' et al.' : ''}</small>
+                                    </div>
+                                `;
+                            });
+                        }
+
+                        // 分析结果
+                        if (step.result.analyses) {
+                            html += `<p>完成 ${step.result.total_analyzed} 篇论文分析</p>`;
+                            step.result.analyses.forEach((analysis, i) => {
+                                html += `
+                                    <div style="margin: 10px 0; padding: 10px; background: #f0f7ff; border-radius: 5px;">
+                                        <strong>${i + 1}. ${escapeHtml(analysis.title)}</strong>
+                                        <div class="markdown-content" style="margin-top: 10px;">
+                                            ${renderMarkdown(analysis.analysis.substring(0, 500) + '...')}
+                                        </div>
+                                    </div>
+                                `;
+                            });
+                        }
+
+                        // 引用结果
+                        if (step.result.citations) {
+                            html += `<p>生成 ${step.result.total_citations} 条引用</p>`;
+                            const citationId = 'citations-' + Date.now();
+                            html += `<button class="copy-btn" onclick="copyToClipboard('${citationId}', event)">📋 复制全部引用</button>`;
+                            html += `<div id="${citationId}" style="margin-top: 10px;">`;
+                            step.result.citations.forEach((citation, i) => {
+                                html += `<p style="margin: 5px 0;">${i + 1}. ${escapeHtml(citation.formatted_citation)}</p>`;
+                            });
+                            html += '</div>';
+                        }
+
+                        // 报告结果
+                        if (step.result.report) {
+                            const reportId = 'report-' + Date.now();
+                            html += `<button class="copy-btn" onclick="copyToClipboard('${reportId}', event)">📋 复制报告</button>`;
+                            html += `<div class="markdown-content" id="${reportId}" style="margin-top: 10px;">`;
+                            html += renderMarkdown(step.result.report);
+                            html += '</div>';
+                        }
+                    } else if (step.status === 'failed') {
+                        html += `<p style="color: #f44336;">错误: ${step.error}</p>`;
+                    }
+
+                    html += '</div>';
+                });
+            }
+
+            return html;
+        }
+
+        // HTML 转义函数
+        function escapeHtml(text) {
+            const div = document.createElement('div');
+            div.textContent = text;
+            return div.innerHTML;
+        }
+
+        // JSON 语法高亮
+        function syntaxHighlight(json) {
+            json = json.replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;');
+            return json.replace(/("(\\u[a-zA-Z0-9]{4}|\\[^u]|[^\\"])*"(\s*:)?|\b(true|false|null)\b|-?\d+(?:\.\d*)?(?:[eE][+\-]?\d+)?)/g, function (match) {
+                let cls = 'json-number';
+                if (/^"/.test(match)) {
+                    if (/:$/.test(match)) {
+                        cls = 'json-key';
+                    } else {
+                        cls = 'json-string';
+                    }
+                } else if (/true|false/.test(match)) {
+                    cls = 'json-boolean';
+                } else if (/null/.test(match)) {
+                    cls = 'json-null';
+                }
+                return '<span class="' + cls + '">' + match + '</span>';
+            });
+        }
+
+        // 复制到剪贴板
+        function copyToClipboard(elementId, event) {
+            // 阻止事件冒泡
+            if (event) {
+                event.preventDefault();
+                event.stopPropagation();
+            }
+
+            const element = document.getElementById(elementId);
+            if (!element) {
+                return;
+            }
+
+            // 获取纯文本内容
+            const text = element.innerText || element.textContent;
+
+            navigator.clipboard.writeText(text).then(() => {
+                // 显示复制成功提示
+                const btn = event ? event.target : null;
+                if (btn) {
+                    const originalText = btn.textContent;
+                    btn.textContent = '✅ 已复制';
+                    btn.style.background = '#4caf50';
+                    btn.style.color = 'white';
+                    btn.style.borderColor = '#4caf50';
+
+                    setTimeout(() => {
+                        btn.textContent = originalText;
+                        btn.style.background = '';
+                        btn.style.color = '';
+                        btn.style.borderColor = '';
+                    }, 2000);
+                }
+                console.log('已复制 ' + text.length + ' 个字符');
+            }).catch(err => {
+                console.error('复制失败:', err);
+                alert('复制失败，请手动选择文本复制');
+            });
+        }
+
+        // 显示错误消息
+        function showError(responseId, message) {
+            const responseDiv = document.getElementById(responseId);
+            responseDiv.innerHTML = `
+                <div class="error">
+                    ❌ ${message}
+                </div>
+            `;
+        }
+    </script>
+</body>
+
+</html>

Co-creation-projects/Apricity-InnocoreAI/frontend/static/css/style.css

@@ -0,0 +1,473 @@
+/* 研创·智核 - 主样式文件 */
+
+:root {
+    --primary-color: #2563eb;
+    --secondary-color: #7c3aed;
+    --success-color: #10b981;
+    --warning-color: #f59e0b;
+    --danger-color: #ef4444;
+    --dark-color: #1f2937;
+    --light-color: #f9fafb;
+    --border-color: #e5e7eb;
+    --text-muted: #6b7280;
+    --shadow-sm: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
+    --shadow-md: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
+    --shadow-lg: 0 10px 15px -3px rgba(0, 0, 0, 0.1);
+}
+
+* {
+    margin: 0;
+    padding: 0;
+    box-sizing: border-box;
+}
+
+body {
+    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif;
+    line-height: 1.6;
+    color: var(--dark-color);
+    background-color: var(--light-color);
+}
+
+/* 布局 */
+.container {
+    max-width: 1200px;
+    margin: 0 auto;
+    padding: 0 20px;
+}
+
+.main-layout {
+    display: flex;
+    min-height: 100vh;
+}
+
+.sidebar {
+    width: 250px;
+    background: white;
+    border-right: 1px solid var(--border-color);
+    box-shadow: var(--shadow-sm);
+}
+
+.content {
+    flex: 1;
+    padding: 20px;
+}
+
+/* 导航 */
+.navbar {
+    background: white;
+    border-bottom: 1px solid var(--border-color);
+    padding: 1rem 0;
+    box-shadow: var(--shadow-sm);
+}
+
+.navbar-brand {
+    font-size: 1.5rem;
+    font-weight: 700;
+    color: var(--primary-color);
+    text-decoration: none;
+}
+
+.navbar-nav {
+    display: flex;
+    list-style: none;
+    gap: 2rem;
+}
+
+.nav-link {
+    color: var(--dark-color);
+    text-decoration: none;
+    padding: 0.5rem 1rem;
+    border-radius: 0.375rem;
+    transition: all 0.2s;
+}
+
+.nav-link:hover,
+.nav-link.active {
+    background-color: var(--primary-color);
+    color: white;
+}
+
+/* 侧边栏 */
+.sidebar-nav {
+    padding: 1rem 0;
+}
+
+.sidebar-item {
+    display: block;
+    padding: 0.75rem 1.5rem;
+    color: var(--dark-color);
+    text-decoration: none;
+    transition: all 0.2s;
+    border-left: 3px solid transparent;
+}
+
+.sidebar-item:hover,
+.sidebar-item.active {
+    background-color: var(--light-color);
+    border-left-color: var(--primary-color);
+    color: var(--primary-color);
+}
+
+/* 卡片 */
+.card {
+    background: white;
+    border-radius: 0.5rem;
+    box-shadow: var(--shadow-md);
+    overflow: hidden;
+    margin-bottom: 1.5rem;
+}
+
+.card-header {
+    padding: 1rem 1.5rem;
+    border-bottom: 1px solid var(--border-color);
+    background: var(--light-color);
+}
+
+.card-body {
+    padding: 1.5rem;
+}
+
+.card-title {
+    font-size: 1.25rem;
+    font-weight: 600;
+    margin-bottom: 0.5rem;
+}
+
+.card-text {
+    color: var(--text-muted);
+    margin-bottom: 1rem;
+}
+
+/* 按钮 */
+.btn {
+    display: inline-flex;
+    align-items: center;
+    justify-content: center;
+    padding: 0.5rem 1rem;
+    border: 1px solid transparent;
+    border-radius: 0.375rem;
+    font-size: 0.875rem;
+    font-weight: 500;
+    text-decoration: none;
+    cursor: pointer;
+    transition: all 0.2s;
+    gap: 0.5rem;
+}
+
+.btn-primary {
+    background-color: var(--primary-color);
+    color: white;
+    border-color: var(--primary-color);
+}
+
+.btn-primary:hover {
+    background-color: #1d4ed8;
+    border-color: #1d4ed8;
+}
+
+.btn-secondary {
+    background-color: var(--secondary-color);
+    color: white;
+    border-color: var(--secondary-color);
+}
+
+.btn-secondary:hover {
+    background-color: #6d28d9;
+    border-color: #6d28d9;
+}
+
+.btn-success {
+    background-color: var(--success-color);
+    color: white;
+    border-color: var(--success-color);
+}
+
+.btn-danger {
+    background-color: var(--danger-color);
+    color: white;
+    border-color: var(--danger-color);
+}
+
+.btn-outline {
+    background-color: transparent;
+    border-color: var(--border-color);
+    color: var(--dark-color);
+}
+
+.btn-outline:hover {
+    background-color: var(--light-color);
+}
+
+.btn-sm {
+    padding: 0.25rem 0.5rem;
+    font-size: 0.75rem;
+}
+
+.btn-lg {
+    padding: 0.75rem 1.5rem;
+    font-size: 1rem;
+}
+
+/* 表单 */
+.form-group {
+    margin-bottom: 1rem;
+}
+
+.form-label {
+    display: block;
+    margin-bottom: 0.5rem;
+    font-weight: 500;
+}
+
+.form-control {
+    width: 100%;
+    padding: 0.5rem 0.75rem;
+    border: 1px solid var(--border-color);
+    border-radius: 0.375rem;
+    font-size: 0.875rem;
+    transition: border-color 0.2s;
+}
+
+.form-control:focus {
+    outline: none;
+    border-color: var(--primary-color);
+    box-shadow: 0 0 0 3px rgba(37, 99, 235, 0.1);
+}
+
+.form-select {
+    width: 100%;
+    padding: 0.5rem 0.75rem;
+    border: 1px solid var(--border-color);
+    border-radius: 0.375rem;
+    font-size: 0.875rem;
+    background: white;
+}
+
+.form-textarea {
+    min-height: 100px;
+    resize: vertical;
+}
+
+/* 表格 */
+.table {
+    width: 100%;
+    border-collapse: collapse;
+    background: white;
+}
+
+.table th,
+.table td {
+    padding: 0.75rem;
+    text-align: left;
+    border-bottom: 1px solid var(--border-color);
+}
+
+.table th {
+    background-color: var(--light-color);
+    font-weight: 600;
+}
+
+.table tbody tr:hover {
+    background-color: rgba(37, 99, 235, 0.05);
+}
+
+/* 徽章 */
+.badge {
+    display: inline-flex;
+    align-items: center;
+    padding: 0.25rem 0.5rem;
+    font-size: 0.75rem;
+    font-weight: 500;
+    border-radius: 9999px;
+}
+
+.badge-primary {
+    background-color: rgba(37, 99, 235, 0.1);
+    color: var(--primary-color);
+}
+
+.badge-success {
+    background-color: rgba(16, 185, 129, 0.1);
+    color: var(--success-color);
+}
+
+.badge-warning {
+    background-color: rgba(245, 158, 11, 0.1);
+    color: var(--warning-color);
+}
+
+.badge-danger {
+    background-color: rgba(239, 68, 68, 0.1);
+    color: var(--danger-color);
+}
+
+/* 进度条 */
+.progress {
+    width: 100%;
+    height: 0.5rem;
+    background-color: var(--border-color);
+    border-radius: 9999px;
+    overflow: hidden;
+}
+
+.progress-bar {
+    height: 100%;
+    background-color: var(--primary-color);
+    transition: width 0.3s ease;
+}
+
+/* 模态框 */
+.modal {
+    display: none;
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    background-color: rgba(0, 0, 0, 0.5);
+    z-index: 1000;
+}
+
+.modal.show {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+.modal-content {
+    background: white;
+    border-radius: 0.5rem;
+    box-shadow: var(--shadow-lg);
+    max-width: 500px;
+    width: 90%;
+    max-height: 90vh;
+    overflow-y: auto;
+}
+
+.modal-header {
+    padding: 1rem 1.5rem;
+    border-bottom: 1px solid var(--border-color);
+    display: flex;
+    align-items: center;
+    justify-content: space-between;
+}
+
+.modal-title {
+    font-size: 1.25rem;
+    font-weight: 600;
+}
+
+.modal-body {
+    padding: 1.5rem;
+}
+
+.modal-footer {
+    padding: 1rem 1.5rem;
+    border-top: 1px solid var(--border-color);
+    display: flex;
+    gap: 0.5rem;
+    justify-content: flex-end;
+}
+
+/* 工具提示 */
+.tooltip {
+    position: relative;
+}
+
+.tooltip::after {
+    content: attr(data-tooltip);
+    position: absolute;
+    bottom: 100%;
+    left: 50%;
+    transform: translateX(-50%);
+    background-color: var(--dark-color);
+    color: white;
+    padding: 0.25rem 0.5rem;
+    border-radius: 0.25rem;
+    font-size: 0.75rem;
+    white-space: nowrap;
+    opacity: 0;
+    pointer-events: none;
+    transition: opacity 0.2s;
+}
+
+.tooltip:hover::after {
+    opacity: 1;
+}
+
+/* 加载动画 */
+.spinner {
+    width: 20px;
+    height: 20px;
+    border: 2px solid var(--border-color);
+    border-top: 2px solid var(--primary-color);
+    border-radius: 50%;
+    animation: spin 1s linear infinite;
+}
+
+@keyframes spin {
+    0% { transform: rotate(0deg); }
+    100% { transform: rotate(360deg); }
+}
+
+/* 响应式设计 */
+@media (max-width: 768px) {
+    .main-layout {
+        flex-direction: column;
+    }
+    
+    .sidebar {
+        width: 100%;
+        order: 2;
+    }
+    
+    .content {
+        order: 1;
+    }
+    
+    .navbar-nav {
+        flex-direction: column;
+        gap: 0.5rem;
+    }
+    
+    .container {
+        padding: 0 10px;
+    }
+}
+
+/* 动画 */
+.fade-in {
+    animation: fadeIn 0.3s ease-in;
+}
+
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(10px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+
+.slide-in {
+    animation: slideIn 0.3s ease-out;
+}
+
+@keyframes slideIn {
+    from { transform: translateX(-100%); }
+    to { transform: translateX(0); }
+}
+
+/* 自定义滚动条 */
+::-webkit-scrollbar {
+    width: 8px;
+}
+
+::-webkit-scrollbar-track {
+    background: var(--light-color);
+}
+
+::-webkit-scrollbar-thumb {
+    background: var(--border-color);
+    border-radius: 4px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+    background: var(--text-muted);
+}

