Initial commit: LightStar project with Next.js frontend and FastAPI backend

.env.example

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+# API密钥
+DEEPSEEK_API_KEY=your_deepseek_api_key_here
+
+# 环境设置
+LIGHTSTAR_ENV=development
+
+# 检索配置
+MAX_SEARCH_RESULTS=15
+ARXIV_SEARCH_TIMEOUT=30
+
+# 聚类配置
+DEFAULT_EMBEDDING_MODEL=all-MiniLM-L6-v2
+DEFAULT_NUM_CLUSTERS=3
+
+# CORS设置
+CORS_ORIGINS=http://localhost:3000,http://localhost:8000

.gitignore

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+# Node.js
+node_modules/
+.next/
+out/
+build/
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+.env
+
+# OS specific
+.DS_Store
+Thumbs.db
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo

README.md

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+# 文献启明星（LightStar）- 科研调研智能体系统
+
+## 项目简介
+
+**文献启明星**是一款面向科研初学者的 AI 智能体系统，致力于帮助用户从模糊的研究意图出发，自动完成科研调研的核心流程，包括：主题拆解、文献检索、文献聚类、结构总结、代表文献推荐与调研报告生成。
+
+用户仅需输入一句话，即可获得一份结构化的初步调研成果，用于选题、开题、综述写作或项目申报。本项目采用前后端分离架构，支持多端适配（Web、移动、桌面），为不同场景下的科研人员提供便捷服务。
+
+## 技术架构
+
+### 后端 (Backend)
+- **框架**: FastAPI
+- **语言**: Python 3.9+
+- **核心依赖**:
+  - arxiv API (文献检索)
+  - sentence-transformers (文本向量化)
+  - scikit-learn (聚类分析)
+
+### 前端 (Frontend)
+- **框架**: Next.js 15
+- **UI构建**: Tailwind CSS 3
+- **动画效果**: Framer Motion
+- **特色功能**:
+  - 响应式设计，适配不同设备
+  - 多主题切换系统 (深邃宇宙、高级深海、现代科技、精致夜光)
+  - 玻璃态UI设计 (Glassmorphism)
+
+## 项目结构
+
+```bash
+lightstar/
+├── backend/                    # FastAPI后端服务
+│   ├── main.py                 # 主入口
+│   ├── config.py               # 配置文件
+│   ├── api/                    # API路由
+│   ├── core/                   # 核心业务逻辑
+│   │   ├── research.py         # 研究流程
+│   │   ├── clustering.py       # 聚类算法
+│   │   └── report.py           # 报告生成
+│   └── utils/                  # 工具函数
+│
+└── lightstar-web/              # Next.js前端应用
+    ├── src/
+    │   ├── app/                # 页面
+    │   ├── components/         # UI组件
+    │   │   ├── ui/             # 基础UI组件
+    │   │   └── forms/          # 表单组件
+    │   └── lib/                # 工具函数
+    └── public/                 # 静态资源
+```
+
+## 使用说明
+
+### 后端服务启动
+
+```bash
+cd backend
+pip install -r requirements.txt
+python main.py
+```
+
+后端服务将在 http://localhost:8000 启动
+
+### 前端应用启动
+
+```bash
+cd lightstar-web
+npm install
+npm run dev
+```
+
+前端应用将在 http://localhost:3000 启动
+
+## 功能特点
+
+- 智能研究主题解析
+- 自动文献检索与分析
+- 文献聚类与可视化
+- 研究报告自动生成
+- 多主题UI，提供舒适的视觉体验
+- 响应式设计，支持各类设备
+
+---
+
+## 二、目标用户
+
+- 初入科研的硕士/博士研究生
+- 缺乏调研经验的年轻教师或科研助理
+- 对某一新领域感兴趣的项目开发人员
+- 需要在多种设备上进行科研调研的研究人员
+
+---
+
+## 三、核心价值
+
+| 用户痛点 | 文献启明星的解决方案 |
+|----------|----------------------|
+| 不会选题、不知从哪开始 | AI 拆解主题，提炼研究方向 |
+| 不会查文献，效率低 | 自动调用开放数据库进行检索 |
+| 无法判断文献质量 | 提供引用数、摘要、推荐标记 |
+| 看完文献却写不出综述 | 聚类归纳 + 自动报告生成 |
+| 时间紧、任务重、无从下手 | 一站式完成调研闭环 |
+| 需要随时随地进行研究 | 多端适配，支持Web/移动/桌面 |
+
+---
+
+## 四、功能流程图
+
+```text
+用户输入研究意图
+         ↓
+AI 拆解关键词与子主题
+         ↓
+调用文献 API 自动检索（arXiv / Semantic Scholar）
+         ↓
+聚类文献主题，归纳关键词与研究路线
+         ↓
+推荐经典/综述文献（摘要 + 链接）
+         ↓
+输出结构化调研报告草稿（可导出）
+```
+
+---
+
+## 五、核心功能模块
+
+| 模块 | 功能描述 |
+|------|----------|
+| 主题解析模块 | 拆分研究意图，生成关键词树与子问题列表 |
+| 文献检索模块 | 接入开放 API，按主题获取高质量文献元数据 |
+| 文献聚类模块 | 使用向量聚类，分析研究方向脉络与热点 |
+| 推荐与标注模块 | 推荐高引用文献与综述，添加标签与摘要 |
+| 报告生成模块 | 调用大模型生成调研报告初稿（1000~1500字） |
+| 可视化模块 | 关键词云、主题聚类图、研究路线图等 |
+
+---
+
+## 六、技术架构（前后端分离多端适配）
+
+### 后端架构
+- **FastAPI**：高性能API框架，提供核心功能接口
+- **Python 3.9+**：后端开发语言
+- **Pydantic**：数据验证和序列化
+- **SQLAlchemy**：数据持久化（可选）
+- **文献API集成**：arXiv/Semantic Scholar/Crossref
+- **大模型调用**：DeepSeek/其他大语言模型
+- **文本向量化**：sentence-transformers + 向量索引
+
+### 前端架构
+- **Web端**：React/Next.js 构建SPA应用
+- **移动端**：React Native/Flutter 实现跨平台移动应用
+- **桌面端**：Electron 打包桌面应用
+- **统一状态管理**：确保各端数据一致性
+- **离线功能支持**：本地缓存和同步机制
+
+---
+
+## 七、项目结构
+lightstar/
+├── backend/ # 后端API服务
+│ ├── main.py # FastAPI入口
+│ ├── config.py # 配置文件
+│ ├── api/ # API路由
+│ ├── core/ # 核心业务逻辑
+│ └── utils/ # 工具函数
+│
+├── frontend/ # 前端应用
+│ ├── web/ # Web应用
+│ ├── mobile/ # 移动应用
+│ └── desktop/ # 桌面应用
+│
+└── README.md # 项目说明文档
+
+---
+
+## 八、用户交互示例
+
+**输入：**
+我想研究碳中和背景下的氢能运输方式
+
+**输出：**
+- 拆解主题为：碳中和 / 氢能 / 储运 / 能效
+- 聚类出三类主流方向：液氢技术、固态储运、长距离管网
+- 推荐 6 篇代表性论文（含摘要与链接）
+- 输出一篇可编辑的调研报告草稿
+
+---
+
+## 九、项目落地优势
+
+- **多端适配**：适用于实验室、课堂、移动场景等多种使用环境
+- **独立开发可行**：模块化设计，便于分阶段实现
+- **前后端分离**：便于团队协作和功能扩展
+- **API驱动设计**：为未来的第三方集成提供基础
+- **无复杂训练需求**：依赖现有开源模型和API，降低部署门槛
+- **实际需求驱动**：直接解决科研人员的真实痛点
+
+---
+
+## 十、后续可拓展方向
+
+- **账户系统**：跨设备同步研究历史和成果
+- **协作功能**：支持多人协同进行文献调研
+- **智能推荐**：基于用户历史研究兴趣推荐相关主题
+- **知识图谱**：构建研究领域知识网络
+- **插件生态**：与Zotero、Notion等工具集成
+- **多语言支持**：扩展至更多科研语言环境
+- **离线模式**：支持无网络环境下基本功能使用
+
+---
+
+## 十一、开发路线图
+
+### 第一阶段：后端核心功能
+- 实现基础API架构
+- 完成关键词提取模块
+- 集成文献检索API
+- 实现基础聚类算法
+- 开发报告生成功能
+
+### 第二阶段：Web端开发
+- 构建Web界面
+- 实现交互功能
+- 完成可视化组件
+- 用户数据管理
+
+### 第三阶段：移动与桌面适配
+- 开发移动应用
+- 构建桌面客户端
+- 实现多端数据同步
+- 支持离线功能
+
+---
+
+## 十二、技术依赖
+
+| 依赖类型 | 主要技术 |
+|---------|---------|
+| 后端框架 | FastAPI, SQLAlchemy |
+| 前端技术 | React/Next.js, React Native/Flutter, Electron |
+| AI模型 | DeepSeek, Sentence-Transformers |
+| 外部API | arXiv, Semantic Scholar, Crossref |
+| 数据处理 | Pandas, FAISS, Scikit-learn |
+| 可视化 | Plotly, D3.js |