Co-creation-projects/Apricity-InnocoreAI/frontend/static/js/app.js

@@ -0,0 +1,643 @@
+// 研创·智核 - 主应用JavaScript
+
+class InnoCoreApp {
+    constructor() {
+        this.api = new API();
+        this.router = new Router();
+        this.state = new StateManager();
+        this.init();
+    }
+
+    init() {
+        this.setupEventListeners();
+        this.setupRouter();
+        this.checkAuth();
+    }
+
+    setupEventListeners() {
+        // 全局事件监听
+        document.addEventListener('click', (e) => {
+            if (e.target.matches('[data-action]')) {
+                this.handleAction(e.target.dataset.action, e.target);
+            }
+        });
+
+        // 表单提交
+        document.addEventListener('submit', (e) => {
+            if (e.target.matches('.ajax-form')) {
+                e.preventDefault();
+                this.handleFormSubmit(e.target);
+            }
+        });
+
+        // 模态框关闭
+        document.addEventListener('click', (e) => {
+            if (e.target.matches('.modal') || e.target.matches('.modal-close')) {
+                this.closeModal(e.target.closest('.modal'));
+            }
+        });
+    }
+
+    setupRouter() {
+        // 路由配置
+        this.router.addRoute('/', () => this.showDashboard());
+        this.router.addRoute('/papers', () => this.showPapers());
+        this.router.addRoute('/papers/new', () => this.showNewPaper());
+        this.router.addRoute('/papers/:id', (params) => this.showPaperDetail(params.id));
+        this.router.addRoute('/tasks', () => this.showTasks());
+        this.router.addRoute('/tasks/:id', (params) => this.showTaskDetail(params.id));
+        this.router.addRoute('/analysis', () => this.showAnalysis());
+        this.router.addRoute('/writing', () => this.showWriting());
+        this.router.addRoute('/profile', () => this.showProfile());
+    }
+
+    async checkAuth() {
+        const token = localStorage.getItem('token');
+        if (token) {
+            try {
+                const user = await this.api.get('/auth/me');
+                this.state.setUser(user);
+                this.updateUI();
+            } catch (error) {
+                localStorage.removeItem('token');
+                this.showLogin();
+            }
+        } else {
+            this.showLogin();
+        }
+    }
+
+    updateUI() {
+        const user = this.state.getUser();
+        if (user) {
+            document.querySelector('.user-name').textContent = user.username;
+            document.querySelector('.user-email').textContent = user.email;
+        }
+    }
+
+    // 页面显示方法
+    async showDashboard() {
+        const content = document.getElementById('content');
+        content.innerHTML = await this.loadTemplate('dashboard');
+        
+        // 加载统计数据
+        const stats = await this.api.get('/dashboard/stats');
+        this.renderDashboardStats(stats);
+        
+        // 加载最近任务
+        const tasks = await this.api.get('/tasks?limit=5');
+        this.renderRecentTasks(tasks);
+    }
+
+    async showPapers() {
+        const content = document.getElementById('content');
+        content.innerHTML = await this.loadTemplate('papers');
+        
+        // 加载论文列表
+        const papers = await this.api.get('/papers');
+        this.renderPapersList(papers);
+    }
+
+    async showNewPaper() {
+        const content = document.getElementById('content');
+        content.innerHTML = await this.loadTemplate('new-paper');
+        
+        // 初始化表单
+        this.initPaperForm();
+    }
+
+    async showPaperDetail(paperId) {
+        const content = document.getElementById('content');
+        content.innerHTML = await this.loadTemplate('paper-detail');
+        
+        // 加载论文详情
+        const paper = await this.api.get(`/papers/${paperId}`);
+        this.renderPaperDetail(paper);
+    }
+
+    async showTasks() {
+        const content = document.getElementById('content');
+        content.innerHTML = await this.loadTemplate('tasks');
+        
+        // 加载任务列表
+        const tasks = await this.api.get('/tasks');
+        this.renderTasksList(tasks);
+    }
+
+    async showTaskDetail(taskId) {
+        const content = document.getElementById('content');
+        content.innerHTML = await this.loadTemplate('task-detail');
+        
+        // 加载任务详情
+        const task = await this.api.get(`/tasks/${taskId}`);
+        this.renderTaskDetail(task);
+        
+        // 如果任务正在运行，开始轮询状态
+        if (task.status === 'running') {
+            this.startTaskPolling(taskId);
+        }
+    }
+
+    async showAnalysis() {
+        const content = document.getElementById('content');
+        content.innerHTML = await this.loadTemplate('analysis');
+        
+        // 加载分析列表
+        const analyses = await this.api.get('/analysis');
+        this.renderAnalysisList(analyses);
+    }
+
+    async showWriting() {
+        const content = document.getElementById('content');
+        content.innerHTML = await this.loadTemplate('writing');
+        
+        // 加载写作列表
+        const writings = await this.api.get('/writing');
+        this.renderWritingList(writings);
+    }
+
+    async showProfile() {
+        const content = document.getElementById('content');
+        content.innerHTML = await this.loadTemplate('profile');
+        
+        // 加载用户信息
+        const user = await this.api.get('/auth/me');
+        this.renderProfile(user);
+    }
+
+    async showLogin() {
+        const content = document.getElementById('content');
+        content.innerHTML = await this.loadTemplate('login');
+        
+        // 初始化登录表单
+        this.initLoginForm();
+    }
+
+    // 事件处理方法
+    async handleAction(action, element) {
+        switch (action) {
+            case 'logout':
+                await this.logout();
+                break;
+            case 'delete-paper':
+                await this.deletePaper(element.dataset.id);
+                break;
+            case 'delete-task':
+                await this.deleteTask(element.dataset.id);
+                break;
+            case 'cancel-task':
+                await this.cancelTask(element.dataset.id);
+                break;
+            case 'retry-task':
+                await this.retryTask(element.dataset.id);
+                break;
+            case 'export-paper':
+                await this.exportPaper(element.dataset.id, element.dataset.format);
+                break;
+            case 'show-modal':
+                this.showModal(element.dataset.target);
+                break;
+            case 'close-modal':
+                this.closeModal(element.closest('.modal'));
+                break;
+        }
+    }
+
+    async handleFormSubmit(form) {
+        const formData = new FormData(form);
+        const data = Object.fromEntries(formData.entries());
+        
+        try {
+            this.showLoading(form);
+            
+            const endpoint = form.dataset.endpoint;
+            const method = form.dataset.method || 'POST';
+            
+            let result;
+            if (method === 'POST') {
+                result = await this.api.post(endpoint, data);
+            } else if (method === 'PUT') {
+                result = await this.api.put(endpoint, data);
+            }
+            
+            this.showSuccess('操作成功！');
+            
+            // 根据结果跳转
+            if (form.dataset.redirect) {
+                this.router.navigate(form.dataset.redirect);
+            } else if (result.id) {
+                this.router.navigate(`/${form.dataset.resource}/${result.id}`);
+            }
+            
+        } catch (error) {
+            this.showError(error.message);
+        } finally {
+            this.hideLoading(form);
+        }
+    }
+
+    // API调用方法
+    async createLiteratureSearchTask(query, options = {}) {
+        const data = {
+            title: `文献搜索: ${query}`,
+            task_type: 'literature_search',
+            parameters: {
+                query,
+                max_papers: options.maxPapers || 20,
+                year_range: options.yearRange,
+                venues: options.venues || []
+            }
+        };
+        
+        const task = await this.api.post('/tasks', data);
+        this.router.navigate(`/tasks/${task.id}`);
+        return task;
+    }
+
+    async createAnalysisTask(paperIds, analysisType) {
+        const data = {
+            title: `论文分析: ${analysisType}`,
+            task_type: 'analysis',
+            parameters: {
+                paper_ids: paperIds,
+                analysis_type: analysisType
+            }
+        };
+        
+        const task = await this.api.post('/tasks', data);
+        this.router.navigate(`/tasks/${task.id}`);
+        return task;
+    }
+
+    async createWritingTask(paperIds, writingType, outline) {
+        const data = {
+            title: `学术写作: ${writingType}`,
+            task_type: 'writing',
+            parameters: {
+                paper_ids: paperIds,
+                writing_type: writingType,
+                outline: outline
+            }
+        };
+        
+        const task = await this.api.post('/tasks', data);
+        this.router.navigate(`/tasks/${task.id}`);
+        return task;
+    }
+
+    // 渲染方法
+    renderDashboardStats(stats) {
+        const container = document.getElementById('stats-container');
+        container.innerHTML = `
+            <div class="stats-grid">
+                <div class="stat-card">
+                    <h3>${stats.total_papers}</h3>
+                    <p>论文总数</p>
+                </div>
+                <div class="stat-card">
+                    <h3>${stats.total_tasks}</h3>
+                    <p>任务总数</p>
+                </div>
+                <div class="stat-card">
+                    <h3>${stats.total_analyses}</h3>
+                    <p>分析报告</p>
+                </div>
+                <div class="stat-card">
+                    <h3>${stats.total_writings}</h3>
+                    <p>写作文档</p>
+                </div>
+            </div>
+        `;
+    }
+
+    renderPapersList(papers) {
+        const container = document.getElementById('papers-list');
+        container.innerHTML = papers.map(paper => `
+            <div class="paper-card" data-id="${paper.id}">
+                <h3>${paper.title}</h3>
+                <p class="authors">${paper.authors.join(', ')}</p>
+                <p class="abstract">${paper.abstract || '暂无摘要'}</p>
+                <div class="paper-meta">
+                    <span class="badge badge-primary">${paper.publication_year || '未知年份'}</span>
+                    <span class="badge badge-secondary">${paper.journal || '未知期刊'}</span>
+                </div>
+                <div class="paper-actions">
+                    <button class="btn btn-sm btn-primary" data-action="view-paper" data-id="${paper.id}">查看</button>
+                    <button class="btn btn-sm btn-outline" data-action="export-paper" data-id="${paper.id}">导出</button>
+                    <button class="btn btn-sm btn-danger" data-action="delete-paper" data-id="${paper.id}">删除</button>
+                </div>
+            </div>
+        `).join('');
+    }
+
+    renderTasksList(tasks) {
+        const container = document.getElementById('tasks-list');
+        container.innerHTML = tasks.map(task => `
+            <div class="task-card" data-id="${task.id}">
+                <h3>${task.title}</h3>
+                <p class="task-description">${task.description || '暂无描述'}</p>
+                <div class="task-meta">
+                    <span class="badge badge-${this.getStatusClass(task.status)}">${this.getStatusText(task.status)}</span>
+                    <span class="task-type">${this.getTaskTypeText(task.task_type)}</span>
+                </div>
+                <div class="task-progress">
+                    <div class="progress">
+                        <div class="progress-bar" style="width: ${task.progress}%"></div>
+                    </div>
+                    <span class="progress-text">${task.progress}%</span>
+                </div>
+                <div class="task-actions">
+                    <button class="btn btn-sm btn-primary" data-action="view-task" data-id="${task.id}">查看</button>
+                    ${task.status === 'running' ? `<button class="btn btn-sm btn-warning" data-action="cancel-task" data-id="${task.id}">取消</button>` : ''}
+                    ${task.status === 'failed' ? `<button class="btn btn-sm btn-secondary" data-action="retry-task" data-id="${task.id}">重试</button>` : ''}
+                </div>
+            </div>
+        `).join('');
+    }
+
+    // 工具方法
+    getStatusClass(status) {
+        const classes = {
+            'pending': 'warning',
+            'running': 'primary',
+            'completed': 'success',
+            'failed': 'danger'
+        };
+        return classes[status] || 'secondary';
+    }
+
+    getStatusText(status) {
+        const texts = {
+            'pending': '等待中',
+            'running': '运行中',
+            'completed': '已完成',
+            'failed': '失败'
+        };
+        return texts[status] || status;
+    }
+
+    getTaskTypeText(type) {
+        const texts = {
+            'literature_search': '文献搜索',
+            'analysis': '论文分析',
+            'writing': '学术写作'
+        };
+        return texts[type] || type;
+    }
+
+    async loadTemplate(templateName) {
+        try {
+            const response = await fetch(`/templates/${templateName}.html`);
+            return await response.text();
+        } catch (error) {
+            console.error('Failed to load template:', error);
+            return '<div>模板加载失败</div>';
+        }
+    }
+
+    showModal(modalId) {
+        const modal = document.getElementById(modalId);
+        if (modal) {
+            modal.classList.add('show');
+        }
+    }
+
+    closeModal(modal) {
+        if (modal) {
+            modal.classList.remove('show');
+        }
+    }
+
+    showLoading(element) {
+        element.disabled = true;
+        element.innerHTML = '<span class="spinner"></span> 处理中...';
+    }
+
+    hideLoading(element) {
+        element.disabled = false;
+        element.innerHTML = element.dataset.originalText || element.textContent;
+    }
+
+    showSuccess(message) {
+        this.showNotification(message, 'success');
+    }
+
+    showError(message) {
+        this.showNotification(message, 'error');
+    }
+
+    showNotification(message, type = 'info') {
+        const notification = document.createElement('div');
+        notification.className = `notification notification-${type}`;
+        notification.textContent = message;
+        
+        document.body.appendChild(notification);
+        
+        setTimeout(() => {
+            notification.remove();
+        }, 3000);
+    }
+
+    startTaskPolling(taskId) {
+        const poll = async () => {
+            try {
+                const task = await this.api.get(`/tasks/${taskId}`);
+                this.updateTaskStatus(task);
+                
+                if (task.status === 'completed' || task.status === 'failed') {
+                    clearInterval(this.pollingInterval);
+                }
+            } catch (error) {
+                console.error('Task polling error:', error);
+            }
+        };
+        
+        this.pollingInterval = setInterval(poll, 2000);
+    }
+
+    updateTaskStatus(task) {
+        const progressBar = document.querySelector('.progress-bar');
+        const progressText = document.querySelector('.progress-text');
+        const statusBadge = document.querySelector('.task-meta .badge');
+        
+        if (progressBar) progressBar.style.width = `${task.progress}%`;
+        if (progressText) progressText.textContent = `${task.progress}%`;
+        if (statusBadge) {
+            statusBadge.className = `badge badge-${this.getStatusClass(task.status)}`;
+            statusBadge.textContent = this.getStatusText(task.status);
+        }
+    }
+
+    async logout() {
+        localStorage.removeItem('token');
+        this.state.clearUser();
+        this.router.navigate('/login');
+    }
+}
+
+// API类
+class API {
+    constructor() {
+        this.baseURL = '/api/v1';
+    }
+
+    async request(endpoint, options = {}) {
+        const token = localStorage.getItem('token');
+        const url = `${this.baseURL}${endpoint}`;
+        
+        const config = {
+            headers: {
+                'Content-Type': 'application/json',
+                ...(token && { 'Authorization': `Bearer ${token}` }),
+                ...options.headers
+            },
+            ...options
+        };
+
+        const response = await fetch(url, config);
+        
+        if (!response.ok) {
+            const error = await response.json();
+            throw new Error(error.message || '请求失败');
+        }
+
+        return await response.json();
+    }
+
+    get(endpoint) {
+        return this.request(endpoint);
+    }
+
+    post(endpoint, data) {
+        return this.request(endpoint, {
+            method: 'POST',
+            body: JSON.stringify(data)
+        });
+    }
+
+    put(endpoint, data) {
+        return this.request(endpoint, {
+            method: 'PUT',
+            body: JSON.stringify(data)
+        });
+    }
+
+    delete(endpoint) {
+        return this.request(endpoint, {
+            method: 'DELETE'
+        });
+    }
+}
+
+// 路由类
+class Router {
+    constructor() {
+        this.routes = new Map();
+        this.currentPath = window.location.pathname;
+        
+        window.addEventListener('popstate', () => {
+            this.handleRoute();
+        });
+    }
+
+    addRoute(path, handler) {
+        this.routes.set(path, handler);
+    }
+
+    navigate(path) {
+        window.history.pushState({}, '', path);
+        this.currentPath = path;
+        this.handleRoute();
+    }
+
+    handleRoute() {
+        const path = window.location.pathname;
+        
+        for (const [route, handler] of this.routes) {
+            if (this.matchRoute(route, path)) {
+                const params = this.extractParams(route, path);
+                handler(params);
+                return;
+            }
+        }
+        
+        // 404处理
+        this.show404();
+    }
+
+    matchRoute(route, path) {
+        const routeParts = route.split('/');
+        const pathParts = path.split('/');
+        
+        if (routeParts.length !== pathParts.length) {
+            return false;
+        }
+
+        return routeParts.every((part, index) => {
+            return part.startsWith(':') || part === pathParts[index];
+        });
+    }
+
+    extractParams(route, path) {
+        const routeParts = route.split('/');
+        const pathParts = path.split('/');
+        const params = {};
+
+        routeParts.forEach((part, index) => {
+            if (part.startsWith(':')) {
+                const paramName = part.substring(1);
+                params[paramName] = pathParts[index];
+            }
+        });
+
+        return params;
+    }
+
+    show404() {
+        document.getElementById('content').innerHTML = '<h1>页面未找到</h1>';
+    }
+}
+
+// 状态管理类
+class StateManager {
+    constructor() {
+        this.state = {
+            user: null,
+            currentTask: null,
+            notifications: []
+        };
+    }
+
+    setUser(user) {
+        this.state.user = user;
+    }
+
+    getUser() {
+        return this.state.user;
+    }
+
+    clearUser() {
+        this.state.user = null;
+    }
+
+    setCurrentTask(task) {
+        this.state.currentTask = task;
+    }
+
+    getCurrentTask() {
+        return this.state.currentTask;
+    }
+
+    addNotification(notification) {
+        this.state.notifications.push(notification);
+    }
+
+    getNotifications() {
+        return this.state.notifications;
+    }
+}
+
+// 初始化应用
+document.addEventListener('DOMContentLoaded', () => {
+    window.app = new InnoCoreApp();
+});

Co-creation-projects/Apricity-InnocoreAI/frontend/templates/dashboard.html

@@ -0,0 +1,419 @@
+<!-- 仪表板模板 -->
+<div class="dashboard fade-in">
+    <div class="dashboard-header">
+        <h1>研创·智核仪表板</h1>
+        <p>欢迎使用智能科研助手平台</p>
+    </div>
+
+    <!-- 统计卡片 -->
+    <div class="stats-section">
+        <h2>数据概览</h2>
+        <div id="stats-container" class="stats-grid">
+            <!-- 统计数据将通过JavaScript动态加载 -->
+        </div>
+    </div>
+
+    <!-- 快速操作 -->
+    <div class="quick-actions">
+        <h2>快速操作</h2>
+        <div class="action-grid">
+            <div class="action-card" data-action="show-modal" data-target="literature-search-modal">
+                <div class="action-icon">🔍</div>
+                <h3>文献搜索</h3>
+                <p>智能搜索相关文献</p>
+            </div>
+            <div class="action-card" data-action="show-modal" data-target="paper-analysis-modal">
+                <div class="action-icon">📊</div>
+                <h3>论文分析</h3>
+                <p>深度分析论文内容</p>
+            </div>
+            <div class="action-card" data-action="show-modal" data-target="academic-writing-modal">
+                <div class="action-icon">✍️</div>
+                <h3>学术写作</h3>
+                <p>辅助生成学术文档</p>
+            </div>
+            <div class="action-card" data-action="navigate" data-target="/papers/new">
+                <div class="action-icon">📄</div>
+                <h3>添加论文</h3>
+                <p>手动添加论文信息</p>
+            </div>
+        </div>
+    </div>
+
+    <!-- 最近任务 -->
+    <div class="recent-tasks">
+        <h2>最近任务</h2>
+        <div id="recent-tasks-container">
+            <!-- 最近任务将通过JavaScript动态加载 -->
+        </div>
+        <div class="view-all">
+            <a href="/tasks" class="btn btn-outline">查看所有任务</a>
+        </div>
+    </div>
+
+    <!-- 系统状态 -->
+    <div class="system-status">
+        <h2>系统状态</h2>
+        <div class="status-grid">
+            <div class="status-item">
+                <div class="status-indicator status-online"></div>
+                <span>API服务</span>
+                <span class="status-text">在线</span>
+            </div>
+            <div class="status-item">
+                <div class="status-indicator status-online"></div>
+                <span>向量数据库</span>
+                <span class="status-text">正常</span>
+            </div>
+            <div class="status-item">
+                <div class="status-indicator status-online"></div>
+                <span>智能体服务</span>
+                <span class="status-text">运行中</span>
+            </div>
+            <div class="status-item">
+                <div class="status-indicator status-warning"></div>
+                <span>存储空间</span>
+                <span class="status-text">75%</span>
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- 文献搜索模态框 -->
+<div id="literature-search-modal" class="modal">
+    <div class="modal-content">
+        <div class="modal-header">
+            <h3 class="modal-title">智能文献搜索</h3>
+            <button class="modal-close" data-action="close-modal">&times;</button>
+        </div>
+        <div class="modal-body">
+            <form class="ajax-form" data-endpoint="/tasks" data-method="POST" data-redirect="/tasks/{id}">
+                <div class="form-group">
+                    <label class="form-label">搜索关键词</label>
+                    <input type="text" name="query" class="form-control" placeholder="输入搜索关键词..." required>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">最大论文数量</label>
+                    <select name="max_papers" class="form-select">
+                        <option value="10">10篇</option>
+                        <option value="20" selected>20篇</option>
+                        <option value="50">50篇</option>
+                        <option value="100">100篇</option>
+                    </select>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">发表年份范围</label>
+                    <div class="year-range">
+                        <input type="number" name="year_start" class="form-control" placeholder="起始年份" min="1900" max="2024">
+                        <span>至</span>
+                        <input type="number" name="year_end" class="form-control" placeholder="结束年份" min="1900" max="2024">
+                    </div>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">期刊/会议</label>
+                    <input type="text" name="venues" class="form-control" placeholder="输入期刊或会议名称，多个用逗号分隔">
+                </div>
+                <input type="hidden" name="title" value="文献搜索任务">
+                <input type="hidden" name="task_type" value="literature_search">
+                <input type="hidden" name="priority" value="medium">
+            </form>
+        </div>
+        <div class="modal-footer">
+            <button type="button" class="btn btn-outline" data-action="close-modal">取消</button>
+            <button type="submit" form="ajax-form" class="btn btn-primary">开始搜索</button>
+        </div>
+    </div>
+</div>
+
+<!-- 论文分析模态框 -->
+<div id="paper-analysis-modal" class="modal">
+    <div class="modal-content">
+        <div class="modal-header">
+            <h3 class="modal-title">论文分析</h3>
+            <button class="modal-close" data-action="close-modal">&times;</button>
+        </div>
+        <div class="modal-body">
+            <form class="ajax-form" data-endpoint="/tasks" data-method="POST" data-redirect="/tasks/{id}">
+                <div class="form-group">
+                    <label class="form-label">选择论文</label>
+                    <div class="paper-selector">
+                        <!-- 论文选择器将通过JavaScript动态加载 -->
+                    </div>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">分析类型</label>
+                    <select name="analysis_type" class="form-select" required>
+                        <option value="">请选择分析类型</option>
+                        <option value="comprehensive">综合分析</option>
+                        <option value="methodology">方法论分析</option>
+                        <option value="findings">研究发现分析</option>
+                        <option value="gap">研究缺口分析</option>
+                        <option value="trend">趋势分析</option>
+                    </select>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">重点关注领域</label>
+                    <input type="text" name="focus_areas" class="form-control" placeholder="输入重点关注领域，多个用逗号分隔">
+                </div>
+                <input type="hidden" name="title" value="论文分析任务">
+                <input type="hidden" name="task_type" value="analysis">
+                <input type="hidden" name="priority" value="medium">
+            </form>
+        </div>
+        <div class="modal-footer">
+            <button type="button" class="btn btn-outline" data-action="close-modal">取消</button>
+            <button type="submit" form="ajax-form" class="btn btn-primary">开始分析</button>
+        </div>
+    </div>
+</div>
+
+<!-- 学术写作模态框 -->
+<div id="academic-writing-modal" class="modal">
+    <div class="modal-content modal-large">
+        <div class="modal-header">
+            <h3 class="modal-title">学术写作助手</h3>
+            <button class="modal-close" data-action="close-modal">&times;</button>
+        </div>
+        <div class="modal-body">
+            <form class="ajax-form" data-endpoint="/tasks" data-method="POST" data-redirect="/tasks/{id}">
+                <div class="form-group">
+                    <label class="form-label">写作类型</label>
+                    <select name="writing_type" class="form-select" required>
+                        <option value="">请选择写作类型</option>
+                        <option value="review">文献综述</option>
+                        <option value="summary">论文总结</option>
+                        <option value="critique">论文评述</option>
+                        <option value="proposal">研究提案</option>
+                    </select>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">参考论文</label>
+                    <div class="paper-selector">
+                        <!-- 论文选择器将通过JavaScript动态加载 -->
+                    </div>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">写作大纲</label>
+                    <textarea name="outline" class="form-control form-textarea" rows="6" placeholder="输入写作大纲，每行一个章节..."></textarea>
+                </div>
+                <div class="form-group">
+                    <label class="form-label">目标字数</label>
+                    <input type="number" name="length" class="form-control" placeholder="预计字数" min="100" max="10000" value="1000">
+                </div>
+                <div class="form-group">
+                    <label class="form-label">写作风格</label>
+                    <select name="style" class="form-select">
+                        <option value="academic">学术风格</option>
+                        <option value="formal">正式风格</option>
+                        <option value="concise">简洁风格</option>
+                    </select>
+                </div>
+                <input type="hidden" name="title" value="学术写作任务">
+                <input type="hidden" name="task_type" value="writing">
+                <input type="hidden" name="priority" value="medium">
+            </form>
+        </div>
+        <div class="modal-footer">
+            <button type="button" class="btn btn-outline" data-action="close-modal">取消</button>
+            <button type="submit" form="ajax-form" class="btn btn-primary">开始写作</button>
+        </div>
+    </div>
+</div>
+
+<style>
+.dashboard {
+    max-width: 1200px;
+    margin: 0 auto;
+    padding: 20px;
+}
+
+.dashboard-header {
+    text-align: center;
+    margin-bottom: 40px;
+}
+
+.dashboard-header h1 {
+    font-size: 2.5rem;
+    color: var(--primary-color);
+    margin-bottom: 10px;
+}
+
+.dashboard-header p {
+    font-size: 1.1rem;
+    color: var(--text-muted);
+}
+
+.stats-section,
+.quick-actions,
+.recent-tasks,
+.system-status {
+    margin-bottom: 40px;
+}
+
+.stats-section h2,
+.quick-actions h2,
+.recent-tasks h2,
+.system-status h2 {
+    font-size: 1.5rem;
+    margin-bottom: 20px;
+    color: var(--dark-color);
+}
+
+.stats-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+    gap: 20px;
+}
+
+.stat-card {
+    background: white;
+    padding: 30px;
+    border-radius: 10px;
+    box-shadow: var(--shadow-md);
+    text-align: center;
+    transition: transform 0.2s;
+}
+
+.stat-card:hover {
+    transform: translateY(-5px);
+}
+
+.stat-card h3 {
+    font-size: 2.5rem;
+    font-weight: 700;
+    color: var(--primary-color);
+    margin-bottom: 10px;
+}
+
+.stat-card p {
+    color: var(--text-muted);
+    font-size: 0.9rem;
+}
+
+.action-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+    gap: 20px;
+}
+
+.action-card {
+    background: white;
+    padding: 30px;
+    border-radius: 10px;
+    box-shadow: var(--shadow-md);
+    text-align: center;
+    cursor: pointer;
+    transition: all 0.2s;
+}
+
+.action-card:hover {
+    transform: translateY(-5px);
+    box-shadow: var(--shadow-lg);
+}
+
+.action-icon {
+    font-size: 3rem;
+    margin-bottom: 15px;
+}
+
+.action-card h3 {
+    font-size: 1.2rem;
+    margin-bottom: 10px;
+    color: var(--dark-color);
+}
+
+.action-card p {
+    color: var(--text-muted);
+    font-size: 0.9rem;
+}
+
+.status-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+    gap: 20px;
+}
+
+.status-item {
+    background: white;
+    padding: 20px;
+    border-radius: 10px;
+    box-shadow: var(--shadow-md);
+    display: flex;
+    align-items: center;
+    gap: 10px;
+}
+
+.status-indicator {
+    width: 12px;
+    height: 12px;
+    border-radius: 50%;
+}
+
+.status-online {
+    background-color: var(--success-color);
+}
+
+.status-warning {
+    background-color: var(--warning-color);
+}
+
+.status-offline {
+    background-color: var(--danger-color);
+}
+
+.status-text {
+    margin-left: auto;
+    font-weight: 500;
+}
+
+.year-range {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+}
+
+.year-range input {
+    flex: 1;
+}
+
+.modal-large {
+    max-width: 800px;
+}
+
+.paper-selector {
+    max-height: 200px;
+    overflow-y: auto;
+    border: 1px solid var(--border-color);
+    border-radius: 0.375rem;
+    padding: 10px;
+}
+
+.view-all {
+    text-align: center;
+    margin-top: 20px;
+}
+
+@media (max-width: 768px) {
+    .dashboard {
+        padding: 10px;
+    }
+    
+    .dashboard-header h1 {
+        font-size: 2rem;
+    }
+    
+    .stats-grid,
+    .action-grid,
+    .status-grid {
+        grid-template-columns: 1fr;
+    }
+    
+    .year-range {
+        flex-direction: column;
+    }
+    
+    .year-range span {
+        display: none;
+    }
+}
+</style>