backend/api/init.py

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+"""
+API路由包
+"""

backend/api/research.py

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+"""
+研究相关API路由
+"""
+
+from fastapi import APIRouter, HTTPException, BackgroundTasks, Query
+from pydantic import BaseModel, Field
+from typing import List, Dict, Any, Optional
+import logging
+
+from backend.core.research import ResearchAgent
+from backend.core.clustering import PaperClusterer
+from backend.core.report import ReportGenerator
+from backend.config import MAX_SEARCH_RESULTS
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/research", tags=["research"])
+
+# 数据模型
+class ResearchRequest(BaseModel):
+    research_intent: str = Field(..., description="用户的研究意图")
+    max_results: int = Field(MAX_SEARCH_RESULTS, description="最大检索结果数量")
+
+class KeywordsRequest(BaseModel):
+    research_intent: str = Field(..., description="用户的研究意图")
+
+class PaperSearchRequest(BaseModel):
+    keywords: List[str] = Field(..., description="检索关键词")
+    max_results: int = Field(MAX_SEARCH_RESULTS, description="最大检索结果数量")
+
+class ClusterRequest(BaseModel):
+    papers: List[Dict[str, Any]] = Field(..., description="要聚类的论文")
+    num_clusters: int = Field(0, description="聚类数量，0表示自动确定")
+
+class ReportRequest(BaseModel):
+    research_intent: str = Field(..., description="研究意图")
+    keywords: List[str] = Field(..., description="关键词")
+    papers: List[Dict[str, Any]] = Field(..., description="检索到的论文")
+    clusters: Optional[Dict[str, Any]] = Field(None, description="聚类结果")
+
+# 全局实例
+research_agent = ResearchAgent()
+paper_clusterer = PaperClusterer()
+report_generator = ReportGenerator()
+
+# 路由定义
+@router.post("/process")
+async def process_research(request: ResearchRequest):
+    """处理完整的研究流程"""
+    try:
+        logger.info(f"Processing research intent: {request.research_intent}")
+        result = await research_agent.process_research_intent(
+            research_intent=request.research_intent,
+            max_results=request.max_results
+        )
+        return result
+    except Exception as e:
+        logger.error(f"Error in research process: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/extract-keywords")
+async def extract_keywords(request: KeywordsRequest):
+    """从研究意图中提取关键词"""
+    try:
+        logger.info(f"Extracting keywords from: {request.research_intent}")
+        result = await research_agent.llm_client.extract_keywords(
+            research_topic=request.research_intent
+        )
+        return {"keywords": result}
+    except Exception as e:
+        logger.error(f"Error extracting keywords: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/search-papers")
+async def search_papers(request: PaperSearchRequest):
+    """根据关键词检索论文"""
+    try:
+        logger.info(f"Searching papers with keywords: {request.keywords}")
+        papers = []
+        for keyword in request.keywords:
+            results = await research_agent.arxiv_client.search_papers(
+                query=keyword,
+                max_results=max(3, request.max_results // len(request.keywords))
+            )
+            papers.extend(results)
+        
+        # 去重
+        unique_papers = []
+        paper_ids = set()
+        for paper in papers:
+            if paper["id"] not in paper_ids:
+                unique_papers.append(paper)
+                paper_ids.add(paper["id"])
+        
+        return {"papers": unique_papers, "count": len(unique_papers)}
+    except Exception as e:
+        logger.error(f"Error searching papers: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/cluster-papers")
+async def cluster_papers(request: ClusterRequest):
+    """对论文进行聚类分析"""
+    try:
+        logger.info(f"Clustering {len(request.papers)} papers")
+        result = paper_clusterer.cluster_papers(
+            papers=request.papers,
+            num_clusters=request.num_clusters
+        )
+        return result
+    except Exception as e:
+        logger.error(f"Error clustering papers: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+@router.post("/generate-report")
+async def generate_report(request: ReportRequest):
+    """生成研究报告"""
+    try:
+        logger.info(f"Generating report for: {request.research_intent}")
+        report = await report_generator.generate_report(
+            research_intent=request.research_intent,
+            keywords=request.keywords,
+            papers=request.papers,
+            clusters=request.clusters
+        )
+        return report
+    except Exception as e:
+        logger.error(f"Error generating report: {str(e)}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))

backend/config.py

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+"""
+文献启明星配置文件
+管理API密钥、模型参数和其他配置选项
+"""
+
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+
+# 加载环境变量
+load_dotenv()
+
+# 基础路径
+BASE_DIR = Path(__file__).resolve().parent
+DATA_DIR = BASE_DIR / "data"
+CACHE_DIR = DATA_DIR / "cache"
+
+# 确保目录存在
+os.makedirs(CACHE_DIR, exist_ok=True)
+
+# API配置
+DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY", "")
+DEEPSEEK_API_URL = os.getenv("DEEPSEEK_API_URL", "https://api.deepseek.com/v1")
+DEEPSEEK_MODEL = os.getenv("DEEPSEEK_MODEL", "deepseek-chat")
+
+# 检索配置
+MAX_SEARCH_RESULTS = int(os.getenv("MAX_SEARCH_RESULTS", "15"))
+ARXIV_SEARCH_TIMEOUT = int(os.getenv("ARXIV_SEARCH_TIMEOUT", "30"))
+
+# 聚类配置
+DEFAULT_EMBEDDING_MODEL = os.getenv("DEFAULT_EMBEDDING_MODEL", "all-MiniLM-L6-v2")
+DEFAULT_NUM_CLUSTERS = int(os.getenv("DEFAULT_NUM_CLUSTERS", "3"))
+MIN_PAPERS_FOR_CLUSTERING = int(os.getenv("MIN_PAPERS_FOR_CLUSTERING", "5"))
+
+# 报告生成配置
+REPORT_MAX_LENGTH = int(os.getenv("REPORT_MAX_LENGTH", "1500"))
+REPORT_TEMPERATURE = float(os.getenv("REPORT_TEMPERATURE", "0.3"))
+
+# CORS设置 - 允许前端访问API
+CORS_ORIGINS = os.getenv("CORS_ORIGINS", "http://localhost:3000,http://localhost:8000").split(",")
+
+# 缓存设置
+ENABLE_CACHE = os.getenv("ENABLE_CACHE", "True").lower() == "true"
+CACHE_EXPIRY = int(os.getenv("CACHE_EXPIRY", "86400"))  # 默认24小时

backend/core/clustering.py

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+"""
+文献聚类模块
+对文献进行向量化和聚类分析
+"""
+
+from typing import Dict, List, Any, Optional
+import numpy as np
+from sklearn.cluster import KMeans
+from sklearn.manifold import TSNE
+import logging
+from sentence_transformers import SentenceTransformer
+
+from backend.config import DEFAULT_EMBEDDING_MODEL, DEFAULT_NUM_CLUSTERS
+
+logger = logging.getLogger(__name__)
+
+class PaperClusterer:
+    """文献聚类引擎"""
+    
+    def __init__(self, embedding_model: str = DEFAULT_EMBEDDING_MODEL):
+        """
+        初始化聚类引擎
+        
+        Args:
+            embedding_model: 使用的嵌入模型名称
+        """
+        self.model_name = embedding_model
+        self._model = None  # 延迟加载
+    
+    @property
+    def model(self):
+        """延迟加载嵌入模型"""
+        if self._model is None:
+            logger.info(f"Loading embedding model: {self.model_name}")
+            try:
+                self._model = SentenceTransformer(self.model_name)
+            except Exception as e:
+                logger.error(f"Failed to load embedding model: {str(e)}")
+                raise
+        return self._model
+    
+    def _extract_texts(self, papers: List[Dict[str, Any]]) -> List[str]:
+        """从论文中提取文本用于嵌入"""
+        texts = []
+        for paper in papers:
+            # 合并标题和摘要以获得更好的嵌入
+            title = paper.get("title", "")
+            summary = paper.get("summary", "")
+            text = f"{title} {summary}"
+            texts.append(text)
+        return texts
+    
+    def cluster_papers(
+        self, 
+        papers: List[Dict[str, Any]],
+        num_clusters: int = DEFAULT_NUM_CLUSTERS
+    ) -> Dict[str, Any]:
+        """
+        对论文进行聚类分析
+        
+        Args:
+            papers: 论文列表
+            num_clusters: 聚类数量，0表示自动确定
+            
+        Returns:
+            聚类结果字典
+        """
+        if len(papers) < 3:
+            logger.warning("Too few papers for clustering")
+            return {
+                "clusters": [],
+                "cluster_info": {},
+                "visualization_data": None,
+                "error": "Too few papers for clustering"
+            }
+        
+        try:
+            # 1. 提取文本
+            texts = self._extract_texts(papers)
+            
+            # 2. 计算嵌入
+            logger.info(f"Computing embeddings for {len(texts)} papers")
+            embeddings = self.model.encode(texts, show_progress_bar=True)
+            
+            # 3. 确定聚类数量（如果未指定）
+            if num_clusters <= 0:
+                # 使用论文数量的平方根作为默认聚类数
+                num_clusters = min(max(2, int(np.sqrt(len(papers)))), 8)
+            
+            # 4. 聚类分析
+            logger.info(f"Clustering into {num_clusters} clusters")
+            kmeans = KMeans(n_clusters=num_clusters, random_state=42)
+            cluster_labels = kmeans.fit_predict(embeddings)
+            
+            # 5. 可视化数据准备（使用t-SNE降维）
+            logger.info("Generating 2D visualization data with t-SNE")
+            tsne = TSNE(n_components=2, random_state=42)
+            vis_data = tsne.fit_transform(embeddings)
+            
+            # 6. 整理聚类结果
+            clusters = [[] for _ in range(num_clusters)]
+            for i, (paper, label) in enumerate(zip(papers, cluster_labels)):
+                paper_with_cluster = paper.copy()
+                paper_with_cluster["cluster"] = int(label)
+                paper_with_cluster["vis_x"] = float(vis_data[i][0])
+                paper_with_cluster["vis_y"] = float(vis_data[i][1])
+                clusters[label].append(paper_with_cluster)
+            
+            # 7. 提取聚类信息
+            cluster_info = {}
+            for i in range(num_clusters):
+                cluster_info[i] = {
+                    "size": len(clusters[i]),
+                    "papers": clusters[i]
+                }
+            
+            # 8. 构造返回结果
+            result = {
+                "clusters": clusters,
+                "cluster_info": cluster_info,
+                "visualization_data": {
+                    "x": vis_data[:, 0].tolist(),
+                    "y": vis_data[:, 1].tolist(),
+                    "labels": cluster_labels.tolist()
+                }
+            }
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"Clustering error: {str(e)}", exc_info=True)
+            return {
+                "clusters": [],
+                "cluster_info": {},
+                "visualization_data": None,
+                "error": str(e)
+            }