Co-creation-projects/Apricity-InnocoreAI/frontend/templates/login.html

@@ -0,0 +1,456 @@
+<!-- 登录模板 -->
+<div class="login-container">
+    <div class="login-card">
+        <div class="login-header">
+            <div class="logo">
+                <h1>研创·智核</h1>
+                <p>InnoCore AI</p>
+            </div>
+            <h2>用户登录</h2>
+            <p>登录您的智能科研助手账户</p>
+        </div>
+
+        <form class="login-form ajax-form" data-endpoint="/auth/login" data-method="POST" data-redirect="/">
+            <div class="form-group">
+                <label class="form-label" for="email">邮箱地址</label>
+                <input type="email" id="email" name="email" class="form-control" placeholder="请输入邮箱地址" required>
+            </div>
+
+            <div class="form-group">
+                <label class="form-label" for="password">密码</label>
+                <input type="password" id="password" name="password" class="form-control" placeholder="请输入密码" required>
+                <div class="password-toggle" data-action="toggle-password">
+                    <span class="eye-icon">👁️</span>
+                </div>
+            </div>
+
+            <div class="form-options">
+                <label class="checkbox-label">
+                    <input type="checkbox" name="remember">
+                    <span class="checkmark"></span>
+                    记住我
+                </label>
+                <a href="#" class="forgot-password">忘记密码？</a>
+            </div>
+
+            <button type="submit" class="btn btn-primary btn-full">
+                <span class="btn-text">登录</span>
+                <span class="btn-loading" style="display: none;">
+                    <span class="spinner"></span>
+                    登录中...
+                </span>
+            </button>
+        </form>
+
+        <div class="login-footer">
+            <p>还没有账户？ <a href="#" data-action="show-register">立即注册</a></p>
+        </div>
+
+        <div class="demo-info">
+            <h3>演示账户</h3>
+            <div class="demo-accounts">
+                <div class="demo-account">
+                    <strong>普通用户：</strong>
+                    <span>demo@example.com / demo123</span>
+                </div>
+                <div class="demo-account">
+                    <strong>高级用户：</strong>
+                    <span>premium@example.com / premium123</span>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <div class="login-features">
+        <h3>平台特色</h3>
+        <div class="feature-list">
+            <div class="feature-item">
+                <div class="feature-icon">🤖</div>
+                <div class="feature-content">
+                    <h4>智能多智能体</h4>
+                    <p>猎手、矿工、教练、验证器四大智能体协同工作</p>
+                </div>
+            </div>
+            <div class="feature-item">
+                <div class="feature-icon">🔍</div>
+                <div class="feature-content">
+                    <h4>智能文献搜索</h4>
+                    <p>基于语义理解的精准文献检索</p>
+                </div>
+            </div>
+            <div class="feature-item">
+                <div class="feature-icon">📊</div>
+                <div class="feature-content">
+                    <h4>深度论文分析</h4>
+                    <p>多维度分析论文内容，提取关键信息</p>
+                </div>
+            </div>
+            <div class="feature-item">
+                <div class="feature-icon">✍️</div>
+                <div class="feature-content">
+                    <h4>学术写作助手</h4>
+                    <p>AI辅助生成高质量的学术文档</p>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- 注册模态框 -->
+<div id="register-modal" class="modal">
+    <div class="modal-content">
+        <div class="modal-header">
+            <h3 class="modal-title">用户注册</h3>
+            <button class="modal-close" data-action="close-modal">&times;</button>
+        </div>
+        <div class="modal-body">
+            <form class="register-form ajax-form" data-endpoint="/auth/register" data-method="POST" data-redirect="/">
+                <div class="form-group">
+                    <label class="form-label" for="reg-username">用户名</label>
+                    <input type="text" id="reg-username" name="username" class="form-control" placeholder="请输入用户名" required>
+                </div>
+
+                <div class="form-group">
+                    <label class="form-label" for="reg-email">邮箱地址</label>
+                    <input type="email" id="reg-email" name="email" class="form-control" placeholder="请输入邮箱地址" required>
+                </div>
+
+                <div class="form-group">
+                    <label class="form-label" for="reg-password">密码</label>
+                    <input type="password" id="reg-password" name="password" class="form-control" placeholder="请输入密码（至少6位）" required>
+                </div>
+
+                <div class="form-group">
+                    <label class="form-label" for="reg-confirm-password">确认密码</label>
+                    <input type="password" id="reg-confirm-password" name="confirm_password" class="form-control" placeholder="请再次输入密码" required>
+                </div>
+
+                <div class="form-group">
+                    <label class="form-label" for="reg-fullname">姓名（可选）</label>
+                    <input type="text" id="reg-fullname" name="full_name" class="form-control" placeholder="请输入您的姓名">
+                </div>
+
+                <div class="form-group">
+                    <label class="form-label" for="reg-institution">机构（可选）</label>
+                    <input type="text" id="reg-institution" name="institution" class="form-control" placeholder="请输入您的机构名称">
+                </div>
+
+                <div class="form-group">
+                    <label class="form-label" for="reg-field">研究领域（可选）</label>
+                    <select id="reg-field" name="research_field" class="form-select">
+                        <option value="">请选择研究领域</option>
+                        <option value="computer-science">计算机科学</option>
+                        <option value="artificial-intelligence">人工智能</option>
+                        <option value="machine-learning">机器学习</option>
+                        <option value="data-science">数据科学</option>
+                        <option value="biology">生物学</option>
+                        <option value="medicine">医学</option>
+                        <option value="physics">物理学</option>
+                        <option value="chemistry">化学</option>
+                        <option value="mathematics">数学</option>
+                        <option value="engineering">工程学</option>
+                        <option value="other">其他</option>
+                    </select>
+                </div>
+
+                <div class="form-group">
+                    <label class="checkbox-label">
+                        <input type="checkbox" name="agree_terms" required>
+                        <span class="checkmark"></span>
+                        我同意 <a href="#" class="terms-link">服务条款</a> 和 <a href="#" class="privacy-link">隐私政策</a>
+                    </label>
+                </div>
+            </form>
+        </div>
+        <div class="modal-footer">
+            <button type="button" class="btn btn-outline" data-action="close-modal">取消</button>
+            <button type="submit" form="register-form" class="btn btn-primary">注册</button>
+        </div>
+    </div>
+</div>
+
+<style>
+.login-container {
+    display: flex;
+    min-height: 100vh;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+}
+
+.login-card {
+    flex: 1;
+    max-width: 500px;
+    margin: auto;
+    background: white;
+    padding: 40px;
+    box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1);
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+}
+
+.login-header {
+    text-align: center;
+    margin-bottom: 30px;
+}
+
+.logo h1 {
+    font-size: 2rem;
+    color: var(--primary-color);
+    margin-bottom: 5px;
+}
+
+.logo p {
+    color: var(--text-muted);
+    font-size: 0.9rem;
+    margin-bottom: 20px;
+}
+
+.login-header h2 {
+    font-size: 1.8rem;
+    color: var(--dark-color);
+    margin-bottom: 10px;
+}
+
+.login-header p {
+    color: var(--text-muted);
+    font-size: 0.95rem;
+}
+
+.form-group {
+    position: relative;
+    margin-bottom: 20px;
+}
+
+.form-options {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: 25px;
+}
+
+.checkbox-label {
+    display: flex;
+    align-items: center;
+    cursor: pointer;
+    font-size: 0.9rem;
+}
+
+.checkbox-label input[type="checkbox"] {
+    display: none;
+}
+
+.checkmark {
+    width: 18px;
+    height: 18px;
+    border: 2px solid var(--border-color);
+    border-radius: 3px;
+    margin-right: 8px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    transition: all 0.2s;
+}
+
+.checkbox-label input[type="checkbox"]:checked + .checkmark {
+    background-color: var(--primary-color);
+    border-color: var(--primary-color);
+}
+
+.checkbox-label input[type="checkbox"]:checked + .checkmark::after {
+    content: '✓';
+    color: white;
+    font-size: 12px;
+}
+
+.forgot-password {
+    color: var(--primary-color);
+    text-decoration: none;
+    font-size: 0.9rem;
+}
+
+.forgot-password:hover {
+    text-decoration: underline;
+}
+
+.btn-full {
+    width: 100%;
+    padding: 12px;
+    font-size: 1rem;
+    margin-bottom: 20px;
+}
+
+.login-footer {
+    text-align: center;
+    margin-bottom: 30px;
+}
+
+.login-footer p {
+    color: var(--text-muted);
+    font-size: 0.9rem;
+}
+
+.login-footer a {
+    color: var(--primary-color);
+    text-decoration: none;
+}
+
+.login-footer a:hover {
+    text-decoration: underline;
+}
+
+.demo-info {
+    background: var(--light-color);
+    padding: 20px;
+    border-radius: 8px;
+    border-left: 4px solid var(--primary-color);
+}
+
+.demo-info h3 {
+    font-size: 1rem;
+    color: var(--dark-color);
+    margin-bottom: 15px;
+}
+
+.demo-accounts {
+    display: flex;
+    flex-direction: column;
+    gap: 10px;
+}
+
+.demo-account {
+    font-size: 0.85rem;
+    color: var(--text-muted);
+}
+
+.demo-account strong {
+    color: var(--dark-color);
+}
+
+.demo-account span {
+    font-family: monospace;
+    background: white;
+    padding: 2px 6px;
+    border-radius: 3px;
+    border: 1px solid var(--border-color);
+}
+
+.login-features {
+    flex: 1;
+    padding: 60px 40px;
+    color: white;
+    display: flex;
+    flex-direction: column;
+    justify-content: center;
+}
+
+.login-features h3 {
+    font-size: 1.8rem;
+    margin-bottom: 30px;
+    text-align: center;
+}
+
+.feature-list {
+    display: flex;
+    flex-direction: column;
+    gap: 30px;
+}
+
+.feature-item {
+    display: flex;
+    align-items: flex-start;
+    gap: 20px;
+}
+
+.feature-icon {
+    font-size: 2.5rem;
+    flex-shrink: 0;
+}
+
+.feature-content h4 {
+    font-size: 1.2rem;
+    margin-bottom: 8px;
+}
+
+.feature-content p {
+    font-size: 0.95rem;
+    opacity: 0.9;
+    line-height: 1.5;
+}
+
+.password-toggle {
+    position: absolute;
+    right: 12px;
+    top: 50%;
+    transform: translateY(-50%);
+    cursor: pointer;
+    color: var(--text-muted);
+    transition: color 0.2s;
+}
+
+.password-toggle:hover {
+    color: var(--primary-color);
+}
+
+.eye-icon {
+    font-size: 1.2rem;
+}
+
+.terms-link,
+.privacy-link {
+    color: var(--primary-color);
+    text-decoration: none;
+}
+
+.terms-link:hover,
+.privacy-link:hover {
+    text-decoration: underline;
+}
+
+@media (max-width: 768px) {
+    .login-container {
+        flex-direction: column;
+    }
+    
+    .login-card {
+        max-width: 100%;
+        padding: 30px 20px;
+    }
+    
+    .login-features {
+        padding: 40px 20px;
+    }
+    
+    .feature-icon {
+        font-size: 2rem;
+    }
+    
+    .feature-content h4 {
+        font-size: 1.1rem;
+    }
+    
+    .demo-accounts {
+        font-size: 0.8rem;
+    }
+}
+
+@media (max-width: 480px) {
+    .login-header h2 {
+        font-size: 1.5rem;
+    }
+    
+    .form-options {
+        flex-direction: column;
+        gap: 15px;
+        align-items: flex-start;
+    }
+    
+    .demo-info {
+        padding: 15px;
+    }
+    
+    .demo-account {
+        flex-direction: column;
+        gap: 5px;
+    }
+}
+</style>

Co-creation-projects/Apricity-InnocoreAI/install.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""
+InnoCore AI - Installation Script
+研创·智核 - 安装脚本
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+
+def install_core_deps():
+    """Install only core dependencies"""
+    print("Installing core dependencies...")
+    
+    core_deps = [
+        "fastapi==0.104.1",
+        "uvicorn[standard]==0.24.0",
+        "python-multipart==0.0.6",
+        "python-dotenv==1.0.0",
+        "pydantic==2.5.0",
+        "httpx==0.25.2",
+        "requests==2.31.0"
+    ]
+    
+    for dep in core_deps:
+        try:
+            print(f"  Installing {dep}...")
+            subprocess.check_call([sys.executable, "-m", "pip", "install", dep])
+            print(f"  [OK] {dep} installed")
+        except subprocess.CalledProcessError as e:
+            print(f"  [ERROR] Failed to install {dep}: {e}")
+            return False
+    
+    print("[OK] Core dependencies installed successfully")
+    return True
+
+def create_env_file():
+    """Create .env file"""
+    env_file = Path(".env")
+    if not env_file.exists():
+        print("Creating .env file...")
+        env_content = """# InnoCore AI Configuration
+OPENAI_API_KEY=your_openai_api_key_here
+DATABASE_URL=sqlite:///./innocore.db
+SECRET_KEY=your_secret_key_here_change_this_in_production
+DEBUG=True
+"""
+        env_file.write_text(env_content)
+        print("[OK] .env file created")
+        print("[WARNING] Please edit .env file and add your OpenAI API key")
+    else:
+        print("[OK] .env file already exists")
+
+def create_directories():
+    """Create necessary directories"""
+    dirs = ["data", "logs"]
+    for dir_path in dirs:
+        Path(dir_path).mkdir(exist_ok=True)
+    print("[OK] Directories created")
+
+def main():
+    print("InnoCore AI - Installation")
+    print("=" * 40)
+    
+    # Install core dependencies
+    if not install_core_deps():
+        print("[ERROR] Installation failed")
+        return
+    
+    # Create environment file
+    create_env_file()
+    
+    # Create directories
+    create_directories()
+    
+    print("\n[SUCCESS] Installation completed!")
+    print("Next steps:")
+    print("1. Edit .env file and add your OpenAI API key")
+    print("2. Run: python run.py")
+    print("3. Open: http://localhost:8000")
+
+if __name__ == "__main__":
+    main()

Co-creation-projects/Apricity-InnocoreAI/main.py

@@ -0,0 +1,218 @@
+"""
+研创·智核 - 主应用入口
+"""
+
+import asyncio
+import logging
+from contextlib import asynccontextmanager
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.middleware.trustedhost import TrustedHostMiddleware
+from fastapi.responses import JSONResponse
+from fastapi.staticfiles import StaticFiles
+import uvicorn
+
+from innocore_ai.core.config import settings
+from innocore_ai.core.database import engine, Base
+from innocore_ai.core.exceptions import InnoCoreException
+from innocore_ai.api.routes import papers, users, tasks, analysis, writing
+from innocore_ai.agents.controller import AgentController
+
+# 配置日志
+logging.basicConfig(
+    level=getattr(logging, settings.LOG_LEVEL),
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# 全局智能体控制器
+agent_controller = None
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """应用生命周期管理"""
+    # 启动时执行
+    logger.info("Starting InnoCore AI application...")
+    
+    # 创建数据库表
+    Base.metadata.create_all(bind=engine)
+    logger.info("Database tables created")
+    
+    # 初始化智能体控制器
+    global agent_controller
+    agent_controller = AgentController()
+    await agent_controller.initialize()
+    logger.info("Agent controller initialized")
+    
+    yield
+    
+    # 关闭时执行
+    logger.info("Shutting down InnoCore AI application...")
+    if agent_controller:
+        await agent_controller.shutdown()
+    logger.info("Application shutdown complete")
+
+# 创建FastAPI应用
+app = FastAPI(
+    title="研创·智核 API",
+    description="基于多智能体架构的智能科研助手平台",
+    version="1.0.0",
+    docs_url="/docs" if settings.DEBUG else None,
+    redoc_url="/redoc" if settings.DEBUG else None,
+    lifespan=lifespan
+)
+
+# 添加中间件
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=settings.ALLOWED_HOSTS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.add_middleware(
+    TrustedHostMiddleware,
+    allowed_hosts=settings.ALLOWED_HOSTS
+)
+
+# 挂载静态文件
+try:
+    app.mount("/static", StaticFiles(directory="innocore_ai/frontend/static"), name="static")
+except Exception:
+    # 如果路径不存在，尝试相对路径
+    app.mount("/static", StaticFiles(directory="frontend/static"), name="static")
+
+# 注册路由
+app.include_router(users.router, prefix="/api/v1/auth", tags=["认证"])
+app.include_router(papers.router, prefix="/api/v1/papers", tags=["论文管理"])
+app.include_router(tasks.router, prefix="/api/v1/tasks", tags=["任务管理"])
+app.include_router(analysis.router, prefix="/api/v1/analysis", tags=["分析报告"])
+app.include_router(writing.router, prefix="/api/v1/writing", tags=["学术写作"])
+
+# 前端路由
+@app.get("/")
+async def read_root():
+    """前端主页"""
+    try:
+        from fastapi.responses import FileResponse
+        return FileResponse("innocore_ai/frontend/index.html")
+    except Exception:
+        return FileResponse("frontend/index.html")
+
+@app.get("/dashboard")
+async def dashboard():
+    """仪表板页面"""
+    try:
+        from fastapi.responses import FileResponse
+        return FileResponse("innocore_ai/frontend/templates/dashboard.html")
+    except Exception:
+        return FileResponse("frontend/templates/dashboard.html")
+
+@app.get("/login")
+async def login():
+    """登录页面"""
+    try:
+        from fastapi.responses import FileResponse
+        return FileResponse("innocore_ai/frontend/templates/login.html")
+    except Exception:
+        return FileResponse("frontend/templates/login.html")
+
+# 处理前端路由的通配符（用于SPA）
+@app.get("/frontend/{path:path}")
+async def frontend_files(path: str):
+    """前端静态文件"""
+    try:
+        from fastapi.responses import FileResponse
+        file_path = f"innocore_ai/frontend/{path}"
+        return FileResponse(file_path)
+    except Exception:
+        file_path = f"frontend/{path}"
+        return FileResponse(file_path)
+
+@app.get("/health")
+async def health_check():
+    """健康检查"""
+    return {
+        "status": "healthy",
+        "version": "1.0.0",
+        "service": "InnoCore AI"
+    }
+
+@app.get("/api/v1/dashboard/stats")
+async def get_dashboard_stats(request: Request):
+    """获取仪表板统计数据"""
+    # 这里应该从数据库获取真实数据
+    return {
+        "total_papers": 156,
+        "total_tasks": 42,
+        "total_analyses": 28,
+        "total_writings": 15,
+        "recent_activities": [
+            {"type": "paper_added", "title": "深度学习在医学图像分析中的应用", "time": "2小时前"},
+            {"type": "task_completed", "title": "文献搜索：机器学习", "time": "4小时前"},
+            {"type": "analysis_generated", "title": "10篇论文综合分析", "time": "1天前"}
+        ]
+    }
+
+# 全局异常处理
+@app.exception_handler(InnoCoreException)
+async def innocore_exception_handler(request: Request, exc: InnoCoreException):
+    """处理自定义异常"""
+    return JSONResponse(
+        status_code=exc.status_code,
+        content={
+            "error": exc.error_code,
+            "message": exc.message,
+            "details": exc.details
+        }
+    )
+
+@app.exception_handler(Exception)
+async def general_exception_handler(request: Request, exc: Exception):
+    """处理通用异常"""
+    logger.error(f"Unhandled exception: {exc}", exc_info=True)
+    return JSONResponse(
+        status_code=500,
+        content={
+            "error": "INTERNAL_SERVER_ERROR",
+            "message": "服务器内部错误",
+            "details": str(exc) if settings.DEBUG else None
+        }
+    )
+
+# 请求日志中间件
+@app.middleware("http")
+async def log_requests(request: Request, call_next):
+    """记录请求日志"""
+    start_time = asyncio.get_event_loop().time()
+    
+    # 记录请求
+    logger.info(f"Request: {request.method} {request.url}")
+    
+    # 处理请求
+    response = await call_next(request)
+    
+    # 计算处理时间
+    process_time = asyncio.get_event_loop().time() - start_time
+    
+    # 记录响应
+    logger.info(f"Response: {response.status_code} - {process_time:.4f}s")
+    
+    # 添加处理时间到响应头
+    response.headers["X-Process-Time"] = str(process_time)
+    
+    return response
+
+def create_app():
+    """创建应用实例"""
+    return app
+
+if __name__ == "__main__":
+    uvicorn.run(
+        "innocore_ai.main:app",
+        host=settings.HOST,
+        port=settings.PORT,
+        reload=settings.DEBUG,
+        log_level=settings.LOG_LEVEL.lower()
+    )

Co-creation-projects/Apricity-InnocoreAI/models/__init__.py

@@ -0,0 +1,11 @@
+"""
+数据模型定义
+"""
+
+from .user import User
+from .paper import Paper
+from .task import Task
+from .analysis import Analysis
+from .writing import Writing
+
+__all__ = ['User', 'Paper', 'Task', 'Analysis', 'Writing']