backend/core/init.py

@@ -0,0 +1,3 @@
+"""
+核心业务逻辑包
+"""

backend/core/report.py

@@ -0,0 +1,114 @@
+"""
+报告生成模块
+根据研究结果生成结构化报告
+"""
+
+from typing import Dict, List, Any, Optional
+import logging
+from backend.utils.api_client import LLMClient
+from backend.config import REPORT_MAX_LENGTH, REPORT_TEMPERATURE
+
+logger = logging.getLogger(__name__)
+
+class ReportGenerator:
+    """研究报告生成器"""
+    
+    def __init__(self):
+        self.llm_client = LLMClient()
+    
+    async def generate_report(
+        self, 
+        research_intent: str,
+        keywords: List[str],
+        papers: List[Dict[str, Any]],
+        clusters: Optional[Dict[str, Any]] = None,
+        max_length: int = REPORT_MAX_LENGTH,
+        temperature: float = REPORT_TEMPERATURE
+    ) -> Dict[str, Any]:
+        """
+        生成研究报告
+        
+        Args:
+            research_intent: 用户的研究意图
+            keywords: 提取的关键词
+            papers: 检索到的论文
+            clusters: 聚类结果
+            max_length: 报告最大长度
+            temperature: 生成参数
+            
+        Returns:
+            包含报告内容的字典
+        """
+        try:
+            # 1. 准备论文信息
+            paper_info = []
+            for i, paper in enumerate(papers[:10]):  # 只使用前10篇
+                paper_info.append(
+                    f"{i+1}. {paper['title']} ({', '.join(paper['authors'][:3])})"
+                    f"\n摘要: {paper['summary'][:300]}..."
+                )
+            
+            paper_text = "\n\n".join(paper_info)
+            
+            # 2. 准备聚类信息
+            cluster_text = ""
+            if clusters and "cluster_info" in clusters:
+                for cluster_id, info in clusters["cluster_info"].items():
+                    papers_in_cluster = info["papers"]
+                    if papers_in_cluster:
+                        titles = [p["title"] for p in papers_in_cluster[:3]]
+                        cluster_text += f"聚类 {cluster_id+1} (包含 {len(papers_in_cluster)} 篇论文):\n"
+                        cluster_text += "代表性论文: " + "; ".join(titles) + "\n\n"
+            
+            # 3. 构造提示
+            cluster_section = f"## 文献聚类\n{cluster_text}" if cluster_text else ""
+            
+            prompt = f"""
+            作为一位科研助手，请基于以下信息为研究主题生成一份结构化的调研报告。
+            
+            ## 研究主题
+            {research_intent}
+            
+            ## 关键词
+            {', '.join(keywords)}
+            
+            ## 检索到的文献
+            {paper_text}
+            
+            {cluster_section}
+            
+            请生成一份完整的调研报告，包括以下部分：
+            1. 研究背景与意义（简要介绍该领域的重要性和研究现状）
+            2. 研究方向分析（基于文献聚类识别主要研究方向）
+            3. 关键技术与方法（总结该领域的主要技术和方法论）
+            4. 代表性研究工作（列举2-3项代表性研究及其贡献）
+            5. 未来研究展望（分析领域挑战与未来可能的突破点）
+            
+            报告应当学术严谨、结构清晰、信息准确。长度控制在1000-1500字。
+            """
+            
+            # 4. 生成报告
+            logger.info("Generating research report")
+            report_content = await self.llm_client.generate_text(
+                prompt=prompt,
+                temperature=temperature,
+                max_tokens=max_length
+            )
+            
+            # 5. 构造结果
+            report = {
+                "research_intent": research_intent,
+                "content": report_content,
+                "keywords": keywords,
+                "paper_count": len(papers)
+            }
+            
+            return report
+            
+        except Exception as e:
+            logger.error(f"Report generation error: {str(e)}", exc_info=True)
+            return {
+                "research_intent": research_intent,
+                "content": f"报告生成失败: {str(e)}",
+                "error": str(e)
+            }

backend/core/research.py

@@ -0,0 +1,116 @@
+"""
+研究流程管理模块
+整合关键词提取、文献检索和聚类分析
+"""
+
+import asyncio
+from typing import Dict, List, Any, Optional
+import logging
+import time
+import json
+from pathlib import Path
+
+from backend.utils.api_client import LLMClient, ArxivClient
+from backend.utils.keywords import extract_keywords, expand_search_queries
+from backend.config import (
+    MAX_SEARCH_RESULTS,
+    CACHE_DIR,
+    ENABLE_CACHE
+)
+
+logger = logging.getLogger(__name__)
+
+class ResearchAgent:
+    """研究智能体，管理整个研究流程"""
+    
+    def __init__(self):
+        self.llm_client = LLMClient()
+        self.arxiv_client = ArxivClient()
+    
+    async def process_research_intent(
+        self, 
+        research_intent: str,
+        max_results: int = MAX_SEARCH_RESULTS
+    ) -> Dict[str, Any]:
+        """
+        处理研究意图，执行完整的研究流程
+        
+        Args:
+            research_intent: 用户输入的研究意图
+            max_results: 最大检索结果数量
+            
+        Returns:
+            包含研究结果的字典
+        """
+        start_time = time.time()
+        
+        # 初始化结果字典
+        result = {
+            "research_intent": research_intent,
+            "timestamp": time.time(),
+            "keywords": [],
+            "papers": [],
+            "clusters": [],
+            "status": "processing"
+        }
+        
+        try:
+            # 1. 提取关键词
+            logger.info(f"Extracting keywords from: {research_intent}")
+            keywords = await extract_keywords(research_intent, self.llm_client)
+            result["keywords"] = keywords
+            logger.info(f"Extracted keywords: {keywords}")
+            
+            # 2. 扩展搜索查询
+            search_queries = await expand_search_queries(keywords, self.llm_client)
+            result["search_queries"] = search_queries
+            logger.info(f"Generated search queries: {search_queries}")
+            
+            # 3. 检索文献
+            all_papers = []
+            for query in search_queries:
+                logger.info(f"Searching papers with query: {query}")
+                papers = await self.arxiv_client.search_papers(
+                    query=query,
+                    max_results=max_results // len(search_queries)
+                )
+                all_papers.extend(papers)
+            
+            # 去重
+            unique_papers = []
+            paper_ids = set()
+            for paper in all_papers:
+                if paper["id"] not in paper_ids:
+                    unique_papers.append(paper)
+                    paper_ids.add(paper["id"])
+            
+            result["papers"] = unique_papers
+            logger.info(f"Found {len(unique_papers)} unique papers")
+            
+            # 4. 聚类分析（这部分将在clustering.py中实现）
+            # TODO: 实现文献聚类
+            
+            result["status"] = "completed"
+            result["processing_time"] = time.time() - start_time
+            
+            # 缓存结果
+            if ENABLE_CACHE:
+                self._cache_result(result)
+            
+            return result
+        
+        except Exception as e:
+            logger.error(f"Error in research process: {str(e)}", exc_info=True)
+            result["status"] = "error"
+            result["error"] = str(e)
+            return result
+    
+    def _cache_result(self, result: Dict[str, Any]) -> None:
+        """缓存研究结果"""
+        try:
+            cache_file = Path(CACHE_DIR) / f"research_{int(time.time())}.json"
+            with open(cache_file, "w", encoding="utf-8") as f:
+                json.dump(result, f, ensure_ascii=False, indent=2)
+            logger.info(f"Cached result to {cache_file}")
+        except Exception as e:
+            logger.error(f"Failed to cache result: {str(e)}")

backend/lightstar_api.log

@@ -0,0 +1,7 @@
+2025-04-15 22:38:10,103 - __main__ - INFO - Starting LightStar API server
+2025-04-15 22:43:18,634 - backend.api.research - INFO - Extracting keywords from: string
+2025-04-15 22:43:18,634 - backend.api.research - ERROR - Error extracting keywords: 'LLMClient' object has no attribute 'extract_keywords'
+Traceback (most recent call last):
+  File "C:\Users\ÐìÃ÷µÏ\Desktop\Lightstar\backend\api\research.py", line 66, in extract_keywords
+    result = await research_agent.llm_client.extract_keywords(
+AttributeError: 'LLMClient' object has no attribute 'extract_keywords'

backend/main.py

@@ -0,0 +1,76 @@
+"""
+文献启明星 API 服务器
+FastAPI应用程序入口
+"""
+
+import logging
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pathlib import Path
+import json
+import sys
+import os
+
+# 添加项目根目录到Python路径
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from backend.api.research import router as research_router
+from backend.config import CORS_ORIGINS, BASE_DIR
+
+# 配置日志
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    handlers=[
+        logging.StreamHandler(),
+        logging.FileHandler(Path(BASE_DIR) / "lightstar_api.log")
+    ]
+)
+
+logger = logging.getLogger(__name__)
+
+# 创建FastAPI应用
+app = FastAPI(
+    title="LightStar API",
+    description="文献启明星 - 科研调研智能体API服务",
+    version="0.1.0",
+)
+
+# 配置CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=CORS_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 注册路由
+app.include_router(research_router)
+
+# 健康检查端点
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy", "version": "0.1.0"}
+
+# API信息端点
+@app.get("/")
+async def root():
+    return {
+        "name": "LightStar API",
+        "description": "文献启明星 - 科研调研智能体API服务",
+        "version": "0.1.0",
+        "endpoints": [
+            "/api/research/process",
+            "/api/research/extract-keywords",
+            "/api/research/search-papers",
+            "/api/research/cluster-papers",
+            "/api/research/generate-report"
+        ]
+    }
+
+# 启动服务器
+if __name__ == "__main__":
+    logger.info("Starting LightStar API server")
+    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)

backend/utils/api_client.py

@@ -0,0 +1,120 @@
+"""
+API客户端工具
+用于与外部API（如LLM API、文献检索API）进行交互
+"""
+
+import httpx
+import json
+import os
+import time
+from typing import Dict, List, Any, Optional
+import logging
+
+from backend.config import DEEPSEEK_API_KEY, DEEPSEEK_API_URL, DEEPSEEK_MODEL
+
+logger = logging.getLogger(__name__)
+
+class LLMClient:
+    """大型语言模型API客户端"""
+    
+    def __init__(
+        self, 
+        api_key: Optional[str] = None,
+        model: Optional[str] = None,
+        api_url: Optional[str] = None
+    ):
+        self.api_key = api_key or DEEPSEEK_API_KEY
+        self.model = model or DEEPSEEK_MODEL
+        self.api_url = api_url or DEEPSEEK_API_URL
+        
+        if not self.api_key:
+            logger.warning("No LLM API key provided. API calls will fail.")
+    
+    async def generate_text(
+        self, 
+        prompt: str, 
+        temperature: float = 0.3,
+        max_tokens: int = 1000
+    ) -> str:
+        """
+        调用LLM生成文本
+        
+        Args:
+            prompt: 提示文本
+            temperature: 温度参数，控制随机性
+            max_tokens: 最大生成token数
+            
+        Returns:
+            生成的文本
+        """
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}"
+        }
+        
+        payload = {
+            "model": self.model,
+            "messages": [{"role": "user", "content": prompt}],
+            "temperature": temperature,
+            "max_tokens": max_tokens
+        }
+        
+        try:
+            async with httpx.AsyncClient(timeout=60.0) as client:
+                response = await client.post(
+                    f"{self.api_url}/chat/completions",
+                    headers=headers,
+                    json=payload
+                )
+                response.raise_for_status()
+                result = response.json()
+                return result["choices"][0]["message"]["content"]
+        except Exception as e:
+            logger.error(f"Error calling LLM API: {str(e)}")
+            return f"Error generating text: {str(e)}"
+
+
+class ArxivClient:
+    """arXiv API客户端"""
+    
+    async def search_papers(
+        self, 
+        query: str, 
+        max_results: int = 10
+    ) -> List[Dict[str, Any]]:
+        """
+        搜索arXiv文献
+        
+        Args:
+            query: 搜索查询
+            max_results: 最大结果数量
+            
+        Returns:
+            文献列表
+        """
+        try:
+            import arxiv
+            
+            search = arxiv.Search(
+                query=query,
+                max_results=max_results,
+                sort_by=arxiv.SortCriterion.Relevance
+            )
+            
+            results = []
+            for paper in await search.results():
+                results.append({
+                    "id": paper.get_short_id(),
+                    "title": paper.title,
+                    "authors": [author.name for author in paper.authors],
+                    "summary": paper.summary,
+                    "published": paper.published.isoformat() if paper.published else None,
+                    "updated": paper.updated.isoformat() if paper.updated else None,
+                    "link": paper.pdf_url,
+                    "source": "arxiv"
+                })
+            
+            return results
+        except Exception as e:
+            logger.error(f"Error searching arXiv: {str(e)}")
+            return []

backend/utils/init.py

@@ -0,0 +1,3 @@
+"""
+工具函数包
+"""

backend/utils/keywords.py

@@ -0,0 +1,82 @@
+"""
+关键词提取和处理工具
+"""
+
+import re
+from typing import List, Dict, Any
+from .api_client import LLMClient
+
+async def extract_keywords(
+    research_topic: str,
+    llm_client: LLMClient
+) -> List[str]:
+    """
+    从研究主题中提取关键词
+    
+    Args:
+        research_topic: 用户输入的研究主题
+        llm_client: LLM客户端实例
+        
+    Returns:
+        关键词列表
+    """
+    prompt = f"""
+    作为一个科研助手，请从以下研究主题中提取5-8个关键的检索词，这些词应该能够用于学术文献检索。
+    请考虑主题的核心概念、方法论、应用领域等方面。返回格式为逗号分隔的关键词列表，不要有编号或其他文本。
+
+    研究主题: {research_topic}
+    
+    关键词:
+    """
+    
+    response = await llm_client.generate_text(prompt, temperature=0.1)
+    
+    # 清理和解析响应
+    # 移除多余的空格和标点
+    response = re.sub(r'[\n\r]+', ',', response)
+    response = re.sub(r'\s*,\s*', ',', response)
+    response = re.sub(r'^\s+|\s+$', '', response)
+    
+    # 分割为关键词列表
+    keywords = [kw.strip() for kw in response.split(',') if kw.strip()]
+    
+    return keywords
+
+async def expand_search_queries(
+    keywords: List[str],
+    llm_client: LLMClient
+) -> List[str]:
+    """
+    扩展搜索查询，生成更全面的检索语句
+    
+    Args:
+        keywords: 关键词列表
+        llm_client: LLM客户端实例
+        
+    Returns:
+        扩展后的搜索查询列表
+    """
+    kw_text = ", ".join(keywords)
+    
+    prompt = f"""
+    作为科研检索专家，我需要你帮助构建有效的学术文献检索语句。
+    基于以下关键词：{kw_text}
+    
+    请生成3个不同的arXiv搜索查询语句，这些查询应当：
+    1. 使用布尔运算符(AND, OR)来组合关键词
+    2. 考虑同义词和相关概念
+    3. 针对不同的研究角度
+    
+    只需返回这些查询语句，每行一个，不要有编号或其他文本。
+    """
+    
+    response = await llm_client.generate_text(prompt, temperature=0.2)
+    
+    # 清理和解析响应
+    queries = [q.strip() for q in response.splitlines() if q.strip()]
+    
+    # 确保至少有一个查询
+    if not queries and keywords:
+        queries = [" AND ".join(keywords[:3])]
+    
+    return queries

frontend/mobile/README.md

@@ -0,0 +1,15 @@
+# 文献启明星 移动客户端
+
+此目录将包含移动应用客户端代码。
+
+## 计划技术栈
+- React Native / Flutter
+- 移动端UI组件库
+- 本地存储解决方案
+
+## 开发计划
+1. 设计移动端UI原型
+2. 实现基础页面结构
+3. 连接后端API
+4. 开发离线功能
+5. 实现数据同步

frontend/web/README.md

@@ -0,0 +1,15 @@
+# 文献启明星 Web 前端
+
+此目录将包含Web应用前端代码。
+
+## 计划技术栈
+- React/Next.js
+- Tailwind CSS
+- 数据可视化库（如Plotly、D3.js）
+
+## 开发计划
+1. 设计用户界面原型
+2. 实现基础页面结构
+3. 连接后端API
+4. 开发交互功能
+5. 实现数据可视化

lightstar-web/.gitignore

@@ -0,0 +1,41 @@
+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.*
+.yarn/*
+!.yarn/patches
+!.yarn/plugins
+!.yarn/releases
+!.yarn/versions
+
+# testing
+/coverage
+
+# next.js
+/.next/
+/out/
+
+# production
+/build
+
+# misc
+.DS_Store
+*.pem
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+.pnpm-debug.log*
+
+# env files (can opt-in for committing if needed)
+.env*
+
+# vercel
+.vercel
+
+# typescript
+*.tsbuildinfo
+next-env.d.ts

lightstar-web/README.md

@@ -0,0 +1,36 @@
+This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app).
+
+## Getting Started
+
+First, run the development server:
+
+```bash
+npm run dev
+# or
+yarn dev
+# or
+pnpm dev
+# or
+bun dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
+
+You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
+
+This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel.
+
+## Learn More
+
+To learn more about Next.js, take a look at the following resources:
+
+- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
+- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
+
+You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome!
+
+## Deploy on Vercel
+
+The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.
+
+Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details.