Co-creation-projects/Apricity-InnocoreAI/models/analysis.py

@@ -0,0 +1,108 @@
+"""
+分析模型
+"""
+
+from datetime import datetime
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel, Field
+from sqlalchemy import Column, Integer, String, DateTime, Text, Boolean, Float, JSON
+from sqlalchemy.ext.declarative import declarative_base
+
+Base = declarative_base()
+
+class AnalysisDB(Base):
+    """分析数据库模型"""
+    __tablename__ = "analysis"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    title = Column(String(200), nullable=False)
+    analysis_type = Column(String(50), nullable=False)
+    paper_ids = Column(JSON)  # 分析的论文ID列表
+    methodology = Column(Text)
+    findings = Column(JSON)  # 分析发现
+    insights = Column(Text)
+    limitations = Column(Text)
+    recommendations = Column(Text)
+    confidence_score = Column(Float, default=0.0)
+    novelty_score = Column(Float, default=0.0)
+    impact_score = Column(Float, default=0.0)
+    metadata = Column(JSON)
+    user_id = Column(Integer, index=True)
+    task_id = Column(Integer, index=True)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+
+class Analysis(BaseModel):
+    """分析响应模型"""
+    id: int
+    title: str
+    analysis_type: str
+    methodology: Optional[str] = None
+    findings: Dict[str, Any] = {}
+    insights: Optional[str] = None
+    limitations: Optional[str] = None
+    recommendations: Optional[str] = None
+    confidence_score: float = 0.0
+    novelty_score: float = 0.0
+    impact_score: float = 0.0
+    created_at: datetime
+    
+    class Config:
+        from_attributes = True
+
+class AnalysisCreate(BaseModel):
+    """分析创建模型"""
+    title: str = Field(..., min_length=1, max_length=200)
+    analysis_type: str = Field(..., regex=r'^(comprehensive|methodology|findings|gap|trend)$')
+    paper_ids: List[int] = []
+    methodology: Optional[str] = None
+
+class AnalysisUpdate(BaseModel):
+    """分析更新模型"""
+    title: Optional[str] = None
+    methodology: Optional[str] = None
+    findings: Optional[Dict[str, Any]] = None
+    insights: Optional[str] = None
+    limitations: Optional[str] = None
+    recommendations: Optional[str] = None
+    confidence_score: Optional[float] = Field(None, ge=0.0, le=1.0)
+    novelty_score: Optional[float] = Field(None, ge=0.0, le=1.0)
+    impact_score: Optional[float] = Field(None, ge=0.0, le=1.0)
+
+class ComprehensiveAnalysis(BaseModel):
+    """综合分析结果"""
+    summary: str
+    key_findings: List[str]
+    methodological_trends: List[str]
+    research_gaps: List[str]
+    future_directions: List[str]
+    quality_assessment: Dict[str, float]
+    citation_network: Dict[str, Any]
+
+class MethodologyAnalysis(BaseModel):
+    """方法论分析结果"""
+    common_methods: List[str]
+    method_comparison: Dict[str, Any]
+    strengths_weaknesses: Dict[str, List[str]]
+    best_practices: List[str]
+
+class FindingsAnalysis(BaseModel):
+    """研究发现分析"""
+    consensus_points: List[str]
+    controversial_points: List[str]
+    emerging_patterns: List[str]
+    evidence_strength: Dict[str, float]
+
+class GapAnalysis(BaseModel):
+    """研究缺口分析"""
+    identified_gaps: List[str]
+    gap_categories: Dict[str, List[str]]
+    opportunity_areas: List[str]
+    research_questions: List[str]
+
+class TrendAnalysis(BaseModel):
+    """趋势分析结果"""
+    temporal_trends: Dict[str, Any]
+    topic_evolution: List[str]
+    emerging_topics: List[str]
+    citation_trends: Dict[str, Any]

Co-creation-projects/Apricity-InnocoreAI/models/paper.py

@@ -0,0 +1,99 @@
+"""
+论文模型
+"""
+
+from datetime import datetime
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel, Field
+from sqlalchemy import Column, Integer, String, DateTime, Text, Boolean, Float, JSON
+from sqlalchemy.ext.declarative import declarative_base
+
+Base = declarative_base()
+
+class PaperDB(Base):
+    """论文数据库模型"""
+    __tablename__ = "papers"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    title = Column(String(500), nullable=False, index=True)
+    authors = Column(Text)  # JSON格式存储作者列表
+    abstract = Column(Text)
+    keywords = Column(Text)  # JSON格式存储关键词
+    publication_year = Column(Integer)
+    journal = Column(String(200))
+    doi = Column(String(100), unique=True, index=True)
+    arxiv_id = Column(String(50), unique=True, index=True)
+    pdf_url = Column(String(500))
+    pdf_path = Column(String(500))
+    full_text = Column(Text)
+    embeddings = Column(JSON)  # 存储向量嵌入
+    metadata = Column(JSON)  # 存储额外的元数据
+    quality_score = Column(Float, default=0.0)
+    relevance_score = Column(Float, default=0.0)
+    is_processed = Column(Boolean, default=False)
+    user_id = Column(Integer, index=True)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+
+class Paper(BaseModel):
+    """论文响应模型"""
+    id: int
+    title: str
+    authors: List[str]
+    abstract: Optional[str] = None
+    keywords: List[str] = []
+    publication_year: Optional[int] = None
+    journal: Optional[str] = None
+    doi: Optional[str] = None
+    arxiv_id: Optional[str] = None
+    pdf_url: Optional[str] = None
+    quality_score: float = 0.0
+    relevance_score: float = 0.0
+    is_processed: bool = False
+    created_at: datetime
+    
+    class Config:
+        from_attributes = True
+
+class PaperCreate(BaseModel):
+    """论文创建模型"""
+    title: str = Field(..., min_length=1, max_length=500)
+    authors: List[str] = []
+    abstract: Optional[str] = None
+    keywords: List[str] = []
+    publication_year: Optional[int] = None
+    journal: Optional[str] = None
+    doi: Optional[str] = None
+    arxiv_id: Optional[str] = None
+    pdf_url: Optional[str] = None
+
+class PaperUpdate(BaseModel):
+    """论文更新模型"""
+    title: Optional[str] = None
+    authors: Optional[List[str]] = None
+    abstract: Optional[str] = None
+    keywords: Optional[List[str]] = None
+    publication_year: Optional[int] = None
+    journal: Optional[str] = None
+    quality_score: Optional[float] = None
+    relevance_score: Optional[float] = None
+
+class PaperSearch(BaseModel):
+    """论文搜索模型"""
+    query: str = Field(..., min_length=1)
+    filters: Dict[str, Any] = {}
+    sort_by: str = "relevance"
+    limit: int = Field(default=20, ge=1, le=100)
+    offset: int = Field(default=0, ge=0)
+
+class PaperAnalysis(BaseModel):
+    """论文分析结果"""
+    paper_id: int
+    summary: str
+    key_findings: List[str]
+    methodology: str
+    limitations: List[str]
+    future_work: List[str]
+    novelty_score: float
+    impact_score: float
+    confidence_score: float

Co-creation-projects/Apricity-InnocoreAI/models/task.py

@@ -0,0 +1,88 @@
+"""
+任务模型
+"""
+
+from datetime import datetime
+from typing import Optional, Dict, Any, List
+from pydantic import BaseModel, Field
+from sqlalchemy import Column, Integer, String, DateTime, Text, Boolean, JSON
+from sqlalchemy.ext.declarative import declarative_base
+
+Base = declarative_base()
+
+class TaskDB(Base):
+    """任务数据库模型"""
+    __tablename__ = "tasks"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    title = Column(String(200), nullable=False)
+    description = Column(Text)
+    task_type = Column(String(50), nullable=False)  # literature_search, analysis, writing
+    status = Column(String(20), default="pending")  # pending, running, completed, failed
+    priority = Column(String(10), default="medium")  # low, medium, high
+    parameters = Column(JSON)  # 任务参数
+    results = Column(JSON)  # 任务结果
+    error_message = Column(Text)
+    progress = Column(Integer, default=0)  # 0-100
+    user_id = Column(Integer, index=True)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+    completed_at = Column(DateTime)
+
+class Task(BaseModel):
+    """任务响应模型"""
+    id: int
+    title: str
+    description: Optional[str] = None
+    task_type: str
+    status: str
+    priority: str
+    progress: int = 0
+    results: Optional[Dict[str, Any]] = None
+    error_message: Optional[str] = None
+    created_at: datetime
+    updated_at: datetime
+    completed_at: Optional[datetime] = None
+    
+    class Config:
+        from_attributes = True
+
+class TaskCreate(BaseModel):
+    """任务创建模型"""
+    title: str = Field(..., min_length=1, max_length=200)
+    description: Optional[str] = None
+    task_type: str = Field(..., regex=r'^(literature_search|analysis|writing)$')
+    priority: str = Field(default="medium", regex=r'^(low|medium|high)$')
+    parameters: Dict[str, Any] = {}
+
+class TaskUpdate(BaseModel):
+    """任务更新模型"""
+    title: Optional[str] = None
+    description: Optional[str] = None
+    status: Optional[str] = None
+    priority: Optional[str] = None
+    progress: Optional[int] = Field(None, ge=0, le=100)
+    results: Optional[Dict[str, Any]] = None
+    error_message: Optional[str] = None
+
+class LiteratureSearchTask(BaseModel):
+    """文献搜索任务参数"""
+    query: str
+    max_papers: int = 20
+    year_range: Optional[tuple] = None
+    venues: List[str] = []
+    quality_threshold: float = 0.5
+
+class AnalysisTask(BaseModel):
+    """分析任务参数"""
+    paper_ids: List[int]
+    analysis_type: str = "comprehensive"  # comprehensive, methodology, findings
+    focus_areas: List[str] = []
+
+class WritingTask(BaseModel):
+    """写作任务参数"""
+    paper_ids: List[int]
+    writing_type: str = "review"  # review, summary, critique
+    outline: Optional[List[str]] = None
+    style: str = "academic"
+    length: int = 1000

Co-creation-projects/Apricity-InnocoreAI/models/user.py

@@ -0,0 +1,67 @@
+"""
+用户模型
+"""
+
+from datetime import datetime
+from typing import Optional, List
+from pydantic import BaseModel, Field
+from sqlalchemy import Column, Integer, String, DateTime, Text, Boolean
+from sqlalchemy.ext.declarative import declarative_base
+
+Base = declarative_base()
+
+class UserDB(Base):
+    """用户数据库模型"""
+    __tablename__ = "users"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    username = Column(String(50), unique=True, index=True, nullable=False)
+    email = Column(String(100), unique=True, index=True, nullable=False)
+    hashed_password = Column(String(255), nullable=False)
+    full_name = Column(String(100))
+    institution = Column(String(200))
+    research_field = Column(String(100))
+    preferences = Column(Text)  # JSON格式存储用户偏好
+    is_active = Column(Boolean, default=True)
+    is_premium = Column(Boolean, default=False)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+
+class User(BaseModel):
+    """用户响应模型"""
+    id: int
+    username: str
+    email: str
+    full_name: Optional[str] = None
+    institution: Optional[str] = None
+    research_field: Optional[str] = None
+    is_active: bool = True
+    is_premium: bool = False
+    created_at: datetime
+    
+    class Config:
+        from_attributes = True
+
+class UserCreate(BaseModel):
+    """用户创建模型"""
+    username: str = Field(..., min_length=3, max_length=50)
+    email: str = Field(..., regex=r'^[^@]+@[^@]+\.[^@]+$')
+    password: str = Field(..., min_length=6)
+    full_name: Optional[str] = None
+    institution: Optional[str] = None
+    research_field: Optional[str] = None
+
+class UserUpdate(BaseModel):
+    """用户更新模型"""
+    full_name: Optional[str] = None
+    institution: Optional[str] = None
+    research_field: Optional[str] = None
+    preferences: Optional[str] = None
+
+class UserPreferences(BaseModel):
+    """用户偏好设置"""
+    research_interests: List[str] = []
+    citation_style: str = "APA"
+    language: str = "zh"
+    notification_enabled: bool = True
+    auto_save: bool = True

Co-creation-projects/Apricity-InnocoreAI/models/writing.py

@@ -0,0 +1,111 @@
+"""
+写作模型
+"""
+
+from datetime import datetime
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel, Field
+from sqlalchemy import Column, Integer, String, DateTime, Text, Boolean, Float, JSON
+from sqlalchemy.ext.declarative import declarative_base
+
+Base = declarative_base()
+
+class WritingDB(Base):
+    """写作数据库模型"""
+    __tablename__ = "writing"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    title = Column(String(200), nullable=False)
+    writing_type = Column(String(50), nullable=False)  # review, summary, critique, proposal
+    content = Column(Text)
+    outline = Column(JSON)  # 大纲结构
+    sections = Column(JSON)  # 章节内容
+    citations = Column(JSON)  # 引用信息
+    metadata = Column(JSON)  # 额外元数据
+    quality_score = Column(Float, default=0.0)
+    word_count = Column(Integer, default=0)
+    status = Column(String(20), default="draft")  # draft, reviewing, completed
+    paper_ids = Column(JSON)  # 参考论文ID列表
+    user_id = Column(Integer, index=True)
+    task_id = Column(Integer, index=True)
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+
+class Writing(BaseModel):
+    """写作响应模型"""
+    id: int
+    title: str
+    writing_type: str
+    content: Optional[str] = None
+    outline: List[Dict[str, Any]] = []
+    sections: Dict[str, str] = {}
+    citations: List[Dict[str, Any]] = []
+    quality_score: float = 0.0
+    word_count: int = 0
+    status: str = "draft"
+    created_at: datetime
+    
+    class Config:
+        from_attributes = True
+
+class WritingCreate(BaseModel):
+    """写作创建模型"""
+    title: str = Field(..., min_length=1, max_length=200)
+    writing_type: str = Field(..., regex=r'^(review|summary|critique|proposal)$')
+    paper_ids: List[int] = []
+    outline: Optional[List[Dict[str, Any]]] = None
+
+class WritingUpdate(BaseModel):
+    """写作更新模型"""
+    title: Optional[str] = None
+    content: Optional[str] = None
+    outline: Optional[List[Dict[str, Any]]] = None
+    sections: Optional[Dict[str, str]] = None
+    citations: Optional[List[Dict[str, Any]]] = None
+    status: Optional[str] = None
+    quality_score: Optional[float] = Field(None, ge=0.0, le=1.0)
+
+class LiteratureReview(BaseModel):
+    """文献综述"""
+    introduction: str
+    methodology_review: str
+    findings_synthesis: str
+    discussion: str
+    conclusion: str
+    references: List[Dict[str, Any]]
+
+class PaperSummary(BaseModel):
+    """论文总结"""
+    background: str
+    methods: str
+    results: str
+    conclusions: str
+    significance: str
+
+class PaperCritique(BaseModel):
+    """论文评述"""
+    strengths: List[str]
+    weaknesses: List[str]
+    methodological_issues: List[str]
+    interpretation_concerns: List[str]
+    suggestions: List[str]
+
+class ResearchProposal(BaseModel):
+    """研究提案"""
+    background: str
+    problem_statement: str
+    research_questions: List[str]
+    methodology: str
+    expected_outcomes: str
+    significance: str
+    timeline: str
+
+class WritingSection(BaseModel):
+    """写作章节"""
+    title: str
+    content: str
+    subsections: List['WritingSection'] = []
+    citations: List[str] = []
+
+# 解决前向引用
+WritingSection.model_rebuild()

Co-creation-projects/Apricity-InnocoreAI/requirements.txt

@@ -0,0 +1,82 @@
+# InnoCore AI - Core Dependencies
+# 核心依赖列表 - 已验证可用
+
+# Web Framework
+fastapi==0.121.3
+uvicorn[standard]==0.38.0
+python-multipart==0.0.20
+starlette==0.50.0
+
+# Database
+sqlalchemy==2.0.44
+asyncpg==0.30.0
+redis==7.1.0
+
+# AI & ML Framework
+hello-agents[all]>=0.2.7  # HelloAgent 框架（包含所有功能）
+openai>=1.0.0  # OpenAI API 客户端
+tiktoken>=0.5.0  # Token 计数工具
+
+# Vector Database
+chromadb==1.3.5
+qdrant-client==1.16.0
+
+# Deep Learning
+torch==2.9.1
+transformers==4.57.1
+sentence-transformers==5.1.2
+safetensors==0.7.0
+
+# Data Processing
+numpy==2.2.6
+scipy==1.15.3
+scikit-learn==1.7.2
+pandas==2.1.4
+
+# HTTP Client
+httpx==0.28.1
+aiohttp==3.13.2
+requests==2.32.5
+
+# Data Validation
+pydantic==2.12.4
+pydantic-core==2.41.5
+
+# Utilities
+python-dotenv==1.2.1
+pyyaml==6.0.3
+tenacity==9.1.2
+tqdm==4.67.1
+click==8.3.1
+
+# Monitoring & Telemetry
+opentelemetry-api==1.38.0
+opentelemetry-sdk==1.38.0
+opentelemetry-exporter-otlp-proto-grpc==1.38.0
+
+# Additional Dependencies
+huggingface-hub==0.36.0
+tokenizers==0.22.1
+jinja2==3.1.6
+rich==14.2.0
+typer==0.20.0
+jsonpatch==1.33
+orjson==3.11.4
+protobuf==6.33.1
+grpcio==1.76.0
+kubernetes==34.1.0
+onnxruntime==1.23.2
+bcrypt==5.0.0
+
+# Literature Search & Processing
+feedparser==6.0.12
+beautifulsoup4==4.14.2
+lxml==6.0.2
+arxiv==2.3.1
+scholarly==1.7.11
+selenium==4.38.0
+
+# PDF Processing
+PyPDF2==3.0.1
+pdfplumber==0.11.0
+pypdf==3.17.4

Co-creation-projects/Apricity-InnocoreAI/run.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+"""
+InnoCore AI - Simple Run Script
+研创·智核 - 简单运行脚本
+"""
+
+import os
+import sys
+import uvicorn
+from pathlib import Path
+
+def main():
+    """Run the full InnoCore AI application"""
+    print("Starting InnoCore AI...")
+    
+    # Add current directory to Python path
+    current_dir = Path(__file__).parent
+    sys.path.insert(0, str(current_dir))
+    
+    # Start server with the full API
+    print("Server will be available at: http://localhost:8000")
+    print("API docs at: http://localhost:8000/docs")
+    print("Health check at: http://localhost:8000/health")
+    
+    uvicorn.run(
+        "api.main:app",
+        host="0.0.0.0",
+        port=8000,
+        reload=True,
+        log_level="info"
+    )
+
+if __name__ == "__main__":
+    main()

Co-creation-projects/Apricity-InnocoreAI/services/__init__.py

@@ -0,0 +1,11 @@
+"""
+服务层
+"""
+
+from .paper_service import PaperService
+from .task_service import TaskService
+from .analysis_service import AnalysisService
+from .writing_service import WritingService
+from .user_service import UserService
+
+__all__ = ['PaperService', 'TaskService', 'AnalysisService', 'WritingService', 'UserService']

Co-creation-projects/Apricity-InnocoreAI/services/analysis_service.py

@@ -0,0 +1,172 @@
+"""
+分析服务
+"""
+
+from typing import Optional, List, Dict, Any
+from sqlalchemy.orm import Session
+from ..core.database import get_db
+from ..models.analysis import AnalysisDB, Analysis, AnalysisCreate, AnalysisUpdate
+from ..core.exceptions import AnalysisNotFoundError
+from ..services.paper_service import PaperService
+import json
+
+class AnalysisService:
+    """分析服务类"""
+    
+    def __init__(self, db: Session):
+        self.db = db
+        self.paper_service = PaperService(db)
+    
+    def get_analysis_by_id(self, analysis_id: int) -> Optional[Analysis]:
+        """根据ID获取分析"""
+        analysis_db = self.db.query(AnalysisDB).filter(AnalysisDB.id == analysis_id).first()
+        if not analysis_db:
+            raise AnalysisNotFoundError(f"Analysis with id {analysis_id} not found")
+        return Analysis.from_orm(analysis_db)
+    
+    def get_analyses_by_user(self, user_id: int, skip: int = 0, limit: int = 20) -> List[Analysis]:
+        """获取用户的分析列表"""
+        analyses_db = self.db.query(AnalysisDB).filter(
+            AnalysisDB.user_id == user_id
+        ).order_by(AnalysisDB.created_at.desc()).offset(skip).limit(limit).all()
+        return [Analysis.from_orm(analysis) for analysis in analyses_db]
+    
+    def create_analysis(self, analysis_create: AnalysisCreate, user_id: int, task_id: Optional[int] = None) -> Analysis:
+        """创建分析"""
+        analysis_db = AnalysisDB(
+            title=analysis_create.title,
+            analysis_type=analysis_create.analysis_type,
+            paper_ids=json.dumps(analysis_create.paper_ids),
+            methodology=analysis_create.methodology,
+            user_id=user_id,
+            task_id=task_id
+        )
+        
+        self.db.add(analysis_db)
+        self.db.commit()
+        self.db.refresh(analysis_db)
+        
+        return Analysis.from_orm(analysis_db)
+    
+    def update_analysis(self, analysis_id: int, analysis_update: AnalysisUpdate) -> Analysis:
+        """更新分析"""
+        analysis_db = self.db.query(AnalysisDB).filter(AnalysisDB.id == analysis_id).first()
+        if not analysis_db:
+            raise AnalysisNotFoundError(f"Analysis with id {analysis_id} not found")
+        
+        # 更新字段
+        update_data = analysis_update.dict(exclude_unset=True)
+        for field, value in update_data.items():
+            if field == 'findings':
+                setattr(analysis_db, field, json.dumps(value))
+            else:
+                setattr(analysis_db, field, value)
+        
+        self.db.commit()
+        self.db.refresh(analysis_db)
+        
+        return Analysis.from_orm(analysis_db)
+    
+    def delete_analysis(self, analysis_id: int) -> bool:
+        """删除分析"""
+        analysis_db = self.db.query(AnalysisDB).filter(AnalysisDB.id == analysis_id).first()
+        if not analysis_db:
+            raise AnalysisNotFoundError(f"Analysis with id {analysis_id} not found")
+        
+        self.db.delete(analysis_db)
+        self.db.commit()
+        
+        return True
+    
+    def get_analysis_statistics(self, user_id: int) -> Dict[str, Any]:
+        """获取分析统计信息"""
+        total_analyses = self.db.query(AnalysisDB).filter(AnalysisDB.user_id == user_id).count()
+        
+        # 按类型统计
+        type_stats = self.db.query(
+            AnalysisDB.analysis_type,
+            self.db.func.count(AnalysisDB.id)
+        ).filter(AnalysisDB.user_id == user_id).group_by(AnalysisDB.analysis_type).all()
+        
+        # 平均分数
+        avg_scores = self.db.query(
+            self.db.func.avg(AnalysisDB.confidence_score),
+            self.db.func.avg(AnalysisDB.novelty_score),
+            self.db.func.avg(AnalysisDB.impact_score)
+        ).filter(AnalysisDB.user_id == user_id).first()
+        
+        return {
+            'total_analyses': total_analyses,
+            'type_distribution': dict(type_stats),
+            'average_confidence': float(avg_scores[0] or 0),
+            'average_novelty': float(avg_scores[1] or 0),
+            'average_impact': float(avg_scores[2] or 0)
+        }
+    
+    def get_related_analyses(self, analysis_id: int, limit: int = 5) -> List[Analysis]:
+        """获取相关分析"""
+        analysis_db = self.db.query(AnalysisDB).filter(AnalysisDB.id == analysis_id).first()
+        if not analysis_db:
+            raise AnalysisNotFoundError(f"Analysis with id {analysis_id} not found")
+        
+        # 获取相同类型的分析
+        related_analyses = self.db.query(AnalysisDB).filter(
+            and_(
+                AnalysisDB.user_id == analysis_db.user_id,
+                AnalysisDB.analysis_type == analysis_db.analysis_type,
+                AnalysisDB.id != analysis_id
+            )
+        ).order_by(AnalysisDB.created_at.desc()).limit(limit).all()
+        
+        return [Analysis.from_orm(analysis) for analysis in related_analyses]
+    
+    def export_analysis(self, analysis_id: int, format: str = "json") -> Dict[str, Any]:
+        """导出分析结果"""
+        analysis = self.get_analysis_by_id(analysis_id)
+        
+        if format == "json":
+            return analysis.dict()
+        elif format == "markdown":
+            return self._export_to_markdown(analysis)
+        elif format == "pdf":
+            return self._export_to_pdf(analysis)
+        else:
+            raise ValueError(f"Unsupported export format: {format}")
+    
+    def _export_to_markdown(self, analysis: Analysis) -> str:
+        """导出为Markdown格式"""
+        markdown = f"# {analysis.title}\n\n"
+        markdown += f"**分析类型**: {analysis.analysis_type}\n\n"
+        markdown += f"**创建时间**: {analysis.created_at.strftime('%Y-%m-%d %H:%M:%S')}\n\n"
+        
+        if analysis.methodology:
+            markdown += f"## 方法论\n\n{analysis.methodology}\n\n"
+        
+        if analysis.findings:
+            markdown += "## 主要发现\n\n"
+            for key, value in analysis.findings.items():
+                markdown += f"### {key}\n\n{value}\n\n"
+        
+        if analysis.insights:
+            markdown += f"## 洞察\n\n{analysis.insights}\n\n"
+        
+        if analysis.limitations:
+            markdown += f"## 局限性\n\n{analysis.limitations}\n\n"
+        
+        if analysis.recommendations:
+            markdown += f"## 建议\n\n{analysis.recommendations}\n\n"
+        
+        # 添加评分
+        markdown += "## 评分\n\n"
+        markdown += f"- **置信度**: {analysis.confidence_score:.2f}\n"
+        markdown += f"- **新颖性**: {analysis.novelty_score:.2f}\n"
+        markdown += f"- **影响力**: {analysis.impact_score:.2f}\n"
+        
+        return markdown
+    
+    def _export_to_pdf(self, analysis: Analysis) -> bytes:
+        """导出为PDF格式"""
+        # 这里可以使用reportlab或其他PDF生成库
+        # 暂时返回Markdown内容的字节
+        markdown_content = self._export_to_markdown(analysis)
+        return markdown_content.encode('utf-8')

Co-creation-projects/Apricity-InnocoreAI/services/paper_service.py

@@ -0,0 +1,248 @@
+"""
+论文服务
+"""
+
+from typing import Optional, List, Dict, Any
+from sqlalchemy.orm import Session
+from sqlalchemy import and_, or_, desc
+from ..core.database import get_db
+from ..core.vector_store import VectorStore
+from ..models.paper import PaperDB, Paper, PaperCreate, PaperUpdate, PaperSearch
+from ..core.exceptions import PaperNotFoundError, PaperAlreadyExistsError
+from ..utils.pdf_parser import PDFParser
+from ..utils.embedding import EmbeddingService
+import json
+
+class PaperService:
+    """论文服务类"""
+    
+    def __init__(self, db: Session):
+        self.db = db
+        self.vector_store = VectorStore()
+        self.pdf_parser = PDFParser()
+        self.embedding_service = EmbeddingService()
+    
+    def get_paper_by_id(self, paper_id: int) -> Optional[Paper]:
+        """根据ID获取论文"""
+        paper_db = self.db.query(PaperDB).filter(PaperDB.id == paper_id).first()
+        if not paper_db:
+            raise PaperNotFoundError(f"Paper with id {paper_id} not found")
+        return Paper.from_orm(paper_db)
+    
+    def get_papers_by_user(self, user_id: int, skip: int = 0, limit: int = 20) -> List[Paper]:
+        """获取用户的论文列表"""
+        papers_db = self.db.query(PaperDB).filter(
+            PaperDB.user_id == user_id
+        ).offset(skip).limit(limit).all()
+        return [Paper.from_orm(paper) for paper in papers_db]
+    
+    def create_paper(self, paper_create: PaperCreate, user_id: int) -> Paper:
+        """创建论文记录"""
+        # 检查DOI是否已存在
+        if paper_create.doi:
+            existing = self.db.query(PaperDB).filter(PaperDB.doi == paper_create.doi).first()
+            if existing:
+                raise PaperAlreadyExistsError(f"Paper with DOI {paper_create.doi} already exists")
+        
+        # 检查arXiv ID是否已存在
+        if paper_create.arxiv_id:
+            existing = self.db.query(PaperDB).filter(PaperDB.arxiv_id == paper_create.arxiv_id).first()
+            if existing:
+                raise PaperAlreadyExistsError(f"Paper with arXiv ID {paper_create.arxiv_id} already exists")
+        
+        # 创建论文记录
+        paper_db = PaperDB(
+            title=paper_create.title,
+            authors=json.dumps(paper_create.authors),
+            abstract=paper_create.abstract,
+            keywords=json.dumps(paper_create.keywords),
+            publication_year=paper_create.publication_year,
+            journal=paper_create.journal,
+            doi=paper_create.doi,
+            arxiv_id=paper_create.arxiv_id,
+            pdf_url=paper_create.pdf_url,
+            user_id=user_id
+        )
+        
+        self.db.add(paper_db)
+        self.db.commit()
+        self.db.refresh(paper_db)
+        
+        # 异步处理PDF和嵌入
+        self._process_paper_async(paper_db.id)
+        
+        return Paper.from_orm(paper_db)
+    
+    def update_paper(self, paper_id: int, paper_update: PaperUpdate) -> Paper:
+        """更新论文信息"""
+        paper_db = self.db.query(PaperDB).filter(PaperDB.id == paper_id).first()
+        if not paper_db:
+            raise PaperNotFoundError(f"Paper with id {paper_id} not found")
+        
+        # 更新字段
+        update_data = paper_update.dict(exclude_unset=True)
+        for field, value in update_data.items():
+            if field in ['authors', 'keywords']:
+                setattr(paper_db, field, json.dumps(value))
+            else:
+                setattr(paper_db, field, value)
+        
+        self.db.commit()
+        self.db.refresh(paper_db)
+        
+        return Paper.from_orm(paper_db)
+    
+    def delete_paper(self, paper_id: int) -> bool:
+        """删除论文"""
+        paper_db = self.db.query(PaperDB).filter(PaperDB.id == paper_id).first()
+        if not paper_db:
+            raise PaperNotFoundError(f"Paper with id {paper_id} not found")
+        
+        # 从向量存储中删除
+        if paper_db.embeddings:
+            self.vector_store.delete_document(paper_id)
+        
+        self.db.delete(paper_db)
+        self.db.commit()
+        
+        return True
+    
+    def search_papers(self, search: PaperSearch, user_id: int) -> List[Paper]:
+        """搜索论文"""
+        query = self.db.query(PaperDB).filter(PaperDB.user_id == user_id)
+        
+        # 文本搜索
+        if search.query:
+            search_filter = or_(
+                PaperDB.title.contains(search.query),
+                PaperDB.abstract.contains(search.query),
+                PaperDB.keywords.contains(search.query)
+            )
+            query = query.filter(search_filter)
+        
+        # 应用过滤器
+        filters = search.filters
+        if 'year_range' in filters:
+            start_year, end_year = filters['year_range']
+            query = query.filter(
+                and_(
+                    PaperDB.publication_year >= start_year,
+                    PaperDB.publication_year <= end_year
+                )
+            )
+        
+        if 'venues' in filters:
+            query = query.filter(PaperDB.journal.in_(filters['venues']))
+        
+        if 'authors' in filters:
+            author_filter = or_(*[
+                PaperDB.authors.contains(author) for author in filters['authors']
+            ])
+            query = query.filter(author_filter)
+        
+        # 排序
+        if search.sort_by == "relevance":
+            query = query.order_by(desc(PaperDB.relevance_score))
+        elif search.sort_by == "quality":
+            query = query.order_by(desc(PaperDB.quality_score))
+        elif search.sort_by == "year":
+            query = query.order_by(desc(PaperDB.publication_year))
+        else:
+            query = query.order_by(desc(PaperDB.created_at))
+        
+        # 分页
+        papers_db = query.offset(search.offset).limit(search.limit).all()
+        return [Paper.from_orm(paper) for paper in papers_db]
+    
+    def semantic_search(self, query: str, user_id: int, limit: int = 10) -> List[Paper]:
+        """语义搜索论文"""
+        # 生成查询向量
+        query_embedding = self.embedding_service.get_embedding(query)
+        
+        # 在向量存储中搜索
+        results = self.vector_store.search(query_embedding, user_id, limit)
+        
+        # 获取对应的论文
+        paper_ids = [result['id'] for result in results]
+        papers_db = self.db.query(PaperDB).filter(
+            and_(
+                PaperDB.id.in_(paper_ids),
+                PaperDB.user_id == user_id
+            )
+        ).all()
+        
+        # 按相似度排序
+        paper_dict = {paper.id: paper for paper in papers_db}
+        sorted_papers = []
+        for result in results:
+            if result['id'] in paper_dict:
+                paper = Paper.from_orm(paper_dict[result['id']])
+                paper.relevance_score = result['score']
+                sorted_papers.append(paper)
+        
+        return sorted_papers
+    
+    def _process_paper_async(self, paper_id: int):
+        """异步处理论文（PDF解析和嵌入生成）"""
+        try:
+            paper_db = self.db.query(PaperDB).filter(PaperDB.id == paper_id).first()
+            if not paper_db:
+                return
+            
+            # 如果有PDF URL，下载并解析
+            if paper_db.pdf_url and not paper_db.full_text:
+                full_text = self.pdf_parser.parse_pdf_from_url(paper_db.pdf_url)
+                if full_text:
+                    paper_db.full_text = full_text
+            
+            # 生成嵌入
+            text_to_embed = paper_db.title + " " + (paper_db.abstract or "")
+            if paper_db.full_text:
+                text_to_embed += " " + paper_db.full_text
+            
+            embedding = self.embedding_service.get_embedding(text_to_embed)
+            paper_db.embeddings = embedding.tolist()
+            
+            # 添加到向量存储
+            self.vector_store.add_document(
+                doc_id=paper_id,
+                embedding=embedding,
+                metadata={
+                    'title': paper_db.title,
+                    'user_id': paper_db.user_id
+                }
+            )
+            
+            paper_db.is_processed = True
+            self.db.commit()
+            
+        except Exception as e:
+            print(f"Error processing paper {paper_id}: {e}")
+            # 可以在这里添加错误日志记录
+    
+    def get_paper_statistics(self, user_id: int) -> Dict[str, Any]:
+        """获取论文统计信息"""
+        total_papers = self.db.query(PaperDB).filter(PaperDB.user_id == user_id).count()
+        processed_papers = self.db.query(PaperDB).filter(
+            and_(PaperDB.user_id == user_id, PaperDB.is_processed == True)
+        ).count()
+        
+        # 按年份统计
+        year_stats = self.db.query(
+            PaperDB.publication_year,
+            self.db.func.count(PaperDB.id)
+        ).filter(PaperDB.user_id == user_id).group_by(PaperDB.publication_year).all()
+        
+        # 按期刊统计
+        journal_stats = self.db.query(
+            PaperDB.journal,
+            self.db.func.count(PaperDB.id)
+        ).filter(PaperDB.user_id == user_id).group_by(PaperDB.journal).all()
+        
+        return {
+            'total_papers': total_papers,
+            'processed_papers': processed_papers,
+            'processing_rate': processed_papers / total_papers if total_papers > 0 else 0,
+            'year_distribution': dict(year_stats),
+            'journal_distribution': dict(journal_stats)
+        }

Co-creation-projects/Apricity-InnocoreAI/services/task_service.py

@@ -0,0 +1,309 @@
+"""
+任务服务
+"""
+
+from typing import Optional, List, Dict, Any
+from sqlalchemy.orm import Session
+from datetime import datetime
+from ..core.database import get_db
+from ..models.task import TaskDB, Task, TaskCreate, TaskUpdate
+from ..core.exceptions import TaskNotFoundError
+from ..agents.controller import AgentController
+import json
+import asyncio
+
+class TaskService:
+    """任务服务类"""
+    
+    def __init__(self, db: Session):
+        self.db = db
+        self.agent_controller = AgentController()
+    
+    def get_task_by_id(self, task_id: int) -> Optional[Task]:
+        """根据ID获取任务"""
+        task_db = self.db.query(TaskDB).filter(TaskDB.id == task_id).first()
+        if not task_db:
+            raise TaskNotFoundError(f"Task with id {task_id} not found")
+        return Task.from_orm(task_db)
+    
+    def get_tasks_by_user(self, user_id: int, skip: int = 0, limit: int = 20) -> List[Task]:
+        """获取用户的任务列表"""
+        tasks_db = self.db.query(TaskDB).filter(
+            TaskDB.user_id == user_id
+        ).order_by(TaskDB.created_at.desc()).offset(skip).limit(limit).all()
+        return [Task.from_orm(task) for task in tasks_db]
+    
+    def create_task(self, task_create: TaskCreate, user_id: int) -> Task:
+        """创建任务"""
+        task_db = TaskDB(
+            title=task_create.title,
+            description=task_create.description,
+            task_type=task_create.task_type,
+            priority=task_create.priority,
+            parameters=task_create.parameters,
+            user_id=user_id
+        )
+        
+        self.db.add(task_db)
+        self.db.commit()
+        self.db.refresh(task_db)
+        
+        # 异步执行任务
+        self._execute_task_async(task_db.id)
+        
+        return Task.from_orm(task_db)
+    
+    def update_task(self, task_id: int, task_update: TaskUpdate) -> Task:
+        """更新任务"""
+        task_db = self.db.query(TaskDB).filter(TaskDB.id == task_id).first()
+        if not task_db:
+            raise TaskNotFoundError(f"Task with id {task_id} not found")
+        
+        # 更新字段
+        update_data = task_update.dict(exclude_unset=True)
+        for field, value in update_data.items():
+            setattr(task_db, field, value)
+        
+        # 如果任务完成，设置完成时间
+        if task_update.status == "completed":
+            task_db.completed_at = datetime.utcnow()
+        
+        self.db.commit()
+        self.db.refresh(task_db)
+        
+        return Task.from_orm(task_db)
+    
+    def delete_task(self, task_id: int) -> bool:
+        """删除任务"""
+        task_db = self.db.query(TaskDB).filter(TaskDB.id == task_id).first()
+        if not task_db:
+            raise TaskNotFoundError(f"Task with id {task_id} not found")
+        
+        self.db.delete(task_db)
+        self.db.commit()
+        
+        return True
+    
+    def cancel_task(self, task_id: int) -> Task:
+        """取消任务"""
+        return self.update_task(task_id, TaskUpdate(status="failed", error_message="Task cancelled by user"))
+    
+    def retry_task(self, task_id: int) -> Task:
+        """重试任务"""
+        # 重置任务状态
+        task = self.update_task(task_id, TaskUpdate(
+            status="pending",
+            progress=0,
+            error_message=None
+        ))
+        
+        # 重新执行任务
+        self._execute_task_async(task_id)
+        
+        return task
+    
+    def get_task_statistics(self, user_id: int) -> Dict[str, Any]:
+        """获取任务统计信息"""
+        total_tasks = self.db.query(TaskDB).filter(TaskDB.user_id == user_id).count()
+        
+        # 按状态统计
+        status_stats = self.db.query(
+            TaskDB.status,
+            self.db.func.count(TaskDB.id)
+        ).filter(TaskDB.user_id == user_id).group_by(TaskDB.status).all()
+        
+        # 按类型统计
+        type_stats = self.db.query(
+            TaskDB.task_type,
+            self.db.func.count(TaskDB.id)
+        ).filter(TaskDB.user_id == user_id).group_by(TaskDB.task_type).all()
+        
+        # 成功率
+        completed_tasks = self.db.query(TaskDB).filter(
+            and_(TaskDB.user_id == user_id, TaskDB.status == "completed")
+        ).count()
+        
+        success_rate = completed_tasks / total_tasks if total_tasks > 0 else 0
+        
+        return {
+            'total_tasks': total_tasks,
+            'success_rate': success_rate,
+            'status_distribution': dict(status_stats),
+            'type_distribution': dict(type_stats)
+        }
+    
+    def _execute_task_async(self, task_id: int):
+        """异步执行任务"""
+        try:
+            # 获取任务信息
+            task_db = self.db.query(TaskDB).filter(TaskDB.id == task_id).first()
+            if not task_db:
+                return
+            
+            # 更新任务状态为运行中
+            task_db.status = "running"
+            task_db.progress = 0
+            self.db.commit()
+            
+            # 根据任务类型执行相应的智能体
+            if task_db.task_type == "literature_search":
+                result = asyncio.run(self._execute_literature_search(task_db))
+            elif task_db.task_type == "analysis":
+                result = asyncio.run(self._execute_analysis(task_db))
+            elif task_db.task_type == "writing":
+                result = asyncio.run(self._execute_writing(task_db))
+            else:
+                raise ValueError(f"Unknown task type: {task_db.task_type}")
+            
+            # 更新任务结果
+            task_db.status = "completed"
+            task_db.progress = 100
+            task_db.results = result
+            task_db.completed_at = datetime.utcnow()
+            self.db.commit()
+            
+        except Exception as e:
+            # 更新任务状态为失败
+            task_db.status = "failed"
+            task_db.error_message = str(e)
+            self.db.commit()
+    
+    async def _execute_literature_search(self, task_db: TaskDB) -> Dict[str, Any]:
+        """执行文献搜索任务"""
+        parameters = task_db.parameters or {}
+        query = parameters.get('query', '')
+        max_papers = parameters.get('max_papers', 20)
+        
+        # 使用猎手智能体进行文献搜索
+        hunter_agent = self.agent_controller.get_agent('hunter')
+        
+        # 更新进度
+        await self._update_task_progress(task_db.id, 20)
+        
+        # 执行搜索
+        search_results = await hunter_agent.search_papers(query, max_papers)
+        
+        # 更新进度
+        await self._update_task_progress(task_db.id, 60)
+        
+        # 使用矿工智能体进行深度挖掘
+        miner_agent = self.agent_controller.get_agent('miner')
+        enriched_results = await miner_agent.enrich_papers(search_results)
+        
+        # 更新进度
+        await self._update_task_progress(task_db.id, 90)
+        
+        # 保存论文到数据库
+        paper_service = PaperService(self.db)
+        saved_papers = []
+        for paper_data in enriched_results:
+            try:
+                paper = paper_service.create_paper(
+                    PaperCreate(**paper_data),
+                    task_db.user_id
+                )
+                saved_papers.append(paper.dict())
+            except Exception as e:
+                print(f"Error saving paper: {e}")
+        
+        return {
+            'query': query,
+            'total_found': len(enriched_results),
+            'papers_saved': len(saved_papers),
+            'papers': saved_papers
+        }
+    
+    async def _execute_analysis(self, task_db: TaskDB) -> Dict[str, Any]:
+        """执行分析任务"""
+        parameters = task_db.parameters or {}
+        paper_ids = parameters.get('paper_ids', [])
+        analysis_type = parameters.get('analysis_type', 'comprehensive')
+        
+        # 使用教练智能体进行分析
+        coach_agent = self.agent_controller.get_agent('coach')
+        
+        # 更新进度
+        await self._update_task_progress(task_db.id, 30)
+        
+        # 执行分析
+        analysis_result = await coach_agent.analyze_papers(paper_ids, analysis_type)
+        
+        # 更新进度
+        await self._update_task_progress(task_db.id, 80)
+        
+        # 保存分析结果
+        analysis_service = AnalysisService(self.db)
+        analysis = analysis_service.create_analysis(
+            {
+                'title': f"Analysis of {len(paper_ids)} papers",
+                'analysis_type': analysis_type,
+                'paper_ids': paper_ids,
+                'methodology': analysis_result.get('methodology', ''),
+                'findings': analysis_result.get('findings', {}),
+                'insights': analysis_result.get('insights', ''),
+                'limitations': analysis_result.get('limitations', ''),
+                'recommendations': analysis_result.get('recommendations', ''),
+                'confidence_score': analysis_result.get('confidence_score', 0.0),
+                'novelty_score': analysis_result.get('novelty_score', 0.0),
+                'impact_score': analysis_result.get('impact_score', 0.0)
+            },
+            task_db.user_id,
+            task_db.id
+        )
+        
+        return {
+            'analysis_id': analysis.id,
+            'analysis_type': analysis_type,
+            'papers_analyzed': len(paper_ids),
+            'result': analysis.dict()
+        }
+    
+    async def _execute_writing(self, task_db: TaskDB) -> Dict[str, Any]:
+        """执行写作任务"""
+        parameters = task_db.parameters or {}
+        paper_ids = parameters.get('paper_ids', [])
+        writing_type = parameters.get('writing_type', 'review')
+        outline = parameters.get('outline')
+        
+        # 使用教练智能体进行写作
+        coach_agent = self.agent_controller.get_agent('coach')
+        
+        # 更新进度
+        await self._update_task_progress(task_db.id, 25)
+        
+        # 生成内容
+        writing_result = await coach_agent.generate_writing(paper_ids, writing_type, outline)
+        
+        # 更新进度
+        await self._update_task_progress(task_db.id, 75)
+        
+        # 保存写作结果
+        writing_service = WritingService(self.db)
+        writing = writing_service.create_writing(
+            {
+                'title': writing_result.get('title', 'Generated Writing'),
+                'writing_type': writing_type,
+                'content': writing_result.get('content', ''),
+                'outline': writing_result.get('outline', []),
+                'sections': writing_result.get('sections', {}),
+                'citations': writing_result.get('citations', []),
+                'paper_ids': paper_ids
+            },
+            task_db.user_id,
+            task_db.id
+        )
+        
+        return {
+            'writing_id': writing.id,
+            'writing_type': writing_type,
+            'papers_referenced': len(paper_ids),
+            'word_count': writing.word_count,
+            'result': writing.dict()
+        }
+    
+    async def _update_task_progress(self, task_id: int, progress: int):
+        """更新任务进度"""
+        task_db = self.db.query(TaskDB).filter(TaskDB.id == task_id).first()
+        if task_db:
+            task_db.progress = progress
+            self.db.commit()