lightstar-web/eslint.config.mjs

@@ -0,0 +1,16 @@
+import { dirname } from "path";
+import { fileURLToPath } from "url";
+import { FlatCompat } from "@eslint/eslintrc";
+
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = dirname(__filename);
+
+const compat = new FlatCompat({
+  baseDirectory: __dirname,
+});
+
+const eslintConfig = [
+  ...compat.extends("next/core-web-vitals", "next/typescript"),
+];
+
+export default eslintConfig;

lightstar-web/next.config.ts

@@ -0,0 +1,7 @@
+import type { NextConfig } from "next";
+
+const nextConfig: NextConfig = {
+  /* config options here */
+};
+
+export default nextConfig;

lightstar-web/package-lock.json

@@ -0,0 +1,5868 @@
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+        "@types/react": "^19",
+        "@types/react-dom": "^19",
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+        "eslint-config-next": "15.3.0",
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+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
+    "node_modules/which-typed-array": {
+      "version": "1.1.19",
+      "resolved": "https://registry.npmmirror.com/which-typed-array/-/which-typed-array-1.1.19.tgz",
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+      "dev": true,
+      "dependencies": {
+        "available-typed-arrays": "^1.0.7",
+        "call-bind": "^1.0.8",
+        "call-bound": "^1.0.4",
+        "for-each": "^0.3.5",
+        "get-proto": "^1.0.1",
+        "gopd": "^1.2.0",
+        "has-tostringtag": "^1.0.2"
+      },
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+        "node": ">= 0.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
+    "node_modules/word-wrap": {
+      "version": "1.2.5",
+      "resolved": "https://registry.npmmirror.com/word-wrap/-/word-wrap-1.2.5.tgz",
+      "integrity": "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==",
+      "dev": true,
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/wrap-ansi": {
+      "version": "8.1.0",
+      "resolved": "https://registry.npmmirror.com/wrap-ansi/-/wrap-ansi-8.1.0.tgz",
+      "integrity": "sha512-si7QWI6zUMq56bESFvagtmzMdGOtoxfR+Sez11Mobfc7tm+VkUckk9bW2UeffTGVUbOksxmSw0AA2gs8g71NCQ==",
+      "dependencies": {
+        "ansi-styles": "^6.1.0",
+        "string-width": "^5.0.1",
+        "strip-ansi": "^7.0.1"
+      },
+      "engines": {
+        "node": ">=12"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
+      }
+    },
+    "node_modules/wrap-ansi-cjs": {
+      "name": "wrap-ansi",
+      "version": "7.0.0",
+      "resolved": "https://registry.npmmirror.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz",
+      "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==",
+      "dependencies": {
+        "ansi-styles": "^4.0.0",
+        "string-width": "^4.1.0",
+        "strip-ansi": "^6.0.0"
+      },
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/ansi-regex": {
+      "version": "5.0.1",
+      "resolved": "https://registry.npmmirror.com/ansi-regex/-/ansi-regex-5.0.1.tgz",
+      "integrity": "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==",
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/emoji-regex": {
+      "version": "8.0.0",
+      "resolved": "https://registry.npmmirror.com/emoji-regex/-/emoji-regex-8.0.0.tgz",
+      "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A=="
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/string-width": {
+      "version": "4.2.3",
+      "resolved": "https://registry.npmmirror.com/string-width/-/string-width-4.2.3.tgz",
+      "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==",
+      "dependencies": {
+        "emoji-regex": "^8.0.0",
+        "is-fullwidth-code-point": "^3.0.0",
+        "strip-ansi": "^6.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi-cjs/node_modules/strip-ansi": {
+      "version": "6.0.1",
+      "resolved": "https://registry.npmmirror.com/strip-ansi/-/strip-ansi-6.0.1.tgz",
+      "integrity": "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==",
+      "dependencies": {
+        "ansi-regex": "^5.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/wrap-ansi/node_modules/ansi-styles": {
+      "version": "6.2.1",
+      "resolved": "https://registry.npmmirror.com/ansi-styles/-/ansi-styles-6.2.1.tgz",
+      "integrity": "sha512-bN798gFfQX+viw3R7yrGWRqnrN2oRkEkUjjl4JNn4E8GxxbjtG3FbrEIIY3l8/hrwUwIeCZvi4QuOTP4MErVug==",
+      "engines": {
+        "node": ">=12"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/ansi-styles?sponsor=1"
+      }
+    },
+    "node_modules/yaml": {
+      "version": "1.10.2",
+      "resolved": "https://registry.npmmirror.com/yaml/-/yaml-1.10.2.tgz",
+      "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==",
+      "engines": {
+        "node": ">= 6"
+      }
+    },
+    "node_modules/yocto-queue": {
+      "version": "0.1.0",
+      "resolved": "https://registry.npmmirror.com/yocto-queue/-/yocto-queue-0.1.0.tgz",
+      "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
+      "dev": true,
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    }
+  }
+}

lightstar-web/package.json

@@ -0,0 +1,33 @@
+{
+  "name": "lightstar-web",
+  "version": "0.1.0",
+  "private": true,
+  "scripts": {
+    "dev": "next dev",
+    "build": "next build",
+    "start": "next start",
+    "lint": "next lint"
+  },
+  "dependencies": {
+    "@radix-ui/react-icons": "^1.3.2",
+    "autoprefixer": "^10.4.16",
+    "clsx": "^2.1.1",
+    "framer-motion": "^12.7.3",
+    "lucide-react": "^0.488.0",
+    "next": "15.3.0",
+    "postcss": "^8.4.31",
+    "react": "^19.0.0",
+    "react-dom": "^19.0.0",
+    "tailwind-merge": "^3.2.0",
+    "tailwindcss": "^3.3.0"
+  },
+  "devDependencies": {
+    "@eslint/eslintrc": "^3",
+    "@types/node": "^20",
+    "@types/react": "^19",
+    "@types/react-dom": "^19",
+    "eslint": "^9",
+    "eslint-config-next": "15.3.0",
+    "typescript": "^5"
+  }
+}

lightstar-web/postcss.config.js

@@ -0,0 +1,6 @@
+module.exports = {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+};

lightstar-web/public/file.svg

@@ -0,0 +1 @@
+<svg fill="none" viewBox="0 0 16 16" xmlns="http://www.w3.org/2000/svg"><path d="M14.5 13.5V5.41a1 1 0 0 0-.3-.7L9.8.29A1 1 0 0 0 9.08 0H1.5v13.5A2.5 2.5 0 0 0 4 16h8a2.5 2.5 0 0 0 2.5-2.5m-1.5 0v-7H8v-5H3v12a1 1 0 0 0 1 1h8a1 1 0 0 0 1-1M9.5 5V2.12L12.38 5zM5.13 5h-.62v1.25h2.12V5zm-.62 3h7.12v1.25H4.5zm.62 3h-.62v1.25h7.12V11z" clip-rule="evenodd" fill="#666" fill-rule="evenodd"/></svg>

lightstar-web/public/globe.svg

@@ -0,0 +1 @@
+<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><g clip-path="url(#a)"><path fill-rule="evenodd" clip-rule="evenodd" d="M10.27 14.1a6.5 6.5 0 0 0 3.67-3.45q-1.24.21-2.7.34-.31 1.83-.97 3.1M8 16A8 8 0 1 0 8 0a8 8 0 0 0 0 16m.48-1.52a7 7 0 0 1-.96 0H7.5a4 4 0 0 1-.84-1.32q-.38-.89-.63-2.08a40 40 0 0 0 3.92 0q-.25 1.2-.63 2.08a4 4 0 0 1-.84 1.31zm2.94-4.76q1.66-.15 2.95-.43a7 7 0 0 0 0-2.58q-1.3-.27-2.95-.43a18 18 0 0 1 0 3.44m-1.27-3.54a17 17 0 0 1 0 3.64 39 39 0 0 1-4.3 0 17 17 0 0 1 0-3.64 39 39 0 0 1 4.3 0m1.1-1.17q1.45.13 2.69.34a6.5 6.5 0 0 0-3.67-3.44q.65 1.26.98 3.1M8.48 1.5l.01.02q.41.37.84 1.31.38.89.63 2.08a40 40 0 0 0-3.92 0q.25-1.2.63-2.08a4 4 0 0 1 .85-1.32 7 7 0 0 1 .96 0m-2.75.4a6.5 6.5 0 0 0-3.67 3.44 29 29 0 0 1 2.7-.34q.31-1.83.97-3.1M4.58 6.28q-1.66.16-2.95.43a7 7 0 0 0 0 2.58q1.3.27 2.95.43a18 18 0 0 1 0-3.44m.17 4.71q-1.45-.12-2.69-.34a6.5 6.5 0 0 0 3.67 3.44q-.65-1.27-.98-3.1" fill="#666"/></g><defs><clipPath id="a"><path fill="#fff" d="M0 0h16v16H0z"/></clipPath></defs></svg>