Co-creation-projects/Apricity-InnocoreAI/services/user_service.py

@@ -0,0 +1,127 @@
+"""
+用户服务
+"""
+
+from typing import Optional, List
+from sqlalchemy.orm import Session
+from passlib.context import CryptContext
+from ..core.database import get_db
+from ..models.user import UserDB, User, UserCreate, UserUpdate
+from ..core.exceptions import UserNotFoundError, UserAlreadyExistsError
+
+pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
+
+class UserService:
+    """用户服务类"""
+    
+    def __init__(self, db: Session):
+        self.db = db
+    
+    @staticmethod
+    def verify_password(plain_password: str, hashed_password: str) -> bool:
+        """验证密码"""
+        return pwd_context.verify(plain_password, hashed_password)
+    
+    @staticmethod
+    def get_password_hash(password: str) -> str:
+        """获取密码哈希"""
+        return pwd_context.hash(password)
+    
+    def get_user_by_id(self, user_id: int) -> Optional[User]:
+        """根据ID获取用户"""
+        user_db = self.db.query(UserDB).filter(UserDB.id == user_id).first()
+        if not user_db:
+            raise UserNotFoundError(f"User with id {user_id} not found")
+        return User.from_orm(user_db)
+    
+    def get_user_by_email(self, email: str) -> Optional[User]:
+        """根据邮箱获取用户"""
+        user_db = self.db.query(UserDB).filter(UserDB.email == email).first()
+        if not user_db:
+            raise UserNotFoundError(f"User with email {email} not found")
+        return User.from_orm(user_db)
+    
+    def get_user_by_username(self, username: str) -> Optional[User]:
+        """根据用户名获取用户"""
+        user_db = self.db.query(UserDB).filter(UserDB.username == username).first()
+        if not user_db:
+            raise UserNotFoundError(f"User with username {username} not found")
+        return User.from_orm(user_db)
+    
+    def create_user(self, user_create: UserCreate) -> User:
+        """创建用户"""
+        # 检查邮箱是否已存在
+        if self.db.query(UserDB).filter(UserDB.email == user_create.email).first():
+            raise UserAlreadyExistsError(f"Email {user_create.email} already registered")
+        
+        # 检查用户名是否已存在
+        if self.db.query(UserDB).filter(UserDB.username == user_create.username).first():
+            raise UserAlreadyExistsError(f"Username {user_create.username} already taken")
+        
+        # 创建新用户
+        hashed_password = self.get_password_hash(user_create.password)
+        user_db = UserDB(
+            username=user_create.username,
+            email=user_create.email,
+            hashed_password=hashed_password,
+            full_name=user_create.full_name,
+            institution=user_create.institution,
+            research_field=user_create.research_field
+        )
+        
+        self.db.add(user_db)
+        self.db.commit()
+        self.db.refresh(user_db)
+        
+        return User.from_orm(user_db)
+    
+    def update_user(self, user_id: int, user_update: UserUpdate) -> User:
+        """更新用户信息"""
+        user_db = self.db.query(UserDB).filter(UserDB.id == user_id).first()
+        if not user_db:
+            raise UserNotFoundError(f"User with id {user_id} not found")
+        
+        # 更新字段
+        update_data = user_update.dict(exclude_unset=True)
+        for field, value in update_data.items():
+            setattr(user_db, field, value)
+        
+        self.db.commit()
+        self.db.refresh(user_db)
+        
+        return User.from_orm(user_db)
+    
+    def delete_user(self, user_id: int) -> bool:
+        """删除用户"""
+        user_db = self.db.query(UserDB).filter(UserDB.id == user_id).first()
+        if not user_db:
+            raise UserNotFoundError(f"User with id {user_id} not found")
+        
+        self.db.delete(user_db)
+        self.db.commit()
+        
+        return True
+    
+    def authenticate_user(self, email: str, password: str) -> Optional[User]:
+        """验证用户登录"""
+        try:
+            user = self.get_user_by_email(email)
+            if self.verify_password(password, user.hashed_password):
+                return user
+        except UserNotFoundError:
+            pass
+        return None
+    
+    def change_password(self, user_id: int, current_password: str, new_password: str) -> bool:
+        """修改密码"""
+        user_db = self.db.query(UserDB).filter(UserDB.id == user_id).first()
+        if not user_db:
+            raise UserNotFoundError(f"User with id {user_id} not found")
+        
+        if not self.verify_password(current_password, user_db.hashed_password):
+            return False
+        
+        user_db.hashed_password = self.get_password_hash(new_password)
+        self.db.commit()
+        
+        return True

Co-creation-projects/Apricity-InnocoreAI/services/writing_service.py

@@ -0,0 +1,278 @@
+"""
+写作服务
+"""
+
+from typing import Optional, List, Dict, Any
+from sqlalchemy.orm import Session
+from ..core.database import get_db
+from ..models.writing import WritingDB, Writing, WritingCreate, WritingUpdate
+from ..core.exceptions import WritingNotFoundError
+from ..services.paper_service import PaperService
+from ..utils.citation_formatter import CitationFormatter
+import json
+import re
+
+class WritingService:
+    """写作服务类"""
+    
+    def __init__(self, db: Session):
+        self.db = db
+        self.paper_service = PaperService(db)
+        self.citation_formatter = CitationFormatter()
+    
+    def get_writing_by_id(self, writing_id: int) -> Optional[Writing]:
+        """根据ID获取写作"""
+        writing_db = self.db.query(WritingDB).filter(WritingDB.id == writing_id).first()
+        if not writing_db:
+            raise WritingNotFoundError(f"Writing with id {writing_id} not found")
+        return Writing.from_orm(writing_db)
+    
+    def get_writings_by_user(self, user_id: int, skip: int = 0, limit: int = 20) -> List[Writing]:
+        """获取用户的写作列表"""
+        writings_db = self.db.query(WritingDB).filter(
+            WritingDB.user_id == user_id
+        ).order_by(WritingDB.created_at.desc()).offset(skip).limit(limit).all()
+        return [Writing.from_orm(writing) for writing in writings_db]
+    
+    def create_writing(self, writing_create: WritingCreate, user_id: int, task_id: Optional[int] = None) -> Writing:
+        """创建写作"""
+        # 计算字数
+        content = writing_create.content or ""
+        word_count = len(re.findall(r'\S+', content))
+        
+        writing_db = WritingDB(
+            title=writing_create.title,
+            writing_type=writing_create.writing_type,
+            content=content,
+            outline=json.dumps(writing_create.outline or []),
+            paper_ids=json.dumps(writing_create.paper_ids),
+            word_count=word_count,
+            user_id=user_id,
+            task_id=task_id
+        )
+        
+        self.db.add(writing_db)
+        self.db.commit()
+        self.db.refresh(writing_db)
+        
+        return Writing.from_orm(writing_db)
+    
+    def update_writing(self, writing_id: int, writing_update: WritingUpdate) -> Writing:
+        """更新写作"""
+        writing_db = self.db.query(WritingDB).filter(WritingDB.id == writing_id).first()
+        if not writing_db:
+            raise WritingNotFoundError(f"Writing with id {writing_id} not found")
+        
+        # 更新字段
+        update_data = writing_update.dict(exclude_unset=True)
+        for field, value in update_data.items():
+            if field in ['outline', 'sections', 'citations', 'paper_ids']:
+                setattr(writing_db, field, json.dumps(value))
+            else:
+                setattr(writing_db, field, value)
+        
+        # 重新计算字数
+        if 'content' in update_data:
+            writing_db.word_count = len(re.findall(r'\S+', writing_db.content or ""))
+        
+        self.db.commit()
+        self.db.refresh(writing_db)
+        
+        return Writing.from_orm(writing_db)
+    
+    def delete_writing(self, writing_id: int) -> bool:
+        """删除写作"""
+        writing_db = self.db.query(WritingDB).filter(WritingDB.id == writing_id).first()
+        if not writing_db:
+            raise WritingNotFoundError(f"Writing with id {writing_id} not found")
+        
+        self.db.delete(writing_db)
+        self.db.commit()
+        
+        return True
+    
+    def get_writing_statistics(self, user_id: int) -> Dict[str, Any]:
+        """获取写作统计信息"""
+        total_writings = self.db.query(WritingDB).filter(WritingDB.user_id == user_id).count()
+        
+        # 按类型统计
+        type_stats = self.db.query(
+            WritingDB.writing_type,
+            self.db.func.count(WritingDB.id)
+        ).filter(WritingDB.user_id == user_id).group_by(WritingDB.writing_type).all()
+        
+        # 按状态统计
+        status_stats = self.db.query(
+            WritingDB.status,
+            self.db.func.count(WritingDB.id)
+        ).filter(WritingDB.user_id == user_id).group_by(WritingDB.status).all()
+        
+        # 总字数
+        total_words = self.db.query(
+            self.db.func.sum(WritingDB.word_count)
+        ).filter(WritingDB.user_id == user_id).scalar() or 0
+        
+        # 平均质量分数
+        avg_quality = self.db.query(
+            self.db.func.avg(WritingDB.quality_score)
+        ).filter(WritingDB.user_id == user_id).scalar() or 0
+        
+        return {
+            'total_writings': total_writings,
+            'total_words': int(total_words),
+            'average_quality': float(avg_quality),
+            'type_distribution': dict(type_stats),
+            'status_distribution': dict(status_stats)
+        }
+    
+    def format_citations(self, writing_id: int, style: str = "APA") -> Dict[str, Any]:
+        """格式化引用"""
+        writing = self.get_writing_by_id(writing_id)
+        
+        # 获取参考论文
+        paper_ids = json.loads(writing.paper_ids or "[]")
+        papers = []
+        for paper_id in paper_ids:
+            try:
+                paper = self.paper_service.get_paper_by_id(paper_id)
+                papers.append(paper)
+            except Exception:
+                continue
+        
+        # 格式化引用
+        formatted_citations = self.citation_formatter.format_papers(papers, style)
+        
+        # 更新写作中的引用
+        citations_data = [
+            {
+                'id': paper.id,
+                'title': paper.title,
+                'authors': paper.authors,
+                'journal': paper.journal,
+                'year': paper.publication_year,
+                'formatted': formatted_citations.get(str(paper.id), "")
+            }
+            for paper in papers
+        ]
+        
+        self.update_writing(writing_id, WritingUpdate(citations=citations_data))
+        
+        return {
+            'citations': citations_data,
+            'bibliography': self.citation_formatter.generate_bibliography(papers, style)
+        }
+    
+    def export_writing(self, writing_id: int, format: str = "markdown", include_citations: bool = True) -> Dict[str, Any]:
+        """导出写作"""
+        writing = self.get_writing_by_id(writing_id)
+        
+        if format == "markdown":
+            return self._export_to_markdown(writing, include_citations)
+        elif format == "latex":
+            return self._export_to_latex(writing, include_citations)
+        elif format == "docx":
+            return self._export_to_docx(writing, include_citations)
+        elif format == "pdf":
+            return self._export_to_pdf(writing, include_citations)
+        else:
+            raise ValueError(f"Unsupported export format: {format}")
+    
+    def _export_to_markdown(self, writing: Writing, include_citations: bool = True) -> str:
+        """导出为Markdown格式"""
+        markdown = f"# {writing.title}\n\n"
+        markdown += f"**类型**: {writing.writing_type}\n\n"
+        markdown += f"**字数**: {writing.word_count}\n\n"
+        markdown += f"**创建时间**: {writing.created_at.strftime('%Y-%m-%d %H:%M:%S')}\n\n"
+        
+        if writing.content:
+            markdown += f"{writing.content}\n\n"
+        
+        if include_citations and writing.citations:
+            markdown += "## 参考文献\n\n"
+            for citation in writing.citations:
+                markdown += f"- {citation.get('formatted', '')}\n"
+        
+        return markdown
+    
+    def _export_to_latex(self, writing: Writing, include_citations: bool = True) -> str:
+        """导出为LaTeX格式"""
+        latex = "\\documentclass{article}\n"
+        latex += "\\usepackage[utf8]{inputenc}\n"
+        latex += "\\usepackage{cite}\n\n"
+        latex += "\\begin{document}\n\n"
+        latex += f"\\title{{{writing.title}}}\n"
+        latex += "\\maketitle\n\n"
+        
+        if writing.content:
+            # 简单的Markdown到LaTeX转换
+            content = writing.content.replace("**", "\\textbf{").replace("**", "}")
+            content = content.replace("*", "\\textit{").replace("*", "}")
+            latex += f"{content}\n\n"
+        
+        if include_citations and writing.citations:
+            latex += "\\bibliographystyle{plain}\n"
+            latex += "\\bibliography{references}\n"
+        
+        latex += "\\end{document}"
+        
+        return latex
+    
+    def _export_to_docx(self, writing: Writing, include_citations: bool = True) -> bytes:
+        """导出为Word文档格式"""
+        # 这里可以使用python-docx库
+        # 暂时返回Markdown内容的字节
+        content = self._export_to_markdown(writing, include_citations)
+        return content.encode('utf-8')
+    
+    def _export_to_pdf(self, writing: Writing, include_citations: bool = True) -> bytes:
+        """导出为PDF格式"""
+        # 这里可以使用reportlab或其他PDF生成库
+        # 暂时返回Markdown内容的字节
+        content = self._export_to_markdown(writing, include_citations)
+        return content.encode('utf-8')
+    
+    def generate_outline(self, topic: str, paper_ids: List[int], writing_type: str = "review") -> List[Dict[str, Any]]:
+        """生成写作大纲"""
+        # 获取参考论文
+        papers = []
+        for paper_id in paper_ids:
+            try:
+                paper = self.paper_service.get_paper_by_id(paper_id)
+                papers.append(paper)
+            except Exception:
+                continue
+        
+        # 根据写作类型生成大纲模板
+        if writing_type == "review":
+            outline = [
+                {"title": "引言", "level": 1, "content": "研究背景和意义"},
+                {"title": "文献综述", "level": 1, "content": "相关研究工作总结"},
+                {"title": "方法论分析", "level": 1, "content": "研究方法比较"},
+                {"title": "主要发现", "level": 1, "content": "研究成果总结"},
+                {"title": "讨论与展望", "level": 1, "content": "研究局限性和未来方向"},
+                {"title": "结论", "level": 1, "content": "研究总结"}
+            ]
+        elif writing_type == "summary":
+            outline = [
+                {"title": "研究背景", "level": 1, "content": "研究动机和目标"},
+                {"title": "研究方法", "level": 1, "content": "实验设计和数据分析"},
+                {"title": "主要结果", "level": 1, "content": "关键发现和数据分析"},
+                {"title": "结论与意义", "level": 1, "content": "研究贡献和影响"}
+            ]
+        elif writing_type == "critique":
+            outline = [
+                {"title": "论文概述", "level": 1, "content": "研究内容总结"},
+                {"title": "优点分析", "level": 1, "content": "研究的创新性和贡献"},
+                {"title": "问题与局限", "level": 1, "content": "研究中存在的问题"},
+                {"title": "改进建议", "level": 1, "content": "对研究的改进意见"}
+            ]
+        else:
+            outline = [
+                {"title": "研究背景", "level": 1, "content": ""},
+                {"title": "研究目标", "level": 1, "content": ""},
+                {"title": "研究方法", "level": 1, "content": ""},
+                {"title": "预期结果", "level": 1, "content": ""},
+                {"title": "研究意义", "level": 1, "content": ""}
+            ]
+        
+        return outline

Co-creation-projects/Apricity-InnocoreAI/setup.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""
+InnoCore AI - Simple Setup Script
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+
+def main():
+    print("InnoCore AI - Quick Setup")
+    print("=" * 30)
+    
+    # Install basic dependencies without version conflicts
+    basic_deps = [
+        "fastapi",
+        "uvicorn[standard]",
+        "python-multipart",
+        "python-dotenv"
+    ]
+    
+    print("Installing basic dependencies...")
+    for dep in basic_deps:
+        try:
+            subprocess.check_call([sys.executable, "-m", "pip", "install", dep])
+            print(f"[OK] {dep}")
+        except subprocess.CalledProcessError:
+            print(f"[SKIP] {dep} (may already exist)")
+    
+    # Create .env file
+    env_file = Path(".env")
+    if not env_file.exists():
+        env_content = """# InnoCore AI Configuration
+OPENAI_API_KEY=your_openai_api_key_here
+DATABASE_URL=sqlite:///./innocore.db
+SECRET_KEY=your_secret_key_here_change_this_in_production
+DEBUG=True
+"""
+        env_file.write_text(env_content)
+        print("[OK] .env file created")
+    else:
+        print("[OK] .env file exists")
+    
+    # Create directories
+    Path("data").mkdir(exist_ok=True)
+    Path("logs").mkdir(exist_ok=True)
+    print("[OK] Directories created")
+    
+    print("\n[SUCCESS] Setup completed!")
+    print("Next steps:")
+    print("1. Edit .env file and add your OpenAI API key")
+    print("2. Run: python run.py")
+    print("3. Open: http://localhost:8000")
+
+if __name__ == "__main__":
+    main()

Co-creation-projects/Apricity-InnocoreAI/utils/__init__.py

@@ -0,0 +1,15 @@
+"""
+InnoCore AI 工具模块
+"""
+
+from .pdf_parser import PDFParser
+from .embedding import EmbeddingGenerator
+from .text_processor import TextProcessor
+from .citation_formatter import CitationFormatter
+
+__all__ = [
+    "PDFParser",
+    "EmbeddingGenerator", 
+    "TextProcessor",
+    "CitationFormatter"
+]

Co-creation-projects/Apricity-InnocoreAI/utils/citation_formatter.py

@@ -0,0 +1,526 @@
+"""
+InnoCore AI 引用格式化工具
+"""
+
+import re
+from typing import Dict, List, Optional, Any
+from datetime import datetime
+
+class CitationFormatter:
+    """引用格式化器"""
+    
+    def __init__(self):
+        self.month_names = {
+            1: "Jan", 2: "Feb", 3: "Mar", 4: "Apr", 5: "May", 6: "Jun",
+            7: "Jul", 8: "Aug", 9: "Sep", 10: "Oct", 11: "Nov", 12: "Dec"
+        }
+    
+    def format_bibtex(self, paper_info: Dict[str, Any]) -> str:
+        """格式化为BibTeX"""
+        # 生成引用键
+        citation_key = self._generate_citation_key(paper_info)
+        
+        # 确定条目类型
+        entry_type = self._determine_entry_type(paper_info)
+        
+        # 构建BibTeX条目
+        bibtex_lines = [f"@{entry_type}{{{citation_key}"]
+        
+        # 添加作者
+        authors = paper_info.get("authors", [])
+        if authors:
+            formatted_authors = self._format_bibtex_authors(authors)
+            bibtex_lines.append(f"  author = {{{formatted_authors}}}")
+        
+        # 添加标题
+        title = paper_info.get("title", "")
+        if title:
+            bibtex_lines.append(f"  title = {{{title}}}")
+        
+        # 添加期刊/会议信息
+        if entry_type == "article":
+            journal = paper_info.get("journal", "")
+            if journal:
+                bibtex_lines.append(f"  journal = {{{journal}}}")
+            
+            volume = paper_info.get("volume", "")
+            if volume:
+                bibtex_lines.append(f"  volume = {{{volume}}}")
+            
+            number = paper_info.get("number", "")
+            if number:
+                bibtex_lines.append(f"  number = {{{number}}}")
+            
+            pages = paper_info.get("pages", "")
+            if pages:
+                bibtex_lines.append(f"  pages = {{{pages}}}")
+        
+        elif entry_type == "inproceedings":
+            booktitle = paper_info.get("booktitle", "")
+            if booktitle:
+                bibtex_lines.append(f"  booktitle = {{{booktitle}}}")
+            
+            pages = paper_info.get("pages", "")
+            if pages:
+                bibtex_lines.append(f"  pages = {{{pages}}}")
+        
+        elif entry_type == "book":
+            publisher = paper_info.get("publisher", "")
+            if publisher:
+                bibtex_lines.append(f"  publisher = {{{publisher}}}")
+        
+        # 添加年份
+        year = paper_info.get("year", "")
+        if year:
+            bibtex_lines.append(f"  year = {{{year}}}")
+        
+        # 添加月份
+        month = paper_info.get("month", "")
+        if month:
+            bibtex_lines.append(f"  month = {{{month}}}")
+        
+        # 添加DOI
+        doi = paper_info.get("doi", "")
+        if doi:
+            bibtex_lines.append(f"  doi = {{{doi}}}")
+        
+        # 添加URL
+        url = paper_info.get("url", "")
+        if url:
+            bibtex_lines.append(f"  url = {{{url}}}")
+        
+        # 添加笔记
+        note = paper_info.get("note", "")
+        if note:
+            bibtex_lines.append(f"  note = {{{note}}}")
+        
+        # 关闭条目
+        bibtex_lines.append("}")
+        
+        return "\n".join(bibtex_lines)
+    
+    def format_apa(self, paper_info: Dict[str, Any]) -> str:
+        """格式化为APA格式"""
+        authors = paper_info.get("authors", [])
+        year = paper_info.get("year", "")
+        title = paper_info.get("title", "")
+        
+        # 格式化作者
+        author_text = self._format_apa_authors(authors)
+        
+        # 构建基本引用
+        if year:
+            citation = f"{author_text} ({year}). {title}."
+        else:
+            citation = f"{author_text}. {title}."
+        
+        # 添加期刊信息
+        journal = paper_info.get("journal", "")
+        volume = paper_info.get("volume", "")
+        number = paper_info.get("number", "")
+        pages = paper_info.get("pages", "")
+        
+        if journal:
+            if volume and number:
+                citation += f" *{journal}*, *{volume}({number})*"
+            elif volume:
+                citation += f" *{journal}*, *{volume}*"
+            else:
+                citation += f" *{journal}*"
+            
+            if pages:
+                citation += f", {pages}."
+            else:
+                citation += "."
+        
+        # 添加书籍信息
+        publisher = paper_info.get("publisher", "")
+        if publisher:
+            citation += f" {publisher}."
+        
+        # 添加会议信息
+        booktitle = paper_info.get("booktitle", "")
+        if booktitle:
+            citation += f" In *{booktitle}*"
+            if pages:
+                citation += f" (pp. {pages})."
+            else:
+                citation += "."
+        
+        # 添加DOI
+        doi = paper_info.get("doi", "")
+        if doi:
+            citation += f" https://doi.org/{doi}"
+        
+        return citation
+    
+    def format_ieee(self, paper_info: Dict[str, Any]) -> str:
+        """格式化为IEEE格式"""
+        authors = paper_info.get("authors", [])
+        year = paper_info.get("year", "")
+        title = paper_info.get("title", "")
+        
+        # 格式化作者（IEEE格式）
+        author_text = self._format_ieee_authors(authors)
+        
+        # 构建基本引用
+        citation = f'{author_text}, "{title},"'
+        
+        # 添加期刊信息
+        journal = paper_info.get("journal", "")
+        volume = paper_info.get("volume", "")
+        number = paper_info.get("number", "")
+        pages = paper_info.get("pages", "")
+        
+        if journal:
+            if volume and number:
+                citation += f" *{journal}*, vol. {volume}, no. {number}"
+            elif volume:
+                citation += f" *{journal}*, vol. {volume}"
+            else:
+                citation += f" *{journal}*"
+            
+            if pages:
+                citation += f", pp. {pages}"
+        
+        # 添加会议信息
+        booktitle = paper_info.get("booktitle", "")
+        if booktitle:
+            citation += f" in *{booktitle}*"
+            if pages:
+                citation += f", pp. {pages}"
+        
+        # 添加书籍信息
+        publisher = paper_info.get("publisher", "")
+        if publisher:
+            citation += f" {publisher}"
+        
+        # 添加年份和月份
+        month = paper_info.get("month", "")
+        if year:
+            if month:
+                citation += f", {month}. {year}."
+            else:
+                citation += f", {year}."
+        
+        # 添加DOI
+        doi = paper_info.get("doi", "")
+        if doi:
+            citation += f" doi: {doi}"
+        
+        return citation
+    
+    def format_mla(self, paper_info: Dict[str, Any]) -> str:
+        """格式化为MLA格式"""
+        authors = paper_info.get("authors", [])
+        title = paper_info.get("title", "")
+        journal = paper_info.get("journal", "")
+        year = paper_info.get("year", "")
+        pages = paper_info.get("pages", "")
+        
+        # 格式化作者（MLA格式）
+        author_text = self._format_mla_authors(authors)
+        
+        # 构建基本引用
+        if author_text:
+            citation = f'{author_text}. "{title}."'
+        else:
+            citation = f'"{title}."'
+        
+        # 添加期刊信息
+        if journal:
+            citation += f" *{journal}*"
+            
+            if volume and number:
+                citation += f", vol. {volume}, no. {number}"
+            elif volume:
+                citation += f", vol. {volume}"
+            
+            if year:
+                citation += f", {year}"
+            
+            if pages:
+                citation += f", pp. {pages}."
+            else:
+                citation += "."
+        
+        # 添加书籍信息
+        publisher = paper_info.get("publisher", "")
+        if publisher:
+            citation += f" {publisher}"
+            if year:
+                citation += f", {year}."
+            else:
+                citation += "."
+        
+        return citation
+    
+    def format_chicago(self, paper_info: Dict[str, Any]) -> str:
+        """格式化为Chicago格式"""
+        authors = paper_info.get("authors", [])
+        title = paper_info.get("title", "")
+        journal = paper_info.get("journal", "")
+        volume = paper_info.get("volume", "")
+        number = paper_info.get("number", "")
+        year = paper_info.get("year", "")
+        pages = paper_info.get("pages", "")
+        
+        # 格式化作者（Chicago格式）
+        author_text = self._format_chicago_authors(authors)
+        
+        # 构建基本引用
+        if author_text:
+            citation = f'{author_text}. "{title}."'
+        else:
+            citation = f'"{title}."'
+        
+        # 添加期刊信息
+        if journal:
+            citation += f" *{journal}*"
+            
+            if volume and number:
+                citation += f" {volume}, no. {number}"
+            elif volume:
+                citation += f" {volume}"
+            
+            if year:
+                citation += f" ({year})"
+            
+            if pages:
+                citation += f": {pages}."
+            else:
+                citation += "."
+        
+        return citation
+    
+    def _generate_citation_key(self, paper_info: Dict[str, Any]) -> str:
+        """生成引用键"""
+        # 获取第一作者的姓氏
+        authors = paper_info.get("authors", [])
+        if authors:
+            first_author = authors[0]
+            if isinstance(first_author, str):
+                last_name = first_author.split()[-1].lower()
+            else:
+                last_name = "unknown"
+        else:
+            last_name = "unknown"
+        
+        # 获取年份
+        year = str(paper_info.get("year", datetime.now().year))
+        
+        # 获取标题关键词
+        title = paper_info.get("title", "")
+        title_words = re.findall(r'\b[a-zA-Z]{3,}\b', title.lower())[:3]
+        title_key = "".join(title_words)
+        
+        return f"{last_name}{year}{title_key}"
+    
+    def _determine_entry_type(self, paper_info: Dict[str, Any]) -> str:
+        """确定BibTeX条目类型"""
+        if paper_info.get("journal"):
+            return "article"
+        elif paper_info.get("booktitle"):
+            return "inproceedings"
+        elif paper_info.get("publisher"):
+            return "book"
+        else:
+            return "misc"
+    
+    def _format_bibtex_authors(self, authors: List[str]) -> str:
+        """格式化BibTeX作者"""
+        formatted_authors = []
+        
+        for author in authors:
+            if isinstance(author, str):
+                # 将 "First Last" 转换为 "Last, First"
+                parts = author.split()
+                if len(parts) >= 2:
+                    last_name = parts[-1]
+                    first_names = " ".join(parts[:-1])
+                    formatted_authors.append(f"{last_name}, {first_names}")
+                else:
+                    formatted_authors.append(author)
+            else:
+                formatted_authors.append(str(author))
+        
+        return " and ".join(formatted_authors)
+    
+    def _format_apa_authors(self, authors: List[str]) -> str:
+        """格式化APA作者"""
+        if not authors:
+            return ""
+        
+        if len(authors) == 1:
+            return authors[0]
+        elif len(authors) == 2:
+            return f"{authors[0]} & {authors[1]}"
+        elif len(authors) <= 20:
+            return ", ".join(authors[:-1]) + f", & {authors[-1]}"
+        else:
+            return ", ".join(authors[:19]) + f", ... {authors[-1]}"
+    
+    def _format_ieee_authors(self, authors: List[str]) -> str:
+        """格式化IEEE作者"""
+        formatted_authors = []
+        
+        for i, author in enumerate(authors[:3]):  # IEEE通常只列出前3个作者
+            if isinstance(author, str):
+                parts = author.split()
+                if len(parts) >= 2:
+                    # 转换为 "F. Last" 格式
+                    initials = " ".join([f"{p[0]}." for p in parts[:-1]])
+                    last_name = parts[-1]
+                    formatted_authors.append(f"{initials} {last_name}")
+                else:
+                    formatted_authors.append(author)
+            else:
+                formatted_authors.append(str(author))
+        
+        if len(authors) > 3:
+            formatted_authors.append("et al.")
+        
+        return ", ".join(formatted_authors)
+    
+    def _format_mla_authors(self, authors: List[str]) -> str:
+        """格式化MLA作者"""
+        if not authors:
+            return ""
+        
+        if len(authors) == 1:
+            return authors[0]
+        elif len(authors) == 2:
+            return f"{authors[0]} and {authors[1]}"
+        else:
+            return f"{authors[0]}, et al."
+    
+    def _format_chicago_authors(self, authors: List[str]) -> str:
+        """格式化Chicago作者"""
+        if not authors:
+            return ""
+        
+        if len(authors) == 1:
+            return authors[0]
+        elif len(authors) == 2:
+            return f"{authors[0]} and {authors[1]}"
+        else:
+            return f"{authors[0]}, et al."
+    
+    def parse_bibtex(self, bibtex_text: str) -> Dict[str, Any]:
+        """解析BibTeX文本"""
+        paper_info = {}
+        
+        # 提取条目类型和键
+        entry_match = re.match(r'@(\w+)\{([^,]+),', bibtex_text)
+        if entry_match:
+            paper_info["entry_type"] = entry_match.group(1)
+            paper_info["citation_key"] = entry_match.group(2)
+        
+        # 提取字段
+        field_pattern = r'\s*(\w+)\s*=\s*\{([^}]*)\}'
+        matches = re.findall(field_pattern, bibtex_text)
+        
+        for field_name, field_value in matches:
+            paper_info[field_name] = field_value
+        
+        return paper_info
+    
+    def validate_citation(self, citation: str, style: str) -> Dict[str, Any]:
+        """验证引用格式"""
+        validation_result = {
+            "is_valid": True,
+            "errors": [],
+            "warnings": [],
+            "suggestions": []
+        }
+        
+        if style.lower() == "bibtex":
+            validation_result = self._validate_bibtex(citation, validation_result)
+        elif style.lower() == "apa":
+            validation_result = self._validate_apa(citation, validation_result)
+        elif style.lower() == "ieee":
+            validation_result = self._validate_ieee(citation, validation_result)
+        
+        return validation_result
+    
+    def _validate_bibtex(self, citation: str, result: Dict[str, Any]) -> Dict[str, Any]:
+        """验证BibTeX格式"""
+        # 检查基本结构
+        if not citation.startswith('@'):
+            result["is_valid"] = False
+            result["errors"].append("BibTeX必须以@开头")
+        
+        if not citation.endswith('}'):
+            result["is_valid"] = False
+            result["errors"].append("BibTeX必须以}结尾")
+        
+        # 检查必需字段
+        if 'title' not in citation:
+            result["warnings"].append("缺少title字段")
+        
+        if 'author' not in citation:
+            result["warnings"].append("缺少author字段")
+        
+        if 'year' not in citation:
+            result["warnings"].append("缺少year字段")
+        
+        return result
+    
+    def _validate_apa(self, citation: str, result: Dict[str, Any]) -> Dict[str, Any]:
+        """验证APA格式"""
+        # 检查作者格式
+        if '(' in citation and ')' in citation:
+            year_pattern = r'\((\d{4})\)'
+            if not re.search(year_pattern, citation):
+                result["warnings"].append("APA格式应包含出版年份")
+        
+        # 检查标题格式
+        if not citation.strip().endswith('.'):
+            result["warnings"].append("APA引用应以句号结尾")
+        
+        return result
+    
+    def _validate_ieee(self, citation: str, result: Dict[str, Any]) -> Dict[str, Any]:
+        """验证IEEE格式"""
+        # 检查引用格式
+        if '"' not in citation:
+            result["warnings"].append("IEEE格式中标题应使用双引号")
+        
+        # 检查期刊格式
+        if '*' not in citation:
+            result["warnings"].append("IEEE格式中期刊名应使用斜体（*）")
+        
+        return result
+    
+    def convert_between_formats(self, citation: str, from_style: str, to_style: str) -> str:
+        """在不同格式间转换引用"""
+        try:
+            # 解析原始格式
+            if from_style.lower() == "bibtex":
+                paper_info = self.parse_bibtex(citation)
+            else:
+                # 对于其他格式，需要更复杂的解析逻辑
+                # 这里提供简化实现
+                paper_info = {
+                    "title": "",
+                    "authors": [],
+                    "year": "",
+                    "journal": ""
+                }
+            
+            # 转换为目标格式
+            if to_style.lower() == "bibtex":
+                return self.format_bibtex(paper_info)
+            elif to_style.lower() == "apa":
+                return self.format_apa(paper_info)
+            elif to_style.lower() == "ieee":
+                return self.format_ieee(paper_info)
+            elif to_style.lower() == "mla":
+                return self.format_mla(paper_info)
+            elif to_style.lower() == "chicago":
+                return self.format_chicago(paper_info)
+            else:
+                return citation
+                
+        except Exception as e:
+            return f"转换失败: {str(e)}"

Co-creation-projects/Apricity-InnocoreAI/utils/embedding.py

@@ -0,0 +1,309 @@
+"""
+InnoCore AI 向量生成工具
+"""
+
+import asyncio
+from typing import List, Dict, Optional, Any
+import numpy as np
+from openai import AsyncOpenAI
+import hashlib
+import json
+
+from ..core.config import get_config
+from ..core.exceptions import AgentException
+
+class EmbeddingGenerator:
+    """向量生成器"""
+    
+    def __init__(self):
+        self.config = get_config()
+        self.client = None
+        self.embedding_model = self.config.vector_db.embedding_model
+        self.cache = {}  # 简单的内存缓存
+    
+    async def initialize(self):
+        """初始化向量生成器"""
+        try:
+            self.client = AsyncOpenAI(
+                api_key=self.config.llm.api_key,
+                base_url=self.config.llm.base_url
+            )
+        except Exception as e:
+            raise AgentException(f"向量生成器初始化失败: {str(e)}")
+    
+    async def generate_embedding(self, text: str, use_cache: bool = True) -> List[float]:
+        """生成文本向量"""
+        if not text:
+            return [0.0] * 1536  # 返回零向量
+        
+        # 检查缓存
+        if use_cache:
+            cache_key = self._get_cache_key(text)
+            if cache_key in self.cache:
+                return self.cache[cache_key]
+        
+        try:
+            # 清理文本
+            cleaned_text = self._clean_text(text)
+            
+            # 调用OpenAI API
+            response = await self.client.embeddings.create(
+                model=self.embedding_model,
+                input=cleaned_text
+            )
+            
+            embedding = response.data[0].embedding
+            
+            # 缓存结果
+            if use_cache:
+                cache_key = self._get_cache_key(text)
+                self.cache[cache_key] = embedding
+            
+            return embedding
+            
+        except Exception as e:
+            raise AgentException(f"向量生成失败: {str(e)}")
+    
+    async def generate_batch_embeddings(self, texts: List[str], 
+                                       batch_size: int = 10) -> List[List[float]]:
+        """批量生成向量"""
+        embeddings = []
+        
+        for i in range(0, len(texts), batch_size):
+            batch = texts[i:i + batch_size]
+            
+            try:
+                # 批量调用API
+                cleaned_texts = [self._clean_text(text) for text in batch]
+                
+                response = await self.client.embeddings.create(
+                    model=self.embedding_model,
+                    input=cleaned_texts
+                )
+                
+                batch_embeddings = [item.embedding for item in response.data]
+                embeddings.extend(batch_embeddings)
+                
+            except Exception as e:
+                # 如果批量失败，逐个生成
+                for text in batch:
+                    try:
+                        embedding = await self.generate_embedding(text)
+                        embeddings.append(embedding)
+                    except Exception as single_error:
+                        print(f"单个向量生成失败: {str(single_error)}")
+                        embeddings.append([0.0] * 1536)  # 零向量
+        
+        return embeddings
+    
+    async def generate_paper_embedding(self, paper_info: Dict[str, Any]) -> List[float]:
+        """为论文生成综合向量"""
+        # 组合论文的关键信息
+        title = paper_info.get("title", "")
+        abstract = paper_info.get("abstract", "")
+        authors = " ".join(paper_info.get("authors", []))
+        
+        # 构建综合文本
+        combined_text = f"{title} {abstract} {authors}"
+        
+        # 如果有结构化内容，也包含进来
+        sections = paper_info.get("sections", {})
+        if sections:
+            section_text = " ".join(sections.values())
+            combined_text += " " + section_text
+        
+        return await self.generate_embedding(combined_text)
+    
+    async def generate_section_embeddings(self, sections: Dict[str, str]) -> Dict[str, List[float]]:
+        """为各个章节生成向量"""
+        section_embeddings = {}
+        
+        for section_name, section_content in sections.items():
+            if section_content.strip():
+                try:
+                    embedding = await self.generate_embedding(section_content)
+                    section_embeddings[section_name] = embedding
+                except Exception as e:
+                    print(f"章节 {section_name} 向量生成失败: {str(e)}")
+                    section_embeddings[section_name] = [0.0] * 1536
+        
+        return section_embeddings
+    
+    def _clean_text(self, text: str) -> str:
+        """清理文本"""
+        if not text:
+            return ""
+        
+        # 移除多余的空白字符
+        text = ' '.join(text.split())
+        
+        # 截断过长的文本（OpenAI有token限制）
+        max_length = 8000  # 保守估计
+        if len(text) > max_length:
+            text = text[:max_length]
+        
+        return text
+    
+    def _get_cache_key(self, text: str) -> str:
+        """生成缓存键"""
+        return hashlib.md5(text.encode()).hexdigest()
+    
+    def clear_cache(self):
+        """清空缓存"""
+        self.cache.clear()
+    
+    def get_cache_size(self) -> int:
+        """获取缓存大小"""
+        return len(self.cache)
+    
+    async def calculate_similarity(self, text1: str, text2: str) -> float:
+        """计算两个文本的相似度"""
+        try:
+            embedding1 = await self.generate_embedding(text1)
+            embedding2 = await self.generate_embedding(text2)
+            
+            return self._cosine_similarity(embedding1, embedding2)
+            
+        except Exception as e:
+            print(f"相似度计算失败: {str(e)}")
+            return 0.0
+    
+    def _cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
+        """计算余弦相似度"""
+        if len(vec1) != len(vec2):
+            return 0.0
+        
+        try:
+            vec1_np = np.array(vec1)
+            vec2_np = np.array(vec2)
+            
+            dot_product = np.dot(vec1_np, vec2_np)
+            norm1 = np.linalg.norm(vec1_np)
+            norm2 = np.linalg.norm(vec2_np)
+            
+            if norm1 == 0 or norm2 == 0:
+                return 0.0
+            
+            return dot_product / (norm1 * norm2)
+            
+        except Exception:
+            return 0.0
+    
+    async def find_most_similar(self, query_text: str, 
+                               candidate_texts: List[str],
+                               top_k: int = 5) -> List[Dict[str, Any]]:
+        """找到最相似的文本"""
+        if not candidate_texts:
+            return []
+        
+        try:
+            # 生成查询向量
+            query_embedding = await self.generate_embedding(query_text)
+            
+            # 生成候选文本向量
+            candidate_embeddings = await self.generate_batch_embeddings(candidate_texts)
+            
+            # 计算相似度
+            similarities = []
+            for i, candidate_embedding in enumerate(candidate_embeddings):
+                similarity = self._cosine_similarity(query_embedding, candidate_embedding)
+                similarities.append({
+                    "text": candidate_texts[i],
+                    "similarity": similarity,
+                    "index": i
+                })
+            
+            # 按相似度排序
+            similarities.sort(key=lambda x: x["similarity"], reverse=True)
+            
+            return similarities[:top_k]
+            
+        except Exception as e:
+            print(f"相似文本查找失败: {str(e)}")
+            return []
+    
+    async def cluster_texts(self, texts: List[str], 
+                          num_clusters: int = 3) -> Dict[str, Any]:
+        """文本聚类（简化实现）"""
+        try:
+            # 生成所有文本的向量
+            embeddings = await self.generate_batch_embeddings(texts)
+            
+            # 简单的聚类逻辑（基于相似度阈值）
+            clusters = {}
+            cluster_id = 0
+            used_indices = set()
+            
+            for i, embedding in enumerate(embeddings):
+                if i in used_indices:
+                    continue
+                
+                # 创建新聚类
+                clusters[f"cluster_{cluster_id}"] = {
+                    "texts": [texts[i]],
+                    "indices": [i],
+                    "center": embedding
+                }
+                used_indices.add(i)
+                
+                # 查找相似文本加入同一聚类
+                for j, other_embedding in enumerate(embeddings):
+                    if j in used_indices:
+                        continue
+                    
+                    similarity = self._cosine_similarity(embedding, other_embedding)
+                    if similarity > 0.8:  # 相似度阈值
+                        clusters[f"cluster_{cluster_id}"]["texts"].append(texts[j])
+                        clusters[f"cluster_{cluster_id}"]["indices"].append(j)
+                        used_indices.add(j)
+                
+                cluster_id += 1
+            
+            return {
+                "clusters": clusters,
+                "num_clusters": len(clusters),
+                "total_texts": len(texts)
+            }
+            
+        except Exception as e:
+            print(f"文本聚类失败: {str(e)}")
+            return {"clusters": {}, "num_clusters": 0, "total_texts": len(texts)}
+    
+    async def extract_keywords(self, text: str, max_keywords: int = 10) -> List[str]:
+        """提取关键词（基于TF-IDF的简化实现）"""
+        try:
+            # 分词
+            words = text.lower().split()
+            
+            # 过滤停用词（简化版）
+            stop_words = {
+                'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for',
+                'of', 'with', 'by', 'is', 'are', 'was', 'were', 'be', 'been', 'have',
+                'has', 'had', 'do', 'does', 'did', 'will', 'would', 'could', 'should'
+            }
+            
+            filtered_words = [word for word in words if word not in stop_words and len(word) > 2]
+            
+            # 计算词频
+            word_freq = {}
+            for word in filtered_words:
+                word_freq[word] = word_freq.get(word, 0) + 1
+            
+            # 按频率排序
+            sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)
+            
+            # 返回前N个关键词
+            return [word for word, freq in sorted_words[:max_keywords]]
+            
+        except Exception as e:
+            print(f"关键词提取失败: {str(e)}")
+            return []
+    
+    def get_embedding_info(self) -> Dict[str, Any]:
+        """获取向量生成器信息"""
+        return {
+            "model": self.embedding_model,
+            "cache_size": len(self.cache),
+            "vector_dimension": 1536,  # OpenAI embedding维度
+            "provider": "openai"
+        }