lightstar-web/public/next.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 394 80"><path fill="#000" d="M262 0h68.5v12.7h-27.2v66.6h-13.6V12.7H262V0ZM149 0v12.7H94v20.4h44.3v12.6H94v21h55v12.6H80.5V0h68.7zm34.3 0h-17.8l63.8 79.4h17.9l-32-39.7 32-39.6h-17.9l-23 28.6-23-28.6zm18.3 56.7-9-11-27.1 33.7h17.8l18.3-22.7z"/><path fill="#000" d="M81 79.3 17 0H0v79.3h13.6V17l50.2 62.3H81Zm252.6-.4c-1 0-1.8-.4-2.5-1s-1.1-1.6-1.1-2.6.3-1.8 1-2.5 1.6-1 2.6-1 1.8.3 2.5 1a3.4 3.4 0 0 1 .6 4.3 3.7 3.7 0 0 1-3 1.8zm23.2-33.5h6v23.3c0 2.1-.4 4-1.3 5.5a9.1 9.1 0 0 1-3.8 3.5c-1.6.8-3.5 1.3-5.7 1.3-2 0-3.7-.4-5.3-1s-2.8-1.8-3.7-3.2c-.9-1.3-1.4-3-1.4-5h6c.1.8.3 1.6.7 2.2s1 1.2 1.6 1.5c.7.4 1.5.5 2.4.5 1 0 1.8-.2 2.4-.6a4 4 0 0 0 1.6-1.8c.3-.8.5-1.8.5-3V45.5zm30.9 9.1a4.4 4.4 0 0 0-2-3.3 7.5 7.5 0 0 0-4.3-1.1c-1.3 0-2.4.2-3.3.5-.9.4-1.6 1-2 1.6a3.5 3.5 0 0 0-.3 4c.3.5.7.9 1.3 1.2l1.8 1 2 .5 3.2.8c1.3.3 2.5.7 3.7 1.2a13 13 0 0 1 3.2 1.8 8.1 8.1 0 0 1 3 6.5c0 2-.5 3.7-1.5 5.1a10 10 0 0 1-4.4 3.5c-1.8.8-4.1 1.2-6.8 1.2-2.6 0-4.9-.4-6.8-1.2-2-.8-3.4-2-4.5-3.5a10 10 0 0 1-1.7-5.6h6a5 5 0 0 0 3.5 4.6c1 .4 2.2.6 3.4.6 1.3 0 2.5-.2 3.5-.6 1-.4 1.8-1 2.4-1.7a4 4 0 0 0 .8-2.4c0-.9-.2-1.6-.7-2.2a11 11 0 0 0-2.1-1.4l-3.2-1-3.8-1c-2.8-.7-5-1.7-6.6-3.2a7.2 7.2 0 0 1-2.4-5.7 8 8 0 0 1 1.7-5 10 10 0 0 1 4.3-3.5c2-.8 4-1.2 6.4-1.2 2.3 0 4.4.4 6.2 1.2 1.8.8 3.2 2 4.3 3.4 1 1.4 1.5 3 1.5 5h-5.8z"/></svg>

lightstar-web/public/vercel.svg

@@ -0,0 +1 @@
+<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1155 1000"><path d="m577.3 0 577.4 1000H0z" fill="#fff"/></svg>

lightstar-web/public/window.svg

@@ -0,0 +1 @@
+<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path fill-rule="evenodd" clip-rule="evenodd" d="M1.5 2.5h13v10a1 1 0 0 1-1 1h-11a1 1 0 0 1-1-1zM0 1h16v11.5a2.5 2.5 0 0 1-2.5 2.5h-11A2.5 2.5 0 0 1 0 12.5zm3.75 4.5a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5M7 4.75a.75.75 0 1 1-1.5 0 .75.75 0 0 1 1.5 0m1.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5" fill="#666"/></svg>

lightstar-web/src/app/globals.css

@@ -0,0 +1,108 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+@layer base {
+  :root {
+    --background: 224 71% 4%;
+    --foreground: 213 31% 91%;
+
+    --primary: 266 100% 64%;
+    --primary-foreground: 210 40% 98%;
+ 
+    --card: 224 71% 4%;
+    --card-foreground: 213 31% 91%;
+  }
+}
+
+/* 动画效果 */
+@keyframes float {
+  0% { transform: translateY(0px); }
+  50% { transform: translateY(-20px); }
+  100% { transform: translateY(0px); }
+}
+
+@keyframes pulse {
+  0%, 100% { opacity: 0.6; }
+  50% { opacity: 0.8; }
+}
+
+@keyframes blob {
+  0% {
+    transform: translate(0px, 0px) scale(1);
+  }
+  33% {
+    transform: translate(30px, -50px) scale(1.2);
+  }
+  66% {
+    transform: translate(-20px, 20px) scale(0.8);
+  }
+  100% {
+    transform: translate(0px, 0px) scale(1);
+  }
+}
+
+/* 星星背景效果 */
+.stars {
+  background-image: radial-gradient(2px 2px at 20px 30px, white, rgba(0, 0, 0, 0)), 
+                    radial-gradient(2px 2px at 40px 70px, white, rgba(0, 0, 0, 0)), 
+                    radial-gradient(2px 2px at 60px 110px, white, rgba(0, 0, 0, 0)),
+                    radial-gradient(2px 2px at 80px 150px, white, rgba(0, 0, 0, 0)),
+                    radial-gradient(2px 2px at 100px 20px, white, rgba(0, 0, 0, 0));
+  background-size: 200px 200px;
+  opacity: 0.3;
+}
+
+.animate-float {
+  animation: float 6s ease-in-out infinite;
+}
+
+.animate-blob {
+  animation: blob 10s infinite ease-in-out;
+}
+
+.animation-delay-2000 {
+  animation-delay: 2s;
+}
+
+.animation-delay-4000 {
+  animation-delay: 4s;
+}
+
+/* 主题变量 */
+.theme-space {
+  --theme-bg-from: theme('colors.space.dark');
+  --theme-bg-via: theme('colors.space.mid');
+  --theme-bg-to: theme('colors.space.light');
+  --theme-accent: theme('colors.space.accent');
+  --theme-highlight: theme('colors.space.highlight');
+}
+
+.theme-ocean {
+  --theme-bg-from: theme('colors.ocean.dark');
+  --theme-bg-via: theme('colors.ocean.mid');
+  --theme-bg-to: theme('colors.ocean.light');
+  --theme-accent: theme('colors.ocean.accent');
+  --theme-highlight: theme('colors.ocean.highlight');
+}
+
+.theme-tech {
+  --theme-bg-from: theme('colors.tech.dark');
+  --theme-bg-via: theme('colors.tech.mid');
+  --theme-bg-to: theme('colors.tech.light');
+  --theme-accent: theme('colors.tech.accent');
+  --theme-highlight: theme('colors.tech.highlight');
+}
+
+.theme-nightglow {
+  --theme-bg-from: theme('colors.nightglow.dark');
+  --theme-bg-via: theme('colors.nightglow.mid');
+  --theme-bg-to: theme('colors.nightglow.light');
+  --theme-accent: theme('colors.nightglow.accent');
+  --theme-highlight: theme('colors.nightglow.highlight');
+}
+
+body {
+  @apply bg-gradient-to-br from-indigo-900 via-violet-800 to-purple-900;
+  min-height: 100vh;
+}

lightstar-web/src/app/layout.tsx

@@ -0,0 +1,34 @@
+import type { Metadata } from "next";
+import { Geist, Geist_Mono } from "next/font/google";
+import "./globals.css";
+
+const geistSans = Geist({
+  variable: "--font-geist-sans",
+  subsets: ["latin"],
+});
+
+const geistMono = Geist_Mono({
+  variable: "--font-geist-mono",
+  subsets: ["latin"],
+});
+
+export const metadata: Metadata = {
+  title: "Create Next App",
+  description: "Generated by create next app",
+};
+
+export default function RootLayout({
+  children,
+}: Readonly<{
+  children: React.ReactNode;
+}>) {
+  return (
+    <html lang="en">
+      <body
+        className={`${geistSans.variable} ${geistMono.variable} antialiased`}
+      >
+        {children}
+      </body>
+    </html>
+  );
+}