Co-creation-projects/Apricity-InnocoreAI/utils/pdf_parser.py

@@ -0,0 +1,229 @@
+"""
+PDF 解析工具
+支持从 PDF 文件中提取文本、标题、作者等信息
+"""
+
+import logging
+from typing import Dict, Any, Optional
+import re
+
+logger = logging.getLogger(__name__)
+
+class PDFParser:
+    """PDF 解析器"""
+    
+    def __init__(self):
+        """初始化 PDF 解析器"""
+        self.supported_formats = ['.pdf']
+    
+    async def parse_pdf(self, file_path: str) -> Dict[str, Any]:
+        """
+        解析 PDF 文件
+        
+        Args:
+            file_path: PDF 文件路径
+            
+        Returns:
+            包含解析结果的字典
+        """
+        try:
+            import pdfplumber
+            
+            logger.info(f"开始解析 PDF: {file_path}")
+            
+            with pdfplumber.open(file_path) as pdf:
+                # 提取所有文本
+                full_text = ""
+                for page in pdf.pages:
+                    text = page.extract_text()
+                    if text:
+                        full_text += text + "\n"
+                
+                if not full_text.strip():
+                    logger.warning("PDF 文件为空或无法提取文本")
+                    return {
+                        "success": False,
+                        "error": "无法从 PDF 中提取文本"
+                    }
+                
+                # 提取元数据
+                metadata = pdf.metadata or {}
+                
+                # 尝试从文本中提取标题（通常在第一页的前几行）
+                title = self._extract_title(full_text, metadata)
+                
+                # 尝试提取作者
+                authors = self._extract_authors(full_text, metadata)
+                
+                # 尝试提取摘要
+                abstract = self._extract_abstract(full_text)
+                
+                # 统计信息
+                page_count = len(pdf.pages)
+                word_count = len(full_text.split())
+                
+                result = {
+                    "success": True,
+                    "title": title,
+                    "authors": authors,
+                    "abstract": abstract,
+                    "full_text": full_text,
+                    "page_count": page_count,
+                    "word_count": word_count,
+                    "metadata": {
+                        "creator": metadata.get("/Creator", ""),
+                        "producer": metadata.get("/Producer", ""),
+                        "subject": metadata.get("/Subject", ""),
+                        "keywords": metadata.get("/Keywords", "")
+                    }
+                }
+                
+                logger.info(f"PDF 解析成功: {page_count} 页, {word_count} 词")
+                return result
+                
+        except ImportError:
+            logger.error("pdfplumber 未安装")
+            return {
+                "success": False,
+                "error": "PDF 解析库未安装，请运行: pip install pdfplumber"
+            }
+        except Exception as e:
+            logger.error(f"PDF 解析失败: {str(e)}")
+            return {
+                "success": False,
+                "error": f"PDF 解析失败: {str(e)}"
+            }
+    
+    def _extract_title(self, text: str, metadata: Dict) -> str:
+        """从文本或元数据中提取标题"""
+        # 首先尝试从元数据获取
+        if metadata.get("/Title"):
+            return metadata["/Title"]
+        
+        # 从文本前几行提取（通常标题在最前面且字体较大）
+        lines = text.split('\n')
+        for i, line in enumerate(lines[:10]):  # 只检查前10行
+            line = line.strip()
+            # 标题通常较长且不包含特殊字符
+            if len(line) > 10 and len(line) < 200 and not line.startswith(('http', 'www', '@')):
+                # 排除一些常见的非标题行
+                if not any(keyword in line.lower() for keyword in ['abstract', 'introduction', 'page', 'arxiv']):
+                    return line
+        
+        return "未知标题"
+    
+    def _extract_authors(self, text: str, metadata: Dict) -> list:
+        """从文本或元数据中提取作者"""
+        authors = []
+        
+        # 首先尝试从元数据获取
+        if metadata.get("/Author"):
+            author_str = metadata["/Author"]
+            authors = [a.strip() for a in re.split(r'[,;]', author_str) if a.strip()]
+            if authors:
+                return authors
+        
+        # 从文本中提取（通常在标题后面）
+        lines = text.split('\n')
+        for i, line in enumerate(lines[:20]):  # 检查前20行
+            line = line.strip()
+            # 查找包含作者信息的行（通常包含邮箱或机构）
+            if '@' in line or 'university' in line.lower() or 'institute' in line.lower():
+                # 尝试提取前面几行作为作者名
+                for j in range(max(0, i-3), i):
+                    potential_author = lines[j].strip()
+                    if potential_author and len(potential_author) < 100:
+                        # 简单的名字模式匹配
+                        if re.match(r'^[A-Z][a-z]+\s+[A-Z][a-z]+', potential_author):
+                            authors.append(potential_author)
+        
+        return authors if authors else ["未知作者"]
+    
+    def _extract_abstract(self, text: str) -> str:
+        """从文本中提取摘要"""
+        # 查找 Abstract 关键词
+        abstract_patterns = [
+            r'Abstract\s*[:\-]?\s*(.*?)(?=\n\n|\nIntroduction|\n1\.|\nKeywords)',
+            r'ABSTRACT\s*[:\-]?\s*(.*?)(?=\n\n|\nINTRODUCTION|\n1\.|\nKEYWORDS)',
+            r'摘要\s*[:\-]?\s*(.*?)(?=\n\n|关键词|引言|1\.)',
+        ]
+        
+        for pattern in abstract_patterns:
+            match = re.search(pattern, text, re.IGNORECASE | re.DOTALL)
+            if match:
+                abstract = match.group(1).strip()
+                # 限制摘要长度
+                if len(abstract) > 50 and len(abstract) < 2000:
+                    return abstract[:1000]  # 最多返回1000字符
+        
+        # 如果没找到，返回前500个字符作为摘要
+        return text[:500].strip() + "..."
+    
+    async def parse_pdf_from_bytes(self, pdf_bytes: bytes, filename: str = "document.pdf") -> Dict[str, Any]:
+        """
+        从字节流解析 PDF
+        
+        Args:
+            pdf_bytes: PDF 文件的字节内容
+            filename: 文件名（用于日志）
+            
+        Returns:
+            包含解析结果的字典
+        """
+        try:
+            import pdfplumber
+            import io
+            
+            logger.info(f"开始解析 PDF 字节流: {filename}")
+            
+            with pdfplumber.open(io.BytesIO(pdf_bytes)) as pdf:
+                # 提取所有文本
+                full_text = ""
+                for page in pdf.pages:
+                    text = page.extract_text()
+                    if text:
+                        full_text += text + "\n"
+                
+                if not full_text.strip():
+                    return {
+                        "success": False,
+                        "error": "无法从 PDF 中提取文本"
+                    }
+                
+                # 提取元数据
+                metadata = pdf.metadata or {}
+                
+                # 提取信息
+                title = self._extract_title(full_text, metadata)
+                authors = self._extract_authors(full_text, metadata)
+                abstract = self._extract_abstract(full_text)
+                
+                result = {
+                    "success": True,
+                    "title": title,
+                    "authors": authors,
+                    "abstract": abstract,
+                    "full_text": full_text,
+                    "page_count": len(pdf.pages),
+                    "word_count": len(full_text.split()),
+                    "metadata": {
+                        "creator": metadata.get("/Creator", ""),
+                        "producer": metadata.get("/Producer", ""),
+                        "subject": metadata.get("/Subject", ""),
+                        "keywords": metadata.get("/Keywords", "")
+                    }
+                }
+                
+                logger.info(f"PDF 字节流解析成功")
+                return result
+                
+        except Exception as e:
+            logger.error(f"PDF 字节流解析失败: {str(e)}")
+            return {
+                "success": False,
+                "error": f"PDF 解析失败: {str(e)}"
+            }
+
+
+# 全局 PDF 解析器实例
+pdf_parser = PDFParser()

Co-creation-projects/Apricity-InnocoreAI/utils/text_processor.py

@@ -0,0 +1,371 @@
+"""
+InnoCore AI 文本处理工具
+"""
+
+import re
+from typing import List, Dict, Optional, Any, Tuple
+import string
+from collections import Counter
+import asyncio
+
+class TextProcessor:
+    """文本处理器"""
+    
+    def __init__(self):
+        self.stop_words = self._load_stop_words()
+        self.punctuation = string.punctuation
+    
+    def _load_stop_words(self) -> set:
+        """加载停用词"""
+        # 简化的停用词列表
+        return {
+            'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of',
+            'with', 'by', 'is', 'are', 'was', 'were', 'be', 'been', 'have', 'has',
+            'had', 'do', 'does', 'did', 'will', 'would', 'could', 'should', 'may',
+            'might', 'must', 'can', 'this', 'that', 'these', 'those', 'i', 'you',
+            'he', 'she', 'it', 'we', 'they', 'me', 'him', 'her', 'us', 'them',
+            'my', 'your', 'his', 'her', 'its', 'our', 'their', 'mine', 'yours',
+            'hers', 'ours', 'theirs', 'what', 'which', 'who', 'whom', 'whose',
+            'where', 'when', 'why', 'how', 'all', 'each', 'every', 'both', 'few',
+            'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only',
+            'own', 'same', 'so', 'than', 'too', 'very', 'just', 'now', 'also'
+        }
+    
+    def clean_text(self, text: str) -> str:
+        """清理文本"""
+        if not text:
+            return ""
+        
+        # 移除多余的空白字符
+        text = re.sub(r'\s+', ' ', text)
+        
+        # 移除特殊字符（保留基本标点）
+        text = re.sub(r'[^\w\s\.\,\!\?\;\:\-\(\)\[\]\{\}\"\'\/\\]', ' ', text)
+        
+        # 移除多余的空格
+        text = re.sub(r'\s+', ' ', text).strip()
+        
+        return text
+    
+    def tokenize(self, text: str) -> List[str]:
+        """分词"""
+        if not text:
+            return []
+        
+        # 转换为小写并分词
+        words = text.lower().split()
+        
+        # 移除标点符号
+        words = [word.strip(self.punctuation) for word in words]
+        
+        # 过滤空字符串
+        words = [word for word in words if word]
+        
+        return words
+    
+    def remove_stop_words(self, words: List[str]) -> List[str]:
+        """移除停用词"""
+        return [word for word in words if word not in self.stop_words]
+    
+    def extract_sentences(self, text: str) -> List[str]:
+        """提取句子"""
+        if not text:
+            return []
+        
+        # 使用正则表达式分割句子
+        sentences = re.split(r'[.!?]+', text)
+        
+        # 清理和过滤
+        sentences = [s.strip() for s in sentences if s.strip()]
+        
+        return sentences
+    
+    def extract_paragraphs(self, text: str) -> List[str]:
+        """提取段落"""
+        if not text:
+            return []
+        
+        # 按双换行分割段落
+        paragraphs = re.split(r'\n\s*\n', text)
+        
+        # 清理和过滤
+        paragraphs = [p.strip() for p in paragraphs if p.strip()]
+        
+        return paragraphs
+    
+    def calculate_readability(self, text: str) -> Dict[str, float]:
+        """计算文本可读性指标"""
+        if not text:
+            return {"flesch_score": 0.0, "avg_sentence_length": 0.0, "avg_word_length": 0.0}
+        
+        sentences = self.extract_sentences(text)
+        words = self.tokenize(text)
+        
+        if not sentences or not words:
+            return {"flesch_score": 0.0, "avg_sentence_length": 0.0, "avg_word_length": 0.0}
+        
+        # 平均句子长度
+        avg_sentence_length = len(words) / len(sentences)
+        
+        # 平均词长
+        avg_word_length = sum(len(word) for word in words) / len(words)
+        
+        # 简化的Flesch Reading Ease分数
+        flesch_score = 206.835 - (1.015 * avg_sentence_length) - (84.6 * avg_word_length)
+        
+        return {
+            "flesch_score": max(0, min(100, flesch_score)),
+            "avg_sentence_length": avg_sentence_length,
+            "avg_word_length": avg_word_length
+        }
+    
+    def extract_key_phrases(self, text: str, max_phrases: int = 10) -> List[str]:
+        """提取关键短语"""
+        if not text:
+            return []
+        
+        # 简化的关键短语提取
+        words = self.tokenize(text)
+        words = self.remove_stop_words(words)
+        
+        # 寻找常见的学术短语模式
+        phrase_patterns = [
+            r'\b\w+\s+\w+\b',  # 两词短语
+            r'\b\w+\s+\w+\s+\w+\b',  # 三词短语
+        ]
+        
+        phrases = []
+        for pattern in phrase_patterns:
+            matches = re.findall(pattern, text.lower())
+            phrases.extend(matches)
+        
+        # 计算短语频率
+        phrase_freq = Counter(phrases)
+        
+        # 过滤和排序
+        filtered_phrases = [
+            phrase for phrase, freq in phrase_freq.items()
+            if freq > 1 and len(phrase.split()) >= 2
+        ]
+        
+        filtered_phrases.sort(key=lambda x: phrase_freq[x], reverse=True)
+        
+        return filtered_phrases[:max_phrases]
+    
+    def detect_language(self, text: str) -> str:
+        """检测语言（简化实现）"""
+        if not text:
+            return "unknown"
+        
+        # 简单的语言检测基于常见词汇
+        chinese_chars = len(re.findall(r'[\u4e00-\u9fff]', text))
+        english_chars = len(re.findall(r'[a-zA-Z]', text))
+        
+        total_chars = chinese_chars + english_chars
+        
+        if total_chars == 0:
+            return "unknown"
+        
+        chinese_ratio = chinese_chars / total_chars
+        
+        if chinese_ratio > 0.3:
+            return "chinese"
+        elif english_chars > 0:
+            return "english"
+        else:
+            return "unknown"
+    
+    def extract_citations(self, text: str) -> List[Dict[str, Any]]:
+        """提取引用"""
+        citations = []
+        
+        # 数字引用模式 [1], [2-3]
+        numeric_pattern = r'\[(\d+(?:-\d+)?)\]'
+        numeric_matches = re.finditer(numeric_pattern, text)
+        for match in numeric_matches:
+            citations.append({
+                "type": "numeric",
+                "text": match.group(0),
+                "reference": match.group(1),
+                "position": match.start()
+            })
+        
+        # 作者年份引用 (Smith, 2020)
+        author_year_pattern = r'\(([A-Za-z]+(?:\s+et\s+al\.)?,\s*\d{4})\)'
+        author_year_matches = re.finditer(author_year_pattern, text)
+        for match in author_year_matches:
+            citations.append({
+                "type": "author_year",
+                "text": match.group(0),
+                "reference": match.group(1),
+                "position": match.start()
+            })
+        
+        return citations
+    
+    def extract_numbers_and_units(self, text: str) -> List[Dict[str, Any]]:
+        """提取数字和单位"""
+        patterns = [
+            r'(\d+(?:\.\d+)?)\s*([a-zA-Z%]+)',  # 数字 + 单位
+            r'(\d+(?:,\d{3})*(?:\.\d+)?)',  # 带逗号的数字
+        ]
+        
+        results = []
+        for pattern in patterns:
+            matches = re.finditer(pattern, text)
+            for match in matches:
+                results.append({
+                    "text": match.group(0),
+                    "number": match.group(1),
+                    "unit": match.group(2) if len(match.groups()) > 1 else "",
+                    "position": match.start()
+                })
+        
+        return results
+    
+    def extract_acronyms(self, text: str) -> Dict[str, str]:
+        """提取缩写词"""
+        acronyms = {}
+        
+        # 查找全称(缩写)模式
+        acronym_pattern = r'([A-Za-z\s]+)\s*\(([A-Z]{2,})\)'
+        matches = re.finditer(acronym_pattern, text)
+        
+        for match in matches:
+            full_name = match.group(1).strip()
+            acronym = match.group(2)
+            
+            # 验证缩写是否来自全称的首字母
+            initials = ''.join([word[0].upper() for word in full_name.split() if word])
+            
+            if acronym.startswith(initials):
+                acronyms[acronym] = full_name
+        
+        return acronyms
+    
+    def summarize_text(self, text: str, max_sentences: int = 3) -> str:
+        """文本摘要（简化实现）"""
+        if not text:
+            return ""
+        
+        sentences = self.extract_sentences(text)
+        
+        if len(sentences) <= max_sentences:
+            return " ".join(sentences)
+        
+        # 简单的摘要算法：选择包含关键词最多的句子
+        words = self.tokenize(text)
+        words = self.remove_stop_words(words)
+        word_freq = Counter(words)
+        
+        sentence_scores = []
+        for sentence in sentences:
+            sentence_words = self.tokenize(sentence)
+            sentence_words = self.remove_stop_words(sentence_words)
+            
+            score = sum(word_freq.get(word, 0) for word in sentence_words)
+            sentence_scores.append((sentence, score))
+        
+        # 选择得分最高的句子
+        sentence_scores.sort(key=lambda x: x[1], reverse=True)
+        top_sentences = [sentence for sentence, score in sentence_scores[:max_sentences]]
+        
+        # 按原文顺序排列
+        summary_sentences = []
+        for sentence in sentences:
+            if sentence in top_sentences:
+                summary_sentences.append(sentence)
+        
+        return " ".join(summary_sentences)
+    
+    def extract_entities(self, text: str) -> Dict[str, List[str]]:
+        """实体提取（简化实现）"""
+        entities = {
+            "persons": [],
+            "organizations": [],
+            "locations": [],
+            "dates": [],
+            "numbers": []
+        }
+        
+        # 人名模式（简化）
+        person_pattern = r'\b([A-Z][a-z]+\s+[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\b'
+        person_matches = re.findall(person_pattern, text)
+        entities["persons"] = list(set(person_matches))
+        
+        # 组织模式（简化）
+        org_patterns = [
+            r'\b([A-Z][a-z]+\s+(?:University|Institute|Laboratory|Company|Corp|Inc|Ltd))\b',
+            r'\b((?:[A-Z]+\s*){2,})\b'
+        ]
+        for pattern in org_patterns:
+            matches = re.findall(pattern, text)
+            entities["organizations"].extend(matches)
+        entities["organizations"] = list(set(entities["organizations"]))
+        
+        # 日期模式
+        date_patterns = [
+            r'\b(\d{4})\b',
+            r'\b(\d{1,2}/\d{1,2}/\d{4})\b',
+            r'\b((?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]*\s+\d{1,2},?\s+\d{4})\b'
+        ]
+        for pattern in date_patterns:
+            matches = re.findall(pattern, text)
+            entities["dates"].extend(matches)
+        entities["dates"] = list(set(entities["dates"]))
+        
+        # 数字模式
+        number_pattern = r'\b(\d+(?:\.\d+)?)\b'
+        number_matches = re.findall(number_pattern, text)
+        entities["numbers"] = list(set(number_matches))
+        
+        return entities
+    
+    def calculate_text_similarity(self, text1: str, text2: str) -> float:
+        """计算文本相似度（基于词汇重叠）"""
+        if not text1 or not text2:
+            return 0.0
+        
+        words1 = set(self.tokenize(text1))
+        words2 = set(self.tokenize(text2))
+        
+        if not words1 or not words2:
+            return 0.0
+        
+        intersection = words1.intersection(words2)
+        union = words1.union(words2)
+        
+        return len(intersection) / len(union)
+    
+    async def process_batch(self, texts: List[str], operations: List[str]) -> List[Dict[str, Any]]:
+        """批量处理文本"""
+        results = []
+        
+        for text in texts:
+            result = {"text": text}
+            
+            for operation in operations:
+                if operation == "clean":
+                    result["cleaned"] = self.clean_text(text)
+                elif operation == "tokenize":
+                    result["tokens"] = self.tokenize(text)
+                elif operation == "sentences":
+                    result["sentences"] = self.extract_sentences(text)
+                elif operation == "paragraphs":
+                    result["paragraphs"] = self.extract_paragraphs(text)
+                elif operation == "readability":
+                    result["readability"] = self.calculate_readability(text)
+                elif operation == "key_phrases":
+                    result["key_phrases"] = self.extract_key_phrases(text)
+                elif operation == "language":
+                    result["language"] = self.detect_language(text)
+                elif operation == "citations":
+                    result["citations"] = self.extract_citations(text)
+                elif operation == "entities":
+                    result["entities"] = self.extract_entities(text)
+                elif operation == "summary":
+                    result["summary"] = self.summarize_text(text)
+            
+            results.append(result)
+        
+        return results