lightstar-web/src/app/page.tsx

@@ -0,0 +1,40 @@
+import { GlassCard } from "@/components/ui/glass-card";
+import { ResearchForm } from "@/components/forms/research-form";
+import { ThemeSelector } from "@/components/theme-selector";
+
+export default function Home() {
+  return (
+    <main className="relative flex min-h-screen flex-col items-center justify-center p-4 sm:p-8 bg-gradient-to-br from-[var(--theme-bg-from)] via-[var(--theme-bg-via)] to-[var(--theme-bg-to)]">
+      {/* 主题选择器 */}
+      <ThemeSelector />
+      
+      {/* 背景装饰 */}
+      <div className="absolute inset-0 overflow-hidden pointer-events-none">
+        <div className="stars absolute inset-0 z-0"></div>
+        <div className="absolute top-1/4 left-1/3 w-[30rem] h-[30rem] bg-[var(--theme-accent)] rounded-full mix-blend-screen filter blur-[150px] opacity-20 animate-blob"></div>
+        <div className="absolute top-1/3 right-1/4 w-[25rem] h-[25rem] bg-[var(--theme-highlight)] rounded-full mix-blend-screen filter blur-[120px] opacity-20 animate-blob animation-delay-2000"></div>
+        <div className="absolute bottom-1/4 right-1/3 w-[20rem] h-[20rem] bg-white rounded-full mix-blend-screen filter blur-[100px] opacity-10 animate-blob animation-delay-4000"></div>
+      </div>
+      
+      <div className="container max-w-xl mx-auto z-10">
+        <div className="text-center mb-8">
+          <h1 className="text-5xl md:text-6xl font-bold text-center text-transparent bg-clip-text bg-gradient-to-r from-white to-white/70 mb-3 drop-shadow-lg animate-pulse">
+            文献启明星
+          </h1>
+          <p className="text-xl text-center text-white/80 mb-2">
+            科研调研智能体系统
+          </p>
+          <div className="w-16 h-1 mx-auto bg-gradient-to-r from-[var(--theme-accent)] to-[var(--theme-highlight)] rounded-full"></div>
+        </div>
+        
+        <GlassCard className="w-full">
+          <ResearchForm />
+        </GlassCard>
+        
+        <p className="text-white/50 text-center text-xs mt-8">
+          基于人工智能的论文检索与分析系统
+        </p>
+      </div>
+    </main>
+  );
+}

lightstar-web/src/components/forms/research-form.tsx

@@ -0,0 +1,84 @@
+"use client";
+
+import { useState } from "react";
+import { motion } from "framer-motion";
+
+export function ResearchForm({ onSubmit }: { onSubmit?: (data: any) => void }) {
+  const [loading, setLoading] = useState(false);
+  const [researchTopic, setResearchTopic] = useState("");
+  const [maxResults, setMaxResults] = useState(15);
+
+  const handleSubmit = async (e: React.FormEvent) => {
+    e.preventDefault();
+    if (!researchTopic.trim()) return;
+    
+    setLoading(true);
+    
+    try {
+      // 模拟API调用
+      console.log("Submitted:", { researchTopic, maxResults });
+      onSubmit?.({ researchTopic, maxResults });
+      
+      // 实际项目中这里会调用后端API
+      // const response = await fetch("http://localhost:8000/api/research/process", {...});
+    } catch (error) {
+      console.error("Error:", error);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  return (
+    <form onSubmit={handleSubmit} className="space-y-6">
+      <div className="space-y-2">
+        <h2 className="text-xl font-semibold text-white/90">研究主题</h2>
+        <p className="text-white/70 text-sm">
+          输入您想探索的研究领域，AI将帮您分析相关文献
+        </p>
+        <textarea
+          placeholder="例如：人工智能在医疗诊断中的应用"
+          className="w-full min-h-[120px] rounded-xl p-4 bg-white/10 border border-white/20 text-white placeholder:text-white/40 focus:outline-none focus:ring-2 focus:ring-[var(--theme-accent)] focus:border-transparent shadow-inner transition-all duration-200"
+          value={researchTopic}
+          onChange={(e) => setResearchTopic(e.target.value)}
+          disabled={loading}
+        />
+      </div>
+
+      <div className="space-y-2">
+        <div className="flex justify-between items-center">
+          <label className="text-sm font-medium text-white/80">
+            文献数量上限: {maxResults}
+          </label>
+          <span className="text-xs text-white/60">推荐: 10-20</span>
+        </div>
+        <input
+          type="range"
+          min={5}
+          max={30}
+          value={maxResults}
+          onChange={(e) => setMaxResults(Number(e.target.value))}
+          className="w-full accent-[var(--theme-accent)]"
+          disabled={loading}
+        />
+      </div>
+
+      <button 
+        type="submit" 
+        className="w-full py-4 px-6 rounded-xl bg-gradient-to-r from-[var(--theme-accent)] to-[var(--theme-highlight)] hover:brightness-110 text-white font-medium shadow-lg transform hover:scale-[1.02] transition-all duration-200"
+        disabled={loading || !researchTopic.trim()}
+      >
+        {loading ? (
+          <span className="flex items-center justify-center">
+            <svg className="animate-spin -ml-1 mr-2 h-5 w-5 text-white" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24">
+              <circle className="opacity-25" cx="12" cy="12" r="10" stroke="currentColor" strokeWidth="4"></circle>
+              <path className="opacity-75" fill="currentColor" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path>
+            </svg>
+            处理中...
+          </span>
+        ) : (
+          "开始研究"
+        )}
+      </button>
+    </form>
+  );
+}

lightstar-web/src/components/theme-selector.tsx

@@ -0,0 +1,40 @@
+"use client";
+
+import { useState, useEffect } from "react";
+import { motion } from "framer-motion";
+
+const themes = [
+  { id: "space", name: "深邃宇宙", color: "#4D61FC" },
+  { id: "ocean", name: "高级深海", color: "#26C5F3" },
+  { id: "tech", name: "现代科技", color: "#FF2D72" },
+  { id: "nightglow", name: "精致夜光", color: "#875EFF" },
+];
+
+export function ThemeSelector() {
+  const [selectedTheme, setSelectedTheme] = useState("space");
+
+  useEffect(() => {
+    // 应用选中的主题到body元素
+    document.body.className = `theme-${selectedTheme}`;
+  }, [selectedTheme]);
+
+  return (
+    <div className="absolute top-4 right-4 z-20">
+      <div className="flex gap-2 backdrop-blur-md bg-white/10 p-2 rounded-full">
+        {themes.map((theme) => (
+          <button
+            key={theme.id}
+            onClick={() => setSelectedTheme(theme.id)}
+            className={`w-6 h-6 rounded-full transition-all ${
+              selectedTheme === theme.id
+                ? "scale-110 shadow-lg"
+                : "opacity-70"
+            }`}
+            style={{ backgroundColor: theme.color }}
+            title={theme.name}
+          />
+        ))}
+      </div>
+    </div>
+  );
+}

lightstar-web/src/components/ui/glass-card.tsx

@@ -0,0 +1,42 @@
+"use client";
+
+import { cn } from "@/lib/utils";
+import { motion } from "framer-motion";
+import React from "react";
+
+interface GlassCardProps extends React.HTMLAttributes<HTMLDivElement> {
+  children: React.ReactNode;
+  className?: string;
+  hoverEffect?: boolean;
+}
+
+export function GlassCard({
+  children,
+  className,
+  hoverEffect = true,
+  ...props
+}: GlassCardProps) {
+  return (
+    <motion.div
+      className={cn(
+        "relative rounded-2xl backdrop-blur-xl bg-white/15 dark:bg-slate-900/20 p-8 shadow-[0_8px_30px_rgba(0,0,0,0.12)] border border-white/10 dark:border-slate-700/10",
+        hoverEffect && "hover:bg-white/20 dark:hover:bg-slate-800/20 transition-all duration-300",
+        className
+      )}
+      initial={{ opacity: 0, y: 20 }}
+      animate={{ opacity: 1, y: 0 }}
+      transition={{ duration: 0.4, ease: "easeOut" }}
+      whileHover={
+        hoverEffect
+          ? { y: -8, boxShadow: "0 20px 40px rgba(0,0,0,0.2)" }
+          : {}
+      }
+      {...props}
+    >
+      {/* 装饰元素 */}
+      <div className="absolute top-0 right-0 -mt-4 -mr-4 h-10 w-10 rounded-full bg-gradient-to-br from-[var(--theme-highlight)] to-[var(--theme-accent)] opacity-70 blur-xl"></div>
+      <div className="absolute bottom-0 left-0 -mb-4 -ml-4 h-8 w-8 rounded-full bg-gradient-to-tr from-[var(--theme-accent)] to-[var(--theme-highlight)] opacity-60 blur-xl"></div>
+      {children}
+    </motion.div>
+  );
+}

lightstar-web/src/lib/utils.ts

@@ -0,0 +1,6 @@
+import { type ClassValue, clsx } from "clsx";
+import { twMerge } from "tailwind-merge";
+
+export function cn(...inputs: ClassValue[]) {
+  return twMerge(clsx(inputs));
+}

lightstar-web/tailwind.config.ts

@@ -0,0 +1,50 @@
+import type { Config } from "tailwindcss";
+
+const config: Config = {
+  content: [
+    "./src/pages/**/*.{js,ts,jsx,tsx,mdx}",
+    "./src/components/**/*.{js,ts,jsx,tsx,mdx}",
+    "./src/app/**/*.{js,ts,jsx,tsx,mdx}",
+  ],
+  theme: {
+    extend: {
+      colors: {
+        // 颜色方案1: 深邃宇宙
+        space: {
+          dark: '#0B1026',
+          mid: '#1A1C4B',
+          light: '#21133A',
+          accent: '#4D61FC',
+          highlight: '#8C5CF4',
+        },
+        // 颜色方案2: 高级深海
+        ocean: {
+          dark: '#051937',
+          mid: '#004261',
+          light: '#004F7A',
+          accent: '#26C5F3',
+          highlight: '#1A7BF5',
+        },
+        // 颜色方案3: 现代科技
+        tech: {
+          dark: '#0F1117',
+          mid: '#151922',
+          light: '#131621',
+          accent: '#FF2D72',
+          highlight: '#0088FF',
+        },
+        // 颜色方案4: 夜光
+        nightglow: {
+          dark: '#09090B',
+          mid: '#18181B',
+          light: '#27272A',
+          accent: '#875EFF',
+          highlight: '#9D7EFF',
+        },
+      },
+    },
+  },
+  plugins: [],
+};
+
+export default config;

lightstar-web/tsconfig.json

@@ -0,0 +1,27 @@
+{
+  "compilerOptions": {
+    "target": "ES2017",
+    "lib": ["dom", "dom.iterable", "esnext"],
+    "allowJs": true,
+    "skipLibCheck": true,
+    "strict": true,
+    "noEmit": true,
+    "esModuleInterop": true,
+    "module": "esnext",
+    "moduleResolution": "bundler",
+    "resolveJsonModule": true,
+    "isolatedModules": true,
+    "jsx": "preserve",
+    "incremental": true,
+    "plugins": [
+      {
+        "name": "next"
+      }
+    ],
+    "paths": {
+      "@/*": ["./src/*"]
+    }
+  },
+  "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
+  "exclude": ["node_modules"]
+}

package-lock.json

@@ -0,0 +1,1473 @@
+{
+  "name": "Lightstar",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
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+        "clsx": "^2.1.1",
+        "framer-motion": "^12.7.3",
+        "postcss": "^8.4.31",
+        "tailwind-merge": "^3.2.0",
+        "tailwindcss": "^3.3.0"
+      }
+    },
+    "node_modules/@isaacs/cliui": {
+      "version": "8.0.2",
+      "resolved": "https://registry.npmmirror.com/@isaacs/cliui/-/cliui-8.0.2.tgz",
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+      "dependencies": {
+        "string-width": "^5.1.2",
+        "string-width-cjs": "npm:string-width@^4.2.0",
+        "strip-ansi": "^7.0.1",
+        "strip-ansi-cjs": "npm:strip-ansi@^6.0.1",
+        "wrap-ansi": "^8.1.0",
+        "wrap-ansi-cjs": "npm:wrap-ansi@^7.0.0"
+      },
+      "engines": {
+        "node": ">=12"
+      }
+    },
+    "node_modules/@jridgewell/gen-mapping": {
+      "version": "0.3.8",
+      "resolved": "https://registry.npmmirror.com/@jridgewell/gen-mapping/-/gen-mapping-0.3.8.tgz",
+      "integrity": "sha512-imAbBGkb+ebQyxKgzv5Hu2nmROxoDOXHh80evxdoXNOrvAnVx7zimzc1Oo5h9RlfV4vPXaE2iM5pOFbvOCClWA==",
+      "dependencies": {
+        "@jridgewell/set-array": "^1.2.1",
+        "@jridgewell/sourcemap-codec": "^1.4.10",
+        "@jridgewell/trace-mapping": "^0.3.24"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
+    },
+    "node_modules/@jridgewell/resolve-uri": {
+      "version": "3.1.2",
+      "resolved": "https://registry.npmmirror.com/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz",
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+    },
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+      "dependencies": {
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+        "@jridgewell/sourcemap-codec": "^1.4.14"
+      }
+    },
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+      "resolved": "https://registry.npmmirror.com/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz",
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+      "dependencies": {
+        "@nodelib/fs.stat": "2.0.5",
+        "run-parallel": "^1.1.9"
+      },
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+      },
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+      "optional": true,
+      "engines": {
+        "node": ">=14"
+      }
+    },
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+      },
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+        "node": ">=12"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/ansi-styles?sponsor=1"
+      }
+    },
+    "node_modules/any-promise": {
+      "version": "1.3.0",
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+    },
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+        "picomatch": "^2.0.4"
+      },
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+      }
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+    },
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+      "funding": [
+        {
+          "type": "opencollective",
+          "url": "https://opencollective.com/postcss/"
+        },
+        {
+          "type": "tidelift",
+          "url": "https://tidelift.com/funding/github/npm/autoprefixer"
+        },
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/ai"
+        }
+      ],
+      "dependencies": {
+        "browserslist": "^4.21.10",
+        "caniuse-lite": "^1.0.30001538",
+        "fraction.js": "^4.3.6",
+        "normalize-range": "^0.1.2",
+        "picocolors": "^1.0.0",
+        "postcss-value-parser": "^4.2.0"
+      },
+      "bin": {
+        "autoprefixer": "bin/autoprefixer"
+      },
+      "engines": {
+        "node": "^10 || ^12 || >=14"
+      },
+      "peerDependencies": {
+        "postcss": "^8.1.0"
+      }
+    },
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+    },
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+      "engines": {
+        "node": ">=8"
+      },
+      "funding": {
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+      }
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+      "dependencies": {
+        "balanced-match": "^1.0.0"
+      }
+    },
+    "node_modules/braces": {
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+      "resolved": "https://registry.npmmirror.com/braces/-/braces-3.0.3.tgz",
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+      "dependencies": {
+        "fill-range": "^7.1.1"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/browserslist": {
+      "version": "4.24.4",
+      "resolved": "https://registry.npmmirror.com/browserslist/-/browserslist-4.24.4.tgz",
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+      "funding": [
+        {
+          "type": "opencollective",
+          "url": "https://opencollective.com/browserslist"
+        },
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+          "type": "tidelift",
+          "url": "https://tidelift.com/funding/github/npm/browserslist"
+        },
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/ai"
+        }
+      ],
+      "dependencies": {
+        "caniuse-lite": "^1.0.30001688",
+        "electron-to-chromium": "^1.5.73",
+        "node-releases": "^2.0.19",
+        "update-browserslist-db": "^1.1.1"
+      },
+      "bin": {
+        "browserslist": "cli.js"
+      },
+      "engines": {
+        "node": "^6 || ^7 || ^8 || ^9 || ^10 || ^11 || ^12 || >=13.7"
+      }
+    },
+    "node_modules/camelcase-css": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmmirror.com/camelcase-css/-/camelcase-css-2.0.1.tgz",
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+      "engines": {
+        "node": ">= 6"
+      }
+    },
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+    "node_modules/yaml": {
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+}

package.json

@@ -0,0 +1,10 @@
+{
+  "dependencies": {
+    "autoprefixer": "^10.4.16",
+    "clsx": "^2.1.1",
+    "framer-motion": "^12.7.3",
+    "postcss": "^8.4.31",
+    "tailwind-merge": "^3.2.0",
+    "tailwindcss": "^3.3.0"
+  }
+}

requirements.txt

@@ -0,0 +1,43 @@
+# 核心依赖
+requests==2.31.0
+arxiv==1.4.8
+python-dotenv==1.0.0
+
+# Web 界面
+streamlit>=1.26.0
+pandas>=1.3.0
+
+# 文本处理与向量化
+scikit-learn>=1.0.0
+sentence-transformers>=2.2.0
+numpy>=1.20.0
+
+# 数据处理
+pandas>=1.3.0
+
+# 可视化
+plotly>=5.5.0
+matplotlib>=3.4.0
+
+# 降维与聚类
+scipy>=1.7.0
+
+# 用户系统
+passlib>=1.7.4  # 密码哈希处理
+bcrypt>=3.2.0   # 安全密码加密
+
+# 开发依赖 (可选，使用 pip install -e ".[dev]" 安装)
+pytest>=7.0.0,<8.0.0
+black>=22.0.0
+flake8>=5.0.0
+
+fastapi>=0.95.0
+uvicorn>=0.21.0
+pydantic>=1.10.7
+httpx>=0.24.0
+arxiv>=1.4.7
+sentence-transformers>=2.2.2
+scikit-learn>=1.2.2
+numpy>=1.24.3
+pandas>=2.0.0
+python-multipart>=0.0.6 

src/lib/utils.ts

@@ -0,0 +1,6 @@
+import { type ClassValue, clsx } from "clsx";
+import { twMerge } from "tailwind-merge";
+
+export function cn(...inputs: ClassValue[]) {
+  return twMerge(clsx(inputs));
+}